From 1bf4ddee672f1e0fc3be59c9de4886bc1cf72f43 Mon Sep 17 00:00:00 2001
From: Eric Firing <efiring@hawaii.edu>
Date: Sun, 5 Aug 2012 15:54:05 -1000
Subject: [PATCH 1/6] update contour_hatching test images for change caused by
 #1022

Filled contours now use more of the available colormap range than
previously.
---
 .../test_axes/contour_hatching.pdf            | Bin 83963 -> 83987 bytes
 .../test_axes/contour_hatching.png            | Bin 59180 -> 58475 bytes
 .../test_axes/contour_hatching.svg            | 166 +++++++++---------
 3 files changed, 83 insertions(+), 83 deletions(-)

diff --git a/lib/matplotlib/tests/baseline_images/test_axes/contour_hatching.pdf b/lib/matplotlib/tests/baseline_images/test_axes/contour_hatching.pdf
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+L72 18
+M0 6
+L72 6" style="fill:#000000;stroke:#000000;stroke-linecap:butt;stroke-linejoin:miter;stroke-width:1.0;"/>
+  </pattern>
  </defs>
 </svg>

From 8c582e47f3d5e00f68017c54db3523419adbced8 Mon Sep 17 00:00:00 2001
From: Eric Firing <efiring@hawaii.edu>
Date: Sun, 5 Aug 2012 20:53:53 -1000
Subject: [PATCH 2/6] colors: avoid problems with bad values under the mask
 when color mapping

This change should have no effect on any plot, but it avoids
warnings that can arise when an inf occurs under a mask.
---
 lib/matplotlib/colors.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
index 5e80abcf6675..7bd62e90051f 100644
--- a/lib/matplotlib/colors.py
+++ b/lib/matplotlib/colors.py
@@ -510,11 +510,10 @@ def __call__(self, X, alpha=None, bytes=False):
             xa = np.array([X])
         else:
             vtype = 'array'
-            xma = ma.array(X, copy=False)
-            mask_bad = xma.mask
-            xa = xma.data.copy()   # Copy here to avoid side effects.
+            xma = ma.array(X, copy=True)  # Copy here to avoid side effects.
+            mask_bad = xma.mask           # Mask will be used below.
+            xa = xma.filled()             # Fill to avoid infs, etc.
             del xma
-            # masked values are substituted below; no need to fill them here
 
         if xa.dtype.char in np.typecodes['Float']:
             # Treat 1.0 as slightly less than 1.

From 9cba6fb3a7aa83d5276eb208edfa82480bb4fa89 Mon Sep 17 00:00:00 2001
From: Eric Firing <efiring@hawaii.edu>
Date: Wed, 1 Aug 2012 09:03:31 -1000
Subject: [PATCH 3/6] contour: clarify and improve support for norm.vmin,
 norm.vmax

Previously, although vmin, vmax could be specified manually
in a norm used to initialize a ContourSet, the values would
be used only if both were specified.  Now one can set either
or both of vmin, vmax in the norm, and they will take precedence
over the default.
---
 lib/matplotlib/contour.py | 72 ++++++++++++++++++++++++++-------------
 1 file changed, 48 insertions(+), 24 deletions(-)

diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
index b018c8086089..bd06b4993f43 100644
--- a/lib/matplotlib/contour.py
+++ b/lib/matplotlib/contour.py
@@ -682,7 +682,7 @@ class ContourSet(cm.ScalarMappable, ContourLabeler):
 
       layers:
         same as levels for line contours; half-way between
-        levels for filled contours.  See _process_colors method.
+        levels for filled contours.  See :meth:`_process_colors`.
     """
     def __init__(self, ax, *args, **kwargs):
         """
@@ -1029,40 +1029,65 @@ def _contour_level_args(self, z, args):
             raise ValueError("Filled contours require at least 2 levels.")
 
     def _process_levels(self):
-        # Color mapping range (norm vmin, vmax) is based on levels.
+        """
+        Assign values to :attr:`layers` based on :attr:`levels`,
+        adding extended layers as needed if contours are filled.
+
+        For line contours, layers simply coincide with levels;
+        a line is a thin layer.  No extended levels are needed
+        with line contours.
+        """
+        # The following attributes are no longer needed, and
+        # should be deprecated and removed to reduce confusion.
         self.vmin = np.amin(self.levels)
         self.vmax = np.amax(self.levels)
-        # Make a private _levels to include extended regions.
+
+        # Make a private _levels to include extended regions; we
+        # want to leave the original levels attribute unchanged.
+        # (Colorbar needs this even for line contours.)
         self._levels = list(self.levels)
+
+        if not self.filled:
+            self.layers = self.levels
+            return
+
         if self.extend in ('both', 'min'):
             self._levels.insert(0, min(self.levels[0],self.zmin) - 1)
         if self.extend in ('both', 'max'):
             self._levels.append(max(self.levels[-1],self.zmax) + 1)
         self._levels = np.asarray(self._levels)
-        if self.filled:
-            # layer values are mid-way between levels
-            self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
-            # ...except that extended layers must be outside the
-            # normed range:
-            if self.extend in ('both', 'min'):
-                self.layers[0] = -np.inf
-            if self.extend in ('both', 'max'):
-                self.layers[-1] = np.inf
-        else:
-            self.layers = self.levels # contour: a line is a thin layer
-                         #  Use only original levels--no extended levels
+
+        # layer values are mid-way between levels
+        self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
+        # ...except that extended layers must be outside the
+        # normed range:
+        if self.extend in ('both', 'min'):
+            self.layers[0] = -np.inf
+        if self.extend in ('both', 'max'):
+            self.layers[-1] = np.inf
 
     def _process_colors(self):
         """
         Color argument processing for contouring.
 
-        Note that we base the color mapping on the contour levels,
-        not on the actual range of the Z values.  This means we
-        don't have to worry about bad values in Z, and we always have
-        the full dynamic range available for the selected levels.
+        Note that we base the color mapping on the contour levels
+        and layers, not on the actual range of the Z values.  This
+        means we don't have to worry about bad values in Z, and we
+        always have the full dynamic range available for the selected
+        levels.
 
         The color is based on the midpoint of the layer, except for
-        an extended end layers.
+        extended end layers.  By default, the norm vmin and vmax
+        are the extreme values of the non-extended levels.  Hence,
+        the layer color extremes are not the extreme values of
+        the colormap itself, but approach those values as the number
+        of levels increases.  An advantage of this scheme is that
+        line contours, when added to filled contours, take on
+        colors that are consistent with those of the filled regions;
+        for example, a contour line on the boundary between two
+        regions will have a color intermediate between the those
+        of the regions.
+
         """
         self.monochrome = self.cmap.monochrome
         if self.colors is not None:
@@ -1079,12 +1104,11 @@ def _process_colors(self):
             self.set_norm(colors.NoNorm())
         else:
             self.cvalues = self.layers
-        if not self.norm.scaled():
-            self.set_clim(self.vmin, self.vmax)
+        self.set_array(self.levels)
+        self.autoscale_None()
         if self.extend in ('both', 'max', 'min'):
             self.norm.clip = False
-        self.set_array(self.layers) # Required by colorbar, but not
-                                    # actually used.
+
         # self.tcolors are set by the "changed" method
 
     def _process_linewidths(self):

From f5c85d8d7bce1e83d6b72c31713f2ad566f19663 Mon Sep 17 00:00:00 2001
From: Eric Firing <efiring@hawaii.edu>
Date: Sun, 5 Aug 2012 22:29:10 -1000
Subject: [PATCH 4/6] contour: support vmin, vmax kwargs for consistency with
 pcolor etc.

This changeset also simplifies and clarifies the vmin, vmax
explanation and handling for pcolor and pcolormesh.
---
 CHANGELOG                 | 10 +++++++++-
 lib/matplotlib/axes.py    | 32 ++++++++++++++++----------------
 lib/matplotlib/contour.py | 14 +++++++++++++-
 3 files changed, 38 insertions(+), 18 deletions(-)

diff --git a/CHANGELOG b/CHANGELOG
index 9bc96a63385a..6eff3d3e6e50 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -1,9 +1,17 @@
+2012-08-05 When a norm is passed to contourf, either or both of the
+           vmin, vmax attributes of that norm are now respected.
+           Formerly they were respected only if both were
+           specified. In addition, vmin and/or vmax can now
+           be passed to contourf directly as kwargs. - EF
+
 2012-07-24 Contourf handles the extend kwarg by mapping the extended
            ranges outside the normed 0-1 range so that they are
            handled by colormap colors determined by the set_under
            and set_over methods.  Previously the extended ranges
            were mapped to 0 or 1 so that the "under" and "over"
-           colormap colors were ignored. - EF
+           colormap colors were ignored. This change also increases
+           slightly the color contrast for a given set of contour
+           levels. - EF
 
 2012-06-24 Make use of mathtext in tick labels configurable - DSD
 
diff --git a/lib/matplotlib/axes.py b/lib/matplotlib/axes.py
index f631543b7fe2..3ccf7cdacd11 100644
--- a/lib/matplotlib/axes.py
+++ b/lib/matplotlib/axes.py
@@ -3754,7 +3754,7 @@ def vlines(self, x, ymin, ymax, colors='k', linestyles='solid',
                                     linestyles=linestyles, label=label)
         self.add_collection(coll)
         coll.update(kwargs)
-      
+
         if len(x) > 0:
             minx = min( x )
             maxx = max( x )
@@ -6955,16 +6955,18 @@ def pcolor(self, *args, **kwargs):
             A :class:`matplotlib.colors.Colormap` instance. If *None*, use
             rc settings.
 
-          norm: [ *None* | Normalize ]
+          *norm*: [ *None* | Normalize ]
             An :class:`matplotlib.colors.Normalize` instance is used
             to scale luminance data to 0,1. If *None*, defaults to
             :func:`normalize`.
 
           *vmin*/*vmax*: [ *None* | scalar ]
             *vmin* and *vmax* are used in conjunction with *norm* to
-            normalize luminance data.  If either are *None*, the min
-            and max of the color array *C* is used.  If you pass a
-            *norm* instance, *vmin* and *vmax* will be ignored.
+            normalize luminance data.  If either is *None*, it
+            is autoscaled to the respective min or max
+            of the color array *C*.  If not *None*, *vmin* or
+            *vmax* passed in here override any pre-existing values
+            supplied in the *norm* instance.
 
           *shading*: [ 'flat' | 'faceted' ]
             If 'faceted', a black grid is drawn around each rectangle; if
@@ -7121,10 +7123,8 @@ def pcolor(self, *args, **kwargs):
         if norm is not None: assert(isinstance(norm, mcolors.Normalize))
         collection.set_cmap(cmap)
         collection.set_norm(norm)
-        if vmin is not None or vmax is not None:
-            collection.set_clim(vmin, vmax)
-        else:
-            collection.autoscale_None()
+        collection.set_clim(vmin, vmax)
+        collection.autoscale_None()
         self.grid(False)
 
         x = X.compressed()
@@ -7167,9 +7167,11 @@ def pcolormesh(self, *args, **kwargs):
 
           *vmin*/*vmax*: [ *None* | scalar ]
             *vmin* and *vmax* are used in conjunction with *norm* to
-            normalize luminance data.  If either are *None*, the min
-            and max of the color array *C* is used.  If you pass a
-            *norm* instance, *vmin* and *vmax* will be ignored.
+            normalize luminance data.  If either is *None*, it
+            is autoscaled to the respective min or max
+            of the color array *C*.  If not *None*, *vmin* or
+            *vmax* passed in here override any pre-existing values
+            supplied in the *norm* instance.
 
           *shading*: [ 'flat' | 'gouraud' ]
             'flat' indicates a solid color for each quad.  When
@@ -7235,10 +7237,8 @@ def pcolormesh(self, *args, **kwargs):
         if norm is not None: assert(isinstance(norm, mcolors.Normalize))
         collection.set_cmap(cmap)
         collection.set_norm(norm)
-        if vmin is not None or vmax is not None:
-            collection.set_clim(vmin, vmax)
-        else:
-            collection.autoscale_None()
+        collection.set_clim(vmin, vmax)
+        collection.autoscale_None()
 
         self.grid(False)
 
diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
index bd06b4993f43..3150ecae4821 100644
--- a/lib/matplotlib/contour.py
+++ b/lib/matplotlib/contour.py
@@ -742,6 +742,8 @@ def __init__(self, ax, *args, **kwargs):
         cmap = kwargs.get('cmap', None)
         self.colors = kwargs.get('colors', None)
         norm = kwargs.get('norm', None)
+        vmin = kwargs.get('vmin', None)
+        vmax = kwargs.get('vmax', None)
         self.extend = kwargs.get('extend', 'neither')
         self.antialiased = kwargs.get('antialiased', None)
         if self.antialiased is None and self.filled:
@@ -789,7 +791,11 @@ def __init__(self, ax, *args, **kwargs):
         kw = {'cmap': cmap}
         if norm is not None:
             kw['norm'] = norm
-        cm.ScalarMappable.__init__(self, **kw) # sets self.cmap;
+        cm.ScalarMappable.__init__(self, **kw) # sets self.cmap, norm if needed;
+        if vmin is not None:
+            self.norm.vmin = vmin
+        if vmax is not None:
+            self.norm.vmax = vmax
         self._process_colors()
 
         self.allsegs, self.allkinds = self._get_allsegs_and_allkinds()
@@ -1488,6 +1494,12 @@ def _initialize_x_y(self, z):
             scaling data values to colors. If *norm* is *None* and
             *colors* is *None*, the default linear scaling is used.
 
+          *vmin*/*vmax*: [ *None* | scalar ]
+            If not *None*, either or both of these values will be
+            supplied to the :class:`matplotlib.colors.Normalize`
+            instance, overriding the default color scaling based on
+            *levels*.
+
           *levels*: [level0, level1, ..., leveln]
             A list of floating point numbers indicating the level
             curves to draw; eg to draw just the zero contour pass

From 413581cac500ec22e10579eff293037154fe4b56 Mon Sep 17 00:00:00 2001
From: Eric Firing <efiring@hawaii.edu>
Date: Mon, 6 Aug 2012 14:36:48 -1000
Subject: [PATCH 5/6] colorbar: improve handling of manually-set vmin, vmax in
 contourf, contour

Formerly, colorbar would not place ticks at contour levels outside the
vmin-vmax range, and would not add line contour marks at such levels.
---
 lib/matplotlib/colorbar.py | 49 ++++++++++++++++++++++----------------
 lib/matplotlib/contour.py  |  2 +-
 2 files changed, 30 insertions(+), 21 deletions(-)

diff --git a/lib/matplotlib/colorbar.py b/lib/matplotlib/colorbar.py
index 2f8a72092300..ba0f4f19c2df 100644
--- a/lib/matplotlib/colorbar.py
+++ b/lib/matplotlib/colorbar.py
@@ -470,11 +470,18 @@ def _add_solids(self, X, Y, C):
     def add_lines(self, levels, colors, linewidths):
         '''
         Draw lines on the colorbar.
+
+        *colors* and *linewidths* must be scalars or
+        sequences the same length as *levels*.
         '''
-        N = len(levels)
-        dummy, y = self._locate(levels)
-        if len(y) != N:
-            raise ValueError("levels are outside colorbar range")
+        y = self._locate(levels)
+        igood = (y < 1.001) & (y > -0.001)
+        y = y[igood]
+        if cbook.iterable(colors):
+            colors = np.asarray(colors)[igood]
+        if cbook.iterable(linewidths):
+            linewidths = np.asarray(linewidths)[igood]
+        N = len(y)
         x = np.array([0.0, 1.0])
         X, Y = np.meshgrid(x,y)
         if self.orientation == 'vertical':
@@ -528,7 +535,10 @@ def _ticker(self):
         locator.axis.get_minpos = lambda : intv[0]
         formatter.axis.get_minpos = lambda : intv[0]
         b = np.array(locator())
-        b, ticks = self._locate(b)
+        ticks = self._locate(b)
+        inrange = (ticks > -0.001) & (ticks < 1.001)
+        ticks = ticks[inrange]
+        b = b[inrange]
         formatter.set_locs(b)
         ticklabels = [formatter(t, i) for i, t in enumerate(b)]
         offset_string = formatter.get_offset()
@@ -746,37 +756,36 @@ def _mesh(self):
 
     def _locate(self, x):
         '''
-        Given a possible set of color data values, return the ones
-        within range, together with their corresponding colorbar
-        data coordinates.
+        Given a set of color data values, return their
+        corresponding colorbar data coordinates.
         '''
         if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
             b = self._boundaries
             xn = x
-            xout = x
         else:
             # Do calculations using normalized coordinates so
             # as to make the interpolation more accurate.
             b = self.norm(self._boundaries, clip=False).filled()
-            # We do our own clipping so that we can allow a tiny
-            # bit of slop in the end point ticks to allow for
-            # floating point errors.
             xn = self.norm(x, clip=False).filled()
-            in_cond = (xn > -0.001) & (xn < 1.001)
-            xn = np.compress(in_cond, xn)
-            xout = np.compress(in_cond, x)
-        # The rest is linear interpolation with clipping.
+        # The rest is linear interpolation with extrapolation at ends.
         y = self._y
         N = len(b)
-        ii = np.minimum(np.searchsorted(b, xn), N-1)
-        i0 = np.maximum(ii - 1, 0)
+        ii = np.searchsorted(b, xn)
+        i0 = ii - 1
+        itop = (ii == N)
+        ibot = (ii == 0)
+        i0[itop] -= 1
+        ii[itop] -= 1
+        i0[ibot] += 1
+        ii[ibot] += 1
+
         #db = b[ii] - b[i0]
         db = np.take(b, ii) - np.take(b, i0)
-        db = np.where(i0==ii, 1.0, db)
         #dy = y[ii] - y[i0]
         dy = np.take(y, ii) - np.take(y, i0)
         z = np.take(y, i0) + (xn-np.take(b,i0))*dy/db
-        return xout, z
+
+        return z
 
     def set_alpha(self, alpha):
         self.alpha = alpha
diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
index 3150ecae4821..b7b166a011a5 100644
--- a/lib/matplotlib/contour.py
+++ b/lib/matplotlib/contour.py
@@ -1091,7 +1091,7 @@ def _process_colors(self):
         line contours, when added to filled contours, take on
         colors that are consistent with those of the filled regions;
         for example, a contour line on the boundary between two
-        regions will have a color intermediate between the those
+        regions will have a color intermediate between those
         of the regions.
 
         """

From 08902460597bb79acdc00ced8fa1287feec865b3 Mon Sep 17 00:00:00 2001
From: Eric Firing <efiring@hawaii.edu>
Date: Mon, 6 Aug 2012 17:56:40 -1000
Subject: [PATCH 6/6] colorbar: allow more than one set of contour line marks

---
 lib/matplotlib/colorbar.py                    |    25 +-
 lib/matplotlib/contour.py                     |    12 +-
 .../test_axes/contour_colorbar.pdf            |   Bin 0 -> 265253 bytes
 .../test_axes/contour_colorbar.png            |   Bin 0 -> 151031 bytes
 .../test_axes/contour_colorbar.svg            | 20114 ++++++++++++++++
 lib/matplotlib/tests/test_axes.py             |    38 +-
 6 files changed, 20167 insertions(+), 22 deletions(-)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/contour_colorbar.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/contour_colorbar.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/contour_colorbar.svg

diff --git a/lib/matplotlib/colorbar.py b/lib/matplotlib/colorbar.py
index ba0f4f19c2df..149d536d742a 100644
--- a/lib/matplotlib/colorbar.py
+++ b/lib/matplotlib/colorbar.py
@@ -255,7 +255,7 @@ def __init__(self, ax, cmap=None,
         self.filled = filled
         self.extendfrac = extendfrac
         self.solids = None
-        self.lines = None
+        self.lines = list()
         self.outline = None
         self.patch = None
         self.dividers = None
@@ -467,14 +467,18 @@ def _add_solids(self, X, Y, C):
                               )
             self.ax.add_collection(self.dividers)
 
-    def add_lines(self, levels, colors, linewidths):
+    def add_lines(self, levels, colors, linewidths, erase=True):
         '''
         Draw lines on the colorbar.
 
         *colors* and *linewidths* must be scalars or
         sequences the same length as *levels*.
+
+        Set *erase* to False to add lines without first
+        removing any previously added lines.
         '''
         y = self._locate(levels)
+        nlevs = len(levels)
         igood = (y < 1.001) & (y > -0.001)
         y = y[igood]
         if cbook.iterable(colors):
@@ -490,9 +494,10 @@ def add_lines(self, levels, colors, linewidths):
             xy = [zip(Y[i], X[i]) for i in xrange(N)]
         col = collections.LineCollection(xy, linewidths=linewidths)
 
-        if self.lines:
-            self.lines.remove()
-        self.lines = col
+        if erase and self.lines:
+            for lc in self.lines.pop():
+                lc.remove()
+        self.lines.append(col)
         col.set_color(colors)
         self.ax.add_collection(col)
 
@@ -843,10 +848,13 @@ def on_mappable_changed(self, mappable):
         self.set_clim(mappable.get_clim())
         self.update_normal(mappable)
 
-    def add_lines(self, CS):
+    def add_lines(self, CS, erase=True):
         '''
         Add the lines from a non-filled
         :class:`~matplotlib.contour.ContourSet` to the colorbar.
+
+        Set *erase* to False if these lines should be added to
+        any pre-existing lines.
         '''
         if not isinstance(CS, contour.ContourSet) or CS.filled:
             raise ValueError('add_lines is only for a ContourSet of lines')
@@ -860,7 +868,8 @@ def add_lines(self, CS):
         #tcolors = [col.get_colors()[0] for col in CS.collections]
         #tlinewidths = [col.get_linewidth()[0] for lw in CS.collections]
         #print 'tlinewidths:', tlinewidths
-        ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths)
+        ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths,
+                                erase=erase)
 
     def update_normal(self, mappable):
         '''
@@ -893,7 +902,7 @@ def update_bruteforce(self, mappable):
         self.outline = None
         self.patch = None
         self.solids = None
-        self.lines = None
+        self.lines = list()
         self.dividers = None
         self.set_alpha(mappable.get_alpha())
         self.cmap = mappable.cmap
diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
index b7b166a011a5..5cfd7a074d07 100644
--- a/lib/matplotlib/contour.py
+++ b/lib/matplotlib/contour.py
@@ -1494,7 +1494,7 @@ def _initialize_x_y(self, z):
             scaling data values to colors. If *norm* is *None* and
             *colors* is *None*, the default linear scaling is used.
 
-          *vmin*/*vmax*: [ *None* | scalar ]
+          *vmin*, *vmax*: [ *None* | scalar ]
             If not *None*, either or both of these values will be
             supplied to the :class:`matplotlib.colors.Normalize`
             instance, overriding the default color scaling based on
@@ -1561,18 +1561,16 @@ def _initialize_x_y(self, z):
             linewidths in the order specified
 
           *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
-            If *linestyles* is *None*, the 'solid' is used.
+            If *linestyles* is *None*, the default is 'solid' unless
+            the lines are monochrome.  In that case, negative
+            contours will take their linestyle from the ``matplotlibrc``
+            ``contour.negative_linestyle`` setting.
 
             *linestyles* can also be an iterable of the above strings
             specifying a set of linestyles to be used. If this
             iterable is shorter than the number of contour levels
             it will be repeated as necessary.
 
-            If contour is using a monochrome colormap and the contour
-            level is less than 0, then the linestyle specified
-            in ``contour.negative_linestyle`` in ``matplotlibrc``
-            will be used.
-
         contourf-only keyword arguments:
 
           *nchunk*: [ 0 | integer ]
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/contour_colorbar.pdf b/lib/matplotlib/tests/baseline_images/test_axes/contour_colorbar.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..35c75cc90bd554b28327d6b76d4187445b8f20ad
GIT binary patch
literal 265253
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diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index cd1769095e07..378a4c9e3436 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -666,14 +666,15 @@ def test_hist_log():
     ax = fig.add_subplot(111)
     ax.hist(data, fill=False, log=True)
 
+def contour_dat():
+    x = np.linspace(-3, 5, 150)
+    y = np.linspace(-3, 5, 120)
+    z = np.cos(x) + np.sin(y[:, np.newaxis])
+    return x, y, z
+
 @image_comparison(baseline_images=['contour_hatching'])
 def test_contour_hatching():
-    x = np.linspace(-3, 5, 150).reshape(1, -1)
-    y = np.linspace(-3, 5, 120).reshape(-1, 1)
-    z = np.cos(x) + np.sin(y)
-
-    # we no longer need x and y to be 2 dimensional, so flatten them.
-    x, y = x.flatten(), y.flatten()
+    x, y, z = contour_dat()
 
     fig = plt.figure()
     ax = fig.add_subplot(111)
@@ -681,6 +682,29 @@ def test_contour_hatching():
                       cmap=plt.get_cmap('gray'),
                       extend='both', alpha=0.5)
 
+@image_comparison(baseline_images=['contour_colorbar'])
+def test_contour_colorbar():
+    x, y, z = contour_dat()
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    cs = ax.contourf(x, y, z, levels=np.arange(-1.8, 1.801, 0.2),
+                      cmap=plt.get_cmap('RdBu'),
+                      vmin=-0.6,
+                      vmax=0.6,
+                      extend='both')
+    cs1 = ax.contour(x, y, z, levels=np.arange(-2.2, -0.599, 0.2),
+                              colors=['y'],
+                              linestyles='solid',
+                              linewidths=2)
+    cs2 = ax.contour(x, y, z, levels=np.arange(0.6, 2.2, 0.2),
+                              colors=['c'],
+                              linewidths=2)
+    cbar = fig.colorbar(cs, ax=ax)
+    cbar.add_lines(cs1)
+    cbar.add_lines(cs2, erase=False)
+
+
 @image_comparison(baseline_images=['hist2d'])
 def test_hist2d():
     np.random.seed(0)
@@ -765,7 +789,7 @@ def test_log_scales():
     plt.plot(np.log(np.linspace(0.1, 100)))
     ax.set_yscale('log', basey=5.5)
     ax.set_xscale('log', basex=9.0)
-    
+
 
 @image_comparison(baseline_images=['stackplot_test_image'])
 def test_stackplot():