From d9f71fbbc8d17c9cd492ac59d8c4537deac9efc6 Mon Sep 17 00:00:00 2001
From: Per <wisalam@live.com>
Date: Sat, 29 Mar 2014 20:42:13 -0400
Subject: [PATCH 01/22] Added functionality for plotting the actual violin
 bodies to violinplot.

---
 lib/matplotlib/axes/_axes.py | 82 ++++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index e0949cc9e8b9..b7ad7ecf9730 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6725,6 +6725,88 @@ def matshow(self, Z, **kwargs):
                                                  integer=True))
         return im
 
+
+    def violinplot(self, x, positions=None, width=0.5):
+        """
+        Make a violin plot.
+
+        Call signature::
+
+          violinplot(x, positions=None)
+
+        Make a violin plot for each column of *x* or each
+        vector in sequence *x*.  Each filled area extends to represent the
+        entire data range, with three lines at the mean, the minimum, and
+        the maximum.
+
+        Parameters
+        ----------
+
+          x : Array or a sequence of vectors.
+            The input data.
+
+          positions : array-like, default = [1, 2, ..., n]
+            Sets the positions of the violins. The ticks and limits are
+            automatically set to match the positions.
+
+          width : array-like, default = 0.5
+            Either a scalar or a vector that sets the maximal width of
+            each violin. The default is 0.5, which uses about half of the
+            available horizontal space.
+
+        Returns
+        -------
+
+        A dictionary mapping each component of the violinplot to a list of the
+        corresponding collection instances created. The dictionary has
+        the following keys:
+
+            - bodies: A list of the 
+              :class:`matplotlib.collections.PolyCollection` instances
+              containing the filled area of each violin.
+            - means: A list of the :class:`matplotlib.lines.Line2D` instances
+              created to identify the mean values for each of the violins.
+            - caps: A list of the :class:`matplotlib.lines.Line2D` instances
+              created to identify the extremal values of each violin's
+              data set.
+
+        """
+
+        bodies = []
+        means = []
+        caps = []
+
+        if positions == None:
+            positions = range(1, len(x) + 1)
+        elif len(positions) != len(x):
+            raise ValueError(datashape_message.format("positions"))
+
+        # TODO: Use kde estimation function on x
+        # These numbers are contrived
+        coords = np.arange(0.0, np.pi, np.pi/100.)
+        datasets = map(lambda i: np.sin(coords) ** i, range(1,len(x) + 1))
+        
+        for d,x in zip(datasets,positions):
+            # Since each data point p is plotted from x-p to x+p,
+            # we need to scale it by an additional 0.5 factor so that we get
+            # correct width in the end.
+            d = 0.5 * widths * d/d.max()
+            m = d.min() # This should actually be the min for the dataset
+            M = d.max() # likewise
+            # bodies += [self.fill_betweenx(np.arange(m,M,(M-m)/100.),
+            bodies += [self.fill_betweenx(coords,
+                                          -d+x,
+                                          d+x,
+                                          facecolor='y',
+                                          alpha=0.3)]
+
+        return {
+            'bodies' : bodies,
+            'means' : means,
+            'caps' : caps
+        }
+
+
     def tricontour(self, *args, **kwargs):
         return mtri.tricontour(self, *args, **kwargs)
     tricontour.__doc__ = mtri.TriContourSet.tricontour_doc

From da40c9db23825cef9b431f7f05f188d6519677b6 Mon Sep 17 00:00:00 2001
From: Kevin Chan <kvii.chan@gmail.com>
Date: Sat, 29 Mar 2014 22:32:09 -0400
Subject: [PATCH 02/22] Added ksdensity (KDE) function in mlab and adapted
 _axes violinplot function to accept new KDE. Added basic violinplot demo in
 examples

---
 examples/statistics/violinplot_demo.py |  51 +++++++++
 lib/matplotlib/axes/_axes.py           |  43 ++++----
 lib/matplotlib/mlab.py                 | 145 +++++++++++++++++++++++++
 lib/matplotlib/pylab.py                |   8 +-
 4 files changed, 223 insertions(+), 24 deletions(-)
 create mode 100644 examples/statistics/violinplot_demo.py

diff --git a/examples/statistics/violinplot_demo.py b/examples/statistics/violinplot_demo.py
new file mode 100644
index 000000000000..840715ae9537
--- /dev/null
+++ b/examples/statistics/violinplot_demo.py
@@ -0,0 +1,51 @@
+"""
+Demo of the new violinplot functionality
+"""
+
+import random
+import numpy as np
+import matplotlib.pyplot as plt
+
+# fake data
+fs = 10 # fontsize
+pos = range(5)
+data = [np.random.normal(size=100) for i in pos]
+
+# TODO: future customizability dicts go here
+
+# (From boxplot demo)
+# demonstrate how to customize the display different elements: 
+# boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
+# flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
+#                   linestyle='none')
+# medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')
+# meanpointprops = dict(marker='D', markeredgecolor='black',
+#                       markerfacecolor='firebrick')
+# meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
+
+fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6,6))
+
+axes[0, 0].violinplot(data, pos, width=0.1)
+axes[0, 0].set_title('Custom violinplot 1', fontsize=fs)
+
+axes[0, 1].violinplot(data, pos, width=0.3)
+axes[0, 1].set_title('Custom violinplot 2', fontsize=fs)
+
+axes[0, 2].violinplot(data, pos, width=0.5)
+axes[0, 2].set_title('Custom violinplot 3', fontsize=fs)
+
+axes[1, 0].violinplot(data, pos, width=0.7)
+axes[1, 0].set_title('Custom violinplot 4', fontsize=fs)
+
+axes[1, 1].violinplot(data, pos, width=0.9)
+axes[1, 1].set_title('Custom violinplot 5', fontsize=fs)
+
+axes[1, 2].violinplot(data, pos, width=1.1)
+axes[1, 2].set_title('Custom violinplot 6', fontsize=fs)
+
+for ax in axes.flatten():
+    ax.set_yticklabels([])
+
+fig.suptitle("Violin Plotting Examples")
+fig.subplots_adjust(hspace=0.4)
+plt.show()
\ No newline at end of file
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index b7ad7ecf9730..1745e77b1184 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6725,24 +6725,23 @@ def matshow(self, Z, **kwargs):
                                                  integer=True))
         return im
 
-
-    def violinplot(self, x, positions=None, width=0.5):
+    def violinplot(self, dataset, positions=None, width=0.5):
         """
         Make a violin plot.
 
         Call signature::
 
-          violinplot(x, positions=None)
+          violinplot(dataset, positions=None)
 
-        Make a violin plot for each column of *x* or each
-        vector in sequence *x*.  Each filled area extends to represent the
+        Make a violin plot for each column of *dataset* or each vector in 
+        sequence *dataset*.  Each filled area extends to represent the
         entire data range, with three lines at the mean, the minimum, and
         the maximum.
 
         Parameters
         ----------
 
-          x : Array or a sequence of vectors.
+          dataset : Array or a sequence of vectors.
             The input data.
 
           positions : array-like, default = [1, 2, ..., n]
@@ -6777,26 +6776,28 @@ def violinplot(self, x, positions=None, width=0.5):
         caps = []
 
         if positions == None:
-            positions = range(1, len(x) + 1)
-        elif len(positions) != len(x):
+            positions = range(1, len(dataset) + 1)
+        elif len(positions) != len(dataset):
             raise ValueError(datashape_message.format("positions"))
 
-        # TODO: Use kde estimation function on x
-        # These numbers are contrived
-        coords = np.arange(0.0, np.pi, np.pi/100.)
-        datasets = map(lambda i: np.sin(coords) ** i, range(1,len(x) + 1))
-        
-        for d,x in zip(datasets,positions):
-            # Since each data point p is plotted from x-p to x+p,
+        for d,p in zip(dataset,positions):            
+            # Calculate the kernel density
+            kde = mlab.ksdensity(d)
+            m = kde['xmin']
+            M = kde['xmax']
+            mean = kde['mean']
+            median = kde['median']
+            v = kde['result']
+            coords = np.arange(m,M,(M-m)/100.)
+
+            # Since each data point p is plotted from v-p to v+p,
             # we need to scale it by an additional 0.5 factor so that we get
             # correct width in the end.
-            d = 0.5 * widths * d/d.max()
-            m = d.min() # This should actually be the min for the dataset
-            M = d.max() # likewise
-            # bodies += [self.fill_betweenx(np.arange(m,M,(M-m)/100.),
+            v = 0.5 * width * v/v.max()
+
             bodies += [self.fill_betweenx(coords,
-                                          -d+x,
-                                          d+x,
+                                          -v+p,
+                                          v+p,
                                           facecolor='y',
                                           alpha=0.3)]
 
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index 468058d3229e..ee4b5439a908 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3656,6 +3656,151 @@ def stineman_interp(xi,x,y,yp=None):
                                   1/(dy1+dy2),))
     return yi
 
+def ksdensity(dataset, bw_method=None):
+    """
+    Representation of a kernel-density estimate using Gaussian kernels.
+
+    Call signature::
+    xmin, xmax, result = ksdensity(dataset, 'scott')
+
+    Parameters
+    ----------
+    dataset : array_like
+        Datapoints to estimate from. In case of univariate data this is a 1-D
+        array, otherwise a 2-D array with shape (# of dims, # of data).
+    bw_method : str or scalar, optional
+        The method used to calculate the estimator bandwidth.  This can be
+        'scott', 'silverman', or a scalar constant.  If a scalar, this will
+        be used directly as `kde.factor`. If None (default), 'scott' is used.
+
+    Attributes
+    ----------
+    dataset : ndarray
+        The dataset with which `ksdensity` was initialized.
+    d : int
+        Number of dimensions.
+    n : int
+        Number of datapoints.
+    factor : float
+        The bandwidth factor, obtained from `kde.covariance_factor`, with which
+        the covariance matrix is multiplied.
+    covariance : ndarray
+        The covariance matrix of `dataset`, scaled by the calculated bandwidth
+        (`kde.factor`).
+    inv_cov : ndarray
+        The inverse of `covariance`.
+
+    Returns
+    -------
+    A dictionary mapping each various aspects of the computed KDE.
+    The dictionary has the following keys:
+
+        xmin : number
+            The min of the input dataset
+        xmax : number
+            The max of the input dataset
+        mean : number
+            The mean of the result
+        median: number
+            The median of the result
+        result: (# of points,)-array
+            The array of the evaluated PDF estimation
+
+    Raises
+    ------
+    ValueError : if the dimensionality of the input points is different than
+                 the dimensionality of the KDE.
+
+    """
+
+    # This implementation with minor modification was too good to pass up.
+    # from scipy: https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py 
+
+    dataset = np.atleast_2d(dataset)
+    xmin = dataset.min()
+    xmax = dataset.max()
+
+    if not dataset.size > 1:
+        raise ValueError("`dataset` input should have multiple elements.")
+
+    d, n = dataset.shape
+
+    # ----------------------------------------------
+    # Set Bandwidth, defaulted to Scott's Factor
+    # ----------------------------------------------
+    scotts_factor = lambda: np.power(n, -1./(d+4))
+    silverman_factor = lambda: np.power(n*(d+2.0)/4.0, -1./(d+4))
+
+    # Default method to calculate bandwidth, can be overwritten by subclass
+    covariance_factor = scotts_factor
+
+    if bw_method is None:
+        pass
+    elif bw_method == 'scott':
+        covariance_factor = scotts_factor
+    elif bw_method == 'silverman':
+        covariance_factor = silverman_factor
+    elif np.isscalar(bw_method) and not isinstance(bw_method, string_types):
+        covariance_factor = lambda: bw_method
+    else:
+        msg = "`bw_method` should be 'scott', 'silverman', or a scalar"
+        raise ValueError(msg)
+
+    # ---------------------------------------------------------------
+    # Computes covariance matrix for each Gaussian kernel with factor
+    # ---------------------------------------------------------------
+    factor = covariance_factor()
+
+    # Cache covariance and inverse covariance of the data
+    data_covariance = np.atleast_2d(np.cov(dataset, rowvar=1,bias=False))
+    data_inv_cov = np.linalg.inv(data_covariance)
+
+    covariance = data_covariance * factor**2
+    inv_cov = data_inv_cov / factor**2
+    norm_factor = np.sqrt(np.linalg.det(2*np.pi*covariance)) * n
+
+    # ----------------------------------------------
+    # Evaluate the estimated pdf on a set of points.
+    # ----------------------------------------------
+    points = np.atleast_2d(np.arange(xmin,xmax, (xmax-xmin)/100.))
+
+    d1, m1 = points.shape
+    if d1 != d:
+        if d1 == 1 and m1 == d:
+            # points was passed in as a row vector
+            points = np.reshape(points, (d, 1))
+            m1 = 1
+        else:
+            msg = "points have dimension %s, dataset has dimension %s" % (d1, d)
+            raise ValueError(msg)
+
+    result = np.zeros((m1,), dtype=np.float)
+
+    if m1 >= n:
+        # there are more points than data, so loop over data
+        for i in range(n):
+            diff = dataset[:, i, np.newaxis] - points
+            tdiff = np.dot(inv_cov, diff)
+            energy = np.sum(diff*tdiff,axis=0) / 2.0
+            result = result + np.exp(-energy)
+    else:
+        # loop over points
+        for i in range(m):
+            diff = dataset - points[:, i, newaxis]
+            tdiff = np.dot(inv_cov, diff)
+            energy = np.sum(diff * tdiff, axis=0) / 2.0
+            result[i] = np.sum(np.exp(-energy), axis=0)
+
+    result = result / norm_factor
+
+    return {
+        'xmin' : xmin,
+        'xmax' : xmax,
+        'mean' : np.mean(result),
+        'median' : np.median(result),
+        'result' : result
+    }
+
 ##################################################
 # Code related to things in and around polygons
 ##################################################
diff --git a/lib/matplotlib/pylab.py b/lib/matplotlib/pylab.py
index dd0a52f12276..86fe482ad65f 100644
--- a/lib/matplotlib/pylab.py
+++ b/lib/matplotlib/pylab.py
@@ -23,6 +23,7 @@
   broken_barh - a set of horizontal bars with gaps
   box       - set the axes frame on/off state
   boxplot   - make a box and whisker plot
+  violinplot - make a violin plot
   cla       - clear current axes
   clabel    - label a contour plot
   clf       - clear a figure window
@@ -162,8 +163,8 @@
 
 _Statistics
 
-  amax       - the maximum along dimension m
-  amin       - the minimum along dimension m
+  amax      - the maximum along dimension m
+  amin      - the minimum along dimension m
   corrcoef  - correlation coefficient
   cov       - covariance matrix
   mean      - the mean along dimension m
@@ -172,7 +173,8 @@
   prod      - the product along dimension m
   ptp       - the max-min along dimension m
   std       - the standard deviation along dimension m
-  asum       - the sum along dimension m
+  asum      - the sum along dimension m
+  ksdensity - the kernel density estimate
 
 _Time series analysis
 

From b9d7e036a14f63e9717a91f9c96591e8c4b90fbf Mon Sep 17 00:00:00 2001
From: solvents <wisalam@live.com>
Date: Sun, 30 Mar 2014 21:40:53 -0400
Subject: [PATCH 03/22] Added statistics lines to the violinplot function.

---
 lib/matplotlib/axes/_axes.py | 95 +++++++++++++++++++++++++++++++-----
 lib/matplotlib/mlab.py       | 55 +++++++++++----------
 2 files changed, 110 insertions(+), 40 deletions(-)

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 1745e77b1184..716c00e39749 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6725,7 +6725,8 @@ def matshow(self, Z, **kwargs):
                                                  integer=True))
         return im
 
-    def violinplot(self, dataset, positions=None, width=0.5):
+    def violinplot(self, dataset, positions=None, widths=0.5, showmeans=False,
+                   showextrema=True, showmedians=False):
         """
         Make a violin plot.
 
@@ -6748,11 +6749,20 @@ def violinplot(self, dataset, positions=None, width=0.5):
             Sets the positions of the violins. The ticks and limits are
             automatically set to match the positions.
 
-          width : array-like, default = 0.5
+          widths : array-like, default = 0.5
             Either a scalar or a vector that sets the maximal width of
             each violin. The default is 0.5, which uses about half of the
             available horizontal space.
 
+          showmeans : bool, default = False
+            If true, will toggle rendering of the means.
+
+          showextrema : bool, default = True
+            If true, will toggle rendering of the extrema.
+
+          showmedians : bool, default = False
+            If true, will toggle rendering of the medians.
+
         Returns
         -------
 
@@ -6763,24 +6773,58 @@ def violinplot(self, dataset, positions=None, width=0.5):
             - bodies: A list of the 
               :class:`matplotlib.collections.PolyCollection` instances
               containing the filled area of each violin.
-            - means: A list of the :class:`matplotlib.lines.Line2D` instances
-              created to identify the mean values for each of the violins.
-            - caps: A list of the :class:`matplotlib.lines.Line2D` instances
-              created to identify the extremal values of each violin's
-              data set.
+            - means: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the mean values of each of the violin's
+              distribution.
+            - mins: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the bottom of each violin's distribution.
+            - maxes: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the top of each violin's distribution.
+            - bars: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the centers of each violin's distribution.
+            - medians: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the median values of each of the violin's
+              distribution.
 
         """
 
-        bodies = []
+        # Statistical quantities to be plotted on the violins
         means = []
-        caps = []
+        mins = []
+        maxes = []
+        medians = []
+
+        # Collections to be returned
+        bodies = []
+        cmeans = None
+        cmaxes = None
+        cmins = None
+        cbars = None
+        cmedians = None
 
+        # Validate positions
         if positions == None:
             positions = range(1, len(dataset) + 1)
         elif len(positions) != len(dataset):
             raise ValueError(datashape_message.format("positions"))
 
-        for d,p in zip(dataset,positions):            
+        # Validate widths
+        if np.isscalar(widths):
+            widths = [widths] * len(dataset)
+        elif len(widths) != len(dataset):
+            raise ValueError(datashape_message.format("widths"))
+
+        # Calculate mins and maxes for statistics lines
+        pmins = -0.25 * np.array(widths) + positions
+        pmaxes = 0.25 * np.array(widths) + positions
+
+        # Check hold status
+        if not self._hold:
+            self.cla()
+        holdStatus = self._hold
+
+        # Render violins
+        for d,p,w in zip(dataset,positions,widths):            
             # Calculate the kernel density
             kde = mlab.ksdensity(d)
             m = kde['xmin']
@@ -6793,7 +6837,7 @@ def violinplot(self, dataset, positions=None, width=0.5):
             # Since each data point p is plotted from v-p to v+p,
             # we need to scale it by an additional 0.5 factor so that we get
             # correct width in the end.
-            v = 0.5 * width * v/v.max()
+            v = 0.5 * w * v/v.max()
 
             bodies += [self.fill_betweenx(coords,
                                           -v+p,
@@ -6801,10 +6845,35 @@ def violinplot(self, dataset, positions=None, width=0.5):
                                           facecolor='y',
                                           alpha=0.3)]
 
+            means.append(mean)
+            mins.append(m)
+            maxes.append(M)
+            medians.append(median)
+
+        # Render means
+        if showmeans:
+            cmeans = self.hlines(means, pmins, pmaxes, colors='r')
+
+        # Render extrema
+        if showextrema:
+            cmaxes = self.hlines(maxes, pmins, pmaxes, colors='r')
+            cmins = self.hlines(mins, pmins, pmaxes, colors='r')
+            cbars = self.vlines(positions, mins, maxes, colors='r')
+
+        # Render medians
+        if showmedians:
+            cmedians = self.hlines(medians, pmins, pmaxes, colors='r')
+
+        # Reset hold
+        self.hold(holdStatus)
+
         return {
             'bodies' : bodies,
-            'means' : means,
-            'caps' : caps
+            'means' : cmeans,
+            'mins' : cmins,
+            'maxes' : cmaxes,
+            'bars' : cbars,
+            'medians' : cmedians
         }
 
 
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index ee4b5439a908..af8cc9a79734 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3661,7 +3661,7 @@ def ksdensity(dataset, bw_method=None):
     Representation of a kernel-density estimate using Gaussian kernels.
 
     Call signature::
-    xmin, xmax, result = ksdensity(dataset, 'scott')
+    kde_dict = ksdensity(dataset, 'silverman')
 
     Parameters
     ----------
@@ -3714,22 +3714,22 @@ def ksdensity(dataset, bw_method=None):
     """
 
     # This implementation with minor modification was too good to pass up.
-    # from scipy: https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py 
+    # from scipy: https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py
 
-    dataset = np.atleast_2d(dataset)
+    dataset = np.array(np.atleast_2d(dataset))
     xmin = dataset.min()
     xmax = dataset.max()
 
     if not dataset.size > 1:
         raise ValueError("`dataset` input should have multiple elements.")
 
-    d, n = dataset.shape
+    dim, num_dp = dataset.shape
 
     # ----------------------------------------------
     # Set Bandwidth, defaulted to Scott's Factor
     # ----------------------------------------------
-    scotts_factor = lambda: np.power(n, -1./(d+4))
-    silverman_factor = lambda: np.power(n*(d+2.0)/4.0, -1./(d+4))
+    scotts_factor = lambda: np.power(num_dp, -1./(dim+4))
+    silverman_factor = lambda: np.power(num_dp*(dim+2.0)/4.0, -1./(dim+4))
 
     # Default method to calculate bandwidth, can be overwritten by subclass
     covariance_factor = scotts_factor
@@ -3740,7 +3740,7 @@ def ksdensity(dataset, bw_method=None):
         covariance_factor = scotts_factor
     elif bw_method == 'silverman':
         covariance_factor = silverman_factor
-    elif np.isscalar(bw_method) and not isinstance(bw_method, string_types):
+    elif np.isscalar(bw_method) and not isinstance(bw_method, six.string_types):
         covariance_factor = lambda: bw_method
     else:
         msg = "`bw_method` should be 'scott', 'silverman', or a scalar"
@@ -3752,41 +3752,42 @@ def ksdensity(dataset, bw_method=None):
     factor = covariance_factor()
 
     # Cache covariance and inverse covariance of the data
-    data_covariance = np.atleast_2d(np.cov(dataset, rowvar=1,bias=False))
+    data_covariance = np.atleast_2d(np.cov(dataset, rowvar=1, bias=False))
     data_inv_cov = np.linalg.inv(data_covariance)
 
     covariance = data_covariance * factor**2
     inv_cov = data_inv_cov / factor**2
-    norm_factor = np.sqrt(np.linalg.det(2*np.pi*covariance)) * n
+    norm_factor = np.sqrt(np.linalg.det(2*np.pi*covariance)) * num_dp
 
     # ----------------------------------------------
     # Evaluate the estimated pdf on a set of points.
     # ----------------------------------------------
-    points = np.atleast_2d(np.arange(xmin,xmax, (xmax-xmin)/100.))
+    points = np.atleast_2d(np.arange(xmin, xmax, (xmax-xmin)/100.))
 
-    d1, m1 = points.shape
-    if d1 != d:
-        if d1 == 1 and m1 == d:
+    dim_pts, num_dp_pts = np.array(points).shape
+    if dim_pts != dim:
+        if dim_pts == 1 and num_dp_pts == num_dp:
             # points was passed in as a row vector
-            points = np.reshape(points, (d, 1))
-            m1 = 1
+            points = np.reshape(points, (dim, 1))
+            num_dp_pts = 1
         else:
-            msg = "points have dimension %s, dataset has dimension %s" % (d1, d)
+            msg = "points have dimension %s,\
+                   dataset has dimension %s" % (dim_pts, dim)
             raise ValueError(msg)
 
-    result = np.zeros((m1,), dtype=np.float)
+    result = np.zeros((num_dp_pts,), dtype=np.float)
 
-    if m1 >= n:
+    if num_dp_pts >= num_dp:
         # there are more points than data, so loop over data
-        for i in range(n):
+        for i in range(num_dp):
             diff = dataset[:, i, np.newaxis] - points
             tdiff = np.dot(inv_cov, diff)
-            energy = np.sum(diff*tdiff,axis=0) / 2.0
+            energy = np.sum(diff*tdiff, axis=0) / 2.0
             result = result + np.exp(-energy)
     else:
         # loop over points
-        for i in range(m):
-            diff = dataset - points[:, i, newaxis]
+        for i in range(num_dp_pts):
+            diff = dataset - points[:, i, np.newaxis]
             tdiff = np.dot(inv_cov, diff)
             energy = np.sum(diff * tdiff, axis=0) / 2.0
             result[i] = np.sum(np.exp(-energy), axis=0)
@@ -3794,11 +3795,11 @@ def ksdensity(dataset, bw_method=None):
     result = result / norm_factor
 
     return {
-        'xmin' : xmin,
-        'xmax' : xmax,
-        'mean' : np.mean(result),
-        'median' : np.median(result),
-        'result' : result
+        'xmin': xmin,
+        'xmax': xmax,
+        'mean': np.mean(dataset),
+        'median': np.median(dataset),
+        'result': result
     }
 
 ##################################################

From 3c4c6198980c6664fee977c02c22c065f346fb32 Mon Sep 17 00:00:00 2001
From: donald <donald@donald-ThinkPad-X230.(none)>
Date: Tue, 1 Apr 2014 17:29:32 -0400
Subject: [PATCH 04/22] Added horizontal violin plot feature compatible with
 means, extremas and medians.

---
 lib/matplotlib/axes/_axes.py | 61 ++++++++++++++++++++++++++++--------
 1 file changed, 48 insertions(+), 13 deletions(-)

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 716c00e39749..fb0e21b54362 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6725,7 +6725,7 @@ def matshow(self, Z, **kwargs):
                                                  integer=True))
         return im
 
-    def violinplot(self, dataset, positions=None, widths=0.5, showmeans=False,
+    def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=False,
                    showextrema=True, showmedians=False):
         """
         Make a violin plot.
@@ -6749,6 +6749,10 @@ def violinplot(self, dataset, positions=None, widths=0.5, showmeans=False,
             Sets the positions of the violins. The ticks and limits are
             automatically set to match the positions.
 
+          vert : bool, default = True.
+            If true, creates vertical violin plot
+            Else, creates horizontal violin plot
+
           widths : array-like, default = 0.5
             Either a scalar or a vector that sets the maximal width of
             each violin. The default is 0.5, which uses about half of the
@@ -6839,30 +6843,61 @@ def violinplot(self, dataset, positions=None, widths=0.5, showmeans=False,
             # correct width in the end.
             v = 0.5 * w * v/v.max()
 
-            bodies += [self.fill_betweenx(coords,
+            # create vertical violin plot
+            if vert:
+                bodies += [self.fill_betweenx(coords,
                                           -v+p,
                                           v+p,
                                           facecolor='y',
                                           alpha=0.3)]
+            # create horizontal violin plot
+            else:
+                bodies += [self.fill_between(coords,
+                                              -v+p,
+                                              v+p,
+                                              facecolor='y',
+                                              alpha=0.3)]
 
             means.append(mean)
             mins.append(m)
             maxes.append(M)
             medians.append(median)
 
-        # Render means
-        if showmeans:
-            cmeans = self.hlines(means, pmins, pmaxes, colors='r')
+        # respective means, extrema median on vertical violin plot
+        if vert:
+            # Render means
+            if showmeans:
+                cmeans = self.hlines(means, pmins, pmaxes, colors='r')
+
+            # Render extrema
+            if showextrema:
+                cmaxes = self.hlines(maxes, pmins, pmaxes, colors='r')
+                cmins = self.hlines(mins, pmins, pmaxes, colors='r')
+                cbars = self.vlines(positions, mins, maxes, colors='r')
+
+            # Render medians
+            if showmedians:
+                cmedians = self.hlines(medians, pmins, pmaxes, colors='r')
+
+        # respective means, extrema median on horizontal violin plot
+        else:
+            # Render means
+            if showmeans:
+                cmeans = self.vlines(means, pmins, pmaxes, colors='r')
+
+            # Render extrema
+            if showextrema:
+                cmaxes = self.vlines(maxes, pmins, pmaxes, colors='r')
+                cmins = self.vlines(mins, pmins, pmaxes, colors='r')
+                cbars = self.hlines(positions, mins, maxes, colors='r')
+
+            # Render medians
+            if showmedians:
+                cmedians = self.vlines(medians, pmins, pmaxes, colors='r')
+
+
 
-        # Render extrema
-        if showextrema:
-            cmaxes = self.hlines(maxes, pmins, pmaxes, colors='r')
-            cmins = self.hlines(mins, pmins, pmaxes, colors='r')
-            cbars = self.vlines(positions, mins, maxes, colors='r')
 
-        # Render medians
-        if showmedians:
-            cmedians = self.hlines(medians, pmins, pmaxes, colors='r')
 
         # Reset hold
         self.hold(holdStatus)

From e6f1b3828350868a754987cc775b3bb07cfb1d09 Mon Sep 17 00:00:00 2001
From: Kevin Chan <kvii.chan@gmail.com>
Date: Tue, 1 Apr 2014 20:42:18 -0400
Subject: [PATCH 05/22] Fixed bug for regression test #1181 in scipy unit
 tests; ksdensity is now referred to as gaussian_kde and exists as a class in
 mlab. Fixed list comp position bug and updated examples

---
 examples/statistics/violinplot_demo.py |  20 ++-
 lib/matplotlib/axes/_axes.py           |  35 ++--
 lib/matplotlib/mlab.py                 | 216 +++++++++++++------------
 3 files changed, 149 insertions(+), 122 deletions(-)

diff --git a/examples/statistics/violinplot_demo.py b/examples/statistics/violinplot_demo.py
index 840715ae9537..93aa50e44e5f 100644
--- a/examples/statistics/violinplot_demo.py
+++ b/examples/statistics/violinplot_demo.py
@@ -8,7 +8,7 @@
 
 # fake data
 fs = 10 # fontsize
-pos = range(5)
+pos = [1,2,4,5,7,8]
 data = [np.random.normal(size=100) for i in pos]
 
 # TODO: future customizability dicts go here
@@ -25,22 +25,28 @@
 
 fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6,6))
 
-axes[0, 0].violinplot(data, pos, width=0.1)
+axes[0, 0].violinplot(data, pos, points=20, widths=0.1, showmeans=True,
+                      showextrema=True, showmedians=True)
 axes[0, 0].set_title('Custom violinplot 1', fontsize=fs)
 
-axes[0, 1].violinplot(data, pos, width=0.3)
+axes[0, 1].violinplot(data, pos, points=40, widths=0.3, showmeans=True,
+                      showextrema=True, showmedians=True)
 axes[0, 1].set_title('Custom violinplot 2', fontsize=fs)
 
-axes[0, 2].violinplot(data, pos, width=0.5)
+axes[0, 2].violinplot(data, pos, points=60, widths=0.5, showmeans=True,
+                      showextrema=True, showmedians=True)
 axes[0, 2].set_title('Custom violinplot 3', fontsize=fs)
 
-axes[1, 0].violinplot(data, pos, width=0.7)
+axes[1, 0].violinplot(data, pos, points=80, vert=False, widths=0.7,
+                      showmeans=True, showextrema=True, showmedians=True)
 axes[1, 0].set_title('Custom violinplot 4', fontsize=fs)
 
-axes[1, 1].violinplot(data, pos, width=0.9)
+axes[1, 1].violinplot(data, pos, points=100, vert=False, widths=0.9,
+                      showmeans=True, showextrema=True, showmedians=True)
 axes[1, 1].set_title('Custom violinplot 5', fontsize=fs)
 
-axes[1, 2].violinplot(data, pos, width=1.1)
+axes[1, 2].violinplot(data, pos, points=200, vert=False, widths=1.1,
+                      showmeans=True, showextrema=True, showmedians=True)
 axes[1, 2].set_title('Custom violinplot 6', fontsize=fs)
 
 for ax in axes.flatten():
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index fb0e21b54362..e7834b5c7b5a 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6725,7 +6725,7 @@ def matshow(self, Z, **kwargs):
                                                  integer=True))
         return im
 
-    def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=False,
+    def violinplot(self, dataset, positions=None, points=100, vert=True, widths=0.5, showmeans=False,
                    showextrema=True, showmedians=False):
         """
         Make a violin plot.
@@ -6748,6 +6748,9 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
           positions : array-like, default = [1, 2, ..., n]
             Sets the positions of the violins. The ticks and limits are
             automatically set to match the positions.
+          
+          points: array-like, default = 100
+            Number of points to evaluate pdf estimation for Gaussian kernel
 
           vert : bool, default = True.
             If true, creates vertical violin plot
@@ -6806,6 +6809,9 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
         cbars = None
         cmedians = None
 
+        datashape_message = ("List of violinplot statistics and `{0}` "
+                             "values must have same the length")
+
         # Validate positions
         if positions == None:
             positions = range(1, len(dataset) + 1)
@@ -6830,13 +6836,14 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
         # Render violins
         for d,p,w in zip(dataset,positions,widths):            
             # Calculate the kernel density
-            kde = mlab.ksdensity(d)
-            m = kde['xmin']
-            M = kde['xmax']
-            mean = kde['mean']
-            median = kde['median']
-            v = kde['result']
-            coords = np.arange(m,M,(M-m)/100.)
+            kde = mlab.gaussian_kde(d)
+            m = kde.dataset.min()
+            M = kde.dataset.max()
+            mean = np.mean(kde.dataset)
+            median = np.median(kde.dataset)
+            coords = np.arange(m,M,(M-m)/float(points))
+
+            v = kde.evaluate(coords)
 
             # Since each data point p is plotted from v-p to v+p,
             # we need to scale it by an additional 0.5 factor so that we get
@@ -6846,10 +6853,10 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
             # create vertical violin plot
             if vert:
                 bodies += [self.fill_betweenx(coords,
-                                          -v+p,
-                                          v+p,
-                                          facecolor='y',
-                                          alpha=0.3)]
+                                              -v+p,
+                                              v+p,
+                                              facecolor='y',
+                                              alpha=0.3)]
             # create horizontal violin plot
             else:
                 bodies += [self.fill_between(coords,
@@ -6895,10 +6902,6 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
             if showmedians:
                 cmedians = self.vlines(medians, pmins, pmaxes, colors='r')
 
-
-
-
-
         # Reset hold
         self.hold(holdStatus)
 
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index af8cc9a79734..bf5a41a1fba9 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3656,12 +3656,12 @@ def stineman_interp(xi,x,y,yp=None):
                                   1/(dy1+dy2),))
     return yi
 
-def ksdensity(dataset, bw_method=None):
+class gaussian_kde(object):    
     """
     Representation of a kernel-density estimate using Gaussian kernels.
 
     Call signature::
-    kde_dict = ksdensity(dataset, 'silverman')
+    kde = gaussian_kde(dataset, 'silverman')
 
     Parameters
     ----------
@@ -3676,10 +3676,10 @@ def ksdensity(dataset, bw_method=None):
     Attributes
     ----------
     dataset : ndarray
-        The dataset with which `ksdensity` was initialized.
-    d : int
+        The dataset with which `gaussian_kde` was initialized.
+    dim : int
         Number of dimensions.
-    n : int
+    num_dp : int
         Number of datapoints.
     factor : float
         The bandwidth factor, obtained from `kde.covariance_factor`, with which
@@ -3690,117 +3690,135 @@ def ksdensity(dataset, bw_method=None):
     inv_cov : ndarray
         The inverse of `covariance`.
 
-    Returns
+    Methods
     -------
-    A dictionary mapping each various aspects of the computed KDE.
-    The dictionary has the following keys:
-
-        xmin : number
-            The min of the input dataset
-        xmax : number
-            The max of the input dataset
-        mean : number
-            The mean of the result
-        median: number
-            The median of the result
-        result: (# of points,)-array
-            The array of the evaluated PDF estimation
-
-    Raises
-    ------
-    ValueError : if the dimensionality of the input points is different than
-                 the dimensionality of the KDE.
+    kde.evaluate(points) : ndarray
+        Evaluate the estimated pdf on a provided set of points.
+    kde(points) : ndarray
+        Same as kde.evaluate(points)
+    kde.set_bandwidth(bw_method='scott') : None
+        Computes the bandwidth, i.e. the coefficient that multiplies the data
+        covariance matrix to obtain the kernel covariance matrix.
+        .. versionadded:: 0.11.0
+    kde.covariance_factor : float
+        Computes the coefficient (`kde.factor`) that multiplies the data
+        covariance matrix to obtain the kernel covariance matrix.
+        The default is `scotts_factor`.  A subclass can overwrite this method
+        to provide a different method, or set it through a call to
+        `kde.set_bandwidth`.
 
     """
 
     # This implementation with minor modification was too good to pass up.
     # from scipy: https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py
 
-    dataset = np.array(np.atleast_2d(dataset))
-    xmin = dataset.min()
-    xmax = dataset.max()
+    def __init__(self, dataset, bw_method=None):
+        self.dataset = np.atleast_2d(dataset)
+        if not self.dataset.size > 1:
+            raise ValueError("`dataset` input should have multiple elements.")
 
-    if not dataset.size > 1:
-        raise ValueError("`dataset` input should have multiple elements.")
+        self.dim, self.num_dp = self.dataset.shape
+        self.set_bandwidth(bw_method=bw_method)
 
-    dim, num_dp = dataset.shape
+    def scotts_factor(self):
+        return np.power(self.num_dp, -1./(self.dim+4))
 
-    # ----------------------------------------------
-    # Set Bandwidth, defaulted to Scott's Factor
-    # ----------------------------------------------
-    scotts_factor = lambda: np.power(num_dp, -1./(dim+4))
-    silverman_factor = lambda: np.power(num_dp*(dim+2.0)/4.0, -1./(dim+4))
+    def silverman_factor(self):
+        return np.power(self.num_dp*(self.dim+2.0)/4.0, -1./(self.dim+4))
 
-    # Default method to calculate bandwidth, can be overwritten by subclass
+    #  Default method to calculate bandwidth, can be overwritten by subclass
     covariance_factor = scotts_factor
 
-    if bw_method is None:
-        pass
-    elif bw_method == 'scott':
-        covariance_factor = scotts_factor
-    elif bw_method == 'silverman':
-        covariance_factor = silverman_factor
-    elif np.isscalar(bw_method) and not isinstance(bw_method, six.string_types):
-        covariance_factor = lambda: bw_method
-    else:
-        msg = "`bw_method` should be 'scott', 'silverman', or a scalar"
-        raise ValueError(msg)
-
-    # ---------------------------------------------------------------
-    # Computes covariance matrix for each Gaussian kernel with factor
-    # ---------------------------------------------------------------
-    factor = covariance_factor()
-
-    # Cache covariance and inverse covariance of the data
-    data_covariance = np.atleast_2d(np.cov(dataset, rowvar=1, bias=False))
-    data_inv_cov = np.linalg.inv(data_covariance)
-
-    covariance = data_covariance * factor**2
-    inv_cov = data_inv_cov / factor**2
-    norm_factor = np.sqrt(np.linalg.det(2*np.pi*covariance)) * num_dp
-
-    # ----------------------------------------------
-    # Evaluate the estimated pdf on a set of points.
-    # ----------------------------------------------
-    points = np.atleast_2d(np.arange(xmin, xmax, (xmax-xmin)/100.))
-
-    dim_pts, num_dp_pts = np.array(points).shape
-    if dim_pts != dim:
-        if dim_pts == 1 and num_dp_pts == num_dp:
-            # points was passed in as a row vector
-            points = np.reshape(points, (dim, 1))
-            num_dp_pts = 1
+    def set_bandwidth(self, bw_method=None):
+        if bw_method is None:
+            pass
+        elif bw_method == 'scott':
+            self.covariance_factor = self.scotts_factor
+        elif bw_method == 'silverman':
+            self.covariance_factor = self.silverman_factor
+        elif np.isscalar(bw_method) and not isinstance(bw_method, six.string_types):
+            self._bw_method = 'use constant'
+            self.covariance_factor = lambda: bw_method
+        elif callable(bw_method):
+            self._bw_method = bw_method
+            self.covariance_factor = lambda: self._bw_method(self)
         else:
-            msg = "points have dimension %s,\
-                   dataset has dimension %s" % (dim_pts, dim)
+            msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
+                  "or a callable."
             raise ValueError(msg)
 
-    result = np.zeros((num_dp_pts,), dtype=np.float)
+        self._compute_covariance()
 
-    if num_dp_pts >= num_dp:
-        # there are more points than data, so loop over data
-        for i in range(num_dp):
-            diff = dataset[:, i, np.newaxis] - points
-            tdiff = np.dot(inv_cov, diff)
-            energy = np.sum(diff*tdiff, axis=0) / 2.0
-            result = result + np.exp(-energy)
-    else:
-        # loop over points
-        for i in range(num_dp_pts):
-            diff = dataset - points[:, i, np.newaxis]
-            tdiff = np.dot(inv_cov, diff)
-            energy = np.sum(diff * tdiff, axis=0) / 2.0
-            result[i] = np.sum(np.exp(-energy), axis=0)
-
-    result = result / norm_factor
-
-    return {
-        'xmin': xmin,
-        'xmax': xmax,
-        'mean': np.mean(dataset),
-        'median': np.median(dataset),
-        'result': result
-    }
+    def _compute_covariance(self):
+        """Computes the covariance matrix for each Gaussian kernel using
+        covariance_factor().
+        """
+        self.factor = self.covariance_factor()
+        # Cache covariance and inverse covariance of the data
+        if not hasattr(self, '_data_inv_cov'):
+            self._data_covariance = np.atleast_2d(np.cov(self.dataset, rowvar=1,
+                                               bias=False))
+            self._data_inv_cov = np.linalg.inv(self._data_covariance)
+
+        self.covariance = self._data_covariance * self.factor**2
+        self.inv_cov = self._data_inv_cov / self.factor**2
+        self._norm_factor = np.sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.num_dp        
+
+    def evaluate(self, points):
+        """Evaluate the estimated pdf on a set of points.
+
+        Parameters
+        ----------
+        points : (# of dimensions, # of points)-array
+            Alternatively, a (# of dimensions,) vector can be passed in and
+            treated as a single point.
+
+        Returns
+        -------
+        values : (# of points,)-array
+            The values at each point.
+
+        Raises
+        ------
+        ValueError : if the dimensionality of the input points is different than
+                     the dimensionality of the KDE.
+
+        """
+        points = np.atleast_2d(points)
+
+        d, m = points.shape
+        if d != self.dim:
+            if d == 1 and m == self.dim:
+                # points was passed in as a row vector
+                points = np.reshape(points, (self.dim, 1))
+                m = 1
+            else:
+                msg = "points have dimension %s, dataset has dimension %s" % (d,
+                    self.dim)
+                raise ValueError(msg)
+
+        result = np.zeros((m,), dtype=np.float)
+
+        if m >= self.num_dp:
+            # there are more points than data, so loop over data
+            for i in range(self.num_dp):
+                diff = self.dataset[:, i, np.newaxis] - points
+                tdiff = np.dot(self.inv_cov, diff)
+                energy = np.sum(diff*tdiff,axis=0) / 2.0
+                result = result + np.exp(-energy)
+        else:
+            # loop over points
+            for i in range(m):
+                diff = self.dataset - points[:, i, np.newaxis]
+                tdiff = np.dot(self.inv_cov, diff)
+                energy = np.sum(diff * tdiff, axis=0) / 2.0
+                result[i] = np.sum(np.exp(-energy), axis=0)
+
+        result = result / self._norm_factor
+
+        return result
+
+    __call__ = evaluate
 
 ##################################################
 # Code related to things in and around polygons

From e8b30415630274f781924e5e888a04106788fa97 Mon Sep 17 00:00:00 2001
From: Per Parker <wisalam@live.com>
Date: Tue, 1 Apr 2014 21:25:26 -0400
Subject: [PATCH 06/22] Reverted 69b304c.

---
 examples/statistics/violinplot_demo.py |   2 +-
 lib/matplotlib/axes/_axes.py           |  35 ++--
 lib/matplotlib/mlab.py                 | 216 ++++++++++++-------------
 3 files changed, 116 insertions(+), 137 deletions(-)

diff --git a/examples/statistics/violinplot_demo.py b/examples/statistics/violinplot_demo.py
index 93aa50e44e5f..8e1c2917b2ca 100644
--- a/examples/statistics/violinplot_demo.py
+++ b/examples/statistics/violinplot_demo.py
@@ -54,4 +54,4 @@
 
 fig.suptitle("Violin Plotting Examples")
 fig.subplots_adjust(hspace=0.4)
-plt.show()
\ No newline at end of file
+plt.show()
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index e7834b5c7b5a..fb0e21b54362 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6725,7 +6725,7 @@ def matshow(self, Z, **kwargs):
                                                  integer=True))
         return im
 
-    def violinplot(self, dataset, positions=None, points=100, vert=True, widths=0.5, showmeans=False,
+    def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=False,
                    showextrema=True, showmedians=False):
         """
         Make a violin plot.
@@ -6748,9 +6748,6 @@ def violinplot(self, dataset, positions=None, points=100, vert=True, widths=0.5,
           positions : array-like, default = [1, 2, ..., n]
             Sets the positions of the violins. The ticks and limits are
             automatically set to match the positions.
-          
-          points: array-like, default = 100
-            Number of points to evaluate pdf estimation for Gaussian kernel
 
           vert : bool, default = True.
             If true, creates vertical violin plot
@@ -6809,9 +6806,6 @@ def violinplot(self, dataset, positions=None, points=100, vert=True, widths=0.5,
         cbars = None
         cmedians = None
 
-        datashape_message = ("List of violinplot statistics and `{0}` "
-                             "values must have same the length")
-
         # Validate positions
         if positions == None:
             positions = range(1, len(dataset) + 1)
@@ -6836,14 +6830,13 @@ def violinplot(self, dataset, positions=None, points=100, vert=True, widths=0.5,
         # Render violins
         for d,p,w in zip(dataset,positions,widths):            
             # Calculate the kernel density
-            kde = mlab.gaussian_kde(d)
-            m = kde.dataset.min()
-            M = kde.dataset.max()
-            mean = np.mean(kde.dataset)
-            median = np.median(kde.dataset)
-            coords = np.arange(m,M,(M-m)/float(points))
-
-            v = kde.evaluate(coords)
+            kde = mlab.ksdensity(d)
+            m = kde['xmin']
+            M = kde['xmax']
+            mean = kde['mean']
+            median = kde['median']
+            v = kde['result']
+            coords = np.arange(m,M,(M-m)/100.)
 
             # Since each data point p is plotted from v-p to v+p,
             # we need to scale it by an additional 0.5 factor so that we get
@@ -6853,10 +6846,10 @@ def violinplot(self, dataset, positions=None, points=100, vert=True, widths=0.5,
             # create vertical violin plot
             if vert:
                 bodies += [self.fill_betweenx(coords,
-                                              -v+p,
-                                              v+p,
-                                              facecolor='y',
-                                              alpha=0.3)]
+                                          -v+p,
+                                          v+p,
+                                          facecolor='y',
+                                          alpha=0.3)]
             # create horizontal violin plot
             else:
                 bodies += [self.fill_between(coords,
@@ -6902,6 +6895,10 @@ def violinplot(self, dataset, positions=None, points=100, vert=True, widths=0.5,
             if showmedians:
                 cmedians = self.vlines(medians, pmins, pmaxes, colors='r')
 
+
+
+
+
         # Reset hold
         self.hold(holdStatus)
 
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index bf5a41a1fba9..af8cc9a79734 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3656,12 +3656,12 @@ def stineman_interp(xi,x,y,yp=None):
                                   1/(dy1+dy2),))
     return yi
 
-class gaussian_kde(object):    
+def ksdensity(dataset, bw_method=None):
     """
     Representation of a kernel-density estimate using Gaussian kernels.
 
     Call signature::
-    kde = gaussian_kde(dataset, 'silverman')
+    kde_dict = ksdensity(dataset, 'silverman')
 
     Parameters
     ----------
@@ -3676,10 +3676,10 @@ class gaussian_kde(object):
     Attributes
     ----------
     dataset : ndarray
-        The dataset with which `gaussian_kde` was initialized.
-    dim : int
+        The dataset with which `ksdensity` was initialized.
+    d : int
         Number of dimensions.
-    num_dp : int
+    n : int
         Number of datapoints.
     factor : float
         The bandwidth factor, obtained from `kde.covariance_factor`, with which
@@ -3690,135 +3690,117 @@ class gaussian_kde(object):
     inv_cov : ndarray
         The inverse of `covariance`.
 
-    Methods
+    Returns
     -------
-    kde.evaluate(points) : ndarray
-        Evaluate the estimated pdf on a provided set of points.
-    kde(points) : ndarray
-        Same as kde.evaluate(points)
-    kde.set_bandwidth(bw_method='scott') : None
-        Computes the bandwidth, i.e. the coefficient that multiplies the data
-        covariance matrix to obtain the kernel covariance matrix.
-        .. versionadded:: 0.11.0
-    kde.covariance_factor : float
-        Computes the coefficient (`kde.factor`) that multiplies the data
-        covariance matrix to obtain the kernel covariance matrix.
-        The default is `scotts_factor`.  A subclass can overwrite this method
-        to provide a different method, or set it through a call to
-        `kde.set_bandwidth`.
+    A dictionary mapping each various aspects of the computed KDE.
+    The dictionary has the following keys:
+
+        xmin : number
+            The min of the input dataset
+        xmax : number
+            The max of the input dataset
+        mean : number
+            The mean of the result
+        median: number
+            The median of the result
+        result: (# of points,)-array
+            The array of the evaluated PDF estimation
+
+    Raises
+    ------
+    ValueError : if the dimensionality of the input points is different than
+                 the dimensionality of the KDE.
 
     """
 
     # This implementation with minor modification was too good to pass up.
     # from scipy: https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py
 
-    def __init__(self, dataset, bw_method=None):
-        self.dataset = np.atleast_2d(dataset)
-        if not self.dataset.size > 1:
-            raise ValueError("`dataset` input should have multiple elements.")
+    dataset = np.array(np.atleast_2d(dataset))
+    xmin = dataset.min()
+    xmax = dataset.max()
 
-        self.dim, self.num_dp = self.dataset.shape
-        self.set_bandwidth(bw_method=bw_method)
+    if not dataset.size > 1:
+        raise ValueError("`dataset` input should have multiple elements.")
 
-    def scotts_factor(self):
-        return np.power(self.num_dp, -1./(self.dim+4))
+    dim, num_dp = dataset.shape
 
-    def silverman_factor(self):
-        return np.power(self.num_dp*(self.dim+2.0)/4.0, -1./(self.dim+4))
+    # ----------------------------------------------
+    # Set Bandwidth, defaulted to Scott's Factor
+    # ----------------------------------------------
+    scotts_factor = lambda: np.power(num_dp, -1./(dim+4))
+    silverman_factor = lambda: np.power(num_dp*(dim+2.0)/4.0, -1./(dim+4))
 
-    #  Default method to calculate bandwidth, can be overwritten by subclass
+    # Default method to calculate bandwidth, can be overwritten by subclass
     covariance_factor = scotts_factor
 
-    def set_bandwidth(self, bw_method=None):
-        if bw_method is None:
-            pass
-        elif bw_method == 'scott':
-            self.covariance_factor = self.scotts_factor
-        elif bw_method == 'silverman':
-            self.covariance_factor = self.silverman_factor
-        elif np.isscalar(bw_method) and not isinstance(bw_method, six.string_types):
-            self._bw_method = 'use constant'
-            self.covariance_factor = lambda: bw_method
-        elif callable(bw_method):
-            self._bw_method = bw_method
-            self.covariance_factor = lambda: self._bw_method(self)
+    if bw_method is None:
+        pass
+    elif bw_method == 'scott':
+        covariance_factor = scotts_factor
+    elif bw_method == 'silverman':
+        covariance_factor = silverman_factor
+    elif np.isscalar(bw_method) and not isinstance(bw_method, six.string_types):
+        covariance_factor = lambda: bw_method
+    else:
+        msg = "`bw_method` should be 'scott', 'silverman', or a scalar"
+        raise ValueError(msg)
+
+    # ---------------------------------------------------------------
+    # Computes covariance matrix for each Gaussian kernel with factor
+    # ---------------------------------------------------------------
+    factor = covariance_factor()
+
+    # Cache covariance and inverse covariance of the data
+    data_covariance = np.atleast_2d(np.cov(dataset, rowvar=1, bias=False))
+    data_inv_cov = np.linalg.inv(data_covariance)
+
+    covariance = data_covariance * factor**2
+    inv_cov = data_inv_cov / factor**2
+    norm_factor = np.sqrt(np.linalg.det(2*np.pi*covariance)) * num_dp
+
+    # ----------------------------------------------
+    # Evaluate the estimated pdf on a set of points.
+    # ----------------------------------------------
+    points = np.atleast_2d(np.arange(xmin, xmax, (xmax-xmin)/100.))
+
+    dim_pts, num_dp_pts = np.array(points).shape
+    if dim_pts != dim:
+        if dim_pts == 1 and num_dp_pts == num_dp:
+            # points was passed in as a row vector
+            points = np.reshape(points, (dim, 1))
+            num_dp_pts = 1
         else:
-            msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
-                  "or a callable."
+            msg = "points have dimension %s,\
+                   dataset has dimension %s" % (dim_pts, dim)
             raise ValueError(msg)
 
-        self._compute_covariance()
-
-    def _compute_covariance(self):
-        """Computes the covariance matrix for each Gaussian kernel using
-        covariance_factor().
-        """
-        self.factor = self.covariance_factor()
-        # Cache covariance and inverse covariance of the data
-        if not hasattr(self, '_data_inv_cov'):
-            self._data_covariance = np.atleast_2d(np.cov(self.dataset, rowvar=1,
-                                               bias=False))
-            self._data_inv_cov = np.linalg.inv(self._data_covariance)
-
-        self.covariance = self._data_covariance * self.factor**2
-        self.inv_cov = self._data_inv_cov / self.factor**2
-        self._norm_factor = np.sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.num_dp        
-
-    def evaluate(self, points):
-        """Evaluate the estimated pdf on a set of points.
-
-        Parameters
-        ----------
-        points : (# of dimensions, # of points)-array
-            Alternatively, a (# of dimensions,) vector can be passed in and
-            treated as a single point.
-
-        Returns
-        -------
-        values : (# of points,)-array
-            The values at each point.
-
-        Raises
-        ------
-        ValueError : if the dimensionality of the input points is different than
-                     the dimensionality of the KDE.
+    result = np.zeros((num_dp_pts,), dtype=np.float)
 
-        """
-        points = np.atleast_2d(points)
-
-        d, m = points.shape
-        if d != self.dim:
-            if d == 1 and m == self.dim:
-                # points was passed in as a row vector
-                points = np.reshape(points, (self.dim, 1))
-                m = 1
-            else:
-                msg = "points have dimension %s, dataset has dimension %s" % (d,
-                    self.dim)
-                raise ValueError(msg)
-
-        result = np.zeros((m,), dtype=np.float)
-
-        if m >= self.num_dp:
-            # there are more points than data, so loop over data
-            for i in range(self.num_dp):
-                diff = self.dataset[:, i, np.newaxis] - points
-                tdiff = np.dot(self.inv_cov, diff)
-                energy = np.sum(diff*tdiff,axis=0) / 2.0
-                result = result + np.exp(-energy)
-        else:
-            # loop over points
-            for i in range(m):
-                diff = self.dataset - points[:, i, np.newaxis]
-                tdiff = np.dot(self.inv_cov, diff)
-                energy = np.sum(diff * tdiff, axis=0) / 2.0
-                result[i] = np.sum(np.exp(-energy), axis=0)
-
-        result = result / self._norm_factor
-
-        return result
-
-    __call__ = evaluate
+    if num_dp_pts >= num_dp:
+        # there are more points than data, so loop over data
+        for i in range(num_dp):
+            diff = dataset[:, i, np.newaxis] - points
+            tdiff = np.dot(inv_cov, diff)
+            energy = np.sum(diff*tdiff, axis=0) / 2.0
+            result = result + np.exp(-energy)
+    else:
+        # loop over points
+        for i in range(num_dp_pts):
+            diff = dataset - points[:, i, np.newaxis]
+            tdiff = np.dot(inv_cov, diff)
+            energy = np.sum(diff * tdiff, axis=0) / 2.0
+            result[i] = np.sum(np.exp(-energy), axis=0)
+
+    result = result / norm_factor
+
+    return {
+        'xmin': xmin,
+        'xmax': xmax,
+        'mean': np.mean(dataset),
+        'median': np.median(dataset),
+        'result': result
+    }
 
 ##################################################
 # Code related to things in and around polygons

From 91a33e8beade1c84d1bbd6230bf169310601fad1 Mon Sep 17 00:00:00 2001
From: Per Parker <wisalam@live.com>
Date: Tue, 1 Apr 2014 21:37:58 -0400
Subject: [PATCH 07/22] Re-added points parameter to violinplot. Fixed several
 style issues.

---
 lib/matplotlib/axes/_axes.py | 153 ++++++++++++++++-------------------
 1 file changed, 69 insertions(+), 84 deletions(-)

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index fb0e21b54362..0171b407f29e 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6725,16 +6725,19 @@ def matshow(self, Z, **kwargs):
                                                  integer=True))
         return im
 
-    def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=False,
-                   showextrema=True, showmedians=False):
+    def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
+                   showmeans=False, showextrema=True, showmedians=False,
+                   points=100):
         """
         Make a violin plot.
 
         Call signature::
 
-          violinplot(dataset, positions=None)
+          violinplot(dataset, positions=None, vert=True, widths=0.5,
+                     showmeans=False, showextrema=True, showmedians=False,
+                     points=100):
 
-        Make a violin plot for each column of *dataset* or each vector in 
+        Make a violin plot for each column of *dataset* or each vector in
         sequence *dataset*.  Each filled area extends to represent the
         entire data range, with three lines at the mean, the minimum, and
         the maximum.
@@ -6750,8 +6753,8 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
             automatically set to match the positions.
 
           vert : bool, default = True.
-            If true, creates vertical violin plot
-            Else, creates horizontal violin plot
+            If true, creates a vertical violin plot.
+            Otherwise, creates a horizontal violin plot.
 
           widths : array-like, default = 0.5
             Either a scalar or a vector that sets the maximal width of
@@ -6767,6 +6770,10 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
           showmedians : bool, default = False
             If true, will toggle rendering of the medians.
 
+          points : scalar, default = 100
+            Defines the number of points to evaluate each of the gaussian
+            kernel density estimations at.
+
         Returns
         -------
 
@@ -6774,7 +6781,7 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
         corresponding collection instances created. The dictionary has
         the following keys:
 
-            - bodies: A list of the 
+            - bodies: A list of the
               :class:`matplotlib.collections.PolyCollection` instances
               containing the filled area of each violin.
             - means: A :class:`matplotlib.collections.LineCollection` instance
@@ -6786,9 +6793,9 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
               created to identify the top of each violin's distribution.
             - bars: A :class:`matplotlib.collections.LineCollection` instance
               created to identify the centers of each violin's distribution.
-            - medians: A :class:`matplotlib.collections.LineCollection` instance
-              created to identify the median values of each of the violin's
-              distribution.
+            - medians: A :class:`matplotlib.collections.LineCollection`
+              instance created to identify the median values of each of the
+              violin's distribution.
 
         """
 
@@ -6799,15 +6806,20 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
         medians = []
 
         # Collections to be returned
-        bodies = []
-        cmeans = None
-        cmaxes = None
-        cmins = None
-        cbars = None
-        cmedians = None
+        artists = {
+            'bodies': [],
+            'cmeans': None,
+            'cmaxes': None,
+            'cmins': None,
+            'cbars': None,
+            'cmedians': None
+        }
+
+        datashape_message = ("List of violinplot statistics and `{0}` "
+                             "values must have the same length")
 
         # Validate positions
-        if positions == None:
+        if positions is None:
             positions = range(1, len(dataset) + 1)
         elif len(positions) != len(dataset):
             raise ValueError(datashape_message.format("positions"))
@@ -6825,91 +6837,64 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=F
         # Check hold status
         if not self._hold:
             self.cla()
-        holdStatus = self._hold
+        hold_status = self._hold
+
+        # Check whether we are rendering vertically or horizontally
+        if vert:
+            fill = self.fill_betweenx
+            rlines = self.hlines
+            blines = self.vlines
+        else:
+            fill = self.fill_between
+            rlines = self.vlines
+            blines = self.hlines
 
         # Render violins
-        for d,p,w in zip(dataset,positions,widths):            
+        for data, pos, width in zip(dataset, positions, widths):
             # Calculate the kernel density
-            kde = mlab.ksdensity(d)
-            m = kde['xmin']
-            M = kde['xmax']
+            kde = mlab.ksdensity(data)
+            min_val = kde['xmin']
+            max_val = kde['xmax']
             mean = kde['mean']
             median = kde['median']
-            v = kde['result']
-            coords = np.arange(m,M,(M-m)/100.)
+            vals = kde['result']
+            coords = np.arange(min_val, max_val, (max_val - min_val)/points)
 
             # Since each data point p is plotted from v-p to v+p,
             # we need to scale it by an additional 0.5 factor so that we get
             # correct width in the end.
-            v = 0.5 * w * v/v.max()
-
-            # create vertical violin plot
-            if vert:
-                bodies += [self.fill_betweenx(coords,
-                                          -v+p,
-                                          v+p,
-                                          facecolor='y',
-                                          alpha=0.3)]
-            # create horizontal violin plot
-            else:
-                bodies += [self.fill_between(coords,
-                                              -v+p,
-                                              v+p,
-                                              facecolor='y',
-                                              alpha=0.3)]
+            vals = 0.5 * width * vals/vals.max()
+
+            # create the violin bodies
+            artists['bodies'] += [fill(coords,
+                                       -vals + pos,
+                                       vals + pos,
+                                       facecolor='y',
+                                       alpha=0.3)]
 
             means.append(mean)
-            mins.append(m)
-            maxes.append(M)
+            mins.append(min_val)
+            maxes.append(max_val)
             medians.append(median)
 
-        # respective means, extrema median on vertical violin plot
-        if vert:
-            # Render means
-            if showmeans:
-                cmeans = self.hlines(means, pmins, pmaxes, colors='r')
-
-            # Render extrema
-            if showextrema:
-                cmaxes = self.hlines(maxes, pmins, pmaxes, colors='r')
-                cmins = self.hlines(mins, pmins, pmaxes, colors='r')
-                cbars = self.vlines(positions, mins, maxes, colors='r')
-
-            # Render medians
-            if showmedians:
-                cmedians = self.hlines(medians, pmins, pmaxes, colors='r')
-
-        # respective means, extrema median on horizontal violin plot
-        else:
-            # Render means
-            if showmeans:
-                cmeans = self.vlines(means, pmins, pmaxes, colors='r')
-
-            # Render extrema
-            if showextrema:
-                cmaxes = self.vlines(maxes, pmins, pmaxes, colors='r')
-                cmins = self.vlines(mins, pmins, pmaxes, colors='r')
-                cbars = self.hlines(positions, mins, maxes, colors='r')
-
-            # Render medians
-            if showmedians:
-                cmedians = self.vlines(medians, pmins, pmaxes, colors='r')
-
-
+        # Render means
+        if showmeans:
+            artists['cmeans'] = rlines(means, pmins, pmaxes, colors='r')
 
+        # Render extrema
+        if showextrema:
+            artists['cmaxes'] = rlines(maxes, pmins, pmaxes, colors='r')
+            artists['cmins'] = rlines(mins, pmins, pmaxes, colors='r')
+            artists['cbars'] = blines(positions, mins, maxes, colors='r')
 
+        # Render medians
+        if showmedians:
+            artists['cmedians'] = rlines(medians, pmins, pmaxes, colors='r')
 
         # Reset hold
-        self.hold(holdStatus)
+        self.hold(hold_status)
 
-        return {
-            'bodies' : bodies,
-            'means' : cmeans,
-            'mins' : cmins,
-            'maxes' : cmaxes,
-            'bars' : cbars,
-            'medians' : cmedians
-        }
+        return artists
 
 
     def tricontour(self, *args, **kwargs):

From 690c770181fac7ba561b0059d763cfc3a4fd07c7 Mon Sep 17 00:00:00 2001
From: Kevin Chan <kvii.chan@gmail.com>
Date: Tue, 1 Apr 2014 23:11:36 -0400
Subject: [PATCH 08/22] Merging for pep8 compliance for mlab's GaussianKDE

---
 lib/matplotlib/axes/_axes.py |  13 ++-
 lib/matplotlib/mlab.py       | 212 ++++++++++++++++++-----------------
 2 files changed, 118 insertions(+), 107 deletions(-)

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 0171b407f29e..8c855371da9e 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6852,14 +6852,15 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
         # Render violins
         for data, pos, width in zip(dataset, positions, widths):
             # Calculate the kernel density
-            kde = mlab.ksdensity(data)
-            min_val = kde['xmin']
-            max_val = kde['xmax']
-            mean = kde['mean']
-            median = kde['median']
-            vals = kde['result']
+            kde = mlab.GaussianKDE(data)
+            min_val = kde.dataset.min()
+            max_val = kde.dataset.max()
+            mean = np.mean(kde.dataset)
+            median = np.median(kde.dataset)
             coords = np.arange(min_val, max_val, (max_val - min_val)/points)
 
+            vals = kde.evaluate(coords)
+
             # Since each data point p is plotted from v-p to v+p,
             # we need to scale it by an additional 0.5 factor so that we get
             # correct width in the end.
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index af8cc9a79734..94a12cc69195 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3656,12 +3656,12 @@ def stineman_interp(xi,x,y,yp=None):
                                   1/(dy1+dy2),))
     return yi
 
-def ksdensity(dataset, bw_method=None):
+class GaussianKDE(object):
     """
     Representation of a kernel-density estimate using Gaussian kernels.
 
     Call signature::
-    kde_dict = ksdensity(dataset, 'silverman')
+    kde = gaussian_kde(dataset, 'silverman')
 
     Parameters
     ----------
@@ -3676,10 +3676,10 @@ def ksdensity(dataset, bw_method=None):
     Attributes
     ----------
     dataset : ndarray
-        The dataset with which `ksdensity` was initialized.
-    d : int
+        The dataset with which `gaussian_kde` was initialized.
+    dim : int
         Number of dimensions.
-    n : int
+    num_dp : int
         Number of datapoints.
     factor : float
         The bandwidth factor, obtained from `kde.covariance_factor`, with which
@@ -3690,117 +3690,127 @@ def ksdensity(dataset, bw_method=None):
     inv_cov : ndarray
         The inverse of `covariance`.
 
-    Returns
+    Methods
     -------
-    A dictionary mapping each various aspects of the computed KDE.
-    The dictionary has the following keys:
-
-        xmin : number
-            The min of the input dataset
-        xmax : number
-            The max of the input dataset
-        mean : number
-            The mean of the result
-        median: number
-            The median of the result
-        result: (# of points,)-array
-            The array of the evaluated PDF estimation
-
-    Raises
-    ------
-    ValueError : if the dimensionality of the input points is different than
-                 the dimensionality of the KDE.
+    kde.evaluate(points) : ndarray
+        Evaluate the estimated pdf on a provided set of points.
+    kde(points) : ndarray
+        Same as kde.evaluate(points)
 
     """
 
     # This implementation with minor modification was too good to pass up.
     # from scipy: https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py
 
-    dataset = np.array(np.atleast_2d(dataset))
-    xmin = dataset.min()
-    xmax = dataset.max()
+    def __init__(self, dataset, bw_method=None):
+        self.dataset = np.atleast_2d(dataset)
+        if not np.array(self.dataset).size > 1:
+            raise ValueError("`dataset` input should have multiple elements.")
+
+        self.dim, self.num_dp = np.array(self.dataset).shape
+
+        if bw_method is None:
+            pass
+        elif bw_method == 'scott':
+            self.covariance_factor = self.scotts_factor
+        elif bw_method == 'silverman':
+            self.covariance_factor = self.silverman_factor
+        elif np.isscalar(bw_method):
+            if not isinstance(bw_method, six.string_types):
+                self._bw_method = 'use constant'
+                self.covariance_factor = lambda: bw_method
+        elif callable(bw_method):
+            self._bw_method = bw_method
+            self.covariance_factor = lambda: self._bw_method(self)
+        else:
+            msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
+                  "or a callable."
+            raise ValueError(msg)
+        
+        # Computes the covariance matrix for each Gaussian kernel using
+        # covariance_factor().
+        
+        self.factor = self.covariance_factor()
+        # Cache covariance and inverse covariance of the data
+        if not hasattr(self, '_data_inv_cov'):
+            self.data_covariance = np.atleast_2d(
+                np.cov(
+                    self.dataset,
+                    rowvar=1,
+                    bias=False))
+            self.data_inv_cov = np.linalg.inv(self.data_covariance)
+
+        self.covariance = self.data_covariance * self.factor ** 2
+        self.inv_cov = self.data_inv_cov / self.factor ** 2
+        self.norm_factor = np.sqrt(
+            np.linalg.det(
+                2 * np.pi * self.covariance)) * self.num_dp
+
+    def scotts_factor(self):
+        return np.power(self.num_dp, -1. / (self.dim + 4))
+
+    def silverman_factor(self):
+        return np.power(
+            self.num_dp * (self.dim + 2.0) / 4.0, -1. / (self.dim + 4))
+
+    #  Default method to calculate bandwidth, can be overwritten by subclass
+    covariance_factor = scotts_factor
 
-    if not dataset.size > 1:
-        raise ValueError("`dataset` input should have multiple elements.")
+    def evaluate(self, points):
+        """Evaluate the estimated pdf on a set of points.
 
-    dim, num_dp = dataset.shape
+        Parameters
+        ----------
+        points : (# of dimensions, # of points)-array
+            Alternatively, a (# of dimensions,) vector can be passed in and
+            treated as a single point.
 
-    # ----------------------------------------------
-    # Set Bandwidth, defaulted to Scott's Factor
-    # ----------------------------------------------
-    scotts_factor = lambda: np.power(num_dp, -1./(dim+4))
-    silverman_factor = lambda: np.power(num_dp*(dim+2.0)/4.0, -1./(dim+4))
+        Returns
+        -------
+        values : (# of points,)-array
+            The values at each point.
 
-    # Default method to calculate bandwidth, can be overwritten by subclass
-    covariance_factor = scotts_factor
+        Raises
+        ------
+        ValueError : if the dimensionality of the input points is different
+                     than the dimensionality of the KDE.
 
-    if bw_method is None:
-        pass
-    elif bw_method == 'scott':
-        covariance_factor = scotts_factor
-    elif bw_method == 'silverman':
-        covariance_factor = silverman_factor
-    elif np.isscalar(bw_method) and not isinstance(bw_method, six.string_types):
-        covariance_factor = lambda: bw_method
-    else:
-        msg = "`bw_method` should be 'scott', 'silverman', or a scalar"
-        raise ValueError(msg)
-
-    # ---------------------------------------------------------------
-    # Computes covariance matrix for each Gaussian kernel with factor
-    # ---------------------------------------------------------------
-    factor = covariance_factor()
-
-    # Cache covariance and inverse covariance of the data
-    data_covariance = np.atleast_2d(np.cov(dataset, rowvar=1, bias=False))
-    data_inv_cov = np.linalg.inv(data_covariance)
-
-    covariance = data_covariance * factor**2
-    inv_cov = data_inv_cov / factor**2
-    norm_factor = np.sqrt(np.linalg.det(2*np.pi*covariance)) * num_dp
-
-    # ----------------------------------------------
-    # Evaluate the estimated pdf on a set of points.
-    # ----------------------------------------------
-    points = np.atleast_2d(np.arange(xmin, xmax, (xmax-xmin)/100.))
-
-    dim_pts, num_dp_pts = np.array(points).shape
-    if dim_pts != dim:
-        if dim_pts == 1 and num_dp_pts == num_dp:
-            # points was passed in as a row vector
-            points = np.reshape(points, (dim, 1))
-            num_dp_pts = 1
+        """
+        points = np.atleast_2d(points)
+
+        dim, num_m = np.array(points).shape
+        if dim != self.dim:
+            if dim == 1 and num_m == self.dim:
+                # points was passed in as a row vector
+                points = np.reshape(points, (self.dim, 1))
+                num_m = 1
+            else:
+                msg = "points have dimension %s, dataset has dimension %s" % (
+                    dim, self.dim)
+                raise ValueError(msg)
+
+        result = np.zeros((num_m,), dtype=np.float)
+
+        if num_m >= self.num_dp:
+            # there are more points than data, so loop over data
+            for i in range(self.num_dp):
+                diff = self.dataset[:, i, np.newaxis] - points
+                tdiff = np.dot(self.inv_cov, diff)
+                energy = np.sum(diff * tdiff, axis=0) / 2.0
+                result = result + np.exp(-energy)
         else:
-            msg = "points have dimension %s,\
-                   dataset has dimension %s" % (dim_pts, dim)
-            raise ValueError(msg)
+            # loop over points
+            for i in range(num_m):
+                diff = self.dataset - points[:, i, np.newaxis]
+                tdiff = np.dot(self.inv_cov, diff)
+                energy = np.sum(diff * tdiff, axis=0) / 2.0
+                result[i] = np.sum(np.exp(-energy), axis=0)
 
-    result = np.zeros((num_dp_pts,), dtype=np.float)
+        result = result / self.norm_factor
 
-    if num_dp_pts >= num_dp:
-        # there are more points than data, so loop over data
-        for i in range(num_dp):
-            diff = dataset[:, i, np.newaxis] - points
-            tdiff = np.dot(inv_cov, diff)
-            energy = np.sum(diff*tdiff, axis=0) / 2.0
-            result = result + np.exp(-energy)
-    else:
-        # loop over points
-        for i in range(num_dp_pts):
-            diff = dataset - points[:, i, np.newaxis]
-            tdiff = np.dot(inv_cov, diff)
-            energy = np.sum(diff * tdiff, axis=0) / 2.0
-            result[i] = np.sum(np.exp(-energy), axis=0)
-
-    result = result / norm_factor
-
-    return {
-        'xmin': xmin,
-        'xmax': xmax,
-        'mean': np.mean(dataset),
-        'median': np.median(dataset),
-        'result': result
-    }
+        return result
+
+    __call__ = evaluate
 
 ##################################################
 # Code related to things in and around polygons

From b98b5911c9cdb21f67ed4aa2b4dee2bc1ef0861b Mon Sep 17 00:00:00 2001
From: limtaesu <limtaesu@gmail.com>
Date: Wed, 2 Apr 2014 02:32:23 -0400
Subject: [PATCH 09/22] Added violinplot tests.

---
 .../test_vert_violinplot_baseline.pdf         |  Bin 0 -> 16822 bytes
 .../test_vert_violinplot_baseline.png         |  Bin 0 -> 33764 bytes
 .../test_vert_violinplot_baseline.svg         | 1374 +++++++++++++++
 .../test_vert_violinplot_showall.pdf          |  Bin 0 -> 21613 bytes
 .../test_vert_violinplot_showall.png          |  Bin 0 -> 34713 bytes
 .../test_vert_violinplot_showall.svg          | 1514 +++++++++++++++++
 .../test_vert_violinplot_showextrema.pdf      |  Bin 0 -> 19687 bytes
 .../test_vert_violinplot_showextrema.png      |  Bin 0 -> 34192 bytes
 .../test_vert_violinplot_showextrema.svg      | 1458 ++++++++++++++++
 .../test_vert_violinplot_showmeans.pdf        |  Bin 0 -> 17799 bytes
 .../test_vert_violinplot_showmeans.png        |  Bin 0 -> 33932 bytes
 .../test_vert_violinplot_showmeans.svg        | 1402 +++++++++++++++
 .../test_vert_violinplot_showmedians.pdf      |  Bin 0 -> 17792 bytes
 .../test_vert_violinplot_showmedians.png      |  Bin 0 -> 34104 bytes
 .../test_vert_violinplot_showmedians.svg      | 1402 +++++++++++++++
 lib/matplotlib/tests/test_axes.py             |  169 ++
 16 files changed, 7319 insertions(+)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.svg

diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.pdf
new file mode 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.png b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.png
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.png b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.png
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.png b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.png
new file mode 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.pdf
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+     <g id="line2d_9">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="295.2" xlink:href="#m93b0483c22" y="388.8"/>
+      </g>
+     </g>
+     <g id="line2d_10">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="295.2" xlink:href="#m741efc42ff" y="43.2"/>
+      </g>
+     </g>
+     <g id="text_5">
+      <!-- 1.5 -->
+      <g transform="translate(286.84125 401.918125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-31"/>
+       <use x="63.623046875" xlink:href="#BitstreamVeraSans-Roman-2e"/>
+       <use x="95.41015625" xlink:href="#BitstreamVeraSans-Roman-35"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_6">
+     <g id="line2d_11">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="351.0" xlink:href="#m93b0483c22" y="388.8"/>
+      </g>
+     </g>
+     <g id="line2d_12">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="351.0" xlink:href="#m741efc42ff" y="43.2"/>
+      </g>
+     </g>
+     <g id="text_6">
+      <!-- 2.0 -->
+      <defs>
+       <path d="
+M19.1875 8.29688
+L53.6094 8.29688
+L53.6094 0
+L7.32812 0
+L7.32812 8.29688
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+Q44.1875 33.9844 37.6406 27.2188
+Q31.1094 20.4531 19.1875 8.29688" id="BitstreamVeraSans-Roman-32"/>
+      </defs>
+      <g transform="translate(342.29625 401.918125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-32"/>
+       <use x="63.623046875" xlink:href="#BitstreamVeraSans-Roman-2e"/>
+       <use x="95.41015625" xlink:href="#BitstreamVeraSans-Roman-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_7">
+     <g id="line2d_13">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="406.8" xlink:href="#m93b0483c22" y="388.8"/>
+      </g>
+     </g>
+     <g id="line2d_14">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="406.8" xlink:href="#m741efc42ff" y="43.2"/>
+      </g>
+     </g>
+     <g id="text_7">
+      <!-- 2.5 -->
+      <g transform="translate(398.221875 401.918125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-32"/>
+       <use x="63.623046875" xlink:href="#BitstreamVeraSans-Roman-2e"/>
+       <use x="95.41015625" xlink:href="#BitstreamVeraSans-Roman-35"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_8">
+     <g id="line2d_15">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="462.6" xlink:href="#m93b0483c22" y="388.8"/>
+      </g>
+     </g>
+     <g id="line2d_16">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="462.6" xlink:href="#m741efc42ff" y="43.2"/>
+      </g>
+     </g>
+     <g id="text_8">
+      <!-- 3.0 -->
+      <defs>
+       <path d="
+M40.5781 39.3125
+Q47.6562 37.7969 51.625 33
+Q55.6094 28.2188 55.6094 21.1875
+Q55.6094 10.4062 48.1875 4.48438
+Q40.7656 -1.42188 27.0938 -1.42188
+Q22.5156 -1.42188 17.6562 -0.515625
+Q12.7969 0.390625 7.625 2.20312
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+Q15.4375 72.6562 20.3438 73.4375
+Q25.25 74.2188 29.5938 74.2188
+Q40.8281 74.2188 47.3594 69.1094
+Q53.9062 64.0156 53.9062 55.3281
+Q53.9062 49.2656 50.4375 45.0938
+Q46.9688 40.9219 40.5781 39.3125" id="BitstreamVeraSans-Roman-33"/>
+      </defs>
+      <g transform="translate(453.9140625 401.918125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-33"/>
+       <use x="63.623046875" xlink:href="#BitstreamVeraSans-Roman-2e"/>
+       <use x="95.41015625" xlink:href="#BitstreamVeraSans-Roman-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_9">
+     <g id="line2d_17">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#m93b0483c22" y="388.8"/>
+      </g>
+     </g>
+     <g id="line2d_18">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#m741efc42ff" y="43.2"/>
+      </g>
+     </g>
+     <g id="text_9">
+      <!-- 3.5 -->
+      <g transform="translate(509.8396875 401.918125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-33"/>
+       <use x="63.623046875" xlink:href="#BitstreamVeraSans-Roman-2e"/>
+       <use x="95.41015625" xlink:href="#BitstreamVeraSans-Roman-35"/>
+      </g>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_2">
+    <g id="ytick_1">
+     <g id="line2d_19">
+      <defs>
+       <path d="
+M0 0
+L4 0" id="m728421d6d4" style="stroke:#000000;stroke-width:0.5;"/>
+      </defs>
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="388.8"/>
+      </g>
+     </g>
+     <g id="line2d_20">
+      <defs>
+       <path d="
+M0 0
+L-4 0" id="mcb0005524f" style="stroke:#000000;stroke-width:0.5;"/>
+      </defs>
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="388.8"/>
+      </g>
+     </g>
+     <g id="text_10">
+      <!-- −3 -->
+      <g transform="translate(52.5425 392.11125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-2212"/>
+       <use x="83.7890625" xlink:href="#BitstreamVeraSans-Roman-33"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_2">
+     <g id="line2d_21">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="331.2"/>
+      </g>
+     </g>
+     <g id="line2d_22">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="331.2"/>
+      </g>
+     </g>
+     <g id="text_11">
+      <!-- −2 -->
+      <g transform="translate(52.7825 334.51125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-2212"/>
+       <use x="83.7890625" xlink:href="#BitstreamVeraSans-Roman-32"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_3">
+     <g id="line2d_23">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="273.6"/>
+      </g>
+     </g>
+     <g id="line2d_24">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="273.6"/>
+      </g>
+     </g>
+     <g id="text_12">
+      <!-- −1 -->
+      <g transform="translate(52.68875 276.91125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-2212"/>
+       <use x="83.7890625" xlink:href="#BitstreamVeraSans-Roman-31"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_4">
+     <g id="line2d_25">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="216.0"/>
+      </g>
+     </g>
+     <g id="line2d_26">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="216.0"/>
+      </g>
+     </g>
+     <g id="text_13">
+      <!-- 0 -->
+      <g transform="translate(61.953125 219.31125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_5">
+     <g id="line2d_27">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="158.4"/>
+      </g>
+     </g>
+     <g id="line2d_28">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="158.4"/>
+      </g>
+     </g>
+     <g id="text_14">
+      <!-- 1 -->
+      <g transform="translate(62.79125 161.71125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-31"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_6">
+     <g id="line2d_29">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="100.8"/>
+      </g>
+     </g>
+     <g id="line2d_30">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="100.8"/>
+      </g>
+     </g>
+     <g id="text_15">
+      <!-- 2 -->
+      <g transform="translate(62.44625 104.11125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-32"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_7">
+     <g id="line2d_31">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="43.2"/>
+      </g>
+     </g>
+     <g id="line2d_32">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="43.2"/>
+      </g>
+     </g>
+     <g id="text_16">
+      <!-- 3 -->
+      <g transform="translate(62.241875 46.51125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-33"/>
+      </g>
+     </g>
+    </g>
+   </g>
+   <g id="patch_3">
+    <path d="
+M72 43.2
+L518.4 43.2" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+   <g id="patch_4">
+    <path d="
+M518.4 388.8
+L518.4 43.2" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+   <g id="patch_5">
+    <path d="
+M72 388.8
+L518.4 388.8" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+   <g id="patch_6">
+    <path d="
+M72 388.8
+L72 43.2" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="p7ff5b81e1d">
+   <rect height="345.6" width="446.4" x="72.0" y="43.2"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 2cbb7054f832..be2dade15ebe 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1564,6 +1564,175 @@ def test_boxplot_bad_ci_2():
     fig, ax = plt.subplots()
     assert_raises(ValueError, ax.boxplot, [x, x],
                   conf_intervals=[[1, 2], [1]])
+# violin plot data initialization
+ax = plt.axes()
+data = [([ 0.07902449, -0.16769639,  1.1572525 ,  0.71400729, -0.17916727,
+       -1.15346725, -0.5298936 ,  1.16570619,  1.13612837, -0.66830221,
+       -0.76738509,  0.85911678,  0.56446469,  0.64772651, -1.97432723,
+       -1.11794413,  0.4094635 ,  2.52767469, -0.81092698, -0.23422668,
+        0.423861  ,  0.01702886, -0.58954823, -1.05303546,  0.22632754,
+       -1.88620214,  0.06759594, -0.51663253, -0.38821442, -0.5462294 ,
+       -0.39967334, -1.2690421 , -0.271953  ,  0.19494831,  1.0674446 ,
+        0.06632929,  0.9051155 , -0.06507299, -0.58885588,  0.03405925,
+        0.60666877, -0.25755542,  1.06387913, -0.50576651, -0.51104135,
+       -0.65366091, -1.10801137,  0.55746182,  0.27206281,  0.25658797,
+        0.008253  , -0.07254077, -0.77980703, -1.5707303 , -0.74731452,
+       -0.38364682,  1.37653142, -0.04123221, -0.84737153,  0.26552353,
+        0.80039697,  0.17446856,  0.32860543,  0.79574814, -1.88942134]), 
+     ([ 3.99586977e-01,   1.09626020e+00,   2.64974356e-01,5.49065532e-01,
+       -1.86679220e+00,  -4.23951661e-01, -3.66858136e-01,7.39441772e-02,
+       -1.25772592e+00, -1.14864510e+00,  -7.59625813e-01,-2.67830782e-01,
+       -2.68205909e-01,  -2.64119550e-02,  -3.00092210e-01,-1.17080290e-03,
+        1.25324397e+00,   1.97518726e-01, 9.74395138e-01,  -2.52217468e-01,
+       -2.00424239e+00, -2.20525681e+00,   6.32069078e-01,  -5.59674009e-02,
+       -1.13007054e+00,   8.47680697e-01,  -1.41563783e+00, 6.84885681e-02,  
+        8.06629024e-01,   1.06561293e+00, 1.48755064e-01,   1.06241336e+00, 
+       -1.53742677e+00, -9.40116707e-01,  -2.35342351e-01,   4.07790960e-01, 
+        9.59066810e-01,   1.83262266e+00,  -1.44675794e-01, -1.61663789e+00, 
+       -3.34055942e-01,  -1.65081542e+00, 6.54573563e-01,  -4.80998938e-01,
+       -4.77104620e-01, 4.35836897e-01,   1.54488583e-01,   1.90264111e+00,
+       -1.73584727e+00,   2.84097580e-01,  -6.67013428e-01, -5.47647643e-01, 
+       -1.77584471e-01,  -6.54191064e-01, 1.02366976e+00,   1.57777769e+00,  
+        2.10098337e-01, -5.34631915e-02, 4.28913084e-01,  -5.56544884e-02, 
+        1.64250239e-01,  -4.77299164e-01,  -8.40402132e-01, -1.58474541e-01]), 
+     ([-0.00975961, -0.9572654 , -0.02331628, -0.88758431,  0.36594918,
+        0.58733922,  0.12169127, -0.17451044, -1.48322656, -0.64203124,
+        1.01373274, -0.77332978, -1.64093613,  0.07944897,  1.79420792,
+       -0.95589844, -2.19618124,  0.99478738, -1.98933911,  0.21046525,
+       -2.31831045,  1.11045528, -0.51981581,  0.49740564, -0.40365721,
+       -0.30515722, -0.60601737, -1.05976064,  1.43356283, -0.59014164,
+        0.58822025,  1.80100922, -1.40905671,  0.74553523, -1.57655404,
+        0.29342432,  0.35548625, -0.99138976, -1.37339981,  0.63871936,
+       -0.60010678, -0.73597695, -0.12228469,  0.2467333 ,  0.03750118,
+       -0.45755544, -0.8648646 ,  0.13883081, -0.11239293, -0.7661388 ,
+       -0.70841112, -0.51668825,  2.2590876 ,  0.61731299, -0.33742898,
+        1.40708783, -1.43371511, -1.20425544,  0.79551956, -0.38148021,
+       -0.05703633, -0.42718744,  1.86441201, -0.36006341, -2.23769144]), 
+     ([ 0.28379466,  0.31202331,  0.54110464,  0.79957469,  0.02825945,
+        1.39430266,  0.38945253,  0.25840893, -1.03405387,  0.3951418 ,
+       -0.32782812, -0.49764761,  1.67314785,  0.57207158,  0.42868172,
+       -0.66405633,  0.49477738, -0.24707622, -0.91179434, -0.88450974,
+        1.47387423,  1.27147423, -1.28664994,  0.84428091,  0.19419244,
+       -1.27527008,  1.44462176,  1.21255381,  1.74448494, -1.47661372,
+       -1.00577117, -0.68746569, -0.85283125, -0.87339905, -0.05053922,
+        1.79110014, -0.99663248,  0.52435397,  1.17699107, -1.51437376,
+        0.52402067, -0.68885234,  1.84101899,  1.09318846,  0.66686321,
+       -1.14796045,  0.54247117, -2.21273401, -0.44526518,  1.08591603,
+       -1.86173825, -1.31016714,  0.7782744 ,  0.76330906, -0.96452241,
+       -1.34983597, -0.90317774,  0.20187156, -2.03515866,  1.35603702,
+        1.01390851,  0.29328188, -0.2223719 , -1.29928072,  0.59399753])]
+
+# violin plot test starts here
+@image_comparison(baseline_images=['test_vert_violinplot_baseline'])
+def test_vert_violinplot_baseline():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0)
+
+@image_comparison(baseline_images=['test_vert_violinplot_showmeans'])
+def test_vert_violinplot_showmeans():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),showmeans=1,showextrema=0,showmedians=0)
+
+@image_comparison(baseline_images=['test_vert_violinplot_showextrema'])
+def test_vert_violinplot_showextrema():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),showmeans=0,showextrema=1,showmedians=0)
+
+@image_comparison(baseline_images=['test_vert_violinplot_showmedians'])
+def test_vert_violinplot_showmedians():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=1)
+
+@image_comparison(baseline_images=['test_vert_violinplot_showall'])
+def test_vert_violinplot_showall():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),showmeans=1,showextrema=1,showmedians=1)
+
+
+@image_comparison(baseline_images=['test_vert_violinplot_customwidths'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_vert_violinplot_customwidths():
+    pass
+
+
+@image_comparison(baseline_images=['test_vert_violinplot_custompoints_10'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_vert_violinplot_custompoints_10():
+    pass
+
+
+@image_comparison(baseline_images=['test_vert_violinplot_custompoints_200'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_vert_violinplot_custompoints_200():
+    pass
+
+@image_comparison(baseline_images=['test_horiz_violinplot_baseline'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_baseline():
+    pass
+
+@image_comparison(baseline_images=['test_horiz_violinplot_showmedian'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_showmedian():
+    pass
+
+@image_comparison(baseline_images=['test_horiz_violinplot_showmean'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_showmean():
+    pass
+
+
+@image_comparison(baseline_images=['test_horiz_violinplot_showextrema'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_showextrema():
+    pass
+
+
+@image_comparison(baseline_images=['test_horiz_violinplot_show_mme'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_show_mme():
+    pass
+
+
+@image_comparison(baseline_images=['test_horiz_violinplot_customwidths'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_customwidths():
+    pass
+
+
+@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_10'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_custompoints_10():
+    pass
+
+
+@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_200'],
+                  extensions=['png'],
+                  savefig_kwarg={'dpi': 40})
+def test_horiz_violinplot_custompoints_200():
+    pass
+
+# test error
+def test_violinplot_bad_positions():
+    ax = plt.axes()
+    assert_raises(ValueError, ax.violinplot, data, positions=range(5))
+
+def test_violinplot_bad_widths():
+    ax = plt.axes()
+    assert_raises(ValueError, ax.violinplot, data, 
+                  positions=range(4), widths=[1,2,3])
+
+# violin plot test ends here
 
 
 @cleanup

From 2fa212af99d98da1a0a27d4a15517dcfbc3dd883 Mon Sep 17 00:00:00 2001
From: DonaldSeo <deokjae.seo@gmail.com>
Date: Wed, 2 Apr 2014 17:33:11 -0400
Subject: [PATCH 10/22] updated version of test_axes.py with violinplot tests

---
 lib/matplotlib/tests/test_axes.py | 84 +++++++++++--------------------
 1 file changed, 30 insertions(+), 54 deletions(-)

diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index be2dade15ebe..93038fc051ba 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1649,78 +1649,54 @@ def test_vert_violinplot_showall():
     ax.violinplot(data,range(4),showmeans=1,showextrema=1,showmedians=1)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_customwidths'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
-def test_vert_violinplot_customwidths():
-    pass
-
-
-@image_comparison(baseline_images=['test_vert_violinplot_custompoints_10'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
+@image_comparison(baseline_images=['test_vert_violinplot_custompoints_10'])
 def test_vert_violinplot_custompoints_10():
-    pass
+    ax = plt.axes()
+    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,10)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_custompoints_200'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
+@image_comparison(baseline_images=['test_vert_violinplot_custompoints_200'])
 def test_vert_violinplot_custompoints_200():
-    pass
+    ax = plt.axes()
+    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,200)
 
-@image_comparison(baseline_images=['test_horiz_violinplot_baseline'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
+@image_comparison(baseline_images=['test_horiz_violinplot_baseline'])
 def test_horiz_violinplot_baseline():
-    pass
-
-@image_comparison(baseline_images=['test_horiz_violinplot_showmedian'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
-def test_horiz_violinplot_showmedian():
-    pass
+    ax = plt.axes()
+    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0)
 
-@image_comparison(baseline_images=['test_horiz_violinplot_showmean'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
-def test_horiz_violinplot_showmean():
-    pass
+@image_comparison(baseline_images=['test_horiz_violinplot_showmedians'])
+def test_horiz_violinplot_showmedians():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=1)
 
+@image_comparison(baseline_images=['test_horiz_violinplot_showmeans'])
+def test_horiz_violinplot_showmeans():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),0,showmeans=1,showextrema=0,showmedians=0)
 
-@image_comparison(baseline_images=['test_horiz_violinplot_showextrema'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
+@image_comparison(baseline_images=['test_horiz_violinplot_showextrema'])
 def test_horiz_violinplot_showextrema():
-    pass
-
-
-@image_comparison(baseline_images=['test_horiz_violinplot_show_mme'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
-def test_horiz_violinplot_show_mme():
-    pass
+    ax = plt.axes()
+    ax.violinplot(data,range(4),0,showmeans=0,showextrema=1,showmedians=0)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_customwidths'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
-def test_horiz_violinplot_customwidths():
-    pass
+@image_comparison(baseline_images=['test_horiz_violinplot_showall'])
+def test_horiz_violinplot_showall():
+    ax = plt.axes()
+    ax.violinplot(data,range(4),0,showmeans=1,showextrema=1,showmedians=1)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_10'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
+@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_10'])
 def test_horiz_violinplot_custompoints_10():
-    pass
+    ax = plt.axes()
+    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,10)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_200'],
-                  extensions=['png'],
-                  savefig_kwarg={'dpi': 40})
+@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_200'])
 def test_horiz_violinplot_custompoints_200():
-    pass
+    ax = plt.axes()
+    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,200)
 
 # test error
 def test_violinplot_bad_positions():

From 14e6402e54ce91b2e7e353a88a062076d1fd618a Mon Sep 17 00:00:00 2001
From: limtaesu <limtaesu@gmail.com>
Date: Wed, 2 Apr 2014 17:55:42 -0400
Subject: [PATCH 11/22] Finished violin plot tests with horizontal feature

---
 .../test_horiz_violinplot_baseline.pdf        |  Bin 0 -> 16992 bytes
 .../test_horiz_violinplot_baseline.png        |  Bin 0 -> 33652 bytes
 .../test_horiz_violinplot_baseline.svg        | 1376 +++++++++++
 .../test_horiz_violinplot_custompoints_10.pdf |  Bin 0 -> 7566 bytes
 .../test_horiz_violinplot_custompoints_10.png |  Bin 0 -> 30585 bytes
 .../test_horiz_violinplot_custompoints_10.svg |  656 +++++
 ...test_horiz_violinplot_custompoints_200.pdf |  Bin 0 -> 27528 bytes
 ...test_horiz_violinplot_custompoints_200.png |  Bin 0 -> 33684 bytes
 ...test_horiz_violinplot_custompoints_200.svg | 2176 +++++++++++++++++
 .../test_horiz_violinplot_medianse.pdf        |  Bin 0 -> 21724 bytes
 .../test_horiz_violinplot_medianse.png        |  Bin 0 -> 40079 bytes
 .../test_horiz_violinplot_medianse.svg        | 1576 ++++++++++++
 .../test_horiz_violinplot_showall.pdf         |  Bin 0 -> 21708 bytes
 .../test_horiz_violinplot_showall.png         |  Bin 0 -> 34764 bytes
 .../test_horiz_violinplot_showall.svg         | 1516 ++++++++++++
 .../test_horiz_violinplot_showextrema.pdf     |  Bin 0 -> 19827 bytes
 .../test_horiz_violinplot_showextrema.png     |  Bin 0 -> 34313 bytes
 .../test_horiz_violinplot_showextrema.svg     | 1460 +++++++++++
 .../test_horiz_violinplot_showmeans.pdf       |  Bin 0 -> 17948 bytes
 .../test_horiz_violinplot_showmeans.png       |  Bin 0 -> 33910 bytes
 .../test_horiz_violinplot_showmeans.svg       | 1404 +++++++++++
 .../test_horiz_violinplot_showmedians.pdf     |  Bin 0 -> 17936 bytes
 .../test_horiz_violinplot_showmedians.png     |  Bin 0 -> 33913 bytes
 .../test_horiz_violinplot_showmedians.svg     | 1404 +++++++++++
 .../test_vert_violinplot_custompoints_10.pdf  |  Bin 0 -> 7575 bytes
 .../test_vert_violinplot_custompoints_10.png  |  Bin 0 -> 31172 bytes
 .../test_vert_violinplot_custompoints_10.svg  |  654 +++++
 .../test_vert_violinplot_custompoints_200.pdf |  Bin 0 -> 27157 bytes
 .../test_vert_violinplot_custompoints_200.png |  Bin 0 -> 33912 bytes
 .../test_vert_violinplot_custompoints_200.svg | 2174 ++++++++++++++++
 lib/matplotlib/tests/test_axes.py             |    8 +-
 31 files changed, 14400 insertions(+), 4 deletions(-)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmeans.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmeans.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmeans.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_10.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_10.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_10.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.svg

diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.png b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.png
new file mode 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmeans.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmeans.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_10.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_10.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.pdf
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+     <g id="line2d_27">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="158.4"/>
+      </g>
+     </g>
+     <g id="line2d_28">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="158.4"/>
+      </g>
+     </g>
+     <g id="text_14">
+      <!-- 1 -->
+      <g transform="translate(62.79125 161.71125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-31"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_6">
+     <g id="line2d_29">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="100.8"/>
+      </g>
+     </g>
+     <g id="line2d_30">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="100.8"/>
+      </g>
+     </g>
+     <g id="text_15">
+      <!-- 2 -->
+      <g transform="translate(62.44625 104.11125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-32"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_7">
+     <g id="line2d_31">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="72.0" xlink:href="#m728421d6d4" y="43.2"/>
+      </g>
+     </g>
+     <g id="line2d_32">
+      <g>
+       <use style="stroke:#000000;stroke-width:0.5;" x="518.4" xlink:href="#mcb0005524f" y="43.2"/>
+      </g>
+     </g>
+     <g id="text_16">
+      <!-- 3 -->
+      <g transform="translate(62.241875 46.51125)scale(0.12 -0.12)">
+       <use xlink:href="#BitstreamVeraSans-Roman-33"/>
+      </g>
+     </g>
+    </g>
+   </g>
+   <g id="patch_3">
+    <path d="
+M72 43.2
+L518.4 43.2" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+   <g id="patch_4">
+    <path d="
+M518.4 388.8
+L518.4 43.2" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+   <g id="patch_5">
+    <path d="
+M72 388.8
+L518.4 388.8" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+   <g id="patch_6">
+    <path d="
+M72 388.8
+L72 43.2" style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;"/>
+   </g>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="p7ff5b81e1d">
+   <rect height="345.6" width="446.4" x="72.0" y="43.2"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 93038fc051ba..5ac6b3cbc5c5 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1652,13 +1652,13 @@ def test_vert_violinplot_showall():
 @image_comparison(baseline_images=['test_vert_violinplot_custompoints_10'])
 def test_vert_violinplot_custompoints_10():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,10)
+    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,points=10)
 
 
 @image_comparison(baseline_images=['test_vert_violinplot_custompoints_200'])
 def test_vert_violinplot_custompoints_200():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,200)
+    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,points=200)
 
 @image_comparison(baseline_images=['test_horiz_violinplot_baseline'])
 def test_horiz_violinplot_baseline():
@@ -1690,13 +1690,13 @@ def test_horiz_violinplot_showall():
 @image_comparison(baseline_images=['test_horiz_violinplot_custompoints_10'])
 def test_horiz_violinplot_custompoints_10():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,10)
+    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,points=10)
 
 
 @image_comparison(baseline_images=['test_horiz_violinplot_custompoints_200'])
 def test_horiz_violinplot_custompoints_200():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,200)
+    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,points=200)
 
 # test error
 def test_violinplot_bad_positions():

From 8761ed4593b3dfd4670a49cc353b5610afa6b397 Mon Sep 17 00:00:00 2001
From: blah blah <blah@blah-VirtualBox.(none)>
Date: Wed, 2 Apr 2014 19:56:36 -0400
Subject: [PATCH 12/22] Initial tests for ksdensity

---
 lib/matplotlib/tests/test_mlab.py | 103 ++++++++++++++++++++++++++++++
 1 file changed, 103 insertions(+)

diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index 3a863edb0a21..1f73ba449e5d 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -2757,7 +2757,110 @@ def get_z(x, y):
     np.testing.assert_array_equal(np.ma.getmask(zi),
                                   np.ma.getmask(correct_zi_masked))
 
+#-----------------------------------------------------------------
+#-----------------------------------------------------------------
 
+class ksdensity_test():
+    def test_kde_integer_input(self):
+        """Regression test for #1181."""
+        x1 = np.arange(5)
+        kde = mlab.ksdensity(x1 + x1)
+        y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869, 0.13480721]
+        assert_array_almost_equal(kde["result"], y_expected, decimal=6)
+	
+    @knownfailureif(True)
+    def test_gaussian_kde_covariance_caching(self):
+        x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
+        xs = np.linspace(-10, 10, num=5)
+        # These expected values are from scipy 0.10, before some changes to
+        # gaussian_kde. They were not compared with any external reference.
+        y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754, 0.01664475]
+
+        # Set the bandwidth, then reset it to the default.
+        kde = mlab.ksdensity(x1, 0.5)
+        y2 = kde['result']
+
+        assert_array_almost_equal(y_expected, y2, decimal=7)
+
+        kde = mlab.ksdensity(xs, 'scott')
+        y2 = kde['result']
+
+        assert_array_almost_equal(y_expected, y2, decimal=7)
+
+    @knownfailureif(True)
+    def test_gaussian_kde_subclassing(self):
+        x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
+        xs = np.linspace(-10, 10, num=50)
+
+        # gaussian_kde itself
+        kde = mlab.ksdensity(xs)
+        ys = kde['result']
+
+        y_expected = [0.06292987, 0.06346938, 0.05860291, 0.08657652, 0.07904017]
+
+        assert_array_almost_equal(y_expected, ys, decimal=6)
+
+
+    @knownfailureif(True)
+    def test_kde_1d(self):
+        #some basic tests comparing to normal distribution
+        np.random.seed(8765678)
+        n_basesample = 500
+        xn = np.random.randn(n_basesample)
+        xnmean = xn.mean()
+        xnstd = xn.std(ddof=1)
+
+        xs = np.linspace(-7, 7, 501)
+
+        # get kde for original sample
+        gkde = mlab.ksdensity(xn)
+
+        # evaluate the density function for the kde for some points
+        kdepdf = gkde['result']
+        intervall = xs[1] - xs[0]
+
+        normpdf = np.random.normal(loc=xnmean, scale=xnstd)
+
+        assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01)
+        prob1 = gkde.integrate_box_1d(xnmean, np.inf)
+        prob2 = gkde.integrate_box_1d(-np.inf, xnmean)
+        assert_almost_equal(prob1, 0.5, decimal=1)
+        assert_almost_equal(prob2, 0.5, decimal=1)
+        assert_almost_equal(gkde.integrate_box(xnmean, np.inf), prob1, decimal=13)
+        assert_almost_equal(gkde.integrate_box(-np.inf, xnmean), prob2, decimal=13)
+
+        assert_almost_equal(gkde.integrate_kde(gkde),
+                        (kdepdf**2).sum()*intervall, decimal=2)
+        assert_almost_equal(gkde.integrate_gaussian(xnmean, xnstd**2),
+                        (kdepdf*normpdf).sum()*intervall, decimal=2)
+
+    def test_kde_bandwidth_method(self):
+
+        np.random.seed(8765678)
+        n_basesample = 50
+        xn = np.random.randn(n_basesample)
+
+        # Default
+        gkde = mlab.ksdensity(xn)
+        # Supply a callable
+        gkde2 =mlab.ksdensity(xn, 'scott')
+        assert_almost_equal(gkde['result'].all(), gkde2['result'].all())
+        # Supply a scalar
+        #gkde3 = mlab.ksdensity(xn, bw_method=gkde.factor)
+
+        xs = np.linspace(-7,7,51)
+        kdepdf = mlab.ksdensity(xs)
+        kdepdf2 = mlab.ksdensity(xs, 'scott')
+        #kdepdf3 = gkde3.evaluate(xs)
+        #assert_almost_equal(kdepdf, kdepdf3)
+
+        assert_raises(ValueError, mlab.ksdensity, xn, bw_method='wrongstring')
+
+
+
+
+#-----------------------------------------------------------------
+#-----------------------------------------------------------------
 if __name__ == '__main__':
     import nose
     import sys

From ef89a14ce3848d59c54990978b9ba464a99ad908 Mon Sep 17 00:00:00 2001
From: grdlok <adam.ortiz@utoronto.ca>
Date: Wed, 2 Apr 2014 21:48:41 -0400
Subject: [PATCH 13/22] re worked the tests in test_mlab to work with the
 modified GaussianKDE

---
 lib/matplotlib/tests/test_mlab.py | 91 +++++++------------------------
 1 file changed, 20 insertions(+), 71 deletions(-)

diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index 1f73ba449e5d..075ede97c8fc 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -2757,18 +2757,20 @@ def get_z(x, y):
     np.testing.assert_array_equal(np.ma.getmask(zi),
                                   np.ma.getmask(correct_zi_masked))
 
-#-----------------------------------------------------------------
-#-----------------------------------------------------------------
+#*****************************************************************
+#*****************************************************************
+
 
 class ksdensity_test():
+    
+
     def test_kde_integer_input(self):
         """Regression test for #1181."""
         x1 = np.arange(5)
-        kde = mlab.ksdensity(x1 + x1)
+        kde = mlab.GaussianKDE(x1)
         y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869, 0.13480721]
-        assert_array_almost_equal(kde["result"], y_expected, decimal=6)
+        assert_array_almost_equal(kde(x1), y_expected, decimal=6)
 	
-    @knownfailureif(True)
     def test_gaussian_kde_covariance_caching(self):
         x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
         xs = np.linspace(-10, 10, num=5)
@@ -2776,64 +2778,12 @@ def test_gaussian_kde_covariance_caching(self):
         # gaussian_kde. They were not compared with any external reference.
         y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754, 0.01664475]
 
-        # Set the bandwidth, then reset it to the default.
-        kde = mlab.ksdensity(x1, 0.5)
-        y2 = kde['result']
-
-        assert_array_almost_equal(y_expected, y2, decimal=7)
-
-        kde = mlab.ksdensity(xs, 'scott')
-        y2 = kde['result']
+        # set it to the default bandwidth.
+        kde2 = mlab.GaussianKDE(x1, 'scott')
+        y2 = kde2(xs)
 
         assert_array_almost_equal(y_expected, y2, decimal=7)
 
-    @knownfailureif(True)
-    def test_gaussian_kde_subclassing(self):
-        x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
-        xs = np.linspace(-10, 10, num=50)
-
-        # gaussian_kde itself
-        kde = mlab.ksdensity(xs)
-        ys = kde['result']
-
-        y_expected = [0.06292987, 0.06346938, 0.05860291, 0.08657652, 0.07904017]
-
-        assert_array_almost_equal(y_expected, ys, decimal=6)
-
-
-    @knownfailureif(True)
-    def test_kde_1d(self):
-        #some basic tests comparing to normal distribution
-        np.random.seed(8765678)
-        n_basesample = 500
-        xn = np.random.randn(n_basesample)
-        xnmean = xn.mean()
-        xnstd = xn.std(ddof=1)
-
-        xs = np.linspace(-7, 7, 501)
-
-        # get kde for original sample
-        gkde = mlab.ksdensity(xn)
-
-        # evaluate the density function for the kde for some points
-        kdepdf = gkde['result']
-        intervall = xs[1] - xs[0]
-
-        normpdf = np.random.normal(loc=xnmean, scale=xnstd)
-
-        assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01)
-        prob1 = gkde.integrate_box_1d(xnmean, np.inf)
-        prob2 = gkde.integrate_box_1d(-np.inf, xnmean)
-        assert_almost_equal(prob1, 0.5, decimal=1)
-        assert_almost_equal(prob2, 0.5, decimal=1)
-        assert_almost_equal(gkde.integrate_box(xnmean, np.inf), prob1, decimal=13)
-        assert_almost_equal(gkde.integrate_box(-np.inf, xnmean), prob2, decimal=13)
-
-        assert_almost_equal(gkde.integrate_kde(gkde),
-                        (kdepdf**2).sum()*intervall, decimal=2)
-        assert_almost_equal(gkde.integrate_gaussian(xnmean, xnstd**2),
-                        (kdepdf*normpdf).sum()*intervall, decimal=2)
-
     def test_kde_bandwidth_method(self):
 
         np.random.seed(8765678)
@@ -2841,26 +2791,25 @@ def test_kde_bandwidth_method(self):
         xn = np.random.randn(n_basesample)
 
         # Default
-        gkde = mlab.ksdensity(xn)
+        gkde = mlab.GaussianKDE(xn)
         # Supply a callable
-        gkde2 =mlab.ksdensity(xn, 'scott')
-        assert_almost_equal(gkde['result'].all(), gkde2['result'].all())
+        gkde2 = mlab.GaussianKDE(xn, 'scott')
         # Supply a scalar
-        #gkde3 = mlab.ksdensity(xn, bw_method=gkde.factor)
+        gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)
 
         xs = np.linspace(-7,7,51)
-        kdepdf = mlab.ksdensity(xs)
-        kdepdf2 = mlab.ksdensity(xs, 'scott')
-        #kdepdf3 = gkde3.evaluate(xs)
-        #assert_almost_equal(kdepdf, kdepdf3)
+        kdepdf = gkde.evaluate(xs)
+        kdepdf2 = gkde2.evaluate(xs)
+        assert_almost_equal(kdepdf.all(), kdepdf2.all())
+        kdepdf3 = gkde3.evaluate(xs)
+        assert_almost_equal(kdepdf.all(), kdepdf3.all())
 
-        assert_raises(ValueError, mlab.ksdensity, xn, bw_method='wrongstring')
 
 
+#*****************************************************************
+#*****************************************************************
 
 
-#-----------------------------------------------------------------
-#-----------------------------------------------------------------
 if __name__ == '__main__':
     import nose
     import sys

From 0555ef11e581038813dbd178d1523c893db8b6a9 Mon Sep 17 00:00:00 2001
From: Per Parker <wisalam@live.com>
Date: Wed, 2 Apr 2014 22:55:41 -0400
Subject: [PATCH 14/22] Fixed some style issues. Added violinplot to
 boilerplate.py Added comments for test cases referencing the origins.

---
 boilerplate.py                    |   1 +
 lib/matplotlib/pyplot.py          |  22 ++++
 lib/matplotlib/tests/test_axes.py | 169 +++++++++++++++++-------------
 lib/matplotlib/tests/test_mlab.py |  17 +--
 4 files changed, 130 insertions(+), 79 deletions(-)

diff --git a/boilerplate.py b/boilerplate.py
index 5bcbf03f741a..1791c808192d 100644
--- a/boilerplate.py
+++ b/boilerplate.py
@@ -146,6 +146,7 @@ def boilerplate_gen():
         'tricontourf',
         'tripcolor',
         'triplot',
+        'violinplot',
         'vlines',
         'xcorr',
         'barbs',
diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py
index da553fe212c6..a33e577d785c 100644
--- a/lib/matplotlib/pyplot.py
+++ b/lib/matplotlib/pyplot.py
@@ -3396,6 +3396,28 @@ def triplot(*args, **kwargs):
 
     return ret
 
+# This function was autogenerated by boilerplate.py.  Do not edit as
+# changes will be lost
+@_autogen_docstring(Axes.violinplot)
+def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
+               showextrema=True, showmedians=False, points=100, hold=None):
+    ax = gca()
+    # allow callers to override the hold state by passing hold=True|False
+    washold = ax.ishold()
+
+    if hold is not None:
+        ax.hold(hold)
+    try:
+        ret = ax.violinplot(dataset, positions=positions, vert=vert,
+                            widths=widths, showmeans=showmeans,
+                            showextrema=showextrema, showmedians=showmedians,
+                            points=points)
+        draw_if_interactive()
+    finally:
+        ax.hold(washold)
+
+    return ret
+
 # This function was autogenerated by boilerplate.py.  Do not edit as
 # changes will be lost
 @_autogen_docstring(Axes.vlines)
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 5ac6b3cbc5c5..5f6ca42def11 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1564,149 +1564,176 @@ def test_boxplot_bad_ci_2():
     fig, ax = plt.subplots()
     assert_raises(ValueError, ax.boxplot, [x, x],
                   conf_intervals=[[1, 2], [1]])
+
+
 # violin plot data initialization
 ax = plt.axes()
-data = [([ 0.07902449, -0.16769639,  1.1572525 ,  0.71400729, -0.17916727,
-       -1.15346725, -0.5298936 ,  1.16570619,  1.13612837, -0.66830221,
-       -0.76738509,  0.85911678,  0.56446469,  0.64772651, -1.97432723,
-       -1.11794413,  0.4094635 ,  2.52767469, -0.81092698, -0.23422668,
-        0.423861  ,  0.01702886, -0.58954823, -1.05303546,  0.22632754,
-       -1.88620214,  0.06759594, -0.51663253, -0.38821442, -0.5462294 ,
-       -0.39967334, -1.2690421 , -0.271953  ,  0.19494831,  1.0674446 ,
-        0.06632929,  0.9051155 , -0.06507299, -0.58885588,  0.03405925,
-        0.60666877, -0.25755542,  1.06387913, -0.50576651, -0.51104135,
-       -0.65366091, -1.10801137,  0.55746182,  0.27206281,  0.25658797,
-        0.008253  , -0.07254077, -0.77980703, -1.5707303 , -0.74731452,
-       -0.38364682,  1.37653142, -0.04123221, -0.84737153,  0.26552353,
-        0.80039697,  0.17446856,  0.32860543,  0.79574814, -1.88942134]), 
-     ([ 3.99586977e-01,   1.09626020e+00,   2.64974356e-01,5.49065532e-01,
-       -1.86679220e+00,  -4.23951661e-01, -3.66858136e-01,7.39441772e-02,
-       -1.25772592e+00, -1.14864510e+00,  -7.59625813e-01,-2.67830782e-01,
-       -2.68205909e-01,  -2.64119550e-02,  -3.00092210e-01,-1.17080290e-03,
-        1.25324397e+00,   1.97518726e-01, 9.74395138e-01,  -2.52217468e-01,
-       -2.00424239e+00, -2.20525681e+00,   6.32069078e-01,  -5.59674009e-02,
-       -1.13007054e+00,   8.47680697e-01,  -1.41563783e+00, 6.84885681e-02,  
-        8.06629024e-01,   1.06561293e+00, 1.48755064e-01,   1.06241336e+00, 
-       -1.53742677e+00, -9.40116707e-01,  -2.35342351e-01,   4.07790960e-01, 
-        9.59066810e-01,   1.83262266e+00,  -1.44675794e-01, -1.61663789e+00, 
-       -3.34055942e-01,  -1.65081542e+00, 6.54573563e-01,  -4.80998938e-01,
-       -4.77104620e-01, 4.35836897e-01,   1.54488583e-01,   1.90264111e+00,
-       -1.73584727e+00,   2.84097580e-01,  -6.67013428e-01, -5.47647643e-01, 
-       -1.77584471e-01,  -6.54191064e-01, 1.02366976e+00,   1.57777769e+00,  
-        2.10098337e-01, -5.34631915e-02, 4.28913084e-01,  -5.56544884e-02, 
-        1.64250239e-01,  -4.77299164e-01,  -8.40402132e-01, -1.58474541e-01]), 
-     ([-0.00975961, -0.9572654 , -0.02331628, -0.88758431,  0.36594918,
-        0.58733922,  0.12169127, -0.17451044, -1.48322656, -0.64203124,
-        1.01373274, -0.77332978, -1.64093613,  0.07944897,  1.79420792,
-       -0.95589844, -2.19618124,  0.99478738, -1.98933911,  0.21046525,
-       -2.31831045,  1.11045528, -0.51981581,  0.49740564, -0.40365721,
-       -0.30515722, -0.60601737, -1.05976064,  1.43356283, -0.59014164,
-        0.58822025,  1.80100922, -1.40905671,  0.74553523, -1.57655404,
-        0.29342432,  0.35548625, -0.99138976, -1.37339981,  0.63871936,
-       -0.60010678, -0.73597695, -0.12228469,  0.2467333 ,  0.03750118,
-       -0.45755544, -0.8648646 ,  0.13883081, -0.11239293, -0.7661388 ,
-       -0.70841112, -0.51668825,  2.2590876 ,  0.61731299, -0.33742898,
-        1.40708783, -1.43371511, -1.20425544,  0.79551956, -0.38148021,
-       -0.05703633, -0.42718744,  1.86441201, -0.36006341, -2.23769144]), 
-     ([ 0.28379466,  0.31202331,  0.54110464,  0.79957469,  0.02825945,
-        1.39430266,  0.38945253,  0.25840893, -1.03405387,  0.3951418 ,
-       -0.32782812, -0.49764761,  1.67314785,  0.57207158,  0.42868172,
-       -0.66405633,  0.49477738, -0.24707622, -0.91179434, -0.88450974,
-        1.47387423,  1.27147423, -1.28664994,  0.84428091,  0.19419244,
-       -1.27527008,  1.44462176,  1.21255381,  1.74448494, -1.47661372,
-       -1.00577117, -0.68746569, -0.85283125, -0.87339905, -0.05053922,
-        1.79110014, -0.99663248,  0.52435397,  1.17699107, -1.51437376,
-        0.52402067, -0.68885234,  1.84101899,  1.09318846,  0.66686321,
-       -1.14796045,  0.54247117, -2.21273401, -0.44526518,  1.08591603,
-       -1.86173825, -1.31016714,  0.7782744 ,  0.76330906, -0.96452241,
-       -1.34983597, -0.90317774,  0.20187156, -2.03515866,  1.35603702,
-        1.01390851,  0.29328188, -0.2223719 , -1.29928072,  0.59399753])]
+data = [([+0.07902449, -0.16769639, +1.1572525,  +0.71400729, -0.17916727,
+          -1.15346725, -0.5298936,  +1.16570619, +1.13612837, -0.66830221,
+          -0.76738509, +0.85911678, +0.56446469, +0.64772651, -1.97432723,
+          -1.11794413, +0.4094635,  +2.52767469, -0.81092698, -0.23422668,
+          +0.423861,   +0.01702886, -0.58954823, -1.05303546, +0.22632754,
+          -1.88620214, +0.06759594, -0.51663253, -0.38821442, -0.5462294,
+          -0.39967334, -1.2690421,  -0.271953,   +0.19494831, +1.0674446,
+          +0.06632929, +0.9051155,  -0.06507299, -0.58885588, +0.03405925,
+          +0.60666877, -0.25755542, +1.06387913, -0.50576651, -0.51104135,
+          -0.65366091, -1.10801137, +0.55746182, +0.27206281, +0.25658797,
+          +0.008253,   -0.07254077, -0.77980703, -1.5707303,  -0.74731452,
+          -0.38364682, +1.37653142, -0.04123221, -0.84737153, +0.26552353,
+          +0.80039697, +0.17446856, +0.32860543, +0.79574814, -1.88942134]),
+        ([+3.99586977e-01, +1.09626020e+00, +2.64974356e-01, +5.49065532e-01,
+          -1.86679220e+00, -4.23951661e-01, -3.66858136e-01, +7.39441772e-02,
+          -1.25772592e+00, -1.14864510e+00, -7.59625813e-01, -2.67830782e-01,
+          -2.68205909e-01, -2.64119550e-02, -3.00092210e-01, -1.17080290e-03,
+          +1.25324397e+00, +1.97518726e-01, +9.74395138e-01, -2.52217468e-01,
+          -2.00424239e+00, -2.20525681e+00, +6.32069078e-01, -5.59674009e-02,
+          -1.13007054e+00, +8.47680697e-01, -1.41563783e+00, +6.84885681e-02,
+          +8.06629024e-01, +1.06561293e+00, +1.48755064e-01, +1.06241336e+00,
+          -1.53742677e+00, -9.40116707e-01, -2.35342351e-01, +4.07790960e-01,
+          +9.59066810e-01, +1.83262266e+00, -1.44675794e-01, -1.61663789e+00,
+          -3.34055942e-01, -1.65081542e+00, +6.54573563e-01, -4.80998938e-01,
+          -4.77104620e-01, +4.35836897e-01, +1.54488583e-01, +1.90264111e+00,
+          -1.73584727e+00, +2.84097580e-01, -6.67013428e-01, -5.47647643e-01,
+          -1.77584471e-01, -6.54191064e-01, +1.02366976e+00, +1.57777769e+00,
+          +2.10098337e-01, -5.34631915e-02, +4.28913084e-01, -5.56544884e-02,
+          +1.64250239e-01, -4.77299164e-01, -8.40402132e-01, -1.58474541e-01]),
+        ([-0.00975961, -0.9572654,  -0.02331628, -0.88758431, +0.36594918,
+          +0.58733922, +0.12169127, -0.17451044, -1.48322656, -0.64203124,
+          +1.01373274, -0.77332978, -1.64093613, +0.07944897, +1.79420792,
+          -0.95589844, -2.19618124, +0.99478738, -1.98933911, +0.21046525,
+          -2.31831045, +1.11045528, -0.51981581, +0.49740564, -0.40365721,
+          -0.30515722, -0.60601737, -1.05976064, +1.43356283, -0.59014164,
+          +0.58822025, +1.80100922, -1.40905671, +0.74553523, -1.57655404,
+          +0.29342432, +0.35548625, -0.99138976, -1.37339981, +0.63871936,
+          -0.60010678, -0.73597695, -0.12228469, +0.2467333,  +0.03750118,
+          -0.45755544, -0.8648646,  +0.13883081, -0.11239293, -0.7661388,
+          -0.70841112, -0.51668825, +2.2590876,  +0.61731299, -0.33742898,
+          +1.40708783, -1.43371511, -1.20425544, +0.79551956, -0.38148021,
+          -0.05703633, -0.42718744, +1.86441201, -0.36006341, -2.23769144]),
+        ([+0.28379466, +0.31202331, +0.54110464, +0.79957469, +0.02825945,
+          +1.39430266, +0.38945253, +0.25840893, -1.03405387, +0.3951418,
+          -0.32782812, -0.49764761, +1.67314785, +0.57207158, +0.42868172,
+          -0.66405633, +0.49477738, -0.24707622, -0.91179434, -0.88450974,
+          +1.47387423, +1.27147423, -1.28664994, +0.84428091, +0.19419244,
+          -1.27527008, +1.44462176, +1.21255381, +1.74448494, -1.47661372,
+          -1.00577117, -0.68746569, -0.85283125, -0.87339905, -0.05053922,
+          +1.79110014, -0.99663248, +0.52435397, +1.17699107, -1.51437376,
+          +0.52402067, -0.68885234, +1.84101899, +1.09318846, +0.66686321,
+          -1.14796045, +0.54247117, -2.21273401, -0.44526518, +1.08591603,
+          -1.86173825, -1.31016714, +0.7782744,  +0.76330906, -0.96452241,
+          -1.34983597, -0.90317774, +0.20187156, -2.03515866, +1.35603702,
+          +1.01390851, +0.29328188, -0.2223719,  -1.29928072, +0.59399753])]
+
 
 # violin plot test starts here
 @image_comparison(baseline_images=['test_vert_violinplot_baseline'])
 def test_vert_violinplot_baseline():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0)
+    ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
+                  showmedians=0)
+
 
 @image_comparison(baseline_images=['test_vert_violinplot_showmeans'])
 def test_vert_violinplot_showmeans():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=1,showextrema=0,showmedians=0)
+    ax.violinplot(data, positions=range(4), showmeans=1, showextrema=0,
+                  showmedians=0)
+
 
 @image_comparison(baseline_images=['test_vert_violinplot_showextrema'])
 def test_vert_violinplot_showextrema():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=0,showextrema=1,showmedians=0)
+    ax.violinplot(data, positions=range(4), showmeans=0, showextrema=1,
+                  showmedians=0)
+
 
 @image_comparison(baseline_images=['test_vert_violinplot_showmedians'])
 def test_vert_violinplot_showmedians():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=1)
+    ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
+                  showmedians=1)
+
 
 @image_comparison(baseline_images=['test_vert_violinplot_showall'])
 def test_vert_violinplot_showall():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=1,showextrema=1,showmedians=1)
+    ax.violinplot(data, positions=range(4), showmeans=1, showextrema=1,
+                  showmedians=1)
 
 
 @image_comparison(baseline_images=['test_vert_violinplot_custompoints_10'])
 def test_vert_violinplot_custompoints_10():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,points=10)
+    ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
+                  showmedians=0, points=10)
 
 
 @image_comparison(baseline_images=['test_vert_violinplot_custompoints_200'])
 def test_vert_violinplot_custompoints_200():
     ax = plt.axes()
-    ax.violinplot(data,range(4),showmeans=0,showextrema=0,showmedians=0,points=200)
+    ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
+                  showmedians=0, points=200)
+
 
 @image_comparison(baseline_images=['test_horiz_violinplot_baseline'])
 def test_horiz_violinplot_baseline():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0)
+    ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
+                  showextrema=0, showmedians=0)
+
 
 @image_comparison(baseline_images=['test_horiz_violinplot_showmedians'])
 def test_horiz_violinplot_showmedians():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=1)
+    ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
+                  showextrema=0, showmedians=1)
+
 
 @image_comparison(baseline_images=['test_horiz_violinplot_showmeans'])
 def test_horiz_violinplot_showmeans():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=1,showextrema=0,showmedians=0)
+    ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
+                  showextrema=0, showmedians=0)
+
 
 @image_comparison(baseline_images=['test_horiz_violinplot_showextrema'])
 def test_horiz_violinplot_showextrema():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=0,showextrema=1,showmedians=0)
+    ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
+                  showextrema=1, showmedians=0)
 
 
 @image_comparison(baseline_images=['test_horiz_violinplot_showall'])
 def test_horiz_violinplot_showall():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=1,showextrema=1,showmedians=1)
+    ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
+                  showextrema=1, showmedians=1)
 
 
 @image_comparison(baseline_images=['test_horiz_violinplot_custompoints_10'])
 def test_horiz_violinplot_custompoints_10():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,points=10)
+    ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
+                  showextrema=0, showmedians=0, points=10)
 
 
 @image_comparison(baseline_images=['test_horiz_violinplot_custompoints_200'])
 def test_horiz_violinplot_custompoints_200():
     ax = plt.axes()
-    ax.violinplot(data,range(4),0,showmeans=0,showextrema=0,showmedians=0,points=200)
+    ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
+                  showextrema=0, showmedians=0, points=200)
+
 
 # test error
 def test_violinplot_bad_positions():
     ax = plt.axes()
     assert_raises(ValueError, ax.violinplot, data, positions=range(5))
 
+
 def test_violinplot_bad_widths():
     ax = plt.axes()
-    assert_raises(ValueError, ax.violinplot, data, 
-                  positions=range(4), widths=[1,2,3])
+    assert_raises(ValueError, ax.violinplot, data, positions=range(4),
+                  widths=[1, 2, 3])
 
 # violin plot test ends here
 
diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index 075ede97c8fc..f8b86b8f7204 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -2758,25 +2758,28 @@ def get_z(x, y):
                                   np.ma.getmask(correct_zi_masked))
 
 #*****************************************************************
+# These Tests where taken from SCIPY with some minor modifications
+# this can be retreived from: 
+# https://github.com/scipy/scipy/blob/master/scipy/stats/tests/test_kdeoth.py
 #*****************************************************************
 
-
 class ksdensity_test():
-    
 
     def test_kde_integer_input(self):
         """Regression test for #1181."""
         x1 = np.arange(5)
         kde = mlab.GaussianKDE(x1)
-        y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869, 0.13480721]
+        y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869,
+                      0.13480721]
         assert_array_almost_equal(kde(x1), y_expected, decimal=6)
-	
+
     def test_gaussian_kde_covariance_caching(self):
         x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
         xs = np.linspace(-10, 10, num=5)
         # These expected values are from scipy 0.10, before some changes to
         # gaussian_kde. They were not compared with any external reference.
-        y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754, 0.01664475]
+        y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754,
+                      0.01664475]
 
         # set it to the default bandwidth.
         kde2 = mlab.GaussianKDE(x1, 'scott')
@@ -2797,7 +2800,7 @@ def test_kde_bandwidth_method(self):
         # Supply a scalar
         gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)
 
-        xs = np.linspace(-7,7,51)
+        xs = np.linspace(-7, 7, 51)
         kdepdf = gkde.evaluate(xs)
         kdepdf2 = gkde2.evaluate(xs)
         assert_almost_equal(kdepdf.all(), kdepdf2.all())
@@ -2805,11 +2808,9 @@ def test_kde_bandwidth_method(self):
         assert_almost_equal(kdepdf.all(), kdepdf3.all())
 
 
-
 #*****************************************************************
 #*****************************************************************
 
-
 if __name__ == '__main__':
     import nose
     import sys

From 62776249dfefe26715cb028d922d16248fe9aecd Mon Sep 17 00:00:00 2001
From: kelsiegr <gregory.kelsie@mail.utoronto.ca>
Date: Thu, 3 Apr 2014 13:57:50 -0400
Subject: [PATCH 15/22] Added silverman and scott tests.

---
 lib/matplotlib/tests/test_mlab.py | 116 ++++++++++++++++++++++++++++++
 1 file changed, 116 insertions(+)

diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index f8b86b8f7204..0d950e6ece03 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -2806,6 +2806,122 @@ def test_kde_bandwidth_method(self):
         assert_almost_equal(kdepdf.all(), kdepdf2.all())
         kdepdf3 = gkde3.evaluate(xs)
         assert_almost_equal(kdepdf.all(), kdepdf3.all())
+        
+#CUSTOM TESTS
+class ksdensity_custom_tests(object):
+class ksdensity_custom_tests(object):
+    def test_no_data(self):
+        """Pass no data into the GaussianKDE class."""
+        mygauss = mlab.GaussianKDE([])
+        self.assertRaises(ValueError,
+                          "`dataset` input should have multiple elements.")
+
+    def test_single_dataset_element(self):
+        """Pass a single dataset element into the GaussianKDE class."""
+        myguass = mlab.GuassianKDE([42])
+        self.assertRaises(ValueError,
+                          "`dataset` input should have multiple elements.")
+
+    def test_silverman_multidim_dataset(self):
+        """Use a multi-dimensional array as the dataset and test silverman's
+        output"""
+        x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        mygauss = mlab.GaussianKDE(x1, "silverman")
+        othergauss = stats.gaussian_kde(x1)
+        expected_output = othergauss.covariance_factor()
+        assert mygauss.covariance_factor() == expected_output
+
+    def test_silverman_singledim_dataset(self):
+        """Use a single dimension list as the dataset and test silverman's
+        output."""
+        x1 = np.array([-7, -5, 1, 4, 5])
+        mygauss = mlab.GaussianKDE(x1, "silverman")
+        othergauss = stats.gaussian_kde(x1)
+        expected_output = othergauss.covariance_factor()
+        assert mygauss.covariance_factor() == expected_output
+
+    def test_scott_multidim_dataset(self):
+        """Use a multi-dimensional array as the dataset and test scott's output
+        """
+        x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        mygauss = mlab.GaussianKDE(x1, "scott")
+        othergauss = stats.gaussian_kde(x1)
+        expected_output = othergauss.covariance_factor()
+        assert mygauss.covariance_factor() == expected_output
+
+    def test_scott_singledim_dataset(self):
+        """Use a single-dimensional array as the dataset and test scott's
+        output"""
+        x1 = np.array([-7, -5, 1, 4, 5])
+        mygauss = mlab.GaussianKDE(x1, "scott")
+        othergauss = stats.gaussian_kde(x1)
+        expected_output = othergauss.covariance_factor()
+        assert mygauss.covariance_factor() == expected_output
+
+    def test_scalar_multidim_dataset(self):
+        """Use a multi-dimensional array as the dataset and test a scalar's
+        output"""
+        pass
+
+    def test_scalar_singledim_dataset(self):
+        """Use a single-dimensional array as the dataset and test the scalar's
+        output"""
+        pass
+
+    def test_callable_empty_dataset(self):
+        """Use an empty array as the dataset and test the callable's cov factor
+        """
+        pass
+
+    def test_callable_multidim_dataset(self):
+        """Use an multi-dimensional array as the dataset and test the
+        callable's cov factor"""
+        pass
+
+    def test_callable_singledim_dataset(self):
+        """Use a single-dimensional array as the dataset and test the
+        callable's  cov factor"""
+        pass
+
+    def test_wrong_bw_method(self):
+        """Test the error message that should be called when bw is invalid."""
+        pass
+
+
+class evaluate_tests(object):
+    def test_evaluate_diff_dim(self):
+        """Test the evaluate method when the dim's of dataset and points are
+        different dimensions"""
+        pass
+
+    def test_evaluate_inv_dim(self):
+        """ Invert the dimensions. I.e Give the dataset a dimension of 1
+        [3,2,4], and the points will have a dimension of 3 [[3],[2],[4]]"""
+        pass
+
+    def test_evaluate_point_dim_not_one(self):
+        """Test"""
+        pass
+
+    def test_evaluate_numm_equal_dataset_dim(self):
+        pass
+
+    def test_evaluate_equal_dim_and_numm_lt(self):
+        """Test when line 3810 fails"""
+        pass
+
+    def test_evaluate_equal_dim_and_numm_gte(self):
+        """Test when line 3810 passes"""
+        pass
+
+    def test_evaluate_nequal_dim_and_numm_lt(self):
+        """Test when line 3810 fails"""
+        pass
+
+    def test_evaluate_nequal_dim_and_numm_gte(self):
+        """Test when line 3810 passes"""
+        pass
+
 
 
 #*****************************************************************

From 39c66a98722588d00216af04c51bbc2977362721 Mon Sep 17 00:00:00 2001
From: grdlok <adam.ortiz@utoronto.ca>
Date: Thu, 3 Apr 2014 15:54:52 -0400
Subject: [PATCH 16/22] implementations of the evaluate tests

Conflicts:
	lib/matplotlib/tests/test_mlab.py
---
 lib/matplotlib/tests/test_mlab.py | 73 ++++++++++++++++++++-----------
 1 file changed, 47 insertions(+), 26 deletions(-)

diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index 0d950e6ece03..20070272473e 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -2889,39 +2889,60 @@ def test_wrong_bw_method(self):
 
 
 class evaluate_tests(object):
+
+    @knownfailureif(True)
     def test_evaluate_diff_dim(self):
         """Test the evaluate method when the dim's of dataset and points are
-        different dimensions"""
-        pass
-
+        different dimensions"""	
+        x1 = np.arange(3, 10, 2)
+	kde = mlab.GaussianKDE(x1)
+	x2 = np.arange(3, 12, 2)
+	y_expected = [0, 0, 0, 0, 0]
+	y = kde.evaluate(x2)
+
+	assert_array_almost_equal(y, y_expected, 7)
+	
     def test_evaluate_inv_dim(self):
-        """ Invert the dimensions. I.e Give the dataset a dimension of 1
-        [3,2,4], and the points will have a dimension of 3 [[3],[2],[4]]"""
-        pass
-
+        """ Invert the dimensions. I.e Give the dataset a dimension of 1 [3,2,4],
+        and the points will have a dimension of 3 [[3],[2],[4]]. ValueError 
+        should be raised"""
+        x1 = np.arange(3, 10, 2)
+	kde = mlab.GaussianKDE(x1)
+	x2 = np.array([[3],[5],[7],[9]])
+	
+        assert_raises(ValueError, kde.evaluate, x2)
+		
+    @knownfailureif(True)
+    def test_evaluate_dim_and_num(self):
+	""" Tests if evaluated against a one by one array"""
+	x1 = np.arange(3, 10, 2)
+        x2 = np.array([3])
+	kde = mlab.GaussianKDE(x1)
+	y_expected = [0]
+	y = kde.evaluate(x2)
+	
+	assert_array_almost_equal(y, y_expected, 7)
+   
+ 
     def test_evaluate_point_dim_not_one(self):
         """Test"""
-        pass
-
-    def test_evaluate_numm_equal_dataset_dim(self):
-        pass
-
+        x1 = np.arange(3, 10, 2)
+	x2 = [np.arange(3, 10, 2), np.arange(3, 10, 2)]
+	kde = mlab.GaussianKDE(x1)
+	
+        assert_raises(ValueError, kde.evaluate, x2)
+    
+    @knownfailureif(True)
     def test_evaluate_equal_dim_and_numm_lt(self):
         """Test when line 3810 fails"""
-        pass
-
-    def test_evaluate_equal_dim_and_numm_gte(self):
-        """Test when line 3810 passes"""
-        pass
-
-    def test_evaluate_nequal_dim_and_numm_lt(self):
-        """Test when line 3810 fails"""
-        pass
-
-    def test_evaluate_nequal_dim_and_numm_gte(self):
-        """Test when line 3810 passes"""
-        pass
-
+        x1 = np.arange(3, 10, 2)
+	x2 = np.arange(3, 8, 2)
+	kde = mlab.GaussianKDE(x1)
+        y_expected = [0, 0, 0]
+	y = kde.evaluate(x2)
+
+	assert_array_almost_equal(y, y_expected, 7)
+    
 
 
 #*****************************************************************

From ca7d605a7a95d1f493c68dd86ba17c5b49b3b44f Mon Sep 17 00:00:00 2001
From: Kevin Chan <kvii.chan@gmail.com>
Date: Thu, 3 Apr 2014 20:53:41 -0400
Subject: [PATCH 17/22] Fixed kde for invalid bw_methods, misc test formatting.
 Some course related tests were added. These are removed later.

---
 lib/matplotlib/__init__.py                    |    1 +
 lib/matplotlib/mlab.py                        |   23 +-
 .../baseline_images/vp_coverage/blank.pdf     |  Bin 0 -> 5902 bytes
 .../baseline_images/vp_coverage/blank.png     |  Bin 0 -> 10483 bytes
 .../baseline_images/vp_coverage/blank.svg     |  509 ++++++
 .../vp_coverage/violinplot_hold_true.pdf      |  Bin 0 -> 18943 bytes
 .../vp_coverage/violinplot_hold_true.png      |  Bin 0 -> 19702 bytes
 .../vp_coverage/violinplot_hold_true.svg      | 1586 +++++++++++++++++
 .../vp_coverage/violinplot_means.pdf          |  Bin 0 -> 13141 bytes
 .../vp_coverage/violinplot_means.png          |  Bin 0 -> 18437 bytes
 .../vp_coverage/violinplot_means.svg          | 1050 +++++++++++
 .../vp_coverage/violinplot_medians.pdf        |  Bin 0 -> 13141 bytes
 .../vp_coverage/violinplot_medians.png        |  Bin 0 -> 18439 bytes
 .../vp_coverage/violinplot_medians.svg        | 1050 +++++++++++
 .../vp_coverage/violinplot_noextrema.pdf      |  Bin 0 -> 9605 bytes
 .../vp_coverage/violinplot_noextrema.png      |  Bin 0 -> 14630 bytes
 .../vp_coverage/violinplot_noextrema.svg      |  933 ++++++++++
 .../vp_coverage/violinplot_scalar_width.pdf   |  Bin 0 -> 11433 bytes
 .../vp_coverage/violinplot_scalar_width.png   |  Bin 0 -> 17207 bytes
 .../vp_coverage/violinplot_scalar_width.svg   | 1096 ++++++++++++
 .../vp_coverage/violinplot_simple_dist.pdf    |  Bin 0 -> 10860 bytes
 .../vp_coverage/violinplot_simple_dist.png    |  Bin 0 -> 15059 bytes
 .../vp_coverage/violinplot_simple_dist.svg    |  981 ++++++++++
 .../vp_coverage/violinplot_simple_pos.pdf     |  Bin 0 -> 11344 bytes
 .../vp_coverage/violinplot_simple_pos.png     |  Bin 0 -> 14723 bytes
 .../vp_coverage/violinplot_simple_pos.svg     | 1082 +++++++++++
 .../vp_coverage/violinplot_simple_widths.pdf  |  Bin 0 -> 10940 bytes
 .../vp_coverage/violinplot_simple_widths.png  |  Bin 0 -> 15700 bytes
 .../vp_coverage/violinplot_simple_widths.svg  | 1021 +++++++++++
 .../vp_coverage/violinplot_vert_false.pdf     |  Bin 0 -> 10839 bytes
 .../vp_coverage/violinplot_vert_false.png     |  Bin 0 -> 12162 bytes
 .../vp_coverage/violinplot_vert_false.svg     |  981 ++++++++++
 lib/matplotlib/tests/test_axes.py             |    1 -
 lib/matplotlib/tests/test_mlab.py             |  149 +-
 lib/matplotlib/tests/test_vpcoverage.py       |  142 ++
 35 files changed, 10519 insertions(+), 86 deletions(-)
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/blank.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/blank.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/blank.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_scalar_width.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_scalar_width.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_scalar_width.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_dist.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_dist.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_dist.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_pos.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_pos.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_pos.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_widths.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_widths.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_widths.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.png
 create mode 100644 lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.svg
 create mode 100644 lib/matplotlib/tests/test_vpcoverage.py

diff --git a/lib/matplotlib/__init__.py b/lib/matplotlib/__init__.py
index de59b5faa546..26ed350a1398 100644
--- a/lib/matplotlib/__init__.py
+++ b/lib/matplotlib/__init__.py
@@ -1360,6 +1360,7 @@ def tk_window_focus():
     'matplotlib.tests.test_tightlayout',
     'matplotlib.tests.test_transforms',
     'matplotlib.tests.test_triangulation',
+    'matplotlib.tests.test_vpcoverage',
     ]
 
 
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index 94a12cc69195..49db78e4b7f4 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3656,12 +3656,13 @@ def stineman_interp(xi,x,y,yp=None):
                                   1/(dy1+dy2),))
     return yi
 
+
 class GaussianKDE(object):
     """
     Representation of a kernel-density estimate using Gaussian kernels.
 
     Call signature::
-    kde = gaussian_kde(dataset, 'silverman')
+    kde = GaussianKDE(dataset, bw_method='silverman')
 
     Parameters
     ----------
@@ -3715,8 +3716,8 @@ def __init__(self, dataset, bw_method=None):
             self.covariance_factor = self.scotts_factor
         elif bw_method == 'silverman':
             self.covariance_factor = self.silverman_factor
-        elif np.isscalar(bw_method):
-            if not isinstance(bw_method, six.string_types):
+        elif (np.isscalar(bw_method) and not
+              isinstance(bw_method, six.string_types)):
                 self._bw_method = 'use constant'
                 self.covariance_factor = lambda: bw_method
         elif callable(bw_method):
@@ -3726,10 +3727,10 @@ def __init__(self, dataset, bw_method=None):
             msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
                   "or a callable."
             raise ValueError(msg)
-        
+
         # Computes the covariance matrix for each Gaussian kernel using
         # covariance_factor().
-        
+
         self.factor = self.covariance_factor()
         # Cache covariance and inverse covariance of the data
         if not hasattr(self, '_data_inv_cov'):
@@ -3780,14 +3781,9 @@ def evaluate(self, points):
 
         dim, num_m = np.array(points).shape
         if dim != self.dim:
-            if dim == 1 and num_m == self.dim:
-                # points was passed in as a row vector
-                points = np.reshape(points, (self.dim, 1))
-                num_m = 1
-            else:
-                msg = "points have dimension %s, dataset has dimension %s" % (
-                    dim, self.dim)
-                raise ValueError(msg)
+            msg = "points have dimension %s, dataset has dimension %s" % (
+                dim, self.dim)
+            raise ValueError(msg)
 
         result = np.zeros((num_m,), dtype=np.float)
 
@@ -3812,6 +3808,7 @@ def evaluate(self, points):
 
     __call__ = evaluate
 
+
 ##################################################
 # Code related to things in and around polygons
 ##################################################
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/blank.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/blank.pdf
new file mode 100644
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_scalar_width.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_scalar_width.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_dist.svg b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_dist.svg
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_pos.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_pos.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_widths.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_widths.pdf
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new file mode 100644
index 000000000000..fd148993320a
--- /dev/null
+++ b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_simple_widths.svg
@@ -0,0 +1,1021 @@
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.pdf b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.svg b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.svg
new file mode 100644
index 000000000000..4ef5e32cf8c1
--- /dev/null
+++ b/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_vert_false.svg
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diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 5f6ca42def11..a0499c63acb8 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1734,7 +1734,6 @@ def test_violinplot_bad_widths():
     ax = plt.axes()
     assert_raises(ValueError, ax.violinplot, data, positions=range(4),
                   widths=[1, 2, 3])
-
 # violin plot test ends here
 
 
diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index 20070272473e..9283b7b650bc 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -2757,13 +2757,14 @@ def get_z(x, y):
     np.testing.assert_array_equal(np.ma.getmask(zi),
                                   np.ma.getmask(correct_zi_masked))
 
+
 #*****************************************************************
 # These Tests where taken from SCIPY with some minor modifications
-# this can be retreived from: 
+# this can be retreived from:
 # https://github.com/scipy/scipy/blob/master/scipy/stats/tests/test_kdeoth.py
 #*****************************************************************
 
-class ksdensity_test():
+class gaussian_kde_tests():
 
     def test_kde_integer_input(self):
         """Regression test for #1181."""
@@ -2806,10 +2807,9 @@ def test_kde_bandwidth_method(self):
         assert_almost_equal(kdepdf.all(), kdepdf2.all())
         kdepdf3 = gkde3.evaluate(xs)
         assert_almost_equal(kdepdf.all(), kdepdf3.all())
-        
-#CUSTOM TESTS
-class ksdensity_custom_tests(object):
-class ksdensity_custom_tests(object):
+
+
+class gaussian_kde_custom_tests(object):
     def test_no_data(self):
         """Pass no data into the GaussianKDE class."""
         mygauss = mlab.GaussianKDE([])
@@ -2826,19 +2826,16 @@ def test_silverman_multidim_dataset(self):
         """Use a multi-dimensional array as the dataset and test silverman's
         output"""
         x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
-        mygauss = mlab.GaussianKDE(x1, "silverman")
-        othergauss = stats.gaussian_kde(x1)
-        expected_output = othergauss.covariance_factor()
-        assert mygauss.covariance_factor() == expected_output
+        assert_raises(np.linalg.LinAlgError, mlab.GaussianKDE, x1, "silverman")
+    
 
     def test_silverman_singledim_dataset(self):
         """Use a single dimension list as the dataset and test silverman's
         output."""
         x1 = np.array([-7, -5, 1, 4, 5])
         mygauss = mlab.GaussianKDE(x1, "silverman")
-        othergauss = stats.gaussian_kde(x1)
-        expected_output = othergauss.covariance_factor()
-        assert mygauss.covariance_factor() == expected_output
+        y_expected = 0.76770389927475502
+        assert_almost_equal(mygauss.covariance_factor(), y_expected, 7)
 
     def test_scott_multidim_dataset(self):
         """Use a multi-dimensional array as the dataset and test scott's output
@@ -2858,91 +2855,99 @@ def test_scott_singledim_dataset(self):
         expected_output = othergauss.covariance_factor()
         assert mygauss.covariance_factor() == expected_output
 
-    def test_scalar_multidim_dataset(self):
-        """Use a multi-dimensional array as the dataset and test a scalar's
-        output"""
-        pass
-
-    def test_scalar_singledim_dataset(self):
-        """Use a single-dimensional array as the dataset and test the scalar's
-        output"""
-        pass
+    def test_scalar_empty_dataset(self):
+        """Use an empty array as the dataset and test the scalar's cov factor
+        """
+        assert_raises(ValueError, mlab.GaussianKDE, [], bw_method=5)
 
-    def test_callable_empty_dataset(self):
-        """Use an empty array as the dataset and test the callable's cov factor
+    def test_scalar_covariance_dataset(self):
+        """Use a dataset and test a scalar's cov factor
         """
-        pass
+        np.random.seed(8765678)
+        n_basesample = 50
+        multidim_data = [np.random.randn(n_basesample) for i in range(5)]
 
-    def test_callable_multidim_dataset(self):
-        """Use an multi-dimensional array as the dataset and test the
-        callable's cov factor"""
-        pass
+        kde = mlab.GaussianKDE(multidim_data, bw_method=0.5)
+        assert_equal(kde.covariance_factor(), 0.5)
+
+    def test_callable_covariance_dataset(self):
+        """Use a multi-dimensional array as the dataset and test the callable's
+        cov factor"""
+        np.random.seed(8765678)
+        n_basesample = 50
+        multidim_data = [np.random.randn(n_basesample) for i in range(5)]
+        callable_fun = lambda (x): 0.55
+        kde = mlab.GaussianKDE(multidim_data, bw_method=callable_fun)
+        assert_equal(kde.covariance_factor(), 0.55)
 
     def test_callable_singledim_dataset(self):
         """Use a single-dimensional array as the dataset and test the
-        callable's  cov factor"""
-        pass
+        callable's cov factor"""
+        np.random.seed(8765678)
+        n_basesample = 50
+        multidim_data = np.random.randn(n_basesample)
+
+        kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
+        y_expected = 0.48438841363348911
+        assert_almost_equal(kde.covariance_factor(), y_expected, 7)
 
     def test_wrong_bw_method(self):
         """Test the error message that should be called when bw is invalid."""
-        pass
+        np.random.seed(8765678)
+        n_basesample = 50
+        data = np.random.randn(n_basesample)
+        assert_raises(ValueError, mlab.GaussianKDE, data, bw_method="invalid")
 
 
-class evaluate_tests(object):
+class gaussian_kde_evaluate_tests(object):
 
-    @knownfailureif(True)
     def test_evaluate_diff_dim(self):
         """Test the evaluate method when the dim's of dataset and points are
-        different dimensions"""	
+        different dimensions"""
         x1 = np.arange(3, 10, 2)
-	kde = mlab.GaussianKDE(x1)
-	x2 = np.arange(3, 12, 2)
-	y_expected = [0, 0, 0, 0, 0]
-	y = kde.evaluate(x2)
+        kde = mlab.GaussianKDE(x1)
+        x2 = np.arange(3, 12, 2)
+        y_expected = [
+            0.08797252, 0.11774109, 0.11774109, 0.08797252, 0.0370153
+        ]
+        y = kde.evaluate(x2)
+        assert_array_almost_equal(y, y_expected, 7)
 
-	assert_array_almost_equal(y, y_expected, 7)
-	
     def test_evaluate_inv_dim(self):
-        """ Invert the dimensions. I.e Give the dataset a dimension of 1 [3,2,4],
-        and the points will have a dimension of 3 [[3],[2],[4]]. ValueError 
-        should be raised"""
-        x1 = np.arange(3, 10, 2)
-	kde = mlab.GaussianKDE(x1)
-	x2 = np.array([[3],[5],[7],[9]])
-	
+        """ Invert the dimensions. ie, Give the dataset a dimension of
+        1 [3,2,4], and the points will have a dimension of 3 [[3],[2],[4]].
+        ValueError should be raised"""
+        np.random.seed(8765678)
+        n_basesample = 50
+        multidim_data = np.random.randn(n_basesample)
+        kde = mlab.GaussianKDE(multidim_data)
+        x2 = [[1], [2], [3]]
         assert_raises(ValueError, kde.evaluate, x2)
-		
-    @knownfailureif(True)
+
     def test_evaluate_dim_and_num(self):
-	""" Tests if evaluated against a one by one array"""
-	x1 = np.arange(3, 10, 2)
+        """ Tests if evaluated against a one by one array"""
+        x1 = np.arange(3, 10, 2)
         x2 = np.array([3])
-	kde = mlab.GaussianKDE(x1)
-	y_expected = [0]
-	y = kde.evaluate(x2)
-	
-	assert_array_almost_equal(y, y_expected, 7)
-   
- 
+        kde = mlab.GaussianKDE(x1)
+        y_expected = [0.08797252]
+        y = kde.evaluate(x2)
+        assert_array_almost_equal(y, y_expected, 7)
+
     def test_evaluate_point_dim_not_one(self):
         """Test"""
         x1 = np.arange(3, 10, 2)
-	x2 = [np.arange(3, 10, 2), np.arange(3, 10, 2)]
-	kde = mlab.GaussianKDE(x1)
-	
+        x2 = [np.arange(3, 10, 2), np.arange(3, 10, 2)]
+        kde = mlab.GaussianKDE(x1)
         assert_raises(ValueError, kde.evaluate, x2)
-    
-    @knownfailureif(True)
-    def test_evaluate_equal_dim_and_numm_lt(self):
+
+    def test_evaluate_equal_dim_and_num_lt(self):
         """Test when line 3810 fails"""
         x1 = np.arange(3, 10, 2)
-	x2 = np.arange(3, 8, 2)
-	kde = mlab.GaussianKDE(x1)
-        y_expected = [0, 0, 0]
-	y = kde.evaluate(x2)
-
-	assert_array_almost_equal(y, y_expected, 7)
-    
+        x2 = np.arange(3, 8, 2)
+        kde = mlab.GaussianKDE(x1)
+        y_expected = [0.08797252, 0.11774109, 0.11774109]
+        y = kde.evaluate(x2)
+        assert_array_almost_equal(y, y_expected, 7)
 
 
 #*****************************************************************
diff --git a/lib/matplotlib/tests/test_vpcoverage.py b/lib/matplotlib/tests/test_vpcoverage.py
new file mode 100644
index 000000000000..b76eb54f3aa4
--- /dev/null
+++ b/lib/matplotlib/tests/test_vpcoverage.py
@@ -0,0 +1,142 @@
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import six
+from six.moves import xrange
+
+from nose.tools import assert_equal, assert_raises
+import datetime
+
+import numpy as np
+from numpy import ma
+
+import matplotlib
+from matplotlib.testing.decorators import image_comparison, cleanup
+import matplotlib.pyplot as plt
+
+
+simple_dataset = [[0.9, 1.1, 0.9, 1.1, 1], [2.2, 2, 1.8, 1.8, 2, 2.2]]
+other_dataset = [[3.3, 2.2, 1.1], [4.4, 8.8, 2.2]]
+
+
+#test for positions is none
+@image_comparison(baseline_images=['violinplot_simple_dist'])
+def test_violinplot_positions_none():
+    plt.violinplot(simple_dataset, positions=None)
+
+
+#test for positions is invalid length
+@cleanup
+def test_violinplot_positions_invalid():
+    assert_raises(ValueError, plt.violinplot, simple_dataset, positions=[5])
+
+
+#test for positions is valid
+@image_comparison(baseline_images=['violinplot_simple_pos'])
+def test_violinplot_positions_valid():
+    plt.violinplot(simple_dataset, positions=[1, 5])
+
+
+#test for widths is valid
+@image_comparison(baseline_images=['violinplot_simple_widths'])
+def test_violinplot_widths_valid():
+    plt.violinplot(simple_dataset, widths=[0.5, 1])
+
+
+#test for widths is a scalar
+@image_comparison(baseline_images=['violinplot_scalar_width'])
+def test_violinplot_widths_scalar():
+    plt.violinplot(simple_dataset, widths=1.5)
+
+
+#test for widths is invalid length
+@cleanup
+def test_violinplot_widths_invalid():
+    assert_raises(ValueError, plt.violinplot, simple_dataset, widths=[1])
+
+
+#test for hold status true
+@image_comparison(baseline_images=['violinplot_hold_true'])
+def test_violinplot_hold_status_true():
+    ax = plt.axes()
+    ax.hold(True)
+    ax.violinplot(simple_dataset)
+    ax.violinplot(other_dataset)
+
+
+#test for hold status false
+@image_comparison(baseline_images=['violinplot_simple_dist'])
+def test_violinplot_hold_status_false():
+    ax = plt.axes()
+    ax.hold(False)
+    ax.violinplot(other_dataset)
+    ax.violinplot(simple_dataset)
+
+
+#test vert = true
+@image_comparison(baseline_images=['violinplot_simple_dist'])
+def test_violinplot_vert_true():
+    plt.violinplot(simple_dataset, vert=True)
+
+
+#test vert = false
+@image_comparison(baseline_images=['violinplot_vert_false'])
+def test_violinplot_vert_false():
+    plt.violinplot(simple_dataset, vert=False)
+
+
+#test show means = false
+@image_comparison(baseline_images=['violinplot_simple_dist'])
+def test_violinplot_means_false():
+    plt.violinplot(simple_dataset, showmeans=False)
+
+
+#test show means = true
+@image_comparison(baseline_images=['violinplot_means'])
+def test_violinplot_means_false():
+    plt.violinplot(other_dataset, showmeans=True)
+
+
+#test showmedians = false
+@image_comparison(baseline_images=['violinplot_simple_dist'])
+def test_violinplot_medians_false():
+    plt.violinplot(simple_dataset, showmedians=False)
+
+
+#test showmedians = true
+@image_comparison(baseline_images=['violinplot_medians'])
+def test_violinplot_medians_false():
+    plt.violinplot(other_dataset, showmedians=True)
+
+
+#test showextrema = true
+@image_comparison(baseline_images=['violinplot_simple_dist'])
+def test_violinplot_extrema_true():
+    plt.violinplot(simple_dataset, showextrema=True)
+
+
+#test showextrema = false
+@image_comparison(baseline_images=['violinplot_noextrema'])
+def test_violinplot_extrema_true():
+    plt.violinplot(simple_dataset, showextrema=False)
+
+
+#test empty dataset
+@cleanup
+def test_violinplot_nodata():
+    artists = plt.violinplot([], showextrema=False)
+    for k in artists:
+        if k == 'bodies':
+            assert len(artists[k]) == 0
+        else:
+            assert artists[k] is None
+
+
+if __name__ == '__main__':
+    import nose
+    import sys
+
+    args = ['-s', '--with-doctest']
+    argv = sys.argv
+    argv = argv[:1] + args + argv[1:]
+    nose.runmodule(argv=argv, exit=False)

From 11aee8628a864e4f2d9a0f7cb024cb9073472e54 Mon Sep 17 00:00:00 2001
From: Kevin Chan <kvii.chan@gmail.com>
Date: Fri, 4 Apr 2014 11:33:15 -0400
Subject: [PATCH 18/22] Renamed vp_coverage baseline images, added bw_method
 paramater to violinplot and updated demo

---
 examples/statistics/violinplot_demo.py        |  29 ++++++------------
 lib/matplotlib/axes/_axes.py                  |  11 +++++--
 lib/matplotlib/mlab.py                        |  15 +++++++--
 .../blank.pdf                                 | Bin
 .../blank.png                                 | Bin
 .../blank.svg                                 |   0
 .../violinplot_hold_true.pdf                  | Bin
 .../violinplot_hold_true.png                  | Bin
 .../violinplot_hold_true.svg                  |   0
 .../violinplot_means.pdf                      | Bin
 .../violinplot_means.png                      | Bin
 .../violinplot_means.svg                      |   0
 .../violinplot_medians.pdf                    | Bin
 .../violinplot_medians.png                    | Bin
 .../violinplot_medians.svg                    |   0
 .../violinplot_noextrema.pdf                  | Bin
 .../violinplot_noextrema.png                  | Bin
 .../violinplot_noextrema.svg                  |   0
 .../violinplot_scalar_width.pdf               | Bin
 .../violinplot_scalar_width.png               | Bin
 .../violinplot_scalar_width.svg               |   0
 .../violinplot_simple_dist.pdf                | Bin
 .../violinplot_simple_dist.png                | Bin
 .../violinplot_simple_dist.svg                |   0
 .../violinplot_simple_pos.pdf                 | Bin
 .../violinplot_simple_pos.png                 | Bin
 .../violinplot_simple_pos.svg                 |   0
 .../violinplot_simple_widths.pdf              | Bin
 .../violinplot_simple_widths.png              | Bin
 .../violinplot_simple_widths.svg              |   0
 .../violinplot_vert_false.pdf                 | Bin
 .../violinplot_vert_false.png                 | Bin
 .../violinplot_vert_false.svg                 |   0
 33 files changed, 31 insertions(+), 24 deletions(-)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/blank.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/blank.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/blank.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_hold_true.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_hold_true.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_hold_true.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_means.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_means.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_means.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_medians.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_medians.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_medians.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_noextrema.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_noextrema.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_noextrema.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_scalar_width.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_scalar_width.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_scalar_width.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_dist.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_dist.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_dist.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_pos.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_pos.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_pos.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_widths.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_widths.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_simple_widths.svg (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_vert_false.pdf (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_vert_false.png (100%)
 rename lib/matplotlib/tests/baseline_images/{vp_coverage => test_vpcoverage}/violinplot_vert_false.svg (100%)

diff --git a/examples/statistics/violinplot_demo.py b/examples/statistics/violinplot_demo.py
index 8e1c2917b2ca..448abc976081 100644
--- a/examples/statistics/violinplot_demo.py
+++ b/examples/statistics/violinplot_demo.py
@@ -11,30 +11,19 @@
 pos = [1,2,4,5,7,8]
 data = [np.random.normal(size=100) for i in pos]
 
-# TODO: future customizability dicts go here
-
-# (From boxplot demo)
-# demonstrate how to customize the display different elements: 
-# boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
-# flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
-#                   linestyle='none')
-# medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')
-# meanpointprops = dict(marker='D', markeredgecolor='black',
-#                       markerfacecolor='firebrick')
-# meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
-
 fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6,6))
 
-axes[0, 0].violinplot(data, pos, points=20, widths=0.1, showmeans=True,
-                      showextrema=True, showmedians=True)
+axes[0, 0].violinplot(data, pos, points=20, widths=0.1, 
+                      showmeans=True, showextrema=True, showmedians=True)
 axes[0, 0].set_title('Custom violinplot 1', fontsize=fs)
 
-axes[0, 1].violinplot(data, pos, points=40, widths=0.3, showmeans=True,
-                      showextrema=True, showmedians=True)
+axes[0, 1].violinplot(data, pos, points=40, widths=0.3, 
+                      showmeans=True, showextrema=True, showmedians=True,
+                      bw_method='silverman')
 axes[0, 1].set_title('Custom violinplot 2', fontsize=fs)
 
 axes[0, 2].violinplot(data, pos, points=60, widths=0.5, showmeans=True,
-                      showextrema=True, showmedians=True)
+                      showextrema=True, showmedians=True, bw_method=0.5)
 axes[0, 2].set_title('Custom violinplot 3', fontsize=fs)
 
 axes[1, 0].violinplot(data, pos, points=80, vert=False, widths=0.7,
@@ -42,11 +31,13 @@
 axes[1, 0].set_title('Custom violinplot 4', fontsize=fs)
 
 axes[1, 1].violinplot(data, pos, points=100, vert=False, widths=0.9,
-                      showmeans=True, showextrema=True, showmedians=True)
+                      showmeans=True, showextrema=True, showmedians=True,
+                      bw_method='silverman')
 axes[1, 1].set_title('Custom violinplot 5', fontsize=fs)
 
 axes[1, 2].violinplot(data, pos, points=200, vert=False, widths=1.1,
-                      showmeans=True, showextrema=True, showmedians=True)
+                      showmeans=True, showextrema=True, showmedians=True,
+                      bw_method=0.5)
 axes[1, 2].set_title('Custom violinplot 6', fontsize=fs)
 
 for ax in axes.flatten():
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 8c855371da9e..45b13a3d26b1 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6727,7 +6727,7 @@ def matshow(self, Z, **kwargs):
 
     def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
                    showmeans=False, showextrema=True, showmedians=False,
-                   points=100):
+                   points=100, bw_method=None):
         """
         Make a violin plot.
 
@@ -6774,6 +6774,13 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
             Defines the number of points to evaluate each of the gaussian
             kernel density estimations at.
 
+          bw_method : str, scalar or callable, optional
+            The method used to calculate the estimator bandwidth.  This can be
+            'scott', 'silverman', a scalar constant or a callable.  If a
+            scalar, this will be used directly as `kde.factor`.  If a 
+            callable, it should take a `GaussianKDE` instance as only 
+            parameter and return a scalar. If None (default), 'scott' is used.
+
         Returns
         -------
 
@@ -6852,7 +6859,7 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
         # Render violins
         for data, pos, width in zip(dataset, positions, widths):
             # Calculate the kernel density
-            kde = mlab.GaussianKDE(data)
+            kde = mlab.GaussianKDE(data, bw_method)
             min_val = kde.dataset.min()
             max_val = kde.dataset.max()
             mean = np.mean(kde.dataset)
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index 49db78e4b7f4..ffdec181fe22 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3669,25 +3669,33 @@ class GaussianKDE(object):
     dataset : array_like
         Datapoints to estimate from. In case of univariate data this is a 1-D
         array, otherwise a 2-D array with shape (# of dims, # of data).
-    bw_method : str or scalar, optional
+
+    bw_method : str, scalar or callable, optional
         The method used to calculate the estimator bandwidth.  This can be
-        'scott', 'silverman', or a scalar constant.  If a scalar, this will
-        be used directly as `kde.factor`. If None (default), 'scott' is used.
+        'scott', 'silverman', a scalar constant or a callable.  If a
+        scalar, this will be used directly as `kde.factor`.  If a 
+        callable, it should take a `GaussianKDE` instance as only 
+        parameter and return a scalar. If None (default), 'scott' is used.
 
     Attributes
     ----------
     dataset : ndarray
         The dataset with which `gaussian_kde` was initialized.
+
     dim : int
         Number of dimensions.
+
     num_dp : int
         Number of datapoints.
+
     factor : float
         The bandwidth factor, obtained from `kde.covariance_factor`, with which
         the covariance matrix is multiplied.
+
     covariance : ndarray
         The covariance matrix of `dataset`, scaled by the calculated bandwidth
         (`kde.factor`).
+
     inv_cov : ndarray
         The inverse of `covariance`.
 
@@ -3695,6 +3703,7 @@ class GaussianKDE(object):
     -------
     kde.evaluate(points) : ndarray
         Evaluate the estimated pdf on a provided set of points.
+        
     kde(points) : ndarray
         Same as kde.evaluate(points)
 
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/blank.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.pdf
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/blank.pdf
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.pdf
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/blank.png b/lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.png
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/blank.png
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.png
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/blank.svg b/lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.svg
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/blank.svg
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.svg
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.pdf
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.pdf
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.pdf
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.png b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.png
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.png
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.png
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.svg b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.svg
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_hold_true.svg
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.svg
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.pdf
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.pdf
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.pdf
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.png b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.png
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.png
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.png
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.svg b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.svg
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_means.svg
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.svg
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.pdf
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.pdf
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.pdf
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.png b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.png
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.png
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.png
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.svg b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.svg
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_medians.svg
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.svg
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.pdf
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.pdf
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.pdf
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.png b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.png
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.png
rename to lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.png
diff --git a/lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.svg b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.svg
similarity index 100%
rename from lib/matplotlib/tests/baseline_images/vp_coverage/violinplot_noextrema.svg
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similarity index 100%
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From f18932556c01ecaf2e7f7582704fb37459ff3a55 Mon Sep 17 00:00:00 2001
From: solvents <wisalam@live.com>
Date: Fri, 4 Apr 2014 13:02:00 -0400
Subject: [PATCH 19/22] Updated kde tests in test_mlab.py to reflect changes to
 imports in test_mlab.py Fixed a syntax error in python 3 and fixed up some
 violinplot tests. Fixed some style problems. Removed course-related test from
 list of tests.

---
 lib/matplotlib/__init__.py                    |    1 -
 lib/matplotlib/axes/_axes.py                  |    5 +-
 lib/matplotlib/mlab.py                        |    6 +-
 .../baseline_images/test_vpcoverage/blank.pdf |  Bin 5902 -> 0 bytes
 .../baseline_images/test_vpcoverage/blank.png |  Bin 10483 -> 0 bytes
 .../baseline_images/test_vpcoverage/blank.svg |  509 ------
 .../test_vpcoverage/violinplot_hold_true.pdf  |  Bin 18943 -> 0 bytes
 .../test_vpcoverage/violinplot_hold_true.png  |  Bin 19702 -> 0 bytes
 .../test_vpcoverage/violinplot_hold_true.svg  | 1586 -----------------
 .../test_vpcoverage/violinplot_means.pdf      |  Bin 13141 -> 0 bytes
 .../test_vpcoverage/violinplot_means.png      |  Bin 18437 -> 0 bytes
 .../test_vpcoverage/violinplot_means.svg      | 1050 -----------
 .../test_vpcoverage/violinplot_medians.pdf    |  Bin 13141 -> 0 bytes
 .../test_vpcoverage/violinplot_medians.png    |  Bin 18439 -> 0 bytes
 .../test_vpcoverage/violinplot_medians.svg    | 1050 -----------
 .../test_vpcoverage/violinplot_noextrema.pdf  |  Bin 9605 -> 0 bytes
 .../test_vpcoverage/violinplot_noextrema.png  |  Bin 14630 -> 0 bytes
 .../test_vpcoverage/violinplot_noextrema.svg  |  933 ----------
 .../violinplot_scalar_width.pdf               |  Bin 11433 -> 0 bytes
 .../violinplot_scalar_width.png               |  Bin 17207 -> 0 bytes
 .../violinplot_scalar_width.svg               | 1096 ------------
 .../violinplot_simple_dist.pdf                |  Bin 10860 -> 0 bytes
 .../violinplot_simple_dist.png                |  Bin 15059 -> 0 bytes
 .../violinplot_simple_dist.svg                |  981 ----------
 .../test_vpcoverage/violinplot_simple_pos.pdf |  Bin 11344 -> 0 bytes
 .../test_vpcoverage/violinplot_simple_pos.png |  Bin 14723 -> 0 bytes
 .../test_vpcoverage/violinplot_simple_pos.svg | 1082 -----------
 .../violinplot_simple_widths.pdf              |  Bin 10940 -> 0 bytes
 .../violinplot_simple_widths.png              |  Bin 15700 -> 0 bytes
 .../violinplot_simple_widths.svg              | 1021 -----------
 .../test_vpcoverage/violinplot_vert_false.pdf |  Bin 10839 -> 0 bytes
 .../test_vpcoverage/violinplot_vert_false.png |  Bin 12162 -> 0 bytes
 .../test_vpcoverage/violinplot_vert_false.svg |  981 ----------
 lib/matplotlib/tests/test_axes.py             |    3 +-
 lib/matplotlib/tests/test_mlab.py             |   31 +-
 lib/matplotlib/tests/test_vpcoverage.py       |  142 --
 36 files changed, 18 insertions(+), 10459 deletions(-)
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_hold_true.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_means.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_medians.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_scalar_width.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_scalar_width.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_scalar_width.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_dist.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_dist.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_dist.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_pos.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_pos.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_pos.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_widths.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_widths.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_widths.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_vert_false.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_vert_false.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_vert_false.svg
 delete mode 100644 lib/matplotlib/tests/test_vpcoverage.py

diff --git a/lib/matplotlib/__init__.py b/lib/matplotlib/__init__.py
index 26ed350a1398..de59b5faa546 100644
--- a/lib/matplotlib/__init__.py
+++ b/lib/matplotlib/__init__.py
@@ -1360,7 +1360,6 @@ def tk_window_focus():
     'matplotlib.tests.test_tightlayout',
     'matplotlib.tests.test_transforms',
     'matplotlib.tests.test_triangulation',
-    'matplotlib.tests.test_vpcoverage',
     ]
 
 
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 45b13a3d26b1..5a81d809f4e7 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6777,8 +6777,8 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
           bw_method : str, scalar or callable, optional
             The method used to calculate the estimator bandwidth.  This can be
             'scott', 'silverman', a scalar constant or a callable.  If a
-            scalar, this will be used directly as `kde.factor`.  If a 
-            callable, it should take a `GaussianKDE` instance as only 
+            scalar, this will be used directly as `kde.factor`.  If a
+            callable, it should take a `GaussianKDE` instance as only
             parameter and return a scalar. If None (default), 'scott' is used.
 
         Returns
@@ -6904,7 +6904,6 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
 
         return artists
 
-
     def tricontour(self, *args, **kwargs):
         return mtri.tricontour(self, *args, **kwargs)
     tricontour.__doc__ = mtri.TriContourSet.tricontour_doc
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index ffdec181fe22..bcf79d84d82b 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -3673,8 +3673,8 @@ class GaussianKDE(object):
     bw_method : str, scalar or callable, optional
         The method used to calculate the estimator bandwidth.  This can be
         'scott', 'silverman', a scalar constant or a callable.  If a
-        scalar, this will be used directly as `kde.factor`.  If a 
-        callable, it should take a `GaussianKDE` instance as only 
+        scalar, this will be used directly as `kde.factor`.  If a
+        callable, it should take a `GaussianKDE` instance as only
         parameter and return a scalar. If None (default), 'scott' is used.
 
     Attributes
@@ -3703,7 +3703,7 @@ class GaussianKDE(object):
     -------
     kde.evaluate(points) : ndarray
         Evaluate the estimated pdf on a provided set of points.
-        
+
     kde(points) : ndarray
         Same as kde.evaluate(points)
 
diff --git a/lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/blank.pdf
deleted file mode 100644
index f42e2bf869544fc8356dce5a6449e60f140c6cc8..0000000000000000000000000000000000000000
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diff --git a/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.svg b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_noextrema.svg
deleted file mode 100644
index 03cd562b5ebf..000000000000
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diff --git a/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_scalar_width.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_scalar_width.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_dist.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_dist.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_widths.pdf b/lib/matplotlib/tests/baseline_images/test_vpcoverage/violinplot_simple_widths.pdf
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diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index a0499c63acb8..6235f9902557 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1567,7 +1567,6 @@ def test_boxplot_bad_ci_2():
 
 
 # violin plot data initialization
-ax = plt.axes()
 data = [([+0.07902449, -0.16769639, +1.1572525,  +0.71400729, -0.17916727,
           -1.15346725, -0.5298936,  +1.16570619, +1.13612837, -0.66830221,
           -0.76738509, +0.85911678, +0.56446469, +0.64772651, -1.97432723,
@@ -1725,11 +1724,13 @@ def test_horiz_violinplot_custompoints_200():
 
 
 # test error
+@cleanup
 def test_violinplot_bad_positions():
     ax = plt.axes()
     assert_raises(ValueError, ax.violinplot, data, positions=range(5))
 
 
+@cleanup
 def test_violinplot_bad_widths():
     ax = plt.axes()
     assert_raises(ValueError, ax.violinplot, data, positions=range(4),
diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index 9283b7b650bc..0cc991f05ebf 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -2772,7 +2772,7 @@ def test_kde_integer_input(self):
         kde = mlab.GaussianKDE(x1)
         y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869,
                       0.13480721]
-        assert_array_almost_equal(kde(x1), y_expected, decimal=6)
+        np.testing.assert_array_almost_equal(kde(x1), y_expected, decimal=6)
 
     def test_gaussian_kde_covariance_caching(self):
         x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
@@ -2786,7 +2786,7 @@ def test_gaussian_kde_covariance_caching(self):
         kde2 = mlab.GaussianKDE(x1, 'scott')
         y2 = kde2(xs)
 
-        assert_array_almost_equal(y_expected, y2, decimal=7)
+        np.testing.assert_array_almost_equal(y_expected, y2, decimal=7)
 
     def test_kde_bandwidth_method(self):
 
@@ -2812,22 +2812,17 @@ def test_kde_bandwidth_method(self):
 class gaussian_kde_custom_tests(object):
     def test_no_data(self):
         """Pass no data into the GaussianKDE class."""
-        mygauss = mlab.GaussianKDE([])
-        self.assertRaises(ValueError,
-                          "`dataset` input should have multiple elements.")
+        assert_raises(ValueError, mlab.GaussianKDE, [])
 
     def test_single_dataset_element(self):
         """Pass a single dataset element into the GaussianKDE class."""
-        myguass = mlab.GuassianKDE([42])
-        self.assertRaises(ValueError,
-                          "`dataset` input should have multiple elements.")
+        assert_raises(ValueError, mlab.GaussianKDE, [42])
 
     def test_silverman_multidim_dataset(self):
         """Use a multi-dimensional array as the dataset and test silverman's
         output"""
         x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
         assert_raises(np.linalg.LinAlgError, mlab.GaussianKDE, x1, "silverman")
-    
 
     def test_silverman_singledim_dataset(self):
         """Use a single dimension list as the dataset and test silverman's
@@ -2841,19 +2836,15 @@ def test_scott_multidim_dataset(self):
         """Use a multi-dimensional array as the dataset and test scott's output
         """
         x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
-        mygauss = mlab.GaussianKDE(x1, "scott")
-        othergauss = stats.gaussian_kde(x1)
-        expected_output = othergauss.covariance_factor()
-        assert mygauss.covariance_factor() == expected_output
+        assert_raises(np.linalg.LinAlgError, mlab.GaussianKDE, x1, "scott")
 
     def test_scott_singledim_dataset(self):
         """Use a single-dimensional array as the dataset and test scott's
         output"""
         x1 = np.array([-7, -5, 1, 4, 5])
         mygauss = mlab.GaussianKDE(x1, "scott")
-        othergauss = stats.gaussian_kde(x1)
-        expected_output = othergauss.covariance_factor()
-        assert mygauss.covariance_factor() == expected_output
+        y_expected = 0.72477966367769553
+        assert_almost_equal(mygauss.covariance_factor(), y_expected, 7)
 
     def test_scalar_empty_dataset(self):
         """Use an empty array as the dataset and test the scalar's cov factor
@@ -2876,7 +2867,7 @@ def test_callable_covariance_dataset(self):
         np.random.seed(8765678)
         n_basesample = 50
         multidim_data = [np.random.randn(n_basesample) for i in range(5)]
-        callable_fun = lambda (x): 0.55
+        callable_fun = lambda x: 0.55
         kde = mlab.GaussianKDE(multidim_data, bw_method=callable_fun)
         assert_equal(kde.covariance_factor(), 0.55)
 
@@ -2911,7 +2902,7 @@ def test_evaluate_diff_dim(self):
             0.08797252, 0.11774109, 0.11774109, 0.08797252, 0.0370153
         ]
         y = kde.evaluate(x2)
-        assert_array_almost_equal(y, y_expected, 7)
+        np.testing.assert_array_almost_equal(y, y_expected, 7)
 
     def test_evaluate_inv_dim(self):
         """ Invert the dimensions. ie, Give the dataset a dimension of
@@ -2931,7 +2922,7 @@ def test_evaluate_dim_and_num(self):
         kde = mlab.GaussianKDE(x1)
         y_expected = [0.08797252]
         y = kde.evaluate(x2)
-        assert_array_almost_equal(y, y_expected, 7)
+        np.testing.assert_array_almost_equal(y, y_expected, 7)
 
     def test_evaluate_point_dim_not_one(self):
         """Test"""
@@ -2947,7 +2938,7 @@ def test_evaluate_equal_dim_and_num_lt(self):
         kde = mlab.GaussianKDE(x1)
         y_expected = [0.08797252, 0.11774109, 0.11774109]
         y = kde.evaluate(x2)
-        assert_array_almost_equal(y, y_expected, 7)
+        np.testing.assert_array_almost_equal(y, y_expected, 7)
 
 
 #*****************************************************************
diff --git a/lib/matplotlib/tests/test_vpcoverage.py b/lib/matplotlib/tests/test_vpcoverage.py
deleted file mode 100644
index b76eb54f3aa4..000000000000
--- a/lib/matplotlib/tests/test_vpcoverage.py
+++ /dev/null
@@ -1,142 +0,0 @@
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import six
-from six.moves import xrange
-
-from nose.tools import assert_equal, assert_raises
-import datetime
-
-import numpy as np
-from numpy import ma
-
-import matplotlib
-from matplotlib.testing.decorators import image_comparison, cleanup
-import matplotlib.pyplot as plt
-
-
-simple_dataset = [[0.9, 1.1, 0.9, 1.1, 1], [2.2, 2, 1.8, 1.8, 2, 2.2]]
-other_dataset = [[3.3, 2.2, 1.1], [4.4, 8.8, 2.2]]
-
-
-#test for positions is none
-@image_comparison(baseline_images=['violinplot_simple_dist'])
-def test_violinplot_positions_none():
-    plt.violinplot(simple_dataset, positions=None)
-
-
-#test for positions is invalid length
-@cleanup
-def test_violinplot_positions_invalid():
-    assert_raises(ValueError, plt.violinplot, simple_dataset, positions=[5])
-
-
-#test for positions is valid
-@image_comparison(baseline_images=['violinplot_simple_pos'])
-def test_violinplot_positions_valid():
-    plt.violinplot(simple_dataset, positions=[1, 5])
-
-
-#test for widths is valid
-@image_comparison(baseline_images=['violinplot_simple_widths'])
-def test_violinplot_widths_valid():
-    plt.violinplot(simple_dataset, widths=[0.5, 1])
-
-
-#test for widths is a scalar
-@image_comparison(baseline_images=['violinplot_scalar_width'])
-def test_violinplot_widths_scalar():
-    plt.violinplot(simple_dataset, widths=1.5)
-
-
-#test for widths is invalid length
-@cleanup
-def test_violinplot_widths_invalid():
-    assert_raises(ValueError, plt.violinplot, simple_dataset, widths=[1])
-
-
-#test for hold status true
-@image_comparison(baseline_images=['violinplot_hold_true'])
-def test_violinplot_hold_status_true():
-    ax = plt.axes()
-    ax.hold(True)
-    ax.violinplot(simple_dataset)
-    ax.violinplot(other_dataset)
-
-
-#test for hold status false
-@image_comparison(baseline_images=['violinplot_simple_dist'])
-def test_violinplot_hold_status_false():
-    ax = plt.axes()
-    ax.hold(False)
-    ax.violinplot(other_dataset)
-    ax.violinplot(simple_dataset)
-
-
-#test vert = true
-@image_comparison(baseline_images=['violinplot_simple_dist'])
-def test_violinplot_vert_true():
-    plt.violinplot(simple_dataset, vert=True)
-
-
-#test vert = false
-@image_comparison(baseline_images=['violinplot_vert_false'])
-def test_violinplot_vert_false():
-    plt.violinplot(simple_dataset, vert=False)
-
-
-#test show means = false
-@image_comparison(baseline_images=['violinplot_simple_dist'])
-def test_violinplot_means_false():
-    plt.violinplot(simple_dataset, showmeans=False)
-
-
-#test show means = true
-@image_comparison(baseline_images=['violinplot_means'])
-def test_violinplot_means_false():
-    plt.violinplot(other_dataset, showmeans=True)
-
-
-#test showmedians = false
-@image_comparison(baseline_images=['violinplot_simple_dist'])
-def test_violinplot_medians_false():
-    plt.violinplot(simple_dataset, showmedians=False)
-
-
-#test showmedians = true
-@image_comparison(baseline_images=['violinplot_medians'])
-def test_violinplot_medians_false():
-    plt.violinplot(other_dataset, showmedians=True)
-
-
-#test showextrema = true
-@image_comparison(baseline_images=['violinplot_simple_dist'])
-def test_violinplot_extrema_true():
-    plt.violinplot(simple_dataset, showextrema=True)
-
-
-#test showextrema = false
-@image_comparison(baseline_images=['violinplot_noextrema'])
-def test_violinplot_extrema_true():
-    plt.violinplot(simple_dataset, showextrema=False)
-
-
-#test empty dataset
-@cleanup
-def test_violinplot_nodata():
-    artists = plt.violinplot([], showextrema=False)
-    for k in artists:
-        if k == 'bodies':
-            assert len(artists[k]) == 0
-        else:
-            assert artists[k] is None
-
-
-if __name__ == '__main__':
-    import nose
-    import sys
-
-    args = ['-s', '--with-doctest']
-    argv = sys.argv
-    argv = argv[:1] + args + argv[1:]
-    nose.runmodule(argv=argv, exit=False)

From ad0bcd41e3c67977562c8c72261a8067c24302a7 Mon Sep 17 00:00:00 2001
From: solvents <wisalam@live.com>
Date: Sun, 20 Apr 2014 15:27:24 -0400
Subject: [PATCH 20/22] Fixed an issue in violinplot where the top end of
 violins would be truncated. Updated test images and reran boilerplate.py

---
 lib/matplotlib/axes/_axes.py                  |    4 +-
 lib/matplotlib/pyplot.py                      |    5 +-
 .../test_horiz_violinplot_baseline.pdf        |  Bin 16992 -> 0 bytes
 .../test_horiz_violinplot_baseline.png        |  Bin 33652 -> 0 bytes
 .../test_horiz_violinplot_baseline.svg        | 1376 ----------
 .../test_horiz_violinplot_custompoints_10.png |  Bin 30585 -> 0 bytes
 ...test_horiz_violinplot_custompoints_200.pdf |  Bin 27528 -> 0 bytes
 ...test_horiz_violinplot_custompoints_200.png |  Bin 33684 -> 0 bytes
 ...test_horiz_violinplot_custompoints_200.svg | 2176 ----------------
 .../test_horiz_violinplot_medianse.pdf        |  Bin 21724 -> 0 bytes
 .../test_horiz_violinplot_medianse.png        |  Bin 40079 -> 0 bytes
 .../test_horiz_violinplot_medianse.svg        | 1576 ------------
 .../test_horiz_violinplot_showall.pdf         |  Bin 21708 -> 0 bytes
 .../test_horiz_violinplot_showall.png         |  Bin 34764 -> 0 bytes
 .../test_horiz_violinplot_showall.svg         | 1516 -----------
 .../test_horiz_violinplot_showextrema.pdf     |  Bin 19827 -> 0 bytes
 .../test_horiz_violinplot_showextrema.png     |  Bin 34313 -> 0 bytes
 .../test_horiz_violinplot_showextrema.svg     | 1460 -----------
 .../test_horiz_violinplot_showmeans.pdf       |  Bin 17948 -> 0 bytes
 .../test_horiz_violinplot_showmeans.png       |  Bin 33910 -> 0 bytes
 .../test_horiz_violinplot_showmedians.pdf     |  Bin 17936 -> 0 bytes
 .../test_horiz_violinplot_showmedians.png     |  Bin 33913 -> 0 bytes
 .../test_horiz_violinplot_showmedians.svg     | 1404 -----------
 .../test_vert_violinplot_baseline.pdf         |  Bin 16822 -> 0 bytes
 .../test_vert_violinplot_baseline.png         |  Bin 33764 -> 0 bytes
 .../test_vert_violinplot_custompoints_10.png  |  Bin 31172 -> 0 bytes
 .../test_vert_violinplot_custompoints_200.pdf |  Bin 27157 -> 0 bytes
 .../test_vert_violinplot_custompoints_200.png |  Bin 33912 -> 0 bytes
 .../test_vert_violinplot_custompoints_200.svg | 2174 ----------------
 .../test_vert_violinplot_showall.pdf          |  Bin 21613 -> 0 bytes
 .../test_vert_violinplot_showall.png          |  Bin 34713 -> 0 bytes
 .../test_vert_violinplot_showall.svg          | 1514 -----------
 .../test_vert_violinplot_showextrema.pdf      |  Bin 19687 -> 0 bytes
 .../test_vert_violinplot_showextrema.png      |  Bin 34192 -> 0 bytes
 .../test_vert_violinplot_showmeans.pdf        |  Bin 17799 -> 0 bytes
 .../test_vert_violinplot_showmeans.png        |  Bin 33932 -> 0 bytes
 .../test_vert_violinplot_showmeans.svg        | 1402 -----------
 .../test_vert_violinplot_showmedians.pdf      |  Bin 17792 -> 0 bytes
 .../test_vert_violinplot_showmedians.png      |  Bin 34104 -> 0 bytes
 .../test_vert_violinplot_showmedians.svg      | 1402 -----------
 .../test_axes/violinplot_horiz_baseline.pdf   |  Bin 0 -> 17330 bytes
 .../test_axes/violinplot_horiz_baseline.png   |  Bin 0 -> 33649 bytes
 .../test_axes/violinplot_horiz_baseline.svg   | 1455 +++++++++++
 ...f => violinplot_horiz_custompoints_10.pdf} |  Bin 7566 -> 7844 bytes
 .../violinplot_horiz_custompoints_10.png      |  Bin 0 -> 31286 bytes
 ...g => violinplot_horiz_custompoints_10.svg} |  426 ++--
 .../violinplot_horiz_custompoints_200.pdf     |  Bin 0 -> 27763 bytes
 .../violinplot_horiz_custompoints_200.png     |  Bin 0 -> 33607 bytes
 .../violinplot_horiz_custompoints_200.svg     | 2234 +++++++++++++++++
 .../test_axes/violinplot_horiz_showall.pdf    |  Bin 0 -> 22081 bytes
 .../test_axes/violinplot_horiz_showall.png    |  Bin 0 -> 34381 bytes
 .../test_axes/violinplot_horiz_showall.svg    | 1594 ++++++++++++
 .../violinplot_horiz_showextrema.pdf          |  Bin 0 -> 20174 bytes
 .../violinplot_horiz_showextrema.png          |  Bin 0 -> 34540 bytes
 .../violinplot_horiz_showextrema.svg          | 1538 ++++++++++++
 .../test_axes/violinplot_horiz_showmeans.pdf  |  Bin 0 -> 17936 bytes
 .../test_axes/violinplot_horiz_showmeans.png  |  Bin 0 -> 36109 bytes
 ...ans.svg => violinplot_horiz_showmeans.svg} | 1696 ++++++-------
 .../violinplot_horiz_showmedians.pdf          |  Bin 0 -> 18278 bytes
 .../violinplot_horiz_showmedians.png          |  Bin 0 -> 36379 bytes
 .../violinplot_horiz_showmedians.svg          | 1443 +++++++++++
 .../test_axes/violinplot_vert_baseline.pdf    |  Bin 0 -> 16964 bytes
 .../test_axes/violinplot_vert_baseline.png    |  Bin 0 -> 36620 bytes
 ...eline.svg => violinplot_vert_baseline.svg} | 1672 ++++++------
 ...df => violinplot_vert_custompoints_10.pdf} |  Bin 7575 -> 7836 bytes
 .../violinplot_vert_custompoints_10.png       |  Bin 0 -> 34075 bytes
 ...vg => violinplot_vert_custompoints_10.svg} |  307 ++-
 .../violinplot_vert_custompoints_200.pdf      |  Bin 0 -> 27664 bytes
 .../violinplot_vert_custompoints_200.png      |  Bin 0 -> 35211 bytes
 .../violinplot_vert_custompoints_200.svg      | 2214 ++++++++++++++++
 .../test_axes/violinplot_vert_showall.pdf     |  Bin 0 -> 22032 bytes
 .../test_axes/violinplot_vert_showall.png     |  Bin 0 -> 34996 bytes
 .../test_axes/violinplot_vert_showall.svg     | 1572 ++++++++++++
 .../test_axes/violinplot_vert_showextrema.pdf |  Bin 0 -> 19877 bytes
 .../test_axes/violinplot_vert_showextrema.png |  Bin 0 -> 35709 bytes
 ...ma.svg => violinplot_vert_showextrema.svg} | 1744 ++++++-------
 .../test_axes/violinplot_vert_showmeans.pdf   |  Bin 0 -> 18114 bytes
 .../test_axes/violinplot_vert_showmeans.png   |  Bin 0 -> 35856 bytes
 .../test_axes/violinplot_vert_showmeans.svg   | 1441 +++++++++++
 .../test_axes/violinplot_vert_showmedians.pdf |  Bin 0 -> 18161 bytes
 .../test_axes/violinplot_vert_showmedians.png |  Bin 0 -> 34655 bytes
 .../test_axes/violinplot_vert_showmedians.svg | 1441 +++++++++++
 lib/matplotlib/tests/test_axes.py             |  137 +-
 83 files changed, 17970 insertions(+), 18953 deletions(-)
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmeans.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmeans.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_baseline.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_10.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.png
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_baseline.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_baseline.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_baseline.svg
 rename lib/matplotlib/tests/baseline_images/test_axes/{test_horiz_violinplot_custompoints_10.pdf => violinplot_horiz_custompoints_10.pdf} (57%)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.png
 rename lib/matplotlib/tests/baseline_images/test_axes/{test_horiz_violinplot_custompoints_10.svg => violinplot_horiz_custompoints_10.svg} (77%)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_200.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_200.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_200.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.png
 rename lib/matplotlib/tests/baseline_images/test_axes/{test_horiz_violinplot_showmeans.svg => violinplot_horiz_showmeans.svg} (52%)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_baseline.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_baseline.png
 rename lib/matplotlib/tests/baseline_images/test_axes/{test_vert_violinplot_baseline.svg => violinplot_vert_baseline.svg} (52%)
 rename lib/matplotlib/tests/baseline_images/test_axes/{test_vert_violinplot_custompoints_10.pdf => violinplot_vert_custompoints_10.pdf} (57%)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_10.png
 rename lib/matplotlib/tests/baseline_images/test_axes/{test_vert_violinplot_custompoints_10.svg => violinplot_vert_custompoints_10.svg} (80%)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_200.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_200.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_200.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showall.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showall.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showall.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showextrema.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showextrema.png
 rename lib/matplotlib/tests/baseline_images/test_axes/{test_vert_violinplot_showextrema.svg => violinplot_vert_showextrema.svg} (53%)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmeans.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmeans.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmeans.svg
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmedians.pdf
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmedians.png
 create mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmedians.svg

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 5a81d809f4e7..e0d583eb8710 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6735,7 +6735,7 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
 
           violinplot(dataset, positions=None, vert=True, widths=0.5,
                      showmeans=False, showextrema=True, showmedians=False,
-                     points=100):
+                     points=100, bw_method=None):
 
         Make a violin plot for each column of *dataset* or each vector in
         sequence *dataset*.  Each filled area extends to represent the
@@ -6864,7 +6864,7 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
             max_val = kde.dataset.max()
             mean = np.mean(kde.dataset)
             median = np.median(kde.dataset)
-            coords = np.arange(min_val, max_val, (max_val - min_val)/points)
+            coords = np.linspace(min_val, max_val, points)
 
             vals = kde.evaluate(coords)
 
diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py
index a33e577d785c..a6f0a82dd8d1 100644
--- a/lib/matplotlib/pyplot.py
+++ b/lib/matplotlib/pyplot.py
@@ -3400,7 +3400,8 @@ def triplot(*args, **kwargs):
 # changes will be lost
 @_autogen_docstring(Axes.violinplot)
 def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
-               showextrema=True, showmedians=False, points=100, hold=None):
+               showextrema=True, showmedians=False, points=100, bw_method=None,
+               hold=None):
     ax = gca()
     # allow callers to override the hold state by passing hold=True|False
     washold = ax.ishold()
@@ -3411,7 +3412,7 @@ def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
         ret = ax.violinplot(dataset, positions=positions, vert=vert,
                             widths=widths, showmeans=showmeans,
                             showextrema=showextrema, showmedians=showmedians,
-                            points=points)
+                            points=points, bw_method=bw_method)
         draw_if_interactive()
     finally:
         ax.hold(washold)
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_baseline.pdf
deleted file mode 100644
index 7c67843c4665da0536dce384ac9d53b9b91c8d06..0000000000000000000000000000000000000000
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_200.pdf
deleted file mode 100644
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GIT binary patch
literal 0
HcmV?d00001

literal 27528
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_medianse.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showall.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.png b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showextrema.png
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_showmedians.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_custompoints_200.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showall.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.png b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showextrema.png
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmeans.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.pdf b/lib/matplotlib/tests/baseline_images/test_axes/test_vert_violinplot_showmedians.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.pdf
similarity index 57%
rename from lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.pdf
rename to lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.pdf
index 1562273344a95758f30ee3d08078d6af6ad505fc..9a88f3a783c6ce26bdd881a0c5d498c5b5e1af18 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.svg b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.svg
similarity index 77%
rename from lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.svg
rename to lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.svg
index 3e91a231d111..cec484ed4de8 100644
--- a/lib/matplotlib/tests/baseline_images/test_axes/test_horiz_violinplot_custompoints_10.svg
+++ b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.svg
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.png b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.png
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similarity index 52%
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rename to lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.svg
index 7b062a4302f3..ea23a3563c1e 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.png b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.png
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_baseline.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_baseline.pdf
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similarity index 52%
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index e1e8fb532705..375239505902 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showall.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showall.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showextrema.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showextrema.pdf
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similarity index 53%
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index 28ed16eac79c..0e7b4676502c 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmedians.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmedians.pdf
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+  <clipPath id="p7ff5b81e1d">
+   <rect height="345.6" width="446.4" x="72.0" y="43.2"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 6235f9902557..67cb92f9d005 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1566,176 +1566,163 @@ def test_boxplot_bad_ci_2():
                   conf_intervals=[[1, 2], [1]])
 
 
-# violin plot data initialization
-data = [([+0.07902449, -0.16769639, +1.1572525,  +0.71400729, -0.17916727,
-          -1.15346725, -0.5298936,  +1.16570619, +1.13612837, -0.66830221,
-          -0.76738509, +0.85911678, +0.56446469, +0.64772651, -1.97432723,
-          -1.11794413, +0.4094635,  +2.52767469, -0.81092698, -0.23422668,
-          +0.423861,   +0.01702886, -0.58954823, -1.05303546, +0.22632754,
-          -1.88620214, +0.06759594, -0.51663253, -0.38821442, -0.5462294,
-          -0.39967334, -1.2690421,  -0.271953,   +0.19494831, +1.0674446,
-          +0.06632929, +0.9051155,  -0.06507299, -0.58885588, +0.03405925,
-          +0.60666877, -0.25755542, +1.06387913, -0.50576651, -0.51104135,
-          -0.65366091, -1.10801137, +0.55746182, +0.27206281, +0.25658797,
-          +0.008253,   -0.07254077, -0.77980703, -1.5707303,  -0.74731452,
-          -0.38364682, +1.37653142, -0.04123221, -0.84737153, +0.26552353,
-          +0.80039697, +0.17446856, +0.32860543, +0.79574814, -1.88942134]),
-        ([+3.99586977e-01, +1.09626020e+00, +2.64974356e-01, +5.49065532e-01,
-          -1.86679220e+00, -4.23951661e-01, -3.66858136e-01, +7.39441772e-02,
-          -1.25772592e+00, -1.14864510e+00, -7.59625813e-01, -2.67830782e-01,
-          -2.68205909e-01, -2.64119550e-02, -3.00092210e-01, -1.17080290e-03,
-          +1.25324397e+00, +1.97518726e-01, +9.74395138e-01, -2.52217468e-01,
-          -2.00424239e+00, -2.20525681e+00, +6.32069078e-01, -5.59674009e-02,
-          -1.13007054e+00, +8.47680697e-01, -1.41563783e+00, +6.84885681e-02,
-          +8.06629024e-01, +1.06561293e+00, +1.48755064e-01, +1.06241336e+00,
-          -1.53742677e+00, -9.40116707e-01, -2.35342351e-01, +4.07790960e-01,
-          +9.59066810e-01, +1.83262266e+00, -1.44675794e-01, -1.61663789e+00,
-          -3.34055942e-01, -1.65081542e+00, +6.54573563e-01, -4.80998938e-01,
-          -4.77104620e-01, +4.35836897e-01, +1.54488583e-01, +1.90264111e+00,
-          -1.73584727e+00, +2.84097580e-01, -6.67013428e-01, -5.47647643e-01,
-          -1.77584471e-01, -6.54191064e-01, +1.02366976e+00, +1.57777769e+00,
-          +2.10098337e-01, -5.34631915e-02, +4.28913084e-01, -5.56544884e-02,
-          +1.64250239e-01, -4.77299164e-01, -8.40402132e-01, -1.58474541e-01]),
-        ([-0.00975961, -0.9572654,  -0.02331628, -0.88758431, +0.36594918,
-          +0.58733922, +0.12169127, -0.17451044, -1.48322656, -0.64203124,
-          +1.01373274, -0.77332978, -1.64093613, +0.07944897, +1.79420792,
-          -0.95589844, -2.19618124, +0.99478738, -1.98933911, +0.21046525,
-          -2.31831045, +1.11045528, -0.51981581, +0.49740564, -0.40365721,
-          -0.30515722, -0.60601737, -1.05976064, +1.43356283, -0.59014164,
-          +0.58822025, +1.80100922, -1.40905671, +0.74553523, -1.57655404,
-          +0.29342432, +0.35548625, -0.99138976, -1.37339981, +0.63871936,
-          -0.60010678, -0.73597695, -0.12228469, +0.2467333,  +0.03750118,
-          -0.45755544, -0.8648646,  +0.13883081, -0.11239293, -0.7661388,
-          -0.70841112, -0.51668825, +2.2590876,  +0.61731299, -0.33742898,
-          +1.40708783, -1.43371511, -1.20425544, +0.79551956, -0.38148021,
-          -0.05703633, -0.42718744, +1.86441201, -0.36006341, -2.23769144]),
-        ([+0.28379466, +0.31202331, +0.54110464, +0.79957469, +0.02825945,
-          +1.39430266, +0.38945253, +0.25840893, -1.03405387, +0.3951418,
-          -0.32782812, -0.49764761, +1.67314785, +0.57207158, +0.42868172,
-          -0.66405633, +0.49477738, -0.24707622, -0.91179434, -0.88450974,
-          +1.47387423, +1.27147423, -1.28664994, +0.84428091, +0.19419244,
-          -1.27527008, +1.44462176, +1.21255381, +1.74448494, -1.47661372,
-          -1.00577117, -0.68746569, -0.85283125, -0.87339905, -0.05053922,
-          +1.79110014, -0.99663248, +0.52435397, +1.17699107, -1.51437376,
-          +0.52402067, -0.68885234, +1.84101899, +1.09318846, +0.66686321,
-          -1.14796045, +0.54247117, -2.21273401, -0.44526518, +1.08591603,
-          -1.86173825, -1.31016714, +0.7782744,  +0.76330906, -0.96452241,
-          -1.34983597, -0.90317774, +0.20187156, -2.03515866, +1.35603702,
-          +1.01390851, +0.29328188, -0.2223719,  -1.29928072, +0.59399753])]
-
-
-# violin plot test starts here
-@image_comparison(baseline_images=['test_vert_violinplot_baseline'])
+@image_comparison(baseline_images=['violinplot_vert_baseline'])
 def test_vert_violinplot_baseline():
+    # First 9 digits of frac(sqrt(2))
+    np.random.seed(414213562)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax = plt.axes()
     ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
                   showmedians=0)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_showmeans'])
+@image_comparison(baseline_images=['violinplot_vert_showmeans'])
 def test_vert_violinplot_showmeans():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(3))
+    np.random.seed(732050807)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), showmeans=1, showextrema=0,
                   showmedians=0)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_showextrema'])
+@image_comparison(baseline_images=['violinplot_vert_showextrema'])
 def test_vert_violinplot_showextrema():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(5))
+    np.random.seed(236067977)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), showmeans=0, showextrema=1,
                   showmedians=0)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_showmedians'])
+@image_comparison(baseline_images=['violinplot_vert_showmedians'])
 def test_vert_violinplot_showmedians():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(7))
+    np.random.seed(645751311)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
                   showmedians=1)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_showall'])
+@image_comparison(baseline_images=['violinplot_vert_showall'])
 def test_vert_violinplot_showall():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(11))
+    np.random.seed(316624790)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), showmeans=1, showextrema=1,
                   showmedians=1)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_custompoints_10'])
+@image_comparison(baseline_images=['violinplot_vert_custompoints_10'])
 def test_vert_violinplot_custompoints_10():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(13))
+    np.random.seed(605551275)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
                   showmedians=0, points=10)
 
 
-@image_comparison(baseline_images=['test_vert_violinplot_custompoints_200'])
+@image_comparison(baseline_images=['violinplot_vert_custompoints_200'])
 def test_vert_violinplot_custompoints_200():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(17))
+    np.random.seed(123105625)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
                   showmedians=0, points=200)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_baseline'])
+@image_comparison(baseline_images=['violinplot_horiz_baseline'])
 def test_horiz_violinplot_baseline():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(19))
+    np.random.seed(358898943)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
                   showextrema=0, showmedians=0)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_showmedians'])
+@image_comparison(baseline_images=['violinplot_horiz_showmedians'])
 def test_horiz_violinplot_showmedians():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(23))
+    np.random.seed(795831523)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
                   showextrema=0, showmedians=1)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_showmeans'])
+@image_comparison(baseline_images=['violinplot_horiz_showmeans'])
 def test_horiz_violinplot_showmeans():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(29))
+    np.random.seed(385164807)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
                   showextrema=0, showmedians=0)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_showextrema'])
+@image_comparison(baseline_images=['violinplot_horiz_showextrema'])
 def test_horiz_violinplot_showextrema():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(31))
+    np.random.seed(567764362)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
                   showextrema=1, showmedians=0)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_showall'])
+@image_comparison(baseline_images=['violinplot_horiz_showall'])
 def test_horiz_violinplot_showall():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(37))
+    np.random.seed(82762530)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
                   showextrema=1, showmedians=1)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_10'])
+@image_comparison(baseline_images=['violinplot_horiz_custompoints_10'])
 def test_horiz_violinplot_custompoints_10():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(41))
+    np.random.seed(403124237)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
                   showextrema=0, showmedians=0, points=10)
 
 
-@image_comparison(baseline_images=['test_horiz_violinplot_custompoints_200'])
+@image_comparison(baseline_images=['violinplot_horiz_custompoints_200'])
 def test_horiz_violinplot_custompoints_200():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(43))
+    np.random.seed(557438524)
+    data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
                   showextrema=0, showmedians=0, points=200)
 
 
-# test error
 @cleanup
 def test_violinplot_bad_positions():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(47))
+    np.random.seed(855654600)
+    data = [np.random.normal(size=100) for i in range(4)]
     assert_raises(ValueError, ax.violinplot, data, positions=range(5))
 
 
 @cleanup
 def test_violinplot_bad_widths():
     ax = plt.axes()
+    # First 9 digits of frac(sqrt(53))
+    np.random.seed(280109889)
+    data = [np.random.normal(size=100) for i in range(4)]
     assert_raises(ValueError, ax.violinplot, data, positions=range(4),
                   widths=[1, 2, 3])
-# violin plot test ends here
 
 
 @cleanup

From b8db6d24f65972eee3eb50b46887d1adae63fa79 Mon Sep 17 00:00:00 2001
From: solvents <wisalam@live.com>
Date: Tue, 22 Apr 2014 13:27:02 -0400
Subject: [PATCH 21/22] Refactored axes.violinplot into cbook.violin_stats
 (arranges violin plot data for drawing), axes.violin (draws pre-arranged
 violin plot data), and axes.violinplot (uses cbook.violin_stats to draw
 violin plots via axes.violin) Updated whats_new.rst. Updated CHANGELOG.

---
 CHANGELOG                    |   4 ++
 doc/users/whats_new.rst      |  21 ++++++
 lib/matplotlib/axes/_axes.py | 135 +++++++++++++++++++++++++++--------
 lib/matplotlib/cbook.py      | 125 +++++++++++++++++++++++++-------
 4 files changed, 230 insertions(+), 55 deletions(-)

diff --git a/CHANGELOG b/CHANGELOG
index 1f9d4a7cc33c..1b24d88f661d 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -30,6 +30,10 @@
            interpolation = 'none' and interpolation = 'nearest' in
            `imshow()` when saving vector graphics files.
 
+2014-04-22 Added violin plotting functions. See `Axes.violinplot`,
+           `Axes.violin`, `cbook.violin_stats` and `mlab.GaussianKDE` for
+           details.
+
 2014-04-10 Fixed the triangular marker rendering error. The "Up" triangle was
            rendered instead of "Right" triangle and vice-versa.
 
diff --git a/doc/users/whats_new.rst b/doc/users/whats_new.rst
index f6706fcdd083..95e64afedb8d 100644
--- a/doc/users/whats_new.rst
+++ b/doc/users/whats_new.rst
@@ -172,6 +172,27 @@ Added the Axes method :meth:`~matplotlib.axes.Axes.add_image` to put image
 handling on a par with artists, collections, containers, lines, patches,
 and tables.
 
+Violin Plots
+````````````
+Per Parker, Gregory Kelsie, Adam Ortiz, Kevin Chan, Geoffrey Lee, Deokjae
+Donald Seo, and Taesu Terry Lim added a basic implementation for violin
+plots. Violin plots can be used to represent the distribution of sample data.
+They are similar to box plots, but use a kernel density estimation function to
+present a smooth approximation of the data sample used. The added features are:
+
+:func:`~matplotlib.Axes.violin` - Renders a violin plot from a collection of
+statistics.
+:func:`~matplotlib.cbook.violin_stats` - Produces a collection of statistics
+suitable for rendering a violin plot.
+:func:`~matplotlib.pyplot.violinplot` - Creates a violin plot from a set of
+sample data. This method makes use of :func:`~matplotlib.cbook.violin_stats`
+to process the input data, and :func:`~matplotlib.cbook.violin_stats` to
+do the actual rendering. Users are also free to modify or replace the output of
+:func:`~matplotlib.cbook.violin_stats` in order to customize the violin plots
+to their liking.
+
+This feature was implemented for a software engineering course at the
+University of Toronto, Scarborough, run in Winter 2014 by Anya Tafliovich.
 
 More `markevery` options to show only a subset of markers
 `````````````````````````````````````````````````````````
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index e0d583eb8710..aac79d17c4b1 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6739,8 +6739,8 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
 
         Make a violin plot for each column of *dataset* or each vector in
         sequence *dataset*.  Each filled area extends to represent the
-        entire data range, with three lines at the mean, the minimum, and
-        the maximum.
+        entire data range, with optional lines at the mean, the median,
+        the minimum, and the maximum.
 
         Parameters
         ----------
@@ -6778,7 +6778,7 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
             The method used to calculate the estimator bandwidth.  This can be
             'scott', 'silverman', a scalar constant or a callable.  If a
             scalar, this will be used directly as `kde.factor`.  If a
-            callable, it should take a `GaussianKDE` instance as only
+            callable, it should take a `GaussianKDE` instance as its only
             parameter and return a scalar. If None (default), 'scott' is used.
 
         Returns
@@ -6806,6 +6806,91 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
 
         """
 
+        def _kde_method(X, coords):
+            kde = mlab.GaussianKDE(X, bw_method)
+            return kde.evaluate(coords)
+
+        vpstats = cbook.violin_stats(dataset, _kde_method, points=points)
+        return self.violin(vpstats, positions=positions, vert=vert,
+                           widths=widths, showmeans=showmeans,
+                           showextrema=showextrema, showmedians=showmedians)
+
+    def violin(self, vpstats, positions=None, vert=True, widths=0.5,
+               showmeans=False, showextrema=True, showmedians=False):
+        """
+        Drawing function for violin plots.
+
+        Call signature::
+
+          violin(vpstats, positions=None, vert=True, widths=0.5,
+                 showmeans=False, showextrema=True, showmedians=False):
+
+        Draw a violin plot for each column of `vpstats`. Each filled area
+        extends to represent the entire data range, with optional lines at the
+        mean, the median, the minimum, and the maximum.
+
+        Parameters
+        ----------
+
+          vpstats : list of dicts
+            A list of dictionaries containing stats for each violin plot.
+            Required keys are:
+              - coords: A list of scalars containing the coordinates that
+                the violin's kernel density estimate were evaluated at.
+              - vals: A list of scalars containing the values of the kernel
+                density estimate at each of the coordinates given in `coords`.
+              - mean: The mean value for this violin's dataset.
+              - median: The median value for this violin's dataset.
+              - min: The minimum value for this violin's dataset.
+              - max: The maximum value for this violin's dataset.
+
+          positions : array-like, default = [1, 2, ..., n]
+            Sets the positions of the violins. The ticks and limits are
+            automatically set to match the positions.
+
+          vert : bool, default = True.
+            If true, plots the violins veritcally.
+            Otherwise, plots the violins horizontally.
+
+          widths : array-like, default = 0.5
+            Either a scalar or a vector that sets the maximal width of
+            each violin. The default is 0.5, which uses about half of the
+            available horizontal space.
+
+          showmeans : bool, default = False
+            If true, will toggle rendering of the means.
+
+          showextrema : bool, default = True
+            If true, will toggle rendering of the extrema.
+
+          showmedians : bool, default = False
+            If true, will toggle rendering of the medians.
+
+        Returns
+        -------
+
+        A dictionary mapping each component of the violinplot to a list of the
+        corresponding collection instances created. The dictionary has
+        the following keys:
+
+            - bodies: A list of the
+              :class:`matplotlib.collections.PolyCollection` instances
+              containing the filled area of each violin.
+            - means: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the mean values of each of the violin's
+              distribution.
+            - mins: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the bottom of each violin's distribution.
+            - maxes: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the top of each violin's distribution.
+            - bars: A :class:`matplotlib.collections.LineCollection` instance
+              created to identify the centers of each violin's distribution.
+            - medians: A :class:`matplotlib.collections.LineCollection`
+              instance created to identify the median values of each of the
+              violin's distribution.
+
+        """
+
         # Statistical quantities to be plotted on the violins
         means = []
         mins = []
@@ -6822,22 +6907,23 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
             'cmedians': None
         }
 
+        N = len(vpstats)
         datashape_message = ("List of violinplot statistics and `{0}` "
                              "values must have the same length")
 
         # Validate positions
         if positions is None:
-            positions = range(1, len(dataset) + 1)
-        elif len(positions) != len(dataset):
+            positions = range(1, N + 1)
+        elif len(positions) != N:
             raise ValueError(datashape_message.format("positions"))
 
         # Validate widths
         if np.isscalar(widths):
-            widths = [widths] * len(dataset)
-        elif len(widths) != len(dataset):
+            widths = [widths] * N
+        elif len(widths) != N:
             raise ValueError(datashape_message.format("widths"))
 
-        # Calculate mins and maxes for statistics lines
+        # Calculate ranges for statistics lines
         pmins = -0.25 * np.array(widths) + positions
         pmaxes = 0.25 * np.array(widths) + positions
 
@@ -6857,33 +6943,20 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
             blines = self.hlines
 
         # Render violins
-        for data, pos, width in zip(dataset, positions, widths):
-            # Calculate the kernel density
-            kde = mlab.GaussianKDE(data, bw_method)
-            min_val = kde.dataset.min()
-            max_val = kde.dataset.max()
-            mean = np.mean(kde.dataset)
-            median = np.median(kde.dataset)
-            coords = np.linspace(min_val, max_val, points)
-
-            vals = kde.evaluate(coords)
-
-            # Since each data point p is plotted from v-p to v+p,
-            # we need to scale it by an additional 0.5 factor so that we get
-            # correct width in the end.
-            vals = 0.5 * width * vals/vals.max()
-
-            # create the violin bodies
-            artists['bodies'] += [fill(coords,
+        for stats, pos, width in zip(vpstats, positions, widths):
+            # The 0.5 factor reflects the fact that we plot from v-p to
+            # v+p
+            vals = np.array(stats['vals'])
+            vals = 0.5 * width * vals / vals.max()
+            artists['bodies'] += [fill(stats['coords'],
                                        -vals + pos,
                                        vals + pos,
                                        facecolor='y',
                                        alpha=0.3)]
-
-            means.append(mean)
-            mins.append(min_val)
-            maxes.append(max_val)
-            medians.append(median)
+            means.append(stats['mean'])
+            mins.append(stats['min'])
+            maxes.append(stats['max'])
+            medians.append(stats['median'])
 
         # Render means
         if showmeans:
diff --git a/lib/matplotlib/cbook.py b/lib/matplotlib/cbook.py
index a22a60ebf559..98e967dbc253 100644
--- a/lib/matplotlib/cbook.py
+++ b/lib/matplotlib/cbook.py
@@ -1944,28 +1944,7 @@ def _compute_conf_interval(data, med, iqr, bootstrap):
     bxpstats = []
 
     # convert X to a list of lists
-    if hasattr(X, 'shape'):
-        # one item
-        if len(X.shape) == 1:
-            if hasattr(X[0], 'shape'):
-                X = list(X)
-            else:
-                X = [X, ]
-
-        # several items
-        elif len(X.shape) == 2:
-            nrows, ncols = X.shape
-            if nrows == 1:
-                X = [X]
-            elif ncols == 1:
-                X = [X.ravel()]
-            else:
-                X = [X[:, i] for i in xrange(ncols)]
-        else:
-            raise ValueError("input `X` must have 2 or fewer dimensions")
-
-    if not hasattr(X[0], '__len__'):
-        X = [X]
+    X = _reshape_2D(X)
 
     ncols = len(X)
     if labels is None:
@@ -1982,7 +1961,7 @@ def _compute_conf_interval(data, med, iqr, bootstrap):
         stats['mean'] = np.mean(x)
 
         # medians and quartiles
-        q1, med, q3 =  np.percentile(x, [25, 50, 75])
+        q1, med, q3 = np.percentile(x, [25, 50, 75])
 
         # interquartile range
         stats['iqr'] = q3 - q1
@@ -2004,7 +1983,7 @@ def _compute_conf_interval(data, med, iqr, bootstrap):
                 hival = np.max(x)
             else:
                 whismsg = ('whis must be a float, valid string, or '
-                          'list of percentiles')
+                           'list of percentiles')
                 raise ValueError(whismsg)
         else:
             loval = np.percentile(x, whis[0])
@@ -2157,6 +2136,104 @@ def is_math_text(s):
     return even_dollars
 
 
+def _reshape_2D(X):
+    if hasattr(X, 'shape'):
+        # one item
+        if len(X.shape) == 1:
+            if hasattr(X[0], 'shape'):
+                X = list(X)
+            else:
+                X = [X, ]
+
+        # several items
+        elif len(X.shape) == 2:
+            nrows, ncols = X.shape
+            if nrows == 1:
+                X = [X]
+            elif ncols == 1:
+                X = [X.ravel()]
+            else:
+                X = [X[:, i] for i in xrange(ncols)]
+        else:
+            raise ValueError("input `X` must have 2 or fewer dimensions")
+
+    if not hasattr(X[0], '__len__'):
+        X = [X]
+
+    return X
+
+
+def violin_stats(X, method, points=100):
+    '''
+    Returns a list of dictionaries of data which can be used to draw a series
+    of violin plots. See the `Returns` section below to view the required keys
+    of the dictionary. Users can skip this function and pass a user-defined set
+    of dictionaries to the `axes.vplot` method instead of using MPL to do the
+    calculations.
+
+    Parameters
+    ----------
+    X : array-like
+        Sample data that will be used to produce the gaussian kernel density
+        estimates. Must have 2 or fewer dimensions.
+
+    method : callable
+        The method used to calculate the kernel density estimate for each
+        column of data. When called via `method(v, coords)`, it should
+        return a vector of the values of the KDE evaluated at the values
+        specified in coords.
+
+    points : scalar, default = 100
+        Defines the number of points to evaluate each of the gaussian kernel
+        density estimates at.
+
+    Returns
+    -------
+
+    A list of dictionaries containing the results for each column of data.
+    The dictionaries contain at least the following:
+
+        - coords: A list of scalars containing the coordinates this particular
+          kernel density estimate was evaluated at.
+        - vals: A list of scalars containing the values of the kernel density
+          estimate at each of the coordinates given in `coords`.
+        - mean: The mean value for this column of data.
+        - median: The median value for this column of data.
+        - min: The minimum value for this column of data.
+        - max: The maximum value for this column of data.
+    '''
+
+    # List of dictionaries describing each of the violins.
+    vpstats = []
+
+    # Want X to be a list of data sequences
+    X = _reshape_2D(X)
+
+    for x in X:
+        # Dictionary of results for this distribution
+        stats = {}
+
+        # Calculate basic stats for the distribution
+        min_val = np.min(x)
+        max_val = np.max(x)
+
+        # Evaluate the kernel density estimate
+        coords = np.linspace(min_val, max_val, points)
+        stats['vals'] = method(x, coords)
+        stats['coords'] = coords
+
+        # Store additional statistics for this distribution
+        stats['mean'] = np.mean(x)
+        stats['median'] = np.median(x)
+        stats['min'] = min_val
+        stats['max'] = max_val
+
+        # Append to output
+        vpstats.append(stats)
+
+    return vpstats
+
+
 class _NestedClassGetter(object):
     # recipe from http://stackoverflow.com/a/11493777/741316
     """

From 01c3176fa61c621e7a6f0d1240bac1f222c19750 Mon Sep 17 00:00:00 2001
From: Per Parker <wisalam@live.com>
Date: Sat, 24 May 2014 16:47:09 -0400
Subject: [PATCH 22/22] Updated violinplot. Removed pdf and svg test images.

---
 lib/matplotlib/axes/_axes.py                  |   50 +-
 lib/matplotlib/cbook.py                       |    8 +
 .../test_axes/violinplot_horiz_baseline.pdf   |  Bin 17330 -> 0 bytes
 .../test_axes/violinplot_horiz_baseline.svg   | 1455 -----------
 .../violinplot_horiz_custompoints_10.pdf      |  Bin 7844 -> 0 bytes
 .../violinplot_horiz_custompoints_10.svg      |  714 ------
 .../violinplot_horiz_custompoints_200.pdf     |  Bin 27763 -> 0 bytes
 .../violinplot_horiz_custompoints_200.svg     | 2234 -----------------
 .../test_axes/violinplot_horiz_showall.pdf    |  Bin 22081 -> 0 bytes
 .../test_axes/violinplot_horiz_showall.svg    | 1594 ------------
 .../violinplot_horiz_showextrema.pdf          |  Bin 20174 -> 0 bytes
 .../violinplot_horiz_showextrema.svg          | 1538 ------------
 .../test_axes/violinplot_horiz_showmeans.pdf  |  Bin 17936 -> 0 bytes
 .../test_axes/violinplot_horiz_showmeans.svg  | 1404 -----------
 .../violinplot_horiz_showmedians.pdf          |  Bin 18278 -> 0 bytes
 .../violinplot_horiz_showmedians.svg          | 1443 -----------
 .../test_axes/violinplot_vert_baseline.pdf    |  Bin 16964 -> 0 bytes
 .../test_axes/violinplot_vert_baseline.svg    | 1374 ----------
 .../violinplot_vert_custompoints_10.pdf       |  Bin 7836 -> 0 bytes
 .../violinplot_vert_custompoints_10.svg       |  693 -----
 .../violinplot_vert_custompoints_200.pdf      |  Bin 27664 -> 0 bytes
 .../violinplot_vert_custompoints_200.svg      | 2214 ----------------
 .../test_axes/violinplot_vert_showall.pdf     |  Bin 22032 -> 0 bytes
 .../test_axes/violinplot_vert_showall.svg     | 1572 ------------
 .../test_axes/violinplot_vert_showextrema.pdf |  Bin 19877 -> 0 bytes
 .../test_axes/violinplot_vert_showextrema.svg | 1458 -----------
 .../test_axes/violinplot_vert_showmeans.pdf   |  Bin 18114 -> 0 bytes
 .../test_axes/violinplot_vert_showmeans.svg   | 1441 -----------
 .../test_axes/violinplot_vert_showmedians.pdf |  Bin 18161 -> 0 bytes
 .../test_axes/violinplot_vert_showmedians.svg | 1441 -----------
 lib/matplotlib/tests/test_axes.py             |   42 +-
 31 files changed, 56 insertions(+), 20619 deletions(-)
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_baseline.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_baseline.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_10.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_200.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_custompoints_200.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_baseline.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_baseline.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_10.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_10.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_200.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_200.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showall.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showall.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showextrema.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showextrema.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmeans.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmeans.svg
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmedians.pdf
 delete mode 100644 lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmedians.svg

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index aac79d17c4b1..40f230d9a650 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6898,14 +6898,7 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
         medians = []
 
         # Collections to be returned
-        artists = {
-            'bodies': [],
-            'cmeans': None,
-            'cmaxes': None,
-            'cmins': None,
-            'cbars': None,
-            'cmedians': None
-        }
+        artists = {}
 
         N = len(vpstats)
         datashape_message = ("List of violinplot statistics and `{0}` "
@@ -6927,53 +6920,50 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
         pmins = -0.25 * np.array(widths) + positions
         pmaxes = 0.25 * np.array(widths) + positions
 
-        # Check hold status
-        if not self._hold:
-            self.cla()
-        hold_status = self._hold
-
         # Check whether we are rendering vertically or horizontally
         if vert:
             fill = self.fill_betweenx
-            rlines = self.hlines
-            blines = self.vlines
+            perp_lines = self.hlines
+            par_lines = self.vlines
         else:
             fill = self.fill_between
-            rlines = self.vlines
-            blines = self.hlines
+            perp_lines = self.vlines
+            par_lines = self.hlines
 
         # Render violins
+        bodies = []
         for stats, pos, width in zip(vpstats, positions, widths):
             # The 0.5 factor reflects the fact that we plot from v-p to
             # v+p
             vals = np.array(stats['vals'])
             vals = 0.5 * width * vals / vals.max()
-            artists['bodies'] += [fill(stats['coords'],
-                                       -vals + pos,
-                                       vals + pos,
-                                       facecolor='y',
-                                       alpha=0.3)]
+            bodies += [fill(stats['coords'],
+                            -vals + pos,
+                            vals + pos,
+                            facecolor='y',
+                            alpha=0.3)]
             means.append(stats['mean'])
             mins.append(stats['min'])
             maxes.append(stats['max'])
             medians.append(stats['median'])
+        artists['bodies'] = bodies
 
         # Render means
         if showmeans:
-            artists['cmeans'] = rlines(means, pmins, pmaxes, colors='r')
+            artists['cmeans'] = perp_lines(means, pmins, pmaxes, colors='r')
 
         # Render extrema
         if showextrema:
-            artists['cmaxes'] = rlines(maxes, pmins, pmaxes, colors='r')
-            artists['cmins'] = rlines(mins, pmins, pmaxes, colors='r')
-            artists['cbars'] = blines(positions, mins, maxes, colors='r')
+            artists['cmaxes'] = perp_lines(maxes, pmins, pmaxes, colors='r')
+            artists['cmins'] = perp_lines(mins, pmins, pmaxes, colors='r')
+            artists['cbars'] = par_lines(positions, mins, maxes, colors='r')
 
         # Render medians
         if showmedians:
-            artists['cmedians'] = rlines(medians, pmins, pmaxes, colors='r')
-
-        # Reset hold
-        self.hold(hold_status)
+            artists['cmedians'] = perp_lines(medians,
+                                             pmins,
+                                             pmaxes,
+                                             colors='r')
 
         return artists
 
diff --git a/lib/matplotlib/cbook.py b/lib/matplotlib/cbook.py
index 98e967dbc253..49bdf3fb2534 100644
--- a/lib/matplotlib/cbook.py
+++ b/lib/matplotlib/cbook.py
@@ -2137,6 +2137,14 @@ def is_math_text(s):
 
 
 def _reshape_2D(X):
+    """
+    Converts a non-empty list or an ndarray of two or fewer dimensions
+    into a list of iterable objects so that in
+
+        for v in _reshape_2D(X):
+
+    v is iterable and can be used to instantiate a 1D array.
+    """
     if hasattr(X, 'shape'):
         # one item
         if len(X.shape) == 1:
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_baseline.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_baseline.pdf
deleted file mode 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showall.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showextrema.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmeans.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_horiz_showmedians.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_10.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_10.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_200.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_custompoints_200.pdf
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmeans.pdf b/lib/matplotlib/tests/baseline_images/test_axes/violinplot_vert_showmeans.pdf
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diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 67cb92f9d005..7acc2566e33c 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1566,7 +1566,8 @@ def test_boxplot_bad_ci_2():
                   conf_intervals=[[1, 2], [1]])
 
 
-@image_comparison(baseline_images=['violinplot_vert_baseline'])
+@image_comparison(baseline_images=['violinplot_vert_baseline'],
+                  extensions=['png'])
 def test_vert_violinplot_baseline():
     # First 9 digits of frac(sqrt(2))
     np.random.seed(414213562)
@@ -1576,7 +1577,8 @@ def test_vert_violinplot_baseline():
                   showmedians=0)
 
 
-@image_comparison(baseline_images=['violinplot_vert_showmeans'])
+@image_comparison(baseline_images=['violinplot_vert_showmeans'],
+                  extensions=['png'])
 def test_vert_violinplot_showmeans():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(3))
@@ -1586,7 +1588,8 @@ def test_vert_violinplot_showmeans():
                   showmedians=0)
 
 
-@image_comparison(baseline_images=['violinplot_vert_showextrema'])
+@image_comparison(baseline_images=['violinplot_vert_showextrema'],
+                  extensions=['png'])
 def test_vert_violinplot_showextrema():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(5))
@@ -1596,7 +1599,8 @@ def test_vert_violinplot_showextrema():
                   showmedians=0)
 
 
-@image_comparison(baseline_images=['violinplot_vert_showmedians'])
+@image_comparison(baseline_images=['violinplot_vert_showmedians'],
+                  extensions=['png'])
 def test_vert_violinplot_showmedians():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(7))
@@ -1606,7 +1610,8 @@ def test_vert_violinplot_showmedians():
                   showmedians=1)
 
 
-@image_comparison(baseline_images=['violinplot_vert_showall'])
+@image_comparison(baseline_images=['violinplot_vert_showall'],
+                  extensions=['png'])
 def test_vert_violinplot_showall():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(11))
@@ -1616,7 +1621,8 @@ def test_vert_violinplot_showall():
                   showmedians=1)
 
 
-@image_comparison(baseline_images=['violinplot_vert_custompoints_10'])
+@image_comparison(baseline_images=['violinplot_vert_custompoints_10'],
+                  extensions=['png'])
 def test_vert_violinplot_custompoints_10():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(13))
@@ -1626,7 +1632,8 @@ def test_vert_violinplot_custompoints_10():
                   showmedians=0, points=10)
 
 
-@image_comparison(baseline_images=['violinplot_vert_custompoints_200'])
+@image_comparison(baseline_images=['violinplot_vert_custompoints_200'],
+                  extensions=['png'])
 def test_vert_violinplot_custompoints_200():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(17))
@@ -1636,7 +1643,8 @@ def test_vert_violinplot_custompoints_200():
                   showmedians=0, points=200)
 
 
-@image_comparison(baseline_images=['violinplot_horiz_baseline'])
+@image_comparison(baseline_images=['violinplot_horiz_baseline'],
+                  extensions=['png'])
 def test_horiz_violinplot_baseline():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(19))
@@ -1646,7 +1654,8 @@ def test_horiz_violinplot_baseline():
                   showextrema=0, showmedians=0)
 
 
-@image_comparison(baseline_images=['violinplot_horiz_showmedians'])
+@image_comparison(baseline_images=['violinplot_horiz_showmedians'],
+                  extensions=['png'])
 def test_horiz_violinplot_showmedians():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(23))
@@ -1656,7 +1665,8 @@ def test_horiz_violinplot_showmedians():
                   showextrema=0, showmedians=1)
 
 
-@image_comparison(baseline_images=['violinplot_horiz_showmeans'])
+@image_comparison(baseline_images=['violinplot_horiz_showmeans'],
+                  extensions=['png'])
 def test_horiz_violinplot_showmeans():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(29))
@@ -1666,7 +1676,8 @@ def test_horiz_violinplot_showmeans():
                   showextrema=0, showmedians=0)
 
 
-@image_comparison(baseline_images=['violinplot_horiz_showextrema'])
+@image_comparison(baseline_images=['violinplot_horiz_showextrema'],
+                  extensions=['png'])
 def test_horiz_violinplot_showextrema():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(31))
@@ -1676,7 +1687,8 @@ def test_horiz_violinplot_showextrema():
                   showextrema=1, showmedians=0)
 
 
-@image_comparison(baseline_images=['violinplot_horiz_showall'])
+@image_comparison(baseline_images=['violinplot_horiz_showall'],
+                  extensions=['png'])
 def test_horiz_violinplot_showall():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(37))
@@ -1686,7 +1698,8 @@ def test_horiz_violinplot_showall():
                   showextrema=1, showmedians=1)
 
 
-@image_comparison(baseline_images=['violinplot_horiz_custompoints_10'])
+@image_comparison(baseline_images=['violinplot_horiz_custompoints_10'],
+                  extensions=['png'])
 def test_horiz_violinplot_custompoints_10():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(41))
@@ -1696,7 +1709,8 @@ def test_horiz_violinplot_custompoints_10():
                   showextrema=0, showmedians=0, points=10)
 
 
-@image_comparison(baseline_images=['violinplot_horiz_custompoints_200'])
+@image_comparison(baseline_images=['violinplot_horiz_custompoints_200'],
+                  extensions=['png'])
 def test_horiz_violinplot_custompoints_200():
     ax = plt.axes()
     # First 9 digits of frac(sqrt(43))