Fixed some style issues. Added violinplot to boilerplate.py

Added comments for test cases referencing the origins.

Author: solvents

boilerplate.py

@@ -146,6 +146,7 @@ def boilerplate_gen():
         'tricontourf',
         'tripcolor',
         'triplot',
+        'violinplot',
         'vlines',
         'xcorr',
         'barbs',

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)

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
 

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')
@@ -2797,19 +2800,17 @@ 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())
         kdepdf3 = gkde3.evaluate(xs)
         assert_almost_equal(kdepdf.all(), kdepdf3.all())
 
 
-
 #*****************************************************************
 #*****************************************************************
 
-
 if __name__ == '__main__':
     import nose
     import sys