Merge pull request #13198 from anntzer/skewt

dstansby · web-flow · commit b28c2e60e9fc · 2019-01-16T21:06:08.000Z
Cleanup SkewT example.
diff --git a/examples/specialty_plots/skewt.py b/examples/specialty_plots/skewt.py
@@ -3,15 +3,14 @@
 SkewT-logP diagram: using transforms and custom projections
 ===========================================================
 
-This serves as an intensive exercise of matplotlib's transforms and custom
+This serves as an intensive exercise of Matplotlib's transforms and custom
 projection API. This example produces a so-called SkewT-logP diagram, which is
 a common plot in meteorology for displaying vertical profiles of temperature.
-As far as matplotlib is concerned, the complexity comes from having X and Y
+As far as Matplotlib is concerned, the complexity comes from having X and Y
 axes that are not orthogonal. This is handled by including a skew component to
 the basic Axes transforms. Additional complexity comes in handling the fact
 that the upper and lower X-axes have different data ranges, which necessitates
 a bunch of custom classes for ticks, spines, and axis to handle this.
-
 """
 
 from contextlib import ExitStack
@@ -108,25 +107,30 @@ def _set_lim_and_transforms(self):
         rot = 30
 
         # Get the standard transform setup from the Axes base class
-        Axes._set_lim_and_transforms(self)
+        super()._set_lim_and_transforms()
 
         # Need to put the skew in the middle, after the scale and limits,
         # but before the transAxes. This way, the skew is done in Axes
         # coordinates thus performing the transform around the proper origin
         # We keep the pre-transAxes transform around for other users, like the
         # spines for finding bounds
-        self.transDataToAxes = self.transScale + \
-            self.transLimits + transforms.Affine2D().skew_deg(rot, 0)
-
+        self.transDataToAxes = (
+            self.transScale
+            + self.transLimits
+            + transforms.Affine2D().skew_deg(rot, 0)
+        )
         # Create the full transform from Data to Pixels
         self.transData = self.transDataToAxes + self.transAxes
 
         # Blended transforms like this need to have the skewing applied using
         # both axes, in axes coords like before.
-        self._xaxis_transform = (transforms.blended_transform_factory(
-            self.transScale + self.transLimits,
-            transforms.IdentityTransform()) +
-            transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
+        self._xaxis_transform = (
+            transforms.blended_transform_factory(
+                self.transScale + self.transLimits,
+                transforms.IdentityTransform())
+            + transforms.Affine2D().skew_deg(rot, 0)
+            + self.transAxes
+        )
 
     @property
     def lower_xlim(self):
@@ -138,8 +142,7 @@ def upper_xlim(self):
         return self.transDataToAxes.inverted().transform(pts)[:, 0]
 
 
-# Now register the projection with matplotlib so the user can select
-# it.
+# Now register the projection with matplotlib so the user can select it.
 register_projection(SkewXAxes)
 
 if __name__ == '__main__':
@@ -150,86 +153,86 @@ def upper_xlim(self):
     import matplotlib.pyplot as plt
     import numpy as np
 
-    # Some examples data
+    # Some example data.
     data_txt = '''
-  978.0    345    7.8    0.8     61   4.16    325     14  282.7  294.6  283.4
-  971.0    404    7.2    0.2     61   4.01    327     17  282.7  294.2  283.4
-  946.7    610    5.2   -1.8     61   3.56    335     26  282.8  293.0  283.4
-  944.0    634    5.0   -2.0     61   3.51    336     27  282.8  292.9  283.4
-  925.0    798    3.4   -2.6     65   3.43    340     32  282.8  292.7  283.4
-  911.8    914    2.4   -2.7     69   3.46    345     37  282.9  292.9  283.5
-  906.0    966    2.0   -2.7     71   3.47    348     39  283.0  293.0  283.6
-  877.9   1219    0.4   -3.2     77   3.46      0     48  283.9  293.9  284.5
-  850.0   1478   -1.3   -3.7     84   3.44      0     47  284.8  294.8  285.4
-  841.0   1563   -1.9   -3.8     87   3.45    358     45  285.0  295.0  285.6
-  823.0   1736    1.4   -0.7     86   4.44    353     42  290.3  303.3  291.0
-  813.6   1829    4.5    1.2     80   5.17    350     40  294.5  309.8  295.4
-  809.0   1875    6.0    2.2     77   5.57    347     39  296.6  313.2  297.6
-  798.0   1988    7.4   -0.6     57   4.61    340     35  299.2  313.3  300.1
-  791.0   2061    7.6   -1.4     53   4.39    335     33  300.2  313.6  301.0
-  783.9   2134    7.0   -1.7     54   4.32    330     31  300.4  313.6  301.2
-  755.1   2438    4.8   -3.1     57   4.06    300     24  301.2  313.7  301.9
-  727.3   2743    2.5   -4.4     60   3.81    285     29  301.9  313.8  302.6
-  700.5   3048    0.2   -5.8     64   3.57    275     31  302.7  313.8  303.3
-  700.0   3054    0.2   -5.8     64   3.56    280     31  302.7  313.8  303.3
-  698.0   3077    0.0   -6.0     64   3.52    280     31  302.7  313.7  303.4
-  687.0   3204   -0.1   -7.1     59   3.28    281     31  304.0  314.3  304.6
-  648.9   3658   -3.2  -10.9     55   2.59    285     30  305.5  313.8  305.9
-  631.0   3881   -4.7  -12.7     54   2.29    289     33  306.2  313.6  306.6
-  600.7   4267   -6.4  -16.7     44   1.73    295     39  308.6  314.3  308.9
-  592.0   4381   -6.9  -17.9     41   1.59    297     41  309.3  314.6  309.6
-  577.6   4572   -8.1  -19.6     39   1.41    300     44  310.1  314.9  310.3
-  555.3   4877  -10.0  -22.3     36   1.16    295     39  311.3  315.3  311.5
-  536.0   5151  -11.7  -24.7     33   0.97    304     39  312.4  315.8  312.6
-  533.8   5182  -11.9  -25.0     33   0.95    305     39  312.5  315.8  312.7
-  500.0   5680  -15.9  -29.9     29   0.64    290     44  313.6  315.9  313.7
-  472.3   6096  -19.7  -33.4     28   0.49    285     46  314.1  315.8  314.1
-  453.0   6401  -22.4  -36.0     28   0.39    300     50  314.4  315.8  314.4
-  400.0   7310  -30.7  -43.7     27   0.20    285     44  315.0  315.8  315.0
-  399.7   7315  -30.8  -43.8     27   0.20    285     44  315.0  315.8  315.0
-  387.0   7543  -33.1  -46.1     26   0.16    281     47  314.9  315.5  314.9
-  382.7   7620  -33.8  -46.8     26   0.15    280     48  315.0  315.6  315.0
-  342.0   8398  -40.5  -53.5     23   0.08    293     52  316.1  316.4  316.1
-  320.4   8839  -43.7  -56.7     22   0.06    300     54  317.6  317.8  317.6
-  318.0   8890  -44.1  -57.1     22   0.05    301     55  317.8  318.0  317.8
-  310.0   9060  -44.7  -58.7     19   0.04    304     61  319.2  319.4  319.2
-  306.1   9144  -43.9  -57.9     20   0.05    305     63  321.5  321.7  321.5
-  305.0   9169  -43.7  -57.7     20   0.05    303     63  322.1  322.4  322.1
-  300.0   9280  -43.5  -57.5     20   0.05    295     64  323.9  324.2  323.9
-  292.0   9462  -43.7  -58.7     17   0.05    293     67  326.2  326.4  326.2
-  276.0   9838  -47.1  -62.1     16   0.03    290     74  326.6  326.7  326.6
-  264.0  10132  -47.5  -62.5     16   0.03    288     79  330.1  330.3  330.1
-  251.0  10464  -49.7  -64.7     16   0.03    285     85  331.7  331.8  331.7
-  250.0  10490  -49.7  -64.7     16   0.03    285     85  332.1  332.2  332.1
-  247.0  10569  -48.7  -63.7     16   0.03    283     88  334.7  334.8  334.7
-  244.0  10649  -48.9  -63.9     16   0.03    280     91  335.6  335.7  335.6
-  243.3  10668  -48.9  -63.9     16   0.03    280     91  335.8  335.9  335.8
-  220.0  11327  -50.3  -65.3     15   0.03    280     85  343.5  343.6  343.5
-  212.0  11569  -50.5  -65.5     15   0.03    280     83  346.8  346.9  346.8
-  210.0  11631  -49.7  -64.7     16   0.03    280     83  349.0  349.1  349.0
-  200.0  11950  -49.9  -64.9     15   0.03    280     80  353.6  353.7  353.6
-  194.0  12149  -49.9  -64.9     15   0.03    279     78  356.7  356.8  356.7
-  183.0  12529  -51.3  -66.3     15   0.03    278     75  360.4  360.5  360.4
-  164.0  13233  -55.3  -68.3     18   0.02    277     69  365.2  365.3  365.2
-  152.0  13716  -56.5  -69.5     18   0.02    275     65  371.1  371.2  371.1
-  150.0  13800  -57.1  -70.1     18   0.02    275     64  371.5  371.6  371.5
-  136.0  14414  -60.5  -72.5     19   0.02    268     54  376.0  376.1  376.0
-  132.0  14600  -60.1  -72.1     19   0.02    265     51  380.0  380.1  380.0
-  131.4  14630  -60.2  -72.2     19   0.02    265     51  380.3  380.4  380.3
-  128.0  14792  -60.9  -72.9     19   0.02    266     50  381.9  382.0  381.9
-  125.0  14939  -60.1  -72.1     19   0.02    268     49  385.9  386.0  385.9
-  119.0  15240  -62.2  -73.8     20   0.01    270     48  387.4  387.5  387.4
-  112.0  15616  -64.9  -75.9     21   0.01    265     53  389.3  389.3  389.3
-  108.0  15838  -64.1  -75.1     21   0.01    265     58  394.8  394.9  394.8
-  107.8  15850  -64.1  -75.1     21   0.01    265     58  395.0  395.1  395.0
-  105.0  16010  -64.7  -75.7     21   0.01    272     50  396.9  396.9  396.9
-  103.0  16128  -62.9  -73.9     21   0.02    277     45  402.5  402.6  402.5
-  100.0  16310  -62.5  -73.5     21   0.02    285     36  406.7  406.8  406.7
+        978.0    345    7.8    0.8
+        971.0    404    7.2    0.2
+        946.7    610    5.2   -1.8
+        944.0    634    5.0   -2.0
+        925.0    798    3.4   -2.6
+        911.8    914    2.4   -2.7
+        906.0    966    2.0   -2.7
+        877.9   1219    0.4   -3.2
+        850.0   1478   -1.3   -3.7
+        841.0   1563   -1.9   -3.8
+        823.0   1736    1.4   -0.7
+        813.6   1829    4.5    1.2
+        809.0   1875    6.0    2.2
+        798.0   1988    7.4   -0.6
+        791.0   2061    7.6   -1.4
+        783.9   2134    7.0   -1.7
+        755.1   2438    4.8   -3.1
+        727.3   2743    2.5   -4.4
+        700.5   3048    0.2   -5.8
+        700.0   3054    0.2   -5.8
+        698.0   3077    0.0   -6.0
+        687.0   3204   -0.1   -7.1
+        648.9   3658   -3.2  -10.9
+        631.0   3881   -4.7  -12.7
+        600.7   4267   -6.4  -16.7
+        592.0   4381   -6.9  -17.9
+        577.6   4572   -8.1  -19.6
+        555.3   4877  -10.0  -22.3
+        536.0   5151  -11.7  -24.7
+        533.8   5182  -11.9  -25.0
+        500.0   5680  -15.9  -29.9
+        472.3   6096  -19.7  -33.4
+        453.0   6401  -22.4  -36.0
+        400.0   7310  -30.7  -43.7
+        399.7   7315  -30.8  -43.8
+        387.0   7543  -33.1  -46.1
+        382.7   7620  -33.8  -46.8
+        342.0   8398  -40.5  -53.5
+        320.4   8839  -43.7  -56.7
+        318.0   8890  -44.1  -57.1
+        310.0   9060  -44.7  -58.7
+        306.1   9144  -43.9  -57.9
+        305.0   9169  -43.7  -57.7
+        300.0   9280  -43.5  -57.5
+        292.0   9462  -43.7  -58.7
+        276.0   9838  -47.1  -62.1
+        264.0  10132  -47.5  -62.5
+        251.0  10464  -49.7  -64.7
+        250.0  10490  -49.7  -64.7
+        247.0  10569  -48.7  -63.7
+        244.0  10649  -48.9  -63.9
+        243.3  10668  -48.9  -63.9
+        220.0  11327  -50.3  -65.3
+        212.0  11569  -50.5  -65.5
+        210.0  11631  -49.7  -64.7
+        200.0  11950  -49.9  -64.9
+        194.0  12149  -49.9  -64.9
+        183.0  12529  -51.3  -66.3
+        164.0  13233  -55.3  -68.3
+        152.0  13716  -56.5  -69.5
+        150.0  13800  -57.1  -70.1
+        136.0  14414  -60.5  -72.5
+        132.0  14600  -60.1  -72.1
+        131.4  14630  -60.2  -72.2
+        128.0  14792  -60.9  -72.9
+        125.0  14939  -60.1  -72.1
+        119.0  15240  -62.2  -73.8
+        112.0  15616  -64.9  -75.9
+        108.0  15838  -64.1  -75.1
+        107.8  15850  -64.1  -75.1
+        105.0  16010  -64.7  -75.7
+        103.0  16128  -62.9  -73.9
+        100.0  16310  -62.5  -73.5
     '''
 
     # Parse the data
     sound_data = StringIO(data_txt)
-    p, h, T, Td = np.loadtxt(sound_data, usecols=range(0, 4), unpack=True)
+    p, h, T, Td = np.loadtxt(sound_data, unpack=True)
 
     # Create a new figure. The dimensions here give a good aspect ratio
     fig = plt.figure(figsize=(6.5875, 6.2125))
