BUG: Fix drawing gridlines in skew-T example (Fixes #6873)

dopplershift · dopplershift · commit da9e5d5ec021 · 2016-11-17T11:09:04.000-07:00
For some reason, `_adjust_location()` on the `SkewSpine` was not
being called; this resulted in `SkewXAxis` having the default (and out
of date) bounds for the upper x-axis. Not sure what the root cause is,
but refactoring this so that it isn't relying on the Spine to modify
attributes on the XAxis both fixes the problem and cleans up the design
greatly.
diff --git a/examples/api/skewt.py b/examples/api/skewt.py
@@ -18,13 +18,11 @@
 import matplotlib.transforms as transforms
 import matplotlib.axis as maxis
 import matplotlib.spines as mspines
-import matplotlib.path as mpath
 from matplotlib.projections import register_projection
 
+
 # The sole purpose of this class is to look at the upper, lower, or total
 # interval as appropriate and see what parts of the tick to draw, if any.
-
-
 class SkewXTick(maxis.XTick):
     def draw(self, renderer):
         if not self.get_visible():
@@ -56,38 +54,31 @@ def draw(self, renderer):
 # This class exists to provide two separate sets of intervals to the tick,
 # as well as create instances of the custom tick
 class SkewXAxis(maxis.XAxis):
-    def __init__(self, *args, **kwargs):
-        maxis.XAxis.__init__(self, *args, **kwargs)
-        self.upper_interval = 0.0, 1.0
-
     def _get_tick(self, major):
         return SkewXTick(self.axes, 0, '', major=major)
 
     @property
     def lower_interval(self):
         return self.axes.viewLim.intervalx
 
+    @property
+    def upper_interval(self):
+        return self.axes.upper_xlim
+
     def get_view_interval(self):
-        return self.upper_interval[0], self.axes.viewLim.intervalx[1]
+        return self.upper_interval[0], self.lower_interval[1]
 
 
 # This class exists to calculate the separate data range of the
 # upper X-axis and draw the spine there. It also provides this range
 # to the X-axis artist for ticking and gridlines
 class SkewSpine(mspines.Spine):
     def _adjust_location(self):
-        trans = self.axes.transDataToAxes.inverted()
+        pts = self._path.vertices
         if self.spine_type == 'top':
-            yloc = 1.0
+            pts[:, 0] = self.axis.upper_interval
         else:
-            yloc = 0.0
-        left = trans.transform_point((0.0, yloc))[0]
-        right = trans.transform_point((1.0, yloc))[0]
-
-        pts = self._path.vertices
-        pts[0, 0] = left
-        pts[1, 0] = right
-        self.axis.upper_interval = (left, right)
+            pts[:, 0] = self.axis.lower_interval
 
 
 # This class handles registration of the skew-xaxes as a projection as well
@@ -143,6 +134,12 @@ def _set_lim_and_transforms(self):
             transforms.IdentityTransform()) +
             transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
 
+    @property
+    def upper_xlim(self):
+        pts = [[0., 1.], [1., 1.]]
+        return self.transDataToAxes.inverted().transform(pts)[:, 0]
+
+
 # Now register the projection with matplotlib so the user can select
 # it.
 register_projection(SkewXAxes)
@@ -242,7 +239,7 @@ def _set_lim_and_transforms(self):
     plt.grid(True)
 
     # Plot the data using normal plotting functions, in this case using
-    # log scaling in Y, as dicatated by the typical meteorological plot
+    # log scaling in Y, as dictated by the typical meteorological plot
     ax.semilogy(T, p)
     ax.semilogy(Td, p)
 
