Hi,Can anyone explain how the clipping code works? Or maybe how the backends decide whether to draw a line? In working on drawing skewT's, I need slanted gridlines. Through proper setting of transforms, I can get them to draw slanted fine, but because they are slanted, the data x range at the top of the plot is not equal to the data x range at the bottom.
One issue that crops up as a result (which I've mentioned before) is that the tickmarks at the top get drawn off past the right end of the plot (enabling clipping of ticklines fixes this).
The second issue, which is the focus here, is that it seems impossible to get matplotlib to draw any gridlines that start before the left side of the chart. I can tweak Axis.draw() and get it to draw *all* the currently set tick marks and I can disable clipping so that the gridlines draw past the *right* side of the plot, but I can't seem to figure out how to draw a grid lines that starts from off the left side.
I've attached a small example of what I'm trying to do and an example of the results I'm getting with a few of the mods I've done to MPL (also attached).
Can anybody point me where I've gone wrong? I traced the calls down to the renderer (in this case GTK), but they for some reason won't trace down any further.
Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma
--- lib/matplotlib/axis.py (revision 5905)
+++ lib/matplotlib/axis.py (working copy)
@@ -123,7 +123,7 @@
artist.Artist.set_clip_path(self, clippath, transform)
#self.tick1line.set_clip_path(clippath, transform)
#self.tick2line.set_clip_path(clippath, transform)
- self.gridline.set_clip_path(clippath, transform)
+ #self.gridline.set_clip_path(clippath, transform)
set_clip_path.__doc__ = artist.Artist.set_clip_path.__doc__
def get_pad_pixels(self):
@@ -711,7 +711,7 @@
interval = self.get_view_interval()
for tick, loc, label in self.iter_ticks():
if tick is None: continue
- if not mtransforms.interval_contains(interval, loc): continue
+ #if not mtransforms.interval_contains(interval, loc): continue
tick.update_position(loc)
tick.set_label1(label)
tick.set_label2(label)
<<inline: skew.png>>
from matplotlib.axes import Axes
from matplotlib.lines import Line2D
from matplotlib.collections import LineCollection
from matplotlib.ticker import FixedLocator, AutoLocator, ScalarFormatter, FixedLocator
from matplotlib import transforms
from matplotlib.projections import register_projection
import numpy as np
class SkewXAxes(Axes):
# The projection must specify a name. This will be used be the
# user to select the projection, i.e. ``subplot(111,
# projection='skewx')``.
name = 'skewx'
def set_xlim(self, *args):
Axes.set_xlim(self, *args)
def draw(self, *args):
'''
draw() is overridden here to allow the data transform to be updated
before calling the Axes.draw() method. This allows resizes to be
properly handled without registering callbacks. The amount of
work done here is kept to a minimum.
'''
self._update_data_transform()
Axes.draw(self, *args)
def _update_data_transform(self):
'''
This separates out the creating of the data transform so that
it alone is updated at draw time.
'''
# This transforms x in pixel space to be x + the offset in y from
# the lower left corner - producing an x-axis sloped 45 degrees
# down, or x-axis grid lines sloped 45 degrees to the right
self.transProjection = transforms.Affine2D(
np.array([[1, 1, -self.bbox.ymin], [0, 1, 0], [0, 0, 1]]))
# Full data transform
self.transData.set(self._transDataNonskew + self.transProjection)
def _set_lim_and_transforms(self):
"""
This is called once when the plot is created to set up all the
transforms for the data, text and grids.
"""
#Get the standard transform setup from the Axes base class
Axes._set_lim_and_transforms(self)
#Save the unskewed data transform for our own use when regenerating
#the data transform. The user might want this as well
self._transDataNonskew = self.transData
#Create a wrapper for the data transform, so that any object that
#grabs this transform will see an updated version when we change it
self.transData = transforms.TransformWrapper(
transforms.IdentityTransform())
#Use the helper method to actually set the skewed data transform
self._update_data_transform()
def get_xaxis_transform(self):
"""
Get the transformation used for drawing x-axis labels, ticks
and gridlines. The x-direction is in data coordinates and the
y-direction is in axis coordinates.
We override here so that the x-axis gridlines get properly
transformed for the skewed plot.
"""
return self._xaxis_transform + self.transProjection
# Now register the projection with matplotlib so the user can select
# it.
register_projection(SkewXAxes)
# Now make a simple example using the custom projection.
import matplotlib.pyplot as plt
fig = plt.figure(1, figsize=(6.5875, 6.2125))
ax = fig.add_subplot(111, projection='skewx')
plt.grid(True)
ax.set_yticks(np.linspace(100,1000,10))
ax.yaxis.set_major_formatter(ScalarFormatter())
ax.xaxis.set_major_locator(FixedLocator(np.arange(-80,65,10)))
ax.set_xlim(-40,45)
ax.set_ylim(1050,100)
plt.savefig('skew.png', dpi=100)
plt.show()
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