Similarly to a previous question of mine, I'd like to control the capstyle of lines being drawn using matplotlib. However, I have an extremely large number of lines, and drawing with anything other than a line collection takes way too long. Are there any workarounds to control the capstyle of lines in a line collection in a generic way (or alternatively, super fast ways of drawing a large number of Line2D
lines). For instance, I've tried using the matplotlib rc settings via:
import matplotlib as mpl
mpl.rcParams['lines.solid_capstyle'] = 'round'
mpl.rcParams['lines.solid_joinstyle'] = 'round'
But this doesn't appear to have any affect. From the docstring for collections.py
:
The classes are not meant to be as flexible as their single element counterparts (e.g. you may not be able to select all line styles) but they are meant to be fast for common use cases (e.g. a large set of solid line segemnts)
Which explains why I can't seem to control various parameters, but I still want to do it! I've had a look at the code for the AGG backend (_backend_agg.cpp
: not that I really understand it), and it appears that line_cap and line_join are controlled by gc.cap
and gc.join
, where gc comes from the GCAgg
class. Does anyone know how one can control this from Python? Am I asking the right question here? Perhaps that are easier ways to control these parameters?
Any help is greatly appreciated... I'm desperate to get this working, so even crazy hacks are welcome!
Thanks,
Carson
MatPlotLib with Python PyLab is a procedural interface to the Matplotlib object-oriented plotting library. Matplotlib is the whole package; matplotlib. pyplot is a module in Matplotlib; and PyLab is a module that gets installed alongside Matplotlib. PyLab is a convenience module that bulk imports matplotlib.
x: X-axis points on the line. y: Y-axis points on the line. linestyle: Change the style of the line.
A line style identifies a particular line of text in the report file that contains information to be extracted. Each line style must be defined such that Extract Editor can identify the same line throughout the report file.
Since you mention in your question that you don't mind "dirty" solutions, one option would as follows.
The "drawing process" of a particular LineCollection
is handled by the draw
method defined in the Collection
class (the base of LineCollection
). This method creates an instance of GraphicsContextBase
(defined in backend_bases.py
) via the statement gc = renderer.new_gc()
. It seems to be exactly this object which governs among other things the properties controlling the capstyle
(property _capstyle
). Therefore, one could subclass GraphicsContextBase
, override the _capstyle
property, and inject a new new_gc
method into the RendererBase
class so that consequent calls to new_gc
return the customized instance:
Borrowing the example from the answer by @florisvb (assuming Python3):
#!/usr/bin/env python
import types
import numpy as np
from matplotlib.backend_bases import GraphicsContextBase, RendererBase
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
class GC(GraphicsContextBase):
def __init__(self):
super().__init__()
self._capstyle = 'round'
def custom_new_gc(self):
return GC()
RendererBase.new_gc = types.MethodType(custom_new_gc, RendererBase)
#----------------------------------------------------------------------
np.random.seed(42)
x = np.random.random(10)
y = np.random.random(10)
points = np.array([x, y]).T.reshape((-1, 1, 2))
segments = np.concatenate([points[:-1], points[1:]], axis=1)
fig = plt.figure()
ax = fig.add_subplot(111)
linewidth = 10
lc = LineCollection(segments, linewidths=linewidth)
ax.add_collection(lc)
fig.savefig('fig.png')
This produces:
To update the answer from @ewcz as this thread still comes up in search results.
One can now use path_effects
instead of defining their own GraphicsContextBase.
e.g.
import numpy as np
import matplotlib.patheffects as path_effects
from matplotlib.collections import LineCollection
np.random.seed(42)
x = np.random.random(10)
y = np.random.random(10)
points = np.array([x, y]).T.reshape((-1, 1, 2))
segments = np.concatenate([points[:-1], points[1:]], axis=1)
fig = plt.figure()
ax = fig.add_subplot(111)
linewidth = 10
### Stroke redraws the segment passing kwargs down to the GC renderer
lc = LineCollection(segments, linewidths=linewidth,
path_effects=[path_effects.Stroke(capstyle="round")])
ax.add_collection(lc)
fig.show()
Example png output with smooth lines and it also seems to work well with pdf output
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