Matplotlib - Control Capstyle Of Line Collection/large Number Of Lines
Solution 1:
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')
Solution 2:
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
Solution 3:
I was struggling with the same issue. I ended up plotting a scatter plot on top of my line collection. It's not perfect, but it may work for your application. There's a few subtleties - below is a working example.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
x = np.random.random(10)
y = np.random.random(10)
z = np.arange(0,10)
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
cmap = plt.get_cmap('jet')
norm = plt.Normalize(np.min(z), np.max(z))
color = cmap(norm(z))
lc = LineCollection(segments, linewidths=linewidth, cmap=cmap, norm=norm)
lc.set_array(z)
lc.set_zorder(z.tolist())
ax.add_collection(lc)
ax.scatter(x,y,color=color,s=linewidth**2,edgecolor='none', zorder=(z+2).tolist())
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