# Matplotlib: Plotting numerous disconnected line segments with different colors

## Question:

I have a set of data records like this:

``````(s1, t1), (u1, v1), color1
(s2, t2), (u2, v2), color2
.
.
.
(sN, tN), (uN, vN), colorN
``````

In any record, the first two values are the end-points of a line segment, the third value is the color of that line segment. More specifically, `(sn, tn)` are the x-y coordinates of the first end-point, `(un, vn)` are the x-y coordinates of the second-endpoint. Also, color is an rgb with alpha value.

In general, any two line segments are disconnected (meaning that their end-points do not necessarily coincide).

How to plot this data using matplotlib with a single `plot` call (or as few as possible) as there could be potentially thousands of records.

# Attempts

Preparing the data in one big list and calling `plot` against it is way too slow. For example the following code couldn’t finish in a reasonable amount of time:

``````import numpy as np
import matplotlib.pyplot as plt

data = []
for _ in xrange(60000):
data.append((np.random.rand(), np.random.rand()))
data.append((np.random.rand(), np.random.rand()))
data.append('r')

print 'now plotting...' # from now on, takes too long
plt.plot(*data)
print 'done'
#plt.show()
``````

I was able to speed-up the plot rendering by using the None insertion trick as follows:

``````import numpy as np
import matplotlib.pyplot as plt
from timeit import timeit

N = 60000
_s = np.random.rand(N)
_t = np.random.rand(N)
_u = np.random.rand(N)
_v = np.random.rand(N)
x = []
y = []
for s, t, u, v in zip(_s, _t, _u, _v):
x.append(s)
x.append(u)
x.append(None)
y.append(t)
y.append(v)
y.append(None)
print timeit(lambda:plt.plot(x, y), number=1)
``````

This executes in under a second on my machine. I still have to figure out how to embed the color values (RGB with alpha channel).

function `plot` allows to draw multiple lines in one call, if your data is just in a list, just unpack it when passing it to `plot`:

``````In [315]: data=[(1, 1), (2, 3), 'r', #assuming points are (1,2) (1,3) actually and,
#here they are in form of (x1, x2), (y1, y2)
...: (2, 2), (4, 5), 'g',
...: (5, 5), (6, 7), 'b',]

In [316]: plot(*data)
Out[316]:
[<matplotlib.lines.Line2D at 0x8752870>,
<matplotlib.lines.Line2D at 0x8752a30>,
<matplotlib.lines.Line2D at 0x8752db0>]
``````

OK, I ended up rasterising the lines on a PIL image before converting it to a numpy array:

``````from PIL import Image
from PIL import ImageDraw
import random as rnd
import numpy as np
import matplotlib.pyplot as plt

N = 60000
s = (500, 500)

im = Image.new('RGBA', s, (255,255,255,255))
draw = ImageDraw.Draw(im)

for i in range(N):
x1 = rnd.random() * s[0]
y1 = rnd.random() * s[1]
x2 = rnd.random() * s[0]
y2 = rnd.random() * s[1]
alpha = rnd.random()
color  = (int(rnd.random() * 256), int(rnd.random() * 256), int(rnd.random() * 256), int(alpha * 256))
draw.line(((x1,y1),(x2,y2)), fill=color, width=1)

plt.imshow(np.asarray(im),
origin='lower')
plt.show()
``````

This is by far the fastest solution and it fits my real-time needs perfectly. One caveat though is the lines are drawn without anti-aliasing.

``````import numpy as np
import pylab as pl
from matplotlib import collections  as mc

lines = [[(0, 1), (1, 1)], [(2, 3), (3, 3)], [(1, 2), (1, 3)]]
c = np.array([(1, 0, 0, 1), (0, 1, 0, 1), (0, 0, 1, 1)])

lc = mc.LineCollection(lines, colors=c, linewidths=2)
fig, ax = pl.subplots()
ax.autoscale()
ax.margins(0.1)
``````

here is the output:

I have tried a good few 2D rendering engines available on Python 3, while looking for a fast solution for an output stage in image-oriented Deep Learning & GAN.

Using the following benchmark: Time to render 99 lines into a 256×256 off-screen image (or whatever is more effective) with and without anti-alias.

The results, in order of efficiency on my oldish x301 laptop:

• PyGtk2: ~2500 FPS, (Python 2, GTK 2, not sure how to get AA)
• PyQt5: ~1200 FPS, ~350 with Antialias
• PyQt4: ~1100 FPS, ~380 with AA
• Cairo: ~750 FPS, ~250 with AA (only slightly faster with ‘FAST’ AA)
• PIL: ~600 FPS

The baseline is a loop which takes ~0.1 ms (10,000 FPS) retrieving random numbers and calling the primitives.

Basic code for PyGtk2:

``````from gtk import gdk
import random

WIDTH = 256
def r255(): return int(256.0*random.random())

cmap = gdk.Colormap(gdk.visual_get_best_with_depth(24), True)
black = cmap.alloc_color('black')
white = cmap.alloc_color('white')
pixmap = gdk.Pixmap(None, WIDTH, WIDTH, 24)
pixmap.set_colormap(cmap)
gc = pixmap.new_gc(black, line_width=2)
pixmap.draw_rectangle(gc, True, -1, -1, WIDTH+2, WIDTH+2);
gc.set_foreground(white)
for n in range(99):
pixmap.draw_line(gc, r255(), r255(), r255(), r255())

gdk.Pixbuf(gdk.COLORSPACE_RGB, False, 8, WIDTH, WIDTH
).get_from_drawable(pixmap, cmap, 0,0, 0,0, WIDTH, WIDTH
).save('Gdk2-lines.png','png')
``````

And here is for PyQt5:

``````from PyQt5.QtCore import Qt
from PyQt5.QtGui import *
import random

WIDTH = 256.0
def r255(): return WIDTH*random.random()

image = QImage(WIDTH, WIDTH, QImage.Format_RGB16)
painter = QPainter()
image.fill(Qt.black)
painter.begin(image)
painter.setPen(QPen(Qt.white, 2))
#painter.setRenderHint(QPainter.Antialiasing)
for n in range(99):
painter.drawLine(WIDTH*r0to1(),WIDTH*r0to1(),WIDTH*r0to1(),WIDTH*r0to1())
painter.end()
image.save('Qt5-lines.png', 'png')
``````

And here is Python3-Cairo for completeness:

``````import cairo
from random import random as r0to1

WIDTH, HEIGHT = 256, 256

surface = cairo.ImageSurface(cairo.FORMAT_A8, WIDTH, HEIGHT)
ctx = cairo.Context(surface)
ctx.scale(WIDTH, HEIGHT)  # Normalizing the canvas
ctx.set_line_width(0.01)
ctx.set_source_rgb(1.0, 1.0, 1.0)
ctx.set_antialias(cairo.ANTIALIAS_NONE)
#ctx.set_antialias(cairo.ANTIALIAS_FAST)

ctx.set_operator(cairo.OPERATOR_CLEAR)
ctx.paint()
ctx.set_operator(cairo.OPERATOR_SOURCE)
for n in range(99):
ctx.move_to(r0to1(), r0to1())
ctx.line_to(r0to1(), r0to1())
ctx.stroke()

surface.write_to_png('Cairo-lines.png')
``````
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