Is it possible to ignore Matplotlib first default color for plotting?

Question

Matplotlib plots each column of my matrix a with 4 columns by blue, yellow, green, red.

Then, I plot only the second, third, fourth columns from matrix a[:,1:4]. Is it possible to make Matplotlib ignore blue from default and start from yellow (so my every lines get the same color as previous)?

a = np.cumsum(np.cumsum(np.random.randn(7,4), axis=0), axis=1)

lab = np.array(["A","B","C","E"])

fig, ax = plt.subplots()
ax.plot(a)
ax.legend(labels=lab )
# plt.show()
fig, ax = plt.subplots()
ax.plot(a[:,1:4])
ax.legend(labels=lab[1:4])
plt.show()

Related: stackoverflow.com/questions/37890412/…

Cris Luengo
– Cris Luengo

2019-04-22 23:03:30 +00:00
Commented Apr 22, 2019 at 23:03 — Cris Luengo
– Cris Luengo, Commented Apr 22, 2019 at 23:03

ImportanceOfBeingErnest · Accepted Answer · 2017-10-12 12:01:42Z

9

The colors used for the successive lines are the one from a color cycler. In order to skip a color in this color cycle, you may call

ax._get_lines.prop_cycler.next()  # python 2
next(ax._get_lines.prop_cycler)   # python 2 or 3

The complete example would look like:

import numpy as np
import matplotlib.pyplot as plt

a = np.cumsum(np.cumsum(np.random.randn(7,4), axis=0), axis=1)
lab = np.array(["A","B","C","E"])

fig, ax = plt.subplots()
ax.plot(a)
ax.legend(labels=lab )

fig, ax = plt.subplots()
# skip first color
next(ax._get_lines.prop_cycler)
ax.plot(a[:,1:4])
ax.legend(labels=lab[1:4])
plt.show()

edited Oct 12, 2017 at 12:01

answered Oct 10, 2017 at 16:55

ImportanceOfBeingErnest

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Comments

DavidG · Accepted Answer · 2017-10-10 15:57:09Z

5

In order to skip the first color I would suggest getting a list of the current colors by using

plt.rcParams['axes.prop_cycle'].by_key()['color']

As shown in this question/answer. Then set the color cycle for the current axes by using:

plt.gca().set_color_cycle()

Therefore your full example would be:

a = np.cumsum(np.cumsum(np.random.randn(7,4), axis=0), axis=1)

lab = np.array(["A","B","C","E"])
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']

fig, ax = plt.subplots()
ax.plot(a)
ax.legend(labels=lab )

fig1, ax1 = plt.subplots()
plt.gca().set_color_cycle(colors[1:4])
ax1.plot(a[:,1:4])
ax1.legend(labels=lab[1:4])
plt.show()

Which gives:

answered Oct 10, 2017 at 15:57

DavidG

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1 Comment

ImportanceOfBeingErnest Over a year ago

The use of set_color_cycle is deprecated as of version 2.0. It should be replaced by set_prop_cycle.

saintsfan342000 · Accepted Answer · 2017-10-10 15:44:37Z

4

You can insert an extra call to ax.plot([],[]) before calling ax.plot(a[:,1:4]).

a = np.cumsum(np.cumsum(np.random.randn(7,4), axis=0), axis=1)

lab = np.array(["A","B","C","E"])

fig, ax = plt.subplots()
ax.plot(a)
ax.legend(labels=lab )
# plt.show()
fig, ax = plt.subplots()
ax.plot([],[])
ax.plot(a[:,1:4])
ax.legend(labels=lab[1:4])
plt.show()

answered Oct 10, 2017 at 15:44

saintsfan342000

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Comments

Ludo Schmidt · Accepted Answer · 2017-10-10 15:47:48Z

1

I have the impression that you want to make sure that every colone keeps a defined color. To do this you can create a color vector that matches each column to display. You can create a color vector. color = ["blue", "yellow", "green", "red"]

a = np.cumsum(np.cumsum(np.random.randn(7,4), axis=0), axis=1)

lab = np.array(["A","B","C","E"])
color = ["blue", "yellow", "green", "red"]

fig, ax = plt.subplots()
ax.plot(a, color = color)
ax.legend(labels=lab )
# plt.show()
fig, ax = plt.subplots()
ax.plot(a[:,1:4])
ax.legend(labels=lab[1:4], color = color[1:4])
plt.show()

answered Oct 10, 2017 at 15:47

Ludo Schmidt

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