Plotting a line in Python with a variable linestyle

Question

I have a data file containing three columns of data; x and y, which represent the coordinates of data points to be plotted; and z, which is an integer that is either 0 or 1. When z=0, I wish to plot the data with a solid line, and when z=1 I want to plot with a dashed line.

My reason for wanting to do this is that my data (x,y) represent the solutions to a differential equation, and z encodes the stability of those solutions - z=0 denotes a stable solution and z=1 denotes unstable. The conventional way to represent stability is with solid and dashed lines.

If I use matplotlib to plot (x,y) while ignoring z I get the following plot:

for which I used the following code:

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

data = np.genfromtxt('bif.txt')
x, y, z = np.hsplit(data, 3)

font = {'size'   : 18}
matplotlib.rc('font', **font)

plt.plot(x, y, linestyle='-',color='b', linewidth=3.0)

plt.ylabel('$||u||_{2}$', fontsize=24, rotation=0, labelpad = 26)
plt.xlabel('$h$', fontsize=24)
plt.tight_layout()

plt.show()

Now essentially, the above plot should have alternating branches of dashed and solid lines depending on the value of z. Is there a way to do this in Python?

Edit

Here is some sample data:

  0.39464808441470212        7.8834557350383436                         0
  0.39463345228324243        7.8863501873675874                         0
  0.39462029602635296        7.8892429033300591                         0
  0.39460867186795834        7.8921339011986227                         0
  0.39459862088291187        7.8950232017888187                         0
  0.39459016704381161        7.8979108278326740                         0
  0.39458331611262704        7.9007968032693112                         0
  0.39457805541136221        7.9036811524879083                         0
  0.39457435446228745        7.9065638995593925                         0
  0.39457216644773424        7.9094450674925740                         0
  0.39457143040382697        7.9123246775481046                         0
  0.39457207403295502        7.9152027486399952                         1
  0.39457401699683936        7.9180792968496210                         1
  0.39457717453584740        7.9209543350720102                         1
  0.39458146125058674        7.9238278728080029                         1
  0.39458679487846998        7.9266999161098797                         1
  0.39459309990056007        7.9295704676815966                         1
  0.39460031082233094        7.9324395271284001                         1
  0.39460837498580414        7.9353070913444350                         1
  0.39461725478969767        7.9381731550211647                         1
  0.39462692921851011        7.9410377112539612                         1
  0.39463739461045982        7.9439007522198146                         1
  0.39464866462720694        7.9467622698954790                         1

That's just a small sample - I have about 8000 data points in total — O Smith
– O Smith, Commented Sep 4, 2015 at 11:54

unddoch · Accepted Answer · 2015-09-04 12:00:52Z

4

itertools.groupby is a handy function, although I don't know how much using it can affect perfomance.

For example (written from memory):

from itertools import groupby
...
for g_z, group in groupby(zip(zip(x, y), z), lambda p: p[1]):
    g_x, g_y = [], []
    for i in group:
        g_x.append(i[0][0])
        g_y.append(i[0][1])
    if g_z:
        plt.plot(g_x, g_y, linestyle='-')
    else:
        plt.plot(g_x, g_y, linestyle='--')
...

edited Sep 4, 2015 at 12:00

answered Sep 4, 2015 at 11:38

unddoch

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3 Comments

O Smith Over a year ago

Unfortunately I get an error using this: ValueError: too many values to unpack

unddoch Over a year ago

Sorry, I confused how groupby works.. does it work now?

O Smith Over a year ago

Yes it does! Thanks, you really helped me out here.

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