plot with marker color coded according to z-value

Community matplotlib-users
1

Hi,

is there a way to adjust the marker color in a xy-plot in relation to the value of a third parameter. Something as the following - not working - example 1.

Example 2 is working but rather slow for large arrays.

cheers
Elmar

# example 1

import matplotlib.pyplot as plt

x = [1,2,3,4]
y = x
c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3))

plt.plot(x,y, color=c, marker='s')
plt.show()

example 2:

import matplotlib.pyplot as plt

x = [1,2,3,4]
y = x
c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3))

for i in range(len(x)):
     plt.plot(x[i], y[i], color=c[i], marker='s')

plt.show()

2

That’s what scatter is intended for.

Basically, you want something like:

plt.scatter(x, y, c=z, marker='s')
plt.colorbar()

Note that you can also vary the markers by size based on an additional parameter, as well.

Have a look at this example: http://matplotlib.org/examples/pylab_examples/scatter_demo.html

Hope that helps,
-Joe

···

On Fri, Oct 19, 2012 at 2:19 PM, elmar werling <elmar@…3497…> wrote:

Hi,

is there a way to adjust the marker color in a xy-plot in relation to

the value of a third parameter. Something as the following - not working

  • example 1.

Example 2 is working but rather slow for large arrays.

cheers

Elmar

example 1

import matplotlib.pyplot as plt

x = [1,2,3,4]

y = x

c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3))

plt.plot(x,y, color=c, marker=‘s’)

plt.show()

example 2:

import matplotlib.pyplot as plt

x = [1,2,3,4]

y = x

c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3))

for i in range(len(x)):

 plt.plot(x[i], y[i], color=c[i], marker='s')

plt.show()

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3

thanks for help,

finally I found the following solution

elmar

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

N = 200
x = np.linspace(0,1,N)
y = np.random.randn(N)
z = np.random.randn(N)*2+5

cm = mpl.cm.get_cmap('RdYlBu')
sc = plt.scatter(x, y, c=z, vmin=min(z), vmax=max(z), s=35, cmap=cm)
plt.colorbar(sc)

plt.show()

···

Am 19.10.2012 21:59, schrieb Joe Kington:

     plt.scatter(x, y, c=z, marker='s')
     plt.colorbar()

4

A suggestion, when dealing with arrays, it is generally faster to use
the numpy function to compute the max and min, either np.max(z) or
z.max(), than the standard Python one.

···

On Fri, Oct 19, 2012 at 11:08 PM, elmar werling <elmar@...3497...> wrote:

vmin=min(z), vmax=max(z)

5

Correct me if I'm wrong, but I don't even think you need them. I think
the default cmap behaviour is to normalise to the min and max of the
data.

···

On Fri, Oct 19, 2012 at 10:23 PM, Daπid <davidmenhur@...287...> wrote:

On Fri, Oct 19, 2012 at 11:08 PM, elmar werling <elmar@...3497...> wrote:

vmin=min(z), vmax=max(z)

A suggestion, when dealing with arrays, it is generally faster to use
the numpy function to compute the max and min, either np.max(z) or
z.max(), than the standard Python one.

--
Damon McDougall
http://www.damon-is-a-geek.com
B2.39
Mathematics Institute
University of Warwick
Coventry
West Midlands
CV4 7AL
United Kingdom

6

yes, default cmap behaviour will normalise to the min and max of the
data.

···

Am 19.10.2012 23:26, schrieb Damon McDougall:

Correct me if I'm wrong, but I don't even think you need them. I think
the default cmap behaviour is to normalise to the min and max of the
data.