Thread: [Matplotlib-users] plot with marker color coded according to z-value

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()

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 <el...@ne...> 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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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()

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

On Fri, Oct 19, 2012 at 10:23 PM, Daπid <dav...@gm...> wrote:
> On Fri, Oct 19, 2012 at 11:08 PM, elmar werling <el...@ne...> 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.

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.

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

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.

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