changing the shade of a color depending on a value

Hi,

I am currently plotting cpu utilization over time (plot_time). I would like the color of my line to be red when at 100%. 80-90% a bit less red, more yellow, and lower numbers will be green. Any thoughts of doing this?

···


— Get your facts first, then you can distort them as you please.–

Hi

have a look at http://matplotlib.org/examples/pylab_examples/multicolored_line.html and
http://matplotlib.org/examples/pylab_examples/show_colormaps.html.

br
Jakob

···

On 02/27/2013 10:49 AM, Rita wrote:

Hi,

I am currently plotting cpu utilization over time (plot_time). I would like the color of my line to be
red when at 100%. 80-90% a bit less red, more yellow, and lower numbers will be green. Any thoughts of
doing this?

--
--- Get your facts first, then you can distort them as you please.--

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Joe Kington’s answer is the best solution I’ve seen to this problem:

http://stackoverflow.com/questions/13622909/matplotlib-how-to-colorize-a-large-number-of-line-segments-as-independent-gradi

There is also an example in the gallery:

http://matplotlib.org/examples/pylab_examples/multicolored_line.html

HTH

···

On 27 February 2013 09:49, Rita <rmorgan466@…287…> wrote:

Hi,

I am currently plotting cpu utilization over time (plot_time). I would like the color of my line to be red when at 100%. 80-90% a bit less red, more yellow, and lower numbers will be green. Any thoughts of doing this?


— Get your facts first, then you can distort them as you please.–


Everyone hates slow websites. So do we.

Make your web apps faster with AppDynamics

Download AppDynamics Lite for free today:

http://p.sf.net/sfu/appdyn_d2d_feb


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A few years ago, Gökhan Sever posted this technique, which is the simplest
and best I've seen:

···

On Wed, Feb 27, 2013 at 1:49 AM, Rita <rmorgan466@...287...> wrote:

Hi,

I am currently plotting cpu utilization over time (plot_time). I would
like the color of my line to be red when at 100%. 80-90% a bit less red,
more yellow, and lower numbers will be green. Any thoughts of doing this?

##

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
x = np.linspace(0, 3 * np.pi, 5000)
y = np.sin(x)
z = np.cos(0.5 * (x[:-1] + x[1:])) # 1st derivative
cmap_z = cm.coolwarm(z)

fig, ax1 = plt.subplots(nrows=1, ncols=1)
ax1.scatter(x, y, c=cmap_z, marker='_', s=5)
fig.show()
##