Reset color cycle in Matplotlib

Question

Say I have data about 3 trading strategies, each with and without transaction costs. I want to plot, on the same axes, the time series of each of the 6 variants (3 strategi

Answer 1

You can get the colors from seaborn like this: colors = sns.color_palette(). Ffisegydd's answer would then work great. You could also get the color to plot using the modulus/remainder operater (%): mycolor = colors[icolumn % len(colors]. I use often use this approach myself. So you could do:

for icol, column in enumerate(with_transaction_frame.columns): mycolor = colors[icol % len(colors] ax.plot(with_transaction_frame[col], label=col, alpha=1.0, color=mycolor)

Ffisegydd's answer may be more 'pythonic', though.

Answer 2

As the answer given by @pelson uses set_color_cycle and this is deprecated in Matplotlib 1.5, I thought it would be useful to have an updated version of his solution using set_prop_cycle:

import matplotlib.pyplot as plt
import numpy as np

for i in range(3):
    plt.plot(np.arange(10) + i)

plt.gca().set_prop_cycle(None)

for i in range(3):
    plt.plot(np.arange(10, 0, -1) + i)

plt.show()

Remark also that I had to change np.arange(10,1,-1) to np.arange(10,0,-1). The former gave an array of only 9 elements. This probably arises from using different Numpy versions. Mine is 1.10.2.

EDIT: Removed the need to use rcParams. Thanks to @divenex for pointing that out in a comment.

Answer 3

Simply choose your colours and assign them to a list, then when you plot your data iterate over a zip object containing your column and the colour you wish.

colors = ['red', 'blue', 'green']

for col, color in zip(colors, with_transaction_frame.columns):
    ax.plot(with_transaction_frame[col], label=col, alpha=1.0, linewidth=1.0, color=color)

for col, color in zip(no_transaction_frame.columns):
    ax.plot(no_transaction_frame[col], label=col, alpha=0.25, linewidth=5, color=color)

zip creates a list that aggregates the elements from each of your lists. This allows you to iterate over both easily at the same time.

Answer 4

You can reset the colorcycle to the original with Axes.set_color_cycle. Looking at the code for this, there is a function to do the actual work:

def set_color_cycle(self, clist=None):
    if clist is None:
        clist = rcParams['axes.color_cycle']
    self.color_cycle = itertools.cycle(clist

And a method on the Axes which uses it:

def set_color_cycle(self, clist):
    """
    Set the color cycle for any future plot commands on this Axes.

    *clist* is a list of mpl color specifiers.
    """
    self._get_lines.set_color_cycle(clist)
    self._get_patches_for_fill.set_color_cycle(clist)

This basically means you can call the set_color_cycle with None as the only argument, and it will be replaced with the default cycle found in rcParams['axes.color_cycle'].

I tried this with the following code and got the expected result:

import matplotlib.pyplot as plt
import numpy as np

for i in range(3):
    plt.plot(np.arange(10) + i)

# for Matplotlib version < 1.5
plt.gca().set_color_cycle(None)
# for Matplotlib version >= 1.5
plt.gca().set_prop_cycle(None)

for i in range(3):
    plt.plot(np.arange(10, 1, -1) + i)

plt.show()

Answer 5

Since you mentioned you're using seaborn, what I would recommend doing is:

with sns.color_palette(n_colors=3):

    ax.plot(...)
    ax.plot(...)

This will set the color palette to use the currently active color cycle, but only the first three colors from it. It's also a general purpose solution for any time you want to set a temporary color cycle.

Note that the only thing that actually needs to be under the with block is whatever you are doing to create the Axes object (i.e. plt.subplots, fig.add_subplot(), etc.). This is just because of how the matplotlib color cycle itself works.

Doing what you specifically want, "resetting" the color cycle, is possible, but it's a hack and I wouldn't do it in any kind of production code. Here, though, is how it could happen:

f, ax = plt.subplots()
ax.plot(np.random.randn(10, 3))
ax._get_lines.color_cycle = itertools.cycle(sns.color_palette())
ax.plot(np.random.randn(10, 3), lw=5, alpha=.25)

Answer 6

As an addition to the already excellent answers, you can consider using a colormap:

import matplotlib.pyplot as plt
import numpy as np

cmap = plt.cm.viridis

datarange = np.arange(4)

for d in datarange:
    # generate colour by feeding float between 0 and 1 to colormap
    color = cmap(d/np.max(datarange)) 
    plt.plot(np.arange(5)+d, c=color)

for d in datarange:
    # generate colour by feeding float between 0 and 1 to colormap
    color = cmap(d/np.max(datarange))
    plt.plot(-np.arange(5)+d, c=color)