Reset color cycle in Matplotlib

问题:

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 strategies * 2 trading costs). I would like the "with transaction cost" lines to be plotted with alpha=1 and linewidth=1 while I want the "no transaction costs" to be plotted with alpha=0.25 and linewidth=5. But I would like the color to be the same for both versions of each strategy.

I would like something along the lines of:

fig, ax = plt.subplots(1, 1, figsize=(10, 10))  for c in with_transaction_frame.columns:     ax.plot(with_transaction_frame[c], label=c, alpha=1, linewidth=1)  ****SOME MAGIC GOES HERE TO RESET THE COLOR CYCLE  for c in no_transaction_frame.columns:     ax.plot(no_transaction_frame[c], label=c, alpha=0.25, linewidth=5)  ax.legend() 

What is the appropriate code to put on the indicated line to reset the color cycle so it is "back to the start" when the second loop is invoked?

回答1:

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)  plt.gca().set_color_cycle(None)  for i in range(3):     plt.plot(np.arange(10, 1, -1) + i)  plt.show() 

回答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.

回答3:

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) 

回答4:

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.

回答5:

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.

文章来源: Reset color cycle in Matplotlib