Customizing color matrices in matplotlib

In this post I intend to pass on some tricks on matplotlib color matrix customization.  I am guilty of beautifying some of my color matrices with Adobe Illustrator in the past, re-arranging labels, titles, colormaps, etc.  However, this time I had to generate way too many of them and I could see the beautifying process becoming extremely painful.  I will simply demonstrate how to do the following three plots simultaneously with relatively few lines of code in the hopes of providing useful elements for your own plot cutomization.

plot1a.png

plo2.png

plt3.png

Plot 1- Plot 3  were generated with the following script which I will explain in detail later int this post:

import glob
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

#Listing your files
files = glob.glob('./attainment_matrices/*.out')

#Organizing your files
data_plot1=[np.genfromtxt(f) for f in files[8:12]]
data_plot2=[np.genfromtxt(f) for f in files[0:4]]
data_plot3=[np.genfromtxt(f) for f in files[16:20]]
data=[data_plot1,data_plot2,data_plot3]

#Organizing titles and labels
plot_titles=['Plot 1','Plot 2', 'Plot 3']
subplot_titles= ['Subplot 1','Subplot 2', 'Subplot 3','Subplot 4']
labels= ['Item 1', 'Item 2', 'Item 3', 'Item 4', 'Item 5']
y_labels= ['Y Title a$\longrightarrow$','Y Title b $\longrightarrow$','Y Title c $\longrightarrow$']
cmap_labels=['Colormap label a$\longrightarrow$', 'Colormap label b$\longrightarrow$', 'Colormap label c$\longrightarrow$']

# Some variables to adjust subplots if necessary
left = 0.125 # the left side of the subplots of the figure
right = 0.9 # the right side of the subplots of the figure
bottom = 0.3 # the bottom of the subplots of the figure
top = 0.82 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots
hspace = 0.5 # the amount of height reserved for white space between subplots

#Font sizes
plot_fontsize=40
subplot_fontsize=32
tick_label_fontsize=22 # Ticks, colormap, x and y labels use this fontsize

#x-label adjustments
rotation= 45 # rotation of labels
adjust=0 #if you want the x labels to be displayed right at the middle then adjust=0.5

x=np.arange(0,5.5)
y=np.linspace(0,100,1001)

#colormaps
colormap=['Set3_r', 'YlGnBu','Paired']

# the j is the iteration variable for each subplot, and the l is the iteration variable
# for each plot.
for l in range(len(plot_titles)):
fig, ax=plt.subplots(1,len(subplot_titles),sharey=True)
plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
#setting the titles wrapped by a transparent grey box at position=(x,y)
fig.suptitle(plot_titles[l], fontsize=plot_fontsize,
bbox={'facecolor':'grey', 'alpha':0.1, 'pad':12}, position=(0.1827, .95))

for j in range(len(subplot_titles)):
a= ax[j].pcolor(x,y,data[l][j], cmap=colormap[l])
ax[j].set_title(subplot_titles[j], fontsize= subplot_fontsize, y=1.03)
#Set the y-label only in the first subplot
ax[0].set_ylabel(y_labels[l], fontsize=tick_label_fontsize)
ax[j].set_xticks(x + adjust, minor=False)
#ax[j].set_xlim(left=0, right=5)
#ax[j].set_ylim(0,100)
ax[j].set_xticklabels(labels[:], rotation=rotation)
ax[j].tick_params(labelsize=tick_label_fontsize)

#colorbar settings:
leftc= 0.12504
bottomc=.13
width_c=.775
height_c=0.04
cbar_ax= fig.add_axes([leftc,bottomc,width_c,height_c])
#cbar= fig.colorbar(a, cax=cbar_ax, orientation='horizontal')
cbar = fig.colorbar(a,cax=cbar_ax, ticks=[0, 0.5, 1], orientation='horizontal')
cbar.ax.set_xticklabels(['Low', 'Medium', 'High'])
cbar.set_label(cmap_labels[l], fontsize=tick_label_fontsize, labelpad=25)
cbar.ax.tick_params(labelsize=tick_label_fontsize)

plt.show()
plt.show()

First, in lines 1 though 4 I specify the required libraries.  I use glob.glob to list the files for the analysis with their full path in line 8.  Then if you want to see the order in which the files are listed you can simply run the print command as follows:

print files[:]

And you should be able to see the order of the files like so:

[‘./data_directory/file1.out’, ‘./data_directory/file10.out’, … ‘./data_directory/file24.out’]

I used the numpy genfromtxt function in lines 11-13 to load the data from the specified files while organizing the data that would be used in plot 1, plot 2 and plot 3.   I then made an array of the previous data on line 14 so I could use it in a loop later on.

I organized the titles of the main plots, subplots, the x and y labels, as well as the colormap labels in lines 17-21.  All the parameters required to adjust the aspect ratio of the subplots are listed in lines 24 to 29.    If you simply want all of your subplots to be squared, you can add the aspect=’equal’  parameter directly in the plt.subplot() function.

The font for the plots, subplots, ticks and labels are specified in lines 32 to 34.  The x-labels can be adjusted in multiple ways.  In line 27 I set the rotation of the x-labels to 45 degrees.  If you want the labels to be completely vertical then you would do: rotation=90.  If you want horizontal labels, you don’t need to specify a rotation parameter.  Then, I used the adjust variable to specify the position of the x-label,  adjust=0 specifies that the label will be written starting at the left corner of the bar, if you want the label to be centered, then you can do adjust=0.5.

In line 44,  I list the different colormaps to be used by each plot. The outer loop in line 48, iterates through the 3 plots,  while the inner loop in line 55, iterates through the 4 subplots generated in each plot.    In line 49 we specify the number of rows and columns of subplots that will be generated.  I want them to share the  y axis, hence, sharey=True.   If you want your subplots to also share the x axis, you would simply add ‘sharex=True‘ in line 49.  The plt.subplots_adjust function in line 50, allows you to specify the exact aspect ratio of your subplots, including the white space between them and their location in the figure canvas, this is detailed in lines 24 to 29.  In line 52, I specified the title of the plot as a whole, since I have three different plots, I loop through each of the different titles.  The title is shown in a grey transparent box at the upper left corner of the canvas which was specified by position(x,y).

Lines 56 to 64 show the subplots’ code.  I use the pcolor function to generate the color matrices.  However, there are other methods to create them, such as pcolormesh, imshow, contour, etc.  In line 57 I loop through the subplot titles, then I assign their font size.  Here, the y=1.03 specifies the distance from the subplot title to the plot.  The more distance I want to create the larger this y value should be.  In line 59 I set the y-label, since I only want the y-label to be shown in the left most plot, I fix ax[0].set_ylabel(…), if you want each subplot to have their own y-labels then you can loop through each of them with the subplot iteration variable j, such as ax[j].set_ylabel(…).  Lines 61 to 62 (commented out), show how you could set the x and y axis limits.  In line 63, I set the x_ticklabels; similarly you could set the y_ticklabels if necessary.  The fontsize across all the ticks in line 64.

The colorbar settings are shown in lines 67 through 76.  Observe how you can specify the position of the left bottom corner of the colorbar, and from there you can assign the width and the height of the colorbar.  Note that there’s a couple of ways to specify the colorbar, the first one is shown in line 72, it will generate a colorbar with the default ticks.  However, if you want to cutomize or add text to your colorbar, you would have to do so as shown in lines 73-74.  The ticks parameter in line 73, specifies the position were the labels written in line 74 are displayed.  You can set the colorbar label with .set_label.   I loop through the colormap labels for each plot and assign their fontsize in line 75.  The labelpad allows you to specify the distance between the colorbar and the label.   Finally,  the font size of the colormap ticks are specified in line 76.

I hope you can find some of the previous elements useful when designing your own color matrices ;).

 

 

 

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3 thoughts on “Customizing color matrices in matplotlib

  1. Pingback: Water Programming Blog Guide (Part I) – Water Programming: A Collaborative Research Blog

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