For example, by using patchworklib and seaborn, you can easily make the following figure. #Figure 2 ax3 = pw.Brick("ax3",(2,1)) ax4 = pw.Brick("ax4",(2,2)) ax3.set_title("ax3") ax4.set_title("ax4") ax34 = ax3/ax4 ax1234 = ax12 | ax34 ax1234.savefig()īricks class is the subclass of class, so seaborn plot functions with the ax parameter can take a Brick class object. Bricks class objects can also be joined with other Brick and Bricks class objects as the following example code. The value ax1|ax2 is returned as the Bricksclass object that holds multiple Brick class objects. If a filename is given, the figure object can be output to the file. The savefig(filename=str) method returns class object. Therefore, the size ax1 has been automatically expanded to match ax2. In the above case, the original height ax2 is larger than ax1. When arranging multiple Brick class objects, the actual figure size of each Brick object will be resized and aligned with each other's axes. The value means not figure size but the aspect ratio of the subplot. The figsize parameter can also be specified. When creating a Brick class object, the label the parameter should be provided with a unique name (if not, a unique name will be automatically specified.). #Source code for Figure 1 import patchworklib as pw ax1 = pw.Brick(label="ax1", figsize=(1,1)) ax2 = pw.Brick(label="ax2", figsize=(1,3)) ax1.set_title("ax1") ax2.set_title("ax2") ax12 = ax1 | ax2 ax12.savefig()įigure 1: A simple example of patchworklib. Using patchworklib, you can quickly arrange two subplots with simple python code, as follows. Each Brick class object can be joined with other Brick class objects by using | or / operators. The patchworklib module provides the Brick class implemented as the subclass of class. To solve these problems in the Matplotlib ecosystem, I have recently developed a new subplot manager, “ Patchworklib,” that allows users to arrange multiple Matplotlib plots quickly using only the | and / operators. Therefore, when handling multiple plotnine and seaborn plots, you need to arrange them manually using another GUI software package such as Keynote, Powerpoint, and illustrator. However, some of the plots produced by both packages cannot be treated as Matplotlib subplots (Although both packages are implemented based on Matplotlib.). Seaborn and plotnine are great data visualization packages that allow you to create beautiful and complex plots with a few lines of python code. Additionally, the syntax for subplots is complicated (although the subplot_mosiac function enables users to define subplot layouts quickly it still does not allow dynamic layout changes of subplots). For example, the native subplot manager in Matplotlib forces the user to determine the entire layout of the figure before drawing each subplot and does not allow to arrange the subplot layouts dynamically even in interactive programming environments as Jupyter-lab. Also, the subplot functions are not sophisticated enough. Matplotlib is the most utilized and famous visualization package in python, but it requires learning heavy syntax to create complex plots.
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