Matplotlib Floating Axes

Introduction

In this lab, we will learn how to use the floating_axes module of Matplotlib to create custom plots with non-rectangular shapes. This module is useful when we need to plot data on non-Cartesian coordinate systems, such as polar or logarithmic plots. We will demonstrate how to create a scatter plot, a bar plot, and a sector plot using different coordinate systems.

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Importing necessary libraries

First, we need to import the necessary libraries. We will use Matplotlib, NumPy, and some modules from mpl_toolkits.axisartist and mpl_toolkits.axisartist.grid_finder.

import matplotlib.pyplot as plt
import numpy as np

from matplotlib.projections import PolarAxes
from matplotlib.transforms import Affine2D
import mpl_toolkits.axisartist.angle_helper as angle_helper
import mpl_toolkits.axisartist.floating_axes as floating_axes
from mpl_toolkits.axisartist.grid_finder import (DictFormatter, FixedLocator,
                                                 MaxNLocator)

Creating a simple floating axes plot

In this step, we will create a simple floating axes plot using GridHelperCurveLinear. We will create a scatter plot and a bar plot with a non-rectangular shape.

def setup_axes1(fig, rect):
    tr = Affine2D().scale(2, 1).rotate_deg(30)

    grid_helper = floating_axes.GridHelperCurveLinear(
        tr, extremes=(-0.5, 3.5, 0, 4),
        grid_locator1=MaxNLocator(nbins=4),
        grid_locator2=MaxNLocator(nbins=4))

    ax1 = fig.add_subplot(
        rect, axes_class=floating_axes.FloatingAxes, grid_helper=grid_helper)
    ax1.grid()

    aux_ax = ax1.get_aux_axes(tr)

    return ax1, aux_ax

fig = plt.figure(figsize=(8, 4))
fig.subplots_adjust(wspace=0.3, left=0.05, right=0.95)

ax1, aux_ax1 = setup_axes1(fig, 131)
aux_ax1.bar([0, 1, 2, 3], [3, 2, 1, 3])

Creating a polar plot

In this step, we will create a polar plot using GridHelperCurveLinear. We will create a scatter plot with a non-rectangular shape.

def setup_axes2(fig, rect):
    tr = PolarAxes.PolarTransform()

    pi = np.pi
    angle_ticks = [(0, r"$0$"),
                   (.25*pi, r"$\frac{1}{4}\pi$"),
                   (.5*pi, r"$\frac{1}{2}\pi$")]
    grid_locator1 = FixedLocator([v for v, s in angle_ticks])
    tick_formatter1 = DictFormatter(dict(angle_ticks))

    grid_locator2 = MaxNLocator(2)

    grid_helper = floating_axes.GridHelperCurveLinear(
        tr, extremes=(.5*pi, 0, 2, 1),
        grid_locator1=grid_locator1,
        grid_locator2=grid_locator2,
        tick_formatter1=tick_formatter1,
        tick_formatter2=None)

    ax1 = fig.add_subplot(
        rect, axes_class=floating_axes.FloatingAxes, grid_helper=grid_helper)
    ax1.grid()

    aux_ax = ax1.get_aux_axes(tr)

    aux_ax.patch = ax1.patch
    ax1.patch.zorder = 0.9

    return ax1, aux_ax

ax2, aux_ax2 = setup_axes2(fig, 132)
theta = np.random.rand(10)*.5*np.pi
radius = np.random.rand(10) + 1.
aux_ax2.scatter(theta, radius)

Creating a sector plot

In this step, we will create a sector plot using GridHelperCurveLinear. We will create a scatter plot with a non-rectangular shape.

def setup_axes3(fig, rect):
    tr_rotate = Affine2D().translate(-95, 0)
    tr_scale = Affine2D().scale(np.pi/180., 1.)
    tr = tr_rotate + tr_scale + PolarAxes.PolarTransform()

    grid_locator1 = angle_helper.LocatorHMS(4)
    tick_formatter1 = angle_helper.FormatterHMS()

    grid_locator2 = MaxNLocator(3)

    ra0, ra1 = 8.*15, 14.*15
    cz0, cz1 = 0, 14000
    grid_helper = floating_axes.GridHelperCurveLinear(
        tr, extremes=(ra0, ra1, cz0, cz1),
        grid_locator1=grid_locator1,
        grid_locator2=grid_locator2,
        tick_formatter1=tick_formatter1,
        tick_formatter2=None)

    ax1 = fig.add_subplot(
        rect, axes_class=floating_axes.FloatingAxes, grid_helper=grid_helper)

    ax1.axis["left"].set_axis_direction("bottom")
    ax1.axis["right"].set_axis_direction("top")

    ax1.axis["bottom"].set_visible(False)
    ax1.axis["top"].set_axis_direction("bottom")
    ax1.axis["top"].toggle(ticklabels=True, label=True)
    ax1.axis["top"].major_ticklabels.set_axis_direction("top")
    ax1.axis["top"].label.set_axis_direction("top")

    ax1.axis["left"].label.set_text(r"cz [km$^{-1}$]")
    ax1.axis["top"].label.set_text(r"$\alpha_{1950}$")
    ax1.grid()

    aux_ax = ax1.get_aux_axes(tr)

    aux_ax.patch = ax1.patch
    ax1.patch.zorder = 0.9

    return ax1, aux_ax

ax3, aux_ax3 = setup_axes3(fig, 133)
theta = (8 + np.random.rand(10)*(14 - 8))*15.
radius = np.random.rand(10)*14000.
aux_ax3.scatter(theta, radius)

Displaying the plot

Finally, we need to display the plot using the show() function of Matplotlib.

plt.show()

Summary

In this lab, we learned how to use the floating_axes module of Matplotlib to create custom plots with non-rectangular shapes. We created a scatter plot, a bar plot, and a sector plot using different coordinate systems. We used GridHelperCurveLinear to create the necessary transformations, FixedLocator and MaxNLocator to set the grid lines, and DictFormatter and FormatterHMS to format the tick labels.