Based on using a floating axis (as in this answer) and the example here, I have the following solution:
import numpy as np import matplotlib.pyplot as plt from matplotlib.transforms import Affine2D import mpl_toolkits.axisartist.floating_axes as floating_axes from mpl_toolkits.axisartist.grid_finder import MaxNLocator # create a figure and original axis fig, ax_orig = plt.subplots(figsize=(7, 7)) # data for plotting in subplot subplot_xdata = np.linspace(0, 80, 100) subplot_ydata = np.linspace(0, 80, 100) # extents of the subplot (based on data) plot_extents = ( subplot_xdata[0], subplot_xdata[-1], subplot_ydata[0], subplot_ydata[-1], ) # create the floating subplot rotation = 145 # rotation of subplot (degrees) transform = Affine2D().rotate_deg(rotation) # transform with rotation applied # set the subplot grid to allow ticks at multiples of 5 or 10 grid_locator = MaxNLocator(steps=[5, 10]) helper = floating_axes.GridHelperCurveLinear( transform, plot_extents, grid_locator1=grid_locator, grid_locator2=grid_locator, ) ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=helper) # position and scale the subplot (play about with these) width = 0.35 # width relative to original axis height = 0.35 # height relative to original axis xloc = 0.4 # x-location (in axis coordinates between 0-1) of bottom left corner of (unrotated) subplot yloc = 0.5 # y-location of bottom left corner of (unrotated) subplot ax.set_position((xloc, yloc, width, height)) # get auxilary axis to for actually plotting the subplot data aux_ax = ax.get_aux_axes(transform) aux_ax.plot(subplot_xdata, subplot_ydata) # add subplot to the figure fig.add_subplot(ax) # plot something on the original axes ax_orig.plot(np.linspace(0, 2, 100), np.linspace(0, 2, 100))
Note that this solution requires you to specify the x-y extent of your subplot.
