Is it possible to add a datashader image to a set of matplotlib subplots?
As a concrete example,
import numpy as np import pandas as pd import matplotlib.pylab as plt import datashader as ds import datashader.transfer_functions as tf from datashader.utils import export_image from functools import partial background = "white" export = partial(export_image, background = background, export_path="export") N = 10000 df = pd.DataFrame(np.random.random((N, 3)), columns = ['x','y', 'z']) f, ax = plt.subplots(2, 2) ax_r = ax.ravel() ax_r[0].scatter(df['x'], df['y'], df['z'].mean()) ax_r[1].hist(df['x']) ax_r[2].hist(df['y']) ax_r[3].plot(df['z']) cvs = ds.Canvas(plot_width=100, plot_height=100) agg = cvs.points(df, 'x', 'y', ds.mean('z')) a = export(tf.shade(agg, cmap=['lightblue', 'darkblue'], how='eq_hist'), 'test')
Where I have a two by two array of matplotlib subplots and would like to replace the [0,0] plot ax_r[0]
in the above example with the datashader image a
. Is this possible, and if so, how?
Thanks!
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Answer
Update [January 2021] Datashader 0.12 now includes native Matplotlib support as per comment from James A. Bednar below.
As of right now [May 2017] the best way to accomplish adding a datashader image to a matplotlib subplot is to use the pull request linked to above. It defines a DSArtist
class. Assuming the DSArtist
class exists, the code would be as follows:
N = 10000 df = pd.DataFrame(np.random.random((N, 3)), columns = ['x','y', 'z']) f, ax = plt.subplots(2, 2) ax_r = ax.ravel() da = DSArtist(ax_r[0], df, 'x', 'y', ds.mean('z'), norm = mcolors.LogNorm()) ax_r[0].add_artist(da) ax_r[1].hist(df['x']) ax_r[2].hist(df['y']) ax_r[3].plot(df['z']) plt.tight_layout() plt.show()