Does anyone know how to convert matplotlib data units into normalized units?
The reason that I need it is that I need to create a subplot on top of another plot. And the default syntax:
plt.axes([0.1,0.1,0.3,0.3])
requires normalized coordinates, but I want to use the data coordinates:
For example this code:
plt.plot([0,2],[2,4]);
plt.axes([0.3,.3,0.4,.4])
produces this:
But I'd like to be able to define the location of the subplot using the data coordinates of it , something like [0.7,2.5,1.7,3.5]. I've tried to fiddle with axes.transData, axes.get_transform() and the like but didn't succeed to find the right function to do the job
Here's one way to do it:
inner axes
printed at 0.5, 2.5, 1.0, 0.3
(in outer axes
coords)
You basically need two transformations -- one from src-coords to display, and one from display to dest-coord. From the docs there seems to be no direct way:
http://matplotlib.org/users/transforms_tutorial.html
bb_data = Bbox.from_bounds(0.5, 2.5, 1.0, 0.3)
disp_coords = ax.transData.transform(bb_data)
fig_coords = fig.transFigure.inverted().transform(disp_coords)
ax
and fig
both carry transformer with them -- to display-coords!
If you call inverted
on them, you get an transformer for the inverse direction.
Here's the full code for the above example:
import matplotlib.pyplot as plt
from matplotlib.transforms import Bbox
plt.plot([0,2], [2,4])
fig = plt.gcf()
ax = plt.gca()
bb_data = Bbox.from_bounds(0.5, 2.5, 1.0, 0.3)
disp_coords = ax.transData.transform(bb_data)
fig_coords = fig.transFigure.inverted().transform(disp_coords)
fig.add_axes(Bbox(fig_coords))
plt.show()
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