Blending maps using mplcairo#

This example shows how to blend two maps using mplcairo.

matplotlib by itself provides only alpha-based transparency for superimposing one image onto another, which can be restrictive when trying to create visually appealing composite images. mplcairo is an enhancement for matplotlib that provides support for cairo’s compositing operators, which include a wide range of blend modes for image overlays.

Note

This example requires mplcairo to be installed. Installation via pip will work in most cases, but you may need to refer to OS-specific installation notes.

We need to tell matplotlib to use a backend from mplcairo. The backend formally needs to be set prior to importing matplotlib.pyplot. The mplcairo.qt GUI backend should work on Linux and Windows, but you will need to use something different on macOS or Jupyter (see mplcairo’s usage notes).

import matplotlib

if matplotlib.get_backend() == "agg":
    # This is the non-GUI backend for when building the documentation
    matplotlib.use("module://mplcairo.base")
else:
    # This is a GUI backend that you would normally use
    matplotlib.use("module://mplcairo.qt")
Traceback (most recent call last):
  File "/home/docs/checkouts/readthedocs.org/user_builds/sunpy/checkouts/stable/examples/plotting/mplcairo_plotting.py", line 36, in <module>
    matplotlib.use("module://mplcairo.base")
  File "/home/docs/checkouts/readthedocs.org/user_builds/sunpy/conda/stable/lib/python3.12/site-packages/matplotlib/__init__.py", line 1255, in use
    plt.switch_backend(name)
  File "/home/docs/checkouts/readthedocs.org/user_builds/sunpy/conda/stable/lib/python3.12/site-packages/matplotlib/pyplot.py", line 415, in switch_backend
    module = backend_registry.load_backend_module(newbackend)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/docs/checkouts/readthedocs.org/user_builds/sunpy/conda/stable/lib/python3.12/site-packages/matplotlib/backends/registry.py", line 323, in load_backend_module
    return importlib.import_module(module_name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/docs/checkouts/readthedocs.org/user_builds/sunpy/conda/stable/lib/python3.12/importlib/__init__.py", line 90, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1310, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 935, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 995, in exec_module
  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
  File "/home/docs/checkouts/readthedocs.org/user_builds/sunpy/conda/stable/lib/python3.12/site-packages/mplcairo/__init__.py", line 27, in <module>
    from . import _mplcairo
ImportError: cannot import name '_mplcairo' from partially initialized module 'mplcairo' (most likely due to a circular import) (/home/docs/checkouts/readthedocs.org/user_builds/sunpy/conda/stable/lib/python3.12/site-packages/mplcairo/__init__.py)

We can now import everything else.

import matplotlib.pyplot as plt
from mplcairo import operator_t

import astropy.units as u

import sunpy.data.sample
import sunpy.map
from sunpy.coordinates import SphericalScreen

Let’s load two maps for blending. We reproject the second map to the coordinate frame of the first map for proper compositing, taking care to use the SphericalScreen context manager in order to preserve off-disk data.

a171 = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)
a131 = sunpy.map.Map(sunpy.data.sample.AIA_131_IMAGE)
with SphericalScreen(a171.observer_coordinate):
    a131 = a131.reproject_to(a171.wcs)

Let’s first plot the two maps individually.

fig1 = plt.figure(figsize=(10, 4))
ax1 = fig1.add_subplot(121, projection=a171)
ax2 = fig1.add_subplot(122, projection=a131)

a171.plot(axes=ax1, clip_interval=(1, 99.9995)*u.percent)
a131.plot(axes=ax2, clip_interval=(1, 99.95)*u.percent)

We now plot the two maps on the same axes. If the plot were rendered at this point, the second map would completely obscure the first map. We save the matplotlib artist returned when plotting the second map (im131) for future use.

fig2 = plt.figure()
ax = fig2.add_subplot(projection=a171)

a171.plot(axes=ax, clip_interval=(1, 99.9995)*u.percent)
im131 = a131.plot(axes=ax, clip_interval=(1, 99.95)*u.percent)

We invoke the mplcairo operator for the screen blend mode to modify the artist for the second map. The second map will now be composited onto the first map using that blend mode.

operator_t.SCREEN.patch_artist(im131)

Finally, we set the title and render the plot.

ax.set_title('mplcairo composite using screen blending')
plt.show()

Total running time of the script: (0 minutes 0.006 seconds)

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