This example shows how to mask off emission from the disk.

from __future__ import print_function, division

import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt

import astropy.units as u

import sunpy.map
from sunpy.data.sample import AIA_171_IMAGE


We first create the Map using the sample data.

aia = sunpy.map.Map(AIA_171_IMAGE)


Next we build two arrays which include all of the x and y pixel indices. We must not forget to add the correct units because we will next pass into a SunPy function which all require them.

x, y = np.meshgrid(*[np.arange(v.value) for v in aia.dimensions]) * u.pixel


Now we can convert this to helioprojective coordinates and create a new array which contains the normalized radial position for each pixel

hpc_coords = aia.pixel_to_data(x, y)
r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / aia.rsun_obs


Finally, we create a mask where all values which are less then Rsun are masked. We also make a slight change to the colormap so that masked values are shown as black instead of the default white.

mask = ma.masked_less_equal(r, 1)
palette = aia.plot_settings['cmap']


Now we create a new custom aia with our new mask and plot the result using our modified colormap

scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask.mask)

fig = plt.figure()
plt.subplot(projection=scaled_map)
scaled_map.plot(cmap=palette)
scaled_map.draw_limb()
plt.show()


Total running time of the script: ( 0 minutes 0.972 seconds)

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