Enhancing Off-limb emission

This example shows how to enhance emission above the limb.

from __future__ import print_function, division

import numpy as np

import matplotlib.pyplot as plt

import astropy.units as u
from astropy.visualization.mpl_normalize import ImageNormalize

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 this into a function which requires them.

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

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

Let’s check how emission above the limb depends on distance

rsun_step_size = 0.01
rsun_array = np.arange(1, r.max(), rsun_step_size)
y = np.array([aia.data[(r > this_r) * (r < this_r + rsun_step_size)].mean()
              for this_r in rsun_array])

Next let’s plot it along with a fit to the data. We perform the fit in linear-log space. We fit the logarithm of the intensity since the intensity drops of very quickly as a function of distance from the limb.

params = np.polyfit(rsun_array[rsun_array < 1.5],
                    np.log(y[rsun_array < 1.5]), 1)

Tell matplotlib to use LaTeX for all the text, make the fontsize bigger, and then plot the data and the fit.

fontsize = 14
plt.plot(rsun_array, y, label='data')
best_fit = np.exp(np.poly1d(params)(rsun_array))
label = r'best fit: {:.2f}$e^{{{:.2f}r}}$'.format(best_fit[0], params[0])
plt.plot(rsun_array, best_fit, label=label)
plt.ylabel(r'mean DN', fontsize=fontsize)
plt.xlabel(r'radius r ($R_{\odot}$)', fontsize=fontsize)
plt.title(r'observed off limb mean DN and best fit', fontsize=fontsize)

We now create our scaling array. At the solar radius, the scale factor is 1. Moving away from the disk, the scaling array increases in value. Finally, in order to not affect the emission on the disk, we set the scale factor to unity for values of r less than 1.

scale_factor = np.exp((r-1)*-params[0])
scale_factor[r < 1] = 1

Let’s now plot and compare the results. The scaled map uses the same image stretching function as the original image (set by the keyword ‘stretch’) clipped to the same range (set by the keywords ‘vmin’ and ‘vmax’).

scaled_map = sunpy.map.Map(aia.data * scale_factor, aia.meta)
scaled_map.plot_settings['norm'] = ImageNormalize(stretch=aia.plot_settings['norm'].stretch,
                                                  vmin=aia.data.min(), vmax=aia.data.max())

fig = plt.figure(figsize=(12, 5))
ax = fig.add_subplot(121, projection=aia)
ax = fig.add_subplot(122, projection=aia)

Total running time of the script: ( 0 minutes 2.248 seconds)

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