Finding Local Peaks in Solar Data

Detecting intensity peaks in solar images can be useful, for example as a simple flare identification mechanism. This example illustrates detection of areas where there is a spike in solar intensity. We use the peak_local_max function in the scikit-image library to find those regions in the map data where the intensity values form a local maxima. Then we plot those peaks in the original AIA plot.

import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
from mpl_toolkits.mplot3d import Axes3D
from skimage.feature import peak_local_max

from import AIA_193_IMAGE
from import all_pixel_indices_from_map

We will first create a Map using some sample data and display it.

aiamap =

Before we find regions of local maxima, we need to create some variables to store pixel values for the 2D SDO/AIA data we are using. These variables are used for plotting in 3D later on.

X, Y = all_pixel_indices_from_map(aiamap)

We will only consider peaks within the AIA data that have minimum intensity value equal to threshold_rel * max(Intensity) which is 20% of the maximum intensity. The next step is to calculate the pixel locations of local maxima positions where peaks are separated by at least min_distance = 60 pixels. This function comes from scikit image and the documenation is found here peak_local_max.

coordinates = peak_local_max(, min_distance=60, threshold_rel=0.2)

We now check for the indices at which we get such a local maxima and plot those positions marked red in the aiamap data.

fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y,
ax.view_init(elev=39, azim=64)
peaks_pos =[coordinates[:, 0], coordinates[:, 1]]
ax.scatter(coordinates[:, 1], coordinates[:, 0], peaks_pos, color='r')
ax.set_xlabel('X Coordinates')
ax.set_ylabel('Y Coordinates')

Now we need to turn the pixel coordinates into the world location so they can be easily overlaid on the Map.

hpc_max = aiamap.pixel_to_world(coordinates[:, 1]*u.pixel, coordinates[:, 0]*u.pixel)

Finally we do an AIA plot to check for the local maxima locations which will be marked with a blue x label.

fig = plt.figure()
ax = plt.subplot(projection=aiamap)
ax.plot_coord(hpc_max, 'bx')

Total running time of the script: ( 0 minutes 1.816 seconds)

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