# 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

import sunpy.map
from sunpy.data.sample import AIA_193_IMAGE
from sunpy.map.maputils import all_pixel_indices_from_map
```

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

```aiamap = sunpy.map.Map(AIA_193_IMAGE)
plt.figure()
aiamap.plot()
plt.colorbar()
``` 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(aiamap.data, 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.plot_surface(X, Y, aiamap.data)
ax.view_init(elev=39, azim=64)
peaks_pos = aiamap.data[coordinates[:, 0], coordinates[:, 1]]
ax.scatter(coordinates[:, 1], coordinates[:, 0], peaks_pos, color='r')
ax.set_xlabel('X Coordinates')
ax.set_ylabel('Y Coordinates')
ax.set_zlabel('Intensity')
``` 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)
aiamap.plot()
ax.plot_coord(hpc_max, 'bx')
plt.show()
``` Total running time of the script: ( 0 minutes 1.816 seconds)

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