# Finding and masking bright pixels¶

How to find and overplot the location of the brightest pixel and then mask pixels around that region.

```# sphinx_gallery_thumbnail_number = 2

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
from sunpy.map.maputils import all_coordinates_from_map
```

We start with the sample data.

```aia = sunpy.map.Map(AIA_171_IMAGE)
```

To find the brightest pixel, we find the maximum in the AIA image data then transform that pixel coordinate to a map coordinate.

```pixel_pos = np.argwhere(aia.data == aia.data.max()) * u.pixel
hpc_max = aia.pixel_to_world(pixel_pos[:, 1], pixel_pos[:, 0])
```

Let’s plot the results.

```fig = plt.figure()
ax = plt.subplot(projection=aia)
aia.plot()
ax.plot_coord(hpc_max, 'bx', color='white', marker='x', markersize=15)
plt.show()
```

A utility function gives us access to the helioprojective coordinate of each pixels. We create a new array which contains the normalized radial position for each pixel adjusted for the position of the brightest pixel (using `hpc_max`) and then create a new map.

```hpc_coords = all_coordinates_from_map(aia)
r_mask = np.sqrt((hpc_coords.Tx-hpc_max.Tx) ** 2 + (hpc_coords.Ty-hpc_max.Ty) ** 2) / aia.rsun_obs
mask = ma.masked_less_equal(r_mask, 0.1)
scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask.mask)
```

Let’s plot the results.

```fig = plt.figure()
ax = plt.subplot(projection=scaled_map)
scaled_map.plot()
plt.show()
```

Total running time of the script: ( 0 minutes 1.132 seconds)

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