old_contents: """
  =================================
  Finding and masking bright pixels
  =================================
  
  How to find and overplot the location of the brightest
  pixel and then mask any pixels out the area around this region.
  """
  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 and import the coordinate
  # functionality.
  aia = sunpy.map.Map(AIA_171_IMAGE)
  
  ###############################################################################
  # Now to find the single brightest pixel, we will maximium of the AIA image.
  # Note that we transform from pixel space to the world corodiate system.
  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 now plot the results. We will overlay the SunPy lon/lat grid.
  fig = plt.figure()
  ax = plt.subplot(projection=aia)
  aia.plot()
  ax.plot_coord(hpc_max, 'bx', color='white', marker='x', markersize=15)
  plt.show()
  
  ###############################################################################
  # Now we create a new custom AIAMap with a mask around the brightest pixel.
  # We have to build two arrays which include all of the x and y pixel indices.
  # These indices are for the entire image.
  # 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 adjusted
  # for the position of the brightest pixel (using `hpc_max`).
  hpc_mask = aia.pixel_to_world(x, y)
  r_mask = np.sqrt((hpc_mask.Tx-hpc_max.Tx) ** 2 + (hpc_mask.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 now plot the new Map!
  fig = plt.figure()
  ax = plt.subplot(projection=scaled_map)
  scaled_map.plot()
  plt.show()
  
new_contents: # -*- coding: utf-8 -*-
  """
  =================================
  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
  
  ###############################################################################
  # 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()
  
  ###############################################################################
  # Next, we create arrays for all of the pixels in the map.
  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 adjusted
  # for the position of the brightest pixel (using `hpc_max`) and then define a
  # new map.
  hpc_mask = aia.pixel_to_world(x, y)
  r_mask = np.sqrt((hpc_mask.Tx-hpc_max.Tx) ** 2 + (hpc_mask.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()
  
subject: Update finding_masking_bright_pixels.py