prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Update "Masking out the solar disk" example to use maputils function
  Example: https://docs.sunpy.org/en/stable/generated/gallery/computer_vision_techniques/mask_disk.html
  
  Update to use `sunpy.map.coordinate_is_on_solar_disk()`
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `examples/computer_vision_techniques/mask_disk.py`
  Content:
  ```
  1 """
  2 ==========================
  3 Masking out the solar disk
  4 ==========================
  5 
  6 How to mask out all emission from the solar disk.
  7 """
  8 import matplotlib.pyplot as plt
  9 import numpy as np
  10 import numpy.ma as ma
  11 
  12 import sunpy.map
  13 from sunpy.data.sample import AIA_171_IMAGE
  14 from sunpy.map.maputils import all_coordinates_from_map
  15 
  16 ###############################################################################
  17 # We start with the sample data
  18 aia = sunpy.map.Map(AIA_171_IMAGE)
  19 
  20 ###############################################################################
  21 # A utility function gives us access to the helioprojective coordinate of each
  22 # pixels. We can use that to create a new array which
  23 # contains the normalized radial position for each pixel.
  24 hpc_coords = all_coordinates_from_map(aia)
  25 r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / aia.rsun_obs
  26 
  27 ###############################################################################
  28 # With this information, we create a mask where all values which are less then
  29 # the solar radius are masked. We also make a slight change to the colormap
  30 # so that masked values are shown as black instead of the default white.
  31 mask = ma.masked_less_equal(r, 1)
  32 palette = aia.cmap
  33 palette.set_bad('black')
  34 
  35 ###############################################################################
  36 # Finally we create a new map with our new mask.
  37 scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask.mask)
  38 
  39 ###############################################################################
  40 # Let's plot the results using our modified colormap
  41 fig = plt.figure()
  42 plt.subplot(projection=scaled_map)
  43 scaled_map.plot(cmap=palette)
  44 scaled_map.draw_limb()
  45 plt.show()
  46
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/examples/computer_vision_techniques/mask_disk.py b/examples/computer_vision_techniques/mask_disk.py
  --- a/examples/computer_vision_techniques/mask_disk.py
  +++ b/examples/computer_vision_techniques/mask_disk.py
  @@ -6,12 +6,10 @@
   How to mask out all emission from the solar disk.
   """
   import matplotlib.pyplot as plt
  -import numpy as np
  -import numpy.ma as ma
   
   import sunpy.map
   from sunpy.data.sample import AIA_171_IMAGE
  -from sunpy.map.maputils import all_coordinates_from_map
  +from sunpy.map.maputils import all_coordinates_from_map, coordinate_is_on_solar_disk
   
   ###############################################################################
   # We start with the sample data
  @@ -19,22 +17,22 @@
   
   ###############################################################################
   # A utility function gives us access to the helioprojective coordinate of each
  -# pixels. We can use that to create a new array which
  -# contains the normalized radial position for each pixel.
  +# pixels. We can use that to create a new array of all the coordinates
  +# that are on the solar disk.
   hpc_coords = all_coordinates_from_map(aia)
  -r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / aia.rsun_obs
   
   ###############################################################################
  -# With this information, we create a mask where all values which are less then
  -# the solar radius are masked. We also make a slight change to the colormap
  -# so that masked values are shown as black instead of the default white.
  -mask = ma.masked_less_equal(r, 1)
  +# Now, we can create a mask from the coordinates by using another utility
  +# function that gives us a mask that has `True` for those coordinates that are
  +# on the solar disk. We also make a slight change to the colormap so that
  +# masked values are shown as black instead of the default white.
  +mask = coordinate_is_on_solar_disk(hpc_coords)
   palette = aia.cmap
   palette.set_bad('black')
   
   ###############################################################################
   # Finally we create a new map with our new mask.
  -scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask.mask)
  +scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask)
   
   ###############################################################################
   # Let's plot the results using our modified colormap
  
verification_info: {"golden_diff": "diff --git a/examples/computer_vision_techniques/mask_disk.py b/examples/computer_vision_techniques/mask_disk.py\n--- a/examples/computer_vision_techniques/mask_disk.py\n+++ b/examples/computer_vision_techniques/mask_disk.py\n@@ -6,12 +6,10 @@\n How to mask out all emission from the solar disk.\n \"\"\"\n import matplotlib.pyplot as plt\n-import numpy as np\n-import numpy.ma as ma\n \n import sunpy.map\n from sunpy.data.sample import AIA_171_IMAGE\n-from sunpy.map.maputils import all_coordinates_from_map\n+from sunpy.map.maputils import all_coordinates_from_map, coordinate_is_on_solar_disk\n \n ###############################################################################\n # We start with the sample data\n@@ -19,22 +17,22 @@\n \n ###############################################################################\n # A utility function gives us access to the helioprojective coordinate of each\n-# pixels. We can use that to create a new array which\n-# contains the normalized radial position for each pixel.\n+# pixels. We can use that to create a new array of all the coordinates\n+# that are on the solar disk.\n hpc_coords = all_coordinates_from_map(aia)\n-r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / aia.rsun_obs\n \n ###############################################################################\n-# With this information, we create a mask where all values which are less then\n-# the solar radius are masked. We also make a slight change to the colormap\n-# so that masked values are shown as black instead of the default white.\n-mask = ma.masked_less_equal(r, 1)\n+# Now, we can create a mask from the coordinates by using another utility\n+# function that gives us a mask that has `True` for those coordinates that are\n+# on the solar disk. We also make a slight change to the colormap so that\n+# masked values are shown as black instead of the default white.\n+mask = coordinate_is_on_solar_disk(hpc_coords)\n palette = aia.cmap\n palette.set_bad('black')\n \n ###############################################################################\n # Finally we create a new map with our new mask.\n-scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask.mask)\n+scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask)\n \n ###############################################################################\n # Let's plot the results using our modified colormap\n", "issue": "Update \"Masking out the solar disk\" example to use maputils function\nExample: https://docs.sunpy.org/en/stable/generated/gallery/computer_vision_techniques/mask_disk.html\r\n\r\nUpdate to use `sunpy.map.coordinate_is_on_solar_disk()`\n", "before_files": [{"content": "\"\"\"\n==========================\nMasking out the solar disk\n==========================\n\nHow to mask out all emission from the solar disk.\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport numpy.ma as ma\n\nimport sunpy.map\nfrom sunpy.data.sample import AIA_171_IMAGE\nfrom sunpy.map.maputils import all_coordinates_from_map\n\n###############################################################################\n# We start with the sample data\naia = sunpy.map.Map(AIA_171_IMAGE)\n\n###############################################################################\n# A utility function gives us access to the helioprojective coordinate of each\n# pixels. We can use that to create a new array which\n# contains the normalized radial position for each pixel.\nhpc_coords = all_coordinates_from_map(aia)\nr = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / aia.rsun_obs\n\n###############################################################################\n# With this information, we create a mask where all values which are less then\n# the solar radius are masked. We also make a slight change to the colormap\n# so that masked values are shown as black instead of the default white.\nmask = ma.masked_less_equal(r, 1)\npalette = aia.cmap\npalette.set_bad('black')\n\n###############################################################################\n# Finally we create a new map with our new mask.\nscaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask.mask)\n\n###############################################################################\n# Let's plot the results using our modified colormap\nfig = plt.figure()\nplt.subplot(projection=scaled_map)\nscaled_map.plot(cmap=palette)\nscaled_map.draw_limb()\nplt.show()\n", "path": "examples/computer_vision_techniques/mask_disk.py"}], "after_files": [{"content": "\"\"\"\n==========================\nMasking out the solar disk\n==========================\n\nHow to mask out all emission from the solar disk.\n\"\"\"\nimport matplotlib.pyplot as plt\n\nimport sunpy.map\nfrom sunpy.data.sample import AIA_171_IMAGE\nfrom sunpy.map.maputils import all_coordinates_from_map, coordinate_is_on_solar_disk\n\n###############################################################################\n# We start with the sample data\naia = sunpy.map.Map(AIA_171_IMAGE)\n\n###############################################################################\n# A utility function gives us access to the helioprojective coordinate of each\n# pixels. We can use that to create a new array of all the coordinates\n# that are on the solar disk.\nhpc_coords = all_coordinates_from_map(aia)\n\n###############################################################################\n# Now, we can create a mask from the coordinates by using another utility\n# function that gives us a mask that has `True` for those coordinates that are\n# on the solar disk. We also make a slight change to the colormap so that\n# masked values are shown as black instead of the default white.\nmask = coordinate_is_on_solar_disk(hpc_coords)\npalette = aia.cmap\npalette.set_bad('black')\n\n###############################################################################\n# Finally we create a new map with our new mask.\nscaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask)\n\n###############################################################################\n# Let's plot the results using our modified colormap\nfig = plt.figure()\nplt.subplot(projection=scaled_map)\nscaled_map.plot(cmap=palette)\nscaled_map.draw_limb()\nplt.show()\n", "path": "examples/computer_vision_techniques/mask_disk.py"}]}