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<section id="External-Neighbours">
<h1>External Neighbours<a class="headerlink" href="#External-Neighbours" title="Link to this heading"></a></h1>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Astropy tools
import astropy.units as u
from astropy.coordinates import Angle, SkyCoord

# Moc and HEALPix tools
import cdshealpix
import mocpy
from mocpy import MOC, WCS

# For plots
import matplotlib.pyplot as plt

import numpy as np

print(&quot;healpix version : &quot;, cdshealpix.__version__)
print(&quot;mocpy version : &quot;, mocpy.__version__)
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healpix version : 0.6.4
mocpy version : 0.13.0
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ipix = np.arange(12 * 4**3, dtype=np.uint64)
depth = 3
delta_depth = 2
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>help(cdshealpix.external_neighbours)
edges, corners = cdshealpix.external_neighbours(ipix, depth, delta_depth)
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Help on function external_neighbours in module cdshealpix.nested.healpix:

external_neighbours(ipix, depth, delta_depth, num_threads=0)
Get the neighbours of specific healpix cells.

This method returns two arrays. One containing the healpix cells
located on the external borders of the cells (at depth: `depth` + `delta_depth`).
The other containing the healpix cells located on the external corners of the cells
(at depth: `depth` + `delta_depth`). Please note that some pixels do not have 4 external corners
e.g. the 12 base pixels have each only 2 external corners.

Parameters
----------
ipix : `numpy.ndarray`
The healpix cells from which the external neighbours will be computed
depth : int
The depth of the input healpix cells
delta_depth : int
The depth of the returned external neighbours will be equal to: `depth` + `delta_depth`
num_threads : int, optional
Specifies the number of threads to use for the computation. Default to 0 means
it will choose the number of threads based on the RAYON_NUM_THREADS environment variable (if set),
or the number of logical CPUs (otherwise)

Returns
-------
external_border_cells, external_corner_cells : (`numpy.ndarray`, `numpy.ndarray`)
external_border_cells will store the pixels located at the external borders of `ipix`.
It will be of shape: (N, 4 * 2 ** (`delta_depth`)) for N input pixels and because each cells have 4 borders.
external_corner_cells will store the pixels located at the external corners of `ipix`
It will be of shape: (N, 4) for N input pixels. -1 values will be put in the array when the pixels have no corners for specific directions.

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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ipix_corner_cells = corners[corners &gt;= 0].ravel().astype(int)
ipix_border_cells = edges.ravel().astype(int)
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Help on method from_healpix_cells in module mocpy.moc.moc:

from_healpix_cells(ipix, depth, max_depth) method of builtins.type instance
Create a MOC from a set of HEALPix cells at various depths.

Parameters
----------
ipix : `numpy.ndarray`
HEALPix cell indices in the NESTED notation. dtype must be np.uint64
depth : `numpy.ndarray`
Depth of the HEALPix cells. Must be of the same size of `ipix`.
dtype must be np.uint8. Corresponds to the `level` of an HEALPix cell in astropy.healpix.
max_depth : int, The resolution of the MOC (degrades on the fly input cells if necessary)

Raises
------
IndexError
When `ipix` and `depth` do not have the same shape

Returns
-------
moc : `~mocpy.moc.MOC`
The MOC

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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Create the moc from corner cells
depth_corner_cells = np.ones(ipix_corner_cells.shape, dtype=np.uint8) * (
depth + delta_depth
)
moc_from_corner_cells = MOC.from_healpix_cells(
ipix=ipix_corner_cells, depth=depth_corner_cells, max_depth=depth + delta_depth
)
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Create the moc from border cells
depth_border_cells = np.ones(ipix_border_cells.shape, dtype=np.uint8) * (
depth + delta_depth
)
moc_from_border_cells = MOC.from_healpix_cells(
ipix=ipix_border_cells, depth=depth_border_cells, max_depth=depth + delta_depth
)
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Plot the MOC using matplotlib
fig = plt.figure(111, figsize=(10, 10))
# Define a astropy WCS from the mocpy.WCS class
with WCS(
fig,
fov=120 * u.deg,
center=SkyCoord(0, 0, unit=&quot;deg&quot;, frame=&quot;icrs&quot;),
coordsys=&quot;icrs&quot;,
rotation=Angle(0, u.degree),
projection=&quot;SIN&quot;,
) as wcs:
ax = fig.add_subplot(1, 1, 1, projection=wcs)

moc_from_corner_cells.fill(ax=ax, wcs=wcs, alpha=0.5, fill=True, color=&quot;green&quot;)
moc_from_border_cells.fill(ax=ax, wcs=wcs, alpha=0.5, fill=True, color=&quot;red&quot;)

plt.xlabel(&quot;ra&quot;)
plt.ylabel(&quot;dec&quot;)
plt.title(&quot;neighbours&quot;)
plt.grid(color=&quot;black&quot;, linestyle=&quot;dotted&quot;)
plt.show()
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