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Commit 75944c67 authored by Leo Pound Singer's avatar Leo Pound Singer

Add sample code for finding credible regions

* Add sample code to test whether a sky position is in the 90%
  credible region.
* Add sample code to find the area of the 90% credible region.
parent 0b6f575f
......@@ -18,6 +18,10 @@ Version 9 (unreleased)
* Add tutorial on working with multi-resolution sky maps.
* Add sample code to test whether a sky position is in the 90% credible region.
* Add sample code to find the area of the 90% credible region.
Version 8 (2019-05-22)
git+ #
......@@ -243,6 +243,73 @@ Let's find which pixel contains the point RA=194.95, Dec=27.98.
>>> ipix
Test if a Sky Location is in the 90% Credible Region
You can easily test if a given sky position is in the 90% credible region.
Let's continue using the sky position from the previous example, for which we
have already determined the pixel index.
Use the following simple algorithm to construct a map that gives the *credible
level of each pixel*:
1. Sort the pixels by descending probability density.
2. Cumulatively sum the pixels' probability.
3. Return the pixels to their original order.
In Python, you can use this simple recipe:
>>> i = np.flipud(np.argsort(hpx))
>>> sorted_credible_levels = np.cumsum(hpx[i])
>>> credible_levels = np.empty_like(sorted_credible_levels)
>>> credible_levels[i] = sorted_credible_levels
>>> credible_levels
array([1., 1., 1., ..., 1., 1., 1.])
.. note::
Observe that the values in the resulting *credible level map* vary inversely
with probability density: the most probable pixel is assigned to the
credible level 0.0, and the least likely pixel is assigned the credible
level 1.0.
.. tip::
This recipe is implemented in the package
:doc:`ligo.skymap <ligo.skymap:index>` as the function
>>> from ligo.skymap.postprocess import find_greedy_credible_levels
>>> credible_levels = find_greedy_credible_levels(hpx)
>>> credible_levels
array([1., 1., 1., ..., 1., 1., 1.])
To check if the pixel that we identified in the previous section is within the
90% credible level, simply test if the value of the credible level map is less
than or equal to 0.9 at that pixel:
>>> credible_levels[ipix]
>>> credible_levels[ipix] <= 0.9
The credible level map has a value greater than 0.9 at that sky location,
therefore the sky location is outside the 90% credible region.
Find the Area of the 90% Credible Region
Since we just found the credible level map, it's easy to compute the 90%
credible area by counting the number of pixels inside the 90% credible region
and multiplying by the area per pixel.
In the Python expression below, note that ``(credible_levels <= 0.9)``
evaluates to a binary array; when it is summed over, true values are treated as
1 and false values are treated as 0.
>>> np.sum(credible_levels <= 0.9) * hp.nside2pixarea(nside, degrees=True)
Most Probable Sky Location
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