diff --git a/bilby/gw/prior.py b/bilby/gw/prior.py
index 5a07c8e944970071b4b08667e5717d34f9c2a137..7981b5af8c40f67729934ca879371ef6cdf5e17c 100644
--- a/bilby/gw/prior.py
+++ b/bilby/gw/prior.py
@@ -767,22 +767,30 @@ ConditionalUniformSourceFrame = conditional_prior_factory(UniformSourceFrame)
class HealPixMapPriorDist(BaseJointPriorDist):
- def __init__(self, hp_file, names=None, bounds=None, distance=False):
- """
- Class defining prior according to given HealPix Map, defaults to 2D in ra and dec but can be set to include
- Distance as well. This only works with skymaps that include the 2D joint probability in ra/dec and that use the
- normal LALInference type skymaps where each pixel has a DISTMU, DISTSIGMA, and DISTNORM defining the conditional
- distance distribution along a given line of sight.
-
- hp_file: file path to .fits file
- .fits file that containes the 2D or 3D Healpix Map
- names: list (optional)
- list of names of parameters included in the JointPriorDist, defaults to ['ra', 'dec']
- bounds: dict or list (optional)
- dictionary or list with given prior bounds. defaults to normal bounds on ra, dev and 0, inf for distance
- if this is for a 3D map
- """
+ """
+ Class defining prior according to given HealPix Map, defaults to 2D in ra and dec but can be set to include
+ Distance as well. This only works with skymaps that include the 2D joint probability in ra/dec and that use the
+ normal LALInference type skymaps where each pixel has a DISTMU, DISTSIGMA, and DISTNORM defining the conditional
+ distance distribution along a given line of sight.
+
+ Parameters
+ ----------
+
+ hp_file : file path to .fits file
+ .fits file that containes the 2D or 3D Healpix Map
+ names : list (optional)
+ list of names of parameters included in the JointPriorDist, defaults to ['ra', 'dec']
+ bounds : dict or list (optional)
+ dictionary or list with given prior bounds. defaults to normal bounds on ra, dev and 0, inf for distance
+ if this is for a 3D map
+ Returns
+ -------
+
+ PriorDist : `bilby.gw.prior.HealPixMapPriorDist`
+ A JointPriorDist object to store the joint prior distribution according to passed healpix map
+ """
+ def __init__(self, hp_file, names=None, bounds=None, distance=False):
self.hp = self._check_imports()
self.hp_file = hp_file
if names is None:
@@ -854,14 +862,14 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- samp: float, int
+ samp : float, int
must take in single value for pixel on unitcube to recale onto ra, dec (distance), for the map Prior
- kwargs: dict
+ kwargs : dict
kwargs are all passed to _rescale() method
Returns
- ----------
- array_like
+ -------
+ rescaled_sample : array_like
sample to rescale onto the prior
"""
if self.distance:
@@ -889,12 +897,15 @@ class HealPixMapPriorDist(BaseJointPriorDist):
JointPrior Parameters. This function updates the current distance pdf, inverse_cdf, and sampler according to
given pixel or line of sight.
+ Parameters
----------
- pix_idx: int
+ pix_idx : int
pixel index value to create the distribtuion for
+
Returns
- ----------
- None - just updates these functions at new pixel values
+ -------
+ None : None
+ just updates these functions at new pixel values
"""
self.distance_pdf = lambda r: self.distnorm[pix_idx] * norm(
loc=self.distmu[pix_idx], scale=self.distsigma[pix_idx]
@@ -918,12 +929,12 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- array: array_like
+ array : array_like
input array we want to renormalize if not already normalized
Returns
- ---------
- array_like:
+ -------
+ normed_array : array_like
returns input array normalized
"""
norm = np.linalg.norm(array, ord=1)
@@ -940,14 +951,14 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- size: int
+ size : int
number of samples we want to draw
- kwargs: dict
+ kwargs : dict
kwargs are all passed to be used
Returns
- ----------
- array_like
+ -------
+ sample : array_like
sample of ra, and dec (and distance if 3D=True)
"""
pixel_choices = np.arange(self.npix)
@@ -971,9 +982,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Method to recursively draw a distance value from the given set distance distribution and check that it is in
the bounds
- Returns
+ Parameters
----------
- dist: sample drawn from the distance distribution at set pixel index
+
+ pix : int
+ integer for pixel to draw a distance from
+
+ Returns
+ -------
+ dist : float
+ sample drawn from the distance distribution at set pixel index
"""
if self.distmu[pix] == np.inf or self.distmu[pix] <= 0:
return 0
@@ -990,16 +1008,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- ra: float, int
+ ra : float, int
value drawn for rightascension
- dec: float, int
+ dec : float, int
value drawn for declination
- pix: int
+ pix : int
pixel index for given pixel we want to get ra, and dec from
Returns
- ---------
- tuple:
+ -------
+ ra_dec : tuple
this returns a tuple of ra, and dec sampled uniformly that are in the pixel given
"""
if not self.check_in_pixel(ra, dec, pix):
@@ -1017,16 +1035,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- ra: float, int
+ ra : float, int
rightascension value to check
- dec: float, int
+ dec : float, int
declination value to check
- pix: int
+ pix : int
index for pixel we want to check in
Returns
- --------
- bool:
+ -------
+ bool :
returns True if values inside pixel, False if not
"""
for val, name in zip([ra, dec], self.names):
@@ -1042,16 +1060,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- samp: array_like
+ samp : array_like
samples of ra, dec to evaluate the lnprob at
- lnprob: array_like
+ lnprob : array_like
array of correct length we want to populate with lnprob values
- outbounds: boolean array
+ outbounds : boolean array
boolean array that flags samples that are out of the given bounds
Returns
- ----------
- array_like
+ -------
+ lnprob : array_like
lnprob values at each sample
"""
for i in range(samp.shape[0]):
diff --git a/docs/gw_prior.txt b/docs/gw_prior.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1167ec7edf9aa407575b4a37e5e60796095e6874
--- /dev/null
+++ b/docs/gw_prior.txt
@@ -0,0 +1,32 @@
+.. gw_prior:
+
+===================================
+Transient Graviatiaonal wave priors
+===================================
+
+A Cosmological GW prior, :code:`Cosmological`:
+
+.. autoclass:: bilby.gw.prior.Cosmological
+ :members:
+
+Uniform in Comoving Volume GW Prior (inherited from Cosmological) :code:`UniformComovingVolume`:
+
+.. autoclass:: bilby.gw.prior.UniformComovingVolume
+ :members:
+
+Uniform in Source Frame GW Prior :code:`UniformSourceFrame`:
+
+.. autoclass:: bilby.gw.prior.UniformSourceFrame
+ :members:
+
+Aligned Spine GW Prior :code:`AlignedSpin`:
+
+.. autoclass:: bilby.gw.prior.AlignedSpin
+ :members:
+
+HealPixMap JointPriorDist (See JointPriors in bilby.core.prior.joint) :code:`HealPixMapPriorDist`:
+
+.. autoclass:: bilby.gw.prior.HealPixMapPriorDist
+ :members:
+
+
diff --git a/docs/index.txt b/docs/index.txt
index eb0cb84acb78e42e0d323f60d552fecd8c9dd440..45c4b119810b707ae6e36bea30752ea2815d231f 100644
--- a/docs/index.txt
+++ b/docs/index.txt
@@ -20,6 +20,7 @@ Welcome to bilby's documentation!
compact-binary-coalescence-parameter-estimation
transient-gw-data
gw_likelihood
+ gw_prior
conversion
gw_references
writing-documentation