JokerSamples

JokerSamples#

class thejoker.JokerSamples(samples=None, t_ref=None, n_offsets=None, poly_trend=None, **kwargs)#

Bases: object

Attributes Summary

`isscalar`
`n_offsets`
`orbits`	A generator that successively returns twobody.KeplerOrbit objects for each sample.
`par_names`
`poly_trend`
`t_ref`

Methods Summary

`copy`()	Return a copy of this instance
`from_inference_data`(prior, idata, data[, ...])	Create a `JokerSamples` instance from an arviz object.
`get_orbit`([index])	Get a twobody.KeplerOrbit object for the samples at the specified index.
`get_t0`([t_ref])
`get_time_with_phase`([phase, t_ref])	Use the phase at reference time to convert to a time with given phase.
`ln_unmarginalized_likelihood`(data)	Compute the log (unmarginalized) likelihood of the data for each sample
`mean`()	Return a new scalar object by taking the mean across all samples
`median_period`()	Return a new scalar object by taking the median in period, and returning the values for that sample
`pack`([units, names, nonlinear_only])	Pack the sample data into a single numpy array (i.e. strip the units and return those separately).
`read`(filename[, path])	Read the samples data to a file.
`std`()	Return a new scalar object by taking the standard deviation across all samples
`unpack`(packed_samples, units, **kwargs)	Unpack the array of packed (prior) samples and return a ~thejoker.JokerSamples instance.
`wrap_K`()	Change negative K values to positive K values and wrap omega to adjust
`write`(output[, overwrite, append])	Write the samples data to a file.

Attributes Documentation

isscalar#

n_offsets#

orbits#: A generator that successively returns twobody.KeplerOrbit objects for each sample. See docstring for thejoker.JokerSamples.get_orbit for more information.

par_names#

poly_trend#

t_ref#

Methods Documentation

copy()#: Return a copy of this instance

classmethod from_inference_data(prior, idata, data, prune_divergences=True)#

Create a JokerSamples instance from an arviz object.

Parameters:

prior (thejoker.JokerPrior) –
idata (arviz.InferenceData) –
data (thejoker.RVData) –
prune_divergences (bool (optional)) –

get_orbit(index=None, **kwargs)#

Get a twobody.KeplerOrbit object for the samples at the specified index.

Parameters:

index (int (optional)) – The index of the samples to turn into a twobody.KeplerOrbit instance. If the samples object is scalar, no index is necessary.
**kwargs – Other keyword arguments are passed to the twobody.KeplerOrbit initializer. For example, you can specify the inclination by passing i=...`, or longitude of the ascending node by passing ``Omega=....

Returns:

orbit – The samples converted to an orbit object. The barycenter position and distance are set to arbitrary values.

Return type:

twobody.KeplerOrbit

get_t0(t_ref=None)#

get_time_with_phase(phase=<Quantity 0. rad>, t_ref=None)#

Use the phase at reference time to convert to a time with given phase.

Parameters:

phase (quantity_like [angle] (optional)) – Get the time at which the phase is equal to this value.
t_ref (~astropy.time.Time (optional)) – The reference time.

ln_unmarginalized_likelihood(data)#: Compute the log (unmarginalized) likelihood of the data for each sample

mean()#: Return a new scalar object by taking the mean across all samples

median_period()#: Return a new scalar object by taking the median in period, and returning the values for that sample

pack(units=None, names=None, nonlinear_only=True)#

Pack the sample data into a single numpy array (i.e. strip the units and return those separately).

Parameters:

units (dict (optional)) – If specified, this controls the units that the samples are converted to before packing into a single array.
names (list (optional)) – The order of names to pack into.
nonlinear_only (bool (optional)) – Only pack the data for the nonlinear parameters into the returned array.

Returns:

packed_samples (numpy.ndarray) – The samples data packed into a single numpy array with no units.
units (~collections.OrderedDict) – The units of the packed sample data.

classmethod read(filename, path=None)#

Read the samples data to a file.

Currently, we only support writing to / reading from HDF5 and FITS files, so the filename must end in a .hdf5, .h5, or .fits extension.

Parameters:: filename (str, h5py.File, h5py.Group) – The output filename or HDF5 group.

std()#: Return a new scalar object by taking the standard deviation across all samples

classmethod unpack(packed_samples, units, **kwargs)#

Unpack the array of packed (prior) samples and return a ~thejoker.JokerSamples instance.

Parameters:

packed_samples (array_like) –
units (~collections.OrderedDict, dict_like) – The units of each column in the packed samples array. The order of columns in this dictionary determines the assumed order of the columns in the packed samples array.
**kwargs – Additional keyword arguments are passed through to the initializer, so this supports any arguments accepted by the initializer (e.g., t_ref, n_offsets, poly_trend)

Returns:

samples – The unpacked samples.

Return type:

~thejoker.JokerSamples

wrap_K()#: Change negative K values to positive K values and wrap omega to adjust

write(output, overwrite=False, append=False)#

Write the samples data to a file.

Currently, we only support writing to / reading from HDF5 files, so the filename must end in a .hdf5 or .h5 extension.

Parameters:

output (str, h5py.File, h5py.Group) – The output filename or h5py group.
overwrite (bool (optional)) – Overwrite the existing file.
append (bool (optional)) – Append the samples to an existing table in the specified filename.

JokerSamples

Contents

JokerSamples#