flip.data_vector.galaxypv_vectors

flip.data_vector.galaxypv_vectors#

Attributes#

jax_installed

Classes#

`VelFromLogDist`	Helper class that provides a standard way to create an ABC using
`VelFromTullyFisher`	Helper class that provides a standard way to create an ABC using
`VelFromFundamentalPlane`	Helper class that provides a standard way to create an ABC using

Module Contents#

flip.data_vector.galaxypv_vectors.jax_installed = True#

class flip.data_vector.galaxypv_vectors.VelFromLogDist(data, covariance_observation=None, velocity_estimator='full')[source]#

Bases: flip.data_vector.basic.DataVector

Helper class that provides a standard way to create an ABC using inheritance.

_kind = 'velocity'#

_needed_keys = ['eta']#

property conditional_needed_keys#

Returns a list of keys needed for the data vector calculation, including any additional keys that are conditionally required.

Returns:: A list of keys needed for the data vector calculation.
Return type:: list

give_data_and_variance(parameter_values_dict, *args)[source]#

Returns the data and variance for the velocity.

If the covariance observation is available, it returns the velocity data and the covariance observation. Otherwise, it returns the velocity data and the squared velocity error.

Parameters:: *args – Additional arguments (not used in this method).
Returns:: A tuple containing the velocity data and the variance.
Return type:: tuple

velocity_estimator = 'full'#

class flip.data_vector.galaxypv_vectors.VelFromTullyFisher(data, h, covariance_observation=None, velocity_estimator='full')[source]#

Bases: flip.data_vector.basic.DataVector

Helper class that provides a standard way to create an ABC using inheritance.

_kind = 'velocity'#

_needed_keys = ['zobs', 'logW', 'm_mean', 'rcom_zobs']#

_free_par = ['a', 'b']#

property conditional_needed_keys#

Returns a list of conditional keys based on the availability of covariance observation.

If the covariance observation is None, the method adds “e_logW” and “e_m_mean” to the list of conditional keys.

Returns:: A list of conditional keys.
Return type:: list

compute_observed_distance_modulus(parameter_values_dict)[source]#

Compute the observed distance modulus based on the given parameter values.

Parameters:: parameter_values_dict (dict) – A dictionary containing the parameter values.
Returns:: The observed distance modulus.
Return type:: float

compute_distance_modulus_difference(parameter_values_dict)[source]#

Compute the difference in distance modulus.

This method calculates the difference in distance modulus based on the provided parameter values.

Parameters:: parameter_values_dict (dict) – A dictionary containing the parameter values.
Returns:: The difference in distance modulus.
Return type:: float

compute_observed_distance_modulus_variance(parameter_values_dict)[source]#

Compute the variance of the observed distance modulus.

Parameters:: parameter_values_dict (dict) – A dictionary containing parameter values.
Returns:: The variance of the observed distance modulus.
Return type:: float or ndarray

give_data_and_variance(parameter_values_dict)[source]#

Compute the velocities and velocity variances based on the given parameter values.

Parameters:: parameter_values_dict (dict) – A dictionary containing the parameter values.
Returns:: A tuple containing the velocities and velocity variances.
Return type:: tuple

_init_A()[source]#

Initializes the matrix A for the galaxypv_vectors class.

Returns:: The initialized matrix A.
Return type:: A (ndarray)

velocity_estimator = 'full'#

h#

_A = None#

_host_matrix = None#

class flip.data_vector.galaxypv_vectors.VelFromFundamentalPlane(data, h, covariance_observation=None, velocity_estimator='full')[source]#

Bases: flip.data_vector.basic.DataVector

Helper class that provides a standard way to create an ABC using inheritance.

_kind = 'velocity'#

_needed_keys = ['zobs', 'logRe', 'logsig', 'logI', 'rcom_zobs']#

_free_par = ['a', 'b', 'c']#

property conditional_needed_keys#

Returns a list of conditional keys based on the availability of covariance observation.

If the covariance observation is None, the method adds “e_logW” and “e_m_mean” to the list of conditional keys.

Returns:: A list of conditional keys.
Return type:: list

compute_observed_distance_modulus(parameter_values_dict)[source]#

Compute the observed distance modulus based on the given parameter values.

Parameters:: parameter_values_dict (dict) – A dictionary containing the parameter values.
Returns:: The observed distance modulus.
Return type:: float

compute_distance_modulus_difference(parameter_values_dict)[source]#

Compute the difference in distance modulus.

This method calculates the difference in distance modulus based on the provided parameter values.

Parameters:: parameter_values_dict (dict) – A dictionary containing the parameter values.
Returns:: The difference in distance modulus.
Return type:: float

compute_observed_distance_modulus_variance(parameter_values_dict)[source]#

Compute the variance of the observed distance modulus.

Parameters:: parameter_values_dict (dict) – A dictionary containing parameter values.
Returns:: The variance of the observed distance modulus.
Return type:: float or ndarray

give_data_and_variance(parameter_values_dict)[source]#

Compute the velocities and velocity variances based on the given parameter values.

Parameters:: parameter_values_dict (dict) – A dictionary containing the parameter values.
Returns:: A tuple containing the velocities and velocity variances.
Return type:: tuple

_init_A()[source]#

Initializes the matrix A for the galaxypv_vectors class.

Returns:: The initialized matrix A.
Return type:: A (ndarray)

velocity_estimator = 'full'#

h#

_A = None#

_host_matrix = None#