sherlockpipe.system_stability.mr_forecast.MrForecast

class sherlockpipe.system_stability.mr_forecast.MrForecast

Bases: object

Mass-radius forecasting model based on the probabilistic broken power-law relation of Chen & Kipping (2017, ApJ, 834, 17).

Provides methods to convert between mass and radius distributions for planets across four populations: Terran, Neptunian, Jovian, and Stellar.

__init__()

Methods

`Mpost2R`(mass[, classify])	Forecast the Radius distribution given the mass distribution.
`Mstat2R`(mean, std[, sample_size, classify])	Forecast the mean and standard deviation of radius given the mena and standard deviation of the mass.
`Rpost2M`(radius[, grid_size, classify])	Forecast the mass distribution given the radius distribution.
`Rstat2M`(mean, std[, sample_size, grid_size, ...])	Forecast the mean and standard deviation of mass given the mean and standard deviation of the radius.
`__init__`()
`read_parameters`()	Read the M-R forecaster hyperparameter file.

Attributes

`M_LOWER`
`M_UPPER`
`N_POPS`

static Mpost2R(mass: float, classify='No')

Forecast the Radius distribution given the mass distribution.

Parameters

mass: one dimensional array: The mass distribution in earth radii
classify: string (optional): If you want the object to be classifed. Options are ‘Yes’ and ‘No’. Default is ‘No’. Result will be printed, not returned.

Returns

radius: one dimensional array: Predicted radius distribution in the input unit.

static Mstat2R(mean: float, std: float, sample_size: int = 1000, classify='No')

Forecast the mean and standard deviation of radius given the mena and standard deviation of the mass. Assuming normal distribution with the mean and standard deviation truncated at the mass range limit of the model.

Parameters

mean: float: Mean (average) of mass in earth radii
std: float: Standard deviation of mass.
sample_size: int (optional): Number of mass samples to draw with the mean and std provided.

Returns

mean: float: Predicted mean of radius in the input unit.
std: float: Predicted standard deviation of radius.

static Rpost2M(radius: float, grid_size: int = 1000.0, classify='No')

Forecast the mass distribution given the radius distribution.

Parameters

radius: one dimensional array: The radius distribution in earth radii
grid_size: int (optional): Number of grid in the mass axis when sampling mass from radius. The more the better results, but slower process.
classify: string (optional): If you want the object to be classifed. Options are ‘Yes’ and ‘No’. Default is ‘No’. Result will be printed, not returned.

Returns

mass: one dimensional array: Predicted mass distribution in the input unit.

static Rstat2M(mean: float, std: float, sample_size: int = 1000.0, grid_size: int = 1000.0, classify='No')

Forecast the mean and standard deviation of mass given the mean and standard deviation of the radius.

Parameters

mean: float: Mean (average) of radius in earth radii
std: float: Standard deviation of radius.
sample_size: int (optional): Number of radius samples to draw with the mean and std provided.
grid_size: int (optional): Number of grid in the mass axis when sampling mass from radius. The more the better results, but slower process.

Returns

mean: float: Predicted mean of mass in the input unit.
std: float: Predicted standard deviation of mass.

static read_parameters()

Read the M-R forecaster hyperparameter file.

Returns

ndarray: Array of hyperparameters from the fitted posterior distribution.