Data¶

class vega.data.Data(corr_item)[source]¶

Class for handling lya forest correlation function data.

An instance of this is required for each cf component

Parameters:	corr_item (CorrelationItem) – Item object with the component config

blind¶

Blinding flag property

Returns:	Blinding flag
Return type:	bool

cov_mat¶

Covariance matrix property

Returns:	Covariance matrix
Return type:	2D array

create_monte_carlo(fiducial_model, scale=1.0, seed=0, forecast=False)[source]¶

Create monte carlo mock of data using a fiducial model.

Parameters:	fiducial_model (1D Array) – Fiducial model of the data scale (float, optional) – Scaling for the covariance, by default 1. seed (int, optional) – Seed for the random number generator, by default 0 forecast (boolean, optional) – Forecast option. If true, we don’t add noise to the mock, by default False
Returns:	Monte Carlo mock of the data
Return type:	1D Array

data_vec¶

Full data vector property

Returns:	Full data vector (xi)
Return type:	1D array

distortion_mat¶

Distortion matrix property

Returns:	Distortion matrix
Return type:	2D array

has_cov_mat¶

Covariance matrix flag

Returns:	Covariance matrix flag
Return type:	bool

has_distortion¶

Distortion matrix flag

Returns:	Distortion matrix flag
Return type:	bool

inv_masked_cov¶

Inverse masked covariance matrix property

Returns:	Inverse masked covariance matrix
Return type:	2D array

log_cov_det¶

Logarithm of the determinant of the covariance matrix property

Returns:	Logarithm of the determinant of the covariance matrix
Return type:	float

masked_data_vec¶

Masked data vector property

Returns:	Masked data vector (xi[mask])
Return type:	1D array