breidablik.analysis.format_read

Functions

pixel_format(data, wavelength[, center, lower, upper, ...])

Changes the data from read into a machine learning format. This function is for machine learning over pixels.

rew_format(data, wavelength[, predict, center, upper, ...])

Changes the data from read into a machine learning format. This function is for machine learning over REWs.

Module Contents

breidablik.analysis.format_read.pixel_format(data, wavelength, center=670.9659, lower=0.4, upper=0.4, ftype='flux')

Changes the data from read into a machine learning format. This function is for machine learning over pixels.

Parameters:

  • data (dict) – Flux data from the read functions. The outermost keys are the stellar parameters for the models. The next keys are the lithium abundances. The innermost keys are ‘flux’ which retreives the NLTE flux or ‘fluxl’ which retreives the LTE flux. All data must be located at the same wavelength points.

  • wavelength (List[Real] or 1darray) – The wavelengths that correspond to the data. From read.get_wavelengths().

  • center (Real, optional) – The center of the wavelengths where the cut should be taken, in the same units as the wavelength. The 3 lithium lines are centered at 610.5298, 670.9659, and 812.8606 nm in the Balder results.

  • upper (Real, optional) – The amount to go above the center when taking the cut, in the same units as the wavelength.

  • lower (Real, optional) – The amount to go below the center when taking the cut, in the same units as the wavelength.

  • ftype (str, optional) – Which type of flux to convert from the data. Accepted options are: ‘flux’ for NLTE or ‘fluxl’ for LTE.

Returns:

Xy – The X and y data sets in the form (X, y). X contains [num of objects x num of parameters], and y contains [num of objects x num of pixels].

Return type:

tuple of 2darrays

breidablik.analysis.format_read.rew_format(data, wavelength, predict='rew', center=670.9659, upper=10, lower=10, ftype='flux', num=10000)

Changes the data from read into a machine learning format. This function is for machine learning over REWs.

Parameters:

  • data (dict) – Flux data from the read functions. The outermost keys are the stellar parameters for the models. The next keys are the lithium abundances. The innermost keys are ‘flux’ which retreives the NLTE flux or ‘fluxl’ which retreives the LTE flux. All data must be located at the same wavelength points.

  • wavelength (List[Real] or 1darray) – The wavelengths that correspond to the data. From read.get_wavelengths().

  • predict (str, optional) – Determines what varlue is placed in the y data. Accepted options are ‘rew’ and ‘li’.

  • center (Real, optional) – The center of the wavelengths where the cut should be taken, in the same units as the wavelength. The 3 lithium lines are centered at 610.5298, 670.9659, and 812.8606 nm in the Balder results.

  • upper (Real, optional) – The amount to go above the center when taking the cut, in the same units as the wavelength.

  • lower (Real, optional) – The amount to go below the center when taking the cut, in the same units as the wavelength.

  • ftype (str, optional) – Which type of flux to convert from the data. Possible options are: ‘flux’ for NLTE or ‘fluxl’ for LTE.

  • num (Int, optional) – The number of points in the interpolation. Before calculating the REW, the line profile is interpolated to finer wavelength points.

Returns:

Xy – The X and y data sets in the form (X, y). X contains [num of objects x num of parameters], and y contains [num of objects].

Return type:

tuple of 2darrays