lezargus.container.spectra module#
Spectra data container.
This module and class primarily deals with spectral data.
- class lezargus.container.spectra.LezargusSpectra(wavelength: ndarray, data: ndarray, uncertainty: ndarray | None = None, wavelength_unit: str | Unit | None = None, data_unit: str | Unit | None = None, pixel_scale: float | None = None, slice_scale: float | None = None, mask: ndarray | None = None, flags: ndarray | None = None, header: Header | None = None)[source]#
Bases:
LezargusContainerArithmetic
Container to hold spectral data and perform operations on it.
- For all available attributes, see :py:class:`LezargusContainerArithmetic`.
- __init__(wavelength: ndarray, data: ndarray, uncertainty: ndarray | None = None, wavelength_unit: str | Unit | None = None, data_unit: str | Unit | None = None, pixel_scale: float | None = None, slice_scale: float | None = None, mask: ndarray | None = None, flags: ndarray | None = None, header: Header | None = None) None [source]#
Instantiate the spectra class.
- Parameters:
wavelength (ndarray) – The wavelength axis of the spectral component of the data, if any. The unit of wavelength is typically in meters; but, check the
wavelength_unit
value.data (ndarray) – The data stored in this container. The unit of the flux is typically in W m^-2 m^-1; but, check the
data_unit
value.uncertainty (ndarray) – The uncertainty in the data of the spectra. The unit of the uncertainty is the same as the data value; per
uncertainty_unit
.wavelength_unit (Astropy Unit) – The unit of the wavelength array. If None, we assume unit-less.
data_unit (Astropy Unit) – The unit of the data array. If None, we assume unit-less.
pixel_scale (float, default = None) – The E-W, “x” dimension, pixel plate scale of the spatial component, if any. Must be in radians per pixel. Scale is None if none is provided.
slice_scale (float, default = None) – The N-S, “y” dimension, pixel slice scale of the spatial component, if any. Must be in radians per slice/pixel. Scale is None if none is provided.
mask (ndarray, default = None) – A mask of the data, used to remove problematic areas. Where True, the values of the data is considered masked. If None, we assume the mask is all clear.
flags (ndarray, default = None) – Flags of the data. These flags store metadata about the data. If None, we assume that there are no harmful flags.
header (Header, default = None) – A set of header data describing the data. Note that when saving, this header is written to disk with minimal processing. We highly suggest writing of the metadata to conform to the FITS Header specification as much as possible. If None, we just use an empty header.
- Return type:
None
- convolve(kernel: ndarray) Self [source]#
Convolve the spectra with a custom kernel.
We compute the convolution and return a near copy of the spectra after convolution. The wavelength is not affected.
- Parameters:
kernel (ndarray) – The kernel which we are using to convolve.
- Returns:
convolved_spectra – A near copy of the spectra after convolution.
- Return type:
ndarray
- interpolate(wavelength: ndarray, skip_mask: bool = True, skip_flags: bool = True) tuple[ndarray, ndarray, ndarray | None, ndarray | None] [source]#
Interpolation calling function for spectra.
Each entry is considered a single point to interpolate over.
- Parameters:
wavelength (ndarray) – The wavelength values which we are going to interpolate to. The units of the data of this array should be the same as the wavelength unit stored.
skip_mask (bool, default = True) – If provided, the propagation of data mask through the interpolation is skipped. It is computationally a little expensive otherwise.
skip_flags (bool, default = True) – If provided, the propagation of data flags through the interpolation is skipped. It is computationally a little expensive otherwise.
- Returns:
interp_data (ndarray) – The interpolated data.
interp_uncertainty (ndarray) – The interpolated uncertainty.
interp_mask (ndarray or None) – A best guess attempt at finding the appropriate mask for the interpolated data. If skip_mask=True, then we skip the computation and return None instead.
interp_flags (ndarray or None) – A best guess attempt at finding the appropriate flags for the interpolated data. If skip_flags=True, then we skip the computation and return None instead.
- classmethod read_fits_file(filename: str) Self [source]#
Read a Lezargus spectra FITS file.
We load a Lezargus FITS file from disk. Note that this should only be used for 1-D spectra files.
- Parameters:
filename (str) – The filename to load.
- Returns:
spectra – The LezargusSpectra class instance.
- Return type:
Self-like
- stitch(*spectra: LezargusSpectra, weight: list[ndarray] | str = 'uniform', average_routine: Callable[[ndarray, ndarray, ndarray], tuple[float, float]] = None) Self [source]#
Stitch together different spectra; we do not scaling.
We stitch this spectra with input spectra. If the spectra are not already to the same scale however, this will result in wildly incorrect results. The header information is preserved, though we take what we can from the other objects.
- Parameters:
*spectra (LezargusSpectra) – A set of Lezargus spectra which we will stitch to this one.
weight (list[ndarray] or str, default = None) –
A list of the weights in the data for stitching. Each entry in the list must have a corresponding entry in the wavelength and data list, or None. For convenience, we provide short-cut inputs for the following:
uniform : Uniform weights.
invar : Inverse variance weights.
average_routine (Callable, str, default = None) – The function used to average all of the spectra together. It must also be able to accept weights and propagate uncertainties. If None, we default to the weighted mean. Namely, it must be of the form f(val, uncert, weight) = avg, uncert.
- Returns:
stitch_spectra – The spectra after stitching.
- Return type: