ftsdata.DoubleSidedOpdData

class ftsdata.DoubleSidedOpdData(*args, hits=<object object>, **kwargs)

Interferogram cube with a symmetric OPD axis, ZPD at the centre.

This is the usual case for a full double-sided FTS scan. The spectrum is obtained by a direct (centred) FFT of the whole interferogram, see to_spectra().

__init__(*args, hits=<object object>, **kwargs)

Methods

__init__(*args[, hits])
add(operand[, operand2])	Performs addition by evaluating self + operand.
convert_unit_to(unit[, equivalencies])	Returns a new NDData object whose values have been converted to a new unit.
divide(operand[, operand2])	Performs division by evaluating self / operand.
from_array(opd, data[, hits, mask])	Construct FTS data from arrays.
get_axis_from_repr(plane)	Retrieve data axis from representation
max(**kwargs)
mean(**kwargs)
min(**kwargs)
mip([axes, level, attr])	Return a 'Multum In Parvo' of a given level on the data along given axes.
multiply(operand[, operand2])	Performs multiplication by evaluating self * operand.
opd_trim([fraction])
subtract(operand[, operand2])	Performs subtraction by evaluating self - operand.
sum(**kwargs)
to_dset([dset_mask, dset_uncertainty, ...])	Create the IDIA HDF5 datasets and attributes from a FTSData object.
to_hdu([hdu_data, hdu_mask, ...])	Creates an HDUList object from a FTSData object.
to_spectra([apodization, output_shape, ...])	Invert a double-sided interferogram cube with a direct FFT.

Attributes

data	ndarray-like : The stored dataset.
dtype	numpy.dtype of this object's data.
flags
hits	ndarra : Hits in the dataset, if any.
mask	any type : Mask for the dataset, if any.
meta
naxis_opd
ndim	integer dimensions of this object's data.
opd_axis
opd_wcs
psf	Image representation of the PSF for the dataset.
read
shape	shape tuple of this object's data.
size	integer size of this object's data.
uncertainty	any type : Uncertainty in the dataset, if any.
unit	Unit : Unit for the dataset, if any.
wcs	any type : A world coordinate system (WCS) for the dataset, if any.
weights	Return the weights as inverse variance regardless of the uncertainty type
write