nenupy.io.xst.XST

class nenupy.io.xst.XST(file_name)[source]

Bases: StatisticsData, Crosslet

Crosslet STatistics reading class.

See also

__init__(file_name)[source]

Methods

`__init__`(file_name)
`get`([polarization, miniarray_selection, ...])	Main data selection method.
`get_beamform`(pointing[, ...])	Perform beamforming operation on XST-like NenuFAR visibilities.
`get_stokes`([stokes, miniarray_selection, ...])	Converts cross-correlation visibilities to Stokes parameter.
`info`()	Print the basic file information.
`rephase`(phase_center)	_summary_

Attributes

`frequencies`	Returns the mid frequency of the recorded subbands.
`mini_arrays`	Retrieves the list of Mini-Arrays used to get the cross-correlations.
`phase_center`	_summary_

property frequencies: Returns the mid frequency of the recorded subbands.

get(polarization='XX', miniarray_selection=None, frequency_selection=None, time_selection=None)

Main data selection method.

Parameters:

polarization (str, optional) – Cross-correlation polarization selection (can either be “XX”, “XY”, “YX”, “YY”), for Stokes parameters see get_stokes(), by default “XX”
miniarray_selection (ndarray, optional) – Mini-Arrays selection, the visibilities will be filter to only take into account cross-correlations involving Mini-Arrays from the list provided, by default None (i.e., all available Mini-Arrays)
frequency_selection (str, optional) – Frequency selection code (e.g., “>=40MHz & <51.1MHz”), by default None (i.e., all available frequencies)
time_selection (str, optional) – Time selection code (e.g., “<2024-06-21T12:32:40”), by default None (i.e., all available time samples)

Returns:

Data selection.

Return type:

XST_Slice

Example

>>> from nenupy.io.xst import XST
>>> import numpy as np

>>> xst = XST(".../nenupy/tests/test_data/XST.fits")
>>> xx_data = xst.get(
        polarization="XX",
        miniarray_selection=np.array([0, 2, 3]),
        frequency_selection=">73MHz & <75MHz",
        time_selection="<2020-02-19T19:00:00"
    )
>>> xx_data.value.shape, xx_data.time.shape, xx_data.frequency.shape
((1, 7, 6), (1,), (7,))

get_beamform(pointing, frequency_selection=None, time_selection=None, mini_arrays=array([0, 1]), polarization='NW', calibration='default')

Perform beamforming operation on XST-like NenuFAR visibilities. In a nutsheel, this method transforms XST into BST.

Parameters:

pointing (Pointing) – Pointing instance, describing where the beamforming must be applied across time
frequency_selection (str, optional) – Frequency selection (see get()), by default None
time_selection (str, optional) – Time selection (see get()), by default None
mini_arrays (ndarray, optional) – Mini-Array selection (see get()), by default np.array([0, 1])
polarization (str, optional) – Polarization selection of the output data (can either be “NW” or “NE”), by default “NW”
calibration (str, optional) – Residual delay calibration file to be used during beamforming, “none” does not apply any calibration, by default “default”

Returns:

Beamformed data from XST-like visibilities.

Return type:

XST_Slice

Raises:

IndexError – Raised if the Mini Array selection is not valid.

Example

>>> from nenupy.io.bst import BST, XST
>>> from nenupy.astro.pointing import Pointing
>>> bst = BST("20191129_141900_BST.fits")
>>> xst = XST("20191129_141900_XST.fits")
>>> bf_cal = xst.get_beamform(
        pointing = Pointing.from_bst(bst, beam=0, analog=False),
        mini_arrays=bst.mini_arrays,
        calibration="default"
    )

See also

Beamforming from cross-correlations

get_stokes(stokes='I', miniarray_selection=None, frequency_selection=None, time_selection=None)

Converts cross-correlation visibilities to Stokes parameter. Available Stokes parameters “I”, “Q”, “U”, “V”, “FL” or “FV” are respectively computed as follows:

\[\begin{split}\begin{cases} \rm{I} = \frac{1}{2}(\rm{XX} + \rm{YY})\\ \rm{Q} = \frac{1}{2}(\rm{XX} - \rm{YY})\\ \rm{U} = \frac{1}{2}(\rm{XY} + \rm{YX})\\ \rm{V} = \frac{-i}{2}(\rm{XY} - \rm{YX})\\ \frac{\rm{L}}{\rm{I}} = \frac{\sqrt{\rm{Q}^2 + \rm{U}^2}}{\rm{I}}\\ \frac{\rm{V}}{\rm{I}} = \frac{\rm{V}}{\rm{I}}\\ \end{cases}\end{split}\]

Parameters:

stokes (str, optional) – Stokes parameter to synthetize (can either be “I”, “Q”, “U”, “V”, “FL” or “FV”), by default “I”
miniarray_selection (ndarray, optional) – Mini-Arrays selection, the visibilities will be filter to only take into account cross-correlations involving Mini-Arrays from the list provided, by default None (i.e., all available Mini-Arrays)
frequency_selection (str, optional) – Frequency selection code (e.g., “>=40MHz & <51.1MHz”), by default None (i.e., all available frequencies)
time_selection (str, optional) – Time selection code (e.g., “<2024-06-21T12:32:40”), by default None (i.e., all available time samples)

Returns:

Data selection converted to desired Stokes parameter.

Return type:

XST_Slice

Example

>>> from nenupy.io.xst import XST
>>> import numpy as np

>>> xst = XST(".../nenupy/tests/test_data/XST.fits")
>>> u_data = xst.get_stokes(
        polarization="U",
        miniarray_selection=np.array([0, 2, 3]),
        frequency_selection=">73MHz & <75MHz",
        time_selection="<2020-02-19T19:00:00"
    )
>>> u_data.value.shape, u_data.time.shape, u_data.frequency.shape
((1, 7, 6), (1,), (7,))

info()

Print the basic file information.

Example

>>> from nenupy.io.bst import BST

>>> bst = BST("/path/to/20231201_021200_BST.fits")
>>> bst.info()
file: '/path/to/20231201_021200_BST.fits'
frequency (384,): 11.9140625 MHz -- 86.71875 MHz
time (13600,): 2023-12-01T02:13:10.000 -- 2023-12-01T05:59:49.000
data: (13600, 2, 768)

property mini_arrays

Retrieves the list of Mini-Arrays used to get the cross-correlations.

Getter:: Mini-Arrays list.
Type:: ndarray

property phase_center

_summary_

Returns:: _description_
Return type:: SkyCoord

rephase(phase_center)

_summary_

Parameters:: phase_center (SkyCoord) – _description_
Returns:: _description_
Return type:: XST_Slice
Raises:: ValueError – _description_