nenupy.io.xst.NenufarTV

class nenupy.io.xst.NenufarTV(file_name)[source]

Bases: StatisticsData, Crosslet

Class to load and process NenuFAR TV data. Results of such operations can be seen live on NenuFAR-TV where the current view of the Sky observed by NenuFAR is displayed.

__init__(file_name)[source]

Generate an instance of NenufarTV

Parameters:

file_name (str) – NenuFAR-TV data file (must end with .dat)

Raises:

AssertionError – Raised if the data file is not correctly formated

Methods

__init__(file_name)

Generate an instance of NenufarTV

compute_nearfield_tv([sources, stokes, ...])

Compute the near-field from NenuFAR-TV data.

compute_nenufar_tv([analog_pointing_file, ...])

Compute the NenuFAR-TV image.

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.

phase_center

_summary_
compute_nearfield_tv(sources=[], stokes='I', radius=<Quantity 400. m>, npix=64)[source]

Compute the near-field from NenuFAR-TV data.

Parameters:

  • sources (list, optional) – List of celestial sources for which a near-field imprint will be computed (passed to make_nearfield()), by default []

  • stokes (str, optional) – Stokes parameter to display (passed to get_stokes()), by default “I”

  • radius (Quantity, optional) – Ground radius on which the near-field projection is computed (passed to make_nearfield()), by default 400 meters

  • npix (int, optional) – Number of pixels of the image size (passed to make_nearfield()), by default 64.

Returns:

The near-field

Return type:

TV_Nearfield

Example

>>> from nenupy.io.xst import NenufarTV

>>> tv = NenufarTV("20191204_132113_nenufarTV.dat")
>>> nf_obj = tv.compute_nearfield_tv(
        sources=["Cyg A", "Cas A", "Vir A", "Tau A", "Sun"],
        npix=64
    )
compute_nenufar_tv(analog_pointing_file=None, fov_radius=<Quantity 27. deg>, resolution=<Quantity 0.5 deg>, stokes='I')[source]

Compute the NenuFAR-TV image.

Parameters:

  • analog_pointing_file (str, optional) – NenuFAR analog pointing file (as read by from_file()), by default None (i.e., a zenithal pointing is assumed)

  • fov_radius (Quantity, optional) – Radius of the image field of view (passed to make_image()), by default 27 degrees

  • resolution (Quantity, optional) – Resolution of the image (passed to make_image()), by default 0.5 degree

  • stokes (str, optional) – Stokes parameter to display (passed to get_stokes()), by default “I”

Returns:

The NenuFAR-TV image

Return type:

TV_Image

Example

>>> from nenupy.io.xst import NenufarTV

>>> tv = NenufarTV("20191204_132113_nenufarTV.dat")
>>> tv_obj = tv.compute_nenufar_tv()
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)
abstract 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_