arrakis.makecat

Make an Arrakis catalogue

Attributes

TQDM_OUT

Classes

SpectralIndices

Functions

`add_metadata`(vo_table, filename)	Add metadata to VO Table for CASDA
`cat_parser`(→ argparse.ArgumentParser)
`cli`()	Command-line interface
`combinate`(→ Tuple[arrakis.utils.typing.ArrayLike, ...)	Return all combinations of data with itself
`compute_local_rm_flag`(→ astropy.table.Table)	Compute the local RM flag
`cuts_and_flags`(→ arrakis.utils.typing.TableLike)	Cut out bad sources, and add flag columns
`fix_blank_units`(→ arrakis.utils.typing.TableLike)	Fix blank units in table
`flag_blended_components`(→ arrakis.utils.typing.TableLike)	Identify blended components in a catalogue and flag them.
`get_alpha`(→ SpectralIndices)
`get_fit_func`(→ Tuple[Callable, matplotlib.pyplot.Figure])	Fit an envelope to define leakage sources
`get_integration_time`(cat, field_col[, sbid])
`is_leakage`(→ bool)	Determine if a source is leakage
`lognorm_from_percentiles`(x1, p1, x2, p2)	Return a log-normal distribuion X parametrized by:
`main`(→ None)	Make a catalogue from the Arrakis database flow
`replace_nans`(filename)	Replace NaNs in a XML table with a string
`sigma_add_fix`(→ arrakis.utils.typing.TableLike)
`update_tile_separations`(→ arrakis.utils.typing.TableLike)	Update the tile separations in the catalogue
`write_votable`(→ None)

Module Contents

class arrakis.makecat.SpectralIndices[source]

Bases: NamedTuple

alphas: numpy.ndarray[source]

alphas_err: numpy.ndarray[source]

betas: numpy.ndarray[source]

betas_err: numpy.ndarray[source]

arrakis.makecat.add_metadata(vo_table: astropy.io.votable.tree.VOTableFile, filename: str)[source]

Add metadata to VO Table for CASDA

Parameters:: vo_table (vot) – VO Table object
Returns:: VO Table object with metadata
Return type:: vot

arrakis.makecat.cat_parser(parent_parser: bool = False) → argparse.ArgumentParser[source]

arrakis.makecat.cli()[source]: Command-line interface

arrakis.makecat.combinate(data: arrakis.utils.typing.ArrayLike) → Tuple[arrakis.utils.typing.ArrayLike, arrakis.utils.typing.ArrayLike][source]

Return all combinations of data with itself

Parameters:: data (ArrayLike) – Data to combine.
Returns:: Data_1 matched with Data_2
Return type:: Tuple[ArrayLike, ArrayLike]

arrakis.makecat.compute_local_rm_flag(good_cat: astropy.table.Table, big_cat: astropy.table.Table) → astropy.table.Table[source]

Compute the local RM flag

Parameters:

good_cat (Table) – Table with just good RMs
big_cat (Table) – Overall table

Returns:

Table with local RM flag

Return type:

Table

arrakis.makecat.cuts_and_flags(cat: arrakis.utils.typing.TableLike, leakage_degree: int = 4, leakage_bins: int = 16, leakage_snr: float = 30.0) → arrakis.utils.typing.TableLike[source]

Cut out bad sources, and add flag columns

A flag of ‘True’ means the source is bad.

Parameters:: cat (rmt) – Catalogue to cut and flag

arrakis.makecat.fix_blank_units(rmtab: arrakis.utils.typing.TableLike) → arrakis.utils.typing.TableLike[source]

Fix blank units in table

Parameters:: rmtab (TableLike) – TableLike

arrakis.makecat.flag_blended_components(cat: arrakis.utils.typing.TableLike) → arrakis.utils.typing.TableLike[source]

Identify blended components in a catalogue and flag them.

Parameters:: cat (TableLike) – Input catalogue
Returns:: Output catalogue with minor components flagged
Return type:: TableLike

arrakis.makecat.get_alpha(cat: arrakis.utils.typing.TableLike) → SpectralIndices[source]

arrakis.makecat.get_fit_func(tab: arrakis.utils.typing.TableLike, nbins: int = 21, offset: float = 0.002, degree: int = 2, do_plot: bool = False, high_snr_cut: float = 30.0) → Tuple[Callable, matplotlib.pyplot.Figure][source]

Fit an envelope to define leakage sources

Parameters:

tab (TableLike) – Catalogue to fit
nbins (int, optional) – Number of bins along seperation axis. Defaults to 21.

Returns:

3rd order polynomial fit.

Return type:

Callable

arrakis.makecat.get_integration_time(cat: rmtable.RMTable, field_col: pymongo.collection.Collection, sbid: int | None = None)[source]

arrakis.makecat.is_leakage(frac: float, sep: float, fit: Callable) → bool[source]

Determine if a source is leakage

Parameters:

frac (float) – Polarised fraction
sep (float) – Separation from tile centre
fit (function) – Fitting function

Returns:

True if source is leakage

Return type:

bool

arrakis.makecat.lognorm_from_percentiles(x1, p1, x2, p2)[source]

Return a log-normal distribuion X parametrized by:

P(X < p1) = x1 P(X < p2) = x2

arrakis.makecat.main(field: str, host: str, epoch: int, sbid: int | None = None, leakage_degree: int = 4, leakage_bins: int = 16, leakage_snr: float = 30.0, username: str | None = None, password: str | None = None, verbose: bool = True, outfile: str | None = None) → None[source]

Make a catalogue from the Arrakis database flow

Parameters:

field (str) – RACS field name
host (str) – MongoDB host IP
username (str, optional) – Mongo username. Defaults to None.
password (str, optional) – Mongo password. Defaults to None.
verbose (bool, optional) – Verbose output. Defaults to True.
outfile (str, optional) – Output file name. Defaults to None.
cat_format (str, optional) – Type of catalogue .e.g. fits. Defaults to None.

arrakis.makecat.replace_nans(filename: str)[source]

Replace NaNs in a XML table with a string

Parameters:: filename (str) – File name

arrakis.makecat.sigma_add_fix(tab: arrakis.utils.typing.TableLike) → arrakis.utils.typing.TableLike[source]

arrakis.makecat.update_tile_separations(rmtab: arrakis.utils.typing.TableLike, field_col: pymongo.collection.Collection) → arrakis.utils.typing.TableLike[source]

Update the tile separations in the catalogue

Parameters:

rmtab (TableLike) – Table to update
field_col (Collection) – Field collection

Returns:

Updated table

Return type:

TableLike

arrakis.makecat.write_votable(rmtab: arrakis.utils.typing.TableLike, outfile: str) → None[source]

arrakis.makecat.TQDM_OUT[source]