orca.transform.flagging
Flagging transforms for measurement sets.
Provides high-level flagging operations that combine detection and application, including AOFlagger integration and autocorrelation-based antenna flagging.
Functions
- flag_with_aoflagger
Run AOFlagger RFI detection on a measurement set.
- flag_ants
Flag specified antennas in a measurement set.
- flag_ant_chan_from_autocorr
Flag antennas and channels based on autocorrelation anomalies.
- flag_on_autocorr
Identify and flag bad antennas from autocorrelation statistics.
- identify_bad_ants
Identify bad antennas without applying flags.
- save_flag_metadata
Save flag statistics in a compact binary format.
Attributes
Functions
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Run AOFlagger RFI detection on a measurement set. |
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Flag all visibilities involving specified antennas. |
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Flag antennas and channels based on autocorrelation anomalies. |
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Identify and flag bad antennas from autocorrelation statistics. |
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Identify bad antennas from autocorrelation statistics. |
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Save FLAG column in a compact bit-packed binary format. |
Module Contents
- orca.transform.flagging.flag_with_aoflagger(ms: str, strategy: str = '/opt/share/aoflagger/strategies/nenufar-lite.lua', in_memory: bool = False, n_threads: int = 5) str[source]
Run AOFlagger RFI detection on a measurement set.
- Parameters:
ms – Path to the measurement set.
strategy – Path to the AOFlagger Lua strategy file.
in_memory – If True, load data into memory for processing.
n_threads – Number of threads for AOFlagger.
- Returns:
Path to the flagged measurement set.
- Raises:
RuntimeError – If AOFlagger returns a non-zero exit code.
- orca.transform.flagging.flag_ants(ms: str, ants: List[int]) str[source]
Flag all visibilities involving specified antennas.
- Parameters:
ms – Path to the measurement set.
ants – List of antenna indices (0-indexed) to flag.
- Returns:
Path to the modified measurement set.
- orca.transform.flagging.flag_ant_chan_from_autocorr(ms: str, threshold: float = 5.0) str[source]
Flag antennas and channels based on autocorrelation anomalies.
- Parameters:
ms – Path to the measurement set.
threshold – Sigma threshold for outlier detection.
- Returns:
Path to the flagged measurement set.
Note
Currently only works on single spectral window data.
- orca.transform.flagging.flag_on_autocorr(ms, date: datetime.date | None = None, thresh: float = 7.0, column='DATA') str[source]
Identify and flag bad antennas from autocorrelation statistics.
Optionally loads a priori bad antenna list for the given date before performing autocorrelation-based detection.
- Parameters:
ms – Path to the measurement set.
date – Observation date for loading a priori bad antennas.
thresh – Sigma threshold for flagging (default 7.0).
column – Data column to analyze (‘DATA’ or ‘CORRECTED_DATA’).
- Returns:
Path to the flagged measurement set.
- orca.transform.flagging.identify_bad_ants(t: casacore.tables.table, thresh: float = 7, column='DATA') List[int][source]
Identify bad antennas from autocorrelation statistics.
Compares each antenna’s autocorrelation bandpass to a median template for core and outrigger antennas separately. Antennas with normalized deviations exceeding the threshold are flagged.
- Parameters:
t – Open casacore table object for the measurement set.
thresh – Sigma threshold for flagging (default 7.0).
column – Data column to analyze (‘DATA’ or ‘CORRECTED_DATA’).
- Returns:
List of antenna indices identified as bad.
- Raises:
AssertionError – If data contains multiple timestamps or spectral windows.
- orca.transform.flagging.save_flag_metadata(ms: str, output_dir: str = '/lustre/pipeline/slow-averaged/') str[source]
Save FLAG column in a compact bit-packed binary format.
Creates a binary file containing the packed flag data, which can be unpacked later using numpy.unpackbits().
- Parameters:
ms – Path to the measurement set.
output_dir – Directory for output file.
- Returns:
Path to the measurement set (unchanged).