orca.metadata package

Submodules

orca.metadata.pathsmanagers module

class orca.metadata.pathsmanagers.OfflinePathsManager(utc_times_txt_path: str, dadafile_dir: Optional[str] = None, working_dir: Optional[str] = None, gaintable_dir: str = None, flag_npy_paths: Optional[Union[str, Dict[datetime.datetime, str]]] = None)

Bases: orca.metadata.pathsmanagers.PathsManager

PathsManager for offline transient processing.

This could potentially work for processing the buffer too. A config file reader will probably parse a config file into this object.

Assumes that the bandpass calibration table is named like bcal_dir/00.bcal’

chunks_by_integration(chunk_size: int) → List[List[datetime.datetime]]

Chunk the datetime array by number of integrations such that each chunk contains data spanning equal or less than the chunk size. Note that the last chunk may be smaller, if the total number of integrations is not divisible by the chunk size.

Parameters

chunk_size – number of integrations per chunk

Returns: A list whose elements are the ordered chunks, which are each a list of ordered timestamps.

chunks_by_time(chunk_time: datetime.timedelta) → List[List[datetime.datetime]]

Chunk the datetime array by time such that each chunk contains data spanning equal or less than chunk_time amount of time. All of the data will be chunked. Note that

1. The last chunk may be smaller, if the span of the data is not divisible by the chunk time

2) Chunking is based on time and not by the number of integrations. Therefore, some chunks might have more or fewer integrations than some other chunks, if the chunk time is not divisible by the integration time.

Parameters

chunk_time – a timedelta object for the chunk time.

Returns: A list whose elements are the ordered chunks, which are each a list of ordered timestamps.

dpp(timestamp: datetime.datetime, product: str, file_suffix: str, file_prefix: Optional[str] = None) → str

Generate path for generic data product. Looks like /path/to/working_dir/<product>/2018-03-02/hh=02/<file_prefix>_2018-03-02T02:02:02<file_suffix>. The first underscore is not there is file_prefix=None

Parameters

• timestamp – Timestamp of observation.

• product – Name of the data product to be used for top-level directory

• file_suffix – Suffix to data file. For example for fits file it’d be ‘.fits’. You can also have something like ‘_v2.fits’

• file_prefix – Prefix of the file. Can be spectral window. If none specified then no prefix.

Returns: Full path to the data product.

property gaintable_dir

get_bcal_path(bandpass_date: datetime.date, spw: str) → str

Return bandpass calibration path in /gaintable/path/2018-03-02/00.bcal.

Parameters

• bandpass_date – Date of the bandpass solution requested.

• spw – Spectral window

Returns

Bandpass calibration path.

get_data_product_path(timestamp: datetime.datetime, product: str, file_suffix: str, file_prefix: Optional[str] = None) → str

Generate path for generic data product. Looks like /path/to/working_dir/<product>/2018-03-02/hh=02/<file_prefix>_2018-03-02T02:02:02<file_suffix>. The first underscore is not there is file_prefix=None

Parameters

• timestamp – Timestamp of observation.

• product – Name of the data product to be used for top-level directory

• file_suffix – Suffix to data file. For example for fits file it’d be ‘.fits’. You can also have something like ‘_v2.fits’

• file_prefix – Prefix of the file. Can be spectral window. If none specified then no prefix.

Returns: Full path to the data product.

get_flag_npy_path(timestamp: datetime.datetime) → str

Return the a priori npy for the flags column for a given time.

Parameters

timestamp –

Returns

If only one flag_npy was supplied, the flag_npy; if a Dict[datetime, str] is supplied, the closest one in time to the supplied timestamp.

get_gaintable_path(gaintable_date: datetime.date, spw: str, extension: str) → str

Get the path to a certain CASA gaintable.

Parameters

• gaintable_date – date of the table requested

• spw – spw of the gaintable requested

• extension – extension of the gaintable (bcal etc)

Returns

The path to the requested gaintable.

get_ms_parent_path(timestamp: datetime.datetime) → str

Generate measurement set parent paths that look like /path/to/working_dir/msfiles/2018-03-02/hh=02/2018-03-02T02:02:02/.

Parameters

timestamp – Timestamp of the ms.

Returns

Path to the measurement set.

get_ms_path(timestamp: datetime.datetime, spw: str) → str

Generate measurement set paths that look like /path/to/working_dir/msfiles/2018-03-02/hh=02/2018-03-02T02:02:02/00_2018-03-02T02:02:02.ms.

Parameters

• timestamp – Timestamp of the ms.

• spw – Spectral window of the ms.

Returns

Path to the measurement set.

time_filter(start_time: datetime.datetime, end_time: datetime.datetime) → orca.metadata.pathsmanagers.OfflinePathsManager

Returns another PathsManager object with only utc_times between start_time (inclusive) and end_time (exclusive).

Parameters

• start_time –

• end_time –

Returns

New PathsManager object with time filtered.

Return type

new_paths_manager

property working_dir

class orca.metadata.pathsmanagers.PathsManager(utc_times_txt_path: str, dadafile_dir: Optional[str])

Bases: object

Base PathsManager class. It contains functionality to manipulate datetime objects and find dada files. Maybe in the future it will evolve into an interface with abstract methods.

utc_times_mapping

An ordered dictionary mapping datetime objects to dada files.

property dadafile_dir

get_dada_path(spw: str, timestamp: datetime.datetime)

Module contents