adam_core.observations.detections module

class adam_core.observations.detections.PointSourceDetections(table: Table, **kwargs: int | float | str)[source]

Bases: Table

PointSourceDetections is a table of data about point source detections.

id

A column for storing large strings (over 231 bytes long). Large string data is stored in variable-length chunks.

exposure_id

A column for storing large strings (over 231 bytes long). Large string data is stored in variable-length chunks.

time

A column which represents an embedded quivr table.

Parameters:

  • table_type – The type of the table to embed.

  • nullable – Whether the column can contain null values.

  • metadata – A dictionary of metadata to attach to the column.

ra

A column for storing 64-bit floating point numbers.

ra_sigma

A column for storing 64-bit floating point numbers.

dec

A column for storing 64-bit floating point numbers.

dec_sigma

A column for storing 64-bit floating point numbers.

mag

A column for storing 64-bit floating point numbers.

mag_sigma

A column for storing 64-bit floating point numbers.

group_by_exposure() → Iterator[PointSourceDetections][source]

Returns an iterator of PointSourceDetections, each grouped by exposure_id.

healpixels(nside: int, nest: bool = True) → ndarray[tuple[Any, ...], dtype[int64]][source]

Returns an array of healpixels for each observation.

group_by_healpixel(nside: int, nest: bool = True) → Iterator[tuple[int, PointSourceDetections]][source]

Returns an iterator of PointSourceDetections, each grouped by healpixel.

Links the detections to the exposures.

schema: ClassVar[pa.Schema] = id: large_string not null exposure_id: large_string time: struct<days: int64, nanos: int64>   child 0, days: int64   child 1, nanos: int64 ra: double not null ra_sigma: double dec: double not null dec_sigma: double mag: double mag_sigma: double
table: pa.Table