empyrean.StationBiases

class StationBiases(table, **kwargs)

Bases: Table

Per-station fitted nuisance biases.

Mirrors a vector of scott::results::StationBias. Returned in DetermineResult.station_biases when ODConfig.fit_station_biases is enabled. Stations whose min_obs_per_station threshold wasn’t met are absent from the table.

Marginalized over the orbit fit, so the σ values include orbit uncertainty inherited through the Schur coupling (N_{ob},(N_{bb}+P_b)^{-1}).

Methods

__init__(table, **kwargs)
apply_mask(mask)	Return a new table with rows filtered to match a boolean mask.
as_column([nullable, metadata])	Embed the Table as a column in another Table.
attributes()	Return a dictionary of the table's attributes.
chunk_counts()	Returns the number of discrete memory chunks that make up each of the Table's underlying arrays.
column(column_name)	Returns the column with the given name as a raw pyarrow ChunkedArray.
drop_duplicates([subset, keep])	Drop duplicate rows from a ~quivr.Table.
empty(**kwargs)	Create an empty instance of the table.
flattened_table()	Completely flatten the Table's underlying Arrow table, taking into account any nested structure, and return the data table itself.
fragmented()	Returns true if the Table has any fragmented arrays.
from_csv(input_file[, validate])	Read a table from a CSV file.
from_dataframe(df[, validate])	Load a DataFrame into the Table.
from_feather(path[, validate])	Read a table from a Feather file.
from_flat_dataframe(df[, validate])	Load a flattened DataFrame into the Table.
from_kwargs([validate, permit_nulls])	Create a Table instance from keyword arguments.
from_parquet(path[, memory_map, ...])	Read a table from a Parquet file.
from_pyarrow(table[, validate, permit_nulls])	Create a new table from a pyarrow Table.
invalid_mask()	Return a boolean mask indicating which rows are invalid.
is_valid()	Validate the table against the schema.
null_mask()	Return a boolean mask indicating which rows of the entire table are null.
nulls(size, **kwargs)	Create a table with nulls.
select(column_name, value)	Select from the table by exact match, returning a new Table which only contains rows for which the value in column_name equals value.
separate_invalid()	Separates rows that have invalid data from those that have valid data.
set_column(name, data)	Return a copy of the table with a particular column replaced with new data.
significant([n_sigma])	Rows whose significance clears n_sigma.
sort_by(by)	Sorts the Table by the given column name (or multiple columns).
take(row_indices)	Return a new Table with only the rows at the given indices.
to_csv(path[, attribute_columns])	Write the table to a CSV file.
to_dataframe([flatten, attr_handling])	Returns self as a pandas DataFrame.
to_feather(path, **kwargs)	Write the table to a Feather file.
to_parquet(path, **kwargs)	Write the table to a Parquet file.
to_structarray()	Returns self as a StructArray.
unique_indices([subset, keep])	Get the indices of the first or last occurrence of each unique row in the table.
validate()	Validate the table against the schema, raising an exception if invalid.
where(expr)	Return a new table with rows filtered to match an expression.
with_table(table)

Attributes

bias_dec_arcsec	Fitted Dec offset (arcsec).
bias_ra_arcsec	Fitted RA·cos(δ) offset (arcsec).
bias_timing_sec	Fitted timing offset (seconds), populated only when a BiasKind::StationTiming nuisance was active.
n_obs	Pre-rejection observation count from this station.
obs_code	MPC observatory code.
schema
sigma_dec_arcsec	1-σ uncertainty on the Dec bias (arcsec).
sigma_ra_arcsec	1-σ uncertainty on the RA bias (arcsec).
sigma_timing_sec	1-σ on the timing bias, matching bias_timing_sec.
significance	max of $|b_i| / \sigma_i$ across populated components.
table

Parameters:

• table (Table)

• kwargs (AttributeValueType)

obs_code

MPC observatory code.

schema: ClassVar[Schema] = obs_code: large_string not null n_obs: uint64 not null bias_ra_arcsec: double not null sigma_ra_arcsec: double not null bias_dec_arcsec: double not null sigma_dec_arcsec: double not null bias_timing_sec: double sigma_timing_sec: double significance: double not null

n_obs

Pre-rejection observation count from this station.

bias_ra_arcsec

Fitted RA·cos(δ) offset (arcsec).

sigma_ra_arcsec

1-σ uncertainty on the RA bias (arcsec).

bias_dec_arcsec

Fitted Dec offset (arcsec).

sigma_dec_arcsec

1-σ uncertainty on the Dec bias (arcsec).

bias_timing_sec

Fitted timing offset (seconds), populated only when a BiasKind::StationTiming nuisance was active.

sigma_timing_sec

1-σ on the timing bias, matching bias_timing_sec.

significance

max of $|b_i| / \sigma_i$ across populated components. $\geq 3$ indicates a real systematic worth keeping fitted; NaN when no component has a usable $\sigma$.

Type:

Scalar significance

significant(n_sigma=3.0)

Rows whose significance clears n_sigma.

Default of 3σ matches the conventional “real systematic worth flagging” threshold used by the OD pipeline’s bias-fitting diagnostics. Rows with NaN significance are excluded.

Return type:

StationBiases

Parameters:

n_sigma (float)