empyrean.SphericalCovariance

class SphericalCovariance(table, **kwargs)

Bases: Table

Covariance matrix for spherical coords [rho, lon, lat, vrho, vlon, vlat].

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_matrix(matrix)	Create from covariance matrices.
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.
from_sigmas(sigmas)	Create diagonal-only covariances from sigma values.
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.
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_matrix()	Return (N, 6, 6) numpy array.
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

cov_lat_lat	A column for storing 64-bit floating point numbers.
cov_lat_vlat	A column for storing 64-bit floating point numbers.
cov_lat_vlon	A column for storing 64-bit floating point numbers.
cov_lat_vrho	A column for storing 64-bit floating point numbers.
cov_lon_lat	A column for storing 64-bit floating point numbers.
cov_lon_lon	A column for storing 64-bit floating point numbers.
cov_lon_vlat	A column for storing 64-bit floating point numbers.
cov_lon_vlon	A column for storing 64-bit floating point numbers.
cov_lon_vrho	A column for storing 64-bit floating point numbers.
cov_rho_lat	A column for storing 64-bit floating point numbers.
cov_rho_lon	A column for storing 64-bit floating point numbers.
cov_rho_rho	A column for storing 64-bit floating point numbers.
cov_rho_vlat	A column for storing 64-bit floating point numbers.
cov_rho_vlon	A column for storing 64-bit floating point numbers.
cov_rho_vrho	A column for storing 64-bit floating point numbers.
cov_vlat_vlat	A column for storing 64-bit floating point numbers.
cov_vlon_vlat	A column for storing 64-bit floating point numbers.
cov_vlon_vlon	A column for storing 64-bit floating point numbers.
cov_vrho_vlat	A column for storing 64-bit floating point numbers.
cov_vrho_vlon	A column for storing 64-bit floating point numbers.
cov_vrho_vrho	A column for storing 64-bit floating point numbers.
schema
sigmas	Return (N, 6) array of 1-sigma uncertainties (sqrt of diagonal).
table

Parameters:

• table (Table)

• kwargs (AttributeValueType)

cov_lat_lat

A column for storing 64-bit floating point numbers.

cov_lat_vlat

A column for storing 64-bit floating point numbers.

cov_lat_vlon

A column for storing 64-bit floating point numbers.

cov_lat_vrho

A column for storing 64-bit floating point numbers.

cov_lon_lat

A column for storing 64-bit floating point numbers.

cov_lon_lon

A column for storing 64-bit floating point numbers.

cov_lon_vlat

A column for storing 64-bit floating point numbers.

cov_lon_vlon

A column for storing 64-bit floating point numbers.

cov_lon_vrho

A column for storing 64-bit floating point numbers.

cov_rho_lat

A column for storing 64-bit floating point numbers.

cov_rho_lon

A column for storing 64-bit floating point numbers.

cov_rho_rho

A column for storing 64-bit floating point numbers.

cov_rho_vlat

A column for storing 64-bit floating point numbers.

cov_rho_vlon

A column for storing 64-bit floating point numbers.

cov_rho_vrho

A column for storing 64-bit floating point numbers.

cov_vlat_vlat

A column for storing 64-bit floating point numbers.

cov_vlon_vlat

A column for storing 64-bit floating point numbers.

cov_vlon_vlon

A column for storing 64-bit floating point numbers.

cov_vrho_vlat

A column for storing 64-bit floating point numbers.

cov_vrho_vlon

A column for storing 64-bit floating point numbers.

cov_vrho_vrho

A column for storing 64-bit floating point numbers.

classmethod from_matrix(matrix)

Create from covariance matrices.

Parameters:

matrix (ndarray[Any, dtype[double]]) – Covariance matrices with shape (N, 6, 6) or (6, 6).

Return type:

_CovarianceTable

classmethod from_sigmas(sigmas)

Create diagonal-only covariances from sigma values.

Parameters:

sigmas (ndarray[Any, dtype[double]]) – Standard deviations with shape (N, 6) or (6,).

Return type:

_CovarianceTable

schema: ClassVar[Schema] = cov_rho_rho: double cov_rho_lon: double cov_lon_lon: double cov_rho_lat: double cov_lon_lat: double cov_lat_lat: double cov_rho_vrho: double cov_lon_vrho: double cov_lat_vrho: double cov_vrho_vrho: double cov_rho_vlon: double cov_lon_vlon: double cov_lat_vlon: double cov_vrho_vlon: double cov_vlon_vlon: double cov_rho_vlat: double cov_lon_vlat: double cov_lat_vlat: double cov_vrho_vlat: double cov_vlon_vlat: double cov_vlat_vlat: double

property sigmas: ndarray[Any, dtype[float64]]

Return (N, 6) array of 1-sigma uncertainties (sqrt of diagonal).

to_matrix()

Return (N, 6, 6) numpy array.

Return type:

ndarray[Any, dtype[double]]

Parameters:

self (_CovarianceTable)