empyrean.CartesianCovariance¶

class CartesianCovariance(table, **kwargs)

Bases: Table

Covariance matrix for Cartesian state [x, y, z, vx, vy, vz].

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_vx_vx`	A column for storing 64-bit floating point numbers.
`cov_vx_vy`	A column for storing 64-bit floating point numbers.
`cov_vx_vz`	A column for storing 64-bit floating point numbers.
`cov_vy_vy`	A column for storing 64-bit floating point numbers.
`cov_vy_vz`	A column for storing 64-bit floating point numbers.
`cov_vz_vz`	A column for storing 64-bit floating point numbers.
`cov_x_vx`	A column for storing 64-bit floating point numbers.
`cov_x_vy`	A column for storing 64-bit floating point numbers.
`cov_x_vz`	A column for storing 64-bit floating point numbers.
`cov_x_x`	A column for storing 64-bit floating point numbers.
`cov_x_y`	A column for storing 64-bit floating point numbers.
`cov_x_z`	A column for storing 64-bit floating point numbers.
`cov_y_vx`	A column for storing 64-bit floating point numbers.
`cov_y_vy`	A column for storing 64-bit floating point numbers.
`cov_y_vz`	A column for storing 64-bit floating point numbers.
`cov_y_y`	A column for storing 64-bit floating point numbers.
`cov_y_z`	A column for storing 64-bit floating point numbers.
`cov_z_vx`	A column for storing 64-bit floating point numbers.
`cov_z_vy`	A column for storing 64-bit floating point numbers.
`cov_z_vz`	A column for storing 64-bit floating point numbers.
`cov_z_z`	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_vx_vx: A column for storing 64-bit floating point numbers.

cov_vx_vy: A column for storing 64-bit floating point numbers.

cov_vx_vz: A column for storing 64-bit floating point numbers.

cov_vy_vy: A column for storing 64-bit floating point numbers.

cov_vy_vz: A column for storing 64-bit floating point numbers.

cov_vz_vz: A column for storing 64-bit floating point numbers.

cov_x_vx: A column for storing 64-bit floating point numbers.

cov_x_vy: A column for storing 64-bit floating point numbers.

cov_x_vz: A column for storing 64-bit floating point numbers.

cov_x_x: A column for storing 64-bit floating point numbers.

cov_x_y: A column for storing 64-bit floating point numbers.

cov_x_z: A column for storing 64-bit floating point numbers.

cov_y_vx: A column for storing 64-bit floating point numbers.

cov_y_vy: A column for storing 64-bit floating point numbers.

cov_y_vz: A column for storing 64-bit floating point numbers.

cov_y_y: A column for storing 64-bit floating point numbers.

cov_y_z: A column for storing 64-bit floating point numbers.

cov_z_vx: A column for storing 64-bit floating point numbers.

cov_z_vy: A column for storing 64-bit floating point numbers.

cov_z_vz: A column for storing 64-bit floating point numbers.

cov_z_z: 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_x_x: double cov_x_y: double cov_y_y: double cov_x_z: double cov_y_z: double cov_z_z: double cov_x_vx: double cov_y_vx: double cov_z_vx: double cov_vx_vx: double cov_x_vy: double cov_y_vy: double cov_z_vy: double cov_vx_vy: double cov_vy_vy: double cov_x_vz: double cov_y_vz: double cov_z_vz: double cov_vx_vz: double cov_vy_vz: double cov_vz_vz: 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)