empyrean.NonGravParams¶

class NonGravParams(table, **kwargs)[source]

Bases: Table

Marsden-Sekanina non-gravitational acceleration parameters.

Model types:: “marsden_water” – Marsden-Sekanina with standard H2O sublimation g(r) “inverse_square” – Marsden-Sekanina with g(r) = 1/r^2 (Yarkovsky) “marsden” – Marsden-Sekanina with custom g(r) exponents
For “marsden”, the g(r) function is:: g(r) = alpha * (r/r0)^{-m} * (1 + (r/r0)^n)^{-k}

Solar radiation pressure is a separate, additive force slot — see SRPParams (orbits.srp). It is NOT a NonGravParams model; a1/a2/a3 here are always radial / transverse / normal Marsden accelerations (AU/day^2).

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.
`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

`a1`	A column for storing 64-bit floating point numbers.
`a2`	A column for storing 64-bit floating point numbers.
`a3`	A column for storing 64-bit floating point numbers.
`alpha`	A column for storing 64-bit floating point numbers.
`covariance`	A column for storing large lists of values (over 2³¹ objects).
`dt`	A column for storing 64-bit floating point numbers.
`dt_variance`	A column for storing 64-bit floating point numbers.
`k`	A column for storing 64-bit floating point numbers.
`m`	A column for storing 64-bit floating point numbers.
`model`	A column for storing large strings (over 2³¹ bytes long).
`n`	A column for storing 64-bit floating point numbers.
`r0`	A column for storing 64-bit floating point numbers.
`schema`
`table`

Parameters:

table (Table)
kwargs (AttributeValueType)

a1: A column for storing 64-bit floating point numbers.

a2: A column for storing 64-bit floating point numbers.

a3: A column for storing 64-bit floating point numbers.

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

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

r0: A column for storing 64-bit floating point numbers.

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

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

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

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

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

covariance

A column for storing large lists of values (over 2³¹ objects).

Unless you need to represent data with more than 2**31 elements, prefer ListColumn.

The values in the list can be of any type.

Note that all quivr Tables are storing lists of values, so this column type is only useful for storing lists of lists.

Parameters:

value_type – The type of the values in the list.
nullable – Whether the list can contain null values.
metadata – A dictionary of metadata to attach to the column.
validator – A validator to run against the column’s values.

schema: ClassVar[Schema] = a1: double not null a2: double not null a3: double not null model: large_string not null alpha: double r0: double m: double n: double k: double dt: double dt_variance: double covariance: large_list<item: double> child 0, item: double