empyrean.PhotometricParams

class PhotometricParams(table, **kwargs)

Bases: Table

Photometric parameters for apparent magnitude computation.

Phase function models:

“hg” – Classical HG model (Bowell et al., 1989): H, G “hg1g2” – Three-parameter model (Muinonen et al., 2010): H, G1, G2 “hg12” – Two-parameter model (Muinonen et al., 2010): H, G12

The apparent V-band magnitude is:

V(alpha) = H + 5*log10(r*Delta) + phi(alpha)

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

g	A column for storing 64-bit floating point numbers.
g1	A column for storing 64-bit floating point numbers.
g12	A column for storing 64-bit floating point numbers.
g2	A column for storing 64-bit floating point numbers.
h	A column for storing 64-bit floating point numbers.
model	A column for storing large strings (over 231 bytes long).
schema
table

Parameters:

• table (Table)

• kwargs (AttributeValueType)

model

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

h

A column for storing 64-bit floating point numbers.

g

A column for storing 64-bit floating point numbers.

g1

A column for storing 64-bit floating point numbers.

g2

A column for storing 64-bit floating point numbers.

g12

A column for storing 64-bit floating point numbers.

schema: ClassVar[Schema] = model: large_string not null h: double not null g: double g1: double g2: double g12: double