fips.Vector#

class fips.Vector(data, name=None, index=None, dtype=None, copy=False)[source]#

State or observation vector composed of one or more Block objects.

A Vector organizes one or more Blocks (prior, posterior, observations, etc.) into a single hierarchical structure.

Vectors are used to represent the full state or observation space and are the 1D matrix components in the inversion framework.

Parameters:

data (pd.Series, Vector, Block, Sequence[Block | pd.Series], or array-like) – Data for the vector.
name (str, optional) – Name for the Vector.
index (pd.Index, optional) – Index for the Vector. If None, uses data.index. Index must have a ‘block’ level if data is a Series.
dtype (dtype, optional) – Data type to force.
copy (bool, default False) – Whether to copy the underlying data.

Attributes

`blocks`	Accessor for retrieving Block instances from the Vector.
`index`	Return the index of the underlying data.
`name`	The name of the underlying Series.
`shape`	Return the shape of the underlying data.
`values`	Return the values as a numpy array.
`data`	The underlying data, which must be numeric and non-NaN.

Methods

`copy`([deep])	Create a copy of the structure.
`from_file`(path)	Load object from a pickle file.
`reindex`(index[, columns, verify_overlap, ...])	Return a new instance with data reindexed to the specified index and columns.
`round_index`(decimals[, axis, inplace])	Round float indices on the specified axis to given decimals.
`to_file`(path)	Save object to a pickle file.
`to_numpy`()	Get the underlying data as a NumPy array.
`to_series`()	Get the underlying Series data.
`to_xarray`()	Convert the Vector to xarray.
`xs`(key[, axis, level, drop_level])	Cross-select data based on index/column values.