cuvis_ai.distance.distance.GFC

class cuvis_ai.distance.distance.GFC(ref_spectra: list = [])[source]

Bases: AbstractDistance

Goodness-of-fit Coefficient (GFC)

Citation:

Hernández-Andrés, J., Romero, J., García-Beltrán, A., & Nieves, J. L. (1998). Testing linear models on spectral daylight measurements. Applied Optics, 37(6), 971-977.

Parameters:: AbstractDistance (AbstractDistance) – Defines the node as AbstractDistance node type

__init__(ref_spectra: list = [])[source]

Construct GFC distance node.

Parameters:: ref_spectra (list, optional) – Reference spectra to compare against, by default []

Methods

`__init__`([ref_spectra])	Construct GFC distance node.
`check_input_dim`(X)	Check that the parameters for the input data data match user expectations
`check_output_dim`(X)	Check that the parameters for the output data data match user expectations
`fit`(X)
`forward`(X[, ref_spectra])	Pass the data through comparative function
`get_fit_requested_meta`()
`get_forward_requested_meta`()
`load`(params[, filepath])	Load dumped parameters to recreate the distance object
`score`(data, ref_spectra)	Score new datacubes against reference spectra.
`serialize`(working_dir)	Convert distance node to serializable format
`set_fit_meta_request`(**kwargs)
`set_forward_meta_request`(**kwargs)
`spectra_to_array`(ref_spectra)	Convert list of spectra to a numpy array

Attributes

`input_dim`	Get required input dimension
`output_dim`	Get required output dimension

check_input_dim(X)

Check that the parameters for the input data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

check_output_dim(X)

Check that the parameters for the output data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

fit(X)

forward(X: ndarray, ref_spectra: list | None = None) → ndarray

Pass the data through comparative function

Parameters:

X (np.ndarray) – Input data.
ref_spectra (list, optional) – List of spectra to compare against

Returns:

Distance maps for each of the reference spectra.

Return type:

np.ndarray

Raises:

ValueError – Mismatch in input data and reference spectra provided on function call.
ValueError – Mismatch in input data and reference spectra provided on node initialization.
ValueError – No reference spectra provided in init or on forward function pass.

get_fit_requested_meta()

get_forward_requested_meta()

property input_dim: list

Get required input dimension

Returns:: List defining which input dimensions should be checked in graph.
Return type:: list

load(params: dict, filepath: str | None = None)

Load dumped parameters to recreate the distance object

Parameters:

params (dict) – Dictionary containing node values
filepath (str, optional) – Directory containing node metadata, by default None

property output_dim

Get required output dimension

Returns:: List defining which input dimensions should be checked in graph.
Return type:: list

score(data: ndarray, ref_spectra: ndarray) → ndarray[source]

Score new datacubes against reference spectra.

Parameters:

data (np.ndarray) – Input data.
ref_spectra (np.ndarray) – Reference spectra to compare against.

Returns:

Distance scores

Return type:

np.ndarray

serialize(working_dir: str) → str

Convert distance node to serializable format

Parameters:: working_dir (str) – Directory where node metadata should be saved.
Returns:: YAML parameterization of node.
Return type:: str

set_fit_meta_request(**kwargs)

set_forward_meta_request(**kwargs)

static spectra_to_array(ref_spectra: ndarray | list) → ndarray

Convert list of spectra to a numpy array

Parameters:: ref_spectra (np.ndarray | list) – Object of reference spectra
Returns:: Spectra stored in singular, indexable, sequential array.
Return type:: np.ndarray