cuvis_ai.distance.distance.ECS

class cuvis_ai.distance.distance.ECS(wavelengths: ndarray | list, ref_spectra: list = [])

Bases: AbstractDistance

Euclidean Distance of Cumulative Spectrum (ECS)

Parameters:

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

__init__(wavelengths: ndarray | list, ref_spectra: list = [])

Initialize an ECS distance node.

Parameters:

• wavelengths (np.ndarray | list) – Array defining positioning of wavelength channels (typically given in nm). This length of this vector must equal the number of channels in inputted datacubes.

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

Methods

__init__(wavelengths[, ref_spectra])	Initialize an ECS 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	Return the 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

Return the required input dimension

Returns:

Required input shape, which can vary in datacube height and width, but must be of consistent channel size.

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

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