nirs4all.operators.augmentation.splines module

class nirs4all.operators.augmentation.splines.Spline_Curve_Simplification(spline_points=None, uniform=False, random_state=None)[source]

Bases: TransformerMixin, BaseEstimator

Class to simplify a 1D signal using B-spline interpolation along the curve.

Optimized implementation with pre-allocated output arrays.

Parameters:

  • spline_points (int, optional) – Number of spline points for simplification. Default is None: the length of the sample / 4.

  • uniform (bool, optional) – If True, the spline points are uniformly spaced. Default is False.

  • random_state (int or None, optional) – Seed for the random number generator.

fit(X, y=None, **kwargs)[source]
transform(X, **kwargs)[source]

Select regularly spaced points on the x-axis and adjust a spline.

Optimized with pre-allocated output array.

Parameters:

X (ndarray) – Input data.

Returns:

Transformed data.

Return type:

ndarray

class nirs4all.operators.augmentation.splines.Spline_Smoothing(random_state=None)[source]

Bases: TransformerMixin, BaseEstimator

Class to apply a smoothing spline to a 1D signal.

Parameters:

random_state (int or None, optional) – Seed for the random number generator (unused, kept for API consistency).

fit(X, y=None, **kwargs)[source]
transform(X, **kwargs)[source]

Apply a smoothing spline to the data.

Optimized implementation with pre-allocated output array.

Parameters:

X (ndarray) – Input data.

Returns:

Transformed data.

Return type:

ndarray

class nirs4all.operators.augmentation.splines.Spline_X_Perturbations(spline_degree=3, perturbation_density=0.05, perturbation_range=(-10, 10), random_state=None)[source]

Bases: TransformerMixin, BaseEstimator

Class to apply a perturbation to a 1D signal using B-spline interpolation.

Optimized implementation with pre-generated random parameters.

Parameters:

  • spline_degree (int, optional) – Degree of the spline. Default is 3 (cubic).

  • perturbation_density (float, optional) – Density of perturbation points relative to data size. Default is 0.05.

  • perturbation_range (tuple, optional) – Range of perturbation values (min, max). Default is (-10, 10).

  • random_state (int or None, optional) – Seed for the random number generator.

fit(X, y=None, **kwargs)[source]
transform(X, **kwargs)[source]

Apply a perturbation using B-spline interpolation.

Optimized with pre-allocated arrays and batch random generation.

Parameters:

X (ndarray) – Input data to be transformed.

Returns:

Transformed data.

Return type:

ndarray

class nirs4all.operators.augmentation.splines.Spline_X_Simplification(spline_points=None, uniform=False, random_state=None)[source]

Bases: TransformerMixin, BaseEstimator

Class to simplify a 1D signal using B-spline interpolation along the x-axis.

Optimized implementation with pre-generated random parameters.

Parameters:

  • spline_points (int, optional) – Number of spline points for simplification. Default is None: the length of the sample / 4.

  • uniform (bool, optional) – If True, the spline points are uniformly spaced. Default is False.

  • random_state (int or None, optional) – Seed for the random number generator.

fit(X, y=None, **kwargs)[source]
transform(X, **kwargs)[source]

Select randomly spaced points along the x-axis and adjust a spline.

Optimized with pre-allocated arrays and batch random generation.

Parameters:

X (ndarray) – Input data.

Returns:

Transformed data.

Return type:

ndarray

class nirs4all.operators.augmentation.splines.Spline_Y_Perturbations(spline_points=None, perturbation_intensity=0.005, random_state=None)[source]

Bases: TransformerMixin, BaseEstimator

Augment the data with a perturbation on the y-axis using B-spline interpolation.

Optimized implementation with pre-generated random parameters.

Parameters:

  • spline_points (int, optional) – Number of spline points. Default is None (uses sample length / 2).

  • perturbation_intensity (float, optional) – Intensity of perturbation relative to max value. Default is 0.005.

  • random_state (int or None, optional) – Seed for the random number generator.

fit(X, y=None, **kwargs)[source]
transform(X, **kwargs)[source]

Apply a perturbation on the y-axis using B-spline interpolation.

Optimized with pre-allocated arrays and batch random generation.

Parameters:

X (ndarray) – Input data to be transformed.

Returns:

Transformed data.

Return type:

ndarray

nirs4all.operators.augmentation.splines.X_length(x, y)[source]

Compute the total length, segment lengths, and cumulative segment lengths of a curve.

Vectorized implementation without np.vectorize.

Parameters:

  • x (ndarray) – Array of x-coordinates of the curve.

  • y (ndarray) – Array of y-coordinates of the curve.

Returns:

A tuple containing the total length, segment lengths, and cumulative segment lengths.

Return type:

tuple

nirs4all.operators.augmentation.splines.interval_selection(n_l, CumVect)[source]

Select the interval indices that bound a given value in an array.

Parameters:

  • n_l (float) – Value to be bounded.

  • CumVect (ndarray) – Cumulative array of values.

Returns:

A tuple containing the minimum and maximum indices of the bounding interval.

Return type:

tuple

nirs4all.operators.augmentation.splines.segment_length(x1, y1, x2, y2)[source]

Compute the length of a line segment given its coordinates.

Parameters:

  • x1 (float) – x-coordinate of the first point.

  • y1 (float) – y-coordinate of the first point.

  • x2 (float) – x-coordinate of the second point.

  • y2 (float) – y-coordinate of the second point.

Returns:

Length of the line segment.

Return type:

float

nirs4all.operators.augmentation.splines.segment_pt_coord(x1, y1, x2, y2, fracL, L)[source]

Compute the coordinates of a point on a line segment given the fraction of its length.

Parameters:

  • x1 (float) – x-coordinate of the first point of the line segment.

  • y1 (float) – y-coordinate of the first point of the line segment.

  • x2 (float) – x-coordinate of the second point of the line segment.

  • y2 (float) – y-coordinate of the second point of the line segment.

  • fracL (float) – Fraction of the length of the line segment.

  • L (float) – Length of the line segment.

Returns:

A tuple containing the x and y coordinates of the point on the line segment.

Return type:

tuple