colour.algebra.interpolation Module¶

Interpolation¶

Defines classes for interpolating variables.

class colour.algebra.interpolation.LinearInterpolator(x=None, y=None)[source]

Bases: object

Linearly interpolates a 1-D function.

Parameters: x (ndarray) – Independent $$x$$ variable values corresponding with $$y$$ variable. y (ndarray) – Dependent and already known $$y$$ variable values to interpolate.
__call__()[source]

Notes

This class is a wrapper around numpy.interp definition.

Examples

Interpolating a single numeric variable:

>>> y = np.array([5.9200,
...               9.3700,
...               10.8135,
...               4.5100,
...               69.5900,
...               27.8007,
...               86.0500])
>>> x = np.arange(len(y))
>>> f = LinearInterpolator(x, y)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> f(0.5)
7.64...

Interpolating an array_like variable:

>>> f([0.25, 0.75])
array([ 6.7825,  8.5075])
__call__(x)[source]

Evaluates the interpolating polynomial at given point(s).

Parameters: x (numeric or array_like) – Point(s) to evaluate the interpolant at. Interpolated value(s). float or ndarray
x[source]

Property for self.__x private attribute.

Returns: self.__x array_like
y[source]

Property for self.__y private attribute.

Returns: self.__y array_like
class colour.algebra.interpolation.SpragueInterpolator(x=None, y=None)[source]

Bases: object

Constructs a fifth-order polynomial that passes through $$y$$ dependent variable.

Sprague (1880) method is recommended by the CIE for interpolating functions having a uniformly spaced independent variable.

Parameters: x (array_like) – Independent $$x$$ variable values corresponding with $$y$$ variable. y (array_like) – Dependent and already known $$y$$ variable values to interpolate.
__call__()[source]

Notes

The minimum number $$k$$ of data points required along the interpolation axis is $$k=6$$.

References

  CIE TC 1-38. (2005). 9.2.4 Method of interpolation for uniformly spaced independent variable. In CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations (pp. 1–27). ISBN:978-3-901-90641-1
  Westland, S., Ripamonti, C., & Cheung, V. (2012). Interpolation Methods. In Computational Colour Science Using MATLAB (2nd ed., pp. 29–37). ISBN:978-0-470-66569-5

Examples

Interpolating a single numeric variable:

>>> y = np.array([5.9200,
...               9.3700,
...               10.8135,
...               4.5100,
...               69.5900,
...               27.8007,
...               86.0500])
>>> x = np.arange(len(y))
>>> f = SpragueInterpolator(x, y)
>>> f(0.5)
7.2185025...

Interpolating an array_like variable:

>>> f([0.25, 0.75])
array([ 6.7295161...,  7.8140625...])
SPRAGUE_C_COEFFICIENTS = array([[ 884, -1960, 3033, -2648, 1080, -180], [ 508, -540, 488, -367, 144, -24], [ -24, 144, -367, 488, -540, 508], [ -180, 1080, -2648, 3033, -1960, 884]])

Defines the coefficients used to generate extra points for boundaries interpolation.

SPRAGUE_C_COEFFICIENTS : array_like, (4, 6)

References

  CIE TC 1-38. (2005). Table V. Values of the c-coefficients of Equ.s 6 and 7. In CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations (p. 19). ISBN:978-3-901-90641-1
__call__(x)[source]

Evaluates the interpolating polynomial at given point(s).

Parameters: x (numeric or array_like) – Point(s) to evaluate the interpolant at. Interpolated value(s). numeric or ndarray
x[source]

Property for self.__x private attribute.

Returns: self.__x array_like
y[source]

Property for self.__y private attribute.

Returns: self.__y array_like
class colour.algebra.interpolation.CubicSplineInterpolator(*args, **kwargs)[source]

Bases: scipy.interpolate.interpolate.interp1d

Interpolates a 1-D function using cubic spline interpolation.

Notes

This class is a wrapper around scipy.interpolate.interp1d class.

class colour.algebra.interpolation.PchipInterpolator(x, y, axis=0, extrapolate=None)

Bases: object

PCHIP 1-d monotonic cubic interpolation

x and y are arrays of values used to approximate some function f, with y = f(x). The interpolant uses monotonic cubic splines to find the value of new points. (PCHIP stands for Piecewise Cubic Hermite Interpolating Polynomial).

Parameters: x (ndarray) – A 1-D array of monotonically increasing real values. x cannot include duplicate values (otherwise f is overspecified) y (ndarray) – A 1-D array of real values. y‘s length along the interpolation axis must be equal to the length of x. If N-D array, use axis parameter to select correct axis. axis (int, optional) – Axis in the y array corresponding to the x-coordinate values. extrapolate (bool, optional) – Whether to extrapolate to ouf-of-bounds points based on first and last intervals, or to return NaNs.
__call__()
derivative()

Notes

The first derivatives are guaranteed to be continuous, but the second derivatives may jump at x_k.

Preserves monotonicity in the interpolation data and does not overshoot if the data is not smooth.

Determines the derivatives at the points x_k, d_k, by using PCHIP algorithm:

Let m_k be the slope of the kth segment (between k and k+1) If m_k=0 or m_{k-1}=0 or sgn(m_k) != sgn(m_{k-1}) then d_k == 0 else use weighted harmonic mean:

w_1 = 2h_k + h_{k-1}, w_2 = h_k + 2h_{k-1} 1/d_k = 1/(w_1 + w_2)*(w_1 / m_k + w_2 / m_{k-1})

where h_k is the spacing between x_k and x_{k+1}.

__call__(x, der=0, extrapolate=None)

Evaluate the PCHIP interpolant or its derivative.

Parameters: x (array-like) – Points to evaluate the interpolant at. der (int, optional) – Order of derivative to evaluate. Must be non-negative. extrapolate (bool, optional) – Whether to extrapolate to ouf-of-bounds points based on first and last intervals, or to return NaNs. y – Interpolated values. Shape is determined by replacing the interpolation axis in the original array with the shape of x. array-like
derivative(der=1)

Construct a piecewise polynomial representing the derivative.

Parameters: der (int, optional) – Order of derivative to evaluate. (Default: 1) If negative, the antiderivative is returned. Piecewise polynomial of order k2 = k - der representing the derivative of this polynomial.
roots()

Return the roots of the interpolated function.