ostk.mathematics.curve_fitting.interpolator.Linear¶

class Linear( self: ostk.mathematics.curve_fitting.interpolator.Linear, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], )¶

Bases: Interpolator

Create a linear interpolator with data points.

Parameters:

x (numpy.array) -- The x-coordinates of data points.
y (numpy.array) -- The y-coordinates of data points.

Example

>>> x = numpy.array([0.0, 1.0, 2.0])
>>> y = numpy.array([0.0, 2.0, 4.0])
>>> interpolator = Linear(x, y)

Methods

`compute_derivative`	Overloaded function.
`evaluate`	Overloaded function.
`generate_interpolator`	Generate an interpolator of specified type with data points.
`get_interpolation_type`	Get the interpolation type of this interpolator.

class Type(self: ostk.mathematics.curve_fitting.Interpolator.Type, value: int)¶

Bases: pybind11_object

Members:

BarycentricRational

CubicSpline

Linear

property name¶

compute_derivative(*args, **kwargs)¶

Overloaded function.

compute_derivative(self: ostk.mathematics.curve_fitting.interpolator.Linear, x: float) -> float
Compute the derivative of the linear interpolation at a single point.
Args:
x (float): The x-coordinate to compute derivative at.

Returns:
float: The derivative value.

Example:
>>> interpolator = Linear([0.0, 1.0], [0.0, 2.0]) >>> derivative = interpolator.compute_derivative(0.5) # 2.0
compute_derivative(self: ostk.mathematics.curve_fitting.interpolator.Linear, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]
Compute the derivative of the linear interpolation at multiple points.
Args:
x (numpy.array): The x-coordinates to compute derivatives at.

Returns:
(numpy.array): The derivative values.

Example:
>>> interpolator = Linear([0.0, 1.0], [0.0, 2.0]) >>> derivatives = interpolator.compute_derivative([0.2, 0.8])

evaluate(*args, **kwargs)¶

Overloaded function.

evaluate(self: ostk.mathematics.curve_fitting.interpolator.Linear, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]
Evaluate the linear interpolation at multiple points.
Args:
x (numpy.array): The x-coordinates to evaluate at.

Returns:
(numpy.array): The interpolated y-values.

Example:
>>> interpolator = Linear([0.0, 1.0], [0.0, 2.0]) >>> result = interpolator.evaluate([0.5, 1.5])
evaluate(self: ostk.mathematics.curve_fitting.interpolator.Linear, x: float) -> float
Evaluate the linear interpolation at a single point.
Args:
x (float): The x-coordinate to evaluate at.

Returns:
float: The interpolated y-value.

Example:
>>> interpolator = Linear([0.0, 1.0], [0.0, 2.0]) >>> result = interpolator.evaluate(0.5) # 1.0

static generate_interpolator( interpolation_type: ostk.mathematics.curve_fitting.Interpolator.Type, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], ) → ostk.mathematics.curve_fitting.Interpolator¶

Generate an interpolator of specified type with data points.

Parameters:

interpolation_type (Interpolator.Type) -- The type of interpolation.
x (numpy.array) -- The x-coordinates of data points.
y (numpy.array) -- The y-coordinates of data points.

Returns:

The created interpolator.

Return type:

Interpolator

Example

>>> x = numpy.array([0.0, 1.0, 2.0])
>>> y = numpy.array([0.0, 2.0, 4.0])
>>> interpolator = Interpolator.generate_interpolator(
...     Interpolator.Type.CubicSpline, x, y
... )

get_interpolation_type( self: ostk.mathematics.curve_fitting.Interpolator, ) → ostk.mathematics.curve_fitting.Interpolator.Type¶

Get the interpolation type of this interpolator.

Returns:: The interpolation type.
Return type:: Interpolator.Type

Example

>>> interpolator = Interpolator(Interpolator.Type.CubicSpline)
>>> type = interpolator.get_interpolation_type()