LinearInterpolation

class pyopenms.LinearInterpolation

Bases: object

Cython implementation of _LinearInterpolation[double,double]

Original C++ documentation is available here

Provides access to linearly interpolated values (and derivatives) from discrete data points. Values beyond the given range of data points are implicitly taken as zero.

The input is just a vector of values (“Data”). These are interpreted as the y-coordinates at the x-coordinate positions 0,…,data_.size-1.

The interpolated data can also be scaled and shifted in the x-dimension by an affine mapping. That is, we have “inside” and “outside” x-coordinates. The affine mapping can be specified in two ways: - using setScale() and setOffset(), - using setMapping()

By default the identity mapping (scale=1, offset=0) is used.

Using the value() and derivative() methods you can sample linearly interpolated values for a given x-coordinate position of the data and the derivative of the data

__init__()

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: LinearInterpolation) → None

Overload:

__init__(self, scale: float, offset: float) → None

Methods

__init__	Overload:
addValue(self, arg_pos, arg_value)	Performs linear resampling.
derivative(self, arg_pos)	Returns the interpolated derivative
empty(self)	Returns true if getData() is empty
getData(self)	Returns the internal random access container from which interpolated values are being sampled
getInsideReferencePoint(self)
getOffset(self)	"Offset" is the point (in "outside" units) which corresponds to "Data[0]"
getOutsideReferencePoint(self)
getScale(self)	"Scale" is the difference (in "outside" units) between consecutive entries in "Data"
index2key(self, pos)	The transformation from "inside" to "outside" coordinates
key2index(self, pos)	The transformation from "outside" to "inside" coordinates
setData(self, data)	Assigns data to the internal random access container from which interpolated values are being sampled
setMapping	Overload:
setOffset(self, offset)	"Offset" is the point (in "outside" units) which corresponds to "Data[0]"
setScale(self, scale)	"Scale" is the difference (in "outside" units) between consecutive entries in "Data"
supportMax(self)
supportMin(self)
value(self, arg_pos)	Returns the interpolated value

addValue(self, arg_pos: float, arg_value: float) → None

Performs linear resampling. The arg_value is split up and added to the data points around arg_pos

derivative(self, arg_pos: float) → float

Returns the interpolated derivative

empty(self) → bool

Returns true if getData() is empty

getData(self) → List[float]

Returns the internal random access container from which interpolated values are being sampled

getInsideReferencePoint(self) → float

getOffset(self) → float

“Offset” is the point (in “outside” units) which corresponds to “Data[0]”

getOutsideReferencePoint(self) → float

getScale(self) → float

“Scale” is the difference (in “outside” units) between consecutive entries in “Data”

index2key(self, pos: float) → float

The transformation from “inside” to “outside” coordinates

key2index(self, pos: float) → float

The transformation from “outside” to “inside” coordinates

setData(self, data: List[float]) → None

Assigns data to the internal random access container from which interpolated values are being sampled

setMapping()

Overload:

setMapping(self, scale: float, inside: float, outside: float) → None

Overload:

setMapping(self, inside_low: float, outside_low: float, inside_high: float, outside_high: float) → None

setOffset(self, offset: float) → None

“Offset” is the point (in “outside” units) which corresponds to “Data[0]”

setScale(self, scale: float) → None

“Scale” is the difference (in “outside” units) between consecutive entries in “Data”

supportMax(self) → float

supportMin(self) → float

value(self, arg_pos: float) → float

Returns the interpolated value