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.

__init__()#

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: LinearInterpolation) → None

Overload:

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

Methods

`__init__`
`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`
`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