Type Alias ndarray_interp::interp1d::Interp1DDataView
source · pub type Interp1DDataView<A, D, S> = Interp1D<ViewRepr<A>, OwnedRepr<A>, D, S>;
Expand description
one-dimensional interpolant for data views and owned axis
Aliased Type§
struct Interp1DDataView<A, D, S> { /* private fields */ }
Implementations§
source§impl<Sd, Sx, Strat> Interp1D<Sd, Sx, Ix1, Strat>where
Sd: Data,
Sd::Elem: Num + PartialOrd + NumCast + Copy + Debug + Sub,
Sx: Data<Elem = Sd::Elem>,
Strat: Interp1DStrategy<Sd, Sx, Ix1>,
impl<Sd, Sx, Strat> Interp1D<Sd, Sx, Ix1, Strat>where Sd: Data, Sd::Elem: Num + PartialOrd + NumCast + Copy + Debug + Sub, Sx: Data<Elem = Sd::Elem>, Strat: Interp1DStrategy<Sd, Sx, Ix1>,
sourcepub fn interp_scalar(&self, x: Sx::Elem) -> Result<Sd::Elem, InterpolateError>
pub fn interp_scalar(&self, x: Sx::Elem) -> Result<Sd::Elem, InterpolateError>
convinient interpolation function for interpolation at one point
when the data dimension is Ix1
let data = array![1.0, 1.5, 2.0];
let x = array![1.0, 2.0, 3.0];
let query = 1.5;
let expected = 1.25;
let interpolator = Interp1DBuilder::new(data).x(x).build().unwrap();
let result = interpolator.interp_scalar(query).unwrap();
source§impl<Sd, D> Interp1D<Sd, OwnedRepr<Sd::Elem>, D, Linear>where
Sd: Data,
Sd::Elem: Num + PartialOrd + NumCast + Copy + Debug,
D: Dimension + RemoveAxis,
impl<Sd, D> Interp1D<Sd, OwnedRepr<Sd::Elem>, D, Linear>where Sd: Data, Sd::Elem: Num + PartialOrd + NumCast + Copy + Debug, D: Dimension + RemoveAxis,
sourcepub fn builder(
data: ArrayBase<Sd, D>
) -> Interp1DBuilder<Sd, OwnedRepr<Sd::Elem>, D, Linear>
pub fn builder( data: ArrayBase<Sd, D> ) -> Interp1DBuilder<Sd, OwnedRepr<Sd::Elem>, D, Linear>
Get the Interp1DBuilder
source§impl<Sd, Sx, D, Strat> Interp1D<Sd, Sx, D, Strat>where
Sd: Data,
Sd::Elem: Num + PartialOrd + NumCast + Copy + Debug + Sub,
Sx: Data<Elem = Sd::Elem>,
D: Dimension + RemoveAxis,
Strat: Interp1DStrategy<Sd, Sx, D>,
impl<Sd, Sx, D, Strat> Interp1D<Sd, Sx, D, Strat>where Sd: Data, Sd::Elem: Num + PartialOrd + NumCast + Copy + Debug + Sub, Sx: Data<Elem = Sd::Elem>, D: Dimension + RemoveAxis, Strat: Interp1DStrategy<Sd, Sx, D>,
sourcepub fn new_unchecked(
x: ArrayBase<Sx, Ix1>,
data: ArrayBase<Sd, D>,
strategy: Strat
) -> Self
pub fn new_unchecked( x: ArrayBase<Sx, Ix1>, data: ArrayBase<Sd, D>, strategy: Strat ) -> Self
Create a interpolator without any data validation. This is fast and cheap.
Safety
The following data properties are assumed, but not checked:
x
is stricktly monotonic risingdata.shape()[0] == x.len()
- the
strategy
is porperly initialized with the data
sourcepub fn interp(
&self,
x: Sx::Elem
) -> Result<Array<Sd::Elem, D::Smaller>, InterpolateError>
pub fn interp( &self, x: Sx::Elem ) -> Result<Array<Sd::Elem, D::Smaller>, InterpolateError>
Calculate the interpolated values at x
.
Returns the interpolated data in an array one dimension smaller than
the data dimension.
// data has 2 dimension:
let data = array![
[0.0, 2.0, 4.0],
[0.5, 2.5, 3.5],
[1.0, 3.0, 3.0],
];
let query = 0.5;
let expected = array![0.25, 2.25, 3.75];
let interpolator = Interp1DBuilder::new(data).build().unwrap();
let result = interpolator.interp(query).unwrap();
Concider using interp_scalar(x)
when the data dimension is Ix1
sourcepub fn interp_array<Sq, Dq>(
&self,
xs: &ArrayBase<Sq, Dq>
) -> Result<Array<Sd::Elem, <Dq as DimAdd<D::Smaller>>::Output>, InterpolateError>where
Sq: Data<Elem = Sd::Elem>,
Dq: Dimension + DimAdd<D::Smaller>,
pub fn interp_array<Sq, Dq>( &self, xs: &ArrayBase<Sq, Dq> ) -> Result<Array<Sd::Elem, <Dq as DimAdd<D::Smaller>>::Output>, InterpolateError>where Sq: Data<Elem = Sd::Elem>, Dq: Dimension + DimAdd<D::Smaller>,
Calculate the interpolated values at all points in xs
let data = array![0.0, 0.5, 1.0 ];
let x = array![0.0, 1.0, 2.0 ];
let query = array![0.5, 1.0, 1.5 ];
let expected = array![0.25, 0.5, 0.75];
let interpolator = Interp1DBuilder::new(data)
.x(x)
.strategy(Linear::new())
.build().unwrap();
let result = interpolator.interp_array(&query).unwrap();
Dimensions
given the data dimension is N
and the dimension of xs
is M
the return array will have dimension M + N - 1
where the first
M
dimensions correspond to the dimensions of xs
.
// data has 2 dimension:
let data = array![
[0.0, 2.0],
[0.5, 2.5],
[1.0, 3.0],
];
let x = array![
0.0,
1.0,
2.0,
];
// query with 2 dimensions:
let query = array![
[0.0, 0.5],
[1.0, 1.5],
];
// expecting 3 dimensions!
let expected = array![
[[0.0, 2.0], [0.25, 2.25]], // result for x=[0.0, 0.5]
[[0.5, 2.5], [0.75, 2.75]], // result for x=[1.0, 1.5]
];
let interpolator = Interp1DBuilder::new(data)
.x(x)
.strategy(Linear::new())
.build().unwrap();
let result = interpolator.interp_array(&query).unwrap();
sourcepub fn get_index_left_of(&self, x: Sx::Elem) -> usize
pub fn get_index_left_of(&self, x: Sx::Elem) -> usize
The index of a known value left of, or at x.
This will never return the right most index,
so calling index_point(idx+1)
is always safe.