Deprecate CamelCase convenience for snake_case constructors (#501)

Specifically, deprecate LinearInterpolation, ConstantInterpolation,
and CubicSplineInterpolation in favor of linear_interpolation,
constant_interpolation, and cubic_spline_interpolation, respectively
since these are merely functions and not types.

Author: mkitti

README.md

@@ -39,14 +39,14 @@ A = log.(xs)
 ```
 Create linear interpolation object without extrapolation
 ```julia
-interp_linear = LinearInterpolation(xs, A)
+interp_linear = linear_interpolation(xs, A)
 interp_linear(3) # exactly log(3)
 interp_linear(3.1) # approximately log(3.1)
 interp_linear(0.9) # outside grid: error
 ```
 Create linear interpolation object with extrapolation
 ```julia
-interp_linear_extrap = LinearInterpolation(xs, A,extrapolation_bc=Line()) 
+interp_linear_extrap = linear_interpolation(xs, A,extrapolation_bc=Line()) 
 interp_linear_extrap(0.9) # outside grid: linear extrapolation
 ```
 

docs/src/convenience-construction.md

@@ -1,7 +1,7 @@
 
 ## Convenience notation
 
-For constant, linear, and cubic spline interpolations, `ConstantInterpolation`, `LinearInterpolation`, and `CubicSplineInterpolation`
+For constant, linear, and cubic spline interpolations, `constant_interpolation`, `linear_interpolation`, and `cubic_spline_interpolation`
 can be used to create interpolating and extrapolating objects handily.
 
 ### Motivating Example
@@ -11,7 +11,7 @@ extrap_full = extrapolate(scale(interpolate(A, BSpline(Linear())), xs), Line())
 ```
 can be written as the more readable
 ```julia
-extrap = LinearInterpolation(xs, A, extrapolation_bc = Line())
+extrap = linear_interpolation(xs, A, extrapolation_bc = Line())
 ```
  by using the convenience constructor.
 
@@ -23,12 +23,12 @@ xs = 1:0.2:5
 A = [f(x) for x in xs]
 
 # linear interpolation
-interp_linear = LinearInterpolation(xs, A)
+interp_linear = linear_interpolation(xs, A)
 interp_linear(3) # exactly log(3)
 interp_linear(3.1) # approximately log(3.1)
 
 # cubic spline interpolation
-interp_cubic = CubicSplineInterpolation(xs, A)
+interp_cubic = cubic_spline_interpolation(xs, A)
 interp_cubic(3) # exactly log(3)
 interp_cubic(3.1) # approximately log(3.1)
 ```
@@ -40,12 +40,12 @@ ys = 2:0.1:5
 A = [f(x,y) for x in xs, y in ys]
 
 # linear interpolation
-interp_linear = LinearInterpolation((xs, ys), A)
+interp_linear = linear_interpolation((xs, ys), A)
 interp_linear(3, 2) # exactly log(3 + 2)
 interp_linear(3.1, 2.1) # approximately log(3.1 + 2.1)
 
 # cubic spline interpolation
-interp_cubic = CubicSplineInterpolation((xs, ys), A)
+interp_cubic = cubic_spline_interpolation((xs, ys), A)
 interp_cubic(3, 2) # exactly log(3 + 2)
 interp_cubic(3.1, 2.1) # approximately log(3.1 + 2.1)
 ```
@@ -57,25 +57,25 @@ xs = 1:0.2:5
 A = [f(x) for x in xs]
 
 # extrapolation with linear boundary conditions
-extrap = LinearInterpolation(xs, A, extrapolation_bc = Line())
+extrap = linear_interpolation(xs, A, extrapolation_bc = Line())
 
 @test extrap(1 - 0.2) # ≈ f(1) - (f(1.2) - f(1))
 @test extrap(5 + 0.2) # ≈ f(5) + (f(5) - f(4.8))
 ```
 You can also use a "fill" value, which gets returned whenever you ask for out-of-range values:
 
 ```julia
-extrap = LinearInterpolation(xs, A, extrapolation_bc = NaN)
+extrap = linear_interpolation(xs, A, extrapolation_bc = NaN)
 @test isnan(extrap(5.2))
 ```
 
-Irregular grids are supported as well; note that presently only `ConstantInterpolation` and `LinearInterpolation` supports irregular grids.
+Irregular grids are supported as well; note that presently only `constant_interpolation` and `linear_interpolation` supports irregular grids.
 ```julia
 xs = [x^2 for x = 1:0.2:5]
 A = [f(x) for x in xs]
 
 # linear interpolation
-interp_linear = LinearInterpolation(xs, A)
+interp_linear = linear_interpolation(xs, A)
 interp_linear(1) # exactly log(1)
 interp_linear(1.05) # approximately log(1.05)
 ```
@@ -96,8 +96,8 @@ x = a:1.0:b
 # length(x) == length(y)
 y = @. cos(x^2 / 9.0) # Function application by broadcasting
 # Interpolations
-itp_linear = LinearInterpolation(x, y)
-itp_cubic = CubicSplineInterpolation(x, y)
+itp_linear = linear_interpolation(x, y)
+itp_cubic = cubic_spline_interpolation(x, y)
 # Interpolation functions
 f_linear(x) = itp_linear(x)
 f_cubic(x) = itp_cubic(x)

docs/src/index.md

@@ -44,14 +44,14 @@ A = log.(xs)
 ```
 Create linear interpolation object without extrapolation
 ```julia
-interp_linear = LinearInterpolation(xs, A)
+interp_linear = linear_interpolation(xs, A)
 interp_linear(3) # exactly log(3)
 interp_linear(3.1) # approximately log(3.1)
 interp_linear(0.9) # outside grid: error
 ```
 Create linear interpolation object with extrapolation
 ```julia
-interp_linear_extrap = LinearInterpolation(xs, A, extrapolation_bc=Line()) 
+interp_linear_extrap = linear_interpolation(xs, A,extrapolation_bc=Line()) 
 interp_linear_extrap(0.9) # outside grid: linear extrapolation
 ```
 

docs/src/iterate.md

@@ -59,7 +59,7 @@ last knot being co-located. (ie. in the example below `etp(2.0) = 1.0` not
 ```jldoctest periodic-demo; setup = :(using Interpolations)
 julia> x = [1.0, 1.5, 1.75, 2.0];
 
-julia> etp = LinearInterpolation(x, x.^2, extrapolation_bc=Periodic());
+julia> etp = linear_interpolation(x, x.^2, extrapolation_bc=Periodic());
 
 julia> kiter = knots(etp);
 
@@ -101,7 +101,7 @@ k[1], k[2], ..., k[end-1], k[end], k[end+1], ... k[2], k[1], k[2], ...
 ```jldoctest reflect-demo; setup = :(using Interpolations)
 julia> x = [1.0, 1.5, 1.75, 2.0];
 
-julia> etp = LinearInterpolation(x, x.^2, extrapolation_bc=Reflect());
+julia> etp = linear_interpolation(x, x.^2, extrapolation_bc=Reflect());
 
 julia> kiter = knots(etp);
 
@@ -139,7 +139,7 @@ multi-dimensional extrapolation also supported.
 ```jldoctest; setup = :(using Interpolations)
 julia> x = [1.0, 1.5, 1.75, 2.0];
 
-julia> etp = LinearInterpolation((x, x), x.*x');
+julia> etp = linear_interpolation((x, x), x.*x');
 
 julia> knots(etp) |> collect
 4×4 Matrix{Tuple{Float64, Float64}}:
@@ -156,7 +156,7 @@ iteration over knots can end up "stuck" iterating along a single axis:
 ```jldoctest; setup = :(using Interpolations)
 julia> x = [1.0, 1.5, 1.75, 2.0];
 
-julia> etp = LinearInterpolation((x, x), x.*x', extrapolation_bc=(Periodic(), Throw()));
+julia> etp = linear_interpolation((x, x), x.*x', extrapolation_bc=(Periodic(), Throw()));
 
 julia> knots(etp) |> k -> Iterators.take(k, 6) |> collect
 6-element Vector{Tuple{Float64, Float64}}:
@@ -174,7 +174,7 @@ Rearranging the axes so non-repeating knots are first can address this issue:
 ```jldoctest; setup = :(using Interpolations)
 julia> x = [1.0, 1.5, 1.75, 2.0];
 
-julia> etp = LinearInterpolation((x, x), x.*x', extrapolation_bc=(Throw(), Periodic()));
+julia> etp = linear_interpolation((x, x), x.*x', extrapolation_bc=(Throw(), Periodic()));
 
 julia> knots(etp) |> k -> Iterators.take(k, 6) |> collect
 6-element Vector{Tuple{Float64, Float64}}:
@@ -199,7 +199,7 @@ result, the iterator will generate an infinite sequence of knots starting at `1.
 ```jldoctest iterate-directional-unbounded; setup = :(using Interpolations)
 julia> x = [1.0, 1.2, 1.3, 2.0];
 
-julia> etp = LinearInterpolation(x, x.^2, extrapolation_bc=((Throw(), Periodic()),));
+julia> etp = linear_interpolation(x, x.^2, extrapolation_bc=((Throw(), Periodic()),));
 
 julia> kiter = knots(etp);
 
@@ -227,7 +227,7 @@ Swapping the boundary conditions, results in a finite sequence of knots from
 ```jldoctest iterate-directional-bounded; setup = :(using Interpolations)
 julia> x = [1.0, 1.2, 1.3, 2.0];
 
-julia> etp = LinearInterpolation(x, x.^2, extrapolation_bc=((Periodic(), Throw()),));
+julia> etp = linear_interpolation(x, x.^2, extrapolation_bc=((Periodic(), Throw()),));
 
 julia> kiter = knots(etp);
 
@@ -272,7 +272,7 @@ We can iterate over all knots greater than `start` by omitting `stop`
 ```jldoctest knotsbetween-usage; setup = :(using Interpolations)
 julia> x = [1.0, 1.5, 1.75, 2.0];
 
-julia> etp = LinearInterpolation(x, x.^2, extrapolation_bc=Periodic());
+julia> etp = linear_interpolation(x, x.^2, extrapolation_bc=Periodic());
 
 julia> krange = knotsbetween(etp; start=4.0);
 
@@ -322,7 +322,7 @@ dimensions.
 ```jldoctest; setup = :(using Interpolations)
 julia> x = [1.0, 1.5, 1.75, 2.0];
 
-julia> etp = LinearInterpolation((x, x), x.*x');
+julia> etp = linear_interpolation((x, x), x.*x');
 
 julia> knotsbetween(etp; start=(1.2, 1.5), stop=(1.8, 3.0)) |> collect
 2×2 Matrix{Tuple{Float64, Float64}}:

src/Interpolations.jl

@@ -23,9 +23,9 @@ export
     InPlaceQ,
     Throw,
 
-    ConstantInterpolation,
-    LinearInterpolation,
-    CubicSplineInterpolation,
+    constant_interpolation,
+    linear_interpolation,
+    cubic_spline_interpolation,
 
     knots,
     knotsbetween

src/convenience-constructors.jl

@@ -1,36 +1,36 @@
 # convenience copnstructors for constant / linear / cubic spline interpolations
 # 1D version
-ConstantInterpolation(range::AbstractRange, vs::AbstractVector; extrapolation_bc = Throw()) =
+constant_interpolation(range::AbstractRange, vs::AbstractVector; extrapolation_bc = Throw()) =
     extrapolate(scale(interpolate(vs, BSpline(Constant())), range), extrapolation_bc)
-ConstantInterpolation(range::AbstractVector, vs::AbstractVector; extrapolation_bc = Throw()) =
+constant_interpolation(range::AbstractVector, vs::AbstractVector; extrapolation_bc = Throw()) =
     extrapolate(interpolate((range, ), vs, Gridded(Constant())), extrapolation_bc)
-LinearInterpolation(range::AbstractRange, vs::AbstractVector; extrapolation_bc = Throw()) =
+linear_interpolation(range::AbstractRange, vs::AbstractVector; extrapolation_bc = Throw()) =
     extrapolate(scale(interpolate(vs, BSpline(Linear())), range), extrapolation_bc)
-LinearInterpolation(range::AbstractVector, vs::AbstractVector; extrapolation_bc = Throw()) =
+linear_interpolation(range::AbstractVector, vs::AbstractVector; extrapolation_bc = Throw()) =
     extrapolate(interpolate((range, ), vs, Gridded(Linear())), extrapolation_bc)
-CubicSplineInterpolation(range::AbstractRange, vs::AbstractVector;
+cubic_spline_interpolation(range::AbstractRange, vs::AbstractVector;
                          bc = Line(OnGrid()), extrapolation_bc = Throw()) =
     extrapolate(scale(interpolate(vs, BSpline(Cubic(bc))), range), extrapolation_bc)
 
 # multivariate versions
-ConstantInterpolation(ranges::NTuple{N,AbstractRange}, vs::AbstractArray{T,N};
+constant_interpolation(ranges::NTuple{N,AbstractRange}, vs::AbstractArray{T,N};
                     extrapolation_bc = Throw()) where {N,T} =
     extrapolate(scale(interpolate(vs, BSpline(Constant())), ranges...), extrapolation_bc)
-ConstantInterpolation(ranges::NTuple{N,AbstractVector}, vs::AbstractArray{T,N};
+constant_interpolation(ranges::NTuple{N,AbstractVector}, vs::AbstractArray{T,N};
                     extrapolation_bc = Throw()) where {N,T} =
     extrapolate(interpolate(ranges, vs, Gridded(Constant())), extrapolation_bc)
-LinearInterpolation(ranges::NTuple{N,AbstractRange}, vs::AbstractArray{T,N};
+linear_interpolation(ranges::NTuple{N,AbstractRange}, vs::AbstractArray{T,N};
                     extrapolation_bc = Throw()) where {N,T} =
     extrapolate(scale(interpolate(vs, BSpline(Linear())), ranges...), extrapolation_bc)
-LinearInterpolation(ranges::NTuple{N,AbstractVector}, vs::AbstractArray{T,N};
+linear_interpolation(ranges::NTuple{N,AbstractVector}, vs::AbstractArray{T,N};
                     extrapolation_bc = Throw()) where {N,T} =
     extrapolate(interpolate(ranges, vs, Gridded(Linear())), extrapolation_bc)
-CubicSplineInterpolation(ranges::NTuple{N,AbstractRange}, vs::AbstractArray{T,N};
+cubic_spline_interpolation(ranges::NTuple{N,AbstractRange}, vs::AbstractArray{T,N};
                          bc = Line(OnGrid()), extrapolation_bc = Throw()) where {N,T} =
     extrapolate(scale(interpolate(vs, BSpline(Cubic(bc))), ranges...), extrapolation_bc)
 
 """
-    etp = LinearInterpolation(knots, A; extrapolation_bc=Throw())
+    etp = linear_interpolation(knots, A; extrapolation_bc=Throw())
 
 A shorthand for one of the following.
 * `extrapolate(scale(interpolate(A, BSpline(Linear())), knots), extrapolation_bc)`
@@ -41,10 +41,10 @@ Consider using `interpolate`, `scale`, or `extrapolate` explicitly as needed
 rather than using this convenience constructor. Performance will improve
 without scaling or extrapolation.
 """
-LinearInterpolation
+linear_interpolation
 
 """
-    etp = ConstantInterpolation(knots, A; extrapolation_bc=Throw())
+    etp = constant_interpolation(knots, A; extrapolation_bc=Throw())
 
 A shorthand for `extrapolate(interpolate(knots, A, scheme), extrapolation_bc)`,
 where `scheme` is either `BSpline(Constant())` or `Gridded(Constant())` depending on whether
@@ -54,15 +54,15 @@ Consider using `interpolate` or `extrapolate` explicitly as needed
 rather than using this convenience constructor. Performance will improve
 without extrapolation.
 """
-ConstantInterpolation
+constant_interpolation
 
 """
-    etp = CubicSplineInterpolation(knots, A; bc=Line(OnGrid()), extrapolation_bc=Throw())
+    etp = cubic_spline_interpolation(knots, A; bc=Line(OnGrid()), extrapolation_bc=Throw())
 
 A shorthand for `extrapolate(scale(interpolate(A, BSpline(Cubic(bc))),knots), extrapolation_bc)`.
 
 Consider using `interpolate`, `scale`, or `extrapolate` explicitly as needed
 rather than using this convenience constructor. Performance will improve
 without scaling or extrapolation.
 """
-CubicSplineInterpolation
+cubic_spline_interpolation

src/deprecations.jl

@@ -62,3 +62,8 @@ create_bcs(it::Flag, gt::Tuple) = map(t->it(t), gt)
 replace_linear_line(::Linear) = Line()
 replace_linear_line(bc::BoundaryCondition) = bc
 replace_linear_line(etpflag::Tuple) = replace_linear_line.(etpflag)
+
+# Old convenience constructors
+@deprecate LinearInterpolation linear_interpolation
+@deprecate ConstantInterpolation constant_interpolation
+@deprecate CubicSplineInterpolation cubic_spline_interpolation

src/iterate.jl

@@ -51,7 +51,7 @@ multiple KnotIterator within `Iterators.product`.
 ```jldoctest
 julia> using Interpolations;
 
-julia> etp = LinearInterpolation([1.0, 1.2, 2.3, 3.0], rand(4); extrapolation_bc=Periodic());
+julia> etp = linear_interpolation([1.0, 1.2, 2.3, 3.0], rand(4); extrapolation_bc=Periodic());
 
 julia> kiter = knots(etp);
 
@@ -121,7 +121,7 @@ infinite sequence of knots.
 ```jldoctest
 julia> using Interpolations;
 
-julia> etp = LinearInterpolation([1.0, 1.2, 2.3, 3.0], rand(4); extrapolation_bc=Periodic());
+julia> etp = linear_interpolation([1.0, 1.2, 2.3, 3.0], rand(4); extrapolation_bc=Periodic());
 
 julia> Iterators.take(knots(etp), 5) |> collect
 5-element $(Array{typeof(1.0),1}):
@@ -424,7 +424,7 @@ If no such knots exists will return a KnotIterator with length 0
 ```jldoctest
 julia> using Interpolations;
 
-julia> etp = LinearInterpolation([1.0, 1.2, 2.3, 3.0], rand(4); extrapolation_bc=Periodic());
+julia> etp = linear_interpolation([1.0, 1.2, 2.3, 3.0], rand(4); extrapolation_bc=Periodic());
 
 julia> knotsbetween(etp; start=38, stop=42) |> collect
 6-element $(Array{typeof(1.0),1}):