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Merge pull request #231 from JuliaMath/teh/news
Add NEWS about release 0.9
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NEWS.md

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# v0.9.0
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Breaking changes:
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- `gradient` and `hessian` are no longer exported; use `Interpolations.gradient` and
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`Interpolations.hessian`.
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- `interpolate` objects now check bounds, and throw an error if you try to evaluate them
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at locations beyond the edge of their interpolation domain; use `extrapolate` if you need out-of-bounds evaluation
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- For quadratic and cubic interpolation, `interpolate!` now returns an object whose axes
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are narrowed by the amount of padding needed on the array edges. This preserves correspondence
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between input indices and output indices. See https://julialang.org/blog/2017/04/offset-arrays
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for more information.
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- The parametrization of some types has changed; this does not affect users of the "exported"
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interface, but does break packages that performed manual construction of explicit types.
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Changes with deprecation warnings:
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- `itp[i...]` should be replaced with `itp(i...)`.
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- `OnGrid` and `OnCell` should now be placed inside the boundary condition (e.g., `Flat(OnGrid())`),
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and should only be used for quadratic and cubic interpolation.
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- the extrapolation boundary condition `Linear` was changed to `Line`, to be consistent
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with interpolation boundary conditions.
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Advance notice of future changes:
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- In future versions `itp[i...]` may be interpreted with reference to the parent array's
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indices rather than the knots supplied by the user (relevant for `scale` and `Gridded`).
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If you fix the existing deprecation warnings then you should be prepared for this change.

README.md

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[![PkgEval Status](http://pkg.julialang.org/badges/Interpolations_0.4.svg)](http://pkg.julialang.org/?pkg=Interpolations)
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[![Interpolations](http://pkg.julialang.org/badges/Interpolations_0.5.svg)](http://pkg.julialang.org/?pkg=Interpolations)
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**NEWS** v0.9 was a breaking release. See the [news](NEWS.md) for details on how to update.
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This package implements a variety of interpolation schemes for the
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Julia language. It has the goals of ease-of-use, broad algorithmic
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support, and exceptional performance.

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