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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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+
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+ Changes with deprecation warnings:
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+
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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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+
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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.
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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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