@@ -7,7 +7,7 @@ Nearly everything in DimensionalData.jl is designed to be extensible.
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- ` AbstractDimStack ` is easily extended to custom mixed array datasets.
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` RasterStack ` or ` ArViZ.Dataset ` are examples.
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- ` Lookup ` can have new types added, e.g. to ` AbstractSampled ` or
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- ` AbstractCategorical ` . ` Rasters.Projected ` is a lookup that knows
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+ ` AbstractCategorical ` . For example, ` Rasters.Projected ` is a lookup that knows
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its coordinate reference system, but otherwise behaves as a regular
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` Sampled ` lookup.
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@@ -16,7 +16,7 @@ Nearly everything in DimensionalData.jl is designed to be extensible.
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## ` dims `
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Objects extending DimensionalData.jl that have dimensions must return
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- a ` Tuple ` of constructed ` Dimension ` s from ` dims(obj) ` .
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+ a ` Tuple ` of constructed ` Dimension ` s from ` dims(obj) ` , like ` (X(), Y()) ` .
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### ` Dimension ` axes
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@@ -44,7 +44,7 @@ implemented.
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## ` rebuild `
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Rebuild methods are used to rebuild immutable objects with new field values,
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- in a way that is more flexible and extensible than just using ConstructionBase.jl
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+ in a more flexible and extensible way than just using ConstructionBase.jl
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reconstruction. Developers can choose to ignore some of the fields passed
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by ` rebuild ` .
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@@ -85,11 +85,14 @@ format(dims, array)
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This lets DimensionalData detect the lookup properties, fill in missing fields
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of a ` Lookup ` , pass keywords from ` Dimension ` to detected ` Lookup `
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constructors, and accept a wider range of dimension inputs like tuples of ` Symbol `
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- and ` Type ` .
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+ and ` Type ` . The way you indicate that something needs to be filled is by using the ` Auto ` types,
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+ like [ ` AutoOrder ` ] ( @ref ) or [ ` AutoSampling ` ] ( @ref )
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Not calling ` format ` in the outer constructors of an ` AbstractDimArray `
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has undefined behaviour.
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+ When creating lookup types, you need to define ` DimensionalData.format ` on your lookup type.
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+
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## Interfaces.jl interface testing
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