Documentation updates

Author: jbisits

.gitignore

@@ -4,3 +4,4 @@
 docs/build
 test/data
 Manifest.toml
+.vscode

docs/src/index.md

@@ -16,11 +16,11 @@ e.g. certain X/Y coordinates. The available selectors are listed here:
 
 |                        |                                                                    |
 | :--------------------- | :----------------------------------------------------------------- |
-| `At(x)`                | get the index exactly matching the passed in value(s)              |
-| `Near(x)`              | get the closest index to the passed in value(s)                    |
+| `At(x)`                | get the index exactly matching the passed in value(s).             |
+| `Near(x)`              | get the closest index to the passed in value(s).                   |
 | `Where(f::Function)`   | filter the array axis by a function of the dimension index values. |
 | `a..b`/`Between(a, b)` | get all indices between two values, excluding the high value.      |
-| `Contains(x)`          | get indices where the value x falls within an interval             |
+| `Contains(x)`          | get indices where the value x falls within an interval.            |
 
 
 Use the `..` selector to take a `view` of madagascar:
@@ -56,7 +56,7 @@ Note that most regular Julia methods, such as `replace`, work as for a standard
 |                           |                                                                              |
 | :------------------------ | :--------------------------------------------------------------------------- |
 | [`classify`](@ref)        | classify values into categories.                                             |
-| [`mask`](@ref)            | mask and object by a polygon or `Raster` along `X/Y`, or other dimensions. |
+| [`mask`](@ref)            | mask an object by a polygon or `Raster` along `X/Y`, or other dimensions.    |
 | [`replace_missing`](@ref) | replace all missing values in an object and update `missingval`.             |
 
 
@@ -158,7 +158,7 @@ We can also reassign dimensions, here `X` becomes `Z`:
 set(A, X => Z)
 ```
 
-`setcrs(A, crs)` and `setmappedcrs(A, crs)` will set the crs value/s of and
+`setcrs(A, crs)` and `setmappedcrs(A, crs)` will set the crs value/s of an
 object to any `GeoFormat` from GeoFormatTypes.jl.
 
 
@@ -202,9 +202,9 @@ Here we plot every third month in the first year, just using the regular index:
 A[Ti=1:3:12] |> plot
 ```
 
-Now plot the ocean temperatures areound the Americas in the first month of 2001.
+Now plot the ocean temperatures around the Americas in the first month of 2001.
 Notice we are using lat/lon coordinates and date/time instead of regular
-indexes: The time dimension uses `DateTime360Day`, so we need to load CFTime.jl
+indexes. The time dimension uses `DateTime360Day`, so we need to load CFTime.jl
 to index it with `Near`.
 
 ```@example nc
@@ -269,14 +269,14 @@ Extract the longitude/latitude value to a Vector of Tuple:
 coords = [(r.longitude, r.latitude) for r in records if !ismissing(r.longitude)]
 ```
 
-Get BioClim layers and subset to south-east australia
+Get BioClim layers and subset to south-east Australia
 
 ```@example sdm
 A = RasterStack(WorldClim{BioClim}, (1, 3, 7, 12))
 SE_aus = A[X=138..155, Y=-40..(-25), Band=1]
 ```
 
-And plot BioClim predictors and scatter occurrence points on all subplots
+Plot BioClim predictors and scatter occurrence points on all subplots
 
 ```@example sdm
 p = plot(SE_aus);
@@ -302,7 +302,7 @@ df[1:5, :]
 
 ## Polygon masking, mosaic and plot
 
-In this example we wil l `mask` the scandinavian countries with border polygons,
+In this example we will `mask` the Scandinavian countries with border polygons,
 then `mosaic` together to make a single plot. 
 
 First, get the country boundary shape files using GADM.jl.
@@ -322,14 +322,14 @@ norway_border = shapes.shapes[53]
 sweden_border = shapes.shapes[54]
 ```
 
-Then load raster data. We load some worldclim layers using RasterDataSources via
+Then load raster data. We load some worldclim layers using `RasterDataSources` via
 Rasters.jl, and drop the Band dimension.
 
 ```@example mask
 climate = RasterStack(WorldClim{Climate}, (:tmin, :tmax, :prec, :wind); month=July)[Band(1)]
 ```
 
-`mask` denmark, norway and sweden from the global dataset using their border polygon,
+`mask` Denmark, Norway and Sweden from the global dataset using their border polygon,
 then trim the missing values. We pad `trim` with a 10 pixel margin.
 
 ```@example mask
@@ -419,7 +419,7 @@ arrays of common raster file types. These methods also work for entire
 
 Spatial raster data is essentially just an `Array`. But `Raster` wrappers
 allow treating them as an array that maintains its spatial index, crs and other
-metadata through all transformations. This means the can always be plotted and
+metadata through all transformations. This means they can always be plotted and
 written to disk after applying most base Julia methods, and most `broadcast`s.
 
 ```@docs
@@ -441,7 +441,7 @@ RasterStack(T::Type{<:RasterDataSources.RasterDataSource})
 
 ### RasterSeries
 
-A series is an meta-array that holds other files/data that is distributed over
+A series is a meta-array that holds other files/data that is distributed over
 some dimension, often time. These files/data can be `Raster`s or `RasterStack`s.
 
 ```@docs
@@ -529,7 +529,7 @@ smapseries
 
 Files can be written to disk in all formats other than SMAP HDF5 using
 `write("filename.ext", A)`. See the docs for [`write`](@ref). They can (with
-some caveats) be written to different formats than they were loaded in,
+some caveats) be written to different formats than they were loaded in as,
 providing file-type conversion for spatial data.
 
 Some metadata may be lost in formats that store little metadata, or where
@@ -603,7 +603,7 @@ zonal
 
 ### File operations
 
-These Base and DimensionalData methods have specific Rasters.jl versions:
+These `Base` and `DimensionalData` methods have specific Rasters.jl versions:
 
 ```@docs
 modify

src/lookup.jl

@@ -101,7 +101,7 @@ end
     Mapped(; order=AutoOrder(), span=AutoSpan(), sampling=AutoSampling(), crs=nothing, mappedcrs)
 
 An [`AbstractSampled`]($DDabssampleddocs) `LookupArray`, where the dimension index has
-been mapped to another projection, usually lat/lon or `EPSG(4326)`. 
+been mapped to another projection, usually lat/lon or `EPSG(4326)`.
 `Mapped` matches the dimension format commonly used in netcdf files.
 
 Fields and behaviours are identical to [`Sampled`]($DDsampleddocs) with the addition of
@@ -144,7 +144,7 @@ struct AffineProjected{T,F,A<:AbstractVector{T},M,C,MC} <: LA.Unaligned{T,1}
     crs::C
     mappedcrs::MC
 end
-function AffineProjected(f; 
+function AffineProjected(f;
     data=AutoIndex(), metadata=NoMetadata(), crs=nothing, mappedcrs=nothing, dim=AutoDim()
 )
     AffineProjected(f, data, metadata, crs, mappedcrs)
@@ -154,7 +154,7 @@ crs(lookup::AffineProjected) = lookup.crs
 mappedcrs(lookup::AffineProjected) = lookup.mappedcrs
 
 DD.metadata(lookup::AffineProjected) = lookup.metadata
-function DD.rebuild(l::AffineProjected; 
+function DD.rebuild(l::AffineProjected;
     affinemap=l.affinemap, data=l.data, metadata=metadata(l),
     crs=crs(l), mappedcrs=mappedcrs(l), dim=dims(l), args...
 )
@@ -174,12 +174,12 @@ function Dimensions.sliceunalligneddims(
     I = map((ud1, ud2), I) do d, i
         i isa Colon ? parent(lookup(d)) : i
     end
-    
+
     af = lookup(ud1).affinemap
     # New resolution for step size changes
     M = af.linear * [step(I[1]) 0; 0 step(I[2])]
     # New of origin for slice
-    v = af(collect(first.(I) .- 1))  
+    v = af(collect(first.(I) .- 1))
     # Create a new affine map
     affinemap = CoordinateTransformations.AffineMap(M, v)
     # Build new lookups with the affine map. Probably should define `set` to do this.
@@ -204,7 +204,7 @@ Other dimension lookups pass through unchanged.
 
 This is used to e.g. save a netcdf file to GeoTiff.
 """
-convertlookup(T::Type{<:LookupArray}, A::AbstractDimArray) = 
+convertlookup(T::Type{<:LookupArray}, A::AbstractDimArray) =
     rebuild(A; dims=convertlookup(T, dims(A)))
 convertlookup(T::Type{<:LookupArray}, dims::Tuple) = map(d -> convertlookup(T, d), dims)
 convertlookup(T::Type{<:LookupArray}, d::Dimension) = rebuild(d, convertlookup(T, lookup(d)))
@@ -257,7 +257,7 @@ end
 """
     setcrs(x, crs)
 
-Set the crs of a `Raster`, `RasterStack`, `Tuple` of `Dimension`,or a `Dimension`.
+Set the crs of a `Raster`, `RasterStack`, `Tuple` of `Dimension`, or a `Dimension`.
 """
 setcrs(dims::DimTuple, crs) = map(d -> setcrs(d, crs), dims)
 function setcrs(dim::Dimension, crs)

src/methods/aggregate.jl

@@ -8,7 +8,7 @@ struct DisAg end
 """
     aggregate(method, object, scale; filename, progress, skipmissing)
 
-Aggregate a Raster, or all arrays in a RasterStack or RasterSeries, by `scale` using
+Aggregate a `Raster`, or all arrays in a `RasterStack` or `RasterSeries`, by `scale` using
 `method`.
 
 # Arguments
@@ -28,7 +28,7 @@ When the aggregation `scale` of is larger than the array axis, the length of the
 # Keywords
 
 - `progress`: show a progress bar.
-- `skipmissingval`: if `true`, any `missingval` will be skipped during aggregation, so that 
+- `skipmissingval`: if `true`, any `missingval` will be skipped during aggregation, so that
     only areas of all missing values will be aggregated to `missingval`. If `false`, any
     aggegrated area containing a `missingval` will be assigned `missingval`.
 - `filename`: a filename to write to directly, useful for large files.
@@ -39,18 +39,18 @@ When the aggregation `scale` of is larger than the array axis, the length of the
 
 ```jldoctest
 using Rasters, Statistics, Plots
-using Rasters: Center 
+using Rasters: Center
 st = read(RasterStack(WorldClim{Climate}; month=1))
 ag = aggregate(Center(), st, (Y(20), X(20)); skipmissingval=true, progress=false)
 plot(ag)
-savefig("build/aggregate_example.png") 
+savefig("build/aggregate_example.png")
 # output
 
 ```
 
 ![aggregate](aggregate_example.png)
 
-Note: currently it is faster to aggregate over memory-backed arrays. 
+Note: currently it is faster to aggregate over memory-backed arrays.
 Use [`read`](@ref) on `src` before use where required.
 """
 function aggregate end
@@ -120,11 +120,11 @@ When the aggregation `scale` of is larger than the array axis, the length of the
 # Keywords
 
 - `progress`: show a progress bar.
-- `skipmissingval`: if `true`, any `missingval` will be skipped during aggregation, so that 
+- `skipmissingval`: if `true`, any `missingval` will be skipped during aggregation, so that
     only areas of all missing values will be aggregated to `missingval`. If `false`, any
     aggegrated area containing a `missingval` will be assigned `missingval`.
 
-Note: currently it is _much_ faster to aggregate over memory-backed arrays. 
+Note: currently it is _much_ faster to aggregate over memory-backed arrays.
 Use [`read`](@ref) on `src` before use where required.
 """
 function aggregate!(locus::Locus, dst::AbstractRaster, src, scale; kw...)
@@ -190,7 +190,7 @@ Disaggregate array, or all arrays in a stack or series, by some scale.
 
 - `progress`: show a progress bar.
 
-Note: currently it is faster to aggregate over memory-backed arrays. 
+Note: currently it is faster to aggregate over memory-backed arrays.
 Use [`read`](@ref) on `src` before use where required.
 
 """
@@ -257,7 +257,7 @@ Disaggregate array `src` to array `dst` by some scale, using `method`.
   usually use in `getindex`. Using a `Selector` will determine the scale by the
   distance from the start of the index in the `src` array.
 
-Note: currently it is faster to aggregate over memory-backed arrays. 
+Note: currently it is faster to aggregate over memory-backed arrays.
 Use [`read`](@ref) on `src` before use where required.
 """
 function disaggregate!(locus::Locus, dst::AbstractRaster, src, scale)
@@ -310,7 +310,7 @@ end
 
 # Handle how methods like `mean` can change the type
 ag_eltype(method::Tuple{<:Locus,Vararg}, A) = eltype(A)
-function ag_eltype(method::Tuple{<:Any}, A) 
+function ag_eltype(method::Tuple{<:Any}, A)
     method_returntype = typeof(method[1](zero(eltype(A))))
     promote_type(eltype(A), method_returntype)
 end

src/methods/classify.jl

@@ -8,7 +8,7 @@ Create a new array with values in `x` classified by the values in `pairs`.
 of `Fix2` e.g. `(>=(4), <(7))`, or an IntervalSets.jl interval, e.g. `3..9` or `OpenInterval(10, 12)`.
 `pairs` can also be a `n * 3` matrix where each row is lower bounds, upper bounds, replacement.
 
-If if tuples or a `Matrix` are used, the `lower` and `upper` keywords define
+If tuples or a `Matrix` are used, the `lower` and `upper` keywords define
 how the lower and upper boundaries are chosen.
 
 If `others` is set other values not covered in `pairs` will be set to that values.
@@ -137,7 +137,7 @@ savefig("build/classify_bang_example.png")
 
 $EXPERIMENTAL
 """
-classify!(A::AbstractRaster, p1::Pair, pairs::Pair...; kw...) = 
+classify!(A::AbstractRaster, p1::Pair, pairs::Pair...; kw...) =
     classify!(A, (p1, pairs...); kw...)
 function classify!(A::AbstractRaster, pairs;
     lower=(>=), upper=(<), others=nothing, missingval=missingval(A)
@@ -211,5 +211,3 @@ _compare(find::Base.Fix2, x, lower, upper) = find(x)
 _compare((l, u)::Tuple, x, lower, upper) = lower(x, l) && upper(x, u)
 _compare((l, u)::Tuple{<:Base.Fix2,<:Base.Fix2}, x, lower, upper) = l(x) && u(x)
 _compare(interval::LA.IntervalSets.Interval, x, lower, upper) = x in interval
-
-

src/methods/crop_extend.jl

@@ -19,7 +19,7 @@ As `crop` is lazy, `filename` and `suffix` keywords don't apply.
 
 # Example
 
-Cropt to another raster:
+Crop to another raster:
 
 ```jldoctest
 using Rasters, Plots
@@ -34,7 +34,7 @@ nz_evenness = evenness[nz_bounds...]
 nz_range = crop(rnge; to=nz_evenness)
 plot(nz_range)
 
-savefig("build/crop_example.png")
+savefig("build/nz_crop_example.png")
 # output
 ```
 
@@ -77,8 +77,8 @@ crop(x::RasterStackOrArray; to, kw...) = _crop_to(x, to; kw...)
 # crop `A` to values of dims of `to`
 function _crop_to(x, to; kw...)
     ext = _extent(to)
-    if isnothing(ext) 
-        if isnothing(dims(to)) 
+    if isnothing(ext)
+        if isnothing(dims(to))
             throw(ArgumentError("No dims or extent available on `to` object of type $(typeof(to))"))
         else
             return _crop_to(x, dims(to); kw...)
@@ -90,7 +90,7 @@ end
 _crop_to(A, to::RasterStackOrArray; kw...) = _crop_to(A, dims(to); kw...)
 function _crop_to(x, to::DimTuple; atol=maybe_eps(to))
     # We can only crop to sampled dims (e.g. not categorical dims like Band)
-    sampled = reduce(to; init=()) do acc, d 
+    sampled = reduce(to; init=()) do acc, d
         lookup(d) isa AbstractSampled ? (acc..., d) : acc
     end
     return _crop_to(x, Extents.extent(to))
@@ -247,7 +247,7 @@ _choosebounds((f1, f2), (a,)::Tuple{<:Dimension}, b::Tuple{<:Dimension}) = _choo
 _choosebounds((f1, f2), ::Tuple{}, ::Tuple{}) = ()
 _choosebounds((f1, f2), ::Tuple{}, (b,)::DimTuple) = bounds(b)
 _choosebounds((f1, f2), a::Tuple, ::Tuple{}) = a
-function _choosebounds((f1, f2), (a1, a2)::Tuple{<:Any,<:Any}, (b,)::Tuple{<:Dimension}) 
+function _choosebounds((f1, f2), (a1, a2)::Tuple{<:Any,<:Any}, (b,)::Tuple{<:Dimension})
     b1, b2 = bounds(b)
     return f1(a1, b1), f2(a2, b2)
 end