Rename package (#274)

* Rename package

* Make sure to require latest YAXArrays

Project.toml

@@ -1,4 +1,4 @@
-name = "ESDL"
+name = "EarthDataLab"
 uuid = "359177bc-a543-11e8-11b7-bb015dba3358"
 authors = ["Fabian Gans <[email protected]>"]
 version = "0.9.2"
@@ -36,7 +36,7 @@ Shapefile = "0.6, 0.7"
 StatsBase = "0.32, 0.33"
 Tables = "0.2, 1.0"
 WeightedOnlineStats = "0.5"
-YAXArrays = "0.2"
+YAXArrays = "0.2.1"
 Zarr = "0.6.2, 0.7"
 julia = "1.3"
 

README.md

@@ -1,20 +1,20 @@
-# ESDL.jl
+# EarthDataLab.jl
 
 *Julia Package for the Earth System Data Cube*
 
 | **Documentation**                                                                                                        | **Build Status**                                                                                |
 |:-------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|
-| [![](https://img.shields.io/badge/docs-latest-blue.svg)](https://juliadatacubes.github.io/ESDL.jl/latest/) | [![][ci-img]][ci-url] [![codecov][codecov-img]](https://codecov.io/github/esa-esdl/ESDL.jl?branch=master) |
+| [![](https://img.shields.io/badge/docs-latest-blue.svg)](https://juliadatacubes.github.io/EarthDataLab.jl/latest/) | [![][ci-img]][ci-url] [![codecov][codecov-img]](https://codecov.io/github/esa-EarthDataLab/EarthDataLab.jl?branch=master) |
 
-[codecov-img]: https://img.shields.io/codecov/c/github/esa-esdl/ESDL.jl/master.svg?label=codecov
+[codecov-img]: https://img.shields.io/codecov/c/github/esa-EarthDataLab/EarthDataLab.jl/master.svg?label=codecov
 # Installation
 
-- Add the ESDL package
-`Pkg.add(PackageSpec(url="https://github.com/esa-esdl/ESDL.jl"))`
+- Add the EarthDataLab package
+`Pkg.add(PackageSpec(url="https://github.com/juliadatacubes/EarthDataLab.jl"))`
 
 # Documentation
 
-The Documentation is available [here](https://juliadatacubes.github.io/ESDL.jl/latest/).
+The Documentation is available [here](https://juliadatacubes.github.io/EarthDataLab.jl/latest/).
 
-[ci-img]: https://github.com/esa-esdl/ESDL.jl/workflows/CI/badge.svg
-[ci-url]: https://github.com/esa-esdl/ESDL.jl/actions?query=workflow%3ACI
+[ci-img]: https://github.com/juliadatacubes/EarthDataLab.jl/workflows/CI/badge.svg
+[ci-url]: https://github.com/juliadatacubes/EarthDataLab.jl/actions?query=workflow%3ACI

docs/make.jl

@@ -1,11 +1,11 @@
-using Documenter, ESDL
+using Documenter, EarthDataLab
 
 makedocs(
-    modules  = [ESDL],
+    modules  = [EarthDataLab],
     clean    = true,
     doctest  = false,
     format   = Documenter.HTML(),
-    sitename = "ESDL.jl",
+    sitename = "EarthDataLab.jl",
     authors  = "Fabian Gans",
     pages    = [ 
         "Home" => "index.md",
@@ -24,6 +24,6 @@ makedocs(
 )
 
 deploydocs(
-    repo   = "github.com/JuliaDataCubes/ESDL.jl.git",
+    repo   = "github.com/JuliaDataCubes/EarthDataLab.jl.git",
     push_preview = true
 )

docs/src/API/analysis.md

@@ -1,6 +1,6 @@
 # Analysis
 
-The ESDL package comes with a list of predefined methods for statistical analysis.
+The EarthDataLab package comes with a list of predefined methods for statistical analysis.
 The functions are defined to work on specific axes. For example a function that removes the
 mean annual cycle, will always extract one time series after the other from a cube, process them, store the results and concatenate the resulting time series to a new output cube. It does not matter which other axes are defined
 in the input cube, the function will simply iterate over these.
@@ -19,19 +19,19 @@ that might be of interest to a broader community, please feel free to open a pul
 All of these functions take a data cube as an argument, process the **input axis** and replace it with the **output axis**.
 
 ```docs
-    ESDL.Proc.removeMSC
-    ESDL.Proc.gapFillMSC
-    ESDL.Proc.gapfillpoly!
-    ESDL.Proc.getMSC
-    ESDL.Proc.getMedSC
+    EarthDataLab.Proc.removeMSC
+    EarthDataLab.Proc.gapFillMSC
+    EarthDataLab.Proc.gapfillpoly!
+    EarthDataLab.Proc.getMSC
+    EarthDataLab.Proc.getMedSC
 ```
 
 ### Time series decomposition
 
 This function takes a data cube as an argument. It adds an additional dimension to the cube and returns it. Every variable, time step and location will have a set of four values in the new dimension instead of just one.
 
 ```docs
-    ESDL.Proc.filterTSFFT
+    EarthDataLab.Proc.filterTSFFT
 ```
 
 ## Simple Statistics
@@ -54,8 +54,8 @@ call one of the [(Weighted-)OnlineStats](@ref) methods.
 An additional simple statistic function is:
 
 ```@docs
-    ESDL.Proc.normalizeTS
-    ESDL.Proc.quantile
+    EarthDataLab.Proc.normalizeTS
+    EarthDataLab.Proc.quantile
 ```
 
 ## (Weighted-)OnlineStats
@@ -166,7 +166,7 @@ In order to understand how these principles are applied, let us walk through a v
 2. So the function will have to accept two arguments, which will be called `xin` for the input time series and `xout` for the output time series. Such a function can be defined like this:
 
 ```julia
-using ESDL
+using EarthDataLab
 
 function mynorm(xout, xin)
 
@@ -302,13 +302,13 @@ In some cases one needs to have access to the value of an axis. For example when
 are important to determine grid cell weights. To do this, one can pass a cube axis to mapCube as if it was a cube having only one dimension. The values will then correspond to the axis values (the latitudes in degrees in this case).
 
 ```julia
-using ESDL # hide
+using EarthDataLab # hide
 function spatialAggregation(xout::Array{T,0}, xin::Matrix, latitudes::AbstractVector) where T
   #code goes here
 end
 
 #Extract the latitude axis
-latitudecube = ESDL.getAxis("Lat",cube)
+latitudecube = EarthDataLab.getAxis("Lat",cube)
 
 indims_map = InDims(LonAxis, LatAxis)
 indims_lat = InDims(LatAxis)
@@ -345,7 +345,7 @@ So far in our examples we always re-used axes from the input cube as output cube
 So for each pixel we want to do the polynomial regression on the two variables and then return a vector of coefficients. We define the function that does the calculation as:
 
 ```julia
-using ESDL
+using EarthDataLab
 using Polynomials
 function fit_npoly(xout, var1, var2, n)
   p = polyfit(var1, var2, n)
@@ -409,4 +409,4 @@ Total size: 320.0 bytes
 ```
 
 
-This is exactly the way the built-in functions in [Analysis](@ref) were generated. So in case you want to contribute some functionality that you feel would benefit this package, please open a pull request at https://github.com/esa-esdl/ESDL.jl
+This is exactly the way the built-in functions in [Analysis](@ref) were generated. So in case you want to contribute some functionality that you feel would benefit this package, please open a pull request at https://github.com/juliadatacubes/EarthDataLab.jl

docs/src/API/cube_access.md

@@ -6,11 +6,11 @@ Before one can read data from a cube, it has to be opened. To open a data cube
 which is accesible through the file system, use the `Cube` constructor:
 
  ```julia
-using ESDL
+using EarthDataLab
 c = Cube("/patch/to/cube")
 ```
 ```
-ESDL data cube at /Net/Groups/BGI/scratch/DataCube/v0.2.0/high-res/
+EarthDataLab data cube at /Net/Groups/BGI/scratch/DataCube/v0.2.0/high-res/
 Spatial resolution:  4320x2160 at 0.083333 degrees.
 Temporal resolution: 2001-01-01T00:00:00 to 2012-01-01T00:00:00 at 8daily time steps
 Variables:           aerosol_optical_thickness_1610 aerosol_optical_thickness_550 aerosol_optical_thickness_555 aerosol_optical_thickness_659 aerosol_optical_thickness_865 air_temperature_2m bare_soil_evaporation black_sky_albedo burnt_area c_emissions country_mask evaporation evaporative_stress fractional_snow_cover gross_primary_productivity interception_loss land_surface_temperature latent_energy net_ecosystem_exchange open_water_evaporation ozone potential_evaporation precipitation root_moisture sensible_heat snow_sublimation snow_water_equivalent soil_moisture surface_moisture terrestrial_ecosystem_respiration transpiration water_mask water_vapour white_sky_albedo
@@ -21,7 +21,7 @@ This returns a `Cube` object that contains some basics information about the cub
 ## Getting a data handle
 
 ```julia
-using ESDL
+using EarthDataLab
 c=Cube()
 ```
 
@@ -50,7 +50,7 @@ needed for the analysis. One can extract such a list by first creating a cube vi
 containing all the needed variables and then apply the `extractLonLats` function.
 
 ```@docs
-    ESDL.Proc.extractLonLats
+    EarthDataLab.Proc.extractLonLats
 ```
 
 Here is an example how to apply the function:
@@ -71,7 +71,7 @@ The returned type will depend on the size of the returned cube. If it is small e
 
 ## Cube Dimensions
 
-Dimensions are an essential part of each Cube in ESDL. Every dimension that a cube has is associated
+Dimensions are an essential part of each Cube in EarthDataLab. Every dimension that a cube has is associated
 with an axis that stores the values of the dimension. For example, a `LatAxis` will contain a
 field `values` representing the chosen latitudes. Similarly, a `VariableAxis` will contain a list of
 Variable names. Axes types are divided in categorical axes and axes represented by ranges. All of them
@@ -80,5 +80,5 @@ are subtypes of the abstract type `CubeAxis`.
 ## List of known regions
 
 ```@docs
-    ESDL.known_regions
+    EarthDataLab.known_regions
 ```

docs/src/API/iotools.md

@@ -1,3 +1,3 @@
 # Loading and saving results
 
-    ESDL.ESDLTools.@loadOrGenerate
+    EarthDataLab.EarthDataLabTools.@loadOrGenerate

docs/src/examples/distributed.md

@@ -10,7 +10,7 @@ addprocs(2)
 
 @everywhere using Pkg
 @everywhere Pkg.activate(".")
-@everywhere using ESDL
+@everywhere using EarthDataLab
 @everywhere using Statistics
 
 @everywhere function mymean(output, pixel)
@@ -45,7 +45,7 @@ addprocs(SlurmManager(10))
 
 @everywhere using Pkg
 @everywhere Pkg.activate(".")
-@everywhere using ESDL
+@everywhere using EarthDataLab
 @everywhere using Statistics
 
 inpath="zg1000_AERday_CanESM5_esm-hist_r6i1p1f1_gn_18500101-20141231.nc"

docs/src/examples/time_mean.md

@@ -1,7 +1,7 @@
 ## How to calculate a time mean
 
 ````@jldoctest
-using ESDL
+using EarthDataLab
 c = Cube()
 citaly = c[var = ["air_temperature_2m", "evaporation"], region="Italy", time=2001:2003]
 mapslices(mean ∘ skipmissing, c, dims="Time")

docs/src/index.md

@@ -1,4 +1,4 @@
-# ESDL.jl
+# EarthDataLab.jl
 
 *A package to explore and analyze the Earth System Data Cube*
 
@@ -13,7 +13,7 @@ for more information.
 
 ## Example Jupyter Notebooks
 
-You can find some example Jupyter Notebooks using *ESDL.jl* [here](https://github.com/esa-esdl/esdl-shared/tree/master/notebooks)
+You can find some example Jupyter Notebooks using *ESDL.jl* [here](https://github.com/juliadatacubes/esdl-shared/tree/master/notebooks)
 
 ## Manual Outline
 

src/ESDLTools.jl

@@ -1,6 +1,6 @@
 module ESDLTools
 using Distributed
-import ..ESDL: ESDLdir
+import ..EarthDataLab: ESDLdir
 export freshworkermodule, passobj, @everywhereelsem,
 @loadOrGenerate, PickAxisArray
 struct PickAxisArray{T,N,AT<:AbstractArray,P,PERM}
@@ -68,7 +68,7 @@ Base.getindex(p::PickAxisArray,i::CartesianIndex) = p[i.I...]
 Takes a list of `variablename=>"Storage Name"` pairs. Checks if all datasets can be found
 on disk and loads them. If not, the datasets will be regenerated by evaluating the given expression.
 
-To force recalculation, call `ESDL.recalculate(true)` before evaluating the macro.
+To force recalculation, call `EarthDataLab.recalculate(true)` before evaluating the macro.
 
 ### Example
 
@@ -104,7 +104,7 @@ macro loadOrGenerate(x...)
   end
   rmEx=Expr(:block,rmEx...)
   esc(quote
-    if !ESDL.recalculate() && all(i->isdir(joinpath(ESDLdir(),i)),$xnames)
+    if !EarthDataLab.recalculate() && all(i->isdir(joinpath(ESDLdir(),i)),$xnames)
       $loadEx
     else
       $rmEx

src/ESDL.jl → src/EarthDataLab.jl

@@ -4,7 +4,7 @@
 
 Some info on the project...
 """
-module ESDL
+module EarthDataLab
 
 include("Proc.jl")
 include("countrydict.jl")

test/Project.toml

@@ -4,7 +4,7 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 DiskArrayTools = "fcd2136c-9f69-4db6-97e5-f31981721d63"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
-ESDL = "359177bc-a543-11e8-11b7-bb015dba3358"
+EarthDataLab = "359177bc-a543-11e8-11b7-bb015dba3358"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 NetCDF = "30363a11-5582-574a-97bb-aa9a979735b9"
 OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e"

test/analysis.jl

@@ -1,11 +1,11 @@
-using ESDL
+using EarthDataLab
 using Test
 using Dates
 import Base.Iterators
 using Distributed
 using Statistics
 addprocs(2)
-@everywhere using ESDL, Statistics, NetCDF
+@everywhere using EarthDataLab, Statistics, NetCDF
 
 @everywhere function sub_and_return_mean(xout1,xout2,xin)
     m=mean(skipmissing(xin))

test/artype.jl

@@ -1,4 +1,4 @@
-using ESDL
+using EarthDataLab
 using Test
 import DataFrames: DataFrame,aggregate
 import Dates: year

test/online.jl

@@ -1,4 +1,4 @@
-using ESDL
+using EarthDataLab
 using Test
 using Random
 using Statistics
@@ -39,7 +39,7 @@ covmat,means = mapCube(CovMatrix,dm,MDAxis=VariableAxis)
 
 @test all(isapprox.(covmat.data,cov(reshape(dm.data,length(dm.data) ÷ 2,2))))
 
-using ESDL
+using EarthDataLab
 using DataStructures
 c=Cube()
 d2 = getCubeData(c,variable=["air_temperature_2m","gross_primary_productivity"],longitude=(30,31),latitude=(50,51),

test/runtests.jl

@@ -1,4 +1,4 @@
-using ESDL, NetCDF, YAXArrays
+using EarthDataLab, NetCDF, YAXArrays
 using Test
 
 newcubedir = mktempdir()

test/tabletocube.jl

@@ -1,4 +1,4 @@
-using ESDL
+using EarthDataLab
 using Test
 using Dates
 using IterTools

test/transform.jl

@@ -1,4 +1,4 @@
-using ESDL
+using EarthDataLab
 using Test
 using Statistics
 
@@ -14,8 +14,8 @@ d3 = subsetcube(c,variable="net_ecosystem_exchange",lon=(10,11),lat=(50,51),
                 time=(Date("2002-01-01"),Date("2007-12-31")))
 conccube = concatenatecubes([d1,d2],CategoricalAxis("NewAxis",["v1","v2"]))
 @test size(conccube)==(4,4,322,2)
-@test ESDL.caxes(conccube)[1:3]==ESDL.caxes(d1)
-@test ESDL.caxes(conccube)[4]==CategoricalAxis("NewAxis",["v1","v2"])
+@test EarthDataLab.caxes(conccube)[1:3]==EarthDataLab.caxes(d1)
+@test EarthDataLab.caxes(conccube)[4]==CategoricalAxis("NewAxis",["v1","v2"])
 @test_throws ErrorException concatenatecubes([d1,d3],CategoricalAxis("NewAxis",["v1","v2"]))
 dd1 = readcubedata(d1)
 dd2 = readcubedata(d2)
@@ -24,10 +24,10 @@ mout = zeros(UInt8,4,4,322,2)
 ddconc = readcubedata(conccube)
 @test ddconc.data[:,:,:,1] == dd1.data
 @test ddconc.data[:,:,:,2] == dd2.data
-#@test isa(ESDL.Cubes.gethandle(conccube,(2,2,2,2)),ESDL.CubeAPI.CachedArrays.CachedArray)
+#@test isa(EarthDataLab.Cubes.gethandle(conccube,(2,2,2,2)),EarthDataLab.CubeAPI.CachedArrays.CachedArray)
 @test mean(ddconc.data,dims=1)[1,:,:,:]==mapslices(mean∘skipmissing,conccube,dims="Lon").data
 conccube2 = concatenatecubes([dd1,dd2],CategoricalAxis("NewAxis",["v1","v2"]))
-#@test isa(ESDL.Cubes.gethandle(conccube2,(2,2,2,2)),Tuple{AbstractArray,AbstractArray})
+#@test isa(EarthDataLab.Cubes.gethandle(conccube2,(2,2,2,2)),Tuple{AbstractArray,AbstractArray})
 @test mean(ddconc.data,dims=1)[1,:,:,:]==mapslices(mean∘skipmissing,conccube2,dims="Lon").data
 end
 end