Merge pull request #242 from JuliaClimate/Balinus-yaxarrays

Update ClimateTools to use YAXArrays backend

.github/workflows/ci-nightly.yml

@@ -38,6 +38,6 @@ jobs:
             ${{ runner.os }}-test-${{ env.cache-name }}-
             ${{ runner.os }}-test-
             ${{ runner.os }}-
-      - run: pip3 install  scipy      
+      #- run: pip3 install  scipy      
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1

.github/workflows/ci.yml

@@ -23,16 +23,16 @@ jobs:
     steps:
       - uses: actions/checkout@v2           
 
-      - name: Display Python version
-        run: python -c "import sys; print(sys.version)"
+      #- name: Display Python version
+      #  run: python -c "import sys; print(sys.version)"
 
-      - name: Set ENV Variables for 🐍 📞
-        run: echo ENV["PYTHON"] = "${{ env.pythonLocation }}/bin/python" >> $GITHUB_ENV
+      #- name: Set ENV Variables for 🐍 📞
+      #  run: echo ENV["PYTHON"] = "${{ env.pythonLocation }}/bin/python" >> $GITHUB_ENV
 
-      - name: Install dependencies 🔧
-        run: |
-          python -m pip install --upgrade pip
-          pip install scipy
+      #- name: Install dependencies 🔧
+      #  run: |
+      #    python -m pip install --upgrade pip
+      #    pip install scipy
 
       - uses: julia-actions/setup-julia@v1
         with:
@@ -49,8 +49,8 @@ jobs:
             ${{ runner.os }}-test-
             ${{ runner.os }}-      
       - uses: julia-actions/julia-buildpkg@v1
-        env:
-          PYTHON : "${{ env.pythonLocation }}/bin/python"
+        #env:
+        #  PYTHON : "${{ env.pythonLocation }}/bin/python"
 
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1

Project.toml

@@ -2,50 +2,29 @@ name = "ClimateTools"
 uuid = "4f4ee721-4970-5af2-8560-6c1d960e3231"
 authors = ["Philippe Roy <[email protected]>"]
 repo = "https://github.com/JuliaClimate/ClimateTools.jl.git"
-version = "0.24.1"
+version = "0.24.2"
 
 [deps]
-ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
-AxisArrays = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
-#ClimateBase = "35604d93-0fb8-4872-9436-495b01d137e2"
+CFTime = "179af706-886a-5703-950a-314cd64e0468"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
-Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-Extremes = "fe3fe864-1b39-11e9-20b8-1f96fa57382d"
-HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
+DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
-IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
-NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab"
-NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
+LombScargle = "fc60dff9-86e7-5f2f-a8a0-edeadbb75bd9"
+LongMemory = "f5f8e4a8-cb56-40ea-a13e-090cc212d358"
+MarSwitching = "b2b8a7f1-da70-4e5f-beaa-e2774ec39c2f"
 NetCDF = "30363a11-5582-574a-97bb-aa9a979735b9"
-Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Polynomials = "f27b6e38-b328-58d1-80ce-0feddd5e7a45"
-ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
-PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-RollingFunctions = "b0e4dd01-7b14-53d8-9b45-175a3e362653"
-Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+YAXArrays = "c21b50f5-aa40-41ea-b809-c0f5e47bfa5c"
+Zarr = "0a941bbe-ad1d-11e8-39d9-ab76183a1d99"
 
 [compat]
-ArgCheck = "1, 2"
-AxisArrays = "0.3, 0.4"
-#ClimateBase = "0.6"
-DataFrames = "0.19, 0.20, 0.21, 1"
-Distances = "0.7, 0.8, 0.9, 0.10"
-Extremes = "0.3"
-HDF5 = "0.15, 0.16"
-Interpolations = "0.12, 0.13, 0.14"
-IterTools = "1"
-NCDatasets = "0.9, 0.10, 0.11, 0.12"
-NaNMath = "0.3, 1"
-NetCDF = "0.7, 0.8, 0.9, 0.10, 0.11"
-Polynomials = "0.5, 0.6, 0.7, 0.8, 1"
-ProgressMeter = "1"
-PyCall = "1"
-Reexport = "0.2, 1"
-RollingFunctions = "0.6, 0.7"
-Shapefile = "0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.10"
-julia = "1.5, 1.6, 1.7, 1.8, 1.9"
+julia = "1.10"
+
+[extras]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Test"]

appveyor.yml

@@ -1,7 +1,7 @@
 environment:
   matrix:  
   - julia_version: 1.6
-  - julia_version: 1.9
+  - julia_version: 1.10
   - julia_version: nightly
 
 platform:
@@ -11,7 +11,7 @@ platform:
 # (tests will run but not make your overall status red)
 matrix:
   allow_failures:
-  - julia_version: 1.6
+  - julia_version: 1.10
   - julia_version: nightly
 
 branches:

deps/build.jl

@@ -1,9 +1,9 @@
-using Pkg
-using PyCall
+# using Pkg
+# using PyCall
 
-if lowercase(get(ENV, "CI", "false")) == "true"
+# if lowercase(get(ENV, "CI", "false")) == "true"
 
-        ENV["PYTHON"] = ""
-        Pkg.build("PyCall")
+#         ENV["PYTHON"] = ""
+#         Pkg.build("PyCall")
 
-end
+# end

src/ClimateTools.jl

@@ -1,209 +1,38 @@
 module ClimateTools
 
-# External modules
-using Reexport
+using CFTime
+using Dates
+using DimensionalData
+using LombScargle
+using LongMemory
+using Statistics
+using YAXArrays
 using NetCDF
-@reexport using NCDatasets
-using Shapefile
-using AxisArrays
-using NaNMath
-const axes = Base.axes
-using ArgCheck
-using DataFrames
-using Interpolations
-using ProgressMeter
+using Zarr
 using Polynomials
-using IterTools
-using Statistics
-using Random
-using Dates
-# using GeoStats
-# using InverseDistanceWeighting
-using Extremes
-using Distances
-using PyCall
-using RollingFunctions
-import Base.vcat
-import Base.getindex
-import Base.show
-import Base.size
-import Base.setindex!
-import Base.similar
-import Base.write
-import Statistics.minimum
-import Statistics.maximum
-import Statistics.std
-import Statistics.var
-import Statistics.mean
-import Statistics.quantile
-import Base: +
-import Base: -
-import Base: *
-import Base: /
-import Base.findmax
-
-const scipy = PyNULL()
-
-function __init__()
-    copy!(scipy, pyimport_conda("scipy.interpolate", "scipy"))
-end
-
-
-# TYPES
-
-"""
-    ClimGrid{A <: AxisArray}
-In-memory representation of Climate Forecast netCDF files.
-struct ClimGrid\n
-  data::AxisArray # Data\n
-  longrid::AbstractArray{N,2} where N # the longitude grid\n
-  latgrid::AbstractArray{N,2} where N # the latitude grid\n
-  msk::Array{N, 2} where N # Data mask (NaNs and 1.0)\n
-  grid_mapping::Dict#{String, Any} # bindings for native grid\n
-  dimension_dict::Dict\n
-  model::String\n
-  frequency::String # Day, month, years\n
-  experiment::String # Historical, RCP4.5, RCP8.5, etc.\n
-  run::String\n
-  project::String # CORDEX, CMIP5, etc.\n
-  institute::String # UQAM, DMI, etc.\n
-  filename::String # Path of the original file\n
-  dataunits::String # Celsius, kelvin, etc.\n
-  latunits::String # latitude coordinate unit\n
-  lonunits::String # longitude coordinate unit\n
-  variable::String # Type of variable (i.e. can be the same as "typeofvar", but it is changed when calculating indices)\n
-  typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)\n
-  typeofcal::String # Calendar type\n
-  timeattrib::Dict # Time attributes (e.g. days since ... )\n
-  varattribs::Dict # Variable attributes dictionary\n
-  globalattribs::Dict # Global attributes dictionary\n
-end\n
-"""
-struct ClimGrid{A <: AxisArray}
-  data::A
-  longrid::Array{N,T} where T where N
-  latgrid::Array{N,T} where T where N
-  msk::Array{N,T} where T where N
-  grid_mapping::Dict # information of native grid
-  dimension_dict::Dict
-  timeattrib::Dict
-  model::String
-  frequency::String
-  experiment::String
-  run::String
-  project::String
-  institute::String
-  filename::String
-  dataunits::String
-  latunits::String # of the coordinate variable
-  lonunits::String # of the coordinate variable
-  variable::String # Type of variable
-  typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)
-  typeofcal::String # Calendar type
-  varattribs::Dict # Variable attributes
-  globalattribs::Dict # Global attributes
-
-end
-
-"""
-    ClimGrid(data; longrid=[], latgrid=[], msk=[], grid_mapping=Dict(), dimension_dict=Dict(), model="NA", frequency="NA", experiment="NA", run="NA", project="NA", institute="NA", filename="NA", dataunits="NA", latunits="NA", lonunits="NA", variable="NA", typeofvar="NA", typeofcal="NA", varattribs=Dict(), globalattribs=Dict())
-Constructor of the ClimGrid function. Data is an AxisArray. Everything else is optional, but usually needed for further processing (mapping, interpolation, etc...).
-struct ClimGrid\n
-  data::AxisArray # Data \n
-  longrid::AbstractArray{N,2} where N # the longitude grid \n
-  latgrid::AbstractArray{N,2} where N # the latitude grid \n
-  msk::Array{N, 2} where N # Data mask (NaNs and 1.0) \n
-  grid_mapping::Dict#{String, Any} # bindings for native grid \n
-  dimension_dict::Dict\n
-  model::String\n
-  frequency::String # Day, month, years\n
-  experiment::String # Historical, RCP4.5, RCP8.5, etc.\n
-  run::String\n
-  project::String # CORDEX, CMIP5, etc.\n
-  institute::String # UQAM, DMI, etc.\n
-  filename::String # Path of the original file\n
-  dataunits::String # Celsius, kelvin, etc.\n
-  latunits::String # latitude coordinate unit\n
-  lonunits::String # longitude coordinate unit\n
-  variable::String # Type of variable (i.e. can be the same as "typeofvar", but it is changed when calculating indices)\n
-  typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)\n
-  typeofcal::String # Calendar type\n
-  timeattrib::Dict # Time attributes (e.g. days since ... )\n
-  varattribs::Dict # Variable attributes dictionary\n
-  globalattribs::Dict # Global attributes dictionary\n
-end\n
-"""
-function ClimGrid(data; longrid=[], latgrid=[], msk=[], grid_mapping=Dict(), dimension_dict=Dict(), timeattrib=Dict(), model="NA", frequency="NA", experiment="NA", run="NA", project="NA", institute="NA", filename="NA", dataunits="NA", latunits="NA", lonunits="NA", variable="NA", typeofvar="NA", typeofcal="NA", varattribs=Dict(), globalattribs=Dict())
-
-    if isempty(dimension_dict)
-        dimension_dict = Dict(["lon" => "lon", "lat" => "lat"])
-    end
-
-    if isempty(msk)
-        msk = Array{Float64}(ones((size(data, 1), size(data, 2))))
-    end
-
-    ClimGrid(data, longrid, latgrid, msk, grid_mapping, dimension_dict, timeattrib, model, frequency, experiment, run, project, institute, filename, dataunits, latunits, lonunits, variable, typeofvar, typeofcal, varattribs, globalattribs)
-end
+using Interpolations
+using DataFrames
+using MarSwitching
 
-# Included files
-include("functions.jl")
-include("indices.jl")
-include("indicators.jl")
-include("extract.jl")
-include("interface.jl")
-include("cf_conventions.jl")
+include("aggregate.jl")
+include("autocorrelation.jl")
 include("biascorrect.jl")
-include("export.jl")
-include("time.jl")
-include("spatial.jl")
-include("analysis.jl")
+include("climatology.jl")
+include("ensembles.jl")
+#include("functions.jl")
+include("markov.jl")
+include("power.jl")
+include("plotting.jl")
+include("processERA5.jl")
+include("utils.jl")
 
-export ClimGrid
-export periodmean
-export finitemean
-export temporalsubset
-export verticalmean
-export get_timevec
-export buildtimetype
-export timeindex
-export buildarray_climato
-export buildarrayinterface
-export buildarray_annual
-export buildarray_resample
-export inpoly, inpolygrid, meshgrid, inpolyvec, ndgrid
-export findmax, findmin
-export frostdays, summerdays, icingdays, tropicalnights
-export daysabove10 #, daysbelow0, degdaysabove, degdaysbelow
-export RXday
-export vaporpressure, approx_surfacepressure
-export customthresover, customthresunder, annualmax, annualmin
-export annualmean, annualsum, prcp1
-export wbgt, diurnaltemperature, meantemperature
-export drought_dc
-export ensemble_mean, ensemble_std, ensemble_max, ensemble_min
-export load, load2D
-export regrid, applymask
-export griddata
-export shapefile_coords, shapefile_coords_poly
-export resample, spatialsubset, timestep
-export qqmap, qqmaptf
-export biascorrect_extremes
-export permute_west_east
-export getdim_lat, getdim_lon, getdim_tim, isdefined, extractpoly
-export get_dimname, get_calendar
-export polyfit, polyval
-export @isdefined
-export merge
-export minimum, maximum, std, var, mean
-export quantile
-export get_max_clusters
-export get_position_clusters
-export daymean, daysum
-export monthmean, monthsum, temporalmean
-export yearmonthdayhour
-export write, findmindist
 
+export daily_fct, climato_tp, subsample, dates_builder_yearmonth, dates_builder_yearmonth_hardcode, dates_builder_yearmonthday, dates_builder_yearmonthday_hardcode, diff, cumsum, yearly_clim
+export yearly_clim
+export qqmap, qqmap_bulk
+export ensemble_fct
+export autocorrelation
+export MSModel
 
 
-end #module
+end