updates v org change, add Argopy demo, streamline docs

Project.toml

@@ -11,8 +11,8 @@ Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 FTPClient = "01fcc997-4f28-56b8-8a06-30002c134abb"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
-MeshArrays = "cb8c808f-1acf-59a3-9d2b-6e38d009f683"
 MITgcmTools = "62725fbc-3a66-4df3-9000-e33e85b3a198"
+MeshArrays = "cb8c808f-1acf-59a3-9d2b-6e38d009f683"
 NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab"
 OceanStateEstimation = "891f6deb-a4f5-4bc5-a2e3-1e8f649cdd2c"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
@@ -27,8 +27,8 @@ DataFrames = "0.20, 0.21, 0.22, 1"
 FTPClient = "1"
 Glob = "1"
 Interpolations = "0.13, 0.14"
-MeshArrays = "0.2"
 MITgcmTools = "0.2"
+MeshArrays = "0.2"
 NCDatasets = "0.10, 0.11, 0.12"
 OceanStateEstimation = "0.3"
 OrderedCollections = "1"

README.md

@@ -1,17 +1,23 @@
 # ArgoData
 
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaOcean.github.io/ArgoData.jl/stable)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaOcean.github.io/ArgoData.jl/dev)
-[![CI](https://github.com/JuliaOcean/ArgoData.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/JuliaOcean/ArgoData.jl/actions/workflows/ci.yml)
-[![Codecov](https://codecov.io/gh/JuliaOcean/ArgoData.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaOcean/ArgoData.jl)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://euroargodev.github.io/ArgoData.jl/stable)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://euroargodev.github.io/ArgoData.jl/dev)
+[![CI](https://github.com/euroargodev/ArgoData.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/euroargodev/ArgoData.jl/actions/workflows/ci.yml)
+[![Codecov](https://codecov.io/gh/euroargodev/ArgoData.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/euroargodev/ArgoData.jl)
 [![DOI](https://zenodo.org/badge/237021498.svg)](https://zenodo.org/badge/latestdoi/237021498)
 
 Argo data processing and analysis. 
 
-- The `GDAC` module functions access and retrieve files from the Argo data servers. 
-- `MITprof` supports the format of [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015) for standard depth data sets like [this one](https://doi.org/10.7910/DVN/EE3C40).
-- `AnalysisMethods` compute e.g. cost functions and geospatial statistics.
-- The `MITprof_plots` module in `examples/` is to plot results.
+Analysis and Processing of 
+[Argo](https://argopy.readthedocs.io/en/latest/what_is_argo.html#what-is-argo) ocean data sets.
+
+## Contents
+
+- `GDAC` module to access and retrieve files from Argo server. 
+- `MITprof` format of [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015) for [standard depth data](https://doi.org/10.7910/DVN/EE3C40).
+- `MITprof_plots` module (in `examples/`) for `MITprof`.
+- `AnalysisMethods` for cost functions and geospatial statistics.
+- notebooks : [argopy interface](Argo_argopy.html), and more.
 
 _This package is in early developement stage when breaking changes can be expected._
 

docs/Project.toml

@@ -1,2 +1,3 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+PlutoSliderServer = "2fc8631c-6f24-4c5b-bca7-cbb509c42db4"

docs/make.jl

@@ -1,21 +1,36 @@
-using Documenter, ArgoData
+using Documenter, ArgoData, PlutoSliderServer
 
+#make docs
 makedocs(;
     modules=[ArgoData],
     format=Documenter.HTML(),
     pages=[
         "Home" => "index.md",
+        "Modules" => "modules.md",
+        "Reference" => "functions.md",
     ],
-    repo="https://github.com/JuliaOcean/ArgoData.jl/blob/{commit}{path}#L{line}",
+    repo="https://github.com/euroargodev/ArgoData.jl/blob/{commit}{path}#L{line}",
     sitename="ArgoData.jl",
     authors="gaelforget <[email protected]>",
     assets=String[],
 )
 
+#create csv list of floats
 files_list=GDAC.files_list()
 GDAC.CSV.write("Argo_float_files.csv",files_list)
 mv("Argo_float_files.csv",joinpath(@__DIR__,"build", "Argo_float_files.csv"))
 
+#run notebooks
+lst=("Argo_argopy.jl",)
+for i in lst
+    fil_in=joinpath(@__DIR__,"..", "examples",i)
+    fil_out=joinpath(@__DIR__,"build", i[1:end-2]*"html")
+    PlutoSliderServer.export_notebook(fil_in)
+    mv(fil_in[1:end-2]*"html",fil_out)
+    cp(fil_in,fil_out[1:end-4]*"jl")
+end
+
+#deploy docs
 deploydocs(;
-    repo="github.com/JuliaOcean/ArgoData.jl",
+    repo="github.com/euroargodev/ArgoData.jl",
 )

docs/src/functions.md

@@ -0,0 +1,17 @@
+
+## Functions
+
+```@index
+```
+
+```@docs
+ProfileNative
+ProfileStandard
+MITprofStandard
+```
+
+```@autodocs
+Modules = [GDAC,MITprof,AnalysisMethods]
+Order   = [:type,:function]
+```
+

docs/src/index.md

@@ -1,69 +1,23 @@
 # ArgoData.jl
 
-Argo data processing and analysis. Currently provides:
+Analysis and Processing of 
+[Argo](https://argopy.readthedocs.io/en/latest/what_is_argo.html#what-is-argo) ocean data sets.
 
-- The `GDAC` module functions access and retrieve files from the Argo data servers. 
-- `MITprof` supports the format of [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015) for standard depth data sets like [this one](https://doi.org/10.7910/DVN/EE3C40).
-- `AnalysisMethods` compute e.g. cost functions and geospatial statistics.
-- The `MITprof_plots` module in `examples/` is to plot results.
+## Contents
 
-_This package is in early developement stage when breaking changes can be expected._
-
-## Workflows
-
-Downloading and accessing an Argo file (`wmo=13857` from `folder="aoml"`) is done like this.
-
-```
-using ArgoData
-input_file=GDAC.download_file("aoml",13857)
-
-using NCDatasets
-Dataset(input_file)
-```
-
-A list of all `folder,wmo` pairs can be obtained using `files_list=GDAC.files_list()`. And a method to download files in bulk & parallel is presented in [examples/Argo\_distributed\_download.jl](https://github.com/JuliaOcean/ArgoData.jl/blob/master/examples/Argo_distributed_download.jl).
-
-### MITprof Format
-
-Formatting an Argo file (`input_file`) into an MITprof file (`output_file`) proceeds as follows.
-
-```
-gridded_fields=GriddedFields.load()
-output_file=MITprof.format(gridded_fields,input_file)
-```
+- `GDAC` module to access and retrieve files from Argo server. 
+- `MITprof` format of [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015) for [standard depth data](https://doi.org/10.7910/DVN/EE3C40).
+- `MITprof_plots` module (in `examples/`) for `MITprof`.
+- `AnalysisMethods` for cost functions and geospatial statistics.
+- notebooks : [argopy interface](Argo_argopy.html), and more.
 
-The file generated by the previous command can be accessed normally as a NetCDF file (e.g., `Dataset(output_file)`) or using the convenient `MITprofStandard` data structure.
-
-```
-mp=MITprofStandard(output_file)
-```
-
-For additional detail, please refer to the [examples/ArgoToMITprof.jl](https://github.com/JuliaOcean/ArgoData.jl/blob/master/examples/ArgoToMITprof.jl) example.
-
-### Download MITprof files
-
-The original collection of MITprof files from [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015) is archived [here](https://doi.org/10.7910/DVN/EE3C40). These files can be retrieved as follows.
-
-```
-using CSV, DataFrames
-tmp = CSV.File("examples/dataverse_files.csv") |> DataFrame
-url0="https://dataverse.harvard.edu/api/access/datafile/"
-run(`wget --content-disposition $(url0)$(tmp[1,:ID])`)
-```
-
-## Functions
-
-```@index
-```
+_This package is in early developement stage when breaking changes can be expected._
 
-```@docs
-ProfileNative
-ProfileStandard
-MITprofStandard
-```
+Argo Float Positions            | Argo Float Profiles (T, S, ...)
+:------------------------------:|:---------------------------------:
+![float positions](https://user-images.githubusercontent.com/20276764/150622726-61169b99-4320-4069-b113-5edabb9b64fe.png) | ![salinity profiles](https://user-images.githubusercontent.com/20276764/150622766-aee5773d-7fea-4360-9b47-05f68e235499.png)   
 
-```@autodocs
-Modules = [GDAC,MITprof,AnalysisMethods]
-Order   = [:type,:function]
-```
+Argo Profile Distributions |  Cost Funtions & Uncertainties
+:------------------------------:|:---------------------------------:
+![distributions](https://user-images.githubusercontent.com/20276764/162872972-dd7fc775-5303-4264-8277-142c02bc1b83.png)  |  ![cost pdf](https://user-images.githubusercontent.com/20276764/162803583-13891235-4809-4a57-b5f6-098083190d6d.png)
 

docs/src/modules.md

@@ -0,0 +1,42 @@
+
+## Workflows
+
+Downloading and accessing an Argo file (`wmo=13857` from `folder="aoml"`) is done like this.
+
+```
+using ArgoData
+input_file=GDAC.download_file("aoml",13857)
+
+using NCDatasets
+Dataset(input_file)
+```
+
+A list of all `folder,wmo` pairs can be obtained using `files_list=GDAC.files_list()`. And a method to download files in bulk & parallel is presented in [examples/Argo\_distributed\_download.jl](https://github.com/euroargodev/ArgoData.jl/blob/master/examples/Argo_distributed_download.jl).
+
+### MITprof Format
+
+Formatting an Argo file (`input_file`) into an MITprof file (`output_file`) proceeds as follows.
+
+```
+gridded_fields=GriddedFields.load()
+output_file=MITprof.format(gridded_fields,input_file)
+```
+
+The file generated by the previous command can be accessed normally as a NetCDF file (e.g., `Dataset(output_file)`) or using the convenient `MITprofStandard` data structure.
+
+```
+mp=MITprofStandard(output_file)
+```
+
+For additional detail, please refer to the [examples/ArgoToMITprof.jl](https://github.com/euroargodev/ArgoData.jl/blob/master/examples/ArgoToMITprof.jl) example.
+
+### Download MITprof files
+
+The original collection of MITprof files from [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015) is archived [here](https://doi.org/10.7910/DVN/EE3C40). These files can be retrieved as follows.
+
+```
+using CSV, DataFrames
+tmp = CSV.File("examples/dataverse_files.csv") |> DataFrame
+url0="https://dataverse.harvard.edu/api/access/datafile/"
+run(`wget --content-disposition $(url0)$(tmp[1,:ID])`)
+```