Merge pull request #48 from euroargodev/v0p1p16a

V0p1p16a

Project.toml

@@ -12,8 +12,8 @@ FTPClient = "01fcc997-4f28-56b8-8a06-30002c134abb"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
-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"
@@ -25,12 +25,13 @@ YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 [compat]
 CSV = "0.6, 0.7, 0.8, 0.9, 0.10"
 DataFrames = "0.20, 0.21, 0.22, 1"
+Downloads = "1"
 FTPClient = "1"
 Glob = "1"
 Interpolations = "0.13, 0.14"
 JLD2 = "0.4"
-MITgcmTools = "0.2"
 MeshArrays = "0.2, 0.3"
+MITgcmTools = "0.2"
 NCDatasets = "0.10, 0.11, 0.12, 0.13"
 OceanStateEstimation = "0.3"
 OrderedCollections = "1"

docs/Project.toml

@@ -1,3 +1,5 @@
 [deps]
+Dataverse = "9c0b9be8-e31e-490f-90fe-77697562404d"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab"
 PlutoSliderServer = "2fc8631c-6f24-4c5b-bca7-cbb509c42db4"

docs/src/examples.md

@@ -7,52 +7,80 @@ The `One Argo Float` notebook demonstrates various functionalities of the `ArgoD
 
 Downloading and accessing an Argo file (`wmo=13857` from `folder="aoml"`) is done like this.
 
-```
+```@example main
 using ArgoData
 input_file=GDAC.download_file("aoml",13857)
+```
 
+You can then simply access the file content using [NCDatasets.jl](https://github.com/Alexander-Barth/NCDatasets.jl#readme).
+
+```@example main
 using NCDatasets
-Dataset(input_file)
+ds=Dataset(input_file)
+keys(ds)
 ```
 
 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).
 
 ## Argo on Standard Depth Levels
 
-### The `MITprof` Format
+A more complete version of the workflow presented below is in this notebook:
 
 👉 [from Argo to MITprof](../ArgoToMITprof.html) 👈 [(code)](https://raw.githubusercontent.com/euroargodev/ArgoData.jl/master/examples/ArgoToMITprof.jl)
 
-The MITprof format is a simple to use version of Argo where profiles have been converted to potential temperature and interpolated to [standard depth levels](https://juliaocean.github.io/OceanRobots.jl/dev/Float_Argo.html).
+### The `MITprof` Format
 
-Turning an Argo file (`input_file`) into an MITprof file (`output_file`) proceeds as follows.
+The MITprof format is a simple to use version of Argo where profiles have been converted to potential temperature and interpolated to [standard depth levels](https://juliaocean.github.io/OceanRobots.jl/dev/examples/Float_Argo.html).
 
-```
+Turning an Argo file (`input_file`) into an MITprof file (`output_file`) proceeds as follows. 
+
+1. gridded fields are retrieved. These climatologies enable quality control of the data and scientific applications in step 2.
+2. read the Argo file and process it. The observed profiles are interpolated to standard depth levels, converted to potential temperature, quality controled, and appended climatological profiles. 
+
+!!! note
+    For more detail on the use of climatologies, representation error estimation, and model-data `cost functions`, see Forget et al 2015, Forget 2011, Forget and Wunsch 2007.
+
+```@example main
+output_file=input_file[1:end-7]*"MITprof.nc" # hide
+isfile(output_file) ? mv(output_file,tempname()) : nothing # hide
 gridded_fields=GriddedFields.load()
 output_file=MITprof.format(gridded_fields,input_file)
+ds2=Dataset(output_file)
+keys(ds2)
 ```
 
 ### Associated Data Structure
 
 The generated file can be accessed normally as a NetCDF file (e.g., `Dataset(output_file)`) or using the convenient `MITprofStandard` data structure.
 
-```
+```@example main
 mp=MITprofStandard(output_file)
 ```
 
 ### Sample `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.
+The full set of MITprof profiles processed in 2023 from the [Argo](https://argo.ucsd.edu/) data base is available in [this Dataverse](https://doi.org/10.7910/DVN/7HLV09). This dataset can be explored and retrieved using [Dataverse.jl](https://github.com/gdcc/Dataverse.jl#readme).
 
+```@example
+using Dataverse
+doi="https://doi.org/10.7910/DVN/7HLV09"
+lst=Dataverse.file_list(doi)
+Dataverse.file_download(lst,lst.filename[2],tempdir())
 ```
-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])`)
+
+Another example is the original collection of MITprof files from [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015) is archived in [this Dataverse](https://doi.org/10.7910/DVN/EE3C40). This contains an earlier versio of Argo along with complementary datasets.
+
+```@example
+using Dataverse
+doi="https://doi.org/10.7910/DVN/EE3C40"
+lst=Dataverse.file_list(doi)
 ```
 
-## `argopy` Python API
+## Argo via Python API
+
+The python library called [argopy](https://img.shields.io/readthedocs/argopy?logo=readthedocs) provides more ways to access, manipulate, and visualize [Argo data](https://argopy.readthedocs.io/en/latest/what_is_argo.html#what-is-argo). The notebook below demonstrates how you can : 
 
-[argopy](https://img.shields.io/readthedocs/argopy?logo=readthedocs) is a python library to access, manipulate, and visualize [Argo data](https://argopy.readthedocs.io/en/latest/what_is_argo.html#what-is-argo). The notebook demonstrate how to (1) install `argopy` into `Julia` via [Conda.jl](https://github.com/JuliaPy/Conda.jl) and (2) use `argopy` via [PyCall.jl](https://github.com/JuliaPy/PyCall.jl).
+1. install `argopy` into `Julia` via [Conda.jl](https://github.com/JuliaPy/Conda.jl)
+2. use `argopy` from Julia via [PyCall.jl](https://github.com/JuliaPy/PyCall.jl)
 
 👉 [Notebook](http://gaelforget.net/notebooks/Argo_argopy.html) 👈 [(code)](https://raw.githubusercontent.com/euroargodev/ArgoData.jl/master/examples/Argo_argopy.jl)