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Merge pull request #35 from euroargodev/argopy
updates v org change, add Argopy demo, streamline docs
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| [deps] | ||
| Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" | ||
| PlutoSliderServer = "2fc8631c-6f24-4c5b-bca7-cbb509c42db4" |
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| using Documenter, ArgoData | ||
| using Documenter, ArgoData, PlutoSliderServer | ||
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| #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[], | ||
| ) | ||
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| #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")) | ||
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| #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 | ||
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| #deploy docs | ||
| deploydocs(; | ||
| repo="github.com/JuliaOcean/ArgoData.jl", | ||
| repo="github.com/euroargodev/ArgoData.jl", | ||
| ) |
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|
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| ## Functions | ||
|
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| ```@index | ||
| ``` | ||
|
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| ```@docs | ||
| ProfileNative | ||
| ProfileStandard | ||
| MITprofStandard | ||
| ``` | ||
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| ```@autodocs | ||
| Modules = [GDAC,MITprof,AnalysisMethods] | ||
| Order = [:type,:function] | ||
| ``` | ||
|
|
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| # ArgoData.jl | ||
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| 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. | ||
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| - 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 | ||
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| _This package is in early developement stage when breaking changes can be expected._ | ||
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| ## Workflows | ||
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| Downloading and accessing an Argo file (`wmo=13857` from `folder="aoml"`) is done like this. | ||
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| ``` | ||
| using ArgoData | ||
| input_file=GDAC.download_file("aoml",13857) | ||
| using NCDatasets | ||
| Dataset(input_file) | ||
| ``` | ||
|
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| 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). | ||
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| ### MITprof Format | ||
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| Formatting an Argo file (`input_file`) into an MITprof file (`output_file`) proceeds as follows. | ||
|
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| ``` | ||
| 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. | ||
|
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| 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. | ||
|
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| ``` | ||
| mp=MITprofStandard(output_file) | ||
| ``` | ||
|
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| For additional detail, please refer to the [examples/ArgoToMITprof.jl](https://github.com/JuliaOcean/ArgoData.jl/blob/master/examples/ArgoToMITprof.jl) example. | ||
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| ### Download MITprof files | ||
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| 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. | ||
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| ``` | ||
| 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])`) | ||
| ``` | ||
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| ## Functions | ||
|
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| ```@index | ||
| ``` | ||
| _This package is in early developement stage when breaking changes can be expected._ | ||
|
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| ```@docs | ||
| ProfileNative | ||
| ProfileStandard | ||
| MITprofStandard | ||
| ``` | ||
| Argo Float Positions | Argo Float Profiles (T, S, ...) | ||
| :------------------------------:|:---------------------------------: | ||
|  |  | ||
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| ```@autodocs | ||
| Modules = [GDAC,MITprof,AnalysisMethods] | ||
| Order = [:type,:function] | ||
| ``` | ||
| Argo Profile Distributions | Cost Funtions & Uncertainties | ||
| :------------------------------:|:---------------------------------: | ||
|  |  | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,42 @@ | ||
|
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| ## 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). | ||
|
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| ### MITprof Format | ||
|
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| 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) | ||
| ``` | ||
|
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||
| 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. | ||
|
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| ``` | ||
| mp=MITprofStandard(output_file) | ||
| ``` | ||
|
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| For additional detail, please refer to the [examples/ArgoToMITprof.jl](https://github.com/euroargodev/ArgoData.jl/blob/master/examples/ArgoToMITprof.jl) example. | ||
|
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| ### Download MITprof files | ||
|
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| 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])`) | ||
| ``` |
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