Merge pull request #46 from euroargodev/extended_workflow

Extended workflow

Project.toml

@@ -1,7 +1,7 @@
 name = "ArgoData"
 uuid = "9eb831cf-c491-48dc-bed4-6aca718df73c"
 authors = ["gaelforget <[email protected]>"]
-version = "0.1.15"
+version = "0.1.16"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
@@ -11,8 +11,9 @@ Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 FTPClient = "01fcc997-4f28-56b8-8a06-30002c134abb"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
-MITgcmTools = "62725fbc-3a66-4df3-9000-e33e85b3a198"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 MeshArrays = "cb8c808f-1acf-59a3-9d2b-6e38d009f683"
+MITgcmTools = "62725fbc-3a66-4df3-9000-e33e85b3a198"
 NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab"
 OceanStateEstimation = "891f6deb-a4f5-4bc5-a2e3-1e8f649cdd2c"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
@@ -27,10 +28,12 @@ DataFrames = "0.20, 0.21, 0.22, 1"
 FTPClient = "1"
 Glob = "1"
 Interpolations = "0.13, 0.14"
+JLD2 = "0.4"
 MITgcmTools = "0.2"
-MeshArrays = "0.2"
-NCDatasets = "0.10, 0.11, 0.12"
+MeshArrays = "0.2, 0.3"
+NCDatasets = "0.10, 0.11, 0.12, 0.13"
 OceanStateEstimation = "0.3"
 OrderedCollections = "1"
+Statistics = "1"
 YAML = "0.4"
 julia = "1.7"

docs/make.jl

@@ -20,13 +20,13 @@ makedocs(;
     format=Documenter.HTML(),
     pages=[
         "Home" => "index.md",
-        "Modules" => "modules.md",
-        "Reference" => "functions.md",
+        "Examples" => "examples.md",
+        "Reference" => "Functionalities.md",
     ],
     repo="https://github.com/euroargodev/ArgoData.jl/blob/{commit}{path}#L{line}",
     sitename="ArgoData.jl",
     authors="gaelforget <[email protected]>",
-    assets=String[],
+    warnonly = [:cross_references,:missing_docs],
 )
 
 #create csv list of floats

docs/src/Functionalities.md

@@ -0,0 +1,53 @@
+
+
+Content of this section:
+
+- Data Structures
+- Modules (1): GDAC, MITprof, GriddedFields
+- Modules (2): MITprofAnalysis, MITprofStat
+
+## Data Structures
+
+```@docs
+ProfileNative
+ProfileStandard
+MITprofStandard
+```
+
+## Module: GDAC
+
+```@autodocs
+Modules = [GDAC]
+Order   = [:type,:function]
+```
+
+## Module: MITprof
+
+```@autodocs
+Modules = [MITprof]
+Order   = [:type,:function]
+```
+
+## Module: GriddedFields
+
+```@autodocs
+Modules = [GriddedFields]
+Order   = [:type,:function]
+```
+
+
+## Module: MITprofAnalysis
+
+```@autodocs
+Modules = [MITprofAnalysis]
+Order   = [:type,:function]
+```
+
+## Module: MITprofStat
+
+```@autodocs
+Modules = [MITprofStat]
+Order   = [:type,:function]
+```
+
+

docs/src/modules.md → docs/src/examples.md

@@ -1,7 +1,9 @@
 
-## Workflows
+The `One Argo Float` notebook demonstrates various functionalities of the `ArgoData.jl` package, which are further documented below.
 
-### Direct Download
+👉 [One Argo Float](https://juliaocean.github.io/OceanRobots.jl/dev/examples/Float_Argo.html) 👈 [(code)](https://raw.githubusercontent.com/juliaocean/OceanRobots.jl/master/examples/Float_Argo.jl)
+
+## Download From Argo Data Center
 
 Downloading and accessing an Argo file (`wmo=13857` from `folder="aoml"`) is done like this.
 
@@ -15,30 +17,30 @@ 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
+## Argo on Standard Depth Levels
 
-👉 [Notebook 1](https://juliaocean.github.io/OceanRobots.jl/dev/Float_Argo.html) 👈 [(code)](https://raw.githubusercontent.com/juliaocean/OceanRobots.jl/master/examples/Float_Argo.jl)
+### The `MITprof` Format
 
-👉 [Notebook 2](../ArgoToMITprof.html) 👈 [(code)](https://raw.githubusercontent.com/euroargodev/ArgoData.jl/master/examples/ArgoToMITprof.jl)
+👉 [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).
 
-Formatting an Argo file (`input_file`) into an MITprof file (`output_file`) proceeds as follows.
+Turning 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)
 ```
 
-**`MITprofStandard` Data Structure**
+### 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.
 
 ```
 mp=MITprofStandard(output_file)
 ```
 
-### `MITprof` Sample Files
+### 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.
 
@@ -49,10 +51,8 @@ url0="https://dataverse.harvard.edu/api/access/datafile/"
 run(`wget --content-disposition $(url0)$(tmp[1,:ID])`)
 ```
 
-### `argopy` Python API
-
-👉 [Notebook](http://gaelforget.net/notebooks/Argo_argopy.html) 👈 [(code)](https://raw.githubusercontent.com/euroargodev/ArgoData.jl/master/examples/Argo_argopy.jl)
+## `argopy` Python API
 
-[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). 
+[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).
 
-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).
+👉 [Notebook](http://gaelforget.net/notebooks/Argo_argopy.html) 👈 [(code)](https://raw.githubusercontent.com/euroargodev/ArgoData.jl/master/examples/Argo_argopy.jl)

docs/src/index.md

@@ -1,15 +1,19 @@
 # ArgoData.jl
 
-Analysis and Processing of 
+Tools to access, visualize, process, and analyze of 
 [Argo](https://argopy.readthedocs.io/en/latest/what_is_argo.html#what-is-argo) ocean data sets.
 
-## Contents
+### 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` 
+- `MITprof` module for the format of [Forget, et al 2015](http://dx.doi.org/10.5194/gmd-8-3071-2015)
+- `MITprofPlots` module (in `examples/`) for `MITprof`
+- `MITprofAnalysis` module for model-data comparison
+- `MITprofAnalysis` module for tabular data access
+- `MITprofStat` module geospatial statistics
+
+### Notebooks
+
   - [ArgoData](https://juliaocean.github.io/OceanRobots.jl/dev/examples/Float_Argo.html) 🚀 interactive vizualisation
   - [MITprof](https://euroargodev.github.io/ArgoData.jl/dev/ArgoToMITprof.html) 🚀 simplified format
   - [argopy](http://gaelforget.net/notebooks/Argo_argopy.html) 🚀 python API via Julia

examples/ArgoToMITprof_step2.jl

@@ -90,13 +90,13 @@ prof_std.Sweight[1:5]
 # +
 #4. spatio-temporal interpolation
 
-fac,rec=ArgoTools.monthly_climatology_factors(prof.date[1])
+(fac0,fac1,rec0,rec1)=ArgoTools.monthly_climatology_factors(prof.date[1])
 # -
 
-prof_T1=[GriddedFields.Interpolate(gridded_fields.T[rec[1]][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50]
-prof_T2=[GriddedFields.Interpolate(gridded_fields.T[rec[2]][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50];
+prof_T0=[GriddedFields.Interpolate(gridded_fields.T[rec0][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50]
+prof_T1=[GriddedFields.Interpolate(gridded_fields.T[rec1][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50];
 
-prof_std.Testim.=ArgoTools.interp_z(-gridded_fields.Γ.RC,fac[1]*prof_T1+fac[2]*prof_T2,z_std)
+prof_std.Testim.=ArgoTools.interp_z(-gridded_fields.Γ.RC,fac0*prof_T0+fac1*prof_T1,z_std)
 prof_std.Testim[1:5]
 
 if do_plot
@@ -107,11 +107,11 @@ end
 # +
 #4. spatio-temporal interpolation
 
-prof_S1=[GriddedFields.Interpolate(gridded_fields.S[rec[1]][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50]
-prof_S2=[GriddedFields.Interpolate(gridded_fields.S[rec[2]][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50];
+prof_S0=[GriddedFields.Interpolate(gridded_fields.S[rec0][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50]
+prof_S1=[GriddedFields.Interpolate(gridded_fields.S[rec1][:,k],📚.f,📚.i,📚.j,📚.w)[1] for k=1:50];
 # -
 
-prof_std.Sestim.=ArgoTools.interp_z(-gridded_fields.Γ.RC,fac[1]*prof_S1+fac[2]*prof_S2,z_std)
+prof_std.Sestim.=ArgoTools.interp_z(-gridded_fields.Γ.RC,fac0*prof_S0+fac1*prof_S1,z_std)
 prof_std.Sestim[1:5]
 
 ArgoTools.prof_test_set2!(prof_std,meta);