Merge pull request #142 from gaelforget/rename_pkg

Rename pkg

Project.toml

@@ -1,7 +1,7 @@
-name = "IndividualDisplacements"
-uuid = "b92f0c32-5b7e-11e9-1d7b-238b2da8b0e6"
+name = "Drifters"
+uuid = "bd752fb7-3f37-44cb-a8fb-f461137b623f"
 authors = ["gaelforget <[email protected]>"]
-version = "0.5.4"
+version = "0.6.0"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
@@ -22,9 +22,9 @@ Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 MITgcm = "dce5fa8e-68ce-4431-a242-9469c69627a0"
 
 [extensions]
-IndividualDisplacementsClimatologyExt = ["Climatology"]
-IndividualDisplacementsMakieExt = ["Makie"]
-IndividualDisplacementsMITgcmExt = ["MITgcm"]
+DriftersClimatologyExt = ["Climatology"]
+DriftersMakieExt = ["Makie"]
+DriftersMITgcmExt = ["MITgcm"]
 
 [compat]
 CSV = "0.10"

docs/make.jl

@@ -1,8 +1,8 @@
-using Documenter, Literate, PlutoSliderServer, IndividualDisplacements
+using Documenter, Literate, PlutoSliderServer, Drifters
 using Climatology
 
 #download data dependencies if needed
-IndividualDisplacements.datadeps.getdata("flt_example")
+Drifters.datadeps.getdata("flt_example")
 Climatology.get_ecco_velocity_if_needed();
 Climatology.get_occa_velocity_if_needed();
 Climatology.get_ecco_variable_if_needed("THETA")
@@ -35,8 +35,8 @@ ismd(f) = splitext(f)[2] == ".md"
 pages(folder) = [joinpath(folder, f) for f in readdir(joinpath(src, folder)) if ismd(f)]
 
 makedocs(;
-    sitename = "IndividualDisplacements.jl",
-    repo = Remotes.GitHub("JuliaClimate", "IndividualDisplacements.jl"),
+    sitename = "Drifters.jl",
+    repo = Remotes.GitHub("JuliaClimate", "Drifters.jl"),
     authors="JuliaClimate <[email protected]>",
     format = Documenter.HTML(),
     pages = [
@@ -46,7 +46,7 @@ makedocs(;
         "Tool Box" => "API.md"],
     doctest = false,
     warnonly = [:cross_references,:missing_docs],
-    modules = [IndividualDisplacements]
+    modules = [Drifters]
 )
 
 pth_in = joinpath(@__DIR__, "..","examples")
@@ -66,5 +66,5 @@ end
 # See "Hosting Documentation" and deploydocs() in the Documenter manual
 # for more information.
 deploydocs(
-    repo = "github.com/JuliaClimate/IndividualDisplacements.jl.git",
+    repo = "github.com/JuliaClimate/Drifters.jl.git",
 )

docs/src/examples.md

@@ -1,15 +1,15 @@
 
 ## Examples
 
-The four examples outlined below form a tutorial of sorts that complements the [User Guide](@ref). They hopefully provide a useful jumping off point in order to configure `IndividualDisplacements.jl` for new problems.
+The four examples outlined below form a tutorial of sorts that complements the [User Guide](@ref). They hopefully provide a useful jumping off point in order to configure `Drifters.jl` for new problems.
 
-Output generated by `IndividualDisplacements.jl` is in [DataFrames.jl](https://juliadata.github.io/DataFrames.jl/latest/) tabular format which comes with powerful and convenient analysis methods. They can  readily be plotted in space and time using, e.g. , [Plots.jl](https://docs.juliaplots.org/stable/) or [Makie.jl](https://makie.juliaplots.org/stable/).
+Output generated by `Drifters.jl` is in [DataFrames.jl](https://juliadata.github.io/DataFrames.jl/latest/) tabular format which comes with powerful and convenient analysis methods. They can  readily be plotted in space and time using, e.g. , [Plots.jl](https://docs.juliaplots.org/stable/) or [Makie.jl](https://makie.juliaplots.org/stable/).
 
 To rerun an example yourself, the recommended method is to copy the corresponding `notebook (code)` link, paste it into the [Pluto.jl](https://github.com/fonsp/Pluto.jl/wiki/🔎-Basic-Commands-in-Pluto) prompt, and click `open`.
 
 ## Simple Two-Dimensional Flow
 
-[notebook (html)](random_flow_field.html) ➭ [notebook (code)](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/basics/random_flow_field.jl)
+[notebook (html)](random_flow_field.html) ➭ [notebook (code)](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/basics/random_flow_field.jl)
 
 Simulate an ensemble of displacements (and trajectories) in a simple 2D configuration. 
 
@@ -21,7 +21,7 @@ Exercises include the non-periodic domain case, statistics made easy via `DataFr
 
 ## Simple Three-Dimensional Flow
 
-[notebook (html)](solid_body_rotation.html) ➭ [notebook (code)](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/basics/solid_body_rotation.jl)
+[notebook (html)](solid_body_rotation.html) ➭ [notebook (code)](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/basics/solid_body_rotation.jl)
 
 Set up a three-dimensional flow field `u,v,w`, initialize a single particle at position `📌`, and wrap everything up within an `Individuals` data structure `I`.
 
@@ -31,26 +31,26 @@ The flow field consists of [rigid body rotation](https://en.wikipedia.org/wiki/R
 
 ## Global Ocean Circulation
 
-[notebook (html)](global_ocean_circulation.html) ➭ [notebook (code)](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/worldwide/global_ocean_circulation.jl)
+[notebook (html)](global_ocean_circulation.html) ➭ [notebook (code)](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/worldwide/global_ocean_circulation.jl)
 
 A simulation of floating particles over the Global Ocean which illustrates (1) using time variable velocity fields, (2) global connections, (3) particle re-seeding, and (4) output statistics. 
 
 The flow field is based on a data-constrained ocean model solution. The problem is configured in a way to mimic, albeit very crudely, the near-surface tranport of plastics or planktons.
 
 ## Three Dimensional Pathways
 
-[notebook (html)](three_dimensional_ocean.html) ➭ [notebook (code)](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/worldwide/three_dimensional_ocean.jl)
+[notebook (html)](three_dimensional_ocean.html) ➭ [notebook (code)](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/worldwide/three_dimensional_ocean.jl)
 
 A simulation of particles that follow the three-dimensional ocean circulation. This example illustrates (1) the 3D case in a relatistic configuration, (2) tracking the advent or origin of a water patch, and (3) multifacted visualizations in 3D.
 
 The flow field is based on a data-constrained, realistic, ocean model. The problem configuration mimics, albeit very approximately, ocean tracers / coumpounds transported by water masses.
 
 ## Additional Examples
 
-- Interactivity : [notebook (html)](interactive_UI.html) ➭ [notebook (code)](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/worldwide/interactive_UI.jl)
+- Interactivity : [notebook (html)](interactive_UI.html) ➭ [notebook (code)](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/worldwide/interactive_UI.jl)
 - Atmosphere : [notebook (html)](https://gaelforget.github.io/MITgcm.jl/dev/examples/HS94_particles.html) ➭ [notebook (code)](https://raw.githubusercontent.com/gaelforget/MITgcm.jl/master/examples/HS94_particles.jl)
 - MITgcm : [Particle cloud](../particle_cloud/index.html), [Detailed look](../detailed_look/index.html) 
-- Plotting : [Plots.jl](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/more/recipes_plots.jl), [Makie.jl](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/more/recipes_makie.jl), [PyPlot.jl](https://github.com/JuliaClimate/IndividualDisplacements.jl/blob/master/examples/more/recipes_pyplot.jl)
+- Plotting : [Plots.jl](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/more/recipes_plots.jl), [Makie.jl](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/more/recipes_makie.jl), [PyPlot.jl](https://github.com/JuliaClimate/Drifters.jl/blob/master/examples/more/recipes_pyplot.jl)
 
 ## Animations
 

docs/src/index.md

@@ -1,10 +1,10 @@
-# IndividualDisplacements.jl
+# Drifters.jl
 
-[IndividualDisplacements.jl](https://github.com/JuliaClimate/IndividualDisplacements.jl) computes elementary point displacements over gridded domains. Inter-operability with global climate model output (on [Arakawa](https://en.wikipedia.org/wiki/Arakawa_grids) `C-grids` in general) that can be represented via [MeshArrays.jl](https://github.com/JuliaClimate/MeshArrays.jl) (see [these docs](https://juliaclimate.github.io/MeshArrays.jl/dev/)) is a central goal of [IndividualDisplacements.jl](https://github.com/JuliaClimate/IndividualDisplacements.jl). 
+[Drifters.jl](https://github.com/JuliaClimate/Drifters.jl) computes elementary point displacements over gridded domains. Inter-operability with global climate model output (on [Arakawa](https://en.wikipedia.org/wiki/Arakawa_grids) `C-grids` in general) that can be represented via [MeshArrays.jl](https://github.com/JuliaClimate/MeshArrays.jl) (see [these docs](https://juliaclimate.github.io/MeshArrays.jl/dev/)) is a central goal of [Drifters.jl](https://github.com/JuliaClimate/Drifters.jl). 
 
 The initial example suite relies on gridded ocean transports estimated in [OCCA](https://doi.org/10.7910/DVN/RNXA2A) ([Forget 2010](http://dx.doi.org/10.1175/2009JPO4043.1)), [ECCOv4](https://eccov4.readthedocs.io/en/latest/) ([Forget et al. 2015](https://doi.org/10.5194/gmd-8-3071-2015)), and [CBIOMES](https://cbiomes.readthedocs.io/en/latest/) ([Forget, 2018](http://doi.org/10.5281/zenodo.1343303)). For the Atmosphere, an additional example based on an idealized model simulation is provided in [MITgcm.jl](https://gaelforget.github.io/MITgcm.jl/dev/). 
 
-Typical applications include the simulation and analysis of materials moving through atmospheric flows (e.g. dust or chemicals) or oceanic flows (e.g. plastics or planktons). An illustration of the type of observations that [IndividualDisplacements.jl](https://github.com/JuliaClimate/IndividualDisplacements.jl) can simulate is provided below. These data collected by [Ocean Drifting Buoy](https://doi.org/10.1002/2016JC011716) can be accessed via [OceanRobots.jl](https://gaelforget.github.io/OceanRobots.jl/dev/).
+Typical applications include the simulation and analysis of materials moving through atmospheric flows (e.g. dust or chemicals) or oceanic flows (e.g. plastics or planktons). An illustration of the type of observations that [Drifters.jl](https://github.com/JuliaClimate/Drifters.jl) can simulate is provided below. These data collected by [Ocean Drifting Buoy](https://doi.org/10.1002/2016JC011716) can be accessed via [OceanRobots.jl](https://gaelforget.github.io/OceanRobots.jl/dev/).
 
 [![Global Drifter Program data](https://user-images.githubusercontent.com/20276764/90924860-41799580-e3be-11ea-96bd-9a5784d00ecc.png)](https://youtu.be/82HPnYBtoVo)
 

docs/src/workflow.md

@@ -16,7 +16,7 @@ A central goal of this package is to support scientific analysis of model simula
 
 As a starting point, the package supports all types of gridded model output (on Arakawa C-grids) from the [MIT General Circulation Model](https://mitgcm.readthedocs.io/en/latest/?badge=latest) via the [MeshArrays.jl](https://github.com/JuliaClimate/MeshArrays.jl) package ([docs found here](https://juliaclimate.github.io/MeshArrays.jl/dev/)). 
 
-By convention, `IndividualDisplacements.jl` expects input flow fields to be provided in a uniform fashion (see [`FlowFields`](@ref)) summarized below: 
+By convention, `Drifters.jl` expects input flow fields to be provided in a uniform fashion (see [`FlowFields`](@ref)) summarized below: 
 
 1. normalized to grid index units (i.e. in 1/s rather than m/s units)
 1. positive towards increasing indices
@@ -31,7 +31,7 @@ For an overview of the examples, please refer to the **example guide**. The rest
 The `Individuals` struct contains a `FlowFields` struct (incl. e.g. arrays), initial positions for the individuals, and the other elements (e.g. functions) involved in `∫!(I,T)` as documented hereafter.
 
 ```@autodocs
-Modules = [IndividualDisplacements]
+Modules = [Drifters]
 Order   = [:type]
 ```
 

examples/more/detailed_look.jl

@@ -5,7 +5,7 @@
 #
 # A more detailed look at spatial interpolation, integration through time, and I/O. 
 # For additional documentation e.g. see
-# [1](https://JuliaClimate.github.io/IndividualDisplacements.jl/dev/),
+# [1](https://JuliaClimate.github.io/Drifters.jl/dev/),
 # [2](https://JuliaClimate.github.io/MeshArrays.jl/dev/),
 # [3](https://docs.juliadiffeq.org/latest/solvers/ode_solve.html),
 # [4](https://en.wikipedia.org/wiki/Displacement_(vector)). 
@@ -21,18 +21,18 @@
 
 # ## 1. Import Software
 
-using IndividualDisplacements, MITgcm
-import IndividualDisplacements.OrdinaryDiffEq as OrdinaryDiffEq
-import IndividualDisplacements.DataFrames as DataFrames
-p=dirname(pathof(IndividualDisplacements))
+using Drifters, MITgcm
+import Drifters.OrdinaryDiffEq as OrdinaryDiffEq
+import Drifters.DataFrames as DataFrames
+p=dirname(pathof(Drifters))
 include(joinpath(p,"../examples/more/example123.jl"))
 #md include(joinpath(p,"../examples/more/recipes_plots.jl"))
 
 # ## 2. Read Trajectory Output
 #
 # from `MITgcm/pkg/flt`
 
-flt_example_path = IndividualDisplacements.datadeps.getdata("flt_example")
+flt_example_path = Drifters.datadeps.getdata("flt_example")
 prec=Float32
 df=read_flt(flt_example_path*"/",prec);
 

examples/more/example123.jl

@@ -1,5 +1,5 @@
-using IndividualDisplacements, MeshArrays, MITgcm, Statistics
-import IndividualDisplacements.OrdinaryDiffEq as OrdinaryDiffEq
+using Drifters, MeshArrays, MITgcm, Statistics
+import Drifters.OrdinaryDiffEq as OrdinaryDiffEq
 
 """
     read_mds(filRoot::String,x::MeshArray)
@@ -39,13 +39,13 @@ earlier, rather than computing trajectories as in the other examples.
 ```
 df=example1()
 
-p=dirname(pathof(IndividualDisplacements))
+p=dirname(pathof(Drifters))
 include(joinpath(p,"../examples/recipes_pyplot.jl"))
 PyPlot.figure(); PlotMapProj(df,300); gcf()
 ```
 """
 function example1()
-   p=dirname(pathof(IndividualDisplacements))
+   p=dirname(pathof(Drifters))
    dirIn=joinpath(p,"../examples/run_offflt/")
    prec=Float32
    df=read_flt(dirIn,prec)
@@ -60,7 +60,7 @@ extended and modified configuration of the standard MITgcm test case.
 ```
 (𝐼,df,ref)=example2();
 
-p=dirname(pathof(IndividualDisplacements))
+p=dirname(pathof(Drifters))
 include(joinpath(p,"../examples/recipes_plots.jl"))
 PlotBasic(df,300,100000.0)
 
@@ -92,7 +92,7 @@ example2_xy()
 Read MITgcm/pkg/flt output
 """
 function example2_xy(𝑃)
-flt_example_path = IndividualDisplacements.datadeps.getdata("flt_example")
+flt_example_path = Drifters.datadeps.getdata("flt_example")
 prec=Float32
 df=read_flt(flt_example_path*"/",prec)
 #
@@ -123,7 +123,7 @@ function example2_setup()
 
    ###### 1) Get gridded variables via MeshArrays.jl
 
-   flt_example_path = IndividualDisplacements.datadeps.getdata("flt_example")
+   flt_example_path = Drifters.datadeps.getdata("flt_example")
    γ=gcmgrid(flt_example_path*"/","PeriodicChannel",1,[(80,42)], [80 42], Float32, read, write)
    nr=8
 

examples/more/example_CyclicArray.jl

@@ -1,7 +1,7 @@
 
-using IndividualDisplacements
-import IndividualDisplacements.CyclicArrays as CyclicArrays
-import IndividualDisplacements.OrdinaryDiffEq as OrdinaryDiffEq 
+using Drifters
+import Drifters.CyclicArrays as CyclicArrays
+import Drifters.OrdinaryDiffEq as OrdinaryDiffEq 
 
 function cyclicarray_example()
     𝑃=cyclicarray_setup()

examples/more/particle_cloud.jl

@@ -6,19 +6,19 @@
 # Using the same setup as `detailed_look.jl` or `example2()`, here we simulate
 # a point cloud getting advected by the flow field. 
 # For additional documentation e.g. see :
-# [1](https://JuliaClimate.github.io/IndividualDisplacements.jl/dev/),
+# [1](https://JuliaClimate.github.io/Drifters.jl/dev/),
 # [2](https://JuliaClimate.github.io/MeshArrays.jl/dev/),
 # [3](https://docs.juliadiffeq.org/latest/solvers/ode_solve.html),
 # [4](https://en.wikipedia.org/wiki/Displacement_(vector))
 
 # ## 1. Import Software
 
-using IndividualDisplacements, Statistics
+using Drifters, Statistics
 
-import IndividualDisplacements.OrdinaryDiffEq: solve, Tsit5
-import IndividualDisplacements.DataFrames: DataFrame
+import Drifters.OrdinaryDiffEq: solve, Tsit5
+import Drifters.DataFrames: DataFrame
 
-p=dirname(pathof(IndividualDisplacements))
+p=dirname(pathof(Drifters))
 include(joinpath(p,"../examples/more/example123.jl"));
 #md include(joinpath(p,"../examples/more/recipes_plots.jl"))
 
@@ -31,7 +31,7 @@ x=vec([x-0.5 for x in ii1, y in ii2])
 y=vec([y-0.5 for x in ii1, y in ii2])
 xy = permutedims([[x[i];y[i];1.0] for i in eachindex(x)])
 
-solv(prob) = IndividualDisplacements.ensemble_solver(prob,solver=Tsit5(),reltol=1e-6,abstol=1e-6)
+solv(prob) = Drifters.ensemble_solver(prob,solver=Tsit5(),reltol=1e-6,abstol=1e-6)
 tr = DataFrame(ID=Int[], x=Float64[], y=Float64[], t=Float64[])
 
 #𝐼 = Individuals{Float64,2}(📌=xy[:,:], 🔴=tr, 🆔=collect(1:size(xy,2)),
@@ -54,6 +54,6 @@ I=(position=xy,record=deepcopy(tr),velocity=dxdt!,
 
 # Compare with trajectory output from `MITgcm`
 
-#flt_example_path = IndividualDisplacements.datadeps.getdata("flt_example")
+#flt_example_path = Drifters.datadeps.getdata("flt_example")
 #df=read_flt(flt_example_path*"/",Float32)
 #ref=plot_paths(df,size(xy,2),100000.0)

examples/more/recipes_makie.jl

@@ -1,6 +1,6 @@
 
 using Makie, MeshArrays, ColorSchemes, Statistics
-import IndividualDisplacements.DataFrames: DataFrame, groupby
+import Drifters.DataFrames: DataFrame, groupby
 
 """
     plot(𝐼::Individuals)
@@ -25,7 +25,7 @@ end
 Plot random subset of size nn trajectories.
 """
 function plot_paths(df::DataFrame,nn::Integer,dMax::Float64=0.)
-   IDs = IndividualDisplacements.randperm(maximum(df.ID))
+   IDs = Drifters.randperm(maximum(df.ID))
    COs=[:gray76 :gold :limegreen :black]
 
    #scene=Scene(limits=FRect(0, 0, 40, 40),show_axis = false)
@@ -57,8 +57,8 @@ MakieScatterMovie(scene,df,tt,fil::String)
 Animate positions, according to time vector tt, and save movie to mp4 file.
 
 ```
-using IndividualDisplacements
-p=dirname(pathof(IndividualDisplacements))
+using Drifters
+p=dirname(pathof(Drifters))
 include(joinpath(p,"../examples/more/recipes_Makie.jl"))
 module ex3
     fil="../examples/worldwide/three_dimensional_ocean.jl"

examples/more/recipes_plots.jl

@@ -1,5 +1,5 @@
 using Plots
-import IndividualDisplacements.DataFrames: DataFrame, groupby
+import Drifters.DataFrames: DataFrame, groupby
 
 """
     plot(𝐼::Individuals)
@@ -24,7 +24,7 @@ end
 Plot random subset of size nn trajectories / paths.
 """
 function plot_paths(df::DataFrame,nn::Integer,dMax::Float64=0.)
-   IDs = IndividualDisplacements.randperm(maximum(df.ID))
+   IDs = Drifters.randperm(maximum(df.ID))
    COs=["w" "y" "g" "k"]
 
    plt=plot(leg=false)

examples/more/recipes_pyplot.jl

@@ -1,6 +1,6 @@
 
 using PyPlot, PyCall
-import IndividualDisplacements.DataFrames: DataFrame, groupby
+import Drifters.DataFrames: DataFrame, groupby
 
 """
     PlotMapProj(df::DataFrame,nn::Integer)
@@ -19,7 +19,7 @@ function PlotMapProj(df::DataFrame,nn::Integer)
    ax.stock_img()
 
    # Draw trajectories
-   IDs = IndividualDisplacements.randperm(maximum(df.ID))
+   IDs = Drifters.randperm(maximum(df.ID))
    COs=["w" "y" "g" "k"]
 
    #for global ocean case:

ext/IndividualDisplacementsClimatologyExt.jl → ext/DriftersClimatologyExt.jl

@@ -1,7 +1,7 @@
-module IndividualDisplacementsClimatologyExt
+module DriftersClimatologyExt
 
-using Climatology, IndividualDisplacements
-import IndividualDisplacements: data_path
+using Climatology, Drifters
+import Drifters: data_path
 
 
 function data_path(name::Symbol)

ext/IndividualDisplacementsMITgcmExt.jl → ext/DriftersMITgcmExt.jl

@@ -1,8 +1,8 @@
 
-module IndividualDisplacementsMITgcmExt
+module DriftersMITgcmExt
 
-using MITgcm, IndividualDisplacements
-import IndividualDisplacements: read_data_ECCO, NetCDF
+using MITgcm, Drifters
+import Drifters: read_data_ECCO, NetCDF
 
 function read_data_ECCO(m0,v0,path,grid,k)
     MITgcm.read_nctiles(joinpath(path,v0),v0,grid,I=(:,:,k,m0))