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View file @ 57f0b540
......@@ -24,7 +24,7 @@ docs/site/
# It records a fixed state of all packages used by the project. As such, it should not be
# committed for packages, but should be committed for applications that require a static
# environment.
# Manifest.toml
Manifest.toml
# Input/output
input/
......
Manifest.toml deleted 100644 → 0
View file @ 210b135d
# This file is machine-generated - editing it directly is not advised
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Project.toml
View file @ 57f0b540
name = "CryoGrid"
uuid = "a535b82e-5f3d-4d97-8b0b-d6483f5bebd5"
authors = ["Brian Groenke <brian.groenke@awi.de>", "Moritz Langer <moritz.langer@awi.de>"]
version = "0.5.1"
version = "0.9.0"
[deps]
ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
......@@ -12,7 +12,6 @@ DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
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Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
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IfElse = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
......@@ -21,10 +20,11 @@ IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
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NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
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Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
PreallocationTools = "d236fae5-4411-538c-8e31-a6e3d9e00b46"
RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
......@@ -34,15 +34,17 @@ Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
SimulationLogs = "e3c6736c-93d9-479d-93f3-96ff5e8836d5"
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TimeSeries = "9e3dc215-6440-5c97-bce1-76c03772f85e"
Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
[compat]
julia = ">= 1.6"
[extras]
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ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[targets]
......
README.md
View file @ 57f0b540
......@@ -5,7 +5,7 @@
[docs-dev-img]: https://img.shields.io/badge/docs-latest-blue.svg
[docs-dev-url]: https://cryogrid.github.io/CryoGrid.jl/dev/
Julia implementation of the CryoGrid land surface model using `DifferentialEquations.jl` and the [SciML](https://github.com/SciML)
Julia implementation of the CryoGrid permafrost model using `DifferentialEquations.jl` and the [SciML](https://github.com/SciML)
package ecosystem.
Part of the broader research project: [Quantifying and explaining uncertainty in permafrost modeling under a warming climate](https://drive.google.com/file/d/1wB_EXtlO_PMXFSzZ-bRV8cg0a0DGDtAB/view?usp=sharing)
......@@ -42,13 +42,14 @@ forcings = loadforcings(CryoGrid.Presets.Forcings.Samoylov_ERA_obs_fitted_1979_2
tair = TimeSeriesForcing(ustrip.(forcings.data.Tair), forcings.timestamps, :Tair);
# get preset soil and initial temperature profile for Samoylov
soilprofile, tempprofile = CryoGrid.Presets.SamoylovDefault
# initializer for temperature
initT = initializer(:T, tempprofile)
# basic 1-layer heat conduction model (defaults to free water freezing scheme)
model = CryoGrid.Presets.SoilHeatColumn(TemperatureGradient(tair), soilprofile)
model = CryoGrid.Presets.SoilHeatColumn(TemperatureGradient(tair), soilprofile, initT)
# define time span (1 year)
tspan = (DateTime(2010,10,30),DateTime(2011,10,30))
p = parameters(model)
initT = initializer(:T, tempprofile)
u0 = initialcondition!(model, tspan, p, initT)
u0, du0 = initialcondition!(model, tspan, p)
# CryoGrid front-end for ODEProblem
prob = CryoGridProblem(model,u0,tspan,p,savevars=(:T,))
# solve discretized system, saving every 6 hours;
......@@ -74,8 +75,7 @@ prob = CryoGridProblem(model,u0,tspan,p,savevars=(:T,))
out = @time solve(prob, Euler(), dt=120.0, saveat=6*3600.0, progress=true) |> CryoGridOutput;
plot(out.T[Z(Near(zs))], color=cg[LinRange(0.0,1.0,length(zs))]', ylabel="Temperature", leg=false)
```
Note that `SoilHeat` uses energy as the state variable by default. To use temperature as the state variable instead:
Note that `SoilHeatColumn` uses energy as the state variable by default. To use temperature as the state variable instead:
```julia
# :T is the variable name for temperature, :H represents enthalpy/energy.
......
docs/Manifest.toml deleted 100644 → 0
View file @ 210b135d
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docs/make.jl
View file @ 57f0b540
......@@ -7,15 +7,15 @@ const modules = [
CryoGrid,
CryoGrid.Utils,
CryoGrid.Numerics,
CryoGrid.Layers,
CryoGrid.Physics,
CryoGrid.Boundaries,
CryoGrid.HeatConduction,
CryoGrid.WaterBalance,
CryoGrid.Soils,
CryoGrid.SEB,
CryoGrid.Land,
CryoGrid.Strat,
CryoGrid.Presets,
CryoGrid.Diagnostics,
CryoGrid.Callbacks,
]
makedocs(modules=modules,
......@@ -35,11 +35,12 @@ makedocs(modules=modules,
"Utilities" => "api/utils.md",
"Physics" => [
"Heat Conduction" => "api/heat_conduction.md",
"Water Balance" => "api/water_balance.md",
"Soils" => "api/soils.md",
"Surface Energy Balance" => "api/seb.md",
],
"Land" => "api/land.md",
"Stratigraphy" => "api/strat.md",
"Diagnostics" => "api/diagnostics.md",
"Callbacks" => "api/callbacks.md",
"Presets" => "api/presets.md",
],
"Contributing" => "contributing.md",
......
docs/src/api/land.md → docs/src/api/soils.md
View file @ 57f0b540
# Land
# Soils
```@autodocs
Modules = [Land]
Modules = [Soils]
Private = false
Order = [:type, :function, :macro]
```
```
\ No newline at end of file
docs/src/api/strat.md 0 → 100644
View file @ 57f0b540
# Strat
This module contains the `Stratigraphy`, `Tile`, and related types for building and evaluating a CryoGrid 1-D process model.
```@autodocs
Modules = [Strat]
Private = false
Order = [:type, :function, :macro]
```
docs/src/api/callbacks.md → docs/src/api/water_balance.md
View file @ 57f0b540
# Callbacks
# Water balance
```@autodocs
Modules = [CryoGrid.Callbacks]
Modules = [WaterBalance]
Private = false
Order = [:type, :function, :macro]
```
\ No newline at end of file
docs/src/manual/overview.md
View file @ 57f0b540
......@@ -12,19 +12,19 @@ strat = Stratigraphy(
1000.0u"m" => bottom(GeothermalHeatFlux(0.053u"J/s/m^2"))
);
grid = CryoGrid.Presets.DefaultGrid_5cm
model = LandModel(strat,grid);
# define initial conditions for temperature using a given profile;
# The default initializer linearly interpolates between profile points.
initT = initializer(:T, tempprofile)
tile = Tile(strat, grid, initT);
```
This model can then be used to construct an `ODEProblem` (from `DiffEqBase.jl`) via the `CryoGridProblem` constructor:
```julia
tspan = (DateTime(2010,10,30),DateTime(2011,10,30))
p = parameters(model)
# define initial conditions for temperature using a given profile;
# The default initializer linearly interpolates between profile points.
initT = initializer(:T, tempprofile)
u0 = initialcondition!(model, tspan, p, initT)
prob = CryoGridProblem(model, u0, tspan, p, savevars=(:T,)) # produces an ODEProblem with problem type CryoGridODEProblem
p = parameters(tile)
u0 = initialcondition!(tile, tspan, p, initT)
prob = CryoGridProblem(tile, u0, tspan, p, savevars=(:T,)) # produces an ODEProblem with problem type CryoGridODEProblem
```
It can then be solved simply using the `solve` function (also from `DiffEqBase` and `OrdinaryDiffEq`):
......@@ -50,19 +50,19 @@ Notice that, in the example above, it is types such as `Soil`, `Heat`, `SFCC`, e
```julia
""" Variable definitions for heat conduction (enthalpy) on soil layer. """
variables(soil::Soil, heat::Heat{:H}) = (
Prognostic(:H, Float"J/m^3", OnGrid(Cells)),
Diagnostic(:T, Float"°C", OnGrid(Cells)),
Diagnostic(:C, Float"J//K*/m^3", OnGrid(Cells)),
Diagnostic(:Ceff, Float"J/K/m^3", OnGrid(Cells)),
Diagnostic(:k, Float"W/m/K", OnGrid(Edges)),
Diagnostic(:kc, Float"W//m/K", OnGrid(Cells)),
Prognostic(:H, OnGrid(Cells), u"J/m^3"),
Diagnostic(:T, OnGrid(Cells), u"°C"),
Diagnostic(:C, OnGrid(Cells), u"J//K*/m^3"),
Diagnostic(:dHdT, OnGrid(Cells), u"J/K/m^3"),
Diagnostic(:k, OnGrid(Edges), u"W/m/K"),
Diagnostic(:kc, OnGrid(Cells), u"W//m/K"),
# this last line just appends any state variables or parameters
# defined by the freeze curve to the tuple.
variables(soil, heat, freezecurve(heat))...,
)
```
When the `Heat` process is assigned to a `Soil` layer, `LandModel` will invoke this method and create state variables corresponding to each [`Var`](@ref). [`Prognostic`](@ref) variables are assigned derivatives (in this case, `dH`, since `H` is the prognostic state variable) and integrated over time. `Diagnostic` variables provide in-place caches for intermediary variables/computations and can be automatically tracked by the modeling engine.
When the `Heat` process is assigned to a `Soil` layer, `Tile` will invoke this method and create state variables corresponding to each [`Var`](@ref). [`Prognostic`](@ref) variables are assigned derivatives (in this case, `dH`, since `H` is the prognostic state variable) and integrated over time. `Diagnostic` variables provide in-place caches for intermediary variables/computations and can be automatically tracked by the modeling engine.
Each variable definition consists of a name (a Julia `Symbol`), a type, and a shape. For variables discretized on the grid, the shape is specified by `OnGrid`, which will generate an array of the appropriate size when the model is compiled. The arguments `Cells` and `Edges` specify whether the variable should be defined on the grid cells or edges respecitvely.
......@@ -82,6 +82,6 @@ end
!!! warning
Prognostic state variables like `H` in the example above **should not be directly modified** in user code. This is especially important when using higher order or implicit integrators as unexpected changes to the underlying state may destroy the accuracy of their internal interpolators. For modleing discontinuities, use [`Callbacks`](@ref) instead.
Prognostic state variables like `H` in the example above **should not be directly modified** in user code. This is especially important when using higher order or implicit integrators as unexpected changes to prognostic state may destroy the accuracy of their internal interpolators. For modeling discontinuities, use [`Callbacks`](@ref) instead.
Note that `state` is of type [`LayerState`](@ref) with fields corresponding to the variables declared by the `variables` function for `Soil` and `Heat`. Additionally, output arrays for the time derivatives are provided (here `dH`), as well as the current timestep, layer boundary depths, and variable grids (accessible via `state.t`, `state.z`, and `state.grids` respectively). Note that `state` will also contain other variables declared on this `Soil` layer by other `SubSurfaceProcess`es, allowing for implicit coupling between processes where appropriate.
Note that `state` is of type [`LayerState`](@ref) with fields corresponding to the variables declared by the `variables` function for `Soil` and `Heat`. Additionally, output arrays for the time derivatives are provided (here `dH`), as well as the current timestep, layer boundary depths, and variable grids (accessible via `state.t`, `state.bounds`, and `state.grids` respectively). Note that `state` will also contain other variables declared on this `Soil` layer by other `SubSurfaceProcess`es, allowing for implicit coupling between processes where appropriate.
examples/heat_freeW_samoylov_simple.jl
View file @ 57f0b540
......@@ -4,14 +4,16 @@ using Plots
forcings = loadforcings(CryoGrid.Presets.Forcings.Samoylov_ERA_obs_fitted_1979_2014_spinup_extended_2044, :Tair => u"°C");
# use air temperature as upper boundary forcing;
tair = TimeSeriesForcing(ustrip.(forcings.data.Tair), forcings.timestamps, :Tair);
# use default profiles for samoylov
soilprofile, tempprofile = CryoGrid.Presets.SamoylovDefault
# "simple" heat conduction model w/ 5 cm grid spacing (defaults to free water freezing scheme)
grid = CryoGrid.Presets.DefaultGrid_5cm
model = CryoGrid.Presets.SoilHeatColumn(:H, TemperatureGradient(tair), CryoGrid.Presets.SamoylovDefault; grid=grid)
initT = initializer(:T, tempprofile)
model = CryoGrid.Presets.SoilHeatColumn(:H, TemperatureGradient(tair), soilprofile, initT; grid=grid)
# define time span
tspan = (DateTime(2010,10,30),DateTime(2011,10,30))
p = parameters(model)
initT = initializer(:T, tempprofile)
u0 = initialcondition!(model, tspan, p, initT)
u0, du0 = initialcondition!(model, tspan, p)
# CryoGrid front-end for ODEProblem
prob = CryoGridProblem(model,u0,tspan,p,savevars=(:T,))
# solve with Crank-Nicolson (Trapezoid) and construct CryoGridOutput from solution
......
examples/heat_vgfc_samoylov_simple.jl
View file @ 57f0b540
......@@ -7,12 +7,12 @@ tair = TimeSeriesForcing(ustrip.(forcings.data.Tair), forcings.timestamps, :Tair
# "simple" heat conduction model w/ 5 cm grid spacing
grid = CryoGrid.Presets.DefaultGrid_5cm
soilprofile, tempprofile = CryoGrid.Presets.SamoylovDefault
model = CryoGrid.Presets.SoilHeatColumn(:H, TemperatureGradient(tair), soilprofile; grid=grid, freezecurve=SFCC(DallAmico()))
initT = initializer(:T, tempprofile)
model = CryoGrid.Presets.SoilHeatColumn(:H, TemperatureGradient(tair), soilprofile, initT; grid=grid, freezecurve=SFCC(DallAmico()))
# define time span
tspan = (DateTime(2010,10,30),DateTime(2011,10,30))
p = parameters(model)
initT = initializer(:T, tempprofile)
u0 = initialcondition!(model, tspan, p, initT)
u0, du0 = initialcondition!(model, tspan, p)
# CryoGrid front-end for ODEProblem
prob = CryoGridProblem(model,u0,tspan,p,savevars=(:T,))
# solve with forward Euler (fixed 10 minute timestep) and construct CryoGridOutput from solution
......
examples/heat_vgfc_seb_samoylov_custom.jl
View file @ 57f0b540
......@@ -7,11 +7,11 @@ const gridvals = vcat([0:0.02:2...,2.05:0.05:4.0...,
35:5:50...,60:10:100...,200:100:1000...]...)u"m"
# soil profile: depth => (excess ice, natural porosity, saturation, organic fraction)
soilprofile = SoilProfile(
0.0u"m" => soilparameters(xic=0.0,por=0.80,sat=1.0,org=0.75), #(θw=0.80,θm=0.05,θo=0.15,ϕ=0.80),
0.1u"m" => soilparameters(xic=0.0,por=0.80,sat=1.0,org=0.25), #(θw=0.80,θm=0.15,θo=0.05,ϕ=0.80),
0.4u"m" => soilparameters(xic=0.30,por=0.55,sat=1.0,org=0.25), #(θw=0.80,θm=0.15,θo=0.05,ϕ=0.55),
3.0u"m" => soilparameters(xic=0.0,por=0.50,sat=1.0,org=0.0), #(θw=0.50,θm=0.50,θo=0.0,ϕ=0.50),
10.0u"m" => soilparameters(xic=0.0,por=0.30,sat=1.0,org=0.0), #(θw=0.30,θm=0.70,θo=0.0,ϕ=0.30),
0.0u"m" => CharacteristicFractions(xic=0.0,por=0.80,sat=1.0,org=0.75), #(θw=0.80,θm=0.05,θo=0.15,ϕ=0.80),
0.1u"m" => CharacteristicFractions(xic=0.0,por=0.80,sat=1.0,org=0.25), #(θw=0.80,θm=0.15,θo=0.05,ϕ=0.80),
0.4u"m" => CharacteristicFractions(xic=0.30,por=0.55,sat=1.0,org=0.25), #(θw=0.80,θm=0.15,θo=0.05,ϕ=0.55),
3.0u"m" => CharacteristicFractions(xic=0.0,por=0.50,sat=1.0,org=0.0), #(θw=0.50,θm=0.50,θo=0.0,ϕ=0.50),
10.0u"m" => CharacteristicFractions(xic=0.0,por=0.30,sat=1.0,org=0.0), #(θw=0.30,θm=0.70,θo=0.0,ϕ=0.30),
),
# mid-winter temperature profile
tempprofile = TemperatureProfile(
......@@ -33,19 +33,19 @@ Lin = TimeSeriesForcing(forcings.data.Lin, forcings.timestamps, :Lin);
Sin = TimeSeriesForcing(forcings.data.Sin, forcings.timestamps, :Sin);
z = 2.; # height [m] for which the forcing variables (Temp, humidity, wind, pressure) are provided
tspan = (DateTime(2010,1,1), DateTime(2011,1,1))
soilprofile, tempprofile = CryoGrid.Presets.SamoylovDefault
initT = initializer(:T, tempprofile)
strat = Stratigraphy(
-2.0u"m" => top(SurfaceEnergyBalance(Tair,pr,q,wind,Lin,Sin,z)),
# Note: You can change Heat{:H} to Heat{:T} to use temperature as the prognostic state variable.
0.0u"m" => subsurface(:soil, Soil(soilprofile), Heat{:H}(freezecurve=SFCC(DallAmico()))),
1000.0u"m" => bottom(GeothermalHeatFlux(0.053u"J/s/m^2"))
Tuple(z => subsurface(:soil1, Soil(para=para), Heat(:H, freezecurve=SFCC(DallAmico()))) for (z,para) in soilprofile),
1000.0u"m" => bottom(GeothermalHeatFlux(0.053u"J/s/m^2")),
);
grid = Grid(gridvals);
model = LandModel(strat,grid);
model = Tile(strat, grid, initT);
# define time span
tspan = (DateTime(2010,10,30),DateTime(2011,10,30))
p = parameters(model)
initT = initializer(:T, tempprofile)
u0 = initialcondition!(model, tspan, p, initT)
u0, du0 = initialcondition!(model, tspan, p)
# CryoGrid front-end for ODEProblem
prob = CryoGridProblem(model,u0,tspan,p,savevars=(:T,))
# solve with forward Euler (w/ CFL) and construct CryoGridOutput from solution
......
examples/readme_examples.jl
View file @ 57f0b540
......@@ -8,13 +8,13 @@ forcings = loadforcings(CryoGrid.Presets.Forcings.Samoylov_ERA_obs_fitted_1979_2
tair = TimeSeriesForcing(ustrip.(forcings.data.Tair), forcings.timestamps, :Tair);
# get preset soil and initial temperature profile for Samoylov
soilprofile, tempprofile = CryoGrid.Presets.SamoylovDefault
initT = initializer(:T, tempprofile)
# basic 1-layer heat conduction model (defaults to free water freezing scheme)
model = CryoGrid.Presets.SoilHeatColumn(TemperatureGradient(tair), soilprofile)
model = CryoGrid.Presets.SoilHeatColumn(TemperatureGradient(tair), soilprofile, initT)
# define time span (1 year)
tspan = (DateTime(2010,10,30),DateTime(2011,10,30))
p = parameters(model)
initT = initializer(:T, tempprofile)
u0 = initialcondition!(model, tspan, p, initT)
u0, du0 = initialcondition!(model, tspan, p, initT)
# CryoGrid front-end for ODEProblem
prob = CryoGridProblem(model,u0,tspan,p,savevars=(:T,))
# solve discretized system, saving every 6 hours;
......
src/Callbacks/Callbacks.jl deleted 100644 → 0
View file @ 210b135d
module Callbacks
import CryoGrid
using CryoGrid.Drivers: HeatOnlyLandModel
using CryoGrid.Numerics
using CryoGrid.Land: LandModel, getvar
using CryoGrid.Utils
using DiffEqCallbacks
using IfElse
"""
CryoGridCallbackFunction{TState,TSetup}(setup, state)
Helper type for defining callbacks on CryoGrid models. Given a LandModel and some additional user-defined state type
`TState`, the user can provide dispatches for `CryoGridCallbackFunction{TState}` that satisfy the relevant `DifferentialEquations.jl`
callback function signature. For example:
```julia
struct MyState
# some state variables
...
end
function (fn::CryoGridCallbackFunction{MyState})(u,p,t)
...
end
function MyCallback(setup::LandModel)
state = MyState(...)
fn = CryoGridCallbackFunction(setup, state)
# create and return SciML callback here
end
```
"""
struct CryoGridCallbackFunction{TState,TSetup}
setup::TSetup
state::TState
CryoGridCallbackFunction(setup::LandModel, state::TState) where TState = new{TState, typeof(setup)}(setup, state)
end
(fn::CryoGridCallbackFunction)(u,p,t) = error("no method dispatch provided for callback function $(typeof(fn))")
export CryoGridCallbackFunction
include("courant_step.jl")
end
src/Callbacks/courant_step.jl deleted 100644 → 0
View file @ 210b135d
struct CFLHeatState{A}
Δt::A
default_dt::Float64
end
function (fn::CryoGridCallbackFunction{<:CFLHeatState{<:AbstractArray}})(u,p,t)
let Δt = fn.state.Δt,
Δx = dustrip(Δ(fn.setup.grid)),
Ceff = getvar(:Ceff, fn.setup, u), # apparent heat capacity
kc = getvar(:kc, fn.setup, u); # thermal cond. at grid centers
@. Δt = Utils.adstrip(Δx^2 * Ceff / kc)
Δt_min = minimum(Δt)
IfElse.ifelse(isfinite(Δt_min) && Δt_min > 0, Δt_min, fn.state.default_dt)
end
end
function (fn::CryoGridCallbackFunction{<:CFLHeatState{Nothing}})(u,p,t)
let Δx = dustrip(Δ(fn.setup.grid)),
Ceff = getvar(:Ceff, fn.setup, u), # apparent heat capacity
kc = getvar(:kc, fn.setup, u); # thermal cond. at grid centers
Δt = Utils.adstrip(Δx^2 * Ceff / kc)
Δt_min = minimum(Δt)
IfElse.ifelse(isfinite(Δt_min) && Δt_min > 0, Δt_min, fn.state.default_dt)
end
end
function CFLStepLimiter(setup::HeatOnlyLandModel; courant_number=1//2, default_dt=60.0, iip=true)
state = iip ? CFLHeatState(zero(dustrip(Δ(setup.grid))), default_dt) : CFLHeatState(nothing, default_dt)
fn = CryoGridCallbackFunction(setup, state)
StepsizeLimiter(fn; safety_factor=courant_number, max_step=true)
end
export CFLStepLimiter
\ No newline at end of file
src/CryoGrid.jl
View file @ 57f0b540
......@@ -9,41 +9,31 @@ end
using Base: @propagate_inbounds
using Reexport
export Layer, SubSurface, Top, Bottom
export Process, SubSurfaceProcess, BoundaryProcess, CompoundProcess, Coupled
export BoundaryStyle, Dirichlet, Neumann
export AbstractParameterization, Parameterization
export variables, initialcondition!, diagnosticstep!, prognosticstep!, interact!, boundaryflux, boundaryvalue, observe
# Common types and methods
export Layer, SubSurface, Top, Bottom
export Process, SubSurfaceProcess, BoundaryProcess, CoupledProcesses, Coupled
include("types.jl")
export variables, initialcondition!, diagnosticstep!, prognosticstep!, interact!
export boundaryflux, boundaryvalue, criterion, affect!, observe
include("methods.jl")
# Submodules
include("Utils/Utils.jl")
include("Numerics/Numerics.jl")
include("Layers/Layers.jl")
include("Physics/Physics.jl")
include("Land/Land.jl")
include("IO/InputOutput.jl")
include("Physics/Physics.jl")
include("Strat/Strat.jl")
include("Diagnostics/Diagnostics.jl")
include("Drivers/Drivers.jl")
include("Callbacks/Callbacks.jl")
using .Utils
using .Numerics
# re-export initializer function from Numerics module
export initializer
export Grid, cells, edges, subgridinds, Δ, volume, area, initializer, getvar
using .Utils
export convert_t, convert_tspan, pstrip, @pstrip, @sym_str
# Re-exported submodules
@reexport using .Utils: convert_tspan
@reexport using .Numerics: Grid
@reexport using .Layers
@reexport using .Physics
@reexport using .Land
@reexport using .Drivers
@reexport using .Strat
@reexport using .InputOutput
@reexport using .Diagnostics
@reexport using .Callbacks
# Re-exported packages
@reexport using Dates: Dates, DateTime
......@@ -51,7 +41,9 @@ export initializer
@reexport using IntervalSets
@reexport using Unitful
# Include Presets submodule last to allow dependence on other submodules.
# include dependent submodules
include("Drivers/Drivers.jl")
@reexport using .Drivers
include("Presets/Presets.jl")
end # module
src/Diagnostics/Diagnostics.jl
View file @ 57f0b540
module Diagnostics
using CryoGrid.Numerics
using CryoGrid.Land
using CryoGrid.Strat
using CryoGrid.Utils
using DimensionalData
......@@ -27,8 +27,8 @@ function zero_annual_amplitude(T::AbstractDimArray{<:TempQuantity}; threshold=0.
Tarr = TimeArray(dims(T, Ti).val, permutedims(T, (Ti,Z)))
annual_min = collapse(Tarr, year, first, minimum)
annual_max = collapse(Tarr, year, first, maximum)
z_inds = argmax(abs.(values(annual_max) .- values(annual_min)) .< threshold, dims=2)
rebuild(T, dims(T, Z)[[i[2] for i in z_inds]], (Ti(timestamp(annual_max)),))[:,1]
z_inds = vec(argmax(abs.(values(annual_max) .- values(annual_min)) .< threshold, dims=2))
rebuild(T, collect(dims(T, Z)[[i[2] for i in z_inds]]), (Ti(timestamp(annual_max)),))
end
"""
permafrosttable(T::AbstractDimArray{<:TempQuantity})
......@@ -42,8 +42,8 @@ function permafrosttable(T::AbstractDimArray{<:TempQuantity})
Tarr = TimeArray(dims(T, Ti).val, permutedims(T, (Ti,Z)))
annual_max = collapse(Tarr, year, first, maximum)
# find argmax (first one/"true" bit) of annual_max < 0
z_inds = argmax(values(annual_max) .< 0u"°C", dims=2)
rebuild(T, dims(T, Z)[[i[2] for i in z_inds]], (Ti(timestamp(annual_max)),))[:,1]
z_inds = vec(argmax(values(annual_max) .< 0u"°C", dims=2))
rebuild(T, collect(dims(T, Z)[[i[2] for i in z_inds]]), (Ti(timestamp(annual_max)),))
end
"""
permafrostbase(T::AbstractDimArray{<:TempQuantity})
......@@ -57,8 +57,8 @@ function permafrostbase(T::AbstractDimArray{<:TempQuantity})
Tarr = TimeArray(dims(T, Ti).val, permutedims(T, (Ti,Z)))
annual_max = collapse(Tarr, year, first, maximum)
# find argmax (first one/"true" bit) of annual_max < 0
z_inds = size(Tarr,2) .- [i[2] for i in argmax(reverse(values(annual_max) .< 0u"°C", dims=2), dims=2)]
rebuild(T, dims(T, Z)[[i for i in z_inds]], (Ti(timestamp(annual_max)),))[:,1]
z_inds = vec(size(Tarr,2) .- [i[2] for i in argmax(reverse(values(annual_max) .< 0u"°C", dims=2), dims=2)])
rebuild(T, collect(dims(T, Z)[[i for i in z_inds]]), (Ti(timestamp(annual_max)),))
end
"""
thawdepth(T::AbstractDimArray{<:TempQuantity})
......@@ -69,8 +69,8 @@ and `Z` (depth) in any order.
function thawdepth(T::AbstractDimArray{<:TempQuantity})
_check_arr_dims(T)
T = permutedims(T, (Z,Ti))
z_inds = argmax((T .<= 0u"°C").data, dims=1)
rebuild(T, dims(T, Z)[[i[1] for i in z_inds]], (dims(T,Ti),))[1,:]
z_inds = vec(argmax((T .<= 0u"°C").data, dims=1))
rebuild(T, collect(dims(T, Z)[[i[1] for i in z_inds]]), (dims(T,Ti),))
end
"""
active_layer_thickness(T::AbstractDimArray{<:TempQuantity})
......
src/Diagnostics/spinup.jl
View file @ 57f0b540
"""
spinup(setup::LandModel, tspan::NTuple{2,DateTime}, p, tol, layername; kwargs...)
spinup(setup::Tile, tspan::NTuple{2,DateTime}, p, tol, layername; kwargs...)
Implements a simple, iterative spin-up procedure.
Runs the model specified by `setup` over `tspan` until the profile mean up to `maxdepth` over the whole time span changes only within the given tolerance `tol`.
Returns the `ODESolution` generated by the final iteration.
"""
function spinup(setup::LandModel, tspan::NTuple{2,DateTime}, p, tol, layername; maxdepth=100u"m", maxiter=1000, saveat=3*3600.0, solver=Euler(), dt=60.0, solve_args...)
function spinup(setup::Tile, tspan::NTuple{2,DateTime}, p, tol, layername; maxdepth=100u"m", maxiter=1000, saveat=3*3600.0, solver=Euler(), dt=60.0, solve_args...)
prob = CryoGridProblem(setup, tspan, p)
@info "Running initial solve ..."
sol = solve(prob, solver, dt=dt, saveat=saveat, solve_args...)
......
src/Drivers/diffeq.jl → src/Drivers/DiffEq/DiffEq.jl
View file @ 57f0b540
"""
Driver module SciML diffeq solvers.
"""
module DiffEq
using CryoGrid.Drivers
using CryoGrid: Strat, SubSurface, CoupledProcesses, Callback, CallbackStyle, Discrete, Continuous
using CryoGrid.InputOutput
using CryoGrid.Numerics
using CryoGrid.Physics: Heat
using CryoGrid.Utils
import CryoGrid: variables, callbacks, criterion, affect!
import CryoGrid.Strat: Tile, Stratigraphy, StratComponent
using ComponentArrays
using Dates
using DimensionalData
using Flatten
using IfElse
using IntervalSets
using ModelParameters
using LinearAlgebra
using LinearSolve
using Reexport
using Unitful
using DiffEqBase
using DiffEqBase.SciMLBase
using DiffEqCallbacks
@reexport using OrdinaryDiffEq
@reexport using DiffEqBase: solve, init, ODEProblem, SciMLBase
export CryoGridProblem
export CFLStepLimiter
include("steplimiters.jl")
export TDMASolver
include("solvers.jl")
"""
Specialized problem type for CryoGrid `ODEProblem`s.
"""
struct CryoGridODEProblem end
function Strat.Tile(integrator::SciMLBase.DEIntegrator)
tile = integrator.sol.prob.f.f
return Strat.updateparams(tile, Strat.withaxes(integrator.u, tile), integrator.p, integrator.t)
end
"""
CryoGridProblem(setup::LandModel, tspan::NTuple{2,Float64}, p=nothing;kwargs...)
CryoGridProblem(setup::Tile, tspan::NTuple{2,Float64}, p=nothing;kwargs...)
CryoGrid specialized constructor for ODEProblem that automatically generates the initial
condition and necessary callbacks.
"""
function CryoGridProblem(
landmodel::LandModel,
tile::Tile,
u0::ComponentVector,
tspan::NTuple{2,Float64},
p=nothing;
saveat=3600.0,
savevars=(),
save_everystep=false,
save_start=true,
save_end=true,
callback=nothing,
kwargs...
)
......@@ -30,22 +73,27 @@ function CryoGridProblem(
# we have to manually expand single-number `saveat` (i.e. time interval for saving) to a step-range.
expandtstep(tstep::Number) = tspan[1]:tstep:tspan[end]
expandtstep(tstep::AbstractVector) = tstep
getsavestate(model::LandModel, u, du) = deepcopy(Land.getvars(model.state, u, du, savevars...))
savefunc(u, t, integrator) = getsavestate(integrator.f.f, Land.withaxes(u, integrator.f.f), get_du(integrator))
pmodel = Model(landmodel)
p = isnothing(p) ? dustrip.(collect(pmodel[:val])) : p
getsavestate(tile::Tile, u, du) = deepcopy(Strat.getvars(tile.state, Strat.withaxes(u, tile), Strat.withaxes(du, tile), savevars...))
savefunc(u, t, integrator) = getsavestate(Tile(integrator), Strat.withaxes(u, Tile(integrator)), get_du(integrator))
# remove units
tile = stripunits(tile)
# collect parameters
p = isnothing(p) ? dustrip.(collect(Model(tile)[:val])) : collect(p)
du0 = zero(u0)
# set up saving callback
stateproto = getsavestate(landmodel, u0, du0)
stateproto = getsavestate(tile, u0, du0)
savevals = SavedValues(Float64, typeof(stateproto))
savingcallback = SavingCallback(savefunc, savevals; saveat=expandtstep(saveat), save_everystep=save_everystep)
callbacks = isnothing(callback) ? savingcallback : CallbackSet(savingcallback, callback)
savingcallback = SavingCallback(savefunc, savevals; saveat=expandtstep(saveat), save_start=save_start, save_end=save_end, save_everystep=save_everystep)
layercallbacks = _makecallbacks(tile)
usercallbacks = isnothing(callback) ? () : callback
callbacks = CallbackSet(savingcallback, layercallbacks..., usercallbacks...)
# note that this implicitly discards any existing saved values in the model setup's state history
landmodel.hist.vals = savevals
# set up default mass matrix
M_diag = similar(landmodel.state.uproto)
tile.hist.vals = savevals
# set up default mass matrix, M:
# M⋅∂u∂t = f(u)
M_diag = similar(tile.state.uproto)
M_idxmap = ComponentArrays.indexmap(getaxes(M_diag)[1])
allvars = Flatten.flatten(landmodel.state.vars, Flatten.flattenable, Var)
allvars = Flatten.flatten(tile.state.vars, Flatten.flattenable, Var)
progvars = map(varname, filter(isprognostic, allvars))
algvars = map(varname, filter(isalgebraic, allvars))
for name in keys(M_idxmap)
......@@ -59,36 +107,33 @@ function CryoGridProblem(
# if no algebraic variables are present, use identity matrix
num_algebraic = length(M_diag) - sum(M_diag)
M = num_algebraic > 0 ? Diagonal(M_diag) : I
func = odefunction(landmodel, M, u0, p, tspan; kwargs...)
func = odefunction(tile, M, u0, p, tspan; kwargs...)
ODEProblem(func, u0, tspan, p, CryoGridODEProblem(); callback=callbacks, kwargs...)
end
"""
CryoGridProblem(setup::LandModel, tspan::NTuple{2,DateTime}, args...;kwargs...)
CryoGridProblem(setup::Tile, tspan::NTuple{2,DateTime}, args...;kwargs...)
"""
CryoGridProblem(setup::LandModel, u0::ComponentVector, tspan::NTuple{2,DateTime}, args...;kwargs...) = CryoGridProblem(setup,u0,convert_tspan(tspan),args...;kwargs...)
export CryoGridProblem
CryoGridProblem(setup::Tile, u0::ComponentVector, tspan::NTuple{2,DateTime}, args...;kwargs...) = CryoGridProblem(setup,u0,convert_tspan(tspan),args...;kwargs...)
"""
odefunction(setup::LandModel, M, u0, p, tspan; kwargs...)
odefunction(setup::Tile, M, u0, p, tspan; kwargs...)
Constructs a SciML `ODEFunction` given the model setup, mass matrix M, initial state u0, parameters p, and tspan.
Can (and should) be overridden by users to provide customized ODEFunction configurations for specific problem setups, e.g:
```
model = LandModel(strat,grid)
function CryoGrid.Setup.odefunction(::DefaultJac, setup::typeof(model), M, u0, p, tspan)
tile = Tile(strat,grid)
function CryoGrid.Setup.odefunction(::DefaultJac, setup::typeof(tile), M, u0, p, tspan)
...
# make sure to return an instance of ODEFunction
end
...
prob = CryoGridProblem(model, tspan, p)
prob = CryoGridProblem(tile, tspan, p)
```
`JacobianStyle` can also be extended to create custom traits which can then be applied to compatible `LandModel`s.
`JacobianStyle` can also be extended to create custom traits which can then be applied to compatible `Tile`s.
"""
odefunction(setup::TSetup, M, u0, p, tspan; kwargs...) where {TSetup<:LandModel} = odefunction(JacobianStyle(TSetup), setup, M, u0, p, tspan; kwargs...)
odefunction(::DefaultJac, setup::TSetup, M, u0, p, tspan; kwargs...) where {TSetup<:LandModel} = ODEFunction(setup, mass_matrix=M; kwargs...)
function odefunction(::TridiagJac, setup::LandModel, M, u0, p, tspan; kwargs...)
odefunction(setup::TSetup, M, u0, p, tspan; kwargs...) where {TSetup<:Tile} = odefunction(JacobianStyle(TSetup), setup, M, u0, p, tspan; kwargs...)
odefunction(::DefaultJac, setup::TSetup, M, u0, p, tspan; kwargs...) where {TSetup<:Tile} = ODEFunction(setup, mass_matrix=M; kwargs...)
function odefunction(::TridiagJac, setup::Tile, M, u0, p, tspan; kwargs...)
if :jac_prototype in keys(kwargs)
@warn "using user specified jac_prorotype instead of tridiagonal"
ODEFunction(setup, mass_matrix=M; kwargs...)
......@@ -102,32 +147,39 @@ function odefunction(::TridiagJac, setup::LandModel, M, u0, p, tspan; kwargs...)
ODEFunction(setup, mass_matrix=M, jac_prototype=J, kwargs...)
end
end
"""
getstate(integrator::SciMLBase.DEIntegrator)
getstate(layername::Symbol, integrator::SciMLBase.DEIntegrator)
Builds the state named tuple for `layername` given an initialized integrator.
"""
Land.getstate(layername::Symbol, integrator::SciMLBase.DEIntegrator) = getstate(Val{layername}(), integrator)
Land.getstate(::Val{layername}, integrator::SciMLBase.DEIntegrator) where {layername} = Land.getstate(integrator.f.f, integrator.u, get_du(integrator), integrator.t)
Strat.getstate(integrator::SciMLBase.DEIntegrator) = Strat.getstate(Tile(integrator), integrator.u, get_du(integrator), integrator.t)
Strat.getstate(layername::Symbol, integrator::SciMLBase.DEIntegrator) = Strat.getstate(Val{layername}(), integrator)
Strat.getstate(::Val{layername}, integrator::SciMLBase.DEIntegrator) where {layername} = Strat.getstate(Val{layername}(), Tile(integrator), integrator.u, get_du(integrator), integrator.t)
"""
getvar(var::Symbol, integrator::SciMLBase.DEIntegrator)
"""
Land.getvar(var::Symbol, integrator::SciMLBase.DEIntegrator) = Land.getvar(Val{var}(), integrator.f.f, integrator.u)
Numerics.getvar(var::Symbol, integrator::SciMLBase.DEIntegrator) = Numerics.getvar(Val{var}(), Tile(integrator), integrator.u)
"""
Constructs a `CryoGridOutput` from the given `ODESolution`.
CryoGridOutput(sol::TSol, tspan::NTuple{2,Float64}=(-Inf,Inf)) where {TSol<:SciMLBase.AbstractODESolution}
Constructs a `CryoGridOutput` from the given `ODESolution`. Optional argument `tspan` restricts the time span of the output.
"""
function InputOutput.CryoGridOutput(sol::TSol) where {TSol <: SciMLBase.AbstractODESolution}
InputOutput.CryoGridOutput(sol::TSol, tspan::NTuple{2,DateTime}) where {TSol<:SciMLBase.AbstractODESolution} = CryoGridOutput(sol, convert_tspan(tspan))
function InputOutput.CryoGridOutput(sol::TSol, tspan::NTuple{2,Float64}=(-Inf,Inf)) where {TSol<:SciMLBase.AbstractODESolution}
# Helper functions for mapping variables to appropriate DimArrays by grid/shape.
withdims(::Var{name,T,<:OnGrid{Cells}}, arr, grid, ts) where {name,T} = DimArray(arr*oneunit(T), (Z(round.(typeof(1.0u"m"), cells(grid), digits=5)),Ti(ts)))
withdims(::Var{name,T,<:OnGrid{Edges}}, arr, grid, ts) where {name,T} = DimArray(arr*oneunit(T), (Z(round.(typeof(1.0u"m"), edges(grid), digits=5)),Ti(ts)))
withdims(::Var{name,T}, arr, zs, ts) where {name,T} = DimArray(arr*oneunit(T), (Ti(ts),))
model = sol.prob.f.f # LandModel
withdims(var::Var{name,<:OnGrid{Cells}}, arr, grid, ts) where {name} = DimArray(arr*one(vartype(var))*varunits(var), (Z(round.(typeof(1.0u"m"), cells(grid), digits=5)),Ti(ts)))
withdims(var::Var{name,<:OnGrid{Edges}}, arr, grid, ts) where {name} = DimArray(arr*one(vartype(var))*varunits(var), (Z(round.(typeof(1.0u"m"), edges(grid), digits=5)),Ti(ts)))
withdims(var::Var{name}, arr, zs, ts) where {name} = DimArray(arr*one(vartype(var))*varunits(var), (Ti(ts),))
save_interval = ClosedInterval(tspan...)
model = sol.prob.f.f # Tile
ts = model.hist.vals.t # use save callback time points
ts_datetime = Dates.epochms2datetime.(round.(ts*1000.0))
u_all = reduce(hcat, sol.(ts))
t_mask = map(∈(save_interval), ts) # indices within t interval
u_all = reduce(hcat, sol.(ts[t_mask])) # build prognostic state from continuous solution
pax = ComponentArrays.indexmap(getaxes(model.state.uproto)[1])
savedstates = model.hist.vals.saveval
# get saved diagnostic states and timestamps only in given interval
savedstates = model.hist.vals.saveval[t_mask]
ts_datetime = Dates.epochms2datetime.(round.(ts[t_mask]*1000.0))
allvars = variables(model)
progvars = tuplejoin(filter(isprognostic, allvars), filter(isalgebraic, allvars))
diagvars = filter(isdiagnostic, allvars)
......@@ -140,7 +192,7 @@ function InputOutput.CryoGridOutput(sol::TSol) where {TSol <: SciMLBase.Abstract
for var in filter(isongrid, tuplejoin(diagvars, fluxvars))
name = varname(var)
states = collect(skipmissing([name ∈ keys(state) ? state[name] : missing for state in savedstates]))
if length(states) == length(ts)
if length(states) == length(ts_datetime)
arr = reduce(hcat, states)
outputs[name] = withdims(var, arr, model.grid, ts_datetime)
end
......@@ -168,3 +220,59 @@ Evaluates the continuous solution at time `t`.
"""
(out::CryoGridOutput{<:ODESolution})(t::Real) = withaxes(out.res(t), out.res.prob.f.f)
(out::CryoGridOutput{<:ODESolution})(t::DateTime) = out(Dates.datetime2epochms(t)/1000.0)
# callback building functions
function _criterionfunc(::Val{name}, i_layer, i_proc, i_cb) where name
function _condition(u,t,integrator)
let tile = Tile(integrator),
comp = tile.strat[i_layer],
layer = comp.layer,
process = comp.processes[i_proc],
cb = tile.callbacks[name][i_cb],
u = Strat.withaxes(u, tile),
du = Strat.withaxes(get_du(integrator), tile),
t = t;
criterion(cb, layer, process, Strat.getstate(Val{name}(), tile, u, du, t))
end
end
end
function _affectfunc(::Val{name}, i_layer, i_proc, i_cb) where name
function _affect!(integrator)
let tile=Tile(integrator),
comp = tile.strat[i_layer],
layer = comp.layer,
process = comp.processes[i_proc],
cb = tile.callbacks[name][i_cb],
u = Strat.withaxes(integrator.u, tile),
du = Strat.withaxes(get_du(integrator), tile),
t = integrator.t;
affect!(cb, layer, process, Strat.getstate(Val{name}(), tile, u, du, t))
end
end
end
_diffeqcallback(::Discrete, ::Val{name}, i_layer, i_proc, i_cb) where name = DiffEqCallbacks.DiscreteCallback(
_criterionfunc(Val{name}(), i_layer, i_proc, i_cb),
_affectfunc(Val{name}(), i_layer, i_proc, i_cb),
# todo: initialize and finalize?
)
_diffeqcallback(::Continuous, ::Val{name}, i_layer, i_proc, i_cb) where name = DiffEqCallbacks.ContinuousCallback(
_criterionfunc(Val{name}(), i_layer, i_proc, i_cb),
_affectfunc(Val{name}(), i_layer, i_proc, i_cb),
# todo: initialize and finalize?
)
function _makecallbacks(component::StratComponent{L,P,name}, i_layer) where {L,P,name}
cbs = []
i_cb = 1
for (i_proc, proc) in enumerate(component.processes)
for callback in callbacks(component.layer, proc)
push!(cbs, _diffeqcallback(CallbackStyle(callback), Val{name}(), i_layer, i_proc, i_cb))
i_cb += 1
end
end
return Tuple(cbs)
end
function _makecallbacks(tile::Tile)
diffeq_callbacks = tuplejoin((_makecallbacks(comp, i) for (i,comp) in enumerate(tile.strat))...)
return diffeq_callbacks
end
end