API Documentation

Macro that takes an expression corresponding to a reaction network and outputs an instance of TheoryReactionNetwork that can be converted to a DiscreteProblem or solved directly.

Most arrows accepted (both right, left, and bi-drectional arrows). Use 0 or ∅ for annihilation/creation to/from nothing.

Custom functions and sampleable objects can be used as numeric parameters. Note that these have to be accessible from ReactiveDynamics's source code.

Examples

acs = @ReactionNetwork begin
    1.0, X ⟶ Y
    1.0, X ⟶ Y, priority=>6., prob=>.7, capacity=>3.
    1.0, ∅ --> (Poisson(.3γ)X, Poisson(.5)Y)
    (XY > 100) && (XY -= 1)
end
@push acs 1.0 X ⟶ Y 
@prob_init acs X=1 Y=2 XY=α
@prob_params acs γ=1 α=4
@solve_and_plot acs

Add new species to a model.

Examples

@add_species acs S I R

Alias object name in an acs.

Default names

Examples

@aka acs species=resource transition=reaction

Set species modality.

Supported modalities

• nonblock
• conserved
• rate

Examples

@mode acs (r"proj\w+", r"experimental\w+") conserved
@mode acs (S, I) conserved
@mode acs S conserved

Set name of a transition in the model.

Examples

@name_transition acs 1="name"
@name_transition acs name="transition_name"
@name_transition acs "name"="transition_name"

Set cost.

Examples

@cost model experimental1=2 experimental2=3

Set valuation.

Examples

@valuation model experimental1=2 experimental2=3

Set reward.

Examples

@reward model experimental1=2 experimental2=3

Add reactions to an acset.

Examples

@push sir_acs β*S*I*tdecay(@time()) S+I --> 2I name=>SI2I
@push sir_acs begin 
    ν*I, I --> R, name=>I2R
    γ, R --> S, name=>R2S
end

Add a jump process (with specified Poisson intensity per unit time step) to a model.

Examples

@jump acs λ Z += rand(Poisson(1.))

Add a periodic callback to a model.

Examples

@periodic acs 1. X += 1

Set initial values of species in an acset.

Examples

@prob_init acs X=1 Y=2 Z=h(α)
@prob_init acs [1., 2., 3.]

Set uncertainty in initial values of species in an acset (stderr).

Examples

@prob_uncertainty acs X=.1 Y=.2
@prob_uncertainty acs [.1, .2,]

Set parameter values in an acset.

Examples

@prob_params acs α=1. β=2.

Set model metadata (e.g. solver arguments)

Examples

@prob_meta acs tspan=(0, 100.) schedule=schedule_weighted!
@prob_meta sir_acs tspan=250 tstep=1

@join models... [equalize...]

Performs join of models and identifies model variables, as specified.

Model variables / parameter values and metadata are propagated; the last model takes precedence.

Examples

@join acs1 acs2 @catchall(A)=acs2.Z @catchall(XY) @catchall(B)

Identify (collapse) a set of species in a model.

Examples

@join acs acs1.A=acs2.A B=C

Import a model from a file: this can be either a single TOML file encoding the entire model, or a batch of CSV files (a root file and a number of files, each per a class of objects).

See tutorials/loadsave for an example.

Examples

@import_network "model.toml"
@import_network "csv/model.toml"

Export model to a file: this can be either a single TOML file encoding the entire model, or a batch of CSV files (a root file and a number of files, each per a class of objects).

See tutorials/loadsave for an example.

Examples

@export_network acs "acs_data.toml" # as a TOML
@export_network acs "csv/model.csv" # as a CSV

@import_solution "sol.jld2"
@import_solution "sol.jld2" sol

Import a solution from a file.

Examples

@import_solution "sir_acs_sol/serialized/sol.jld2"

@export_solution_as_table sol

Export a solution as a DataFrame.

Examples

@export_solution_as_table sol

@export_solution_as_csv sol
@export_solution_as_csv sol "sol.csv"

Export a solution to a file.

Examples

@export_solution_as_csv sol "sol.csv"

@export_solution sol
@export_solution sol "sol.jld2"

Export a solution to a file.

Examples

@export_solution sol "sol.jdl2"

Convert a model to a DiscreteProblem. If passed a problem instance, return the instance.

Examples

@problematize acs tspan=1:100

Solve the problem. Solverargs passed at the calltime take precedence.

Examples

@solve prob
@solve prob tspan=1:100
@solve prob tspan=100 trajectories=20

Plot the solution (summary).

Examples

@plot sol plot_type=summary
@plot sol plot_type=allocation # not supported for ensemble solutions!
@plot sol plot_type=valuations # not supported for ensemble solutions!
@plot sol plot_type=new_transitions # not supported for ensemble solutions!

@optimize acset objective <free_var=[init_val]>... <free_prm=[init_val]>... opts...

Take an acset and optimize given functional.

Objective is an expression which may reference the model's variables and parameters, i.e., A+β. The values to optimized are listed using their symbolic names; unless specified, the initial value is inferred from the model. The vector of free variables passed to the NLopt solver has the form [free_vars; free_params]; order of vars and params, respectively, is preserved.

By default, the functional is minimized. Specify objective=max to perform maximization.

Propagates NLopt solver arguments; see NLopt documentation.

Examples

@optimize acs abs(A-B) A B=20. α=2. lower_bounds=0 upper_bounds=100
@optimize acss abs(A-B) A B=20. α=2. upper_bounds=[200,300,400] maxeval=200 objective=min

@fit acset data_points time_steps empiric_variables <free_var=[init_val]>... <free_prm=[init_val]>... opts...

Take an acset and fit initial values and parameters to empirical data.

The values to optimized are listed using their symbolic names; unless specified, the initial value is inferred from the model. The vector of free variables passed to the NLopt solver has the form [free_vars; free_params]; order of vars and params, respectively, is preserved.

Propagates NLopt solver arguments; see NLopt documentation.

Examples

t = [1, 50, 100]
data = [80 30 20]
@fit acs data t vars=A B=20 A α # fit B, A, α; empirical data is for variable A

@fit acset data_points time_steps empiric_variables <free_var=[init_val]>... <free_prm=[init_val]>... opts...

Take an acset, fit initial values and parameters to empirical data, and plot the result.

The values to optimized are listed using their symbolic names; unless specified, the initial value is inferred from the model. The vector of free variables passed to the NLopt solver has the form [free_vars; free_params]; order of vars and params, respectively, is preserved.

Propagates NLopt solver arguments; see NLopt documentation.

Examples

t = [1, 50, 100]
data = [80 30 20]
@fit acs data t vars=A B=20 A α # fit B, A, α; empirical data is for variable A

@build_solver acset <free_var=[init_val]>... <free_prm=[init_val]>... opts...

Take an acset and export a solution as a function of free vars and free parameters.

Examples

solver = @build_solver acs S α β # function of variable S and parameters α, β
solver([S, α, β])