From bb425863b3710c781a6407e521842d0730f64dfc Mon Sep 17 00:00:00 2001 From: abhro <5664668+abhro@users.noreply.github.com> Date: Sun, 7 Sep 2025 21:34:42 -0400 Subject: [PATCH] Dedent docstrings in JumpProblemLibrary Make sure that sentence text is not picked up as code examples by doc formatters and viewers. --- .../src/JumpProblemLibrary.jl | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/lib/JumpProblemLibrary/src/JumpProblemLibrary.jl b/lib/JumpProblemLibrary/src/JumpProblemLibrary.jl index 5faa061..f3ae84c 100644 --- a/lib/JumpProblemLibrary/src/JumpProblemLibrary.jl +++ b/lib/JumpProblemLibrary/src/JumpProblemLibrary.jl @@ -15,9 +15,9 @@ export prob_jump_dnarepressor, prob_jump_constproduct, prob_jump_nonlinrxs, prob_jump_diffnetwork """ - General structure to hold JumpProblem info. Needed since - the JumpProblem constructor requires the algorithm, so we - don't create the JumpProblem here. +General structure to hold JumpProblem info. Needed since +the JumpProblem constructor requires the algorithm, so we +don't create the JumpProblem here. """ struct JumpProblemNetwork network::Any # Catalyst network @@ -49,7 +49,7 @@ Nsims = 8000 expected_avg = 5.926553750000000e+02 prob_data = Dict("num_sims_for_mean" => Nsims, "expected_mean" => expected_avg) """ - DNA negative feedback autoregulatory model. Protein acts as repressor. +DNA negative feedback autoregulatory model. Protein acts as repressor. """ prob_jump_dnarepressor = JumpProblemNetwork(dna_rs, rates, tf, u0, prob, prob_data) @@ -65,7 +65,7 @@ Nsims = 16000 expected_avg = t -> rates[1] / rates[2] .* (1.0 - exp.(-rates[2] * t)) prob_data = Dict("num_sims_for_mean" => Nsims, "expected_mean_at_t" => expected_avg) """ - Simple birth-death process with constant production and degradation. +Simple birth-death process with constant production and degradation. """ prob_jump_constproduct = JumpProblemNetwork(bd_rs, rates, tf, u0, prob, prob_data) @@ -84,7 +84,7 @@ Nsims = 32000 expected_avg = 84.876015624999994 prob_data = Dict("num_sims_for_mean" => Nsims, "expected_mean" => expected_avg) """ - Example with a mix of nonlinear reactions, including third order +Example with a mix of nonlinear reactions, including third order """ prob_jump_nonlinrxs = JumpProblemNetwork(nonlin_rs, rates, tf, u0, prob, prob_data) @@ -105,7 +105,7 @@ u0 = [:X => 200.0, :Y => 60.0, :Z => 120.0, :R => 100.0, :S => 50.0, :SP => 50.0 tf = 4000.0 prob = DiscreteProblem(oscil_rs, u0, (0.0, tf), eval_module = @__MODULE__) """ - Oscillatory system, uses a mixture of jump types. +Oscillatory system, uses a mixture of jump types. """ prob_jump_osc_mixed_jumptypes = JumpProblemNetwork(oscil_rs, nothing, tf, u0, prob, nothing) @@ -153,10 +153,10 @@ u0 = [:S1 => params[1], :S2 => params[2], :S3 => params[3], :S4 => 0, :S5 => 0, tf = 100.0 prob = DiscreteProblem(rs, u0, (0.0, tf), rates, eval_module = @__MODULE__) """ - Multistate model from Gupta and Mendes, - "An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems", - Computation 2018, 6, 9; doi:10.3390/computation6010009 - Translated from supplementary data file: Models/Multi-state/fixed_multistate.xml +Multistate model from Gupta and Mendes, +"An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems", +Computation 2018, 6, 9; doi:10.3390/computation6010009 +Translated from supplementary data file: Models/Multi-state/fixed_multistate.xml """ prob_jump_multistate = JumpProblemNetwork(rs, rates, tf, u0, prob, Dict("specs_to_sym_name" => specs_sym_to_name, @@ -206,9 +206,9 @@ tf = 2000.0 prob = DiscreteProblem(rs, u0, (0.0, tf), eval_module = @__MODULE__) """ - Twenty-gene model from McCollum et al, - "The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior" - Comp. Bio. and Chem., 30, pg. 39-49 (2006). +Twenty-gene model from McCollum et al, +"The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior" +Comp. Bio. and Chem., 30, pg. 39-49 (2006). """ prob_jump_twentygenes = JumpProblemNetwork(rs, nothing, tf, u0, prob, nothing) @@ -229,10 +229,10 @@ u0 = [:G => 1000, :M => 0, :P => 0, :P2 => 0, :P2G => 0] tf = 4000.0 prob = DiscreteProblem(rn, u0, (0.0, tf), rnpar, eval_module = @__MODULE__) """ - Negative feedback autoregulatory gene expression model. Dimer is the repressor. - Taken from Marchetti, Priami and Thanh, - "Simulation Algorithms for Comptuational Systems Biology", - Springer (2017). +Negative feedback autoregulatory gene expression model. Dimer is the repressor. +Taken from Marchetti, Priami and Thanh, +"Simulation Algorithms for Comptuational Systems Biology", +Springer (2017). """ prob_jump_dnadimer_repressor = JumpProblemNetwork(rn, rnpar, tf, u0, prob, Dict("specs_names" => varlabels)) @@ -257,10 +257,10 @@ function getDiffu0(diffnetwork, N) end tf = 10.0 """ - Continuous time random walk (i.e. diffusion approximation) example. - Here the network in the JumpProblemNetwork is a function that returns a - network given the number of lattice sites. - u0 is a similar function that returns the initial condition vector. +Continuous time random walk (i.e. diffusion approximation) example. +Here the network in the JumpProblemNetwork is a function that returns a +network given the number of lattice sites. +u0 is a similar function that returns the initial condition vector. """ prob_jump_diffnetwork = JumpProblemNetwork(getDiffNetwork, params, tf, getDiffu0, nothing, nothing)