Merge pull request #37 from himoto/develop

Release v0.4.3

Author: Hiroaki Imoto

Project.toml

@@ -1,7 +1,7 @@
 name = "BioMASS"
 uuid = "324734c7-f323-4536-9335-775d9be9d101"
 authors = ["Hiroaki Imoto <himoto@protein.osaka-u.ac.jp>"]
-version = "0.4.2"
+version = "0.4.3"
 
 [deps]
 CMAEvolutionStrategy = "8d3b24bd-414e-49e0-94fb-163cc3a3e411"

README.md

@@ -2,10 +2,11 @@
 
 [![Actions Status](https://github.com/himoto/BioMASS.jl/workflows/CI/badge.svg)](https://github.com/himoto/BioMASS.jl/actions)
 [![version](https://juliahub.com/docs/BioMASS/version.svg)](https://juliahub.com/ui/Packages/BioMASS/acq1V)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://himoto.github.io/BioMASS.jl/stable)
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://himoto.github.io/BioMASS.jl/dev)
-[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 
 This module provides a Julia interface to the [BioMASS](https://github.com/okadalabipr/biomass) parameter estimation.
 
@@ -33,7 +34,7 @@ optimize(model, 1, max_generation=20000, allowable_error=0.5)
 visualize(model, viz_type="best", show_all=true)
 ```
 
-## Convert optimized parameters to BioMASS format
+### Conversion of optimized parameters into BioMASS format
 
 ![](docs/src/assets/conversion.png)
 

docs/src/assets/Subp_1NMPP1.png

@@ -2,6 +2,8 @@
 
 A numerical study of the changes in the dynamics and stability of a system upon variations in its parameters.
 
+![](../assets/Subp_1NMPP1.png)
+
 ## Procedure for stability analysis at fixed points
 
 Consider the following system of ordinary differential equations:

docs/src/usage/bifurcation_analysis.md

@@ -6,15 +6,83 @@ using StatsBase
 using Statistics
 using DelimitedFiles
 
-export optimize,
+export load_model,
+    optimize,
     optimize_continue,
     param2biomass,
-    load_model,
     visualize,
     create_diffeq,
     new_curve!,
     get_bistable_regime
 
+
+const requirements = [
+    joinpath("name2idx", "parameters.jl"),
+    joinpath("name2idx", "species.jl"),
+    "set_model.jl",
+    "observable.jl",
+    "simulation.jl",
+    "experimental_data.jl",
+    "set_search_param.jl",
+    "fitness.jl",
+]
+
+struct ExecModel
+    path::String
+    parameters::Module
+    species::Module
+    observables::Vector{String}
+    sim::Module
+    exp::Module
+    obj_func::Function
+    cond2idx::Function
+    search_idx::Function
+    search_region::Function
+    update_param::Function
+    gene2val::Function
+    val2gene::Function
+    bestIndivVal2randGene::Function
+end
+function ExecModel(model_path::String, show_info::Bool)
+    for req in requirements
+        include(joinpath(model_path, req))
+    end
+    if show_info
+        print(
+            "Model information\n"
+            * "-----------------\n"
+            * @sprintf(
+                "%d species\n%d parameters, of which %d to be estimated",
+                length(V.NAMES), length(C.NAMES), length(get_search_index()[1])
+            )
+        )
+    end
+    ExecModel(
+        model_path,
+        C,
+        V,
+        observables,
+        Sim,
+        Exp,
+        objective,
+        conditions_index,
+        get_search_index,
+        get_search_region,
+        update_param,
+        decode_gene2val,
+        encode_val2gene,
+        encode_bestIndivVal2randGene,
+    )
+end
+
+function load_model(path_to_model::String; show_info::Bool=false)
+    if isdir(path_to_model)
+        ExecModel(path_to_model, show_info)
+    else
+        error("$path_to_model: No such directory")
+    end
+end
+
 function isinstalled(pymodule::String)::Bool
     try
         pyimport(pymodule)
@@ -24,13 +92,12 @@ function isinstalled(pymodule::String)::Bool
     end
 end
 
-include("exec_model.jl")
 include("convert.jl")
 include("optimize.jl")
-include("ga/initial_population.jl")
-include("ga/converging.jl")
-include("ga/local_search.jl")
-include("ga/v2.jl")
+include("estimation/initial_population.jl")
+include("estimation/converging.jl")
+include("estimation/local_search.jl")
+include("estimation/ga.jl")
 if isinstalled("matplotlib")
     include("visualize.jl")
 else

src/BioMASS.jl

@@ -284,7 +284,7 @@ function ga_v2_continue(;
         )
     )[:, 1]
     best_indiv_gene::Vector{Float64} = model.val2gene(best_indiv)
-    best_fitness::Float64 = objective(best_indiv_gene)
+    best_fitness::Float64 = model.obj_func(best_indiv_gene)
 
     population::Matrix{Float64} = get_initial_population_continue(
         model, nth_param_set, n_population, n_gene, initial_threshold, p0_bounds

src/ga/converging.jl renamed to src/estimation/converging.jl

@@ -56,7 +56,7 @@ function local_search!(
             verbosity=0,
             multi_threading=true,
             maxiter=maxiter,
-            maxfevals=100 * n_gene,
+            maxfevals=1000 * n_gene,
         )
         x_best_fit = xbest(result)
         obj_val = objective(x_best_fit)

src/ga/v2.jl renamed to src/estimation/ga.jl

@@ -37,7 +37,7 @@ function __init__()
                 'xtol': 0.1,
                 'ftol': 1.0,
                 'maxiter': maxiter,
-                'maxfev': 100 * n_gene,
+                'maxfev': 1000 * n_gene,
                 'direc': direc,
             }
         )
@@ -61,7 +61,7 @@ function __init__()
             objective,
             tuple(zip(lower, upper)),
             strategy='best2bin',
-            mutation=0.1,
+            mutation=(0.0, 0.3),
             recombination=0.9,
             maxiter=maxiter,
             popsize=1,