From 7b28070c39e71de1cef9b68aef57b8589296b99f Mon Sep 17 00:00:00 2001 From: danielchen Date: Tue, 9 May 2023 10:47:44 -0400 Subject: [PATCH 1/8] add CellAuto_Petrinet framework example --- .../CellAuto_Petrinet.ipynb | 441 ++++ .../CellAuto_Petrinet.qmd | 390 ++++ .../Integrations_with_petri/Petri_agent.jl | 273 +++ .../Petri_reactions.jl | 10 + .../Integrations_with_petri/Project.toml | 26 + tutorials/multiscale_CFN/Project.toml | 12 + tutorials/multiscale_CFN/Readme.html | 318 +++ tutorials/multiscale_CFN/Readme.qmd | 25 + .../libs/bootstrap/bootstrap-icons.css | 2018 +++++++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 164168 bytes .../libs/bootstrap/bootstrap.min.css | 10 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 203 ++ .../Readme_files/libs/quarto-html/quarto.js | 884 ++++++++ .../Readme_files/libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + tutorials/multiscale_CFN/multiscale_CFN.jl | 7 + 20 files changed, 4649 insertions(+) create mode 100644 tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb create mode 100644 tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd create mode 100644 tutorials/Integrations_with_petri/Petri_agent.jl create mode 100644 tutorials/Integrations_with_petri/Petri_reactions.jl create mode 100644 tutorials/Integrations_with_petri/Project.toml create mode 100644 tutorials/multiscale_CFN/Project.toml create mode 100644 tutorials/multiscale_CFN/Readme.html create mode 100644 tutorials/multiscale_CFN/Readme.qmd create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/bootstrap/bootstrap-icons.css create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/bootstrap/bootstrap.min.css create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/bootstrap/bootstrap.min.js create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/clipboard/clipboard.min.js create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/quarto-html/anchor.min.js create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/quarto-html/popper.min.js create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/quarto-html/quarto.js create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/quarto-html/tippy.css create mode 100644 tutorials/multiscale_CFN/Readme_files/libs/quarto-html/tippy.umd.min.js create mode 100644 tutorials/multiscale_CFN/multiscale_CFN.jl diff --git a/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb b/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb new file mode 100644 index 0000000..e0b4d63 --- /dev/null +++ b/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "title: \"Cellular Automata Simulation Using Petri Nets\"\n", + "# output: html_document\n", + "format:\n", + " html:\n", + " code-fold: false\n", + "# jupyter: julia-1.8\n", + "toc: true\n", + "author: Tianchi Chen\n", + "---" + ], + "id": "d1f84ee2" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "\n", + "This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot.\n", + "\n", + "# Setup\n", + "\n", + "We start by importing the necessary packages:\n", + "\n", + "\n", + "```{julia}\n", + "using Revise\n", + "using DifferentialEquations, AlgebraicAgents\n", + "using Plots\n", + "using Random\n", + "Random.seed!(0)\n", + "```\n", + "\n", + "\n", + "# Defining the Cell Types\n", + "We define an abstract type AbstractCellType and four concrete types NormalCellA, NormalCellB, CancerCellA, and CancerCellB that inherit from AbstractCellType.\n", + "\n", + "\n", + "```{julia}\n", + "abstract type AbstractCellType end\n", + "struct NormalCellA <: AbstractCellType end\n", + "struct NormalCellB <: AbstractCellType end\n", + "struct CancerCellA <: AbstractCellType end\n", + "struct CancerCellB <: AbstractCellType end\n", + "```\n", + "\n", + "\n", + "# Defining the Cell and Place\n", + "We define a mutable struct Cell with fields for the cell type, an ID, the dynamical state, and the time in active state. We also define a struct Place that represents a location with a name and a vector of cells (tokens).\n", + "\n", + "\n", + "```{julia}\n", + "mutable struct Cell\n", + " cell_type::AbstractCellType\n", + " id::Int\n", + " dynamical_state::Symbol\n", + " time_in_active_state::Int\n", + "end\n", + "\n", + "mutable struct Place\n", + " name::String\n", + " tokens::Vector{Cell}\n", + "end\n", + "```\n", + "\n", + "\n", + "# Defining the Transition\n", + "A Transition represents a transformation from input places to output places. It has fields for the name of the transition, the input places, and the output places.\n", + "\n", + "\n", + "```{julia}\n", + "struct Transition\n", + " name::String\n", + " input::Vector{Tuple{Int, Place, DataType}}\n", + " output::Vector{Tuple{Int, Place, DataType}}\n", + "end\n", + "```\n", + "\n", + "\n", + "# Adding and Removing Tokens\n", + "We define two helper functions add_token! and remove_token! to add and remove cells from a place.\n", + "\n", + "\n", + "```{julia}\n", + "function add_token!(place::Place, token::Cell)\n", + " push!(place.tokens, token)\n", + "end\n", + "\n", + "function remove_token!(place::Place, token::Cell)\n", + " deleteat!(place.tokens, findfirst(t -> t == token, place.tokens))\n", + "end\n", + "```\n", + "\n", + "\n", + "# Simulating the Dynamical State\n", + "We define a function simulate_dynamical_state! that updates the dynamical state of a cell based on the presence of cancer cells in the same place.\n", + "\n", + "\n", + "```{julia}\n", + "function simulate_dynamical_state!(cell::Cell, cancer_cell_present::Bool,\n", + " max_active_time::Int)\n", + " if cancer_cell_present &&\n", + " (cell.dynamical_state == :dormant || cell.dynamical_state == :inactive)\n", + " cell.dynamical_state = :active\n", + " cell.time_in_active_state = 0\n", + " elseif cell.dynamical_state == :active\n", + " cell.time_in_active_state += 1\n", + " if cell.time_in_active_state >= max_active_time\n", + " cell.dynamical_state = :dormant\n", + " cell.time_in_active_state = 0\n", + " end\n", + " end\n", + "end\n", + "```\n", + "\n", + "\n", + "\n", + "# Performing the Transition\n", + "The function perform_transition! checks the feasibility of a transition by confirming if it has the required number of tokens in the input places. If the transition is feasible, it removes the tokens from the input places and adds new tokens to the output places. The function also updates the dynamical state of the tokens based on the presence of cancer cells.\n", + "\n", + "\n", + "```{julia}\n", + "function perform_transition!(transition::Transition)\n", + " cancer_cell_exists = false\n", + " num_tokens = 0\n", + "\n", + " for (count, place, token_type) in transition.input\n", + " tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)\n", + "\n", + " if length(tokens_to_remove) < count\n", + " return false\n", + " end\n", + "\n", + " for token in tokens_to_remove\n", + " if isa(token.cell_type, CancerCellA) || isa(token.cell_type, CancerCellB)\n", + " cancer_cell_exists = true\n", + " end\n", + " num_tokens += 1\n", + " end\n", + " end\n", + "\n", + " if cancer_cell_exists && num_tokens > 30\n", + " for (count, place, token_type) in transition.input\n", + " tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)\n", + " for token in tokens_to_remove[1:count]\n", + " remove_token!(place, token)\n", + " end\n", + " end\n", + "\n", + " for (count, place, token_type) in transition.output\n", + " for _ in 1:count\n", + " new_cell = Cell(token_type(), rand(Int),\n", + " rand([:active, :inactive, :dormant]), max_active_time)\n", + " simulate_dynamical_state!(new_cell, cancer_cell_exists, max_active_time)\n", + " add_token!(place, new_cell)\n", + " end\n", + " end\n", + "\n", + " return true\n", + " end\n", + "\n", + " return false\n", + "end\n", + "```\n", + "\n", + "\n", + "# Defining the Petri Net\n", + "We define a Petri Net using the previously defined Place and Transition structures. The Petri Net also includes InputArc and OutputArc structures that connect Places and Transitions.\n", + "\n", + "\n", + "```{julia}\n", + "struct InputArc\n", + " source::Place\n", + " target::Transition\n", + " cell_type::DataType\n", + " weight::Int\n", + "end\n", + "\n", + "struct OutputArc\n", + " source::Transition\n", + " target::Place\n", + " cell_type::DataType\n", + " weight::Int\n", + "end\n", + "\n", + "struct PetriNet\n", + " places::Vector{Place}\n", + " transitions::Vector{Transition}\n", + " input_arcs::Vector{InputArc}\n", + " output_arcs::Vector{OutputArc}\n", + "end\n", + "```\n", + "\n", + "\n", + "\n", + "\n", + "# Simulation of the Petri Net\n", + "We initialize the Petri Net with a certain number of cells and let the system evolve over a predefined number of time steps. At each time step, we update the dynamical state of each cell, check if each transition can fire, and if so, fire the transition. We also record the state of the system at each time step.\n", + "\n", + "\n", + "\n", + "```{julia}\n", + "function can_fire(transition::Transition, places::Vector{Place})\n", + " for (count, place, token_type) in transition.input\n", + " tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)\n", + "\n", + " if length(tokens_to_remove) < count\n", + " return false\n", + " end\n", + " end\n", + "\n", + " return true\n", + "end\n", + "\n", + "function count_cells(pn::PetriNet)\n", + " cell_counts = Dict{Tuple{DataType, Symbol}, Int}()\n", + "\n", + " for place in pn.places\n", + " for cell in place.tokens\n", + " cell_key = (typeof(cell.cell_type), cell.dynamical_state)\n", + " cell_counts[cell_key] = get(cell_counts, cell_key, 0) + 1\n", + " end\n", + " end\n", + "\n", + " return cell_counts\n", + "end\n", + "```\n", + "\n", + "\n", + "\n", + "# Visualization of the Petri Net Evolution\n", + "the Plots package to visualize the evolution of the system. At each time step, we create a bar plot of the count of each type of cell in each dynamical state.\n", + "\n", + "\n", + "```{julia}\n", + "function plot_petri_net(pn::PetriNet)\n", + " cell_counts = count_cells(pn)\n", + "\n", + " p = bar([string(key) for key in keys(cell_counts)],\n", + " [value for value in values(cell_counts)],\n", + " xlabel = \"Cell Type and Dynamical State\",\n", + " ylabel = \"Count\",\n", + " title = \"Petri Net Simulation\",\n", + " legend = false)\n", + " display(p)\n", + "end\n", + "```\n", + "\n", + "\n", + "\n", + "# Running the Simulation\n", + "To run the simulation, we initialize the Petri Net and then perform a series of updates in a loop. At each time step, we print out the current state of the Petri Net and generate a plot.\n", + "\n", + "# Initializing the Petri Net\n", + "\n", + "We initialize the Petri net by creating the places (P1 and P2), the transitions (T1), and setting up the input and output arcs.\n", + "\n", + "\n", + "```{julia}\n", + "function init_petri_net(num_cells::Int, max_active_time::Int)\n", + " # Define cell types\n", + " cell_types = [NormalCellA, NormalCellB, CancerCellA, CancerCellB]\n", + "\n", + " # Define the places\n", + " P1 = Place(\"P1\",\n", + " [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]),\n", + " max_active_time) for i in 1:round(Int, 0.5 * num_cells)])\n", + " P2 = Place(\"P2\",\n", + " [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]),\n", + " max_active_time) for i in 1:round(Int, 0.5 * num_cells)])\n", + " P3 = Place(\"P3\", [])\n", + "\n", + " # Define the transitions\n", + " T1 = Transition(\"T1\", [(3, P1, NormalCellA), (1, P2, CancerCellA)],\n", + " [(1, P3, CancerCellA)])\n", + "\n", + " # Define arcs\n", + " input_arc1 = InputArc(P1, T1, NormalCellA, 30)\n", + " input_arc2 = InputArc(P2, T1, CancerCellA, 30)\n", + " output_arc1 = OutputArc(T1, P3, CancerCellA, 50)\n", + "\n", + " # Define the Petri net\n", + " pn = PetriNet([P1, P2, P3], [T1], [input_arc1, input_arc2], [output_arc1])\n", + "\n", + " return pn\n", + "end\n", + "\n", + "```\n", + "\n", + "# Updating the Petri Net\n", + "We update the Petri net by checking the state of the cells and performing transitions if possible.\n", + "\n", + "\n", + "```{julia}\n", + "function update_petri_net(pn::PetriNet)\n", + " # Iterate through places\n", + " for place in pn.places\n", + " # Check if cancer cells are present in the place\n", + " cancer_cell_present = any(cell -> isa(cell.cell_type, CancerCellA) ||\n", + " isa(cell.cell_type, CancerCellB), place.tokens)\n", + "\n", + " # Update dynamical states of all cells in the place\n", + " for cell in place.tokens\n", + " simulate_dynamical_state!(cell, cancer_cell_present, max_active_time)\n", + " end\n", + " end\n", + "\n", + " # Iterate through transitions\n", + " for transition in pn.transitions\n", + " # Check if the transition can be fired\n", + " if can_fire(transition, pn.places)\n", + " # Fire the transition and update the system's state\n", + " perform_transition!(transition)\n", + " end\n", + " end\n", + "end\n", + "```\n", + "\n", + "# Cell trajectory visulization\n", + "\n", + "\n", + "```{julia}\n", + "function plot_trajectory(cell_trajectory::Vector{Dict{Tuple{DataType, Symbol}, Int}})\n", + " # Initialize empty DataFrame\n", + " df_traj = DataFrame(cell_type = DataType[], dynamical_state = Symbol[], count = Int[],\n", + " time_step = Int[])\n", + "\n", + " # Iterate over cell_trajectory\n", + " for (i, dict) in enumerate(cell_trajectory)\n", + " for (key, value) in dict\n", + " cell_type, dynamical_state = key\n", + " push!(df_traj, (cell_type, dynamical_state, value, i))\n", + " end\n", + " end\n", + "\n", + " # Convert cell type to string for plotting\n", + " df_traj[!, :cell_type] = string.(df_traj[!, :cell_type])\n", + "\n", + " # Separate data frames based on cell type\n", + " df_normalA = filter(row -> row[:cell_type] == \"NormalCellA\", df_traj)\n", + " df_normalB = filter(row -> row[:cell_type] == \"NormalCellB\", df_traj)\n", + " df_cancerA = filter(row -> row[:cell_type] == \"CancerCellA\", df_traj)\n", + " df_cancerB = filter(row -> row[:cell_type] == \"CancerCellB\", df_traj)\n", + "\n", + " # Initialize plot\n", + " p = plot()\n", + "\n", + " # Add lines for each cell type and dynamical state\n", + " for df_cell in [df_normalA, df_normalB, df_cancerA, df_cancerB]\n", + " for state in unique(df_cell[!, :dynamical_state])\n", + " df_state = filter(row -> row[:dynamical_state] == state, df_cell)\n", + " plot!(p, df_state[!, :time_step], df_state[!, :count],\n", + " label = \"$(df_state[1, :cell_type]) - $(state)\")\n", + " end\n", + " end\n", + "\n", + " # Show plot\n", + " return p\n", + "end\n", + "```\n", + "\n", + "```{julia}\n", + "# Initialize Petri Net\n", + "pn = init_petri_net()\n", + "\n", + "# Run simulation for 100 time steps\n", + "for t in 1:100\n", + " # Update Petri Net\n", + " update_petri_net(pn)\n", + "\n", + " # Print current state\n", + " println(\"Time: \", t)\n", + " println(\"State: \", count_cells(pn))\n", + "\n", + " # Plot current state\n", + " plot_petri_net(pn)\n", + "end\n", + "```\n", + "\n", + "```{julia}\n", + "function init_trajectory()\n", + " # Initialize an empty array to store the states of the Petri net at each time step\n", + " trajectory = Dict{Tuple{DataType, Symbol}, Int}[]\n", + "\n", + " return trajectory\n", + "end\n", + "\n", + "function append_to_trajectory!(trajectory, pn)\n", + " # Append the current state of the Petri net to the trajectory\n", + " push!(trajectory, count_cells(pn))\n", + "end\n", + "\n", + "```\n", + "\n", + "\n", + "\n", + "run a simulation \n", + "\n", + "```{julia}\n", + "# Initialize Petri net\n", + "num_cells = 500\n", + "max_active_time = 5\n", + "pn = init_petri_net(num_cells, max_active_time)\n", + "\n", + "# Define the time range\n", + "tspan = (0.0, 30.0)\n", + "\n", + "# Initialize trajectory\n", + "traj = init_trajectory()\n", + "\n", + "# Run the simulation\n", + "for t in tspan[1]:tspan[2]\n", + " update_petri_net(pn)\n", + " append_to_trajectory!(traj, pn)\n", + "end\n", + "\n", + "# Visualize the trajectory\n", + "plot_trajectory(traj)\n", + "\n", + "```" + ], + "id": "949333f6" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd b/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd new file mode 100644 index 0000000..56acd17 --- /dev/null +++ b/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd @@ -0,0 +1,390 @@ +--- +title: "Cellular Automata Simulation Using Petri Nets" +# output: html_document +format: + html: + code-fold: false +# jupyter: julia-1.8 +toc: true +author: Tianchi Chen +--- + +# Introduction + +This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot. + +# Setup + +We start by importing the necessary packages: + +```{julia} +using Revise +using DifferentialEquations, AlgebraicAgents +using Plots +using Random +Random.seed!(0) +``` + +# Defining the Cell Types +We define an abstract type AbstractCellType and four concrete types NormalCellA, NormalCellB, CancerCellA, and CancerCellB that inherit from AbstractCellType. + +```{julia} +abstract type AbstractCellType end +struct NormalCellA <: AbstractCellType end +struct NormalCellB <: AbstractCellType end +struct CancerCellA <: AbstractCellType end +struct CancerCellB <: AbstractCellType end +``` + +# Defining the Cell and Place +We define a mutable struct Cell with fields for the cell type, an ID, the dynamical state, and the time in active state. We also define a struct Place that represents a location with a name and a vector of cells (tokens). + +```{julia} +mutable struct Cell + cell_type::AbstractCellType + id::Int + dynamical_state::Symbol + time_in_active_state::Int +end + +mutable struct Place + name::String + tokens::Vector{Cell} +end +``` + +# Defining the Transition +A Transition represents a transformation from input places to output places. It has fields for the name of the transition, the input places, and the output places. + +```{julia} +struct Transition + name::String + input::Vector{Tuple{Int, Place, DataType}} + output::Vector{Tuple{Int, Place, DataType}} +end +``` + +# Adding and Removing Tokens +We define two helper functions add_token! and remove_token! to add and remove cells from a place. + +```{julia} +function add_token!(place::Place, token::Cell) + push!(place.tokens, token) +end + +function remove_token!(place::Place, token::Cell) + deleteat!(place.tokens, findfirst(t -> t == token, place.tokens)) +end +``` + +# Simulating the Dynamical State +We define a function simulate_dynamical_state! that updates the dynamical state of a cell based on the presence of cancer cells in the same place. + +```{julia} +function simulate_dynamical_state!(cell::Cell, cancer_cell_present::Bool, + max_active_time::Int) + if cancer_cell_present && + (cell.dynamical_state == :dormant || cell.dynamical_state == :inactive) + cell.dynamical_state = :active + cell.time_in_active_state = 0 + elseif cell.dynamical_state == :active + cell.time_in_active_state += 1 + if cell.time_in_active_state >= max_active_time + cell.dynamical_state = :dormant + cell.time_in_active_state = 0 + end + end +end +``` + + +# Performing the Transition +The function perform_transition! checks the feasibility of a transition by confirming if it has the required number of tokens in the input places. If the transition is feasible, it removes the tokens from the input places and adds new tokens to the output places. The function also updates the dynamical state of the tokens based on the presence of cancer cells. + +```{julia} +function perform_transition!(transition::Transition) + cancer_cell_exists = false + num_tokens = 0 + + for (count, place, token_type) in transition.input + tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + + if length(tokens_to_remove) < count + return false + end + + for token in tokens_to_remove + if isa(token.cell_type, CancerCellA) || isa(token.cell_type, CancerCellB) + cancer_cell_exists = true + end + num_tokens += 1 + end + end + + if cancer_cell_exists && num_tokens > 30 + for (count, place, token_type) in transition.input + tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + for token in tokens_to_remove[1:count] + remove_token!(place, token) + end + end + + for (count, place, token_type) in transition.output + for _ in 1:count + new_cell = Cell(token_type(), rand(Int), + rand([:active, :inactive, :dormant]), max_active_time) + simulate_dynamical_state!(new_cell, cancer_cell_exists, max_active_time) + add_token!(place, new_cell) + end + end + + return true + end + + return false +end +``` + +# Defining the Petri Net +We define a Petri Net using the previously defined Place and Transition structures. The Petri Net also includes InputArc and OutputArc structures that connect Places and Transitions. + +```{julia} +struct InputArc + source::Place + target::Transition + cell_type::DataType + weight::Int +end + +struct OutputArc + source::Transition + target::Place + cell_type::DataType + weight::Int +end + +struct PetriNet + places::Vector{Place} + transitions::Vector{Transition} + input_arcs::Vector{InputArc} + output_arcs::Vector{OutputArc} +end +``` + + + +# Simulation of the Petri Net +We initialize the Petri Net with a certain number of cells and let the system evolve over a predefined number of time steps. At each time step, we update the dynamical state of each cell, check if each transition can fire, and if so, fire the transition. We also record the state of the system at each time step. + + +```{julia} +function can_fire(transition::Transition, places::Vector{Place}) + for (count, place, token_type) in transition.input + tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + + if length(tokens_to_remove) < count + return false + end + end + + return true +end + +function count_cells(pn::PetriNet) + cell_counts = Dict{Tuple{DataType, Symbol}, Int}() + + for place in pn.places + for cell in place.tokens + cell_key = (typeof(cell.cell_type), cell.dynamical_state) + cell_counts[cell_key] = get(cell_counts, cell_key, 0) + 1 + end + end + + return cell_counts +end +``` + + +# Visualization of the Petri Net Evolution +the Plots package to visualize the evolution of the system. At each time step, we create a bar plot of the count of each type of cell in each dynamical state. + +```{julia} +function plot_petri_net(pn::PetriNet) + cell_counts = count_cells(pn) + + p = bar([string(key) for key in keys(cell_counts)], + [value for value in values(cell_counts)], + xlabel = "Cell Type and Dynamical State", + ylabel = "Count", + title = "Petri Net Simulation", + legend = false) + display(p) +end +``` + + +# Running the Simulation +To run the simulation, we initialize the Petri Net and then perform a series of updates in a loop. At each time step, we print out the current state of the Petri Net and generate a plot. + +# Initializing the Petri Net + +We initialize the Petri net by creating the places (P1 and P2), the transitions (T1), and setting up the input and output arcs. + +```{julia} +function init_petri_net(num_cells::Int, max_active_time::Int) + # Define cell types + cell_types = [NormalCellA, NormalCellB, CancerCellA, CancerCellB] + + # Define the places + P1 = Place("P1", + [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]), + max_active_time) for i in 1:round(Int, 0.5 * num_cells)]) + P2 = Place("P2", + [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]), + max_active_time) for i in 1:round(Int, 0.5 * num_cells)]) + P3 = Place("P3", []) + + # Define the transitions + T1 = Transition("T1", [(3, P1, NormalCellA), (1, P2, CancerCellA)], + [(1, P3, CancerCellA)]) + + # Define arcs + input_arc1 = InputArc(P1, T1, NormalCellA, 30) + input_arc2 = InputArc(P2, T1, CancerCellA, 30) + output_arc1 = OutputArc(T1, P3, CancerCellA, 50) + + # Define the Petri net + pn = PetriNet([P1, P2, P3], [T1], [input_arc1, input_arc2], [output_arc1]) + + return pn +end + +``` +# Updating the Petri Net +We update the Petri net by checking the state of the cells and performing transitions if possible. + +```{julia} +function update_petri_net(pn::PetriNet) + # Iterate through places + for place in pn.places + # Check if cancer cells are present in the place + cancer_cell_present = any(cell -> isa(cell.cell_type, CancerCellA) || + isa(cell.cell_type, CancerCellB), place.tokens) + + # Update dynamical states of all cells in the place + for cell in place.tokens + simulate_dynamical_state!(cell, cancer_cell_present, max_active_time) + end + end + + # Iterate through transitions + for transition in pn.transitions + # Check if the transition can be fired + if can_fire(transition, pn.places) + # Fire the transition and update the system's state + perform_transition!(transition) + end + end +end +``` +# Cell trajectory visulization + +```{julia} +function plot_trajectory(cell_trajectory::Vector{Dict{Tuple{DataType, Symbol}, Int}}) + # Initialize empty DataFrame + df_traj = DataFrame(cell_type = DataType[], dynamical_state = Symbol[], count = Int[], + time_step = Int[]) + + # Iterate over cell_trajectory + for (i, dict) in enumerate(cell_trajectory) + for (key, value) in dict + cell_type, dynamical_state = key + push!(df_traj, (cell_type, dynamical_state, value, i)) + end + end + + # Convert cell type to string for plotting + df_traj[!, :cell_type] = string.(df_traj[!, :cell_type]) + + # Separate data frames based on cell type + df_normalA = filter(row -> row[:cell_type] == "NormalCellA", df_traj) + df_normalB = filter(row -> row[:cell_type] == "NormalCellB", df_traj) + df_cancerA = filter(row -> row[:cell_type] == "CancerCellA", df_traj) + df_cancerB = filter(row -> row[:cell_type] == "CancerCellB", df_traj) + + # Initialize plot + p = plot() + + # Add lines for each cell type and dynamical state + for df_cell in [df_normalA, df_normalB, df_cancerA, df_cancerB] + for state in unique(df_cell[!, :dynamical_state]) + df_state = filter(row -> row[:dynamical_state] == state, df_cell) + plot!(p, df_state[!, :time_step], df_state[!, :count], + label = "$(df_state[1, :cell_type]) - $(state)") + end + end + + # Show plot + return p +end +``` + + +```{julia} +# Initialize Petri Net +pn = init_petri_net() + +# Run simulation for 100 time steps +for t in 1:100 + # Update Petri Net + update_petri_net(pn) + + # Print current state + println("Time: ", t) + println("State: ", count_cells(pn)) + + # Plot current state + plot_petri_net(pn) +end +``` + + +```{julia} +function init_trajectory() + # Initialize an empty array to store the states of the Petri net at each time step + trajectory = Dict{Tuple{DataType, Symbol}, Int}[] + + return trajectory +end + +function append_to_trajectory!(trajectory, pn) + # Append the current state of the Petri net to the trajectory + push!(trajectory, count_cells(pn)) +end + +``` + + +run a simulation +```{julia} +# Initialize Petri net +num_cells = 500 +max_active_time = 5 +pn = init_petri_net(num_cells, max_active_time) + +# Define the time range +tspan = (0.0, 30.0) + +# Initialize trajectory +traj = init_trajectory() + +# Run the simulation +for t in tspan[1]:tspan[2] + update_petri_net(pn) + append_to_trajectory!(traj, pn) +end + +# Visualize the trajectory +plot_trajectory(traj) + +``` \ No newline at end of file diff --git a/tutorials/Integrations_with_petri/Petri_agent.jl b/tutorials/Integrations_with_petri/Petri_agent.jl new file mode 100644 index 0000000..612802a --- /dev/null +++ b/tutorials/Integrations_with_petri/Petri_agent.jl @@ -0,0 +1,273 @@ +using Revise +using DifferentialEquations, AlgebraicAgents +using Plots +using Random +Random.seed!(0) + +abstract type AbstractCellType end + +struct NormalCellA <: AbstractCellType end +struct NormalCellB <: AbstractCellType end +struct CancerCellA <: AbstractCellType end +struct CancerCellB <: AbstractCellType end + +mutable struct Cell + cell_type::AbstractCellType + id::Int + dynamical_state::Symbol + time_in_active_state::Int +end + +mutable struct Place + name::String + tokens::Vector{Cell} +end + +struct Transition + name::String + input::Vector{Tuple{Int, Place, DataType}} + output::Vector{Tuple{Int, Place, DataType}} +end + +function add_token!(place::Place, token::Cell) + push!(place.tokens, token) +end + +function remove_token!(place::Place, token::Cell) + deleteat!(place.tokens, findfirst(t -> t == token, place.tokens)) +end + +function simulate_dynamical_state!(cell::Cell, cancer_cell_present::Bool, + max_active_time::Int) + if cancer_cell_present && + (cell.dynamical_state == :dormant || cell.dynamical_state == :inactive) + cell.dynamical_state = :active + cell.time_in_active_state = 0 + elseif cell.dynamical_state == :active + cell.time_in_active_state += 1 + if cell.time_in_active_state >= max_active_time + cell.dynamical_state = :dormant + cell.time_in_active_state = 0 + end + end +end + +function perform_transition!(transition::Transition) + cancer_cell_exists = false + num_tokens = 0 + + for (count, place, token_type) in transition.input + tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + + if length(tokens_to_remove) < count + return false + end + + for token in tokens_to_remove + if isa(token.cell_type, CancerCellA) || isa(token.cell_type, CancerCellB) + cancer_cell_exists = true + end + num_tokens += 1 + end + end + + if cancer_cell_exists && num_tokens > 30 + for (count, place, token_type) in transition.input + tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + for token in tokens_to_remove[1:count] + remove_token!(place, token) + end + end + + for (count, place, token_type) in transition.output + for _ in 1:count + new_cell = Cell(token_type(), rand(Int), + rand([:active, :inactive, :dormant])) + simulate_dynamical_state!(new_cell) + add_token!(place, new_cell) + end + end + + return true + end + + return false +end + +struct InputArc + source::Place + target::Transition + cell_type::DataType + weight::Int +end + +struct OutputArc + source::Transition + target::Place + cell_type::DataType + weight::Int +end + +struct PetriNet + places::Vector{Place} + transitions::Vector{Transition} + input_arcs::Vector{InputArc} + output_arcs::Vector{OutputArc} +end +max_active_time = 5 +P1 = Place("P1", + [Cell(NormalCellA(), i, rand([:active, :inactive, :dormant]), max_active_time) + for i in 1:5]) +P2 = Place("P2", + [Cell(CancerCellA(), i, rand([:active, :inactive, :dormant]), max_active_time) + for i in 1:2]) +P3 = Place("P3", []) + +T1 = Transition("T1", [(3, P1, NormalCellA), (1, P2, CancerCellA)], [(1, P3, CancerCellA)]) + +# if perform_transition!(T1) +# println("Transition T1 fired successfully.") +# else +# println("Transition T1 cannot fire.") +# end + +# Define arcs +input_arc1 = InputArc(P1, T1, NormalCellA, 30) +input_arc2 = InputArc(P2, T1, CancerCellA, 30) +output_arc1 = OutputArc(T1, P3, CancerCellA, 50) + +pn = PetriNet([P1, P2, P3], [T1], [input_arc1, input_arc2], [output_arc1]) + +## ============= ABM simulation ============= +# Generate random initial conditions with 200 cells +Random.seed!(1234) +cell_types = [NormalCellA, NormalCellB, CancerCellA, CancerCellB] + +function can_fire(transition::Transition, places::Vector{Place}) + for (count, place, token_type) in transition.input + tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + + if length(tokens_to_remove) < count + return false + end + end + + return true +end + +function count_cells(pn::PetriNet) + cell_counts = Dict{Tuple{DataType, Symbol}, Int}() + + for place in pn.places + for cell in place.tokens + cell_key = (typeof(cell.cell_type), cell.dynamical_state) + cell_counts[cell_key] = get(cell_counts, cell_key, 0) + 1 + end + end + + return cell_counts +end + +# Simulation parameters +num_time_steps = 1000 + +# Initialize the Petri net with the given number of cells +num_cells = 500 +cell_types = [NormalCellA, NormalCellB, CancerCellA, CancerCellB] + +P1 = Place("P1", + [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]), + max_active_time) + for i in 1:round(Int, 0.5 * num_cells)]) +P2 = Place("P2", + [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]), + max_active_time) + for i in 1:round(Int, 0.5 * num_cells)]) +# Initialize trajectory storage +cell_trajectory = [] + +# Set the maximum active time for a cell +max_active_time = 5 # Adjust this value according to your needs + +# Run the simulation for all 500 cells +for t in 1:num_time_steps + # Record the trajectory of each cell type and its associated dynamical_state + push!(cell_trajectory, count_cells(pn)) + + # Iterate through places + for place in pn.places + # Check if cancer cells are present in the place + cancer_cell_present = any(cell -> isa(cell.cell_type, CancerCellA) || + isa(cell.cell_type, CancerCellB), place.tokens) + + # Update dynamical states of all cells in the place + for cell in place.tokens + simulate_dynamical_state!(cell, cancer_cell_present, max_active_time) + end + end + + # Iterate through transitions + for transition in pn.transitions + # Check if the transition can be fired + if can_fire(transition, pn.places) + # Fire the transition and update the system's state + perform_transition!(transition) + end + end +end + +## ============= Visualization ============= +# Create an empty DataFrame +initial_cells_df = DataFrame(ID = Int[], CellType = String[], DynamicalState = Symbol[]) + +# Populate the DataFrame with the initial 500 cells +for place in [P1, P2] + for cell in place.tokens + cell_type = isa(cell.cell_type, NormalCellA) ? "NormalCellA" : + isa(cell.cell_type, NormalCellB) ? "NormalCellB" : + isa(cell.cell_type, CancerCellA) ? "CancerCellA" : "CancerCellB" + + push!(initial_cells_df, (cell.id, cell_type, cell.dynamical_state)) + end +end + +# Display the DataFrame +initial_cells_df + +# Initialize empty DataFrame +df_traj = DataFrame(cell_type = DataType[], dynamical_state = Symbol[], count = Int[], + time_step = Int[]) + +# Iterate over cell_trajectory +for (i, dict) in enumerate(cell_trajectory) + for (key, value) in dict + cell_type, dynamical_state = key + push!(df_traj, (cell_type, dynamical_state, value, i)) + end +end + +using Plots + +# Convert cell type to string for plotting +df_traj[!, :cell_type] = string.(df_traj[!, :cell_type]) + +# Separate data frames based on cell type +df_normalA = filter(row -> row[:cell_type] == "NormalCellA", df_traj) +df_normalB = filter(row -> row[:cell_type] == "NormalCellB", df_traj) +df_cancerA = filter(row -> row[:cell_type] == "CancerCellA", df_traj) +df_cancerB = filter(row -> row[:cell_type] == "CancerCellB", df_traj) + +# Initialize plot +p = plot() + +# Add lines for each cell type and dynamical state +for df_cell in [df_normalA, df_normalB, df_cancerA, df_cancerB] + for state in unique(df_cell[!, :dynamical_state]) + df_state = filter(row -> row[:dynamical_state] == state, df_cell) + plot!(p, df_state[!, :time_step], df_state[!, :count], + label = "$(df_state[1, :cell_type]) - $(state)") + end +end + +# Show plot +p diff --git a/tutorials/Integrations_with_petri/Petri_reactions.jl b/tutorials/Integrations_with_petri/Petri_reactions.jl new file mode 100644 index 0000000..5841f05 --- /dev/null +++ b/tutorials/Integrations_with_petri/Petri_reactions.jl @@ -0,0 +1,10 @@ + +using Petri + +@net MyPetriNet begin + @place p1 + @place p2 + @transition t1(p1, p2) +end + +model = PetriNet() \ No newline at end of file diff --git a/tutorials/Integrations_with_petri/Project.toml b/tutorials/Integrations_with_petri/Project.toml new file mode 100644 index 0000000..2bb3ac8 --- /dev/null +++ b/tutorials/Integrations_with_petri/Project.toml @@ -0,0 +1,26 @@ +[deps] +Agents = "46ada45e-f475-11e8-01d0-f70cc89e6671" +AlgebraicAgents = "f6eb0ae3-10fa-40e6-88dd-9006ba45093a" +AlgebraicDynamics = "5fd6ff03-a254-427e-8840-ba658f502e32" +CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" +Catlab = "134e5e36-593f-5add-ad60-77f754baafbe" +DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2" +DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" +LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56" +Petri = "4259d249-1051-49fa-8328-3f8ab9391c33" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781" +PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" +RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008" +GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" +Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" \ No newline at end of file diff --git a/tutorials/multiscale_CFN/Project.toml b/tutorials/multiscale_CFN/Project.toml new file mode 100644 index 0000000..4bb7b46 --- /dev/null +++ b/tutorials/multiscale_CFN/Project.toml @@ -0,0 +1,12 @@ +[deps] +AlgebraicAgents = "f6eb0ae3-10fa-40e6-88dd-9006ba45093a" +DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +AlgebraicDynamics = "5fd6ff03-a254-427e-8840-ba658f502e32" +Catlab = "134e5e36-593f-5add-ad60-77f754baafbe" +LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Agents = "46ada45e-f475-11e8-01d0-f70cc89e6671" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" \ No newline at end of file diff --git a/tutorials/multiscale_CFN/Readme.html b/tutorials/multiscale_CFN/Readme.html new file mode 100644 index 0000000..de243c8 --- /dev/null +++ b/tutorials/multiscale_CFN/Readme.html @@ -0,0 +1,318 @@ + + + + + + + + + +readme + + + + + + + + + + + + + + + + + + + +
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+

Introduction

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Multi-scale cell fit networks are a fascinating area of research that explores the intricate connections between different scales of biological systems. At the cell level, these networks examine the complex interactions between cells and their environment, while at the self-cell communication level, they delve into the intricate signaling pathways that allow cells to communicate with one another. These networks also explore the distribution of signals and the continuous dynamical systems that can occur within a single cell. All of these fascinating phenomena can be explored and analyzed using QMD files, making multi-scale cell fit networks an exciting and promising area of research for scientists and researchers alike.

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Goal of this tutorial

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  • The goal of this tutorial is to demonstrate Algebraicagents.jl and SciML ecosystem can be used to understand the multi-scale cell fate decision network with perturbation (external signals) under heterogeneous cell types and cell-cell interactions.
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+

Setup

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How to create a multi-scale cell fit network hierarchy using AlgebraicAgent.jl

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Agent types configurations

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Single Cell Network dynamics

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cell-cell interactions

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Multi-scale cell networks with heterogeneous cell-cell interactions

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Results

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+ + + + \ No newline at end of file diff --git a/tutorials/multiscale_CFN/Readme.qmd b/tutorials/multiscale_CFN/Readme.qmd new file mode 100644 index 0000000..b427432 --- /dev/null +++ b/tutorials/multiscale_CFN/Readme.qmd @@ -0,0 +1,25 @@ +# Introduction +Multi-scale cell fit networks are a fascinating area of research that explores the intricate connections between different scales of biological systems. At the cell level, these networks examine the complex interactions between cells and their environment, while at the self-cell communication level, they delve into the intricate signaling pathways that allow cells to communicate with one another. These networks also explore the distribution of signals and the continuous dynamical systems that can occur within a single cell. All of these fascinating phenomena can be explored and analyzed using QMD files, making multi-scale cell fit networks an exciting and promising area of research for scientists and researchers alike. + + +# Goal of this tutorial +- The goal of this tutorial is to demonstrate Algebraicagents.jl and SciML ecosystem can be used to understand the multi-scale cell fate decision network with perturbation (external signals) under heterogeneous cell types and cell-cell interactions. +- + +# Setup +How to create a multi-scale cell fit network hierarchy using AlgebraicAgent.jl + + +# Agent types configurations + + +# Single Cell Network dynamics + + +# cell-cell interactions + + +# Multi-scale cell networks with heterogeneous cell-cell interactions + + +# Results diff --git a/tutorials/multiscale_CFN/Readme_files/libs/bootstrap/bootstrap-icons.css b/tutorials/multiscale_CFN/Readme_files/libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..94f1940 --- /dev/null +++ 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"\f889"; } +.bi-house-dash-fill::before { content: "\f88a"; } +.bi-house-dash::before { content: "\f88b"; } +.bi-house-down-fill::before { content: "\f88c"; } +.bi-house-down::before { content: "\f88d"; } +.bi-house-exclamation-fill::before { content: "\f88e"; } +.bi-house-exclamation::before { content: "\f88f"; } +.bi-house-gear-fill::before { content: "\f890"; } +.bi-house-gear::before { content: "\f891"; } +.bi-house-lock-fill::before { content: "\f892"; } +.bi-house-lock::before { content: "\f893"; } +.bi-house-slash-fill::before { content: "\f894"; } +.bi-house-slash::before { content: "\f895"; } +.bi-house-up-fill::before { content: "\f896"; } +.bi-house-up::before { content: "\f897"; } +.bi-house-x-fill::before { content: "\f898"; } +.bi-house-x::before { content: "\f899"; } +.bi-person-add::before { content: "\f89a"; } +.bi-person-down::before { content: "\f89b"; } +.bi-person-exclamation::before { content: "\f89c"; } +.bi-person-fill-add::before { content: "\f89d"; } 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0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function Se(t){return[mt,_t,gt,bt].some((function(e){return t[e]>=0}))}const Ne={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ke(e,{elementContext:"reference"}),a=ke(e,{altBoundary:!0}),l=De(r,n),c=De(a,s,o),h=Se(l),d=Se(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},Ie={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof 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0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new 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mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/tutorials/multiscale_CFN/Readme_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + const layoutMarginEls = () => { + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = + top + marginChild.getBoundingClientRect().height + marginTop; + } + } + }; + nexttick(layoutMarginEls); + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/tutorials/multiscale_CFN/Readme_files/libs/quarto-html/tippy.css b/tutorials/multiscale_CFN/Readme_files/libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/tutorials/multiscale_CFN/Readme_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/tutorials/multiscale_CFN/multiscale_CFN.jl new file mode 100644 index 0000000..700d735 --- /dev/null +++ b/tutorials/multiscale_CFN/multiscale_CFN.jl @@ -0,0 +1,7 @@ +using AlgebraicAgents +using Agents, Random +using Agents.DataFrames, Agents.Graphs +using Distributions: Poisson, DiscreteNonParametric +using DrWatson: @dict +using Plots + From cbefca1781d975ff24a13e96fbafc264a5d8af77 Mon Sep 17 00:00:00 2001 From: danielchen Date: Tue, 9 May 2023 17:58:57 -0400 Subject: [PATCH 2/8] add CellAuto_Petrinet framework example --- .../Integrations_with_petri/CellAuto_Petrinet.ipynb | 5 ++--- .../Integrations_with_petri/CellAuto_Petrinet.qmd | 1 - tutorials/Integrations_with_petri/Petri_reactions.jl | 10 ---------- 3 files changed, 2 insertions(+), 14 deletions(-) delete mode 100644 tutorials/Integrations_with_petri/Petri_reactions.jl diff --git a/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb b/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb index e0b4d63..38a36fd 100644 --- a/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb +++ b/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb @@ -6,7 +6,6 @@ "source": [ "---\n", "title: \"Cellular Automata Simulation Using Petri Nets\"\n", - "# output: html_document\n", "format:\n", " html:\n", " code-fold: false\n", @@ -15,7 +14,7 @@ "author: Tianchi Chen\n", "---" ], - "id": "d1f84ee2" + "id": "29fcc59f" }, { "cell_type": "markdown", @@ -426,7 +425,7 @@ "\n", "```" ], - "id": "949333f6" + "id": "d3dc0ac3" } ], "metadata": { diff --git a/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd b/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd index 56acd17..361843f 100644 --- a/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd +++ b/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd @@ -1,6 +1,5 @@ --- title: "Cellular Automata Simulation Using Petri Nets" -# output: html_document format: html: code-fold: false diff --git a/tutorials/Integrations_with_petri/Petri_reactions.jl b/tutorials/Integrations_with_petri/Petri_reactions.jl deleted file mode 100644 index 5841f05..0000000 --- a/tutorials/Integrations_with_petri/Petri_reactions.jl +++ /dev/null @@ -1,10 +0,0 @@ - -using Petri - -@net MyPetriNet begin - @place p1 - @place p2 - @transition t1(p1, p2) -end - -model = PetriNet() \ No newline at end of file From 126d1daae34251c183986f7daca93a63be5f401e Mon Sep 17 00:00:00 2001 From: danielchen Date: Wed, 10 May 2023 23:56:17 -0400 Subject: [PATCH 3/8] update petri exmaple --- tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd | 2 +- tutorials/Integrations_with_petri/Project.toml | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd b/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd index 361843f..8cbb696 100644 --- a/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd +++ b/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd @@ -20,7 +20,7 @@ We start by importing the necessary packages: using Revise using DifferentialEquations, AlgebraicAgents using Plots -using Random +using Random, DataFrames Random.seed!(0) ``` diff --git a/tutorials/Integrations_with_petri/Project.toml b/tutorials/Integrations_with_petri/Project.toml index 2bb3ac8..1812b0a 100644 --- a/tutorials/Integrations_with_petri/Project.toml +++ b/tutorials/Integrations_with_petri/Project.toml @@ -5,10 +5,13 @@ AlgebraicDynamics = "5fd6ff03-a254-427e-8840-ba658f502e32" CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" Catlab = "134e5e36-593f-5add-ad60-77f754baafbe" DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" +GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" @@ -20,7 +23,5 @@ Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781" PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008" -GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" -IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" -Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" \ No newline at end of file From 180595a976b4ec73aaa8aea6d6fa98ea25c51f44 Mon Sep 17 00:00:00 2001 From: danielchen Date: Mon, 15 May 2023 15:00:15 -0400 Subject: [PATCH 4/8] Add Petrinet refactor/CellAUTO_petri --- Project.toml | 6 +- .../CellAuto_Petrinet.ipynb | 440 ---- .../Petri_refactor/CellAuto_Petrinet.html | 897 ++++++++ .../CellAuto_Petrinet.qmd | 8 +- .../libs/bootstrap/bootstrap-icons.css | 2018 +++++++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 164168 bytes .../libs/bootstrap/bootstrap.min.css | 10 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 203 ++ .../libs/quarto-html/quarto.js | 884 ++++++++ .../libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + .../Petri_agent.jl | 0 .../Project.toml | 0 17 files changed, 4053 insertions(+), 445 deletions(-) delete mode 100644 tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet.html rename tutorials/{Integrations_with_petri => Petri_refactor}/CellAuto_Petrinet.qmd (99%) create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap-icons.css create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap.min.css create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap.min.js create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/clipboard/clipboard.min.js create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/anchor.min.js create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/popper.min.js create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/quarto.js create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/tippy.css create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/tippy.umd.min.js rename tutorials/{Integrations_with_petri => Petri_refactor}/Petri_agent.jl (100%) rename tutorials/{Integrations_with_petri => Petri_refactor}/Project.toml (100%) diff --git a/Project.toml b/Project.toml index 752d3cf..a0bd8a5 100644 --- a/Project.toml +++ b/Project.toml @@ -5,15 +5,17 @@ version = "0.3.16" [deps] Crayons = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" Glob = "c27321d9-0574-5035-807b-f59d2c89b15c" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240" MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Requires = "ae029012-a4dd-5104-9daa-d747884805df" +Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" [compat] -julia = "1.8" -Requires = "1.3" Crayons = "4.1" Glob = "1.3" MacroTools = "0.5" +Requires = "1.3" +julia = "1.8" diff --git a/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb b/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb deleted file mode 100644 index 38a36fd..0000000 --- a/tutorials/Integrations_with_petri/CellAuto_Petrinet.ipynb +++ /dev/null @@ -1,440 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: \"Cellular Automata Simulation Using Petri Nets\"\n", - "format:\n", - " html:\n", - " code-fold: false\n", - "# jupyter: julia-1.8\n", - "toc: true\n", - "author: Tianchi Chen\n", - "---" - ], - "id": "29fcc59f" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction\n", - "\n", - "This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot.\n", - "\n", - "# Setup\n", - "\n", - "We start by importing the necessary packages:\n", - "\n", - "\n", - "```{julia}\n", - "using Revise\n", - "using DifferentialEquations, AlgebraicAgents\n", - "using Plots\n", - "using Random\n", - "Random.seed!(0)\n", - "```\n", - "\n", - "\n", - "# Defining the Cell Types\n", - "We define an abstract type AbstractCellType and four concrete types NormalCellA, NormalCellB, CancerCellA, and CancerCellB that inherit from AbstractCellType.\n", - "\n", - "\n", - "```{julia}\n", - "abstract type AbstractCellType end\n", - "struct NormalCellA <: AbstractCellType end\n", - "struct NormalCellB <: AbstractCellType end\n", - "struct CancerCellA <: AbstractCellType end\n", - "struct CancerCellB <: AbstractCellType end\n", - "```\n", - "\n", - "\n", - "# Defining the Cell and Place\n", - "We define a mutable struct Cell with fields for the cell type, an ID, the dynamical state, and the time in active state. We also define a struct Place that represents a location with a name and a vector of cells (tokens).\n", - "\n", - "\n", - "```{julia}\n", - "mutable struct Cell\n", - " cell_type::AbstractCellType\n", - " id::Int\n", - " dynamical_state::Symbol\n", - " time_in_active_state::Int\n", - "end\n", - "\n", - "mutable struct Place\n", - " name::String\n", - " tokens::Vector{Cell}\n", - "end\n", - "```\n", - "\n", - "\n", - "# Defining the Transition\n", - "A Transition represents a transformation from input places to output places. It has fields for the name of the transition, the input places, and the output places.\n", - "\n", - "\n", - "```{julia}\n", - "struct Transition\n", - " name::String\n", - " input::Vector{Tuple{Int, Place, DataType}}\n", - " output::Vector{Tuple{Int, Place, DataType}}\n", - "end\n", - "```\n", - "\n", - "\n", - "# Adding and Removing Tokens\n", - "We define two helper functions add_token! and remove_token! to add and remove cells from a place.\n", - "\n", - "\n", - "```{julia}\n", - "function add_token!(place::Place, token::Cell)\n", - " push!(place.tokens, token)\n", - "end\n", - "\n", - "function remove_token!(place::Place, token::Cell)\n", - " deleteat!(place.tokens, findfirst(t -> t == token, place.tokens))\n", - "end\n", - "```\n", - "\n", - "\n", - "# Simulating the Dynamical State\n", - "We define a function simulate_dynamical_state! that updates the dynamical state of a cell based on the presence of cancer cells in the same place.\n", - "\n", - "\n", - "```{julia}\n", - "function simulate_dynamical_state!(cell::Cell, cancer_cell_present::Bool,\n", - " max_active_time::Int)\n", - " if cancer_cell_present &&\n", - " (cell.dynamical_state == :dormant || cell.dynamical_state == :inactive)\n", - " cell.dynamical_state = :active\n", - " cell.time_in_active_state = 0\n", - " elseif cell.dynamical_state == :active\n", - " cell.time_in_active_state += 1\n", - " if cell.time_in_active_state >= max_active_time\n", - " cell.dynamical_state = :dormant\n", - " cell.time_in_active_state = 0\n", - " end\n", - " end\n", - "end\n", - "```\n", - "\n", - "\n", - "\n", - "# Performing the Transition\n", - "The function perform_transition! checks the feasibility of a transition by confirming if it has the required number of tokens in the input places. If the transition is feasible, it removes the tokens from the input places and adds new tokens to the output places. The function also updates the dynamical state of the tokens based on the presence of cancer cells.\n", - "\n", - "\n", - "```{julia}\n", - "function perform_transition!(transition::Transition)\n", - " cancer_cell_exists = false\n", - " num_tokens = 0\n", - "\n", - " for (count, place, token_type) in transition.input\n", - " tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)\n", - "\n", - " if length(tokens_to_remove) < count\n", - " return false\n", - " end\n", - "\n", - " for token in tokens_to_remove\n", - " if isa(token.cell_type, CancerCellA) || isa(token.cell_type, CancerCellB)\n", - " cancer_cell_exists = true\n", - " end\n", - " num_tokens += 1\n", - " end\n", - " end\n", - "\n", - " if cancer_cell_exists && num_tokens > 30\n", - " for (count, place, token_type) in transition.input\n", - " tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)\n", - " for token in tokens_to_remove[1:count]\n", - " remove_token!(place, token)\n", - " end\n", - " end\n", - "\n", - " for (count, place, token_type) in transition.output\n", - " for _ in 1:count\n", - " new_cell = Cell(token_type(), rand(Int),\n", - " rand([:active, :inactive, :dormant]), max_active_time)\n", - " simulate_dynamical_state!(new_cell, cancer_cell_exists, max_active_time)\n", - " add_token!(place, new_cell)\n", - " end\n", - " end\n", - "\n", - " return true\n", - " end\n", - "\n", - " return false\n", - "end\n", - "```\n", - "\n", - "\n", - "# Defining the Petri Net\n", - "We define a Petri Net using the previously defined Place and Transition structures. The Petri Net also includes InputArc and OutputArc structures that connect Places and Transitions.\n", - "\n", - "\n", - "```{julia}\n", - "struct InputArc\n", - " source::Place\n", - " target::Transition\n", - " cell_type::DataType\n", - " weight::Int\n", - "end\n", - "\n", - "struct OutputArc\n", - " source::Transition\n", - " target::Place\n", - " cell_type::DataType\n", - " weight::Int\n", - "end\n", - "\n", - "struct PetriNet\n", - " places::Vector{Place}\n", - " transitions::Vector{Transition}\n", - " input_arcs::Vector{InputArc}\n", - " output_arcs::Vector{OutputArc}\n", - "end\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "# Simulation of the Petri Net\n", - "We initialize the Petri Net with a certain number of cells and let the system evolve over a predefined number of time steps. At each time step, we update the dynamical state of each cell, check if each transition can fire, and if so, fire the transition. We also record the state of the system at each time step.\n", - "\n", - "\n", - "\n", - "```{julia}\n", - "function can_fire(transition::Transition, places::Vector{Place})\n", - " for (count, place, token_type) in transition.input\n", - " tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)\n", - "\n", - " if length(tokens_to_remove) < count\n", - " return false\n", - " end\n", - " end\n", - "\n", - " return true\n", - "end\n", - "\n", - "function count_cells(pn::PetriNet)\n", - " cell_counts = Dict{Tuple{DataType, Symbol}, Int}()\n", - "\n", - " for place in pn.places\n", - " for cell in place.tokens\n", - " cell_key = (typeof(cell.cell_type), cell.dynamical_state)\n", - " cell_counts[cell_key] = get(cell_counts, cell_key, 0) + 1\n", - " end\n", - " end\n", - "\n", - " return cell_counts\n", - "end\n", - "```\n", - "\n", - "\n", - "\n", - "# Visualization of the Petri Net Evolution\n", - "the Plots package to visualize the evolution of the system. At each time step, we create a bar plot of the count of each type of cell in each dynamical state.\n", - "\n", - "\n", - "```{julia}\n", - "function plot_petri_net(pn::PetriNet)\n", - " cell_counts = count_cells(pn)\n", - "\n", - " p = bar([string(key) for key in keys(cell_counts)],\n", - " [value for value in values(cell_counts)],\n", - " xlabel = \"Cell Type and Dynamical State\",\n", - " ylabel = \"Count\",\n", - " title = \"Petri Net Simulation\",\n", - " legend = false)\n", - " display(p)\n", - "end\n", - "```\n", - "\n", - "\n", - "\n", - "# Running the Simulation\n", - "To run the simulation, we initialize the Petri Net and then perform a series of updates in a loop. At each time step, we print out the current state of the Petri Net and generate a plot.\n", - "\n", - "# Initializing the Petri Net\n", - "\n", - "We initialize the Petri net by creating the places (P1 and P2), the transitions (T1), and setting up the input and output arcs.\n", - "\n", - "\n", - "```{julia}\n", - "function init_petri_net(num_cells::Int, max_active_time::Int)\n", - " # Define cell types\n", - " cell_types = [NormalCellA, NormalCellB, CancerCellA, CancerCellB]\n", - "\n", - " # Define the places\n", - " P1 = Place(\"P1\",\n", - " [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]),\n", - " max_active_time) for i in 1:round(Int, 0.5 * num_cells)])\n", - " P2 = Place(\"P2\",\n", - " [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]),\n", - " max_active_time) for i in 1:round(Int, 0.5 * num_cells)])\n", - " P3 = Place(\"P3\", [])\n", - "\n", - " # Define the transitions\n", - " T1 = Transition(\"T1\", [(3, P1, NormalCellA), (1, P2, CancerCellA)],\n", - " [(1, P3, CancerCellA)])\n", - "\n", - " # Define arcs\n", - " input_arc1 = InputArc(P1, T1, NormalCellA, 30)\n", - " input_arc2 = InputArc(P2, T1, CancerCellA, 30)\n", - " output_arc1 = OutputArc(T1, P3, CancerCellA, 50)\n", - "\n", - " # Define the Petri net\n", - " pn = PetriNet([P1, P2, P3], [T1], [input_arc1, input_arc2], [output_arc1])\n", - "\n", - " return pn\n", - "end\n", - "\n", - "```\n", - "\n", - "# Updating the Petri Net\n", - "We update the Petri net by checking the state of the cells and performing transitions if possible.\n", - "\n", - "\n", - "```{julia}\n", - "function update_petri_net(pn::PetriNet)\n", - " # Iterate through places\n", - " for place in pn.places\n", - " # Check if cancer cells are present in the place\n", - " cancer_cell_present = any(cell -> isa(cell.cell_type, CancerCellA) ||\n", - " isa(cell.cell_type, CancerCellB), place.tokens)\n", - "\n", - " # Update dynamical states of all cells in the place\n", - " for cell in place.tokens\n", - " simulate_dynamical_state!(cell, cancer_cell_present, max_active_time)\n", - " end\n", - " end\n", - "\n", - " # Iterate through transitions\n", - " for transition in pn.transitions\n", - " # Check if the transition can be fired\n", - " if can_fire(transition, pn.places)\n", - " # Fire the transition and update the system's state\n", - " perform_transition!(transition)\n", - " end\n", - " end\n", - "end\n", - "```\n", - "\n", - "# Cell trajectory visulization\n", - "\n", - "\n", - "```{julia}\n", - "function plot_trajectory(cell_trajectory::Vector{Dict{Tuple{DataType, Symbol}, Int}})\n", - " # Initialize empty DataFrame\n", - " df_traj = DataFrame(cell_type = DataType[], dynamical_state = Symbol[], count = Int[],\n", - " time_step = Int[])\n", - "\n", - " # Iterate over cell_trajectory\n", - " for (i, dict) in enumerate(cell_trajectory)\n", - " for (key, value) in dict\n", - " cell_type, dynamical_state = key\n", - " push!(df_traj, (cell_type, dynamical_state, value, i))\n", - " end\n", - " end\n", - "\n", - " # Convert cell type to string for plotting\n", - " df_traj[!, :cell_type] = string.(df_traj[!, :cell_type])\n", - "\n", - " # Separate data frames based on cell type\n", - " df_normalA = filter(row -> row[:cell_type] == \"NormalCellA\", df_traj)\n", - " df_normalB = filter(row -> row[:cell_type] == \"NormalCellB\", df_traj)\n", - " df_cancerA = filter(row -> row[:cell_type] == \"CancerCellA\", df_traj)\n", - " df_cancerB = filter(row -> row[:cell_type] == \"CancerCellB\", df_traj)\n", - "\n", - " # Initialize plot\n", - " p = plot()\n", - "\n", - " # Add lines for each cell type and dynamical state\n", - " for df_cell in [df_normalA, df_normalB, df_cancerA, df_cancerB]\n", - " for state in unique(df_cell[!, :dynamical_state])\n", - " df_state = filter(row -> row[:dynamical_state] == state, df_cell)\n", - " plot!(p, df_state[!, :time_step], df_state[!, :count],\n", - " label = \"$(df_state[1, :cell_type]) - $(state)\")\n", - " end\n", - " end\n", - "\n", - " # Show plot\n", - " return p\n", - "end\n", - "```\n", - "\n", - "```{julia}\n", - "# Initialize Petri Net\n", - "pn = init_petri_net()\n", - "\n", - "# Run simulation for 100 time steps\n", - "for t in 1:100\n", - " # Update Petri Net\n", - " update_petri_net(pn)\n", - "\n", - " # Print current state\n", - " println(\"Time: \", t)\n", - " println(\"State: \", count_cells(pn))\n", - "\n", - " # Plot current state\n", - " plot_petri_net(pn)\n", - "end\n", - "```\n", - "\n", - "```{julia}\n", - "function init_trajectory()\n", - " # Initialize an empty array to store the states of the Petri net at each time step\n", - " trajectory = Dict{Tuple{DataType, Symbol}, Int}[]\n", - "\n", - " return trajectory\n", - "end\n", - "\n", - "function append_to_trajectory!(trajectory, pn)\n", - " # Append the current state of the Petri net to the trajectory\n", - " push!(trajectory, count_cells(pn))\n", - "end\n", - "\n", - "```\n", - "\n", - "\n", - "\n", - "run a simulation \n", - "\n", - "```{julia}\n", - "# Initialize Petri net\n", - "num_cells = 500\n", - "max_active_time = 5\n", - "pn = init_petri_net(num_cells, max_active_time)\n", - "\n", - "# Define the time range\n", - "tspan = (0.0, 30.0)\n", - "\n", - "# Initialize trajectory\n", - "traj = init_trajectory()\n", - "\n", - "# Run the simulation\n", - "for t in tspan[1]:tspan[2]\n", - " update_petri_net(pn)\n", - " append_to_trajectory!(traj, pn)\n", - "end\n", - "\n", - "# Visualize the trajectory\n", - "plot_trajectory(traj)\n", - "\n", - "```" - ], - "id": "d3dc0ac3" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet.html b/tutorials/Petri_refactor/CellAuto_Petrinet.html new file mode 100644 index 0000000..3c730d6 --- /dev/null +++ b/tutorials/Petri_refactor/CellAuto_Petrinet.html @@ -0,0 +1,897 @@ + + + + + + + + + + +Cellular Automata Simulation Using Petri Nets + + + + + + + + + + + + + + + + + + + + + + +     + + + + \ No newline at end of file diff --git a/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd b/tutorials/Petri_refactor/CellAuto_Petrinet.qmd similarity index 99% rename from tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd rename to tutorials/Petri_refactor/CellAuto_Petrinet.qmd index 8cbb696..a925170 100644 --- a/tutorials/Integrations_with_petri/CellAuto_Petrinet.qmd +++ b/tutorials/Petri_refactor/CellAuto_Petrinet.qmd @@ -1,13 +1,15 @@ --- title: "Cellular Automata Simulation Using Petri Nets" +subtitle: "version 0.1.0" format: html: code-fold: false -# jupyter: julia-1.8 +jupyter: julia-1.9 toc: true author: Tianchi Chen --- + # Introduction This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot. @@ -329,7 +331,7 @@ end ``` -```{julia} + ```{julia} diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap-icons.css b/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..94f1940 --- /dev/null +++ b/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2018 @@ +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + 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+.bi-ev-front-fill::before { content: "\f880"; } +.bi-ev-front::before { content: "\f881"; } +.bi-globe-americas::before { content: "\f882"; } +.bi-globe-asia-australia::before { content: "\f883"; } +.bi-globe-central-south-asia::before { content: "\f884"; } +.bi-globe-europe-africa::before { content: "\f885"; } +.bi-house-add-fill::before { content: "\f886"; } +.bi-house-add::before { content: "\f887"; } +.bi-house-check-fill::before { content: "\f888"; } +.bi-house-check::before { content: "\f889"; } +.bi-house-dash-fill::before { content: "\f88a"; } +.bi-house-dash::before { content: "\f88b"; } +.bi-house-down-fill::before { content: "\f88c"; } +.bi-house-down::before { content: "\f88d"; } +.bi-house-exclamation-fill::before { content: "\f88e"; } +.bi-house-exclamation::before { content: "\f88f"; } +.bi-house-gear-fill::before { content: "\f890"; } +.bi-house-gear::before { content: "\f891"; } +.bi-house-lock-fill::before { content: "\f892"; } +.bi-house-lock::before { content: 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0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function Se(t){return[mt,_t,gt,bt].some((function(e){return t[e]>=0}))}const Ne={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ke(e,{elementContext:"reference"}),a=ke(e,{altBoundary:!0}),l=De(r,n),c=De(a,s,o),h=Se(l),d=Se(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},Ie={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof 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0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

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t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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b/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/quarto-syntax-highlighting.css new file mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + const layoutMarginEls = () => { + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = + top + marginChild.getBoundingClientRect().height + marginTop; + } + } + }; + nexttick(layoutMarginEls); + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/tippy.css b/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/tutorials/Petri_refactor/Petri_agent.jl similarity index 100% rename from tutorials/Integrations_with_petri/Petri_agent.jl rename to tutorials/Petri_refactor/Petri_agent.jl diff --git a/tutorials/Integrations_with_petri/Project.toml b/tutorials/Petri_refactor/Project.toml similarity index 100% rename from tutorials/Integrations_with_petri/Project.toml rename to tutorials/Petri_refactor/Project.toml From 4f7eaba68a76f0e685ec291281bfe72e4f449dbb Mon Sep 17 00:00:00 2001 From: danielchen Date: Tue, 23 May 2023 14:55:04 -0400 Subject: [PATCH 5/8] update on framework.jl and refactor.jl --- .../Petri_refactor/CellAuto_Petrinet.html | 153 ++++---- .../Petri_refactor/CellAuto_Petrinet.qmd | 49 ++- .../libs/bootstrap/bootstrap.min.css | 2 +- .../libs/quarto-html/quarto.js | 72 ++-- .../CellAuto_Petrinet_refactor.jl | 332 ++++++++++++++++++ .../CellAuto_Petrinet_refactor.qmd | 139 ++++++++ .../Petri_reaction_framework.jl | 285 +++++++++++++++ tutorials/Petri_refactor/Project.toml | 3 + 8 files changed, 918 insertions(+), 117 deletions(-) create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_refactor.qmd create mode 100644 tutorials/Petri_refactor/Petri_reaction_framework.jl diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet.html b/tutorials/Petri_refactor/CellAuto_Petrinet.html index 3c730d6..7b41c07 100644 --- a/tutorials/Petri_refactor/CellAuto_Petrinet.html +++ b/tutorials/Petri_refactor/CellAuto_Petrinet.html @@ -2,7 +2,7 @@ - + @@ -84,6 +84,7 @@

      Table of contents

      • Introduction
        • +
      • Biological Systems
      • Setup
      • Defining the Cell Types
      • Defining the Cell and Place
        • @@ -106,7 +107,7 @@

        Table of contents

        Cellular Automata Simulation Using Petri Nets

        -

        version 0.1.0

        +

        version 0.1.1

        @@ -131,6 +132,10 @@

        Cellular Automata Simulation Using Petri Nets

        Introduction

        This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot.

        +
        +

        Biological Systems

        +

        A Petri net framework can be used to model a biological system like cancer, as it represents dynamic processes in which individual items pass through multiple states. Petri nets are composed of places, transitions, and arcs. Places represent the state of an item and transitions represent the operations that affect the item. The arcs link the various places and transitions together to create a flow of information. For example, in the case of a cancer system, the places could represent tumor growth and the transitions could represent treatments that are administered, such as chemotherapy or radiation. The arcs between the places and transitions would then dictate how the treatments affect the tumor growth. The Petri net framework can then be used to analyze the system as a whole and identify potential areas for improvement or areas of focus.

        +

        Setup

        We start by importing the necessary packages:

        @@ -525,129 +530,129 @@

        Cell trajectory visulization

        - + - + - + - + - + - - - - - - - - - - - - - - + + + + + + + + + + + + + + 5 - + 10 - + 15 - + 20 - + 25 - + 30 - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + 0 - + 20 - + 40 - + 60 - + 80 - + 100 - + 120 - - - - - - - - - - - - + + + + + + + + + + + + NormalCellA - dormant - - + + NormalCellA - active - - + + NormalCellB - dormant - - + + NormalCellB - active - - + + CancerCellA - dormant - - + + CancerCellA - active - - + + CancerCellB - dormant - - + + CancerCellB - active diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet.qmd b/tutorials/Petri_refactor/CellAuto_Petrinet.qmd index a925170..50f3bf3 100644 --- a/tutorials/Petri_refactor/CellAuto_Petrinet.qmd +++ b/tutorials/Petri_refactor/CellAuto_Petrinet.qmd @@ -1,6 +1,6 @@ --- title: "Cellular Automata Simulation Using Petri Nets" -subtitle: "version 0.1.0" +subtitle: "version 0.1.2" format: html: code-fold: false @@ -14,8 +14,27 @@ author: Tianchi Chen This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot. -# Setup +# Biological Systems +A Petri net framework can be used to model a biological system like cancer, as it represents dynamic processes in which individual items pass through multiple states. Petri nets are composed of places, transitions, and arcs. Places represent the state of an item and transitions represent the operations that affect the item. The arcs link the various places and transitions together to create a flow of information. For example, in the case of a cancer system, the places could represent tumor growth and the transitions could represent treatments that are administered, such as chemotherapy or radiation. The arcs between the places and transitions would then dictate how the treatments affect the tumor growth. The Petri net framework can then be used to analyze the system as a whole and identify potential areas for improvement or areas of focus. + +# Theoretical and computational framework +In this document, we will show how to use the Petri Net framework to simulate a cellular automata model. We will also see how to use the Petri Net framework to analyze the system as a whole. +The Petri Net framework consists several key elements: +1. A Petri Net + 1. Input Arc + 1. connect Places to a transition + 2. with arc weight to be the number of tokens required for the transition + 3. contains + 2. Output Arc + 3. Place + 4. Transition + 5. Marking +2. Tokens +3. Dynamical state + + +# Simulation examples for heterogeneous hierarchical dynamic cancer networks using the Petri Net framework We start by importing the necessary packages: ```{julia} @@ -26,7 +45,7 @@ using Random, DataFrames Random.seed!(0) ``` -# Defining the Cell Types +## Defining the Cell Types We define an abstract type AbstractCellType and four concrete types NormalCellA, NormalCellB, CancerCellA, and CancerCellB that inherit from AbstractCellType. ```{julia} @@ -37,7 +56,7 @@ struct CancerCellA <: AbstractCellType end struct CancerCellB <: AbstractCellType end ``` -# Defining the Cell and Place +## Defining the Cell and Place We define a mutable struct Cell with fields for the cell type, an ID, the dynamical state, and the time in active state. We also define a struct Place that represents a location with a name and a vector of cells (tokens). ```{julia} @@ -54,7 +73,7 @@ mutable struct Place end ``` -# Defining the Transition +## Defining the Transition A Transition represents a transformation from input places to output places. It has fields for the name of the transition, the input places, and the output places. ```{julia} @@ -65,7 +84,7 @@ struct Transition end ``` -# Adding and Removing Tokens +## Adding and Removing Tokens We define two helper functions add_token! and remove_token! to add and remove cells from a place. ```{julia} @@ -78,7 +97,7 @@ function remove_token!(place::Place, token::Cell) end ``` -# Simulating the Dynamical State +## Simulating the Dynamical State We define a function simulate_dynamical_state! that updates the dynamical state of a cell based on the presence of cancer cells in the same place. ```{julia} @@ -99,7 +118,7 @@ end ``` -# Performing the Transition +## Performing the Transition The function perform_transition! checks the feasibility of a transition by confirming if it has the required number of tokens in the input places. If the transition is feasible, it removes the tokens from the input places and adds new tokens to the output places. The function also updates the dynamical state of the tokens based on the presence of cancer cells. ```{julia} @@ -146,7 +165,7 @@ function perform_transition!(transition::Transition) end ``` -# Defining the Petri Net +## Defining the Petri Net We define a Petri Net using the previously defined Place and Transition structures. The Petri Net also includes InputArc and OutputArc structures that connect Places and Transitions. ```{julia} @@ -174,7 +193,7 @@ end -# Simulation of the Petri Net +## Simulation of the Petri Net We initialize the Petri Net with a certain number of cells and let the system evolve over a predefined number of time steps. At each time step, we update the dynamical state of each cell, check if each transition can fire, and if so, fire the transition. We also record the state of the system at each time step. @@ -206,7 +225,7 @@ end ``` -# Visualization of the Petri Net Evolution +## Visualization of the Petri Net Evolution the Plots package to visualize the evolution of the system. At each time step, we create a bar plot of the count of each type of cell in each dynamical state. ```{julia} @@ -224,10 +243,10 @@ end ``` -# Running the Simulation +## Running the Simulation To run the simulation, we initialize the Petri Net and then perform a series of updates in a loop. At each time step, we print out the current state of the Petri Net and generate a plot. -# Initializing the Petri Net +## Initializing the Petri Net We initialize the Petri net by creating the places (P1 and P2), the transitions (T1), and setting up the input and output arcs. @@ -261,7 +280,7 @@ function init_petri_net(num_cells::Int, max_active_time::Int) end ``` -# Updating the Petri Net +## Updating the Petri Net We update the Petri net by checking the state of the cells and performing transitions if possible. ```{julia} @@ -288,7 +307,7 @@ function update_petri_net(pn::PetriNet) end end ``` -# Cell trajectory visulization +## Cell trajectory visulization ```{julia} function plot_trajectory(cell_trajectory::Vector{Dict{Tuple{DataType, Symbol}, Int}}) diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap.min.css b/tutorials/Petri_refactor/CellAuto_Petrinet_files/libs/bootstrap/bootstrap.min.css index 0c07a5b..a87b434 100644 --- 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".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); const sidebarEl = window.document.getElementById("quarto-sidebar"); const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); @@ -431,32 +476,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { }; }; - // Find any conflicting margin elements and add margins to the - // top to prevent overlap - const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > * " - ); - - const layoutMarginEls = () => { - let lastBottom = 0; - for (const marginChild of marginChildren) { - if (marginChild.offsetParent !== null) { - // clear the top margin so we recompute it - marginChild.style.marginTop = null; - const top = marginChild.getBoundingClientRect().top + window.scrollY; - if (top < lastBottom) { - const margin = lastBottom - top; - marginChild.style.marginTop = `${margin}px`; - } - const styles = window.getComputedStyle(marginChild); - const marginTop = parseFloat(styles["marginTop"]); - lastBottom = - top + marginChild.getBoundingClientRect().height + marginTop; - } - } - }; - nexttick(layoutMarginEls); - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); for (const tabEl of tabEls) { const id = tabEl.getAttribute("data-bs-target"); @@ -466,7 +485,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { ); if (columnEl) tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; if (el) { const visibleCls = `${el.id}-margin-content`; diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl new file mode 100644 index 0000000..3e2cb01 --- /dev/null +++ b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl @@ -0,0 +1,332 @@ +using Distributions, StatsBase + +# Define the places +places = ["P1", "P2", "P3"] + +# Define the transitions +transitions = ["T1", "T2", "T3"] + +# Define the token types +token_types = ["TokenA", "TokenB", "TokenC"] + +# Define the arc multiplicity matrix +#! this can be essentially determine by the stoichiometric formula from catalyst.jl or reactive_dynamics +# TODO: +arc_multiplicity = [2 0 1; + 1 1 0; + 0 2 1] + +# Define the token counts for each place and token type +token_counts = Dict("P1" => Dict("TokenA" => 3, "TokenB" => 2, "TokenC" => 1), + "P2" => Dict("TokenA" => 1, "TokenB" => 2, "TokenC" => 3), + "P3" => Dict("TokenA" => 2, "TokenB" => 1, "TokenC" => 3)) + +# Create the tensor with the specified dimensions +# This function takes in arc_multiplicity, places, transitions and token_types as inputs, and returns a 3D tensor of integers. +# arc_multiplicity: a 2D array containing the number of tokens an arc connects between each place and token type +# places: an array of strings representing the different places in the system +# transitions: an array of strings representing the different transitions (events) in the system +# token_types: an array of strings representing the different types of tokens in the system + +function create_tensor(arc_multiplicity, places, transitions, token_types) + + # The length of places is calculated and stored in num_places variable + num_places = length(places) + + # The length of transitions is calculated and stored in num_transitions variable + num_transitions = length(transitions) + + # The length of token_types is calculated and stored in num_token_types variable + num_token_types = length(token_types) + + # A 3D tensor of zeros with dimensions num_places x num_transitions x num_token_types is created and stored in the tensor variable + # Each element in this tensor initially has a value of 0 + tensor = zeros(Int, num_places, num_transitions, num_token_types) + + # Arc multiplicity values are assigned to the elements of the 3D tensor using three nested for-loops + for i in 1:num_places, j in 1:num_transitions, k in 1:num_token_types + + # The arc_multiplicity value for the ith place and kth token type is assigned to the corresponding element in the 3D tensor + # Here, we are accessing the ith row, jth column and kth depth of the tensor and assigning it the value arc_multiplicity[i, k] + tensor[i, j, k] = arc_multiplicity[i, k] + end + + # The final 3D tensor is returned + return tensor +end + + +tensor = create_tensor(arc_multiplicity, places, transitions, token_types) + +# Determine the available transitions based on token availability and arc multiplicity +function find_transition_index(transition, transitions) + findfirst(x -> x == transition, transitions) +end + +function check_token_counts(token_counts, tensor, places, token_types, transition, + transitions) + for (i, place) in enumerate(places) + for (k, token_type) in enumerate(token_types) + j = find_transition_index(transition, transitions) + if j != nothing && token_counts[place][token_type] < tensor[i, j, k] + return false + end + end + end + return true +end + +function get_available_transitions(token_counts, tensor, places, token_types, transitions) + [transition + for transition in transitions + if check_token_counts(token_counts, tensor, places, token_types, + transition, transitions)] +end + +available_transitions = get_available_transitions(token_counts, tensor, places, token_types, + transitions) + +# Compute the effective arc multiplicity based on token availability +function compute_effective_multiplicity(token_counts, tensor, places, token_types, + transitions) + num_places = length(places) + num_transitions = length(transitions) + num_token_types = length(token_types) + effective_multiplicity = zeros(Int, num_places, num_transitions, num_token_types) + + for i in 1:num_places, j in 1:num_transitions, k in 1:num_token_types + effective_multiplicity[i, j, k] = min(token_counts[places[i]][token_types[k]], + tensor[i, j, k]) + end + + return effective_multiplicity +end + +effective_multiplicity = compute_effective_multiplicity(token_counts, tensor, places, + token_types, transitions) + +# Randomly select the transitions to fire based on their probabilities and effective multiplicity +function select_transitions(available_transitions, effective_multiplicity) + if isempty(available_transitions) + return [] + end + + sample(available_transitions, + weights(sum(effective_multiplicity[:, available_transitions, + :], dims = 3))) +end + +selected_transitions = select_transitions(available_transitions, effective_multiplicity) + +# Update the token counts based on the selected transitions +function update_token_counts(token_counts, places, token_types, selected_transitions, + effective_multiplicity, transitions) + for transition in selected_transitions + for i in 1:num_places + for k in 1:num_token_types + token_counts[places[i]][token_types[k]] -= effective_multiplicity[i, + findfirst(x -> x == + transition, + transitions), + k] + end + end + end +end + +update_token_counts(token_counts, places, token_types, selected_transitions, + effective_multiplicity, transitions) + +# Print the selected transitions, effective multiplicity, and updated token counts +println("Selected transitions: ", selected_transitions) +println("Effective multiplicity: ", effective_multiplicity) +println("Updated token counts: ", token_counts) + + + + + + + + + + +## ======== version 2 ================================= +# Required Libraries +using Revise +using AlgebraicAgents +using ReactiveDynamics +using DifferentialEquations +using Distributions, Plots, DataFrames, Random +## + +# Define an abstract transition +abstract type AbstractTransition end +# Define the structure of a cell +struct Cell + cell_type::Symbol # Normal or Cancer + dynamical_state::Symbol # Active or Inactive +end + +# Define the structure of a place +struct Place + id::String + cells::Dict{Cell, Int} +end + +# Define the structure for an input arc +struct InputArc + id::String + source::Place + target::AbstractTransition + token_type::Cell + multiplicity::Int +end + +# Define the structure for an output arc +struct OutputArc + id::String + source::AbstractTransition + target::Place + token_type::Cell + multiplicity::Int +end + + +# Define the structure of a generalized Petri net +mutable struct GeneralizedPetriNet + places::Vector{Place} + transitions::Vector{AbstractTransition} + marking::Dict{Place, Dict{Cell, Int}} +end + + +# Define the structure for a stochastic transition +mutable struct StochasticTransition + id::String + input_arcs::Vector{InputArc} + output_arcs::Vector{OutputArc} + rate_fn::Function + function StochasticTransition(id::String, input_arcs::Vector{InputArc}, + output_arcs::Vector{OutputArc}, rate_fn::Function) + function precondition(gpn::GeneralizedPetriNet) + all(arc -> gpn.marking[arc.source][arc.token_type] >= arc.multiplicity, + filter(arc -> arc.target.id == id, input_arcs)) + end + function postcondition(gpn::GeneralizedPetriNet) + begin + for input_arc in input_arcs + gpn.marking[input_arc.source][input_arc.token_type] -= input_arc.multiplicity + end + for output_arc in output_arcs + gpn.marking[output_arc.target][output_arc.token_type] += output_arc.multiplicity + end + end + end + new{id, input_arcs, output_arcs, rate_fn} + end +end + +# Define a function to execute a transition +function execute_transition!(t::StochasticTransition, gpn::GeneralizedPetriNet) + if t.precondition(gpn) + t.postcondition(gpn) + end +end + +# Define a function to update the Petri net +function update_petri_net!(gpn::GeneralizedPetriNet) + can_fire_transitions = filter(t -> t.precondition(gpn), gpn.transitions) + if isempty(can_fire_transitions) + return + end + # Order transitions based on their rates + transition_rates = map(t -> t.rate_fn(gpn), can_fire_transitions) + probabilities = transition_rates ./ sum(transition_rates) + selected_transition = sample(can_fire_transitions, Weights(probabilities)) + execute_transition!(selected_transition, gpn) +end + +# Define a function to simulate the Petri net +function simulate_petri_net!(gpn::GeneralizedPetriNet, simulation_time::Int) + for _ in 1:simulation_time + update_petri_net!(gpn) + end +end + + +function add_place!(gpn::GeneralizedPetriNet, place::Place) + push!(gpn.places, place) + gpn.marking[place] = place.cells +end + +function add_transition!(gpn::GeneralizedPetriNet, transition::StochasticTransition) + push!(gpn.transitions, transition) +end + +function add_arc!(gpn::GeneralizedPetriNet, arc::Union{InputArc, OutputArc}) + transition = arc.source isa AbstractTransition ? arc.source : arc.target + if arc isa InputArc + push!(transition.input_arcs, arc) + else + push!(transition.output_arcs, arc) + end +end + + + + +# Initialize an empty Generalized Petri Net +gpn = GeneralizedPetriNet([], [], Dict()) + +# Define cell types and dynamical states +Normal_Active = Cell(:Normal, :Active) +Normal_Inactive = Cell(:Normal, :Inactive) +Cancer_Active = Cell(:Cancer, :Active) +Cancer_Inactive = Cell(:Cancer, :Inactive) + +# Define Places +place1 = Place("Place1", Dict(Normal_Active => 50)) +place2 = Place("Place2", Dict(Normal_Inactive => 50)) +place3 = Place("Place3", Dict(Cancer_Active => 50)) +place4 = Place("Place4", Dict(Cancer_Inactive => 50)) + +# Add Places to the Generalized Petri Net +add_place!(gpn, place1) +add_place!(gpn, place2) +add_place!(gpn, place3) +add_place!(gpn, place4) + +# Define rate function +rate_fn = _ -> 0.5 + +# Define Transitions +Transition1 = StochasticTransition("Transition1", [], [], rate_fn) +Transition2 = StochasticTransition("Transition2", [], [], rate_fn) + +# Add Transitions to the Generalized Petri Net +add_transition!(gpn, Transition1) +add_transition!(gpn, Transition2) + +# Define Input and Output Arcs +input_arc1 = InputArc("InputArc1", place1, Transition1, Normal_Active, 1) +output_arc1 = OutputArc("OutputArc1", Transition1, place2, Normal_Inactive, 1) +input_arc2 = InputArc("InputArc2", place3, Transition2, Cancer_Active, 1) +output_arc2 = OutputArc("OutputArc2", Transition2, place4, Cancer_Inactive, 1) + +# Add Arcs to the Generalized Petri Net +add_arc!(gpn, input_arc1) +add_arc!(gpn, output_arc1) +add_arc!(gpn, input_arc2) +add_arc!(gpn, output_arc2) + +# Update Transitions with their Input and Output Arcs +Transition1.input_arcs = [input_arc1] +Transition1.output_arcs = [output_arc1] +Transition2.input_arcs = [input_arc2] +Transition2.output_arcs = [output_arc2] + +# Simulate the Generalized Petri Net +simulate_petri_net!(gpn, 100) + + diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.qmd b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.qmd new file mode 100644 index 0000000..f84703f --- /dev/null +++ b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.qmd @@ -0,0 +1,139 @@ +--- +title: "Cellular Automata Simulation Using Petri Nets" +subtitle: "version 0.1.2" +format: + html: + code-fold: false +jupyter: julia-1.9 +toc: true +author: Tianchi Chen +--- + +# Aim of this document +Trying to build the module for stochastic petric net with arc multiplicity and implicit dynamical state without internal dynamics inside the token agent. + +# Introduction + +This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot. + + +# Biological Systems +A Petri net framework can be used to model a biological system like cancer, as it represents dynamic processes in which individual items pass through multiple states. Petri nets are composed of places, transitions, and arcs. Places represent the state of an item and transitions represent the operations that affect the item. The arcs link the various places and transitions together to create a flow of information. For example, in the case of a cancer system, the places could represent tumor growth and the transitions could represent treatments that are administered, such as chemotherapy or radiation. The arcs between the places and transitions would then dictate how the treatments affect the tumor growth. The Petri net framework can then be used to analyze the system as a whole and identify potential areas for improvement or areas of focus. + +# Theoretical and computational framework +In this document, we will show how to use the Petri Net framework to simulate a cellular automata model. We will also see how to use the Petri Net framework to analyze the system as a whole. +The Petri Net framework consists several key elements: +1. A Petri Net + 1. Input Arc + 1. connect Places to a transition + 2. with arc weight to be the number of tokens required for the transition + 3. contains + 2. Output Arc + 3. Place + 4. Transition + 5. Marking +2. Tokens +3. Dynamical state + + + +# how to estimate the transition rate in a SPN +A Stochastic Petri Net (SPN) models complex systems with concurrent, asynchronous, distributed, parallel, nondeterministic, and/or stochastic activity. In an SPN, the firing rate of a transition (event) is often modeled as a stochastic or random process. + +If the firing rate of a transition depends on the token's density in a place, and there may be multiple input arcs to a transition from different places, each with potentially different token densities, then you're dealing with a more complex situation. Here are a few ways to approach it: + +Weighted Average Approach: You can treat the firing rate of a transition as a weighted average of the token densities from each place (input arc). The weight can be proportional to the token's multiplicity in each place. This would result in a firing rate that is influenced more by the places with higher token multiplicity. +Maximum Density Approach: Alternatively, you could use the maximum token density from all the places connected to a transition. This makes the firing rate sensitive to the place with the most tokens. +Minimum Density Approach: Another approach would be to use the minimum token density from all the places connected to a transition. This would make the firing rate sensitive to the place with the fewest tokens, modeling a "bottleneck" situation. +Probabilistic Approach: If the SPN is being simulated, another approach is to generate a random number for each firing and choose the firing rate according to a probability distribution that is a function of the token densities. +Remember that these are approximations and might not fully capture the actual behavior of your system. The appropriate approach to use depends on the specific characteristics and requirements of the system you are modeling. + +Also, it is worth mentioning that the firing rate function should satisfy Markovian properties (memorylessness) to maintain the stochastic nature of SPN. However, in complex systems with multiple inputs and token densities, this might be challenging to implement, and non-Markovian Stochastic Petri Nets (NMSPN) might be a more suitable choice. + + + + +```{julia} +using Revise +using DifferentialEquations, AlgebraicAgents +using Plots +using Random, DataFrames, TensorOperations +Random.seed!(0) +``` + +```{julia} +using Distributions + +# Define the places +places = ["P1", "P2", "P3"] + +# Define the transitions +transitions = ["T1", "T2", "T3"] + +# Define the token types +token_types = ["TokenA", "TokenB", "TokenC"] + +# Create the tensor with the specified dimensions +num_places = length(places) +num_transitions = length(transitions) +num_token_types = length(token_types) +tensor = zeros(Int, num_places, num_transitions, num_token_types) + +# Define the arc multiplicity matrix +arc_multiplicity = [2 0 1; + 1 1 0; + 0 2 1] + +# Fill the tensor with the arc multiplicity values +for i in 1:num_places, j in 1:num_transitions, k in 1:num_token_types + tensor[i, j, k] = arc_multiplicity[i, k] +end + +# Define the token counts for each place and token type +token_counts = Dict("P1" => Dict("TokenA" => 3, "TokenB" => 2, "TokenC" => 1), + "P2" => Dict("TokenA" => 1, "TokenB" => 2, "TokenC" => 3), + "P3" => Dict("TokenA" => 2, "TokenB" => 1, "TokenC" => 3)) + +# Determine the available transitions based on token availability and arc multiplicity +available_transitions = [transition + for transition in transitions + if all(token_counts[place][token_type] >= tensor[i, j, k] + for (i, place) in enumerate(places) + for (k, token_type) in enumerate(token_types) + if (j = findfirst(x -> x == transition, transitions)) != + nothing)] + +# Compute the effective arc multiplicity based on token availability +effective_multiplicity = zeros(Int, num_places, num_transitions, num_token_types) +for i in 1:num_places, j in 1:num_transitions, k in 1:num_token_types + effective_multiplicity[i, j, k] = min(token_counts[places[i]][token_types[k]], + tensor[i, j, k]) +end + +# Randomly select the transitions to fire based on their probabilities and effective multiplicity +selected_transitions = sample(available_transitions, + weights(sum(effective_multiplicity[:, available_transitions, + :], dims = 3))) + +# Update the token counts based on the selected transitions +for transition in selected_transitions + for i in 1:num_places + for k in 1:num_token_types + token_counts[places[i]][token_types[k]] -= effective_multiplicity[i, + findfirst(x -> x == + transition, + transitions), + k] + end + end +end + +# Print the selected transitions, effective multiplicity, and updated token counts +println("Selected transitions: ", selected_transitions) +println("Effective multiplicity: ", effective_multiplicity) +println("Updated token counts: ", token_counts) + +``` + + +Discard the muplicity tensor way \ No newline at end of file diff --git a/tutorials/Petri_refactor/Petri_reaction_framework.jl b/tutorials/Petri_refactor/Petri_reaction_framework.jl new file mode 100644 index 0000000..5cbec7d --- /dev/null +++ b/tutorials/Petri_refactor/Petri_reaction_framework.jl @@ -0,0 +1,285 @@ +# Required Libraries +using Revise +using AlgebraicAgents +using ReactiveDynamics +using DifferentialEquations +using Distributions, Plots, DataFrames, Random +## +# Define the structure of a cell +struct Cell + # id::String + cell_type::Symbol # Normal or Cancer + dynamical_state::Symbol # Active or Inactive +end + +# Define the structure of a place +struct Place + id::String + cells::Dict{Cell, Int} +end + +# Define the structure for a stochastic transition +struct StochasticTransition + id::String + precondition::Function + postcondition::Function + rate_fn::Function +end + + +# Define the structure for an input arc +struct InputArc + id::String + source::Place + target::StochasticTransition + token_type::Cell + multiplicity::Int +end + +# Define the structure for an output arc +struct OutputArc + id::String + source::StochasticTransition + target::Place + token_type::Cell + multiplicity::Int +end + +# Define the structure of a generalized Petri net +struct GeneralizedPetriNet + places::Vector{Place} + transitions::Vector{StochasticTransition} + marking::Dict{Place, Dict{Cell, Int}} +end + +# Function to check if a transition can fire +function can_fire(t::StochasticTransition, gpn::GeneralizedPetriNet) + all(input_arc -> gpn.marking[input_arc.source][input_arc.token_type] >= + input_arc.multiplicity, t.input_arcs) +end + +# Function to execute a transition +function execute_transition(t::StochasticTransition, + gpn::GeneralizedPetriNet) + if can_fire(t, gpn) + # Consume tokens according to the input arcs + for arc in t.input_arcs + gpn.marking[arc.source][arc.token_type] -= arc.multiplicity + end + # Generate tokens according to the output arcs + for arc in t.output_arcs + gpn.marking[arc.target][arc.token_type] += arc.multiplicity + end + end +end + +# # Function to update the Petri net +# function update_petri_net(gpn::GeneralizedPetriNet) +# # Get all transitions that can fire +# can_fire_transitions = [t for t in gpn.transitions if can_fire(t, gpn)] + +# # If there are no transitions that can fire, return +# if isempty(can_fire_transitions) +# return gpn +# end + +# # If there are stochastic transitions, sort them by their rates +# if any(t -> isa(t, StochasticTransition), can_fire_transitions) +# sort!(can_fire_transitions, by = t -> isa(t, StochasticTransition) ? t.rate : 0, +# rev = true) +# end + +# # Attempt to fire each transition +# for t in can_fire_transitions +# # If the transition can still fire, execute it +# if can_fire(t, gpn) +# execute_transition(t, gpn) +# end +# end + +# return gpn +# end + + + + + + +# Updated function to calculate the weighted rate for each transition +function transition_rate(t::StochasticTransition, gpn::GeneralizedPetriNet) + total_weight = 0 + total_rate = 0 + for arc in t.input_arcs + cell_density = gpn.marking[arc.source][arc.token_type] + total_rate += arc.multiplicity * cell_density + total_weight += arc.multiplicity + end + # Ensure that we do not divide by zero + if total_weight != 0 + return total_rate / total_weight + else + return 0 + end +en + + + +# Define a function to update the Petri net +function update_petri_net!(gpn::GeneralizedPetriNet) + can_fire_transitions = [t for t in gpn.transitions if can_fire(t, gpn)] + if isempty(can_fire_transitions) + return + end + # Order transitions based on their rates + transition_rates = [t.rate for t in can_fire_transitions] + probabilities = transition_rates ./ sum(transition_rates) + selected_transition = sample(can_fire_transitions, Weights(probabilities)) + execute_transition!(selected_transition, gpn) +end + + + +# Define a function to simulate the Petri net +function simulate_petri_net!(gpn::GeneralizedPetriNet, simulation_time::Int) + for _ in 1:simulation_time + update_petri_net!(gpn) + end +end + + + + + + + + + + + + + + + + + +## simulation +# Simulation +active_normal_cell = Cell("ANC", :normal, :active) +inactive_normal_cell = Cell("INC", :normal, :inactive) +active_cancer_cell = Cell("ACC", :cancer, :active) +inactive_cancer_cell = Cell("ICC", :cancer, :inactive) + +place1 = Place("P1", Dict(active_normal_cell => 10, inactive_normal_cell => 5, active_cancer_cell => 5, inactive_cancer_cell => 2)) +place2 = Place("P2", Dict(active_normal_cell => 5, inactive_normal_cell => 10, active_cancer_cell => 10, inactive_cancer_cell => 3)) +place3 = Place("P3", Dict(active_normal_cell => 8, inactive_normal_cell => 8, active_cancer_cell => 6, inactive_cancer_cell => 4)) +place4 = Place("P4", Dict(active_normal_cell => 6, inactive_normal_cell => 6, active_cancer_cell => 8, inactive_cancer_cell => 4)) + +# Transition 1: An active cancer cell and an active normal cell react to create two active cancer cells. +input_arc1 = InputArc("IA1", place1, transition1, active_cancer_cell, 1) +input_arc2 = InputArc("IA2", place1, transition1, active_normal_cell, 1) +output_arc1 = OutputArc("OA1", transition1, place2, active_cancer_cell, 2) +transition1 = GeneralTransition("T1", [input_arc1, input_arc2]) + +# Transition 1: An active cancer cell and an active normal cell react to create two active cancer cells. +rate1 = gpn -> place_rate(gpn.places[1], active_cancer_cell, 0.1) # rate function depends on the number of active cancer cells in P1 +input_arc1 = InputArc("IA1", place1, transition1, active_cancer_cell, 1) +input_arc2 = InputArc("IA2", place1, transition1, active_normal_cell, 1) +output_arc1 = OutputArc("OA1", transition1, place2, active_cancer_cell, 2) +transition1 = StochasticTransition("T1", [input_arc1, input_arc2], rate1) + + + + +# Transition 2: An active cancer cell and an inactive normal cell react to create two active cancer cells. +input_arc3 = InputArc("IA3", place3, transition2, active_cancer_cell, 1) +input_arc4 = InputArc("IA4", place3, transition2, inactive_normal_cell, 1) +output_arc2 = OutputArc("OA2", transition2, place4, active_cancer_cell, 2) +transition2 = GeneralTransition("T2", [input_arc3, input_arc4]) + +gpn = GeneralizedPetriNet( + [place1, place2, place3, place4], + [transition1, transition2], + [input_arc1, input_arc2, input_arc3, input_arc4], + [output_arc1, output_arc2], + Dict(place1 => place1.cells, place2 => place2.cells, place3 => place3.cells, place4 => place4.cells), + zeros(Int, 4, 2) # arc_multiplicity_matrix of size 4x2 as we have 4 places and 2 transitions +) + +# Number of time steps +n_steps = 100 + +# Simulate the system for n_steps +for t in 1:n_steps + update_petri_net(gpn) +end + + +# ================================================================= +# Define the rate function for each transition +rate1 = gpn -> place_rate(gpn.places[1], active_cancer_cell, 0.1) +rate2 = gpn -> place_rate(gpn.places[3], active_cancer_cell, 0.2) +# Define the ODE system for the Petri net +function petri_net_ode(du, u, p, t) + update_petri_net(p.gpn) + du .= 0 + for i in 1:length(p.transitions) + t = p.transitions[i] + du[p.marking[t.source], i] -= t.rate(p.gpn) + du[p.marking[t.target], i] += t.rate(p.gpn) + end +end +# Set up the initial conditions for the ODE solver +u0 = zeros(Int, length(gpn.marking), length(gpn.transitions)) +u0[1, 1] = 1 # initial marking for P1 +# Set up the ODE problem and solver +tspan = (0.0, 10.0) +prob = ODEProblem(petri_net_ode, u0, tspan, + (gpn = gpn, transitions = [transition1, transition2], + marking = gpn.marking)) +sol = solve(prob, Tsit5()) +# Plot the results +using Plots +plot(sol, vars = 1:4, title = ["P1" "P2" "P3" "P4"], xlabel = "Time", + ylabel = "Number of cells") + +# ================================================================= + + + + +## ==== visualization================================================ +using Plots + +function visualize_trajectory(gpn::GeneralizedPetriNet, n_steps::Int, cell_types::Vector{Cell}) + data = Dict() # This will hold the number of each cell type for each time step + + for cell_type in cell_types + data[cell_type] = [] + end + + # Simulate the system for n_steps + for t in 1:n_steps + update_petri_net(gpn) + # Store the number of each cell type + for cell_type in cell_types + push!(data[cell_type], sum([gpn.marking[place][cell_type] for place in gpn.places])) + end + end + + # Plot the number of each cell type over time + p = plot() + for cell_type in cell_types + plot!(p, data[cell_type], label = "$(cell_type.cell_type) - $(cell_type.dynamical_state)") + end + title!(p, "Number of Cells Over Time") + xlabel!(p, "Time") + ylabel!(p, "Number of Cells") + return p +end + +# Define your cell types +cell_types = [active_normal_cell, inactive_normal_cell, active_cancer_cell, inactive_cancer_cell] + +# Run the simulation and visualize the results +p = visualize_trajectory(gpn, 100, cell_types) + diff --git a/tutorials/Petri_refactor/Project.toml b/tutorials/Petri_refactor/Project.toml index 1812b0a..6ea71e0 100644 --- a/tutorials/Petri_refactor/Project.toml +++ b/tutorials/Petri_refactor/Project.toml @@ -23,5 +23,8 @@ Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781" PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +ReactiveDynamics = "c7456e7d-545a-4b79-91ea-6e93d96dd4d4" Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" +TensorOperations = "6aa20fa7-93e2-5fca-9bc0-fbd0db3c71a2" WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008" From 21aa18ea84105cb2dd14ad70a53da7f0dfa2c85a Mon Sep 17 00:00:00 2001 From: danielchen Date: Tue, 21 Nov 2023 14:16:52 -0500 Subject: [PATCH 6/8] debuging project.tml --- .../Petri_refactor/CellAuto_Petrinet.html | 213 +++++++++--------- .../CellAuto_Petrinet_refactor.ipynb | 177 +++++++++++++++ .../CellAuto_Petrinet_refactor.jl | 1 + .../Petri_reaction_framework.jl | 11 +- tutorials/Petri_refactor/Project.toml | 27 +-- .../old project file/Project.toml | 24 ++ tutorials/testing/Project.toml | 7 + tutorials/testing/ode_test.jl | 40 ++++ 8 files changed, 368 insertions(+), 132 deletions(-) create mode 100644 tutorials/Petri_refactor/CellAuto_Petrinet_refactor.ipynb create mode 100644 tutorials/Petri_refactor/old project file/Project.toml create mode 100644 tutorials/testing/Project.toml create mode 100644 tutorials/testing/ode_test.jl diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet.html b/tutorials/Petri_refactor/CellAuto_Petrinet.html index 7b41c07..ece06e4 100644 --- a/tutorials/Petri_refactor/CellAuto_Petrinet.html +++ b/tutorials/Petri_refactor/CellAuto_Petrinet.html @@ -85,7 +85,9 @@

        Table of contents

        @@ -107,7 +110,7 @@

        Table of contents

        Cellular Automata Simulation Using Petri Nets

        -

        version 0.1.1

        +

        version 0.1.2

        @@ -136,8 +139,12 @@

        Introduction

        Biological Systems

        A Petri net framework can be used to model a biological system like cancer, as it represents dynamic processes in which individual items pass through multiple states. Petri nets are composed of places, transitions, and arcs. Places represent the state of an item and transitions represent the operations that affect the item. The arcs link the various places and transitions together to create a flow of information. For example, in the case of a cancer system, the places could represent tumor growth and the transitions could represent treatments that are administered, such as chemotherapy or radiation. The arcs between the places and transitions would then dictate how the treatments affect the tumor growth. The Petri net framework can then be used to analyze the system as a whole and identify potential areas for improvement or areas of focus.

        -
        -

        Setup

        +
        +

        Theoretical and computational framework

        +

        In this document, we will show how to use the Petri Net framework to simulate a cellular automata model. We will also see how to use the Petri Net framework to analyze the system as a whole. The Petri Net framework consists several key elements: 1. A Petri Net 1. Input Arc 1. connect Places to a transition 2. with arc weight to be the number of tokens required for the transition 3. contains 2. Output Arc 3. Place 4. Transition 5. Marking 2. Tokens 3. Dynamical state

        +
        +
        +

        Simulation examples for heterogeneous hierarchical dynamic cancer networks using the Petri Net framework

        We start by importing the necessary packages:

        using Revise
@@ -149,9 +156,8 @@ 
        Setup
        
 
        TaskLocalRNG()
        -
        -
        -

        Defining the Cell Types

        +
        +

        Defining the Cell Types

        We define an abstract type AbstractCellType and four concrete types NormalCellA, NormalCellB, CancerCellA, and CancerCellB that inherit from AbstractCellType.

        abstract type AbstractCellType end
@@ -161,8 +167,8 @@ 
        Defining the Cell Types
        
 struct CancerCellB <: AbstractCellType end
        -
        -

        Defining the Cell and Place

        +
        +

        Defining the Cell and Place

        We define a mutable struct Cell with fields for the cell type, an ID, the dynamical state, and the time in active state. We also define a struct Place that represents a location with a name and a vector of cells (tokens).

        mutable struct Cell
@@ -178,8 +184,8 @@ 
        Defining the Cell and Place
        
 end
        -
        -

        Defining the Transition

        +
        +

        Defining the Transition

        A Transition represents a transformation from input places to output places. It has fields for the name of the transition, the input places, and the output places.

        struct Transition
@@ -189,8 +195,8 @@ 
        Defining the Transition
        
 end
        -
        -

        Adding and Removing Tokens

        +
        +

        Adding and Removing Tokens

        We define two helper functions add_token! and remove_token! to add and remove cells from a place.

        function add_token!(place::Place, token::Cell)
@@ -205,8 +211,8 @@ 
        Adding and Removing Tokens
        -
        -

        Simulating the Dynamical State

        +
        +

        Simulating the Dynamical State

        We define a function simulate_dynamical_state! that updates the dynamical state of a cell based on the presence of cancer cells in the same place.

        function simulate_dynamical_state!(cell::Cell, cancer_cell_present::Bool,
@@ -228,8 +234,8 @@ 
        Simulating the Dynamical State
        -
        -

        Performing the Transition

        +
        +

        Performing the Transition

        The function perform_transition! checks the feasibility of a transition by confirming if it has the required number of tokens in the input places. If the transition is feasible, it removes the tokens from the input places and adds new tokens to the output places. The function also updates the dynamical state of the tokens based on the presence of cancer cells.

        function perform_transition!(transition::Transition)
@@ -278,8 +284,8 @@ 
        Performing the Transition
        -
        -

        Defining the Petri Net

        +
        +

        Defining the Petri Net

        We define a Petri Net using the previously defined Place and Transition structures. The Petri Net also includes InputArc and OutputArc structures that connect Places and Transitions.

        struct InputArc
@@ -304,8 +310,8 @@ 
        Defining the Petri Net
        
 end
        -
        -

        Simulation of the Petri Net

        +
        +

        Simulation of the Petri Net

        We initialize the Petri Net with a certain number of cells and let the system evolve over a predefined number of time steps. At each time step, we update the dynamical state of each cell, check if each transition can fire, and if so, fire the transition. We also record the state of the system at each time step.

        function can_fire(transition::Transition, places::Vector{Place})
@@ -337,8 +343,8 @@ 
        Simulation of the Petri Net
        -
        -

        Visualization of the Petri Net Evolution

        +
        +

        Visualization of the Petri Net Evolution

        the Plots package to visualize the evolution of the system. At each time step, we create a bar plot of the count of each type of cell in each dynamical state.

        function plot_petri_net(pn::PetriNet)
@@ -357,12 +363,12 @@ 
        Visualization of the Petri Net Evolution
        -
        -

        Running the Simulation

        +
        +

        Running the Simulation

        To run the simulation, we initialize the Petri Net and then perform a series of updates in a loop. At each time step, we print out the current state of the Petri Net and generate a plot.

        -
        -

        Initializing the Petri Net

        +
        +

        Initializing the Petri Net

        We initialize the Petri net by creating the places (P1 and P2), the transitions (T1), and setting up the input and output arcs.

        function init_petri_net(num_cells::Int, max_active_time::Int)
@@ -397,8 +403,8 @@ 
        Initializing the Petri Net
        -
        -

        Updating the Petri Net

        +
        +

        Updating the Petri Net

        We update the Petri net by checking the state of the cells and performing transitions if possible.

        function update_petri_net(pn::PetriNet)
@@ -428,8 +434,8 @@ 
        Updating the Petri Net
        -
        -

        Cell trajectory visulization

        +
        +

        Cell trajectory visulization

        function plot_trajectory(cell_trajectory::Vector{Dict{Tuple{DataType, Symbol}, Int}})
     # Initialize empty DataFrame
@@ -530,135 +536,136 @@ 
        Cell trajectory visulization
        
 
 
 
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        diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.ipynb b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.ipynb new file mode 100644 index 0000000..a269926 --- /dev/null +++ b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "title: Cellular Automata Simulation Using Petri Nets\n", + "subtitle: version 0.1.2\n", + "format:\n", + " html:\n", + " code-fold: false\n", + "toc: true\n", + "author: Tianchi Chen\n", + "---" + ], + "id": "5f62dea4" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aim of this document\n", + "Trying to build the module for stochastic petric net with arc multiplicity and implicit dynamical state without internal dynamics inside the token agent. \n", + "\n", + "# Introduction\n", + "\n", + "This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot.\n", + "\n", + "\n", + "# Biological Systems\n", + "A Petri net framework can be used to model a biological system like cancer, as it represents dynamic processes in which individual items pass through multiple states. Petri nets are composed of places, transitions, and arcs. Places represent the state of an item and transitions represent the operations that affect the item. The arcs link the various places and transitions together to create a flow of information. For example, in the case of a cancer system, the places could represent tumor growth and the transitions could represent treatments that are administered, such as chemotherapy or radiation. The arcs between the places and transitions would then dictate how the treatments affect the tumor growth. The Petri net framework can then be used to analyze the system as a whole and identify potential areas for improvement or areas of focus.\n", + "\n", + "# Theoretical and computational framework\n", + "In this document, we will show how to use the Petri Net framework to simulate a cellular automata model. We will also see how to use the Petri Net framework to analyze the system as a whole.\n", + "The Petri Net framework consists several key elements:\n", + "1. A Petri Net\n", + " 1. Input Arc\n", + " 1. connect Places to a transition\n", + " 2. with arc weight to be the number of tokens required for the transition\n", + " 3. contains \n", + " 2. Output Arc\n", + " 3. Place\n", + " 4. Transition\n", + " 5. Marking\n", + "2. Tokens\n", + "3. Dynamical state\n", + "\n", + "\n", + "\n", + "# how to estimate the transition rate in a SPN\n", + "A Stochastic Petri Net (SPN) models complex systems with concurrent, asynchronous, distributed, parallel, nondeterministic, and/or stochastic activity. In an SPN, the firing rate of a transition (event) is often modeled as a stochastic or random process.\n", + "\n", + "If the firing rate of a transition depends on the token's density in a place, and there may be multiple input arcs to a transition from different places, each with potentially different token densities, then you're dealing with a more complex situation. Here are a few ways to approach it:\n", + "\n", + "Weighted Average Approach: You can treat the firing rate of a transition as a weighted average of the token densities from each place (input arc). The weight can be proportional to the token's multiplicity in each place. This would result in a firing rate that is influenced more by the places with higher token multiplicity.\n", + "Maximum Density Approach: Alternatively, you could use the maximum token density from all the places connected to a transition. This makes the firing rate sensitive to the place with the most tokens.\n", + "Minimum Density Approach: Another approach would be to use the minimum token density from all the places connected to a transition. This would make the firing rate sensitive to the place with the fewest tokens, modeling a \"bottleneck\" situation.\n", + "Probabilistic Approach: If the SPN is being simulated, another approach is to generate a random number for each firing and choose the firing rate according to a probability distribution that is a function of the token densities.\n", + "Remember that these are approximations and might not fully capture the actual behavior of your system. The appropriate approach to use depends on the specific characteristics and requirements of the system you are modeling.\n", + "\n", + "Also, it is worth mentioning that the firing rate function should satisfy Markovian properties (memorylessness) to maintain the stochastic nature of SPN. However, in complex systems with multiple inputs and token densities, this might be challenging to implement, and non-Markovian Stochastic Petri Nets (NMSPN) might be a more suitable choice.\n" + ], + "id": "4759c84c" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "using Revise\n", + "using DifferentialEquations, AlgebraicAgents\n", + "using Plots\n", + "using Random, DataFrames, TensorOperations\n", + "Random.seed!(0)" + ], + "id": "bc7401ad", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "using Distributions\n", + "\n", + "# Define the places\n", + "places = [\"P1\", \"P2\", \"P3\"]\n", + "\n", + "# Define the transitions\n", + "transitions = [\"T1\", \"T2\", \"T3\"]\n", + "\n", + "# Define the token types\n", + "token_types = [\"TokenA\", \"TokenB\", \"TokenC\"]\n", + "\n", + "# Create the tensor with the specified dimensions\n", + "num_places = length(places)\n", + "num_transitions = length(transitions)\n", + "num_token_types = length(token_types)\n", + "tensor = zeros(Int, num_places, num_transitions, num_token_types)\n", + "\n", + "# Define the arc multiplicity matrix\n", + "arc_multiplicity = [2 0 1;\n", + " 1 1 0;\n", + " 0 2 1]\n", + "\n", + "# Fill the tensor with the arc multiplicity values\n", + "for i in 1:num_places, j in 1:num_transitions, k in 1:num_token_types\n", + " tensor[i, j, k] = arc_multiplicity[i, k]\n", + "end\n", + "\n", + "# Define the token counts for each place and token type\n", + "token_counts = Dict(\"P1\" => Dict(\"TokenA\" => 3, \"TokenB\" => 2, \"TokenC\" => 1),\n", + " \"P2\" => Dict(\"TokenA\" => 1, \"TokenB\" => 2, \"TokenC\" => 3),\n", + " \"P3\" => Dict(\"TokenA\" => 2, \"TokenB\" => 1, \"TokenC\" => 3))\n", + "\n", + "# Determine the available transitions based on token availability and arc multiplicity\n", + "available_transitions = [transition\n", + " for transition in transitions\n", + " if all(token_counts[place][token_type] >= tensor[i, j, k]\n", + " for (i, place) in enumerate(places)\n", + " for (k, token_type) in enumerate(token_types)\n", + " if (j = findfirst(x -> x == transition, transitions)) !=\n", + " nothing)]\n", + "\n", + "# Compute the effective arc multiplicity based on token availability\n", + "effective_multiplicity = zeros(Int, num_places, num_transitions, num_token_types)\n", + "for i in 1:num_places, j in 1:num_transitions, k in 1:num_token_types\n", + " effective_multiplicity[i, j, k] = min(token_counts[places[i]][token_types[k]],\n", + " tensor[i, j, k])\n", + "end\n", + "\n", + "# Randomly select the transitions to fire based on their probabilities and effective multiplicity\n", + "selected_transitions = sample(available_transitions,\n", + " weights(sum(effective_multiplicity[:, available_transitions,\n", + " :], dims = 3)))\n", + "\n", + "# Update the token counts based on the selected transitions\n", + "for transition in selected_transitions\n", + " for i in 1:num_places\n", + " for k in 1:num_token_types\n", + " token_counts[places[i]][token_types[k]] -= effective_multiplicity[i,\n", + " findfirst(x -> x ==\n", + " transition,\n", + " transitions),\n", + " k]\n", + " end\n", + " end\n", + "end\n", + "\n", + "# Print the selected transitions, effective multiplicity, and updated token counts\n", + "println(\"Selected transitions: \", selected_transitions)\n", + "println(\"Effective multiplicity: \", effective_multiplicity)\n", + "println(\"Updated token counts: \", token_counts)" + ], + "id": "56e01cb9", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Discard the muplicity tensor way" + ], + "id": "f0c8c8e8" + } + ], + "metadata": { + "kernelspec": { + "name": "julia-1.9", + "language": "julia", + "display_name": "Julia 1.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl index 3e2cb01..3607ed0 100644 --- a/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl +++ b/tutorials/Petri_refactor/CellAuto_Petrinet_refactor.jl @@ -330,3 +330,4 @@ Transition2.output_arcs = [output_arc2] simulate_petri_net!(gpn, 100) + diff --git a/tutorials/Petri_refactor/Petri_reaction_framework.jl b/tutorials/Petri_refactor/Petri_reaction_framework.jl index 5cbec7d..10823e9 100644 --- a/tutorials/Petri_refactor/Petri_reaction_framework.jl +++ b/tutorials/Petri_refactor/Petri_reaction_framework.jl @@ -1,10 +1,13 @@ -# Required Libraries +## ============== Load the necessary packages using Revise using AlgebraicAgents -using ReactiveDynamics +# using ReactiveDynamics #! compiling error using DifferentialEquations using Distributions, Plots, DataFrames, Random -## + + + +## ============== Define the type hierarchy # Define the structure of a cell struct Cell # id::String @@ -120,7 +123,7 @@ function transition_rate(t::StochasticTransition, gpn::GeneralizedPetriNet) else return 0 end -en +end diff --git a/tutorials/Petri_refactor/Project.toml b/tutorials/Petri_refactor/Project.toml index 6ea71e0..f1a0c46 100644 --- a/tutorials/Petri_refactor/Project.toml +++ b/tutorials/Petri_refactor/Project.toml @@ -1,30 +1,7 @@ [deps] -Agents = "46ada45e-f475-11e8-01d0-f70cc89e6671" AlgebraicAgents = "f6eb0ae3-10fa-40e6-88dd-9006ba45093a" -AlgebraicDynamics = "5fd6ff03-a254-427e-8840-ba658f502e32" -CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" -Catlab = "134e5e36-593f-5add-ad60-77f754baafbe" -DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83" DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" -Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" -DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" -GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" -GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" -LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800" -LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" -MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" -Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" -NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56" -Petri = "4259d249-1051-49fa-8328-3f8ab9391c33" -Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781" -PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" -RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" -Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" ReactiveDynamics = "c7456e7d-545a-4b79-91ea-6e93d96dd4d4" -Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" -StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" -TensorOperations = "6aa20fa7-93e2-5fca-9bc0-fbd0db3c71a2" -WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008" diff --git a/tutorials/Petri_refactor/old project file/Project.toml b/tutorials/Petri_refactor/old project file/Project.toml new file mode 100644 index 0000000..974395e --- /dev/null +++ b/tutorials/Petri_refactor/old project file/Project.toml @@ -0,0 +1,24 @@ +[deps] +Agents = "46ada45e-f475-11e8-01d0-f70cc89e6671" +AlgebraicAgents = "f6eb0ae3-10fa-40e6-88dd-9006ba45093a" +CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" +Catlab = "134e5e36-593f-5add-ad60-77f754baafbe" +DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" +GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" +LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +Petri = "4259d249-1051-49fa-8328-3f8ab9391c33" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +ReactiveDynamics = "c7456e7d-545a-4b79-91ea-6e93d96dd4d4" +Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" diff --git a/tutorials/testing/Project.toml b/tutorials/testing/Project.toml new file mode 100644 index 0000000..f1a0c46 --- /dev/null +++ b/tutorials/testing/Project.toml @@ -0,0 +1,7 @@ +[deps] +AlgebraicAgents = "f6eb0ae3-10fa-40e6-88dd-9006ba45093a" +Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83" +DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" +OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" +ReactiveDynamics = "c7456e7d-545a-4b79-91ea-6e93d96dd4d4" diff --git a/tutorials/testing/ode_test.jl b/tutorials/testing/ode_test.jl new file mode 100644 index 0000000..efe7cd4 --- /dev/null +++ b/tutorials/testing/ode_test.jl @@ -0,0 +1,40 @@ +using AlgebraicAgents +using DifferentialEquations + +# Define the ODE system (e.g., a simple exponential decay) +function cell_ode!(du, u, p, t) + du[1] = -p[1] * u[1] +end + +# Define the agent type +mutable struct CellAgent + state::Vector{Float64} + parameters::Vector{Float64} +end + +# Define the update rule using the ODE solver +function update_cell!(agent::CellAgent, dt) + # Define the ODE problem + prob = ODEProblem(cell_ode!, agent.state, (0.0, dt), agent.parameters) + # Solve the ODE problem + sol = solve(prob, Tsit5(), saveat=dt) + # Update the agent's state with the last solution step + agent.state = sol[end] +end + +# Initialize agents with initial state and parameters +agents = [CellAgent([1.0], [0.1]) for _ in 1:10] + +# Define the simulation +function run_simulation(agents, timesteps) + for _ in 1:timesteps + # Update each agent + for agent in agents + update_cell!(agent, 1.0) # Assuming a dt of 1.0 for simplicity + end + # Here you can add synchronization logic if needed + end +end + +# Run the simulation for 100 timesteps +run_simulation(agents, 100) \ No newline at end of file From 374a189aeed9194ce1fee793c4059330dd8bb2ef Mon Sep 17 00:00:00 2001 From: danielchen Date: Thu, 30 Nov 2023 21:07:24 -0500 Subject: [PATCH 7/8] added a hetergenous cell system sysmtem bio example, with Diffeqagent inside a general CellAgent --- tutorials/sciml/Project.toml | 5 + tutorials/sciml/systembio.jl | 440 +++++++++++++++++++++++++++++++++++ 2 files changed, 445 insertions(+) create mode 100644 tutorials/sciml/systembio.jl diff --git a/tutorials/sciml/Project.toml b/tutorials/sciml/Project.toml index 25806f3..8440e52 100644 --- a/tutorials/sciml/Project.toml +++ b/tutorials/sciml/Project.toml @@ -1,3 +1,8 @@ [deps] AlgebraicAgents = "f6eb0ae3-10fa-40e6-88dd-9006ba45093a" +Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" diff --git a/tutorials/sciml/systembio.jl b/tutorials/sciml/systembio.jl new file mode 100644 index 0000000..39f2e90 --- /dev/null +++ b/tutorials/sciml/systembio.jl @@ -0,0 +1,440 @@ +using Revise +using AlgebraicAgents +using DifferentialEquations +using DataFrames + +# Define the ODE function for cell dynamics +function cell_dynamics!(du, u, p, t) + du[1] = -p[1] * u[1] # Example dynamics +end + +abstract type AbstractCellType end +struct NormalCellA <: AbstractCellType end +struct NormalCellB <: AbstractCellType end +struct CancerCellA <: AbstractCellType end +struct CancerCellB <: AbstractCellType end + +# Define the CellAgent with a DiffEqAgent field for dynamical_state +@aagent struct CellAgent + id::Int + cell_type::AbstractCellType + dynamical_state_agent::DiffEqAgent + time_in_active_state::Int +end +# CellAgent1 = CellAgent("1" , 1, NormalCellA(), diffeq_agent, 0) + + +mutable struct Place + name::String + tokens::Vector{CellAgent} +end + + +function map_state_to_value(state::Symbol) + if state == :active + return 1.0 + elseif state == :inactive + return 0.5 + elseif state == :dormant + return 0.0 + else + error("Invalid state") + end +end + +# Function to create a DiffEqAgent +function create_diffeq_agent(id::Int, + initial_state::Symbol, + params::Vector{Float64}, + t_end::Float64) + state_value = map_state_to_value(initial_state) # Convert the symbolic state to a numerical value + u0 = [state_value] + tspan = (0.0, t_end) # Example time span + prob = ODEProblem(cell_dynamics!, u0, tspan, params) + return DiffEqAgent("$id", prob) +end + + +diffeq_agent = create_diffeq_agent(1, :inactive, [1.1], 20.0) + +function create_cell_agent(id::Int, cell_type::AbstractCellType, initial_state::Symbol, params::Vector{Float64}, t_end::Float64) + # create the DiffEqAgent with parames + diffeq_agent = create_diffeq_agent(id, initial_state, params, t_end) + # get the state value from the DiffEqAgent + return CellAgent("$id", id, cell_type, diffeq_agent, 0) +end + + +# # function to simulate the DiffEqAgent at a given time with default params +# function simulate_agent(diffeq_agent::DiffEqAgent, +# t_end::Float64, +# params::Vector{Float64} = diffeq_agent.prob.p) +# new_prob = remake(diffeq_agent, p = params, tspan = (0.0, t_end)) +# sol = solve(new_prob) +# return sol(t_end) +# end + +# cell_agent = create_cell_agent(1, NormalCellA(), :active, [0.1], 20.0) + +# simulate_agent(cell_agent.dynamical_state_agent, 30.0) + + + +struct Transition + name::String + input::Vector{Tuple{Int, Place, DataType}} + output::Vector{Tuple{Int, Place, DataType}} +end + + + +function add_token!(place::Place, token::CellAgent) + push!(place.tokens, token) +end + +function remove_token!(place::Place, token::CellAgent) + deleteat!(place.tokens, findfirst(t -> t == token, place.tokens)) +end + + + + +function simulate_dynamical_state!(cell_agent::CellAgent, + cancer_cell_present::Bool, + max_active_time::Int) + # Access the state from the DiffEqAgent + state = cell_agent.dynamical_state_agent.integrator.u[1] + + # Logic to update the dynamical state based on the presence of cancer cells + if cancer_cell_present && state == :dormant || state == :inactive + state = :active + cell_agent.time_in_active_state = 0 + elseif state == :active + cell_agent.time_in_active_state += 1 + if cell_agent.time_in_active_state >= max_active_time + state = :dormant + cell_agent.time_in_active_state = 0 + end + end + + # Update the state in the DiffEqAgent + cell_agent.dynamical_state_agent.integrator.u[1] = state +end + + +# ## Performing the Transition +# The function perform_transition! checks the feasibility of a transition by confirming if it has the required number of tokens in the input places. If the transition is feasible, it removes the tokens from the input places and adds new tokens to the output places. The function also updates the dynamical state of the tokens based on the presence of cancer cells. + + + +# function perform_transition!(transition::Transition) +# cancer_cell_exists = false +# num_tokens = 0 + +# for (count, place, token_type) in transition.input +# tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + +# if length(tokens_to_remove) < count +# return false +# end + +# for token in tokens_to_remove +# if isa(token.cell_type, CancerCellA) || isa(token.cell_type, CancerCellB) +# cancer_cell_exists = true +# end +# num_tokens += 1 +# end +# end + +# if cancer_cell_exists && num_tokens > 30 +# for (count, place, token_type) in transition.input +# tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) +# for token in tokens_to_remove[1:count] +# remove_token!(place, token) +# end +# end + +# for (count, place, token_type) in transition.output +# for _ in 1:count +# new_cell_agent = create_cell_agent(rand(Int), +# token_type(), +# rand([:active, :inactive, :dormant]), +# [0.1]) +# simulate_dynamical_state!(new_cell_agent, +# cancer_cell_exists, +# max_active_time) +# add_token!(place, new_cell_agent) +# end +# end + +# return true +# end + +# return false +# end + +function perform_transition!(transition::Transition, + cancer_cell_exists::Bool, + max_active_time::Int) + for (count, place, token_type) in transition.output + for _ in 1:count + # Determine the initial state based on the model's rules + initial_state = if cancer_cell_exists + :active + else + rand([:active, :inactive, :dormant]) + end + + # Create a new CellAgent with the determined initial state + new_cell_agent = create_cell_agent( + # Generate a unique ID + rand(Int), # Generate a unique ID for the cell + token_type(), # The cell type + initial_state, # The initial state as a Symbol + [0.1]) + + # Simulate the dynamical state of the new cell agent + simulate_dynamical_state!(new_cell_agent, cancer_cell_exists, max_active_time) + + # Add the new cell agent to the place + add_token!(place, new_cell_agent) + end + end + + return true +end + +# ## Defining the Petri Net +# We define a Petri Net using the previously defined Place and Transition structures. The Petri Net also includes InputArc and OutputArc structures that connect Places and Transitions. + + + +struct InputArc + source::Place + target::Transition + cell_type::DataType + weight::Int +end + +struct OutputArc + source::Transition + target::Place + cell_type::DataType + weight::Int +end + +struct PetriNet + places::Vector{Place} + transitions::Vector{Transition} + input_arcs::Vector{InputArc} + output_arcs::Vector{OutputArc} +end + + + +# ## Simulation of the Petri Net +# We initialize the Petri Net with a certain number of cells and let the system evolve over a predefined number of time steps. At each time step, we update the dynamical state of each cell, check if each transition can fire, and if so, fire the transition. We also record the state of the system at each time step. + + + +function can_fire(transition::Transition, places::Vector{Place}) + for (count, place, token_type) in transition.input + tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens) + + if length(tokens_to_remove) < count + return false + end + end + + return true +end + +function count_cells(pn::PetriNet) + cell_counts = Dict{Tuple{DataType, Symbol}, Int}() + + for place in pn.places + for cell in place.tokens + cell_key = (typeof(cell.cell_type), + # simulate(cell_agent.dynamical_state_agent).integrator.u) + rand([:active, :inactive, :dormant])) + cell_counts[cell_key] = get(cell_counts, cell_key, 0) + 1 + end + end + + return cell_counts +end + + + +# ## Visualization of the Petri Net Evolution +# the Plots package to visualize the evolution of the system. At each time step, we create a bar plot of the count of each type of cell in each dynamical state. + + +function plot_petri_net(pn::PetriNet) + cell_counts = count_cells(pn) + + p = bar([string(key) for key in keys(cell_counts)], + [value for value in values(cell_counts)], + xlabel = "Cell Type and Dynamical State", + ylabel = "Count", + title = "Petri Net Simulation", + legend = false) + display(p) +end + + + +# ## Running the Simulation +# To run the simulation, we initialize the Petri Net and then perform a series of updates in a loop. At each time step, we print out the current state of the Petri Net and generate a plot. + + + +# ## Initializing the Petri Net + +# We initialize the Petri net by creating the places (P1 and P2), the transitions (T1), and setting up the input and output arcs. + +function init_petri_net(num_cells::Int, max_active_time::Int) + # Define cell types + cell_types = [NormalCellA, NormalCellB, CancerCellA, CancerCellB] + # Define the places with CellAgents + P1 = Place("P1", + [create_cell_agent( + i, + rand(cell_types)(), + rand([:active, :inactive, :dormant]), + [0.1]) + for i in 1:round(Int, 0.5 * num_cells)]) + P2 = Place("P2", + [create_cell_agent( + i, + rand(cell_types)(), + rand([:active, :inactive, :dormant]), + [0.1]) + for i in 1:round(Int, 0.5 * num_cells)]) + P3 = Place("P3", []) + + # Define the transitions + T1 = Transition("T1", [(3, P1, NormalCellA), (1, P2, CancerCellA)], + [(1, P3, CancerCellA)]) + + # Define arcs + input_arc1 = InputArc(P1, T1, NormalCellA, 30) + input_arc2 = InputArc(P2, T1, CancerCellA, 30) + output_arc1 = OutputArc(T1, P3, CancerCellA, 50) + + # Define the Petri net + pn = PetriNet([P1, P2, P3], [T1], [input_arc1, input_arc2], [output_arc1]) + + return pn +end + + + + + +# ## Updating the Petri Net +# We update the Petri net by checking the state of the cells and performing transitions if possible. + +function update_petri_net(pn::PetriNet) + # Iterate through places + for place in pn.places + # Check if cancer cells are present in the place + cancer_cell_present = any(cell -> isa(cell.cell_type, CancerCellA) || + isa(cell.cell_type, CancerCellB), place.tokens) + + # Update dynamical states of all cells in the place + for cell in place.tokens + simulate_dynamical_state!(cell, cancer_cell_present, max_active_time) + end + end + + # Iterate through transitions + for transition in pn.transitions + # Check if the transition can be fired + if can_fire(transition, pn.places) + # Fire the transition and update the system's state + perform_transition!(transition, false, 2) + end + end +end + + + + + +## Cell trajectory visulization +function plot_trajectory(cell_trajectory::Vector{Dict{Tuple{DataType, Symbol}, Int}}) + # Initialize empty DataFrame + df_traj = DataFrame(cell_type = DataType[], dynamical_state = Symbol[], count = Int[], + time_step = Int[]) + + # Iterate over cell_trajectory + for (i, dict) in enumerate(cell_trajectory) + for (key, value) in dict + cell_type, dynamical_state = key + push!(df_traj, (cell_type, dynamical_state, value, i)) + end + end + + # Convert cell type to string for plotting + df_traj[!, :cell_type] = string.(df_traj[!, :cell_type]) + + # Separate data frames based on cell type + df_normalA = filter(row -> row[:cell_type] == "NormalCellA", df_traj) + df_normalB = filter(row -> row[:cell_type] == "NormalCellB", df_traj) + df_cancerA = filter(row -> row[:cell_type] == "CancerCellA", df_traj) + df_cancerB = filter(row -> row[:cell_type] == "CancerCellB", df_traj) + + # Initialize plot + p = plot() + + # Add lines for each cell type and dynamical state + for df_cell in [df_normalA, df_normalB, df_cancerA, df_cancerB] + for state in unique(df_cell[!, :dynamical_state]) + df_state = filter(row -> row[:dynamical_state] == state, df_cell) + plot!(p, df_state[!, :time_step], df_state[!, :count], + label = "$(df_state[1, :cell_type]) - $(state)") + end + end + + # Show plot + return p +end + + + +function init_trajectory() + # Initialize an empty array to store the states of the Petri net at each time step + trajectory = Dict{Tuple{DataType, Symbol}, Int}[] + + return trajectory +end + +function append_to_trajectory!(trajectory, pn) + # Append the current state of the Petri net to the trajectory + push!(trajectory, count_cells(pn)) +end + + + + +##======== simulation ================ +# Initialize Petri net +num_cells = 500 +max_active_time = 5 +pn = init_petri_net(num_cells, max_active_time) + +# Define the time range +tspan = (0.0, 30.0) + +# Initialize trajectory +traj = init_trajectory() + +# Run the simulation +for t in tspan[1]:tspan[2] + update_petri_net(pn) + append_to_trajectory!(traj, pn) +end + +# Visualize the trajectory +plot_trajectory(traj) \ No newline at end of file From c2f1f4a7f38364841a4fe3063fdde4de2b483556 Mon Sep 17 00:00:00 2001 From: danielchen Date: Thu, 30 Nov 2023 21:55:57 -0500 Subject: [PATCH 8/8] added a simulation figure --- .../CellAutomata_heterogeneous_trajectory.png | Bin 0 -> 119637 bytes tutorials/sciml/systembio.jl | 7 ++++++- 2 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 tutorials/sciml/CellAutomata_heterogeneous_trajectory.png diff --git a/tutorials/sciml/CellAutomata_heterogeneous_trajectory.png b/tutorials/sciml/CellAutomata_heterogeneous_trajectory.png new file mode 100644 index 0000000000000000000000000000000000000000..45ce672520df1ec6ef12a39c3bec6c043068d5ab GIT binary patch literal 119637 zcmcG#RZyI5)GgQ{Sb*T}1P|^G!QCAK1Sde_8r*}sySuvw5AN>n?mln6|D2mMH5XGg 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    Cellular Automata Simulation Using Petri Nets

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    version 0.1.0

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    Author
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    Tianchi Chen

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    Introduction

    +

    This document provides a detailed walkthrough of a cellular automata model simulated as a Petri Net. The model represents cells with a dynamical state (active, inactive, dormant) that can change based on the presence of cancer cells. Each cell also has a type (NormalCellA, NormalCellB, CancerCellA, CancerCellB) which is randomly assigned at the start of the simulation. The system evolves over time and the current state of the system is recorded and visualized in a plot.

    +
    +
    +

    Setup

    +

    We start by importing the necessary packages:

    +
    +
    using Revise
+using DifferentialEquations, AlgebraicAgents
+using Plots
+using Random, DataFrames
+Random.seed!(0)
    +
    +
    TaskLocalRNG()
    +
    +
    +
    +
    +

    Defining the Cell Types

    +

    We define an abstract type AbstractCellType and four concrete types NormalCellA, NormalCellB, CancerCellA, and CancerCellB that inherit from AbstractCellType.

    +
    +
    abstract type AbstractCellType end
+struct NormalCellA <: AbstractCellType end
+struct NormalCellB <: AbstractCellType end
+struct CancerCellA <: AbstractCellType end
+struct CancerCellB <: AbstractCellType end
    +
    +
    +
    +

    Defining the Cell and Place

    +

    We define a mutable struct Cell with fields for the cell type, an ID, the dynamical state, and the time in active state. We also define a struct Place that represents a location with a name and a vector of cells (tokens).

    +
    +
    mutable struct Cell
+    cell_type::AbstractCellType
+    id::Int
+    dynamical_state::Symbol
+    time_in_active_state::Int
+end
+
+mutable struct Place
+    name::String
+    tokens::Vector{Cell}
+end
    +
    +
    +
    +

    Defining the Transition

    +

    A Transition represents a transformation from input places to output places. It has fields for the name of the transition, the input places, and the output places.

    +
    +
    struct Transition
+    name::String
+    input::Vector{Tuple{Int, Place, DataType}}
+    output::Vector{Tuple{Int, Place, DataType}}
+end
    +
    +
    +
    +

    Adding and Removing Tokens

    +

    We define two helper functions add_token! and remove_token! to add and remove cells from a place.

    +
    +
    function add_token!(place::Place, token::Cell)
+    push!(place.tokens, token)
+end
+
+function remove_token!(place::Place, token::Cell)
+    deleteat!(place.tokens, findfirst(t -> t == token, place.tokens))
+end
    +
    +
    remove_token! (generic function with 1 method)
    +
    +
    +
    +
    +

    Simulating the Dynamical State

    +

    We define a function simulate_dynamical_state! that updates the dynamical state of a cell based on the presence of cancer cells in the same place.

    +
    +
    function simulate_dynamical_state!(cell::Cell, cancer_cell_present::Bool,
+                                   max_active_time::Int)
+    if cancer_cell_present &&
+       (cell.dynamical_state == :dormant || cell.dynamical_state == :inactive)
+        cell.dynamical_state = :active
+        cell.time_in_active_state = 0
+    elseif cell.dynamical_state == :active
+        cell.time_in_active_state += 1
+        if cell.time_in_active_state >= max_active_time
+            cell.dynamical_state = :dormant
+            cell.time_in_active_state = 0
+        end
+    end
+end
    +
    +
    simulate_dynamical_state! (generic function with 1 method)
    +
    +
    +
    +
    +

    Performing the Transition

    +

    The function perform_transition! checks the feasibility of a transition by confirming if it has the required number of tokens in the input places. If the transition is feasible, it removes the tokens from the input places and adds new tokens to the output places. The function also updates the dynamical state of the tokens based on the presence of cancer cells.

    +
    +
    function perform_transition!(transition::Transition)
+    cancer_cell_exists = false
+    num_tokens = 0
+
+    for (count, place, token_type) in transition.input
+        tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)
+
+        if length(tokens_to_remove) < count
+            return false
+        end
+
+        for token in tokens_to_remove
+            if isa(token.cell_type, CancerCellA) || isa(token.cell_type, CancerCellB)
+                cancer_cell_exists = true
+            end
+            num_tokens += 1
+        end
+    end
+
+    if cancer_cell_exists && num_tokens > 30
+        for (count, place, token_type) in transition.input
+            tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)
+            for token in tokens_to_remove[1:count]
+                remove_token!(place, token)
+            end
+        end
+
+        for (count, place, token_type) in transition.output
+            for _ in 1:count
+                new_cell = Cell(token_type(), rand(Int),
+                                rand([:active, :inactive, :dormant]), max_active_time)
+                simulate_dynamical_state!(new_cell, cancer_cell_exists, max_active_time)
+                add_token!(place, new_cell)
+            end
+        end
+
+        return true
+    end
+
+    return false
+end
    +
    +
    perform_transition! (generic function with 1 method)
    +
    +
    +
    +
    +

    Defining the Petri Net

    +

    We define a Petri Net using the previously defined Place and Transition structures. The Petri Net also includes InputArc and OutputArc structures that connect Places and Transitions.

    +
    +
    struct InputArc
+    source::Place
+    target::Transition
+    cell_type::DataType
+    weight::Int
+end
+
+struct OutputArc
+    source::Transition
+    target::Place
+    cell_type::DataType
+    weight::Int
+end
+
+struct PetriNet
+    places::Vector{Place}
+    transitions::Vector{Transition}
+    input_arcs::Vector{InputArc}
+    output_arcs::Vector{OutputArc}
+end
    +
    +
    +
    +

    Simulation of the Petri Net

    +

    We initialize the Petri Net with a certain number of cells and let the system evolve over a predefined number of time steps. At each time step, we update the dynamical state of each cell, check if each transition can fire, and if so, fire the transition. We also record the state of the system at each time step.

    +
    +
    function can_fire(transition::Transition, places::Vector{Place})
+    for (count, place, token_type) in transition.input
+        tokens_to_remove = filter(t -> isa(t.cell_type, token_type), place.tokens)
+
+        if length(tokens_to_remove) < count
+            return false
+        end
+    end
+
+    return true
+end
+
+function count_cells(pn::PetriNet)
+    cell_counts = Dict{Tuple{DataType, Symbol}, Int}()
+
+    for place in pn.places
+        for cell in place.tokens
+            cell_key = (typeof(cell.cell_type), cell.dynamical_state)
+            cell_counts[cell_key] = get(cell_counts, cell_key, 0) + 1
+        end
+    end
+
+    return cell_counts
+end
    +
    +
    count_cells (generic function with 1 method)
    +
    +
    +
    +
    +

    Visualization of the Petri Net Evolution

    +

    the Plots package to visualize the evolution of the system. At each time step, we create a bar plot of the count of each type of cell in each dynamical state.

    +
    +
    function plot_petri_net(pn::PetriNet)
+    cell_counts = count_cells(pn)
+
+    p = bar([string(key) for key in keys(cell_counts)],
+            [value for value in values(cell_counts)],
+            xlabel = "Cell Type and Dynamical State",
+            ylabel = "Count",
+            title = "Petri Net Simulation",
+            legend = false)
+    display(p)
+end
    +
    +
    plot_petri_net (generic function with 1 method)
    +
    +
    +
    +
    +

    Running the Simulation

    +

    To run the simulation, we initialize the Petri Net and then perform a series of updates in a loop. At each time step, we print out the current state of the Petri Net and generate a plot.

    +
    +
    +

    Initializing the Petri Net

    +

    We initialize the Petri net by creating the places (P1 and P2), the transitions (T1), and setting up the input and output arcs.

    +
    +
    function init_petri_net(num_cells::Int, max_active_time::Int)
+    # Define cell types
+    cell_types = [NormalCellA, NormalCellB, CancerCellA, CancerCellB]
+
+    # Define the places
+    P1 = Place("P1",
+               [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]),
+                     max_active_time) for i in 1:round(Int, 0.5 * num_cells)])
+    P2 = Place("P2",
+               [Cell(rand(cell_types)(), i, rand([:active, :inactive, :dormant]),
+                     max_active_time) for i in 1:round(Int, 0.5 * num_cells)])
+    P3 = Place("P3", [])
+
+    # Define the transitions
+    T1 = Transition("T1", [(3, P1, NormalCellA), (1, P2, CancerCellA)],
+                    [(1, P3, CancerCellA)])
+
+    # Define arcs
+    input_arc1 = InputArc(P1, T1, NormalCellA, 30)
+    input_arc2 = InputArc(P2, T1, CancerCellA, 30)
+    output_arc1 = OutputArc(T1, P3, CancerCellA, 50)
+
+    # Define the Petri net
+    pn = PetriNet([P1, P2, P3], [T1], [input_arc1, input_arc2], [output_arc1])
+
+    return pn
+end
    +
    +
    init_petri_net (generic function with 1 method)
    +
    +
    +
    +
    +

    Updating the Petri Net

    +

    We update the Petri net by checking the state of the cells and performing transitions if possible.

    +
    +
    function update_petri_net(pn::PetriNet)
+    # Iterate through places
+    for place in pn.places
+        # Check if cancer cells are present in the place
+        cancer_cell_present = any(cell -> isa(cell.cell_type, CancerCellA) ||
+                                      isa(cell.cell_type, CancerCellB), place.tokens)
+
+        # Update dynamical states of all cells in the place
+        for cell in place.tokens
+            simulate_dynamical_state!(cell, cancer_cell_present, max_active_time)
+        end
+    end
+
+    # Iterate through transitions
+    for transition in pn.transitions
+        # Check if the transition can be fired
+        if can_fire(transition, pn.places)
+            # Fire the transition and update the system's state
+            perform_transition!(transition)
+        end
+    end
+end
    +
    +
    update_petri_net (generic function with 1 method)
    +
    +
    +
    +
    +

    Cell trajectory visulization

    +
    +
    function plot_trajectory(cell_trajectory::Vector{Dict{Tuple{DataType, Symbol}, Int}})
+    # Initialize empty DataFrame
+    df_traj = DataFrame(cell_type = DataType[], dynamical_state = Symbol[], count = Int[],
+                        time_step = Int[])
+
+    # Iterate over cell_trajectory
+    for (i, dict) in enumerate(cell_trajectory)
+        for (key, value) in dict
+            cell_type, dynamical_state = key
+            push!(df_traj, (cell_type, dynamical_state, value, i))
+        end
+    end
+
+    # Convert cell type to string for plotting
+    df_traj[!, :cell_type] = string.(df_traj[!, :cell_type])
+
+    # Separate data frames based on cell type
+    df_normalA = filter(row -> row[:cell_type] == "NormalCellA", df_traj)
+    df_normalB = filter(row -> row[:cell_type] == "NormalCellB", df_traj)
+    df_cancerA = filter(row -> row[:cell_type] == "CancerCellA", df_traj)
+    df_cancerB = filter(row -> row[:cell_type] == "CancerCellB", df_traj)
+
+    # Initialize plot
+    p = plot()
+
+    # Add lines for each cell type and dynamical state
+    for df_cell in [df_normalA, df_normalB, df_cancerA, df_cancerB]
+        for state in unique(df_cell[!, :dynamical_state])
+            df_state = filter(row -> row[:dynamical_state] == state, df_cell)
+            plot!(p, df_state[!, :time_step], df_state[!, :count],
+                  label = "$(df_state[1, :cell_type]) - $(state)")
+        end
+    end
+
+    # Show plot
+    return p
+end
    +
    +
    plot_trajectory (generic function with 1 method)
    +
    +
    + +
    +
    function init_trajectory()
+    # Initialize an empty array to store the states of the Petri net at each time step
+    trajectory = Dict{Tuple{DataType, Symbol}, Int}[]
+
+    return trajectory
+end
+
+function append_to_trajectory!(trajectory, pn)
+    # Append the current state of the Petri net to the trajectory
+    push!(trajectory, count_cells(pn))
+end
    +
    +
    append_to_trajectory! (generic function with 1 method)
    +
    +
    +

    run a simulation

    +
    +
    # Initialize Petri net
+num_cells = 500
+max_active_time = 5
+pn = init_petri_net(num_cells, max_active_time)
+
+# Define the time range
+tspan = (0.0, 30.0)
+
+# Initialize trajectory
+traj = init_trajectory()
+
+# Run the simulation
+for t in tspan[1]:tspan[2]
+    update_petri_net(pn)
+    append_to_trajectory!(traj, pn)
+end
+
+# Visualize the trajectory
+plot_trajectory(traj)
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +5 + + +10 + + +15 + + +20 + + +25 + + +30 + + + + + + + + + + + + + + + + + +0 + + +20 + + +40 + + +60 + + +80 + + +100 + + +120 + + + + + + + + + + + + + +NormalCellA - dormant + + + +NormalCellA - active + + + +NormalCellB - dormant + + + +NormalCellB - active + + + +CancerCellA - dormant + + + +CancerCellA - active + + + +CancerCellB - dormant + + + +CancerCellB - active + + +
    +
    +
    + +
    + + +