{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving all Task / Method Settings\n",
    "This notebook, lists all the settings available for all the tasks available. To change the settings the approach is\n",
    "\n",
    "* find all the methods available for a task using `get_valid_methods` (added in basico 0.77)\n",
    "* retrieve the current settings using `get_task_settings` \n",
    "* change the the task by specifying a different method using `set_task_settings(task, settings={'method': {'name': name}} )`\n",
    "* retrieve the new method parameters using `get_task_settings` again\n",
    "\n",
    "The `print_task_info` function below does that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from basico import *\n",
    "new_model()\n",
    "\n",
    "from IPython.display import display, Markdown\n",
    "import json\n",
    "def print_task_info(task: str):\n",
    "    display(Markdown(f\"### {task}\"))\n",
    "    method_names = get_valid_methods(task)\n",
    "    display(Markdown(f\"**Valid Methods**\"))\n",
    "    for name in method_names:\n",
    "        display(Markdown(f\"- {name}\"))\n",
    "\n",
    "    settings = get_task_settings(task)\n",
    "    display(Markdown(f\"**Problem**\"))\n",
    "    problem = json.dumps(settings['problem'], indent=4)\n",
    "    display(Markdown(f\"```python\\n {problem}\\n```\"))\n",
    "    for name in method_names:\n",
    "        display(Markdown(f\"#### Method: {name}\"))\n",
    "        set_task_settings(task, settings={'method': {'name': name}} )\n",
    "        settings = json.dumps(get_task_settings(task)['method'], indent=4)\n",
    "        display(Markdown(f\"```python\\n {settings}\\n```\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without further ado, here all the tasks and their method settings currently availble: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Steady-State"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Enhanced Newton"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"JacobianRequested\": true,\n",
       "    \"StabilityAnalysisRequested\": true\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Enhanced Newton"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Resolution\": 1e-09,\n",
       "    \"Derivation Factor\": 0.001,\n",
       "    \"Use Newton\": true,\n",
       "    \"Use Integration\": true,\n",
       "    \"Use Back Integration\": false,\n",
       "    \"Accept Negative Concentrations\": false,\n",
       "    \"Iteration Limit\": 50,\n",
       "    \"Maximum duration for forward integration\": 1000000000.0,\n",
       "    \"Maximum duration for backward integration\": 1000000.0,\n",
       "    \"Target Criterion\": \"Distance and Rate\",\n",
       "    \"name\": \"Enhanced Newton\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Time-Course"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Deterministic (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Deterministic (RADAU5)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Stochastic (Gibson + Bruck)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Stochastic (Direct method)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Stochastic (τ-Leap)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Stochastic (Adaptive SSA/τ-Leap)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Hybrid (Runge-Kutta)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Hybrid (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Hybrid (RK-45)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- SDE Solver (RI5)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"AutomaticStepSize\": false,\n",
       "    \"StepNumber\": 100,\n",
       "    \"StepSize\": 0.01,\n",
       "    \"Duration\": 1.0,\n",
       "    \"TimeSeriesRequested\": true,\n",
       "    \"OutputStartTime\": 0.0,\n",
       "    \"Output Event\": false,\n",
       "    \"Start in Steady State\": false,\n",
       "    \"Use Values\": false,\n",
       "    \"Values\": \"\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Deterministic (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Integrate Reduced Model\": false,\n",
       "    \"Relative Tolerance\": 1e-06,\n",
       "    \"Absolute Tolerance\": 1e-12,\n",
       "    \"Max Internal Steps\": 100000,\n",
       "    \"Max Internal Step Size\": 0.0,\n",
       "    \"name\": \"Deterministic (LSODA)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Deterministic (RADAU5)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Integrate Reduced Model\": false,\n",
       "    \"Relative Tolerance\": 0.0001,\n",
       "    \"Absolute Tolerance\": 1e-06,\n",
       "    \"Max Internal Steps\": 1000000000,\n",
       "    \"Initial Step Size\": 0.001,\n",
       "    \"name\": \"Deterministic (RADAU5)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Stochastic (Gibson + Bruck)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Max Internal Steps\": 1000000,\n",
       "    \"Subtype\": 2,\n",
       "    \"Use Random Seed\": false,\n",
       "    \"Random Seed\": 1,\n",
       "    \"name\": \"Stochastic (Gibson + Bruck)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Stochastic (Direct method)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Max Internal Steps\": 1000000,\n",
       "    \"Use Random Seed\": false,\n",
       "    \"Random Seed\": 1,\n",
       "    \"name\": \"Stochastic (Direct method)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Stochastic (τ-Leap)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Epsilon\": 0.001,\n",
       "    \"Max Internal Steps\": 10000,\n",
       "    \"Use Random Seed\": false,\n",
       "    \"Random Seed\": 1,\n",
       "    \"name\": \"Stochastic (\\u03c4-Leap)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Stochastic (Adaptive SSA/τ-Leap)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Epsilon\": 0.03,\n",
       "    \"Max Internal Steps\": 1000000,\n",
       "    \"Use Random Seed\": false,\n",
       "    \"Random Seed\": 1,\n",
       "    \"name\": \"Stochastic (Adaptive SSA/\\u03c4-Leap)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Hybrid (Runge-Kutta)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Max Internal Steps\": 1000000,\n",
       "    \"Lower Limit\": 800.0,\n",
       "    \"Upper Limit\": 1000.0,\n",
       "    \"Partitioning Interval\": 1,\n",
       "    \"Use Random Seed\": false,\n",
       "    \"Random Seed\": 1,\n",
       "    \"Runge Kutta Stepsize\": 0.001,\n",
       "    \"name\": \"Hybrid (Runge-Kutta)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Hybrid (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Max Internal Steps\": 1000000,\n",
       "    \"Lower Limit\": 800.0,\n",
       "    \"Upper Limit\": 1000.0,\n",
       "    \"Partitioning Interval\": 1,\n",
       "    \"Use Random Seed\": false,\n",
       "    \"Random Seed\": 1,\n",
       "    \"Integrate Reduced Model\": false,\n",
       "    \"Relative Tolerance\": 1e-06,\n",
       "    \"Absolute Tolerance\": 1e-12,\n",
       "    \"Max Internal Step Size\": 0.0,\n",
       "    \"name\": \"Hybrid (LSODA)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Hybrid (RK-45)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Max Internal Steps\": 100000,\n",
       "    \"Relative Tolerance\": 1e-06,\n",
       "    \"Absolute Tolerance\": 1e-09,\n",
       "    \"Partitioning Strategy\": \"User specified Partition\",\n",
       "    \"Use Random Seed\": false,\n",
       "    \"Random Seed\": 1,\n",
       "    \"name\": \"Hybrid (RK-45)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: SDE Solver (RI5)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Internal Steps Size\": 0.0001,\n",
       "    \"Max Internal Steps\": 10000,\n",
       "    \"Force Physical Correctness\": true,\n",
       "    \"Absolute Tolerance\": 1e-06,\n",
       "    \"Tolerance for Root Finder\": 1e-06,\n",
       "    \"name\": \"SDE Solver (RI5)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Scan"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Scan Framework"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Subtask\": 1,\n",
       "    \"Subtask Output\": \"subTaskDuring\",\n",
       "    \"Adjust initial conditions\": false,\n",
       "    \"Continue on Error\": false\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Scan Framework"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"Scan Framework\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Elementary Flux Modes"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- EFM Algorithm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: EFM Algorithm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"EFM Algorithm\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Optimization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Current Solution Statistics"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Differential Evolution"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Evolution Strategy (SRES)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Evolutionary Programming"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Genetic Algorithm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Genetic Algorithm SR"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Hooke & Jeeves"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Levenberg - Marquardt"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Nelder - Mead"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Particle Swarm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Praxis"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Random Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Scatter Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Simulated Annealing"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Steepest Descent"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Truncated Newton"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Subtask\": \"CN=Root,Vector=TaskList[Steady-State]\",\n",
       "    \"Maximize\": false,\n",
       "    \"Randomize Start Values\": false,\n",
       "    \"Calculate Statistics\": true\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Current Solution Statistics"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"Current Solution Statistics\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Differential Evolution"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 2000,\n",
       "    \"Population Size\": 10,\n",
       "    \"name\": \"Differential Evolution\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Evolution Strategy (SRES)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"Pf\": 0.475,\n",
       "    \"name\": \"Evolution Strategy (SRES)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Evolutionary Programming"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"name\": \"Evolutionary Programming\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Genetic Algorithm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"name\": \"Genetic Algorithm\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Genetic Algorithm SR"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"Pf\": 0.475,\n",
       "    \"name\": \"Genetic Algorithm SR\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Hooke & Jeeves"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 50,\n",
       "    \"Tolerance\": 1e-05,\n",
       "    \"Rho\": 0.2,\n",
       "    \"name\": \"Hooke & Jeeves\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Levenberg - Marquardt"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 2000,\n",
       "    \"Tolerance\": 1e-06,\n",
       "    \"name\": \"Levenberg - Marquardt\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Nelder - Mead"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 200,\n",
       "    \"Tolerance\": 1e-05,\n",
       "    \"Scale\": 10.0,\n",
       "    \"name\": \"Nelder - Mead\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Particle Swarm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 2000,\n",
       "    \"Swarm Size\": 50,\n",
       "    \"Std. Deviation\": 1e-06,\n",
       "    \"name\": \"Particle Swarm\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Praxis"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Tolerance\": 1e-05,\n",
       "    \"name\": \"Praxis\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Random Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Iterations\": 100000,\n",
       "    \"name\": \"Random Search\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Scatter Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Iterations\": 200,\n",
       "    \"name\": \"Scatter Search\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Simulated Annealing"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Start Temperature\": 1.0,\n",
       "    \"Cooling Factor\": 0.85,\n",
       "    \"Tolerance\": 1e-06,\n",
       "    \"name\": \"Simulated Annealing\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Steepest Descent"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 100,\n",
       "    \"Tolerance\": 1e-06,\n",
       "    \"name\": \"Steepest Descent\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Truncated Newton"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"Truncated Newton\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Parameter Estimation"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Current Solution Statistics"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Differential Evolution"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Evolution Strategy (SRES)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Evolutionary Programming"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Genetic Algorithm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Genetic Algorithm SR"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Hooke & Jeeves"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Levenberg - Marquardt"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- NL2SOL"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Nelder - Mead"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Particle Swarm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Praxis"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Random Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Scatter Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Simulated Annealing"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Steepest Descent"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Truncated Newton"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Maximize\": false,\n",
       "    \"Randomize Start Values\": false,\n",
       "    \"Calculate Statistics\": true,\n",
       "    \"Steady-State\": \"CN=Root,Vector=TaskList[Steady-State]\",\n",
       "    \"Time-Course\": \"CN=Root,Vector=TaskList[Time-Course]\",\n",
       "    \"Create Parameter Sets\": false,\n",
       "    \"Use Time Sens\": false,\n",
       "    \"Time-Sens\": \"\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Current Solution Statistics"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"Current Solution Statistics\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Differential Evolution"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 2000,\n",
       "    \"Population Size\": 10,\n",
       "    \"name\": \"Differential Evolution\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Evolution Strategy (SRES)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"Pf\": 0.475,\n",
       "    \"name\": \"Evolution Strategy (SRES)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Evolutionary Programming"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"name\": \"Evolutionary Programming\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Genetic Algorithm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"name\": \"Genetic Algorithm\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Genetic Algorithm SR"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Generations\": 200,\n",
       "    \"Population Size\": 20,\n",
       "    \"Pf\": 0.475,\n",
       "    \"name\": \"Genetic Algorithm SR\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Hooke & Jeeves"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 50,\n",
       "    \"Tolerance\": 1e-05,\n",
       "    \"Rho\": 0.2,\n",
       "    \"name\": \"Hooke & Jeeves\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Levenberg - Marquardt"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 2000,\n",
       "    \"Tolerance\": 1e-06,\n",
       "    \"name\": \"Levenberg - Marquardt\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: NL2SOL"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 2000,\n",
       "    \"name\": \"NL2SOL\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Nelder - Mead"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 200,\n",
       "    \"Tolerance\": 1e-05,\n",
       "    \"Scale\": 10.0,\n",
       "    \"name\": \"Nelder - Mead\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Particle Swarm"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 2000,\n",
       "    \"Swarm Size\": 50,\n",
       "    \"Std. Deviation\": 1e-06,\n",
       "    \"name\": \"Particle Swarm\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Praxis"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Tolerance\": 1e-05,\n",
       "    \"name\": \"Praxis\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Random Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Iterations\": 100000,\n",
       "    \"name\": \"Random Search\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Scatter Search"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Number of Iterations\": 200,\n",
       "    \"name\": \"Scatter Search\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Simulated Annealing"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Start Temperature\": 1.0,\n",
       "    \"Cooling Factor\": 0.85,\n",
       "    \"Tolerance\": 1e-06,\n",
       "    \"name\": \"Simulated Annealing\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Steepest Descent"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Iteration Limit\": 100,\n",
       "    \"Tolerance\": 1e-06,\n",
       "    \"name\": \"Steepest Descent\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Truncated Newton"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"Truncated Newton\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Metabolic Control Analysis"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- MCA Method (Reder)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: MCA Method (Reder)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Modulation Factor\": 1e-09,\n",
       "    \"Use Reder\": true,\n",
       "    \"Use Smallbone\": true,\n",
       "    \"name\": \"MCA Method (Reder)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Lyapunov Exponents"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Wolf Method"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"ExponentNumber\": 3,\n",
       "    \"DivergenceRequested\": true,\n",
       "    \"TransientTime\": 0.0\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Wolf Method"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Orthonormalization Interval\": 1.0,\n",
       "    \"Overall time\": 1000.0,\n",
       "    \"Relative Tolerance\": 1e-06,\n",
       "    \"Absolute Tolerance\": 1e-12,\n",
       "    \"Max Internal Steps\": 10000,\n",
       "    \"name\": \"Wolf Method\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Time Scale Separation Analysis"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- ILDM (LSODA,Deuflhard)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- ILDM (LSODA,Modified)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- CSP (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"StepNumber\": 100,\n",
       "    \"StepSize\": 0.01,\n",
       "    \"Duration\": 1.0,\n",
       "    \"TimeSeriesRequested\": true,\n",
       "    \"OutputStartTime\": 0.0\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: ILDM (LSODA,Deuflhard)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Deuflhard Tolerance\": 0.0001,\n",
       "    \"name\": \"ILDM (LSODA,Deuflhard)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: ILDM (LSODA,Modified)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Deuflhard Tolerance\": 0.0001,\n",
       "    \"name\": \"ILDM (LSODA,Modified)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: CSP (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Integrate Reduced Model\": true,\n",
       "    \"Ratio of Modes Separation\": 0.9,\n",
       "    \"Maximum Relative Error\": 0.001,\n",
       "    \"Maximum Absolute Error\": 1e-06,\n",
       "    \"Refinement Iterations Number\": 1000,\n",
       "    \"name\": \"CSP (LSODA)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Sensitivities"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Sensitivities Method"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"SubtaskType\": 1\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Sensitivities Method"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Delta factor\": 0.001,\n",
       "    \"Delta minimum\": 1e-12,\n",
       "    \"name\": \"Sensitivities Method\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Moieties"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Householder Reduction"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Householder Reduction"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"Householder Reduction\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Cross Section"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Deterministic (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"AutomaticStepSize\": false,\n",
       "    \"StepNumber\": 100,\n",
       "    \"StepSize\": 0.01,\n",
       "    \"Duration\": 1.0,\n",
       "    \"TimeSeriesRequested\": true,\n",
       "    \"OutputStartTime\": 0.0,\n",
       "    \"Output Event\": false,\n",
       "    \"Start in Steady State\": false,\n",
       "    \"Use Values\": false,\n",
       "    \"Values\": \"\",\n",
       "    \"LimitCrossings\": false,\n",
       "    \"NumCrossingsLimit\": 0,\n",
       "    \"LimitOutTime\": false,\n",
       "    \"LimitOutCrossings\": false,\n",
       "    \"PositiveDirection\": true,\n",
       "    \"NumOutCrossingsLimit\": 0,\n",
       "    \"LimitUntilConvergence\": false,\n",
       "    \"ConvergenceTolerance\": 1e-06,\n",
       "    \"Threshold\": 0.0,\n",
       "    \"DelayOutputUntilConvergence\": false,\n",
       "    \"OutputConvergenceTolerance\": 1e-06,\n",
       "    \"SingleVariable\": \"\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Deterministic (LSODA)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Integrate Reduced Model\": false,\n",
       "    \"Relative Tolerance\": 1e-06,\n",
       "    \"Absolute Tolerance\": 1e-12,\n",
       "    \"Max Internal Steps\": 100000,\n",
       "    \"Max Internal Step Size\": 0.0,\n",
       "    \"name\": \"Deterministic (LSODA)\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Linear Noise Approximation"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Linear Noise Approximation"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: Linear Noise Approximation"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"name\": \"Linear Noise Approximation\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Time-Course Sensitivities"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Valid Methods**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- LSODA Sensitivities"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Problem**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"AutomaticStepSize\": false,\n",
       "    \"StepNumber\": 100,\n",
       "    \"StepSize\": 0.01,\n",
       "    \"Duration\": 1.0,\n",
       "    \"TimeSeriesRequested\": true,\n",
       "    \"OutputStartTime\": 0.0,\n",
       "    \"Output Event\": false,\n",
       "    \"Start in Steady State\": false,\n",
       "    \"Use Values\": false,\n",
       "    \"Values\": \"\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Method: LSODA Sensitivities"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```python\n",
       " {\n",
       "    \"Integrate Reduced Model\": false,\n",
       "    \"Relative Tolerance\": 1e-06,\n",
       "    \"Absolute Tolerance\": 1e-12,\n",
       "    \"Max Internal Steps\": 10000,\n",
       "    \"Max Internal Step Size\": 0.0,\n",
       "    \"name\": \"LSODA Sensitivities\"\n",
       "}\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for task in T.all_task_names():\n",
    "    print_task_info(task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "petab-edit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
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 "nbformat": 4,
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