QUASIP.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: `quasip` Function\n",
    "\n",
    "The `quasip` function simulates a dynamical system based on a set of parameters. It explores different behaviors of the system under various conditions.\n",
    "\n",
    "## Parameters\n",
    "\n",
    "- **Map** (int): Determines the type of system to simulate. Set to `0` for differential equation mode, `1` for map mode.\n",
    "- **alpha**, **beta**, **gamma** (float): Parameters influencing the system's behavior in differential equation mode.\n",
    "- **A**, **B** (float): Amplitude parameters used in the differential equation.\n",
    "- **Omega** (float): Frequency parameter for the system.\n",
    "- **Picture** (int): Determines the type of plot. `1` for a 1D map, `2` for theta_dot vs theta, and `3` for a 2D torus plot.\n",
    "- **k**, **b** (float): Parameters used in map mode. k = coupling constant   \n",
    "\n",
    "## Returns\n",
    "\n",
    "- **Pendulum** (list of tuples): Contains positions (b1, b2) for plotting the pendulum's orientation.\n",
    "- **Circle_map** (list of tuples): Contains positions for plotting the 1D map if `Picture == 1`.\n",
    "- **Theta_dot** (list of tuples): Contains positions for plotting theta_dot vs theta if `Picture == 2`.\n",
    "- **Torus** (list of tuples): Contains line segments for the 2D torus plot if `Picture == 3`.\n",
    "\n",
    "\n",
    "## Example\n",
    "Pendulum, Circle_map, Theta_dot, Torus = `quasip_test` (Map, alpha, beta, gamma, A, B, Omega, Picture, k, b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define parameters\n",
    "from QUASIP_functions import quasip_test, quasip_anim\n",
    "import numpy as np\n",
    "\n",
    "# Define parameters\n",
    "Map = 0\n",
    "alpha = 1\n",
    "beta = 1.576\n",
    "gamma = 1\n",
    "A = 1.4\n",
    "B = 1\n",
    "Omega = 1.76 #0.292 #1.76\n",
    "Picture = 2 # 1 Circle, 2 Theta_dot, 3 Torus\n",
    "k = 0.25\n",
    "b = 0.25\n",
    "\n",
    "# Call the function with the parameters\n",
    "Pendulum, Circle_map, Theta_dot, Torus = quasip_test(Map, alpha, beta, gamma, A, B, Omega, Picture, k, b)\n",
    "\n",
    "quasip_anim(Pendulum, Circle_map, Theta_dot, Torus, Map, Picture)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (opendrift_env)",
   "language": "python",
   "name": "opendrift_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}