Python-DT_Slot_3/e-RK4-Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mv:\\georg\\Documents\\Persönliche Dokumente\\Arbeit\\Kelag\\Coding\\Python\\DT_Slot_3\\Kelag_DT_Slot_3\\e-RK4-Test.ipynb Cell 2'\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/e-RK4-Test.ipynb#ch0000001?line=11'>12</a>\u001b[0m \u001b[39mwhile\u001b[39;00m V\u001b[39m.\u001b[39mlevel \u001b[39m>\u001b[39m \u001b[39m0.005\u001b[39m:\n\u001b[0;32m     <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/e-RK4-Test.ipynb#ch0000001?line=12'>13</a>\u001b[0m     t \u001b[39m=\u001b[39m t\u001b[39m+\u001b[39mV\u001b[39m.\u001b[39mtimestep\n\u001b[1;32m---> <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/e-RK4-Test.ipynb#ch0000001?line=13'>14</a>\u001b[0m     V\u001b[39m.\u001b[39;49me_RK_4()\n\u001b[0;32m     <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/e-RK4-Test.ipynb#ch0000001?line=14'>15</a>\u001b[0m     V\u001b[39m.\u001b[39mlevel \u001b[39m=\u001b[39m V\u001b[39m.\u001b[39mupdate_level(V\u001b[39m.\u001b[39mtimestep)\n\u001b[0;32m     <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/e-RK4-Test.ipynb#ch0000001?line=15'>16</a>\u001b[0m     V\u001b[39m.\u001b[39mset_volume()\n",
      "File \u001b[1;32mv:\\georg\\Documents\\Persönliche Dokumente\\Arbeit\\Kelag\\Coding\\Python\\DT_Slot_3\\Kelag_DT_Slot_3\\Ausgleichsbecken_class_file.py:81\u001b[0m, in \u001b[0;36mAusgleichsbecken_class.e_RK_4\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     <a href='file:///v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Ausgleichsbecken_class_file.py?line=78'>79</a>\u001b[0m Y3 \u001b[39m=\u001b[39m yn \u001b[39m+\u001b[39m dt\u001b[39m/\u001b[39m\u001b[39m2\u001b[39m\u001b[39m*\u001b[39mFODE_function(Y2, h_hs, alpha, p_hs)\n\u001b[0;32m     <a href='file:///v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Ausgleichsbecken_class_file.py?line=79'>80</a>\u001b[0m Y4 \u001b[39m=\u001b[39m yn \u001b[39m+\u001b[39m dt\u001b[39m*\u001b[39mFODE_function(Y3, h_hs, alpha, p_hs)\n\u001b[1;32m---> <a href='file:///v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Ausgleichsbecken_class_file.py?line=80'>81</a>\u001b[0m ynp1 \u001b[39m=\u001b[39m yn \u001b[39m+\u001b[39m dt\u001b[39m/\u001b[39m\u001b[39m6\u001b[39m\u001b[39m*\u001b[39m(FODE_function(Y1, h, alpha, p)\u001b[39m+\u001b[39m\u001b[39m2\u001b[39;49m\u001b[39m*\u001b[39;49mFODE_function(Y2, h_hs, alpha, p_hs)\u001b[39m+\u001b[39m \\\n\u001b[0;32m     <a href='file:///v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Ausgleichsbecken_class_file.py?line=81'>82</a>\u001b[0m     \u001b[39m2\u001b[39m\u001b[39m*\u001b[39mFODE_function(Y3, h_hs, alpha, p_hs)\u001b[39m+\u001b[39m FODE_function(Y4, h, alpha, p))\n\u001b[0;32m     <a href='file:///v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Ausgleichsbecken_class_file.py?line=83'>84</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutflux \u001b[39m=\u001b[39m ynp1\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39marea_outflux\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "V = Ausgleichsbecken_class(1.,0.5,0.,10.,timestep=0.001)\n",
    "V.set_initial_level(5.) \n",
    "V.set_influx(1.)\n",
    "V.set_outflux(0.)\n",
    "\n",
    "V.p0 = 0.\n",
    "\n",
    "outflux_vec = []\n",
    "level_vec   = []\n",
    "\n",
    "t = 0\n",
    "while V.level > 0.005:\n",
    "    t = t+V.timestep\n",
    "    V.e_RK_4()\n",
    "    V.level = V.update_level(V.timestep)\n",
    "    V.set_volume()\n",
    "    outflux_vec.append(V.outflux)\n",
    "    level_vec.append(V.level)\n",
    "    \n",
    "plt.plot(level_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2800a31f4f0>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "V = Ausgleichsbecken_class(1.,0.1,0.,10.,timestep=0.001)\n",
    "\n",
    "V.set_initial_level(5.) \n",
    "V.set_influx(0.75)\n",
    "V.set_outflux(0.)\n",
    "\n",
    "V.p0 = 0.\n",
    "\n",
    "outflux_vec = []\n",
    "level_vec   = []\n",
    "\n",
    "t = 0\n",
    "while V.level > 0.01:\n",
    "    t = t+V.timestep\n",
    "    V.e_RK_4()\n",
    "    V.level = V.update_level(V.timestep)\n",
    "    V.set_volume()\n",
    "    outflux_vec.append(V.outflux)\n",
    "    level_vec.append(V.level)\n",
    "    if t > 100:\n",
    "        break\n",
    "    \n",
    "plt.plot(level_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28008e85880>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "V = Ausgleichsbecken_class(1.,0.9,0.,10.,timestep=0.001)\n",
    "\n",
    "V.set_initial_level(10.) \n",
    "V.set_influx(3.2)\n",
    "V.set_outflux(0.)\n",
    "\n",
    "V.p0 = 0.\n",
    "\n",
    "outflux_vec = []\n",
    "level_vec   = []\n",
    "\n",
    "t = 0\n",
    "while V.level > 0.005:\n",
    "    t = t+V.timestep\n",
    "    V.e_RK_4()\n",
    "    V.level = V.update_level(V.timestep)\n",
    "    V.set_volume()\n",
    "    outflux_vec.append(V.outflux)\n",
    "    level_vec.append(V.level)\n",
    "    \n",
    "plt.plot(level_vec)\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48"
  },
  "kernelspec": {
   "display_name": "Python 3.8.13 ('DT_Slot_3')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
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  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}