{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # only need to import the function you want to use\n",
    "#     # secondary functions, that are called within the imported one, don't need to be importex explicitly\n",
    "# from volume_change import V_von_h\n",
    "\n",
    "# V_von_h(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create in and outflux vectors\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import cos,sin\n",
    "from volume_change import V_h_test_1,h_V_test_1,V_h_test_2,h_V_test_2\n",
    "from flow_patterns import return_flux_profiles,make_flux_df\n",
    "\n",
    "\n",
    "t_max = 1000\n",
    "timestep = 00.1\n",
    "time = np.arange(0,t_max,timestep)\n",
    "#input identifiers\n",
    "i_i = 'st_0010_0010'\n",
    "#output identifiers\n",
    "o_i = 'st_0010_0010'\n",
    "# influx and outflux offset\n",
    "i_o = 7.5\n",
    "o_o = 8.\n",
    "#outflux delay\n",
    "o_d = 5\n",
    "\n",
    "influx, outflux = return_flux_profiles(len(time),i_i,o_i,i_o,o_o,o_d)\n",
    "\n",
    "\n",
    "h_0 = 0.\n",
    "\n",
    "V_t = np.empty_like(time,dtype=float)\n",
    "h_t = np.empty_like(time,dtype=float)\n",
    "delta_Q = np.empty_like(time,dtype=float)\n",
    "delta_V = np.empty_like(time,dtype=float)\n",
    "\n",
    "for i in range(len(time)):\n",
    "    delta_Q[i] = influx[i]-outflux[i]\n",
    "    delta_V[i] = delta_Q[i]*timestep\n",
    "    if i == 0:\n",
    "        V_t[0] = V_h_test_2(h_0)\n",
    "    else:\n",
    "        V_t[i] = V_t[i-1]+delta_V[i]\n",
    "    \n",
    "    h_t[i] = h_V_test_2(V_t[i])\n",
    "\n",
    "df = pd.DataFrame(np.transpose([time,influx,outflux,h_t,V_t]),columns=['time','influx','outflux','h_t','V_t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "from matplotlib import pyplot as plt\n",
    "df.plot(x='time',y='h_t')^\n",
    "\n",
    "fig = plt(df[])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"vscode\"\n",
    "\n",
    "\n",
    "fig1 = make_subplots(3,1,subplot_titles=('Height','Volume','Fluxes'))\n",
    "\n",
    "fig1.add_trace(go.Scatter(x=df['time'],y=df['h_t'],name='height',mode='lines+markers',marker=dict(size=5)),row=1,col=1)\n",
    "fig1.add_trace(go.Scatter(x=df['time'],y=df['V_t'],name='volume',mode='lines+markers',marker=dict(size=5)),row=2,col=1)\n",
    "fig1.add_trace(go.Scatter(x=df['time'],y=df['influx'],name='influx',mode='lines+markers',marker=dict(size=5)),row=3,col=1)\n",
    "fig1.add_trace(go.Scatter(x=df['time'],y=df['outflux'],name='outlfux',mode='lines+markers',marker=dict(size=5)),row=3,col=1)\n",
    "\n",
    "fig1.update_xaxes(title_text = 'time',row=1,col=1)\n",
    "fig1.update_xaxes(title_text = 'time',row=2,col=1)\n",
    "fig1.update_xaxes(title_text = 'time',row=3,col=1)\n",
    "fig1.update_yaxes(title_text = 'h(t)',row=1,col=1)\n",
    "fig1.update_yaxes(title_text = 'V(t)',row=2,col=1)\n",
    "fig1.update_yaxes(title_text = 'Q(t)',row=3,col=1)\n",
    "\n",
    "fig1.update_layout(height=700)\n",
    "\n",
    "fig2 = px.scatter(df,x='time',y='h_t',animation_frame='time')\n",
    "fig2.update_xaxes(range=[0,100])\n",
    "fig2.update_yaxes(range=[0,5])\n",
    "fig1.show()\n",
    "fig2.show('notebook')"
   ]
  }
 ],
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