{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05de63af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from numba import jit \n",
    "from numba.experimental import jitclass\n",
    "from numba.typed import List\n",
    "import xarray as xr\n",
    "\n",
    "import source.riversection as sectorg\n",
    "import source.s1driverflow as model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b81c9ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeSect(classsect, gdfsorted):\n",
    "    \n",
    "    def from3Dto2D(pointz, porg=None):\n",
    "        point = pointz[:,:2]\n",
    "        if porg is None : porg = pointz[0]\n",
    "        v = point[-1] - point[0]\n",
    "        e = v/np.linalg.norm(v)\n",
    "        d = point - porg[:2]\n",
    "        L = np.dot(d, e)\n",
    "        Z = pointz[:,2]\n",
    "        return L, Z\n",
    "    \n",
    "    channel = List()\n",
    "    for calc, dist in zip( gdfsorted['calc-input'].values, gdfsorted['distancefromDB'].values ):\n",
    "        \n",
    "        typed_ps = List()\n",
    "        typed_ns = List()\n",
    "        for i, c in enumerate(calc['point-data']):\n",
    "            p3d = np.array( c['coordinates'] )\n",
    "            n   = np.array( c['manning'] )\n",
    "            if len(n) == 1 : n = np.repeat(n, (len(p3d) - 1))\n",
    "            \n",
    "            if i == 0 : porg = p3d[0]\n",
    "                \n",
    "            L, Z = from3Dto2D(p3d, porg)\n",
    "            p = np.c_[L, Z]\n",
    "            \n",
    "            typed_ps.append(p)\n",
    "            typed_ns.append(n)\n",
    "            \n",
    "        channel.append( classsect.section(typed_ps, typed_ns, dist) )\n",
    "        \n",
    "    return channel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df524cf9",
   "metadata": {},
   "source": [
    "#  read section data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1aac9e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fin = '8909090001CalcData.geojson'\n",
    "gdfsect = gpd.read_file(fin)\n",
    "\n",
    "# Since the downstream end is at point 3.000, the data downstream of that point will be deleted.\n",
    "val = gdfsect[gdfsect['name'] == '3.000']['distancefromDB'].values[0]\n",
    "gdfsect = gdfsect[gdfsect['distancefromDB'] >= val]\n",
    "gdfsect = gdfsect.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d07b5de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdfsectsorted = gdfsect.sort_values('distancefromDB', ascending=True)\n",
    "chD2U = makeSect(sectorg, gdfsectsorted)\n",
    "\n",
    "gdfsectsorted = gdfsect.sort_values('distancefromDB', ascending=False)\n",
    "chU2D = makeSect(sectorg, gdfsectsorted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8afe5bc",
   "metadata": {},
   "source": [
    "#  read upstream boundary Q and downstream boundary H data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e3c1d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfhydro = pd.read_csv('hydrologicData.csv',index_col='date',parse_dates=True)\n",
    "\n",
    "dt = float(10)\n",
    "dfhydroip = dfhydro.resample(str(dt) + 'S').interpolate()\n",
    "Qup = dfhydroip['代継橋_時間流量'].values\n",
    "Hdown = dfhydroip['小島_時間水位'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66d15bb7",
   "metadata": {},
   "source": [
    "# set initial condition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef69047a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.41 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# nonuniform flow\n",
    "Qt = np.full(len(chD2U), Qup[0], dtype=np.float64)\n",
    "Huni = model.NonUniformflow(chD2U, Qt, Hdown[0])\n",
    "Auni = np.array( [chD2U[i].H2ABS(hh)[0] for i, hh in enumerate(Huni)] )\n",
    "A0 = Auni[0]\n",
    "\n",
    "# unsteady flow : 20hr\n",
    "Q = np.full_like(Auni, Qup[0])\n",
    "A, H = Auni[::-1], Huni[::-1]\n",
    "for n in range(1, int(3600*20/dt)):\n",
    "    A, Q, H = model.UnSteadyflow(chU2D, A, Q, H, A0, Qup[0], dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdfe2152",
   "metadata": {},
   "source": [
    "# main simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8193b24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 14.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nmax = len(chU2D)\n",
    "ntmax = len(Qup)\n",
    "Amat, Qmat, Hmat = np.zeros((ntmax, nmax)), np.zeros((ntmax, nmax)), np.zeros((ntmax, nmax))\n",
    "\n",
    "Amat[0], Qmat[0], Hmat[0] = A, Q, H\n",
    "\n",
    "for n in range(1, ntmax):\n",
    "    A0, _, _ = chU2D[-1].H2ABS(Hdown[n])\n",
    "    A, Q, H = model.UnSteadyflow(chU2D, A, Q, H, A0, Qup[n], dt)\n",
    "    Amat[n], Qmat[n], Hmat[n] = A, Q, H"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4639b5",
   "metadata": {},
   "source": [
    "# export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d55bfae",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdfsectsorted = gdfsect.sort_values('distancefromDB', ascending=False)\n",
    "\n",
    "ds = xr.Dataset( {'A': (['t','x'], Amat)\n",
    "                ,'Q': (['t','x'], Qmat)\n",
    "                ,'H': (['t','x'], Hmat)\n",
    "                ,'zbmin': (['x'], [c.zbmin() for c in chU2D] )\n",
    "                 }\n",
    "                , coords={'x':gdfsectsorted['distancefromDB'].values\n",
    "                          , 't':dfhydroip.index.values} \n",
    "                ,attrs = {'name':gdfsectsorted['name'].values.tolist()}\n",
    "               )\n",
    "\n",
    "dout = ds.to_netcdf(r'calout.nc')\n",
    "del dout"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 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": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}