advection_solver_easy_run.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02477789-eef0-4fad-aa68-60c6aae055a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final phys time =  1.0\n",
      "END OF SIMULATION\n",
      "time of simulation =  4.953470945358276  secs\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "parameters of simulations\n",
    "Nx1 - число точек в первом измерении\n",
    "Nx2 - число точек во втором измерении\n",
    "setup - модель\n",
    "setup = '2D' - Nx1 = Nx2 = xx\n",
    "setup = '1D', Nx1 = xx, Nx2 = 1 \n",
    "flux_type = 'adv', 'LW'\n",
    "rec_type = 'PCM', 'PLM', 'PPM', 'PPMorig', 'WENO'\n",
    "RK_integr = 'RK1', 'RK2', 'RK3' \n",
    "'''\n",
    "from piastra2D_easy_advection import *\n",
    "Nx1 = 64\n",
    "Nx2 = 1\n",
    "setup = '1D'\n",
    "CFL = 0.4 \n",
    "flux_type = 'adv'\n",
    "rec_type = 'PPM'\n",
    "RK_integr = 'RK3'\n",
    "\n",
    "easy_advection_solver_call(Nx1, Nx2, setup, CFL, flux_type, rec_type, RK_integr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff855a12-70b2-4ea2-ad6a-048e0598988d",
   "metadata": {},
   "outputs": [],
   "source": [
    "''' \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}