FiniteVolumeGPU/HIPTestSchemes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:14.302756Z",
     "start_time": "2025-07-07T09:47:12.367247Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot\n",
    "from numpy.ma.core import multiply\n",
    "\n",
    "\n",
    "# Add line profiler\n",
    "%load_ext line_profiler\n",
    "\n",
    "#Import packages we need\n",
    "from matplotlib import animation, rc\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#Lets have matplotlib \"inline\"\n",
    "matplotlib.pyplot.ion()\n",
    "\n",
    "#Set large figure sizes\n",
    "#Note, this prevents nice figures for articles...\n",
    "rc('figure', figsize=(16.0, 12.0))\n",
    "rc('animation', html='html5')\n",
    "\n",
    "import importlib\n",
    "\n",
    "try:\n",
    "    from StringIO import StringIO\n",
    "except ImportError:\n",
    "    from io import StringIO\n",
    "\n",
    "from GPUSimulators import IPythonMagic\n",
    "from GPUSimulators.common import Timer\n",
    "from GPUSimulators.helpers import InitialConditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:14.322375Z",
     "start_time": "2025-07-07T09:47:14.311538Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Console logger using level INFO\n",
      "File logger using level DEBUG to test_schemes.log\n",
      "Python version 3.13.5 | packaged by conda-forge | (main, Jun 16 2025, 08:27:50) [GCC 13.3.0]\n"
     ]
    }
   ],
   "source": [
    "%setup_logging --out=test_schemes.log TestSchemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:14.399887Z",
     "start_time": "2025-07-07T09:47:14.368540Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registering my_context in user workspace\n",
      "Using cache dir /home/smyalygames/Repositories/FiniteVolumeGPU/.fvm_cache/hipcontext\n",
      "HIP Python version 6.4.43483-a187df25c\n",
      "ROCm version 6.4.1\n",
      "Using device 0/1 'AMD Radeon RX 6950 XT (gfx1030)' (3)\n",
      "Autotuning enabled. It may take several minutes to run the code the first time: have patience\n"
     ]
    }
   ],
   "source": [
    "%cuda_context_handler my_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:14.539607Z",
     "start_time": "2025-07-07T09:47:14.415958Z"
    }
   },
   "outputs": [],
   "source": [
    "nx = 256\n",
    "ny = 128\n",
    "g = 9.81\n",
    "\n",
    "h0, hu0, hv0, dx, dy = InitialConditions.bump(nx, ny, 100, 100, 15)\n",
    "\n",
    "# print(my_context.autotuner.get)\n",
    "\n",
    "arguments = {\n",
    "    'context': my_context,\n",
    "    'h0': h0, 'hu0': hu0, 'hv0': hv0,\n",
    "    'nx': nx, 'ny': ny,\n",
    "    'dx': dx, 'dy': dy, \n",
    "    'g': g,\n",
    "    'compile_opts': []\n",
    "}\n",
    "\n",
    "t_end = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:14.548316Z",
     "start_time": "2025-07-07T09:47:14.546565Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot(sim):\n",
    "    h = sim.u0[0].download(sim.stream)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.title(str(sim) + \", t=\" + str(sim.sim_time()) + \", nt=\" + str(sim.sim_steps()))\n",
    "    extent = [0, sim.dx*sim.nx, 0, sim.dy*sim.ny]\n",
    "    plt.imshow(h, vmin=0.49, vmax=0.52, extent=extent)\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:16.967566Z",
     "start_time": "2025-07-07T09:47:14.603022Z"
    }
   },
   "outputs": [
    {
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tRM3hakSKW+3WiRSP+vQ+K+x21HxJf8aYY68WTS4iQWy47jOai/rlXV3Xm5/ouHHVrh8bnnWKI8WtuHGRMO67a0SM6zI9Fw2iygeIFPcjs/V6Vmy41U5ZmT+vtyLFozIVBe7HiuuY8KAdXdZrGCmuynXdYG6sd0E0r/d/rrad3YlF/qVN/PjY44ocAAAAAACARHAiBwAAAAAAIBGcyAEAAAAAAEhE0s/IAQAAAAAA7an8LNoxktp4B8UVOQAAAAAAAIngRA4AAAAAAEAikr61Kpue+TezUHGdGrGXOk3Qis+M4s6C2HAdA6j79CIWdV0/RjEavG7H+1mfovPqlsVeht3UiCaPNpi0IrNi1Y0o9UwVumBAOp7dGHu0r4sH4KKMau9HK5pcxyRGdYsjxfU2CcqNPqNo6yh+vDju21WMFO9bXlAWpXfq8VSNKi+JAg/WLYtO97fRkGLM7fVKYs2L+iht16pb441bK9bcyiau3qXdR0tGnF45tNe1gLQWDW5pqY9aY20aDW6040qiyiv3WTZ/Mtq02mkaGx69rhpR5bViw425lVU36qNGjHkw97Pix43Y8pl1/dhwXdfqw4gY12VWxLiq60eOR3HjOtLbixWPIsW95Y6OFFftZEH8uIoU99bVc1grYrxp3Lhe14oQLyu3IsWjCG/v5zhSfG45Vy9Lx4hP+3V1bLhVpuLH/bE7PafVdf22orlfxTKt6pxoaj6+aMYY8eNjjytyAAAAAADAovLpT39a1q5dK5OTk7J+/Xq5++67K6333e9+VyYmJuSCCy4Ifj81NSU33nijnHPOOTI5OSnPfe5z5Y477gjqbNu2TbIsC/6tXr269tg5kQMAAAAAABaN2267TTZv3izXXXed7N27V174whfK5ZdfLvv37zfXO3LkiFx99dXyspe9LCq7/vrr5bOf/ax88pOflPvvv182btwor33ta2Xv3r1Bvec85zly8ODB2X/33Xdf7fFzIgcAAAAAACwaO3bskLe97W3y9re/XZ797GfLxz/+cTnjjDPk5ptvNtd75zvfKVdeeaVcfPHFUdktt9wiH/zgB+WKK66Qs88+W971rnfJZZddJh/72MeCehMTE7J69erZf6eddlrt8XMiBwAAAAAAzHCJ/hORo0ePBv+OHz8evbwTJ07Inj17ZMOGDcHvN2zYIPfee2/hZtm1a5f8/Oc/lxtuuKFv+fHjx2VycjL43fLly+Wee+4Jfvfggw/KmjVrZO3atfKGN7xBHnroocI+i3AiBwAAAAAAJO+MM86QlStXzv7bvn17VOfRRx+VXq8nq1atCn6/atUqOXToUN92H3zwQdm6dav8y7/8i0xM9M+Muuyyy2THjh3y4IMPSp7nsnv3brn99tvl4MGDs3Uuuugi+cIXviD/8R//IZ/73Ofk0KFDcskll8hjjz1W63UmnVoFAAAAAAAgIvLII4/IySefPLu8bNmywrqZSkB0zkW/ExHp9Xpy5ZVXyoc+9CF55jOfWdjeJz7xCXnHO94h5513nmRZJuecc4685S1vkV27ds3Wufzyy2d/Pv/88+Xiiy+Wc845Rz7/+c/Lli1bKr1GkcRP5GS9TDq9eEPbkZhGTLhetsr0tUxWfLSKQovioztZUdVwuFF8po589Cr01PiC8RhlYseEVo4m77Nu2E6zeDi9nt2njqAsLIrT6oIM0RrR5Dr+0N+3TaPJRcJ9pI81I0Y8Op7y4sjHTMWWBpHnRjR4psZjRpXXKGsc923Elut1S2O5veNgsHb8usXHU+nbomrdWnHj0W8qrVc6hrIxtdFmwz7KxzDi+MrFlZ45ZwRJr6XR3EUarlYaIV6x3Trt1OqzTvx5jYjxYDsP1I5RViNS3FrXep212mkYKR6NwWjHijgXqRExrl+zESmu48ebRoxHZdGyET9uRIHrOHJ/atxVZZnRzrA+jvxo8GgOa9S1IsWjMrXsR4z38uKyKMa8YcS47j/Xf6v5c1GjbGbZ+1lvr2AOaawnEm4wY7sHh8EUN66k6uSTTw5O5PRz6qmnSrfbja6+OXz4cHSVjojI448/Lj/60Y9k7969smnTJhERyfNcnHMyMTEhd955p7z0pS+V0047Tb72ta/JsWPH5LHHHpM1a9bI1q1bZe3atYVjOemkk+T888+XBx98sNbr5AgFAAAAAAAiMvOfWin+q2rp0qWyfv162b17d/D73bt3yyWXXBLVP/nkk+W+++6Tffv2zf7buHGjPOtZz5J9+/bJRRddFNSfnJyUpz/96TI9PS1f/vKX5dWvfnXhWI4fPy4//elP5WlPe1rl8YskfkUOAAAAAABAHVu2bJGrrrpKLrzwQrn44otl586dsn//ftm4caOIiFx77bVy4MAB+cIXviCdTkfWrVsXrH/66afL5ORk8Pvvf//7cuDAAbngggvkwIEDsm3bNsnzXN7//vfP1nnf+94nr3rVq+TMM8+Uw4cPy4c//GE5evSovPnNb641fk7kAAAAAACAReP1r3+9PPbYY3LjjTfKwYMHZd26dfKNb3xDzjrrLBEROXjwoOzfv79Wm8eOHZPrr79eHnroIXnyk58sV1xxhdxyyy3ylKc8ZbbOL37xC3njG98ojz76qJx22mnyx3/8x/K9731vtt+qMudG/TCA+o4ePSorV66Uc669Sboq3kuk3jNyzHu+rXuz9b3Gup/gPmn7uTzWPdVi3MMcPyNHD6JaO/p+53AbGM+ZKXuYxRCekaOZ9xebzxDR9xMb5dbzRkrvyTXG1/DeXv1Mmuh5MblV5q2rnyVjjs8oK3lGTuV2xvIZOcNoZ9yfkVNtvdJ1a7bVqM2GfZSPgWfkjATPyOEZOaXtGGU8I4dn5MjwnpFj9VnUZhmekTPez8jJjx2T/R+4Xo4cOVL6vJWF5Im/s//P+mtlYiL+O3ucTU8fk2/v2b5o9hnPyAEAAAAAAEgEJ3IAAAAAAAASwYkcAAAAAACARCT9sOMsF8l6/QqslTJrUd1/bTw7Rt8Ca/Wp78XUdf3nMRjPatHPxInuB/Wb0c+98e851a9LtxPcN50VV9UvK9oGxfcJO2OD1bm/WNcN7ss1bz3W90LrF+Pdg24+P8d+/lF4T64eg7dvdaE6xeo/hybat3rRqxvt6twZZeq94d/rr5870+3fZr92w+f76LLMKFN9Vn0OTp3nuljPF1LltZ6Roxn3Zld+9o/VZr/loB2jsOnzcwZdt2I7Q1lPysbX0sNaFuuzboZhSM/PKX1+TJE669Woaz/jr531zOcCWc+HqfNsG6vdkmfbWPMMazzR82KK+u9X13gOTrCs5xxWXetZitbzasraMZ+1Y8wl9IeeX6aeiaOfX+PPC6Nn4jR8Do6ev1nPyOlGz2ucW+7q8eh2M6NPv0yksEzTz5LpBHPGzKxbtUzEfg6O/2yb+Jk4zZ6D4z8DR5eJ1HgOjlrPfA6OLovmZcXPdhRjvtt4nujPS6dG8OC2ceIk3ubjbpHNu7giBwAAAAAAIBGcyAEAAAAAAEgEJ3IAAAAAAAASkfQzcgAAAAAAQHsy5+xnLI6h1MY7KK7IAQAAAAAASAQncgAAAAAAABKR9K1VWW8ufrxy6nRJfGYYkVkcVe7qxE4bMZczdf2GdZnrX69PO/6YdDS5MyLF6429YjS5aretaHJNR0lmRgRk0dhmlosjvRtHk+sKVmR9SZS0mVCpIxb92HCj3ShuXFX2I8ejzRNEimeFZbrPOMK7OA7djAavEeFtRnrXiB+3lI694vjqxH3XiyYvPoBqtVNn3Zpt1a4nLV5CO09X4tbaXgtQ46jvutpKj7diupv2WVJvPuLHm8aGl8WPV61behxU7bNOpHhUt3gOV6edKP7bLzcixeM+iqPBG0eK6zFYseF6mmPU1bHcTSPG/XjxPkMI+tF1O8H8sjhSXJdHZVLMihjXpvPq/y9uRYpb0eU6UtwFZSo2vGHEuB8v3m88/rzQ6bpG/LgVMZ5F0eThohU/bs4La8znCo+DXvE6i4KTPpP6MZfYcAfFFTkAAAAAAACJ4EQOAAAAAABAIjiRAwAAAAAAkIikn5EDAAAAAABa5FyCz8hJbLwD4oocAAAAAACARHAiBwAAAAAAIBFp31rlZDZmrHKsa43YSyvaUsd765RSVzV2WiQ8nWbFBpdGnmfFVY1IcRfFV3oN6+i9IPZSlel2vI3SVjR5HLneLKq8dL3gODCiya3odim5wq9GZLZZ10g8t/rU0YxmVLlxXGZ58fYRCTef3U5x/yLqZeqGzIjz4uW4brNY84H6tNYzlPZp1a3a5yDx44O8FktQt0Y89JCutl3skeKDGFocedN26xxOdfpoKza8aplud74iz635kzUes25xhHdpjHrT6PSG0eTRulaMudHHzLLRjhUpHs0vvShwo66O8M50NLhXtxPFjVt1iyPGrZhwEZGuFWNufPDWqVuHH+lttVgWKe6LY8PD8qoR47mK+7Yixp1R1+l5oY4Y99e1YsNVH1kUR26UGRHjVt2yOZE5nytYL5sy6i0GuTT/Ph0V/bfEAscVOQAAAAAAAIngRA4AAAAAAEAiOJEDAAAAAACQiLSfkQMAAAAAAFqTOSdZYnHeqY13UFyRAwAAAAAAkAhO5AAAAAAAACQi6VursrxPZHHfit7POtpPxaoF8XRWtLQRTT7Tjp8zqeuqyEUjGjxcUQ9WlxuR50E0uY581C/G+1FHZPrRf0bceDQ+I+I8ugpONxPmTovJikr1o65rxFFG0ZFmVKuxb6OG/T7KBlHwc9myFSleFilotGNHiqvFoB0d7+361yvp0zq+a0WBiy4bINbcKMvMbVC9nfmOGC99m7R0BWvz+PF56rOqxXVFb3/zFFXaOLp8WBHjLfXfVhx5rXasmO6gnooCH1b8uFFmR4pXjyo3Y8MbRpNHy9G8p2KZKtex4X4fVqT4zLLfTnFMeBTZHbXrKpWp4ZkR43FMePHYy6LKw3ZqzO8q14xjxYN2/CjwkvhxP2I8igmP6la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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators.model.lxf import LxF\n",
    "\n",
    "with Timer(\"construct\") as t:\n",
    "    sim = LxF(**arguments)\n",
    "\n",
    "with Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:17.117251Z",
     "start_time": "2025-07-07T09:47:16.986696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators.model import Force\n",
    "\n",
    "with Timer(\"construct\") as t:\n",
    "    sim = Force(**arguments)\n",
    "\n",
    "with Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:17.275746Z",
     "start_time": "2025-07-07T09:47:17.134142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators.model import HLL\n",
    "\n",
    "with Timer(\"construct\") as t:\n",
    "    sim = HLL(**arguments)\n",
    "\n",
    "with Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:17.456667Z",
     "start_time": "2025-07-07T09:47:17.290729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators.model import HLL2\n",
    "\n",
    "with Timer(\"construct\") as t:\n",
    "    sim = HLL2(**arguments)\n",
    "\n",
    "with Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:17.635076Z",
     "start_time": "2025-07-07T09:47:17.473962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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giRjzOJq8eQ4+EtrEobtNEPLmbZDnzds59/HQLolbd/Zx435+Omo6y3zetxrStaNPI5oYXDdOpR5n8M9uahxPjZvMqxYJOqLSR8eOVKR4x8WoJuLIg4sm93Hkme7r4scz9VyHwm0Dvx+oZYN/rvVOPWa/TEWVp/b9iaLKtURs+dIcEsNGEeOJzmuNMZ9gnM1IAt/ycd8n2nHaHMcrNnzN61hrhLjn3w5Sc11rpLiI2QYTRYr7OHIdDe7XqWqZixTXtaju379VLXpvT0WMD+xBNRkx7saRqvk4utWZ42Hhjne93sr9RKJIcRM57iPFo4jxrqrZdQYdKR7Fj9vjZqnalY8Y1/HjPm7ctXU0eLomtub+vNCx4lE0uam5/du1M9X2ceO9vt33+t16v9Vx4yIiO3p1xPcOFze+07Vni3p/13HjIiK7Tfx4c9y4iMguVZ/J3DpU5LiPG++7c9FCvZn4t6O5YF+r3VDvJ93E3zI4Dogf3/L4RA4AAAAAAEBLcCEHAAAAAACgJbiQAwAAAAAA0BKt/o0cAAAAAACwsbZbnHfb8IkcAAAAAACAluBCDgAAAAAAQEu0+qtV+xd2SbdYygD0Ed9aKhp8Ehs1ThxHrmL43Dp6KsIvFxu7F42jMtd83Lge1S+XjdYY5+eTiX17mDfW9LJxpLiP167nm6cil916/DpNInRiHSI2Ijp6XGYcW4vbahuM/POuY6ddzLRr57kKaPSR4lFEtZ57YlxfiyJh6/VEr65M9R2XzKrnnooiT8aUi0il4ix9LHZZryOUbkI+clVFjEeRq4WLDS9VrLmLFNcx5j5uXBJx5JmLMdf7WpY310Rk1VHlmYu6T0WVRzH0uu+4aOZJ3hJX+d8G/vWW/FhvKtI8GniCrpsQVX6yOG4fw15jlPu65nOcYsTtOKuPFE/2XWukuFtPHFWuY8P9con4cXc8SdV8xHhW6uOLez9XMeLBx4+PXPy4ihgPvnayRIzn7vjmj3cqGjxzkeKijn8mXlwkih83EeNR3LiPI1cR4z0fMa5r9qBQde0bb9mv66WvqRhxHxMexYirvqmIcR8pXvZDoq87xuqIcRc3nvdc9LaKHO+7uPEpFzE+reLHZ7oDU9OR4z5ufGfHxojvKOr6bhc/riPHU3HjIjZyXMeNi4jMZvVcfdx4L/NvULXSva0suDe+oqrXM5PbJ7ufuScfGyuENR97N03b5rtOfCIHAAAAAACgJbiQAwAAAAAA0BJcyAEAAAAAAGiJVv9GDgAAAAAA2DhZaF/8eNvmu158IgcAAAAAAKAluJADAAAAAADQEq3+atUD87PSyftj++Umvro5sltEJEv1TdQ6Lmov1Tc1v5RO7tYRmuP8stTcy3TcuF7Wj2NizV3Nx4hXqq6jyEVEgr6GmEhYXlqnusPHKrtLkVE8uSnqQV3Nx5Gr9cTx483ZzT4uWo+Tu7nr7Rc9DhdRreO2oyRp33ek+/qY9eZaFAlr+tp9L0SZ8bro42vVg8vdOsZFjjeJllPz86+L0rbNc+Rjb138uIly7bjXjd6nfS3346gY+o6rqb5RXPwkUeXmdTImwzvxOsnMvt8cW+6bUV8vEQkZml9SMf3Y1hEzmYoY37CP5k4Sj77VnIAIzw3bzhO8j2xUbHg87upixJP9RGyMeKLvRkWKi7hY8VSkuB+n9OOoyftocFXLotjw5hhxHxtuaiqKXCSOFA8jVW9zJG10XKiPN1nHnca7GHETOe76mshxHyne9ePW7SqKG3cx4r1U/HjdN44bd21V13HjS8tKcy2KH9djNtd8pHjVdW0VRx5cTUeO+7jxbs/ulz3V9nHjM92ha9eR4zuiiHEVBd6xtV0ufnynihHfXczZdagYcR837uPIZ7N6PlOZm3tet7uTvHk6Q7foUfWmuFDaxzllIs/tfunbRcZnF3DyafWFHAAAAAAAsIGCTPQfGltC2+a7TlyeBAAAAAAAaAku5AAAAAAAALQEX60CAAAAAAAiIpJVS//apG3zXS8+kQMAAAAAANASXMgBAAAAAABoCS7kAAAAAAAAtESrfyPnR3MzUkhfREQydX+e2eyxTLWL3NaKvGpsd5O10k6msM2O1HU/H99ea60KmesxUn3thPSy4+aTqWVzt4rM9LPLjfx0svo6YeVqQV1CDFnmaradqXHUQ/zxfPwE1ZzcOGGo19k8HxGRXC3ra6L2IT9Xv3+FxDi6ned+As3j5H77jOx+muv91m1b0esp3DpHdp/O9LKub6b7+uev9F9QVX0r/zgTX2atml8LISTyBcvm17+fjrjXcRi5+enHPbKvqUxty6xwbwB+2+q6r5lxbC24/SIrslXVoufE7+96Pf51q1+sftv5/UmX/D6b6Bu9bFe7nIjdL8Z0tfNJTmHt/JukWek2ycFMvFYnkTj8pU2wXDbJc5LomhzHv60l+kaPWff1Lz9dc9s8PY7r694jTd2/f1bqwfj39tIdM3TdHU9kVB+8Q6K2VFft4bCxFtxyJ5Oso07P3fFF17Jezy7Ysaf1Wa9bN7q2FnQ7qhWN7apna3Fbnft17ftjmayJbat65Wp6Wb9c3FevIzTWKlcLUbvev7OeO+/q1ft0t2v3717X7qfTvXqfnu7a/Xu2OzDtHd3F5ds7O4u2r2rv7szb5YoF096Z1+2dUa1edjZ368jsfGbyer5TmX2cXfWGVUxwgPVvR0N3dF7wf0Aolelr51NGb8TN4r+nljy8uM1+cMUjfnzL4xM5AAAAAAAALcGFHAAAAAAAgJZo9VerAAAAAADAxsnCOr7yvEnaNt/14hM5AAAAAAAALcGFHAAAAAAAgJbgQg4AAAAAAEBLtPo3cuaP9SUPU0sN9aW4OAW7jo8rChc/XjRHjPc6Nsqu31ERgm7LRZHeKoq3ctGfUd81ZqVF46jHlld+HTpSPL1+Hdc8TEa52+uAuYsfHqm45tKNU+rtk/kYbB/BXD8nlY/ejqLBdTyzf05W7rfUVxrbPhlcTBR4c0y4iE2ajteh5ur2yzB0213Vo6h0n/Ooxs1czUSVD/22TERf+5hZHRHtY7AzFyWroyPdOCYT2kcY+2hyVc8SEdU+mnx9UeVqvrl7XHrnS8WNywRR5eNizFcbVe6fdx9HXiQi6lU7GVsuIonkz3idmSlKEx9j7k0Sa77K0vjI85Q1xqG32nH6DvpGRYNPNGYqXXaS2PDUcnoX8bVUjHg0jo4fd/Opqsa+Udy476tjw10tGSnu+po4cB8NPmyODQ9D19bvu5Vb58kid8cIF/9tIsZdTUyta2s911bHlOAjxvsdVWuOGxcRqUz8uD0OlK5d9VRsuI8Y7yZqydhw21dHjFfuIcfx4yHRV9X67nXRda8FFTnecRHj3V69D/cTceMiIjMqcny24+K9XXtnt44K39WxseE7ChVNXviabe9SEeMzLmJ8l4om9zUfMa7bXfcmvNbIcf8WXLoTj7lQ76fHgn1y56p+XVO3RUQWgn2ydX3R7QjDoP5eUQfyhbmRiNwfP4DtIoTkcXBLatt814lP5AAAAAAAALQEF3IAAAAAAABaggs5AAAAAAAALdHq38gBAAAAAAAbJwtjfg9uC2rbfNeLT+QAAAAAAAC0BBdyAAAAAAAAWqLVX60qj3UkVD9+CDqtzsfXqstVo44Nustdu6MixyufvZvgI71tza4jGlcnObs4v9S4EdW1l9vIQD1OXuWNNREbVRpFleto8ih2ujnKOfd9TTS5KUnlI71VHHkUE+zGLROx4SEZTd4cRx7Fhhe65mKeXXq03hcrl82o00d9qnMcMW4m6+bjtpdaTz7yEePZirdFRDL3WshUPHkWRcKrGGwfSVv451pHeLtxdHxtKpp8qYM08tHlazRJVLkOzcz8TrzGqHIf2a0jaKNl/Q6+2ppbT4j6Ziv2ExEJ7r3M5I2m4sbF7m8TRYj7baLe6JJv0eMixdXDzibI015XVHnKZkeXb4VIcS0VC+6tNSZ8zLLJ2HBPvQdF69TLRvHjtmlixEPzsTkap3QDVfr9qbkWLeujwavmmvjYcFUP7v3cRIyfrJHiXiJi3L+3JyPG/XFAR477SPFEO44Yr98Eq15zTcRGjFeuVrlocBsxLs21KCa8OY486tvRNftaSMaPu75B1aTn/j7o2f1UR473enbfn1KR49NdHzfeHDG+o+uiwDu2PdtZXcT4ztzWdhbzpj2rYsV936lMxaFn7nG5+PHucThODdwJr44bFxE5Wk0t3z5U7jA13X5guNPUHhzNmPaRUT3OsZGNKh9U9f4/Un8jDY/Z527bCXLczg2Om7bNd534RA4AAAAAAEBLcCEHAAAAAACgJbiQAwAAAAAA0BKt/o0cAAAAAACwcYgf3/r4RA4AAAAAAEBLcCEHAAAAAACgJVr91ap8vpD8kSxoFYnno6VFx0W7GN6ya+MGg4pRDBPEj0dU5GIcIW7j/Tp5leirx0nnseo06crF+XVUxmnu4qpzF92sI9D9fDpqDkVl4yp930I9D0OXy52Z1GK7XOn66jjyysdX+0uRaiNUPl47X7nf0jhuGxSpvs3rz6P4cRXlHMWE63Gao8mX2ipS3D9/IzfuUMUzu8jzTO3/+dDFjbuIWh0ZHwrXt6Me+MhN1sWRZ/o5K10Mtl5nKppcxO40Pk5XvYbGvEzSEeMpfj56zOgOv2Mmosqljv70+4GOJhcRu2NE+7eOOHfL+chz09fV9HZ2z0mW6Bvtaz6mW/f1tXzlfiu19dty5reX6Tfm/XuN0eU+qnw9h4m1rH9DrfW1kLCujzWvMgo8XmeiWKUnZObrx0mOm5iDf6+oGvqt2Ld5HPN+mYoQ93UfBe5jxNWyca1eNvi4cTfutogVz/wx1b/X1u1kxLivTRIx3imaa1HEeN32EeOViR93keIuZ1q3y0RNRKRU59GTxY83t5OR4n65KI5cvRa6tpapiPHC/T2g48ZFRHo6YrxnI8Z15PhsIm5cRGSnihSf7fj4cR8jXrd3uPjxnXkdMT6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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators.model import KP07\n",
    "\n",
    "with Timer(\"construct\") as t:\n",
    "    sim = KP07(**arguments)\n",
    "\n",
    "with Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:47:17.817515Z",
     "start_time": "2025-07-07T09:47:17.648272Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X/t73vlduv/12+cQnPiGvfOUrZc2aNXL11VfL//pf/0vuvvvu0b6Ptze+8Y1y//3399RGu/cqUvw96+SWoW6+v5044ogjZOuttx6dnjlzZvAdi1myZIm8//3vb1tPL8P6/e9/L8cdd5zsvPPO8rnPfc4r27g9n3zyyXLllVeO9nnWrFly/PHHy6233jp6ZdWpp54avLcjjjhCFi5cKFdddZV88pOf7Og9AQAwFXEiBwD64OKLL5ZXvOIVcumll0qe53LLLbcUnsz53ve+J9tss43U63V5yUteIjvssINXvsMOOwS3GomI/Pd//7eIjJwgGcvnP/95EZHSt1Vt9Oijj8rg4GBh+xttscUWwYmigYGBMecbGBjwIor/z//5P/KSl7xEvvrVr8qVV14p06ZNk6OPPlquvvpq2WOPPUbr1Wq1YL1sPAE21rqZaCtXrpSjjjpK9thjD7njjjtkcHCw7TzbbrutHHfccfKZz3xGXnjhBZk+ffroe9x77729EyFJksjRRx8tV1xxhaxdu9Z7vsuxxx4rM2fOlD/+8Y9yzjnnRE8admrDhg2jyVn/+q//KgcddJBXfuedd8rnP/95+drXviYnnXTS6O8XLVokc+fOlSVLlozeDjXett9+++BZPWW0e68bP5OnnnrKS7ISGfkOtvt+7LDDDrJ+/XpZt26dbLHFFsH8CxYs6Lrv9jshIjI4OCgvvPBCR/Pvsssu8pKXvKRtvaKTTY8++qgcccQRUqvV5N/+7d+CdbGxfxtPVG509NFHS5Ik8rOf/Sy63L/4i7+QPffcU+677762fQQAYCrjGTkA0CeXXHKJfPjDH5bbbrtNTjnllMJndLz85S+XAw88UF7+8peP+YfZ/vvvL7/61a+C+R988EERCeOrRUSGhobkS1/6kixYsKCruObHH39cHnjggdGHNY+XLbfcUi655BJ5+OGHZc2aNXL99dfLfffdJ2984xu9eo1GIzhhs2bNGhEZ+4/ZibRy5Up53eteJ7vuuqt897vfLXVSYeNVTxv/UN5tt92CP/ZtXfvg4jPPPFOeffZZmT9/vpx99tny9NNPd/M2Rm3YsEGOP/54ufvuu+Ub3/iGHHnkkUGdn//85yIiow8h3mjbbbeV3XffvafnvpR10003Sb1e7+jH6uS9bny2zcbv20aNRkMefvjhMb9/ncy/Zs0aefLJJ9vOP57+5m/+pqP1NtZ6efTRR+Xwww8X55zcfffdY54Qss8Vsjp5CLdzjghyAADa4IocAOijJUuWSJqm8uEPf1icc3LrrbeWPjFywgknyGc/+1lZtmyZ93Dfm266SebMmRNcQSAi8i//8i/y5JNPyqWXXlq6zy+88IKcccYZ0mg05Lzzzis9f7dmzpwpp59+uvziF7+QpUuXBlcwfPnLX5azzz57dPrWW28VEZHDDz+81HI2Xi3T6VULMT//+c/lda97nbzkJS+Ru+66S7bbbruO53366aflX//1X+UVr3jF6BVNtVpN3vzmN8s///M/y+9//3uZO3euiIz8MXvnnXfKbrvtJjvuuONoG5/73OfklltukS984Qty2GGHyStf+Up55zvf2XUS0sarU77//e/L17/+9eBKio3mzJkjIiOJTvrKoaeeekp+85vfjPmHfy9in1m3t1Z1+l4POuggmT17ttx4443e9++f//mf5bnnnpMTTzwxupzXv/71Mm3aNLnxxhu97+qNN94oSZLI8ccfX7rvZcTWXbe3Vq1atUoOP/xwaTabsnz58sLb6E444QS58MIL5dvf/raccMIJo7//9re/Lc65tsls9913nzzyyCPe9x4AAIQ4kQMAffahD31I0jSViy++WJxz8pWvfKXUyZxjjjlGjjrqKHnve98rzzzzjOy+++7yla98Re68887CW7Y+//nPy/Tp0+WUU06Jtr1q1Sq57777JM9z+dOf/iQrV66UL3zhC/Loo4/Kxz/+cVm0aFHp91vGQQcdJMcdd5y87GUvk+22205+9atfyZe+9CVZuHChdxJnYGBAPv7xj8tzzz0nr3rVq2TFihXy0Y9+VI455hh5zWteU2qZG6+Q+OQnPymnnXaa1Ot12WuvvaLPARnLr3/9a3nd614nIiKXXXaZPPLII/LII4+Mlu+2226jyWOnnHKK7LLLLnLggQfKjjvuKI888oh8/OMflz/+8Y+j0egbfeQjH5Fvf/vb8vrXv16WLFkiM2bMkM997nPyi1/8Qv7pn/5ptN6DDz4oZ599tpx22mmj0daf//zn5aSTTpKlS5fK4sWLR+v+4Ac/kP/6r/8SkZEEoUcffVT++Z//WUREDjvssNF+nnTSSfLtb39bLrzwQtlhhx28W1pmzJgh++67r4iInHjiifKhD31I3vve98of/vAHeeUrXymrV6+Wq6++WtatWycf+MAHOlqHN954o7zzne+UL37xi3L66acX1tttt91k+vTp8uUvf1n22Wcf2WqrrWTOnDkyZ84c2WGHHbq6KqvT95plmVx11VVy6qmnynve8x55+9vfLo888oicd955ctRRR8nrX//60fl+8IMfyJFHHikf+tCH5EMf+pCIjNz6ddFFF8nFF18s22+/vSxatEjuv/9+WbJkiZxxxhmjyxEZed7MvHnz5LTTTgu2i27Ftve5c+eOnizs1Nq1a+WII46Q1atXy+c//3lZu3atrF27drT8JS95yejVOXvvvbe8733vk+uuu0623nprOeaYY+Q3v/mNXHTRRXLAAQfIW9/61tH5Xv7yl8tf//Vfyz777CPTpk2Tn/zkJ3L11VfLrFmzJvSEMgAAlbTJHrMMAJuBWDLPxvSbE0880Q0NDY2mVv3Xf/1X23afffZZd/bZZ7tZs2a5gYEB97KXvcx95StfGbPuqlWrXJqm7h3veEdhexvTnDb+ZFnmtttuO7dgwQK3ePFi99BDDwXzFKVWbbnllkHdww47zM2fPz/4/a677uqOPfbY0enzzz/fHXjggW677bZzg4OD7qUvfan74Ac/6J588slgGf/+7//uDj/8cDd9+nS3/fbbu/e+973uueeeC9pvl1rlnHMXXHCBmzNnjkvTNHhPY72XsVJ72qUl6WVeccUV7hWveIXbZpttXJZlbqeddnInnHCC+8lPfjLmMh988EF37LHHuq233tpNmzbNHXzwwe7//t//O1r+3HPPub333tvtu+++XvKPc869733vc/V63f34xz/23kNRP/V7j70fuw5Wr17t3v/+97vdd9/dTZs2zc2ZM8cde+yx7t577x3zPY313fjUpz7lRMRL7iryla98xe29996uXq87EXEf/vCH284TU+a9Oufcrbfe6l72spe5gYEBN2vWLHf22WcHSV4bvyNj9e2Tn/yk23PPPd3AwIDbZZdd3Ic//GE3NDTk1XnwwQediLjzzz+/o/6/733vC35vvwPOldve29n4Hot+7HtvNBruYx/7mNt9991dvV53s2fPdu9973vd008/7dV729ve5nbffXe35ZZbunq97nbddVd35plnuieeeKLrvgIAMFUkznUYUwIAwAQ4/fTTR29j2RQ2Pgfk3/7t3yRNU57XUZJzTprNptx8883yrne9S+6//3458MADRUTkrW99q/zud7/rOXVqc3HdddfJeeedJ//v//2/4MHKAAAARRidAgBg/PCHP5R6vS5vetObNnVXKueb3/ym1Ot1ede73uX93jkny5cvl8suu2wT9Wzyufvuu+Xss8/mJA4AACiFZ+QAAKD84z/+ozz77LMiMpLKhHIOP/xw74qbjc+ESZLEe7YKRL72ta9t6i4AAIAK4tYqAAAAAACAiih9a9UPf/hDeeMb3yhz5syRJEmCyFPnnCxZskTmzJkj06dPl8MPP1weeughr86GDRvkb//2b2XHHXeULbfcUt70pjfJH/7wh57eCAAAAAAAQCeuu+46mTdvnkybNk0WLFggP/rRjwrrLl++XJIkCX4efvjh0Tqf/exn5dBDD5XttttOtttuO3nd614nP/nJT3pabpHSJ3Kef/55efnLXy6f/vSnxyy/6qqr5Nprr5VPf/rTcv/998usWbPkqKOOGr1MXURk8eLFcvvtt8ttt90m99xzjzz33HNy3HHHSbPZLP0GAAAAAAAAOvXVr35VFi9eLBdeeKGsXLlSDj30UDnmmGNk1apV0fl+/etfy+rVq0d/9thjj9Gy5cuXy9vf/na5++675d5775VddtlFFi1aJI8//njPy7V6urUqSRK5/fbb5fjjjxeRkatx5syZI4sXL5a///u/F5GRq29mzpwpV155pbznPe+RP/3pT7LTTjvJl770JTn55JNFROSJJ56QnXfeWe644w45+uiju+0OAAAAAABA1EEHHSSvfOUr5frrrx/93T777CPHH3+8XHHFFUH95cuXyxFHHCFPP/10x89QbDabst1228mnP/1pecc73tHVcov09WHHv/vd72TNmjWyaNGi0d8NDg7KYYcdJitWrJD3vOc98sADD8jw8LBXZ86cObLffvvJihUrxjyRs2HDBtmwYcPodJ7n8t///d+yww47SJIk/XwLAAAAAIApzDknzz77rMyZM0fSdGoFPa9fv16GhoY2dTe64pwLzg8MDg7K4OCg97uhoSF54IEH5Pzzz/d+v2jRIlmxYkV0GQcccICsX79e9t13X7nooovkiCOOKKy7bt06GR4elu23377n5Vp9PZGzZs0aEZEgRnPmzJny6KOPjtYZGBiQ7bbbLqizcX7riiuukEsuuaSfXQUAAAAAoNBjjz0mL3nJSzZ1NybM+vXrZd6uW8matdV85MlWW20lzz33nPe7D3/4w7JkyRLvd08++aQ0m80xz1sUnZOYPXu23HDDDbJgwQLZsGGDfOlLX5IjjzxSli9fLq997WvHnOf888+XF7/4xfK6172u6+UWGZf4cXsWbKwzY1aszgUXXCDnnHPO6PSf/vQn2WWXXWTfv7pYsoFpvXcY6FaJC8JcrK4pc/a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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators.model import KP07Dimsplit\n",
    "\n",
    "with Timer(\"construct\") as t:\n",
    "    sim = KP07Dimsplit(**arguments)\n",
    "\n",
    "with Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T09:51:32.447912Z",
     "start_time": "2025-07-07T09:47:17.824893Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Benchmarking WAF to autotuning_data_tofu.npz\n",
      "Using cache dir /home/smyalygames/Repositories/FiniteVolumeGPU/.fvm_cache/hipcontext\n",
      "HIP Python version 6.4.43483-a187df25c\n",
      "ROCm version 6.4.1\n",
      "Using device 0/1 'AMD Radeon RX 6950 XT (gfx1030)' (3)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f5f7c0aa1a44f5693db38e493d71fa4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Autotuner Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e6d1f635d3b41eb9f0e771296f4deed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 0 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f0e8c2608674153a702aa3966477103",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 1 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d678312e46842d7854609c7eee3e776",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 2 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 3 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 4 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 5 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "5fd4d83925bf44d2bf793692154dd749",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 6 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e01b9d3fd4ed411abd8ad0b4bd5007e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 7 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "10991b72c13940f9858e6a51a3f5871d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 8 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d200cdc2b0e442c8aa952384cb3e31fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 9 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2528c05dae9e48bcae094698c75b61ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 10 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0937f3fb792a4958a4e4490af387e2a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 11 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0c115102f914119a76f17cc4d34af20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 12 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d26de591bcfd48219275092051a6a087",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 13 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d07fb863eb8f453b857b32e40af48e00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 14 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23e5407bf71c45efa65fd99d2f88ff7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 15 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "024aca7a9aca413c9a616302eff623e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 16 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "e38ee153f5cd486fbb5bafbbe803201b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 17 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "207b66ecf6874cef8c0e0a00f9b87b83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 18 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1faa0e7847464b0c8d2788ff7685f8ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 19 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "65d1e1abdc68496a88535b3ff82d9417",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 20 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "15df406bd9ab4461816c04dcd178b2b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 21 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "3fdedce2ca134ed5b0494af2622b34ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 22 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iteration 23 Progress:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators.model import WAF\n",
    "\n",
    "with Timer(\"construct\") as t:\n",
    "    sim = WAF(**arguments)\n",
    "\n",
    "with Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "\n",
    "with Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "FiniteVolumeGPU_HPC",
   "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}