{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Couette (Startup)\n",
    "\n",
    "The simulation of a Couette flow is mainly used for validation of the transient flow description through the current collision operator implemented.\n",
    "In this case, the moving wall boundary condition is employed at the $y=h$ and the halfway bounce-back BC $y=0$, those BC are therefore also validated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nassu.cfg.model import ConfigScheme\n",
    "\n",
    "filename = \"tests/validation/cases/01_couette_flow.nassu.yaml\"\n",
    "\n",
    "sim_cfgs = ConfigScheme.sim_cfgs_from_file_dct(filename)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation parameters are shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NX</th>\n",
       "      <th>NY</th>\n",
       "      <th>tau</th>\n",
       "      <th>time_steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "\n",
    "sim_cfg = sim_cfgs[\"startupCouette\", 0]\n",
    "dct = {\n",
    "    \"NX\": [sim_cfg.domain.domain_size.x],\n",
    "    \"NY\": [sim_cfg.domain.domain_size.y],\n",
    "    \"tau\": [sim_cfg.models.LBM.tau],\n",
    "    \"time_steps\": sim_cfg.n_steps,\n",
    "}\n",
    "df = pd.DataFrame(dct, index=None)\n",
    "\n",
    "HTML(df.to_html())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "The plots of the evolution of velocity profile compared with the analytical solution are shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from typing import Callable\n",
    "from nassu.cfg.schemes.simul import SimulationConfigs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing functions for Couette Flow case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_couette_analytical_func(u_wall: float) -> Callable[[float], float]:\n",
    "    \"\"\"Couette analytical velocity function\"\"\"\n",
    "    return lambda pos: u_wall * pos\n",
    "\n",
    "\n",
    "def get_adimensional_time(sim_cfg: SimulationConfigs, time_step: int) -> float:\n",
    "    \"\"\"Adimensional time\"\"\"\n",
    "    nu = sim_cfg.models.LBM.kinematic_viscosity\n",
    "    h = sim_cfg.domain.domain_size.y\n",
    "    return nu * time_step / (h**2)\n",
    "\n",
    "\n",
    "def get_couette_transient_analytical_func(\n",
    "    sim_cfg: SimulationConfigs, time_step: int, u_wall: float\n",
    ") -> Callable[[float], float]:\n",
    "    adim_time = get_adimensional_time(sim_cfg, time_step)\n",
    "\n",
    "    def func(y: float):\n",
    "        nonlocal u_wall, adim_time\n",
    "\n",
    "        fix_term = u_wall * y\n",
    "        mul_term = 2 * u_wall / np.pi\n",
    "        terms = []\n",
    "        for n in range(1, 15):\n",
    "            exp_term = -(n**2) * (np.pi**2) * adim_time\n",
    "            sin_term = n * np.pi * (1 - y)\n",
    "            term = ((-(1**n)) / n) * np.exp(exp_term) * np.sin(sin_term)\n",
    "            terms.append(term)\n",
    "        series_sum = sum(terms)\n",
    "        res = fix_term + mul_term * series_sum\n",
    "        return res\n",
    "\n",
    "    return func\n",
    "\n",
    "\n",
    "def post_proc_couette_time_step(sim_cfg: SimulationConfigs, time_step: int, ax):\n",
    "    line = sim_cfg.output.series[\"default\"].lines[\"velocity_profile\"]\n",
    "\n",
    "    points_df = pd.read_csv(line.points_filename)\n",
    "    data_df = line.read_full_data(\"ux\")\n",
    "\n",
    "    u_wall = sim_cfg.models.BC.BC_map[0].ux\n",
    "    adim_time = get_adimensional_time(sim_cfg, time_step)\n",
    "    transient_func = get_couette_transient_analytical_func(sim_cfg, time_step, u_wall)\n",
    "    ax.set_title(r\"Couette $\\nu u/h^2=$\" + f\"{adim_time:.4f}\")\n",
    "\n",
    "    x_abs = points_df[\"y\"].to_numpy(dtype=np.float32)\n",
    "    x = (x_abs) / (len(x_abs) - 1)\n",
    "    num_y = data_df[data_df[\"time_step\"] == time_step]\n",
    "    num_y = num_y.drop(columns=\"time_step\").to_numpy().T\n",
    "    ax.plot(x, num_y, \"ok\", label=\"Numerical\", fillstyle=\"none\")\n",
    "\n",
    "    analytical_y = transient_func(x)\n",
    "    ax.plot(x, analytical_y, \"--k\", label=\"Analytical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process velocity profiles for some time steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2)\n",
    "fig.set_size_inches(12, 9)\n",
    "\n",
    "time_steps_proc = [80, 400, 800, 4000]\n",
    "ax_ticks = [0, 0.0005, 0.001, 0.0015, 0.002]\n",
    "ax_ticks_label = [f\"{x:4.1e}\" for x in ax_ticks]\n",
    "for idx, t in enumerate(time_steps_proc):\n",
    "    i, j = idx % 2, idx // 2\n",
    "    post_proc_couette_time_step(sim_cfg, t, ax[i, j])\n",
    "    ax[i, j].set_yticks(ax_ticks)\n",
    "    ax[i, j].set_yticklabels(ax_ticks_label)\n",
    "    ax[i, j].legend()\n",
    "\n",
    "\n",
    "plt.show(fig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The walls have a slight inaccuracy due to the halfway bounce-back not being \"rightly\" implemented in moments representation.\n",
    "\n",
    "Despite that, results show a very satisfatory agreement between numerical model and analytical solution, confirming the solver capability to represent a transient flow.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version: 1.6.33\n",
      "Commit hash: fbc0edb5260d2734f0a290e1806c26ac6d865ff4\n"
     ]
    }
   ],
   "source": [
    "sim_info = sim_cfg.output.read_info()\n",
    "\n",
    "nassu_commit = sim_info[\"commit\"]\n",
    "nassu_version = sim_info[\"version\"]\n",
    "print(\"Version:\", nassu_version)\n",
    "print(\"Commit hash:\", nassu_commit)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"nt\">simulations</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">  </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">startupCouette</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">save_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">./tests/validation/results/01_couette_flow/startup_couette</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">n_steps</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">4000</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">report</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">500</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">domain_size</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">32</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">y</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">32</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">block_size</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">8</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">data</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">monitors</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">fields</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">macrs_stats</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">macrs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">u</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">stats</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">min</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">max</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">mean</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">pos</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">100</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">divergence</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">50</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">instantaneous</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">80</span><span class=\"w\"> </span><span class=\"p p-Indicator\">},</span><span class=\"nt\"> macrs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">u</span><span class=\"p p-Indicator\">]</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">statistics</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">probes</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">historic_series</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">default</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">80</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> lvl</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">macrs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">u</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">lines</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">velocity_profile</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">4</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">4</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">32</span><span class=\"p p-Indicator\">]</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">models</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">precision</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">single</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">LBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">tau</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.9</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">vel_set</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">D2Q9</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">coll_oper</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">RRBGK</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">engine</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">CUDA</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">BC</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">periodic_dims</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">true</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">false</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">BC_map</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">VelocityBounceBack</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">ux</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">2e-3</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">uy</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">VelocityBounceBack</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">ux</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">uy</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{n+nt}{simulations}\\PY{p}{:}\n",
       "\\PY{+w}{  }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{startupCouette}\n",
       "\\PY{+w}{    }\\PY{n+nt}{save\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{./tests/validation/results/01\\PYZus{}couette\\PYZus{}flow/startup\\PYZus{}couette}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{4000}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{report}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{500}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{domain}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{domain\\PYZus{}size}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{32}\n",
       "\\PY{+w}{        }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{32}\n",
       "\\PY{+w}{      }\\PY{n+nt}{block\\PYZus{}size}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{data}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{monitors}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{fields}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{macrs\\PYZus{}stats}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{rho}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{u}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{stats}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{min}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{max}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{mean}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{pos}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{interval}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{ frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{100}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{      }\\PY{n+nt}{divergence}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{ frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{50}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{      }\\PY{n+nt}{instantaneous}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{default}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{ interval}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{ frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{80}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZcb{}}\\PY{p+pIndicator}{,}\\PY{n+nt}{ macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{rho}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{u}\\PY{p+pIndicator}{]}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{      }\\PY{n+nt}{statistics}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{interval}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{ frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{      }\\PY{n+nt}{probes}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{historic\\PYZus{}series}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{default}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{interval}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{ frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{80}\\PY{p+pIndicator}{,}\\PY{n+nt}{ lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{            }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{rho}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{u}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{lines}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{n+nt}{velocity\\PYZus{}profile}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{n+nt}{dist}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{                }\\PY{n+nt}{start\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{end\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{32}\\PY{p+pIndicator}{]}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{models}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{precision}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{default}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{single}\n",
       "\n",
       "\\PY{+w}{      }\\PY{n+nt}{LBM}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{tau}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.9}\n",
       "\\PY{+w}{        }\\PY{n+nt}{vel\\PYZus{}set}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{D2Q9}\n",
       "\\PY{+w}{        }\\PY{n+nt}{coll\\PYZus{}oper}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RRBGK}\n",
       "\n",
       "\\PY{+w}{      }\\PY{n+nt}{engine}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{CUDA}\n",
       "\n",
       "\\PY{+w}{      }\\PY{n+nt}{BC}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{periodic\\PYZus{}dims}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{true}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{false}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{        }\\PY{n+nt}{BC\\PYZus{}map}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{N}\n",
       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{VelocityBounceBack}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{N}\n",
       "\\PY{+w}{            }\\PY{n+nt}{ux}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2e\\PYZhy{}3}\n",
       "\\PY{+w}{            }\\PY{n+nt}{uy}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\n",
       "\\PY{+w}{          }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{S}\n",
       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{VelocityBounceBack}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{S}\n",
       "\\PY{+w}{            }\\PY{n+nt}{ux}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{            }\\PY{n+nt}{uy}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "simulations:\n",
       "  - name: startupCouette\n",
       "    save_path: ./tests/validation/results/01_couette_flow/startup_couette\n",
       "\n",
       "    n_steps: 4000\n",
       "\n",
       "    report:\n",
       "      frequency: 500\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 32\n",
       "        y: 32\n",
       "      block_size: 8\n",
       "\n",
       "    data:\n",
       "      monitors:\n",
       "        fields:\n",
       "          macrs_stats:\n",
       "            macrs: [rho, u]\n",
       "            stats: [min, max, mean, pos]\n",
       "            interval: { frequency: 100 }\n",
       "      divergence: { frequency: 50 }\n",
       "      instantaneous:\n",
       "        default: { interval: { frequency: 80 }, macrs: [rho, u] }\n",
       "      statistics:\n",
       "        interval: { frequency: 0 }\n",
       "      probes:\n",
       "        historic_series:\n",
       "          default:\n",
       "            interval: { frequency: 80, lvl: 0 }\n",
       "            macrs: [rho, u]\n",
       "            lines:\n",
       "              velocity_profile:\n",
       "                dist: 1\n",
       "                start_pos: [4, 0]\n",
       "                end_pos: [4, 32]\n",
       "\n",
       "    models:\n",
       "      precision:\n",
       "        default: single\n",
       "\n",
       "      LBM:\n",
       "        tau: 0.9\n",
       "        vel_set: D2Q9\n",
       "        coll_oper: RRBGK\n",
       "\n",
       "      engine:\n",
       "        name: CUDA\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [true, false]\n",
       "        BC_map:\n",
       "          - pos: N\n",
       "            BC: VelocityBounceBack\n",
       "            wall_normal: N\n",
       "            ux: 2e-3\n",
       "            uy: 0\n",
       "\n",
       "          - pos: S\n",
       "            BC: VelocityBounceBack\n",
       "            wall_normal: S\n",
       "            ux: 0\n",
       "            uy: 0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Code\n",
    "\n",
    "Code(filename=filename)"
   ]
  }
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