{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Turbulent Channel (Reynolds 2003)\n",
    "\n",
    "The simulation of a periodic turbulent channel is also used for validation of the equilibrium wall model implemented with the thin boundary layer (TBL) equation. A friction Reynolds number of 2003 is fixed for this case. The reference article used for comparison of results is [Han et al., 2020](https://iopscience.iop.org/article/10.1088/1873-7005/ac1782/meta). The pressure gradient is estabilished through a constant body force."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:41.795868Z",
     "iopub.status.busy": "2026-06-29T17:45:41.795800Z",
     "iopub.status.idle": "2026-06-29T17:45:42.318425Z",
     "shell.execute_reply": "2026-06-29T17:45:42.318049Z"
    }
   },
   "outputs": [],
   "source": [
    "from nassu.cfg.model import ConfigScheme\n",
    "\n",
    "filename = (\n",
    "    \"validation/turbulence/02_turbulent_channel_flow/02.1_turbulent_channel_flow_wm.nassu.yaml\"\n",
    ")\n",
    "\n",
    "sim_cfgs = ConfigScheme.sim_cfgs_from_file_dct(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:42.319559Z",
     "iopub.status.busy": "2026-06-29T17:45:42.319451Z",
     "iopub.status.idle": "2026-06-29T17:45:42.321370Z",
     "shell.execute_reply": "2026-06-29T17:45:42.321117Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_cfg = next(\n",
    "    sim_cfg\n",
    "    for (name, _), sim_cfg in sim_cfgs.items()\n",
    "    if sim_cfg.name == \"periodicTurbulentChannel\"\n",
    ")\n",
    "sim_cfg_wm = next(\n",
    "    sim_cfg\n",
    "    for (name, _), sim_cfg in sim_cfgs.items()\n",
    "    if sim_cfg.name == \"periodicTurbulentChannelMultilevel\"\n",
    ")\n",
    "sim_cfg_wm_mb = next(\n",
    "    sim_cfg\n",
    "    for (name, _), sim_cfg in sim_cfgs.items()\n",
    "    if sim_cfg.name == \"periodicTurbulentChannelNoWM\"\n",
    ")\n",
    "sim_cfgs_use = {\"ref\": sim_cfg, \"wm\": sim_cfg_wm, \"mb\": sim_cfg_wm_mb}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions to use for turbulence channel processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:42.322147Z",
     "iopub.status.busy": "2026-06-29T17:45:42.322080Z",
     "iopub.status.idle": "2026-06-29T17:45:42.902798Z",
     "shell.execute_reply": "2026-06-29T17:45:42.902467Z"
    }
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import nassu.viz as common\n",
    "\n",
    "common.use_style()\n",
    "\n",
    "\n",
    "def get_experimental_profiles(reynolds_tau: float) -> dict[str, pd.DataFrame]:\n",
    "    files_tau: dict[float, dict[str, str]] = {\n",
    "        2003: {\n",
    "            \"ux\": \"Re_tau_2003_u_avg.csv\",\n",
    "            \"ux_rms\": \"Re_tau_2003_u_rms.csv\",\n",
    "            \"uy_rms\": \"Re_tau_2003_v_rms.csv\",\n",
    "            \"uz_rms\": \"Re_tau_2003_w_rms.csv\",\n",
    "        },\n",
    "    }\n",
    "\n",
    "    files_get = files_tau[reynolds_tau]\n",
    "    vals_exp: dict[str, pd.DataFrame] = {}\n",
    "    for name, comp_file in files_get.items():\n",
    "        filename = (\n",
    "            pathlib.Path(\"validation/turbulence/02_turbulent_channel_flow/reference\") / comp_file\n",
    "        )\n",
    "        df = pd.read_csv(filename, delimiter=\",\")\n",
    "        vals_exp[name] = df\n",
    "    return vals_exp\n",
    "\n",
    "\n",
    "# One cached wall-normal (z) centre-line probe per simulation's statistics\n",
    "# export, reused for every macroscopic instead of re-probing per call. The\n",
    "# wall-modelled channel walls are inset by 4 cells, so the line spans z in\n",
    "# [4, z/2] over z-8 samples (no cell-centre offset, matching the original).\n",
    "_line_probes: dict[str, common.LineProbe] = {}\n",
    "\n",
    "\n",
    "def _line_probe(sim_cfg) -> common.LineProbe:\n",
    "    if sim_cfg.name not in _line_probes:\n",
    "        ds = sim_cfg.domain.domain_size\n",
    "        _line_probes[sim_cfg.name] = common.LineProbe.from_export(\n",
    "            sim_cfg.output.exports[\"default_stats\"].volumes[\"default_stats\"].stats,\n",
    "            sim_cfg.n_steps + 1,\n",
    "            (ds.x // 2, ds.y // 2, 4),\n",
    "            (ds.x // 2, ds.y // 2, ds.z // 2),\n",
    "            ds.z - 8,\n",
    "            cell_offset=0.0,\n",
    "        )\n",
    "    return _line_probes[sim_cfg.name]\n",
    "\n",
    "\n",
    "def get_macr_compressed(sim_cfg, macr_name: str, is_2nd_order: bool) -> np.ndarray:\n",
    "    probe = _line_probe(sim_cfg)\n",
    "    ds = sim_cfg.domain.domain_size\n",
    "    norm_pos = (probe.sample_points[:, 2] - 4) / (ds.z - 8)\n",
    "    name = macr_name if not is_2nd_order else f\"{macr_name}_2nd\"\n",
    "    return np.array([norm_pos, probe.sample(name)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:42.904052Z",
     "iopub.status.busy": "2026-06-29T17:45:42.903898Z",
     "iopub.status.idle": "2026-06-29T17:45:42.959385Z",
     "shell.execute_reply": "2026-06-29T17:45:42.959002Z"
    }
   },
   "outputs": [],
   "source": [
    "y, ux_avg = get_macr_compressed(sim_cfg, \"ux\", is_2nd_order=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "The average velocity profile is shown for the case. It can be seen a good approximation of desired profile when using the wall model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:42.960545Z",
     "iopub.status.busy": "2026-06-29T17:45:42.960466Z",
     "iopub.status.idle": "2026-06-29T17:45:43.549865Z",
     "shell.execute_reply": "2026-06-29T17:45:43.549558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "reynolds_tau = 2003\n",
    "# TODO(#750): verify this normalization before migrating to common.friction_velocity.\n",
    "# u_ref = 0.00225 is ~9% below the force-implied friction velocity\n",
    "# u* = sqrt(F * delta) ~ 0.00246 (F = 1.898e-7, half-height delta ~ 28-32 in z\n",
    "# for the wall-inset channel). This is the residual normalization question\n",
    "# flagged on #750; re-run on GPU and check against the DNS reference, then\n",
    "# replace with common.friction_velocity(sim_cfg, geometry=\"channel\", length=...).\n",
    "u_ref = 0.00246\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "\n",
    "sim_colors = [common.colors.sim, common.colors.green, common.colors.blue]\n",
    "\n",
    "\n",
    "for i, (sim_cfg, c) in enumerate(zip(sim_cfgs_use.values(), sim_colors)):\n",
    "    height_scale = common.wall_units_scale(sim_cfg, u_ref)\n",
    "    name = sim_cfg.name.removeprefix(\"periodic\")\n",
    "\n",
    "    analytical_values = get_experimental_profiles(reynolds_tau)\n",
    "\n",
    "    y, ux_avg = get_macr_compressed(sim_cfg, \"ux\", is_2nd_order=False)\n",
    "    if not sim_cfg.name.endswith(\"Multilevel\"):\n",
    "        y, ux_avg = y[::2], ux_avg[::2]\n",
    "\n",
    "    ux_avg /= u_ref\n",
    "    y *= height_scale * (sim_cfg.domain.domain_size.z - 8)\n",
    "\n",
    "    exp_y = analytical_values[\"ux\"][\"y+\"]\n",
    "    exp_ux_avg = analytical_values[\"ux\"][\"u/u*\"]\n",
    "\n",
    "    ax.plot(\n",
    "        y,\n",
    "        ux_avg,\n",
    "        marker=\"v\",\n",
    "        fillstyle=\"none\",\n",
    "        linestyle=\"none\",\n",
    "        color=c,\n",
    "        markeredgewidth=1.7,\n",
    "        label=f\"{name}\",\n",
    "    )\n",
    "\n",
    "ax.plot(exp_y, exp_ux_avg, **common.markers.exp(shape=\"o\"), label=\"Reference\")\n",
    "\n",
    "ax.set_title(\"Turbulent Channel\")\n",
    "\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xlim(5, 2000)\n",
    "ax.set_ylim(0, 25)\n",
    "ax.set_xscale(\"symlog\")\n",
    "ax.set_ylabel(\"$u/u*$\")\n",
    "ax.set_xlabel(\"$y^+$\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of the ${\\mathrm{u_{rms}}}$ velocity profiles shown below also indicate a good representation with the multilevel approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:43.565953Z",
     "iopub.status.busy": "2026-06-29T17:45:43.565855Z",
     "iopub.status.idle": "2026-06-29T17:45:43.746451Z",
     "shell.execute_reply": "2026-06-29T17:45:43.746121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = common.fig_triple()\n",
    "\n",
    "vel_name_map = {\"ux\": \"u\", \"uy\": \"v\", \"uz\": \"w\"}\n",
    "vel_colors = {\"ux\": common.colors.sim, \"uy\": common.colors.blue, \"uz\": common.colors.green}\n",
    "vel_exp_markers = {\"ux\": \"o\", \"uy\": \"^\", \"uz\": \"s\"}\n",
    "\n",
    "for i, sim_cfg in enumerate(sim_cfgs_use.values()):\n",
    "    name = sim_cfg.name.removeprefix(\"periodic\")\n",
    "    for macr_name in (\"ux\", \"uy\", \"uz\"):\n",
    "        macr_compr_rms = get_macr_compressed(sim_cfg, macr_name, is_2nd_order=True)\n",
    "        macr_compr_avg = get_macr_compressed(sim_cfg, macr_name, is_2nd_order=False)\n",
    "\n",
    "        y = macr_compr_rms[0].copy()\n",
    "        vel_2nd = macr_compr_rms[1].copy()\n",
    "        vel_avg = macr_compr_avg[1].copy()\n",
    "        vel_rms = (vel_2nd - vel_avg**2) ** 0.5\n",
    "\n",
    "        vel_rms /= u_ref\n",
    "        y *= height_scale * (sim_cfg.domain.domain_size.z - 8)\n",
    "        # Remove wall value\n",
    "        vel_rms = vel_rms[::2]\n",
    "        y = y[::2]\n",
    "\n",
    "        c = vel_colors[macr_name]\n",
    "        name_vel = vel_name_map[macr_name]\n",
    "        df = analytical_values[f\"{macr_name}_rms\"]\n",
    "        exp_y = df[\"y+\"]\n",
    "        exp_rms = df[f\"{name_vel}'/u*\"]\n",
    "\n",
    "        axes[i].plot(\n",
    "            y,\n",
    "            vel_rms,\n",
    "            marker=\"v\",\n",
    "            linestyle=\"none\",\n",
    "            color=c,\n",
    "            markeredgewidth=1.7,\n",
    "            label=f\"{name} {name_vel.upper()}\",\n",
    "        )\n",
    "        axes[i].plot(\n",
    "            exp_y,\n",
    "            exp_rms,\n",
    "            marker=vel_exp_markers[macr_name],\n",
    "            fillstyle=\"none\",\n",
    "            linestyle=\"none\",\n",
    "            color=c,\n",
    "            markeredgewidth=1.7,\n",
    "            alpha=0.6,\n",
    "            label=f\"Ref. {name_vel.upper()}\",\n",
    "        )\n",
    "\n",
    "    axes[i].set_title(f\"{name}\")\n",
    "    axes[i].legend()\n",
    "    axes[i].set_xlim(10, 2000)\n",
    "    axes[i].set_ylim(0, 3.5)\n",
    "    axes[i].set_xlabel(\"$y^+$\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen good approach of results with the use of wall model and subsequent combination with multigrid approach. As the shell point becomes nearer the surface for a high refinement, the first point velocity becomes a little smaller than for a coarser refinement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flow field\n",
    "\n",
    "Instantaneous velocity magnitude on the wall-modelled channel planes (`plane_series`): the streamwise / wall-normal mid-plane and the wall-parallel plane above the wall-model matching height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:43.747279Z",
     "iopub.status.busy": "2026-06-29T17:45:43.747196Z",
     "iopub.status.idle": "2026-06-29T17:45:43.965641Z",
     "shell.execute_reply": "2026-06-29T17:45:43.965314Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[33m2026-06-29 14:45:43.780 (   1.502s) [    7FD75AB63740]vtkXOpenGLRenderWindow.:1460  WARN| bad X server connection. DISPLAY=\u001b[0m\n",
      "/tmp/ipykernel_1980273/83295274.py:31: UserWarning: Using static image for notebook display.\n",
      "Install trame for interactive backends: pip install \"pyvista[jupyter]\"\n",
      "  plotter.show()\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAFABpADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD0P/hbWg/8+mpf9+0/+Lo/4W1oP/PpqX/fuP8A+LrwXVUWd7COTJU3PIzjPyPxUEdjbNqc8DRAwJEjrEfuKzFgSB/wEVfKeOsZV5bt/ge+t8XtBWdIvsOqncpO4Qx7RjHBO/qc8fQ0lz8YvD1ray3D2eplI1LELFHnA/4HXzyJrhYoTEWaaOC7WPPJ+V1C/U8CjUodOXRJJoZBvZPllD5eQ+hPfPfNLlNFia3Mk3+HmfRf/C2tB/59NS/79p/8XR/wtrQf+fTUv+/af/F14ReT36QXBhhiwI2KMJCWzjj5duP1qssOmJFDPHLtdmXbMr5dz6H1z3FPlMli69rt/hc96t/jF4euUZks9TAV2jOYo+qkg/x+1S/8La0D/n01L/v2n/xdfPUNlBLZ3kzqTN505WTJymHbG09vWno8d7cwrfEMjWyPGjfddjnccdyOPzo5S3iqt3Z7eR9Bf8La0D/n01L/AL9p/wDF0f8AC2tA/wCfTUv+/af/ABdfOEqQF9Rit5GMe+2X5WPy/vOQD/nFafkRWWo2v2ZBGsu9ZFXo2BkE+/HX3o5Qliqy6/h5Jnvn/C2tA/59NS/79p/8XR/wtrQP+fTUv+/af/F186wW0UHh2C7jUi5SJHEufm7cZ9O2OlTTB7jUrpZoLaYJjy1nmK7VwOQNpHXPPt7UcofWa13Z7eX/AAT39vjF4eS6itzZ6nvkVmB8qPGFxn+P/aFS/wDC2tA/59NS/wC/af8AxdfPdg8jXlmXdXUJOEKsW+XcmBkgZ+tbEjuIyY1Vn7BmwD+OD/KhRInjK8Wldf0z2z/hbWgf8+mpf9+0/wDi6P8AhbWgf8+mpf8AftP/AIuvEoZJmTM8aI+eiOWGPqQKzNXWN5UZzHJ5cTMYXYrkcfMp7MMfr2o5BQxleUuVv8D3sfGLw8btrb7Hqe9UEhPlR4wSR/f9jU3/AAtrQf8An01L/v2n/wAXXzvJHGLq5ubW3Tz/ALGssZKDdvO/n61GFMcUE0CWiSsyhZ1uHd3Oeh+TLZ/zijlNfrNZ7P8AD/gn0X/wtrQP+fTUv+/af/F0f8Lb0Af8uuo/9+0/+Lr56sobG4F41yVkdLiXPmN/qxvOMen1q/psrSachkYuuWCs/VkydpP1GKFEieLrR6/ge0x/G7w3I0Z/s/WFikbbHM0MYRiemP3mee2RSN8b/Dal2/s/WDAjFXnEEewEcH/lpnA9QMV4irC/eNYgEsoXBBAx5hXoF/2QQOe+KgSeNPD1xG7KHVZUZSed2Tx9T/WjlNPrlX+v63Pd5PjX4dSV0TTtYm8v/WNFBGQvGcf6zk47DNDfGzw5uRYLDVrgsgk/dQxnCnoSTIBz+deHabMlul0kzqjJJubccYBUc/Sq+iOISyyHYzwROobjK4NHKH1uqk32/E95Pxt8NFIjFY6tM8mdsaQx7hjg5y4xg8c0f8Lt8NeVu+xasZN/l+T5Cb92M4xv9Oc9MV4Lp8qrqRlLARzed5bHo37zPH86BKn9tefuHk+eU3543eUB1/SjlH9aq3a+f/APf7b4y+HrhnT7DqsUqY3RyRRggHoeHII4PT0qx/wtrQf+fTUv+/af/F14NHIr6zMyEEJAisR2O5jiru+nyGE8bWT0PbP+FtaD/wA+mpf9+4//AIuj/hbWg/8APpqX/ftP/i68U8yjzKPZk/Xq57X/AMLZ0H/n01L/AL9p/wDF0f8AC2dB/wCfTUv+/af/ABdeKeZR5lL2bF9erntf/C2dB/59NS/79p/8XR/wtnQf+fTUv+/af/F14p5lL5lHIw+v1z2r/hbOg/8APpqX/ftP/i6P+Fs6D/z6al/37T/4uvFfMo8z3o5GH1+ue1f8LZ0H/n01L/v2n/xdH/C2dB/59NR/79p/8XXivmUeZRyMPr9c9r/4WzoP/PpqP/ftP/i6P+Fs6D/z66j/AN+0/wDi68V8yjzaORh9frntX/C2NB/59dR/79p/8XR/wtjQf+fXUf8Av2n/AMXXivm0ebRyMPr9c9q/4WxoP/PrqP8A37T/AOLo/wCFsaD/AM+uo/8AftP/AIuvFfNo82lyMPr9c9q/4WxoP/PrqP8A37T/AOLo/wCFsaD/AM+uo/8AftP/AIuvFfMo8yjkYfX657V/wtjQf+fXUf8Av2n/AMXR/wALY0H/AJ9dR/79p/8AF14r5lHmUcjD6/XPav8AhbGg/wDPrqP/AH7T/wCLo/4WxoP/AD6aj/37T/4uvFfMo8yjkYfX657V/wALY0H/AJ9NR/79p/8AF0f8LZ0H/n11H/v2n/xdeK+ZR5lHIw+v1z2n/hbOg/8APpqP/ftP/i6P+Fs6D/z6al/37T/4uvFvMo8ynyMPr9c9p/4WzoP/AD6al/37T/4uj/hbOg/8+mpf9+0/+LrxbzKPMo5GH1+ue0/8LZ0H/n01L/v2n/xdH/C2dB/59NS/79p/8XXi3mUeYaORh9frntP/AAtnQf8An01L/v2n/wAXR/wtnQf+fTUv+/af/F14t5ho30cjD6/XPaf+Fs6D/wA+mpf9+0/+Lo/4WzoP/PpqX/ftP/i68W30b6ORh9frntP/AAtnQf8An01L/v2n/wAXR/wtrQf+fTUv+/af/F14tvo30cgfX657T/wtrQf+fTUv+/af/F0f8La0H/n01L/v3H/8XXiu+jfRyB9frntX/C2tB/59NS/79p/8XR/wtrQf+fTUv+/af/F14rvo30cgfX657V/wtrQf+fTUv+/af/F0f8La0H/n01L/AL9p/wDF14rvo30cgfX657V/wtrQf+fTUv8Av2n/AMXR/wALa0H/AJ9NS/79p/8AF14rvo30cgfX657V/wALZ0H/AJ9NS/79p/8AF0f8LZ0H/n01L/v2n/xdeLb6N9HIH1+ue0/8LZ0H/n01L/v2n/xdH/C2dB/59NS/79p/8XXi2+jeaORh9frntP8AwtnQf+fTUv8Av2n/AMXR/wALZ0H/AJ9NS/79p/8AF14vvo30uRh9frntH/C2dB/59NS/79p/8XR/wtnQf+fTUv8Av2n/AMXXi++jfT5GH1+ue0f8LZ0H/n01L/v2n/xdH/C2tB/59NS/79p/8XXi2+jdRyB9frntP/C2tB/59NS/79p/8XR/wtrQf+fTUv8Av2n/AMXXi26jdRyB9frntP8AwtrQf+fTUv8Av2n/AMXSf8La0H/n01L/AL9p/wDF14vuo3UcgfX657R/wtrQf+fTUv8Av2n/AMXS/wDC2tB/59NS/wC/af8AxdeK7qXdRyB9frntP/C2tB/59NS/79x//F0f8La0H/n01L/v2n/xdeLbqN1HKH1+ue0/8La0H/n01L/v2n/xdH/C2tB/59NS/wC/af8AxdeLbqN1HIH1+ue0/wDC2dB/59NS/wC/af8AxdH/AAtnQf8An01L/v2n/wAXXi26jfRyB9frntP/AAtnQf8An01H/v2n/wAXS/8AC2dB/wCfTUf+/af/ABdeLbzRvNLkYfX657T/AMLZ0H/n01H/AL9p/wDF0n/C2dB/59NR/wC/af8AxdeL7zRvNHIw+v1z2j/hbOg/8+mpf9+0/wDi6P8AhbOg/wDPpqX/AH7T/wCLrxbeaN9PkYfX657T/wALZ0H/AJ9NS/79p/8AF0f8LZ0H/n01L/v2n/xdeLbqNxo5A+v1z2n/AIWzoP8Az6al/wB+0/8Ai6P+FtaD/wA+mpf9+0/+LrxbcaNxo5A+v1z6O8O+IrTxLp8l7ZRzxxpKYiJlAOQAexPHzCqHjkZ0SH/r4X/0Fqx/hGc+FLr/AK/n/wDQI6j+Ldsl5pOgW8hcI+sxhtjFSR5M2Rkc89K48bDmw84t9D2cNNzhGT3ZzRU+lJg1zurW48O3kn9iw+V5+m3UnkJ93zI9hVgvr8x+tUJ9NuINGXUIbLT7eYIrpqZ1N2cscYZj5Xzgk/d6HOK+QhQ5kmno9tv8/wArnY6ep2WDSgGsS30621rW9UXVUWZrR444rZiSiKY1beB3JYsM/wCzisK4soIrXxNZ21xI0SXtlGreYWaP5o/lDHn5ScD0x7UoUFN8t9dOndrz8x+zaO7CE9qeIj6VTiYaU0On2ej3bWy4AljaMouTkk7nDHkkng/jXO3Mt5aWl34c06Yw3pu8Wjr1WFwZc/QbXT8BWUKLm9H/AMN3faxpypbnYCGjya5KfVptUhn1ezTLadpjSxrjOLiRM4x6qo6f7dLq+haRD4Pu7+OVvPFo0gv/ADT5kpK9S2fmDZxjpzgVSw1mlN2b02vr9/oPTojXv9XttPuZYJY5i0aROSgBGJJPLXv6jn2rSMB9K4jUuv8A3D9N/wDSmvQKVamqcU1/Wi/zBQTZU8g+lKID6Uye+nivFgTTLyaNiAZ4zFsXPrlw3H0/OrtYPmW4/ZRIFhx1qNb2H+1G08K/nLAJy2Bt2liv55Fcz4jt4l1WbU7hVu7W2hj8wRT7Z7Ehi3mKOnIxnocL3qyulaePHU06WFsZhZLOreWAfMMjfNn1PrXQqEeXmb6fjp5+YLR2R1FFee2NndX+hLql1ptlJcPGZZL+TUnjlibnOP3Z8vaRjaDgYxzVy0tU1bxHaxatOs7nR4ZGiRiI523vlyOMjnoRjnpwKqWEUb3ltv8A1f8AOw1O521UNR1WLTnhiMM9xcT58qCBQXYDqeSAAMjkkdRWdpsMem+JrjTrIlbM2qztCGJWF9xA2/3Qwzx/s5rQ1O6stMC388e+4wYYVRcySFiDsUd8kD8s9qx9mlNLe5V9Cv8A8JJZC1MpjuROJvs/2Ty/33mYzt2/TnOcY5zQPElnHFcG6iubSaBVZoJo/wB4wY4XaFJDZPHBPNZCWVxbeIdKv9Q2rdXt1IzopysX7ghEB7kAHnuc1D4oIHi7SGz8sYjMh7KDcRhc/iDXTGhSlNRXVX/4H4Ecztc3k8RWqu6XsFzp7LE0w+1IAGReWIKkjgdR19qLfxDBLNAk1pe2iXBxDLcxBVkOMgcElSRnhgOlYXxD5tbIDkr57sB/cETbj9ORWh40zLolusR3SPdw+Xt7ndnj8AaUaNOSg7fFf5WHzPXyLCeKLRwk32a8WykYKl60QETZOAeu4AnGCRjkc0svie2jadltbyW2gYpNdRxZjQj73fJxzkgHGDWZfFH+F6lGBDafGqYPUlVAH54qzohRPBsxkYAR/aRJn+EiR85pOlTUea32rf8AB9Rc0r2OijlWVFdGDKwBVgeCPWn1l+H0ZfDelq4IYWkQIPrsFaXSuSceWTXYtPQdRSbqNwqRi0UmR60tABRRRQMKKKKAAjNMKU+igTVyHyz6UCKpqKCeREflCjyhUlFA+VEflCkMVS0UByogMdNKGrNJtFBLgimVNMKmrxjBphhBqlJozlRuUSpphBq+YKb9nrVVrGEsPJlHBpMGr32aj7NV+3RH1aRSwaUKfSrn2c0eQaTrjWGkVNppdlWvJo8qodYpUGVwlOCVP5dKEqHUbLVEiCVIsdSBcU8DFRds1jTQxYhTwgFLmkLYoNbJC4ApjMBTWkqFnoSuRKokPZ6bvqIvSBq0VM53VJg2acATUackVaRcCoasawXMNEXrThEtPopGyihuxfSlwB2paKB2CiiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAh6VE6mpqKCWrnmzWe4qWQMVOVyM4PTI/M0fY8OXCAOQAWxyQOn8zXV/wBmD0pP7NHpX2CzmkfJf2fWOTWxRCCsSKVzghemTk/maj/suDez/Zod7cM3ljJ+tdf/AGaPSj+zR6U/7YpB9RrI5f7O/pUK6bEkxmW3jEp6uEAY/jXW/wBmj0pDpw9KazemT9SrI5YWYVSojUKxJIA4JPWmPp8ckQikgjeMdEZQQPwrqjp49KT7APSrWa02L6pVRyw0+MLtEEYXjjaMcHI/I082m5lYoCy/dJHI+ldJ9gHpR9gHpT/tSAvqtU5n7CnkiHyk8oDGzaMY9MUk2nxXGPOgjkx03qDj866j+zx6Uv8AZ49KX9q0wWEqnM/YxuVvLG5QQpxyAew/IflTvs7eldKNOHpTxpw9Kl5vTRSwNVnMfZn9Kjl05J9vnQpJtORvUHB/GutGmj0pw0welQ86pItZfW6HJfYvnLhBvIwWxzj0/WmLpkaSmVLeNZD1cIAT+NdmNKH92lGlD+7U/wBuUilltc4SLRI1UiWKOZvMeQM8YO3cxbH61bNoxUqVBBGCMV2P9kj+7S/2QPSl/btIp5biHqzg10S0R1dLG3VlOQREoIP5VK2mxtMJmt4zKOjlBuH41239kD+7Sf2QP7tNZ5SB5diTiZdOjmZWlt43ZehZASKWbT0nAE0EcgHQOoOPzrtDpA/u006QP7tNZ5SJ/s7EI42SwSWPy5IUeMfwsoI/Kk+wp5Pk+SnlYxs2jH5V2J0kf3aadKHpVrOqLJeAxCOQislgTZFEkaf3UXAp/kN6V1R0oelJ/ZY9KpZzR7kPBV+qOW8hvSj7O/pXU/2WPSnDSh6UPOaS6gsDWfQ5T7O/pR9nf0rrhpIPanjRwf4ah55RRosurvocd9nf0pPs7+ldp/Yw/u0h0Zf7tL+3qI/7MxHY4zyH9KTyX9K7FtGH92om0celXHO6L6kSy/ELocl5TelHlt6V07aSPSo20r2reObUZdTJ4Wst0c35belJsat9tMx2qM6djtW0cwpPqZulUXQxNjUbDWybA+lNNj7Vf12n3J5Z9jI2mjaa1vsPtSfYvaq+twFaXYytpo2mtT7H7Un2P2p/WoBaXYzNppdhrUFl7U4WXtSeLgHLLsZOxqXY1a4sfanCw9qh42A+SfYxvLb0o8tvStv7B7Uv9n+1T9fpj9lPsYflt6UeW3pW7/Z/tR/Z/tS/tCmP2NTsYXlt6UeW3pW7/Z/tR/Z/tS/tCmHsanYw/Lajy2rd/s/2o/s/2o/tCmHsanYwvLb0o8tq3f7P9qP7PHpR/aFMPY1OxheW1Hlmtz7B7UhsfamsfTF7KfYxPLajYa2TY+1NNl7VSxsGL2czI2GjYa1jZ+1J9k9qf1uAuSRl+WaPLPpWp9k9qUWntR9biHJIyvLb0pfKb0rWFp7U4Wg9Kl42KH7ORkeS3pR5LelbQsx6VILIVm8fFFKhNmF5DelL9nf0roBYj0qQWA9KyeaQRosJUZzf2d/Sl+zP6V0w08elPGnL6CsnnFNFrAVWct9mf0o+zP6V1Y05fQU7+zV9Kh53TLWW1Tkvsr+lL9lf0rrRpq+lL/Zg9Kn+3KZX9mVjkfsr+lH2V/Suv/swegpp0xfSj+3KYf2ZWOR+zP6UfZ39K6w6avoKYdNHpVrOqbIeXVUcr5D+lHkt6V0racPSoWsB6VtHNKcjKWDqI5/yW9KPJb0rcNl7Un2L2rX6/Az9hMxfJb0o8lvStv7F7UfY/al9fgH1eZieS3pS+Q3pW19k9qeLQelJ5hFDWHmzDFu3pS/ZX9K6BbMelTpYg9qwnm0YmscFUkcz9kf0pfscnpXWLp4PYVKNNB7Cud57FHRHKqrOO+xSf3aPsUn92u0GmL7Uf2YntUf29HsX/Y9U4v7HJ/dpPsknpXaHS09qadLX0FUs+j2E8oqnGfZJPSj7K/pXYnSh6Uw6WPSqWeUyHlVZHIfZX9KPsz+ldadLH92kOlj+7VrO6ZP9mVux2/wmjMfhW6B/5/XP/jiVqeObSC60uyeZNzQXiyxnJG1tjjPHXhj19aj+H0H2fQZ0xjN0x/8AHUq34x/5BEX/AF3H/oLVvi63tMDKpHqj2MJTcFGEuhwMlpBLeQ3bpmeFXSNsngNjcMdOdo/KqMfhzSYrlJ0s1DI/mIm9jGjf3gmdoPuBWrRXxqqTSsmz1LIoX2jWGozLNcwEzKu0SxyNG+303KQSPbpUcfh/SobaS3is0SKRkd1UkZZCCp6+oB9++a06KFVmlZSdvULIKqtptm+qR6k0AN5HEYVlychCc4x0q1RUqTWwFSx02z02OWOzgWJJpWmcAk5dup5qkfCuiMTmxUoST5XmN5YJ6kJnaDz1ArYoqlVqJtqT18wsjPl0TTpjmS3z+7ji++w+WNt6Dr2bn+daFFFS5Se7CwUUUUhmde6DpmoXS3N1aiSUAAnewDgHIDAHDAHsc1bFpAL43gT/AEgxiIvk/dBJAx06k1NRVOcmrNisjLm8OaTcXDzy2YLSNvkQOwR29WQHax+oqC68Pw3+vSXt1GjwfZY4o9rMsiOrOSQRgjhgMg+tbdFWq9RdWLlRVsdNs9NjdLSER723OxJZnPqzHJJ+pqDUNDsNUuYbi6jmM0AKxvFcSRFQev3GHWtGipVSalzJu/cdlsZo0HT/ALI9q8cs0LsHInuJJSGHQgsxKn6YpYdB0yC3uIFtVdLkYm81mkaQdgWYkn860aKPa1P5n94WRnWmhadZSPJDb5kdPLLSyNIdv90FicD2HFNtPD+l2Vwk8FriSMER7pGYRg/3FJIX8AK0s0bqHVqa+89fMVkZi+HNJS6W4W0AZX8xU3t5avnO4JnaDnnOKLjw7pV1cPPNaBmkYNIodgkh9WQHa3TuDWlupN1P21S9+Z/eFojuAMdBSE00tTSayE5AzVGXNKaYVpoyk2HmH1pRMR3qMrTCprRRizFzkiyJ6eJxVHkUbiKr2XYFiJLc0RIp704EHvWcJDUiymodNo1jiE9y9RUCTetTAg9Kg3Uk9haKKKCgooooAKKKKACiiigAooooAKKKKACiiigAooooAKMUUUAJgUbaWigQ2kJpxFMIpCY0tio2anMKYRTRlJsYxphNPK0m2tYtI55Jsjwacq08JUqR0SqBCk2xYk5qxSKu0UtZHbGPKrBRRRQUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFNNAhc0tMzS0BcdVdr62XUUsDJi6eIyqm08oCATnp1IqxXJ+INQg0LxXp2rXwkSxa1ltmmVCwRyysM455wa1o0/aS5etnYUnZXOjhv7a4vLm0ik3T2u3zk2kbdwyvPQ8elWa5PwrfJqXiTxFdxxSxxSG2KCVdrFfLIBwemeo9iK6yivT9nPl8l+KTCLurhRRRWRQUUUUAGB6Um0egpaKBCbV9BSbF9BTqKAshuxfQUhiQ9qfRQFkRGBTTDbCrFFF2S6cX0Kht/ak8j2q5RT5n3I9jEqiD2p4tx6VPRRdlKlFEQhUU4RqO1PopFKKQgVR2FLgUUUDCiiigYUUUUAFFFFABRRRQAUYHpRRQITaPQUmxfQU6igLDdi/3RS7QOwpaKAsgxRRRQMKKKKACjFFFADSinqopphQ/w1JRQS4pldrVD0qF7IelXqKpVJLZmcqEJboy2tPaozaj0rXIB7U0xKa1WJqLqYSwcHsY5tR6Uw2w9K2DAKabetVjJoxlgUY5tvaj7N7Vr/Z6X7MKv69Ij+zzIFr7U8WvtWsLdacIUFQ8bNlxwCMoWvtTxa+1agjUdqXavpWbxNRmqwUEZgtval+ze1aWB6UuB6VHt59y/qkDN+ze1H2b2rSwPSkwPSj20+4/qsDO+z+1H2celaO0elG1fSj20+4fVYGb5A9KPIHpWj5a+lNMQo9tMX1WJnmEelNMQ9K0TCKYYKpV5EPCmeYh6U0xD0rQMHtTDD7VosSzGWF8igYh6U0wj0q+Yfam+T7VosUZvC+RR8kelJ5A9Kv8Ak0eVVfW/Mn6r5FDyB6Uvke1X/Ko8mj64w+qeRn+SPSjyhV8w+1IYfamsWL6q0UwgpwUVYMPtSeUfSh10+oKg10GKBUqgUgjPpTwh9KwnNPqbQg10HKBUqgUxUPpUyRmuaTOuEWOVR6U8IPSlC4pak6ErCbR6UYHpS0UDDA9KTA9KWigBpRT2ppiHpUlFAmkys0I9KiaAelXqTaPSmpNbGcqMWZxtx6Un2b2rR2il2j0rT20+5n9VgZ32b2pDbe1aeB6UYHpR7afcPqsDKNt7UCCtTavpSbF9BT+sTJ+qRKCxYqZI6s7F9KXA9KzlNy3NY0FEaigU+iipNkrBRRRQMKKKKACiiigAooooA7fwd/yCJf8Aruf/AEFaPGP/ACCIv+u4/wDQWo8Hf8giX/ruf/QVo8Y/8giL/ruP/QWr6z/mVf8Abpxf8vjiKKKK+TO0KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiimlqBCk4ppao2eoyxoIc7ExcU0vUJY03dT5TJ1CffSbqhyaXNPlF7QmzSjmogakU1LRadyQLS+WKVadSNUkR+UKQwipaKYcqK5gNRmA+lXKKak0Q6UWUDEaTyyKv7R6UhRT2qudkfV0UwCKmRiKl8oU0x4qG7jVNxHB807I9aiIxTdxFIvmtuWKKgDmnq9MakmSUUmQaWgoKKKKBhRRRQAUUUUAFFFFABRRketJketAhaKTcPWjcPWgLi0U3eKQyCgLofSGojNTDNRYh1EiU0wgVF5vvR5vvT5WZupEk20uymCSpFbNLVDTTFVKkAxQOlLQapWCiiigoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACm06kxQIbilpcGpLeB7idYkIDNnBbp0zSemoWIwcVk61fXUUljY2LpFcXsxQTOu4RqqlmIHc4GB9a6f+xLn/AJ6Q/mf8K5PxRFJpmqaXeSqXhs5289owW2K8ZAbHXAJGa0w8ozqWWu/320/EU7pDba5v9P1uDTry8+2w3ULyRStGqOjIVyp24BBDccdqXU729m1a20qxuFti8LzzT7A7KqlVAUHjJLdT6VRW/t9X8S2lzYyedbWVvMZZlB2bn2AKD3OFJqOfUYI9X07XGZhp1xZNGJ9pwhZldS3oCAefWupU3zJuOtu3XW2np0MXM0LLVLiyv72x1S6SZLe3W6S6KBD5ZLAhgOMgr1HY0zw9r8eqWV5fvfQS5JlS1jdC1vEBwGxzuOMnPQnHasDUz/bV3d6haRSXOnRJbRS7EJ89FlLyBf7wAI6deRV+8msddvtOi0VkkmiL+bNEhCxQmNlKsccZJXC9eM9q0dGLWq1e/lZX+V/67BGbJvtWujw9/wAJCb+Mn7P9r+w+Svl+Xjds3fe3be+cZ7V1MMqzQxyr911DDPoRXGtrNsPBX9jEP/a/2H7H9i2HzPM2bOn93PO7pjmuwtYjBaQwk5MaKpP0GKxxMbLVW1dtLaf11NIslooorkNAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACjFFFACYHpRtHpS0UCG7B6UbB6U6igLDdg9KNg9KdRQFkN2D0o2L6U6igLIZ5a0nlLUlFAuVEfkrSiJRT6KA5UIFA7UtFFAwooooGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHb+Dv+QRL/13P/oK0eMf+QRF/wBdx/6C1Hg7/kES/wDXc/8AoK1H43bbosJH/Pwv/oLV9Z/zKv8At04m7VbnFlgO9IXWqhkNJuNfKcrNnWRcDg04HNVFJqwmaWxcZ8w+iiig0CiiigAooooAKKKKACiiigApCaOaMUCGk5ph5qXbRgUhNXK5U0nlmrOB6UuBTJ9mip5Zo8o1bop3YvZIqeWaXyzVrA9KTA9KV2HskVwhp4XFS4HpRgUDUEhFFOoooLCiiigYUUUUAFFFFABRRRQAhXNQstT0hGaCXG5VNAbFSslRlDQYuLQokpfNpm00m007IV5D/NNHmmm7DSiM0aBeQ4SmlEppBGaXZSLXML52KQz1GwxUTmqjG5E6kokxucd6jNz71WdjULMa6oYZM454qSLhufem/afeqJY0bjXQsJEweLmXxce9OE/vWeGNSBjUSwsSo4mRc873pDLVcE04ZrJ0UjRVpMkMlMMlG0mjyyacVBCbkxhegOaXyjSiI+laXhYztMcr1ZjNQpEatRREcmuSq49DsoRl1Jl6U6iisTuCiiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRmjNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRSbhRuFAhaKM5ooGFWtOOL+I/X+RqrVixOLyM/X+RqJ/CxN2VzoN9c/qY3X8p+n8hWxvrHvjm8kP0/kK5sPG0jOM+Z2KZjpPKqWiuwvlQwRgU/pRRQNKwUUU1nxQDdh1FFFAwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDt/B3/IIl/wCu5/8AQVpnjZd+jQj/AKeF/wDQWp/g7/kES/8AXc/+grR4x/5BEX/Xcf8AoLV9Z/zKv+3TiavVsefGE+lKIDVmivk7s6fZRIlhA61KBiiigtJLYKKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFBOKYTQIfmjIqMmmlqQuYmzRUG+jzKYudE9FV/NpfNoDnRPRUIlp4kFA1JMfSbRSbhS7hQPQTYKNlLuFNMgpC0F2ilwKiMoqMzU7EucUWSQKjZwKrtL71GZPeqUGzKVdErvUDtSEk96btJreEFHc5Z1HLYjY5phXNWPLJo8o10KrGJzulKRV2Uvl1aEVOENN4lAsM2VRHTxHVtbcntUy2/rWMsV2NoYRlJYvaplh9qtrCop4UDtXPKrJnVDDJblZYKeIKnorO7N1TiiHyBSiAVLRQPkj2GiNRTqKKCrWCik3Ad6QyKO9AXQ6iozMtIZhQLmRLRUPnilEwNAudEtFNDA06goKKKKBhRRRQAU006kNAhhNJupTTDSIbHbqUPTKKBXZMGzS1Fuo34oK5iWiovNppmpg5pE9IWA71WM3vUZl96ai2Q60UWzIBTTMKqeZ70m+q9mzN1y0ZqaZag3UZo5Be1bJvMpQ9QjNOFJxBTbLCtUtV0qdelQdEXcWprQ4uUP1/lUNSQHEyn6/yp2voKo7Qb8jV31mXZzcufp/Kre+qU5zMx+n8qlU+XU48LPmm15EdFFFUegFFFFABTWTNOooFa4UUUUDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAO38Hf8giX/AK7n/wBBWjxj/wAgiL/ruP8A0FqPB3/IIl/67n/0FaPGP/IIi/67j/0Fq+s/5lX/AG6cX/L44iiiivkztCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBpptSUmKQmiIikIqbbSbaCXErkU0g1ZKUnl07kODK2KSrHlUhip8xDpsgpwJp5jppWi6YuVoA9L5hphFJTshczQ8yGmF6Sk25qkkS5NiFjTCSalEeaesJNVzRRHJKRXwTThGT2q0tv61KsarSdV9DSOHfUrLAT2qVbcd6norNybN40ooiEK0vkrUlFIvlQzylpQijtTqKB2QUUUUDCiiigAoopCwFAhScUwvimM9Rk0ESn2JDJTTIajxmnBM0EczYFiaac1MIqd5QouHI2VTmmEmrhhFRtCatSRnKlIq7jShzUphPpSeUavni0Zck0ORzVlGyKrhCKkXisXa+h0021uT0UwPS7xQbXQ6im7xSGQUBdDycUwtTS9MLZpEOQpam5pKKZDYZpN1IRSEU0iG2KXppekIphU1aijOUpCmSmGQ0uw0nlmtoqCMZObGFzSbjUnlGlERrTngiOSbIwTTwDTxFUixVlKquhpClIYFp4WpVjp4SsHO50xpEASnhKl204LU3ZoqY1VqQDAoooNUrBToziQGm0A4OaqCvJIyxDtSk/J/kWt9V5DmQmjfVa+v7XT7Zrm7mWGIEAsx7noB6n2Fddek1FW7nk5bO9V+n6onoqjYaxYam0i2k+548F43RkdQehKsAce+Kff6nZ6XEkl5OIw7bUGCzOfQKMk/gK5PZz5uW2vY9u63LdFVLDUrPU4mks5xIEba4wVZT6FTgg/WkOqWQ1ZdL88fbTEZvKAJOzOMk9BRySu1bVBdFyisn/AISTTDdvbK9y8iSmJilpMyhwcEbgu3g+9a1EoSj8SsCaewUUUVIwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDt/B3/IIl/wCu5/8AQVo8Y/8AIIi/67j/ANBajwd/yCJf+u5/9BWjxj/yCIv+u4/9BavrP+ZV/wBunF/y+OIooor5M7QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEKg0wpUlFAmkyAx0nlmrFFBHIiAQmniICpKKBqCQ0IB2p1FFBQUUUUDCiiigAooooAKKKKACims22oHkJoIlJInLqO9NMwqqWJpOTVcpk6z6FgzZppcmowpPapViNJ2QJykN5NOCE1KIwKfjFI0UO5GsdPCgUtFBaSQUUUUDCiiigAxSYHpS0UCE2imMuKkoIzQDRWY4qMuandKgZDTjbqc800J5lG+mFTSYrXliYc0iUNTgc1GoqVFrOSSNYNscFzTxHT1XAp1QdCiR+XR5VSUUx8qIvJFHkipaKA5UR+StL5S+lPooDlQzy19KXy19KdRQOyG7F9KXaKWigLITAoxS0UAJiloooGFFFFABTXOFJp1RzHETGtaCvVivNHPitKE/R/kN3+9Y+rYk13w8jDKfaJXwem4RPg/hWhvqK+00albW7JO9vc28nmwTIASjcg8HqCCQR717GLiqcU36femeBlEuau15fqitfAL4y0d1GGe2uUYjuo8sgH8aJQH8c2oYZ8vTpWTPYmRASPw4qW00i5S/N/f3/2q6WIww7YRGkSkgkhcnJOByT2ps2i3MkdjMupMup2kRjN15QIlBA3Bkz0JAPB4I615qlBWXNsmuvW//DH0lmZ9/qEWj+Kb+6ZSxfTocRJ96aTzHVFHqTnH/wCqobLT5LDxRpk126vfXNtdTXUg6bsw/KP9lRgD6e9acXhq3ma4l1ho9Tnn2bjLCAihc7Qq84+8x6nrTF8H6PFq9rfW9jawrBHIpiSADeW24bPtgjp/Ea0Vakla/S22+ll+Pl2J5WZ9quv+H9FmvJWsJ4InluZYEVt7Izs7ESZxnB4G3tjNdbHIssSSIcq6hgfUGuf/AOEZufsX9lnVpP7Ixs+z+UPM8v8A55+Zn7uOOmccZroVUKoVQAAMADtWOInGWqabu9lb+v61KimhaKKK5iwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDt/B3/ACCJf+u5/wDQVo8Y/wDIIi/67j/0FqPB3/IIl/67n/0FaPGP/IIi/wCu4/8AQWr6z/mVf9unF/y+OIooor5M7QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoopDmgBaKbilAoELRRRQMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAGMhNRmEmp6KCXFMhEHvThEoqSigSgkIFA7UtFFBQUUUUDCiiigAooooAKKKKACiiigAooooAKaUBp1FArEJhpvk1YooJcIsgEVSqmKdRQNRSCiiigoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqG6OLdz9P51NUF6cWjn6fzrfCq9eHqvzObGf7tU/wAL/Iob60LU5t0P1/nWPvrXsjm0Q/X+de5nELUF6/oz5zIpXxMv8L/NE9FFFfOH1gUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB2/g7/kES/9dz/6CtHjH/kERf8AXcf+gtR4O/5BEv8A13P/AKCtHjH/AJBEX/Xcf+gtX1n/ADKv+3Ti/wCXxxFFFFfJnaFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAITRupCKTFAh+aKj5oyRSFckoqPzMUebTDmRJRTBKKUOpoHdDqKTcPWjIoAWiiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFVtQOLGQ/T+YqzVTU+NPl/D+Yrpwf+80/8S/M5cb/ALtU/wAL/Ixd1bmnnNjGfr/M1zu6ug0znT4vx/ma+jz6NsNH/EvyZ8zw/wD7zL/C/wA0W6KKK+TPsAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDt/B3/IIl/67n/0FaPGP/IIi/67j/0FqPB3/IIl/wCu5/8AQVo8Y/8AIIi/67j/ANBavrP+ZV/26cX/AC+OIooor5M7QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKbzQIdRSAUtAwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAMCk2ilooEMMYNRtEanooE4plQoRQAat4FJtHpRcj2ZXGacCam2ik2Uh8rQ0NTwc03bSgYoKVx1FFFMoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKp6rxps34fzFXKpav8A8gub/gP/AKEK6sD/AL1T/wAS/NHLjf8Adqn+F/kc3urpdK502H8f5muW3VpSXt1FYaXY2LpFcXszxiZ13CNVDMxA7nAwPrX1Wf03LDxS/m/RnzWQ6YmT/u/qjo6Kw7ae/wBP1yDTr28+2w3ULyRStGqOjIVyp24BBDZHHanandXs2r2ulWNwtsZIXuJp9gdlVSqgKDxklupzwK+Q9i+ZK+lr38vzPreY2qK52K91a3uNR0syR3t5FbLcWkrqI9+4suHA44K9sZBpllqN1aa1NZXGprqUUdo08ziNVaB1YDadvGDk4B5+U1X1eWtmv6/rrYXMjpaK5P7Vro8Pf8JCb+Mn7P8Aa/sPkr5fl43bN33t23vnGe1dTDKs0Mcq/ddQwz6EVNSk4db9Bp3H0UUVkUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAdv4O/5BEv/AF3P/oK0eMjjSIf+u4/9Bajwd/yCJf8Aruf/AEFaj8bHGjQ/9fC/+gtX1n/Mq/7dOJu1U4zNFQB6eGr5I6lJMkopm6lzTKuOopAaWgAooooGFFFFABRRSHpQAtJuHrTGzULE0ESnYsb19aQyqKqMx9aiLn1q402zCWIsXjOoppuBWeZD60wy+9bRwzZjLGWNE3VNN39KzDL70wyn1raOCuYvHM1ftdH2usnzT60eafWr+okfX5Gyt0DUqyq1YizH1qeOf3rCphJR2N6eOvubGc0VSin96tJIGrlcWtGd0KkZ7D6KKKRoFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSZHrQIWik3D1oyPWgBaKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUmaAFopMik3igVx1FMMgpPNoFzIkoqPzRS+aKA5kPopnmLR5i+tA7ofRTPNX1pPOT1oFzIkoqPz0pPtCUBzx7ktFRfaEpfPWgOePckopglU96UOp70Duh1FJketLQAUUUUDCiiigAooooAKo6z/yCpv8AgP8A6EKvVn60f+JTP/wH/wBCFdeA/wB7pf4o/mjmxn+71PR/kctmrtwxtoND1Jkdre1nk88opYorq67sDsCRn61nbq6fSXxpkP8AwL/0I19dxDLkw0X/AHvzUkfN5MrV36fqjOTULbWPE1nc2MnnWtlbzGWdQdm5ygCg9zhWPHSmz6lbx6xp2uszDTbmxeIT7ThCzI6lvQEA8+tdB5lG+vjfaxTWmlrb9NfLzPprnLnU3l1e91rToGurSG3hgLKp/eDzGaQp/e2qwPHU8VC72Gq65C+hFC0kUwvpYU2oVZSFDnGC28gjvwa64nNJVLEJapdLb+Vte/foSzjzrcA8GHRyH/tX7F9j+x7D5nmbNnT+7nnd0xXYWmYLSGEnJjRVJ+gxTSDRk1NWopq0VbW4KTRcooorA6QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDt/B3/ACCJf+u5/wDQVqPxx/yBYf8Ar4X/ANBapPB3/IIl/wCu5/8AQVqPxvzosP8A18L/AOgtX1n/ADKv+3Thl/EOBzTg1Rnik3Yr5S1zTmsThqcDVcPUgek0y4zJc0uaYGBp1I0THbqXIpuKOlA7j6Kj3Y70hlx3phzIlpCQKrtP71E1x701FvYzlWiiyzrUDuKrPcVA85NdFPDSkclXFxLLyCoHkFV2lJqIsT2Nd9LC23PPqYm+xM0lRmSmYJ7GlERPautQjE5nOUg3E0cmpFh9qlWColVhEqNKTK+00u2rXk+1BirP6wjT2DK2MU5SRUhjpu2nzqQuRolR8Vajl96ojipVbFctWkpHTSquLNWOUMMGpazY5MVcilyMGvOnBxZ6tKqpImoooqTcKKKKACiiigAooooAKKKKACiiigBGOKhaQ1MwzUDpQZzv0GNKfWozMfWlZaiZK1govc5JymKZjSeefWmFKaUroVODMHOZYW4PrUqXJ7mqO3FOBxUyoxexUa80aazK1SAg9Ky1fFTJOR3rCVJo6oYhPcvUVAtxnrUgkU96zN1NPYfRSZB70tBQUUUUDCiiigAooooAKKKKACiiigAooooAKKCQKaXAoFcdSEU3zBSeYPWgXMhTUbZp28etNJBpESaImJphY1IRUbLWkbGE7jS9J5hpGFRmt4wiznlOSJPNNIZj61ATTSa2VCJi60iczH1ppmPrUBNJWqoRM3WkTGY+tHnH1qHFG2r9lBEe0kTecfWnCU1CEp4SolCBcZTJRKfWnCY+tRBKXZWLhA1U5kwnI708XB9aqlTTTkUvYQZXt5o0VufepVnU1k7iKespFRLCvoawxj6muGB6GlrMW4x3qVbr3rB0prodMcTBl6iqn2r3ppuvep5Jdi3Xh3LbOAKzNacHS5h/u/8AoQp7XPvWfqk+7T5R9P5iu7L6E/rVJv8AmX5o48ViYulKK6pmBmuj0x8abEPr/M1zGa3tOf8A0GIfX+Zr6/P6fPhor+8vyZ4eBfJUb8v8jVElPVs1VQ5qzGK+JqwUT26U3Jk6jNP2UsacVLXMd8Y6akJjphXFWaay5oBwHUUUUGgUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB2/g7/AJBEv/Xc/wDoK0njMZ0eIf8ATcf+gtS+Dv8AkES/9dz/AOgrR4x/5BEX/Xcf+gtX1n/Mq/7dOL/l8cA8ZqBlIq/1qNog1fKJ2N50r7FAkigSYqw9ufSq7xEdq3jKEtzjnCcRwmxUguBVNkYdqYSw7Vr9XjLYz+sTiaH2gUhuBWfub0pNzelNYRdxPGSLrXAqJp/eq3zHtShGPatVh4R3MniJyHtNnvTC5PenCEntUq2/tVc1OBKjUmVsMe9KImNXktvap1tRWcsYlsaxwcpbmYLcmni1PpWoIFFPEajtWMsZN7HRHAR6matp7VKtr7Vf2gdqWsXXm+pvHCQRTW29qf8AZ/arNFZuTfU1VGCKph9qiaP2q/jNROlCk0TKirFBo6iKVcdagYV1U6rOGpSSIMUU8imV1KVzmasPU1YjeqwqRTXPVjdG9OVmaMb5GDUlUkfFWUcEVxtWPSpz5kSUUUUjUKKKKACiiigAopCQKaZAKBXSH0VCZqaZaCXNE5IFMYiovMpN1FiXNCtioyKUmkNNGUmMIFMIFSGmkVtFmEkRmmmnlTSFTW0ZIxkhlJuxTitIVrRNMzaYokx3p4m96hKmkwaHTjIFUlEtrP71Itx71n5IpPMIqXhU9jRYqSNUXFPEwNZImxT1uPespYWS2No41dTWDg0oINZq3HvUyz+9YSpyjudEcRGRdoqBZhUglU1BupJj6KZ5i0hmUUBzIkoqA3ApjXBppNkurFFksB1NRtMB0qo059aheY1pGjKRhPEpbFlp/eomn96qtKahaU12U8Hfc4amLZdNx70faPes4ymm+Ya6Fgkc7xkjUE/vTxNmstZDUySGsp4NI0hi2zRD5p3WqiPVhGzXDUp8p206vMKVzUbJVgc0FM1Cm0aOnzFJkphSrrR0wxV0QrnNOgykVo21aMVAirb6wjL2DK4SniOpxF7VIsXtWUsQaQw7IBHUgiqcR08IBXPKq2dMaCRAIvaneT7VYC07AqOZmyooqGA+lRtAfStCkwPSmpyQPDxZltAaiaIitgop7UwwIa2jiZLc554NPYxyrCmksO9azWgPTFRNZ+1bxxceqOeWDmtjNLt600u3rWgbP2pPsf8As1qsVSMnhapn5Y96q34IsZCT6fzFbYs/aqWs2/l6RO+Om3/0IV1YPFweJpxXWS/MznhKig2zlt1dFpUZewiP1/ma5jNdroEIfRrdj33f+hGvoeI5unhItfzL8mY4Gh7SpbyJo4TVuOLHWpRGq06vgpzctz36dFQAcUUUVJuFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB2/g7/kES/9dz/6CtHjH/kERf8AXcf+gtR4O/5BEv8A13P/AKCtJ4yIGkRZ/wCe4/8AQWr6z/mVf9unF/y+OJopMj1pa+TOwKaUVuop1FAEDW4PSoWtfartFNSa2M5UoS3Rnm1PpSC1PpWjRV+1n3M/q0CitofSpFtcVaoqXOT3ZaoQXQhWBRUgjUdqdRUmiilsGKKKKCgooooAKKKKACiiigApG6UtFAirIKgZavMmaiaKqjKxz1KVyiVppWrhiPpTDF7V0RrI5ZUGVsYpRUpjpChq/aJmfs2hFbFSrJiotpowRUSjGRcZOJbWfFSCcGqGTTgxrJ0jeNdl8SKe9O3D1qiGNSqTWbjY2jVuWdwppamqCaftqTW7ZCzZqIk1aMeaaYqa0M5QbKpzRzVjyaPJquYz9lIgpcVN5dL5dLmGqbIcUban8v2pQlK5XsyDZR5dWQgpcCi7H7JFbyjSeSat4oxTux+yiUjF7U0xe1XsD0pCgNNTaIdBGeY6YY60DEDUbQ1cazRlLDmeyVEy1oNF7VC0VdVPEI46mHZQIIpm4irjRVC0RruhWizjnSkiMSEVKk3vUZjNAUiqlGEkTFzTLiS+9TLJVJAaspXn1qcUd1KpJk240hJpyinba5LpM6+VtFck0wk1YKU0x1tGpFGMqcisc1G2atmOo2jrohWiYTpSKbCoypq4Y6aYq6o1kcsqTKew0CM1c8qlEPtVvEpErDtlVYzUqoasCKpBF7VhPEpm0MOyJFqygoWOpVSuCrVUjupUmh6CpgtNRMVLXMd8Y2Q3YKTyxT6KC7IZ5S0eWtPooFyobsHpS7RS0UDsJgU004niomNITdh4NP61ADUqmgUXcdRRRTLCiiigAooooAKKKKACsvxFxoNz/wAB/wDQhWpWV4k/5AF1/wAA/wDQhXZl3++Uv8UfzRlX/hS9GcJurtNKvrXT/DEFzdzLDECwLN6lzgD1PtXD5ro7QCT/AIRVHGU+0XD4PTcEkwfwr7XiSClhYp/zflGTPKwC5ajfl+qOjsNYsNTaRbSfc8eC8boyOoPQlWAOPfFPv9Ts9LiSS8nEYdtqDBZnPoFGSfwFZ18AvjLR3UYZ7a5RiO6jyyAfxomAfxzahxny9OlZM9iZEBI/DivhvZQbT1tZv7r/AOXY9e7NGx1Oz1GB5bScSKh2uNpDKfQqRkH2IpunatZ6qJvsjyMYWCSLJC8bKSMjhgD0INZ0WI/HF3sGBJp8LPjuRJIAT+HFJpMgXX/EJJwPtEXP/bBKJU4Wlbsmvnb/ADFzFn/hJNMN29sr3LyJKYmKWkzKHBwRuC7eD71rVxtqde0DRpryRrCeCN5bmWBFbeys7OxEmcZwem3tjNdZFcJLEkiHKuAwPsaVenGHwarvf/gIIzvuTUUUVgaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHb+Dv+QRL/wBdz/6CtReOW26JCf8Ap5X/ANBapfB3/IIl/wCu5/8AQVqt4+ONCg/6+V/9BavroK+WJeR59V2m2cIJvenif3qiXpPMxXzf1e5CxLRpCcU8SqayxL704T+9S8NI0ji11NQOp70uR61mif3qVZvesnSkjaOJiy9RVZZvepBLUGymmS0VEZPemFyaAc0TFwKTzKg5NOCmkTztkwanVGqmngYpmiFooooGFFFFABRRRQAUUUUAFFFFACYHpSFAadRQKxGYgaaYKmooE4plcwUzyD6Vbop3ZDpRZT8g+lKID6Vboo5mL2MSusFSrEBT6KRagkAGKKKKCwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAGlAaiaOp6QjIoJcUym0dRmKrjLTClUqjRzypJlIw00xVdKU0pWyxEkYSw6KgjxUijFSFaaRim6nMT7PlHhsU4NUNPXNZyiaRmyUc08JmmoKnUVkdMVchMdMaOrWBSFaLtDdNMpGOmeXVxkqMrVqo0YSoogEdOEdS7aeq0OowjSREI6kWKplWnVF2zeNNIiEQp4QCnUUFpJBRRRQUFFFFABRRRQAUUUhoAaxqI9akNNxSMpajQKlXimgUooHFWJKKZuxRvpl3Q+io/NFKJFoFzIfRSBge9LQMKKKKBhWT4mOPD11/wD/0MVrVkeKOPDl3/wAA/wDQ1rsy7/fKP+KP5ozq/BL0PPd1dppumjUvDWnsk729zbyNLBMgBKNuYHg9QQSCPeuG3V6P4X58OWn/AAP/ANDavtOKJOOEg1/MvykcGEj779AtNIuUvzf39/8AarpYjDDthEaRKSCSFyck4HJPamz6LcyRWMy6iy6naRGM3XlAiUEDcGTPQkA9eCOtbNFfCe2ne/6I9HlRj6fpclnc3F5dXZurycKrSbAiqq5wqrzgZJPU9aS308W97qNw0gkW9dWKFfugIqY984z+NajioGpe0k22+v8AX6GM1Y54+Hrj7H/Zp1ST+ysbPI8oeZ5f/PPzM/dxx0zjjNboYKoVRgDgAUNUZNbOUqnxHM5NGpRRRXKeiFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAdv4O/5BEv8A13P/AKCtV/HwzoUH/Xyv/oLVY8Hf8giX/ruf/QVqLxyM6JCP+nlf/QWr66Ltll/I8+qrzaPNCtMKmr3lZpphr56OISMZYdlAg0nNXTBTDBW8cRFmDw8kVwTUisaf5NOWKpnVgxxpSQ5CamXNMWOrEcdcVSSb0O6lCQKpPapBH608AClrI61BIaEUdqXA9KWigqwUUUUDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAaRTcVJSYFITQzFNK5qXApcUC5SsUNMMZ9KuYFJgelNNoh00yoIj6U8R1YwPSjAp3YKkkMVcVIOKKKRolYKKKKBjWFRsKlaompESG05TTDQDigzTsWFNLUKvUgeg1Ukx1FGc0UygooooAKKKKACiml8VGZKCXJIlzSVGGzUqikCdxuKQipKRhxQDRGTSbqGqMmgzbsOLU0tTSaQmqSIcgJpu6g0hFWkjJtjhIRUyTetVcUoOKbggjUki+rhqdVJXIqZJfWs2mjpjVTJ6xvFRx4au/+Af8Aoa1rhwaxvFh/4pi8/wCAf+hrXXlv++Uf8UfzRU9Ys833V6V4VOfDVp/wP/0Nq8v3V6d4TYf8IxZ/8D/9Davs+Kv9zj/iX5SOehG0jboJxTC4FRtJXwB0uSQrmoWoL0wtVKLOec0xDTCKfS4rROxg1cv0UUVieiFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAdv4O/5BEv/AF3P/oK0zxr/AMgeH/r4X/0Fqf4O/wCQRL/13P8A6CtR+NjjRof+vhf/AEFq+s/5lX/bpwy/inEqopdooQ5FOr5M7ElYaY1pphFSUUByohMIo8mpqKBckSMRgVIBiiigpJIKKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFGcUxpAKBXsPpCQKhaf0qBpqai3sZyqxRcMiim+ctUTLTPNNaKjJmDxSNETLTg6nvWaJTT1lpOlJDjiUzRoqqkpqwrhqz2OiM1IdRRRQWFFFJk0ALRSA0tABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACEZppWn0UCauQlKaVNWMUm0UiXAr4NAJFTlBTTHQTyMYGNSB6b5dLtxQNXQ8NS5pmKKCrjiRTGeg1G1ApSGs1NyaD1pAKpGDbbJkqcdKgSpx0pG8NgooooLI2WoitWaaVpESjcrFaTFWClNKU7mbgQYpMVMVpNtPmIcCLFJtqXbSbafMS4EWKUGn7aTbT5kLlaAORWR4rkP8AwjN4P9z/ANDWtbbWL4tOPDF4f9z/ANDWuzLkvrlL/FH80Um9jzfdXpvhR/8AimbP/gf/AKG1eV769N8Jt/xTFn/wP/0Nq+w4pV8HH/EvyZUvdVzeL0wtTC1NJr4aNMwlUHFqbuptJWygjFzZKGp6moAaerVE4FQmadIaCwHemF19a5j020Lk0Zpu4etKCPWkTcfmjNAxS0ygooooGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFIWA7igQtFMMijvSGZfWgXMiSiovNHrThIPWgOZD6KbvX1pdy+tA7oWikyPWloAKKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFGaMj1oEFFGR60mR60ALRSblHcU0yoO9AXSH0VA1yo6VGbv3pqMnsjN1YLqW6KpfavenC596bhJdBKvB9S3RUAuB604TqamzLU4vqS0UzzU9aPNT1oHzIfRTPNX1pPNFAcyJKKaHBpcigdxaKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHb+Dv+QRL/wBdz/6CtReOP+QLD/18r/6C1S+Dv+QRL/13P/oK1H4450WH/r4X/wBBavrP+ZX/ANunBP8AiHDxGpagj4qevkzshsFFFFBYUUUUAFFFFABRRRQAUUUUAFFFFABRQaYxNAm7D8ikyPWoGY0wuaEQ6li1ketLkVS8w0olNPlZPtkXKKrrKakWTNItTTJKKQEGloKCiiigYUm4U00w5pEt2JNwpPMFQkmmnNMhzZOZRTDNUXNJg00iHUY5pSe9Rs59aUrTStXFIxk5MjZie9MOalKikwK3jJIwlFshING01LgetHHrWntDPkIwpp4BpwxTgBWcplxgC5qxG1QgVItc89Tqp6FpTkUtMSn1B1oKKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFJilooAaRUbCpj0qNqREkQkUnSnNTSaaMWOU4qVXqvmlBosOMrFoHNLUKmpQc0GydxaKKCcUFBTCRSM9Rs5pESkkOJFJmoixoyadjLnJRTsVECakDUik0O20hQUuaCaC9CNlrnvGfy+Fb0/7n/oa10LNWXrmmnWNJnsfO8nzdvz7d2MMD0yPSurA1I08TTnN2Skm/kzN2TPG/MrvtIvbmPw1o1nZukc95LInmsu4RqpdmOO54wPrVb/hW7f8AQW/8l/8A7KtF9MbQLXRXd3ngsZpPOkWM5VXV/m2jPALDP519dmuZYTF0o06UuZ3vaz7Puu9ia8oyjaJct576x1mGwu7r7XFcxO8crRhHVkK5B24BBDccdqdqNxeTarb6ZZzi3LxPPLNsDMqggAKDxklup9Khju7fVvEdpcWT+dbWdvMZZlB2bm2gKD3OFJpJr23i1bT9bYsNOuLNoxPtOELFXUt6AgHn1r577S01t266209DkUGyvc6pqOmzT6bJLHc3TeT9mndNv+scp84HHykE8YzViee/0S4tXu777bbXDmJ90So0bbWYEbe3ykYPqOaz9QJ1TUpdXsY5J7Sz+y/MiH97slZ32f3sKR0q7qd1a+Ip7Cy0yUTskzSyuoO2JRG4+Y9iSwGOtXdaXWnXTbT8CvZkPn60NC/t03qf6j7V9i8kbNmN23d97djvnr2rpYXEsSSrna6hhn0Ncu2q248H/wBkkONV+x/ZPsew+Z5mzZ0/u55z0xXWW1uYLWGI8lECk/QVhiJWWqtq/u/rqCpkB1AetJ9vHqK5f7U/rR9qb1r2f7GgeJ/aVQ6kX49acL9fWuU+1P60v2t/Wk8lgxrM6iOtGoD1p41EeorkPtj+tH2yT1qHkcS1m1RHY/2kvqKX+0l9q437ZJ60v2yT1qf7CXcr+2Kh2P8AaSe1NOpr7VyH2yT1pPtb+tP+w49w/tiqdcdUHqKadUHrXJ/an9aPtT+tUskgS82qs6o6n/tUn9pe9cr9pf1o+0v61X9i0yf7UqnU/wBpe9L/AGl71yv2l/Wj7S/rR/YtMX9qVTq/7S/2qP7T965X7U/rR9qb1pf2LAf9qVTqv7T96P7T965X7U3rR9qf1o/sWAf2rVOq/tP3pf7T965T7U/rR9qf1o/sSAf2rVOr/tP/AGqP7T/2q5T7U/rR9qf1o/sSA/7VqnV/2n/tUh1P/arlftT+tJ9pf1o/sSAv7VqnUHUs/wAVMOoA965r7S/rR9pf1q1k1NEvMqrOk+3j1o+3j1rmvtL+tH2l/Wn/AGRAn+0Kh0wvx60v28etcz9pf1o+1P60v7HgP+0ah0/28etKL8etcv8Aan9aPtT+tL+xoD/tKodUNQHrTxqI9a5L7W/rS/bH9ah5JFlrNKiOwGpD1FKNSX2rjvtj+tH22T1qf7CXctZvUOz/ALST2o/tFPauM+2yetH22T1pf2Eu4/7Yqdjs/wC0k9qT+0k9q437bJ60n2yT1p/2Eu4f2xU7HZHU19qadTX1Fcf9sk/vUn2t/Wmsij3Jeb1TrzqY9aYdSHrXJ/a39aT7U/rVLI4EvNap1n9pD1pP7SHrXKfan9aPtT+tV/YkCf7UqnV/2kPWj+0h61yn2p/Wj7S/rR/YlMP7UqnV/wBpj1o/tMetcp9pf1o+0v60f2JTD+06p1f9pj1pP7SH96uV+1P60fan9af9iUxf2nVOr/tMetOGpj1rkvtT+tH2p/Wk8kpjWaVTrxqg9acNUHqK477U/rR9qf1qf7DgV/a1Y7H+1V9RR/aq+1cd9qf1o+1P60f2FDuP+16x2H9qr6ik/tUeorkPtT+tH2p/Wn/YdMX9rVjrjqw9aYdVH96uT+0v60faX9aayOmS81rM6o6qP71N/tQetct9of1o+0P61ayWkS8yrdzqf7THrSf2mPWuX+0P60faH9af9jUhf2jW7nUf2mPWk/tMetcx9of1o89/Wj+xqXYX9o1u50p1MetRtqWe9c95zetJ5zetaRymkuhLx1V9TdbUM96Z9u96xfNb1pPNNarLqa6GTxNR9TcF9708X3vWD5x9aXzm9aTy6DGsVNHQC/HrTxf+9c757etH2hvWs3lcGWsbUR0gvx604X49a5r7Q3rR9pb1rN5RAtY+odOL8etOF8PWuX+1P60fa39ah5NBlLMaiOrF8vrTxfj1rkvtj+tH21/Ws3kkWaLNKiOwGoD1FPGor6iuN+3SetH26T+9UPIl3LWb1EdoNQX2pf7QT2ri/t0n96j7fJ/epf2F5lf2zPsdr9vj9qPt8dcV9vk/vUfb5P71L+wX3H/bM+x2n9oR+1IdRT2rjPt8n96j7dJ/ep/2D5h/bM+x2R1FPam/2kvtXHfbpP71H21/Wn/YS7k/2xUOx/tJfUUv9pJ7Vxv21/Wj7a/rT/sJdxf2xUOz/tJPaj+0k9q4z7bJ60fbZPWl/YS7j/tip2OyOpJ7Uh1NfauO+2yetJ9sf1prIl3E84qHXnUx6imnUx61yX2t/Wj7W/rVLI4EvNqp1n9pj1o/tIetcn9rf1o+1P60/wCxIC/tWqe4eAZ/P0KdvS5Yf+OrU3jUZ0aH/r4X/wBBasn4UyGTwvck/wDP64/8cStnxkM6RF/13H/oLV1YqkqWAlTXRHq4WbqqMn1OCAxUimgrSdK+LPSSsPopoalyKZQtFFFAwooyPWkyPWgQtFJuHrSbxQF0OopnmCjzFoC6H0VEZR60wzCglzSJyaYahM3vSebRysh1USMKjK0ofNOHNGqJ0kQlaTbVnZmk8uq5yfZEAFPBp5jppGKV7hyuI8PThJVcnFN8zFCi2P2ti6Hpd1URL708Te9PkkUqyLeM0m2oVmFSCVamxopxYpSkMdL5i0hlX1pA+UaUFNIxSNMKgeYetVGDexjOpFErHFRNIBUDze9QtITXXTwze5x1MSuhYaYVE04quxY1GQ9dsMNBbnHPESexYNxSfaKrFXphVhXQsPTMHXmXhcCpFmBrNywp6yEVE8JF7FRxMluaqyZqZWrLSU1ajlrgrYZx2O6jib7mkjVLVKOSrCyCuBprc9OFRNEtFN3il3Cg0uLRRkUUDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAaTUbGpCtJspENNkBpuKseXS+WKdyPZsrbaeqVMEFOCgUXGqYxUp4GKWig0SsFNIp1FAERQmk8o1NRQLlRD5NHk1NRQLkRF5VHl1LRQPlRFtpCtTUYpBykGzNHlZqeimLkRD5NHk1NRnFAciIfLo2CnlhTdwpCskKFpdlIDTwc0FJIZs9qQx1LRTDlR5rtpNtT7RRtFfo/OfBEOyjZU2BRgUudgRbKNlTYowKOdgQ7KXZUnFHFHMwI9lG2pOKSjmYDNtG2n0UcwDNtG2n0vFHMwI9tG2pKOKXMwI9tG2pOKOKOZgR7aNlS0UczAi2UbKloo5mBFso21LRRzMCLbRtqWijmYEWyk21NijFHOwIdlGypsUYp84EOyk21NgUYFHOBDto2VNgUYFHOBDso2VNgUuKOcCHZRsqbApcCjnYEGyjZU+KMUudgQbKNlTYowKOdgQ7KNlTYoxT52BDso2VNijFHOwIdlGypsUYo52BBspdlTYoxRzsCHZSbKnxRijnAg2UbamxRgUc7Ah2UbKmxRijnYEWyjZU2KMUc7Ah2UbKmxScUc7Ai2UbKlwKOKOdgRbKTZU3FHFHOwIdtGypsCjFHOwItlGypsUYo52BDso2VNijFHOwIdtJtqbFGKOYCHbRsqXFLijnAh2UbKmwKMCjnAi2UmypsCjAo52BDso2VNgUYFHOBBso2VPgUYHpT5wINlGyp8D0owPSjnAg2UbKnwPSjA9KOcCDZRsqbAowKOcCHZRsqbAowKOcCHZSbKn20bRRzgQbKXZU22jbRzgQ7KNlTbaNoo5wIdlGyp9tJto5wIdlGyptvtRto5wIdtG01LtpdtHOBDtNLtqXb7UbaOcD2D4SDHhW6/6/n/8AQErc8Y/8giL/AK7j/wBBasX4UDHha6/6/X/9AStnxmcaPEf+ngf+gtXDmTvhZ+h9NgP4cDiaYxpjSVC0nvXwyVz1JVEiRnxTDLjvULSVEXrWNFs5p17FozH1ppnPrVUvTSxrZYdGDxLLRuD6003PvVUk1GxPrW0cLFmUsVIuG596T7V71nsx9ajLt610RwUGYSxk0af2v3pPtXvWUZD60nmH1rT+z4Gf16Zqm596abis4OfWnBj60/qcUH1uTL3n0omqmCakXNRKhFIqNaTLySZqzG2aoR5q7EOlebXgo7Ho4ebZbUcU6kHSlrmPREIzUTrU1IwyKBNXKTioGJq68dRNCTWtOaW5x1KUnsUyxpvmEVaNuT2phtjXVGrT6nJKjUWxCJiKUXBpWtmqM27VqvYyM/3sST7VSG5NReQ1Agan7Oihe0rMcZyabvJ7GpFtmqdLU+lKVWlDYcaVWe5VCFuxqRbfPar6WuOtTrEq9q554t/ZOuGC/mM5bTPan/Y/atDA9KWsHXqPqdCwlNGabP2qJrX2rXwKjeMHtTjiKi6kywcHsYr22O1QtDitl4h6VWeL2rspY19Thq4NLYzgpFSI2KmeKoimK6/aKaOT2bgydJKnWWqAJFPEmK56mHUjeFdxL/ne9L5/vWf5tIZvesvqdzb63Y0hce9PFz71kef70faPej6iwWOsbQuBSicVjC596etz71EsFNGscembHnCl8zNZiT571ZSTPeuadKUNzohiFMtbqTeaReRTttZHQrsPMpQwNMK0mDQF2TZoqME08HIplJ3FooooGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRQTigAoppcUnmCgV0PopnmClDZoC6HUUUUDCiiigBCcVGz05qiakRJjS1N3UjUzNWonNKRMr1Kr1VBqRWpONi4TLQOaWoVapA1SbqVz/2Q==",
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1680x320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nassu import viz\n",
    "\n",
    "viz.enable_offscreen()\n",
    "\n",
    "PANEL = (840, 320)\n",
    "cfg = sim_cfgs[\"periodicTurbulentChannel\", 0]\n",
    "domain = (168.0, 168.0, 64.0)\n",
    "\n",
    "panels = [\n",
    "    viz.Panel(\n",
    "        \"mid-channel\",\n",
    "        viz.PlaneSource.from_cfg(cfg, series=\"plane_series\", plane=\"mid_channel\"),\n",
    "        viz.frame_domain(domain, \"y\", panel=PANEL, slice_coord=84.0),\n",
    "    ),\n",
    "    viz.Panel(\n",
    "        \"near-wall\",\n",
    "        viz.PlaneSource.from_cfg(cfg, series=\"plane_series\", plane=\"near_wall\"),\n",
    "        viz.frame_domain(domain, \"z\", panel=PANEL, slice_coord=8.0),\n",
    "    ),\n",
    "]\n",
    "steps = [panels[0].source.steps[-1]]\n",
    "plotter = viz.render_grid(\n",
    "    panels,\n",
    "    steps=steps,\n",
    "    scalar=\"u_mag\",\n",
    "    cmap=\"viridis\",\n",
    "    clim=(0.0, 0.06),\n",
    "    bar_title=\"|u|\",\n",
    "    panel_size=PANEL,\n",
    ")\n",
    "plotter.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:43.966598Z",
     "iopub.status.busy": "2026-06-29T17:45:43.966523Z",
     "iopub.status.idle": "2026-06-29T17:45:44.001923Z",
     "shell.execute_reply": "2026-06-29T17:45:44.001690Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.1a0"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Commit hash:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beddd3464d5add9153956c71d380b82cb4629570"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-29T17:45:44.002717Z",
     "iopub.status.busy": "2026-06-29T17:45:44.002648Z",
     "iopub.status.idle": "2026-06-29T17:45:44.036748Z",
     "shell.execute_reply": "2026-06-29T17:45:44.036506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>pre { line-height: 125%; }\n",
       "td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       ".output_html .hll { background-color: #ffffcc }\n",
       ".output_html { background: #f8f8f8; }\n",
       ".output_html .c { color: #3D7B7B; font-style: italic } /* Comment */\n",
       ".output_html .err { border: 1px solid #F00 } /* Error */\n",
       ".output_html .k { color: #008000; font-weight: bold } /* Keyword */\n",
       ".output_html .o { color: #666 } /* Operator */\n",
       ".output_html .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */\n",
       ".output_html .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */\n",
       ".output_html .cp { color: #9C6500 } /* Comment.Preproc */\n",
       ".output_html .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */\n",
       ".output_html .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */\n",
       ".output_html .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */\n",
       ".output_html .gd { color: #A00000 } /* Generic.Deleted */\n",
       ".output_html .ge { font-style: italic } /* Generic.Emph */\n",
       ".output_html .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */\n",
       ".output_html .gr { color: #E40000 } /* Generic.Error */\n",
       ".output_html .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       ".output_html .gi { color: #008400 } /* Generic.Inserted */\n",
       ".output_html .go { color: #717171 } /* Generic.Output */\n",
       ".output_html .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       ".output_html .gs { font-weight: bold } /* Generic.Strong */\n",
       ".output_html .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       ".output_html .gt { color: #04D } /* Generic.Traceback */\n",
       ".output_html .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       ".output_html .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       ".output_html .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       ".output_html .kp { color: #008000 } /* Keyword.Pseudo */\n",
       ".output_html .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       ".output_html .kt { color: #B00040 } /* Keyword.Type */\n",
       ".output_html .m { color: #666 } /* Literal.Number */\n",
       ".output_html .s { color: #BA2121 } /* Literal.String */\n",
       ".output_html .na { color: #687822 } /* Name.Attribute */\n",
       ".output_html .nb { color: #008000 } /* Name.Builtin */\n",
       ".output_html .nc { color: #00F; font-weight: bold } /* Name.Class */\n",
       ".output_html .no { color: #800 } /* Name.Constant */\n",
       ".output_html .nd { color: #A2F } /* Name.Decorator */\n",
       ".output_html .ni { color: #717171; font-weight: bold } /* Name.Entity */\n",
       ".output_html .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */\n",
       ".output_html .nf { color: #00F } /* Name.Function */\n",
       ".output_html .nl { color: #767600 } /* Name.Label */\n",
       ".output_html .nn { color: #00F; font-weight: bold } /* Name.Namespace */\n",
       ".output_html .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       ".output_html .nv { color: #19177C } /* Name.Variable */\n",
       ".output_html .ow { color: #A2F; font-weight: bold } /* Operator.Word */\n",
       ".output_html .w { color: #BBB } /* Text.Whitespace */\n",
       ".output_html .mb { color: #666 } /* Literal.Number.Bin */\n",
       ".output_html .mf { color: #666 } /* Literal.Number.Float */\n",
       ".output_html .mh { color: #666 } /* Literal.Number.Hex */\n",
       ".output_html .mi { color: #666 } /* Literal.Number.Integer */\n",
       ".output_html .mo { color: #666 } /* Literal.Number.Oct */\n",
       ".output_html .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       ".output_html .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       ".output_html .sc { color: #BA2121 } /* Literal.String.Char */\n",
       ".output_html .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       ".output_html .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       ".output_html .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       ".output_html .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */\n",
       ".output_html .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       ".output_html .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */\n",
       ".output_html .sx { color: #008000 } /* Literal.String.Other */\n",
       ".output_html .sr { color: #A45A77 } /* Literal.String.Regex */\n",
       ".output_html .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       ".output_html .ss { color: #19177C } /* Literal.String.Symbol */\n",
       ".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".output_html .fm { color: #00F } /* Name.Function.Magic */\n",
       ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"nt\">variables</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># Domain size</span>\n",
       "<span class=\"w\">  </span><span class=\"nt\">ds</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\">168</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\">168</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">z</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">64</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># Equation-based (&quot;cold&quot;) start - self-contained, no external</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># artefact. The streamwise field is an analytic Reichardt mean</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># profile mirrored across both walls (wall distance in viscous</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># units y+ = (u*/nu) * min(y, NY - y) = 4.5 * min(y, NY - y)),</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># seeded with wall-enveloped multi-mode sinusoidal perturbations</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># (streamwise streaks plus cross-flow rolls carrying wall-normal</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># velocity) at ~10% intensity to break the x/z symmetry and trip</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># transition to turbulence. The sin(pi*y/NY) envelope vanishes at</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># both no-slip walls. The scalar is initialised separately via its</span>\n",
       "<span class=\"w\">  </span><span class=\"c1\"># `initial_field` ramp.</span>\n",
       "<span class=\"w\">  </span><span class=\"nt\">init</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">ds</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">ux</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!sub</span><span class=\"w\"> </span><span class=\"s\">&quot;0.005</span><span class=\"nv\"> </span><span class=\"s\">*</span><span class=\"nv\"> </span><span class=\"s\">(2.5*log(1</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">0.41*4.5*Min(y,</span><span class=\"nv\"> </span><span class=\"s\">${ds.y}</span><span class=\"nv\"> </span><span class=\"s\">-</span><span class=\"nv\"> </span><span class=\"s\">y))</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">7.8*(1</span><span class=\"nv\"> </span><span class=\"s\">-</span><span class=\"nv\"> </span><span class=\"s\">exp(-4.5*min(y,</span><span class=\"nv\"> </span><span class=\"s\">${ds.y}</span><span class=\"nv\"> </span><span class=\"s\">-</span><span class=\"nv\"> </span><span class=\"s\">y)/11)</span><span class=\"nv\"> </span><span class=\"s\">-</span><span class=\"nv\"> </span><span class=\"s\">(4.5*min(y,</span><span class=\"nv\"> </span><span class=\"s\">${ds.y}</span><span class=\"nv\"> </span><span class=\"s\">-</span><span class=\"nv\"> </span><span class=\"s\">y)/11)*exp(-4.5*min(y,</span><span class=\"nv\"> </span><span class=\"s\">${ds.y}</span><span class=\"nv\"> </span><span class=\"s\">-</span><span class=\"nv\"> </span><span class=\"s\">y)/3)))</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">0.010*sin(pi*y/${ds.y})*cos(2*pi*5*z/${ds.z})</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">0.006*sin(pi*y/${ds.y})*sin(2*pi*3*x/${ds.x})*cos(2*pi*4*z/${ds.z})&quot;</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">uy</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!sub</span><span class=\"w\"> </span><span class=\"s\">&quot;0.006*sin(pi*y/${ds.y})*sin(2*pi*3*x/${ds.x})*cos(2*pi*4*z/${ds.z})</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">0.004*sin(pi*y/${ds.y})*sin(2*pi*2*x/${ds.x})*sin(2*pi*3*z/${ds.z})&quot;</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">uz</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!sub</span><span class=\"w\"> </span><span class=\"s\">&quot;0.006*sin(pi*y/${ds.y})*sin(2*pi*3*x/${ds.x})*sin(2*pi*4*z/${ds.z})</span><span class=\"nv\"> </span><span class=\"s\">-</span><span class=\"nv\"> </span><span class=\"s\">0.004*sin(pi*y/${ds.y})*cos(2*pi*2*x/${ds.x})*cos(2*pi*3*z/${ds.z})&quot;</span>\n",
       "\n",
       "<span class=\"nt\">simulations</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">  </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">save_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">./validation/turbulence/02_turbulent_channel_flow/results/periodic</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">run_simul</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">n_steps</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">200000</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">report</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1000</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">domain_size</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${ds.x}</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">y</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${ds.y}</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">z</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${ds.z}</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",
       "<span class=\"w\">      </span><span class=\"nt\">bodies</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">plane_floor</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">run</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">cfg_use</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">plane_cfg</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</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\">geometry_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">fixture/stl/abl/ground.stl</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">transformation</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">scale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">translation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">4</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">plane_ceil</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">run</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">cfg_use</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">plane_cfg</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</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\">geometry_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">fixture/stl/abl/ground.stl</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">transformation</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">scale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">fixed_point</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">80</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\">rotation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">radians(180)</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</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\">translation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">60</span><span class=\"p p-Indicator\">]</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">data</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">export_IBM_nodes</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">ibm_exp</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">body_name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">plane_floor</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\">5000</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">exports</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\">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=\"nv\">omega_LES</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">f_IBM</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">interval</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\">50000</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">lvl</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\">target</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">volume</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{}</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">outputs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">instantaneous</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default_stats</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">macrs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">rho</span>\n",
       "<span class=\"w\">            </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">u</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">interval</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\">100</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">start_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">100000</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">lvl</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\">target</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">volume</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{}</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">outputs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">instantaneous</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">false</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">stats</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">macrs_1st_order</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">rho</span>\n",
       "<span class=\"w\">                </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">u</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">macrs_2nd_order</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">u</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">plane_series</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\">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\">25000</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=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">target</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">planes</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\"># Streamwise / wall-normal mid-plane (IBM walls are z-normal).</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">mid_channel</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">axis</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">y</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">axis_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">84</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=\"c1\"># Wall-parallel plane above the wall-model matching height.</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">near_wall</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">axis</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">z</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">axis_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">8</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\">outputs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">instantaneous</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</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\">0.500096682620786</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">F</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\">1.89820067659664E-07</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\">0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">z</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\">vel_set</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">D3Q27</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",
       "<span class=\"w\">      </span><span class=\"nt\">initialization</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">equations</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">rho</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"s\">&quot;1&quot;</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">ux</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!sub</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${init.ds.ux}</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">uy</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!sub</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${init.ds.uy}</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">uz</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!sub</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${init.ds.uz}</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\">LES</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">model</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Smagorinsky</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">sgs_cte</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.17</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\">true</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">true</span><span class=\"p p-Indicator\">]</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">dirac_delta</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">3_points</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">forces_accomodate_time</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\">reset_forces</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">body_cfgs</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\">n_iterations</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\">forces_factor</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1.0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">plane_cfg</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">n_iterations</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\">forces_factor</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_model</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\">EqTBL</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">dist_ref</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">2.0</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">dist_shell</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">start_step</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\">params</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">z0</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.00155</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_error</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1e-04</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_iters</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">10</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_min_div</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">51</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_div</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">51</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">multiblock</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">overlap_F2C</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">2</span>\n",
       "\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\">periodicTurbulentChannelMultilevel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">parent</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">run_simul</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">refinement</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">static</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\">volumes_refine</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</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</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">16</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">lvl</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\">is_abs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">              </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">48</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">64</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">lvl</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\">is_abs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\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\">periodicTurbulentChannelNoWM</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">parent</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">run_simul</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">models</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">body_cfgs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">plane_cfg</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_model</span><span class=\"p\">:</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{n+nt}{variables}\\PY{p}{:}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} Domain size}\n",
       "\\PY{+w}{  }\\PY{n+nt}{ds}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{168}\n",
       "\\PY{+w}{    }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{168}\n",
       "\\PY{+w}{    }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{64}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} Equation\\PYZhy{}based (\\PYZdq{}cold\\PYZdq{}) start \\PYZhy{} self\\PYZhy{}contained, no external}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} artefact. The streamwise field is an analytic Reichardt mean}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} profile mirrored across both walls (wall distance in viscous}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} units y+ = (u*/nu) * min(y, NY \\PYZhy{} y) = 4.5 * min(y, NY \\PYZhy{} y)),}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} seeded with wall\\PYZhy{}enveloped multi\\PYZhy{}mode sinusoidal perturbations}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} (streamwise streaks plus cross\\PYZhy{}flow rolls carrying wall\\PYZhy{}normal}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} velocity) at \\PYZti{}10\\PYZpc{} intensity to break the x/z symmetry and trip}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} transition to turbulence. The sin(pi*y/NY) envelope vanishes at}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} both no\\PYZhy{}slip walls. The scalar is initialised separately via its}\n",
       "\\PY{+w}{  }\\PY{c+c1}{\\PYZsh{} `initial\\PYZus{}field` ramp.}\n",
       "\\PY{+w}{  }\\PY{n+nt}{init}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{n+nt}{ds}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{ux}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!sub}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{0.005}\\PY{n+nv}{ }\\PY{l+s}{*}\\PY{n+nv}{ }\\PY{l+s}{(2.5*log(1}\\PY{n+nv}{ }\\PY{l+s}{+}\\PY{n+nv}{ }\\PY{l+s}{0.41*4.5*Min(y,}\\PY{n+nv}{ }\\PY{l+s}{\\PYZdl{}\\PYZob{}ds.y\\PYZcb{}}\\PY{n+nv}{ }\\PY{l+s}{\\PYZhy{}}\\PY{n+nv}{ }\\PY{l+s}{y))}\\PY{n+nv}{ }\\PY{l+s}{+}\\PY{n+nv}{ }\\PY{l+s}{7.8*(1}\\PY{n+nv}{ }\\PY{l+s}{\\PYZhy{}}\\PY{n+nv}{ }\\PY{l+s}{exp(\\PYZhy{}4.5*min(y,}\\PY{n+nv}{ }\\PY{l+s}{\\PYZdl{}\\PYZob{}ds.y\\PYZcb{}}\\PY{n+nv}{ }\\PY{l+s}{\\PYZhy{}}\\PY{n+nv}{ }\\PY{l+s}{y)/11)}\\PY{n+nv}{ }\\PY{l+s}{\\PYZhy{}}\\PY{n+nv}{ }\\PY{l+s}{(4.5*min(y,}\\PY{n+nv}{ }\\PY{l+s}{\\PYZdl{}\\PYZob{}ds.y\\PYZcb{}}\\PY{n+nv}{ }\\PY{l+s}{\\PYZhy{}}\\PY{n+nv}{ }\\PY{l+s}{y)/11)*exp(\\PYZhy{}4.5*min(y,}\\PY{n+nv}{ }\\PY{l+s}{\\PYZdl{}\\PYZob{}ds.y\\PYZcb{}}\\PY{n+nv}{ }\\PY{l+s}{\\PYZhy{}}\\PY{n+nv}{ }\\PY{l+s}{y)/3)))}\\PY{n+nv}{ }\\PY{l+s}{+}\\PY{n+nv}{ }\\PY{l+s}{0.010*sin(pi*y/\\PYZdl{}\\PYZob{}ds.y\\PYZcb{})*cos(2*pi*5*z/\\PYZdl{}\\PYZob{}ds.z\\PYZcb{})}\\PY{n+nv}{ }\\PY{l+s}{+}\\PY{n+nv}{ }\\PY{l+s}{0.006*sin(pi*y/\\PYZdl{}\\PYZob{}ds.y\\PYZcb{})*sin(2*pi*3*x/\\PYZdl{}\\PYZob{}ds.x\\PYZcb{})*cos(2*pi*4*z/\\PYZdl{}\\PYZob{}ds.z\\PYZcb{})}\\PY{l+s}{\\PYZdq{}}\n",
       "\\PY{+w}{      }\\PY{n+nt}{uy}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!sub}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{0.006*sin(pi*y/\\PYZdl{}\\PYZob{}ds.y\\PYZcb{})*sin(2*pi*3*x/\\PYZdl{}\\PYZob{}ds.x\\PYZcb{})*cos(2*pi*4*z/\\PYZdl{}\\PYZob{}ds.z\\PYZcb{})}\\PY{n+nv}{ }\\PY{l+s}{+}\\PY{n+nv}{ }\\PY{l+s}{0.004*sin(pi*y/\\PYZdl{}\\PYZob{}ds.y\\PYZcb{})*sin(2*pi*2*x/\\PYZdl{}\\PYZob{}ds.x\\PYZcb{})*sin(2*pi*3*z/\\PYZdl{}\\PYZob{}ds.z\\PYZcb{})}\\PY{l+s}{\\PYZdq{}}\n",
       "\\PY{+w}{      }\\PY{n+nt}{uz}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!sub}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{0.006*sin(pi*y/\\PYZdl{}\\PYZob{}ds.y\\PYZcb{})*sin(2*pi*3*x/\\PYZdl{}\\PYZob{}ds.x\\PYZcb{})*sin(2*pi*4*z/\\PYZdl{}\\PYZob{}ds.z\\PYZcb{})}\\PY{n+nv}{ }\\PY{l+s}{\\PYZhy{}}\\PY{n+nv}{ }\\PY{l+s}{0.004*sin(pi*y/\\PYZdl{}\\PYZob{}ds.y\\PYZcb{})*cos(2*pi*2*x/\\PYZdl{}\\PYZob{}ds.x\\PYZcb{})*cos(2*pi*3*z/\\PYZdl{}\\PYZob{}ds.z\\PYZcb{})}\\PY{l+s}{\\PYZdq{}}\n",
       "\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}{periodicTurbulentChannel}\n",
       "\\PY{+w}{    }\\PY{n+nt}{save\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{./validation/turbulence/02\\PYZus{}turbulent\\PYZus{}channel\\PYZus{}flow/results/periodic}\n",
       "\\PY{+w}{    }\\PY{n+nt}{run\\PYZus{}simul}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{200000}\n",
       "\\PY{+w}{    }\\PY{n+nt}{report}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1000}\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{k+kt}{!math}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{\\PYZdl{}\\PYZob{}ds.x\\PYZcb{}}\n",
       "\\PY{+w}{        }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!math}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{\\PYZdl{}\\PYZob{}ds.y\\PYZcb{}}\n",
       "\\PY{+w}{        }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!math}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{\\PYZdl{}\\PYZob{}ds.z\\PYZcb{}}\n",
       "\\PY{+w}{      }\\PY{n+nt}{block\\PYZus{}size}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n",
       "\\PY{+w}{      }\\PY{n+nt}{bodies}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{plane\\PYZus{}floor}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{IBM}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{run}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\\PY{+w}{            }\\PY{n+nt}{cfg\\PYZus{}use}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{plane\\PYZus{}cfg}\n",
       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{          }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/abl/ground.stl}\n",
       "\\PY{+w}{          }\\PY{n+nt}{transformation}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{scale}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{2}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{2}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{2}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{        }\\PY{n+nt}{plane\\PYZus{}ceil}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{IBM}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{run}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\\PY{+w}{            }\\PY{n+nt}{cfg\\PYZus{}use}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{plane\\PYZus{}cfg}\n",
       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{          }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/abl/ground.stl}\n",
       "\\PY{+w}{          }\\PY{n+nt}{transformation}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{scale}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{2}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{2}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{2}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{fixed\\PYZus{}point}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{80}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{rotation}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!math}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{radians(180)}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{60}\\PY{p+pIndicator}{]}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{data}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{export\\PYZus{}IBM\\PYZus{}nodes}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{ibm\\PYZus{}exp}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{body\\PYZus{}name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{plane\\PYZus{}floor}\n",
       "\\PY{+w}{          }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{5000}\n",
       "\\PY{+w}{      }\\PY{n+nt}{exports}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{default}\\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}{,}\\PY{+w}{ }\\PY{n+nv}{omega\\PYZus{}LES}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{f\\PYZus{}IBM}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{          }\\PY{n+nt}{interval}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{50000}\n",
       "\\PY{+w}{            }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{          }\\PY{n+nt}{target}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{volume}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{          }\\PY{n+nt}{outputs}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{instantaneous}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\\PY{+w}{        }\\PY{n+nt}{default\\PYZus{}stats}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{macrs}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{rho}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{u}\n",
       "\\PY{+w}{          }\\PY{n+nt}{interval}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{100}\n",
       "\\PY{+w}{            }\\PY{n+nt}{start\\PYZus{}step}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{100000}\n",
       "\\PY{+w}{            }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{          }\\PY{n+nt}{target}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{volume}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{          }\\PY{n+nt}{outputs}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{instantaneous}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{false}\n",
       "\\PY{+w}{            }\\PY{n+nt}{stats}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{n+nt}{macrs\\PYZus{}1st\\PYZus{}order}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{rho}\n",
       "\\PY{+w}{                }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{u}\n",
       "\\PY{+w}{              }\\PY{n+nt}{macrs\\PYZus{}2nd\\PYZus{}order}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{u}\n",
       "\\PY{+w}{        }\\PY{n+nt}{plane\\PYZus{}series}\\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}{interval}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{25000}\\PY{p+pIndicator}{,}\\PY{n+nt}{ lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{          }\\PY{n+nt}{target}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{planes}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{c+c1}{\\PYZsh{} Streamwise / wall\\PYZhy{}normal mid\\PYZhy{}plane (IBM walls are z\\PYZhy{}normal).}\n",
       "\\PY{+w}{              }\\PY{n+nt}{mid\\PYZus{}channel}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{n+nt}{axis}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{y}\n",
       "\\PY{+w}{                }\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{84}\n",
       "\\PY{+w}{                }\\PY{n+nt}{dist}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{              }\\PY{c+c1}{\\PYZsh{} Wall\\PYZhy{}parallel plane above the wall\\PYZhy{}model matching height.}\n",
       "\\PY{+w}{              }\\PY{n+nt}{near\\PYZus{}wall}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{n+nt}{axis}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{z}\n",
       "\\PY{+w}{                }\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n",
       "\\PY{+w}{                }\\PY{n+nt}{dist}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{          }\\PY{n+nt}{outputs}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{instantaneous}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\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}{0.500096682620786}\n",
       "\\PY{+w}{        }\\PY{n+nt}{F}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.89820067659664E\\PYZhy{}07}\n",
       "\\PY{+w}{          }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{          }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{        }\\PY{n+nt}{vel\\PYZus{}set}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{D3Q27}\n",
       "\\PY{+w}{        }\\PY{n+nt}{coll\\PYZus{}oper}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RRBGK}\n",
       "\\PY{+w}{      }\\PY{n+nt}{initialization}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{equations}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{1}\\PY{l+s}{\\PYZdq{}}\n",
       "\\PY{+w}{          }\\PY{n+nt}{ux}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!sub}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{\\PYZdl{}\\PYZob{}init.ds.ux\\PYZcb{}}\n",
       "\\PY{+w}{          }\\PY{n+nt}{uy}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!sub}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{\\PYZdl{}\\PYZob{}init.ds.uy\\PYZcb{}}\n",
       "\\PY{+w}{          }\\PY{n+nt}{uz}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!sub}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{\\PYZdl{}\\PYZob{}init.ds.uz\\PYZcb{}}\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}{LES}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{model}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Smagorinsky}\n",
       "\\PY{+w}{        }\\PY{n+nt}{sgs\\PYZus{}cte}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.17}\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}{true}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{true}\\PY{p+pIndicator}{]}\n",
       "\n",
       "\\PY{+w}{      }\\PY{n+nt}{IBM}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{dirac\\PYZus{}delta}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{3\\PYZus{}points}\n",
       "\\PY{+w}{        }\\PY{n+nt}{forces\\PYZus{}accomodate\\PYZus{}time}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{        }\\PY{n+nt}{reset\\PYZus{}forces}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\\PY{+w}{        }\\PY{n+nt}{body\\PYZus{}cfgs}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{default}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{n\\PYZus{}iterations}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{            }\\PY{n+nt}{forces\\PYZus{}factor}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.0}\n",
       "\\PY{+w}{          }\\PY{n+nt}{plane\\PYZus{}cfg}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{n\\PYZus{}iterations}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{            }\\PY{n+nt}{forces\\PYZus{}factor}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.25}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}model}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{EqTBL}\n",
       "\\PY{+w}{              }\\PY{n+nt}{dist\\PYZus{}ref}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2.0}\n",
       "\\PY{+w}{              }\\PY{n+nt}{dist\\PYZus{}shell}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.25}\n",
       "\\PY{+w}{              }\\PY{n+nt}{start\\PYZus{}step}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{              }\\PY{n+nt}{params}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{n+nt}{z0}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.00155}\n",
       "\\PY{+w}{                }\\PY{n+nt}{TDMA\\PYZus{}max\\PYZus{}error}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1e\\PYZhy{}04}\n",
       "\\PY{+w}{                }\\PY{n+nt}{TDMA\\PYZus{}max\\PYZus{}iters}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{10}\n",
       "\\PY{+w}{                }\\PY{n+nt}{TDMA\\PYZus{}min\\PYZus{}div}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{51}\n",
       "\\PY{+w}{                }\\PY{n+nt}{TDMA\\PYZus{}max\\PYZus{}div}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{51}\n",
       "\n",
       "\\PY{+w}{      }\\PY{n+nt}{multiblock}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{overlap\\PYZus{}F2C}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2}\n",
       "\n",
       "\\PY{+w}{  }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicTurbulentChannelMultilevel}\n",
       "\\PY{+w}{    }\\PY{n+nt}{parent}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicTurbulentChannel}\n",
       "\\PY{+w}{    }\\PY{n+nt}{run\\PYZus{}simul}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{domain}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{refinement}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{static}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{default}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{volumes\\PYZus{}refine}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{start}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{end}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{168}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{168}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{16}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{                }\\PY{n+nt}{is\\PYZus{}abs}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\\PY{+w}{              }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{start}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{48}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{end}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{168}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{168}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{64}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{                }\\PY{n+nt}{is\\PYZus{}abs}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\n",
       "\\PY{+w}{  }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicTurbulentChannelNoWM}\n",
       "\\PY{+w}{    }\\PY{n+nt}{parent}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicTurbulentChannel}\n",
       "\\PY{+w}{    }\\PY{n+nt}{run\\PYZus{}simul}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{models}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{IBM}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{body\\PYZus{}cfgs}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{plane\\PYZus{}cfg}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}model}\\PY{p}{:}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "variables:\n",
       "  # Domain size\n",
       "  ds:\n",
       "    x: 168\n",
       "    y: 168\n",
       "    z: 64\n",
       "  # Equation-based (\"cold\") start - self-contained, no external\n",
       "  # artefact. The streamwise field is an analytic Reichardt mean\n",
       "  # profile mirrored across both walls (wall distance in viscous\n",
       "  # units y+ = (u*/nu) * min(y, NY - y) = 4.5 * min(y, NY - y)),\n",
       "  # seeded with wall-enveloped multi-mode sinusoidal perturbations\n",
       "  # (streamwise streaks plus cross-flow rolls carrying wall-normal\n",
       "  # velocity) at ~10% intensity to break the x/z symmetry and trip\n",
       "  # transition to turbulence. The sin(pi*y/NY) envelope vanishes at\n",
       "  # both no-slip walls. The scalar is initialised separately via its\n",
       "  # `initial_field` ramp.\n",
       "  init:\n",
       "    ds:\n",
       "      ux: !sub \"0.005 * (2.5*log(1 + 0.41*4.5*Min(y, ${ds.y} - y)) + 7.8*(1 - exp(-4.5*min(y, ${ds.y} - y)/11) - (4.5*min(y, ${ds.y} - y)/11)*exp(-4.5*min(y, ${ds.y} - y)/3))) + 0.010*sin(pi*y/${ds.y})*cos(2*pi*5*z/${ds.z}) + 0.006*sin(pi*y/${ds.y})*sin(2*pi*3*x/${ds.x})*cos(2*pi*4*z/${ds.z})\"\n",
       "      uy: !sub \"0.006*sin(pi*y/${ds.y})*sin(2*pi*3*x/${ds.x})*cos(2*pi*4*z/${ds.z}) + 0.004*sin(pi*y/${ds.y})*sin(2*pi*2*x/${ds.x})*sin(2*pi*3*z/${ds.z})\"\n",
       "      uz: !sub \"0.006*sin(pi*y/${ds.y})*sin(2*pi*3*x/${ds.x})*sin(2*pi*4*z/${ds.z}) - 0.004*sin(pi*y/${ds.y})*cos(2*pi*2*x/${ds.x})*cos(2*pi*3*z/${ds.z})\"\n",
       "\n",
       "simulations:\n",
       "  - name: periodicTurbulentChannel\n",
       "    save_path: ./validation/turbulence/02_turbulent_channel_flow/results/periodic\n",
       "    run_simul: true\n",
       "\n",
       "    n_steps: 200000\n",
       "    report:\n",
       "      frequency: 1000\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: !math ${ds.x}\n",
       "        y: !math ${ds.y}\n",
       "        z: !math ${ds.z}\n",
       "      block_size: 8\n",
       "      bodies:\n",
       "        plane_floor:\n",
       "          IBM:\n",
       "            run: true\n",
       "            cfg_use: plane_cfg\n",
       "            order: 0\n",
       "          geometry_path: fixture/stl/abl/ground.stl\n",
       "          transformation:\n",
       "            scale: [2, 2, 2]\n",
       "            translation: [0, 0, 4]\n",
       "        plane_ceil:\n",
       "          IBM:\n",
       "            run: true\n",
       "            cfg_use: plane_cfg\n",
       "            order: 0\n",
       "          geometry_path: fixture/stl/abl/ground.stl\n",
       "          transformation:\n",
       "            scale: [2, 2, 2]\n",
       "            fixed_point: [0, 80, 0]\n",
       "            rotation: !math [radians(180), 0, 0]\n",
       "            translation: [0, 0, 60]\n",
       "\n",
       "    data:\n",
       "      export_IBM_nodes:\n",
       "        ibm_exp:\n",
       "          body_name: plane_floor\n",
       "          frequency: 5000\n",
       "      exports:\n",
       "        default:\n",
       "          macrs: [rho, u, omega_LES, f_IBM]\n",
       "          interval:\n",
       "            frequency: 50000\n",
       "            lvl: 0\n",
       "          target:\n",
       "            volume: {}\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "        default_stats:\n",
       "          macrs:\n",
       "            - rho\n",
       "            - u\n",
       "          interval:\n",
       "            frequency: 100\n",
       "            start_step: 100000\n",
       "            lvl: 0\n",
       "          target:\n",
       "            volume: {}\n",
       "          outputs:\n",
       "            instantaneous: false\n",
       "            stats:\n",
       "              macrs_1st_order:\n",
       "                - rho\n",
       "                - u\n",
       "              macrs_2nd_order:\n",
       "                - u\n",
       "        plane_series:\n",
       "          macrs: [rho, u]\n",
       "          interval: {frequency: 25000, lvl: 0}\n",
       "          target:\n",
       "            planes:\n",
       "              # Streamwise / wall-normal mid-plane (IBM walls are z-normal).\n",
       "              mid_channel:\n",
       "                axis: y\n",
       "                axis_pos: 84\n",
       "                dist: 1\n",
       "              # Wall-parallel plane above the wall-model matching height.\n",
       "              near_wall:\n",
       "                axis: z\n",
       "                axis_pos: 8\n",
       "                dist: 1\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "\n",
       "    models:\n",
       "      precision:\n",
       "        default: single\n",
       "\n",
       "      LBM:\n",
       "        tau: 0.500096682620786\n",
       "        F:\n",
       "          x: 1.89820067659664E-07\n",
       "          y: 0\n",
       "          z: 0\n",
       "        vel_set: D3Q27\n",
       "        coll_oper: RRBGK\n",
       "      initialization:\n",
       "        equations:\n",
       "          rho: \"1\"\n",
       "          ux: !sub ${init.ds.ux}\n",
       "          uy: !sub ${init.ds.uy}\n",
       "          uz: !sub ${init.ds.uz}\n",
       "\n",
       "      engine:\n",
       "        name: CUDA\n",
       "\n",
       "      LES:\n",
       "        model: Smagorinsky\n",
       "        sgs_cte: 0.17\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [true, true, true]\n",
       "\n",
       "      IBM:\n",
       "        dirac_delta: 3_points\n",
       "        forces_accomodate_time: 0\n",
       "        reset_forces: true\n",
       "        body_cfgs:\n",
       "          default:\n",
       "            n_iterations: 1\n",
       "            forces_factor: 1.0\n",
       "          plane_cfg:\n",
       "            n_iterations: 1\n",
       "            forces_factor: 0.25\n",
       "            wall_model:\n",
       "              name: EqTBL\n",
       "              dist_ref: 2.0\n",
       "              dist_shell: 0.25\n",
       "              start_step: 0\n",
       "              params:\n",
       "                z0: 0.00155\n",
       "                TDMA_max_error: 1e-04\n",
       "                TDMA_max_iters: 10\n",
       "                TDMA_min_div: 51\n",
       "                TDMA_max_div: 51\n",
       "\n",
       "      multiblock:\n",
       "        overlap_F2C: 2\n",
       "\n",
       "  - name: periodicTurbulentChannelMultilevel\n",
       "    parent: periodicTurbulentChannel\n",
       "    run_simul: true\n",
       "\n",
       "    domain:\n",
       "      refinement:\n",
       "        static:\n",
       "          default:\n",
       "            volumes_refine:\n",
       "              - start: [0, 0, 0]\n",
       "                end: [168, 168, 16]\n",
       "                lvl: 1\n",
       "                is_abs: true\n",
       "              - start: [0, 0, 48]\n",
       "                end: [168, 168, 64]\n",
       "                lvl: 1\n",
       "                is_abs: true\n",
       "\n",
       "  - name: periodicTurbulentChannelNoWM\n",
       "    parent: periodicTurbulentChannel\n",
       "    run_simul: true\n",
       "\n",
       "    models:\n",
       "      IBM:\n",
       "        body_cfgs:\n",
       "          plane_cfg:\n",
       "            wall_model:"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Code\n",
    "\n",
    "Code(filename=filename)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nassu",
   "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": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}