{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flow Over Stationary Sphere\n",
    "\n",
    "The simulation of a laminar flow over a stationary sphere is used for the validation of force spreading aspects of the immersed boundary method (IBM). For that purpose, the drag coefficient is measured through the forces calculated at the Lagrangian mesh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Total drag from the direct IBM force.** The sphere drag reported below is the *full* drag (form pressure + skin friction), obtained by summing the IBM spreading force the solver applied to enforce the no-slip condition over the body's Lagrangian nodes (`nassu.viz.read_body_ibm_force`). Each node's force is rescaled from its level-local lattice units to global units - a `(2^-lvl)^2` factor, the `dx^2` area ratio - before summing (issue #1016). This direct force needs no surface-pressure or wall-stress reconstruction and matches the reference correlations to within a few percent. The decomposed pressure + viscous-traction route (`nassu.viz.pressure_force_coefficients` + `nassu.viz.force_coefficients`) is kept only as a diagnostic split; on this sphere it under-predicts the total by ~30%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:54.579749Z",
     "iopub.status.busy": "2026-07-03T13:54:54.579647Z",
     "iopub.status.idle": "2026-07-03T13:54:55.137760Z",
     "shell.execute_reply": "2026-07-03T13:54:55.137398Z"
    }
   },
   "outputs": [],
   "source": [
    "from nassu.cfg.model import ConfigScheme\n",
    "\n",
    "filename = \"validation/external_aero/01_flow_over_sphere/01_flow_over_sphere.nassu.yaml\"\n",
    "\n",
    "sim_cfgs = ConfigScheme.sim_cfgs_from_file_dct(filename)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A multilevel configuration is adopted to allow a large ratio between the sphere's and domain's sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:55.139135Z",
     "iopub.status.busy": "2026-07-03T13:54:55.139013Z",
     "iopub.status.idle": "2026-07-03T13:54:55.141172Z",
     "shell.execute_reply": "2026-07-03T13:54:55.140817Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_laminar = [sim_cfg for (name, _), sim_cfg in sim_cfgs.items() if name.startswith(\"laminar\")]\n",
    "sim_turb = next(\n",
    "    sim_cfg for (name, _), sim_cfg in sim_cfgs.items() if name == \"turbulentFlowOverSphere\"\n",
    ")\n",
    "sim_turb_les = next(sim_cfg for (name, _), sim_cfg in sim_cfgs.items() if name.endswith(\"LES\"))\n",
    "sim_cfgs_use = sim_laminar + [sim_turb] + [sim_turb_les]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions to use for flow over sphere processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:55.142074Z",
     "iopub.status.busy": "2026-07-03T13:54:55.141999Z",
     "iopub.status.idle": "2026-07-03T13:54:55.589000Z",
     "shell.execute_reply": "2026-07-03T13:54:55.588617Z"
    }
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import nassu.viz as viz\n",
    "from nassu.cfg.schemes.simul import SimulationConfigs\n",
    "\n",
    "\n",
    "def get_experimental_profile_Cp(reynolds: float) -> pd.DataFrame:\n",
    "    files_tau: dict[float, str] = {\n",
    "        4200: \"Cp_vs_theta_Re_4200.csv\",\n",
    "        50000: \"Cp_vs_theta_Re_50000.csv\",\n",
    "        300000: \"Cp_vs_theta_Re_300000.csv\",\n",
    "        400000: \"Cp_vs_theta_Re_400000.csv\",\n",
    "        1140000: \"Cp_vs_theta_Re_1140000.csv\",\n",
    "    }\n",
    "    filename = (\n",
    "        pathlib.Path(\"validation/external_aero/01_flow_over_sphere/reference\")\n",
    "        / files_tau[reynolds]\n",
    "    )\n",
    "    # ([theta], [Cp])\n",
    "    return pd.read_csv(filename, delimiter=\",\")\n",
    "\n",
    "\n",
    "def get_experimental_profile_Cd() -> pd.DataFrame:\n",
    "    filename = (\n",
    "        pathlib.Path(\"validation/external_aero/01_flow_over_sphere/reference\")\n",
    "        / \"drag_coefficient.csv\"\n",
    "    )\n",
    "    # ([Author], [Re], [Cd])\n",
    "    return pd.read_csv(filename, delimiter=\",\")\n",
    "\n",
    "\n",
    "def mean_pressure_form_drag(\n",
    "    sim_cfg: SimulationConfigs,\n",
    "    *,\n",
    "    u_inf: float,\n",
    "    normal: np.ndarray,\n",
    "    area: np.ndarray,\n",
    "    a_ref: float,\n",
    "    rho_inf: float = 1.0,\n",
    "    body_name: str = \"sphere\",\n",
    "    series: str = \"default_series\",\n",
    "    stats_start: float = 0.0,\n",
    "):\n",
    "    \"\"\"Time-mean form (pressure) drag/lift coefficients from a body pressure series.\n",
    "\n",
    "    Reads the surface-density history sampled over the body nodes, forms the\n",
    "    per-node mean ``Cp = (rho - rho_inf) / (1.5 rho_inf u_inf**2)`` and integrates\n",
    "    the pressure force ``C_F = -sum_i Cp_i n_i A_i / a_ref`` over the sphere with\n",
    "    ``nassu.viz.pressure_force_coefficients``. The per-node outward ``normal`` and\n",
    "    ``area`` come from the ``body_nodes`` friction export, which shares the exact\n",
    "    same deterministic per-triangle ordering as the surface-pressure series (both\n",
    "    keyed by the body-node index), so the form and viscous drag use identical\n",
    "    geometry weights and add to the full drag. Returns ``(Cd_pressure, Cl_pressure)``.\n",
    "    \"\"\"\n",
    "    hs = sim_cfg.output.exports[series].series.bodies[body_name]\n",
    "    df = hs.read_full_data(\"rho\")\n",
    "    df = df[df[\"time_step\"] >= stats_start].drop(columns=\"time_step\")\n",
    "    rho_mean = df.mean().to_numpy()  # (n_nodes,), body-node index order\n",
    "\n",
    "    assert len(rho_mean) == len(area) == len(normal), (len(rho_mean), len(area), len(normal))\n",
    "    cp = (rho_mean - rho_inf) / (1.5 * rho_inf * u_inf**2)\n",
    "    coeffs = viz.pressure_force_coefficients(cp, normal, area, a_ref=a_ref)\n",
    "    return float(coeffs[\"Cd_pressure\"]), float(coeffs[\"Cl_pressure\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "The drag coefficient reported here is the **total** drag from the direct IBM force: the spreading force summed over the sphere's Lagrangian nodes (form pressure + skin friction together), matching the Schiller / Concha / Morrison correlations to within a few percent across the Reynolds sweep. The decomposed pressure + viscous-traction reconstruction is overlaid as a diagnostic; it systematically under-predicts the total by ~30% (issue #1016). The stacked bar shows the reconstruction split (form + friction + wall-normal viscous) with the direct-force total marked, so the shortfall is visible per Reynolds number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:55.590137Z",
     "iopub.status.busy": "2026-07-03T13:54:55.589984Z",
     "iopub.status.idle": "2026-07-03T13:54:56.315443Z",
     "shell.execute_reply": "2026-07-03T13:54:56.315070Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3072641/476860046.py:59: UserWarning: IBM node export for body 'sphere' has no `lvl` field (pre-#1016 export); rescaling all force-bearing nodes at level 1. Exact only if the body sits entirely at that level; re-run to regenerate `lvl`.\n",
      "  bif = common.read_body_ibm_force(sim_cfg, \"sphere\", start_step=sim_cfg.n_steps // 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3072641/476860046.py:59: UserWarning: IBM node export for body 'sphere' has no `lvl` field (pre-#1016 export); rescaling all force-bearing nodes at level 1. Exact only if the body sits entirely at that level; re-run to regenerate `lvl`.\n",
      "  bif = common.read_body_ibm_force(sim_cfg, \"sphere\", start_step=sim_cfg.n_steps // 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3072641/476860046.py:59: UserWarning: IBM node export for body 'sphere' has no `lvl` field (pre-#1016 export); rescaling all force-bearing nodes at level 1. Exact only if the body sits entirely at that level; re-run to regenerate `lvl`.\n",
      "  bif = common.read_body_ibm_force(sim_cfg, \"sphere\", start_step=sim_cfg.n_steps // 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import nassu.viz as common\n",
    "\n",
    "common.use_style()\n",
    "\n",
    "fig, ax = common.fig_single()\n",
    "\n",
    "df_exp = get_experimental_profile_Cd()\n",
    "authors = [\"Schiller\", \"Concha\"]\n",
    "exp_shapes = [\"o\", \"s\"]\n",
    "for author, shape in zip(authors, exp_shapes):\n",
    "    ax.plot(df_exp[\"Re\"], df_exp[author], **common.markers.exp(shape=shape), label=author)\n",
    "\n",
    "# Continuous standard-drag reference (Morrison 2013 correlation), restricted to\n",
    "# the laminar sweep's Reynolds range for a like-for-like overlay.\n",
    "df_morrison = pd.read_csv(\n",
    "    \"validation/external_aero/01_flow_over_sphere/reference/sphere_drag_Cd_vs_Re.csv\",\n",
    "    comment=\"#\",\n",
    ")\n",
    "m = (df_morrison[\"Re\"] >= 20) & (df_morrison[\"Re\"] <= 600)\n",
    "ax.plot(\n",
    "    df_morrison[\"Re\"][m],\n",
    "    df_morrison[\"Cd\"][m],\n",
    "    **common.markers.exp_line(linestyle=\"-\"),\n",
    "    label=\"Morrison (2013)\",\n",
    ")\n",
    "\n",
    "# TOTAL drag from the DIRECT IBM force (issue #1016): the spreading force the\n",
    "# solver applied to the fluid to enforce no-slip, summed over the sphere's\n",
    "# Lagrangian nodes (form pressure + skin friction together, no reconstruction).\n",
    "# `nassu.viz.read_body_ibm_force` rescales each node's force from its level-local\n",
    "# lattice units to global units - (2^-lvl)^2, the dx^2 area ratio - and sums it;\n",
    "# `body_force_coefficients` normalizes by q*A_ref. On this sphere the direct force\n",
    "# matches Schiller / Concha / Morrison to within a few percent, whereas the\n",
    "# decomposed pressure + viscous-traction reconstruction (overlaid as a diagnostic\n",
    "# curve, and split in the bar chart below) under-predicts the total by ~30%.\n",
    "RHO_INF = 1.0\n",
    "num_vals = {\n",
    "    \"Re\": [],\n",
    "    \"Cd_direct\": [],\n",
    "    \"Cd_pressure\": [],\n",
    "    \"Cd_viscous\": [],\n",
    "    \"Cd_friction\": [],\n",
    "    \"Cd_normal_viscous\": [],\n",
    "}\n",
    "for sim_cfg in sim_laminar:\n",
    "    bf = common.read_body_friction(sim_cfg, \"sphere\", series=\"friction\")\n",
    "\n",
    "    # Shared reference geometry (frontal area) from the source-triangle centroids.\n",
    "    diameter = float(np.ptp(bf.centroid[:, 0]))\n",
    "    a_ref = np.pi * diameter**2 / 4\n",
    "\n",
    "    # Freestream speed from the inlet BC (lattice units).\n",
    "    u_inf = max(bc.ux for bc in sim_cfg.models.BC.BC_map if \"ux\" in bc.model_dump())\n",
    "    reynolds = u_inf * diameter / sim_cfg.models.LBM.kinematic_viscosity\n",
    "\n",
    "    # Headline total drag: the direct IBM force over the steady window.\n",
    "    bif = common.read_body_ibm_force(sim_cfg, \"sphere\", start_step=sim_cfg.n_steps // 2)\n",
    "    f_body = common.time_mean_ibm_force(bif)\n",
    "    cd_direct = float(\n",
    "        common.body_force_coefficients(f_body, rho_ref=RHO_INF, u_ref=u_inf, a_ref=a_ref)[\"Cd\"]\n",
    "    )\n",
    "\n",
    "    # Diagnostic reconstruction: viscous (skin-friction + wall-normal) drag from\n",
    "    # the `body_nodes` traction split, plus the form (pressure) drag from the mean\n",
    "    # surface Cp. Both use the friction export's per-triangle normals/areas.\n",
    "    t_mean = common.time_mean_friction(bf)\n",
    "    vcoef = common.force_coefficients(\n",
    "        t_mean,\n",
    "        bf.normal,\n",
    "        bf.area,\n",
    "        rho_ref=RHO_INF,\n",
    "        u_ref=u_inf,\n",
    "        a_ref=a_ref,\n",
    "        flow_dir=np.array([1.0, 0.0, 0.0]),\n",
    "    )\n",
    "    cd_viscous = float(vcoef[\"Cd_total\"])\n",
    "    cd_pressure, _ = mean_pressure_form_drag(\n",
    "        sim_cfg,\n",
    "        u_inf=u_inf,\n",
    "        normal=bf.normal,\n",
    "        area=bf.area,\n",
    "        a_ref=a_ref,\n",
    "        rho_inf=RHO_INF,\n",
    "        series=\"default_series\",\n",
    "        stats_start=sim_cfg.n_steps // 2,\n",
    "    )\n",
    "\n",
    "    num_vals[\"Re\"].append(reynolds)\n",
    "    num_vals[\"Cd_direct\"].append(cd_direct)\n",
    "    num_vals[\"Cd_pressure\"].append(cd_pressure)\n",
    "    num_vals[\"Cd_viscous\"].append(cd_viscous)\n",
    "    num_vals[\"Cd_friction\"].append(float(vcoef[\"Cd_friction\"]))\n",
    "    num_vals[\"Cd_normal_viscous\"].append(float(vcoef[\"Cd_normal_viscous\"]))\n",
    "\n",
    "order = np.argsort(num_vals[\"Re\"])\n",
    "Re_sorted = np.array(num_vals[\"Re\"])[order]\n",
    "ax.plot(\n",
    "    Re_sorted,\n",
    "    np.array(num_vals[\"Cd_direct\"])[order],\n",
    "    **common.markers.sim(shape=\"^\"),\n",
    "    label=\"AeroSim (direct force)\",\n",
    ")\n",
    "# Diagnostic overlay: the decomposed pressure + viscous-traction reconstruction,\n",
    "# which systematically under-predicts the direct-force total (issue #1016).\n",
    "recon = np.array(num_vals[\"Cd_pressure\"])[order] + np.array(num_vals[\"Cd_viscous\"])[order]\n",
    "ax.plot(\n",
    "    Re_sorted,\n",
    "    recon,\n",
    "    **common.markers.sim_line(linestyle=\"--\"),\n",
    "    label=\"pressure+traction (diag.)\",\n",
    ")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xscale(\"log\")\n",
    "# Log axis: plain-number ticks (the AeroSim style only covers linear axes).\n",
    "common.plain_number_axis(ax, \"x\", ticks=[20, 50, 100, 200, 500])\n",
    "ax.set_xlabel(\"Re\")\n",
    "ax.set_ylabel(\"$C_D$\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig)\n",
    "\n",
    "# Drag decomposition as a STACKED BAR per Reynolds number: the reconstruction\n",
    "# split into form (pressure) + skin friction + wall-normal viscous traction, with\n",
    "# the DIRECT-force total (the headline Cd) drawn as a marker. The gap between the\n",
    "# stack top and the marker is the reconstruction shortfall (issue #1016).\n",
    "fig_dec, ax_dec = common.fig_single()\n",
    "re_labels = [f\"{r:.0f}\" for r in Re_sorted]\n",
    "pres = np.array(num_vals[\"Cd_pressure\"])[order]\n",
    "fric = np.array(num_vals[\"Cd_friction\"])[order]\n",
    "nvis = np.array(num_vals[\"Cd_normal_viscous\"])[order]\n",
    "direct = np.array(num_vals[\"Cd_direct\"])[order]\n",
    "xb = np.arange(len(re_labels))\n",
    "ax_dec.bar(xb, pres, label=\"form (pressure)\", color=common.colors.sim)\n",
    "ax_dec.bar(xb, fric, bottom=pres, label=\"skin friction\", color=common.colors.blue)\n",
    "ax_dec.bar(xb, nvis, bottom=pres + fric, label=\"wall-normal viscous\", color=common.colors.green)\n",
    "ax_dec.plot(\n",
    "    xb, direct, \"o\", color=common.colors.exp, markersize=8, label=\"direct force (total)\", zorder=5\n",
    ")\n",
    "for k, tot in enumerate(direct):\n",
    "    ax_dec.text(xb[k], tot, f\"{tot:.2f}\", ha=\"center\", va=\"bottom\", fontsize=10)\n",
    "ax_dec.set_xticks(xb)\n",
    "ax_dec.set_xticklabels(re_labels)\n",
    "common.bar_axis(ax_dec)\n",
    "ax_dec.set_xlabel(\"Re\")\n",
    "ax_dec.set_ylabel(\"$C_D$ contribution\")\n",
    "ax_dec.set_title(\"Drag: direct force (total) vs decomposed reconstruction\")\n",
    "ax_dec.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig_dec)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pressure coefficient for the case of turbulent flow around a stationary sphere is performed for a Re=4,200 simulation to check capacity of measuring the pressure coefficient using the historic series function for a body. A posterior LES simulation is then performed for a Re=50,000 to also verify the solver stability for high Reynolds simulations. In both cases, excellent agreement with experimental data was obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:56.316308Z",
     "iopub.status.busy": "2026-07-03T13:54:56.316199Z",
     "iopub.status.idle": "2026-07-03T13:54:57.325427Z",
     "shell.execute_reply": "2026-07-03T13:54:57.325080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = common.fig_double()\n",
    "\n",
    "\n",
    "def post_proc_crossflow_sphere_Cp(\n",
    "    sim_cfg: SimulationConfigs, ax, reynolds: float, u_inf: float = 0.05\n",
    "):\n",
    "    hs = sim_cfg.output.exports[\"default_series\"].series.bodies[\"sphere\"]\n",
    "\n",
    "    df_points = pd.read_csv(hs.points_filename)\n",
    "    df_hs = hs.read_full_data(\"rho\")\n",
    "\n",
    "    sphere_center = tuple(df_points[d].mean() for d in (\"x\", \"y\", \"z\"))\n",
    "\n",
    "    # Use only points in z=0 to calculate Cp\n",
    "    bool_arr = np.zeros((len(df_points),), dtype=np.bool_)\n",
    "    df_points = df_points.loc[\n",
    "        (df_points[\"z\"] <= sphere_center[2] + 0.5) & (df_points[\"z\"] >= sphere_center[2] - 0.5)\n",
    "    ]\n",
    "    bool_arr[df_points[\"idx\"]] = True\n",
    "\n",
    "    # TODO: This comes from sim_cfg, now hand written,\n",
    "    # but update to calculate it automatically\n",
    "    rho_inf = 1\n",
    "    df_hs = df_hs[df_hs[\"time_step\"] >= 10000]\n",
    "    df_rho = df_hs.drop(columns=\"time_step\")\n",
    "    df_cp = (df_rho - rho_inf) / (1.5 * rho_inf * (u_inf**2))\n",
    "    Cp_avg = df_cp.mean().to_numpy().T\n",
    "    Cp_avg = Cp_avg[bool_arr]\n",
    "\n",
    "    # Get angles for points, use acos because it goes from 0 to 180\n",
    "    x = df_points[\"x\"] - df_points.mean()[\"x\"]\n",
    "    y = df_points[\"y\"] - df_points.mean()[\"y\"]\n",
    "    points_angles: np.ndarray = np.arccos(-x / (x**2 + y**2) ** 0.5)\n",
    "    # Convert to angles\n",
    "    points_angles *= 180 / np.pi\n",
    "    points_angles = np.array(points_angles)\n",
    "\n",
    "    # # Sort arrays\n",
    "    argsort = points_angles.argsort()\n",
    "    points_angles = points_angles[argsort]\n",
    "    cp_points = Cp_avg[argsort]\n",
    "\n",
    "    ax.plot(points_angles, cp_points, **common.markers.sim_line(linestyle=\"--\"), label=\"AeroSim\")\n",
    "\n",
    "    df_exp = get_experimental_profile_Cp(reynolds)\n",
    "    ax.plot(df_exp[\"theta\"], df_exp[\"Cp\"], **common.markers.exp(shape=\"o\"), label=\"Experimental\")\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "\n",
    "for i, (sim_cfg, reynolds) in enumerate([(sim_turb, 4200), (sim_turb_les, 50000)]):\n",
    "    post_proc_crossflow_sphere_Cp(sim_cfg, ax[i], reynolds, u_inf=0.05)\n",
    "    ax[i].set_xlim((0, 180))\n",
    "    ax[i].set_ylim((-0.6, 1.2))\n",
    "    ax[i].set_ylabel(\"$C_p$\")\n",
    "    ax[i].set_xlabel(r\"$\\theta$ (deg)\")\n",
    "    ax[i].set_title(f\"$Re={reynolds}$\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pressure coefficient curve is very similar for both Re = 4200 and Re= 50,000. It can be seen better proximity of results in the LES simulation with a higher Reynolds number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Force coefficients, peaks and shedding\n",
    "\n",
    "The turbulent sphere's mean drag is the **total** drag from the direct IBM force (`nassu.viz.read_body_ibm_force`), summed over the body and rescaled per refinement level (issue #1016). The fluctuating loads and the vortex-shedding signal still come from the surface-pressure force history $C_d(t)$, $C_l(t)$ - the instantaneous IBM force is too noisy to resolve them - so the design peak (Cook-Mayne / Gumbel via `nassu.viz.design_peak`) is the pressure signal's fluctuation lifted onto the direct mean drag, and the Strouhal number comes from the lift spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:57.326246Z",
     "iopub.status.busy": "2026-07-03T13:54:57.326152Z",
     "iopub.status.idle": "2026-07-03T13:54:57.330457Z",
     "shell.execute_reply": "2026-07-03T13:54:57.330117Z"
    }
   },
   "outputs": [],
   "source": [
    "# --- Pressure-integrated force coefficients -------------------------------\n",
    "# Drag and lift of the turbulent sphere are obtained by integrating the surface\n",
    "# pressure over the body; the IBM spreading force is too noisy to resolve the\n",
    "# fluctuating loads. For each body node i, Cp_i = (rho_i - rho_inf)/(1.5 rho_inf U^2),\n",
    "# and the force-coefficient vector is C_F = -sum_i Cp_i n_i A_i / A_ref with\n",
    "# A_ref = pi D^2 / 4 the frontal area. The surface pressure history comes from\n",
    "# the body historic series (`default_series`); the outward unit normals and the\n",
    "# per-node areas come from the `body_nodes` friction export (`bf`), which shares\n",
    "# the same deterministic per-triangle ordering as the pressure series (both keyed\n",
    "# by the body-node index), so the form and viscous drag use identical geometry\n",
    "# weights.\n",
    "\n",
    "U_INF = 0.05\n",
    "RHO_INF = 1.0\n",
    "STATS_START = 10000  # drop the initial transient (matches the Cp analysis above)\n",
    "\n",
    "\n",
    "def force_coeff_series(sim_cfg, bf, body_name=\"sphere\", series=\"default_series\"):\n",
    "    \"\"\"Time series of the pressure-integrated force coefficient.\n",
    "\n",
    "    Returns ``(time, C_F[:, 3], D)`` with ``C_F`` the (drag, y, z) coefficient\n",
    "    components per exported time step and ``D`` the sphere diameter. The per-node\n",
    "    outward ``normal`` and ``area`` come from the friction export ``bf``.\n",
    "    \"\"\"\n",
    "    hs = sim_cfg.output.exports[series].series.bodies[body_name]\n",
    "    df = hs.read_full_data(\"rho\")\n",
    "    df = df[df[\"time_step\"] >= STATS_START]\n",
    "    time = df[\"time_step\"].to_numpy()\n",
    "    rho = df.drop(columns=\"time_step\").to_numpy()  # (n_t, n_nodes), body-node index order\n",
    "\n",
    "    normal = bf.normal\n",
    "    area = bf.area\n",
    "    diameter = float(np.ptp(bf.centroid[:, 0]))\n",
    "    assert rho.shape[1] == len(area) == len(normal), (rho.shape, len(area), len(normal))\n",
    "\n",
    "    cp = (rho - RHO_INF) / (1.5 * RHO_INF * U_INF**2)  # (n_t, n_nodes)\n",
    "    a_ref = np.pi * diameter**2 / 4\n",
    "    cF = -np.einsum(\"tn,n,nd->td\", cp, area, normal) / a_ref  # (n_t, 3)\n",
    "    return time, cF, diameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:57.331127Z",
     "iopub.status.busy": "2026-07-03T13:54:57.331046Z",
     "iopub.status.idle": "2026-07-03T13:54:58.547676Z",
     "shell.execute_reply": "2026-07-03T13:54:58.547266Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3072641/3450118258.py:26: UserWarning: IBM node export for body 'sphere' has no `lvl` field (pre-#1016 export); rescaling all force-bearing nodes at level 3. Exact only if the body sits entirely at that level; re-run to regenerate `lvl`.\n",
      "  bif = viz.read_body_ibm_force(scfg, \"sphere\", start_step=STATS_START)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3072641/3450118258.py:26: UserWarning: IBM node export for body 'sphere' has no `lvl` field (pre-#1016 export); rescaling all force-bearing nodes at level 3. Exact only if the body sits entirely at that level; re-run to regenerate `lvl`.\n",
      "  bif = viz.read_body_ibm_force(scfg, \"sphere\", start_step=STATS_START)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nassu.viz import design_peak\n",
    "\n",
    "runs = [(sim_turb, 4200, \"Re = 4200\"), (sim_turb_les, 50000, \"Re = 50000 (LES)\")]\n",
    "\n",
    "fig, ax = common.fig_double()\n",
    "force_stats = {}\n",
    "for i, (scfg, Re, title) in enumerate(runs):\n",
    "    # Friction export: shared per-triangle normals/areas for the pressure-force\n",
    "    # history and the diagnostic viscous drag split.\n",
    "    bf = viz.read_body_friction(scfg, \"sphere\", series=\"friction\")\n",
    "\n",
    "    t, cF, D = force_coeff_series(scfg, bf)\n",
    "    Cd_form = cF[:, 0]  # form (pressure) drag history\n",
    "    Cl = np.hypot(cF[:, 1], cF[:, 2])  # transverse (lift) magnitude\n",
    "\n",
    "    ax[i].plot(t - t[0], Cd_form, color=common.colors.sim, lw=1.0, label=\"$C_D$ (form)\")\n",
    "    ax[i].plot(t - t[0], Cl, color=common.colors.exp, lw=1.0, alpha=0.8, label=\"$C_L$\")\n",
    "    ax[i].set_title(title)\n",
    "    ax[i].set_xlabel(\"time step\")\n",
    "    ax[i].set_ylabel(\"force coefficient\")\n",
    "    ax[i].legend()\n",
    "\n",
    "    # Headline mean drag: the DIRECT IBM force summed over the body and rescaled\n",
    "    # per refinement level (issue #1016), time-averaged over the steady window.\n",
    "    a_ref = np.pi * D**2 / 4\n",
    "    bif = viz.read_body_ibm_force(scfg, \"sphere\", start_step=STATS_START)\n",
    "    f_body = viz.time_mean_ibm_force(bif)\n",
    "    cd_direct = float(\n",
    "        viz.body_force_coefficients(f_body, rho_ref=RHO_INF, u_ref=U_INF, a_ref=a_ref)[\"Cd\"]\n",
    "    )\n",
    "\n",
    "    # Diagnostic reconstruction: viscous (skin-friction + wall-normal) drag from\n",
    "    # the `body_nodes` traction, and the mean form drag from the pressure signal.\n",
    "    vcoef = viz.force_coefficients(\n",
    "        viz.time_mean_friction(bf, stats_start=STATS_START),\n",
    "        bf.normal,\n",
    "        bf.area,\n",
    "        rho_ref=RHO_INF,\n",
    "        u_ref=U_INF,\n",
    "        a_ref=a_ref,\n",
    "        flow_dir=np.array([1.0, 0.0, 0.0]),\n",
    "    )\n",
    "    cd_viscous = float(vcoef[\"Cd_total\"])\n",
    "    cd_form_mean = float(Cd_form.mean())\n",
    "\n",
    "    # Cook-Mayne / Gumbel design peak of the fluctuating (form) loads. The\n",
    "    # instantaneous IBM force is too noisy for the fluctuation, so the design peak\n",
    "    # is the pressure signal's fluctuation about its own mean, lifted onto the\n",
    "    # direct mean drag: Cd_peak = Cd_direct + (form_peak - form_mean).\n",
    "    cd_peak = design_peak(Cd_form, n_epochs=10, sign=1)\n",
    "    cl_peak = design_peak(Cl, n_epochs=10, sign=1)\n",
    "    force_stats[Re] = {\n",
    "        \"Cd_mean\": cd_direct,  # total drag from the direct IBM force\n",
    "        \"Cd_form_mean\": cd_form_mean,  # diagnostic: pressure-only mean\n",
    "        \"Cd_viscous_mean\": cd_viscous,  # diagnostic: traction viscous drag\n",
    "        \"Cd_rms\": float(Cd_form.std()),\n",
    "        \"Cl_rms\": float(Cl.std()),\n",
    "        \"Cd_peak\": cd_direct + (cd_peak[\"peak\"] - cd_form_mean),  # direct mean + form fluctuation\n",
    "        \"Cl_peak\": cl_peak[\"peak\"],\n",
    "        \"Cl_peak_factor\": cl_peak[\"peak_factor\"],\n",
    "        \"cF\": cF,\n",
    "        \"time\": t,\n",
    "        \"D\": D,\n",
    "    }\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig)\n",
    "\n",
    "# Summary force coefficients as GROUPED BARS per Reynolds number (mean/peak drag,\n",
    "# its direct/form/viscous diagnostics, and the lift fluctuation/peak) - one bar\n",
    "# group per Re. Each metric is shown with its conventional subscript notation.\n",
    "fig_sum, ax_sum = common.fig_single()\n",
    "metric_labels = {\n",
    "    \"Cd_mean\": r\"$\\overline{C_D}$\",\n",
    "    \"Cd_form_mean\": r\"$\\overline{C_{D,\\mathrm{form}}}$\",\n",
    "    \"Cd_viscous_mean\": r\"$\\overline{C_{D,\\mathrm{visc}}}$\",\n",
    "    \"Cd_peak\": r\"$C_{D,\\mathrm{peak}}$\",\n",
    "    \"Cd_rms\": r\"$C_{D,\\mathrm{rms}}$\",\n",
    "    \"Cl_rms\": r\"$C_{L,\\mathrm{rms}}$\",\n",
    "    \"Cl_peak\": r\"$C_{L,\\mathrm{peak}}$\",\n",
    "}\n",
    "metrics = list(metric_labels)\n",
    "res = list(force_stats)\n",
    "xg = np.arange(len(metrics))\n",
    "width = 0.8 / len(res)\n",
    "for j, Re in enumerate(res):\n",
    "    vals = [force_stats[Re][mk] for mk in metrics]\n",
    "    bars = ax_sum.bar(xg + j * width, vals, width, label=f\"Re = {Re}\")\n",
    "    ax_sum.bar_label(bars, fmt=\"%.3f\", fontsize=7, padding=2)\n",
    "ax_sum.set_xticks(xg + width * (len(res) - 1) / 2)\n",
    "ax_sum.set_xticklabels([metric_labels[mk] for mk in metrics])\n",
    "common.bar_axis(ax_sum)\n",
    "ax_sum.set_ylabel(\"coefficient\")\n",
    "ax_sum.set_title(\"Turbulent sphere force-coefficient summary\")\n",
    "ax_sum.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integral quantities (Re = 3700 benchmark)\n",
    "\n",
    "The subcritical turbulent sphere has well-established integral quantities at Re = 3700 (DNS, LES and experiment). The run is at Re = 4200; these quantities are nearly Re-flat across that range, so the comparison carries a ~13% Reynolds offset (see `reference/REFERENCES.md`). The drag is the total (direct IBM force) drag from the run and the base pressure is taken from the run's surface pressure; the recirculation length and separation angle need the wake field and are shown as references only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:58.548751Z",
     "iopub.status.busy": "2026-07-03T13:54:58.548629Z",
     "iopub.status.idle": "2026-07-03T13:54:59.080996Z",
     "shell.execute_reply": "2026-07-03T13:54:59.080536Z"
    }
   },
   "outputs": [
    {
     "data": {
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N0a5dO0yePDnLC947d+5gyJAhaNGiBZYsWYIlS5bA19cXaWlpEIvFqF+/PubOnQsXFxeV1/Lp06dYt24drly5IszjY21tjQ4dOmDYsGFKbSF8bOutW7cOvr6+QgDQzMwMHh4emDRpEuzs7LKUsWvXLmzbtk0I2ojFYjg6OqJz584YMGCAymNUiY6OxsGDB3HixAm8fPlSCO6UKlUK33zzDcaPH6/ULtizZw+WLl0KfAxKfjo31N27d2FmZqZW2dnJfH1UvShMSkrCxo0b4ePjIwQljYyM4O7ujsmTJ6NixYp5Ki+3F8zZKV26dJ6PUcXQ0BBly5ZFTEyM2sFIANi/fz/wccGAT/n7+yM9PR3Ozs7Cy8rM+vTpg3379gk/w4rpFx4+fIioqCgYGBigZ8+eWY779ttv8fvvv+P169d49epVlmvt5OSEcuXK4eXLlwgODkb58uXVPp/iiEEpKnSCg4Nx9OhRAMC8efNyDEjp2q1btwAAtWvXzjGdRCLB999/jydPnkBfXx+Ojo5K5/Xo0SMMHTpUCNCYmJjAwMAA79+/x549e3Dt2jXs2bNHaLDY29tDIpEgNDQUcrkcTk5OwpvRzHPZzJw5U3g7IBKJYGtri/j4eDx69Ajjxo3DzJkzMXjwYJV1Xrp0Kc6dOwd8/INsaGgIff3//cpITk7GsGHDcO/ePeDjRNqWlpaIjY3FlStXcOvWLfz1118qAykymQxjxozB5cuXoaenBxsbG8TGxuLVq1cYM2YMtm/fjvr16ysd8+TJEwwbNkyYYNDU1BQmJiaIiYnB2bNnERsbi127dgnpExMTMXjwYDx8+FCoX4kSJRAbG4vLly/j1q1b+Oeff9CwYUPhGH9/fwwfPhwymQxisRhWVlbCCh0HDx5EcnIyVq9eneN9Vnj48KEwnl0kEqFkyZLCqpHHjx/HlStXsH//fqGxYWRkBGdnZ+ENl+J6KxgZGalVbk4y91L79M1wRkYGxo0bhwsXLgAfG3TW1taIi4vDvXv3MHToUCxZsiRP8xls2LAhX3OsaWreJKlUKvx8qjtnx4cPH4TJMVU1bBQBKUdHR5U9Mw0MDFCjRg3cvHkTz549E4JSwcHBSEtLg56eXrZ1qVu3LoKCgvDs2TOV+2vXro0jR47g1q1bDEoRFRPnz5/Ho0ePoKenh6VLlxaLVVS1QfG3oVatWjmme/PmDfr27Yv379/D2NgYdnZ2ShOS+/j4YObMmULPWEtLS6SlpSE8PByrV69GQEAA/vrrL4jFYojFYjg7OyM9PR2RkZEwMDBQCpYo5qTJT/tFISMjA8OHD8edO3egp6cHBwcHpYU3goKC8P333wu9942NjWFsbIz4+HgcOXIE165dw+7du1VO2B0XF4e+ffvi7du3MDQ0hKWlJRISEnDr1i0MGjQIJ06cyDIn4okTJzBt2jQhuKNYsSwuLg67du2CiYkJpk+fLqR/8eIFhgwZIrx4NDY2hpGRkdDTRdEWzrygz7p167Bq1SrhWllbWyMpKQkhISH466+/YGlpiWHDhuV4nxWOHDkiTKRtYGCAUqVK4f3794iKisKWLVtw8+ZN7N69W2iTWVhYwNnZWXhx++l108S8boq5jMRicZbAT3R0NAYNGiQE4wwMDGBubo64uDicOXMG169fx7Zt21S+hM3OuHHj8N9//+W5ntm1XfIqNjYWL168APLQdlP0cDc2NkaXLl2y7Fe03T59ea3g5uYGQ0NDSCQSvHr1Snj5rjjO1dVV5QI21tbWKF++PF69eoVnz56pDADWqlULwcHBuHXrFoNSuWBQigqdvXv3QiqVolKlSmjbtq2uq5Oj+/fvAx8nOcxJSEgIkpOTsXLlSrRr106pYZmWlobx48cjPj4eLVu2xNSpU1GlShUAQGBgIH7++WfcunULixcvxrJlywAAW7duBT7+gk1JScGxY8eyvI3ZuXMnDh48CHNzc0ybNg3du3eHkZERJBIJ9u/fj0WLFmHZsmVwd3dX+Yv03LlzGD58OEaMGIESJUoo7Vu4cCHu3buHMmXKYNasWfjqq6+gr6+PxMRErF27Fps3b8a0adNw7ty5LAGV5ORk3Lt3D4sXL0bnzp1haGiIiIgITJo0CXfu3MHff/+tFJSSSCQYN24cYmJiUK1aNcyZMwf16tWDSCRCSkoKrl69irt37yqVMW/ePDx8+BDly5fHrFmz0Lx5c+jp6SEhIQGrV6/G9u3bMW3aNJw+fVp4i7ljxw7IZDL07NkTU6dOFRpX8fHxOH/+fJYle3NiamoqrHTn4uIi3O/g4GCsWLECp0+fxp9//im8YatZsybOnz8vTHS+fv36z5r8NCYmRuhSnpSUJLypVPxcffqmaN26dbhw4QJsbW0xc+ZMeHh4wNDQEKmpqfD29sbvv/+OuXPnonnz5krD5HJiaWmZr5Vo8huAi4qKQlpaGtLS0vD27Vts27YNDx8+RJ06ddQO4pw8eRKJiYkoX768yga/Ysitk5NTtnkozjnz86I47tNgY2aKPLN7zhS/YxS/c4joy7d7924AQNu2bVGpUiVdV+eLoW7b7ebNm6hcuTI2btyI2rVrKwUZAgMDMWvWLGRkZGDgwIEYOXIkSpUqBblcjhs3bmDWrFm4dOkSdu3ahf79+8Pc3Bznz5/H8+fP0aVLF5QvXx7Hjh3LUmZ+2i8K8fHxePr0KRYtWoQuXboo/b2RyWSYMGECIiIi0LBhQ8yYMQNubm4QiUR49+4dFixYAD8/P/z8888qJ5N+9OgRypUrh61bt6JRo0YQi8V4+PAhxo4di/DwcOzduxejRo0S0gcHB2P69OlIT09Hu3bt4OXlhQoVKgAfg1InT55Uyj8jIwPjx49HVFQUmjZtihkzZqBq1aoQiUR48+YN5s2bhytXrmDBggXCqtMAsH37duDjxNjfffed8GI2PDwcPj4+aveSwse/0ePHj0eXLl1QpkwZ4X7fu3cPP//8Mx4/foz9+/cLPdt79+6N3r17CxOdnz9/Xu2yVAkNDRWGN8bFxeHWrVtYv349AKBr165Zgn7Tp09HYGAgqlWrhlmzZqF+/foQi8WIjo7G0qVLceTIEUybNk1Y6VHda5Cftlt+hYSEQC6XIykpCS9evMDGjRuRlJSEfv36Zdv77lOKHu7t27eHhYVFlv25td3EYjEcHBzw5s0bhIWFCUEpRfA2pzafk5MTXr16le2ULK6urvDx8cF///2Hb7/9Vq3zKa4YlKJCRzHMpWPHjvnOY9iwYcJcAGKxGGZmZqhQoQLatWuH5s2ba6yuirc56izl+9NPP6mco+bo0aMICQmBm5sb1qxZo9SIqFSpEtauXYv27dvjxIkT+Pnnn9VeYfDvv/8GPg6F9PDwELYbGhqiX79+iI2NxZ9//on9+/dj2rRpWY7v1KkTpkyZkmV7REQEDh8+DAMDA/z9999CIwMf3xrNmDEDwcHBuHDhAs6dO4dOnTplyWPmzJlKXWHt7e0xd+5cdOvWLUuA6cSJE3j37h1KlSqFLVu2CG/a8DH407ZtW6Xg5bt373DixAkYGxvjn3/+Uer6bGlpiTlz5iA4OBj+/v64dOmS0N1a0a186NChSn/4raysVHbbzUn16tXx888/A5nmdUhNTYW+vj4mTJiAc+fOKQ3d1LQZM2Zk2SYSidC6dWvMnz9faZWQtLQ0Ycz8n3/+qRQQNDExwciRIxEWFoZdu3bBx8cny2oo2Rk1apRSA7WgTZ48GTdv3hQ+GxoaYvz48fjhhx9ULteriqJh06tXL5X7FcMwVQ3VVVDsy7yKTH6Py0zxO0bxO4eIvmwSiUT4nabq76i2nD17FuvWrcO2bds+eyhSYZCWliZ88c+t7WZgYIA1a9YotXMUtmzZAolEgm+//VZppS+RSIQmTZrg999/x7fffovdu3crDc/PSX7bL5lNnjxZ5d+wCxcu4Pnz5yhXrhw2btyo9PeodOnSWL16NTp16oTLly8jJCQkS2BCT08Pq1evVpqvp0aNGhgzZgzmzp2bZa7KrVu3QiKRoFGjRli9erVSQM/a2hr9+vVTSn/69GkEBQWhUqVK2LBhg1Kv/7Jly+LPP/9Ehw4dcO7cOURFRaFUqVLAx7+ZpqamWdomDg4OeR7Kmrk3eHp6OmJjY5GWloZSpUphxIgR8PLywuXLl9W+n3nVrVu3LNuMjIzQr18/Yc5KhUePHsHf3x9WVlb4559/lEZflCxZEr/99hsCAwPx8OFD3Lt3T2ml4pxkDvhpQ4cOHZSGC5YsWRKLFy9Wu92dWw93fEYbTPHvnH7vKfZl13ZTfGfJPCcbqcagFBUqMpkML1++BPLQbfNT8fHx8Pf3x9ChQ9GpUydh4vDr169j9OjRaNWqFVasWKH2F9WcKAJfuS31qa+vrzIghY/zCACAu7u7MH5eLpcLEyTK5XJUrVoVN27cwKNHj9C4ceNc6xUUFITQ0FCYmZmhRo0aCA8PV8oPgNCd98GDByrzyG6o1s2bNyGVSlGzZk2YmJhkyVsul6N27dq4cOECHjx4oLIx3aFDhyzbqlSpAn19fSQkJCAjI0MYKqjoutyrVy+lgFR2rl+/DplMhlq1asHAwEBl/WrVqgV/f3/cv39faNQ1aNAAN2/exB9//IEff/xR6K2WX3fv3sWGDRtw69YtlZOBxsbGQiKRFMhwDFtbW2FS8+joaKGcpk2bKnV7x8c3xsnJyXB2doazs7PKZ8XNzU1IW1iVLFkSzs7OSE1NRVxcHCQSCbZt2wZzc/Nsh6hm9urVK2HIQ3Y9qxSN6pwmL1XMgaB4fjMfl9PcVqqOy0zxOyYhIUEYYkpEX67Xr18Lc8h8Ts/ZzxUXF4dHjx7laX6XwkzRbjMwMMg1yFazZk2VASl8XNYeAFq2bKny76atrS1KlCiBFy9eKM1Tk5P8tl8yy67tpmhrNm/eHO/fv8f79++ztDXd3NwQEhKCBw8eZAlKVahQIcsE0sjUlvx0AnjFJNVDhgxR6++Von5fffWVMJXFp/WrXr06Ll68iIcPHwrzj9avXx9XrlzB8uXLMXDgwCxtnLw6evQoduzYgYcPH6qcMDwyMvKz8s+Jo6MjxGIx0tPThTmVLCws0KZNmyxtRcXz17BhQ2RkZGTbFn/48CHu37+vdlBK25ycnJCeno73798jMTER0dHRWLduHWxtbdGyZctcj1f0cC9btqwwZcKn1GmDKfblt+2W+WVvZoq2GxepyR2DUlSoxMfHCz/gn3ZTVdeTJ08AAC1atFBqyLVs2RL169fH2LFjUbVqVWHVhM+h+OX16Upbn7K3t8/2y6ZiuM769euFbrrZybwSTE4U3UiTk5OF1SLymmd2kxYq6hsQEJDrHwxVeVtYWKjs7aWnpwdDQ0NkZGRAKpUK10vRAFB36ILi3G/evJmn+o0cORLPnj3D2bNncfbsWdja2sLNzQ2NGjXCN998k2P33U+dP38e48aNE55lU1NTWFhYCOekmAssPT29QIJSv/32G9zd3YGPfzCPHj2KuXPnYtGiRShVqpRSoFBxP0NCQvJ1P7Oj7YnOf//9d+HfqampOHHiBBYtWoTFixdDJBLh+++/z/F4RS8pd3f3bLv7K77A5LR6kKLHXeYvO4rnPafroeq4zBS/Y/T09BiQIioGMn+JUXfYNOVO8XdYKpUKi79kJ6fJmxVDe8aNG5drmfHx8WqtEJ3f9otCiRIlsky3oKD4W79z507s3Lkzz3lnV39FsO3TdrCiV2/lypVzLOvT+m3ZskXova1O/ebPn49x48bh77//xt9//43SpUujZs2aaNasGTp27KhyOFd2Vq9erdRTqESJEjA1NYWenp4wF5gmVvbLjo+Pj3D/kpOTsXHjRmzYsAFjx47FwYMHldrBimflzJkzOHPmTI755qXtpu2Jzn19fYV/x8TEYMeOHVi3bh1GjhyJXbt2ZTsPlELmHu7Z/Syr0wZT7FPVdsupzafquMwUPxea6AjxpWNQigqVzF9G87sc/ePHjwFA5VhkxbwMR44c0UhQysbGBiEhIdkuAayQ05dsxS9RRe+W/OajyqcTaaqSXWM3u7IU9TUzM8u151J2jaO8UDfw9ylzc/Nce7Blrp+xsTHWrVuHwMBAXL58Gffv38d///2HS5cu4Y8//sCsWbOydDfPzpIlSyCVSuHp6Ylx48bB0dFR2CeTyVC7du0Cbdhkpuj5k5ycjAULFuDXX39Fy5YthT+gmSfIz+2LjzrDVBV0OdG5iYkJevXqBX19fUybNg1bt27NMSiVnp6OI0eOADl0/8bHIQT4OPltdl6/fg18smKO4t8xMTFISkpSGZRVdVxmit8xebkHRFR0Zf4Sk5qaqlZPm08dOXIEu3btwooVK4RVeEuUKIE+ffqo1QshM7lcDm9vb5w9exZGRkbo0aOH0gsOPz8/YcJpPT092Nvbo0GDBujXr59Se+LZs2fYvn07goODYWFhgUaNGmVJ8+TJE+zcuROBgYEwNjZGq1at0K9fv2xf7uWFtbU1RCIRZDIZ3r9/n2M7QZ02l6J3iybltf2ioE5b09raOsdhTPj4N/RzKXqOqOptlFP9bGxsci0/889CmTJlcPjwYdy9exfXr1/HgwcPcP36dZw8eRLLly/H2rVrVc4R+anY2FhhUvopU6bA09NT6R48evQoz1M5fA4zMzNMmjQJYWFhOHLkCObPnw9vb29hv+J6lShRItfAW16G3epyonNbW1tMmDAB8fHx2LFjB7Zt25ZjUCpzD/dPVy3OTNGuUrSzPvXhwwfhBXjmNpg6bT7FPkXaTyUkJACf0dGiOGFQigoVc3NzlCpVClFRUbh9+3a2XTEz+3Qoy5MnT2BtbS2MN/9U+fLlceHCBY0MgXFycsKDBw8+a6xwmTJlcPv2bYwYMUKtYUbqUPxyNDU1xZkzZ7LtVpofil/YNWvWFCZcL0iK8gICAtRqECjS169fHxs3bsxzeZUqVVJ6G3Xv3j0MGzYMCxcuRJs2bXLtGv7+/XsEBwfD1NQUCxYsyHLtg4KCsg1IqTsRZX707dsXO3bsQGBgILZt2ybM96S4Xg4ODkpvrD6Xtic6V0Ux7DAsLCzHN+Lnz59HTEwMSpYsiVatWmWbX7Vq1aCvry9MJvvpUIaEhAShp2bmXprOzs7CKpPXr1/PsoBDRkYGbt++DXyco0MVxe+YvPTYI6Kiq3z58hCJRJDL5bhz506uC7+oatNcuXIFgYGBQvuidevW2L17t9q9EDJbs2YNTE1NMXr0aNy6dQteXl6Ii4sT5tepXbs25s+fD3zshRQcHIwNGzbAz88PmzdvBj5Oft23b1906dIFY8eORVpaGq5fv46FCxcKx54+fRqTJ09G9+7dMWbMGMTHx2PFihW4ffu22ivg5kRPTw92dnaIiIhAdHR0rsGf7JQuXRpBQUH4/fff83Qdc/K57Rd18u7Tpw8mTZqk0bxVKVu2LOLj4xEQEKBWT3dF/QYMGJDnl8YikQj169cX5sSUyWQ4dOgQfvrpJ8yYMUNYSTonDx8+REZGBho3bqxytT5NrS6XV15eXjh16hRu3LiBa9euoWnTpkCmnklff/01lixZorHytD3RuSqKicYVqxpmR9FL6quvvsqxba5oV929e1fltBk3btyAVCpFiRIllFanVhwXGBiIyMjILC/5g4ODERYWBpFIJNT5U4oeg5lfTpNqHANAhU779u2Bj5Mkvn37Ntt0YWFhGDt2bJb5ep4+fZrrfECaGgKjGKOtWLo3PxQNzR07duQ45ljdt034+CXY1dUVCQkJ2LZtW45p85IvADRr1gympqa4ffu2MGeAKnK5PM+9m1RRzJlw5MgRYXnWT2Uu56uvvoKRkRGuXr2aZeLNT+uXeY6M7K5D3bp1UaFCBWRkZAjzneVEMaZfJpOpzFPxJlkVxR/K/Ax7y42enh5GjhwJfOwer/i5qVmzJuzt7REcHCxMFpmdvDwro0aNwvnz5/P8X17mTsmtPqdOnQI+NgZyCvgpGjbdu3fP8U18iRIl0KRJEwBQ+YXh33//RUZGBsqVK6cUsBKJRGjXrh0A4J9//skyP8GhQ4cQHR0NMzOzLCsjKih+xxTWeSGISLNsbGyEHh6rVq0Shviq8vTpU5WBhsePH0MikeDPP//Et99+iyZNmmDp0qWwtLTEoUOH8lQfPT09TJo0CU2bNsWECRMwePBgrF69WqiXlZUVatasiZo1awqrnq5atQpXr14V5q68ePEixGIxFixYgCZNmqBly5aYPn06fvrpJ+DjkKXZs2ejS5cu+PXXX9GiRQt07twZa9asga+vL65cuZKnOmdH8Xs0uzk11aFou61fvz7H+bby8nczv+0XdSjaUgcOHEBoaKhG6psTxdQRf/31l9Bb5FOZ226K67lv3z5haGRu9ZPJZCrn+xGLxejSpQv09PTw7t27HH92FBRtt7S0tCz7UlNT8ddff2V7rJGREaRSabaTXX8Oe3t7oQf3unXrhO1ff/019PT0cObMmWzbxvgYIM5pTqRPrV27Nl9tt7zI6RmTy+U4ffo0kGk14+zyUKeHOwA0adIElpaWiI+PF9p7mcvbtGkT8PEZzPwiuVy5cqhatSrkcrnKFSkVC0rVq1cv21EpbLupjz2lqNAZNWoUfH19ER0dDU9PTwwePBhNmzaFtbU1EhMTERgYCD8/P5w+fRp2dnZKQ2HS0tLw6tWrHIdZRUREaOwtgKLB+DkTQLdp0wYNGzbErVu30KtXLwwYMAC1a9eGra0t4uLiEBoaikuXLuHhw4c4ceKE2vlOmzYNP/zwA3777Tfcu3cPXbp0QenSpSEWixEWFoZnz57hxIkT6N+/f56WKS1RogRGjx6NFStWYNSoUejVqxdat24NBwcHpKWlISwsDP/99x+OHz+OtWvXCpNg5lfTpk3RvHlzXLlyBQMGDMDw4cOFwNi7d+9w5coVBAUFCX8cbGxsMGLECPz555/44Ycf4OnpiVatWsHBwQGpqakIDw/H3bt3ceLECWzatEl4g9e1a1fUqFED7u7uSj1bTp48iYcPH0IkEmXbPTczS0tLlC9fHsHBwRg1ahRGjhwJOzs7vHnzBt7e3rh69arw9vtT5cqVw82bN+Ht7Q1LS0vhDW6pUqU00oOoc+fOWLt2LV6/fo1du3Zh2LBh0NPTw5QpUzB16lRMnz4d/v7+6NChAxwcHCCXyxEWFoZHjx7h+PHjmDZtmjC5aGGwZs0a3L9/H506dUKZMmVgZ2eH1NRUvHv3DseOHRMaNn379s02j7CwMOGLTnar7mU2duxYXLlyBcePH4eJiQm+++476Ovr48SJE8IzqOot7/Dhw3H48GHcu3cP48aNww8//IASJUooDXn54Ycfsh2io/gd87lDG4mo6Jg6dSr69++P58+fo3v37hg8eDDq1q0Lc3NzxMfH4/Hjxzh79iz8/f2z9PL88OEDXr16hYEDB6JixYrCdkNDQ5QvXz7HL/6qfLpYS5cuXfDvv//iv//+E1Y1vnbtGnx8fBASEoKkpCTh71xwcDBq1qwJW1tbJCcnY9euXejRo4fw+07x9+3atWtISEjI8rvYzc0NTk5O8Pf318gKyg0bNsTJkyfx4MGDbBe2yM3QoUNx+PBh+Pn5oXfv3ujXrx8qV64MCwsLREZG4u3btzhz5gzMzc2xcuVKtfLMb/tFHQ0aNEDbtm1x9uxZob4NGjRAyZIl8f79e4SGhuLq1au4ePGisAr25+jfvz/27NmD169fw9PTEyNGjEDt2rUhl8vx+vVrnDx5EqVLl4aXlxfw8YWnu7s7Ll26BE9PTwwcOBB169aFra0tEhISEBYWBn9/f1y/fl0IgkRGRsLT0xMdO3ZEw4YN4eTkBFNTU4SFhWHbtm2QSqUoVaqUWsPXatSoAQMDAwQEBGDu3Lnw9PSEkZERnj17hnXr1mU79Asf227Pnz/HunXr0L17d2H4obOzs0Z6wA8fPhx79+7FzZs3cfv2bTRo0ABly5bFd999h+3bt2PgwIHo27cvmjVrBjs7OyQnJyM8PBw3b97EyZMncfTo0UI1fGz06NGwtLRE69at4ejoCBsbG7x//x4vXrzAvn37EBAQAJFIhD59+mSbh6KHu62tbY493PHxd96IESOwbNkyLF68WJhvNzExEZs3b8aNGzdgYGCgcrXG8ePHY9y4cfD29oZYLEb37t0hk8mwb98+7N+/H8hlXjnF9we23XLHoBQVOnZ2dti0aRPGjx+PN2/e4I8//sAff/yhMq1ieI7Cs2fPIJVKVc4nhY+TCT979kytL5/qcHNzQ4UKFfD48WNERETka9UPkUiEP//8ExMnTsSNGzewdOlSlenyOolg8+bNsWTJEsyZMwenTp0Seo18Kj+T740YMQJJSUnYuHEjtm/fju3bt2dJIxKJNDL/AwCsXLkSY8aMwZ07d7By5cosDbxPh3mOHTsWSUlJ2Lx5M7y9vZXG4WdXP4lEAh8fH/j4+Kisw9ixY7Od7+dT06dPx7hx43Dt2jVhRRmF0aNHY/v27Sp7Q3Xr1g379+/HhQsXcOHCBWH75s2b0axZM7XKzomenh6GDx+O2bNnY/PmzRgwYACMjIzQtWtXxMXFYenSpTh48CAOHjyo8vjCNlGjWCzG1atXhVVoPiUSidCvXz+VXfEVDhw4AJlMhvr16yt9cctOvXr1MGPGDPz222/Yv3+/0ChRGDhwoMplncuUKYMlS5Zg6tSpOHfuXJbhBO3atRN6sn3qw4cPuHbtGqysrLLtSUVEX55atWphzZo1mDZtGt6+fYtffvkl27SftoeePHkCqVSqcoW29+/fo3z58nmqy6cTXSs+K4bY7Nq1C7/88gtGjRqFLl26wNzcHCkpKfj++++FOUI7deqEa9eu4ZdffsFvv/2GevXqoU2bNujduzcMDQ2FHjwLFizI0n6Ii4sThsJ8Lg8PDyxevBgXLlzA7Nmz8xU4sLGxwaZNmzB27Fg8fPhQ6O31qc6dO+cp3/y0X9S1dOlSTJkyBefPn8+213Ze5h/KSYkSJfDXX39h1KhRePPmDWbPnp0lzadBgN9//x0//vgjLl++jBUrVqjMN3NwRSwWIyYmJtvrZGBggPnz56t1f21tbTFixAisXbsWe/bswZ49e5TymTRpUrbBxW7dumHZsmX4559/lHrU3L17VyPX09HREd27d8e+ffuwfv16oWfPzJkzkZqaigMHDmDjxo0qe3Dr6elpdAoPTZDJZDh27BiOHTumcr+RkRF++umnHFcbV/R46tatm1pt06FDh+LRo0c4ceIEVqxYofR8GRgYYNGiRSpX2mzXrh1GjBiBjRs3YvPmzcJQZIUff/wx2/b5/fv3ERUVhSZNmqBkyZK51rG4Y1CKCiVXV1ccP34cx44dg5+fH4KDg/HhwwdYW1ujZMmScHV1hbu7e5ahPoourNkFpfbv34/09PQce07kVZ8+ffDbb7/h+PHjGDp0qNI+fX19ODs75xqssra2xtatW3H+/HmcOnUKL1++REpKCkqWLAknJye4u7urXEXPyckJqamp2Q5F7Nq1Kxo3box9+/bh5s2biIyMhJGRERwcHODq6oqOHTtmmRfH3t4eEokk1wbP5MmT8c033+DAgQO4f/8+4uLiYG5uDgcHB9SrVw+dOnVSGkMtFovh7Oyc4x9oxfl82oCwsrLC9u3bcfLkSfj6+iIoKAhyuRylS5dG8+bNs8w1JRKJMGPGDHTr1g0HDhzAgwcPEB8fDwsLCzg6Ogr1y3xffHx8cOHCBfj5+eHt27eIj49HqVKlUKlSJfTp0ydPPb6+/vpr7NixA5s3b8bz589hYGCAChUqwNPTE+7u7jh37hxKlCiR5b41bNgQGzduxJ49exAcHIzU1FQgD/MsOTo6Ql9fP8cJcbt3747du3cjLi4OZ8+eFd5+f//99/j666+xd+9e3L17F9HR0TA1NYW9vT1q1qyJTp06Zbs0tq5MmDABX331FXx9ffHixQth9Z5SpUqhRo0a6NKlS6737caNG3B2dsZ3332ndrmDBw9GrVq1sHPnTjx79gwymQwVK1ZEr169cnxj17FjR1SuXBne3t548OAB0tLSUKZMGXTu3BldunTJtuF87tw5pKSk4Ntvv9XonFtEVPi1bNkSZ86cwcGDB3H9+nWEhIQgPT0dtra2KFWqFGrXrg13d/csvWYUi75kniMFH3uHBgUF5RisVyUpKUlprk7FkChFj94tW7bA09MTEyZMENJ8OuRdT08PixYtwtSpU3Hjxg1cvnwZS5cuxenTp+Ht7S30MJk4caLK4TC5La6irpIlS6JNmzY4deoU7t69K8xFpGBqagpnZ+dcF5aoWrUqjh8/jiNHjuDixYt4+/YtpFIp7OzsUK5cObRt21aYA0jBwMAgx3Zhftovenp6atXXzMwM69evh7+/P44fP45nz54hOTkZ1tbWcHZ2RrNmzYSh5gpGRkZwdnbO9ku14nxU3S/F9dm3bx8uXbqEsLAwGBkZoWzZsujQoYMwXYeCubk5/vnnH/j5+eHkyZN49uwZUlJSYGNjAycnJ7Ro0UKpfnZ2drh06RJ8fX1x8+ZNvHv3DmlpabC3t0etWrXQt29ftVY9VJgwYQIqVaqEAwcOICQkBGZmZqhevToGDhwIExMT7NmzR+V9GzZsGMzNzeHr64vQ0FBheJo604Qo7l1u6UeOHImrV68iKCgIgYGBqFSpEvT19bFo0SJ8++23OHLkCB4+fIjExERYWlrCwcEBjRs3RocOHfI9b1pB2bBhA/z9/XH+/Hm8fv0akZGRMDAwgJOTE+rVq4fu3bvneN9iY2MRHBwMZ2fnXIfuKYjFYvz+++9o27YtfHx88ObNGxgZGcHNzQ0DBw7M8l0oMy8vLzRu3Bh79+7Fq1evIBKJUKVKFfTt2zfHuY+PHj0KfPyeSGqQE31Bfv75Z3nVqlXliYmJWfY9f/5cXrduXfmsWbM0WmZ8fLy8fv368m+++UYuk8k0mjcR0dChQ+XVq1eXBwUF6boqRFREzJw5U+7i4iJ/+fKl0vbFixfLmzRpIk9OTlYrn71798pdXFzkBw4cUNp+6NAhuYuLi/zVq1dyuVwur1Onjnzt2rVKaf7991+5i4uLfO/evdnm//fff8tdXFzkcXFx8pcvX8pdXFzkBw8ezMOZ5s+dO3fkLi4u8hkzZhR4WURUvHz48EHerFkzecuWLeVpaWm6rk6RwInO6Yvy9OlTODk5Kc0zlZycjN27d6N///5o3rw55syZo9EyLS0tMXLkSLx48UKYw4aISBPu378Pf39/9O7dO8/DbYio+Hr06BHEYjG8vb2FuZ1Onz6N7du3Y8GCBTA1NRXS9u/fH4sWLcoxv3PnzglD9cLCwrB27Vq0a9dO6EFbq1Yt+Pr6Cj2oHj16lGVuokOHDuHSpUtCfaRSKV68eIFSpUrB0tISlSpVQteuXbF8+XLcvXtXOC4tLQ27d+/GvXv3NHZ96tWrh3bt2uHo0aN49+6dxvIlItqzZw+io6Px448/Zlntj1Tj8D36YshkMmHJVsVwruTkZERFRcHNzQ3z589Hhw4dNDLp4KcGDRqEU6dO4fDhw/Dw8NB4/kRUPB04cADly5fPcSJNos/h4+ODEydOICYmBo6OjujVqxdatmxZYHloojzKmUQiQWBgIIYMGYKbN2+iZcuW0NfXh0QiwdKlS5WGQMnlcjx48EBpaJ4qY8eOxahRoyCRSPDu3TvUrVsXCxYsEPbPnTsX48ePR8uWLWFrawtzc3MsXbpUaYJ0V1dXrFq1Cj/++COcnJwQFRUFe3t7rF27VmibLVy4EH/88YcwJMrU1BRRUVHo0qVLlqFln8vLywtPnjzBwYMHlYYdEhHll1QqxYkTJ9C0aVOVc4ySaiK5qiWgiIogqVQqzKGAj/M5WVhYwM7OjlFqIiKiT8yaNSvLZP34+GVd1UpEn5uHJsqj3D148ACenp7CIhlv3rxBeno6ypcvn2XS42fPnqFr167YunUrmjRpkiWv2NhYhISEoHr16hCJRAgODhbmGfqUTCYTelOVKVMGMpkMjx49Ela0VVCskmplZZVtMCw9PR1v3rwR5iwqbJM1ExGR5jAoRURERFTMXLp0CcOHD4eVlRWmTp2KKlWqICAgAL///jskEgmOHj2a65LzeclDE+WRenbv3o158+bh5s2bKFGiRI5pt2/fjuPHj2PXrl1aqx8REVFmnFOKiIiIqJhRLKm9ePFieHp6onbt2vj+++8xbdo0SKVSlT2aPicPTZRH6nn8+DHKlSuXa0AKANq0aYPVq1drpV5ERESqsKcUERERUTHTvHlzSKVSXLt2TWmuxaSkJDRu3Bg1a9bE7t27NZaHJsoj9QQHBwMAF0cgIqIigROda0B0dDR8fX1Rvnx5mJiY6Lo6REREpKbU1FQEBwfDw8MDJUuW1HV1tOLDhw+Ijo5Gw4YNsyz+YW5uDkdHR7x9+1ZjeXxOedHR0Th+/DicnZ1hbGwsbDcwMICBgUGez704iY2N1XUViIioGFO3jcWglAb4+vpiwIABuq4GERER5dP27dvRv39/XVdDK1JSUoCPASFVzM3NERMTo7E8Pqc8X19fDB48OMe6EBERUeGVWxuLQSkNUHSP3r59O6pVq6br6nxRpFIpnj9/DhcXF668QjrBZ5AKAz6HBefJkycYMGBAsRrqpOhhlJ6ernJ/enp6rqvW5iWPzylPcV/WrVuHKlWqKJWvr/95zdh58+Z91vG6ZGBggD59+mDPnj3ZXtfCrKCvPe+t7vDeZo/3NmexZzoWaP4FSSY2Qny55bB6PQViWZquq5NnNu1OFki+z549w7Bhw3JtYzEopQGKIXvVqlVDvXr1dF2dL4pUKoVYLEadOnX4RYx0gs8gadr79+8REhKCcuXKwdTUNNf0SUlJeP78OSpUqIB69epBT08PT58+xYcPHwAArq6uSsOa8HHYTlxcHMqWLcshTmoqTsPvzc3NYWxsjJCQkCz75HI5wsLC4ODgoLE8Pqc8xX1p3LixxttYRfmeGxoaokaNGjh69CgkEomuq5NnzZs3L9D8eW91h/c2e7y3OQt7kVig+RckmViGcLcacBClQixL1nV18syxgO6t4uc1t59brr5HRESkJSdPnkSrVq0wadIktG7dGrdv3871mJ9//hmDBg1CWFiYsO2vv/7CokWLMGDAALx580bYnpSUhClTpqBz584YPXo0PDw88OTJkwI7HyqaRCIRqlatiqCgoCxzOd25cwfJycm59vzOSx6aKI+IiIi+TAxKERERaUFGRgYWLVqEP/74A6dOncL06dOxePHiHI85deoUbGxsYGNjo7T9999/x969e2Fpaam0PS4uDu7u7rh69SpOnz6Njh074vfffy+Q86GirV27dsDH4RhJSUkAgJiYGCxatAgA4OHhodE8NFEeERERfXkYlCIiItKCZ8+eAQDc3d0BAF26dMHz58+zXSErNjYWW7ZsweTJk9Uuo0yZMujatavwuWLFillWOyMCgH79+qF06dLw9/fHV199hU6dOqFVq1Z49OgR6tatKwSRAMDPzw/dunXDrl278p1HXtISERFR8cGgFBERkRZERUXB3t5e+GxgYAAbGxtERkaqTD9//nxMmTIl3/NnxMfH4++//+bKZaSSmZkZtmzZgiZNmiAlJQWBgYGQSqXw8PDA+vXrlYKZ8fHxePr0KaKjo/OdR17SEhERUfHBic6JiIi0wMjICGlpyiuypKWlqQw6+fr6IiUlBfr6+ggICEB6ejrevn2L+Ph42Nra5lpWXFwchg8fjlGjRqFp06YaPQ/6cpQpUwZbt24VJsa3s7ODhYVFlnStWrXC4cOHUbJkyXznkde0REREVDwwKEVERKQF5cqVw9u3b5GSkgJTU1OEh4fjw4cPKlcdi4uLQ0JCgjDfTmJiIo4dO4ZGjRqhWbNmOZYTHR2NYcOGYciQIejevXuBnQ99OVTNW5aZpaVllvnL8ppHftMSERHRl41BKSIiIi1wcnJC/fr1MXv2bHTv3h3e3t7o2rUrjIyMAACPHz9G2bJlYW5ujr59+6Jv377Cse7u7hg9ejQaN24MAAgKCkJCQgIyMjLw7NkzpKWloWbNmoiNjcWAAQPQvHlzlC9fHgEBATA2Noarq6vOzpuIiIiIKDsMShEREWnJsmXL8Mcff2Djxo2oUaMGJkyYIOxbu3YtRo4ciVq1amU5rnr16kLwCgD279+PW7duoVy5cti2bRvMzMywefNmREZGokSJEnjw4AEePHgAAHB2duYKfERERERUKDEoRUREpCU2NjZYsGCByn1r167N9ri1a9ciICBA+Dx16lSV6VxdXbF3714N1JSIiIiIqOBx9T0iIiIiIiIiItI6BqWIiIiIiIiIiEjrGJQiIiIiIiIiIiKtY1CKiIiIiIiIiIi0jhOdk1bIZDL4+Pjg5cuXcHNzQ8eOHbNNK5FIcOzYMbx+/RpVqlSBnZ2dsC8pKQlHjhxBeHg4GjRogJYtW2Y5/vHjxzh06BCGDh0KR0fHAjsnItKesC0iXVdBp2RiM8DND+E7LCGWJeu6OjrjOFiu6yoQERERkQaxpxRpxaJFi7Bp0yYYGRnhjz/+wIYNG7JNO3jwYNy7dw8mJibYuHEjNm3aBABIS0tD7969ce3aNZiammLx4sXYuXOn0rFpaWn49ddfceTIEURHRxf4eRERERERERFR/rCnFBW49+/fY//+/Th58iQcHR3RoUMHDBgwAMOGDYOBgUGW9MuWLYOzszMAoFatWvjpp58AAHfu3IFMJsOaNWsAAK1bt8bIkSPx3XffQST6Xy+KP/74A/369cPKlSu1eo5ERERERERElDdFoqfU8ePHMXjwYLRr1w69e/fGpk2bkJ6enq+8IiIi4OHhAXd3d+zevbvAyyPg1atXsLOzE4bSValSBQAQHh6uMr2zszP++ecfzJ07VwgyAYC+vj4kEgmkUikAIDU1FeHh4YiNjQUA3L17FyEhIejcubOWzoyIiIiIiIiI8qvQ95RavXo11q5dK3x+8+YN/vvvP9y8eRMbNmwQesioa968eYiNjcX79++RnJx1Xg5Nl0dAQkICzM3NlbZZWFggPj4eZcqUUXmMnZ0dJBIJ7t69i9evXwMA6tWrBzs7O/Tt2xeurq549eoVjIyMkJCQADMzMyxdulToRUVEREREREREhVuh7ikVGBiIDRs2wNTUFAsXLsSZM2fw999/o0yZMrh48SKOHTuWp/x8fHxw+/ZtTJw4USvl0f9YW1sjPj5eaVtCQgJsbGyyPaZr164YM2YM/vrrLxw8eBAfPnyAvr4+tm3bhpEjR6JWrVpYsGAB0tLS4ODggN27dyM9PR1//fUXFi5ciPj4eGzZsgWPHz/WwhkSERERERERUV4V6qDU4cOHIZVKMWnSJHh6eqJs2bJwd3fHqlWrAAD79+9XO6+YmBgsXLgQs2bNgoODQ4GXR/+vcuXKSEhIwIsXL4CPc0OZmJiovA/R0dEICwsTPr9//x76+vrC3FMGBgZo27Ytvv32Wxw/fhxNmzaFqakp6tSpgy5dusDZ2RnOzs7Q19dHqVKlYGJiosUzJSIiIiIiIiJ1FerhewEBAQCQZY4gNzc3VKhQAffv34dcLldrSN38+fPRsGFDdO/eHWfPni3w8uj/mZqaYsiQIRg2bBiaNWuGS5cuYeLEidDT0wMArF+/Hu3bt0elSpUgl8sxduxYlCtXDoaGhvDz80OvXr2EtBs3bkRUVBSCgoLw8OFDeHt7AwDq1KmDOnXqCGV6e3vjm2++QYUKFXR01kRERERERESUk0IdlAoNDYW1tbXKYV6VKlVCUFAQ4uLichwGBgC+vr64fft2rsPvPre8xMREJCQkCJ8NDQ1haGiYY5nFxejRo9GgQQMEBgaid+/eqF27tjBhealSpWBgYACpVAobGxts3boVly9fRmpqKoYOHYqkpCSltIaGhqhevTqWLFkCKysrYV9mo0aNgr29vcp9RHmheIb4LOmWTGym6yrolExsqvT/4qogfg75s01ERESkO4U6KJWamgozM9VfRExN/9cwT0lJyTEoFR8fjwULFmD+/Pm5Bq8+t7xBgwbB2NhY+NyzZ094enrmWGZxYmBgAFdXV8jlcqFXGgBUqFABUVFRiIqKErbZ29sDAJKSkgAADx48AACULVtWSBMcHJxtWZUqVcLbt2/x9u3bAjkXKn4UzyDpiJufrmtQKERWO6nrKuhUeKa/HZry/PlzjedJREREROop1EEpQ0NDpKWlqdyn2G5kZJRjHr/++iuaN2+Odu3aFXh53t7eSkPI2FPq80mlUjx48AA1a9YUhvARaROfwcIhfIelrqugUzKxKSKrnYTdk44Qy1J0XR2dceifoEaqvJHJZBrPk4iIiIjUU6iDUqVKlcLDhw+RmpqaZcLqd+/ewcDAAFZWVtke//TpUxw9ehTW1tZwd3cXtisCTOvXr8fWrVvxyy+/oGXLlp9dnoWFBSwti/cXp4Kip6fHgADpFJ9B3RLLknVdhUJBLEsp1teiIH4G+XNNREREpDuFevW96tWrQyaTwc9PedhGREQEnj59ChcXF2FVNlUUQ7/i4uIQEREh/BcfHw98nAMqIiICqampGimPiIiIiIiIiIjUU6iDUh07dgQALF26FC9evAAAxMbGYvr06ZBKpcL+7NSqVQt+fn5Z/vv111+Bj5Nv+/n5oVWrVhopj4iIiIiIiIiI1FOoh+81adIEbdq0wblz59C5c2dYW1sjMTERGRkZKF++PAYMGCCkffnyJYYOHYpWrVphwYIFwMc5nRwcHLLka21tDXwcbpd5f17K05auXbtqvczCxNDQEDNnzkSfPn0gkUh0XR2d8fHx0XUViIiIiIiIiDSqUPeUAoAVK1Zg4MCBMDMzQ1xcHACgXbt28Pb2Vpr3KT09XWloXkGXR0RERERERERE+Veoe0oBgImJCWbPno2ZM2ciISEB5ubmKle0q1y5Mvz8/GBsbJxrnl999RX8/PxgYWGR7/KIiIiIiIiIiCj/Cn1QSkFPTw82NjbZ7jcwMFA5VE8VIyOjXNPmVh4REREREREREeVfoR++R0REREREREREXx4GpYiIiIiIiIiISOsYlCIiIiIiIiIiIq1jUIqIiIiIiIiIiLSOQSkiIiIiIiIiItI6BqWIiIiIiIiIiEjrGJQiIiIiIiIiIiKtY1CKiIiIiIiIiIi0jkEpIiIiIiIiIiLSOgaliIiIiIiIiIhI6xiUIiIiIiIiIiIirWNQioiIiIiIiIiItE5f1xUgItKWuLg4XLhwAXp6emjTpg3Mzc2zTRsdHQ1/f3/o6+vD0tJS2B4UFAQ/Pz/hs7GxMfr27ZvrPiIiIiIiIlLGoBQRFQsxMTHo3r07qlevDolEgnXr1uHw4cMwMTHJkvbatWv49ddfUatWLURHRyMgIACHDh1C6dKl8fjxY+zatQvu7u4AAFNTU+G4nPYRERERERGRMgaliKhY2L17N+rVq4dVq1YBAIYMGQIfHx/06dMnS1obGxscPHgQRkZGkEql6NmzJ+7cuYPSpUsDAKpXr45Zs2apLCenfURERERERPT/OKcUERUL//33H1q1aiV8btmyJQICAlSmrVq1KiIiIrBlyxYsWrQIANCiRQthf0REBHbt2oXz588jPT1d6dic9hEREREREdH/Y1CKiIqFuLg4WFlZCZ+trKwQGxubbfrU1FS8e/cOL1++hJ6enrC9QoUKcHNzw/Pnz7FmzRr06NEDSUlJue4jIiIiIiIiZRy+R0TFQokSJZCYmCh8TkpKUprA/FNVq1bF7NmzIZVKMXToUOzatQvjxo1D9erVUb16dQCAXC7HgAEDcPLkSXz77bc57iMiIiIiIiJl7ClFRMVCtWrVcPPmTeHzzZs3Ua1aNZVpQ0JClD4bGhpCKpVmSScSiWBmZqZymF5O+4iIiIiIiIg9pYiomOjTpw+6d+8OIyMjpKWl4datW1iwYAEAIDAwEM+ePUOnTp0AADt27EBISAiqV6+Oly9f4u7du/jpp58AAOfPn8ebN2+QkZGBgIAAPH78WJh3Kqd9REREREREpIw9pYioWChTpgz27dsHS0tLODo6Yv/+/cIcU6mpqYiMjBTSTps2Dd27d0dKSgpq1qyJ5cuXo2zZsgCAmJgYhISEIDY2Fs2aNcOJEydQsmTJXPcRERERERGRMvaUIqJio2LFipgwYUKW7TVq1ECNGjWUtrVu3RqtW7eGVCpVWqUvp/mhOHcUERERERGR+thTioiIiIiIiIiItK7I9JR6+/YtQkNDYW1tDRcXlzwfL5FIEBwcjPfv38PR0RFOTk4QiURKaZKTk3Hnzh2Vx1tYWKBu3br5rj8REREREREREf2/Qh+UioqKwtSpU3Ht2jVhW8WKFbF8+XK4ubnlenxoaCjWrFmDkydPIiUlRdhepUoVTJkyBa1atRK2hYSEYPjw4SrzcXNzw8GDBz/7fIiIiIiIiIiIqJAHpWQyGUaPHo0HDx6gRIkScHV1xbt37/Dq1SsMGzYMJ06cgI2NTY553Lt3DwcOHICxsTGqVasGc3NzBAcH48WLFxg9ejT27NmDWrVqKR1ToUIFODs7Z9lGRERERERERESaUaiDUmfPnsWDBw9Qo0YNbNmyBRYWFpBKpZg7dy7279+PzZs3w8vLK8c8nJ2d8fvvv6Nt27YwNDQEPga7/vjjD/z11184depUlqDUt99+i2HDhhXouREVJV27dtV1FXTG0NAQM2fORJ8+fSCRSHRdHZ3x8fHRdRWIiIiIiOgLU6gnOj937hwAYMqUKbCwsAAA6OnpYcaMGTAyMsKZM2dyzaNOnTro1KmTEJACALFYjCZNmgAATE1NsxwjlUpx//593Lp1C9HR0Ro8IyIiIqLCITU1FStWrECbNm1Qp04ddOzYEf/88w+kUqnG8zhx4gSqV6+u8r+ffvqpAM6OiIiIioJC3VPq2bNnMDAwQIMGDZS2W1hYwM3NDQEBAUhLS4ORkVGueb19+xZBQUHChOfbtm2DtbU1PD09s6T9/fffIZPJhM8tWrTAL7/8AicnJw2dGREREZHuyGQyjBw5Ejdu3BC2vXr1CsuWLcOrV6+waNEijeYhl8uzDXZlbnMRERFR8VKog1KxsbGwtbWFgYFBln0ODg6QyWSIj4+Hvb19rnmdPHkSK1asED43aNAAy5cvh4ODQ5a0FhYWqFSpEpKSkhAUFAR/f38MGDAAR44cEXpsqZKYmIiEhAThs6GhoVIPrfz43OOLOsX5F/frkJe31gWhOF9/PoP/o+tnUCY202n5uiYTmyr9v7gqiOdQ18+2rvj4+ODGjRuoUKECli5disqVK+O///7D1KlTceDAAXh6eqJevXoaz2Pjxo1o3ry50jaxuFB33CciIqICVKiDUlKpNNteUPr6/6t6RkaGWnmVLl0aLVq0QGpqKoKDg3H79m1MnDgRGzZsECZLt7CwwOrVq9GuXTuhgRQaGorJkyfj3r172LVrF0aMGJFtGYMGDYKxsbHwuWfPnip7YuXFzJkzP+v4L0Vuc4d96QICAnRaPp9DPoO6fgbh5qfb8guJyGondV0FnQovgOfw+fPnGs+zKFDME7d06VJhbs2mTZti7ty5GD9+PA4fPqxWUCqveYhEIqENR0RERFSoWwVmZmaIjY1Vue/9+/dCGnV06tQJnTp1Aj4Gu7y9vfHbb79h5cqV+PXXXwEAjo6OcHR0VDrOyckJv/76K7755hvcvXs3xzK8vb1Rp04d4bMmekr16dPns44v6gwNDeHl5YUVK1YU60mm9+zZo9Pyi/NzyGfwf3T9DIbvsNRp+bomE5sistpJ2D3pCLEsRdfV0RmH/glqpMqb4jp07MGDB3B0dMyy2EurVq1gaGiIhw8fFkgey5cvx6RJk6Cnp4eqVatiwIAB8PDw0MAZERERUVFUqINSZcqUwevXrxEaGpplPqdnz57B0tISVlZWec5XT08PQ4YMwYYNG3Dr1q1c05csWRIA8OHDhxzTWVhYwNJSs1+civOX4MwkEkmxvhZ6eno6Lb84X3sFPoO6fQbFsmSdll9YiGUpxfpaFMRzqOtnWxdSUlLw/v17VK9ePcs+Q0NDODk5ISwsrEDyePbsmfDvmzdv4ubNmxg5ciQmT56cY3mcIkFZUR9aXtDDZovqdQHvba6K6nUB722uivJUDUV9moWCurfq5luog1INGzaEv78/du/erdRYuXjxIsLCwtCmTZtc84iNjRWG52UWHByMhIQE2NnZCdtiYmJga2ubJe3BgwcBAGXLlv2MsyEiIiLSnMOHD6u9cp2DgwPOnz8PZHrJZmJiojKtiYkJIiMjc8wvr3kYGRnhhx9+QKdOnVCuXDnExsbixIkTWLNmDTZu3IhOnTrB1dU12/I4RYJqRXVoeUEPCee91R3e29zx3mbjC5iqoahOs1AQ0yMgD1MkFOqgVI8ePbB+/Xr8/fffSEtLQ8OGDfH69WusX78eANCvXz8hbVJSEu7evYtSpUqhWrVqwvYpU6ZAX18fzZo1g7OzM9LS0vD8+XPs378fcrlcKbA1ffp0yOVyNG/eHKVLl0ZiYiL8/f1x6tQpAED37t21ev5ERERE2ZHJZGq/hcw8B6fiLX12PcBTU1NzXdk4r3m0bdsWbdu2FT6bm5tj1KhR0NPTw/Lly3HhwoUcg1KcIkFZUR9aXtBDwnlvdYf3Nnu8tzkrylM1FPVpFgpiegTkYYqEQh2Usre3x7x58zBr1ixs2bIFW7ZsEfYNGjQILVq0ED6/ffsWw4cPh4eHB1avXi1st7W1hY+PD/z8skZeW7VqhVGjRgmfS5YsiUOHDsHf318pnZ6eHmbOnJnrhJ9ERERE2tKjRw907dpVrbQikUj4t7m5OSwsLPD69ess6dLT0xEWFoaKFSvmmJ8m8gCA+vXrAx97q+eEUySoVlSHlhf0sNmieE0+xXurWlG8Jp/ivVXtS5ieoKhOs1BQ91bdfAt1UAofG1xVq1bFwYMHERISAmtra3h4eKBly5ZK6czNzdGiRQulXlIAsGzZMvTt2xcXL17E69evIZfLUbp0abi7u6Np06ZKaX/77Td4enriwoULePPmDUQiEapUqYJvvvlGrYYVERERkbZ8zkp2NWrUwLVr1/Do0SO4ubkJ2y9duoS0tDTUqFFDK3n8999/AKByqgUiIiL68hX6oBQAVK9eXeVEmpmVKVMGmzZtUrmvfv36wpu43DRo0AANGjTIVz2JiIiIioJvvvkG165dw/Tp07F8+XJUqVIFAQEBWLBgAQCo1QMrL3mMHj0aHTt2RN26deHo6IiYmBicOHECK1euBIAsLxuJiIioeCgSQSkiIiIi0pwePXrgwIEDuHfvHrp166a0r3PnzmjUqJHw+ejRo5g+fTrGjBmDcePG5SuPR48eCROtf2rw4MFKPa2IiIio+BDrugJEREREpF36+vr4559/MHDgQJQqVQpisRjOzs4YP348Fi9erJRWMaG6XC7Pdx4bN25E7969UbZsWejr68PU1BQNGzbEypUrv4jVtIiIiCh/2FOKiIiIqBgyNzfH7NmzMXv27BzTde3aFd988w3E4qzvMtXNw9XVFb/88stn15mIiIi+LAxKEREREVG2PmdCdSIiIqKccPgeERERERERERFpHYNSRERERERERESkdQxKERERERERERGR1jEoRUREREREREREWsegFBERERERERERaR2DUkREREREREREpHUMShERERERERERkdYxKEVERERERERERFrHoBQREREREREREWkdg1JERERERERERKR1DEoREREREREREZHWMShFRERERERERERax6AUERERERERERFpHYNSRERERERERESkdQxKERERERERERGR1jEoRUREREREREREWsegFBERERERERERaR2DUkREREREREREpHUMShERERERERERkdYxKEVERERERERERFqnr+sKqOPVq1c4cuQIQkJCYG1tDQ8PDzRo0EDt45OSknD69Gk8efIE79+/h6OjI9zd3VGvXr0CKY+IiIiIiIiIiHJW6INSvr6+8PLyQnp6urDN29sbY8eOxYQJE3I9/saNGxg1ahRSUlKUtq9fvx4eHh5YuXIl9PX//zJ8bnlERERERERERJS7Qh2UiomJwYwZM5CRkYHevXujYcOGeP36Nf7991+sXbsWTZs2RcOGDXPMIzo6GsbGxujSpQuqVq0KMzMzBAUFYffu3fD19cWhQ4fw7bffaqw8IiIiIiIiIiLKXaEOSh08eBApKSkYPHgwZs6cKWx3cXHBhAkTsG3btlyDRE2aNIGfnx8MDQ2VtterVw8jRozAixcvNFoeERERERERERHlrlAHpW7evAkAGDBggNJ2Dw8P2NnZ4caNG7nmYWtrq3L7s2fPAACurq4aLY+IiIiIiIiIiHJXqINSb968gbm5OcqUKZNlX7Vq1eDn54eEhARYWlrmmtfZs2fh4+MDiUSCoKAgBAcHo2vXrujWrZvGyktMTERCQoLw2dDQMEsPrbz63OOLOsX5F/frIJVKdVp+cb7+fAb/R9fPoExsptPydU0mNlX6f3FVEM+hrp9tIiIiouKsUAelkpOTsw0AKbYnJSWpFZR69eoVfH19hc/169fHoEGDoKenp7HyBg0aBGNjY+Fzz5494enpmWvdcpJ5GGFx5uXlpesq6FRAQIBOy+dzyGdQ188g3Px0W34hEVntpK6roFPhBfAcPn/+XON5EhEREZF6CnVQSiwWQyaTqdyneLOZeeW8nLRt2xZly5ZFamoqAgMDceDAAXz33Xf4+++/0bRpU42U5+3tjTp16gifNdFTqk+fPp91fFFnaGgILy8vrFixAhKJRNfV0Zk9e/botPzi/BzyGfwfXT+D4Ttyf/nwJZOJTRFZ7STsnnSEWJaixhFfJof+CWqkypvs/u4TERERUcEr1EEpa2trBAYGQiqVKvVoAoCIiAiIRCK1ekkBQMWKFVGxYkXh87fffosOHTpg/fr1QlDqc8uzsLBQuz7qKs5fgjOTSCTF+lp8+jxqW3G+9gp8BnX7DIplyTotv7AQy1KK9bUoiOdQ1882ERERUXEm1nUFclKlShWkp6fj7t27StuTk5Px8OFDlC1bVmm4XF6UK1cOFhYWePfunVbKIyIiIiIiIiKi/1eog1KtW7cGAKxcuRIpKf8briCXy7Fy5Up8+PABX3/9da55HD58GGFhYUrbZDIZNm/ejISEBJQuXVqj5RERERERERERUe4K9fA9Dw8PuLi44O7du2jfvj1q1KiB169f49WrV7CwsMCQIUOEtO/evcPSpUtRp04dDB06VNh++PBhzJo1C+XLl4ezszPS0tIQGBiIqKgoiEQipbR5KY+IiIiIiIiIiPKvUAel9PX1sWHDBvz444+4f/8+Lly4AABwdHTE8uXLYW9vL6RNTExUWl1PoVevXoiMjMTLly/x8uVLYXuZMmUwbdo0tGrVKl/lERERERERERFR/hXqoBQAODs7Y9++fXj69ClCQ0NhbW2NmjVrZlkFr3Tp0li1ahUcHByUtnfp0gVdunTBmzdvEBwcDLlcjtKlS6NixYoQiUT5Lo+IiIiIiIiIiPKvyERaXF1d4erqmu1+CwsLdOjQIdv9ZcuWRdmyZTVWHhERERERERER5V+hnuiciIiIiIiIiIi+TAxKERERERERERGR1jEoRUREREREREREWsegFBERERERERERaR2DUkREREREREREpHUMShERERERERERkdYxKEVERERERERERFrHoBQREREREREREWkdg1JERERERERERKR1DEoREREREREREZHWMShFRERERERERERax6AUERERERERERFpHYNSRERERERERESkdQxKERERERERERGR1jEoRUREREREREREWsegFBERERERERERaR2DUkRERETFWFJSEt6+fYsPHz7kO4/3798jMDAQUVFRWimPiIiIvgz6uq4AEREREWlfeHg45s2bBz8/P8hkMhgYGOCbb77B7NmzYWFhkevxiYmJOHbsGM6cOYObN28iPT0dnp6eWLhwYYGUR0RERF8eBqWIiIiIipkPHz5g8ODBCAoKgqGhIUqVKoWIiAgcPnwY4eHh2Lp1a6553L59G/PmzQMAGBsbIz09vUDLIyIioi8Ph+8RERERFTO7d+9GUFAQGjVqhMuXL+P8+fM4d+4cXFxccP36dZw7dy7XPCwsLNC3b19s2rQJBw8eLPDyiIiI6MvDoBQRERFRMXPq1CkAwMKFC2FlZQUAcHBwwJw5cwAAJ0+ezDWPBg0aYP78+WjRogX09XPufK+J8oiIiOjLw6AUERERUTEil8vx9OlTlC9fHmXLllXa16BBA5iamuLJkydFtjwiIiIqOjinFBEREZGWpKen48yZM7h79y6SkpLw22+/AQAuX76Mpk2b5trjKLPExERERkaqldbAwEAICCUnJyM1NRWlS5fOkk4sFsPR0RHR0dFq1yM3migvMTERCQkJwmdDQ0MYGhp+Vr0+93hdUtS9qJ6DVCot0PyL6nUB722uiup1Ae9trmRiswLNvyDJxKZK/y9qCureqptvkQpKpaWlwcjIKN/HKy6Knp6eyv0ymQxJSUkq9+np6cHMrOj+oBAREZFuxcTEYMiQIXj58iUcHR0RFhYmBKWOHj2K+Ph4dOnSRe38zpw5g5kzZ6qV1t7eHpcuXQIASCQS4GOgShUjIyMhjSZoorxBgwbB2NhY+NyzZ094enp+Vr3UvXaFmZeXl66rkC8BAQEFmj/vre7w3uaO9zYbbn4Fm78WRFYrmkPRwwvo3j5//lytdIU+KCWRSLB27Vrs378f0dHRMDU1hYeHB6ZNmwYbG5tcj3/79i0OHDiAs2fPIjg4GHK5HE5OTujRowe+//57pUDTy5cvs20Murm55TqJJxEREVF2Fi1ahBIlSuDs2bOws7NDrVq1hH29e/fGypUr8xSUsrCwQIUKFdRKW6pUKeHfJiYmwMceTKokJSXB1FRzb3s1UZ63tzfq1KkjfNZET6k+ffp81vG6ZGhoCC8vL6xYsUKjAURt2bNnT4Hmz3urO7y32eO9zVn4DssCzb8gycSmiKx2EnZPOkIsS9F1dfLMoX+CGqnyTiaTqZWu0AelfvrpJxw9ehT4+CYtJSUFhw4dwpMnT7Bv375cGyS//vorLl68CHzsIi4Wi/HmzRusWrUK58+fx65du7K8uTMyMsqSL3tJERER0ee4cOECjhw5AicnJ2RkZCjtq1y5Mp4+fZqn/Nq1a4d27drluR4mJiawsbFBYGAg5HI5RCKRsC8lJQWhoaFwc3PLc74FWZ6FhQUsLTX7haUofin8lEQiKZLnkd2oBU0pitfkU7y3qhXFa/Ip3lvVxDLVLy6KErEspUieR0HdW3XzLdQTnd+9exdHjx6FnZ0d9u3bh/v37+PixYuoV68enj59il27duWaR9myZTFx4kQcP34c//33H/777z9s3LgRVlZWePDgAS5cuJDlmIkTJ+L27dtK/23btq2AzpKIiIiKg7S0tGx7eaempkIs1l6zrG7duoiJicHly5eVth8/fhwZGRmoW7dukS6PiIiIioZCHZQ6fvw4AGD69OlCF3dHR0csX74cYrEYx44dyzWPWbNmYcyYMahcuTIMDQ2hr6+Pli1bYty4cQCA0NBQlcdJpdIiGcEmIiKiwqlChQo4c+aMyn2+vr5wdXXVWl169eoFAJgxYwaOHj2Kp0+fYvfu3Vi0aBHEYjF69uwppE1KSkJgYCBiY2OV8pDL5QgMDERgYCDevn0LfJyQXLEtc/q8lEdERETFR6Eevvfw4UOIRCK0bt1aabuzszNcXV3x5MkTZGRk5GmlGgXF28iqVatm2eft7Y2VK1ciIyMDzs7O6NGjB0aOHFlkV0ogIiIi3Rs4cCDmzZuH0NBQtGrVCgDw5s0bnDp1CmvWrMHy5cu1Vpc2bdqgc+fOOHbsGKZMmaK0b8yYMUrto3PnzmHatGkYN24cxo8fL2xPSUlBp06dlI719fWFr68vAGDUqFGYNGlSnssjIiKi4qNQB6UiIyNRsmRJlfM5lSlTBo8fP0Z8fDxKliyZp3zfv3+Pf//9F40aNULTpk2z7A8PDxfmmQoJCcGaNWtw8+ZNbN68OccAGJcr1ryivnSqphT0Eqy5Kc7Xn8/g/+j6GSzKywRrQlFfalhTCuI51Oaz3adPH4SFhWHt2rVYtWoV8HFeKAMDA0yYMAHt27fXWl0AYNmyZWjcuDFOnjyJmJgYODo6omfPnvDw8FBKp5hQ3draWmm7WCzOcaL1T4cqqlseERERFR+FOiiVlpYmrNjyKcWywB8+fMhTnklJSRgxYgREIhFWrlyptE9fXx9Dhw6Fp6cnKlSogLS0NPj7++PXX3/FzZs3ceTIEaH7uSpcrrjgFNWlUzWlwJdgzQWfQz6Dun4Gv4RlgjWhqC41rCkFsWSxussVa8qPP/6I7777DteuXUNsbCysrKzQrFkzODg4aLUe+BhU6t27N3r37p1juq+//hpff/11lu0mJiY4deqUxssjIiKi4qNQB6WMjY2RkqJ6SUXF9rwsWRwZGYkRI0bgw4cP2LFjh9LyyABQsWJFTJ8+XfhsYmKCdu3aoUSJEhg0aBAuXbqUY1CKyxVrXlFfOlVTCnoJ1twU5+eQz+D/6PoZLMrLBGtCUV9qWFMKYslidZcr1iR7e3t0795d+Jyamqr1OhAREREVBoU6KOXo6Ijbt28jPj4eVlZWSvuCgoJgbGycZXt2Xr58ieHDh8PCwgI7duyAra2t2vVQLFMcFxeXYzouV1xwiurSqZpS0Euw5qY4X3sFPoO6fQaL4vK6BaGoLjWsKQXxHGrz2Q4JCcGhQ4eExVZCQkIwfPhwBAYGol69eli7dm22q/MRERERfYkK9ep7ihX3Tp5UHq7w/PlzvHz5EjVr1lRr+eRbt26hX79+KFmyJLZt25ZtQEoul6vcfv36dQDI0rOKiIiISF0rV65UmoNp1apVSExMxIwZMyCVSrFmzRqd1o+IiIhI2wp1T6lu3bph8+bNWLFiBSwtLdGoUSMEBwdjzpw5AKDU9V0qlSI5ORkGBgZK81CdOnUKU6dOhYuLC1avXg2RSIT3798L+42MjGBkZAQAmDZtGsqUKYPmzZujdOnSSEpKwuXLl4VGYrt27bR49kRERPQluXHjBmbPng18bLdcuHABXl5e6Nu3L1q0aIHRo0fruopEREREWlWog1Kurq74/vvvsWXLFmFJYYVGjRqhR48ewufnz5+je/fu8PDwwOrVq4XtGzduhEQiwcOHD4XllzMbOnSoMI9UfHw8fHx8sHbt2izpOnfuzNVhiIiIKN8SEhKEuTAfPXqE9+/fw93dHQDg5OSE2NhYHdeQiIiISLsKdVAKAGbMmIFKlSph7969CA0NhbW1Ndq3b4+RI0cqzQMhFothYWGRZbU+MzMzWFhYZJu/opcUPi5VvH//fly8eBGvX7+GWCxG5cqV0bVrV3Tt2hUikaiAzpKIiIi+dOXKlcPp06fRpUsX7Nu3D+XLl4eTkxMA4O3btyhTpoyuq0hERESkVYU+KCUSidRaPrhq1aq4fft2lu3btm1TuywrKyv88MMP+OGHH/JVVyIiIqLsDB06FNOmTcPixYsRExODuXPnCvvOnj3LaQKIiIio2Cn0QSkiIiKiL0HPnj3h5OSEu3fvwtXVFV9//bWwLz09HUOGDNFp/YiIiIi0jUEpIiIiIi1p0qQJmjRpkmX7p3NnEhERERUHDEoRERERaUl6ejqePHmCd+/eIT09XWmfWCxGly5ddFY3IiIiIm1jUIqIiIhIC0JCQjBq1Cg8f/5c5X49PT0GpYiIiKhYEeu6AkRERETFwcqVK+Hg4IATJ05AT08Ply5dwq5du9C/f3907NgRFy5c0HUViYiIiLRK4z2lPnz4gPv37yM+Ph6WlpaoUqUKbGxsNF0MERERUZFy48YNbN++HaVLl4ZcLoe9vT3s7e1Rr1497N27F3///Tdmz56t62oSERERaY1Gg1J79uzB0qVLkZSUJGwTi8WoW7cufvjhB6VVZoiIiIiKC5lMhtjYWJQtWxZisRgGBgZITU2FiYkJAKBTp05YtWoVg1JERERUrGgsKHX27FnMnTsXjRo1gru7OxwcHJCcnIyXL1/i8uXLGD16NHr16oVff/0VYjFHDRIREVHxIZPJgI8v6wDA0dERd+/eRfPmzQEAr169EtIQERERFRcaC0pt2LABw4YNw7Rp07Lsk8vl8PHxwfz581GyZElMnjxZU8USERERFTldunSBl5cX+vfvD319fezatQtfffWVrqtFREREpFUaCUolJSXh6dOn2LJli8r9IpEI3bp1g4ODA4YPH47+/fvD3t5eE0UTERERFXp6enpYsGCB8Hn48OGIiYnB9u3bIZFI4O7ujp9++kmndSQiIiLSNo0EpSIiIuDs7Axzc/Mc0zVu3Bht2rSBr68vBg0apImiiYiIiAo9kUgET09P4bORkRF+/vln/Pzzz5DL5RCJRDqtHxEREZEuaGRyp/T0dBgbG6uVtm3btggICNBEsURERERFTmxsLK5cuYLDhw/j2rVriI+P13WViIiIiHRCIz2l5HK52mmdnZ0RFRWliWKJiIiIioyMjAysXLkS27dvR1pamrDdxMQEgwcPxoQJE7gYDBERERUrGpvoPDg4GGPGjIGLi4vwX/ny5aGvr1yEkZERkpKSNFUsERERUZGwYsUK7Nu3D2PHjoW7uztsbGwQExOD8+fPY9OmTRCJRJg4caKuq0lERESkNRoJStna2qJt27Z48uQJLl68CKlUCgAwMDBApUqV4OLigipVqqBq1aqQy+Vc8piIiIiKnYMHD2LZsmVo3bq1sM3e3h7Vq1dHxYoV8dtvvzEoRURERMWKRoJSdnZ2WLFiBQDgw4cPePbsGZ48eSL8d+bMGfj4+AjpXV1dNVEsERERUZGRmpqKJk2aqNzXtGlTpKamar1ORERERLqkseF7CsbGxqhduzZq164tbJPJZAgKChKCVJnnUSAiIiIqDtzd3XH79m189dVXWfbduXMHrVq10km9iIiIiHQlz0GpQ4cOwdTUFI0bN4aVlZVax4jFYlSqVAmVKlVC586d81NPIiIioiIn84u4n3/+GUuWLMHr16+V5pS6cOECHj16hJkzZ+q0rkRERETalqeg1JkzZzBjxgw4Ojpi//79BVcrIiIioiIuIyMDtWrVyrL96NGjKtMfP34cjx8/1kLNiIiIiAoHtYNSUqkUS5YsAQDMnz8fJUuWzDH969ev8ebNG7i4uMDe3v7za0pERERUhOjp6WHOnDlqpxeJRAVaHyIiIqLCRu2g1N27d/H27VtUrFgRLVu2zDW9g4MDRowYgeDgYPTp0wcLFiz43LoSERERFRkikQgDBgzQdTWIiIiICi2xugkV3ckbNWqkVnojIyOMHz8eAHDhwoX81o+IiIiIiIiIiL5AagelEhMTAQA2NjZqZ96xY0dYWloiMjISz549y18NiYiIiIiIiIjoi6N2UMra2hoAEBsbq3bmenp6aNiwIQDg7du3+akfERERERERERF9gdSeU8rZ2RkA8tzjydHREQAQFRWV17oJ0tLScPXqVYSEhMDa2hrNmzeHlZWV2sfLZDLcv38fr169glQqRdmyZVG/fn3o66s+/c8tj4iIiIiIiIiIcqZ2UKpRo0YwNDTEvXv38Pz5c7i4uKh1nIGBAQAgOTk5XxV8+vQpxowZg5CQEGGbmZkZli5dirZt2+Z6/M6dO7FhwwZEREQobS9btizmzZuH5s2ba7Q8IiIioryQyWQYN24cSpUqhSFDhqB8+fK6rhIRERGRVqg9fM/U1BSdOnUCAMyYMQNpaWlqHacI7lhaWua5cmlpaUKAqFq1ahgwYABatGiB5ORkTJo0Ca9fv841Dz8/P0RHR6Nu3bro0aMHPD094ezsjDdv3mDs2LEIDw/XaHlEREREeSGXy3H58mXs27cPnTp1wrRp03RdJSIiIiKtUDsoBQBeXl6wsLDAo0ePMHz4cERHR+eY/v379/D39wcAVKtWLc+VO3r0KEJCQtC6dWscPHgQc+bMwaZNmzB58mRIJBJs2rQp1zz69euHCxcuYPfu3fjtt9+wcOFC+Pr6olmzZkhNTcXFixc1Wh4RERFRXujp6eHBgwe4d+8etmzZggoVKui6SkRERERakaeglJ2dHdavXw8TExPcuHED3bt3x65du5CampolbUpKCqZOnYrk5GSUL18e1atXz3PlLl26BAAYN24cxOL/r+qQIUNgYWEh7M9Jy5YtYW9vr7TNwMAA3bp1Az6+ndRkeURERET5YWRkhEaNGmH06NG6rgoRERGRVqg9p5RCw4YN8e+//2LKlCkICQnBvHnzsGzZMjRs2BAVK1aEqakpwsPDceHCBcTExAAApk+frhTkUderV69gZGQENzc3pe2GhoaoUaMGrl27hpSUFJiamuY570ePHsHAwADu7u5aKY+IiIjo3Llz8PPzQ3x8PBwcHODu7o7mzZtDJBLpumpEREREWpfnoBQA1KtXD0eOHMG6deuwe/duJCcn4+LFi0pD4fCxR9KcOXPw9ddf56ty8fHxsLW1VdlQs7W1BT4OEcxrkOjq1avYuXMnJkyYIKwqqInyEhMTkZCQIHw2NDSEoaFhnur2qc89vqhTnH9xvw5SqVSn5Rfn689n8H90/QzKxGY6LV/XZGJTpf8XVwXxHGrz2V6/fj3++OMPlCxZEnZ2drh9+za2bt0KNzc3LFu2DJUqVdJaXYiIiIgKg3wFpQDAwsIC06dPx6hRo3Dx4kXcuHEDoaGhSEpKgqWlJerUqYNevXrBycnpsyqY3ZtDxfbMw+/UcfnyZYwfPx59+vTBiBEjNFreoEGDYGxsLHzu2bMnPD0981S/T82cOfOzjv9SeHl56boKOhUQEKDT8vkc8hnU9TMINz/dll9IRFY7qesq6FR4ATyHz58/13ie2dm+fTsmTpyIUaNGCT3I7969i40bN6JPnz7Yt28f55MiIiKiYiXfQSkFS0tLdOvWTZijSZMsLCyUVsfLLC4uTkijrkOHDmHOnDkYNGiQypVtPrc8b29v1KlTR/isiZ5Sffr0+azjizpDQ0N4eXlhxYoVkEgkuq6OzuzZs0en5Rfn55DP4P/o+hkM35H3FVy/JDKxKSKrnYTdk44Qy1J0XR2dceifoEaqvJHJZBrPMzsJCQkYOnSo0pQG9erVw4YNG7Bw4UL8/vvvWL16tdbqQ0RERKRrnx2UKkgVKlTAq1ev8OLFC1SpUkXYLpVK8fDhQ5QqVQrm5uZq5bV27VqsXr0a48ePx7hx4wqkPAsLC1haavaLU3H+EpyZRCIp1tdCT09Pp+UX52uvwGdQt8+gWJas0/ILC7EspVhfi4J4DrX5bFerVg3h4eEoX758ln3Dhw9Hp06dtFYXIiIiosIg77OPa1GzZs0AAH///bfS9n379iE+Ph7NmzfPNQ+pVIo5c+Zg9erVmDFjRrYBKU2VR0RERKTK2LFjMXfuXCQlJWXZJ5PJYGBgoJN6EREREelKoe4p1a1bN6xZswZHjhxBdHQ0GjZsiNevX8PHxwdisRiDBw8W0sbGxuLQoUOoUKGC0sTqP/30Ew4fPgw3NzfIZDJs2rRJqYyaNWuiUaNGeS6PiIiISF0ZGRmYPn060tLS0KVLF3z//fdo1qwZ7O3tER4ejmXLlqFjx466riYRERGRVhXqoJSFhQVWr16NcePG4cqVK7hy5QrwcVW/2bNno1q1akLaiIgILF26FB4eHkpBqRcvXgAAHj16hEePHmUpY+jQoUJQKi/lEREREalLT08P/fr1w6NHj/D48WMsXrxYab+5uTlat26NJ0+eoEqVKtDXL9RNNCIiIiKNKPQtnkaNGsHX1xenT59GaGgorK2t8fXXX6Ns2bJK6WxsbDB06FBUrVpVaXu3bt3QuHHjHPPPT3lERERE6hKJRJg4caLwOTIyUnhh9vjxYzx69AgLFiwAABgbG6NGjRrYsWOHDmtMREREVPAKfVAKAKytrXNd/cve3h7Tp0/Psv37778vkPKIiIiIFORyOUQikdrp7ezsYGdnh9atWwvbYmNjhUDVs2fPCqimRERERIVHkQhKERERERVWGRkZGDx4MCIiIjB79my0bNkyX/nY2Njgq6++wldffaXxOhIREREVRoV69T0iIiKiws7b2xu3bt2CnZ0dWrRooevqEBERERUZ7ClFRERElE8ymQz//vsvAGDGjBnQ09PLMX1sbCwmTZoEe3t7uLu7o3PnzlqqKREREVHhw55SRERERPn09OlTREVFoUyZMqhZs2au6W1sbNC+fXscOXIECxcuhEwm00o9iYiIiAojBqWIiIiI8ik0NBQAULFiRbWP8fT0hLW1NWJjY/HgwYMCrB0RERFR4cagFBEREVE+GRgYAECeVt4zMjISJjMPCAgosLoRERERFXacU4qIiIgon2xtbYFMPabU5ebmBh8fH0RFRRVQzdQjk8nw8OFDREdHw8nJCa6urnnOQy6X48GDBwgNDUXp0qVRo0aNLGnCw8OzDcA5OzurNfSRiIiIvjwMShERERHlU5UqVWBiYoIXL17gzZs3KFu2rFrHWVlZAQCio6MLuIbZe/DgASZPnow3b94I29zc3LBq1SqUKVMm1+PDw8OxceNGnD17FhEREcDHoYkLFy7MkvbOnTuYPHmyynx69OiB33777bPOhYiIiIomBqWIiIiI8snIyAgtWrTAmTNnsHbtWixZskSt45KTk4FMw/+0LTY2Fj/88APi4+NRrlw5VKxYEU+ePMGjR48wYsQI+Pj45Fq3J0+eYMeOHRCJRKhcuTJevnyZa7n169dHyZIllbaxlxQREVHxxaAUERER0WcYN24czp8/j8OHD6NatWoYPHhwrscohrI5OjpqoYZZeXt7Iz4+Ht27d8fixYshFoshkUgwevRo+Pv74+jRo+jZs2eOeTg6OmLu3Llo27YtPnz4gPbt2+da7qhRo+Du7q7BMyEiIqKijBOdExEREX0GV1dXjBkzBgCwePFizJw5E4mJidmmDwwMhK+vLwCgSZMmWqtnZhcuXICenh6mT58Osfh/zUFDQ0NMnToVAHD+/Plc83B1dUX//v1hb2+vdrnv37+Hn58f/P39dT6fFhEREekee0oRERERfaZx48YhLS0NGzduxMGDB3Hu3Dn07NkTbdq0QdWqVWFhYYG4uDj4+flh2bJlSEtLQ61atVCvXj2t11UmkyEwMBCVK1eGjY2N0j5XV1dYWlri+fPnBVK2l5eX8G+RSIT27dtj/vz5sLa2LpDyiIiIqHBjUIqIiIhIA7y8vFCrVi3MnTsXsbGx2Lx5MzZv3qwyrY2NDZYuXfpZ5YWEhODBgwdqpTUxMUHLli0BAElJSUhPT8+2h5O9vT3Cw8M/q27ZKV++PMqVK4eYmBg8efIEvr6+CAsLw549e4QeW6okJiYiISFB+GxoaAhDQ8PPqsvnHq9LiroX1XOQSqUFmn9RvS7gvc1VUb0u4L3NlUxsVqD5FySZ2FTp/0VNQd1bdfNlUIqIiIhIQ9q1a4fmzZtjz549OHz4MJ49ewa5XC7s19PTw9dff40ZM2agdOnSn1XWjRs3MHPmTLXS2tvb49KlSwCAjIwMoS6q6OnpCWk0pXz58jh06BCqV68ubAsODsaIESNw//59nD9/Hm3bts32+EGDBsHY2Fj43LNnT3h6en5WndS9doVZ5p5nRYliTrWCwnurO7y3ueO9zYabX8HmrwWR1U7qugr5El5A91bdXtcMShERERFpkKmpKYYMGYIhQ4YgNjYWb968QUJCAszMzODi4oISJUpopBxnZ2d4eHioldbKykr4t7m5OQAgPj5eZdqEhAQhjaa4ubll2Va+fHlMmTIF48ePR0BAQI5BKW9vb9SpU0f4rImeUn369Pms43XJ0NAQXl5eWLFiBSQSia6rk2d79uwp0Px5b3WH9zZ7vLc5C99hWaD5FySZ2BSR1U7C7klHiGUpuq5Onjn0T1AjVd7JZDK10jEoRURERFRAbGxssszbpCmNGzdG48aN83ycoaEhHB0d8eLFC2RkZEBf//+bg7GxsQgLC0PDhg01XFvVFMGy3L6gWVhYwNJSs19YiuKXwk9JJJIieR7Z9dLTlKJ4TT7Fe6taUbwmn+K9VU0sSy7Q/LVBLEspkudRUPdW3Xy5+h4RERFRMdOwYUMkJSXh8OHDStt37twJuVyORo0aabS80NBQldsVb97LlSun0fKIiIioaGBPKSIiIqJipn///vDx8cGCBQvw5s0bVKlSBQEBAdi5cyeMjIyU5msKDQ3F/fv3UblyZVSuXFnYLpVKcebMGQBAdHQ08HHy9VOnTgEAKlWqhCpVqgAA+vXrB1dXV9StWxcODg6IjY3FiRMncP/+fZibm6Njx45avgJERERUGDAoRURERFTM1KlTBxMnTsSqVavw119/Cdv19fWxYMECODo6Cttu3bqFadOmYdy4cRg/fryw/cOHD5g4caJSvteuXcO1a9cAAKNGjcKkSZMAAE5OTrhw4QIuXLiglN7S0hJ//PFHgQ1xJCIiosKNQSkiIiKiYmjMmDFo2rQpTp48iZiYGDg6OqJr165wcXFRSqeYUL1SpUpK2/X19XOcaD1zPjt37sS1a9dw/fp1hISEwNjYGNWrV0enTp2UJmEnIiKi4oVBKSIiIqJiqm7duqhbt26OaRo0aIAGDRpk2W5kZITVq1erXVbTpk3RtGnTfNWTiIiIvkyc6JyIiIiIiIiIiLSOQSkiIiIiIiIiItI6BqWIiIiIiIiIiEjrisScUhcuXMC+ffsQEhICa2treHh4oE+fPhCL1YupvX//HhcuXMDZs2fx8uVLuLq64vfff8+SLjg4GKNGjVKZh4uLS57mTSAiIiIiIiIiouwV+qDUxo0bsWLFCqVtitVbVq1alevxERERaNOmDdLT04VtFhYWKtNKJBIEBQWp3GdqaprnuhMRERERERERkWqFOigVHByMVatWwcjICFOmTEGjRo0QHByMxYsX49SpUzh58iQ6duyYYx7p6ekwMTFBhw4d0K5dO3h5eeVa7g8//ICePXsqbTM2Nv7s8yEiIiIiIiIiov8p1EGpw4cPIyMjA5MnT8agQYMAAK6urnBwcECfPn2wf//+XINSDg4OuHr1KgwMDAAAU6ZMybVcGxsbVKpUSUNnQUREREREREREnyrUQal79+4BALp37660vU6dOihXrhzu3bsHuVwOkUiUbR76+nk/xbNnz+LMmTNIS0tDhQoV0L17d7i7u+fjDIiIiIiIiIiISJVCHZQKCQmBlZUVbG1ts+yrXLkyXr9+jbi4ONjY2Gi03Lt37wr/fvz4MY4fP47Bgwdj5syZOR6XmJiIhIQE4bOhoSEMDQ0/qy6fe3xRpzj/4n4dpFKpTssvztefz+D/6PoZlInNdFq+rsnEpkr/L64K4jnU9bNNREREVJwV6qBUSkoKzM3NVe5TbE9JSdFoUKpBgwbo1asXqlSpgqSkJPj7+2Pbtm3YsmULmjdvnmOPqUGDBinNPdWzZ094enp+Vn1yC4QVF+rMBfYlCwgI0Gn5fA75DOr6GYSbn27LLyQiq53UdRV0KrwAnsPnz59rPE8iIiIiUk+hDkoZGhpCIpGo3JeWlgYAMDIy0lh5FStWxI4dO5S2NW3aFFWqVMH06dNx4sSJHINS3t7eqFOnjlL9P7d3RZ8+fT7r+KLO0NAQXl5eWLFiRbbPQnGwZ88enZZfnJ9DPoP/o+tnMHyHpU7L1zWZ2BSR1U7C7klHiGUpuq6Ozjj0T1AjVd7IZDKN50lERERE6inUQamSJUvi8ePHSEtLyxJ8CgkJgb6+PqysrDRWXnbzT7Vp0wYAEBYWluPxFhYWsLTU7Ben4vwlODOJRFKsr4Wenp5Oyy/O116Bz6Bun0GxLFmn5RcWYllKsb4WBfEc6vrZJiIiIirOxLquQE6qVasGqVSKy5cvK22PiorCkydPULlyZWFVvYIUFBQEfAw6ERERERERERHR5yvUQSkPDw8AwNKlS/H27VsAQFJSEubMmYOMjAx06NBBo+WtW7cOfn5+Sr0hAgICMG3aNABAkyZNNFoeEREREREREVFxVaiH77Vo0QItWrSAv78/PDw84ODggJiYGHz48AHOzs4YOHCgkDYwMBBjx45FixYtMHv2bKV8vv/+e0RERAAA0tPT8fTpUyGg1axZM8ydOxcAcOfOHaxatQp6enqws7NDYmIikpKSAACurq749ttvtXj2RERERERERERfrkLdUwoAVq1ahV69ekFPTw8hISFIS0tD8+bNsXXrVqWV+SQSCYKCghAZGZklj7dv3yIoKAhBQUGQy+VIS0sTPoeHhwvpxo0bh/bt20NfXx9hYWFISkpCiRIl0L9/f2zfvl2jk6oTERERERERERVnhbqnFACYm5tj0aJFmDt3LqKiomBlZaVybqdKlSrhxIkTSoEqhS1btiA9PT3b/BXq1q2LP//8EzKZDBERERCLxbCzs4NIJNLwWRERERERERERFW+FPiilYGxsjDJlymS739DQEJUqVVK5r2zZsnkqSywWw9HRMc91JCIiIiIiIiIi9RT64XtERERERERERPTlYVCKiIiIiIiIiIi0jkEpIiIiIiIiIiLSOgaliIiIiIiIiIhI6xiUIiIiIiIiIiIirWNQioiIiIiIiIiItI5BKSIiIiIiIiIi0joGpYiIiIiIiIiISOsYlCIiIiIiIiIiIq1jUIqIiIiIiIiIiLSOQSkiIiIiIiIiItI6BqWIiIiIiIiIiEjrGJQiIiIiIiIiIiKtY1CKiIiIiIiIiIi0jkEpIiIiIiIiIiLSOgaliIiIiIiIiIhI6xiUIiIiIiIiIiIirWNQioiIiIiIiIiItI5BKSIiIiIiIiIi0joGpYiIiIiIiIiISOsYlCIiIiIiIiIiIq1jUIqIiIiIiIiIiLSOQSkiIiIiIiIiItI6fV1XQF0xMTEIDQ2FtbU1Spcuna88YmNjERQUBDMzM7i6uhZ4eUREREREREREpFqhD0rFxcVh1qxZOH/+PORyOQDAzc0NS5YsQZUqVXI9Pi0tDXv27MGZM2dw584dSKVS1K5dG3v37i2Q8oiIiIiIiIiIKHeFevieXC7H2LFjce7cORgbG6NGjRqwtrbGo0ePMGTIECQkJOSaR1RUFBYuXIibN2+iRIkSEIlEBVoeERERERERERHlrlAHpS5cuIA7d+6gatWqOH/+PA4cOIDLly+jc+fOiIqKwpYtW3LNw9jYGAMHDoS3tzeuXLkCAwODAi2PiIiIiIiIiIhyV6iDUmfOnAEATJ06FTY2NgAAAwMDzJkzBwYGBjh9+nSueZQsWRKzZ89G48aNoaenV+DlERERERERERFR7gp1UOrZs2fQ19dH48aNlbZbWVmhRo0aCAoKgkQiKbLlEREREREREREVV4V6ovOYmBjY2trC0NAwyz5HR0fcu3cP8fHxsLOzKxTlJSYmKs07ZWhoqDKvvPjc44s6xfkX9+sglUp1Wn5xvv58Bv9H18+gTGym0/J1TSY2Vfp/cVUQz6Gun20iIiKi4qxQB6UyMjJgbGyscp++/v+qnp6eXmjKGzRokNLxPXv2hKen52fVaebMmZ91/JfCy8tL11XQqYCAAJ2Wz+eQz6Cun0G4+em2/EIistpJXVdBp8IL4Dl8/vy5xvMkIiIiIvUU6qCUqalptiveJSYmCmkKS3ne3t6oU6eO8FkTPaX69OnzWccXdYaGhvDy8sKKFSuK9dDJPXv26LT84vwc8hn8H10/g+E7LHVavq7JxKaIrHYSdk86QixL0XV1dMahv+ZXwZXJZBrPk4iIiIjUU6iDUmXKlMGbN28QEREBe3t7pX3Pnz+HhYUFrK2tC015FhYWsLTU7Ben4vwlODOJRFKsr0Vuk/QXtOJ87RX4DOr2GRTLknVafmEhlqUU62tREM+hrp9tIiIiouKsUAel6tWrhytXrmDfvn0YN26csP3q1asICQlBq1atinR5RERERESkWT4+PrquQr5JpVIEBARgz549DJoTUbFQqINSPXr0wIYNG7B+/XpIpVI0atQIwcHBWLVqFQCgb9++QtqUlBQ8fvwY1tbWqFSpklI+Dx48QFpaGgBALpcjOTkZt2/fBgCl9Hkpj4iIiKioi46OxsWLFxETEwNHR0e0bt0aFhYWah+flJSEmzdv4vXr1zAyMkKFChXQuHFjiMWqF3j+3PKIiIjoy1Kog1LOzs746aefsGDBAqxbtw7r1q0T9nl6eqJ169bC59evX6N///7w8PDA6tWrlfKZOHEiQkJChM8vX75E//79AQBt2rQR8s1LeURERERFma+vL2bMmIGUlP+fp8zGxgZr165FvXr1cj1+wYIFOHjwIFJTU5W2V6lSBUuWLIGbm5tGyyMqqpKTkxEfHw8nJyeIRKJs0wUGBkImkyEkJERpnlqFpKQkJCYmwtHRUeXxiYmJiIyMRMWKFXMsh4ioMCnUQSkA6NevH6pWrYr9+/cjNDQU1tbWaN++PTp16qSUztTUFPXq1UPFihWz5FGjRo0sc0QpVK5cOV/lERERERVVb968wZQpUyCRSNC6dWtUqVIFAQEBuHnzJsaOHYszZ87A3Nw8xzwuXrwIPT09tG7dGuXKlUNaWhrOnj2LFy9eYNSoUTh37pyw4IsmyiMqinbu3ImlS5fCxMQE1tbW2LRpU7ZBpbFjx0Imk+H169fw8PAQhu/JZDL89NNP8PX1hZmZGRwcHLBx40bY2NgIx8pkMowbNw7Xr1/H/fv3YWRkpLVzJCL6HIU+KAUA9evXR/369XNMU65cOezatUvlvk97TmmiPCIiIqKiauvWrZBIJBg/frzSPJqzZs3C/v37sX//fgwePDjHPObOnYsmTZrA2NhY2DZlyhT07t0bgYGBuH//Pho0aKCx8oiKmri4OCxbtgy7d++Gq6srfvnlF6xZswYLFy5Umf7UqVOIi4tDkyZNsmx/8OABLl++DHNzcyxatAh//fUXZs6cKaTZunUrGjZsiOvXrxf4eRERaZLqAf9ERERE9MXy9/eHkZERhg4dqrR9zJgxAIDLly/nmkerVq2UAlIAYG5ujq+//hoAlOaV0kR5REXN1atXUaNGDbi6ugIfR2ScO3cuz/m8e/cOtWrVEnoTNm3aFGfPnhX2BwUFwc/Pj4FdIiqSikRPKSIiIiLSjIyMDLx58wbVq/9fe/cdX/P5/3/8kU1EiL1rx6a2ohQVYscotWKv2EQlihoRm9iziK01au9de8WIUav2CpIg+/z++Mb5NR+0Zk7G83679VbnfV3ner/eeZ+TXOd1rlEAW1vbGGWZM2cmbdq0XL9+/aPbP336NBkzZqRw4cKxcj6RuOLBgwcEBwdD9Gv74cOHZMiQwVieMWNGnj59SlhYmHFq6/soWbIk8+bNY8eOHaRNm5ZFixbx8OFDiN6tb+jQoQwdOlTrSIlIvKSklIiIiEg85O/vH2O0xL+xs7OjTZs2EL1YclRUFKlTp35r3VSpUsXYIOZDzJ49mxMnTjB79mysrKw+2/mCgoJ4/vy58bG1tfUHfah/m099vim9jj2+XkNkZKSpQ/giZs+ezcGDBwEYO3YslpaWhIWFGa/31atXWFpaYmZm9s6fwevjkZGRxn8XLVoUT09PFixYQEREBLVr1+bChQtERkby66+/kjNnTiIiIrh27RpEL5ieI0eOePv6+Dfx+Zr0vv13UebJvmj7X1KUuW2M/8c3X+revm+7SkqJiIiIxEP+/v5MnTr1veqmT5/emJR67V2jKgwGw0fFM3fuXCZNmsTIkSOpWLHiG+Wfcr5WrVrFmCro4uJCo0aNPirO1/65Hk981bdvX1OH8FFOnz5t6hC+CGdnZ+PmSGFhYYSFhXH27Fnj9V68eJHUqVPj5+f3zjZevHgBwLlz54wLnRM98qp3794AbNy4kcyZM3P69Gn8/Pzw8/Nj9+7dxrpdu3alX79+71xQPT7T+9Z0vvj7tuDeL9t+LHiYf7OpQ/go97/Qvb18+fJ71VNSSkRERCQeyp8/f4xFw//NP3e2s7Ozw9zcnMePH7+17tOnT0mRIsV7xxEVFYWXlxfLli1j3Lhxb+xY/DnOt2jRIooVK2Z8/DlGSv3www+f9HxTsra2pm/fvowfP56wsDBTh/PBVqxYYeoQYkXBggVZuHAhhw8fpkyZMqxbt45mzZpRrFgxDAYD169fJ1u2bFha/t9Hstu3bxuTt3Z2dtjZ2ZE5c2YArl27RlRUFGfPnmX9+vVMnTqVYsWKxXhfvHjxglKlSrFp06YEu/ue3rem86Xft/eXvP/fnbgmytyWh/k3k86/JuZRL00dzgfL0Pz5e9T6cFFRUe9VT0kpERERkXgof/785M+f/4OfZ2lpSbZs2bhy5QovX76Msc7TnTt3ePToERUqVHivtkJDQ+nXrx979uxhypQpxkXOP/f5kidP/kGJsvcRHz8U/q/Xo3Him3+OAErILCwsmD17NuPHj2fbtm1UqFCB9u3bY2FhYdyNcvHixaRJkwaAESNGcOvWLTJmzEifPn3Inz8/EydOBKB79+6Ym5uTPn16xo4dS5kyZd44n5WVFTly5MDS0jLB/ozj4+v9f+l9+3bmUS++aPuxwTzqZby8ji91b9+3XSWlRERERBKZ8uXLs2TJEubPnx9jtNX06dMB3jr97n89e/aMzp07c+nSJWbNmsU333zzRc8nEh/ly5ePOXPmvHHc2tqaLVu2xDg2Z84cIiMjOX36NMWKFYvxge5/675N0qRJ36ueiEhcoqSUiIiISCLTqlUrVq5cyZQpUzh37hx58+bl1KlTHD16FAcHBxo2bGise/HiRbZv307p0qVjjM5o0aIFV65coWLFipw4cYITJ07EOEe1atWMI7k+5HwiIiKSeCgpJSIiIpLIZM+enTFjxuDh4cHu3buNiyQ7ODgwdepUkidPbqx76dIlpk6dipubW4yk1M2bNwHYv38/+/fvf+McmTNnNialPuR8IiIikngoKSUiIiKSCDk7O1OqVCn27NlDQEAAGTJk4LvvvsPe3j5GPUdHR9zc3ChdunSM4126dPnX7Z7/d72r9z2fiIiIJB5KSomIiIgkUmnTpqVx48b/Widfvnzky5fvjeNdu3b9IucTERGRxMPc1AGIiIiIiIiIiEjio5FSIiIiIpLo/PHHH6YO4aO93qFtxYoVX3ybdhERkS9JI6VERERERERERCTWKSklIiIiIiIiIiKxTtP3RERERERM6ObNm/j4+PDw4UMqVqxI+/btMTd/+3fHu3fv5vfff+fx48e4uLjQpEmT/2znxIkTjB49OkY7WbNmZfz48V/82kRERP6NklIiIiIiIiYSFRVF+/btqVatGnXq1GHixInY2NjQunXrN+ru3LmTVatWUb9+fa5evcrUqVOxsLCgYcOG/9pOnjx58PDwMLYza9YsMmTIEMtXKiIi8iYlpURERERETOTQoUOYm5szYMAAAGxsbBg2bNhbk1Lly5enatWqREZGkjZtWuOC5w0bNvzXduzt7SlWrBgAYWFhnDx5kv79+8fylYqIiLxJa0qJiIiIiJjIjRs3yJcvn/Fxvnz5uHXrFgaD4Y26SZIkMf47KiqKQ4cOUbp06Q9qZ9u2beTKlYucOXN+oSsSERF5fxopJSIiIiISSyZMmMDhw4cBGDZsGKGhodjY2BjLbWxsCA8PJzw8HGtr67e2YTAY+PXXX8mWLRt16tQBeO92Vq1aRaNGjb7gFYqIiLw/JaVERERERGKJi4sLVapUASBLliykTZuWvXv3GssfPnxIypQp35mQioqKYvDgwYSFhTFixAjj8fdp59atW5w/f54ZM2Z8oasTERH5MJq+JyIiIiISS7Jnz06xYsUoVqwYdnZ2lC1bFj8/P65fvw7Ab7/9RsWKFd/63MjISNzd3YmMjKRTp04xduh7n3Z+++03nJ2dsbW1/aLXKCIi8r40UkpERERExETSpk1L586dcXFxIV26dISGhrJgwQKIXpS8RYsWzJw5k1SpUrF+/Xo2bNhAoUKFGDp0KLa2tpQuXZr+/fv/aztEJ7TWrFnD1KlTTXi1IiIiMSkpJSIiIiJiQp06dcLFxYXHjx+TK1cu45Q7KysrPDw8sLOzA+Dbb79l+fLlREVFcfnyZfLmzYuDg8N/tgMQERHB1KlTKVKkiAmuUERE5O3iRVLq7t27bNiwgdu3b+Pg4ED16tUpWLDgZ2/jwYMHTJw48a3Pz5IlC25ubp90HSIiIiIib5M2bVrSpk0b45iZmRnFihUzPk6VKhWpUqUiMjISg8FA0aJFsbCw+M92iF74XAkpERGJa+J8UmrPnj306tWLV69eGY/NmjWLvn370qFDh8/axvPnz1mzZs1b2yhYsKCSUiIiIiIiIiIin0mcTko9ffqUfv368erVK2rXrk2pUqW4efMmixcvZty4cZQqVSrGt0efqw1nZ+c3Fob859BoERERERERERH5NHE6KbV27VqCgoL48ccfGTJkiPF4gQIF6NevH4sWLfrPpNTHtFGoUCFcXFy+wBWJiIiIiIiIiAiA+XvUMZnDhw8D0Lp16xjHa9euTdq0aTl06NAXaePChQuMGjWKoUOHsnDhQh4+fPiJVyIiIiIiIiIiIv8Up0dK3bhxg2TJkpE9e/YYx83MzMiXLx/79+8nKCiI5MmTf9Y2NmzYEKPuhAkTGD58OHXr1v1s1yYiIiIiIiIikpjF6aTUixcvSJky5VvLXh//r6TUh7aRMmVKnJycyJs3L8HBwRw4cIBjx44xcOBA8ubNS758+d55rqCgIJ4/f258bG1tHWMr3o/xqc+P715ff2L/OURGRpr0/In556/X4P8x9WswyjyZSc9valHmtjH+n1h9idehqV/bIiIiIolZnE5KmZmZERUV9dYyg8EA8MY2uJ/SRqZMmdixY0eMJFfnzp2ZOHEiM2fOZNWqVfz888/vPFerVq1IkiSJ8bGLiwuNGjX61/j+y8CBAz/p+QlF3759TR2CSZ0+fdqk59frUK9BU78GKbjXtOePIx7m32zqEEzq/hd4HV6+fPmztykiIiIi7ydOJ6VSpkzJjRs3iIqKwtw85vJXDx8+xMzMDHt7+8/Whp2d3VvbaN26NTNnzuTatWv/eq7/XTT9c4yU+uGHHz7p+fGdtbU1ffv2Zfz48YSFhZk6HJNZsWKFSc+fmF+Heg3+H1O/Bu8vSWHS85talLktD/NvJp1/TcyjXpo6HJPJ0Pz5e9T6MO/64kpEREREvrw4nZTKkycPly9f5vTp0xQvXtx4/NWrV5w7d44sWbKQNGnSL95GcHAwAFZWVv9aL3ny5KRI8Xk/OCXmD8H/FBYWlqh/Fv81IvBLS8w/+9f0GjTta9A86oVJzx9XmEe9TNQ/iy/xOjT1a1tEREQkMYvTu+9VqlQJgMmTJ8f4MDh16lRevnxJ5cqVP2sbW7Zs4dGjRzGeHxQUxPDhwwEoXLjwZ7gqERERERERERGJ0yOlatasyfTp0zl8+DA1atSgWLFi3Lhxg/Pnz2Nra0vbtm2Nde/evYuPjw+FCxemefPmH9XGqlWr6N+/P3nz5iVLliwEBwdz+vRpgoODSZUqFc2aNYv1n4GIiIiIiIjAH3/8YeoQPlpkZCSnT59mxYoVGqUr8g9xOillbW3NrFmz6N69O5cvX+bOnTsApE6dmnHjxpEpUyZj3efPn7NmzRpevnwZIyn1IW04Oztz7do1zp07x7lz54zHCxUqxKhRo0iTJk0sXbmIiIiIiIiISMIWp5NSANmzZ2fdunWcPn2au3fv4uDgQMmSJbGxsYlRL2PGjIwaNYrMmTN/dBsNGzakfv36XLp0iZs3b2Jubk7u3LnJlSvXF79OERERERERiT8MBgNhYWFvfK58m8jIyHdurhEZGYmZmdkbG3O9Fh4ezqtXr/5zky+R+CjOJ6UAzM3NKV68eIyFyv9XypQpcXFx+aQ2iF7wtECBAhQoUOCTYhYREREREZGEafPmzQwdOpTg4GCKFy+Oj48PDg4Ob9QLDg5mwoQJrF+/npCQEL777ju8vb2xtbWNURYWFkblypUZNWoUtra2Mdro3r07u3fvxs/P770SYCLxSZxe6FxEREREREQkLgkODmbQoEFMnDiRU6dOkSFDBqZNm/bWuidOnCBXrlzs3LmTadOm8ejRI+bNmxejbPfu3ezfvz9G2Wu//fYb2bJli5XrEjEFJaVERERERERE3tOBAwfIkycP33zzDdbW1rRr144tW7a8tW6lSpVo3rw5yZIlI1myZJQuXZqnT5/GKLOzs8Pe3j5GGcD9+/dZvXo1Xbt2jbVrE4ltSkqJiIiIiIiIvMOrV68IDAwkMDCQyMhI7t+/T5YsWYzlWbJk4fHjx4SHh/9rO4GBgWzcuJH69eu/URYQEMCGDRtilA0ZMgRPT0+srKw+8xWJxB3xYk0pEREREREREVMYOnQoO3fuBGDu3LmYm5vHWLQ8KirqXxcqB3j27Bljxoyha9euFClS5I2yjh074ubmZixbuXIlX331FVmzZiU4OBiAoKAgrKys/vU8IvGNklIiIiIiIiIi7zB69OgYj588ecKaNWuMj2/cuEGGDBmwsLB46/OfPHlC27ZtcXJyol69em+UtWvXDldX1xijpE6cOMHOnTtZvXq18ViNGjVYsWKFdoeXBEUpVhEREREREZH3VKFCBe7du8eqVau4desWkydPpm7dusbywMBADAYDAA8fPqRly5Y0bNiQ4sWLExgYyMuXL2OUNWrUiCpVqsQoGz16NMePH+f48ePs3bsXgIMHDyohJQmOklIiIiIiIiIi78nGxobJkyezdOlSmjZtSpo0aejcuTMAYWFhVKlShYcPHwLw559/8vDhQ3x8fOjZsyfff/89P//8c4yySZMmUaVKFapUqWIs+yczMzOSJ0+OmZlZLF+pyJen6XsiIiIiIiIiH6BMmTIxpvC9Zm1tzfHjx42P69evT/369YmMjOT06dMUK1bMOM3vddl/sbW1jdGmSEKikVIiIiIiIiIiIhLrlJQSEREREREREZFYp6SUiIiIiIiIiIjEOiWlREREREREREQk1ikpJSIiIiIiIiIisU5JKRERERERERERiXWWpg5ARERERERERBKvjK4GU4fw0SIjI7l/+jQZmj/HwsLC1OHEOxopJSIiIiIiIiIisU5JKRERERERERERiXWaviciIiIiIiIiAoSEhLB69WoePnxIhQoVKFmy5Dvr3r9/n02bNnHz5k2SJ09O3rx5jWVBQUGsX7+egIAASpQoQbly5YxlDx48YPPmzTx//py8efPy/fffY2mZONMzGiklIiIiIiIiIgJ06dKFTZs2ERkZSbdu3di1a9db6506dYq2bdty//59nj59SpMmTTh8+DAAz549o2XLlly7do3w8HDc3d1ZuHChsax+/fpcunQJKysrpk+fzsiRI2P1GuOSxJmKExERERERERH5h/Pnz3Px4kX27t2LtbU1jo6OzJkzhypVqrxRN3369Pz222/Y2Nhw+vRpsmbNyqZNmyhbtiyWlpbMnz+fVKlSAZAmTRqOHj1K69at+fvvv0maNCmjRo0CoGDBgkyePDnWrzWuUFJKRERERERERBK9Cxcu8PXXX2NtbQ1AmTJl8PT0fGvdTJkyQfTuewB3796lcOHCANjZ2QEwduxYAgMDuXLlCoMGDQKgcOHCfPfdd3Tu3JlMmTLh7+9P3759Y+X64iJN3xMRERERERGRRGft2rWMHj2a0aNHc/v2bZ4/f07y5MmN5cmTJyckJITQ0NB/bWfv3r38/fff/PjjjzGOp0mThhQpUvDo0SNu374N0WtWXb58GVtbW1KlSkVwcDA3b978QlcY92mklIiIiIiIiIgkOvb29qRJkwYAKysrHBwceP78ubH8+fPn2NraYmNj8842fv/9d7Zv386SJUtIkiRJjLI2bdoAUKpUKby8vKhRowZbt24FYMKECQDUrFmTRo0a0bRpU8zNE9+4ISWlRERERERERCTR+d+1oooUKYK3tzfBwcHY2dmxd+9eihYt+s7nL1myhNWrV/PTTz+RIkUK4/EbN26QNm1akiVLBkBwcDBJkyY1loeFhRn//XoUlpmZ2We9tvgizielIiIimD9/PqtWreLOnTs4ODjg5ORE7969Ywyr+1xtfI7ziYiIiIiIiEj8kidPHsqVK0ezZs0oVKgQ27dvZ+rUqRCdKxg/fjxdu3YlefLk7N27l+HDh+Pi4sK6dev4888/cXR0xMXFhcDAQHr27EmhQoUICQlh3759xh32qlSpgo+PD66urmTPnp2dO3fSsmVLJaXiqqFDh7Jq1Srj48ePH7NkyRLOnj3L0qVLsbKy+qxtfI7ziYiIiIiIiEj8M3HiRHbu3MnDhw9p164duXPnhuiRTGnSpDFOscuQIQP9+/cnKiqKu3fvkiZNGuzt7SF6xNW8efPYt28f5ubm9OvXj4wZM0L0lMENGzawZ88enj59St26dSlevLgJr9i04nRSys/Pj1WrVpEqVSomTJhAqVKluHnzJu7u7vj5+bFy5UqaN2/+2dr4HOcTERERERERkfjJwsKC6tWrv/V4u3btjI8dHR1xdHQkMjKS06dPU6xYMSwsLIzladKkwcXF5a3nsLW1xdnZ+QtdQfwSp1fRWr9+PQADBgygXLlyWFpakitXLiZMmICZmZmx/HO18TnOJyIiIiIiIiIi/y1OJ6XOnj2LmZkZ1apVi3H8q6++Im/evJw/f57IyMjP1sbnOJ+IiIiIiIiIiPy3OD1978GDB6ROnRo7O7s3yr766isuXbrE06dPjVs4fmobH3u+V69eAXDkyBECAgKMx62srLC0/LQf8eu2E6uIiAjOnTvHy5cvCQ8PN3U4JnPw4EGTnj8xvw71Gvw/pn4NBtxI3BtNRJnb8Mxwjts3k2IeFae/T/qiUn2B1+GlS5cgkf+ei8te3xd/f39ThxKnREZGcvnyZaKiomJMFZH4T/c24dK9Tbh0b9/u9d/u/+pjxemkVGhoaIxtE/8pSZIkAISEhHy2Nj72fDdu3ACga9eu/xqLfJzNmzebOgST27Fjh6lDSNT0GtRr0PSCAFdTB2F6v1T4Yk3fuHGD8uXLf7H25eO87mO1aNHC1KGIiIjIR/ivPlacTkolSZLknVm118mhdyWRPqaNjz2fk5MTCxYsIHPmzMbkFdEjpbRbn4iISNz16tUrbty4gZOTk6lDkbdwcnJi8eLFZM+e/T/7fCIiIhJ3vG8fK04npdKnT8+pU6cICgoiefKYUzdu3LiBjY0NDg4On62Njz1fmjRpaN269SdcqYiIiJiKRkjFXWnSpNHOxyIiIvHU+/Sx4vTCFEWKFMFgMLB169YYx69du8aVK1coWLAg5ub/fgkf0sbnOJ+IiIiIiIiIiPy3OJ1hqV27NgBjx45l7969vHz5En9/f3r37o3BYKBu3box6kdERBAVFfXRbXzo+URERERERERE5OOYGQwGg6mD+DeDBw9mxYoVbxwvVqwYixcvNq7Z5O/vT/369XFycsLHx+ej2vjQuiIiIiIiIiIi8nHi9EgpgCFDhjBw4EBy5cpF0qRJyZQpE66ursybNy9GgsjMzAwLC4u3Tq973zY+tK6IxH8GgwFPT09Wr15t6lAkETt9+jS9evXi/v37pg5FRES+oI0bNzJ06FCCgoJMHYp8Zrq3Cde6desYPnw4wcHBpg4lQYrzI6UkYdq5cyeVK1fGwsLC1KGIEBwcjIeHB5aWlnh7e2NtbW3qkCQR2rt3L8OHD2fEiBGULVvW1OGISCwLDw9n3759VK1a1dShyBe2bNkyFi1axJQpU8idO7epw5HPSPc24VqyZAlLlixhypQp5MqVy9ThJChxfqSUJEyLFi1i2bJlpg5DErGwsDD27NkDgJ2dHZMnT8bGxoZevXqhXL2YQqVKlZgyZQr9+vXjxIkTpg5HRGJZZGQkI0eO5MiRI6YORb6AgwcPGkdZNGvWjP79+9O2bVtu3bpl6tDkM9K9TbiaN29Onz59aNu2LXfu3DF1OAmKklJiEh4eHkybNo1nz56ZOhRJhE6ePImzszNr1qwxbo5gZmbG8OHDefjwIb6+vqYOURK4oKAgRo8eTZs2bZg/fz6RkZEA5M+fn6FDh9K3b19evHhh6jBFJBYlSZKEfv364eXl9cbGPRJ/PX/+HDc3N4YNGxZjinaVKlVo0aIF/fv3N2l88vHu3bvHwIEDqVq1Kh4eHkRERIDubYJw9+5dfv75Z9q3b8/atWuNx6tVq0bTpk1xd3c3aXwJjZJSYhKOjo44OTkxZcoUU4ciicyDBw/o3Lkz/fv3Z/LkyTHWobO0tGTo0KHMnDmT0NBQk8YpCVv79u158uQJjRs3ZsOGDfTt29c4Qq9atWrky5ePlStXmjpMEYllzs7O2NnZ6f2fgPTv3x87Ozs2bNjwxnSu9u3b8/z5c/bu3Wuy+OTj3Lp1i0aNGpE1a1YmTZrE6dOnOX78uLFc9zb+evHiBc2aNcPe3p7atWvj4+PD5MmTjeUdO3bk0aNHHDhwwKRxJiRKSonJ9OzZk02bNnHlyhVThyKJyI4dOyhcuDBOTk68evWKP//8k+vXrxvLCxUqRM6cOTl06JBJ45SE6/bt21y+fBlvb2+cnZ3x9fXl/Pnz/Pbbb8Y6rq6ubNiwwaRxiohpeHp6MmXKFAIDA00dinyip0+fsm/fPgYOHIiVlRV+fn4cP37c+CWEubk5LVu21O/7eGjNmjWUK1eOrl274ujoSKpUqciWLZuxXPc2/jpx4gSpUqWif//+1K9fH19fXxYvXsyxY8cAsLCwoGXLlmzcuNHUoSYYSkpJrLlz5w5LliwxZpUdHBzo3LkzXl5eMeqpEyafi8Fg4OzZs/z999/GYxEREURFRXHp0iW+//57Jk2ahIuLS4wpe9WqVeP06dMmiloSmsePH3Py5EnCwsIASJYsGaGhoca1RZIlS8bgwYPx8fExDv0vVaoUd+7cMT5HRBKuS5cusXDhQvz8/AAoUKAA3333HVOnTo1RT/2juO/27dv4+fkZp2RHRkZiMBgICQmhXbt2uLu74+7ujqurK+Hh4RDd53h97yXuunbtGmvXrjX2KUuVKsWBAwcYN24cLi4u3L9/nyZNmjBo0KAYI591b+O+/33f2tnZ8eTJE+N9zJw5M25ubkyaNMn4HH1W+LyUlJIv7tmzZwwbNgwXFxeuXLnCxIkTjQmA5s2b8+DBA3bs2AHA/fv3qV+/vqZOySd78uQJDRs2pE+fPjRo0IDBgwcTFRVF2bJlOX78OOPGjWPSpEmsXLmSRYsWMWHCBF6+fAnRo6X+ue6DyMfy9fWlRo0aDBw4kO+//55z587h4OBAiRIlWLFihbFexYoVsbW1NQ79t7CwwNHRkYcPH5owehH5ku7cuUOPHj3o0KEDd+7coV+/fuzfvx+A3r1788cff3D16lUADh06RJcuXUwcsfybkSNH0rBhQ3r16oWLiwv37t0jTZo05M6dmyFDhpAjRw62bNnCli1bePLkCVu3bgUgXbp0xg/DEvdERkYydOhQWrduzcaNG2nUqBFPnjyhXLlyrFy5kmvXrlGjRg127NjBli1b+PPPP41fwOvexn1ve98WLVoUgF27dhnrNWnShLNnz/LgwQMAMmbMaEwsy6dTUkq+uHPnzhEZGcnmzZvJly8f9+7dw8fHh7t372JpacnAgQMZM2YMYWFhZMiQgY0bN2JjY2PqsCWemzFjBnnz5mX79u1s376ds2fP4uPjg6OjIxUrVuTMmTMUL14coheXjoqKIigoCKI7EVZWVia+AonvHj16xPjx41mzZg1bt26lc+fOdOrUiSdPntCjRw9mzZrF3bt3jfWzZcsWIwmVKVOmGGueiUjCsn//fvLkycPGjRuxs7Pj+fPnDB48mJCQEFKnTk3Hjh3x9vYGoGzZssyfP9/UIcs7HD9+nO3bt7Nt2zZ27txJ2bJl6dSpE2FhYXTt2pXdu3fz7bffAmBtbU3+/Pnf+H0vcdOSJUvw9/dny5YtzJkzh9y5c/PkyROI/rt98uRJGjduDNEjbPLly2dMJqN7G6e9630bGRmJm5sb3t7exi+skyZNStq0aXn06JHx+ZkzZzZh9AmLervyWfzbFJMKFSrwyy+/MHr0aFatWsWvv/5K1apVGTt2LESPEKhdu7ZxJ76kSZPGWtyScP3zm45UqVIxceJEFi5cyI0bNxgyZAg2NjZMnDgRf39/PD09KVGiBOnTp4foDsSQIUNMfAUS3z148AB7e3uyZs0K0dtEV6hQgfHjx1OqVCkaNGhAu3btOHbsGJs2bcLPz4+vv/7a+PwhQ4aoMysSjxkMhn/tHzVt2tSYrD5//jzr1q0jbdq0zJkzB4CWLVuSO3duwsLCMDMz0xd2cdjdu3fJnj07KVKkwMzMjAEDBmBra8uiRYuoVasWtWrVYtq0aZw7d47ly5ezf/9+nJycjM+fPXu2SeOXd3v48CHp06fH0tKSVatWcfv2bbZs2WKcxufo6MjMmTN59OgRK1eu5Pjx41SvXt34fN3buOvf3reNGzcme/bsdOrUCT8/PxYuXEhoaGiMzQpmzZpl0vgTEiWl5JNt374dNzc3AG7evMnjx4/fqHPjxg3279/PkiVLcHR05NWrVxw+fJiTJ08C0KNHD9KlSxfrsUvCsHHjRjp06ICHhwe3b9+G6Cl4Bw8eNNbJnj079erVY8mSJaRPn57ly5dz69Yt3N3dcXBwYNq0aca6lpaWWFpamuRaJH66d+8e48ePp0+fPsbh3rly5SIwMDDGZg49evRg06ZNPHv2DA8PD+rWrYuHhweLFi1i0qRJxgQW0dvDi0j8NX36dOOOTX5+fm+d6rF//37Cw8OZOXMmGTJkwMzMjCVLlvDgwQOsrKwYMGAA1tbWJohe3uXy5cv069ePzp07G3dWK1SoEH5+fsYR1+bm5vTs2RNfX18MBgNjxoyhYsWKeHh4sHfvXnx9fWOMstDv+7jh0qVLDBw4kL59+xqn07dp0wYrKyvq1q3LggUL6Nu3L0QnlQMDA/Hy8uLUqVNUrlyZrVu3snTp0hhfKOnexg2XL19m+PDhuLu7G9eC+rf3LcCUKVPImzcvvXr1Yt++fcyfPz/G/dS9/XzMDK9X8BL5SGFhYdSpU4cCBQrw559/MmbMGCpVqhSjzosXL6hSpQp9+vThypUrnDt3jpEjR5I1a1Z1tuSTrFq1itmzZ9OzZ0/OnDnDhg0bWLFiBUmSJMHJyYnFixdTsGBBiO78T548OcYuZyKf6vbt2/z444/UrVsXe3t7Zs6cydKlS8mXLx/Dhg3j8ePH+Pj4GOs3bNiQ3r17U6FCBZPGLSJf1sOHD6lTpw5ly5bl1KlTzJs3jzx58sSoc/XqVVq1aoW7uzu7du3C1taW1q1b4+joiJmZmclil7e7e/eucZRrihQpmDBhAv369aNx48a4urpStGhRevfuDdEj5UqUKMGGDRs06jUeOHv2LJ06daJDhw4kT56ccePGMWTIEGrWrAnRC1v7+vqSMWNGiE5KNW/enDp16pg4cvkvJ06coGfPnjRv3pyXL1+ybNkydu3ahb29vd63cYSGAsgne72ryM6dO9myZctb38DJkiVj0qRJzJ07l9y5czNr1ixSpEhhknglYdm4cSM9evSgdu3a1K5dG1tbW/r378/y5cvp1KkT7u7u+Pr6kipVKu7du0eyZMlMHbIkMNOmTaN///7Gjunff//NsWPHyJcvH25ubtSqVYsVK1bwww8/EBoaytOnT/U6FEkEQkJCCAsL48KFC2zevPmt7/tcuXLRv39/1q5dS+nSpWnXrp2+rIvD9u7dS6lSpejYsSNEr0np6upKqVKlGDhwIE2bNqVs2bKUK1eOgIAAIiIiNJoinli8eDGtW7emTZs2ED0C2tPTk6+//poMGTLw+PFjgoKCyJgxI0FBQcaF7CXuGzt2LDNmzKBw4cIQnaQ6e/Ys5cuX1/s2jlBSSj5acHAw4eHhODg4sHLlSjw8PNi+fTutW7d+a/1y5cpRrly5WI9TEg6DwcCNGzdwcHAgZcqUEL2o5D+njLq5ubFhwwZ27dpFx44duXr1KvXq1aNgwYKcPn36jS22RT7UmTNnOHnypLHj2qRJkxhrQZmZmRnXJ0uVKhXTpk2jc+fObN26lXv37lGoUKEY9UUkYXn69ClWVlZky5aNP/74A1dXV/z8/N7ZB6pfvz7169eP9Tjlvz169IjQ0FCyZMkCb+lzFClShEaNGjF9+nTGjBnDiBEj6NatG6VLl8bf3582bdqQKlUqE16BvMuzZ8+MfUmiv0C/c+cORPc3//rrL7Jly8a8efPw9PSkXbt2dOjQgUqVKnHgwAGcnJz0uSaO2rVrF0FBQdSrVw8Ad3d3Y0KK/+mnOTo66n0bB2j6nnywsLAwvLy82Lx5M0mTJsXJyYmBAwdy5coVWrVqxaZNm3BwcCAyMhILCwtThysJhJ+fH/379ycoKIigoCAmTZpE1apVWbduHTNnzmTjxo3Gncrmzp2Lv78/48ePB+Do0aPcvXuX8uXLkzZtWhNficR3LVu25Ny5cyxbtox8+fK9UV6nTh0WL14cYzTokydP2L9/PxkyZKBs2bKxHLGIxIanT5/i4eHB6dOnsbKyokuXLjRr1oytW7cydepU1q5di4WFhfpH8UBwcDADBgzgyJEjhIWFUbduXUaMGMHz58+pVKkSy5cvN/7+v3XrFnXq1OH48eNYWlpy69Ytjh49iqOjI4UKFTL1pcj/uHLlCqNGjeLRo0fG9yTR97FRo0YUK1aMR48ekTlzZho0aMDo0aPZunUrBoOBzZs38/fff1OxYkXj0hAS91SpUoXg4GC2bNnyRnIpNDSU2rVrs3379hjH9b41LS10Lh9s7NixvHz5ks2bN+Pi4sKyZcu4evUqefLkoU6dOri7uzNx4kQaNWqEcp7ysW7dusWhQ4cAePXqFf369WPQoEH8+eefDB8+3Lgwea1atTA3N+fXX381PjdbtmwxtlouXbo09evXV0JKPgtzc3NcXV3x8vJ6o+z27dvY2NiQIkUKQkJCjIvgpk6dmvr16yshJZKA9e7dm4IFC/LHH39QtmxZpkyZwosXL3ByciJt2rQMHTqUwYMHG9cukbjDYDCwatUq42Nvb2+yZcvGoUOHjMtT3Lp1ixQpUtC6dWuGDh1KVFQURO/YGx4eblwsOWvWrDRs2FAfbOOIf97bFy9e0LVrV6pVq4a5uTnLly831suaNSt//PEHlStXpmfPnkyaNInkyZMbNygwMzPD2dmZzp07KyEVh0VFRZE9e3bKlSsXYz3P144cOUKxYsUg+ouEo0ePgt63JqeklHywgwcPUrFiRRo1asRff/3Fxo0byZUrFwD9+vUjV65chIWFMXXqVC3SKR/t3LlznDlzBoA7d+5Qu3ZtKlasCNFTQYODgyF6p7xRo0Yxbdo0Fi5cyNmzZ1mwYIE+/MsXExUVRceOHbl58ybbtm2LUbZ3717KlSvHpk2bcHZ2Zv369UrOiyQCL1++5MmTJ9jb21O3bl2SJ0/Opk2bjOtIjR07FoPBQPr06Rk+fLipw5W3WLp0qfHfwcHB9O/fHysrK9KmTUvu3Ll58eIFAF26dCE0NJTevXtz/vx5Jk2aRK5cuXBwcDBh9PJvXt/bZMmSsWXLFn788Uc8PDzw8fHh+fPnxnrp06enWbNmVKpUicjISH799VcqV65swsjlQz1//hw7Ozvc3d1Zt24dly5dilG+b98+ypQpg6+vL7Vq1eLw4cMmi1X+P60pJR/M3t6eGTNmMGrUKMqUKQPAxYsXMTMzw9HRkZ9++snUIUoCYG9vz/Xr1wHInTs3bm5uxrLr16/HmBtepEgR5s6dy5gxY1i8eDF16tShU6dOJolbEoekSZPSr18/vL298fX1ZeDAgRQoUICDBw/i5+fHgQMHGDNmDCVLljR1qCISC6ysrHj48CHbt2/H19eX3LlzQ/QXeY6OjqRJk4YRI0aYOkx5BzMzM548eYLBYMDMzIwJEyYYlwQwGAw8fPiQnDlzQvQ28PPnz8fLyws3NzcKFy7MrFmzTHwF8i7/e29fT9crU6YMZcqUYcqUKQwaNCjGc06ePMnPP/9M1qxZ6devn4kil4/x/Plz7O3tyZw5M66urgwcOBBLS0tmzpxJqlSpOHDgAFu2bMHR0RFfX1/jwAoxLSWl5K2ePHnC2bNn3/rtQJs2bfDy8iI8PJxz586xcuVKdu3axbBhw3B0dDRJvJLw5M6dmyNHjhg7Ea87h0R38suXLx+jfvHixWMMwxb5EoKCgkiWLBkBAQEcPXqUO3fuUKBAAePaIkWKFKFy5co0atQoxmtWRBKG69evExAQQIkSJWIct7KyomXLlsYFdo8fP878+fO5evUqEydO1C5d8UDGjBk5c+YMxYoVi/H7+/z58+TJkyfGrogODg6MHTvWRJHKh/rnvf0nd3d36tSpQ7NmzciVK1eMPmfv3r2pVq2ayWKWj/P8+XOSJ0/OzZs3uXDhAufPn6dDhw7GtaW++eYbypcvT9WqVU0dqvyDFjqXGMLDw/H19WXWrFlkyZKF33///a31Vq1axcKFCzEYDNSsWZPWrVuTPHnyWI9XEra6devSr18/vv322zeOz5o1i+DgYLy8vHB1daVSpUomi1MSj9u3b9OqVStevXpF/fr1+fbbb+nWrRubN2827uQiIglPUFAQ06dPZ+nSpTg7OzNq1Kg36kRFRTF16lQ2bNiAnZ0dDRs2pEmTJlhZWZkkZvkw06ZN4+7du4wcOfKN48mSJTPusvfixQt++eUXk8UpH+5d9xZg4sSJ+Pn5UahQIa5evcr06dNNEqN8Hvv27WPUqFE8e/aMdu3aGXdB3rx5c4zEssQtGiklRidOnMDDw4OsWbMycuRI9u/f/866jRs3pnHjxrEanyQ+rq6uTJw4kW+++QZLy//7dfXgwQMCAgKYO3cumzdvplu3blSoUMHUoUoC9Pob03/KnDkz1atXp2nTpmTPnh2APn36EBkZaaIoReRL27RpE15eXlSsWBF3d/d31jM3N6dHjx706NEjVuOTz6Np06Y4OTnh6upKnjx5jMf3799PsWLFqFGjBpUqVaJPnz4mjVM+3LvubVRUFKlTp+bw4cNkypSJYcOGmTROeX9v66MRPXOiQoUKdOrUyThC9eHDh4SGhiopFYdppJQYXbx4kQcPHlCpUiXWrl1LZGQkDRs2NHVYkoBERkaye/fu9x4ObTAYaNWqFV9//bWxE3jw4EE6dOhA06ZN6dGjBylTpvzCUUtic/XqVUaNGkW1atVo2rSpqcMRERM7cOAADg4OFCxYkDFjxlCjRg2KFCli6rDkPRw/fpwcOXKQOnXq96rv6+vL6tWrWbJkCba2thgMBkqXLk2ePHnw9PTUrmvx2P/eW4B169axcuVKPDw8dG/jkePHjzNy5EgGDRr0xlRqiZ+UlErEgoODWbVqFdeuXaNatWoxpj8NGzaMZs2akSdPHoKDg0mWLJl20pNPFhYWRu3atRk0aNAbU/Le5enTp7Rq1Yo6derQsWNHDAYD169fNy44KvK5BAYGGqfedOrUiTp16mBubq7Ep0gi8+DBA1auXMmTJ09o1KhRjC3C27Vrx4wZM7C2tiYwMBB7e3uTxir/btSoUQQHB7912ta7jBgxgr/++ospU6aQPHlyrl69qsWQ44Hg4GAeP35sHMX8Nv97b6OiorT+Yzxy9+5dxowZg5+fH/3796do0aKkTZtWU6QTAL0LE6mAgAB++OEH/P39KV68OO7u7uzbt89Yfu3aNbJnz87KlSupUaMGp06dMmm8kjBYW1szYMAAvL29iYiIeK/nODg4sGzZMi5dukSPHj149uyZElLyWUVGRrJs2TJq1qxJWFgY69evx8bGhrp168b4vSgiCd/ly5dp0qQJL1++JHfu3Li6unLt2jWInurz+r+pU6fi5OTE48ePTR2y/As3Nzf27NnDhQsX3vs5gwYN4vvvv6dly5acPn1aCak4Jiws7I1je/bsoXLlyjRu3BgPD493Pvd/760SUvHDq1ev8PHxwcXFhTx58rB69WquXLmCi4vLB723Je7SSKlEaurUqVhZWVGsWDG8vLxInjw5gwYNIl++fISFhVGrVi1sbW1Jnjw5np6e5M+f39QhSwLStm1bKlWqROvWrT/oeW/bAUfkUw0ePJibN2/i4eHBs2fP8PLyIkWKFHh6empHUZFEpkePHtSrV4+QkBDGjRtH0aJF+emnn8iQIQOXLl3C3d2dwMBAihUrhru7OxkzZjR1yPIfVqxYwR9//MGSJUs+6Hn379/HzMxMm1jEMe3ataNXr14ULlyYq1evMnfuXG7cuIG3tzcZMmSgVatW/Pjjj9SrV++dbejexi8//vgjGTNmpH///hw/fpzx48cbB1XoHiYMSkolIoMHD6Z169bkypWLIUOGcO7cOZ4+fUq/fv1wdnaG6DV8AgMDad68OV27djUeF/mcrly5QqtWrdi4caNxi1YRUwkODubZs2eMGTOGCxcu4O7uTvXq1U0dlojEgoiICHr16oW3tzd2dnb8+OOPhIaGYjAY8PDwoGTJkhDdP/L392fQoEExjkvcFxUVhYuLCx07dlS/NgF48eIFN27cIE+ePJibm1OnTh1Kly5t3BHR39+fLl26sGnTJuPaURK/BQcHc+PGDUaOHEl4eDienp58/fXXpg5LPiONWUxE8ubNa9zCuGLFity6dYvffvsNZ2dnXr16xeTJk+nSpQspUqRgw4YN+sMtn83ly5dZtGgRZ86cASBPnjw4OzszefLkGPUCAwNNFKEkZnZ2dixbtowCBQqwadMmJaREEhFLS0vjluFE94/CwsJYuXIlJUuWJCAgAE9PT0aPHk2BAgVYvXq1ElJx3LNnz1i1ahWbNm0iMjISc3NzPDw8GDt2LCEhIcZ66nPEXY8fP8bPz++tZcmSJWPKlCksXLgQS0tLBg4cyMGDBwkPDwcgf/78VKxYkVmzZsVy1PKl2NnZMW/ePBo1asSqVauUkEqAlJRKwC5cuMCYMWOMj5s2bcq9e/fYu3cvVatWpWLFijRq1IhevXpRrVo17ty5Y/yWQeRzuHv3Lr169aJdu3bcunULd3d39uzZA9FTJLZv387FixcBOHbsGB07djRxxJIQ7dixg8GDBzNv3ry3rkUB0L9/fzp37qypoSKJwI4dO2JM5erVqxfr1q3j5s2btGnThqRJk9KwYUPc3NyoWbMmKVOmxM3NzaQxy38LCwtj9uzZODk5ceTIEdatW8fPP/8MQOnSpSlcuDBz586F6BFyjRo14saNGyaOWt7m6tWr9OrVi9DQUIgeqXjjxg1jUnHAgAHMnTuXx48f8+2335IzZ04WLVpkfH7v3r2xsbExWfzy/iIiIli1ahWDBg1i9erV76w3ceJEGjZsqI23EihN30uALly4QL58+QgJCaFGjRpMnTrVuHXxrl27GDNmDOvXr8fKyorDhw9z//59SpUqRebMmU0duiQwq1at4u7du7Rt25aFCxfi6+tLkiRJ2Lx5M7a2tixZsoStW7eyaNEiDAYDoaGhJEmSxNRhSwIydOhQzpw5Q/PmzVm/fj3ZsmVj+PDhpg5LREzg3LlzFCpUiJs3b/LDDz+wYcMG0qRJA8CsWbM4deoUM2fOJCIigr179/Ly5UsqVKiAg4ODqUOX93Dnzh28vb3p168fN27cYMSIETx69Ijp06fzzTffcOfOHVxcXFi3bh0ZMmTg1atXJE2a1NRhyzv06NEDR0dHGjRoQLt27QgICMDc3Bxvb28qVaqEt7c3gYGBeHl5cf36dZo1a8bGjRtJnTq1qUOX9xQREUHbtm2xsrKiTp06zJgxg+bNm9OqVStThyaxTCOlEpCnT5+yf/9+BgwYwO+//46trS29e/dm5MiRvM49FitWjOvXr7N48WIAypYtS/369ZWQko/2rpEnAI0bN6Zbt2507dqVM2fOsGbNGjJlymQcUt20aVMKFizIq1evMDMzU0JKPkpAQAAzZsxg4MCBHDt2zHj8ypUr+Pv7M2XKFI4ePcq1a9coUaKESWMVkdh39+5ddu7cSbt27Th9+jRfffUVDRo0YOLEicY6xYoVY/fu3Rw4cABLS0uqVq1KnTp1lJCKY/6tz5E5c2amTJnCrl27+OWXXxg8eDC9evXCy8uLyMhIMmfOTK9evQgICABQQiqO69+/P4sWLWLEiBG0adOGI0eOMHXqVH766Sfu379Pt27d2LdvH+fPnydHjhx07tyZR48emTpseYu///6bsWPHMnjwYK5evWo8vm3bNjJmzMiAAQNYt24dlpaW5MmTx6SximkoKRVPLVq0KMa8+NOnT1OzZk3Wrl2Lg4MDkyZNIjg4mPr162MwGJg3bx7BwcGMHz8eNzc347xrkU8xd+5c4xTRc+fOGYdZ/9PBgwd58eIFs2bNIlOmTBgMBlasWMG9e/ewsLBgwIAB6hjKRwkLC2POnDnUqlWLBw8ekDp1ajp27GicEvr48WMCAwNp2LAhGTJkYMuWLcbfiSKS8ISEhMSYwgOwYcMGGjRowI4dO8iYMaPxi7rXH2h3797NkydPmD59Ov379+f58+cmi1/+3Y0bN6hTpw5hYWE8ffo0xofbf5o8eTILFiygYsWKhISE8PjxY1asWAFAs2bNKFCgQCxHLh/ixYsXzJs3j6xZs9K0aVN2795N48aNAShRogS1atXi999/J3ny5PTo0YMpU6YA4OrqSr58+UwcvfzT8+fPGTlyJE2bNsXMzIyIiAhat25tTAw/evSIS5cu4erqSpUqVVi3bh3lypVTPy0xMki89OuvvxqePHlifNymTRvDkiVLjI979uxpGD16tMFgMBhu3rxp+P777w358+c3uLu7G8LCwkwSsyQ8T548MZQpU8bQq1cvQ/ny5Q0XLlx4o87169cN5cqVM6xZs8bQq1cvQ79+/Qz+/v6GyMhIk8Qs8dvTp08NEyZMMBgMBsPFixcN+fPnNxw+fNhYPnjwYMPQoUMNBoPB8PLlS0Px4sUNGzduNJZv27bNULNmTcOjR49MEL2IfEmRkZGGadOmGUJCQozHypcvbzh27JjBYDAYQkNDDfXr1zesWbPGYDAYDEeOHDGUKVPGULRoUcPkyZNNFre8vy5duhg6depkKFu2rOHXX399a5369esbJk+ebJg5c6bhu+++M1y8eNHw9OnTWI9VPkx4eLjhwoULhk6dOhk8PDwMoaGhhhcvXhgqVKhgOHjwoLHejBkzjH/nIyMjdW/jGH9/f8OyZcsMBoPBsHHjRkOhQoUM9+/fN5Y3a9bM+Jn1r7/+MhQoUMDg7+9vMBgMhoiICMPixYsNDRo0MERERJjoCsQULE2dFJOP4+rqSvfu3fHw8CBjxoyEhISQLVu2GOUtWrTghx9+4KuvvmLLli2EhYVpepR8VqGhoYSFhXHq1Cm2bNmCnZ3dG3WyZ8/OwIEDjTsWtW/fXotPykcLDQ3l3LlzADg6OtKoUSOWLl1KmTJlIHox1NdrxCRNmpThw4czaNAgNm/ezK1btyB6nanXdUQk4TA3N6d169a4ubkxZcoUkiRJQmhoKFmzZgXA2tqaH3/8kfHjx/P9999TunRpDhw4QFRUlDY5iCdevHjBkSNHmDdvHuXLl39rnXHjxjFhwgSSJ0/OggULYvSPJe66ffs2bdq0oVChQowcORKi37N9+/bFw8ODn3/+mdDQUBYvXszYsWMh+j2fMmVKE0cu//To0SPjBgLOzs4sXryYVatWxdgsIm3atADkypWLLl264OrqSrly5fDz8yN79uyMHTsWCwsLk12DxD5N34ujAgICOHXq1BvHg4OD+fXXXwHInTu38Zfy999/H2MnmcePH5MmTRpmz54N0b+0lZCSz+XZs2cEBQWRMWNG1q9fT5IkSTh+/Pg769epU4dff/2Vbt26KSEln8TBwYG//vrL+LhXr14cOnSIrVu3MnjwYFatWsW6desYNWoUz549w9nZmbVr11KlShUGDRrE2rVrKV26tEmvQUQ+3q1bt7hy5cobxx88eMDKlStJliwZtra2xl3WqlWrxtKlS431Hj9+jIWFBatWrQLA0tJSCak4LiQkxLhW0JgxY2jbti0bNmx4Z/1cuXIxbdo0vL29lZCKBwYOHMitW7fInj079erVe2PqVr169UiXLh0zZsxg3759jB07lnLlypksXvl3adKk4fLly8bHnp6ezJs3j8OHD9OpUydOnz7NlClTmD17NmFhYbi5uTF37ly+/fZbZsyYwbx588iVK5dJr0Fin5JScdTt27fp2bMnL1++BCA8PJyLFy/SvXt3bty4QXh4OB07duTEiRMcP36c5s2bExAQQMeOHRkxYgRDhw5l2LBh7N+/39SXIglIYGAg3bt3p0aNGtSqVYtFixaROXNmfvrpJ7y9vY1rlUVGRpo6VEmgrK2tsbe35/z58wCkSpWKrl270qNHDywtLTlw4ABLly4lIiICZ2dnVqxYQbZs2WjQoAElS5Y0dfgi8olOnjyJp6en8YNrcHAwly5dolWrVjx79gwAd3d3fH19uXfvHn379mX9+vW4u7vj4eHB77//Tp8+fdQ/iifmzp1L5cqVadq0KR06dMDBwYGuXbuyf/9+zp49C+pzxHu5c+fG29sbgG7dunHx4kX8/f2N5WZmZgwaNIiGDRvi7e2thFQclzNnTs6dO0dwcDAABQsWxNnZmfbt21O8eHGOHz/O5MmTOXv2LM7Ozuzdu5dChQrRoEED8ubNa+rwxUSUlIqjihQpQrly5Yy7lN24cYM2bdqQJEkSfvnlF6ysrEiaNCl9+/bFy8sLS0tLfH19qVKlCqlSpWLp0qX4+fnx1VdfmfpSJAHp378/OXPmZMOGDVSoUIFp06YRGBhI5cqVyZ49O4MHD2bYsGF069bN1KFKAla9enV8fX2Nj1u0aEGuXLnIly8fqVOnJk2aNPz888/Mnz9fiSiRBKZu3bqYmZmxbt06AA4fPkyrVq2oUqUKHTt2hOhd2Jo1a8bYsWNJkyYNa9aswdHRkdy5c7N06VL27t1L9uzZTXwl8l9Wr17Nrl27WLNmDd26dePo0aPs2bMHOzs745SuefPmUb16dePCyRL3BAQEcO7cOYKCggDw8/OLsfNly5YtuXLlCocOHcLe3p7u3bsbp++9VqRIEZo1axbrscuHs7GxoWzZssbNBQD69OlDkiRJKF26NLa2tuTIkYMpU6YwcuRIbTwgoKRU3Na3b19WrFjBrVu3yJMnD05OTpiZmcWoU7duXaytrVm9ejXW1tY0bdqUmjVrMnLkSLZu3YqXl5fJ4peEJTIykhs3bpAuXTrq1q2LpaUlmzdvxt7eHoBRo0ZhZWWFg4MDo0aNMnW4koA1b96c3bt34+fnB9HTbwYOHMikSZOMnV6AfPnyaQi4SAJjZmaGp6cnEydO5OXLl1SrVg1HR8c31h95PZr8xIkTpEiRgnbt2lGsWDG6dOnCs2fP6Nmzp8muQd7Pzp07qVevHm5ubqxYsQJfX1+qV68OQIMGDahXrx43b97Ex8eHVKlSmTpc+R9+fn506NCB77//nu7du1O+fHmWLVtGjhw5+P3334075VpbW9O7d2+8vLyIjIykSZMmBAUFaTRjPNaxY0fmzJljnHabOnVqunTpwogRI2JMzyxTpoxxfSlJ3MwM2nMxzomMjGTq1Kn8+eefPHz4kEKFCjFlyhQCAgKoVasWvr6+5M6d21j/7Nmz+Pv706RJEwA2bdrE3bt3adWqldZJkM8mKiqKihUrki1bNoYMGWLcdvfw4cPkyJGD9OnTmzpESUQ2bNjA5MmTWb58OalTp4boRP4PP/ygNaNEEqjg4GDGjh3LpUuXuH79Ok2bNqV3795cvHiRtm3bsnnzZlKkSGGsv2PHDqysrKhUqRJETwVLmzatcbSVxG1Dhw5l69atDBw4kDp16mBmZsaDBw+4fv06ZcuWNXV48g6BgYGMGDGCo0eP0qNHD2rXro21tTUHDhzAzc2NyZMnc+/ePTZu3Ggc9Xz//n0qVarE4MGDad68OU+ePCFVqlR6n8ZjEydO5OTJk8ybNw9ra2vCw8Np3749I0aMMG4+IfKaklJx0MSJEzl27BjDhw/n0aNH9OrVi8mTJ1OmTBkWLlzIvn37mDdvnqnDlERozpw5rF+/nqFDh2IwGFi4cCH+/v6MHz+eIkWKmDo8SSCioqI4cuQIKVKk+Ndh3b6+vqxcuZLp06ergyOSCHTv3h1ra2t69erF2bNn+fnnn1m3bh1ZsmRhyJAhWFlZMWjQIFOHKZ/JlStXaNGiBR4eHhQoUICtW7eyZMkSOnXqhKurq6nDk3cICwujdu3adOjQgcaNG8coW7BgAStXrmT9+vU0bNiQhg0b0qBBA4YNG0a2bNmwtbWlbdu2Jotd3k9YWBiHDh3iq6++eudUaIPBwLBhw7h+/TqTJ0+O8YWByP9SUioOatKkCT179jRudbt69WoWLFjAmjVrMBgM1K1bl6FDh2o0gHxWkZGRzJ8/n/Pnz9OiRYu3rsVjMBiYOXMma9euxdbWlvr169OsWTONyJPPqkuXLvz9998EBATg5uZG8+bN31n38OHDjBkzBg8PD60fJZLAlSxZkvXr15MxY0YAxo0bx99//42Pj49xNPmaNWvIkCGDqUOV97B//37WrFlDkSJFaN269VtHxRw8eJDJkyfz9OlTvv32W9q1a0emTJlMEq+8vx07dhhHuiVLlsx4/OXLl5QuXZo1a9ZgYWFBhw4duH//PvXr12fYsGFvTMOVuOfVq1c0a9YMc3Nzbt++zfjx46lYseI7669du5ZFixbh4+NDlixZYjVWiT+UlDKxBw8esHXrVqysrKhXrx62trb079+fIkWK0LJlSwAuXrxIvXr1GDp0KM2aNePx48ekSZPG1KFLAmEwGLhw4QJr167l3r17lCxZkrlz57JgwYIY00RFvqT79+9z4MABUqRIwbZt2xgzZgx3796lUaNGrFy58l9HQoWHh2NlZRWr8YrIl3X9+nV27dpF6tSpqV27NpaWljRs2JA+ffoYv7TbtGkTvXv3ZtGiRZQpU0b9o3jC39+fp0+fMmLECH788UfWrl2Ls7OzRsgkMG3btqVQoUL06dMnxvGSJUsyb948ihYtSlRUFGFhYSRJksRkccr7+euvvzhz5gyvXr0iMDCQrl27cvLkSXr16sXmzZtjJB//l/pp8l+00LkJnTx5kgYNGuDv78+OHTtwcXEhKCgIV1dXZs6cycmTJ3n69CmzZs2ibdu2hIeHA6jDJZ/V48ePadu2LdeuXWPq1Km4urrSp08fhg0bZurQJAEKCQnh2rVrxsehoaFcvXqVpk2bsnHjRnr37k3p0qUxMzMjc+bMtG3b1rhV9LuooyOSsGzatInmzZtz8+ZNFi9eTPv27YmMjKRDhw4MGzaMq1evcv/+fRYtWoSbmxuBgYGg/lG8MX78ePr378+8efNo0aIF06dPZ968edy7d8/UoclnNHDgQHx9fbl165bx2Pr167GzsyN//vwAmJubKyEVxwQEBPDgwQPj49DQUA4dOoSrqytr165lxIgRxtk6xYsXp3z58sycOfNf21Q/Tf6TQb64vXv3Gjw9PQ1TpkwxvHz50ni8ZcuWhpUrVxof9+vXzzBq1CiDwWAwbNu2zVCxYkVDkSJFDIMHDzZERESYJHZJGMLCwt44NnLkSMP58+cNBoPBMGPGDMMPP/xgLIuKijI0bNjQsGXLlliNUxK+vXv3GsaPH2983LJlS0OjRo0MJ06cMBgMBsOKFStivBZDQ0MN1atXNxw8eNAk8YrIlxEeHm5Yt26d4aeffjL4+voaoqKijGXly5c3HD9+3Fjvhx9+MKxYscJgMBgMCxcuNJQsWdJQsmRJw8yZM00Wv7zb2/oce/bsMcyaNctgMBgMV65cMRQoUMDw4MEDY/msWbMMvXv3jtU45cOFh4cbzp07Z7h169Z71R82bJjBzc3NEBYWZpgzZ47hm2++MZw5c+aLxykfb8GCBYbVq1cbDNH3u1q1aoYff/zRcOfOHYPBYDCMGjXK8NNPPxnrP3r0yFC2bFnDzZs3TRazxH8aKfUFnT9/nubNmzNhwgSyZcvGzp07GTx4sLE8MDAwxpSU5s2bs2vXLgC+//579u3bx8mTJ/nll180x1o+2rFjxxgzZswbx/Ply8fIkSMBaNOmDY8ePeLQoUMQveX2oEGD8PHxQTN85VNFRUUZX0fffvstderUYdOmTRA9vP/s2bPGb00bN25MREQE69evh+itokeMGEHy5MlNeAUi8jnt3buXOnXqsGbNGnLkyMHcuXOZM2eOsfyf/SNLS0uaNGli7B+1atWKY8eOceTIETp16mSya5C3Cw8Pp1mzZm8cz58/P7/++it37twhd+7cNG3alOnTpxvLXV1dOXv2LJcvX47liOV9hYWF0aJFC3755RcmTZrE48eP//M5PXr04OjRozg5OXH06FFWrlypjXHioKioKOO/W7duTYYMGTh27BiWlpY0a9aMe/fuGddyc3Nz48CBA5w9exaiR6gOGzZMOyXKJ1FS6guaMWMGNjY2rFmzho4dOzJ9+nQ2bNjA06dPAahSpQpLliwx1r9z5w729vYx2lAySj7V5cuXsbKy4urVq4wYMcJ4vEGDBoSEhLBp0yZsbGxwd3fHy8uLyMhIAIoVK8aSJUv0R0Y+WmhoKF5eXnzzzTc4OzsbP1Sam5szbNgwnjx5QuXKlalQoQIrVqyA6ISop6cn48eP59WrVwCUKlWKwoULm/RaROTziIyMZPTo0VSoUIFff/2Vjh07Mnz4cOPW8Lxn/8jcXF3YuOj58+c8ffoUg8FA3759jX3edOnS4erqavySrHv37mzbto1Lly5B9BcQq1atIm/evCaNX97twIEDhIeHs3LlSsaNG2ecLnv9+nVOnz791uekSJGCIUOGMGTIEGbPnk3mzJljOWr5NwEBAfTt25dy5crh7OzM1atXAXjx4gVDhgwhIiKCli1bYmVlxd69ewGws7Ojd+/exi+2iR5MoV2Q5VPoL/oX5O7uzqlTp4xvcDMzM+zs7EiaNCkA7du35++//6Zt27YMGjSIUaNG4e7ubuKoJb7766+/jJ1AojuIKVKkIGvWrOzevZvDhw9D9Ouxf//+jB07lpCQEJycnEiZMiUbN240PjdlypQmuQZJGH766Sfu3LnDli1b2Lx5M1WqVAEgV65c1K1bl0mTJkH0uhMzZ84kICAAgK+//lq/C0USKAsLCwYOHMiGDRsICgqC6A030qZNa6zz008/8fvvv9OrVy/69u3LmjVr6NKliwmjlncJCgri4sWLxsev+xxmZmY4ODjg4+NjLGvTpg2nTp3i2LFjpEyZkq5du8YoV58jbsuUKRMPHjyIsUYUwNmzZ2P0Hf+Xs7MzlSpVioUI5UNERETQtm1b0qVLx759+9i0aRO5cuUCoFq1aqRPn56lS5diZWXFTz/9hLe3t3F94wYNGtCsWbMYI6xEPoV23/vCxo0bh7+/P82aNcPLy4vAwEAKFChAx44dqVChAiEhIfzxxx/GpED69OlNHbLEYxEREYwdO5Y//viD+vXr065dO+bOnUv27Nlp2rQpW7duZdq0acateENDQylSpAjdu3fHzc2NgIAAUqZMqW+g5ZNFRERQuHBhdu/ebdye3WAwYDAYMDc3JzAwkJo1azJ37lzy58/PiBEjCAsL0wL7IolEp06dyJo1K7ly5WL8+PHY2NhQtGhRunfvTv78+Xn69Cnr168nSZIk1KhR442RUhI3bN68mbFjx5InTx66du1KVFQUPj4+/Prrrzx//pyaNWuyYMEC4wiozp07c//+fVavXo3BYCAwMBAHBwdTX4a8pyVLljB69Gjy5ctH/vz5yZ8/P2FhYdy/f19fJsUzly5dokWLFhw7dsx4LCoqCjMzM8zMzLhy5QqtWrVi06ZNODg40K5dOypWrIirq6tJ45aESUmpL+zFixc4OTlha2uLp6cnFSpUYM+ePYwZM4YcOXIwdOhQ4wc2kc/l8ePHzJ8/nzVr1hin5jk7O0P0ehylSpWiXbt2TJgwgWTJkmFpaUn37t1NHbYkMI0aNaJIkSIULVqUPXv2cPbsWZ49e0br1q3p3r07y5YtY+PGjSxevJjnz59z4cIFypUrZ+qwRSQW3Lhxg9q1a1OoUCE8PT1xdHRkzZo1+Pj4ULVqVQYMGPCvW4xL3BEREcG6deuYPXs2AI6OjsYRUEuWLGH9+vXMnj2b48ePs3z5cvLmzUvbtm1JlSqViSOXD3X9+nUmTpzI0KFDuXDhAhcuXGDNmjUEBARQsGBB3N3dyZcvn6nDlPcQEhJCpUqV6NChA/b29mzfvp1r164RFBSEp6cn9erVY9iwYRgMBoYMGcLNmzd59eqV7q98EUpKxYLVq1cza9Ys1q9fj7W1NUQvFrh//34qV66sdaPki3n27Bk///wz5ubmTJ48GYD79+/Ttm1brl+/jpOTE6NHj8bGxsbUoUoCdO/ePRYtWoS1tTUVK1akRIkS/P3339SoUYNdu3aRLl062rRpw8iRI7UWgUgiNHr0aK5evWpMZhA9/cvPz4+KFSuaNDb5cFFRUaxYsYJJkyaxbds2UqRIgcFgwNPTk99//508efIwfvx4HB0dTR2qfKQ9e/bg6+vLvHnzAPD396d379789ttv3Lhxg0KFCpk6RPkA/v7+/P7776ROnZoqVarg6OjIzp078fDw4M8//yQoKIiOHTvi6+urzwryRSkpFQsMBgONGzemRo0atG/f3tThSCLz5MkTnJycmDFjBqVKlYLo12RoaChJkiQxdXiSgKxevZqtW7eSOnVqunXr9tYFTW/dukX16tXZtWsXGTNmNEmcIhI3BAcHU716dby9vfn2229NHY58Jp07dyZLliwMGjTIeOzVq1fGNVUl7oqKiuLly5fY2dm9tTwwMJB69eqRPXt2kiZNypkzZ5gwYQJlypSJ9Vjlw4SFhbFw4UIOHz5snG77tmnRe/bswd3dnSNHjmizI4k1WjjmMzp48CCnTp164/jr3aT8/PxMEpckbqlTp6Zr166MHDnSuCChmZmZElLyWY0bN47FixdTt25d7OzsaNmyJc+ePYPoD54BAQH88ccfuLq60qNHDyWkRBIJg8HA2rVruXPnzhtldnZ29OnT5507d0n89NNPP/Hbb7/x119/GY8pIRX3XbhwgWrVqlGyZEkmTJjw1jr29vb89ttvODk5Ua1aNTZt2qSEVDzRvXt3jh8/TuPGjbl79y5dunQhIiIComdWPHjwAF9fXzw9Pfnll1+UkJJYpZFS7+Gfi/O+zcOHDxkyZAhXrlxh+PDhWhNF4pzw8HCmTp1Kp06dsLW1NXU4kkDcunWLp0+fUqRIEb7++mv++OMP4zS8fv36kT17dtzc3FiwYAEbNmwgU6ZMNGvWTL8jRRKIfy6K+zaXLl3C09MTMzMzRo0aRe7cuWM9RjGNZcuWUbx4cU3ViwceP36MtbU1vXr1on379uTLl48WLVrQp08fqlWrZurw5BP4+fnh4OCAmZkZDRo04ODBg1hbWxMZGUnjxo3p1KkTTk5ODBs2DD8/P/LmzUurVq20bpTEOo2U+hcGg4E5c+bw7bffUqNGDRYuXPjWera2tpQpU4ZNmzbpw5bEuqNHj9KgQQMuX778zjpWVlb07t1bCSn5LIKDg7l69So//vgjV69eBcDa2pqwsDBjne+++844+sHV1ZXffvsNHx8f/Y4USQBCQkIYMWIE33zzDc7Ozuzdu/et9aytrWnRogUrV65UQiqBePDgAQMGDGDatGn/Wq9Zs2ZKSMUx//wb/drUqVP57rvvqFSpElZWVnzzzTekSpWKoUOH4u3tTWhoqElilU/z8OFDjh8/Trdu3bh7964xEfV6xoSFhQUVK1Y09tMGDx7Mb7/9hpeXlxJSYhKWpg4gLps6dSr79u1j+fLlb10b5TU7OzttjymfXUBAAPv27SM8PJx69eoZF8n/p5UrVzJz5kz69etn3G5Z5EubMGECe/bsoV+/ftSrVw+id9obN24cPj4+WFhYsGPHDvLkyWPqUEXkC3i99fvWrVtJkSLFO+vlyJGDHDlyxGJk8rHCw8P5888/uX//PqVKlSJnzpxv1Ll37x7169enSZMm6vfGI2FhYURERODi4sKaNWtImjQpO3bs4PDhw1y5coVDhw5x4cIFunfvzvPnz0mRIgWlS5emYMGCzJ8/ny5dupj6EuQD9e3blzt37jB79mzy588PQMmSJfHx8cHd3Z2XL19y4MABWrRoYepQRUAjpd505swZQkJCANiwYQO9e/eOkZCKjIw0YXSSWOzatYtatWpx9OhRgoKCuH379lvrOTs7s2nTJpydnWM9Rkm8OnfuzNOnT0mbNq3xWI8ePQgNDaVq1ap89913vHr1im7dupk0ThH5fI4cOQLRH3C3bdvGzz//bExIRUVFGb+Bl/jnwYMHNG7cmPnz5/P8+XOuX7/+1noZM2Zk48aN9O3bl2TJksV6nPLh7ty5Q7du3bC1teX333/n0qVLABQsWJBVq1bRoEED7OzsKF26NM7OzkyZMsX4XHd3d27dumXC6OVj9e7dmzt37pAmTRrjsREjRrB3716qV69O1apVKVq0KHXr1jVpnCKvaU2p/9G2bVuKFy+Om5sb7u7uBAcHU7t2bQ4dOsTRo0d58uQJVapUwdvb+51rTIl8iuDgYCpXrsycOXP4+uuvTR2OJHIvXrzA29ubTZs2kSJFCkaOHEm5cuWYNWsWZ86cYfr06THqX7x4kSRJkpA9e3aTxSwin1dYWBg1atRgwIABODk54eLiQvHixSlUqBB79uzh3LlzPHv2jDZt2igZHQ/16NGDzJkzM2DAAFOHIp/Z3LlzCQgIwN3dnVevXlGjRg0mTJhAiRIl8PHx4a+//sLHxweAp0+fUqtWLXx9fcmVK5epQ5f39ODBA4YOHcrhw4fJkiULkydPJmfOnPTt25csWbLQu3dvY92IiAj8/f1Jly4d6dOnN2ncIv+U6LMqoaGhMUY/DRw4kF9//ZV79+4xePBgHB0dOXv2LNWqVWPjxo3s3r2bffv2cfjwYZPGLQmLn58fAwYMICIigmfPnhEWFmac0/3gwQPWrVvH+vXrNbdfYl2PHj2wsbFh8+bNlClTBnd3d8LDw2nTpg2XL1/m0KFDMerny5dPCSmRBODFixfGf1tbW+Pu7s6YMWMIDQ1l6tSpWFlZcf36dVq2bMn27dtZuXIlU6dO5cGDByaNW/5bQEAAQ4cOxd/fH4Dbt29TuHBhiF4vbO/evSxfvvytOyZK3Pbq1SsmTZpEeHg4RI+8r1KlCkTvgNinTx+8vLwwGAx06NABPz8/jh8/DoCDgwP9+vV75+h8iXvCwsJo2bIlZcqUYcuWLaRMmZIhQ4YA0L9/f5YvXx7jflpaWlK4cGElpCTOSdRJqbt371KjRg0aN27M2bNnAciTJw/16tVj7Nix2NnZ0bNnTwYMGEClSpWwtLTE3NxcW2TKZ/Pw4UMGDhxI9+7dKVeuHBYWFmTJkoVvv/2Wxo0b88MPP9CtWzeuXbvGsmXL6NWrl6lDlgRo586djBgx4o2k56NHjwgODubrr7/mhx9+4OXLlyxfvhwrKyvjh9TXnVsRSTiOHz9O5cqV6dChg/EDTY0aNciUKRPz588nU6ZMDBgwgN69e1OiRAnMzMywsLAwddjyHyIiIli4cCG1a9fG2trauDxF69atGTZsGO3ataNevXrs3buXCxcu4OLiwqNHj0wdtnwAGxsb9u3bx+LFi3n69Ck3btyIMeq+bt26WFpasnr1apImTUq/fv3w8vIyTr91cXGhUqVKJrwCeZvFixczY8aMN/pbx48fp3jx4gDUq1cPR0dH4yYEGTJkoGXLlowfP94kMYt8iEQ9fc9gMPDDDz9gY2PDzZs3qV69Or169SIiIgInJyemT59OiRIlePHiBeHh4Zw6dYopU6aQJ08eRo8eberwJZ5bvXo1Y8eOpUmTJnTq1AlbW1vCwsKwtLTEzMyMy5cv4+DgQLp06QC4du0azs7OnDp1iqRJk5o6fElAevfuzdWrV7GxsWHq1KnGb9ACAwMpX748uXPnxtPTk5IlSwLg7+9Pzpw5sbGxwd/f37iIpogkDMHBwTg5OVG4cGH8/Pxo06YNbdq04a+//qJ58+Zs2rSJ9OnTExQURHh4OPv372fy5Mk0bdqUjh07mjp8eYurV6/i5uZG1qxZ+emnn4wLmb969YqkSZNy9+5dXr16Rc6cOY1fvtapU4f27dsbN7SQ+OHEiRN07tyZzp07c+nSJcaMGROj3M/Pj65du7Jlyxbs7OwYPXo0nTp1ImXKlCaLWf5d7dq1MTc356uvvsLb29u4ptuZM2do2rQp5cuXZ+DAgcZpl6dPn6ZYsWKEhIRw9+7dt25cIBKXJKqRUqGhoezYsYP58+ezd+9ezMzM8PT05PLlyyxZsoTw8HBq167NsWPH6NGjByNHjiQqKoo///yT9u3bs2zZMtq1a6eElHwW6dKlw2Aw0K5dO2xtbdm2bRs1a9bkzz//xMzMDEdHR2NCCmDNmjUUKFBACSn57HLlymUcNdq0aVNOnjwJgL29PTVr1iRr1qzkzJmTW7duMXjwYDp06MC1a9cAlJASSQCeP3/O+vXrmT9/PmfOnMHOzo7evXtz7949VqxYwYkTJ3BxcSEkJITatWszbtw4iN4BtmPHjuzatQsvLy8lpOKwDBkyEBwcTJMmTciZMyfXrl2jY8eOeHl5AZApUyZy5cplTEidOHGCO3fuUKhQIRNHLh+qRIkSVKxYkcmTJ7Nz507atWvHvHnz8Pf3x2AwUKRIEcqXL8/SpUsBGDBggBJScVzOnDlxc3MjW7ZsNG3a1LgAfdGiRcmfPz+Ojo5kyJCBCxcu0LlzZwYOHEhAQABJkiRRQkrihUQxUio8PJylS5cyZ84c8uTJQ/r06dm7dy8lSpRg0qRJeHh4kDx5cn7++WdOnz7Nzz//TKZMmTh9+jT9+/enUaNGpr4ESaA6d+6Mra0tjx49IigoCA8PD0qXLm0s/+2337h//z67du3CysqKiRMnkilTJpPGLAnP5s2b2bFjB+PHj+fo0aP07duXHj160LhxY168eIGHhwfbt2/HwcGBRo0a0aFDB+zs7Ewdtoh8ouDgYGbNmsWqVav4+uuvSZo0Kbt376ZFixb06dOHRo0aGZPVW7duZdSoURQtWpSdO3eyZMkSihYtaupLkA+wdu1apk2bxnfffcf69evp1KkTzZs3x8rKCqKnAp0+fZozZ85w6tQphg8fznfffWfqsOUj3Lt3j1q1ajF79myeP3/On3/+ycGDBwkMDKR69ep069aNlClTGu+9xG0+Pj7Y2NjQqVMn1q5dy7hx4xgzZgzffPMNd+7cYcCAARw/fpxs2bLRokULmjVrpnsr8UqCT0o9e/aMpk2bkiVLFjw8PIzZ4idPntC0aVMaNmxIgwYNqF27NkuXLiVPnjxEREQwf/58pk+fTs2aNRk1apSpL0PiuXHjxlGtWjWKFSsW4/jNmzepVasW7dq1o2fPnm/s6Hj8+HGuXr1Krly5jOt2iHxuly9fxt3dnbVr17J7925GjhxJYGAgtWvXxsPDA0tLS1OHKCKf2cWLF2nXrh3fffcdvXr1Mm4dfunSJX788UfGjBmDg4MD3bp1Y+vWrdjb2xMcHMzEiRNZsWIFvXv3pl27dqa+DHmLAwcOcP78eTp16hTj+OtlK8zMzJgxYwapUqWKUf7gwQP27NlDypQpqVixIra2trEcuXxOU6dO5ciRI/j6+hqP3bt3jyRJkuDg4GDS2OTDbNy4kT179jB69GhWrVrFpEmTiIqKokuXLri6upo6PJFPluCTUgAdOnQgd+7cb2x1u3//fnr37s2BAwdYsGABR48eZf78+cbyoKAgkidPboKIJaGZM2cO27ZtY+XKlW8klsaMGcPly5eZO3cuRC9EumzZMpIlS4aLi4uJIpbEJCwsjDJlylC8eHEePHiAh4cHhQoVomfPnmTLlo1ffvnF1CGKyGcWFhZG7dq16dy58xt/a+bPn8/69etZs2YNffv2JU2aNAwcONBYrv5R3Hbt2jUaNmzImjVr3tgN9cyZM7Rt25YtW7aQNm1aAI4dO8bmzZsZPHiwiSKWLyEkJISaNWvi7u5OzZo1TR2OfIKLFy/StWtX7O3tSZ48OZ6eniRLlsw42rF58+amDlHkkySKpNS7/jgbDAbKlCnDtGnTKFq0KDVr1sTDw4OqVauaNF5JeF53/rt27Ur9+vVjlL1eUNbLywtzc3NGjRpFxowZ8fDwMC5YKPKlOTk54ezsTLdu3YwjoyIiIiB6C2ERSXh27tzJkCFD2Lp1q3HhXKJHzHz77bccOXLEuI7UypUrtTZJPDJq1Chu3rzJzJkz3yhzd3fHwsICNzc3xowZw9mzZ+nfv78SF/HQnTt3GD9+PF27diV37txvlO/ZswczMzPtqBfPhYaGUqxYMcaOHUvt2rVjHLexsTFpbCKfQ6JY6Dxnzpw0adIEb2/vGMcjIiKIjIzExsYGa2trfHx8YmybKvK5WFtbM2DAAMaNG8eLFy9ilL1eULZnz56MGDGC/v37M2/ePCWkJFblzJmTr776KkYCytLSUgkpkQSsatWqODo6vpG4CAsLw9zcHEtLSzJkyMDMmTO1nmE84+bmxpkzZzhw4MAbZX379mXbtm24uLiQN29eNm/erIRUHBMZGcmCBQuMXw69zet7mCNHDjJnzvzWOpUrV1ZCKgGwsbEhc+bMb3wxoISUJBSJ5tOGm5sb1atX58CBA1SoUAGi51rnzJmTwoULA1CwYEETRykJWdWqVVm6dCmzZs2iT58+ED1Kas6cObRq1QozMzPq1q2rhQnFJHLlykVAQICpwxCRWDZw4EAaN25MkyZNyJo1KxEREYwZM4b69esbNzQoWbKkqcOUD5Q8eXJ69erFqFGjWLdunfELho0bN5I0aVLGjh1LwYIFSZ8+valDlbewsLBg3759ADg4OLBr1y4mT54co87XX3/NunXryJAhg4milNiUO3duHj16ZOowRL6IRDF977UVK1awaNEipk2bxpgxY7h//z4zZ84kXbp0pg5NEom//vqLxo0bs27dOo4ePcqkSZP49ttv6dev3xsLjorEpsDAQOzt7U0dhoiYwIgRI7h//z6dOnVi2LBhpE2blvHjx5M0aVJThyafICoqChcXF1xcXPj666/x8vIiLCyMwYMHa+fEeGDdunUMGDCAAgUK8PPPP2s2RyKnfpokZIkqKfX6j/Pff/9Nx44dadeunUalSKwbMWIEv/32GwUKFMDT01Mj9ERExKSeP39O9erVsbKyom/fvjRo0MDUIclncuzYMdq3b4+9vT29evXCxcVFO/nGE+vXr+f3338ne/bsDB061NThiIh8MYkqKQVw5coV7O3tNVxZTOb58+ccPHgQZ2dnU4ciIiICwOnTp8mdO7dxyp4kHH/88QdVqlTRvY0Hli9fzqFDh6hbty5Vq1bl2bNn1KxZk4ULF5I3b15Thyci8kUkuqSUiIiIiIhIXHH27FmOHj3Kvn37+P7775k7dy5jxoyhdOnS+Pr6sn37dhYsWMDu3bspUKAAGTNmNHXIIiKfjZJSIiIiIiIiJvDq1SucnJxIly4dy5cvx9LSkj///JORI0eybt06zMzMaNu2LRcvXiRDhgx4e3uTP39+U4ctIvLZKCklIiIiIiISSyIjI3n8+DFp06bF3NyctWvX4uPjw86dO41rfnXt2pUyZcrQunVrIiIiuHPnDl999ZWpQxcR+ezMTR2AiIiIiIhIYrBt2za+++47WrRoQb169Xj8+DH16tUjderUrFu3zlhv4MCBzJs3j9DQUCwtLZWQEpEES0kpERERERGRL+z8+fOMGTOGWbNmMXToUJ49e8bSpUsxMzPD09OTCRMm8OLFCwCyZs3KH3/8gY2NjanDFhH5ojR9T0RERERE5AubOnUqBoOBCxcu8NdffzFgwACqVatmLO/Xrx958+alY8eOJo1TRCQ2KSklIiIiIiLyGYWEhLBkyRJevHhB69atSZEiBStWrGDEiBF0794dV1dXrK2tCQ4O5vDhw1SrVo3AwECSJEmCtbW1qcMXEYk1mr4nIiIiIiLyGfXq1YszZ85w/fp1OnToQEREBM7OzqRKlYqIiAhu3brF0qVLcXZ2Zt++fQDY29srISUiiY5GSomIiIiIiHyiCxcu4OvrS/bs2Tl79ixTp04FoEOHDpQvXx5XV1euXLmCt7c3169f5+uvv6Zt27YULFjQ1KGLiJiMklIiIiIiIiLvKSAggJ07d9K4cWMA7t69y5MnT+jVqxcNGjRg586d5M2bl9GjRwNw/fp1mjdvzoYNG0iVKpWJoxcRiVs0fU9EREREROQ9WVtbExAQYHy8bNkyOnXqxJAhQ3Bzc2PWrFns3buXW7duAZAjRw7q1auHj4+PCaMWEYmblJQSERERERF5T3Z2djg7O9OzZ08MBgOdOnXCzMzMWJ4uXTratm2Lt7e38Vi3bt20q56IyFsoKSUiIiIiIvIeLl26xNatW8mSJQu3b99m/fr12NnZ0atXL6ZPn26s5+rqyuXLlzl58iREJ7IyZcpkwshFROImJaVERERERET+xaNHj/Dz86NDhw6Eh4djZmaGh4cH48eP5+XLlzRs2JCwsDDWr18P0VP8Fi1axNdff23q0EVE4jQlpURERERERP7FmjVr6NChA927d6d27doAlChRghIlSjB79mzMzc3x8PDAx8eHqKgoADJmzBhjWp+IiLxJSSkREREREZH/8ezZM4KCgiB6Op6dnR0WFhYx6vTv359ly5Zx584dSpYsybJlyzA310csEZH3pd+YIiIiIiIi0QIDA+nevTs1atSgVq1aLFq0CGtra9zd3fn111+JjIw01s2YMSN9+vTh6dOnAKRJk8aEkYuIxD9KSomIiIiIiETr378/OXPmZMOGDVSoUIFp06YRGBiIk5MTKVOmZPny5THq//DDDxQqVMhk8YqIxGdKSomIiIiIiACRkZHcuHGDdOnSUbduXSwtLdm8eTP29vYAeHp6MnPmTMLCwkwdqohIgmBmMBgMpg5CREREREQktp04cYLffvsNBwcH2rVrh4ODAxUrViRbtmwMGTKEfPnyAXD48GFy5MhB+vTpefz4sabpiYh8JhopJSIiIiIiicrff//NkSNH6NOnD7ly5eLly5c0adKEoKAgXF1defHiBS9fvuTEiRP06NGDn3/+mQcPHoDWjRIR+aw0UkpERERERBKV4cOHs2HDBkaPHk3lypUBGDJkCPb29vTp04eZM2eydu1abG1tqV+/Ps2aNcPa2trUYYuIJDhKSomIiIiISIK3f/9+Xr16RfXq1Xn69ClOTk6MHTuWSpUqAXD8+HG8vLxYvXq1qUMVEUk0NH1PREREREQSvPXr12NlZQWAg4MD3bp1Y8aMGcZFy0+ePEnatGlNHKWISOJiaeoAREREREREPreXL1/i7e1N3rx5adCgAffu3SNjxozG8ubNm7NixQpq1apF6tSpefLkCTNnzjRpzCIiiY1GSomIiIiISIJja2tLq1atuHz5Mk5OTpw7d47IyEhjuaWlJQMHDsRgMDBy5Eg2b95Mrly5TBqziEhiozWlREREREQkQdi7dy9r1qwhadKkdO3alaxZswJw+/Zt6tevT8qUKcmdOzetW7emXLlyAHTq1IlSpUrRvn17E0cvIpL4aKSUiIiIiIjEe/PmzWP06NFUq1aNZMmS0b59e0JCQgAIDg7G0dGRrVu34uLiwsqVK/nrr78A+Omnnzhw4ICJoxcRSZy0ppSIiIiIiMRr4eHhrFixgtmzZzN//nx27tyJm5sb1tbWANy7d48MGTJgYWFB9erVqV69uvG5OXLkYMGCBSaMXkQk8VJSSkRERERE4rWIiAgCAwNp2rQp9erVY/Pmzdjb2xMeHo6ZmRmPHz/mm2++MXWYIiLyP5SUEhERERGReC1p0qQULFiQHDlyMHDgQAD27duHl5cXnp6euLi4YGFhYeowRUTkf2ihcxERERERiffu3LlDixYtSJ48OREREVhYWODu7k7FihVNHZqIiLyDklIiIiIiIpIghIWFceLECezs7ChcuLCpwxERkf+gpJSIiIiIiIiIiMQ6c1MHICIiIiIiIiIiiY+SUiIiIiIiIiIiEuuUlBIRERERERERkVinpJSIiIiIiIiIiMQ6JaVERERERERERCTWKSklIiIiIiIiIiKxTkkpERERERERERGJdUpKiYiIiIiIiIhIrFNSSkREREREREREYp2SUiIiIiIiIiIiEuuUlBIRERERERERkVinpJSIiIiIiIiIiMS6/we0wY6zKVQAWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Re=3700 integral-quantity benchmark (subcritical turbulent sphere) vs the\n",
    "# Re=4200 run. The integral quantities are nearly Re-flat across this range, so\n",
    "# the comparison carries a ~13% Re offset (see reference/REFERENCES.md).\n",
    "df_int = pd.read_csv(\n",
    "    \"validation/external_aero/01_flow_over_sphere/reference/sphere_integral_Re3700.csv\",\n",
    "    comment=\"#\",\n",
    ")\n",
    "\n",
    "# Run quantities at Re=4200: the total drag (direct IBM force, from force_stats)\n",
    "# and the base pressure from the time-mean surface pressure.\n",
    "hs = sim_turb.output.exports[\"default_series\"].series.bodies[\"sphere\"]\n",
    "df_rho = hs.read_full_data(\"rho\")\n",
    "df_rho = df_rho[df_rho[\"time_step\"] >= STATS_START].drop(columns=\"time_step\")\n",
    "cp_mean_nodes = (df_rho.mean().to_numpy() - RHO_INF) / (1.5 * RHO_INF * U_INF**2)\n",
    "pts = pd.read_csv(hs.points_filename)\n",
    "base_cp_run = float(cp_mean_nodes[pts[\"x\"].to_numpy().argmax()])  # rearmost (base) node\n",
    "run_vals = {\"Cd\": force_stats[4200][\"Cd_mean\"], \"base_Cp\": base_cp_run}\n",
    "\n",
    "# Conventional-notation axis labels for each integral quantity.\n",
    "q_labels = {\"Cd\": \"$C_D$\", \"base_Cp\": \"$C_{p,\\\\mathrm{base}}$\"}\n",
    "\n",
    "\n",
    "def _short_ref(full: str) -> str:\n",
    "    \"\"\"Compact 'First-author et al. (year)' label from a full attribution string.\"\"\"\n",
    "    full = str(full).strip()\n",
    "    year = \"\"\n",
    "    if \"(\" in full and \")\" in full:\n",
    "        year = \" \" + full[full.index(\"(\") : full.index(\")\") + 1]\n",
    "        full = full[: full.index(\"(\")].strip()\n",
    "    surnames = [p.strip().split()[-1] for p in full.replace(\" & \", \",\").split(\",\") if p.strip()]\n",
    "    if len(surnames) == 1:\n",
    "        base = surnames[0]\n",
    "    elif len(surnames) == 2:\n",
    "        base = f\"{surnames[0]} & {surnames[1]}\"\n",
    "    else:\n",
    "        base = f\"{surnames[0]} et al.\"\n",
    "    return base + year\n",
    "\n",
    "\n",
    "# The run only produces Cd and base_Cp from the surface data; recirculation length\n",
    "# and separation angle need the wake field and are omitted here (references only).\n",
    "# Each reference bar is labelled with its SOURCE (whose DNS/LES/experiment it is),\n",
    "# read from the reference CSV's `reference` column - not a bare \"DNS\"/\"LES\".\n",
    "fig_int, ax_int = common.fig_double()\n",
    "for axq, q in zip(ax_int, [\"Cd\", \"base_Cp\"]):\n",
    "    sub = df_int[df_int[\"quantity\"] == q]\n",
    "    labels = [f\"{r.ref_type} - {_short_ref(r.reference)}\" for r in sub.itertuples()]\n",
    "    values = list(sub[\"value\"])\n",
    "    bar_colors = [common.colors.exp] * len(values)\n",
    "    labels.append(\"AeroSim (Re = 4200)\")\n",
    "    values.append(run_vals[q])\n",
    "    bar_colors.append(common.colors.sim)\n",
    "    xq = np.arange(len(values))\n",
    "    bars = axq.bar(xq, values, color=bar_colors)\n",
    "    axq.bar_label(bars, fmt=\"%.3f\", fontsize=8, padding=2)\n",
    "    axq.axhline(0.0, color=common.colors.refline, lw=0.8)\n",
    "    axq.set_xticks(xq)\n",
    "    axq.set_xticklabels(labels, rotation=30, ha=\"right\", fontsize=8)\n",
    "    common.bar_axis(axq)\n",
    "    axq.set_ylabel(q_labels[q])\n",
    "    axq.set_title(f\"{q_labels[q]}  (references at Re = 3700)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show(fig_int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vortex shedding and spectra\n",
    "\n",
    "The lift-force power spectrum exposes the vortex-shedding peak. The low-mode Strouhal number plateaus near $St \\approx 0.19$ at high Reynolds number (Sakamoto & Haniu, 1990). The wake velocity probe gives an independent, velocity-based estimate of the same frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:54:59.082059Z",
     "iopub.status.busy": "2026-07-03T13:54:59.081884Z",
     "iopub.status.idle": "2026-07-03T13:55:00.181330Z",
     "shell.execute_reply": "2026-07-03T13:55:00.181016Z"
    }
   },
   "outputs": [],
   "source": "from nassu.viz import energy_spectrum, read_series\n\n# Low-mode shedding Strouhal reference (digitized Sakamoto & Haniu 1990 Fig. 7).\ndf_st = pd.read_csv(\n    \"validation/external_aero/01_flow_over_sphere/reference/sphere_strouhal.csv\",\n    comment=\"#\",\n)\n\nfig, ax = common.fig_double()\nfor i, (scfg, Re, title) in enumerate(runs):\n    s = force_stats[Re]\n    cF, t, D = s[\"cF\"], s[\"time\"], s[\"D\"]\n\n    # The transverse (lift) force fluctuation carries the vortex-shedding signal.\n    dt = float(np.median(np.diff(t)))\n    f, S = energy_spectrum(cF[:, 2], dt)\n    St = f * D / U_INF\n    ax[i].loglog(St, S, color=common.colors.sim, alpha=0.8, label=r\"$C_L$ (pressure force)\")\n\n    # Independent, velocity-based check: the wake velocity probe, a max-rate\n    # instantaneous series export sampled every finest-level iteration (#1023);\n    # time_step is in level-0 steps.\n    try:\n        probe = scfg.output.exports[\"spectrum\"].series.points[\"point_velocity_wake\"]\n        dfw = read_series(probe, \"ux\", start_step=STATS_START)\n        dtw = float(np.median(np.diff(dfw[\"time_step\"])))\n        ux_wake = dfw.drop(columns=\"time_step\").iloc[:, 0].to_numpy()\n        fw, Sw = energy_spectrum(ux_wake, dtw)\n        ax[i].loglog(fw * D / U_INF, Sw, color=common.colors.exp, alpha=0.5, label=r\"wake $u_x$\")\n    except (KeyError, AttributeError, FileNotFoundError, ValueError):\n        pass\n\n    # Reference low-mode Strouhal at this Re (interpolated off the digitized curve)\n    # and the dominant peak of the lift spectrum.\n    st_ref = float(np.interp(Re, df_st[\"Re\"], df_st[\"St\"]))\n    st_peak = float(St[np.argmax(S)])\n    ax[i].axvline(\n        st_ref, color=common.colors.refline, ls=\"--\", alpha=0.8, label=f\"St ref = {st_ref:.2f}\"\n    )\n    ax[i].axvline(\n        st_peak, color=common.colors.sim, ls=\":\", alpha=0.8, label=f\"St peak = {st_peak:.2f}\"\n    )\n\n    ax[i].set_title(title)\n    ax[i].set_xlabel(r\"$St = f\\,D/U$\")\n    ax[i].set_ylabel(\"PSD\")\n    ax[i].legend(fontsize=\"small\")\n\nplt.tight_layout()\nplt.show(fig)"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flow field\n",
    "\n",
    "Instantaneous velocity magnitude on the lateral plane through the sphere centre (`plane_series.mid_sphere`): wake view for the turbulent runs, framed from the sphere geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:55:00.182172Z",
     "iopub.status.busy": "2026-07-03T13:55:00.181999Z",
     "iopub.status.idle": "2026-07-03T13:55:00.528914Z",
     "shell.execute_reply": "2026-07-03T13:55:00.528595Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3072641/1996413703.py:29: UserWarning: Using static image for notebook display.\n",
      "Install trame for interactive backends: pip install \"pyvista[jupyter]\"\n",
      "  plotter.show()\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1040x320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz.enable_offscreen()\n",
    "\n",
    "labels = {\n",
    "    \"turbulentFlowOverSphere\": \"turbulent\",\n",
    "    \"turbulentFlowOverSphereLES\": \"turbulent LES\",\n",
    "}\n",
    "cfg0 = sim_cfgs[\"turbulentFlowOverSphere\", 0]\n",
    "body, geom = viz.read_body(cfg0, \"sphere\")\n",
    "view = viz.frame_body(geom, \"y\", slice_coord=64.0, downstream=2.0, half_extent=3.0)\n",
    "\n",
    "panels = [\n",
    "    viz.Panel(\n",
    "        label,\n",
    "        viz.PlaneSource.from_cfg(sim_cfgs[name, 0], series=\"plane_series\", plane=\"mid_sphere\"),\n",
    "        view,\n",
    "    )\n",
    "    for name, label in labels.items()\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, 0.08),\n",
    "    bar_title=\"|u|\",\n",
    "    bodies=[body],\n",
    ")\n",
    "plotter.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:55:00.529971Z",
     "iopub.status.busy": "2026-07-03T13:55:00.529879Z",
     "iopub.status.idle": "2026-07-03T13:55:00.633970Z",
     "shell.execute_reply": "2026-07-03T13:55:00.633441Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0a1"
     ]
    },
    {
     "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": [
      "a9e18ca8bb03b24ce4d631e70482ec8cf66e7cef"
     ]
    },
    {
     "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": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T13:55:00.634917Z",
     "iopub.status.busy": "2026-07-03T13:55:00.634818Z",
     "iopub.status.idle": "2026-07-03T13:55:00.720490Z",
     "shell.execute_reply": "2026-07-03T13:55:00.720135Z"
    }
   },
   "outputs": [
    {
     "data": {
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       ".output_html .sr { color: #A45A77 } /* Literal.String.Regex */\n",
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       ".output_html .fm { color: #00F } /* Name.Function.Magic */\n",
       ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
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       ".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\">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\">laminarFlowOverSphere</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/external_aero/01_flow_over_sphere/results/laminar</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\">40000</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">report</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\">1000</span><span class=\"p p-Indicator\">}</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">domain_size</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">480</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\">160</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\">160</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\">sphere</span><span class=\"p\">:</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/basic/sphere.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\">1.6</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">1.6</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">1.6</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\">112</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72</span><span class=\"p p-Indicator\">]</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\">96</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">64</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\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">160</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">96</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">96</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=\"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\">sphere</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\">body_nodes</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># Per-source-triangle skin-friction primitives (issue #879): the friction</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># velocity `friction_u_tau` and `friction_y_plus`, plus the aggregated</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># viscous-traction vector and centroid/normal/area, written as a flat</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># CSV/HDF time series. `nassu.viz.friction` derives the form/friction drag</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># split from these (issue #876). Sampled late in the steady run. The</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># quantities are listed explicitly so the exported IBM wall values (y+,</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># u_tau, traction) are unambiguous.</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">friction</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\">sphere</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">mode</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">triangle</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\">quantities</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\">friction_u_tau</span>\n",
       "<span class=\"w\">            </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">friction_y_plus</span>\n",
       "<span class=\"w\">            </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">traction_x</span>\n",
       "<span class=\"w\">            </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">traction_y</span>\n",
       "<span class=\"w\">            </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">traction_z</span>\n",
       "<span class=\"w\">            </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">area</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_interp</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>\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\">10000</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=\"c1\"># Surface-pressure history over the sphere nodes: the mechanical form</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># (pressure) drag is integrated from the mean surface `rho` (Cp), then</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># added to the viscous `body_nodes.friction` split for the full drag.</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># Named `default_series` so the analysis reads it through the same</span>\n",
       "<span class=\"w\">        </span><span class=\"c1\"># accessor as the turbulent runs; the turbulent child fully overrides it.</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default_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=\"s\">&quot;rho&quot;</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\">1000</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\">bodies</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">sphere</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\">sphere</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">normal_offset</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\">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\">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=\"s\">&quot;rho&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;u&quot;</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\">5000</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\"># Lateral (y-normal) plane through the sphere centre: wake view.</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\"># Children override axis_pos for their own sphere position.</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">mid_sphere</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\">80</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",
       "\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\">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\">0.51</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;1.0&quot;</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">ux</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!unroll</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;0.007854&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;0.024583&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;0.064583&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">uy</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"s\">&quot;0&quot;</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">uz</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"s\">&quot;0&quot;</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">engine</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">CUDA</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">BC</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">periodic_dims</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">false</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">false</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">false</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">BC_map</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</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\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</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\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">E</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">RegularizedNeumannOutlet</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">rho</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\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">E</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\">2</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">W</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">UniformFlow</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">W</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">rho</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\">ux</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!unroll</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0.007854</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0.024583</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0.064583</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">uy</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">uz</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\">order</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\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">NF</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">NB</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">SF</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">SB</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">NF</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</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\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">NB</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</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\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">SF</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</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\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">SB</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</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\">1</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">IBM</span><span class=\"p\">:</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\">1000</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><span class=\"w\"> </span><span class=\"p p-Indicator\">{}</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\">turbulentFlowOverSphere</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\">laminarFlowOverSphere</span>\n",
       "\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/external_aero/01_flow_over_sphere/results/turbulent</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\">100000</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">domain_size</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">640</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\">128</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\">128</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">block_size</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">8</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">bodies</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!not-inherit</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">sphere</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">small_triangles</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"s\">&quot;add&quot;</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/basic/sphere.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\">0.8</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0.8</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0.8</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\">112</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">60</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\">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\">96</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">32</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">32</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\">224</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">96</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">96</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\">104</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">56</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">56</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\">144</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72</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\">3</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=\"nt\">data</span><span class=\"p\">:</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\">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>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default_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=\"s\">&quot;u&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;rho&quot;</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\">20</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\">bodies</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">sphere</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\">sphere</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">normal_offset</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\">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\">plane_series</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\">frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">10000</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\"># Sphere centre for this domain size.</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">mid_sphere</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\">axis_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">64</span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">spectrum</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=\"s\">&quot;rho&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;u&quot;</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\">0</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\">points</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\"># Centreline wake point ~2 diameters downstream of the sphere</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\"># centre (~(116, 64, 64), D~8), inside the lvl-3 refinement box:</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\"># independent velocity-based shedding-frequency (Strouhal) check.</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">point_velocity_wake</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">132</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">64</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\">outputs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">spectrum</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\">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\">0.500285714285714</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><span class=\"w\"> </span><span class=\"kt\">!not-inherit</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">rho</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\">u</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\">0.05</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\">IBM</span><span class=\"p\">:</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\">5000</span>\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=\"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\">false</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">false</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">false</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">BC_map</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</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\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</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\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">E</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">RegularizedNeumannOutlet</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">rho</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\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">E</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\">2</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">W</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">UniformFlow</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">W</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">rho</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\">ux</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.05</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">uy</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">uz</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\">order</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\">turbulentFlowOverSphereLES</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\">turbulentFlowOverSphere</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">models</span><span class=\"p\">:</span>\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\">0.500024</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",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{n+nt}{simulations}\\PY{p}{:}\n",
       "\\PY{+w}{  }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{laminarFlowOverSphere}\n",
       "\\PY{+w}{    }\\PY{n+nt}{save\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{./validation/external\\PYZus{}aero/01\\PYZus{}flow\\PYZus{}over\\PYZus{}sphere/results/laminar}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{40000}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{report}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{1000}\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{domain}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{domain\\PYZus{}size}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{480}\n",
       "\\PY{+w}{        }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{160}\n",
       "\\PY{+w}{        }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{160}\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}{sphere}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/basic/sphere.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}{1.6}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{1.6}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{1.6}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{112}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{72}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{72}\\PY{p+pIndicator}{]}\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}{96}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{64}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{64}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{end}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{160}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{96}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{96}\\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{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}{sphere}\n",
       "\\PY{+w}{          }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{5000}\n",
       "\\PY{+w}{      }\\PY{n+nt}{body\\PYZus{}nodes}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} Per\\PYZhy{}source\\PYZhy{}triangle skin\\PYZhy{}friction primitives (issue \\PYZsh{}879): the friction}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} velocity `friction\\PYZus{}u\\PYZus{}tau` and `friction\\PYZus{}y\\PYZus{}plus`, plus the aggregated}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} viscous\\PYZhy{}traction vector and centroid/normal/area, written as a flat}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} CSV/HDF time series. `nassu.viz.friction` derives the form/friction drag}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} split from these (issue \\PYZsh{}876). Sampled late in the steady run. The}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} quantities are listed explicitly so the exported IBM wall values (y+,}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} u\\PYZus{}tau, traction) are unambiguous.}\n",
       "\\PY{+w}{        }\\PY{n+nt}{friction}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{body\\PYZus{}name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{sphere}\n",
       "\\PY{+w}{          }\\PY{n+nt}{mode}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{triangle}\n",
       "\\PY{+w}{          }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{5000}\n",
       "\\PY{+w}{          }\\PY{n+nt}{quantities}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{friction\\PYZus{}u\\PYZus{}tau}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{friction\\PYZus{}y\\PYZus{}plus}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{traction\\PYZus{}x}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{traction\\PYZus{}y}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{traction\\PYZus{}z}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{area}\n",
       "\\PY{+w}{            }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{rho\\PYZus{}interp}\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}{]}\n",
       "\\PY{+w}{          }\\PY{n+nt}{interval}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{10000}\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{c+c1}{\\PYZsh{} Surface\\PYZhy{}pressure history over the sphere nodes: the mechanical form}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} (pressure) drag is integrated from the mean surface `rho` (Cp), then}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} added to the viscous `body\\PYZus{}nodes.friction` split for the full drag.}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} Named `default\\PYZus{}series` so the analysis reads it through the same}\n",
       "\\PY{+w}{        }\\PY{c+c1}{\\PYZsh{} accessor as the turbulent runs; the turbulent child fully overrides it.}\n",
       "\\PY{+w}{        }\\PY{n+nt}{default\\PYZus{}series}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{rho}\\PY{l+s}{\\PYZdq{}}\\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}{1000}\\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}{bodies}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{n+nt}{sphere}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{n+nt}{body\\PYZus{}name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{sphere}\n",
       "\\PY{+w}{                }\\PY{n+nt}{normal\\PYZus{}offset}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.25}\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}{plane\\PYZus{}series}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{rho}\\PY{l+s}{\\PYZdq{}}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{u}\\PY{l+s}{\\PYZdq{}}\\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}{5000}\\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{} Lateral (y\\PYZhy{}normal) plane through the sphere centre: wake view.}\n",
       "\\PY{+w}{              }\\PY{c+c1}{\\PYZsh{} Children override axis\\PYZus{}pos for their own sphere position.}\n",
       "\\PY{+w}{              }\\PY{n+nt}{mid\\PYZus{}sphere}\\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}{80}\n",
       "\\PY{+w}{                }\\PY{n+nt}{dist}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\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}{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}{0.51}\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}{1.0}\\PY{l+s}{\\PYZdq{}}\n",
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       "\\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",
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       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Neumann}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{N}\n",
       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\n",
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       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\n",
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       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Neumann}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{F}\n",
       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\n",
       "\\PY{+w}{          }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{B}\n",
       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Neumann}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{B}\n",
       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\n",
       "\\PY{+w}{          }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{E}\n",
       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RegularizedNeumannOutlet}\n",
       "\\PY{+w}{            }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.0}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{E}\n",
       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2}\n",
       "\n",
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       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{UniformFlow}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{W}\n",
       "\\PY{+w}{            }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{            }\\PY{n+nt}{ux}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!unroll}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{0.007854}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0.024583}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0.064583}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{            }\\PY{n+nt}{uy}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
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       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2}\n",
       "\n",
       "\\PY{+w}{          }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{NF}\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\\PY{+w}{        }\\PY{n+nt}{sphere}\\PY{p}{:}\n",
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       "\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",
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       "\\PY{+w}{                }\\PY{n+nt}{is\\PYZus{}abs}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\n",
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       "\\PY{+w}{            }\\PY{n+nt}{bodies}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{n+nt}{sphere}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{n+nt}{body\\PYZus{}name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{sphere}\n",
       "\\PY{+w}{                }\\PY{n+nt}{normal\\PYZus{}offset}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.25}\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",
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       "\\PY{+w}{          }\\PY{n+nt}{target}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{planes}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{c+c1}{\\PYZsh{} Sphere centre for this domain size.}\n",
       "\\PY{+w}{              }\\PY{n+nt}{mid\\PYZus{}sphere}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{64}\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\PY{+w}{        }\\PY{n+nt}{spectrum}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{rho}\\PY{l+s}{\\PYZdq{}}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{u}\\PY{l+s}{\\PYZdq{}}\\PY{p+pIndicator}{]}\n",
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       "\\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}{points}\\PY{p}{:}\n",
       "\\PY{+w}{              }\\PY{c+c1}{\\PYZsh{} Centreline wake point \\PYZti{}2 diameters downstream of the sphere}\n",
       "\\PY{+w}{              }\\PY{c+c1}{\\PYZsh{} centre (\\PYZti{}(116, 64, 64), D\\PYZti{}8), inside the lvl\\PYZhy{}3 refinement box:}\n",
       "\\PY{+w}{              }\\PY{c+c1}{\\PYZsh{} independent velocity\\PYZhy{}based shedding\\PYZhy{}frequency (Strouhal) check.}\n",
       "\\PY{+w}{              }\\PY{n+nt}{point\\PYZus{}velocity\\PYZus{}wake}\\PY{p}{:}\n",
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       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{models}\\PY{p}{:}\n",
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       "\\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}{:}\\PY{+w}{ }\\PY{k+kt}{!not\\PYZhy{}inherit}\n",
       "\\PY{+w}{        }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.0}\n",
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       "\\PY{+w}{            }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
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       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RegularizedNeumannOutlet}\n",
       "\\PY{+w}{            }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.0}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{E}\n",
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       "\\PY{+w}{            }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{UniformFlow}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{W}\n",
       "\\PY{+w}{            }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{            }\\PY{n+nt}{ux}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.05}\n",
       "\\PY{+w}{            }\\PY{n+nt}{uy}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{            }\\PY{n+nt}{uz}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n",
       "\\PY{+w}{            }\\PY{n+nt}{order}\\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}{turbulentFlowOverSphereLES}\n",
       "\\PY{+w}{    }\\PY{n+nt}{parent}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{turbulentFlowOverSphere}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{models}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{LBM}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{tau}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.500024}\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",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "simulations:\n",
       "  - name: laminarFlowOverSphere\n",
       "    save_path: ./validation/external_aero/01_flow_over_sphere/results/laminar\n",
       "\n",
       "    n_steps: 40000\n",
       "\n",
       "    report: {frequency: 1000}\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 480\n",
       "        y: 160\n",
       "        z: 160\n",
       "      block_size: 8\n",
       "      bodies:\n",
       "        sphere:\n",
       "          geometry_path: fixture/stl/basic/sphere.stl\n",
       "          transformation:\n",
       "            scale: [1.6, 1.6, 1.6]\n",
       "            translation: [112, 72, 72]\n",
       "      refinement:\n",
       "        static:\n",
       "          default:\n",
       "            volumes_refine:\n",
       "              - start: [96, 64, 64]\n",
       "                end: [160, 96, 96]\n",
       "                lvl: 1\n",
       "                is_abs: true\n",
       "\n",
       "    data:\n",
       "      export_IBM_nodes:\n",
       "        ibm_exp:\n",
       "          body_name: sphere\n",
       "          frequency: 5000\n",
       "      body_nodes:\n",
       "        # Per-source-triangle skin-friction primitives (issue #879): the friction\n",
       "        # velocity `friction_u_tau` and `friction_y_plus`, plus the aggregated\n",
       "        # viscous-traction vector and centroid/normal/area, written as a flat\n",
       "        # CSV/HDF time series. `nassu.viz.friction` derives the form/friction drag\n",
       "        # split from these (issue #876). Sampled late in the steady run. The\n",
       "        # quantities are listed explicitly so the exported IBM wall values (y+,\n",
       "        # u_tau, traction) are unambiguous.\n",
       "        friction:\n",
       "          body_name: sphere\n",
       "          mode: triangle\n",
       "          frequency: 5000\n",
       "          quantities:\n",
       "            - friction_u_tau\n",
       "            - friction_y_plus\n",
       "            - traction_x\n",
       "            - traction_y\n",
       "            - traction_z\n",
       "            - area\n",
       "            - rho_interp\n",
       "      exports:\n",
       "        default:\n",
       "          macrs: [rho, u]\n",
       "          interval:\n",
       "            frequency: 10000\n",
       "            lvl: 0\n",
       "          target:\n",
       "            volume: {}\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "        # Surface-pressure history over the sphere nodes: the mechanical form\n",
       "        # (pressure) drag is integrated from the mean surface `rho` (Cp), then\n",
       "        # added to the viscous `body_nodes.friction` split for the full drag.\n",
       "        # Named `default_series` so the analysis reads it through the same\n",
       "        # accessor as the turbulent runs; the turbulent child fully overrides it.\n",
       "        default_series:\n",
       "          macrs: [\"rho\"]\n",
       "          interval: {frequency: 1000, lvl: 0}\n",
       "          target:\n",
       "            bodies:\n",
       "              sphere:\n",
       "                body_name: sphere\n",
       "                normal_offset: 0.25\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "        plane_series:\n",
       "          macrs: [\"rho\", \"u\"]\n",
       "          interval: {frequency: 5000, lvl: 0}\n",
       "          target:\n",
       "            planes:\n",
       "              # Lateral (y-normal) plane through the sphere centre: wake view.\n",
       "              # Children override axis_pos for their own sphere position.\n",
       "              mid_sphere:\n",
       "                axis: y\n",
       "                axis_pos: 80\n",
       "                dist: 1\n",
       "\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "    models:\n",
       "      precision:\n",
       "        default: single\n",
       "\n",
       "      LBM:\n",
       "        tau: 0.51\n",
       "        vel_set: D3Q27\n",
       "        coll_oper: RRBGK\n",
       "      initialization:\n",
       "        equations:\n",
       "          rho: \"1.0\"\n",
       "          ux: !unroll [\"0.007854\", \"0.024583\", \"0.064583\"]\n",
       "          uy: \"0\"\n",
       "          uz: \"0\"\n",
       "      engine:\n",
       "        name: CUDA\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [false, false, false]\n",
       "        BC_map:\n",
       "          - pos: N\n",
       "            BC: Neumann\n",
       "            wall_normal: N\n",
       "            order: 0\n",
       "\n",
       "          - pos: S\n",
       "            BC: Neumann\n",
       "            wall_normal: S\n",
       "            order: 0\n",
       "\n",
       "          - pos: F\n",
       "            BC: Neumann\n",
       "            wall_normal: F\n",
       "            order: 1\n",
       "\n",
       "          - pos: B\n",
       "            BC: Neumann\n",
       "            wall_normal: B\n",
       "            order: 1\n",
       "\n",
       "          - pos: E\n",
       "            BC: RegularizedNeumannOutlet\n",
       "            rho: 1.0\n",
       "            wall_normal: E\n",
       "            order: 2\n",
       "\n",
       "          - pos: W\n",
       "            BC: UniformFlow\n",
       "            wall_normal: W\n",
       "            rho: 1\n",
       "            ux: !unroll [0.007854, 0.024583, 0.064583]\n",
       "            uy: 0\n",
       "            uz: 0\n",
       "            order: 2\n",
       "\n",
       "          - pos: NF\n",
       "            BC: Neumann\n",
       "            wall_normal: N\n",
       "            order: 0\n",
       "\n",
       "          - pos: NB\n",
       "            BC: Neumann\n",
       "            wall_normal: N\n",
       "            order: 0\n",
       "\n",
       "          - pos: SF\n",
       "            BC: Neumann\n",
       "            wall_normal: S\n",
       "            order: 0\n",
       "\n",
       "          - pos: SB\n",
       "            BC: Neumann\n",
       "            wall_normal: S\n",
       "            order: 0\n",
       "\n",
       "          - pos: NF\n",
       "            BC: Neumann\n",
       "            wall_normal: F\n",
       "            order: 1\n",
       "\n",
       "          - pos: NB\n",
       "            BC: Neumann\n",
       "            wall_normal: B\n",
       "            order: 1\n",
       "\n",
       "          - pos: SF\n",
       "            BC: Neumann\n",
       "            wall_normal: F\n",
       "            order: 1\n",
       "\n",
       "          - pos: SB\n",
       "            BC: Neumann\n",
       "            wall_normal: B\n",
       "            order: 1\n",
       "\n",
       "      IBM:\n",
       "        forces_accomodate_time: 1000\n",
       "        body_cfgs:\n",
       "          default: {}\n",
       "\n",
       "      multiblock:\n",
       "        overlap_F2C: 2\n",
       "\n",
       "  - name: turbulentFlowOverSphere\n",
       "    parent: laminarFlowOverSphere\n",
       "\n",
       "    save_path: ./validation/external_aero/01_flow_over_sphere/results/turbulent\n",
       "\n",
       "    n_steps: 100000\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 640\n",
       "        y: 128\n",
       "        z: 128\n",
       "      block_size: 8\n",
       "\n",
       "      bodies: !not-inherit\n",
       "        sphere:\n",
       "          small_triangles: \"add\"\n",
       "          geometry_path: fixture/stl/basic/sphere.stl\n",
       "          transformation:\n",
       "            scale: [0.8, 0.8, 0.8]\n",
       "            translation: [112, 60, 60]\n",
       "\n",
       "      refinement:\n",
       "        static:\n",
       "          default:\n",
       "            volumes_refine:\n",
       "              - start: [96, 32, 32]\n",
       "                end: [224, 96, 96]\n",
       "                lvl: 1\n",
       "                is_abs: true\n",
       "              - start: [104, 56, 56]\n",
       "                end: [144, 72, 72]\n",
       "                lvl: 3\n",
       "                is_abs: true\n",
       "\n",
       "    data:\n",
       "      exports:\n",
       "        default:\n",
       "          interval: {frequency: 25000}\n",
       "        default_series:\n",
       "          macrs: [\"u\", \"rho\"]\n",
       "          interval: {frequency: 20, lvl: 0}\n",
       "          target:\n",
       "            bodies:\n",
       "              sphere:\n",
       "                body_name: sphere\n",
       "                normal_offset: 0.25\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "        plane_series:\n",
       "          interval: {frequency: 10000}\n",
       "          target:\n",
       "            planes:\n",
       "              # Sphere centre for this domain size.\n",
       "              mid_sphere: {axis_pos: 64}\n",
       "        spectrum:\n",
       "          macrs: [\"rho\", \"u\"]\n",
       "          interval:\n",
       "            frequency: 0\n",
       "            lvl: 0\n",
       "          target:\n",
       "            points:\n",
       "              # Centreline wake point ~2 diameters downstream of the sphere\n",
       "              # centre (~(116, 64, 64), D~8), inside the lvl-3 refinement box:\n",
       "              # independent velocity-based shedding-frequency (Strouhal) check.\n",
       "              point_velocity_wake:\n",
       "                pos: [132, 64, 64]\n",
       "          outputs:\n",
       "            spectrum: true\n",
       "\n",
       "    models:\n",
       "      LBM:\n",
       "        tau: 0.500285714285714\n",
       "        vel_set: D3Q27\n",
       "        coll_oper: RRBGK\n",
       "      initialization: !not-inherit\n",
       "        rho: 1.0\n",
       "        u:\n",
       "          x: 0.05\n",
       "          y: 0\n",
       "          z: 0\n",
       "      IBM:\n",
       "        forces_accomodate_time: 5000\n",
       "      multiblock:\n",
       "        overlap_F2C: 2\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [false, false, false]\n",
       "        BC_map:\n",
       "          - pos: N\n",
       "            BC: Neumann\n",
       "            wall_normal: N\n",
       "            order: 0\n",
       "\n",
       "          - pos: S\n",
       "            BC: Neumann\n",
       "            wall_normal: S\n",
       "            order: 0\n",
       "\n",
       "          - pos: F\n",
       "            BC: Neumann\n",
       "            wall_normal: F\n",
       "            order: 1\n",
       "\n",
       "          - pos: B\n",
       "            BC: Neumann\n",
       "            wall_normal: B\n",
       "            order: 1\n",
       "\n",
       "          - pos: E\n",
       "            BC: RegularizedNeumannOutlet\n",
       "            rho: 1.0\n",
       "            wall_normal: E\n",
       "            order: 2\n",
       "\n",
       "          - pos: W\n",
       "            BC: UniformFlow\n",
       "            wall_normal: W\n",
       "            rho: 1\n",
       "            ux: 0.05\n",
       "            uy: 0\n",
       "            uz: 0\n",
       "            order: 2\n",
       "\n",
       "  - name: turbulentFlowOverSphereLES\n",
       "    parent: turbulentFlowOverSphere\n",
       "\n",
       "    models:\n",
       "      LBM:\n",
       "        tau: 0.500024\n",
       "\n",
       "      LES:\n",
       "        model: Smagorinsky\n",
       "        sgs_cte: 0.17"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Code\n",
    "\n",
    "Code(filename=filename)"
   ]
  }
 ],
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   "display_name": "nassu (3.12.13.final.0)",
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   "name": "python",
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