{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e151bcbd-93f2-41ec-8c2d-3666cec26af9",
   "metadata": {},
   "source": [
    "**This notebook**: simple chroma pulling over v1.2.0 to try and restore a bit of the vibrancy of the older palette (e.g., <v1.1.1).\n",
    "\n",
    "I think with v1.2.0 we tried to make the mid-lightness as symmetric as possible, resisting the urge to lean into the naturally skewed chroma boundary. So I can fill into that as much as I want, it's just that we'll be increasing the difference in the character between light and dark modes under fixed contrast schemes. It's already not *perfectly balanced*, so we've never strictly relied on that quality to begin with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd89e94a-d2f6-4c91-9c27-bdae00378834",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "from functools import cache\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import interp1d, CubicSpline, BSpline\n",
    "\n",
    "from pprint import pprint\n",
    "from coloraide import Color\n",
    "\n",
    "@cache\n",
    "def max_C_Lh(L, h, space='srgb', eps=1e-6, tol=1e-9):\n",
    "    '''\n",
    "    Binary search for max chroma at fixed lightness and hue\n",
    "\n",
    "    Parameters:\n",
    "        L: lightness percentage\n",
    "    '''\n",
    "    def C_in_gamut(C):\n",
    "        return Color('oklch', [L/100, C, h]).convert(space).in_gamut(tolerance=tol)\n",
    "\n",
    "    lo, hi = 0.0, 0.1\n",
    "    while C_in_gamut(hi): hi *= 2\n",
    "    while hi - lo > eps:\n",
    "        m = (lo + hi) / 2\n",
    "        lo, hi = (m, hi) if C_in_gamut(m) else (lo, m)\n",
    "\n",
    "    Cmax = lo\n",
    "    c_oklch = Color('oklch', [L, Cmax, h])\n",
    "    c_srgb  = c_oklch.convert('srgb')\n",
    "    \n",
    "    return Cmax\n",
    "\n",
    "def quad_bezier_rational(P0, P1, P2, w, t):\n",
    "    t = np.asarray(t)[:, None]\n",
    "    num = (1-t)**2*P0 + 2*w*(1-t)*t*P1 + t**2*P2\n",
    "    den = (1-t)**2 + 2*w*(1-t)*t + t**2\n",
    "    \n",
    "    return num/den\n",
    "    \n",
    "def bezier_y_at_x(P0, P1, P2, w, x_query, n=400):\n",
    "    t = np.linspace(0, 1, n)\n",
    "    B = quad_bezier_rational(P0, P1, P2, w, t)\n",
    "    x_vals, y_vals = B[:, 0], B[:, 1]\n",
    "    \n",
    "    return np.interp(x_query, x_vals, y_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c7d2d1d9-dad9-4f8f-8417-c22d38f089f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SET LIGHTNESS CONTROL POINTS\n",
    "# L_points = [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 98]\n",
    "L_points = list(range(10, 98+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0617a01-cb05-4950-9bc3-f4fd0de63f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIXED MONOBIOME PARAMETERS\n",
    "L_resolution = 5  # step size along lightness dim\n",
    "L_space = np.arange(0, 100+L_resolution, L_resolution)\n",
    "\n",
    "monotone_C_map = {\n",
    "    \"alpine\": 0,\n",
    "    \"badlands\": 0.011,\n",
    "    \"chaparral\": 0.011,\n",
    "    \"savanna\": 0.011,\n",
    "    \"grassland\": 0.011,\n",
    "    \"tundra\": 0.011,\n",
    "}\n",
    "\n",
    "h_weights = {\n",
    "    \"red\": 3.0,\n",
    "    \"orange\": 3.8,  # 3.6\n",
    "    \"yellow\": 3.8,  # 4.0\n",
    "    \"green\": 3.8,\n",
    "    \"blue\": 3.4,  # 3.8\n",
    "}\n",
    "h_L_offsets = {\n",
    "    \"red\": 0,  # -1,\n",
    "    \"orange\": -5.5,  # -3,\n",
    "    \"yellow\": -13.5,  # -8\n",
    "    \"green\": -11,  # -8\n",
    "    \"blue\": 10,  # 14\n",
    "}\n",
    "h_C_offsets = {\n",
    "    \"red\": 0,  # 0\n",
    "    \"orange\": -0.01,  # -0.02\n",
    "    \"yellow\": -0.052,  # -0.08\n",
    "    \"green\": -0.088,  # -0.105\n",
    "    \"blue\": 0.0,  # 0.01\n",
    "}\n",
    "\n",
    "monotone_h_map = {\n",
    "    \"alpine\": 0,\n",
    "    \"badlands\": 29,\n",
    "    \"chaparral\": 62.5,\n",
    "    \"savanna\": 104,\n",
    "    \"grassland\": 148,\n",
    "    \"tundra\": 262,\n",
    "}\n",
    "accent_h_map = {\n",
    "    \"red\": 29,\n",
    "    \"orange\": 62.5,\n",
    "    \"yellow\": 104,\n",
    "    \"green\": 148,\n",
    "    \"blue\": 262,\n",
    "}\n",
    "h_map = {**monotone_h_map, **accent_h_map}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46b619a1-9118-4576-9100-c1265ccd15f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute C max values over each point in L space\n",
    "h_Lspace_Cmax = {\n",
    "    h_str: [max_C_Lh(_L, _h) for _L in L_space]\n",
    "    for h_str, _h in h_map.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0da9d0b5-fa60-4dd7-aa55-0b3c81e503fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute *unbounded* chroma curves for all hues\n",
    "h_L_points_C = {}\n",
    "h_ctrl_L_C = {}\n",
    "\n",
    "for h_str, _h in monotone_h_map.items():\n",
    "    h_L_points_C[h_str] = np.array([monotone_C_map[h_str]]*len(L_points))\n",
    "    \n",
    "for h_str, _h in accent_h_map.items():\n",
    "    Lspace_Cmax = h_Lspace_Cmax[h_str]\n",
    "    \n",
    "    # get L value of max chroma; will be a bezier control\n",
    "    L_Cmax_idx = np.argmax(Lspace_Cmax)\n",
    "    L_Cmax = L_space[L_Cmax_idx]\n",
    "\n",
    "    # offset control point by any preset x-shift\n",
    "    L_Cmax += h_L_offsets[h_str]\n",
    "\n",
    "    # and get max C at the L offset\n",
    "    Cmax = max_C_Lh(L_Cmax, _h)\n",
    "\n",
    "    # set 3 control points; shift by any global linear offest\n",
    "    C_offset = h_C_offsets.get(h_str, 0)\n",
    "    \n",
    "    p_0 = np.array([0, 0])\n",
    "    p_Cmax = np.array([L_Cmax, Cmax + C_offset])\n",
    "    p_100 = np.array([100, 0])\n",
    "    \n",
    "    B_L_points = bezier_y_at_x(p_0, p_Cmax, p_100, h_weights.get(h_str, 1), L_points)\n",
    "    h_L_points_C[h_str] = B_L_points\n",
    "    h_ctrl_L_C[h_str] = np.vstack([p_0, p_Cmax, p_100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56fd3826-c2b7-480e-ac42-a72e96c045f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute full set of final chroma curves; limits every point to in-gamut max\n",
    "h_LC_color_map = {}\n",
    "h_L_points_Cstar = {}\n",
    "\n",
    "for h_str, L_points_C in h_L_points_C.items():\n",
    "    _h = h_map[h_str]\n",
    "\n",
    "    h_L_points_Cstar[h_str] = [\n",
    "        max(0, min(_C, max_C_Lh(_L, _h)))\n",
    "        for _L, _C in zip(L_points, L_points_C)\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c257cdb-7cb4-4aed-b1a4-bb560f4848b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# put together objects for output formats\n",
    "toml_lines = []\n",
    "oklch_hL_dict = {}\n",
    "\n",
    "for h_str, L_points_Cstar in h_L_points_Cstar.items():\n",
    "    _h = h_map[h_str]\n",
    "    toml_lines.append(f\"[{h_str}]\")\n",
    "    oklch_hL_dict[h_str] = {}\n",
    "    \n",
    "    for _L, _C in zip(L_points, L_points_Cstar):\n",
    "        oklch = Color('oklch', [_L/100, _C, _h])\n",
    "        srgb = oklch.convert('srgb')\n",
    "        \n",
    "        hex_str = srgb.to_string(hex=True)\n",
    "        \n",
    "        l, c, h = oklch.convert('oklch').coords()\n",
    "        # oklch_str = oklch.to_string(percent=False)\n",
    "        oklch_str = f\"oklch({l*100:.1f}% {c:.4f} {h:.1f})\"\n",
    "        \n",
    "        toml_lines.append(f'l{_L} = \"{hex_str}\"')\n",
    "        oklch_hL_dict[h_str][_L] = oklch_str\n",
    "        \n",
    "    toml_lines.append(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "281de03a-1ba4-419a-a991-1a9be2a66667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_h_map = {}\n",
    "fig, axes = plt.subplots(\n",
    "    len(monotone_h_map),\n",
    "    1,\n",
    "    sharex=True,\n",
    "    sharey=True,\n",
    "    figsize=(4, 8)\n",
    ")\n",
    "\n",
    "for i, h_str in enumerate(h_L_points_Cstar):\n",
    "    _h = h_map[h_str]\n",
    "    L_points_Cstar = h_L_points_Cstar[h_str]\n",
    "    L_space_Cmax = h_Lspace_Cmax[h_str]\n",
    "    \n",
    "    if _h not in ax_h_map:\n",
    "        ax_h_map[_h] = axes[i]\n",
    "    ax = ax_h_map[_h]\n",
    "\n",
    "    # plot Cmax and Cstar\n",
    "    ax.plot(L_space, L_space_Cmax, c=\"b\", alpha=0.2)\n",
    "    ax.plot(L_points, L_points_Cstar, alpha=0.7)\n",
    "    \n",
    "    ax.title.set_text(f\"Hue [${_h}$]\")\n",
    "    \n",
    "axes[-1].set_xlabel(\"Lightness (%)\")\n",
    "axes[-1].set_xticks([L_points[0], L_points[-1]])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=0.9)\n",
    "\n",
    "plt.suptitle(\"$C^*$ curves for hue groups\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b52e351a-fa3c-4d60-877c-75a938b52289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v111_L_space = list(range(15, 95+1, 5))\n",
    "v111_hC_points = {\n",
    "    \"red\": [0.058, 0.074, 0.092, 0.11, 0.128, 0.147, 0.167, 0.183, 0.193, 0.193, 0.182, 0.164, 0.14, 0.112, 0.081, 0.052, 0.024],\n",
    "    \"orange\": [0.030, 0.038, 0.046, 0.058, 0.07, 0.084, 0.1, 0.114, 0.125, 0.134, 0.138, 0.136, 0.128, 0.112, 0.092, 0.064, 0.032],\n",
    "    \"yellow\": [0.02, 0.024, 0.03, 0.036, 0.044, 0.05, 0.06, 0.068, 0.076, 0.082, 0.088, 0.088, 0.086, 0.082, 0.072, 0.058, 0.04],\n",
    "    \"green\": [0.0401, 0.048, 0.056, 0.064, 0.072, 0.08, 0.09, 0.098, 0.104, 0.108, 0.11, 0.108, 0.102, 0.094, 0.084, 0.072, 0.05],\n",
    "    \"blue\": [0.06, 0.072, 0.084, 0.096, 0.106, 0.116, 0.124, 0.13, 0.132, 0.128, 0.122, 0.11, 0.096, 0.08, 0.064, 0.044, 0.023],\n",
    "}\n",
    "\n",
    "ax_h_map = {}\n",
    "fig, axes = plt.subplots(\n",
    "    len(monotone_h_map),\n",
    "    1,\n",
    "    sharex=True,\n",
    "    sharey=True,\n",
    "    figsize=(5, 10)\n",
    ")\n",
    "\n",
    "for i, h_str in enumerate(h_L_points_Cstar):\n",
    "    _h = h_map[h_str]\n",
    "    L_points_Cstar = h_L_points_Cstar[h_str]\n",
    "    L_space_Cmax = h_Lspace_Cmax[h_str]\n",
    "    \n",
    "    if _h not in ax_h_map:\n",
    "        ax_h_map[_h] = axes[i]\n",
    "    ax = ax_h_map[_h]\n",
    "\n",
    "    # plot Cmax and Cstar\n",
    "    ax.plot(L_space, L_space_Cmax, c=\"b\", alpha=0.2, label='Cmax')\n",
    "    ax.plot(L_points, L_points_Cstar, alpha=0.7, label='C*')\n",
    "\n",
    "    if h_str in v111_hC_points:\n",
    "        ax.scatter(v111_L_space, v111_hC_points[h_str], s=4, label='Cv111')\n",
    "        \n",
    "    if h_str in h_ctrl_L_C:\n",
    "        cpts = h_ctrl_L_C[h_str]\n",
    "        cpt_x, cpt_y = cpts[:, 0], cpts[:, 1]\n",
    "        h_w = h_weights.get(h_str, 1)\n",
    "        \n",
    "        P0, P1, P2 = cpts[0], cpts[1], cpts[2]\n",
    "        d0 = 2 * h_w * (P1 - P0)\n",
    "        d2 = 2 * h_w * (P2 - P1)\n",
    "\n",
    "        handle_scale = 0.25\n",
    "        H0 = P0 + handle_scale * d0\n",
    "        H2 = P2 - handle_scale * d2\n",
    "    \n",
    "        # ax.plot([P0[0], H0[0]], [P0[1], H0[1]], color='tab:blue', lw=1)\n",
    "        # ax.plot([P2[0], H2[0]], [P2[1], H2[1]], color='tab:orange', lw=1)\n",
    "        \n",
    "        ax.plot(cpt_x, cpt_y, '--', color='gray', lw=1, label='Bezier polygon')\n",
    "        ax.scatter(cpt_x, cpt_y, color='red', zorder=5, s=2, label='Control points')\n",
    "        \n",
    "    ax.title.set_text(f\"Hue [${_h}$]\")\n",
    "    \n",
    "axes[-1].set_ylabel(\"Chroma (C)\")\n",
    "axes[-1].set_xlabel(\"Lightness (%)\")\n",
    "axes[-1].set_xticks([L_points[0], 50, 65, L_points[-1]])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=0.9)\n",
    "\n",
    "handles, labels = axes[-1].get_legend_handles_labels()\n",
    "unique = dict(zip(labels, handles))\n",
    "fig.legend(unique.values(), unique.keys(), loc='lower center', bbox_to_anchor=(0.5, -0.06), ncol=3)\n",
    "\n",
    "plt.suptitle(\"$C^*$ curves for hue groups + v111 5% lightness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e2a1a5a-7724-459a-9a1e-69ab0f421c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArEAAAJJCAYAAAC9G29ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAvPZJREFUeJzs3XmYXHWVP/53LXeppZfsnYYsJAETCCQQQgBZNQrIMIMGBURZZAARUIjjMHH8AupPwwjGOIoiPrKooBEQGFAZMCiOEAgmRgwQZghkIZ3urvXu+72/P+LnWtVL0t3p7qpbdV7P0w+kupZPdVffOnXu+ZwTC4IgACGEEEIIIRESr/UCCCGEEEIIGS4KYgkhhBBCSORQEEsIIYQQQiKHglhCCCGEEBI5FMQSQgghhJDIoSCWEEIIIYREDgWxhBBCCCEkciiIJYQQQgghkUNBLCGEEEIIiRwKYgkhhBBCSORQEEsIiYz7778fv//972u9DEIIIXUgFgRBUOtFEELI/jz00ENIJBIwDAOzZs3C1q1bceSRR+L9739/rZdGCCGkRigTSwipexdeeCG6u7uxdu1afPGLX0R7ezsFsAfwjW98A/Pnz4fv+7Veypi5++67MXPmTFiWVeulEEJqgIJYQkhNvPHGG7j22msxb948pNNptLS04L3vfS++//3vw3XdftePxWLhfxOJxHgvN1JkWcZ//Md/4Oabb0Y8PrzDvKqquPXWW3H22Wdj4sSJiMViuP/++4d8+9deew0f/ehHMWfOHKTTaUyePBmnnXYannzyySHfh2VZuPnmm9HZ2YlUKoVly5bh2Wef7Xe9yy+/HLZt4wc/+MGQ75sQ0jgoiCWEjLsf/OAHWLx4MR577DF8+MMfxre//W3ceuutSCaT+MxnPoO1a9dWXX/dunWYOnUqbrzxRnzta19DPp/H+vXra7P4CLj33nvhui4uvvjiYd82n8/jK1/5Ct544w0sWrRo2LffuXMnFEXBZZddhm9/+9v4f//v/wEA/vEf/xH33HPPkO7j8ssvx5o1a3DJJZfg29/+NhKJBD70oQ/hj3/8Y9X1RFHEZZddhjVr1oAq4whpPlQTSwgZVw899BA+8YlP4Nxzz8XPf/5zZDKZft+fPHkyPvjBD/a77f3334/Zs2fjjDPOGKfVHpimaf2eQ60tWrQIxxxzDH7yk58M+7aWZaFUKqGjowN/+tOfsHTpUtx33324/PLLR7wez/OwZMkSmKaJbdu27fe6GzduxLJly3DHHXfgX/7lXwAApmli4cKFmDp1Kl588cWq62/atAnHH3881q9fj/e9730jXiMhJHooE0sIGTfd3d247rrrcOSRR+KRRx4ZMPj7+Mc/PmAAC+zL0A03gN2zZw+uvPJKdHZ2QhAEHHbYYbj22mth23bV/c6ePbvfbW+77bawjKHy36+//jo+/vGPY8KECTjllFPwyCOPIBaL4fnnn+93Hz/4wQ8Qi8WwdevWqjV96lOfwrRp0yAIAo466ijce++9VbdTFAU33ngjZs+eDUEQMHXqVHzgAx/A5s2b9/t833nnHbz66qtYvnx5eNlw1icIAjo6Ovb7GMOVSCQwY8YMlMvlA173kUceQSKRwNVXXx1eJooirrzySmzYsAG7d++uuv6SJUswceJEPPHEE6O6ZkJI/UvWegGEkObx7W9/G+VyGT/72c8gCMKYP15XVxdOOOEElMtlXH311Zg/fz727NmDRx55BLqug+f5Ed3vRz/6URx++OH4+te/jiAIcO655yKbzeIXv/gFTj/99Krrrlu3DkcddRQWLlwIAOjp6cGJJ56IWCyG66+/HlOmTMFvfvMbXHnllZBlGTfeeCMA4NOf/jQeeeQRXH/99TjyyCNRKBTwxz/+EW+88QaOO+64QdfGMpWV1xnO+kaLpmkwDAOSJOG//uu/8Jvf/AYXXnjhAW/35z//GUcccQRaW1urLj/hhBMAAFu2bMGMGTOqvnfcccfhhRdeGL3FE0KiISCEkHHS2dkZzJgxI/A8b1we79JLLw3i8Xjwyiuv9Pue7/vh/1922WXBrFmz+l3n1ltvDSoPk+zfF198cb/rXnzxxcHUqVMD13XDy/bu3RvE4/HgK1/5SnjZlVdeGUyfPj3I5/NVt7/ooouCtra2QNf1IAiCoK2tLbjuuuuG/mT/5ktf+lIAIFAUZUTrq/TKK68EAIL77rtv2Ou45pprAgABgCAejwcXXHBBUCwWD3i7o446Knjf+97X7/LXXnstABDcfffd/b539dVXB6lUathrJIREG5UTEELGxfbt29HV1YVzzz132DvmR8L3fTz++OM477zzcPzxx/f7fmWZwHB9+tOf7nfZhRdeiN7e3qphDI888gh83w8zkEEQ4NFHH8V5552HIAiQz+fDr7POOguSJIXlAu3t7Xj55ZfR1dU1rLUVCgUkk0lks9lhr2803XjjjXj22WfxwAMP4JxzzoHneVUlHIMxDGPALL0oiuH3+5owYQIMw4Cu6we/cEJIZFAQSwgZF6yWcc6cOePyeLlcDrIsj/ppcgA47LDD+l129tlno62tDevWrQsvW7duHRYvXowjjjgiXFO5XMY999yDKVOmVH1dccUVAIDe3l4A+/q8bt26FTNmzMAJJ5yA2267DW+//faI1zyU9Y2m+fPnY/ny5bj00kvx1FNPQVXVMHjfn1QqNWDfV9M0w+/3xe7zYD6YEEKih4JYQsi4YL1fHcep8Ur6Gyz48TxvwMsHCqQEQcD555+Pxx57DK7rYs+ePXjhhReqspxs8MAnPvEJPPvsswN+vfe97wUAfOxjH8Pbb7+N73znO+js7MQdd9yBo446Cr/5zW/2+1wmTZoE13WhKMqw1zeWLrjgArzyyiv43//93/1eb/r06di7d2+/y9llnZ2d/b5XKpWQTqcH/L0QQhoXbewihIyLefPmAUDVLv2xNGXKFLS2tg7p8SZMmDDgzvmdO3cO6zEvvPBCPPDAA1i/fj3eeOMNBEFQFSROmTIFLS0t8DyvqnvAYKZPn47PfOYz+MxnPoPe3l4cd9xx+NrXvoZzzjln0NvMnz8fwL4uBcccc8yw1jeWWBmAJEn7vd7ixYvxu9/9DrIsV23uevnll8Pv9/XOO+9gwYIFo7dYQkgkUCaWEDIuZs+ejaVLl+Lhhx8esE2Uoij45je/OWqPF4/Hcf755+PJJ5/En/70p37frzytPXfuXEiShFdffTW8bO/evXjssceG9ZjLly/HxIkTsW7dOqxbtw4nnHBCVelBIpHAihUr8Oijjw4YXOdyOQD7MsB9g72pU6eis7PzgCNWTzrpJAAY8DkfaH3Dpes6tm3bhnw+H17GyiEqOY6DH//4x0ilUjjyyCP3e/sLLrgAnudVDUawLAv33Xcfli1b1q8zAQBs3rwZJ5988oifByEkmigTSwgZNz/84Q9x+umn473vfS8+9alP4eijj4bruti8eTN++ctf4pRTTsHnP//5UXu8r3/963jmmWdw+umn4+qrr8aCBQuwd+9ePPzww/jjH/+I9vZ2AMBFF12Em2++GR/+8Ifx2c9+Frqu4/vf/z6OOOKIA/ZlrcRxHD7ykY/g5z//OTRNw5133tnvOrfffjt+97vfYdmyZbjqqqtw5JFHolgsYvPmzfjtb3+LYrEIRVFw6KGH4oILLsCiRYuQzWbx29/+Fq+88soBA/05c+Zg4cKF+O1vf4tPfepTw14fAHz3u99FuVwON5U9+eSTePfddwEAN9xwA9ra2gDsG0xw5pln4tZbb8Vtt90GALjmmmsgyzJOO+00HHLIIeju7saDDz6Ibdu24Zvf/GbVhrOBbr9s2TJ89KMfxapVq9Db24t58+bhgQcewI4dO/CjH/2o31o3bdqEYrGIf/qnf9rvz4UQ0oBq2BmBENKE3nrrreCKK64IDjnkkIDjuGDatGnBiSeeGNx2223BW2+9NeqPt3PnzuDSSy8NpkyZEgiCEMyZMye47rrrAsuyqq73zDPPBAsXLgx4ng/e8573BD/96U8HbbGVy+UGfbxnn302ABDEYrFg9+7dA16np6cnuO6664IZM2YEHMcFHR0dwfvf//7gnnvuCYIgCCzLCr7whS8EixYtClpaWoJMJhMsWrQo+N73vjek57xmzZogm82G7bqGu75Zs2aF7bH6fr3zzjvh9X73u98FAIJbb701vOxnP/tZsHz58mDatGlBMpkMJkyYECxfvjx44okn+j3OQLcPgiAwDCP4l3/5l6CjoyMQBCFYunRp8PTTTw+41ptvvjmYOXNmVcs0QkhzoLGzhBDSYCRJwpw5c/CNb3wDV155Za2XM2Ysy8Ls2bPxb//2b/jc5z5X6+UQQsYZ1cQSQkiDaWtrw7/+67/ijjvuCDsiNKL77rsPHMcN2LeXENL4KBNLCCGEEEIihzKxhBBCCCEkciiIJYQQQgghkUNBLCGEEEIIiRwKYgkhhBBCSORQEEsIIYQQQiKHglhCCCGEEBI5FMQSQgghhJDIoSCWEEIIIYREDgWxhBBCCCEkciiIJYQQQgghkUNBLCGEEEIIiRwKYgkhhBBCSORQEEsIIYQQQiKHglhCCCGEEBI5FMQSQgghhJDIoSCWEEIIIYREDgWxhBBCCCEkciiIJYQQQgghkUNBLCGEEEIIiRwKYgkhhBBCSORQEEsIIYQQQiKHglhCCCGEEBI5FMQSQgghhJDIoSCWEEIIIYREDgWxhBBCCCEkciiIJYQQQgghkUNBLCGEEEIIiRwKYgkhhBBCSORQEEsIIYQQQiKHglhCCCGEEBI5FMQSQgghhJDIoSCWEEIIIYREDgWxhBBCCCEkciiIJYQQQgghkUNBLCGEEEIIiRwKYgkhhBBCSORQEEsIIYQQQiKHglhCCCGEEBI5FMQSQgghhJDISdZ6AePF9310dXWhpaUFsVis1sshhBBCCCF9BEEARVHQ2dmJeHz/udamCWK7urowY8aMWi+DEEIIIYQcwO7du3HooYfu9zpNE8S2tLQA2PdDaW1trfFqCCGEEEJIX7IsY8aMGWHctj9NE8SyEoLW1lYKYgkhhBBC6thQSj9pYxchhBBCCIkcCmIJIYQQQkjkUBBLCCGEEEIih4JYQgghhBASORTEEkIIIYSQyKEglhBCCCGERA4FsYQQQgghJHIoiCWEEEIIIZFDQSwhhBBCCIkcCmIJIYQQQkjkUBBLCCGEEEIih4JYQgghhBASORTEEkIIIYSQyKEglhBCCCGERA4FsYQQQgghJHIoiCWEEEIIIZFDQSwhhBBCCImcZK0XQAghpDaCIIDv+/A8D57nwfd9BEEw6PUTiQTi8TgSiQQSiQRisdg4rpYQQqpREEsIIQ0uCAI4jhN+WZYF27bhui583w+/9hfABkGAeDxe9ZVMJsFxHARBAM/z4DgOHMdRcEsIGRcUxBJCSIPxPC8MVE3ThGmacF0XjuMAQJhNZf/lOC4MTPcnCIIwY+v7PizLgmEY8DwPAMBxHJLJJNLpNARBCL8oqCWEjAUKYgkhpAE4jgPTNGEYBjRNg23bCIIAsVgMHMeB53mkUqmDCihjsRiSyYHfNoIggOu6cF0XxWIRQRAgkUhAEAS0tLQglUpBFEUKaAkho4aCWEIIiSjbtmEYBlRVhWmasG0bsVgMPM8jk8kcMLM6mliwzHEcUqkUgL9nhHt6ehCPxyGKIrLZLNLpNAW0hJCDRkEsIYREiOd5YeCqqiocx0EikQDP82htba2rwDCRSCCdTgP4e0Db29uLeDyOTCaDtrY2pNNpJBKJGq+UEBJFFMQSQkidC4IAlmVB0zTIsgzTNBGLxSCKYhgk1ru+Aa2u61AUBalUCm1tbchms+A4rsarJIRECQWxhBBSp4IggK7rkCQJqqrC8zzwPI+WlpZxLRUYbYlEAtlsFr7vwzRNdHd3h5nk1tZWCIJQ6yUSQiKAglhCCKkzvu9D0zRIkgRN0xAEAdLp9KCbqqIqHo8jnU4jCALYto18Pg9JktDe3o62tjbKzBJC9quxjoiEEBJhnudB0zSUSiUYhhEGeY1eMxqLxcJ2XJZlIZfLQZZlTJgwAa2trQ3//AkhI0NBLCGE1Jjv+1BVFcViEYZhIJlMIpvNRrpkYKTY4ARWZsCC2Wb9eRBCBkdBLCGE1EgQBGHmVdM0JBKJyNe7joZYLIZUKgVBEGAYBrq6upDNZjFp0qSwfRchhFAQSwgh4ywIAhiGgXK5DEVREIvFKNM4ANaKi9UIm6aJiRMnor29nX5WhBAKYgkhZDzZto1isQhZlhEEAVKpVMNt2Bpt8XgcLS0t4eAEXdcpK0sIoSCWEELGg+/7kCQJxWIRtm0jnU7T7vthEgQBHMeFWdkJEyagvb2dNn4R0qQoiCWEkDGm6zoKhQJUVYUgCGhra6v1kiKrMivb29sLwzAoK0tIk6IglhBCxojjOCiVSiiXywiCgDZtjaLKrKxlWZgyZQpaWlrqauwuIWRsURBLCCGjLAgCKIqCQqEA0zSpdGCMsKysYRjYu3cvTNPEpEmTqLyAkCYxopTAXXfdhdmzZ0MURSxbtgwbN24c9LqvvfYaVqxYgdmzZyMWi2Ht2rX9rsO+1/fruuuuC69zxhln9Pv+pz/96ZEsnxBCxozjOOjp6UFXVxc8z0NraysFsGMslUpBFEUUCgXs3bsXlmXVekmEkHEw7CB23bp1WLlyJW699VZs3rwZixYtwllnnYXe3t4Br6/rOubMmYPbb78dHR0dA17nlVdewd69e8OvZ599FgDw0Y9+tOp6V111VdX1vvGNbwx3+YQQMiZY9vXdd99FqVRCOp1GOp2O1OltRVFQLpdrvYwR4TgOLS0tUBQFe/bsgaqqtV4SIWSMDTuIXbNmDa666ipcccUVOPLII3H33XcjnU7j3nvvHfD6S5cuxR133IGLLroIgiAMeJ0pU6ago6Mj/Hrqqacwd+5cnH766VXXS6fTVddrbW0d7vIJIWTUOY6D3t5edHV1wXVdtLa2Rq5t1htvvIFzzz0Xv/jFLwDsC8qjJh6Po7W1FZ7noaurC4VCIZLPgxAyNMMKYm3bxqZNm7B8+fK/30E8juXLl2PDhg2jsiDbtvHTn/4Un/rUp/plMB588EFMnjwZCxcuxKpVq6Dr+qD3Y1kWZFmu+iKEkNGmqir27NmDYrGIVCqFTCYTmexrEARwHAcA0NnZifb2diiKAgCReQ59xWIxZDIZ8DyP3t5e5HI5eJ5X62URQsbAsFIF+Xwenudh2rRpVZdPmzYN27ZtG5UFPf744yiXy7j88surLv/4xz+OWbNmobOzE6+++ipuvvlmvPnmm/jlL3854P2sXr0aX/7yl0dlTYQQ0pfv+ygWiygUCojFYmhtba3bwM/3fQDo1xkhFouF9bptbW1ob2/H3r17YRhG5FtW8TyPeDyOQqEA13UxderUyGXHCSH7V3d/0T/60Y9wzjnnoLOzs+ryq6++Ovz/o48+GtOnT8f73/9+bN++HXPnzu13P6tWrcLKlSvDf8uyjBkzZozdwgkhTcOyLOTzeciyjFQqBZ7na72kkOd5iMViVQHrYG29du/ejZ/+9Kd44YUX8OEPfxiCIKBcLqNYLOKQQw4ZryWPmWQyiWw2C0mS4Ps+pk6dWle/K0LIwRlWEDt58mQkEgn09PRUXd7T0zPopq3h2LlzJ377298Oml2ttGzZMgDAW2+9NWAQKwjCoDW4hBAyEkEQQFVV5HI5WJZVl31f+7aX6u3txauvvgpVVXHaaadh4sSJYZ3od77zHWzevBkXXXQRent78fLLL2PatGkoFAoNEcQC+34ebMOX67qYNm1a5LPMhJB9hhXE8jyPJUuWYP369Tj//PMB7DtNtX79elx//fUHvZj77rsPU6dOxbnnnnvA627ZsgUAMH369IN+XEIIORDP81AoFFAqlZBIJOqufMBxHCiKgj/84Q9Yv349Lr/8crz11lv42te+htbWVmQyGTz88MO45pprcNppp+HZZ5/F7373O3zxi1/Ehz/8YQD7Ntl+85vfRHd3N4455hgEQVBXz3Gk2IYvRVHQ1dWFadOmIZvN1npZhJCDNOxygpUrV+Kyyy7D8ccfjxNOOAFr166Fpmm44oorAACXXnopDjnkEKxevRrAvo1ar7/+evj/e/bswZYtW5DNZjFv3rzwfn3fx3333YfLLrusX93S9u3b8dBDD+FDH/oQJk2ahFdffRU33XQTTjvtNBxzzDEjfvKEEDIUlmUhl8tBUZS6HFzw+uuvY9WqVTj88MNRLBYxf/58PPPMM/jVr36Fu+++GyeffDIcx8HXv/513H777TjttNMgyzIcx6lKGpxxxhn4yU9+gl27dtXw2YwNVresaRr27t2LadOmUYcbQiJu2EHshRdeiFwuh1tuuQXd3d1YvHgxnn766XCz165du6pOr3V1deHYY48N/33nnXfizjvvxOmnn47f//734eW//e1vsWvXLnzqU5/q95g8z+O3v/1tGDDPmDEDK1aswJe+9KXhLp8QQoZFVVX09vbCtu26LB8A9p2R2r17N0qlEu655x7Mnz8fq1evxsknn4yTTz4ZL7zwAt566y289tprePXVV7F161a0tbVB0zQEQQDP85BIJMJ9A3v37gUQ3Q4F+5PJZKDrOrq7uwGAAllCIiwWNEkTPVmW0dbWBkmS6KBFCDmgIAhQKpWQz+cRi8XqenCB53n44Ac/iLlz5+Kee+6BYRj47Gc/i+eeey4sfZg9ezYWLFiA448/Hu973/tQKBRwyimn4IknnsCSJUvC+zruuONwwgkn4M4772zoU+6GYYTddtra2mq9HELI3wwnXqu77gSEEFJrrusin8+jVCpBFMW63ySaSCRw6KGHoq2tDaZpIpVKIR6PY8mSJVi9evWAm19bWlqwZMkS/Md//Ae++MUvYvHixVizZg1M00R3dzf27t2Lww8/vAbPZnykUqnwuQZBgPb29loviRAyTPV3XowQQmrINE3s3bsXpVIJmUym7gNYZtGiRSiXyyiVSgD2ZVT37NmDrq6u8Dq+7+PHP/4xXnrpJQDArbfeCkEQ8OlPfxqLFi3Cjh07cOWVV+K4447r1+WgEYmiCI7j0NPTg1KpRNO9CIkYysQSQsjfRKH+dTALFy7Eiy++iEKhgOnTp+OTn/wktm7dis997nP4yEc+gkwmg9dffx1dXV1hN5njjjsOa9aswcsvv4wJEybguOOOa7r2U6IoAtjXiiwIAkyYMKFuy0YIIdUoiCWENL0gCFAul5HL5cJd7FEzb948lMtl7N69GwsXLkQ6ncbq1avxxz/+EQ8++CBs28Zxxx2Hq666qqqry5QpU/AP//APNVx57YmiiFgshlwuBwAUyBISERTEEkKamu/7yOfzKBaL4Hk+zMxFzdSpUzFp0qSqMoBsNouzzz4bZ599dg1XFg2sbCSXyyGRSNBmL0IigIJYQkjTcl0XuVwO5XK5Lvu/Dkc2m8W6detqvYxIEwQBQRCgp6cnshl5QppJdAq+CCFkFFmWhb1796JcLiObzUY6gCWjRxRFxONx9Pb2QlXVWi+HELIfFMQSQpqOruvo6uqCpmloaWlpip34ZOjS6XSYkdV1vdbLIYQMgoJYQkhTURQFe/fuheM4ketAQMZPJpOB53no7u6GYRi1Xg4hZAB09CaENAU2gWvv3r0IggDZbJZ2oJP9ymazsG0bPT09sCyr1sshhPRBQSwhpOEFQYBisYje3l4kk0mk0+laL4lEREtLSzjZy7btWi+HEFKBglhCSEPzfR+5XA69vb0QBCGyLbRIbcRiMbS0tEDXdeRyOXieV+slEUL+hlpsEUIalud5yOVy4QjZZJIOeWT4WCArSRISiQSmTp1KtdSE1AE6ohNCGpLjOOjt7YUsy8hms9SBgByUeDyObDaLUqmEZDKJSZMmUU01ITVGHyUJIQ3Htm10d3dDlmVqoUVGTTKZRCqVQj6fhyRJtV4OIU2PMrGEkIZiWRa6u7uh6zq10CKjjuf5sM46mUwim83WekmENC06uhNCGoZpmti7dy8Mw0BraysFsGRMsM2BPT091EOWkBqiIzwhpCGwKVyWZaGlpYXqFcmYymQycF0Xvb291HqLkBqhIJYQEnmapoVTuGiIARkv2WyWWm8RUkMUxBJCIk1RFHR3d8PzPMrAknHFWm/JsoxCoYAgCGq9JEKaCgWxhJDIkmUZ3d3d4RhZQsZbPB5HOp1GsViELMu1Xg4hTYWCWEJIJMmyjJ6eHsRiMRojS2qK4zjwPI9cLgdd12u9HEKaBgWxhJDIkSQJ3d3dYRaMkFoTRRG+79NGL0LGEQWxhJBIkSQJPT09SCQSSKVStV4OIaFMJgPDMGijFyHjhIJYQkhklMtldHd3h5OTCKkntNGLkPFFQSwhpO4FQYBSqYSenh5wHBc2myek3tBGL0LGDwWxhJC6Vy6X0dvbSwEsiYTKjV400YuQsUNBLCGkrpVKJQpgSeSIogjP89Db2wvXdWu9HEIaEgWxhJC6JUkSBbAksthEr3w+T/WxhIwBCmIJIXWJtdGiAJZEVSwWQyaTQblcRrlcrvVyCGk4FMQSQuoOa6NFASyJumQyCZ7nUSgUaBACIaOMglhCSF1hk7gSiQQFsKQhsPrYXC5H9bGEjCIKYgkhdUOWZXR3d9MgA9JwqD6WkNFHQSwhpC4oioKenh7E43EKYEnDofpYQkYfBbGEkJpTVRW9vb2IxWJIp9O1Xg4hY4LqYwkZXRTEEkJqStd19PT0wPd9CmBJw2P1sYVCgepjCTlIFMQSQmrGMAx0d3fD8zxkMplaL4eQcZHJZKCqKorFItXHEnIQKIglhNSEaZro7u6G4zjIZrO1Xg4h4yYejyOdTqNUKkFV1Vovh5DIoiCWEDLuLMtCd3c3bNumAJY0JY7jkEgkkMvlYNt2rZdDSCRREEsIGVe2baO7uxumaSKbzSIWi9V6SYTURCqVgm3byOfz8H2/1sshJHIoiCWEjBvHcdDT0wNd19HS0kIBLGlqrO2WJEnUdouQEaAglhAyLlzXRW9vL1RVpQCWkL9JJBIQBAHFYhGGYdR6OYRECgWxhJAx53keent7IcsyWlpaEI/ToYcQRhRFuK6LfD5PbbcIGQZ6JyGEjCnf95HL5SBJErLZLAWwhAwgm81CURRqu0XIMNC7CSFkzARBgHw+j1KphEwmg0QiUeslEVKX4vE4MpkMSqUSNE2r9XIIiQQKYgkhYyIIAhQKBRQKBWQyGSSTyVoviZC6xnEcYrEYlRUQMkQjCmLvuusuzJ49G6IoYtmyZdi4ceOg133ttdewYsUKzJ49G7FYDGvXru13ndtuuw2xWKzqa/78+VXXMU0T1113HSZNmoRsNosVK1agp6dnJMsnhIyxIAhQKpWQz+eRSqUogCVkiDKZDAzDQKFQoLICQg5g2EHsunXrsHLlStx6663YvHkzFi1ahLPOOgu9vb0DXl/XdcyZMwe33347Ojo6Br3fo446Cnv37g2//vjHP1Z9/6abbsKTTz6Jhx9+GM8//zy6urrwkY98ZLjLJ4SMA1mWkcvlIAgCeJ6v9XIIiYxYLIZ0Oo1yuUzTvAg5gGEHsWvWrMFVV12FK664AkceeSTuvvtupNNp3HvvvQNef+nSpbjjjjtw0UUXQRCEQe83mUyio6Mj/Jo8eXL4PUmS8KMf/Qhr1qzB+973PixZsgT33XcfXnzxRbz00kvDfQqEkDGkqip6e3uRTCb3+zdPCBkYx3GIx+PI5/NwHKfWyyGkbg0riLVtG5s2bcLy5cv/fgfxOJYvX44NGzYc1EL+7//+D52dnZgzZw4uueQS7Nq1K/zepk2b4DhO1ePOnz8fM2fOHPRxLcuCLMtVX4SQsaXreljmk0qlarwaQqIrnU7DNE0qKyBkP4YVxObzeXieh2nTplVdPm3aNHR3d494EcuWLcP999+Pp59+Gt///vfxzjvv4NRTT4WiKACA7u5u8DyP9vb2IT/u6tWr0dbWFn7NmDFjxOsjhByYaZro6emB67rIZDK1Xg4hkcameZXL5fC9kBBSrS66E5xzzjn46Ec/imOOOQZnnXUWfv3rX6NcLuMXv/jFiO9z1apVkCQp/Nq9e/corpgQUsm2bfT09MC2bWSz2Vovh5CGkEwmkUwmkc/nYdt2rZdDSN0ZVhA7efJkJBKJfl0Benp69rtpa7ja29txxBFH4K233gIAdHR0wLbtfrOl9/e4giCgtbW16osQMvrYOFld15HNZmmcLCGjKJVKwbIsKisgZADDCmJ5nseSJUuwfv368DLf97F+/XqcdNJJo7YoVVWxfft2TJ8+HQCwZMkScBxX9bhvvvkmdu3aNaqPSwgZHjZOVlEUtLS0UABLyChjZQWSJFFZASF9DLt548qVK3HZZZfh+OOPxwknnIC1a9dC0zRcccUVAIBLL70UhxxyCFavXg1g32nG119/Pfz/PXv2YMuWLchms5g3bx4A4F/+5V9w3nnnYdasWejq6sKtt96KRCKBiy++GADQ1taGK6+8EitXrsTEiRPR2tqKG264ASeddBJOPPHEUflBEEKGx/d95PN5GidLyBirLCsQRZHa1hHyN8MOYi+88ELkcjnccsst6O7uxuLFi/H000+Hm7127dpV9WbW1dWFY489Nvz3nXfeiTvvvBOnn346fv/73wMA3n33XVx88cUoFAqYMmUKTjnlFLz00kuYMmVKeLtvfetbiMfjWLFiBSzLwllnnYXvfe97I33ehJCDEAQBisUiisUijZMlZBykUinIsoxisYhp06bRWQ9CAMSCJimykWUZbW1tkCSJ6mMJOUilUgk9PT2UFSJkHLmuC8Mw0NnZiZaWllovh5AxMZx4jc7/EUKGRVEU5HI58DxPASwh4yiZTCIej6NQKNAQBEJAQSwhZBh0XUdvby/i8ThEUaz1cghpOul0GoZhoFQqUbcC0vQoiCWEDAkbZuB5HtLpdK2XQ0hTisViSKfTKJVK0DSt1sshpKYoiCWEHJDjOOjp6YFlWTSNi5Aa4zgOsVgMhUIBruvWejmE1AwFsYSQ/WK9YHVdp16whNSJTCYDXdf7DQEipJlQEEsIGZTv+8jlcpAkiQJYQupILBZDKpVCsViEruu1Xg4hNTHsPrGEkObAesGWSiUaZkBGLAgC+L7f76vvdZhYLIZ4PB7+l33FYjH6ENUHz/PhSFpBEKhfM2k6FMQSQgYkSRLy+TxSqRSSSTpUkP3zfR+u68J1XXieB8dxYNt2GLSyYDYIAgRBEAakLICtDFDZ9/sGsxzHgef5cIIVaznVzDKZDBRFgSRJmDhxYq2XQ8i4oncmQkg/qqpSL1gyqCAI4LpuGKhalhUGr57nhVnTykxqZUA61MeoDHo9z4Nt21BVFUEQIJFIIJFIIJlMQhRFCIIAjuOaLhsZj8chCAJKpRLS6TS1viNNhYJYQkgVwzDQ09MDAPSGSAD0D1oNwwiDVgBIJBKIx+PgeX7UgshYLLbf+2IBs2VZMAwDwL5d+4IghJPk2C7+RieKYjiSdvr06U3xnAkBKIglhFSwbRs9PT1wHIfGMze5IAhg2zZs24amaXAcpypoTSaTEAShZgETy8RWrtdxHGiaBkVRkEgkwPM8stksRFFs+JKYTCYDWZaRzWbpb5c0jcb+qyaEDJnruujt7YVhGPQm2KR83w/LA3RdDwPXRCIRZjnrNcsXi8Wqyl9Y+UEul0MymUQ6nUY6nYYgCA1ZR8s+WBQKBaRSKXAcV+slETLmKIglhMD3feTzeSiKQq20mpBt2zBNE5qmhZuxksnkqJYHjLdEIoFUKhWWQiiKAkVRIAgCMpkM0ul0w2VnU6kUZFlGqVTClClT6O+YNLzG+gsmhAxbZSutTCbTkFkq0h+rJ9V1PaxxZZukGuk1EIvFwHEcOI4LM82FQiE89Z7JZBoma1k5kjaTydB0PdLwKIglpMnJshyegmy0zBTpz7Zt6LoeZl1jsVi4GarRxeNxiKIY1s+WSqXw7EOjBLMcx8GyLBSLRYiiGNlMOiFDQe9YhDQxTdPQ29sb9t8kjcn3fViWBU3ToOs6fN8Hx3HIZDJNecq5sn7Wtu2GC2bT6TQURUG5XMakSZNqvRxCxgwFsYQ0KdM00dPTgyAImiIL14w8z4NhGFBVFaZpAgAEQaCMe4W+wayqqmhtbY30lDrWO7ZcLiOTydDfN2lYdCQjpAk5joPe3l44joNsNlvr5ZBR5rouNE2DqqqwbTvc5BTVoGw8sGCWjXHVdR2tra1IpVKRzFaz3rGlUgkdHR2RfA6EHAgFsYQ0Gc/zkMvloGkaWltb6c2tgfQNXpu5ZGCk2OQv0zTR29uLlpYWtLS0RLLcJp1OQ5IkZLNZtLS01Ho5hIw6CmIJaSJBEKBQKECSJGql1UBYk39VVeE4Dniep+D1IMTjcaTT6bA1l67raGtri1yJQTKZRCKRoI2bpGHRK5qQJlIqlVAsFpFOpyP1ZkwG5rouVFWl4HWMJJNJJJPJsC2XaZpob2+PVFY2nU5DlmWUy2VMnjy51sshZFRREEtIk1BVFfl8PjxdSqLL87xwvKpt2xS8jjGe55FMJqHrOmzbRnt7e2R+3rFYDKIoolwuhyN4CWkUlIohpAmwTgSsJyiJJt/3oaoqenp6UCgUEAQBMpkMeJ6PREAVZfF4PBwekM/nUSwW4bpujVc1NIIgwHVdFItFBEFQ6+UQMmooE0tIg3McBz09PXBdlzZ3RJTv+zAMA7Isw7IsJBKJyGQCGw1rUcZ+F+3t7ZHoYJDJZMIpZa2trbVeDiGjgjKxhDQw1olA13VqpRVRpmkin88jl8vBcRyk02mIolj3QVMjYx8iXNdFLpeDJEnwfb/Wy9qvRCKBZDKJQqEQmQwyIQdCmVhCGhR1Iog2x3GgKApUVUUQBNTntc7EYjGkUqlwfK3jOJgwYUJddwBIpVJh79gpU6bUejmEHLT6/WsjhByUcrlMnQgiiNW9yrIMx3EgimJdB0bNjuM4JBIJqKoK13UxYcKEut08xQJvtskrlUrVekmEHBR6ZyOkAamqilwuB57nqRNBRARBAF3Xw01bwL46Rgpg6x/b9GVZFnK5XJg9r0c8z8P3fdrkRRoCHR0JaTBs0hBrrUPqn23bUBQFiqKEARGVf0RLLBYLA9l8Pg/XddHa2lqXZ0HS6XT4eqNNXiTKKIglpIGwjSa2bdObUwRUlg64rgtRFJFIJGq9LHIQBEFAPB6v6zpZtsmLlRvV2/oIGar6+4hICBkR3/fDU5nUiaD+sYx5ZekABbCNgeM4pNNpqKqKYrEIx3FqvaR+UqkUdF2HJEm1XgohI0YfvwhpAEEQoFgshhs26vEUJtnHdV3IsgxVVeH7Pm28a1DxeBzpdBqapsHzPEyaNKmuxtWyTV6lUgmZTIZKj0gk0ZGTkAagKAoKhQJSqRRl8+oU27jV29sLSZLAcRwymQwFsA2s74Yv0zRrvaQqbJJXqVSiTV4kkujoSUjE6bqOXC6HZDJZV5ke8nds5Gdvby9c16WuA00kFoshnU6H9eq6rtd6SVXS6TRkWYamabVeCiHDRkEsIRFm23YYGFHPx/oTBAFUVUVPTw9kWYYgCJEYUUpGFwtkASCfz9dVC65kMolYLIZCoQDP82q9HEKGhYJYQiKKjZQ1TZM2ctUhx3FQKBSQz+fh+z5lXwlEUUQ8Hkc+n4eiKHUTyKbTadrkRSKJjqiERBAbKSvLMo2UrTNBEEDTNEiSFE7cimqdsu/7VTW7QRDQa+0gCYKAWCyGYrEIAHXx9xuPxyEIQrjJSxCEmq6HkKGiTCwhEVQul1EoFGhjUJ1hta8s+5pOp2sewDqOgw0bNmDnzp37vd5AWUH22ioUCti5c2fNg61GwSbpFYtFyLJcFxlZURRh2zbK5XJdrIeQoaBMLCERo2ka8vk8BEGg09N1gnUeKJfLsG27Jl0iLMsaMINmGAZuuOEG3HTTTZg1a9agt4/FYv0yr3fffTfWrVuHIAgwdepUnHPOOfjYxz6GTCYzJs+hmbBNmKVSCQDQ2tpa8w8J6XQ6bNNHv2MSBZTCISRCLMtCb28vgiCgvo51grUoyuVy8DyvJkMLrr/+eqxZswZBEMD3fbiuG2bTWltbMW/evLC8gTEMo+o+PvnJT+KHP/whfN8HADzwwAN48cUXccstt+CZZ57BddddhyeffBKPPvro+D2xBscysqVSqS4yshzHAdgXWLPXASH1jIJYQiKCbeSyLIuyJHXCMAzkcjlIklTTzgOdnZ2QZRmmaSIej4c7ztlu82nTpmH37t1QVRUA8LGPfQzf+c53ACAMVrLZLLZv3w7P8+B5HrZs2YJzzz0XZ555JnRdx8yZMyEIAn7961+jXC6P+3NsVDzPg+f5uglkM5kMVFUNXyuE1DM6F0lIBARBEO5oroeNIM3O933IshwGHZlMpqa/k0WLFuGpp57CW2+9hb179+KZZ55BZ2cnPvzhD+Owww7D3LlzsXnzZsiyjAkTJmDu3LnYvXs3AITrXrx4Mf77v/8bsVgMO3bsQC6Xw/333497770XiqKgtbUV06dPxwc+8AEqYxlllRlQoLalBexDULFYRDqdpt81qWv06iQkAkqlUrhzmDZy1ZZlWZAkCZqmQRCEMACppcMOOwyO4+Dxxx9HPp/HpEmTsHnzZrz88sv41re+hWOPPRbPPPMMSqUSZs2ahQULFmDdunUwTTMsS1mwYAEeeOABSJKEtrY2+L6POXPm4J//+Z/R0dGByZMn18VzbVSVgWwsFkNra2vN1pJKpSDLMiRJwqRJk2q2DkIOhN4NCalzqqrSRq46EAQBFEVBb28vdF1HOp2um6Du0EMPxY4dO/Dyyy/j2muvxS233ILbbrsNkyZNwpo1a3D00UfDMAzk83kA+wJWVgbBzJ07F4ZhoKurC5MmTcKMGTNgWRaOPfZYTJ8+HRzHwbZt/Pd//3e/eloyOjiOC2tka3k6PxaLQRRFlMtlWJZVs3UQciAUxBJSx9hGrlgsRr0ba8h13XBwQSwWq7uMeCaTQVtbG+bOnYv58+cDAObNm4dzzjkHL7zwAlpbW8HzPHp6egDsy9y6rouurq7wPhKJBDiOw65duxCLxXD55Zdj+/bt+OxnP4sXXngBf/jDH/CDH/wAP/vZz9Dd3V2T59kMeJ5HIpFAsVis6YhaQRBg2zZKpVLN63QJGQyldQipU2zWum3baGlpqfVympZhGCiVSrAsqyats4YikUjgkEMOQXt7e1WJwKxZs2BZFjRNQ2dnJ/bs2QNd1zF58mR0dHTg17/+Nd7znvcgnU7jJz/5CRzHwa5du+A4DhYsWIA777wTTz/9NP6//+//g+u6OOqoo3DhhRdi2rRpNX7GjU0QBJimiUKhgFgsVrOR0plMBpIkoaWlhTaTkro0olTCXXfdhdmzZ0MURSxbtgwbN24c9LqvvfYaVqxYgdmzZyMWi2Ht2rX9rrN69WosXboULS0tmDp1Ks4//3y8+eabVdc544wzEIvFqr4+/elPj2T5hNQ9NpFLURRks1nayFUDvu+jXC4jl8vBcZyatM4ajqOOOgrvvvtuVfZOURRMmzYNqqpi7ty56OnpgaIoAICLLroIL730Ej7/+c/j2muvxYQJE3DiiSdWDUVYsmQJvvCFL+BXv/oVnn32WaxduxbnnHMO0un0uD+/ZiOKYngcME2zJmtg5UvUcovUq2EHsevWrcPKlStx6623YvPmzVi0aBHOOuss9Pb2Dnh9XdcxZ84c3H777ejo6BjwOs8//zyuu+46vPTSS3j22WfhOA4++MEPQtO0qutdddVV2Lt3b/j1jW98Y7jLJyQSaCNXbTmOg3w+j1KphGQyiXQ6XfcfJBYuXIjXXnsNTzzxBGzbBgA8+uij6OjowPTp0zF58mS88cYb4Q74Sy65BKtWrcKhhx6KE044Aeeddx6+853v4Pbbb6+q9eV5nl6DNZJKpeB5HgqFQs1qUzOZDBRFCT/8EFJPhl1OsGbNGlx11VW44oorAOyb6PKrX/0K9957L/7t3/6t3/WXLl2KpUuXAsCA3weAp59+uurf999/P6ZOnYpNmzbhtNNOCy9Pp9ODBsKENApVVVEoFGgjVw0EQRCWD9i2jXQ6HZkAbtasWeA4Dk8//TS2b9+ON954A6Zp4vOf/zwA4NRTT0V7ezs6OzsB7CtBOPnkk3HyySfXctnkAFKpFAzDQLFYrEmHiHg8Ho7IzWQydEwidWVYr0bbtrFp0yasWrUqvCwej2P58uXYsGHDqC2K7ZidOHFi1eUPPvggfvrTn6KjowPnnXce/t//+3+DntayLKvqk6ssy6O2PkLGimVZyOVyAEAbucYZ6/0qSRLi8XjNe78O14QJE3DMMcfg3HPPhed5WLx4MZYuXYpZs2YhCAIsWLAACxYsqPUyyTCxmlhN01AqlTBp0qRxL2tJpVKQJIlabpG6M6wgNp/Pw/O8fkX906ZNw7Zt20ZlQb7v48Ybb8R73/teLFy4MLz84x//OGbNmoXOzk68+uqruPnmm/Hmm2/il7/85YD3s3r1anz5y18elTURMh4qJ3LRRq7xZds2yuVyXfV+Ha5MJoO77rprwO9FKRgn/bGOGJqmIR6PY+LEieN6hoAF0uVyGdlslj5gk7pRd+cFrrvuOmzduhV//OMfqy6/+uqrw/8/+uijMX36dLz//e/H9u3bMXfu3H73s2rVKqxcuTL8tyzLmDFjxtgtnJCDwCZyybJc02k9zUjX9UiWD5DmwgJJRVEQj8cxYcKEcT1OCIIASZJQLpcxdepUOkaRujCsIHby5MlIJBJhr0Gmp6dnVGpVr7/+ejz11FP4wx/+gEMPPXS/1122bBkA4K233howiBUEgT4tksgol8solUrIZrMURI2TyvIBlumiN2ZSzxKJBERRhCRJSCQSaGtrG9fHT6fTYcst6lBB6sGw3i15nseSJUuwfv368DLf97F+/XqcdNJJI15EEAS4/vrr8dhjj+G5557DYYcddsDbbNmyBQAwffr0ET8uIfVA0zTk83nwPE+bJsZJZfcBjuOQSqUogCWRkEwmwfN8WP4ynjiOQxAENACB1I1hv2OuXLkSl112GY4//niccMIJWLt2LTRNC7sVXHrppTjkkEOwevVqAPtqzV5//fXw//fs2YMtW7Ygm81i3rx5APaVEDz00EN44okn0NLSEk6DaWtrQyqVwvbt2/HQQw/hQx/6ECZNmoRXX30VN910E0477TQcc8wxo/KDIKQWbNtGLpdDEARhg3oytnRdD8dpUvkAiSKe5+H7PorFIuLx+LgOQ0in01AUBaqqUu0+qblYMIKPU9/97ndxxx13oLu7G4sXL8Z//ud/hqf3zzjjDMyePRv3338/AGDHjh0DZlZPP/10/P73v9+3iEEyIPfddx8uv/xy7N69G5/4xCewdetWaJqGGTNm4MMf/jC+9KUvobW1dUhrlmUZbW1tkCRpyLchZCx5nofu7m4oioKWlhbKBI4x3/ehKErY/UQURfqZk0jTdR3JZBJTpkwBz/Pj9riqqkIQBBx66KF1PQCERNNw4rURBbFRREEsqSdsI1c+n0dLSwtlA8eY67qQJAmKooDjuHF9wydkrARBAF3XIYoiJk+ePG7lSOwDYUdHByZMmDAuj0max3DiNXrnJKQGFEVBoVCg09njwLKssPODKIoUwJKGEYvFkE6nYRgGJEkat9Gw8XgcPM+jVCrBcZxxeUxCBkLvnoSMM8MwkMvlwHFcJPuRRkUQBFBVFb29vbAsC5lMhk59kobDWm/JsgxZlsdtw5UoirAsKyzPIaQWKIglZBw5joPe3l44jjOumzGaje/7KJfLyOfzYbaK6l9Jo0okEhAEAeVyGbquj8tjVg5AME1zXB6TkL4oiCVknPi+j3w+D13XaVfvGHIcB4VCAeVymfpFk6bBcRwSiQSKxeK4BZU8z8NxHBrrTmqGglhCxkmpVArHNlJWcGyYpol8Pg9N05BOp6nvLmkqoijC8zyUSiW4rjsuj8lKGSzLGpfHI6QSBbGEjANVVVEoFJBKpagucwyw+tdcLkfjY0lTS6fTsCwLxWJxXDZ6sWysqqpj/liE9EVHeULGmGVZ6O3tRSwWo53xY8D3fZRKJap/JQR/r1XVNA3lcnlcNnqxelzqVEDGGwWxhIwhz/OqsoNkdLHxsZIkUf0rIX8Tj8chiiJkWR6X0bSCIMC2bcrGknFHBWOEjJEgCFAoFGgi1xgxTROlUonGxxIA+/7eKr8OdCo9Ho8jFouF/200yWQyrI/lOG5MP+Cxs0zlchmtra1UMkXGDQWxhIwRWZZRLBYpwBplQRBA0zSUSiV4nkflA03E8zz4vh/+13VdeJ4Hz/P6BbEHOo0ei8XCr3g8Hn4lk0kkk0kkEgnE4/FIB2SCIEDXdRSLRUyZMmVMNzpWZn5pKiYZLxTEEjIGdF1HLpcDz/M00GAU+b4PWZZRLpeRTCaRyWRqvSQyRlzXDYNUx3HC/68MUiuDUPZf4O9ZVvbhZqCAtjJbywLjyvtm95FIJMDzPJLJJDiOQzKZjNSHJlYfK0kSJk6cOGZrj8ViSCaTKJVKyGaz9MGdjAsKYgkZZY7jIJfLhVlCMjpc10W5XIaiKBAEgT4cNJAgCMJA1bbtqgwrC0YTiUSYHR1ugDRQ4FYZ9A7E9/3wi9WVskCN5/kwsK33Nm6VE704jhvTLGkqlYKiKNB1HdlsdswehxCmvv/6CImYyoEGdEpt9Ni2jWKxCMMwqE1ZAwiCIMyw2rYN27bDTCgLLlmwWKus50DBMith0DQNmqaF60ylUmFQW49YNrlcLoPjuDGbFsjKL8rlMjKZTKQy1iSa6vMvjpCIooEGo0/XdZRKJTiOQ2+MERYEAWzbhuM4ME0TruvC9/2q7Ga9n4KOx+Nhm7zKQNyyrHD0qyiKdflceJ4P/5ZYacRYYOULhmHQmSgy5iiIJWSUsIEGoihSpnAUBEEARVFQLpcBgOpfI6gy22pZVjhFKpFIhGNSo4oF3yz76rouDMOAruthtrPeyl5SqVQYyE6ePHlMAm32O5UkCalUij50kjFFQSwho8C2beRyOcRiMepVOgp830e5XA7r+GhIRHT4vh8GrSxwZaeZRVFs2KCGBbSsvldRFKiqClEUkU6n6+I1XDkIged5tLe3j8njiKIIRVHQ3t4+ZqULhAAUxBJy0NhAA9M0qQ52FLiui1KpFAYA9VpnSP6OlQqwwNVxHMRisTAj2aiB60AqJ/Ox7KxpmhBFMczO1lI8HocgCJBlGTzPj8kpf47joOs6FEWhIJaMKXp3IOQgsIEGsizTQINRwGa+m6ZJ/XUjwHVdmKYJ0zTDkaNsoxP9Lfw9O+t5XvhzEgQB6XS6psEsx3Fhtw+O48ak5IF1Kmhvb6+LLDRpTBTEEnIQFEVBqVSigGsU0AauaGDlAoZhwLIs+L6PZDIJQRDob2AQrD1YZTCbSqWQzWZrdqZBFEXouh72jx3t3x3HcWGNMAWxZKxQEEvICJmmiVwuF25SISNDG7iige3CNwwDjuNUTbciQ5NIJJBKpeB5XvghIJPJ1KRtXCwWgyiKUFUVHMehra1t1O+f4zhIkoTW1lb6gEPGBB19CBkB13WRy+Xgui5aWlpqvZzIqpzARRu46k8QBLAsK8wesqwrlQscHBbMOo4DWZZhmiay2SwEQRjXnyv7AC7LctgebDSxIJmGH5CxQkEsIcPE6mBVVaUA9iBUTuCiDVz1xfM82LYNTdPCWlc2cpWMHvYztW0bpVIJoigim82O65mdyv6xU6ZMGdXfMcu+KopCQSwZE3REImSYJElCqVRCJpOhU2QjRBO46hPbqKXrOlzXDRv40+t87LC2fL7vwzRN2LaNbDaLdDo9bllZ1naL1ceO5uOmUimoqgrLsmremYE0HgpiCRkGXdeRz+fresRkvTNNE8ViEbZt0wauOsE2apmmCc/zmrI1Vq3F4/GqEgPbttHS0jIuxxnWP5b1ZR7NVoGs3ZaqqhTEklFH78KEDJHjOMjlcvA8j8YpjpCmaSgWi/B9f1wzTWRgbKOWaZoIggAcx1GgUWNskhlrW9bS0jIuvVZZfawkSRAEYVRfBzzPQ5ZltLe301kXMqroHBEhQ+D7PgqFAm1QGKEgCCBJEvL5PABQAFtDQRCE2XBW0sEyr3R2oT6wrCwAlMtlSJIEz/PG/HEFQYDneZAkCb7vj+r9sjIVQkYTHbEIGYJyuRzWwVLwNTxshKwkSeB5njoQ1AjrNKCqKmzbDic3Ub1r/eJ5Hp7nhRvsWlpaxjxTPhZjadnYYVmWkc1m6RhKRg0FsYQcgKZpKBQKtIN+BKgDQe2xzKumaWHwKooiBa8RwdpxWZaFcrmMbDY7pr2UK8fSCoIwaqUMoihC07Rw0AMho4HeUQjZD9u2kcvlEAQB1QoOU2UHAppoNv4qg1fHccLm9vR7iB72u2ObvjzPQzabHbPfJcdxcBwn7N88Gh8+2fhdVVUpiCWjho5mhAzC933k83kYhkFTpIbJNE3k83mYpkmtyMZZEAQwDAOFQgHlchme50EURQpgGwDHceA4DqqqQpIkuK47Zo+VSqVgmiYkSUIQBKNyn6IoQpblMV03aS6UiSVkEKVSCZIkUQ3XMOm6jkKhQB0IxlllzSsbCyuKIv38G0wymUQ8HodpmuHEwNGetAX8ve2WoigQBGFUNrSyLgWapo36mFvSnOhjOSEDUFUVhUKBGvEPQxAEkGWZOhCMs77dBlzXDVsk0c+/MbEPKJ7noVwuQ9O0MXmcRCKBZDKJcrkM27YP+v5isRiSySRkWR617C5pbhTEEtIHq4ONxWK0k36IfN+HJEkoFotIJBJjkhki/bHNPqVSCY7jUNlAE2F1smzXv6qqYxIYCoIQbtAcjbZboihC13Vqt0VGBZUTEFLB933kcjmYpjmqU2saGcsGsd3M4zn3vVnZtg1d12EYRji2lALX5sRxHGKxGBRFge/7aGlpGfUMPGu7JQjCQZcBsDNbiqLQXgNy0CiIJeRvgiBAsViELMtj8kbQiFzXRbFYhKZpVHoxDlzXhaZpMAwDQRCA53n6mRMkk0nEYjGoqhoGsqP5uojH42E9qyAIB32mRRCEsF8xne0iB4M+uhPyN5V1sJTVOjBWdqFpGtLpNAVTY8h1XSiKgkKhAE3TkEwm6UMDqcLKeHRdH5MOADzPh4NLDrasgOd5OI4DVVVHaXWkWdE7NSHYV1uYy+XCjAPZP9M0kcvlYFkWtdAaQ77vQ9M0FItFKIqCRCKBdDpNQyPIgNiGL8MwUC6X4TjOqN4/u29Zlg/6vnieH7dxuqRx0TsPaXqe5yGfz8OyLKTT6Vovp+5pmoZcLgfXdakDwRip7DggSRKAfXWJFLySA4nH40ilUuFmrNEMZCuneZmmeVD3JQgCTNOkDV7koFAQS5paEAQolUpUBzsErIVWoVAAQC20xgrrOMDaZaVSKfA8Tz9rMmRssx8LZEejPRbDcVx43DyYLGo8Hg87K1C7LTJSFMSSpkZ1sENDLbTGnuu6kCQJpVIJpmmG7bIoeCUjwVpwsdfVaAayldO8DoYoitA07aCzuqR50bs2aVqsDjaRSFAd7H6wzRzlchk8z9PPapSxefJ9N23RhypysNjULc/zRjWQZZleRVEOqhwgmUzC8zwoijIq6yLNh46SpCl5nodcLgfbtpFKpWq9nLrlui4KhQIkSYIoitQDdhQFQQDDMMJylng8Tpu2yJionO5lWdao3CfrTytJ0kF1QhBFEYqijPomNNIcKIglTYf1g1UUBdlslk7XDsJxHBSLRaiqSi20RlnlpC3P85BKpegDAhlToiiGZUGjFciKogjTNA+qrpXnedi2PWajc0ljoyCWNB1VVVEsFpFOp+mU7SAsy0I+nw97wNLPaXS4rgtZlqvqXgVBoA9SZFywQHa0MrKs7lZRFBiGMeL74DgOkiSNylhb0lzonYk0lco6WMp8DcwwDOTzedi2TT1gR0kQBGG/V6p7JbUkiiKCIBi1Glk2LexgygpY/1lqt0WGi46gpGlU1sFSP9iBaZqGfD4ftnaiDOHBo36vpN5UlhaMRi3qwZYVxONxxGIxardFhm1EQexdd92F2bNnQxRFLFu2DBs3bhz0uq+99hpWrFiB2bNnIxaLYe3atSO6T9M0cd1112HSpEnIZrNYsWIFenp6RrJ80oT61sGSatQDdvSxHp2lUgmO44T9XgmpB4IghF0LDnZE7WiUFbB2W6NVr0uaw7CD2HXr1mHlypW49dZbsXnzZixatAhnnXUWent7B7y+ruuYM2cObr/9dnR0dIz4Pm+66SY8+eSTePjhh/H888+jq6sLH/nIR4a7fNKkWB0sncLtj51apB6wo8P3/bBllq7r4Hme+r0CUBQFzz//PO666y788Ic/RFdXV62X1NRYmyzHcVAulw86kD3YsgKO4+C6LlRVPah1kOYSC4aZu1+2bBmWLl2K7373uwD2HbBnzJiBG264Af/2b/+239vOnj0bN954I2688cZh3ackSZgyZQoeeughXHDBBQCAbdu2YcGCBdiwYQNOPPHEA65blmW0tbVBkiS0trYO5ymTiLMsC3v27IHv+1RG0Afb5CFJEgRBoDrhg2SaJlRVhW3b4DiuKX+enufh7bffhm3bOOqoowDsq7P+wQ9+gM2bN+Pwww+HLMsoFov45Cc/iTPOOANBEDR9kF8rrNWbIAhob28/qC4krPa7ra0NEyZMGPbvlA09mDlzJpXcNLHhxGvDepXYto1NmzZh1apV4WXxeBzLly/Hhg0bRrTYodznpk2b4DgOli9fHl5n/vz5mDlz5qBBrGVZVaclZFke0fpItHmeF25SamlpqfVy6grrGynLMkRRpDeNg+C6LjRNg67rYYP5ZgrKXn75Zdx3333o6enBxz72MTz11FPo7OzE6tWrkUwm8fjjj+Mvf/kLvv/97yOTyQAA7rvvPjz44IM49dRTqX1bDbHXq2EYYfAw0rNVlWUFqVRq2D24BUGALMthIEzIgQzrXSufz8PzPEybNq3q8mnTpmHbtm0jWsBQ7rO7uxs8z6O9vb3fdbq7uwe839WrV+PLX/7yiNZEGgOb7y3LMlpaWpoqqDgQ13XDnfKpVIqCiBEKggC6rkPTNHieB57nG+ZnWZkdNQwDr776KjZu3IipU6fizDPPxNSpUwEAO3fuxI9+9CPMmjULN954I/7617+ip6cHkyZNQhAEUBQFu3btwqJFi9DV1YXnn38eu3fvxp///Gckk0ns3LkTc+bMqeVTDQVBAN/3EQRB+MXaPlV+j/2bfcVisfALQNW/Y7FYuHGJ/X+9lTSx4NMwDMRiMbS1tY34eJlMJmHbdjjhbzh/D7FYDMlkEuVyGS0tLXX3cyL1p2FTL6tWrcLKlSvDf8uyjBkzZtRwRWS8sbpEqoOt5jgOCoUCDMOgHrAHwbIsqKoKy7LAcVxDTH6zLAuCIADYF1Bs2LAB9913H5YtW4a3334bnudhy5Yt2LBhA+644w5wHIetW7diz549WLt2LdLpNObNmwcA+NGPfgRN08DzPPL5PLZs2YKXXnoJs2bNwty5c3HmmWfisMMOwyGHHDLuz9PzvDAo9X0fnueFX30D1Up9A7vKfw9UmVd5GbsuC2KTySSSyWT470QiUdMP2vF4HKIoQtd1xOPxgyq7S6VS0DQNkiRh4sSJw76tqqowDCPM2hMymGEFsZMnT0YikejXFaCnp2fQTVujcZ8dHR3hJ7vKbOz+HlcQhPBgTJpPZT9Y2hH+d5ZloVgswjRNZDIZyk6PgOd5YekAsG9XddQ+CFiWhXg8DkmS8OSTT2LDhg3I5XJ4z3veg3POOQenn346AOCQQw7BO++8A9M0cdttt2HOnDl47bXX8PnPfx4vvPACzjjjDOzevRszZ84M682TySROOOEEfP/738fevXuxYMECiKKI448/Hl/96lf7la2MZUulgQJVx3HCy9hjV2ZJWeaQZU9HS2WG1/d9mKZZ9fiJRALJZBIcx4X/P95/n/F4HDzPQ1VVxOPxEXdyYZvGVFUddlkB+1uSZZmCWHJAwzry8jyPJUuWYP369eFlvu9j/fr1OOmkk0a0gKHc55IlS8BxXNV13nzzTezatWvEj0sal+/7yOfzsCyrIbJjo8U0zaohBhTADg/bAMNG8SaTycgEsO+++y4efvhh7N69G5dccgkuvfRS7N27F++++y527tyJc889F7fccgvmzZuHr33ta/jf//1fAEBnZycmTpyIxYsXh6f8jzrqKMyaNQtvvvkmgL8nDMrlcvh4bW1tSKVS2LFjBwDghBNOwPbt2/Hcc8+FfUkVRcH3vvc9/PWvfx215xkEAVzXhWmaYSaQbVxk7Z88z0M8HgfHcRBFMZyaxvM8kskkEonEmGRFWYDMAlVBEMLHTiaTCIIAlmVBUZRw3SzTf7CdA4YjmUyC5/mDapcF7Os2wDqfeJ43rNuybCzb6EXIYIZdTrBy5UpcdtllOP7443HCCSdg7dq10DQNV1xxBQDg0ksvxSGHHILVq1cD2Ldx6/XXXw//f8+ePdiyZQuy2Wx42ulA99nW1oYrr7wSK1euxMSJE9Ha2oobbrgBJ5100pA6E5DmUiwWqQ62D13XUSgU4Pt+0206Gg2O44SnOBOJRN3+DPfs2YPf/e53eOONNzB16lSsWLEChx56KP7v//4P3/ve9/DnP/8Zl156KRYvXoz29nbwPI8bbrgBkyZNAgAce+yxePnll/Hss89i5syZEEUREyZMAMdxMAwj/FA4ffp0dHd3w7IsHHbYYXj22Wexd+/e8EzZ9u3bYVlW2EbrvPPOw9tvv437778fzz//PCzLQm9vL9ra2nDKKaeM+PmyoNXzPLiuC8dxwlKByszqaGdVRxPLwlbWjrLMsWmaMAwjDLp5ng8ztWMpmUzC933IshzWy44EKytQFKXfnpb94TgOuq5DVVVq+Uf2a9hB7IUXXohcLodbbrkF3d3dWLx4MZ5++ulwY9auXbuqMhNdXV049thjw3/feeeduPPOO3H66afj97///ZDuEwC+9a1vIR6PY8WKFbAsC2eddRa+973vjfR5kwZFdbDVKsedxmIxajE2TL7vhxu3fN+v68yrpmlYu3YtDMPAkUceiZ07d+Laa6/Fd77zHcyaNQstLS1oa2vDGWecEZZadXR04N1338U3v/lNbNy4Mcx+TZo0CYVCAYcccgg6OzvR29sLXdfDIHbGjBl49dVXkc/ncdJJJ+GJJ57At771Ldx4442YNGkSXnzxRQiCgLfffjsMKD/72c9i27ZteP7555HJZHDUUUdh/vz5w3pNBkFQVRYwUNDKsqlRVrn5i5Uf2LYNy7LCEikW0I5VcM7zfDiFa6RjumOxWJjVZVnvoWKdCtrb26lzChnUsPvERhX1iW18LNPvui7VUuHvU7jK5TISiQTViA9T341b9d7z9f7778czzzyDr3zlK5g3bx5838dnP/tZTJkyBRdffDFuu+02XHDBBVVDYiRJwre+9S2Ypolzzz0XS5YswWOPPYbnnnsON910ExYuXIif/vSneOGFF3DzzTdj9uzZAIDf/e53ePDBB3HNNddg6dKlePPNN/Hggw9i06ZNcF0XF1xwAVpaWvD444/j5z//OXzfH3Hwz7KSjuPAtu1w81XfTGszYEG867rhTn7W33msAj3DMMBx3EEFk+wD0OTJk4f8u2LHr87OTmq31WTGrE8sIfWK1cGapkkfUoDwVGC5XA5PQ5Kh6btxq15LB/r685//jCVLloQBbDwexwc+8AE899xzyOfzmDlzJnbu3AlgX4u1ZDKJrVu3Yvv27bj66qtx6qmnAtj3/HO5HIrFIgDg8MMPx5NPPol8Ph8GsZ2dnVAUJZyu9J73vAe33HILurq6cMghhyCRSOAb3/hG2Ju5MnBhG5xYEDqQyvIAlm31fT887T6WGch6xgJXdrrfdV0oihIGs6y+djSx1luSJGHChAkj+sDARsqKojjk4zO12yJDQa8K0hBKpRIkSUI2m23KN7dKbAoX69NIAezQDbRxKyqvp7a2tjCoZBtpDj300HCU5+zZs/HWW29V3aa9vR22beOdd94BAPzpT3/C//zP/yAWi2H79u0A9k1PisfjVZuLjjjiCNx3330488wzw8vefvtttLW1QVEUPPXUU3jllVfCCYuVBsueshpQVVUhSRJkWYZhGAiCIGxhxvqORuV3MpZYJwH2GtV1HZIkQVXVUd0IxoYhsE1nIzl5y9YqSRJs2x7y7dgQhoPZYEYaG2ViSeRV1sFGvRbuYHmeFw54SKVSVEs2RCzQY5toopJ9rXTEEUfg17/+NXzfD0sfJkyYgK6uLkyfPh2WZeHZZ58F8PfM6OGHH45//Md/xOOPP45HHnkELS0tuOqqq/DOO+/g6KOPBrBvE9fPfvazqscaqL769ddfx5133okdO3Zg8uTJ+PCHP4wPfOAD+11zZW2rbdthmQArf4na76AWKrOznudB13WYphl2PhiNMhjWMkvTNCQSiRG13uJ5PuwYMWnSpCFlVtl1JElCOp2m1wPph97hSKTZto18Pg8ATZ9xrJzClU6nmz6gHwo2cUtVVXieB0EQIvtzO/XUU/HQQw/hgQcewCc+8QkEQYDHHnsMgiDg6KOPhq7rKBaLkCQprDHkeR6XXHIJFi1aBEEQcNhhhyGZTOKMM87od/+VE7wGcsopp+Doo4/G1KlT9zviubJUgNW4skCsWcsERgvrnOF5HgzDgGmaSKVSEEXxoF/XbEOZoijh4wyXKIph79ihBsKsw4FlWdSpgPRDQSyJLN/3w8lTzV4H6zgOisUidF2nKVxDZNs2NE2DaZpIJBKR79wwa9Ys/PM//zMefvhh/OUvf0GhUAAAfPGLXwSwL+t64403DpiZW7hwYdW/2ajVytfRgYLLyZMnY/LkyQN+j9Vv2rbdL3CtRVP/Rsdqh1l9t23bSKVSB53drmy9NZJBMqxPriRJQ84SU7stsj/UnYBEVrFYRE9PD7LZbGSzZ6PBtm0UCgWawjVElW2zgiAAz/MNFfS/+uqrYZ/Yo48+etDAslLl5KjRwnq4Oo4Dy7LCVlisBRa9TsdHZS9dnueRSqUOOuNtmiaSyeSIOhawtn9tbW1DHknLppvNmjWLSqSaAHUnIA2PNe8fjdNkUWaaJorFIk3hGqKotc0aiWOOOQbHHHPMgN9jNad9Xyej+bphda6WZcFxHARBEHYUaKQPC1ERi8XCAQms/pj1bB1pQCgIAgzDgKIoaGtrG9bvtbK+NpvNDimby3rGqqo6rKEJpPFREEsix3Ec5HI5BEHQ1L1P2U56x3Fo08MBRLVt1kgNVA4w0L9HSxAEYY2rZVlhi69GGDzQKOLxOARBCDd/seMGz/PD/ltgU7zYRsjh9nHlOC7sdjBx4sQDPj4LxFlmjj4MEYaCWBIpQRCgUChA1/WmLgupHCNLgx32j7Vtsm0bPM83xenI8XqTr6xzZW2dmrmPaxQkEgmIogjHccJJWiPp7MKC4pF2LGC9YzOZzJBqXdmmMF3XR9QdgTSmxj+ak4bC+p8286lzVVVpjOwQsOyrpmlhr8tmfc2MJpZ1tSwr3KRF5QLRwsbBsqys67phVnY42O+9sq/yUCWTSdi2DUVRhrThLB6PIxaLQZKkpj7+k2oUxJLIMAwDhUKhabJpfQVBAEVRUCqVaIzsATRj9nWsVW7SYlnXZDLZ9K3tooxlZW3bDntLp1KpYX0Y4TgOnueFk8OG87fGMrmZTGZIH8hZuy3WOowQOrKTSHBdF7lcDq7rNmUZQRAEkCSJxsgegOu6Ye1rVIcW1BO2s92yrKpaV8q6Ng620aoyK5vJZIYdjI5koxfrUsHaZx3odqzFl6IoFMQSABTEkghgdbCapu23iXqjYmNkh9NbsRmZpglFUeA4TqSHFtQD3/fDTVqswwDr5kAfChoTCyjZ5LR0Oj3ksz2VG70SicSwEg2iKELXdRiGMaT6flEUoSgK2tvb6cM8oSCW1D9ZllEqlZDJZJou+1MZwB5MS5xG1ggjY+sF26jFSgbYrvBm+7trVmyzFtv05XnekP+e4vF4OFqW47ghZ0rj8Tji8XhYznCg1xrP85AkCaqqDrnPLGlc9I5I6pppmsjn8+A4rukCOM/zUCqVwlNnlFnszzAMqKoK13XB8zz9jEagsj0WayrPaq7pw0DzYZu+WGmO7/tDPv6MdKIX61Qw1M4DLJBta2ujv/kmRx+vSd3yPA/5fB6O4zRd/ZPruigUChTADsJ13bBThe/7TT/0YiR834dpmpBlGbIswzCMcIc5lQ2QZDIJjuPCWle2me9AeJ5HEASQZRme5w3pNmwEsSzLQ3ocURRhmiY0TRvS/ZPGRUEsqUusDlZRlKbrCei6LorFIjRNQzqdpuCsD8MwUCqVYBgGeJ6njOEwua4LwzAgSVJYQ8xxHH0QIP2wjLzrupBlGbZtD+l2giCE7bOGOtleEARYljWkwDQWiyGRSECSpCHfP2lMFMSSuqSqKkqlEtLpdFPV49m2jXw+D13Xm+65HwhlXw8OOz3MxneyiXeCINDrjAyKdS9g2VXTNId0G7Zha6jZUlbGMNSsbyqVgq7r4RQ+0pzoyEXqjmVZyOVyYSPtZmHbNgqFAkzTpAC2D8q+jgyrd1VVFeVyGbquh0EJlQyQ4eB5HvF4PJyadaAMKGvFpqoqLMsa0mNwHBe+Xg+EfYCVZXlI900aU3PtlCF1z/d95PN5WJbVVP1gTdNEsViEbdtIp9MUXPxN384DoijSz2YIgiAIuwzYth22yKLMNTkYHMeFGf0gCA54rKochJBIJA64ObcyGzuUCWJsFK1pmsOaFkYaB6V6SF0plUqQZRktLS1NE6ywSWQUwFaj7Ovwsc1akiSFNYxssxYFsGQ0sA1frFTA9/39Xp+17GIlLAfCxuEOJRvLguqhXJc0JsrEkrqhaRoKhcKwxx5Gma7rKBQK8H1/SI2+m0HfqVuUfT0wz/PgOA5M04TjOGHPzmb5OyLji30gMgwjPHYN9iGJla/ouo5kMjnkFlpsHO2BBi4IghC222qm8jOyDwWxpC44joNcLgcATTOFRdM0FIvF8LQc+fvULer7OjR9hxOwZvUU9JOxxiZ8WZaFIAiQzWYH/XtlH6pUVQ3PDOwPx3GwLAuqqoLn+f2+ngVBCDcrTpgw4aCeE4keCmJJzbF2WoZhNE0drKqqKBaLYaax2bHTh5R9HRrXdWFZVhi8JpNJCl5rwPf98BR5EARV/x7o+5X/BRD+vir/y7Ln7P/Z9yr/v16w4NS2baiqut9ANplMhqf+k8nkAetj2QCETCaz32MkmyonSRJaW1vpg2+ToSCW1Bxrm5TJZOruID3agiCAoigoFovgOK5pss77Y5omVFWFbdsQBIHehPbDdV2YpgnLsuD7fpjVavS/m1pigajneVWBauW/mb41n31/LwP9nvreZrAglwW1iUQiHNXKvmr5++8byGYymUEDVEEQwr/3tra2/a47mUzCsiwoinLAD2iiKEJRFGia1jSJELIPBbGkptimJp7nG36sbBAEkCQJ5XKZAljsy75qmgZN0xCLxYY8o70ZOY4Dy7JgmiZ83wfP803/+hltfYNTz/PgeV54ed/gkn2x0+pj9dplj83W5LougiCoytAmk0kkEonwa7z/jlgZCxtbnM1mBzyeD7c+lmVjs9nsfqc2suBekqSm2hRMKIglNeS6LvL5PFzXbfhPz5UBLM/zTb8BoTL72gwfYEYiCIKqzCu1yRo9LIPKgtS+AStQnf0cyyD1QPb32L7vw/f9sI0aWy8LapPJ5LhlatmAA/a3PVggW9k/luO4/W7cYq91RVEOeMahcvgBbZJtHvTOQWqC1cGqqoqWlpZaL2dM+b6PcrkMWZYhCEJTB2y+74e1rwAo+zoANqCA1bxS8HrwKrOrrusOGLCy4C9Kr0dWTsCw51gZ1LKWWOMR0LJAlm3KGiyQrewfywLuwbDg1DCM/W6ArRx+QEFs82jed1NSU4qioFQqIZPJNHQbIN/3w963oig2dQDL6tso+zowFryaphnOqD/QGzwZWGWGlQWt7JR839rSRsKCVob9DBzHCTdAsU1VY/W6YiUDBwpkBUGAYRhhfexgWODNjqH7+53R8IPmQ+8iZNyxsbJD2aEaZZ7noVQqQVEUpFKppg1GgiCApmlhQ3LKvlaj4PXgsdPqLGh1XTfccFW5+anZXneVmVrf9+E4DmzbDk/pszZ2o/1zYYGsaZphTWvf13NlfSzP8/uteRVFMSwV2F8dLcdxMAwjDHhJ42vcCILUJc/zkMvl4DhOQ9fBep6HYrEIVVWbOoC1bRuKosCyLMq+9kHB68ixmlYWsLLNTizTOpQRp82mb0DLRhKzcgOO40Y1M82CVNu2w0C27/2zjWiyLIfrGGztiURiSAkBQRCgKAomTJjQ9HsPmgH9lZNxxTKTjVwH67ouisUiNE1DOp1uuFOWQ8Gyr2zG+oFOAzYTCl5HhpUIsKCVlQhUlgc0W6Z1pFhbLPbzdBwHiUQirNkfrdciexzTNBGLxQYsH+N5vqqsYLDjBOtUoOv6ft8/eJ6HLMtQFAUTJ04cledB6hcFsWTcsAb/jTxW1nEcFItF6LretAGs67pQFAWGYYQZHjJw8Dra2a9GMlC2tbJEoBFrWsdbZXbWdd1w2IggCKM2triy/RYLZPt+2GD1sclkctAAldX0yrKMVCo1aKY9FouB5/lwFC19OGxsFMSScdEMY2Vt20axWIRpmk0xuGEghmFAURR4nkfZ17+h4HXoKvuzOo4TBq4s2GrUY0c9YEGh67owDCMcPjIar1VWg2sYBgD0Oz6y362maeA4btB6VnYdTdP2uxmMlRTQ8IPGR0EsGXO+7yOfz8M0zYY9oNi2jUKh0LQBLBsnyZqY72+TRrOg4HVoKltfOY4TlgmwbCtl8sdXZTDL/p5ZMHswxzWWETUMA7FYDOl0uur+2Fha1nZrsGEJPM9DURSk0+n91tAmk0lIkjRgLS5pHBTEkjHHmvxns9mGDO4sy0KhUIBt200ZwJqmCUVR4LoujY0F1bwORWUnAcdxwu4CbENWs/0N1aNkMhkO3NA0DTzPH3Sfa/Y3wMoW+n7YrRxL297ePuB9cBwXZmMHuw5QXUN7oMlgJLooiCVjio2VbdTgxjRNFAoFOI7TL7PQ6CrHxsbj8QNO1Gl0fSdsARS8VmIZV8dxwsCVZVwpcK1PrA6VfTBzHAeCIEAQhBFnNxOJRLjxkw1HqHw8nueh6zoEQRjwjE7fbOxgJSZsox8bfkCvr8ZEQSwZM67rIpfLwfO8/U5aiSoWoLPn10wHSRpcUI1lXil4rUaBa2NgwazneTBNE67rhsNbRvI7TCaT8H0fmqb1Kxlh2XhZlsPhDH2x2lhVVffbgSCVSoXDD6jEqTE19zsPGTNBEIRtphqxnZau6ygUCvB9vyED9MH4vg9d12lwwd+w8bCmadJ42L9RVTXMgrEaVyoVaAysr6vjOFUlBiN5zfM8H071amlpqQpWWdstRVHQ3t4+4GtGEASoqopMJgNBEAZ8DBYss44GpPFQEEvGhKIoKBaLDdlmStM0FItFBEHQVAEsDS74O1Y20OzBq+M42LRpE9LpNLq6uvCjH/0IPM/jtNNOwwc/+EFMnDiRNmc1II7jwoEJLCs7ko1ffQPZyr8h1naL47gBa1qTyWR4Rojn+UEfWxTFMBgeLNgl0dW870JkzFSOlW20Ny/W6zYWizXNJ/sgCMLsaxAETZ19rax59TwvHNvZqNgwgWKxiK1bt2LHjh047LDDsHTpUoiiiFKphEcffRS7d+/Gaaedhi996UvYuXMnHn/8cQRBgE9+8pNhb1fSWFhbLNbFYCS1smyql2VZ/aZ6sftXVRUcxw0YgLLNW5lMZtDjcWVWl4LYxkNBLBlVrJ1WI46VZQEsa97dDFjrLNaIvNE+lAyV67ph2YDv++Hc+UbDahRFUYTneUgkEti1axe++93vwjAMTJkyBX/961/x9NNP49///d8Ri8Vw2GGHYefOnXjve9+LefPmYdasWZAkCc899xw++clP1vopkTHGTtmbpgnP84Y9Zptt1LIsC/F4vGoTVt+2W33vl20SUxRlvxtLBUEIhx806zGsUVEQS0ZVqVSCLMsNVQfLDpKlUgnJZLIhg5eBsFGQjuM07eACz/PC4NXzvIb5/ZumCV3XMXHiROzcuRP33nsvtm3bhgkTJuCYY47BP/7jP+LQQw+Fpmn46U9/ilmzZuGaa64JN2veeeedeOyxx3DeeedhypQpCIIA8+bNA7DvVPPMmTNRKBTgum5Tl500CzbMgNVAi6K431P8g91+oB6ygiBA1/VBBxeIoghd12EYxqDlXYIgQJZlqKqKCRMmjPyJkrrTfO9KZMxomoZCodBQY2WDIIAsyygWiw0TwByI53lhb1+2ca1Rfp9D5fs+DMOALMthK6CD7ZFZK6ZpwjAMPPHEE9iyZQvuuOMOXHbZZVi/fj0sy8L3v/99dHZ24stf/jJWrlyJYrGItWvXAti3gfFPf/oTPvjBD2LDhg34yU9+gvvvvx/vvPMO3nzzTQBAR0dHuJGTmTRpEuLxOHbv3l2Lp0xqgGVUgX0fgA3DGFYpCaudNgwj7PLB7lcQBGiaBtM0B7wda6U12OOx7gqSJMHzvGE+M1LPondEJnWpEcfKBkEQBnM8zzfFaSjWaJyNnGzkes+B+L4P27ZhGAYcxwmnFUWlBnj37t34n//5H5RKJSxatAi/+tWvMH/+fHz84x/Hvffei87OTrz3ve/Fd77zHWQyGfz1r3+F67q45pprAOz7/X/gAx/A1772NWzZsiWsc7z55psxadIkdHR0YNasWbjxxhsxe/ZsJBIJtLW1IZVKYefOnWG7o9bWVkycOBHbt2/HYYcdFtbWksbXt7wgnU4P+TiSSCQGbL3FygpYfWzf+xNFEYZh7HewAdvgRaNoGwsFseSgBUGAQqEAwzAa5uDAAthSqRSOXGxkzd46KwiCsGzAcZxIDm+QJAnf/va3AQBHH300nn/+ebz99tuYPn06OI7DggUL0Nvbiw9+8IOYOnUqAGDjxo0wDAM333wzuru7wyDg8MMPRywWg+/74HkeF1xwAc4+++wBH7etrQ0TJ07Ejh07cOyxxwLY9/ppaWnBli1bsHz5cgpimwzbN2DbNjRNQyqVGvIxlOO4qo1e7OwH61agqira2tr6PV4ikYCiKIPW5NIo2sY0ot/iXXfdhdmzZ0MURSxbtgwbN27c7/UffvhhzJ8/H6Io4uijj8avf/3rqu/HYrEBv+64447wOrNnz+73/dtvv30kyyejjGUrG2Uqiu/7KJVKKJVKYeuYRuY4TljLnEwmIxe8HQwWvMqyDEVR4HkeBEEYVj1fvXjsscegqipuvPFGXHbZZfjsZz+Ljo4O7NmzB77vY+rUqejs7Kx6PadSKWiahnnz5uHTn/40vva1r+HOO+/El7/8ZcybNw8zZ87EvHnz8PLLL2PPnj3h7bZv347f/OY36OnpQTabRSqVwquvvhp+n+d5vO9978OiRYsAgAKGJsXzfPgB2bIsBEEw5NvZtg1d18MSAVZWwOpf+2IjazVNG/R+2etd1/WRPSFSd4adiV23bh1WrlyJu+++G8uWLcPatWtx1lln4c033ww/3Vd68cUXcfHFF2P16tX4h3/4Bzz00EM4//zzsXnzZixcuBAAsHfv3qrb/OY3v8GVV16JFStWVF3+la98BVdddVX470baPBRVbOzqYJNVosb3fZTLZUiSFE6kaVR9W2c10+YtNkbTNE3Ytg1g3xtnlJ//tm3bsHjxYhx66KHwPA8tLS048cQT8fLLL6NUKmH27Nno6uoKe3I6joPJkydDFEW85z3vCY/H8Xgcvb29KBaLmD9/Pv7pn/4J999/P/7zP/8TRxxxBLq7u1EsFrFgwQKceuqp4DgOH/rQh6rWkkwmsXz58lr8GEid4TgOruuGNbJD+ZBc2XqrsmNBIpFAPB4Pywoqj8+s7pWNox3o2B2Px2kUbYMZ9hF7zZo1uOqqq3DFFVfgyCOPxN133410Oo177713wOt/+9vfxtlnn40vfOELWLBgAb761a/iuOOOw3e/+93wOh0dHVVfTzzxBM4880zMmTOn6r5aWlqqrpfJZIa7fDKKPM9DPp+H67oN0TOVZWCbIYB1XReSJEGW5fDUeZQDuOFwHAeqqkKWZdi2HfagjPrzb21thaIoABBmr2bNmgVN09DT04PZs2ejXC4jl8uFoziPOeYYHH744bjnnnuwceNG9Pb24k9/+hMef/xx/PnPfwYAzJ07F//+7/+OM888E7ZtY8GCBbjqqqvwyU9+EtlsFolEAkuWLMGSJUv6rYk20RDg72OYWVeMobwuKjsWVG7o4nk+/Bvum9llGVxWFjWQylG0JPqG9S5t2zY2bdqEVatWhZfF43EsX74cGzZsGPA2GzZswMqVK6suO+uss/D4448PeP2enh786le/wgMPPNDve7fffju++tWvYubMmfj4xz+Om266adBAw7Ksqh2Osiwf6OmRYWC7kRVFaYiMuOd5KJVK+62pahSmaUJRFLiu2xDB21A1+pStuXPnYv369QAQlgxMnDgRPT092LlzJ4488kg4joNCoYDDDz8c8XgckydPxkUXXYTW1lY88cQT2Lt3LzKZDBYtWoQTTjghvG9RFAetiWV83+/3Wmqkny85OKwmlZUVDOU4y/rAso1erMyHlRXwPF/VVot1SFAUBZlMZsBSMLbxTJKkhki+NLthBbH5fB6e52HatGlVl0+bNg3btm0b8Dbd3d0DXr+7u3vA6z/wwANoaWnBRz7ykarLP/vZz+K4447DxIkT8eKLL2LVqlXYu3cv1qxZM+D9rF69Gl/+8peH+tTIMLHG/43QTqtZAljf96GqKjRNi+TGpZGqHFTQyFO2TjrpJDz66KN49NFHcf7550NRFDz66KNIp9PYuXMnTj75ZKiqip6enqq/2fb2dnz84x9HsVhEW1vbfn82lS2M+v7dR/04QMYem8LlOE44tvtAf4vJZBKe54XHLZbVTSaT4cjZymQWm/KlKErYLaOvylG0oiiO6nMk46vuzpfee++9uOSSS/q9sCqzuccccwx4nsc111yD1atXDzg9adWqVVW3kWUZM2bMGLuFNxHbtpHP58MDUpR5nodisQhVVRu6H6pt25BlGZZlRbbf6XA16qCCwcycORMf/ehH8eSTT2LDhg3QdR3HHnssZs2aBdM0kUwmccMNN2DWrFkD3p694VOgSsYSy5ayzgWD1a9WYhO9WPcM9t7Dxsm2t7dXfSBnfWUzmcyA8UHlbSmIjbZhvZNNnjwZiUQCPT09VZf39PSgo6NjwNt0dHQM+fr/8z//gzfffBPr1q074FqWLVsG13WxY8cOvOc97+n3fdbfkIwu3/dRKBRgmmbk22m5rotisRgeSBvxDZqdimM1YpWTcBpVZa9X13WRSCQi1et1uHzfh+u6cBwHH/jAB9DR0YF33nkH06ZNwymnnBKekXJdF0uXLj3g/TXi3wGpPywjq+v6AVtwDTaalrXd4nm+ao8Ma9OlquqgnUZEUYQsy2hra2voD7eNblhHK57nsWTJkrDuCth3AF2/fj1OOumkAW9z0kknVV0fAJ599tkBr/+jH/0IS5YsCduy7M+WLVsQj8cH7IhAxk65XEa5XEY2m410UNAMAazruiiXy5BlGYlEouHLB/q2y/J9P+zx24jPm2WaNU2DpmlwHAexWAwLFy7EihUrcMopp2Dz5s3YsWMHZs6cGdYCDrXNESFjjeO4sAUX6xIyGLbRyzTNcL8Lu4yNx64kiiJUVa3aG1OJZYPZZkgSTcM+p7hy5UpcdtllOP7443HCCSdg7dq10DQNV1xxBQDg0ksvxSGHHILVq1cDAD73uc/h9NNPxze/+U2ce+65+PnPf44//elPuOeee6ruV5ZlPPzww/jmN7/Z7zE3bNiAl19+GWeeeSZaWlqwYcMG3HTTTfjEJz5Bc5DHkWEYKBaLkZ/kxDa3sFnbjRjAslNlbI55Iz5HprJdlmVZYdamEZ9zEARh1pXNqWeTjWKxGHRdx0svvYSNGzdCURQUCgUsX74cH/jABwBQlpXUn8oWXEEQ7PcM6kATvVgHA1VVq8oKkslkWEY10JkYlsmVJAltbW1NUWLViIb9W7vwwguRy+Vwyy23oLu7G4sXL8bTTz8dbt7atWtX1YHy5JNPxkMPPYQvfelL+OIXv4jDDz8cjz/+eNiTkPn5z3+OIAhw8cUX93tMQRDw85//HLfddhssy8Jhhx2Gm266qV/XAzJ2XNcN22lFuYygMoBtxD6BnudBVVXouo54PN7wu28rg1cg+r1eB+N5HlzXhW3b8DwPQRAgmUz2e+NNpVKYPn06Zs2ahWnTpuGII47AoYceWqNVEzI0bKwsG2Kwv0C2cqJXS0tLWC40UFlB5XCEyi4Gld+XZTkMgEn0xIImObfEal8kSYp0EFYLQRAgl8uhUCigpaUlskGCbdthPW8jBrCWZUFRFNi23W/HbqNp9HZZjOd5sG07zLrGYjEkk8mGe+0SAuz7u2btt/YXyLLSIVEUw9I2x3Hg+z4mTpxYVV+r6zoEQcDUqVMHfO/SdR3JZBIzZsxoyGNIFA0nXotmNELGlaqqKJVKkT71zgJYy7IaLoANgiD8HbHBE40awHqeB13XIcsyDMMIx+Q20psPK4/QNA2KooRN2Xmeb9j6XkIAhB/QDMMYtJYV+Pt0LtM0w+wtq6/tOwRBFEUYhjHoqFn2/f2NqyX1qzHf6ciosW0buVwurD+KIsuyUCgUYNt2w+3Od10XiqKEp9IaNXjt23GABa+NpG/JAIBwzCYhzWKopQVsEALLpPI8P2BZAestO1gfcPZ9SZLC9l0kOhrzHY+MCt/3kc/nYVlWZEswLMsKa3kbLYBths1bQRCEwavjOA3ZLouVDNi2HU69opIB0syGGsgONAiBdStgZy7Y7VkXj4Hey9goWtaHlkRH473rkVFTLpfDT6dRfEM1TRP5fB6O4yCVSkXyOQzE8zxIkoRyuQwADTE1rS8WvCqKAlmWwxG5jXI6vW/JgGEYYVcFCmAJqS4t2F/7LdZvlnU3GKisgJUfsHHbfcXjccRiMUiSRC3oIoYysWRAuq6jUChEtt7QNE0UCgW4rlu1WzXqKjdvRb3V2WBYBqYROw4MVjIQ1VIdQsZS34zsQEMJWKss0zSRSCSQTqcHLCtg42gH60TAsrGDdTIg9YmCWNKP67rI5XJhs/ioMQwDhUIBnuc1zMGo7+StRsosM67rwrKsqoxKowTplSUDrLcrZVwJObDKQJZlVPtipQSV9bF9ywpYsKsoCtLpdL+AmO0nkGW5IY+vjaox0htk1ARBgEKhENnaIF3Xkc/n4ft+wwSwjT55i03skWU5fBOK6hmASqxkQNf1sMsAeyOlAJaQoUsmkwiCINzYOdh1gH3vAZ7nDVhWwPN82Et7IGwULesIQuofBbGkiqIoKJVKkWxDxUogWJ/BRsCmpJmmCVEUG+q0cxAEME0TsiyH7W0aoUTC9/2qcbC2bSORSIDn+cg/N0JqhQWlhmGEpTh9sfpYXdfD6V+maVa112JlBQO18OI4Dp7nQZblMXseZHRROQEJsZ38A00CqneapqFYLAJAQwSwbMct23XbCM+JYRlKtmEjHo83RMcBz/PgOE5YMkCDCQgZXRzHwbZt6LqOdDrd70NhZf/YZDIZttRSVTU8A8LuQ1EU8Dzf7+8zlUpBURS0t7dHspyu2VAmlgCobqcVtdPwqqqiUCgAQEP0DrVtG6VSCaqqguO4hjqQOo4DVVUhyzIcxwHP8wO+kURFEARwXRe6rocteoIgoMEEhIwRlm01TXPAjCyrN9c0LTzGsBICVlYgiiJUVR2wbIDdP2VjoyFa6TYyZkqlEmRZjlwdrKqqKBaLYTYvytjmLU3TwpKIRgmCGm1MLAte2UjYIAjCHrZk34fiWCzWMK9fUl94ng/LAQZqMVjZP7alpQWCIITjZ1l2NhaLQVEUCILQ7/asNratrW3AjgikflAmlkDXdRSLxchtpmEZ2EYIYF3XhSRJkGUZ8Xi8YTZvVW7aaoQxsazeVVXVMNND9a7olxFjfTcrlcvl/fb7JGSoWE9l27bDD8Z9se8bhlE1tYttDBNFEbquh+27KgmCEJYckPpGQWyTi2I7rSAIIMsyCoUCkslkZNY9GNM0USqVoOt6w2zearRNW57nwTTNsGTA87ywvq5Retgyruvil7/8JXbv3l11eRAE8DwPvu/3u03f3+uOHTuwcePGcBf4888/jzvuuAPvvPPO2C2cNBVWc25Z1oCBLAt0TdOEZVn9OhPE43HE43HIsjzga1oQBEiSBMdxxuX5kJFprKMvGZYottMKggCKoqBYLIb9AKPK933IsoxSqQTf9xti8habtCXLcjgSN8qTtjzPg2EYYRP0Rq13/epXv4rHH388DM4feeQRbNmypeo6sVgMiUSi32vUNE3813/9FzZs2ABgX93zj3/8Y3zrW9/Ca6+9BgBhFr6jo2Ncng9pDizDalnWgFl+Vjag6zpc1wXP8zAMI8y+iqII0zTDD9qVBEEIh8uQ+kU1sU0sau20WAa2VCqB47hIB7Bs4wDLEEStG8RAGmXSVt96V9/3I/+BaTBs8ILrutixYwdc10UikcBRRx2FvXv3Vl23t7cXzz33HF5//XW0tbXhfe97H4499lgIgoDdu3fjj3/8I0466SQoioJJkyaho6MDf/jDH7Bs2TIIgoBCoYC2tjYEQRCJ4w2Jhng8HrbeYtnXShzHwbKsMFkTj8fDTbPsbAobcFB5HGb3JUkSWltbG+IY3Yii9w5DRkXU2mk1UgDL2oE5jtPvwBlFbAOFJElha5uBNkvUO9/3Ydt2uLmO1btGuQyiku/78H2/6rQr+/8jjzwSXV1d4QeQGTNmYOfOneFpVlmW8Ytf/AKvvfYali5dipaWFtxzzz34zW9+g1gshrPPPhumaeIvf/kLkskkyuUyzj//fOzcuRNdXV3wPC/sHEIBLBltrJXdQMMQWDDKyg5Y9wGWfWW1swMNQNhfppbUh2i9y5BREbV2WkEQQJIklEqlsCVTFHmeB0mSIEkSYrFY5Ddv+b5fVffKNqRFLeDzPK9qOIHrug1Z78pqAGOxGFzXDZ8nAMybNw+5XC58I58zZw56e3vDJvFvvfUWXnjhBVx11VU477zzcOmll2L58uX4xS9+AQCYNWsWDj/8cDz//PNoaWnB9u3bcdxxx+GII47Axo0b0dXVhZkzZ9LGLjJmKqd6DbTRkI2lZW23dF0Pp+jxPA9FUfq9Plnf2XK5POiABVJbjXOEJkPGRphGoQ6WBbDlcjmsRYwiy7JQKpWgaRoEQYhsIA7s+51YlhXWvQZBAFEUI5dRZpu1WPDq+z44jot0vetAu7RZYLpnzx6sWbMGV199Nb74xS9i3bp1KJVKAIDZs2fDNE1IkgQAmDlzJlRVDf+9detWHHHEEZg5cyaAfRmqU045BbZt43//93+RTCZx8sknY+vWrdi2bRsmT54Mx3Fw6qmnYufOnfjzn/+MadOmDbiBhpDRwnFc2EO272utciwta/+mqio8z9vvOFrWxYCysfWJgtgmw0azRiFj1jcDG8UANggCqKqKUqkE13XDHoVR5TgOFEUJW9VEbZNT3+EEbLMWm+YTlecxEPbmzAJZy7KwevVq/OIXv4Bt27j//vuRzWbxmc98Bpdeeim2bduGBx54AL7vY9KkSUgmk+jp6UEQBJg6dSo4jkNXVxeAffXObW1tYdDr+z7a2tqQzWbR09MDAFiyZAmmTJmCn/70p+jo6ICmaTj22GPBcRy6u7uRSCQgiuKAgTYho2V/rbdYKYFhGOH1KssKBhpHG4/HkUgkIEkSfQirQxTENpEotdPyfR+lUgmlUimybadc1w2z3qw/alSDJNd1w8ycbdvhJLGonG5no25Z8GpZFuLxeMNsqnv++edxzTXXQJbl8DXGcRy2bduGZcuW4S9/+Qt838enPvUpLF68GAsXLsR5552Hl19+Gf/3f/8HAJg0aRL27NkT1q9OmjQJO3bsAABMmzYNxWIxDGLj8ThKpRIEQag6zXrmmWfi7bffhiRJ4ZmeI488EgDCU7UUxJKxVNl6q29AWjmW1rbtsKzAsixwHAff98OzS5XS6XR4xobUl+gfvcmQBEGAYrEIXdfR2tpa6+Xsl+/7YfAXxdPUAMJaURYQRCXY64vVvbIRj1GbtOX7fthpgG34GKhNVNSdfvrpePLJJ/HEE09gxYoVSKfT2L59O9LpNKZPn47HHnsMmqZhzZo1ePfdd8NJRZ2dnWF2afbs2Xj33Xdh2zaSySQOPfRQ7Nq1CwBw9NFH4+WXX8bDDz+Ma6+9Fq2trfjv//5viKKI+fPnh+s46qijMGvWLGSzWUyYMAEAcNJJJ2HRokWYMGECgiBouJ89qT+sBpbVvFYmbRKJRNg6r6WlBQDCbgVsHG0mk0Eqlaq6v3g8Hn44i2oyohFFLzogI8LaaaXT6br+A6wMYNkp3ijxfT+c5hSPx6sOhFHC6l5N04TjOGEmOSp834fjOGHwyrIz9fzaP1gf+chH8F//9V847LDDcMopp2Dbtm2YMmUKstksWltbsWfPHhx11FE444wz0NnZicmTJ1f9Tg8//HA888wzME0T6XQac+bMwYsvvgjXdTFz5kx89KMfxYMPPoivfvWr0DQNHMfhwgsvREdHR9g2q6OjA1//+terAlVBEMIgopF//qS+sNZbpmkikUhUvZewtluGYSCTycA0zbAF12DjaFOpVJiNjcJ+kmYRrQiBjIht28jn80gkEnV9Wp4FsJIkRTIDy8YURrn3KzvtbhgGbNsOR/pGJfjwPC8MXj3PQzwej1TN7sE4+eST8de//hXPPfccTjzxxHCTCs/zmDx5Mtra2nDyySdj7ty5YdBZLpdRKBQwd+5czJkzB8ViEYqiYOLEiejs7AyzthMmTMDRRx+Nf/3Xf8XWrVsxYcIEzJkzB5lMpqrvK9sw4/s+ZVxJzSWTyfB4lk6nw7NIldO82GZOtulWFEVomoZMJoNMJhPeF7utJEmR6a3eDOgo0+Aq22nVc1aQ1cBKkhS53qlBEEDTNJRKJdi2Hbn1M67rQtM0yLIctqHheT4SB+vKyVq6roeTtRo9+9rXWWedhVgshh//+MfwfR9Tp04FACxatAiHHXYYvv3tb2Pr1q0olUr461//iieeeAKbNm0CsK9NFtv0BiAMWtkpVwCYOHEiTjvtNBx99NHhG/xAP18KYEm94DgOruv22+jFNmxpmhZ+8FJVNZxMx8rBKqVSqfAYQ+pDLGiSKntZltHW1hZO32gWxWIRPT09yGazdVvLyAJYNjWlXtc5EM/zoCgKdF0PP9FHDWs1xdrSRKXuNQgCeJ5XNVkrkUhEYu1jxfd9vPLKK1i7di2mTZuGj33sYzj55JMB7Ju49ctf/hI7duzA3r17IYoiFi5ciLPPPhvz5s1DLBYLJ3gd6DEAClRJdLAzTKIo9ttgywYgZDIZOI6D1tbWcCPXpEmT+sULiqKgpaUF06dPb6oPyONpOPFa9NJFZMh0XUexWKzriUNRDmBN04SqqrBtu65/xoNhda9syk1URqv2HQsbBEFkJs+NtXg8jmXLluGYY47Bq6++innz5gFA2Dbr6quvRnd3N1pbWwes6+v7Gh6oLICCVxI1lR0LWIkUw6Z5sTG0rKyA4zgoioJ0Ol11bEmlUlAUBe3t7ZEYFtTo6KjfoFzXRaFQgOd5dfuH5vt+WIMXpQCWlQ+wmsNUKhWpT+RRrXttlk4DB4MFnccccwwURQmzpux3G4/H0dnZWXV9dvlA6GdLGsVgG71Y3byu62hra4PjOFBVFW1tbeFxvr29PbwfNhmMJV7q/bjZ6CiIbUCsnZaqqlX1bPXE8zyUSqXIBbCu60JRlLBZdtSyf67rwjCMsH8iz/N1H6g0Y6eBkWK/yx07dmDKlCno6OgY0vUJaQaDbfRKJpPwPC/sPGAYBkRRDMfRptPpqrNUqVQqPOVdz3tNmgEdwRoQmxCVTqfr8k0qqgGsaZoolUowTTNy3RPYAVqSJJimiWQyWffDCjzPg2VZ4UaKRhgLO9Z0XcdvfvMb7NixAwsWLABAwwUIqTTYRi82zYuVHKiqikQiESYuKq/LcRw8z4Msy7V4CqRCdN6FyZCwdlrsFEm98TwvzBLXa5DdV5R7v/q+D9u2YRgGHMcBx3F1X/fK2mRZlhWeHq/3NdeLfD4fttg69dRTAVBvVkL64jgOtm2Ho5CBv0/zYkMQ2Ehatskrk8lU9VWuzMZGqYd2o6EgtoH4vo9CoQDTNOuyAwMLYBVFQSaTiUQA6zgOZFmOXO/XyjGrjuMgHo/X/dhblnllnQYoeB2+mTNn4o477qj1Mgipa6yNlmmaVccZlnll5Qa6rkMQhLAGtnLvAOszyyZLktqIxjsyGRJJklAul+tyLJ7ruigWi+En2igEsIZhQJZlBEEQqQL+KNW9VrbJsm0bvu9HpksCISS6EokEgiAIN3qxsjYWnCaTSSQSiXCTl67rYXDLiKIYZmMrOx6Q8UNBbIMwDAOFQqEuWz1VBrBRKCHwPC+sw0wkEpE5OEWp3+tgbbLqsQSGENKYkskkbNuGaZpIpVKIx+NV07yy2Ww4fjsej4dZV/YexvM8DMMIx9SS8Vff0QQZEs/zkM/n4Xle3Z3WiFoAa9s2SqUSNE0LJ1bVO5ZNkGU5rNsVRbEuA9ggCGDbNnRdh6ZpcBwHiUSirrPFhJDGxepjLcsKN2+xYycLXjVNC8sP+k7rEkURkiTBtu1xXzuhTGxDKJVKddlOK0oBbBR7vw7U77Ve616pTRYhpB4NNgiBDUFIJpOIx+PQdT0MWCu70wiCAEmSIMsyJk+eXMun0pTqN6ogQ6KqKgqFQngqpF5EKYB1XReSJEFRlHC3ar0HV67rQtM0yLIMx3HCrHG9rdv3fViWBU3ToOs6PM+jNlmEkLrCyghM0wwHqbBuBSwbaxhGeCZJ07Sq21M2tnbqN7IgB+Q4DvL5fFjDUy+itInLNE2Uy2UYhhGOGqxnnudB13VIkgTDMOq23yv1eCWERAmbxGUYRjjJLpFIIBaLwTAMxGKxsFxLluWqgJXnedi2DUVRarX8pkXlBBEVBAEKhQIMw6irdlqu6yKfz8M0TWQymboNWPqWD9R79jUIAliWBcMw4Lpu3e7gpx6vhJCoYvWxbKMXy8ZalgXXdeH7fpg0UFUVEydOBLAvaysIAsrlMlpbW+s+GdJI6it9Q4ZMlmWUy+W6ChRZZtg0TaTT6bpZV1+u66JcLkOWZSSTyboPYG3bhizLUBQlPIjWW79az/NgGEaYeQUQqb66hBAC/L1jAcu0VgaysVgsPL6pqgrTNMPbCYIQHqvJ+KF3mAgyTRP5fB4cx9VNkOA4TjhooZ4DWNM0oSgKXNetapVSj9hoRHagrMcd/JVtsnzfj1RLMkII6Ssej8P3/apesYlEIiyRYu23BEEIW2vFYjHKxtZIfb0jkgPyfR/5fB6O49TN+FMWwLJG0PUYwPq+D1mWUSqV4Pt+3W2Eq+T7PnRdhyzL0HW97upeWY9XXdehqmq4q5fn+bps60UIIcORTCbh+35VfSzHcWFJASuX0jQNhmGEt2PZWKqNHT/1kcYjQ1YqlaAoSt2006osIain0oZKURkdy3a+GoYBx3GQTCbr5oMKMPiAgnoJrgkhZLRwHBfW97OSM1ZWkEqlwrrZygEIfbOx9fpe00joJxwhmqbVVTst27bDEoJ6DWDZ6FiWfa3HNQKo6vfKDoT1slbWj5Z9Aft27dbDa5AQQsZCZf9YNpAlkUjAdd3wQzzHcdB1HbquI5vNAtiXjZVlGbIshxu/yNihIDYi2K5/AHWx25sFsJZl1WUA23d0bD1lNCv1rXvlOK5ugkMWvLIBBQAFr4SQ5lFZH8tqY1lNbDKZhGEYVeNo2QAXnucpGztO6N0oAlg7LV3Xkclkar2cqgC2HmtgozA6lh0YFUWpu36vrOZLVVVomha29KqnAJsQQsZDMpmE53kwTRO+74dBqmma/cZoM6IowrIsqo0dB/QRIQIURUGpVKqLjGe9B7Cs92u9lg/0rXtlu/nrYZ19R8PG43EaTkAIaXqsfyyb6FhZVgDsO0MlyzJSqVQ4OZFlY1taWigbO4ZGlFa56667MHv2bIiiiGXLlmHjxo37vf7DDz+M+fPnQxRFHH300fj1r39d9f3LL788bFHBvs4+++yq6xSLRVxyySVobW1Fe3s7rrzyyrBRfSOzbRv5fB7JZLLmfwj1HMB6nodyuQxJkhCPx+sygHVdF6qqhi2+eJ6viyBxsOla7NQYIYQ0s1gshkQiAdM0w8CV5/mwtaDjOGG3FkYUxfBsGxk7ww5i161bh5UrV+LWW2/F5s2bsWjRIpx11lno7e0d8PovvvgiLr74Ylx55ZX485//jPPPPx/nn38+tm7dWnW9s88+G3v37g2/fvazn1V9/5JLLsFrr72GZ599Fk899RT+8Ic/4Oqrrx7u8iOFtdNiuyFrqZ4DWMuyUCqVoOt6XY6O9TwPmqZBkqSwlqoeSgfYKTJN06DrOoIgCLs31NPvlxBCao21DzRNE57nhdlW1q3FdV3Ishzub6jMxrI9BWT0xYIgCIZzg2XLlmHp0qX47ne/C2BfoDVjxgzccMMN+Ld/+7d+17/wwguhaRqeeuqp8LITTzwRixcvxt133w1gXya2XC7j8ccfH/Ax33jjDRx55JF45ZVXcPzxxwMAnn76aXzoQx/Cu+++i87OzgOuW5ZltLW1QZKkuhrTuj/FYhE9PT3IZrM17b/JssG2bddVANt3dGy9nJZnBhoVW+tsOjDwaNh6WBchhNQ727YhCEJ4ts+yrPDYLggCpkyZgsmTJyMWiyEIAsiyjI6ODkyYMKHWS4+M4cRrw0oF2baNTZs2Yfny5X+/g3gcy5cvx4YNGwa8zYYNG6quDwBnnXVWv+v//ve/x9SpU/Ge97wH1157LQqFQtV9tLe3hwEsACxfvhzxeBwvv/zygI9rWVbY5oJ9RYlhGCgWixAEgQLYAbiuC0mSoChKWKdUL2sD9rXMUhSlrkbFVo6GNQwjzBTUel2EEBIVfcfSsrICz/PC9yU2AIGysWNvWEFsPp+H53mYNm1a1eXTpk1Dd3f3gLfp7u4+4PXPPvts/PjHP8b69evxH//xH3j++edxzjnnwPO88D6mTp1adR/JZBITJ04c9HFXr16Ntra28GvGjBnDeao15Xle+LMWRbFm67Asqy4DWNM0US6XYRhG3ZUPsLpXSZJg23bYHaGWPzvP88J6LdM0wwMrTdcihJDhYUMNKssKkslk2Ee7sjc5QLWxY60uUjAXXXRR+P9HH300jjnmGMydOxe///3v8f73v39E97lq1SqsXLky/Lcsy5EIZIMgQKlUgqqqNZ3KZVkWCoUCHMepmwC2b/lAPWVfWVsqwzDgeR44jqt5kMima9m2XbVZixBCyMixbCyb2sXacPm+D9d1USqVkM1mkc1mqVPBGBtWJnby5MlIJBLo6empurynpwcdHR0D3qajo2NY1weAOXPmYPLkyXjrrbfC++i7ccx1XRSLxUHvRxAEtLa2Vn1FQT1M5aoMYOtll7/ruiiXy5BlGclksm4CWFb3Ksty+Em7liUgbDQsy7yyaTP1sJGMEEIaBWu7xcoK2JhaljwolUphCQFlY8fOsN7VeJ7HkiVLsH79+vAy3/exfv16nHTSSQPe5qSTTqq6PgA8++yzg14fAN59910UCgVMnz49vI9yuYxNmzaF13nuuefg+z6WLVs2nKdQ1xzHQT6fDz+51UI9BrCmaaJUKsEwjHAqSj3o2zJLFMWatcwaLHjleZ6CV0IIGWV9226xDbKu64a1seysIdXGjp1hv7utXLkSP/zhD/HAAw/gjTfewLXXXgtN03DFFVcAAC699FKsWrUqvP7nPvc5PP300/jmN7+Jbdu24bbbbsOf/vQnXH/99QAAVVXxhS98AS+99BJ27NiB9evX45/+6Z8wb948nHXWWQCABQsW4Oyzz8ZVV12FjRs34oUXXsD111+Piy66aEidCaKATeUyDKNmU7kqa2DrIYANggCqqqJcLofDC+ohIBusZVatgtfKHoVsgAIFr4QQMrb6tt1KJpMIgiDsAFMsFsNMLWVjx8awU1oXXnghcrkcbrnlFnR3d2Px4sV4+umnw81bu3btqnrzPPnkk/HQQw/hS1/6Er74xS/i8MMPx+OPP46FCxcC2PciePXVV/HAAw+gXC6js7MTH/zgB/HVr34VgiCE9/Pggw/i+uuvx/vf/37E43GsWLEC//mf/3mwz79uyLJc06lcLICtlxpY13XDkaz1soO+smWW4zjgOK5mGXOWeWU9CoF9f0sUuBJCyPjhOA6WZYX93Fnv2Hg8DkVRIMsyJk2ahFgsBo7jqDZ2lA27T2xU1XOfWMuy8O677wJATYYa1FsAa5omZFmG53l1U8vJdp1allXTcaws88pGwwIUvBJCSC35vg/P85BOp6smeXEch5aWFsyaNQuiKIZ9Y6dPn4729vZaL7tuDSdeo48CNcamcjmOU5Pg2jTNuulC4Ps+VFWFpmnh6Nhac10XpmmGU1hqdZqeBa+WZYWt52iyFiGE1F48Hofv+zBNE4lEAslkEqZphu9pbBM6S4Cw7gWUjT14lL6psVKpBFmWkc1mx/2x6ymAdRwH5XIZqqqC47iqUpJa8H0/7PdnGEbNRsWy1l0suGd1V7XKBBNCCOmPbeqqTHiwTV6FQgG6rgPYd7aVamNHD30MqCFd11EsFiGK4rgHR/UUwBqGAUVRwuEOtTw1HgQBbNsO615Ze6rx/vn4vl9VNsDqqShwJYSQ+sTabrGkh+d5CIIAhmEgn88jlUqFG2+pNnZ0UCa2RlzXRT6fD0eSjqd6CWB934csyyiXywBQ8+4DfVtm8Tw/7oEjy7xqmgZd18O6KgpgCSGkvsXjccTj8bBbAcdx4RCEUqkUZl+pU8HooY8ANRAEAYrFIjRNG/epXCyAdV23Zq28gH3lA7Isw7Ksmncf8Dwv7DrAgsbxHlbA5m6zmtd4PF6zzgeEEEJGpu80L1ZWYFkWent7kclkws42pVKJsrEHiTKxNaCqKkqlEtLp9LhmHisD2HQ6PW6P25dhGCgWi+FAhVr9AQdBEH4aZpvJRFEc1wCWBdCapkHTNARBUPOgnhBCyMhVTvNi7ye+76NcLqNYLALYl41l0x7JyP3/7d15dFXlvT7wZ5/5ZDiZgAzILDOBgJEU8IKWXAYpSrWCSCsIFW3BgtheBBXEVmFpcblQW6rXK8uqYO2yFFmKIoJDSZlRUYYAuQwhJwPJmcd99vv7g9/ZNwfCkOQMOeH5rJUl2dP77gwnj+/57vflX8o4CwQCqKurU59SjJe2EGAbzz4QrjVNlIunzIp33Wt4MuxAIKCOvCb6YTYiImq9xqt5hR/E9fv9ahlhRkYGTCYTjEajWhsbzzzQnnAkNo7CZQQ+ny+uQbItBNjwWtLh2QcS9VZ5KBSCy+WC3W5HIBCAwWCAwWCIW4ANhULw+XxwuVzwer0ceSUiaocar+YFQH2uwW63o7a2FkIIGI1G1sa2Ev9yxlH4IaZ4rsrVFgKs2+2Gy+VSl45NxANKiqKosw7Ishz3utfwyGu45jX8hCoREbVP4fpYrVYLs9kMWZYhhEBdXR2ysrKQlpYGk8nE0dhW4EhsnPh8PtTV1UGv18dt1C3RATYUCsFut8PhcECSpIQE2PCUWU6nE06nU/2/33gF2FAoBK/XC5fLBY/HA0mSYDQaOfJKRNTOSZIEnU6nlhKE3/XzeDywWq3q7ESsjW05htg4aLwqV7xWoUp0gA2XD7jdbvUt+3iTZRlutxsOhwPBYDCuU2Y1Dq8+n08Nr/Ge9YCIiBIn/Jrv9XojPj9//jxsNpv6t8FmsyEYDCasn8mKITYOwvPDxWtVrkQHWLfbjYaGBjW0xzu4KYoCj8cDh8MBj8cT19W2mgqvBoOB4ZWI6Dql1+vVabbCy4UHAgFYrVYEg0EYjUYEAgGOxrYAQ2yMxXtVrkQG2HD5gN1uh0ajiXv5gBBCfVvG5XIBQNxCdCgUgsfjYXglIqJLNC4rCL8j2NDQgLq6uojR2EAgkOiuJhWG2BiSZRm1tbVxW5UrkQG2cfmA0WiMe4H6xattmUymuPQhHF6dTid8Pp+6SAHDKxERhYUHscKzFWi1WiiKAqvVCq/Xy9HYFmKIjSG73Q6PxxOXMoLwg2OJCLDh8gFZluNePhAOkXa7XZ2TLx5zvsqyrIZXv9+vznubyGVziYio7dLr9eosNXq9HhqNBk6nE9XV1QAuLIAQnv6Rrg0fkY4hWZah0WhiHqjCATYUCsU1wIZCITidTng8Huj1+riOvoZLB3w+H4LBIHQ6XVweHpNlWV2JRQgBnU7H4EpERNckvJpXeBGEUCiE6upqZGZmIjMzE3a7HTabDZ06dUp0V5MCQ2ySS1SADb/tEQgE4v7UfbxX2xJCIBQKMbwSEVGraDQadbnxlJQU6HQ6eL1eWK1WpKenw2w2w+FwICMjg6s4XgOG2CSWiAArhFAfYBJCxPXhrfBqV41XuoplkAyHV7/fj2AwyPBKREStFl6G1u/3w2g0IhQKoa6uDjk5OejYsSO8Xi/sdjtHY68BQ2ySSkSADZcPeL1e9a2QeAiXDni9XnW+11iO/DK8EhFRLIXLCsLLsHs8HlRWVsJiscBsNsNut8NiscBkMiW6q20a/yonoUQEWL/fj4aGBng8HnXRgHgIBoNwOBxwOp1QFAUmkylmAVYIoT6w5XK5EAwG1eVhGWCJiChaGs9WoNFooNfrYbPZUF1dDYPBAFmWYbfbE9zLto8jsUkm3gFWCAG32x0x72o8ygdkWYbP51OnI4llkAyH10AgoK6YotVqGVyJiChmmiorsFqtyM7ORkpKijoaG6+VPpMRQ2wSiXeATUT5gKIoCAQC8Hg8CIVC0Ov1MR95ZXglIqJEuLiswOVy4dy5c7jxxhuhKArsdjtMJlNcFw5KJgyxSSLeAdbv98PpdMZt9gEhBILBIDwej/o2fqxmHWB4JSKitiA8W4HP51NnK6iurkZOTg4yMjLgcDhgsVgSsoR8MmCITQLxDLCJKB+IV+kAwysREbU1jcsKTCYTnE4nzp49i/T0dAghYLPZ4r6Me7JgiG3jwkvJxiPAxrt8QFEUNbzKshyzWQcYXomIqC1rXFZgNBpRX1+P2tpa5Obmwul0IiMjA6mpqYnuZpvDENuGhQNsPJaSjWf5gBACgUAAXq9XXbkkFoXr4RKF8AcA6HQ6/t8sERG1KY3LCsxmM4LBICorK5GVlaWOxqakpPDv10UYYtuoeAXYeJcPyLKsrrYFICYF6+HwGggEIMsyAIZXIiJq28KjscFgUC0rOHfuHLp37w6XywW32420tLREd7NNYYhtg+IVYONZPhAuHfB6vVAUJSazDjC8EhFRMtPpdBFL0lZVVaFDhw7QarXqaCxL4f4PvxJtTLwCbLwWLwivtuVwOOB2u6HRaKK+YEG4PMHtdsPj8UCWZTWUM8ASEVGyCAfU8ENefr8fp0+fhtFohNPphNvtTnAP2xaOxLYh8Qiw8SwfaFw6IElS1KfMajzyGgwGIUkSR16JiCip6XQ6BINBhEIhmEwmnD9/HufPn0d6ejrq6+uRmprK0dj/j1+FNiIeATYUCsFut8PpdEKr1cZsAuVQKAS32w273Q6fzwedTgeDwRC1thqPvLrdbsiyDL1ez5FXIiJKepIkQavVqn8/FUXB2bNnIUmSuiw6XcCR2DYgHgE2HrMPhEsHvF6v+pa+wWCI6vWDwSD8fj9kWYYkSQyuRETU7mi1WnUFS7PZDLvdjurqauTm5qqjsbFehCgZMMQmWKwDbLzKB4LBoFo6oNFoolo6wPBKRETXm3BZgVarhVarxblz55CVlQVZluF0OpGZmZnoLiYcQ2wCxTrAxmP2gfC8dl6vF0B0V9sKlw2EZxtgeCUioutFuKwgEAjAZDLB5XLh3Llz6NatGxoaGpCWlgad7vqOcdf33SdQOMAGg8GYrMIR6/KBxqUDwWAwqqttMbwSERFBDbGyLMNkMqGmpgYdOnSAyWSCw+FAdnZ2oruYUHywKwFiGWCFEHC5XGhoaIAsyzCbzVEPsMFgEE6nE06nE4qiRG3KrHAwdrlc8Hg8CIVCfGCLiIiua+FFEHQ6HUKhEM6cOQMAaGhoUFejvF5xJDbOGgfYaJcQxLp8IBQKwev1wufzAYhe6YCiKBGLFHDklYiI6AJJkqDRaNQlaRsaGlBfX4+srCzY7XZ06NAh0V1MGIbYOPL7/REBNpohLRAIwOFwxKR84OJZB6K12hbDKxER0dXpdDoEAgEoiqI+5GWxWGC322GxWKI6E1AyYYiNE7/fj7q6upgE2PDsA0KIqM8+EAwG4fF4EAgEoNVqozLrAMMrERFR84SDrMlkgtfrRU1NDXJzc2Gz2dCpU6dEdy8hGGLjIFYBNhQKweVywe12q7Wj0SLLMnw+X1RLBxheiYiIWkaj0UCj0SAYDEKv16O6uhoZGRnqaKzJZEp0F+OOITbGAoEAnE5n1ANsrMoHwpMrR7N0gOGViIio9cKjsXq9HsFgEFVVVTAYDLDZbMjNzb3u/q4yxMaQ3+9HQ0MD9Hp9VANsrMoHwuE1WqUDDK9ERETRpdPpIMuyGl6zs7NhNBphsVhitupnW8UQG0NutxuBQAAWiyUqwS1W5QPRLh1geCUiIooNjUaDUCgEIQSEEKiqqkJKSgpsNlvMVuVsqxhiY0gIAY1GE5UfqFiUD4RLBxrPydqa614cXjUaDcMrERFRlOn1evj9fuj1erjdbtTX18NkMsFisSAtLS3R3YsbhtgkEC4fUBQlKv+XJYRAMBiMWukAwysREVF86fV6hEIhaLVa1NTUIDU1Fenp6UhJSYna8u9tHUNsG3Zx+YDRaGz1NaNZOsDwSkRElBjhsgJJktRZkNLS0mCxWGCxWBLdvbhoUXp59dVX0b17d5hMJpSUlGD37t1XPP79999Hv379YDKZUFhYiI8++kjdFwwGsXjxYhQWFiI1NRUFBQW4//77ce7cuYhrdO/eHZIkRXysWrWqJd1PCoFAAA0NDXC73TAaja2uf1UUBV6vFw6HQ13Ry2g0tijAKooCv98Pt9sNj8cDRVGg1+uh0+kYYImIiOIkvBStTqeDzWZDfX096uvrEQqFEt21uGh2gnnvvfewaNEiLF++HPv378eQIUMwfvx41NTUNHn8zp07MX36dMyZMwcHDhzAlClTMGXKFBw6dAgA4PF4sH//fjz11FPYv38/PvjgAxw9ehR33HHHJdd65plnUFVVpX488sgjze1+UvB4POqayGazuVV1qkIItZ7W5XIBQItrahleiYiI2g5JkqDTXXhTPRQKoa6uDg0NDerf+/ZOEkKI5pxQUlKCm2++Ga+88gqAC8GmS5cueOSRR/D4449fcvy0adPgdruxefNmdduPfvQjFBUVYe3atU22sWfPHgwfPhynTp1C165dAVwYiV24cCEWLlzYnO6qHA5HxKTA8fDDDz/AarUiJyfnmo4Plw94PB5otdpWLyN3cemAXq9v8cjrxWUDWq2WwZWIiKgNCAQC0Gg0kGUZ+fn56NWrF7p166YG3GTSnLzWrEQTCASwb98+lJaW/t8FNBqUlpairKysyXPKysoijgeA8ePHX/Z4ALDb7ZAkCZmZmRHbV61ahZycHAwdOhQvvPACZFm+7DX8fj8cDkfER1sWDAZhs9ngdrthMBhaFWAblw54PJ4Wlw5cPPIansGAI69ERERth16vh6IokCQJ9fX1qK2tbfO5JxqaFdHr6uoQCoWQm5sbsT03NxdHjhxp8hyr1drk8VartcnjfT4fFi9ejOnTp0ck8N/85jcYNmwYsrOzsXPnTixZsgRVVVV48cUXm7zOypUrsWLFiubcXsKEA2drFy8Izzrg8XgQDAah1WphMpmafT3O80pERJQ8JEmCRqOBEAI+nw+1tbXIyspCWlpaq9/Vbcva1DhzMBjE1KlTIYTAn//854h9ixYtUv89ePBgGAwGPPTQQ1i5cmWTT+0vWbIk4hyHw4EuXbrErvMtoCiKOvtAeJqrlorGrAPh+lmGVyIiouQSXpJWq9XCbrfDarUiOzsbHTt2THTXYqZZIbZDhw7QarWorq6O2F5dXY28vLwmz8nLy7um48MB9tSpU/j888+vWgdRUlICWZbxv//7v+jbt+8l+41GY1SmpIqVYDAIh8MBv98Pg8HQ4rqV8Fv+Xq9XXYauuQ9tMbwSERElv/CStLIso66uDtXV1UhPT4fJZEp012KiWUN1BoMBN910E7Zt26ZuUxQF27Ztw4gRI5o8Z8SIERHHA8DWrVsjjg8H2PLycnz22WfX9CDUwYMHodFo0KlTp+bcQpvg9XpRX1+vzj7QkgDb1KwDzZ3JQAgBv9+vPkwWrnllgCUiIko+4VVCNRoNXC4XKisr0dDQgGY+w580mp2eFi1ahJkzZ6K4uBjDhw/HSy+9BLfbjQceeAAAcP/996Nz585YuXIlAGDBggUYM2YMVq9ejUmTJmHDhg3Yu3cvXnvtNQAXAuzPfvYz7N+/H5s3b0YoFFLrZbOzs2EwGFBWVoZdu3bhtttuQ3p6OsrKyvDoo4/i5z//ObKysqL1tYi5aJUPtLZ0IFw76/f7OfJKRETUjuj1egQCASiKApvNhsrKSmRkZCAlJSXRXYu6ZofYadOmoba2FsuWLYPVakVRURG2bNmiPrx1+vTpiEA1cuRIvPvuu3jyySexdOlS9O7dGxs3bsSgQYMAAJWVldi0aRMAoKioKKKt7du349Zbb4XRaMSGDRvw9NNPw+/3o0ePHnj00Ucjal7bOlmWYbfbW1U+oCiKGl5bUjrA8EpERNT+6XQ6KIoCt9uNc+fOoVOnTujSpUu7+3vf7Hlik1Wi5omtqqpCamoqnE4nQqFQi2YLaDzrQCAQgE6na9YKXk2FV06TRURE1H4Fg0HIsgytVosePXqgqKgIaWlpie7WVTUnr7Wp2QnaG0VR1OmuNBoNzGZzs69xcelAc+Z75cgrERHR9Sk8GivLMqqrq2G1WtGzZ88WLXrUVjHExpDL5YLX60VKSkqzywdaUzoQDq+BQADBYJDhlYiI6DojSRK0Wi0URYHD4UBFRQU6dOhwyUJSyYwhNobCq2c0J8CGZx3wer1q6cC1juAyvBIREVFYeMotIQTq6upw9uxZpKWlJeVytE1pH3fRTrS0dKBxeA0vxcvwSkRERAaDQR0cq6ioQG5ubrtZAIEhtg1oaelAU+GVD2wRERFRmEajUetj6+vrUVFRgczMzGY9IN5WtZ/q3iTU0gULwue53W54PB7IsqzOWMAAS0RERI3pdDpoNBrIsozTp0+jpqYm0V2KCo7EJkhLSgc48kpEREQtYTAYEAqF4HA4cPz4ceTk5CT9crQciY0zRVHg9XrhcDjg8Xig0+muGmAbzxPLkVciIiJqLo1GA71eDyEEKisrcfbs2aRfjpYjsXHS1IIFV5t1QAgBWZbV2QYAjrwSERFRy+j1esiyDI/Hg2PHjiE3Nxfp6emJ7laLcSQ2DmRZhtvthsPhgCzLMBqNVyyobhx43W43gsEgR16JiIioVSRJgsFggCRJqK2tRUVFBRRFSXS3WowjsTHU+MGta5l1oKmRV61W265W1yAiIqLE0el00Gq18Pv9OHHiBDp37oycnJxEd6tFmI5iyOv1qg9uXWnWgaZGXrVaLfR6PQMsERERRZXRaIRWq0V9fT3Ky8sRCoUS3aUWYUKKofAPxeVKBxheiYiIKN40Gg0MBgMURcHJkydhtVoT3aUWYVJKAIZXIiIiSiSDwQCdTgeXy4UjR44gEAgkukvNxsQUR+GaV4ZXIiIiSjSj0QhJknD27FmcOnUq0d1pNianOGgcXl0uF8MrERERJZxWq4XRaITX68WRI0fU1UOTBRNUjCmKwvBKREREbVK4rMBqtaK8vDypFkBgkoohn8+HQCCAQCDA8EpERERtjiRJMJlMCIVCKC8vR319faK7dM2YqGIoFApBCAGDwcDwSkRERG2SXq+H0WhEfX09fvjhh6RZAIHJioiIiOg6F37I6+TJkzh37lyiu3NNGGKJiIiIrnNarRYmkwlutxvfffddUky5xRBLRERERDCZTDAYDDh79ixOnjyZ6O5cFUMsEREREQEAzGYzZFnGoUOH2vyUWwyxRERERAQA0Ol0MBgMqK2txZEjRxLdnStiiCUiIiIildlshiRJOHz4MOrq6hLdnctiiCUiIiIilVarhdlshsPhwDfffNNmp9xiiCUiIiKiCCaTCXq9HhUVFThz5kyiu9MkhlgiIiIiukRqair8fj8OHDiAYDCY6O5cgiGWiIiIiC4RfsirpqamTS5HyxBLRERERE3S6/UIhUJtcvEDhlgiIiIiapIkSRBCJLobTWKIJSIiIqKkwxBLREREREmHIZaIiIiIkg5DLBERERElHYZYIiIiIko6DLFERERElHQYYomIiIgo6TDEEhEREVHSYYglIiIioqTDEEtERERESYchloiIiIiSDkMsERERESUdhlgiIiIiSjoMsURERESUdFoUYl999VV0794dJpMJJSUl2L179xWPf//999GvXz+YTCYUFhbio48+itgvhMCyZcuQn58Ps9mM0tJSlJeXRxxTX1+PGTNmwGKxIDMzE3PmzIHL5WpJ94mIiIgoyTU7xL733ntYtGgRli9fjv3792PIkCEYP348ampqmjx+586dmD59OubMmYMDBw5gypQpmDJlCg4dOqQe8/zzz2PNmjVYu3Ytdu3ahdTUVIwfPx4+n089ZsaMGfj++++xdetWbN68GV9++SXmzp3bglsmIiIiomQnCSFEc04oKSnBzTffjFdeeQUAoCgKunTpgkceeQSPP/74JcdPmzYNbrcbmzdvVrf96Ec/QlFREdauXQshBAoKCvDYY4/ht7/9LQDAbrcjNzcX69atw7333ovDhw9jwIAB2LNnD4qLiwEAW7Zswe23346zZ8+ioKDgqv12OBzIyMiA3W6HxWJpzi232AcffIBz586hY8eOcWmPiIiIKJp8Ph/cbjcmT56MLl26xLy95uS1Zo3EBgIB7Nu3D6Wlpf93AY0GpaWlKCsra/KcsrKyiOMBYPz48erxFRUVsFqtEcdkZGSgpKREPaasrAyZmZlqgAWA0tJSaDQa7Nq1qzm3QERERETtgK45B9fV1SEUCiE3Nzdie25uLo4cOdLkOVartcnjrVaruj+87UrHdOrUKbLjOh2ys7PVYy7m9/vh9/vVzx0Ox9VuLyZCoRAaGhoS0jYRERFRayiKkuguXFazQmwyWblyJVasWJHQPmRnZ8Ptdie0D0REREStYTAY4laK2RzNCrEdOnSAVqtFdXV1xPbq6mrk5eU1eU5eXt4Vjw//t7q6Gvn5+RHHFBUVqcdc/OCYLMuor6+/bLtLlizBokWL1M8dDkdcajkau/XWW+PaHhEREdH1olk1sQaDATfddBO2bdumblMUBdu2bcOIESOaPGfEiBERxwPA1q1b1eN79OiBvLy8iGMcDgd27dqlHjNixAjYbDbs27dPPebzzz+HoigoKSlpsl2j0QiLxRLxQURERETtQ7PLCRYtWoSZM2eiuLgYw4cPx0svvQS3240HHngAAHD//fejc+fOWLlyJQBgwYIFGDNmDFavXo1JkyZhw4YN2Lt3L1577TUAgCRJWLhwIf7whz+gd+/e6NGjB5566ikUFBRgypQpAID+/ftjwoQJePDBB7F27VoEg0HMnz8f99577zXNTEBERERE7UuzQ+y0adNQW1uLZcuWwWq1oqioCFu2bFEfzDp9+jQ0mv8b4B05ciTeffddPPnkk1i6dCl69+6NjRs3YtCgQeox//Vf/wW32425c+fCZrPhlltuwZYtW2AymdRj3nnnHcyfPx9jx46FRqPB3XffjTVr1rTm3omIiIgoSTV7nthklYh5YomIiIjo2sVsnlgiIiIioraAIZaIiIiIkg5DLBERERElHYZYIiIiIko6DLFERERElHQYYomIiIgo6TDEEhEREVHSYYglIiIioqTDEEtERERESYchloiIiIiSDkMsERERESUdhlgiIiIiSjoMsURERESUdBhiiYiIiCjp6BLdgXgRQgAAHA5HgntCRERERE0J57RwbruS6ybEOp1OAECXLl0S3BMiIiIiuhKn04mMjIwrHiOJa4m67YCiKDh37hzS09MhSVLM23M4HOjSpQvOnDkDi8US8/aIKFKifwfZPtvn3wBqD+L9syyEgNPpREFBATSaK1e9XjcjsRqNBjfccEPc27VYLHwBI0qgRP8Osn22z78B1B7E82f5aiOwYXywi4iIiIiSDkMsERERESUdhtgYMRqNWL58OYxGY6K7QnRdSvTvINtn+/wbQO1BW/5Zvm4e7CIiIiKi9oMjsURERESUdBhiiYiIiCjpMMQSERERUdJhiCUiIiKipMMQ20pffvklJk+ejIKCAkiShI0bN0bsF0Jg2bJlyM/Ph9lsRmlpKcrLyxPTWaJ2btWqVZAkCQsXLlS33XrrrZAkKeLj4Ycfjkp7Tz/99CXX7tevn7rf5/Nh3rx5yMnJQVpaGu6++25UV1dHpe1raT+W9x5WWVmJn//858jJyYHZbEZhYSH27t2r7o/1a+DV2p81a9YlX4MJEyZEpe3u3btfcm1JkjBv3jwAsf/+E0WT0+nEwoUL0a1bN5jNZowcORJ79uxR97tcLsyfPx833HADzGYzBgwYgLVr1yawxwyxreZ2uzFkyBC8+uqrTe5//vnnsWbNGqxduxa7du1Camoqxo8fD5/PF+eeErVve/bswV/+8hcMHjz4kn0PPvggqqqq1I/nn38+au0OHDgw4tpff/21uu/RRx/Fhx9+iPfffx9ffPEFzp07h7vuuitqbV+tfSC2997Q0IBRo0ZBr9fj448/xg8//IDVq1cjKytLPSaWr4HX0j4ATJgwIeJrsH79+la3DVz4mWt83a1btwIA7rnnHgDx+f4TRcsvf/lLbN26FX/961/x3XffYdy4cSgtLUVlZSUAYNGiRdiyZQvefvttHD58GAsXLsT8+fOxadOmxHVaUNQAEP/4xz/UzxVFEXl5eeKFF15Qt9lsNmE0GsX69esT0EOi9snpdIrevXuLrVu3ijFjxogFCxao+y7+PJqWL18uhgwZ0uQ+m80m9Hq9eP/999Vthw8fFgBEWVlZzNsXIrb3LoQQixcvFrfccstl98f6NfBq7QshxMyZM8Wdd97Z6rauxYIFC0SvXr2Eoihx+f4TRYvH4xFarVZs3rw5YvuwYcPEE088IYQQYuDAgeKZZ5657P5E4EhsDFVUVMBqtaK0tFTdlpGRgZKSEpSVlSWwZ0Tty7x58zBp0qSI37XG3nnnHXTo0AGDBg3CkiVL4PF4otZ2eXk5CgoK0LNnT8yYMQOnT58GAOzbtw/BYDCiT/369UPXrl2j+vt/ufbDYnnvmzZtQnFxMe655x506tQJQ4cOxeuvv67uj/Vr4NXaD9uxYwc6deqEvn374le/+hXOnz/f6rYvFggE8Pbbb2P27NmQJClu33+iaJBlGaFQCCaTKWK72WxW390ZOXIkNm3ahMrKSgghsH37dhw7dgzjxo1LRJcBALqEtXwdsFqtAIDc3NyI7bm5ueo+ImqdDRs2YP/+/RG1W43dd9996NatGwoKCvDtt99i8eLFOHr0KD744INWt11SUoJ169ahb9++qKqqwooVK/Af//EfOHToEKxWKwwGAzIzMyPOiebv/5XaT09Pj+m9A8DJkyfx5z//GYsWLcLSpUuxZ88e/OY3v4HBYMDMmTNj/hp4tfaBC6UEd911F3r06IETJ05g6dKlmDhxIsrKyqDValvdh7CNGzfCZrNh1qxZABCX7z9RtKSnp2PEiBH4/e9/j/79+yM3Nxfr169HWVkZbrzxRgDAyy+/jLlz5+KGG26ATqeDRqPB66+/jtGjRyes3wyxRJS0zpw5gwULFmDr1q2XjCCEzZ07V/13YWEh8vPzMXbsWJw4cQK9evVqVfsTJ05U/z148GCUlJSgW7du+Nvf/gaz2dyqa7e2/Tlz5sT03gFAURQUFxfjueeeAwAMHToUhw4dwtq1a9UQGUvX0v69996rHl9YWIjBgwejV69e2LFjB8aOHRu1vrzxxhuYOHEiCgoKonZNonj661//itmzZ6Nz587QarUYNmwYpk+fjn379gG4EGL//e9/Y9OmTejWrRu+/PJLzJs3DwUFBZd9FyzWWE4QQ3l5eQBwydOo1dXV6j4iarl9+/ahpqYGw4YNg06ng06nwxdffIE1a9ZAp9MhFApdck5JSQkA4Pjx41HvT2ZmJvr06YPjx48jLy8PgUAANpst4phY/v43br8p0b73/Px8DBgwIGJb//791ZKGWL8GXq39pvTs2RMdOnSI6vf/1KlT+Oyzz/DLX/5S3ZaI7z9Ra/Tq1QtffPEFXC4Xzpw5g927dyMYDKJnz57wer1YunQpXnzxRUyePBmDBw/G/PnzMW3aNPzxj39MWJ8ZYmOoR48eyMvLw7Zt29RtDocDu3btwogRIxLYM6L2YezYsfjuu+9w8OBB9aO4uBgzZszAwYMHm3y7+ODBgwAuBKBoc7lcOHHiBPLz83HTTTdBr9dH/P4fPXoUp0+fjtnvf+P2mxLtex81ahSOHj0ase3YsWPo1q0bgNi/Bl6t/aacPXsW58+fj+r3/80330SnTp0wadIkdVsivv9E0ZCamor8/Hw0NDTgk08+wZ133olgMIhgMAiNJjI2arVaKIqSoJ6CsxO0ltPpFAcOHBAHDhwQAMSLL74oDhw4IE6dOiWEEGLVqlUiMzNT/POf/xTffvutuPPOO0WPHj2E1+tNcM+J2qfGT+QfP35cPPPMM2Lv3r2ioqJC/POf/xQ9e/YUo0ePjkpbjz32mNixY4eoqKgQ//rXv0Rpaano0KGDqKmpEUII8fDDD4uuXbuKzz//XOzdu1eMGDFCjBgxIiptX639WN+7EELs3r1b6HQ68eyzz4ry8nLxzjvviJSUFPH222+rx8TyNfBq7TudTvHb3/5WlJWViYqKCvHZZ5+JYcOGid69ewufz9fq9oUQIhQKia5du4rFixdfsi/W33+iaNqyZYv4+OOPxcmTJ8Wnn34qhgwZIkpKSkQgEBBCXHhtHThwoNi+fbs4efKkePPNN4XJZBJ/+tOfEtZnhthW2r59uwBwycfMmTOFEBemmHnqqadEbm6uMBqNYuzYseLo0aOJ7TRRO9Y4xJ4+fVqMHj1aZGdnC6PRKG688Ubxu9/9Ttjt9qi0NW3aNJGfny8MBoPo3LmzmDZtmjh+/Li63+v1il//+tciKytLpKSkiJ/+9KeiqqoqKm1frf1Y33vYhx9+KAYNGiSMRqPo16+feO211yL2x/o18ErtezweMW7cONGxY0eh1+tFt27dxIMPPiisVmvU2v/kk08EgCbvKdbff6Joeu+990TPnj2FwWAQeXl5Yt68ecJms6n7q6qqxKxZs0RBQYEwmUyib9++YvXq1UJRlIT1WRJCiMSNAxMRERERNR9rYomIiIgo6TDEEhEREVHSYYglIiIioqTDEEtERERESYchloiIiIiSDkMsERERESUdhlgiIiIiSjoMsURE10CSJGzcuPGaj9+xYwckSYLNZotZn+LpjTfewLhx41p1jbVr12Ly5MlR6hERXe8YYomIAMyaNQtTpky57P6qqipMnDgxqm0+/fTTKCoqiuo1Y8Hn8+Gpp57C8uXL1W1bt25Fnz59YLFY8Itf/AKBQEDdZ7fb0adPH5w6dSriOrNnz8b+/fvx1Vdfxa3vRNR+McQSEV2DvLw8GI3GRHcjIf7+97/DYrFg1KhRAABFUXDffffh4YcfRllZGfbu3YvXXntNPf7xxx/Hww8/jG7dukVcx2Aw4L777sOaNWvi2n8iap8YYomIrsHF5QQ7d+5EUVERTCYTiouLsXHjRkiShIMHD0act2/fPhQXFyMlJQUjR47E0aNHAQDr1q3DihUr8M0330CSJEiShHXr1qlt/fd//zd++tOfIiUlBb1798amTZsirnvo0CFMnDgRaWlpyM3NxS9+8QvU1dWp+//+97+jsLAQZrMZOTk5KC0thdvtBnCh1GH48OFITU1FZmYmRo0adcmoaWMbNmyIKAOoq6tDXV0dfv3rX2PgwIG44447cPjwYfXrsmfPHixYsKDJa02ePBmbNm2C1+u98heciOgqGGKJiJrJ4XBg8uTJKCwsxP79+/H73/8eixcvbvLYJ554AqtXr8bevXuh0+kwe/ZsAMC0adPw2GOPYeDAgaiqqkJVVRWmTZumnrdixQpMnToV3377LW6//XbMmDED9fX1AACbzYYf//jHGDp0KPbu3YstW7aguroaU6dOBXCh9GH69OmYPXs2Dh8+jB07duCuu+6CEAKyLGPKlCkYM2YMvv32W5SVlWHu3LmQJOmy9/v111+juLhY/bxjx47Iz8/Hp59+Co/Hg6+++gqDBw9GMBjEr371K/zlL3+BVqtt8lrFxcWQZRm7du1q3hediOgiukR3gIgo2bz77ruQJAmvv/46TCYTBgwYgMrKSjz44IOXHPvss89izJgxAC68zT5p0iT4fD6YzWakpaVBp9MhLy/vkvNmzZqF6dOnAwCee+45rFmzBrt378aECRPwyiuvYOjQoXjuuefU4//nf/4HXbp0wbFjx+ByuSDLMu666y71Lf3CwkIAQH19Pex2O37yk5+gV69eAID+/ftf9l5tNhvsdjsKCgrUbZIk4W9/+xseffRRLFiwALfffjtmz56NVatW4bbbboPJZMKoUaNQV1eHRx55BPPnz1fPTUlJQUZGxhVHfomIrgVDLBFRMx09ehSDBw+GyWRStw0fPrzJYwcPHqz+Oz8/HwBQU1ODrl27XrGNxuelpqbCYrGgpqYGAPDNN99g+/btSEtLu+S8EydOYNy4cRg7diwKCwsxfvx4jBs3Dj/72c+QlZWF7OxszJo1C+PHj8d//ud/orS0FFOnTlX7drHw2/6N7xUAbrnlFuzZs0f9/NixY3jrrbdw4MABjB49GgsWLMDEiRMxaNAgjB49OuJ+zGYzPB7PFe+fiOhqWE5ARBRDer1e/Xf4LXtFUZp1Xvjc8HkulwuTJ0/GwYMHIz7Ky8sxevRoaLVabN26FR9//DEGDBiAl19+GX379kVFRQUA4M0330RZWRlGjhyJ9957D3369MG///3vJvuRk5MDSZLQ0NBwxf4+9NBDWL16NRRFwYEDB3DPPfegU6dOGDNmDL744ouIY+vr69GxY8erfg2IiK6EIZaIqJn69u2L7777Dn6/X93WeFTyWhkMBoRCoWafN2zYMHz//ffo3r07brzxxoiP1NRUABdC76hRo7BixQocOHAABoMB//jHP9RrDB06FEuWLMHOnTsxaNAgvPvuu5ft44ABA/DDDz9ctj9vvPEGsrOzcccdd6j3EwwG1f82vscTJ07A5/Nh6NChzb5vIqLGGGKJiP4/u91+yejmmTNnLjnuvvvug6IomDt3Lg4fPoxPPvkEf/zjHwHgig9IXax79+6oqKjAwYMHUVdXFxGKr2TevHmor6/H9OnTsWfPHpw4cQKffPIJHnjgAYRCIezatQvPPfcc9u7di9OnT+ODDz5AbW0t+vfvj4qKCixZsgRlZWU4deoUPv30U5SXl1+xLnb8+PH4+uuvm9xXU1ODP/zhD3j55ZcBAFlZWejfvz9eeukllJWVYdu2berUXADw1VdfoWfPnmo9LhFRSzHEEhH9fzt27MDQoUMjPlasWHHJcRaLBR9++CEOHjyIoqIiPPHEE1i2bBmAS2tHr+Tuu+/GhAkTcNttt6Fjx45Yv379NZ1XUFCAf/3rXwiFQhg3bhwKCwuxcOFCZGZmQqPRwGKx4Msvv8Ttt9+OPn364Mknn8Tq1asxceJEpKSk4MiRI7j77rvRp08fzJ07F/PmzcNDDz102fbmzJmDjz76CHa7/ZJ9CxYswGOPPRbx4Ne6deuwYcMG/OQnP8Hvfvc73Hzzzeq+9evXN/kAHBFRc0lCCJHoThARJbt33nkHDzzwAOx2O8xmc6K7E3X33HMPhg0bhiVLlrT4Gt9//z1+/OMf49ixY8jIyIhi74joesSRWCKiFnjrrbfw9ddfo6KiAhs3bsTixYsxderUdhlgAeCFF15ocjaE5qiqqsJbb73FAEtEUcGRWCKiFnj++efxpz/9CVarFfn5+ZgyZQqeffZZpKSkJLprRETXBYZYIiIiIko6LCcgIiIioqTDEEtERERESYchloiIiIiSDkMsERERESUdhlgiIiIiSjoMsURERESUdBhiiYiIiCjpMMQSERERUdJhiCUiIiKipPP/AB2X27eLxitRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import arctan2, degrees\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "\n",
    "for h_str in h_L_points_Cstar:\n",
    "    if h_str not in accent_h_map:\n",
    "        continue\n",
    "    ax.fill_between(L_points, h_L_points_Cstar[h_str], alpha=0.2, color='grey', label=h_str)\n",
    "\n",
    "    x, y = L_points, h_L_points_Cstar[h_str]\n",
    "    n = int(0.5*len(x))\n",
    "    ax.text(x[n], y[n]-0.01, h_str, rotation=10, va='center', ha='left')\n",
    "    \n",
    "ax.set_xlabel(\"Lightness (%)\")\n",
    "ax.set_xticks([L_points[0], 45, 50, 55, 60, 65, 70, L_points[-1]])\n",
    "plt.suptitle(\"$C^*$ curves (v1.3.0)\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d137161e-1628-4789-b6ff-b82925b989e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import arctan2, degrees\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "\n",
    "for h_str in h_L_points_Cstar:\n",
    "    if h_str not in accent_h_map:\n",
    "        continue\n",
    "    ax.fill_between(v111_L_space, v111_hC_points[h_str], alpha=0.2, color='grey', label=h_str)\n",
    "\n",
    "    x, y = v111_L_space, v111_hC_points[h_str]\n",
    "    n = int(0.46*len(x))\n",
    "    ax.text(x[n], y[n]-0.005, h_str, rotation=30, va='center', ha='left')\n",
    "    \n",
    "ax.set_xlabel(\"Lightness (%)\")\n",
    "ax.set_xticks([L_points[0], 45, 50, 55, 60, 65, 70, L_points[-1]])\n",
    "plt.suptitle(\"$C^*$ curves (v1.1.1)\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d72e7f0b-a1a4-46f1-84fb-752229afafb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "\n",
    "for h_str in h_L_points_Cstar:\n",
    "    if h_str not in accent_h_map:\n",
    "        continue\n",
    "    ax.fill_between(L_points, h_L_points_Cstar[h_str], alpha=0.2, color=h_str)\n",
    "    \n",
    "    \n",
    "ax.set_xlabel(\"Lightness (%)\")\n",
    "ax.set_xticks([L_points[0], 45, 50, 55,   60, 65, 70, L_points[-1]])\n",
    "ax.set_xlim(45, 70)\n",
    "plt.suptitle(\"$C^*$ curves -- mid-range close-up (v1.3.0)\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef0dd0d9-a267-494f-b9c6-90d9ab0e6564",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TOML] written\n",
      "[JSON] written\n"
     ]
    }
   ],
   "source": [
    "# write files -- QBR = \"quadratic bezier rational\"\n",
    "PALETTE_DIR = \"palettes\"\n",
    "\n",
    "toml_content = '\\n'.join(toml_lines)\n",
    "with Path(PALETTE_DIR, 'monobiome-vQBRsn-130.toml').open('w') as f:\n",
    "    f.write(toml_content)\n",
    "print(\"[TOML] written\")\n",
    "    \n",
    "with Path(PALETTE_DIR, 'monobiome-vQBRsn-130-oklch.json').open('w') as f:\n",
    "    json.dump(oklch_hL_dict, f)\n",
    "print(\"[JSON] written\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e14f54-32d3-4027-beb6-496ae129fc8a",
   "metadata": {},
   "source": [
    "### Build scheme maps with fixed OKLCH distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e12883f-54aa-4b16-8e49-42fa8b1aee3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unused; computes exact L1 to hit distance `d`. Not\n",
    "# useful currently b/c it can't be used to get colors\n",
    "# along the chroma curve: it solves for lightness, but\n",
    "# at that lightness the chroma will change (and therefore\n",
    "# the distance). *Could* be adapt for more complexity to\n",
    "# optimize along chroma curves, but discrete opt is easier.\n",
    "def solve_L(d, l1, c1, h1, c2, h2):\n",
    "    return l1 + (d**2 - (c1-c2)**2 - (h1-h2)**2)**0.5\n",
    "    \n",
    "# l1, c1, h1 = oklch_color_map[\"alpine\"][0].coords()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9a66c06d-fe35-425f-a093-48433f25585b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def compute_dma_map(dT, metric = None):\n",
    "    \"\"\"\n",
    "    For threshold `dT`, compute the nearest accent shades\n",
    "    that exceed that threshold for every monotone shade.\n",
    "\n",
    "    Returns:\n",
    "\n",
    "    Map like\n",
    "    { \"alpine\": {\n",
    "        \"oklch( ... )\": {\n",
    "            \"red\": *nearest oklch >= dT from M base*\n",
    "    \"\"\"\n",
    "\n",
    "    if metric is None:\n",
    "        metric = lambda mc,ac: mc.distance(ac, space=\"oklch\")\n",
    "    \n",
    "    oklch_color_map = {\n",
    "        c_name: [Color(c_str) for c_str in c_str_dict.values()]\n",
    "        for c_name, c_str_dict in oklch_hL_dict.items()\n",
    "    }\n",
    "    \n",
    "    dT_mL_acol_map = {}\n",
    "    for m_name in monotone_h_map:\n",
    "        mL_acol_map = {}\n",
    "        m_colors = oklch_color_map[m_name]\n",
    "        \n",
    "        for m_color in m_colors:\n",
    "            acol_min_map = {}\n",
    "            \n",
    "            for a_name in accent_h_map:\n",
    "                a_colors = oklch_color_map[a_name]\n",
    "                oklch_dists = filter(\n",
    "                    lambda d: (d[1] - dT) > 0,\n",
    "                    [\n",
    "                        (ac, metric(m_color, ac))\n",
    "                        for ac in a_colors\n",
    "                    ]\n",
    "                )\n",
    "                oklch_dists = list(oklch_dists)\n",
    "                if oklch_dists:\n",
    "                    min_a_color = min(oklch_dists, key=lambda t: t[1])[0]\n",
    "                    acol_min_map[a_name] = min_a_color\n",
    "    \n",
    "            # make sure the current monotone level has *all* accents; o/w ignore\n",
    "            if len(acol_min_map) < len(accent_h_map):\n",
    "                continue\n",
    "\n",
    "            mL = m_color.coords()[0]\n",
    "            mL_acol_map[int(mL*100)] = acol_min_map\n",
    "        dT_mL_acol_map[m_name] = mL_acol_map\n",
    "    return dT_mL_acol_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5d3f15e9-73cf-4808-adff-3f57f7855c09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partial scheme coverage for mL=45@dT=7\n",
      "{'alpine': {'blue': color(--oklch 0.98 0.0094 262 / 1),\n",
      "            'green': color(--oklch 0.98 0.0097 148 / 1),\n",
      "            'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
      "            'red': color(--oklch 0.98 0.0097 29 / 1),\n",
      "            'yellow': color(--oklch 0.98 0.0093 104 / 1)},\n",
      " 'badlands': {'blue': color(--oklch 0.98 0.0094 262 / 1),\n",
      "              'green': color(--oklch 0.98 0.0097 148 / 1),\n",
      "              'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
      "              'red': color(--oklch 0.98 0.0097 29 / 1),\n",
      "              'yellow': color(--oklch 0.98 0.0093 104 / 1)},\n",
      " 'chaparral': {'blue': color(--oklch 0.98 0.0094 262 / 1),\n",
      "               'green': color(--oklch 0.98 0.0097 148 / 1),\n",
      "               'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
      "               'red': color(--oklch 0.98 0.0097 29 / 1),\n",
      "               'yellow': color(--oklch 0.98 0.0093 104 / 1)},\n",
      " 'tundra': {'blue': color(--oklch 0.98 0.0094 262 / 1),\n",
      "            'green': color(--oklch 0.98 0.0097 148 / 1),\n",
      "            'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
      "            'red': color(--oklch 0.98 0.0097 29 / 1),\n",
      "            'yellow': color(--oklch 0.98 0.0093 104 / 1)}}\n"
     ]
    }
   ],
   "source": [
    "mL = 45\n",
    "\n",
    "# dT = 40 / 100\n",
    "# dT_mL_acol_map = compute_dma_map(dT)\n",
    "\n",
    "dT = 7\n",
    "dT_mL_acol_map = compute_dma_map(\n",
    "    dT,\n",
    "    metric=lambda mc,ac: ac.contrast(mc, method='wcag21')\n",
    ")\n",
    "\n",
    "Lma_map = {\n",
    "    m_name: mL_acol_dict[mL]\n",
    "    for m_name, mL_acol_dict in dT_mL_acol_map.items()\n",
    "    if mL in mL_acol_dict\n",
    "}\n",
    "\n",
    "# the `mL_acol_dict` only includes lightnesses where all accent\n",
    "# colors were within threshold. Coverage here will be partial if,\n",
    "# at the `mL`, there is some monotone base that doesn't have all\n",
    "# accents within threshold. This can happen at the edge, e.g., alpine@L15\n",
    "# has all accents w/in the distance, but the red accent was too far under\n",
    "# tundra@L15, so there's no entry. This particular case is fairly rare; it's\n",
    "# more likely that *all* monotones are undefined. Either way, both such\n",
    "# cases lead to partial scheme coverage.\n",
    "if len(Lma_map) < len(monotone_h_map):\n",
    "    print(f\"Partial scheme coverage for {mL=}@{dT=}\")\n",
    "pprint(Lma_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7a2c2f40-e736-4f04-8970-ccbce81a8493",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpine': {10: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  11: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  12: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  13: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  14: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  15: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  16: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  17: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  18: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  19: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  20: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  21: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  22: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  23: {'red': color(--oklch 0.75 0.1402 29 / 1),\n",
       "   'orange': color(--oklch 0.74 0.1083 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.73 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  24: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.73 0.0981 148 / 1),\n",
       "   'blue': color(--oklch 0.74 0.1226 262 / 1)},\n",
       "  25: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.75 0.1192 262 / 1)},\n",
       "  26: {'red': color(--oklch 0.77 0.1308 29 / 1),\n",
       "   'orange': color(--oklch 0.76 0.103 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.76 0.1155 262 / 1)},\n",
       "  27: {'red': color(--oklch 0.78 0.1259 29 / 1),\n",
       "   'orange': color(--oklch 0.77 0.1 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.76 0.0842 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.77 0.1117 262 / 1)},\n",
       "  28: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.79 0.0934 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.78 0.0814 104 / 1),\n",
       "   'green': color(--oklch 0.77 0.0921 148 / 1),\n",
       "   'blue': color(--oklch 0.78 0.1077 262 / 1)},\n",
       "  30: {'red': color(--oklch 0.81 0.1079 29 / 1),\n",
       "   'orange': color(--oklch 0.8 0.0898 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.79 0.0796 104 / 1),\n",
       "   'green': color(--oklch 0.78 0.0899 148 / 1),\n",
       "   'blue': color(--oklch 0.79 0.1036 262 / 1)},\n",
       "  31: {'red': color(--oklch 0.82 0.1012 29 / 1),\n",
       "   'orange': color(--oklch 0.81 0.0861 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.8 0.0776 104 / 1),\n",
       "   'green': color(--oklch 0.79 0.0874 148 / 1),\n",
       "   'blue': color(--oklch 0.8 0.0994 262 / 1)},\n",
       "  32: {'red': color(--oklch 0.83 0.0947 29 / 1),\n",
       "   'orange': color(--oklch 0.82 0.0822 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.81 0.0752 104 / 1),\n",
       "   'green': color(--oklch 0.8 0.0846 148 / 1),\n",
       "   'blue': color(--oklch 0.81 0.095 262 / 1)},\n",
       "  33: {'red': color(--oklch 0.84 0.0883 29 / 1),\n",
       "   'orange': color(--oklch 0.83 0.0782 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.82 0.0726 104 / 1),\n",
       "   'green': color(--oklch 0.82 0.0784 148 / 1),\n",
       "   'blue': color(--oklch 0.83 0.0845 262 / 1)},\n",
       "  34: {'red': color(--oklch 0.85 0.082 29 / 1),\n",
       "   'orange': color(--oklch 0.84 0.0741 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.83 0.0697 104 / 1),\n",
       "   'green': color(--oklch 0.83 0.0749 148 / 1),\n",
       "   'blue': color(--oklch 0.84 0.0792 262 / 1)},\n",
       "  35: {'red': color(--oklch 0.86 0.0758 29 / 1),\n",
       "   'orange': color(--oklch 0.85 0.0699 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.85 0.0633 104 / 1),\n",
       "   'green': color(--oklch 0.84 0.0713 148 / 1),\n",
       "   'blue': color(--oklch 0.85 0.0739 262 / 1)},\n",
       "  36: {'red': color(--oklch 0.87 0.0697 29 / 1),\n",
       "   'orange': color(--oklch 0.86 0.0656 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.86 0.0598 104 / 1),\n",
       "   'green': color(--oklch 0.85 0.0675 148 / 1),\n",
       "   'blue': color(--oklch 0.86 0.0687 262 / 1)},\n",
       "  37: {'red': color(--oklch 0.88 0.0638 29 / 1),\n",
       "   'orange': color(--oklch 0.88 0.0567 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.87 0.0562 104 / 1),\n",
       "   'green': color(--oklch 0.87 0.0595 148 / 1),\n",
       "   'blue': color(--oklch 0.87 0.0636 262 / 1)},\n",
       "  38: {'red': color(--oklch 0.89 0.0579 29 / 1),\n",
       "   'orange': color(--oklch 0.89 0.0522 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.88 0.0524 104 / 1),\n",
       "   'green': color(--oklch 0.88 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.89 0.0534 262 / 1)},\n",
       "  39: {'red': color(--oklch 0.9 0.0522 29 / 1),\n",
       "   'orange': color(--oklch 0.9 0.0477 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.9 0.0444 104 / 1),\n",
       "   'green': color(--oklch 0.89 0.0511 148 / 1),\n",
       "   'blue': color(--oklch 0.9 0.0483 262 / 1)},\n",
       "  40: {'red': color(--oklch 0.92 0.041 29 / 1),\n",
       "   'orange': color(--oklch 0.91 0.043 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.91 0.0402 104 / 1),\n",
       "   'green': color(--oklch 0.91 0.0423 148 / 1),\n",
       "   'blue': color(--oklch 0.91 0.0433 262 / 1)},\n",
       "  41: {'red': color(--oklch 0.93 0.0356 29 / 1),\n",
       "   'orange': color(--oklch 0.93 0.0337 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.93 0.0317 104 / 1),\n",
       "   'green': color(--oklch 0.92 0.0377 148 / 1),\n",
       "   'blue': color(--oklch 0.93 0.0334 262 / 1)},\n",
       "  42: {'red': color(--oklch 0.94 0.0302 29 / 1),\n",
       "   'orange': color(--oklch 0.94 0.029 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.94 0.0273 104 / 1),\n",
       "   'green': color(--oklch 0.94 0.0286 148 / 1),\n",
       "   'blue': color(--oklch 0.94 0.0285 262 / 1)},\n",
       "  43: {'red': color(--oklch 0.96 0.0198 29 / 1),\n",
       "   'orange': color(--oklch 0.96 0.0194 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.95 0.0229 104 / 1),\n",
       "   'green': color(--oklch 0.95 0.0239 148 / 1),\n",
       "   'blue': color(--oklch 0.95 0.0237 262 / 1)},\n",
       "  44: {'red': color(--oklch 0.97 0.0147 29 / 1),\n",
       "   'orange': color(--oklch 0.97 0.0146 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.97 0.0138 104 / 1),\n",
       "   'green': color(--oklch 0.97 0.0144 148 / 1),\n",
       "   'blue': color(--oklch 0.97 0.0141 262 / 1)},\n",
       "  45: {'red': color(--oklch 0.98 0.0097 29 / 1),\n",
       "   'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.98 0.0093 104 / 1),\n",
       "   'green': color(--oklch 0.98 0.0097 148 / 1),\n",
       "   'blue': color(--oklch 0.98 0.0094 262 / 1)},\n",
       "  68: {'red': color(--oklch 0.13 0.0526 29 / 1),\n",
       "   'orange': color(--oklch 0.12 0.0256 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.12 0.0171 104 / 1),\n",
       "   'green': color(--oklch 0.12 0.0204 148 / 1),\n",
       "   'blue': color(--oklch 0.12 0.0342 262 / 1)},\n",
       "  69: {'red': color(--oklch 0.16 0.0645 29 / 1),\n",
       "   'orange': color(--oklch 0.16 0.0341 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.15 0.0214 104 / 1),\n",
       "   'green': color(--oklch 0.15 0.0254 148 / 1),\n",
       "   'blue': color(--oklch 0.16 0.0454 262 / 1)},\n",
       "  70: {'red': color(--oklch 0.19 0.0763 29 / 1),\n",
       "   'orange': color(--oklch 0.18 0.0383 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.18 0.0256 104 / 1),\n",
       "   'green': color(--oklch 0.17 0.0288 148 / 1),\n",
       "   'blue': color(--oklch 0.18 0.051 262 / 1)},\n",
       "  71: {'red': color(--oklch 0.21 0.0841 29 / 1),\n",
       "   'orange': color(--oklch 0.2 0.0425 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.2 0.0284 104 / 1),\n",
       "   'green': color(--oklch 0.19 0.0321 148 / 1),\n",
       "   'blue': color(--oklch 0.2 0.0566 262 / 1)},\n",
       "  72: {'red': color(--oklch 0.22 0.0879 29 / 1),\n",
       "   'orange': color(--oklch 0.22 0.0467 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.21 0.0298 104 / 1),\n",
       "   'green': color(--oklch 0.21 0.0355 148 / 1),\n",
       "   'blue': color(--oklch 0.22 0.0621 262 / 1)},\n",
       "  73: {'red': color(--oklch 0.24 0.0956 29 / 1),\n",
       "   'orange': color(--oklch 0.23 0.0488 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.23 0.0326 104 / 1),\n",
       "   'green': color(--oklch 0.22 0.0371 148 / 1),\n",
       "   'blue': color(--oklch 0.23 0.0648 262 / 1)},\n",
       "  74: {'red': color(--oklch 0.25 0.0994 29 / 1),\n",
       "   'orange': color(--oklch 0.25 0.0529 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.24 0.034 104 / 1),\n",
       "   'green': color(--oklch 0.24 0.0405 148 / 1),\n",
       "   'blue': color(--oklch 0.24 0.0676 262 / 1)},\n",
       "  75: {'red': color(--oklch 0.27 0.1068 29 / 1),\n",
       "   'orange': color(--oklch 0.26 0.055 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.25 0.0354 104 / 1),\n",
       "   'green': color(--oklch 0.25 0.0421 148 / 1),\n",
       "   'blue': color(--oklch 0.26 0.073 262 / 1)},\n",
       "  76: {'red': color(--oklch 0.28 0.1106 29 / 1),\n",
       "   'orange': color(--oklch 0.27 0.0571 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.26 0.0368 104 / 1),\n",
       "   'green': color(--oklch 0.26 0.0438 148 / 1),\n",
       "   'blue': color(--oklch 0.27 0.0757 262 / 1)},\n",
       "  77: {'red': color(--oklch 0.29 0.1142 29 / 1),\n",
       "   'orange': color(--oklch 0.28 0.0591 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.28 0.0396 104 / 1),\n",
       "   'green': color(--oklch 0.27 0.0454 148 / 1),\n",
       "   'blue': color(--oklch 0.28 0.0784 262 / 1)},\n",
       "  78: {'red': color(--oklch 0.3 0.1179 29 / 1),\n",
       "   'orange': color(--oklch 0.29 0.0612 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.29 0.041 104 / 1),\n",
       "   'green': color(--oklch 0.28 0.0471 148 / 1),\n",
       "   'blue': color(--oklch 0.29 0.0811 262 / 1)},\n",
       "  79: {'red': color(--oklch 0.31 0.1215 29 / 1),\n",
       "   'orange': color(--oklch 0.3 0.0632 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.3 0.0424 104 / 1),\n",
       "   'green': color(--oklch 0.29 0.0487 148 / 1),\n",
       "   'blue': color(--oklch 0.3 0.0838 262 / 1)},\n",
       "  80: {'red': color(--oklch 0.32 0.125 29 / 1),\n",
       "   'orange': color(--oklch 0.31 0.0653 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.3 0.0504 148 / 1),\n",
       "   'blue': color(--oklch 0.31 0.0864 262 / 1)},\n",
       "  81: {'red': color(--oklch 0.33 0.1286 29 / 1),\n",
       "   'orange': color(--oklch 0.32 0.0673 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.31 0.052 148 / 1),\n",
       "   'blue': color(--oklch 0.32 0.0891 262 / 1)},\n",
       "  82: {'red': color(--oklch 0.34 0.132 29 / 1),\n",
       "   'orange': color(--oklch 0.33 0.0693 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.32 0.0536 148 / 1),\n",
       "   'blue': color(--oklch 0.33 0.0917 262 / 1)},\n",
       "  83: {'red': color(--oklch 0.35 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.34 0.0714 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.33 0.0465 104 / 1),\n",
       "   'green': color(--oklch 0.33 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  84: {'red': color(--oklch 0.36 0.1389 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.34 0.0478 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  85: {'red': color(--oklch 0.37 0.1422 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.35 0.0492 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  86: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.35 0.0585 148 / 1),\n",
       "   'blue': color(--oklch 0.36 0.0994 262 / 1)},\n",
       "  87: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.37 0.0773 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.36 0.0601 148 / 1),\n",
       "   'blue': color(--oklch 0.37 0.1019 262 / 1)},\n",
       "  88: {'red': color(--oklch 0.39 0.1486 29 / 1),\n",
       "   'orange': color(--oklch 0.38 0.0793 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  89: {'red': color(--oklch 0.4 0.1517 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.38 0.0533 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  90: {'red': color(--oklch 0.41 0.1548 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  91: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.39 0.0649 148 / 1),\n",
       "   'blue': color(--oklch 0.4 0.1094 262 / 1)},\n",
       "  92: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.41 0.0851 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  93: {'red': color(--oklch 0.43 0.1606 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.41 0.0573 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  94: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.41 0.068 148 / 1),\n",
       "   'blue': color(--oklch 0.42 0.1142 262 / 1)},\n",
       "  95: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.43 0.0889 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.43 0.1165 262 / 1)},\n",
       "  96: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.43 0.0599 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  97: {'red': color(--oklch 0.46 0.1685 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.43 0.0711 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  98: {'red': color(--oklch 0.47 0.1709 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.44 0.0726 148 / 1),\n",
       "   'blue': color(--oklch 0.45 0.121 262 / 1)}},\n",
       " 'badlands': {10: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  11: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  12: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  13: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  14: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  15: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  16: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  17: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  18: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  19: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  20: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  21: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  22: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  23: {'red': color(--oklch 0.75 0.1402 29 / 1),\n",
       "   'orange': color(--oklch 0.74 0.1083 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.73 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  24: {'red': color(--oklch 0.75 0.1402 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.73 0.0981 148 / 1),\n",
       "   'blue': color(--oklch 0.74 0.1226 262 / 1)},\n",
       "  25: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.75 0.1192 262 / 1)},\n",
       "  26: {'red': color(--oklch 0.77 0.1308 29 / 1),\n",
       "   'orange': color(--oklch 0.76 0.103 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.76 0.1155 262 / 1)},\n",
       "  27: {'red': color(--oklch 0.78 0.1259 29 / 1),\n",
       "   'orange': color(--oklch 0.77 0.1 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.76 0.0842 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.76 0.1155 262 / 1)},\n",
       "  28: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.79 0.0934 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.78 0.0814 104 / 1),\n",
       "   'green': color(--oklch 0.77 0.0921 148 / 1),\n",
       "   'blue': color(--oklch 0.78 0.1077 262 / 1)},\n",
       "  30: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.8 0.0898 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.79 0.0796 104 / 1),\n",
       "   'green': color(--oklch 0.78 0.0899 148 / 1),\n",
       "   'blue': color(--oklch 0.79 0.1036 262 / 1)},\n",
       "  31: {'red': color(--oklch 0.81 0.1079 29 / 1),\n",
       "   'orange': color(--oklch 0.81 0.0861 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.8 0.0776 104 / 1),\n",
       "   'green': color(--oklch 0.79 0.0874 148 / 1),\n",
       "   'blue': color(--oklch 0.8 0.0994 262 / 1)},\n",
       "  32: {'red': color(--oklch 0.82 0.1012 29 / 1),\n",
       "   'orange': color(--oklch 0.82 0.0822 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.81 0.0752 104 / 1),\n",
       "   'green': color(--oklch 0.8 0.0846 148 / 1),\n",
       "   'blue': color(--oklch 0.81 0.095 262 / 1)},\n",
       "  33: {'red': color(--oklch 0.83 0.0947 29 / 1),\n",
       "   'orange': color(--oklch 0.83 0.0782 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.82 0.0726 104 / 1),\n",
       "   'green': color(--oklch 0.81 0.0816 148 / 1),\n",
       "   'blue': color(--oklch 0.82 0.0898 262 / 1)},\n",
       "  34: {'red': color(--oklch 0.84 0.0883 29 / 1),\n",
       "   'orange': color(--oklch 0.84 0.0741 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.83 0.0697 104 / 1),\n",
       "   'green': color(--oklch 0.83 0.0749 148 / 1),\n",
       "   'blue': color(--oklch 0.84 0.0792 262 / 1)},\n",
       "  35: {'red': color(--oklch 0.86 0.0758 29 / 1),\n",
       "   'orange': color(--oklch 0.85 0.0699 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.84 0.0666 104 / 1),\n",
       "   'green': color(--oklch 0.84 0.0713 148 / 1),\n",
       "   'blue': color(--oklch 0.85 0.0739 262 / 1)},\n",
       "  36: {'red': color(--oklch 0.87 0.0697 29 / 1),\n",
       "   'orange': color(--oklch 0.86 0.0656 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.86 0.0598 104 / 1),\n",
       "   'green': color(--oklch 0.85 0.0675 148 / 1),\n",
       "   'blue': color(--oklch 0.86 0.0687 262 / 1)},\n",
       "  37: {'red': color(--oklch 0.88 0.0638 29 / 1),\n",
       "   'orange': color(--oklch 0.88 0.0567 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.87 0.0562 104 / 1),\n",
       "   'green': color(--oklch 0.86 0.0636 148 / 1),\n",
       "   'blue': color(--oklch 0.87 0.0636 262 / 1)},\n",
       "  38: {'red': color(--oklch 0.89 0.0579 29 / 1),\n",
       "   'orange': color(--oklch 0.89 0.0522 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.88 0.0524 104 / 1),\n",
       "   'green': color(--oklch 0.88 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.88 0.0584 262 / 1)},\n",
       "  39: {'red': color(--oklch 0.9 0.0522 29 / 1),\n",
       "   'orange': color(--oklch 0.9 0.0477 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.9 0.0444 104 / 1),\n",
       "   'green': color(--oklch 0.89 0.0511 148 / 1),\n",
       "   'blue': color(--oklch 0.9 0.0483 262 / 1)},\n",
       "  40: {'red': color(--oklch 0.92 0.041 29 / 1),\n",
       "   'orange': color(--oklch 0.91 0.043 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.91 0.0402 104 / 1),\n",
       "   'green': color(--oklch 0.91 0.0423 148 / 1),\n",
       "   'blue': color(--oklch 0.91 0.0433 262 / 1)},\n",
       "  41: {'red': color(--oklch 0.93 0.0356 29 / 1),\n",
       "   'orange': color(--oklch 0.93 0.0337 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.92 0.036 104 / 1),\n",
       "   'green': color(--oklch 0.92 0.0377 148 / 1),\n",
       "   'blue': color(--oklch 0.92 0.0384 262 / 1)},\n",
       "  42: {'red': color(--oklch 0.94 0.0302 29 / 1),\n",
       "   'orange': color(--oklch 0.94 0.029 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.94 0.0273 104 / 1),\n",
       "   'green': color(--oklch 0.94 0.0286 148 / 1),\n",
       "   'blue': color(--oklch 0.94 0.0285 262 / 1)},\n",
       "  43: {'red': color(--oklch 0.96 0.0198 29 / 1),\n",
       "   'orange': color(--oklch 0.95 0.0242 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.95 0.0229 104 / 1),\n",
       "   'green': color(--oklch 0.95 0.0239 148 / 1),\n",
       "   'blue': color(--oklch 0.95 0.0237 262 / 1)},\n",
       "  44: {'red': color(--oklch 0.97 0.0147 29 / 1),\n",
       "   'orange': color(--oklch 0.97 0.0146 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.97 0.0138 104 / 1),\n",
       "   'green': color(--oklch 0.97 0.0144 148 / 1),\n",
       "   'blue': color(--oklch 0.97 0.0141 262 / 1)},\n",
       "  45: {'red': color(--oklch 0.98 0.0097 29 / 1),\n",
       "   'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.98 0.0093 104 / 1),\n",
       "   'green': color(--oklch 0.98 0.0097 148 / 1),\n",
       "   'blue': color(--oklch 0.98 0.0094 262 / 1)},\n",
       "  68: {'red': color(--oklch 0.12 0.0486 29 / 1),\n",
       "   'orange': color(--oklch 0.12 0.0256 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.12 0.0171 104 / 1),\n",
       "   'green': color(--oklch 0.12 0.0204 148 / 1),\n",
       "   'blue': color(--oklch 0.12 0.0342 262 / 1)},\n",
       "  69: {'red': color(--oklch 0.16 0.0645 29 / 1),\n",
       "   'orange': color(--oklch 0.15 0.032 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.15 0.0214 104 / 1),\n",
       "   'green': color(--oklch 0.15 0.0254 148 / 1),\n",
       "   'blue': color(--oklch 0.15 0.0426 262 / 1)},\n",
       "  70: {'red': color(--oklch 0.18 0.0724 29 / 1),\n",
       "   'orange': color(--oklch 0.18 0.0383 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.17 0.0242 104 / 1),\n",
       "   'green': color(--oklch 0.17 0.0288 148 / 1),\n",
       "   'blue': color(--oklch 0.18 0.051 262 / 1)},\n",
       "  71: {'red': color(--oklch 0.21 0.0841 29 / 1),\n",
       "   'orange': color(--oklch 0.2 0.0425 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.19 0.027 104 / 1),\n",
       "   'green': color(--oklch 0.19 0.0321 148 / 1),\n",
       "   'blue': color(--oklch 0.2 0.0566 262 / 1)},\n",
       "  72: {'red': color(--oklch 0.22 0.0879 29 / 1),\n",
       "   'orange': color(--oklch 0.22 0.0467 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.21 0.0298 104 / 1),\n",
       "   'green': color(--oklch 0.21 0.0355 148 / 1),\n",
       "   'blue': color(--oklch 0.21 0.0593 262 / 1)},\n",
       "  73: {'red': color(--oklch 0.24 0.0956 29 / 1),\n",
       "   'orange': color(--oklch 0.23 0.0488 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.23 0.0326 104 / 1),\n",
       "   'green': color(--oklch 0.22 0.0371 148 / 1),\n",
       "   'blue': color(--oklch 0.23 0.0648 262 / 1)},\n",
       "  74: {'red': color(--oklch 0.25 0.0994 29 / 1),\n",
       "   'orange': color(--oklch 0.24 0.0509 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.24 0.034 104 / 1),\n",
       "   'green': color(--oklch 0.24 0.0405 148 / 1),\n",
       "   'blue': color(--oklch 0.24 0.0676 262 / 1)},\n",
       "  75: {'red': color(--oklch 0.27 0.1068 29 / 1),\n",
       "   'orange': color(--oklch 0.26 0.055 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.25 0.0354 104 / 1),\n",
       "   'green': color(--oklch 0.25 0.0421 148 / 1),\n",
       "   'blue': color(--oklch 0.26 0.073 262 / 1)},\n",
       "  76: {'red': color(--oklch 0.28 0.1106 29 / 1),\n",
       "   'orange': color(--oklch 0.27 0.0571 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.26 0.0368 104 / 1),\n",
       "   'green': color(--oklch 0.26 0.0438 148 / 1),\n",
       "   'blue': color(--oklch 0.27 0.0757 262 / 1)},\n",
       "  77: {'red': color(--oklch 0.29 0.1142 29 / 1),\n",
       "   'orange': color(--oklch 0.28 0.0591 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.27 0.0382 104 / 1),\n",
       "   'green': color(--oklch 0.27 0.0454 148 / 1),\n",
       "   'blue': color(--oklch 0.28 0.0784 262 / 1)},\n",
       "  78: {'red': color(--oklch 0.3 0.1179 29 / 1),\n",
       "   'orange': color(--oklch 0.29 0.0612 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.28 0.0396 104 / 1),\n",
       "   'green': color(--oklch 0.28 0.0471 148 / 1),\n",
       "   'blue': color(--oklch 0.29 0.0811 262 / 1)},\n",
       "  79: {'red': color(--oklch 0.31 0.1215 29 / 1),\n",
       "   'orange': color(--oklch 0.3 0.0632 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.29 0.041 104 / 1),\n",
       "   'green': color(--oklch 0.29 0.0487 148 / 1),\n",
       "   'blue': color(--oklch 0.3 0.0838 262 / 1)},\n",
       "  80: {'red': color(--oklch 0.32 0.125 29 / 1),\n",
       "   'orange': color(--oklch 0.31 0.0653 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.3 0.0424 104 / 1),\n",
       "   'green': color(--oklch 0.3 0.0504 148 / 1),\n",
       "   'blue': color(--oklch 0.31 0.0864 262 / 1)},\n",
       "  81: {'red': color(--oklch 0.33 0.1286 29 / 1),\n",
       "   'orange': color(--oklch 0.32 0.0673 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.31 0.052 148 / 1),\n",
       "   'blue': color(--oklch 0.32 0.0891 262 / 1)},\n",
       "  82: {'red': color(--oklch 0.34 0.132 29 / 1),\n",
       "   'orange': color(--oklch 0.33 0.0693 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.32 0.0536 148 / 1),\n",
       "   'blue': color(--oklch 0.33 0.0917 262 / 1)},\n",
       "  83: {'red': color(--oklch 0.35 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.34 0.0714 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.33 0.0465 104 / 1),\n",
       "   'green': color(--oklch 0.33 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  84: {'red': color(--oklch 0.36 0.1389 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.34 0.0478 104 / 1),\n",
       "   'green': color(--oklch 0.33 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  85: {'red': color(--oklch 0.37 0.1422 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.35 0.0492 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  86: {'red': color(--oklch 0.37 0.1422 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.35 0.0585 148 / 1),\n",
       "   'blue': color(--oklch 0.36 0.0994 262 / 1)},\n",
       "  87: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.37 0.0773 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.36 0.0601 148 / 1),\n",
       "   'blue': color(--oklch 0.37 0.1019 262 / 1)},\n",
       "  88: {'red': color(--oklch 0.39 0.1486 29 / 1),\n",
       "   'orange': color(--oklch 0.38 0.0793 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  89: {'red': color(--oklch 0.4 0.1517 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.38 0.0533 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  90: {'red': color(--oklch 0.41 0.1548 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  91: {'red': color(--oklch 0.41 0.1548 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.39 0.0649 148 / 1),\n",
       "   'blue': color(--oklch 0.4 0.1094 262 / 1)},\n",
       "  92: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.41 0.0851 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  93: {'red': color(--oklch 0.43 0.1606 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.41 0.0573 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  94: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.41 0.068 148 / 1),\n",
       "   'blue': color(--oklch 0.42 0.1142 262 / 1)},\n",
       "  95: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.43 0.0889 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.43 0.1165 262 / 1)},\n",
       "  96: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.43 0.0599 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.43 0.1165 262 / 1)},\n",
       "  97: {'red': color(--oklch 0.46 0.1685 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.43 0.0711 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  98: {'red': color(--oklch 0.47 0.1709 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.44 0.0726 148 / 1),\n",
       "   'blue': color(--oklch 0.45 0.121 262 / 1)}},\n",
       " 'chaparral': {10: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  11: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  12: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  13: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  14: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  15: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  16: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  17: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  18: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  19: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  20: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  21: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  22: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  23: {'red': color(--oklch 0.75 0.1402 29 / 1),\n",
       "   'orange': color(--oklch 0.74 0.1083 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.73 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  24: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.73 0.0981 148 / 1),\n",
       "   'blue': color(--oklch 0.74 0.1226 262 / 1)},\n",
       "  25: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.75 0.1192 262 / 1)},\n",
       "  26: {'red': color(--oklch 0.77 0.1308 29 / 1),\n",
       "   'orange': color(--oklch 0.76 0.103 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.76 0.1155 262 / 1)},\n",
       "  27: {'red': color(--oklch 0.78 0.1259 29 / 1),\n",
       "   'orange': color(--oklch 0.77 0.1 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.76 0.0842 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.77 0.1117 262 / 1)},\n",
       "  28: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.79 0.0934 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.78 0.0814 104 / 1),\n",
       "   'green': color(--oklch 0.77 0.0921 148 / 1),\n",
       "   'blue': color(--oklch 0.78 0.1077 262 / 1)},\n",
       "  30: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.8 0.0898 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.79 0.0796 104 / 1),\n",
       "   'green': color(--oklch 0.78 0.0899 148 / 1),\n",
       "   'blue': color(--oklch 0.79 0.1036 262 / 1)},\n",
       "  31: {'red': color(--oklch 0.81 0.1079 29 / 1),\n",
       "   'orange': color(--oklch 0.81 0.0861 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.8 0.0776 104 / 1),\n",
       "   'green': color(--oklch 0.79 0.0874 148 / 1),\n",
       "   'blue': color(--oklch 0.8 0.0994 262 / 1)},\n",
       "  32: {'red': color(--oklch 0.82 0.1012 29 / 1),\n",
       "   'orange': color(--oklch 0.82 0.0822 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.81 0.0752 104 / 1),\n",
       "   'green': color(--oklch 0.8 0.0846 148 / 1),\n",
       "   'blue': color(--oklch 0.81 0.095 262 / 1)},\n",
       "  33: {'red': color(--oklch 0.83 0.0947 29 / 1),\n",
       "   'orange': color(--oklch 0.83 0.0782 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.82 0.0726 104 / 1),\n",
       "   'green': color(--oklch 0.82 0.0784 148 / 1),\n",
       "   'blue': color(--oklch 0.83 0.0845 262 / 1)},\n",
       "  34: {'red': color(--oklch 0.85 0.082 29 / 1),\n",
       "   'orange': color(--oklch 0.84 0.0741 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.83 0.0697 104 / 1),\n",
       "   'green': color(--oklch 0.83 0.0749 148 / 1),\n",
       "   'blue': color(--oklch 0.84 0.0792 262 / 1)},\n",
       "  35: {'red': color(--oklch 0.86 0.0758 29 / 1),\n",
       "   'orange': color(--oklch 0.85 0.0699 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.85 0.0633 104 / 1),\n",
       "   'green': color(--oklch 0.84 0.0713 148 / 1),\n",
       "   'blue': color(--oklch 0.85 0.0739 262 / 1)},\n",
       "  36: {'red': color(--oklch 0.87 0.0697 29 / 1),\n",
       "   'orange': color(--oklch 0.86 0.0656 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.86 0.0598 104 / 1),\n",
       "   'green': color(--oklch 0.85 0.0675 148 / 1),\n",
       "   'blue': color(--oklch 0.86 0.0687 262 / 1)},\n",
       "  37: {'red': color(--oklch 0.88 0.0638 29 / 1),\n",
       "   'orange': color(--oklch 0.88 0.0567 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.87 0.0562 104 / 1),\n",
       "   'green': color(--oklch 0.87 0.0595 148 / 1),\n",
       "   'blue': color(--oklch 0.87 0.0636 262 / 1)},\n",
       "  38: {'red': color(--oklch 0.89 0.0579 29 / 1),\n",
       "   'orange': color(--oklch 0.89 0.0522 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.88 0.0524 104 / 1),\n",
       "   'green': color(--oklch 0.88 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.89 0.0534 262 / 1)},\n",
       "  39: {'red': color(--oklch 0.9 0.0522 29 / 1),\n",
       "   'orange': color(--oklch 0.9 0.0477 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.9 0.0444 104 / 1),\n",
       "   'green': color(--oklch 0.89 0.0511 148 / 1),\n",
       "   'blue': color(--oklch 0.9 0.0483 262 / 1)},\n",
       "  40: {'red': color(--oklch 0.92 0.041 29 / 1),\n",
       "   'orange': color(--oklch 0.91 0.043 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.91 0.0402 104 / 1),\n",
       "   'green': color(--oklch 0.91 0.0423 148 / 1),\n",
       "   'blue': color(--oklch 0.91 0.0433 262 / 1)},\n",
       "  41: {'red': color(--oklch 0.93 0.0356 29 / 1),\n",
       "   'orange': color(--oklch 0.93 0.0337 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.92 0.036 104 / 1),\n",
       "   'green': color(--oklch 0.92 0.0377 148 / 1),\n",
       "   'blue': color(--oklch 0.93 0.0334 262 / 1)},\n",
       "  42: {'red': color(--oklch 0.94 0.0302 29 / 1),\n",
       "   'orange': color(--oklch 0.94 0.029 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.94 0.0273 104 / 1),\n",
       "   'green': color(--oklch 0.94 0.0286 148 / 1),\n",
       "   'blue': color(--oklch 0.94 0.0285 262 / 1)},\n",
       "  43: {'red': color(--oklch 0.96 0.0198 29 / 1),\n",
       "   'orange': color(--oklch 0.96 0.0194 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.95 0.0229 104 / 1),\n",
       "   'green': color(--oklch 0.95 0.0239 148 / 1),\n",
       "   'blue': color(--oklch 0.95 0.0237 262 / 1)},\n",
       "  44: {'red': color(--oklch 0.97 0.0147 29 / 1),\n",
       "   'orange': color(--oklch 0.97 0.0146 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.97 0.0138 104 / 1),\n",
       "   'green': color(--oklch 0.97 0.0144 148 / 1),\n",
       "   'blue': color(--oklch 0.97 0.0141 262 / 1)},\n",
       "  45: {'red': color(--oklch 0.98 0.0097 29 / 1),\n",
       "   'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.98 0.0093 104 / 1),\n",
       "   'green': color(--oklch 0.98 0.0097 148 / 1),\n",
       "   'blue': color(--oklch 0.98 0.0094 262 / 1)},\n",
       "  68: {'red': color(--oklch 0.13 0.0526 29 / 1),\n",
       "   'orange': color(--oklch 0.12 0.0256 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.12 0.0171 104 / 1),\n",
       "   'green': color(--oklch 0.12 0.0204 148 / 1),\n",
       "   'blue': color(--oklch 0.12 0.0342 262 / 1)},\n",
       "  69: {'red': color(--oklch 0.16 0.0645 29 / 1),\n",
       "   'orange': color(--oklch 0.16 0.0341 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.15 0.0214 104 / 1),\n",
       "   'green': color(--oklch 0.15 0.0254 148 / 1),\n",
       "   'blue': color(--oklch 0.15 0.0426 262 / 1)},\n",
       "  70: {'red': color(--oklch 0.19 0.0763 29 / 1),\n",
       "   'orange': color(--oklch 0.18 0.0383 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.18 0.0256 104 / 1),\n",
       "   'green': color(--oklch 0.17 0.0288 148 / 1),\n",
       "   'blue': color(--oklch 0.18 0.051 262 / 1)},\n",
       "  71: {'red': color(--oklch 0.21 0.0841 29 / 1),\n",
       "   'orange': color(--oklch 0.2 0.0425 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.2 0.0284 104 / 1),\n",
       "   'green': color(--oklch 0.19 0.0321 148 / 1),\n",
       "   'blue': color(--oklch 0.2 0.0566 262 / 1)},\n",
       "  72: {'red': color(--oklch 0.22 0.0879 29 / 1),\n",
       "   'orange': color(--oklch 0.22 0.0467 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.21 0.0298 104 / 1),\n",
       "   'green': color(--oklch 0.21 0.0355 148 / 1),\n",
       "   'blue': color(--oklch 0.21 0.0593 262 / 1)},\n",
       "  73: {'red': color(--oklch 0.24 0.0956 29 / 1),\n",
       "   'orange': color(--oklch 0.23 0.0488 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.23 0.0326 104 / 1),\n",
       "   'green': color(--oklch 0.22 0.0371 148 / 1),\n",
       "   'blue': color(--oklch 0.23 0.0648 262 / 1)},\n",
       "  74: {'red': color(--oklch 0.25 0.0994 29 / 1),\n",
       "   'orange': color(--oklch 0.24 0.0509 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.24 0.034 104 / 1),\n",
       "   'green': color(--oklch 0.24 0.0405 148 / 1),\n",
       "   'blue': color(--oklch 0.24 0.0676 262 / 1)},\n",
       "  75: {'red': color(--oklch 0.27 0.1068 29 / 1),\n",
       "   'orange': color(--oklch 0.26 0.055 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.25 0.0354 104 / 1),\n",
       "   'green': color(--oklch 0.25 0.0421 148 / 1),\n",
       "   'blue': color(--oklch 0.26 0.073 262 / 1)},\n",
       "  76: {'red': color(--oklch 0.28 0.1106 29 / 1),\n",
       "   'orange': color(--oklch 0.27 0.0571 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.26 0.0368 104 / 1),\n",
       "   'green': color(--oklch 0.26 0.0438 148 / 1),\n",
       "   'blue': color(--oklch 0.27 0.0757 262 / 1)},\n",
       "  77: {'red': color(--oklch 0.29 0.1142 29 / 1),\n",
       "   'orange': color(--oklch 0.28 0.0591 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.28 0.0396 104 / 1),\n",
       "   'green': color(--oklch 0.27 0.0454 148 / 1),\n",
       "   'blue': color(--oklch 0.28 0.0784 262 / 1)},\n",
       "  78: {'red': color(--oklch 0.3 0.1179 29 / 1),\n",
       "   'orange': color(--oklch 0.29 0.0612 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.29 0.041 104 / 1),\n",
       "   'green': color(--oklch 0.28 0.0471 148 / 1),\n",
       "   'blue': color(--oklch 0.29 0.0811 262 / 1)},\n",
       "  79: {'red': color(--oklch 0.31 0.1215 29 / 1),\n",
       "   'orange': color(--oklch 0.3 0.0632 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.3 0.0424 104 / 1),\n",
       "   'green': color(--oklch 0.29 0.0487 148 / 1),\n",
       "   'blue': color(--oklch 0.3 0.0838 262 / 1)},\n",
       "  80: {'red': color(--oklch 0.32 0.125 29 / 1),\n",
       "   'orange': color(--oklch 0.31 0.0653 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.3 0.0424 104 / 1),\n",
       "   'green': color(--oklch 0.3 0.0504 148 / 1),\n",
       "   'blue': color(--oklch 0.31 0.0864 262 / 1)},\n",
       "  81: {'red': color(--oklch 0.33 0.1286 29 / 1),\n",
       "   'orange': color(--oklch 0.32 0.0673 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.31 0.052 148 / 1),\n",
       "   'blue': color(--oklch 0.32 0.0891 262 / 1)},\n",
       "  82: {'red': color(--oklch 0.34 0.132 29 / 1),\n",
       "   'orange': color(--oklch 0.33 0.0693 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.32 0.0536 148 / 1),\n",
       "   'blue': color(--oklch 0.33 0.0917 262 / 1)},\n",
       "  83: {'red': color(--oklch 0.35 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.34 0.0714 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.33 0.0465 104 / 1),\n",
       "   'green': color(--oklch 0.33 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  84: {'red': color(--oklch 0.36 0.1389 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.34 0.0478 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  85: {'red': color(--oklch 0.37 0.1422 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.35 0.0492 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  86: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.35 0.0585 148 / 1),\n",
       "   'blue': color(--oklch 0.36 0.0994 262 / 1)},\n",
       "  87: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.37 0.0773 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.36 0.0601 148 / 1),\n",
       "   'blue': color(--oklch 0.37 0.1019 262 / 1)},\n",
       "  88: {'red': color(--oklch 0.39 0.1486 29 / 1),\n",
       "   'orange': color(--oklch 0.38 0.0793 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  89: {'red': color(--oklch 0.4 0.1517 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.38 0.0533 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  90: {'red': color(--oklch 0.41 0.1548 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  91: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.39 0.0649 148 / 1),\n",
       "   'blue': color(--oklch 0.4 0.1094 262 / 1)},\n",
       "  92: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.41 0.0851 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  93: {'red': color(--oklch 0.43 0.1606 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.41 0.0573 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  94: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.41 0.068 148 / 1),\n",
       "   'blue': color(--oklch 0.42 0.1142 262 / 1)},\n",
       "  95: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.43 0.0889 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.43 0.1165 262 / 1)},\n",
       "  96: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.43 0.0599 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  97: {'red': color(--oklch 0.46 0.1685 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.43 0.0711 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  98: {'red': color(--oklch 0.47 0.1709 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.44 0.0726 148 / 1),\n",
       "   'blue': color(--oklch 0.45 0.121 262 / 1)}},\n",
       " 'savanna': {10: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  11: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  12: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  13: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  14: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  15: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  16: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  17: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  18: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  19: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  20: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  21: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  22: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  23: {'red': color(--oklch 0.75 0.1402 29 / 1),\n",
       "   'orange': color(--oklch 0.74 0.1083 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.73 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  24: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.73 0.0981 148 / 1),\n",
       "   'blue': color(--oklch 0.74 0.1226 262 / 1)},\n",
       "  25: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.75 0.1192 262 / 1)},\n",
       "  26: {'red': color(--oklch 0.77 0.1308 29 / 1),\n",
       "   'orange': color(--oklch 0.76 0.103 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.76 0.1155 262 / 1)},\n",
       "  27: {'red': color(--oklch 0.78 0.1259 29 / 1),\n",
       "   'orange': color(--oklch 0.77 0.1 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.76 0.0842 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.77 0.1117 262 / 1)},\n",
       "  28: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.79 0.0934 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.78 0.0814 104 / 1),\n",
       "   'green': color(--oklch 0.77 0.0921 148 / 1),\n",
       "   'blue': color(--oklch 0.78 0.1077 262 / 1)},\n",
       "  30: {'red': color(--oklch 0.81 0.1079 29 / 1),\n",
       "   'orange': color(--oklch 0.8 0.0898 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.79 0.0796 104 / 1),\n",
       "   'green': color(--oklch 0.78 0.0899 148 / 1),\n",
       "   'blue': color(--oklch 0.79 0.1036 262 / 1)},\n",
       "  31: {'red': color(--oklch 0.82 0.1012 29 / 1),\n",
       "   'orange': color(--oklch 0.81 0.0861 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.8 0.0776 104 / 1),\n",
       "   'green': color(--oklch 0.79 0.0874 148 / 1),\n",
       "   'blue': color(--oklch 0.8 0.0994 262 / 1)},\n",
       "  32: {'red': color(--oklch 0.83 0.0947 29 / 1),\n",
       "   'orange': color(--oklch 0.82 0.0822 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.81 0.0752 104 / 1),\n",
       "   'green': color(--oklch 0.81 0.0816 148 / 1),\n",
       "   'blue': color(--oklch 0.82 0.0898 262 / 1)},\n",
       "  33: {'red': color(--oklch 0.84 0.0883 29 / 1),\n",
       "   'orange': color(--oklch 0.83 0.0782 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.82 0.0726 104 / 1),\n",
       "   'green': color(--oklch 0.82 0.0784 148 / 1),\n",
       "   'blue': color(--oklch 0.83 0.0845 262 / 1)},\n",
       "  34: {'red': color(--oklch 0.85 0.082 29 / 1),\n",
       "   'orange': color(--oklch 0.84 0.0741 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.83 0.0697 104 / 1),\n",
       "   'green': color(--oklch 0.83 0.0749 148 / 1),\n",
       "   'blue': color(--oklch 0.84 0.0792 262 / 1)},\n",
       "  35: {'red': color(--oklch 0.86 0.0758 29 / 1),\n",
       "   'orange': color(--oklch 0.85 0.0699 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.85 0.0633 104 / 1),\n",
       "   'green': color(--oklch 0.84 0.0713 148 / 1),\n",
       "   'blue': color(--oklch 0.85 0.0739 262 / 1)},\n",
       "  36: {'red': color(--oklch 0.87 0.0697 29 / 1),\n",
       "   'orange': color(--oklch 0.86 0.0656 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.86 0.0598 104 / 1),\n",
       "   'green': color(--oklch 0.85 0.0675 148 / 1),\n",
       "   'blue': color(--oklch 0.86 0.0687 262 / 1)},\n",
       "  37: {'red': color(--oklch 0.88 0.0638 29 / 1),\n",
       "   'orange': color(--oklch 0.88 0.0567 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.87 0.0562 104 / 1),\n",
       "   'green': color(--oklch 0.87 0.0595 148 / 1),\n",
       "   'blue': color(--oklch 0.87 0.0636 262 / 1)},\n",
       "  38: {'red': color(--oklch 0.89 0.0579 29 / 1),\n",
       "   'orange': color(--oklch 0.89 0.0522 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.88 0.0524 104 / 1),\n",
       "   'green': color(--oklch 0.88 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.89 0.0534 262 / 1)},\n",
       "  39: {'red': color(--oklch 0.91 0.0466 29 / 1),\n",
       "   'orange': color(--oklch 0.9 0.0477 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.9 0.0444 104 / 1),\n",
       "   'green': color(--oklch 0.89 0.0511 148 / 1),\n",
       "   'blue': color(--oklch 0.9 0.0483 262 / 1)},\n",
       "  40: {'red': color(--oklch 0.92 0.041 29 / 1),\n",
       "   'orange': color(--oklch 0.92 0.0384 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.91 0.0402 104 / 1),\n",
       "   'green': color(--oklch 0.91 0.0423 148 / 1),\n",
       "   'blue': color(--oklch 0.91 0.0433 262 / 1)},\n",
       "  41: {'red': color(--oklch 0.93 0.0356 29 / 1),\n",
       "   'orange': color(--oklch 0.93 0.0337 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.93 0.0317 104 / 1),\n",
       "   'green': color(--oklch 0.92 0.0377 148 / 1),\n",
       "   'blue': color(--oklch 0.93 0.0334 262 / 1)},\n",
       "  42: {'red': color(--oklch 0.94 0.0302 29 / 1),\n",
       "   'orange': color(--oklch 0.94 0.029 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.94 0.0273 104 / 1),\n",
       "   'green': color(--oklch 0.94 0.0286 148 / 1),\n",
       "   'blue': color(--oklch 0.94 0.0285 262 / 1)},\n",
       "  43: {'red': color(--oklch 0.96 0.0198 29 / 1),\n",
       "   'orange': color(--oklch 0.96 0.0194 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.95 0.0229 104 / 1),\n",
       "   'green': color(--oklch 0.95 0.0239 148 / 1),\n",
       "   'blue': color(--oklch 0.96 0.0189 262 / 1)},\n",
       "  44: {'red': color(--oklch 0.97 0.0147 29 / 1),\n",
       "   'orange': color(--oklch 0.97 0.0146 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.97 0.0138 104 / 1),\n",
       "   'green': color(--oklch 0.97 0.0144 148 / 1),\n",
       "   'blue': color(--oklch 0.97 0.0141 262 / 1)},\n",
       "  68: {'red': color(--oklch 0.13 0.0526 29 / 1),\n",
       "   'orange': color(--oklch 0.13 0.0278 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.12 0.0171 104 / 1),\n",
       "   'green': color(--oklch 0.12 0.0204 148 / 1),\n",
       "   'blue': color(--oklch 0.13 0.037 262 / 1)},\n",
       "  69: {'red': color(--oklch 0.16 0.0645 29 / 1),\n",
       "   'orange': color(--oklch 0.16 0.0341 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.16 0.0228 104 / 1),\n",
       "   'green': color(--oklch 0.15 0.0254 148 / 1),\n",
       "   'blue': color(--oklch 0.16 0.0454 262 / 1)},\n",
       "  70: {'red': color(--oklch 0.19 0.0763 29 / 1),\n",
       "   'orange': color(--oklch 0.18 0.0383 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.18 0.0256 104 / 1),\n",
       "   'green': color(--oklch 0.18 0.0305 148 / 1),\n",
       "   'blue': color(--oklch 0.18 0.051 262 / 1)},\n",
       "  71: {'red': color(--oklch 0.21 0.0841 29 / 1),\n",
       "   'orange': color(--oklch 0.2 0.0425 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.2 0.0284 104 / 1),\n",
       "   'green': color(--oklch 0.19 0.0321 148 / 1),\n",
       "   'blue': color(--oklch 0.2 0.0566 262 / 1)},\n",
       "  72: {'red': color(--oklch 0.23 0.0918 29 / 1),\n",
       "   'orange': color(--oklch 0.22 0.0467 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.21 0.0298 104 / 1),\n",
       "   'green': color(--oklch 0.21 0.0355 148 / 1),\n",
       "   'blue': color(--oklch 0.22 0.0621 262 / 1)},\n",
       "  73: {'red': color(--oklch 0.24 0.0956 29 / 1),\n",
       "   'orange': color(--oklch 0.23 0.0488 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.23 0.0326 104 / 1),\n",
       "   'green': color(--oklch 0.22 0.0371 148 / 1),\n",
       "   'blue': color(--oklch 0.23 0.0648 262 / 1)},\n",
       "  74: {'red': color(--oklch 0.25 0.0994 29 / 1),\n",
       "   'orange': color(--oklch 0.25 0.0529 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.24 0.034 104 / 1),\n",
       "   'green': color(--oklch 0.24 0.0405 148 / 1),\n",
       "   'blue': color(--oklch 0.24 0.0676 262 / 1)},\n",
       "  75: {'red': color(--oklch 0.27 0.1068 29 / 1),\n",
       "   'orange': color(--oklch 0.26 0.055 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.25 0.0354 104 / 1),\n",
       "   'green': color(--oklch 0.25 0.0421 148 / 1),\n",
       "   'blue': color(--oklch 0.26 0.073 262 / 1)},\n",
       "  76: {'red': color(--oklch 0.28 0.1106 29 / 1),\n",
       "   'orange': color(--oklch 0.27 0.0571 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.27 0.0382 104 / 1),\n",
       "   'green': color(--oklch 0.26 0.0438 148 / 1),\n",
       "   'blue': color(--oklch 0.27 0.0757 262 / 1)},\n",
       "  77: {'red': color(--oklch 0.29 0.1142 29 / 1),\n",
       "   'orange': color(--oklch 0.28 0.0591 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.28 0.0396 104 / 1),\n",
       "   'green': color(--oklch 0.27 0.0454 148 / 1),\n",
       "   'blue': color(--oklch 0.28 0.0784 262 / 1)},\n",
       "  78: {'red': color(--oklch 0.3 0.1179 29 / 1),\n",
       "   'orange': color(--oklch 0.29 0.0612 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.29 0.041 104 / 1),\n",
       "   'green': color(--oklch 0.28 0.0471 148 / 1),\n",
       "   'blue': color(--oklch 0.29 0.0811 262 / 1)},\n",
       "  79: {'red': color(--oklch 0.31 0.1215 29 / 1),\n",
       "   'orange': color(--oklch 0.3 0.0632 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.3 0.0424 104 / 1),\n",
       "   'green': color(--oklch 0.29 0.0487 148 / 1),\n",
       "   'blue': color(--oklch 0.3 0.0838 262 / 1)},\n",
       "  80: {'red': color(--oklch 0.32 0.125 29 / 1),\n",
       "   'orange': color(--oklch 0.31 0.0653 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.3 0.0504 148 / 1),\n",
       "   'blue': color(--oklch 0.31 0.0864 262 / 1)},\n",
       "  81: {'red': color(--oklch 0.33 0.1286 29 / 1),\n",
       "   'orange': color(--oklch 0.32 0.0673 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.31 0.052 148 / 1),\n",
       "   'blue': color(--oklch 0.32 0.0891 262 / 1)},\n",
       "  82: {'red': color(--oklch 0.34 0.132 29 / 1),\n",
       "   'orange': color(--oklch 0.33 0.0693 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.32 0.0536 148 / 1),\n",
       "   'blue': color(--oklch 0.33 0.0917 262 / 1)},\n",
       "  83: {'red': color(--oklch 0.35 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.34 0.0714 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.33 0.0465 104 / 1),\n",
       "   'green': color(--oklch 0.33 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  84: {'red': color(--oklch 0.36 0.1389 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.34 0.0478 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  85: {'red': color(--oklch 0.37 0.1422 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.35 0.0492 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  86: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.35 0.0585 148 / 1),\n",
       "   'blue': color(--oklch 0.36 0.0994 262 / 1)},\n",
       "  87: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.37 0.0773 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.36 0.0601 148 / 1),\n",
       "   'blue': color(--oklch 0.37 0.1019 262 / 1)},\n",
       "  88: {'red': color(--oklch 0.39 0.1486 29 / 1),\n",
       "   'orange': color(--oklch 0.38 0.0793 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  89: {'red': color(--oklch 0.4 0.1517 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.38 0.0533 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  90: {'red': color(--oklch 0.41 0.1548 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  91: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.39 0.0649 148 / 1),\n",
       "   'blue': color(--oklch 0.4 0.1094 262 / 1)},\n",
       "  92: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.41 0.0851 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  93: {'red': color(--oklch 0.43 0.1606 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.41 0.0573 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  94: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.41 0.068 148 / 1),\n",
       "   'blue': color(--oklch 0.42 0.1142 262 / 1)},\n",
       "  95: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.43 0.0889 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.43 0.1165 262 / 1)},\n",
       "  96: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.43 0.0599 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  97: {'red': color(--oklch 0.46 0.1685 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.43 0.0711 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  98: {'red': color(--oklch 0.47 0.1709 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.44 0.0726 148 / 1),\n",
       "   'blue': color(--oklch 0.45 0.121 262 / 1)}},\n",
       " 'grassland': {10: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  11: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  12: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  13: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  14: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  15: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  16: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  17: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  18: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  19: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  20: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  21: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  22: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  23: {'red': color(--oklch 0.75 0.1402 29 / 1),\n",
       "   'orange': color(--oklch 0.74 0.1083 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.73 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.74 0.1226 262 / 1)},\n",
       "  24: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.73 0.0981 148 / 1),\n",
       "   'blue': color(--oklch 0.74 0.1226 262 / 1)},\n",
       "  25: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.75 0.1192 262 / 1)},\n",
       "  26: {'red': color(--oklch 0.77 0.1308 29 / 1),\n",
       "   'orange': color(--oklch 0.76 0.103 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.76 0.1155 262 / 1)},\n",
       "  27: {'red': color(--oklch 0.78 0.1259 29 / 1),\n",
       "   'orange': color(--oklch 0.77 0.1 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.76 0.0842 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.77 0.1117 262 / 1)},\n",
       "  28: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.79 0.0934 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.78 0.0814 104 / 1),\n",
       "   'green': color(--oklch 0.77 0.0921 148 / 1),\n",
       "   'blue': color(--oklch 0.79 0.1036 262 / 1)},\n",
       "  30: {'red': color(--oklch 0.81 0.1079 29 / 1),\n",
       "   'orange': color(--oklch 0.8 0.0898 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.79 0.0796 104 / 1),\n",
       "   'green': color(--oklch 0.78 0.0899 148 / 1),\n",
       "   'blue': color(--oklch 0.8 0.0994 262 / 1)},\n",
       "  31: {'red': color(--oklch 0.82 0.1012 29 / 1),\n",
       "   'orange': color(--oklch 0.81 0.0861 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.8 0.0776 104 / 1),\n",
       "   'green': color(--oklch 0.79 0.0874 148 / 1),\n",
       "   'blue': color(--oklch 0.81 0.095 262 / 1)},\n",
       "  32: {'red': color(--oklch 0.83 0.0947 29 / 1),\n",
       "   'orange': color(--oklch 0.82 0.0822 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.81 0.0752 104 / 1),\n",
       "   'green': color(--oklch 0.81 0.0816 148 / 1),\n",
       "   'blue': color(--oklch 0.82 0.0898 262 / 1)},\n",
       "  33: {'red': color(--oklch 0.84 0.0883 29 / 1),\n",
       "   'orange': color(--oklch 0.83 0.0782 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.82 0.0726 104 / 1),\n",
       "   'green': color(--oklch 0.82 0.0784 148 / 1),\n",
       "   'blue': color(--oklch 0.83 0.0845 262 / 1)},\n",
       "  34: {'red': color(--oklch 0.85 0.082 29 / 1),\n",
       "   'orange': color(--oklch 0.84 0.0741 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.84 0.0666 104 / 1),\n",
       "   'green': color(--oklch 0.83 0.0749 148 / 1),\n",
       "   'blue': color(--oklch 0.84 0.0792 262 / 1)},\n",
       "  35: {'red': color(--oklch 0.86 0.0758 29 / 1),\n",
       "   'orange': color(--oklch 0.85 0.0699 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.85 0.0633 104 / 1),\n",
       "   'green': color(--oklch 0.84 0.0713 148 / 1),\n",
       "   'blue': color(--oklch 0.85 0.0739 262 / 1)},\n",
       "  36: {'red': color(--oklch 0.87 0.0697 29 / 1),\n",
       "   'orange': color(--oklch 0.87 0.0612 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.86 0.0598 104 / 1),\n",
       "   'green': color(--oklch 0.85 0.0675 148 / 1),\n",
       "   'blue': color(--oklch 0.86 0.0687 262 / 1)},\n",
       "  37: {'red': color(--oklch 0.88 0.0638 29 / 1),\n",
       "   'orange': color(--oklch 0.88 0.0567 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.87 0.0562 104 / 1),\n",
       "   'green': color(--oklch 0.87 0.0595 148 / 1),\n",
       "   'blue': color(--oklch 0.88 0.0584 262 / 1)},\n",
       "  38: {'red': color(--oklch 0.89 0.0579 29 / 1),\n",
       "   'orange': color(--oklch 0.89 0.0522 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.89 0.0484 104 / 1),\n",
       "   'green': color(--oklch 0.88 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.89 0.0534 262 / 1)},\n",
       "  39: {'red': color(--oklch 0.91 0.0466 29 / 1),\n",
       "   'orange': color(--oklch 0.9 0.0477 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.9 0.0444 104 / 1),\n",
       "   'green': color(--oklch 0.9 0.0467 148 / 1),\n",
       "   'blue': color(--oklch 0.9 0.0483 262 / 1)},\n",
       "  40: {'red': color(--oklch 0.92 0.041 29 / 1),\n",
       "   'orange': color(--oklch 0.92 0.0384 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.91 0.0402 104 / 1),\n",
       "   'green': color(--oklch 0.91 0.0423 148 / 1),\n",
       "   'blue': color(--oklch 0.91 0.0433 262 / 1)},\n",
       "  41: {'red': color(--oklch 0.93 0.0356 29 / 1),\n",
       "   'orange': color(--oklch 0.93 0.0337 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.93 0.0317 104 / 1),\n",
       "   'green': color(--oklch 0.92 0.0377 148 / 1),\n",
       "   'blue': color(--oklch 0.93 0.0334 262 / 1)},\n",
       "  42: {'red': color(--oklch 0.95 0.025 29 / 1),\n",
       "   'orange': color(--oklch 0.94 0.029 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.94 0.0273 104 / 1),\n",
       "   'green': color(--oklch 0.94 0.0286 148 / 1),\n",
       "   'blue': color(--oklch 0.94 0.0285 262 / 1)},\n",
       "  43: {'red': color(--oklch 0.96 0.0198 29 / 1),\n",
       "   'orange': color(--oklch 0.96 0.0194 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.96 0.0184 104 / 1),\n",
       "   'green': color(--oklch 0.95 0.0239 148 / 1),\n",
       "   'blue': color(--oklch 0.96 0.0189 262 / 1)},\n",
       "  44: {'red': color(--oklch 0.97 0.0147 29 / 1),\n",
       "   'orange': color(--oklch 0.97 0.0146 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.97 0.0138 104 / 1),\n",
       "   'green': color(--oklch 0.97 0.0144 148 / 1),\n",
       "   'blue': color(--oklch 0.97 0.0141 262 / 1)},\n",
       "  68: {'red': color(--oklch 0.13 0.0526 29 / 1),\n",
       "   'orange': color(--oklch 0.13 0.0278 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.13 0.0185 104 / 1),\n",
       "   'green': color(--oklch 0.12 0.0204 148 / 1),\n",
       "   'blue': color(--oklch 0.13 0.037 262 / 1)},\n",
       "  69: {'red': color(--oklch 0.17 0.0685 29 / 1),\n",
       "   'orange': color(--oklch 0.16 0.0341 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.16 0.0228 104 / 1),\n",
       "   'green': color(--oklch 0.16 0.0271 148 / 1),\n",
       "   'blue': color(--oklch 0.16 0.0454 262 / 1)},\n",
       "  70: {'red': color(--oklch 0.19 0.0763 29 / 1),\n",
       "   'orange': color(--oklch 0.18 0.0383 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.18 0.0256 104 / 1),\n",
       "   'green': color(--oklch 0.18 0.0305 148 / 1),\n",
       "   'blue': color(--oklch 0.18 0.051 262 / 1)},\n",
       "  71: {'red': color(--oklch 0.21 0.0841 29 / 1),\n",
       "   'orange': color(--oklch 0.2 0.0425 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.2 0.0284 104 / 1),\n",
       "   'green': color(--oklch 0.2 0.0338 148 / 1),\n",
       "   'blue': color(--oklch 0.2 0.0566 262 / 1)},\n",
       "  72: {'red': color(--oklch 0.23 0.0918 29 / 1),\n",
       "   'orange': color(--oklch 0.22 0.0467 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.21 0.0298 104 / 1),\n",
       "   'green': color(--oklch 0.21 0.0355 148 / 1),\n",
       "   'blue': color(--oklch 0.22 0.0621 262 / 1)},\n",
       "  73: {'red': color(--oklch 0.24 0.0956 29 / 1),\n",
       "   'orange': color(--oklch 0.23 0.0488 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.23 0.0326 104 / 1),\n",
       "   'green': color(--oklch 0.23 0.0388 148 / 1),\n",
       "   'blue': color(--oklch 0.23 0.0648 262 / 1)},\n",
       "  74: {'red': color(--oklch 0.26 0.1031 29 / 1),\n",
       "   'orange': color(--oklch 0.25 0.0529 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.24 0.034 104 / 1),\n",
       "   'green': color(--oklch 0.24 0.0405 148 / 1),\n",
       "   'blue': color(--oklch 0.25 0.0703 262 / 1)},\n",
       "  75: {'red': color(--oklch 0.27 0.1068 29 / 1),\n",
       "   'orange': color(--oklch 0.26 0.055 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.25 0.0354 104 / 1),\n",
       "   'green': color(--oklch 0.25 0.0421 148 / 1),\n",
       "   'blue': color(--oklch 0.26 0.073 262 / 1)},\n",
       "  76: {'red': color(--oklch 0.28 0.1106 29 / 1),\n",
       "   'orange': color(--oklch 0.27 0.0571 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.27 0.0382 104 / 1),\n",
       "   'green': color(--oklch 0.26 0.0438 148 / 1),\n",
       "   'blue': color(--oklch 0.27 0.0757 262 / 1)},\n",
       "  77: {'red': color(--oklch 0.29 0.1142 29 / 1),\n",
       "   'orange': color(--oklch 0.28 0.0591 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.28 0.0396 104 / 1),\n",
       "   'green': color(--oklch 0.27 0.0454 148 / 1),\n",
       "   'blue': color(--oklch 0.28 0.0784 262 / 1)},\n",
       "  78: {'red': color(--oklch 0.3 0.1179 29 / 1),\n",
       "   'orange': color(--oklch 0.29 0.0612 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.29 0.041 104 / 1),\n",
       "   'green': color(--oklch 0.28 0.0471 148 / 1),\n",
       "   'blue': color(--oklch 0.29 0.0811 262 / 1)},\n",
       "  79: {'red': color(--oklch 0.31 0.1215 29 / 1),\n",
       "   'orange': color(--oklch 0.3 0.0632 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.3 0.0424 104 / 1),\n",
       "   'green': color(--oklch 0.29 0.0487 148 / 1),\n",
       "   'blue': color(--oklch 0.3 0.0838 262 / 1)},\n",
       "  80: {'red': color(--oklch 0.32 0.125 29 / 1),\n",
       "   'orange': color(--oklch 0.31 0.0653 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.3 0.0504 148 / 1),\n",
       "   'blue': color(--oklch 0.31 0.0864 262 / 1)},\n",
       "  81: {'red': color(--oklch 0.33 0.1286 29 / 1),\n",
       "   'orange': color(--oklch 0.32 0.0673 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.31 0.052 148 / 1),\n",
       "   'blue': color(--oklch 0.32 0.0891 262 / 1)},\n",
       "  82: {'red': color(--oklch 0.34 0.132 29 / 1),\n",
       "   'orange': color(--oklch 0.33 0.0693 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.32 0.0536 148 / 1),\n",
       "   'blue': color(--oklch 0.33 0.0917 262 / 1)},\n",
       "  83: {'red': color(--oklch 0.35 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.34 0.0714 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.33 0.0465 104 / 1),\n",
       "   'green': color(--oklch 0.33 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  84: {'red': color(--oklch 0.36 0.1389 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.34 0.0478 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  85: {'red': color(--oklch 0.37 0.1422 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.35 0.0492 104 / 1),\n",
       "   'green': color(--oklch 0.35 0.0585 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  86: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.35 0.0585 148 / 1),\n",
       "   'blue': color(--oklch 0.36 0.0994 262 / 1)},\n",
       "  87: {'red': color(--oklch 0.39 0.1486 29 / 1),\n",
       "   'orange': color(--oklch 0.37 0.0773 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.36 0.0601 148 / 1),\n",
       "   'blue': color(--oklch 0.37 0.1019 262 / 1)},\n",
       "  88: {'red': color(--oklch 0.39 0.1486 29 / 1),\n",
       "   'orange': color(--oklch 0.38 0.0793 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  89: {'red': color(--oklch 0.4 0.1517 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.38 0.0533 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  90: {'red': color(--oklch 0.41 0.1548 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  91: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.39 0.0649 148 / 1),\n",
       "   'blue': color(--oklch 0.4 0.1094 262 / 1)},\n",
       "  92: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.41 0.0851 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  93: {'red': color(--oklch 0.43 0.1606 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.41 0.0573 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.42 0.1142 262 / 1)},\n",
       "  94: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.41 0.068 148 / 1),\n",
       "   'blue': color(--oklch 0.42 0.1142 262 / 1)},\n",
       "  95: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.43 0.0889 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.43 0.1165 262 / 1)},\n",
       "  96: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.43 0.0599 104 / 1),\n",
       "   'green': color(--oklch 0.43 0.0711 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  97: {'red': color(--oklch 0.46 0.1685 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.43 0.0711 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  98: {'red': color(--oklch 0.47 0.1709 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.44 0.0726 148 / 1),\n",
       "   'blue': color(--oklch 0.45 0.121 262 / 1)}},\n",
       " 'tundra': {10: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  11: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.68 0.1384 262 / 1)},\n",
       "  12: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  13: {'red': color(--oklch 0.7 0.1608 29 / 1),\n",
       "   'orange': color(--oklch 0.69 0.1166 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.68 0.0861 104 / 1),\n",
       "   'green': color(--oklch 0.67 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  14: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  15: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.69 0.1364 262 / 1)},\n",
       "  16: {'red': color(--oklch 0.71 0.1571 29 / 1),\n",
       "   'orange': color(--oklch 0.7 0.1155 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.69 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.68 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  17: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.7 0.1342 262 / 1)},\n",
       "  18: {'red': color(--oklch 0.72 0.1531 29 / 1),\n",
       "   'orange': color(--oklch 0.71 0.1141 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.7 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.69 0.0999 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  19: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.71 0.1317 262 / 1)},\n",
       "  20: {'red': color(--oklch 0.73 0.149 29 / 1),\n",
       "   'orange': color(--oklch 0.72 0.1125 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.71 0.0867 104 / 1),\n",
       "   'green': color(--oklch 0.7 0.0998 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  21: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.72 0.1289 262 / 1)},\n",
       "  22: {'red': color(--oklch 0.74 0.1447 29 / 1),\n",
       "   'orange': color(--oklch 0.73 0.1105 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.72 0.0866 104 / 1),\n",
       "   'green': color(--oklch 0.71 0.0995 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  23: {'red': color(--oklch 0.75 0.1402 29 / 1),\n",
       "   'orange': color(--oklch 0.74 0.1083 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.73 0.0864 104 / 1),\n",
       "   'green': color(--oklch 0.72 0.0989 148 / 1),\n",
       "   'blue': color(--oklch 0.73 0.1259 262 / 1)},\n",
       "  24: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.73 0.0981 148 / 1),\n",
       "   'blue': color(--oklch 0.74 0.1226 262 / 1)},\n",
       "  25: {'red': color(--oklch 0.76 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.75 0.1058 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.74 0.0859 104 / 1),\n",
       "   'green': color(--oklch 0.74 0.0971 148 / 1),\n",
       "   'blue': color(--oklch 0.75 0.1192 262 / 1)},\n",
       "  26: {'red': color(--oklch 0.77 0.1308 29 / 1),\n",
       "   'orange': color(--oklch 0.76 0.103 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.75 0.0852 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.76 0.1155 262 / 1)},\n",
       "  27: {'red': color(--oklch 0.78 0.1259 29 / 1),\n",
       "   'orange': color(--oklch 0.77 0.1 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.76 0.0842 104 / 1),\n",
       "   'green': color(--oklch 0.75 0.0957 148 / 1),\n",
       "   'blue': color(--oklch 0.77 0.1117 262 / 1)},\n",
       "  28: {'red': color(--oklch 0.8 0.1147 29 / 1),\n",
       "   'orange': color(--oklch 0.79 0.0934 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.78 0.0814 104 / 1),\n",
       "   'green': color(--oklch 0.77 0.0921 148 / 1),\n",
       "   'blue': color(--oklch 0.78 0.1077 262 / 1)},\n",
       "  30: {'red': color(--oklch 0.81 0.1079 29 / 1),\n",
       "   'orange': color(--oklch 0.8 0.0898 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.79 0.0796 104 / 1),\n",
       "   'green': color(--oklch 0.78 0.0899 148 / 1),\n",
       "   'blue': color(--oklch 0.79 0.1036 262 / 1)},\n",
       "  31: {'red': color(--oklch 0.82 0.1012 29 / 1),\n",
       "   'orange': color(--oklch 0.81 0.0861 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.8 0.0776 104 / 1),\n",
       "   'green': color(--oklch 0.79 0.0874 148 / 1),\n",
       "   'blue': color(--oklch 0.8 0.0994 262 / 1)},\n",
       "  32: {'red': color(--oklch 0.83 0.0947 29 / 1),\n",
       "   'orange': color(--oklch 0.82 0.0822 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.81 0.0752 104 / 1),\n",
       "   'green': color(--oklch 0.8 0.0846 148 / 1),\n",
       "   'blue': color(--oklch 0.81 0.095 262 / 1)},\n",
       "  33: {'red': color(--oklch 0.84 0.0883 29 / 1),\n",
       "   'orange': color(--oklch 0.83 0.0782 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.82 0.0726 104 / 1),\n",
       "   'green': color(--oklch 0.82 0.0784 148 / 1),\n",
       "   'blue': color(--oklch 0.83 0.0845 262 / 1)},\n",
       "  34: {'red': color(--oklch 0.85 0.082 29 / 1),\n",
       "   'orange': color(--oklch 0.84 0.0741 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.83 0.0697 104 / 1),\n",
       "   'green': color(--oklch 0.83 0.0749 148 / 1),\n",
       "   'blue': color(--oklch 0.84 0.0792 262 / 1)},\n",
       "  35: {'red': color(--oklch 0.86 0.0758 29 / 1),\n",
       "   'orange': color(--oklch 0.85 0.0699 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.85 0.0633 104 / 1),\n",
       "   'green': color(--oklch 0.84 0.0713 148 / 1),\n",
       "   'blue': color(--oklch 0.85 0.0739 262 / 1)},\n",
       "  36: {'red': color(--oklch 0.87 0.0697 29 / 1),\n",
       "   'orange': color(--oklch 0.86 0.0656 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.86 0.0598 104 / 1),\n",
       "   'green': color(--oklch 0.85 0.0675 148 / 1),\n",
       "   'blue': color(--oklch 0.86 0.0687 262 / 1)},\n",
       "  37: {'red': color(--oklch 0.88 0.0638 29 / 1),\n",
       "   'orange': color(--oklch 0.88 0.0567 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.87 0.0562 104 / 1),\n",
       "   'green': color(--oklch 0.87 0.0595 148 / 1),\n",
       "   'blue': color(--oklch 0.87 0.0636 262 / 1)},\n",
       "  38: {'red': color(--oklch 0.89 0.0579 29 / 1),\n",
       "   'orange': color(--oklch 0.89 0.0522 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.88 0.0524 104 / 1),\n",
       "   'green': color(--oklch 0.88 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.89 0.0534 262 / 1)},\n",
       "  39: {'red': color(--oklch 0.9 0.0522 29 / 1),\n",
       "   'orange': color(--oklch 0.9 0.0477 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.9 0.0444 104 / 1),\n",
       "   'green': color(--oklch 0.89 0.0511 148 / 1),\n",
       "   'blue': color(--oklch 0.9 0.0483 262 / 1)},\n",
       "  40: {'red': color(--oklch 0.92 0.041 29 / 1),\n",
       "   'orange': color(--oklch 0.92 0.0384 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.91 0.0402 104 / 1),\n",
       "   'green': color(--oklch 0.91 0.0423 148 / 1),\n",
       "   'blue': color(--oklch 0.91 0.0433 262 / 1)},\n",
       "  41: {'red': color(--oklch 0.93 0.0356 29 / 1),\n",
       "   'orange': color(--oklch 0.93 0.0337 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.93 0.0317 104 / 1),\n",
       "   'green': color(--oklch 0.92 0.0377 148 / 1),\n",
       "   'blue': color(--oklch 0.93 0.0334 262 / 1)},\n",
       "  42: {'red': color(--oklch 0.94 0.0302 29 / 1),\n",
       "   'orange': color(--oklch 0.94 0.029 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.94 0.0273 104 / 1),\n",
       "   'green': color(--oklch 0.94 0.0286 148 / 1),\n",
       "   'blue': color(--oklch 0.94 0.0285 262 / 1)},\n",
       "  43: {'red': color(--oklch 0.96 0.0198 29 / 1),\n",
       "   'orange': color(--oklch 0.96 0.0194 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.95 0.0229 104 / 1),\n",
       "   'green': color(--oklch 0.95 0.0239 148 / 1),\n",
       "   'blue': color(--oklch 0.95 0.0237 262 / 1)},\n",
       "  44: {'red': color(--oklch 0.97 0.0147 29 / 1),\n",
       "   'orange': color(--oklch 0.97 0.0146 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.97 0.0138 104 / 1),\n",
       "   'green': color(--oklch 0.97 0.0144 148 / 1),\n",
       "   'blue': color(--oklch 0.97 0.0141 262 / 1)},\n",
       "  45: {'red': color(--oklch 0.98 0.0097 29 / 1),\n",
       "   'orange': color(--oklch 0.98 0.0097 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.98 0.0093 104 / 1),\n",
       "   'green': color(--oklch 0.98 0.0097 148 / 1),\n",
       "   'blue': color(--oklch 0.98 0.0094 262 / 1)},\n",
       "  68: {'red': color(--oklch 0.13 0.0526 29 / 1),\n",
       "   'orange': color(--oklch 0.12 0.0256 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.12 0.0171 104 / 1),\n",
       "   'green': color(--oklch 0.12 0.0204 148 / 1),\n",
       "   'blue': color(--oklch 0.12 0.0342 262 / 1)},\n",
       "  69: {'red': color(--oklch 0.16 0.0645 29 / 1),\n",
       "   'orange': color(--oklch 0.16 0.0341 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.15 0.0214 104 / 1),\n",
       "   'green': color(--oklch 0.15 0.0254 148 / 1),\n",
       "   'blue': color(--oklch 0.16 0.0454 262 / 1)},\n",
       "  70: {'red': color(--oklch 0.19 0.0763 29 / 1),\n",
       "   'orange': color(--oklch 0.18 0.0383 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.18 0.0256 104 / 1),\n",
       "   'green': color(--oklch 0.17 0.0288 148 / 1),\n",
       "   'blue': color(--oklch 0.18 0.051 262 / 1)},\n",
       "  71: {'red': color(--oklch 0.21 0.0841 29 / 1),\n",
       "   'orange': color(--oklch 0.2 0.0425 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.2 0.0284 104 / 1),\n",
       "   'green': color(--oklch 0.19 0.0321 148 / 1),\n",
       "   'blue': color(--oklch 0.2 0.0566 262 / 1)},\n",
       "  72: {'red': color(--oklch 0.22 0.0879 29 / 1),\n",
       "   'orange': color(--oklch 0.22 0.0467 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.21 0.0298 104 / 1),\n",
       "   'green': color(--oklch 0.21 0.0355 148 / 1),\n",
       "   'blue': color(--oklch 0.22 0.0621 262 / 1)},\n",
       "  73: {'red': color(--oklch 0.24 0.0956 29 / 1),\n",
       "   'orange': color(--oklch 0.23 0.0488 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.23 0.0326 104 / 1),\n",
       "   'green': color(--oklch 0.22 0.0371 148 / 1),\n",
       "   'blue': color(--oklch 0.23 0.0648 262 / 1)},\n",
       "  74: {'red': color(--oklch 0.25 0.0994 29 / 1),\n",
       "   'orange': color(--oklch 0.25 0.0529 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.24 0.034 104 / 1),\n",
       "   'green': color(--oklch 0.24 0.0405 148 / 1),\n",
       "   'blue': color(--oklch 0.24 0.0676 262 / 1)},\n",
       "  75: {'red': color(--oklch 0.27 0.1068 29 / 1),\n",
       "   'orange': color(--oklch 0.26 0.055 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.25 0.0354 104 / 1),\n",
       "   'green': color(--oklch 0.25 0.0421 148 / 1),\n",
       "   'blue': color(--oklch 0.26 0.073 262 / 1)},\n",
       "  76: {'red': color(--oklch 0.28 0.1106 29 / 1),\n",
       "   'orange': color(--oklch 0.27 0.0571 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.26 0.0368 104 / 1),\n",
       "   'green': color(--oklch 0.26 0.0438 148 / 1),\n",
       "   'blue': color(--oklch 0.27 0.0757 262 / 1)},\n",
       "  77: {'red': color(--oklch 0.29 0.1142 29 / 1),\n",
       "   'orange': color(--oklch 0.28 0.0591 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.28 0.0396 104 / 1),\n",
       "   'green': color(--oklch 0.27 0.0454 148 / 1),\n",
       "   'blue': color(--oklch 0.28 0.0784 262 / 1)},\n",
       "  78: {'red': color(--oklch 0.3 0.1179 29 / 1),\n",
       "   'orange': color(--oklch 0.29 0.0612 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.29 0.041 104 / 1),\n",
       "   'green': color(--oklch 0.28 0.0471 148 / 1),\n",
       "   'blue': color(--oklch 0.29 0.0811 262 / 1)},\n",
       "  79: {'red': color(--oklch 0.31 0.1215 29 / 1),\n",
       "   'orange': color(--oklch 0.3 0.0632 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.3 0.0424 104 / 1),\n",
       "   'green': color(--oklch 0.29 0.0487 148 / 1),\n",
       "   'blue': color(--oklch 0.3 0.0838 262 / 1)},\n",
       "  80: {'red': color(--oklch 0.32 0.125 29 / 1),\n",
       "   'orange': color(--oklch 0.31 0.0653 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.3 0.0504 148 / 1),\n",
       "   'blue': color(--oklch 0.31 0.0864 262 / 1)},\n",
       "  81: {'red': color(--oklch 0.33 0.1286 29 / 1),\n",
       "   'orange': color(--oklch 0.32 0.0673 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.31 0.0437 104 / 1),\n",
       "   'green': color(--oklch 0.31 0.052 148 / 1),\n",
       "   'blue': color(--oklch 0.32 0.0891 262 / 1)},\n",
       "  82: {'red': color(--oklch 0.34 0.132 29 / 1),\n",
       "   'orange': color(--oklch 0.33 0.0693 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.32 0.0451 104 / 1),\n",
       "   'green': color(--oklch 0.32 0.0536 148 / 1),\n",
       "   'blue': color(--oklch 0.33 0.0917 262 / 1)},\n",
       "  83: {'red': color(--oklch 0.35 0.1355 29 / 1),\n",
       "   'orange': color(--oklch 0.34 0.0714 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.33 0.0465 104 / 1),\n",
       "   'green': color(--oklch 0.33 0.0553 148 / 1),\n",
       "   'blue': color(--oklch 0.34 0.0943 262 / 1)},\n",
       "  84: {'red': color(--oklch 0.36 0.1389 29 / 1),\n",
       "   'orange': color(--oklch 0.35 0.0734 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.34 0.0478 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  85: {'red': color(--oklch 0.37 0.1422 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.35 0.0492 104 / 1),\n",
       "   'green': color(--oklch 0.34 0.0569 148 / 1),\n",
       "   'blue': color(--oklch 0.35 0.0969 262 / 1)},\n",
       "  86: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.36 0.0753 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.35 0.0585 148 / 1),\n",
       "   'blue': color(--oklch 0.36 0.0994 262 / 1)},\n",
       "  87: {'red': color(--oklch 0.38 0.1454 29 / 1),\n",
       "   'orange': color(--oklch 0.37 0.0773 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.36 0.0506 104 / 1),\n",
       "   'green': color(--oklch 0.36 0.0601 148 / 1),\n",
       "   'blue': color(--oklch 0.37 0.1019 262 / 1)},\n",
       "  88: {'red': color(--oklch 0.39 0.1486 29 / 1),\n",
       "   'orange': color(--oklch 0.38 0.0793 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.37 0.0519 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.38 0.1045 262 / 1)},\n",
       "  89: {'red': color(--oklch 0.4 0.1517 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.38 0.0533 104 / 1),\n",
       "   'green': color(--oklch 0.37 0.0617 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  90: {'red': color(--oklch 0.41 0.1548 29 / 1),\n",
       "   'orange': color(--oklch 0.39 0.0813 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.39 0.0546 104 / 1),\n",
       "   'green': color(--oklch 0.38 0.0633 148 / 1),\n",
       "   'blue': color(--oklch 0.39 0.1069 262 / 1)},\n",
       "  91: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.4 0.0832 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.39 0.0649 148 / 1),\n",
       "   'blue': color(--oklch 0.4 0.1094 262 / 1)},\n",
       "  92: {'red': color(--oklch 0.42 0.1577 29 / 1),\n",
       "   'orange': color(--oklch 0.41 0.0851 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.4 0.0559 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  93: {'red': color(--oklch 0.43 0.1606 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.41 0.0573 104 / 1),\n",
       "   'green': color(--oklch 0.4 0.0664 148 / 1),\n",
       "   'blue': color(--oklch 0.41 0.1118 262 / 1)},\n",
       "  94: {'red': color(--oklch 0.44 0.1634 29 / 1),\n",
       "   'orange': color(--oklch 0.42 0.087 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.41 0.068 148 / 1),\n",
       "   'blue': color(--oklch 0.42 0.1142 262 / 1)},\n",
       "  95: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.43 0.0889 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.42 0.0586 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.43 0.1165 262 / 1)},\n",
       "  96: {'red': color(--oklch 0.45 0.166 29 / 1),\n",
       "   'orange': color(--oklch 0.44 0.0908 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.43 0.0599 104 / 1),\n",
       "   'green': color(--oklch 0.42 0.0696 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  97: {'red': color(--oklch 0.46 0.1685 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.43 0.0711 148 / 1),\n",
       "   'blue': color(--oklch 0.44 0.1188 262 / 1)},\n",
       "  98: {'red': color(--oklch 0.47 0.1709 29 / 1),\n",
       "   'orange': color(--oklch 0.45 0.0926 62.5 / 1),\n",
       "   'yellow': color(--oklch 0.44 0.0612 104 / 1),\n",
       "   'green': color(--oklch 0.44 0.0726 148 / 1),\n",
       "   'blue': color(--oklch 0.45 0.121 262 / 1)}}}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dT_mL_acol_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b66b95-2c86-452f-bef4-1e3716ab9555",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "monobiome",
   "language": "python",
   "name": "monobiome"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}