Mountain images

README.md

Mountain summit images

Images of the summits of mountains

GPX.ipynb

Munro summit images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = requests.request(method=\"GET\", url=\"https://data.daniel-williams.co.uk/summit/summit\")\n",
    "summits = c.json()\n",
    "summits = [summit for summit in summits if \"munro\" in summit['types']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import srtm\n",
    "geo_elevation_data = srtm.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.filters import gaussian_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.patheffects as pe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_munro(munro, ax=None):\n",
    "    center = munro['lat'], munro['lon']\n",
    "    name = munro['name']\n",
    "    data = np.zeros((200,200))\n",
    "    \n",
    "    lons = np.linspace(center[0]-0.025, center[0]+0.025, 200)\n",
    "    lats = np.linspace(center[1]-0.025, center[1]+0.025, 200)\n",
    "\n",
    "    aa, bb = np.meshgrid(lons, lats)\n",
    "    \n",
    "    levels = np.arange(0, 1500, 100)\n",
    "    \n",
    "    for i in range(len(lons)):\n",
    "        for j in range(len(lats)):\n",
    "            data[i, j] = geo_elevation_data.get_elevation(aa[i, j], bb[i, j], approximate=False)\n",
    "    \n",
    "    nans = np.isnan(data)\n",
    "    x = lambda z: z.nonzero()[0]\n",
    "    \n",
    "    f = interpolate.interp2d(aa[~nans], bb[~nans], data[~nans], kind='cubic')\n",
    "    \n",
    "    data = f(aa, bb)\n",
    "    \n",
    "    #data[nans] = np.interp(x(nans), x(~nans), data[~nans])\n",
    "    \n",
    "    blurred = gaussian_filter(data.T, sigma=2.5)\n",
    "    \n",
    "    if not ax:\n",
    "        f, ax = plt.subplots(dpi=300)\n",
    "        \n",
    "    ax.contourf(lats, lons, blurred, levels=levels, alpha=1, cmap=\"Greys\")#, cmap=\"gist_earth\")\n",
    "    \n",
    "    ax.text(x= 0.97, y =0.8, s=name, \n",
    "            horizontalalignment=\"right\", \n",
    "            color=\"black\",\n",
    "            fontdict={'fontname':'Source Code Pro', 'fontweight': 'bold', \"fontsize\": 7},\n",
    "            path_effects=[pe.Stroke(linewidth=2, foreground='w'), pe.Normal()],\n",
    "            transform=axis.transAxes)\n",
    "    \n",
    "    ax.text(x= 0.99, y =0.7, s=f\"{munro['height']} m\", \n",
    "            horizontalalignment=\"right\", \n",
    "            fontdict={'fontname':'Source Code Pro', \"fontsize\": 6},\n",
    "            color=\"white\",\n",
    "            transform=axis.transAxes)\n",
    "    \n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_frame_on(False)\n",
    "    ax.set_aspect(1)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "summits = c.json()\n",
    "summits = [summit for summit in summits if \"munro\" in summit['types']]\n",
    "summits.sort(reverse=True, key=lambda x: x['height'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Cannot retrieve http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N56W006.hgt.zip",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-d3337212e0b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msummit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummits\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mplot_munro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-8-0f745a1a4c0b>\u001b[0m in \u001b[0;36mplot_munro\u001b[0;34m(munro, ax)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlons\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeo_elevation_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_elevation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maa\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapproximate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mnans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gaston/sandbox/local/lib/python3.7/site-packages/srtm/data.py\u001b[0m in \u001b[0;36mget_elevation\u001b[0;34m(self, latitude, longitude, approximate)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_elevation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatitude\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapproximate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mgeo_elevation_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlatitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlongitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;31m#mod_logging.debug('File for ({0}, {1}) -> {2}'.format(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gaston/sandbox/local/lib/python3.7/site-packages/srtm/data.py\u001b[0m in \u001b[0;36mget_file\u001b[0;34m(self, latitude, longitude)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve_or_load_file_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gaston/sandbox/local/lib/python3.7/site-packages/srtm/data.py\u001b[0m in \u001b[0;36mretrieve_or_load_file_data\u001b[0;34m(self, file_name)\u001b[0m\n\u001b[1;32m    137\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Connection to %s failed (timeout)'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m200\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;36m300\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot retrieve %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0mmod_logging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Retrieving {0}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Cannot retrieve http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N56W006.hgt.zip"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family [\"'URW Bookman L'\"] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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zw1CXdqlNew4fPjztXcrsMXndDteeNqlLu9SmTVPObHndIbPb4NrTLrVpk7q0aQa/Yy+FFhoMJ3bG90+4TXe9E9dda3OP64y/ZfzvV5Lsq7W+q7tiKeV/TPLfk5ya5Pgkv1FK+eZa64PbmP9RLr744tx77729trn00ktz2WWXTfMwWMMtt9wy9CGwBnVpl9oM7+GHH572LmX2mLxul2tPm9SlXWrThilntrzukNltcu1pl9q0SV3aMIPfsZdCC9/B0P1TgklfSDzQGR+3jblPWGPZD6x+4ZMktdb3JXlZkpVn0pOTfP825gaARSOzAaB98hoAmJsWGgzd9/gcM+E23S+BmvQvMjabO0k+VGv9rfVWrrV+KMm7O4u+bxtzA8CikdkA0D55DQDMTQsfkXSwM570LyW66x1cd61+cyfJui98Vq3zPePx87cx95quueYanw/ZiEOHDh3xFrSzzz67d22YPnVpl9q05/Dhw7nxxql+X6LMHpPX7XDtaZO6tEtt2jTlzJbXHTK7Da497VKbNqlLm2bwO/ZSaKHBcFdnfMaE25zZGd89pbmT5G8m2ObjnfFjSimPqbX+wzaO4QjnnXdeTjrppGntjinavXt3jjtuO+8WZhbUpV1qM7xdu3ZNe5cye0xet8u1p03q0i61acOUM1ted8jsNrn2tEtt2qQubZjB79hLoYWPSPpEZ/ykCbfZ0xnftI25V287yV9qrH6h85htzA8Ai0RmA0D75DUAMDctNBi6f61wXillkndVPHOd7fv661U/nzjBNqtf7Ny7jfkBYJHIbABon7wGAOamhQbDDUkeGI9PSPKsjVYupRyb5LmdRe/d6sS11k8n+XRn0TdPsNlTO+O7a633bXV+AFgwMhsA2ievAYC5GbzBUGs9mOS6zqJ9m2zyijzyFw53J/nANg/h3Z3xd0+wfned7c4NAAtDZgNA++Q1ADBPgzcYxt7SGe8rpZy71kqllOOTvLGz6K211oe2Ofd/S3J4PH5+KeVl661YSnl2Ri++Vuzf5twAsGhkNgC0T14DAHPRRIOh1npNkg+Ofzw2yXtKKU/rrlNKOSXJ1Um+cbzo7iRvWmt/pZS9pZTaue3bYO6bc+SLrytKKa9YvV4p5UVJ3pNk5evC/zTJb2923wBgmchsAGifvAYA5mWSL3ual9ck+XCSs5LsTfLRUsr7k9yc5LQkFyU5frzuQ0leWWu9Z0pz/3hGX2r1gow+o/KqUsrHk9yY5KtJnpbkWzvr3zGev05pfgBYJDIbANonrwGAmWumwVBr/Wwp5YIkVyZ5epKS5PzxreuLSS6ptV6XKam1PlBK+a6M3sr56vHip+bIL5ta8WdJvrfW+plpzQ8Ai0RmA0D75DUAMA9NfETSilrrTUmek+R1SX4vyWeSPJjkCxm9XfINSb55/HbPac99b631NUlelOSXk3wiycEk9ye5Nck7M/psyOd54QPATiezAaB98hoAmLVm3sGwotb6YJJ3jG9b3cetGf11xla2/UCSD2x1bgDYKWQ2ALRPXgMAs9TUOxgAAAAAAIDFoMEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD0psEAAAAAAAD01lyDoZRyTCnltaWUa0spt5VSDpVS7iil3FBK+bFSyqlzPp6fK6XUzu3Wec4PAK2S2QDQPnkNAMzSUUMfQFcp5ZwkVyZ5+qr/OnN8e16S15dSLqm1XjuH43l2kv9l1vMAwKKR2QDQPnkNAMxaM+9gKKU8Icl1eeSFT03y/iS/kuR3ktw/Xn56kqtLKRfM+HiOTvJLaegxAoAWyGwAaJ+8BgDmoaVgvyLJ48fj25I8o9Z6fq31X9VaX5ZkT0YvjpLk6CTvKqU8bobH8+NJzuscGwAwIrMBoH3yGgCYuSYaDKWUlyZ5wfjHB5N8V631Y911aq13Jnl5klvGi05O8oYZHc85Sf6v8Y+/nuQPZzEPACwamQ0A7ZPXAMC8NNFgSPIjnfHba60H1lqp1npfkp/qLLqslDLV75EopZSM3rZ5bJIvJfnfprl/AFhwMhsA2ievAYC5GLzBUEo5McmFnUVv22STq5IcHI9PTvLCKR/SDyf5tvH49bXWL0x5/wCwkGQ2ALRPXgMA8zR4gyHJ8zP6S4YkuS/JjRutXGs9lORDnUVT+yKqUsoTk/zs+McPZvTlVwDAiMwGgPbJawBgblpoMDy1Mz5Qa31ogm0+ss722/WWJI/J6DMqL6u11inuGwAWncwGgPbJawBgblpoMDylM75twm1u74zPmcZBlFJeleQ7xz++qdb68WnsFwCWiMwGgPbJawBgbqb65U1bdEpn/PkJt/lcZ3zydg+glHJKkv8y/vGTSf7Ddve5VQcOHMju3bt7bXPqqafm9NNPn9ER7VyHDh3a8GeGoS7tUpv2HD58eNq7lNlj8rodrj1tUpd2qU2bppzZ8rpDZrfBtaddatMmdWnTDH7HXgotNBhO7Izvn3Cb7nonrrvW5P6/JKeNxz9Ua31gCvvckosvvjj33ntvr20uvfTSXHbZZTM6IlbccsstQx8Ca1CXdqnN8B5++OFp71Jmj8nrdrn2tEld2qU2bZhyZsvrDpndJteedqlNm9SlDTP4HXsptPARSd0/JXhwwm26L06O287kpZTvSPLa8Y9vr7W+bzv7A4AlJrMBoH3yGgCYmxYaDN33+Bwz4TbHdsaT/kXGo5RSTkjyC+Mf70ryY1vdFwDsADIbANonrwGAuWnhI5IOdsaT/qVEd72D6661uf+QZO94/L/XWu/cxr6m4pprrvH5kI04dOjQEW9BO/vss3vXhulTl3apTXsOHz6cG2+8cZq7lNlj8rodrj1tUpd2qU2bppzZ8rpDZrfBtaddatMmdWnTDH7HXgotNBju6ozPmHCbMzvju7cyaSnlmUn+5/GP76u1vn0r+5m28847LyeddNLQh8Eadu/eneOO29a7hZkBdWmX2gxv165d096lzB6T1+1y7WmTurRLbdow5cyW1x0yu02uPe1SmzapSxtm8Dv2UmihwfCJzvhJE26zpzO+aYvzPi2PfETUnlLKn26w7mmd8Vmr1v33tdZrtngMALBIZDYAtE9eAwBz00KD4eOd8XmllKNqrQ9tss0z19l+q548vk3imCTP6fx82norAsCSkdkA0D55DQDMTQtf8nxDkgfG4xOSPGujlUspxyZ5bmfRe2d0XADAkWQ2ALRPXgMAczN4g6HWejDJdZ1F+zbZ5BVJHjMe353kA1ucd3+ttUxyS3JJZ9PbVv3//q3MDwCLRmYDQPvkNQAwT4M3GMbe0hnvK6Wcu9ZKpZTjk7yxs+itE7zVEwCYHpkNAO2T1wDAXDTRYBh/gdMHxz8em+Q9pZSnddcppZyS5Ook3zhedHeSN621v1LK3lJK7dz2zebIAWBnkdkA0D55DQDMSwtf8rziNUk+nOSsJHuTfLSU8v4kN2f0JU8XJTl+vO5DSV5Za71ngOMEgJ1OZgNA++Q1ADBzzTQYaq2fLaVckOTKJE9PUpKcP751fTHJJbXW6wIAzJ3MBoD2yWsAYB6aaTAkSa31plLKc5K8Ksmrk5yb5Iwk9yS5Jcm7k7yt1nrncEcJAMhsAGifvAYAZq2pBkOS1FofTPKO8W2r+7g1o7/OmNYx7U+yf1r7A4BlILMBoH3yGgCYpSa+5BkAAAAAAFgsGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvGgwAAAAAAEBvRw19AKuVUo5J8n1JXp3k3CRnJPlSkk8neXeS/bXWO6c8594kL07yoiTnJdmT5MQk/5Dks0k+lOSKWuv7pzkvACwymQ0A7ZPXAMAsNdVgKKWck+TKJE9f9V9njm/PS/L6UsoltdZrpzDfM5L8fJJnr7PKPxrfzkvyr0sp1yd5Xa319u3ODQCLTGYDQPvkNQAwa800GEopT0hyXZLHjxfVJB9IcnOS05JclOS4JKcnubqU8pJa63u3Oe1T8ugXPp9M8tdJ7kzyuCTPT/KE8f+dn+RDpZQX1Fpv2ebcALCQZDYAtE9eAwDz0EyDIckVeeSFz21JXl5r/djKf5ZSTk3yziQXJjk6ybtKKU+utd4zhbn/NskvJfm1Wuvfdf+jlPJ1SfYl+a9Jjh8f46+XUp5fa61TmBsAFo3MBoD2yWsAYOaa+JLnUspLk7xg/OODSb6r+8InScafCfnyJCt/1XBykjdsc+o7klyS5Jxa65tWv/AZz/twrfVXkvxAZ/Fzk3zHNucGgIUjswGgffIaAJiXJhoMSX6kM357rfXAWivVWu9L8lOdRZeVUrb8Loxa6/trrftrrV+dYN3fSvLhzqKLtzovACwwmQ0A7ZPXAMBcDN5gKKWcmNFbMle8bZNNrkpycDw+OckLZ3Fc6/iTznjvHOcFgMHJbABon7wGAOZp8AZDRl/wdOx4fF+SGzdaudZ6KMmHOosumNFxrTl9Z7xrjvMCQAtkNgC0T14DAHPTQoPhqZ3xgVrrQxNs85F1tp+18zrjz8xxXgBogcwGgPbJawBgbrb82YpT9JTO+LYJt7m9Mz5niseyrlLKnhz5lxx/NIt5Dhw4kN27d/fa5tRTT83pp58+i8PZ0Q4dOrThzwxDXdqlNu05fPjwtHcps8fkdTtce9qkLu1SmzZNObPldYfMboNrT7vUpk3q0qYZ/I69FFpoMJzSGX9+wm0+1xmfPMVj2cjP5ZG3bN6e5HdmMcnFF1+ce++9t9c2l156aS677LJZHA4dt9xyy9CHwBrUpV1qM7yHH3542ruU2WPyul2uPW1Sl3apTRumnNnyukNmt8m1p11q0yZ1acMMfsdeCi18RNKJnfH9E27TXe/EddeaklLK65L8886in6i1PjDreQGgMTIbANonrwGAuWmhwdB9r+KDE27TfeFx3BSP5VFKKc9K8vOdRVfWWq+Y5ZwA0CiZDQDtk9cAwNy08BFJ3Q8RO2bCbY7tjCf9i4zeSinfkNHbNFdeoP1Vkh+a1XxJcs011/h8yEYcOnToiLegnX322b1rw/SpS7vUpj2HDx/OjTfeOM1dyuwxed0O1542qUu71KZNU85sed0hs9vg2tMutWmTurRpBr9jL4UWGgwHO+NJ/1Kiu97BddfahlLKWUn+MMmZ40W3JHlJrfXLs5hvxXnnnZeTTjppllOwRbt3785xx830j3nYAnVpl9oMb9euXZuv1I/MHpPX7XLtaZO6tEtt2jDlzJbXHTK7Ta497VKbNqlLG2bwO/ZSaOEjku7qjM+YcJszO+O7p3gsSZJSyikZvfB58njRHUkuqrXeMe25AGCByGwAaJ+8BgDmpoUGwyc64ydNuM2ezvimKR5LSiknJfn9JOeOF92Z0QufT09zHgBYQDIbANonrwGAuWmhwfDxzvi8UsokH9v0zHW235ZSyglJrk3yreNF92b0ls2/mdYcALDAZDYAtE9eAwBz00KD4YYkD4zHJyR51kYrl1KOTfLczqL3TuMgSim7k/x2km8bL/pKkotrrX8xjf0DwBKQ2QDQPnkNAMzN4A2GWuvBJNd1Fu3bZJNXJHnMeHx3kg9s9xhKKUcnuSrJBeNFDyR5ea31T7a7bwBYFjIbANonrwGAeRq8wTD2ls54Xynl3LVWKqUcn+SNnUVvrbU+tJ2JSym7klyR5KXjRQ8leWWt9Y+2s18AWFIyGwDaJ68BgLloosFQa70myQfHPx6b5D2llKd11ymlnJLk6iTfOF50d5I3rbW/UsreUkrt3Pats15J8stJvme86OEkr621/vZ27g8ALCuZDQDtk9cAwLxM8mVP8/KaJB9OclaSvUk+Wkp5f5Kbk5yW5KIkx4/XXfkLiHu2OecPJ3ld5+ebk3x7KeXbJ9m41vqj25wfABaRzAaA9slrAGDmmmkw1Fo/W0q5IMmVSZ6epCQ5f3zr+mKSS2qt12X7Tl/18zeNb5Py4geAHUdmA0D75DUAMA/NNBiSpNZ6UynlOUleleTVSc5NckaSe5LckuTdSd5Wa71zuKMEAGQ2ALRPXgMAs9ZUgyFJaq0PJnnH+LbVfdya0V9nbLbe5Uku3+o8ALCTyWwAaJ+8BgBmqYkveQYAAAAAABaLBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANCbBgMAAAAAANDbUUMfAEe68847c9JJJw19GCT5whe+kF/4hV/42s8/+ZM/mSc96UkDHhGJurRMbRbDr/7qr5564YUXfnHo41h08rodrj1tUpd2qc3ikNnTIbPb4NrTLrVpk7osDnmtwdCcu+66K2efffbQh0FGL0R/8Rd/8Ws/X3bZZS7mDVCXdqnNYrj99ttPGfoYloG8bodrT5vUpV1qszhk9nTI7Da49rRLbdqkLotDXvuIJAAAAAAAYAs0GAAAAAAAgN6aazCUUo4ppby2lHJtKeW2UsqhUsodpZQbSik/Vko5dRnnBoBFI7MBoH3yGgCYpaa+g6GUck6SK5M8fdV/nTm+PS/J60spl9Rar12WuQFg0chsAGifvAYAZq2ZdzCUUp6Q5Lo88uKjJnl/kl9J8jtJ7h8vPz3J1aWUC5ZhbgBYNDIbANonrwGAeWjpHQxXJHn8eHxbkpfXWj+28p/jt06+M8mFSY5O8q5SypNrrfcs+NwAsGhkNgC0T14DADPXxDsYSikvTfKC8Y8PJvmu7ouPJKm13pnk5UluGS86OckbFnluAFg0MhsA2ievAYB5aaLBkORHOuO311oPrLVSrfW+JD/VWXRZKWW778IYcm4AWDQyGwDaJ68BgLkYvMFQSjkxo7dFrnjbJptcleTgeHxykhcu4twAsGhkNgC0T14DAPM0eIMhyfOTHDse35fkxo1WrrUeSvKhzqLtfBnUkHMDwKKR2QDQPnkNAMxNC28/fGpnfKDW+tAE23wkyYvX2H6R5k6SsnrBww8/nAcffHCbu2UaHn744Tz2sY894me1GZ66tEtt2rPW479r165HZU8POzWz5XXDXHvapC7tUps2TTmzd2peJzK7Wa497VKbNqlLm2bwO/ZSaKHB8JTO+LYJt7m9Mz5nQedORm8BPcLBgwfzwQ9+cJu7ZVquuuqqr42//OUvq00j1KVdatO+PXv2PHbztda1UzNbXjfOtadN6tIutVkM28jsnZrXicxummtPu9SmTeqyGLb5O/ZSaOEjkk7pjD8/4Taf64wf9QJiQeYGgEUjswGgffIaAJibFhoMJ3bG90+4TXe9E9ddq+25AWDu9uzZc/c2NpfZADAn28hseQ0Ac7LN37GXQgsNht2d8aQfJvZAZ3zcgs4NAHP37d/+7dt58SOzAWBOtpHZ8hoA5mSbv2MvhRa+g+FQZ3zMhNsc2xlP+lcRrc2dJJ/K+Eus/viP//jk22+//eTPfOYz9z700EO1z0727Nlz12tf+9o7t3ksACyfkkd/1MCntrG/nZrZ8hqAWZtmZu/UvE5kNgCzNe3fsZdCCw2Gg53xpH+t0F3v4LprtT13Lrzwwq8muWk83s6uAGA9X5jivnZkZstrAOZkWpm9I/M6kdkAzMU0f8deCi18RNJdnfEZE25zZme8nbehDDk3ACwamQ0A7ZPXAMDctNBg+ERn/KQJt9nTGd+0oHMDwKKR2QDQPnkNAMxNCw2Gj3fG55VSJvnYpmeus/0izQ0Ai0ZmA0D75DUAMDctNBhuSPLAeHxCkmdttHIp5dgkz+0seu+Czg0Ai0ZmA0D75DUAMDeDNxhqrQeTXNdZtG+TTV6R5DHj8d1JPrCIcwPAopHZANA+eQ0AzNPgDYaxt3TG+0op5661Uinl+CRv7Cx6a631oQWeGwAWjcwGgPbJawBgLppoMNRar0nywfGPxyZ5Tynlad11SimnJLk6yTeOF92d5E1r7a+UsreUUju3ffOaGwCWmcwGgPbJawBgXib5wqV5eU2SDyc5K8neJB8tpbw/yc1JTktyUZLjx+s+lOSVtdZ7lmBuAFg0MhsA2ievAYCZK7XWoY/ha0op5yS5MsnTN1jti0kuGf9VxHr72Zvk051Fl9Ra989jbgDYCWQ2ALRPXgMAs9bSOxhSa72plPKcJK9K8uok5yY5I8k9SW5J8u4kb6u13rlMcwPAopHZANA+eQ0AzFpT72AAAAAAAAAWQxNf8gwAAAAAACwWDQYAAAAAAKA3DYaOUsoxpZTXllKuLaXcVko5VEq5o5RyQynlx0oppy7a3KWUC0sp7yilfLKUcl8p5e5Syl+VUv7f8ZduNW+IupRS9pZSLi2l/Fop5WOllC+VUg53Hr9fKKW8qOc+a8/bQ9O+X9M279qUUvZt4XH8pZ5zLPw5k8y3NqWU87dQl+5t3zr73buFff3ttO7XtJVSdpVSnlZK+VellP9WSvnzUsqDnWO/fg7HsBRZM+9rzzzmdu3Z1pwyexPzrkuR1xObZ22KvJ5IkddTNe/rzzzmXobrzxB1KfJ6IvOuTZHZE5lnXYq8nkiR122ptbqNvofinCR/maRucPt8kpcuwtxJTkryzk32+WCSnxj6sW+pLkmekeTPNpmve3tfkj0T7nvSfa7cHhr68W+pNuM5923hcfylCfe9FOfMELVJcv4W6tK9vWSd/e7dwr7+dujHf5378t1J7tvk2K9ftOfFEOfNENeeZXsMF+k5tsl8MrvBuozn3LeFx1Bez/6cOX8Ldene5PXodv2iPS+GOm+GuP4s4+O4CI/NJvPJ60ZrM55z3xYexx2V2QOcM+dvoSbdm7we3a5ftOfFIp8zR4WUUp6Q5Lokjx8vqkk+kOTmJKcluSjJcUlOT3J1KeUltdb3tjp3KeXoJL+V5ILO4r9O8pEku5O8IMlZSY5O8tOllKNrrW+cxv2ZpoHq8pQkz1617JMZPX53JnlckucnecL4/85P8qFSygtqrbf0mOfNE6zz1R77m6shz5mOm8bHsJkbNlthWc6ZZLDa/F0me06v+I4k3zQefz7JH02wzT8keccE632xx3HM0+OSHD/U5MuSNfLatWcVmb0Jee2cWUVeb05eT+m8kdltXn/kdZt5nchs58wR5PXm5HVr58zQHY4Wbhk9CVY6Qbcm+Ser/v/UjE7QlXXuSvK4VudO8sbO+vcnedWq/z8myf/TWefhJC/VC9KwAAARl0lEQVQaug4t1CXJq8b7+lSSH0/y9Wus83VJ/mWO7JZ+KEnZZN9f6zgO/dguYm3G+93X2ef+Kd6fpThnhqxNj+PbleSOzvz/aYN193bvy9CP7Tbv98pz93NJfifJTyX5p0n+c+c+Xr9Iz4shzpshn9/L8hgu0nNsgjlldoN1Ge93X2ef+6d4f5wz8zs+eS2vm7svO/FxXITn2ARzyutGazPe777OPvdP8f44Z+ZzfPJaXg9ej1prBj+AoW9JXtopzgNJzltnvRMy6kStrPvTLc6dUXfsYGfdyzZYt/u2mxuGrkULdUnyovGFatcE6/6zzrw1yf+0yfpL8eJn4HNmX2d/+6d0f5binBm6Nls8xprkaRusu7ez3q1DP77bvN9nZo23eie5vHMfr1+U58UQ583A156leAxndRuqNpHZTdZlvM99nf3tn9L9cc7I63ncb3k9hfNm4OvP0jyOi/Acm3Beed1obcb73NfZ3/4p3R/nzDDHKK/l9XA1GfoAhr4luaZTmLdusu73d9a9K8lRrc2d5PWd9T6RDTr+SfZk9BbBlfWfMXQ9WqhLz+Psfpbkf9lk3WV58TPkObOvs7/9U7o/S3HODF2bHsf4G515P7LJuns769469OM7o8fj8s59vH5RnhdDnDcDX3uW4jGc4fO4+WvPeO4dldkDnzP7OvvbP6X745yR10PW7PLOfbx+UZ4XQ503A19/luZxXITn2IyOc0fl9dC1icxusi49jlFeH3kfL+/cx+sX5XmxDOfM12UHK6WcmOTCzqK3bbLJVRl1lJLk5CQvbHDu7+6M99fxs28ttdbbk3Q/A+yfbXIMczFkXbbgTzrjvXOcdxALVptJLfw5kyxGbUopj0vyss6it896zp1uWbJGXrv2TMmOyewFq8uknDPyemktU9bI7DavP4tw7enYMXmdLFxtJuWckddLSc6sb0c3GDL6IqFjx+P7kty40cq11kMZfQ7gigvWW3eIuUspu5M8t7Po+gmO430b7XMgQ9alr+5Jv2uO8w5lkWqzqSU6Z5LFqM0rM/pyoiQ5nOSKOcy50y1L1shr155p2EmZvUh12ZRzRl7vAMuUNTK7zevPIlx7vjZ9Z7zseZ0sVm025Zz5Gnm9nOTMOo4a+gAG9tTO+ECt9aEJtvlIkhevsX0Lcz8ljzSNapK/nHCfax3TkIasS1/ndcafmXSjUsoLkzw7yRkZvbXpziQfy+jz0+6b6hFOV0u1eVwp5XuTnJvksUm+nOTvM7p4H9io49uxLOdM0lZt1vO6zvjaWusXe2x7VCnlxUmeldEXJh3K6Lz58yQfrrU+ML3DXCrLkjXy2rVnGnZSZrdUF3l9pJZqsx55PX/LlDUyu83rzyJce1bspLxO2qqNzH5ES3VZj7yePzmzjp3eYHhKZ3zbhNvc3hmf09jc3X1+Ydwp67PPk0spp/W8KM3CkHWZWCllT47sFP5Rj83fv87yr5RSfiXJv6+1fmHLBzc7LdXm5ePbWj5VSnlTkl/Z5EXQspwzSVu1eZRSyjdl1O1f0fftm1+f5A/W+b8vlVLekuRna60H11lnp1qWrJHXrj3bsgMzu6W6yOsjtVSbR5HXg1mmrJHZbV5/mr72rNiBeZ20VRuZ/YiW6vIo8nowcmYdO/0jkk7pjD8/4Taf64xPbmzu7e5zvf3O25B16ePn8shbNm9P8jtT2OfxSX40yUdLKc/dbOUBLEptvinJLyX57VLKCRustyznTNJ+bf5FZ3xXRl+MNC3/KMlPJvnzUso/nuJ+l8GyZI28du3Zrp2W2YtSF3k9GXm9/JYpa2R2m9ef1q89K3ZaXieLU5udltmt10VeD0POrGOnNxhO7Izvn3Cb7nonrrvWMHNvd5/r7XfehqzLREopr0vyzzuLfmKCt5A9kOS/J9mX5FuSnJTkmCRnJvnOJL+ZRz5v8qwk1zR4MW+hNrcn+U9JXprkiRl95uAJGXV9/02SmzrrfmeSK0op613rluWcSdqozZpKKSXJD3QWXVFrfXDCzf8hyf4kr8qoxidm9JmHT0zyvTnyr5qekuT3SimnbfeYl8iyZI28du3Zsh2a2S3URV6vrYXarEleD2qZskZmt3n9afbas2KH5nXSRm1k9qO1UJc1yetByZl17PSPSNrdGU96MnYD7rjG5t7uPtfb77wNWZdNlVKeleTnO4uurLVO8mU6X19rvWuN5Z/PqNt8TSnlO5O8K6PH4OQkb0ly0TYPeZqGrs3VSd5Ra314jf/7ZJJPllJ+OaP6XDJe/rIkr0nya2tssyznTDJ8bTbyoiR7Oz9P+vbNO5I8fp23ZX42o18YfrOU8q8zqnlJ8g1JfibJD275aJfLsmSNvHbt2ZIdnNlD10Ver2/o2mxEXg9nmbJGZrd5/Wn52rOT8zoZvjYye21D12Uj8no4cmYdO/0dDN3PtTpmwm2O7Ywn7SzNa+7t7nO9/c7bkHXZUCnlGzJ6m+bKBeCvkvzQJNuu88Jn9TrvSfJvO4suLKV8a9/jnKFBa1NrvWedFz7ddR7MKPw+2Fn84+usviznTNLweZMjv3zqr2utfzHJRrXWByb5zMda61szetGzYl8p5Yyex7isliVr5LVrT287PLPltXNmK+T1cJYpa2R2m9efZq89OzyvE5mdOGf6ktfDkTPr2OkNhu6JNWm3p7vedr7sZBZzb3ef6+133oasy7pKKWcl+cOM3m6ZJLckeUmt9ctTnuqXc+QXtvzTKe9/O5qszWrjF0j/rrPoW0opT1hj1WU5Z5JGa1NKOT5HvtW575dPTepn8kio7kry4hnNs2iWJWvktWtPLzK7zbqsJq/bqY28HtwyZY3MbvP60+q1Z6fnddJobVbbgZndZF3k9eDkzDp2eoOh2+2etBt3Zmd8d2Nzb3ef6+133oasy5pKKadk9MLnyeNFdyS5qNZ6x7TnGgf3ezuLnjrtObahudps4ANJDnd+XutxXJZzJmm3Nq9I8pjx+KtJfn0Wk4z/EuPPOotaOm+GtCxZI69deyYms5M0WJcNyOvNyevlt0xZI7PbvP40d+2R11/TXG02sJMyu9W6yOthyZl17PQGwyc64ydNuM2ezvimddcaZu7uPk8vpexeY52N9nl3rfWLEx7LLA1Zl0cppZyU5PeTnDtedGdGL3w+Pc15Vum+qDp1hvP01VRtNlJrPZxRrVas9TguyzmTtFub7ts3/2AWvzB0tHreDGlZskZeu/ZMRGZ/TVN12Yi8noi8Xn7LlDUyu83rT1PXHnl9hKZqs5Edltmt1kVeD0vOrGOnNxg+3hmfV0qZ5Euvn7nO9i3M/YkkK5+dV5I8fQr7HMKQdTlCKeWEJNcmWfmMxnszesvm30xrjnWc0BnfN+O5+mimNhPa7HFclnMmabA247fMXtBZtH/ac6zS6nkzpGXJGnnt2rMpmX2EZuoyIXm9MXm9/JYpa2R2m9efZq498vpRmqnNhHZKZjdXF3ndBDmzjp3eYLghj3zz9glJnrXRyqWUY5M8t7PoveutO8TctdZDSf60s+j8CY7jRRvtcyBD1qW7391JfjvJt40XfSXJxZN+gc42PaMz/vs5zDepJmoziVLK2UlO6ix61OO4ROdM0mZtfiCP5Mw9GZ1Ps9TqeTOkZckaee3asyGZ/ShN1GUS8rqJ2sjr4S1T1sjsNq8/TVx75PWamqjNJHZYZrdYF3k9PDmzjh3dYBh/pth1nUX7Ntmk+1lnd2f0+XOtzX31pPsspTwxyYXrbDuYIeuyopRydJKr8kh3+IEkL6+1/sl29z3B3OckeX5n0fWznnNSLdSmh3/ZGd+b5KPrrLfw50zSbG26b9/8jXFwzkQp5aIkT+wsun5Wcy2SZckaee3asxGZ/Wgt1KUHeb0xeb0DLFPWyOw2rz8tXHvk9dpaqE0POyazG62LvB6YnNlArXVH35JcnKSOb4eSnLvOescn+VRn3Z9pce4kp2f07eEr6/7gBute0VnvhqFr0VBddiV5V2efh5O8bJv7PHHC9Y7PqHO5MvcXJ9122WvT53HI6MXj/Z25f36DdZfinBmyNuvM8ezO/muS5/bc/pgkx0y47mlJ/rYz198k+bqh6zHhsV/eOe7rF+V5McR5M3AuLMVjOMPnscxuMLOHqkufxyDyWl7L65k+L4Y6bwbOhaV5HBfhOdZjbnndYG36PA7ZgZk95DmzxhzyerJjv7xz3NcvyvNiGc6ZwQ+ghVtGHaSV4nw6ydNW/f8pSf6gs85dSR63zr72rjrp981r7s42b+ys/5Ukr1z1/0cn+dlVx/mioevQQl0y+ryz/Z31vprkVVO4L3eO63LOBut8W5KPrTrOHxq6Dg3VZl+SDyf5F0keu846u5P82/HzfmV/X0py1ib3ZynOmaFqs862b+5s94kt3I+9ST6T5A1JnrTOOiWjcL+1M9fDSV46dB163M/LO8d+fc/HZ8dlzZDP72V5DGf4XJbZDWb2QHXZF3ndZG3W2VZeT3Y/L+8c+/U9H58dmTVDPseX6XGcwXNZXjeY1wPWZl9kdnN1WWdbeT3Z/by8c+zX93x85Mw2bmV8kDva+ItSPpzkrPGimuT9SW7OqHN3UUadpyR5KKMvILpu9X7G+9qb0ZNrxSW11v3zmLuzz6OT/F6O/PKXA0k+klE4vLAzX5L837XWN260zyEMUZdSyr/J6MK94lMZXRQmUmv90XXm755of5/kr5J8PqNu58kZfcHV2as2e/N6+xvaQLXZl+RtnX3eNL59KaO/iPn6JM/LkZ8Jef947g3fnrgs50wy7PWss90xSe7I6LmdJD9Za/3pnvdj9dy3ZlSTOzP6i6fTkjwnyeNXbfr6Wut/7DPXvJRSrs2jj/fMJGeMx/dl9Jciq7201nrEZ17u1KyR1649q9aT2ZuQ186ZTY5BXq9BXk/nvJHZbV5/5HWbeZ3I7DhnNjoGeb0Ged3YOTN0h6OVW5JzkvxljuwErb59IaMvIJpa12uac6/a52OT/MYm+3wwyf859GPfUl1yZLez922D+fvs5+5s8HaoVm4D1GZfz8fxz5I8daedM0PUZo3tXtHZ5qtJnrCF+7B67s1un80232Y9h7rcusVry95p1GZaz4tV+5z7eTPk83tZHsMZPsdldoO3Aeqyr+djKK8HuJ6Nt5PXa9+nW7d4Xdk7jdpM63mxap+DnDdDPseX6XGcwXNcXjd6G6A2+3o+jjsys+ddlzW2k9dr36dbt3hd2TuN2kzrebEs58xRIUlSa72plPKcJK9K8uok52bU9bonyS1J3p3kbbXWOxdh7lrrvUm+r5Tyixl9EczzMup0Hc7obVG/n+SXa60fn+Z9mbYh6zJl/zijGjwvyT/JqKN5apITM/qctS8k+Yskf5TknbXWrwx0nBMboDZXJvlkRp/9+NwkT87oMTwloy+svzejjvOfJvnNWusf99n5spwzSRPnzes64/fWWj+7hX3cluS8jOrw/Izuw0q9j0/y5Yz+iuPGJL+b5LdqrYe3c9DLblmyRl63q4Frz7QsVWbL63Y1cM7I6wYtU9bI7DY1cO2ZlqXK60Rmt6qBc0ZeN0jOHMlHJAEAAAAAAL193dAHAAAAAAAALB4NBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoDcNBgAAAAAAoLf/H9NI3bsPdpUtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1800 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(3,3, dpi=300, figsize=(6,6));\n",
    "for summit, axis in zip(summits[:9], ax.flatten()):\n",
    "    plot_munro(summit, axis);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05000000000000071"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lats[-1] - lats[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04999999999999716"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lons[-1] - lons[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "center = 56.50237, -4.72301 #56.52577, -4.41735# dorain 56.50237, -4.72301 # lui 56.39704, -4.8111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(lons)):\n",
    "    for j in range(len(lats)):\n",
    "        data[i, j] = geo_elevation_data.get_elevation(aa[i, j], bb[i, j], approximate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[data<500] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f77d346c400>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(lons, lats, blurred, levels=levels,)# cmap=\"gist_earth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f77ddcf0f60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

prominence.py

saved_munros.pkl

SRTM Data.ipynb