illustrations.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import folium\n",
    "import h3.api.numpy_int as h3\n",
    "from folium.vector_layers import CircleMarker"
   ]
  },
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    "def get_hexagon(h):\n",
    "    geo_lst = list(h3.h3_to_geo_boundary(h))\n",
    "    geo_lst.append(geo_lst[0])\n",
    "    return np.array(geo_lst)"
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    "center = [38.72533, -9.15]"
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
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       "<folium.folium.Map at 0x7f8338bc53a0>"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map(location=center, zoom_start=18)\n",
    "\n",
    "hh = h3.geo_to_h3(38.72533, -9.15, 11)\n",
    "ring = h3.k_ring(hh, 2)\n",
    "\n",
    "for r in ring:\n",
    "    hexagon = get_hexagon(r)\n",
    "    polygon = folium.vector_layers.Polygon(locations=hexagon)\n",
    "    polygon.add_to(m)\n",
    "m"
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   "source": [
    "## Sorted Search Demonstration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([7, 7, 2, 7, 7, 3, 3, 2, 3, 8, 8, 8, 2, 8, 8, 5, 6, 5, 6, 5, 6])\n",
    "idxs = np.argsort(data)\n",
    "\n",
    "keys = np.array([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 3])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i0 = np.searchsorted(data, keys, side=\"left\", sorter=idxs)\n",
    "i0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 6])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i1 = np.searchsorted(data, keys, side=\"right\", sorter=idxs)\n",
    "i1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2, 2, 2]), array([ 2,  7, 12]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[idxs[0:3]], idxs[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3, 3, 3]), array([5, 6, 8]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[idxs[3:6]], idxs[3:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {\n",
    "    \"init_geo\": [29.76275701, 28.861421639000003, \n",
    "                 29.021140319, 28.71507979, \n",
    "                 28.807593859, 29.268012094, \n",
    "                 28.818752568999997, 28.573495078, \n",
    "                 28.625998347000003, 29.172096508000003],\n",
    "    \"init_h3i\": [29.002119281999995, 31.501784377000007, \n",
    "                 29.913000368000006, 30.084877427000002, \n",
    "                 29.115711202, 29.365764047, \n",
    "                 29.375851799000003, 30.04566253, \n",
    "                 29.513744804999995, 30.07629952],\n",
    "    \"qrad_geo\": [14.455405539000004, 12.925163823999995, \n",
    "                 14.692950778999993, 16.122003127000006, \n",
    "                 18.081273656, 16.782265988000006, \n",
    "                 13.358665484999996, 14.337435540000001, \n",
    "                 16.600762007, 15.374777615],\n",
    "    \"qrad_h3i\": [5.463111709999993, 3.119011846999996, \n",
    "                 5.1980214820000015, 6.469879312000003, \n",
    "                 6.439164724000008, 6.789193827000005, \n",
    "                 4.794309327999997, 4.683093052000004, \n",
    "                 6.548858373999991, 6.293878848000006],\n",
    "    \"qknn_geo\": [7.1081926910000135, 6.342480156999997, \n",
    "                 7.460930053999988, 7.468806442999991, \n",
    "                 8.582897841000005, 8.199856435000001, \n",
    "                 6.301281680999992, 7.061586293000005, \n",
    "                 7.844416269000007, 7.850034356999998],\n",
    "    \"qknn_h3i\": [5.567441251999995, 3.4514994520000073, \n",
    "                 5.014784836000004, 6.786518560999994, \n",
    "                 6.629357628999998, 7.221533246999996, \n",
    "                 4.706059526000004, 4.923216506000003, \n",
    "                 6.839402250000006, 6.238044574999989]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf_df = pd.DataFrame.from_dict(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_idx = perf_df.mean().index.str.endswith(\"geo\")\n",
    "h3i_idx = perf_df.mean().index.str.endswith(\"h3i\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "init_geo    28.962635\n",
       "qrad_geo    15.273070\n",
       "qknn_geo     7.422048\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df.mean()[geo_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "init_h3i    29.799482\n",
       "qrad_h3i     5.579852\n",
       "qknn_h3i     5.737786\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df.mean()[h3i_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_tbl = pd.DataFrame(data=[\n",
    "    perf_df.mean()[geo_idx].to_numpy(),\n",
    "    perf_df.mean()[h3i_idx].to_numpy()\n",
    "], columns=[\"Initialization\", \"Radius\", \"KNN\"], index=[\"TI\", \"H3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initialization</th>\n",
       "      <th>Radius</th>\n",
       "      <th>KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TI</th>\n",
       "      <td>28.962635</td>\n",
       "      <td>15.273070</td>\n",
       "      <td>7.422048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H3</th>\n",
       "      <td>29.799482</td>\n",
       "      <td>5.579852</td>\n",
       "      <td>5.737786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Initialization     Radius       KNN\n",
       "TI       28.962635  15.273070  7.422048\n",
       "H3       29.799482   5.579852  5.737786"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_tbl = pd.DataFrame(data=[\n",
    "    perf_df.std()[geo_idx].to_numpy(),\n",
    "    perf_df.std()[h3i_idx].to_numpy()\n",
    "], columns=[\"Initialization\", \"Radius\", \"KNN\"], index=[\"TI\", \"H3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initialization</th>\n",
       "      <th>Radius</th>\n",
       "      <th>KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TI</th>\n",
       "      <td>0.359285</td>\n",
       "      <td>1.623289</td>\n",
       "      <td>0.742955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H3</th>\n",
       "      <td>0.721261</td>\n",
       "      <td>1.158461</td>\n",
       "      <td>1.204939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Initialization    Radius       KNN\n",
       "TI        0.359285  1.623289  0.742955\n",
       "H3        0.721261  1.158461  1.204939"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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