BDNR2022_Proyecto_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bases de Datos No Relacionales\n",
    "\n",
    "## Proyecto Final - López & Ríos\n",
    "\n",
    "**NOTA:** Es necesario ejecutar las tres consultas en la carpeta import/ previo a correr estas celdas, para crear la base de grafos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\tlink.ref = \"stylesheet\";\n",
       "\tlink.type = \"text/css\";\n",
       "\tlink.href = \"https://cdnjs.cloudflare.com/ajax/libs/vis/4.8.2/vis.css\";\n",
       "\tdocument.head.appendChild(link);\n",
       "require.config({     paths: {         vis: '//cdnjs.cloudflare.com/ajax/libs/vis/4.8.2/vis.min'     } }); require(['vis'], function(vis) {  window.vis = vis; }); "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "py2neo version 2021.2.3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Comenzamos por importar las bibliotecas necesarias \n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "#permite conectarse a la BD \n",
    "#Fuente: https://py2neo.org/2021.1/\n",
    "import py2neo\n",
    "from py2neo import Graph,Node,Relationship\n",
    "\n",
    "#permite representar gráficamente los grafos obtenidos \n",
    "#Fuente: https://github.com/merqurio/neo4jupyter\n",
    "import neo4jupyter\n",
    "neo4jupyter.init_notebook_mode()\n",
    "\n",
    "\n",
    "print ('py2neo version', py2neo.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#estos son los parámetros para la conexión a una instancia de Neo4j que se ejecuta localmente \n",
    "#y en el puerto por defecto\n",
    "#si van a utilizar otra opción deben ajustar los parámetros\n",
    "\n",
    "\n",
    "#indica la url de la BD\n",
    "#dburl='http://localhost:7474'\n",
    "dburl='bolt://127.0.0.1:7687'\n",
    "user='neo4j'\n",
    "pasw='bdnr'\n",
    "\n",
    "graph = Graph(dburl, auth=(user, pasw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_query(query, n=3):\n",
    "    \n",
    "    # Ejecuta n consultas y mide el tiempo de cada una\n",
    "    exec_times = np.zeros(n)\n",
    "    run_result = None\n",
    "    for i in range(n):\n",
    "        st = time.time()\n",
    "        run_result = graph.run(query)\n",
    "        \n",
    "        # get the execution time for this round\n",
    "        exec_times[i] = time.time() - st\n",
    "    \n",
    "    mean_time = exec_times.mean()\n",
    "    std_error = exec_times.std()\n",
    "    \n",
    "    print(f\"Execution times: {list(exec_times)}\")\n",
    "    print(f'Execution time per round: {mean_time:.4f}±{std_error:.4f}s ({100*std_error/mean_time:.2f}%)')\n",
    "    \n",
    "    return run_result.to_data_frame(), exec_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_exec_times(times_list, labels_list):\n",
    "    fig = plt.figure(figsize=(10, 6))\n",
    "    for k, exec_times in enumerate(times_list):\n",
    "        x = list(range(1, len(exec_times)+1))\n",
    "        plt.plot(x, exec_times,label=labels_list[k], linewidth=1.0)\n",
    "    plt.xlabel(\"# run\")\n",
    "    plt.ylabel(\"Execution time (s)\")\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Búsqueda por nodos, sin índices...\n",
      "Execution times: [26.621201038360596, 27.789082288742065, 26.74163269996643, 27.568854093551636, 27.09640860557556]\n",
      "Execution time per round: 27.1634±0.4544s (1.67%)\n",
      "\n",
      "\n",
      "Búsqueda por aristas, sin índices...\n",
      "Execution times: [26.805971384048462, 18.34953761100769, 18.979355096817017, 19.16641592979431, 19.098763942718506]\n",
      "Execution time per round: 20.4800±3.1762s (15.51%)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s</th>\n",
       "      <th>m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'latitude': -20.44444, 'name': 'MS/A756/AGUA ...</td>\n",
       "      <td>{'station_name': 'MS/A756/AGUA CLARA', 'date':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'latitude': -15.902658, 'name': 'GO/A013/ARAG...</td>\n",
       "      <td>{'station_name': 'GO/A013/ARAGARCAS', 'date': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'latitude': -13.41111111, 'name': 'MT/A929/NO...</td>\n",
       "      <td>{'station_name': 'MT/A929/NOVA UBIRATA', 'date...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'latitude': -14.13305554, 'station': 'ALTO PA...</td>\n",
       "      <td>{'date': 2019-01-01, 'station_name': 'GO/A024/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'latitude': -19.69527777, 'name': 'MS/A710/PA...</td>\n",
       "      <td>{'station_name': 'MS/A710/PARANAIBA', 'date': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>{'latitude': -22.57527777, 'name': 'MS/S711/LA...</td>\n",
       "      <td>{'station_name': 'MS/S711/LAGUNA CARAPA', 'dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>{'latitude': -18.802187, 'name': 'MS/A730/CHAP...</td>\n",
       "      <td>{'station_name': 'MS/A730/CHAPADAO DO SUL', 'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>{'latitude': -17.785303, 'name': 'GO/A025/RIO ...</td>\n",
       "      <td>{'station_name': 'GO/A025/RIO VERDE', 'date': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>{'latitude': -22.09277777, 'name': 'MS/S710/IT...</td>\n",
       "      <td>{'station_name': 'MS/S710/ITAPORA', 'date': 20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>{'latitude': -17.81555555, 'name': 'MT/A934/AL...</td>\n",
       "      <td>{'station_name': 'MT/A934/ALTO TAQUARI', 'date...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     s  \\\n",
       "0    {'latitude': -20.44444, 'name': 'MS/A756/AGUA ...   \n",
       "1    {'latitude': -15.902658, 'name': 'GO/A013/ARAG...   \n",
       "2    {'latitude': -13.41111111, 'name': 'MT/A929/NO...   \n",
       "3    {'latitude': -14.13305554, 'station': 'ALTO PA...   \n",
       "4    {'latitude': -19.69527777, 'name': 'MS/A710/PA...   \n",
       "..                                                 ...   \n",
       "107  {'latitude': -22.57527777, 'name': 'MS/S711/LA...   \n",
       "108  {'latitude': -18.802187, 'name': 'MS/A730/CHAP...   \n",
       "109  {'latitude': -17.785303, 'name': 'GO/A025/RIO ...   \n",
       "110  {'latitude': -22.09277777, 'name': 'MS/S710/IT...   \n",
       "111  {'latitude': -17.81555555, 'name': 'MT/A934/AL...   \n",
       "\n",
       "                                                     m  \n",
       "0    {'station_name': 'MS/A756/AGUA CLARA', 'date':...  \n",
       "1    {'station_name': 'GO/A013/ARAGARCAS', 'date': ...  \n",
       "2    {'station_name': 'MT/A929/NOVA UBIRATA', 'date...  \n",
       "3    {'date': 2019-01-01, 'station_name': 'GO/A024/...  \n",
       "4    {'station_name': 'MS/A710/PARANAIBA', 'date': ...  \n",
       "..                                                 ...  \n",
       "107  {'station_name': 'MS/S711/LAGUNA CARAPA', 'dat...  \n",
       "108  {'station_name': 'MS/A730/CHAPADAO DO SUL', 'd...  \n",
       "109  {'station_name': 'GO/A025/RIO VERDE', 'date': ...  \n",
       "110  {'station_name': 'MS/S710/ITAPORA', 'date': 20...  \n",
       "111  {'station_name': 'MT/A934/ALTO TAQUARI', 'date...  \n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate query time without index\n",
    "graph.run(\"DROP INDEX meas_hour IF EXISTS\")\n",
    "graph.run(\"DROP INDEX meas_date IF EXISTS\")\n",
    "\n",
    "# Búsqueda por nodos\n",
    "print(\"Búsqueda por nodos, sin índices...\")\n",
    "query_date_time_nodes = \"\"\"MATCH (s:Station)-->(m:Measurement)\n",
    "                           WHERE m.hour = time(\"2:00\") AND m.date = date(\"2019-01-01\")\n",
    "                           RETURN s, m\"\"\"\n",
    "res_1, times_1 = evaluate_query(query_date_time_nodes, n=5)\n",
    "\n",
    "\n",
    "# Búsqueda por edges\n",
    "print(\"\\n\\nBúsqueda por aristas, sin índices...\")\n",
    "query_date_time_edges = \"\"\"MATCH (s:Station)\n",
    "                        -[:MEASURED{hour: time(\"2:00\"), date:date(\"2019-01-01\")}]\n",
    "                        ->(m:Measurement)\n",
    "                        RETURN m,s\"\"\"\n",
    "res_2, times_2 = evaluate_query(query_date_time_edges, n=5)\n",
    "\n",
    "assert len(res_1) == len(res_2)\n",
    "res_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear subgrafo temporal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def delete_nodes_transactions(node_match=\"Year{name:2000}\"):\n",
    "    query = f\"\"\"MATCH (p:{node_match})\n",
    "                CALL {{\n",
    "                  WITH p\n",
    "                  DETACH DELETE p\n",
    "                }}\n",
    "                IN TRANSACTIONS OF 10000 ROWS\n",
    "                RETURN COUNT(p)\n",
    "                \"\"\"\n",
    "    # print(query)\n",
    "    res = graph.run(query)\n",
    "    res = 0 if res is None else next(res)[0]\n",
    "    print(f\"Deleted nodes matching :{node_match} : {res}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# Create years in range of data\n",
    "graph.run(\"\"\"MATCH (m:Measurement)\n",
    "             WITH MIN(m.date.year) AS min_year, MAX(m.date.year) AS max_year\n",
    "             FOREACH(y in range(min_year, max_year)\n",
    "             | CREATE (:Year{name: y}))\"\"\")\n",
    "\n",
    "# Create months\n",
    "graph.run(\"\"\"FOREACH(m in range(1, 12)\n",
    "             | CREATE (:Month{name: m}))\"\"\")\n",
    "\n",
    "# Create days\n",
    "graph.run(\"\"\"FOREACH(d in range(1, 31)\n",
    "             | CREATE (:Day{name: d}))\"\"\")\n",
    "\n",
    "# Create hours\n",
    "graph.run(\"\"\"FOREACH(h in range(1, 23)\n",
    "             | CREATE (:Hour{name: h}))\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 9min 55s\n"
     ]
    },
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     "execution_count": 8,
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     "output_type": "execute_result"
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   ],
   "source": [
    "%%time\n",
    "graph.run(\"\"\"MATCH (meas:Measurement)\n",
    "             MATCH (y:Year{name: meas.date.year})\n",
    "             MATCH (m:Month{name: meas.date.month})\n",
    "             MATCH (d:Day{name: meas.date.day})\n",
    "             MATCH (h:Hour{name: meas.hour.hour})\n",
    "             CALL{\n",
    "                 WITH meas, y, m, d, h\n",
    "                 MERGE (y)-[:Y_CONTAINS]->(meas)\n",
    "                 MERGE (m)-[:M_CONTAINS]->(meas)\n",
    "                 MERGE (d)-[:D_CONTAINS]->(meas)\n",
    "                 MERGE (h)-[:H_CONTAINS]->(meas)\n",
    "             } IN TRANSACTIONS OF 10000 ROWS\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.6 ms\n",
      "Wall time: 21.9 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table><tr><th>count(*)</th></tr><tr><td style=\"text-align:right\">43803960</td></tr></table>"
      ],
      "text/plain": [
       " count(*) \n",
       "----------\n",
       " 43803960 "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# See number of edges we added for this (surprisingly fast)\n",
    "graph.run(\"MATCH ()-[:Y_CONTAINS|:M_CONTAINS|:D_CONTAINS|:H_CONTAINS]->() RETURN count(*)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Búsqueda usando subgrafo temporal (sin índices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution times: [5.326352596282959, 3.1334645748138428, 3.0220797061920166, 3.1113924980163574, 2.990163564682007]\n",
      "Execution time per round: 3.5167±0.9064s (25.77%)\n"
     ]
    },
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       "      <td>{'station_name': 'MT/A934/ALTO TAQUARI', 'date...</td>\n",
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       "      <th>1</th>\n",
       "      <td>{'station_name': 'MS/S710/ITAPORA', 'date': 20...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'station_name': 'GO/A025/RIO VERDE', 'date': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'station_name': 'MS/S711/LAGUNA CARAPA', 'dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'station_name': 'MS/A730/CHAPADAO DO SUL', 'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>{'station_name': 'MT/A904/SORRISO', 'date': 20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>{'date': 2019-01-01, 'station_name': 'GO/A024/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>{'station_name': 'MT/A929/NOVA UBIRATA', 'date...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>{'station_name': 'GO/A013/ARAGARCAS', 'date': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>{'station_name': 'MS/A756/AGUA CLARA', 'date':...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     m\n",
       "0    {'station_name': 'MT/A934/ALTO TAQUARI', 'date...\n",
       "1    {'station_name': 'MS/S710/ITAPORA', 'date': 20...\n",
       "2    {'station_name': 'GO/A025/RIO VERDE', 'date': ...\n",
       "3    {'station_name': 'MS/S711/LAGUNA CARAPA', 'dat...\n",
       "4    {'station_name': 'MS/A730/CHAPADAO DO SUL', 'd...\n",
       "..                                                 ...\n",
       "107  {'station_name': 'MT/A904/SORRISO', 'date': 20...\n",
       "108  {'date': 2019-01-01, 'station_name': 'GO/A024/...\n",
       "109  {'station_name': 'MT/A929/NOVA UBIRATA', 'date...\n",
       "110  {'station_name': 'GO/A013/ARAGARCAS', 'date': ...\n",
       "111  {'station_name': 'MS/A756/AGUA CLARA', 'date':...\n",
       "\n",
       "[112 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try matching without index\n",
    "query_date_time_subgraph = \"\"\"MATCH (m:Measurement)<--(:Year{name:2019}),\n",
    "                                 (m)<--(:Month{name:1}),\n",
    "                                 (m)<--(:Day{name:1}),\n",
    "                                 (m)<--(:Hour{name:2})\n",
    "                           RETURN m\"\"\"\n",
    "res_3, times_3 = evaluate_query(query_date_time_subgraph, n=5)\n",
    "res_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>state</th>\n",
       "      <th>populationPercent</th>\n",
       "      <th>uniqueness</th>\n",
       "      <th>type</th>\n",
       "      <th>entityType</th>\n",
       "      <th>labelsOrTypes</th>\n",
       "      <th>properties</th>\n",
       "      <th>indexProvider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>UniqueMeasurementStationTime</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>UNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>NODE</td>\n",
       "      <td>[Measurement]</td>\n",
       "      <td>[station_name, name]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>UniqueStationName</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>UNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>NODE</td>\n",
       "      <td>[Station]</td>\n",
       "      <td>[name]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                          name   state  populationPercent uniqueness  \\\n",
       "0   5  UniqueMeasurementStationTime  ONLINE              100.0     UNIQUE   \n",
       "1   3             UniqueStationName  ONLINE              100.0     UNIQUE   \n",
       "\n",
       "    type entityType  labelsOrTypes            properties     indexProvider  \n",
       "0  BTREE       NODE  [Measurement]  [station_name, name]  native-btree-1.0  \n",
       "1  BTREE       NODE      [Station]                [name]  native-btree-1.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure there are no mysterious indices created before proceeding\n",
    "graph.run(\"DROP INDEX index_343aff4e IF EXISTS\")\n",
    "graph.run(\"DROP INDEX index_f7700477 IF EXISTS\")\n",
    "\n",
    "graph.run(\"SHOW INDEXES\").to_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subgrafo temporal con un nodo para cada día del año (crear aristas demora varias horas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "(No data)"
      ],
      "text/plain": [
       "(No data)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Temporal graph but hierarchical\n",
    "\n",
    "#Create years in range of data\n",
    "graph.run(\"\"\"MATCH (d:DateTime) DETACH DELETE d RETURN count(d)\"\"\")\n",
    "\n",
    "graph.run(\"\"\"MATCH (meas:Measurement)\n",
    "             WITH MIN(meas.date.year) AS min_year, MAX(meas.date.year) AS max_year\n",
    "             FOREACH(y in range(min_year, max_year)\n",
    "             | FOREACH(m in range(1, 12)\n",
    "               | FOREACH(d in range(1, 31)\n",
    "                 | FOREACH(h in range(0, 23) |\n",
    "                   CREATE (dt:DateTime{year: y, month: m, day: d, hour: h})))))\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>COUNT(d)</th></tr><tr><td style=\"text-align:right\">196416</td></tr></table>"
      ],
      "text/plain": [
       " COUNT(d) \n",
       "----------\n",
       "   196416 "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.run(\"MATCH (d:DateTime) RETURN COUNT(d)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 14.4 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "(No data)"
      ],
      "text/plain": [
       "(No data)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "graph.run(\"\"\"MATCH (date_time:DateTime)\n",
    "             MATCH (y:Year{name: date_time.year})\n",
    "             MATCH (m:Month{name: date_time.month})\n",
    "             MATCH (d:Day{name: date_time.day})\n",
    "             MATCH (h:Hour{name: date_time.hour})\n",
    "             CALL{\n",
    "                 WITH date_time, y, m, d, h\n",
    "                 MERGE (y)-[:YEAR_OF]->(date_time)\n",
    "                 MERGE (m)-[:MONTH_OF]->(date_time)\n",
    "                 MERGE (d)-[:DAY_OF]->(date_time)\n",
    "                 MERGE (h)-[:HOUR_OF]->(date_time)\n",
    "             } IN TRANSACTIONS OF 10000 ROWS\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# CUIDADO: ESTA CONSULTA DEMORA VARIAS HORAS, Y NO SE PUEDE CORTAR!\n",
    "graph.run(\"\"\"MATCH (meas:Measurement)\n",
    "              MATCH (d:DateTime{year: meas.date.year, month: meas.date.month, day: meas.date.day, hour: meas.hour.hour})\n",
    "              CALL{\n",
    "                  WITH meas, d\n",
    "                  MERGE (d)-[:I_CONTAINS]->(meas)\n",
    "              } IN TRANSACTIONS OF 10000 ROWS\"\"\")\n",
    "\n",
    "\n",
    "## Otra opción, también demora\n",
    "# graph.run(\"\"\"MATCH (meas:Measurement)\n",
    "#              MATCH (:Year{name:meas.date.year})-[:YEAR_OF]->(dt:DateTime),\n",
    "#                    (:Month{name:meas.date.month})-[:MONTH_OF]->(dt:DateTime),\n",
    "#                    (:Day{name:meas.date.day})-[:DAY_OF]->(dt:DateTime),\n",
    "#                    (:Hour{name:meas.hour.hour})-[:HOUR_OF]->(dt:DateTime)\n",
    "#              CALL{\n",
    "#                  WITH meas, dt\n",
    "#                  MERGE (dt)-[:I_CONTAINS]->(meas)\n",
    "#              } IN TRANSACTIONS OF 10000 ROWS\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution times: [0.6917073726654053, 0.6506140232086182, 0.6535942554473877, 0.7937514781951904, 0.7060916423797607]\n",
      "Execution time per round: 0.6992±0.0519s (7.43%)\n"
     ]
    },
    {
     "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>m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'station_name': 'MS/A756/AGUA CLARA', 'date':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'station_name': 'MT/A934/ALTO TAQUARI', 'date...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'station_name': 'MS/S710/ITAPORA', 'date': 20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'station_name': 'GO/A025/RIO VERDE', 'date': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'station_name': 'MS/A730/CHAPADAO DO SUL', 'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>{'station_name': 'MT/A904/SORRISO', 'date': 20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>{'station_name': 'MS/A710/PARANAIBA', 'date': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>{'date': 2019-01-01, 'station_name': 'GO/A024/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>{'station_name': 'MT/A929/NOVA UBIRATA', 'date...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>{'station_name': 'GO/A013/ARAGARCAS', 'date': ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     m\n",
       "0    {'station_name': 'MS/A756/AGUA CLARA', 'date':...\n",
       "1    {'station_name': 'MT/A934/ALTO TAQUARI', 'date...\n",
       "2    {'station_name': 'MS/S710/ITAPORA', 'date': 20...\n",
       "3    {'station_name': 'GO/A025/RIO VERDE', 'date': ...\n",
       "4    {'station_name': 'MS/A730/CHAPADAO DO SUL', 'd...\n",
       "..                                                 ...\n",
       "107  {'station_name': 'MT/A904/SORRISO', 'date': 20...\n",
       "108  {'station_name': 'MS/A710/PARANAIBA', 'date': ...\n",
       "109  {'date': 2019-01-01, 'station_name': 'GO/A024/...\n",
       "110  {'station_name': 'MT/A929/NOVA UBIRATA', 'date...\n",
       "111  {'station_name': 'GO/A013/ARAGARCAS', 'date': ...\n",
       "\n",
       "[112 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_datetime = \"\"\"MATCH (m:Measurement)<--(:DateTime{year:2019, month:1, day:1, hour: 2}) RETURN m\"\"\"\n",
    "res_4, times_4 = evaluate_query(query_datetime, n=5)\n",
    "res_4\n",
    "\n",
    "#query_date_subgraph = \"\"\"MATCH (m:Measurement)<--(dt:DateTime)<--(:Year{name:m.date.year}),\n",
    "#                                (dt:DateTime)<--(:Month{name:m.date.month}),\n",
    "#                                (dt:DateTime)<--(:Day{name:m.date.day}),\n",
    "#                                (dt:DateTime)<--(:Hour{name:m.hour.hour})\n",
    "#                            RETURN m\"\"\"\n",
    "#res_5, times_5 = evaluate_query(query_date_subgraph, n=10)\n",
    "#res_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparación de búsquedas sin índices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_exec_times([times_1, times_2, times_3, times_4], [\"Búsqueda por nodos (sin índices)\", \"Búsqueda por edges (sin índices)\", \"Búsqueda con subgrafo temporal (sin índices)\",\"Búsqueda con subgrafo temporal con un nodo para cada día y hora del año (sin índices)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Búsqueda temporal con índices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>state</th>\n",
       "      <th>populationPercent</th>\n",
       "      <th>uniqueness</th>\n",
       "      <th>type</th>\n",
       "      <th>entityType</th>\n",
       "      <th>labelsOrTypes</th>\n",
       "      <th>properties</th>\n",
       "      <th>indexProvider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>UniqueMeasurementStationTime</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>UNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>NODE</td>\n",
       "      <td>[Measurement]</td>\n",
       "      <td>[station_name, name]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>UniqueStationName</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>UNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>NODE</td>\n",
       "      <td>[Station]</td>\n",
       "      <td>[name]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>date_time</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NONUNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>NODE</td>\n",
       "      <td>[DateTime]</td>\n",
       "      <td>[hour, day, month, year]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>meas_date</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NONUNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>NODE</td>\n",
       "      <td>[Measurement]</td>\n",
       "      <td>[date]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>meas_hour</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NONUNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>NODE</td>\n",
       "      <td>[Measurement]</td>\n",
       "      <td>[hour]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>rel_meas</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NONUNIQUE</td>\n",
       "      <td>BTREE</td>\n",
       "      <td>RELATIONSHIP</td>\n",
       "      <td>[MEASURED]</td>\n",
       "      <td>[date, hour]</td>\n",
       "      <td>native-btree-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                          name   state  populationPercent uniqueness  \\\n",
       "0   5  UniqueMeasurementStationTime  ONLINE              100.0     UNIQUE   \n",
       "1   3             UniqueStationName  ONLINE              100.0     UNIQUE   \n",
       "2   1                     date_time  ONLINE              100.0  NONUNIQUE   \n",
       "3   7                     meas_date  ONLINE              100.0  NONUNIQUE   \n",
       "4   8                     meas_hour  ONLINE              100.0  NONUNIQUE   \n",
       "5   9                      rel_meas  ONLINE              100.0  NONUNIQUE   \n",
       "\n",
       "    type    entityType  labelsOrTypes                properties  \\\n",
       "0  BTREE          NODE  [Measurement]      [station_name, name]   \n",
       "1  BTREE          NODE      [Station]                    [name]   \n",
       "2  BTREE          NODE     [DateTime]  [hour, day, month, year]   \n",
       "3  BTREE          NODE  [Measurement]                    [date]   \n",
       "4  BTREE          NODE  [Measurement]                    [hour]   \n",
       "5  BTREE  RELATIONSHIP     [MEASURED]              [date, hour]   \n",
       "\n",
       "      indexProvider  \n",
       "0  native-btree-1.0  \n",
       "1  native-btree-1.0  \n",
       "2  native-btree-1.0  \n",
       "3  native-btree-1.0  \n",
       "4  native-btree-1.0  \n",
       "5  native-btree-1.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# ATENCIÓN: Correr esta celda hasta que los nodos pasen de POPULATING a ONLINE\n",
    "graph.run(\"\"\"CREATE INDEX meas_hour IF NOT EXISTS\n",
    "             FOR (m:Measurement)\n",
    "             ON (m.hour)\"\"\")\n",
    "\n",
    "graph.run(\"\"\"CREATE INDEX meas_date IF NOT EXISTS\n",
    "             FOR (m:Measurement)\n",
    "             ON (m.date)\"\"\")\n",
    "\n",
    "graph.run(\"\"\"CREATE INDEX rel_meas IF NOT EXISTS\n",
    "             FOR ()-[r:MEASURED]-()\n",
    "             ON (r.date, r.hour)\"\"\")\n",
    "\n",
    "graph.run(\"\"\"CREATE INDEX date_time IF NOT EXISTS\n",
    "             FOR (d:DateTime)\n",
    "             ON (\n",
    "                 d.hour,\n",
    "                 d.day,\n",
    "                 d.month,\n",
    "                 d.year\n",
    "                )\"\"\")\n",
    "\n",
    "graph.run(\"SHOW INDEXES\").to_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Búsqueda por nodos, con índices...\n",
      "Execution times: [0.16565155982971191, 0.03164839744567871, 0.05047488212585449, 0.037896156311035156, 0.039279937744140625]\n",
      "Execution time per round: 0.0650±0.0507s (78.00%)\n",
      "\n",
      "\n",
      "Búsqueda por aristas, con índices (en los nodos)...\n",
      "Execution times: [0.1535022258758545, 0.03245902061462402, 0.030605792999267578, 0.037633657455444336, 0.02393364906311035]\n",
      "Execution time per round: 0.0556±0.0491s (88.33%)\n",
      "\n",
      "\n",
      "Búsqueda con subgrafo temporal, con índices\n",
      "Execution times: [8.920443296432495, 2.818814754486084, 2.3854105472564697, 2.576427936553955, 2.3772075176239014]\n",
      "Execution time per round: 3.8157±2.5575s (67.03%)\n",
      "\n",
      "\n",
      "Búsqueda con subgrafo temporal con un nodo para cada día y hora del año, con índices\n",
      "Execution times: [0.08583974838256836, 0.016954421997070312, 0.015958070755004883, 0.017951011657714844, 0.018949270248413086]\n",
      "Execution time per round: 0.0311±0.0274s (87.93%)\n"
     ]
    }
   ],
   "source": [
    "# WARNING: Correr la celda anterior hasta que los índices pasen de POPULATING a ONLINE\n",
    "query_date_time_nodes = \"\"\"MATCH (s:Station)-->(m:Measurement)\n",
    "                           WHERE m.hour = time(\"2:00\") AND m.date = date(\"2019-01-01\")\n",
    "                           RETURN m\"\"\"\n",
    "\n",
    "query_date_time_edges = \"\"\"MATCH (s:Station)\n",
    "                        -[:MEASURED{hour: time(\"2:00\"), date:date(\"2019-01-01\")}]\n",
    "                        ->(m:Measurement)\n",
    "                        RETURN m\"\"\"\n",
    "\n",
    "query_date_time_subgraph = \"\"\"MATCH (m:Measurement)<--(:Year{name:2019}),\n",
    "                                 (m)<--(:Month{name:1}),\n",
    "                                 (m)<--(:Day{name:1}),\n",
    "                                 (m)<--(:Hour{name:2})\n",
    "                           RETURN m\"\"\"\n",
    "\n",
    "query_date_time_subgraph_date_time = \"\"\"MATCH (m:Measurement)<--(:DateTime{year:2019, month:1, day:1, hour: 2}) RETURN m\"\"\"\n",
    "\n",
    "print(\"Búsqueda por nodos, con índices...\")\n",
    "res_1, times_1 = evaluate_query(query_date_time_nodes, n=5)\n",
    "\n",
    "\n",
    "print(\"\\n\\nBúsqueda por aristas, con índices...\")\n",
    "res_2, times_2 = evaluate_query(query_date_time_edges, n=5)\n",
    "\n",
    "\n",
    "print(\"\\n\\nBúsqueda con subgrafo temporal, con índices\")\n",
    "res_3, times_3 = evaluate_query(query_date_time_subgraph, n=5)\n",
    "\n",
    "print(\"\\n\\nBúsqueda con subgrafo temporal con un nodo para cada día y hora del año, con índices\")\n",
    "res_4, times_4 = evaluate_query(query_date_time_subgraph_date_time, n=5)\n",
    "\n",
    "assert len(res_2) == len(res_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_exec_times([times_1, times_2, times_3, times_4], [\"Búsqueda por nodos (con índices)\", \"Búsqueda por edges (con índices)\", \"Búsqueda con subgrafo temporal (con índices)\",\"Búsqueda con subgrafo temporal con un nodo para cada día y hora del año (con índice)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comentarios sobre las celdas anteriores\n",
    "\n",
    "Luego de crear los índices, deja de tener sentido correr la consulta con el primer subgrafo temporal, ya que enlentece.\n",
    "\n",
    "Había algunos índices que se crean sólos, llamados \"index_...\", con los que la consulta con el subgrafo temporal andaba mucho más rápido.\n",
    "\n",
    "Otra cosa a mencionar es que cuando las consultas se vuelven untractable, no hay forma de cortar la ejecución sin matar los procesos de java, que generan inconsistencias de datos. Darle stop al servidor demora muchísimo, e incluso después deja un proceso huérfano de java que nunca termina y consume significativa CPU (como si continuara ejecutando la consulta)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution times: [133.95534944534302, 136.25075435638428, 133.73389053344727, 131.51721358299255, 129.58871293067932]\n",
      "Execution time per round: 133.0092±2.2739s (1.71%)\n",
      "Execution times: [7.109611511230469, 6.903726577758789, 6.466206312179565, 6.9376959800720215, 7.070847988128662]\n",
      "Execution time per round: 6.8976±0.2292s (3.32%)\n",
      "Execution times: [0.01558542251586914, 0.015621662139892578, 0.01562047004699707, 0.01602029800415039, 0.03125429153442383]\n",
      "Execution time per round: 0.0188±0.0062s (33.04%)\n"
     ]
    }
   ],
   "source": [
    "# Tres consultas interesantes a comparar:\n",
    "\n",
    "# Esta demora ~70 segundos\n",
    "_ = evaluate_query(\n",
    "\"\"\"\n",
    "MATCH (s0:Station{name: \"MS/A756/AGUA CLARA\"})-->(m0:Measurement)\n",
    "MATCH (s:Station)-->(m:Measurement{hour: m0.hour, date: m0.date})\n",
    "WITH s0, s, m0, m, point.distance(point({longitude: s.longitude, latitude: s.latitude}), point({longitude: s0.longitude, latitude: s0.latitude}))/1000 AS travelDistance\n",
    "WHERE travelDistance < 300 AND m0.date = date(\"2019-01-01\") AND m0.hour.hour = 2\n",
    "RETURN s0, s\n",
    "\"\"\"\n",
    ", n=5)\n",
    "\n",
    "# Notar que la distancia se calcula entre estaciones y no entre Measurements. Sin embargo, el hecho de\n",
    "# que el filtrado por distancia se hace sobre los pares (:Station)->(:Measurement)\n",
    "# (en vez de sólamente nodos de (:Station)),\n",
    "# parece hacer que la distancia se ejecute en cada par, resultando en una consulta muy lenta.\n",
    "\n",
    "\n",
    "# Entonces probamos otra opción: primero filtrar las estaciones por distancia, y luego hacer el matching con (:Measurements)\n",
    "# Ahora la consulta demora ~5 segundos\n",
    "_ = evaluate_query(\n",
    "\"\"\"\n",
    "MATCH (s0:Station{name: \"MS/A756/AGUA CLARA\"})\n",
    "MATCH (s:Station)\n",
    "WITH s0, s, point.distance(point({longitude: s.longitude, latitude: s.latitude}), point({longitude: s0.longitude, latitude: s0.latitude}))/1000 AS travelDistance\n",
    "WHERE travelDistance < 300\n",
    "MATCH (s0)-->(m0:Measurement)\n",
    "MATCH (s)-->(m:Measurement{date:m0.date, hour: m0.hour})\n",
    "WHERE m0.date = date(\"2019-01-01\") AND m0.hour.hour = 2\n",
    "RETURN s0, s\n",
    "\"\"\"\n",
    ", n=5)\n",
    "\n",
    "# Sin embargo, aún se tiene que ejecutar la distancia entre todas las ~600 nodos\n",
    "#  (:Station), y no sólo contra las que tienen (:Measurements) para ese día y hora\n",
    "\n",
    "\n",
    "# Probamos entonces primero filtrar los Measurements, y luego las estaciones por distancia\n",
    "# Volvemos a una consulta idéntica a la primera, sólo cambiando el orden de los WITH/WHERE\n",
    "_ = evaluate_query(\n",
    "\"\"\"\n",
    "MATCH (s0:Station{name: \"MS/A756/AGUA CLARA\"})-->(m0:Measurement)\n",
    "MATCH (s:Station)-->(m:Measurement{hour: m0.hour, date: m0.date})\n",
    "WHERE m0.date = date(\"2019-01-01\") AND m0.hour.hour = 2\n",
    "WITH s0, s, m0, m, point.distance(point({longitude: s.longitude, latitude: s.latitude}), point({longitude: s0.longitude, latitude: s0.latitude}))/1000 AS travelDistance\n",
    "WHERE travelDistance < 300\n",
    "RETURN s0, s\n",
    "\"\"\"\n",
    ", n=5)\n",
    "# Esta consulta, demora menos de 50ms :party:\n",
    "\n",
    "\n",
    "# Por lo tanto, aunque a priori parezca un poco anti-intuitivo aplicar el WHERE primero sobre los measurements\n",
    "# (que son muchos más registros que las estaciones), ejecutar una función de distancia es relativamente costoso\n",
    "# y por lo tanto es conveniente hacer ese filtrado al final, sobre la menor cantidad de registros posibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos los EDGES de distancia entre todas las estaciones\n",
    "graph.run(\"\"\"MATCH (s0:Station),(s:Station)\n",
    "             WHERE s0 <> s\n",
    "             WITH s0, s, point.distance(point({longitude: s.longitude, latitude: s.latitude}), point({longitude: s0.longitude, latitude: s0.latitude}))/1000 AS Distance\n",
    "             CALL{\n",
    "                WITH s0, s, Distance\n",
    "                MERGE (s0)-[:S_DISTANCE{distance:Distance}]-(s)\n",
    "             } IN TRANSACTIONS OF 10000 ROWS\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution times: [0.014960289001464844, 0.013998270034790039, 0.0169527530670166, 0.011967658996582031, 0.017918109893798828]\n",
      "Execution time per round: 0.0152±0.0021s (13.96%)\n"
     ]
    }
   ],
   "source": [
    "# Con EDGES de S_DISTANCE:\n",
    "_ = evaluate_query(\n",
    "\"\"\"\n",
    "MATCH (m0:Measurement)<--(s0:Station{name: \"MS/A756/AGUA CLARA\"})-[d:S_DISTANCE]-(s:Station)-->(m:Measurement{hour: m0.hour, date: m0.date})\n",
    "WHERE m0.date = date(\"2019-01-01\") AND m0.hour.hour = 2 and d.distance < 300\n",
    "RETURN s0, s\n",
    "\"\"\"\n",
    ", n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>m0.name</th>\n",
       "      <th>t0</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>AbsoluteError</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-21T13:00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.300000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-21T14:00</td>\n",
       "      <td>16.2</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-21T15:00</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-22T13:00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>15.866667</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-22T14:00</td>\n",
       "      <td>16.3</td>\n",
       "      <td>15.833333</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-04-22T15:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-04-23T13:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-04-23T14:00</td>\n",
       "      <td>15.9</td>\n",
       "      <td>15.166667</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-04-23T15:00</td>\n",
       "      <td>15.2</td>\n",
       "      <td>14.966667</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-04-24T13:00</td>\n",
       "      <td>15.9</td>\n",
       "      <td>14.933333</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-04-24T14:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-04-24T15:00</td>\n",
       "      <td>12.6</td>\n",
       "      <td>13.366667</td>\n",
       "      <td>0.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-04-25T13:00</td>\n",
       "      <td>16.8</td>\n",
       "      <td>16.333333</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-04-25T14:00</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-04-25T15:00</td>\n",
       "      <td>16.1</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>0.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-04-26T13:00</td>\n",
       "      <td>16.7</td>\n",
       "      <td>16.533333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021-04-26T14:00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.333333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021-04-26T15:00</td>\n",
       "      <td>16.4</td>\n",
       "      <td>15.933333</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2021-04-27T13:00</td>\n",
       "      <td>13.9</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-04-27T14:00</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-04-27T15:00</td>\n",
       "      <td>12.3</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021-04-28T13:00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2021-04-28T14:00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2021-04-28T15:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>1.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2021-04-29T13:00</td>\n",
       "      <td>10.9</td>\n",
       "      <td>11.533333</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2021-04-29T14:00</td>\n",
       "      <td>13.6</td>\n",
       "      <td>12.966667</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2021-04-29T15:00</td>\n",
       "      <td>12.7</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2021-04-30T13:00</td>\n",
       "      <td>14.4</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-04-30T14:00</td>\n",
       "      <td>14.3</td>\n",
       "      <td>13.866667</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021-04-30T15:00</td>\n",
       "      <td>14.5</td>\n",
       "      <td>13.766667</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             m0.name    t0      t_avg  AbsoluteError\n",
       "0   2021-04-21T13:00  17.0  16.300000       0.700000\n",
       "1   2021-04-21T14:00  16.2  15.900000       0.300000\n",
       "2   2021-04-21T15:00  15.5  15.500000       0.000000\n",
       "3   2021-04-22T13:00  16.5  15.866667       0.633333\n",
       "4   2021-04-22T14:00  16.3  15.833333       0.466667\n",
       "5   2021-04-22T15:00  14.0  14.400000       0.400000\n",
       "6   2021-04-23T13:00  16.0  15.200000       0.800000\n",
       "7   2021-04-23T14:00  15.9  15.166667       0.733333\n",
       "8   2021-04-23T15:00  15.2  14.966667       0.233333\n",
       "9   2021-04-24T13:00  15.9  14.933333       0.966667\n",
       "10  2021-04-24T14:00  13.0  14.100000       1.100000\n",
       "11  2021-04-24T15:00  12.6  13.366667       0.766667\n",
       "12  2021-04-25T13:00  16.8  16.333333       0.466667\n",
       "13  2021-04-25T14:00  16.9  17.000000       0.100000\n",
       "14  2021-04-25T15:00  16.1  16.666667       0.566667\n",
       "15  2021-04-26T13:00  16.7  16.533333       0.166667\n",
       "16  2021-04-26T14:00  17.0  16.333333       0.666667\n",
       "17  2021-04-26T15:00  16.4  15.933333       0.466667\n",
       "18  2021-04-27T13:00  13.9  13.200000       0.700000\n",
       "19  2021-04-27T14:00  13.1  13.166667       0.066667\n",
       "20  2021-04-27T15:00  12.3  13.100000       0.800000\n",
       "21  2021-04-28T13:00  11.0  10.800000       0.200000\n",
       "22  2021-04-28T14:00  11.0  10.333333       0.666667\n",
       "23  2021-04-28T15:00   8.0   9.566667       1.566667\n",
       "24  2021-04-29T13:00  10.9  11.533333       0.633333\n",
       "25  2021-04-29T14:00  13.6  12.966667       0.633333\n",
       "26  2021-04-29T15:00  12.7  12.900000       0.200000\n",
       "27  2021-04-30T13:00  14.4  13.733333       0.666667\n",
       "28  2021-04-30T14:00  14.3  13.866667       0.433333\n",
       "29  2021-04-30T15:00  14.5  13.766667       0.733333"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_name = \"MS/A756/AGUA CLARA\"\n",
    "\n",
    "\n",
    "dfs = {}\n",
    "for distance in [100, 500, 1000]:\n",
    "    dfs[distance] = graph.run(f\"\"\"\n",
    "        MATCH (s0:Station{{name: \"{station_name}\"}})-->(m0:Measurement)\n",
    "        MATCH (s:Station)-->(m:Measurement{{hour: m0.hour, date: m0.date}})\n",
    "        WITH s0, s, m0, m\n",
    "        WHERE m0.date > date(\"2021-04-20\") AND m.temperature <> -9999\n",
    "              AND m0.hour.hour IN [13, 14, 15]\n",
    "        WITH s0, s, m0, m,\n",
    "        point.distance(point({{longitude: s.longitude, latitude: s.latitude}}),\n",
    "                       point({{longitude: s0.longitude, latitude: s0.latitude}}))/1000 AS distance\n",
    "        WHERE distance < {distance}\n",
    "        RETURN m0.name,\n",
    "               m0.temperature AS t0,\n",
    "               AVG(m.temperature) AS t_avg,\n",
    "               ABS(m0.temperature - AVG(m.temperature)) AS AbsoluteError\n",
    "        ORDER BY m0.name\n",
    "        \"\"\").to_data_frame()\n",
    "\n",
    "dfs[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datetimes = dfs[100][\"m0.name\"]\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 6), sharex=True)\n",
    "fig.tight_layout(pad=1.0)\n",
    "\n",
    "axes[0].set_title(f\"Temperatura (13-15hs) de {station_name} vs. promedio de estaciones cercanas\")\n",
    "axes[0].set_ylabel(\"Temperatura (ºC)\")\n",
    "axes[1].set_ylabel(\"Error absoluto (ºC)\")\n",
    "axes[1].set_xlabel(\"Fecha/Hora\")\n",
    "\n",
    "for distance in dfs:\n",
    "    axes[0].plot(datetimes, dfs[distance][\"t_avg\"], label=f\"T(ºC) promedio [{distance}km]\")\n",
    "    axes[1].plot(datetimes, dfs[distance][\"AbsoluteError\"], label=f\"Error absoluto (ºC) [{distance}km]\")\n",
    "\n",
    "axes[0].plot(datetimes, dfs[100][\"t0\"], \"-k\", label=f\"T(ºC) {station_name}\", linewidth=2)\n",
    "\n",
    "axes[0].legend(loc=\"upper right\")\n",
    "axes[1].legend(loc=\"upper right\")\n",
    "# axes[0].set_xticklabels(ticks=axes[0].get_xticks(), labels=axes[0].get_xlabels(), rotation=90)\n",
    "# _ = axes[1].set_xticklabels(ticks=axes[1].get_xticks(), labels=axes[1].get_xlabels(), rotation=90)\n",
    "axes[0].tick_params(axis=\"x\", labelrotation=45)\n",
    "axes[1].tick_params(axis=\"x\", labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "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>s.name</th>\n",
       "      <th>s.latitude</th>\n",
       "      <th>s.longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MT/A907/RONDONOPOLIS</td>\n",
       "      <td>-16.450000</td>\n",
       "      <td>-54.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO/A027/PARAUNA</td>\n",
       "      <td>-16.945833</td>\n",
       "      <td>-50.425278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MS/S710/ITAPORA</td>\n",
       "      <td>-22.092778</td>\n",
       "      <td>-54.798833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO/A011/SAO SIMAO</td>\n",
       "      <td>-18.966667</td>\n",
       "      <td>-50.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GO/A035/ITUMBIARA</td>\n",
       "      <td>-18.409722</td>\n",
       "      <td>-49.191944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GO/A023/CAIAPONIA</td>\n",
       "      <td>-16.966667</td>\n",
       "      <td>-51.817500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GO/A028/IPORA</td>\n",
       "      <td>-16.423056</td>\n",
       "      <td>-51.148056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MS/A760/COSTA RICA</td>\n",
       "      <td>-18.492665</td>\n",
       "      <td>-53.171261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MS/S715/RIBAS DO RIO PARDO</td>\n",
       "      <td>-20.466667</td>\n",
       "      <td>-53.763028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MS/S701/ANGELICA</td>\n",
       "      <td>-22.148056</td>\n",
       "      <td>-53.763611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MS/A743/RIO BRILHANTE</td>\n",
       "      <td>-21.774722</td>\n",
       "      <td>-54.528056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MS/S702/ARAL MOREIRA</td>\n",
       "      <td>-22.955000</td>\n",
       "      <td>-55.626000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MT/A909/ALTO ARAGUAIA</td>\n",
       "      <td>-17.339444</td>\n",
       "      <td>-53.224167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MS/A720/COXIM</td>\n",
       "      <td>-18.512222</td>\n",
       "      <td>-54.735833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MT/A934/ALTO TAQUARI</td>\n",
       "      <td>-17.815556</td>\n",
       "      <td>-53.287500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GO/A026/MINEIROS</td>\n",
       "      <td>-17.454722</td>\n",
       "      <td>-52.601111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GO/A029/EDEIA</td>\n",
       "      <td>-17.336944</td>\n",
       "      <td>-49.914722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MS/S709/IGUATEMI</td>\n",
       "      <td>-23.644444</td>\n",
       "      <td>-54.570278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MS/S704/BONITO</td>\n",
       "      <td>-21.246667</td>\n",
       "      <td>-56.450442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GO/A003/MORRINHOS</td>\n",
       "      <td>-17.716667</td>\n",
       "      <td>-49.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MS/A758/JARDIM</td>\n",
       "      <td>-21.478333</td>\n",
       "      <td>-56.136944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MS/S711/LAGUNA CARAPA</td>\n",
       "      <td>-22.575278</td>\n",
       "      <td>-55.160278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MS/A709/IVINHEMA</td>\n",
       "      <td>-22.300421</td>\n",
       "      <td>-53.822778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MT/A932/GUIRATINGA</td>\n",
       "      <td>-16.341667</td>\n",
       "      <td>-53.766111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MS/A704/TRES LAGOAS</td>\n",
       "      <td>-20.783333</td>\n",
       "      <td>-51.712222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MS/A756/AGUA CLARA</td>\n",
       "      <td>-20.444440</td>\n",
       "      <td>-52.875556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MS/A752/ITAQUIRAI</td>\n",
       "      <td>-23.449444</td>\n",
       "      <td>-54.181667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MS/A703/PONTA PORA</td>\n",
       "      <td>-22.552448</td>\n",
       "      <td>-55.716317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MS/S706/CAARAPO</td>\n",
       "      <td>-22.656944</td>\n",
       "      <td>-54.819306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MS/S712/NOVA ALVORADA DO SUL</td>\n",
       "      <td>-21.450972</td>\n",
       "      <td>-54.341944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MS/A702/CAMPO GRANDE</td>\n",
       "      <td>-20.447195</td>\n",
       "      <td>-54.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>MS/S716/SANTA RITA DO PARDO</td>\n",
       "      <td>-21.305833</td>\n",
       "      <td>-52.820278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>MS/A742/CASSILANDIA</td>\n",
       "      <td>-19.122471</td>\n",
       "      <td>-51.720732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GO/A016/JATAI</td>\n",
       "      <td>-17.923622</td>\n",
       "      <td>-51.717467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          s.name  s.latitude  s.longitude\n",
       "0           MT/A907/RONDONOPOLIS  -16.450000   -54.566667\n",
       "1                GO/A027/PARAUNA  -16.945833   -50.425278\n",
       "2                MS/S710/ITAPORA  -22.092778   -54.798833\n",
       "3              GO/A011/SAO SIMAO  -18.966667   -50.616667\n",
       "4              GO/A035/ITUMBIARA  -18.409722   -49.191944\n",
       "5              GO/A023/CAIAPONIA  -16.966667   -51.817500\n",
       "6                  GO/A028/IPORA  -16.423056   -51.148056\n",
       "7             MS/A760/COSTA RICA  -18.492665   -53.171261\n",
       "8     MS/S715/RIBAS DO RIO PARDO  -20.466667   -53.763028\n",
       "9               MS/S701/ANGELICA  -22.148056   -53.763611\n",
       "10         MS/A743/RIO BRILHANTE  -21.774722   -54.528056\n",
       "11          MS/S702/ARAL MOREIRA  -22.955000   -55.626000\n",
       "12         MT/A909/ALTO ARAGUAIA  -17.339444   -53.224167\n",
       "13                 MS/A720/COXIM  -18.512222   -54.735833\n",
       "14          MT/A934/ALTO TAQUARI  -17.815556   -53.287500\n",
       "15              GO/A026/MINEIROS  -17.454722   -52.601111\n",
       "16                 GO/A029/EDEIA  -17.336944   -49.914722\n",
       "17              MS/S709/IGUATEMI  -23.644444   -54.570278\n",
       "18                MS/S704/BONITO  -21.246667   -56.450442\n",
       "19             GO/A003/MORRINHOS  -17.716667   -49.100000\n",
       "20                MS/A758/JARDIM  -21.478333   -56.136944\n",
       "21         MS/S711/LAGUNA CARAPA  -22.575278   -55.160278\n",
       "22              MS/A709/IVINHEMA  -22.300421   -53.822778\n",
       "23            MT/A932/GUIRATINGA  -16.341667   -53.766111\n",
       "24           MS/A704/TRES LAGOAS  -20.783333   -51.712222\n",
       "25            MS/A756/AGUA CLARA  -20.444440   -52.875556\n",
       "26             MS/A752/ITAQUIRAI  -23.449444   -54.181667\n",
       "27            MS/A703/PONTA PORA  -22.552448   -55.716317\n",
       "28               MS/S706/CAARAPO  -22.656944   -54.819306\n",
       "29  MS/S712/NOVA ALVORADA DO SUL  -21.450972   -54.341944\n",
       "30          MS/A702/CAMPO GRANDE  -20.447195   -54.600000\n",
       "31   MS/S716/SANTA RITA DO PARDO  -21.305833   -52.820278\n",
       "32           MS/A742/CASSILANDIA  -19.122471   -51.720732\n",
       "33                 GO/A016/JATAI  -17.923622   -51.717467"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_name = \"MS/A756/AGUA CLARA\"\n",
    "search_distance = 500\n",
    "search_date = \"2021-04-30\"\n",
    "search_time = \"14:00\"\n",
    "\n",
    "ref_station = graph.run(f\"\"\"\n",
    "    MATCH (s:Station{{name: \"{station_name}\"}})\n",
    "    RETURN s.name, s.latitude, s.longitude\n",
    "    \"\"\").to_data_frame().iloc[0]\n",
    "\n",
    "df_map = graph.run(f\"\"\"\n",
    "    MATCH (s0:Station{{name: \"{station_name}\"}})\n",
    "           -->(m0:Measurement{{date: date(\"{search_date}\"), hour: time(\"{search_time}\")}})\n",
    "    MATCH (s:Station)-->(m:Measurement{{hour: m0.hour, date: m0.date}})\n",
    "    WHERE m.temperature <> -9999\n",
    "    WITH s0, m0, s, m,\n",
    "         point.distance(point({{longitude: s.longitude, latitude: s.latitude}}),\n",
    "                        point({{longitude: s0.longitude, latitude: s0.latitude}}))/1000\n",
    "                        AS distance\n",
    "    WHERE distance < {search_distance}\n",
    "    RETURN s.name, s.latitude, s.longitude\n",
    "    \"\"\").to_data_frame()\n",
    "df_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "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>s.name</th>\n",
       "      <th>s.latitude</th>\n",
       "      <th>s.longitude</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MT/A907/RONDONOPOLIS</td>\n",
       "      <td>-16.450000</td>\n",
       "      <td>-54.566667</td>\n",
       "      <td>2.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO/A027/PARAUNA</td>\n",
       "      <td>-16.945833</td>\n",
       "      <td>-50.425278</td>\n",
       "      <td>0.014286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MS/S710/ITAPORA</td>\n",
       "      <td>-22.092778</td>\n",
       "      <td>-54.798833</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO/A011/SAO SIMAO</td>\n",
       "      <td>-18.966667</td>\n",
       "      <td>-50.616667</td>\n",
       "      <td>2.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GO/A035/ITUMBIARA</td>\n",
       "      <td>-18.409722</td>\n",
       "      <td>-49.191944</td>\n",
       "      <td>1.431034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GO/A023/CAIAPONIA</td>\n",
       "      <td>-16.966667</td>\n",
       "      <td>-51.817500</td>\n",
       "      <td>1.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GO/A028/IPORA</td>\n",
       "      <td>-16.423056</td>\n",
       "      <td>-51.148056</td>\n",
       "      <td>1.077778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MS/A760/COSTA RICA</td>\n",
       "      <td>-18.492665</td>\n",
       "      <td>-53.171261</td>\n",
       "      <td>3.997059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MS/S715/RIBAS DO RIO PARDO</td>\n",
       "      <td>-20.466667</td>\n",
       "      <td>-53.763028</td>\n",
       "      <td>0.196552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MS/S701/ANGELICA</td>\n",
       "      <td>-22.148056</td>\n",
       "      <td>-53.763611</td>\n",
       "      <td>0.177273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MS/A743/RIO BRILHANTE</td>\n",
       "      <td>-21.774722</td>\n",
       "      <td>-54.528056</td>\n",
       "      <td>1.304545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MS/S702/ARAL MOREIRA</td>\n",
       "      <td>-22.955000</td>\n",
       "      <td>-55.626000</td>\n",
       "      <td>0.894444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MT/A909/ALTO ARAGUAIA</td>\n",
       "      <td>-17.339444</td>\n",
       "      <td>-53.224167</td>\n",
       "      <td>4.520690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MS/A720/COXIM</td>\n",
       "      <td>-18.512222</td>\n",
       "      <td>-54.735833</td>\n",
       "      <td>2.203226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MT/A934/ALTO TAQUARI</td>\n",
       "      <td>-17.815556</td>\n",
       "      <td>-53.287500</td>\n",
       "      <td>5.572414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GO/A026/MINEIROS</td>\n",
       "      <td>-17.454722</td>\n",
       "      <td>-52.601111</td>\n",
       "      <td>2.355172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GO/A029/EDEIA</td>\n",
       "      <td>-17.336944</td>\n",
       "      <td>-49.914722</td>\n",
       "      <td>1.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MS/S709/IGUATEMI</td>\n",
       "      <td>-23.644444</td>\n",
       "      <td>-54.570278</td>\n",
       "      <td>1.241176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MS/S704/BONITO</td>\n",
       "      <td>-21.246667</td>\n",
       "      <td>-56.450442</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GO/A003/MORRINHOS</td>\n",
       "      <td>-17.716667</td>\n",
       "      <td>-49.100000</td>\n",
       "      <td>1.441935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MS/A758/JARDIM</td>\n",
       "      <td>-21.478333</td>\n",
       "      <td>-56.136944</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MS/S711/LAGUNA CARAPA</td>\n",
       "      <td>-22.575278</td>\n",
       "      <td>-55.160278</td>\n",
       "      <td>1.185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MS/A709/IVINHEMA</td>\n",
       "      <td>-22.300421</td>\n",
       "      <td>-53.822778</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MT/A932/GUIRATINGA</td>\n",
       "      <td>-16.341667</td>\n",
       "      <td>-53.766111</td>\n",
       "      <td>1.934783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MS/A704/TRES LAGOAS</td>\n",
       "      <td>-20.783333</td>\n",
       "      <td>-51.712222</td>\n",
       "      <td>0.715625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MS/A756/AGUA CLARA</td>\n",
       "      <td>-20.444440</td>\n",
       "      <td>-52.875556</td>\n",
       "      <td>0.742424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MS/A752/ITAQUIRAI</td>\n",
       "      <td>-23.449444</td>\n",
       "      <td>-54.181667</td>\n",
       "      <td>1.029412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MS/A703/PONTA PORA</td>\n",
       "      <td>-22.552448</td>\n",
       "      <td>-55.716317</td>\n",
       "      <td>0.884211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MS/S706/CAARAPO</td>\n",
       "      <td>-22.656944</td>\n",
       "      <td>-54.819306</td>\n",
       "      <td>0.542105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MS/S712/NOVA ALVORADA DO SUL</td>\n",
       "      <td>-21.450972</td>\n",
       "      <td>-54.341944</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MS/A702/CAMPO GRANDE</td>\n",
       "      <td>-20.447195</td>\n",
       "      <td>-54.600000</td>\n",
       "      <td>1.975862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>MS/S716/SANTA RITA DO PARDO</td>\n",
       "      <td>-21.305833</td>\n",
       "      <td>-52.820278</td>\n",
       "      <td>0.751852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>MS/A742/CASSILANDIA</td>\n",
       "      <td>-19.122471</td>\n",
       "      <td>-51.720732</td>\n",
       "      <td>1.086667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GO/A016/JATAI</td>\n",
       "      <td>-17.923622</td>\n",
       "      <td>-51.717467</td>\n",
       "      <td>1.872222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          s.name  s.latitude  s.longitude     error\n",
       "0           MT/A907/RONDONOPOLIS  -16.450000   -54.566667  2.460000\n",
       "1                GO/A027/PARAUNA  -16.945833   -50.425278  0.014286\n",
       "2                MS/S710/ITAPORA  -22.092778   -54.798833  0.047619\n",
       "3              GO/A011/SAO SIMAO  -18.966667   -50.616667  2.708333\n",
       "4              GO/A035/ITUMBIARA  -18.409722   -49.191944  1.431034\n",
       "5              GO/A023/CAIAPONIA  -16.966667   -51.817500  1.884615\n",
       "6                  GO/A028/IPORA  -16.423056   -51.148056  1.077778\n",
       "7             MS/A760/COSTA RICA  -18.492665   -53.171261  3.997059\n",
       "8     MS/S715/RIBAS DO RIO PARDO  -20.466667   -53.763028  0.196552\n",
       "9               MS/S701/ANGELICA  -22.148056   -53.763611  0.177273\n",
       "10         MS/A743/RIO BRILHANTE  -21.774722   -54.528056  1.304545\n",
       "11          MS/S702/ARAL MOREIRA  -22.955000   -55.626000  0.894444\n",
       "12         MT/A909/ALTO ARAGUAIA  -17.339444   -53.224167  4.520690\n",
       "13                 MS/A720/COXIM  -18.512222   -54.735833  2.203226\n",
       "14          MT/A934/ALTO TAQUARI  -17.815556   -53.287500  5.572414\n",
       "15              GO/A026/MINEIROS  -17.454722   -52.601111  2.355172\n",
       "16                 GO/A029/EDEIA  -17.336944   -49.914722  1.812500\n",
       "17              MS/S709/IGUATEMI  -23.644444   -54.570278  1.241176\n",
       "18                MS/S704/BONITO  -21.246667   -56.450442  0.975000\n",
       "19             GO/A003/MORRINHOS  -17.716667   -49.100000  1.441935\n",
       "20                MS/A758/JARDIM  -21.478333   -56.136944  1.500000\n",
       "21         MS/S711/LAGUNA CARAPA  -22.575278   -55.160278  1.185000\n",
       "22              MS/A709/IVINHEMA  -22.300421   -53.822778  0.133333\n",
       "23            MT/A932/GUIRATINGA  -16.341667   -53.766111  1.934783\n",
       "24           MS/A704/TRES LAGOAS  -20.783333   -51.712222  0.715625\n",
       "25            MS/A756/AGUA CLARA  -20.444440   -52.875556  0.742424\n",
       "26             MS/A752/ITAQUIRAI  -23.449444   -54.181667  1.029412\n",
       "27            MS/A703/PONTA PORA  -22.552448   -55.716317  0.884211\n",
       "28               MS/S706/CAARAPO  -22.656944   -54.819306  0.542105\n",
       "29  MS/S712/NOVA ALVORADA DO SUL  -21.450972   -54.341944  2.400000\n",
       "30          MS/A702/CAMPO GRANDE  -20.447195   -54.600000  1.975862\n",
       "31   MS/S716/SANTA RITA DO PARDO  -21.305833   -52.820278  0.751852\n",
       "32           MS/A742/CASSILANDIA  -19.122471   -51.720732  1.086667\n",
       "33                 GO/A016/JATAI  -17.923622   -51.717467  1.872222"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_map[\"error\"] = 0\n",
    "avg_distance = 500\n",
    "\n",
    "for station in [station_name] + list(df_map[\"s.name\"]):\n",
    "    station_query = graph.run(f\"\"\"\n",
    "        MATCH (s0:Station{{name: \"{station}\"}})\n",
    "               -->(m0:Measurement{{date: date(\"{search_date}\"), hour: time(\"{search_time}\")}})\n",
    "        MATCH (s:Station)-->(m:Measurement{{hour: m0.hour, date: m0.date}})\n",
    "        WHERE m.temperature <> -9999 AND s <> s0\n",
    "        WITH s0, m0, s, m, point.distance(point({{longitude: s.longitude, latitude: s.latitude}}), point({{longitude: s0.longitude, latitude: s0.latitude}}))/1000 AS distance\n",
    "        WHERE distance < {avg_distance}\n",
    "\n",
    "        RETURN AVG(m.temperature) as avg_temperature, ABS(m0.temperature - AVG(m.temperature)) AS AbsoluteError\n",
    "        \"\"\").to_data_frame().iloc[0]\n",
    "    df_map.loc[df_map[\"s.name\"] == station, \"error\"] = station_query[\"AbsoluteError\"]\n",
    "df_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 10))\n",
    "\n",
    "ax.set_xlabel(\"Longitud\")\n",
    "ax.set_ylabel(\"Latitud\")\n",
    "ax.set_title(f\"Error absoluto en las estaciones cercanas ({search_date} / {search_time})\")\n",
    "ax.annotate(station_name.split(\"/\")[-1], (ref_station[\"s.longitude\"], ref_station[\"s.latitude\"]),\n",
    "           textcoords=\"offset pixels\", xytext=(-30, 10), size=14)\n",
    "\n",
    "m = ax.scatter(df_map[\"s.longitude\"], df_map[\"s.latitude\"], c=df_map[\"error\"], cmap=\"coolwarm\", marker=\"o\", s=200)\n",
    "fig.colorbar(m, ax=ax)\n",
    "\n",
    "ax.scatter([ref_station[\"s.longitude\"]], [ref_station[\"s.latitude\"]], c=\"k\", s=300, edgecolor=\"k\")\n",
    "\n",
    "for idx in df_map.index:\n",
    "    name = df_map.loc[idx, \"s.name\"]\n",
    "    if name == station_name:\n",
    "        continue\n",
    "    txt_offset = (-30, -15)\n",
    "    txt_size = 8\n",
    "    station_display = name.split(\"/\")[-1]\n",
    "    ax.annotate(station_display, (df_map.loc[idx, \"s.longitude\"], df_map.loc[idx, \"s.latitude\"]),\n",
    "                textcoords=\"offset pixels\", xytext=txt_offset, size=txt_size)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}