{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be77e42e",
   "metadata": {},
   "source": [
    "# SADAR: Exploratory Data Analysis\n",
    "\n",
    "Smart Anomaly Detection for Aviation Routes. ADS-B (Automatic Dependent Surveillance-Broadcast) surveillance data over Madrid-Barajas (LEMD, the airport's ICAO code), collected from the OpenSky Network.\n",
    "\n",
    "The purpose of this notebook is to understand the raw data before any modelling. We confirm the dataset's basic figures, characterise its quality, locate the few real events it contains, and decide which trajectories should be excluded from the normal training set used later by the one-class anomaly-detection models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2977e59c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:35.254171Z",
     "iopub.status.busy": "2026-05-29T16:46:35.254032Z",
     "iopub.status.idle": "2026-05-29T16:46:35.710666Z",
     "shell.execute_reply": "2026-05-29T16:46:35.710271Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading raw parquet from: /Users/rup/Documents/DevelopmentLocal/SADAR/data/raw\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "from IPython.display import display\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (9, 4)\n",
    "plt.rcParams['axes.grid'] = True\n",
    "pd.set_option('display.max_columns', 30)\n",
    "\n",
    "DATA_DIR = os.environ.get('SADAR_DATA_DIR', 'data/raw')\n",
    "print('Reading raw parquet from:', DATA_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0199f525",
   "metadata": {},
   "source": [
    "## 1. Load and merge\n",
    "\n",
    "The analysis begins by combining the raw days into a single table. We read every daily parquet file for the airport, tag each row with the date it came from so any measurement can be traced back to its day, and concatenate the result. Sorting by flight and time then lets each trajectory be read in order, from its first sample to its last."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd109881",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:35.711809Z",
     "iopub.status.busy": "2026-05-29T16:46:35.711698Z",
     "iopub.status.idle": "2026-05-29T16:46:36.567274Z",
     "shell.execute_reply": "2026-05-29T16:46:36.566861Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily files used: 18\n",
      "Total rows: 3,429,638\n",
      "Distinct flights: 19,057\n",
      "In-memory size (MB): 737.8\n"
     ]
    }
   ],
   "source": [
    "def load_adsb(data_dir):\n",
    "    files = sorted(glob.glob(os.path.join(data_dir, 'lemd_*.parquet')))\n",
    "    frames = []\n",
    "    for f in files:\n",
    "        d = pd.read_parquet(f)\n",
    "        d['source_date'] = os.path.basename(f).split('__')[0].replace('lemd_', '')\n",
    "        frames.append(d)\n",
    "    df = pd.concat(frames, ignore_index=True).sort_values(['flight_id', 'time'])\n",
    "    return df, files\n",
    "\n",
    "df, used_files = load_adsb(DATA_DIR)\n",
    "print('Daily files used:', len(used_files))\n",
    "print('Total rows:', f'{len(df):,}')\n",
    "print('Distinct flights:', f\"{df['flight_id'].nunique():,}\")\n",
    "print('In-memory size (MB):', round(df.memory_usage(deep=True).sum() / 1e6, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f78986",
   "metadata": {},
   "source": [
    "## 2. Schema and types\n",
    "\n",
    "Before relying on any figure we inspect the structure of the data: its 21 columns, the type of each one, and how time is stored (a raw Unix timestamp together with a ready-made UTC datetime). Printing the minimum, median and maximum of the main continuous variables is a fast way to detect physically impossible values, such as negative speeds or altitudes well outside the expected range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9444a818",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:36.568762Z",
     "iopub.status.busy": "2026-05-29T16:46:36.568681Z",
     "iopub.status.idle": "2026-05-29T16:46:36.880485Z",
     "shell.execute_reply": "2026-05-29T16:46:36.880090Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns: 22\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>dtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icao24</th>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baroaltitude</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoaltitude</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heading</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertrate</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>callsign</th>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onground</th>\n",
       "      <td>bool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squawk</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alert</th>\n",
       "      <td>bool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spi</th>\n",
       "      <td>bool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lastcontact</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_id</th>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>operation</th>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_utc</th>\n",
       "      <td>datetime64[ms, UTC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity_kmh</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist_to_runway_m</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_phase</th>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_date</th>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                dtype\n",
       "time                            int64\n",
       "icao24                            str\n",
       "lat                           float64\n",
       "lon                           float64\n",
       "baroaltitude                  float64\n",
       "geoaltitude                   float64\n",
       "velocity                      float64\n",
       "heading                       float64\n",
       "vertrate                      float64\n",
       "callsign                          str\n",
       "onground                         bool\n",
       "squawk                        float64\n",
       "alert                            bool\n",
       "spi                              bool\n",
       "lastcontact                   float64\n",
       "flight_id                         str\n",
       "operation                         str\n",
       "time_utc          datetime64[ms, UTC]\n",
       "velocity_kmh                  float64\n",
       "dist_to_runway_m              float64\n",
       "flight_phase                      str\n",
       "source_date                       str"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>baroaltitude</th>\n",
       "      <td>-365.760000</td>\n",
       "      <td>4366.260000</td>\n",
       "      <td>38648.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoaltitude</th>\n",
       "      <td>-304.800000</td>\n",
       "      <td>4686.300000</td>\n",
       "      <td>39006.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity_kmh</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>527.218114</td>\n",
       "      <td>6153.974158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertrate</th>\n",
       "      <td>-139.476480</td>\n",
       "      <td>-0.650240</td>\n",
       "      <td>165.811200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist_to_runway_m</th>\n",
       "      <td>11.447052</td>\n",
       "      <td>35211.168852</td>\n",
       "      <td>199999.879356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heading</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>197.007947</td>\n",
       "      <td>359.921405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         min           50%            max\n",
       "baroaltitude     -365.760000   4366.260000   38648.640000\n",
       "geoaltitude      -304.800000   4686.300000   39006.780000\n",
       "velocity_kmh        0.000000    527.218114    6153.974158\n",
       "vertrate         -139.476480     -0.650240     165.811200\n",
       "dist_to_runway_m   11.447052  35211.168852  199999.879356\n",
       "heading            -0.000000    197.007947     359.921405"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'time' is just a Unix timestamp; 'time_utc' already comes as a proper date\n",
    "print('Columns:', len(df.columns))\n",
    "display(df.dtypes.to_frame('dtype'))\n",
    "\n",
    "df['dt'] = pd.to_datetime(df['time'], unit='s', utc=True)\n",
    "\n",
    "ranges = df[['baroaltitude', 'geoaltitude', 'velocity_kmh', 'vertrate',\n",
    "             'dist_to_runway_m', 'heading']].describe().T[['min', '50%', 'max']]\n",
    "display(ranges)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79d1b4b",
   "metadata": {},
   "source": [
    "## 3. Null analysis\n",
    "\n",
    "Surveillance data is rarely complete, so we measure how much is missing in each column. This guides the cleaning strategy for Phase 2: short interruptions can be filled by interpolation, whereas trajectories with too many holes are better discarded than patched. It also shows which columns are reliable enough for the models to depend on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6aa2c053",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:36.881605Z",
     "iopub.status.busy": "2026-05-29T16:46:36.881544Z",
     "iopub.status.idle": "2026-05-29T16:46:37.002146Z",
     "shell.execute_reply": "2026-05-29T16:46:37.001748Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>null_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>geoaltitude</th>\n",
       "      <td>24.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baroaltitude</th>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity</th>\n",
       "      <td>13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heading</th>\n",
       "      <td>13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity_kmh</th>\n",
       "      <td>13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertrate</th>\n",
       "      <td>13.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>callsign</th>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squawk</th>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_id</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_date</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_phase</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist_to_runway_m</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_utc</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>operation</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lastcontact</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spi</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alert</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icao24</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onground</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  null_%\n",
       "geoaltitude        24.79\n",
       "baroaltitude       13.48\n",
       "velocity           13.37\n",
       "heading            13.37\n",
       "velocity_kmh       13.37\n",
       "vertrate           13.34\n",
       "callsign            2.45\n",
       "squawk              1.97\n",
       "flight_id           0.00\n",
       "source_date         0.00\n",
       "flight_phase        0.00\n",
       "dist_to_runway_m    0.00\n",
       "time_utc            0.00\n",
       "operation           0.00\n",
       "time                0.00\n",
       "lastcontact         0.00\n",
       "spi                 0.00\n",
       "alert               0.00\n",
       "icao24              0.00\n",
       "onground            0.00\n",
       "lon                 0.00\n",
       "lat                 0.00\n",
       "dt                  0.00"
      ]
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    },
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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_pct = (df.isna().mean() * 100).round(2).sort_values(ascending=False)\n",
    "display(null_pct.to_frame('null_%'))\n",
    "\n",
    "top = null_pct[null_pct > 0]\n",
    "ax = top.plot.barh(color='#1B98E0')\n",
    "ax.set_xlabel('% missing')\n",
    "ax.set_title('Missing values per column')\n",
    "ax.invert_yaxis()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c4bc68",
   "metadata": {},
   "source": [
    "The missing data is concentrated almost entirely in the movement fields: speed, heading, vertical rate and the two altitudes. These are gaps in radio coverage rather than corrupt records. GPS altitude (geoaltitude) is the least complete, so the models will rely on pressure altitude (baroaltitude) instead. Position and identity fields are essentially complete."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7635a088",
   "metadata": {},
   "source": [
    "## 4. Distributions\n",
    "\n",
    "We next examine how the main variables are distributed. The flight-phase bar chart shows how the records split between ground, climb, cruise, descent and approach, while the histograms reveal the typical ranges of altitude, speed, distance to the runway and vertical rate. Heading is shown on a polar chart because it is an angle, and a circular view makes the two dominant runway directions immediately visible. That same circular nature is why heading is later encoded as sin and cos, so that 359 degrees and 1 degree sit beside each other rather than at opposite ends of the scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "961c9136",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:37.003318Z",
     "iopub.status.busy": "2026-05-29T16:46:37.003241Z",
     "iopub.status.idle": "2026-05-29T16:46:37.072097Z",
     "shell.execute_reply": "2026-05-29T16:46:37.071681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>rows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_phase</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>descent</th>\n",
       "      <td>1090369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>climb</th>\n",
       "      <td>731531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_ground</th>\n",
       "      <td>684742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cruise</th>\n",
       "      <td>446580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>approach</th>\n",
       "      <td>265192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>takeoff</th>\n",
       "      <td>211224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 rows\n",
       "flight_phase         \n",
       "descent       1090369\n",
       "climb          731531\n",
       "on_ground      684742\n",
       "cruise         446580\n",
       "approach       265192\n",
       "takeoff        211224"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phase_counts = df['flight_phase'].value_counts()\n",
    "ax = phase_counts.plot.bar(color='#2A9D8F')\n",
    "ax.set_title('Rows per flight phase')\n",
    "ax.set_ylabel('rows')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "display(phase_counts.to_frame('rows'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15d99ca3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:37.073453Z",
     "iopub.status.busy": "2026-05-29T16:46:37.073352Z",
     "iopub.status.idle": "2026-05-29T16:46:37.625715Z",
     "shell.execute_reply": "2026-05-29T16:46:37.625330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(12, 7))\n",
    "\n",
    "df['baroaltitude'].dropna().plot.hist(bins=80, ax=axes[0, 0], color='#1B98E0')\n",
    "axes[0, 0].set_title('baroaltitude (m)')\n",
    "\n",
    "df['velocity_kmh'].dropna().plot.hist(bins=80, ax=axes[0, 1], color='#1B98E0')\n",
    "axes[0, 1].set_title('velocity_kmh')\n",
    "\n",
    "(df['dist_to_runway_m'].dropna() / 1000).plot.hist(bins=80, ax=axes[1, 0], color='#1B98E0')\n",
    "axes[1, 0].set_title('dist_to_runway (km)')\n",
    "\n",
    "df['vertrate'].dropna().clip(-25, 25).plot.hist(bins=80, ax=axes[1, 1], color='#1B98E0')\n",
    "axes[1, 1].set_title('vertrate (m/s, clipped +/-25)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07111153",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:37.626809Z",
     "iopub.status.busy": "2026-05-29T16:46:37.626731Z",
     "iopub.status.idle": "2026-05-29T16:46:37.776458Z",
     "shell.execute_reply": "2026-05-29T16:46:37.776083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = df['heading'].dropna().to_numpy()\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(111, projection='polar')\n",
    "ax.set_theta_zero_location('N')\n",
    "ax.set_theta_direction(-1)\n",
    "ax.hist(np.deg2rad(h), bins=72, color='#2A9D8F')\n",
    "ax.set_title('heading distribution (deg)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23973acd",
   "metadata": {},
   "source": [
    "## 5. Trajectory statistics\n",
    "\n",
    "These statistics describe the rhythm of the airport and the size of a typical trajectory. We count the distinct flights on each sampled day, the number of points that make up a flight, and how long flights remain within the coverage area. Together they inform the length of the input windows and the minimum trajectory length below which a flight is too short to be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e835b45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:37.777650Z",
     "iopub.status.busy": "2026-05-29T16:46:37.777570Z",
     "iopub.status.idle": "2026-05-29T16:46:38.252723Z",
     "shell.execute_reply": "2026-05-29T16:46:38.252340Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flights/day  min/median/max: 912 1054 1217\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights_per_day = df.groupby(df['dt'].dt.date)['flight_id'].nunique()\n",
    "print('Flights/day  min/median/max:',\n",
    "      int(flights_per_day.min()), int(flights_per_day.median()), int(flights_per_day.max()))\n",
    "ax = flights_per_day.plot.bar(color='#2A9D8F')\n",
    "ax.set_title('Distinct flights per sampled day')\n",
    "ax.set_ylabel('flights')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "142f89e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:38.253755Z",
     "iopub.status.busy": "2026-05-29T16:46:38.253698Z",
     "iopub.status.idle": "2026-05-29T16:46:38.479260Z",
     "shell.execute_reply": "2026-05-29T16:46:38.478804Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Points/flight  min/median/max: 4 175 1606\n",
      "Duration min  median/max (min): 30.2 297.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points_per_flight = df.groupby('flight_id').size()\n",
    "duration_min = (df.groupby('flight_id')['time'].max() - df.groupby('flight_id')['time'].min()) / 60\n",
    "\n",
    "print('Points/flight  min/median/max:',\n",
    "      int(points_per_flight.min()), int(points_per_flight.median()), int(points_per_flight.max()))\n",
    "print('Duration min  median/max (min):',\n",
    "      round(float(duration_min.median()), 1), round(float(duration_min.max()), 1))\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "points_per_flight.plot.hist(bins=80, ax=axes[0], color='#1B98E0')\n",
    "axes[0].set_title('points per flight')\n",
    "duration_min.clip(0, 200).plot.hist(bins=80, ax=axes[1], color='#1B98E0')\n",
    "axes[1].set_title('flight duration (min, clipped 200)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9430d7a5",
   "metadata": {},
   "source": [
    "## 6. Real events: emergency squawks\n",
    "\n",
    "The nearest thing to a real label in this dataset is the emergency transponder code a crew can set: 7500 for unlawful interference or hijack, 7600 for a radio failure, and 7700 for a general emergency. Only a handful of flights ever transmit one, and they are far too few and too uncertain to train on, so we use them only as illustrations and as a check that a finished model reacts to genuine trouble. For each case we record the callsign, the day, the code that appeared, how long it was held, the closest approach to the runway, and whether the flight landed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "548064a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:38.480394Z",
     "iopub.status.busy": "2026-05-29T16:46:38.480318Z",
     "iopub.status.idle": "2026-05-29T16:46:38.744499Z",
     "shell.execute_reply": "2026-05-29T16:46:38.744087Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flight_id</th>\n",
       "      <th>callsign</th>\n",
       "      <th>date</th>\n",
       "      <th>squawk(s)</th>\n",
       "      <th>emergency_points</th>\n",
       "      <th>total_points</th>\n",
       "      <th>min_dist_to_rwy_km</th>\n",
       "      <th>landed_on_ground</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3420ca_1501530490</td>\n",
       "      <td>[IBE2845]</td>\n",
       "      <td>20170731</td>\n",
       "      <td>[7500 (hijack)]</td>\n",
       "      <td>3</td>\n",
       "      <td>171</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4b161e_1580715790</td>\n",
       "      <td>[SWR202Y]</td>\n",
       "      <td>20200203</td>\n",
       "      <td>[7700 (general emergency)]</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0.8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4ca564_1548700030</td>\n",
       "      <td>[RYR61AD, ]</td>\n",
       "      <td>20190128</td>\n",
       "      <td>[7600 (radio failure)]</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0.3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>738051_1506930700</td>\n",
       "      <td>[ELY395]</td>\n",
       "      <td>20171002</td>\n",
       "      <td>[7700 (general emergency)]</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>0.8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           flight_id     callsign      date                   squawk(s)  \\\n",
       "0  3420ca_1501530490    [IBE2845]  20170731             [7500 (hijack)]   \n",
       "1  4b161e_1580715790    [SWR202Y]  20200203  [7700 (general emergency)]   \n",
       "2  4ca564_1548700030  [RYR61AD, ]  20190128      [7600 (radio failure)]   \n",
       "3  738051_1506930700     [ELY395]  20171002  [7700 (general emergency)]   \n",
       "\n",
       "   emergency_points  total_points  min_dist_to_rwy_km  landed_on_ground  \n",
       "0                 3           171                 0.8             False  \n",
       "1                 1           156                 0.8              True  \n",
       "2                 1           173                 0.3              True  \n",
       "3                 5           162                 0.8              True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EMERGENCY = {7500: 'hijack', 7600: 'radio failure', 7700: 'general emergency'}\n",
    "emask = df['squawk'].isin(EMERGENCY)\n",
    "rows = []\n",
    "for fid, g in df[df['flight_id'].isin(df.loc[emask, 'flight_id'].unique())].groupby('flight_id'):\n",
    "    codes = sorted(set(g.loc[g['squawk'].isin(EMERGENCY), 'squawk'].astype(int)))\n",
    "    rows.append({\n",
    "        'flight_id': fid,\n",
    "        'callsign': g['callsign'].dropna().unique().tolist(),\n",
    "        'date': g['source_date'].iloc[0],\n",
    "        'squawk(s)': [f'{c} ({EMERGENCY[c]})' for c in codes],\n",
    "        'emergency_points': int(emask[g.index].sum()),\n",
    "        'total_points': len(g),\n",
    "        'min_dist_to_rwy_km': round(float(g['dist_to_runway_m'].min()) / 1000, 1),\n",
    "        'landed_on_ground': bool(g['onground'].any()),\n",
    "    })\n",
    "cases = pd.DataFrame(rows)\n",
    "display(cases)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32d2e33",
   "metadata": {},
   "source": [
    "IBE2845 transmitted the hijack code for only a few points before landing normally, which points to a transponder glitch rather than a real hijack. The most plausible genuine emergency is ELY395 (El Al), which declared 7700 close to the runway and landed. These four flights serve as anchors to sanity-check the models, never as training data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1934280",
   "metadata": {},
   "source": [
    "## 7. Go-arounds (strict rule)\n",
    "\n",
    "A go-around is an aborted landing: the aircraft descends almost to the runway, breaks off the approach and climbs away to try again or divert. We detect it with a deliberately strict rule to avoid over-counting. The flight must come within 5 km of the runway while descending, then show a sustained positive vertical rate and re-enter a climb or takeoff phase. Go-arounds are normal but uncommon, and because they look unusual we remove them from the training set so the models do not learn the manoeuvre as everyday behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33769794",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:38.745572Z",
     "iopub.status.busy": "2026-05-29T16:46:38.745506Z",
     "iopub.status.idle": "2026-05-29T16:46:45.936888Z",
     "shell.execute_reply": "2026-05-29T16:46:45.936527Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go-around flights (strict rule): 100 (0.52% of flights)\n"
     ]
    }
   ],
   "source": [
    "def is_go_around(g):\n",
    "    g = g.sort_values('time')\n",
    "    near = g['dist_to_runway_m'] < 5000  # within 5 km of the runway\n",
    "    if not near.any():\n",
    "        return False\n",
    "    idx = np.where(near.to_numpy())[0]\n",
    "    before = g.iloc[: idx[-1] + 1]   # everything up to the last point near the runway\n",
    "    after = g.iloc[idx[0]:]          # everything from the first point near the runway on\n",
    "    # did it drop toward the runway and then climb away again? (vertrate is + up, - down)\n",
    "    descended = (before['vertrate'].fillna(0) < -1).any()\n",
    "    climbed_after = (after['vertrate'].fillna(0) > 2).sum() >= 3\n",
    "    phase_climb = after['flight_phase'].isin(['climb', 'takeoff']).any()\n",
    "    return bool(descended and climbed_after and phase_climb)\n",
    "\n",
    "go_around = df.groupby('flight_id').apply(is_go_around, include_groups=False)\n",
    "go_around_ids = go_around[go_around].index\n",
    "print('Go-around flights (strict rule):', int(go_around.sum()),\n",
    "      f'({100 * go_around.mean():.2f}% of flights)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423e3ee7",
   "metadata": {},
   "source": [
    "## 8. Transponder gaps\n",
    "\n",
    "A transponder gap is a stretch in which the aircraft stops being heard. For each flight we consider only the airborne samples and flag a gap whenever more than 120 seconds pass between two consecutive points. A dropout can look suspicious, but in this dataset the gaps are almost always holes in ground-receiver coverage, so we treat them as an ordinary characteristic of the data rather than as evidence of an incident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0bdd1a06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:45.938178Z",
     "iopub.status.busy": "2026-05-29T16:46:45.938111Z",
     "iopub.status.idle": "2026-05-29T16:46:49.809171Z",
     "shell.execute_reply": "2026-05-29T16:46:49.808813Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flights with airborne gap > 120 s: 4608 (24.2% of flights)\n"
     ]
    }
   ],
   "source": [
    "def has_airborne_gap(g, threshold_s=120):\n",
    "    air = g.loc[g['onground'] == False, 'time']\n",
    "    if len(air) < 2:\n",
    "        return False\n",
    "    return bool((air.sort_values().diff().dropna() > threshold_s).any())\n",
    "\n",
    "gaps = df.groupby('flight_id').apply(has_airborne_gap, include_groups=False)\n",
    "print('Flights with airborne gap > 120 s:', int(gaps.sum()),\n",
    "      f'({100 * gaps.mean():.1f}% of flights)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef608e7",
   "metadata": {},
   "source": [
    "## 9. Geographic visualization\n",
    "\n",
    "The final step turns the data into a map. We draw a sample of ordinary flights around Madrid-Barajas alongside the four emergency-squawk flights, which makes the shape of the traffic apparent: the funnels aircraft follow on final approach and the fans they spread into after departure. Those corridors are the normal pattern the models will learn, so a trajectory that strays outside them is what we later aim to flag. The first plot is a plain longitude and latitude scatter that captures the overall geometry, and the interactive map below places the same flights on real terrain, so that any single trajectory can be zoomed in and followed point by point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cc870764",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:49.810261Z",
     "iopub.status.busy": "2026-05-29T16:46:49.810205Z",
     "iopub.status.idle": "2026-05-29T16:46:49.982359Z",
     "shell.execute_reply": "2026-05-29T16:46:49.982030Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.default_rng(42)\n",
    "normal_ids = df.loc[~df['flight_id'].isin(go_around_ids), 'flight_id'].unique()\n",
    "sample_ids = rng.choice(normal_ids, size=min(60, len(normal_ids)), replace=False)\n",
    "sample = df[df['flight_id'].isin(sample_ids)]\n",
    "emerg = df[df['flight_id'].isin(cases['flight_id'])]\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(sample['lon'], sample['lat'], s=1, alpha=0.2, color='#1B98E0', label='normal sample')\n",
    "plt.scatter(emerg['lon'], emerg['lat'], s=6, color='#E63946', label='emergency squawk')\n",
    "plt.scatter([-3.5668], [40.4936], marker='*', s=200, color='#E9C46A', label='LEMD')\n",
    "plt.xlabel('lon'); plt.ylabel('lat'); plt.title('Trajectories around LEMD')\n",
    "plt.legend(); plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd83ceae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-29T16:46:49.983554Z",
     "iopub.status.busy": "2026-05-29T16:46:49.983488Z",
     "iopub.status.idle": "2026-05-29T16:46:50.816981Z",
     "shell.execute_reply": "2026-05-29T16:46:50.816535Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "kind=normal<br>flight_id=34134e_1543867370<br>lat=%{lat}<br>lon=%{lon}<extra></extra>",
         "lat": {
          "bdata": "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",
          "dtype": "f8"
         },
         "legendgroup": "normal",
         "line": {
          "color": "#1B98E0"
         },
         "lon": {
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   "source": [
    "map_ids = list(rng.choice(normal_ids, size=min(25, len(normal_ids)), replace=False))\n",
    "map_df = df[df['flight_id'].isin(map_ids + list(cases['flight_id']))].copy()\n",
    "map_df['kind'] = np.where(map_df['flight_id'].isin(cases['flight_id']), 'emergency', 'normal')\n",
    "fig = px.line_map(\n",
    "    map_df, lat='lat', lon='lon', color='kind', line_group='flight_id',\n",
    "    color_discrete_map={'normal': '#1B98E0', 'emergency': '#E63946'},\n",
    "    map_style='open-street-map', zoom=6, height=520,\n",
    ")\n",
    "fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))\n",
    "fig.show()"
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   "source": [
    "## 10. Data-quality conclusions\n",
    "\n",
    "What we verified and what it implies for the modelling phases:\n",
    "\n",
    "- Volume: 18 sampled days, around 3.43M rows and roughly 19,000 distinct flights, which is ample normal data for one-class learning.\n",
    "- Nulls: almost all the missing data is in the movement fields (GPS altitude worst, then pressure altitude, speed, heading and vertical rate). These are coverage gaps, not errors. The plan is to interpolate short gaps, drop flights with too many, and prefer pressure altitude over GPS altitude.\n",
    "- Heading is strongly bimodal along the runway axes, which confirms the sin/cos encoding decision.\n",
    "- Real events: exactly 4 flights with emergency squawks, held out for validation only.\n",
    "- Go-arounds (strict rule): a small set of normal but uncommon flights, removed from the training set so the models do not learn them as normal.\n",
    "- Transponder gaps: a sizeable share of flights have airborne gaps over 120 seconds, treated as a feature, never as an incident label."
   ]
  }
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