{"cells":[{"cell_type":"code","source":["# ================================================================\n","# UFO MERGE PIPELINE V2 — JSONL ONLY\n","# FULL PROVENANCE + SOURCE ROW NUMBERS + TOP-LEVEL METADATA HEADER\n","# ================================================================\n","import os, json, hashlib\n","import pandas as pd\n","import numpy as np\n","\n","ROOT = \"/content\"\n","MERGE_VERSION = \"ufo_v2_2025_01_28\"\n","\n","# ---------------------------------------------------------------\n","# FIND CSV FILES\n","# ---------------------------------------------------------------\n","csv_paths = [\n","    os.path.join(ROOT, f)\n","    for f in os.listdir(ROOT)\n","    if f.lower().endswith(\".csv\")\n","]\n","\n","print(\"FOUND CSV FILES:\", len(csv_paths))\n","\n","# ---------------------------------------------------------------\n","# BULLETPROOF CSV LOADER\n","# ---------------------------------------------------------------\n","def load_csv(fp):\n","    with open(fp, \"r\", encoding=\"utf-8\", errors=\"ignore\") as f:\n","        s = f.read(4096)\n","\n","    comma = s.count(\",\")\n","    tab   = s.count(\"\\t\")\n","    pipe  = s.count(\"|\")\n","\n","    sep = \",\" if comma >= tab and comma >= pipe else (\"\\t\" if tab >= pipe else \"|\")\n","\n","    df = pd.read_csv(fp, dtype=str, sep=sep, engine=\"python\", on_bad_lines=\"skip\")\n","    df = df.loc[:, ~df.columns.duplicated()].copy()\n","\n","    df[\"source_row_nr\"] = df.index\n","    df[\"source_file\"]   = os.path.basename(fp)\n","\n","    df[\"raw_snapshot\"] = df.astype(str).agg(\"|\".join, axis=1)\n","    df[\"raw_hash\"]     = df[\"raw_snapshot\"].apply(lambda x: hashlib.sha1(x.encode()).hexdigest())\n","\n","    return df\n","\n","# ---------------------------------------------------------------\n","# LOAD ALL\n","# ---------------------------------------------------------------\n","dfs = [load_csv(fp) for fp in csv_paths]\n","raw_merged = pd.concat(dfs, ignore_index=True)\n","print(\"RAW MERGED:\", len(raw_merged))\n","\n","# ---------------------------------------------------------------\n","# NORMALIZE\n","# ---------------------------------------------------------------\n","df = raw_merged.copy()\n","df.columns = [c.lower().strip() for c in df.columns]\n","df = df.loc[:, ~df.columns.duplicated()]\n","\n","def pick(*names):\n","    for n in names:\n","        if n in df.columns:\n","            return df[n].astype(str)\n","    return pd.Series([None]*len(df))\n","\n","df[\"date_time\"] = pick(\"datetime\",\"date_time\",\"dateoccurred\",\"reported\",\"occurred\",\"sighted_at\")\n","df[\"city\"]      = pick(\"city\",\"location_city\",\"location.city\")\n","df[\"state\"]     = pick(\"state\",\"location_state\",\"location.state\",\"province\")\n","df[\"country\"]   = pick(\"country\",\"location_country\",\"location.country\")\n","df[\"shape\"]     = pick(\"shape\",\"ufo_shape\",\"form\")\n","df[\"summary\"]   = pick(\"summary\",\"description\",\"desc\",\"text\",\"comments\")\n","\n","df[\"duration_seconds\"] = pd.to_numeric(\n","    pick(\"duration\",\"duration_seconds\",\"encounter_length\").str.replace(\",\", \"\"),\n","    errors=\"coerce\"\n",")\n","\n","df[\"latitude\"]  = pd.to_numeric(pick(\"latitude\",\"lat\",\"city_latitude\"), errors=\"coerce\")\n","df[\"longitude\"] = pd.to_numeric(pick(\"longitude\",\"lon\",\"lng\",\"city_longitude\"), errors=\"coerce\")\n","\n","df[\"full_text\"] = df[\"summary\"].astype(str)\n","\n","# ---------------------------------------------------------------\n","# FIXED YEAR PARSING — VECTORIZED + COERCE\n","# ---------------------------------------------------------------\n","df[\"year\"] = (\n","    pd.to_datetime(df[\"date_time\"], errors=\"coerce\")\n","    .dt.year\n","    .fillna(-1)\n","    .astype(int)\n",")\n","\n","df[\"year_context_available\"] = df[\"year\"].apply(lambda y: y >= 0)\n","\n","# ---------------------------------------------------------------\n","# UUID\n","# ---------------------------------------------------------------\n","def make_uuid(row):\n","    base = (\n","        str(row[\"city\"]) + \"|\" +\n","        str(row[\"state\"]) + \"|\" +\n","        str(row[\"date_time\"]) + \"|\" +\n","        str(row[\"shape\"]) + \"|\" +\n","        str(row[\"summary\"])\n","    )\n","    return hashlib.md5(base.encode()).hexdigest()\n","\n","df[\"ufo_uuid\"] = df.apply(make_uuid, axis=1)\n","\n","# ---------------------------------------------------------------\n","# DEDUPE V2 — VECTOR BASED + FULL LINEAGE\n","# ---------------------------------------------------------------\n","df[\"text_hash\"] = df[\"full_text\"].str.lower().apply(lambda x: hashlib.md5(x.encode()).hexdigest())\n","df[\"duration_bucket\"] = (df[\"duration_seconds\"] // 10).fillna(-1).astype(int)\n","\n","group_cols = [\"city\",\"state\",\"date_time\",\"shape\",\"duration_bucket\",\"text_hash\"]\n","\n","agg = {\n","    \"raw_snapshot\": list,\n","    \"raw_hash\": list,\n","    \"source_file\": list,\n","    \"source_row_nr\": list,\n","    \"summary\": \"first\",\n","    \"full_text\": \"first\",\n","    \"duration_seconds\": \"first\",\n","    \"latitude\": \"first\",\n","    \"longitude\": \"first\",\n","    \"country\": \"first\",\n","    \"year\": \"first\",\n","    \"year_context_available\": \"first\",\n","    \"ufo_uuid\": \"first\",\n","}\n","\n","deduped = df.groupby(group_cols, as_index=False).agg(agg)\n","print(\"DEDUPED ROWS:\", len(deduped))\n","\n","# ---------------------------------------------------------------\n","# BUILD LINEAGE\n","# ---------------------------------------------------------------\n","def build_lineage(row):\n","    return [\n","        {\n","            \"file\": f,\n","            \"row\": int(rn),\n","            \"raw_hash\": h\n","        }\n","        for f, rn, h in zip(\n","            row[\"source_file\"],\n","            row[\"source_row_nr\"],\n","            row[\"raw_hash\"]\n","        )\n","    ]\n","\n","deduped[\"lineage\"] = deduped.apply(build_lineage, axis=1)\n","deduped = deduped.drop(columns=[\"source_file\",\"source_row_nr\"])\n","\n","# ---------------------------------------------------------------\n","# METADATA\n","# ---------------------------------------------------------------\n","deduped[\"merge_version\"] = MERGE_VERSION\n","deduped[\"dedupe_method\"] = \"vectorized_signature_v2\"\n","\n","# ---------------------------------------------------------------\n","# SAVE JSONL\n","# ---------------------------------------------------------------\n","raw_out    = \"/content/ufo_raw_v2.jsonl\"\n","dedupe_out = \"/content/ufo_deduped_v2.jsonl\"\n","\n","df.to_json(raw_out, orient=\"records\", lines=True, force_ascii=False)\n","\n","with open(dedupe_out, \"w\", encoding=\"utf-8\") as f:\n","    header = {\n","        \"dataset\": \"UFO Witness Corpus v2\",\n","        \"merge_version\": MERGE_VERSION,\n","        \"generated_at\": pd.Timestamp(\"now\").isoformat(),\n","        \"sources\": sorted([os.path.basename(p) for p in csv_paths]),\n","        \"dedupe_method\": \"vectorized_signature_v2\",\n","        \"uuid_method\": \"md5(city|state|datetime|shape|summary)\",\n","        \"provenance_fields\": [\"raw_snapshot\",\"raw_hash\",\"lineage\"]\n","    }\n","    f.write(json.dumps(header, ensure_ascii=False) + \"\\n\")\n","    deduped.to_json(f, orient=\"records\", lines=True, force_ascii=False)\n","\n","print(\"\\nDONE.\")\n","print(\"RAW JSONL:     \", raw_out)\n","print(\"DEDUPED JSONL: \", dedupe_out)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lgxj1HvfrUNK","executionInfo":{"status":"ok","timestamp":1763996939222,"user_tz":300,"elapsed":107021,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"17ab49ef-a884-4a61-f8b8-0eec87c87585"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["FOUND CSV FILES: 6\n","RAW MERGED: 486384\n","DEDUPED ROWS: 242842\n","\n","DONE.\n","RAW JSONL:      /content/ufo_raw_v2.jsonl\n","DEDUPED JSONL:  /content/ufo_deduped_v2.jsonl\n"]}]},{"cell_type":"code","source":["# ================================================================\n","# SECTION 1 — DOWNLOAD ALL REFERENCE DATA NEEDED FOR ENRICHMENT\n","# ================================================================\n","import os, requests, hashlib\n","import pandas as pd\n","\n","os.makedirs(\"/content/refdata\", exist_ok=True)\n","\n","def dl(url, path):\n","    print(f\"[DL] {url}\")\n","    r = requests.get(url, timeout=30)\n","    r.raise_for_status()\n","    with open(path, \"wb\") as f:\n","        f.write(r.content)\n","    sha = hashlib.sha256(r.content).hexdigest()\n","    print(f\"[OK] Saved: {path}\\nSHA256: {sha}\\n\")\n","    return path\n","\n","\n","# ================================================================\n","# 1) AIRPORT DATA\n","# ================================================================\n","print(\"\\n=== AIRPORT DATA ===\")\n","\n","openflights_url = \"https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat\"\n","openflights_path = \"/content/refdata/airports_openflights.dat\"\n","dl(openflights_url, openflights_path)\n","\n","airport_code_url = \"https://raw.githubusercontent.com/datasets/airport-codes/master/data/airport-codes.csv\"\n","airport_code_path = \"/content/refdata/airport_codes.csv\"\n","dl(airport_code_url, airport_code_path)\n","\n","print(\"Rows airports_openflights:\", sum(1 for _ in open(openflights_path)))\n","print(\"Rows airport_codes:\", len(pd.read_csv(airport_code_path)))\n","\n","# ================================================================\n","# 3) FINAL REPORT\n","# ================================================================\n","print(\"\\nALL DONE — ALL FILES SUCCESSFULLY DOWNLOADED & VERIFIED.\")\n","print(\"Stored under /content/refdata:\")\n","for f in os.listdir(\"/content/refdata\"):\n","    print(\" •\", f)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rg-LksCG7D54","executionInfo":{"status":"ok","timestamp":1763997051904,"user_tz":300,"elapsed":2251,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"61fc4fa3-fe9f-403e-f1d4-aa71269ba0ca"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","=== AIRPORT DATA ===\n","[DL] https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat\n","[OK] Saved: /content/refdata/airports_openflights.dat\n","SHA256: 9387cdb38df5bd664da823f8ccb69fdd9b33a1888f5b7cca09c34a3cd9ff59f9\n","\n","[DL] https://raw.githubusercontent.com/datasets/airport-codes/master/data/airport-codes.csv\n","[OK] Saved: /content/refdata/airport_codes.csv\n","SHA256: a916fd8b262c9ab87be40229d93bc6ee9756f2a99dcc2b05b8b7a014f7cdeed6\n","\n","Rows airports_openflights: 7698\n","Rows airport_codes: 82808\n","\n","ALL DONE — ALL FILES SUCCESSFULLY DOWNLOADED & VERIFIED.\n","Stored under /content/refdata:\n"," • airports_openflights.dat\n"," • airport_codes.csv\n"]}]},{"cell_type":"code","source":["# ================================================================\n","# MOON METRICS — SKYFIELD (NASA JPL DE421)\n","# ================================================================\n","from skyfield.api import load, wgs84\n","import math\n","import pandas as pd\n","\n","ts   = load.timescale()\n","eph  = load(\"de421.bsp\")   # downloaded automatically\n","moon = eph[\"moon\"]\n","sun  = eph[\"sun\"]\n","\n","def moon_metrics(lat, lon, dt):\n","    \"\"\"\n","    Returns:\n","      - moon_illumination (0–100%)\n","      - altitude (deg)\n","      - azimuth (deg)\n","      - distance (km)\n","      - phase_age_days (0–29.53)\n","    \"\"\"\n","    if pd.isna(lat) or pd.isna(lon) or dt is None:\n","        return {\n","            \"moon_illumination\": None,\n","            \"moon_altitude_deg\": None,\n","            \"moon_azimuth_deg\": None,\n","            \"moon_distance_km\": None,\n","            \"moon_phase_age_days\": None\n","        }\n","\n","    try:\n","        t = ts.from_datetime(dt)\n","        observer = wgs84.latlon(lat, lon)\n","\n","        astrometric = observer.at(t).observe(moon)\n","        alt, az, distance = astrometric.apparent().altaz()\n","\n","        # Sun vector for illumination\n","        sun_app = observer.at(t).observe(sun).apparent()\n","\n","        phase_angle = sun_app.separation_from(astrometric.apparent()).degrees\n","        illumination = (1 + math.cos(math.radians(phase_angle))) / 2\n","\n","        phase_age_days = (phase_angle / 360.0) * 29.530588\n","\n","        return {\n","            \"moon_illumination\": float(illumination * 100.0),\n","            \"moon_altitude_deg\": float(alt.degrees),\n","            \"moon_azimuth_deg\": float(az.degrees),\n","            \"moon_distance_km\": float(distance.km),\n","            \"moon_phase_age_days\": float(phase_age_days)\n","        }\n","\n","    except:\n","        return {\n","            \"moon_illumination\": None,\n","            \"moon_altitude_deg\": None,\n","            \"moon_azimuth_deg\": None,\n","            \"moon_distance_km\": None,\n","            \"moon_phase_age_days\": None\n","        }\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zWKtktMF_haF","executionInfo":{"status":"ok","timestamp":1763997144838,"user_tz":300,"elapsed":575,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"1af20835-0259-474d-af86-cc3381baaa5a"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["[#################################] 100% de421.bsp\n"]}]},{"cell_type":"code","source":["# ================================================================\n","# GEOMAGNETIC LATITUDE + HAVERSINE DISTANCE (DROP-IN MODULE)\n","# ================================================================\n","import math\n","\n","# ----------------------------------------------------------------\n","# CONSTANTS (2020 IGRF dipole pole — scientific & transparent)\n","# ----------------------------------------------------------------\n","GEOMAG_POLE_LAT = math.radians(80.65)     # degrees → radians\n","GEOMAG_POLE_LON = math.radians(-72.68)    # degrees → radians\n","EARTH_RADIUS_KM = 6371.0088               # WGS84 mean radius\n","\n","\n","# ----------------------------------------------------------------\n","# FUNCTION: Compute geomagnetic latitude (degrees)\n","# ----------------------------------------------------------------\n","def geomagnetic_latitude_deg(lat_deg: float, lon_deg: float) -> float:\n","    \"\"\"\n","    Compute geomagnetic latitude using the IGRF dipole pole projection.\n","    Scientifically accurate and requires no external data.\n","    \"\"\"\n","    lat = math.radians(lat_deg)\n","    lon = math.radians(lon_deg)\n","\n","    term = (\n","        math.sin(lat) * math.sin(GEOMAG_POLE_LAT)\n","        + math.cos(lat) * math.cos(GEOMAG_POLE_LAT) * math.cos(lon - GEOMAG_POLE_LON)\n","    )\n","\n","    # result in radians\n","    geomag_lat = math.asin(term)\n","\n","    return math.degrees(geomag_lat)\n","\n","\n","# ----------------------------------------------------------------\n","# FUNCTION: Haversine distance (km)\n","# ----------------------------------------------------------------\n","def haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:\n","    \"\"\"\n","    Great-circle distance using the haversine formula.\n","    \"\"\"\n","    # convert to radians\n","    lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2])\n","\n","    dlat = lat2 - lat1\n","    dlon = lon2 - lon1\n","\n","    a = (\n","        math.sin(dlat / 2)**2 +\n","        math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2)**2\n","    )\n","    c = 2 * math.asin(math.sqrt(a))\n","\n","    return EARTH_RADIUS_KM * c\n","\n","\n","# ----------------------------------------------------------------\n","# OPTIONAL INLINE TESTS\n","# ----------------------------------------------------------------\n","if __name__ == \"__main__\":\n","    # Example: Roswell, NM\n","    print(\"Geomagnetic latitude:\", geomagnetic_latitude_deg(33.3943, -104.5230))\n","    # Example distance: NYC ↔ LA\n","    print(\"NY ↔ LA (km):\", haversine_km(40.7128, -74.0060, 34.0522, -118.2437))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lVkk6oHlBoNZ","executionInfo":{"status":"ok","timestamp":1763997152978,"user_tz":300,"elapsed":5,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"ec8d3d09-d3f7-4a86-f9f8-72840b5f36f4"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Geomagnetic latitude: 41.17134641179337\n","NY ↔ LA (km): 3935.751690893986\n"]}]},{"cell_type":"code","source":["!pip install skyfield numpy\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mQ9LxTcOKevm","executionInfo":{"status":"ok","timestamp":1763997078488,"user_tz":300,"elapsed":8417,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"543532b0-f1b1-4600-8e5e-299d10a8bd3d"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting skyfield\n","  Downloading skyfield-1.53-py3-none-any.whl.metadata (2.4 kB)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from skyfield) (2025.11.12)\n","Collecting jplephem>=2.13 (from skyfield)\n","  Downloading jplephem-2.23-py3-none-any.whl.metadata (23 kB)\n","Collecting sgp4>=2.13 (from skyfield)\n","  Downloading sgp4-2.25-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (33 kB)\n","Downloading skyfield-1.53-py3-none-any.whl (366 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m367.0/367.0 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading jplephem-2.23-py3-none-any.whl (49 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading sgp4-2.25-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (235 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.7/235.7 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: sgp4, jplephem, skyfield\n","Successfully installed jplephem-2.23 sgp4-2.25 skyfield-1.53\n"]}]},{"cell_type":"code","source":["# ================================================================\n","# UFO ENRICHMENT PIPELINE — BallTree + Safe Moon (Local-Time → UTC)\n","# Scientific-grade enrichment:\n","#   • macro context (600+ fields)\n","#   • moon metrics (Skyfield, NASA DE421) — local time → UTC\n","#   • geomagnetic latitude\n","#   • great-circle distance (Haversine via BallTree)\n","#   • nearest airport (BallTree, 1945+ cutoff)\n","#\n","# Design choices:\n","#   - Only use existing fields: latitude, longitude, date_time.\n","#   - No synthetic times or guessed years.\n","#   - Moon metrics are added ONLY when:\n","#       * latitude/longitude are valid real coordinates (not NaN, not 0/0)\n","#       * date_time parses cleanly\n","#       * we can find a real timezone for that location.\n","#   - On failure, we log and leave moon_* as None.\n","# ================================================================\n","\n","import json\n","import math\n","import pandas as pd\n","from datetime import datetime, timezone\n","import numpy as np\n","from sklearn.neighbors import BallTree\n","from dateutil import parser\n","from timezonefinder import TimezoneFinder\n","import pytz\n","\n","# ================================================================\n","# INPUT FILES\n","# ================================================================\n","UFO_PATH   = \"/content/ufo_deduped_v2.jsonl\"\n","MACRO_PATH = \"/content/macro_yearly_context_clean_named.parquet\"\n","OUT_PATH   = \"/content/ufo_enriched_v2.jsonl\"\n","\n","tf = TimezoneFinder()\n","\n","# ================================================================\n","# GEOMAGNETIC LATITUDE + HAVERSINE\n","# ================================================================\n","EARTH_RADIUS_KM = 6371.0088\n","\n","GEOMAG_POLE_LAT = math.radians(80.65)\n","GEOMAG_POLE_LON = math.radians(-72.68)\n","\n","\n","def geomagnetic_latitude_deg(lat, lon):\n","    \"\"\"Compute geomagnetic latitude (IGRF-style approximation).\"\"\"\n","    lat_r = math.radians(lat)\n","    lon_r = math.radians(lon)\n","    term = (\n","        math.sin(lat_r) * math.sin(GEOMAG_POLE_LAT)\n","        + math.cos(lat_r) * math.cos(GEOMAG_POLE_LAT) * math.cos(lon_r - GEOMAG_POLE_LON)\n","    )\n","    return math.degrees(math.asin(term))\n","\n","\n","def is_valid_coord(lat, lon):\n","    \"\"\"Strict coordinate validity check: numeric, not NaN, not (0,0) sentinel.\"\"\"\n","    if not isinstance(lat, (int, float)) or not isinstance(lon, (int, float)):\n","        return False\n","    # math.isnan works for floats; ints will be auto-cast\n","    if math.isnan(lat) or math.isnan(lon):\n","        return False\n","    # Treat (0,0) as \"no data\" sentinel\n","    if lat == 0.0 and lon == 0.0:\n","        return False\n","    return True\n","\n","\n","# ================================================================\n","# MOON METRICS — SKYFIELD (NASA DE421)\n","# ================================================================\n","from skyfield.api import load, wgs84\n","\n","ts = load.timescale()\n","eph = load(\"de421.bsp\")\n","earth, moon, sun = eph[\"earth\"], eph[\"moon\"], eph[\"sun\"]\n","\n","\n","def safe_parse_dt_utc(dt_raw, lat, lon):\n","    \"\"\"\n","    Parse a date_time string into a UTC datetime, using only:\n","      - the given string\n","      - the given coordinates (for timezone lookup)\n","\n","    Rules:\n","      - Reject 'nan', 'none', '', etc. → returns None.\n","      - If parsing fails → returns None.\n","      - If parsed datetime has tzinfo → convert to UTC.\n","      - If no tzinfo:\n","          * require valid coords\n","          * use TimezoneFinder(lat, lon) to get a real tz name\n","          * localize and convert to UTC\n","          * if timezone unavailable/ambiguous → returns None.\n","    \"\"\"\n","    if not isinstance(dt_raw, str):\n","        return None\n","\n","    s = dt_raw.strip()\n","    if not s:\n","        return None\n","    low = s.lower()\n","    if low in (\"nan\", \"none\", \"null\"):\n","        return None\n","\n","    # 1) parse clock time\n","    try:\n","        dt_local = parser.parse(s)\n","    except (ValueError, OverflowError):\n","        return None\n","\n","    # 2) If it already has a timezone, just convert to UTC\n","    if dt_local.tzinfo is not None:\n","        try:\n","            return dt_local.astimezone(pytz.utc)\n","        except Exception:\n","            return None\n","\n","    # 3) No tzinfo: we require valid coordinates + real timezone\n","    if not is_valid_coord(lat, lon):\n","        return None\n","\n","    try:\n","        tzname = tf.timezone_at(lat=lat, lng=lon)\n","    except Exception:\n","        tzname = None\n","\n","    if tzname is None:\n","        return None\n","\n","    try:\n","        local_tz = pytz.timezone(tzname)\n","    except Exception:\n","        return None\n","\n","    try:\n","        local_dt = local_tz.localize(dt_local, is_dst=None)\n","    except (pytz.AmbiguousTimeError, pytz.NonExistentTimeError):\n","        # Ambiguous or nonexistent local times (DST edge cases) → we don't guess.\n","        return None\n","    except Exception:\n","        return None\n","\n","    try:\n","        return local_dt.astimezone(pytz.utc)\n","    except Exception:\n","        return None\n","\n","\n","def compute_moon(lat, lon, dt_utc):\n","    \"\"\"\n","    Compute moon metrics given:\n","      - lat, lon (degrees)\n","      - dt_utc (datetime in UTC or naive-UTC)\n","\n","    Uses ts.utc(...) for maximum clarity.\n","    Only catches ValueError (logs and returns None-fields).\n","    Other exceptions will surface so they aren't silently swallowed.\n","    \"\"\"\n","    if dt_utc is None:\n","        return {\n","            \"moon_altitude_deg\": None,\n","            \"moon_azimuth_deg\": None,\n","            \"moon_distance_km\": None,\n","            \"moon_illumination\": None,\n","            \"moon_phase_age_days\": None,\n","        }\n","\n","    # Normalize to naive UTC\n","    if dt_utc.tzinfo is not None:\n","        dt_utc = dt_utc.astimezone(timezone.utc).replace(tzinfo=None)\n","\n","    sec = dt_utc.second + dt_utc.microsecond / 1e6\n","\n","    try:\n","        t = ts.utc(dt_utc.year, dt_utc.month, dt_utc.day,\n","                   dt_utc.hour, dt_utc.minute, sec)\n","\n","        # Topocentric observer: earth + WGS84 lat/lon\n","        observer = (earth + wgs84.latlon(lat, lon)).at(t)\n","\n","        # Moon position\n","        astrometric = observer.observe(moon)\n","        alt, az, distance = astrometric.apparent().altaz()\n","\n","        # Phase angle from sun→moon\n","        phase_angle = observer.observe(moon).phase_angle(sun).degrees\n","\n","        # Approximate illuminated fraction (%) from phase angle\n","        illum_frac = max(\n","            0.0,\n","            min(100.0, 100.0 * (1.0 + math.cos(math.radians(phase_angle))) / 2.0),\n","        )\n","\n","        # Simple phase-age proxy (not exact physical age, but monotonic)\n","        age_days = 29.530588 * (phase_angle / 180.0)\n","\n","        return {\n","            \"moon_altitude_deg\": alt.degrees,\n","            \"moon_azimuth_deg\": az.degrees,\n","            \"moon_distance_km\": distance.km,\n","            \"moon_illumination\": illum_frac,\n","            \"moon_phase_age_days\": age_days,\n","        }\n","\n","    except ValueError as e:\n","        # Narrow, expected failures (e.g., out-of-range date) → log and return None\n","        print(f\"[compute_moon] ValueError at lat={lat}, lon={lon}, dt_utc={dt_utc}: {e}\")\n","        return {\n","            \"moon_altitude_deg\": None,\n","            \"moon_azimuth_deg\": None,\n","            \"moon_distance_km\": None,\n","            \"moon_illumination\": None,\n","            \"moon_phase_age_days\": None,\n","        }\n","\n","\n","# ================================================================\n","# AIRPORTS — LOAD + BallTree BUILD\n","# ================================================================\n","openflights_path = \"/content/refdata/airports_openflights.dat\"\n","airport_codes_path = \"/content/refdata/airport_codes.csv\"\n","\n","# ---------- OpenFlights ----------\n","of = pd.read_csv(openflights_path, header=None)\n","of = of[[6, 7, 4, 5]]\n","of.columns = [\"lat\", \"lon\", \"iata\", \"icao\"]\n","of[\"code\"] = of[\"iata\"]\n","of = of.dropna(subset=[\"lat\", \"lon\"])\n","\n","# ---------- Airport Codes ----------\n","ac = pd.read_csv(airport_codes_path)\n","\n","\n","def parse_coords(s):\n","    try:\n","        lon, lat = s.split(\",\")\n","        return float(lat), float(lon)\n","    except Exception:\n","        return None, None\n","\n","\n","ac[\"lat\"], ac[\"lon\"] = zip(*ac[\"coordinates\"].apply(parse_coords))\n","ac[\"code\"] = ac[\"iata_code\"]\n","ac = ac.dropna(subset=[\"lat\", \"lon\"])\n","\n","# ---------- COMBINE ----------\n","airport_df = pd.concat(\n","    [\n","        ac[[\"code\", \"lat\", \"lon\"]],\n","        of[[\"code\", \"lat\", \"lon\"]],\n","    ],\n","    ignore_index=True,\n",").dropna(subset=[\"lat\", \"lon\"]).reset_index(drop=True)\n","\n","# ---------- BUILD BallTree ----------\n","airport_points = np.radians(airport_df[[\"lat\", \"lon\"]].to_numpy())\n","airport_tree = BallTree(airport_points, metric=\"haversine\")\n","\n","MIN_AIRPORT_YEAR = 1945\n","\n","# ================================================================\n","# LOAD MACRO CONTEXT\n","# ================================================================\n","print(\"=== Loading macro context parquet (600+ fields) ===\")\n","macro = pd.read_parquet(MACRO_PATH).set_index(\"year\")\n","\n","# ================================================================\n","# STREAMING ENRICHMENT\n","# ================================================================\n","print(\"\\n=== Streaming enrichment start ===\")\n","written = 0\n","\n","with open(OUT_PATH, \"w\", encoding=\"utf-8\") as fout:\n","    with open(UFO_PATH, \"r\", encoding=\"utf-8\") as fin:\n","        for line_idx, line in enumerate(fin):\n","            row = json.loads(line)\n","\n","            # Pass through metadata header unchanged if present\n","            if line_idx == 0 and \"dataset\" in row and \"merge_version\" in row:\n","                fout.write(json.dumps(row, ensure_ascii=False) + \"\\n\")\n","                written += 1\n","                continue\n","\n","            # YEAR + macro context (uses existing 'year' field only)\n","            yr = row.get(\"year\")\n","            if isinstance(yr, float):\n","                yr = int(yr)\n","            if isinstance(yr, int) and yr in macro.index:\n","                row.update(macro.loc[yr].to_dict())\n","\n","            # Coordinates + time\n","            lat = row.get(\"latitude\") or row.get(\"city_latitude\")\n","            lon = row.get(\"longitude\") or row.get(\"city_longitude\")\n","            dt_raw = row.get(\"date_time\")\n","\n","            has_coords = is_valid_coord(lat, lon)\n","\n","            # ---------- Moon metrics ----------\n","            if has_coords:\n","                dt_utc = safe_parse_dt_utc(dt_raw, lat, lon)\n","                if dt_utc is not None:\n","                    moon_dict = compute_moon(lat, lon, dt_utc)\n","                else:\n","                    moon_dict = {\n","                        \"moon_altitude_deg\": None,\n","                        \"moon_azimuth_deg\": None,\n","                        \"moon_distance_km\": None,\n","                        \"moon_illumination\": None,\n","                        \"moon_phase_age_days\": None,\n","                    }\n","            else:\n","                moon_dict = {\n","                    \"moon_altitude_deg\": None,\n","                    \"moon_azimuth_deg\": None,\n","                    \"moon_distance_km\": None,\n","                    \"moon_illumination\": None,\n","                    \"moon_phase_age_days\": None,\n","                }\n","\n","            row.update(moon_dict)\n","\n","            # ---------- Geomagnetic latitude ----------\n","            if has_coords:\n","                row[\"geomagnetic_latitude\"] = geomagnetic_latitude_deg(lat, lon)\n","            else:\n","                row[\"geomagnetic_latitude\"] = None\n","\n","            # ---------- Nearest airport ----------\n","            if has_coords and isinstance(yr, int) and yr >= MIN_AIRPORT_YEAR:\n","                pt = np.radians([[lat, lon]])\n","                dist_rad, idx = airport_tree.query(pt, k=1)\n","                idx = idx[0][0]\n","                dist_km = dist_rad[0][0] * EARTH_RADIUS_KM\n","\n","                row[\"nearest_airport_code\"] = airport_df.iloc[idx].code\n","                row[\"nearest_airport_km\"] = float(dist_km)\n","                row[\"nearest_airport_source\"] = \"openflights+airportcodes_BallTree_1945cut\"\n","            else:\n","                row[\"nearest_airport_code\"] = None\n","                row[\"nearest_airport_km\"] = None\n","                row[\"nearest_airport_source\"] = None\n","\n","            fout.write(json.dumps(row, ensure_ascii=False) + \"\\n\")\n","            written += 1\n","\n","print(\"=== DONE ===\")\n","print(\"Wrote:\", written)\n","print(\"Output:\", OUT_PATH)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SAy4DxBaLJq1","executionInfo":{"status":"ok","timestamp":1763998121353,"user_tz":300,"elapsed":881733,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"1cceb9ba-bfce-477b-b20a-7de4db9e0938"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["=== Loading macro context parquet (600+ fields) ===\n","\n","=== Streaming enrichment start ===\n","=== DONE ===\n","Wrote: 242843\n","Output: /content/ufo_enriched_v2.jsonl\n"]}]},{"cell_type":"code","source":["!pip install timezonefinder"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fkB9Q9E-bXRr","executionInfo":{"status":"ok","timestamp":1763997234586,"user_tz":300,"elapsed":9194,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"c4d0c756-41b2-487f-84c3-68ac48753497"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting timezonefinder\n","  Downloading timezonefinder-8.1.0-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB)\n","Requirement already satisfied: numpy<3,>=1.23 in /usr/local/lib/python3.12/dist-packages (from timezonefinder) (2.0.2)\n","Collecting h3>4 (from timezonefinder)\n","  Downloading h3-4.3.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (18 kB)\n","Requirement already satisfied: cffi<3,>=1.15.1 in /usr/local/lib/python3.12/dist-packages (from timezonefinder) (2.0.0)\n","Requirement already satisfied: flatbuffers>=25.2.10 in /usr/local/lib/python3.12/dist-packages (from timezonefinder) (25.9.23)\n","Requirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi<3,>=1.15.1->timezonefinder) (2.23)\n","Downloading timezonefinder-8.1.0-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (28.2 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.2/28.2 MB\u001b[0m \u001b[31m70.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading h3-4.3.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (1.0 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m49.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: h3, timezonefinder\n","Successfully installed h3-4.3.1 timezonefinder-8.1.0\n"]}]},{"cell_type":"code","source":["!tail /content/ufo_enriched_v2.jsonl\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zuOGA1PLU4IF","executionInfo":{"status":"ok","timestamp":1763853005525,"user_tz":300,"elapsed":165,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"ffaa1311-98ae-478d-ce6f-f3b2a887440f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{\"city\": \"zutphen (netherlands)\", \"state\": \"nan\", \"date_time\": \"nan\", \"shape\": \"nan\", \"duration_bucket\": -1, \"text_hash\": \"a3d2de7675556553a5f08e4c88d2c228\", \"raw_snapshot\": [\"4/18/2010 23:00|zutphen (netherlands)|nan|nan|sphere|120|2 minutes|We saw an orange glowing light in the sky when airspace was closed|4/18/2012|52.142736|6.196058|37622|ufo_sighting_data.csv\"], \"raw_hash\": [\"11b66f14ae1958e977a1a9e76802f2ed07b49a42\"], \"summary\": \"nan\", \"full_text\": \"nan\", \"duration_seconds\": null, \"latitude\": 52.142736, \"longitude\": 6.196058, \"country\": \"nan\", \"year\": -1, \"year_context_available\": false, \"ufo_uuid\": \"9442a05a246962b71a7dcaa1e28fb580\", \"lineage\": [{\"file\": \"ufo_sighting_data.csv\", \"row\": 37622, \"raw_hash\": \"11b66f14ae1958e977a1a9e76802f2ed07b49a42\"}], \"merge_version\": \"ufo_v2_2025_01_28\", \"dedupe_method\": \"vectorized_signature_v2\", \"moon_altitude_deg\": null, \"moon_azimuth_deg\": null, \"moon_distance_km\": null, \"moon_illumination\": null, \"moon_phase_age_days\": null, \"geomagnetic_latitude\": 52.96698200878594, \"nearest_airport_code\": null, \"nearest_airport_km\": null, \"nearest_airport_source\": null}\n","{\"city\": \"zwevegem (belgium)\", \"state\": \"nan\", \"date_time\": \"8/10/2001 20:45\", \"shape\": \"unknown\", \"duration_bucket\": -1, \"text_hash\": \"a3d2de7675556553a5f08e4c88d2c228\", \"raw_snapshot\": [\"8/10/2001 20:45|zwevegem (belgium)|nan|nan|unknown|1500|25 minutes|A small object - first hangs still - then starts moving with BIG clouds - hangs still again and so on.|8/12/2001|50.812926|3.332698|64220|scrubbed.csv\", \"8/10/2001 20:45|zwevegem (belgium)|nan|nan|unknown|1500|25 minutes|A small object - first hangs still - then starts moving with BIG clouds - hangs still again and so on.|8/12/2001|50.812926|3.332698|71056|complete.csv\"], \"raw_hash\": [\"02ef72a490973cb96af03377ba5be7ef87b71124\", \"119d8869e935917e4960cdef0475b810db1647e4\"], \"summary\": \"nan\", \"full_text\": \"nan\", \"duration_seconds\": null, \"latitude\": 50.812926, \"longitude\": 3.332698, \"country\": \"nan\", \"year\": 2001, \"year_context_available\": true, \"ufo_uuid\": \"4f7b386b2a2c2b5376e3ccda11ea7d47\", \"lineage\": [{\"file\": \"scrubbed.csv\", \"row\": 64220, \"raw_hash\": \"02ef72a490973cb96af03377ba5be7ef87b71124\"}, {\"file\": \"complete.csv\", \"row\": 71056, \"raw_hash\": \"119d8869e935917e4960cdef0475b810db1647e4\"}], \"merge_version\": \"ufo_v2_2025_01_28\", \"dedupe_method\": \"vectorized_signature_v2\", \"nchs_nchs_top_five_leading_causes_of_death_united_states_1990_1950_2000_number_of_deaths\": 0.0, \"nchs_nchs_age_adjusted_death_rates_for_selected_major_causes_of_death_age_adjusted_death_rate\": 562.3000000000001, \"nchs_nchs_death_rates_and_life_expectancy_at_birth_average_life_expectancy_years\": 679.0, \"nchs_nchs_death_rates_and_life_expectancy_at_birth_age_adjusted_death_rate\": 8587.499999999998, \"nchs_nchs_leading_causes_of_death_united_states_deaths\": 8631566.0, \"nchs_nchs_leading_causes_of_death_united_states_age_adjusted_death_rate\": 80287.5, \"nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_hhs_region\": 0.0, \"nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_observed_deaths\": 0.0, \"nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_population\": 0.0, \"nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_expected_deaths\": 0.0, \"nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_potentially_excess_deaths\": 0.0, \"nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_percent_potentially_excess_deaths\": 0.0, \"news_articles_total\": NaN, \"news_unique_sources\": NaN, \"news_source_entropy\": NaN, \"news_total_words\": NaN, \"news_mean_words\": NaN, \"news_median_words\": NaN, \"news_vocab_size\": NaN, \"news_vocab_entropy\": NaN, \"news_lexical_diversity\": NaN, \"news_sentiment_mean\": NaN, \"news_sentiment_std\": NaN, \"news_ufo\": NaN, \"news_uap\": NaN, \"news_alien\": NaN, \"news_saucer\": NaN, \"news_disk\": NaN, \"news_balloon\": NaN, \"news_missile\": NaN, \"news_nuclear\": NaN, \"news_articles_total_z\": NaN, \"news_unique_sources_z\": NaN, \"news_source_entropy_z\": NaN, \"news_total_words_z\": NaN, \"news_mean_words_z\": NaN, \"news_vocab_size_z\": NaN, \"news_vocab_entropy_z\": NaN, \"news_sentiment_mean_z\": NaN, \"famous_birth_count\": 10.0, \"famous_death_count\": 9887.0, \"famous_mean_age_of_death\": 76.43056538889451, \"famous_std_age_of_death\": 14.514551464707832, \"famous_manner_entropy\": 1.5040151106983035, \"famous_country_entropy\": 2.321928094887362, \"famous_occupation_entropy\": 1.0, \"famous_gender_entropy\": 0.9852281360342515, \"famous_homicide_rate\": 0.0059674319813897035, \"famous_birth_count_z\": -0.2846059984394959, \"famous_death_count_z\": 5.35958552967062, \"famous_mean_age_of_death_z\": 1.2803202055609642, \"famous_homicide_rate_z\": -0.16700507015879182, \"famous_manner_entropy_z\": 1.5281069628509354, \"famous_country_entropy_z\": 0.2896200752892911, \"famous_occupation_entropy_z\": -0.5338450617849012, \"famous_gender_entropy_z\": 2.1062114778493792, \"gdp_gdp_growth_mean\": 3.2243806404623734, \"gdp_gdp_growth_std\": 5.24605078530132, \"gdp_gdp_growth_min\": -9.4319740700862, \"gdp_gdp_growth_max\": 63.3798754250602, \"gdp_gdp_growth_median\": 3.056857151261025, \"disaster_disaster_deaths_total\": NaN, \"disaster_disaster_deaths_mean\": NaN, \"disaster_disaster_deaths_std\": NaN, \"disaster_disaster_deaths_min\": NaN, \"disaster_disaster_deaths_max\": NaN, \"disaster_disaster_deaths_count\": NaN, \"disaster_disaster_deaths_pct_change\": NaN, \"patent_yearly_contextmerged_patent_count\": 184172.0, \"patent_yearly_contextmerged_cpc_total\": 874414.0, \"patent_yearly_contextmerged_gov_interest\": 3548.0, \"patent_yearly_contextmerged_cit_back\": 2169210.0, \"arxiv_total_papers\": NaN, \"arxiv_category_entropy\": NaN, \"arxiv_math_co\": NaN, \"arxiv_math_nt\": NaN, \"arxiv_hep_th\": NaN, \"arxiv_math_pr\": NaN, \"arxiv_math_ag\": NaN, \"arxiv_nlin_ps\": NaN, \"arxiv_physics_chem_ph\": NaN, \"arxiv_q_bio_mn\": NaN, \"arxiv_math_na\": NaN, \"arxiv_math_ra\": NaN, \"arxiv_q_bio_pe\": NaN, \"arxiv_q_bio_cb\": NaN, \"arxiv_quant_ph\": NaN, \"arxiv_physics_optics\": NaN, \"arxiv_physics_comp_ph\": NaN, \"arxiv_cond_mat_stat_mech\": NaN, 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\"ucdp_state_violence_events\": 16316.0, \"ucdp_non_state_violence_events\": 585.0, \"ucdp_one_sided_violence_events\": 1562.0, \"ucdp_deaths_a_sum\": 25841.0, \"ucdp_deaths_b_sum\": 22898.0, \"ucdp_deaths_civilians_sum\": 28632.0, \"ucdp_deaths_unknown_sum\": 9017.0, \"ucdp_fatalities_best_sum\": 86388.0, \"ucdp_fatalities_high_sum\": 100608.0, \"ucdp_fatalities_low_sum\": 83197.0, \"ucdp_fatalities_best_avg\": 4.678979580783188, \"ucdp_fatalities_high_avg\": 5.449168607485241, \"ucdp_fatalities_low_avg\": 4.506147429995125, \"ucdp_countries_covered\": 47.0, \"ucdp_clear_events\": 0.0, \"ucdp_unclear_events\": 18463.0, \"air_air_passengers_total\": 12274553849.055, \"air_air_passengers_mean\": 70951178.31823699, \"air_air_passengers_std\": 297567906.636648, \"air_air_passengers_min\": 0.0, \"air_air_passengers_max\": 2943248000.0, \"air_air_passengers_count\": 173.0, \"air_air_passengers_pct_change\": 0.03886354854905183, \"moon_altitude_deg\": null, \"moon_azimuth_deg\": null, \"moon_distance_km\": null, \"moon_illumination\": null, \"moon_phase_age_days\": null, \"nearest_airport_code\": null, \"nearest_airport_km\": null, \"nearest_airport_source\": null}\n","{\"city\": \"Çanakkale (Turkey)\", \"state\": \"nan\", \"date_time\": \"nan\", \"shape\": \"triangle\", \"duration_bucket\": -1, \"text_hash\": \"fdc3e0d47cebf1c3777c6a3c139ec67c\", \"raw_snapshot\": [\"Two triangle-tear shaped flying object landed on the ground and just disappeared.|Çanakkale (Turkey)|nan|2002-11-16T06:55:00|triangle|10 mins|Occurred : 11/16/2002 06:55  (Entered as : 11/16/2002 06:55) Reported: 11/19/2002 4:58:30 AM 04:58 Posted: 11/20/2002 Location: Çanakkale (Turkey),  Shape: Triangle Duration:10 mins|http://www.nuforc.org/webreports/026/S26139.html|Two triangle-tear shaped flying object landed on the ground and just disappeared. Two triangle-tear shaped flying object landed on the ground and just disappeared. There were no light on the objects till they got close to ground. Just before landing a bright yellow light shined and than went off. It took a few seconds to disappear on the ground. We found no sign there. When I told the event to my grandfather he also said familiar events has taken place around. ((NUFORC Note:  We do not know whether the event occurred during the morning, or the evening.  PD))|2002-11-20T00:00:00|nan|nan|21717|nuforc_reports.csv\"], \"raw_hash\": [\"88b3c8238ff9913843544881f657c29a9a68487e\"], \"summary\": \"Two triangle-tear shaped flying object landed on the ground and just disappeared.\", \"full_text\": \"Two triangle-tear shaped flying object landed on the ground and just disappeared.\", \"duration_seconds\": null, \"latitude\": null, \"longitude\": null, \"country\": \"nan\", \"year\": -1, \"year_context_available\": false, \"ufo_uuid\": \"68aa6f7b814f39fb6941b841e4353828\", \"lineage\": [{\"file\": \"nuforc_reports.csv\", \"row\": 21717, \"raw_hash\": \"88b3c8238ff9913843544881f657c29a9a68487e\"}], \"merge_version\": \"ufo_v2_2025_01_28\", \"dedupe_method\": \"vectorized_signature_v2\", \"moon_altitude_deg\": null, \"moon_azimuth_deg\": null, \"moon_distance_km\": null, \"moon_illumination\": null, \"moon_phase_age_days\": null, \"nearest_airport_code\": null, \"nearest_airport_km\": null, \"nearest_airport_source\": null}\n","{\"city\": \"Çanakkale/Çan (Turkey)\", \"state\": \"nan\", \"date_time\": \"nan\", \"shape\": \"other\", \"duration_bucket\": -1, \"text_hash\": \"40d79b66868f48f5c5f0a1bd500efde8\", \"raw_snapshot\": [\"Morning landscape with large red and blue lights in the house at sunrise over the side at the rear balcony UFO.  ((anonymous))|Çanakkale/Çan (Turkey)|nan|2009-06-30T06:30:00|other|10 seconds|Occurred : 6/30/2009 06:30  (Entered as : 00/00/2009 6:30) Reported: 4/30/2016 9:47:23 AM 09:47 Posted: 5/6/2016 Location: Çanakkale/Çan (Turkey),  Shape: Other Duration:10 seconds|http://www.nuforc.org/webreports/127/S127125.html|I was only 17 years old.  Morning landscape with large red and blue lights in the house at sunrise over the side at the rear balcony UFO observing the side of the building was a long horizontal and also large UFO.  really great and was very quiet.  Nobody had yet to wake up.  just think for me, and there were now gone. ((NUFORC Note:  Witness elects to remain totally anonymous; provides no contact information.  PD)) ((NUFORC Note:  Witness indicates that the date of the sighting is approximate.  PD))|2016-05-06T00:00:00|nan|nan|110974|nuforc_reports.csv\"], \"raw_hash\": [\"0cd6265cd6c3c150ee96434f9ec8a84448492e9e\"], \"summary\": \"Morning landscape with large red and blue lights in the house at sunrise over the side at the rear balcony UFO.  ((anonymous))\", \"full_text\": \"Morning landscape with large red and blue lights in the house at sunrise over the side at the rear balcony UFO.  ((anonymous))\", \"duration_seconds\": null, \"latitude\": null, \"longitude\": null, \"country\": \"nan\", \"year\": -1, \"year_context_available\": false, \"ufo_uuid\": \"debcce116ebf81624ea7f8a932983cff\", \"lineage\": [{\"file\": \"nuforc_reports.csv\", \"row\": 110974, \"raw_hash\": \"0cd6265cd6c3c150ee96434f9ec8a84448492e9e\"}], \"merge_version\": \"ufo_v2_2025_01_28\", \"dedupe_method\": \"vectorized_signature_v2\", \"moon_altitude_deg\": null, \"moon_azimuth_deg\": null, \"moon_distance_km\": null, \"moon_illumination\": null, \"moon_phase_age_days\": null, \"nearest_airport_code\": null, \"nearest_airport_km\": null, \"nearest_airport_source\": null}\n","{\"city\": \"Ísafjörður (Iceland)\", \"state\": \"nan\", \"date_time\": \"nan\", \"shape\": \"cylinder\", \"duration_bucket\": -1, \"text_hash\": \"4c77701798696675d700dd6d9c84c5ac\", \"raw_snapshot\": [\"Cylinder hiding from fighter jets in west Iceland|Ísafjörður (Iceland)|nan|2004-10-12T16:00:00|cylinder|15 min|Occurred : 10/12/2004 16:00  (Entered as : 10/12/2004 16:00) Reported: 10/13/2004 10:47:50 AM 10:47 Posted: 10/27/2004 Location: Ísafjörður (Iceland),  Shape: Cylinder Duration:15 min|http://www.nuforc.org/webreports/039/S39757.html|Cylinder hiding from fighter jets in west Iceland The 3 of us where walking on a well known footpath. We were enjoying the scenery when one of our party noticed a craft that seemd to be hovering under a cliff. The craft was cylindrical in shape with no obvious doors or windows. The color was some sort of metalic but it's difficult to describe it because looking at it hurt our eyes. (As in some sort of camoflage). Suddenly we heard a sonic boom and 3 fighter jets flew over, in what seemd to be a tight formation. They passed over us about half dozen times. All of us were very afraid, both of the noise and the low passage of the jets. When the jets had finally gone the craft left. It passed directly over us. It made no sound but we all felt terrible pressure in our heads and all our watches stopped. About a half hour passed until we felt strong enough to come down from the mountain. The fighter jets might have been from the U.S. but we are not entirely sure, a large Russian fleet has been manovering on the west coast of our country for a week now, they have not given any expalnations about what they are doing there. There seemed to be no markings what so ever on the jets. ((NUFORC Note:  Report does appear to have been submitted from outside the U. S..  We would welcome other reports from anyone else who might have been witness to the alleged events described above.  PD))|2004-10-27T00:00:00|nan|nan|32935|nuforc_reports.csv\"], \"raw_hash\": [\"f62c0b49759157ee1f0a28b968b9837c22b60f87\"], \"summary\": \"Cylinder hiding from fighter jets in west Iceland\", \"full_text\": \"Cylinder hiding from fighter jets in west Iceland\", \"duration_seconds\": null, \"latitude\": null, \"longitude\": null, \"country\": \"nan\", \"year\": -1, \"year_context_available\": false, \"ufo_uuid\": \"257a32210bc9471ddac184548d3a46be\", \"lineage\": [{\"file\": \"nuforc_reports.csv\", \"row\": 32935, \"raw_hash\": \"f62c0b49759157ee1f0a28b968b9837c22b60f87\"}], \"merge_version\": \"ufo_v2_2025_01_28\", \"dedupe_method\": \"vectorized_signature_v2\", \"moon_altitude_deg\": null, \"moon_azimuth_deg\": null, \"moon_distance_km\": null, \"moon_illumination\": null, \"moon_phase_age_days\": null, \"nearest_airport_code\": null, \"nearest_airport_km\": null, \"nearest_airport_source\": null}\n","{\"city\": \"Ölmstad (Sweden)\", \"state\": \"nan\", \"date_time\": \"nan\", \"shape\": \"circle\", \"duration_bucket\": -1, \"text_hash\": \"35a82f759a4f07a3f530de1c5f4a42ae\", \"raw_snapshot\": [\"We where walking home at 6:20 PM on the third of January 2004 when we saw a circular object in the sky. The object flew very quickly ac|Ölmstad (Sweden)|nan|2004-01-03T06:20:00|circle|2 seconds|Occurred : 1/3/2004 06:20  (Entered as : 01/03/2004 6:20) Reported: 1/3/2004 9:40:42 AM 09:40 Posted: 1/17/2004 Location: Ölmstad (Sweden),  Shape: Circle Duration:2 seconds|http://www.nuforc.org/webreports/034/S34271.html|We where walking home at 6:20 PM on the third of January 2004 when we saw a circular object in the sky. The object flew very quickly across the sky and then disapeared without a trace. The sky was very foggy(we could hardly see the moon) so we beleive that it couldn't have been a falling star but something else.|2004-01-17T00:00:00|nan|nan|28811|nuforc_reports.csv\"], \"raw_hash\": [\"d584ee7005d49fdbf3db73582849ae5a06eb5d05\"], \"summary\": \"We where walking home at 6:20 PM on the third of January 2004 when we saw a circular object in the sky. The object flew very quickly ac\", \"full_text\": \"We where walking home at 6:20 PM on the third of January 2004 when we saw a circular object in the sky. The object flew very quickly ac\", \"duration_seconds\": null, \"latitude\": null, \"longitude\": null, \"country\": \"nan\", \"year\": -1, \"year_context_available\": false, \"ufo_uuid\": \"bc3e1fc68f83bb404b981cef4b3b8f23\", \"lineage\": [{\"file\": \"nuforc_reports.csv\", \"row\": 28811, \"raw_hash\": \"d584ee7005d49fdbf3db73582849ae5a06eb5d05\"}], \"merge_version\": \"ufo_v2_2025_01_28\", \"dedupe_method\": \"vectorized_signature_v2\", \"moon_altitude_deg\": null, \"moon_azimuth_deg\": null, \"moon_distance_km\": null, \"moon_illumination\": null, \"moon_phase_age_days\": null, \"nearest_airport_code\": null, \"nearest_airport_km\": null, \"nearest_airport_source\": null}\n"]}]},{"cell_type":"code","source":["import shutil\n","\n","shutil.make_archive(\n","    \"/content/ufo_enriched_v2\",   # output name (no .zip here)\n","    \"zip\",\n","    \"/content\",                   # root folder\n","    \"ufo_enriched_v2.jsonl\"       # file to include\n",")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"nz5EOFDFlVNf","executionInfo":{"status":"ok","timestamp":1763848965243,"user_tz":300,"elapsed":126543,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"0a0768c6-84d9-4b0b-c25c-97912c992263"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/ufo_enriched_v2.zip'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["import json\n","\n","path = \"/content/ufo_enriched_v2.jsonl\"\n","\n","# read first 5 rows\n","with open(path, \"r\", encoding=\"utf-8\") as f:\n","    for i in range(5):\n","        row = json.loads(next(f))\n","        print(row.keys())\n","        print(\"-------\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SsG8P_EUm8jH","executionInfo":{"status":"ok","timestamp":1763849364770,"user_tz":300,"elapsed":54,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"6627da53-51d6-45fb-91b2-6e52ac9938aa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["dict_keys(['dataset', 'merge_version', 'generated_at', 'sources', 'dedupe_method', 'uuid_method', 'provenance_fields', 'nearest_airport_code', 'nearest_airport_km', 'nearest_airport_source'])\n","-------\n","dict_keys(['city', 'state', 'date_time', 'shape', 'duration_bucket', 'text_hash', 'raw_snapshot', 'raw_hash', 'summary', 'full_text', 'duration_seconds', 'latitude', 'longitude', 'country', 'year', 'year_context_available', 'ufo_uuid', 'lineage', 'merge_version', 'dedupe_method', 'nchs_nchs_top_five_leading_causes_of_death_united_states_1990_1950_2000_number_of_deaths', 'nchs_nchs_age_adjusted_death_rates_for_selected_major_causes_of_death_age_adjusted_death_rate', 'nchs_nchs_death_rates_and_life_expectancy_at_birth_average_life_expectancy_years', 'nchs_nchs_death_rates_and_life_expectancy_at_birth_age_adjusted_death_rate', 'nchs_nchs_leading_causes_of_death_united_states_deaths', 'nchs_nchs_leading_causes_of_death_united_states_age_adjusted_death_rate', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_hhs_region', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_observed_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_population', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_expected_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_potentially_excess_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_percent_potentially_excess_deaths', 'news_articles_total', 'news_unique_sources', 'news_source_entropy', 'news_total_words', 'news_mean_words', 'news_median_words', 'news_vocab_size', 'news_vocab_entropy', 'news_lexical_diversity', 'news_sentiment_mean', 'news_sentiment_std', 'news_ufo', 'news_uap', 'news_alien', 'news_saucer', 'news_disk', 'news_balloon', 'news_missile', 'news_nuclear', 'news_articles_total_z', 'news_unique_sources_z', 'news_source_entropy_z', 'news_total_words_z', 'news_mean_words_z', 'news_vocab_size_z', 'news_vocab_entropy_z', 'news_sentiment_mean_z', 'famous_birth_count', 'famous_death_count', 'famous_mean_age_of_death', 'famous_std_age_of_death', 'famous_manner_entropy', 'famous_country_entropy', 'famous_occupation_entropy', 'famous_gender_entropy', 'famous_homicide_rate', 'famous_birth_count_z', 'famous_death_count_z', 'famous_mean_age_of_death_z', 'famous_homicide_rate_z', 'famous_manner_entropy_z', 'famous_country_entropy_z', 'famous_occupation_entropy_z', 'famous_gender_entropy_z', 'gdp_gdp_growth_mean', 'gdp_gdp_growth_std', 'gdp_gdp_growth_min', 'gdp_gdp_growth_max', 'gdp_gdp_growth_median', 'disaster_disaster_deaths_total', 'disaster_disaster_deaths_mean', 'disaster_disaster_deaths_std', 'disaster_disaster_deaths_min', 'disaster_disaster_deaths_max', 'disaster_disaster_deaths_count', 'disaster_disaster_deaths_pct_change', 'patent_yearly_contextmerged_patent_count', 'patent_yearly_contextmerged_cpc_total', 'patent_yearly_contextmerged_gov_interest', 'patent_yearly_contextmerged_cit_back', 'arxiv_total_papers', 'arxiv_category_entropy', 'arxiv_math_co', 'arxiv_math_nt', 'arxiv_hep_th', 'arxiv_math_pr', 'arxiv_math_ag', 'arxiv_nlin_ps', 'arxiv_physics_chem_ph', 'arxiv_q_bio_mn', 'arxiv_math_na', 'arxiv_math_ra', 'arxiv_q_bio_pe', 'arxiv_q_bio_cb', 'arxiv_quant_ph', 'arxiv_physics_optics', 'arxiv_physics_comp_ph', 'arxiv_cond_mat_stat_mech', 'arxiv_cond_mat_mtrl_sci', 'arxiv_nlin_si', 'arxiv_cs_ne', 'arxiv_cs_ai', 'arxiv_math_fa', 'arxiv_math_cv', 'arxiv_physics_soc_ph', 'arxiv_math_ph', 'arxiv_math_mp', 'arxiv_physics_gen_ph', 'arxiv_math_oa', 'arxiv_math_qa', 'arxiv_math_rt', 'arxiv_math_dg', 'arxiv_physics_data_an', 'arxiv_cs_ce', 'arxiv_cs_ms', 'arxiv_cs_na', 'arxiv_astro_ph', 'arxiv_math_ac', 'arxiv_cs_cc', 'arxiv_cond_mat_other', 'arxiv_math_kt', 'arxiv_math_gt', 'arxiv_math_ds', 'arxiv_math_ap', 'arxiv_cond_mat_mes_hall', 'arxiv_nucl_th', 'arxiv_gr_qc', 'arxiv_physics_class_ph', 'arxiv_hep_ph', 'arxiv_hep_ex', 'arxiv_physics_bio_ph', 'arxiv_q_bio_bm', 'arxiv_nlin_cg', 'arxiv_cs_dm', 'arxiv_cs_lo', 'arxiv_nucl_ex', 'arxiv_math_sg', 'arxiv_cs_it', 'arxiv_math_it', 'arxiv_math_st', 'arxiv_stat_th', 'arxiv_cond_mat_str_el', 'arxiv_q_bio_ot', 'arxiv_physics_flu_dyn', 'arxiv_cond_mat_soft', 'arxiv_cond_mat_dis_nn', 'arxiv_math_ca', 'arxiv_cond_mat_supr_con', 'arxiv_physics_geo_ph', 'arxiv_nlin_cd', 'arxiv_physics_atom_ph', 'arxiv_physics_ed_ph', 'arxiv_math_oc', 'arxiv_math_gr', 'arxiv_math_at', 'arxiv_math_ct', 'arxiv_math_lo', 'arxiv_cs_ni', 'arxiv_math_sp', 'arxiv_q_bio_qm', 'arxiv_q_bio_nc', 'arxiv_cs_pf', 'arxiv_stat_me', 'arxiv_stat_ap', 'arxiv_math_gm', 'arxiv_cs_ds', 'arxiv_cs_cr', 'arxiv_cs_se', 'arxiv_math_gn', 'arxiv_stat_co', 'arxiv_cs_ar', 'arxiv_cs_lg', 'arxiv_cs_sc', 'arxiv_math_mg', 'arxiv_q_bio_gn', 'arxiv_cs_cg', 'arxiv_cs_cv', 'arxiv_hep_lat', 'arxiv_physics_ins_det', 'arxiv_cs_oh', 'arxiv_cs_db', 'arxiv_cs_dl', 'arxiv_cs_hc', 'arxiv_cs_pl', 'arxiv_physics_space_ph', 'arxiv_physics_plasm_ph', 'arxiv_cs_ir', 'arxiv_cs_cy', 'arxiv_cs_gt', 'arxiv_cs_dc', 'arxiv_q_bio_sc', 'arxiv_cs_ma', 'arxiv_cs_cl', 'arxiv_physics_med_ph', 'arxiv_physics_atm_clus', 'arxiv_physics_hist_ph', 'arxiv_math_ho', 'arxiv_nlin_ao', 'arxiv_physics_ao_ph', 'arxiv_physics_acc_ph', 'arxiv_cs_mm', 'arxiv_cs_ro', 'arxiv_stat_ml', 'arxiv_q_bio_to', 'arxiv_physics_pop_ph', 'arxiv_cs_gl', 'arxiv_cs_os', 'arxiv_cs_gr', 'arxiv_cs_sd', 'arxiv_adap_org', 'arxiv_q_bio', 'arxiv_cond_mat', 'arxiv_alg_geom', 'arxiv_dg_ga', 'arxiv_q_alg', 'arxiv_chem_ph', 'arxiv_chao_dyn', 'arxiv_patt_sol', 'arxiv_cmp_lg', 'arxiv_comp_gas', 'arxiv_mtrl_th', 'arxiv_supr_con', 'arxiv_solv_int', 'arxiv_funct_an', 'arxiv_atom_ph', 'arxiv_acc_phys', 'arxiv_plasm_ph', 'arxiv_q_fin_pr', 'arxiv_q_fin_gn', 'arxiv_q_fin_st', 'arxiv_q_fin_cp', 'arxiv_q_fin_pm', 'arxiv_q_fin_rm', 'arxiv_q_fin_tr', 'arxiv_bayes_an', 'arxiv_ao_sci', 'arxiv_astro_ph_ep', 'arxiv_astro_ph_sr', 'arxiv_astro_ph_co', 'arxiv_cond_mat_quant_gas', 'arxiv_astro_ph_he', 'arxiv_cs_fl', 'arxiv_astro_ph_ga', 'arxiv_astro_ph_im', 'arxiv_cs_sy', 'arxiv_cs_si', 'arxiv_stat_ot', 'arxiv_cs_et', 'arxiv_q_fin_ec', 'arxiv_q_fin_mf', 'arxiv_econ_em', 'arxiv_eess_iv', 'arxiv_physics_app_ph', 'arxiv_eess_sp', 'arxiv_eess_as', 'arxiv_econ_th', 'arxiv_econ_gn', 'arxiv_eess_sy', 'space_launch_total', 'space_launch_mean', 'space_launch_std', 'space_countries_reporting', 'space_max_country_launch', 'space_min_country_launch', 'space_launch_total_z', 'space_launch_mean_z', 'space_countries_reporting_z', 'nuclear_yearly_context_number_of_nuclear_weapons_tests_year', 'nuclear_yearly_context_number_of_nuclear_weapons_tests_number_of_nuclear_weapons_tests', 'nuclear_yearly_context_nuclear_warhead_inventories_year', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_deployed_strategic_nuclear_warheads', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_deployed_nonstrategic_nuclear_warheads', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_nondeployed_nuclear_warheads_in_reserve', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_retired_nuclear_warheads', 'energy_country_reporting', 'energy_country_entropy', 'energy_biodiesel_cons_kboed_sum', 'energy_biodiesel_cons_kboed_mean', 'energy_biodiesel_cons_kboed_count', 'energy_biodiesel_cons_pj_sum', 'energy_biodiesel_cons_pj_mean', 'energy_biodiesel_cons_pj_count', 'energy_biofuels_cons_ej_sum', 'energy_biofuels_cons_ej_mean', 'energy_biofuels_cons_ej_count', 'energy_biofuels_cons_kbd_sum', 'energy_biofuels_cons_kbd_mean', 'energy_biofuels_cons_kbd_count', 'energy_biofuels_cons_kboed_sum', 'energy_biofuels_cons_kboed_mean', 'energy_biofuels_cons_kboed_count', 'energy_biofuels_cons_pj_sum', 'energy_biofuels_cons_pj_mean', 'energy_biofuels_cons_pj_count', 'energy_biogeo_ej_sum', 'energy_biogeo_ej_mean', 'energy_biogeo_ej_count', 'energy_biogeo_twh_sum', 'energy_biogeo_twh_mean', 'energy_biogeo_twh_count', 'energy_biogeo_twh_net_sum', 'energy_biogeo_twh_net_mean', 'energy_biogeo_twh_net_count', 'energy_co2_combust_mtco2_sum', 'energy_co2_combust_mtco2_mean', 'energy_co2_combust_mtco2_count', 'energy_co2_combust_pc_sum', 'energy_co2_combust_pc_mean', 'energy_co2_combust_pc_count', 'energy_co2_combust_per_ej_sum', 'energy_co2_combust_per_ej_mean', 'energy_co2_combust_per_ej_count', 'energy_coalcons_ej_sum', 'energy_coalcons_ej_mean', 'energy_coalcons_ej_count', 'energy_electbyfuel_hydro_sum', 'energy_electbyfuel_hydro_mean', 'energy_electbyfuel_hydro_count', 'energy_electbyfuel_nuclear_sum', 'energy_electbyfuel_nuclear_mean', 'energy_electbyfuel_nuclear_count', 'energy_electbyfuel_ren_power_sum', 'energy_electbyfuel_ren_power_mean', 'energy_electbyfuel_ren_power_count', 'energy_ethanol_cons_kboed_sum', 'energy_ethanol_cons_kboed_mean', 'energy_ethanol_cons_kboed_count', 'energy_ethanol_cons_pj_sum', 'energy_ethanol_cons_pj_mean', 'energy_ethanol_cons_pj_count', 'energy_fuel_oil_cons_kbd_sum', 'energy_fuel_oil_cons_kbd_mean', 'energy_fuel_oil_cons_kbd_count', 'energy_gascons_bcfd_sum', 'energy_gascons_bcfd_mean', 'energy_gascons_bcfd_count', 'energy_gascons_bcm_sum', 'energy_gascons_bcm_mean', 'energy_gascons_bcm_count', 'energy_gascons_ej_sum', 'energy_gascons_ej_mean', 'energy_gascons_ej_count', 'energy_hydro_ej_sum', 'energy_hydro_ej_mean', 'energy_hydro_ej_count', 'energy_hydro_twh_sum', 'energy_hydro_twh_mean', 'energy_hydro_twh_count', 'energy_hydro_twh_net_sum', 'energy_hydro_twh_net_mean', 'energy_hydro_twh_net_count', 'energy_light_dist_cons_kbd_sum', 'energy_light_dist_cons_kbd_mean', 'energy_light_dist_cons_kbd_count', 'energy_liqcons_kbd_sum', 'energy_liqcons_kbd_mean', 'energy_liqcons_kbd_count', 'energy_middle_dist_cons_kbd_sum', 'energy_middle_dist_cons_kbd_mean', 'energy_middle_dist_cons_kbd_count', 'energy_nuclear_ej_sum', 'energy_nuclear_ej_mean', 'energy_nuclear_ej_count', 'energy_nuclear_twh_sum', 'energy_nuclear_twh_mean', 'energy_nuclear_twh_count', 'energy_nuclear_twh_net_sum', 'energy_nuclear_twh_net_mean', 'energy_nuclear_twh_net_count', 'energy_oilcons_ej_sum', 'energy_oilcons_ej_mean', 'energy_oilcons_ej_count', 'energy_oilcons_kbd_sum', 'energy_oilcons_kbd_mean', 'energy_oilcons_kbd_count', 'energy_oilcons_mt_sum', 'energy_oilcons_mt_mean', 'energy_oilcons_mt_count', 'energy_oilprod_crudecond_kbd_sum', 'energy_oilprod_crudecond_kbd_mean', 'energy_oilprod_crudecond_kbd_count', 'energy_oilprod_kbd_sum', 'energy_oilprod_kbd_mean', 'energy_oilprod_kbd_count', 'energy_oilprod_mt_sum', 'energy_oilprod_mt_mean', 'energy_oilprod_mt_count', 'energy_oilprod_ngl_kbd_sum', 'energy_oilprod_ngl_kbd_mean', 'energy_oilprod_ngl_kbd_count', 'energy_other_oil_cons_kbd_sum', 'energy_other_oil_cons_kbd_mean', 'energy_other_oil_cons_kbd_count', 'energy_pop_sum', 'energy_pop_mean', 'energy_pop_count', 'energy_refcap_kbd_sum', 'energy_refcap_kbd_mean', 'energy_refcap_kbd_count', 'energy_ren_power_ej_sum', 'energy_ren_power_ej_mean', 'energy_ren_power_ej_count', 'energy_ren_power_twh_sum', 'energy_ren_power_twh_mean', 'energy_ren_power_twh_count', 'energy_ren_power_twh_net_sum', 'energy_ren_power_twh_net_mean', 'energy_ren_power_twh_net_count', 'energy_renewables_ej_sum', 'energy_renewables_ej_mean', 'energy_renewables_ej_count', 'energy_solar_ej_sum', 'energy_solar_ej_mean', 'energy_solar_ej_count', 'energy_solar_twh_sum', 'energy_solar_twh_mean', 'energy_solar_twh_count', 'energy_solar_twh_net_sum', 'energy_solar_twh_net_mean', 'energy_solar_twh_net_count', 'energy_tes_ej_sum', 'energy_tes_ej_mean', 'energy_tes_ej_count', 'energy_tes_gj_pc_sum', 'energy_tes_gj_pc_mean', 'energy_tes_gj_pc_count', 'energy_wind_ej_sum', 'energy_wind_ej_mean', 'energy_wind_ej_count', 'energy_wind_twh_sum', 'energy_wind_twh_mean', 'energy_wind_twh_count', 'energy_wind_twh_net_sum', 'energy_wind_twh_net_mean', 'energy_wind_twh_net_count', 'energy_gasprod_bcfd_sum', 'energy_gasprod_bcfd_mean', 'energy_gasprod_bcfd_count', 'energy_gasprod_bcm_sum', 'energy_gasprod_bcm_mean', 'energy_gasprod_bcm_count', 'energy_gasprod_ej_sum', 'energy_gasprod_ej_mean', 'energy_gasprod_ej_count', 'energy_gasflared_bcm_sum', 'energy_gasflared_bcm_mean', 'energy_gasflared_bcm_count', 'energy_gasflared_mtco2_sum', 'energy_gasflared_mtco2_mean', 'energy_gasflared_mtco2_count', 'energy_diesel_gasoil_cons_kbd_sum', 'energy_diesel_gasoil_cons_kbd_mean', 'energy_diesel_gasoil_cons_kbd_count', 'energy_gasoline_cons_kbd_sum', 'energy_gasoline_cons_kbd_mean', 'energy_gasoline_cons_kbd_count', 'energy_kerosene_cons_kbd_sum', 'energy_kerosene_cons_kbd_mean', 'energy_kerosene_cons_kbd_count', 'energy_lpg_cons_kbd_sum', 'energy_lpg_cons_kbd_mean', 'energy_lpg_cons_kbd_count', 'energy_naphtha_cons_kbd_sum', 'energy_naphtha_cons_kbd_mean', 'energy_naphtha_cons_kbd_count', 'energy_refcaputil_pct_sum', 'energy_refcaputil_pct_mean', 'energy_refcaputil_pct_count', 'energy_refthru_kbd_sum', 'energy_refthru_kbd_mean', 'energy_refthru_kbd_count', 'energy_coalprod_ej_sum', 'energy_coalprod_ej_mean', 'energy_coalprod_ej_count', 'energy_coalprod_mt_sum', 'energy_coalprod_mt_mean', 'energy_coalprod_mt_count', 'energy_elect_twh_sum', 'energy_elect_twh_mean', 'energy_elect_twh_count', 'energy_electbyfuel_coal_sum', 'energy_electbyfuel_coal_mean', 'energy_electbyfuel_coal_count', 'energy_electbyfuel_gas_sum', 'energy_electbyfuel_gas_mean', 'energy_electbyfuel_gas_count', 'energy_electbyfuel_oil_sum', 'energy_electbyfuel_oil_mean', 'energy_electbyfuel_oil_count', 'energy_electbyfuel_other_sum', 'energy_electbyfuel_other_mean', 'energy_electbyfuel_other_count', 'energy_electbyfuel_total_sum', 'energy_electbyfuel_total_mean', 'energy_electbyfuel_total_count', 'energy_biodiesel_prod_kboed_sum', 'energy_biodiesel_prod_kboed_mean', 'energy_biodiesel_prod_kboed_count', 'energy_biodiesel_prod_pj_sum', 'energy_biodiesel_prod_pj_mean', 'energy_biodiesel_prod_pj_count', 'energy_biofuels_prod_kbd_sum', 'energy_biofuels_prod_kbd_mean', 'energy_biofuels_prod_kbd_count', 'energy_biofuels_prod_kboed_sum', 'energy_biofuels_prod_kboed_mean', 'energy_biofuels_prod_kboed_count', 'energy_biofuels_prod_pj_sum', 'energy_biofuels_prod_pj_mean', 'energy_biofuels_prod_pj_count', 'energy_co2_mtco2_sum', 'energy_co2_mtco2_mean', 'energy_co2_mtco2_count', 'energy_ethanol_prod_kboed_sum', 'energy_ethanol_prod_kboed_mean', 'energy_ethanol_prod_kboed_count', 'energy_ethanol_prod_pj_sum', 'energy_ethanol_prod_pj_mean', 'energy_ethanol_prod_pj_count', 'energy_methane_process_mtco2_sum', 'energy_methane_process_mtco2_mean', 'energy_methane_process_mtco2_count', 'energy_cobalt_kt_sum', 'energy_cobalt_kt_mean', 'energy_cobalt_kt_count', 'energy_graphite_kt_sum', 'energy_graphite_kt_mean', 'energy_graphite_kt_count', 'energy_lithium_kt_sum', 'energy_lithium_kt_mean', 'energy_lithium_kt_count', 'energy_rareearths_kt_sum', 'energy_rareearths_kt_mean', 'energy_rareearths_kt_count', 'energy_cobaltres_kt_sum', 'energy_cobaltres_kt_mean', 'energy_cobaltres_kt_count', 'energy_graphiteres_kt_sum', 'energy_graphiteres_kt_mean', 'energy_graphiteres_kt_count', 'energy_lithiumres_kt_sum', 'energy_lithiumres_kt_mean', 'energy_lithiumres_kt_count', 'energy_rareearthsres_kt_sum', 'energy_rareearthsres_kt_mean', 'energy_rareearthsres_kt_count', 'global_temp_temp_anomaly_mean', 'global_temp_temp_anomaly_std', 'global_temp_temp_anomaly_min', 'global_temp_temp_anomaly_max', 'global_temp_temp_anomaly_count', 'global_temp_temp_anomaly_pct_change', 'market_spx_mean', 'market_spx_std', 'market_spx_min', 'market_spx_max', 'market_spx_pct_change', 'market_spx_vol_mean', 'market_dji_mean', 'market_dji_std', 'market_dji_min', 'market_dji_max', 'market_dji_pct_change', 'market_dji_vol_mean', 'market_dax_mean', 'market_dax_std', 'market_dax_min', 'market_dax_max', 'market_dax_pct_change', 'market_dax_vol_mean', 'gdelt_yearly_context_records', 'gdelt_yearly_context_events_total', 'gdelt_yearly_context_articles_total', 'gdelt_yearly_context_goldstein_mean', 'gdelt_yearly_context_goldstein_std', 'gdelt_yearly_context_quad_entropy', 'gdelt_yearly_context_cameo_entropy', 'gdelt_yearly_context_coop_total', 'gdelt_yearly_context_conflict_total', 'gdelt_yearly_context_conflict_ratio', 'gdelt_yearly_context_source_lat_mean', 'gdelt_yearly_context_source_long_mean', 'gdelt_yearly_context_target_lat_mean', 'gdelt_yearly_context_target_long_mean', 'gdelt_yearly_context_geo_spread_mean_km', 'gdelt_yearly_context_cameo_distribution', 'gdelt_yearly_context_events_total_zscore', 'gdelt_yearly_context_articles_total_zscore', 'gdelt_yearly_context_goldstein_mean_zscore', 'gdelt_yearly_context_geo_spread_mean_km_zscore', 'gdelt_yearly_context_conflict_ratio_zscore', 'ucdp_events_total', 'ucdp_unique_conflicts', 'ucdp_unique_dyads', 'ucdp_state_violence_events', 'ucdp_non_state_violence_events', 'ucdp_one_sided_violence_events', 'ucdp_deaths_a_sum', 'ucdp_deaths_b_sum', 'ucdp_deaths_civilians_sum', 'ucdp_deaths_unknown_sum', 'ucdp_fatalities_best_sum', 'ucdp_fatalities_high_sum', 'ucdp_fatalities_low_sum', 'ucdp_fatalities_best_avg', 'ucdp_fatalities_high_avg', 'ucdp_fatalities_low_avg', 'ucdp_countries_covered', 'ucdp_clear_events', 'ucdp_unclear_events', 'air_air_passengers_total', 'air_air_passengers_mean', 'air_air_passengers_std', 'air_air_passengers_min', 'air_air_passengers_max', 'air_air_passengers_count', 'air_air_passengers_pct_change', 'moon_altitude_deg', 'moon_azimuth_deg', 'moon_distance_km', 'moon_illumination', 'moon_phase_age_days', 'geomagnetic_latitude', 'nearest_airport_code', 'nearest_airport_km', 'nearest_airport_source'])\n","-------\n","dict_keys(['city', 'state', 'date_time', 'shape', 'duration_bucket', 'text_hash', 'raw_snapshot', 'raw_hash', 'summary', 'full_text', 'duration_seconds', 'latitude', 'longitude', 'country', 'year', 'year_context_available', 'ufo_uuid', 'lineage', 'merge_version', 'dedupe_method', 'moon_altitude_deg', 'moon_azimuth_deg', 'moon_distance_km', 'moon_illumination', 'moon_phase_age_days', 'geomagnetic_latitude', 'nearest_airport_code', 'nearest_airport_km', 'nearest_airport_source'])\n","-------\n","dict_keys(['city', 'state', 'date_time', 'shape', 'duration_bucket', 'text_hash', 'raw_snapshot', 'raw_hash', 'summary', 'full_text', 'duration_seconds', 'latitude', 'longitude', 'country', 'year', 'year_context_available', 'ufo_uuid', 'lineage', 'merge_version', 'dedupe_method', 'nchs_nchs_top_five_leading_causes_of_death_united_states_1990_1950_2000_number_of_deaths', 'nchs_nchs_age_adjusted_death_rates_for_selected_major_causes_of_death_age_adjusted_death_rate', 'nchs_nchs_death_rates_and_life_expectancy_at_birth_average_life_expectancy_years', 'nchs_nchs_death_rates_and_life_expectancy_at_birth_age_adjusted_death_rate', 'nchs_nchs_leading_causes_of_death_united_states_deaths', 'nchs_nchs_leading_causes_of_death_united_states_age_adjusted_death_rate', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_hhs_region', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_observed_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_population', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_expected_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_potentially_excess_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_percent_potentially_excess_deaths', 'news_articles_total', 'news_unique_sources', 'news_source_entropy', 'news_total_words', 'news_mean_words', 'news_median_words', 'news_vocab_size', 'news_vocab_entropy', 'news_lexical_diversity', 'news_sentiment_mean', 'news_sentiment_std', 'news_ufo', 'news_uap', 'news_alien', 'news_saucer', 'news_disk', 'news_balloon', 'news_missile', 'news_nuclear', 'news_articles_total_z', 'news_unique_sources_z', 'news_source_entropy_z', 'news_total_words_z', 'news_mean_words_z', 'news_vocab_size_z', 'news_vocab_entropy_z', 'news_sentiment_mean_z', 'famous_birth_count', 'famous_death_count', 'famous_mean_age_of_death', 'famous_std_age_of_death', 'famous_manner_entropy', 'famous_country_entropy', 'famous_occupation_entropy', 'famous_gender_entropy', 'famous_homicide_rate', 'famous_birth_count_z', 'famous_death_count_z', 'famous_mean_age_of_death_z', 'famous_homicide_rate_z', 'famous_manner_entropy_z', 'famous_country_entropy_z', 'famous_occupation_entropy_z', 'famous_gender_entropy_z', 'gdp_gdp_growth_mean', 'gdp_gdp_growth_std', 'gdp_gdp_growth_min', 'gdp_gdp_growth_max', 'gdp_gdp_growth_median', 'disaster_disaster_deaths_total', 'disaster_disaster_deaths_mean', 'disaster_disaster_deaths_std', 'disaster_disaster_deaths_min', 'disaster_disaster_deaths_max', 'disaster_disaster_deaths_count', 'disaster_disaster_deaths_pct_change', 'patent_yearly_contextmerged_patent_count', 'patent_yearly_contextmerged_cpc_total', 'patent_yearly_contextmerged_gov_interest', 'patent_yearly_contextmerged_cit_back', 'arxiv_total_papers', 'arxiv_category_entropy', 'arxiv_math_co', 'arxiv_math_nt', 'arxiv_hep_th', 'arxiv_math_pr', 'arxiv_math_ag', 'arxiv_nlin_ps', 'arxiv_physics_chem_ph', 'arxiv_q_bio_mn', 'arxiv_math_na', 'arxiv_math_ra', 'arxiv_q_bio_pe', 'arxiv_q_bio_cb', 'arxiv_quant_ph', 'arxiv_physics_optics', 'arxiv_physics_comp_ph', 'arxiv_cond_mat_stat_mech', 'arxiv_cond_mat_mtrl_sci', 'arxiv_nlin_si', 'arxiv_cs_ne', 'arxiv_cs_ai', 'arxiv_math_fa', 'arxiv_math_cv', 'arxiv_physics_soc_ph', 'arxiv_math_ph', 'arxiv_math_mp', 'arxiv_physics_gen_ph', 'arxiv_math_oa', 'arxiv_math_qa', 'arxiv_math_rt', 'arxiv_math_dg', 'arxiv_physics_data_an', 'arxiv_cs_ce', 'arxiv_cs_ms', 'arxiv_cs_na', 'arxiv_astro_ph', 'arxiv_math_ac', 'arxiv_cs_cc', 'arxiv_cond_mat_other', 'arxiv_math_kt', 'arxiv_math_gt', 'arxiv_math_ds', 'arxiv_math_ap', 'arxiv_cond_mat_mes_hall', 'arxiv_nucl_th', 'arxiv_gr_qc', 'arxiv_physics_class_ph', 'arxiv_hep_ph', 'arxiv_hep_ex', 'arxiv_physics_bio_ph', 'arxiv_q_bio_bm', 'arxiv_nlin_cg', 'arxiv_cs_dm', 'arxiv_cs_lo', 'arxiv_nucl_ex', 'arxiv_math_sg', 'arxiv_cs_it', 'arxiv_math_it', 'arxiv_math_st', 'arxiv_stat_th', 'arxiv_cond_mat_str_el', 'arxiv_q_bio_ot', 'arxiv_physics_flu_dyn', 'arxiv_cond_mat_soft', 'arxiv_cond_mat_dis_nn', 'arxiv_math_ca', 'arxiv_cond_mat_supr_con', 'arxiv_physics_geo_ph', 'arxiv_nlin_cd', 'arxiv_physics_atom_ph', 'arxiv_physics_ed_ph', 'arxiv_math_oc', 'arxiv_math_gr', 'arxiv_math_at', 'arxiv_math_ct', 'arxiv_math_lo', 'arxiv_cs_ni', 'arxiv_math_sp', 'arxiv_q_bio_qm', 'arxiv_q_bio_nc', 'arxiv_cs_pf', 'arxiv_stat_me', 'arxiv_stat_ap', 'arxiv_math_gm', 'arxiv_cs_ds', 'arxiv_cs_cr', 'arxiv_cs_se', 'arxiv_math_gn', 'arxiv_stat_co', 'arxiv_cs_ar', 'arxiv_cs_lg', 'arxiv_cs_sc', 'arxiv_math_mg', 'arxiv_q_bio_gn', 'arxiv_cs_cg', 'arxiv_cs_cv', 'arxiv_hep_lat', 'arxiv_physics_ins_det', 'arxiv_cs_oh', 'arxiv_cs_db', 'arxiv_cs_dl', 'arxiv_cs_hc', 'arxiv_cs_pl', 'arxiv_physics_space_ph', 'arxiv_physics_plasm_ph', 'arxiv_cs_ir', 'arxiv_cs_cy', 'arxiv_cs_gt', 'arxiv_cs_dc', 'arxiv_q_bio_sc', 'arxiv_cs_ma', 'arxiv_cs_cl', 'arxiv_physics_med_ph', 'arxiv_physics_atm_clus', 'arxiv_physics_hist_ph', 'arxiv_math_ho', 'arxiv_nlin_ao', 'arxiv_physics_ao_ph', 'arxiv_physics_acc_ph', 'arxiv_cs_mm', 'arxiv_cs_ro', 'arxiv_stat_ml', 'arxiv_q_bio_to', 'arxiv_physics_pop_ph', 'arxiv_cs_gl', 'arxiv_cs_os', 'arxiv_cs_gr', 'arxiv_cs_sd', 'arxiv_adap_org', 'arxiv_q_bio', 'arxiv_cond_mat', 'arxiv_alg_geom', 'arxiv_dg_ga', 'arxiv_q_alg', 'arxiv_chem_ph', 'arxiv_chao_dyn', 'arxiv_patt_sol', 'arxiv_cmp_lg', 'arxiv_comp_gas', 'arxiv_mtrl_th', 'arxiv_supr_con', 'arxiv_solv_int', 'arxiv_funct_an', 'arxiv_atom_ph', 'arxiv_acc_phys', 'arxiv_plasm_ph', 'arxiv_q_fin_pr', 'arxiv_q_fin_gn', 'arxiv_q_fin_st', 'arxiv_q_fin_cp', 'arxiv_q_fin_pm', 'arxiv_q_fin_rm', 'arxiv_q_fin_tr', 'arxiv_bayes_an', 'arxiv_ao_sci', 'arxiv_astro_ph_ep', 'arxiv_astro_ph_sr', 'arxiv_astro_ph_co', 'arxiv_cond_mat_quant_gas', 'arxiv_astro_ph_he', 'arxiv_cs_fl', 'arxiv_astro_ph_ga', 'arxiv_astro_ph_im', 'arxiv_cs_sy', 'arxiv_cs_si', 'arxiv_stat_ot', 'arxiv_cs_et', 'arxiv_q_fin_ec', 'arxiv_q_fin_mf', 'arxiv_econ_em', 'arxiv_eess_iv', 'arxiv_physics_app_ph', 'arxiv_eess_sp', 'arxiv_eess_as', 'arxiv_econ_th', 'arxiv_econ_gn', 'arxiv_eess_sy', 'space_launch_total', 'space_launch_mean', 'space_launch_std', 'space_countries_reporting', 'space_max_country_launch', 'space_min_country_launch', 'space_launch_total_z', 'space_launch_mean_z', 'space_countries_reporting_z', 'nuclear_yearly_context_number_of_nuclear_weapons_tests_year', 'nuclear_yearly_context_number_of_nuclear_weapons_tests_number_of_nuclear_weapons_tests', 'nuclear_yearly_context_nuclear_warhead_inventories_year', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_deployed_strategic_nuclear_warheads', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_deployed_nonstrategic_nuclear_warheads', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_nondeployed_nuclear_warheads_in_reserve', 'nuclear_yearly_context_nuclear_warhead_inventories_number_of_retired_nuclear_warheads', 'energy_country_reporting', 'energy_country_entropy', 'energy_biodiesel_cons_kboed_sum', 'energy_biodiesel_cons_kboed_mean', 'energy_biodiesel_cons_kboed_count', 'energy_biodiesel_cons_pj_sum', 'energy_biodiesel_cons_pj_mean', 'energy_biodiesel_cons_pj_count', 'energy_biofuels_cons_ej_sum', 'energy_biofuels_cons_ej_mean', 'energy_biofuels_cons_ej_count', 'energy_biofuels_cons_kbd_sum', 'energy_biofuels_cons_kbd_mean', 'energy_biofuels_cons_kbd_count', 'energy_biofuels_cons_kboed_sum', 'energy_biofuels_cons_kboed_mean', 'energy_biofuels_cons_kboed_count', 'energy_biofuels_cons_pj_sum', 'energy_biofuels_cons_pj_mean', 'energy_biofuels_cons_pj_count', 'energy_biogeo_ej_sum', 'energy_biogeo_ej_mean', 'energy_biogeo_ej_count', 'energy_biogeo_twh_sum', 'energy_biogeo_twh_mean', 'energy_biogeo_twh_count', 'energy_biogeo_twh_net_sum', 'energy_biogeo_twh_net_mean', 'energy_biogeo_twh_net_count', 'energy_co2_combust_mtco2_sum', 'energy_co2_combust_mtco2_mean', 'energy_co2_combust_mtco2_count', 'energy_co2_combust_pc_sum', 'energy_co2_combust_pc_mean', 'energy_co2_combust_pc_count', 'energy_co2_combust_per_ej_sum', 'energy_co2_combust_per_ej_mean', 'energy_co2_combust_per_ej_count', 'energy_coalcons_ej_sum', 'energy_coalcons_ej_mean', 'energy_coalcons_ej_count', 'energy_electbyfuel_hydro_sum', 'energy_electbyfuel_hydro_mean', 'energy_electbyfuel_hydro_count', 'energy_electbyfuel_nuclear_sum', 'energy_electbyfuel_nuclear_mean', 'energy_electbyfuel_nuclear_count', 'energy_electbyfuel_ren_power_sum', 'energy_electbyfuel_ren_power_mean', 'energy_electbyfuel_ren_power_count', 'energy_ethanol_cons_kboed_sum', 'energy_ethanol_cons_kboed_mean', 'energy_ethanol_cons_kboed_count', 'energy_ethanol_cons_pj_sum', 'energy_ethanol_cons_pj_mean', 'energy_ethanol_cons_pj_count', 'energy_fuel_oil_cons_kbd_sum', 'energy_fuel_oil_cons_kbd_mean', 'energy_fuel_oil_cons_kbd_count', 'energy_gascons_bcfd_sum', 'energy_gascons_bcfd_mean', 'energy_gascons_bcfd_count', 'energy_gascons_bcm_sum', 'energy_gascons_bcm_mean', 'energy_gascons_bcm_count', 'energy_gascons_ej_sum', 'energy_gascons_ej_mean', 'energy_gascons_ej_count', 'energy_hydro_ej_sum', 'energy_hydro_ej_mean', 'energy_hydro_ej_count', 'energy_hydro_twh_sum', 'energy_hydro_twh_mean', 'energy_hydro_twh_count', 'energy_hydro_twh_net_sum', 'energy_hydro_twh_net_mean', 'energy_hydro_twh_net_count', 'energy_light_dist_cons_kbd_sum', 'energy_light_dist_cons_kbd_mean', 'energy_light_dist_cons_kbd_count', 'energy_liqcons_kbd_sum', 'energy_liqcons_kbd_mean', 'energy_liqcons_kbd_count', 'energy_middle_dist_cons_kbd_sum', 'energy_middle_dist_cons_kbd_mean', 'energy_middle_dist_cons_kbd_count', 'energy_nuclear_ej_sum', 'energy_nuclear_ej_mean', 'energy_nuclear_ej_count', 'energy_nuclear_twh_sum', 'energy_nuclear_twh_mean', 'energy_nuclear_twh_count', 'energy_nuclear_twh_net_sum', 'energy_nuclear_twh_net_mean', 'energy_nuclear_twh_net_count', 'energy_oilcons_ej_sum', 'energy_oilcons_ej_mean', 'energy_oilcons_ej_count', 'energy_oilcons_kbd_sum', 'energy_oilcons_kbd_mean', 'energy_oilcons_kbd_count', 'energy_oilcons_mt_sum', 'energy_oilcons_mt_mean', 'energy_oilcons_mt_count', 'energy_oilprod_crudecond_kbd_sum', 'energy_oilprod_crudecond_kbd_mean', 'energy_oilprod_crudecond_kbd_count', 'energy_oilprod_kbd_sum', 'energy_oilprod_kbd_mean', 'energy_oilprod_kbd_count', 'energy_oilprod_mt_sum', 'energy_oilprod_mt_mean', 'energy_oilprod_mt_count', 'energy_oilprod_ngl_kbd_sum', 'energy_oilprod_ngl_kbd_mean', 'energy_oilprod_ngl_kbd_count', 'energy_other_oil_cons_kbd_sum', 'energy_other_oil_cons_kbd_mean', 'energy_other_oil_cons_kbd_count', 'energy_pop_sum', 'energy_pop_mean', 'energy_pop_count', 'energy_refcap_kbd_sum', 'energy_refcap_kbd_mean', 'energy_refcap_kbd_count', 'energy_ren_power_ej_sum', 'energy_ren_power_ej_mean', 'energy_ren_power_ej_count', 'energy_ren_power_twh_sum', 'energy_ren_power_twh_mean', 'energy_ren_power_twh_count', 'energy_ren_power_twh_net_sum', 'energy_ren_power_twh_net_mean', 'energy_ren_power_twh_net_count', 'energy_renewables_ej_sum', 'energy_renewables_ej_mean', 'energy_renewables_ej_count', 'energy_solar_ej_sum', 'energy_solar_ej_mean', 'energy_solar_ej_count', 'energy_solar_twh_sum', 'energy_solar_twh_mean', 'energy_solar_twh_count', 'energy_solar_twh_net_sum', 'energy_solar_twh_net_mean', 'energy_solar_twh_net_count', 'energy_tes_ej_sum', 'energy_tes_ej_mean', 'energy_tes_ej_count', 'energy_tes_gj_pc_sum', 'energy_tes_gj_pc_mean', 'energy_tes_gj_pc_count', 'energy_wind_ej_sum', 'energy_wind_ej_mean', 'energy_wind_ej_count', 'energy_wind_twh_sum', 'energy_wind_twh_mean', 'energy_wind_twh_count', 'energy_wind_twh_net_sum', 'energy_wind_twh_net_mean', 'energy_wind_twh_net_count', 'energy_gasprod_bcfd_sum', 'energy_gasprod_bcfd_mean', 'energy_gasprod_bcfd_count', 'energy_gasprod_bcm_sum', 'energy_gasprod_bcm_mean', 'energy_gasprod_bcm_count', 'energy_gasprod_ej_sum', 'energy_gasprod_ej_mean', 'energy_gasprod_ej_count', 'energy_gasflared_bcm_sum', 'energy_gasflared_bcm_mean', 'energy_gasflared_bcm_count', 'energy_gasflared_mtco2_sum', 'energy_gasflared_mtco2_mean', 'energy_gasflared_mtco2_count', 'energy_diesel_gasoil_cons_kbd_sum', 'energy_diesel_gasoil_cons_kbd_mean', 'energy_diesel_gasoil_cons_kbd_count', 'energy_gasoline_cons_kbd_sum', 'energy_gasoline_cons_kbd_mean', 'energy_gasoline_cons_kbd_count', 'energy_kerosene_cons_kbd_sum', 'energy_kerosene_cons_kbd_mean', 'energy_kerosene_cons_kbd_count', 'energy_lpg_cons_kbd_sum', 'energy_lpg_cons_kbd_mean', 'energy_lpg_cons_kbd_count', 'energy_naphtha_cons_kbd_sum', 'energy_naphtha_cons_kbd_mean', 'energy_naphtha_cons_kbd_count', 'energy_refcaputil_pct_sum', 'energy_refcaputil_pct_mean', 'energy_refcaputil_pct_count', 'energy_refthru_kbd_sum', 'energy_refthru_kbd_mean', 'energy_refthru_kbd_count', 'energy_coalprod_ej_sum', 'energy_coalprod_ej_mean', 'energy_coalprod_ej_count', 'energy_coalprod_mt_sum', 'energy_coalprod_mt_mean', 'energy_coalprod_mt_count', 'energy_elect_twh_sum', 'energy_elect_twh_mean', 'energy_elect_twh_count', 'energy_electbyfuel_coal_sum', 'energy_electbyfuel_coal_mean', 'energy_electbyfuel_coal_count', 'energy_electbyfuel_gas_sum', 'energy_electbyfuel_gas_mean', 'energy_electbyfuel_gas_count', 'energy_electbyfuel_oil_sum', 'energy_electbyfuel_oil_mean', 'energy_electbyfuel_oil_count', 'energy_electbyfuel_other_sum', 'energy_electbyfuel_other_mean', 'energy_electbyfuel_other_count', 'energy_electbyfuel_total_sum', 'energy_electbyfuel_total_mean', 'energy_electbyfuel_total_count', 'energy_biodiesel_prod_kboed_sum', 'energy_biodiesel_prod_kboed_mean', 'energy_biodiesel_prod_kboed_count', 'energy_biodiesel_prod_pj_sum', 'energy_biodiesel_prod_pj_mean', 'energy_biodiesel_prod_pj_count', 'energy_biofuels_prod_kbd_sum', 'energy_biofuels_prod_kbd_mean', 'energy_biofuels_prod_kbd_count', 'energy_biofuels_prod_kboed_sum', 'energy_biofuels_prod_kboed_mean', 'energy_biofuels_prod_kboed_count', 'energy_biofuels_prod_pj_sum', 'energy_biofuels_prod_pj_mean', 'energy_biofuels_prod_pj_count', 'energy_co2_mtco2_sum', 'energy_co2_mtco2_mean', 'energy_co2_mtco2_count', 'energy_ethanol_prod_kboed_sum', 'energy_ethanol_prod_kboed_mean', 'energy_ethanol_prod_kboed_count', 'energy_ethanol_prod_pj_sum', 'energy_ethanol_prod_pj_mean', 'energy_ethanol_prod_pj_count', 'energy_methane_process_mtco2_sum', 'energy_methane_process_mtco2_mean', 'energy_methane_process_mtco2_count', 'energy_cobalt_kt_sum', 'energy_cobalt_kt_mean', 'energy_cobalt_kt_count', 'energy_graphite_kt_sum', 'energy_graphite_kt_mean', 'energy_graphite_kt_count', 'energy_lithium_kt_sum', 'energy_lithium_kt_mean', 'energy_lithium_kt_count', 'energy_rareearths_kt_sum', 'energy_rareearths_kt_mean', 'energy_rareearths_kt_count', 'energy_cobaltres_kt_sum', 'energy_cobaltres_kt_mean', 'energy_cobaltres_kt_count', 'energy_graphiteres_kt_sum', 'energy_graphiteres_kt_mean', 'energy_graphiteres_kt_count', 'energy_lithiumres_kt_sum', 'energy_lithiumres_kt_mean', 'energy_lithiumres_kt_count', 'energy_rareearthsres_kt_sum', 'energy_rareearthsres_kt_mean', 'energy_rareearthsres_kt_count', 'global_temp_temp_anomaly_mean', 'global_temp_temp_anomaly_std', 'global_temp_temp_anomaly_min', 'global_temp_temp_anomaly_max', 'global_temp_temp_anomaly_count', 'global_temp_temp_anomaly_pct_change', 'market_spx_mean', 'market_spx_std', 'market_spx_min', 'market_spx_max', 'market_spx_pct_change', 'market_spx_vol_mean', 'market_dji_mean', 'market_dji_std', 'market_dji_min', 'market_dji_max', 'market_dji_pct_change', 'market_dji_vol_mean', 'market_dax_mean', 'market_dax_std', 'market_dax_min', 'market_dax_max', 'market_dax_pct_change', 'market_dax_vol_mean', 'gdelt_yearly_context_records', 'gdelt_yearly_context_events_total', 'gdelt_yearly_context_articles_total', 'gdelt_yearly_context_goldstein_mean', 'gdelt_yearly_context_goldstein_std', 'gdelt_yearly_context_quad_entropy', 'gdelt_yearly_context_cameo_entropy', 'gdelt_yearly_context_coop_total', 'gdelt_yearly_context_conflict_total', 'gdelt_yearly_context_conflict_ratio', 'gdelt_yearly_context_source_lat_mean', 'gdelt_yearly_context_source_long_mean', 'gdelt_yearly_context_target_lat_mean', 'gdelt_yearly_context_target_long_mean', 'gdelt_yearly_context_geo_spread_mean_km', 'gdelt_yearly_context_cameo_distribution', 'gdelt_yearly_context_events_total_zscore', 'gdelt_yearly_context_articles_total_zscore', 'gdelt_yearly_context_goldstein_mean_zscore', 'gdelt_yearly_context_geo_spread_mean_km_zscore', 'gdelt_yearly_context_conflict_ratio_zscore', 'ucdp_events_total', 'ucdp_unique_conflicts', 'ucdp_unique_dyads', 'ucdp_state_violence_events', 'ucdp_non_state_violence_events', 'ucdp_one_sided_violence_events', 'ucdp_deaths_a_sum', 'ucdp_deaths_b_sum', 'ucdp_deaths_civilians_sum', 'ucdp_deaths_unknown_sum', 'ucdp_fatalities_best_sum', 'ucdp_fatalities_high_sum', 'ucdp_fatalities_low_sum', 'ucdp_fatalities_best_avg', 'ucdp_fatalities_high_avg', 'ucdp_fatalities_low_avg', 'ucdp_countries_covered', 'ucdp_clear_events', 'ucdp_unclear_events', 'air_air_passengers_total', 'air_air_passengers_mean', 'air_air_passengers_std', 'air_air_passengers_min', 'air_air_passengers_max', 'air_air_passengers_count', 'air_air_passengers_pct_change', 'moon_altitude_deg', 'moon_azimuth_deg', 'moon_distance_km', 'moon_illumination', 'moon_phase_age_days', 'geomagnetic_latitude', 'nearest_airport_code', 'nearest_airport_km', 'nearest_airport_source'])\n","-------\n","dict_keys(['city', 'state', 'date_time', 'shape', 'duration_bucket', 'text_hash', 'raw_snapshot', 'raw_hash', 'summary', 'full_text', 'duration_seconds', 'latitude', 'longitude', 'country', 'year', 'year_context_available', 'ufo_uuid', 'lineage', 'merge_version', 'dedupe_method', 'moon_altitude_deg', 'moon_azimuth_deg', 'moon_distance_km', 'moon_illumination', 'moon_phase_age_days', 'geomagnetic_latitude', 'nearest_airport_code', 'nearest_airport_km', 'nearest_airport_source'])\n","-------\n"]}]},{"cell_type":"code","source":["# ================================================================\n","# UFO ENRICHMENT VALIDATION REPORT\n","# Validates:\n","#   • macro context merge\n","#   • moon metrics\n","#   • geomagnetic latitude\n","#   • nearest airport\n","#   • NO contamination of original fields\n","# ================================================================\n","\n","import json\n","import random\n","import pandas as pd\n","\n","ENRICHED_PATH = \"/content/ufo_enriched_v2.jsonl\"\n","\n","print(\"=== VALIDATION REPORT ===\")\n","print(\"Loading enriched dataset…\")\n","\n","total = 0\n","has_coords = 0\n","has_datetime = 0\n","has_both = 0\n","has_macro = 0\n","has_airport = 0\n","has_moon = 0\n","has_geomag = 0\n","\n","sample_rows = []\n","macro_prefixes = [\n","    \"news_\", \"gdp_\", \"arxiv_\", \"energy_\", \"market_\",\n","    \"ucdp_\", \"gdelt_\", \"global_temp_\", \"disaster_\",\n","    \"famous_\", \"patent_\", \"nchs_\", \"air_\"\n","]\n","\n","with open(ENRICHED_PATH, \"r\", encoding=\"utf-8\") as f:\n","    for line in f:\n","        row = json.loads(line)\n","        total += 1\n","\n","        if len(sample_rows) < 20:\n","            sample_rows.append(row)\n","\n","        # coordinates\n","        lat = row.get(\"latitude\") or row.get(\"city_latitude\")\n","        lon = row.get(\"longitude\") or row.get(\"city_longitude\")\n","        coords_ok = isinstance(lat, (int,float)) and isinstance(lon, (int,float))\n","        if coords_ok:\n","            has_coords += 1\n","\n","        # datetime (accept anything non-empty string)\n","        dt = row.get(\"date_time\")\n","        date_ok = isinstance(dt, str) and len(dt.strip()) > 0\n","        if date_ok:\n","            has_datetime += 1\n","        if coords_ok and date_ok:\n","            has_both += 1\n","\n","        # macro context — any field that starts with any macro prefix\n","        if any(k.startswith(p) for k in row.keys() for p in macro_prefixes):\n","            has_macro += 1\n","\n","        # moon metrics — FIXED: now accepts if altitude exists OR illumination exists\n","        if (\n","            (\"moon_altitude_deg\" in row and row[\"moon_altitude_deg\"] is not None)\n","            or (\"moon_illumination\" in row and row[\"moon_illumination\"] is not None)\n","        ):\n","            has_moon += 1\n","\n","        # geomagnetic lat\n","        if \"geomagnetic_latitude\" in row and row[\"geomagnetic_latitude\"] is not None:\n","            has_geomag += 1\n","\n","        # nearest airport\n","        if row.get(\"nearest_airport_code\") not in (None, \"\"):\n","            has_airport += 1\n","\n","print(\"\\n=== COVERAGE SUMMARY ===\")\n","print(f\"Total rows:                        {total:,}\")\n","print(f\"Rows w/ valid coordinates:         {has_coords:,}\")\n","print(f\"Rows w/ valid datetime:            {has_datetime:,}\")\n","print(f\"Rows w/ BOTH coords + datetime:    {has_both:,}\")\n","print()\n","print(f\"Rows w/ macro context:             {has_macro:,}\")\n","print(f\"Rows w/ moon metrics:              {has_moon:,}\")\n","print(f\"Rows w/ geomagnetic latitude:      {has_geomag:,}\")\n","print(f\"Rows w/ nearest airport:           {has_airport:,}\")\n","\n","def pct(x):\n","    return f\"{(x/total*100):.2f}%\"\n","\n","print(\"\\n=== PERCENTAGES ===\")\n","print(f\"Macro context coverage:            {pct(has_macro)}\")\n","print(f\"Moon metric coverage:              {pct(has_moon)}\")\n","print(f\"Geomagnetic lat coverage:          {pct(has_geomag)}\")\n","print(f\"Nearest airport coverage:          {pct(has_airport)}\")\n","\n","print(\"\\n=== CHECK FOR FIELD CONTAMINATION ===\")\n","\n","ORIGINAL_FIELDS = {\n","    \"city\",\"state\",\"date_time\",\"shape\",\"duration_bucket\",\"text_hash\",\n","    \"raw_snapshot\",\"raw_hash\",\"summary\",\"full_text\",\"duration_seconds\",\n","    \"latitude\",\"longitude\",\"country\",\"year\",\"year_context_available\",\n","    \"ufo_uuid\",\"lineage\",\"merge_version\",\"dedupe_method\"\n","}\n","\n","added_fields = []\n","for k in sample_rows[0].keys():\n","    if k not in ORIGINAL_FIELDS:\n","        added_fields.append(k)\n","\n","print(\"Fields added by enrichment:\", len(added_fields))\n","print(sorted(added_fields)[:50], \"… (truncated)\")\n","\n","print(\"\\n=== RANDOM SAMPLE CHECK (5 rows) ===\")\n","for r in random.sample(sample_rows, 5):\n","    print(\"-\"*50)\n","    print(\"city:\", r.get(\"city\"))\n","    print(\"date_time:\", r.get(\"date_time\"))\n","    print(\"latitude/longitude:\", r.get(\"latitude\"), r.get(\"longitude\"))\n","    print(\"moon_altitude_deg:\", r.get(\"moon_altitude_deg\"))\n","    print(\"geomagnetic_latitude:\", r.get(\"geomagnetic_latitude\"))\n","    print(\"nearest_airport_code:\", r.get(\"nearest_airport_code\"))\n","    print(\"macro example gdp_gdp_growth_mean:\", r.get(\"gdp_gdp_growth_mean\"))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6eRLA3x5umEM","executionInfo":{"status":"ok","timestamp":1763998299158,"user_tz":300,"elapsed":61555,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"57cda182-8aa9-46ce-be20-5ee624e8c7dc"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["=== VALIDATION REPORT ===\n","Loading enriched dataset…\n","\n","=== COVERAGE SUMMARY ===\n","Total rows:                        242,843\n","Rows w/ valid coordinates:         106,374\n","Rows w/ valid datetime:            242,842\n","Rows w/ BOTH coords + datetime:    106,374\n","\n","Rows w/ macro context:             86,750\n","Rows w/ moon metrics:              85,299\n","Rows w/ geomagnetic latitude:      106,374\n","Rows w/ nearest airport:           85,273\n","\n","=== PERCENTAGES ===\n","Macro context coverage:            35.72%\n","Moon metric coverage:              35.13%\n","Geomagnetic lat coverage:          43.80%\n","Nearest airport coverage:          35.11%\n","\n","=== CHECK FOR FIELD CONTAMINATION ===\n","Fields added by enrichment: 5\n","['dataset', 'generated_at', 'provenance_fields', 'sources', 'uuid_method'] … (truncated)\n","\n","=== RANDOM SAMPLE CHECK (5 rows) ===\n","--------------------------------------------------\n","city: &ccedil;anakkale (turkey)\n","date_time: 11/16/2002 06:55\n","latitude/longitude: 40.155312 26.41416\n","moon_altitude_deg: -28.91432093156959\n","geomagnetic_latitude: 38.07322441330124\n","nearest_airport_code: CKZ\n","macro example gdp_gdp_growth_mean: 3.458433530666633\n","--------------------------------------------------\n","city: ((Location unspecified)) (UK/England)\n","date_time: nan\n","latitude/longitude: None None\n","moon_altitude_deg: None\n","geomagnetic_latitude: None\n","nearest_airport_code: None\n","macro example gdp_gdp_growth_mean: None\n","--------------------------------------------------\n","city: &iacute;safj&ouml;r&eth;ur (iceland)\n","date_time: 10/12/2004 16:00\n","latitude/longitude: 66.075833 -23.126667\n","moon_altitude_deg: 13.956305094358\n","geomagnetic_latitude: 70.8530255463422\n","nearest_airport_code: IFJ\n","macro example gdp_gdp_growth_mean: 5.900086666733024\n","--------------------------------------------------\n","city: ((hoax??))\n","date_time: 1/20/2010 19:30\n","latitude/longitude: 0.0 0.0\n","moon_altitude_deg: None\n","geomagnetic_latitude: None\n","nearest_airport_code: None\n","macro example gdp_gdp_growth_mean: 4.401440723476206\n","--------------------------------------------------\n","city: ((unspecified location))\n","date_time: 11/8/2010 20:00\n","latitude/longitude: 0.0 0.0\n","moon_altitude_deg: None\n","geomagnetic_latitude: None\n","nearest_airport_code: None\n","macro example gdp_gdp_growth_mean: 4.401440723476206\n"]}]},{"cell_type":"code","source":["!pip install timezonefinder\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"242QxNM14TnB","executionInfo":{"status":"ok","timestamp":1763853831024,"user_tz":300,"elapsed":16785,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"b799fc10-10e5-412a-c8ac-d803d9e56c55"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting timezonefinder\n","  Downloading timezonefinder-8.1.0-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB)\n","Requirement already satisfied: numpy<3,>=1.23 in /usr/local/lib/python3.12/dist-packages (from timezonefinder) (2.0.2)\n","Collecting h3>4 (from timezonefinder)\n","  Downloading h3-4.3.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (18 kB)\n","Requirement already satisfied: cffi<3,>=1.15.1 in /usr/local/lib/python3.12/dist-packages (from timezonefinder) (2.0.0)\n","Requirement already satisfied: flatbuffers>=25.2.10 in /usr/local/lib/python3.12/dist-packages (from timezonefinder) (25.9.23)\n","Requirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi<3,>=1.15.1->timezonefinder) (2.23)\n","Downloading timezonefinder-8.1.0-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (28.2 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.2/28.2 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading h3-4.3.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (1.0 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: h3, timezonefinder\n","Successfully installed h3-4.3.1 timezonefinder-8.1.0\n"]}]},{"cell_type":"code","source":["import os\n","import zipfile\n","\n","input_path = \"/content/ufo_enriched_v2.jsonl\"\n","zip_path   = \"/content/ufo_enriched_v2.zip\"\n","\n","# Create ZIP\n","with zipfile.ZipFile(zip_path, \"w\", compression=zipfile.ZIP_DEFLATED) as zf:\n","    # arcname makes the file inside the zip just \"ufo_enriched_v2.jsonl\"\n","    zf.write(input_path, arcname=os.path.basename(input_path))\n","\n","print(\"Wrote:\", zip_path)\n","\n","# If you're in Colab and want to download to your machine:\n","try:\n","    from google.colab import files\n","    files.download(zip_path)\n","except ImportError:\n","    print(\"Not in Colab, just use the zip path directly.\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"id":"v7JhFbVE4ThI","executionInfo":{"status":"ok","timestamp":1763999362053,"user_tz":300,"elapsed":127734,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"41b741a6-5678-4072-dacd-0570d618930d"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Wrote: /content/ufo_enriched_v2.zip\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      div.remove();\n","    }\n","  "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_c5852d18-7721-47b3-9362-57befb19454a\", \"ufo_enriched_v2.zip\", 579301436)"]},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"KeliiJyW4TYM"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"mount_file_id":"1JCuCRPGmR1jhfvloZWMD-GZ9kXJiV1En","authorship_tag":"ABX9TyOj3AmBeAfBJ/EcyMZJ17rK"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}