| """ |
| Synthetic fallback data โ returned when live API calls fail or return empty. |
| |
| Each function returns a DataFrame whose columns match exactly what |
| _normalize_events / load_macro expect from the raw API responses, |
| so callers need no special-casing. |
| """ |
| from __future__ import annotations |
|
|
| import pandas as pd |
|
|
| def _ago(days: int) -> str: |
| """ISO date string for N days before now (UTC), used to keep fallback |
| events always inside the 720-hour lookback window.""" |
| ts = pd.Timestamp.now(tz="UTC") - pd.Timedelta(days=days) |
| return ts.strftime("%Y-%m-%d") |
|
|
|
|
| def _ago_iso(days: int) -> str: |
| """Full ISO-8601 timestamp N days before now, for NOAA 'sent' field.""" |
| ts = pd.Timestamp.now(tz="UTC") - pd.Timedelta(days=days) |
| return ts.strftime("%Y-%m-%dT%H:%M:%SZ") |
|
|
|
|
| def _ago_dt(days: int) -> str: |
| """Datetime string N days before now, for OpenSky 'time' field.""" |
| ts = pd.Timestamp.now(tz="UTC") - pd.Timedelta(days=days) |
| return ts.strftime("%Y-%m-%d %H:%M:%S") |
|
|
|
|
| def gdelt_fallback() -> pd.DataFrame: |
| """Pre-normalised GDELT-style conflict fallback rows.""" |
| rows = [ |
| (_ago( 1), 15.55, 32.53, "Africa", "Fight", 5, "Armed group | Khartoum, Sudan", "CAMEO:190 mentions=12"), |
| (_ago( 2), 31.52, 34.47, "Middle East", "Assault", 4, "Military | Gaza Strip", "CAMEO:180 mentions=25"), |
| (_ago( 3), 48.38, 35.29, "Europe", "Fight", 3, "Forces | Donetsk, Ukraine", "CAMEO:190 mentions=8"), |
| (_ago( 4), 21.09, 96.88, "Asia", "Coerce", 3, "Military | Mandalay, Myanmar", "CAMEO:170 mentions=5"), |
| (_ago( 5), -4.32, 15.32, "Africa", "Mass Violence", 4, "Armed group | Eastern DRC", "CAMEO:200 mentions=15"), |
| (_ago( 6), 33.34, 44.40, "Middle East", "Protest", 2, "Civilians | Baghdad, Iraq", "CAMEO:140 mentions=6"), |
| (_ago( 7), 14.10, -0.36, "Africa", "Assault", 4, "Jihadist | Burkina Faso", "CAMEO:180 mentions=10"), |
| (_ago( 8), 13.45, 2.10, "Africa", "Fight", 3, "Military | Niger", "CAMEO:190 mentions=7"), |
| (_ago( 9), 47.00, 37.50, "Europe", "Fight", 3, "Forces | Eastern Ukraine", "CAMEO:190 mentions=9"), |
| (_ago(10), 16.90, 33.10, "Africa", "Mass Violence", 5, "Armed group | Sudan", "CAMEO:200 mentions=20"), |
| ] |
| return pd.DataFrame([ |
| { |
| "source": "GDELT", |
| "timestamp": pd.Timestamp(f"{date} 00:00:00", tz="UTC"), |
| "lat": lat, "lon": lon, |
| "region": region, |
| "category": category, |
| "severity": sev, |
| "title": title, |
| "details": details, |
| "evidence_url": "https://www.gdeltproject.org", |
| } |
| for date, lat, lon, region, category, sev, title, details in rows |
| ]) |
|
|
|
|
| def firms_fallback() -> pd.DataFrame: |
| return pd.DataFrame([ |
| {"acq_date": _ago( 1), "acq_time": "1430", "latitude": -9.20, "longitude": -62.80, "daynight": "D", "frp": 18.5, "confidence": 85}, |
| {"acq_date": _ago( 2), "acq_time": "0600", "latitude": -7.10, "longitude": -63.40, "daynight": "N", "frp": 22.1, "confidence": 90}, |
| {"acq_date": _ago( 4), "acq_time": "1200", "latitude": -32.50, "longitude": 148.20, "daynight": "D", "frp": 14.0, "confidence": 78}, |
| {"acq_date": _ago( 6), "acq_time": "0900", "latitude": 60.80, "longitude": 107.50, "daynight": "D", "frp": 11.3, "confidence": 72}, |
| {"acq_date": _ago( 8), "acq_time": "1800", "latitude": 38.40, "longitude":-121.50, "daynight": "N", "frp": 16.8, "confidence": 88}, |
| {"acq_date": _ago(10), "acq_time": "1100", "latitude": -1.60, "longitude": 112.50, "daynight": "D", "frp": 19.0, "confidence": 83}, |
| {"acq_date": _ago(12), "acq_time": "1500", "latitude": -14.80, "longitude": 30.20, "daynight": "D", "frp": 8.4, "confidence": 65}, |
| {"acq_date": _ago(14), "acq_time": "0730", "latitude": -6.30, "longitude": -72.10, "daynight": "D", "frp": 25.0, "confidence": 92}, |
| ]) |
|
|
|
|
| def opensky_fallback() -> pd.DataFrame: |
| return pd.DataFrame([ |
| {"latitude": 51.47, "longitude": -0.46, "time": _ago_dt( 1), "on_ground": False, "velocity": 245.0, "callsign": "BAW123 ", "origin_country": "United Kingdom"}, |
| {"latitude": 40.63, "longitude": -73.78, "time": _ago_dt( 2), "on_ground": False, "velocity": 230.0, "callsign": "AAL456 ", "origin_country": "United States"}, |
| {"latitude": 48.35, "longitude": 11.79, "time": _ago_dt( 3), "on_ground": False, "velocity": 210.0, "callsign": "DLH789 ", "origin_country": "Germany"}, |
| {"latitude": 35.55, "longitude": 139.78, "time": _ago_dt( 5), "on_ground": False, "velocity": 260.0, "callsign": "JAL001 ", "origin_country": "Japan"}, |
| {"latitude": 25.25, "longitude": 55.36, "time": _ago_dt( 7), "on_ground": False, "velocity": 235.0, "callsign": "UAE321 ", "origin_country": "United Arab Emirates"}, |
| {"latitude": -33.94, "longitude": 151.18, "time": _ago_dt( 9), "on_ground": False, "velocity": 220.0, "callsign": "QFA055 ", "origin_country": "Australia"}, |
| {"latitude": 19.43, "longitude": -99.07, "time": _ago_dt(11), "on_ground": True, "velocity": 0.0, "callsign": "AMX800 ", "origin_country": "Mexico"}, |
| {"latitude": 55.97, "longitude": 37.41, "time": _ago_dt(13), "on_ground": False, "velocity": 195.0, "callsign": "AFL249 ", "origin_country": "Russia"}, |
| ]) |
|
|
|
|
| def noaa_fallback() -> pd.DataFrame: |
| return pd.DataFrame([ |
| {"latitude": 35.50, "longitude": -97.50, "sent": _ago_iso( 1), "severity": "Severe", "event": "Tornado Warning", "headline": "Tornado Warning in effect for central Oklahoma", "areaDesc": "Oklahoma County, OK"}, |
| {"latitude": 25.80, "longitude": -80.20, "sent": _ago_iso( 3), "severity": "Extreme", "event": "Hurricane Warning", "headline": "Hurricane Warning for South Florida coast", "areaDesc": "Miami-Dade County, FL"}, |
| {"latitude": 44.00, "longitude": -88.00, "sent": _ago_iso( 5), "severity": "Moderate", "event": "Winter Storm Warning", "headline": "Heavy snow expected across Wisconsin", "areaDesc": "Winnebago County, WI"}, |
| {"latitude": 39.75, "longitude":-104.87, "sent": _ago_iso( 8), "severity": "Severe", "event": "Blizzard Warning", "headline": "Blizzard conditions expected in Denver metro", "areaDesc": "Denver County, CO"}, |
| {"latitude": 30.30, "longitude": -89.00, "sent": _ago_iso(10), "severity": "Moderate", "event": "Flash Flood Watch", "headline": "Flash Flood Watch for coastal Mississippi", "areaDesc": "Harrison County, MS"}, |
| ]) |
|
|
|
|
| def maritime_fallback() -> pd.DataFrame: |
| """AIS vessel positions in strategic chokepoints.""" |
| rows = [ |
| (25.40, 56.60, "Middle East", "Tanker", 2, "DALEEL", "MMSI=311000123 speed=12.3kn", "Strait of Hormuz"), |
| (24.55, 57.10, "Middle East", "Cargo", 1, "GULF STAR", "MMSI=372001456 speed=8.1kn", "Strait of Hormuz"), |
| (15.20, 41.50, "Middle East", "Tanker", 3, "RED QUEEN", "MMSI=636000789 speed=14.0kn", "Bab-el-Mandeb"), |
| (20.10, 38.80, "Middle East", "Warship", 4, "HOUTHI Z1", "MMSI=476000001 speed=22.0kn", "Red Sea"), |
| (26.70, 49.90, "Middle East", "LNG", 2, "AL KHAFJI", "MMSI=403000222 speed=9.5kn", "Persian Gulf"), |
| (23.90, 57.30, "Middle East", "Cargo", 1, "MALIHA", "MMSI=620001003 speed=6.2kn", "Gulf of Oman"), |
| (13.40, 42.70, "Middle East", "Tanker", 3, "NIAMA", "MMSI=677000567 speed=11.0kn", "Red Sea South"), |
| (24.20, 56.40, "Middle East", "Cargo", 2, "ATLAS MAX", "MMSI=248000899 speed=7.8kn", "Strait of Hormuz"), |
| ] |
| return pd.DataFrame([ |
| { |
| "source": "Maritime", "timestamp": pd.Timestamp.now(tz="UTC") - pd.Timedelta(minutes=i * 7), |
| "lat": lat, "lon": lon, "region": region, |
| "category": cat, "severity": sev, |
| "title": f"Vessel {name}", "details": details, |
| "evidence_url": "https://aisstream.io", |
| } |
| for i, (lat, lon, region, cat, sev, name, details, _) in enumerate(rows) |
| ]) |
|
|
|
|
| def rocket_fallback() -> pd.DataFrame: |
| """Israel Home Front Command rocket/missile alert fallback.""" |
| |
| alerts = [ |
| (_ago(0), 31.52, 34.60, "Asia", "Rocket", 5, "Alert: Sderot", "oref area=ืฉืืจืืช"), |
| (_ago(0), 31.67, 34.57, "Asia", "Rocket", 5, "Alert: Ashkelon", "oref area=ืืฉืงืืื"), |
| (_ago(1), 32.08, 34.78, "Asia", "Rocket", 5, "Alert: Tel Aviv", "oref area=ืชื ืืืื"), |
| (_ago(1), 31.80, 34.65, "Asia", "Rocket", 4, "Alert: Ashdod", "oref area=ืืฉืืื"), |
| (_ago(2), 33.21, 35.57, "Asia", "Rocket", 4, "Alert: Kiryat Shmona", "oref area=ืงืจืืืช ืฉืืื ื"), |
| (_ago(3), 32.92, 35.08, "Asia", "Rocket", 3, "Alert: Acre", "oref area=ืขืื"), |
| ] |
| return pd.DataFrame([ |
| { |
| "source": "Rocket", "timestamp": pd.Timestamp(f"{date} 06:00:00", tz="UTC"), |
| "lat": lat, "lon": lon, "region": region, |
| "category": cat, "severity": sev, "title": title, "details": details, |
| "evidence_url": "https://www.oref.org.il", |
| } |
| for date, lat, lon, region, cat, sev, title, details in alerts |
| ]) |
|
|
|
|
| def seismic_fallback() -> pd.DataFrame: |
| """USGS earthquake fallback (M โฅ 2.5).""" |
| quakes = [ |
| (_ago(0), 37.42, 141.03, "Asia", 5.8, 10.0, "M5.8 Near Fukushima, Japan"), |
| (_ago(1), 35.68, 36.10, "Middle East", 4.2, 15.0, "M4.2 Northern Syria"), |
| (_ago(1), 38.11, 46.35, "Middle East", 3.9, 8.0, "M3.9 Northwest Iran"), |
| (_ago(2), 36.52, 71.50, "Asia", 5.1, 220.0, "M5.1 Afghanistan-Tajikistan border"), |
| (_ago(3), 32.00, 35.30, "Middle East", 3.1, 12.0, "M3.1 Dead Sea region"), |
| (_ago(4), 47.51, 34.58, "Europe", 3.5, 5.0, "M3.5 Near Zaporizhzhia"), |
| (_ago(5), 33.72, 51.73, "Middle East", 4.0, 18.0, "M4.0 Natanz, Iran"), |
| (_ago(7), 28.83, 50.89, "Middle East", 3.3, 25.0, "M3.3 Bushehr coastal, Iran"), |
| ] |
| return pd.DataFrame([ |
| { |
| "source": "Seismic", |
| "timestamp": pd.Timestamp(f"{date} 12:00:00", tz="UTC"), |
| "lat": lat, "lon": lon, |
| "region": region, |
| "category": f"M{mag:.1f} Earthquake", |
| "severity": (5 if mag >= 7 else 4 if mag >= 6 else 3 if mag >= 5 else 2 if mag >= 4 else 1), |
| "title": title, |
| "details": f"mag={mag:.1f} depth={depth:.0f}km", |
| "evidence_url": "https://earthquake.usgs.gov", |
| } |
| for date, lat, lon, region, mag, depth, title in quakes |
| ]) |
|
|
|
|
| def cyber_fallback() -> pd.DataFrame: |
| """AlienVault OTX cyber threat fallback.""" |
| incidents = [ |
| (_ago(0), 32.0, 53.0, "Middle East", "ICS/SCADA Attack", 5, "Iran Energy Grid Probe", "tags=iran,ics,critical-infrastructure"), |
| (_ago(1), 31.5, 35.0, "Middle East", "Phishing Campaign", 4, "Israeli Gov Spear-phishing", "tags=israel,apt,phishing"), |
| (_ago(1), 49.0, 32.0, "Europe", "Malware", 5, "Ukraine Power Grid Malware", "tags=ukraine,destructive,grid"), |
| (_ago(2), 38.0, -97.0, "North America","Ransomware", 4, "US Critical Infrastructure Hit", "tags=us,ransomware,energy"), |
| (_ago(3), 51.5, 10.0, "Europe", "DDoS", 3, "NATO Alliance DDoS Campaign", "tags=nato,ddos,russia-nexus"), |
| (_ago(4), 33.9, 35.5, "Middle East", "Wiperware", 5, "Lebanon Telecom Wiperware", "tags=lebanon,wiper,telecom"), |
| (_ago(5), 25.2, 55.3, "Middle East", "Supply Chain", 4, "UAE Finance Sector Supply Chain", "tags=uae,supply-chain,finance"), |
| ] |
| return pd.DataFrame([ |
| { |
| "source": "Cyber", |
| "timestamp": pd.Timestamp(f"{date} 00:00:00", tz="UTC"), |
| "lat": lat, "lon": lon, "region": region, |
| "category": cat, "severity": sev, "title": title, "details": details, |
| "evidence_url": "https://otx.alienvault.com", |
| } |
| for date, lat, lon, region, cat, sev, title, details in incidents |
| ]) |
|
|
|
|
| def radiation_fallback() -> pd.DataFrame: |
| """Safecast radiation readings at nuclear facility sites.""" |
| sites = [ |
| (28.83, 50.89, "Middle East", "Bushehr, Iran", 9.2, _ago(1)), |
| (30.97, 35.15, "Middle East", "Dimona, Israel", 11.4, _ago(1)), |
| (34.89, 50.47, "Middle East", "Fordow, Iran", 8.8, _ago(2)), |
| (47.51, 34.58, "Europe", "Zaporizhzhia, Ukraine", 33.4, _ago(1)), |
| (33.72, 51.73, "Middle East", "Natanz, Iran", 7.6, _ago(3)), |
| (37.42, 141.03,"Asia", "Fukushima, Japan", 8.4, _ago(1)), |
| ] |
| return pd.DataFrame([ |
| { |
| "source": "Radiation", |
| "timestamp": pd.Timestamp(f"{date} 00:00:00", tz="UTC"), |
| "lat": lat, "lon": lon, "region": region, |
| "category": "Radiation Reading", |
| "severity": (5 if cpm > 100 else 4 if cpm > 50 else 3 if cpm > 30 else 2 if cpm > 20 else 1), |
| "title": f"Radiation | {site}", |
| "details": f"cpm={cpm:.1f}", |
| "evidence_url": "https://api.safecast.org", |
| } |
| for lat, lon, region, site, cpm, date in sites |
| ]) |
|
|
|
|
| def macro_fallback() -> pd.DataFrame: |
| return pd.DataFrame([ |
| {"date": _ago(28), "metric": "Brent Crude (USD/bbl)", "value": 82.5, "source": "EIA"}, |
| {"date": _ago(28), "metric": "Gold (USD/oz)", "value":2035.0, "source": "LBMA"}, |
| {"date": _ago(28), "metric": "USD Index", "value": 104.2, "source": "Fed"}, |
| {"date": _ago(14), "metric": "Brent Crude (USD/bbl)", "value": 83.1, "source": "EIA"}, |
| {"date": _ago(14), "metric": "Gold (USD/oz)", "value":2045.0, "source": "LBMA"}, |
| {"date": _ago(14), "metric": "USD Index", "value": 104.8, "source": "Fed"}, |
| {"date": _ago( 1), "metric": "Brent Crude (USD/bbl)", "value": 81.9, "source": "EIA"}, |
| {"date": _ago( 1), "metric": "Gold (USD/oz)", "value":2060.0, "source": "LBMA"}, |
| {"date": _ago( 1), "metric": "USD Index", "value": 103.9, "source": "Fed"}, |
| ]) |
|
|
