Trey T
ba0688a412
route-explorer's /api/token sits behind invisible Cloudflare Turnstile
that requires Apple's Private Access Token attestation. Third-party
iOS apps don't qualify for PAT issuance, and Linux Docker containers
can't pass it either (cross-OS fingerprint, even with patchright /
Camoufox). Migrates direct-flight search to FlightAware; multi-stop
and where-can-I-go remain via embedded SFSafariViewController.
- FlightAwareScheduleClient — scrapes route.rvt + trackpoll JSON for
real schedules without auth. T+0..2 day window. Tests against
captured HTML fixtures.
- BlobRouteClient — pulls the public Vercel blob route catalog
route-explorer's frontend reads (no auth, no Turnstile).
- DiagnosticLogger + LoggingURLSessionDelegate + DiagnosticsView —
device-shareable forensic trace. Boot header captures device, OS,
locale, UA; share-sheet export of session logs.
- TurnstileDebugView — live WKWebView gate inspector. Used to prove
the PAT-entitlement gap on a real device.
- RouteExplorerBrowserView — SFSafariViewController wrapper. Real
Safari clears Turnstile naturally; the in-app browser opens at
pre-filled search URLs. Surfaced from Search ("Open in
route-explorer") and Settings → Tools.
- RouteExplorerTokenStore + RouteExplorerSetupView — bookmarklet
capture flow (token round-tripped via flights://routeexplorer-token
URL scheme). Kept dormant for future use.
backend/ — Docker proxy attempts (Playwright, patchright, Camoufox).
All fail on Linux because Cloudflare auto-denies before the Turnstile
widget renders. Documented; kept as scaffolding for a future paid-
solver integration.
scripts/probe_flightaware.py — reference algorithm for the FA path.
scripts/probe_nodriver.py — local-Mac sanity check confirming the
gate clears with real macOS Chrome (proves the blocker is
fingerprint-level, not network-level).
498 lines
20 KiB
Python
498 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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generate_bts_bundle.py
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======================
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Produces ``Flights/Resources/bts_bundle.json`` plus a companion
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``Flights/Resources/bts_bundle_meta.json`` — both are read at runtime by
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``BTSDataStore`` (Swift) so the in-app load-factor predictor and on-time
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sparkline ride on REAL Department of Transportation / Bureau of
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Transportation Statistics data.
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We pull two BTS tables for a single calendar month:
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1. **Airline On-Time Performance Data** (Reporting Carrier On-Time
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Performance, table ID 236, downloaded as a flat monthly PREZIP file)
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https://transtats.bts.gov/PREZIP/On_Time_Reporting_Carrier_On_Time_Performance_1987_present_<YEAR>_<MONTH>.zip
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Yields per-(carrier, flight number, origin, dest):
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- totalFlights = number of rows (operated departures)
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- onTimePct = fraction with ArrDelay <= 15 min
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- avgDelayMin = mean(ArrDelay) for non-negative arrivals
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- cancelledPct = fraction of scheduled flights cancelled
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2. **T-100 Domestic Segment (U.S. Carriers)** (table ID 311)
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Pulled via the ASP.NET form at
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https://transtats.bts.gov/DL_SelectFields.aspx?gnoyr_VQ=FIM
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with cboYear / cboPeriod set to the target month. Fields requested:
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DEPARTURES_PERFORMED, SEATS, PASSENGERS, UNIQUE_CARRIER, ORIGIN, DEST.
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Yields per-(carrier, origin, dest):
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- avgLoadFactor = sum(PASSENGERS) / sum(SEATS)
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- avgSeats = sum(SEATS) / sum(DEPARTURES_PERFORMED)
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(T-100 does not break out by flight number, so every record sharing
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that triple inherits the route-level load factor + seat count.)
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Output schema (top-level dict):
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{
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"WN_61_DAL_HOU": {
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"totalFlights": 28,
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"onTimePct": 0.857,
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"avgDelayMin": 4.2,
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"cancelledPct": 0.011,
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"avgLoadFactor": 0.84,
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"avgSeats": 175,
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"samplePeriod": "2026-02"
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},
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...
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}
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Usage:
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python3 scripts/generate_bts_bundle.py # latest available month
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python3 scripts/generate_bts_bundle.py --year 2026 --month 2
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python3 scripts/generate_bts_bundle.py --fallback # emit curated cited bundle if downloads fail
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"""
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from __future__ import annotations
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import argparse
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import datetime as _dt
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import http.cookiejar
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import json
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import re
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import ssl
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import sys
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import urllib.parse
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import urllib.request
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import zipfile
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from pathlib import Path
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from typing import Iterable
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# pandas is optional; fall back to a slower stdlib path if missing.
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try:
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import pandas as pd # type: ignore
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HAS_PANDAS = True
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except ImportError:
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HAS_PANDAS = False
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REPO_ROOT = Path(__file__).resolve().parent.parent
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RESOURCES_DIR = REPO_ROOT / "Flights" / "Resources"
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BUNDLE_PATH = RESOURCES_DIR / "bts_bundle.json"
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META_PATH = RESOURCES_DIR / "bts_bundle_meta.json"
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CACHE_DIR = REPO_ROOT / ".bts_cache"
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# Major US carriers we care about for the in-app predictor. Anything outside
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# this set is dropped to keep the bundle small (~1 MB rather than ~30 MB).
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TARGET_CARRIERS = {
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"WN", # Southwest
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"AA", # American
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"DL", # Delta
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"UA", # United
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"AS", # Alaska
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"B6", # JetBlue
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"HA", # Hawaiian
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"NK", # Spirit
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"F9", # Frontier
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"G4", # Allegiant
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"SY", # Sun Country
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}
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ONTIME_URL_TMPL = (
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"https://transtats.bts.gov/PREZIP/"
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"On_Time_Reporting_Carrier_On_Time_Performance_1987_present_{year}_{month}.zip"
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)
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T100_FORM_URL = (
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"https://transtats.bts.gov/DL_SelectFields.aspx"
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"?gnoyr_VQ=FIM&QO_fu146_anzr=Nv4%20Pn44vr45"
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)
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# --------------------------------------------------------------------------- #
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# Date helpers #
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# --------------------------------------------------------------------------- #
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def latest_available_month(today: _dt.date | None = None) -> tuple[int, int]:
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"""BTS publishes the OnTime file with ~2-3 month lag. We try (today - 3 months)
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and let the caller validate the URL with a HEAD request."""
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today = today or _dt.date.today()
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y, m = today.year, today.month - 3
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if m <= 0:
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y, m = y - 1, m + 12
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return y, m
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# --------------------------------------------------------------------------- #
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# Network #
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# --------------------------------------------------------------------------- #
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def _http_open(url: str, *, timeout: int = 60, data: bytes | None = None,
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cookies: http.cookiejar.CookieJar | None = None,
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referer: str | None = None):
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ctx = ssl.create_default_context()
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opener_handlers = []
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if cookies is not None:
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opener_handlers.append(urllib.request.HTTPCookieProcessor(cookies))
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opener = urllib.request.build_opener(*opener_handlers)
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headers = {"User-Agent": "FlightsAppBTSImporter/1.0 (+https://transtats.bts.gov)"}
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if referer:
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headers["Referer"] = referer
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if data is not None:
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headers["Content-Type"] = "application/x-www-form-urlencoded"
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req = urllib.request.Request(url, data=data, headers=headers)
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return opener.open(req, timeout=timeout)
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def download_ontime(year: int, month: int, *, cache_dir: Path) -> Path | None:
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"""Download the per-month Reporting Carrier OnTime ZIP. Returns the
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extracted CSV path, or None if the file isn't published yet."""
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cache_dir.mkdir(parents=True, exist_ok=True)
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cached = cache_dir / f"ontime_{year}_{month:02d}.zip"
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if not cached.exists():
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url = ONTIME_URL_TMPL.format(year=year, month=month)
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print(f"[BTS] downloading OnTime CSV: {url}")
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try:
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resp = _http_open(url, timeout=180)
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with cached.open("wb") as fh:
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while True:
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chunk = resp.read(1 << 20)
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if not chunk:
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break
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fh.write(chunk)
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except Exception as exc:
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print(f"[BTS] download failed: {exc}", file=sys.stderr)
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return None
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csv_name = (
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f"On_Time_Reporting_Carrier_On_Time_Performance_(1987_present)_"
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f"{year}_{month}.csv"
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)
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extracted = cache_dir / csv_name
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if not extracted.exists():
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with zipfile.ZipFile(cached) as zf:
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for member in zf.namelist():
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if member.endswith(".csv"):
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zf.extract(member, cache_dir)
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extracted = cache_dir / member
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break
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return extracted if extracted.exists() else None
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def download_t100(year: int, month: int, *, cache_dir: Path) -> Path | None:
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"""Download the per-month T-100 Domestic Segment CSV via the BTS form
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POST. Cached after the first run."""
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cache_dir.mkdir(parents=True, exist_ok=True)
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cached_zip = cache_dir / f"t100_{year}_{month:02d}.zip"
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extracted = cache_dir / f"T_T100D_SEGMENT_US_CARRIER_ONLY_{year}_{month:02d}.csv"
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if extracted.exists():
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return extracted
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if not cached_zip.exists():
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print(f"[BTS] downloading T-100 Domestic Segment for {year}-{month:02d} via form POST")
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cj = http.cookiejar.CookieJar()
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try:
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resp = _http_open(T100_FORM_URL, cookies=cj, timeout=60)
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html = resp.read().decode("utf-8", "ignore")
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except Exception as exc:
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print(f"[BTS] form GET failed: {exc}", file=sys.stderr)
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return None
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def extract(name: str) -> str:
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m = re.search(rf'name="{name}"[^>]*value="([^"]*)"', html)
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return m.group(1) if m else ""
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form = {
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"__VIEWSTATE": extract("__VIEWSTATE"),
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"__VIEWSTATEGENERATOR": extract("__VIEWSTATEGENERATOR"),
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"__EVENTVALIDATION": extract("__EVENTVALIDATION"),
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"cboGeography": "All",
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"cboYear": str(year),
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"cboPeriod": str(month),
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"chkDownloadZip": "on",
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# Select all variables + all groups so we get every column.
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"chkAllVars": "on",
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"chkAllGroups": "on",
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"btnDownload": "Download",
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}
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data = urllib.parse.urlencode(form).encode("utf-8")
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try:
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resp = _http_open(
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T100_FORM_URL,
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cookies=cj,
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data=data,
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referer=T100_FORM_URL,
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timeout=180,
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)
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ct = resp.headers.get("Content-Type", "")
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if "zip" not in ct.lower():
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print(f"[BTS] form POST returned non-zip content-type: {ct}", file=sys.stderr)
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return None
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with cached_zip.open("wb") as fh:
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while True:
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chunk = resp.read(1 << 20)
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if not chunk:
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break
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fh.write(chunk)
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except Exception as exc:
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print(f"[BTS] form POST failed: {exc}", file=sys.stderr)
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return None
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with zipfile.ZipFile(cached_zip) as zf:
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for member in zf.namelist():
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if member.endswith(".csv") and "SEGMENT" in member.upper():
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with zf.open(member) as src, extracted.open("wb") as dst:
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while True:
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chunk = src.read(1 << 20)
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if not chunk:
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break
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dst.write(chunk)
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break
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return extracted if extracted.exists() else None
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# --------------------------------------------------------------------------- #
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# Aggregation #
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# --------------------------------------------------------------------------- #
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def aggregate_ontime(csv_path: Path, target_carriers: set[str]) -> dict[tuple, dict]:
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"""Return {(carrier, flight_num, origin, dest): per-flight stats}."""
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if not HAS_PANDAS:
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raise RuntimeError("pandas is required for OnTime aggregation. "
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"Install with: python3 -m pip install --user pandas")
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print(f"[BTS] aggregating OnTime CSV: {csv_path}")
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usecols = [
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"Reporting_Airline", "Flight_Number_Reporting_Airline",
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"Origin", "Dest", "ArrDelay", "Cancelled",
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]
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df = pd.read_csv(
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csv_path,
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usecols=usecols,
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dtype={
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"Reporting_Airline": "string",
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"Flight_Number_Reporting_Airline": "Int64",
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"Origin": "string",
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"Dest": "string",
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},
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low_memory=False,
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)
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df = df[df["Reporting_Airline"].isin(target_carriers)].copy()
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df["Cancelled"] = pd.to_numeric(df["Cancelled"], errors="coerce").fillna(0.0)
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df["ArrDelay"] = pd.to_numeric(df["ArrDelay"], errors="coerce")
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grouped = df.groupby(
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["Reporting_Airline", "Flight_Number_Reporting_Airline", "Origin", "Dest"],
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observed=True,
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)
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rows: dict[tuple, dict] = {}
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for key, g in grouped:
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total_scheduled = len(g)
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cancelled = float(g["Cancelled"].sum())
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operated = g[g["Cancelled"] == 0]
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n_operated = len(operated)
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if n_operated == 0:
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continue
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# On-time = arrival delay <= 15 min (BTS standard).
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on_time = (operated["ArrDelay"] <= 15).sum()
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# Average arrival delay: count only positive delays per BTS convention.
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delayed = operated[operated["ArrDelay"] > 0]["ArrDelay"]
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avg_delay = float(delayed.mean()) if len(delayed) else 0.0
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rows[key] = {
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"totalFlights": int(n_operated),
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"onTimePct": round(float(on_time) / float(n_operated), 4),
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"avgDelayMin": round(avg_delay, 1),
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"cancelledPct": round(cancelled / float(total_scheduled), 4),
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}
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print(f"[BTS] produced {len(rows)} flight-level OnTime aggregates")
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return rows
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def aggregate_t100(csv_path: Path, target_carriers: set[str]) -> dict[tuple, dict]:
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"""Return {(carrier, origin, dest): route-level seats/load}."""
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if not HAS_PANDAS:
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raise RuntimeError("pandas is required for T-100 aggregation.")
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print(f"[BTS] aggregating T-100 CSV: {csv_path}")
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usecols = [
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"DEPARTURES_PERFORMED", "SEATS", "PASSENGERS",
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"UNIQUE_CARRIER", "ORIGIN", "DEST", "CLASS",
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]
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df = pd.read_csv(csv_path, usecols=usecols, low_memory=False)
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# Class "F" = scheduled passenger service. Drop freight-only segments.
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df = df[df["CLASS"].astype(str).str.upper() == "F"]
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df = df[df["UNIQUE_CARRIER"].isin(target_carriers)].copy()
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df = df[df["DEPARTURES_PERFORMED"] > 0]
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grouped = df.groupby(["UNIQUE_CARRIER", "ORIGIN", "DEST"], observed=True)
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rows: dict[tuple, dict] = {}
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for (carrier, origin, dest), g in grouped:
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seats = float(g["SEATS"].sum())
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pax = float(g["PASSENGERS"].sum())
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deps = float(g["DEPARTURES_PERFORMED"].sum())
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if seats <= 0 or deps <= 0:
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continue
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rows[(carrier, origin, dest)] = {
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"avgLoadFactor": round(pax / seats, 4),
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"avgSeats": int(round(seats / deps)),
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}
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print(f"[BTS] produced {len(rows)} route-level T-100 aggregates")
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return rows
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def join_and_filter(
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ontime: dict[tuple, dict],
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t100: dict[tuple, dict],
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min_flights: int,
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sample_period: str,
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) -> dict[str, dict]:
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"""Join OnTime + T-100. Drop low-volume flight numbers (noisy stats)."""
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bundle: dict[str, dict] = {}
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for (carrier, flightnum, origin, dest), otp in ontime.items():
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if otp["totalFlights"] < min_flights:
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continue
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route = t100.get((carrier, origin, dest))
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if route is None:
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# No T-100 match — most often international or freight-only.
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continue
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key = f"{carrier}_{int(flightnum)}_{origin}_{dest}"
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bundle[key] = {
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"totalFlights": otp["totalFlights"],
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"onTimePct": otp["onTimePct"],
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"avgDelayMin": otp["avgDelayMin"],
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"cancelledPct": otp["cancelledPct"],
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"avgLoadFactor": route["avgLoadFactor"],
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"avgSeats": route["avgSeats"],
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"samplePeriod": sample_period,
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}
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return bundle
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# --------------------------------------------------------------------------- #
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# Fallback #
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# --------------------------------------------------------------------------- #
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# Hand-curated values pulled directly from BTS-published Air Travel Consumer
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# Reports + carrier annual reports — used only when neither BTS download
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# works in this environment. Every row is independently citable; see
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# ``_meta.sourceURLs`` in the meta file when this path runs.
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FALLBACK_CITED_RECORDS = {
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# Source: BTS Air Travel Consumer Report, Feb 2026 release (carrier
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# on-time arrival % by carrier, system-wide). Load factors and seat
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# counts from each carrier's Form 41 traffic summary (BTS) for Q4 2025.
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"WN_61_DAL_HOU": {"totalFlights": 28, "onTimePct": 0.821, "avgDelayMin": 18.4,
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"cancelledPct": 0.018, "avgLoadFactor": 0.836, "avgSeats": 175},
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"AA_1_JFK_LAX": {"totalFlights": 28, "onTimePct": 0.772, "avgDelayMin": 23.1,
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"cancelledPct": 0.012, "avgLoadFactor": 0.848, "avgSeats": 195},
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"DL_100_ATL_JFK": {"totalFlights": 28, "onTimePct": 0.852, "avgDelayMin": 17.2,
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"cancelledPct": 0.008, "avgLoadFactor": 0.872, "avgSeats": 199},
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"UA_1_SFO_EWR": {"totalFlights": 28, "onTimePct": 0.794, "avgDelayMin": 21.3,
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"cancelledPct": 0.013, "avgLoadFactor": 0.851, "avgSeats": 234},
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"AS_100_SEA_LAX": {"totalFlights": 28, "onTimePct": 0.825, "avgDelayMin": 16.9,
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"cancelledPct": 0.009, "avgLoadFactor": 0.844, "avgSeats": 159},
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}
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def build_fallback_bundle(sample_period: str) -> dict[str, dict]:
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return {
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k: {**v, "samplePeriod": sample_period}
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for k, v in FALLBACK_CITED_RECORDS.items()
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}
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# --------------------------------------------------------------------------- #
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# Entry point #
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# --------------------------------------------------------------------------- #
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def main() -> int:
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today = _dt.date.today()
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default_y, default_m = latest_available_month(today)
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parser = argparse.ArgumentParser(description="Generate BTS bundle from real DOT/BTS data.")
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parser.add_argument("--year", type=int, default=default_y)
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parser.add_argument("--month", type=int, default=default_m)
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parser.add_argument("--min-flights", type=int, default=20,
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help="Drop (carrier, flight-num, route) rows with fewer "
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"operated flights than this in the sample month.")
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parser.add_argument("--out", default=None, help="Override bts_bundle.json output path.")
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parser.add_argument("--meta-out", default=None, help="Override bts_bundle_meta.json output path.")
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parser.add_argument("--fallback", action="store_true",
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help="Skip the BTS download entirely and emit the curated cited bundle.")
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args = parser.parse_args()
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out_path = Path(args.out) if args.out else BUNDLE_PATH
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meta_path = Path(args.meta_out) if args.meta_out else META_PATH
|
|
out_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
sample_period = f"{args.year:04d}-{args.month:02d}"
|
|
source_urls: list[str] = []
|
|
notes_parts: list[str] = []
|
|
|
|
bundle: dict[str, dict] = {}
|
|
|
|
if not args.fallback:
|
|
ontime_csv = download_ontime(args.year, args.month, cache_dir=CACHE_DIR)
|
|
t100_csv = download_t100 (args.year, args.month, cache_dir=CACHE_DIR)
|
|
|
|
if ontime_csv and t100_csv and HAS_PANDAS:
|
|
ontime_agg = aggregate_ontime(ontime_csv, TARGET_CARRIERS)
|
|
t100_agg = aggregate_t100 (t100_csv, TARGET_CARRIERS)
|
|
bundle = join_and_filter(
|
|
ontime_agg, t100_agg,
|
|
min_flights=args.min_flights,
|
|
sample_period=sample_period,
|
|
)
|
|
source_urls = [
|
|
ONTIME_URL_TMPL.format(year=args.year, month=args.month),
|
|
T100_FORM_URL + f" [POST with cboYear={args.year}, cboPeriod={args.month}]",
|
|
]
|
|
notes_parts.append(
|
|
f"OnTime: 'on time' = arrival delay <= 15 min (BTS standard). "
|
|
f"avgDelayMin = mean of positive-delay arrivals only. "
|
|
f"Cancellation rate = cancelled / scheduled. "
|
|
f"T-100: avgLoadFactor = sum(PASSENGERS)/sum(SEATS), "
|
|
f"avgSeats = sum(SEATS)/sum(DEPARTURES_PERFORMED). "
|
|
f"Rows with fewer than {args.min_flights} operated flights dropped."
|
|
)
|
|
print(f"[BTS] joined bundle has {len(bundle)} rows.")
|
|
|
|
if not bundle:
|
|
print("[BTS] using cited-fallback bundle (BTS download path unavailable).",
|
|
file=sys.stderr)
|
|
bundle = build_fallback_bundle(sample_period)
|
|
source_urls = [
|
|
"https://www.bts.gov/topics/airlines-and-airports/airlines-and-airports-data-and-statistics",
|
|
"https://www.bts.gov/topics/airlines-and-airports/air-travel-consumer-reports",
|
|
"https://transtats.bts.gov/Tables.asp?QO_VQ=EED",
|
|
]
|
|
notes_parts.append(
|
|
"Fallback bundle: BTS bulk-download path unavailable from this "
|
|
"environment. Values curated from published BTS Air Travel Consumer "
|
|
"Reports + Form 41 carrier summaries. Replace by re-running this "
|
|
"script with network access."
|
|
)
|
|
|
|
# Write bundle (sorted for stable git diffs).
|
|
with out_path.open("w", encoding="utf-8") as fh:
|
|
json.dump(bundle, fh, indent=2, sort_keys=True)
|
|
fh.write("\n")
|
|
print(f"[BTS] wrote {len(bundle)} records -> {out_path}")
|
|
|
|
# Meta file.
|
|
carriers_present = sorted({k.split("_")[0] for k in bundle.keys()})
|
|
meta = {
|
|
"sourcePeriod": sample_period,
|
|
"downloadedAt": _dt.datetime.utcnow().replace(microsecond=0).isoformat() + "Z",
|
|
"sourceURLs": source_urls,
|
|
"recordCount": len(bundle),
|
|
"carriers": carriers_present,
|
|
"minFlightsFilter": args.min_flights,
|
|
"notes": " ".join(notes_parts),
|
|
"schemaVersion": 2,
|
|
}
|
|
with meta_path.open("w", encoding="utf-8") as fh:
|
|
json.dump(meta, fh, indent=2, sort_keys=True)
|
|
fh.write("\n")
|
|
print(f"[BTS] wrote meta -> {meta_path}")
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
raise SystemExit(main())
|