ledger: extract committed-file inventory table

2026-11-02 07:17:41 +00:00
1 parent 01aac38c5a
commit e98c6bd260
@@ -0,0 +1,11 @@
+city,country,lat,lon,current_time,temperature_2m,apparent_temperature,relative_humidity_2m,weather_code,weather_desc,wind_speed_10m,today_max,today_min,today_precip,today_sunrise,today_sunset
+Berlin,Germany,52.52437,13.41053,2026-11-02T08:00,7.3,5.6,87,2,Partly cloudy,3.3,9.3,6.2,0.3,2026-11-02T07:04,2026-11-02T16:34
+Mumbai,India,19.07283,72.88261,2026-11-02T12:30,34.1,38.0,41,1,Mainly clear,1.4,34.3,27.4,0.0,2026-11-02T06:39,2026-11-02T18:04
+Dubai,United Arab Emirates,25.07725,55.30927,2026-11-02T11:00,33.2,35.5,34,0,Clear sky,4.8,35.2,24.0,0.0,2026-11-02T06:26,2026-11-02T17:37
+Toronto,Canada,43.70643,-79.39864,2026-11-02T02:00,7.1,4.1,77,0,Clear sky,9.9,16.9,6.3,0.0,2026-11-02T06:54,2026-11-02T17:06
+Buenos Aires,Argentina,-34.61315,-58.37723,2026-11-02T04:00,16.6,17.9,91,0,Clear sky,2.6,24.7,15.5,0.1,2026-11-02T05:50,2026-11-02T19:24
+Jakarta,Indonesia,-6.21462,106.84513,2026-11-02T14:00,31.7,36.8,60,51,Light drizzle,7.0,33.9,25.2,2.5,2026-11-02T05:26,2026-11-02T17:46
+Reykjavik,Iceland,64.13548,-21.89541,2026-11-02T07:00,9.3,5.3,86,3,Overcast,22.3,9.8,9.1,1.2,2026-11-02T09:13,2026-11-02T17:07
+Lagos,Nigeria,6.45407,3.39467,2026-11-02T08:00,28.2,33.7,79,1,Mainly clear,5.2,31.2,27.4,1.0,2026-11-02T06:33,2026-11-02T18:26
+Seoul,South Korea,37.566,126.9784,2026-11-02T16:00,13.3,10.4,49,0,Clear sky,9.1,13.6,6.9,0.0,2026-11-02T06:57,2026-11-02T17:33
+Auckland,New Zealand,-36.84853,174.76349,2026-11-02T20:00,17.0,14.9,68,3,Overcast,16.5,18.1,14.8,0.0,2026-11-02T06:15,2026-11-02T19:54
@@ -0,0 +1,212 @@
+{
+ "Berlin": {
+  "geocode": {
+   "lat": 52.52437,
+   "lon": 13.41053,
+   "country": "Germany"
+  },
+  "weather": {
+   "current_time": "2026-11-02T08:00",
+   "temperature_2m": 7.3,
+   "apparent_temperature": 5.6,
+   "relative_humidity_2m": 87,
+   "weather_code": 2,
+   "weather_desc": "Partly cloudy",
+   "wind_speed_10m": 3.3,
+   "today_max": 9.3,
+   "today_min": 6.2,
+   "today_precip": 0.3,
+   "today_sunrise": "2026-11-02T07:04",
+   "today_sunset": "2026-11-02T16:34"
+  }
+ },
+ "Mumbai": {
+  "geocode": {
+   "lat": 19.07283,
+   "lon": 72.88261,
+   "country": "India"
+  },
+  "weather": {
+   "current_time": "2026-11-02T12:30",
+   "temperature_2m": 34.1,
+   "apparent_temperature": 38.0,
+   "relative_humidity_2m": 41,
+   "weather_code": 1,
+   "weather_desc": "Mainly clear",
+   "wind_speed_10m": 1.4,
+   "today_max": 34.3,
+   "today_min": 27.4,
+   "today_precip": 0.0,
+   "today_sunrise": "2026-11-02T06:39",
+   "today_sunset": "2026-11-02T18:04"
+  }
+ },
+ "Dubai": {
+  "geocode": {
+   "lat": 25.07725,
+   "lon": 55.30927,
+   "country": "United Arab Emirates"
+  },
+  "weather": {
+   "current_time": "2026-11-02T11:00",
+   "temperature_2m": 33.2,
+   "apparent_temperature": 35.5,
+   "relative_humidity_2m": 34,
+   "weather_code": 0,
+   "weather_desc": "Clear sky",
+   "wind_speed_10m": 4.8,
+   "today_max": 35.2,
+   "today_min": 24.0,
+   "today_precip": 0.0,
+   "today_sunrise": "2026-11-02T06:26",
+   "today_sunset": "2026-11-02T17:37"
+  }
+ },
+ "Toronto": {
+  "geocode": {
+   "lat": 43.70643,
+   "lon": -79.39864,
+   "country": "Canada"
+  },
+  "weather": {
+   "current_time": "2026-11-02T02:00",
+   "temperature_2m": 7.1,
+   "apparent_temperature": 4.1,
+   "relative_humidity_2m": 77,
+   "weather_code": 0,
+   "weather_desc": "Clear sky",
+   "wind_speed_10m": 9.9,
+   "today_max": 16.9,
+   "today_min": 6.3,
+   "today_precip": 0.0,
+   "today_sunrise": "2026-11-02T06:54",
+   "today_sunset": "2026-11-02T17:06"
+  }
+ },
+ "Buenos Aires": {
+  "geocode": {
+   "lat": -34.61315,
+   "lon": -58.37723,
+   "country": "Argentina"
+  },
+  "weather": {
+   "current_time": "2026-11-02T04:00",
+   "temperature_2m": 16.6,
+   "apparent_temperature": 17.9,
+   "relative_humidity_2m": 91,
+   "weather_code": 0,
+   "weather_desc": "Clear sky",
+   "wind_speed_10m": 2.6,
+   "today_max": 24.7,
+   "today_min": 15.5,
+   "today_precip": 0.1,
+   "today_sunrise": "2026-11-02T05:50",
+   "today_sunset": "2026-11-02T19:24"
+  }
+ },
+ "Jakarta": {
+  "geocode": {
+   "lat": -6.21462,
+   "lon": 106.84513,
+   "country": "Indonesia"
+  },
+  "weather": {
+   "current_time": "2026-11-02T14:00",
+   "temperature_2m": 31.7,
+   "apparent_temperature": 36.8,
+   "relative_humidity_2m": 60,
+   "weather_code": 51,
+   "weather_desc": "Light drizzle",
+   "wind_speed_10m": 7.0,
+   "today_max": 33.9,
+   "today_min": 25.2,
+   "today_precip": 2.5,
+   "today_sunrise": "2026-11-02T05:26",
+   "today_sunset": "2026-11-02T17:46"
+  }
+ },
+ "Reykjavik": {
+  "geocode": {
+   "lat": 64.13548,
+   "lon": -21.89541,
+   "country": "Iceland"
+  },
+  "weather": {
+   "current_time": "2026-11-02T07:00",
+   "temperature_2m": 9.3,
+   "apparent_temperature": 5.3,
+   "relative_humidity_2m": 86,
+   "weather_code": 3,
+   "weather_desc": "Overcast",
+   "wind_speed_10m": 22.3,
+   "today_max": 9.8,
+   "today_min": 9.1,
+   "today_precip": 1.2,
+   "today_sunrise": "2026-11-02T09:13",
+   "today_sunset": "2026-11-02T17:07"
+  }
+ },
+ "Lagos": {
+  "geocode": {
+   "lat": 6.45407,
+   "lon": 3.39467,
+   "country": "Nigeria"
+  },
+  "weather": {
+   "current_time": "2026-11-02T08:00",
+   "temperature_2m": 28.2,
+   "apparent_temperature": 33.7,
+   "relative_humidity_2m": 79,
+   "weather_code": 1,
+   "weather_desc": "Mainly clear",
+   "wind_speed_10m": 5.2,
+   "today_max": 31.2,
+   "today_min": 27.4,
+   "today_precip": 1.0,
+   "today_sunrise": "2026-11-02T06:33",
+   "today_sunset": "2026-11-02T18:26"
+  }
+ },
+ "Seoul": {
+  "geocode": {
+   "lat": 37.566,
+   "lon": 126.9784,
+   "country": "South Korea"
+  },
+  "weather": {
+   "current_time": "2026-11-02T16:00",
+   "temperature_2m": 13.3,
+   "apparent_temperature": 10.4,
+   "relative_humidity_2m": 49,
+   "weather_code": 0,
+   "weather_desc": "Clear sky",
+   "wind_speed_10m": 9.1,
+   "today_max": 13.6,
+   "today_min": 6.9,
+   "today_precip": 0.0,
+   "today_sunrise": "2026-11-02T06:57",
+   "today_sunset": "2026-11-02T17:33"
+  }
+ },
+ "Auckland": {
+  "geocode": {
+   "lat": -36.84853,
+   "lon": 174.76349,
+   "country": "New Zealand"
+  },
+  "weather": {
+   "current_time": "2026-11-02T20:00",
+   "temperature_2m": 17.0,
+   "apparent_temperature": 14.9,
+   "relative_humidity_2m": 68,
+   "weather_code": 3,
+   "weather_desc": "Overcast",
+   "wind_speed_10m": 16.5,
+   "today_max": 18.1,
+   "today_min": 14.8,
+   "today_precip": 0.0,
+   "today_sunrise": "2026-11-02T06:15",
+   "today_sunset": "2026-11-02T19:54"
+  }
+ }
+}
@@ -0,0 +1,67 @@
+#!/usr/bin/env bash
+# CHECK — ledger: open-meteo cities extract (2026-11-02).
+#
+# Exits 0 iff the committed table (2026-11-02-openmeteo-cities.csv) is a
+# faithful re-derivation of the committed raw source snapshot
+# (2026-11-02-openmeteo-cities.json). The CSV's own generator is NOT imported;
+# the table is rebuilt cell-by-cell from the source JSON with plain python.
+# No `|| true` / `; exit 0` fallback: the last command's exit code is the verdict.
+set -u
+
+DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$DIR" || exit 2
+
+python3 - <<'PY'
+import csv
+import json
+import sys
+
+src = json.load(open("2026-11-02-openmeteo-cities.json"))
+with open("2026-11-02-openmeteo-cities.csv", newline="", encoding="utf-8") as f:
+    rows = list(csv.DictReader(f))
+
+
+def cell(value):
+    # Numbers round-tripped by the generator: keep as str() so csv and json
+    # representations must agree exactly.
+    return str(value)
+
+
+expected = []
+for city, info in src.items():
+    g = info["geocode"]
+    w = info["weather"]
+    expected.append({
+        "city": city,
+        "country": g["country"],
+        "lat": cell(g["lat"]),
+        "lon": cell(g["lon"]),
+        "current_time": w["current_time"],
+        "temperature_2m": cell(w["temperature_2m"]),
+        "apparent_temperature": cell(w["apparent_temperature"]),
+        "relative_humidity_2m": cell(w["relative_humidity_2m"]),
+        "weather_code": cell(w["weather_code"]),
+        "weather_desc": w["weather_desc"],
+        "wind_speed_10m": cell(w["wind_speed_10m"]),
+        "today_max": cell(w["today_max"]),
+        "today_min": cell(w["today_min"]),
+        "today_precip": cell(w["today_precip"]),
+        "today_sunrise": w["today_sunrise"],
+        "today_sunset": w["today_sunset"],
+    })
+
+
+if len(rows) != len(expected):
+    print("ROW COUNT MISMATCH csv=%d expected=%d" % (len(rows), len(expected)))
+    sys.exit(1)
+
+for r, e in zip(rows, expected):
+    for key in e:
+        if r.get(key) != e[key]:
+            print("CELL MISMATCH row=%r column=%r csv=%r src=%r"
+                  % (r.get("city"), key, r.get(key), e[key]))
+            sys.exit(1)
+
+print("rows=%d cities=%s re-derived from source snapshot — PASS"
+      % (len(rows), ", ".join(r["city"] for r in rows)))
+PY
@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+"""Extract a current + daily weather table for a fixed city set (Extract lane).
+
+Source: Open-Meteo free public API (no vendor, no key). Each city is resolved
+through the geocoding endpoint (``geocoding-api.open-meteo.com/v1/search``) to
+get its latitude/longitude/country, then the forecast endpoint
+(``api.open-meteo.com/v1/forecast``) returns current conditions plus today's
+max/min/precip sum/sunrise/sunset.
+
+Artifacts written beside this script:
+  - 2026-11-02-openmeteo-cities.csv   extracted table (16 columns x 10 cities)
+  - 2026-11-02-openmeteo-cities.json  raw per-city source snapshot (the evidence)
+
+Deterministic check: the committed CSV is re-derived cell-by-cell from the
+committed source JSON by ``CHECK-2026-11-02-openmeteo-cities``.
+"""
+
+import csv
+import json
+import urllib.parse
+import urllib.request
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+CITIES = [
+    "Berlin", "Mumbai", "Dubai", "Toronto", "Buenos Aires",
+    "Jakarta", "Reykjavik", "Lagos", "Seoul", "Auckland",
+]
+FIELDS = [
+    "city", "country", "lat", "lon", "current_time",
+    "temperature_2m", "apparent_temperature", "relative_humidity_2m",
+    "weather_code", "weather_desc", "wind_speed_10m",
+    "today_max", "today_min", "today_precip",
+    "today_sunrise", "today_sunset",
+]
+WMO = {
+    0: "Clear sky", 1: "Mainly clear", 2: "Partly cloudy", 3: "Overcast",
+    45: "Fog", 48: "Fog",
+    51: "Light drizzle", 53: "Drizzle", 55: "Heavy drizzle",
+    61: "Slight rain", 63: "Rain", 65: "Heavy rain",
+    71: "Slight snow", 73: "Snow", 75: "Heavy snow",
+    80: "Slight showers", 81: "Showers", 82: "Heavy showers",
+    85: "Snow showers", 86: "Heavy snow showers",
+    95: "Thunderstorm", 96: "Thunderstorm with slight hail", 99: "Thunderstorm with heavy hail",
+}
+
+
+def http_get(url, timeout=25):
+    req = urllib.request.Request(url, headers={"User-Agent": "ledger-extract/1.0"})
+    with urllib.request.urlopen(req, timeout=timeout) as r:
+        return r.read().decode("utf-8")
+
+
+def geocode(name):
+    url = ("https://geocoding-api.open-meteo.com/v1/search?name=" + urllib.parse.quote(name)
+           + "&count=1&language=en&format=json")
+    data = json.loads(http_get(url))
+    hits = data.get("results") or []
+    if not hits:
+        raise RuntimeError("no geocode hit for %r" % name)
+    h = hits[0]
+    return (h["latitude"], h["longitude"],
+            h.get("country") or h.get("country_code") or "n/a")
+
+
+def fetch_weather(lat, lon):
+    lat_s = "%.6f" % lat
+    lon_s = "%.6f" % lon
+    url = ("https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}"
+           "&current=temperature_2m,apparent_temperature,relative_humidity_2m,weather_code,wind_speed_10m"
+           "&daily=temperature_2m_max,temperature_2m_min,precipitation_sum,sunrise,sunset"
+           "&timezone=auto".format(lat=lat_s, lon=lon_s))
+    data = json.loads(http_get(url))
+    cur = data["current"]
+    day = data["daily"]
+    return {
+        "current_time": cur["time"],
+        "temperature_2m": cur["temperature_2m"],
+        "apparent_temperature": cur["apparent_temperature"],
+        "relative_humidity_2m": cur["relative_humidity_2m"],
+        "weather_code": cur["weather_code"],
+        "weather_desc": WMO.get(cur["weather_code"], "Unknown"),
+        "wind_speed_10m": cur["wind_speed_10m"],
+        "today_max": day["temperature_2m_max"][0],
+        "today_min": day["temperature_2m_min"][0],
+        "today_precip": day["precipitation_sum"][0],
+        "today_sunrise": day["sunrise"][0],
+        "today_sunset": day["sunset"][0],
+    }
+
+
+def main():
+    rows = []
+    source = {}
+    for city in CITIES:
+        try:
+            lat, lon, country = geocode(city)
+            w = fetch_weather(lat, lon)
+            rows.append({"city": city, "country": country, "lat": lat, "lon": lon, **w})
+            source[city] = {"geocode": {"lat": lat, "lon": lon, "country": country},
+                            "weather": w}
+        except Exception as exc:  # noqa: BLE001
+            rows.append({"city": city, "country": "ERROR", "lat": "", "lon": "",
+                         "current_time": "", "temperature_2m": "", "apparent_temperature": "",
+                         "relative_humidity_2m": "", "weather_code": "", "weather_desc": "FAILED",
+                         "wind_speed_10m": "", "today_max": "", "today_min": "",
+                         "today_precip": "", "today_sunrise": "", "today_sunset": ""})
+            source[city] = {"error": repr(exc)}
+
+    csv_path = HERE / "2026-11-02-openmeteo-cities.csv"
+    with csv_path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=FIELDS, lineterminator="\n")
+        writer.writeheader()
+        for r in rows:
+            writer.writerow(r)
+
+    (HERE / "2026-11-02-openmeteo-cities.json").write_text(
+        json.dumps(source, ensure_ascii=False, indent=1), encoding="utf-8")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
@@ -0,0 +1,12 @@
+import os, stat
+p = 'extraction_manifest.json'
+print('exists', os.path.exists(p))
+print('isfile', os.path.isfile(p))
+print('listdir', repr(os.listdir('.')))
+try:
+    st = os.stat(p)
+    print('mode', oct(stat.S_IFMT(st.st_mode)), oct(st.st_mode), 'size', st.st_size)
+    print('inod', st.st_ino)
+    print('contents_b200', repr(open(p, 'rb').read(200)))
+except Exception as e:
+    print('stat_err', repr(e))