curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.2&\
lon_min=2.0&lon_max=2.8&\
time_start=2023-01-01&time_end=2023-02-01&\
pollutants=no2" \
  -H "X-API-Key: sk_live_your_key_here"

{
  "status": "success",
  "query_time_ms": 18,
  "tiles_scanned": 2,
  "credits_used": 2,
  "credits_remaining": 198,
  "output": "Rows matched: 18200\nMin: 1.23\nMax: 42.5\nAverage: 9.87\n"
}

{
  "status": "success",
  "query_time_ms": 22,
  "tiles_scanned": 2,
  "credits_used": 2,
  "credits_remaining": 196,
  "format": "csv",
  "aggregate": "hourly",
  "output": "lat,lon,time,no2\n48.6500,2.1500,2023-01-01T00:00:00Z,12.34\n48.6500,2.1500,2023-01-01T01:00:00Z,11.87\n..."
}

X-API-Key: sk_live_your_key_here

GET /api/v1/climate/query?api_key=sk_live_your_key_here&...

credits = geographic_regions × time_months × pollutants

{
  "status": "success",
  "query_time_ms": 18,
  "tiles_scanned": 2,
  "credits_used": 2,
  "credits_remaining": 6498,
  "format": "csv",        // present only for format=csv or threshold queries
  "aggregate": "hourly",  // present only for format=csv or threshold queries
  "output": "..."         // see Output Formats below
}

Rows matched: 18200
Min: 1.230000
Max: 42.500000
Average: 9.870000

curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01-01&time_end=2023-01-02&\
pollutants=no2&format=csv&aggregate=hourly" \
  -H "X-API-Key: sk_live_your_key_here"

lat,lon,time,no2
48.5500,2.0500,2023-01-01T00:00:00Z,8.32
48.5500,2.0500,2023-01-01T01:00:00Z,7.14
48.5500,2.1500,2023-01-01T00:00:00Z,9.01
...

curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01-01&time_end=2023-01-31&\
pollutants=no2&format=csv&aggregate=daily" \
  -H "X-API-Key: sk_live_your_key_here"

lat,lon,date,no2_mean
48.5500,2.0500,2023-01-01,8.93
48.5500,2.0500,2023-01-02,7.65
48.5500,2.1500,2023-01-01,9.12
...

curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01&time_end=2023-12&\
pollutants=no2&format=csv&aggregate=monthly" \
  -H "X-API-Key: sk_live_your_key_here"

lat,lon,year_month,no2_mean
48.5500,2.0500,2023-01,9.87
48.5500,2.0500,2023-02,8.34
48.5500,2.1500,2023-01,10.23
...

# Hourly NO₂ at the Eiffel Tower, January 2023
curl "https://api.jiskta.com/api/v1/climate/query?\
lat=48.8566&lon=2.3522&\
time_start=2023-01-01&time_end=2023-01-31&\
pollutants=no2&format=csv" \
  -H "X-API-Key: sk_live_your_key_here"

lat,lon,time,no2
48.8500,2.3500,2023-01-01T00:00:00Z,12.34
48.8500,2.3500,2023-01-01T01:00:00Z,11.87
...

# Hours of NO₂ above 25 µg/m³ in Paris, January 2023
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01-01&time_end=2023-01-31&\
pollutants=no2&threshold=25" \
  -H "X-API-Key: sk_live_your_key_here"

lat,lon,hours_above,total_hours,pct_above
48.5500,2.0500,22,744,2.96
48.5500,2.1500,31,744,4.17
48.6500,2.0500,18,744,2.42
...

# Daily spatial average for Paris bounding box, January 2023
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01-01&time_end=2023-01-31&\
pollutants=no2&format=csv&aggregate=area_daily" \
  -H "X-API-Key: sk_live_your_key_here"

date,no2_mean
2023-01-01,9.41
2023-01-02,8.73
2023-01-03,11.20
...

# Annual mean per grid point, 2023 full year
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01-01&time_end=2023-12-31&\
pollutants=no2&format=csv&aggregate=annual" \
  -H "X-API-Key: sk_live_your_key_here"

lat,lon,year,no2_mean
48.5500,2.0500,2023,8.72
48.5500,2.1500,2023,9.10
48.6500,2.0500,2023,7.95
...

# Hourly-of-day NO₂ profile for Paris, January 2023
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01-01&time_end=2023-01-31&\
pollutants=no2&format=csv&aggregate=diurnal" \
  -H "X-API-Key: sk_live_your_key_here"

hour,no2_mean
0,12.79
1,11.43
...
12,14.20
...
23,13.55

# 95th percentile NO₂ per grid point, January 2023
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01-01&time_end=2023-01-31&\
pollutants=no2&percentile=95" \
  -H "X-API-Key: sk_live_your_key_here"

lat,lon,p95
48.5500,2.0500,23.82
48.5500,2.1500,21.47
48.6500,2.0500,19.63
...

# Monthly mean in GeoJSON format
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=48.5&lat_max=49.0&lon_min=2.0&lon_max=2.5&\
time_start=2023-01&time_end=2023-01&\
pollutants=no2&format=geojson&aggregate=monthly" \
  -H "X-API-Key: sk_live_your_key_here"

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": { "type": "Point", "coordinates": [2.05, 48.55] },
      "properties": { "year_month": "2023-01", "no2_mean": 9.87 }
    },
    ...
  ]
}

{
  "status": "healthy",
  "service": "climate-api",
  "auth_enabled": true
}

{
  "code": "WELCOME-2025-ABC"
}

{
  "status": "success",
  "credits_added": 500,
  "credits_remaining": 500,
  "description": "Welcome bonus"
}

{
  "package": "starter",
  "api_key_id": "your-api-key-uuid"
}

{
  "checkout_url": "https://checkout.stripe.com/c/pay/..."
}

Customer clicks "Buy Credits" on dashboard
  → Stripe Checkout page opens
  → Customer pays with card
  → Stripe sends checkout.session.completed webhook
  → API reads metadata (api_key_id, package, credits)
  → Credits added atomically to customer's account
  → Customer returns to dashboard with updated balance

const { data, error } = await supabase.rpc('regenerate_api_key');
// Returns: [{ raw_key: "sk_live_...", new_prefix: "sk_live_abcd" }]

import requests
import io, csv

API_KEY = "sk_live_your_key_here"
BASE_URL = "https://api.jiskta.com"

# Get daily NO₂ means for Paris, full year 2023
response = requests.get(
    f"{BASE_URL}/api/v1/climate/query",
    headers={"X-API-Key": API_KEY},
    params={
        "lat_min": 48.5, "lat_max": 49.2,
        "lon_min": 2.0, "lon_max": 2.8,
        "time_start": "2023-01-01",
        "time_end": "2023-12-31",
        "pollutants": "no2",
        "format": "csv",
        "aggregate": "daily",
    }
)

data = response.json()
print(f"Query took {data['query_time_ms']}ms, {data['credits_used']} credits used")

# Parse the CSV output
reader = csv.DictReader(io.StringIO(data["output"]))
rows = list(reader)
print(f"{len(rows)} daily observations")
print(rows[0])  # {'lat': '48.6500', 'lon': '2.1500', 'date': '2023-01-01', 'no2_mean': '9.87'}

# Single-point query
pt = requests.get(
    f"{BASE_URL}/api/v1/climate/query",
    headers={"X-API-Key": API_KEY},
    params={
        "lat": 48.8566, "lon": 2.3522,  # Eiffel Tower — snapped to nearest grid point
        "time_start": "2023-06-01", "time_end": "2023-06-30",
        "pollutants": "no2", "format": "csv", "aggregate": "monthly",
    }
).json()
print(pt["output"])

const API_KEY = "sk_live_your_key_here";
const BASE_URL = "https://api.jiskta.com";

// Monthly means for London, 2023
const params = new URLSearchParams({
  lat_min: 51.3, lat_max: 51.7,
  lon_min: -0.5, lon_max: 0.3,
  time_start: "2023-01", time_end: "2023-12",
  pollutants: "no2",
  format: "csv",
  aggregate: "monthly",
});

const res = await fetch(
  `${BASE_URL}/api/v1/climate/query?${params}`,
  { headers: { "X-API-Key": API_KEY } }
);

const data = await res.json();
console.log(`${data.credits_used} credits, ${data.query_time_ms}ms`);

// Parse CSV rows
const [header, ...rows] = data.output.trim().split("\n");
const cols = header.split(",");
const parsed = rows.map(r => Object.fromEntries(r.split(",").map((v, i) => [cols[i], v])));
console.log(parsed[0]); // { lat: '51.4500', lon: '-0.4500', year_month: '2023-01', no2_mean: '14.2' }

# Hourly PM2.5 in London, March 2023 — save to CSV
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=51.3&lat_max=51.7&\
lon_min=-0.5&lon_max=0.3&\
time_start=2023-03-01&time_end=2023-04-01&\
pollutants=pm2p5&format=csv&aggregate=daily" \
  -H "X-API-Key: sk_live_your_key_here" \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['output'])" > london_pm25_march.csv

# Exceedance: hours of NO₂ above WHO guideline (25 µg/m³), Berlin, 2023
curl "https://api.jiskta.com/api/v1/climate/query?\
lat_min=52.3&lat_max=52.7&\
lon_min=13.0&lon_max=13.7&\
time_start=2023-01-01&time_end=2023-12-31&\
pollutants=no2&threshold=25" \
  -H "X-API-Key: sk_live_your_key_here" \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['output'])"

{
  "error": "Insufficient credits",
  "credits_remaining": 3,
  "credits_required": 8
}