# Point Queries ### Single Point & Init Time Check the weather for Zurich. {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "point", "value": [47.3784, 8.5387], # [longitude, latitude] - Zurich "method": "nearest", }, "variables": [ "air_temperature_at_height_level_2m", "wind_speed_at_height_level_100m", ], "init_time": "latest", "prediction_timedelta": {"start": 0, "end": 72}, # 0-72 hours ahead } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"✅ Retrieved {len(df)} rows") print(df.head()) ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 73 rows model init_time ... air_temperature_at_height_level_2m wind_speed_at_height_level_100m 0 ept2 2025-10-23T06:00:00Z ... 284.600006 4.7 1 ept2 2025-10-23T06:00:00Z ... 285.399994 5.0 2 ept2 2025-10-23T06:00:00Z ... 286.399994 4.8 3 ept2 2025-10-23T06:00:00Z ... 286.500000 9.3 4 ept2 2025-10-23T06:00:00Z ... 284.800018 10.8 [5 rows x 7 columns] ``` {% endtab %} {% endtabs %} ### Multiple Points, Single Init Time Get the latest forecast for three points of interest. {% tabs %} {% tab title="Python (Example)" %} ```python points = [ [47.3784, 8.5387], # Zurich [46.2044, 6.1432], # Geneva [45.4642, 9.1900], # Milan ] query = { "models": ["ept2"], "geo": { "type": "point", "value": points, "method": "bilinear", # Use bilinear interpolation }, "variables": [ "air_temperature_at_height_level_2m", "air_pressure_at_mean_sea_level", ], "init_time": "latest", "prediction_timedelta": [ 0, 1, 2, 3, 24, 48, 72, ], # Specific timesteps: 0, 1h, 2h, 3h } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"✅ Retrieved {len(df)} rows") print(df.head(12)) ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 21 rows model init_time ... air_temperature_at_height_level_2m air_pressure_at_mean_sea_level 0 ept2 2025-10-23T06:00:00Z ... 285.353058 99202.460938 1 ept2 2025-10-23T06:00:00Z ... 286.886932 100174.296875 2 ept2 2025-10-23T06:00:00Z ... 278.207947 101294.437500 3 ept2 2025-10-23T06:00:00Z ... 286.374359 99177.875000 4 ept2 2025-10-23T06:00:00Z ... 286.428955 99310.710938 5 ept2 2025-10-23T06:00:00Z ... 285.010590 99835.976562 6 ept2 2025-10-23T06:00:00Z ... 285.821747 99690.070312 7 ept2 2025-10-23T06:00:00Z ... 279.756836 100894.539062 8 ept2 2025-10-23T06:00:00Z ... 280.985046 100497.390625 9 ept2 2025-10-23T06:00:00Z ... 280.339874 100916.289062 10 ept2 2025-10-23T06:00:00Z ... 287.028015 100000.000000 11 ept2 2025-10-23T06:00:00Z ... 286.628876 100383.093750 [12 rows x 7 columns] ``` {% endtab %} {% endtabs %} ### Multiple Models & Points Request data for mulitple models and locations to compare them. {% tabs %} {% tab title="Python (Example)" %} ```python query = { "models": ["ept2", "aifs"], # Multiple models "geo": { "type": "point", "value": [ [47.3784, 8.5387], # Zurich [46.9479, 7.4474], # Bern ], "method": "nearest", }, "variables": [ "air_temperature_at_height_level_2m", "wind_speed_at_height_level_10m", ], "init_time": "latest", "prediction_timedelta": {"start": 0, "end": 4}, # 0-4 hours "order_by": ["model", "latitude", "longitude", "prediction_timedelta"], } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"✅ Retrieved {len(df)} rows") print(df.head(10)) # Show comparison between models if len(df) > 0: print("\nModel comparison at first timestep:") print( df[df["prediction_timedelta"] == df["prediction_timedelta"].min()][ ["model", "latitude", "longitude", "air_temperature_at_height_level_2m"] ] ) ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 12 rows model init_time ... air_temperature_at_height_level_2m wind_speed_at_height_level_10m 0 aifs 2025-10-23T06:00:00Z ... 286.800018 0.0 1 aifs 2025-10-23T06:00:00Z ... 286.000000 0.0 2 ept2 2025-10-23T06:00:00Z ... 284.600006 5.1 3 ept2 2025-10-23T06:00:00Z ... 285.399994 5.9 4 ept2 2025-10-23T06:00:00Z ... 285.700012 6.8 5 ept2 2025-10-23T06:00:00Z ... 284.500000 10.1 6 ept2 2025-10-23T06:00:00Z ... 283.100006 10.0 7 ept2 2025-10-23T06:00:00Z ... 284.600006 4.7 8 ept2 2025-10-23T06:00:00Z ... 285.399994 5.0 9 ept2 2025-10-23T06:00:00Z ... 286.399994 4.8 [10 rows x 7 columns] Model comparison at first timestep: model latitude longitude air_temperature_at_height_level_2m 0 aifs 47.000000 7.500000 286.800018 1 aifs 47.500000 8.500000 286.000000 2 ept2 46.916668 7.416667 284.600006 7 ept2 47.416668 8.500000 284.600006 ``` {% endtab %} {% endtabs %} ### Historical Data for Single Point Query two months of 3-day forecasts for Zurich {% tabs %} {% tab title="Python (Example)" %} ```python # Query forecasts from March to April 2025 start_time = datetime(2025, 3, 1, 0, 0, 0) end_time = datetime(2025, 4, 30, 23, 59, 59) query = { "models": ["ept2"], "geo": { "type": "point", "value": [47.3784, 8.5387], # Zurich "method": "nearest", }, "variables": [ "air_temperature_at_height_level_2m", "precipitation_amount_sum_1h", ], "init_time": { "start": start_time.strftime("%Y-%m-%dT%H:%M:%S"), "end": end_time.strftime("%Y-%m-%dT%H:%M:%S"), }, "prediction_timedelta": {"start": 0, "end": 72}, "order_by": ["init_time", "prediction_timedelta"], } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"✅ Retrieved {len(df)} rows") print(df.head(10)) if len(df) > 0: print(f"\nInit times range: {df['init_time'].min()} to {df['init_time'].max()}") ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 17812 rows model init_time prediction_timedelta latitude longitude air_temperature_at_height_level_2m precipitation_amount_sum_1h 0 ept2 2025-03-01T00:00:00Z 0 47.416668 8.5 273.700012 NaN 1 ept2 2025-03-01T00:00:00Z 1 47.416668 8.5 273.600006 0.00 2 ept2 2025-03-01T00:00:00Z 2 47.416668 8.5 273.700012 0.00 3 ept2 2025-03-01T00:00:00Z 3 47.416668 8.5 273.800018 0.00 4 ept2 2025-03-01T00:00:00Z 4 47.416668 8.5 273.899994 0.00 5 ept2 2025-03-01T00:00:00Z 5 47.416668 8.5 273.899994 0.00 6 ept2 2025-03-01T00:00:00Z 6 47.416668 8.5 273.899994 0.00 7 ept2 2025-03-01T00:00:00Z 7 47.416668 8.5 274.200012 0.00 8 ept2 2025-03-01T00:00:00Z 8 47.416668 8.5 274.899994 0.01 9 ept2 2025-03-01T00:00:00Z 9 47.416668 8.5 275.700012 0.01 Init times range: 2025-03-01T00:00:00Z to 2025-04-30T18:00:00Z ``` {% endtab %} {% endtabs %}