> For the complete documentation index, see [llms.txt](https://docs.jua.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.jua.ai/api-v2/query-engine/examples/point-queries.md). # 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], # [latitude, longitude] - 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 %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.jua.ai/api-v2/query-engine/examples/point-queries.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.