Metadata-Version: 2.1
Name: meteoblue-dataset-sdk
Version: 1.0.0
Summary: Easy access to the meteoblue dataset API
Home-page: https://github.com/meteoblue/python-dataset-sdk
Author: meteoblue AG
Author-email: patrick.zippenfenig@meteoblue.com
License: MIT License
Description: # meteoblue Python Dataset SDK
        
        [![PyPI version](https://badge.fury.io/py/meteoblue-dataset-sdk.svg)](https://badge.fury.io/py/meteoblue-dataset-sdk)
        
        This library simplifies access to the [meteoblue dataset API](https://docs.meteoblue.com/en/apis/environmental-data/dataset-api).
        
        In order to use this library you need a meteoblue API key.
        
        Features:
        - Fetch any dataset from the meteoblue environmental data archive
        - Transparently integrates job queues to query large datasets
        - Efficiently transfers data using compressed protobuf messages
        - Asynchronous interface to query data in parallel
        - Data can be used as simple floating-point arrays. No further formatting required.
        - Semantic Versioning: The interface for version 1 is declared stable. Breaking interface changes will be published in version 2.
        
        
        ## Installation
        - Ensure that you are using at least Python 3.7 with `python --version` (Sometimes `python3`)
        - Install the module with `pip install 'meteoblue_dataset_sdk >=1.0,<2.0'` (Sometimes `pip3`)
        
        This module will also install the following dependencies automatically:
        - aiohttp >=3.6,<4
        - protobuf >=3.0,<4
        
        
        ## Usage
        See [main.py](./main.py) for a working example. To generate the query JSON it is highly recommended to use the [dataset API web interfaces](https://docs.meteoblue.com/en/apis/environmental-data/web-interfaces).
        
        ```python
        import meteoblue_dataset_sdk
        import logging
        
        # Display information about the current download state
        logging.basicConfig(level=logging.INFO)
        
        query = {
            "units": {
                "temperature": "C",
                "velocity": "km/h",
                "length": "metric",
                "energy": "watts",
            },
            "geometry": {
                "type": "MultiPoint",
                "coordinates": [[7.57327, 47.558399, 279]],
                "locationNames": ["Basel"],
            },
            "format": "protobuf",
            "timeIntervals": ["2019-01-01T+00:00/2019-01-01T+00:00"],
            "timeIntervalsAlignment": "none",
            "queries": [
                {
                    "domain": "NEMSGLOBAL",
                    "gapFillDomain": None,
                    "timeResolution": "hourly",
                    "codes": [{"code": 11, "level": "2 m above gnd"}],
                }
            ],
        }
        client = meteoblue_dataset_sdk.Client(apikey="xxxxxx")
        result = client.querySync(query)
        # result is a structured object containing timestamps and data
        
        timeInterval = result.geometries[0].timeIntervals[0]
        data = result.geometries[0].codes[0].timeIntervals[0].data
        
        print(timeInterval)
        # start: 1546300800
        # end: 1546387200
        # stride: 3600
        ```
        
        NOTE: `timeInterval.end` is the first timestamp that is not included anymore in the time interval.
        
        If your code is using `async/await`, you should use `await client.query()` instead of `client.querySync()`. Asynchronous IO is essential for modern webserver frameworks like Flask or FastAPI.
        
        ```python
        client = meteoblue_dataset_sdk.Client(apikey="xxxxxx")
        result = await client.query(query)
        ```
        
        ## Protobuf format
        Data is transferred using protobuf and defined as [this protobuf structure](./meteoblue_dataset_sdk/Dataset.proto).
        
        A 10 year hourly data series for 1 location requires `350 kb` using protobuf, compared to `1600 kb` using JSON. Additionally the meteoblue Python SDK transfers data using gzip which reduces the size to only `87 kb`. 
        
        More detailed output of the `result` protobuf object:
        
        ```
        geometries {
          domain: "NEMSGLOBAL"
          lats: 47.66651916503906
          lons: 7.5
          asls: 499.7736511230469
          locationNames: "Basel"
          nx: 1
          ny: 1
          timeResolution: "hourly"
          timeIntervals {
            start: 1546300800
            end: 1546387200
            stride: 3600
          }
          codes {
            code: 11
            level: "2 m above gnd"
            unit: "\302\260C"
            aggregation: "none"
            timeIntervals {
              data: 2.890000104904175
              data: 2.690000057220459
              data: 2.549999952316284
              data: 2.380000114440918
              data: 2.2699999809265137
              data: 2.119999885559082
              data: 1.9900000095367432
              data: 1.8300000429153442
              data: 1.8200000524520874
              data: 2.0999999046325684
              data: 2.430000066757202
              data: 2.9200000762939453
              data: 3.7200000286102295
              data: 3.930000066757202
              data: 3.9100000858306885
              data: 3.5299999713897705
              data: 3.130000114440918
              data: 2.880000114440918
              data: 2.6500000953674316
              data: 2.4600000381469727
              data: 2.2799999713897705
              data: 2.0299999713897705
              data: 1.690000057220459
              data: 1.3799999952316284
            }
          }
        }
        ```
        
Keywords: meteoblue
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
