Metadata-Version: 2.1
Name: meteoblue-dataset-sdk
Version: 0.0.14
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.
        
        
        ## Installation
        - Ensure that you are using at least Python 3.7 with `python --version` 
        - Install the module with `pip3 install 'meteoblue_dataset_sdk>=1.0.0,<2'`
        
        This module will also install the following dependencies automatically:
        - aiohttp >=3.6,<4
        - protobuf >=3.0,<4
        
        ## Usage
        ```python
        import meteoblue_dataset_sdk
        
        async def myFunction():
            # Query 1 year of hourly temperature for Basel from meteoblue NEMSGLOBAL
            query = {"geometry": {"type": "MultiPoint", "coordinates": [[7.57327, 47.558399, 279]], "locationNames": ["Basel"]}, "format": "json", "timeIntervals": [
                    "2019-01-01T+00:00/2019-01-01T+00:00"], "queries": [{"domain": "NEMSGLOBAL", "timeResolution": "hourly", "codes": [{"code": 11, "level": "2 m above gnd"}]}]}
        
            client = mbdataset.Client(apikey='XXXXXXXXXXXXXXX')
            result = await client.query(query)
            # result is a structured object containing timestamps and data
        
            timestamps = result.geometries[0].timeIntervals[0].timestamps
            data = result.geometries[0].codes[0].timeIntervals[0].data
        
            print(timestamps)
            # [1546300800, 1546304400, 1546308000, 1546311600, 1546315200, ...
            print(data)
            # [2.89, 2.69, 2.549999, 2.3800001,
        ```
        
        More detailed output:
        
        ```
        geometries {
          domain: "NEMSGLOBAL"
          lats: 47.66651916503906
          lons: 7.5
          asls: 499.7736511230469
          locationNames: "Basel"
          nx: 1
          ny: 1
          timeResolution: "hourly"
          timeIntervals {
            timestamps: 1546300800
            timestamps: 1546304400
            timestamps: 1546308000
            timestamps: 1546311600
            timestamps: 1546315200
            timestamps: 1546318800
            timestamps: 1546322400
            timestamps: 1546326000
            timestamps: 1546329600
            timestamps: 1546333200
            timestamps: 1546336800
            timestamps: 1546340400
            timestamps: 1546344000
            timestamps: 1546347600
            timestamps: 1546351200
            timestamps: 1546354800
            timestamps: 1546358400
            timestamps: 1546362000
            timestamps: 1546365600
            timestamps: 1546369200
            timestamps: 1546372800
            timestamps: 1546376400
            timestamps: 1546380000
            timestamps: 1546383600
          }
          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
            }
          }
        }
        ```
        
        
        ## Developer setup
        ```bash
        python3 -m venv env
        source env/bin/activate
        pip3 install -r requirements.txt
        ```
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
