Simple Python Web Service Client

Data Delivery Web service (the API for retrieving time series data)

if __name__ == '__main__':
    import requests
    import xmltodict  # https://pypi.org/project/xmltodict/
    import pandas as pd

    api_key = 'demo'
    dateFrom = '2024-05-01'
    dateTo = '2024-05-01'
    latitude = 48.61259
    longitude = 20.827079
    tilt = 40
    azimuth = 180

    request_xml = f'''<ws:dataDeliveryRequest dateFrom="{dateFrom}" dateTo="{dateTo}"
    xmlns="http://geomodel.eu/schema/data/request"
    xmlns:ws="http://geomodel.eu/schema/ws/data"
    xmlns:geo="http://geomodel.eu/schema/common/geo"
    xmlns:pv="http://geomodel.eu/schema/common/pv"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <site id="demo" lat="{latitude}" lng="{longitude}">
    <pv:geometry xsi:type="pv:GeometryFixedOneAngle" tilt="{tilt}" azimuth="{azimuth}"/>
    <pv:system installedPower="1" installationType="ROOF_MOUNTED">
        <pv:module type="CSI"/>
        <pv:inverter/>
        <pv:losses/>
    </pv:system>
    </site>
    <processing key="GHI DNI DIF GTI TEMP WS WD RH" summarization="HOURLY">
    <timeZone>GMT+02</timeZone>
    <timestampType>START</timestampType>
    </processing>
    </ws:dataDeliveryRequest>'''

    url = f'https://solargis.info/ws/rest/datadelivery/request?key={api_key}'
    headers = {'Content-Type': 'application/xml'}
    with requests.post(url, data=request_xml, headers=headers) as response:
        if not response.ok:
            print(f'Failed API request with code {response.status_code}:\n{response.text}')
        else:
            datadict = xmltodict.parse(response.text)  # transform XML document into Python dictionary
            columns = datadict['dataDeliveryResponse']['site']['columns'].split()  # get names of columns (GHI, DNI, etc.)
            rows = datadict['dataDeliveryResponse']['site']['row']  # get rows or records from XML
            rows = rows if isinstance(rows, list) else [rows]
            timestamps = [r['@dateTime'] for r in
                        rows]  # use '@dateTime' for sub/hourly data, '@date' for daily data, '@yearMonth' for monthly data
            data_values = [r['@values'].split() for r in rows]
            # create pandas dataframe from the response data:
            df = pd.DataFrame(data_values, index=pd.to_datetime(timestamps), columns=columns)
            df = df.apply(pd.to_numeric, errors='coerce')  # make numbers from original strings in XML
            print(df)
            print(df.describe())
            # plot the response:
            # import matplotlib.pyplot as plt
            # df.plot()
            # plt.savefig('solargi_api_response.png')

LTA Web service (the API for retrieving long-term average data)

if __name__ == '__main__':
    import requests
    import xmltodict  # https://pypi.org/project/xmltodict/

    api_key = 'demo'
    latitude = 48.61259
    longitude = 20.827079
    tilt = 40
    azimuth = 180

    request_xml = f'''<calculateRequest 
          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
          xmlns:geo="http://geomodel.eu/schema/common/geo"
          xmlns:pv="http://geomodel.eu/schema/common/pv"
          xmlns="http://geomodel.eu/schema/ws/pvplanner">
        <site lat="{latitude}" lng="{longitude}">
            <!-- optional terrain data -->
            <geo:terrain elevation="246" azimuth="176" tilt="3.1" />
            <!-- optional custom horizon data can override the natural terrain horizon -->
            <!--<geo:horizon>11.11:18.0 7.5:15.53 15.0:10.94 352.5:17.29</geo:horizon>-->
            <pv:geometry xsi:type="pv:GeometryFixedOneAngle" azimuth="{azimuth}" tilt="{tilt}"/>
            <pv:system installedPower="1" installationType="ROOF_MOUNTED" availability="99">
                <pv:module type="CSI">
                </pv:module>
                <pv:inverter>
                    <pv:efficiency xsi:type="pv:EfficiencyConstant" percent="97.5"/>
                </pv:inverter>
                <pv:losses dc="5.5" ac="1.5"/>
            </pv:system>
        </site>
    </calculateRequest>'''

    url = f'https://solargis.info/ws/rest/pvplanner/calculate?key={api_key}'
    headers = {'Content-Type': 'application/xml'}
    with requests.post(url, data=request_xml, headers=headers) as response:
        if not response.ok:
            print(f'Failed API request with code {response.status_code}:\n{response.text}')
        else:
            datadict = xmltodict.parse(response.text)  # transform XML document into Python dictionary
            response_dict = datadict['ns3:calculateResponse']
            # explore solar and climate data in the response:
            climate_data = response_dict['ns3:irradiation']  
            climate_data_reference = climate_data['ns3:reference'] # general climate data
            solar_data_inplane = climate_data['ns3:inplane'] # POA irradiance components
            geometry_comparison = climate_data['ns3:comparison']  # comparison of the various geometry options with selected one
            optimum_angle = climate_data.get('ns3:optimum')  # optimum fixed angle for the selected location
            # get PV calculation data:
            pv_data = response_dict['ns3:calculation']
            pv_data_output = pv_data['ns3:output']  # PV system output total and specific
            pv_data_losses = pv_data['ns3:losses']  # PV system losses breakdown
            print('Climate data reference:', climate_data_reference)
            print('Solar data in-plane:', solar_data_inplane)
            print('Geometry comparison:', geometry_comparison)
            print('Optimum angle:', optimum_angle)
            print('PV data output:', pv_data_output)
            print('PV data losses:', pv_data_losses)

Public

Simple Python Web Service Client

Data Delivery Web service (the API for retrieving time series data)

LTA Web service (the API for retrieving long-term average data)

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