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Add first round of tests
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‎tests/iotools/test_meteonorm.py

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import pandas as pd
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import numpy as np
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import pytest
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import pvlib
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from tests.conftest import RERUNS, RERUNS_DELAY
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@pytest.fixture
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def demo_api_key():
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# Demo locations:
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# lat=50, lon=10 (Germany)
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# lat=21, lon=79 (India)
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# lat=-3, lon=-60 (Brazil)
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# lat=51, lon=-114 (Canada)
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# lat=24, lon=33 (Egypt)
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return 'demo0000-0000-0000-0000-000000000000'
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@pytest.fixture
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def demo_url():
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return 'https://demo.meteonorm.com/v1/'
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@pytest.fixture
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def expected_meta():
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meta = {
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'altitude': 290,
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'frequency': '1_hour',
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'parameters': [
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{'aggregation_method': 'average',
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'description': 'Diffuse horizontal irradiance',
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'name': 'diffuse_horizontal_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Diffuse horizontal irradiance with shading taken into account',
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'name': 'diffuse_horizontal_irradiance_with_shading',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Diffuse tilted irradiance',
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'name': 'diffuse_tilted_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Diffuse tilted irradiance with shading taken into account',
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'name': 'diffuse_tilted_irradiance_with_shading',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Direct horizontal irradiance',
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'name': 'direct_horizontal_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Direct horizontal irradiance with shading taken into account',
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'name': 'direct_horizontal_irradiance_with_shading',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Direct normal irradiance',
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'name': 'direct_normal_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Direct normal irradiance with shading taken into account',
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'name': 'direct_normal_irradiance_with_shading',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Direct tilted irradiance',
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'name': 'direct_tilted_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Direct tilted irradiance with shading taken into account',
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'name': 'direct_tilted_irradiance_with_shading',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Global horizontal clear sky irradiance',
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'name': 'global_clear_sky_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Global horizontal irradiance',
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'name': 'global_horizontal_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Global horizontal irradiance with shading taken into account',
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'name': 'global_horizontal_irradiance_with_shading',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Global tilted irradiance',
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'name': 'global_tilted_irradiance',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Global tilted irradiance with shading taken into account',
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'name': 'global_tilted_irradiance_with_shading',
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'unit': {'description': 'Watt per square meter', 'name': 'W/m**2'}},
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{'aggregation_method': 'average',
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'description': 'Power output per kWp installed',
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'name': 'pv_production',
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'unit': {'description': 'Watts per kilowatt peak', 'name': 'W/kWp'}},
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{'aggregation_method': 'average',
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'description': 'Power output per kWp installed, with shading taken into account',
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'name': 'pv_production_with_shading',
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'unit': {'description': 'Watts per kilowatt peak', 'name': 'W/kWp'}},
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{'aggregation_method': 'average',
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'description': 'Snow depth',
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'name': 'snow_depth',
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'unit': {'description': 'millimeters', 'name': 'mm'}},
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{'aggregation_method': 'average',
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'description': 'Air temperature, 2 m above ground.',
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'name': 'temperature',
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'unit': {'description': 'degrees Celsius', 'name': '°C'}}],
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'surface_azimuth': 180,
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'surface_tilt': 0,
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'time_zone': 0,
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'latitude': 50,
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'longitude': 10,
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}
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return meta
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@pytest.fixture
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def expected_meteonorm_index():
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expected_meteonorm_index = \
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pd.date_range('2023-01-01', '2024-12-31 23:59', freq='1h', tz='UTC')
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expected_meteonorm_index.freq = None
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return expected_meteonorm_index
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@pytest.fixture
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def expected_metenorm_data():
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# The first 12 rows of data
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columns = ['dhi', 'diffuse_horizontal_irradiance_with_shading', 'poa_diffuse',
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'diffuse_tilted_irradiance_with_shading', 'bhi',
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'direct_horizontal_irradiance_with_shading', 'dni',
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'direct_normal_irradiance_with_shading', 'poa_direct',
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'direct_tilted_irradiance_with_shading', 'ghi_clear', 'ghi',
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'global_horizontal_irradiance_with_shading', 'poa',
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'global_tilted_irradiance_with_shading', 'pv_production',
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'pv_production_with_shading', 'snow_depth', 'temp_air']
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expected = [
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 12.25],
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 11.75],
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 11.75],
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 11.5],
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 11.25],
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 11.],
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 11.],
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[2.5, 2.68309898, 2.67538201, 2.68309898, 0., 0., 0., 0., 0., 0., 0., 2.5,
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2.68309898, 2.67538201, 2.68309898, 2.34649978, 2.35326557, 0., 11.],
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[40.43632435, 40.41304027, 40.43632435, 40.41304027, 37.06367565, 37.06367565,
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288.7781947, 288.7781947, 37.06367565, 37.06367565, 98.10113439, 77.5,
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77.47671591, 77.5, 77.47671591, 67.02141875, 67.00150474, 0., 11.75],
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[60.52591348, 60.51498257, 60.52591348, 60.51498257, 104.47408652, 104.47408652,
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478.10101591, 478.10101591, 104.47408652, 104.47408652, 191.27910925, 165.,
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164.98906908, 165., 164.98906908, 140.23845, 140.22938131, 0., 12.75],
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[71.90169306, 71.89757085, 71.90169306, 71.89757085, 138.84830694, 138.84830694,
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508.02986044, 508.02986044, 138.84830694, 138.84830694, 253.85597777, 210.75,
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210.7458778, 210.75, 210.7458778, 177.07272956, 177.06937293, 0., 13.75],
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[78.20403711, 78.19681926, 78.20403711, 78.19681926, 142.79596289, 142.79596289,
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494.06576548, 494.06576548, 142.79596289, 142.79596289, 272.34275335, 221.,
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220.99278214, 221., 220.99278214, 185.179657, 185.17380523, 0., 14.],
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]
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index = pd.date_range('2023-01-01', periods=12, freq='1h', tz='UTC')
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index.freq = None
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expected = pd.DataFrame(expected, index=index, columns=columns)
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expected['snow_depth'] = expected['snow_depth'].astype(np.int64)
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return expected
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@pytest.mark.remote_data
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@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
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def test_get_meteonorm_training(
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demo_api_key, demo_url, expected_meta, expected_meteonorm_index,
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expected_metenorm_data):
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data, meta = pvlib.iotools.get_meteonorm(
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latitude=50, longitude=10,
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start='2023-01-01', end='2025-01-01',
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api_key=demo_api_key,
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endpoint='observation/training',
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time_step='1h',
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url=demo_url)
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assert meta == expected_meta
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pd.testing.assert_index_equal(data.index, expected_meteonorm_index)
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pd.testing.assert_frame_equal(data.iloc[:12], expected_metenorm_data)
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@pytest.mark.remote_data
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@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
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def test_get_meteonorm_realtime(
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demo_api_key, demo_url, expected_meta, expected_meteonorm_index,
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expected_metenorm_data):
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data, meta = pvlib.iotools.get_meteonorm(
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latitude=21, longitude=79,
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start=pd.Timestamp.now(tz='UTC') - pd.Timedelta(hours=5),
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end=pd.Timestamp.now(tz='UTC') - pd.Timedelta(hours=1),
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surface_tilt=20, surface_azimuth=10,
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parameters=['ghi', 'global_horizontal_irradiance_with_shading'],
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api_key=demo_api_key,
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endpoint='/observation/realtime',
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time_step='1min',
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horizon='flat',
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map_variables=False,
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interval_index=True,
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url=demo_url,
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)
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assert meta['frequency'] == '1_minute'
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assert meta['lat'] == 21
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assert meta['lon'] == 79
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assert meta['surface_tilt'] == 20
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assert meta['surface_azimuth'] == 10
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assert all(data.columns == pd.Index([
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'global_horizontal_irradiance',
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'global_horizontal_irradiance_with_shading']))
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assert data.shape == (241, 2)
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# can't test the specific index as it varies due to the
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# use of pd.Timestamp.now
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assert type(data.index) is pd.core.indexes.interval.IntervalIndex

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