diff --git a/docs/sphinx/source/whatsnew/v0.9.1.rst b/docs/sphinx/source/whatsnew/v0.9.1.rst index d3736f8908..64fc94cfa1 100644 --- a/docs/sphinx/source/whatsnew/v0.9.1.rst +++ b/docs/sphinx/source/whatsnew/v0.9.1.rst @@ -20,6 +20,8 @@ Bug fixes values were returned when the sun is behind the plane of array (:issue:`1348`, :pull:`1349`) * Fixed bug in :py:func:`pvlib.iotools.get_pvgis_hourly` where the ``optimal_surface_tilt`` argument was not being passed to the ``optimalinclination`` request parameter (:pull:`1356`) +* Fixed bug in :py:func:`pvlib.bifacial.pvfactors_timeseries` where scalar ``surface_tilt`` + and ``surface_azimuth`` inputs caused an error (:issue:`1127`, :issue:`1332`, :pull:`1361`) Testing diff --git a/pvlib/bifacial.py b/pvlib/bifacial.py index 342402c006..c7247e2f27 100644 --- a/pvlib/bifacial.py +++ b/pvlib/bifacial.py @@ -82,31 +82,14 @@ def pvfactors_timeseries( Bifacial PV and Diffuse Shade on Single-Axis Trackers." 44th IEEE Photovoltaic Specialist Conference. 2017. """ - # Convert pandas Series inputs (and some lists) to numpy arrays - if isinstance(solar_azimuth, pd.Series): - solar_azimuth = solar_azimuth.values - elif isinstance(solar_azimuth, list): - solar_azimuth = np.array(solar_azimuth) - if isinstance(solar_zenith, pd.Series): - solar_zenith = solar_zenith.values - elif isinstance(solar_zenith, list): - solar_zenith = np.array(solar_zenith) - if isinstance(surface_azimuth, pd.Series): - surface_azimuth = surface_azimuth.values - elif isinstance(surface_azimuth, list): - surface_azimuth = np.array(surface_azimuth) - if isinstance(surface_tilt, pd.Series): - surface_tilt = surface_tilt.values - elif isinstance(surface_tilt, list): - surface_tilt = np.array(surface_tilt) - if isinstance(dni, pd.Series): - dni = dni.values - elif isinstance(dni, list): - dni = np.array(dni) - if isinstance(dhi, pd.Series): - dhi = dhi.values - elif isinstance(dhi, list): - dhi = np.array(dhi) + # Convert Series, list, float inputs to numpy arrays + solar_azimuth = np.array(solar_azimuth) + solar_zenith = np.array(solar_zenith) + dni = np.array(dni) + dhi = np.array(dhi) + # GH 1127, GH 1332 + surface_tilt = np.full_like(solar_zenith, surface_tilt) + surface_azimuth = np.full_like(solar_zenith, surface_azimuth) # Import pvfactors functions for timeseries calculations. from pvfactors.run import run_timeseries_engine diff --git a/pvlib/tests/test_bifacial.py b/pvlib/tests/test_bifacial.py index 25542c4869..92207f905f 100644 --- a/pvlib/tests/test_bifacial.py +++ b/pvlib/tests/test_bifacial.py @@ -5,97 +5,78 @@ import pytest -@requires_pvfactors -def test_pvfactors_timeseries(): - """ Test that pvfactors is functional, using the TLDR section inputs of the - package github repo README.md file: - https://github.com/SunPower/pvfactors/blob/master/README.md#tldr---quick-start""" - - # Create some inputs - timestamps = pd.DatetimeIndex([datetime(2017, 8, 31, 11), - datetime(2017, 8, 31, 12)] - ).set_names('timestamps') - solar_zenith = [20., 10.] - solar_azimuth = [110., 140.] - surface_tilt = [10., 0.] - surface_azimuth = [90., 90.] - axis_azimuth = 0. - dni = [1000., 300.] - dhi = [50., 500.] - gcr = 0.4 - pvrow_height = 1.75 - pvrow_width = 2.44 - albedo = 0.2 - n_pvrows = 3 - index_observed_pvrow = 1 - rho_front_pvrow = 0.03 - rho_back_pvrow = 0.05 - horizon_band_angle = 15. - - # Expected values - expected_ipoa_front = pd.Series([1034.95474708997, 795.4423259036623], - index=timestamps, - name=('total_inc_front')) - expected_ipoa_back = pd.Series([92.12563846416197, 78.05831585685098], - index=timestamps, - name=('total_inc_back')) +@pytest.fixture +def example_values(): + """ + Example values from the pvfactors github repo README file: + https://github.com/SunPower/pvfactors/blob/master/README.rst#quick-start + """ + inputs = dict( + timestamps=pd.DatetimeIndex([datetime(2017, 8, 31, 11), + datetime(2017, 8, 31, 12)]), + solar_zenith=[20., 10.], + solar_azimuth=[110., 140.], + surface_tilt=[10., 0.], + surface_azimuth=[90., 90.], + axis_azimuth=0., + dni=[1000., 300.], + dhi=[50., 500.], + gcr=0.4, + pvrow_height=1.75, + pvrow_width=2.44, + albedo=0.2, + n_pvrows=3, + index_observed_pvrow=1, + rho_front_pvrow=0.03, + rho_back_pvrow=0.05, + horizon_band_angle=15., + ) + outputs = dict( + expected_ipoa_front=pd.Series([1034.95474708997, 795.4423259036623], + index=inputs['timestamps'], + name=('total_inc_front')), + expected_ipoa_back=pd.Series([92.12563846416197, 78.05831585685098], + index=inputs['timestamps'], + name=('total_inc_back')), + ) + return inputs, outputs - # Run calculation - ipoa_inc_front, ipoa_inc_back, _, _ = pvfactors_timeseries( - solar_azimuth, solar_zenith, surface_azimuth, surface_tilt, - axis_azimuth, - timestamps, dni, dhi, gcr, pvrow_height, pvrow_width, albedo, - n_pvrows=n_pvrows, index_observed_pvrow=index_observed_pvrow, - rho_front_pvrow=rho_front_pvrow, rho_back_pvrow=rho_back_pvrow, - horizon_band_angle=horizon_band_angle) - assert_series_equal(ipoa_inc_front, expected_ipoa_front) - assert_series_equal(ipoa_inc_back, expected_ipoa_back) +@requires_pvfactors +def test_pvfactors_timeseries_list(example_values): + """Test basic pvfactors functionality with list inputs""" + inputs, outputs = example_values + ipoa_inc_front, ipoa_inc_back, _, _ = pvfactors_timeseries(**inputs) + assert_series_equal(ipoa_inc_front, outputs['expected_ipoa_front']) + assert_series_equal(ipoa_inc_back, outputs['expected_ipoa_back']) @requires_pvfactors -def test_pvfactors_timeseries_pandas_inputs(): - """ Test that pvfactors is functional, using the TLDR section inputs of the - package github repo README.md file, but converted to pandas Series: - https://github.com/SunPower/pvfactors/blob/master/README.md#tldr---quick-start""" +def test_pvfactors_timeseries_pandas(example_values): + """Test basic pvfactors functionality with Series inputs""" + + inputs, outputs = example_values + for key in ['solar_zenith', 'solar_azimuth', 'surface_tilt', + 'surface_azimuth', 'dni', 'dhi']: + inputs[key] = pd.Series(inputs[key], index=inputs['timestamps']) - # Create some inputs - timestamps = pd.DatetimeIndex([datetime(2017, 8, 31, 11), - datetime(2017, 8, 31, 12)] - ).set_names('timestamps') - solar_zenith = pd.Series([20., 10.]) - solar_azimuth = pd.Series([110., 140.]) - surface_tilt = pd.Series([10., 0.]) - surface_azimuth = pd.Series([90., 90.]) - axis_azimuth = 0. - dni = pd.Series([1000., 300.]) - dhi = pd.Series([50., 500.]) - gcr = 0.4 - pvrow_height = 1.75 - pvrow_width = 2.44 - albedo = 0.2 - n_pvrows = 3 - index_observed_pvrow = 1 - rho_front_pvrow = 0.03 - rho_back_pvrow = 0.05 - horizon_band_angle = 15. + ipoa_inc_front, ipoa_inc_back, _, _ = pvfactors_timeseries(**inputs) + assert_series_equal(ipoa_inc_front, outputs['expected_ipoa_front']) + assert_series_equal(ipoa_inc_back, outputs['expected_ipoa_back']) - # Expected values - expected_ipoa_front = pd.Series([1034.95474708997, 795.4423259036623], - index=timestamps, - name=('total_inc_front')) - expected_ipoa_back = pd.Series([92.12563846416197, 78.05831585685098], - index=timestamps, - name=('total_inc_back')) - # Run calculation - ipoa_inc_front, ipoa_inc_back, _, _ = pvfactors_timeseries( - solar_azimuth, solar_zenith, surface_azimuth, surface_tilt, - axis_azimuth, - timestamps, dni, dhi, gcr, pvrow_height, pvrow_width, albedo, - n_pvrows=n_pvrows, index_observed_pvrow=index_observed_pvrow, - rho_front_pvrow=rho_front_pvrow, rho_back_pvrow=rho_back_pvrow, - horizon_band_angle=horizon_band_angle) +@requires_pvfactors +def test_pvfactors_scalar_orientation(example_values): + """test that surface_tilt and surface_azimuth inputs can be scalars""" + # GH 1127, GH 1332 + inputs, outputs = example_values + inputs['surface_tilt'] = 10. + inputs['surface_azimuth'] = 90. + # the second tilt is supposed to be zero, so we need to + # update the expected irradiances too: + outputs['expected_ipoa_front'].iloc[1] = 800.6524022701132 + outputs['expected_ipoa_back'].iloc[1] = 81.72135884745822 - assert_series_equal(ipoa_inc_front, expected_ipoa_front) - assert_series_equal(ipoa_inc_back, expected_ipoa_back) + ipoa_inc_front, ipoa_inc_back, _, _ = pvfactors_timeseries(**inputs) + assert_series_equal(ipoa_inc_front, outputs['expected_ipoa_front']) + assert_series_equal(ipoa_inc_back, outputs['expected_ipoa_back'])