Allow scalar surface orientation inputs to pvlib.bifacial.pvfactors_timeseries (#1361)

* simplify pvfactors_timeseries, fix scalar bug, rework tests

* whatsnew

* remove overlooked debugging code

* nix is_scalar conditional, use full_like instead

Author: kandersolar

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

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

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'])