statsmodels decorator

Author: cwhanse

pvlib/inverter.py

@@ -14,7 +14,6 @@
 import pandas as pd
 from numpy.polynomial.polynomial import polyfit  # different than np.polyfit
 from scipy.optimize import minimize
-import statsmodels.api as sm
 from pvlib._deprecation import deprecated
 
 
@@ -584,6 +583,11 @@ def _fit_sandia_field_a(pac, pdc, vdc, pac0, vdc0):
     dict
         Parameters Pdco, Pso and C0 for the Sandia inverter model.
     '''
+    try:
+        import statsmodels.api as sm
+    except ImportError:
+        raise ImportError(
+            'Parameter fitting requires statsmodels')
     # select data. Avoid very low power, clipping and DC voltage far from
     # nominal
     u = (pac > 0.05*pac0) & (pac < pac0) & (np.abs(vdc - vdc0)/vdc0 < 0.05)    

tests/test_inverter.py

@@ -1,10 +1,11 @@
 import numpy as np
 import pandas as pd
-
-from .conftest import assert_series_equal
 from numpy.testing import assert_allclose
 
+from .conftest import assert_series_equal
 from .conftest import TESTS_DATA_DIR
+from .conftest import requires_statsmodels
+
 import pytest
 
 from pvlib import inverter
@@ -202,17 +203,18 @@ def test_pvwatts_multi():
                           'Pso': 10., 'C0': 1e-6, 'C1': 1e-4, 'C2': 1e-2,
                           'C3': 1e-3, 'Pnt': 1.}),
 ])
-def test_fit_sandia(infilen, expected):
+def test_fit_sandia_lab(infilen, expected):
     curves = pd.read_csv(infilen)
     dc_power = curves['ac_power'] / curves['efficiency']
-    result = inverter.fit_sandia(ac_power=curves['ac_power'],
-                                 dc_power=dc_power,
-                                 dc_voltage=curves['dc_voltage'],
-                                 dc_voltage_level=curves['dc_voltage_level'],
-                                 p_ac_0=expected['Paco'], p_nt=expected['Pnt'])
+    result = inverter.fit_sandia_lab(
+        ac_power=curves['ac_power'], dc_power=dc_power, 
+        dc_voltage=curves['dc_voltage'],
+        dc_voltage_level=curves['dc_voltage_level'],
+        p_ac_0=expected['Paco'], p_nt=expected['Pnt'])
     assert expected == pytest.approx(result, rel=1e-3)
 
 
+@requires_statsmodels
 def test_fit_sandia_field():
     pdc = np.arange(start=100., stop=1300., step=100.)
     vdc = np.array([550., 600., 650, 550., 600., 650, 550., 600., 650,