fix modelchain with module temperature and arrays (#1193)

* fix modelchain with module temperature and arrays

* refactor to directly build from arrays

* add test

* improve test

* fix test

Author: wholmgren

pvlib/modelchain.py

@@ -1519,10 +1519,14 @@ def _prepare_temperature(self, data=None):
         if not isinstance(data, tuple):
             # broadcast data to all arrays
             data = (data,) * self.system.num_arrays
+        # data is tuple, so temperature_model_parameters must also be
+        # tuple. system.temperature_model_parameters is reduced to a dict
+        # if system.num_arrays == 1, so manually access parameters. GH 1192
+        t_mod_params = tuple(array.temperature_model_parameters
+                             for array in self.system.arrays)
         # find where cell or module temperature is specified in input data
         given_cell_temperature = tuple(itertools.starmap(
-            self._get_cell_temperature,
-            zip(data, poa, self.system.temperature_model_parameters)
+            self._get_cell_temperature, zip(data, poa, t_mod_params)
         ))
         # If cell temperature has been specified for all arrays return
         # immediately and do not try to compute it.

pvlib/tests/test_modelchain.py

@@ -830,6 +830,38 @@ def test__prepare_temperature(sapm_dc_snl_ac_system, location, weather,
     assert_series_equal(mc.results.cell_temperature, data['cell_temperature'])
 
 
+def test__prepare_temperature_len1_weather_tuple(
+        sapm_dc_snl_ac_system, location, weather, total_irrad):
+    # GH 1192
+    weather['module_temperature'] = [40., 30.]
+    data = weather.copy()
+
+    mc = ModelChain(sapm_dc_snl_ac_system, location, aoi_model='no_loss',
+                    spectral_model='no_loss')
+    mc.run_model([data])
+    expected = pd.Series([42.617244212941394, 30.0], index=data.index)
+    assert_series_equal(mc.results.cell_temperature[0], expected)
+
+    data = weather.copy().rename(
+        columns={
+            "ghi": "poa_global", "dhi": "poa_diffuse", "dni": "poa_direct"}
+    )
+    mc = ModelChain(sapm_dc_snl_ac_system, location, aoi_model='no_loss',
+                    spectral_model='no_loss')
+    mc.run_model_from_poa([data])
+    expected = pd.Series([41.5, 30.0], index=data.index)
+    assert_series_equal(mc.results.cell_temperature[0], expected)
+
+    data = weather.copy()[["module_temperature", "ghi"]].rename(
+        columns={"ghi": "effective_irradiance"}
+    )
+    mc = ModelChain(sapm_dc_snl_ac_system, location, aoi_model='no_loss',
+                    spectral_model='no_loss')
+    mc.run_model_from_effective_irradiance([data])
+    expected = pd.Series([41.5, 30.0], index=data.index)
+    assert_series_equal(mc.results.cell_temperature[0], expected)
+
+
 def test__prepare_temperature_arrays_weather(sapm_dc_snl_ac_system_same_arrays,
                                              location, weather,
                                              total_irrad):