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fix run_model_from_effective_irradiance with iterable weather and excluding cell temperature #1165

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merged 8 commits into from
Feb 8, 2021
Merged

fix run_model_from_effective_irradiance with iterable weather and excluding cell temperature #1165

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0eea3a1
check for complete poa in _set_celltemp
cwhanse Feb 6, 2021
27ed23a
test for new location of ValueError, add test for run_from_eff_irrad …
cwhanse Feb 6, 2021
94ca106
handle single data/arrays, fix test
cwhanse Feb 6, 2021
2d20a38
remove old test
cwhanse Feb 8, 2021
aa750e1
add _verify_df to run_from_effective_irradiance
cwhanse Feb 8, 2021
df5b8fd
remove ValueError from _set_celltemp
cwhanse Feb 8, 2021
00eb76f
test for single, list tuple
cwhanse Feb 8, 2021
a3aa924
edits from review
cwhanse Feb 8, 2021
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27 changes: 7 additions & 20 deletions pvlib/modelchain.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1572,11 +1572,13 @@ def _prepare_temperature(self, data=None):
"""
poa = _irrad_for_celltemp(self.results.total_irrad,
self.results.effective_irradiance)
if not isinstance(data, tuple) and self.system.num_arrays > 1:
# handle simple case first, single array, data not iterable
if not isinstance(data, tuple) and self.system.num_arrays == 1:
return self._prepare_temperature_single_array(data, poa)
if not isinstance(data, tuple):
# broadcast data to all arrays
data = (data,) * self.system.num_arrays
elif not isinstance(data, tuple):
return self._prepare_temperature_single_array(data, poa)
# 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)
Expand All @@ -1587,23 +1589,7 @@ def _prepare_temperature(self, data=None):
self.results.cell_temperature = given_cell_temperature
return self
# Calculate cell temperature from weather data. If cell_temperature
# has not been provided for some arrays then it is computed with
# ModelChain.temperature_model(). Because this operates on all Arrays
# simultaneously, 'poa_global' must be known for all arrays, including
# those that have a known cell temperature.
try:
self._verify_df(self.results.total_irrad, ['poa_global'])
except ValueError:
# Provide a more informative error message. Because only
# run_model_from_effective_irradiance() can get to this point
# without known POA we can suggest a very specific remedy in the
# error message.
raise ValueError("Incomplete input data. Data must contain "
"'poa_global'. For systems with multiple Arrays "
"if you have provided 'cell_temperature' for "
"only a subset of Arrays you must provide "
"'poa_global' for all Arrays, including those "
"that have a known 'cell_temperature'.")
# has not been provided for some arrays then it is computed.
self.temperature_model()
# replace calculated cell temperature with temperature given in `data`
# where available.
Expand Down Expand Up @@ -1814,6 +1800,7 @@ def run_model_from_effective_irradiance(self, data=None):
"""
data = _to_tuple(data)
self._check_multiple_input(data)
self._verify_df(data, required=['effective_irradiance'])
self._assign_weather(data)
self._assign_total_irrad(data)
self.results.effective_irradiance = _tuple_from_dfs(
Expand Down
43 changes: 22 additions & 21 deletions pvlib/tests/test_modelchain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -985,26 +985,44 @@ def test_run_model_from_poa_tracking(sapm_dc_snl_ac_system, location,
assert_series_equal(ac, expected)


@pytest.mark.parametrize("input_type", [lambda x: x[0], tuple, list])
def test_run_model_from_effective_irradiance(sapm_dc_snl_ac_system, location,
weather, total_irrad):
weather, total_irrad, input_type):
data = weather.copy()
data[['poa_global', 'poa_diffuse', 'poa_direct']] = total_irrad
data['effective_irradiance'] = data['poa_global']
mc = ModelChain(sapm_dc_snl_ac_system, location, aoi_model='no_loss',
spectral_model='no_loss')
ac = mc.run_model_from_effective_irradiance(data).results.ac
ac = mc.run_model_from_effective_irradiance(input_type((data,))).results.ac
expected = pd.Series(np.array([149.280238, 96.678385]),
index=data.index)
assert_series_equal(ac, expected)


@pytest.mark.parametrize("input_type", [tuple, list])
def test_run_model_from_effective_irradiance_multi_array(
sapm_dc_snl_ac_system_Array, location, weather, total_irrad,
input_type):
data = weather.copy()
data[['poa_global', 'poa_diffuse', 'poa_direct']] = total_irrad
data['effective_irradiance'] = data['poa_global']
mc = ModelChain(sapm_dc_snl_ac_system_Array, location, aoi_model='no_loss',
spectral_model='no_loss')
mc.run_model_from_effective_irradiance(input_type((data, data)))
# arrays have different orientation, but should give same dc power
# because we are the same passing POA irradiance and air
# temperature.
assert_frame_equal(mc.results.dc[0], mc.results.dc[1])


@pytest.mark.parametrize("input_type", [lambda x: x[0], tuple, list])
def test_run_model_from_effective_irradiance_no_poa_global(
sapm_dc_snl_ac_system, location, weather, total_irrad):
sapm_dc_snl_ac_system, location, weather, total_irrad, input_type):
data = weather.copy()
data['effective_irradiance'] = total_irrad['poa_global']
mc = ModelChain(sapm_dc_snl_ac_system, location, aoi_model='no_loss',
spectral_model='no_loss')
ac = mc.run_model_from_effective_irradiance(data).results.ac
ac = mc.run_model_from_effective_irradiance(input_type((data,))).results.ac
expected = pd.Series(np.array([149.280238, 96.678385]),
index=data.index)
assert_series_equal(ac, expected)
Expand Down Expand Up @@ -1087,23 +1105,6 @@ def test_run_model_from_effective_irradiance_minimal_input(
assert not mc.results.ac.empty


def test_run_model_from_effective_irradiance_missing_poa(
sapm_dc_snl_ac_system_Array, location, total_irrad):
data_incomplete = pd.DataFrame(
{'effective_irradiance': total_irrad['poa_global'],
'poa_global': total_irrad['poa_global']},
index=total_irrad.index)
data_complete = pd.DataFrame(
{'effective_irradiance': total_irrad['poa_global'],
'cell_temperature': 30},
index=total_irrad.index)
mc = ModelChain(sapm_dc_snl_ac_system_Array, location)
with pytest.raises(ValueError,
match="you must provide 'poa_global' for all Arrays"):
mc.run_model_from_effective_irradiance(
(data_complete, data_incomplete))


def test_run_model_singleton_weather_single_array(cec_dc_snl_ac_system,
location, weather):
mc = ModelChain(cec_dc_snl_ac_system, location,
Expand Down