def test_invalid_csv_columns(self):
override = {
"locations": {
"2.techs": {
"test_supply_elec": None,
"test_demand_elec": None
},
"3.techs": {
"test_supply_elec": None,
"test_demand_elec": None
},
},
"links": {
"0,1": {
"exists": False
},
"2,3.techs": {
"test_transmission_elec": None
},
},
}
with pytest.raises(exceptions.ModelError) as excinfo:
build_test_model(override_dict=override, scenario="one_day")
assert check_error_or_warning(
excinfo,
[
"column `2` not found in file `demand_elec.csv`, but was requested by loc::tech `2::test_demand_elec`.",
"column `3` not found in file `demand_elec.csv`, but was requested by loc::tech `3::test_demand_elec`.",
],
)
def test_predefined_clusters(self):
override = {
'model.subset_time': ['2005-01-01', '2005-01-04'],
'model.time': {
'function': 'apply_clustering',
'function_options': {
'clustering_func': 'file=clusters.csv:0', 'how': 'mean'
}
}
}
model = build_test_model(override, scenario='simple_supply')
assert np.array_equal(model._model_data.clusters.to_pandas().unique(), [0, 1, 2])
override2 = {**override, **{
'model.time.function_options.clustering_func': 'file=cluster_days.csv:1'
}}
model = build_test_model(override2, scenario='simple_supply')
assert np.array_equal(model._model_data.clusters.to_pandas().unique(), [0, 1, 2])
override3 = {**override, **{
'model.time.function_options.clustering_func': 'file=cluster_days.csv:1',
'model.time.function_options.how': 'closest'
}}
model = build_test_model(override3, scenario='simple_supply')
assert np.array_equal(model._model_data.clusters.to_pandas().unique(), [0])
def test_invalid_csv_columns(self):
override = {
'locations': {
'2.techs': {
'test_supply_elec': None,
'test_demand_elec': None
},
'3.techs': {
'test_supply_elec': None,
'test_demand_elec': None
}
},
'links': {
'0,1': {
'exists': False
},
'2,3.techs': {
'test_transmission_elec': None
}
}
}
with pytest.raises(exceptions.ModelError) as excinfo:
build_test_model(override_dict=override, scenario='one_day')
assert check_error_or_warning(excinfo, [
'column `2` not found in file `demand_elec.csv`, but was requested by loc::tech `2::test_demand_elec`.',
'column `3` not found in file `demand_elec.csv`, but was requested by loc::tech `3::test_demand_elec`.'
])
def test_15min_resampling_to_6h(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
'techs.test_demand_elec.constraints.resource':
'file=demand_elec_15mins.csv',
'model.subset_time': None,
'model.time': {
'function': 'resample',
'function_options': {
'resolution': '6H'
}
}
}
model = build_test_model(override, scenario='simple_supply,one_day')
data = model._model_data
dtindex = pd.DatetimeIndex([
'2005-01-01 00:00:00', '2005-01-01 06:00:00',
'2005-01-01 12:00:00', '2005-01-01 18:00:00',
'2005-01-02 00:00:00', '2005-01-02 06:00:00',
'2005-01-02 12:00:00', '2005-01-02 18:00:00',
'2005-01-03 00:00:00', '2005-01-03 06:00:00',
'2005-01-03 12:00:00', '2005-01-03 18:00:00'
])
assert dtindex.equals(data.timesteps.to_index())
def test_15min_to_2h_resampling_to_2h(self):
"""
CSV has daily timeseries varying from 15min to 2h resolution, resample all to 2h
"""
override = {
'techs.test_demand_elec.constraints.resource': 'file=demand_elec_15T_to_2h.csv',
'model.subset_time': None,
'model.time': {
'function': 'resample', 'function_options': {'resolution': '2H'}
}
}
model = build_test_model(override, scenario='simple_supply,one_day')
data = model._model_data
dtindex = pd.DatetimeIndex([
'2005-01-01 00:00:00', '2005-01-01 02:00:00', '2005-01-01 04:00:00',
'2005-01-01 06:00:00', '2005-01-01 08:00:00', '2005-01-01 10:00:00',
'2005-01-01 12:00:00', '2005-01-01 14:00:00', '2005-01-01 16:00:00',
'2005-01-01 18:00:00', '2005-01-01 20:00:00', '2005-01-01 22:00:00',
'2005-01-02 00:00:00', '2005-01-02 02:00:00', '2005-01-02 04:00:00',
'2005-01-02 06:00:00', '2005-01-02 08:00:00', '2005-01-02 10:00:00',
'2005-01-02 12:00:00', '2005-01-02 14:00:00', '2005-01-02 16:00:00',
'2005-01-02 18:00:00', '2005-01-02 20:00:00', '2005-01-02 22:00:00',
'2005-01-03 00:00:00', '2005-01-03 02:00:00', '2005-01-03 04:00:00',
'2005-01-03 06:00:00', '2005-01-03 08:00:00', '2005-01-03 10:00:00',
'2005-01-03 12:00:00', '2005-01-03 14:00:00', '2005-01-03 16:00:00',
'2005-01-03 18:00:00', '2005-01-03 20:00:00', '2005-01-03 22:00:00'
])
assert dtindex.equals(data.timesteps.to_index())
def test_15min_to_2h_masking_1D(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
'techs.test_demand_elec.constraints.resource': 'file=demand_elec_15T_to_2h.csv',
'model.subset_time': None
}
model = build_test_model(override, scenario='simple_supply,one_day')
data = model._model_data
mask = masks.extreme(
data, 'test_demand_elec', var='resource', how='max', length='1D'
)
dtindex = pd.DatetimeIndex([
'2005-01-02 00:00:00', '2005-01-02 00:15:00', '2005-01-02 00:30:00',
'2005-01-02 00:45:00', '2005-01-02 01:00:00', '2005-01-02 01:30:00',
'2005-01-02 02:00:00', '2005-01-02 02:30:00', '2005-01-02 03:00:00',
'2005-01-02 04:00:00', '2005-01-02 05:00:00', '2005-01-02 06:00:00',
'2005-01-02 08:00:00', '2005-01-02 10:00:00', '2005-01-02 12:00:00',
'2005-01-02 14:00:00', '2005-01-02 16:00:00', '2005-01-02 17:00:00',
'2005-01-02 18:00:00', '2005-01-02 20:00:00', '2005-01-02 22:00:00'
])
assert dtindex.equals(mask)
def test_15min_resampling_to_6h(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
"techs.test_demand_elec.constraints.resource":
"file=demand_elec_15mins.csv",
"model.subset_time": None,
"model.time": {
"function": "resample",
"function_options": {
"resolution": "6H"
},
},
}
model = build_test_model(override, scenario="simple_supply,one_day")
data = model._model_data
dtindex = pd.DatetimeIndex([
"2005-01-01 00:00:00",
"2005-01-01 06:00:00",
"2005-01-01 12:00:00",
"2005-01-01 18:00:00",
"2005-01-02 00:00:00",
"2005-01-02 06:00:00",
"2005-01-02 12:00:00",
"2005-01-02 18:00:00",
"2005-01-03 00:00:00",
"2005-01-03 06:00:00",
"2005-01-03 12:00:00",
"2005-01-03 18:00:00",
])
assert dtindex.equals(data.timesteps.to_index())
def test_15min_to_2h_resampling_to_2h(self):
"""
CSV has daily timeseries varying from 15min to 2h resolution, resample all to 2h
"""
override = {
"techs.test_demand_elec.constraints.resource":
"file=demand_elec_15T_to_2h.csv",
"model.subset_time": None,
"model.time": {
"function": "resample",
"function_options": {
"resolution": "2H"
},
},
}
model = build_test_model(override, scenario="simple_supply,one_day")
data = model._model_data
dtindex = pd.DatetimeIndex([
"2005-01-01 00:00:00",
"2005-01-01 02:00:00",
"2005-01-01 04:00:00",
"2005-01-01 06:00:00",
"2005-01-01 08:00:00",
"2005-01-01 10:00:00",
"2005-01-01 12:00:00",
"2005-01-01 14:00:00",
"2005-01-01 16:00:00",
"2005-01-01 18:00:00",
"2005-01-01 20:00:00",
"2005-01-01 22:00:00",
"2005-01-02 00:00:00",
"2005-01-02 02:00:00",
"2005-01-02 04:00:00",
"2005-01-02 06:00:00",
"2005-01-02 08:00:00",
"2005-01-02 10:00:00",
"2005-01-02 12:00:00",
"2005-01-02 14:00:00",
"2005-01-02 16:00:00",
"2005-01-02 18:00:00",
"2005-01-02 20:00:00",
"2005-01-02 22:00:00",
"2005-01-03 00:00:00",
"2005-01-03 02:00:00",
"2005-01-03 04:00:00",
"2005-01-03 06:00:00",
"2005-01-03 08:00:00",
"2005-01-03 10:00:00",
"2005-01-03 12:00:00",
"2005-01-03 14:00:00",
"2005-01-03 16:00:00",
"2005-01-03 18:00:00",
"2005-01-03 20:00:00",
"2005-01-03 22:00:00",
])
assert dtindex.equals(data.timesteps.to_index())
def test_predefined_clusters(self):
override = {
"model.subset_time": ["2005-01-01", "2005-01-04"],
"model.time": {
"function": "apply_clustering",
"function_options": {
"clustering_func": "file=clusters.csv:0",
"how": "mean",
},
},
}
model = build_test_model(override, scenario="simple_supply")
assert np.array_equal(model._model_data.clusters.to_pandas().unique(),
[0, 1, 2])
override2 = {
**override,
**{
"model.time.function_options.clustering_func":
"file=cluster_days.csv:1"
},
}
model = build_test_model(override2, scenario="simple_supply")
assert np.array_equal(model._model_data.clusters.to_pandas().unique(),
[0, 1, 2])
override3 = {
**override,
**{
"model.time.function_options.clustering_func": "file=cluster_days.csv:1",
"model.time.function_options.how": "closest",
},
}
model = build_test_model(override3, scenario="simple_supply")
assert np.array_equal(model._model_data.clusters.to_pandas().unique(),
[0])
def test_invalid_csv_columns(self):
override = {
"nodes": {
"c.techs": {"test_supply_elec": None, "test_demand_elec": None},
"d.techs": {"test_supply_elec": None, "test_demand_elec": None},
},
"links": {
"a,b": {"exists": False},
"c,d.techs": {"test_transmission_elec": None},
},
}
with pytest.raises(exceptions.ModelError) as excinfo:
build_test_model(override_dict=override, scenario="one_day")
assert check_error_or_warning(
excinfo,
[
"file:column combinations `[('demand_elec.csv', 'c') ('demand_elec.csv', 'd')]` not found, but are requested by parameter `resource`."
],
)
def test_predefined_clusters_fail(self):
override = {
'model.subset_time': ['2005-01-01', '2005-01-04'],
'model.time': {
'function': 'apply_clustering',
'function_options': {
'clustering_func': 'file=clusters.csv:0', 'how': 'mean'
}
}
}
# should fail - no CSV data column defined
override1 = {**override, **{
'model.time.function_options.clustering_func': 'file=clusters.csv'
}}
with pytest.raises(exceptions.ModelError) as error:
build_test_model(override1, scenario='simple_supply')
assert check_error_or_warning(error, 'No time clustering column given')
# should fail - unknown CSV data column defined
override2 = {**override, **{
'model.time.function_options.clustering_func': 'file=clusters.csv:1'
}}
with pytest.raises(KeyError) as error:
build_test_model(override2, scenario='simple_supply')
assert check_error_or_warning(error, 'time clustering column 1 not found')
# should fail - more than one cluster given to any one day
override3 = {**override, **{
'model.time.function_options.clustering_func': 'file=clusters.csv:b'
}}
with pytest.raises(exceptions.ModelError) as error:
build_test_model(override3, scenario='simple_supply')
assert check_error_or_warning(
error, 'More than one cluster value assigned to a day in `clusters.csv:b`'
)
# should fail - not enough data in clusters.csv to cover subset_time
override4 = {**override, **{
'model.subset_time': ['2005-01-01', '2005-01-06'],
'model.time.function_options.clustering_func': 'file=cluster_days.csv:1'
}}
with pytest.raises(exceptions.ModelError) as error:
build_test_model(override4, scenario='simple_supply')
assert check_error_or_warning(error, 'Missing cluster days')
def test_15min_clustering(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
"techs.test_demand_elec.constraints.resource":
"file=demand_elec_15mins.csv",
"model.subset_time": None,
}
model = build_test_model(override, scenario="simple_supply,one_day")
data = model._model_data
data_clustered_kmeans = funcs.apply_clustering(
data,
timesteps=None,
clustering_func="kmeans",
how="mean",
normalize=True,
k=2,
)
data_clustered_hierarchical = funcs.apply_clustering(
data,
timesteps=None,
clustering_func="hierarchical",
how="mean",
normalize=True,
k=2,
)
assert len(data_clustered_kmeans.clusters.to_pandas().unique()) == 2
assert len(
data_clustered_hierarchical.clusters.to_pandas().unique()) == 2
days = np.unique(
data_clustered_kmeans.timesteps.to_index().strftime("%Y-%m-%d"))
# not sure which of '2005-01-01' and '2005-01-03' it will choose to
# label the cluster of those two days
assert "2005-01-02" in days and ("2005-01-01" in days
or "2005-01-03" in days)
assert np.array_equal(
data_clustered_kmeans.timestep_resolution.values,
[0.25 for i in range(24 * 4 * 2)],
)
def test_15min_to_2h_clustering(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
'techs.test_demand_elec.constraints.resource': 'file=demand_elec_15T_to_2h.csv',
'model.subset_time': None
}
model = build_test_model(override, scenario='simple_supply,one_day')
data = model._model_data
data_clustered_kmeans = funcs.apply_clustering(
data,
timesteps=None,
clustering_func='kmeans',
how='mean',
normalize=True,
k=2
)
data_clustered_hierarchical = funcs.apply_clustering(
data,
timesteps=None,
clustering_func='hierarchical',
how='mean',
normalize=True,
k=2
)
assert len(data_clustered_kmeans.clusters.to_pandas().unique()) == 2
assert len(data_clustered_hierarchical.clusters.to_pandas().unique()) == 2
days = np.unique(data_clustered_kmeans.timesteps.to_index().strftime('%Y-%m-%d'))
# not sure which of '2005-01-01' and '2005-01-03' it will choose to
# label the cluster of those two days
assert '2005-01-02' in days and ('2005-01-01' in days or '2005-01-03' in days)
assert np.array_equal(
data_clustered_kmeans.timestep_resolution.values,
[
0.25, 0.25, 0.25, 0.25, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 2, 2, 2, 2,
2, 1, 1, 2, 2, 2, 0.25, 0.25, 0.25, 0.25, 0.5, 0.5, 0.5, 0.5, 1,
1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2
]
)
def test_15min_to_2h_masking_1D(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
"techs.test_demand_elec.constraints.resource":
"file=demand_elec_15T_to_2h.csv",
"model.subset_time": None,
}
model = build_test_model(override, scenario="simple_supply,one_day")
data = model._model_data
mask = masks.extreme(data,
"test_demand_elec",
var="resource",
how="max",
length="1D")
dtindex = pd.DatetimeIndex([
"2005-01-02 00:00:00",
"2005-01-02 00:15:00",
"2005-01-02 00:30:00",
"2005-01-02 00:45:00",
"2005-01-02 01:00:00",
"2005-01-02 01:30:00",
"2005-01-02 02:00:00",
"2005-01-02 02:30:00",
"2005-01-02 03:00:00",
"2005-01-02 04:00:00",
"2005-01-02 05:00:00",
"2005-01-02 06:00:00",
"2005-01-02 08:00:00",
"2005-01-02 10:00:00",
"2005-01-02 12:00:00",
"2005-01-02 14:00:00",
"2005-01-02 16:00:00",
"2005-01-02 17:00:00",
"2005-01-02 18:00:00",
"2005-01-02 20:00:00",
"2005-01-02 22:00:00",
])
assert dtindex.equals(mask)
def test_15min_resampling_to_6h(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
'techs.test_demand_elec.constraints.resource': 'file=demand_elec_15mins.csv',
'model.subset_time': None,
'model.time': {
'function': 'resample', 'function_options': {'resolution': '6H'}
}
}
model = build_test_model(override, scenario='simple_supply,one_day')
data = model._model_data
dtindex = pd.DatetimeIndex([
'2005-01-01 00:00:00', '2005-01-01 06:00:00', '2005-01-01 12:00:00',
'2005-01-01 18:00:00', '2005-01-02 00:00:00', '2005-01-02 06:00:00',
'2005-01-02 12:00:00', '2005-01-02 18:00:00', '2005-01-03 00:00:00',
'2005-01-03 06:00:00', '2005-01-03 12:00:00', '2005-01-03 18:00:00'
])
assert dtindex.equals(data.timesteps.to_index())
def test_15min_masking_1D_resampling_to_2h(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
'techs.test_demand_elec.constraints.resource': 'file=demand_elec_15mins.csv',
'model.subset_time': None,
'model.time': {
'masks': [{
'function': 'extreme',
'options': {'tech': 'test_demand_elec', 'how': 'max'}
}],
'function': 'resample', 'function_options': {'resolution': '2H'}
}
}
model = build_test_model(override, scenario='simple_supply,one_day')
data = model._model_data
dtindex = pd.DatetimeIndex([
'2005-01-01 00:00:00', '2005-01-01 02:00:00', '2005-01-01 04:00:00',
'2005-01-01 06:00:00', '2005-01-01 08:00:00', '2005-01-01 10:00:00',
'2005-01-01 12:00:00', '2005-01-01 14:00:00', '2005-01-01 16:00:00',
'2005-01-01 18:00:00', '2005-01-01 20:00:00', '2005-01-01 22:00:00',
'2005-01-02 00:00:00', '2005-01-02 00:15:00', '2005-01-02 00:30:00',
'2005-01-02 00:45:00', '2005-01-02 01:00:00', '2005-01-02 01:15:00',
'2005-01-02 01:30:00', '2005-01-02 01:45:00', '2005-01-02 02:00:00',
'2005-01-02 02:15:00', '2005-01-02 02:30:00', '2005-01-02 02:45:00',
'2005-01-02 03:00:00', '2005-01-02 03:15:00', '2005-01-02 03:30:00',
'2005-01-02 03:45:00', '2005-01-02 04:00:00', '2005-01-02 04:15:00',
'2005-01-02 04:30:00', '2005-01-02 04:45:00', '2005-01-02 05:00:00',
'2005-01-02 05:15:00', '2005-01-02 05:30:00', '2005-01-02 05:45:00',
'2005-01-02 06:00:00', '2005-01-02 06:15:00', '2005-01-02 06:30:00',
'2005-01-02 06:45:00', '2005-01-02 07:00:00', '2005-01-02 07:15:00',
'2005-01-02 07:30:00', '2005-01-02 07:45:00', '2005-01-02 08:00:00',
'2005-01-02 08:15:00', '2005-01-02 08:30:00', '2005-01-02 08:45:00',
'2005-01-02 09:00:00', '2005-01-02 09:15:00', '2005-01-02 09:30:00',
'2005-01-02 09:45:00', '2005-01-02 10:00:00', '2005-01-02 10:15:00',
'2005-01-02 10:30:00', '2005-01-02 10:45:00', '2005-01-02 11:00:00',
'2005-01-02 11:15:00', '2005-01-02 11:30:00', '2005-01-02 11:45:00',
'2005-01-02 12:00:00', '2005-01-02 12:15:00', '2005-01-02 12:30:00',
'2005-01-02 12:45:00', '2005-01-02 13:00:00', '2005-01-02 13:15:00',
'2005-01-02 13:30:00', '2005-01-02 13:45:00', '2005-01-02 14:00:00',
'2005-01-02 14:15:00', '2005-01-02 14:30:00', '2005-01-02 14:45:00',
'2005-01-02 15:00:00', '2005-01-02 15:15:00', '2005-01-02 15:30:00',
'2005-01-02 15:45:00', '2005-01-02 16:00:00', '2005-01-02 16:15:00',
'2005-01-02 16:30:00', '2005-01-02 16:45:00', '2005-01-02 17:00:00',
'2005-01-02 17:15:00', '2005-01-02 17:30:00', '2005-01-02 17:45:00',
'2005-01-02 18:00:00', '2005-01-02 18:15:00', '2005-01-02 18:30:00',
'2005-01-02 18:45:00', '2005-01-02 19:00:00', '2005-01-02 19:15:00',
'2005-01-02 19:30:00', '2005-01-02 19:45:00', '2005-01-02 20:00:00',
'2005-01-02 20:15:00', '2005-01-02 20:30:00', '2005-01-02 20:45:00',
'2005-01-02 21:00:00', '2005-01-02 21:15:00', '2005-01-02 21:30:00',
'2005-01-02 21:45:00', '2005-01-02 22:00:00', '2005-01-02 22:15:00',
'2005-01-02 22:30:00', '2005-01-02 22:45:00', '2005-01-02 23:00:00',
'2005-01-02 23:15:00', '2005-01-02 23:30:00', '2005-01-02 23:45:00',
'2005-01-03 00:00:00', '2005-01-03 02:00:00', '2005-01-03 04:00:00',
'2005-01-03 06:00:00', '2005-01-03 08:00:00', '2005-01-03 10:00:00',
'2005-01-03 12:00:00', '2005-01-03 14:00:00', '2005-01-03 16:00:00',
'2005-01-03 18:00:00', '2005-01-03 20:00:00', '2005-01-03 22:00:00'
])
assert dtindex.equals(data.timesteps.to_index())
def test_15min_masking_1D_resampling_to_2h(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
"techs.test_demand_elec.constraints.resource":
"file=demand_elec_15mins.csv",
"model.subset_time": None,
"model.time": {
"masks": [{
"function": "extreme",
"options": {
"tech": "test_demand_elec",
"how": "max"
},
}],
"function":
"resample",
"function_options": {
"resolution": "2H"
},
},
}
model = build_test_model(override, scenario="simple_supply,one_day")
data = model._model_data
dtindex = pd.DatetimeIndex([
"2005-01-01 00:00:00",
"2005-01-01 02:00:00",
"2005-01-01 04:00:00",
"2005-01-01 06:00:00",
"2005-01-01 08:00:00",
"2005-01-01 10:00:00",
"2005-01-01 12:00:00",
"2005-01-01 14:00:00",
"2005-01-01 16:00:00",
"2005-01-01 18:00:00",
"2005-01-01 20:00:00",
"2005-01-01 22:00:00",
"2005-01-02 00:00:00",
"2005-01-02 00:15:00",
"2005-01-02 00:30:00",
"2005-01-02 00:45:00",
"2005-01-02 01:00:00",
"2005-01-02 01:15:00",
"2005-01-02 01:30:00",
"2005-01-02 01:45:00",
"2005-01-02 02:00:00",
"2005-01-02 02:15:00",
"2005-01-02 02:30:00",
"2005-01-02 02:45:00",
"2005-01-02 03:00:00",
"2005-01-02 03:15:00",
"2005-01-02 03:30:00",
"2005-01-02 03:45:00",
"2005-01-02 04:00:00",
"2005-01-02 04:15:00",
"2005-01-02 04:30:00",
"2005-01-02 04:45:00",
"2005-01-02 05:00:00",
"2005-01-02 05:15:00",
"2005-01-02 05:30:00",
"2005-01-02 05:45:00",
"2005-01-02 06:00:00",
"2005-01-02 06:15:00",
"2005-01-02 06:30:00",
"2005-01-02 06:45:00",
"2005-01-02 07:00:00",
"2005-01-02 07:15:00",
"2005-01-02 07:30:00",
"2005-01-02 07:45:00",
"2005-01-02 08:00:00",
"2005-01-02 08:15:00",
"2005-01-02 08:30:00",
"2005-01-02 08:45:00",
"2005-01-02 09:00:00",
"2005-01-02 09:15:00",
"2005-01-02 09:30:00",
"2005-01-02 09:45:00",
"2005-01-02 10:00:00",
"2005-01-02 10:15:00",
"2005-01-02 10:30:00",
"2005-01-02 10:45:00",
"2005-01-02 11:00:00",
"2005-01-02 11:15:00",
"2005-01-02 11:30:00",
"2005-01-02 11:45:00",
"2005-01-02 12:00:00",
"2005-01-02 12:15:00",
"2005-01-02 12:30:00",
"2005-01-02 12:45:00",
"2005-01-02 13:00:00",
"2005-01-02 13:15:00",
"2005-01-02 13:30:00",
"2005-01-02 13:45:00",
"2005-01-02 14:00:00",
"2005-01-02 14:15:00",
"2005-01-02 14:30:00",
"2005-01-02 14:45:00",
"2005-01-02 15:00:00",
"2005-01-02 15:15:00",
"2005-01-02 15:30:00",
"2005-01-02 15:45:00",
"2005-01-02 16:00:00",
"2005-01-02 16:15:00",
"2005-01-02 16:30:00",
"2005-01-02 16:45:00",
"2005-01-02 17:00:00",
"2005-01-02 17:15:00",
"2005-01-02 17:30:00",
"2005-01-02 17:45:00",
"2005-01-02 18:00:00",
"2005-01-02 18:15:00",
"2005-01-02 18:30:00",
"2005-01-02 18:45:00",
"2005-01-02 19:00:00",
"2005-01-02 19:15:00",
"2005-01-02 19:30:00",
"2005-01-02 19:45:00",
"2005-01-02 20:00:00",
"2005-01-02 20:15:00",
"2005-01-02 20:30:00",
"2005-01-02 20:45:00",
"2005-01-02 21:00:00",
"2005-01-02 21:15:00",
"2005-01-02 21:30:00",
"2005-01-02 21:45:00",
"2005-01-02 22:00:00",
"2005-01-02 22:15:00",
"2005-01-02 22:30:00",
"2005-01-02 22:45:00",
"2005-01-02 23:00:00",
"2005-01-02 23:15:00",
"2005-01-02 23:30:00",
"2005-01-02 23:45:00",
"2005-01-03 00:00:00",
"2005-01-03 02:00:00",
"2005-01-03 04:00:00",
"2005-01-03 06:00:00",
"2005-01-03 08:00:00",
"2005-01-03 10:00:00",
"2005-01-03 12:00:00",
"2005-01-03 14:00:00",
"2005-01-03 16:00:00",
"2005-01-03 18:00:00",
"2005-01-03 20:00:00",
"2005-01-03 22:00:00",
])
assert dtindex.equals(data.timesteps.to_index())
def test_predefined_clusters_fail(self):
override = {
"model.subset_time": ["2005-01-01", "2005-01-04"],
"model.time": {
"function": "apply_clustering",
"function_options": {
"clustering_func": "file=clusters.csv:0",
"how": "mean",
},
},
}
# should fail - no CSV data column defined
override1 = {
**override,
**{
"model.time.function_options.clustering_func": "file=clusters.csv"
},
}
with pytest.raises(exceptions.ModelError) as error:
build_test_model(override1, scenario="simple_supply")
assert check_error_or_warning(error, "No time clustering column given")
# should fail - unknown CSV data column defined
override2 = {
**override,
**{
"model.time.function_options.clustering_func":
"file=clusters.csv:1"
},
}
with pytest.raises(KeyError) as error:
build_test_model(override2, scenario="simple_supply")
assert check_error_or_warning(error,
"time clustering column 1 not found")
# should fail - more than one cluster given to any one day
override3 = {
**override,
**{
"model.time.function_options.clustering_func":
"file=clusters.csv:b"
},
}
with pytest.raises(exceptions.ModelError) as error:
build_test_model(override3, scenario="simple_supply")
assert check_error_or_warning(
error,
"More than one cluster value assigned to a day in `clusters.csv:b`"
)
# should fail - not enough data in clusters.csv to cover subset_time
override4 = {
**override,
**{
"model.subset_time": ["2005-01-01", "2005-01-06"],
"model.time.function_options.clustering_func":
"file=cluster_days.csv:1",
},
}
with pytest.raises(exceptions.ModelError) as error:
build_test_model(override4, scenario="simple_supply")
assert check_error_or_warning(error, "Missing cluster days")
