forked from calliope-project/calliope
-
Notifications
You must be signed in to change notification settings - Fork 0
/
conftest.py
257 lines (215 loc) · 7.59 KB
/
conftest.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
from itertools import chain, combinations
import numpy as np
import pytest
import xarray as xr
from calliope import AttrDict
from calliope.backend import backends, latex_backend
from calliope.test.common.util import build_test_model as build_model
ALL_DIMS = {"nodes", "techs", "carriers", "costs", "timesteps", "carrier_tiers"}
@pytest.fixture(
scope="session",
params=set(
chain.from_iterable(combinations(ALL_DIMS, i) for i in range(1, len(ALL_DIMS)))
),
)
def foreach(request):
return request.param
@pytest.fixture(scope="session")
def simple_supply():
m = build_model({}, "simple_supply,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def supply_milp():
m = build_model({}, "supply_milp,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def storage_milp():
m = build_model({}, "storage_milp,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def conversion_plus_milp():
m = build_model({}, "conversion_plus_milp,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def supply_and_supply_plus_milp():
m = build_model({}, "supply_and_supply_plus_milp,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def simple_supply_and_supply_plus():
m = build_model({}, "simple_supply_and_supply_plus,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def simple_storage():
m = build_model({}, "simple_storage,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def simple_conversion():
m = build_model({}, "simple_conversion,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def supply_export():
m = build_model({}, "supply_export,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def supply_purchase():
m = build_model({}, "supply_purchase,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def conversion_plus_purchase():
m = build_model({}, "conversion_plus_purchase,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def storage_purchase():
m = build_model({}, "storage_purchase,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="session")
def simple_conversion_plus():
m = build_model({}, "simple_conversion_plus,two_hours,investment_costs")
m.build()
m.solve()
return m
@pytest.fixture(scope="module")
def dummy_model_data():
model_data = xr.Dataset(
coords={
dim: ["foo", "bar"]
if dim != "techs"
else ["foobar", "foobaz", "barfoo", "bazfoo"]
for dim in ALL_DIMS
},
data_vars={
"node_tech": (
["nodes", "techs"],
np.random.choice(a=[np.nan, True], p=[0.05, 0.95], size=(2, 4)),
),
"carrier": (
["carrier_tiers", "carriers", "techs"],
np.random.choice(a=[np.nan, True], p=[0.05, 0.95], size=(2, 2, 4)),
),
"with_inf": (
["nodes", "techs"],
[[1.0, np.nan, 1.0, 3], [np.inf, 2.0, True, np.nan]],
),
"only_techs": (["techs"], [np.nan, 1, 2, 3]),
"no_dims": (2),
"all_inf": (["nodes", "techs"], np.ones((2, 4)) * np.inf, {"is_result": 1}),
"all_nan": (["nodes", "techs"], np.ones((2, 4)) * np.nan),
"all_false": (["nodes", "techs"], np.zeros((2, 4)).astype(bool)),
"all_true": (["nodes", "techs"], np.ones((2, 4)).astype(bool)),
"all_true_carriers": (["carriers", "techs"], np.ones((2, 4)).astype(bool)),
"nodes_true": (["nodes"], [True, True]),
"nodes_false": (["nodes"], [False, False]),
"with_inf_as_bool": (
["nodes", "techs"],
[[True, False, True, True], [False, True, True, False]],
),
"with_inf_as_bool_and_subset_on_bar_in_nodes": (
["nodes", "techs"],
[[False, False, False, False], [False, True, True, False]],
),
"with_inf_as_bool_or_subset_on_bar_in_nodes": (
["nodes", "techs"],
[[True, False, True, True], [True, True, True, True]],
),
"only_techs_as_bool": (["techs"], [False, True, True, True]),
"with_inf_and_only_techs_as_bool": (
["nodes", "techs"],
[[False, False, True, True], [False, True, True, False]],
),
"with_inf_or_only_techs_as_bool": (
["nodes", "techs"],
[[True, True, True, True], [False, True, True, True]],
),
"inheritance": (
["nodes", "techs"],
[
["foo.bar", "boo", "baz", "boo"],
["bar", "ar", "baz.boo", "foo.boo"],
],
),
"boo_inheritance_bool": (
["nodes", "techs"],
[[False, True, False, True], [False, False, True, True]],
),
"primary_carrier_out": (
["carriers", "techs"],
[[1.0, np.nan, 1.0, np.nan], [np.nan, 1.0, np.nan, np.nan]],
),
"lookup_techs": (
["techs"],
["foobar", np.nan, "foobaz", np.nan],
),
"link_remote_nodes": (
["nodes", "techs"],
[["bar", np.nan, "bar", np.nan], ["foo", np.nan, np.nan, np.nan]],
),
"link_remote_techs": (
["nodes", "techs"],
[
["foobar", np.nan, "foobaz", np.nan],
["bazfoo", np.nan, np.nan, np.nan],
],
),
},
attrs={"scenarios": ["foo"]},
)
# xarray forces np.nan to strings if all other values are strings.
for k in ["link_remote_nodes", "link_remote_techs", "lookup_techs"]:
model_data[k] = model_data[k].where(model_data[k] != "nan")
for param in model_data.data_vars.values():
param.attrs["is_result"] = 0
model_data.attrs["run_config"] = AttrDict(
{"foo": True, "bar": {"foobar": "baz"}, "foobar": {"baz": {"foo": np.inf}}}
)
model_data.attrs["model_config"] = AttrDict({"a_b": 0, "b_a": [1, 2]})
model_data.attrs["defaults"] = AttrDict(
{"all_inf": np.inf, "all_nan": np.nan, "with_inf": 100, "only_techs": 5}
)
return model_data
def populate_backend_model(backend, model_data):
backend.add_all_parameters(model_data, model_data.run_config)
backend.add_variable(
model_data,
"multi_dim_var",
{
"foreach": ["nodes", "techs"],
"where": "with_inf",
"bounds": {"min": "0", "max": ".inf"},
},
)
backend.add_variable(
model_data, "no_dim_var", {"bounds": {"min": "-1", "max": "1"}}
)
return backend
@pytest.fixture(scope="module")
def dummy_pyomo_backend_model(dummy_model_data):
backend = backends.PyomoBackendModel()
return populate_backend_model(backend, dummy_model_data)
@pytest.fixture(scope="module")
def dummy_latex_backend_model(dummy_model_data):
backend = latex_backend.LatexBackendModel()
return populate_backend_model(backend, dummy_model_data)