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Add test precalculated
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Juliette-Gerbaux committed Dec 6, 2024
1 parent 8f4d70b commit f60fb98
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317 changes: 317 additions & 0 deletions tests/test_bellman_value_precalculated_reward_two_stocks.py
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from functions_iterative import (
TimeScenarioParameter,
ReservoirManagement,
)
from multi_stock_bellman_value_calculation import *
from calculate_reward_and_bellman_values import (
MultiStockManagement,
)
from read_antares_data import Reservoir
import pytest
import numpy as np


def test_bellman_value_precalculated_multi_stock() -> None:

param = TimeScenarioParameter(len_week=5, len_scenario=1)

reservoir_1 = Reservoir("test_data/two_nodes", "area_1")
reservoir_management_1 = ReservoirManagement(
reservoir=reservoir_1,
penalty_bottom_rule_curve=3000,
penalty_upper_rule_curve=3000,
penalty_final_level=3000,
force_final_level=True,
)

reservoir_2 = Reservoir("test_data/two_nodes", "area_2")
reservoir_management_2 = ReservoirManagement(
reservoir=reservoir_2,
penalty_bottom_rule_curve=3000,
penalty_upper_rule_curve=3000,
penalty_final_level=3000,
force_final_level=True,
)

multi_management = MultiStockManagement(
[reservoir_management_1, reservoir_management_2]
)

levels, _, bellman_costs, bellman_controls, slopes, _ = precalculated_method(
param=param,
multi_stock_management=multi_management,
output_path="test_data/two_nodes",
xNsteps=5,
Nsteps_bellman=5,
name_solver="CLP",
controls_looked_up="line+diagonal",
verbose=True,
)

assert levels == pytest.approx(
np.array(
[
[
[0.0, 628377.6569],
[277853.0681, 0.0],
[175340.436, 628377.6569],
[277853.0681, 396540.564],
[409896.721, 628377.6569],
[277853.0681, 927000.529],
[589466.8605, 628377.6569],
[277853.0681, 1333106.7645],
[769037.0, 628377.6569],
[277853.0681, 1739213.0],
],
[
[0.0, 628377.6569],
[277853.0681, 0.0],
[178416.584, 628377.6569],
[277853.0681, 403497.416],
[486031.384, 628377.6569],
[277853.0681, 1099182.616],
[627534.192, 628377.6569],
[277853.0681, 1419197.808],
[769037.0, 628377.6569],
[277853.0681, 1739213.0],
],
[
[0.0, 628377.6569],
[277853.0681, 0.0],
[180723.695, 628377.6569],
[277853.0681, 408715.055],
[488338.495, 628377.6569],
[277853.0681, 1104400.255],
[628687.7475, 628377.6569],
[277853.0681, 1421806.6275],
[769037.0, 628377.6569],
[277853.0681, 1739213.0],
],
[
[0.0, 628377.6569],
[277853.0681, 0.0],
[183030.806, 628377.6569],
[277853.0681, 413932.694],
[491414.643, 628377.6569],
[277853.0681, 1111357.107],
[630225.8215, 628377.6569],
[277853.0681, 1425285.0535],
[769037.0, 628377.6569],
[277853.0681, 1739213.0],
],
[
[0.0, 628377.6569],
[277853.0681, 0.0],
[185337.917, 628377.6569],
[277853.0681, 419150.333],
[493721.754, 628377.6569],
[277853.0681, 1116574.746],
[631379.377, 628377.6569],
[277853.0681, 1427893.873],
[769037.0, 628377.6569],
[277853.0681, 1739213.0],
],
]
)
)

assert bellman_controls == pytest.approx(
np.array(
[
[
[[0.0], [181530.9463735]],
[[129383.263], [-304244.94783314]],
[[26870.633], [181530.95748161]],
[[129383.263], [61185.617]],
[[160609.60919247], [124748.89981205]],
[[129383.263], [155975.24600452]],
[[219514.397], [124748.89981205]],
[[103827.55582937], [419664.0]],
[[306936.0], [124748.89981205]],
[[103827.55582937], [419664.0]],
],
[
[[13776.0], [263393.096]],
[[217424.96382709], [-28367.50984833]],
[[52773.40570775], [144306.62825704]],
[[116288.634], [78091.78486466]],
[[193278.43809968], [197279.2502553]],
[[116288.634], [203738.091]],
[[259392.28273827], [263393.096]],
[[116288.634], [419664.0]],
[[306936.0], [263393.096]],
[[116288.634], [419664.0]],
],
[
[[0.0], [256443.244]],
[[144342.19194326], [-294442.21953368]],
[[11011.94606618], [184605.26872985]],
[[146538.83712493], [41945.38354514]],
[[175156.44001992], [246067.35263216]],
[[86019.81249675], [419664.0]],
[[206364.00587073], [256443.244]],
[[86019.81249675], [419664.0]],
[[298585.86061946], [256443.244]],
[[113212.486], [419664.0]],
],
[
[[0.0], [251428.605]],
[[138754.63237131], [-310705.16724054]],
[[16174.115], [79192.47066511]],
[[110996.375], [-16964.93476829]],
[[19371.83979269], [85278.13317882]],
[[0.0], [419664.0]],
[[158812.70351082], [95447.16468272]],
[[0.0], [419664.0]],
[[297623.88351082], [95447.16468272]],
[[0.0], [419664.0]],
],
[
[[0.0], [-272320.61380624]],
[[34066.10375969], [-322182.0]],
[[14066.07670537], [-77354.29218551]],
[[12630.48903407], [-200568.15764549]],
[[153336.66361319], [31852.9091698]],
[[15945.4810059], [419664.0]],
[[290994.29361319], [31852.9091698]],
[[15945.4810059], [419664.0]],
[[306936.0], [31993.30673435]],
[[15945.4810059], [419664.0]],
],
]
)
)

assert bellman_costs == pytest.approx(
np.array(
[
[
1.00000010e16,
1.00000013e16,
1.00000000e16,
1.00000000e16,
1.00000000e16,
1.00000000e16,
1.00000005e16,
1.00000014e16,
1.00000014e16,
1.00000039e16,
],
[
1.00000025e16,
1.00000041e16,
1.00000003e16,
1.00000002e16,
1.00000000e16,
1.00000001e16,
1.00000005e16,
1.00000013e16,
1.00000013e16,
1.00000048e16,
],
[
1.00000034e16,
1.00000059e16,
1.00000003e16,
1.00000008e16,
1.00000001e16,
1.00000001e16,
1.00000005e16,
1.00000010e16,
1.00000009e16,
1.00000036e16,
],
[
1.00000042e16,
1.00000061e16,
1.00000004e16,
1.00000011e16,
1.00000001e16,
1.00000001e16,
1.00000005e16,
1.00000010e16,
1.00000009e16,
1.00000034e16,
],
[
1.00000050e16,
1.00000035e16,
1.00000000e16,
1.00000000e16,
1.00000000e16,
1.00000000e16,
1.00000004e16,
1.00000009e16,
1.00000008e16,
1.00000033e16,
],
]
)
)

assert slopes == pytest.approx(
np.array(
[
[
[-6.00000000e03, 0.00000000e00],
[-2.11898256e03, -6.00000000e03],
[-1.15170000e02, 0.00000000e00],
[-1.15170000e02, -1.15170000e02],
[0.00000000e00, 0.00000000e00],
[0.00000000e00, 0.00000000e00],
[3.00000000e03, 0.00000000e00],
[0.00000000e00, 6.00000000e03],
[6.00000000e03, 0.00000000e00],
[0.00000000e00, 6.00000000e03],
],
[
[-1.30000000e04, 0.00000000e00],
[-5.11898000e03, -1.20000000e04],
[-9.00000000e03, 0.00000000e00],
[-2.75281053e03, -3.11517000e03],
[-5.95800000e01, -5.95800000e01],
[-1.19160000e02, 0.00000000e00],
[3.00000000e03, -1.19160000e02],
[-1.19160000e02, 6.00000000e03],
[9.00000000e03, -1.19160000e02],
[-1.19160000e02, 1.20000000e04],
],
[
[-1.90000000e04, 0.00000000e00],
[-7.04933707e03, -1.39835533e04],
[-2.75281035e03, -3.11516983e03],
[-5.85635493e03, -6.06353718e03],
[-1.22642731e02, -1.31127492e02],
[-1.66240000e02, 0.00000000e00],
[2.94042000e03, -3.20801251e02],
[-1.66240000e02, 3.00000000e03],
[3.00000000e03, -5.00000000e02],
[-1.66240000e02, 1.20000000e04],
],
[
[-2.50000000e04, 0.00000000e00],
[-9.04534212e03, -1.33182190e04],
[-3.01822810e03, -3.11517000e03],
[-6.00811264e03, -6.06354000e03],
[-1.58015064e-01, -1.38714297e02],
[-1.66240000e02, 0.00000000e00],
[3.00000000e03, -1.38850000e02],
[-1.66240000e02, 3.00000000e03],
[3.00000000e03, -1.38850000e02],
[-1.66240000e02, 9.00000000e03],
],
[
[-3.10000000e04, 0.00000000e00],
[-9.04534000e03, -1.93182200e04],
[-2.70028202e02, -1.13365718e02],
[-2.70028202e02, -1.13365718e02],
[0.00000000e00, 0.00000000e00],
[-1.66240000e02, 0.00000000e00],
[3.00000000e03, 0.00000000e00],
[-1.66240000e02, 3.00000000e03],
[3.00000000e03, 0.00000000e00],
[-1.66240000e02, 9.00000000e03],
],
]
)
)

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