Remove functions from second test and filter ts on scenarios and time…

…steps

src/andromede/study/data.py

@@ -13,7 +13,7 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Dict, Optional
+from typing import Dict, Optional, List
 
 import pandas as pd
 
@@ -116,6 +116,19 @@ def load_ts_from_txt(
         raise Exception(f"An error has arrived when processing '{ts_path}'")
 
 
+def filter_ts_on_scenarios_and_timesteps(
+    raw_ts: pd.DataFrame,
+    scenarios: Optional[List[int]],
+    timesteps: Optional[List[int]],
+) -> pd.DataFrame:
+    filtered_ts = raw_ts
+    if scenarios is not None:
+        filtered_ts = filtered_ts.filter(items=scenarios, axis=1)
+    if timesteps is not None:
+        filtered_ts = filtered_ts.filter(items=timesteps, axis=0)
+    return filtered_ts.reset_index(drop=True)
+
+
 @dataclass(frozen=True)
 class TimeScenarioSeriesData(AbstractDataStructure):
     """

src/andromede/study/resolve_components.py

@@ -31,6 +31,7 @@
     TimeScenarioIndex,
     TimeScenarioSeriesData,
     load_ts_from_txt,
+    filter_ts_on_scenarios_and_timesteps,
 )
 from andromede.study.parsing import (
     InputComponent,
@@ -142,13 +143,21 @@ def build_network(comp_network: NetworkComponents) -> Network:
 
 
 def build_data_base(
-    input_comp: InputComponents, timeseries_dir: Optional[Path]
+    input_comp: InputComponents,
+    timeseries_dir: Optional[Path],
+    scenarios: Optional[List[int]] = None,
+    timesteps: Optional[List[int]] = None,
 ) -> DataBase:
     database = DataBase()
     for comp in input_comp.components:
         for param in comp.parameters or []:
             param_value = _evaluate_param_type(
-                param.type, param.value, param.timeseries, timeseries_dir
+                param.type,
+                param.value,
+                param.timeseries,
+                timeseries_dir,
+                scenarios,
+                timesteps,
             )
             database.add_data(comp.id, param.name, param_value)
 
@@ -160,11 +169,17 @@ def _evaluate_param_type(
     param_value: Optional[float],
     timeseries_name: Optional[str],
     timeseries_dir: Optional[Path],
+    scenarios: Optional[List[int]],
+    timesteps: Optional[List[int]],
 ) -> AbstractDataStructure:
     if param_type == "constant" and param_value is not None:
         return ConstantData(float(param_value))
 
     elif param_type == "timeseries":
-        return TimeScenarioSeriesData(load_ts_from_txt(timeseries_name, timeseries_dir))
+        return TimeScenarioSeriesData(
+            filter_ts_on_scenarios_and_timesteps(
+                load_ts_from_txt(timeseries_name, timeseries_dir), scenarios, timesteps
+            )
+        )
 
     raise ValueError(f"Data should be either constant or timeseries ")

src/andromede/thermal_heuristic/problem.py

@@ -13,7 +13,7 @@
 import pandas as pd
 import numpy as np
 import ortools.linear_solver.pywraplp as pywraplp
-from typing import List, Dict
+from typing import List, Dict, Optional
 
 from andromede.libs.standard import (
     BALANCE_PORT_TYPE,
@@ -88,7 +88,13 @@ def create_main_problem(
         ],
     )
 
-    network, database = get_network_and_database(data_dir, lib, "components.yml")
+    network, database = get_network_and_database(
+        data_dir,
+        lib,
+        "components.yml",
+        scenarios=[scenario],
+        timesteps=list(range(week * number_hours, (week + 1) * number_hours)),
+    )
 
     modify_parameters_of_cluster(
         lower_bound,
@@ -110,6 +116,7 @@ def create_main_problem(
         time_block,
         scenarios,
         border_management=BlockBorderManagement.CYCLE,
+        solver_id="XPRESS",
     )
 
     return problem
@@ -177,7 +184,11 @@ def get_cluster_id(network: Network, cluster_model_id: str) -> list[str]:
 
 
 def get_network_and_database(
-    data_dir: Path, lib: Library, yml_file: str
+    data_dir: Path,
+    lib: Library,
+    yml_file: str,
+    scenarios: Optional[List[int]],
+    timesteps: Optional[List[int]],
 ) -> tuple[Network, DataBase]:
     compo_file = data_dir / yml_file
 
@@ -186,7 +197,9 @@ def get_network_and_database(
     components_input = resolve_components_and_cnx(components_file, lib)
     network = build_network(components_input)
 
-    database = build_data_base(components_file, data_dir)
+    database = build_data_base(
+        components_file, data_dir, scenarios=scenarios, timesteps=timesteps
+    )
     return network, database
 
 
@@ -219,7 +232,11 @@ def create_problem_accurate_heuristic(
     )
 
     network, database = get_network_and_database(
-        data_dir, lib, "components_heuristic.yml"
+        data_dir,
+        lib,
+        "components_heuristic.yml",
+        scenarios=[scenario],
+        timesteps=list(range(week * number_hours, (week + 1) * number_hours)),
     )
 
     modify_parameters_of_cluster(
@@ -242,6 +259,7 @@ def create_problem_accurate_heuristic(
         time_block,
         scenarios,
         border_management=BlockBorderManagement.CYCLE,
+        solver_id="XPRESS",
     )
 
     return problem
@@ -367,6 +385,7 @@ def create_problem_fast_heuristic(
     parameters = pywraplp.MPSolverParameters()
     parameters.SetIntegerParam(parameters.PRESOLVE, parameters.PRESOLVE_OFF)
     parameters.SetIntegerParam(parameters.SCALING, 0)
+    parameters.SetDoubleParam(parameters.RELATIVE_MIP_GAP, 1e-7)
     problem.solver.EnableOutput()
 
     status = problem.solver.Solve(parameters)

tests/functional/data_complex_case/components.yml

@@ -0,0 +1,181 @@
+# Copyright (c) 2024, RTE (https://www.rte-france.com)
+#
+# See AUTHORS.txt
+#
+# This Source Code Form is subject to the terms of the Mozilla Public
+# License, v. 2.0. If a copy of the MPL was not distributed with this
+# file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#
+# SPDX-License-Identifier: MPL-2.0
+#
+# This file is part of the Antares project.
+study:
+  nodes:
+    - id: N
+      model: NODE_BALANCE_MODEL
+
+  components:
+    - id: D
+      model: FIXED_DEMAND
+      parameters:
+        - name: demand
+          type: timeseries
+          timeseries: demand-ts
+    - id: S
+      model: SPI
+      parameters:
+        - name: cost
+          type: constant
+          value: 1
+    - id: U
+      model: UNSP
+      parameters:
+        - name: cost
+          type: constant
+          value: 10000
+    - id: G1
+      model: GEN
+      parameters:
+        - name: p_max
+          type: constant
+          value: 410
+        - name: p_min
+          type: constant
+          value: 180
+        - name: cost
+          type: constant
+          value: 96
+        - name: startup_cost
+          type: constant
+          value: 100500
+        - name: fixed_cost
+          type: constant
+          value: 1
+        - name: d_min_up
+          type: constant
+          value: 8
+        - name: d_min_down
+          type: constant
+          value: 8
+        - name: nb_units_min
+          type: constant
+          value: 0
+        - name: nb_units_max
+          type: constant
+          value: 1
+        - name: min_generating
+          type: constant
+          value: 0
+        - name: max_generating
+          type: timeseries
+          timeseries: series_G1
+    - id: G2
+      model: GEN
+      parameters:
+        - name: p_max
+          type: constant
+          value: 90
+        - name: p_min
+          type: constant
+          value: 60
+        - name: cost
+          type: constant
+          value: 137
+        - name: startup_cost
+          type: constant
+          value: 24500
+        - name: fixed_cost
+          type: constant
+          value: 1
+        - name: d_min_up
+          type: constant
+          value: 11
+        - name: d_min_down
+          type: constant
+          value: 11
+        - name: nb_units_min
+          type: constant
+          value: 0
+        - name: nb_units_max
+          type: constant
+          value: 3
+        - name: min_generating
+          type: constant
+          value: 0
+        - name: max_generating
+          type: timeseries
+          timeseries: series_G2
+    - id: G3
+      model: GEN
+      parameters:
+        - name: p_max
+          type: constant
+          value: 275
+        - name: p_min
+          type: constant
+          value: 150
+        - name: cost
+          type: constant
+          value: 107
+        - name: startup_cost
+          type: constant
+          value: 69500
+        - name: fixed_cost
+          type: constant
+          value: 1
+        - name: d_min_up
+          type: constant
+          value: 9
+        - name: d_min_down
+          type: constant
+          value: 9
+        - name: nb_units_min
+          type: constant
+          value: 0
+        - name: nb_units_max
+          type: constant
+          value: 4
+        - name: min_generating
+          type: constant
+          value: 0
+        - name: max_generating
+          type: timeseries
+          timeseries: series_G3
+
+
+
+  connections:
+      - component1: N
+        port_1: balance_port
+        component2: D
+        port_2: balance_port
+
+      - component1: N
+        port_1: balance_port
+        component2: G1
+        port_2: balance_port
+
+      - component1: N
+        port_1: balance_port
+        component2: G2
+        port_2: balance_port
+
+      - component1: N
+        port_1: balance_port
+        component2: G3
+        port_2: balance_port
+
+      - component1: N
+        port_1: balance_port
+        component2: U
+        port_2: balance_port
+
+      - component1: N
+        port_1: balance_port
+        component2: S
+        port_2: balance_port
+
+
+
+
+