Improve testing (#5)

* Add coverage test

* Remove coverage

* Update ci

* Run isort

* Change black version

* Black version

---------

Co-authored-by: Juliette-Gerbaux <[email protected]>

.github/workflows/ci.yml

@@ -29,11 +29,31 @@ jobs:
 
       - name: Test
         run: |
-          pytest
+          pytest --cov --cov-report xml
+
+      - name: Archive code coverage results
+        uses: actions/upload-artifact@v4
+        with:
+          name: python-code-coverage-report
+          path: coverage.xml
 
       - name: Check typing
         run: |
           python -m mypy src
 
+      - name: Check imports sort order
+        uses: isort/isort-action@v1
+        with:
+          sort-paths: src, tests
+          configuration: --profile black --check-only --diff
+          requirements-files: "requirements.txt requirements-dev.txt"
+
+      - name: Check formatting
+        uses: psf/black@stable
+        with:
+          # Version of Black should match the versions set in `requirements-dev.txt`
+          version: "~=24.3.0"
+          options: --check --diff
+
 
 

requirements-dev.txt

@@ -1,4 +1,6 @@
-black
+black==24.3.0
 isort
 mypy
 pytest
+coverage
+pytest-cov

src/calculate_reward_and_bellman_values.py

@@ -1,9 +1,11 @@
-from read_antares_data import TimeScenarioParameter, Reservoir, TimeScenarioIndex
-from scipy.interpolate import interp1d
-import numpy as np
-from type_definition import Array1D, Array2D, Optional, Dict, Callable, List, Iterable
 from itertools import product
 
+import numpy as np
+from scipy.interpolate import interp1d
+
+from read_antares_data import Reservoir, TimeScenarioIndex, TimeScenarioParameter
+from type_definition import Array1D, Array2D, Callable, Dict, Iterable, List, Optional
+
 
 class ReservoirManagement:
 

src/display.py

@@ -1,8 +1,9 @@
 import numpy as np
-from tqdm import tqdm
 import plotly.graph_objects as go
-from read_antares_data import TimeScenarioParameter
+from tqdm import tqdm
+
 from multi_stock_bellman_value_calculation import MultiStockManagement
+from read_antares_data import TimeScenarioParameter
 from type_definition import Array1D, Dict
 
 

src/estimation.py

@@ -1,7 +1,9 @@
+from typing import Any, Dict, Optional, Union
+
 import numpy as np
-from typing import Dict, Union, Any, Optional
-from read_antares_data import TimeScenarioParameter
+
 from hyperplane_interpolation import get_interpolation
+from read_antares_data import TimeScenarioParameter
 from type_definition import Array1D
 
 

src/functions_iterative.py

@@ -1,18 +1,17 @@
+from time import time
+
 import numpy as np
 from scipy.interpolate import interp1d
-from time import time
-from optimization import (
-    AntaresProblem,
-    Basis,
-)
-from read_antares_data import TimeScenarioParameter, TimeScenarioIndex
+
 from calculate_reward_and_bellman_values import (
-    RewardApproximation,
-    ReservoirManagement,
     BellmanValueCalculation,
-    MultiStockManagement,
     MultiStockBellmanValueCalculation,
+    MultiStockManagement,
+    ReservoirManagement,
+    RewardApproximation,
 )
+from optimization import AntaresProblem, Basis
+from read_antares_data import TimeScenarioIndex, TimeScenarioParameter
 from type_definition import Array1D, Array2D, Array3D, Array4D, Dict, List
 
 

src/hyperplane_decomposition.py

@@ -1,5 +1,6 @@
 import numpy as np
 from numpy.linalg import norm
+
 from type_definition import Array1D
 
 

src/hyperplane_interpolation.py

@@ -1,6 +1,7 @@
 # Ceci est le ficiher des coupes gourmandes, l'utilité n'a malheuresement pas été prouvée et la méthode n'a pas été retenue
 import numpy as np
 from numpy.linalg import norm
+
 from type_definition import Array1D
 
 

src/launch_calculation.py

@@ -1,10 +1,10 @@
+from calculate_reward_and_bellman_values import ReservoirManagement
+from functions_iterative import itr_control
+from read_antares_data import TimeScenarioParameter
 from simple_bellman_value_calculation import (
     calculate_bellman_value_directly,
     calculate_bellman_value_with_precalculated_reward,
 )
-from functions_iterative import itr_control
-from read_antares_data import TimeScenarioParameter
-from calculate_reward_and_bellman_values import ReservoirManagement
 from type_definition import Array1D, Array2D
 
 

src/multi_stock_bellman_value_calculation.py

@@ -1,34 +1,33 @@
+import os as os
+import pickle as pkl
 from itertools import product
+from pathlib import Path
+from typing import Annotated, Any, Dict, Literal, Optional
+
+import juliacall
+import numpy as np
+import numpy.typing as npt
+import ortools.linear_solver.pywraplp as pywraplp
+from ortools.linear_solver.python import model_builder
+from scipy.stats import random_correlation
+from tqdm import tqdm
+
 from calculate_reward_and_bellman_values import (
-    RewardApproximation,
     BellmanValueCalculation,
-    MultiStockManagement,
     MultiStockBellmanValueCalculation,
+    MultiStockManagement,
+    RewardApproximation,
 )
-
-import ortools.linear_solver.pywraplp as pywraplp
-from ortools.linear_solver.python import model_builder
+from display import ConvergenceProgressBar, draw_usage_values, draw_uvs_sddp
 from estimation import Estimator, LinearCostEstimator, LinearInterpolator
-from read_antares_data import TimeScenarioParameter, TimeScenarioIndex
-
 from optimization import (
     AntaresProblem,
-    WeeklyBellmanProblem,
     Basis,
-    solve_problem_with_multivariate_bellman_values,
+    WeeklyBellmanProblem,
     solve_for_optimal_trajectory,
+    solve_problem_with_multivariate_bellman_values,
 )
-from typing import Annotated, Literal, Dict, Optional, Any
-import numpy.typing as npt
-import numpy as np
-
-from display import ConvergenceProgressBar, draw_usage_values, draw_uvs_sddp
-from scipy.stats import random_correlation
-import pickle as pkl
-from tqdm import tqdm
-from pathlib import Path
-import juliacall
-import os as os
+from read_antares_data import TimeScenarioIndex, TimeScenarioParameter
 
 jl = juliacall.Main
 

src/optimization.py

@@ -1,19 +1,21 @@
+import pickle as pkl
 import re
+from time import time
 from typing import Optional
+
+import numpy as np
+import ortools.linear_solver.pywraplp as pywraplp
+from ortools.linear_solver.python import model_builder
+from scipy.interpolate import interp1d
+
 from calculate_reward_and_bellman_values import (
     BellmanValueCalculation,
     MultiStockBellmanValueCalculation,
     MultiStockManagement,
 )
 from estimation import Estimator, LinearInterpolator
 from read_antares_data import TimeScenarioParameter
-import numpy as np
-from time import time
-from scipy.interpolate import interp1d
-from ortools.linear_solver.python import model_builder
-import ortools.linear_solver.pywraplp as pywraplp
-from type_definition import Array2D, List, Dict, Array1D, npt
-import pickle as pkl
+from type_definition import Array1D, Array2D, Dict, List, npt
 
 
 class Basis:

src/read_antares_data.py

@@ -1,7 +1,8 @@
-from dataclasses import dataclass, field
-from typing import Optional
 import subprocess
 from configparser import ConfigParser
+from dataclasses import dataclass, field
+from typing import Optional
+
 import numpy as np
 
 

src/simple_bellman_value_calculation.py

@@ -1,16 +1,16 @@
+import numpy as np
+from scipy.interpolate import interp1d
+
 from calculate_reward_and_bellman_values import (
-    RewardApproximation,
-    ReservoirManagement,
     BellmanValueCalculation,
-    MultiStockManagement,
     MultiStockBellmanValueCalculation,
+    MultiStockManagement,
+    ReservoirManagement,
+    RewardApproximation,
 )
-from read_antares_data import TimeScenarioParameter, TimeScenarioIndex
-from optimization import AntaresProblem
-from optimization import Basis
-import numpy as np
-from scipy.interpolate import interp1d
 from functions_iterative import compute_upper_bound
+from optimization import AntaresProblem, Basis
+from read_antares_data import TimeScenarioIndex, TimeScenarioParameter
 from type_definition import Array1D, Array2D, Dict
 
 

src/type_definition.py

@@ -1,6 +1,7 @@
-import numpy.typing as npt
+from typing import Annotated, Callable, Dict, Iterable, List, Literal, Optional
+
 import numpy as np
-from typing import Annotated, Literal, Dict, List, Optional, Callable, Iterable
+import numpy.typing as npt
 
 Array1D = Annotated[npt.NDArray[np.float32], Literal["N"]]
 Array2D = Annotated[npt.NDArray[np.float32], Literal["N", "N"]]

tests/test_basis.py

@@ -1,17 +1,18 @@
+import numpy as np
+import ortools.linear_solver.pywraplp as pywraplp
+import pytest
+
+from calculate_reward_and_bellman_values import MultiStockManagement
 from functions_iterative import (
-    TimeScenarioParameter,
+    BellmanValueCalculation,
     ReservoirManagement,
+    RewardApproximation,
     TimeScenarioIndex,
+    TimeScenarioParameter,
     compute_upper_bound,
-    BellmanValueCalculation,
-    RewardApproximation,
 )
-from read_antares_data import Reservoir
 from optimization import AntaresProblem, Basis
-from calculate_reward_and_bellman_values import MultiStockManagement
-import pytest
-import ortools.linear_solver.pywraplp as pywraplp
-import numpy as np
+from read_antares_data import Reservoir
 
 
 def test_basis_with_xpress() -> None:

tests/test_bellman_value_exact.py

@@ -1,13 +1,9 @@
-from functions_iterative import (
-    TimeScenarioParameter,
-    ReservoirManagement,
-)
-from simple_bellman_value_calculation import (
-    calculate_bellman_value_directly,
-)
-from read_antares_data import Reservoir
-import pytest
 import numpy as np
+import pytest
+
+from functions_iterative import ReservoirManagement, TimeScenarioParameter
+from read_antares_data import Reservoir
+from simple_bellman_value_calculation import calculate_bellman_value_directly
 
 
 def test_bellman_value_exact() -> None:

tests/test_bellman_value_exact_two_stocks.py

@@ -1,24 +1,26 @@
+from itertools import product
+
+import numpy as np
+import pytest
+
+from calculate_reward_and_bellman_values import (
+    BellmanValueCalculation,
+    MultiStockBellmanValueCalculation,
+    MultiStockManagement,
+    RewardApproximation,
+)
 from functions_iterative import (
-    TimeScenarioParameter,
     ReservoirManagement,
     TimeScenarioIndex,
+    TimeScenarioParameter,
 )
 from multi_stock_bellman_value_calculation import (
     AntaresProblem,
     Dict,
-    solve_problem_with_multivariate_bellman_values,
     calculate_bellman_value_multi_stock,
-)
-from calculate_reward_and_bellman_values import (
-    MultiStockManagement,
-    BellmanValueCalculation,
-    MultiStockBellmanValueCalculation,
-    RewardApproximation,
+    solve_problem_with_multivariate_bellman_values,
 )
 from read_antares_data import Reservoir
-import pytest
-import numpy as np
-from itertools import product
 
 
 def test_iterate_over_stock_discretization() -> None:

tests/test_bellman_value_precalculated_reward.py

@@ -1,14 +1,15 @@
+import numpy as np
+import pytest
+
 from functions_iterative import (
-    TimeScenarioParameter,
     ReservoirManagement,
     TimeScenarioIndex,
+    TimeScenarioParameter,
 )
+from read_antares_data import Reservoir
 from simple_bellman_value_calculation import (
     calculate_bellman_value_with_precalculated_reward,
 )
-from read_antares_data import Reservoir
-import pytest
-import numpy as np
 
 
 def test_bellman_value_precalculated_reward() -> None:

tests/test_bellman_value_precalculated_reward_two_stocks.py

@@ -1,14 +1,10 @@
-from functions_iterative import (
-    TimeScenarioParameter,
-    ReservoirManagement,
-)
+import numpy as np
+import pytest
+
+from calculate_reward_and_bellman_values import MultiStockManagement
+from functions_iterative import ReservoirManagement, TimeScenarioParameter
 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: