Clean code (#4)

* Remove unused code

* Clean tests

* Clean src

---------

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

src/calculate_reward_and_bellman_values.py

@@ -160,9 +160,6 @@ def get_disc(
             n_pts_below + n_pts_in + n_pts_above
         )  # Make sure total adds up
         if method == "lines":
-            # in_curve_pts = np.linspace(lbs, ubs, xNsteps-2) # Disc-2 * R
-            # all_pts = np.insert(in_curve_pts, 0, empty, axis=0) # (Disc-1) * R
-            # all_pts = np.insert(all_pts, all_pts.shape[0], full, axis=0) # Disc * R
             above_curve_pts = [
                 np.linspace(ubs[r], full[r], n_pts_above[r], endpoint=True)
                 for r in range(n_reservoirs)
@@ -183,7 +180,6 @@ def get_disc(
                     for r in range(n_reservoirs)
                 ]
             ).T
-            # all_pts = np.concatenate((below_curve_pts, in_curve_pts, above_curve_pts), axis=0) # Disc * R
             diffs_to_ref = all_pts[:, None] - reference_pt[None, :]  # Disc * R
             diffs_to_ref = (
                 diffs_to_ref[:, :, None] * np.eye(n_reservoirs)[None, :, :]

src/display.py

@@ -31,8 +31,6 @@ def draw_usage_values(
                 closest_level
             ]
 
-    # z = np.maximum(np.zeros(reinterpolated_usage_values[area].T.shape), np.minimum(ub*np.ones(reinterpolated_usage_values[area].T.shape), reinterpolated_usage_values[area].T))
-    # z = np.maximum(np.zeros(reinterpolated_usage_values[area].T.shape) - ub, np.minimum(ub*np.ones(reinterpolated_usage_values[area].T.shape), reinterpolated_usage_values[area].T))
     usage_values_plot = go.Figure(
         data=[
             go.Heatmap(
@@ -64,9 +62,7 @@ def draw_usage_values(
                 marker=dict(symbol="circle"),
                 showlegend=True,
             )
-            for i, (area, mng) in enumerate(
-                multi_stock_management.dict_reservoirs.items()
-            )
+            for i, (_, _) in enumerate(multi_stock_management.dict_reservoirs.items())
         ]
         + [
             go.Scatter(
@@ -78,9 +74,7 @@ def draw_usage_values(
                 line=dict(dash="dash"),
                 showlegend=True,
             )
-            for i, (area, mng) in enumerate(
-                multi_stock_management.dict_reservoirs.items()
-            )
+            for i, (_, mng) in enumerate(multi_stock_management.dict_reservoirs.items())
         ]
         + [
             go.Scatter(
@@ -92,9 +86,7 @@ def draw_usage_values(
                 line=dict(dash="dash"),
                 showlegend=True,
             )
-            for i, (area, mng) in enumerate(
-                multi_stock_management.dict_reservoirs.items()
-            )
+            for i, (_, mng) in enumerate(multi_stock_management.dict_reservoirs.items())
         ],
         layout=dict(title=f"Usage Values"),
     )
@@ -168,7 +160,7 @@ def draw_uvs_sddp(
                 showlegend=True,
                 visible=(r == 0),
             )
-            for r, res in enumerate(reservoirs)
+            for r, _ in enumerate(reservoirs)
         ]
         + [
             go.Scatter(

src/estimation.py

@@ -311,7 +311,6 @@ def __init__(
         costs: np.ndarray,
         duals: np.ndarray,
         correlations: Optional[np.ndarray] = None,
-        interp_mode: bool = False,
     ) -> None:
         """
         Instanciates a LinearCostEstimator
@@ -324,9 +323,6 @@ def __init__(
             costs:np.ndarray: Cost for every input,
             duals:np.ndarray: Duals for every input first dimension should be the same as inputs,
         """
-        # self.true_controls=controls
-        # self.true_costs=costs
-        # self.true_duals=duals
         self.estimators = np.array(
             [
                 [
@@ -335,7 +331,6 @@ def __init__(
                         costs=costs[week, scenario],
                         duals=duals[week, scenario],
                         correlations=correlations,
-                        # interp_mode=interp_mode,
                     )
                     for scenario in range(param.len_scenario)
                 ]
@@ -389,7 +384,6 @@ def update(
         inputs: np.ndarray,
         costs: np.ndarray,
         duals: np.ndarray,
-        interp_mode: bool = False,
     ) -> None:
         """
         Updates the parameters of the Linear Interpolators
@@ -409,10 +403,9 @@ def update(
                     inputs=inputs,
                     costs=costs,
                     duals=duals,
-                    #    interp_mode=interp_mode,
                 )
 
-    def enrich_estimator(self, n_splits: int = 3) -> None:
+    def enrich_estimator(self) -> None:
         """
         Adds 'mid_cuts' to our cost estimator to smoothen the curves and (hopefully) accelerate convergence
 
@@ -441,8 +434,6 @@ def cleanup_approximations(
         for week_estimators in self.estimators:
             for estimator in week_estimators:
                 estimator.remove_incoherence()
-                # controls=true_controls[week, scenario],
-                # real_costs=true_costs[week, scenario])
 
     def remove_redundants(
         self,

src/functions_iterative.py

@@ -274,7 +274,7 @@ def itr_control(
     ) = init_iterative_calculation(param, reservoir_management, output_path, X, solver)
     i = 0
 
-    while (gap >= tol_gap and gap >= 0) and i < N:  # and (i<3):
+    while (gap >= tol_gap and gap >= 0) and i < N:
         debut = time()
 
         initial_x, controls = compute_x_multi_scenario(
@@ -391,7 +391,6 @@ def init_iterative_calculation(
         stock_discretization=X,
     )
 
-    i = 0
     gap = 1e3
     fin = time()
     tot_t.append(fin - debut)

src/hyperplane_interpolation.py

@@ -41,14 +41,11 @@ def BezierAv(
     if last_axify:
         p[:-1] = (p0[:-1] + p1[:-1]) / 2
         p[-1] = p0[-1] * diff_rate + p1[-1] * (1 - diff_rate)
-        # v_sum = np.sum(v[:-1])
-        # v[-1] /= v_sum
         v = v0 * (t) + v1 * (1 - t)
         v[-1] = v0[-1] * (t * (1 - partage) + partage * diff_rate) + v1[-1] * (
             (1 - t) * (1 - partage) + partage * (1 - diff_rate)
         )
     elif norm(v) > 1e-12:
-        # v = v / sum(v[:-1])
         v = v / norm(v) * (n0 + n1) / 2
     return np.array([p, v])
 
@@ -125,10 +122,6 @@ def interpolate_between(
     return mlr, data
 
 
-mlrs: list = []
-datas: list = []
-
-
 def enrich_by_interpolation(
     controls_init: np.ndarray, costs: np.ndarray, slopes: np.ndarray, n_splits: int = 3
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:

src/launch_calculation.py

@@ -50,7 +50,7 @@ def calculate_bellman_values(
 
     elif method == "precalculated":
         # or with precalulated reward
-        vb, G = calculate_bellman_value_with_precalculated_reward(
+        vb, _ = calculate_bellman_value_with_precalculated_reward(
             len_controls=len_controls,
             param=param,
             reservoir_management=reservoir_management,
@@ -61,7 +61,7 @@ def calculate_bellman_values(
 
     elif method == "iterative":
         # or with iterative algorithm
-        vb, G, _, _, controls_upper, traj = itr_control(
+        vb, _, _, _, _, _ = itr_control(
             param=param,
             reservoir_management=reservoir_management,
             output_path=output_path,