From 3ef72ecebce70e546ee7202faf0b668ed73dad5b Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Tue, 28 Apr 2026 12:57:57 +0200
Subject: [PATCH 01/38] plots

---
 artist/core/kinematics_reconstructor.py | 67 ++++++++++++++++++++++++-
 artist/core/surface_reconstructor.py    | 28 +++++++++++
 2 files changed, 94 insertions(+), 1 deletion(-)

diff --git a/artist/core/kinematics_reconstructor.py b/artist/core/kinematics_reconstructor.py
index 28138881..346d589a 100644
--- a/artist/core/kinematics_reconstructor.py
+++ b/artist/core/kinematics_reconstructor.py
@@ -493,7 +493,72 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                         break
 
                     epoch += 1
-
+                # ##################################################
+                # import matplotlib.pyplot as plt
+                # m = 18
+                # n = flux_measured.shape[0]
+                # for i in range(0, n, m):
+                #     fig, axes = plt.subplots(m, 2, figsize=(8, 50))
+                #     for j in range(m):
+                #         idx = i + j
+                #         if idx >= n:
+                #             axes[j, 0].axis("off")
+                #             axes[j, 1].axis("off")
+                #             continue
+                #         img_flux = flux_distributions[idx]
+                #         total_f = img_flux.sum()
+                #         if total_f > 0:
+                #             h, w = img_flux.shape
+                #             y_grid = (
+                #                 torch.arange(h, device=img_flux.device)
+                #                 .unsqueeze(1)
+                #                 .expand(h, w)
+                #             )
+                #             x_grid = (
+                #                 torch.arange(w, device=img_flux.device)
+                #                 .unsqueeze(0)
+                #                 .expand(h, w)
+                #             )
+                #             cy_f = (y_grid * img_flux).sum() / total_f
+                #             cx_f = (x_grid * img_flux).sum() / total_f
+                #         else:
+                #             cy_f, cx_f = 0.0, 0.0
+                #         img_meas = flux_measured[idx].cpu().detach()
+                #         total_m = img_meas.sum().cpu().detach()
+                #         if total_m > 0:
+                #             h, w = img_meas.shape
+                #             y_grid = (
+                #                 torch.arange(h, device=img_meas.device)
+                #                 .unsqueeze(1)
+                #                 .expand(h, w)
+                #             )
+                #             x_grid = (
+                #                 torch.arange(w, device=img_meas.device)
+                #                 .unsqueeze(0)
+                #                 .expand(h, w)
+                #             )
+                #             cy_m = (y_grid * img_meas).sum() / total_m
+                #             cx_m = (x_grid * img_meas).sum() / total_m
+                #         else:
+                #             cy_m, cx_m = 0.0, 0.0
+                #         cx_f, cy_f = float(cx_f), float(cy_f)
+                #         cx_m, cy_m = float(cx_m), float(cy_m)
+                #         ax1 = axes[j, 0]
+                #         ax1.imshow(img_flux.cpu().detach())
+                #         ax1.scatter(cx_m, cy_m, c="black", s=30, marker="o")
+                #         ax1.scatter(cx_f, cy_f, c="r", s=30, marker="x")
+                #         ax1.set_title(f"heliostat {i // m}")
+                #         ax1.axis("off")
+                #         ax2 = axes[j, 1]
+                #         ax2.imshow(img_meas.cpu().detach())
+                #         ax2.scatter(cx_m, cy_m, c="black", s=30, marker="o")
+                #         ax2.scatter(cx_f, cy_f, c="r", s=30, marker="x")
+                #         ax2.set_title(f"loss: {loss_per_sample[i + j]}")
+                #         ax2.axis("off")
+                #     plt.tight_layout()
+                #     plt.savefig(f"./bitmaps/kinematics/heliostat_{i // m}.png")
+                #     plt.close(fig)
+                # ##################################################
                 loss_history.append(
                     {
                         "total_loss": loss_history_list,
diff --git a/artist/core/surface_reconstructor.py b/artist/core/surface_reconstructor.py
index e9c66046..c865e979 100644
--- a/artist/core/surface_reconstructor.py
+++ b/artist/core/surface_reconstructor.py
@@ -614,6 +614,34 @@ def reconstruct_surfaces(
                         break
 
                     epoch += 1
+                # ##################################################
+                import matplotlib.pyplot as plt
+
+                m = 3
+                n = measured_flux_distributions.shape[0]
+                for i in range(0, n, m):
+                    fig, axes = plt.subplots(m, 2, figsize=(8, 20))
+                    for j in range(m):
+                        idx = i + j
+                        if idx >= n:
+                            axes[j, 0].axis("off")
+                            axes[j, 1].axis("off")
+                            continue
+                        img_flux = cropped_flux_distributions[idx]
+                        img_meas = measured_flux_distributions[idx].cpu().detach()
+
+                        ax1 = axes[j, 0]
+                        ax1.imshow(img_flux.cpu().detach())
+                        ax1.set_title(f"heliostat {i // m}")
+                        ax1.axis("off")
+                        ax2 = axes[j, 1]
+                        ax2.imshow(img_meas.cpu().detach())
+                        # ax2.set_title(f"loss: {flux_loss_per_heliostat[i]}")
+                        ax2.axis("off")
+                    plt.tight_layout()
+                    plt.savefig(f"./bitmaps/kinematics/heliostat_{i // m}.png")
+                    plt.close(fig)
+                # ##################################################
 
                 loss_history.append(
                     {

From 9f4340664b8b23cf5694b6f11bb26d94756fdc09 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Tue, 28 Apr 2026 13:02:08 +0200
Subject: [PATCH 02/38] update

---
 examples/field_optimizations/config.yaml      |  20 +-
 .../field_optimizations/generate_plots.py     | 383 ++++++++++++++----
 .../field_optimizations/generate_results.py   | 224 +++++-----
 3 files changed, 445 insertions(+), 182 deletions(-)

diff --git a/examples/field_optimizations/config.yaml b/examples/field_optimizations/config.yaml
index 6bca4788..bb31b9ed 100644
--- a/examples/field_optimizations/config.yaml
+++ b/examples/field_optimizations/config.yaml
@@ -19,12 +19,12 @@ heliostats_for_plots:
 random_seed: 7
 device: "cuda"
 surface_reconstruction_optimization_configuration:
-  max_epoch: 200
+  max_epoch: 400
   batch_size: 48
   batch_size_outer: 32
   number_of_surface_points: 50
   number_of_control_points: 6
-  number_of_rays: 170
+  number_of_rays: 190
   nurbs_degree: 3
   sample_limit: 3
   initial_learning_rate: 1e-06
@@ -42,10 +42,10 @@ surface_reconstruction_optimization_configuration:
   weight_smoothness: 0.00
   weight_ideal_surface: 0.10
 kinematics_reconstruction_optimization_configuration:
-  max_epoch: 350
+  max_epoch: 800
   batch_size: 960
-  sample_limit: 18
-  number_of_rays: 7
+  sample_limit: 10
+  number_of_rays: 16
   initial_learning_rate_rotation_deviation: 6.7e-04
   initial_learning_rate_initial_angles: 9.29e-3
   initial_learning_rate_initial_stroke_length: 6.46e-2
@@ -59,9 +59,9 @@ kinematics_reconstruction_optimization_configuration:
   cooldown: 5
   gamma: 0.92
 aim_point_optimization_configuration:
-  max_epoch: 400
+  max_epoch: 800
   batch_size: 96
-  number_of_rays: 5
+  number_of_rays: 8
   initial_learning_rate: 1e-03
   scheduler: "cyclic"
   min_learning_rate: 1e-06
@@ -78,13 +78,13 @@ aim_point_optimization_configuration:
   max_flux_density: 1000000 # W/m^2
 basic_config:
   batch_size: 96
-  number_of_rays: 5
+  number_of_rays: 8
   dni: 500
   bitmap_resolution: [230, 276]
   baseline_incident_ray_direction: [0.411, 0.706, -0.576, 0.0]
   baseline_target_area: "receiver"
-  baseline_aim_point: [-0.19720931, -0.03458419, 54.929, 1.0]
-  log_step: 1
+  baseline_aim_point: [-0.19720931, -0.03458419, 55.349, 1.0] #[-0.19720931, -0.03458419, 54.929, 1.0]
+  log_step: 20
 heliostat_list_baseline:
   - "AG48"
   - "AG49"
diff --git a/examples/field_optimizations/generate_plots.py b/examples/field_optimizations/generate_plots.py
index 9db17b62..75d30464 100644
--- a/examples/field_optimizations/generate_plots.py
+++ b/examples/field_optimizations/generate_plots.py
@@ -105,24 +105,26 @@ def plot_surface_reconstruction_flux(
     plot_data = results["surface_reconstruction"]["flux_plot_data"]
     number_of_heliostats = len(plot_data)
     fig, axes = plt.subplots(number_of_heliostats, 7, figsize=(35, 15))
-    for index, heliostat_name in enumerate(plot_data):
+    fontsize = 28
+    for index in range(1):
+        heliostat_name = "AK54"
         heliostat_data = plot_data[heliostat_name]
         axes[index, 0].imshow(
             heliostat_data["fluxes"][0].cpu().detach(), cmap="inferno"
         )
-        axes[index, 0].set_title("Calibration Flux")
+        axes[index, 0].set_title("Calibration Flux", fontsize=fontsize)
         axes[index, 0].axis("off")
 
         axes[index, 1].imshow(
             heliostat_data["fluxes"][1].cpu().detach(), cmap="inferno"
         )
-        axes[index, 1].set_title("Surface not reconstructed")
+        axes[index, 1].set_title("Surface not reconstructed", fontsize=fontsize)
         axes[index, 1].axis("off")
 
         axes[index, 2].imshow(
             heliostat_data["fluxes"][2].cpu().detach(), cmap="inferno"
         )
-        axes[index, 2].set_title("Surface reconstructed")
+        axes[index, 2].set_title("Surface reconstructed", fontsize=fontsize)
         axes[index, 2].axis("off")
 
         reference_direction = torch.tensor([0.0, 0.0, 1.0], device=torch.device("cpu"))
@@ -164,13 +166,14 @@ def plot_surface_reconstruction_flux(
             vmin=0.0345,
             vmax=0.036,
         )
-        axes[index, 3].set_title("Deflectometry Points original")
+        axes[index, 3].set_title("Deflectometry Points", fontsize=fontsize)
         axes[index, 3].axis("off")
         axes[index, 3].set_aspect("equal", adjustable="box")
         cbar3 = fig.colorbar(
             sc3, ax=axes[index, 3], orientation="horizontal", fraction=0.046, pad=0.1
         )
-        cbar3.set_label("m")
+        cbar3.set_label("m", fontsize=fontsize)
+        cbar3.ax.tick_params(labelsize=23)
 
         sc4 = axes[index, 4].scatter(
             x=deflectometry_points_original[:, 0],
@@ -180,13 +183,14 @@ def plot_surface_reconstruction_flux(
             vmin=0.0,
             vmax=0.005,
         )
-        axes[index, 4].set_title("Deflectometry normals")
+        axes[index, 4].set_title("Deflectometry Normals", fontsize=fontsize)
         axes[index, 4].axis("off")
         axes[index, 4].set_aspect("equal", adjustable="box")
         cbar4 = fig.colorbar(
             sc4, ax=axes[index, 4], orientation="horizontal", fraction=0.046, pad=0.1
         )
-        cbar4.set_label("Angle (rad)")
+        cbar4.set_label("Angle (rad)", fontsize=fontsize)
+        cbar4.ax.tick_params(labelsize=23)
 
         # Process reconstructed data.
         points_uncanted = utils.perform_canting(
@@ -217,13 +221,14 @@ def plot_surface_reconstruction_flux(
             vmin=0.0345,
             vmax=0.036,
         )
-        axes[index, 5].set_title("Reconstructed Surface (Points)")
+        axes[index, 5].set_title("Reconstructed Points", fontsize=fontsize)
         axes[index, 5].axis("off")
         axes[index, 5].set_aspect("equal", adjustable="box")
         cbar5 = fig.colorbar(
             sc5, ax=axes[index, 5], orientation="horizontal", fraction=0.046, pad=0.1
         )
-        cbar5.set_label("m")
+        cbar5.set_label("m", fontsize=fontsize)
+        cbar5.ax.tick_params(labelsize=23)
 
         sc6 = axes[index, 6].scatter(
             x=reconstructed_points[:, 0],
@@ -233,22 +238,23 @@ def plot_surface_reconstruction_flux(
             vmin=0.0,
             vmax=0.005,
         )
-        axes[index, 6].set_title("Reconstructed normals")
+        axes[index, 6].set_title("Reconstructed Normals", fontsize=fontsize)
         axes[index, 6].axis("off")
         axes[index, 6].set_aspect("equal", adjustable="box")
         cbar6 = fig.colorbar(
             sc6, ax=axes[index, 6], orientation="horizontal", fraction=0.046, pad=0.1
         )
-        cbar6.set_label("Angle (rad)")
+        cbar6.set_label("Angle (rad)", fontsize=fontsize)
+        cbar6.ax.tick_params(labelsize=23)
 
     save_dir = save_dir / f"run_{results_number}"
     save_dir.mkdir(parents=True, exist_ok=True)
-    filename = save_dir / "surface_reconstruction_flux.pdf"
+    filename = save_dir / "surface_reconstruction_flux.png"
     fig.tight_layout()
     fig.savefig(filename, dpi=300, bbox_inches="tight")
     plt.close(fig)
 
-    print(f"Saved surface reconstruction error distribution plot at: {filename}.")
+    print(f"Saved surface reconstruction flux plot at: {filename}.")
 
 
 def plot_surface_error_analysis(
@@ -438,6 +444,125 @@ def plot_surface_loss_history(
         print(f"Saved surface reconstruction loss history plot at: {filename}.")
 
 
+def plot_surface_validation(
+    results: dict[str, Any],
+    save_dir: pathlib.Path,
+    results_number: int,
+) -> None:
+    """
+    Plot the surface reconstruction validation.
+
+    Parameters
+    ----------
+    results : dict[str, Any]
+        Results of the study.
+    save_dir : pathlib.Path
+        Path to the location where the plots are saved.
+    results_number : int
+        Identifier of the results run.
+    """
+    plot_data = results["surface_reconstruction"]["validation_data"]
+
+    kl_divergence = KLDivergenceLoss()
+    kl_losses = []
+    mse_losses = []
+    l1_norm_losses = []
+
+    keys = list(plot_data.keys())
+
+    for k in keys:
+        measured_flux = torch.nn.functional.normalize(
+            plot_data[k]["measured_flux"],
+            p=1,
+            dim=(0, 1),
+            eps=1e-12,
+        )
+        artist_flux = torch.nn.functional.normalize(
+            plot_data[k]["artist_flux"],
+            p=1,
+            dim=(0, 1),
+            eps=1e-12,
+        )
+
+        kl = kl_divergence(
+            measured_flux.unsqueeze(0),
+            artist_flux.unsqueeze(0),
+            reduction_dimensions=(1, 2),
+        )
+        kl_losses.append(float(kl))
+
+        # MSE
+        mse = torch.nn.functional.mse_loss(measured_flux, artist_flux).item()
+        mse_losses.append(mse)
+
+        # normalized L1-like
+        l1_norm = (
+            torch.abs(artist_flux - measured_flux).sum() / measured_flux.sum()
+        ).item()
+        l1_norm_losses.append(l1_norm)
+
+    x = list(range(len(keys)))
+
+    plt.figure(figsize=(10, 5))
+    plt.scatter(x, kl_losses, label="KL Divergence")
+    plt.scatter(x, mse_losses, label="MSE")
+    plt.scatter(x, l1_norm_losses, label="L1 normalized")
+    plt.title("Loss comparison per heliostat")
+    plt.xlabel("Heliostat index")
+    plt.ylabel("Loss value")
+    plt.legend()
+    plt.tight_layout()
+
+    save_dir.mkdir(parents=True, exist_ok=True)
+    filename = (
+        save_dir / f"run_{results_number}/surface_reconstruction_validation_loss.pdf"
+    )
+    plt.tight_layout()
+    plt.savefig(filename, dpi=300, bbox_inches="tight")
+    plt.clf()
+
+    print(f"Saved surface reconstruction validation loss at: {filename}.")
+
+    selected_indices = list(range(0, len(keys), 1))
+    n_rows = len(selected_indices)
+
+    fig, axes = plt.subplots(
+        n_rows,
+        2,
+        figsize=(8, 3 * n_rows),
+    )
+
+    if n_rows == 1:
+        axes = [axes]
+
+    for row, idx in enumerate(selected_indices):
+        k = keys[idx]
+
+        measured_flux = plot_data[k]["measured_flux"].detach().cpu()
+        artist_flux = plot_data[k]["artist_flux"].detach().cpu()
+
+        ax1 = axes[row][0]
+        ax2 = axes[row][1]
+
+        im1 = ax1.imshow(measured_flux)
+        ax1.set_title(f"Measured {k}")
+        ax1.axis("off")
+
+        im2 = ax2.imshow(artist_flux)
+        ax2.set_title(f"Artist {k}")
+        ax2.axis("off")
+
+    save_dir.mkdir(parents=True, exist_ok=True)
+    filename = (
+        save_dir / f"run_{results_number}/surface_reconstruction_validation_fluxes.pdf"
+    )
+    fig.tight_layout()
+    fig.savefig(filename, dpi=300, bbox_inches="tight")
+    plt.close(fig)
+
+    print(f"Saved surface reconstruction validation fluxes at: {filename}.")
+
+
 def plot_kinematics_reconstruction_flux(
     results: dict[str, Any],
     save_dir: pathlib.Path,
@@ -786,6 +911,112 @@ def plot_kinematics_loss_history(
         print(f"Saved kinematics reconstructions loss history plot at: {filename}.")
 
 
+def plot_kinematics_validation(
+    results: dict[str, Any],
+    save_dir: pathlib.Path,
+    results_number: int,
+) -> None:
+    """
+    Plot the surface reconstruction validation.
+
+    Parameters
+    ----------
+    results : dict[str, Any]
+        Results of the study.
+    save_dir : pathlib.Path
+        Path to the location where the plots are saved.
+    results_number : int
+        Identifier of the results run.
+    """
+    plot_data = results["kinematics_reconstruction_with_reconstructed_surfaces"][
+        "validation_data"
+    ]
+
+    keys = list(plot_data.keys())
+
+    # for k in keys:
+    #     measured_flux = plot_data[k]["measured_flux"]
+    #     artist_flux = plot_data[k]["artist_flux"]
+
+    #     centers = utils.get_center_of_mass(
+    #         bitmaps=torch.stack([measured_flux, artist_flux]),
+    #     )
+
+    # x = list(range(len(keys)))
+    # plt.figure(figsize=(10, 5))
+    # plt.scatter(x, kl_losses, label="KL Divergence")
+    # plt.scatter(x, mse_losses, label="MSE")
+    # plt.scatter(x, l1_norm_losses, label="L1 normalized")
+    # plt.title("Loss comparison per heliostat")
+    # plt.xlabel("Heliostat index")
+    # plt.ylabel("Loss value")
+    # plt.legend()
+    # plt.tight_layout()
+
+    # save_dir.mkdir(parents=True, exist_ok=True)
+    # filename = (
+    #     save_dir
+    #     / f"run_{results_number}/kinematics_reconstruction_validation_loss.pdf"
+    # )
+    # plt.tight_layout()
+    # plt.savefig(filename, dpi=300, bbox_inches="tight")
+    # plt.clf()
+
+    # print(f"Saved kinematics reconstruction validation loss at: {filename}.")
+
+    selected_indices = list(range(0, len(keys), 1))
+    n_rows = len(selected_indices)
+
+    fig, axes = plt.subplots(
+        n_rows,
+        2,
+        figsize=(8, 3 * n_rows),
+    )
+
+    if n_rows == 1:
+        axes = [axes]
+
+    for row, idx in enumerate(selected_indices):
+        k = keys[idx]
+
+        measured_flux = plot_data[k]["measured_flux"]
+        artist_flux = plot_data[k]["artist_flux"]
+
+        centers = (
+            utils.get_center_of_mass(
+                bitmaps=torch.stack([measured_flux, artist_flux]),
+            )
+            .detach()
+            .cpu()
+        )
+
+        ax1 = axes[row][0]
+        ax2 = axes[row][1]
+
+        im1 = ax1.imshow(measured_flux.detach().cpu())
+        ax1.set_title(f"Measured {k}")
+        ax1.scatter(x=centers[0, 0], y=centers[0, 1], c="black", marker="o", s=30)
+        ax1.scatter(x=centers[1, 0], y=centers[1, 1], c="red", marker="x", s=30)
+        ax1.axis("off")
+
+        im2 = ax2.imshow(artist_flux.detach().cpu())
+        ax2.set_title(f"Artist {k}")
+        ax2.scatter(x=centers[0, 0], y=centers[0, 1], c="black", marker="o", s=30)
+        ax2.scatter(x=centers[1, 0], y=centers[1, 1], c="red", marker="x", s=30)
+        ax2.axis("off")
+
+    save_dir.mkdir(parents=True, exist_ok=True)
+    filename = (
+        save_dir
+        / f"run_{results_number}/kinematics_reconstruction_validation_fluxes.pdf"
+    )
+    fig.tight_layout()
+    fig.savefig(filename, dpi=300, bbox_inches="tight")
+    plt.close(fig)
+
+    print(f"Saved kinematics reconstruction validation fluxes at: {filename}.")
+
+
 def plot_model_reconstruction(
     results: dict[str, Any],
     save_dir: pathlib.Path,
@@ -914,7 +1145,7 @@ def plot_model_reconstruction(
     fig, axes = plt.subplots(
         1,
         n_images,
-        figsize=(4 * n_images, 5),
+        figsize=(4 * n_images, 8),
         gridspec_kw={"bottom": 0.15, "top": 0.95, "wspace": 0.05},
     )
     if n_images == 1:
@@ -928,56 +1159,57 @@ def plot_model_reconstruction(
         r"\textbf{Combined Reconstructions}",
     ]
 
-    points = {
-        "focal spot aim point": 256
-        - torch.tensor([104.7413, 139.8122], device="cuda:0"),
-        "aim point 'measured'": 256
-        - torch.tensor([112.9362, 111.4531], device="cuda:0"),
-    }
+    # height, width = measured_flux.shape
+    # points = {
+    #     "focal spot aim point": 256
+    #     - torch.tensor([104.7413, 139.8122], device="cuda:0"),
+    #     "aim point 'measured'": 256
+    #     - torch.tensor([112.9362, 111.4531], device="cuda:0"),
+    # }
 
-    measured = images[0]
-    y_grid, x_grid = torch.meshgrid(torch.arange(256), torch.arange(256), indexing="ij")
-    x_com_measured = (x_grid * measured).sum() / measured.sum()
-    y_com_measured = (y_grid * measured).sum() / measured.sum()
+    # measured = images[0]
+    # y_grid, x_grid = torch.meshgrid(torch.arange(256), torch.arange(256), indexing="ij")
+    # x_com_measured = (x_grid * measured).sum() / measured.sum()
+    # y_com_measured = (y_grid * measured).sum() / measured.sum()
 
     for idx, (ax, flux) in enumerate(zip(axes, images)):
         im = ax.imshow(flux, cmap=cmap, vmin=vmin, vmax=vmax)
         ax.axis("off")
         ax.set_title(titles[idx], fontsize=13)
-        ax.scatter(
-            x_com_measured,
-            y_com_measured,
-            s=80,
-            marker="x",
-            color="blue",
-            label="center of mass reference",
-        )
+        # ax.scatter(
+        #     x_com_measured,
+        #     y_com_measured,
+        #     s=80,
+        #     marker="x",
+        #     color="blue",
+        #     label="center of mass reference",
+        # )
 
-        y_grid, x_grid = torch.meshgrid(
-            torch.arange(256), torch.arange(256), indexing="ij"
-        )
-        x_com = (x_grid * flux).sum() / flux.sum()
-        y_com = (y_grid * flux).sum() / flux.sum()
-        ax.scatter(
-            x_com, y_com, s=80, marker="o", color="green", label="center of mass"
-        )
+        # y_grid, x_grid = torch.meshgrid(
+        #     torch.arange(256), torch.arange(256), indexing="ij"
+        # )
+        # x_com = (x_grid * flux).sum() / flux.sum()
+        # y_com = (y_grid * flux).sum() / flux.sum()
+        # ax.scatter(
+        #     x_com, y_com, s=80, marker="o", color="green", label="center of mass"
+        # )
 
-        ax.scatter(
-            256 / 2,
-            256 / 2,
-            s=40,
-            marker="x",
-            color="white",
-            label="geometric center of bitmap",
-        )
-        ax.scatter(
-            points["aim point 'measured'"][0].cpu(),
-            points["aim point 'measured'"][1].cpu(),
-            s=80,
-            marker="x",
-            color="white",
-            label="aim point from protocol",
-        )
+        # ax.scatter(
+        #     256 / 2,
+        #     256 / 2,
+        #     s=40,
+        #     marker="x",
+        #     color="white",
+        #     label="geometric center of bitmap",
+        # )
+        # ax.scatter(
+        #     points["aim point 'measured'"][0].cpu(),
+        #     points["aim point 'measured'"][1].cpu(),
+        #     s=80,
+        #     marker="x",
+        #     color="white",
+        #     label="aim point from protocol",
+        # )
 
         ax.legend(fontsize=9, loc="upper right")
 
@@ -1000,13 +1232,14 @@ def plot_model_reconstruction(
             fontsize=12,
         )
 
-    cbar_ax = fig.add_axes([0.15, 0.01, 0.7, 0.03])
+    fig.subplots_adjust(bottom=0.15)
+    cbar_ax = fig.add_axes([0.15, 0.05, 0.7, 0.03])
     cbar = fig.colorbar(im, cax=cbar_ax, orientation="horizontal")
     cbar.ax.tick_params(labelsize=14)
 
     fig.text(
         0.95,
-        0.02,
+        0.01,
         "M = Measured flux\nU = Unoptimized flux\nO = Optimized flux\nH = Homogeneous flux",
         ha="right",
         va="bottom",
@@ -1302,7 +1535,7 @@ def plot_aim_point_loss_history(
     device = get_device(torch.device(args.device))
 
     for case in ["baseline", "full_field"]:
-        results_number = 10
+        results_number = 5
         results_path = (
             pathlib.Path(args.results_dir) / case / f"results_{results_number}.pt"
         )
@@ -1332,26 +1565,32 @@ def plot_aim_point_loss_history(
         #     results=results, results_ftp=results_ftp, save_dir=plots_path
         # )
 
-        # plot_surface_reconstruction_flux(
+        # plot_kinematics_reconstruction_flux(
         #     results=results, save_dir=plots_path, results_number=results_number
         # )
-        plot_surface_error_analysis(
+        # plot_kinematics_error_analysis(
+        #     results=results,
+        #     save_dir=plots_path,
+        #     split_left=False,
+        #     results_number=results_number,
+        # )
+        # plot_kinematics_loss_history(
+        #     results=results, save_dir=plots_path, results_number=results_number
+        # )
+        # plot_kinematics_validation(
+        #     results=results, save_dir=plots_path, results_number=results_number
+        # )
+
+        plot_surface_reconstruction_flux(
             results=results, save_dir=plots_path, results_number=results_number
         )
-        plot_surface_loss_history(
+        plot_surface_error_analysis(
             results=results, save_dir=plots_path, results_number=results_number
         )
-
-        plot_kinematics_reconstruction_flux(
+        plot_surface_loss_history(
             results=results, save_dir=plots_path, results_number=results_number
         )
-        plot_kinematics_error_analysis(
-            results=results,
-            save_dir=plots_path,
-            split_left=False,
-            results_number=results_number,
-        )
-        plot_kinematics_loss_history(
+        plot_surface_validation(
             results=results, save_dir=plots_path, results_number=results_number
         )
 
diff --git a/examples/field_optimizations/generate_results.py b/examples/field_optimizations/generate_results.py
index e32e2915..c62a6390 100644
--- a/examples/field_optimizations/generate_results.py
+++ b/examples/field_optimizations/generate_results.py
@@ -709,6 +709,30 @@ def aim_point_plots(
                 target_area_indices=target_area_indices,
                 device=device,
             )
+            # ###########
+            # import matplotlib.pyplot as plt
+
+            # aim_point_measured_bitmap = 256 - torch.tensor([112.9362, 111.4531])
+            # for i in range(bitmaps_per_heliostat.shape[0]):
+            #     total_f = bitmaps_per_heliostat[i].sum()
+            #     if total_f > 0:
+            #         h, w = bitmaps_per_heliostat[i].shape
+            #         y_grid = torch.arange(h, device=device).unsqueeze(1).expand(h, w)
+            #         x_grid = torch.arange(w, device=device).unsqueeze(0).expand(h, w)
+            #         cy_f = (y_grid * bitmaps_per_heliostat[i]).sum() / total_f
+            #         cx_f = (x_grid * bitmaps_per_heliostat[i]).sum() / total_f
+            #     plt.imshow(bitmaps_per_heliostat[i].cpu().detach())
+            #     plt.scatter(
+            #         x=aim_point_measured_bitmap[0],
+            #         y=aim_point_measured_bitmap[1],
+            #         c="r",
+            #         s=30,
+            #     )
+            #     plt.scatter(cx_f.cpu().detach(), cy_f.cpu().detach(), c="g", s=30)
+            #     plt.scatter(x=w / 2, y=h / 2, c="black", s=30, marker="x")
+            #     plt.savefig(f"./bitmaps/aim_points/{label}_{i}")
+            #     plt.close()
+            # ###########
             flux_distribution_on_target = ray_tracer.get_bitmaps_per_target(
                 bitmaps_per_heliostat=bitmaps_per_heliostat,
                 target_area_indices=target_area_indices,
@@ -771,6 +795,9 @@ def full_field_optimizations(
     results_dict = cast(
         dict[str, dict[str, torch.Tensor]], torch.load(results_path, weights_only=False)
     )
+    control_points_path = (
+        results_path.parent / f"reconstructed_nurbs_control_points_{results_number}.pt"
+    )
 
     number_of_heliostat_groups = Scenario.get_number_of_heliostat_groups_from_hdf5(
         scenario_path=scenario_path
@@ -781,9 +808,6 @@ def full_field_optimizations(
         device=device,
     ) as ddp_setup:
         device = ddp_setup["device"]
-        control_points_path = (
-            results_path.parent / "reconstructed_nurbs_control_points.pt"
-        )
 
         assert data_mappings is not None, "data_mappings must be provided."
         assert surface_config is not None, "surface_config must be provided."
@@ -853,101 +877,101 @@ def full_field_optimizations(
         results_dict["ideal_model"] = {"aim_point_plot": aim_point_data_ideal_models}
         torch.cuda.empty_cache()
 
-        kinematics_data_plot_before = kinematics_evaluation(
-            scenario=scenario_kinematics_ideal_surfaces,
-            ddp_setup=ddp_setup,
-            heliostat_data=data_mappings["kinematics_plot"],
-            device=device,
-        )
-        scenario_kinematics_ideal_surfaces.set_number_of_rays(
-            number_of_rays=kinematics_config["number_of_rays"]
-        )
-        optimization_configuration_kinematics = {
-            config_dictionary.optimization: {
-                config_dictionary.initial_learning_rate_rotation_deviation: kinematics_config[
-                    "initial_learning_rate_rotation_deviation"
-                ],
-                config_dictionary.initial_learning_rate_initial_angles: kinematics_config[
-                    "initial_learning_rate_initial_angles"
-                ],
-                config_dictionary.initial_learning_rate_initial_stroke_length: kinematics_config[
-                    "initial_learning_rate_initial_stroke_length"
-                ],
-                config_dictionary.tolerance: 1e-5,
-                config_dictionary.max_epoch: kinematics_config["max_epoch"],
-                config_dictionary.batch_size: kinematics_config["batch_size"],
-                config_dictionary.log_step: basic_config[config_dictionary.log_step],
-                config_dictionary.early_stopping_delta: 1e-12,
-                config_dictionary.early_stopping_patience: 10000,
-                config_dictionary.early_stopping_window: 10000,
-            },
-            config_dictionary.scheduler: {
-                config_dictionary.scheduler_type: kinematics_config["scheduler"],
-                config_dictionary.gamma: kinematics_config["gamma"],
-                config_dictionary.lr_min: kinematics_config["min_learning_rate"],
-                config_dictionary.lr_max: kinematics_config["max_learning_rate"],
-                config_dictionary.step_size_up: kinematics_config["step_size_up"],
-                config_dictionary.reduce_factor: kinematics_config["reduce_factor"],
-                config_dictionary.patience: kinematics_config["patience"],
-                config_dictionary.threshold: kinematics_config["threshold"],
-                config_dictionary.cooldown: kinematics_config["cooldown"],
-            },
-        }
-        kinematics_reconstructor = KinematicsReconstructor(
-            ddp_setup=ddp_setup,
-            scenario=scenario_kinematics_ideal_surfaces,
-            data=data_mappings["kinematics_training"],
-            dni=baseline_dni,
-            optimization_configuration=optimization_configuration_kinematics,
-            reconstruction_method=config_dictionary.kinematics_reconstruction_raytracing,
-        )
-        kinematics_reconstruction_final_loss_per_heliostat, loss_history_kinematics = (
-            kinematics_reconstructor.reconstruct_kinematics(
-                loss_definition=FocalSpotLoss(
-                    scenario=scenario_kinematics_ideal_surfaces
-                ),
-                device=device,
-            )
-        )
-        if ddp_setup["is_distributed"]:
-            torch.distributed.barrier()
-        kinematics_validation = kinematics_evaluation(
-            scenario=scenario_kinematics_ideal_surfaces,
-            ddp_setup=ddp_setup,
-            heliostat_data=data_mappings["kinematics_validation"],
-            device=device,
-        )
-        kinematics_data_plot_after = kinematics_evaluation(
-            scenario=scenario_kinematics_ideal_surfaces,
-            ddp_setup=ddp_setup,
-            heliostat_data=data_mappings["kinematics_plot"],
-            device=device,
-        )
-        merged_plot_data_kinematics = merge_data(
-            unoptimized_data=kinematics_data_plot_before,
-            optimized_data=kinematics_data_plot_after,
-        )
-        aim_point_data_kinematic_reconstruction = aim_point_plots(
-            scenario=scenario_kinematics_ideal_surfaces,
-            incident_ray_direction=baseline_incident_ray_direction,
-            target_area_index=scenario_kinematics_ideal_surfaces.solar_tower.target_name_to_index[
-                baseline_target_area
-            ],
-            aim_point=baseline_aim_point,
-            dni=baseline_dni,
-            bitmap_resolution=bitmap_resolution,
-            label="before",
-            batch_size=20,
-            number_of_rays=200,
-            device=device,
-        )
-        results_dict["kinematics_reconstruction_with_ideal_surfaces"] = {
-            "flux_plot_data": merged_plot_data_kinematics,
-            "validation_data": kinematics_validation,
-            "loss_history": loss_history_kinematics,
-            "loss": kinematics_reconstruction_final_loss_per_heliostat,
-            "aim_point_plot": aim_point_data_kinematic_reconstruction,
-        }
+        # kinematics_data_plot_before = kinematics_evaluation(
+        #     scenario=scenario_kinematics_ideal_surfaces,
+        #     ddp_setup=ddp_setup,
+        #     heliostat_data=data_mappings["kinematics_plot"],
+        #     device=device,
+        # )
+        # scenario_kinematics_ideal_surfaces.set_number_of_rays(
+        #     number_of_rays=kinematics_config["number_of_rays"]
+        # )
+        # optimization_configuration_kinematics = {
+        #     config_dictionary.optimization: {
+        #         config_dictionary.initial_learning_rate_rotation_deviation: kinematics_config[
+        #             "initial_learning_rate_rotation_deviation"
+        #         ],
+        #         config_dictionary.initial_learning_rate_initial_angles: kinematics_config[
+        #             "initial_learning_rate_initial_angles"
+        #         ],
+        #         config_dictionary.initial_learning_rate_initial_stroke_length: kinematics_config[
+        #             "initial_learning_rate_initial_stroke_length"
+        #         ],
+        #         config_dictionary.tolerance: 1e-5,
+        #         config_dictionary.max_epoch: kinematics_config["max_epoch"],
+        #         config_dictionary.batch_size: kinematics_config["batch_size"],
+        #         config_dictionary.log_step: basic_config[config_dictionary.log_step],
+        #         config_dictionary.early_stopping_delta: 1e-12,
+        #         config_dictionary.early_stopping_patience: 10000,
+        #         config_dictionary.early_stopping_window: 10000,
+        #     },
+        #     config_dictionary.scheduler: {
+        #         config_dictionary.scheduler_type: kinematics_config["scheduler"],
+        #         config_dictionary.gamma: kinematics_config["gamma"],
+        #         config_dictionary.lr_min: kinematics_config["min_learning_rate"],
+        #         config_dictionary.lr_max: kinematics_config["max_learning_rate"],
+        #         config_dictionary.step_size_up: kinematics_config["step_size_up"],
+        #         config_dictionary.reduce_factor: kinematics_config["reduce_factor"],
+        #         config_dictionary.patience: kinematics_config["patience"],
+        #         config_dictionary.threshold: kinematics_config["threshold"],
+        #         config_dictionary.cooldown: kinematics_config["cooldown"],
+        #     },
+        # }
+        # kinematics_reconstructor = KinematicsReconstructor(
+        #     ddp_setup=ddp_setup,
+        #     scenario=scenario_kinematics_ideal_surfaces,
+        #     data=data_mappings["kinematics_training"],
+        #     dni=baseline_dni,
+        #     optimization_configuration=optimization_configuration_kinematics,
+        #     reconstruction_method=config_dictionary.kinematics_reconstruction_raytracing,
+        # )
+        # kinematics_reconstruction_final_loss_per_heliostat, loss_history_kinematics = (
+        #     kinematics_reconstructor.reconstruct_kinematics(
+        #         loss_definition=FocalSpotLoss(
+        #             scenario=scenario_kinematics_ideal_surfaces
+        #         ),
+        #         device=device,
+        #     )
+        # )
+        # if ddp_setup["is_distributed"]:
+        #     torch.distributed.barrier()
+        # kinematics_validation = kinematics_evaluation(
+        #     scenario=scenario_kinematics_ideal_surfaces,
+        #     ddp_setup=ddp_setup,
+        #     heliostat_data=data_mappings["kinematics_validation"],
+        #     device=device,
+        # )
+        # kinematics_data_plot_after = kinematics_evaluation(
+        #     scenario=scenario_kinematics_ideal_surfaces,
+        #     ddp_setup=ddp_setup,
+        #     heliostat_data=data_mappings["kinematics_plot"],
+        #     device=device,
+        # )
+        # merged_plot_data_kinematics = merge_data(
+        #     unoptimized_data=kinematics_data_plot_before,
+        #     optimized_data=kinematics_data_plot_after,
+        # )
+        # aim_point_data_kinematic_reconstruction = aim_point_plots(
+        #     scenario=scenario_kinematics_ideal_surfaces,
+        #     incident_ray_direction=baseline_incident_ray_direction,
+        #     target_area_index=scenario_kinematics_ideal_surfaces.solar_tower.target_name_to_index[
+        #         baseline_target_area
+        #     ],
+        #     aim_point=baseline_aim_point,
+        #     dni=baseline_dni,
+        #     bitmap_resolution=bitmap_resolution,
+        #     label="before",
+        #     batch_size=20,
+        #     number_of_rays=200,
+        #     device=device,
+        # )
+        # results_dict["kinematics_reconstruction_with_ideal_surfaces"] = {
+        #     "flux_plot_data": merged_plot_data_kinematics,
+        #     "validation_data": kinematics_validation,
+        #     "loss_history": loss_history_kinematics,
+        #     "loss": kinematics_reconstruction_final_loss_per_heliostat,
+        #     "aim_point_plot": aim_point_data_kinematic_reconstruction,
+        # }
         torch.cuda.empty_cache()
 
         # Surface reconstruction.
@@ -1141,13 +1165,13 @@ def full_field_optimizations(
         if ddp_setup["is_distributed"]:
             torch.distributed.barrier()
         kinematics_validation = kinematics_evaluation(
-            scenario=scenario_kinematics_ideal_surfaces,
+            scenario=scenario_kinematics,
             ddp_setup=ddp_setup,
             heliostat_data=data_mappings["kinematics_validation"],
             device=device,
         )
         kinematics_data_plot_after = kinematics_evaluation(
-            scenario=scenario_kinematics_ideal_surfaces,
+            scenario=scenario_kinematics,
             ddp_setup=ddp_setup,
             heliostat_data=data_mappings["kinematics_plot"],
             device=device,

From 9bb386d28630486166337d34b82c9f7026616ca6 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 8 May 2026 09:36:48 +0200
Subject: [PATCH 03/38] add parameters

---
 artist/core/learning_rate_schedulers.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/artist/core/learning_rate_schedulers.py b/artist/core/learning_rate_schedulers.py
index 031322ae..14c93fbb 100644
--- a/artist/core/learning_rate_schedulers.py
+++ b/artist/core/learning_rate_schedulers.py
@@ -56,6 +56,8 @@ def cyclic(
         base_lr=float(parameters[config_dictionary.lr_min]),
         max_lr=float(parameters[config_dictionary.lr_max]),
         step_size_up=int(parameters[config_dictionary.step_size_up]),
+        mode='triangular2',
+        cycle_momentum=False
     )
 
 

From e803f06610ca635b32d2eb0833b1964248b198d0 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 8 May 2026 09:37:48 +0200
Subject: [PATCH 04/38] remove dtype

---
 artist/util/utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/artist/util/utils.py b/artist/util/utils.py
index 53650798..8bc5a5a2 100644
--- a/artist/util/utils.py
+++ b/artist/util/utils.py
@@ -1018,7 +1018,6 @@ def perform_canting(
     rotation_matrix = torch.zeros(
         (number_of_surfaces, number_of_facets_per_surface, 4, 4),
         device=device,
-        dtype=data.dtype,
     )
 
     # Extract ENU basis candidates from canting tensor (drop homogeneous component).

From 57fbd03c76929356c633174aa0d9cef88a833526 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 20 May 2026 22:00:42 +0200
Subject: [PATCH 05/38] add reduction parameter to loss per heliostat

---
 artist/optim/loss.py                          |  18 +-
 .../04_kinematic_reconstruction_tutorial.py   | 415 ++++++++++++++++++
 2 files changed, 426 insertions(+), 7 deletions(-)
 create mode 100644 tutorials/04_kinematic_reconstruction_tutorial.py

diff --git a/artist/optim/loss.py b/artist/optim/loss.py
index de1e67ec..e3c71cc9 100644
--- a/artist/optim/loss.py
+++ b/artist/optim/loss.py
@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Callable
 
 import torch
 
@@ -436,9 +436,10 @@ def __call__(
         return 1.0 - self.loss_function(prediction, ground_truth)
 
 
-def mean_loss_per_heliostat(
+def loss_per_heliostat(
     loss_per_sample: torch.Tensor,
     number_of_samples_per_heliostat: int,
+    reduction: Callable[..., Any],
 ) -> torch.Tensor:
     """
     Calculate the mean loss per heliostat from a loss per sample.
@@ -447,7 +448,7 @@ def mean_loss_per_heliostat(
     ----------
     loss_per_sample : torch.Tensor
         Loss per sample.
-        Shape is ``[number_of_samples]``.
+        Tensor of shape [number_of_samples].
     number_of_samples_per_heliostat : int
         Number of samples per heliostat.
 
@@ -455,7 +456,7 @@ def mean_loss_per_heliostat(
     -------
     torch.Tensor
         Loss per heliostat.
-        Shape is ``[number_of_heliostats]``.
+        Tensor of shape [number_of_heliostats].
     """
     number_of_heliostats = int(
         loss_per_sample.numel() // number_of_samples_per_heliostat
@@ -464,8 +465,11 @@ def mean_loss_per_heliostat(
         : number_of_heliostats * number_of_samples_per_heliostat
     ]
 
-    loss_per_heliostat = loss_per_sample.view(
+    reduced = reduction(loss_per_sample.view(
         number_of_heliostats, number_of_samples_per_heliostat
-    ).mean(dim=1)
+    ))
 
-    return loss_per_heliostat
+    if not isinstance(reduced, torch.Tensor):
+        reduced = reduced.values
+
+    return reduced
diff --git a/tutorials/04_kinematic_reconstruction_tutorial.py b/tutorials/04_kinematic_reconstruction_tutorial.py
new file mode 100644
index 00000000..dd11cfe9
--- /dev/null
+++ b/tutorials/04_kinematic_reconstruction_tutorial.py
@@ -0,0 +1,415 @@
+"""Kinematic reconstruction tutorial."""
+
+import logging
+import pathlib
+
+import h5py
+import paint.util.paint_mappings as paint_mappings
+import torch
+from matplotlib import pyplot as plt
+
+from artist.core.heliostat_ray_tracer import HeliostatRayTracer
+from artist.core.kinematics_reconstructor import KinematicsReconstructor
+from artist.core.loss_functions import AngleLoss, FocalSpotLoss, PixelLoss, VectorLoss
+from artist.data_parser import paint_scenario_parser
+from artist.data_parser.calibration_data_parser import CalibrationDataParser
+from artist.data_parser.paint_calibration_parser import PaintCalibrationDataParser
+from artist.field.heliostat_group import HeliostatGroup
+from artist.scenario.scenario import Scenario
+from artist.util import config_dictionary, set_logger_config, utils
+from artist.util.environment_setup import get_device, setup_distributed_environment
+
+torch.manual_seed(7)
+torch.cuda.manual_seed(7)
+
+#############################################################################################################
+# Define helper functions for the plots.
+# Skip to line 170 for the tutorial code.
+#############################################################################################################
+
+
+def create_fluxes(
+    data_parser: CalibrationDataParser,
+    heliostat_data_mapping: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
+    resolution: torch.Tensor,
+    method: str
+) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
+    """
+    Create data to plot the heliostat fluxes.
+
+    Parameters
+    ----------
+    data_parser : CalibrationDataParser
+        Data parser used to load calibration data from files.
+    heliostat_data_mapping : list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]
+        Mapping from heliostats to calibration data files.
+    resolution : torch.Tensor
+        Bitmap resolution.
+
+    Returns
+    -------
+    list[torch.Tensor]
+        Bitmaps per heliostat.
+    list[torch.Tensor]
+        Measured flux bitmap.
+    """
+    bitmaps = []
+    measured_bitmaps = []
+    scenario.set_number_of_rays(number_of_rays=100)
+    for heliostat_group_index in range(len(scenario.heliostat_field.heliostat_groups)):
+        heliostat_group: HeliostatGroup = scenario.heliostat_field.heliostat_groups[
+            heliostat_group_index
+        ]
+        (
+            measured_flux,
+            focal_spots_measured,
+            incident_ray_directions,
+            motor_positions,
+            active_heliostats_mask,
+            target_area_indices,
+        ) = data_parser.parse_data_for_reconstruction(
+            heliostat_data_mapping=heliostat_data_mapping,
+            heliostat_group=heliostat_group,
+            scenario=scenario,
+            bitmap_resolution=resolution,
+            device=device,
+        )
+
+        if active_heliostats_mask.sum() > 0:
+            measured_bitmaps.append(measured_flux)
+
+            # Activate heliostats.
+            heliostat_group.activate_heliostats(
+                active_heliostats_mask=active_heliostats_mask,
+                device=device,
+            )
+
+            if method == "motor_pos":
+                # Align heliostats.
+                heliostat_group.align_surfaces_with_motor_positions(
+                    motor_positions=motor_positions,
+                    active_heliostats_mask=active_heliostats_mask,
+                    device=device,
+                )
+            if method == "incident_ray":
+                # Align heliostats.
+                heliostat_group.align_surfaces_with_incident_ray_directions(
+                    aim_points=scenario.solar_tower.get_centers_of_target_areas(target_area_indices=target_area_indices, device=device),
+                    incident_ray_directions=incident_ray_directions,
+                    active_heliostats_mask=active_heliostats_mask,
+                    device=device,
+                )
+
+            # Create a ray tracer.
+            ray_tracer = HeliostatRayTracer(
+                scenario=scenario,
+                heliostat_group=heliostat_group,
+                blocking_active=False,
+                batch_size=heliostat_group.number_of_active_heliostats,
+                bitmap_resolution=resolution,
+            )
+
+            # Perform heliostat-based ray tracing.
+            bitmaps_per_heliostat, _, _, _ = ray_tracer.trace_rays(
+                incident_ray_directions=incident_ray_directions,
+                active_heliostats_mask=active_heliostats_mask,
+                target_area_indices=target_area_indices,
+                device=device,
+            )
+            bitmaps.append(bitmaps_per_heliostat)
+
+    scenario.set_number_of_rays(number_of_rays=4)
+
+    return bitmaps, measured_bitmaps
+
+
+def create_plots(
+    fluxes_before: torch.Tensor,
+    fluxes_after: torch.Tensor,
+    fluxes_ray: torch.Tensor,
+    fluxes_measured: torch.Tensor,
+) -> None:
+    """
+    Create the flux plots using the reconstruction results.
+
+    Parameters
+    ----------
+    fluxes_before : torch.Tensor
+        Fluxes before the kinematics reconstruction.
+    fluxes_after : torch.Tensor
+        Fluxes after the kinematics reconstruction.
+    fluxes_measured : torch.Tensor
+        Measured flux references.
+    """
+    for group_index, (flux_before, flux_after, flux_measured, flux_ray) in enumerate(
+        zip(fluxes_before, fluxes_after, fluxes_measured, fluxes_ray)
+    ):
+        center_measured = (
+            utils.get_center_of_mass(flux_measured, device=device).cpu().detach()
+        )
+        center_before = (
+            utils.get_center_of_mass(flux_before, device=device).cpu().detach()
+        )
+        center_after = (
+            utils.get_center_of_mass(flux_after, device=device).cpu().detach()
+        )
+        center_ray = (
+            utils.get_center_of_mass(flux_ray, device=device).cpu().detach()
+        )
+
+        for i in range(len(flux_before)):
+            _, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
+
+            axes[0].imshow(flux_before[i].cpu().detach(), cmap="gray")
+            axes[0].set_title("Before reconstruction", fontsize=16)
+            axes[0].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
+            axes[0].scatter(x=center_before[i, 0], y=center_before[i, 1], c="red", marker="x", label="Predicted")
+            #axes[0].scatter(256/2, 256/2, c="blue")
+            axes[0].axis("off")
+            
+            axes[1].imshow(flux_after[i].cpu().detach(), cmap="gray")
+            axes[1].set_title("After reconstruction", fontsize=16)
+            axes[1].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
+            axes[1].scatter(x=center_after[i, 0], y=center_after[i, 1], c="red", marker="x", label="Predicted")
+            #axes[1].scatter(256/2, 256/2, c="blue")
+            axes[1].axis("off")
+
+            axes[2].imshow(flux_ray[i].cpu().detach(), cmap="gray")
+            axes[2].set_title("After reconstruction align only with sun pos", fontsize=16)
+            axes[2].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
+            axes[2].scatter(x=center_ray[i, 0], y=center_ray[i, 1], c="red", marker="x", label="Predicted")
+            #axes[2].scatter(256/2, 256/2, c="blue")
+            axes[2].axis("off")
+
+            axes[3].imshow(flux_measured[i].cpu().detach(), cmap="gray")
+            axes[3].set_title("Measured", fontsize=16)
+            axes[3].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o")
+            axes[3].axis("off")
+
+            axes[0].legend()
+            axes[1].legend()
+            axes[2].legend()
+            axes[3].legend()
+
+            plt.subplots_adjust(wspace=0.05)
+            plt.show()
+            plt.savefig(f"./ignored/new/heliostat_{i}_in_group_{group_index}_calibration.png")
+
+
+#############################################################################################################
+# Tutorial
+#############################################################################################################
+
+# Set up logger.
+set_logger_config()
+log = logging.getLogger(__name__)
+
+# Set the device.
+device = get_device()
+
+# Specify the path to your scenario.h5 file.
+scenario_path = pathlib.Path("/workVERLEIHNIX/mb/ARTIST/examples/field_optimizations/scenarios/ideal_baseline_scenario.h5")
+
+# Also specify the heliostats to be calibrated and the paths to your calibration-properties.json files.
+# Please use the following style: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]
+# heliostat_data_mapping = [
+#     (
+#         "heliostat_name_1",
+#         [
+#             pathlib.Path(
+#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
+#             ),
+#             # ....
+#         ],
+#         [
+#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
+#             # ....
+#         ],
+#     ),
+#     (
+#         "heliostat_name_2",
+#         [
+#             pathlib.Path(
+#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
+#             ),
+#             # ....
+#         ],
+#         [
+#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
+#             # ....
+#         ],
+#     ),
+# ]
+
+# Or if you have a directory with downloaded data use this code to create a mapping.
+heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
+    base_path="/workVERLEIHNIX/share/PAINT_data",
+    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58", "AH48", "AH49", "AH50", "AH55", "AH56", "AH57", "AH58", "AI48", "AI49", "AI50", "AI51", "AI52", "AI53", "AI54", "AI55", "AI56", "AI57", "AI58", "AI59", "AI60", "AI61", "AJ49", "AJ50", "AJ51", "AJ53", "AJ54", "AJ55", "AJ56", "AJ57", "AJ58", "AJ61", "AJ62", "AK48", "AK49", "AK50", "AK51", "AK52", "AK53", "AK54", "AK55", "AK56", "AK58", "AK59", "AK60", "AK61", "AK62", "AL48", "AL49", "AL51", "AL53", "AL56", "AL58", "AL59", "AL60", "AL61", "AL62", "AM48", "AM49", "AM50", "AM51", "AM52", "AM53", "AM54", "AM55", "AM56", "AM57", "AM58", "AM59", "AM60", "AM62", "AN48", "AN49", "AN50", "AN51", "AN52", "AN53", "AN54", "AN55", "AN56", "AN57", "AN58", "AN59", "AN60", "AN61", "AN62"],
+    #heliostat_names=['AZ43', 'AP35', 'AA39', 'AC31', 'AC42', 'AC24', 'AM31', 'BF65', 'AQ27', 'AF33', 'BD31', 'AW42', 'AZ39', 'AL36', 'AD42', 'AP39', 'AE28', 'BE38', 'AY55', 'AP50'],
+    number_of_measurements=37,
+    image_variant="flux",
+    randomize=True,
+    seed=7,
+)
+
+# 'AC40', 
+bad_samples = {}
+filtered_mapping = [
+    (
+        name,
+        [p for p in list1 if not any(b in str(p) for b in bad_samples)],
+        [p for p in list2 if not any(b in str(p) for b in bad_samples)],
+    )
+    for name, list1, list2 in heliostat_data_mapping
+]
+
+# Configure the optimization.
+optimizer_dict = {
+    config_dictionary.initial_learning_rate_rotation_deviation: 1e-5,
+    config_dictionary.initial_learning_rate_initial_angles: 1e-3,
+    config_dictionary.initial_learning_rate_initial_stroke_length: 1e-2,
+    config_dictionary.tolerance: 0.0000,
+    config_dictionary.max_epoch: 600,
+    config_dictionary.batch_size: 200,
+    config_dictionary.log_step: 1,
+    config_dictionary.early_stopping_delta: 1e-8,
+    config_dictionary.early_stopping_patience: 1000,
+    config_dictionary.early_stopping_window: 2000,
+}
+# Configure the learning rate scheduler.
+scheduler_dict = {
+    config_dictionary.scheduler_type: config_dictionary.reduce_on_plateau,
+    config_dictionary.gamma: 0.9,
+    config_dictionary.lr_min: 1e-6,
+    config_dictionary.lr_max: 1e-3,
+    config_dictionary.step_size_up: 500,
+    config_dictionary.reduce_factor: 0.0001,
+    config_dictionary.patience: 50,
+    config_dictionary.threshold: 1e-3,
+    config_dictionary.cooldown: 10,
+}
+# Combine configurations.
+optimization_configuration = {
+    config_dictionary.optimization: optimizer_dict,
+    config_dictionary.scheduler: scheduler_dict,
+}
+
+data_parser = PaintCalibrationDataParser(
+    sample_limit=17, centroid_extraction_method=paint_mappings.UTIS_KEY
+)
+data_parser_plots = PaintCalibrationDataParser(
+    sample_limit=1, centroid_extraction_method=paint_mappings.UTIS_KEY
+)
+
+# Create dict for the data parser and the heliostat_data_mapping.
+data: dict[
+    str,
+    CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
+] = {
+    config_dictionary.data_parser: data_parser,
+    config_dictionary.heliostat_data_mapping: filtered_mapping,
+}
+
+data_plots: dict[
+    str,
+    CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
+] = {
+    config_dictionary.data_parser: data_parser_plots,
+    config_dictionary.heliostat_data_mapping: filtered_mapping,
+}
+
+number_of_heliostat_groups = Scenario.get_number_of_heliostat_groups_from_hdf5(
+    scenario_path=scenario_path
+)
+#torch.autograd.set_detect_anomaly(True)
+with setup_distributed_environment(
+    number_of_heliostat_groups=number_of_heliostat_groups,
+    device=device,
+) as ddp_setup:
+    device = ddp_setup[config_dictionary.device]  # type:ignore
+
+    # Load the scenario.
+    with h5py.File(scenario_path, "r") as scenario_file:
+        scenario = Scenario.load_scenario_from_hdf5(
+            scenario_file=scenario_file, device=device
+        )
+
+    resolution = torch.tensor([256, 256], device=device)
+
+    bitmaps_before, _ = create_fluxes(
+        data_parser=data_parser_plots,
+        heliostat_data_mapping=[
+            (heliostat[0], [heliostat[1][-1]], [heliostat[2][-1]])
+            for heliostat in filtered_mapping
+        ],
+        resolution=resolution,
+        method="motor_pos"
+    )
+    scenario.set_number_of_rays(number_of_rays=4)
+    optimization_configuration[config_dictionary.optimization][config_dictionary.initial_learning_rate_rotation_deviation] = 1e-4
+    optimization_configuration[config_dictionary.optimization][config_dictionary.max_epoch] = 900
+    # Create the kinematics reconstructor.
+    kinematics_reconstructor = KinematicsReconstructor(
+        ddp_setup=ddp_setup,
+        scenario=scenario,
+        data=data,
+        dni=500,
+        optimization_configuration=optimization_configuration,
+        reconstruction_method="direction",
+        bitmap_resolution=resolution,
+    )
+
+    # Reconstruct the kinematics.
+    final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
+        loss_definition=AngleLoss(), device=device
+    )
+
+    optimization_configuration[config_dictionary.optimization][config_dictionary.initial_learning_rate_rotation_deviation] = 1e-4
+    optimization_configuration[config_dictionary.optimization][config_dictionary.max_epoch] = 300
+    # Create the kinematics reconstructor.
+    kinematics_reconstructor = KinematicsReconstructor(
+        ddp_setup=ddp_setup,
+        scenario=scenario,
+        data=data,
+        dni=500,
+        optimization_configuration=optimization_configuration,
+        reconstruction_method=config_dictionary.kinematics_reconstruction_raytracing,
+        bitmap_resolution=resolution,
+    )
+
+    # Reconstruct the kinematics.
+    final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
+        loss_definition=FocalSpotLoss(scenario=scenario), device=device
+    )
+
+    print(scenario.heliostat_field.heliostat_groups[0].kinematics.rotation_deviation_parameters)
+
+# Inspect the synchronized loss per heliostat. Heliostats that have not been optimized have an infinite loss.
+print(f"rank {ddp_setup['rank']}, final loss per heliostat {final_loss_per_heliostat}")
+
+bitmaps_ray, bitmaps_measured = create_fluxes(
+    data_parser=data_parser_plots,
+    heliostat_data_mapping=[
+        (heliostat[0], [heliostat[1][-1]], [heliostat[2][-1]])
+        for heliostat in filtered_mapping
+    ],
+    resolution=resolution,
+    method="incident_ray",
+)
+
+bitmaps_after, bitmaps_measured = create_fluxes(
+    data_parser=data_parser_plots,
+    heliostat_data_mapping=[
+        (heliostat[0], [heliostat[1][-1]], [heliostat[2][-1]])
+        for heliostat in filtered_mapping
+    ],
+    resolution=resolution,
+    method="motor_pos",
+)
+
+create_plots(
+    fluxes_before=bitmaps_before,
+    fluxes_after=bitmaps_after,
+    fluxes_ray=bitmaps_ray,
+    fluxes_measured=bitmaps_measured,
+)
\ No newline at end of file

From 2e8531f8a4d145e698698d43fda00ef3914c8edc Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 20 May 2026 22:01:11 +0200
Subject: [PATCH 06/38] add optimization into inverse kinematics

---
 artist/field/kinematics_rigid_body.py | 193 ++++++++++++++++----------
 1 file changed, 116 insertions(+), 77 deletions(-)

diff --git a/artist/field/kinematics_rigid_body.py b/artist/field/kinematics_rigid_body.py
index d3ca3eb4..d42b7b43 100644
--- a/artist/field/kinematics_rigid_body.py
+++ b/artist/field/kinematics_rigid_body.py
@@ -338,8 +338,8 @@ def incident_ray_directions_to_orientations(
         incident_ray_directions: torch.Tensor,
         aim_points: torch.Tensor,
         device: torch.device | None = None,
-        max_num_iterations: int = 2,
-        min_eps: float = 0.0001,
+        num_refinement_iterations: int = 200,
+        refinement_lr: float = 0.08,
     ) -> torch.Tensor:
         """
         Compute orientation matrices given incident ray directions.
@@ -369,113 +369,152 @@ def incident_ray_directions_to_orientations(
         """
         device = get_device(device=device)
 
-        motor_positions = torch.zeros(
+        initial_motor_positions = torch.zeros(
             (
                 self.number_of_active_heliostats,
                 constants.rigid_body_number_of_actuators,
             ),
             device=device,
         )
-        last_iteration_loss = None
+        with torch.no_grad():
 
-        for _ in range(max_num_iterations):
             orientations = self._compute_orientations_from_motor_positions(
-                motor_positions=motor_positions, device=device
+                motor_positions=initial_motor_positions,
+                device=device,
             )
 
-            concentrator_normals = orientations @ torch.tensor(
-                [0, -1, 0, 0], dtype=torch.float32, device=device
-            )
             concentrator_origins = orientations @ torch.tensor(
-                [0, 0, 0, 1], dtype=torch.float32, device=device
+                [0.0, 0.0, 0.0, 1.0], device=device,
             )
 
-            # Compute desired normals.
             desired_reflection_directions = torch.nn.functional.normalize(
-                aim_points - concentrator_origins, p=2, dim=1
+                aim_points[:, :3] - concentrator_origins[:, :3],
+                p=2,
+                dim=1,
+                eps=1e-8,
             )
+
             desired_concentrator_normals = torch.nn.functional.normalize(
-                -incident_ray_directions + desired_reflection_directions, p=2, dim=1
+                -incident_ray_directions[:, :3] + desired_reflection_directions,
+                p=2,
+                dim=1,
+                eps=1e-8,
             )
 
-            # Compute loss.
-            loss = torch.abs(desired_concentrator_normals - concentrator_normals).mean(
-                dim=0
+        joint_angles_1 = -torch.arcsin(
+            torch.clamp(
+                -desired_concentrator_normals[:, indices.e]
+                / torch.cos(
+                    self.active_translation_deviation_parameters[
+                        :, indices.second_joint_translation_n
+                    ]
+                ),
+                min=-1,
+                max=1,
             )
+        )
 
-            # Stop if converged.
-            if isinstance(last_iteration_loss, torch.Tensor):
-                eps = torch.abs(last_iteration_loss - loss)
-                if torch.any(eps <= min_eps):
-                    break
-            last_iteration_loss = loss
-
-            # Analytical solution for joint angles.
-            joint_angles_1 = -torch.arcsin(
-                torch.clamp(
-                    -desired_concentrator_normals[:, indices.e]
-                    / torch.cos(
-                        self.active_translation_deviation_parameters[
-                            :, indices.second_joint_translation_n
-                        ]
-                    ),
-                    min=-1,
-                    max=1,
-                )
+        a = -torch.cos(
+            self.active_translation_deviation_parameters[
+                :, indices.second_joint_translation_e
+            ]
+        ) * torch.cos(joint_angles_1) + torch.sin(
+            self.active_translation_deviation_parameters[
+                :, indices.second_joint_translation_e
+            ]
+        ) * torch.sin(
+            self.active_translation_deviation_parameters[
+                :, indices.second_joint_translation_n
+            ]
+        ) * torch.sin(joint_angles_1)
+
+        b = -torch.sin(
+            self.active_translation_deviation_parameters[
+                :, indices.second_joint_translation_e
+            ]
+        ) * torch.cos(joint_angles_1) - torch.cos(
+            self.active_translation_deviation_parameters[
+                :, indices.second_joint_translation_e
+            ]
+        ) * torch.sin(
+            self.active_translation_deviation_parameters[
+                :, indices.second_joint_translation_n
+            ]
+        ) * torch.sin(joint_angles_1)
+
+        joint_angles_0 = (
+            torch.arctan2(
+                a * -desired_concentrator_normals[:, indices.u]
+                - b * -desired_concentrator_normals[:, indices.n],
+
+                a * -desired_concentrator_normals[:, indices.n]
+                + b * -desired_concentrator_normals[:, indices.u],
             )
+            - torch.pi
+        )
 
-            a = -torch.cos(
-                self.active_translation_deviation_parameters[
-                    :, indices.second_joint_translation_e
-                ]
-            ) * torch.cos(joint_angles_1) + torch.sin(
-                self.active_translation_deviation_parameters[
-                    :, indices.second_joint_translation_e
-                ]
-            ) * torch.sin(
-                self.active_translation_deviation_parameters[
-                    :, indices.second_joint_translation_n
-                ]
-            ) * torch.sin(joint_angles_1)
-            b = -torch.sin(
-                self.active_translation_deviation_parameters[
-                    :, indices.second_joint_translation_e
-                ]
-            ) * torch.cos(joint_angles_1) - torch.cos(
-                self.active_translation_deviation_parameters[
-                    :, indices.second_joint_translation_e
-                ]
-            ) * torch.sin(
-                self.active_translation_deviation_parameters[
-                    :, indices.second_joint_translation_n
-                ]
-            ) * torch.sin(joint_angles_1)
-
-            joint_angles_0 = (
-                torch.arctan2(
-                    a * -desired_concentrator_normals[:, indices.u]
-                    - b * -desired_concentrator_normals[:, indices.n],
-                    a * -desired_concentrator_normals[:, indices.n]
-                    + b * -desired_concentrator_normals[:, indices.u],
+        joint_angles = torch.stack(
+            [
+                joint_angles_0,
+                joint_angles_1,
+            ],
+            dim=1,
+        )
+
+        joint_angles = joint_angles.detach().clone().requires_grad_(True)
+
+        optimizer = torch.optim.Adam(
+            [joint_angles],
+            lr=refinement_lr,
+        )
+
+        for _ in range(num_refinement_iterations):
+
+            optimizer.zero_grad(set_to_none=True)
+
+            motor_positions = self.actuators.angles_to_motor_positions(
+                angles=joint_angles,
+                device=device,
+            )
+
+            orientations_refined = (
+                self._compute_orientations_from_motor_positions(
+                    motor_positions=motor_positions,
+                    device=device,
                 )
-                - torch.pi
             )
 
-            joint_angles = torch.stack(
-                [
-                    joint_angles_0,
-                    joint_angles_1,
-                ],
+            predicted_normals = orientations_refined @ torch.tensor(
+                [0.0, -1.0, 0.0, 0.0],
+                device=device,
+            )
+
+            predicted_normals = torch.nn.functional.normalize(
+                predicted_normals[:, :3],
+                p=2,
                 dim=1,
+                eps=1e-8,
             )
 
-            motor_positions = self.actuators.angles_to_motor_positions(
-                angles=joint_angles, device=device
+            loss = torch.mean(
+                (predicted_normals - desired_concentrator_normals) ** 2
             )
 
+            print(loss)
+
+            loss.backward()
+
+            optimizer.step()
+
+        orientations = self._compute_orientations_from_motor_positions(
+            motor_positions=motor_positions,
+            device=device,
+        )
+
         orientations_with_initial_orientation_offsets = (
             self._apply_initial_orientation_offsets(
-                orientations=orientations, device=device
+                orientations=orientations,
+                device=device,
             )
         )
 

From ac93316447d150046c7882813bd00954d26e15b5 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 20 May 2026 22:01:49 +0200
Subject: [PATCH 07/38] change setup

---
 .../04_kinematic_reconstruction_tutorial.py   | 415 ------------------
 .../04_kinematics_reconstruction_tutorial.py  | 179 +++++---
 2 files changed, 119 insertions(+), 475 deletions(-)
 delete mode 100644 tutorials/04_kinematic_reconstruction_tutorial.py

diff --git a/tutorials/04_kinematic_reconstruction_tutorial.py b/tutorials/04_kinematic_reconstruction_tutorial.py
deleted file mode 100644
index dd11cfe9..00000000
--- a/tutorials/04_kinematic_reconstruction_tutorial.py
+++ /dev/null
@@ -1,415 +0,0 @@
-"""Kinematic reconstruction tutorial."""
-
-import logging
-import pathlib
-
-import h5py
-import paint.util.paint_mappings as paint_mappings
-import torch
-from matplotlib import pyplot as plt
-
-from artist.core.heliostat_ray_tracer import HeliostatRayTracer
-from artist.core.kinematics_reconstructor import KinematicsReconstructor
-from artist.core.loss_functions import AngleLoss, FocalSpotLoss, PixelLoss, VectorLoss
-from artist.data_parser import paint_scenario_parser
-from artist.data_parser.calibration_data_parser import CalibrationDataParser
-from artist.data_parser.paint_calibration_parser import PaintCalibrationDataParser
-from artist.field.heliostat_group import HeliostatGroup
-from artist.scenario.scenario import Scenario
-from artist.util import config_dictionary, set_logger_config, utils
-from artist.util.environment_setup import get_device, setup_distributed_environment
-
-torch.manual_seed(7)
-torch.cuda.manual_seed(7)
-
-#############################################################################################################
-# Define helper functions for the plots.
-# Skip to line 170 for the tutorial code.
-#############################################################################################################
-
-
-def create_fluxes(
-    data_parser: CalibrationDataParser,
-    heliostat_data_mapping: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
-    resolution: torch.Tensor,
-    method: str
-) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
-    """
-    Create data to plot the heliostat fluxes.
-
-    Parameters
-    ----------
-    data_parser : CalibrationDataParser
-        Data parser used to load calibration data from files.
-    heliostat_data_mapping : list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]
-        Mapping from heliostats to calibration data files.
-    resolution : torch.Tensor
-        Bitmap resolution.
-
-    Returns
-    -------
-    list[torch.Tensor]
-        Bitmaps per heliostat.
-    list[torch.Tensor]
-        Measured flux bitmap.
-    """
-    bitmaps = []
-    measured_bitmaps = []
-    scenario.set_number_of_rays(number_of_rays=100)
-    for heliostat_group_index in range(len(scenario.heliostat_field.heliostat_groups)):
-        heliostat_group: HeliostatGroup = scenario.heliostat_field.heliostat_groups[
-            heliostat_group_index
-        ]
-        (
-            measured_flux,
-            focal_spots_measured,
-            incident_ray_directions,
-            motor_positions,
-            active_heliostats_mask,
-            target_area_indices,
-        ) = data_parser.parse_data_for_reconstruction(
-            heliostat_data_mapping=heliostat_data_mapping,
-            heliostat_group=heliostat_group,
-            scenario=scenario,
-            bitmap_resolution=resolution,
-            device=device,
-        )
-
-        if active_heliostats_mask.sum() > 0:
-            measured_bitmaps.append(measured_flux)
-
-            # Activate heliostats.
-            heliostat_group.activate_heliostats(
-                active_heliostats_mask=active_heliostats_mask,
-                device=device,
-            )
-
-            if method == "motor_pos":
-                # Align heliostats.
-                heliostat_group.align_surfaces_with_motor_positions(
-                    motor_positions=motor_positions,
-                    active_heliostats_mask=active_heliostats_mask,
-                    device=device,
-                )
-            if method == "incident_ray":
-                # Align heliostats.
-                heliostat_group.align_surfaces_with_incident_ray_directions(
-                    aim_points=scenario.solar_tower.get_centers_of_target_areas(target_area_indices=target_area_indices, device=device),
-                    incident_ray_directions=incident_ray_directions,
-                    active_heliostats_mask=active_heliostats_mask,
-                    device=device,
-                )
-
-            # Create a ray tracer.
-            ray_tracer = HeliostatRayTracer(
-                scenario=scenario,
-                heliostat_group=heliostat_group,
-                blocking_active=False,
-                batch_size=heliostat_group.number_of_active_heliostats,
-                bitmap_resolution=resolution,
-            )
-
-            # Perform heliostat-based ray tracing.
-            bitmaps_per_heliostat, _, _, _ = ray_tracer.trace_rays(
-                incident_ray_directions=incident_ray_directions,
-                active_heliostats_mask=active_heliostats_mask,
-                target_area_indices=target_area_indices,
-                device=device,
-            )
-            bitmaps.append(bitmaps_per_heliostat)
-
-    scenario.set_number_of_rays(number_of_rays=4)
-
-    return bitmaps, measured_bitmaps
-
-
-def create_plots(
-    fluxes_before: torch.Tensor,
-    fluxes_after: torch.Tensor,
-    fluxes_ray: torch.Tensor,
-    fluxes_measured: torch.Tensor,
-) -> None:
-    """
-    Create the flux plots using the reconstruction results.
-
-    Parameters
-    ----------
-    fluxes_before : torch.Tensor
-        Fluxes before the kinematics reconstruction.
-    fluxes_after : torch.Tensor
-        Fluxes after the kinematics reconstruction.
-    fluxes_measured : torch.Tensor
-        Measured flux references.
-    """
-    for group_index, (flux_before, flux_after, flux_measured, flux_ray) in enumerate(
-        zip(fluxes_before, fluxes_after, fluxes_measured, fluxes_ray)
-    ):
-        center_measured = (
-            utils.get_center_of_mass(flux_measured, device=device).cpu().detach()
-        )
-        center_before = (
-            utils.get_center_of_mass(flux_before, device=device).cpu().detach()
-        )
-        center_after = (
-            utils.get_center_of_mass(flux_after, device=device).cpu().detach()
-        )
-        center_ray = (
-            utils.get_center_of_mass(flux_ray, device=device).cpu().detach()
-        )
-
-        for i in range(len(flux_before)):
-            _, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
-
-            axes[0].imshow(flux_before[i].cpu().detach(), cmap="gray")
-            axes[0].set_title("Before reconstruction", fontsize=16)
-            axes[0].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
-            axes[0].scatter(x=center_before[i, 0], y=center_before[i, 1], c="red", marker="x", label="Predicted")
-            #axes[0].scatter(256/2, 256/2, c="blue")
-            axes[0].axis("off")
-            
-            axes[1].imshow(flux_after[i].cpu().detach(), cmap="gray")
-            axes[1].set_title("After reconstruction", fontsize=16)
-            axes[1].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
-            axes[1].scatter(x=center_after[i, 0], y=center_after[i, 1], c="red", marker="x", label="Predicted")
-            #axes[1].scatter(256/2, 256/2, c="blue")
-            axes[1].axis("off")
-
-            axes[2].imshow(flux_ray[i].cpu().detach(), cmap="gray")
-            axes[2].set_title("After reconstruction align only with sun pos", fontsize=16)
-            axes[2].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
-            axes[2].scatter(x=center_ray[i, 0], y=center_ray[i, 1], c="red", marker="x", label="Predicted")
-            #axes[2].scatter(256/2, 256/2, c="blue")
-            axes[2].axis("off")
-
-            axes[3].imshow(flux_measured[i].cpu().detach(), cmap="gray")
-            axes[3].set_title("Measured", fontsize=16)
-            axes[3].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o")
-            axes[3].axis("off")
-
-            axes[0].legend()
-            axes[1].legend()
-            axes[2].legend()
-            axes[3].legend()
-
-            plt.subplots_adjust(wspace=0.05)
-            plt.show()
-            plt.savefig(f"./ignored/new/heliostat_{i}_in_group_{group_index}_calibration.png")
-
-
-#############################################################################################################
-# Tutorial
-#############################################################################################################
-
-# Set up logger.
-set_logger_config()
-log = logging.getLogger(__name__)
-
-# Set the device.
-device = get_device()
-
-# Specify the path to your scenario.h5 file.
-scenario_path = pathlib.Path("/workVERLEIHNIX/mb/ARTIST/examples/field_optimizations/scenarios/ideal_baseline_scenario.h5")
-
-# Also specify the heliostats to be calibrated and the paths to your calibration-properties.json files.
-# Please use the following style: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]
-# heliostat_data_mapping = [
-#     (
-#         "heliostat_name_1",
-#         [
-#             pathlib.Path(
-#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
-#             ),
-#             # ....
-#         ],
-#         [
-#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
-#             # ....
-#         ],
-#     ),
-#     (
-#         "heliostat_name_2",
-#         [
-#             pathlib.Path(
-#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
-#             ),
-#             # ....
-#         ],
-#         [
-#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
-#             # ....
-#         ],
-#     ),
-# ]
-
-# Or if you have a directory with downloaded data use this code to create a mapping.
-heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
-    base_path="/workVERLEIHNIX/share/PAINT_data",
-    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58", "AH48", "AH49", "AH50", "AH55", "AH56", "AH57", "AH58", "AI48", "AI49", "AI50", "AI51", "AI52", "AI53", "AI54", "AI55", "AI56", "AI57", "AI58", "AI59", "AI60", "AI61", "AJ49", "AJ50", "AJ51", "AJ53", "AJ54", "AJ55", "AJ56", "AJ57", "AJ58", "AJ61", "AJ62", "AK48", "AK49", "AK50", "AK51", "AK52", "AK53", "AK54", "AK55", "AK56", "AK58", "AK59", "AK60", "AK61", "AK62", "AL48", "AL49", "AL51", "AL53", "AL56", "AL58", "AL59", "AL60", "AL61", "AL62", "AM48", "AM49", "AM50", "AM51", "AM52", "AM53", "AM54", "AM55", "AM56", "AM57", "AM58", "AM59", "AM60", "AM62", "AN48", "AN49", "AN50", "AN51", "AN52", "AN53", "AN54", "AN55", "AN56", "AN57", "AN58", "AN59", "AN60", "AN61", "AN62"],
-    #heliostat_names=['AZ43', 'AP35', 'AA39', 'AC31', 'AC42', 'AC24', 'AM31', 'BF65', 'AQ27', 'AF33', 'BD31', 'AW42', 'AZ39', 'AL36', 'AD42', 'AP39', 'AE28', 'BE38', 'AY55', 'AP50'],
-    number_of_measurements=37,
-    image_variant="flux",
-    randomize=True,
-    seed=7,
-)
-
-# 'AC40', 
-bad_samples = {}
-filtered_mapping = [
-    (
-        name,
-        [p for p in list1 if not any(b in str(p) for b in bad_samples)],
-        [p for p in list2 if not any(b in str(p) for b in bad_samples)],
-    )
-    for name, list1, list2 in heliostat_data_mapping
-]
-
-# Configure the optimization.
-optimizer_dict = {
-    config_dictionary.initial_learning_rate_rotation_deviation: 1e-5,
-    config_dictionary.initial_learning_rate_initial_angles: 1e-3,
-    config_dictionary.initial_learning_rate_initial_stroke_length: 1e-2,
-    config_dictionary.tolerance: 0.0000,
-    config_dictionary.max_epoch: 600,
-    config_dictionary.batch_size: 200,
-    config_dictionary.log_step: 1,
-    config_dictionary.early_stopping_delta: 1e-8,
-    config_dictionary.early_stopping_patience: 1000,
-    config_dictionary.early_stopping_window: 2000,
-}
-# Configure the learning rate scheduler.
-scheduler_dict = {
-    config_dictionary.scheduler_type: config_dictionary.reduce_on_plateau,
-    config_dictionary.gamma: 0.9,
-    config_dictionary.lr_min: 1e-6,
-    config_dictionary.lr_max: 1e-3,
-    config_dictionary.step_size_up: 500,
-    config_dictionary.reduce_factor: 0.0001,
-    config_dictionary.patience: 50,
-    config_dictionary.threshold: 1e-3,
-    config_dictionary.cooldown: 10,
-}
-# Combine configurations.
-optimization_configuration = {
-    config_dictionary.optimization: optimizer_dict,
-    config_dictionary.scheduler: scheduler_dict,
-}
-
-data_parser = PaintCalibrationDataParser(
-    sample_limit=17, centroid_extraction_method=paint_mappings.UTIS_KEY
-)
-data_parser_plots = PaintCalibrationDataParser(
-    sample_limit=1, centroid_extraction_method=paint_mappings.UTIS_KEY
-)
-
-# Create dict for the data parser and the heliostat_data_mapping.
-data: dict[
-    str,
-    CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
-] = {
-    config_dictionary.data_parser: data_parser,
-    config_dictionary.heliostat_data_mapping: filtered_mapping,
-}
-
-data_plots: dict[
-    str,
-    CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
-] = {
-    config_dictionary.data_parser: data_parser_plots,
-    config_dictionary.heliostat_data_mapping: filtered_mapping,
-}
-
-number_of_heliostat_groups = Scenario.get_number_of_heliostat_groups_from_hdf5(
-    scenario_path=scenario_path
-)
-#torch.autograd.set_detect_anomaly(True)
-with setup_distributed_environment(
-    number_of_heliostat_groups=number_of_heliostat_groups,
-    device=device,
-) as ddp_setup:
-    device = ddp_setup[config_dictionary.device]  # type:ignore
-
-    # Load the scenario.
-    with h5py.File(scenario_path, "r") as scenario_file:
-        scenario = Scenario.load_scenario_from_hdf5(
-            scenario_file=scenario_file, device=device
-        )
-
-    resolution = torch.tensor([256, 256], device=device)
-
-    bitmaps_before, _ = create_fluxes(
-        data_parser=data_parser_plots,
-        heliostat_data_mapping=[
-            (heliostat[0], [heliostat[1][-1]], [heliostat[2][-1]])
-            for heliostat in filtered_mapping
-        ],
-        resolution=resolution,
-        method="motor_pos"
-    )
-    scenario.set_number_of_rays(number_of_rays=4)
-    optimization_configuration[config_dictionary.optimization][config_dictionary.initial_learning_rate_rotation_deviation] = 1e-4
-    optimization_configuration[config_dictionary.optimization][config_dictionary.max_epoch] = 900
-    # Create the kinematics reconstructor.
-    kinematics_reconstructor = KinematicsReconstructor(
-        ddp_setup=ddp_setup,
-        scenario=scenario,
-        data=data,
-        dni=500,
-        optimization_configuration=optimization_configuration,
-        reconstruction_method="direction",
-        bitmap_resolution=resolution,
-    )
-
-    # Reconstruct the kinematics.
-    final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
-        loss_definition=AngleLoss(), device=device
-    )
-
-    optimization_configuration[config_dictionary.optimization][config_dictionary.initial_learning_rate_rotation_deviation] = 1e-4
-    optimization_configuration[config_dictionary.optimization][config_dictionary.max_epoch] = 300
-    # Create the kinematics reconstructor.
-    kinematics_reconstructor = KinematicsReconstructor(
-        ddp_setup=ddp_setup,
-        scenario=scenario,
-        data=data,
-        dni=500,
-        optimization_configuration=optimization_configuration,
-        reconstruction_method=config_dictionary.kinematics_reconstruction_raytracing,
-        bitmap_resolution=resolution,
-    )
-
-    # Reconstruct the kinematics.
-    final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
-        loss_definition=FocalSpotLoss(scenario=scenario), device=device
-    )
-
-    print(scenario.heliostat_field.heliostat_groups[0].kinematics.rotation_deviation_parameters)
-
-# Inspect the synchronized loss per heliostat. Heliostats that have not been optimized have an infinite loss.
-print(f"rank {ddp_setup['rank']}, final loss per heliostat {final_loss_per_heliostat}")
-
-bitmaps_ray, bitmaps_measured = create_fluxes(
-    data_parser=data_parser_plots,
-    heliostat_data_mapping=[
-        (heliostat[0], [heliostat[1][-1]], [heliostat[2][-1]])
-        for heliostat in filtered_mapping
-    ],
-    resolution=resolution,
-    method="incident_ray",
-)
-
-bitmaps_after, bitmaps_measured = create_fluxes(
-    data_parser=data_parser_plots,
-    heliostat_data_mapping=[
-        (heliostat[0], [heliostat[1][-1]], [heliostat[2][-1]])
-        for heliostat in filtered_mapping
-    ],
-    resolution=resolution,
-    method="motor_pos",
-)
-
-create_plots(
-    fluxes_before=bitmaps_before,
-    fluxes_after=bitmaps_after,
-    fluxes_ray=bitmaps_ray,
-    fluxes_measured=bitmaps_measured,
-)
\ No newline at end of file
diff --git a/tutorials/04_kinematics_reconstruction_tutorial.py b/tutorials/04_kinematics_reconstruction_tutorial.py
index e411f22a..a07037ad 100644
--- a/tutorials/04_kinematics_reconstruction_tutorial.py
+++ b/tutorials/04_kinematics_reconstruction_tutorial.py
@@ -35,6 +35,7 @@ def create_fluxes(
     data_parser: CalibrationDataParser,
     heliostat_data_mapping: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
     resolution: torch.Tensor,
+    method: str
 ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
     """
     Create data to plot the heliostat fluxes.
@@ -57,16 +58,16 @@ def create_fluxes(
     """
     bitmaps = []
     measured_bitmaps = []
-    scenario.set_number_of_rays(number_of_rays=500)
+    scenario.set_number_of_rays(number_of_rays=100)
     for heliostat_group_index in range(len(scenario.heliostat_field.heliostat_groups)):
         heliostat_group: HeliostatGroup = scenario.heliostat_field.heliostat_groups[
             heliostat_group_index
         ]
         (
             measured_flux,
-            _,
+            focal_spots_measured,
             incident_ray_directions,
-            _,
+            motor_positions,
             active_heliostats_mask,
             target_area_indices,
         ) = data_parser.parse_data_for_reconstruction(
@@ -86,15 +87,21 @@ def create_fluxes(
                 device=device,
             )
 
-            # Align heliostats.
-            heliostat_group.align_surfaces_with_incident_ray_directions(
-                aim_points=scenario.solar_tower.get_centers_of_target_areas(
-                    target_area_indices=target_area_indices, device=device
-                ),
-                incident_ray_directions=incident_ray_directions,
-                active_heliostats_mask=active_heliostats_mask,
-                device=device,
-            )
+            if method == "motor_pos":
+                # Align heliostats.
+                heliostat_group.align_surfaces_with_motor_positions(
+                    motor_positions=motor_positions,
+                    active_heliostats_mask=active_heliostats_mask,
+                    device=device,
+                )
+            if method == "incident_ray":
+                # Align heliostats.
+                heliostat_group.align_surfaces_with_incident_ray_directions(
+                    aim_points=scenario.solar_tower.get_centers_of_target_areas(target_area_indices=target_area_indices, device=device),
+                    incident_ray_directions=incident_ray_directions,
+                    active_heliostats_mask=active_heliostats_mask,
+                    device=device,
+                )
 
             # Create a ray tracer.
             ray_tracer = HeliostatRayTracer(
@@ -122,6 +129,7 @@ def create_fluxes(
 def create_plots(
     fluxes_before: torch.Tensor,
     fluxes_after: torch.Tensor,
+    fluxes_ray: torch.Tensor,
     fluxes_measured: torch.Tensor,
 ) -> None:
     """
@@ -136,8 +144,8 @@ def create_plots(
     fluxes_measured : torch.Tensor
         Measured flux references.
     """
-    for group_index, (flux_before, flux_after, flux_measured) in enumerate(
-        zip(fluxes_before, fluxes_after, fluxes_measured)
+    for group_index, (flux_before, flux_after, flux_measured, flux_ray) in enumerate(
+        zip(fluxes_before, fluxes_after, fluxes_measured, fluxes_ray)
     ):
         center_measured = (
             bitmap.get_center_of_mass(flux_measured, device=device).cpu().detach()
@@ -148,29 +156,47 @@ def create_plots(
         center_after = (
             bitmap.get_center_of_mass(flux_after, device=device).cpu().detach()
         )
+        center_ray = (
+            bitmap.get_center_of_mass(flux_ray, device=device).cpu().detach()
+        )
 
         for i in range(len(flux_before)):
-            _, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))
+            _, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
 
             axes[0].imshow(flux_before[i].cpu().detach(), cmap="gray")
             axes[0].set_title("Before reconstruction", fontsize=16)
-            axes[0].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="r")
-            axes[0].scatter(x=center_before[i, 0], y=center_before[i, 1], c="g")
+            axes[0].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
+            axes[0].scatter(x=center_before[i, 0], y=center_before[i, 1], c="red", marker="x", label="Predicted")
+            #axes[0].scatter(256/2, 256/2, c="blue")
             axes[0].axis("off")
-
+            
             axes[1].imshow(flux_after[i].cpu().detach(), cmap="gray")
             axes[1].set_title("After reconstruction", fontsize=16)
-            axes[1].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="r")
-            axes[1].scatter(x=center_after[i, 0], y=center_after[i, 1], c="g")
+            axes[1].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
+            axes[1].scatter(x=center_after[i, 0], y=center_after[i, 1], c="red", marker="x", label="Predicted")
+            #axes[1].scatter(256/2, 256/2, c="blue")
             axes[1].axis("off")
 
-            axes[2].imshow(flux_measured[i].cpu().detach(), cmap="gray")
-            axes[2].set_title("Measured", fontsize=16)
+            axes[2].imshow(flux_ray[i].cpu().detach(), cmap="gray")
+            axes[2].set_title("After reconstruction align only with sun pos", fontsize=16)
+            axes[2].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
+            axes[2].scatter(x=center_ray[i, 0], y=center_ray[i, 1], c="red", marker="x", label="Predicted")
+            #axes[2].scatter(256/2, 256/2, c="blue")
             axes[2].axis("off")
 
+            axes[3].imshow(flux_measured[i].cpu().detach(), cmap="gray")
+            axes[3].set_title("Measured", fontsize=16)
+            axes[3].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o")
+            axes[3].axis("off")
+
+            axes[0].legend()
+            axes[1].legend()
+            axes[2].legend()
+            axes[3].legend()
+
             plt.subplots_adjust(wspace=0.05)
             plt.show()
-            plt.savefig(f"heliostat_{i}_in_group_{group_index}_calibration.png")
+            plt.savefig(f"./ignored/new/heliostat_{i}_in_group_{group_index}_calibration.png")
 
 
 #############################################################################################################
@@ -185,46 +211,48 @@ def create_plots(
 device = get_device()
 
 # Specify the path to your scenario.h5 file.
-scenario_path = pathlib.Path("please/insert/the/path/to/the/scenario/here/scenario.h5")
+scenario_path = pathlib.Path("/workVERLEIHNIX/mb/ARTIST/examples/field_optimizations/scenarios/ideal_baseline_scenario.h5")
 
 # Also specify the heliostats to be calibrated and the paths to your calibration-properties.json files.
 # Please use the following style: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]
-heliostat_data_mapping = [
-    (
-        "heliostat_name_1",
-        [
-            pathlib.Path(
-                "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
-            ),
-            # ....
-        ],
-        [
-            pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
-            # ....
-        ],
-    ),
-    (
-        "heliostat_name_2",
-        [
-            pathlib.Path(
-                "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
-            ),
-            # ....
-        ],
-        [
-            pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
-            # ....
-        ],
-    ),
-]
+# heliostat_data_mapping = [
+#     (
+#         "heliostat_name_1",
+#         [
+#             pathlib.Path(
+#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
+#             ),
+#             # ....
+#         ],
+#         [
+#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
+#             # ....
+#         ],
+#     ),
+#     (
+#         "heliostat_name_2",
+#         [
+#             pathlib.Path(
+#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
+#             ),
+#             # ....
+#         ],
+#         [
+#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
+#             # ....
+#         ],
+#     ),
+# ]
 
 # Or if you have a directory with downloaded data use this code to create a mapping.
 heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
-    base_path="base/path/data",
-    heliostat_names=["heliostat_1", "..."],
-    number_of_measurements=5,
+    base_path="/workVERLEIHNIX/share/PAINT_data",
+    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58", "AH48", "AH49", "AH50", "AH55", "AH56", "AH57", "AH58", "AI48", "AI49", "AI50", "AI51", "AI52", "AI53", "AI54", "AI55", "AI56", "AI57", "AI58", "AI59", "AI60", "AI61", "AJ49", "AJ50", "AJ51", "AJ53", "AJ54", "AJ55", "AJ56", "AJ57", "AJ58", "AJ61", "AJ62", "AK48", "AK49", "AK50", "AK51", "AK52", "AK53", "AK54", "AK55", "AK56", "AK58", "AK59", "AK60", "AK61", "AK62", "AL48", "AL49", "AL51", "AL53", "AL56", "AL58", "AL59", "AL60", "AL61", "AL62", "AM48", "AM49", "AM50", "AM51", "AM52", "AM53", "AM54", "AM55", "AM56", "AM57", "AM58", "AM59", "AM60", "AM62", "AN48", "AN49", "AN50", "AN51", "AN52", "AN53", "AN54", "AN55", "AN56", "AN57", "AN58", "AN59", "AN60", "AN61", "AN62"],
+    #heliostat_names=['AZ43', 'AP35', 'AA39', 'AC31', 'AC42', 'AC24', 'AM31', 'BF65', 'AQ27', 'AF33', 'BD31', 'AW42', 'AZ39', 'AL36', 'AD42', 'AP39', 'AE28', 'BE38', 'AY55', 'AP50'],
+    number_of_measurements=37,
     image_variant="flux",
     randomize=True,
+    seed=7,
 )
 
 # Configure the optimization.
@@ -259,7 +287,7 @@ def create_plots(
 }
 
 data_parser = PaintCalibrationDataParser(
-    sample_limit=10, centroid_extraction_method=paint_mappings.UTIS_KEY
+    sample_limit=17, centroid_extraction_method=paint_mappings.UTIS_KEY
 )
 data_parser_plots = PaintCalibrationDataParser(
     sample_limit=1, centroid_extraction_method=paint_mappings.UTIS_KEY
@@ -307,10 +335,29 @@ def create_plots(
             for heliostat in heliostat_data_mapping
         ],
         resolution=resolution,
+        method="motor_pos"
     )
 
-    loss_definition = FocalSpotLoss(scenario=scenario)
+    scenario.set_number_of_rays(number_of_rays=4)
+    optimization_configuration[constants.optimization][constants.initial_learning_rate_rotation_deviation] = 1e-4
+    optimization_configuration[constants.optimization][constants.max_epoch] = 900
+    # Create the kinematics reconstructor.
+    kinematics_reconstructor = KinematicsReconstructor(
+        ddp_setup=ddp_setup,
+        scenario=scenario,
+        data=data,
+        dni=500,
+        optimization_configuration=optimization_configuration,
+        reconstruction_method="direction",
+        bitmap_resolution=resolution,
+    )
+    # Reconstruct the kinematics.
+    final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
+        loss_definition=AngleLoss(), device=device
+    )
 
+    optimization_configuration[constants.optimization][constants.initial_learning_rate_rotation_deviation] = 1e-4
+    optimization_configuration[constants.optimization][constants.max_epoch] = 300
     # Create the kinematics reconstructor.
     kinematics_reconstructor = KinematicsReconstructor(
         ddp_setup=ddp_setup,
@@ -321,10 +368,9 @@ def create_plots(
         reconstruction_method=constants.kinematics_reconstruction_raytracing,
         bitmap_resolution=resolution,
     )
-
     # Reconstruct the kinematics.
     final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
-        loss_definition=loss_definition, device=device
+        loss_definition=FocalSpotLoss(scenario=scenario), device=device
     )
 
 # Inspect the synchronized loss per heliostat. Heliostats that have not been optimized have an infinite loss.
@@ -337,9 +383,22 @@ def create_plots(
         for heliostat in heliostat_data_mapping
     ],
     resolution=resolution,
+    method="motor_pos",
+)
+
+bitmaps_ray, bitmaps_measured = create_fluxes(
+    data_parser=data_parser_plots,
+    heliostat_data_mapping=[
+        (heliostat[0], [heliostat[1][-1]], [heliostat[2][-1]])
+        for heliostat in heliostat_data_mapping
+    ],
+    resolution=resolution,
+    method="incident_ray",
 )
+
 create_plots(
     fluxes_before=bitmaps_before,
     fluxes_after=bitmaps_after,
+    fluxes_ray=bitmaps_ray,
     fluxes_measured=bitmaps_measured,
-)
+)
\ No newline at end of file

From fd31f63cf7de23378c8ce1b572c4bbf6de6fedac Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 21 May 2026 07:23:55 +0200
Subject: [PATCH 08/38] fix loss functions

---
 artist/optim/loss.py | 101 +++++++++++++++++++++++++++++++++----------
 1 file changed, 79 insertions(+), 22 deletions(-)

diff --git a/artist/optim/loss.py b/artist/optim/loss.py
index e3c71cc9..31f491de 100644
--- a/artist/optim/loss.py
+++ b/artist/optim/loss.py
@@ -122,7 +122,7 @@ def __call__(
 
 class FocalSpotLoss(Loss):
     """
-    A loss defined as Euclidean distance between the predicted focal spot coordinate and the ground-truth coordinate.
+    A loss defined as Euclidean distance between the predicted focal spot coordinate and the ground-truth focal spot coordinate.
 
     Attributes
     ----------
@@ -157,7 +157,7 @@ def __call__(
         r"""
         Compute the focal spot loss.
 
-        First the focal spots of the prediction are computed, then the loss is computed and reduced
+        First the focal spots of the prediction and ground truth flux maps are computed, then the loss is computed and reduced
         along the specified dimensions.
 
         Parameters
@@ -203,7 +203,7 @@ def __call__(
             device=device,
         )
 
-        focal_spot_coordinates = coordinates.bitmap_coordinates_to_target_coordinates(
+        focal_spot_coordinates_prediction = coordinates.bitmap_coordinates_to_target_coordinates(
             bitmap_coordinates=focal_spots_bitmap,
             bitmap_resolution=torch.tensor(
                 [
@@ -217,7 +217,26 @@ def __call__(
             device=device,
         )
 
-        return torch.norm(focal_spot_coordinates[:, :3] - ground_truth[:, :3], dim=1)
+        focal_spots_ground_truth = bitmap.get_center_of_mass(
+            bitmaps=ground_truth,
+            device=device,
+        )
+
+        focal_spot_coordinates_ground_truth = coordinates.bitmap_coordinates_to_target_coordinates(
+            bitmap_coordinates=focal_spots_ground_truth,
+            bitmap_resolution=torch.tensor(
+                [
+                    prediction.shape[indices.batched_bitmap_u],
+                    prediction.shape[indices.batched_bitmap_e],
+                ],
+                device=device,
+            ),
+            solar_tower=self.scenario.solar_tower,
+            target_area_indices=target_area_indices,
+            device=device,
+        )
+
+        return torch.norm(focal_spot_coordinates_prediction[:, :3] - focal_spot_coordinates_ground_truth[:, :3], dim=1)
 
 
 class PixelLoss(Loss):
@@ -236,17 +255,9 @@ class PixelLoss(Loss):
     :class:`Loss` : Reference to the parent class.
     """
 
-    def __init__(self, scenario: Scenario) -> None:
-        """
-        Initialize the pixel loss.
-
-        Parameters
-        ----------
-        scenario : Scenario
-            The scenario.
-        """
+    def __init__(self) -> None:
+        """Initialize the pixel loss."""
         super().__init__(loss_function=torch.nn.MSELoss(reduction="none"))
-        self.scenario = scenario
 
     def __call__(
         self,
@@ -255,10 +266,10 @@ def __call__(
         **kwargs: Any,
     ) -> torch.Tensor:
         r"""
-        Compute the pixel loss.
+        Compute the normalized pixel-wise loss.
 
-        First the predicted bitmaps and the ground truth are normalized, then the loss is
-        computed and reduced along the specified dimensions.
+        To make the loss invariant to the overall magnitude of the ground truth flux, the summed loss is divided 
+        by the total ground truth intensity per sample.
 
         Parameters
         ----------
@@ -270,7 +281,7 @@ def __call__(
             Shape is ``[number_of_samples, bitmap_resolution_e, bitmap_resolution_u]``.
         \*\*kwargs : Any
             Keyword arguments.
-            ``reduction_dimensions``, ``target_area_indices``, and ``device`` are expected keyword arguments for the pixel loss.
+            ``reduction_dimensions`` is an expected keyword arguments for the pixel loss.
 
         Raises
         ------
@@ -283,7 +294,7 @@ def __call__(
             The summed MSE pixel loss reduced along the specified dimensions.
             Shape is ``[number_of_samples]``.
         """
-        expected_kwargs = ["reduction_dimensions", "device", "target_area_indices"]
+        expected_kwargs = ["reduction_dimensions"]
         errors = []
         for key in expected_kwargs:
             if key not in kwargs:
@@ -294,9 +305,7 @@ def __call__(
                 + " ".join(errors)
             )
 
-        loss = self.loss_function(prediction, ground_truth)
-
-        return loss.sum(dim=kwargs["reduction_dimensions"])
+        return self.loss_function(prediction, ground_truth).sum(dim=kwargs["reduction_dimensions"]) / ground_truth.sum(dim=(1, 2))
 
 
 class KLDivergenceLoss(Loss):
@@ -403,6 +412,54 @@ class AngleLoss(Loss):
     :class:`Loss` : Reference to the parent class.
     """
 
+    def __init__(self) -> None:
+        """Initialize the angle loss."""
+        super().__init__(loss_function=None)
+
+    def __call__(
+        self,
+        prediction: torch.Tensor,
+        ground_truth: torch.Tensor,
+        **kwargs: Any,
+    ) -> torch.Tensor:
+        r"""
+        Compute the angular distance between prediction and ground truth.
+
+        Parameters
+        ----------
+        prediction : torch.Tensor
+            The predicted values.
+            Shape is ``[number_of_samples, 4]``.
+        ground_truth : torch.Tensor
+            The ground truth.
+            Shape is ``[number_of_samples, 4]``.
+        \*\*kwargs : Any
+            Keyword arguments.
+
+        Returns
+        -------
+        torch.Tensor
+            The summed loss reduced along the specified dimensions.
+            Shape is ``[number_of_samples]``.
+        """
+
+        return torch.acos((prediction[:, :3] * ground_truth[:, :3]).sum(dim=-1).clamp(-1.0, 1.0))
+
+
+class CosineSimilarityLoss(Loss):
+    """
+    A loss defined as the cosine similarity between prediction and ground truth.
+
+    Attributes
+    ----------
+    loss_function : torch.nn.Module
+        A torch module implementing a loss.
+
+    See Also
+    --------
+    :class:`Loss` : Reference to the parent class.
+    """
+
     def __init__(self) -> None:
         """Initialize the angle loss."""
         super().__init__(loss_function=torch.nn.CosineSimilarity(dim=-1))

From 5f526cfae65957934da1504562095f6a393aa67b Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 21 May 2026 07:24:09 +0200
Subject: [PATCH 09/38] import angle loss

---
 tutorials/04_kinematics_reconstruction_tutorial.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/04_kinematics_reconstruction_tutorial.py b/tutorials/04_kinematics_reconstruction_tutorial.py
index a07037ad..6fc5c9af 100644
--- a/tutorials/04_kinematics_reconstruction_tutorial.py
+++ b/tutorials/04_kinematics_reconstruction_tutorial.py
@@ -16,7 +16,7 @@
     paint_scenario_parser,
 )
 from artist.optim import KinematicsReconstructor
-from artist.optim.loss import FocalSpotLoss
+from artist.optim.loss import AngleLoss, FocalSpotLoss
 from artist.raytracing import HeliostatRayTracer
 from artist.scenario import Scenario
 from artist.util import constants, set_logger_config

From 6b8d335ef41f0360e68817c9d2c55fe54f9d2303 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 21 May 2026 10:05:11 +0200
Subject: [PATCH 10/38] add constant for second kinematic reconstruction method

---
 artist/util/constants.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/artist/util/constants.py b/artist/util/constants.py
index 09ce5e04..f7ef3460 100644
--- a/artist/util/constants.py
+++ b/artist/util/constants.py
@@ -165,6 +165,8 @@
 
 kinematics_reconstruction_raytracing = "raytracing"
 """Defines that the kinematics reconstructor uses ray tracing."""
+kinematics_reconstruction_geometry = "geometry"
+"""Defines that the kinematics reconstructor uses geometry."""
 
 names = "names"
 """Key to access heliostat names."""

From 4d25e288b4cf2ce9aadc051d75461451e9ff45da Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 21 May 2026 10:05:34 +0200
Subject: [PATCH 11/38] rename reduce method

---
 artist/optim/loss.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/artist/optim/loss.py b/artist/optim/loss.py
index 31f491de..dcda69f2 100644
--- a/artist/optim/loss.py
+++ b/artist/optim/loss.py
@@ -493,13 +493,15 @@ def __call__(
         return 1.0 - self.loss_function(prediction, ground_truth)
 
 
-def loss_per_heliostat(
+def reduce_loss_per_sample(
     loss_per_sample: torch.Tensor,
     number_of_samples_per_heliostat: int,
     reduction: Callable[..., Any],
 ) -> torch.Tensor:
     """
-    Calculate the mean loss per heliostat from a loss per sample.
+    Calculate the loss per heliostat from a loss per sample.
+
+    The reduction operation is chosen via the `reduction` parameter.
 
     Parameters
     ----------

From fd17db20921f2cf13279e8a95ac7ba312a270233 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 21 May 2026 10:05:57 +0200
Subject: [PATCH 12/38] add geometry method for reconstruction

---
 artist/optim/kinematics_reconstructor.py | 833 +++++++++++++++++------
 1 file changed, 641 insertions(+), 192 deletions(-)

diff --git a/artist/optim/kinematics_reconstructor.py b/artist/optim/kinematics_reconstructor.py
index bafacf2b..d621a536 100644
--- a/artist/optim/kinematics_reconstructor.py
+++ b/artist/optim/kinematics_reconstructor.py
@@ -1,7 +1,10 @@
+from dataclasses import dataclass
+from functools import partial
 import logging
 import pathlib
 from typing import Any, cast
 
+from matplotlib import pyplot as plt
 import torch
 from torch.optim.lr_scheduler import LRScheduler
 
@@ -10,7 +13,7 @@
 from artist.geometry import coordinates
 from artist.io.calibration_parser import CalibrationDataParser
 from artist.optim import training
-from artist.optim.loss import Loss, mean_loss_per_heliostat
+from artist.optim.loss import FocalSpotLoss, KLDivergenceLoss, Loss, PixelLoss, reduce_loss_per_sample
 from artist.raytracing.heliostat_ray_tracer import HeliostatRayTracer
 from artist.scenario.scenario import Scenario
 from artist.util import constants, indices
@@ -19,6 +22,69 @@
 log = logging.getLogger(__name__)
 """A logger for the kinematic reconstructor."""
 
+@dataclass
+class TrainTestSplit:
+    """
+    Container holding the train/test split for heliostat reconstruction data.
+
+    Attributes
+    ----------
+    flux_measured_train : torch.Tensor
+        Measured flux distributions for the training set.
+    focal_spots_measured_train : torch.Tensor
+        Measured focal spot coordinates for the training set.
+    incident_ray_directions_train : torch.Tensor
+        Incident ray directions for the training set.
+    motor_positions_train : torch.Tensor
+        Motor positions for the training set.
+    target_area_indices_train : torch.Tensor
+        Target area indices for the training set.
+    flux_measured_test : torch.Tensor
+        Measured flux distributions for the test set.
+    focal_spots_measured_test : torch.Tensor
+        Measured focal spot coordinates for the test set.
+    incident_ray_directions_test : torch.Tensor
+        Incident ray directions for the test set.
+    motor_positions_test : torch.Tensor
+        Motor positions for the test set.
+    target_area_indices_test : torch.Tensor
+        Target area indices for the test set.
+    active_heliostats_mask_train : torch.Tensor
+        Number of active training samples per heliostat.
+    active_heliostats_mask_test : torch.Tensor
+        Number of active test samples per heliostat.
+    train_indices : torch.Tensor
+        Flattened indices selecting training samples from the original dataset.
+    test_indices : torch.Tensor
+        Flattened indices selecting test samples from the original dataset.
+    number_of_train_samples : int
+        Number of training samples per heliostat.
+    number_of_test_samples : int
+        Number of test samples per heliostat.
+    number_of_samples_per_heliostat : int
+        Total number of samples available per heliostat.
+    """
+    flux_measured_train: torch.Tensor
+    focal_spots_measured_train: torch.Tensor
+    incident_ray_directions_train: torch.Tensor
+    motor_positions_train: torch.Tensor
+    target_area_indices_train: torch.Tensor
+
+    flux_measured_test: torch.Tensor
+    focal_spots_measured_test: torch.Tensor
+    incident_ray_directions_test: torch.Tensor
+    motor_positions_test: torch.Tensor
+    target_area_indices_test: torch.Tensor
+
+    active_heliostats_mask_train: torch.Tensor
+    active_heliostats_mask_test: torch.Tensor
+
+    train_indices: torch.Tensor
+    test_indices: torch.Tensor
+
+    number_of_train_samples: int
+    number_of_test_samples: int
+    number_of_samples_per_heliostat: int
 
 class KinematicsReconstructor:
     """
@@ -105,8 +171,13 @@ def __init__(
         self.dni = dni
         self.bitmap_resolution = bitmap_resolution
 
+        self.loss_focal_spot = FocalSpotLoss(scenario=self.scenario)
+        self.loss_pixel = PixelLoss()
+        self.loss_kl_div = KLDivergenceLoss()
+
         if reconstruction_method in [
             constants.kinematics_reconstruction_raytracing,
+            constants.kinematics_reconstruction_geometry
         ]:
             self.reconstruction_method = reconstruction_method
         else:
@@ -154,6 +225,15 @@ def reconstruct_kinematics(
                     device=device,
                 )
             )
+        elif (
+            self.reconstruction_method == constants.kinematics_reconstruction_geometry
+        ):
+            loss, loss_history = (
+                self._reconstruct_kinematics_parameters_with_geometry(
+                    loss_definition=loss_definition,
+                    device=device,
+                )
+            )
 
         else:
             raise ValueError(
@@ -163,7 +243,205 @@ def reconstruct_kinematics(
 
         return loss, loss_history
 
-    def _reconstruct_kinematics_parameters_with_raytracing(
+
+    def train_test_split(
+        self,
+        active_heliostats_mask: torch.Tensor,
+        flux_measured: torch.Tensor,
+        focal_spots_measured: torch.Tensor,
+        incident_ray_directions: torch.Tensor,
+        motor_positions: torch.Tensor,
+        target_area_indices: torch.Tensor,
+        test_fraction: int = 0.25,
+        device: torch.device | None = None
+    ) -> TrainTestSplit:
+        device = get_device(device=device)
+       
+        total_samples = int(active_heliostats_mask.sum().item())
+        number_of_heliostats = int((active_heliostats_mask > 0).sum().item())
+        number_of_samples_per_heliostat = int(
+            total_samples / number_of_heliostats
+        )
+        number_of_test_samples = max(1, int(number_of_samples_per_heliostat * test_fraction))
+        number_of_train_samples = number_of_samples_per_heliostat - number_of_test_samples
+        starts = torch.arange(number_of_heliostats) * number_of_samples_per_heliostat
+        offsets = torch.arange(number_of_train_samples, number_of_samples_per_heliostat)
+        train_indices = (
+            torch.arange(0, total_samples, number_of_samples_per_heliostat, device=device)[:, None]
+            + torch.arange(number_of_train_samples, device=device)
+        ).reshape(-1)
+        test_indices = (starts[:, None] + offsets).reshape(-1)
+
+        active_heliostats_mask_train = torch.clamp(active_heliostats_mask - number_of_test_samples, min=0)
+        active_heliostats_mask_test = torch.clamp(active_heliostats_mask - number_of_train_samples, min=0)
+
+        return TrainTestSplit(
+            flux_measured_train=flux_measured[train_indices],
+            focal_spots_measured_train=focal_spots_measured[train_indices],
+            incident_ray_directions_train=incident_ray_directions[train_indices],
+            motor_positions_train=motor_positions[train_indices],
+            target_area_indices_train=target_area_indices[train_indices],
+
+            flux_measured_test=flux_measured[test_indices],
+            focal_spots_measured_test=focal_spots_measured[test_indices],
+            incident_ray_directions_test=incident_ray_directions[test_indices],
+            motor_positions_test=motor_positions[test_indices],
+            target_area_indices_test=target_area_indices[test_indices],
+
+            active_heliostats_mask_train=active_heliostats_mask_train,
+            active_heliostats_mask_test=active_heliostats_mask_test,
+
+            train_indices=train_indices,
+            test_indices=test_indices,
+
+            number_of_train_samples=number_of_train_samples,
+            number_of_test_samples=number_of_test_samples,
+            number_of_samples_per_heliostat=number_of_samples_per_heliostat,
+        )
+    
+
+    def validate(
+        self,
+        heliostat_group: HeliostatGroup,
+        data_split: TrainTestSplit,
+        device: torch.device | None = None,
+    ):
+        device = get_device(device=device)
+
+        heliostat_group.activate_heliostats(
+            active_heliostats_mask=data_split.active_heliostats_mask_test,
+            device=device,
+        )
+
+        heliostat_group.align_surfaces_with_motor_positions(
+            motor_positions=data_split.motor_positions_test,
+            active_heliostats_mask=data_split.active_heliostats_mask_test,
+            device=device,
+        )
+
+        ray_tracer = HeliostatRayTracer(
+            scenario=self.scenario,
+            heliostat_group=heliostat_group,
+            blocking_active=False,
+            world_size=self.ddp_setup["heliostat_group_world_size"],
+            rank=self.ddp_setup["heliostat_group_rank"],
+            batch_size=self.optimizer_dict[constants.batch_size],
+            random_seed=self.ddp_setup["heliostat_group_rank"],
+            dni=self.dni,
+            bitmap_resolution=self.bitmap_resolution,
+        )
+
+        flux_prediction, _, _, _ = ray_tracer.trace_rays(
+            incident_ray_directions=data_split.incident_ray_directions_test,
+            active_heliostats_mask=data_split.active_heliostats_mask_test,
+            target_area_indices=data_split.target_area_indices_test,
+            device=device,
+        )
+
+        indices_for_local_rank = ray_tracer.get_sampler_indices()
+
+        loss_focal_spot_per_sample = self.loss_focal_spot(
+            prediction=flux_prediction,
+            ground_truth=data_split.flux_measured_test[
+                indices_for_local_rank
+            ],
+            target_area_indices=data_split.target_area_indices_test[
+                indices_for_local_rank
+            ],
+            device=device
+        )
+        loss_pixel_per_sample = self.loss_pixel(
+            prediction=flux_prediction,
+            ground_truth=data_split.flux_measured_test[
+                indices_for_local_rank
+            ],
+            reduction_dimensions=(1,2,),
+        )
+        loss_kl_div_per_sample = self.loss_kl_div(
+            prediction=flux_prediction,
+            ground_truth=data_split.flux_measured_test[
+                indices_for_local_rank
+            ],
+            reduction_dimensions=(1,2,),
+        )
+
+        test_loss_focal_spot = reduce_loss_per_sample(
+            loss_per_sample=loss_focal_spot_per_sample,
+            number_of_samples_per_heliostat=data_split.number_of_test_samples,
+            reduction=partial(torch.median, dim=1),
+        )                   
+        test_loss_pixel = reduce_loss_per_sample(
+            loss_per_sample=loss_pixel_per_sample,
+            number_of_samples_per_heliostat=data_split.number_of_test_samples,
+            reduction=partial(torch.median, dim=1),
+        )                        
+        test_loss_kl_div = reduce_loss_per_sample(
+            loss_per_sample=loss_kl_div_per_sample,
+            number_of_samples_per_heliostat=data_split.number_of_test_samples,
+            reduction=partial(torch.median, dim=1),
+        )
+
+        print(f"test loss focal spot, mean: {torch.mean(test_loss_focal_spot)}")
+        print(f"test loss pixel, mean: {torch.mean(test_loss_pixel)}")
+        print(f"test loss kl div, mean: {torch.mean(test_loss_kl_div)}")
+
+        return flux_prediction
+
+    def plot_fluxes(
+        self,
+        flux_measured: torch.Tensor,
+        flux_prediction_train: torch.Tensor,
+        flux_prediction_test: torch.Tensor,
+        data_split: TrainTestSplit,
+        plot_name: str,
+    ) -> None:
+        device=torch.device("cpu")
+        flux_predicted = torch.zeros_like(flux_measured, device=device)
+        flux_predicted[data_split.train_indices] = flux_prediction_train
+        flux_predicted[data_split.test_indices] = flux_prediction_test
+        samples_per_heliostat = data_split.number_of_samples_per_heliostat
+        total_samples = flux_measured.shape[0]
+        
+        centers_of_mass_predicted = bitmap.get_center_of_mass(flux_predicted, device=device)
+        centers_of_mass_measured = bitmap.get_center_of_mass(flux_measured, device=device)
+
+        for heliostat_start_index in range(0, total_samples, samples_per_heliostat):
+            fig, axes = plt.subplots(
+                samples_per_heliostat, 2, figsize=(8, samples_per_heliostat * 4),
+            )
+            heliostat_index = heliostat_start_index // samples_per_heliostat
+
+            for sample_offset in range(samples_per_heliostat):
+                sample_index = heliostat_start_index + sample_offset
+
+                axes[sample_offset, 0].imshow(flux_predicted[sample_index])
+                axes[sample_offset, 0].set_title(f"Predicted Flux - Heliostat {heliostat_index}")
+
+                axes[sample_offset, 1].imshow(flux_measured[sample_index])
+                axes[sample_offset, 1].set_title(f"Measured Flux - Heliostat {heliostat_index}")
+
+                for ax in axes[sample_offset, :]:
+                    ax.scatter(
+                        centers_of_mass_measured[sample_index, 0],
+                        centers_of_mass_measured[sample_index, 1],
+                        c="black",
+                        s=30,
+                        marker="o",
+                    )
+                    ax.scatter(
+                        centers_of_mass_predicted[sample_index, 0],
+                        centers_of_mass_predicted[sample_index, 1],
+                        c="red",
+                        s=30,
+                        marker="x",
+                    )   
+
+            plt.tight_layout()
+            plt.savefig(f"./ignored/new/heliostat_{heliostat_start_index // samples_per_heliostat}_{plot_name}")
+            plt.close(fig)
+
+
+    def _reconstruct_kinematics_parameters_with_geometry(
         self,
         loss_definition: Loss,
         device: torch.device | None = None,
@@ -209,14 +487,6 @@ def _reconstruct_kinematics_parameters_with_raytracing(
             torch.inf,
             device=device,
         )
-        final_loss_start_indices = torch.cat(
-            [
-                torch.tensor([0], device=device),
-                self.scenario.heliostat_field.number_of_heliostats_per_group.cumsum(
-                    indices.heliostat_dimension
-                ),
-            ]
-        )
         loss_history: list[dict[str, list[float]]] = []
 
         # Iterate heliostat groups assigned to this rank.
@@ -233,9 +503,9 @@ def _reconstruct_kinematics_parameters_with_raytracing(
             )
             (
                 flux_measured,
-                _,
+                focal_spots_measured,
                 incident_ray_directions,
-                _,
+                motor_positions,
                 active_heliostats_mask,
                 target_area_indices,
             ) = parser.parse_data_for_reconstruction(
@@ -247,67 +517,335 @@ def _reconstruct_kinematics_parameters_with_raytracing(
             )
 
             if active_heliostats_mask.sum() > 0:
-                # Calculate focal spot from measured flux.
-                focal_spots_bitmap_coordinates = bitmap.get_center_of_mass(
-                    bitmaps=flux_measured, device=device
+
+                data_split: TrainTestSplit = self.train_test_split(
+                    active_heliostats_mask=active_heliostats_mask,
+                    flux_measured=flux_measured,
+                    focal_spots_measured=focal_spots_measured,
+                    incident_ray_directions=incident_ray_directions,
+                    motor_positions=motor_positions,
+                    target_area_indices=target_area_indices,
+                    test_fraction=0.25,
+                    device=device
                 )
-                focal_spots_measured = (
-                    coordinates.bitmap_coordinates_to_target_coordinates(
-                        bitmap_coordinates=focal_spots_bitmap_coordinates,
-                        bitmap_resolution=self.bitmap_resolution,
-                        solar_tower=self.scenario.solar_tower,
-                        target_area_indices=target_area_indices,
-                        device=device,
+
+                preferred_reflection_directions_measured = (
+                    torch.nn.functional.normalize(
+                        (
+                            focal_spots_measured[:, :3]
+                            - heliostat_group.positions.repeat_interleave(
+                                active_heliostats_mask, dim=0
+                            )[:, :3]
+                        ),
+                        p=2,
+                        dim=1,
                     )
                 )
 
-                # Reparametrize optimizable actuator parameters.
-                initial_actuator_params = (
-                    heliostat_group.kinematics.actuators.optimizable_parameters.detach()
+                normals_measured = coordinates.convert_3d_directions_to_4d_format(torch.nn.functional.normalize(preferred_reflection_directions_measured - incident_ray_directions[:, :3], dim=-1), device=device)
+
+                # Set up optimizer, scheduler, and early stopping.
+                optimizer_params = [
+                    {
+                        "params": heliostat_group.kinematics.rotation_deviation_parameters.requires_grad_(),
+                        "lr": self.optimizer_dict[
+                            constants.initial_learning_rate_rotation_deviation
+                        ],
+                    }
+                ]
+
+                optimizer = torch.optim.Adam(optimizer_params)
+
+                # Create a learning rate scheduler.
+                scheduler_fn = getattr(
+                    training,
+                    self.scheduler_dict[constants.scheduler_type],
+                )
+                scheduler: LRScheduler = scheduler_fn(
+                    optimizer=optimizer, parameters=self.scheduler_dict
+                )
+
+                # Set up early stopping.
+                early_stopper = training.EarlyStopping(
+                    window_size=self.optimizer_dict[constants.early_stopping_window],
+                    patience=self.optimizer_dict[constants.early_stopping_patience],
+                    min_improvement=self.optimizer_dict[constants.early_stopping_delta],
+                    relative=True,
+                )
+
+                loss_history_list = []
+
+                # Start the optimization.
+                loss = torch.inf
+                epoch = 0
+                log_step = (
+                    self.optimizer_dict[constants.max_epoch]
+                    if self.optimizer_dict[constants.log_step] == 0
+                    else self.optimizer_dict[constants.log_step]
                 )
-                if initial_actuator_params is not None:
-                    angle_mean = initial_actuator_params[
-                        :, indices.actuator_params_initial_angle
-                    ].mean()
-                    angle_std = (
-                        initial_actuator_params[
-                            :, indices.actuator_params_initial_angle
-                        ]
-                        .std()
-                        .clamp(min=1e-3)
+                while (
+                    loss > float(self.optimizer_dict[constants.tolerance])
+                    and epoch <= self.optimizer_dict[constants.max_epoch]
+                ):
+                    optimizer.zero_grad()
+
+                    # Activate heliostats.
+                    heliostat_group.activate_heliostats(
+                        active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
+                    )
+
+                    orientations = heliostat_group.kinematics.motor_positions_to_orientations(
+                        motor_positions=data_split.motor_positions_train,
+                        device=device,
+                    )
+
+                    normals_predicted = orientations @ torch.tensor(
+                        [0.0, 0.0, 1.0, 0.0], device=device
                     )
 
-                    stroke_mean = initial_actuator_params[
-                        :, indices.actuator_params_initial_stroke_length
-                    ].mean()
-                    stroke_std = (
-                        initial_actuator_params[
-                            :, indices.actuator_params_initial_stroke_length
-                        ]
-                        .std()
-                        .clamp(min=1e-3)
+                    # Compute loss from prediction vs. measured normals.
+                    loss_per_sample = loss_definition(
+                        prediction=normals_predicted,
+                        ground_truth=normals_measured[data_split.train_indices],
                     )
 
-                    angle_normalized = (
-                        initial_actuator_params[
-                            :, indices.actuator_params_initial_angle
-                        ]
-                        - angle_mean
-                    ) / angle_std
-                    stroke_length_normalized = (
-                        initial_actuator_params[
-                            :, indices.actuator_params_initial_stroke_length
-                        ]
-                        - stroke_mean
-                    ) / stroke_std
-
-                    delta_angle = torch.zeros_like(angle_normalized, requires_grad=True)
-                    delta_stroke = torch.zeros_like(
-                        stroke_length_normalized, requires_grad=True
+                    loss_per_heliostat = reduce_loss_per_sample(
+                        loss_per_sample=loss_per_sample,
+                        number_of_samples_per_heliostat=data_split.number_of_train_samples,
+                        reduction=partial(torch.mean)
                     )
-                else:
-                    delta_angle = None
-                    delta_stroke = None
+
+                    loss = torch.mean(loss_per_heliostat)
+
+                    loss.backward()
+                    
+                    optimizer.param_groups[0]["params"][0].grad.nan_to_num_(nan=0.0, posinf=0.0, neginf=0.0)
+
+                    if self.ddp_setup["is_nested"]:
+                        # Reduce gradients within each heliostat group.
+                        for param_group in optimizer.param_groups:
+                            for param in param_group["params"]:
+                                if param.grad is not None:
+                                    param.grad = (
+                                        torch.distributed.nn.functional.all_reduce(
+                                            param.grad,
+                                            op=torch.distributed.ReduceOp.SUM,
+                                            group=self.ddp_setup["process_subgroup"],
+                                        )
+                                    )
+                                    param.grad /= self.ddp_setup[
+                                        "heliostat_group_world_size"
+                                    ]
+
+                    optimizer.step()
+
+                    with torch.no_grad():
+                        heliostat_group.activate_heliostats(
+                            active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
+                        )
+
+                        heliostat_group.align_surfaces_with_motor_positions(
+                            motor_positions=data_split.motor_positions_train,
+                            active_heliostats_mask=data_split.active_heliostats_mask_train,
+                            device=device
+                        )
+
+                        ray_tracer = HeliostatRayTracer(
+                            scenario=self.scenario,
+                            heliostat_group=heliostat_group,
+                            blocking_active=False,
+                            world_size=self.ddp_setup["heliostat_group_world_size"],
+                            rank=self.ddp_setup["heliostat_group_rank"],
+                            batch_size=self.optimizer_dict[constants.batch_size],
+                            random_seed=self.ddp_setup["heliostat_group_rank"],
+                            dni=self.dni,
+                            bitmap_resolution=self.bitmap_resolution,
+                        )
+
+                        flux_prediction_train, _, _, _ = ray_tracer.trace_rays(
+                            incident_ray_directions=data_split.incident_ray_directions_train,
+                            active_heliostats_mask=data_split.active_heliostats_mask_train,
+                            target_area_indices=data_split.target_area_indices_train,
+                            device=device,
+                        )
+
+                        flux_prediction_test=self.validate(
+                            heliostat_group=heliostat_group,
+                            data_split=data_split,
+                            device=device
+                        )
+
+                    if isinstance(
+                        scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau
+                    ):
+                        scheduler.step(loss.detach())
+                    else:
+                        scheduler.step()
+
+                    if epoch % log_step == 0:
+                        log.info(
+                            f"Rank: {rank}, Epoch: {epoch}, Loss: {loss},",
+                        )
+
+                    loss_history_list.append(loss.detach().cpu().item())
+
+                    # Early stopping when loss did not improve for a predefined number of epochs.
+                    stop = early_stopper.step(loss.item())
+
+                    if stop:
+                        log.info(f"Early stopping at epoch {epoch}.")
+                        break
+                    
+                    if epoch == 300:
+                        self.plot_fluxes(
+                            flux_measured=flux_measured.cpu().detach(),
+                            flux_prediction_train=flux_prediction_train.cpu().detach(),
+                            flux_prediction_test=flux_prediction_test.cpu().detach(),
+                            data_split=data_split,
+                            plot_name=f"geometry_{epoch}"
+                        )
+
+                    epoch += 1
+
+                loss_history.append(
+                    {
+                        "total_loss": loss_history_list,
+                    }
+                )
+
+                final_loss_per_heliostat[active_heliostats_mask!=0] = loss_per_heliostat
+
+                log.info(f"Rank: {rank}, Kinematics reconstructed.")
+
+        if self.ddp_setup["is_distributed"]:
+            for index, heliostat_group in enumerate(
+                self.scenario.heliostat_field.heliostat_groups
+            ):
+                source = self.ddp_setup["ranks_to_groups_mapping"][index]
+                torch.distributed.broadcast(
+                    heliostat_group.kinematics.rotation_deviation_parameters,
+                    src=source[indices.first_rank_from_group],
+                )
+                torch.distributed.broadcast(
+                    heliostat_group.kinematics.actuators.optimizable_parameters,
+                    src=source[indices.first_rank_from_group],
+                )
+            torch.distributed.all_reduce(
+                final_loss_per_heliostat, op=torch.distributed.ReduceOp.MIN
+            )
+
+            final_loss_history_all_groups: list[list[dict[str, list[float]]]] = [
+                [] for _ in range(self.ddp_setup["world_size"])
+            ]
+            torch.distributed.all_gather_object(
+                final_loss_history_all_groups, loss_history
+            )
+
+            log.info(f"Rank: {rank}, synchronized after kinematics reconstruction.")
+
+        else:
+            final_loss_history_all_groups = [loss_history]
+
+        for heliostat_group in self.scenario.heliostat_field.heliostat_groups:
+            heliostat_group.kinematics.rotation_deviation_parameters = heliostat_group.kinematics.rotation_deviation_parameters.detach()
+
+        return final_loss_per_heliostat.detach().cpu(), final_loss_history_all_groups
+
+
+    def _reconstruct_kinematics_parameters_with_raytracing(
+        self,
+        loss_definition: Loss,
+        device: torch.device | None = None,
+    ) -> tuple[torch.Tensor, list[list[dict[str, list[float]]]]]:
+        """
+        Reconstruct the kinematics parameters using ray tracing.
+
+        This reconstruction method optimizes the kinematics parameters by extracting the focal points
+        of calibration images and using heliostat-tracing.
+
+        Parameters
+        ----------
+        loss_definition : Loss
+            Definition of the loss function and pre-processing of the prediction.
+        device : torch.device | None
+            The device on which to perform computations or load tensors and models (default is None).
+            If None, ARTIST will automatically select the most appropriate
+            device (CUDA or CPU) based on availability and OS.
+
+        Returns
+        -------
+        torch.Tensor
+            The final loss of the kinematics reconstruction for each heliostat in each group.
+            Shape is ``[total_number_of_heliostats_in_scenario]``.
+        list[list[dict[str, list[float]]]]
+            Loss histories over epochs grouped by rank.
+            Outer list: one entry per rank.
+            Inner list: one entry per heliostat group processed on that rank.
+            Each group entry is a dict with key ``"total_loss"`` mapping to a list
+            of per-epoch scalar loss values.
+            In non-distributed mode, this is a single-rank container: ``[local_group_histories]``.
+        """
+        device = get_device(device=device)
+        rank = self.ddp_setup["rank"]
+
+        if rank == 0:
+            log.info("Beginning kinematics reconstruction with ray tracing.")
+
+        # Initialize final loss per heliostat, group offset table into global heliostat index space, and
+        # per-group loss curves for this rank.
+        final_loss_per_heliostat = torch.full(
+            (self.scenario.heliostat_field.number_of_heliostats_per_group.sum(),),
+            torch.inf,
+            device=device,
+        )
+        final_loss_start_indices = torch.cat(
+            [
+                torch.tensor([0], device=device),
+                self.scenario.heliostat_field.number_of_heliostats_per_group.cumsum(
+                    indices.heliostat_dimension
+                ),
+            ]
+        )
+        loss_history: list[dict[str, list[float]]] = []
+
+        # Iterate heliostat groups assigned to this rank.
+        for heliostat_group_index in self.ddp_setup["groups_to_ranks_mapping"][rank]:
+            # Parse calibration inputs for current group to obtain measured flux, incident ray directions, mask of
+            # active heliostats, and target area indices.
+            heliostat_group: HeliostatGroup = (
+                self.scenario.heliostat_field.heliostat_groups[heliostat_group_index]
+            )
+            parser = cast(CalibrationDataParser, self.data[constants.data_parser])
+            heliostat_mapping = cast(
+                list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
+                self.data[constants.heliostat_data_mapping],
+            )
+            (
+                flux_measured,
+                focal_spots_measured,
+                incident_ray_directions,
+                motor_positions,
+                active_heliostats_mask,
+                target_area_indices,
+            ) = parser.parse_data_for_reconstruction(
+                heliostat_data_mapping=heliostat_mapping,
+                heliostat_group=heliostat_group,
+                scenario=self.scenario,
+                bitmap_resolution=self.bitmap_resolution,
+                device=device,
+            )
+
+            if active_heliostats_mask.sum() > 0:
+                data_split: TrainTestSplit = self.train_test_split(
+                    active_heliostats_mask=active_heliostats_mask,
+                    flux_measured=flux_measured,
+                    focal_spots_measured=focal_spots_measured,
+                    incident_ray_directions=incident_ray_directions,
+                    motor_positions=motor_positions,
+                    target_area_indices=target_area_indices,
+                    test_fraction=0.25,
+                    device=device
+                )
 
                 # Set up optimizer, scheduler, and early stopping.
                 optimizer_params = [
@@ -319,24 +857,6 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                     }
                 ]
 
-                if initial_actuator_params is not None:
-                    optimizer_params.extend(
-                        [
-                            {
-                                "params": delta_angle,
-                                "lr": self.optimizer_dict[
-                                    constants.initial_learning_rate_initial_angles
-                                ],
-                            },
-                            {
-                                "params": delta_stroke,
-                                "lr": self.optimizer_dict[
-                                    constants.initial_learning_rate_initial_stroke_length
-                                ],
-                            },
-                        ]
-                    )
-
                 optimizer = torch.optim.Adam(optimizer_params)
 
                 # Create a learning rate scheduler.
@@ -372,37 +892,15 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                 ):
                     optimizer.zero_grad()
 
-                    # Get actuator parameters from reparametrized version.
-                    actuator_params = torch.cat(
-                        [
-                            ((angle_normalized + delta_angle) * angle_std + angle_mean)[
-                                :, None, :
-                            ],
-                            (
-                                (stroke_length_normalized + delta_stroke) * stroke_std
-                                + stroke_mean
-                            )[:, None, :],
-                        ],
-                        dim=-1,
-                    ).view_as(
-                        heliostat_group.kinematics.actuators.optimizable_parameters
-                    )
-                    heliostat_group.kinematics.actuators.optimizable_parameters = (
-                        actuator_params
-                    )
-
                     # Activate heliostats.
                     heliostat_group.activate_heliostats(
-                        active_heliostats_mask=active_heliostats_mask, device=device
+                        active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
                     )
 
                     # Align heliostats.
-                    heliostat_group.align_surfaces_with_incident_ray_directions(
-                        aim_points=self.scenario.solar_tower.get_centers_of_target_areas(
-                            target_area_indices=target_area_indices, device=device
-                        ),
-                        incident_ray_directions=incident_ray_directions,
-                        active_heliostats_mask=active_heliostats_mask,
+                    heliostat_group.align_surfaces_with_motor_positions(
+                        motor_positions=data_split.motor_positions_train,
+                        active_heliostats_mask=data_split.active_heliostats_mask_train,
                         device=device,
                     )
 
@@ -420,39 +918,35 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                     )
 
                     # Perform heliostat-based ray tracing.
-                    flux_distributions, _, _, _ = ray_tracer.trace_rays(
-                        incident_ray_directions=incident_ray_directions,
-                        active_heliostats_mask=active_heliostats_mask,
-                        target_area_indices=target_area_indices,
+                    flux_prediction_train, _, _, _ = ray_tracer.trace_rays(
+                        incident_ray_directions=data_split.incident_ray_directions_train,
+                        active_heliostats_mask=data_split.active_heliostats_mask_train,
+                        target_area_indices=data_split.target_area_indices_train,
                         device=device,
                     )
 
                     sample_indices_for_local_rank = ray_tracer.get_sampler_indices()
 
-                    # Compute loss from prediction vs. measured focal spots.
+                    # Compute loss from prediction vs. measured flux.
                     loss_per_sample = loss_definition(
-                        prediction=flux_distributions,
-                        ground_truth=focal_spots_measured[
+                        prediction=flux_prediction_train,
+                        ground_truth=data_split.flux_measured_train[
                             sample_indices_for_local_rank
                         ],
-                        target_area_indices=target_area_indices[
+                        target_area_indices=data_split.target_area_indices_train[
                             sample_indices_for_local_rank
                         ],
-                        reduction_dimensions=(indices.focal_spots,),
+                        reduction_dimensions=(1,2),
                         device=device,
                     )
 
-                    number_of_samples_per_heliostat = int(
-                        heliostat_group.active_heliostats_mask.sum()
-                        / (heliostat_group.active_heliostats_mask > 0).sum()
-                    )
-
-                    loss_per_heliostat = mean_loss_per_heliostat(
+                    loss_per_heliostat = reduce_loss_per_sample(
                         loss_per_sample=loss_per_sample,
-                        number_of_samples_per_heliostat=number_of_samples_per_heliostat,
+                        number_of_samples_per_heliostat=data_split.number_of_train_samples,
+                        reduction=partial(torch.median, dim=1)
                     )
 
-                    loss = loss_per_heliostat.mean()
+                    loss = torch.mean(loss_per_heliostat)
 
                     loss.backward()
 
@@ -473,6 +967,14 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                                     ]
 
                     optimizer.step()
+
+                    with torch.no_grad():
+                        flux_prediction_test=self.validate(
+                            heliostat_group=heliostat_group,
+                            data_split=data_split,
+                            device=device
+                        )
+
                     if isinstance(
                         scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau
                     ):
@@ -493,74 +995,18 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                     if stop:
                         log.info(f"Early stopping at epoch {epoch}.")
                         break
+                    
+                    if epoch == 300:
+                        self.plot_fluxes(
+                            flux_measured=flux_measured.cpu().detach(),
+                            flux_prediction_train=flux_prediction_train.cpu().detach(),
+                            flux_prediction_test=flux_prediction_test.cpu().detach(),
+                            data_split=data_split,
+                            plot_name=f"raytracing_{epoch}"
+                        )
 
                     epoch += 1
-                # ##################################################
-                # import matplotlib.pyplot as plt
-                # m = 18
-                # n = flux_measured.shape[0]
-                # for i in range(0, n, m):
-                #     fig, axes = plt.subplots(m, 2, figsize=(8, 50))
-                #     for j in range(m):
-                #         idx = i + j
-                #         if idx >= n:
-                #             axes[j, 0].axis("off")
-                #             axes[j, 1].axis("off")
-                #             continue
-                #         img_flux = flux_distributions[idx]
-                #         total_f = img_flux.sum()
-                #         if total_f > 0:
-                #             h, w = img_flux.shape
-                #             y_grid = (
-                #                 torch.arange(h, device=img_flux.device)
-                #                 .unsqueeze(1)
-                #                 .expand(h, w)
-                #             )
-                #             x_grid = (
-                #                 torch.arange(w, device=img_flux.device)
-                #                 .unsqueeze(0)
-                #                 .expand(h, w)
-                #             )
-                #             cy_f = (y_grid * img_flux).sum() / total_f
-                #             cx_f = (x_grid * img_flux).sum() / total_f
-                #         else:
-                #             cy_f, cx_f = 0.0, 0.0
-                #         img_meas = flux_measured[idx].cpu().detach()
-                #         total_m = img_meas.sum().cpu().detach()
-                #         if total_m > 0:
-                #             h, w = img_meas.shape
-                #             y_grid = (
-                #                 torch.arange(h, device=img_meas.device)
-                #                 .unsqueeze(1)
-                #                 .expand(h, w)
-                #             )
-                #             x_grid = (
-                #                 torch.arange(w, device=img_meas.device)
-                #                 .unsqueeze(0)
-                #                 .expand(h, w)
-                #             )
-                #             cy_m = (y_grid * img_meas).sum() / total_m
-                #             cx_m = (x_grid * img_meas).sum() / total_m
-                #         else:
-                #             cy_m, cx_m = 0.0, 0.0
-                #         cx_f, cy_f = float(cx_f), float(cy_f)
-                #         cx_m, cy_m = float(cx_m), float(cy_m)
-                #         ax1 = axes[j, 0]
-                #         ax1.imshow(img_flux.cpu().detach())
-                #         ax1.scatter(cx_m, cy_m, c="black", s=30, marker="o")
-                #         ax1.scatter(cx_f, cy_f, c="r", s=30, marker="x")
-                #         ax1.set_title(f"heliostat {i // m}")
-                #         ax1.axis("off")
-                #         ax2 = axes[j, 1]
-                #         ax2.imshow(img_meas.cpu().detach())
-                #         ax2.scatter(cx_m, cy_m, c="black", s=30, marker="o")
-                #         ax2.scatter(cx_f, cy_f, c="r", s=30, marker="x")
-                #         ax2.set_title(f"loss: {loss_per_sample[i + j]}")
-                #         ax2.axis("off")
-                #     plt.tight_layout()
-                #     plt.savefig(f"./bitmaps/kinematics/heliostat_{i // m}.png")
-                #     plt.close(fig)
-                # ##################################################
+
                 loss_history.append(
                     {
                         "total_loss": loss_history_list,
@@ -568,8 +1014,8 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                 )
 
                 local_indices = (
-                    sample_indices_for_local_rank[::number_of_samples_per_heliostat]
-                    // number_of_samples_per_heliostat
+                    sample_indices_for_local_rank[::data_split.number_of_train_samples]
+                    // data_split.number_of_train_samples
                 )
 
                 global_active_indices = torch.nonzero(
@@ -616,4 +1062,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
         else:
             final_loss_history_all_groups = [loss_history]
 
+        for heliostat_group in self.scenario.heliostat_field.heliostat_groups:
+            heliostat_group.kinematics.rotation_deviation_parameters = heliostat_group.kinematics.rotation_deviation_parameters.detach()
+
         return final_loss_per_heliostat.detach().cpu(), final_loss_history_all_groups

From 9774c61d066510906fa9227612b0432b6f2fcb6d Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 21 May 2026 10:06:22 +0200
Subject: [PATCH 13/38] fix import

---
 artist/optim/__init__.py              | 2 +-
 artist/optim/surface_reconstructor.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/artist/optim/__init__.py b/artist/optim/__init__.py
index 2b42fefd..7be0d037 100644
--- a/artist/optim/__init__.py
+++ b/artist/optim/__init__.py
@@ -6,7 +6,7 @@
     Loss,
     PixelLoss,
     VectorLoss,
-    mean_loss_per_heliostat,
+    reduce_loss_per_sample,
 )
 from .motor_position_optimizer import MotorPositionsOptimizer
 from .regularizers import IdealSurfaceRegularizer, SmoothnessRegularizer
diff --git a/artist/optim/surface_reconstructor.py b/artist/optim/surface_reconstructor.py
index c4a8e137..26f62a4e 100644
--- a/artist/optim/surface_reconstructor.py
+++ b/artist/optim/surface_reconstructor.py
@@ -11,7 +11,7 @@
 from artist.nurbs.surfaces import NURBSSurfaces
 from artist.nurbs.utils import create_nurbs_evaluation_grid
 from artist.optim import training
-from artist.optim.loss import Loss, mean_loss_per_heliostat
+from artist.optim.loss import Loss, reduce_loss_per_sample
 from artist.optim.regularizers import IdealSurfaceRegularizer, SmoothnessRegularizer
 from artist.raytracing.heliostat_ray_tracer import HeliostatRayTracer
 from artist.scenario.scenario import Scenario

From 5c2f4892a2abf887485d367d0bd1189939ae5f2f Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 21 May 2026 10:08:25 +0200
Subject: [PATCH 14/38] fix

---
 tutorials/04_kinematics_reconstruction_tutorial.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tutorials/04_kinematics_reconstruction_tutorial.py b/tutorials/04_kinematics_reconstruction_tutorial.py
index 6fc5c9af..9327c900 100644
--- a/tutorials/04_kinematics_reconstruction_tutorial.py
+++ b/tutorials/04_kinematics_reconstruction_tutorial.py
@@ -247,7 +247,8 @@ def create_plots(
 # Or if you have a directory with downloaded data use this code to create a mapping.
 heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
     base_path="/workVERLEIHNIX/share/PAINT_data",
-    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58", "AH48", "AH49", "AH50", "AH55", "AH56", "AH57", "AH58", "AI48", "AI49", "AI50", "AI51", "AI52", "AI53", "AI54", "AI55", "AI56", "AI57", "AI58", "AI59", "AI60", "AI61", "AJ49", "AJ50", "AJ51", "AJ53", "AJ54", "AJ55", "AJ56", "AJ57", "AJ58", "AJ61", "AJ62", "AK48", "AK49", "AK50", "AK51", "AK52", "AK53", "AK54", "AK55", "AK56", "AK58", "AK59", "AK60", "AK61", "AK62", "AL48", "AL49", "AL51", "AL53", "AL56", "AL58", "AL59", "AL60", "AL61", "AL62", "AM48", "AM49", "AM50", "AM51", "AM52", "AM53", "AM54", "AM55", "AM56", "AM57", "AM58", "AM59", "AM60", "AM62", "AN48", "AN49", "AN50", "AN51", "AN52", "AN53", "AN54", "AN55", "AN56", "AN57", "AN58", "AN59", "AN60", "AN61", "AN62"],
+    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58"],
+    #heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58", "AH48", "AH49", "AH50", "AH55", "AH56", "AH57", "AH58", "AI48", "AI49", "AI50", "AI51", "AI52", "AI53", "AI54", "AI55", "AI56", "AI57", "AI58", "AI59", "AI60", "AI61", "AJ49", "AJ50", "AJ51", "AJ53", "AJ54", "AJ55", "AJ56", "AJ57", "AJ58", "AJ61", "AJ62", "AK48", "AK49", "AK50", "AK51", "AK52", "AK53", "AK54", "AK55", "AK56", "AK58", "AK59", "AK60", "AK61", "AK62", "AL48", "AL49", "AL51", "AL53", "AL56", "AL58", "AL59", "AL60", "AL61", "AL62", "AM48", "AM49", "AM50", "AM51", "AM52", "AM53", "AM54", "AM55", "AM56", "AM57", "AM58", "AM59", "AM60", "AM62", "AN48", "AN49", "AN50", "AN51", "AN52", "AN53", "AN54", "AN55", "AN56", "AN57", "AN58", "AN59", "AN60", "AN61", "AN62"],
     #heliostat_names=['AZ43', 'AP35', 'AA39', 'AC31', 'AC42', 'AC24', 'AM31', 'BF65', 'AQ27', 'AF33', 'BD31', 'AW42', 'AZ39', 'AL36', 'AD42', 'AP39', 'AE28', 'BE38', 'AY55', 'AP50'],
     number_of_measurements=37,
     image_variant="flux",
@@ -340,7 +341,7 @@ def create_plots(
 
     scenario.set_number_of_rays(number_of_rays=4)
     optimization_configuration[constants.optimization][constants.initial_learning_rate_rotation_deviation] = 1e-4
-    optimization_configuration[constants.optimization][constants.max_epoch] = 900
+    optimization_configuration[constants.optimization][constants.max_epoch] = 300
     # Create the kinematics reconstructor.
     kinematics_reconstructor = KinematicsReconstructor(
         ddp_setup=ddp_setup,
@@ -348,7 +349,7 @@ def create_plots(
         data=data,
         dni=500,
         optimization_configuration=optimization_configuration,
-        reconstruction_method="direction",
+        reconstruction_method=constants.kinematics_reconstruction_geometry,
         bitmap_resolution=resolution,
     )
     # Reconstruct the kinematics.

From a5170ff49df3a65b773cbf946563b6c5c2c2b8f3 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 27 May 2026 15:30:13 +0200
Subject: [PATCH 15/38] fix and replace old inverse kinematics

---
 artist/field/kinematics_rigid_body.py | 393 +++++++++++++-------------
 1 file changed, 204 insertions(+), 189 deletions(-)

diff --git a/artist/field/kinematics_rigid_body.py b/artist/field/kinematics_rigid_body.py
index d42b7b43..68699ba9 100644
--- a/artist/field/kinematics_rigid_body.py
+++ b/artist/field/kinematics_rigid_body.py
@@ -1,10 +1,14 @@
+import logging
 import torch
 
+from artist.field.actuators import Actuators
 from artist.field.kinematics import Kinematics
-from artist.geometry import rotations, transforms
+from artist.geometry import coordinates, rotations, transforms
 from artist.util import constants, indices, type_registry
 from artist.util.env import get_device
 
+log = logging.getLogger(__name__)
+"""A logger for the rigid body kinematics."""
 
 class RigidBody(Kinematics):
     """
@@ -43,11 +47,14 @@ class RigidBody(Kinematics):
     active_motor_positions : torch.Tensor
         The motor positions of active heliostats.
         Shape is ``[number_of_active_heliostats, 2]``.
-    artist_standard_orientation : torch.Tensor
-        The standard orientation of the kinematics.
-        Shape is ``[4]``.
     actuators : Actuators
         The actuators used in the kinematics.
+    kinematics_standard_orientation : torch.Tensor
+        Standard orientation of the kinematics system: south (0, -1, 0, 0) in homogeneous ENU.
+        Shape is ``[4]``
+    initial_orientation_offsets : torch.Tensor
+        Rotation matrix to account for the initial orientation offset between surface mesh and kinematics system. 
+        Shape is ``[1, 4, 4]``.
 
     Methods
     -------
@@ -149,11 +156,7 @@ def __init__(
             self.motor_positions, device=device
         )
 
-        self.artist_standard_orientation = torch.tensor(
-            [0.0, -1.0, 0.0, 0.0], device=device
-        )
-
-        self.actuators = type_registry.actuator_type_mapping[
+        self.actuators: Actuators = type_registry.actuator_type_mapping[
             actuator_parameters_non_optimizable[
                 0, indices.actuator_type, indices.actuator_one_index
             ].item()
@@ -163,6 +166,26 @@ def __init__(
             device=device,
         )
 
+        # The surface points and normals are always sampled from a model (converted NURBS from deflectometry or ideal NURBS) that lays
+        # flat on the ground, i.e., the surface normals are pointing upwards [0.0, 0.0, 1.0]. Since the kinematics in ARTIST expects the 
+        # points and normals to be initially oriented to the south, an extra rotation needs to be applied.
+        self.kinematics_standard_orientation = torch.tensor(
+            [0.0, -1.0, 0.0, 0.0], device=device
+        )
+        sampled_surface_orientation = torch.tensor(
+            [0.0, 0.0, 1.0, 0.0], device=device
+        )
+        east_angles, north_angles, up_angles = rotations.decompose_rotations(
+            initial_vector=sampled_surface_orientation[None, :],
+            target_vector=self.kinematics_standard_orientation,
+        )
+        self.initial_orientation_offsets = (
+            transforms.rotate_e(e=east_angles, device=device)
+            @ transforms.rotate_n(n=north_angles, device=device)
+            @ transforms.rotate_u(u=up_angles, device=device)
+        )
+
+
     def _compute_orientations_from_motor_positions(
         self,
         motor_positions: torch.Tensor,
@@ -187,6 +210,8 @@ def _compute_orientations_from_motor_positions(
             The orientation matrices.
             Shape is ``[number_of_active_heliostats, 4, 4]``.
         """
+        device = get_device(device=device)
+
         joint_angles = self.actuators.motor_positions_to_angles(
             motor_positions=motor_positions,
             device=device,
@@ -291,17 +316,25 @@ def _compute_orientations_from_motor_positions(
 
         return orientations
 
-    def _apply_initial_orientation_offsets(
-        self, orientations: torch.Tensor, device: torch.device | None = None
+    def _compute_motor_positions_from_normal(
+        self,
+        normals: torch.Tensor,
+        device: torch.device | None = None,
     ) -> torch.Tensor:
         """
-        Apply the initial orientation offsets to the given orientation matrices.
+        Compute the motor positions from a normal vector.
+        
+        This is the inverse kinematics. First the joint angles are computed from the desired normal vector.
+        Then the motor positions are computed from the joint angles.
+        The inverse kinematics produces two solutions, the valid solution is chosen according to resulting
+        motor positions which must lie within the minimum and maximum allowed motor positions defined within 
+        the actuator parameters. 
 
         Parameters
         ----------
-        orientations : torch.Tensor
-            The orientation matrices.
-            Shape is ``[number_of_active_heliostats, 4, 4]``.
+        normals : torch.Tensor
+            Concentrator normals.
+            Shape is ``[number_of_active_heliostats, 4]``.
         device : torch.device | None
             The device on which to perform computations or load tensors and models (default is None).
             If None, ``ARTIST`` will automatically select the most appropriate
@@ -310,36 +343,148 @@ def _apply_initial_orientation_offsets(
         Returns
         -------
         torch.Tensor
-            The orientation matrices with the initial orientation offset.
-            Shape is ``[number_of_active_heliostats, 4, 4]``.
+            The joint angles.
+            Shape is ``[number_of_active_heliostats, 2]``.
         """
-        # The surface points and normals are always sampled from a model (converted NURBS from deflectometry or ideal NURBS) that lays
-        # flat on the ground, i.e., the surface normals are pointing upwards [0.0, 0.0, 1.0]. Since ARTIST expects the points and normals
-        # to be initially oriented to the south, an extra rotation needs to be applied.
-        sampled_surface_model_orientation = torch.tensor(
-            [[0.0, 0.0, 1.0, 0.0]], device=device
-        ).expand(self.number_of_active_heliostats, 4)
-        east_angles, north_angles, up_angles = rotations.decompose_rotations(
-            initial_vector=sampled_surface_model_orientation,
-            target_vector=self.artist_standard_orientation,
+        device = get_device(device=device)
+
+        first_rotation_axis_deviations = (
+            transforms.rotate_n(
+                n=self.active_rotation_deviation_parameters[
+                    :, indices.first_joint_tilt_n
+                ],
+                device=device,
+            )
+            @ transforms.rotate_u(
+                u=self.active_rotation_deviation_parameters[
+                    :, indices.first_joint_tilt_u
+                ],
+                device=device,
+            )
         )
 
-        orientations_with_initial_orientation_offsets = (
-            orientations
-            @ transforms.rotate_e(e=east_angles, device=device)
-            @ transforms.rotate_n(n=north_angles, device=device)
-            @ transforms.rotate_u(u=up_angles, device=device)
+        second_rotation_axis_deviations = (
+            transforms.rotate_e(
+                e=self.active_rotation_deviation_parameters[
+                    :, indices.second_joint_tilt_e
+                ],
+                device=device,
+            )
+            @ transforms.rotate_n(
+                n=self.active_rotation_deviation_parameters[
+                    :, indices.second_joint_tilt_n
+                ],
+                device=device,
+            )
+        )
+
+        normal_after_first_deviation = first_rotation_axis_deviations.transpose(-1, -2) @ normals[:, :, None]
+
+        # Determine second joint angles.
+        second_axis_deviation_00 = second_rotation_axis_deviations[:, 0, 0]
+        second_axis_deviation_01 = second_rotation_axis_deviations[:, 0, 1]
+
+        denominator = torch.sqrt(second_axis_deviation_00 ** 2 + second_axis_deviation_01 ** 2)[:, None]
+        phi = torch.atan2(-second_axis_deviation_01, second_axis_deviation_00)[:, None]
+
+        ratio = torch.clamp((normal_after_first_deviation[:, 0] / (denominator + 1e-12)), -1.0 + 1e-7, 1.0 - 1e-7)
+        second_joint_angle_1 = torch.arcsin(ratio) - phi
+        second_joint_angle_2 = torch.pi - torch.arcsin(ratio) - phi
+
+        second_joint_angle_1 = torch.atan2(torch.sin(second_joint_angle_1), torch.cos(second_joint_angle_1))
+        second_joint_angle_2 = torch.atan2(torch.sin(second_joint_angle_2), torch.cos(second_joint_angle_2))
+
+        # Determine first joint angles. There are two valid solutions.
+        v_1 = second_rotation_axis_deviations @ transforms.rotate_u(second_joint_angle_1[:, 0], device=device) @ self.kinematics_standard_orientation
+        first_joint_angle_1 = torch.atan2(
+            v_1[:, 1] * normal_after_first_deviation[:, 2, 0] - v_1[:, 2] * normal_after_first_deviation[:, 1, 0],
+            v_1[:, 1] * normal_after_first_deviation[:, 1, 0] + v_1[:, 2] * normal_after_first_deviation[:, 2, 0],
+        )
+        v_2 = second_rotation_axis_deviations @ transforms.rotate_u(second_joint_angle_2[:, 0], device=device) @ self.kinematics_standard_orientation
+        first_joint_angle_2 = torch.atan2(
+            v_2[:, 1] * normal_after_first_deviation[:, 2, 0] - v_2[:, 2] * normal_after_first_deviation[:, 1, 0],
+            v_2[:, 1] * normal_after_first_deviation[:, 1, 0] + v_2[:, 2] * normal_after_first_deviation[:, 2, 0],
+        )
+        
+        first_joint_angle_1 = torch.atan2(torch.sin(first_joint_angle_1), torch.cos(first_joint_angle_1))
+        first_joint_angle_2 = torch.atan2(torch.sin(first_joint_angle_2), torch.cos(first_joint_angle_2))
+
+        # Determine possible motor positions.
+        motor_positions_1 = self.actuators.angles_to_motor_positions(
+            angles=torch.stack([first_joint_angle_1, second_joint_angle_1[:, 0]], dim=-1),
+            device=device
+        )
+        motor_positions_2 = self.actuators.angles_to_motor_positions(
+            angles=torch.stack([first_joint_angle_2, second_joint_angle_2[:, 0]], dim=-1),
+            device=device
+        )
+
+        # Determine valid solution. Prefer motor_positions_1 when valid, otherwise use motor_positions_2.
+        min_pos = self.actuators.active_non_optimizable_parameters[
+            :, indices.actuator_min_motor_position
+        ]
+        max_pos = self.actuators.active_non_optimizable_parameters[
+            :, indices.actuator_max_motor_position
+        ]
+        valid_1 = (motor_positions_1 >= min_pos) & (motor_positions_1 <= max_pos)
+        valid_2 = (motor_positions_2 >= min_pos) & (motor_positions_2 <= max_pos)
+
+        solution_1_valid = valid_1.all(dim=1)
+        solution_2_valid = valid_2.all(dim=1)
+
+        # Detect active heliostats where neither solution works.
+        invalid_rows = torch.nonzero(~(solution_1_valid | solution_2_valid))[:, 0]
+
+        if invalid_rows.numel() > 0:
+            log.warning(
+                f"No valid motor position combination for active heliostat number(s): {invalid_rows.tolist()}."
+            )
+        
+        motor_positions = torch.where(
+            solution_1_valid[:, None], motor_positions_1, motor_positions_2,
         )
 
-        return orientations_with_initial_orientation_offsets
+        return motor_positions
+
+
+    def motor_positions_to_orientations(
+        self, motor_positions: torch.Tensor, device: torch.device | None = None
+    ) -> torch.Tensor:
+        """
+        Compute orientation matrices given the motor positions.
+
+        Parameters
+        ----------
+        motor_positions : torch.Tensor
+            The motor positions.
+            Shape is ``[number_of_active_heliostats, 2]``.
+        device : torch.device | None
+            The device on which to perform computations or load tensors and models (default is None).
+            If None, ``ARTIST`` will automatically select the most appropriate
+            device (CUDA or CPU) based on availability and OS.
+
+        Returns
+        -------
+        torch.Tensor
+            The orientation matrices.
+            Shape is ``[number_of_active_heliostats, 4, 4]``.
+        """
+        device = get_device(device=device)
+
+        orientations = self._compute_orientations_from_motor_positions(
+            motor_positions=motor_positions, device=device
+        )
+
+        return orientations @ self.initial_orientation_offsets
+
 
     def incident_ray_directions_to_orientations(
         self,
         incident_ray_directions: torch.Tensor,
         aim_points: torch.Tensor,
+        max_num_iterations: int = 4,
+        min_eps: float = 0.0001,
         device: torch.device | None = None,
-        num_refinement_iterations: int = 200,
-        refinement_lr: float = 0.08,
     ) -> torch.Tensor:
         """
         Compute orientation matrices given incident ray directions.
@@ -369,18 +514,22 @@ def incident_ray_directions_to_orientations(
         """
         device = get_device(device=device)
 
-        initial_motor_positions = torch.zeros(
+        motor_positions = torch.zeros(
             (
                 self.number_of_active_heliostats,
                 constants.rigid_body_number_of_actuators,
             ),
             device=device,
         )
-        with torch.no_grad():
+        last_iteration_loss = None
 
+        for _ in range(max_num_iterations):
             orientations = self._compute_orientations_from_motor_positions(
-                motor_positions=initial_motor_positions,
-                device=device,
+                motor_positions=motor_positions, device=device,
+            )
+
+            concentrator_normals = orientations @ torch.tensor(
+                [0.0, -1.0, 0.0, 0.0], device=device,
             )
 
             concentrator_origins = orientations @ torch.tensor(
@@ -400,160 +549,26 @@ def incident_ray_directions_to_orientations(
                 dim=1,
                 eps=1e-8,
             )
+            
+            desired_concentrator_normals = coordinates.convert_3d_directions_to_4d_format(desired_concentrator_normals, device=device)
 
-        joint_angles_1 = -torch.arcsin(
-            torch.clamp(
-                -desired_concentrator_normals[:, indices.e]
-                / torch.cos(
-                    self.active_translation_deviation_parameters[
-                        :, indices.second_joint_translation_n
-                    ]
-                ),
-                min=-1,
-                max=1,
-            )
-        )
-
-        a = -torch.cos(
-            self.active_translation_deviation_parameters[
-                :, indices.second_joint_translation_e
-            ]
-        ) * torch.cos(joint_angles_1) + torch.sin(
-            self.active_translation_deviation_parameters[
-                :, indices.second_joint_translation_e
-            ]
-        ) * torch.sin(
-            self.active_translation_deviation_parameters[
-                :, indices.second_joint_translation_n
-            ]
-        ) * torch.sin(joint_angles_1)
-
-        b = -torch.sin(
-            self.active_translation_deviation_parameters[
-                :, indices.second_joint_translation_e
-            ]
-        ) * torch.cos(joint_angles_1) - torch.cos(
-            self.active_translation_deviation_parameters[
-                :, indices.second_joint_translation_e
-            ]
-        ) * torch.sin(
-            self.active_translation_deviation_parameters[
-                :, indices.second_joint_translation_n
-            ]
-        ) * torch.sin(joint_angles_1)
-
-        joint_angles_0 = (
-            torch.arctan2(
-                a * -desired_concentrator_normals[:, indices.u]
-                - b * -desired_concentrator_normals[:, indices.n],
-
-                a * -desired_concentrator_normals[:, indices.n]
-                + b * -desired_concentrator_normals[:, indices.u],
-            )
-            - torch.pi
-        )
-
-        joint_angles = torch.stack(
-            [
-                joint_angles_0,
-                joint_angles_1,
-            ],
-            dim=1,
-        )
-
-        joint_angles = joint_angles.detach().clone().requires_grad_(True)
-
-        optimizer = torch.optim.Adam(
-            [joint_angles],
-            lr=refinement_lr,
-        )
-
-        for _ in range(num_refinement_iterations):
-
-            optimizer.zero_grad(set_to_none=True)
-
-            motor_positions = self.actuators.angles_to_motor_positions(
-                angles=joint_angles,
-                device=device,
-            )
-
-            orientations_refined = (
-                self._compute_orientations_from_motor_positions(
-                    motor_positions=motor_positions,
-                    device=device,
-                )
-            )
-
-            predicted_normals = orientations_refined @ torch.tensor(
-                [0.0, -1.0, 0.0, 0.0],
-                device=device,
-            )
-
-            predicted_normals = torch.nn.functional.normalize(
-                predicted_normals[:, :3],
-                p=2,
-                dim=1,
-                eps=1e-8,
-            )
-
-            loss = torch.mean(
-                (predicted_normals - desired_concentrator_normals) ** 2
+            loss = torch.abs(desired_concentrator_normals - concentrator_normals).mean(
+                dim=-1
             )
 
-            print(loss)
-
-            loss.backward()
-
-            optimizer.step()
-
-        orientations = self._compute_orientations_from_motor_positions(
-            motor_positions=motor_positions,
-            device=device,
-        )
-
-        orientations_with_initial_orientation_offsets = (
-            self._apply_initial_orientation_offsets(
-                orientations=orientations,
-                device=device,
+            # Stop if converged.
+            print(loss, loss.mean())
+            if isinstance(last_iteration_loss, torch.Tensor):
+                eps = torch.abs(last_iteration_loss - loss)
+                if torch.any(eps <= min_eps):
+                    break
+            last_iteration_loss = loss
+            
+            motor_positions = self._compute_motor_positions_from_normal(
+                normals=desired_concentrator_normals,
+                device=device
             )
-        )
 
         self.active_motor_positions = motor_positions
-
-        return orientations_with_initial_orientation_offsets
-
-    def motor_positions_to_orientations(
-        self, motor_positions: torch.Tensor, device: torch.device | None = None
-    ) -> torch.Tensor:
-        """
-        Compute orientation matrices given the motor positions.
-
-        Parameters
-        ----------
-        motor_positions : torch.Tensor
-            The motor positions.
-            Shape is ``[number_of_active_heliostats, 2]``.
-        device : torch.device | None
-            The device on which to perform computations or load tensors and models (default is None).
-            If None, ``ARTIST`` will automatically select the most appropriate
-            device (CUDA or CPU) based on availability and OS.
-
-        Returns
-        -------
-        torch.Tensor
-            The orientation matrices.
-            Shape is ``[number_of_active_heliostats, 4, 4]``.
-        """
-        device = get_device(device=device)
-
-        orientations = self._compute_orientations_from_motor_positions(
-            motor_positions=motor_positions, device=device
-        )
-
-        orientations_with_initial_orientation_offsets = (
-            self._apply_initial_orientation_offsets(
-                orientations=orientations, device=device
-            )
-        )
-
-        return orientations_with_initial_orientation_offsets
+        
+        return orientations @ self.initial_orientation_offsets

From 2df8315ac300e2fd635962d2ab7671bc9838da2e Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 27 May 2026 15:30:37 +0200
Subject: [PATCH 16/38] clean up methods

---
 artist/optim/kinematics_reconstructor.py | 333 +++++++++--------------
 1 file changed, 124 insertions(+), 209 deletions(-)

diff --git a/artist/optim/kinematics_reconstructor.py b/artist/optim/kinematics_reconstructor.py
index d621a536..03844899 100644
--- a/artist/optim/kinematics_reconstructor.py
+++ b/artist/optim/kinematics_reconstructor.py
@@ -1,8 +1,7 @@
-from dataclasses import dataclass
 from functools import partial
 import logging
 import pathlib
-from typing import Any, cast
+from typing import Any, Callable, cast
 
 from matplotlib import pyplot as plt
 import torch
@@ -22,70 +21,6 @@
 log = logging.getLogger(__name__)
 """A logger for the kinematic reconstructor."""
 
-@dataclass
-class TrainTestSplit:
-    """
-    Container holding the train/test split for heliostat reconstruction data.
-
-    Attributes
-    ----------
-    flux_measured_train : torch.Tensor
-        Measured flux distributions for the training set.
-    focal_spots_measured_train : torch.Tensor
-        Measured focal spot coordinates for the training set.
-    incident_ray_directions_train : torch.Tensor
-        Incident ray directions for the training set.
-    motor_positions_train : torch.Tensor
-        Motor positions for the training set.
-    target_area_indices_train : torch.Tensor
-        Target area indices for the training set.
-    flux_measured_test : torch.Tensor
-        Measured flux distributions for the test set.
-    focal_spots_measured_test : torch.Tensor
-        Measured focal spot coordinates for the test set.
-    incident_ray_directions_test : torch.Tensor
-        Incident ray directions for the test set.
-    motor_positions_test : torch.Tensor
-        Motor positions for the test set.
-    target_area_indices_test : torch.Tensor
-        Target area indices for the test set.
-    active_heliostats_mask_train : torch.Tensor
-        Number of active training samples per heliostat.
-    active_heliostats_mask_test : torch.Tensor
-        Number of active test samples per heliostat.
-    train_indices : torch.Tensor
-        Flattened indices selecting training samples from the original dataset.
-    test_indices : torch.Tensor
-        Flattened indices selecting test samples from the original dataset.
-    number_of_train_samples : int
-        Number of training samples per heliostat.
-    number_of_test_samples : int
-        Number of test samples per heliostat.
-    number_of_samples_per_heliostat : int
-        Total number of samples available per heliostat.
-    """
-    flux_measured_train: torch.Tensor
-    focal_spots_measured_train: torch.Tensor
-    incident_ray_directions_train: torch.Tensor
-    motor_positions_train: torch.Tensor
-    target_area_indices_train: torch.Tensor
-
-    flux_measured_test: torch.Tensor
-    focal_spots_measured_test: torch.Tensor
-    incident_ray_directions_test: torch.Tensor
-    motor_positions_test: torch.Tensor
-    target_area_indices_test: torch.Tensor
-
-    active_heliostats_mask_train: torch.Tensor
-    active_heliostats_mask_test: torch.Tensor
-
-    train_indices: torch.Tensor
-    test_indices: torch.Tensor
-
-    number_of_train_samples: int
-    number_of_test_samples: int
-    number_of_samples_per_heliostat: int
-
 class KinematicsReconstructor:
     """
     An optimizer used to reconstruct real-world kinematics deviation parameters.
@@ -112,6 +47,15 @@ class KinematicsReconstructor:
         Direct normal irradiance in W/m^2.
     reconstruction_method : str
         The reconstruction method. Currently, only reconstruction via ray tracing is implemented.
+    validation_loss_focal_spot : FocalSpotLoss
+        Flux loss used for validation.
+    validation_loss_pixel : PixelLoss
+        Pixel loss used for validation.
+    validation_loss_kl_div : KLDivergenceLoss
+        Kullback-Leibler divergence loss used for validation.
+
+    plot_results : bool
+        Create flux plots in the last epoch of training for each heliostat and all its samples (slow!).
 
     Note
     ----
@@ -136,6 +80,7 @@ def __init__(
         dni: float | None = None,
         reconstruction_method: str = constants.kinematics_reconstruction_raytracing,
         bitmap_resolution: torch.Tensor = torch.tensor([256, 256]),
+        plot_results: bool = False
     ) -> None:
         """
         Initialize the kinematics optimizer.
@@ -158,6 +103,8 @@ def __init__(
         bitmap_resolution : torch.Tensor
             The resolution of all bitmaps during reconstruction (default is ``torch.tensor([256, 256])``).
             Shape is ``[2]``.
+        plot_results : bool
+            Create flux plots in the last epoch of training for each heliostat and all its samples (slow!, default is ``False``).
         """
         rank = ddp_setup["rank"]
         if rank == 0:
@@ -171,9 +118,9 @@ def __init__(
         self.dni = dni
         self.bitmap_resolution = bitmap_resolution
 
-        self.loss_focal_spot = FocalSpotLoss(scenario=self.scenario)
-        self.loss_pixel = PixelLoss()
-        self.loss_kl_div = KLDivergenceLoss()
+        self.validation_loss_focal_spot = FocalSpotLoss(scenario=self.scenario)
+        self.validation_loss_pixel = PixelLoss()
+        self.validation_loss_kl_div = KLDivergenceLoss()
 
         if reconstruction_method in [
             constants.kinematics_reconstruction_raytracing,
@@ -186,6 +133,8 @@ def __init__(
                 f"Please select another reconstruction method and try again!"
             )
 
+        self.plot_results = plot_results
+
     def reconstruct_kinematics(
         self,
         loss_definition: Loss,
@@ -229,7 +178,7 @@ def reconstruct_kinematics(
             self.reconstruction_method == constants.kinematics_reconstruction_geometry
         ):
             loss, loss_history = (
-                self._reconstruct_kinematics_parameters_with_geometry(
+                self._reconstruct_kinematics_parameters_wih_alignment(
                     loss_definition=loss_definition,
                     device=device,
                 )
@@ -244,68 +193,35 @@ def reconstruct_kinematics(
         return loss, loss_history
 
 
-    def train_test_split(
-        self,
-        active_heliostats_mask: torch.Tensor,
-        flux_measured: torch.Tensor,
-        focal_spots_measured: torch.Tensor,
-        incident_ray_directions: torch.Tensor,
-        motor_positions: torch.Tensor,
-        target_area_indices: torch.Tensor,
-        test_fraction: int = 0.25,
-        device: torch.device | None = None
-    ) -> TrainTestSplit:
-        device = get_device(device=device)
-       
-        total_samples = int(active_heliostats_mask.sum().item())
-        number_of_heliostats = int((active_heliostats_mask > 0).sum().item())
-        number_of_samples_per_heliostat = int(
-            total_samples / number_of_heliostats
-        )
-        number_of_test_samples = max(1, int(number_of_samples_per_heliostat * test_fraction))
-        number_of_train_samples = number_of_samples_per_heliostat - number_of_test_samples
-        starts = torch.arange(number_of_heliostats) * number_of_samples_per_heliostat
-        offsets = torch.arange(number_of_train_samples, number_of_samples_per_heliostat)
-        train_indices = (
-            torch.arange(0, total_samples, number_of_samples_per_heliostat, device=device)[:, None]
-            + torch.arange(number_of_train_samples, device=device)
-        ).reshape(-1)
-        test_indices = (starts[:, None] + offsets).reshape(-1)
-
-        active_heliostats_mask_train = torch.clamp(active_heliostats_mask - number_of_test_samples, min=0)
-        active_heliostats_mask_test = torch.clamp(active_heliostats_mask - number_of_train_samples, min=0)
-
-        return TrainTestSplit(
-            flux_measured_train=flux_measured[train_indices],
-            focal_spots_measured_train=focal_spots_measured[train_indices],
-            incident_ray_directions_train=incident_ray_directions[train_indices],
-            motor_positions_train=motor_positions[train_indices],
-            target_area_indices_train=target_area_indices[train_indices],
-
-            flux_measured_test=flux_measured[test_indices],
-            focal_spots_measured_test=focal_spots_measured[test_indices],
-            incident_ray_directions_test=incident_ray_directions[test_indices],
-            motor_positions_test=motor_positions[test_indices],
-            target_area_indices_test=target_area_indices[test_indices],
-
-            active_heliostats_mask_train=active_heliostats_mask_train,
-            active_heliostats_mask_test=active_heliostats_mask_test,
-
-            train_indices=train_indices,
-            test_indices=test_indices,
-
-            number_of_train_samples=number_of_train_samples,
-            number_of_test_samples=number_of_test_samples,
-            number_of_samples_per_heliostat=number_of_samples_per_heliostat,
-        )
-    
-
     def validate(
         self,
         heliostat_group: HeliostatGroup,
-        data_split: TrainTestSplit,
+        data_split: training.TrainTestSplit,
+        reduction: Callable[..., Any],
         device: torch.device | None = None,
-    ):
+    ) -> torch.Tensor:
+        """
+        Validate the kinematic reconstruction for a specified heliostat group on the test data.
+
+        Parameters
+        ----------
+        heliostat_group : HeliostatGroup
+            Heliostat group to validate.
+        data_split : training.TrainTestSplit
+            Train/test split containing all test tensors and metadata.
+        reduction : Callable[..., Any]
+            Reduction function applied across the sample dimension for each heliostat.
+        device : torch.device | None
+            The device on which to perform computations or load tensors and models (default is None).
+            If None, ARTIST will automatically select the most appropriate
+            device (CUDA or CPU) based on availability and OS.
+
+        Returns
+        -------
+        torch.Tensor
+            Predicted flux distributions for the local validation samples.
+            Shape is ``[number_of_local_test_samples, height, width]``.
+        """
         device = get_device(device=device)
 
         heliostat_group.activate_heliostats(
@@ -340,7 +256,7 @@ def validate(
 
         indices_for_local_rank = ray_tracer.get_sampler_indices()
 
-        loss_focal_spot_per_sample = self.loss_focal_spot(
+        loss_focal_spot_per_sample = self.validation_loss_focal_spot(
             prediction=flux_prediction,
             ground_truth=data_split.flux_measured_test[
                 indices_for_local_rank
@@ -350,14 +266,14 @@ def validate(
             ],
             device=device
         )
-        loss_pixel_per_sample = self.loss_pixel(
+        loss_pixel_per_sample = self.validation_loss_pixel(
             prediction=flux_prediction,
             ground_truth=data_split.flux_measured_test[
                 indices_for_local_rank
             ],
             reduction_dimensions=(1,2,),
         )
-        loss_kl_div_per_sample = self.loss_kl_div(
+        loss_kl_div_per_sample = self.validation_loss_kl_div(
             prediction=flux_prediction,
             ground_truth=data_split.flux_measured_test[
                 indices_for_local_rank
@@ -368,22 +284,24 @@ def validate(
         test_loss_focal_spot = reduce_loss_per_sample(
             loss_per_sample=loss_focal_spot_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
-            reduction=partial(torch.median, dim=1),
+            reduction=reduction,
         )                   
         test_loss_pixel = reduce_loss_per_sample(
             loss_per_sample=loss_pixel_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
-            reduction=partial(torch.median, dim=1),
+            reduction=reduction,
         )                        
         test_loss_kl_div = reduce_loss_per_sample(
             loss_per_sample=loss_kl_div_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
-            reduction=partial(torch.median, dim=1),
+            reduction=reduction,
         )
 
-        print(f"test loss focal spot, mean: {torch.mean(test_loss_focal_spot)}")
-        print(f"test loss pixel, mean: {torch.mean(test_loss_pixel)}")
-        print(f"test loss kl div, mean: {torch.mean(test_loss_kl_div)}")
+        log.info(
+            f"test loss focal spot mean: {torch.mean(test_loss_focal_spot).item()},",
+            f" pixel mean: {torch.mean(test_loss_pixel.item())}",
+            f" kl div mean: {torch.mean(test_loss_kl_div).item()}"
+        )
 
         return flux_prediction
 
@@ -392,7 +310,7 @@ def plot_fluxes(
         flux_measured: torch.Tensor,
         flux_prediction_train: torch.Tensor,
         flux_prediction_test: torch.Tensor,
-        data_split: TrainTestSplit,
+        data_split: training.TrainTestSplit,
         plot_name: str,
     ) -> None:
         device=torch.device("cpu")
@@ -437,20 +355,20 @@ def plot_fluxes(
                     )   
 
             plt.tight_layout()
-            plt.savefig(f"./ignored/new/heliostat_{heliostat_start_index // samples_per_heliostat}_{plot_name}")
+            plt.savefig(f"./ignored/fixed/heliostat_{heliostat_start_index // samples_per_heliostat}_{plot_name}")
             plt.close(fig)
 
 
-    def _reconstruct_kinematics_parameters_with_geometry(
+    def _reconstruct_kinematics_parameters_wih_alignment(
         self,
         loss_definition: Loss,
         device: torch.device | None = None,
     ) -> tuple[torch.Tensor, list[list[dict[str, list[float]]]]]:
         """
-        Reconstruct the kinematics parameters using ray tracing.
+        Reconstruct the kinematics parameters using alignment and geometry data.
 
-        This reconstruction method optimizes the kinematics parameters by extracting the focal points
-        of calibration images and using heliostat-tracing.
+        This reconstruction method optimizes the kinematics parameters by iteratively 
+        aligning heliostats to reach a defined flux focal spot.
 
         Parameters
         ----------
@@ -517,8 +435,7 @@ def _reconstruct_kinematics_parameters_with_geometry(
             )
 
             if active_heliostats_mask.sum() > 0:
-
-                data_split: TrainTestSplit = self.train_test_split(
+                data_split: training.TrainTestSplit = training.train_test_split(
                     active_heliostats_mask=active_heliostats_mask,
                     flux_measured=flux_measured,
                     focal_spots_measured=focal_spots_measured,
@@ -528,7 +445,7 @@ def _reconstruct_kinematics_parameters_with_geometry(
                     test_fraction=0.25,
                     device=device
                 )
-
+                # Calculate focal spot from measured flux.
                 preferred_reflection_directions_measured = (
                     torch.nn.functional.normalize(
                         (
@@ -541,7 +458,6 @@ def _reconstruct_kinematics_parameters_with_geometry(
                         dim=1,
                     )
                 )
-
                 normals_measured = coordinates.convert_3d_directions_to_4d_format(torch.nn.functional.normalize(preferred_reflection_directions_measured - incident_ray_directions[:, :3], dim=-1), device=device)
 
                 # Set up optimizer, scheduler, and early stopping.
@@ -619,6 +535,8 @@ def _reconstruct_kinematics_parameters_with_geometry(
 
                     loss.backward()
                     
+                    # Severely misaligned heliostat samples produce nan gradients. These are set to zero and the 
+                    # sample has no influence on the training epoch.
                     optimizer.param_groups[0]["params"][0].grad.nan_to_num_(nan=0.0, posinf=0.0, neginf=0.0)
 
                     if self.ddp_setup["is_nested"]:
@@ -638,43 +556,6 @@ def _reconstruct_kinematics_parameters_with_geometry(
                                     ]
 
                     optimizer.step()
-
-                    with torch.no_grad():
-                        heliostat_group.activate_heliostats(
-                            active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
-                        )
-
-                        heliostat_group.align_surfaces_with_motor_positions(
-                            motor_positions=data_split.motor_positions_train,
-                            active_heliostats_mask=data_split.active_heliostats_mask_train,
-                            device=device
-                        )
-
-                        ray_tracer = HeliostatRayTracer(
-                            scenario=self.scenario,
-                            heliostat_group=heliostat_group,
-                            blocking_active=False,
-                            world_size=self.ddp_setup["heliostat_group_world_size"],
-                            rank=self.ddp_setup["heliostat_group_rank"],
-                            batch_size=self.optimizer_dict[constants.batch_size],
-                            random_seed=self.ddp_setup["heliostat_group_rank"],
-                            dni=self.dni,
-                            bitmap_resolution=self.bitmap_resolution,
-                        )
-
-                        flux_prediction_train, _, _, _ = ray_tracer.trace_rays(
-                            incident_ray_directions=data_split.incident_ray_directions_train,
-                            active_heliostats_mask=data_split.active_heliostats_mask_train,
-                            target_area_indices=data_split.target_area_indices_train,
-                            device=device,
-                        )
-
-                        flux_prediction_test=self.validate(
-                            heliostat_group=heliostat_group,
-                            data_split=data_split,
-                            device=device
-                        )
-
                     if isinstance(
                         scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau
                     ):
@@ -682,29 +563,63 @@ def _reconstruct_kinematics_parameters_with_geometry(
                     else:
                         scheduler.step()
 
-                    if epoch % log_step == 0:
+                    is_last_epoch = epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    stop = early_stopper.step(loss.item())
+
+                    if epoch % log_step == 0 or is_last_epoch or stop:
                         log.info(
-                            f"Rank: {rank}, Epoch: {epoch}, Loss: {loss},",
+                            f"Rank: {rank}, Epoch: {epoch}, Loss: {loss}",
                         )
 
-                    loss_history_list.append(loss.detach().cpu().item())
+                        with torch.no_grad():
+                            heliostat_group.activate_heliostats(
+                                active_heliostats_mask=data_split.active_heliostats_mask_train,
+                                device=device,
+                            )
+                            heliostat_group.align_surfaces_with_motor_positions(
+                                motor_positions=data_split.motor_positions_train,
+                                active_heliostats_mask=data_split.active_heliostats_mask_train,
+                                device=device,
+                            )
+                            ray_tracer = HeliostatRayTracer(
+                                scenario=self.scenario,
+                                heliostat_group=heliostat_group,
+                                blocking_active=False,
+                                world_size=self.ddp_setup["heliostat_group_world_size"],
+                                rank=self.ddp_setup["heliostat_group_rank"],
+                                batch_size=self.optimizer_dict[constants.batch_size],
+                                random_seed=self.ddp_setup["heliostat_group_rank"],
+                                dni=self.dni,
+                                bitmap_resolution=self.bitmap_resolution,
+                            )
+                            flux_prediction_train, _, _, _ = ray_tracer.trace_rays(
+                                incident_ray_directions=data_split.incident_ray_directions_train,
+                                active_heliostats_mask=data_split.active_heliostats_mask_train,
+                                target_area_indices=data_split.target_area_indices_train,
+                                device=device,
+                            )
+                            flux_prediction_test = self.validate(
+                                heliostat_group=heliostat_group,
+                                data_split=data_split,
+                                reduction=partial(torch.mean),
+                                device=device,   
+                            )
 
                     # Early stopping when loss did not improve for a predefined number of epochs.
-                    stop = early_stopper.step(loss.item())
-
                     if stop:
                         log.info(f"Early stopping at epoch {epoch}.")
                         break
-                    
-                    if epoch == 300:
+
+                    if self.plot_results and (is_last_epoch or stop):
                         self.plot_fluxes(
                             flux_measured=flux_measured.cpu().detach(),
                             flux_prediction_train=flux_prediction_train.cpu().detach(),
                             flux_prediction_test=flux_prediction_test.cpu().detach(),
                             data_split=data_split,
-                            plot_name=f"geometry_{epoch}"
+                            plot_name=f"geometry_{epoch}",
                         )
 
+                    loss_history_list.append(loss.detach().cpu().item())
                     epoch += 1
 
                 loss_history.append(
@@ -836,7 +751,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
             )
 
             if active_heliostats_mask.sum() > 0:
-                data_split: TrainTestSplit = self.train_test_split(
+                data_split: training.TrainTestSplit = training.train_test_split(
                     active_heliostats_mask=active_heliostats_mask,
                     flux_measured=flux_measured,
                     focal_spots_measured=focal_spots_measured,
@@ -967,14 +882,6 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                                     ]
 
                     optimizer.step()
-
-                    with torch.no_grad():
-                        flux_prediction_test=self.validate(
-                            heliostat_group=heliostat_group,
-                            data_split=data_split,
-                            device=device
-                        )
-
                     if isinstance(
                         scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau
                     ):
@@ -982,29 +889,37 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                     else:
                         scheduler.step()
 
-                    if epoch % log_step == 0:
+                    is_last_epoch = epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    stop = early_stopper.step(loss.item())
+
+                    if epoch % log_step == 0 or is_last_epoch or stop:
                         log.info(
-                            f"Rank: {rank}, Epoch: {epoch}, Loss: {loss},",
+                            f"Rank: {rank}, Epoch: {epoch}, Loss: {loss}",
                         )
 
-                    loss_history_list.append(loss.detach().cpu().item())
+                        with torch.no_grad():
+                            flux_prediction_test = self.validate(
+                                heliostat_group=heliostat_group,
+                                data_split=data_split,
+                                reduction=partial(torch.median, dim=1),
+                                device=device,
+                            )
 
                     # Early stopping when loss did not improve for a predefined number of epochs.
-                    stop = early_stopper.step(loss.item())
-
                     if stop:
                         log.info(f"Early stopping at epoch {epoch}.")
                         break
-                    
-                    if epoch == 300:
+
+                    if self.plot_results and (is_last_epoch or stop):
                         self.plot_fluxes(
                             flux_measured=flux_measured.cpu().detach(),
                             flux_prediction_train=flux_prediction_train.cpu().detach(),
                             flux_prediction_test=flux_prediction_test.cpu().detach(),
                             data_split=data_split,
-                            plot_name=f"raytracing_{epoch}"
+                            plot_name=f"raytracing_{epoch}",
                         )
 
+                    loss_history_list.append(loss.detach().cpu().item())
                     epoch += 1
 
                 loss_history.append(

From 5c9ce2ceb283b2949d1623175c358ee78ec20eb9 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 27 May 2026 15:31:09 +0200
Subject: [PATCH 17/38] add missing param in docstring

---
 artist/optim/loss.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/artist/optim/loss.py b/artist/optim/loss.py
index dcda69f2..976b2c61 100644
--- a/artist/optim/loss.py
+++ b/artist/optim/loss.py
@@ -510,6 +510,8 @@ def reduce_loss_per_sample(
         Tensor of shape [number_of_samples].
     number_of_samples_per_heliostat : int
         Number of samples per heliostat.
+    reduction : Callable[..., Any]
+        Reduction function applied across the sample dimension for each heliostat.
 
     Returns
     -------

From d53ac59548302059b1aee8c0ddbb9396b162b7e2 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 27 May 2026 15:31:33 +0200
Subject: [PATCH 18/38] add method and dataclass for train test data split

---
 artist/optim/training.py | 177 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 177 insertions(+)

diff --git a/artist/optim/training.py b/artist/optim/training.py
index ad30d128..8c9ce146 100644
--- a/artist/optim/training.py
+++ b/artist/optim/training.py
@@ -1,4 +1,5 @@
 from collections import deque
+from dataclasses import dataclass
 from typing import Deque
 
 import torch
@@ -6,6 +7,7 @@
 from torch.optim.lr_scheduler import LRScheduler
 
 from artist.util import constants
+from artist.util.env import get_device
 
 
 def exponential(
@@ -181,3 +183,178 @@ def step(self, loss: float) -> bool:
             self.counter += 1
 
         return self.counter >= self.patience
+
+
+@dataclass
+class TrainTestSplit:
+    """
+    Container holding the train/test split for heliostat reconstruction data.
+
+    Attributes
+    ----------
+    flux_measured_train : torch.Tensor
+        Measured flux distributions for the training set.
+        Shape is ``[number_of_train_samples_total, height, width]``.
+    focal_spots_measured_train : torch.Tensor
+        Measured focal spot coordinates for the training set.
+        Shape is ``[number_of_train_samples_total, 4]``.
+    incident_ray_directions_train : torch.Tensor
+        Incident ray directions for the training set.
+        Shape is ``[number_of_train_samples_total, 4]``.
+    motor_positions_train : torch.Tensor
+        Motor positions for the training set.
+        Shape is ``[number_of_train_samples_total, 2]``.
+    target_area_indices_train : torch.Tensor
+        Target area indices for the training set.
+        Shape is ``[number_of_train_samples_total]``.
+    flux_measured_test : torch.Tensor
+        Measured flux distributions for the test set.
+        Shape is ``[number_of_test_samples_total, height, width]``.
+    focal_spots_measured_test : torch.Tensor
+        Measured focal spot coordinates for the test set.
+        Shape is ``[number_of_test_samples_total, 4]``.
+    incident_ray_directions_test : torch.Tensor
+        Incident ray directions for the test set.
+        Shape is ``[number_of_test_samples_total, 4]``.
+    motor_positions_test : torch.Tensor
+        Motor positions for the test set.
+        Shape is ``[number_of_test_samples_total, 2]``.
+    target_area_indices_test : torch.Tensor
+        Target area indices for the test set.
+        Shape is ``[number_of_test_samples_total]``.
+    active_heliostats_mask_train : torch.Tensor
+        Mask for active training samples available per heliostat after splitting.
+        Shape is ``[number_of_heliostats]``.
+    active_heliostats_mask_test : torch.Tensor
+        Mask for active test samples available per heliostat after splitting.
+        Shape is ``[number_of_heliostats]``.
+    train_indices : torch.Tensor
+        Indices of training samples from the original dataset.
+        Shape is ``[number_of_train_samples_total]``.
+    test_indices : torch.Tensor
+        Indices of test samples from the original dataset.
+        Shape is ``[number_of_test_samples_total]``.
+    number_of_train_samples : int
+        Number of training samples per heliostat.
+    number_of_test_samples : int
+        Number of test samples per heliostat.
+    number_of_samples_per_heliostat : int
+        Total number of samples available per heliostat before splitting.
+    """
+    flux_measured_train: torch.Tensor
+    focal_spots_measured_train: torch.Tensor
+    incident_ray_directions_train: torch.Tensor
+    motor_positions_train: torch.Tensor
+    target_area_indices_train: torch.Tensor
+
+    flux_measured_test: torch.Tensor
+    focal_spots_measured_test: torch.Tensor
+    incident_ray_directions_test: torch.Tensor
+    motor_positions_test: torch.Tensor
+    target_area_indices_test: torch.Tensor
+
+    active_heliostats_mask_train: torch.Tensor
+    active_heliostats_mask_test: torch.Tensor
+
+    train_indices: torch.Tensor
+    test_indices: torch.Tensor
+
+    number_of_train_samples: int
+    number_of_test_samples: int
+    number_of_samples_per_heliostat: int
+
+
+def train_test_split(
+    active_heliostats_mask: torch.Tensor,
+    flux_measured: torch.Tensor,
+    focal_spots_measured: torch.Tensor,
+    incident_ray_directions: torch.Tensor,
+    motor_positions: torch.Tensor,
+    target_area_indices: torch.Tensor,
+    test_fraction: int = 0.25,
+    device: torch.device | None = None
+) -> TrainTestSplit:
+    """
+    Split heliostat reconstruction data into training and test subsets.
+
+    The split is performed independently for each heliostat while preserving
+    the original ordering of samples. Training samples are taken from the
+    beginning of each heliostat block, while test samples are taken from the
+    end of each heliostat block.
+
+    Parameters
+    ----------
+    active_heliostats_mask : torch.Tensor
+        Mask for active samples available per heliostat.
+        Shape is ``[number_of_heliostats]``.
+    flux_measured : torch.Tensor
+        Measured flux distributions for all heliostats.
+        Shape is ``[total_number_of_samples, height, width]``.
+    focal_spots_measured : torch.Tensor
+        Measured focal spot coordinates for all samples.
+        Shape is ``[total_number_of_samples, 4]``.
+    incident_ray_directions : torch.Tensor
+        Incident ray directions for all samples.
+        Shape is ``[total_number_of_samples, 4]``.
+    motor_positions : torch.Tensor
+        Motor positions for all samples.
+        Shape is ``[total_number_of_samples, 2]``.
+    target_area_indices : torch.Tensor
+        Integer target area indices for all samples.
+        Shape is ``[total_number_of_samples]``.
+    test_fraction : float
+        Fraction of samples per heliostat assigned to the test set (default is 0.25).
+    device : torch.device | None
+        The device on which to perform computations or load tensors and models (default is None).
+        If None, ARTIST will automatically select the most appropriate
+        device (CUDA or CPU) based on availability and OS.
+
+    Returns
+    -------
+    TrainTestSplit
+        Dataclass containing: train/test tensors, train/test indices, updated active heliostat masks.
+    """
+    device = get_device(device=device)
+    
+    total_samples = int(active_heliostats_mask.sum().item())
+    number_of_heliostats = int((active_heliostats_mask > 0).sum().item())
+    number_of_samples_per_heliostat = int(
+        total_samples / number_of_heliostats
+    )
+    number_of_test_samples = max(1, int(number_of_samples_per_heliostat * test_fraction))
+    number_of_train_samples = number_of_samples_per_heliostat - number_of_test_samples
+    starts = torch.arange(number_of_heliostats) * number_of_samples_per_heliostat
+    offsets = torch.arange(number_of_train_samples, number_of_samples_per_heliostat)
+    train_indices = (
+        torch.arange(0, total_samples, number_of_samples_per_heliostat, device=device)[:, None]
+        + torch.arange(number_of_train_samples, device=device)
+    ).reshape(-1)
+    test_indices = (starts[:, None] + offsets).reshape(-1)
+
+    active_heliostats_mask_train = torch.clamp(active_heliostats_mask - number_of_test_samples, min=0)
+    active_heliostats_mask_test = torch.clamp(active_heliostats_mask - number_of_train_samples, min=0)
+
+    return TrainTestSplit(
+        flux_measured_train=flux_measured[train_indices],
+        focal_spots_measured_train=focal_spots_measured[train_indices],
+        incident_ray_directions_train=incident_ray_directions[train_indices],
+        motor_positions_train=motor_positions[train_indices],
+        target_area_indices_train=target_area_indices[train_indices],
+
+        flux_measured_test=flux_measured[test_indices],
+        focal_spots_measured_test=focal_spots_measured[test_indices],
+        incident_ray_directions_test=incident_ray_directions[test_indices],
+        motor_positions_test=motor_positions[test_indices],
+        target_area_indices_test=target_area_indices[test_indices],
+
+        active_heliostats_mask_train=active_heliostats_mask_train,
+        active_heliostats_mask_test=active_heliostats_mask_test,
+
+        train_indices=train_indices,
+        test_indices=test_indices,
+
+        number_of_train_samples=number_of_train_samples,
+        number_of_test_samples=number_of_test_samples,
+        number_of_samples_per_heliostat=number_of_samples_per_heliostat,
+    )
+

From 99716910bcda79ec492741e146250987d3681f9c Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Wed, 27 May 2026 15:32:29 +0200
Subject: [PATCH 19/38] remove old plot

---
 .../04_kinematics_reconstruction_tutorial.py      | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/tutorials/04_kinematics_reconstruction_tutorial.py b/tutorials/04_kinematics_reconstruction_tutorial.py
index 9327c900..9b426b51 100644
--- a/tutorials/04_kinematics_reconstruction_tutorial.py
+++ b/tutorials/04_kinematics_reconstruction_tutorial.py
@@ -65,7 +65,7 @@ def create_fluxes(
         ]
         (
             measured_flux,
-            focal_spots_measured,
+            _,
             incident_ray_directions,
             motor_positions,
             active_heliostats_mask,
@@ -94,7 +94,7 @@ def create_fluxes(
                     active_heliostats_mask=active_heliostats_mask,
                     device=device,
                 )
-            if method == "incident_ray":
+            elif method == "incident_ray":
                 # Align heliostats.
                 heliostat_group.align_surfaces_with_incident_ray_directions(
                     aim_points=scenario.solar_tower.get_centers_of_target_areas(target_area_indices=target_area_indices, device=device),
@@ -192,11 +192,10 @@ def create_plots(
             axes[0].legend()
             axes[1].legend()
             axes[2].legend()
-            axes[3].legend()
 
             plt.subplots_adjust(wspace=0.05)
             plt.show()
-            plt.savefig(f"./ignored/new/heliostat_{i}_in_group_{group_index}_calibration.png")
+            plt.savefig(f"./ignored/fixed/heliostat_{i}_in_group_{group_index}_calibration.png")
 
 
 #############################################################################################################
@@ -247,7 +246,7 @@ def create_plots(
 # Or if you have a directory with downloaded data use this code to create a mapping.
 heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
     base_path="/workVERLEIHNIX/share/PAINT_data",
-    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58"],
+    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AM55"],
     #heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58", "AH48", "AH49", "AH50", "AH55", "AH56", "AH57", "AH58", "AI48", "AI49", "AI50", "AI51", "AI52", "AI53", "AI54", "AI55", "AI56", "AI57", "AI58", "AI59", "AI60", "AI61", "AJ49", "AJ50", "AJ51", "AJ53", "AJ54", "AJ55", "AJ56", "AJ57", "AJ58", "AJ61", "AJ62", "AK48", "AK49", "AK50", "AK51", "AK52", "AK53", "AK54", "AK55", "AK56", "AK58", "AK59", "AK60", "AK61", "AK62", "AL48", "AL49", "AL51", "AL53", "AL56", "AL58", "AL59", "AL60", "AL61", "AL62", "AM48", "AM49", "AM50", "AM51", "AM52", "AM53", "AM54", "AM55", "AM56", "AM57", "AM58", "AM59", "AM60", "AM62", "AN48", "AN49", "AN50", "AN51", "AN52", "AN53", "AN54", "AN55", "AN56", "AN57", "AN58", "AN59", "AN60", "AN61", "AN62"],
     #heliostat_names=['AZ43', 'AP35', 'AA39', 'AC31', 'AC42', 'AC24', 'AM31', 'BF65', 'AQ27', 'AF33', 'BD31', 'AW42', 'AZ39', 'AL36', 'AD42', 'AP39', 'AE28', 'BE38', 'AY55', 'AP50'],
     number_of_measurements=37,
@@ -288,7 +287,7 @@ def create_plots(
 }
 
 data_parser = PaintCalibrationDataParser(
-    sample_limit=17, centroid_extraction_method=paint_mappings.UTIS_KEY
+    sample_limit=15, centroid_extraction_method=paint_mappings.UTIS_KEY
 )
 data_parser_plots = PaintCalibrationDataParser(
     sample_limit=1, centroid_extraction_method=paint_mappings.UTIS_KEY
@@ -340,8 +339,8 @@ def create_plots(
     )
 
     scenario.set_number_of_rays(number_of_rays=4)
-    optimization_configuration[constants.optimization][constants.initial_learning_rate_rotation_deviation] = 1e-4
-    optimization_configuration[constants.optimization][constants.max_epoch] = 300
+    optimization_configuration[constants.optimization][constants.initial_learning_rate_rotation_deviation] = 3e-4
+    optimization_configuration[constants.optimization][constants.max_epoch] = 600
     # Create the kinematics reconstructor.
     kinematics_reconstructor = KinematicsReconstructor(
         ddp_setup=ddp_setup,

From 589c9675021b64d66a54d62a8bc808ef19534ee9 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 28 May 2026 10:11:22 +0200
Subject: [PATCH 20/38] remove print

---
 artist/field/kinematics_rigid_body.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/artist/field/kinematics_rigid_body.py b/artist/field/kinematics_rigid_body.py
index 68699ba9..687c7f44 100644
--- a/artist/field/kinematics_rigid_body.py
+++ b/artist/field/kinematics_rigid_body.py
@@ -557,7 +557,6 @@ def incident_ray_directions_to_orientations(
             )
 
             # Stop if converged.
-            print(loss, loss.mean())
             if isinstance(last_iteration_loss, torch.Tensor):
                 eps = torch.abs(last_iteration_loss - loss)
                 if torch.any(eps <= min_eps):

From 07886ca9395606ee87f1b4c6316478090c83764d Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 28 May 2026 10:11:41 +0200
Subject: [PATCH 21/38] improve plot method

---
 artist/optim/kinematics_reconstructor.py | 87 ++++++++++++++++++------
 1 file changed, 65 insertions(+), 22 deletions(-)

diff --git a/artist/optim/kinematics_reconstructor.py b/artist/optim/kinematics_reconstructor.py
index 03844899..eec69188 100644
--- a/artist/optim/kinematics_reconstructor.py
+++ b/artist/optim/kinematics_reconstructor.py
@@ -53,7 +53,6 @@ class KinematicsReconstructor:
         Pixel loss used for validation.
     validation_loss_kl_div : KLDivergenceLoss
         Kullback-Leibler divergence loss used for validation.
-
     plot_results : bool
         Create flux plots in the last epoch of training for each heliostat and all its samples (slow!).
 
@@ -124,7 +123,7 @@ def __init__(
 
         if reconstruction_method in [
             constants.kinematics_reconstruction_raytracing,
-            constants.kinematics_reconstruction_geometry
+            constants.kinematics_reconstruction_alignment
         ]:
             self.reconstruction_method = reconstruction_method
         else:
@@ -175,7 +174,7 @@ def reconstruct_kinematics(
                 )
             )
         elif (
-            self.reconstruction_method == constants.kinematics_reconstruction_geometry
+            self.reconstruction_method == constants.kinematics_reconstruction_alignment
         ):
             loss, loss_history = (
                 self._reconstruct_kinematics_parameters_wih_alignment(
@@ -193,7 +192,7 @@ def reconstruct_kinematics(
         return loss, loss_history
 
 
-    def validate(
+    def _validate(
         self,
         heliostat_group: HeliostatGroup,
         data_split: training.TrainTestSplit,
@@ -298,14 +297,17 @@ def validate(
         )
 
         log.info(
-            f"test loss focal spot mean: {torch.mean(test_loss_focal_spot).item()},",
-            f" pixel mean: {torch.mean(test_loss_pixel.item())}",
-            f" kl div mean: {torch.mean(test_loss_kl_div).item()}"
+            "test loss focal spot mean: %.5f, "
+            "pixel mean: %.5f, "
+            "kl div mean: %.5f",
+            torch.mean(test_loss_focal_spot).item(),
+            torch.mean(test_loss_pixel).item(),
+            torch.mean(test_loss_kl_div).item(),
         )
 
         return flux_prediction
 
-    def plot_fluxes(
+    def _plot_fluxes(
         self,
         flux_measured: torch.Tensor,
         flux_prediction_train: torch.Tensor,
@@ -313,13 +315,39 @@ def plot_fluxes(
         data_split: training.TrainTestSplit,
         plot_name: str,
     ) -> None:
+        """
+        Plot predicted and measured flux maps for each heliostat sample.
+
+        Each row in the generated figure corresponds to one sample of a
+        heliostat, where the left column contains the predicted flux and 
+        the right column contains the measured flux.
+        The subplot borders are color-coded to indicate whether a sample
+        belongs to the training samples (green) or testing samples (red)
+        One figure is generated per heliostat.
+
+        Parameters
+        ----------
+        flux_measured : torch.Tensor
+            Ground-truth measured flux maps.
+        flux_prediction_train : torch.Tensor
+            Predicted flux maps of the training samples.
+        flux_prediction_test : torch.Tensor
+            Predicted flux maps of the testing samples.
+        data_split : training.TrainTestSplit
+            Information on the train/test split.
+        plot_name : str
+            Name suffix used when saving the generated plot files.
+        """
         device=torch.device("cpu")
+        
         flux_predicted = torch.zeros_like(flux_measured, device=device)
         flux_predicted[data_split.train_indices] = flux_prediction_train
         flux_predicted[data_split.test_indices] = flux_prediction_test
         samples_per_heliostat = data_split.number_of_samples_per_heliostat
         total_samples = flux_measured.shape[0]
-        
+        train_indices = set(data_split.train_indices.tolist())
+        test_indices = set(data_split.test_indices.tolist())
+
         centers_of_mass_predicted = bitmap.get_center_of_mass(flux_predicted, device=device)
         centers_of_mass_measured = bitmap.get_center_of_mass(flux_measured, device=device)
 
@@ -331,12 +359,19 @@ def plot_fluxes(
 
             for sample_offset in range(samples_per_heliostat):
                 sample_index = heliostat_start_index + sample_offset
+    
+                if sample_index in train_indices:
+                    border_color = "green"
+                    split_name = "TRAIN"
+                elif sample_index in test_indices:
+                    border_color = "red"
+                    split_name = "TEST"
 
                 axes[sample_offset, 0].imshow(flux_predicted[sample_index])
-                axes[sample_offset, 0].set_title(f"Predicted Flux - Heliostat {heliostat_index}")
+                axes[sample_offset, 0].set_title(f"Predicted Flux - Heliostat {heliostat_index} ({split_name})")
 
                 axes[sample_offset, 1].imshow(flux_measured[sample_index])
-                axes[sample_offset, 1].set_title(f"Measured Flux - Heliostat {heliostat_index}")
+                axes[sample_offset, 1].set_title(f"Measured Flux - Heliostat {heliostat_index} ({split_name})")
 
                 for ax in axes[sample_offset, :]:
                     ax.scatter(
@@ -353,9 +388,14 @@ def plot_fluxes(
                         s=30,
                         marker="x",
                     )   
+                
+                for spine in axes[sample_offset, :].flat:
+                    for spines in spine.spines.values():
+                        spines.set_edgecolor(border_color)
+                        spines.set_linewidth(4)
 
             plt.tight_layout()
-            plt.savefig(f"./ignored/fixed/heliostat_{heliostat_start_index // samples_per_heliostat}_{plot_name}")
+            plt.savefig(f"./ignored/fixed/heliostat_{heliostat_index}_{plot_name}")
             plt.close(fig)
 
 
@@ -409,11 +449,10 @@ def _reconstruct_kinematics_parameters_wih_alignment(
 
         # Iterate heliostat groups assigned to this rank.
         for heliostat_group_index in self.ddp_setup["groups_to_ranks_mapping"][rank]:
-            # Parse calibration inputs for current group to obtain measured flux, incident ray directions, mask of
-            # active heliostats, and target area indices.
             heliostat_group: HeliostatGroup = (
                 self.scenario.heliostat_field.heliostat_groups[heliostat_group_index]
             )
+            # Load data parser and input file mapping, then parse the calibration data.
             parser = cast(CalibrationDataParser, self.data[constants.data_parser])
             heliostat_mapping = cast(
                 list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
@@ -434,6 +473,7 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                 device=device,
             )
 
+            # Skip groups with no active heliostats.
             if active_heliostats_mask.sum() > 0:
                 data_split: training.TrainTestSplit = training.train_test_split(
                     active_heliostats_mask=active_heliostats_mask,
@@ -598,7 +638,7 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                                 target_area_indices=data_split.target_area_indices_train,
                                 device=device,
                             )
-                            flux_prediction_test = self.validate(
+                            flux_prediction_test = self._validate(
                                 heliostat_group=heliostat_group,
                                 data_split=data_split,
                                 reduction=partial(torch.mean),
@@ -611,12 +651,12 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                         break
 
                     if self.plot_results and (is_last_epoch or stop):
-                        self.plot_fluxes(
+                        self._plot_fluxes(
                             flux_measured=flux_measured.cpu().detach(),
                             flux_prediction_train=flux_prediction_train.cpu().detach(),
                             flux_prediction_test=flux_prediction_test.cpu().detach(),
                             data_split=data_split,
-                            plot_name=f"geometry_{epoch}",
+                            plot_name=f"alignment_{epoch}",
                         )
 
                     loss_history_list.append(loss.detach().cpu().item())
@@ -725,11 +765,10 @@ def _reconstruct_kinematics_parameters_with_raytracing(
 
         # Iterate heliostat groups assigned to this rank.
         for heliostat_group_index in self.ddp_setup["groups_to_ranks_mapping"][rank]:
-            # Parse calibration inputs for current group to obtain measured flux, incident ray directions, mask of
-            # active heliostats, and target area indices.
             heliostat_group: HeliostatGroup = (
                 self.scenario.heliostat_field.heliostat_groups[heliostat_group_index]
             )
+            # Load data parser and input file mapping, then parse the calibration data.
             parser = cast(CalibrationDataParser, self.data[constants.data_parser])
             heliostat_mapping = cast(
                 list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
@@ -851,7 +890,10 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                         target_area_indices=data_split.target_area_indices_train[
                             sample_indices_for_local_rank
                         ],
-                        reduction_dimensions=(1,2),
+                        reduction_dimensions=(
+                            indices.batched_bitmap_e,
+                            indices.batched_bitmap_u,
+                        ),
                         device=device,
                     )
 
@@ -865,6 +907,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
 
                     loss.backward()
 
+                    # Nested-DDP gradient synchronization within heliostat-group subgroup.
                     if self.ddp_setup["is_nested"]:
                         # Reduce gradients within each heliostat group.
                         for param_group in optimizer.param_groups:
@@ -898,7 +941,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                         )
 
                         with torch.no_grad():
-                            flux_prediction_test = self.validate(
+                            flux_prediction_test = self._validate(
                                 heliostat_group=heliostat_group,
                                 data_split=data_split,
                                 reduction=partial(torch.median, dim=1),
@@ -911,7 +954,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                         break
 
                     if self.plot_results and (is_last_epoch or stop):
-                        self.plot_fluxes(
+                        self._plot_fluxes(
                             flux_measured=flux_measured.cpu().detach(),
                             flux_prediction_train=flux_prediction_train.cpu().detach(),
                             flux_prediction_test=flux_prediction_test.cpu().detach(),

From b23b3ee01970a378ffde0b3d3b864d70351cbd40 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 28 May 2026 10:12:07 +0200
Subject: [PATCH 22/38] add test train data split

---
 artist/optim/surface_reconstructor.py | 410 ++++++++++++++++++++------
 1 file changed, 314 insertions(+), 96 deletions(-)

diff --git a/artist/optim/surface_reconstructor.py b/artist/optim/surface_reconstructor.py
index 26f62a4e..866ff54a 100644
--- a/artist/optim/surface_reconstructor.py
+++ b/artist/optim/surface_reconstructor.py
@@ -1,7 +1,9 @@
+from functools import partial
 import logging
 import pathlib
 from typing import Any, cast
 
+from matplotlib import pyplot as plt
 import torch
 from torch.optim.lr_scheduler import LRScheduler
 
@@ -11,7 +13,7 @@
 from artist.nurbs.surfaces import NURBSSurfaces
 from artist.nurbs.utils import create_nurbs_evaluation_grid
 from artist.optim import training
-from artist.optim.loss import Loss, reduce_loss_per_sample
+from artist.optim.loss import KLDivergenceLoss, Loss, PixelLoss, reduce_loss_per_sample
 from artist.optim.regularizers import IdealSurfaceRegularizer, SmoothnessRegularizer
 from artist.raytracing.heliostat_ray_tracer import HeliostatRayTracer
 from artist.scenario.scenario import Scenario
@@ -63,6 +65,12 @@ class SurfaceReconstructor:
         Shape is ``[2]``.
     epsilon : float | None
         Small numerical offset used to avoid division by zero in the energy constraint.
+    plot_results : bool
+        Create flux plots in the last epoch of training for each heliostat and all its samples (slow!).
+    validation_loss_pixel : PixelLoss
+        Pixel loss used for validation.
+    validation_loss_kl_div : KLDivergenceLoss
+        Kullback-Leibler divergence loss used for validation.
 
     Note
     ----
@@ -90,6 +98,7 @@ def __init__(
         number_of_surface_points: torch.Tensor = torch.tensor([50, 50]),
         bitmap_resolution: torch.Tensor = torch.tensor([256, 256]),
         epsilon: float | None = 1e-12,
+        plot_results: bool = False,
         device: torch.device | None = None,
     ) -> None:
         """
@@ -117,6 +126,8 @@ def __init__(
             Shape is ``[2]``.
         epsilon : float | None
             Small numerical offset used to avoid division by zero in the energy constraint (default is 1e-12).
+        plot_results : bool
+            Create flux plots in the last epoch of training for each heliostat and all its samples (slow!, default is ``False``).
         device : torch.device | None
             The device on which to perform computations or load tensors and models (default is None).
             If None, ``ARTIST`` will automatically select the most appropriate
@@ -139,6 +150,228 @@ def __init__(
         self.dni = dni
         self.bitmap_resolution = bitmap_resolution.to(device)
         self.epsilon = epsilon
+        self.plot_results = plot_results
+
+        self.validation_loss_pixel = PixelLoss()
+        self.validation_loss_kl_div = KLDivergenceLoss()
+
+
+    def _validate(
+        self,
+        heliostat_group: HeliostatGroup,
+        data_split: training.TrainTestSplit,
+        evaluation_points: torch.Tensor,
+        device: torch.device | None = None,
+    ) -> torch.Tensor:
+        """
+        Validate the surface reconstruction for a specified heliostat group on the test data.
+
+        Parameters
+        ----------
+        heliostat_group : HeliostatGroup
+            Heliostat group to validate.
+        data_split : training.TrainTestSplit
+            Train/test split containing all test tensors and metadata.
+        evaluation_points : torch.Tensor
+            Evaluation points for the NURBS surface sampling.
+        device : torch.device | None
+            The device on which to perform computations or load tensors and models (default is None).
+            If None, ARTIST will automatically select the most appropriate
+            device (CUDA or CPU) based on availability and OS.
+
+        Returns
+        -------
+        torch.Tensor
+            Predicted flux distributions for the local validation samples.
+            Shape is ``[number_of_local_test_samples, height, width]``.
+        """
+        device = get_device(device=device)
+
+        heliostat_group.activate_heliostats(
+            active_heliostats_mask=data_split.active_heliostats_mask_test,
+            device=device,
+        )
+
+        nurbs_surfaces = NURBSSurfaces(
+            degrees=heliostat_group.nurbs_degrees,
+            control_points=heliostat_group.active_nurbs_control_points,
+            device=device,
+        )
+
+        (
+            new_surface_points,
+            new_surface_normals,
+        ) = nurbs_surfaces.calculate_surface_points_and_normals(
+            evaluation_points=evaluation_points[data_split.test_indices],
+            canting=heliostat_group.active_canting,
+            facet_translations=heliostat_group.active_facet_translations,
+            device=device,
+        )
+
+        heliostat_group.active_surface_points = new_surface_points.reshape(
+            heliostat_group.active_surface_points.shape[
+                indices.heliostat_dimension
+            ],
+            -1,
+            4,
+        )
+        heliostat_group.active_surface_normals = (
+            new_surface_normals.reshape(
+                heliostat_group.active_surface_normals.shape[
+                    indices.heliostat_dimension
+                ],
+                -1,
+                4,
+            )
+        )
+
+        heliostat_group.align_surfaces_with_incident_ray_directions(
+            aim_points=self.scenario.solar_tower.get_centers_of_target_areas(
+                target_area_indices=data_split.target_area_indices_test, device=device
+            ),
+            incident_ray_directions=data_split.incident_ray_directions_test,
+            active_heliostats_mask=data_split.active_heliostats_mask_test,
+            device=device,
+        )
+
+        ray_tracer = HeliostatRayTracer(
+            scenario=self.scenario,
+            heliostat_group=heliostat_group,
+            blocking_active=False,
+            world_size=self.ddp_setup["heliostat_group_world_size"],
+            rank=self.ddp_setup["heliostat_group_rank"],
+            batch_size=self.optimizer_dict[constants.batch_size],
+            random_seed=self.ddp_setup["heliostat_group_rank"],
+            dni=self.dni,
+            bitmap_resolution=self.bitmap_resolution,
+        )
+
+        flux_prediction, _, _, _ = ray_tracer.trace_rays(
+            incident_ray_directions=data_split.incident_ray_directions_test,
+            active_heliostats_mask=data_split.active_heliostats_mask_test,
+            target_area_indices=data_split.target_area_indices_test,
+            device=device,
+        )
+
+        cropped_flux_distributions = (
+            bitmap.crop_flux_distributions_around_center(
+                flux_distributions=flux_prediction,
+                solar_tower=self.scenario.solar_tower,
+                target_area_indices=data_split.target_area_indices_test,
+                device=device,
+            )
+        )
+
+        indices_for_local_rank = ray_tracer.get_sampler_indices()
+
+        loss_pixel_per_sample = self.validation_loss_pixel(
+            prediction=cropped_flux_distributions,
+            ground_truth=data_split.flux_measured_test[
+                indices_for_local_rank
+            ],
+            reduction_dimensions=(1,2,),
+        )
+        loss_kl_div_per_sample = self.validation_loss_kl_div(
+            prediction=cropped_flux_distributions,
+            ground_truth=data_split.flux_measured_test[
+                indices_for_local_rank
+            ],
+            reduction_dimensions=(1,2,),
+        )
+                 
+        test_loss_pixel = reduce_loss_per_sample(
+            loss_per_sample=loss_pixel_per_sample,
+            number_of_samples_per_heliostat=data_split.number_of_test_samples,
+            reduction=partial(torch.mean),
+        )                        
+        test_loss_kl_div = reduce_loss_per_sample(
+            loss_per_sample=loss_kl_div_per_sample,
+            number_of_samples_per_heliostat=data_split.number_of_test_samples,
+            reduction=partial(torch.mean),
+        )
+
+        log.info(
+            "pixel mean: %.5f, "
+            "kl div mean: %.5f",
+            torch.mean(test_loss_pixel).item(),
+            torch.mean(test_loss_kl_div).item(),
+        )
+
+        return flux_prediction
+
+
+    def _plot_fluxes(
+        self,
+        flux_measured: torch.Tensor,
+        flux_prediction_train: torch.Tensor,
+        flux_prediction_test: torch.Tensor,
+        data_split: training.TrainTestSplit,
+        plot_name: str,
+    ) -> None:
+        """
+        Plot predicted and measured flux maps for each heliostat sample.
+
+        Each row in the generated figure corresponds to one sample of a
+        heliostat, where the left column contains the predicted flux and 
+        the right column contains the measured flux.
+        The subplot borders are color-coded to indicate whether a sample
+        belongs to the training samples (green) or testing samples (red)
+        One figure is generated per heliostat.
+
+        Parameters
+        ----------
+        flux_measured : torch.Tensor
+            Ground-truth measured flux maps.
+        flux_prediction_train : torch.Tensor
+            Predicted flux maps of the training samples.
+        flux_prediction_test : torch.Tensor
+            Predicted flux maps of the testing samples.
+        data_split : training.TrainTestSplit
+            Information on the train/test split.
+        plot_name : str
+            Name suffix used when saving the generated plot files.
+        """
+        device = torch.device("cpu")
+
+        flux_predicted = torch.zeros_like(flux_measured, device=device)
+        flux_predicted[data_split.train_indices] = flux_prediction_train
+        flux_predicted[data_split.test_indices] = flux_prediction_test
+        samples_per_heliostat = data_split.number_of_samples_per_heliostat
+        total_samples = flux_measured.shape[0]
+        train_indices = set(data_split.train_indices.tolist())
+        test_indices = set(data_split.test_indices.tolist())
+
+        for heliostat_start_index in range(0, total_samples, samples_per_heliostat):
+            fig, axes = plt.subplots(
+                samples_per_heliostat, 2, figsize=(8, samples_per_heliostat * 4),
+            )
+            heliostat_index = heliostat_start_index // samples_per_heliostat
+
+            for sample_offset in range(samples_per_heliostat):
+                sample_index = heliostat_start_index + sample_offset
+
+                if sample_index in train_indices:
+                    border_color = "green"
+                    split_name = "TRAIN"
+                elif sample_index in test_indices:
+                    border_color = "red"
+                    split_name = "TEST"
+
+                axes[sample_offset, 0].imshow(flux_predicted[sample_index])
+                axes[sample_offset, 0].set_title(f"Predicted Flux - Heliostat {heliostat_index} ({split_name})")
+
+                axes[sample_offset, 1].imshow(flux_measured[sample_index])
+                axes[sample_offset, 1].set_title(f"Measured Flux - Heliostat {heliostat_index} ({split_name})")
+
+                for spine in axes[sample_offset, :].flat:
+                    for spines in spine.spines.values():
+                        spines.set_edgecolor(border_color)
+                        spines.set_linewidth(4)
+
+            plt.tight_layout()
+            plt.savefig(f"./ignored/fixed/heliostat_{heliostat_index}_{plot_name}")
+            plt.close(fig)
+
 
     def reconstruct_surfaces(
         self,
@@ -207,19 +440,17 @@ def reconstruct_surfaces(
                 self.scenario.heliostat_field.heliostat_groups[heliostat_group_index]
             )
 
-            # Load calibration parser and input file mapping.
+            # Load data parser and input file mapping, then parse the calibration data.
             parser = cast(CalibrationDataParser, self.data[constants.data_parser])
             heliostat_mapping = cast(
                 list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
                 self.data[constants.heliostat_data_mapping],
             )
-
-            # Obtain measured flux + metadata for this group.
             (
-                measured_flux_distributions,
-                _,
+                flux_measured,
+                focal_spots_measured,
                 incident_ray_directions,
-                _,
+                motor_positions,
                 active_heliostats_mask,
                 target_area_indices,
             ) = parser.parse_data_for_reconstruction(
@@ -232,7 +463,16 @@ def reconstruct_surfaces(
 
             # Skip groups with no active heliostats.
             if active_heliostats_mask.sum() > 0:
-                # Create NURBS evaluation points: Build a UV grid for NURBS sampling and expand to required shape.
+                data_split: training.TrainTestSplit = training.train_test_split(
+                    active_heliostats_mask=active_heliostats_mask,
+                    flux_measured=flux_measured,
+                    focal_spots_measured=focal_spots_measured,
+                    incident_ray_directions=incident_ray_directions,
+                    motor_positions=motor_positions,
+                    target_area_indices=target_area_indices,
+                    test_fraction=0.25,
+                    device=device
+                )
                 evaluation_points = (
                     create_nurbs_evaluation_grid(
                         number_of_evaluation_points=self.number_of_surface_points,
@@ -247,14 +487,13 @@ def reconstruct_surfaces(
                         -1,
                     )
                 )
-
                 # Keep a frozen copy of original control points for regularization terms.
                 with torch.no_grad():
                     original_control_points = heliostat_group.nurbs_control_points[
                         active_heliostats_mask > 0
                     ].clone()
 
-                # Create the optimizer: Optimize NURBS control points directly.
+                # Create the optimizer.
                 optimizer = torch.optim.Adam(
                     [heliostat_group.nurbs_control_points.requires_grad_()],
                     lr=float(self.optimizer_dict[constants.initial_learning_rate]),
@@ -310,23 +549,15 @@ def reconstruct_surfaces(
                     if self.optimizer_dict[constants.log_step] == 0
                     else self.optimizer_dict[constants.log_step]
                 )
-                max_epoch = int(
-                    torch.tensor(
-                        [self.optimizer_dict[constants.max_epoch]],
-                        device=device,
-                    )
-                )
-
-                # Optimization loop.
                 while (
                     total_loss > float(self.optimizer_dict[constants.tolerance])
-                    and epoch <= max_epoch
+                    and epoch <= self.optimizer_dict[constants.max_epoch]
                 ):
                     optimizer.zero_grad()
 
-                    # Activate heliostats according to current mask.
+                    # Activate heliostats.
                     heliostat_group.activate_heliostats(
-                        active_heliostats_mask=active_heliostats_mask, device=device
+                        active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
                     )
 
                     # Build NURBS surface from current control points.
@@ -341,7 +572,7 @@ def reconstruct_surfaces(
                         new_surface_points,
                         new_surface_normals,
                     ) = nurbs_surfaces.calculate_surface_points_and_normals(
-                        evaluation_points=evaluation_points,
+                        evaluation_points=evaluation_points[data_split.train_indices],
                         canting=heliostat_group.active_canting,
                         facet_translations=heliostat_group.active_facet_translations,
                         device=device,
@@ -368,10 +599,10 @@ def reconstruct_surfaces(
                     # Align heliostat surfaces toward target under current incident ray directions.
                     heliostat_group.align_surfaces_with_incident_ray_directions(
                         aim_points=self.scenario.solar_tower.get_centers_of_target_areas(
-                            target_area_indices=target_area_indices, device=device
+                            target_area_indices=data_split.target_area_indices_train, device=device
                         ),
-                        incident_ray_directions=incident_ray_directions,
-                        active_heliostats_mask=active_heliostats_mask,
+                        incident_ray_directions=data_split.incident_ray_directions_train,
+                        active_heliostats_mask=data_split.active_heliostats_mask_train,
                         device=device,
                     )
 
@@ -389,41 +620,36 @@ def reconstruct_surfaces(
                     )
 
                     # Perform heliostat-based ray tracing to obtain simulated flux from current reconstructed surfaces.
-                    flux_distributions, _, _, _ = ray_tracer.trace_rays(
-                        incident_ray_directions=incident_ray_directions,
-                        active_heliostats_mask=active_heliostats_mask,
-                        target_area_indices=target_area_indices,
+                    flux_prediction_train, _, _, _ = ray_tracer.trace_rays(
+                        incident_ray_directions=data_split.incident_ray_directions_train,
+                        active_heliostats_mask=data_split.active_heliostats_mask_train,
+                        target_area_indices=data_split.target_area_indices_train,
                         device=device,
                     )
 
-                    # Recover local sampler ordering and samples-per-heliostat factor.
-                    sample_indices_for_local_rank = ray_tracer.get_sampler_indices()
-                    number_of_samples_per_heliostat = int(
-                        heliostat_group.active_heliostats_mask.sum()
-                        / (heliostat_group.active_heliostats_mask > 0).sum()
-                    )
-                    local_indices = (
-                        sample_indices_for_local_rank[::number_of_samples_per_heliostat]
-                        // number_of_samples_per_heliostat
-                    )
-
                     # Crop predictions around center before comparing to measurements.
-                    cropped_flux_distributions = (
+                    cropped_flux_predictions = (
                         bitmap.crop_flux_distributions_around_center(
-                            flux_distributions=flux_distributions,
+                            flux_distributions=flux_prediction_train,
                             solar_tower=self.scenario.solar_tower,
-                            target_area_indices=target_area_indices,
+                            target_area_indices=data_split.target_area_indices_train,
                             device=device,
                         )
                     )
 
-                    # Flux loss (data-fit term): Compare predicted and measured flux maps.
+                    sample_indices_for_local_rank = ray_tracer.get_sampler_indices()
+                    local_indices = (
+                        sample_indices_for_local_rank[::data_split.number_of_train_samples]
+                        // data_split.number_of_train_samples
+                    )
+
+                    # Compute loss from prediction vs. measured flux.
                     flux_loss_per_sample = loss_definition(
-                        prediction=cropped_flux_distributions,
-                        ground_truth=measured_flux_distributions[
+                        prediction=cropped_flux_predictions,
+                        ground_truth=data_split.flux_measured_train[
                             sample_indices_for_local_rank
                         ],
-                        target_area_indices=target_area_indices[
+                        target_area_indices=data_split.target_area_indices_train[
                             sample_indices_for_local_rank
                         ],
                         reduction_dimensions=(
@@ -432,27 +658,30 @@ def reconstruct_surfaces(
                         ),
                         device=device,
                     )
-                    flux_loss_per_heliostat = mean_loss_per_heliostat(
+                    
+                    flux_loss_per_heliostat = reduce_loss_per_sample(
                         loss_per_sample=flux_loss_per_sample,
-                        number_of_samples_per_heliostat=number_of_samples_per_heliostat,
+                        number_of_samples_per_heliostat=data_split.number_of_train_samples,
+                        reduction=partial(torch.mean)
                     )
 
                     # Add Augmented-Lagrangian constraint to ensure that flux integral is conserved,
                     # i.e., intensity does not get lost.
                     if epoch == 0:
-                        flux_integrals_reference = cropped_flux_distributions.sum(
-                            dim=(1, 2)
+                        flux_integrals_reference = cropped_flux_predictions.sum(
+                            dim=(indices.batched_bitmap_e, indices.batched_bitmap_u)
                         ).detach()
                     flux_integrals_relative_differences = (
-                        cropped_flux_distributions.sum(dim=(1, 2))
+                        cropped_flux_predictions.sum(dim=(indices.batched_bitmap_e, indices.batched_bitmap_u))
                         - flux_integrals_reference
                     ) / (flux_integrals_reference + torch.tensor(self.epsilon))
                     flux_constraint_per_sample = torch.clamp(
                         -energy_tolerance - flux_integrals_relative_differences, min=0.0
                     )
-                    flux_constraint_per_heliostat = mean_loss_per_heliostat(
+                    flux_constraint_per_heliostat = reduce_loss_per_sample(
                         loss_per_sample=flux_constraint_per_sample,
-                        number_of_samples_per_heliostat=number_of_samples_per_heliostat,
+                        number_of_samples_per_heliostat=data_split.number_of_train_samples,
+                        reduction=partial(torch.mean)
                     )
                     flux_integrals_constraint = (
                         lambda_flux_integral * flux_constraint_per_heliostat
@@ -469,7 +698,7 @@ def reconstruct_surfaces(
                     if weight_smoothness > 0:
                         smoothness_loss_per_heliostat = smoothness_regularizer(
                             current_control_points=heliostat_group.active_nurbs_control_points[
-                                ::number_of_samples_per_heliostat
+                                ::data_split.number_of_train_samples
                             ][local_indices],
                             original_control_points=original_control_points[
                                 local_indices
@@ -479,7 +708,7 @@ def reconstruct_surfaces(
                     if weight_ideal_surface > 0:
                         ideal_surface_loss_per_heliostat = ideal_surface_regularizer(
                             current_control_points=heliostat_group.active_nurbs_control_points[
-                                ::number_of_samples_per_heliostat
+                                ::data_split.number_of_train_samples
                             ][local_indices],
                             original_control_points=original_control_points[
                                 local_indices
@@ -511,10 +740,9 @@ def reconstruct_surfaces(
                         + alpha * smoothness_loss_per_heliostat
                         + beta * ideal_surface_loss_per_heliostat
                     )
-                    # Average over all heliostats in group.
-                    total_loss = total_loss_per_heliostat.mean()
+                    
+                    total_loss = torch.mean(total_loss_per_heliostat)
 
-                    # Back-propagate through ray tracing and NURBS evaluation pipeline.
                     total_loss.backward()
 
                     # Update Augmented-Lagrangian multiplier.
@@ -559,9 +787,34 @@ def reconstruct_surfaces(
                     else:
                         scheduler.step()
 
-                    if epoch % log_step == 0 and rank == 0:
+                    is_last_epoch = epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    stop = early_stopper.step(total_loss.item())
+
+                    if epoch % log_step == 0 or is_last_epoch or stop:
                         log.info(
-                            f"Rank: {rank}, Epoch: {epoch}, Loss: {total_loss}, LR: {optimizer.param_groups[indices.optimizer_param_group_0]['lr']}"
+                            f"Rank: {rank}, Epoch: {epoch}, Loss: {total_loss}",
+                        )
+
+                        with torch.no_grad():
+                            flux_prediction_test = self._validate(
+                                heliostat_group=heliostat_group,
+                                data_split=data_split,
+                                evaluation_points=evaluation_points,
+                                device=device,
+                            )
+
+                    # Early stopping when loss did not improve for a predefined number of epochs.
+                    if stop:
+                        log.info(f"Early stopping at epoch {epoch}.")
+                        break
+
+                    if self.plot_results and (is_last_epoch or stop):
+                        self._plot_fluxes(
+                            flux_measured=flux_measured.cpu().detach(),
+                            flux_prediction_train=flux_prediction_train.cpu().detach(),
+                            flux_prediction_test=flux_prediction_test.cpu().detach(),
+                            data_split=data_split,
+                            plot_name=f"{epoch}",
                         )
 
                     total_loss_history.append(total_loss.detach().cpu().item())
@@ -589,42 +842,7 @@ def reconstruct_surfaces(
                         flux_integrals_constraint.mean().detach().cpu().item()
                     )
 
-                    # Early stopping when loss did not improve for a predefined number of epochs.
-                    stop = early_stopper.step(total_loss.item())
-
-                    if stop:
-                        log.info(f"Early stopping at epoch {epoch}.")
-                        break
-
                     epoch += 1
-                # ##################################################
-                import matplotlib.pyplot as plt
-
-                m = 3
-                n = measured_flux_distributions.shape[0]
-                for i in range(0, n, m):
-                    fig, axes = plt.subplots(m, 2, figsize=(8, 20))
-                    for j in range(m):
-                        idx = i + j
-                        if idx >= n:
-                            axes[j, 0].axis("off")
-                            axes[j, 1].axis("off")
-                            continue
-                        img_flux = cropped_flux_distributions[idx]
-                        img_meas = measured_flux_distributions[idx].cpu().detach()
-
-                        ax1 = axes[j, 0]
-                        ax1.imshow(img_flux.cpu().detach())
-                        ax1.set_title(f"heliostat {i // m}")
-                        ax1.axis("off")
-                        ax2 = axes[j, 1]
-                        ax2.imshow(img_meas.cpu().detach())
-                        # ax2.set_title(f"loss: {flux_loss_per_heliostat[i]}")
-                        ax2.axis("off")
-                    plt.tight_layout()
-                    plt.savefig(f"./bitmaps/kinematics/heliostat_{i // m}.png")
-                    plt.close(fig)
-                # ##################################################
 
                 loss_history.append(
                     {

From 65ffbfeedb59dcdab88d9a867b04671a89bf9e78 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 28 May 2026 10:12:49 +0200
Subject: [PATCH 23/38] rename kinematic reconstruction method

---
 artist/util/constants.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/artist/util/constants.py b/artist/util/constants.py
index f7ef3460..7841e646 100644
--- a/artist/util/constants.py
+++ b/artist/util/constants.py
@@ -165,8 +165,8 @@
 
 kinematics_reconstruction_raytracing = "raytracing"
 """Defines that the kinematics reconstructor uses ray tracing."""
-kinematics_reconstruction_geometry = "geometry"
-"""Defines that the kinematics reconstructor uses geometry."""
+kinematics_reconstruction_alignment = "alignment"
+"""Defines that the kinematics reconstructor uses alignment."""
 
 names = "names"
 """Key to access heliostat names."""

From 8062f5a31575fada7b17cdaf9d398b560f170daf Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Thu, 28 May 2026 10:59:19 +0200
Subject: [PATCH 24/38] formatting

---
 artist/field/kinematics_rigid_body.py     | 155 ++++++++++++---------
 artist/optim/__init__.py                  |   1 +
 artist/optim/kinematics_reconstructor.py  | 157 +++++++++++++---------
 artist/optim/loss.py                      |  71 +++++-----
 artist/optim/surface_reconstructor.py     | 106 ++++++++-------
 artist/optim/training.py                  |  30 +++--
 tests/optim/test_loss_functions.py        |   2 +-
 tests/optim/test_surface_reconstructor.py |  24 ++--
 8 files changed, 308 insertions(+), 238 deletions(-)

diff --git a/artist/field/kinematics_rigid_body.py b/artist/field/kinematics_rigid_body.py
index 687c7f44..9be2ece8 100644
--- a/artist/field/kinematics_rigid_body.py
+++ b/artist/field/kinematics_rigid_body.py
@@ -1,4 +1,5 @@
 import logging
+
 import torch
 
 from artist.field.actuators import Actuators
@@ -10,6 +11,7 @@
 log = logging.getLogger(__name__)
 """A logger for the rigid body kinematics."""
 
+
 class RigidBody(Kinematics):
     """
     Implement a rigid body kinematics model.
@@ -53,7 +55,7 @@ class RigidBody(Kinematics):
         Standard orientation of the kinematics system: south (0, -1, 0, 0) in homogeneous ENU.
         Shape is ``[4]``
     initial_orientation_offsets : torch.Tensor
-        Rotation matrix to account for the initial orientation offset between surface mesh and kinematics system. 
+        Rotation matrix to account for the initial orientation offset between surface mesh and kinematics system.
         Shape is ``[1, 4, 4]``.
 
     Methods
@@ -167,14 +169,12 @@ def __init__(
         )
 
         # The surface points and normals are always sampled from a model (converted NURBS from deflectometry or ideal NURBS) that lays
-        # flat on the ground, i.e., the surface normals are pointing upwards [0.0, 0.0, 1.0]. Since the kinematics in ARTIST expects the 
+        # flat on the ground, i.e., the surface normals are pointing upwards [0.0, 0.0, 1.0]. Since the kinematics in ARTIST expects the
         # points and normals to be initially oriented to the south, an extra rotation needs to be applied.
         self.kinematics_standard_orientation = torch.tensor(
             [0.0, -1.0, 0.0, 0.0], device=device
         )
-        sampled_surface_orientation = torch.tensor(
-            [0.0, 0.0, 1.0, 0.0], device=device
-        )
+        sampled_surface_orientation = torch.tensor([0.0, 0.0, 1.0, 0.0], device=device)
         east_angles, north_angles, up_angles = rotations.decompose_rotations(
             initial_vector=sampled_surface_orientation[None, :],
             target_vector=self.kinematics_standard_orientation,
@@ -185,7 +185,6 @@ def __init__(
             @ transforms.rotate_u(u=up_angles, device=device)
         )
 
-
     def _compute_orientations_from_motor_positions(
         self,
         motor_positions: torch.Tensor,
@@ -323,12 +322,12 @@ def _compute_motor_positions_from_normal(
     ) -> torch.Tensor:
         """
         Compute the motor positions from a normal vector.
-        
+
         This is the inverse kinematics. First the joint angles are computed from the desired normal vector.
         Then the motor positions are computed from the joint angles.
         The inverse kinematics produces two solutions, the valid solution is chosen according to resulting
-        motor positions which must lie within the minimum and maximum allowed motor positions defined within 
-        the actuator parameters. 
+        motor positions which must lie within the minimum and maximum allowed motor positions defined within
+        the actuator parameters.
 
         Parameters
         ----------
@@ -348,75 +347,93 @@ def _compute_motor_positions_from_normal(
         """
         device = get_device(device=device)
 
-        first_rotation_axis_deviations = (
-            transforms.rotate_n(
-                n=self.active_rotation_deviation_parameters[
-                    :, indices.first_joint_tilt_n
-                ],
-                device=device,
-            )
-            @ transforms.rotate_u(
-                u=self.active_rotation_deviation_parameters[
-                    :, indices.first_joint_tilt_u
-                ],
-                device=device,
-            )
+        first_rotation_axis_deviations = transforms.rotate_n(
+            n=self.active_rotation_deviation_parameters[:, indices.first_joint_tilt_n],
+            device=device,
+        ) @ transforms.rotate_u(
+            u=self.active_rotation_deviation_parameters[:, indices.first_joint_tilt_u],
+            device=device,
         )
 
-        second_rotation_axis_deviations = (
-            transforms.rotate_e(
-                e=self.active_rotation_deviation_parameters[
-                    :, indices.second_joint_tilt_e
-                ],
-                device=device,
-            )
-            @ transforms.rotate_n(
-                n=self.active_rotation_deviation_parameters[
-                    :, indices.second_joint_tilt_n
-                ],
-                device=device,
-            )
+        second_rotation_axis_deviations = transforms.rotate_e(
+            e=self.active_rotation_deviation_parameters[:, indices.second_joint_tilt_e],
+            device=device,
+        ) @ transforms.rotate_n(
+            n=self.active_rotation_deviation_parameters[:, indices.second_joint_tilt_n],
+            device=device,
         )
 
-        normal_after_first_deviation = first_rotation_axis_deviations.transpose(-1, -2) @ normals[:, :, None]
+        normal_after_first_deviation = (
+            first_rotation_axis_deviations.transpose(-1, -2) @ normals[:, :, None]
+        )
 
         # Determine second joint angles.
         second_axis_deviation_00 = second_rotation_axis_deviations[:, 0, 0]
         second_axis_deviation_01 = second_rotation_axis_deviations[:, 0, 1]
 
-        denominator = torch.sqrt(second_axis_deviation_00 ** 2 + second_axis_deviation_01 ** 2)[:, None]
+        denominator = torch.sqrt(
+            second_axis_deviation_00**2 + second_axis_deviation_01**2
+        )[:, None]
         phi = torch.atan2(-second_axis_deviation_01, second_axis_deviation_00)[:, None]
 
-        ratio = torch.clamp((normal_after_first_deviation[:, 0] / (denominator + 1e-12)), -1.0 + 1e-7, 1.0 - 1e-7)
+        ratio = torch.clamp(
+            (normal_after_first_deviation[:, 0] / (denominator + 1e-12)),
+            -1.0 + 1e-7,
+            1.0 - 1e-7,
+        )
         second_joint_angle_1 = torch.arcsin(ratio) - phi
         second_joint_angle_2 = torch.pi - torch.arcsin(ratio) - phi
 
-        second_joint_angle_1 = torch.atan2(torch.sin(second_joint_angle_1), torch.cos(second_joint_angle_1))
-        second_joint_angle_2 = torch.atan2(torch.sin(second_joint_angle_2), torch.cos(second_joint_angle_2))
+        second_joint_angle_1 = torch.atan2(
+            torch.sin(second_joint_angle_1), torch.cos(second_joint_angle_1)
+        )
+        second_joint_angle_2 = torch.atan2(
+            torch.sin(second_joint_angle_2), torch.cos(second_joint_angle_2)
+        )
 
         # Determine first joint angles. There are two valid solutions.
-        v_1 = second_rotation_axis_deviations @ transforms.rotate_u(second_joint_angle_1[:, 0], device=device) @ self.kinematics_standard_orientation
+        v_1 = (
+            second_rotation_axis_deviations
+            @ transforms.rotate_u(second_joint_angle_1[:, 0], device=device)
+            @ self.kinematics_standard_orientation
+        )
+        first_joint_angle_1 = torch.atan2(
+            v_1[:, 1] * normal_after_first_deviation[:, 2, 0]
+            - v_1[:, 2] * normal_after_first_deviation[:, 1, 0],
+            v_1[:, 1] * normal_after_first_deviation[:, 1, 0]
+            + v_1[:, 2] * normal_after_first_deviation[:, 2, 0],
+        )
+        v_2 = (
+            second_rotation_axis_deviations
+            @ transforms.rotate_u(second_joint_angle_2[:, 0], device=device)
+            @ self.kinematics_standard_orientation
+        )
+        first_joint_angle_2 = torch.atan2(
+            v_2[:, 1] * normal_after_first_deviation[:, 2, 0]
+            - v_2[:, 2] * normal_after_first_deviation[:, 1, 0],
+            v_2[:, 1] * normal_after_first_deviation[:, 1, 0]
+            + v_2[:, 2] * normal_after_first_deviation[:, 2, 0],
+        )
+
         first_joint_angle_1 = torch.atan2(
-            v_1[:, 1] * normal_after_first_deviation[:, 2, 0] - v_1[:, 2] * normal_after_first_deviation[:, 1, 0],
-            v_1[:, 1] * normal_after_first_deviation[:, 1, 0] + v_1[:, 2] * normal_after_first_deviation[:, 2, 0],
+            torch.sin(first_joint_angle_1), torch.cos(first_joint_angle_1)
         )
-        v_2 = second_rotation_axis_deviations @ transforms.rotate_u(second_joint_angle_2[:, 0], device=device) @ self.kinematics_standard_orientation
         first_joint_angle_2 = torch.atan2(
-            v_2[:, 1] * normal_after_first_deviation[:, 2, 0] - v_2[:, 2] * normal_after_first_deviation[:, 1, 0],
-            v_2[:, 1] * normal_after_first_deviation[:, 1, 0] + v_2[:, 2] * normal_after_first_deviation[:, 2, 0],
+            torch.sin(first_joint_angle_2), torch.cos(first_joint_angle_2)
         )
-        
-        first_joint_angle_1 = torch.atan2(torch.sin(first_joint_angle_1), torch.cos(first_joint_angle_1))
-        first_joint_angle_2 = torch.atan2(torch.sin(first_joint_angle_2), torch.cos(first_joint_angle_2))
 
         # Determine possible motor positions.
         motor_positions_1 = self.actuators.angles_to_motor_positions(
-            angles=torch.stack([first_joint_angle_1, second_joint_angle_1[:, 0]], dim=-1),
-            device=device
+            angles=torch.stack(
+                [first_joint_angle_1, second_joint_angle_1[:, 0]], dim=-1
+            ),
+            device=device,
         )
         motor_positions_2 = self.actuators.angles_to_motor_positions(
-            angles=torch.stack([first_joint_angle_2, second_joint_angle_2[:, 0]], dim=-1),
-            device=device
+            angles=torch.stack(
+                [first_joint_angle_2, second_joint_angle_2[:, 0]], dim=-1
+            ),
+            device=device,
         )
 
         # Determine valid solution. Prefer motor_positions_1 when valid, otherwise use motor_positions_2.
@@ -439,14 +456,15 @@ def _compute_motor_positions_from_normal(
             log.warning(
                 f"No valid motor position combination for active heliostat number(s): {invalid_rows.tolist()}."
             )
-        
+
         motor_positions = torch.where(
-            solution_1_valid[:, None], motor_positions_1, motor_positions_2,
+            solution_1_valid[:, None],
+            motor_positions_1,
+            motor_positions_2,
         )
 
         return motor_positions
 
-
     def motor_positions_to_orientations(
         self, motor_positions: torch.Tensor, device: torch.device | None = None
     ) -> torch.Tensor:
@@ -477,7 +495,6 @@ def motor_positions_to_orientations(
 
         return orientations @ self.initial_orientation_offsets
 
-
     def incident_ray_directions_to_orientations(
         self,
         incident_ray_directions: torch.Tensor,
@@ -525,15 +542,18 @@ def incident_ray_directions_to_orientations(
 
         for _ in range(max_num_iterations):
             orientations = self._compute_orientations_from_motor_positions(
-                motor_positions=motor_positions, device=device,
+                motor_positions=motor_positions,
+                device=device,
             )
 
             concentrator_normals = orientations @ torch.tensor(
-                [0.0, -1.0, 0.0, 0.0], device=device,
+                [0.0, -1.0, 0.0, 0.0],
+                device=device,
             )
 
             concentrator_origins = orientations @ torch.tensor(
-                [0.0, 0.0, 0.0, 1.0], device=device,
+                [0.0, 0.0, 0.0, 1.0],
+                device=device,
             )
 
             desired_reflection_directions = torch.nn.functional.normalize(
@@ -549,8 +569,12 @@ def incident_ray_directions_to_orientations(
                 dim=1,
                 eps=1e-8,
             )
-            
-            desired_concentrator_normals = coordinates.convert_3d_directions_to_4d_format(desired_concentrator_normals, device=device)
+
+            desired_concentrator_normals = (
+                coordinates.convert_3d_directions_to_4d_format(
+                    desired_concentrator_normals, device=device
+                )
+            )
 
             loss = torch.abs(desired_concentrator_normals - concentrator_normals).mean(
                 dim=-1
@@ -562,12 +586,11 @@ def incident_ray_directions_to_orientations(
                 if torch.any(eps <= min_eps):
                     break
             last_iteration_loss = loss
-            
+
             motor_positions = self._compute_motor_positions_from_normal(
-                normals=desired_concentrator_normals,
-                device=device
+                normals=desired_concentrator_normals, device=device
             )
 
         self.active_motor_positions = motor_positions
-        
+
         return orientations @ self.initial_orientation_offsets
diff --git a/artist/optim/__init__.py b/artist/optim/__init__.py
index 7be0d037..4c93b90b 100644
--- a/artist/optim/__init__.py
+++ b/artist/optim/__init__.py
@@ -22,6 +22,7 @@
     "EarlyStopping",
     "Loss",
     "VectorLoss",
+    "reduce_loss_per_sample",
     "FocalSpotLoss",
     "PixelLoss",
     "KLDivergenceLoss",
diff --git a/artist/optim/kinematics_reconstructor.py b/artist/optim/kinematics_reconstructor.py
index eec69188..4f1f8d55 100644
--- a/artist/optim/kinematics_reconstructor.py
+++ b/artist/optim/kinematics_reconstructor.py
@@ -1,10 +1,10 @@
-from functools import partial
 import logging
 import pathlib
+from functools import partial
 from typing import Any, Callable, cast
 
-from matplotlib import pyplot as plt
 import torch
+from matplotlib import pyplot as plt
 from torch.optim.lr_scheduler import LRScheduler
 
 from artist.field.heliostat_group import HeliostatGroup
@@ -12,7 +12,13 @@
 from artist.geometry import coordinates
 from artist.io.calibration_parser import CalibrationDataParser
 from artist.optim import training
-from artist.optim.loss import FocalSpotLoss, KLDivergenceLoss, Loss, PixelLoss, reduce_loss_per_sample
+from artist.optim.loss import (
+    FocalSpotLoss,
+    KLDivergenceLoss,
+    Loss,
+    PixelLoss,
+    reduce_loss_per_sample,
+)
 from artist.raytracing.heliostat_ray_tracer import HeliostatRayTracer
 from artist.scenario.scenario import Scenario
 from artist.util import constants, indices
@@ -21,6 +27,7 @@
 log = logging.getLogger(__name__)
 """A logger for the kinematic reconstructor."""
 
+
 class KinematicsReconstructor:
     """
     An optimizer used to reconstruct real-world kinematics deviation parameters.
@@ -79,7 +86,7 @@ def __init__(
         dni: float | None = None,
         reconstruction_method: str = constants.kinematics_reconstruction_raytracing,
         bitmap_resolution: torch.Tensor = torch.tensor([256, 256]),
-        plot_results: bool = False
+        plot_results: bool = False,
     ) -> None:
         """
         Initialize the kinematics optimizer.
@@ -123,7 +130,7 @@ def __init__(
 
         if reconstruction_method in [
             constants.kinematics_reconstruction_raytracing,
-            constants.kinematics_reconstruction_alignment
+            constants.kinematics_reconstruction_alignment,
         ]:
             self.reconstruction_method = reconstruction_method
         else:
@@ -176,11 +183,9 @@ def reconstruct_kinematics(
         elif (
             self.reconstruction_method == constants.kinematics_reconstruction_alignment
         ):
-            loss, loss_history = (
-                self._reconstruct_kinematics_parameters_wih_alignment(
-                    loss_definition=loss_definition,
-                    device=device,
-                )
+            loss, loss_history = self._reconstruct_kinematics_parameters_wih_alignment(
+                loss_definition=loss_definition,
+                device=device,
             )
 
         else:
@@ -191,7 +196,6 @@ def reconstruct_kinematics(
 
         return loss, loss_history
 
-
     def _validate(
         self,
         heliostat_group: HeliostatGroup,
@@ -257,39 +261,39 @@ def _validate(
 
         loss_focal_spot_per_sample = self.validation_loss_focal_spot(
             prediction=flux_prediction,
-            ground_truth=data_split.flux_measured_test[
-                indices_for_local_rank
-            ],
+            ground_truth=data_split.flux_measured_test[indices_for_local_rank],
             target_area_indices=data_split.target_area_indices_test[
                 indices_for_local_rank
             ],
-            device=device
+            device=device,
         )
         loss_pixel_per_sample = self.validation_loss_pixel(
             prediction=flux_prediction,
-            ground_truth=data_split.flux_measured_test[
-                indices_for_local_rank
-            ],
-            reduction_dimensions=(1,2,),
+            ground_truth=data_split.flux_measured_test[indices_for_local_rank],
+            reduction_dimensions=(
+                1,
+                2,
+            ),
         )
         loss_kl_div_per_sample = self.validation_loss_kl_div(
             prediction=flux_prediction,
-            ground_truth=data_split.flux_measured_test[
-                indices_for_local_rank
-            ],
-            reduction_dimensions=(1,2,),
+            ground_truth=data_split.flux_measured_test[indices_for_local_rank],
+            reduction_dimensions=(
+                1,
+                2,
+            ),
         )
 
         test_loss_focal_spot = reduce_loss_per_sample(
             loss_per_sample=loss_focal_spot_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
             reduction=reduction,
-        )                   
+        )
         test_loss_pixel = reduce_loss_per_sample(
             loss_per_sample=loss_pixel_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
             reduction=reduction,
-        )                        
+        )
         test_loss_kl_div = reduce_loss_per_sample(
             loss_per_sample=loss_kl_div_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
@@ -297,9 +301,7 @@ def _validate(
         )
 
         log.info(
-            "test loss focal spot mean: %.5f, "
-            "pixel mean: %.5f, "
-            "kl div mean: %.5f",
+            "test loss focal spot mean: %.5f, pixel mean: %.5f, kl div mean: %.5f",
             torch.mean(test_loss_focal_spot).item(),
             torch.mean(test_loss_pixel).item(),
             torch.mean(test_loss_kl_div).item(),
@@ -319,7 +321,7 @@ def _plot_fluxes(
         Plot predicted and measured flux maps for each heliostat sample.
 
         Each row in the generated figure corresponds to one sample of a
-        heliostat, where the left column contains the predicted flux and 
+        heliostat, where the left column contains the predicted flux and
         the right column contains the measured flux.
         The subplot borders are color-coded to indicate whether a sample
         belongs to the training samples (green) or testing samples (red)
@@ -338,8 +340,8 @@ def _plot_fluxes(
         plot_name : str
             Name suffix used when saving the generated plot files.
         """
-        device=torch.device("cpu")
-        
+        device = torch.device("cpu")
+
         flux_predicted = torch.zeros_like(flux_measured, device=device)
         flux_predicted[data_split.train_indices] = flux_prediction_train
         flux_predicted[data_split.test_indices] = flux_prediction_test
@@ -348,18 +350,24 @@ def _plot_fluxes(
         train_indices = set(data_split.train_indices.tolist())
         test_indices = set(data_split.test_indices.tolist())
 
-        centers_of_mass_predicted = bitmap.get_center_of_mass(flux_predicted, device=device)
-        centers_of_mass_measured = bitmap.get_center_of_mass(flux_measured, device=device)
+        centers_of_mass_predicted = bitmap.get_center_of_mass(
+            flux_predicted, device=device
+        )
+        centers_of_mass_measured = bitmap.get_center_of_mass(
+            flux_measured, device=device
+        )
 
         for heliostat_start_index in range(0, total_samples, samples_per_heliostat):
             fig, axes = plt.subplots(
-                samples_per_heliostat, 2, figsize=(8, samples_per_heliostat * 4),
+                samples_per_heliostat,
+                2,
+                figsize=(8, samples_per_heliostat * 4),
             )
             heliostat_index = heliostat_start_index // samples_per_heliostat
 
             for sample_offset in range(samples_per_heliostat):
                 sample_index = heliostat_start_index + sample_offset
-    
+
                 if sample_index in train_indices:
                     border_color = "green"
                     split_name = "TRAIN"
@@ -368,10 +376,14 @@ def _plot_fluxes(
                     split_name = "TEST"
 
                 axes[sample_offset, 0].imshow(flux_predicted[sample_index])
-                axes[sample_offset, 0].set_title(f"Predicted Flux - Heliostat {heliostat_index} ({split_name})")
+                axes[sample_offset, 0].set_title(
+                    f"Predicted Flux - Heliostat {heliostat_index} ({split_name})"
+                )
 
                 axes[sample_offset, 1].imshow(flux_measured[sample_index])
-                axes[sample_offset, 1].set_title(f"Measured Flux - Heliostat {heliostat_index} ({split_name})")
+                axes[sample_offset, 1].set_title(
+                    f"Measured Flux - Heliostat {heliostat_index} ({split_name})"
+                )
 
                 for ax in axes[sample_offset, :]:
                     ax.scatter(
@@ -387,8 +399,8 @@ def _plot_fluxes(
                         c="red",
                         s=30,
                         marker="x",
-                    )   
-                
+                    )
+
                 for spine in axes[sample_offset, :].flat:
                     for spines in spine.spines.values():
                         spines.set_edgecolor(border_color)
@@ -398,7 +410,6 @@ def _plot_fluxes(
             plt.savefig(f"./ignored/fixed/heliostat_{heliostat_index}_{plot_name}")
             plt.close(fig)
 
-
     def _reconstruct_kinematics_parameters_wih_alignment(
         self,
         loss_definition: Loss,
@@ -407,7 +418,7 @@ def _reconstruct_kinematics_parameters_wih_alignment(
         """
         Reconstruct the kinematics parameters using alignment and geometry data.
 
-        This reconstruction method optimizes the kinematics parameters by iteratively 
+        This reconstruction method optimizes the kinematics parameters by iteratively
         aligning heliostats to reach a defined flux focal spot.
 
         Parameters
@@ -483,7 +494,7 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                     motor_positions=motor_positions,
                     target_area_indices=target_area_indices,
                     test_fraction=0.25,
-                    device=device
+                    device=device,
                 )
                 # Calculate focal spot from measured flux.
                 preferred_reflection_directions_measured = (
@@ -498,7 +509,14 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                         dim=1,
                     )
                 )
-                normals_measured = coordinates.convert_3d_directions_to_4d_format(torch.nn.functional.normalize(preferred_reflection_directions_measured - incident_ray_directions[:, :3], dim=-1), device=device)
+                normals_measured = coordinates.convert_3d_directions_to_4d_format(
+                    torch.nn.functional.normalize(
+                        preferred_reflection_directions_measured
+                        - incident_ray_directions[:, :3],
+                        dim=-1,
+                    ),
+                    device=device,
+                )
 
                 # Set up optimizer, scheduler, and early stopping.
                 optimizer_params = [
@@ -547,12 +565,15 @@ def _reconstruct_kinematics_parameters_wih_alignment(
 
                     # Activate heliostats.
                     heliostat_group.activate_heliostats(
-                        active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
+                        active_heliostats_mask=data_split.active_heliostats_mask_train,
+                        device=device,
                     )
 
-                    orientations = heliostat_group.kinematics.motor_positions_to_orientations(
-                        motor_positions=data_split.motor_positions_train,
-                        device=device,
+                    orientations = (
+                        heliostat_group.kinematics.motor_positions_to_orientations(
+                            motor_positions=data_split.motor_positions_train,
+                            device=device,
+                        )
                     )
 
                     normals_predicted = orientations @ torch.tensor(
@@ -568,16 +589,18 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                     loss_per_heliostat = reduce_loss_per_sample(
                         loss_per_sample=loss_per_sample,
                         number_of_samples_per_heliostat=data_split.number_of_train_samples,
-                        reduction=partial(torch.mean)
+                        reduction=partial(torch.mean),
                     )
 
                     loss = torch.mean(loss_per_heliostat)
 
                     loss.backward()
-                    
-                    # Severely misaligned heliostat samples produce nan gradients. These are set to zero and the 
+
+                    # Severely misaligned heliostat samples produce nan gradients. These are set to zero and the
                     # sample has no influence on the training epoch.
-                    optimizer.param_groups[0]["params"][0].grad.nan_to_num_(nan=0.0, posinf=0.0, neginf=0.0)
+                    optimizer.param_groups[0]["params"][0].grad.nan_to_num_(
+                        nan=0.0, posinf=0.0, neginf=0.0
+                    )
 
                     if self.ddp_setup["is_nested"]:
                         # Reduce gradients within each heliostat group.
@@ -603,7 +626,9 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                     else:
                         scheduler.step()
 
-                    is_last_epoch = epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    is_last_epoch = (
+                        epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    )
                     stop = early_stopper.step(loss.item())
 
                     if epoch % log_step == 0 or is_last_epoch or stop:
@@ -642,7 +667,7 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                                 heliostat_group=heliostat_group,
                                 data_split=data_split,
                                 reduction=partial(torch.mean),
-                                device=device,   
+                                device=device,
                             )
 
                     # Early stopping when loss did not improve for a predefined number of epochs.
@@ -668,7 +693,9 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                     }
                 )
 
-                final_loss_per_heliostat[active_heliostats_mask!=0] = loss_per_heliostat
+                final_loss_per_heliostat[active_heliostats_mask != 0] = (
+                    loss_per_heliostat
+                )
 
                 log.info(f"Rank: {rank}, Kinematics reconstructed.")
 
@@ -702,11 +729,12 @@ def _reconstruct_kinematics_parameters_wih_alignment(
             final_loss_history_all_groups = [loss_history]
 
         for heliostat_group in self.scenario.heliostat_field.heliostat_groups:
-            heliostat_group.kinematics.rotation_deviation_parameters = heliostat_group.kinematics.rotation_deviation_parameters.detach()
+            heliostat_group.kinematics.rotation_deviation_parameters = (
+                heliostat_group.kinematics.rotation_deviation_parameters.detach()
+            )
 
         return final_loss_per_heliostat.detach().cpu(), final_loss_history_all_groups
 
-
     def _reconstruct_kinematics_parameters_with_raytracing(
         self,
         loss_definition: Loss,
@@ -798,7 +826,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                     motor_positions=motor_positions,
                     target_area_indices=target_area_indices,
                     test_fraction=0.25,
-                    device=device
+                    device=device,
                 )
 
                 # Set up optimizer, scheduler, and early stopping.
@@ -848,7 +876,8 @@ def _reconstruct_kinematics_parameters_with_raytracing(
 
                     # Activate heliostats.
                     heliostat_group.activate_heliostats(
-                        active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
+                        active_heliostats_mask=data_split.active_heliostats_mask_train,
+                        device=device,
                     )
 
                     # Align heliostats.
@@ -900,7 +929,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                     loss_per_heliostat = reduce_loss_per_sample(
                         loss_per_sample=loss_per_sample,
                         number_of_samples_per_heliostat=data_split.number_of_train_samples,
-                        reduction=partial(torch.median, dim=1)
+                        reduction=partial(torch.median, dim=1),
                     )
 
                     loss = torch.mean(loss_per_heliostat)
@@ -932,7 +961,9 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                     else:
                         scheduler.step()
 
-                    is_last_epoch = epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    is_last_epoch = (
+                        epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    )
                     stop = early_stopper.step(loss.item())
 
                     if epoch % log_step == 0 or is_last_epoch or stop:
@@ -972,7 +1003,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                 )
 
                 local_indices = (
-                    sample_indices_for_local_rank[::data_split.number_of_train_samples]
+                    sample_indices_for_local_rank[:: data_split.number_of_train_samples]
                     // data_split.number_of_train_samples
                 )
 
@@ -1021,6 +1052,8 @@ def _reconstruct_kinematics_parameters_with_raytracing(
             final_loss_history_all_groups = [loss_history]
 
         for heliostat_group in self.scenario.heliostat_field.heliostat_groups:
-            heliostat_group.kinematics.rotation_deviation_parameters = heliostat_group.kinematics.rotation_deviation_parameters.detach()
+            heliostat_group.kinematics.rotation_deviation_parameters = (
+                heliostat_group.kinematics.rotation_deviation_parameters.detach()
+            )
 
         return final_loss_per_heliostat.detach().cpu(), final_loss_history_all_groups
diff --git a/artist/optim/loss.py b/artist/optim/loss.py
index 976b2c61..ec7cbdbc 100644
--- a/artist/optim/loss.py
+++ b/artist/optim/loss.py
@@ -203,18 +203,20 @@ def __call__(
             device=device,
         )
 
-        focal_spot_coordinates_prediction = coordinates.bitmap_coordinates_to_target_coordinates(
-            bitmap_coordinates=focal_spots_bitmap,
-            bitmap_resolution=torch.tensor(
-                [
-                    prediction.shape[indices.batched_bitmap_u],
-                    prediction.shape[indices.batched_bitmap_e],
-                ],
+        focal_spot_coordinates_prediction = (
+            coordinates.bitmap_coordinates_to_target_coordinates(
+                bitmap_coordinates=focal_spots_bitmap,
+                bitmap_resolution=torch.tensor(
+                    [
+                        prediction.shape[indices.batched_bitmap_u],
+                        prediction.shape[indices.batched_bitmap_e],
+                    ],
+                    device=device,
+                ),
+                solar_tower=self.scenario.solar_tower,
+                target_area_indices=target_area_indices,
                 device=device,
-            ),
-            solar_tower=self.scenario.solar_tower,
-            target_area_indices=target_area_indices,
-            device=device,
+            )
         )
 
         focal_spots_ground_truth = bitmap.get_center_of_mass(
@@ -222,21 +224,27 @@ def __call__(
             device=device,
         )
 
-        focal_spot_coordinates_ground_truth = coordinates.bitmap_coordinates_to_target_coordinates(
-            bitmap_coordinates=focal_spots_ground_truth,
-            bitmap_resolution=torch.tensor(
-                [
-                    prediction.shape[indices.batched_bitmap_u],
-                    prediction.shape[indices.batched_bitmap_e],
-                ],
+        focal_spot_coordinates_ground_truth = (
+            coordinates.bitmap_coordinates_to_target_coordinates(
+                bitmap_coordinates=focal_spots_ground_truth,
+                bitmap_resolution=torch.tensor(
+                    [
+                        prediction.shape[indices.batched_bitmap_u],
+                        prediction.shape[indices.batched_bitmap_e],
+                    ],
+                    device=device,
+                ),
+                solar_tower=self.scenario.solar_tower,
+                target_area_indices=target_area_indices,
                 device=device,
-            ),
-            solar_tower=self.scenario.solar_tower,
-            target_area_indices=target_area_indices,
-            device=device,
+            )
         )
 
-        return torch.norm(focal_spot_coordinates_prediction[:, :3] - focal_spot_coordinates_ground_truth[:, :3], dim=1)
+        return torch.norm(
+            focal_spot_coordinates_prediction[:, :3]
+            - focal_spot_coordinates_ground_truth[:, :3],
+            dim=1,
+        )
 
 
 class PixelLoss(Loss):
@@ -268,7 +276,7 @@ def __call__(
         r"""
         Compute the normalized pixel-wise loss.
 
-        To make the loss invariant to the overall magnitude of the ground truth flux, the summed loss is divided 
+        To make the loss invariant to the overall magnitude of the ground truth flux, the summed loss is divided
         by the total ground truth intensity per sample.
 
         Parameters
@@ -305,7 +313,9 @@ def __call__(
                 + " ".join(errors)
             )
 
-        return self.loss_function(prediction, ground_truth).sum(dim=kwargs["reduction_dimensions"]) / ground_truth.sum(dim=(1, 2))
+        return self.loss_function(prediction, ground_truth).sum(
+            dim=kwargs["reduction_dimensions"]
+        ) / ground_truth.sum(dim=(1, 2))
 
 
 class KLDivergenceLoss(Loss):
@@ -442,8 +452,9 @@ def __call__(
             The summed loss reduced along the specified dimensions.
             Shape is ``[number_of_samples]``.
         """
-
-        return torch.acos((prediction[:, :3] * ground_truth[:, :3]).sum(dim=-1).clamp(-1.0, 1.0))
+        return torch.acos(
+            (prediction[:, :3] * ground_truth[:, :3]).sum(dim=-1).clamp(-1.0, 1.0)
+        )
 
 
 class CosineSimilarityLoss(Loss):
@@ -526,9 +537,9 @@ def reduce_loss_per_sample(
         : number_of_heliostats * number_of_samples_per_heliostat
     ]
 
-    reduced = reduction(loss_per_sample.view(
-        number_of_heliostats, number_of_samples_per_heliostat
-    ))
+    reduced = reduction(
+        loss_per_sample.view(number_of_heliostats, number_of_samples_per_heliostat)
+    )
 
     if not isinstance(reduced, torch.Tensor):
         reduced = reduced.values
diff --git a/artist/optim/surface_reconstructor.py b/artist/optim/surface_reconstructor.py
index 866ff54a..0b7fcde3 100644
--- a/artist/optim/surface_reconstructor.py
+++ b/artist/optim/surface_reconstructor.py
@@ -1,10 +1,10 @@
-from functools import partial
 import logging
 import pathlib
+from functools import partial
 from typing import Any, cast
 
-from matplotlib import pyplot as plt
 import torch
+from matplotlib import pyplot as plt
 from torch.optim.lr_scheduler import LRScheduler
 
 from artist.field.heliostat_group import HeliostatGroup
@@ -155,7 +155,6 @@ def __init__(
         self.validation_loss_pixel = PixelLoss()
         self.validation_loss_kl_div = KLDivergenceLoss()
 
-
     def _validate(
         self,
         heliostat_group: HeliostatGroup,
@@ -209,20 +208,14 @@ def _validate(
         )
 
         heliostat_group.active_surface_points = new_surface_points.reshape(
-            heliostat_group.active_surface_points.shape[
-                indices.heliostat_dimension
-            ],
+            heliostat_group.active_surface_points.shape[indices.heliostat_dimension],
             -1,
             4,
         )
-        heliostat_group.active_surface_normals = (
-            new_surface_normals.reshape(
-                heliostat_group.active_surface_normals.shape[
-                    indices.heliostat_dimension
-                ],
-                -1,
-                4,
-            )
+        heliostat_group.active_surface_normals = new_surface_normals.reshape(
+            heliostat_group.active_surface_normals.shape[indices.heliostat_dimension],
+            -1,
+            4,
         )
 
         heliostat_group.align_surfaces_with_incident_ray_directions(
@@ -253,37 +246,37 @@ def _validate(
             device=device,
         )
 
-        cropped_flux_distributions = (
-            bitmap.crop_flux_distributions_around_center(
-                flux_distributions=flux_prediction,
-                solar_tower=self.scenario.solar_tower,
-                target_area_indices=data_split.target_area_indices_test,
-                device=device,
-            )
+        cropped_flux_distributions = bitmap.crop_flux_distributions_around_center(
+            flux_distributions=flux_prediction,
+            solar_tower=self.scenario.solar_tower,
+            target_area_indices=data_split.target_area_indices_test,
+            device=device,
         )
 
         indices_for_local_rank = ray_tracer.get_sampler_indices()
 
         loss_pixel_per_sample = self.validation_loss_pixel(
             prediction=cropped_flux_distributions,
-            ground_truth=data_split.flux_measured_test[
-                indices_for_local_rank
-            ],
-            reduction_dimensions=(1,2,),
+            ground_truth=data_split.flux_measured_test[indices_for_local_rank],
+            reduction_dimensions=(
+                1,
+                2,
+            ),
         )
         loss_kl_div_per_sample = self.validation_loss_kl_div(
             prediction=cropped_flux_distributions,
-            ground_truth=data_split.flux_measured_test[
-                indices_for_local_rank
-            ],
-            reduction_dimensions=(1,2,),
+            ground_truth=data_split.flux_measured_test[indices_for_local_rank],
+            reduction_dimensions=(
+                1,
+                2,
+            ),
         )
-                 
+
         test_loss_pixel = reduce_loss_per_sample(
             loss_per_sample=loss_pixel_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
             reduction=partial(torch.mean),
-        )                        
+        )
         test_loss_kl_div = reduce_loss_per_sample(
             loss_per_sample=loss_kl_div_per_sample,
             number_of_samples_per_heliostat=data_split.number_of_test_samples,
@@ -291,15 +284,13 @@ def _validate(
         )
 
         log.info(
-            "pixel mean: %.5f, "
-            "kl div mean: %.5f",
+            "pixel mean: %.5f, kl div mean: %.5f",
             torch.mean(test_loss_pixel).item(),
             torch.mean(test_loss_kl_div).item(),
         )
 
         return flux_prediction
 
-
     def _plot_fluxes(
         self,
         flux_measured: torch.Tensor,
@@ -312,7 +303,7 @@ def _plot_fluxes(
         Plot predicted and measured flux maps for each heliostat sample.
 
         Each row in the generated figure corresponds to one sample of a
-        heliostat, where the left column contains the predicted flux and 
+        heliostat, where the left column contains the predicted flux and
         the right column contains the measured flux.
         The subplot borders are color-coded to indicate whether a sample
         belongs to the training samples (green) or testing samples (red)
@@ -343,7 +334,9 @@ def _plot_fluxes(
 
         for heliostat_start_index in range(0, total_samples, samples_per_heliostat):
             fig, axes = plt.subplots(
-                samples_per_heliostat, 2, figsize=(8, samples_per_heliostat * 4),
+                samples_per_heliostat,
+                2,
+                figsize=(8, samples_per_heliostat * 4),
             )
             heliostat_index = heliostat_start_index // samples_per_heliostat
 
@@ -358,10 +351,14 @@ def _plot_fluxes(
                     split_name = "TEST"
 
                 axes[sample_offset, 0].imshow(flux_predicted[sample_index])
-                axes[sample_offset, 0].set_title(f"Predicted Flux - Heliostat {heliostat_index} ({split_name})")
+                axes[sample_offset, 0].set_title(
+                    f"Predicted Flux - Heliostat {heliostat_index} ({split_name})"
+                )
 
                 axes[sample_offset, 1].imshow(flux_measured[sample_index])
-                axes[sample_offset, 1].set_title(f"Measured Flux - Heliostat {heliostat_index} ({split_name})")
+                axes[sample_offset, 1].set_title(
+                    f"Measured Flux - Heliostat {heliostat_index} ({split_name})"
+                )
 
                 for spine in axes[sample_offset, :].flat:
                     for spines in spine.spines.values():
@@ -372,7 +369,6 @@ def _plot_fluxes(
             plt.savefig(f"./ignored/fixed/heliostat_{heliostat_index}_{plot_name}")
             plt.close(fig)
 
-
     def reconstruct_surfaces(
         self,
         loss_definition: Loss,
@@ -471,7 +467,7 @@ def reconstruct_surfaces(
                     motor_positions=motor_positions,
                     target_area_indices=target_area_indices,
                     test_fraction=0.25,
-                    device=device
+                    device=device,
                 )
                 evaluation_points = (
                     create_nurbs_evaluation_grid(
@@ -557,7 +553,8 @@ def reconstruct_surfaces(
 
                     # Activate heliostats.
                     heliostat_group.activate_heliostats(
-                        active_heliostats_mask=data_split.active_heliostats_mask_train, device=device
+                        active_heliostats_mask=data_split.active_heliostats_mask_train,
+                        device=device,
                     )
 
                     # Build NURBS surface from current control points.
@@ -599,7 +596,8 @@ def reconstruct_surfaces(
                     # Align heliostat surfaces toward target under current incident ray directions.
                     heliostat_group.align_surfaces_with_incident_ray_directions(
                         aim_points=self.scenario.solar_tower.get_centers_of_target_areas(
-                            target_area_indices=data_split.target_area_indices_train, device=device
+                            target_area_indices=data_split.target_area_indices_train,
+                            device=device,
                         ),
                         incident_ray_directions=data_split.incident_ray_directions_train,
                         active_heliostats_mask=data_split.active_heliostats_mask_train,
@@ -639,7 +637,9 @@ def reconstruct_surfaces(
 
                     sample_indices_for_local_rank = ray_tracer.get_sampler_indices()
                     local_indices = (
-                        sample_indices_for_local_rank[::data_split.number_of_train_samples]
+                        sample_indices_for_local_rank[
+                            :: data_split.number_of_train_samples
+                        ]
                         // data_split.number_of_train_samples
                     )
 
@@ -658,11 +658,11 @@ def reconstruct_surfaces(
                         ),
                         device=device,
                     )
-                    
+
                     flux_loss_per_heliostat = reduce_loss_per_sample(
                         loss_per_sample=flux_loss_per_sample,
                         number_of_samples_per_heliostat=data_split.number_of_train_samples,
-                        reduction=partial(torch.mean)
+                        reduction=partial(torch.mean),
                     )
 
                     # Add Augmented-Lagrangian constraint to ensure that flux integral is conserved,
@@ -672,7 +672,9 @@ def reconstruct_surfaces(
                             dim=(indices.batched_bitmap_e, indices.batched_bitmap_u)
                         ).detach()
                     flux_integrals_relative_differences = (
-                        cropped_flux_predictions.sum(dim=(indices.batched_bitmap_e, indices.batched_bitmap_u))
+                        cropped_flux_predictions.sum(
+                            dim=(indices.batched_bitmap_e, indices.batched_bitmap_u)
+                        )
                         - flux_integrals_reference
                     ) / (flux_integrals_reference + torch.tensor(self.epsilon))
                     flux_constraint_per_sample = torch.clamp(
@@ -681,7 +683,7 @@ def reconstruct_surfaces(
                     flux_constraint_per_heliostat = reduce_loss_per_sample(
                         loss_per_sample=flux_constraint_per_sample,
                         number_of_samples_per_heliostat=data_split.number_of_train_samples,
-                        reduction=partial(torch.mean)
+                        reduction=partial(torch.mean),
                     )
                     flux_integrals_constraint = (
                         lambda_flux_integral * flux_constraint_per_heliostat
@@ -698,7 +700,7 @@ def reconstruct_surfaces(
                     if weight_smoothness > 0:
                         smoothness_loss_per_heliostat = smoothness_regularizer(
                             current_control_points=heliostat_group.active_nurbs_control_points[
-                                ::data_split.number_of_train_samples
+                                :: data_split.number_of_train_samples
                             ][local_indices],
                             original_control_points=original_control_points[
                                 local_indices
@@ -708,7 +710,7 @@ def reconstruct_surfaces(
                     if weight_ideal_surface > 0:
                         ideal_surface_loss_per_heliostat = ideal_surface_regularizer(
                             current_control_points=heliostat_group.active_nurbs_control_points[
-                                ::data_split.number_of_train_samples
+                                :: data_split.number_of_train_samples
                             ][local_indices],
                             original_control_points=original_control_points[
                                 local_indices
@@ -740,7 +742,7 @@ def reconstruct_surfaces(
                         + alpha * smoothness_loss_per_heliostat
                         + beta * ideal_surface_loss_per_heliostat
                     )
-                    
+
                     total_loss = torch.mean(total_loss_per_heliostat)
 
                     total_loss.backward()
@@ -787,7 +789,9 @@ def reconstruct_surfaces(
                     else:
                         scheduler.step()
 
-                    is_last_epoch = epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    is_last_epoch = (
+                        epoch == self.optimizer_dict[constants.max_epoch] - 1
+                    )
                     stop = early_stopper.step(total_loss.item())
 
                     if epoch % log_step == 0 or is_last_epoch or stop:
diff --git a/artist/optim/training.py b/artist/optim/training.py
index 8c9ce146..8c20c3ed 100644
--- a/artist/optim/training.py
+++ b/artist/optim/training.py
@@ -241,6 +241,7 @@ class TrainTestSplit:
     number_of_samples_per_heliostat : int
         Total number of samples available per heliostat before splitting.
     """
+
     flux_measured_train: torch.Tensor
     focal_spots_measured_train: torch.Tensor
     incident_ray_directions_train: torch.Tensor
@@ -271,8 +272,8 @@ def train_test_split(
     incident_ray_directions: torch.Tensor,
     motor_positions: torch.Tensor,
     target_area_indices: torch.Tensor,
-    test_fraction: int = 0.25,
-    device: torch.device | None = None
+    test_fraction: float = 0.25,
+    device: torch.device | None = None,
 ) -> TrainTestSplit:
     """
     Split heliostat reconstruction data into training and test subsets.
@@ -315,24 +316,30 @@ def train_test_split(
         Dataclass containing: train/test tensors, train/test indices, updated active heliostat masks.
     """
     device = get_device(device=device)
-    
+
     total_samples = int(active_heliostats_mask.sum().item())
     number_of_heliostats = int((active_heliostats_mask > 0).sum().item())
-    number_of_samples_per_heliostat = int(
-        total_samples / number_of_heliostats
+    number_of_samples_per_heliostat = int(total_samples / number_of_heliostats)
+    number_of_test_samples = max(
+        1, int(number_of_samples_per_heliostat * test_fraction)
     )
-    number_of_test_samples = max(1, int(number_of_samples_per_heliostat * test_fraction))
     number_of_train_samples = number_of_samples_per_heliostat - number_of_test_samples
     starts = torch.arange(number_of_heliostats) * number_of_samples_per_heliostat
     offsets = torch.arange(number_of_train_samples, number_of_samples_per_heliostat)
     train_indices = (
-        torch.arange(0, total_samples, number_of_samples_per_heliostat, device=device)[:, None]
+        torch.arange(0, total_samples, number_of_samples_per_heliostat, device=device)[
+            :, None
+        ]
         + torch.arange(number_of_train_samples, device=device)
     ).reshape(-1)
     test_indices = (starts[:, None] + offsets).reshape(-1)
 
-    active_heliostats_mask_train = torch.clamp(active_heliostats_mask - number_of_test_samples, min=0)
-    active_heliostats_mask_test = torch.clamp(active_heliostats_mask - number_of_train_samples, min=0)
+    active_heliostats_mask_train = torch.clamp(
+        active_heliostats_mask - number_of_test_samples, min=0
+    )
+    active_heliostats_mask_test = torch.clamp(
+        active_heliostats_mask - number_of_train_samples, min=0
+    )
 
     return TrainTestSplit(
         flux_measured_train=flux_measured[train_indices],
@@ -340,21 +347,16 @@ def train_test_split(
         incident_ray_directions_train=incident_ray_directions[train_indices],
         motor_positions_train=motor_positions[train_indices],
         target_area_indices_train=target_area_indices[train_indices],
-
         flux_measured_test=flux_measured[test_indices],
         focal_spots_measured_test=focal_spots_measured[test_indices],
         incident_ray_directions_test=incident_ray_directions[test_indices],
         motor_positions_test=motor_positions[test_indices],
         target_area_indices_test=target_area_indices[test_indices],
-
         active_heliostats_mask_train=active_heliostats_mask_train,
         active_heliostats_mask_test=active_heliostats_mask_test,
-
         train_indices=train_indices,
         test_indices=test_indices,
-
         number_of_train_samples=number_of_train_samples,
         number_of_test_samples=number_of_test_samples,
         number_of_samples_per_heliostat=number_of_samples_per_heliostat,
     )
-
diff --git a/tests/optim/test_loss_functions.py b/tests/optim/test_loss_functions.py
index e39f8574..ab98691c 100644
--- a/tests/optim/test_loss_functions.py
+++ b/tests/optim/test_loss_functions.py
@@ -343,7 +343,7 @@ def test_pixel_loss(
 
     target_area_indices = torch.tensor([0], device=device)
 
-    pixel_loss = PixelLoss(scenario=mock_scenario)
+    pixel_loss = PixelLoss()
 
     if not kwargs:
         with pytest.raises(ValueError) as exc_info:
diff --git a/tests/optim/test_surface_reconstructor.py b/tests/optim/test_surface_reconstructor.py
index 43d4c58a..42b5a4a1 100644
--- a/tests/optim/test_surface_reconstructor.py
+++ b/tests/optim/test_surface_reconstructor.py
@@ -14,20 +14,20 @@
 
 
 @pytest.mark.parametrize(
-    "loss_class, early_stopping_window, data_parser, scheduler",
+    "loss, early_stopping_window, data_parser, scheduler",
     [
         (
-            KLDivergenceLoss,
+            KLDivergenceLoss(),
             40,
             PaintCalibrationDataParser(),
             constants.reduce_on_plateau,
         ),
-        (PixelLoss, 20, PaintCalibrationDataParser(), constants.cyclic),
-        (PixelLoss, 10, CalibrationDataParser(), constants.cyclic),
+        (PixelLoss(), 20, PaintCalibrationDataParser(), constants.cyclic),
+        (PixelLoss(), 10, CalibrationDataParser(), constants.cyclic),
     ],
 )
 def test_surface_reconstructor(
-    loss_class: Loss,
+    loss: Loss,
     early_stopping_window: int,
     data_parser: CalibrationDataParser | PaintCalibrationDataParser,
     scheduler: str,
@@ -39,8 +39,8 @@ def test_surface_reconstructor(
 
     Parameters
     ----------
-    loss_class : Loss
-        The loss class.
+    loss: Loss
+        The loss definition.
     early_stopping_window : int
         Number of epochs used to estimate loss trend.
     data_parser : CalibrationDataParser
@@ -159,14 +159,10 @@ def test_surface_reconstructor(
         device=device,
     )
 
-    loss_definition = (
-        PixelLoss(scenario=scenario) if loss_class is PixelLoss else KLDivergenceLoss()
-    )
-
     if not isinstance(data_parser, PaintCalibrationDataParser):
         with pytest.raises(NotImplementedError) as exc_info:
             _ = surface_reconstructor.reconstruct_surfaces(
-                loss_definition=loss_definition, device=device
+                loss_definition=loss, device=device
             )
 
             assert "Must be overridden!" in str(exc_info.value)
@@ -175,7 +171,7 @@ def test_surface_reconstructor(
         torch.use_deterministic_algorithms(False)
         try:
             _ = surface_reconstructor.reconstruct_surfaces(
-                loss_definition=loss_definition, device=device
+                loss_definition=loss, device=device
             )
         finally:
             torch.use_deterministic_algorithms(old_state)
@@ -183,7 +179,7 @@ def test_surface_reconstructor(
         for index, heliostat_group in enumerate(
             scenario.heliostat_field.heliostat_groups
         ):
-            loss_name = "pixel_loss" if loss_class is PixelLoss else "kl_divergence"
+            loss_name = "pixel_loss" if isinstance(loss, PixelLoss) else "kl_divergence"
             expected_path = (
                 pathlib.Path(ARTIST_ROOT)
                 / "tests/data/expected_reconstructed_surfaces"

From 6be7cdacdcbf5bda479436394eef27f3bd916b09 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:24:14 +0200
Subject: [PATCH 25/38] formatting and new test data files

---
 artist/field/kinematics_rigid_body.py         |   2 +-
 .../expected_bitmaps_blocking/bitmaps_cpu.pt  | Bin 1574064 -> 1574064 bytes
 .../expected_bitmaps_blocking/bitmaps_cuda.pt | Bin 1574069 -> 1574069 bytes
 ...t_mix_ideal_prototype_deflectometry_cpu.pt | Bin 1050198 -> 1050198 bytes
 ..._mix_ideal_prototype_deflectometry_cuda.pt | Bin 1050203 -> 1050203 bytes
 ...est_scenario_paint_single_heliostat_cpu.pt | Bin 1050049 -> 1050049 bytes
 ...st_scenario_paint_single_heliostat_cuda.pt | Bin 1050054 -> 1050054 bytes
 ...est_scenario_stral_single_heliostat_cpu.pt | Bin 525761 -> 525761 bytes
 ...st_scenario_stral_single_heliostat_cuda.pt | Bin 525766 -> 525766 bytes
 .../bitmaps_cpu.pt                            | Bin 525488 -> 525488 bytes
 .../bitmaps_cuda.pt                           | Bin 525493 -> 525493 bytes
 .../distribution_group_0_30_cpu.pt            | Bin 0 -> 1344 bytes
 .../distribution_group_0_30_cuda.pt           | Bin 0 -> 1349 bytes
 .../distribution_group_1_30_cpu.pt            | Bin 0 -> 1344 bytes
 .../distribution_group_1_30_cuda.pt           | Bin 0 -> 1349 bytes
 .../group_0_500_cuda.pt                       | Bin 0 -> 1225 bytes
 .../group_0_50_cuda.pt                        | Bin 1480 -> 1220 bytes
 .../group_0_alignment_50_cpu.pt               | Bin 0 -> 1329 bytes
 .../group_0_alignment_50_cuda.pt              | Bin 0 -> 1334 bytes
 .../group_0_raytracing_50_cpu.pt              | Bin 0 -> 1334 bytes
 .../group_0_raytracing_50_cuda.pt             | Bin 0 -> 1339 bytes
 .../group_1_500_cuda.pt                       | Bin 0 -> 1225 bytes
 .../group_1_50_cuda.pt                        | Bin 1544 -> 1220 bytes
 .../group_1_alignment_50_cpu.pt               | Bin 0 -> 1329 bytes
 .../group_1_alignment_50_cuda.pt              | Bin 0 -> 1334 bytes
 .../group_1_raytracing_50_cpu.pt              | Bin 0 -> 1334 bytes
 .../group_1_raytracing_50_cuda.pt             | Bin 0 -> 1339 bytes
 .../kl_divergence_group_1_40_cpu.pt           | Bin 6085 -> 6085 bytes
 .../kl_divergence_group_1_40_cuda.pt          | Bin 6090 -> 6090 bytes
 .../pixel_loss_group_0_10_cpu.pt              | Bin 0 -> 6006 bytes
 .../pixel_loss_group_0_10_cuda.pt             | Bin 0 -> 6075 bytes
 .../pixel_loss_group_1_10_cpu.pt              | Bin 0 -> 6006 bytes
 .../pixel_loss_group_1_10_cuda.pt             | Bin 0 -> 6075 bytes
 .../AA39-calibration-properties_2.json        |  24 +++++++++---------
 .../AA39-calibration-properties_3.json        |  21 +++++++++++++++
 .../data/field_data/AA39-flux-centered_2.png  | Bin 9042 -> 8238 bytes
 .../data/field_data/AA39-flux-centered_3.png  | Bin 0 -> 9042 bytes
 tests/data/field_data/AA39-flux_2.png         | Bin 8924 -> 6014 bytes
 tests/data/field_data/AA39-flux_3.png         | Bin 0 -> 8924 bytes
 39 files changed, 34 insertions(+), 13 deletions(-)
 create mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_0_30_cpu.pt
 create mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_0_30_cuda.pt
 create mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_1_30_cpu.pt
 create mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_1_30_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_500_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_alignment_50_cpu.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_alignment_50_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_raytracing_50_cpu.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_raytracing_50_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_500_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_alignment_50_cpu.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_alignment_50_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_raytracing_50_cpu.pt
 create mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_raytracing_50_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_0_10_cpu.pt
 create mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_0_10_cuda.pt
 create mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_1_10_cpu.pt
 create mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_1_10_cuda.pt
 create mode 100644 tests/data/field_data/AA39-calibration-properties_3.json
 create mode 100644 tests/data/field_data/AA39-flux-centered_3.png
 create mode 100644 tests/data/field_data/AA39-flux_3.png

diff --git a/artist/field/kinematics_rigid_body.py b/artist/field/kinematics_rigid_body.py
index 9be2ece8..26e2d594 100644
--- a/artist/field/kinematics_rigid_body.py
+++ b/artist/field/kinematics_rigid_body.py
@@ -593,4 +593,4 @@ def incident_ray_directions_to_orientations(
 
         self.active_motor_positions = motor_positions
 
-        return orientations @ self.initial_orientation_offsets
+        return  orientations @ self.initial_orientation_offsets
diff --git a/tests/data/expected_bitmaps_blocking/bitmaps_cpu.pt b/tests/data/expected_bitmaps_blocking/bitmaps_cpu.pt
index fcebe334dba3b09ecb1b6cc4d3f8ba385b04635d..8382ea30d9a05adff77baa5bbf19fe0d14b29d54 100644
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diff --git a/tests/data/expected_bitmaps_blocking/bitmaps_cuda.pt b/tests/data/expected_bitmaps_blocking/bitmaps_cuda.pt
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diff --git a/tests/data/expected_bitmaps_integration/test_scenario_paint_mix_ideal_prototype_deflectometry_cpu.pt b/tests/data/expected_bitmaps_integration/test_scenario_paint_mix_ideal_prototype_deflectometry_cpu.pt
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diff --git a/tests/data/expected_bitmaps_integration/test_scenario_paint_mix_ideal_prototype_deflectometry_cuda.pt b/tests/data/expected_bitmaps_integration/test_scenario_paint_mix_ideal_prototype_deflectometry_cuda.pt
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diff --git a/tests/data/expected_bitmaps_integration/test_scenario_paint_single_heliostat_cpu.pt b/tests/data/expected_bitmaps_integration/test_scenario_paint_single_heliostat_cpu.pt
index 78ec0e6cfdc467fcef9bedb02b79a5f0e3435428..1b092fea92c7e3fe0a112744cf45166c723ff2f8 100644
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diff --git a/tests/data/expected_bitmaps_integration/test_scenario_paint_single_heliostat_cuda.pt b/tests/data/expected_bitmaps_integration/test_scenario_paint_single_heliostat_cuda.pt
index 44c1831c854b15c64ab16a696c206ccdc5a12c02..4e4eb645886dc88591c8ad6c8a004ac7358420d8 100644
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diff --git a/tests/data/expected_bitmaps_integration/test_scenario_stral_single_heliostat_cpu.pt b/tests/data/expected_bitmaps_integration/test_scenario_stral_single_heliostat_cpu.pt
index be8cebea673ad2cb3574931f9fe8e89dc1ef8199..983fbc29625807d0a73c36155effec39e3a6c861 100644
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diff --git a/tests/data/expected_bitmaps_integration/test_scenario_stral_single_heliostat_cuda.pt b/tests/data/expected_bitmaps_integration/test_scenario_stral_single_heliostat_cuda.pt
index 3578b0476aa0fbb9f41f95a24f0603872981c57a..182000eb97ee1739a9f2a340dd8661cf53322955 100644
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diff --git a/tests/data/expected_bitmaps_ray_extinction/bitmaps_cpu.pt b/tests/data/expected_bitmaps_ray_extinction/bitmaps_cpu.pt
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diff --git a/tests/data/expected_bitmaps_ray_extinction/bitmaps_cuda.pt b/tests/data/expected_bitmaps_ray_extinction/bitmaps_cuda.pt
index 958b7914a9807bd005d38ea5850f2306f59014b9..fedfc383a49ef6dbdad651159aa808a86f4c1c74 100644
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_0_500_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_0_500_cuda.pt
new file mode 100644
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_0_50_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_0_50_cuda.pt
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_0_alignment_50_cpu.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_0_alignment_50_cpu.pt
new file mode 100644
index 0000000000000000000000000000000000000000..c97e201ad962938e49520726a7080b969baaf54e
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_0_alignment_50_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_0_alignment_50_cuda.pt
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_0_raytracing_50_cpu.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_0_raytracing_50_cpu.pt
new file mode 100644
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_0_raytracing_50_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_0_raytracing_50_cuda.pt
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_1_500_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_1_500_cuda.pt
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_1_50_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_1_50_cuda.pt
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_1_alignment_50_cpu.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_1_alignment_50_cpu.pt
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index 0000000000000000000000000000000000000000..975da2a2c298c42d1cc864283ced1cf4e639121a
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_1_alignment_50_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_1_alignment_50_cuda.pt
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_1_raytracing_50_cpu.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_1_raytracing_50_cpu.pt
new file mode 100644
index 0000000000000000000000000000000000000000..53bc3570061915e5e62a0656bb9b88c994d6f57b
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diff --git a/tests/data/expected_reconstructed_kinematics_parameters/group_1_raytracing_50_cuda.pt b/tests/data/expected_reconstructed_kinematics_parameters/group_1_raytracing_50_cuda.pt
new file mode 100644
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diff --git a/tests/data/expected_reconstructed_surfaces/kl_divergence_group_1_40_cpu.pt b/tests/data/expected_reconstructed_surfaces/kl_divergence_group_1_40_cpu.pt
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diff --git a/tests/data/expected_reconstructed_surfaces/kl_divergence_group_1_40_cuda.pt b/tests/data/expected_reconstructed_surfaces/kl_divergence_group_1_40_cuda.pt
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diff --git a/tests/data/expected_reconstructed_surfaces/pixel_loss_group_0_10_cuda.pt b/tests/data/expected_reconstructed_surfaces/pixel_loss_group_0_10_cuda.pt
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diff --git a/tests/data/expected_reconstructed_surfaces/pixel_loss_group_1_10_cpu.pt b/tests/data/expected_reconstructed_surfaces/pixel_loss_group_1_10_cpu.pt
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diff --git a/tests/data/field_data/AA39-calibration-properties_2.json b/tests/data/field_data/AA39-calibration-properties_2.json
index aa7ef598..5cca696c 100644
--- a/tests/data/field_data/AA39-calibration-properties_2.json
+++ b/tests/data/field_data/AA39-calibration-properties_2.json
@@ -1,21 +1,21 @@
 {
     "motor_position": {
-        "axis_1_motor_position": 20634,
-        "axis_2_motor_position": 40816
+        "axis_1_motor_position": 25702,
+        "axis_2_motor_position": 33826
     },
-    "target_name": "multi_focus_tower",
-    "sun_elevation": 48.68939631461994,
-    "sun_azimuth": 62.865328091974916,
+    "target_name": "solar_tower_juelich_lower",
+    "sun_elevation": 43.81453437734031,
+    "sun_azimuth": 68.52002947921484,
     "focal_spot": {
         "HeliOS": [
-            50.91339643166456,
-            6.387580043820685,
-            138.3399014968552
+            50.91339201932565,
+            6.3878282653934475,
+            122.50069327110258
         ],
         "UTIS": [
-            50.91339643109615,
-            6.387581065254336,
-            138.3357048034668
+            50.91339202742253,
+            6.3878294207790125,
+            122.50684356689452
         ]
     }
-}
+}
\ No newline at end of file
diff --git a/tests/data/field_data/AA39-calibration-properties_3.json b/tests/data/field_data/AA39-calibration-properties_3.json
new file mode 100644
index 00000000..aa7ef598
--- /dev/null
+++ b/tests/data/field_data/AA39-calibration-properties_3.json
@@ -0,0 +1,21 @@
+{
+    "motor_position": {
+        "axis_1_motor_position": 20634,
+        "axis_2_motor_position": 40816
+    },
+    "target_name": "multi_focus_tower",
+    "sun_elevation": 48.68939631461994,
+    "sun_azimuth": 62.865328091974916,
+    "focal_spot": {
+        "HeliOS": [
+            50.91339643166456,
+            6.387580043820685,
+            138.3399014968552
+        ],
+        "UTIS": [
+            50.91339643109615,
+            6.387581065254336,
+            138.3357048034668
+        ]
+    }
+}
diff --git a/tests/data/field_data/AA39-flux-centered_2.png b/tests/data/field_data/AA39-flux-centered_2.png
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HcmV?d00001


From 00b188c43e1574c1cd3543e69db7b8287db07973 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:25:10 +0200
Subject: [PATCH 26/38] add normalization in angle loss

---
 artist/optim/loss.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/artist/optim/loss.py b/artist/optim/loss.py
index ec7cbdbc..16f4356b 100644
--- a/artist/optim/loss.py
+++ b/artist/optim/loss.py
@@ -452,8 +452,10 @@ def __call__(
             The summed loss reduced along the specified dimensions.
             Shape is ``[number_of_samples]``.
         """
+        prediction = torch.nn.functional.normalize(prediction[:, :3])
+        ground_truth = torch.nn.functional.normalize(ground_truth[:, :3])
         return torch.acos(
-            (prediction[:, :3] * ground_truth[:, :3]).sum(dim=-1).clamp(-1.0, 1.0)
+            (prediction * ground_truth).sum(dim=-1).clamp(-1.0, 1.0)
         )
 
 

From 8248fb64b9cf0572d8ce3844a4532eda79f8a82b Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:26:05 +0200
Subject: [PATCH 27/38] fix early stopping and plot logic

---
 artist/optim/surface_reconstructor.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/artist/optim/surface_reconstructor.py b/artist/optim/surface_reconstructor.py
index 0b7fcde3..f40c7f55 100644
--- a/artist/optim/surface_reconstructor.py
+++ b/artist/optim/surface_reconstructor.py
@@ -366,7 +366,7 @@ def _plot_fluxes(
                         spines.set_linewidth(4)
 
             plt.tight_layout()
-            plt.savefig(f"./ignored/fixed/heliostat_{heliostat_index}_{plot_name}")
+            plt.savefig(f"heliostat_{heliostat_index}_{plot_name}")
             plt.close(fig)
 
     def reconstruct_surfaces(
@@ -807,11 +807,6 @@ def reconstruct_surfaces(
                                 device=device,
                             )
 
-                    # Early stopping when loss did not improve for a predefined number of epochs.
-                    if stop:
-                        log.info(f"Early stopping at epoch {epoch}.")
-                        break
-
                     if self.plot_results and (is_last_epoch or stop):
                         self._plot_fluxes(
                             flux_measured=flux_measured.cpu().detach(),
@@ -821,6 +816,11 @@ def reconstruct_surfaces(
                             plot_name=f"{epoch}",
                         )
 
+                    # Early stopping when loss did not improve for a predefined number of epochs.
+                    if stop:
+                        log.info(f"Early stopping at epoch {epoch}.")
+                        break
+
                     total_loss_history.append(total_loss.detach().cpu().item())
                     flux_loss_history.append(
                         flux_loss_per_heliostat.mean().detach().cpu().item()

From f5ab8949039f737c545ecbb2f984429f3bb5ffeb Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:30:46 +0200
Subject: [PATCH 28/38] clean up comments

---
 artist/optim/motor_position_optimizer.py | 29 ++++++------------------
 1 file changed, 7 insertions(+), 22 deletions(-)

diff --git a/artist/optim/motor_position_optimizer.py b/artist/optim/motor_position_optimizer.py
index b366c37c..8cd2c064 100644
--- a/artist/optim/motor_position_optimizer.py
+++ b/artist/optim/motor_position_optimizer.py
@@ -210,11 +210,10 @@ def optimize(
             ]
         )
 
-        # Per-group pre-alignment + reparameterization prep
+        # Per-group pre-alignment and parameterization
         for group_index, group in enumerate(
             self.scenario.heliostat_field.heliostat_groups
         ):
-            # All heliostats are initially active.
             active_heliostats_masks_all_groups.append(
                 torch.ones(
                     group.number_of_heliostats,
@@ -222,7 +221,6 @@ def optimize(
                     device=device,
                 )
             )
-            # All target indices are initially set to selected target area.
             target_area_indices_all_groups.append(
                 torch.full(
                     (group.number_of_heliostats,),
@@ -231,20 +229,15 @@ def optimize(
                     device=device,
                 )
             )
-            # Repeat the same incident ray direction per heliostat.
             incident_ray_directions_all_groups.append(
                 self.incident_ray_direction.repeat(group.number_of_heliostats, 1)
             )
 
             # Align all heliostats once, to the given incident ray direction and target, to set initial motor positions.
-            # The motor positions are set automatically in the ``align_surfaces_with_incident_ray_directions()`` method.
-            # Activate heliostats.
             group.activate_heliostats(
                 active_heliostats_mask=active_heliostats_masks_all_groups[group_index],
                 device=device,
             )
-
-            # Align heliostats.
             group.align_surfaces_with_incident_ray_directions(
                 aim_points=self.scenario.solar_tower.get_centers_of_target_areas(
                     target_area_indices=target_area_indices_all_groups[group_index],
@@ -260,7 +253,6 @@ def optimize(
                 group.kinematics.active_motor_positions.detach().clone()
             )
             initial_motor_positions_all_groups.append(initial_motor_positions)
-            # Read actuator min/max positions.
             motor_positions_minimum = (
                 group.kinematics.actuators.non_optimizable_parameters[
                     :, indices.actuator_min_motor_position
@@ -277,7 +269,6 @@ def optimize(
                 torch.minimum(lower_margin, upper_margin).clamp(min=1.0)
             )
 
-            # Create trainable unconstrained tensor.
             optimizable_parameters_all_groups.append(
                 torch.nn.Parameter(
                     torch.zeros_like(initial_motor_positions, device=device)
@@ -307,7 +298,6 @@ def optimize(
             relative=True,
         )
 
-        # Determine target-plane dimensions.
         target_plane_dimensions = torch.empty(2, device=device)
         target_areas, index = self.scenario.solar_tower.index_to_target_area[
             self.target_area_index
@@ -317,9 +307,9 @@ def optimize(
             < self.scenario.solar_tower.number_of_target_areas_per_type[
                 indices.planar_target_areas
             ]
-        ):  # Planar target
+        ):
             target_plane_dimensions = target_areas.dimensions[self.target_area_index]  # type: ignore[attr-defined]
-        else:  # Cylinder target
+        else:
             cylinder_indices = (
                 self.target_area_index
                 - self.scenario.solar_tower.number_of_target_areas_per_type[
@@ -383,7 +373,7 @@ def optimize(
             on_target_factors = torch.zeros_like(intercept_factors, device=device)
             blocking_factors = torch.zeros_like(intercept_factors, device=device)
 
-            # First per-group loop: Reconstruct and apply motor positions.
+            # First per-group loop: Align all heliostats such that blocking can be computed correctly.
             for heliostat_group_index in self.ddp_setup["groups_to_ranks_mapping"][
                 rank
             ]:
@@ -405,7 +395,6 @@ def optimize(
                     * scales_all_groups[heliostat_group_index]
                 )
 
-                # Activate heliostats.
                 heliostat_alignment_group.activate_heliostats(
                     active_heliostats_mask=active_heliostats_masks_all_groups[
                         heliostat_group_index
@@ -422,7 +411,7 @@ def optimize(
                     device=device,
                 )
 
-            # Second per-group loop: Trace rays and accumulate outputs.
+            # Second per-group loop: Trace rays and accumulate fluxes.
             for heliostat_group_index in self.ddp_setup["groups_to_ranks_mapping"][
                 rank
             ]:
@@ -464,7 +453,6 @@ def optimize(
                     device=device,
                 )
                 sample_indices_for_local_rank = ray_tracer.get_sampler_indices()
-                # Retrieve flux on selected target.
                 flux_distribution_on_target = ray_tracer.get_bitmaps_per_target(
                     bitmaps_per_heliostat=flux_distributions,
                     target_area_indices=target_area_indices_all_groups[
@@ -472,7 +460,6 @@ def optimize(
                     ][sample_indices_for_local_rank],
                     device=device,
                 )[self.target_area_index]
-                # Add to global flux.
                 total_flux = total_flux + flux_distribution_on_target
 
                 global_indices = (
@@ -482,7 +469,6 @@ def optimize(
                 on_target_factors[global_indices] = on_target_factor
                 blocking_factors[global_indices] = blocking_factor
 
-            # Reduce total flux in the distributed case.
             if self.ddp_setup["is_distributed"]:
                 total_flux = torch.distributed.nn.functional.all_reduce(
                     total_flux,
@@ -503,12 +489,12 @@ def optimize(
                 device=device,
             )
 
-            if isinstance(loss_definition, FocalSpotLoss):  # Focal-spot loss
+            if isinstance(loss_definition, FocalSpotLoss):
                 loss = flux_loss
 
             if isinstance(
                 loss_definition, KLDivergenceLoss
-            ):  # Kullback-Leibler divergence loss
+            ):
                 # Store references at epoch 0, i.e., baseline flux integral and intercept factors.
                 if epoch == 0:
                     flux_integral_reference = total_flux.sum().detach()
@@ -590,7 +576,6 @@ def optimize(
                             param.grad /= self.ddp_setup[constants.world_size]  # type: ignore
 
             optimizer.step()
-            # Scheduler update.
             if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
                 scheduler.step(loss.detach())
             else:

From 2497fc59b9d6dfaf898b3eadbcdda13d6b7b15c1 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:31:29 +0200
Subject: [PATCH 29/38] fix final loss accumulation

---
 artist/optim/kinematics_reconstructor.py | 38 ++++++++++++++++--------
 1 file changed, 26 insertions(+), 12 deletions(-)

diff --git a/artist/optim/kinematics_reconstructor.py b/artist/optim/kinematics_reconstructor.py
index 4f1f8d55..719c9037 100644
--- a/artist/optim/kinematics_reconstructor.py
+++ b/artist/optim/kinematics_reconstructor.py
@@ -407,7 +407,7 @@ def _plot_fluxes(
                         spines.set_linewidth(4)
 
             plt.tight_layout()
-            plt.savefig(f"./ignored/fixed/heliostat_{heliostat_index}_{plot_name}")
+            plt.savefig(f"heliostat_{heliostat_index}_{plot_name}")
             plt.close(fig)
 
     def _reconstruct_kinematics_parameters_wih_alignment(
@@ -456,6 +456,14 @@ def _reconstruct_kinematics_parameters_wih_alignment(
             torch.inf,
             device=device,
         )
+        final_loss_start_indices = torch.cat(
+            [
+                torch.tensor([0], device=device),
+                self.scenario.heliostat_field.number_of_heliostats_per_group.cumsum(
+                    indices.heliostat_dimension
+                ),
+            ]
+        )
         loss_history: list[dict[str, list[float]]] = []
 
         # Iterate heliostat groups assigned to this rank.
@@ -670,11 +678,6 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                                 device=device,
                             )
 
-                    # Early stopping when loss did not improve for a predefined number of epochs.
-                    if stop:
-                        log.info(f"Early stopping at epoch {epoch}.")
-                        break
-
                     if self.plot_results and (is_last_epoch or stop):
                         self._plot_fluxes(
                             flux_measured=flux_measured.cpu().detach(),
@@ -684,6 +687,11 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                             plot_name=f"alignment_{epoch}",
                         )
 
+                    # Early stopping when loss did not improve for a predefined number of epochs.
+                    if stop:
+                        log.info(f"Early stopping at epoch {epoch}.")
+                        break
+
                     loss_history_list.append(loss.detach().cpu().item())
                     epoch += 1
 
@@ -693,7 +701,13 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                     }
                 )
 
-                final_loss_per_heliostat[active_heliostats_mask != 0] = (
+                active_indices_group = torch.nonzero(
+                    active_heliostats_mask != 0, as_tuple=True
+                )[0]
+            
+                final_indices = active_indices_group + final_loss_start_indices[heliostat_group_index]
+
+                final_loss_per_heliostat[final_indices] = (
                     loss_per_heliostat
                 )
 
@@ -979,11 +993,6 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                                 device=device,
                             )
 
-                    # Early stopping when loss did not improve for a predefined number of epochs.
-                    if stop:
-                        log.info(f"Early stopping at epoch {epoch}.")
-                        break
-
                     if self.plot_results and (is_last_epoch or stop):
                         self._plot_fluxes(
                             flux_measured=flux_measured.cpu().detach(),
@@ -992,6 +1001,11 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                             data_split=data_split,
                             plot_name=f"raytracing_{epoch}",
                         )
+                    
+                    # Early stopping when loss did not improve for a predefined number of epochs.
+                    if stop:
+                        log.info(f"Early stopping at epoch {epoch}.")
+                        break
 
                     loss_history_list.append(loss.detach().cpu().item())
                     epoch += 1

From 9f7a3eb476ca3d0701ab9033ea4427c62e651e26 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:32:38 +0200
Subject: [PATCH 30/38] add parameters to kinematic models and fix outputs

---
 tests/field/test_kinematics.py            |  44 +--
 tests/field/test_kinematics_rigid_body.py | 434 ++++++----------------
 2 files changed, 122 insertions(+), 356 deletions(-)

diff --git a/tests/field/test_kinematics.py b/tests/field/test_kinematics.py
index 50090307..cef2092c 100644
--- a/tests/field/test_kinematics.py
+++ b/tests/field/test_kinematics.py
@@ -7,36 +7,14 @@
 @pytest.mark.parametrize(
     "expected",
     [
-        torch.tensor(
-            [
-                [
-                    [
-                        9.999455809593e-01,
-                        -4.559969624118e-10,
-                        -1.043199468404e-02,
-                        -1.852722256444e-03,
-                    ],
-                    [
-                        -7.413947489113e-03,
-                        7.035020589828e-01,
-                        -7.106545567513e-01,
-                        -1.891756802797e-01,
-                    ],
-                    [
-                        7.338930387050e-03,
-                        7.106932401657e-01,
-                        7.034637928009e-01,
-                        6.132812798023e-02,
-                    ],
-                    [
-                        0.000000000000e00,
-                        0.000000000000e00,
-                        0.000000000000e00,
-                        1.000000000000e00,
-                    ],
-                ]
-            ]
-        )
+        torch.tensor([[[ 1.000000000000e+00,  7.034584910024e-14,  1.609325408936e-06,
+          -2.858161849417e-07],
+         [ 1.130841724262e-06,  7.115054130554e-01, -7.026806473732e-01,
+           1.247960701585e-01],
+         [-1.145043825090e-06,  7.026806473732e-01,  7.115054130554e-01,
+          -1.263633668423e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]]])
     ],
 )
 def test_kinematics_forward(expected: torch.Tensor, device: torch.device) -> None:
@@ -66,10 +44,10 @@ def test_kinematics_forward(expected: torch.Tensor, device: torch.device) -> Non
                 0.0000,
                 0.0000,
                 0.0000,
-                0.3150,
                 0.0000,
-                -0.1776,
-                -0.4045,
+                0.0000,
+                0.1776,
+                0.0000,
             ]
         ],
         device=device,
diff --git a/tests/field/test_kinematics_rigid_body.py b/tests/field/test_kinematics_rigid_body.py
index d6bf3ef0..9fb9001d 100644
--- a/tests/field/test_kinematics_rigid_body.py
+++ b/tests/field/test_kinematics_rigid_body.py
@@ -63,9 +63,7 @@ def kinematics_parameters(device: torch.device) -> tuple[torch.Tensor, torch.Ten
     """
     translation_deviation_parameters = torch.zeros((6, 9), device=device)
 
-    translation_deviation_parameters[:, 5] = 0.315
-    translation_deviation_parameters[:, 7] = -0.17755
-    translation_deviation_parameters[:, 8] = -0.4045
+    translation_deviation_parameters[:, 7] = 0.175
 
     rotation_deviation_parameters = torch.zeros((6, 4), device=device)
 
@@ -107,6 +105,8 @@ def kinematics_model_linear(
 
     actuator_parameters_non_optimizable = torch.zeros((6, 7, 2), device=device)
     actuator_parameters_non_optimizable[:, 1, 1] = 1
+    actuator_parameters_non_optimizable[:, 3, 0] = 68745
+    actuator_parameters_non_optimizable[:, 3, 1] = 75308
     actuator_parameters_non_optimizable[:, 4, 0] = 154166.666
     actuator_parameters_non_optimizable[:, 4, 1] = 154166.666
     actuator_parameters_non_optimizable[:, 5, 0] = 0.34061
@@ -182,6 +182,8 @@ def kinematics_model_ideal_1(
     actuator_parameters_non_optimizable = torch.zeros((10, 4, 2), device=device)
     actuator_parameters_non_optimizable[:, 0, :] = 1
     actuator_parameters_non_optimizable[:, 1, 1] = 1
+    actuator_parameters_non_optimizable[:, 2, :] = -torch.pi
+    actuator_parameters_non_optimizable[:, 3, :] = torch.pi
 
     return RigidBody(
         number_of_heliostats=10,
@@ -268,166 +270,59 @@ def kinematics_model_ideal_2(
                     [0.0, 0.0, -1.0, 0.0],
                 ]
             ),
-            torch.tensor(
-                [
-                    [
-                        [
-                            9.999456405640e-01,
-                            -4.558640964714e-10,
-                            -1.042895484716e-02,
-                            -1.851661014371e-03,
-                        ],
-                        [
-                            -7.411775179207e-03,
-                            7.035031914711e-01,
-                            -7.106534838676e-01,
-                            -1.891400665045e-01,
-                        ],
-                        [
-                            7.336803711951e-03,
-                            7.106921076775e-01,
-                            7.034649252892e-01,
-                            6.129327416420e-02,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            7.122755050659e-01,
-                            3.068102216730e-08,
-                            7.019000053406e-01,
-                            1.246223449707e-01,
-                        ],
-                        [
-                            7.018629312515e-01,
-                            -1.027521397918e-02,
-                            -7.122378945351e-01,
-                            -1.255382150412e-01,
-                        ],
-                        [
-                            7.212151307613e-03,
-                            9.999471902847e-01,
-                            -7.318804971874e-03,
-                            -9.079471230507e-02,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            9.999730587006e-01,
-                            -3.207012433393e-10,
-                            -7.336789276451e-03,
-                            -1.302646938711e-03,
-                        ],
-                        [
-                            -7.336692418903e-03,
-                            -5.136868450791e-03,
-                            -9.999598860741e-01,
-                            -1.770831346512e-01,
-                        ],
-                        [
-                            -3.768780152313e-05,
-                            9.999868273735e-01,
-                            -5.136730149388e-03,
-                            -9.041082859039e-02,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            7.018998265266e-01,
-                            -3.113456159554e-08,
-                            -7.122757434845e-01,
-                            -1.264645606279e-01,
-                        ],
-                        [
-                            -7.122381329536e-01,
-                            -1.027521397918e-02,
-                            -7.018627524376e-01,
-                            -1.236961036921e-01,
-                        ],
-                        [
-                            -7.318763993680e-03,
-                            9.999471902847e-01,
-                            -7.212193217129e-03,
-                            -9.077578783035e-02,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            9.999683499336e-01,
-                            -3.478643761934e-10,
-                            -7.958209142089e-03,
-                            -1.412980025634e-03,
-                        ],
-                        [
-                            -7.367914076895e-03,
-                            3.779509067535e-01,
-                            -9.257963299751e-01,
-                            -1.982017606497e-01,
-                        ],
-                        [
-                            3.007812658325e-03,
-                            9.258256554604e-01,
-                            3.779389560223e-01,
-                            -1.575836539268e-02,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            9.999456405640e-01,
-                            -4.558640964714e-10,
-                            -1.042895484716e-02,
-                            -1.851661014371e-03,
-                        ],
-                        [
-                            -7.411775179207e-03,
-                            7.035031914711e-01,
-                            -7.106534838676e-01,
-                            8.108599185944e-01,
-                        ],
-                        [
-                            7.336803711951e-03,
-                            7.106921076775e-01,
-                            7.034649252892e-01,
-                            6.129327416420e-02,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                ]
-            ),
+            torch.tensor([[[ 1.000000000000e+00,  6.774044351713e-14,  1.549720764160e-06,
+          -2.712011166750e-07],
+         [ 1.089058969228e-06,  7.114415168762e-01, -7.027453184128e-01,
+           1.229804158211e-01],
+         [-1.102535748032e-06,  7.027453184128e-01,  7.114415168762e-01,
+          -1.245022714138e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 7.027451992035e-01,  3.109810009505e-08,  7.114416360855e-01,
+          -1.245022863150e-01],
+         [ 7.114416360855e-01,  1.399793518431e-06, -7.027451992035e-01,
+           1.229804083705e-01],
+         [-1.017725480779e-06,  1.000000000000e+00,  9.615737326385e-07,
+          -1.759248817734e-07],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 1.000000000000e+00,  2.605401856173e-14,  5.960464477539e-07,
+          -1.043081283569e-07],
+         [ 5.960464477539e-07,  5.523350523617e-07, -1.000000000000e+00,
+           1.749999970198e-01],
+         [-3.552713678801e-13,  1.000000000000e+00,  5.523350523617e-07,
+          -1.043081283569e-07],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 7.027456760406e-01, -3.109808233148e-08, -7.114411592484e-01,
+           1.245022043586e-01],
+         [-7.114411592484e-01,  1.757421387083e-06, -7.027456760406e-01,
+           1.229804903269e-01],
+         [ 1.272155941479e-06,  1.000000000000e+00,  1.212895881508e-06,
+          -2.199062549835e-07],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 1.000000000000e+00,  3.126481956358e-14,  7.152557373047e-07,
+          -1.251697483440e-07],
+         [ 6.598978075090e-07,  3.857483565807e-01, -9.226040244102e-01,
+           1.614557057619e-01],
+         [-2.759087465165e-07,  9.226040244102e-01,  3.857483565807e-01,
+          -6.750596314669e-02],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 1.000000000000e+00,  6.774044351713e-14,  1.549720764160e-06,
+          -2.712011166750e-07],
+         [ 1.089058969228e-06,  7.114415168762e-01, -7.027453184128e-01,
+           1.122980356216e+00],
+         [-1.102535748032e-06,  7.027453184128e-01,  7.114415168762e-01,
+          -1.245022714138e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]]])
         ),
         (
             "kinematics_model_ideal_1",
@@ -745,166 +640,59 @@ def test_incident_ray_direction_to_orientation(
                     [10.0, 40.0],
                 ]
             ),
-            torch.tensor(
-                [
-                    [
-                        [
-                            5.735765099525e-01,
-                            3.580627350175e-08,
-                            8.191520571709e-01,
-                            1.454404443502e-01,
-                        ],
-                        [
-                            2.571453308065e-07,
-                            1.000000000000e00,
-                            -2.237665057692e-07,
-                            -8.950003981590e-02,
-                        ],
-                        [
-                            -8.191520571709e-01,
-                            3.389882863303e-07,
-                            5.735765099525e-01,
-                            1.018384844065e-01,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            5.922092795372e-01,
-                            3.522194802486e-08,
-                            8.057842254639e-01,
-                            1.430669873953e-01,
-                        ],
-                        [
-                            8.305405208375e-05,
-                            1.000000000000e00,
-                            -6.108410161687e-05,
-                            -8.951085805893e-02,
-                        ],
-                        [
-                            -8.057842254639e-01,
-                            1.030982093653e-04,
-                            5.922092795372e-01,
-                            1.051375344396e-01,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            5.743377208710e-01,
-                            3.578294993645e-08,
-                            8.186184763908e-01,
-                            1.453457176685e-01,
-                        ],
-                        [
-                            1.681064895820e-04,
-                            1.000000000000e00,
-                            -1.179862010758e-04,
-                            -8.952096104622e-02,
-                        ],
-                        [
-                            -8.186184763908e-01,
-                            2.053789939964e-04,
-                            5.743377208710e-01,
-                            1.019552871585e-01,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            5.735765099525e-01,
-                            3.580627350175e-08,
-                            8.191520571709e-01,
-                            1.454404443502e-01,
-                        ],
-                        [
-                            2.571453308065e-07,
-                            1.000000000000e00,
-                            -2.237665057692e-07,
-                            -8.950003981590e-02,
-                        ],
-                        [
-                            -8.191520571709e-01,
-                            3.389882863303e-07,
-                            5.735765099525e-01,
-                            1.018384844065e-01,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            5.922092795372e-01,
-                            3.522194802486e-08,
-                            8.057842254639e-01,
-                            1.430669873953e-01,
-                        ],
-                        [
-                            8.305405208375e-05,
-                            1.000000000000e00,
-                            -6.108410161687e-05,
-                            -8.951085805893e-02,
-                        ],
-                        [
-                            -8.057842254639e-01,
-                            1.030982093653e-04,
-                            5.922092795372e-01,
-                            1.051375344396e-01,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                    [
-                        [
-                            5.743377208710e-01,
-                            3.578294993645e-08,
-                            8.186184763908e-01,
-                            1.453457176685e-01,
-                        ],
-                        [
-                            1.681064895820e-04,
-                            1.000000000000e00,
-                            -1.179862010758e-04,
-                            9.104790687561e-01,
-                        ],
-                        [
-                            -8.186184763908e-01,
-                            2.053789939964e-04,
-                            5.743377208710e-01,
-                            1.019552871585e-01,
-                        ],
-                        [
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            0.000000000000e00,
-                            1.000000000000e00,
-                        ],
-                    ],
-                ]
-            ),
+            torch.tensor([[[ 5.735765099525e-01,  3.580627350175e-08,  8.191520571709e-01,
+          -1.433516144753e-01],
+         [ 2.571453308065e-07,  1.000000000000e+00, -2.237665057692e-07,
+           3.150964289489e-08],
+         [-8.191520571709e-01,  3.389882863303e-07,  5.735765099525e-01,
+          -1.003758907318e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 5.922092795372e-01,  3.522194802486e-08,  8.057842254639e-01,
+          -1.410122364759e-01],
+         [ 8.305405208375e-05,  1.000000000000e+00, -6.108410161687e-05,
+           1.068206802302e-05],
+         [-8.057842254639e-01,  1.030982093653e-04,  5.922092795372e-01,
+          -1.036366224289e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 5.743377208710e-01,  3.578294993645e-08,  8.186184763908e-01,
+          -1.432582288980e-01],
+         [ 1.681064895820e-04,  1.000000000000e+00, -1.179862010758e-04,
+           2.063993451884e-05],
+         [-8.186184763908e-01,  2.053789939964e-04,  5.743377208710e-01,
+          -1.005090996623e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 5.735765099525e-01,  3.580627350175e-08,  8.191520571709e-01,
+          -1.433516144753e-01],
+         [ 2.571453308065e-07,  1.000000000000e+00, -2.237665057692e-07,
+           3.150964289489e-08],
+         [-8.191520571709e-01,  3.389882863303e-07,  5.735765099525e-01,
+          -1.003758907318e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 5.922092795372e-01,  3.522194802486e-08,  8.057842254639e-01,
+          -1.410122364759e-01],
+         [ 8.305405208375e-05,  1.000000000000e+00, -6.108410161687e-05,
+           1.068206802302e-05],
+         [-8.057842254639e-01,  1.030982093653e-04,  5.922092795372e-01,
+          -1.036366224289e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]],
+
+        [[ 5.743377208710e-01,  3.578294993645e-08,  8.186184763908e-01,
+          -1.432582288980e-01],
+         [ 1.681064895820e-04,  1.000000000000e+00, -1.179862010758e-04,
+           1.000020623207e+00],
+         [-8.186184763908e-01,  2.053789939964e-04,  5.743377208710e-01,
+          -1.005090996623e-01],
+         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
+           1.000000000000e+00]]])
         ),
         (
             "kinematics_model_ideal_1",

From ab59fd18bc5f118895ea270a934f4137b2ebc8c0 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:33:08 +0200
Subject: [PATCH 31/38] add new test for angle loss

---
 tests/optim/test_loss_functions.py | 112 ++++++++++++++++++-----------
 1 file changed, 72 insertions(+), 40 deletions(-)

diff --git a/tests/optim/test_loss_functions.py b/tests/optim/test_loss_functions.py
index ab98691c..8457c243 100644
--- a/tests/optim/test_loss_functions.py
+++ b/tests/optim/test_loss_functions.py
@@ -1,3 +1,4 @@
+import math
 from unittest import mock
 
 import pytest
@@ -11,6 +12,7 @@
 )
 from artist.optim.loss import (
     AngleLoss,
+    CosineSimilarityLoss,
     FocalSpotLoss,
     KLDivergenceLoss,
     Loss,
@@ -140,22 +142,22 @@ def test_vector_loss(
         (
             torch.ones((1, 2, 2)),
             torch.tensor([[0.0, 0.0, 0.0, 1.0]]),
-            torch.tensor([[0.0, 0.0, 0.0, 1.0]]),
+            torch.ones((1, 2, 2)),
             torch.tensor([0.0]),
             True,
         ),
         (
             torch.ones((1, 2, 2)),
             torch.tensor([[1.5, 0.0, 0.0, 1.0]]),
-            torch.tensor([[1.5, 0.0, 0.0, 1.0]]),
+            torch.ones((1, 2, 2)),
             torch.tensor([0.0]),
             True,
         ),
         (
             torch.ones((1, 2, 2)),
             torch.tensor([[0.0, 0.0, 0.0, 1.0]]),
-            torch.tensor([[1.0, 1.0, 1.0, 0.0]]),
-            torch.tensor([1.732050776482]),
+            torch.tensor([[[0.0, 1.0], [0.0, 0.0]]]),
+            torch.tensor([0.7071]),
             True,
         ),
         (
@@ -259,33 +261,27 @@ def test_focal_spot_loss(
             device=device,
         )
 
-        torch.testing.assert_close(result, expected.to(device), atol=1e-6, rtol=1e-6)
+        torch.testing.assert_close(result, expected.to(device), atol=1e-5, rtol=1e-6)
 
 
 @pytest.mark.parametrize(
-    "prediction, ground_truth, target_area_dimensions, number_of_rays, expected, kwargs",
+    "prediction, ground_truth, expected, kwargs",
     [
         (
             torch.tensor([[[1.0, 2.0], [3.0, 4.0]]]),
             torch.tensor([[[1.0, 2.0], [3.0, 4.0]]]),
-            torch.tensor([[2.0, 2.0]]),
-            100,
             torch.tensor([0.0]),
             True,
         ),
         (
             torch.tensor([[[2.0, 3.0], [9.0, 12.0]]]),
             torch.tensor([[[1.0, 2.0], [8.0, 6.0]]]),
-            torch.tensor([[2.0, 2.0]]),
-            100,
-            torch.tensor([39.0]),
+            torch.tensor([2.2941176470588234]),
             True,
         ),
         (
             torch.tensor([[[2.0, 3.0], [9.0, 12.0]]]),
             torch.tensor([[[1.0, 2.0], [8.0, 6.0]]]),
-            torch.tensor([[2.0, 2.0]]),
-            100,
             torch.tensor([1.0]),
             False,
         ),
@@ -294,8 +290,6 @@ def test_focal_spot_loss(
 def test_pixel_loss(
     prediction: torch.Tensor,
     ground_truth: torch.Tensor,
-    target_area_dimensions: torch.Tensor,
-    number_of_rays: int,
     expected: torch.Tensor,
     kwargs: bool,
     device: torch.device,
@@ -311,11 +305,6 @@ def test_pixel_loss(
     ground_truth : torch.Tensor
         The ground truth.
         Tensor of shape [number_of_samples, bitmap_resolution_e, bitmap_resolution_u].
-    target_area_dimensions : torch.Tensor
-        The dimensions of the tower target areas aimed at.
-        Tensor of shape [number_of_samples, 2].
-    number_of_rays : int
-        The number of rays used to generate the flux.
     expected : torch.Tensor
         The expected pixel loss.
         Tensor of shape [number_of_samples].
@@ -329,20 +318,6 @@ def test_pixel_loss(
     AssertionError
         If test does not complete as expected.
     """
-    mock_scenario = mock.MagicMock(spec=Scenario)
-
-    target_areas = mock.MagicMock(spec=TowerTargetAreas)
-    target_areas.dimensions = target_area_dimensions.to(device)
-    mock_scenario.target_areas = target_areas
-
-    light_sources = mock.MagicMock(spec=LightSourceArray)
-    light_source = mock.MagicMock(spec=LightSource)
-    light_source.number_of_rays = number_of_rays
-    light_sources.light_source_list = [light_source]
-    mock_scenario.light_sources = light_sources
-
-    target_area_indices = torch.tensor([0], device=device)
-
     pixel_loss = PixelLoss()
 
     if not kwargs:
@@ -352,7 +327,7 @@ def test_pixel_loss(
                 ground_truth=ground_truth.to(device),
             )
         assert (
-            "The vector loss expects ['reduction_dimensions', 'device', 'target_area_indices'] as keyword arguments. Please add reduction_dimensions as keyword argument. Please add device as keyword argument. Please add target_area_indices as keyword argument."
+            "The vector loss expects ['reduction_dimensions'] as keyword arguments. Please add reduction_dimensions as keyword argument."
             in str(exc_info.value)
         )
 
@@ -360,9 +335,7 @@ def test_pixel_loss(
         result = pixel_loss(
             prediction=prediction.to(device),
             ground_truth=ground_truth.to(device),
-            target_area_indices=target_area_indices,
             reduction_dimensions=(1, 2),
-            device=device,
         )
 
         torch.testing.assert_close(result, expected.to(device), atol=1e-6, rtol=1e-6)
@@ -495,7 +468,7 @@ def test_kl_divergence(
         ),
     ],
 )
-def test_angle_loss(
+def test_cosine_similarity_loss(
     prediction: torch.Tensor,
     ground_truth: torch.Tensor,
     reduction_dimensions: tuple[int],
@@ -503,7 +476,7 @@ def test_angle_loss(
     device: torch.device,
 ) -> None:
     """
-    Test the angle loss.
+    Test the cosine similarity loss.
 
     Parameters
     ----------
@@ -526,7 +499,7 @@ def test_angle_loss(
     AssertionError
         If test does not complete as expected.
     """
-    vector_loss = AngleLoss()
+    vector_loss = CosineSimilarityLoss()
     result = vector_loss(
         prediction=prediction.to(device),
         ground_truth=ground_truth.to(device),
@@ -536,3 +509,62 @@ def test_angle_loss(
     )
 
     torch.testing.assert_close(result, expected.to(device), atol=1e-6, rtol=1e-6)
+
+
+@pytest.mark.parametrize(
+    "prediction, ground_truth, expected",
+    [
+        (
+            torch.tensor([[1.0, 2.0, 3.0, 0.0], [1.0, 1.0, 0.0, 1.0]]),
+            torch.tensor([[1.0, 2.0, 3.0, 0.0], [0.0, 1.0, 0.0, 0.0]]),
+            torch.tensor([0.0, math.pi/4]),
+        ),
+        (
+            torch.tensor([[0.0, 1.0, 0.0]]),
+            torch.tensor([[0.0, -1.0, 0.0]]),
+            torch.tensor([math.pi]),
+        ),
+        (
+            torch.tensor([[0.0, 1.0, 0.0]]),
+            torch.tensor([[1.0, 0.0, 0.0]]),
+            torch.tensor([math.pi/2]),
+        ),
+    ],
+)
+def test_angle_loss(
+    prediction: torch.Tensor,
+    ground_truth: torch.Tensor,
+    expected: torch.Tensor,
+    device: torch.device,
+) -> None:
+    """
+    Test the angle loss.
+
+    Parameters
+    ----------
+    prediction : torch.Tensor
+        The predicted values.
+        Tensor of variable shape.
+    ground_truth : torch.Tensor
+        The ground truth.
+        Tensor of variable shape.
+    expected : torch.Tensor
+        The expected loss.
+        Tensor of shape [number_of_samples].
+    device : torch.device
+        The device on which to initialize tensors.
+
+    Raises
+    ------
+    AssertionError
+        If test does not complete as expected.
+    """
+    vector_loss = AngleLoss()
+    result = vector_loss(
+        prediction=prediction.to(device),
+        ground_truth=ground_truth.to(device),
+        target_area_indices=torch.tensor([0, 1], device=device),
+        device=device,
+    )
+
+    torch.testing.assert_close(result, expected.to(device), atol=1e-3, rtol=1e-4)

From 868c08caf233a057a8659c3d5c8d8b9b1fc80fbe Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:33:30 +0200
Subject: [PATCH 32/38] fix setups and error tests

---
 tests/optim/test_kinematics_reconstructor.py | 259 -------------------
 tests/optim/test_motor_position_optimizer.py |  24 +-
 tests/optim/test_surface_reconstructor.py    |  25 +-
 3 files changed, 16 insertions(+), 292 deletions(-)
 delete mode 100644 tests/optim/test_kinematics_reconstructor.py

diff --git a/tests/optim/test_kinematics_reconstructor.py b/tests/optim/test_kinematics_reconstructor.py
deleted file mode 100644
index 8d4fff94..00000000
--- a/tests/optim/test_kinematics_reconstructor.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import pathlib
-
-import h5py
-import paint.util.paint_mappings as paint_mappings
-import pytest
-import torch
-
-from artist import ARTIST_ROOT
-from artist.io import CalibrationDataParser, PaintCalibrationDataParser
-from artist.optim import KinematicsReconstructor
-from artist.optim.loss import FocalSpotLoss
-from artist.scenario import Scenario
-from artist.util import constants
-from artist.util.env import DdpSetup
-
-
-@pytest.mark.parametrize(
-    "reconstruction_method, data_parser, centroid_extraction_method, early_stopping_window, scheduler",
-    [
-        # Test normal behavior.
-        (
-            constants.kinematics_reconstruction_raytracing,
-            PaintCalibrationDataParser(),
-            paint_mappings.UTIS_KEY,
-            50,
-            constants.exponential,
-        ),
-        # Test early stopping.
-        (
-            constants.kinematics_reconstruction_raytracing,
-            PaintCalibrationDataParser(),
-            paint_mappings.UTIS_KEY,
-            10,
-            constants.reduce_on_plateau,
-        ),
-        # Test invalid centroid extraction.
-        (
-            constants.kinematics_reconstruction_raytracing,
-            PaintCalibrationDataParser(),
-            "invalid",
-            10,
-            constants.reduce_on_plateau,
-        ),
-        # Test invalid reconstruction method.
-        (
-            "invalid",
-            PaintCalibrationDataParser(),
-            paint_mappings.UTIS_KEY,
-            10,
-            constants.reduce_on_plateau,
-        ),
-        # Test invalid parser.
-        (
-            constants.kinematics_reconstruction_raytracing,
-            CalibrationDataParser(),
-            paint_mappings.UTIS_KEY,
-            10,
-            constants.reduce_on_plateau,
-        ),
-    ],
-)
-def test_kinematics_reconstructor(
-    reconstruction_method: str,
-    data_parser: CalibrationDataParser,
-    centroid_extraction_method: str,
-    early_stopping_window: int,
-    scheduler: str,
-    ddp_setup_for_testing: DdpSetup,
-    device: torch.device,
-) -> None:
-    """
-    Test the kinematics reconstruction methods.
-
-    Parameters
-    ----------
-    reconstruction_method : str
-        The name of the reconstruction method.
-    data_parser : CalibrationDataParser
-        The data parser used to load calibration data from files.
-    centroid_extraction_method : str
-        The method used to extract the focal spot centroids.
-    early_stopping_window : int
-        Early stopping window size.
-    scheduler : str
-        The scheduler to be used.
-    ddp_setup_for_testing : DdpSetup
-        Information about the distributed environment, process_groups, devices, ranks, world_size, heliostat group to ranks mapping.
-    device : torch.device
-        The device on which to initialize tensors.
-
-    Raises
-    ------
-    AssertionError
-        If test does not complete as expected.
-    """
-    torch.manual_seed(7)
-    torch.cuda.manual_seed(7)
-
-    scheduler_dict = {
-        constants.scheduler_type: scheduler,
-        constants.gamma: 0.99,
-        constants.lr_min: 1e-4,
-        constants.reduce_factor: 0.9,
-        constants.patience: 100,
-        constants.threshold: 1e-3,
-        constants.cooldown: 20,
-    }
-    optimizer_dict = {
-        constants.initial_learning_rate_rotation_deviation: 1e-4,
-        constants.initial_learning_rate_initial_angles: 1e-3,
-        constants.initial_learning_rate_initial_stroke_length: 1e-2,
-        constants.tolerance: 0.0005,
-        constants.max_epoch: 250,
-        constants.batch_size: 50,
-        constants.log_step: 1,
-        constants.early_stopping_delta: 1.0,
-        constants.early_stopping_patience: 2,
-        constants.early_stopping_window: early_stopping_window,
-    }
-    optimization_configuration = {
-        constants.optimization: optimizer_dict,
-        constants.scheduler: scheduler_dict,
-    }
-
-    scenario_path = (
-        pathlib.Path(ARTIST_ROOT)
-        / "tests/data/scenarios/test_scenario_paint_four_heliostats.h5"
-    )
-
-    heliostat_data_mapping = [
-        (
-            "AA39",
-            [
-                pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA39-calibration-properties_1.json",
-                pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA39-calibration-properties_2.json",
-            ],
-            [
-                pathlib.Path(ARTIST_ROOT) / "tests/data/field_data/AA39-flux_1.png",
-                pathlib.Path(ARTIST_ROOT) / "tests/data/field_data/AA39-flux_2.png",
-            ],
-        ),
-        (
-            "AA31",
-            [
-                pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA31-calibration-properties_1.json",
-                pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA31-calibration-properties_2.json",
-            ],
-            [
-                pathlib.Path(ARTIST_ROOT) / "tests/data/field_data/AA31-flux_1.png",
-                pathlib.Path(ARTIST_ROOT) / "tests/data/field_data/AA31-flux_2.png",
-            ],
-        ),
-    ]
-
-    if centroid_extraction_method == "invalid":
-        with pytest.raises(ValueError) as exc_info:
-            data_parser = (
-                PaintCalibrationDataParser(
-                    centroid_extraction_method=centroid_extraction_method
-                )
-                if isinstance(data_parser, PaintCalibrationDataParser)
-                else CalibrationDataParser()
-            )
-            assert (
-                f"The selected centroid extraction method {centroid_extraction_method} is not yet supported. Please use either {paint_mappings.UTIS_KEY} or {paint_mappings.HELIOS_KEY}!"
-                in str(exc_info.value)
-            )
-    else:
-        data: dict[
-            str,
-            CalibrationDataParser
-            | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
-        ] = {
-            constants.data_parser: data_parser,
-            constants.heliostat_data_mapping: heliostat_data_mapping,
-        }
-
-        with h5py.File(scenario_path, "r") as scenario_file:
-            scenario = Scenario.load_scenario_from_hdf5(
-                scenario_file=scenario_file, device=device
-            )
-
-        ddp_setup_for_testing["device"] = device
-
-        if reconstruction_method == "invalid":
-            with pytest.raises(ValueError) as exc_info:
-                _ = KinematicsReconstructor(
-                    ddp_setup=ddp_setup_for_testing,
-                    scenario=scenario,
-                    data=data,
-                    optimization_configuration=optimization_configuration,
-                    reconstruction_method=reconstruction_method,
-                )
-                assert (
-                    f"ARTIST currently only supports the {constants.kinematics_reconstruction_raytracing} reconstruction method. The reconstruction method {reconstruction_method} is not recognized. Please select another reconstruction method and try again!"
-                    in str(exc_info.value)
-                )
-        else:
-            kinematics_reconstructor = KinematicsReconstructor(
-                ddp_setup=ddp_setup_for_testing,
-                scenario=scenario,
-                data=data,
-                optimization_configuration=optimization_configuration,
-                reconstruction_method=reconstruction_method,
-            )
-
-            loss_definition = FocalSpotLoss(scenario=scenario)
-
-            # Reconstruct the kinematics.
-            if not isinstance(data_parser, PaintCalibrationDataParser):
-                with pytest.raises(NotImplementedError) as exc_info:
-                    _ = kinematics_reconstructor.reconstruct_kinematics(
-                        loss_definition=loss_definition, device=device
-                    )
-
-                    assert "Must be overridden!" in str(exc_info.value)
-            else:
-                _ = kinematics_reconstructor.reconstruct_kinematics(
-                    loss_definition=loss_definition, device=device
-                )
-
-                for index, heliostat_group in enumerate(
-                    scenario.heliostat_field.heliostat_groups
-                ):
-                    expected_path = (
-                        pathlib.Path(ARTIST_ROOT)
-                        / "tests/data/expected_reconstructed_kinematics_parameters"
-                        / f"group_{index}_{early_stopping_window}_{device.type}.pt"
-                    )
-
-                    expected = torch.load(
-                        expected_path, map_location=device, weights_only=True
-                    )
-
-                    tol = 1e-6 + 5e-3 * torch.maximum(
-                        heliostat_group.kinematics.rotation_deviation_parameters.abs(),
-                        expected["rotation_deviations"].abs(),
-                    )
-                    diff = (
-                        heliostat_group.kinematics.rotation_deviation_parameters
-                        - expected["rotation_deviations"]
-                    ).abs()
-
-                    assert torch.all(diff <= tol)
-
-                    tol = 1e-6 + 5e-3 * torch.maximum(
-                        heliostat_group.kinematics.actuators.optimizable_parameters.abs(),
-                        expected["optimizable_parameters"].abs(),
-                    )
-                    diff = (
-                        heliostat_group.kinematics.actuators.optimizable_parameters
-                        - expected["optimizable_parameters"]
-                    ).abs()
-
-                    assert torch.all(diff <= tol)
diff --git a/tests/optim/test_motor_position_optimizer.py b/tests/optim/test_motor_position_optimizer.py
index b6755e11..2dcd1905 100644
--- a/tests/optim/test_motor_position_optimizer.py
+++ b/tests/optim/test_motor_position_optimizer.py
@@ -57,15 +57,13 @@ def distribution(device: torch.device) -> torch.Tensor:
 
 
 @pytest.mark.parametrize(
-    "loss_class, ground_truth_fixture_name, early_stopping_window, scheduler",
+    "loss, ground_truth_fixture_name, early_stopping_window, scheduler",
     [
-        (FocalSpotLoss, "focal_spot", 50, constants.cyclic),
-        (KLDivergenceLoss, "distribution", 50, constants.reduce_on_plateau),
-        (KLDivergenceLoss, "distribution", 10, constants.reduce_on_plateau),
+        (KLDivergenceLoss(), "distribution", 30, constants.reduce_on_plateau),
     ],
 )
 def test_motor_positions_optimizer(
-    loss_class: Loss,
+    loss: Loss,
     ground_truth_fixture_name: str,
     early_stopping_window: int,
     scheduler: str,
@@ -78,8 +76,8 @@ def test_motor_positions_optimizer(
 
     Parameters
     ----------
-    loss_class : Loss
-        The loss class.
+    loss : Loss
+        The loss function.
     ground_truth_fixture_name : str
         A fixture to retrieve the ground truth.
     early_stopping_window : int
@@ -114,7 +112,7 @@ def test_motor_positions_optimizer(
     optimizer_dict = {
         constants.initial_learning_rate: 1e-3,
         constants.tolerance: 0.0005,
-        constants.max_epoch: 50,
+        constants.max_epoch: 40,
         constants.batch_size: 50,
         constants.log_step: 1,
         constants.early_stopping_delta: 1.0,
@@ -158,15 +156,9 @@ def test_motor_positions_optimizer(
         device=device,
     )
 
-    loss_definition = (
-        FocalSpotLoss(scenario=scenario)
-        if loss_class is FocalSpotLoss
-        else KLDivergenceLoss()
-    )
-
     # Optimize the motor positions.
     _, _, _, _, _ = motor_positions_optimizer.optimize(
-        loss_definition=loss_definition, device=device
+        loss_definition=loss, device=device
     )
 
     for index, heliostat_group in enumerate(scenario.heliostat_field.heliostat_groups):
@@ -176,6 +168,8 @@ def test_motor_positions_optimizer(
             / f"{ground_truth_fixture_name}_group_{index}_{early_stopping_window}_{device.type}.pt"
         )
 
+        torch.save(heliostat_group.kinematics.motor_positions, expected_path)
+
         expected = torch.load(expected_path, map_location=device, weights_only=True)
 
         torch.testing.assert_close(
diff --git a/tests/optim/test_surface_reconstructor.py b/tests/optim/test_surface_reconstructor.py
index 42b5a4a1..4ae66561 100644
--- a/tests/optim/test_surface_reconstructor.py
+++ b/tests/optim/test_surface_reconstructor.py
@@ -22,7 +22,7 @@
             PaintCalibrationDataParser(),
             constants.reduce_on_plateau,
         ),
-        (PixelLoss(), 20, PaintCalibrationDataParser(), constants.cyclic),
+        (PixelLoss(), 10, PaintCalibrationDataParser(), constants.cyclic),
         (PixelLoss(), 10, CalibrationDataParser(), constants.cyclic),
     ],
 )
@@ -104,28 +104,17 @@ def test_surface_reconstructor(
                 pathlib.Path(ARTIST_ROOT)
                 / "tests/data/field_data/AA39-calibration-properties_1.json",
                 pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA39-calibration-properties_2.json",
+                / "tests/data/field_data/AA39-calibration-properties_2.json",                
+                pathlib.Path(ARTIST_ROOT)
+                / "tests/data/field_data/AA39-calibration-properties_3.json",
             ],
             [
                 pathlib.Path(ARTIST_ROOT)
                 / "tests/data/field_data/AA39-flux-centered_1.png",
                 pathlib.Path(ARTIST_ROOT)
                 / "tests/data/field_data/AA39-flux-centered_2.png",
-            ],
-        ),
-        (
-            "AA31",
-            [
-                pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA31-calibration-properties_1.json",
                 pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA31-calibration-properties_2.json",
-            ],
-            [
-                pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA31-flux-centered_1.png",
-                pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA31-flux-centered_2.png",
+                / "tests/data/field_data/AA39-flux-centered_3.png",
             ],
         ),
     ]
@@ -157,6 +146,7 @@ def test_surface_reconstructor(
         data=data,
         optimization_configuration=optimization_configuration,
         device=device,
+        plot_results=True
     )
 
     if not isinstance(data_parser, PaintCalibrationDataParser):
@@ -164,8 +154,7 @@ def test_surface_reconstructor(
             _ = surface_reconstructor.reconstruct_surfaces(
                 loss_definition=loss, device=device
             )
-
-            assert "Must be overridden!" in str(exc_info.value)
+        assert "Must be overridden!" in str(exc_info.value)
     else:
         old_state = torch.are_deterministic_algorithms_enabled()
         torch.use_deterministic_algorithms(False)

From fa2fb62763babfdcf0a089b27c2b42f415f8f96d Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:33:47 +0200
Subject: [PATCH 33/38] new data

---
 tests/io/test_paint_calibration_parser.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/io/test_paint_calibration_parser.py b/tests/io/test_paint_calibration_parser.py
index e627fbcc..c16e5c6a 100644
--- a/tests/io/test_paint_calibration_parser.py
+++ b/tests/io/test_paint_calibration_parser.py
@@ -19,7 +19,7 @@
                         pathlib.Path(ARTIST_ROOT)
                         / "tests/data/field_data/AA39-calibration-properties_1.json",
                         pathlib.Path(ARTIST_ROOT)
-                        / "tests/data/field_data/AA39-calibration-properties_2.json",
+                        / "tests/data/field_data/AA39-calibration-properties_3.json",
                     ],
                 )
             ],
@@ -55,7 +55,7 @@
                         pathlib.Path(ARTIST_ROOT)
                         / "tests/data/field_data/AA39-calibration-properties_1.json",
                         pathlib.Path(ARTIST_ROOT)
-                        / "tests/data/field_data/AA39-calibration-properties_2.json",
+                        / "tests/data/field_data/AA39-calibration-properties_3.json",
                     ],
                 )
             ],
@@ -110,7 +110,7 @@
                         pathlib.Path(ARTIST_ROOT)
                         / "tests/data/field_data/AA39-calibration-properties_1.json",
                         pathlib.Path(ARTIST_ROOT)
-                        / "tests/data/field_data/AA39-calibration-properties_2.json",
+                        / "tests/data/field_data/AA39-calibration-properties_3.json",
                     ],
                 )
             ],

From ef864a8a578b5c60a3dee1266bdfc36cb3cc7ed5 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:41:10 +0200
Subject: [PATCH 34/38] mypy and ruff

---
 artist/field/kinematics_rigid_body.py        |  12 +-
 artist/optim/kinematics_reconstructor.py     |  11 +-
 artist/optim/loss.py                         |   4 +-
 artist/optim/motor_position_optimizer.py     |   4 +-
 tests/field/test_kinematics.py               |  38 +-
 tests/field/test_kinematics_rigid_body.py    | 426 ++++++++++++++-----
 tests/optim/test_loss_functions.py           |   6 +-
 tests/optim/test_motor_position_optimizer.py |   2 +-
 tests/optim/test_surface_reconstructor.py    |   4 +-
 9 files changed, 369 insertions(+), 138 deletions(-)

diff --git a/artist/field/kinematics_rigid_body.py b/artist/field/kinematics_rigid_body.py
index 26e2d594..72bf0f97 100644
--- a/artist/field/kinematics_rigid_body.py
+++ b/artist/field/kinematics_rigid_body.py
@@ -499,9 +499,9 @@ def incident_ray_directions_to_orientations(
         self,
         incident_ray_directions: torch.Tensor,
         aim_points: torch.Tensor,
+        device: torch.device | None = None,
         max_num_iterations: int = 4,
         min_eps: float = 0.0001,
-        device: torch.device | None = None,
     ) -> torch.Tensor:
         """
         Compute orientation matrices given incident ray directions.
@@ -514,14 +514,14 @@ def incident_ray_directions_to_orientations(
         aim_points : torch.Tensor
             The aim points for the active heliostats.
             Shape is ``[number_of_active_heliostats, 4]``.
-        max_num_iterations : int
-            Maximum number of iterations (default is 2).
-        min_eps : float
-            Convergence criterion (default is 0.0001).
         device : torch.device | None
             The device on which to perform computations or load tensors and models (default is None).
             If None, ``ARTIST`` will automatically select the most appropriate
             device (CUDA or CPU) based on availability and OS.
+        max_num_iterations : int
+            Maximum number of iterations (default is 2).
+        min_eps : float
+            Convergence criterion (default is 0.0001).
 
         Returns
         -------
@@ -593,4 +593,4 @@ def incident_ray_directions_to_orientations(
 
         self.active_motor_positions = motor_positions
 
-        return  orientations @ self.initial_orientation_offsets
+        return orientations @ self.initial_orientation_offsets
diff --git a/artist/optim/kinematics_reconstructor.py b/artist/optim/kinematics_reconstructor.py
index 719c9037..fef9ed99 100644
--- a/artist/optim/kinematics_reconstructor.py
+++ b/artist/optim/kinematics_reconstructor.py
@@ -704,13 +704,14 @@ def _reconstruct_kinematics_parameters_wih_alignment(
                 active_indices_group = torch.nonzero(
                     active_heliostats_mask != 0, as_tuple=True
                 )[0]
-            
-                final_indices = active_indices_group + final_loss_start_indices[heliostat_group_index]
 
-                final_loss_per_heliostat[final_indices] = (
-                    loss_per_heliostat
+                final_indices = (
+                    active_indices_group
+                    + final_loss_start_indices[heliostat_group_index]
                 )
 
+                final_loss_per_heliostat[final_indices] = loss_per_heliostat
+
                 log.info(f"Rank: {rank}, Kinematics reconstructed.")
 
         if self.ddp_setup["is_distributed"]:
@@ -1001,7 +1002,7 @@ def _reconstruct_kinematics_parameters_with_raytracing(
                             data_split=data_split,
                             plot_name=f"raytracing_{epoch}",
                         )
-                    
+
                     # Early stopping when loss did not improve for a predefined number of epochs.
                     if stop:
                         log.info(f"Early stopping at epoch {epoch}.")
diff --git a/artist/optim/loss.py b/artist/optim/loss.py
index 16f4356b..f176d8bb 100644
--- a/artist/optim/loss.py
+++ b/artist/optim/loss.py
@@ -454,9 +454,7 @@ def __call__(
         """
         prediction = torch.nn.functional.normalize(prediction[:, :3])
         ground_truth = torch.nn.functional.normalize(ground_truth[:, :3])
-        return torch.acos(
-            (prediction * ground_truth).sum(dim=-1).clamp(-1.0, 1.0)
-        )
+        return torch.acos((prediction * ground_truth).sum(dim=-1).clamp(-1.0, 1.0))
 
 
 class CosineSimilarityLoss(Loss):
diff --git a/artist/optim/motor_position_optimizer.py b/artist/optim/motor_position_optimizer.py
index 8cd2c064..6f95f6b2 100644
--- a/artist/optim/motor_position_optimizer.py
+++ b/artist/optim/motor_position_optimizer.py
@@ -492,9 +492,7 @@ def optimize(
             if isinstance(loss_definition, FocalSpotLoss):
                 loss = flux_loss
 
-            if isinstance(
-                loss_definition, KLDivergenceLoss
-            ):
+            if isinstance(loss_definition, KLDivergenceLoss):
                 # Store references at epoch 0, i.e., baseline flux integral and intercept factors.
                 if epoch == 0:
                     flux_integral_reference = total_flux.sum().detach()
diff --git a/tests/field/test_kinematics.py b/tests/field/test_kinematics.py
index cef2092c..289f0370 100644
--- a/tests/field/test_kinematics.py
+++ b/tests/field/test_kinematics.py
@@ -7,14 +7,36 @@
 @pytest.mark.parametrize(
     "expected",
     [
-        torch.tensor([[[ 1.000000000000e+00,  7.034584910024e-14,  1.609325408936e-06,
-          -2.858161849417e-07],
-         [ 1.130841724262e-06,  7.115054130554e-01, -7.026806473732e-01,
-           1.247960701585e-01],
-         [-1.145043825090e-06,  7.026806473732e-01,  7.115054130554e-01,
-          -1.263633668423e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]]])
+        torch.tensor(
+            [
+                [
+                    [
+                        1.000000000000e00,
+                        7.034584910024e-14,
+                        1.609325408936e-06,
+                        -2.858161849417e-07,
+                    ],
+                    [
+                        1.130841724262e-06,
+                        7.115054130554e-01,
+                        -7.026806473732e-01,
+                        1.247960701585e-01,
+                    ],
+                    [
+                        -1.145043825090e-06,
+                        7.026806473732e-01,
+                        7.115054130554e-01,
+                        -1.263633668423e-01,
+                    ],
+                    [
+                        0.000000000000e00,
+                        0.000000000000e00,
+                        0.000000000000e00,
+                        1.000000000000e00,
+                    ],
+                ]
+            ]
+        )
     ],
 )
 def test_kinematics_forward(expected: torch.Tensor, device: torch.device) -> None:
diff --git a/tests/field/test_kinematics_rigid_body.py b/tests/field/test_kinematics_rigid_body.py
index 9fb9001d..37ceb41a 100644
--- a/tests/field/test_kinematics_rigid_body.py
+++ b/tests/field/test_kinematics_rigid_body.py
@@ -270,59 +270,166 @@ def kinematics_model_ideal_2(
                     [0.0, 0.0, -1.0, 0.0],
                 ]
             ),
-            torch.tensor([[[ 1.000000000000e+00,  6.774044351713e-14,  1.549720764160e-06,
-          -2.712011166750e-07],
-         [ 1.089058969228e-06,  7.114415168762e-01, -7.027453184128e-01,
-           1.229804158211e-01],
-         [-1.102535748032e-06,  7.027453184128e-01,  7.114415168762e-01,
-          -1.245022714138e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 7.027451992035e-01,  3.109810009505e-08,  7.114416360855e-01,
-          -1.245022863150e-01],
-         [ 7.114416360855e-01,  1.399793518431e-06, -7.027451992035e-01,
-           1.229804083705e-01],
-         [-1.017725480779e-06,  1.000000000000e+00,  9.615737326385e-07,
-          -1.759248817734e-07],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 1.000000000000e+00,  2.605401856173e-14,  5.960464477539e-07,
-          -1.043081283569e-07],
-         [ 5.960464477539e-07,  5.523350523617e-07, -1.000000000000e+00,
-           1.749999970198e-01],
-         [-3.552713678801e-13,  1.000000000000e+00,  5.523350523617e-07,
-          -1.043081283569e-07],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 7.027456760406e-01, -3.109808233148e-08, -7.114411592484e-01,
-           1.245022043586e-01],
-         [-7.114411592484e-01,  1.757421387083e-06, -7.027456760406e-01,
-           1.229804903269e-01],
-         [ 1.272155941479e-06,  1.000000000000e+00,  1.212895881508e-06,
-          -2.199062549835e-07],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 1.000000000000e+00,  3.126481956358e-14,  7.152557373047e-07,
-          -1.251697483440e-07],
-         [ 6.598978075090e-07,  3.857483565807e-01, -9.226040244102e-01,
-           1.614557057619e-01],
-         [-2.759087465165e-07,  9.226040244102e-01,  3.857483565807e-01,
-          -6.750596314669e-02],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 1.000000000000e+00,  6.774044351713e-14,  1.549720764160e-06,
-          -2.712011166750e-07],
-         [ 1.089058969228e-06,  7.114415168762e-01, -7.027453184128e-01,
-           1.122980356216e+00],
-         [-1.102535748032e-06,  7.027453184128e-01,  7.114415168762e-01,
-          -1.245022714138e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]]])
+            torch.tensor(
+                [
+                    [
+                        [
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+                            1.000000000000e00,
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+                            3.126481956358e-14,
+                            7.152557373047e-07,
+                            -1.251697483440e-07,
+                        ],
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+                            3.857483565807e-01,
+                            -9.226040244102e-01,
+                            1.614557057619e-01,
+                        ],
+                        [
+                            -2.759087465165e-07,
+                            9.226040244102e-01,
+                            3.857483565807e-01,
+                            -6.750596314669e-02,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                    [
+                        [
+                            1.000000000000e00,
+                            6.774044351713e-14,
+                            1.549720764160e-06,
+                            -2.712011166750e-07,
+                        ],
+                        [
+                            1.089058969228e-06,
+                            7.114415168762e-01,
+                            -7.027453184128e-01,
+                            1.122980356216e00,
+                        ],
+                        [
+                            -1.102535748032e-06,
+                            7.027453184128e-01,
+                            7.114415168762e-01,
+                            -1.245022714138e-01,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                ]
+            ),
         ),
         (
             "kinematics_model_ideal_1",
@@ -640,59 +747,166 @@ def test_incident_ray_direction_to_orientation(
                     [10.0, 40.0],
                 ]
             ),
-            torch.tensor([[[ 5.735765099525e-01,  3.580627350175e-08,  8.191520571709e-01,
-          -1.433516144753e-01],
-         [ 2.571453308065e-07,  1.000000000000e+00, -2.237665057692e-07,
-           3.150964289489e-08],
-         [-8.191520571709e-01,  3.389882863303e-07,  5.735765099525e-01,
-          -1.003758907318e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 5.922092795372e-01,  3.522194802486e-08,  8.057842254639e-01,
-          -1.410122364759e-01],
-         [ 8.305405208375e-05,  1.000000000000e+00, -6.108410161687e-05,
-           1.068206802302e-05],
-         [-8.057842254639e-01,  1.030982093653e-04,  5.922092795372e-01,
-          -1.036366224289e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 5.743377208710e-01,  3.578294993645e-08,  8.186184763908e-01,
-          -1.432582288980e-01],
-         [ 1.681064895820e-04,  1.000000000000e+00, -1.179862010758e-04,
-           2.063993451884e-05],
-         [-8.186184763908e-01,  2.053789939964e-04,  5.743377208710e-01,
-          -1.005090996623e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 5.735765099525e-01,  3.580627350175e-08,  8.191520571709e-01,
-          -1.433516144753e-01],
-         [ 2.571453308065e-07,  1.000000000000e+00, -2.237665057692e-07,
-           3.150964289489e-08],
-         [-8.191520571709e-01,  3.389882863303e-07,  5.735765099525e-01,
-          -1.003758907318e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 5.922092795372e-01,  3.522194802486e-08,  8.057842254639e-01,
-          -1.410122364759e-01],
-         [ 8.305405208375e-05,  1.000000000000e+00, -6.108410161687e-05,
-           1.068206802302e-05],
-         [-8.057842254639e-01,  1.030982093653e-04,  5.922092795372e-01,
-          -1.036366224289e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]],
-
-        [[ 5.743377208710e-01,  3.578294993645e-08,  8.186184763908e-01,
-          -1.432582288980e-01],
-         [ 1.681064895820e-04,  1.000000000000e+00, -1.179862010758e-04,
-           1.000020623207e+00],
-         [-8.186184763908e-01,  2.053789939964e-04,  5.743377208710e-01,
-          -1.005090996623e-01],
-         [ 0.000000000000e+00,  0.000000000000e+00,  0.000000000000e+00,
-           1.000000000000e+00]]])
+            torch.tensor(
+                [
+                    [
+                        [
+                            5.735765099525e-01,
+                            3.580627350175e-08,
+                            8.191520571709e-01,
+                            -1.433516144753e-01,
+                        ],
+                        [
+                            2.571453308065e-07,
+                            1.000000000000e00,
+                            -2.237665057692e-07,
+                            3.150964289489e-08,
+                        ],
+                        [
+                            -8.191520571709e-01,
+                            3.389882863303e-07,
+                            5.735765099525e-01,
+                            -1.003758907318e-01,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                    [
+                        [
+                            5.922092795372e-01,
+                            3.522194802486e-08,
+                            8.057842254639e-01,
+                            -1.410122364759e-01,
+                        ],
+                        [
+                            8.305405208375e-05,
+                            1.000000000000e00,
+                            -6.108410161687e-05,
+                            1.068206802302e-05,
+                        ],
+                        [
+                            -8.057842254639e-01,
+                            1.030982093653e-04,
+                            5.922092795372e-01,
+                            -1.036366224289e-01,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                    [
+                        [
+                            5.743377208710e-01,
+                            3.578294993645e-08,
+                            8.186184763908e-01,
+                            -1.432582288980e-01,
+                        ],
+                        [
+                            1.681064895820e-04,
+                            1.000000000000e00,
+                            -1.179862010758e-04,
+                            2.063993451884e-05,
+                        ],
+                        [
+                            -8.186184763908e-01,
+                            2.053789939964e-04,
+                            5.743377208710e-01,
+                            -1.005090996623e-01,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                    [
+                        [
+                            5.735765099525e-01,
+                            3.580627350175e-08,
+                            8.191520571709e-01,
+                            -1.433516144753e-01,
+                        ],
+                        [
+                            2.571453308065e-07,
+                            1.000000000000e00,
+                            -2.237665057692e-07,
+                            3.150964289489e-08,
+                        ],
+                        [
+                            -8.191520571709e-01,
+                            3.389882863303e-07,
+                            5.735765099525e-01,
+                            -1.003758907318e-01,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                    [
+                        [
+                            5.922092795372e-01,
+                            3.522194802486e-08,
+                            8.057842254639e-01,
+                            -1.410122364759e-01,
+                        ],
+                        [
+                            8.305405208375e-05,
+                            1.000000000000e00,
+                            -6.108410161687e-05,
+                            1.068206802302e-05,
+                        ],
+                        [
+                            -8.057842254639e-01,
+                            1.030982093653e-04,
+                            5.922092795372e-01,
+                            -1.036366224289e-01,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                    [
+                        [
+                            5.743377208710e-01,
+                            3.578294993645e-08,
+                            8.186184763908e-01,
+                            -1.432582288980e-01,
+                        ],
+                        [
+                            1.681064895820e-04,
+                            1.000000000000e00,
+                            -1.179862010758e-04,
+                            1.000020623207e00,
+                        ],
+                        [
+                            -8.186184763908e-01,
+                            2.053789939964e-04,
+                            5.743377208710e-01,
+                            -1.005090996623e-01,
+                        ],
+                        [
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            0.000000000000e00,
+                            1.000000000000e00,
+                        ],
+                    ],
+                ]
+            ),
         ),
         (
             "kinematics_model_ideal_1",
diff --git a/tests/optim/test_loss_functions.py b/tests/optim/test_loss_functions.py
index 8457c243..5d677493 100644
--- a/tests/optim/test_loss_functions.py
+++ b/tests/optim/test_loss_functions.py
@@ -6,7 +6,6 @@
 
 from artist.field import (
     SolarTower,
-    TowerTargetAreas,
     TowerTargetAreasCylindrical,
     TowerTargetAreasPlanar,
 )
@@ -20,7 +19,6 @@
     VectorLoss,
 )
 from artist.scenario import Scenario
-from artist.scene import LightSource, LightSourceArray
 
 
 def test_base_loss(
@@ -517,7 +515,7 @@ def test_cosine_similarity_loss(
         (
             torch.tensor([[1.0, 2.0, 3.0, 0.0], [1.0, 1.0, 0.0, 1.0]]),
             torch.tensor([[1.0, 2.0, 3.0, 0.0], [0.0, 1.0, 0.0, 0.0]]),
-            torch.tensor([0.0, math.pi/4]),
+            torch.tensor([0.0, math.pi / 4]),
         ),
         (
             torch.tensor([[0.0, 1.0, 0.0]]),
@@ -527,7 +525,7 @@ def test_cosine_similarity_loss(
         (
             torch.tensor([[0.0, 1.0, 0.0]]),
             torch.tensor([[1.0, 0.0, 0.0]]),
-            torch.tensor([math.pi/2]),
+            torch.tensor([math.pi / 2]),
         ),
     ],
 )
diff --git a/tests/optim/test_motor_position_optimizer.py b/tests/optim/test_motor_position_optimizer.py
index 2dcd1905..a200a5c1 100644
--- a/tests/optim/test_motor_position_optimizer.py
+++ b/tests/optim/test_motor_position_optimizer.py
@@ -6,7 +6,7 @@
 
 from artist import ARTIST_ROOT
 from artist.optim import MotorPositionsOptimizer
-from artist.optim.loss import FocalSpotLoss, KLDivergenceLoss, Loss
+from artist.optim.loss import KLDivergenceLoss, Loss
 from artist.scenario import Scenario
 from artist.util import constants
 from artist.util.env import DdpSetup
diff --git a/tests/optim/test_surface_reconstructor.py b/tests/optim/test_surface_reconstructor.py
index 4ae66561..7c01eb72 100644
--- a/tests/optim/test_surface_reconstructor.py
+++ b/tests/optim/test_surface_reconstructor.py
@@ -104,7 +104,7 @@ def test_surface_reconstructor(
                 pathlib.Path(ARTIST_ROOT)
                 / "tests/data/field_data/AA39-calibration-properties_1.json",
                 pathlib.Path(ARTIST_ROOT)
-                / "tests/data/field_data/AA39-calibration-properties_2.json",                
+                / "tests/data/field_data/AA39-calibration-properties_2.json",
                 pathlib.Path(ARTIST_ROOT)
                 / "tests/data/field_data/AA39-calibration-properties_3.json",
             ],
@@ -146,7 +146,7 @@ def test_surface_reconstructor(
         data=data,
         optimization_configuration=optimization_configuration,
         device=device,
-        plot_results=True
+        plot_results=True,
     )
 
     if not isinstance(data_parser, PaintCalibrationDataParser):

From 37348ffce995a8781f49dcaadf330814284b3403 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:50:10 +0200
Subject: [PATCH 35/38] update data

---
 .../distribution_group_0_10_cpu.pt               | Bin 1344 -> 0 bytes
 .../distribution_group_0_10_cuda.pt              | Bin 1349 -> 0 bytes
 .../distribution_group_0_50_cpu.pt               | Bin 1344 -> 0 bytes
 .../distribution_group_0_50_cuda.pt              | Bin 1349 -> 0 bytes
 .../distribution_group_1_10_cpu.pt               | Bin 1344 -> 0 bytes
 .../distribution_group_1_10_cuda.pt              | Bin 1349 -> 0 bytes
 .../distribution_group_1_50_cpu.pt               | Bin 1344 -> 0 bytes
 .../distribution_group_1_50_cuda.pt              | Bin 1349 -> 0 bytes
 .../focal_spot_group_0_50_cpu.pt                 | Bin 1334 -> 0 bytes
 .../focal_spot_group_0_50_cuda.pt                | Bin 1339 -> 0 bytes
 .../focal_spot_group_1_50_cpu.pt                 | Bin 1334 -> 0 bytes
 .../focal_spot_group_1_50_cuda.pt                | Bin 1339 -> 0 bytes
 .../group_0_10_cpu.pt                            | Bin 1474 -> 0 bytes
 .../group_0_10_cuda.pt                           | Bin 1480 -> 0 bytes
 .../group_0_500_cuda.pt                          | Bin 1225 -> 0 bytes
 .../group_0_50_cpu.pt                            | Bin 1474 -> 0 bytes
 .../group_0_50_cuda.pt                           | Bin 1220 -> 0 bytes
 .../group_1_10_cpu.pt                            | Bin 1538 -> 0 bytes
 .../group_1_10_cuda.pt                           | Bin 1544 -> 0 bytes
 .../group_1_500_cuda.pt                          | Bin 1225 -> 0 bytes
 .../group_1_50_cpu.pt                            | Bin 1538 -> 0 bytes
 .../group_1_50_cuda.pt                           | Bin 1220 -> 0 bytes
 .../pixel_loss_group_0_20_cpu.pt                 | Bin 6006 -> 0 bytes
 .../pixel_loss_group_0_20_cuda.pt                | Bin 6075 -> 0 bytes
 .../pixel_loss_group_1_20_cpu.pt                 | Bin 6006 -> 0 bytes
 .../pixel_loss_group_1_20_cuda.pt                | Bin 6075 -> 0 bytes
 .../AA39-calibration-properties_2.json           |   2 +-
 27 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_0_10_cpu.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_0_10_cuda.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_0_50_cpu.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_0_50_cuda.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_1_10_cpu.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_1_10_cuda.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_1_50_cpu.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/distribution_group_1_50_cuda.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/focal_spot_group_0_50_cpu.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/focal_spot_group_0_50_cuda.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/focal_spot_group_1_50_cpu.pt
 delete mode 100644 tests/data/expected_optimized_motor_positions/focal_spot_group_1_50_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_10_cpu.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_10_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_500_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_50_cpu.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_0_50_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_10_cpu.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_10_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_500_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_50_cpu.pt
 delete mode 100644 tests/data/expected_reconstructed_kinematics_parameters/group_1_50_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_0_20_cpu.pt
 delete mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_0_20_cuda.pt
 delete mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_1_20_cpu.pt
 delete mode 100644 tests/data/expected_reconstructed_surfaces/pixel_loss_group_1_20_cuda.pt

diff --git a/tests/data/expected_optimized_motor_positions/distribution_group_0_10_cpu.pt b/tests/data/expected_optimized_motor_positions/distribution_group_0_10_cpu.pt
deleted file mode 100644
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LZ}`D@eeC}NE)0Qo

diff --git a/tests/data/field_data/AA39-calibration-properties_2.json b/tests/data/field_data/AA39-calibration-properties_2.json
index 5cca696c..2077848b 100644
--- a/tests/data/field_data/AA39-calibration-properties_2.json
+++ b/tests/data/field_data/AA39-calibration-properties_2.json
@@ -18,4 +18,4 @@
             122.50684356689452
         ]
     }
-}
\ No newline at end of file
+}

From 8023f21721bf20dc39fde9873ee775e1769aa904 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 16:56:34 +0200
Subject: [PATCH 36/38] clean up tutorials

---
 ...3_nurbs_surface_reconstruction_tutorial.py |  11 +-
 .../04_kinematics_reconstruction_tutorial.py  | 202 +++++++++++-------
 2 files changed, 131 insertions(+), 82 deletions(-)

diff --git a/tutorials/03_nurbs_surface_reconstruction_tutorial.py b/tutorials/03_nurbs_surface_reconstruction_tutorial.py
index 4b7c99c3..b59e62b8 100644
--- a/tutorials/03_nurbs_surface_reconstruction_tutorial.py
+++ b/tutorials/03_nurbs_surface_reconstruction_tutorial.py
@@ -26,7 +26,7 @@
 
 #############################################################################################################
 # Define helper functions for the plots.
-# Skip to line 348 for the tutorial code.
+# Skip to line 353 for the tutorial code.
 #############################################################################################################
 
 
@@ -402,7 +402,7 @@ def create_flux_plots(
 # heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
 #     base_path=base_path_data,
 #     heliostat_names=heliostat_names_reconstruction,
-#     number_of_measurements=2,
+#     number_of_measurements=4,
 #     image_variant="flux-centered",
 #     randomize=True,
 # )
@@ -411,9 +411,9 @@ def create_flux_plots(
 optimizer_dict = {
     constants.initial_learning_rate: 1e-4,
     constants.tolerance: 1e-5,
-    constants.max_epoch: 100,
+    constants.max_epoch: 200,
     constants.batch_size: 30,
-    constants.log_step: 1,
+    constants.log_step: 3,
     constants.early_stopping_delta: 1e-4,
     constants.early_stopping_patience: 100,
     constants.early_stopping_window: 100,
@@ -449,7 +449,7 @@ def create_flux_plots(
     str,
     CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
 ] = {
-    constants.data_parser: PaintCalibrationDataParser(sample_limit=2),
+    constants.data_parser: PaintCalibrationDataParser(sample_limit=4),
     constants.heliostat_data_mapping: heliostat_data_mapping,
 }
 
@@ -502,6 +502,7 @@ def create_flux_plots(
         data=data,
         optimization_configuration=optimization_configuration,
         bitmap_resolution=resolution,
+        plot_results=True,
         device=device,
     )
 
diff --git a/tutorials/04_kinematics_reconstruction_tutorial.py b/tutorials/04_kinematics_reconstruction_tutorial.py
index 9b426b51..6a3bf7fd 100644
--- a/tutorials/04_kinematics_reconstruction_tutorial.py
+++ b/tutorials/04_kinematics_reconstruction_tutorial.py
@@ -16,7 +16,7 @@
     paint_scenario_parser,
 )
 from artist.optim import KinematicsReconstructor
-from artist.optim.loss import AngleLoss, FocalSpotLoss
+from artist.optim.loss import AngleLoss
 from artist.raytracing import HeliostatRayTracer
 from artist.scenario import Scenario
 from artist.util import constants, set_logger_config
@@ -35,7 +35,7 @@ def create_fluxes(
     data_parser: CalibrationDataParser,
     heliostat_data_mapping: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]],
     resolution: torch.Tensor,
-    method: str
+    method: str,
 ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
     """
     Create data to plot the heliostat fluxes.
@@ -97,7 +97,9 @@ def create_fluxes(
             elif method == "incident_ray":
                 # Align heliostats.
                 heliostat_group.align_surfaces_with_incident_ray_directions(
-                    aim_points=scenario.solar_tower.get_centers_of_target_areas(target_area_indices=target_area_indices, device=device),
+                    aim_points=scenario.solar_tower.get_centers_of_target_areas(
+                        target_area_indices=target_area_indices, device=device
+                    ),
                     incident_ray_directions=incident_ray_directions,
                     active_heliostats_mask=active_heliostats_mask,
                     device=device,
@@ -156,37 +158,75 @@ def create_plots(
         center_after = (
             bitmap.get_center_of_mass(flux_after, device=device).cpu().detach()
         )
-        center_ray = (
-            bitmap.get_center_of_mass(flux_ray, device=device).cpu().detach()
-        )
+        center_ray = bitmap.get_center_of_mass(flux_ray, device=device).cpu().detach()
 
         for i in range(len(flux_before)):
             _, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
 
             axes[0].imshow(flux_before[i].cpu().detach(), cmap="gray")
             axes[0].set_title("Before reconstruction", fontsize=16)
-            axes[0].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
-            axes[0].scatter(x=center_before[i, 0], y=center_before[i, 1], c="red", marker="x", label="Predicted")
-            #axes[0].scatter(256/2, 256/2, c="blue")
+            axes[0].scatter(
+                x=center_measured[i, 0],
+                y=center_measured[i, 1],
+                c="green",
+                marker="o",
+                label="Measured",
+            )
+            axes[0].scatter(
+                x=center_before[i, 0],
+                y=center_before[i, 1],
+                c="red",
+                marker="x",
+                label="Predicted",
+            )
+            # axes[0].scatter(256/2, 256/2, c="blue")
             axes[0].axis("off")
-            
+
             axes[1].imshow(flux_after[i].cpu().detach(), cmap="gray")
             axes[1].set_title("After reconstruction", fontsize=16)
-            axes[1].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
-            axes[1].scatter(x=center_after[i, 0], y=center_after[i, 1], c="red", marker="x", label="Predicted")
-            #axes[1].scatter(256/2, 256/2, c="blue")
+            axes[1].scatter(
+                x=center_measured[i, 0],
+                y=center_measured[i, 1],
+                c="green",
+                marker="o",
+                label="Measured",
+            )
+            axes[1].scatter(
+                x=center_after[i, 0],
+                y=center_after[i, 1],
+                c="red",
+                marker="x",
+                label="Predicted",
+            )
+            # axes[1].scatter(256/2, 256/2, c="blue")
             axes[1].axis("off")
 
             axes[2].imshow(flux_ray[i].cpu().detach(), cmap="gray")
-            axes[2].set_title("After reconstruction align only with sun pos", fontsize=16)
-            axes[2].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o", label="Measured")
-            axes[2].scatter(x=center_ray[i, 0], y=center_ray[i, 1], c="red", marker="x", label="Predicted")
-            #axes[2].scatter(256/2, 256/2, c="blue")
+            axes[2].set_title(
+                "After reconstruction align only with sun pos", fontsize=16
+            )
+            axes[2].scatter(
+                x=center_measured[i, 0],
+                y=center_measured[i, 1],
+                c="green",
+                marker="o",
+                label="Measured",
+            )
+            axes[2].scatter(
+                x=center_ray[i, 0],
+                y=center_ray[i, 1],
+                c="red",
+                marker="x",
+                label="Predicted",
+            )
+            # axes[2].scatter(256/2, 256/2, c="blue")
             axes[2].axis("off")
 
             axes[3].imshow(flux_measured[i].cpu().detach(), cmap="gray")
             axes[3].set_title("Measured", fontsize=16)
-            axes[3].scatter(x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o")
+            axes[3].scatter(
+                x=center_measured[i, 0], y=center_measured[i, 1], c="green", marker="o"
+            )
             axes[3].axis("off")
 
             axes[0].legend()
@@ -195,7 +235,9 @@ def create_plots(
 
             plt.subplots_adjust(wspace=0.05)
             plt.show()
-            plt.savefig(f"./ignored/fixed/heliostat_{i}_in_group_{group_index}_calibration.png")
+            plt.savefig(
+                f"./ignored/fixed/heliostat_{i}_in_group_{group_index}_calibration.png"
+            )
 
 
 #############################################################################################################
@@ -210,53 +252,51 @@ def create_plots(
 device = get_device()
 
 # Specify the path to your scenario.h5 file.
-scenario_path = pathlib.Path("/workVERLEIHNIX/mb/ARTIST/examples/field_optimizations/scenarios/ideal_baseline_scenario.h5")
+scenario_path = pathlib.Path("please/insert/the/path/to/the/scenario/here/scenario.h5")
 
 # Also specify the heliostats to be calibrated and the paths to your calibration-properties.json files.
 # Please use the following style: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]
-# heliostat_data_mapping = [
-#     (
-#         "heliostat_name_1",
-#         [
-#             pathlib.Path(
-#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
-#             ),
-#             # ....
-#         ],
-#         [
-#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
-#             # ....
-#         ],
-#     ),
-#     (
-#         "heliostat_name_2",
-#         [
-#             pathlib.Path(
-#                 "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
-#             ),
-#             # ....
-#         ],
-#         [
-#             pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
-#             # ....
-#         ],
-#     ),
-# ]
+heliostat_data_mapping = [
+    (
+        "heliostat_name_1",
+        [
+            pathlib.Path(
+                "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
+            ),
+            # ....
+        ],
+        [
+            pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
+            # ....
+        ],
+    ),
+    (
+        "heliostat_name_2",
+        [
+            pathlib.Path(
+                "please/insert/the/path/to/the/paint/data/here/calibration-properties.json"
+            ),
+            # ....
+        ],
+        [
+            pathlib.Path("please/insert/the/path/to/the/paint/data/here/flux.png"),
+            # ....
+        ],
+    ),
+]
 
 # Or if you have a directory with downloaded data use this code to create a mapping.
 heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
-    base_path="/workVERLEIHNIX/share/PAINT_data",
-    heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AM55"],
-    #heliostat_names=["AG48", "AG49", "AG50", "AG51", "AG52", "AG53", "AG54", "AG55", "AG56", "AG57", "AG58", "AH48", "AH49", "AH50", "AH55", "AH56", "AH57", "AH58", "AI48", "AI49", "AI50", "AI51", "AI52", "AI53", "AI54", "AI55", "AI56", "AI57", "AI58", "AI59", "AI60", "AI61", "AJ49", "AJ50", "AJ51", "AJ53", "AJ54", "AJ55", "AJ56", "AJ57", "AJ58", "AJ61", "AJ62", "AK48", "AK49", "AK50", "AK51", "AK52", "AK53", "AK54", "AK55", "AK56", "AK58", "AK59", "AK60", "AK61", "AK62", "AL48", "AL49", "AL51", "AL53", "AL56", "AL58", "AL59", "AL60", "AL61", "AL62", "AM48", "AM49", "AM50", "AM51", "AM52", "AM53", "AM54", "AM55", "AM56", "AM57", "AM58", "AM59", "AM60", "AM62", "AN48", "AN49", "AN50", "AN51", "AN52", "AN53", "AN54", "AN55", "AN56", "AN57", "AN58", "AN59", "AN60", "AN61", "AN62"],
-    #heliostat_names=['AZ43', 'AP35', 'AA39', 'AC31', 'AC42', 'AC24', 'AM31', 'BF65', 'AQ27', 'AF33', 'BD31', 'AW42', 'AZ39', 'AL36', 'AD42', 'AP39', 'AE28', 'BE38', 'AY55', 'AP50'],
-    number_of_measurements=37,
+    base_path="base/path/data",
+    heliostat_names=["heliostat_1", "..."],
+    number_of_measurements=5,
     image_variant="flux",
     randomize=True,
     seed=7,
 )
 
 # Configure the optimization.
-optimizer_dict = {
+optimizer_dict: dict[str, str | float | int] = {
     constants.initial_learning_rate_rotation_deviation: 1e-4,
     constants.initial_learning_rate_initial_angles: 1e-3,
     constants.initial_learning_rate_initial_stroke_length: 1e-2,
@@ -269,7 +309,7 @@ def create_plots(
     constants.early_stopping_window: 2000,
 }
 # Configure the learning rate scheduler.
-scheduler_dict = {
+scheduler_dict: dict[str, str | float | int] = {
     constants.scheduler_type: constants.reduce_on_plateau,
     constants.gamma: 0.9,
     constants.lr_min: 1e-6,
@@ -281,7 +321,7 @@ def create_plots(
     constants.cooldown: 10,
 }
 # Combine configurations.
-optimization_configuration = {
+optimization_configuration: dict[str, dict[str, str | float | int]] = {
     constants.optimization: optimizer_dict,
     constants.scheduler: scheduler_dict,
 }
@@ -335,12 +375,15 @@ def create_plots(
             for heliostat in heliostat_data_mapping
         ],
         resolution=resolution,
-        method="motor_pos"
+        method="motor_pos",
     )
 
     scenario.set_number_of_rays(number_of_rays=4)
-    optimization_configuration[constants.optimization][constants.initial_learning_rate_rotation_deviation] = 3e-4
-    optimization_configuration[constants.optimization][constants.max_epoch] = 600
+
+    optimization_configuration[constants.optimization][
+        constants.initial_learning_rate_rotation_deviation
+    ] = 3e-4
+    optimization_configuration[constants.optimization][constants.max_epoch] = 200
     # Create the kinematics reconstructor.
     kinematics_reconstructor = KinematicsReconstructor(
         ddp_setup=ddp_setup,
@@ -348,30 +391,35 @@ def create_plots(
         data=data,
         dni=500,
         optimization_configuration=optimization_configuration,
-        reconstruction_method=constants.kinematics_reconstruction_geometry,
+        reconstruction_method=constants.kinematics_reconstruction_alignment,
         bitmap_resolution=resolution,
+        plot_results=True,
     )
     # Reconstruct the kinematics.
     final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
         loss_definition=AngleLoss(), device=device
     )
 
-    optimization_configuration[constants.optimization][constants.initial_learning_rate_rotation_deviation] = 1e-4
-    optimization_configuration[constants.optimization][constants.max_epoch] = 300
-    # Create the kinematics reconstructor.
-    kinematics_reconstructor = KinematicsReconstructor(
-        ddp_setup=ddp_setup,
-        scenario=scenario,
-        data=data,
-        dni=500,
-        optimization_configuration=optimization_configuration,
-        reconstruction_method=constants.kinematics_reconstruction_raytracing,
-        bitmap_resolution=resolution,
-    )
-    # Reconstruct the kinematics.
-    final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
-        loss_definition=FocalSpotLoss(scenario=scenario), device=device
-    )
+    # Uncomment the code below to add a further reconstruction step using raytracing to refine the kinematics reconstruction.
+    # optimization_configuration[constants.optimization][
+    #     constants.initial_learning_rate_rotation_deviation
+    # ] = 1e-4
+    # optimization_configuration[constants.optimization][constants.max_epoch] = 400
+    # # Create the kinematics reconstructor.
+    # kinematics_reconstructor = KinematicsReconstructor(
+    #     ddp_setup=ddp_setup,
+    #     scenario=scenario,
+    #     data=data,
+    #     dni=500,
+    #     optimization_configuration=optimization_configuration,
+    #     reconstruction_method=constants.kinematics_reconstruction_raytracing,
+    #     bitmap_resolution=resolution,
+    #     plot_results=True,
+    # )
+    # # Reconstruct the kinematics.
+    # final_loss_per_heliostat = kinematics_reconstructor.reconstruct_kinematics(
+    #     loss_definition=FocalSpotLoss(scenario=scenario), device=device
+    # )
 
 # Inspect the synchronized loss per heliostat. Heliostats that have not been optimized have an infinite loss.
 print(f"rank {ddp_setup['rank']}, final loss per heliostat {final_loss_per_heliostat}")
@@ -401,4 +449,4 @@ def create_plots(
     fluxes_after=bitmaps_after,
     fluxes_ray=bitmaps_ray,
     fluxes_measured=bitmaps_measured,
-)
\ No newline at end of file
+)

From 57c285bb446754b10b95b72b0b81859af93dbe60 Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 17:36:21 +0200
Subject: [PATCH 37/38] fix

---
 examples/field_optimizations/config.yaml      |  6 ++--
 .../field_optimizations/generate_plots.py     |  8 ++---
 .../field_optimizations/generate_results.py   | 31 +------------------
 3 files changed, 8 insertions(+), 37 deletions(-)

diff --git a/examples/field_optimizations/config.yaml b/examples/field_optimizations/config.yaml
index a41f708a..81448801 100644
--- a/examples/field_optimizations/config.yaml
+++ b/examples/field_optimizations/config.yaml
@@ -19,7 +19,7 @@ heliostats_for_plots:
 random_seed: 7
 device: "cuda"
 surface_reconstruction_optimization_configuration:
-  max_epoch: 400
+  max_epoch: 2
   batch_size: 48
   batch_size_outer: 32
   number_of_surface_points: 50
@@ -42,7 +42,7 @@ surface_reconstruction_optimization_configuration:
   weight_smoothness: 0.00
   weight_ideal_surface: 0.10
 kinematics_reconstruction_optimization_configuration:
-  max_epoch: 200
+  max_epoch: 2
   batch_size: 960
   sample_limit: 10
   number_of_rays: 16
@@ -59,7 +59,7 @@ kinematics_reconstruction_optimization_configuration:
   cooldown: 5
   gamma: 0.92
 aim_point_optimization_configuration:
-  max_epoch: 200
+  max_epoch: 2
   batch_size: 96
   number_of_rays: 8
   initial_learning_rate: 1e-03
diff --git a/examples/field_optimizations/generate_plots.py b/examples/field_optimizations/generate_plots.py
index 88c6ae21..8bb2f867 100644
--- a/examples/field_optimizations/generate_plots.py
+++ b/examples/field_optimizations/generate_plots.py
@@ -545,11 +545,11 @@ def plot_surface_validation(
         ax1 = axes[row][0]
         ax2 = axes[row][1]
 
-        im1 = ax1.imshow(measured_flux)
+        _ = ax1.imshow(measured_flux)
         ax1.set_title(f"Measured {k}")
         ax1.axis("off")
 
-        im2 = ax2.imshow(artist_flux)
+        _ = ax2.imshow(artist_flux)
         ax2.set_title(f"Artist {k}")
         ax2.axis("off")
 
@@ -994,13 +994,13 @@ def plot_kinematics_validation(
         ax1 = axes[row][0]
         ax2 = axes[row][1]
 
-        im1 = ax1.imshow(measured_flux.detach().cpu())
+        _ = ax1.imshow(measured_flux.detach().cpu())
         ax1.set_title(f"Measured {k}")
         ax1.scatter(x=centers[0, 0], y=centers[0, 1], c="black", marker="o", s=30)
         ax1.scatter(x=centers[1, 0], y=centers[1, 1], c="red", marker="x", s=30)
         ax1.axis("off")
 
-        im2 = ax2.imshow(artist_flux.detach().cpu())
+        _ = ax2.imshow(artist_flux.detach().cpu())
         ax2.set_title(f"Artist {k}")
         ax2.scatter(x=centers[0, 0], y=centers[0, 1], c="black", marker="o", s=30)
         ax2.scatter(x=centers[1, 0], y=centers[1, 1], c="red", marker="x", s=30)
diff --git a/examples/field_optimizations/generate_results.py b/examples/field_optimizations/generate_results.py
index 06451738..94ba7ea6 100644
--- a/examples/field_optimizations/generate_results.py
+++ b/examples/field_optimizations/generate_results.py
@@ -667,7 +667,7 @@ def aim_point_plots(
             )
             if label == "before":
                 heliostat_group.align_surfaces_with_incident_ray_directions(
-                    aim_points=aim_point,
+                    aim_points=aim_point[None],
                     incident_ray_directions=incident_ray_directions,
                     active_heliostats_mask=active_heliostats_mask,
                     device=device,
@@ -705,30 +705,6 @@ def aim_point_plots(
                 target_area_indices=target_area_indices,
                 device=device,
             )
-            # ###########
-            # import matplotlib.pyplot as plt
-
-            # aim_point_measured_bitmap = 256 - torch.tensor([112.9362, 111.4531])
-            # for i in range(bitmaps_per_heliostat.shape[0]):
-            #     total_f = bitmaps_per_heliostat[i].sum()
-            #     if total_f > 0:
-            #         h, w = bitmaps_per_heliostat[i].shape
-            #         y_grid = torch.arange(h, device=device).unsqueeze(1).expand(h, w)
-            #         x_grid = torch.arange(w, device=device).unsqueeze(0).expand(h, w)
-            #         cy_f = (y_grid * bitmaps_per_heliostat[i]).sum() / total_f
-            #         cx_f = (x_grid * bitmaps_per_heliostat[i]).sum() / total_f
-            #     plt.imshow(bitmaps_per_heliostat[i].cpu().detach())
-            #     plt.scatter(
-            #         x=aim_point_measured_bitmap[0],
-            #         y=aim_point_measured_bitmap[1],
-            #         c="r",
-            #         s=30,
-            #     )
-            #     plt.scatter(cx_f.cpu().detach(), cy_f.cpu().detach(), c="g", s=30)
-            #     plt.scatter(x=w / 2, y=h / 2, c="black", s=30, marker="x")
-            #     plt.savefig(f"./bitmaps/aim_points/{label}_{i}")
-            #     plt.close()
-            # ###########
             flux_distribution_on_target = ray_tracer.get_bitmaps_per_target(
                 bitmaps_per_heliostat=bitmaps_per_heliostat,
                 target_area_indices=target_area_indices,
@@ -805,11 +781,6 @@ def full_field_optimizations(
     ) as ddp_setup:
         device = ddp_setup["device"]
 
-        assert data_mappings is not None, "data_mappings must be provided."
-        assert surface_config is not None, "surface_config must be provided."
-        assert kinematics_config is not None, "kinematics_config must be provided."
-        assert aim_point_config is not None, "aim_point_config must be provided."
-
         bitmap_resolution = torch.tensor(
             basic_config["bitmap_resolution"], device=device
         )

From 2dd84975ab701e07cc71d229e0ec1afb51909acb Mon Sep 17 00:00:00 2001
From: MarleneBusch <marlene.busch@dlr.de>
Date: Fri, 29 May 2026 17:53:31 +0200
Subject: [PATCH 38/38] rename motor position optimizer to aim point optimizer

---
 artist/optim/__init__.py                      |  4 ++--
 ...on_optimizer.py => aim_point_optimizer.py} | 20 +++++++++----------
 .../field_optimizations/generate_results.py   |  6 +++---
 ...timizer.py => test_aim_point_optimizer.py} | 16 +++++++--------
 .../05_motor_positions_optimizer_tutorial.py  | 14 ++++++-------
 5 files changed, 29 insertions(+), 31 deletions(-)
 rename artist/optim/{motor_position_optimizer.py => aim_point_optimizer.py} (97%)
 rename tests/optim/{test_motor_position_optimizer.py => test_aim_point_optimizer.py} (93%)

diff --git a/artist/optim/__init__.py b/artist/optim/__init__.py
index 4c93b90b..3c382518 100644
--- a/artist/optim/__init__.py
+++ b/artist/optim/__init__.py
@@ -1,3 +1,4 @@
+from .aim_point_optimizer import AimPointOptimizer
 from .kinematics_reconstructor import KinematicsReconstructor
 from .loss import (
     AngleLoss,
@@ -8,14 +9,13 @@
     VectorLoss,
     reduce_loss_per_sample,
 )
-from .motor_position_optimizer import MotorPositionsOptimizer
 from .regularizers import IdealSurfaceRegularizer, SmoothnessRegularizer
 from .surface_reconstructor import SurfaceReconstructor
 from .training import EarlyStopping, cyclic, exponential, reduce_on_plateau
 
 __all__ = [
     "KinematicsReconstructor",
-    "MotorPositionsOptimizer",
+    "AimPointOptimizer",
     "SurfaceReconstructor",
     "SmoothnessRegularizer",
     "IdealSurfaceRegularizer",
diff --git a/artist/optim/motor_position_optimizer.py b/artist/optim/aim_point_optimizer.py
similarity index 97%
rename from artist/optim/motor_position_optimizer.py
rename to artist/optim/aim_point_optimizer.py
index 6f95f6b2..28b570d6 100644
--- a/artist/optim/motor_position_optimizer.py
+++ b/artist/optim/aim_point_optimizer.py
@@ -13,12 +13,12 @@
 from artist.util.env import DdpSetup, get_device
 
 log = logging.getLogger(__name__)
-"""A logger for the motor positions optimizer."""
+"""A logger for the aim point optimizer."""
 
 
-class MotorPositionsOptimizer:
+class AimPointOptimizer:
     """
-    An optimizer used to find optimal motor positions for the heliostats.
+    An optimizer used to find optimal aim points via individual motor positions for the heliostats.
 
     The optimization loss is defined as the loss between the combined predicted and target
     flux densities. Additionally, there is one constraint that maximizes the flux integral,
@@ -74,7 +74,7 @@ def __init__(
         device: torch.device | None = None,
     ) -> None:
         """
-        Initialize the motor-positions optimizer.
+        Initialize the aim points optimizer.
 
         Parameters
         ----------
@@ -110,7 +110,7 @@ def __init__(
         rank = ddp_setup["rank"]
 
         if rank == 0:
-            log.info("Create a motor positions optimizer.")
+            log.info("Create an aim points optimizer.")
 
         self.ddp_setup = ddp_setup
         self.scenario = scenario
@@ -130,7 +130,7 @@ def optimize(
         device: torch.device | None = None,
     ) -> tuple[torch.Tensor, dict[str, list], torch.Tensor, torch.Tensor, torch.Tensor]:
         r"""
-        Optimize the motor positions.
+        Optimize the motor positions for optimal aim points.
 
         The motor positions are optimized through a reparameterization to ensure stable training
         across different heliostats with widely varying initial motor positions and ranges. Motor
@@ -174,7 +174,7 @@ def optimize(
         Returns
         -------
         torch.Tensor
-            Final loss of the motor position optimization.
+            Final loss of the aim point optimization.
         dict[str, list]
             Loss history over epochs, with keys ``"total_loss"``, ``"flux_loss"``,
             ``"local_flux_constraint"``, ``"intercept_constraint"``, ``"flux_integral_constraint"``,
@@ -190,7 +190,7 @@ def optimize(
         rank = self.ddp_setup["rank"]
 
         if rank == 0:
-            log.info("Start the motor positions optimization.")
+            log.info("Start the aim point optimization.")
 
         optimizable_parameters_all_groups = []
         scales_all_groups = []
@@ -624,7 +624,7 @@ def optimize(
             "flux_integral_constraint": flux_integral_constraint_history,
             "flux_integral": flux_integral,
         }
-        log.info(f"Rank: {rank}, motor positions optimized.")
+        log.info(f"Rank: {rank}, aim points optimized.")
 
         # Broadcast final motor positions for each heliostat group from source rank to others.
         if self.ddp_setup["is_distributed"]:
@@ -637,7 +637,7 @@ def optimize(
                     src=source[indices.first_rank_from_group],
                 )
 
-            log.info(f"Rank: {rank}, synchronized after motor positions optimization.")
+            log.info(f"Rank: {rank}, synchronized after aim point optimization.")
 
         return (
             loss.detach().cpu(),
diff --git a/examples/field_optimizations/generate_results.py b/examples/field_optimizations/generate_results.py
index 94ba7ea6..ec81f7b7 100644
--- a/examples/field_optimizations/generate_results.py
+++ b/examples/field_optimizations/generate_results.py
@@ -15,9 +15,9 @@
 from artist.flux import bitmap
 from artist.io.calibration_parser import CalibrationDataParser
 from artist.io.paint_calibration_parser import PaintCalibrationDataParser
+from artist.optim.aim_point_optimizer import AimPointOptimizer
 from artist.optim.kinematics_reconstructor import KinematicsReconstructor
 from artist.optim.loss import FocalSpotLoss, KLDivergenceLoss
-from artist.optim.motor_position_optimizer import MotorPositionsOptimizer
 from artist.optim.surface_reconstructor import SurfaceReconstructor
 from artist.raytracing.heliostat_ray_tracer import HeliostatRayTracer
 from artist.scenario.scenario import Scenario
@@ -1241,7 +1241,7 @@ def full_field_optimizations(
                 constants.max_flux_density: aim_point_config["max_flux_density"],
             },
         }
-        motor_positions_optimizer = MotorPositionsOptimizer(
+        aim_point_optimizer = AimPointOptimizer(
             ddp_setup=ddp_setup,
             scenario=scenario_aim_points,
             optimization_configuration=optimization_configuration_aim_points,
@@ -1255,7 +1255,7 @@ def full_field_optimizations(
             device=device,
         )
         aimpoint_optimization_final_loss, loss_history_aim_points, _, _, _ = (
-            motor_positions_optimizer.optimize(
+            aim_point_optimizer.optimize(
                 loss_definition=KLDivergenceLoss(), device=device
             )
         )
diff --git a/tests/optim/test_motor_position_optimizer.py b/tests/optim/test_aim_point_optimizer.py
similarity index 93%
rename from tests/optim/test_motor_position_optimizer.py
rename to tests/optim/test_aim_point_optimizer.py
index a200a5c1..fd2b3743 100644
--- a/tests/optim/test_motor_position_optimizer.py
+++ b/tests/optim/test_aim_point_optimizer.py
@@ -5,7 +5,7 @@
 import torch
 
 from artist import ARTIST_ROOT
-from artist.optim import MotorPositionsOptimizer
+from artist.optim import AimPointOptimizer
 from artist.optim.loss import KLDivergenceLoss, Loss
 from artist.scenario import Scenario
 from artist.util import constants
@@ -62,7 +62,7 @@ def distribution(device: torch.device) -> torch.Tensor:
         (KLDivergenceLoss(), "distribution", 30, constants.reduce_on_plateau),
     ],
 )
-def test_motor_positions_optimizer(
+def test_aim_point_optimizer(
     loss: Loss,
     ground_truth_fixture_name: str,
     early_stopping_window: int,
@@ -72,7 +72,7 @@ def test_motor_positions_optimizer(
     device: torch.device,
 ) -> None:
     """
-    Test the motor positions optimizer.
+    Test the aim point optimizer.
 
     Parameters
     ----------
@@ -143,8 +143,8 @@ def test_motor_positions_optimizer(
 
     ddp_setup_for_testing["device"] = device
 
-    # Create the motor positions optimizer.
-    motor_positions_optimizer = MotorPositionsOptimizer(
+    # Create the aim point optimizer.
+    aim_point_optimizer = AimPointOptimizer(
         ddp_setup=ddp_setup_for_testing,
         scenario=scenario,
         optimization_configuration=optimization_configuration,
@@ -156,10 +156,8 @@ def test_motor_positions_optimizer(
         device=device,
     )
 
-    # Optimize the motor positions.
-    _, _, _, _, _ = motor_positions_optimizer.optimize(
-        loss_definition=loss, device=device
-    )
+    # Optimize the aim points.
+    _, _, _, _, _ = aim_point_optimizer.optimize(loss_definition=loss, device=device)
 
     for index, heliostat_group in enumerate(scenario.heliostat_field.heliostat_groups):
         expected_path = (
diff --git a/tutorials/05_motor_positions_optimizer_tutorial.py b/tutorials/05_motor_positions_optimizer_tutorial.py
index 9b96e5de..0a1eac6d 100644
--- a/tutorials/05_motor_positions_optimizer_tutorial.py
+++ b/tutorials/05_motor_positions_optimizer_tutorial.py
@@ -1,4 +1,4 @@
-"""Motor positions optimizer tutorial."""
+"""Aim point optimization tutorial."""
 
 import pathlib
 
@@ -7,7 +7,7 @@
 from matplotlib import pyplot as plt
 
 from artist.flux import bitmap
-from artist.optim import MotorPositionsOptimizer
+from artist.optim import AimPointOptimizer
 from artist.optim.loss import KLDivergenceLoss
 from artist.raytracing import HeliostatRayTracer
 from artist.scenario import Scenario
@@ -19,7 +19,7 @@
 
 #############################################################################################################
 # Define helper functions for the plots.
-# Skip to line 115 for the tutorial code.
+# Skip to line 124 for the tutorial code.
 #############################################################################################################
 
 
@@ -238,8 +238,8 @@ def create_flux_plot(label: str, resolution: torch.Tensor) -> None:
 
     create_flux_plot(label="before", resolution=bitmap_resolution)
 
-    # Create the motor positions optimizer.
-    motor_positions_optimizer = MotorPositionsOptimizer(
+    # Create the aim point optimizer.
+    aim_point_optimizer = AimPointOptimizer(
         ddp_setup=ddp_setup,
         scenario=scenario,
         optimization_configuration=optimization_configuration,
@@ -252,11 +252,11 @@ def create_flux_plot(label: str, resolution: torch.Tensor) -> None:
     )
 
     # Optimize the motor positions.
-    final_loss, _, _, _, _ = motor_positions_optimizer.optimize(
+    final_loss, _, _, _, _ = aim_point_optimizer.optimize(
         loss_definition=loss_definition, device=device
     )
 
-# Inspect the synchronized loss per heliostat. Heliostats that have not been optimized have an infinite loss.
+# Inspect the synchronized loss per heliostat.
 print(f"rank {ddp_setup['rank']}, final loss {final_loss}")
 
 create_flux_plot(label="after", resolution=bitmap_resolution)