Merge branch 'main' into joss_paper

Author: kalebphipps

.github/pull_request_template.md

@@ -1,12 +1,13 @@
 # Description
 
-Please include a summary of the changes and the related issue. Please also include relevant motivation and context. List any dependencies that are required for this change.
+<!--
+Please include a summary of the changes and the related issue. Please also include relevant motivation and context. List any dependencies that are required for this change. -->
 
 Fixes # (issue)
 
 ## Type of change
 
-Please delete options that are not relevant.
+<!-- Please delete options that are not relevant. -->
 
 - [ ] Bug fix (non-breaking change which fixes an issue)
 - [ ] New feature (non-breaking change which adds functionality)
@@ -15,6 +16,8 @@ Please delete options that are not relevant.
 
 # Checklist:
 
+<!-- If you are unable to check all the boxes, your pull request may not be eligible for review. -->
+
 - [ ] My code follows the style guidelines of this project
 - [ ] I have performed a self-review of my code
 - [ ] I have commented my code, particularly in hard-to-understand areas

.gitignore

@@ -161,3 +161,17 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+#data files.
+.pt
+.png
+.jpg
+.pdf
+.json
+.csv
+.vscode/settings.json
+examples/paint_plots/data/
+examples/paint_plots/plots/
+
+# Local config files.
+**/config.local.json

.pre-commit-config.yaml

@@ -1,9 +1,11 @@
 fail_fast: false
 repos:
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.18.1 # Use the sha / tag you want to point at
+    rev: v1.18.2 # Use the sha / tag you want to point at
     hooks:
       - id: mypy
+        additional_dependencies:
+          - types-PyYAML
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v6.0.0
     hooks:
@@ -12,7 +14,7 @@ repos:
       - id: check-yaml
       - id: check-added-large-files
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.13.0
+    rev: v0.13.3
     hooks:
       - id: ruff
         args: [ --fix ]

artist/__init__.py

@@ -2,14 +2,14 @@
 
 import os
 
-from . import core, data_loader, field, scenario, scene, util
+from . import core, data_parser, field, scenario, scene, util
 
 ARTIST_ROOT = f"{os.sep}".join(__file__.split(os.sep)[:-2])
-"""Reference to the root directory of ARTIST."""
+"""Reference to the root directory of ``ARTIST``."""
 
 __all__ = [
     "core",
-    "data_loader",
+    "data_parser",
     "field",
     "scenario",
     "scene",

artist/core/__init__.py

@@ -1,19 +1,19 @@
-"""Bundle all classes responsible for the core functions in ARTIST."""
+"""Bundle all classes responsible for the core functions in ``ARTIST``."""
 
 from artist.core.heliostat_ray_tracer import (
     DistortionsDataset,
     HeliostatRayTracer,
     RestrictedDistributedSampler,
 )
-from artist.core.kinematic_calibrator import KinematicCalibrator
+from artist.core.kinematic_reconstructor import KinematicReconstructor
 from artist.core.motor_position_optimizer import MotorPositionsOptimizer
 from artist.core.surface_reconstructor import SurfaceReconstructor
 
 __all__ = [
     "HeliostatRayTracer",
     "DistortionsDataset",
     "RestrictedDistributedSampler",
-    "KinematicCalibrator",
+    "KinematicReconstructor",
     "SurfaceReconstructor",
     "MotorPositionsOptimizer",
 ]

artist/core/core_utils.py

@@ -11,7 +11,7 @@ def per_heliostat_reduction(
     """
     Compute mean losses for each heliostat with multiple samples.
 
-    If the active heliostats of one group have different amounts of samples to train on, i.e.
+    If the active heliostats of one group have different amounts of samples to train on, i.e.,
     one heliostat is trained with more samples than another, this function makes sure that
     each heliostat still contributes equally to the overall loss of the group. This function
     computes the mean loss for each heliostat.
@@ -20,13 +20,13 @@ def per_heliostat_reduction(
     ----------
     per_sample_values : torch.Tensor
         The per sample values to be reduced.
-        Tensor of shape [number_of_samples]
+        Tensor of shape [number_of_samples].
     active_heliostats_mask : torch.Tensor
         A mask defining which heliostats are activated.
         Tensor of shape [number_of_heliostats].
     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
+        If None, ``ARTIST`` will automatically select the most appropriate
         device (CUDA or CPU) based on availability and OS.
 
     Returns
@@ -48,7 +48,7 @@ def per_heliostat_reduction(
         0, heliostat_ids, per_sample_values
     )
 
-    # Compute mean MSE per heliostat on each rank.
+    # Compute MSE loss per heliostat on each rank.
     number_of_samples_per_heliostat = torch.zeros(
         len(active_heliostats_mask), device=device
     )
@@ -76,7 +76,7 @@ def scale_loss(
     loss : torch.Tensor
         The loss to be scaled.
         Tensor of shape [1].
-    reference_loss :  torch.Tensor
+    reference : torch.Tensor
         The reference loss.
         Tensor of shape [1].
     weight : float
@@ -90,4 +90,9 @@ def scale_loss(
     """
     epsilon = 1e-12
     scale = (reference * weight) / (loss + epsilon)
-    return loss * scale
+    scaled_loss = loss * scale
+
+    inf_mask = torch.isinf(loss)
+    scaled_loss[inf_mask] = loss[inf_mask]
+
+    return scaled_loss