Update README.md

README.md

@@ -490,197 +490,131 @@ With the release of this dataset, we aim to lower the barrier for optimization a
 Load the dataset and convert to a Pandas Dataframe:
 ```python
 import datasets
+import torch
+from torch.utils.data import DataLoader
 
-full_flat_ds = datasets.load_dataset("proxima-fusion/constellaration", "full_flat")["train"]
-full_flat_df = full_flat_ds.to_pandas().set_index("plasma_config_id")
+ds = datasets.load_dataset("proxima-fusion/constellaration", "full_flat")["train"]
+ds = ds.select_columns([c for c in ds.column_names
+                        if c.startswith("boundary.")
+                        or c.startswith("metrics.")])
 
-for column in full_flat_df.columns:
-    print(column)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ds_torch = ds.with_format("torch", device=device)  # other options: "jax", "tensorflow" etc.
+
+dataloader = DataLoader(ds_torch, batch_size=4)
+
+for batch in dataloader:
+    print(batch)
+    break
 ```
 <details>
 <summary>Output</summary>
 
 ```python
-boundary.n_field_periods
-boundary.is_stellarator_symmetric
-boundary.r_cos(0, 0)
-boundary.r_cos(0, 1)
-boundary.r_cos(0, 2)
-boundary.r_cos(0, 3)
-boundary.r_cos(0, 4)
-boundary.r_cos(1, -4)
-boundary.r_cos(1, -3)
-boundary.r_cos(1, -2)
-boundary.r_cos(1, -1)
-boundary.r_cos(1, 0)
-boundary.r_cos(1, 1)
-boundary.r_cos(1, 2)
-boundary.r_cos(1, 3)
-boundary.r_cos(1, 4)
-boundary.r_cos(2, -4)
-boundary.r_cos(2, -3)
-boundary.r_cos(2, -2)
-boundary.r_cos(2, -1)
-boundary.r_cos(2, 0)
-boundary.r_cos(2, 1)
-boundary.r_cos(2, 2)
-boundary.r_cos(2, 3)
-boundary.r_cos(2, 4)
-boundary.r_cos(3, -4)
-boundary.r_cos(3, -3)
-boundary.r_cos(3, -2)
-boundary.r_cos(3, -1)
-boundary.r_cos(3, 0)
-boundary.r_cos(3, 1)
-boundary.r_cos(3, 2)
-boundary.r_cos(3, 3)
-boundary.r_cos(3, 4)
-boundary.r_cos(4, -4)
-boundary.r_cos(4, -3)
-boundary.r_cos(4, -2)
-boundary.r_cos(4, -1)
-boundary.r_cos(4, 0)
-boundary.r_cos(4, 1)
-boundary.r_cos(4, 2)
-boundary.r_cos(4, 3)
-boundary.r_cos(4, 4)
-boundary.z_sin(0, 1)
-boundary.z_sin(0, 2)
-boundary.z_sin(0, 3)
-boundary.z_sin(0, 4)
-boundary.z_sin(1, -4)
-boundary.z_sin(1, -3)
-boundary.z_sin(1, -2)
-boundary.z_sin(1, -1)
-boundary.z_sin(1, 0)
-boundary.z_sin(1, 1)
-boundary.z_sin(1, 2)
-boundary.z_sin(1, 3)
-boundary.z_sin(1, 4)
-boundary.z_sin(2, -4)
-boundary.z_sin(2, -3)
-boundary.z_sin(2, -2)
-boundary.z_sin(2, -1)
-boundary.z_sin(2, 0)
-boundary.z_sin(2, 1)
-boundary.z_sin(2, 2)
-boundary.z_sin(2, 3)
-boundary.z_sin(2, 4)
-boundary.z_sin(3, -4)
-boundary.z_sin(3, -3)
-boundary.z_sin(3, -2)
-boundary.z_sin(3, -1)
-boundary.z_sin(3, 0)
-boundary.z_sin(3, 1)
-boundary.z_sin(3, 2)
-boundary.z_sin(3, 3)
-boundary.z_sin(3, 4)
-boundary.z_sin(4, -4)
-boundary.z_sin(4, -3)
-boundary.z_sin(4, -2)
-boundary.z_sin(4, -1)
-boundary.z_sin(4, 0)
-boundary.z_sin(4, 1)
-boundary.z_sin(4, 2)
-boundary.z_sin(4, 3)
-boundary.z_sin(4, 4)
-metrics.qi
-metrics.vacuum_well
-metrics.aspect_ratio
-metrics.max_elongation
-metrics.average_triangularity
-metrics.axis_magnetic_mirror_ratio
-metrics.edge_magnetic_mirror_ratio
-metrics.aspect_ratio_over_edge_rotational_transform
-metrics.flux_compression_in_regions_of_bad_curvature
-metrics.axis_rotational_transform_over_n_field_periods
-metrics.edge_rotational_transform_over_n_field_periods
-metrics.minimum_normalized_magnetic_gradient_scale_length
-metrics.id
-omnigenous_field_and_targets.aspect_ratio
-omnigenous_field_and_targets.major_radius
-omnigenous_field_and_targets.max_elongation
-omnigenous_field_and_targets.rotational_transform
-omnigenous_field_and_targets.id
-omnigenous_field_and_targets.omnigenous_field.x_lmn
-omnigenous_field_and_targets.omnigenous_field.n_field_periods
-omnigenous_field_and_targets.omnigenous_field.poloidal_winding
-omnigenous_field_and_targets.omnigenous_field.torodial_winding
-omnigenous_field_and_targets.omnigenous_field.modB_spline_knot_coefficients
-desc_omnigenous_field_optimization_settings.id
-desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.name
-desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.weight
-desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.target_kind
-desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.name
-desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.weight
-desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eta_weight
-desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_rho
-desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_M_factor
-desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_N_factor
-desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.name
-desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.weight
-desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.target_kind
-desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.name
-desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.weight
-desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.target_kind
-desc_omnigenous_field_optimization_settings.optimizer_settings.name
-desc_omnigenous_field_optimization_settings.optimizer_settings.maxiter
-desc_omnigenous_field_optimization_settings.optimizer_settings.verbose
-desc_omnigenous_field_optimization_settings.equilibrium_settings.psi
-desc_omnigenous_field_optimization_settings.equilibrium_settings.check_orientation
-desc_omnigenous_field_optimization_settings.equilibrium_settings.max_poloidal_mode
-desc_omnigenous_field_optimization_settings.equilibrium_settings.max_toroidal_mode
-desc_omnigenous_field_optimization_settings.initial_guess_settings.torsion
-desc_omnigenous_field_optimization_settings.initial_guess_settings.elongation
-desc_omnigenous_field_optimization_settings.initial_guess_settings.aspect_ratio
-desc_omnigenous_field_optimization_settings.initial_guess_settings.major_radius
-desc_omnigenous_field_optimization_settings.initial_guess_settings.mirror_ratio
-desc_omnigenous_field_optimization_settings.initial_guess_settings.n_field_periods
-desc_omnigenous_field_optimization_settings.initial_guess_settings.is_iota_positive
-desc_omnigenous_field_optimization_settings.initial_guess_settings.is_stellarator_symmetric
-desc_omnigenous_field_optimization_settings.initial_guess_settings.max_elongation
-desc_omnigenous_field_optimization_settings.initial_guess_settings.max_poloidal_mode
-desc_omnigenous_field_optimization_settings.initial_guess_settings.max_toroidal_mode
-desc_omnigenous_field_optimization_settings.initial_guess_settings.rotational_transform
-vmec_omnigenous_field_optimization_settings.verbose
-vmec_omnigenous_field_optimization_settings.max_poloidal_mode
-vmec_omnigenous_field_optimization_settings.max_toroidal_mode
-vmec_omnigenous_field_optimization_settings.n_inner_optimizations
-vmec_omnigenous_field_optimization_settings.gradient_free_max_time
-vmec_omnigenous_field_optimization_settings.infinity_norm_spectrum_scaling
-vmec_omnigenous_field_optimization_settings.gradient_free_budget_per_design_variable
-vmec_omnigenous_field_optimization_settings.use_continuation_method_in_fourier_space
-vmec_omnigenous_field_optimization_settings.gradient_free_optimization_hypercube_bounds
-vmec_omnigenous_field_optimization_settings.gradient_based_relative_objectives_tolerance
-vmec_omnigenous_field_optimization_settings.id
-qp_init_omnigenous_field_optimization_settings.torsion
-qp_init_omnigenous_field_optimization_settings.elongation
-qp_init_omnigenous_field_optimization_settings.aspect_ratio
-qp_init_omnigenous_field_optimization_settings.major_radius
-qp_init_omnigenous_field_optimization_settings.mirror_ratio
-qp_init_omnigenous_field_optimization_settings.n_field_periods
-qp_init_omnigenous_field_optimization_settings.is_iota_positive
-qp_init_omnigenous_field_optimization_settings.is_stellarator_symmetric
-qp_init_omnigenous_field_optimization_settings.id
-nae_init_omnigenous_field_optimization_settings.aspect_ratio
-nae_init_omnigenous_field_optimization_settings.mirror_ratio
-nae_init_omnigenous_field_optimization_settings.max_elongation
-nae_init_omnigenous_field_optimization_settings.n_field_periods
-nae_init_omnigenous_field_optimization_settings.max_poloidal_mode
-nae_init_omnigenous_field_optimization_settings.max_toroidal_mode
-nae_init_omnigenous_field_optimization_settings.rotational_transform
-nae_init_omnigenous_field_optimization_settings.id
-misc.vmecpp_wout_id
-misc.has_optimize_boundary_omnigenity_vmec_error
-misc.has_optimize_boundary_omnigenity_desc_error
-misc.has_generate_qp_initialization_from_targets_error
-misc.has_generate_nae_initialization_from_targets_error
-misc.has_neurips_2025_forward_model_error
+{'boundary.is_stellarator_symmetric': tensor([True, True, True, True]),
+ 'boundary.n_field_periods': tensor([5., 2., 5., 3.]),
+ 'boundary.r_cos(0, 0)': tensor([0.9995, 0.9933, 1.0000, 1.0000]),
+ 'boundary.r_cos(0, 1)': tensor([ 0.0008,  0.0957,  0.0978, -0.0658]),
+ 'boundary.r_cos(0, 2)': tensor([-0.0166, -0.0522, -0.0358, -0.0385]),
+ 'boundary.r_cos(0, 3)': tensor([ 0.0006, -0.0098,  0.0004,  0.0022]),
+ 'boundary.r_cos(0, 4)': tensor([ 2.7140e-06, -5.2107e-04,  4.1021e-04,  4.6007e-04]),
+ 'boundary.r_cos(1, -1)': tensor([-0.0774, -0.0480,  0.0674,  0.0142]),
+ 'boundary.r_cos(1, -2)': tensor([-0.0254,  0.0015,  0.0327,  0.0565]),
+ 'boundary.r_cos(1, -3)': tensor([-0.0036, -0.0002,  0.0047, -0.0110]),
+ 'boundary.r_cos(1, -4)': tensor([ 0.0009, -0.0007,  0.0019, -0.0007]),
+ 'boundary.r_cos(1, 0)': tensor([0.2136, 0.1506, 0.0668, 0.0835]),
+ 'boundary.r_cos(1, 1)': tensor([-0.0430, -0.0009, -0.0421, -0.0468]),
+ 'boundary.r_cos(1, 2)': tensor([ 0.0070,  0.0099,  0.0148, -0.0137]),
+ 'boundary.r_cos(1, 3)': tensor([-0.0019,  0.0014, -0.0013,  0.0040]),
+ 'boundary.r_cos(1, 4)': tensor([-0.0001, -0.0001, -0.0001,  0.0001]),
+ 'boundary.r_cos(2, -1)': tensor([ 0.0222, -0.0189, -0.0043, -0.0197]),
+ 'boundary.r_cos(2, -2)': tensor([ 0.0252, -0.0124, -0.0106,  0.0008]),
+ 'boundary.r_cos(2, -3)': tensor([ 0.0063, -0.0054, -0.0039,  0.0047]),
+ 'boundary.r_cos(2, -4)': tensor([ 0.0011,  0.0021, -0.0010, -0.0004]),
+ 'boundary.r_cos(2, 0)': tensor([-0.0170, -0.0226, -0.0083, -0.0095]),
+ 'boundary.r_cos(2, 1)': tensor([-1.9742e-03,  2.4878e-03, -6.7278e-05, -5.8859e-03]),
+ 'boundary.r_cos(2, 2)': tensor([0.0030, 0.0017, 0.0003, 0.0010]),
+ 'boundary.r_cos(2, 3)': tensor([-0.0009,  0.0002, -0.0010,  0.0009]),
+ 'boundary.r_cos(2, 4)': tensor([ 0.0002,  0.0002,  0.0002, -0.0002]),
+ 'boundary.r_cos(3, -1)': tensor([-0.0052, -0.0055,  0.0013, -0.0080]),
+ 'boundary.r_cos(3, -2)': tensor([-0.0019, -0.0035,  0.0026, -0.0004]),
+ 'boundary.r_cos(3, -3)': tensor([-0.0030,  0.0013,  0.0007,  0.0002]),
+ 'boundary.r_cos(3, -4)': tensor([-0.0011,  0.0003,  0.0003,  0.0029]),
+ 'boundary.r_cos(3, 0)': tensor([-0.0057, -0.0003, -0.0014,  0.0013]),
+ 'boundary.r_cos(3, 1)': tensor([ 0.0021,  0.0029,  0.0010, -0.0005]),
+ 'boundary.r_cos(3, 2)': tensor([-0.0001,  0.0011, -0.0008, -0.0007]),
+ 'boundary.r_cos(3, 3)': tensor([1.6793e-04, 3.4548e-04, 4.4246e-04, 3.4558e-05]),
+ 'boundary.r_cos(3, 4)': tensor([-4.5783e-05,  1.1368e-04, -1.1460e-04,  1.4845e-04]),
+ 'boundary.r_cos(4, -1)': tensor([ 7.2199e-05,  1.2212e-04,  5.9814e-05, -5.6161e-04]),
+ 'boundary.r_cos(4, -2)': tensor([ 0.0004,  0.0004, -0.0001, -0.0003]),
+ 'boundary.r_cos(4, -3)': tensor([-0.0002, -0.0002, -0.0001, -0.0005]),
+ 'boundary.r_cos(4, -4)': tensor([ 2.3317e-04,  7.7059e-05,  3.5259e-05, -1.2475e-04]),
+ 'boundary.r_cos(4, 0)': tensor([ 1.3698e-04, -2.1361e-04, -5.7761e-04,  8.3187e-05]),
+ 'boundary.r_cos(4, 1)': tensor([-7.0448e-05, -5.2118e-04,  3.7886e-04, -1.2714e-04]),
+ 'boundary.r_cos(4, 2)': tensor([ 0.0003, -0.0002, -0.0002, -0.0002]),
+ 'boundary.r_cos(4, 3)': tensor([-9.0838e-05, -1.6513e-04,  6.7852e-05,  4.1940e-06]),
+ 'boundary.r_cos(4, 4)': tensor([-6.2693e-06,  1.4236e-06, -3.0395e-05, -4.5643e-05]),
+ 'boundary.z_sin(0, 1)': tensor([-0.1963, -0.4660,  0.0626, -0.0143]),
+ 'boundary.z_sin(0, 2)': tensor([ 0.0197, -0.0487,  0.0186,  0.0149]),
+ 'boundary.z_sin(0, 3)': tensor([-0.0022, -0.0056,  0.0017, -0.0066]),
+ 'boundary.z_sin(0, 4)': tensor([ 6.1945e-04, -2.5693e-03,  3.3028e-05, -3.0652e-04]),
+ 'boundary.z_sin(1, -1)': tensor([-0.0737, -0.0783,  0.0069, -0.0228]),
+ 'boundary.z_sin(1, -2)': tensor([-0.0346,  0.0154,  0.0488,  0.0576]),
+ 'boundary.z_sin(1, -3)': tensor([-0.0038, -0.0004,  0.0128, -0.0016]),
+ 'boundary.z_sin(1, -4)': tensor([-5.1524e-04, -2.2537e-03,  1.7765e-03,  9.6958e-05]),
+ 'boundary.z_sin(1, 0)': tensor([-0.0947, -0.1091, -0.1901, -0.1610]),
+ 'boundary.z_sin(1, 1)': tensor([0.0144, 0.0656, 0.0731, 0.0166]),
+ 'boundary.z_sin(1, 2)': tensor([-0.0049,  0.0008, -0.0216,  0.0150]),
+ 'boundary.z_sin(1, 3)': tensor([ 0.0011, -0.0014,  0.0032, -0.0012]),
+ 'boundary.z_sin(1, 4)': tensor([ 0.0002,  0.0015, -0.0005, -0.0004]),
+ 'boundary.z_sin(2, -1)': tensor([-0.0140,  0.0100,  0.0049, -0.0189]),
+ 'boundary.z_sin(2, -2)': tensor([ 0.0140,  0.0016, -0.0162, -0.0114]),
+ 'boundary.z_sin(2, -3)': tensor([ 0.0048, -0.0015, -0.0107,  0.0036]),
+ 'boundary.z_sin(2, -4)': tensor([ 0.0009,  0.0024, -0.0018, -0.0010]),
+ 'boundary.z_sin(2, 0)': tensor([ 0.0142, -0.0110,  0.0052, -0.0008]),
+ 'boundary.z_sin(2, 1)': tensor([ 0.0004, -0.0072, -0.0033,  0.0095]),
+ 'boundary.z_sin(2, 2)': tensor([-0.0008, -0.0030,  0.0036, -0.0005]),
+ 'boundary.z_sin(2, 3)': tensor([-2.4587e-04, -1.4641e-03, -6.1073e-05, -5.5180e-04]),
+ 'boundary.z_sin(2, 4)': tensor([-4.4745e-05,  1.1328e-04, -5.3629e-05,  3.2248e-04]),
+ 'boundary.z_sin(3, -1)': tensor([ 0.0137,  0.0037, -0.0037,  0.0090]),
+ 'boundary.z_sin(3, -2)': tensor([-0.0037, -0.0089, -0.0025, -0.0084]),
+ 'boundary.z_sin(3, -3)': tensor([-0.0052,  0.0009, -0.0015, -0.0025]),
+ 'boundary.z_sin(3, -4)': tensor([-0.0011,  0.0014, -0.0002,  0.0023]),
+ 'boundary.z_sin(3, 0)': tensor([ 0.0012, -0.0005, -0.0001, -0.0020]),
+ 'boundary.z_sin(3, 1)': tensor([-0.0007,  0.0040,  0.0003, -0.0016]),
+ 'boundary.z_sin(3, 2)': tensor([-0.0006, -0.0003,  0.0005,  0.0008]),
+ 'boundary.z_sin(3, 3)': tensor([ 0.0002,  0.0003, -0.0004,  0.0004]),
+ 'boundary.z_sin(3, 4)': tensor([-5.6412e-05,  1.0018e-04,  5.2259e-05, -6.8400e-05]),
+ 'boundary.z_sin(4, -1)': tensor([ 3.2419e-05, -4.5468e-04,  1.1803e-03,  5.0270e-04]),
+ 'boundary.z_sin(4, -2)': tensor([ 3.0922e-04, -2.3282e-03, -6.1121e-05,  6.0890e-04]),
+ 'boundary.z_sin(4, -3)': tensor([ 0.0006,  0.0007, -0.0003, -0.0002]),
+ 'boundary.z_sin(4, -4)': tensor([ 1.5477e-04,  2.9187e-04,  9.7048e-05, -3.6228e-04]),
+ 'boundary.z_sin(4, 0)': tensor([ 0.0002,  0.0010, -0.0004, -0.0007]),
+ 'boundary.z_sin(4, 1)': tensor([-6.8060e-04,  6.0775e-04,  4.5423e-04, -7.6356e-05]),
+ 'boundary.z_sin(4, 2)': tensor([ 0.0002, -0.0004, -0.0003,  0.0002]),
+ 'boundary.z_sin(4, 3)': tensor([-5.1384e-05,  5.7630e-05,  9.2855e-05, -3.2524e-05]),
+ 'boundary.z_sin(4, 4)': tensor([ 3.9863e-05, -1.2590e-04, -4.8167e-06,  5.3396e-05]),
+ 'metrics.aspect_ratio': tensor([7.7955, 8.7009, 8.3073, 9.6474]),
+ 'metrics.aspect_ratio_over_edge_rotational_transform': tensor([ 4.7622, 39.3853,  7.4686,  9.3211]),
+ 'metrics.average_triangularity': tensor([ 0.6554, -0.6067, -0.3194, -0.5184]),
+ 'metrics.axis_magnetic_mirror_ratio': tensor([0.2098, 0.2465, 0.5243, 0.2823]),
+ 'metrics.axis_rotational_transform_over_n_field_periods': tensor([0.3285, 0.2195, 0.2377, 0.2333]),
+ 'metrics.edge_magnetic_mirror_ratio': tensor([0.3518, 0.2744, 0.7742, 0.4869]),
+ 'metrics.edge_rotational_transform_over_n_field_periods': tensor([0.3274, 0.1105, 0.2225, 0.3450]),
+ 'metrics.flux_compression_in_regions_of_bad_curvature': tensor([2.0642, 1.6771, 1.6702, 1.4084]),
+ 'metrics.id': ['DfpsEPzxXHTUgveVPNFVxyw',
+                'D6ydRp85utx9ZXqZVXbez8G',
+                'DLfSjAoEr26nCNet4S84XET',
+                'DBVNupAx3tW54uUz5bGQfyT'],
+ 'metrics.max_elongation': tensor([7.9947, 5.7292, 5.6427, 6.7565]),
+ 'metrics.minimum_normalized_magnetic_gradient_scale_length': tensor([5.0333, 4.4903, 2.7130, 5.9777]),
+ 'metrics.qi': tensor([0.0101, 0.0007, 0.0249, 0.0148]),
+ 'metrics.vacuum_well': tensor([-0.0216, -0.0756, -0.1321, -0.2297])}
 ```
 </details>
 
 ### Advanced Usage
-Install `constellaration` from [here](https://github.com/proximafusion/constellaration).
+For functionality around computation, manipulation and visualization of data contained in this dataset, install `constellaration` from [here](https://github.com/proximafusion/constellaration).
 
 Load and instantiate plasma boundaries:
 ```python
@@ -711,13 +645,15 @@ from constellaration.mhd import vmec_utils
 
 ds = datasets.load_dataset("proxima-fusion/constellaration", "vmecpp_ideal_mhd_equilibria", streaming=True)["train"]
 
-for batch in ds.filter(lambda row: row["vmecpp_wout_json"] is not None).iter(batch_size=1):
-    break
+ds = ds.filter(lambda row: row["vmecpp_wout_json"] is not None)
+
+row = next(ds.__iter__())
 
-vmecpp_wout_json = batch["vmecpp_wout_json"][0]
+vmecpp_wout_json = row["vmecpp_wout_json"]
 vmecpp_wout = vmec_utils.VmecppWOut.model_validate_json(vmecpp_wout_json)
 
-plasma_config_id = batch["plasma_config_id"][0]
+# Fetch corresponding boundary
+plasma_config_id = row["plasma_config_id"]
 boundary_json = full_json_df.loc[plasma_config_id]["boundary.surface"]
 boundary = surface_rz_fourier.SurfaceRZFourier.model_validate_json(boundary_json)
 ```