Update README.md

README.md

@@ -513,10 +513,10 @@ configs:
   data_files:
   - split: train
     path: full_json/train-*
-- config_name: vmecpp_ideal_mhd_equilibria
+- config_name: vmecpp_wout
   data_files:
   - split: train
-    path: vmecpp_ideal_mhd_equilibria/part-*
+    path: vmecpp_wout/part*
 license: mit
 language:
 - en
@@ -597,18 +597,31 @@ Load the dataset and convert to a Pandas Dataframe (here, `torch` is used as an
 ```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_ds = full_flat_ds.select_columns([c for c in full_flat_ds.column_names
+ds = datasets.load_dataset(
+    "proxima-fusion/constellaration",
+    split="train",
+    num_proc=4,
+)
+ds = ds.select_columns([c for c in ds.column_names
                         if c.startswith("boundary.")
                         or c.startswith("metrics.")])
+ds = ds.filter(
+    lambda x: x == 3,
+    input_columns=["boundary.n_field_periods"],
+    num_proc=4,
+)
+ml_ds = ds.remove_columns([
+    "boundary.n_field_periods", "boundary.is_stellarator_symmetric",  # all same value
+    "boundary.r_sin", "boundary.z_cos",  # empty
+    "boundary.json", "metrics.json", "metrics.id",  # not needed
+])
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-ds_torch = full_flat_ds.with_format("torch", device=device)  # other options: "jax", "tensorflow" etc.
+torch_ds = ml_ds.with_format("torch", device=device)  # other options: "jax", "tensorflow" etc.
 
-for batch in DataLoader(ds_torch, batch_size=4, num_workers=4):
-    print(batch)
+for batch in torch.utils.data.DataLoader(torch_ds, batch_size=4, num_workers=4):
+    pprint(batch)
     break
 ```
 <div style="margin-left: 1em;">
@@ -616,56 +629,7 @@ for batch in DataLoader(ds_torch, batch_size=4, num_workers=4):
 <summary>Output</summary>
 
 ```python
-{'metrics.id': ['DfpsEPzxXHTUgveVPNFVxyw',
-                'D6ydRp85utx9ZXqZVXbez8G',
-                'DLfSjAoEr26nCNet4S84XET',
-                'DBVNupAx3tW54uUz5bGQfyT'],
- 'metrics.qi': tensor([0.0101, 0.0007, 0.0249, 0.0148]),
- 'metrics.vacuum_well': tensor([-0.0216, -0.0756, -0.1321, -0.2297]),
- 'metrics.aspect_ratio': tensor([7.7955, 8.7009, 8.3073, 9.6474]),
- 'metrics.max_elongation': tensor([7.9947, 5.7292, 5.6427, 6.7565]),
- '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.edge_magnetic_mirror_ratio': tensor([0.3518, 0.2744, 0.7742, 0.4869]),
- 'metrics.aspect_ratio_over_edge_rotational_transform': tensor([ 4.7622, 39.3853,  7.4686,  9.3211]),
- 'metrics.flux_compression_in_regions_of_bad_curvature': tensor([2.0642, 1.6771, 1.6702, 1.4084]),
- 'metrics.axis_rotational_transform_over_n_field_periods': tensor([0.3285, 0.2195, 0.2377, 0.2333]),
- 'metrics.edge_rotational_transform_over_n_field_periods': tensor([0.3274, 0.1105, 0.2225, 0.3450]),
- 'metrics.minimum_normalized_magnetic_gradient_scale_length': tensor([5.0333, 4.4903, 2.7130, 5.9777]),
- 'boundary.r_cos': tensor([[[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  9.9949e-01,
-           7.6796e-04, -1.6605e-02,  5.5780e-04,  2.7140e-06],
-         [ 9.0751e-04, -3.6262e-03, -2.5385e-02, -7.7356e-02,  2.1363e-01,
-          -4.3046e-02,  7.0488e-03, -1.9437e-03, -1.4148e-04],
-         [ 1.1192e-03,  6.2549e-03,  2.5220e-02,  2.2216e-02, -1.7012e-02,
-          -1.9742e-03,  3.0245e-03, -8.5555e-04,  1.9455e-04],
-         [-1.0992e-03, -3.0390e-03, -1.9017e-03, -5.2155e-03, -5.7048e-03,
-           2.0575e-03, -1.1494e-04,  1.6793e-04, -4.5783e-05],
-         [ 2.3317e-04, -2.0280e-04,  4.0296e-04,  7.2199e-05,  1.3698e-04,
-          -7.0448e-05,  2.9516e-04, -9.0838e-05, -6.2693e-06]],
-
-        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  9.9334e-01,
-           9.5655e-02, -5.2197e-02, -9.8426e-03, -5.2107e-04],
-         [-6.9068e-04, -2.0027e-04,  1.4961e-03, -4.8040e-02,  1.5062e-01,
-          -8.6001e-04,  9.8727e-03,  1.3859e-03, -1.4257e-04],
-         [ 2.1268e-03, -5.4152e-03, -1.2367e-02, -1.8891e-02, -2.2634e-02,
-           2.4878e-03,  1.7472e-03,  1.7854e-04,  1.5837e-04],
-         [ 2.9863e-04,  1.3137e-03, -3.4623e-03, -5.5232e-03, -2.7664e-04,
-           2.9050e-03,  1.1163e-03,  3.4548e-04,  1.1368e-04],
-         [ 7.7059e-05, -2.3845e-04,  4.4021e-04,  1.2212e-04, -2.1361e-04,
-          -5.2118e-04, -1.5413e-04, -1.6513e-04,  1.4236e-06]],
-
-        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
-           9.7826e-02, -3.5796e-02,  4.3583e-04,  4.1021e-04],
-         [ 1.8546e-03,  4.6983e-03,  3.2687e-02,  6.7351e-02,  6.6784e-02,
-          -4.2107e-02,  1.4765e-02, -1.3257e-03, -1.3003e-04],
-         [-9.6934e-04, -3.8986e-03, -1.0552e-02, -4.2583e-03, -8.3177e-03,
-          -6.7278e-05,  3.3263e-04, -1.0408e-03,  2.2868e-04],
-         [ 2.6668e-04,  6.7729e-04,  2.6165e-03,  1.2503e-03, -1.4094e-03,
-           9.9661e-04, -8.1563e-04,  4.4246e-04, -1.1460e-04],
-         [ 3.5259e-05, -1.1595e-04, -1.3741e-04,  5.9814e-05, -5.7761e-04,
-           3.7886e-04, -1.9925e-04,  6.7852e-05, -3.0395e-05]],
-
-        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
+{'boundary.r_cos': tensor([[[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
           -6.5763e-02, -3.8500e-02,  2.2178e-03,  4.6007e-04],
          [-6.6648e-04, -1.0976e-02,  5.6475e-02,  1.4193e-02,  8.3476e-02,
           -4.6767e-02, -1.3679e-02,  3.9562e-03,  1.0087e-04],
@@ -674,41 +638,41 @@ for batch in DataLoader(ds_torch, batch_size=4, num_workers=4):
          [ 2.9056e-03,  1.6125e-04, -4.0626e-04, -8.0189e-03,  1.3228e-03,
           -5.3636e-04, -7.3536e-04,  3.4558e-05,  1.4845e-04],
          [-1.2475e-04, -4.9942e-04, -2.6091e-04, -5.6161e-04,  8.3187e-05,
-          -1.2714e-04, -2.1174e-04,  4.1940e-06, -4.5643e-05]]]),
- 'boundary.z_sin': tensor([[[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
-          -1.9626e-01,  1.9675e-02, -2.2323e-03,  6.1945e-04],
-         [-5.1524e-04, -3.8208e-03, -3.4611e-02, -7.3697e-02, -9.4725e-02,
-           1.4401e-02, -4.8538e-03,  1.1153e-03,  1.8119e-04],
-         [ 8.8862e-04,  4.7645e-03,  1.4034e-02, -1.3992e-02,  1.4160e-02,
-           3.7312e-04, -7.9215e-04, -2.4587e-04, -4.4745e-05],
-         [-1.1121e-03, -5.1679e-03, -3.6563e-03,  1.3666e-02,  1.2211e-03,
-          -7.2069e-04, -6.1250e-04,  2.2509e-04, -5.6412e-05],
-         [ 1.5477e-04,  6.2145e-04,  3.0922e-04,  3.2419e-05,  2.4620e-04,
-          -6.8060e-04,  1.9615e-04, -5.1384e-05,  3.9863e-05]],
+          -1.2714e-04, -2.1174e-04,  4.1940e-06, -4.5643e-05]],
+
+        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  9.9909e-01,
+          -6.8512e-02, -8.1567e-02,  2.5140e-02, -2.4035e-03],
+         [-3.4328e-03,  1.6768e-02,  1.2305e-02, -3.6708e-02,  1.0285e-01,
+           1.1224e-02, -2.3418e-02, -5.4137e-04,  9.3986e-04],
+         [-2.8389e-03,  1.4652e-03,  1.0112e-03,  9.8102e-04, -2.3162e-02,
+          -6.1180e-03,  1.5327e-03,  9.4122e-04, -1.2781e-03],
+         [ 3.9240e-04, -2.3131e-04,  4.5690e-04, -3.8244e-03, -1.5314e-03,
+           1.8863e-03,  1.1882e-03, -5.2338e-04,  2.6766e-04],
+         [-2.8441e-04, -3.4162e-04,  5.4013e-05,  7.4252e-04,  4.9895e-04,
+          -6.1110e-04, -8.7185e-04, -1.1714e-04,  9.9285e-08]],
 
-        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
-          -4.6604e-01, -4.8706e-02, -5.5774e-03, -2.5693e-03],
-         [-2.2537e-03, -3.5303e-04,  1.5423e-02, -7.8331e-02, -1.0906e-01,
-           6.5595e-02,  8.1187e-04, -1.4336e-03,  1.4737e-03],
-         [ 2.4240e-03, -1.4512e-03,  1.5746e-03,  1.0027e-02, -1.0979e-02,
-          -7.1742e-03, -3.0459e-03, -1.4641e-03,  1.1328e-04],
-         [ 1.3788e-03,  9.4350e-04, -8.9247e-03,  3.6596e-03, -5.4934e-04,
-           4.0136e-03, -2.9544e-04,  3.2837e-04,  1.0018e-04],
-         [ 2.9187e-04,  7.0291e-04, -2.3282e-03, -4.5468e-04,  1.0021e-03,
-           6.0775e-04, -3.5128e-04,  5.7630e-05, -1.2590e-04]],
-
-        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
-           6.2592e-02,  1.8552e-02,  1.7140e-03,  3.3028e-05],
-         [ 1.7765e-03,  1.2827e-02,  4.8849e-02,  6.8718e-03, -1.9011e-01,
-           7.3130e-02, -2.1610e-02,  3.1883e-03, -5.4797e-04],
-         [-1.7520e-03, -1.0653e-02, -1.6171e-02,  4.8786e-03,  5.1656e-03,
-          -3.2699e-03,  3.5506e-03, -6.1073e-05, -5.3629e-05],
-         [-2.1859e-04, -1.4518e-03, -2.4928e-03, -3.6855e-03, -1.1030e-04,
-           2.5252e-04,  4.9944e-04, -3.5102e-04,  5.2259e-05],
-         [ 9.7048e-05, -3.4866e-04, -6.1121e-05,  1.1803e-03, -4.2218e-04,
-           4.5423e-04, -2.6355e-04,  9.2855e-05, -4.8167e-06]],
+        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
+           6.9176e-02, -1.8489e-02, -6.5094e-03, -7.6238e-04],
+         [ 1.4062e-03,  4.2645e-03, -1.0647e-02, -8.1579e-02,  1.0522e-01,
+           1.6914e-02,  6.5321e-04,  6.9397e-04,  2.0881e-04],
+         [-6.5155e-05, -1.2232e-03, -3.3660e-03,  9.8742e-03, -1.4611e-02,
+           6.0985e-03,  9.5693e-04, -1.0049e-04,  5.4173e-05],
+         [-4.3969e-04, -5.1155e-04,  6.9611e-03, -2.8698e-04, -5.8589e-03,
+          -5.4844e-05, -7.3797e-04, -5.4401e-06, -3.3698e-05],
+         [-1.9741e-04,  1.0003e-04, -2.0176e-04,  4.9546e-04, -1.6201e-04,
+          -1.9169e-04, -3.9886e-04,  3.3773e-05, -3.5972e-05]],
 
-        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
+        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
+           1.1652e-01, -1.5593e-02, -1.0215e-02, -1.8656e-03],
+         [ 3.1697e-03,  2.1618e-02,  2.7072e-02, -2.4032e-02,  8.6125e-02,
+          -7.1168e-04, -1.2433e-02, -2.0902e-03,  1.5868e-04],
+         [-2.3877e-04, -4.9871e-03, -2.4145e-02, -2.1623e-02, -3.1477e-02,
+          -8.3460e-03, -8.8675e-04, -5.3290e-04, -2.2784e-04],
+         [-1.0006e-03,  2.1055e-05, -1.7186e-03, -5.2886e-03,  4.5186e-03,
+          -1.1530e-03,  6.2732e-05,  1.4212e-04,  4.3367e-05],
+         [ 7.8993e-05, -3.9503e-04,  1.5458e-03, -4.9707e-04, -3.9470e-04,
+           6.0808e-04, -3.6447e-04,  1.2936e-04,  6.3461e-07]]]),
+ 'boundary.z_sin': tensor([[[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
           -1.4295e-02,  1.4929e-02, -6.6461e-03, -3.0652e-04],
          [ 9.6958e-05, -1.6067e-03,  5.7568e-02, -2.2848e-02, -1.6101e-01,
            1.6560e-02,  1.5032e-02, -1.2463e-03, -4.0128e-04],
@@ -717,11 +681,52 @@ for batch in DataLoader(ds_torch, batch_size=4, num_workers=4):
          [ 2.3465e-03, -2.4885e-03, -8.4212e-03,  8.9649e-03, -1.9880e-03,
           -1.6269e-03,  8.4700e-04,  3.7171e-04, -6.8400e-05],
          [-3.6228e-04, -1.8575e-04,  6.0890e-04,  5.0270e-04, -6.9953e-04,
-          -7.6356e-05,  2.3796e-04, -3.2524e-05,  5.3396e-05]]]),
- 'boundary.r_sin': tensor([nan, nan, nan, nan]),
- 'boundary.z_cos': tensor([nan, nan, nan, nan]),
- 'boundary.n_field_periods': tensor([5., 2., 5., 3.]),
- 'boundary.is_stellarator_symmetric': tensor([True, True, True, True])}
+          -7.6356e-05,  2.3796e-04, -3.2524e-05,  5.3396e-05]],
+
+        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
+          -8.5341e-02,  2.4825e-02,  8.0996e-03, -7.1501e-03],
+         [-1.3470e-03,  4.6367e-03,  4.1579e-02, -3.6802e-02, -1.5076e-01,
+           7.1852e-02, -1.9793e-02,  8.2575e-03, -3.8958e-03],
+         [-2.3956e-03, -5.7497e-03,  5.8264e-03,  9.4471e-03, -3.5171e-03,
+          -1.0481e-02, -3.2885e-03,  4.0624e-03,  4.3130e-04],
+         [ 6.3403e-05, -9.2162e-04, -2.4765e-03,  5.4090e-04,  1.9999e-03,
+          -1.1500e-03,  2.7581e-03, -5.7271e-04,  3.0363e-04],
+         [ 4.6278e-04,  4.3696e-04,  8.0524e-05, -2.4660e-04, -2.3747e-04,
+           5.5060e-05, -1.3221e-04, -5.4823e-05,  1.6025e-04]],
+
+        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
+          -1.6090e-01, -1.4364e-02,  3.7923e-03,  1.8234e-03],
+         [ 1.2118e-03,  3.1261e-03,  3.2037e-03, -5.7482e-02, -1.5461e-01,
+          -1.8058e-03, -5.7149e-03, -7.4521e-04,  2.9463e-04],
+         [ 8.7049e-04, -3.2717e-04, -1.0188e-02,  1.1215e-02, -7.4697e-03,
+          -1.3592e-03, -1.4984e-03, -3.1362e-04,  1.5780e-06],
+         [ 1.2617e-04, -1.2257e-04, -6.9928e-04,  8.7431e-04, -2.5848e-03,
+           1.2087e-03, -2.4723e-04, -1.6535e-05, -6.4372e-05],
+         [-4.3932e-04, -1.8130e-04,  7.4368e-04, -6.1396e-04, -4.1518e-04,
+           4.8132e-04,  1.6036e-04,  5.3081e-05,  1.6636e-05]],
+
+        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
+          -1.1264e-02, -1.8349e-03,  7.2464e-03,  2.3807e-03],
+         [ 3.2969e-03,  1.9590e-02,  2.8355e-02, -1.0493e-02, -1.3216e-01,
+           1.7804e-02,  7.9768e-03,  2.1362e-03, -6.9118e-04],
+         [-5.2572e-04, -4.1409e-03, -3.6560e-02,  2.1644e-02,  1.6418e-02,
+           9.3557e-03,  3.3846e-03,  7.4172e-05,  1.8406e-04],
+         [-1.4907e-03,  2.0496e-03, -4.8581e-03,  3.5471e-03, -2.9191e-03,
+          -1.5056e-03,  7.7168e-04, -2.3136e-04, -1.2064e-05],
+         [-2.3742e-04,  4.5083e-04, -1.2933e-03, -4.4028e-04,  6.4168e-04,
+          -8.2755e-04,  4.1233e-04, -1.1037e-04, -6.3762e-06]]]),
+ 'metrics.aspect_ratio': tensor([9.6474, 9.1036, 9.4119, 9.5872]),
+ 'metrics.aspect_ratio_over_edge_rotational_transform': tensor([  9.3211, 106.7966,  13.8752,   8.9834]),
+ 'metrics.average_triangularity': tensor([-0.6456, -0.5325, -0.6086, -0.6531]),
+ 'metrics.axis_magnetic_mirror_ratio': tensor([0.2823, 0.4224, 0.2821, 0.2213]),
+ 'metrics.axis_rotational_transform_over_n_field_periods': tensor([0.2333, 0.0818, 0.1887, 0.1509]),
+ 'metrics.edge_magnetic_mirror_ratio': tensor([0.4869, 0.5507, 0.3029, 0.2991]),
+ 'metrics.edge_rotational_transform_over_n_field_periods': tensor([0.3450, 0.0284, 0.2261, 0.3557]),
+ 'metrics.flux_compression_in_regions_of_bad_curvature': tensor([1.4084, 0.9789, 1.5391, 1.1138]),
+ 'metrics.max_elongation': tensor([6.7565, 6.9036, 5.6105, 5.8703]),
+ 'metrics.minimum_normalized_magnetic_gradient_scale_length': tensor([5.9777, 4.2971, 8.5928, 4.8531]),
+ 'metrics.qi': tensor([0.0148, 0.0157, 0.0016, 0.0248]),
+ 'metrics.vacuum_well': tensor([-0.2297, -0.1146, -0.0983, -0.1738])}
 ```
 </details>
 </div>
@@ -732,14 +737,18 @@ For advanced manipulation and visualization of data contained in this dataset, i
 
 Load and instantiate plasma boundaries:
 ```python
-import datasets
 from constellaration.geometry import surface_rz_fourier
 
-full_json_ds = datasets.load_dataset("proxima-fusion/constellaration", "full_json")["train"]
-full_json_df = full_json_ds.to_pandas().set_index("plasma_config_id")
+ds = datasets.load_dataset(
+    "proxima-fusion/constellaration",
+    columns=["plasma_config_id", "boundary.json"],
+    split="train",
+    num_proc=4,
+)
+pandas_ds = ds.to_pandas().set_index("plasma_config_id")
 
 plasma_config_id = "DQ4abEQAQjFPGp9nPQN9Vjf"
-boundary_json = full_json_df.loc[plasma_config_id]["boundary.json"]
+boundary_json = pandas_ds.loc[plasma_config_id]["boundary.json"]
 boundary = surface_rz_fourier.SurfaceRZFourier.model_validate_json(boundary_json)
 ```
 Plot boundary: