darcy_2d_li2021 v1.0.0.post1: card, Parquet copies

README.md

@@ -1,26 +1,65 @@
 ---
+pretty_name: 2-D Darcy flow, Li et al. (2021) operator-learning benchmark (1200 coefficient / pressure pairs, 128 x 128)
 license: cc-by-4.0
+task_categories:
+- other
+size_categories:
+- 1K<n<10K
 tags:
 - pinneapple
-- operator learning
-- neural operator
-- Darcy
+- physics
+- scientific-machine-learning
+- operator-learning
+- neural-operator
+- darcy
 - benchmark
-- FNO
+- fno
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: data/default/train-*.parquet
+  - split: test
+    path: data/default/test-*.parquet
+dataset_info:
+- config_name: default
+  features:
+  - name: a
+    list:
+      list: float32
+      length: 128
+    length: 128
+  - name: index
+    dtype: int32
+  - name: u
+    list:
+      list: float32
+      length: 128
+    length: 128
+  splits:
+  - name: train
+    num_bytes: 131076000
+    num_examples: 1000
+  - name: test
+    num_bytes: 26215200
+    num_examples: 200
 ---
 
 # 2-D Darcy flow, Li et al. (2021) operator-learning benchmark (1200 coefficient / pressure pairs, 128 x 128)
 
 -div(a grad u) = 1 on the unit square, u = 0 on the boundary. Coefficients a = 12 where g >= 0 and 3 where g < 0, g ~ N(0, (-Laplacian + 9 I)^-2) (Li et al. 2021, section 5.2). g is drawn as a periodic Gaussian random field on the 128 x 128 cell grid (Li et al. use a cosine expansion on 421 nodes), so a is piecewise constant on the cells of the stored grid. The task maps a to u. Cell centres ((i + 1/2) / 128, (j + 1/2) / 128); u is the cell average of the solution.
 
-`darcy_2d_li2021` v1.0.0 · licence cc-by-4.0 · Hub `PINNeAPPle-Labs/darcy-2d-li2021` @ v1.0.0 · 2 file(s), 71.1 MB, downloaded on first use
+`darcy_2d_li2021` v1.0.0 (revision `v1.0.0.post1`) · licence cc-by-4.0 · part of the [PINNeAPPle](https://github.com/PINNeAPPle-Labs/PINNeAPPle) zoo
 
-## Splits
+## Configs and splits
 
-| Split | Rows | Content |
-|---|---|---|
-| `train` | 1000 | 1000 pairs |
-| `test` | 200 | 200 pairs (held out) |
+| Config | Split | Samples | Parquet rows | Parquet files | Content |
+|---|---|---|---|---|---|
+| `default` | `train` | 1000 | 1000 | 1 | 1000 pairs |
+| `default` | `test` | 200 | 200 | 1 | 200 pairs (held out) |
+
+Split rule: rows 0-999 are train, 1000-1199 test; inputs are i.i.d. draws, so the contiguous split is a random split
 
 ## Columns
 
@@ -30,6 +69,33 @@ tags:
 | `index` | - | int32 | sample index (row of the generated set) |
 | `u` | - | float32[128, 128] | pressure, cell averages on the 128 x 128 cells |
 
+## Load
+
+With the library (the npz files, SHA-256 checked; arrays with their full shape):
+
+```bash
+pip install "pinneapple @ git+https://github.com/PINNeAPPle-Labs/PINNeAPPle"
+```
+
+```python
+import pinneapple as pp
+
+d = pp.datasets.load("darcy_2d_li2021", split="test")
+print(d.n_rows, d.columns)
+print(d["a"].shape)
+```
+
+With `datasets` (the Parquet files of this repo):
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("PINNeAPPle-Labs/darcy-2d-li2021", split="test", revision="v1.0.0.post1")
+print(ds)
+ds = ds.with_format("numpy")
+print(ds[0]["a"].shape)
+```
+
 ## Provenance
 
 python scripts/zoo/operator_data.py generate --out DIR (inputs drawn from fixed seeds in the parent process, so the data do not depend on the number of workers); solvers in pinneapple_simulation.numerical_solvers.operator_benchmarks, written from the equations; seed 20212
@@ -38,20 +104,20 @@ python scripts/zoo/operator_data.py generate --out DIR (inputs drawn from fixed
 
 Solver (cell-centred two-point-flux finite volumes, harmonic face coefficients, sparse direct solve) checked in tests/test_operator_benchmarks.py on a manufactured variable-coefficient solution (second order, max error < 2e-3 at 64^2). Each sample is solved on the 512^2 grid (the piecewise-constant coefficient is represented exactly on every refinement) and averaged onto its 128^2 cells. Grid study on 4 held-aside inputs against 1024^2: max rel. L2 error 4.6e-3 (128^2), 1.6e-3 (256^2), 4.3e-4 (512^2, the dataset); observed order 1.36 / 1.21 (reduced from 2 by the coefficient jumps); Richardson estimate of the dataset's discretisation error ~8e-4 rel. L2, ten times below the ~1e-2 operator-learning methods reach on this task. u > 0 and below the a = 3 bound 1/24 in every sample.
 
-Split rule: rows 0-999 are train, 1000-1199 test; inputs are i.i.d. draws, so the contiguous split is a random split
+## Files
 
-## Citation
-
-Li Z., Kovachki N., Azizzadenesheli K., Liu B., Bhattacharya K., Stuart A., Anandkumar A. (2021). Fourier Neural Operator for Parametric Partial Differential Equations. ICLR 2021, arXiv:2010.08895. Data generated with PINNeAPPle's reference solvers (pinneapple_simulation.numerical_solvers.operator_benchmarks); PINNeAPPle, Zenodo DOI 10.5281/zenodo.23343062.
+The npz files are the library's format and the source of truth: `pp.datasets.load` downloads them from this repo at the tagged revision and refuses any file whose SHA-256 differs from the manifest of the package. The Parquet files under `data/` are derived from them by `pinneapple_hub.zoo.parquet.write_dataset` and hold the same numbers (a test round-trips npz -> Parquet -> arrays bit for bit); they are what the dataset viewer and `load_dataset` read.
 
+| File | Size | SHA-256 |
+|---|---|---|
+| `train.npz` | 59.2 MB | `e32d8114482e9bcef831ab291938bbe0fd9306a24fb675d95a5735a98d1cc72d` |
+| `test.npz` | 11.8 MB | `c7f7b49deb5eff530405f44a8b2f8246d8c666b898b32ebeb6a18e82b049e89e` |
 
-## Use
+Total 71.1 MB of npz.
 
-```python
-import pinneapple as pp
-d = pp.datasets.load("darcy_2d_li2021", split="test")
-```
+## Citation
 
+Li Z., Kovachki N., Azizzadenesheli K., Liu B., Bhattacharya K., Stuart A., Anandkumar A. (2021). Fourier Neural Operator for Parametric Partial Differential Equations. ICLR 2021, arXiv:2010.08895. Data generated with PINNeAPPle's reference solvers (pinneapple_simulation.numerical_solvers.operator_benchmarks); PINNeAPPle, Zenodo DOI 10.5281/zenodo.23343062.
 
 ## Cite
 

data/default/test-00000.parquet

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+size 11983791

data/default/train-00000.parquet

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+size 59902919