---
dataset_info:
- config_name: large_100
  features:
  - name: lrs
    sequence:
      array4_d:
        shape:
        - 3
        - 16
        - 16
        - 16
        dtype: float32
  - name: hr
    dtype:
      array4_d:
        shape:
        - 3
        - 64
        - 64
        - 64
        dtype: float32
  splits:
  - name: train
    num_bytes: 268237120
    num_examples: 80
  - name: validation
    num_bytes: 33529640
    num_examples: 10
  - name: test
    num_bytes: 33529640
    num_examples: 10
  download_size: 329464088
  dataset_size: 335296400
- config_name: large_50
  features:
  - name: lrs
    sequence:
      array4_d:
        shape:
        - 3
        - 16
        - 16
        - 16
        dtype: float32
  - name: hr
    dtype:
      array4_d:
        shape:
        - 3
        - 64
        - 64
        - 64
        dtype: float32
  splits:
  - name: train
    num_bytes: 134118560
    num_examples: 40
  - name: validation
    num_bytes: 16764820
    num_examples: 5
  - name: test
    num_bytes: 16764820
    num_examples: 5
  download_size: 164732070
  dataset_size: 167648200
- config_name: small_50
  features:
  - name: lrs
    sequence:
      array4_d:
        shape:
        - 3
        - 4
        - 4
        - 4
        dtype: float32
  - name: hr
    dtype:
      array4_d:
        shape:
        - 3
        - 16
        - 16
        - 16
        dtype: float32
  splits:
  - name: train
    num_bytes: 2220320
    num_examples: 40
  - name: validation
    num_bytes: 277540
    num_examples: 5
  - name: test
    num_bytes: 277540
    num_examples: 5
  download_size: 2645696
  dataset_size: 2775400
---


# Super-resolution of Velocity Fields in Three-dimensional Fluid Dynamics

This dataset loader attempts to reproduce the data of Wang et al. (2024)'s experiments on Super-resolution of 3D Turbulence. 

References:
- Wang et al. (2024): "Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution"

## Usage

For a given configuration  (e.g. `large_50`):

```py
>>> ds = datasets.load_dataset("dl2-g32/jhtdb", name="large_50")
>>> ds
DatasetDict({
    train: Dataset({
        features: ['lrs', 'hr'],
        num_rows: 40
    })
    validation: Dataset({
        features: ['lrs', 'hr'],
        num_rows: 5
    })
    test: Dataset({
        features: ['lrs', 'hr'],
        num_rows: 5
    })
})
```

Each split contains the input `lrs` which corresponds on a sequence of low resolution samples from time `t - ws/2, ..., t, ... ts + ws/2` (ws = window size) and `hr` corresponds to the high resolution sample at time `t`. All the parameters per data point are specified in the corresponding `metadata_*.csv`.

Specifically, for the default configuration, for each datapoint we have `3` low resolution samples and `1` high resolution sample. Each of the former have shapes `(3, 16, 16, 16)` and the latter has shape `(3, 64, 64, 64)`. 

## Replication

This dataset is entirely generated by `scripts/generate.py` and each configuration is fully specified in their corresponding `scripts/*.yaml`.

### Usage

```sh
python -m scripts.generate --config scripts/small_100.yaml --token edu.jhu.pha.turbulence.testing-201311
``` 

This will create two folders on `datasets/jhtdb`:
1. A `tmp` folder that will store all samples accross runs to serve as a cache. 
2. The corresponding subset, `small_50` for example. This folder will contain a `metadata_*.csv` and data `*.zip` for each split.

Note:
- For the small variants, the default token is enough, but for the large variants a token has to be requested. More details [here](https://turbulence.pha.jhu.edu/authtoken.aspx).
- For reference, the `large_100` takes ~15 minutes to generate for a total of ~300MB.