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Datasets:
dl2-g32
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jhtdb

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dgcnz commited on
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·
1 Parent(s): 1747a0a

feat: add large_50 and large_100

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  1. README.md +53 -0
  2. datasets/jhtdb/.gitattributes +6 -0
  3. datasets/jhtdb/large_100/metadata_test.csv +11 -0
  4. datasets/jhtdb/large_100/metadata_train.csv +81 -0
  5. datasets/jhtdb/large_100/metadata_val.csv +11 -0
  6. datasets/jhtdb/large_100/test.zip +3 -0
  7. datasets/jhtdb/large_100/train.zip +3 -0
  8. datasets/jhtdb/large_100/val.zip +3 -0
  9. datasets/jhtdb/large_50/metadata_test.csv +6 -0
  10. datasets/jhtdb/large_50/metadata_train.csv +41 -0
  11. datasets/jhtdb/large_50/metadata_val.csv +6 -0
  12. datasets/jhtdb/large_50/test.zip +3 -0
  13. datasets/jhtdb/large_50/train.zip +3 -0
  14. datasets/jhtdb/large_50/val.zip +3 -0
  15. jhtdb.py +30 -2
README.md CHANGED
@@ -33,3 +33,56 @@ dataset_info:
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  download_size: 2645696
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  dataset_size: 2775400
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  download_size: 2645696
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  dataset_size: 2775400
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  ---
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+
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+
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+ # Super-resolution of Velocity Fields in Three-dimensional Fluid Dynamics
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+
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+ This dataset loader attempts to reproduce the data of Wang et al. (2024)'s experiments on Super-resolution of 3D Turbulence.
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+
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+ References:
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+ - Wang et al. (2024): "Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution"
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+
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+ ## Usage
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+
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+ For a given configuration (e.g. `large_50`):
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+
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+ ```py
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+ >>> ds = datasets.load_dataset("dl2-g32/jhtdb", name="large_50")
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+ >>> ds
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+ DatasetDict({
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+ train: Dataset({
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+ features: ['lrs', 'hr'],
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+ num_rows: 40
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+ })
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+ validation: Dataset({
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+ features: ['lrs', 'hr'],
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+ num_rows: 5
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+ })
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+ test: Dataset({
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+ features: ['lrs', 'hr'],
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+ num_rows: 5
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+ })
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+ })
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+ ```
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+
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+ 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`.
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+
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+ 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)`.
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+
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+ ## Replication
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+
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+ This dataset is entirely generated by `scripts/generate.py` and each configuration is fully specified in their corresponding `scripts/*.yaml`.
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+
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+ ### Usage
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+
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+ ```sh
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+ python -m scripts.generate --config scripts/small_100.yaml --token edu.jhu.pha.turbulence.testing-201311
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+ ```
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+
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+ This will create two folders on `datasets/jhtdb`:
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+ 1. A `tmp` folder that will store all samples accross runs to serve as a cache.
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+ 2. The corresponding subset, `small_50` for example. This folder will contain a `metadata_*.csv` and data `*.zip` for each split.
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+
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+ Note:
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+ - 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).
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+ - For reference, the `large_100` takes ~15 minutes to generate for a total of ~300MB.
datasets/jhtdb/.gitattributes ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ large_100/test.zip filter=lfs diff=lfs merge=lfs -text
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+ large_100/train.zip filter=lfs diff=lfs merge=lfs -text
3
+ large_100/val.zip filter=lfs diff=lfs merge=lfs -text
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+ large_50/test.zip filter=lfs diff=lfs merge=lfs -text
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+ large_50/train.zip filter=lfs diff=lfs merge=lfs -text
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+ large_50/val.zip filter=lfs diff=lfs merge=lfs -text
datasets/jhtdb/large_100/metadata_test.csv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
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+ time_step,window_size,sx,sy,sz,ex,ey,ez,lr_factor,hr_path,lr_paths
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datasets/jhtdb/large_100/metadata_train.csv ADDED
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+ time_step,window_size,sx,sy,sz,ex,ey,ez,lr_factor,hr_path,lr_paths
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20
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21
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22
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23
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24
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25
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28
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29
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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63
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64
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65
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66
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datasets/jhtdb/large_50/metadata_val.csv ADDED
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jhtdb.py CHANGED
@@ -52,7 +52,35 @@ _URLS = {
52
  "datasets/jhtdb/small_50/metadata_test.csv",
53
  "datasets/jhtdb/small_50/test.zip",
54
  ),
55
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
56
  }
57
 
58
 
@@ -64,7 +92,7 @@ class JHTDB(datasets.GeneratorBasedBuilder):
64
  datasets.BuilderConfig(name="small_50", version=VERSION, description=""),
65
  ]
66
 
67
- DEFAULT_CONFIG_NAME = "small_50"
68
 
69
  def _info(self):
70
  if self.config.name.startswith("small"):
 
52
  "datasets/jhtdb/small_50/metadata_test.csv",
53
  "datasets/jhtdb/small_50/test.zip",
54
  ),
55
+ },
56
+ "large_50": {
57
+ "train": (
58
+ "datasets/jhtdb/large_50/metadata_train.csv",
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+ "test": (
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+ "datasets/jhtdb/large_50/metadata_test.csv",
67
+ "datasets/jhtdb/large_50/test.zip",
68
+ ),
69
+ },
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+ "large_100": {
71
+ "train": (
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+ "datasets/jhtdb/large_100/metadata_train.csv",
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+ "datasets/jhtdb/large_100/train.zip",
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+ "val": (
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+ "datasets/jhtdb/large_100/val.zip",
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+ ),
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+ "test": (
80
+ "datasets/jhtdb/large_100/metadata_test.csv",
81
+ "datasets/jhtdb/large_100/test.zip",
82
+ ),
83
+ },
84
  }
85
 
86
 
 
92
  datasets.BuilderConfig(name="small_50", version=VERSION, description=""),
93
  ]
94
 
95
+ DEFAULT_CONFIG_NAME = "large_50"
96
 
97
  def _info(self):
98
  if self.config.name.startswith("small"):