feat: add data

.gitignore

@@ -0,0 +1,5 @@
+.venv
+**/__pycache__
+
+datasets/jhtdb/tmp
+**/.DS_Store

README.md

@@ -1,3 +1,35 @@
----
-license: mit
----
+---
+dataset_info:
+  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
+---

datasets/jhtdb/small_50/metadata_test.csv

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datasets/jhtdb/small_50/metadata_train.csv

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datasets/jhtdb/small_50/metadata_val.csv

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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/small_50/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f601e08c5b36fd2966f82ce201b943aed1964a79e52d5d9260c8efdcc12f8182
+size 262950

datasets/jhtdb/small_50/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83a53edfcac9a88ed6b80ad7fe26735f9b96c5b311d17c604c63fd88609ac173
+size 2103236

datasets/jhtdb/small_50/val.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f601e08c5b36fd2966f82ce201b943aed1964a79e52d5d9260c8efdcc12f8182
+size 262950

jhtdb.py

@@ -0,0 +1,123 @@
+"""TODO: Add a description here."""
+
+import csv
+import json
+import os
+import numpy as np
+from pathlib import Path
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+    "small_50": {
+        "train": (
+            "datasets/jhtdb/small_50/metadata_train.csv",
+            "datasets/jhtdb/small_50/train.zip",
+        ),
+        "val": (
+            "datasets/jhtdb/small_50/metadata_val.csv",
+            "datasets/jhtdb/small_50/val.zip",
+        ),
+        "test": (
+            "datasets/jhtdb/small_50/metadata_test.csv",
+            "datasets/jhtdb/small_50/test.zip",
+        ),
+    }
+}
+
+
+class JHTDB(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="small_50", version=VERSION, description=""),
+    ]
+
+    DEFAULT_CONFIG_NAME = "small_50"
+
+    def _info(self):
+        if self.config.name.startswith("small"):
+            features = datasets.Features(
+                {
+                    "lrs": datasets.Sequence(
+                        datasets.Array4D(shape=(3, 4, 4, 4), dtype="float32"),
+                    ),
+                    "hr": datasets.Array4D(shape=(3, 16, 16, 16), dtype="float32"),
+                }
+            )
+        elif self.config.name.startswith("large"):
+            features = datasets.Features(
+                {
+                    "lrs": datasets.Sequence(
+                        datasets.Array4D(shape=(3, 16, 16, 16), dtype="float32"),
+                    ),
+                    "hr": datasets.Array4D(shape=(3, 64, 64, 64), dtype="float32"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        named_splits = {
+            "train": datasets.Split.TRAIN,
+            "val": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+        return [
+            datasets.SplitGenerator(
+                name=named_splits[split],
+                gen_kwargs={
+                    "metadata_path": Path(metadata_path),
+                    "data_path": Path(data_path),
+                },
+            )
+            for split, (metadata_path, data_path) in data_dir.items()
+        ]
+
+    def _generate_examples(self, metadata_path: Path, data_path: Path):
+        with open(metadata_path) as f:
+            reader = csv.DictReader(f)
+            for key, data in enumerate(reader):
+                yield key, {
+                    "lrs": [
+                        np.load(data_path / Path(p).name)
+                        for p in json.loads(data["lr_paths"])
+                    ],
+                    "hr": np.load(data_path / Path(data["hr_path"]).name),
+                }

pyproject.toml

@@ -0,0 +1,25 @@
+[tool.poetry]
+name = "jhtdb"
+version = "0.1.0"
+description = ""
+authors = ["Diego Canez <[email protected]>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.12"
+tqdm = "^4.66.4"
+torch = "^2.3.0"
+datasets = "^2.19.1"
+
+
+[tool.poetry.group.dev.dependencies]
+pyjhtdb = {git = "https://github.com/dgcnz/pyJHTDB"}
+isort = "^5.13.2"
+black = "^24.4.2"
+flake8 = "^7.0.0"
+jsonargparse = "^4.28.0"
+pandas = "^2.2.2"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

scripts/generate.py

@@ -0,0 +1,263 @@
+import numpy as np
+import json
+from jsonargparse import CLI
+import pandas as pd
+
+import pyJHTDB
+import pyJHTDB.dbinfo
+from tqdm import tqdm
+from pathlib import Path
+from itertools import chain
+import zipfile
+
+
+def get_filename(
+    time_step: int,
+    start: np.ndarray,  # [x, y, z]
+    end: np.ndarray,  # [x, y, z]
+    step: np.ndarray,  # [x, y, z]
+    filter_width: int,
+):
+    """Serializes jhtdb params into a filename."""
+    return "{0}_{1}_{2}_{3}_{4}_{5}_{6}_{7}_{8}_{9}_{10}.npy".format(
+        time_step,
+        start[0],
+        start[1],
+        start[2],
+        end[0],
+        end[1],
+        end[2],
+        step[0],
+        step[1],
+        step[2],
+        filter_width,
+    )
+
+
+def download_jhtdb(
+    loader: pyJHTDB.libJHTDB,
+    time_step: int,
+    start: np.ndarray,
+    end: np.ndarray,
+    step: np.ndarray,
+    filter_width: int,
+    path: Path,
+    dataset: str = "isotropic1024coarse",
+    field: str = "u",
+):
+    """
+    :param loader: pyJHTDB.libJHTDB object
+    :param time_step: time step to download
+    :param start: start [x, y, z] of the cutout
+    :param end: end [x, y, z] of the cutout
+    :param step: step size of the cutout
+    :param filter_width: filter width of the cutout
+    :param path: path to save the data
+    :param dataset: dataset to download from. Default is "isotropic1024coarse"
+    :param field: velocity ("u") or pressure ("p") field
+    """
+    if not path.exists():
+        results: np.ndarray = loader.getCutout(
+            data_set=dataset,
+            field=field,
+            time_step=time_step,
+            start=start,
+            end=end,
+            step=step,
+            filter_width=filter_width,
+        )
+        if results is None:
+            raise Exception("Could not download data from JHTDB")
+        results = np.rollaxis(
+            results, -1, 0
+        )  # Move the [x, y, z] dimensions to the front
+        np.save(path, results)
+    return np.load(path)
+
+
+def download_all(params: dict, loader: pyJHTDB.libJHTDB):
+    """Download all the data from the JHTDB database.
+    TODO: parallelize this function
+    """
+    for p in tqdm(params):
+        download_jhtdb(loader=loader, **p)
+
+
+def get_params(
+    total_samples: int,
+    domain_size: int,
+    lr_factor: int,
+    time_range: list[int],
+    window_size: int,
+) -> tuple[dict, dict]:
+    dt = np.arange(window_size) - window_size // 2
+    time_steps_hr = np.random.randint(time_range[0], time_range[1], size=total_samples)
+    # reshape time_steps to (total_samples, len(dt)) so that for each i we can get time_steps[i] + dt[j]
+    time_steps_lr = np.repeat(time_steps_hr[:, np.newaxis], len(dt), axis=1) + dt
+    # time_steps.shape = [total_samples, window_size]
+    starts = np.random.randint(1, 1024 - domain_size, size=(total_samples, 3))
+    ends = starts + domain_size - 1
+    all_params_lr = [
+        [
+            {
+                "time_step": time_steps_lr[i, j],
+                "start": starts[i],
+                "end": ends[i],
+                "step": np.full(3, lr_factor, dtype=int),
+                "filter_width": lr_factor,
+            }
+            for j in range(len(dt))
+        ]
+        for i in range(total_samples)
+    ]
+    all_params_hr = [
+        {
+            "time_step": time_steps_hr[i],
+            "start": starts[i],
+            "end": ends[i],
+            "step": np.ones(3, dtype=int),
+            "filter_width": 1,
+        }
+        for i in range(total_samples)
+    ]
+    return all_params_lr, all_params_hr
+
+
+def download_generic(
+    total_samples: int,
+    domain_size: int,
+    lr_factor: int,
+    time_range: tuple[int, int],
+    window_size: int,
+    tmp_data_dir: Path,
+    token: str,
+):
+    """Download all the data from the JHTDB database."""
+    # initialize runner
+    lJHTDB = pyJHTDB.libJHTDB()
+    lJHTDB.initialize()
+    lJHTDB.add_token(token)
+    tmp_data_dir.mkdir(parents=True, exist_ok=True)
+
+    all_params_lr, all_params_hr = get_params(
+        total_samples, domain_size, lr_factor, time_range, window_size
+    )
+    # add path to all the params
+    all_params_hr = [
+        dict(p, path=tmp_data_dir / get_filename(**p)) for p in all_params_hr
+    ]
+    all_params_lr = [
+        [dict(p, path=tmp_data_dir / get_filename(**p)) for p in lr]
+        for lr in all_params_lr
+    ]
+
+    # all_params_lr.shape = [total_samples, window_size]
+    # all_params_hr.shape = [total_samples]
+    # flatten nested params
+    all_params = list(chain.from_iterable(all_params_lr)) + all_params_hr
+    download_all(all_params, lJHTDB)
+    return all_params_lr, all_params_hr, all_params
+
+
+def make_jhtdb_dataset(
+    name: str,
+    total_samples: int = 128,
+    train_split: float = 0.8,
+    val_split: float = 0.1,
+    test_split: float = 0.1,
+    domain_size: int = 64,
+    lr_factor: int = 4,
+    root: Path = Path("dataset/jhtdb"),
+    time_range: tuple[int, int] = (2, 1023),
+    window_size: int = 3,
+    seed: int = 123,
+    token: str = "edu.jhu.pha.turbulence.testing-201311",
+) -> tuple[np.ndarray, np.ndarray]:
+    """Creates low and high res dataset from JHTDB database.
+
+    Where:
+    low_res.shape = [nr_samples, 3, domain_size / lr_factor, domain_size / lr_factor, domain_size / lr_factor]
+    high_res.shape = [nr_samples, 3, domain_size, domain_size, domain_size]
+    And 3 corresponds to the x, y, z components of the velocity field.
+
+    Make a dataset from the JHTDB database.
+    :param: name: name of the dataset
+    :param: total_samples: total number of samples to generate
+    :param: train_split: percentage of samples to use for training
+    :param: val_split: percentage of samples to use for validation
+    :param: test_split: percentage of samples to use for testing
+    :param: domain_size: size of the domain to generate
+    :param: lr_factor: factor to downsample the data
+    :param: root: root directory to store the dataset
+    :param: time_range: range of time steps to sample from
+    :param: seed: seed to generate the dataset
+    :param: window_size: size of the window to sample from
+    :param: token: token to access the JHTDB database
+    :return: tuple of low res and high res data
+    """
+    assert window_size % 2 == 1, "Window size must be odd"
+    assert time_range[0] - window_size // 2 >= 1, "Time step out of range"
+    assert time_range[1] + window_size // 2 <= 1024, "Time step out of range"
+    assert time_range[0] >= 1 and time_range[1] <= 1024, "Time step out of range"
+
+    np.random.seed(seed)
+    # download all the data
+    tmp_data_dir = root / "tmp"
+    all_params_lr, all_params_hr, _ = download_generic(
+        total_samples,
+        domain_size,
+        lr_factor,
+        time_range,
+        window_size,
+        tmp_data_dir,
+        token,
+    )
+    assert len(all_params_lr) == len(all_params_hr), "Length mismatch"
+
+    # split the data
+    cur_root = root / name
+    cur_root.mkdir(parents=True, exist_ok=True)
+    splits_ratios = [("train", train_split), ("val", val_split), ("test", test_split)]
+
+    for split, split_ratio in splits_ratios:
+        # split data
+        split_dir = cur_root / split
+        split_params_lr = all_params_lr[: int(total_samples * split_ratio)]
+        split_params_hr = all_params_hr[: int(total_samples * split_ratio)]
+
+        # compress all the data to a zip
+        split_paths = [
+            p["path"]
+            for p in split_params_hr + list(chain.from_iterable(split_params_lr))
+        ]
+        with zipfile.ZipFile(cur_root / f"{split}.zip", "w") as z:
+            for p in split_paths:
+                z.write(p, p.name)
+
+        # create metadata
+        metadata = []
+        for lr, hr in zip(split_params_lr, split_params_hr):
+            metadata.append(
+                {
+                    "time_step": hr["time_step"],
+                    "window_size": window_size,
+                    "sx": hr["start"][0],
+                    "sy": hr["start"][1],
+                    "sz": hr["start"][2],
+                    "ex": hr["end"][0],
+                    "ey": hr["end"][1],
+                    "ez": hr["end"][2],
+                    "lr_factor": lr_factor,
+                    "hr_path": str(split_dir / hr["path"].name),
+                    "lr_paths": json.dumps(
+                        [str(split_dir / p["path"].name) for p in lr]
+                    ),
+                }
+            )
+
+        metadata_df = pd.DataFrame(metadata)
+        metadata_df.to_csv(cur_root / f"metadata_{split}.csv", index=False)
+
+
+if __name__ == "__main__":
+    CLI(make_jhtdb_dataset)

scripts/large_100.yaml

@@ -0,0 +1,14 @@
+total_samples: 100
+train_split: 0.8
+val_split: 0.1
+test_split: 0.1
+domain_size: 64
+lr_factor: 4
+root: "datasets/jhtdb"
+name: "large_100"
+time_range: 
+  - 2
+  - 1023
+window_size: 3
+seed: 123
+token: "edu.jhu.pha.turbulence.testing-201311"

scripts/large_50.yaml

@@ -0,0 +1,14 @@
+total_samples: 50
+train_split: 0.8
+val_split: 0.1
+test_split: 0.1
+domain_size: 64
+lr_factor: 4
+root: "datasets/jhtdb"
+name: "large_50"
+time_range: 
+  - 2
+  - 1023
+window_size: 3
+seed: 123
+token: "edu.jhu.pha.turbulence.testing-201311"

scripts/small_100.yaml

@@ -0,0 +1,14 @@
+total_samples: 100
+train_split: 0.8
+val_split: 0.1
+test_split: 0.1
+domain_size: 16
+lr_factor: 4
+root: "datasets/jhtdb"
+name: "small_100"
+time_range: 
+  - 2
+  - 1023
+window_size: 3
+seed: 123
+token: "edu.jhu.pha.turbulence.testing-201311"

scripts/small_50.yaml

@@ -0,0 +1,14 @@
+name: small_50
+total_samples: 50
+train_split: 0.8
+val_split: 0.1
+test_split: 0.1
+domain_size: 16
+lr_factor: 4
+root: "datasets/jhtdb"
+time_range: 
+  - 2
+  - 1023
+window_size: 3
+seed: 123
+token: "edu.jhu.pha.turbulence.testing-201311"