test.py

import huggingface_hub
import requests.exceptions
import zarr
import numpy as np
import datasets
import fsspec
from huggingface_hub import HfFileSystem
from fsspec.implementations.zip import ZipFileSystem





# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{ocf:mrms,
title = {MRMS Archival Precipitation Rate Radar Dataset},
author={Jacob Bieker
},
year={2022}
}
"""

# You can copy an official description
_DESCRIPTION = """\
This dataset consists of MRMS precipitation radar data for the continental United States, 
sampled at a 1kmx1km area and 2-mimntely spatial resolution. 
"""

_HOMEPAGE = "https://mtarchive.geol.iastate.edu/"

_LICENSE = "US Government data, Open license, no restrictions"


class ZarrTest:

    @staticmethod
    def create():
        store1 = zarr.DirectoryStore('data.zarr')
        root1 = zarr.group(store=store1)
        z1 = root1.zeros('trace', shape=(100, 100), chunks=(100, 10), dtype='i1', overwrite=True)
        z1[:] = 42
        z1[0, :] = np.arange(100)
        z1[:, 0] = np.arange(100)
        z2 = root1.zeros('plaintext', shape=100, dtype='u1', overwrite=True)
        z3 = root1.zeros('ciphertext', shape=100, dtype='u1', overwrite=True)
        z4 = root1.zeros('key', shape=100, dtype='u1', overwrite=True)
        z2[:] = 22
        z3[:] = 33
        z4[:] = 44
        zarr.consolidate_metadata(store1)

        store2 = zarr.DirectoryStore('data.zarr')
        root2 = zarr.group(store=store2)
        test = np.all(z1[:] == root2['trace'][:])
        print(test)
        store1.close()
        store2.close()

    @staticmethod
    def create_in_hf():
        try:
            ...
            # huggingface_hub.delete_folder(path_in_repo="data.zarr", repo_id="spikingneurons/test", repo_type="dataset")
        except requests.exceptions.HTTPError as e:
            ...
        store1 = zarr.DirectoryStore('hf://datasets/spikingneurons/test/data.zarr')
        root1 = zarr.group(store=store1)
        z1 = root1.zeros('trace', shape=(100, 100), chunks=(100, 10), dtype='i1', overwrite=True)
        z1[:] = 42
        z1[0, :] = np.arange(100)
        z1[:, 0] = np.arange(100)
        z2 = root1.zeros('plaintext', shape=100, dtype='u1', overwrite=True)
        z3 = root1.zeros('ciphertext', shape=100, dtype='u1', overwrite=True)
        z4 = root1.zeros('key', shape=100, dtype='u1', overwrite=True)
        z2[:] = 22
        z3[:] = 33
        z4[:] = 44
        zarr.consolidate_metadata(store1)
        print(z1[:])
        store1.close()


        store2 = zarr.DirectoryStore('hf://datasets/spikingneurons/test/data.zarr')
        root2 = zarr.open_consolidated(store2)
        print(root2['trace'][:])
        store2.close()

    @staticmethod
    def create_in_zip():
        store1 = zarr.ZipStore('data.zarr.zip')
        root1 = zarr.group(store=store1)
        z1 = root1.zeros('trace', shape=(100, 100), chunks=(100, 10), dtype='i1', overwrite=True)
        z1[:] = 42
        z1[0, :] = np.arange(100)
        z1[:, 0] = np.arange(100)
        z2 = root1.zeros('plaintext', shape=100, dtype='u1', overwrite=True)
        z3 = root1.zeros('ciphertext', shape=100, dtype='u1', overwrite=True)
        z4 = root1.zeros('key', shape=100, dtype='u1', overwrite=True)
        z2[:] = 22
        z3[:] = 33
        z4[:] = 44
        zarr.consolidate_metadata(store1)
        print(z1[:])
        store1.close()

        store2 = zarr.ZipStore('data.zarr.zip')
        root2 = zarr.open_consolidated(store2)
        print(root2['trace'][:])
        store2.close()

    @staticmethod
    def load_from_hf():
        """
        Note that we load data generated via create_in_zip to upload to HF
        """
        from datasets import load_dataset
        import datasets
        # ds = load_dataset("spikingneurons/test", streaming=True, trust_remote_code=True)
        # print(ds)
        # _fs = HfFileSystem()
        # _files = _fs.ls("datasets/spikingneurons/test", detail=False)
        # print(_files)
        # import xarray as xr
        # _arr = xr.open_dataset('hf://datasets/spikingneurons/test/data.zarr', engine='zarr', chunks={})
        from zarr.storage import FSStore
        store = FSStore('hf://datasets/spikingneurons/test/data.zarr')
        root = zarr.group(store=store)
        print(root['trace'][:])
        # store = FSStore('zip://*::hf://datasets/spikingneurons/test/example.zip')
        # store11 = FSStore('zip://hf://datasets/spikingneurons/test/example.zip')
        # store22 = FSStore('zip+hf://datasets/spikingneurons/test/example.zip')
        # store = zarr.DirectoryStore(UPath('hf://datasets/spikingneurons/test') / 'data.zarr')
        # store1 = zarr.DirectoryStore('data.zarr')
        # root1 = zarr.group(store=store1)
        with fsspec.open('hf://datasets/spikingneurons/test/data.zarr.zip', 'rb') as zip_file:
            _zip_fs = ZipFileSystem(zip_file)
            store4 = FSStore(url="", fs=_zip_fs)
            root4 = zarr.open_consolidated(store=store4)
            print(root4['trace'][:])

ZarrTest.create()
# ZarrTest.create_in_zip()
# ZarrTest.load_from_hf()
# ZarrTest.create_in_hf()

class Test(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = []
    # DEFAULT_CONFIG_NAME = "default"

    def _info(self):
        _features = datasets.Features({
            "data": datasets.Value("string"),
            "label": datasets.ClassLabel(names=["0", "1"]),
        })

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=_features,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        from fsspec.implementations.zip import ZipFileSystem
        print(dl_manager)
        print(self.config.name)
        urls = ["example.zip"]
        data_dir = dl_manager.download_and_extract(urls)
        print(data_dir)

        # Open the zip file using fsspec
        with fsspec.open(data_dir[0]) as f:
            with fsspec.filesystem("zip", fo=f) as fs:
                # List all files in the zip
                all_files = fs.glob("*")
                print("All files in the zip:", all_files)


        streaming = dl_manager.is_streaming
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls if streaming else data_dir["train"],
                    "split": "train",
                    "streaming": False,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls if streaming else data_dir["test"],
                    "split": "test",
                    "streaming": False,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls if streaming else data_dir["valid"],
                    "split": "valid",
                    "streaming": False,
                },
            ),
        ]