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IO

danling.utils.io

save

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save(obj: Any, file: PathStr, *args: List[Any], **kwargs: Dict[str, Any]) -> File

Save any file with supported extensions.

Source code in danling/utils/io.py
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def save(obj: Any, file: PathStr, *args: List[Any], **kwargs: Dict[str, Any]) -> File:
    r"""
    Save any file with supported extensions.
    """
    extension = os.path.splitext(file)[-1].lower()[1:]
    if extension in PYTORCH:
        if not TORCH_AVAILABLE:
            raise ImportError(f"Trying to save {obj} to {file!r} but torch is not installed.")
        torch.save(obj, file, *args, **kwargs)
    elif extension in NUMPY:
        if not NUMPY_AVAILABLE:
            raise ImportError(f"Trying to save {obj} to {file!r} but numpy is not installed.")
        numpy.save(file, obj, *args, **kwargs)
    elif extension in PANDAS:
        if not PANDAS_AVAILABLE:
            raise ImportError(f"Trying to save {obj} to {file!r} but pandas is not installed.")
        pandas.to_pickle(obj, file, *args, **kwargs)
    elif extension in CSV:
        if isinstance(obj, pandas.DataFrame):
            obj.to_csv(file, *args, **kwargs)
        else:
            raise NotImplementedError(f"Trying to save {obj} to {file!r} but is not supported")
    elif extension in JSON:
        if isinstance(obj, FlatDict):
            obj.json(file)
        else:
            with open(file, "w") as fp:
                json.dump(obj, fp, *args, **kwargs)  # type: ignore
    elif extension in YAML:
        if isinstance(obj, FlatDict):
            obj.yaml(file)
        else:
            with open(file, "w") as fp:
                yaml.dump(obj, fp, *args, **kwargs)  # type: ignore
    elif extension in PICKLE:
        with open(file, "wb") as fp:
            pickle.dump(obj, fp, *args, **kwargs)  # type: ignore
    else:
        raise ValueError(f"Tying to save {obj} to {file!r} with unsupported extension={extension!r}")
    return file

load

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load(file: PathStr, *args: List[Any], **kwargs: Dict[str, Any]) -> Any

Load any file with supported extensions.

Source code in danling/utils/io.py
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def load(file: PathStr, *args: List[Any], **kwargs: Dict[str, Any]) -> Any:
    r"""
    Load any file with supported extensions.
    """
    if not os.path.isfile(file):
        raise ValueError(f"Trying to load {file!r} but it is not a file.")
    extension = os.path.splitext(file)[-1].lower()[1:]
    if extension in PYTORCH:
        if not TORCH_AVAILABLE:
            raise ImportError(f"Trying to load {file!r} but torch is not installed.")
        return torch.load(file, *args, **kwargs)
    if extension in NUMPY:
        if not NUMPY_AVAILABLE:
            raise ImportError(f"Trying to load {file!r} but numpy is not installed.")
        return numpy.load(file, *args, **kwargs)
    if extension in JSON:
        with open(file) as fp:
            return json.load(fp, *args, **kwargs)  # type: ignore
    if extension in YAML:
        with open(file) as fp:
            return yaml.load(fp, *args, **kwargs)  # type: ignore
    if extension in PICKLE:
        with open(file, "rb") as fp:
            return pickle.load(fp, *args, **kwargs)  # type: ignore
    if extension in PANDAS_SUPPORTED:
        return load_pandas(file, *args, **kwargs)
    raise ValueError(f"Tying to load {file!r} with unsupported extension={extension!r}")

load_pandas

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load_pandas(file: PathStr, *args: List[Any], **kwargs: Dict[str, Any]) -> Any

Load any pandas data file with supported extensions.

Source code in danling/utils/io.py
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def load_pandas(file: PathStr, *args: List[Any], **kwargs: Dict[str, Any]) -> Any:
    r"""
    Load any pandas data file with supported extensions.
    """
    if not PANDAS_AVAILABLE:
        raise ImportError(f"Trying to load {file!r} but pandas is not installed.")
    if not os.path.isfile(file):
        raise ValueError(f"Trying to load {file!r} but it is not a file.")
    extension = os.path.splitext(file)[-1].lower()[1:]
    if extension in PANDAS or extension in PICKLE:
        return pandas.read_pickle(file, *args, **kwargs)
    if extension in H5:
        return pandas.read_hdf(file, *args, **kwargs)
    if extension in CSV:
        return pandas.read_csv(file, *args, **kwargs)
    if extension in JSON:
        return pandas.read_json(file, *args, **kwargs)
    if extension in EXCEL:
        return pandas.read_excel(file, *args, **kwargs)
    if extension in XML:
        return pandas.read_xml(file, *args, **kwargs)
    if extension in SQL:
        return pandas.read_sql(file, *args, **kwargs)
    raise ValueError(f"Tying to load {file!r} with unsupported extension={extension!r}")

is_json_serializable

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is_json_serializable(obj: Any) -> bool

Check if obj is JSON serializable.

Source code in danling/utils/io.py
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def is_json_serializable(obj: Any) -> bool:
    r"""
    Check if `obj` is JSON serializable.
    """
    try:
        json.dumps(obj)
        return True
    except (TypeError, OverflowError):
        return False