# encoding: utf-8 # module h5py.h5d # from C:\Users\xukai\Downloads\发票2\venv\Lib\site-packages\h5py\h5d.cp311-win_amd64.pyd # by generator 1.147 """ Provides access to the low-level HDF5 "H5D" dataset interface. """ # imports import builtins as __builtins__ # from h5py._objects import with_phil import h5py._objects as __h5py__objects # Variables with simple values ALLOC_TIME_DEFAULT = 0 ALLOC_TIME_EARLY = 1 ALLOC_TIME_INCR = 3 ALLOC_TIME_LATE = 2 CHUNKED = 2 COMPACT = 0 CONTIGUOUS = 1 FILL_TIME_ALLOC = 0 FILL_TIME_IFSET = 2 FILL_TIME_NEVER = 1 FILL_VALUE_DEFAULT = 1 FILL_VALUE_UNDEFINED = 0 FILL_VALUE_USER_DEFINED = 2 SPACE_STATUS_ALLOCATED = 2 SPACE_STATUS_NOT_ALLOCATED = 0 SPACE_STATUS_PART_ALLOCATED = 1 VDS_FIRST_MISSING = 0 VDS_LAST_AVAILABLE = 1 VIRTUAL = 3 # functions def create(*args, **kwargs): # real signature unknown """ (objectID loc, STRING name or None, TypeID tid, SpaceID space, PropDCID dcpl=None, PropID lcpl=None) => DatasetID Create a new dataset. If "name" is None, the dataset will be anonymous. """ pass def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None): # reliably restored by inspect """ Returns a new subclass of tuple with named fields. >>> Point = namedtuple('Point', ['x', 'y']) >>> Point.__doc__ # docstring for the new class 'Point(x, y)' >>> p = Point(11, y=22) # instantiate with positional args or keywords >>> p[0] + p[1] # indexable like a plain tuple 33 >>> x, y = p # unpack like a regular tuple >>> x, y (11, 22) >>> p.x + p.y # fields also accessible by name 33 >>> d = p._asdict() # convert to a dictionary >>> d['x'] 11 >>> Point(**d) # convert from a dictionary Point(x=11, y=22) >>> p._replace(x=100) # _replace() is like str.replace() but targets named fields Point(x=100, y=22) """ pass def open(*args, **kwargs): # real signature unknown """ (ObjectID loc, STRING name, PropID dapl=None) => DatasetID Open an existing dataset attached to a group or file object, by name. If specified, dapl may be a dataset access property list. """ pass def __pyx_unpickle_DatasetID(*args, **kwargs): # real signature unknown pass # classes class DatasetID(__h5py__objects.ObjectID): """ Represents an HDF5 dataset identifier. Objects of this class may be used in any HDF5 function which expects a dataset identifier. Also, all H5D* functions which take a dataset instance as their first argument are presented as methods of this class. Properties: dtype: Numpy dtype representing the dataset type shape: Numpy-style shape tuple representing the dataspace rank: Integer giving dataset rank * Hashable: Yes, unless anonymous * Equality: True HDF5 identity if unless anonymous """ def extend(self, *args, **kwargs): # real signature unknown """ (TUPLE shape) Extend the given dataset so it's at least as big as "shape". Note that a dataset may only be extended up to the maximum dimensions of its dataspace, which are fixed when the dataset is created. """ pass def flush(self, *args, **kwargs): # real signature unknown """ no return Flushes all buffers associated with a dataset to disk. This function causes all buffers associated with a dataset to be immediately flushed to disk without removing the data from the cache. Use this in SWMR write mode to allow readers to be updated with the dataset changes. Feature requires: 1.9.178 HDF5 """ pass def get_access_plist(self, *args, **kwargs): # real signature unknown """ () => PropDAID Create an return a new copy of the dataset access property list. """ pass def get_chunk_info(self, *args, **kwargs): # real signature unknown """ (hsize_t index, SpaceID space=None) => StoreInfo Retrieve storage information about a chunk specified by its index. Feature requires: HDF5 1.10.5 .. versionadded:: 3.0 """ pass def get_chunk_info_by_coord(self, *args, **kwargs): # real signature unknown """ (TUPLE chunk_offset) => StoreInfo Retrieve information about a chunk specified by the array address of the chunk’s first element in each dimension. Feature requires: HDF5 1.10.5 .. versionadded:: 3.0 """ pass def get_create_plist(self, *args, **kwargs): # real signature unknown """ () => PropDCID Create an return a new copy of the dataset creation property list used when this dataset was created. """ pass def get_num_chunks(self, *args, **kwargs): # real signature unknown """ (SpaceID space=None) => INT num_chunks Retrieve the number of chunks that have nonempty intersection with a specified dataspace. Currently, this function only gets the number of all written chunks, regardless of the dataspace. Feature requires: HDF5 1.10.5 .. versionadded:: 3.0 """ pass def get_offset(self, *args, **kwargs): # real signature unknown """ () => LONG offset or None Get the offset of this dataset in the file, in bytes, or None if it doesn't have one. This is always the case for datasets which use chunked storage, compact datasets, and datasets for which space has not yet been allocated in the file. """ pass def get_space(self, *args, **kwargs): # real signature unknown """ () => SpaceID Create and return a new copy of the dataspace for this dataset. """ pass def get_space_status(self, *args, **kwargs): # real signature unknown """ () => INT space_status_code Determine if space has been allocated for a dataset. Return value is one of: * SPACE_STATUS_NOT_ALLOCATED * SPACE_STATUS_PART_ALLOCATED * SPACE_STATUS_ALLOCATED """ pass def get_storage_size(self, *args, **kwargs): # real signature unknown """ () => LONG storage_size Report the size of storage, in bytes, that is allocated in the file for the dataset's raw data. The reported amount is the storage allocated in the written file, which will typically differ from the space required to hold a dataset in working memory (any associated HDF5 metadata is excluded). For contiguous datasets, the returned size equals the current allocated size of the raw data. For unfiltered chunked datasets, the returned size is the number of allocated chunks times the chunk size. For filtered chunked datasets, the returned size is the space required to store the filtered data. """ pass def get_type(self, *args, **kwargs): # real signature unknown """ () => TypeID Create and return a new copy of the datatype for this dataset. """ pass def read(self, *args, **kwargs): # real signature unknown """ (SpaceID mspace, SpaceID fspace, NDARRAY arr_obj, TypeID mtype=None, PropDXID dxpl=None) Read data from an HDF5 dataset into a Numpy array. It is your responsibility to ensure that the memory dataspace provided is compatible with the shape of the Numpy array. Since a wide variety of dataspace configurations are possible, this is not checked. You can easily crash Python by reading in data from too large a dataspace. If a memory datatype is not specified, one will be auto-created based on the array's dtype. The provided Numpy array must be writable and C-contiguous. If this is not the case, ValueError will be raised and the read will fail. Keyword dxpl may be a dataset transfer property list. """ pass def read_direct_chunk(self, *args, **kwargs): # real signature unknown """ (offsets, PropID dxpl=None) Reads data to a bytes array directly from a chunk at position specified by the `offsets` argument and bypasses any filters HDF5 would normally apply to the written data. However, the written data may be compressed or not. Returns a tuple containing the `filter_mask` and the bytes data which are the raw data storing this chuck. `filter_mask` is a bit field of up to 32 values. It records which filters have been applied to this chunk, of the filter pipeline defined for that dataset. Each bit set to `1` means that the filter in the corresponding position in the pipeline was not applied to compute the raw data. So the default value of `0` means that all defined filters have been applied to the raw data. Feature requires: 1.10.2 HDF5 """ pass def refresh(self, *args, **kwargs): # real signature unknown """ no return Refreshes all buffers associated with a dataset. This function causes all buffers associated with a dataset to be cleared and immediately re-loaded with updated contents from disk. This function essentially closes the dataset, evicts all metadata associated with it from the cache, and then re-opens the dataset. The reopened dataset is automatically re-registered with the same ID. Use this in SWMR read mode to poll for dataset changes. Feature requires: 1.9.178 HDF5 """ pass def set_extent(self, *args, **kwargs): # real signature unknown """ (TUPLE shape) Set the size of the dataspace to match the given shape. If the new size is larger in any dimension, it must be compatible with the maximum dataspace size. """ pass def write(self, *args, **kwargs): # real signature unknown """ (SpaceID mspace, SpaceID fspace, NDARRAY arr_obj, TypeID mtype=None, PropDXID dxpl=None) Write data from a Numpy array to an HDF5 dataset. Keyword dxpl may be a dataset transfer property list. It is your responsibility to ensure that the memory dataspace provided is compatible with the shape of the Numpy array. Since a wide variety of dataspace configurations are possible, this is not checked. You can easily crash Python by writing data from too large a dataspace. If a memory datatype is not specified, one will be auto-created based on the array's dtype. The provided Numpy array must be C-contiguous. If this is not the case, ValueError will be raised and the read will fail. """ pass def write_direct_chunk(self, *args, **kwargs): # real signature unknown """ (offsets, data, uint32_t filter_mask=0x00000000, PropID dxpl=None) This function bypasses any filters HDF5 would normally apply to written data. However, calling code may apply filters (e.g. gzip compression) itself before writing the data. `data` is a Python object that implements the Py_buffer interface. In case of a ndarray the shape and dtype are ignored. It's the user's responsibility to make sure they are compatible with the dataset. `filter_mask` is a bit field of up to 32 values. It records which filters have been applied to this chunk, of the filter pipeline defined for that dataset. Each bit set to `1` means that the filter in the corresponding position in the pipeline was not applied. So the default value of `0` means that all defined filters have been applied to the data before calling this function. Feature requires: 1.8.11 HDF5 """ pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass dtype = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """ Numpy dtype object representing the dataset type """ rank = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """ Integer giving the dataset rank (0 = scalar) """ shape = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """ Numpy-style shape tuple representing the dataspace """ class StoreInfo(tuple): """ StoreInfo(chunk_offset, filter_mask, byte_offset, size) """ def _asdict(self): # reliably restored by inspect """ Return a new dict which maps field names to their values. """ pass @classmethod def _make(cls, *args, **kwargs): # real signature unknown """ Make a new StoreInfo object from a sequence or iterable """ pass def _replace(self, **kwds): # reliably restored by inspect """ Return a new StoreInfo object replacing specified fields with new values """ pass def __getnewargs__(self): # reliably restored by inspect """ Return self as a plain tuple. Used by copy and pickle. """ pass def __init__(self, chunk_offset, filter_mask, byte_offset, size): # real signature unknown; restored from __doc__ pass @staticmethod # known case of __new__ def __new__(_cls, chunk_offset, filter_mask, byte_offset, size): # reliably restored by inspect """ Create new instance of StoreInfo(chunk_offset, filter_mask, byte_offset, size) """ pass def __repr__(self): # reliably restored by inspect """ Return a nicely formatted representation string """ pass byte_offset = _tuplegetter(2, 'Alias for field number 2') chunk_offset = _tuplegetter(0, 'Alias for field number 0') filter_mask = _tuplegetter(1, 'Alias for field number 1') size = _tuplegetter(3, 'Alias for field number 3') _fields = ( 'chunk_offset', 'filter_mask', 'byte_offset', 'size', ) _field_defaults = {} __match_args__ = ( 'chunk_offset', 'filter_mask', 'byte_offset', 'size', ) __slots__ = () # variables with complex values phil = None # (!) real value is '' __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x0000021577ABBED0>' __spec__ = None # (!) real value is "ModuleSpec(name='h5py.h5d', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x0000021577ABBED0>, origin='C:\\\\Users\\\\xukai\\\\Downloads\\\\??2\\\\venv\\\\Lib\\\\site-packages\\\\h5py\\\\h5d.cp311-win_amd64.pyd')" __test__ = {}