packages/vaex-hdf5/vaex/hdf5/dataset.py
def create(cls, path, N, column_names, dtypes=None, write=True):
        """Create a new (empty) hdf5 file with columns given by column names, of length N

        Optionally, numpy dtypes can be passed, default is floats
        """

        dtypes = dtypes or [np.float] * len(column_names)

        if N == 0:
            raise ValueError("Cannot export empty table")
        with h5py.File(path, "w") as h5file_output:
            h5data_output = h5file_output.require_group("data")
            for column_name, dtype in zip(column_names, dtypes):
                shape = (N,)
                print(dtype)
                if dtype.type == np.datetime64:
                    array = h5file_output.require_dataset("/data/%s" % column_name, shape=shape, dtype=np.int64)
                    array.attrs["dtype"] = dtype.name
                else:
                    array = h5file_output.require_dataset("/data/%s" % column_name, shape=shape, dtype=dtype)
                array[0] = array[0]  # make sure the array really exists
        return Hdf5MemoryMapped(path, write=write)
Create a new (empty) hdf5 file with columns given by column names, of length N

        Optionally, numpy dtypes can be passed, default is floats
        
Create a new (empty) hdf5 file with columns given by column names, of length N
Optionally, numpy dtypes can be passed, default is floats
Create a new ( empty ) hdf5 file with columns given by column names of length N Optionally numpy dtypes can be passed default is floats
def create(cls, path, N, column_names, dtypes=None, write=True):
        dtypes = dtypes or [np.float] * len(column_names)
        if N == 0:
            raise ValueError("Cannot export empty table")
        with h5py.File(path, "w") as h5file_output:
            h5data_output = h5file_output.require_group("data")
            for column_name, dtype in zip(column_names, dtypes):
                shape = (N,)
                print(dtype)
                if dtype.type == np.datetime64:
                    array = h5file_output.require_dataset("/data/%s" % column_name, shape=shape, dtype=np.int64)
                    array.attrs["dtype"] = dtype.name
                else:
                    array = h5file_output.require_dataset("/data/%s" % column_name, shape=shape, dtype=dtype)
                array[0] = array[0]  
        return Hdf5MemoryMapped(path, write=write)
def create ( cls , path , N , column_names , dtypes = None , write = True ) : dtypes = dtypes or [ np . float ] * len ( column_names ) if N == 0 : raise ValueError ( "Cannot export empty table" ) with h5py . File ( path , "w" ) as h5file_output : h5data_output = h5file_output . require_group ( "data" ) for column_name , dtype in zip ( column_names , dtypes ) : shape = ( N , ) print ( dtype ) if dtype . type == np . datetime64 : array = h5file_output . require_dataset ( "/data/%s" % column_name , shape = shape , dtype = np . int64 ) array . attrs [ "dtype" ] = dtype . name else : array = h5file_output . require_dataset ( "/data/%s" % column_name , shape = shape , dtype = dtype ) array [ 0 ] = array [ 0 ] return Hdf5MemoryMapped ( path , write = write )
Create a new (empty) hdf5 file with columns given by column names, of length N
Optionally, numpy dtypes can be passed, default is floats
Create a new ( empty ) hdf5 file with columns given by column names of length N Optionally numpy dtypes can be passed default is floats
"""Create a new (empty) hdf5 file with columns given by column names, of length N

        Optionally, numpy dtypes can be passed, default is floats
        """ # make sure the array really exists
cls
path
N
column_names
dtypes
write