packages/vaex-astro/vaex/astro/export.py
def export_hdf5_v1(dataset, path, column_names=None, byteorder="=", shuffle=False, selection=False, progress=None, virtual=True):
    """
    :param DatasetLocal dataset: dataset to export
    :param str path: path for file
    :param lis[str] column_names: list of column names to export or None for all columns
    :param str byteorder: = for native, < for little endian and > for big endian
    :param bool shuffle: export rows in random order
    :param bool selection: export selection or not
    :param progress: progress callback that gets a progress fraction as argument and should return True to continue,
        or a default progress bar when progress=True
    :param: bool virtual: When True, export virtual columns
    :return:
    """

    if selection:
        if selection == True:  # easier to work with the name
            selection = "default"
    # first open file using h5py api
    with h5py.File(path, "w") as h5file_output:

        h5data_output = h5file_output.require_group("data")
        # i1, i2 = dataset.current_slice
        N = len(dataset) if not selection else dataset.selected_length(selection)
        if N == 0:
            raise ValueError("Cannot export empty table")
        logger.debug("virtual=%r", virtual)
        logger.debug("exporting %d rows to file %s" % (N, path))
        # column_names = column_names or (dataset.get_column_names() + (list(dataset.virtual_columns.keys()) if virtual else []))
        column_names = column_names or dataset.get_column_names(virtual=virtual, strings=True)

        logger.debug("exporting columns(hdf5): %r" % column_names)
        for column_name in column_names:
            if column_name in dataset.get_column_names(strings=True):
                column = dataset.columns[column_name]
                shape = (N,) + column.shape[1:]
                dtype = column.dtype
            else:
                dtype = np.float64().dtype
                shape = (N,)
            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:
                try:
                    array = h5file_output.require_dataset("/data/%s" % column_name, shape=shape, dtype=dtype.newbyteorder(byteorder))
                except:
                    logging.exception("error creating dataset for %r, with type %r " % (column_name, dtype))
            array[0] = array[0]  # make sure the array really exists
        random_index_name = None
        column_order = list(column_names)  # copy
        if shuffle:
            random_index_name = "random_index"
            while random_index_name in dataset.get_column_names():
                random_index_name += "_new"
            shuffle_array = h5file_output.require_dataset("/data/" + random_index_name, shape=(N,), dtype=byteorder + "i8")
            shuffle_array[0] = shuffle_array[0]
            column_order.append(random_index_name)  # last item
        h5data_output.attrs["column_order"] = ",".join(column_order)  # keep track or the ordering of columns

    # after this the file is closed,, and reopen it using out class
    dataset_output = vaex.hdf5.dataset.Hdf5MemoryMapped(path, write=True)

    column_names = vaex.export._export(dataset_input=dataset,
                                       dataset_output=dataset_output,
                                       path=path,
                                       random_index_column=random_index_name,
                                       column_names=column_names,
                                       selection=selection,
                                       shuffle=shuffle,
                                       byteorder=byteorder,
                                       progress=progress)
    import getpass
    import datetime
    user = getpass.getuser()
    date = str(datetime.datetime.now())
    source = dataset.path
    description = "file exported by vaex, by user %s, on date %s, from source %s" % (user, date, source)
    if dataset.description:
        description += "previous description:\n" + dataset.description
    dataset_output.copy_metadata(dataset)
    dataset_output.description = description
    logger.debug("writing meta information")
    dataset_output.write_meta()
    dataset_output.close_files()
    return
def export_hdf5_v1(dataset, path, column_names=None, byteorder="=", shuffle=False, selection=False, progress=None, virtual=True):
    """
    :param DatasetLocal dataset: dataset to export
    :param str path: path for file
    :param lis[str] column_names: list of column names to export or None for all columns
    :param str byteorder: = for native, < for little endian and > for big endian
    :param bool shuffle: export rows in random order
    :param bool selection: export selection or not
    :param progress: progress callback that gets a progress fraction as argument and should return True to continue,
        or a default progress bar when progress=True
    :param: bool virtual: When True, export virtual columns
    :return:
    """

    if selection:
        if selection == True:  # easier to work with the name
            selection = "default"
    # first open file using h5py api
    with h5py.File(path, "w") as h5file_output:

        h5data_output = h5file_output.require_group("data")
        # i1, i2 = dataset.current_slice
        N = len(dataset) if not selection else dataset.selected_length(selection)
        if N == 0:
            raise ValueError("Cannot export empty table")
        logger.debug("virtual=%r", virtual)
        logger.debug("exporting %d rows to file %s" % (N, path))
        # column_names = column_names or (dataset.get_column_names() + (list(dataset.virtual_columns.keys()) if virtual else []))
        column_names = column_names or dataset.get_column_names(virtual=virtual, strings=True)

        logger.debug("exporting columns(hdf5): %r" % column_names)
        for column_name in column_names:
            if column_name in dataset.get_column_names(strings=True):
                column = dataset.columns[column_name]
                shape = (N,) + column.shape[1:]
                dtype = column.dtype
            else:
                dtype = np.float64().dtype
                shape = (N,)
            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:
                try:
                    array = h5file_output.require_dataset("/data/%s" % column_name, shape=shape, dtype=dtype.newbyteorder(byteorder))
                except:
                    logging.exception("error creating dataset for %r, with type %r " % (column_name, dtype))
            array[0] = array[0]  # make sure the array really exists
        random_index_name = None
        column_order = list(column_names)  # copy
        if shuffle:
            random_index_name = "random_index"
            while random_index_name in dataset.get_column_names():
                random_index_name += "_new"
            shuffle_array = h5file_output.require_dataset("/data/" + random_index_name, shape=(N,), dtype=byteorder + "i8")
            shuffle_array[0] = shuffle_array[0]
            column_order.append(random_index_name)  # last item
        h5data_output.attrs["column_order"] = ",".join(column_order)  # keep track or the ordering of columns

    # after this the file is closed,, and reopen it using out class
    dataset_output = vaex.hdf5.dataset.Hdf5MemoryMapped(path, write=True)

    column_names = vaex.export._export(dataset_input=dataset,
                                       dataset_output=dataset_output,
                                       path=path,
                                       random_index_column=random_index_name,
                                       column_names=column_names,
                                       selection=selection,
                                       shuffle=shuffle,
                                       byteorder=byteorder,
                                       progress=progress)
    import getpass
    import datetime
    user = getpass.getuser()
    date = str(datetime.datetime.now())
    source = dataset.path
    description = "file exported by vaex, by user %s, on date %s, from source %s" % (user, date, source)
    if dataset.description:
        description += "previous description:\n" + dataset.description
    dataset_output.copy_metadata(dataset)
    dataset_output.description = description
    logger.debug("writing meta information")
    dataset_output.write_meta()
    dataset_output.close_files()
    return
def export_hdf5_v1 ( dataset , path , column_names = None , byteorder = "=" , shuffle = False , selection = False , progress = None , virtual = True ) : if selection : if selection == True : # easier to work with the name selection = "default" # first open file using h5py api with h5py . File ( path , "w" ) as h5file_output : h5data_output = h5file_output . require_group ( "data" ) # i1, i2 = dataset.current_slice N = len ( dataset ) if not selection else dataset . selected_length ( selection ) if N == 0 : raise ValueError ( "Cannot export empty table" ) logger . debug ( "virtual=%r" , virtual ) logger . debug ( "exporting %d rows to file %s" % ( N , path ) ) # column_names = column_names or (dataset.get_column_names() + (list(dataset.virtual_columns.keys()) if virtual else [])) column_names = column_names or dataset . get_column_names ( virtual = virtual , strings = True ) logger . debug ( "exporting columns(hdf5): %r" % column_names ) for column_name in column_names : if column_name in dataset . get_column_names ( strings = True ) : column = dataset . columns [ column_name ] shape = ( N , ) + column . shape [ 1 : ] dtype = column . dtype else : dtype = np . float64 ( ) . dtype shape = ( N , ) 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 : try : array = h5file_output . require_dataset ( "/data/%s" % column_name , shape = shape , dtype = dtype . newbyteorder ( byteorder ) ) except : logging . exception ( "error creating dataset for %r, with type %r " % ( column_name , dtype ) ) array [ 0 ] = array [ 0 ] # make sure the array really exists random_index_name = None column_order = list ( column_names ) # copy if shuffle : random_index_name = "random_index" while random_index_name in dataset . get_column_names ( ) : random_index_name += "_new" shuffle_array = h5file_output . require_dataset ( "/data/" + random_index_name , shape = ( N , ) , dtype = byteorder + "i8" ) shuffle_array [ 0 ] = shuffle_array [ 0 ] column_order . append ( random_index_name ) # last item h5data_output . attrs [ "column_order" ] = "," . join ( column_order ) # keep track or the ordering of columns # after this the file is closed,, and reopen it using out class dataset_output = vaex . hdf5 . dataset . Hdf5MemoryMapped ( path , write = True ) column_names = vaex . export . _export ( dataset_input = dataset , dataset_output = dataset_output , path = path , random_index_column = random_index_name , column_names = column_names , selection = selection , shuffle = shuffle , byteorder = byteorder , progress = progress ) import getpass import datetime user = getpass . getuser ( ) date = str ( datetime . datetime . now ( ) ) source = dataset . path description = "file exported by vaex, by user %s, on date %s, from source %s" % ( user , date , source ) if dataset . description : description += "previous description:\n" + dataset . description dataset_output . copy_metadata ( dataset ) dataset_output . description = description logger . debug ( "writing meta information" ) dataset_output . write_meta ( ) dataset_output . close_files ( ) return
:param DatasetLocal dataset: dataset to export
    :param str path: path for file
    :param lis[str] column_names: list of column names to export or None for all columns
    :param str byteorder: = for native, < for little endian and > for big endian
    :param bool shuffle: export rows in random order
    :param bool selection: export selection or not
    :param progress: progress callback that gets a progress fraction as argument and should return True to continue,
        or a default progress bar when progress=True
    :param: bool virtual: When True, export virtual columns
    :return:
: param DatasetLocal dataset : dataset to export : param str path : path for file : param lis [ str ] column_names : list of column names to export or None for all columns : param str byteorder : = for native < for little endian and > for big endian : param bool shuffle : export rows in random order : param bool selection : export selection or not : param progress : progress callback that gets a progress fraction as argument and should return True to continue or a default progress bar when progress = True : param : bool virtual : When True export virtual columns : return :