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#!/usr/bin/env python3
"""inmemory.py.
Example usage of creating in-memory HDF5 file with a specified chunksize
using PyTables 3.0.0+
See also Cookbook page
http://pytables.github.io/cookbook/inmemory_hdf5_files.html and available
drivers
http://pytables.github.io/usersguide/parameter_files.html#hdf5-driver-management
"""
import numpy as np
import tables as tb
CHUNKY = 30
CHUNKX = 4320
if __name__ == "__main__":
# create dataset and add global attrs
file_path = "demofile_chunk%sx%d.h5" % (CHUNKY, CHUNKX)
with tb.open_file(
file_path,
"w",
title="PyTables HDF5 In-memory example",
driver="H5FD_CORE",
) as h5f:
# dummy some data
lats = np.empty([2160])
lons = np.empty([4320])
# create some simple arrays
lat_node = h5f.create_array("/", "lat", lats, title="latitude")
lon_node = h5f.create_array("/", "lon", lons, title="longitude")
# create a 365 x 4320 x 8640 CArray of 32bit float
shape = (5, 2160, 4320)
atom = tb.Float32Atom(dflt=np.nan)
# chunk into daily slices and then further chunk days
sst_node = h5f.create_carray(
h5f.root, "sst", atom, shape, chunkshape=(1, CHUNKY, CHUNKX)
)
# dummy up an ndarray
sst = np.empty([2160, 4320], dtype=np.float32)
sst.fill(30.0)
# write ndarray to a 2D plane in the HDF5
sst_node[0] = sst
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