hdf5_clifford

hdf5_clifford.py

import h5py
import numpy as np

# This is our function that will do the writing
def write_mv_array(file_name, mv_array, metric, basis_names, compression=False):
    n_datapoints = mv_array.shape[0]
    mv_dims = mv_array.shape[1]
    with h5py.File(file_name, "w") as f:
        if compression:
            dset_coefs = f.create_dataset("coefs", data=mv_array, compression="gzip", compression_opts=9)
        else:
            dset_coefs = f.create_dataset("coefs", data=mv_array)
        dset_ip = f.create_dataset("metric", data=metric)
        dset_basis_names = f.create_dataset("basis_names", data=basis_names)
        

from clifford.g3c import *
from clifford.tools.g3c import *

# We will test it with clifford
basis_names = np.array(list(sorted(layout.basis_vectors.keys())),dtype=bytes)

# TODO make this into a function in clifford
layout.metric = np.zeros((len(layout.basis_vectors),len(layout.basis_vectors)))
for i,v in enumerate(layout.basis_vectors_lst):
    for j,v2 in enumerate(layout.basis_vectors_lst):
        layout.metric[i,j] = (v|v2)[0]

# Make some data
mv_array = ConformalMVArray([random_point_pair() for i in range(500)]).value


# Write the file with and without compression

# This is 128K
write_mv_array("mytestfile.hdf5", mv_array, layout.metric, basis_names)

# This is 48K
write_mv_array("mytestfile_compressed.hdf5", mv_array, layout.metric, basis_names, compression=True)