Serialization with h5py

• HDF5 - Hierarchical Data Format - supports n-dimensional datasets and each element in the dataset may itself be a complex object.
• docs

TODO: fix these

import h5py
import os
import sys
import numpy as np

filename = 'counter.h5'

if len(sys.argv) == 1:
    if not os.path.exists(filename):
        print("counter does not exist yet")
        exit(1)
    with h5py.File(filename, 'r') as hdf:
        for name in hdf.keys():
            print(f"{name}: {hdf[name][0]}")
    exit()

if not os.path.exists(filename):
    with h5py.File(filename, 'w') as hdf:
        pass

with h5py.File(filename, 'r+') as hdf:
    for name in sys.argv[1:]:
        if name not in hdf:
            hdf[name] = np.zeros(1, dtype=int)
        hdf[name][0] += 1
        print(f"{name}: {hdf[name][0]}")

import h5py
#import numpy as np
#
#original_data = []
#
#count = 10
#size = (2, 5)
filename = 'data.h5'
#
#for _ in range(count):
#    row = np.random.random(size)
#    print(row)
#    original_data.append(row)

with h5py.File(filename, 'w') as hdf:
    hdf["a"] = 23
    hdf["b"] = 19

with h5py.File(filename, 'r') as hdf:
    print(hdf)        # <HDF5 file "data.h5" (mode r)>
    print(hdf.keys()) # <KeysViewHDF5 ['a', 'b']>
    for key in hdf.keys():
        print(key, hdf[key])

import sys
import h5py

filename = sys.argv[1]

with h5py.File(filename, 'r') as hdf:
    loaded = hdf['data'][:]

print(len(loaded))
print(type(loaded))
print(loaded.size)
print(loaded.shape)

print(type(loaded[0]))
print(loaded[0].size)
print(loaded[0].shape)