pip install fast5_research

from fast5_research import Fast5

filename='my.fast5'

with Fast5(filename) as fh:
    raw = fh.get_read(raw=True)
    summary = fh.summary()
print('Raw is {} samples long.'.format(len(raw)))
print('Summary {}.'.format(summary))

from uuid import uuid4
import numpy as np
from fast5_research import Fast5

filename='my_new.fast5'
mean, stdv, n = 40.0, 2.0, 10000
raw_data = np.random.laplace(mean, stdv/np.sqrt(2), int(dwell))

# example of how to digitize data
start, stop = int(min(raw_data - 1)), int(max(raw_data + 1))
rng = stop - start
digitisation = 8192.0
bins = np.arange(start, stop, rng / digitisation)
# np.int16 is required, the library will refuse to write anything other
raw_data = np.digitize(raw_data, bins).astype(np.int16)

# The following are required meta data
channel_id = {
    'digitisation': digitisation,
    'offset': 0,
    'range': rng,
    'sampling_rate': 4000,
    'channel_number': 1,
    }
read_id = {
    'start_time': 0,
    'duration': len(raw_data),
    'read_number': 1,
    'start_mux': 1,
    'read_id': str(uuid4()),
    'scaling_used': 1,
    'median_before': 0,
}
tracking_id = {
    'exp_start_time': '1970-01-01T00:00:00Z',
    'run_id': str(uuid4()).replace('-',''),
    'flow_cell_id': 'FAH00000',
}
context_tags = {}

with Fast5.New(filename, 'w', tracking_id=tracking_id, context_tags=context_tags, channel_id=channel_id) as h:
    h.set_raw(raw_data, meta=read_id, read_number=1)