source: ReferenceDesigns/w3_802.11/python/examples/chan_est_viewer/chan_ests_to_HDF5.py

import sys
import os
import datetime
import numpy as np
import wlan_exp.util as wlan_exp_util
import wlan_exp.log.util as log_util
import wlan_exp.log.util_hdf as hdf_util
import wlan_exp.log.util_sample_data as sample_data_util

DEFAULT_LOGFILE = 'ap_one_node_capture.hdf5'
logfile_error   = False

# Use log file given as command line argument, if present
if(len(sys.argv) > 1):
    LOGFILE = str(sys.argv[1])

    # Check if the string argument matchs a local file
    if not os.path.isfile(LOGFILE):
        # User specified non-existant file - give up and exit
        logfile_error = True

else:
    # No command line argument - check if default file name exists locally
    LOGFILE = DEFAULT_LOGFILE

    if not os.path.isfile(LOGFILE):
        # No local file specified or found - check for matching sample data file
        try:
            LOGFILE = sample_data_util.get_sample_data_file(DEFAULT_LOGFILE)
            print("Local log file not found - Using sample data file!")
        except IOError as e:
            logfile_error = True

if logfile_error:
    print("ERROR: Logfile {0} not found".format(LOGFILE))
    sys.exit()
else:
    print("Reading log file '{0}' ({1:5.1f} MB)\n".format(LOGFILE, (os.path.getsize(LOGFILE)/2**20)))

if len(sys.argv) == 3:
    HDF5_FILE_OUT = str(sys.argv[2])
else:
    HDF5_FILE_OUT = 'np_rx_ofdm_entries.hdf5'

print("WLAN Exp Log Example: OFDM Rx Entry Exporter")


# Extract the raw log data and log index from the HDF5 file
log_data      = hdf_util.hdf5_to_log_data(filename=LOGFILE)
raw_log_index = hdf_util.hdf5_to_log_index(filename=LOGFILE)

# Generate indexes with only Rx_OFDM events
log_index_rx = log_util.filter_log_index(raw_log_index, include_only=['RX_OFDM'],
                                         merge={'RX_OFDM': ['RX_OFDM', 'RX_OFDM_LTG']})

# Generate numpy array of all OFDM Rx entries
log_np = log_util.log_data_to_np_arrays(log_data, log_index_rx)
log_rx_ofdm = log_np['RX_OFDM']

#################################################################
# Filter the OFDM Rx Entries
#  Find the source address for the most receptions

# Extract unique values for address 2 (transmitting address in received MAC headers)
uniq_addrs = np.unique(log_rx_ofdm['addr2'])

# Count the number of receptions per source address
num_rx = list()
for ii,ua in enumerate(uniq_addrs):
    num_rx.append(np.sum(log_rx_ofdm['addr2'] == ua))

# Find the source address responsible for the most receptions
most_rx = max(num_rx)
most_common_addr = uniq_addrs[num_rx.index(most_rx)]

print("Found {0} receptions from {1}".format(most_rx, wlan_exp_util.mac_addr_to_str(most_common_addr)))

# Create new numpy array of all receptions from most-common source
arr_rx_one_src = np.empty(most_rx, dtype=log_rx_ofdm.dtype)
arr_rx_one_src[:] = log_rx_ofdm[(log_rx_ofdm['addr2'] == most_common_addr)]

# Create some strings to use as attributes in the HDF5 file
#     - attributes are only for convenience - they aren't required for writing or reading HDF5 files
root_desc = 'Source Log file: {0}\n'.format(LOGFILE)
root_desc += 'HDF5 file written at {0}\n'.format(datetime.datetime.now())

ds_desc = 'All Rx OFDM entries from addr {0}\n'.format(wlan_exp_util.mac_addr_to_str(most_common_addr))

# Construct dictionaries to feed the HDF5 exporter
#     - The dict key names are used as HDF5 dataset names
log_dict = {'RX_OFDM': arr_rx_one_src}
attr_dict = {'/': root_desc, 'RX_OFDM': ds_desc}

print('Generating HDF5 file {0}'.format(HDF5_FILE_OUT))
hdf_util.np_arrays_to_hdf5(HDF5_FILE_OUT, np_log_dict=log_dict, attr_dict=attr_dict)