def main():
parser = argparse.ArgumentParser(
description='Get folder location to look for DAQ files')
parser.add_argument('-dir', action="store", dest="dir_daq")
parser.add_argument('-elemlist', action="store", dest="elemlist")
results = parser.parse_args()
current_directory = os.getcwd()
target_directory = results.dir_daq # Replace with the folder that holds all the HDF5 files or the top level folder of where your HDF5 files are
with open(results.elemlist) as fname:
elemlist = fname.read().split('\n')
for root, dirs, files in os.walk(
os.path.join(current_directory, target_directory)):
for fname in files:
if fname.endswith('.daq'):
try:
daq = Daq()
print("Trying to read {}".format(fname))
daq.read_daq(os.path.join(root, fname),
process_dynobjs=False,
interpolate_missing_frames=True)
#daq.write_hd5(os.path.join(root,fname + '.hdf5'))
# Daq Size
data_length = daq['CFS_Accelerator_Pedal_Position'].shape[
1]
# Date Index
data_run_start = ''.join(
re.findall(
r'\d+',
np.array2string(
np.array(daq['RunInst']).flatten())))
data_run_start = dt.datetime.strptime(
data_run_start, '%Y%m%d%H%M%S')
data_run_start = pd.date_range(
pd.to_datetime(data_run_start),
periods=data_length,
freq='0.01667S')
data_list = []
col_list = []
for elem in elemlist:
data = np.array(daq[elem]).T
num_col = data.shape[1]
data_list.append(data)
for i in range(num_col):
col_list.append(elem + '_' + str(i))
data = np.concatenate(data_list, axis=1)
df = pd.DataFrame(data=data,
index=data_run_start,
columns=col_list)
df.index.rename('timestamp', inplace=True)
df.to_csv(os.path.join(root, fname + '.csv'))
except AssertionError:
print("Could not read {}".format(fname))
