def filter_mdf(file, signals):
mdf_file = MDF(file)
# Lenkradwinkel
lenkradwinkel = mdf_file.get('can0_LWI_Lenkradwinkel')
samples = list(lenkradwinkel.samples)
vz_samples = list(mdf_file.get('can0_LWI_VZ_Lenkradwinkel').samples)
for i in range(0, len(samples)):
if not int(vz_samples[i]):
samples[i] = samples[i] * -1
lenkradwinkel.samples = np.asarray(samples)
# Lenkradwinkel geschw.
lenkrad_gesch = mdf_file.get('can0_LWI_Lenkradw_Geschw')
samples = list(lenkrad_gesch.samples)
vz_samples = list(mdf_file.get('can0_LWI_VZ_Lenkradw_Geschw').samples)
for i in range(0, len(samples)):
if not int(vz_samples[i]):
samples[i] = samples[i] * -1
lenkrad_gesch.samples = np.asarray(samples)
# Gierrate
gierrate = mdf_file.get('can0_ESP_Gierrate')
samples = list(gierrate.samples)
vz_samples = list(mdf_file.get('can0_ESP_VZ_Gierrate').samples)
for i in range(0, len(samples)):
if not int(vz_samples[i]):
samples[i] = samples[i] * -1
gierrate.samples = np.asarray(samples)
# filter file with VZ signals
filtered_mdf = mdf_file.filter(signals)
return filtered_mdf
def main():
file = sys.argv[1]
mdf_file = MDF(file)
mdf_file = mdf_file.filter([
'can0_LWI_Lenkradwinkel', 'can0_LWI_Lenkradw_Geschw',
'can0_ESP_Fahrer_bremst', 'can0_ESP_Bremsdruck',
'can0_MO_Fahrpedalrohwert_01', 'can0_MO_Kuppl_schalter',
'can0_MO_Drehzahl_01', 'can0_MO_Gangposition', 'can0_ESP_v_Signal',
'can0_ESP_HL_Fahrtrichtung', 'can0_ESP_HR_Fahrtrichtung',
'can0_ESP_Laengsbeschl', 'can0_ESP_Gierrate'
])
signals = mdf_file.iter_channels(skip_master=True)
sf = dict()
for signal in signals:
f = len(
signal.timestamps) / (signal.timestamps[-1] - signal.timestamps[0])
sf[signal.name[5:]] = f
values = list(map(lambda x: int(math.ceil(x) / 10) * 10, sf.values()))
signal_names = list(map(lambda x: '_'.join(x.split('_')[1:]), sf.keys()))
fig, ax = plt.subplots(figsize=[8, 4])
plt.bar(range(len(sf)), values, align='center')
plt.yticks(np.arange(0, max(values) + 10, 10))
plt.ylabel('Frequency [Hz]')
plt.xticks(range(len(sf)), signal_names, rotation='vertical')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(color='gray', linestyle='-', linewidth=0.25, alpha=0.5)
plt.savefig('../Thesis/images/signal_frequences.png', bbox_inches='tight')
from asammdf import MDF
import sys
import matplotlib.pyplot as plt
mdf = MDF('Dauerzeitschrieb_20171218_053831.mf4', memory='full')
sig_list = []
while 1:
choice_1 = input("Do you want to add a signal?(y/n)\n")
if choice_1 == "n":
if len(sig_list) > 0:
filter = mdf.filter(sig_list)
file_name = input("Enter the file name to be saved\n")
filter.convert('4.10').save(file_name + '.mf4')
mdf_new = MDF((file_name + '.mf4'), memory='full')
data_frame = mdf_new.export('pandas', filename=file_name)
data_frame.to_csv(file_name + '.csv')
print("CSV and mf4 file created.\n")
choice_2 = input("Do you want to plot the signal(s)?(y/n)\n")
if choice_2 == "y":
for i in range(len(sig_list)):
plt.figure(i)
plt.xlabel('Time [s]')
plt.ylabel('[{}]'.format(mdf.get(sig_list[i]).unit))
plt.plot(
mdf.get(sig_list[i]).timestamps,
mdf.get(sig_list[i]).samples, 'b')
plt.plot(
mdf.get(sig_list[i]).timestamps,
mdf.get(sig_list[i]).samples, 'b.')
plt.grid(True)
break
def main():
my_parser = argparse.ArgumentParser(
description='Transform signals with sign')
my_parser.add_argument('-i',
'--input',
action='store',
metavar='mf4_input',
type=str,
required=True,
help='Input directory with source mf4 files')
my_parser.add_argument('-o',
'--output',
action='store',
metavar='mf4_output',
type=str,
required=True,
help='Output directory')
# Execute the parse_args() method
args = my_parser.parse_args()
mf4_input = args.input
mf4_output = args.output
if not os.path.isdir(mf4_input):
print('The mf4 input path specified is not a directory')
sys.exit()
if not os.path.isdir(mf4_output):
print('The mf4 ouput path specified is not a directory')
sys.exit()
for file in os.listdir(mf4_input):
if not file.endswith('.mf4'):
continue
print('process %s' % file)
mdf_file = MDF(os.path.join(mf4_input, file))
# Lenkradwinkel
lenkradwinkel = mdf_file.get('can0_LWI_Lenkradwinkel')
samples = list(lenkradwinkel.samples)
vz_samples = list(mdf_file.get('can0_LWI_VZ_Lenkradwinkel').samples)
for i in range(0, len(samples)):
if not int(vz_samples[i]):
samples[i] = samples[i] * -1
lenkradwinkel.samples = np.asarray(samples)
# Lenkradwinkel geschw.
lenkradwinkel = mdf_file.get('can0_LWI_Lenkradw_Geschw')
samples = list(lenkradwinkel.samples)
vz_samples = list(mdf_file.get('can0_LWI_VZ_Lenkradw_Geschw').samples)
for i in range(0, len(samples)):
if not int(vz_samples[i]):
samples[i] = samples[i] * -1
lenkradwinkel.samples = np.asarray(samples)
# Gierrate
lenkradwinkel = mdf_file.get('can0_ESP_Gierrate')
samples = list(lenkradwinkel.samples)
vz_samples = list(mdf_file.get('can0_ESP_VZ_Gierrate').samples)
for i in range(0, len(samples)):
if not int(vz_samples[i]):
samples[i] = samples[i] * -1
lenkradwinkel.samples = np.asarray(samples)
# filter file with VZ signals
filtered_mdf = mdf_file.filter(interesting_signals)
filtered_mdf.save(os.path.join(mf4_output, file))
import matplotlib
# import matplotlib.pyplot as plt
# MDF 파일을 읽어옵니다.
path = "./"
data = MDF(path + "Acceleration_StandingStart.MDF")
### CAN 신호 리스트를 가져 옵니다.
signal_list = list(data.channels_db)
# 가져온 리스트에서 시간축은 신호가 아니므로 제외합니다.
signal_list.remove('t')
print(signal_list) # 로깅된 CAN 신호 전체를 볼 수 있습니다.
### 그래프 출력
#speed = data.get('VehicleSpeed')
#speed.plot()
### 필요한 신호만 필터링
filtered_signal_list = ['VehicleSpeed', 'Throttle']
### 여러 그래프 출력
for signal in data.select(filtered_signal_list):
signal.plot()
# 10초 ~ 12초 사이의 데이터만 필터링
filtered_data = data.filter(filtered_signal_list).cut(start=10, stop=12)
### 엑셀 파일 또는 CSV 파일로 출력
#signals_data_frame = data.to_dataframe()
#signals_data_frame.to_excel(path + "signals_data_frame.xlsx")
#signals_data_frame.to_csv(path + "signals_data_frame.csv")
# create empty MDf version 4.00 file
mdf4 = MDF(version='4.10')
# append the 3 signals to the new file
signals = [s_uint8, s_int32, s_float64]
mdf4.append(signals, 'Created by Python')
# save new file
mdf4.save('my_new_file.mf4', overwrite=True)
# convert new file to mdf version 3.10 with lowest possible RAM usage
mdf3 = mdf4.convert('3.10', memory='minimum')
print(mdf3.version)
# get the float signal
sig = mdf3.get('Float64_Signal')
print(sig)
# cut measurement from 0.3s to end of measurement
mdf4_cut = mdf4.cut(start=0.3)
mdf4_cut.get('Float64_Signal').plot()
# cut measurement from start of measurement to 0.4s
mdf4_cut = mdf4.cut(stop=0.45)
mdf4_cut.get('Float64_Signal').plot()
# filter some signals from the file
mdf4 = mdf4.filter(['Int32_Signal', 'Uint8_Signal'])
# save using zipped transpose deflate blocks
mdf4.save('out.mf4', compression=2, overwrite=True)
for files in range(len(srcFiles)): #
print(
f'\nRead mf4 files started. {files+1}/{len(srcFiles)} File selected {srcFiles[files].name}'
)
sleep(1)
try:
mf4_data = MDF(srcFiles[files])
csvname = srcFiles[files].name
outfilename_Comment = srcFiles[0].parent.as_posix().replace(
'/', '\\') + '\\' + csvname + '_Comments.csv'
outfilename_Gps = srcFiles[0].parent.as_posix().replace(
'/', '\\') + '\\' + csvname + '_GPS.csv'
outfilename_merged = srcFiles[0].parent.as_posix().replace(
'/', '\\') + '\\' + srcFiles[0].parts[-2] + '_Merged.csv'
filtered = mf4_data.filter(['Comment'])
sdata = filtered.get('Comment')
commentSignals.append(sdata)
comment = sdata.samples.astype(str).tolist()
timestamp = sdata.timestamps.tolist()
fname = [srcFiles[files].name] * len(comment)
gpsLatitude = mf4_data.get('GPSLatitude')
gpsLongitude = mf4_data.get('GPSLongitude')
gpsTs = gpsLatitude.timestamps.tolist()
gpsLat = gpsLatitude.samples.tolist()
gpsLon = gpsLongitude.samples.tolist()
gpsLatwgs = [(Lat / 1000000) - 90 for Lat in gpsLat]
gpsLonwgs = [(Lon / 1000000) - 180 for Lon in gpsLon]
gpsList = {'ts': gpsTs, 'lat': gpsLatwgs, 'lon': gpsLonwgs}
gpsFrame = pd.DataFrame(gpsList).to_csv(outfilename_Gps)