def test_mixed(self):
t = np.arange(15, dtype="<f8")
s1 = Signal(
np.frombuffer(b"\x00\x00\x00\x02" * 15, dtype=">u4"), t, name="Motorola"
)
s2 = Signal(
np.frombuffer(b"\x04\x00\x00\x00" * 15, dtype="<u4"), t, name="Intel"
)
for version in ("3.30", "4.10"):
mdf = MDF(version=version)
mdf.append([s1, s2], common_timebase=True)
outfile = mdf.save(
Path(TestEndianess.tempdir.name) / f"out", overwrite=True
)
mdf.close()
with MDF(outfile) as mdf:
self.assertTrue(np.array_equal(mdf.get("Motorola").samples, [2] * 15))
self.assertTrue(np.array_equal(mdf.get("Intel").samples, [4] * 15))
for version in ("3.30", "4.10"):
mdf = MDF(version=version)
mdf.append([s2, s1], common_timebase=True)
outfile = mdf.save(
Path(TestEndianess.tempdir.name) / f"out", overwrite=True
)
mdf.close()
with MDF(outfile) as mdf:
self.assertTrue(np.array_equal(mdf.get("Motorola").samples, [2] * 15))
self.assertTrue(np.array_equal(mdf.get("Intel").samples, [4] * 15))
def test_mixed(self):
t = np.arange(15, dtype='<f8')
s1 = Signal(
np.frombuffer(b'\x00\x00\x00\x02' * 15, dtype='>u4'),
t,
name='Motorola'
)
s2 = Signal(
np.frombuffer(b'\x04\x00\x00\x00' * 15, dtype='<u4'),
t,
name='Intel'
)
for version in ('3.30', '4.10'):
mdf = MDF(version=version)
mdf.append([s1, s2], common_timebase=True)
outfile = mdf.save(
Path(TestEndianess.tempdir.name) / f"out",
overwrite=True,
)
mdf.close()
with MDF(outfile) as mdf:
self.assertTrue(
np.array_equal(
mdf.get('Motorola').samples, [2,] * 15
)
)
self.assertTrue(
np.array_equal(
mdf.get('Intel').samples, [4,] * 15
)
)
for version in ('3.30', '4.10'):
mdf = MDF(version=version)
mdf.append([s2, s1], common_timebase=True)
outfile = mdf.save(
Path(TestEndianess.tempdir.name) / f"out",
overwrite=True,
)
mdf.close()
with MDF(outfile) as mdf:
self.assertTrue(
np.array_equal(
mdf.get('Motorola').samples, [2,] * 15
)
)
self.assertTrue(
np.array_equal(
mdf.get('Intel').samples, [4,] * 15
)
)
def test_attachment_blocks_wo_filename(self):
original_data = b"Testing attachemnt block\nTest line 1"
mdf = MDF()
mdf.attach(
original_data,
file_name=None,
comment=None,
compression=True,
mime=r"text/plain",
embedded=True,
)
outfile = mdf.save(
Path(TestMDF4.tempdir.name) / "attachment.mf4", overwrite=True,
)
with MDF(outfile) as attachment_mdf:
data, filename, md5_sum = attachment_mdf.extract_attachment(index=0)
self.assertEqual(data, original_data)
self.assertEqual(filename, Path("bin.bin"))
mdf.close()
# keep_arrays=False,
use_display_names=True,
time_as_date=True,
reduce_memory_usage=True,
raw=False,
ignore_value2text_conversions=False)
#remove \CCP from column names of dataframe
df.columns = [col.split('\\')[0] for col in df.columns]
#resampling the data to 1 second
df = df.resample('S').mean()
n_i = list(range(1,len(df['egr_b_operate_valve'])+1))
df['Sl.no'] = n_i
df.set_index('Sl.no',inplace=True)
mdf.close()
#%%
def exh_pressure_sampling():
RPM = [3400,3200,3000,2800,2600,2400,2200,2000,1800,1600,1400,1200]
mean_pres = []
for i in RPM:
df_i = df[(df['cps_n_engine'] > i-50) & (df['cps_n_engine'] < i+50)]
a = df_i['egr_P_exhaustp'].mean()
mean_pres.append(a)
pres_dict = {'Engine Speed':RPM,'Mean Pressure':mean_pres}
df_mean_pres = pd.DataFrame(pres_dict)
df_mean_pres.set_index(df_mean_pres['Engine Speed'])
df_mean_pres.plot(x='Engine Speed',y='Mean Pressure',xticks= RPM,kind = 'line',grid=True)
class openMDF:
def __init__(self, path, roadtype=0):
self.filename = path.split('/')[-1].split('.')[0]
self.roadType = roadtype
if self.filename not in loser_file_data:
self.open_file(path)
self.task_1()
def open_file(self, path):
print('开启mf4文件')
self.mf = MDF(path)
def close_file(self):
print('关闭mf4文件')
self.mf.close()
# 统计转向灯开启时间
def task_1(self):
signal_left = '_g_PL_AD_fw_PL_AD_FCT_RunnableSppHmi_RunnableSppHmi_m_sppHmiInput_out_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_dirIndL'
signal_right = '_g_PL_AD_fw_PL_AD_FCT_RunnableSppHmi_RunnableSppHmi_m_sppHmiInput_out_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_dirIndR'
signal_speed = '_g_PL_AD_fw_PL_AD_FCT_RunnableFsm_RunnableFsm._m_fsmController._m_fip._m_displayedSpeedCalculator._m_displaySpeed._m_value'
# 轮端转角
signal_RoadWheelAngle = '_g_PL_AD_fw_VMC_VMC_FW_MvpVse_VseSes_VseSes_m_portVehicleStateEstimation_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_estimation_RoadWheelAngle_Front._m_value'
# 左航向角
Left_heading_angle_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._0_._VFC_Line_HeadingAngle'
# 左侧车道线
Left_lane_line_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._0_._VFC_Line_Dy'
# 右航向角
Right_heading_angle_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._1_._VFC_Line_HeadingAngle'
# 右侧车道线
Right_lane_line_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._1_._VFC_Line_Dy'
# vxvRef
vxvRef_signal = '_g_PL_AD_fw_DACoreCyclic_HV_PerPmeRunnable_PerPmeRunnable_m_pmePort_out_local.TChangeableMemPool._._._m_arrayPool._0_._elem._vxvRef_sw'
odometer_signal = 'ICM_TotalOdometer'
# Line ID 255
left_line_signal = 'VFC_Line01_LineID'
right_line_signal = 'VFC_Line02_LineID'
if self.mf:
try:
left = self.mf.get(signal_left)
right = self.mf.get(signal_right)
speed = self.mf.get(signal_speed)
RoadWheelAngle = self.mf.get(signal_RoadWheelAngle).samples.tolist()
Left_heading_angle = self.mf.get(Left_heading_angle_signal).samples.tolist()
Left_lane_line = self.mf.get(Left_lane_line_signal).samples.tolist()
print(len(Left_lane_line))
print(len(left.samples.tolist()))
Right_heading_angle = self.mf.get(Right_heading_angle_signal).samples.tolist()
Right_lane_line = self.mf.get(Right_lane_line_signal).samples.tolist()
vxvRef = self.mf.get(vxvRef_signal)
odometer = self.mf.get(odometer_signal)
left_line = self.mf.get(left_line_signal).samples.tolist()
right_line = self.mf.get(right_line_signal).samples.tolist()
except Exception as e:
print(e)
loser_file_data.append(self.filename)
else:
def func(data):
num = data.samples.tolist()[0]
index_list = [0]
for index in range(len(data.samples.tolist())):
if num != data.samples.tolist()[index]:
num = data.samples.tolist()[index]
index_list.append(index)
if len(data.samples.tolist())-1 not in index_list:
index_list.append(len(data.samples.tolist())-1)
return index_list
# 处理数据长度问题 speed, vxvRef
st = speed.timestamps.tolist()
start_time = 0
stop_time = -2
# 计算差值
left_data = []
left_time = left.timestamps.tolist()
old_spd = speed.samples.tolist()
old_vxv = vxvRef.samples.tolist()
old_spd_time = speed.timestamps.tolist()
old_vxvRef = vxvRef.samples.tolist()
new_spd = ['' for x in left_time]
new_vxvRef = ['' for x in left_time]
for tt in range(len(old_spd_time)):
this_index = index_number(left_time, old_spd_time[tt])
new_spd[this_index] = old_spd[tt]
new_vxvRef[this_index] = old_vxvRef[tt]
for i in range(len(new_spd)):
if new_spd[i] == '':
if i==0:
if new_spd[i+1] != 0:
new_spd[i] = new_spd[i+1]
else:
new_spd[i] = 0
else:
new_spd[i] = new_spd[i-1]
if new_vxvRef[i] == '':
if i==0:
if new_vxvRef[i+1] != 0:
new_vxvRef[i] = new_vxvRef[i+1]
else:
new_vxvRef[i] = 0
else:
new_vxvRef[i] = new_vxvRef[i-1]
left_index_list = func(left)
right_index_list = func(right)
if left_index_list == [] and right_index_list == []:
self.close_file()
return False
new_data = {
'left': [],
'left_time': [],
'right': [],
'right_time': [],
'spd': [],
'wheel': [],
'Left_heading_angle': [],
'left_lane': [],
'Right_heading_angle': [],
'right_lane': [],
'vxvRef': [],
'left_line': [],
'right_line': []
}
#
for index in range(len(left_index_list)-1):
new_data['left'].append(left.samples.tolist()[left_index_list[index]:left_index_list[index+1]])
new_data['left_time'].append(left.timestamps.tolist()[left_index_list[index]:left_index_list[index+1]])
new_data['right'].append([])
new_data['right_time'].append([])
new_data['spd'].append(new_spd[left_index_list[index]:left_index_list[index+1]])
new_data['wheel'].append(RoadWheelAngle[left_index_list[index]:left_index_list[index+1]])
new_data['Left_heading_angle'].append(Left_heading_angle[left_index_list[index]:left_index_list[index+1]])
new_data['left_lane'].append(Left_lane_line[left_index_list[index]:left_index_list[index+1]])
new_data['Right_heading_angle'].append(Right_heading_angle[left_index_list[index]:left_index_list[index+1]])
new_data['right_lane'].append(Right_lane_line[left_index_list[index]:left_index_list[index+1]])
new_data['vxvRef'].append(new_vxvRef[left_index_list[index]:left_index_list[index+1]])
new_data['left_line'].append(left_line[left_index_list[index]:left_index_list[index+1]])
new_data['right_line'].append([])
for index in range(len(right_index_list)-1):
new_data['left'].append([])
new_data['left_time'].append([])
new_data['right'].append(right.samples.tolist()[right_index_list[index]:right_index_list[index+1]])
new_data['right_time'].append(right.timestamps.tolist()[right_index_list[index]:right_index_list[index+1]])
new_data['spd'].append(new_spd[right_index_list[index]:right_index_list[index+1]])
new_data['wheel'].append(RoadWheelAngle[right_index_list[index]:right_index_list[index+1]])
new_data['Left_heading_angle'].append(Left_heading_angle[right_index_list[index]:right_index_list[index+1]])
new_data['left_lane'].append(Left_lane_line[right_index_list[index]:right_index_list[index+1]])
new_data['Right_heading_angle'].append(Right_heading_angle[right_index_list[index]:right_index_list[index+1]])
new_data['right_lane'].append(Right_lane_line[right_index_list[index]:right_index_list[index+1]])
new_data['vxvRef'].append(new_vxvRef[right_index_list[index]:right_index_list[index+1]])
new_data['left_line'].append([])
new_data['right_line'].append(right_line[right_index_list[index]:right_index_list[index+1]])
# for key,value in new_data.items():
# print(len(value))
# 车道线判断逻辑函数
def func2(i,direction,speed,max_spd,min_spd):
print(direction)
return_data = 0
lane_list = []
overrun = []
for lane_line in range(len(new_data[f'{direction}_lane'][i])-1):
if new_data[f'{direction}_lane'][i][lane_line] == 2048:
print('无信号')
continue
this_num = new_data[f'{direction}_lane'][i][lane_line] * 0.015625 - 32
next_num = new_data[f'{direction}_lane'][i][lane_line + 1] * 0.015625 - 32
print(next_num - this_num)
if abs(next_num - this_num) >= 2.4:
lane_list.append(lane_line)
print(lane_list)
if lane_list != []:
if len(lane_list) > 1:
print('多次变道， 不做计算')
else:
print(f'单次变道{direction}')
new_lane = new_data[f'{direction}_lane'][i][lane_list[0]+1:]
this_len = int(len(new_lane)/5)
new_list = [sum(new_lane[:this_len])/len(new_lane[:this_len]),sum(new_lane[this_len:this_len*2])/len(new_lane[this_len:this_len*2]),sum(new_lane[this_len*2:this_len*3])/len(new_lane[this_len*2:this_len*3]),sum(new_lane[this_len*3:this_len*4])/len(new_lane[this_len*3:this_len*4]),sum(new_lane[this_len*4:])/len(new_lane[this_len*4:])]
if new_list[0] > new_list[1] > new_list[2] > new_list[3] > new_list[4]:
# 变道到车轮压线时间
Time_from_lighting_to_pressing_line = new_data[f'{direction}_time'][i][lane_list[0]] - new_data[f'{direction}_time'][i][0]
Line_pressing_data = [self.filename,speed,max_spd,min_spd,Time_from_lighting_to_pressing_line, self.roadType]
if Line_pressing_data not in Line_pressing:
Line_pressing.append(Line_pressing_data)
return_data = 1
else:
this_len = int(len(new_data[f'{direction}_lane'][i])/5)
new_list = [
sum(new_data[f'{direction}_lane'][i][:this_len])/len(new_data[f'{direction}_lane'][i][:this_len]),
sum(new_data[f'{direction}_lane'][i][this_len:this_len*2])/len(new_data[f'{direction}_lane'][i][this_len:this_len*2]),
sum(new_data[f'{direction}_lane'][i][this_len*2:this_len*3])/len(new_data[f'{direction}_lane'][i][this_len*2:this_len*3]),
sum(new_data[f'{direction}_lane'][i][this_len*3:this_len*4])/len(new_data[f'{direction}_lane'][i][this_len*3:this_len*4]),
sum(new_data[f'{direction}_lane'][i][this_len*4:])/len(new_data[f'{direction}_lane'][i][this_len*4:])]
if min(new_list) == new_list[-1] and new_data[f'{direction}_lane'][i] != new_data[f'{direction}_lane'][-1]:
this_lane_list = new_data[f'{direction}_lane'][i][this_len*4:]
if len(new_data[f'{direction}_lane'][i+1]) >= 200:
this_lane_list.extend(new_data[f'{direction}_lane'][i+1][:200])
else:
this_lane_list.extend(new_data[f'{direction}_lane'][i+1])
for lane_line in range(len(this_lane_list)-1):
if this_lane_list[lane_line] == 2048:
print('无信号')
continue
this_num = this_lane_list[lane_line] * 0.015625 - 32
next_num = this_lane_list[lane_line + 1] * 0.015625 - 32
print(next_num - this_num)
if abs(next_num - this_num) >= 2.4:
lane_list.append(lane_line)
if lane_list != []:
if len(lane_list) > 1:
print('多次变道， 不做计算')
else:
print(f'单次变道{direction}')
new_lane = new_data[f'{direction}_lane'][i][lane_list[0]+1:]
this_len = int(len(new_lane)/5)
new_list = [sum(new_lane[:this_len])/len(new_lane[:this_len]),sum(new_lane[this_len:this_len*2])/len(new_lane[this_len:this_len*2]),sum(new_lane[this_len*2:this_len*3])/len(new_lane[this_len*2:this_len*3]),sum(new_lane[this_len*3:this_len*4])/len(new_lane[this_len*3:this_len*4]),sum(new_lane[this_len*4:])/len(new_lane[this_len*4:])]
if new_list[0] > new_list[1] > new_list[2] > new_list[3] > new_list[4]:
# 变道到车轮压线时间
Time_from_lighting_to_pressing_line = new_data[f'{direction}_time'][i][lane_list[0]] - new_data[f'{direction}_time'][i][0]
Line_pressing_data = [self.filename,speed,max_spd,min_spd,Time_from_lighting_to_pressing_line, self.roadType]
if Line_pressing_data not in Line_pressing:
Line_pressing.append(Line_pressing_data)
return_data = 1
return return_data
def func3(key,i,speed,max_spd,min_spd,direction):
result_dict[key][direction][0] += 1
this_time = new_data['{}_time'.format(direction)][i][-1] - new_data['{}_time'.format(direction)][i][0]
#################################
this_index = index_number(time_1,this_time)
new_result_dict[key][this_index] += 1
#################################
mysql_data_sql = [self.filename,direction, speed,max_spd,min_spd, this_time, self.roadType]
if mysql_data_sql not in mysql_data:
mysql_data.append(mysql_data_sql)
#################################
result_dict[key][direction][1].append(this_time)
# 数据规整完成， 长度一致，无变化
for i in range(len(new_data['spd'])):
judgment_basis = 0
for j in new_data['wheel'][i]:
if j > 0.1:
judgment_basis = 1
break
if judgment_basis == 0:
try:
# 车速计算
speed = int(sum(new_data['spd'][i]))/len(new_data['spd'][i]) * 3.6
# 最大车速留档
max_spd = int(max(new_data['spd'][i])) * 3.6
# 最小车速留档
min_spd = int(min(new_data['spd'][i])) * 3.6
except IndexError:
break
else:
if speed >=80:
if 1 in new_data['left'][i] and 1 not in new_data['right'][i]:
# 判断车道线情况
# Time_from_lighting_to_pressing_line 为开启变道灯到压线的时间
# 这里的判断逻辑需要重新计算， 考虑变道中跳变仅存在一次，并且确实变道完成
# 如：
# 1.跨过车道线完成跳变之后， 继续变动完成变道操作
# 2.跨过车到线之后的数据迅速达到正常车位， 即车道线扣除车身之后最大值应该为60公分，否则视为未变道成功， 需要继续查看后续数据来判断
lane = func2(i,'left',speed,max_spd,min_spd)
# 车道线检测
if lane == 1:
func3('80-100',i,speed,max_spd,min_spd,'left')
elif 1 not in new_data['left'][i] and 1 in new_data['right'][i]:
lane = func2(i,'right',speed,max_spd,min_spd)
if lane == 1:
func3('80-100',i,speed,max_spd,min_spd,'right')
else:
continue
elif speed >=60:
if 1 in new_data['left'][i] and 1 not in new_data['right'][i]:
# 判断车道线情况
lane = func2(i,'left',speed,max_spd,min_spd)
if lane == 1:
func3('60-80',i,speed,max_spd,min_spd,'left')
elif 1 not in new_data['left'][i] and 1 in new_data['right'][i]:
# 判断车道线情况
lane = func2(i,'right',speed,max_spd,min_spd)
if lane == 1:
func3('60-80',i,speed,max_spd,min_spd,'right')
else:
continue
elif speed >=40:
if 1 in new_data['left'][i] and 1 not in new_data['right'][i]:
# 判断车道线情况
lane = func2(i,'left',speed,max_spd,min_spd)
if lane == 1:
func3('40-60',i,speed,max_spd,min_spd,'left')
elif 1 not in new_data['left'][i] and 1 in new_data['right'][i]:
# 判断车道线情况
lane = func2(i,'right',speed,max_spd,min_spd)
if lane == 1:
func3('40-60',i,speed,max_spd,min_spd,'right')
else:
continue
elif speed > 0:
if 1 in new_data['left'][i] and 1 not in new_data['right'][i]:
# 判断车道线情况
lane = func2(i,'left',speed,max_spd,min_spd)
if lane == 1:
func3('0~40',i,speed,max_spd,min_spd,'left')
elif 1 not in new_data['left'][i] and 1 in new_data['right'][i]:
# 判断车道线情况
lane = func2(i,'right',speed,max_spd,min_spd)
if lane == 1:
func3('0~40',i,speed,max_spd,min_spd,'right')
# result_dict['0~40']['right'][1].append(this_time)
else:
continue
else:
continue
this_odometer = odometer.samples.tolist()
this_time = odometer.timestamps.tolist()
odo = this_odometer[-1] - this_odometer[0]
t = this_time[-1] - this_time[0]
global all_odometer
all_odometer += odo
global all_time
all_time += t
self.close_file()
return new_result_dict
finally:
self.close_file()
def generate_test_file(tmpdir, version="4.10"):
mdf = MDF(version=version)
if version <= "3.30":
filename = Path(tmpdir) / f"big_test_{version}.mdf"
else:
filename = Path(tmpdir) / f"big_test_{version}.mf4"
if filename.exists():
return filename
t = np.arange(cycles, dtype=np.float64)
cls = v4b.ChannelConversion if version >= "4.00" else v3b.ChannelConversion
# no conversion
sigs = []
for i in range(channels_count):
sig = Signal(
np.ones(cycles, dtype=np.uint64) * i,
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
conversion=None,
comment="Unsigned int 16bit channel {}".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# linear
sigs = []
for i in range(channels_count):
conversion = {
"conversion_type": v4c.CONVERSION_TYPE_LIN
if version >= "4.00"
else v3c.CONVERSION_TYPE_LINEAR,
"a": float(i),
"b": -0.5,
}
sig = Signal(
np.ones(cycles, dtype=np.int64),
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
conversion=cls(**conversion),
comment="Signed 16bit channel {} with linear conversion".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# algebraic
sigs = []
for i in range(channels_count):
conversion = {
"conversion_type": v4c.CONVERSION_TYPE_ALG
if version >= "4.00"
else v3c.CONVERSION_TYPE_FORMULA,
"formula": "{} * sin(X)".format(i),
}
sig = Signal(
np.arange(cycles, dtype=np.int32) / 100.0,
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
conversion=cls(**conversion),
comment="Sinus channel {} with algebraic conversion".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# rational
sigs = []
for i in range(channels_count):
conversion = {
"conversion_type": v4c.CONVERSION_TYPE_RAT
if version >= "4.00"
else v3c.CONVERSION_TYPE_RAT,
"P1": 0,
"P2": i,
"P3": -0.5,
"P4": 0,
"P5": 0,
"P6": 1,
}
sig = Signal(
np.ones(cycles, dtype=np.int64),
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
conversion=cls(**conversion),
comment="Channel {} with rational conversion".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# string
sigs = []
encoding = "latin-1" if version < "4.00" else "utf-8"
for i in range(channels_count):
sig = [
"Channel {} sample {}".format(i, j).encode(encoding) for j in range(cycles)
]
sig = Signal(
np.array(sig),
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
comment="String channel {}".format(i),
raw=True,
encoding=encoding,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# byte array
sigs = []
ones = np.ones(cycles, dtype=np.dtype("(8,)u1"))
for i in range(channels_count):
sig = Signal(
ones * i,
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
comment="Byte array channel {}".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# value to text
sigs = []
ones = np.ones(cycles, dtype=np.uint64)
conversion = {
"raw": np.arange(255, dtype=np.float64),
"phys": np.array(["Value {}".format(i).encode("ascii") for i in range(255)]),
"conversion_type": v4c.CONVERSION_TYPE_TABX
if version >= "4.00"
else v3c.CONVERSION_TYPE_TABX,
"links_nr": 260,
"ref_param_nr": 255,
}
for i in range(255):
conversion["val_{}".format(i)] = conversion[
"param_val_{}".format(i)
] = conversion["raw"][i]
conversion["text_{}".format(i)] = conversion["phys"][i]
conversion["text_{}".format(255)] = "Default"
for i in range(channels_count):
sig = Signal(
ones * i,
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
comment="Value to text channel {}".format(i),
conversion=cls(**conversion),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
name = mdf.save(filename, overwrite=True)
mdf.close()
def generate_arrays_test_file(tmpdir):
version = "4.10"
mdf = MDF(version=version)
filename = Path(tmpdir) / f"arrays_test_{version}.mf4"
if filename.exists():
return filename
t = np.arange(cycles, dtype=np.float64)
# lookup tabel with axis
sigs = []
for i in range(array_channels_count):
samples = [
np.ones((cycles, 2, 3), dtype=np.uint64) * i,
np.ones((cycles, 2), dtype=np.uint64) * i,
np.ones((cycles, 3), dtype=np.uint64) * i,
]
types = [
("Channel_{}".format(i), "(2, 3)<u8"),
("channel_{}_axis_1".format(i), "(2, )<u8"),
("channel_{}_axis_2".format(i), "(3, )<u8"),
]
sig = Signal(
np.core.records.fromarrays(samples, dtype=np.dtype(types)),
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
conversion=None,
comment="Array channel {}".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# lookup tabel with default axis
sigs = []
for i in range(array_channels_count):
samples = [np.ones((cycles, 2, 3), dtype=np.uint64) * i]
types = [("Channel_{}".format(i), "(2, 3)<u8")]
sig = Signal(
np.core.records.fromarrays(samples, dtype=np.dtype(types)),
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
conversion=None,
comment="Array channel {} with default axis".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
# structure channel composition
sigs = []
for i in range(array_channels_count):
samples = [
np.ones(cycles, dtype=np.uint8) * i,
np.ones(cycles, dtype=np.uint16) * i,
np.ones(cycles, dtype=np.uint32) * i,
np.ones(cycles, dtype=np.uint64) * i,
np.ones(cycles, dtype=np.int8) * i,
np.ones(cycles, dtype=np.int16) * i,
np.ones(cycles, dtype=np.int32) * i,
np.ones(cycles, dtype=np.int64) * i,
]
types = [
("struct_{}_channel_0".format(i), np.uint8),
("struct_{}_channel_1".format(i), np.uint16),
("struct_{}_channel_2".format(i), np.uint32),
("struct_{}_channel_3".format(i), np.uint64),
("struct_{}_channel_4".format(i), np.int8),
("struct_{}_channel_5".format(i), np.int16),
("struct_{}_channel_6".format(i), np.int32),
("struct_{}_channel_7".format(i), np.int64),
]
sig = Signal(
np.core.records.fromarrays(samples, dtype=np.dtype(types)),
t,
name="Channel_{}".format(i),
unit="unit_{}".format(i),
conversion=None,
comment="Structure channel composition {}".format(i),
raw=True,
)
sigs.append(sig)
mdf.append(sigs, common_timebase=True)
name = mdf.save(filename, overwrite=True)
mdf.close()
class openMDF:
def __init__(self, path, roadtype=0):
self.filename = path.split('/')[-1].split('.')[0]
self.roadType = roadtype
if self.filename not in loser_file_data:
self.open_file(path)
self.task_1()
def open_file(self, path):
print('开启mf4文件')
self.mf = MDF(path)
def close_file(self):
print('关闭mf4文件')
self.mf.close()
# 统计转向灯开启时间
def task_1(self):
signal_left = '_g_PL_AD_fw_PL_AD_FCT_RunnableSppHmi_RunnableSppHmi_m_sppHmiInput_out_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_dirIndL'
signal_right = '_g_PL_AD_fw_PL_AD_FCT_RunnableSppHmi_RunnableSppHmi_m_sppHmiInput_out_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_dirIndR'
signal_speed = '_g_PL_AD_fw_PL_AD_FCT_RunnableFsm_RunnableFsm._m_fsmController._m_fip._m_displayedSpeedCalculator._m_displaySpeed._m_value'
# 轮端转角
signal_RoadWheelAngle = '_g_PL_AD_fw_VMC_VMC_FW_MvpVse_VseSes_VseSes_m_portVehicleStateEstimation_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_estimation_RoadWheelAngle_Front._m_value'
# 左航向角
Left_heading_angle_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._0_._VFC_Line_HeadingAngle'
# 左侧车道线
Left_lane_line_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._0_._VFC_Line_Dy'
# 右航向角
Right_heading_angle_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._1_._VFC_Line_HeadingAngle'
# 右侧车道线
Right_lane_line_signal = '_g_GAC_A18_NET_net_apl_g_netRunnable_rbCanRxLD_serializer_m_CNetVFC_Line_SenderPort_1_local.TChangeableMemPool._._._m_arrayPool._0_._elem._m_vfc_lineInformation._1_._VFC_Line_Dy'
# vxvRef
vxvRef_signal = '_g_PL_AD_fw_DACoreCyclic_HV_PerPmeRunnable_PerPmeRunnable_m_pmePort_out_local.TChangeableMemPool._._._m_arrayPool._0_._elem._vxvRef_sw'
odometer_signal = 'ICM_TotalOdometer'
# Line ID 255
left_line_signal = 'VFC_Line01_LineID'
right_line_signal = 'VFC_Line02_LineID'
if self.mf:
try:
left = self.mf.get(signal_left)
right = self.mf.get(signal_right)
speed = self.mf.get(signal_speed)
RoadWheelAngle = self.mf.get(
signal_RoadWheelAngle).samples.tolist()
Left_heading_angle = self.mf.get(
Left_heading_angle_signal).samples.tolist()
Left_lane_line = self.mf.get(
Left_lane_line_signal).samples.tolist()
Right_heading_angle = self.mf.get(
Right_heading_angle_signal).samples.tolist()
Right_lane_line = self.mf.get(
Right_lane_line_signal).samples.tolist()
vxvRef = self.mf.get(vxvRef_signal)
odometer = self.mf.get(odometer_signal)
left_line = self.mf.get(left_line_signal).samples.tolist()
right_line = self.mf.get(right_line_signal).samples.tolist()
except Exception as e:
print(e)
loser_file_data.append(self.filename)
else:
lane = 0
for i in range(len(Left_lane_line) - 1):
if abs((Left_lane_line[i] - Left_lane_line[i + 1]) *
0.015625 - 32) >= 2.4:
lane = 1
break
if lane == 0:
return
else:
def func(data):
num = data.samples.tolist()[0]
index_list = [0]
for index in range(len(data.samples.tolist())):
if num != data.samples.tolist()[index]:
num = data.samples.tolist()[index]
index_list.append(index)
if len(data.samples.tolist()) - 1 not in index_list:
index_list.append(len(data.samples.tolist()) - 1)
return index_list