def read_data():
tc = [
'TC13-130', 'TC13-133', 'TC13-207', 'TC16-111', 'TC16-113', 'TC16-104',
'TC16-104', 'TC12-212', 'TC17-113', 'TC15-111', 'TC14-016', 'TC12-004',
'TC17-205', 'TC16-127', 'TC18-110', 'TC16-129', 'TC18-105', 'TC17-207',
'TC16-140'
]
channels = ['14CHST0000Y7ACXC', '16CHST0000Y7ACXC']
tests, _ = get_test_info()
tc = [i for i in tc if i in tests]
_, t, chdata = initialize('P:\\', channels, cutoff, tc=tc)
return t, chdata
'11NECKUP00THFOXA', '11NECKUP00THFOZA', '11NECKLO00THFOXA',
'11NECKLO00THFOZA', '11HEAD003SH3ACRA', '11CHST003SH3ACRC',
'11HEAD0000H3ACXA', '11HEAD0000H3ACRA', '11CHST0000H3ACXC',
'11CHST0000H3ACRC', '11PELV0000H3ACXA', '11HICR0015H3ACRA',
'11CHST0000H3DSXB', '11FEMRLE00H3FOZB', '11FEMRRI00H3FOZB',
'11SEBE0000B3FO0D', '11SEBE0000B6FO0D', '11NECKUP00H3FOZA',
'13HEAD0000HFACXA', '13HEAD0000HFACRA', '13NECKUP00HFFOXA',
'13NECKUP00HFFOYA', '13NECKUP00HFFOZA', '13NECKUP00HFFORA',
'13CHST0000HFACXC', '13CHST0000HFACRC', '13CHST0000HFDSXB',
'13LUSP0000HFFOXA', '13ILACLE00HFFOXA', '13ILACRI00HFFOXA',
'13PELV0000HFACXA', '13FEMRLE00HFFOZB', '13FEMRRI00HFFOZB',
'13SEBE0000B3FO0D', '13SEBE0000B6FO0D', '13HEAD003SHFACRA',
'13CHST003SHFACRC', '13BRIC0000HFAV0D'
]
table, t, chdata = initialize(directory, channels, cutoff, verbose=False)
#%% preprocessing
preprocess_channels = [
'11CHSTLEUPTHDSXB', '11CHSTRIUPTHDSXB', '11CHSTLELOTHDSXB',
'11CHSTRILOTHDSXB'
]
chdata[preprocess_channels] = chdata[preprocess_channels].applymap(
lambda x: x - x[0])
if 'TC13-006' in chdata.index:
chdata.at['TC13-006',
'11SEBE0000B6FO0D'] = -chdata.at['TC13-006', '11SEBE0000B6FO0D']
chdata.at['TC18-030', '10SIMELE00INACXD'] = np.tile(np.nan, len(cutoff))
#%% feature extraction
cutoff = range(100, 1600)
channels = [
'11HICR0000TH00RA', '11HICR0036TH00RA', '11HICR0015TH00RA',
'11HICR0000THACRA', '11HICR0036THACRA', '11HICR0015THACRA',
'11BRIC0000THAV0D', '11HEAD0000THACXA', '11HEAD0000THACZA',
'11HEAD0000THACRA', '11NECKUP00THFOXA', '11NECKUP00THFOYA',
'11NECKUP00THFOZA', '11NECKLO00THFOXA', '11NECKLO00THFOYA',
'11NECKLO00THFOZA', '11CLAVLEOUTHFOXA', '11CLAVLEINTHFOXA',
'11SPIN0100THACXC', '11SPIN0100THACYC', '11CHST0000THACXC',
'11CHST0000THACYC', '11CHSTLEUPTHDSXB', '11CHSTRILOTHDSXB',
'11CHSTLELOTHDSXB', '11CHSTRIUPTHDSXB', '11THSP0100THAVZA',
'11PELV0000THACXA', '11FEMRLE00THFOZB', '11FEMRRI00THFOZB'
]
table, t, chdata = initialize(directory, channels, cutoff)
#%% feature extraction
def get_all_features(write_csv=False):
i_to_t = get_i_to_t(t)
feature_funs = {
'Min_': [get_min],
'Max_': [get_max],
'Tmin_': [get_argmin, i_to_t],
'Tmax_': [get_argmax, i_to_t]
}
features = pd.concat(chdata.chdata.get_features(feature_funs).values(),
axis=1,
sort=True)
'11PELV0000H3ACXA',
'11PELV0000H3ACZA',
'11PELV0000H3ACRA',
'11HICR0015H3ACRA',
'11NIJCIPTEH300YX',
'11NIJCIPTFH300YX',
'11NIJCIPCFH300YX',
'11NIJCIPCEH300YX',
'11CHST0000H3DSXB',
'11FEMRLE00H3FOZB',
'11FEMRRI00H3FOZB',
'10SIMELE00INACXD',
'10SIMERI00INACXD']
drop = []
table, t, chdata, se_names = initialize(directory,channels,cutoff)
table = table.drop(drop)
preprocess = ['11CHSTLEUPTHDSRB','11CHSTRIUPTHDSRB','11CHSTLELOTHDSRB','11CHSTRILOTHDSRB']
chdata[preprocess] = chdata[preprocess].applymap(lambda x: x-x[0])
chdata['11CHST0000THDSRB'] = chdata[preprocess].applymap(max).max(axis=1).apply(lambda x: [x])
#chdata = chdata.drop(preprocess,axis=1)
#%%
if __name__=='__main__':
def i_to_t(i):
if not np.isnan(i):
return t[int(i)]
else:
return np.nan
'S0SLED000000VEXD'
]
query_list = [['DUMMY', ['Y2', 'Y7']],
['INSTALL', ['H1', 'H3', 'HB', 'LB', 'C1', 'B0', 'B11', 'B12']]]
exclude = [
'SE16-0343', # <- this is Summit H3
'SE16-0395',
'SE16-0399',
'SE17-0018_2'
]
table, t, chdata = initialize(directory,
channels,
cutoff,
drop=exclude,
query_list=query_list)
append = [chdata]
#%% angle stuff
displacement = get_se_angle(chdata.index)
se_angles = displacement.apply(
get_angle, axis=1).apply(lambda x: x - x[0]).rename('Angle')
abs_angles = displacement.apply(get_angle, axis=1).rename('Abs_Angle')
angle_t = displacement['Time'] / 1000
position = displacement.drop('Time', axis=1)
displacement = position.applymap(lambda x: x - x[0]).rename(lambda x: 'D' + x,
axis=1)
# shift certain signals
from PMG.COM.easyname import renameISO
from PMG.COM.arrange import sep_by_peak, t_ch_from_test_ch
directory = 'C:\\Users\\tangk\\Desktop\\AHEC EV\\'
dummy = 'H3'
cutoff = range(100, 1600)
n = 1
channels = [
'13HEAD0000HFACXA', '13CHST0000HFACXC', '13PELV0000HFACXA',
'13FEMRLE00HFFOZB', '13FEMRRI00HFFOZB', '13SEBE0000B3FO0D',
'13SEBE0000B6FO0D', '13ILACLE00HFFOXA', '13ILACRI00HFFOXA'
]
table, t, chdata, se_names = initialize(directory,
channels,
cutoff,
query='ID13==\'HF\'')
#%%
pairs = {
'Cruze': ['TC17-025', 'TC15-035'],
'Camry': ['TC15-162', 'TC17-028'],
'Civic': ['TC15-155', 'TC17-203'],
'Sentra': ['TC16-205', 'TC17-206'],
'Explorer': ['TC11-233', 'TC11-234'],
'Tiguan': ['TC15-029', 'TC15-030']
}
lp_abs = {}
up_abs = {}
for ch in channels:
chlist = [
'11HEAD0000THACXA', '11HEAD0000THACRA', '11NECKUP00THFOXA',
'11CHST0000THACXC', '11CHST0000THACRC', '11PELV0000THACXA',
'11HEAD0000H3ACXA', '11HEAD0000H3ACRA', '11NECKUP00H3FOXA',
'11CHST0000H3ACXC', '11CHST0000H3ACRC', '11PELV0000H3ACXA',
'Max_11HEAD0000THACRA', 'Max_11HEAD0000H3ACRA', 'Min_11HEAD0000THACXA',
'Min_11HEAD0000H3ACXA', 'Max_11NECKUP00THFOZA', 'Max_11NECKUP00H3FOZA',
'Max_11CHST0000THACRC', 'Max_11CHST0000H3ACRC', 'Min_11CHST0000THACXC',
'Min_11CHST0000H3ACXC', 'Min_11PELV0000THACXA', 'Min_11PELV0000H3ACXA',
'10CVEHCG0000ACXD', '10SIMELE00INACXD', '10SIMERI00INACXD'
]
chlist = list(set(chlist))
table, t, chdata = initialize('P:/Data Analysis/Projects/AHEC EV/', chlist,
range(100, 1600))
#%%
table.Type_2 = table.Type_2.fillna(table.Type)
pairs = table[table.Type.isin(['ICE']) & table.Speed.isin([48])
& table.Series_1.isin([1])].Pair.reset_index()
pairs.columns = ['ICE', 'EV']
series2 = table[table.Speed.isin([48])
& table.Series_2.isin([1])].iloc[:, :10].reset_index()
s_left = series2.loc[series2.Type.isin(['ICE']),
['TC', 'Model', 'Counterpart']]
s_right = series2.loc[:, ['TC', 'Model', 'Counterpart']]
pairs2 = s_left.merge(s_right, left_on='Model',
right_on='Counterpart').loc[:, ['TC_x', 'TC_y']]
pairs2.columns = ['ICE', 'EV']
'11CHST0000H3ACXC',
'11CHST0000H3ACYC',
'11CHST0000H3ACZC',
'11PELV0000H3ACXA',
'11HICR0015H3ACRA',
'11CHST0000H3DSXB',
'11FEMRLE00H3FOZB',
'11FEMRRI00H3FOZB',
'11SEBE0000B3FO0D',
'11SEBE0000B6FO0D',
'11NIJCIPTEH300YX',
'11NIJCIPTFH300YX',
'11NIJCIPCFH300YX',
'11NIJCIPCEH300YX']
table, t, chdata = initialize(directory,channels,cutoff,query='Subset==\'PAIRED\'')
preprocess = ['11CHSTLEUPTHDSXB','11CHSTRIUPTHDSXB','11CHSTLELOTHDSXB','11CHSTRILOTHDSXB']
chdata[preprocess] = chdata[preprocess].applymap(lambda x: x-x[0])
if 'TC13-006' in chdata.index:
chdata['11SEBE0000B6FO0D']['TC13-006'] = -chdata['11SEBE0000B6FO0D']['TC13-006']
#chdata['11CHST0000THDSXB'] = chdata[preprocess]
#%% define testing parameters
#if __init__ == '__main__':
# to_JSON = {'project_name': 'THOR_vs_H3_Driver',
# 'directory' : directory,
# 'cat' : {'THOR_paired' : list(table.query('Subset==\'PAIRED\' and ID11==\'TH\'').index),
# 'H3_paired' : list(table.query('Subset==\'PAIRED\' and ID11==\'H3\'').index),
'11CHSTLELOTHDSXB',
'11HEAD0000H3ACXA',
'11HEAD0000H3ACZA',
'11HEAD0000H3ACRA',
'11NECKUP00H3FOZA',
'11NECKUP00H3MOYB',
'11CHST0000H3ACXC',
'11PELV0000H3ACXA',
'11HICR0015H3ACRA',
'11CHST0000H3DSXB',
'11FEMRLE00H3FOZB',
'11FEMRRI00H3FOZB',
'11SEBE0000B3FO0D',
'11SEBE0000B6FO0D']
table, t, chdata, se_names = initialize(directory,channels,cutoff,query='SUBSET==\'PAIRED\'')
preprocess = ['11CHSTLEUPTHDSXB','11CHSTRIUPTHDSXB','11CHSTLELOTHDSXB','11CHSTRILOTHDSXB']
chdata[preprocess] = chdata[preprocess].applymap(lambda x: x-x[0])
#chdata['11CHST0000THDSXB'] = chdata[preprocess]
#%%
for ch in channels:
if len(chdata[ch].iloc[0])==1 or 'DSX' in ch or 'H3' in ch:
continue
fig, axs = plt.subplots(nrows=1,ncols=2,sharey=True,figsize=(15,5))
if 'TH' in ch:
th = chdata[ch]
h3 = chdata[ch[:10] + 'H3' + ch[12:]]
else: