def process_file(conn, path):
'''Parse CSV file, process data within and put to DB
Parameters
----------
conn : DBobject
Connection object to temporary database
path : str
defines path to the files
'''
#import mnemonic data and append dict to variable below
m_raw_data = apt.mnemonics(path)
#process raw data with once a day routine
returndata = once_a_day_routine(m_raw_data)
#put all data in a database that uses a condition
for key, value in returndata.items():
m = m_raw_data.mnemonic(key)
length = len(value)
mean = statistics.mean(value)
deviation = statistics.stdev(value)
dataset = (float(m.meta['start']), float(m.meta['end']), length, mean,
deviation)
sql.add_data(conn, key, dataset)
#add rest of the data to database
for identifier in mn.mnemSet_15min:
m = m_raw_data.mnemonic(identifier)
temp = []
#look for all values that fit to the given conditions
for element in m:
temp.append(float(element['value']))
#return None if no applicable data was found
if len(temp) > 2:
length = len(temp)
mean = statistics.mean(temp)
deviation = statistics.stdev(temp)
dataset = (float(m.meta['start']), float(m.meta['end']), length,
mean, deviation)
sql.add_data(conn, identifier, dataset)
elif len(temp) == 2:
dataset = (float(element['time']), float(element['time']), 1,
temp[0], 0)
sql.add_data(conn, identifier, dataset)
else:
print('No data for {}'.format(identifier))
print(temp)
del temp
def process_file(conn, path):
#import mnemonic data and append dict to variable below
m_raw_data = apt.mnemonics(path)
#process raw data with once a day routine
FW, GWX, GWY = wheelpos_routine(m_raw_data)
for key, values in FW.items():
for data in values:
sql.add_wheel_data(conn, 'INRSI_C_FWA_POSITION_{}'.format(key),
data)
for key, values in GWX.items():
for data in values:
sql.add_wheel_data(conn, 'INRSI_C_GWA_X_POSITION_{}'.format(key),
data)
for key, values in GWY.items():
for data in values:
sql.add_wheel_data(conn, 'INRSI_C_GWA_Y_POSITION_{}'.format(key),
data)
def process_file(conn, path):
'''Parse CSV file, process data within and put to DB
Parameters
----------
conn : DBobject
Connection object to temporary database
path : str
defines path to the files
'''
#import mnemonic data and append dict to variable below
m_raw_data = apt.mnemonics(path)
#process raw data with once a day routine
return_data, lamp_data = whole_day_routine(m_raw_data)
FW, GWX, GWY = wheelpos_routine(m_raw_data)
for key, values in FW.items():
for data in values:
sql.add_wheel_data(conn, 'INRSI_C_FWA_POSITION_{}'.format(key),
data)
for key, values in GWX.items():
for data in values:
sql.add_wheel_data(conn, 'INRSI_C_GWA_X_POSITION_{}'.format(key),
data)
for key, values in GWY.items():
for data in values:
sql.add_wheel_data(conn, 'INRSI_C_GWA_Y_POSITION_{}'.format(key),
data)
#put all data to a database that uses a condition
for key, value in return_data.items():
m = m_raw_data.mnemonic(key)
length = len(value)
if length > 2:
mean = statistics.mean(value)
deviation = statistics.stdev(value)
dataset = (float(m.meta['start']), float(m.meta['end']), length,
mean, deviation)
sql.add_data(conn, key, dataset)
#add rest of the data to database -> no conditions applied
for identifier in mn.mnemSet_day:
m = m_raw_data.mnemonic(identifier)
temp = []
#look for all values that fit to the given conditions
for element in m:
temp.append(float(element['value']))
#return None if no applicable data was found
if len(temp) > 2:
length = len(temp)
mean = statistics.mean(temp)
deviation = statistics.stdev(temp)
dataset = (float(m.meta['start']), float(m.meta['end']), length,
mean, deviation)
sql.add_data(conn, identifier, dataset)
else:
print('No data for {}'.format(identifier))
print(temp)
del temp
#add lamp data to database -> distiction over lamps
for key, values in lamp_data.items():
for data in values:
dataset_volt = (data[0], data[1], data[5], data[6], data[7])
dataset_curr = (data[0], data[1], data[2], data[3], data[4])
sql.add_data(conn, 'LAMP_{}_VOLT'.format(key), dataset_volt)
sql.add_data(conn, 'LAMP_{}_CURR'.format(key), dataset_curr)