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Thomas Vliagkoftis authoredThomas Vliagkoftis authored
Code owners
Assign users and groups as approvers for specific file changes. Learn more.
config.py 10.88 KiB
import secrets
import numpy as np, pandas as pd, getpass as gt
class Config(secrets.Secrets):
PUBLIC_SAVE_PATH = f'/eos/jeodpp/data/projects/LEGENT/transfer/{gt.getuser()}/'
EEA_2013 = {
'delimiter': '\t',
'encoding': 'utf-8',
'year': 2013,
'column_names': [
'id',
'country',
'oem_group',
'oem_mh',
'oem_manufacturer',
'oem_ms',
'type_approval_number',
'type',
'variant',
'version',
'oem_make',
'commercial_name',
'vehicle_category_type_approved',
'registrations',
'co2_nedc_declared',
'mass_in_running_order',
'wheel_base',
'axle_width_steering_axle',
'axle_width_other_axle',
'fuel_type',
'fuel_mode',
'engine_capacity',
'engine_max_power',
'electric_energy_consumption',
'eco_innovative_technology',
'eco_co2_reduction_nedc'
]
}
EEA_2014 = {
'delimiter': '\t',
'encoding': 'utf-8',
'year': 2014,
'column_names': [
'id',
'country',
'oem_group',
'oem_mh',
'oem_manufacturer',
'oem_ms',
'type_approval_number',
'type',
'variant',
'version',
'oem_make',
'commercial_name',
'vehicle_category_type_approved',
'registrations',
'co2_nedc_declared',
'mass_in_running_order',
'wheel_base',
'axle_width_steering_axle',
'axle_width_other_axle',
'fuel_type',
'fuel_mode',
'engine_capacity',
'engine_max_power',
'electric_energy_consumption',
'eco_innovative_technology',
'eco_co2_reduction_nedc'
]
}
EEA_2015 = {
'delimiter': '\t',
'encoding': 'utf-8',
'year': 2015,
'column_names': [
'id',
'country',
'oem_group',
'oem_mh',
'oem_manufacturer',
'oem_ms',
'type_approval_number',
'type',
'variant',
'version',
'oem_make',
'commercial_name',
'vehicle_category_type_approved',
'registrations',
'co2_nedc_declared',
'mass_in_running_order',
'wheel_base',
'axle_width_steering_axle',
'axle_width_other_axle',
'fuel_type',
'fuel_mode',
'engine_capacity',
'engine_max_power',
'electric_energy_consumption',
'eco_innovative_technology',
'eco_co2_reduction_nedc'
]
}
EEA_2016 = {
'delimiter': '\t',
'encoding': 'utf-16',
'year': 2016,
'column_names': [
'id',
'country',
'oem_group',
'oem_mh',
'oem_manufacturer',
'oem_ms',
'type_approval_number',
'type',
'variant',
'version',
'oem_make',
'commercial_name',
'vehicle_category_type_approved',
'registrations',
'co2_nedc_declared',
'mass_in_running_order',
'wheel_base',
'axle_width_steering_axle',
'axle_width_other_axle',
'fuel_type',
'fuel_mode',
'engine_capacity',
'engine_max_power',
'electric_energy_consumption',
'eco_innovative_technology',
'eco_co2_reduction_nedc'
]
}
EEA_2017 = {
'delimiter': '\t',
'encoding': 'utf-16',
'year': 2017,
'column_names': [
'id',
'country',
'oem_group',
'vehicle_family_id',
'oem_mh',
'oem_manufacturer',
'oem_ms',
'type_approval_number',
'type',
'variant',
'version',
'oem_make',
'commercial_name',
'vehicle_category_type_approved',
'vehicle_category_register',
'mass_in_running_order',
'mass_wltp',
'co2_nedc_declared',
'co2_wltp_declared',
'wheel_base',
'axle_width_steering_axle',
'axle_width_other_axle',
'fuel_type',
'fuel_mode',
'engine_capacity',
'engine_max_power',
'electric_energy_consumption',
'eco_innovative_technology',
'eco_co2_reduction_nedc',
'eco_co2_reduction_wltp',
'deviation_factor',
'verification_factor',
'registrations'
]
}
EEA_2018 = {
'delimiter': '\t',
'encoding': 'utf-8',
'year': 2018,
'column_properties': pd.DataFrame([
['id', 'ID', np.int32],
['country', 'MS', np.object],
['oem_group', 'Mp', np.object],
['vehicle_family_id', 'VFN', np.object],
['oem_mh', 'Mh', np.object],
['oem_manufacturer', 'Man', np.object],
['oem_ms', 'MMS', np.object],
['type_approval_number', 'Tan', np.object],
['type', 'T', np.object],
['variant', 'Va', np.object],
['version', 'Ve', np.object],
['oem_make', 'Mk', np.object],
['commercial_name', 'Cn', np.object],
['vehicle_category_type_approved', 'Ct', np.object],
['vehicle_category_register', 'Cr', np.object],
['mass_in_running_order', 'm (kg)', np.float16],
['mass_wltp', 'Mt', np.float16],
['co2_nedc_declared', 'Enedc (g/km)', np.float16],
['co2_wltp_declared', 'Ewltp (g/km)', np.float16],
['wheel_base', 'W (mm)', np.float16],
['axle_width_steering_axle', 'At1 (mm)', np.float16],
['axle_width_other_axle', 'At2 (mm)', np.float16],
['fuel_type', 'Ft', np.object],
['fuel_mode', 'Fm', np.object],
['engine_capacity', 'ec (cm3)', np.float16],
['engine_max_power', 'ep (KW)', np.float16],
['electric_energy_consumption', 'z (Wh/km)', np.float16],
['eco_innovative_technology', 'It', np.object],
['eco_co2_reduction_nedc', 'Ernedc (g/km)', np.float16],
['eco_co2_reduction_wltp', 'Erwltp (g/km)', np.float16],
['deviation_factor', 'De', np.float16],
['verification_factor', 'Vf', np.float16],
['registrations', 'r', np.int32]
], columns=['db_names', 'names', 'coltype'])
}
EEA_2019 = {
'delimiter': ',',
'encoding': 'utf-8',
'year': 2019,
'column_properties': pd.DataFrame([
['id', 'ID', np.int32],
['country', 'Country', np.object],
['vehicle_family_id', 'VFN', np.object],
['oem_group', 'Mp', np.object],
['oem_mh', 'Mh', np.object],
['oem_manufacturer', 'Man', np.object],
['oem_ms', 'MMS', np.object],
['type_approval_number', 'Tan', np.object],
['type', 'T', np.object],
['variant', 'Va', np.object],
['version', 'Ve', np.object],
['oem_make', 'Mk', np.object],
['commercial_name', 'Cn', np.object],
['vehicle_category_type_approved', 'Ct', np.object],
['vehicle_category_register', 'Cr', np.object],
['registrations', 'r', np.int32],
['mass_in_running_order', 'm (kg)', np.float16],
['mass_wltp', 'Mt', np.float16],
['co2_nedc_declared', 'Enedc (g/km)', np.float16],
['co2_wltp_declared', 'Ewltp (g/km)', np.float16],
['wheel_base', 'W (mm)', np.float16],
['axle_width_steering_axle', 'At1 (mm)', np.float16],
['axle_width_other_axle', 'At2 (mm)', np.float16],
['fuel_type', 'Ft', np.object],
['fuel_mode', 'Fm', np.object],
['engine_capacity', 'ec (cm3)', np.float16],
['engine_max_power', 'ep (KW)', np.float16],
['electric_energy_consumption', 'z (Wh/km)', np.float16],
['eco_innovative_technology', 'IT', np.object],
['eco_co2_reduction_nedc', 'Ernedc (g/km)', np.float16],
['eco_co2_reduction_wltp', 'Erwltp (g/km)', np.float16],
['deviation_factor', 'De', np.float16],
['verification_factor', 'Vf', np.float16],
['status', 'Status', np.object],
['year', 'year', np.int32],
['electric_range', 'Electric range (km)', np.float16]
], columns=['db_names', 'names', 'coltype'])
}
EEA_2020 = {
'delimiter': ',',
'encoding': 'utf-8',
'year': 2020,
'column_properties': pd.DataFrame([
['id', 'ID', np.int32],
['country', 'Country', np.object],
['vehicle_family_id', 'VFN', np.object],
['oem_group', 'Mp', np.object],
['oem_mh', 'Mh', np.object],
['oem_manufacturer', 'Man', np.object],
['oem_ms', 'MMS', np.object],
['type_approval_number', 'Tan', np.object],
['type', 'T', np.object],
['variant', 'Va', np.object],
['version', 'Ve', np.object],
['oem_make', 'Mk', np.object],
['commercial_name', 'Cn', np.object],
['vehicle_category_type_approved', 'Ct', np.object],
['vehicle_category_register', 'Cr', np.object],
['registrations', 'r', np.int32],
['mass_in_running_order', 'm (kg)', np.float16],
['mass_wltp', 'Mt', np.float16],
['co2_nedc_declared', 'Enedc (g/km)', np.float16],
['co2_wltp_declared', 'Ewltp (g/km)', np.float16],
['wheel_base', 'W (mm)', np.float16],
['axle_width_steering_axle', 'At1 (mm)', np.float16],
['axle_width_other_axle', 'At2 (mm)', np.float16],
['fuel_type', 'Ft', np.object],
['fuel_mode', 'Fm', np.object],
['engine_capacity', 'ec (cm3)', np.float16],
['engine_max_power', 'ep (KW)', np.float16],
['electric_energy_consumption', 'z (Wh/km)', np.float16],
['eco_innovative_technology', 'IT', np.object],
['eco_co2_reduction_nedc', 'Ernedc (g/km)', np.float16],
['eco_co2_reduction_wltp', 'Erwltp (g/km)', np.float16],
['deviation_factor', 'De', np.float16],
['verification_factor', 'Vf', np.float16],
['status', 'Status', np.object],
['year', 'year', np.int32],
['electric_range', 'Electric range (km)', np.float16]
], columns=['db_names', 'names', 'coltype'])
}