"""
**INPUT FILE FORMAT**
The file format consists of a one-row data header and subsequent data rows.
The data represent the percentage of engines complying with the new standard, associated with standardyear_id, and in
the given year, indicated by the column headers. In the table below, all engines shown comply with the 2027 standards
in 2027 (and continue to do so) while no engines meet the 2031 standards in 2027 since 2027 engines would be sold prior
to the 2031 standards being implemented. However, all 2031 engines comply with 2031 and later standards.
File Type
comma-separated values (CSV)
Sample Data Columns
.. csv-table::
:widths: auto
optionID,regClassName,regClassID,FuelName,fuelTypeID,standardyear_id,2027,2031
0,LHD45,42,Gasoline,1,2027,1,1
0,LHD45,42,Diesel,2,2027,1,1
0,LHD45,42,CNG,3,2027,1,1
0,LHD45,42,Gasoline,1,2031,,1
0,LHD45,42,Diesel,2,2031,,1
0,LHD45,42,CNG,3,2031,,1
Data Column Name and Description
:optionID:
The option or alternative number.
:regClassName:
The MOVES regulatory class name corresponding to the regClassID.
:regClassID:
The MOVES regClass ID, an integer.
:FuelName:
The MOVES fuel type name corresponding to the fuelTypeID.
:fuelTypeID:
The MOVES fuel type ID, an integer, where 1=Gasoline, 2=Diesel, etc.
:standardyear_id:
The year in which a new standard or provision (for which a corresponding cost exists) is implemented.
:2027:
The share of engines meeting a new standard that begins in the standardyear_id.
:2031:
The share of engines meeting a new standard that begins in the standardyear_id.
----
**CODE**
"""
import sys
import pandas as pd
from bca_tool_code.general_input_modules.general_functions import read_input_file
from bca_tool_code.general_input_modules.input_files import InputFiles
[docs]class TechPenetrations:
"""
The TechPenetrations class reads the tech penetrations file and provides methods to query its contents.
"""
[docs] def __init__(self):
self._dict = dict()
self.start_years = list()
self.value_name = 'techpen'
self.unit_id = None
[docs] def init_from_file(self, filepath, unit_id):
"""
Parameters:
filepath: Path to the specified file.\n
unit_id: str; 'engine_id' or 'vehicle_id'.
Returns:
Reads file at filepath; converts monetized values to analysis dollars (if applicable); creates a dictionary
and other attributes specified in the class __init__.
"""
df = read_input_file(filepath, skiprows=1)
self.unit_id = unit_id
if unit_id == 'engine_id':
df.insert(0,
unit_id,
pd.Series(zip(
df['regClassID'],
df['fuelTypeID']
)))
id_vars = ['optionID', unit_id, 'regClassID', 'fuelTypeID', 'standardyear_id']
elif unit_id == 'vehicle_id':
df.insert(0,
unit_id,
pd.Series(zip(
df['sourceTypeID'],
df['regClassID'],
df['fuelTypeID']
)))
id_vars = ['optionID', unit_id, 'sourceTypeID', 'regClassID', 'fuelTypeID', 'standardyear_id']
else:
print(f'\nImproper unit_id passed to {self}')
sys.exit()
df = pd.melt(df,
id_vars=id_vars,
value_vars=[col for col in df.columns if '20' in col],
var_name='modelyear_id',
value_name=self.value_name)
df['modelyear_id'] = pd.to_numeric(df['modelyear_id'])
self.start_years = df['modelyear_id'].unique()
key = pd.Series(zip(
df[unit_id],
df['optionID'],
df['modelyear_id'],
df['standardyear_id'],
))
df.set_index(key, inplace=True)
self._dict = df.to_dict('index')
# update input_files_pathlist if this class is used
InputFiles.update_pathlist(filepath)
[docs] def get_attribute_value(self, vehicle, standardyear_id):
"""
Parameters:
vehicle: object; an object of the Vehicle class.
standardyear_id: int; the year in which a new standard starts.
Returns:
A single tech penetration value for the given vehicle.
"""
unit_id = vehicle.engine_id
if self.unit_id == 'vehicle_id':
unit_id = vehicle.vehicle_id
option_id, modelyear_id = vehicle.option_id, vehicle.modelyear_id
year = max([int(year) for year in self.start_years if int(year) <= modelyear_id])
return self._dict[unit_id, option_id, year, standardyear_id][self.value_name]