"""
**INPUT FILE FORMAT**
The file format consists of a one-row data header and subsequent data rows.
The data represent indirect cost contributors to the Retail Price Equivalent markup factor. The contributors are used
to calculate the indirect cost portion of the tech cost. The data also represent the scalers, the scaling metric and the
number of years of applicability that can be applied to each individual contribution factor to estimate increases due
to the associated policy option.
File Type
comma-separated values (CSV)
Sample Data Columns
.. csv-table::
:widths: auto
optionID,fuelTypeID,Markup_Factor,Value,Scaler,Scaled_by,NumberOfYears
0,1,Warranty,0,None,None,
0,1,RnD,0.05,None,None,
0,1,Other,0.36,None,None,
0,1,Profit,0.06,None,None,
Data Column Name and Description
:optionID:
The option or alternative number.
:fuelTypeID:
The MOVES fuel type ID, an integer, where 1=Gasoline, 2=Diesel, etc.
:Markup_Factor:
The indirect cost contribution factor name.
:Value:
The indirect cost contribution factor value.
:Scaler:
The scaling approach to use, whether absolute or relative.
:Scaled_by:
The policy provision that impacts the scaling of the contribution factor, i.e., 'Warranty' or 'R&D'.
:NumberOfYears:
The number of year, an integer, to including the scaling factor.
----
**CODE**
"""
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 Markups:
"""
The Markups class reads the Markups input file and provides methods to query its contents.
"""
[docs] def __init__(self):
self._dict = dict()
self.contribution_factors = dict()
self.markup_factor_names = list()
[docs] def init_from_file(self, filepath):
"""
Parameters:
filepath: Path to the specified file.
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, usecols=lambda x: 'Notes' not in x)
key = pd.Series(
zip(
df['fuelTypeID'],
df['optionID'],
df['Markup_Factor'],
))
df.set_index(key, inplace=True)
self.markup_factor_names = [arg for arg in df['Markup_Factor'].unique()]
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, key, attribute_name):
"""
Parameters:
key: tuple; (fueltype_id, option_id, markup_factor), where markup_factor is, e.g., 'Warranty', RnD'.\n
attribute_name: str; the attribute name for which a value is sought.
Returns:
A single value associated with the attribute name for the given key.
"""
return self._dict[key][attribute_name]
[docs] def get_attribute_values(self, key):
"""
Parameters:
key: tuple; (fueltype_id, option_id, markup_factor), where markup_factor is, e.g., 'Warranty', 'RnD'.
Returns:
A list of values for the given key.
"""
values_list = list()
for attribute_name in ['Value', 'Scaler', 'Scaled_by', 'NumberOfYears']:
values_list.append(self.get_attribute_value(key, attribute_name))
return values_list
[docs] def update_contribution_factors(self, vehicle, update_dict):
"""
Parameters:
vehicle: object; a vehicle object of the Vehicles class.\n
update_dict: Dictionary; represents the attribute-value pairs to be updated.
Returns:
Updates the object dictionary with each attribute updated with the appropriate value.
"""
key = vehicle.vehicle_id, vehicle.engine_id, vehicle.option_id, vehicle.modelyear_id
if key in self.contribution_factors:
for attribute_name, attribute_value in update_dict.items():
self.contribution_factors[key][attribute_name] = attribute_value
else:
self.contribution_factors.update({key: {}})
for attribute_name, attribute_value in update_dict.items():
self.contribution_factors[key].update({attribute_name: attribute_value})
[docs] def get_contribution_factors_data(self, key, attribute_name):
"""
Parameters:
key: tuple; the object contribution_factors dictionary key.
attribute_name: str; the name of the attribute (data) sought.
Returns:
The value associated with the passed attribute name.
"""
return self.contribution_factors[key][attribute_name]