"""
**INPUT FILE FORMAT**
The file format consists of a one-row data header and subsequent data rows.
The data represent the warranty provisions for the associated optionID.
File Type
comma-separated values (CSV)
Sample Data Columns
.. csv-table::
:widths: auto
optionID,regClassName,regClassID,fuelTypeID,period_id,2024,2027,2031
0,HHD8,47,1,Miles,50000,50000,50000,
0,HHD8,47,1,Age,5,5,5,
0,HHD8,47,3,Hours,,,,
1,HHD8,47,3,Miles,100000,450000,450000,
1,HHD8,47,3,Age,5,7,7,
1,HHD8,47,1,Hours,,8000,8000,
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.
:fuelTypeID:
The MOVES fuel type ID, an integer, where 1=Gasoline, 2=Diesel, etc.
:period_id:
The identifier, i.e., 'Miles', 'Age' or 'Hours'
:2024:
The miles/age/hours provision for MY2024 and later.
:2027:
The miles/age/hours provision for MY2027 and later.
:2031:
The miles/age/hours provision for MY2031 and later.
----
**CODE**
"""
import pandas as pd
import numpy as np
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 Warranty:
"""
The Warranty class reads the warranty input file and provides methods to query its contents.
"""
[docs] def __init__(self):
self._dict = dict()
self.start_years = list()
self.value_name = 'period_value'
[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)
df = df.replace(np.nan, None)
# value_name = 'period_value'
df = pd.melt(df,
id_vars=['optionID', 'regClassName', 'regClassID', 'fuelTypeID', 'period_id'],
value_vars=[col for col in df.columns if '20' in col],
var_name='start_year',
value_name=self.value_name
)
df['start_year'] = pd.to_numeric(df['start_year'])
self.start_years = df['start_year'].unique()
key = pd.Series(
zip(
zip(
df['regClassID'],
df['fuelTypeID']),
df['optionID'],
df['start_year'],
df['period_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, key, attribute_name):
"""
Parameters:
key: tuple; ((regclass_id, fueltype_id), option_id, modelyear_id, period_id), where period_id is 'Miles' or 'Age' or 'Hours'.\n
attribute_name: str; the attribute name for which a value is sought (e.g., period_value).
Returns:
A single value associated with the period_id for the given key.
"""
engine_id, option_id, my_id, period_id = key
if my_id == min(self.start_years):
year = my_id
else:
year = max([int(year) for year in self.start_years if int(year) <= my_id])
new_key = (engine_id, option_id, year, period_id)
return self._dict[new_key][attribute_name]