"""
The methods and classes that are not defined in Python are defined in C++ py_main.cpp.
"""
from ..lib.BTLS import Vehicle, _Vehicle
import pandas as pd
__all__ = ["vehicle_list_to_df", "df_to_vehicle_list"]
[docs]
def vehicle_list_to_df(vehicle_list: list[_Vehicle]) -> pd.DataFrame:
"""
Convert a list of Vehicle objects to a pandas DataFrame.
Parameters
----------
vehicle_list : list[_Vehicle]\n
List of Vehicle objects. \n
_Vehicle and Vehicle are the same.
Returns
-------
pd.DataFrame\n
A DataFrame containing all the vehicle properties.
"""
if not all(isinstance(vehicle, (Vehicle, _Vehicle)) for vehicle in vehicle_list):
raise ValueError("All vehicles in the list must be of type Vehicle.")
data_list = [vehicle.get_all_properties() for vehicle in vehicle_list]
column_names = [
"Head", # vehicle id
"Year",
"Month",
"Day",
"Hour",
"Min",
"Sec", # the only float value related to time
"NoAxles",
"NoAxleGroups",
"GVW", # in kN
"Velocity", # in m/s
"Length", # in m
"Lane", # 1-based local lane number
"Dir", # 1 or 2
"Trns", # in meters, transverse position on lane
"AxleWeights", # list, all in kN
"AxleSpacings", # list, all in m
"AxleWidths", # list, all in m, 1.98m by default
]
return pd.DataFrame(data_list, columns=column_names)
[docs]
def df_to_vehicle_list(df: pd.DataFrame) -> list[Vehicle]:
"""
Convert a pandas DataFrame to a list of Vehicle objects.
Parameters
----------
df : pd.DataFrame\n
A DataFrame containing vehicle properties.
Returns
-------
list[Vehicle]\n
A list of Vehicle objects.
"""
if not all(
col in df.columns
for col in [
"Head",
"Year",
"Month",
"Day",
"Hour",
"Min",
"Sec",
"NoAxles",
"NoAxleGroups",
"GVW",
"Velocity",
"Length",
"Lane",
"Dir",
"Trns",
"AxleWeights",
"AxleSpacings",
"AxleWidths",
]
):
raise ValueError("DataFrame must contain all the required columns.")
# Recalculate the GVW and Length just in case if AxleWeights and AxleSpacings are modified. The NoAxles should not be modified; instead, a new vehicle should be created if user wants to change the number of axles.
df["GVW"] = df["AxleWeights"].apply(lambda x: sum(x))
df["Length"] = df["AxleSpacings"].apply(lambda x: sum(x))
vehicle_list = []
for row in df.itertuples(index=False):
vehicle = Vehicle(0) # 0 does not matter
vehicle.set_all_properties(tuple(row))
vehicle_list.append(vehicle)
return vehicle_list
