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TCO Stock and Emissions module

t3co.tco.tco_stock_emissions

dropCols(df: pd.DataFrame) -> pd.DataFrame

This helper method drops columns if any row contains ['*']

Parameters:

Name Type Description Default
df pd.DataFrame

Input dataframe

 required

Returns:

Name Type Description
df pd.DataFrame

Output dataframe with dropped dummy columns
Source code in t3co/tco/tco_stock_emissions.py
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def dropCols(df: pd.DataFrame) -> pd.DataFrame:
    """
    This helper method drops columns if any row contains ['*']

    Args:
        df (pd.DataFrame): Input dataframe

    Returns:
        df (pd.DataFrame): Output dataframe with dropped dummy columns
    """
    if df is not None:
        droplist = [i for i in df.columns if df[i].isin(["*"]).any()]
        df = df.drop(droplist, axis=1)
    return df

stockModel(sales: pd.DataFrame, marketShares: pd.DataFrame, survival: pd.DataFrame, annualTravel: pd.DataFrame, fuelSplit: pd.DataFrame, fuelEfficiency: pd.DataFrame, emissions: pd.DataFrame, vehicleCosts: pd.DataFrame = None, travelCosts: pd.DataFrame = None, fuelCosts: pd.DataFrame = None, insuranceCosts: pd.DataFrame = None, residualCosts: pd.DataFrame = None, downtimeCosts: pd.DataFrame = None, write_files: bool = False) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]

This function generates the ownershipCosts dataframe from the dataframes for each cost category

Parameters:

Name Type Description Default
sales pd.DataFrame

Dataframe of yearly number of vehicles sales

 required
marketShares pd.DataFrame

Dataframe of yearly Market Share of selection's vocation per vehicle [veh/veh]

 required
survival pd.DataFrame

Dataframe of yearly Surviving vehicle per each vehicle [veh/veh]

 required
annualTravel pd.DataFrame

Dataframe of vehicle's vmt: Annual Travel [mi/yr]

 required
fuelSplit pd.DataFrame

Dataframe of fraction of travel using each fuel [mi/mi]

 required
fuelEfficiency pd.DataFrame

Dataframe of vehicle's yearly average fuel efficiency [mi/gge]

 required
emissions pd.DataFrame

Dataframe of vehicle's yearly average emissions

 required
vehicleCosts pd.DataFrame

Dataframe of vehicle components costs [dol]. Defaults to None.

 None
travelCosts pd.DataFrame

Dataframe of maintenance costs [dol/mi]. Defaults to None.

 None
fuelCosts pd.DataFrame

Dataframe of fuel operating costs [dol/gge]. Defaults to None.

 None
insuranceCosts pd.DataFrame

Dataframe of yearly insurance costs [dol]. Defaults to None.

 None
residualCosts pd.DataFrame

Dataframe of yearly residual costs [dol]. Defaults to None.

 None
downtimeCosts pd.DataFrame

Dataframe of yearly downtime costs [dol]. Defaults to None.

 None
write_files bool

if True, save vehicleCosts, travelCosts, fuelCosts, insuranceCosts,residualCosts, downtimeCosts . Defaults to False.

 False

Returns:

Name Type Description
stock pd.DataFrame

Dataframe of stock model of vehicles in the market
emissions pd.DataFrame

Dataframe of total emissions
ownershipCosts pd.DataFrame

Dataframe of all ownership costs for given selection
Source code in t3co/tco/tco_stock_emissions.py
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def stockModel(
    sales: pd.DataFrame,
    marketShares: pd.DataFrame,
    survival: pd.DataFrame,
    annualTravel: pd.DataFrame,
    fuelSplit: pd.DataFrame,
    fuelEfficiency: pd.DataFrame,
    emissions: pd.DataFrame,
    vehicleCosts: pd.DataFrame = None,
    travelCosts: pd.DataFrame = None,
    fuelCosts: pd.DataFrame = None,
    insuranceCosts: pd.DataFrame = None,
    residualCosts: pd.DataFrame = None,
    downtimeCosts: pd.DataFrame = None,
    write_files: bool = False,
) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
    """
    This function generates the ownershipCosts dataframe from the dataframes for each cost category

    Args:
        sales (pd.DataFrame): Dataframe of yearly number of vehicles sales
        marketShares (pd.DataFrame): Dataframe of yearly Market Share of selection's vocation per vehicle [veh/veh]
        survival (pd.DataFrame): Dataframe of yearly Surviving vehicle per each vehicle [veh/veh]
        annualTravel (pd.DataFrame): Dataframe of vehicle's vmt: Annual Travel [mi/yr]
        fuelSplit (pd.DataFrame): Dataframe of fraction of travel using each fuel [mi/mi]
        fuelEfficiency (pd.DataFrame): Dataframe of vehicle's yearly average fuel efficiency [mi/gge]
        emissions (pd.DataFrame): Dataframe of vehicle's yearly average emissions
        vehicleCosts (pd.DataFrame, optional): Dataframe of vehicle components costs [dol]. Defaults to None.
        travelCosts (pd.DataFrame, optional): Dataframe of maintenance costs [dol/mi]. Defaults to None.
        fuelCosts (pd.DataFrame, optional): Dataframe of fuel operating costs [dol/gge]. Defaults to None.
        insuranceCosts (pd.DataFrame, optional): Dataframe of yearly insurance costs [dol]. Defaults to None.
        residualCosts (pd.DataFrame, optional): Dataframe of yearly residual costs [dol]. Defaults to None.
        downtimeCosts (pd.DataFrame, optional): Dataframe of yearly downtime costs [dol]. Defaults to None.
        write_files (bool, optional): if True, save vehicleCosts, travelCosts, fuelCosts, insuranceCosts,residualCosts, downtimeCosts . Defaults to False.

    Returns:
        stock (pd.DataFrame): Dataframe of stock model of vehicles in the market
        emissions (pd.DataFrame): Dataframe of total emissions
        ownershipCosts (pd.DataFrame): Dataframe of all ownership costs for given selection

    """
    # drop columns with *
    sales = dropCols(sales)
    marketShares = dropCols(marketShares)
    survival = dropCols(survival)
    annualTravel = dropCols(annualTravel)
    fuelSplit = dropCols(fuelSplit)
    fuelEfficiency = dropCols(fuelEfficiency)
    emissions = dropCols(emissions)
    vehicleCosts = dropCols(vehicleCosts)
    travelCosts = dropCols(travelCosts)
    fuelCosts = dropCols(fuelCosts)
    insuranceCosts = dropCols(insuranceCosts)
    residualCosts = dropCols(residualCosts)
    downtimeCosts = dropCols(downtimeCosts)

    # compute stock
    years = marketShares["Model Year"].unique()
    regionalSales = pd.merge(
        sales, marketShares
    )  # on='Model Year'  # seems to default to cross method
    regionalSales["Sales [veh]"] = (
        regionalSales["Sales [veh]"] * regionalSales["Market Share [veh/veh]"]
    )
    regionalSales = regionalSales.drop(["Market Share [veh/veh]"], axis=1)

    stock = []
    for y in years:
        df = regionalSales[regionalSales["Model Year"] <= y].copy()
        df["Year"] = y
        stock.append(df)

    stock = pd.concat(stock)
    stock["Age [yr]"] = stock["Year"] - stock["Model Year"]
    stock = pd.merge(survival, stock)
    stock["Stock [veh]"] = stock["Sales [veh]"] * stock["Surviving Vehicles [veh/veh]"]
    stock = stock.drop(["Surviving Vehicles [veh/veh]"], axis=1)

    # compute vmt
    stock = pd.merge(stock, annualTravel)
    stock["Travel [mi]"] = stock["Annual Travel [mi/yr]"] * stock["Stock [veh]"]
    # stock['Maintenance Cost [dol/mi]'] = stock['Maintenance Cost [dol/mi]'] * stock['Stock [veh]']
    stock = stock.drop(["Annual Travel [mi/yr]", "Age [yr]"], axis=1)

    # compute energy
    energy = pd.merge(pd.merge(stock, fuelEfficiency), fuelSplit)
    energy["Travel [mi]"] = (
        energy["Travel [mi]"] * energy["Fraction of Travel [mi/mi]"]
    )  # travel [mi] by fuel type
    energy["Energy [gge]"] = energy["Travel [mi]"] / energy["Fuel Efficiency [mi/gge]"]

    df = energy.copy()  # store for ownership costs calculations
    df["Age [yr]"] = df["Year"] - df["Model Year"]

    energy = energy.groupby(
        ["Region", "Vehicle", "Vocation", "Model Year", "Year"], as_index=False
    ).agg({"Energy [gge]": "sum"})
    stock = pd.merge(stock, energy).sort_values(
        by=["Year", "Model Year", "Region", "Vocation", "Vehicle"]
    )
    stock = stock[
        [
            "Year",
            "Region",
            "Vocation",
            "Vehicle",
            "Model Year",
            "Sales [veh]",
            "Stock [veh]",
            "Travel [mi]",
            "Energy [gge]",
        ]
    ]

    # # compute emissions if it specified
    # if emissions is not None: # quickly wrote this on 6/19/19 at 10pm at night. Probably a better way to make this more robust and optional inputs for other cost files as well
    #     emissions = pd.merge(df, emissions)
    #     emissions['Emission [g]'] = emissions['Emission Rate [g/gge]'] * emissions['Energy [gge]']
    #     emissions = emissions.drop(['Emission Rate [g/gge]'], axis=1)
    #     emissions = emissions.groupby(['Year','Region','Vocation','Vehicle','Fuel','Pollutant'], as_index=False).agg({'Stock [veh]' : np.sum, 'Travel [mi]' : np.sum, 'Energy [gge]' : np.sum, 'Emission [g]' : np.sum})

    # compute ownership costs
    vehicleCosts = pd.merge(vehicleCosts, df[df["Age [yr]"] < 0.1])
    vehicleCosts["Cost [$]"] = (
        vehicleCosts["Stock [veh]"] * vehicleCosts["Cost [$/veh]"]
    )
    vehicleCosts = vehicleCosts.drop(["Cost [$/veh]"], axis=1)

    travelCosts = pd.merge(travelCosts, df)

    travelCosts["Cost [$]"] = travelCosts["Travel [mi]"] * travelCosts["Cost [$/mi]"]
    travelCosts = travelCosts.drop(["Cost [$/mi]"], axis=1)

    fuelCosts = pd.merge(fuelCosts, df)
    fuelCosts["Cost [$]"] = fuelCosts["Energy [gge]"] * fuelCosts["Cost [$/gge]"]
    fuelCosts = fuelCosts.drop(["Cost [$/gge]"], axis=1)

    insuranceCosts = pd.merge(insuranceCosts, df)
    insuranceCosts["Cost [$]"] = insuranceCosts["Cost [$/Yr]"]
    insuranceCosts = insuranceCosts.drop(["Cost [$/Yr]"], axis=1)

    residualCosts = pd.merge(residualCosts, df)
    residualCosts["Cost [$]"] = residualCosts["Cost [$/Yr]"]
    residualCosts = residualCosts.drop(["Cost [$/Yr]"], axis=1)

    downtimeCosts = pd.merge(downtimeCosts, df)
    downtimeCosts["Cost [$]"] = downtimeCosts["Cost [$/Yr]"]
    downtimeCosts = downtimeCosts.drop(["Cost [$/Yr]"], axis=1)

    if write_files:
        vehicleCosts.to_csv(gl.TCO_INTERMEDIATES / "vehicle_costs.csv", index=False)
        travelCosts.to_csv(gl.TCO_INTERMEDIATES / "travel_costs.csv", index=False)
        fuelCosts.to_csv(gl.TCO_INTERMEDIATES / "fuel_costs.csv", index=False)
        insuranceCosts.to_csv(gl.TCO_INTERMEDIATES / "insurance_costs.csv", index=False)
        residualCosts.to_csv(gl.TCO_INTERMEDIATES / "residual_costs.csv", index=False)
        downtimeCosts.to_csv(gl.TCO_INTERMEDIATES / "downtime_costs.csv", index=False)

    ownershipCosts = pd.concat(
        [
            vehicleCosts,
            travelCosts,
            fuelCosts,
            insuranceCosts,
            residualCosts,
            downtimeCosts,
        ]
    )
    ownershipCosts = ownershipCosts.groupby(
        ["Region", "Vehicle", "Vocation", "Model Year", "Year", "Category"],
        as_index=False,
    ).agg({"Cost [$]": "sum"})
    ownershipCosts = ownershipCosts.sort_values(
        by=["Year", "Model Year", "Region", "Vocation", "Vehicle"]
    )[["Year", "Region", "Vocation", "Vehicle", "Model Year", "Category", "Cost [$]"]]
    return stock, emissions, ownershipCosts