0 
Depreciation_DM = model.constant("Depreciation_DM")
Depreciation_DM.equation = 0.0708

DMPIncRate = model.constant("DMPIncRate")
DMPIncRate.equation = 0.0284
DMPInc = model.flow("DMPInc")
DM_Price = model.stock("DM_Price")
DM_Price.initial_value = 4.2e+07
DM_Price.equation = DMPInc
DMPInc.equation = DM_Price * DMPIncRate

Average_Expected_Years_Kept_Battery_DM = model.constant("Average_Expected_Years_Kept_Battery_DM")
Average_Expected_Years_Kept_Battery_DM.equation = 6.0
Depreciation_Battery_DM = model.constant("Depreciation_Battery_DM")
Depreciation_Battery_DM.equation = 0.00033
Battery_Price_DM = model.converter("Battery_Price_DM")
Battery_Price_DM.equation = 0.25 * DM_Price

Battery_Resale_DM = model.converter("Battery_Resale_DM")
Battery_Resale_DM.equation = ((Charging_Frequency_DM * Average_Expected_Years_Kept_Battery_DM)* Depreciation_Battery_DM) * Battery_Price_DM

Discount_Rate_DM = model.constant("Discount_Rate_DM")
Discount_Rate_DM.equation = 0.0838

Battery_Resale_DM = model.converter("Battery_Resale_DM")
Battery_Resale_DM.equation = (
    (Charging_Frequency_DM * Average_Expected_Years_Kept_Battery_DM) *
    Depreciation_Battery_DM
) * Battery_Price_DM


Resale_Value_DM = model.converter("Resale_Value_DM")
Resale_Value_DM._function_string = (
    "lambda model, t: ("
    "pow(1.0 - model.memoize('Depreciation_DM', t), "
    "model.memoize('Average_Expected_Years_Kept_DM', t))"
    "* model.memoize('DM_Price', t)"
    "+ model.memoize('Battery_Resale_DM', t)"
    ") / "
    "pow(1.0 + model.memoize('Discount_Rate_DM', t), "
    "(model.memoize('Average_Expected_Years_Kept_DM', t) - 1.0))"
)

Resale_Value_DM.generate_function()

Purchase_Tax_Subsidy_DM_pct = model.converter("Purchase_Tax_Subsidy_DM_pct")
Purchase_Tax_Subsidy_DM_pct._function_string = (
    "lambda model, t: 0.11 if t < 2023.0 else 0.01"
)

Purchase_Tax_Subsidy_DM_pct.generate_function()

Price_Subsidy_DM = model.converter("Price_Subsidy_DM")
Price_Subsidy_DM._function_string = (
    "lambda model, t: 0.0 if t < 2023.0 else 7e+06"
)
Price_Subsidy_DM.generate_function()

Purchase_Cost_After_Subsidies_DM = model.converter("Purchase_Cost_After_Subsidies_DM")
Purchase_Cost_After_Subsidies_DM.equation = ((DM_Price - Price_Subsidy_DM) + (Purchase_Tax_Subsidy_DM_pct * DM_Price))

model.points["PKB_%_DM_Subsidy"] = [
        [2015, 0.02],
        [2016, 0.02],
        [2017, 0.02],
        [2018, 0.02],
        [2019, 0.02],
        [2020, 0.02],
        [2021, 0.002],
        [2022, 0.002],
        [2023, 0.0],
        [2024, 0.0],
        [2025, 0.0],
        [2026, 0.0],
        [2027, 0.0],
        [2028, 0.0],
        [2029, 0.0],
        [2030, 0.0],
        [2031, 0.0],
        [2032, 0.0],
        [2033, 0.0],
        [2034, 0.0],
        [2035, 0.0],
]
PKB_pct_DM_Subsidy = model.converter("PKB_%_DM_Subsidy")
PKB_pct_DM_Subsidy.equation = sd.lookup(sd.time(), "PKB_%_DM_Subsidy")

SWDKLLJ = model.constant("SWDKLLJ")
SWDKLLJ.equation = 35000.0

Yearly_Tax_Cost_DM_After_Subsidies = model.converter("Yearly_Tax_Cost_DM_After_Subsidies")
Yearly_Tax_Cost_DM_After_Subsidies._function_string = (
    "lambda model, t: ("
    "model.memoize('PKB_%_DM_Subsidy', t) * "
    "pow(1.0 - model.memoize('Depreciation_DM', t), (t - 2014.0 - 1.0)) * "
    "model.memoize('Resale_Value_DM', t)"
    ") + model.memoize('SWDKLLJ', t)"
)

Yearly_Tax_Cost_DM_After_Subsidies.generate_function()

First I used the first script and the result was an error,...

bptk.plot_scenarios(
    scenarios="base",
    scenario_managers="smDm vs cm",
    equations=["Yearly_Tax_Cost_DM_After_Subsidies"],
    series_names={},
    return_df=True)

2025-11-03 16:00:25.551125, [ERROR] No output data produced.**
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipython-input-2983896965.py in <cell line: 0>()
     30 #PKB_pct_DM_Subsidy* ((1.0 - Depreciation_DM) ** (Time - 2014 - 1))* Resale_Value_DM) + SWDKLLJ
     31 
---> 32 bptk.plot_scenarios(
     33     scenarios="base",
     34     scenario_managers="smDm vs cm",
________________________________________
1 frames
________________________________________
/usr/local/lib/python3.12/dist-packages/BPTK_Py/visualizations/visualize.py in plot(self, df, return_df, visualize_from_period, visualize_to_period, stacked, kind, title, alpha, x_label, y_label, start_date, freq, series_names, format)
     63 
     64             new_columns = {}
---> 65             for column in df.columns:
     66                 for series_names_key in series_names_keys:
     67                     if series_names_key in column:
AttributeError: 'NoneType' object has no attribute 'columns'

After I was diagnosed with the second script, it turned out that the value for "Yearly_Tax_Cost_DM_After_Subsidies" actually exists.

print("PKB 2015→", model.memoize("PKB_%_DM_Subsidy", 2015.0))
print("Resale 2015→", model.memoize("Resale_Value_DM", 2015.0))
print("SWDKLLJ 2015→", model.memoize("SWDKLLJ", 2015.0))
print("Tax DM 2015→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2015.0))
print("Tax DM 2016→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2016.0))
print("Tax DM 2017→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2017.0))
print("Tax DM 2018→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2018.0))
print("Tax DM 2019→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2019.0))
print("Tax DM 2020→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2020.0))
print("Tax DM 2021→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2021.0))
print("Tax DM 2022→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2022.0))
print("Tax DM 2035→", model.memoize("Yearly_Tax_Cost_DM_After_Subsidies", 2035.0))

PKB 2015→ 0.02
Resale 2015→ 20247218.066840753
SWDKLLJ 2015→ 35000.0
Tax DM 2015→ 439944.36133681505
Tax DM 2016→ 421960.49068990693
Tax DM 2017→ 404775.2966868149
Tax DM 2018→ 388353.30952273984
Tax DM 2019→ 372660.63463245216
Tax DM 2020→ 357664.882732768
Tax DM 2021→ 65833.51029718903
Tax DM 2022→ 64464.17190476344
Tax DM 2035→ 35000.0

However, I need the first script for my simulation because there are still many other columns in my simulation that depend on the value in the "Yearly_Tax_Cost_DM_After_Subsidies" column. Why does the error AttributeError: 'NoneType' object has no attribute 'columns' occur???

Improve this question
7
  • next time use ``` to format block of code/output/error (instead of > and **) to make them more readable. And use ` to format inline code. Commented Nov 3 at 16:52
  • 1
    error suggests that df is None. You could check code beforer df.columns in file visualize.py to see how it creates df. Maybe this can explain why it is None. Commented Nov 3 at 17:14
  • 1
    code in run_scenarios() shows that it writes some messages in log. And it seems you get it in your output [ERROR] No output data produced.. (and it seems it sends this messsage when len(df) == 0) You can debug this function to see why it didn't create output data. Commented Nov 3 at 17:23
  • 1
    This code is incomplete, and I'm not familiar with the libraries you're using, but the code also seems to be overly long. Please make a minimal reproducible example. Commented Nov 3 at 18:14
  • 1
    Please write a title that describes the actual problem. Maybe start by thinking through the error message: Why is df = None? Have you considered stepping through your code to help debug? Check out How to Ask for tips on writing a good title and more. Commented Nov 3 at 18:16
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