from banking_breakdown import document_builder
import pandas as pd
from banking_breakdown import types
import numpy as np


def _generate_dummy_data() -> types.ReportData:
    categories = ["A", "B", "C", "D", "E", "F", "G"]
    values = np.array([10, 12, 53, 12, 90, 23, 32])
    values = values / values.sum() * 100
    values = np.round(values, decimals=1)
    values[-1] += 100 - np.sum(values)

    category_overview_df = pd.DataFrame(
        {"category": categories, "value": values})

    t = ["2023-01-01",
         "2023-02-01",
         "2023-03-01",
         "2023-04-01",
         "2023-05-01",
         "2023-06-01",
         "2023-07-01",
         "2023-08-01",
         "2023-09-01",
         "2023-10-01",
         "2023-11-01",
         "2023-12-01"]

    net_income = 200 * np.random.normal(size=len(t)) + 100
    net_income_df = pd.DataFrame({"t": t, "value": net_income})

    total_value = np.cumsum(net_income)

    total_value_df = pd.DataFrame({"t": t, "value": total_value})

    report_data = types.ReportData(category_overview=category_overview_df,
                                   net_income=net_income_df,
                                   total_value=total_value_df)

    return report_data


def main():
    report_data = _generate_dummy_data()
    document_builder.build_document(report_data)


if __name__ == "__main__":
    main()