51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
from banking_breakdown import document_builder
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import pandas as pd
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from banking_breakdown import types
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import numpy as np
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def _generate_dummy_data() -> types.ReportData:
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categories = ["A", "B", "C", "D", "E", "F", "G"]
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values = np.array([10, 12, 53, 12, 90, 23, 32])
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values = values / values.sum() * 100
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values = np.round(values, decimals=1)
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values[-1] += 100 - np.sum(values)
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category_overview_df = pd.DataFrame(
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{"category": categories, "value": values})
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t = ["2023-01-01",
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"2023-02-01",
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"2023-03-01",
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"2023-04-01",
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"2023-05-01",
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"2023-06-01",
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"2023-07-01",
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"2023-08-01",
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"2023-09-01",
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"2023-10-01",
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"2023-11-01",
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"2023-12-01"]
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net_income = 200 * np.random.normal(size=len(t)) + 100
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net_income_df = pd.DataFrame({"t": t, "value": net_income})
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total_value = np.cumsum(net_income)
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total_value_df = pd.DataFrame({"t": t, "value": total_value})
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report_data = types.ReportData(category_overview=category_overview_df,
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net_income=net_income_df,
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total_value=total_value_df)
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return report_data
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def main():
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report_data = _generate_dummy_data()
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document_builder.build_document(report_data)
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if __name__ == "__main__":
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main()
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