mirror of
https://github.com/fhswf/aki_prj23_transparenzregister.git
synced 2025-04-22 06:52:54 +02:00
Philipp Horstenkamp
f8c111d7e2
SQL Creation is now done dynamicly by the definition of the enumeration type.
89 lines
3.9 KiB
Python
89 lines
3.9 KiB
Python
"""Tests for data elements."""
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import pandas as pd
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from sqlalchemy.orm import Session
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from aki_prj23_transparenzregister.ui import data_elements
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def test_import() -> None:
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"""Checks if an import co ui_elements can be made."""
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assert data_elements is not None
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def test_get_company_data(full_db: Session) -> None:
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"""Checks if data from the company and district court tables can be accessed."""
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company_df = data_elements.get_company_data(full_db)
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test_data = pd.DataFrame(
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{
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"company_id": {0: 1, 1: 2, 2: 3},
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"company_hr": {0: "HRB 123", 1: "HRB 123", 2: "HRB 12"},
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"company_court_id": {0: 2, 1: 1, 2: 2},
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"company_name": {
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0: "Some Company GmbH",
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1: "Other Company GmbH",
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2: "Third Company GmbH",
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},
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"company_company_type": {0: None, 1: None, 2: None},
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"company_founding_date": {0: "2010-08-07"},
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"company_business_purpose": {
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0: 'Say "Hello World"',
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1: "Some purpose",
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},
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"company_street": {0: "Sesamstr.", 1: "Sesamstr."},
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"company_house_number": {0: "4", 1: "8"},
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"company_zip_code": {0: "58644", 1: "58636"},
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"company_city": {0: "TV City", 1: "TV City"},
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"company_longitude": {0: 7.6968, 1: 7.7032},
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"company_latitude": {0: 51.3246, 1: 51.38},
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"company_pos_accuracy": {0: 4.0, 1: 4.0},
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"company_capital_value": {0: 1000000.0, 2: 10000.0},
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"company_original_currency": {0: "DEUTSCHE_MARK", 2: "EURO"},
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"company_capital_type": {0: "HAFTEINLAGE", 2: "GRUNDKAPITAL"},
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"company_last_update": {
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0: "2023-01-01",
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1: "2023-01-01",
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2: "2023-01-01",
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},
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"company_sector": {2: "Electronic"},
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"district_court_name": {
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0: "Amtsgericht Dortmund",
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1: "Amtsgericht Bochum",
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2: "Amtsgericht Dortmund",
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},
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}
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)
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test_data = test_data.set_index("company_id")
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pd.testing.assert_frame_equal(company_df, test_data)
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def test_get_finance_data(full_db: Session) -> None:
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"""Checks if data from the company and finance tables can be accessed."""
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finance_df = data_elements.get_finance_data(full_db)
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test_data = pd.DataFrame(
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{
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"annual_finance_statement_id": {0: 1, 1: 2},
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"annual_finance_statement_company_id": {0: 1, 1: 1},
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"annual_finance_statement_date": {0: "2023-01-01", 1: "2022-01-01"},
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"annual_finance_statement_revenue": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_net_income": {0: 100.0},
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"annual_finance_statement_ebit": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_ebitda": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_gross_profit": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_operating_profit": {1: 1.0},
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"annual_finance_statement_assets": {0: 1000.0, 1: 1100},
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"annual_finance_statement_liabilities": {0: 0.0},
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"annual_finance_statement_equity": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_current_assets": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_current_liabilities": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_long_term_debt": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_short_term_debt": {0: 1000.0, 1: 1100.0},
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"annual_finance_statement_cash_and_cash_equivalents": {0: 1.0},
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"annual_finance_statement_dividends": {0: 0.0},
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"annual_finance_statement_cash_flow": {0: 1000.0, 1: 1100.0},
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"company_name": {0: "Some Company GmbH", 1: "Some Company GmbH"},
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"company_id": {0: 1, 1: 1},
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}
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)
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pd.testing.assert_frame_equal(finance_df, test_data)
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