update data based on selected company (#122)

Added UI elements to select a company and update shown data depending on chosen company



---------

Co-authored-by: Philipp Horstenkamp <philipp@horstenkamp.de>

tests/conftest.py

@@ -3,6 +3,7 @@ import datetime
 import os
 from collections.abc import Generator
 from inspect import getmembers, isfunction
+from typing import Any
 
 import pytest
 from sqlalchemy.engine import Engine
@@ -49,7 +50,60 @@ def empty_db() -> Generator[Session, None, None]:
 
 
 @pytest.fixture()
-def full_db(empty_db: Session) -> Session:
+def finance_statements() -> list[dict[str, Any]]:
+    """Creates a list of finance statements."""
+    return [
+        {
+            "id": 1,
+            "company_id": 1,
+            "date": datetime.date.fromisoformat("2023-01-01"),
+            "total_volume": 1000.0,
+            "ebit": 1000.0,
+            "ebitda": 1000.0,
+            "ebit_margin": 1000.0,
+            "total_balance": 1000.0,
+            "equity": 1000.0,
+            "debt": 1000.0,
+            "return_on_equity": 1000.0,
+            "capital_turnover_rate": 1000.0,
+            "current_liabilities": 1000.0,
+            "dividends": float("NaN"),
+            "net_income": float("NaN"),
+            "assets": 1000.0,
+            "long_term_debt": 1000.0,
+            "short_term_debt": 1000.0,
+            "revenue": 1000.0,
+            "cash_flow": 1000.0,
+            "current_assets": 1000.0,
+        },
+        {
+            "id": 2,
+            "company_id": 1,
+            "date": datetime.date.fromisoformat("2022-01-01"),
+            "total_volume": 1100.0,
+            "ebit": 1100.0,
+            "ebitda": 1100.0,
+            "ebit_margin": 1100.0,
+            "total_balance": 1100.0,
+            "equity": 1100.0,
+            "debt": 1100.0,
+            "return_on_equity": 1100.0,
+            "capital_turnover_rate": 1100.0,
+            "current_liabilities": 1100.0,
+            "dividends": float("NaN"),
+            "net_income": float("NaN"),
+            "assets": 1100.0,
+            "long_term_debt": 1100.0,
+            "short_term_debt": 1100.0,
+            "revenue": 1100.0,
+            "cash_flow": 1100.0,
+            "current_assets": 1100.0,
+        },
+    ]
+
+
+@pytest.fixture()
+def full_db(empty_db: Session, finance_statements: list[dict[str, Any]]) -> Session:
     """Fills a db with some test data."""
     empty_db.add_all(
         [
@@ -112,5 +166,13 @@ def full_db(empty_db: Session) -> Session:
         ]
     )
     empty_db.commit()
+
+    empty_db.add_all(
+        [
+            entities.AnnualFinanceStatement(**finance_statement)
+            for finance_statement in finance_statements
+        ]
+    )
+    empty_db.commit()
     # print(pd.read_sql_table("company", empty_db.bind).to_string())
     return empty_db

tests/ui/ui_elements_test.py

@@ -0,0 +1,118 @@
+"""Tests for ui elements."""
+
+import pandas as pd
+from sqlalchemy.orm import Session
+
+from aki_prj23_transparenzregister.ui import ui_elements
+
+
+def test_import() -> None:
+    """Checks if an import co ui_elements can be made."""
+    assert ui_elements is not None
+
+
+def test_get_company_data(full_db: Session) -> None:
+    """Checks if data from the company and district court tables can be accessed."""
+    company_df = ui_elements.get_company_data(full_db)
+
+    test_data = pd.DataFrame(
+        {
+            "company_id": {0: 1, 1: 2, 2: 3},
+            "company_hr": {0: "HRB 123", 1: "HRB 123", 2: "HRB 12"},
+            "company_court_id": {0: 2, 1: 1, 2: 2},
+            "company_name": {
+                0: "Some Company GmbH",
+                1: "Other Company GmbH",
+                2: "Third Company GmbH",
+            },
+            "company_street": {0: "Sesamstr.", 1: "Sesamstr.", 2: None},
+            "company_zip_code": {0: "12345", 1: "12345", 2: None},
+            "company_city": {0: "TV City", 1: "TV City", 2: None},
+            "company_last_update": {
+                0: "2023-01-01",
+                1: "2023-01-01",
+                2: "2023-01-01",
+            },
+            "company_sector": {0: None, 1: None, 2: None},
+            "district_court_name": {
+                0: "Amtsgericht Dortmund",
+                1: "Amtsgericht Bochum",
+                2: "Amtsgericht Dortmund",
+            },
+        }
+    )
+    test_data = test_data.set_index("company_id")
+    pd.testing.assert_frame_equal(company_df, test_data)
+
+
+def test_get_finance_data(full_db: Session) -> None:
+    """Checks if data from the company and finance tables can be accessed."""
+    finance_df = ui_elements.get_finance_data(full_db)
+    test_data = pd.DataFrame(
+        {
+            "annual_finance_statement_id": {0: 1, 1: 2},
+            "annual_finance_statement_company_id": {0: 1, 1: 1},
+            "annual_finance_statement_date": {0: "2023-01-01", 1: "2022-01-01"},
+            "annual_finance_statement_total_volume": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_ebit": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_ebitda": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_ebit_margin": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_total_balance": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_equity": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_debt": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_return_on_equity": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_capital_turnover_rate": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_current_liabilities": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_dividends": {0: None, 1: None},
+            "annual_finance_statement_net_income": {0: None, 1: None},
+            "annual_finance_statement_assets": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_long_term_debt": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_short_term_debt": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_revenue": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_cash_flow": {0: 1000.0, 1: 1100.0},
+            "annual_finance_statement_current_assets": {0: 1000.0, 1: 1100.0},
+            "company_name": {0: "Some Company GmbH", 1: "Some Company GmbH"},
+            "company_id": {0: 1, 1: 1},
+        }
+    )
+    pd.testing.assert_frame_equal(finance_df, test_data)
+
+
+def test_create_header() -> None:
+    """Checks if the header can be created."""
+    options = {1: "a", 2: "b"}
+    ui_elements.create_header(options)
+
+
+def test_create_company_header() -> None:
+    """Checks if the company header can be created."""
+    selected_company = "Test GmbH"
+    ui_elements.create_company_header(selected_company)
+
+
+def test_create_company_stats(full_db: Session) -> None:
+    """Checks if the company widgets can be created."""
+    company_df = ui_elements.get_company_data(full_db)
+    value_chosen = 1
+    selected_company_stats = company_df.loc[value_chosen]
+    ui_elements.create_company_stats(selected_company_stats)
+
+
+def test_create_tabs(full_db: Session) -> None:
+    """Checks if the tabs of the company page can be created."""
+    selected_company_id = 1
+    finance_df = ui_elements.get_finance_data(full_db)
+    selected_finance_df = finance_df.loc[
+        finance_df["company_id"] == selected_company_id
+    ]
+    ui_elements.create_tabs(selected_finance_df)
+
+
+def test_kennzahlen_layout(full_db: Session) -> None:
+    """Checks if the financial metric layout of the company page can be created."""
+    selected_company_id = 1
+    finance_df = ui_elements.get_finance_data(full_db)
+    selected_finance_df = finance_df.loc[
+        finance_df["company_id"] == selected_company_id
+    ]
+    ui_elements.kennzahlen_layout(selected_finance_df)

tests/utils/data_transfer_test.py

@@ -168,7 +168,7 @@ def test_get_person_id_value_check(
         data_transfer.get_person_id(
             firstname,
             surname,
-            date.fromisoformat(date_str) if date_str else None,  # type: ignore
+            date.fromisoformat(date_str) if date_str else None,
             full_db,
         )
 
@@ -941,7 +941,11 @@ def test_add_annual_report_to_unknown_company(
 @pytest.mark.parametrize("year", [2023, 2025, 2020])
 @pytest.mark.parametrize("short_term_debt", [2023.2, 2025.5, 2020.5, float("NaN")])
 def test_add_annual_report(
-    short_term_debt: float, company_id: int, year: int, full_db: Session
+    short_term_debt: float,
+    company_id: int,
+    year: int,
+    finance_statements: list[dict[str, Any]],
+    full_db: Session,
 ) -> None:
     """Tests the addition of annual financial records."""
     data_transfer.add_annual_report(
@@ -961,34 +965,38 @@ def test_add_annual_report(
     df_prior = pd.read_sql_table(
         entities.AnnualFinanceStatement.__tablename__, full_db.bind  # type: ignore
     )
+    expected_results = pd.DataFrame(
+        finance_statements
+        + [
+            {
+                "id": 3,
+                "company_id": company_id,
+                "date": pd.to_datetime(date(year, 1, 1)),
+                "total_volume": float("NaN"),
+                "ebit": 123.0,
+                "ebitda": 235.0,
+                "ebit_margin": float("NaN"),
+                "total_balance": float("NaN"),
+                "equity": float("NaN"),
+                "debt": float("NaN"),
+                "return_on_equity": float("NaN"),
+                "capital_turnover_rate": float("NaN"),
+                "current_liabilities": float("NaN"),
+                "dividends": float("NaN"),
+                "net_income": float("NaN"),
+                "assets": float("NaN"),
+                "long_term_debt": float("NaN"),
+                "short_term_debt": short_term_debt,
+                "revenue": float("NaN"),
+                "cash_flow": float("NaN"),
+                "current_assets": float("NaN"),
+            }
+        ]
+    )
+
+    expected_results["date"] = pd.to_datetime(expected_results["date"])
     pd.testing.assert_frame_equal(
-        pd.DataFrame(
-            [
-                {
-                    "id": 1,
-                    "company_id": company_id,
-                    "date": pd.to_datetime(date(year, 1, 1)),
-                    "total_volume": float("NaN"),
-                    "ebit": 123.0,
-                    "ebitda": 235.0,
-                    "ebit_margin": float("NaN"),
-                    "total_balance": float("NaN"),
-                    "equity": float("NaN"),
-                    "debt": float("NaN"),
-                    "return_on_equity": float("NaN"),
-                    "capital_turnover_rate": float("NaN"),
-                    "current_liabilities": float("NaN"),
-                    "dividends": float("NaN"),
-                    "net_income": float("NaN"),
-                    "assets": float("NaN"),
-                    "long_term_debt": float("NaN"),
-                    "short_term_debt": short_term_debt,
-                    "revenue": float("NaN"),
-                    "cash_flow": float("NaN"),
-                    "current_assets": float("NaN"),
-                }
-            ]
-        ),
+        expected_results,
         df_prior,
     )