
import unittest
import user_code
import ast
import re   
import unittest
import importlib
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os

class TestTask(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        # Create a test CSV file
        data = {
            'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
            'Revenue': [1000, 1200, 1100, 1300, 1250],
            'Expenses': [700, 800, 900, 850, 1000],
        }
        df = pd.DataFrame(data)
        df.to_csv('monthly_data.csv', index=False)

    @classmethod
    def tearDownClass(cls):
        os.remove('monthly_data.csv')

    def test_profit_calculation(self):
        import user_code
        importlib.reload(user_code)
        df = pd.read_csv('monthly_data.csv')
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        user_code.analyze_profit_trends('monthly_data.csv')
        plt.show = plt_show_backup
        # Check profit column
        expected_profit = df['Revenue'] - df['Expenses']
        df['Profit'] = expected_profit
        _dynamic_test(
            self,
            all(df['Profit'] == expected_profit),
            'Monthly profit is correctly calculated as Revenue - Expenses',
            f'Profit calculation incorrect. Expected {list(expected_profit)}, got {list(df["Profit"])}',
        )

    def test_moving_average(self):
        import user_code
        importlib.reload(user_code)
        df = pd.read_csv('monthly_data.csv')
        df['Profit'] = df['Revenue'] - df['Expenses']
        expected_ma = df['Profit'].rolling(window=3, min_periods=1).mean().tolist()
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        user_code.analyze_profit_trends('monthly_data.csv')
        plt.show = plt_show_backup
        # Recompute in user code
        df['Profit_MA'] = df['Profit'].rolling(window=3, min_periods=1).mean()
        _dynamic_test(
            self,
            np.allclose(df['Profit_MA'], expected_ma),
            '3-month moving average is correctly computed',
            f'Expected moving averages: {expected_ma}, got {list(df["Profit_MA"])}',
        )

    def test_plot_lines_and_labels(self):
        import user_code
        importlib.reload(user_code)
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        plt.figure()
        user_code.analyze_profit_trends('monthly_data.csv')
        # Get current axes
        ax = plt.gca()
        lines = ax.get_lines()
        labels = [line.get_label() for line in lines]
        _dynamic_test(
            self,
            'Monthly Profit' in labels and '3-Month Moving Average' in labels,
            'Both lines for profit and moving average are plotted and labeled',
            f'Expected line labels: Monthly Profit and 3-Month Moving Average. Got: {labels}',
        )
        plt.show = plt_show_backup
        plt.close('all')

    def test_x_axis_month_labels(self):
        import user_code
        importlib.reload(user_code)
        df = pd.read_csv('monthly_data.csv')
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        plt.figure()
        user_code.analyze_profit_trends('monthly_data.csv')
        ax = plt.gca()
        x_tick_labels = [tick.get_text() for tick in ax.get_xticklabels()]
        # Remove empty labels
        x_tick_labels = [l for l in x_tick_labels if l]
        months = list(df['Month'])
        found = all(month in ''.join(x_tick_labels) for month in months)
        _dynamic_test(
            self,
            found,
            'X-axis displays month labels from the CSV file',
            f'Expected months: {months} in x-axis labels: {x_tick_labels}',
        )
        plt.show = plt_show_backup
        plt.close('all')

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

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Delve into financial data analysis using Python, including trend analysis, ratio calculations, and benchmarking.

Learn to retrieve financial data from online sources and create automated reports for accounting purposes.

Challenge: Analyze Profit Trends

Lösung

Challenge: Analyze Profit Trends

Lösung