In engineering analysis, understanding the relationship between two variables—such as **voltage** and **current** in a circuit—is a key application of **correlation analysis** and **scatter plotting**. Correlation quantifies the degree to which changes in one variable are associated with changes in another. A **scatter plot** visually displays this relationship, allowing you to quickly assess whether the variables move together in a predictable way. In this challenge, you will explore the relationship between voltage and current using real-world engineering data.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import sys
import io
import matplotlib
matplotlib.use('Agg')

class TestTask(unittest.TestCase):
    def test_correlation_coefficient_and_relationship(self):
        import user_code
        importlib.reload(user_code)
        voltage = [2, 4, 6, 8, 10, 12, 14, 16]
        current = [0.2, 0.4, 0.61, 0.8, 1.01, 1.19, 1.39, 1.6]
        func = getattr(user_code, 'analyze_voltage_current', None)
        _dynamic_test(self, callable(func), "Function is defined.", "Function analyze_voltage_current is not defined.")
        if not callable(func):
            return
        result = func(voltage, current)
        _dynamic_test(self, result is not None and isinstance(result, tuple) and len(result)==2,
                      "Function returns a tuple of length 2.",
                      f"Expected tuple of length 2, got {result}.")
        if not (result is not None and isinstance(result, tuple) and len(result)==2):
            return
        corr, rel = result
        # Compute expected correlation
        import numpy as np
        expected_corr = np.corrcoef(voltage, current)[0,1]
        _dynamic_test(self, abs(corr - expected_corr) < 1e-6,
                      "Correlation coefficient is correct.",
                      f"Expected correlation: {expected_corr}, got: {corr}")
        expected_rel = "strong" if abs(expected_corr)>0.7 else ("weak" if abs(expected_corr)>0.3 else "negligible")
        _dynamic_test(self, rel == expected_rel,
                      f"Relationship description is correct: {expected_rel}",
                      f"Expected relationship: '{expected_rel}', got: '{rel}'")

    def test_scatter_plot_generated(self):
        import user_code
        importlib.reload(user_code)
        import matplotlib.pyplot as plt
        voltage = [2, 4, 6, 8, 10, 12, 14, 16]
        current = [0.2, 0.4, 0.61, 0.8, 1.01, 1.19, 1.39, 1.6]
        func = getattr(user_code, 'analyze_voltage_current', None)
        _dynamic_test(self, callable(func), "Function is defined.", "Function analyze_voltage_current is not defined.")
        if not callable(func):
            return
        # Clear any previous figures
        plt.close('all')
        # Capture the number of figures before
        num_figs_before = len(plt.get_fignums())
        func(voltage, current)
        num_figs_after = len(plt.get_fignums())
        _dynamic_test(self, num_figs_after > num_figs_before,
                      "Scatter plot figure was generated.",
                      "No scatter plot figure was generated.")
        # Check axis labels and title
        fig = plt.gcf()
        ax = fig.gca()
        xlabel = ax.get_xlabel()
        ylabel = ax.get_ylabel()
        title = ax.get_title()
        _dynamic_test(self, xlabel and "voltage" in xlabel.lower(),
                      "X-axis label includes 'Voltage'.",
                      f"X-axis label missing or incorrect: {xlabel}")
        _dynamic_test(self, ylabel and "current" in ylabel.lower(),
                      "Y-axis label includes 'Current'.",
                      f"Y-axis label missing or incorrect: {ylabel}")
        _dynamic_test(self, title and "voltage" in title.lower() and "current" in title.lower(),
                      "Plot title includes both 'Voltage' and 'Current'.",
                      f"Plot title missing or incorrect: {title}")

    def test_negative_correlation(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'analyze_voltage_current', None)
        _dynamic_test(self, callable(func), "Function is defined.", "Function analyze_voltage_current is not defined.")
        if not callable(func):
            return
        voltage = [5, 4, 3, 2, 1]
        current = [1, 2, 3, 4, 5]
        result = func(voltage, current)
        _dynamic_test(self, result is not None and isinstance(result, tuple) and len(result)==2,
                      "Function returns a tuple of length 2 for negative correlation.",
                      f"Expected tuple of length 2, got {result}.")
        if not (result is not None and isinstance(result, tuple) and len(result)==2):
            return
        corr, rel = result
        _dynamic_test(self, corr < 0,
                      "Correlation coefficient is negative as expected.",
                      f"Expected negative correlation, got: {corr}")
        expected_rel = "strong" if abs(corr)>0.7 else ("weak" if abs(corr)>0.3 else "negligible")
        _dynamic_test(self, rel == expected_rel,
                      f"Relationship description is correct: {expected_rel}",
                      f"Expected relationship: '{expected_rel}', got: '{rel}'")

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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Apply data science techniques to engineering problems, including classification, clustering, and predictive modeling using Python's scikit-learn and seaborn libraries.

Challenge: Explore Voltage and Current Relationship

解答