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

A practical course designed for engineers who want to leverage Python to solve real-world engineering problems. Explore data analysis, mathematical modeling, and simulation using Python's powerful libraries and tools, all through engaging, hands-on examples and challenges.

Learn how to use Python to analyze, summarize, and visualize engineering data. This section covers essential data handling and visualization techniques tailored for engineering applications.

Dive into mathematical modeling and simulation techniques for engineering systems using Python. Learn to model physical processes, solve equations, and simulate system behavior.

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

Solution

Challenge: Explore Voltage and Current Relationship

Solution