In engineering, analyzing sensor data is essential for monitoring system performance and detecting anomalies. When you have a series of measurements—such as pump pressure readings from a water treatment plant—**descriptive statistics** like `mean`, `median`, and `standard deviation` help you summarize and understand the data's central tendency and spread. Identifying **outliers**, especially those that are more than two standard deviations from the mean, is a standard approach to flag potential faults or abnormal events that may require further investigation.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import user_code
import importlib
import math

class TestTask(unittest.TestCase):
    def setUp(self):
        self.pressure_readings = [250, 255, 253, 249, 260, 252, 248, 300, 251, 254, 265, 247, 250, 400]
        self.n = len(self.pressure_readings)
        self.mean = sum(self.pressure_readings) / self.n
        sorted_readings = sorted(self.pressure_readings)
        if self.n % 2 == 1:
            self.median = sorted_readings[self.n // 2]
        else:
            self.median = (sorted_readings[self.n // 2 - 1] + sorted_readings[self.n // 2]) / 2
        self.variance = sum((x - self.mean) ** 2 for x in self.pressure_readings) / self.n
        self.std_dev = math.sqrt(self.variance)
        self.outliers = [x for x in self.pressure_readings if abs(x - self.mean) > 2 * self.std_dev]

    def test_mean_computation(self):
        importlib.reload(user_code)
        result = user_code.analyze_pressure_readings(self.pressure_readings)
        _dynamic_test(
            self,
            result is not None and 'mean' in result and abs(result['mean'] - self.mean) < 1e-8,
            "Mean is correctly computed and returned in dictionary.",
            f"Expected mean: {self.mean}, got: {None if result is None else result.get('mean', None)}",
        )

    def test_median_computation(self):
        importlib.reload(user_code)
        result = user_code.analyze_pressure_readings(self.pressure_readings)
        _dynamic_test(
            self,
            result is not None and 'median' in result and abs(result['median'] - self.median) < 1e-8,
            "Median is correctly computed and returned in dictionary.",
            f"Expected median: {self.median}, got: {None if result is None else result.get('median', None)}",
        )

    def test_stddev_computation(self):
        importlib.reload(user_code)
        result = user_code.analyze_pressure_readings(self.pressure_readings)
        _dynamic_test(
            self,
            result is not None and 'std_dev' in result and abs(result['std_dev'] - self.std_dev) < 1e-8,
            "Standard deviation is correctly computed and returned in dictionary.",
            f"Expected std_dev: {self.std_dev}, got: {None if result is None else result.get('std_dev', None)}",
        )

    def test_outlier_detection(self):
        importlib.reload(user_code)
        result = user_code.analyze_pressure_readings(self.pressure_readings)
        _dynamic_test(
            self,
            result is not None and 'outliers' in result and isinstance(result['outliers'], list),
            "Outliers list is present and is a list.",
            f"Expected outliers as list, got: {None if result is None else result.get('outliers', None)}",
        )
        expected_set = set(self.outliers)
        actual_set = set(result['outliers']) if result is not None and 'outliers' in result else set()
        _dynamic_test(
            self,
            expected_set == actual_set,
            "Outliers are correctly detected and returned in dictionary.",
            f"Expected outliers: {sorted(expected_set)}, got: {sorted(actual_set)}",
        )

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: Analyze Pump Pressure Data

Ratkaisu

Challenge: Analyze Pump Pressure Data

Ratkaisu