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

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Challenge: Analyze Pump Pressure Data

Solución

Challenge: Analyze Pump Pressure Data

Solución