In previous chapters, you explored how to store engineering sensor data efficiently using Python lists and how to create basic plots to visualize that data. These skills are essential for engineers who need to monitor, analyze, and present sensor readings from equipment or processes. By combining structured data storage with clear visualizations, you can quickly spot patterns, trends, and anomalies in your measurements. This chapter challenges you to apply these concepts to a real-world scenario: visualizing a day's worth of temperature sensor data from a factory.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import user_code

class TestTask(unittest.TestCase):
    def setUp(self):
        importlib.reload(user_code)
        self.temperatures = [22.1, 21.8, 21.5, 21.3, 21.0, 20.8, 20.7, 21.2, 22.5, 24.0, 25.5, 27.1,
                             28.3, 29.0, 29.4, 29.1, 28.0, 26.7, 25.3, 24.2, 23.7, 23.0, 22.5, 22.2]
        plt.close('all')

    def test_plot_all_points(self):
        plt.close('all')
        user_code.plot_temperature_profile(self.temperatures)
        fig = plt.gcf()
        ax = plt.gca()
        lines = ax.get_lines()
        # Check that there is at least one line with 24 points
        line_points = [line for line in lines if len(line.get_xdata()) == 24]
        _dynamic_test(
            self,
            len(line_points) > 0,
            "Plot includes all 24 temperature readings.",
            f"Expected a line with 24 data points, found {len(line_points)} such lines."
        )
        # x-axis should represent hours 0..23
        xdata = line_points[0].get_xdata()
        _dynamic_test(
            self,
            list(xdata) == list(range(24)),
            "X-axis represents hours 0 to 23.",
            f"Expected x-axis to be 0..23, got {list(xdata)}."
        )

    def test_labels_and_title(self):
        plt.close('all')
        user_code.plot_temperature_profile(self.temperatures)
        ax = plt.gca()
        xlabel = ax.get_xlabel()
        ylabel = ax.get_ylabel()
        title = ax.get_title()
        _dynamic_test(
            self,
            xlabel == 'Hour',
            "X-axis label is correct.",
            f"Expected x-axis label 'Hour', got '{xlabel}'."
        )
        _dynamic_test(
            self,
            ylabel == 'Temperature in °C',
            "Y-axis label is correct.",
            f"Expected y-axis label 'Temperature in °C', got '{ylabel}'."
        )
        _dynamic_test(
            self,
            title == '24-Hour Temperature Profile',
            "Plot title is correct.",
            f"Expected title '24-Hour Temperature Profile', got '{title}'."
        )

    def test_highlight_max_min_points(self):
        plt.close('all')
        user_code.plot_temperature_profile(self.temperatures)
        ax = plt.gca()
        # Find all scatter points (collections)
        collections = [c for c in ax.collections]
        # There should be at least two collections (for max and min)
        _dynamic_test(
            self,
            len(collections) >= 2,
            "Scatter points for max and min temperatures are present.",
            f"Expected at least two scatter collections, got {len(collections)}."
        )
        max_temp = max(self.temperatures)
        min_temp = min(self.temperatures)
        max_hour = self.temperatures.index(max_temp)
        min_hour = self.temperatures.index(min_temp)
        found_max = False
        found_min = False
        # Check for red and blue scatter points at correct locations
        for c in collections:
            offsets = c.get_offsets()
            facecolors = c.get_facecolor()
            for i, (x, y) in enumerate(offsets):
                color = facecolors[i][:3]
                # Red is (1,0,0), blue is (0,0,1)
                if int(x) == max_hour and abs(y - max_temp) < 1e-6:
                    found_max = (color[0] > 0.9 and color[1] < 0.2 and color[2] < 0.2)
                if int(x) == min_hour and abs(y - min_temp) < 1e-6:
                    found_min = (color[0] < 0.2 and color[1] < 0.2 and color[2] > 0.9)
        _dynamic_test(
            self,
            found_max,
            "Maximum temperature point is highlighted in red.",
            "Maximum temperature point is not correctly highlighted in red."
        )
        _dynamic_test(
            self,
            found_min,
            "Minimum temperature point is highlighted in blue.",
            "Minimum temperature point is not correctly highlighted in blue."
        )

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: Visualize Temperature Sensor Data

Ratkaisu

Challenge: Visualize Temperature Sensor Data

Ratkaisu