Visualizing motivational message categories helps you understand your team's morale and where to focus your coaching efforts. By seeing the distribution of athletes across categories like **"Excellent"**, **"Good"**, and **"Needs Improvement"**, you gain insights into overall performance and can tailor your motivational strategies accordingly.


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

class TestTask(unittest.TestCase):
    def setUp(self):
        self.user_code = importlib.import_module('user_code')
        importlib.reload(self.user_code)

    def _get_bar_chart_info(self, categories):
        plt.clf()
        self.user_code.plot_category_counts(categories)
        ax = plt.gca()
        bars = ax.patches
        xlabels = [tick.get_text() for tick in ax.get_xticklabels()]
        ylabels = [tick.get_text() for tick in ax.get_yticklabels()]
        xlabel = ax.get_xlabel()
        ylabel = ax.get_ylabel()
        title = ax.get_title()
        bar_heights = [bar.get_height() for bar in bars]
        bar_x = [bar.get_x() + bar.get_width()/2 for bar in bars]
        bar_labels = [bar.get_x() + bar.get_width()/2 for bar in bars]
        return {
            'bars': bars,
            'xlabels': xlabels,
            'ylabels': ylabels,
            'xlabel': xlabel,
            'ylabel': ylabel,
            'title': title,
            'bar_heights': bar_heights,
            'bar_x': bar_x,
        }

    def test_category_counts(self):
        categories = ["Excellent", "Good", "Good", "Needs Improvement", "Excellent", "Good"]
        info = self._get_bar_chart_info(categories)
        expected_counts = {"Excellent": 2, "Good": 3, "Needs Improvement": 1}
        found = {}
        for i, label in enumerate(info['xlabels']):
            if label in expected_counts:
                found[label] = info['bar_heights'][i]
        _dynamic_test(
            self,
            found == expected_counts,
            "Bar heights match the counts for each category.",
            f"Expected counts {expected_counts}, got {found}.",
        )

    def test_unique_categories_on_x_axis(self):
        categories = ["Excellent", "Excellent", "Excellent"]
        info = self._get_bar_chart_info(categories)
        _dynamic_test(
            self,
            "Excellent" in info['xlabels'] and len([x for x in info['xlabels'] if x]),
            "X-axis includes unique category from input list.",
            f"Expected 'Excellent' on x-axis. Got {info['xlabels']}.",
        )
        categories = ["Needs Improvement", "Good", "Needs Improvement", "Good", "Needs Improvement"]
        info = self._get_bar_chart_info(categories)
        _dynamic_test(
            self,
            set([x for x in info['xlabels'] if x]) == set(["Needs Improvement", "Good"]),
            "X-axis includes all unique categories from input list.",
            f"Expected 'Needs Improvement' and 'Good' on x-axis. Got {info['xlabels']}.",
        )

    def test_axis_labels_and_title(self):
        categories = ["Good", "Excellent"]
        info = self._get_bar_chart_info(categories)
        _dynamic_test(
            self,
            info['xlabel'] == "Performance Category",
            "X-axis label is set correctly.",
            f"Expected x-axis label 'Performance Category', got '{info['xlabel']}'.",
        )
        _dynamic_test(
            self,
            info['ylabel'] == "Number of Athletes",
            "Y-axis label is set correctly.",
            f"Expected y-axis label 'Number of Athletes', got '{info['ylabel']}'.",
        )
        _dynamic_test(
            self,
            info['title'] == "Athletes by Performance Category",
            "Chart title is set correctly.",
            f"Expected title 'Athletes by Performance Category', got '{info['title']}'.",
        )

    def test_bar_height_matches_count(self):
        categories = ["Good", "Good", "Excellent"]
        info = self._get_bar_chart_info(categories)
        heights = dict(zip(info['xlabels'], info['bar_heights']))
        _dynamic_test(
            self,
            heights.get("Good", 0) == 2 and heights.get("Excellent", 0) == 1,
            "Bar heights match counts for 'Good' and 'Excellent' categories.",
            f"Expected bar heights 2 for 'Good' and 1 for 'Excellent', got {heights}.",
        )

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 Python course designed specifically for coaches, trainers, and sports professionals. Learn how to leverage Python to analyze performance, track progress, and make data-driven decisions to enhance coaching outcomes.

Learn how to use Python to monitor, analyze, and visualize individual athlete progress and performance metrics.

Use Python to analyze and compare team-level data, uncover trends, and support strategic coaching decisions.

Explore how Python can automate repetitive coaching tasks, generate reports, and provide actionable insights.

Challenge: Visualize Motivational Messages

Ratkaisu

Challenge: Visualize Motivational Messages

Ratkaisu