Visualizing team progression helps coaches and athletes see improvements and identify patterns over a season. By plotting the scores from each game, you gain a clear picture of how the team is performing over time, which can be invaluable for planning future training and identifying strengths or weaknesses in performance trends.


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)

    def test_plot_points_count(self):
        plt.clf()
        scores = [10, 20, 15, 18]
        user_code.plot_team_progression(scores)
        lines = plt.gca().get_lines()
        _dynamic_test(
            self,
            len(lines) == 1,
            "There is one line on the plot graph",
            f"Expected 1 line on the graph, got {len(lines)}",
        )
        points = lines[0].get_xydata()
        _dynamic_test(
            self,
            len(points) == len(scores),
            f"Number of points matches input list length ({len(scores)})",
            f"Expected {len(scores)} points, got {len(points)}",
        )

    def test_x_axis_game_number_start(self):
        plt.clf()
        scores = [5, 7, 9]
        user_code.plot_team_progression(scores)
        lines = plt.gca().get_lines()
        points = lines[0].get_xydata()
        x_values = [pt[0] for pt in points]
        _dynamic_test(
            self,
            x_values == [1, 2, 3],
            "X-axis values start at 1 and increase by 1 for each game",
            f"Expected x-axis: [1, 2, 3], got: {x_values}",
        )

    def test_y_axis_team_scores(self):
        plt.clf()
        scores = [3, 8, 12, 6]
        user_code.plot_team_progression(scores)
        lines = plt.gca().get_lines()
        points = lines[0].get_xydata()
        y_values = [pt[1] for pt in points]
        _dynamic_test(
            self,
            y_values == scores,
            "Y-axis values match input team scores",
            f"Expected y-axis: {scores}, got: {y_values}",
        )

    def test_plot_title(self):
        plt.clf()
        user_code.plot_team_progression([1, 2, 3])
        title = plt.gca().get_title()
        expected = "Team Score Progression Over the Season"
        _dynamic_test(
            self,
            title == expected,
            f"Graph title is '{expected}'",
            f"Expected title: '{expected}', got: '{title}'",
        )

    def test_axis_labels(self):
        plt.clf()
        user_code.plot_team_progression([1, 2, 3])
        ax = plt.gca()
        xlab = ax.get_xlabel()
        ylab = ax.get_ylabel()
        _dynamic_test(
            self,
            xlab == "Game Number",
            "X-axis is labeled 'Game Number'",
            f"Expected x-axis label 'Game Number', got: '{xlab}'",
        )
        _dynamic_test(
            self,
            ylab == "Team Score",
            "Y-axis is labeled 'Team Score'",
            f"Expected y-axis label 'Team Score', got: '{ylab}'",
        )

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 Team Progression

Рішення

Challenge: Visualize Team Progression

Рішення