Coaches often want to recognize athletes who show the most progress. Automate the process of finding the most improved athlete.


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

class TestTask(unittest.TestCase):
    def test_returns_most_improved(self):
        import user_code
        importlib.reload(user_code)
        athletes = [
            {'name': 'Alex', 'first_half': [10, 12, 11], 'second_half': [14, 16, 15]},
            {'name': 'Jordan', 'first_half': [15, 14, 13], 'second_half': [16, 15, 16]},
            {'name': 'Casey', 'first_half': [8, 9, 7], 'second_half': [12, 13, 14]},
        ]
        result = None
        if hasattr(user_code, 'find_most_improved_athlete'):
            result = user_code.find_most_improved_athlete(athletes)
        _dynamic_test(
            self,
            result == 'Casey',
            "Returns 'Casey' as the most improved athlete for provided data",
            f"Expected 'Casey', got {result}",
        )

    def test_correct_average_calculation(self):
        import user_code
        importlib.reload(user_code)
        athletes = [
            {'name': 'Taylor', 'first_half': [2, 2, 2], 'second_half': [4, 4, 4]}, # improvement 2
            {'name': 'Morgan', 'first_half': [3, 3, 3], 'second_half': [5, 5, 5]}, # improvement 2
            {'name': 'Sam', 'first_half': [1, 1, 1], 'second_half': [2, 2, 2]},   # improvement 1
        ]
        result = None
        if hasattr(user_code, 'find_most_improved_athlete'):
            result = user_code.find_most_improved_athlete(athletes)
        _dynamic_test(
            self,
            result in ['Taylor', 'Morgan'],
            "Returns an athlete with the largest improvement (Taylor or Morgan)",
            f"Expected 'Taylor' or 'Morgan', got {result}",
        )

    def test_negative_and_zero_improvements(self):
        import user_code
        importlib.reload(user_code)
        athletes = [
            {'name': 'A', 'first_half': [10, 10], 'second_half': [9, 9]},  # -1
            {'name': 'B', 'first_half': [5, 5], 'second_half': [5, 5]},    # 0
            {'name': 'C', 'first_half': [1, 2], 'second_half': [2, 3]},    # +1
        ]
        result = None
        if hasattr(user_code, 'find_most_improved_athlete'):
            result = user_code.find_most_improved_athlete(athletes)
        _dynamic_test(
            self,
            result == 'C',
            "Returns athlete with greatest (even if small) positive improvement",
            f"Expected 'C', got {result}",
        )

    def test_handles_all_negative_improvements(self):
        import user_code
        importlib.reload(user_code)
        athletes = [
            {'name': 'X', 'first_half': [10, 10], 'second_half': [5, 5]},  # -5
            {'name': 'Y', 'first_half': [10, 10], 'second_half': [6, 6]},  # -4
            {'name': 'Z', 'first_half': [10, 10], 'second_half': [7, 7]},  # -3
        ]
        result = None
        if hasattr(user_code, 'find_most_improved_athlete'):
            result = user_code.find_most_improved_athlete(athletes)
        _dynamic_test(
            self,
            result == 'Z',
            "Returns athlete with least negative improvement if all negative",
            f"Expected 'Z', got {result}",
        )

    def test_handles_single_athlete(self):
        import user_code
        importlib.reload(user_code)
        athletes = [
            {'name': 'Solo', 'first_half': [1, 2, 3], 'second_half': [4, 5, 6]},
        ]
        result = None
        if hasattr(user_code, 'find_most_improved_athlete'):
            result = user_code.find_most_improved_athlete(athletes)
        _dynamic_test(
            self,
            result == 'Solo',
            "Returns the single athlete's name if only one athlete",
            f"Expected 'Solo', got {result}",
        )

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: Find Most Improved Athlete

Løsning

Challenge: Find Most Improved Athlete

Løsning