Säähavainnot

import random

def get_forecast(previous_temperatures):
    forecast = previous_temperatures[:12]

    total_change = previous_temperatures[-1] - previous_temperatures[0]
    forecast = (t + total_change for t in forecast)

    fluctuation = max(previous_temperatures) - min(previous_temperatures)
    cutoff = 0.45 * fluctuation
    forecast = [(forecast if i < cutoff else None) for i, forecast in enumerate(forecast)]

    return forecast

def load_test_data():
    parsed_data = []
    with open("test-data.txt", "r", encoding="utf-8") as file:
        for line in file:
            temperatures = [float(s) for s in line.strip().split(" ")]
            forecast_input = temperatures[:24]
            correct_forecast = temperatures[24:]

            parsed_data.append((forecast_input, correct_forecast))

    random.shuffle(parsed_data)
    return parsed_data

def solve_task():
    day_count = int(input(""))

    data = ([float(s) for s in input("").split(" ")] for _ in range(day_count))

    for day in data:
        forecast = get_forecast(day)

        print(" ".join(("?" if h is None else str(h)) for h in forecast))

def get_point_forecast_quality(correct_temperature, forecast_temperature):
    if forecast_temperature is None:
        return 1

    deviation = abs(correct_temperature - forecast_temperature)

    if deviation > 2.05:
        return 0
    elif deviation > 0.75:
        return 1
    else:
        return 2

def get_point_forecast_score(correct_temperature, forecast_temperature):
    possible_scores = [-1, 0, 1]
    return possible_scores[get_point_forecast_quality(correct_temperature, forecast_temperature)]

def get_random_color():
    return (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))

def plot():
    import matplotlib.pyplot as plt
    import numpy as np

    color_codes = ["#F00", "#FF0", "#0F0"]

    fig = plt.figure()
    x = np.arange(36)

    test_data = load_test_data()
    for day in test_data[:20]:
        line_color = get_random_color()
        forecast = get_forecast(day[0])

        full_day = day[0] + day[1]
        plt.plot(x, full_day, color=line_color)

        points = [p for p in zip(x[24:], forecast, day[1]) if p[1] is not None]
        filtered_t = [t for t, forecast, correct in points]
        filtered_forecast = [forecast for t, forecast, correct in points]
        filtered_correct = [correct for t, forecast, correct in points]

        colors = [color_codes[get_point_forecast_quality(correct, forecast)] for t, forecast, correct in points]
        plt.scatter(filtered_t, filtered_forecast, c = colors)

        plt.plot(filtered_t, filtered_forecast, color=line_color)

    plt.show()

def test_perfomance():
    test_data = load_test_data()

    correct_forecasts = 0
    incorrect_forecasts = 0
    skipped_forecasts = 0

    for day in test_data:
        forecast = get_forecast(day[0])

        for f, c in zip(forecast, day[1]):
            score = get_point_forecast_score(c, f)

            if f is None:
                skipped_forecasts += 1
            elif score == 1:
                correct_forecasts += 1
            elif score == -1:
                incorrect_forecasts += 1

    final_score = 25 * (correct_forecasts - incorrect_forecasts) / len(test_data)
    print(f"{final_score = }")
    print(f"{correct_forecasts = }")
    print(f"{incorrect_forecasts = }")
    print(f"{skipped_forecasts = }")
    print("")

solve_task()

Test 1

Verdict: ACCEPTED

Test 2

Verdict: ACCEPTED

Test 3

Verdict: ACCEPTED

Test 4

Verdict: ACCEPTED

Test 5

Verdict: ACCEPTED

Test 6

Verdict: ACCEPTED

Test 7

Verdict: ACCEPTED

Test 8

Verdict: ACCEPTED