from datetime import timedelta

import pytest

from progressbar import algorithms

def test_ema_initialization() -> None:

ema = algorithms.ExponentialMovingAverage()

assert ema.alpha == 0.5

assert ema.value is None

@pytest.mark.parametrize(

'alpha, new_value, expected',

[

(0.5, 10, 5),

(0.1, 20, 2),

(0.9, 30, 27),

(0.3, 15, 4.5),

(0.7, 40, 28),

(0.5, 0, 0),

(0.2, 100, 20),

(0.8, 50, 40),

],

)

def test_ema_update(alpha, new_value: float, expected) -> None:

# The first update seeds the average, so blending starts from an

# explicit zero observation: alpha * new_value + (1 - alpha) * 0

ema = algorithms.ExponentialMovingAverage(alpha)

ema.update(0, timedelta(seconds=1))

result = ema.update(new_value, timedelta(seconds=1))

assert result == expected

def test_dema_initialization() -> None:

dema = algorithms.DoubleExponentialMovingAverage()

assert dema.alpha == 0.5

assert dema.ema1 is None

assert dema.ema2 is None

@pytest.mark.parametrize(

'alpha, new_value, expected',

[

(0.5, 10, 7.5),

(0.1, 20, 3.8),

(0.9, 30, 29.7),

(0.3, 15, 7.65),

(0.5, 0, 0),

(0.2, 100, 36.0),

(0.8, 50, 48.0),

],

)

def test_dema_update(alpha, new_value: float, expected) -> None:

# Seeded with an explicit zero observation, a single update yields

# ema1 = alpha * v, ema2 = alpha^2 * v, so the result is

# alpha * v * (2 - alpha) which matches the historical values

dema = algorithms.DoubleExponentialMovingAverage(alpha)

dema.update(0, timedelta(seconds=1))

result = dema.update(new_value, timedelta(seconds=1))

assert result == pytest.approx(expected)

# Additional test functions can be added here as needed.

def test_ema_seeds_from_first_value() -> None:

# Regression: B8 - the average started at 0, biasing early values

# toward zero instead of the first observation.

ema = algorithms.ExponentialMovingAverage(0.5)

assert ema.update(100, timedelta(seconds=1)) == 100

assert ema.update(50, timedelta(seconds=1)) == 75

def test_dema_seeds_from_first_value() -> None:

# Regression: B8 - same zero bias for the double EMA.

dema = algorithms.DoubleExponentialMovingAverage(0.5)

assert dema.update(100, timedelta(seconds=1)) == 100

assert dema.update(50, timedelta(seconds=1)) == 62.5