Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Ensure correct float types with numpy 2.0 #34

Merged
merged 1 commit into from
Jul 4, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 19 additions & 5 deletions auglib/core/utils.py
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ def from_db(x_db: typing.Union[float, observe.Base]) -> float:
"""
x_db = observe.observe(x_db)
x = pow(10.0, x_db / 20.0)
return x
return float(x)


def get_peak(signal: np.ndarray) -> float:
Expand All @@ -34,28 +34,42 @@ def get_peak(signal: np.ndarray) -> float:
Returns:
peak as positive value

Examples:
>>> get_peak(np.array([1, 2, 3]))
3.0

"""
minimum = np.min(signal)
maximum = np.max(signal)
if abs(minimum) > maximum:
peak = abs(minimum)
else:
peak = maximum
return peak
return float(peak)


def rms_db(signal: np.ndarray):
def rms_db(signal: np.ndarray) -> float:
r"""Root mean square in dB.

Very soft signals are limited
to a value of -120 dB.

Args:
signal: input signal

Returns:
root mean square in decibel

Examples:
>>> rms_db(np.zeros((1, 4)))
-120.0

"""
# It is:
# 20 * log10(rms) = 10 * log10(power)
# which saves us from calculating sqrt()
power = np.mean(np.square(signal))
return 10 * np.log10(max(1e-12, power))
return float(10 * np.log10(max(1e-12, power)))


def to_db(x: typing.Union[float, observe.Base]) -> float:
Expand All @@ -75,7 +89,7 @@ def to_db(x: typing.Union[float, observe.Base]) -> float:
x = observe.observe(x)
assert x > 0, "cannot convert gain {} to decibels".format(x)
x_db = 20 * np.log10(x)
return x_db
return float(x_db)


def to_samples(
Expand Down
6 changes: 2 additions & 4 deletions tests/test_transform_babble_noise.py
Original file line number Diff line number Diff line change
Expand Up @@ -45,10 +45,8 @@ def test_babble_noise_1(
if snr_db is not None:
gain_db = -120 - snr_db
gain = audmath.inverse_db(gain_db)
expected_babble = gain * np.ones(
(1, int(duration * sampling_rate)),
dtype=auglib.core.transform.DTYPE,
)
expected_babble = gain * np.ones((1, int(duration * sampling_rate)))
expected_babble = expected_babble.astype(auglib.core.transform.DTYPE)

babble = transform(signal)
assert babble.dtype == expected_babble.dtype
Expand Down
2 changes: 1 addition & 1 deletion tests/test_transform_pink_noise.py
Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ def test_pink_noise(duration, sampling_rate, gain_db, snr_db):
)
assert noise.shape == expected_noise.shape
assert noise.dtype == expected_noise.dtype
np.testing.assert_almost_equal(noise, expected_noise)
np.testing.assert_almost_equal(noise, expected_noise, decimal=6)


@pytest.mark.parametrize(
Expand Down
Loading