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Fixed deprecation warning. #378

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Merged
merged 3 commits into from
Oct 8, 2025
Merged

Fixed deprecation warning. #378

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453e7f2
Fixed deprecation warning.
cgohil8 Sep 28, 2025
351bd10
Merge branch 'main' into pkg-resources
cgohil8 Oct 8, 2025
858250f
Formatted with black.
cgohil8 Oct 8, 2025
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8 changes: 4 additions & 4 deletions osl_dynamics/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,16 +1,16 @@
import logging
from pkg_resources import DistributionNotFound, get_distribution
from importlib.metadata import PackageNotFoundError, version

from osl_dynamics.config_api.pipeline import run_pipeline


# Setup the version
try:
__version__ = get_distribution("osl-dynamics").version
except DistributionNotFound:
__version__ = version("osl-dynamics")
except PackageNotFoundError:
__version__ = "unknown"
finally:
del get_distribution, DistributionNotFound
del version, PackageNotFoundError

# Configure logging
logging.basicConfig(
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16 changes: 8 additions & 8 deletions osl_dynamics/analysis/spectral.py
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Expand Up @@ -1260,15 +1260,15 @@
n_lags = autocorr_func.shape[-1]
nfft = max(nfft, 2 ** nextpow2(n_lags))

# Calculate the argments to keep for the given frequency range

Check failure on line 1263 in osl_dynamics/analysis/spectral.py

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argments ==> arguments
f = np.arange(nfft // 2 + 1) * sampling_frequency / nfft
[min_arg, max_arg] = get_frequency_args_range(f, frequency_range)
f = f[min_arg:max_arg + 1]
f = f[min_arg : max_arg + 1]

# Calculate cross power spectra as the Fourier transform of the
# auto/cross-correlation function
psd = np.fft.fft(autocorr_func, nfft)
psd = psd[..., min_arg:max_arg + 1]
psd = psd[..., min_arg : max_arg + 1]
psd = abs(psd)

# Normalise the power spectra
Expand Down Expand Up @@ -1385,7 +1385,7 @@

# Only keep a particular frequency range
[min_arg, max_arg] = get_frequency_args_range(f, frequency_range)
f = f[min_arg:max_arg + 1]
f = f[min_arg : max_arg + 1]

# Number of frequency bins
n_freq = max_arg - min_arg + 1
Expand Down Expand Up @@ -1428,7 +1428,7 @@

# Calculate cross spectra for the sub-window
X = np.fft.fft(x_sub_window, nfft)
X = X[..., min_arg:max_arg + 1]
X = X[..., min_arg : max_arg + 1]
XY = np.conj(X)[np.newaxis, :, :] * X[:, np.newaxis, :]
XY_sub_window[k] = XY[m, n]

Expand Down Expand Up @@ -1463,7 +1463,7 @@

# Calculate PSD for the sub-window
X = np.fft.fft(x_sub_window, nfft)
X = X[..., min_arg:max_arg + 1]
X = X[..., min_arg : max_arg + 1]
XX_sub_window[k] = np.real(np.conj(X) * X)

# Average the cross spectra for each sub-window
Expand Down Expand Up @@ -1696,7 +1696,7 @@

# Only keep a particular frequency range
[min_arg, max_arg] = get_frequency_args_range(f, frequency_range)
f = f[min_arg:max_arg + 1]
f = f[min_arg : max_arg + 1]

# Number of frequency bins
n_freq = max_arg - min_arg + 1
Expand Down Expand Up @@ -1739,7 +1739,7 @@

# Fourier transform
X = np.fft.fft(x_sub_window, nfft)
X = X[..., min_arg:max_arg + 1]
X = X[..., min_arg : max_arg + 1]

# Calculate cross spectra for the sub-window
XY = np.conj(X)[:, :, np.newaxis, :] * X[:, np.newaxis, :, :]
Expand Down Expand Up @@ -1776,7 +1776,7 @@

# Fourier transform
X = np.fft.fft(x_sub_window, nfft)
X = X[..., min_arg:max_arg + 1]
X = X[..., min_arg : max_arg + 1]

# Calculate spectra for the sub-window
XX = np.real(np.conj(X) * X)
Expand Down