diff --git a/osl_dynamics/__init__.py b/osl_dynamics/__init__.py
index aca090f2..d535e083 100644
--- a/osl_dynamics/__init__.py
+++ b/osl_dynamics/__init__.py
@@ -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(
diff --git a/osl_dynamics/analysis/spectral.py b/osl_dynamics/analysis/spectral.py
index 1edfe91f..36834ab1 100644
--- a/osl_dynamics/analysis/spectral.py
+++ b/osl_dynamics/analysis/spectral.py
@@ -1263,12 +1263,12 @@ def autocorr_to_spectra(
     # Calculate the argments to keep for the given frequency range
     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
@@ -1385,7 +1385,7 @@ def _welch_spectrogram(
 
     # 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
@@ -1428,7 +1428,7 @@ def _welch_spectrogram(
 
                 # 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]
 
@@ -1463,7 +1463,7 @@ def _welch_spectrogram(
 
                 # 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
@@ -1696,7 +1696,7 @@ def _multitaper_spectrogram(
 
     # 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
@@ -1739,7 +1739,7 @@ def _multitaper_spectrogram(
 
                 # 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, :, :]
@@ -1776,7 +1776,7 @@ def _multitaper_spectrogram(
 
                 # 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)