first type fixes

pyrasa/utils/aperiodic_utils.py

@@ -1,7 +1,7 @@
 """Utilities for slope fitting."""
 
 import warnings
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
 
 import numpy as np
 import pandas as pd
@@ -30,7 +30,7 @@ def knee_model(x, b0, k, b1, b2):
     return y_hat
 
 
-def _get_gof(psd: np.ndarray, psd_pred: np.ndarray, fit_func: Callable) -> tuple[pd.DataFrame, pd.DataFrame]:
+def _get_gof(psd: np.ndarray, psd_pred: np.ndarray, fit_func: str) -> pd.DataFrame:
     """
     get goodness of fit (i.e. mean squared error and R2)
     BIC and AIC currently assume OLS

pyrasa/utils/peak_utils.py

@@ -11,7 +11,7 @@
 def get_peak_params(
     periodic_spectrum: np.ndarray,
     freqs: np.ndarray,
-    ch_names: Iterable = (),
+    ch_names: Iterable | None = None,
     smooth: bool = True,
     smoothing_window: int = 1,
     polyorder: int = 1,
@@ -57,7 +57,7 @@ def get_peak_params(
     freq_step = freqs[1] - freqs[0]
 
     # generate channel names if not given
-    if len(ch_names) == 0:
+    if ch_names is None:
         ch_names = np.arange(periodic_spectrum.shape[0])
 
     # cut data