figured out weird warning

pyrasa/irasa_mne/mne_objs.py

@@ -1,4 +1,5 @@
 # %% inherit from spectrum array
+
 import matplotlib
 import mne
 import numpy as np
@@ -10,6 +11,8 @@
 from pyrasa.utils.peak_utils import get_peak_params
 from pyrasa.utils.types import SlopeFit
 
+# FutureWarning:
+
 
 class PeriodicSpectrumArray(SpectrumArray):
     """Subclass of SpectrumArray"""
@@ -170,7 +173,10 @@ def __init__(
         )
 
     def get_slopes(
-        self: SpectrumArray, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None
+        self: SpectrumArray,
+        fit_func: str = 'fixed',
+        scale: bool = False,
+        fit_bounds: tuple[float, float] | None = None,
     ) -> SlopeFit:
         """
         This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA.
@@ -393,7 +399,10 @@ def __init__(
         )
 
     def get_slopes(
-        self: SpectrumArray, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None
+        self: SpectrumArray,
+        fit_func: str = 'fixed',
+        scale: bool = False,
+        fit_bounds: tuple[float, float] | None = None,
     ) -> SlopeFit:
         """
         This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA.

pyrasa/utils/fit_funcs.py

@@ -9,7 +9,7 @@
 from scipy.optimize import curve_fit
 
 
-def get_args(f: Callable) -> list:
+def _get_args(f: Callable) -> list:
     return inspect.getfullargspec(f)[0][2:]
 
 
@@ -59,7 +59,12 @@ def func(self, x: np.ndarray, *args: float) -> np.ndarray:
 
     @property
     def curve_kwargs(self) -> dict[str, Any]:
-        return {}
+        return {
+            'maxfev': 10_000,
+            'ftol': 1e-5,
+            'xtol': 1e-5,
+            'gtol': 1e-5,
+        }
 
     def add_infos_to_df(self, df_params: pd.DataFrame) -> pd.DataFrame:
         return df_params
@@ -75,7 +80,7 @@ def fit_func(self) -> tuple[pd.DataFrame, pd.DataFrame]:
         curve_kwargs = self.curve_kwargs
         p, _ = curve_fit(self.func, self.freq, self.aperiodic_spectrum, **curve_kwargs)
 
-        my_args = get_args(self.func)
+        my_args = _get_args(self.func)
         df_params = pd.DataFrame(dict(zip(my_args, p)), index=[0])
         df_params['fit_type'] = self.label
 

tests/test_mne.py

@@ -1,5 +1,6 @@
 import pytest
 from neurodsp.utils.sim import set_random_seed
+from scipy.optimize import OptimizeWarning
 
 from pyrasa.irasa_mne import irasa_epochs, irasa_raw
 
@@ -13,8 +14,9 @@ def test_mne(gen_mne_data_raw):
 
     # test raw
     irasa_raw_result = irasa_raw(mne_data, band=(0.25, 50), duration=2, hset_info=(1.0, 2.0, 0.05))
-    irasa_raw_result.aperiodic.get_slopes(fit_func='fixed')
-    irasa_raw_result.periodic.get_peaks(smoothing_window=2)
+    with pytest.warns(OptimizeWarning):
+        irasa_raw_result.aperiodic.get_slopes(fit_func='fixed')
+        irasa_raw_result.periodic.get_peaks(smoothing_window=2)
 
     # test epochs
     irasa_epoched_result = irasa_epochs(epochs, band=(0.5, 50), hset_info=(1.0, 2.0, 0.05))