Merge pull request #54 from schmidtfa/minor_fixes

Minor fixes to main
schmidtfa · Aug 16, 2024 · 382cf54 · 382cf54
2 parents 114b4df + cd2e484
commit 382cf54
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diff --git a/README.md b/README.md
@@ -12,6 +12,7 @@ PyRASA is a Python library designed to separate and parametrize aperiodic (fract
 - **Aperiodic and Periodic Decomposition:** Utilize the IRASA algorithm to decompose power spectra into aperiodic and periodic components, enabling better interpretation of neurophysiological signals.
 - **Time Resolved Spectral Parametrization:** Perform time resolved spectral parametrizazion, allowing you to track changes in spectral components over time.
 - **Support for Raw and Epoched MNE Objects:** PyRASA provides functions designed for both continuous (Raw) and event-related (Epochs) data, making it versatile for various types of EEG/MEG analyses.
+- **Consistent Ontology:** PyRASA uses the same jargon to label parameters as specparam, the most commonly used tool to parametrize power spectra, to allow users to easily switch between tools depending on their needs, while keeping the labeling of features consistent.
 - **Custom Aperiodic Fit Models:** In addition to the built-in "fixed" and "knee" models for aperiodic fitting, users can specify their custom aperiodic fit functions, offering flexibility in how aperiodic components are modeled.
 
 
@@ -53,7 +54,7 @@ from pyrasa.irasa import irasa
 
 irasa_out = irasa(sig, 
                     fs=fs, 
-                    band=(1, 100), 
+                    band=(.1, 200), 
                     psd_kwargs={'nperseg': duration*fs, 
                                 'noverlap': duration*fs*overlap
                                 },
@@ -72,7 +73,7 @@ irasa_out.get_peaks()
 ```
 |   ch_name |   cf |      bw |     pw |
 |----------:|-----:|--------:|-------:|
-|         0 |  9.5 | 1.44337 | 0.4146 |
+|         0 |  9.5 | 1.4426 | 0.4178 |
 
 Extract aperiodic parameters
 
@@ -82,9 +83,9 @@ irasa_out.fit_aperiodic_model(fit_func='knee').aperiodic_params
 
 ```
 
-|   Offset |   Knee |   Exponent_1 |   Exponent_2 | fit_type   |   Knee Frequency (Hz) |   ch_name |
-|---------:|-------:|-------------:|-------------:|:-----------|----------------------:|----------:|
-|  1.38098 | 532.91 |     0.511999 |      1.89448 | knee       |               8.59554 |         0 |
+|   Offset |   Knee |   Exponent_1 |   Exponent_2 | fit_type   |   Knee Frequency (Hz) |   tau |   ch_name |
+|---------:|-------:|-------------:|-------------:|:-----------|----------------------:|----------:|----------:|
+|  	1.737402e-16 | 60.942144 |     0.039556 |      1.472746	 | knee       |               14.131277 |         0.011263 |         0 |
 
 And the goodness of fit
 
@@ -96,7 +97,7 @@ irasa_out.fit_aperiodic_model(fit_func='knee').gof
 
 |         mse |   r_squared |      BIC |      AIC | fit_type   |   ch_name |
 |------------:|------------:|---------:|---------:|:-----------|----------:|
-| 3.02402e-05 |    0.999894 | -2049.69 | -2062.86 | knee       |         0 |
+| 0.000051 |    0.999751 | -3931.840246 | -3947.806104 | knee       |         0 |
 
 
 ### How to Contribute

diff --git a/examples/basic_functionality.ipynb b/examples/basic_functionality.ipynb
diff --git a/pyrasa/utils/fit_funcs.py b/pyrasa/utils/fit_funcs.py
@@ -296,6 +296,7 @@ def add_infos_to_df(self, df_params: pd.DataFrame) -> pd.DataFrame:
         df_params['Knee Frequency (Hz)'] = df_params['Knee'] ** (
             1.0 / (2 * df_params['Exponent_1'] + df_params['Exponent_2'])
         )
+        df_params['tau'] = 1.0 / (2 * np.pi * df_params['Knee Frequency (Hz)'])
         return df_params
 
     @property

diff --git a/simulations/example_knee.png b/simulations/example_knee.png
diff --git a/tests/test_irasa_knee.py b/tests/test_irasa_knee.py
@@ -28,7 +28,13 @@ def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee):
     # test whether we can get the first exponent correctly
     assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE))
     # test whether we can get the second exponent correctly
-    assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE))
+    assert bool(
+        np.isclose(
+            slope_fit_k.aperiodic_params['Exponent_2'][0],
+            np.abs(exponent),
+            atol=TOLERANCE,
+        )
+    )
     # test whether we can get the knee correctly
     knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** (
         1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0])
@@ -59,7 +65,13 @@ def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq):
     # test whether we can get the first exponent correctly
     assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE))
     # test whether we can get the second exponent correctly
-    assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE))
+    assert bool(
+        np.isclose(
+            slope_fit_k.aperiodic_params['Exponent_2'][0],
+            np.abs(exponent),
+            atol=TOLERANCE,
+        )
+    )
     # test whether we can get the knee correctly
     knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** (
         1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0])