alias conversion functions to model objects

Author: TomDonoghue

fooof/objs/fit.py

@@ -78,6 +78,7 @@
 from fooof.utils.data import trim_spectrum
 from fooof.utils.params import compute_gauss_std
 from fooof.data import FOOOFResults, FOOOFSettings, FOOOFMetaData
+from fooof.data.conversions import model_to_dataframe
 from fooof.sim.gen import gen_freqs, gen_aperiodic, gen_periodic, gen_model
 
 ###################################################################################################
@@ -716,6 +717,25 @@ def set_check_data_mode(self, check_data):
         self._check_data = check_data
 
 
+    def to_df(self, peak_org):
+        """Convert and extract the model results as a pandas object.
+
+        Parameters
+        ----------
+        peak_org : int or Bands
+            How to organize peaks.
+            If int, extracts the first n peaks.
+            If Bands, extracts peaks based on band definitions.
+
+        Returns
+        -------
+        pd.Series
+            Model results organized into a pandas object.
+        """
+
+        return model_to_dataframe(self.get_results(), peak_org)
+
+
     def _check_width_limits(self):
         """Check and warn about peak width limits / frequency resolution interaction."""
 

fooof/objs/group.py

@@ -21,6 +21,7 @@
 from fooof.core.strings import gen_results_fg_str
 from fooof.core.io import save_fg, load_jsonlines
 from fooof.core.modutils import copy_doc_func_to_method, safe_import
+from fooof.data.conversions import group_to_dataframe
 
 ###################################################################################################
 ###################################################################################################
@@ -543,6 +544,25 @@ def print_results(self, concise=False):
         print(gen_results_fg_str(self, concise))
 
 
+    def to_df(self, peak_org):
+        """Convert and extract the model results as a pandas object.
+
+        Parameters
+        ----------
+        peak_org : int or Bands
+            How to organize peaks.
+            If int, extracts the first n peaks.
+            If Bands, extracts peaks based on band definitions.
+
+        Returns
+        -------
+        pd.DataFrame
+            Model results organized into a pandas object.
+        """
+
+        return group_to_dataframe(self.get_results(), peak_org)
+
+
     def _fit(self, *args, **kwargs):
         """Create an alias to FOOOF.fit for FOOOFGroup object, for internal use."""
 

fooof/tests/objs/test_fit.py

@@ -12,9 +12,12 @@
 from fooof.core.items import OBJ_DESC
 from fooof.core.errors import FitError
 from fooof.core.utils import group_three
+from fooof.core.modutils import safe_import
+from fooof.core.errors import DataError, NoDataError, InconsistentDataError
 from fooof.sim import gen_freqs, gen_power_spectrum
 from fooof.data import FOOOFSettings, FOOOFMetaData, FOOOFResults
-from fooof.core.errors import DataError, NoDataError, InconsistentDataError
+
+pd = safe_import('pandas')
 
 from fooof.tests.settings import TEST_DATA_PATH
 from fooof.tests.tutils import get_tfm, plot_test
@@ -425,3 +428,10 @@ def test_fooof_check_data():
     # Model fitting should execute, but return a null model fit, given the NaNs, without failing
     tfm.fit()
     assert not tfm.has_model
+
+def test_fooof_to_df(tfm, tbands, skip_if_no_pandas):
+
+    df1 = tfm.to_df(2)
+    assert isinstance(df1, pd.Series)
+    df2 = tfm.to_df(tbands)
+    assert isinstance(df2, pd.Series)

fooof/tests/objs/test_group.py

@@ -9,10 +9,14 @@
 import numpy as np
 from numpy.testing import assert_equal
 
-from fooof.data import FOOOFResults
 from fooof.core.items import OBJ_DESC
+from fooof.core.modutils import safe_import
+from fooof.core.errors import DataError, NoDataError, InconsistentDataError
+from fooof.data import FOOOFResults
 from fooof.sim import gen_group_power_spectra
 
+pd = safe_import('pandas')
+
 from fooof.tests.settings import TEST_DATA_PATH
 from fooof.tests.tutils import default_group_params, plot_test
 
@@ -349,3 +353,10 @@ def test_fg_get_group(tfg):
     # Check that the correct results are extracted
     assert [tfg.group_results[ind] for ind in inds1] == nfg1.group_results
     assert [tfg.group_results[ind] for ind in inds2] == nfg2.group_results
+
+def test_fg_to_df(tfg, tbands, skip_if_no_pandas):
+
+    df1 = tfg.to_df(2)
+    assert isinstance(df1, pd.DataFrame)
+    df2 = tfg.to_df(tbands)
+    assert isinstance(df2, pd.DataFrame)