Merge branch 'main' into shade

Author: TomDonoghue

.github/workflows/build.yml

@@ -0,0 +1,36 @@
+# This workflow runs CI tests: it installs Python dependencies and run tests across a variety of Python versions
+
+name: build
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest
+        pip install pytest-cov
+        pip install -r requirements.txt
+        pip install -r optional-requirements.txt
+    - name: Test with pytest
+      run: |
+        pytest --cov=./
+    - name: Upload coverage to Codecov
+      uses: codecov/codecov-action@v1

.travis.yml

@@ -10,8 +10,8 @@ FOOOF - fitting oscillations & one over f
 .. |Version| image:: https://img.shields.io/pypi/v/fooof.svg
 .. _Version: https://pypi.python.org/pypi/fooof/
 
-.. |BuildStatus| image:: https://travis-ci.com/fooof-tools/fooof.svg
-.. _BuildStatus: https://travis-ci.com/fooof-tools/fooof
+.. |BuildStatus| image:: https://github.com/fooof-tools/fooof/actions/workflows/build.yml/badge.svg
+.. _BuildStatus: https://github.com/fooof-tools/fooof/actions/workflows/build.yml
 
 .. |Coverage| image:: https://codecov.io/gh/fooof-tools/fooof/branch/main/graph/badge.svg
 .. _Coverage: https://codecov.io/gh/fooof-tools/fooof
@@ -71,7 +71,7 @@ This documentation includes:
 Dependencies
 ------------
 
-FOOOF is written in Python, and requires Python >= 3.5 to run.
+FOOOF is written in Python, and requires Python >= 3.6 to run.
 
 It has the following required dependencies:
 

README.rst

@@ -13,13 +13,13 @@ def group_three(vec):
 
     Parameters
     ----------
-    vec : 1d array
-        Array of items to group by 3. Length of array must be divisible by three.
+    vec : list or 1d array
+        List or array of items to group by 3. Length of array must be divisible by three.
 
     Returns
     -------
-    list of list
-        List of lists, each with three items.
+    array or list of list
+        Array or list of lists, each with three items. Output type will match input type.
 
     Raises
     ------
@@ -30,7 +30,11 @@ def group_three(vec):
     if len(vec) % 3 != 0:
         raise ValueError("Wrong size array to group by three.")
 
-    return [list(vec[ii:ii+3]) for ii in range(0, len(vec), 3)]
+    # Reshape, if an array, as it's faster, otherwise asssume lise
+    if isinstance(vec, np.ndarray):
+        return np.reshape(vec, (-1, 3))
+    else:
+        return [list(vec[ii:ii+3]) for ii in range(0, len(vec), 3)]
 
 
 def nearest_ind(array, value):

fooof/core/utils.py

@@ -74,7 +74,6 @@
                                 gen_issue_str, gen_width_warning_str)
 
 from fooof.plts.fm import plot_fm
-from fooof.plts.style import style_spectrum_plot
 from fooof.utils.data import trim_spectrum
 from fooof.utils.params import compute_gauss_std
 from fooof.data import FOOOFResults, FOOOFSettings, FOOOFMetaData
@@ -586,9 +585,7 @@ def get_params(self, name, col=None):
 
         Notes
         -----
-        For further description of the data you can extract, check the FOOOFResults documentation.
-
-        If there is no data on periodic features, this method will return NaN.
+        If there are no fit peak (no peak parameters), this method will return NaN.
         """
 
         if not self.has_model:
@@ -635,12 +632,13 @@ def get_results(self):
     @copy_doc_func_to_method(plot_fm)
     def plot(self, plot_peaks=None, plot_aperiodic=True, plt_log=False,
              add_legend=True, save_fig=False, file_name=None, file_path=None,
-             ax=None, plot_style=style_spectrum_plot,
-             data_kwargs=None, model_kwargs=None, aperiodic_kwargs=None, peak_kwargs=None):
+             ax=None, data_kwargs=None, model_kwargs=None,
+             aperiodic_kwargs=None, peak_kwargs=None, **plot_kwargs):
 
-        plot_fm(self, plot_peaks, plot_aperiodic, plt_log, add_legend,
-                save_fig, file_name, file_path, ax, plot_style,
-                data_kwargs, model_kwargs, aperiodic_kwargs, peak_kwargs)
+        plot_fm(self, plot_peaks=plot_peaks, plot_aperiodic=plot_aperiodic, plt_log=plt_log,
+                add_legend=add_legend, save_fig=save_fig, file_name=file_name,
+                file_path=file_path, ax=ax, data_kwargs=data_kwargs, model_kwargs=model_kwargs,
+                aperiodic_kwargs=aperiodic_kwargs, peak_kwargs=peak_kwargs, **plot_kwargs)
 
 
     @copy_doc_func_to_method(save_report_fm)
@@ -1007,18 +1005,16 @@ def _create_peak_params(self, gaus_params):
         with `freqs`, `fooofed_spectrum_` and `_ap_fit` all required to be available.
         """
 
-        peak_params = np.empty([0, 3])
+        peak_params = np.empty((len(gaus_params), 3))
 
         for ii, peak in enumerate(gaus_params):
 
             # Gets the index of the power_spectrum at the frequency closest to the CF of the peak
-            ind = min(range(len(self.freqs)), key=lambda ii: abs(self.freqs[ii] - peak[0]))
+            ind = np.argmin(np.abs(self.freqs - peak[0]))
 
             # Collect peak parameter data
-            peak_params = np.vstack((peak_params,
-                                     [peak[0],
-                                      self.fooofed_spectrum_[ind] - self._ap_fit[ind],
-                                      peak[2] * 2]))
+            peak_params[ii] = [peak[0], self.fooofed_spectrum_[ind] - self._ap_fit[ind],
+                               peak[2] * 2]
 
         return peak_params
 
@@ -1037,8 +1033,8 @@ def _drop_peak_cf(self, guess):
             Guess parameters for gaussian peak fits. Shape: [n_peaks, 3].
         """
 
-        cf_params = [item[0] for item in guess]
-        bw_params = [item[2] * self._bw_std_edge for item in guess]
+        cf_params = guess[:, 0]
+        bw_params = guess[:, 2] * self._bw_std_edge
 
         # Check if peaks within drop threshold from the edge of the frequency range
         keep_peak = \

fooof/objs/fit.py

@@ -354,7 +354,8 @@ def get_params(self, name, col=None):
 
         Notes
         -----
-        For further description of the data you can extract, check the FOOOFResults documentation.
+        When extracting peak information ('peak_params' or 'gaussian_params'), an additional column
+        is appended to the returned array, indicating the index of the model that the peak came from.
         """
 
         if not self.has_model:
@@ -375,21 +376,18 @@ def get_params(self, name, col=None):
         # As a special case, peak_params are pulled out in a way that appends
         #  an extra column, indicating which FOOOF run each peak comes from
         if name in ('peak_params', 'gaussian_params'):
-            out = np.array([np.insert(getattr(data, name), 3, index, axis=1)
-                            for index, data in enumerate(self.group_results)])
+
+            # Collect peak data, appending the index of the model it comes from
+            out = np.vstack([np.insert(getattr(data, name), 3, index, axis=1)
+                             for index, data in enumerate(self.group_results)])
+
             # This updates index to grab selected column, and the last column
             #  This last column is the 'index' column (FOOOF object source)
             if col is not None:
                 col = [col, -1]
         else:
             out = np.array([getattr(data, name) for data in self.group_results])
 
-        # Some data can end up as a list of separate arrays
-        #   If so, concatenate it all into one 2d array
-        if isinstance(out[0], np.ndarray):
-            out = np.concatenate([arr.reshape(1, len(arr)) \
-                if arr.ndim == 1 else arr for arr in out], 0)
-
         # Select out a specific column, if requested
         if col is not None:
             out = out[:, col]
@@ -398,9 +396,9 @@ def get_params(self, name, col=None):
 
 
     @copy_doc_func_to_method(plot_fg)
-    def plot(self, save_fig=False, file_name=None, file_path=None):
+    def plot(self, save_fig=False, file_name=None, file_path=None, **plot_kwargs):
 
-        plot_fg(self, save_fig, file_name, file_path)
+        plot_fg(self, save_fig=save_fig, file_name=file_name, file_path=file_path, **plot_kwargs)
 
 
     @copy_doc_func_to_method(save_report_fg)

fooof/objs/group.py

@@ -219,9 +219,14 @@ def fit_fooof_3d(fg, freqs, power_spectra, freq_range=None, n_jobs=1):
     >>> fgs = fit_fooof_3d(fg, freqs, power_spectra, freq_range=[3, 30])  # doctest:+SKIP
     """
 
-    fgs = []
-    for cond_spectra in power_spectra:
-        fg.fit(freqs, cond_spectra, freq_range, n_jobs)
-        fgs.append(fg.copy())
+    # Reshape 3d data to 2d and fit, in order to fit with a single group model object
+    shape = np.shape(power_spectra)
+    powers_2d = np.reshape(power_spectra, (shape[0] * shape[1], shape[2]))
+
+    fg.fit(freqs, powers_2d, freq_range, n_jobs)
+
+    # Reorganize 2d results into a list of model group objects, to reflect original shape
+    fgs = [fg.get_group(range(dim_a * shape[1], (dim_a + 1) * shape[1])) \
+        for dim_a in range(shape[0])]
 
     return fgs

fooof/objs/utils.py

@@ -7,10 +7,10 @@
 from fooof.core.funcs import gaussian_function
 from fooof.core.modutils import safe_import, check_dependency
 from fooof.sim.gen import gen_aperiodic
-from fooof.plts.utils import check_ax
+from fooof.plts.utils import check_ax, savefig
 from fooof.plts.spectra import plot_spectrum
 from fooof.plts.settings import PLT_FIGSIZES, PLT_COLORS
-from fooof.plts.style import check_n_style, style_spectrum_plot
+from fooof.plts.style import style_spectrum_plot
 from fooof.analysis.periodic import get_band_peak_fm
 from fooof.utils.params import compute_knee_frequency, compute_fwhm
 
@@ -20,16 +20,15 @@
 ###################################################################################################
 ###################################################################################################
 
+@savefig
 @check_dependency(plt, 'matplotlib')
-def plot_annotated_peak_search(fm, plot_style=style_spectrum_plot):
+def plot_annotated_peak_search(fm):
     """Plot a series of plots illustrating the peak search from a flattened spectrum.
 
     Parameters
     ----------
     fm : FOOOF
         FOOOF object, with model fit, data and settings available.
-    plot_style : callable, optional, default: style_spectrum_plot
-        A function to call to apply styling & aesthetics to the plots.
     """
 
     # Recalculate the initial aperiodic fit and flattened spectrum that
@@ -46,14 +45,12 @@ def plot_annotated_peak_search(fm, plot_style=style_spectrum_plot):
         # This forces the creation of a new plotting axes per iteration
         ax = check_ax(None, PLT_FIGSIZES['spectral'])
 
-        plot_spectrum(fm.freqs, flatspec, ax=ax, plot_style=None,
-                      label='Flattened Spectrum', color=PLT_COLORS['data'], linewidth=2.5)
-        plot_spectrum(fm.freqs, [fm.peak_threshold * np.std(flatspec)]*len(fm.freqs),
-                      ax=ax, plot_style=None, label='Relative Threshold',
-                      color='orange', linewidth=2.5, linestyle='dashed')
-        plot_spectrum(fm.freqs, [fm.min_peak_height]*len(fm.freqs),
-                      ax=ax, plot_style=None, label='Absolute Threshold',
-                      color='red', linewidth=2.5, linestyle='dashed')
+        plot_spectrum(fm.freqs, flatspec, ax=ax, linewidth=2.5,
+                      label='Flattened Spectrum', color=PLT_COLORS['data'])
+        plot_spectrum(fm.freqs, [fm.peak_threshold * np.std(flatspec)]*len(fm.freqs), ax=ax,
+                      label='Relative Threshold', color='orange', linewidth=2.5, linestyle='dashed')
+        plot_spectrum(fm.freqs, [fm.min_peak_height]*len(fm.freqs), ax=ax,
+                      label='Absolute Threshold', color='red', linewidth=2.5, linestyle='dashed')
 
         maxi = np.argmax(flatspec)
         ax.plot(fm.freqs[maxi], flatspec[maxi], '.',
@@ -65,18 +62,18 @@ def plot_annotated_peak_search(fm, plot_style=style_spectrum_plot):
         if ind < fm.n_peaks_:
 
             gauss = gaussian_function(fm.freqs, *fm.gaussian_params_[ind, :])
-            plot_spectrum(fm.freqs, gauss, ax=ax, plot_style=None,
-                          label='Gaussian Fit', color=PLT_COLORS['periodic'],
-                          linestyle=':', linewidth=3.0)
+            plot_spectrum(fm.freqs, gauss, ax=ax, label='Gaussian Fit',
+                          color=PLT_COLORS['periodic'], linestyle=':', linewidth=3.0)
 
             flatspec = flatspec - gauss
 
-        check_n_style(plot_style, ax, False, True)
+        style_spectrum_plot(ax, False, True)
 
 
+@savefig
 @check_dependency(plt, 'matplotlib')
-def plot_annotated_model(fm, plt_log=False, annotate_peaks=True, annotate_aperiodic=True,
-                         ax=None, plot_style=style_spectrum_plot):
+def plot_annotated_model(fm, plt_log=False, annotate_peaks=True,
+                         annotate_aperiodic=True, ax=None):
     """Plot a an annotated power spectrum and model, from a FOOOF object.
 
     Parameters
@@ -91,8 +88,6 @@ def plot_annotated_model(fm, plt_log=False, annotate_peaks=True, annotate_aperio
         Whether to annotate the aperiodic components of the model fit.
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
-    plot_style : callable, optional, default: style_spectrum_plot
-        A function to call to apply styling & aesthetics to the plots.
 
     Raises
     ------
@@ -112,7 +107,7 @@ def plot_annotated_model(fm, plt_log=False, annotate_peaks=True, annotate_aperio
 
     # Create the baseline figure
     ax = check_ax(ax, PLT_FIGSIZES['spectral'])
-    fm.plot(plot_peaks='dot-shade-width', plt_log=plt_log, ax=ax, plot_style=None,
+    fm.plot(plot_peaks='dot-shade-width', plt_log=plt_log, ax=ax,
             data_kwargs={'lw' : lw1, 'alpha' : 0.6},
             aperiodic_kwargs={'lw' : lw1, 'zorder' : 10},
             model_kwargs={'lw' : lw1, 'alpha' : 0.5},
@@ -133,7 +128,7 @@ def plot_annotated_model(fm, plt_log=False, annotate_peaks=True, annotate_aperio
     #   See: https://github.com/matplotlib/matplotlib/issues/12820. Fixed in 3.2.1.
     bug_buff = 0.000001
 
-    if annotate_peaks:
+    if annotate_peaks and fm.n_peaks_:
 
         # Extract largest peak, to annotate, grabbing gaussian params
         gauss = get_band_peak_fm(fm, fm.freq_range, attribute='gaussian_params')
@@ -219,7 +214,7 @@ def plot_annotated_model(fm, plt_log=False, annotate_peaks=True, annotate_aperio
                     color=PLT_COLORS['aperiodic'], fontsize=fontsize)
 
     # Apply style to plot & tune grid styling
-    check_n_style(plot_style, ax, plt_log, True)
+    style_spectrum_plot(ax, plt_log, True)
     ax.grid(True, alpha=0.5)
 
     # Add labels to plot in the legend