[ENH] RESS implem sklearn interface

.github/workflows/testing.yml

@@ -19,7 +19,7 @@ jobs:
     steps:
     - uses: actions/checkout@v1
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install meegkit and dependencies

Makefile

@@ -4,7 +4,7 @@ PYTHON ?= python
 PYTESTS ?= py.test
 CTAGS ?= ctags
 CODESPELL_SKIPS ?= "*.html,*.fif,*.eve,*.gz,*.tgz,*.zip,*.mat,*.stc,*.label,*.w,*.bz2,*.annot,*.sulc,*.log,*.local-copy,*.orig_avg,*.inflated_avg,*.gii,*.pyc,*.doctree,*.pickle,*.inv,*.png,*.edf,*.touch,*.thickness,*.nofix,*.volume,*.defect_borders,*.mgh,lh.*,rh.*,COR-*,FreeSurferColorLUT.txt,*.examples,.xdebug_mris_calc,bad.segments,BadChannels,*.hist,empty_file,*.orig,*.js,*.map,*.ipynb,searchindex.dat"
-CODESPELL_DIRS ?= meegkit/ doc/
+CODESPELL_DIRS ?= meegkit/ examples/
 
 help:
 	@echo "Please use \`make <target>' where <target> is one of:"
@@ -43,9 +43,8 @@ build-doc:
 	cd doc; make html
 
 build-examples:
-	cd examples;
-	find . -name "example_*.py" | xargs sphx_glr_python_to_jupyter.py
-	find . -name "example_*.ipynb" | xargs jupyter nbconvert --execute --to notebook --inplace
+	cd examples; find . -name "example_*.py" | xargs sphx_glr_python_to_jupyter.py
+	cd examples; find . -name "example_*.ipynb" | xargs jupyter nbconvert --execute --to notebook --inplace
 
 # Style
 # =============================================================================

doc/Makefile

@@ -24,6 +24,7 @@ help:
 clean:
 	-rm -rf _build/*
 	-rm -rf _source/*
+	-rm -rf auto_examples/*
 	-rm -rf generated
 	-rm -rf *.stc
 	-rm -rf *.fif

examples/example_ress.py

@@ -14,7 +14,7 @@
 import scipy.signal as ss
 
 from meegkit import ress
-from meegkit.utils import fold, matmul3d, rms, snr_spectrum, unfold
+from meegkit.utils import fold, matmul3d, snr_spectrum, unfold
 
 # import config
 
@@ -48,8 +48,8 @@
 noise = fold(noise, n_times)
 
 # mix signal and noise
-signal = SNR * signal / rms(signal.flatten())
-noise = noise / rms(noise.flatten())
+signal = SNR * signal /  np.sqrt(np.mean(signal ** 2))
+noise = noise / np.sqrt(np.mean(noise ** 2))
 data = signal + noise
 
 # Plot
@@ -64,14 +64,14 @@
 ###############################################################################
 # Enhance oscillatory activity using RESS
 # -----------------------------------------------------------------------------
-
 # Apply RESS
-out, maps, _ = ress.RESS(data, sfreq=sfreq, peak_freq=target, return_maps=True)
+r = ress.RESS(sfreq=sfreq, peak_freq=target, compute_unmixing=True)
+out = r.fit_transform(data)
 
 # Compute PSD
 nfft = 250
 df = sfreq / nfft  # frequency resolution
-bins, psd = ss.welch(out.squeeze(1), sfreq, window="hamming", nperseg=nfft,
+bins, psd = ss.welch(np.squeeze(out), sfreq, window="hamming", nperseg=nfft,
                      noverlap=125, axis=0)
 psd = psd.mean(axis=1, keepdims=True)  # average over trials
 snr = snr_spectrum(psd, bins, skipbins=2, n_avg=2)
@@ -88,12 +88,12 @@
 ###############################################################################
 # Project components back into sensor space to see the effects of RESS on the
 # average SSVEP.
-
-proj = matmul3d(out, maps)
+fromress = r.from_ress
+proj = matmul3d(out, fromress)
 f, ax = plt.subplots(n_chans, 2, sharey="col")
 for c in range(n_chans):
-    ax[c, 0].plot(data[:, c].mean(-1), lw=.5)
-    ax[c, 1].plot(proj[:, c].mean(-1), lw=.5)
+    ax[c, 0].plot(data[:, c].mean(-1), lw=.5, label="data")
+    ax[c, 1].plot(proj[:, c].mean(-1), lw=.5, label="projection")
     ax[c, 0].set_ylabel(f"ch{c}")
     if c < n_chans:
         ax[c, 0].set_xticks([])
@@ -102,3 +102,4 @@
 ax[0, 0].set_title("Trial average (before)")
 ax[0, 1].set_title("Trial average (after)")
 plt.show()
+