add example + add asr to doc

Author: nbara

.travis.yml

@@ -16,6 +16,7 @@ install:
   - pip install sphinx-gallery
   - pip install numpydoc
   - pip install sphinx_bootstrap_theme
+  - pip install git+https://github.com/pymanopt/pymanopt
 script:
   - mkdir docs
   - cd doc

doc/conf.py

@@ -55,7 +55,7 @@
 exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'config.py']
 
 # generate autosummary even if no references
-# autosummary_generate = True
+autosummary_generate = True
 autodoc_default_flags = ['members', 'undoc-members', 'show-inheritance', 'inherited-members']
 numpydoc_show_class_members = True
 

doc/index.rst

@@ -19,6 +19,7 @@ Contents
 .. autosummary::
    :toctree: modules/
 
+   ~meegkit.asr
    ~meegkit.cca
    ~meegkit.dss
    ~meegkit.detrend

examples/example_asr.py

@@ -0,0 +1,66 @@
+"""
+ASR example
+===========
+
+Denoise data using Artifact Subspace Reconstruction.
+
+Uses meegkit.ASR().
+"""
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+from meegkit.asr import ASR
+from meegkit.utils.asr import yulewalk_filter
+from meegkit.utils.matrix import sliding_window
+
+# THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))
+raw = np.load(os.path.join('..', 'tests', 'data', 'eeg_raw.npy'))
+sfreq = 250
+
+###############################################################################
+# Calibration and processing
+# -----------------------------------------------------------------------------
+
+# Train on a clean portion of data
+asr = ASR(method='euclid')
+train_idx = np.arange(0 * sfreq, 30 * sfreq, dtype=int)
+_, sample_mask = asr.fit(raw[:, train_idx])
+
+# Apply filter using sliding (non-overlapping) windows
+X = sliding_window(raw, window=int(sfreq), step=int(sfreq))
+Y = np.zeros_like(X)
+for i in range(X.shape[1]):
+    Y[:, i, :] = asr.transform(X[:, i, :])
+
+raw = X.reshape(8, -1)  # reshape to (n_chans, n_times)
+clean = Y.reshape(8, -1)
+
+###############################################################################
+# Plot the results
+# -----------------------------------------------------------------------------
+#
+# Data was trained on a 40s window from 5s to 45s onwards (gray filled area).
+# The algorithm then removes portions of this data with high amplitude
+# artifacts before running the calibration (hatched area = good).
+
+times = np.arange(raw.shape[-1]) / sfreq
+f, ax = plt.subplots(8, sharex=True, figsize=(8, 5))
+for i in range(8):
+    ax[i].fill_between(train_idx / sfreq, 0, 1, color='grey', alpha=.3,
+                       transform=ax[i].get_xaxis_transform(),
+                       label='calibration window')
+    ax[i].fill_between(train_idx / sfreq, 0, 1, where=sample_mask.flat,
+                       transform=ax[i].get_xaxis_transform(),
+                       facecolor='none', hatch='...', edgecolor='k',
+                       label='selected window')
+    ax[i].plot(times, raw[i], lw=.5, label='before ASR')
+    ax[i].plot(times, clean[i], label='after ASR', lw=.5)
+    ax[i].set_ylim([-50, 50])
+    ax[i].set_ylabel(f'ch{i}')
+    ax[i].set_yticks([])
+ax[i].set_xlabel('Time (s)')
+ax[0].legend(fontsize='small', bbox_to_anchor=(1.04, 1), borderaxespad=0)
+plt.subplots_adjust(hspace=0, right=0.75)
+plt.suptitle('Before/after ASR')
+plt.show()

meegkit/asr.py

@@ -191,6 +191,8 @@ def fit(self, X, y=None, **kwargs):
         self.state_ = dict(M=M, T=T, R=None)
         self._fitted = True
 
+        return clean, sample_mask
+
     def transform(self, X, y=None, **kwargs):
         """Apply Artifact Subspace Reconstruction.