Add example on ERP

doc/api.rst

@@ -108,7 +108,7 @@ Channel selection
     :template: class.rst
 
     ElectrodeSelection
-    FlatChannelRemover         
+    FlatChannelRemover
 
 Stats
 ------------------
@@ -156,7 +156,6 @@ Covariance preprocessing
     normalize
     get_nondiag_weight
 
-
 Distances
 ~~~~~~~~~~~~~~~~~~~~~~
 .. _distance_api:
@@ -174,7 +173,6 @@ Distances
     distance_kullback_sym
     distance_wasserstein
 
-
 Mean
 ~~~~~~~~~~~~~~~~~~~~~~
 .. _mean_api:
@@ -194,7 +192,6 @@ Mean
     mean_harmonic
     mean_kullback_sym
 
-
 Geodesic
 ~~~~~~~~~~~~~~~~~~~~~~
 .. _geodesic_api:
@@ -208,7 +205,6 @@ Geodesic
     geodesic_euclid
     geodesic_logeuclid
 
-
 Tangent Space
 ~~~~~~~~~~~~~~~~~~~~~~
 .. _ts_base_api:
@@ -246,3 +242,15 @@ Aproximate Joint Diagonalization
     ajd_pham
     uwedge
 
+Visualization
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. _viz_api:
+.. currentmodule:: pyriemann.utils.viz
+
+.. autosummary::
+    :toctree: generated/
+
+    plot_confusion_matrix
+    plot_embedding
+    plot_cospectra
+    plot_waveforms

doc/requirements.txt

@@ -1,7 +1,7 @@
 sphinx-gallery
 sphinx-bootstrap_theme
 numpydoc
-mne
+mne==0.23.4
 scikit-learn
 seaborn
 pandas

doc/whatsnew.rst

@@ -30,6 +30,8 @@ v0.2.8.dev
 
 - Add new function :func:`pyriemann.datasets.make_gaussian_blobs` for generating random datasets with SPD matrices
 
+- Add module ``pyriemann.utils.viz`` in API, add :func:`pyriemann.utils.viz.plot_waveforms`, and add an example on ERP visualization
+
 v0.2.7 (June 2021)
 ------------------
 

examples/ERP/plot_ERP.py

@@ -0,0 +1,102 @@
+"""
+===============================================================================
+Display ERP
+===============================================================================
+
+Different ways to display a multichannel event-related potential (ERP).
+
+"""
+# Authors: Quentin Barthélemy
+#
+# License: BSD (3-clause)
+
+import numpy as np
+import mne
+from matplotlib import pyplot as plt
+from pyriemann.utils.viz import plot_waveforms
+
+
+###############################################################################
+# Load EEG data
+# -------------
+
+# Set filenames
+data_path = mne.datasets.sample.data_path()
+raw_fname = data_path + "/MEG/sample/sample_audvis_filt-0-40_raw.fif"
+event_fname = data_path + "/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif"
+
+# Read raw data, select occipital channels and high-pass filter signal
+raw = mne.io.Raw(raw_fname, preload=True, verbose=False)
+raw.pick_channels(['EEG 057', 'EEG 058', 'EEG 059'], ordered=True)
+raw.rename_channels({'EEG 057': 'O1', 'EEG 058': 'Oz', 'EEG 059': 'O2'})
+n_channels = len(raw.ch_names)
+raw.filter(1.0, None, method="iir")
+
+# Read epochs and get responses to left visual field stimulus
+tmin, tmax = -0.1, 0.8
+epochs = mne.Epochs(
+    raw, mne.read_events(event_fname), {'vis_l': 3}, tmin, tmax, proj=False,
+    baseline=None, preload=True, verbose=False)
+X = 5e5 * epochs.get_data()
+print('Number of trials:', X.shape[0])
+times = np.linspace(tmin, tmax, num=X.shape[2])
+
+plt.rcParams["figure.figsize"] = (7, 12)
+ylims = []
+
+
+###############################################################################
+# Plot all trials
+# ---------------
+#
+# This kind of plot is a little bit messy.
+
+fig = plot_waveforms(X, 'all', times=times, alpha=0.3)
+fig.suptitle('Plot all trials', fontsize=16)
+for i_channel in range(n_channels):
+    fig.axes[i_channel].set(ylabel=raw.ch_names[i_channel])
+    fig.axes[i_channel].set_xlim(tmin, tmax)
+    ylims.append(fig.axes[i_channel].get_ylim())
+fig.axes[n_channels - 1].set(xlabel='Time')
+plt.show()
+
+
+###############################################################################
+# Plot central tendency and dispersion of trials
+# ----------------------------------------------
+#
+# This kind of plot is well-spread, but mean and standard deviation can be
+# contaminated by artifacts, and they make a symmetric assumption on amplitude
+# distribution.
+
+fig = plot_waveforms(X, 'mean+/-std', times=times)
+fig.suptitle('Plot mean+/-std of trials', fontsize=16)
+for i_channel in range(n_channels):
+    fig.axes[i_channel].set(ylabel=raw.ch_names[i_channel])
+    fig.axes[i_channel].set_xlim(tmin, tmax)
+    fig.axes[i_channel].set_ylim(ylims[i_channel])
+fig.axes[n_channels - 1].set(xlabel='Time')
+plt.show()
+
+
+###############################################################################
+# Plot histogram of trials
+# ------------------------
+#
+# This plot estimates a 2D histogram of trials [1]_.
+
+fig = plot_waveforms(X, 'hist', times=times, n_bins=25, cmap=plt.cm.Greys)
+fig.suptitle('Plot histogram of trials', fontsize=16)
+for i_channel in range(n_channels):
+    fig.axes[i_channel].set(ylabel=raw.ch_names[i_channel])
+    fig.axes[i_channel].set_ylim(ylims[i_channel])
+fig.axes[n_channels - 1].set(xlabel='Time')
+plt.show()
+
+
+###############################################################################
+# References
+# ----------
+# .. [1] A. Souloumiac and B. Rivet, "Improved estimation of EEG evoked
+#    potentials by jitter compensation and enhancing spatial filters", ICASSP,
+#    2013.

examples/ERP/plot_embedding_EEG.py → examples/ERP/plot_embedding_MEG.py

@@ -1,6 +1,6 @@
 """
 =====================================================================
-Embedding ERP EEG data in 2D Euclidean space with Laplacian Eigenmaps
+Embedding ERP MEG data in 2D Euclidean space with Laplacian Eigenmaps
 =====================================================================
 
 Spectral embedding via Laplacian Eigenmaps of a set of ERP data.

pyriemann/utils/viz.py

@@ -2,8 +2,8 @@
 import pandas as pd
 import numpy as np
 from sklearn.metrics import confusion_matrix
-from pyriemann.embedding import Embedding
-from pyriemann.utils import deprecated
+from ..embedding import Embedding
+from . import deprecated
 
 
 @deprecated(
@@ -63,8 +63,8 @@ def plot_embedding(
     return fig
 
 
-def plot_cospectra(cosp, freqs, ylabels=None, title="Cospectra"):
-    """Plot cospectral matrices
+def plot_cospectra(cosp, freqs, *, ylabels=None, title="Cospectra"):
+    """Plot cospectral matrices.
 
     Parameters
     ----------
@@ -77,14 +77,25 @@ def plot_cospectra(cosp, freqs, ylabels=None, title="Cospectra"):
     -------
     fig : matplotlib figure
         Figure of cospectra.
+
+    Notes
+    -----
+    .. versionadded:: 0.2.7
     """
     try:
         import matplotlib.pyplot as plt
     except ImportError:
         raise ImportError("Install matplotlib to plot cospectra")
+
+    if cosp.ndim != 3:
+        raise Exception('Input cosp has not 3 dimensions')
+    n_freqs, n_channels, _ = cosp.shape
+    if freqs.shape != (n_freqs,):
+        raise Exception(
+            'Input freqs has not the same number of frequencies as cosp')
+
     fig = plt.figure(figsize=(12, 7))
     fig.suptitle(title)
-    n_freqs = min(cosp.shape[0], freqs.shape[0])
     for f in range(n_freqs):
         ax = plt.subplot((n_freqs - 1) // 8 + 1, 8, f + 1)
         plt.imshow(cosp[f], cmap=plt.get_cmap("Reds"))
@@ -100,3 +111,94 @@ def plot_cospectra(cosp, freqs, ylabels=None, title="Cospectra"):
             plt.yticks([])
 
     return fig
+
+
+def plot_waveforms(X, display, *, times=None, color='gray', alpha=0.5,
+                   linewidth=1.5, color_mean='k', color_std='gray', n_bins=50,
+                   cmap=None):
+    ''' Display repetitions of a multichannel waveform.
+
+    Parameters
+    ----------
+    X : ndarray, shape (n_reps, n_channels, n_times)
+        Repetitions of the multichannel waveform.
+    display : {'all', 'mean', 'mean+/-std', 'hist'}
+        Type of display:
+
+        * 'all' for all the repetitions;
+        * 'mean' for the mean of the repetitions;
+        * 'mean+/-std' for the mean +/- standard deviation of the repetitions;
+        * 'hist' for the 2D histogram of the repetitions.
+    time : None | ndarray, shape (n_times,) (default None)
+        Values to display on x-axis.
+    color : matplotlib color, optional
+        Color of the lines, when ``display=all``.
+    alpha : float, optional
+        Alpha value used to cumulate repetitions, when ``display=all``.
+    linewidth : float, optional
+        Line width in points, when ``display=mean``.
+    color_mean : matplotlib color, optional
+        Color of the mean line, when ``display=mean``.
+    color_std : matplotlib color, optional
+        Color of the standard deviation area, when ``display=mean+/-std``.
+    n_bins : int, optional
+        Number of vertical bins for the 2D histogram, when ``display=hist``.
+    cmap : Colormap or str, optional
+        Color map for the histogram, when ``display=hist``.
+
+    Returns
+    -------
+    fig : matplotlib figure
+        Figure of waveform (one subplot by channel).
+
+    Notes
+    -----
+    .. versionadded:: 0.2.8
+    '''
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:
+        raise ImportError("Install matplotlib to plot waveforms")
+
+    if X.ndim != 3:
+        raise Exception('Input X has not 3 dimensions')
+    n_reps, n_channels, n_times = X.shape
+    if times is None:
+        times = np.arange(n_times)
+    elif times.shape != (n_times,):
+        raise Exception(
+            'Parameter times has not the same number of values as X')
+
+    fig, axes = plt.subplots(nrows=n_channels, ncols=1)
+    if n_channels == 1:
+        axes = [axes]
+    channels = np.arange(n_channels)
+
+    if display == 'all':
+        for (channel, ax) in zip(channels, axes):
+            for i_rep in range(n_reps):
+                ax.plot(times, X[i_rep, channel], c=color, alpha=alpha)
+
+    elif display in ['mean', 'mean+/-std']:
+        mean = np.mean(X, axis=0)
+        for (channel, ax) in zip(channels, axes):
+            ax.plot(times, mean[channel], c=color_mean, lw=linewidth)
+        if display == 'mean+/-std':
+            std = np.std(X, axis=0)
+            for (channel, ax) in zip(channels, axes):
+                ax.fill_between(times, mean[channel] - std[channel],
+                                mean[channel] + std[channel], color=color_std)
+
+    elif display == 'hist':
+        times_rep = np.repeat(times[np.newaxis, :], n_reps, axis=0)
+        for (channel, ax) in zip(channels, axes):
+            ax.hist2d(times_rep.ravel(), X[:, channel, :].ravel(),
+                      bins=(n_times, n_bins), cmap=cmap)
+
+    else:
+        raise Exception('Parameter display unknown %s' % display)
+
+    if n_channels > 1:
+        for ax in axes[:-1]:
+            ax.set_xticklabels([])  # remove xticklabels
+    return fig

tests/test_viz.py

@@ -6,6 +6,7 @@
     plot_confusion_matrix,
     plot_embedding,
     plot_cospectra,
+    plot_waveforms
 )
 
 
@@ -36,3 +37,16 @@ def test_cospectra():
     cosp = np.random.randn(n_freqs, n_channels, n_channels)
     freqs = np.random.randn(n_freqs)
     plot_cospectra(cosp, freqs)
+
+
+@requires_matplotlib
+@pytest.mark.parametrize("display", ["all", "mean", "mean+/-std", "hist"])
+def test_plot_waveforms(display):
+    """Test plot_waveforms"""
+    n_matrices, n_channels, n_times = 16, 3, 50
+    X = np.random.randn(n_matrices, n_channels, n_times)
+    plot_waveforms(X, display)
+    plot_waveforms(X, display, times=np.arange(n_times))
+
+    X = np.random.randn(n_matrices, 1, n_times)
+    plot_waveforms(X, display)