Merge branch 'master' into log_euclidean_framework

.github/workflows/pypi-publish.yml

@@ -0,0 +1,34 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@e53eb8b103ffcb59469888563dc324e3c8ba6f06
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2015-2023, authors of pyRiemann
+Copyright (c) 2015-2024, authors of pyRiemann
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

doc/api.rst

@@ -17,9 +17,10 @@ SPD Matrices Estimation
     ERPCovariances
     XdawnCovariances
     BlockCovariances
-    CospCovariances
+    CrossSpectra
+    CoSpectra
     Coherences
-    HankelCovariances
+    TimeDelayCovariances
     Kernels
     Shrinkage
 
@@ -182,7 +183,6 @@ Datasets
 
     make_gaussian_blobs
     make_outliers
-    make_covariances
     make_matrices
     make_masks
     sample_gaussian_spd
@@ -192,10 +192,10 @@ Datasets
 Utils function
 --------------
 
-Utils functions are low level functions that implement most base components of Riemannian Geometry.
+Utils functions are low level functions that implement most base components of Riemannian geometry.
 
-Covariance preprocessing
-~~~~~~~~~~~~~~~~~~~~~~~~
+Covariance processing
+~~~~~~~~~~~~~~~~~~~~~
 .. _covariance_api:
 .. currentmodule:: pyriemann.utils.covariance
 
@@ -338,6 +338,7 @@ Aproximate Joint Diagonalization
 .. autosummary::
     :toctree: generated/
 
+    ajd
     ajd_pham
     rjd
     uwedge
@@ -371,6 +372,9 @@ Visualization
 .. autosummary::
     :toctree: generated/
 
-    plot_embedding
+    plot_bihist
+    plot_biscatter
     plot_cospectra
+    plot_cov_ellipse
+    plot_embedding
     plot_waveforms

doc/conf.py

@@ -27,10 +27,7 @@
 curdir = os.path.dirname(__file__)
 sys.path.append(os.path.abspath(os.path.join(curdir, 'sphinxext')))
 
-
 matplotlib.use('Agg')
-import shlex
-import sphinx_gallery
 import sphinx_bootstrap_theme
 
 # If extensions (or modules to document with autodoc) are in another directory,
@@ -97,7 +94,7 @@
 
 # General information about the project.
 project = u'pyRiemann'
-copyright = u'2015-2023, pyRiemann Contributors'
+copyright = u'2015-2024, pyRiemann Contributors'
 author = u'Alexandre Barachant'
 
 # The version info for the project you're documenting, acts as replacement for

doc/whatsnew.rst

@@ -7,9 +7,14 @@ What's new in the package
 
 A catalog of new features, improvements, and bug-fixes in each release.
 
-v0.6.dev
+v0.7.dev
 --------
 
+- Add kernel option to :class:`pyriemann.embedding.LocallyLinearEmbedding`. :pr:`293` by :user:`gabelstein`
+
+v0.6 (April 2024)
+-----------------
+
 - Update pyRiemann from Python 3.7 to 3.8. :pr:`254` by :user:`qbarthelemy`
 
 - Speedup pairwise distance function :func:`pyriemann.utils.distance.pairwise_distance`
@@ -22,6 +27,30 @@ v0.6.dev
 
 - Correct :func:`pyriemann.utils.distance.distance_wasserstein` and :func:`pyriemann.utils.distance.distance_kullback`, keeping only real part. :pr:`267` by :user:`qbarthelemy`
 
+- Deprecate input ``covmats`` for mean functions, renamed into ``X``. :pr:`252` by :user:`qbarthelemy`
+
+- Add support for complex covariance estimation for 'lwf', 'mcd', 'oas' and 'sch' estimators. :pr:`274` by :user:`gabelstein`
+
+- Deprecate input ``covtest`` for predict of :class:`pyriemann.classification.KNearestNeighbor`, renamed into ``X``. :pr:`259` by :user:`qbarthelemy`
+
+- Correct check for `kernel_fct` param of :class:`pyriemann.classification.SVC`. :pr:`272` by :user:`qbarthelemy`
+
+- Add ``sample_weight`` parameter in TLCenter, TLStretch and TLRotate. :pr:`273` by :user:`apmellot`
+
+- Deprecate ``HankelCovariances``, renamed into :class:`pyriemann.estimation.TimeDelayCovariances`. :pr:`275` by :user:`qbarthelemy`
+
+- Add an example on augmented covariance matrix. :pr:`276` by :user:`carraraig`
+
+- Remove function `make_covariances`. :pr:`280` by :user:`qbarthelemy`
+
+- Speedup :class:`pyriemann.estimation.TimeDelayCovariances`. :pr:`281` by :user:`qbarthelemy`
+
+- Enhance ajd module and add a generic :func:`pyriemann.utils.ajd.ajd` function. :pr:`238` by :user:`qbarthelemy`
+
+- Add :func:`pyriemann.utils.viz.plot_bihist`, :func:`pyriemann.utils.viz.plot_biscatter` and :func:`pyriemann.utils.viz.plot_cov_ellipse` for display. :pr:`287` 
+  by :user:`qbarthelemy` and :user:`gcattan`
+
+- Add :class:`pyriemann.estimation.CrossSpectra` and deprecate `CospCovariances` renamed into :class:`pyriemann.estimation.CoSpectra`. :pr:`288` by :user:`qbarthelemy`
 
 v0.5 (Jun 2023)
 ---------------

examples/ERP/plot_ERP.py

@@ -37,7 +37,7 @@
 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()
+X = 5e5 * epochs.get_data(copy=False)
 print('Number of trials:', X.shape[0])
 times = np.linspace(tmin, tmax, num=X.shape[2])
 

examples/ERP/plot_classify_EEG_tangentspace.py

@@ -13,26 +13,25 @@
 # License: BSD (3-clause)
 
 import numpy as np
-
-from pyriemann.estimation import XdawnCovariances
-from pyriemann.tangentspace import TangentSpace
-
+from matplotlib import pyplot as plt
 import mne
 from mne import io
 from mne.datasets import sample
-
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import KFold
 from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
 from sklearn.pipeline import make_pipeline
 
-from matplotlib import pyplot as plt
+from pyriemann.estimation import XdawnCovariances
+from pyriemann.tangentspace import TangentSpace
 
 print(__doc__)
 
 
 ###############################################################################
 # Set parameters and read data
+# ----------------------------
+
 data_path = str(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"
@@ -65,16 +64,16 @@
 
 labels = epochs.events[:, -1]
 evoked = epochs.average()
+epochs_data = epochs.get_data(copy=False)
 
 ###############################################################################
 # Decoding in tangent space with a logistic regression
+# ----------------------------------------------------
 
 n_components = 2  # pick some components
 
 # Define a monte-carlo cross-validation generator (reduce variance):
 cv = KFold(n_splits=10, shuffle=True, random_state=42)
-epochs_data = epochs.get_data()
-
 
 clf = make_pipeline(
     XdawnCovariances(n_components),

examples/ERP/plot_classify_MEG_mdm.py

@@ -17,13 +17,9 @@
 
 import numpy as np
 from matplotlib import pyplot as plt
-from pyriemann.estimation import XdawnCovariances
-from pyriemann.classification import MDM
-
 import mne
 from mne import io
 from mne.datasets import sample
-
 from sklearn.metrics import (
     classification_report,
     confusion_matrix,
@@ -32,6 +28,9 @@
 from sklearn.model_selection import KFold
 from sklearn.pipeline import make_pipeline
 
+from pyriemann.classification import MDM
+from pyriemann.estimation import XdawnCovariances
+
 print(__doc__)
 
 

examples/ERP/plot_embedding_MEG.py

@@ -20,23 +20,22 @@
 #           Gabriel Wagner vom Berg <gabriel@bccn-berlin.de>
 # License: BSD (3-clause)
 
-from pyriemann.estimation import XdawnCovariances
-from pyriemann.utils.viz import plot_embedding
-
+import matplotlib.pyplot as plt
 import mne
 from mne import io
 from mne.datasets import sample
-
 from sklearn.model_selection import train_test_split
 
-import matplotlib.pyplot as plt
-
+from pyriemann.estimation import XdawnCovariances
+from pyriemann.utils.viz import plot_embedding
 
 print(__doc__)
 
 
 ###############################################################################
 # Set parameters and read data
+# ----------------------------
+
 data_path = str(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'
@@ -56,11 +55,12 @@
 epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,
                     picks=picks, baseline=None, preload=True, verbose=False)
 
-X = epochs.get_data()
+X = epochs.get_data(copy=False)
 y = epochs.events[:, -1]
 
 ###############################################################################
 # Embedding of Xdawn covariance matrices
+# --------------------------------------
 
 nfilter = 4
 xdwn = XdawnCovariances(estimator='scm', nfilter=nfilter)

examples/SSVEP/plot_classify_ssvep_mdm.py

@@ -14,7 +14,6 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-
 from mne import find_events, Epochs
 from mne.io import Raw
 from sklearn.model_selection import cross_val_score, RepeatedKFold
@@ -36,7 +35,7 @@
 # Read data in MNE Raw and numpy format
 raw = Raw(destination, preload=True, verbose='ERROR')
 events = find_events(raw, shortest_event=0, verbose=False)
-raw = raw.pick_types(eeg=True)
+raw = raw.pick("eeg")
 
 event_id = {'13 Hz': 2, '17 Hz': 4, '21 Hz': 3, 'resting-state': 1}
 sfreq = int(raw.info['sfreq'])
@@ -100,15 +99,17 @@
 ###############################################################################
 # Building Epochs and plotting 3 s of the signal from electrode Oz for a trial
 
-epochs = Epochs(raw_ext, events, event_id, tmin=2, tmax=5, baseline=None)
+epochs = Epochs(
+    raw_ext, events, event_id, tmin=2, tmax=5, baseline=None
+).get_data(copy=False)
 
 n_seconds = 3
 time = np.linspace(0, n_seconds, n_seconds * sfreq,
                    endpoint=False)[np.newaxis, :]
 channels = range(0, len(raw_ext.ch_names), len(raw.ch_names))
 plt.figure(figsize=(7, 5))
 for f, c in zip(frequencies, channels):
-    plt.plot(epochs.get_data()[5, c, :].T, label=str(int(f))+' Hz')
+    plt.plot(epochs[5, c, :].T, label=str(int(f))+' Hz')
 plt.xlabel("Time (s)")
 plt.ylabel(r"Oz after filtering ($\mu$V)")
 plt.legend(loc='upper right')
@@ -125,8 +126,9 @@
 # The covariance matrices will be estimated using the Ledoit-Wolf shrinkage
 # estimator on the extended signal.
 
-cov_ext_trials = BlockCovariances(estimator='lwf',
-                                  block_size=8).transform(epochs.get_data())
+cov_ext_trials = BlockCovariances(
+    estimator='lwf', block_size=8
+).transform(epochs)
 
 # This plot shows an example of a covariance matrix observed for each class:
 ch_names = raw_ext.info['ch_names']

examples/SSVEP/plot_classify_ssvep_pga.py

@@ -16,7 +16,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.animation import FuncAnimation
-
 from mne import find_events, Epochs, make_fixed_length_epochs
 from mne.io import Raw
 from sklearn.pipeline import make_pipeline
@@ -64,23 +63,27 @@ def plot_pga(ax, data, labels, centers):
 # Training set
 raw = Raw(download_data(subject=12, session=1), preload=True, verbose=False)
 events = find_events(raw, shortest_event=0, verbose=False)
-raw = raw.pick_types(eeg=True)
+raw = raw.pick("eeg")
 ch_count = len(raw.info['ch_names'])
 raw_ext = extend_signal(raw, frequencies, freq_band)
 epochs = Epochs(
-    raw_ext, events, events_id, tmin=2, tmax=5, baseline=None, verbose=False)
+    raw_ext, events, events_id, tmin=2, tmax=5, baseline=None, verbose=False
+).get_data(copy=False)
 x_train = BlockCovariances(
-    estimator='lwf', block_size=ch_count).transform(epochs.get_data())
+    estimator='lwf', block_size=ch_count
+).transform(epochs)
 y_train = events[:, 2]
 
 # Testing set
 raw = Raw(download_data(subject=12, session=4), preload=True, verbose=False)
 raw = raw.pick_types(eeg=True)
 raw_ext = extend_signal(raw, frequencies, freq_band)
 epochs = make_fixed_length_epochs(
-    raw_ext, duration=duration, overlap=duration - interval, verbose=False)
+    raw_ext, duration=duration, overlap=duration - interval, verbose=False
+).get_data(copy=False)
 x_test = BlockCovariances(
-    estimator='lwf', block_size=ch_count).transform(epochs.get_data())
+    estimator='lwf', block_size=ch_count
+).transform(epochs)
 
 
 ###############################################################################

examples/artifacts/plot_correct_ajdc_EEG.py

@@ -13,13 +13,12 @@
 
 import gzip
 import numpy as np
-from scipy.signal import welch
 from matplotlib import pyplot as plt
-
 from mne import create_info
 from mne.io import RawArray
 from mne.viz import plot_topomap
 from mne.preprocessing import ICA
+from scipy.signal import welch
 
 from pyriemann.spatialfilters import AJDC
 from pyriemann.utils.viz import plot_cospectra