Merge branch 'develop' into n_subjects

.github/workflows/test-braindecode.yml

@@ -68,9 +68,8 @@ jobs:
       - name: Install and test braindecode
         run: |
           source $VENV
-          poetry run pip install torch
           cd braindecode
-          poetry run pip install -e .[tests]
-          poetry run pytest test/
+          pip install -e .[tests]
+          pytest test/
 
       # poetry run pip install -U https://api.github.com/repos/braindecode/braindecode/zipball/master

.github/workflows/whats-new.yml

@@ -0,0 +1,38 @@
+name: Check What's new update
+
+on:
+  push:
+    branches: [develop]
+  pull_request:
+    branches: [develop]
+
+jobs:
+  check-whats-news:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check for file changes in PR
+        run: |
+          pr_number=${{ github.event.pull_request.number }}
+          response=$(curl -s -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
+            "https://api.github.com/repos/${{ github.repository }}/pulls/${pr_number}/files")
+
+          file_changed=false
+          file_to_check="docs/source/whats_new.rst" # Specify the path to your file
+
+          for file in $(echo "${response}" | jq -r '.[] | .filename'); do
+            if [ "$file" == "$file_to_check" ]; then
+              file_changed=true
+              break
+            fi
+          done
+
+          if $file_changed; then
+            echo "File ${file_to_check} has been changed in the PR."
+          else
+            echo "File ${file_to_check} has not been changed in the PR."
+            echo "::error::File ${file_to_check} has not been changed in the PR."
+            exit 1
+          fi
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, you do not need to create your own token

docs/source/install/install_pip.rst

@@ -5,6 +5,9 @@ Installing from PyPI
 
 MOABB can be installed via pip from `PyPI <https://pypi.org/project/moabb>`__.
 
+.. warning::
+    MOABB is only compatible with **Python 3.8, 3.9, 3.10 and 3.11**.
+
 .. note::
     We recommend the most updated version of pip to install from PyPI.
 

docs/source/whats_new.rst

@@ -19,11 +19,13 @@ Enhancements
 ~~~~~~~~~~~~
 
 - Adding cache option to the evaluation (:gh:`517` by `Bruno Aristimunha`_)
+- Option to interpolate channel in paradigms' `match_all` method (:gh:`480` by `Gregoire Cattan`_)
 
 Bugs
 ~~~~
 
-- None
+- Fix TRCA implementation for different stimulation freqs and for signal filtering (:gh:522 by `Sylvain Chevallier`_)
+- Fix saving to BIDS runs with a description string in their name (:gh:`530` by `Pierre Guetschel`_)
 
 API changes
 ~~~~~~~~~~~

examples/plot_cross_subject_ssvep.py

@@ -104,9 +104,7 @@
 pipelines["CCA"] = make_pipeline(SSVEP_CCA(interval=interval, freqs=freqs, n_harmonics=2))
 
 pipelines_TRCA = {}
-pipelines_TRCA["TRCA"] = make_pipeline(
-    SSVEP_TRCA(interval=interval, freqs=freqs, n_fbands=5)
-)
+pipelines_TRCA["TRCA"] = make_pipeline(SSVEP_TRCA(interval=interval, freqs=freqs))
 
 pipelines_MSET_CCA = {}
 pipelines_MSET_CCA["MSET_CCA"] = make_pipeline(SSVEP_MsetCCA(freqs=freqs))

moabb/datasets/bids_interface.py

@@ -56,6 +56,23 @@ def subject_bids_to_moabb(subject: str):
     return int(subject)
 
 
+def run_moabb_to_bids(run: str):
+    """Convert the run to run index plus eventually description."""
+    p = r"([0-9]+)(|[a-zA-Z]+[a-zA-Z0-9]*)"
+    idx, desc = re.fullmatch(p, run).groups()
+    out = {"run": idx}
+    if desc:
+        out["recording"] = desc
+    return out
+
+
+def run_bids_to_moabb(path: mne_bids.BIDSPath):
+    """Extracts the run index plus eventually description from a path."""
+    if path.recording is None:
+        return path.run
+    return f"{path.run}{path.recording}"
+
+
 @dataclass
 class BIDSInterfaceBase(abc.ABC):
     """Base class for BIDSInterface.
@@ -173,7 +190,7 @@ def load(self, preload=False):
             session_moabb = path.session
             session = sessions_data.setdefault(session_moabb, {})
             run = self._load_file(path, preload=preload)
-            session[path.run] = run
+            session[run_bids_to_moabb(path)] = run
         log.info("Finished reading cache of %s", repr(self))
         return sessions_data
 
@@ -223,12 +240,13 @@ def save(self, sessions_data):
                     )
                     continue
 
+                run_kwargs = run_moabb_to_bids(run)
                 bids_path = mne_bids.BIDSPath(
                     root=self.root,
                     subject=subject_moabb_to_bids(self.subject),
                     session=session,
                     task=self.dataset.paradigm,
-                    run=run,
+                    **run_kwargs,
                     description=self.desc,
                     extension=self._extension,
                     datatype=self._datatype,

moabb/datasets/compound_dataset/__init__.py

@@ -8,7 +8,7 @@
     BI_Il,
     Cattan2019_VR_Il,
 )
-from .utils import _init_compound_dataset_list
+from .utils import _init_compound_dataset_list, compound  # noqa: F401
 
 
 _init_compound_dataset_list()

moabb/datasets/compound_dataset/base.py

@@ -36,13 +36,12 @@ class CompoundDataset(BaseDataset):
     interval: list with 2 entries
         See `BaseDataset`.
 
-    paradigm: ['p300','imagery', 'ssvep', 'rstate']
-        Defines what sort of dataset this is
     """
 
-    def __init__(self, subjects_list: list, code: str, interval: list, paradigm: str):
+    def __init__(self, subjects_list: list, code: str, interval: list):
         self._set_subjects_list(subjects_list)
         dataset, _, _, _ = self.subjects_list[0]
+        paradigm = self._get_paradigm()
         super().__init__(
             subjects=list(range(1, self.count + 1)),
             sessions_per_subject=self._get_sessions_per_subject(),
@@ -52,6 +51,17 @@ def __init__(self, subjects_list: list, code: str, interval: list, paradigm: str
             paradigm=paradigm,
         )
 
+    @property
+    def datasets(self):
+        all_datasets = [entry[0] for entry in self.subjects_list]
+        found_flags = set()
+        filtered_dataset = []
+        for dataset in all_datasets:
+            if dataset.code not in found_flags:
+                filtered_dataset.append(dataset)
+                found_flags.add(dataset.code)
+        return filtered_dataset
+
     @property
     def count(self):
         return len(self.subjects_list)
@@ -78,6 +88,16 @@ def _set_subjects_list(self, subjects_list: list):
             for compoundDataset in subjects_list:
                 self.subjects_list.extend(compoundDataset.subjects_list)
 
+    def _get_paradigm(self):
+        dataset, _, _, _ = self.subjects_list[0]
+        paradigm = dataset.paradigm
+        # Check all of the datasets have the same paradigm
+        for i in range(1, len(self.subjects_list)):
+            entry = self.subjects_list[i]
+            dataset = entry[0]
+            assert dataset.paradigm == paradigm
+        return paradigm
+
     def _with_data_origin(self, data: dict, shopped_subject):
         data_origin = self.subjects_list[shopped_subject - 1]
 

moabb/datasets/compound_dataset/bi_illiteracy.py

@@ -15,7 +15,6 @@ def __init__(self, subjects_list, dataset=None, code=None):
             subjects_list=subjects_list,
             code=code,
             interval=[0, 1.0],
-            paradigm="p300",
         )
 
 

moabb/datasets/compound_dataset/utils.py

@@ -1,6 +1,8 @@
 import inspect
+from typing import List
 
 import moabb.datasets.compound_dataset as db
+from moabb.datasets.base import BaseDataset
 from moabb.datasets.compound_dataset.base import CompoundDataset
 
 
@@ -11,3 +13,16 @@ def _init_compound_dataset_list():
     for ds in inspect.getmembers(db, inspect.isclass):
         if issubclass(ds[1], CompoundDataset) and not ds[0] == "CompoundDataset":
             compound_dataset_list.append(ds[1])
+
+
+def compound(*datasets: List[BaseDataset], interval=[0, 1.0]):
+    subjects_list = [
+        (d, subject, None, None) for d in datasets for subject in d.subject_list
+    ]
+    code = "".join([d.code for d in datasets])
+    ret = CompoundDataset(
+        subjects_list=subjects_list,
+        code=code,
+        interval=interval,
+    )
+    return ret

moabb/datasets/gigadb.py

@@ -13,7 +13,7 @@
 
 
 log = logging.getLogger(__name__)
-GIGA_URL = "ftp://parrot.genomics.cn/gigadb/pub/10.5524/100001_101000/100295/mat_data/"
+GIGA_URL = "https://ftp.cngb.org/pub/gigadb/pub/10.5524/100001_101000/100295/mat_data/"
 
 
 class Cho2017(BaseDataset):

moabb/datasets/preprocessing.py

@@ -2,6 +2,7 @@
 from collections import OrderedDict
 from operator import methodcaller
 from typing import Dict, List, Tuple, Union
+from warnings import warn
 
 import mne
 import numpy as np
@@ -199,13 +200,15 @@ def __init__(
         tmax: float,
         baseline: Tuple[float, float],
         channels: List[str] = None,
+        interpolate_missing_channels: bool = False,
     ):
         assert isinstance(event_id, dict)  # not None
         self.event_id = event_id
         self.tmin = tmin
         self.tmax = tmax
         self.baseline = baseline
         self.channels = channels
+        self.interpolate_missing_channels = interpolate_missing_channels
 
     def transform(self, X, y=None):
         raw = X["raw"]
@@ -218,9 +221,40 @@ def transform(self, X, y=None):
         if self.channels is None:
             picks = mne.pick_types(raw.info, eeg=True, stim=False)
         else:
+            available_channels = raw.info["ch_names"]
+            if self.interpolate_missing_channels:
+                missing_channels = list(set(self.channels).difference(available_channels))
+
+                # add missing channels (contains only zeros by default)
+                try:
+                    raw.add_reference_channels(missing_channels)
+                except IndexError:
+                    # Index error can occurs if the channels we add are not part of this epoch montage
+                    # Then log a warning
+                    montage = raw.info["dig"]
+                    warn(
+                        f"Montage disabled as one of these channels, {missing_channels}, is not part of the montage {montage}"
+                    )
+                    # and disable the montage
+                    raw.info.pop("dig")
+                    # run again with montage disabled
+                    raw.add_reference_channels(missing_channels)
+
+                # Trick: mark these channels as bad
+                raw.info["bads"].extend(missing_channels)
+                # ...and use mne bad channel interpolation to generate the value of the missing channels
+                try:
+                    raw.interpolate_bads(origin="auto")
+                except ValueError:
+                    # use default origin if montage info not available
+                    raw.interpolate_bads(origin=(0, 0, 0.04))
+                # update the name of the available channels
+                available_channels = self.channels
+
             picks = mne.pick_channels(
-                raw.info["ch_names"], include=self.channels, ordered=True
+                available_channels, include=self.channels, ordered=True
             )
+            assert len(picks) == len(self.channels)
 
         epochs = mne.Epochs(
             raw,
@@ -236,6 +270,7 @@ def transform(self, X, y=None):
             event_repeated="drop",
             on_missing="ignore",
         )
+        warn(f"warnEpochs {epochs}")
         return epochs
 
 

moabb/evaluations/base.py

@@ -1,6 +1,7 @@
 import logging
 from abc import ABC, abstractmethod
 
+import pandas as pd
 from sklearn.base import BaseEstimator
 from sklearn.model_selection import GridSearchCV
 
@@ -172,6 +173,8 @@ def process(self, pipelines, param_grid=None, postprocess_pipeline=None):
         for _, pipeline in pipelines.items():
             if not (isinstance(pipeline, BaseEstimator)):
                 raise (ValueError("pipelines must only contains Pipelines " "instance"))
+
+        res_per_db = []
         for dataset in self.datasets:
             log.info("Processing dataset: {}".format(dataset.code))
             process_pipeline = self.paradigm.make_process_pipelines(
@@ -191,10 +194,13 @@ def process(self, pipelines, param_grid=None, postprocess_pipeline=None):
             )
             for res in results:
                 self.push_result(res, pipelines, process_pipeline)
+            res_per_db.append(
+                self.results.to_dataframe(
+                    pipelines=pipelines, process_pipeline=process_pipeline
+                )
+            )
 
-        return self.results.to_dataframe(
-            pipelines=pipelines, process_pipeline=process_pipeline
-        )
+        return pd.concat(res_per_db, ignore_index=True)
 
     def push_result(self, res, pipelines, process_pipeline):
         message = "{} | ".format(res["pipeline"])