Archiving library versions 0.6 and 0.7.

Author: bruAristimunha

0.6/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 75c590fa0e9b50d7ddb42c80fc31816e
+tags: 645f666f9bcd5a90fca523b33c5a78b7

0.6/.nojekyll

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+braindecode.org
+www.braindecode.org

0.6/CNAME

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+"""
+Custom Dataset Example
+======================
+
+This example shows how to convert data X and y as numpy arrays to a braindecode
+compatible data format.
+"""
+
+# Authors: Lukas Gemein <[email protected]>
+#
+# License: BSD (3-clause)
+
+import mne
+
+from braindecode.datasets import create_from_X_y
+
+###############################################################################
+# To set up the example, we first fetch some data using mne:
+
+# 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet
+subject_id = 22
+event_codes = [5, 6, 9, 10, 13, 14]
+# event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
+
+# This will download the files if you don't have them yet,
+# and then return the paths to the files.
+physionet_paths = mne.datasets.eegbci.load_data(
+    subject_id, event_codes, update_path=False)
+
+# Load each of the files
+parts = [mne.io.read_raw_edf(path, preload=True, stim_channel='auto')
+         for path in physionet_paths]
+
+###############################################################################
+# We take the required data, targets and additional information sampling
+# frequency and channel names from the loaded data. Note that this data and
+# information can originate from any source.
+X = [raw.get_data() for raw in parts]
+y = event_codes
+sfreq = parts[0].info["sfreq"]
+ch_names = parts[0].info["ch_names"]
+
+###############################################################################
+# Convert to data format compatible with skorch and braindecode:
+windows_dataset = create_from_X_y(
+    X, y, drop_last_window=False, sfreq=sfreq, ch_names=ch_names,
+    window_stride_samples=500,
+    window_size_samples=500,
+)
+
+windows_dataset.description  # look as dataset description
+
+###############################################################################
+# You can manipulate the dataset
+print(len(windows_dataset))  # get the number of samples
+
+###############################################################################
+# You can now index the data
+i = 0
+x_i, y_i, window_ind = windows_dataset[0]
+n_channels, n_times = x_i.shape  # the EEG data
+_, start_ind, stop_ind = window_ind
+print(f"n_channels={n_channels}  -- n_times={n_times} -- y_i={y_i}")
+print(f"start_ind={start_ind} -- stop_ind={stop_ind}")

0.6/_downloads/039c2490138a56f628199b79e22e54fd/plot_custom_dataset_example.py

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+"""
+Load and save dataset example
+=============================
+
+In this example, we show how to load and save braindecode datasets.
+"""
+
+# Authors: Lukas Gemein <[email protected]>
+#
+# License: BSD (3-clause)
+
+import tempfile
+
+from braindecode.datasets import MOABBDataset
+from braindecode.preprocessing import preprocess, Preprocessor
+from braindecode.datautil import load_concat_dataset
+from braindecode.preprocessing import create_windows_from_events
+
+
+###############################################################################
+# First, we load some dataset using MOABB.
+dataset = MOABBDataset(
+    dataset_name='BNCI2014001',
+    subject_ids=[1],
+)
+
+###############################################################################
+# We can apply preprocessing steps to the dataset. It is also possible to skip
+# this step and not apply any preprocessing.
+preprocess(
+    concat_ds=dataset,
+    preprocessors=[Preprocessor(fn='resample', sfreq=10)]
+)
+
+###############################################################################
+# We save the dataset to a an existing directory. It will create a '.fif' file
+# for every dataset in the concat dataset. Additionally it will create two
+# JSON files, the first holding the description of the dataset, the second
+# holding the name of the target. If you want to store to the same directory
+# several times, for example due to trying different preprocessing, you can
+# choose to overwrite the existing files.
+
+tmpdir = tempfile.mkdtemp()  # write in a temporary directory
+dataset.save(
+    path=tmpdir,
+    overwrite=False,
+)
+
+##############################################################################
+# We load the saved dataset from a directory. Signals can be preloaded in
+# compliance with mne. Optionally, only specific '.fif' files can be loaded
+# by specifying their ids. The target name can be changed, if the dataset
+# supports it (TUHAbnormal for example supports 'pathological', 'age', and
+# 'gender'. If you stored a preprocessed version with target 'pathological'
+# it is possible to change the target upon loading).
+dataset_loaded = load_concat_dataset(
+    path=tmpdir,
+    preload=True,
+    ids_to_load=[1, 3],
+    target_name=None,
+)
+
+##############################################################################
+# The serialization utility also supports WindowsDatasets, so we create
+# compute windows next.
+windows_dataset = create_windows_from_events(
+    concat_ds=dataset_loaded,
+    trial_start_offset_samples=0,
+    trial_stop_offset_samples=0,
+)
+
+windows_dataset.description
+
+##############################################################################
+# Again, we save the dataset to an existing directory. It will create a
+# '-epo.fif' file for every dataset in the concat dataset. Additionally it
+# will create a JSON file holding the description of the dataset. If you
+# want to store to the same directory several times, for example due to
+# trying different windowing parameters, you can choose to overwrite the
+# existing files.
+windows_dataset.save(
+    path=tmpdir,
+    overwrite=True,
+)
+
+##############################################################################
+# Load the saved dataset from a directory. Signals can be preloaded in
+# compliance with mne. Optionally, only specific '-epo.fif' files can be
+# loaded by specifying their ids.
+windows_dataset_loaded = load_concat_dataset(
+    path=tmpdir,
+    preload=False,
+    ids_to_load=[0],
+    target_name=None,
+)
+
+windows_dataset_loaded.description