"""
Load and save dataset example
=============================

In this example, we show how to load and save braindecode datasets.
"""

# Authors: Lukas Gemein <l.gemein@gmail.com>
#
# 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