kraken/backend/signal_visualization.py

'''
模型测试脚本，
测试AUC，
绘制Confusion matrix, ERSD map
'''
import os
import argparse
import logging

import mne
import yaml
import joblib
import numpy as np
from scipy import signal
from sklearn.metrics import accuracy_score, f1_score
import matplotlib.pyplot as plt

from dataloaders import neo
import bci_core.utils as bci_utils
import bci_core.pipeline as bci_pipeline
import bci_core.viz as bci_viz
from settings.config import settings


logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

config_info = settings.CONFIG_INFO


def parse_args():
    parser = argparse.ArgumentParser(
        description='Model validation'
    )
    parser.add_argument(
        '--subj',
        dest='subj',
        help='Subject name',
        default=None,
        type=str
    )
    return parser.parse_args()


if __name__ == '__main__':
    args = parse_args()
    subj_name = args.subj

    data_dir = os.path.join(settings.DATA_PATH, subj_name)
    
    with open(os.path.join(data_dir, 'val_info.yml'), 'r') as f:
        info = yaml.safe_load(f)
    sessions = info['sessions']
    
    ori_epoch_length = info.get('ori_epoch_length', 5.)

    # preprocess raw
    raw, event_id = neo.raw_loader(data_dir, sessions, 
                                ori_epoch_length=ori_epoch_length,
                                reref_method=config_info['reref'],
                                upsampled_epoch_length=None)
    
    fs = raw.info['sfreq']
    
    events, _ = mne.events_from_annotations(raw, event_id)

    # ersd map
    fig_erds = bci_viz.plot_ersd(raw.get_data(), events, fs, (0, 2.5), event_id, 0)

    # tfr plot
    fig_tfr = bci_viz.plot_time_frequency(raw.get_data(), events, fs, np.arange(5, 200, 20), (-1, 4), {v: k for k, v in event_id.items()})

    
    fig_erds.savefig(os.path.join(data_dir, 'erds.pdf'))
    fig_tfr.savefig(os.path.join(data_dir, 'tfr.pdf'))

    plt.show()