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Remove dysfunctional PREP tests / examples #163

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202 changes: 0 additions & 202 deletions tests/test_prep_pipeline.py
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Expand Up @@ -3,216 +3,14 @@
# Authors: The PyPREP developers
# SPDX-License-Identifier: MIT

import matplotlib.pyplot as plt
import numpy as np
import pytest
import scipy.io as sio

from pyprep.prep_pipeline import PrepPipeline

from .conftest import make_random_mne_object


@pytest.mark.usefixtures("raw", "montage")
def test_prep_pipeline(raw, montage):
"""Test prep pipeline."""
eeg_index = np.array(
[idx for idx, typ in enumerate(raw.get_channel_types()) if typ == "eeg"]
)
raw_copy = raw.copy()
ch_names = raw_copy.info["ch_names"]
ch_names_eeg = list(np.asarray(ch_names)[eeg_index])
sample_rate = raw_copy.info["sfreq"]
prep_params = {
"ref_chs": ch_names_eeg,
"reref_chs": ch_names_eeg,
"line_freqs": np.arange(60, sample_rate / 2, 60),
}
prep = PrepPipeline(raw_copy, prep_params, montage, random_state=42)
prep.fit()

EEG_raw = raw_copy.get_data(picks="eeg") * 1e6
EEG_raw_max = np.max(abs(EEG_raw), axis=None)
EEG_raw_matlab = sio.loadmat("./examples/matlab_results/EEG_raw.mat")
EEG_raw_matlab = EEG_raw_matlab["save_data"]
EEG_raw_diff = EEG_raw - EEG_raw_matlab
# EEG_raw_mse = (EEG_raw_diff / EEG_raw_max ** 2).mean(axis=None)

fig, axs = plt.subplots(5, 3, sharex="all")
plt.setp(fig, facecolor=[1, 1, 1])
fig.suptitle("Python versus Matlab PREP results", fontsize=16)

im = axs[0, 0].imshow(
EEG_raw / EEG_raw_max,
aspect="auto",
extent=[0, (EEG_raw.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[0, 0].set_title("Python", fontsize=14)
axs[0, 1].imshow(
EEG_raw_matlab / EEG_raw_max,
aspect="auto",
extent=[0, (EEG_raw_matlab.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[0, 1].set_title("Matlab", fontsize=14)
axs[0, 2].imshow(
EEG_raw_diff / EEG_raw_max,
aspect="auto",
extent=[0, (EEG_raw_diff.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[0, 2].set_title("Difference", fontsize=14)
# axs[0, 0].set_title('Original EEG', loc='left', fontsize=14)
# axs[0, 0].set_ylabel('Channel Number', fontsize=14)
cb = fig.colorbar(im, ax=axs, fraction=0.05, pad=0.04)
cb.set_label("\u03bcVolt", fontsize=14)

EEG_new_matlab = sio.loadmat("./examples/matlab_results/EEGNew.mat")
EEG_new_matlab = EEG_new_matlab["save_data"]
EEG_new = prep.EEG_new
EEG_new_max = np.max(abs(EEG_new), axis=None)
EEG_new_diff = EEG_new - EEG_new_matlab
# EEG_new_mse = ((EEG_new_diff / EEG_new_max) ** 2).mean(axis=None)
axs[1, 0].imshow(
EEG_new / EEG_new_max,
aspect="auto",
extent=[0, (EEG_new.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[1, 1].imshow(
EEG_new_matlab / EEG_new_max,
aspect="auto",
extent=[0, (EEG_new_matlab.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[1, 2].imshow(
EEG_new_diff / EEG_new_max,
aspect="auto",
extent=[0, (EEG_new_diff.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
# axs[1, 0].set_title('High pass filter', loc='left', fontsize=14)
# axs[1, 0].set_ylabel('Channel Number', fontsize=14)

EEG_clean_matlab = sio.loadmat("./examples/matlab_results/EEG.mat")
EEG_clean_matlab = EEG_clean_matlab["save_data"]
EEG_clean = prep.EEG
EEG_max = np.max(abs(EEG_clean), axis=None)
EEG_diff = EEG_clean - EEG_clean_matlab
# EEG_mse = ((EEG_diff / EEG_max) ** 2).mean(axis=None)
axs[2, 0].imshow(
EEG_clean / EEG_max,
aspect="auto",
extent=[0, (EEG_clean.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[2, 1].imshow(
EEG_clean_matlab / EEG_max,
aspect="auto",
extent=[0, (EEG_clean_matlab.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[2, 2].imshow(
EEG_diff / EEG_max,
aspect="auto",
extent=[0, (EEG_diff.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
# axs[2, 0].set_title('Line-noise removal', loc='left', fontsize=14)
axs[2, 0].set_ylabel("Channel Number", fontsize=14)

EEG = prep.EEG_before_interpolation
EEG_max = np.max(abs(EEG), axis=None)
EEG_ref_mat = sio.loadmat("./examples/matlab_results/EEGref.mat")
EEG_ref_matlab = EEG_ref_mat["save_EEG"]
# reference_matlab = EEG_ref_mat["save_reference"]
EEG_ref_diff = EEG - EEG_ref_matlab
# EEG_ref_mse = ((EEG_ref_diff / EEG_max) ** 2).mean(axis=None)
# reference_signal = prep.reference_before_interpolation
# reference_max = np.max(abs(reference_signal), axis=None)
# reference_diff = reference_signal - reference_matlab
# reference_mse = ((reference_diff / reference_max) ** 2).mean(axis=None)
axs[3, 0].imshow(
EEG / EEG_max,
aspect="auto",
extent=[0, (EEG.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[3, 1].imshow(
EEG_ref_matlab / EEG_max,
aspect="auto",
extent=[0, (EEG_ref_matlab.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[3, 2].imshow(
EEG_ref_diff / EEG_max,
aspect="auto",
extent=[0, (EEG_ref_diff.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
# axs[3, 0].set_title('Referencing', loc='left', fontsize=14)
# axs[3, 0].set_ylabel('Channel Number', fontsize=14)

EEG_final = prep.raw.get_data() * 1e6
EEG_final_max = np.max(abs(EEG_final), axis=None)
EEG_final_matlab = sio.loadmat("./examples/matlab_results/EEGinterp.mat")
EEG_final_matlab = EEG_final_matlab["save_data"]
EEG_final_diff = EEG_final - EEG_final_matlab
# EEG_final_mse = ((EEG_final_diff / EEG_final_max) ** 2).mean(axis=None)
axs[4, 0].imshow(
EEG_final / EEG_final_max,
aspect="auto",
extent=[0, (EEG_final.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[4, 1].imshow(
EEG_final_matlab / EEG_final_max,
aspect="auto",
extent=[0, (EEG_final_matlab.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
axs[4, 2].imshow(
EEG_final_diff / EEG_final_max,
aspect="auto",
extent=[0, (EEG_final_diff.shape[1] / sample_rate), 63, 0],
vmin=-1,
vmax=1,
cmap=plt.get_cmap("RdBu"),
)
# axs[4, 0].set_title('Interpolation', loc='left', fontsize=14)
# axs[4, 0].set_ylabel('Channel Number', fontsize=14)
axs[4, 1].set_xlabel("Time(s)", fontsize=14)


@pytest.mark.usefixtures("raw", "montage")
def test_prep_pipeline_non_eeg(raw, montage):
"""Test prep pipeline with non eeg channels."""
Expand Down