def test_proj_raw_duration(duration, sfreq):
"""Test equivalence of `duration` options."""
n_ch, n_dim = 30, 3
rng = np.random.RandomState(0)
signals = rng.randn(n_dim, 10000)
mixing = rng.randn(n_ch, n_dim) + [0, 1, 2]
data = np.dot(mixing, signals)
raw = RawArray(data, create_info(n_ch, sfreq, 'eeg'))
raw.set_eeg_reference(projection=True)
n_eff = int(round(raw.info['sfreq'] * duration))
# crop to an even "duration" number of epochs
stop = ((len(raw.times) // n_eff) * n_eff - 1) / raw.info['sfreq']
raw.crop(0, stop)
proj_def = compute_proj_raw(raw, n_eeg=n_dim)
proj_dur = compute_proj_raw(raw, duration=duration, n_eeg=n_dim)
proj_none = compute_proj_raw(raw, duration=None, n_eeg=n_dim)
assert len(proj_dur) == len(proj_none) == len(proj_def) == n_dim
# proj_def is not in here because it does not necessarily evenly divide
# the signal length:
for pu, pn in zip(proj_dur, proj_none):
assert_allclose(pu['data']['data'], pn['data']['data'])
# but we can test it here since it should still be a small subspace angle:
for proj in (proj_dur, proj_none, proj_def):
computed = np.concatenate([p['data']['data'] for p in proj], 0)
angle = np.rad2deg(linalg.subspace_angles(computed.T, mixing)[0])
assert angle < 1e-5
def test_proj_raw_duration(duration, sfreq):
"""Test equivalence of `duration` options."""
n_ch, n_dim = 30, 3
rng = np.random.RandomState(0)
signals = rng.randn(n_dim, 10000)
mixing = rng.randn(n_ch, n_dim) + [0, 1, 2]
data = np.dot(mixing, signals)
raw = RawArray(data, create_info(n_ch, sfreq, 'eeg'))
raw.set_eeg_reference(projection=True)
n_eff = int(round(raw.info['sfreq'] * duration))
# crop to an even "duration" number of epochs
stop = ((len(raw.times) // n_eff) * n_eff - 1) / raw.info['sfreq']
raw.crop(0, stop)
proj_def = compute_proj_raw(raw, n_eeg=n_dim)
proj_dur = compute_proj_raw(raw, duration=duration, n_eeg=n_dim)
proj_none = compute_proj_raw(raw, duration=None, n_eeg=n_dim)
assert len(proj_dur) == len(proj_none) == len(proj_def) == n_dim
# proj_def is not in here because it does not necessarily evenly divide
# the signal length:
for pu, pn in zip(proj_dur, proj_none):
assert_allclose(pu['data']['data'], pn['data']['data'])
# but we can test it here since it should still be a small subspace angle:
for proj in (proj_dur, proj_none, proj_def):
computed = np.concatenate([p['data']['data'] for p in proj], 0)
angle = np.rad2deg(linalg.subspace_angles(computed.T, mixing)[0])
assert angle < 1e-5
def stream(self):
print("starting debug stream thread")
debug_index = 0
while True:
while self.target is None:
pass
sleep(2.5)
# grab debug epoch
chunk = debug_data[debug_index]
debug_index += 1
# turn into mne object with RawArray
# apply info from self.stream_info above to get channel info
raw = RawArray(data=chunk, info=create_info(debug_pilot.ch_names[:-3], 500, 'eeg'))
# print(raw.info)
# print(debug_pilot.info)
# bandpass filter
raw = raw.filter(LO_FREQ, HI_FREQ, method='fir', fir_design='firwin', phase='zero')
# raw = raw.notch_filter(50, method='iir')
# raw = raw.reorder_channels(sorted(raw.ch_names))
# get processed data and split into 4
# raw.crop(tmin=2.)
# raw_data = raw.get_data(picks=sorted(GOODS))*1000
# to_classify = np.stack([raw_data[:,i::4] for i in range(4)])
# or resample
raw.crop(tmin=2.)
raw = raw.resample(125)
to_classify = np.stack([raw.get_data(picks=sorted(GOODS))*1000])
print(to_classify.shape)
# print(to_classify)
# print(to_classify)
# classify each individually
# reshape to [epochs (4), kernels (1), channels (?), samples (1000)]
probs = self.model.predict(to_classify.reshape(to_classify.shape[0], 1, to_classify.shape[1], to_classify.shape[2]))
# print(probs)
probs = np.sum(probs, axis=0) / 1
print(probs)
result = np.where(probs > 0.66)
print("debug target:", debug_labels[debug_index], f"({debug_label_map[debug_labels[debug_index]]})")
# self.game.sendall(bytes([self.target + 1]))
if len(result[0]) == 0:
# send unknown
print('unknown')
self.game.sendall(bytes([25]))
else:
# send index of result
print('classified:', result[0][0], f"({debug_label_map[result[0][0]]})")
self.game.sendall(bytes([result[0][0] + 1]))
self.target = None
print('quitting stream and cleaning up')
self.to_classify.clear()
events, event_id = events_from_annotations(debug_pilot, event_id={'Stimulus/left': 0, 'Stimulus/right': 1, 'Stimulus/feet': 2})
picks = pick_types(debug_pilot.info, meg=False, eeg=True, stim=False, eog=False)
epochs = Epochs(debug_pilot, events, event_id, proj=False, picks=picks, baseline=None, preload=True, verbose=False, tmin=-1.5, tmax=2.5)
debug_data = epochs.get_data()
debug_data = debug_data[:,:,:-1]
# print(_pilotX[0].shape)
# print(debug_data[0].shape)
# print(debug_data[0])
# print()
stream_info = create_info(debug_pilot.ch_names[:-3], 500, 'eeg')
signal = debug_data[4]
raw = RawArray(data=signal, info=stream_info)
raw = raw.filter(LO_FREQ, HI_FREQ, method='fir', fir_design='firwin', phase='zero')
raw = raw.crop(tmin=2.)
raw = raw.resample(125)
realtime = raw.get_data(picks=sorted(GOODS))*1000
print(sorted(GOODS))
print(realtime.shape)
print(realtime)
print()
import numpy as np
from matplotlib import pyplot as plt
t = np.arange(0, 2, 0.008)
fig, axs = plt.subplots(3, 3)
from mne import create_info
from integration import stream_channels
raw = RawArray(data=debug_data[0],
info=create_info(stream_channels, 500, 'eeg'))
raw = raw.filter(LO_FREQ,
HI_FREQ,
method='fir',
fir_design='firwin',
phase='zero')
raw = raw.notch_filter(50)
# raw = raw.drop_channels([ch for ch in raw.ch_names if ch not in GOODS])
# print(raw.ch_names)
# raw = raw.reorder_channels(sorted(raw.ch_names))
# print(raw.ch_names)
raw = raw.crop(tmin=11.)
raw = raw.resample(125)
# raw = raw.reorder_channels(sorted(raw.ch_names))
# print(raw.ch_names)
realtime = raw.get_data(picks=sorted(GOODS)) * 1000
raw = raw.reorder_channels(sorted(raw.ch_names))
# print(raw.ch_names)
ordered = raw.get_data(picks=GOODS) * 1000
# print(realtime[0])
# print(ordered[0])
# print(_pilotX[0].shape, _pilotX[0][0])
# print(realtime.shape, realtime[0])
