1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
|
"""
.. _tut_modifying_data_inplace:
Modifying data in-place
=======================
"""
from __future__ import print_function
import mne
import os.path as op
import numpy as np
from matplotlib import pyplot as plt
###############################################################################
# It is often necessary to modify data once you have loaded it into memory.
# Common examples of this are signal processing, feature extraction, and data
# cleaning. Some functionality is pre-built into MNE-python, though it is also
# possible to apply an arbitrary function to the data.
# Load an example dataset, the preload flag loads the data into memory now
data_path = op.join(mne.datasets.sample.data_path(), 'MEG',
'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(data_path, preload=True, add_eeg_ref=False)
raw = raw.crop(0, 10)
print(raw)
###############################################################################
# Signal processing
# -----------------
#
# Most MNE objects have in-built methods for filtering:
filt_bands = [(1, 3), (3, 10), (10, 20), (20, 60)]
f, (ax, ax2) = plt.subplots(2, 1, figsize=(15, 10))
_ = ax.plot(raw._data[0])
for fband in filt_bands:
raw_filt = raw.copy()
raw_filt.filter(*fband, h_trans_bandwidth='auto', l_trans_bandwidth='auto',
filter_length='auto', phase='zero')
_ = ax2.plot(raw_filt[0][0][0])
ax2.legend(filt_bands)
ax.set_title('Raw data')
ax2.set_title('Band-pass filtered data')
###############################################################################
# In addition, there are functions for applying the Hilbert transform, which is
# useful to calculate phase / amplitude of your signal.
# Filter signal with a fairly steep filter, then take hilbert transform
raw_band = raw.copy()
raw_band.filter(12, 18, l_trans_bandwidth=2., h_trans_bandwidth=2.,
filter_length='auto', phase='zero')
raw_hilb = raw_band.copy()
hilb_picks = mne.pick_types(raw_band.info, meg=False, eeg=True)
raw_hilb.apply_hilbert(hilb_picks)
print(raw_hilb._data.dtype)
###############################################################################
# Finally, it is possible to apply arbitrary functions to your data to do
# what you want. Here we will use this to take the amplitude and phase of
# the hilbert transformed data.
#
# .. note:: You can also use ``amplitude=True`` in the call to
# :meth:`mne.io.Raw.apply_hilbert` to do this automatically.
#
# Take the amplitude and phase
raw_amp = raw_hilb.copy()
raw_amp.apply_function(np.abs, hilb_picks, float, 1)
raw_phase = raw_hilb.copy()
raw_phase.apply_function(np.angle, hilb_picks, float, 1)
f, (a1, a2) = plt.subplots(2, 1, figsize=(15, 10))
a1.plot(raw_band._data[hilb_picks[0]])
a1.plot(raw_amp._data[hilb_picks[0]])
a2.plot(raw_phase._data[hilb_picks[0]])
a1.set_title('Amplitude of frequency band')
a2.set_title('Phase of frequency band')
|
