forked from NeuroTechX/moabb
-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
161 lines (129 loc) · 4.52 KB
/
utils.py
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
"""Utils for easy database selection."""
import inspect
import moabb.datasets as db
from moabb.datasets.base import BaseDataset
dataset_list = []
def _init_dataset_list():
for ds in inspect.getmembers(db, inspect.isclass):
if issubclass(ds[1], BaseDataset):
dataset_list.append(ds[1])
def dataset_search( # noqa: C901
paradigm=None,
multi_session=False,
events=None,
has_all_events=False,
interval=None,
min_subjects=1,
channels=(),
):
"""Returns a list of datasets that match a given criteria.
Parameters
----------
paradigm: str | None
'imagery', 'p300', 'ssvep', None
multi_session: bool
if True only returns datasets with more than one session per subject.
If False return all
events: list of strings
events to select
has_all_events: bool
skip datasets that don't have all events in events
interval:
Length of motor imagery interval, in seconds. Only used in imagery
paradigm
min_subjects: int,
minimum subjects in dataset
channels: list of str
list or set of channels
"""
if len(dataset_list) == 0:
_init_dataset_list()
channels = set(channels)
out_data = []
if events is not None and has_all_events:
n_classes = len(events)
else:
n_classes = None
assert paradigm in ["imagery", "p300", "ssvep", None]
for type_d in dataset_list:
d = type_d()
skip_dataset = False
if multi_session and d.n_sessions < 2:
continue
if len(d.subject_list) < min_subjects:
continue
if paradigm is not None and paradigm != d.paradigm:
continue
if interval is not None and d.interval[1] - d.interval[0] < interval:
continue
keep_event_dict = {}
if events is None:
keep_event_dict = d.event_id.copy()
else:
n_events = 0
for e in events:
if n_classes is not None:
if n_events == n_classes:
break
if e in d.event_id.keys():
keep_event_dict[e] = d.event_id[e]
n_events += 1
else:
if has_all_events:
skip_dataset = True
if keep_event_dict and not skip_dataset:
if len(channels) > 0:
s1 = d.get_data([1])[1]
sess1 = s1[list(s1.keys())[0]]
raw = sess1[list(sess1.keys())[0]]
raw.pick_types(eeg=True)
if channels <= set(raw.info["ch_names"]):
out_data.append(d)
else:
out_data.append(d)
return out_data
def find_intersecting_channels(datasets, verbose=False):
"""Given a list of dataset instances return a list of channels shared by
all datasets. Skip datasets which have 0 overlap with the others.
returns: set of common channels, list of datasets with valid channels
"""
allchans = set()
dset_chans = []
keep_datasets = []
for d in datasets:
print("Searching dataset: {:s}".format(type(d).__name__))
s1 = d.get_data([1])[1]
sess1 = s1[list(s1.keys())[0]]
raw = sess1[list(sess1.keys())[0]]
raw.pick_types(eeg=True)
processed = []
for ch in raw.info["ch_names"]:
ch = ch.upper()
if ch.find("EEG") == -1:
# TODO: less hacky way of finding poorly labeled datasets
processed.append(ch)
allchans.update(processed)
if len(processed) > 0:
if verbose:
print("Found EEG channels: {}".format(processed))
dset_chans.append(processed)
keep_datasets.append(d)
else:
print(
"Dataset {:s} has no recognizable EEG channels".format(type(d).__name__)
) # noqa
allchans.intersection_update(*dset_chans)
allchans = [s.replace("Z", "z") for s in allchans]
return allchans, keep_datasets
def _download_all(update_path=True, verbose=None):
"""Download all data.
This function is mainly used to generate the data cache.
"""
# iterate over dataset
for ds in dataset_list:
# call download
ds().download(update_path=True, verbose=verbose, accept=True)
def block_rep(block: int, rep: int):
return f"block_{block}-repetition_{rep}"
def blocks_reps(blocks: list, reps: list):
return [block_rep(b, r) for b in blocks for r in reps]