/
_export.py
222 lines (177 loc) · 5.87 KB
/
_export.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
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
# Authors: MNE Developers
#
# License: BSD-3-Clause
import os.path as op
from ._egimff import export_evokeds_mff
from ..utils import logger, verbose, warn, _check_fname, _validate_type
@verbose
def export_raw(
fname,
raw,
fmt="auto",
physical_range="auto",
add_ch_type=False,
*,
overwrite=False,
verbose=None,
):
"""Export Raw to external formats.
%(export_fmt_support_raw)s
%(export_warning)s
Parameters
----------
%(fname_export_params)s
raw : instance of Raw
The raw instance to export.
%(export_fmt_params_raw)s
%(physical_range_export_params)s
%(add_ch_type_export_params)s
%(overwrite)s
.. versionadded:: 0.24.1
%(verbose)s
Notes
-----
.. versionadded:: 0.24
%(export_warning_note_raw)s
%(export_eeglab_note)s
%(export_edf_note)s
"""
fname = str(_check_fname(fname, overwrite=overwrite))
supported_export_formats = { # format : (extensions,)
"eeglab": ("set",),
"edf": ("edf",),
"brainvision": (
"eeg",
"vmrk",
"vhdr",
),
}
fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats)
# check for unapplied projectors
if any(not proj["active"] for proj in raw.info["projs"]):
warn(
"Raw instance has unapplied projectors. Consider applying "
"them before exporting with raw.apply_proj()."
)
if fmt == "eeglab":
from ._eeglab import _export_raw
_export_raw(fname, raw)
elif fmt == "edf":
from ._edf import _export_raw
_export_raw(fname, raw, physical_range, add_ch_type)
elif fmt == "brainvision":
from ._brainvision import _export_raw
_export_raw(fname, raw, overwrite)
@verbose
def export_epochs(fname, epochs, fmt="auto", *, overwrite=False, verbose=None):
"""Export Epochs to external formats.
%(export_fmt_support_epochs)s
%(export_warning)s
Parameters
----------
%(fname_export_params)s
epochs : instance of Epochs
The epochs to export.
%(export_fmt_params_epochs)s
%(overwrite)s
.. versionadded:: 0.24.1
%(verbose)s
Notes
-----
.. versionadded:: 0.24
%(export_warning_note_epochs)s
%(export_eeglab_note)s
"""
fname = str(_check_fname(fname, overwrite=overwrite))
supported_export_formats = {
"eeglab": ("set",),
}
fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats)
# check for unapplied projectors
if any(not proj["active"] for proj in epochs.info["projs"]):
warn(
"Epochs instance has unapplied projectors. Consider applying "
"them before exporting with epochs.apply_proj()."
)
if fmt == "eeglab":
from ._eeglab import _export_epochs
_export_epochs(fname, epochs)
@verbose
def export_evokeds(fname, evoked, fmt="auto", *, overwrite=False, verbose=None):
"""Export evoked dataset to external formats.
This function is a wrapper for format-specific export functions. The export
function is selected based on the inferred file format. For additional
options, use the format-specific functions.
%(export_fmt_support_evoked)s
%(export_warning)s
Parameters
----------
%(fname_export_params)s
evoked : Evoked instance, or list of Evoked instances
The evoked dataset, or list of evoked datasets, to export to one file.
Note that the measurement info from the first evoked instance is used,
so be sure that information matches.
%(export_fmt_params_evoked)s
%(overwrite)s
.. versionadded:: 0.24.1
%(verbose)s
See Also
--------
mne.write_evokeds
mne.export.export_evokeds_mff
Notes
-----
.. versionadded:: 0.24
%(export_warning_note_evoked)s
"""
fname = str(_check_fname(fname, overwrite=overwrite))
supported_export_formats = {
"mff": ("mff",),
}
fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats)
if not isinstance(evoked, list):
evoked = [evoked]
logger.info(f"Exporting evoked dataset to {fname}...")
if fmt == "mff":
export_evokeds_mff(fname, evoked, overwrite=overwrite)
def _infer_check_export_fmt(fmt, fname, supported_formats):
"""Infer export format from filename extension if auto.
Raises error if fmt is auto and no file extension found,
then checks format against supported formats, raises error if format is not
supported.
Parameters
----------
fmt : str
Format of the export, will only infer the format from filename if fmt
is auto.
fname : str
Name of the target export file, only used when fmt is auto.
supported_formats : dict of str : tuple/list
Dictionary containing supported formats (as keys) and each format's
corresponding file extensions in a tuple (e.g., {'eeglab': ('set',)})
"""
_validate_type(fmt, str, "fmt")
fmt = fmt.lower()
if fmt == "auto":
fmt = op.splitext(fname)[1]
if fmt:
fmt = fmt[1:].lower()
# find fmt in supported formats dict's tuples
fmt = next(
(k for k, v in supported_formats.items() if fmt in v), fmt
) # default to original fmt for raising error later
else:
raise ValueError(
f"Couldn't infer format from filename {fname}" " (no extension found)"
)
if fmt not in supported_formats:
supported = []
for format, extensions in supported_formats.items():
ext_str = ", ".join(f"*.{ext}" for ext in extensions)
supported.append(f"{format} ({ext_str})")
supported_str = ", ".join(supported)
raise ValueError(
f"Format '{fmt}' is not supported. "
f"Supported formats are {supported_str}."
)
return fmt