mne.io.read_raw_fif#
- mne.io.read_raw_fif(fname, allow_maxshield=False, preload=False, on_split_missing='raise', verbose=None)[source]#
Reader function for Raw FIF data.
- Parameters:
- fname
str
| file-like The raw filename to load. For files that have automatically been split, the split part will be automatically loaded. Filenames should end with raw.fif, raw.fif.gz, raw_sss.fif, raw_sss.fif.gz, raw_tsss.fif, raw_tsss.fif.gz, or _meg.fif. If a file-like object is provided, preloading must be used.
Changed in version 0.18: Support for file-like objects.
- allow_maxshield
bool
|str
(defaultFalse
) If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be “yes” to load without eliciting a warning.
- preload
bool
orstr
(defaultFalse
) Preload data into memory for data manipulation and faster indexing. If True, the data will be preloaded into memory (fast, requires large amount of memory). If preload is a string, preload is the file name of a memory-mapped file which is used to store the data on the hard drive (slower, requires less memory).
- on_split_missing
str
Can be
'raise'
(default) to raise an error,'warn'
to emit a warning, or'ignore'
to ignore when split file is missing.New in version 0.22.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- fname
- Returns:
- rawinstance of
Raw
A Raw object containing FIF data.
- rawinstance of
Notes
New in version 0.9.0.
When reading a FIF file, note that the first N seconds annotated
BAD_ACQ_SKIP
are skipped. They are removed fromraw.times
andraw.n_times
parameters butraw.first_samp
andraw.first_time
are updated accordingly.
Examples using mne.io.read_raw_fif
#
Overview of MEG/EEG analysis with MNE-Python
Importing data from MEG devices
Built-in plotting methods for Raw objects
Background on projectors and projections
How to convert 3D electrode positions to a 2D image