eelbrain.load.fiff¶
Tools for importing data through mne.
events ([raw, merge, proj, name, bads, …]) |
Load events from a raw fiff file. |
epochs (ds[, tmin, tmax, baseline, decim, …]) |
Load epochs as NDVar . |
mne_epochs (ds[, tmin, tmax, baseline, …]) |
Load epochs as mne.Epochs . |
add_epochs (ds[, tmin, tmax, baseline, …]) |
Load epochs and add them to a dataset as NDVar . |
add_mne_epochs (ds[, tmin, tmax, baseline, …]) |
Load epochs and add them to a dataset as mne.Epochs . |
epochs_ndvar (epochs[, name, data, exclude, …]) |
Convert an mne.Epochs object to an NDVar . |
evoked_ndvar (evoked[, name, data, exclude, …]) |
Convert one or more mne Evoked objects to an NDVar . |
sensor_dim (info[, picks, sysname, connectivity]) |
Create a Sensor dimension from an mne.Info object. |
stc_ndvar (stc, subject, src[, subjects_dir, …]) |
Convert one or more mne.SourceEstimate objects to an NDVar . |
forward_operator (fwd, src[, subjects_dir, …]) |
Load forward operator as NDVar |
inverse_operator (inv, src[, subjects_dir, …]) |
Load inverse operator as NDVar |
DatasetSTCLoader (data_dir) |
Load source estimates on disk into Dataset for use in statistical tests |
Managing events with a Dataset
¶
To load events as Dataset
:
>>> ds = load.fiff.events(raw_file_path)
By default, the Dataset
contains a variable called "trigger"
with trigger values, and a variable called "i_start"
with the indices of
the events:
>>> print(ds[:10])
trigger i_start
-----------------
2 27977
3 28345
1 28771
4 29219
2 29652
3 30025
1 30450
4 30839
2 31240
3 31665
These events can be modified in ds
(adding event labels as Factor
,
discarding events, modifying i_start
, , …) before being used to load data
epochs.
Epochs can be loaded as NDVar
with load.fiff.epochs()
. Epochs
will be loaded based only on the "i_start"
variable, so any modification
to this variable will affect the epochs that are loaded.:
>>> ds['epochs'] = load.fiff.epochs(ds)
Epochs can also be loaded as mne-python mne.Epochs
object:
>>> mne_epochs = load.fiff.mne_epochs(ds)
Using Threshold Rejection¶
In case threshold rejection is used, the number of the epochs returned by
load.fiff.epochs(ds, reject=reject_options)
might not be the same as the
number of events in ds
(whenever epochs are rejected). For those cases,
load.fiff.add_epochs`()
will automatically resize the Dataset
:
>>> epoch_ds = load.fiff.add_epochs(ds, -0.1, 0.6, reject=reject_options)
The returned epoch_ds
will contain the epochs as NDVar as ds['meg']
.
If no epochs got rejected during loading, the length of epoch_ds
is
identical with the input ds
. If epochs were rejected, epoch_ds
is a
shorter copy of the original ds
.
mne.Epochs
can be added to ds
in the same fashion with:
>>> ds = load.fiff.add_mne_epochs(ds, -0.1, 0.6, reject=reject_options)
Separate events files¶
If events are stored separately form the raw files, they can be loaded in
load.fiff.events()
by supplying the path to the events file as
events
parameter:
>>> ds = load.fiff.events(raw_file_path, events=events_file_path)
Loading source estimates into a Dataset¶
Previously exported stc files can be loaded into a Dataset
with the
DatasetSTCLoader
class. The stcs must reside in
subdirectories named by condition. Supply the path to the data, and the
constructor will detect the factors’ levels from the names of the condition
directories. Call set_factor_names()
to
indicate the names of the experimental conditions, and finally load the data
with make_dataset()
.
>>> loader = load.fiff.DatasetSTCLoader("path/to/exported/stcs")
>>> loader.set_factor_names(["factor1", "factor2"])
>>> ds = loader.make_dataset(subjects_dir="mri/")