touch examples with plot_events

examples/datasets/limo_data.py

@@ -107,7 +107,7 @@
 print(limo_epochs.metadata.head())
 
 # %%
-# Now let's take a closer look at the information in the epochs
+# Now let us take a closer look at the information in the epochs
 # metadata.
 
 # We want include all columns in the summary table

examples/preprocessing/epochs_metadata.py

@@ -35,8 +35,8 @@
 #
 # All experimental events are stored in the :class:`~mne.io.Raw` instance as
 # :class:`~mne.Annotations`. We first need to convert these to events and the
-# corresponding mapping from event codes to event names (``event_id``). We then
-# visualize the events.
+# corresponding mapping from event codes to event names (``event_id``).
+# We then visualize the events.
 all_events, all_event_id = mne.events_from_annotations(raw)
 mne.viz.plot_events(events=all_events, event_id=all_event_id, sfreq=raw.info["sfreq"])
 

tutorials/clinical/60_sleep.py

@@ -48,22 +48,22 @@
 # Load the data
 # -------------
 #
-# Here we download the data from two subjects and the end goal is to obtain
-# :term:`epochs` and its associated ground truth.
+# Here we download the data of two subjects. The end goal is to obtain
+# :term:`epochs` and the associated ground truth.
 #
 # MNE-Python provides us with
 # :func:`mne.datasets.sleep_physionet.age.fetch_data` to conveniently download
 # data from the Sleep Physionet dataset
 # :footcite:`KempEtAl2000,GoldbergerEtAl2000`.
 # Given a list of subjects and records, the fetcher downloads the data and
-# provides us for each subject, a pair of files:
+# provides us with a pair of files for each subject:
 #
 # * ``-PSG.edf`` containing the polysomnography. The :term:`raw` data from the
 #   EEG helmet,
 # * ``-Hypnogram.edf`` containing the :term:`annotations` recorded by an
 #   expert.
 #
-# Combining these two in a :class:`mne.io.Raw` object then we can extract
+# Combining these two in a :class:`mne.io.Raw` object will allow us to extract
 # :term:`events` based on the descriptions of the annotations to obtain the
 # :term:`epochs`.
 #

tutorials/intro/10_overview.py

@@ -208,7 +208,7 @@
 # example of this is shown in the next section. There is also a convenient
 # `~mne.viz.plot_events` function for visualizing the distribution of events
 # across the duration of the recording (to make sure event detection worked as
-# expected). Here we'll also make use of the `~mne.Info` attribute to get the
+# expected). Here we will also make use of the `~mne.Info` attribute to get the
 # sampling frequency of the recording (so our x-axis will be in seconds instead
 # of in samples).
 

tutorials/preprocessing/70_fnirs_processing.py

@@ -172,7 +172,7 @@
 # and the unwanted heart rate component has been removed, we can extract epochs
 # related to each of the experimental conditions.
 #
-# First we extract the events of interest and visualise them to ensure they are
+# First we extract the events of interest and visualize them to ensure they are
 # correct.
 
 events, event_dict = mne.events_from_annotations(raw_haemo)
@@ -181,7 +181,7 @@
 
 # %%
 # Next we define the range of our epochs, the rejection criteria,
-# baseline correction, and extract the epochs. We visualise the log of which
+# baseline correction, and extract the epochs. We visualize the log of which
 # epochs were dropped.
 
 reject_criteria = dict(hbo=80e-6)
@@ -209,7 +209,7 @@
 # -------------------------------------------
 #
 # Now we can view the haemodynamic response for our tapping condition.
-# We visualise the response for both the oxy- and deoxyhaemoglobin, and
+# We visualize the response for both the oxy- and deoxyhaemoglobin, and
 # observe the expected peak in HbO at around 6 seconds consistently across
 # trials, and the consistent dip in HbR that is slightly delayed relative to
 # the HbO peak.
@@ -296,7 +296,7 @@
 # ---------------------------------------
 #
 # Finally we generate topo maps for the left and right conditions to view
-# the location of activity. First we visualise the HbO activity.
+# the location of activity. First we visualize the HbO activity.
 
 times = np.arange(4.0, 11.0, 1.0)
 epochs["Tapping/Left"].average(picks="hbo").plot_topomap(times=times, **topomap_args)

tutorials/raw/20_event_arrays.py

@@ -158,8 +158,8 @@
 # :func:`mne.viz.plot_events` will plot each event versus its sample number
 # (or, if you provide the sampling frequency, it will plot them versus time in
 # seconds). It can also account for the offset between sample number and sample
-# index in Neuromag systems, with the ``first_samp`` parameter. If an event
-# dictionary is provided, it will be used to generate a legend:
+# index in Neuromag systems, with the ``first_samp`` parameter.
+# If an event dictionary is provided, it will be used to generate a legend:
 
 fig = mne.viz.plot_events(
     events, sfreq=raw.info["sfreq"], first_samp=raw.first_samp, event_id=event_dict