# -*- coding: utf-8 -*-
"""
.. _tut-events-vs-annotations:

============================
Parsing events from raw data
============================

This tutorial describes how to read experimental events from raw recordings,
and how to convert between the two different representations of events within
MNE-Python (Events arrays and Annotations objects).

In the :ref:`introductory tutorial <overview-tut-events-section>` we saw an
example of reading experimental events from a :term:`"STIM" channel <stim
channel>`; here we'll discuss :term:`events` and :term:`annotations` more
broadly, give more detailed information about reading from STIM channels, and
give an example of reading events that are in a marker file or included in
the data file as an embedded array. The tutorials :ref:`tut-event-arrays` and
:ref:`tut-annotate-raw` discuss how to plot, combine, load, save, and
export :term:`events` and `~mne.Annotations` (respectively), and the
latter tutorial also covers interactive annotation of `~mne.io.Raw`
objects.

We'll begin by loading the Python modules we need, and loading the same
:ref:`example data <sample-dataset>` we used in the :ref:`introductory tutorial
<tut-overview>`, but to save memory we'll crop the `~mne.io.Raw` object
to just 60 seconds before loading it into RAM:
"""

# %%

import numpy as np
import mne

sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = (sample_data_folder / 'MEG' / 'sample' /
                        'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=60).load_data()

# %%
# The Events and Annotations data structures
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Generally speaking, both the Events and `~mne.Annotations` data
# structures serve the same purpose: they provide a mapping between times
# during an EEG/MEG recording and a description of what happened at those
# times. In other words, they associate a *when* with a *what*. The main
# differences are:
#
# 1. **Units**: the Events data structure represents the *when* in terms of
#    samples, whereas the `~mne.Annotations` data structure represents
#    the *when* in seconds.
# 2. **Limits on the description**: the Events data structure represents the
#    *what* as an integer "Event ID" code, whereas the `~mne.Annotations` data
#    structure represents the *what* as a string.
# 3. **How duration is encoded**: Events in an Event array do not have a
#    duration (though it is possible to represent duration with pairs of
#    onset/offset events within an Events array), whereas each element of an
#    `~mne.Annotations` object necessarily includes a duration (though
#    the duration can be zero if an instantaneous event is desired).
# 4. **Internal representation**: Events are stored as an ordinary
#    :class:`NumPy array <numpy.ndarray>`, whereas `~mne.Annotations` is
#    a :class:`list`-like class defined in MNE-Python.
#
#
# .. _stim-channel-defined:
#
# What is a STIM channel?
# ^^^^^^^^^^^^^^^^^^^^^^^
#
# A :term:`stim channel` (short for "stimulus channel") is a channel that does
# not receive signals from an EEG, MEG, or other sensor. Instead, STIM channels
# record voltages (usually short, rectangular DC pulses of fixed magnitudes
# sent from the experiment-controlling computer) that are time-locked to
# experimental events, such as the onset of a stimulus or a button-press
# response by the subject (those pulses are sometimes called `TTL`_ pulses,
# event pulses, trigger signals, or just "triggers"). In other cases, these
# pulses may not be strictly time-locked to an experimental event, but instead
# may occur in between trials to indicate the type of stimulus (or experimental
# condition) that is about to occur on the upcoming trial.
#
# The DC pulses may be all on one STIM channel (in which case different
# experimental events or trial types are encoded as different voltage
# magnitudes), or they may be spread across several channels, in which case the
# channel(s) on which the pulse(s) occur can be used to encode different events
# or conditions. Even on systems with multiple STIM channels, there is often
# one channel that records a weighted sum of the other STIM channels, in such a
# way that voltage levels on that channel can be unambiguously decoded as
# particular event types. On older Neuromag systems (such as that used to
# record the sample data) this "summation channel" was typically ``STI 014``;
# on newer systems it is more commonly ``STI101``. You can see the STIM
# channels in the raw data file here:

raw.copy().pick_types(meg=False, stim=True).plot(start=3, duration=6)

# %%
# You can see that ``STI 014`` (the summation channel) contains pulses of
# different magnitudes whereas pulses on other channels have consistent
# magnitudes. You can also see that every time there is a pulse on one of the
# other STIM channels, there is a corresponding pulse on ``STI 014``.
#
# .. TODO: somewhere in prev. section, link out to a table of which systems
#    have STIM channels vs. which have marker files or embedded event arrays
#    (once such a table has been created).
#
#
# Converting a STIM channel signal to an Events array
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# If your data has events recorded on a STIM channel, you can convert them into
# an events array using `~mne.find_events`. The sample number of the onset
# (or offset) of each pulse is recorded as the event time, the pulse magnitudes
# are converted into integers, and these pairs of sample numbers plus integer
# codes are stored in :class:`NumPy arrays <numpy.ndarray>` (usually called
# "the events array" or just "the events"). In its simplest form, the function
# requires only the `~mne.io.Raw` object, and the name of the channel(s)
# from which to read events:

events = mne.find_events(raw, stim_channel='STI 014')
print(events[:5])  # show the first 5

# %%
# .. admonition:: The middle column of the Events array
#     :class: sidebar note
#
#     MNE-Python events are actually *three* values: in between the sample
#     number and the integer event code is a value indicating what the event
#     code was on the immediately preceding sample. In practice, that value is
#     almost always ``0``, but it can be used to detect the *endpoint* of an
#     event whose duration is longer than one sample. See the documentation of
#     `~mne.find_events` for more details.
#
# If you don't provide the name of a STIM channel, `~mne.find_events`
# will first look for MNE-Python :ref:`config variables <tut-configure-mne>`
# for variables ``MNE_STIM_CHANNEL``, ``MNE_STIM_CHANNEL_1``, etc. If those are
# not found, channels ``STI 014`` and ``STI101`` are tried, followed by the
# first channel with type "STIM" present in ``raw.ch_names``. If you regularly
# work with data from several different MEG systems with different STIM channel
# names, setting the ``MNE_STIM_CHANNEL`` config variable may not be very
# useful, but for researchers whose data is all from a single system it can be
# a time-saver to configure that variable once and then forget about it.
#
# `~mne.find_events` has several options, including options for aligning
# events to the onset or offset of the STIM channel pulses, setting the minimum
# pulse duration, and handling of consecutive pulses (with no return to zero
# between them). For example, you can effectively encode event duration by
# passing ``output='step'`` to `~mne.find_events`; see the documentation
# of `~mne.find_events` for details. More information on working with
# events arrays (including how to plot, combine, load, and save event arrays)
# can be found in the tutorial :ref:`tut-event-arrays`.
#
#
# Reading embedded events as Annotations
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Some EEG/MEG systems generate files where events are stored in a separate
# data array rather than as pulses on one or more STIM channels. For example,
# the EEGLAB format stores events as a collection of arrays in the :file:`.set`
# file. When reading those files, MNE-Python will automatically convert the
# stored events into an `~mne.Annotations` object and store it as the
# :attr:`~mne.io.Raw.annotations` attribute of the `~mne.io.Raw` object:

testing_data_folder = mne.datasets.testing.data_path()
eeglab_raw_file = testing_data_folder / 'EEGLAB' / 'test_raw.set'
eeglab_raw = mne.io.read_raw_eeglab(eeglab_raw_file)
print(eeglab_raw.annotations)

# %%
# The core data within an `~mne.Annotations` object is accessible
# through three of its attributes: ``onset``, ``duration``, and
# ``description``. Here we can see that there were 154 events stored in the
# EEGLAB file, they all had a duration of zero seconds, there were two
# different types of events, and the first event occurred about 1 second after
# the recording began:

print(len(eeglab_raw.annotations))
print(set(eeglab_raw.annotations.duration))
print(set(eeglab_raw.annotations.description))
print(eeglab_raw.annotations.onset[0])

# %%
# More information on working with `~mne.Annotations` objects, including
# how to add annotations to `~mne.io.Raw` objects interactively, and how
# to plot, concatenate, load, save, and export `~mne.Annotations`
# objects can be found in the tutorial :ref:`tut-annotate-raw`.
#
#
# Converting between Events arrays and Annotations objects
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Once your experimental events are read into MNE-Python (as either an Events
# array or an `~mne.Annotations` object), you can easily convert between
# the two formats as needed. You might do this because, e.g., an Events array
# is needed for epoching continuous data, or because you want to take advantage
# of the "annotation-aware" capability of some functions, which automatically
# omit spans of data if they overlap with certain annotations.
#
# To convert an `~mne.Annotations` object to an Events array, use the
# function `mne.events_from_annotations` on the `~mne.io.Raw` file
# containing the annotations. This function will assign an integer Event ID to
# each unique element of ``raw.annotations.description``, and will return the
# mapping of descriptions to integer Event IDs along with the derived Event
# array. By default, one event will be created at the onset of each annotation;
# this can be modified via the ``chunk_duration`` parameter of
# `~mne.events_from_annotations` to create equally spaced events within
# each annotation span (see :ref:`chunk-duration`, below, or see
# :ref:`fixed-length-events` for direct creation of an Events array of
# equally-spaced events).

events_from_annot, event_dict = mne.events_from_annotations(eeglab_raw)
print(event_dict)
print(events_from_annot[:5])

# %%
# If you want to control which integers are mapped to each unique description
# value, you can pass a :class:`dict` specifying the mapping as the
# ``event_id`` parameter of `~mne.events_from_annotations`; this
# :class:`dict` will be returned unmodified as the ``event_dict``.
#
# Note that this ``event_dict`` can be used when creating `~mne.Epochs` from
# `~mne.io.Raw` objects, as demonstrated in the tutorial
# :ref:`tut-epochs-class`.

custom_mapping = {'rt': 77, 'square': 42}
(events_from_annot,
 event_dict) = mne.events_from_annotations(eeglab_raw, event_id=custom_mapping)
print(event_dict)
print(events_from_annot[:5])

# %%
# To make the opposite conversion (from an Events array to an
# `~mne.Annotations` object), you can create a mapping from integer
# Event ID to string descriptions, use `~mne.annotations_from_events`
# to construct the `~mne.Annotations` object, and call the
# `~mne.io.Raw.set_annotations` method to add the annotations to the
# `~mne.io.Raw` object.
#
# Because the :ref:`sample data <sample-dataset>` was recorded on a Neuromag
# system (where sample numbering starts when the acquisition system is
# initiated, not when the *recording* is initiated), we also need to pass in
# the ``orig_time`` parameter so that the onsets are properly aligned relative
# to the start of recording:

mapping = {1: 'auditory/left', 2: 'auditory/right', 3: 'visual/left',
           4: 'visual/right', 5: 'smiley', 32: 'buttonpress'}
annot_from_events = mne.annotations_from_events(
    events=events, event_desc=mapping, sfreq=raw.info['sfreq'],
    orig_time=raw.info['meas_date'])
raw.set_annotations(annot_from_events)

# %%
# Now, the annotations will appear automatically when plotting the raw data,
# and will be color-coded by their label value:

raw.plot(start=5, duration=5)

# %%
# .. _`chunk-duration`:
#
# Making multiple events per annotation
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# As mentioned above, you can generate equally-spaced events from an
# `~mne.Annotations` object using the ``chunk_duration`` parameter of
# `~mne.events_from_annotations`. For example, suppose we have an
# annotation in our `~mne.io.Raw` object indicating when the subject was
# in REM sleep, and we want to perform a resting-state analysis on those spans
# of data. We can create an Events array with a series of equally-spaced events
# within each "REM" span, and then use those events to generate (potentially
# overlapping) epochs that we can analyze further.

# create the REM annotations
rem_annot = mne.Annotations(onset=[5, 41],
                            duration=[16, 11],
                            description=['REM'] * 2)
raw.set_annotations(rem_annot)
(rem_events,
 rem_event_dict) = mne.events_from_annotations(raw, chunk_duration=1.5)

# %%
# Now we can check that our events indeed fall in the ranges 5-21 seconds and
# 41-52 seconds, and are ~1.5 seconds apart (modulo some jitter due to the
# sampling frequency). Here are the event times rounded to the nearest
# millisecond:

print(np.round((rem_events[:, 0] - raw.first_samp) / raw.info['sfreq'], 3))

# %%
# Other examples of resting-state analysis can be found in the online
# documentation for `~mne.make_fixed_length_events`.
#
# .. LINKS
#
# .. _`TTL`: https://en.wikipedia.org/wiki/Transistor%E2%80%93transistor_logic
