ENH: Collapse BAD_ACQ_SKIP in raw.plot

Feature description

Hi, I would like to know if there is any plan to support missing data / gaps in continous raw, especially regarding:

• annotations: take into account EDGE Boundary duration (time gap) when annotating
• time_as_index: get the timestamp index of a Raw that contains time gaps

Example

Let say I have two successive recordings of 5 and 3 seconds with a 2s gap in between. I have 2 channels at 100Hz, so the recording duration is 10 seconds (in real life), and I have 8 seconds of data (here I use simple sin and cos).

import mne
import numpy as np
from datetime import datetime,timedelta,timezone

# create basic info
info = mne.create_info(
    ch_names=["Ch1", "Ch2"],
    sfreq=100)

# create fake successive recordings 0 and 1
start_datetime = datetime.now().replace(tzinfo=timezone.utc)

x0 = np.linspace(0, 5, 500)
x1 = np.linspace(7, 10, 300)

k = 2 # only for viz purpose
raw0 = mne.io.RawArray(np.array([np.cos(k*x0), np.sin(k*x0)]), info)
raw0.set_meas_date(start_datetime);
raw1 = mne.io.RawArray(np.array([np.cos(k*x1), np.sin(k*x1)]), info)
raw1.set_meas_date(start_datetime + timedelta(0,7)); # I know when 2nd recording started

# concat and plot recordings
raws = mne.concatenate_raws([raw0, raw1])
raws.plot();

Now if I have an event that occured between the 4th and the 8th second, I would like to annotate it regarding the start of the experiment like this:

raws.annotations.append(onset=4, duration=4, description="4s event")
raws.plot(); 

The current behaviour is that the 4 seconds from the onset are annotated regardless of any gap/missing data. The feature would allow to annotate data that are discontinous.

The same happens with time_as_index:

raws.time_as_index(8) # would love result: array([600])

Additional comments

This is something common that recordings have gaps between one another (see the chb-mit database). If the gap is only a few seconds long that does'nt matter, one can fill with NaN or 0 as a workaround. But sometimes gaps are few hours long.

I only used the API for a few weeks, but if you think the feature is interesting and a new user can help on that, please let me know where the changes have to be made so I can fork and test it (but I feel this could have a strong impact).

Thanks a lot !

Start with the raw.plot() and Raw.time_as_index() entry points, then review how concatenate_raws() represents measurement-time gaps and annotations. Define expected gap-aware behavior for both APIs and add coverage using the two-recording example; done means annotations and timestamp indexing honor the missing interval without breaking continuous recordings.