![]() ![]() ForĮxample, if the original event array had 4 events and the second event List of indices of selected events (not dropped or ignored etc.). Names of conditions corresponding to event_ids. The mne.Info object with information about the sensors and methods of measurement. That contain annotations is not supported, and any annotations will To the Epochs object programmatically (via code) or interactively It is not yet possible to add annotations ![]() Raw object will be preserved in the resulting Currently annotations that are present in the There is limited support for Annotations in theĮpochs class. Keys can be any channel type present in the object.Įvent_id ] respectively. The dictionary keys correspond to the different channel types valid If the PTP of any one channel exceeds the rejection threshold, the In each individual epoch, the PTP is calculated for every channel. the absolute difference between the lowest and the highest signal Reject epochs based on maximum peak-to-peak signal amplitude (PTP), Or wait before accessing each epoch (more memoryĮfficient but can be slower). Load all epochs from disk when creating the object Info will be included if their names or indices are String values “all” to pick all channels, or “data” to pick dataĬhannels. In lists, channel type strings (e.g., ) will pick channels of those types, channel name strings (e.g., Slices and lists of integers will be interpreted asĬhannel indices. picks str | array_like | slice | NoneĬhannels to include. Subtract this mean from the entire epoch.ĭefaults to (None, 0), i.e. allĬorrection is applied to each epoch and channel individually in theĬalculate the mean signal of the baseline period. ![]() The baseline (a, b) includes both endpoints, i.e. If (None, None), the entire time interval is used. Is None, it is set to the end of the interval. If a is None, the beginning of the data is used and if b If a tuple (a, b), the interval is between a and b If None, do not apply baseline correction. The time interval to consider as “baseline” when applying baselineĬorrection. The closest or matching samples corresponding to the start and end Start and end time of the epochs in seconds, relative to the time-lockedĮvent. With string integer names corresponding to the event id integers. If None, all events will be used and a dict is created If a list, all events with the IDs specified in the listĪre used. If int, a dict will be created with the id as They will be marked as IGNORED in the drop log. If some events don’t match the events of interest as specified by event_id, The first column contains the event time in Parameters : raw Raw objectĪn instance of Raw. Epochs ( raw, events, event_id = None, tmin = -0.2, tmax = 0.5, baseline = (None, 0), picks = None, preload = False, reject = None, flat = None, proj = True, decim = 1, reject_tmin = None, reject_tmax = None, detrend = None, on_missing = 'raise', reject_by_annotation = True, metadata = None, event_repeated = 'error', verbose = None ) #Įpochs extracted from a Raw instance. Invariably, you'll end up with a bug that you'll miss because you are ignoring the exceptions.Īs for making it run faster, we'd really need to know more about the log file's # class mne. You should never never catch and then ignore all possible exceptions. You don't need the parens, and I'd combine the last two lines machine += diff-tickĭon't ever do this. Create an a class and put stuff in there. ![]() Machine = obj.setdefault(user,)ĭon't use dictionaries random attribute storage. Tick = datetime.timedelta(hours=threshold.tm_hour,minutes=threshold.tm_min,seconds=threshold.tm_sec).total_seconds() On my test file, I'm ending up with roughly 9:30min (Python) and 4:30min (PyPy) on 28 million records (small log). I need to run over a log file with plenty of entries (25+GB) in Python to extract some log times. ![]()
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