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# NOTE: `dataset` is an internal object for data ingest & time alignment

session_start_time = dataset.pkl['startdatetime']
roi_masks = dataset.get_roi_mask_array()
max_projection = dataset.get_max_projection()

dFF, dFF_t = dataset.get_dff_traces()

NA = 'THIS REQUIRED ATTRIBUTE INTENTIONALLY LEFT BLANK.'

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# NOTE: `dataset` is an internal object for data ingest & time alignment

session_start_time = dataset.pkl['startdatetime']
roi_masks = dataset.get_roi_mask_array()
max_projection = dataset.get_max_projection()

dFF, dFF_t = dataset.get_dff_traces()

NA = 'THIS REQUIRED ATTRIBUTE INTENTIONALLY LEFT BLANK.'

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def timeseries_to_xarray(time_series):
    
    attrs = ts.fields.copy()
    data = attrs.pop('data').value
    timestamps = attrs.pop('timestamps').value
    
    for k,v in attrs.iteritems():
        # replace any links with their name
        try:
            attrs[k] = v.name

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## create minimal set of objects for defining a dF/F object

from pynwb.ophys import TwoPhotonSeries, RoiResponseSeries, DfOverF, Fluorescence, PlaneSegmentation, \
    ImageSegmentation, OpticalChannel, ImagingPlane, ROI
from pynwb.image import ImageSeries

w, h = 5, 5
img_mask = [[0 for x in range(w)] for y in range(h)]
w, h = 5, 2
pix_mask = [[0 for x in range(w)] for y in range(h)]