name='my_population',
source='acquisition',
unit='second',
timestamps=np.random.rand(5))
f = NWBFile(source='noone',
session_description='my first synthetic recording',
file_name='tmp.nwb',
identifier='hi',
experimenter='Dr. Bilbo Baggins',
lab='Bag End Labatory',
institution='University of Middle Earth at the Shire',
experiment_description='empty',
session_id='LONELYMTN',
session_start_time=datetime.now())
f.add_raw_timeseries(fs)
test_file_name = 'example.nwb'
manager = get_build_manager()
io = HDF5IO(test_file_name, manager, mode='w')
io.write(f)
io.close()
io = HDF5IO(test_file_name, mode='r')
read_data = io.read()
pst = read_data.get_raw_timeseries('my_population')
print pst.data[2:5]
print pst.timestamps[2:5]
description="This 2D Brownian process generated with \
numpy.cumsum(numpy.random.normal(size=(2,len(spatial_timestamps))), axis=-1).T"
)
# Create experimental epochs
epoch_tags = ('test_example', )
ep1 = f.add_epoch('epoch1',
ephys_timestamps[100],
ephys_timestamps[200],
tags=epoch_tags,
description="the first test epoch")
ep2 = f.add_epoch('epoch2',
ephys_timestamps[600],
ephys_timestamps[700],
tags=epoch_tags,
description="the second test epoch")
# Add the time series data and include the epochs it is apart of
f.add_raw_timeseries(ephys_ts, [ep1, ep2])
f.add_raw_timeseries(spatial_ts, [ep1, ep2])
# Write the NWB file
manager = get_build_manager()
io = HDF5IO(filename, manager, mode='w')
io.write(f)
io.close()
io = HDF5IO(filename, manager, mode='r')
io.read()
io.close()
