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# YASS configuration example (all sections and values) #
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data:
# project's root folder, data will be loaded and saved here
# can be an absolute or relative path
root_folder: data/retina/
# recordings filename (must be a binary file), details about the recordings
# are specified in the recordings section
recordings: data.bin
# channel geometry filename , supports txt (one x, y pair per line,
# separated by spaces) or a npy file with shape (n_channels, 2),
# where every row contains a x, y pair. see yass.geometry.parse for details
geometry: geometry.npy
resources:
# maximum memory per batch allowed (only relevant for preprocess
# and detection step, which perform batch processing)
max_memory: 200MB
# maximum memory per batch allowed (only relevant for detection step
# which uses tensorflow GPU is available)
max_memory_gpu: 1GB
# number of processes to use for operations that support parallel execution,
# 'max' will use all cores, if you as an int, it will use that many cores
processes: max
recordings:
# precision of the recording – must be a valid numpy dtype
dtype: int16
# recording rate (in Hz)
sampling_rate: 20000
# number of channels
n_channels: 49
# channels spatial radius to consider them neighbors, see
# yass.geometry.find_channel_neighbors for details
spatial_radius: 70
# temporal length of waveforms in ms
spike_size_ms: 1.5
# recordings order, one of ('channels', 'samples'). In a dataset with k
# observations per channel and j channels: 'channels' means first k
# contiguous observations come from channel 0, then channel 1, and so on.
# 'sample' means first j contiguous data are the first observations from
# all channels, then the second observations from all channels and so on
order: samples