'/rawDataSample.bin'))
path_to_filtered_data = (os.path.expanduser('~/data/ucl-neuropixel'
'/tmp/filtered.bin'))
# create batch processor for the data
bp = BatchProcessor(path_to_neuropixel_data,
dtype='int16', n_channels=385, data_format='wide',
max_memory='500MB')
# appply a single channel transformation, each batch will be all observations
# from one channel, results are saved to disk
bp.single_channel_apply(butterworth,
mode='disk',
output_path=path_to_filtered_data,
low_freq=300, high_factor=0.1,
order=3, sampling_freq=30000,
channels=[0, 1, 2])
# let's visualize the results
raw = RecordingsReader(path_to_neuropixel_data, dtype='int16',
n_channels=385, data_format='wide')
# you do not need to specify the format since single_channel_apply
# saves a yaml file with such parameters
filtered = RecordingsReader(path_to_filtered_data)
fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(raw[:2000, 0])
ax2.plot(filtered[:2000, 0])
x_long = dummy(big_long[(slice(0, 2000000, None), 1)])
big_long[(slice(0, 2000000, None), 1)] = x_long
big_long.flush()
x_long = dummy(big_long[:, 1])
big_long[:, 1] = x_long
bp_long = BatchProcessor(path_to_long,
dtype='int64',
n_channels=50,
data_format='long',
max_memory='500MB')
path = bp_long.single_channel_apply(dummy, path_to_out)
out = RecordingsReader(path)
out
bp_wide = BatchProcessor(path_to_wide,
dtype='int64',
n_channels=50,
data_format='wide',
max_memory='500MB')
path = bp_wide.single_channel_apply(dummy, path_to_out)
out = RecordingsReader(path)
out
path = bp_long.multi_channel_apply(dummy, path_to_out)
'/rawDataSample.bin'))
path_to_filtered_data = (os.path.expanduser('~/data/ucl-neuropixel'
'/tmp/filtered.bin'))
# create batch processor for the data
bp = BatchProcessor(path_to_neuropixel_data,
dtype='int16',
n_channels=385,
data_format='wide',
max_memory='500MB')
# appply a single channel transformation, each batch will be all observations
# from one channel
bp.single_channel_apply(butterworth,
path_to_filtered_data,
low_freq=300,
high_factor=0.1,
order=3,
sampling_freq=30000)
# let's visualize the results
raw = RecordingsReader(path_to_neuropixel_data,
dtype='int16',
n_channels=385,
data_format='wide')
# you do not need to specify the format since single_channel_apply
# saves a yaml file with such parameters
filtered = RecordingsReader(path_to_filtered_data)
fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(raw[:2000, 0])