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jsiegle avatar jsiegle commented on July 22, 2024

Hi! I'd recommend using the numpy.ndarray.tofile method for this.

Here's an example of how to load the continuous.dat file using memory mapping:

import numpy as np
import os

input_directory = '/path/to/recording'
stream_name = "example_data"

input_file = os.path.join(input_directory, 'continuous', stream_name, 'continuous.dat')

num_channels = 16

data_flat = np.memmap(input_file, mode='r', dtype='int16')
data = np.reshape(data_flat, (data_flat.size // num_channels, num_channels))

And here's how to write blocks from the beginning and end to a new file:

block1size = 10000  # block size in samples
block2size = 5000   # block size in samples

output_file = '/path/to/output/continuous.dat'

f = open(output_file, "wb")

data[:block1size,:].tofile(f)
data[-block2size:,:].tofile(f)

f.close()

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apearson98 avatar apearson98 commented on July 22, 2024

Thank you so much for your help! In your experience with this, does the function generation new timestamps for the second block of data, or does it maintain the original? Thanks again!

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jsiegle avatar jsiegle commented on July 22, 2024

This will only affect the .dat file, not the timestamps.

To get the new timestamps, you can use the following code:

ts = np.load('timestamps.npy')
new_ts = np.concatenate((ts[:block1size], ts[-block2size:]))

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