Comments (3)
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()
from analysis-tools.
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!
from analysis-tools.
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:]))
from analysis-tools.
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