- MNE version: e.g. 1.0.3
- operating system: / Windows 10
When reading in an IPython notebook a large .edf file which is 10Gb size (4 hours/ 150 channels/ 2048Hz sampling frequency) with:
raw_edf = mne.io.read_raw_edf('file.edf', preload=False)
the kernel crashes as the RAM memory, which is 12 Gb, gets full.
I thought the
preload argument was loading only metadata (which should not be more than a few hundred Mbs).
Would there be a way in which I could read the
raw_edf (only the metadata) and then with the
get_data() method, be able to just load into memory a small piece of data (n channels and x time range) without the system failing because of the RAM being full?
If I try the same with a higher RAM (32Gb), there is no problem and once it is loaded, the RAM goes back to normal (e.g: before loading it is 1Gb, when loading it goes up to 15Gb, once loaded back to 1 Gb approx.). We need to make it functional, if possible, with the 12 Gb RAM.
For privacy reasons, I can’t share the file that I am using.
Thanks in advance.