Polarization and Interferometer recording from CVUT campus testbed for IT4inov

Data are stored as compressed Numpy.array by:

import numpy as np
np.savez_compressed(fname, array)

and could be read as follows:

def readBinary(fn):
    """Load arrays from ``.npy`` or ``.npz`` files.

    Args:
        fn (string): Path to the file.

    Returns:
        numpy.array: Loaded array.
    """
    r = np.array([])
    with np.load(fn) as data:
        key = data.files[0]
        r = data[key]
    return r

Sampling frequency as in all cases 20000 Hz.

In case of any question do not hesitate to contact me on slapak{at}cesnet.cz


Original data.shape=(1200000, )
data ... output from interferometer
int_double-clap.npz

This is repeated double-clap by hands near the common patchcord. It is hard to see it on spectrogram.


Original data.shape=(1200000, )
data ... output from interferometer
int_rack-closing-direct.npz

This case contains clearer data of recorder rack-closing a reopenning. These data are acquired in simplified setup without the polarization measurement instruments. As labeled, the red areas are from closing interval and green are from rack opening phase. The question which I'am not able to answer is: Are these 3rd smaller peaks - currently labelled as start of closing - the last part of opening phase?


Original data.shape=(1200000, )
data ... output from interferometer
int_rack-closing-fullconnect.npz

Rack doors close-open. These data are acquired in full setup with the polarization measurement instruments. The events occured repeatedly in following schema: close-open 11 times starting with close and finished by open.


Original data.shape=(1200000, 5)
data[:,0] ... channel 0
...
data[:,3] ... channel 3
data[:,4] ... data from thermal sensor (channels' values may be somehow related to sensor temperature)
pol_rack-closing-direct.npz

Rack doors close-open. These data are acquired by polarilog in simplified setup without the interferometry measurement instruments. The events occured repeatedly in following schema: close-open probably 6 times starting with close and finished by open. Labeling is nearly impossible by pure human approach. May be we should start with some form of preprocessing and clastering.


Original data.shape=(1200000, 5)
data[:,0] ... channel 0
...
data[:,3] ... channel 3
data[:,4] ... data from thermal sensor (channels' values may be somehow related to sensor temperature)
pol_rack-closing-fullconnect.npz

Rack doors close-open. These data are acquired by polarilog in full setup with the interferometry measurement instruments. The events occured repeatedly in following schema: close-open probably 6 times starting with close and finished by open. Labeling is nearly impossible by pure human approach. May be we should start with some form of preprocessing and clastering.