tsaug¶
tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to connect multiple augmenters into a pipeline.
Table of Contents
Installation¶
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of tsaug from PyPI.
$ pip install tsaug
Alternatively, you could install from source code. This will give you the latest, but unstable, version of tsaug.
$ git clone https://github.com/arundo/tsaug.git
$ cd tsaug/
$ git checkout develop
$ pip install ./
Quick Start¶
Augment a batch of multivariate time series¶
Load time series.
>>> import numpy as np
>>> X = np.load("./X.npy")
>>> Y = np.load("./Y.npy")
>>> from tsaug.visualization import plot
>>> plot(X, Y)
Build a pipeline of augmenters as follows:
random time warping 5 times in parallel,
random crop subsequences with length 300,
random quantize to 10-, 20-, or 30- level sets,
with 80% probability , random drift the signal up to 10% - 50%,
with 50% probability, reverse the sequence.
>>> from tsaug import TimeWarp, Crop, Quantize, Drift, Reverse
>>> my_augmenter = (
... TimeWarp() * 5 # random time warping 5 times in parallel
... + Crop(size=300) # random crop subsequences with length 300
... + Quantize(n_levels=[10, 20, 30]) # random quantize to 10-, 20-, or 30- level sets
... + Drift(max_drift=(0.1, 0.5)) @ 0.8 # with 80% probability, random drift the signal up to 10% - 50%
... + Reverse() @ 0.5 # with 50% probability, reverse the sequence
... )
Augment the batch.
>>> X_aug, Y_aug = my_augmenter.augment(X, Y)
>>> plot(X_aug, Y_aug)
Augment a 2-channel audio sequence¶
Load audio sequence.
>>> import librosa
>>> from IPython.display import Audio
>>> beethoven, samplerate = librosa.load("./beethoven.wav", mono=False)
>>> beethoven = np.expand_dims(beethoven.swapaxes(0,1), 0) # reshape the numpy array to input of tsaug
>>> plot(beethoven)
>>> Audio(beethoven.reshape(-1, 2).T, rate=samplerate)
Build a pipeline of augmenters as follows:
random crop subsequences of 10 seconds,
with 50% probability, add random noise up to 1% - 5%,
drop out 10% of the time points (dropped out units are 1 ms, 10 ms, or 100 ms) and fill the dropped out points with zeros.
>>> from tsaug import Crop, AddNoise, Dropout
>>> my_augmenter = (
... Crop(size=samplerate * 10) * 5 # random crop subsequences of 10 seconds
... + AddNoise(scale=(0.01, 0.05)) @ 0.5 # with 50% probability, add random noise up to 1% - 5%
... + Dropout(
... p=0.1,
... fill=0,
... size=[int(0.001 * samplerate), int(0.01 * samplerate), int(0.1 * samplerate)]
... ) # drop out 10% of the time points (dropped out units are 1 ms, 10 ms, or 100 ms) and fill the dropped out points with zeros
... )
Augment the audio sequence.
>>> beethoven_aug = my_augmenter.augment(beethoven)
>>> for i in range(5):
... plot(beethoven_aug[[i]])
... Audio(beethoven_aug[i].reshape(-1, 2).T, rate=samplerate)
