TigerTail is an event analysis library that helps analysts calculate non-stationary time windows prior to events of interest across multivariate time series data sets.
TigerTail implements three classes and attempts to follow pandas. These classes are the TimeSeries, EventSeries, and
TimeFrame. The TimeFrame acts as a pandas-like dataframe except that each data dimension, or column, within the TimeFrame
is allowed to have varying time-based indices. That means that you can store multiple time series with different sample rates,
and different event occurences altogther and window across these together. Users can provide custom aggregation functions for
windowing.
pip install tigertail
from TT import tdf
tdf = tdf.read_data('data_loc.csv')
tdf.window(5, 'seconds').plot()
