Time Series analysis with pandas
Python
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README.md

pyTSA

Many dynamical systems are often analyzed by performing simulation ensembles under different parameter configurations. Automatizing time-series analysis is key to save time and focus on other modeling tasks. pyTSA is an open source Python tool to make data-analysis as intuitive as possible. Its scripts can be processed in a pipeline with any simulation tool outputting time-series, and intuitive commands allow to perform complex analyses.

Current pyTSA version supports the following plots, in many graphical formats: single traces (single panel or multi panel), average and standard deviation of a dataset (with barplot or traces, single panel or multi panel), 2D/3D probability density function of a quantity at some specific time point (with normalization and gaussian fit), 2D/3D time-varying probability density function of a quantity in a time-interval (heatmap or surface) and 2D/3D phase-space.

pyTSA is a Pandas extension that allow you to work on multiple time series.

The source code is currently hosted on GitHub at https://github.com/luca-dex/pyTSA. For the latest version:

git clone https://github.com/luca-dex/pyTSA.git
cd pyTSA

And via easy_install or pip:

sudo pip install .

For more information and examples click here.

There is also a getting started.