  Fuzzy logic is an accepted and well-developed approach for constructing
verbal models. Fuzzy based methods are getting more popular, while the
engineers deal with more daily life tasks. This paper presents a new Python
toolkit for Interval Type 2 Fuzzy Logic Systems (IT2FLS). Developing software
tools is an important issue for facilitating the practical use of theoretical
results. There are limited tools for implementing IT2FLSs in Python. The
developed PyIT2FLS is providing a set of tools for fast and easy modeling of
fuzzy systems. This paper includes a brief description of how developed toolkit
can be used. Also, three examples are given showing the usage of the developed
toolkit for simulating IT2FLSs. First, a simple rule-based system is developed
and it's codes are presented in the paper. The second example is the prediction
of the Mackey-Glass chaotic time series using IT2FLS. In this example, the
Particle Swarm Optimization (PSO) algorithm is used for determining system
parameters while minimizing the mean square error. In the last example, an
IT2FPID is designed and used for controlling a linear time-delay system. The
code for the examples are available on toolkit's GitHub page:
\url{https://github.com/Haghrah/PyIT2FLS}. The simulations and their results
confirm the ability of the developed toolkit to be used in a wide range of the
applications.
