Troubleshooting Guide#

This document includes common ProgPy issues and ways to troubleshoot.

Tensorflow Version Compatibility#

The current datadriven dependencies require tensorflow>=2.18.0 due to compatibility with newer versions of numpy (from 2.0) and numpoly (from 1.3.6). Dependency specifications can be found in pyproject.toml.

Older versions of tensorflow may work with older numpy and numpoly versions. One known version that works is tensorflow==2.16.2 with numpy==1.26.4 and numpoly==1.2.12.

Data-Driven Tools#

If you are using data-driven tools (e.g., LSTM model), make sure the datadriven dependencies are installed using the following command:

$ pip install 'progpy[datadriven]'

Installing ProgPy Data-Driven Tools with Python 3.13#

Tensorflow does not support Python3.13 as of the writing of this (April 2025). Until this is fixed, ProgPy data-driven features may not work correctly. If you are having trouble running data-driven features with Python3.13, try with an earlier version of Python.

Simulation Divergence#

Simulation divergance or instability can occur for a variety of reasons. A few suggestions for debugging are included below:

  • Check that your state transition equations are numerically stable

  • Verify that your time step (dt) is appropriate for your system dynamics

  • Consider using a different integration method if default Euler method fails