This software is a delay differential equation solver that uses Python and C programming languages.
PyDDE is an open source numerical solver for systems of delay differential equations (DDEs), implemented as a Python package and written in both Python and C. The software is built around the back-end of ddesolve, an R package with the same functionality.
PyDDE stands out from other solvers by being able to solve a wide range of ODE and DDE models with discontinuities that may have state-dependent effects but state-independent timings. Its simulation is handled by an adaptively-stepping embedded RK2(3) scheme with cubic Hermite interpolation for calculation of delay terms.
One of the key advantages of PyDDE is its speed and efficiency, allowing rapid prototyping of scriptable models in a free, platform-independent environment. The software is purpose-built to be extremely flexible and extensible.
There is a lack of easily-obtainable numerical solvers of delay differential equations for interpreted languages. PyDDE was developed in response to this need, providing a free and open-source option that's accessible to users of different operating systems and hardware platforms.
PyDDE started as a port of Simon Wood's Solv95. Though Solv95 is fast, efficient and adaptable to different ODE and DDE models, development for it requires knowledge of programming in either C or FORTRAN. Rapid prototyping of models is also hampered by the requirement that models be written in C.
PyDDE was developed to address the limitations of Solv95. It is built directly on the ddesolve back end, which is built directly on the code used in Solv95. PyDDE is just as powerful and flexible as before, but also easier to use. It has better error-handling and makes available the power of Python to process solution data, allowing for much faster model development.
In practical terms, PyDDE should perform comparably in most situations. Though it may be a bit slower than the original Solv95, much of the memory management has been rewritten for better performance. It is designed to be more user-friendly and accessible to Python users, making it an excellent DDE solver for various scientific applications.
PyDDE stands out from other solvers by being able to solve a wide range of ODE and DDE models with discontinuities that may have state-dependent effects but state-independent timings. Its simulation is handled by an adaptively-stepping embedded RK2(3) scheme with cubic Hermite interpolation for calculation of delay terms.
One of the key advantages of PyDDE is its speed and efficiency, allowing rapid prototyping of scriptable models in a free, platform-independent environment. The software is purpose-built to be extremely flexible and extensible.
There is a lack of easily-obtainable numerical solvers of delay differential equations for interpreted languages. PyDDE was developed in response to this need, providing a free and open-source option that's accessible to users of different operating systems and hardware platforms.
PyDDE started as a port of Simon Wood's Solv95. Though Solv95 is fast, efficient and adaptable to different ODE and DDE models, development for it requires knowledge of programming in either C or FORTRAN. Rapid prototyping of models is also hampered by the requirement that models be written in C.
PyDDE was developed to address the limitations of Solv95. It is built directly on the ddesolve back end, which is built directly on the code used in Solv95. PyDDE is just as powerful and flexible as before, but also easier to use. It has better error-handling and makes available the power of Python to process solution data, allowing for much faster model development.
In practical terms, PyDDE should perform comparably in most situations. Though it may be a bit slower than the original Solv95, much of the memory management has been rewritten for better performance. It is designed to be more user-friendly and accessible to Python users, making it an excellent DDE solver for various scientific applications.
What's New
Version 0.2.2: N/A