CyRK 0.8.0

Runge-Kutta ODE Integrator Implemented in Cython and Numba.

CyRK

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Runge-Kutta ODE Integrator Implemented in Cython and Numba

CyRK provides fast integration tools to solve systems of ODEs using an adaptive time stepping scheme. CyRK can accept differential equations that are written in pure Python, njited numba, or cython-based cdef classes. These kinds of functions are generally easier to implement than pure c functions. Using CyRK can speed up development time while not making a huge sacrifice when it comes to performance.

The purpose of this package is to provide some functionality of scipy's solve_ivp with greatly improved performance.

Currently, CyRK's numba (njit-safe) implementation is 10-120+x faster than scipy's solve_ivp function. The cython cyrk_ode function that works with python (or numba) functions is 20-40+x faster than scipy. The cython CySolver class that works with cython-based cdef classes is 30-500+x faster than scipy.

An additional benefit of the two cython implementations is that they are pre-compiled. This avoids most of the start-up performance hit experienced by just-in-time compilers like numba.

Installation

CyRK has been tested on Python 3.8--3.11; Windows, Ubuntu, and MacOS.

To install simply open a terminal and call:

pip install CyRK

If not installing from a wheel, CyRK will attempt to install Cython and Numpy in order to compile the source code. After everything has been compiled, cython will be uninstalled and CyRK's runtime dependencies (see the pyproject.toml file for the latest list) will be installed instead.

A new installation of CyRK can be tested quickly by running the following from a python console.

from CyRK import test_cyrk, test_nbrk, test_cysolver
test_cyrk()
# Should see "CyRK's cyrk_ode was tested successfully."
test_nbrk()
# Should see "CyRK's nbrk_ode was tested successfully."
test_cysolver()
# Should see "CyRK's CySolver was tested successfully."

Troubleshooting Installation and Runtime Problems

Please report installation issues. We will work on a fix and/or add workaround information here.

• If you see a "Can not load module: CyRK.cy" or similar error then the cython extensions likely did not compile during installation. Try running pip install CyRK --no-binary="CyRK" to force python to recompile the cython extensions locally (rather than via a prebuilt wheel).
• On MacOS: If you run into problems installing CyRK then reinstall using the verbose flag (pip install -v .) to look at the installation log. If you see an error that looks like "clang: error: unsupported option '-fopenmp'" then you may have a problem with your llvm or libomp libraries. It is recommended that you install CyRK in an Anaconda environment with the following packages conda install numpy scipy cython llvm-openmp. See more discussion here and the steps taken here.
• CyRK has a number of runtime status codes which can be used to help determine what failed during integration. Learn more about these codes https://github.com/jrenaud90/CyRK/blob/main/Documentation/Status%20and%20Error%20Codes.md.

Development and Testing Dependencies

If you intend to work on CyRK's code base you will want to install the following dependencies in order to run CyRK's test suite and experimental notebooks.

conda install pytest scipy matplotlib jupyter

conda install can be replaced with pip install if you prefer.

Using CyRK

CyRK's API is similar to SciPy's solve_ivp function. A differential equation can be defined in python such as:

import numpy as np

# For even more speed up you can use numba's njit to compile the diffeq
from numba import njit
@njit
def diffeq_nb(t, y):
    dy = np.empty_like(y)
    dy[0] = (1. - 0.01 * y[1]) * y[0]
    dy[1] = (0.02 * y[0] - 1.) * y[1]
    return dy

initial_conds = np.asarray((20., 20.), dtype=np.complex128, order='C')
time_span = (0., 50.)
rtol = 1.0e-7
atol = 1.0e-8

Numba-based nbrk_ode

The system of ODEs can then be solved using CyRK's numba solver by,

from CyRK import nbrk_ode
time_domain, y_results, success, message = \
    nbrk_ode(diffeq_nb, time_span, initial_conds, rk_method=1, rtol=rtol, atol=atol)

Cython-based cyrk_ode

To call the cython version of the integrator you need to slightly edit the differential equation so that it does not return the derivative. Instead, the output is passed as an input argument (a np.ndarray) to the function.

@njit
def diffeq_cy(t, y, dy):
    dy[0] = (1. - 0.01 * y[1]) * y[0]
    dy[1] = (0.02 * y[0] - 1.) * y[1]

Alternatively, you can use CyRK's conversion helper functions to automatically convert between numba/scipy and cyrk function calls.

from CyRK import nb2cy, cy2nb

@njit
def diffeq_nb(t, y):
    dy = np.empty_like(y)
    dy[0] = (1. - 0.01 * y[1]) * y[0]
    dy[1] = (0.02 * y[0] - 1.) * y[1]
    return dy

diffeq_cy = nb2cy(diffeq_nb, use_njit=True)
diffeq_nb2 = cy2nb(diffeq_cy, use_njit=True)

You can then call the ODE solver in a similar fashion as the numba version.

from CyRK import cyrk_ode
time_domain, y_results, success, message = \
    cyrk_ode(diffeq_cy, time_span, initial_conds, rk_method=1, rtol=rtol, atol=atol)

Cython-based CySolver

The cython-based CySolver class requires writing a new cython cdef class. This is done in a new cython .pyx file which must then be cythonized and compiled before it can be used.

"""ODE.pyx"""
# distutils: language = c++
# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False

from CyRK.cy.cysolver cimport CySolver
# Note the `cimport` here^

cdef class MyCyRKDiffeq(CySolver):

    cdef void diffeq(self) noexcept nogil:
        
        # Unpack dependent variables using the `self.y_ptr` variable.
        # In this example we have a system of two dependent variables, but any number can be used.
        cdef double y0, y1
        y0 = self.y_ptr[0]
        y1 = self.y_ptr[1]

        # Unpack any additional arguments that do not change with time using the `self.args_ptr` variable.
        cdef double a, b
        # These must be float64s
        a  = self.args_ptr[0]
        b  = self.args_ptr[1]

        # If needed, unpack the time variable using `self.t_now`
        cdef double t
        t = self.t_now

        # This then updates dydt by setting the values of `self.dy_ptr`
        self.dy_ptr[0] = (1. - a * y1) * y0
        self.dy_ptr[1] = (b * y0 - 1.) * y1

Once you compile the differential equation it can be imported in a regular python file and used in a similar fashion to the other integrators.

"""run.py"""
from ODE import MyCyRKDiffeq

# It is important that any arrays passed to the CySolver are C-contiguous (set with numpy with "order=C")
# Also, currently, CySolver only works with floats/doubles. Not complex.
initial_conds = np.asarray((20., 20.), dtype=np.float64, order='C')

# Need to make an instance of the integrator.
# The diffeq no longer needs to be passed to the class.
MyCyRKDiffeqInst = MyCyRKDiffeq(time_span, initial_conds, args=(0.01, 0.02), rk_method=1, rtol=rtol, atol=atol, auto_solve=True)

# To perform the integration make a call to the solve method.
# Only required if the `auto_solve` flag is set to False (defaults to True)
# MyCyRKDiffeqInst.solve()

# Once complete, you can access the results via...
MyCyRKDiffeqInst.success     # True / False
MyCyRKDiffeqInst.message     # Note about integration
MyCyRKDiffeqInst.t           # Time domain
MyCyRKDiffeqInst.y           # y dependent variables
MyCyRKDiffeqInst.extra       # Extra output that was captured during integration.
# See Documentation/Extra Output.md for more information on `extra`

Optional Arguments

All three integrators can take the following optional inputs:

• rtol: Relative Tolerance (default is 1.0e-6).
• atol: Absolute Tolerance (default is 1.0e-8).
• rtols: A numpy ndarray of relative tolerances set for each y0 (default is None; e.g., use rtol for each).
• atols: A numpy ndarray of absolute tolerances set for each y0 (default is None; e.g., use atol for each).
• max_step: Maximum step size (default is +infinity).
• first_step: Initial step size (default is 0).
  • If 0, then the solver will try to determine an ideal value.
• args: Python tuple of additional arguments passed to the diffeq.
  • For the cython solvers these arguments must be floating point numbers only.
• t_eval: Both solvers uses an adaptive time stepping protocol based on the recent error at each step. This results in a final non-uniform time domain of variable size. If the user would like the results at specific time steps then they can provide a np.ndarray array at the desired steps via t_eval. The solver will then interpolate the results to fit this array.
• rk_method: Runge-Kutta method (default is 1; all of these methods are based off of SciPy implementations):
  • 0 - "RK23" Explicit Runge-Kutta method of order 3(2).
  • 1 - "RK45" Explicit Runge-Kutta method of order 5(4).
  • 2 - "DOP853" Explicit Runge-Kutta method of order 8.
• capture_extra and interpolate_extra: CyRK has the capability of capturing additional parameters during integration. Please see Documentation\Extra Output.md for more details.
• max_num_steps: Maximum number of steps the solver is allowed to use. Defaults to system architecture's max size for ints.

Additional Arguments for cyrk_ode and CySolver

• num_extra : The number of extra outputs the integrator should expect.
  • Please see Documentation\Extra Output.md for more details.
• expected_size : Best guess on the expected size of the final time domain (number of points).
  • The integrator must pre-allocate memory to store results from the integration. It will attempt to use arrays sized to expected_size. However, if this is too small or too large then performance will be negatively impacted. It is recommended you try out different values based on the problem you are trying to solve.
  • If expected_size=0 (the default) then the solver will attempt to guess a best size. Currently this is a very basic guess so it is not recommended.
  • It is better to overshoot than undershoot this guess.

Limitations and Known Issues

• Issue 30: CyRK's CySolver does not allow for complex-valued dependent variables.

Citing CyRK

It is great to see CyRK used in other software or in scientific studies. We ask that you cite back to CyRK's GitHub website so interested parties can learn about this package. It would also be great to hear about the work being done with CyRK, so get in touch!

Renaud, Joe P. (2022). CyRK - ODE Integrator Implemented in Cython and Numba. Zenodo. https://doi.org/10.5281/zenodo.7093266

In addition to citing CyRK, please consider citing SciPy and its references for the specific Runge-Kutta model that was used in your work. CyRK is largely an adaptation of SciPy's functionality. Find more details here.

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3), 261-272.

Contribute to CyRK

Please look here for an up-to-date list of contributors to the CyRK package.

CyRK is open-source and is distributed under the Creative Commons Attribution-ShareAlike 4.0 International license. You are welcome to fork this repository and make any edits with attribution back to this project (please see the Citing CyRK section).

• We encourage users to report bugs or feature requests using GitHub Issues.
• If you would like to contribute but don't know where to start, check out the good first issue tag on GitHub.
• Users are welcome to submit pull requests and should feel free to create them before the final code is completed so that feedback and suggestions can be given early on.