odeint

问题 I am trying to use openmp to parallelize my code. Everything works just fine when I use constant step sizes, however when I run the same code using an adaptative stepper I get errors that I don't understand. Here are the essential parts of the code : using namespace std; using namespace boost::numeric::odeint; const int jmax = 10; typedef double value_type; typedef boost::array<value_type ,2*(jmax+1) > state_type; //The step function void rhs( const state_type A , state_type &dAdt , const

问题 I am working on a physics problem for which I have to evolve parameters according to ODEs. From time to time they have to manipulated so that I would like to have a data type that can be used with routines such as diagonalisation,... Therefore, I implemented a class with eigen::Matrix as members and want to perform the integration with odeint. For a single eigen::matrix this worked fine. I made a minimal example: #include <iostream> #include <fstream> #include <cmath> #include <string>

问题 I'm trying to use numba to boost the python performance of scipy.integrate.odeint. To this end, I have to use @nb.jit(nopython=True) for the function defining the ODE system. However, this function has to take another python-class instance as an argument in my program. I had to jit the class also with @nb.jitclass(spec) with appropriate specs. This worked fine, until I found a serious issue when the specs of the class includes another type of class instance as its method. My code is following

问题 I am totally new to coding and I want to solve these 5 differential equations numerically. I took a python template and applied it to my case. Here's the simplified version of what I wrote: import numpy as np from math import * from matplotlib import rc, font_manager import matplotlib.pyplot as plt from scipy.integrate import odeint #Constants and parameters alpha=1/137. k=1.e-9 T=40. V= 6.e-6 r = 6.9673e12 u = 1.51856e7 #defining dy/dt's def f(y, t): A = y[0] B = y[1] C = y[2] D = y[3] E = y

问题 I am extending the parameter study example from boost's odeint used with thrust, and I do not know how to pass a vector of values to the constructor of the observer, such that those values can be accessed (read-only) from within the observer's functor. The following is the code just for the observer. //// Observes the system, comparing the current state to //// values in unchangingVector struct minimum_perturbation_observer { struct minPerturbFunctor { template< class T > __host__ __device__

问题 For solving simple ODEs using SciPy, I used to use the odeint function, with form: scipy.integrate.odeint(func, y0, t, args=(), Dfun=None, col_deriv=0, full_output=0, ml=None, mu=None, rtol=None, atol=None, tcrit=None, h0=0.0, hmax=0.0, hmin=0.0, ixpr=0, mxstep=0, mxhnil=0, mxordn=12, mxords=5, printmessg=0)[source] where a simple function to be integrated could include additional arguments of the form: def dy_dt(t, y, arg1, arg2): # processing code here In SciPy 1.0, it seems the ode and

问题 Okay so how would i approach to writing a code to optimize the constants a and b in a differential equation, like dy/dt = a*y^2 + b, using curve_fit? I would be using odeint to solve the ODE and then curve_fit to optimize a and b. If you could please provide input on this situation i would greatly appreciate it! 回答1: You might be better served by looking at ODEs with Sympy. Scipy/Numpy are fundamentally numerical packages and aren't really set up to do algebraic/symbolic operations. 回答2: You

问题 I want to solve a system of equations using odeint and I get the following error: File "C:", line 45, in <module> C_B = odeint(dC_Bdt,C_B0,t) File "C:\Anaconda3\envs\ChemEng\lib\site-packages\scipy\integrate\odepack.py", line 233, in odeint int(bool(tfirst))) RuntimeError: The array return by func must be one-dimensional, but got ndim=2. My code is: import numpy as np import matplotlib.pyplot as plt from scipy.integrate import odeint # Create time domain t = np.linspace(0,100,100) #

问题 I'm trying to write an equation to model and then plot an integral control system (specifically regarding cruise control). However I'm receiving two errors whenever I run it: ValueError: object too deep for desired array odepack.error: Result from function call is not a proper array of floats. I've read these questions: scipy curve_fit error: Result from function call is not a proper array of floats How to solve this differential equation using scipy odeint? Object Too Deep for Desired Array

问题 I encountered a problem with the odeint library using a controlled error stepper together with a complex state type. The code from the example with the complex stuart landau equation, I modified such that it includes an adaptive integrator. The code looks like this now: #include <iostream> #include <complex> #include <boost/array.hpp> #include <boost/numeric/odeint.hpp> using namespace std; using namespace boost::numeric::odeint; //[ stuart_landau_system_function typedef complex< double >