ode | Apache ODE is a WS-BPEL compliant web services | Continuous Deployment library

 by   apache Java Version: Current License: Apache-2.0

kandi X-RAY | ode Summary

kandi X-RAY | ode Summary

ode is a Java library typically used in Devops, Continuous Deployment applications. ode has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Apache ODE is a WS-BPEL compliant web services orchestration engine. It organizes web services calls following a process description written in BPEL. Another way to describe it would be a web service capable workflow engine.

            kandi-support Support

              ode has a low active ecosystem.
              It has 42 star(s) with 60 fork(s). There are 14 watchers for this library.
              It had no major release in the last 6 months.
              ode has no issues reported. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of ode is current.

            kandi-Quality Quality

              ode has no bugs reported.

            kandi-Security Security

              ode has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              ode is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              ode releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed ode and discovered the below as its top functions. This is intended to give you an instant insight into ode implemented functionality, and help decide if they suit your requirements.
            • Computes the time after the given time .
            • Creates the Iolation lock list .
            • Convert an Object to a List .
            • Build HQL query .
            • Evaluates an RValue .
            • Issue an Invoke operation .
            • Performs a case insensitive match .
            • Do JAXP compiler .
            • Creates an Axis message exchange .
            • Downcast the value of the given type .
            Get all kandi verified functions for this library.

            ode Key Features

            No Key Features are available at this moment for ode.

            ode Examples and Code Snippets

            No Code Snippets are available at this moment for ode.

            Community Discussions


            How much is too much when being DRY in my engineering computations?
            Asked 2021-Jun-07 at 16:13

            I am an engineering student and I have to turn in projects that run code for simulations. The calculations are all carried out in Python, but the derivations/ setup is done on paper and included in the report. I sometimes find myself copying and pasting code for repeat graphs or numerical simulations, so I want to write functions for these (trying to be better about DRY).

            When writing functions to do these things, I'm worried that I'm "hiding" to much of the code behind the functions. For example, this function would plot two simulations differing only by tolerance level. This would be used to check for convergence in a numerical simulation of an ode.



            Answered 2021-Jun-07 at 16:13

            In my experience the dry principle is more important when writing libraries and code that you want to reuse. However my experience is that if you want to make a report then sometimes making things to dry can actually make things harder to maintain. I.e. sometimes you want to be able to change an individual graph/plot/piece of data in one place without it affecting the rest of the report. It takes a bit of practice and experience to find out what works best for you. In this particular use case be less focussed on following the DRY rule then when writing a library or an application.

            Additionally if I had to make such a report and the situation would allow it. I would make in in a Jupyter notebook. Here you can nicely mix code with text and graphical output.

            Source https://stackoverflow.com/questions/67851186


            Can we modify the solution vector between integrations steps with scipy.integrate.ode, using VODE?
            Asked 2021-Jun-07 at 13:06

            I am trying to get a solution for a stiff ODE problem where at each integration step, i have to modify the solution vector before continuing on the integration. For that, i am using scipy.integrate.ode, with the integrator VODE, in bdf mode. Here is a simplified version of the code i am using. The function is much more complex than that and involve the use of CANTERA.



            Answered 2021-Jun-07 at 13:06

            For those getting the same problem, the issue lies in the Fortran wrapper from Scipy.

            My solution was to change the package used, from ode to solve_ivp. The difference is that solve_ivp is entirely made with Python, and you will be able to hack your way through the implementation. Note that the code will run slowly compared to the vode link that the other package used, even though the code is very well written and use numpy (basically, C level of performances whenever possible).

            Here are the few steps you will have to follow.

            First, to reproduce the already working code :

            Source https://stackoverflow.com/questions/67505932


            deSolve ODE Integration Error, am I using the wrong function?
            Asked 2021-Jun-06 at 16:48

            I'm attempting to solve a set of equations related to biological processes. One equation (of about 5) is for a pharmacokinetic (PK) curve of the form C = Co(exp(k1*t)-exp(k2*t). The need is to simultaneously solve the derivative of this equation along with some enzyme binding equations and initial results where not as expected. After troubleshooting, realized that the PK derivative doesn't numerically integrate by itself, if k is negative using the desolve ode function. I've attempted every method (lsode, lsoda, etc) in the ode function, with no success. I've tried adjusting rtol, it doesn't resolve.

            Is there an alternative to the deSolve ode function I should investigate? Or another way to get at this problem?

            Below is the code with a simplified equation to demonstrate the problem. When k is negative, the integrated solution does not match the analytical result. When k is positive, results are as expected.

            First Image, result with k=0.2: Analytical and Integrated results match when k is positive

            Second Image, result with k=-0.2: Integrated result does not match analytical when k is negative



            Answered 2021-Apr-30 at 15:49

            The initial value should be

            Source https://stackoverflow.com/questions/67335095


            Solve system of recursive differential equation in Python
            Asked 2021-Jun-05 at 09:58

            So I am trying to solve the following system of differential equations in Python.

            System of differential equations

            As you can see, for each n in {0,1,2,3,...} the system depends on the solution to the previous system.

            I have tried solving the system for n=0 and found a solution R(0|t) that I can insert in R(1|t) and Python solves the system without problems. I have defined the solution R(0|t) as r0(t) and implemented the solution for n=1 as follows:



            Answered 2021-May-20 at 10:46

            Your system is coupled, even if in triangular way. So the most compact way is to solve it as a coupled system

            Source https://stackoverflow.com/questions/67503577


            Modifying SIR model to include stochasticity
            Asked 2021-May-31 at 18:25

            I am trying to establish a method of estimating infectious disease parameters by comparing real epidemic curves with simulations of a stochastic SIR model. To construct the stochastic SIR model, I am using the deSolve package and instead of using fixed parameter values I would like to draw the parameter value used in the equations at each time point from a Poisson distribution centered on the original parameter values.

            Using the parameter beta as an example, beta represents the average number of transmission events per capita and is the product of the average number of contacts and the probability that transmission occurs upon contact. Realistically, there is variation in the number of contacts a person will have and since transmission is also a probabilistic event there is variation surrounding this too. So even if the average transmission rate were to be 2.4 (for example), an individual can go on to infect 0, 1, 2 or 3 ... etc. people with varying probabilities.

            I have tried to incorporate this into my code below using the rpois function and reassigning the parameters used in the equations to the outputs of the rpois.

            I have run my code with the same initial values and parameters multiple times and all the curves are different indicating that SOMETHING "stochastic" is going on, but I am unsure whether the code is sampling using the rpois at each time point or just once at the beginning. I have only started coding very recently so do not have much experience.

            I would be grateful if anyone more experienced than myself could verify what my code is ACTUALLY doing and whether it is sampling using rpois at each time point or not. If not I would be grateful for any suggestions for achieving this. Perhaps a loop is needed?



            Answered 2021-May-31 at 18:14

            The code given in the question runs the model with constant parameters over time. Here an example with parameters varying over time. However, this setting assumes that for a given time step, the parameters are equal for all indidividuals of the pupolation. If you want to have individual variability, one can either use a matrix formulation for different sub-populations or use an individual model instead.

            Model with fluctuating population parameters:

            Source https://stackoverflow.com/questions/67774513


            Setting initial (state) values for ODE system in compiled model (deSolve, Rcpp)
            Asked 2021-May-28 at 14:25

            I am struggling with a probably minor problem while calling compiled ODEs to be solved via the R package 'deSolve' and I seeking advice from more expert users.


            I have a couple of ODE systems to be solved with 'deSolve'. I have defined the ODEs in separate C++ functions (one for each model) I am calling through R in conjunction with 'Rcpp'. The initial values of the system change if the function takes input from another model (so basically to have a cascade).

            This works quite nicely, however, for one model I have to set the initial parameters for t < 2. I've tried to do this in the C++ function, but it does not seem to work.

            Running code example ...


            Answered 2021-May-28 at 14:25

            I was able to reproduce your code. It seems to me that this is indeed elegant, even if it does not leverage the full power of the solver. The reason is, that Rcpp creates an interface to the compiled model via an ordinary R function. So back-calls from the slovers (e.g. lsoda) to R are necessary in each time step. Such back-calls are not for the "plain" C/Fortran interface. Here communication between solver and model takes place at the machine code level.

            With this informational, I can see that we don't need to expect initialization issues at the C/C++ level, but it looks like a typical case. As the model function is simply the derivative of the model (and only this). The integration is done by the solver "from outside". It calls the model always with the actual integration state, derived from the time step before (roughly speaking). Therefore, it is not possible to force the state variables to fixed values within the model function.

            However, there are several options how to resolve this:

            • chaining of lsoda calls
            • use of events

            The following shows a chained approach, but I am not yet sure about the initialization of the parameters in the first time segment, so may only be part of the solution.

            Source https://stackoverflow.com/questions/67738643


            How to avoid Stack-overflow error in Fipy PDE solver?
            Asked 2021-May-22 at 14:26

            I am trying to solve a 1D PDE coupled with an ODE (solved as explicit Euler). I am getting a stack-overflow error if I use small dt. I tried looking at the length of the stack but cannot figure out anything useful from there. I am not very experienced with python (old fortran numerics coder).
            The code:



            Answered 2021-May-22 at 14:26

            FiPy makes heavy use of lazy evaluation, so you generally only want to evaluate expressions once, rather than redefining them over and over in a loop.

            The most significant changes I made are:

            • calling gradfunc() with m.value and mesh.x.value to avoid a recursive buildup of an unwieldy lazy equation
            • replacing R0 with a FiPy Variable, enabling...
            • ...writing eq_m, eq_v, and eq only once, in terms of R0, which will change automatically as the problem evolves

            Source https://stackoverflow.com/questions/67630817


            Differences between Rs deSolve and Pythons odeint
            Asked 2021-May-20 at 16:53

            I'm currently exploring the Lorenz system with Python and R and have noticed subtle differences in the ode packages. odeint from Python and ode both say they use lsoda to calculate their derivatives. However, using the lsoda command for both seems to give far different results. I have tried ode45 for the ode function in R to get something more similar to Python but am wondering why I can't get exactly the same results:



            Answered 2021-May-20 at 16:53

            All numerical methods that solve ODEs are approximations that work up to a given precision. The precision of the deSolve solvers is set to atol=1e-6, rtol=1e-6 by default, where atol is absolute and rtol is relative tolerance. Furthermore, ode45 has some additional parameters to fine-tune the automatic step size algorithm, and it can make use of interpolation.

            To increase tolerance, set for example:

            Source https://stackoverflow.com/questions/67624064


            Typescript Alert showing multiple times when not needed
            Asked 2021-May-19 at 05:38

            I am trying to get the button to do a single alert when the button is clicked, but the button odes something strange:

            The first time I click the Login Button, the alert shows once, when I click it again the alert shows 2 times, then I click the button a 3rd time, the alert shows 3 times. After that, If I were to click the Forgot Password Button, the alert assigned to Forgot password would show 4 times.

            I am very confused as to why this happens and how to fix it?




            Answered 2021-May-19 at 05:38

            You are setting up new event listeners in your constructor every time the enter button is clicked. They're compounding on top of each other. Additionally, you're setting the username and password values at the wrong time. They should be set when the user tries to validate their input (clicks the submit button). The constructor should be instantiated after the html and script have loaded, eg. in a window.onload event

            Source https://stackoverflow.com/questions/67594448


            How to adapt RK4 ODE Solver from first order to 2nd order
            Asked 2021-May-18 at 15:28

            I'd like to simulate this system of equations in a matlab program.

            I'm need help to set up the code. I have a Matlab function for doing Runge-Kutta4k approximation for first-order ODE's, and I want to adapt it to work for second-order ODE's.

            Here's some code :

            RK4 function:



            Answered 2021-May-18 at 08:11

            Cleaning up the code you have, for the first-order system that you have correctly given, gives a script (decompose in single modules if you prefer that or for reuse)

            Source https://stackoverflow.com/questions/67567216

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install ode

            You can download it from GitHub.
            You can use ode like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the ode component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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