xarray-simlab | Xarray extension and framework for computer model

 by   benbovy Python Version: 0.5.0 License: BSD-3-Clause

kandi X-RAY | xarray-simlab Summary

kandi X-RAY | xarray-simlab Summary

xarray-simlab is a Python library typically used in Simulation applications. xarray-simlab has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install xarray-simlab' or download it from GitHub, PyPI.

Xarray extension and framework for computer model simulations
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            kandi-support Support

              xarray-simlab has a low active ecosystem.
              It has 45 star(s) with 9 fork(s). There are 4 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 17 open issues and 64 have been closed. On average issues are closed in 179 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of xarray-simlab is 0.5.0

            kandi-Quality Quality

              xarray-simlab has 0 bugs and 0 code smells.

            kandi-Security Security

              xarray-simlab has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              xarray-simlab code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              xarray-simlab is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              xarray-simlab releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              xarray-simlab saves you 3072 person hours of effort in developing the same functionality from scratch.
              It has 7519 lines of code, 596 functions and 40 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed xarray-simlab and discovered the below as its top functions. This is intended to give you an instant insight into xarray-simlab implemented functionality, and help decide if they suit your requirements.
            • Returns a dict of the command class to use
            • Return the value of key
            • Get project root directory
            • Create a ConfigParser from a root
            • Defines a variable
            • Normalize encoding
            • Convert a group to a tuple
            • Convert a list of dimensions to a tuple
            • Create a foreign variable
            • Get all input variables
            • Create runtime executors for each step
            • Define an object
            • Get the dependencies of all processes
            • Get information about the model
            • Scans the setup py file
            • Transpose the input dataset back into another
            • Resolve module name
            • Ensures that there are intent conflicts
            • Creates a property for a given variable
            • Create a dictionary of variables
            • Get the keywords from a git version file
            • Execute the given stage
            • Create the versioneer config file
            • Define on demand
            • Make a property variable variable
            • Create a Process class from a class
            Get all kandi verified functions for this library.

            xarray-simlab Key Features

            No Key Features are available at this moment for xarray-simlab.

            xarray-simlab Examples and Code Snippets

            No Code Snippets are available at this moment for xarray-simlab.

            Community Discussions

            QUESTION

            What is the most appropriate solving method for ODE or PDE based ecosystem models in Python GEKKO?
            Asked 2020-Jul-06 at 22:54

            I have been looking for a while, but could not find the answer to this specific question anywhere, sorry if it is a duplicate!

            I have started to build a python package based on the xarray-simlab framework with the goal to provide a modular toolbox for building reproducible and flexible marine ecosystem models. Xarray-simlab at the moment only supports explicit step-sizes to solve the model functions. In order to solve complex models more safely & efficiently, I have instead started using GEKKO as a solver backend, as the model syntax seems well suited. (Note: At the moment I will only need functionality to solve the model equations over time, but I would like to make use of GEKKO's optimization functionality to fit model parameters to field or lab data at later stages.)

            The current prototype of the package creates a xsimlab process class that passes the GEKKO model instance m to all sub-processes. Process classes that inherit the model instance initialize m.SV, m.Param or define m.Intermediates based on the processes added to the model & parameters (incl. SV dimensions) supplied at runtime. In the next step all initialized intermediates are accumulated to the affected state variables in m.Equations. Once successfully solved, GEKKO variables are repackaged into a xarray data structure, that includes relevant metadata and can be analysed further. The package prototype can solve basic models using IMODE=7, but I have come across one issue related to the time steps of that solver:

            I was expecting functionality similar to scipy's odeint, with adaptive time step evaluation, but obviously this does not seem to be the case and instead it evaluates the model at the discrete time-steps supplied.

            The package is still under heavy development, and there are plenty of features that I am still trying to improve, so below is a minimal code example of a simple chemostat model. The model describes a phytoplankton state variable growing on a nutrient in a simplified flow-through system. The nutrient flows in at a constant rate, and phytoplankton dies and is lost from the system at a constant rate:

            ...

            ANSWER

            Answered 2020-Jul-06 at 22:54

            Try to increase the number of nodes per segment with:

            Source https://stackoverflow.com/questions/62588609

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

            Vulnerabilities

            No vulnerabilities reported

            Install xarray-simlab

            You can install using 'pip install xarray-simlab' or download it from GitHub, PyPI.
            You can use xarray-simlab like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            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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            Install
          • PyPI

            pip install xarray-simlab

          • CLONE
          • HTTPS

            https://github.com/benbovy/xarray-simlab.git

          • CLI

            gh repo clone benbovy/xarray-simlab

          • sshUrl

            git@github.com:benbovy/xarray-simlab.git

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