monod | : notebook : Our cool , secure , and offline-first Markdown | Editor library

 by   TailorDev JavaScript Version: 4.2.0 License: MIT

kandi X-RAY | monod Summary

kandi X-RAY | monod Summary

monod is a JavaScript library typically used in Editor applications. monod has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

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            kandi-support Support

              monod has a medium active ecosystem.
              It has 877 star(s) with 61 fork(s). There are 21 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 28 open issues and 118 have been closed. On average issues are closed in 8 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of monod is 4.2.0

            kandi-Quality Quality

              monod has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              monod is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              monod releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed monod and discovered the below as its top functions. This is intended to give you an instant insight into monod implemented functionality, and help decide if they suit your requirements.
            • synchronize current document
            • Stores document in database
            • Load a document
            • Format an HTML entry for a specific type
            • Normalize an array of authors
            • Parse BEM string to JSON
            • Update the content of the current content
            • Format an array of authors
            • Persist to local database
            • Returns the message with the given action definition .
            Get all kandi verified functions for this library.

            monod Key Features

            No Key Features are available at this moment for monod.

            monod Examples and Code Snippets

            No Code Snippets are available at this moment for monod.

            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

            QUESTION

            Curve fitting of Monod growth/degradation equations to the experimental data
            Asked 2020-Jun-16 at 12:26

            So the problem that is being faced here is the curve fitting of the Monod equations to the experimental data. The model of bacteria growth and degradation of the organic carbon looks like this:

            dX/dt = (u * S * X )/(K + S)

            dS/dt = ((-1/Y) * u * S * X )/(K + S)

            These equations are solved using the scipy odeint function. Results after integration are stored into two vectors, one for growth, and the another one for degradation. The next step is to curve fit this model to the experimentally observed data and estimate the model parameters: u, K and Y. Once the code is run, the following error is produced:

            ...

            ANSWER

            Answered 2020-Jun-16 at 12:26

            The result of f() needs to have the same shape as the experimental data you feed into curve_fit as third parameter. In the last line of f() you just take the t = 0s value of the solution for both ODEs and return that, but you should return the complete solution. When fitting several sets of data at once using curve_fit, just concat them (stack horizontally), i.e.

            Source https://stackoverflow.com/questions/62357192

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

            Vulnerabilities

            No vulnerabilities reported

            Install monod

            You can download it from GitHub.

            Support

            Documentation can be found in the [doc/](doc/writing.md) directory.
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