intro_to_wc_modeling | Whole-cell modeling tutorials

by KarrLab Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | intro_to_wc_modeling Summary

intro_to_wc_modeling is a Python library. intro_to_wc_modeling 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.

This repository provides an introduction to whole-cell (WC) modeling, including a variety of written explanations and interactive tutorials. The goal of this resource is to prepare you to be a productive WC modeler. This repository provides the source markup for the text and the source code for the tutorials. Please visit to read the text which guides you through the tutorials.

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Quality

Security

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Support

intro_to_wc_modeling has a low active ecosystem.

It has 11 star(s) with 3 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 963 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of intro_to_wc_modeling is current.

Quality

intro_to_wc_modeling has 0 bugs and 0 code smells.

Security

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

intro_to_wc_modeling code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

intro_to_wc_modeling is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

intro_to_wc_modeling 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 available. Examples and code snippets are not available.

It has 3043 lines of code, 212 functions and 44 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed intro_to_wc_modeling and discovered the below as its top functions. This is intended to give you an instant insight into intro_to_wc_modeling implemented functionality, and help decide if they suit your requirements.

Read a model from an Excel file
Setup simulation
Return a component with the given id
Set component indices
Setup the simulation
Find component by id
Return True if the formula contains a carbon formula
Initialize simulation
Simulates the simulation
Returns a dictionary of species concentrations
Calculate reaction rates
Calculate the effective reaction boundary limits
Plot a model
Gets the scale for the given units
Plot the results of the simulation
Simulate the waveform
Edit the database
Simulate the DHAP model
Create the database
Plots the results of the model
Insert records into the database
Simulate an SSA experiment
Query the database
Simulate the probability distribution
Generate a deterministic experiment
Create a sqlalchemy session

Get all kandi verified functions for this library.

intro_to_wc_modeling Key Features

No Key Features are available at this moment for intro_to_wc_modeling.

intro_to_wc_modeling Examples and Code Snippets

No Code Snippets are available at this moment for intro_to_wc_modeling.

Community Discussions

No Community Discussions are available at this moment for intro_to_wc_modeling.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install intro_to_wc_modeling

You can download it from GitHub.
You can use intro_to_wc_modeling 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.