comut | Python library | Genomics library

by vanallenlab Python Version: 0.0.3 License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | comut Summary

comut is a Python library typically used in Artificial Intelligence, Genomics applications. comut 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 comut' or download it from GitHub, PyPI.

CoMut is a Python library for creating comutation plots to visualize genomic and phenotypic information.

Support

Quality

Security

License

Reuse

Support

comut has a low active ecosystem.

It has 67 star(s) with 20 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 9 open issues and 8 have been closed. On average issues are closed in 3 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of comut is 0.0.3

Quality

comut has 0 bugs and 0 code smells.

Security

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

comut code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

comut 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

comut releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

comut saves you 296 person hours of effort in developing the same functionality from scratch.

It has 713 lines of code, 24 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Plot CoMut
Plot indicator data
Plot patch data
Determine the default width and comut
Get the default height for the plot
Returns the coordinates of the x y coordinates
Plot data on axis
Calculate the height specification for a plot
Plot bar chart data
Plot side bar chart data
Add categorical data
Parse categorical data
Sort a list by value order
Return the default colormap
Check that the provided samples are present
Add continuous data
Add legend
Add bar plot data
Add sample indicators

Get all kandi verified functions for this library.

comut Key Features

No Key Features are available at this moment for comut.

comut Examples and Code Snippets

CoMut,Dependencies

Python

Lines of Code : 4

License : Permissive (MIT)

Copy

numpy>=1.18.1
pandas>=0.25.3
palettable>=3.3.0
matplotlib>=3.3.1

Community Discussions

Trending Discussions on comut

Correct way to compute 1,2,3 sigma errors

Compute aggregated columns with np.where returns list objects

QUESTION

Correct way to compute 1,2,3 sigma errors

Asked 2021-Aug-11 at 13:46

I wanted to calculated 1, 2, 3 sigma error of a distribution using python. It is described in following 68–95–99.7 rule wikipedia page. So far I have written following code. Is it correct way to compute such kpi's. Thanks.

...

ANSWER

Answered 2021-Aug-11 at 13:28

An easier way could be like so (taken from here):

NumPy's std yields the standard deviation, which is usually denoted with "sigma". To get the 2-sigma or 3-sigma ranges, you can simply multiply sigma with 2 or 3:

print [x.mean() - 3 * x.std(), x.mean() + 3 * x.std()]

result:

[-27.545797458510656, 52.315028227741429]

Source https://stackoverflow.com/questions/68742756

QUESTION

Compute aggregated columns with np.where returns list objects

Asked 2021-Mar-02 at 07:16

I want to make some computations on an aggregated dataframe with if-else-conditions. I've tried to use np.where but the result is a list object in the result column. What I am doing wrong here:

...

ANSWER

Answered 2021-Mar-02 at 07:16

I think you can generate and fill new columns, not dictionary and return x from function:

Source https://stackoverflow.com/questions/66434846

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

Vulnerabilities

No vulnerabilities reported

Install comut

CoMut is available on pypi here and can be installed via pip.
For those who do not want to install Python or other packages, there is a Google Colab notebook where you can simply upload a MAF file and run the notebook to make a basic comutation plot. This file is also available as a jupyter notebook for local use.

Support

There is also a Documentation notebook that provides documentation for CoMut. It describes the fundamentals of creating comutation plots and provides the code used to generate the comut above.

Find more information at: