d_code | 2Photon Imaging and Electrophysiology Analysis code
kandi X-RAY | d_code Summary
kandi X-RAY | d_code Summary
d_code is a Jupyter Notebook library. d_code has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
There are 5 main packages:.
There are 5 main packages:.
Support
Quality
Security
License
Reuse
Support
d_code has a low active ecosystem.
It has 14 star(s) with 8 fork(s). There are 13 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 960 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of d_code is current.
Quality
d_code has no bugs reported.
Security
d_code has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
d_code 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
d_code releases are not available. You will need to build from source code and install.
Installation instructions, examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of d_code
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of d_code
d_code Key Features
No Key Features are available at this moment for d_code.
d_code Examples and Code Snippets
No Code Snippets are available at this moment for d_code.
Community Discussions
No Community Discussions are available at this moment for d_code.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install d_code
This package requires many core elements of the Python scientific stack, including Numpy and Scipy. The simplest way to get these is to install a Python distribution such as Anaconda (recommended) or Enthought. All of this code works on a Python 2.7.x codebase. Porting to Python 3 wouldn't be a bad idea, but right now it runs on 2.7.
Numpy
Scipy (interpolate, stats, ndimage, signal)
Matplotlib
Scikit-learn
IPython
Pyside
Sphinx (for documentation)
pymongo
mahotas
pymorph
image_registration
tifffile
Numpy
Scipy (interpolate, stats, ndimage, signal)
Matplotlib
Scikit-learn
IPython
Pyside
Sphinx (for documentation)
pymongo
mahotas
pymorph
image_registration
tifffile
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 .
Find more information at:
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page