algopy | this is my repository where i am going to practice and code | Learning library

by Pratiknarola Python Version: Current License: MIT

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kandi X-RAY | algopy Summary

algopy is a Python library typically used in Tutorial, Learning, Example Codes applications. algopy has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However algopy build file is not available. You can download it from GitHub.

this is my repository where i am going to practice and code my algorithms in python.

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algopy has a low active ecosystem.

It has 4 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

algopy has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of algopy is current.

Quality

algopy has no bugs reported.

Security

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

License

algopy 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

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

algopy has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Sort a list of elements
R Shift down a list of children
Removes the elements of the list
Merge overlapping arrays
Removes the item from the queue
Pop the item off the stack
Checks if the queue is empty
Remove the right element from the queue
Check if queue is empty
Checks if the current list is empty
Return the current size in bytes
Insert new node after node_data
Search for node data
Insert an item into the queue
Return True if the queue is full
Insert an info item into the queue
Removes the last item from the queue
Sort an array
Return True if the buffer is full

Get all kandi verified functions for this library.

algopy Key Features

No Key Features are available at this moment for algopy.

algopy Examples and Code Snippets

No Code Snippets are available at this moment for algopy.

Community Discussions

Trending Discussions on algopy

Algorithmic Differentiation vs Multiple Explicit Components with Analytical Derivatives

QUESTION

Algorithmic Differentiation vs Multiple Explicit Components with Analytical Derivatives

Asked 2018-Oct-01 at 01:22

I have a problem composed of around 6 mathematical expressions - i.e. (f(g(z(y(x))))) where x are two independent arrays.

I can divide this expression into multiple explicit comps with analytical derivatives or use an algorithmic differentiation method to get the derivatives which reduces the system to a single explicit component.

As far as i understand it is not easy to tell in advance the possible computational performance difference between these 2 approaches. It might depend on the algorithmic differentiation tools capabilities on the reverse mode case but maybe the system will be very large with multiple explicit components that it would still be ok to use algo diff.

my questions is :

Is algo diff. a common tool being used by any of the developers/users ? I found AlgoPY but not sure about other python tools.

...

ANSWER

Answered 2018-Oct-01 at 01:22

As of OpenMDAO v2.4 the OpenMDAO development team has not heavily used AD tools on any pure-python components. We have experimented with it a bit and found roughly a 2x increase in computational vs hand differentiated components. While some computational cost increase is expected, I do not want to indicate that I expect 2x to be the final rule of thumb. We simply don't have enough data to provide such an estimate.

Python based AD tools are much less well developed than those for compiled languages. The dynamic typing and general language flexibility both make it much more challenging to write good AD tools.

We have interfaced OpenMDAO with compiled codes that use AD, such as CFD and FEA tools. In these cases you're always using the matrix-free derivative APIs for OpenMDAO (apply_linear and compute_jacvec_product).

If your component is small enough to fit in memory and fast enough to run on a single process, I suggest you hand differentiate your code. That will give you the best overall performance for now.

AD support for small serial components is something we'll look into supporting in the future, but we don't have anything to offer you in the near term (as of OpenMDAO v2.4)

Source https://stackoverflow.com/questions/52569917

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

Vulnerabilities

No vulnerabilities reported

Install algopy

You can download it from GitHub.
You can use algopy 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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