from-python-to-numpy | access book on numpy vectorization techniques | Learning library

by rougier Python Version: 1.1 License: Non-SPDX

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kandi X-RAY | from-python-to-numpy Summary

from-python-to-numpy is a Python library typically used in Tutorial, Learning applications. from-python-to-numpy has no bugs, it has no vulnerabilities and it has medium support. However from-python-to-numpy build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Copyright (c) 2017 Nicolas P. Rougier License: Creative Commons Attribution 4.0 International (CC BY-NC-SA 4.0). Website: There are already a fair number of books about NumPy (see bibliography) and a legitimate question is to wonder if another book is really necessary. As you may have guessed by reading these lines, my personal answer is yes, mostly because I think there is room for a different approach concentrating on the migration from Python to NumPy through vectorization. There are a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this book is to explain some of these techniques and to provide an opportunity for making this experience in the process.

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from-python-to-numpy has a medium active ecosystem.

It has 1915 star(s) with 326 fork(s). There are 60 watchers for this library.

It had no major release in the last 12 months.

There are 20 open issues and 43 have been closed. On average issues are closed in 20 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of from-python-to-numpy is 1.1

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from-python-to-numpy has 0 bugs and 0 code smells.

Security

from-python-to-numpy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

from-python-to-numpy code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

from-python-to-numpy has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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from-python-to-numpy releases are available to install and integrate.

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

from-python-to-numpy saves you 3218 person hours of effort in developing the same functionality from scratch.

It has 6917 lines of code, 176 functions and 47 files.

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

Top functions reviewed by kandi - BETA

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

Updates a given frame number
Update the mesh
Density step
Compute the target position
Generate a random Poisson disk
Insert data at index
Append data to the end of the list
Display a 2D image
Displays a 2D matrix
Draws the voronoi surface
Compute the voronoi of a triangle
A simple example of DART sampling
Updates the booleans
Iterate over a set of points
The solution of the solution problem
Find the fractional dimension of a matrix
Finds a breadth - first matching path
Append data to the end of the sequence
Print info about a NumPy array
Performs DART sampling
Sampling Sampling
Bellman Ford algorithm
Find the index between the given base and base
Builds a complex maze
Generate a voronoi triangle
Timeit a statement
Run mandelbrot

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from-python-to-numpy Key Features

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from-python-to-numpy Examples and Code Snippets

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Community Discussions

Trending Discussions on from-python-to-numpy

tensorflow - reason there is no numpy out equivalent parameter

QUESTION

tensorflow - reason there is no numpy out equivalent parameter

Asked 2021-Apr-06 at 09:05

Backgrund

In Tensorflow, even when using mutable variables, it looks there is no out option as in numpy to specify the location to store the calculation result. One of the reason why the calculation gets slower is the temporary copy as explained From Python to Numpy and in my understanding re-using the existing buffer would avoid such copies.

Question

Would like to understand why there is no out option equivalent in Tensorflow. For instance matmul appear to have no such option to specify the location.Is it because by design Tensorflow will avoid making temporary copies or does it always create temporary copies.

It appears there is no copy indexing or view indexing concepts that numpy has. When an array is extracted from an existing array, is it a shallow copy (view) or a deep copy or it depends?

Please advise where to look at to understand the internal behavior overview similar to From Python to Numpy that gives good insights into its internal architecture and performance considerations.

...

ANSWER

Answered 2021-Apr-06 at 09:05

Tensorflow produces computations graphs, which are highly optimized in terms of the data flow. For example, if some of the stated computations are not needed to produce the final result, TF would not evaluate them. Moreover, TF compiles procedures to its own low-level operations. Hence out parameter of numpy does not make sense in this context.

Thus, TF internally optimizes all steps of the dataflow, and you do not need to provide any instructions. You can optimize the procedure of getting the result as an algorithm, but not how the algorithmworks internally.

To get familiar with the idea what a computational graph is, consider reading this guide

Source https://stackoverflow.com/questions/66964000

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

Vulnerabilities

No vulnerabilities reported

Install from-python-to-numpy

You can download it from GitHub.
You can use from-python-to-numpy 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.

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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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