python-schoof | Python implementation of Schoof 's algorithm

by pdinges Python Version: Current License: GPL-3.0

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kandi X-RAY | python-schoof Summary

python-schoof is a Python library. python-schoof has no vulnerabilities, it has a Strong Copyleft License and it has low support. However python-schoof has 4 bugs and it build file is not available. You can download it from GitHub.

this is an implementation of schoof’s algorithm for counting the points on elliptic curves over finite fields (schoof, rené. elliptic curves over finite fields and the computation of square roots mod p. mathematics of computation, 44(170):483–494, 1985). elliptic curve cryptographic algorithms base their security guarantees on the number of points on the used elliptic curve. because naive point counting is infeasible, having a fast counting algorithm is important to swiftly decide whether a curve is safe to use in cryptography or not. rené schoof’s algorithm for counting the points on an elliptic curve over a finite field is the foundation for the (asymptotically) fastest [schoof–elkies–atkin][sea-algorithm] counting algorithm. implementation. the implementation is written in [python 3][python] and is kept

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Support

python-schoof has a low active ecosystem.

It has 26 star(s) with 8 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. On average issues are closed in 921 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of python-schoof is current.

Quality

python-schoof has 4 bugs (2 blocker, 0 critical, 2 major, 0 minor) and 82 code smells.

Security

python-schoof has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

python-schoof code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

python-schoof is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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python-schoof releases are not available. You will need to build from source code and install.

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

Installation instructions, examples and code snippets are available.

python-schoof saves you 1830 person hours of effort in developing the same functionality from scratch.

It has 4041 lines of code, 631 functions and 48 files.

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

Top functions reviewed by kandi - BETA

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

Run the algorithm
Generate a name for a dump
True if the thread is running
Dump the call info
Add local names to the class
Flattens all methods of a class
Return the value of a dict key
Copy a function
Generate a set of parameters
Calculate cumulative cumulative time
Return the total call count
The number of primitive calls
Parse command line options
Parse arguments
Merge the call graph by division polynomials
Append comment line
Dump the call graph
Parse input
Return the output file
Parse a file
Create a call graph from a list of profiles
Merges all cast wrappers
Stop the current comment
Calculates the total time of all functions
Merge call graph by field namespaces
Decorator for operator casting

Get all kandi verified functions for this library.

python-schoof Key Features

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python-schoof Examples and Code Snippets

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Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install python-schoof

Installing Python from source code requires a C compiler; on Linux and Unix systems, one is almost always available. The following steps install Python on a Linux system:.
Download. Download the source tar ball of version 3.1 or later from the Python website at http://www.python.org/download/releases/.
Compile. Open a terminal and create a temporary directory, say ${HOME}/tmp/, by executing mkdir ${HOME}/tmp/. Change into the temporary directory and extract the source tar ball: cd ${HOME}/tmp/ and then tar xzvf Python-3.1.2.tgz; adjust the path and file name accordingly. If you downloaded the bzipped source tar ball, use tar xjvf Python-3.1.2.tar.bz2 instead. Next, change into the directory that contains the extracted source code, for instance `${HOME}/tmp/Python-3.1.2/`. Configure the build system by executing `./configure --prefix=${HOME}/python3`. The prefix is the path that will be the root of the Python installation, so adjust it to taste. In case required components are missing, the command will exit with an error message. In this case, please install the components and re-execute `configure`. If everything worked, then the last line of output by `configure` will be `creating Makefile`. To start the compilation, execute `make`. * **Install.** Use `make install` to install Python after the compilation finished. * **Set Up Environment.** To enable the local Python installation, add its interpreter and modules to the respective search paths: execute `export PATH=${HOME}/python3/bin:${PATH}` to tell the shell where to find the `python3` interpreter; adjust the path to your prefix for `configure`. Likewise, execute `export PYTHONPATH=${HOME}/python3/lib/python3.1` to tell Python where to find its modules. Note that the scope of `export` is the current shell. Thus you have to issue both commands in every freshly opened terminal you wish to use for Python 3.1 programs.

Support

The implementation comes with extensive [API documentation][api] that explains the purpose and usage of all classes. Furthermore, the directory _test contains unit tests to verify correct arithmetic. The unit tests are designed to be easily extended to further implementations of the same objects. Thus, mistakes in new designs should be simpler to locate. To run the unit tests, execute python3 -m _test in the repository root directory.

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