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Data Theft Repackaged: A Case Study in Malicious Wrapper Packages on npm
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
.. image:: https://img.shields.io/pypi/v/graphtools.svg :target: https://pypi.org/project/graphtools/ :alt: Latest PyPi version .. image:: https://anaconda.org/conda-forge/graphtools/badges/version.svg :target: https://anaconda.org/conda-forge/graphtools/ :alt: Latest Conda version .. image:: https://img.shields.io/github/workflow/status/KrishnaswamyLab/graphtools/Unit%20Tests/master?label=Github%20Actions :target: https://travis-ci.com/KrishnaswamyLab/graphtools :alt: Github Actions Build .. image:: https://img.shields.io/readthedocs/graphtools.svg :target: https://graphtools.readthedocs.io/ :alt: Read the Docs .. image:: https://coveralls.io/repos/github/KrishnaswamyLab/graphtools/badge.svg?branch=master :target: https://coveralls.io/github/KrishnaswamyLab/graphtools?branch=master :alt: Coverage Status .. image:: https://img.shields.io/twitter/follow/KrishnaswamyLab.svg?style=social&label=Follow :target: https://twitter.com/KrishnaswamyLab :alt: Twitter .. image:: https://img.shields.io/github/stars/KrishnaswamyLab/graphtools.svg?style=social&label=Stars :target: https://github.com/KrishnaswamyLab/graphtools/ :alt: GitHub stars .. image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/psf/black :alt: Code style: black
Tools for building and manipulating graphs in Python.
graphtools is available on pip
. Install by running the following in a terminal::
pip install --user graphtools
Alternatively, graphtools can be installed using Conda <https://conda.io/docs/>
_ (most easily obtained via the Miniconda Python distribution <https://conda.io/miniconda.html>
_)::
conda install -c conda-forge graphtools
Or, to install the latest version from github::
pip install --user git+git://github.com/KrishnaswamyLab/graphtools.git
The graphtools.Graph
class provides an all-in-one interface for k-nearest neighbors, mutual nearest neighbors, exact (pairwise distances) and landmark graphs.
Use it as follows::
from sklearn import datasets
import graphtools
digits = datasets.load_digits()
G = graphtools.Graph(digits['data'])
K = G.kernel
P = G.diff_op
G = graphtools.Graph(digits['data'], n_landmark=300)
L = G.landmark_op
If you have any questions or require assistance using graphtools, please contact us at https://krishnaswamylab.org/get-help
FAQs
graphtools
We found that graphtools demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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