A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

Overview

python_graphs

This package is for computing graph representations of Python programs for machine learning applications. It includes the following modules:

  • control_flow For computing control flow graphs statically from Python programs.
  • data_flow For computing data flow analyses of Python programs.
  • program_graph For computing graphs statically to represent arbitrary Python programs or functions.
  • cyclomatic_complexity For computing the cyclomatic complexity of a Python function.

Installation

To install python_graphs with pip, run: pip install python_graphs.

To install python_graphs from source, run: python setup.py develop.

Common Tasks

Generate a control flow graph from a function fn:

from python_graphs import control_flow
graph = control_flow.get_control_flow_graph(fn)

Generate a program graph from a function fn:

from python_graphs import program_graph
graph = program_graph.get_program_graph(fn)

Compute the cyclomatic complexity of a function fn:

from python_graphs import control_flow
from python_graphs import cyclomatic_complexity
graph = control_flow.get_control_flow_graph(fn)
value = cyclomatic_complexity.cyclomatic_complexity(graph)

This is not an officially supported Google product.

Comments
  • Can you provide a quick start example?

    Can you provide a quick start example?

    Super cool project! Love the idea and think it has a lot of potential.

    it would be awesome to have an examples/ directory containing some sample usages - maybe even just plotting the graphs with networkX and matplotlib.

    question 
    opened by LukeWood 5
  • How do we solve the error when installing python-graphs?

    How do we solve the error when installing python-graphs?

    Hello,

    I encountered an error "fatal error: 'graphviz/cgraph.h' file not found" when trying to install python_graphs. How do I solve this issue, please? Thanks.

    question 
    opened by fraolBatole 2
  • How to generate a Holistic Data Flow Graph for a given Function ?

    How to generate a Holistic Data Flow Graph for a given Function ?

    @dbieber, Thanks for this awesome work.

    Question

    control_flow.get_control_flow_graph, returns a Control Flow Graph for a given Function Object. There is one data_flow class, Is there a way to generate a complete Data Flow Graph given a Function Object?

    Thanks.

    opened by reshinthadithyan 2
  • Rename fn to get_test_components to eliminate extra test from logs

    Rename fn to get_test_components to eliminate extra test from logs

    The function test_components was being registered as an unsupported test, when in reality it was meant as a helper function for tests. Renaming resolves this.

    opened by dbieber 0
  • get_start_control_flow_node, next_from_end, raise edges, and labels in branches

    get_start_control_flow_node, next_from_end, raise edges, and labels in branches

    • Adds get_start_control_flow_node to ControlFlowGraph
    • Adds next_from_end to ControlFlowNode
    • Uses labels (e.g. '' and '' strings) to indicate these special nodes
    • Support keyword only arguments without defaults
    • Add non-interrupting edges from raise statements
    • Bump version number
    opened by dbieber 0
  • Separate branch kinds

    Separate branch kinds

    Splits "branches" into branches, except_branches, and reraise_branches.

    branches are you're usual branch decisions: ifs, fors, and whiles. except_branches are at "except E:" statements, with True indicating the exception matches and False indicating it does not reraise_branches are at the end of "finally:" blocks, with True indicating the path taken after finally if an error has been raised previously, and False indicating the path taken if there's nothing to reraise at the end of the finally.

    opened by dbieber 0
  • Add module frame to catch raises in top-level code.

    Add module frame to catch raises in top-level code.

    Add module frame to catch raises in top-level code. Also marks except expressions and finally blocks as branch points.

    An "except A:"'s branch decision is whether the current exception matches A. At the end of a finally block, the branch decision is whether an exception is currently being raised.

    This includes https://github.com/google-research/python-graphs/pull/3: Splits "branches" into branches, except_branches, and reraise_branches.

    branches are your usual branch decisions: ifs, fors, and whiles. except_branches are at "except E:" statements, with True indicating the exception matches and False indicating it does not reraise_branches are at the end of "finally:" blocks, with True indicating the path taken after finally if an error has been raised previously, and False indicating the path taken if there's nothing to reraise at the end of the finally.

    opened by dbieber 0
  • KeyError when trying to get program_graph

    KeyError when trying to get program_graph

    When I try to create a program graph, I encounter a KeyError. If I remove all the and and or expressions from the python file (buggy.py) the error does not occur.

    This is how I use the library:

    graph = program_graph.get_program_graph(code)
    program_graph_graphviz.render(graph, path='source.png')
    

    where code is simply the code in the attached file buggy.py.txt.

    I have also attached the log file log.txt.

    buggy.py.txt

    log.txt

    More information: python 3.9.5 commit head=44c15b92197f374c3550353ff827997ef1c1d857 gast 0.5.3

    opened by ppashakhanloo 1
Releases(v1.2.3)
  • v1.2.3(Oct 7, 2021)

    get_start_control_flow_node, next_from_end, raise edges, and labels in branches (#6)

    * Adds get_start_control_flow_node to ControlFlowGraph
    * Adds next_from_end to ControlFlowNode
    * Uses labels (e.g. '<exit>' and '<raise>' strings) to indicate these special nodes
    * Support keyword only arguments without defaults
    * Add non-interrupting edges from raise statements
    * Bump version number
    
    Source code(tar.gz)
    Source code(zip)
  • v1.2.0(Oct 5, 2021)

    Introduce get_branches API on control flow nodes. Previously the new branch types (except_branches and reraise_branches) were only accessible on basic blocks, not on individual nodes.

    Source code(tar.gz)
    Source code(zip)
  • v1.1.0(Oct 5, 2021)

    1. Adds a module frame to catch raises in top-level code.
    2. Also marks except expressions and finally blocks as branch points.

    The branch kinds are: branches, except_branches, and reraise_branches.

    • branches are your usual branch decisions: ifs, fors, and whiles.
    • except_branches are at "except E:" statements, with True indicating the exception matches and False indicating it does not
    • reraise_branches are at the end of "finally:" blocks, with True indicating the path taken after finally if an error has been raised previously, and False indicating the path taken if there's nothing to reraise at the end of the finally.
    Source code(tar.gz)
    Source code(zip)
  • v1.0.1(May 7, 2021)

  • v1.0.0(Apr 12, 2021)

    v1.0.0

    Initial public release of the python_graphs library.

    Core features:

    • control flow graph generation
    • data flow analyses
    • program graph construction
    • cyclomatic complexity calculation
    • a solid test suite for all the above
    • visualizations using graphviz for each of the graph representations
    Source code(tar.gz)
    Source code(zip)
Owner
Google Research
Google Research
PyTorch Implementation of SSTNs for hyperspectral image classifications from the IEEE T-GRS paper "Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework."

PyTorch Implementation of SSTN for Hyperspectral Image Classification Paper links: SSTN published on IEEE T-GRS. Also, you can directly find the imple

Zilong Zhong 54 Dec 19, 2022
Official Implementation of DE-DETR and DELA-DETR in "Towards Data-Efficient Detection Transformers"

DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-DETR and DELA-DETR in

Wen Wang 61 Dec 12, 2022
Create Own QR code with Python

Create-Own-QR-code Create Own QR code with Python SO guys in here, you have to install pyqrcode 2. open CMD and type python -m pip install pyqrcode

JehanKandy 10 Jul 13, 2022
AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation

AttentionGAN-v2 for Unpaired Image-to-Image Translation AttentionGAN-v2 Framework The proposed generator learns both foreground and background attenti

Hao Tang 530 Dec 27, 2022
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks Contributions A novel pairwise feature LSP to extract structural

31 Dec 06, 2022
Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral) [Project] [Paper] [Demo] [Related Work: A2RL (for Auto Image Cropping)] [C

Wu Huikai 402 Dec 27, 2022
This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.

st-nerf We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation SIGGRAPH 2021 Jiakai Zha

Diplodocus 258 Jan 02, 2023
yolov5目标检测模型的知识蒸馏(基于响应的蒸馏)

代码地址: https://github.com/Sharpiless/yolov5-knowledge-distillation 教师模型: python train.py --weights weights/yolov5m.pt \ --cfg models/yolov5m.ya

52 Dec 04, 2022
GAN example for Keras. Cuz MNIST is too small and there should be something more realistic.

Keras-GAN-Animeface-Character GAN example for Keras. Cuz MNIST is too small and there should an example on something more realistic. Some results Trai

160 Sep 20, 2022
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection.

Code for the paper: How Effective is Incongruity? Implications for Code-mix Sarcasm Detection - ICON ACL 2021

2 Jun 05, 2022
A repository for interferometer controller code.

dses-interferometer-controller A repository for interferometer controller code, hardware, and simulations. See dses.science for more information on th

Eli Reed 1 Jan 17, 2022
Data and Code for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning"

Introduction Code and data for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning". We cons

Pan Lu 81 Dec 27, 2022
Code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks

Biomedical Entity Linking This repo provides the code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Res

Tuan Manh Lai 24 Oct 24, 2022
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv

Hanxun Huang 11 Nov 30, 2022
NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch

PyTorch implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping Paper: https://arxiv.org/abs/2102.06171.pdf Original code: htt

Vaibhav Balloli 320 Jan 02, 2023
Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Covid-19 Test AI (Deep Learning - NNs) Software I developed a segmentation algorithm to understand whether Covid-19 Test Photos are positive or negati

Emirhan BULUT 28 Dec 04, 2021
Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

Numan 16 Jul 18, 2022
A rule-based log analyzer & filter

Flog 一个根据规则集来处理文本日志的工具。 前言 在日常开发过程中,由于缺乏必要的日志规范,导致很多人乱打一通,一个日志文件夹解压缩后往往有几十万行。 日志泛滥会导致信息密度骤减,给排查问题带来了不小的麻烦。 以前都是用grep之类的工具先挑选出有用的,再逐条进行排查,费时费力。在忍无可忍之后决

上山打老虎 9 Jun 23, 2022
An end-to-end framework for mixed-integer optimization with data-driven learned constraints.

OptiCL OptiCL is an end-to-end framework for mixed-integer optimization (MIO) with data-driven learned constraints. We address a problem setting in wh

Holly Wiberg 57 Dec 26, 2022