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
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-dri

Neural Magic 1.5k Dec 30, 2022
Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection".

A2S-USOD Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection". Code will be released upon

15 Dec 16, 2022
Official PyTorch implementation of CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds Introduction This is the official PyTorch implementation of o

Yijia Weng 96 Dec 07, 2022
Planner_backend - Academic planner application designed for students and counselors.

Planner (backend) Academic planner application designed for students and advisors.

2 Dec 31, 2021
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

MI-AOD Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection (The PDF is not available tem

Tianning Yuan 269 Dec 21, 2022
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization This repository contains the source code for the paper (link wi

Rakuten Group, Inc. 0 Nov 19, 2021
[CVPR'21] Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration

Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration This repository contains the implementation of our paper Locally Aware Pi

sfwang 70 Dec 19, 2022
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

Jia Research Lab 137 Dec 14, 2022
An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.

Raspberry Pi Air Quality Monitor A simple air quality monitoring service for the Raspberry Pi. Installation Clone the repository and run the following

rydercalmdown 24 Dec 09, 2022
PyTorch implementation for our paper "Deep Facial Synthesis: A New Challenge"

FSGAN Here is the official PyTorch implementation for our paper "Deep Facial Synthesis: A New Challenge". This project achieve the translation between

Deng-Ping Fan 32 Oct 10, 2022
A series of convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.

imutils A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, and displ

Adrian Rosebrock 4.3k Jan 08, 2023
Implementation of QuickDraw - an online game developed by Google, combined with AirGesture - a simple gesture recognition application

QuickDraw - AirGesture Introduction Here is my python source code for QuickDraw - an online game developed by google, combined with AirGesture - a sim

Viet Nguyen 89 Dec 18, 2022
OpenMMLab Model Deployment Toolset

Introduction English | 简体中文 MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Major features F

OpenMMLab 1.5k Dec 30, 2022
Image-to-Image Translation in PyTorch

CycleGAN and pix2pix in PyTorch New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that e

Jun-Yan Zhu 19k Jan 07, 2023
A simple but complete full-attention transformer with a set of promising experimental features from various papers

x-transformers A concise but fully-featured transformer, complete with a set of promising experimental features from various papers. Install $ pip ins

Phil Wang 2.3k Jan 03, 2023
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference

RawVSR This repo contains the official codes for our paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference Xiaoh

Xiaohong Liu 23 Oct 08, 2022
Codecov coverage standard for Python

Python-Standard Last Updated: 01/07/22 00:09:25 What is this? This is a Python application, with basic unit tests, for which coverage is uploaded to C

Codecov 10 Nov 04, 2022
code for paper"A High-precision Semantic Segmentation Method Combining Adversarial Learning and Attention Mechanism"

PyTorch implementation of UAGAN(U-net Attention Generative Adversarial Networks) This repository contains the source code for the paper "A High-precis

Tong 8 Apr 25, 2022
PyTorch implementation of CVPR'18 - Perturbative Neural Networks

This is an attempt to reproduce results in Perturbative Neural Networks paper. See original repo for details.

Michael Klachko 57 May 14, 2021
Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Machine Learning University: Accelerated Tabular Data Class This repository contains slides, notebooks, and datasets for the Machine Learning Universi

AWS Samples 916 Dec 23, 2022