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
The deployment framework aims to provide a simple, lightweight, fast integrated, pipelined deployment framework that ensures reliability, high concurrency and scalability of services.

savior是一个能够进行快速集成算法模块并支持高性能部署的轻量开发框架。能够帮助将团队进行快速想法验证(PoC),避免重复的去github上找模型然后复现模型;能够帮助团队将功能进行流程拆解,很方便的提高分布式执行效率;能够有效减少代码冗余,减少不必要负担。

Tao Luo 125 Dec 22, 2022
Simple image captioning model - CLIP prefix captioning.

CLIP prefix captioning. Inference Notebook: 🥳 New: 🥳 Our technical papar is finally out! Official implementation for the paper "ClipCap: CLIP Prefix

688 Jan 04, 2023
Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

Johannes von Lindheim 3 Oct 29, 2022
Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

RealBasicVSR [Paper] This is the official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution, arXiv". This repository contain

Kelvin C.K. Chan 566 Dec 28, 2022
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

100 Dec 28, 2022
Repositório da disciplina de APC, no segundo semestre de 2021

NOTAS FINAIS: https://github.com/fabiommendes/apc2018/blob/master/nota-final.pdf Algoritmos e Programação de Computadores Este é o Git da disciplina A

16 Dec 16, 2022
(CVPR 2022) Energy-based Latent Aligner for Incremental Learning

Energy-based Latent Aligner for Incremental Learning Accepted to CVPR 2022 We illustrate an Incremental Learning model trained on a continuum of tasks

Joseph K J 37 Jan 03, 2023
Code to reproduce the results for Compositional Attention

Compositional-Attention This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal 58 Nov 30, 2022
[EMNLP 2021] MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

MuVER This repo contains the code and pre-trained model for our EMNLP 2021 paper: MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity

24 May 30, 2022
Algorithmic Trading using RNN

Deep-Trading This an implementation adapted from Rachnog Neural networks for algorithmic trading. Part One — Simple time series forecasting and this c

Hazem Nomer 29 Sep 04, 2022
PyTorch implementation of DUL (Data Uncertainty Learning in Face Recognition, CVPR2020)

PyTorch implementation of DUL (Data Uncertainty Learning in Face Recognition, CVPR2020)

Mouxiao Huang 20 Nov 15, 2022
A framework for multi-step probabilistic time-series/demand forecasting models

JointDemandForecasting.py A framework for multi-step probabilistic time-series/demand forecasting models File stucture JointDemandForecasting contains

Stanford Intelligent Systems Laboratory 3 Sep 28, 2022
Simple image captioning model - CLIP prefix captioning.

Simple image captioning model - CLIP prefix captioning.

688 Jan 04, 2023
Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

Direct LiDAR Odometry: Fast Localization with Dense Point Clouds DLO is a lightweight and computationally-efficient frontend LiDAR odometry solution w

VECTR at UCLA 369 Dec 30, 2022
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

Nervana 3.9k Dec 20, 2022
Code accompanying "Learning What To Do by Simulating the Past", ICLR 2021.

Learning What To Do by Simulating the Past This repository contains code that implements the Deep Reward Learning by Simulating the Past (Deep RSLP) a

Center for Human-Compatible AI 24 Aug 07, 2021
Adversarially Learned Inference

Adversarially Learned Inference Code for the Adversarially Learned Inference paper. Compiling the paper locally From the repo's root directory, $ cd p

Mohamed Ishmael Belghazi 308 Sep 24, 2022
Python inverse kinematics for your robot model based on Pinocchio.

Python inverse kinematics for your robot model based on Pinocchio.

Stéphane Caron 50 Dec 22, 2022
MG-GCN: Scalable Multi-GPU GCN Training Framework

MG-GCN MG-GCN: multi-GPU GCN training framework. For more information, please read our paper. After cloning our repository, run git submodule update -

Translational Data Analytics (TDA) Lab @GaTech 6 Oct 24, 2022
An implementation of quantum convolutional neural network with MindQuantum. Huawei, classifying MNIST dataset

关于实现的一点说明 山东大学 2020级 苏博南 www.subonan.com 文件说明 tools.py 这里面主要有两个函数: resize(a, lenb) 这其实是我找同学写的一个小算法hhh。给出一个$28\times 28$的方阵a,返回一个$lenb\times lenb$的方阵。因

ぼっけなす 2 Aug 29, 2022