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
Official Pytorch implementation of RePOSE (ICCV2021)

RePOSE: Iterative Rendering and Refinement for 6D Object Detection (ICCV2021) [Link] Abstract We present RePOSE, a fast iterative refinement method fo

Shun Iwase 68 Nov 15, 2022
Python Single Object Tracking Evaluation

pysot-toolkit The purpose of this repo is to provide evaluation API of Current Single Object Tracking Dataset, including VOT2016 VOT2018 VOT2018-LT OT

348 Dec 22, 2022
This tool converts a Nondeterministic Finite Automata (NFA) into a Deterministic Finite Automata (DFA)

This tool converts a Nondeterministic Finite Automata (NFA) into a Deterministic Finite Automata (DFA)

Quinn Herden 1 Feb 04, 2022
Multi-Template Mouse Brain MRI Atlas (MBMA): both in-vivo and ex-vivo

Multi-template MRI mouse brain atlas (both in vivo and ex vivo) Mouse Brain MRI atlas (both in-vivo and ex-vivo) (repository relocated from the origin

8 Nov 18, 2022
Official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection

Adaptive Class Suppression Loss for Long-Tail Object Detection This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppressio

CASIA-IVA-Lab 67 Dec 04, 2022
Multi-objective gym environments for reinforcement learning.

MO-Gym: Multi-Objective Reinforcement Learning Environments Gym environments for multi-objective reinforcement learning (MORL). The environments follo

Lucas Alegre 74 Jan 03, 2023
A transformer-based method for Healthcare Image Captioning in Vietnamese

vieCap4H Challenge 2021: A transformer-based method for Healthcare Image Captioning in Vietnamese This repo GitHub contains our solution for vieCap4H

Doanh B C 4 May 05, 2022
Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

ProbAI 2021 - Probabilistic Programming and Variational Inference Tutorial with Pryo Day 1 (June 14) Slides Notebook: students_PPLs_Intro Notebook: so

PGM-Lab 46 Nov 01, 2022
My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

machine-learning-with-graphs My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs Course materials can be

Marko Njegomir 7 Dec 14, 2022
Augmented CLIP - Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.

Train aug_clip against laion400m-embeddings found here: https://laion.ai/laion-400-open-dataset/ - note that this used the base ViT-B/32 CLIP model. S

Peter Baylies 55 Sep 13, 2022
Measuring Coding Challenge Competence With APPS

Measuring Coding Challenge Competence With APPS This is the repository for Measuring Coding Challenge Competence With APPS by Dan Hendrycks*, Steven B

Dan Hendrycks 218 Dec 27, 2022
Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection

Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection This material is supplementray code for paper accepted in ICDAR 2021 We h

NCSOFT 30 Dec 21, 2022
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021]

piglet PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021] This repo contains code and data for PIGLeT. If you like

Rowan Zellers 51 Oct 08, 2022
Code for "R-GCN: The R Could Stand for Random"

RR-GCN: Random Relational Graph Convolutional Networks PyTorch Geometric code for the paper "R-GCN: The R Could Stand for Random" RR-GCN is an extensi

PreDiCT.IDLab 31 Sep 07, 2022
A PyTorch implementation of deep-learning-based registration

DiffuseMorph Implementation A PyTorch implementation of deep-learning-based registration. Requirements OS : Ubuntu / Windows Python 3.6 PyTorch 1.4.0

24 Jan 03, 2023
Multi-objective constrained optimization for energy applications via tree ensembles

Multi-objective constrained optimization for energy applications via tree ensembles

C⚙G - Imperial College London 1 Nov 19, 2021
Pretraining on Dynamic Graph Neural Networks

Pretraining on Dynamic Graph Neural Networks Our article is PT-DGNN and the code is modified based on GPT-GNN Requirements python 3.6 Ubuntu 18.04.5 L

7 Dec 17, 2022
A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models with machine learning (NeurIPS 2021 Datasets and Benchmarks Track)

ClimART - A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models Official PyTorch Implementation Using deep le

21 Dec 31, 2022
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

BUPT GAMMA Lab 519 Jan 02, 2023
Plotting points that lie on the intersection of the given curves using gradient descent.

Plotting intersection of curves using gradient descent Webapp Link --- What's the app about Why this app Plotting functions and their intersection. A

Divakar Verma 2 Jan 09, 2022