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

Last update: Dec 29, 2022

Related tags

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?

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?

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 ?

@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

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
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

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. 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
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)
Adds a module frame to catch raises in top-level code.

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)

Includes __init__.py files to allow use of python_graphs library from Python 2.
Source code(tar.gz)
Source code(zip)
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

GitHub Repository

Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

This is the codebase for the paper: Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Directory Structur

19 Aug 21, 2022

A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021)

GDN A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021) Abstract In this paper, we consider an inverse problem i

4 Sep 13, 2022

PlenOctrees: NeRF-SH Training & Conversion

PlenOctrees Official Repo: NeRF-SH training and conversion This repository contains code to train NeRF-SH and to extract the PlenOctree, constituting

323 Dec 29, 2022

we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks.

Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection Overview Localization of anatomical landmarks is essential for clinica

0 Aug 28, 2022

Shitty gaze mouse controller

demo.mp4 shitty_gaze_mouse_cotroller install tensofflow, cv2 run the main.py and as it starts it will collect data so first raise your left eyebrow(bo

16 Aug 30, 2022

Code for our paper "Interactive Analysis of CNN Robustness"

Perturber Code for our paper "Interactive Analysis of CNN Robustness" Datasets Feature visualizations: Google Drive Fine-tuning checkpoints as saved m

0 Aug 17, 2021

Faster Convex Lipschitz Regression

Faster Convex Lipschitz Regression This reepository provides a python implementation of our Faster Convex Lipschitz Regression algorithm with GPU and

0 Nov 19, 2021

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection Main requirements torch = 1.0 torchvision = 0.2.0 Python 3 Environm

15 Apr 04, 2022

Simulator for FRC 2022 challenge: Rapid React

rrsim Simulator for FRC 2022 challenge: Rapid React out-1.mp4 Usage In order to run the simulator use the following: python3 rrsim.py [config_path] wh

1 Jan 18, 2022

Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images.

IAug_CDNet Official Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images. Overview We propose a

53 Dec 02, 2022

Chinese named entity recognization with BiLSTM using Keras

Chinese named entity recognization (Bilstm with Keras) Project Structure ./ ├── README.md ├── data │ ├── README.md │ ├── data 数据集 │ │ ├─

1 Dec 17, 2021

Consecutive-Subsequence - Simple software to calculate susequence with highest sum

Simple software to calculate susequence with highest sum This repository contain

1 Jan 31, 2022

CvT-ASSD: Convolutional vision-Transformerbased Attentive Single Shot MultiBox Detector (ICTAI 2021 CCF-C 会议)The 33rd IEEE International Conference on Tools with Artificial Intelligence

CvT-ASSD including extra CvT, CvT-SSD, VGG-ASSD models original-code-website: https://github.com/albert-jin/CvT-SSD new-code-website: https://github.c

5 Mar 07, 2022

Official Implementation of SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

Official Implementation of SimIPU SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations Since

37 Dec 01, 2022

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

Related tags

Overview

python_graphs

Installation

Common Tasks

Comments

Question

Releases(v1.2.3)

v1.2.3(Oct 7, 2021)

v1.2.0(Oct 5, 2021)

v1.1.0(Oct 5, 2021)

v1.0.1(May 7, 2021)

v1.0.0(Apr 12, 2021)

v1.0.0

Core features: