Bonsai
Bonsai is an attempt to provide a miniature and refined representation
for the
often cumbersome syntax trees and program models.
This idea, of providing a smaller tree that is more or less the same
thing,
is where the name comes from.
This work started as part of an analysis tool that I am developing for
my own
research. I am interested in analysing ROS
robotics applications, which are often written in C++.
Since free C++ analysis tools are rather scarce, I tried
to come up with my own, using the Python bindings of the clang
compiler.
At the moment of this writing, I am aware that these bindings are
incomplete
in terms of AST information they provide.
As this analysis tool developed, I realized that the C++ analysis
features
are independent of ROS or any other framework, and that this kind of
tool
might be useful for someone else, either as is, or as a starting point
for
something else.
Features
Bonsai provides an interface to represent, analyse or manipulate
programs.
The model it uses is abstract enough to serve as a basis for specific
language
implementations, although it focuses more on
imperative/object-oriented
languages for now.
What to expect from bonsai:
classes for the different entities of a program (e.g. variables, functions, etc.);
extended classes for specific programming languages (only C++ for now);
parser implementations, able to take a file and produce a model (e.g. clang for C++);
extensible interface to manipulate and query the resulting model (e.g. find calls for a function);
a console script to use as a standalone application.
Installation
Here are some instructions to help you get bonsai.
Bonsai has been tested with Linux Ubuntu and Python 2.7,
but the platform should not make much of a difference.
Dependencies are minimal, and depend on what you want to analyse.
Since at the moment there is only a single implementation for C++
using clang 3.8, you will need to install libclang and the
(pip install clang) to parse C++ files. Skip this if you want to
use
the library in any other way.
Method 1: Running Without Installation
Open a terminal, and move to a directory where you want to clone this
repository.
git clone https://github.com/git-afsantos/bonsai.gitThere is an executable script in the root of this repository to help
you get started.
It allows you to run bonsai without installing it. Make sure that your
terminal is at
the root of the repository.
cd bonsai
python bonsai-runner.py <args>You can also run it with the executable package syntax.
python -m bonsai <args>Method 2: Installing Bonsai on Your Machine
Bonsai is now available on PyPi.
You can install it from source or from a wheel.
[sudo] pip install bonsai-codeThe above command will install bonsai for you. Alternatively, download
and extract its
source, move to the project’s root directory, and then execute the
following.
python setup.py installAfter installation, you should be able to run the command bonsai
in your terminal
from anywhere.
Examples
The cpp_example.py script at the root of this repository is a
small example on
how to parse a C++ file and then find all references to a variable
a in that file.
In it, you can see parser creation
parser = CppAstParser(workspace = "examples/cpp")access to the global (top level, or root) scope of the program, and
obtaining
a pretty string representation of everything that goes in it
parser.global_scope.pretty_str()getting a list of all references to variable a, starting the
search from
the top of the program (global scope)
CodeQuery(parser.global_scope).all_references.where_name("a").get()and accessing diverse properties from the returned CodeReference
objects,
such as file line and column (cppobj.line, cppobj.column), the
type of the
object (cppobj.result), what is it a reference of
(cppobj.reference,
in this case a CodeVariable) and an attempt to interpret the
program and
resolve the reference to a concrete value
(resolve_reference(cppobj)).
Do note that resolving expressions and references is still
experimental,
and more often that not will not be able to produce anything useful.
This is the pretty string output for a program that defines a class
C
and a couple of functions.
class C:
C():
[declaration]
void m(int a):
[declaration]
int x_ = None
C():
x_ = 0
void m(int a):
a = (a + 2) * 3
this.x_ = a
int main(int argc, char ** argv):
C c = new C()
c.m(42)
C * c1 = new C()
C * c2 = new C()
new C()
delete(c1)
delete(c2)
return 0
The pretty string representation, as seen, is a sort of
pseudo-language, inspired
in the Python syntax, even though the parsed program is originally in
C++.
For more details on what you can get from the various program
entities, check out
the source for the abstract model and then the language-specific
implementation of your choice.
1 maintainer
Metadata
1 maintainer
Release files for bonsai-code 0.6.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| bonsai-code-0.6.8.tar.gz | 43.0 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bonsai_code-0.6.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 95.7 kB
Release files / bonsai-code-0.6.8.tar.gz
| Download URL | bonsai-code-0.6.8.tar.gz |
|---|---|
| Size | 43.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
6ef835e605bd8ac6cba9df68d0c9f176297027ccb277469ffb4baeea837a3602
|
|
BLAKE2b-256 checksum How to use checksums |
cc3c95755937d4dbb6d493408e939445b86b675b623bc87cc4e72d67950523f3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.8.10
|
Release files / bonsai_code-0.6.8-py3-none-any.whl
| Download URL | bonsai_code-0.6.8-py3-none-any.whl |
|---|---|
| Size | 52.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ca880e515a52ff823f5f7e79452867078270c81f18bc7616f4d038941a166d7b
|
|
BLAKE2b-256 checksum How to use checksums |
8730265c6fc841513cf36d17eaaf22cc149286c1901fb036dd381e021041602d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.8.10
|
Security for bonsai-code 0.6.8
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
