• deptree

deptree is a simple utility for printing the reverse depedency tree of a set of source files. By default, deptree is configured to parse TypeScript files, but can be configured to handle other formats.

• Installation

From within an activated Python virtualenv, cd to the deptree project root and run:

./setup.py install

This will install the deptree library, the deptree executable, and all external depedencies. If errors occur, please verify that you have the latest version of pip and virtualenv installed and are able to install third party packages from PyPi. For more information, please see:

https://pip.pypa.io https://virtualenv.pypa.io

• Tests

Once deptree is installed, you may run the unittests against the development code within the project root by running:

./bin/runtests

This will use Python's unittest discovery functionality to locate and execute all unittests.

• Usage

deptree is straightforward. Simply run:

deptree myscript.ts

myscript.ts <- fs.ts <- myscript.ts os.ts <- myscript.ts

Multiple source files may be specified. The regular expression used to find depedencies may be overridden using the --pattern flag. If an alternate expression is specified, it must include a named capturing group called "path" which represents the relative path to the referenced file. Extensions besides ".ts" may be used by employing the --extension option. For more usage information, please see:

deptree -h

• The deptree Library

Deptree may be used within other Python applications:

import deptree dependencies = deptree.get_deptree(['myscript.ts'])

This will reutrn an OrderedDictionary mapping source files to their depedents:

OrderedDict([('myscript.ts', []), ('fs.ts', ['myscript.ts']), ('os.ts', ['myscript.ts'])])

• Implementation notes

• While a more abstract implementation is certainly possible, I've chosen a concrete solution (a simple OrderedDict) which solves the problem at hand. In other words, some type of DepdendencyTree class could have a number of DepdendencyNode children, each with its own children. A DepdendencyVisitor class could then walk this tree in arbitrary ways to produce forward or reverse depdendency graphs, etc.

However, such an abstract design carries its own maintenance cost and risks (primarily a steep learning curve for other developers and additional lines of code, each with the potential for their own additional bugs). Given that the current implementation is so terse, it can easily be swapped later for a more generic and abstract solution if needed.

• deptree was written to be pep8 compliant within reason. Exceptions are occasionally made for readability.

• Tests are written using Python's unittest and mock libraries. Thus, mock and its dependencies will be installed when ./setup.py install is run. mock allows for consistent and coherent 'monkey patching' of code by reassigning objects at runtime. See https://pypi.python.org/pypi/mock for more info.

• The project structure is: ./ <- root ./bin <- executables ./lib <- libraries ./lib/deptree <- the deptree package ./lib/deptree/test <- unittests and test data

• git was used for source control, virtualenv for environment configuration, and setuptools for deployment. flake8 was used for linting.

• The 'deptree' executable is a very thin wrapper around deptree.main.run. Housing most of the executable logic within the 'deptree' package allows for the executable logic to by easily tested.

• To keep code terse, dot notation from imported libraries is avoided as much as possible. Thus, 'from argparse import ArgumentParser' is preferred over 'import argparse'.