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https://github.com/sclabs/treeshaker

A tree-shaking tool for Python.
https://github.com/sclabs/treeshaker

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A tree-shaking tool for Python.

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README 

        
treeshaker

==========



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A tree-shaking tool for Python.



What it does

------------



![](images/treeshaker.png)



If you give treeshaker a root environment (specified by a `requirements.txt`

file), a list of target packages `target_packages` (in this case `mypkg` and

`otherpkg`), and a target module (in this case `mypkg.target`), treeshaker will

create an output directory that contains



 1. the subset of modules among all the modules in all the `target_packages`

    that are needed to import the target module, copied as .py files directly

    into the root of the output directory and

 2. a new `requirements.txt` file containing the subset of the root

    `requirements.txt` lines needed to import all the modules in (1)



An end user can then open the folder, `pip install -r requirements.txt`, and

import the target module, without installing or obtaining the complete code for

any of the `target_packages`.



Quick start

-----------



 1. Install treeshaker:



        $ pip install treeshaker



 2. Define your root environment in a `requirements.txt` file and ensure the

    listed packages are available in your current Python environment. If you

    don't have a `requirements.txt` file yet, you can run



        $ pip install 

        $ pip freeze > requirements.txt

    

    If you already have a `requirements.txt` file, you can instead run



        $ pip install -r requirements.txt



 3. Create a configuration file (see the [example config file](examples/treeshaker.cfg)

    for reference), specifying the location of your root `requirements.txt` file

    as well as the details of what targets should be built and how.



 4. Run treeshaker:



        $ treeshaker



Demo

----



 1. `git clone https://github.com/sclabs/treeshaker.git`

 2. `cd treeshaker`

 3. `virtualenv venv`

 4. `source venv/bin/activate`

 5. `pip install . ./examples/mypkg ./examples/otherpkg ./examples/externaldep ./examples/otherdep`

 6. `treeshaker --config examples/treeshaker.cfg`



To clean up after the demo run



    $ python examples/clean.py



Important features

------------------



### Building multiple targets versus building one target



To build one specific target, run



    $ treeshaker --target 



treeshaker will find the configuration file section `[target:]`

(which must exist) to determine the `outdir` (a required key in every

`[target:]` section) and all other parameters for the build.



To build a list of targets, add the desired targets to the `[targets]` section

(as keys with no values) and run



    $ treeshaker



### Module renaming



The copied modules will be renamed if necessary to avoid name conflicts.



If one copied module is `mypkg.dep` and another is `otherpkg.dep`, these will be

renamed to `dep_mypkg.py` and `dep_otherpkg.py`, respectively.



### Import rewriting



During module renaming, treeshaker will attempt to rewrite import statements in

the copied modules as necessary. The logic for doing this is quite rudimentary

and is only expected to succeed when the import is of the form



    import mypkg.mymod



or



    from mypkg.mymod import not_a_module



In these cases, when `mypkg.mymod` is copied, it will be renamed and a simple

find-and-replace will "fix" the import statement.



The following form of import is expected to fail:



    from mypkg import mymod



Relative imports are generally also expected to fail, though they may work when

importing a sibling module whose name has no conflicts.



### Configuration file inheritance



If a key is missing from a `[target:]` section, treeshaker will

fall back to looking for the key in the `[target]` section.



The [example configuration file](examples/treeshaker.cfg) defines many keys in

the `[target]` section, but all of these can be overridden on a per-target basis

by adding the key to the corresponding `[target:]` section.



### `` interpolation



You can use the special string `` in the `[target]` section or any

`[target:]` section and it will be replaced with the actual

outdir at runtime.



### Automatic documentation



Functions and/or classes listed in a `functions` key and [python-fire](https://github.com/google/python-fire)

components listed in a `fire_components` key in the configuration file will be

documented in `/README.md`, using their docstring or fire help page,

respectively.



You can also manually write a README section that will be included in

`/README.md` by specifying a filename in a `readme` key. If this file

does not exist on the disk, it will not be included.



### Custom configuration file name



To run treeshaker using a specific configuration file, run



    $ treeshaker --config path/to/customconfig.cfg



How does it work?

-----------------



### Dependency graph construction



Python provides [modulefinder](https://docs.python.org/2/library/modulefinder.html) in the

standard library, but it is not quite powerful of flexible enough to accomplish

what we want. Instead, we use the [modulegraph](https://github.com/ronaldoussoren/modulegraph)

library, which is both performant (thanks to the useful `excludes` argument) and

flexible (it creates a dependency graph that we can analyze in detail to

classify imported modules).



### Matching PyPI package names to root module names



How can treeshaker determine that if you import a module from `sklearn`, the

corresponding `requirements.txt` line is e.g., `scikit-learn==0.22.1`?



[pipreqs](https://github.com/bndr/pipreqs) has compiled a mapping of PyPI names

to root module names for many PyPI packages; we use this list to perform this

matching. We considered adding a dependency on pipreqs, but we decided to vendor

it ourselves to avoid an additional seven dependent package installs (we only

use the single file that contains the mapping).



For consistency, we choose to use the actual module name (the one used in

imports and the one that reflects the location of the package on-disk after

install) to specify the `target_packages`.



Caveats

-------



While treeshaking is a cool concept, there's rarely any real need to perform

treeshaking on Python code. This library was created for a highly specific use

case: automatically minimizing the amount of code revealed from a monolithic

private codebase when providing code for one specific module (or more

specifically, the task that module performs).



In a monolithic library design, it's possible to have a very large number of

modules and a large number of tasks that the library can accomplish. It might be

better to apply the "single responsibility principle" and design a set of

libraries that each do one task, but what happens when the monolith already

exists and a redesign is not feasible? In most situations, there is no harm in

distributing the entire library even if the person requesting the code wants to

accomplish just one task. However, when the library code is private, it may be

desirable to minimize the total amount of code "exposed" or "revealed" to the

requestor.