Contents¶
libfuncpy¶
Motivation¶
Functional Programming tools in Python - extending beyond map, filter, reduce, and partial.
Installation¶
Install libfuncpy from PyPI with:
pip install libfuncpy
Install it from GitHub with:
# clone the repository
git clone https://github.com/joaomcteixeira/libfuncpy
# move to the folder
cd libfuncpy
# install in develop mode
python setup.py develop
Usage¶
This is an example page for a real project. Describe here examples on how to use your software!
To use libfuncpy:
import libfuncpy
Here we explain how to contribute to a project that adopted this template. Actually, you can use this same scheme when contributing to this template. If you are completely new to git this might not be the best beginner tutorial, but will be very good still ;-)
You will notice that the text that appears is a mirror of the CONTRIBUTING.rst file. You can also point your community to that file (or the docs) to guide them in the steps required to interact with you project.
Contributing¶
How to contribute to this project.
Fork this repository¶
Fork this repository before contributing.
Clone your fork¶
Next, clone your fork to your local machine, keep it up to date with the upstream, and update the online fork with those updates.
git clone https://github.com/YOUR-USERNAME/libfuncpy.git
cd libfuncpy
git remote add upstream git://github.com/joaomcteixeira/libfuncpy.git
git fetch upstream
git merge upstream/main
git pull origin main
Install for developers¶
Create a dedicated Python environment where to develop the project.
If you are using pip follow the official instructions on
Installing packages using pip and virtual environments, most likely
what you want is:
python3 -m venv libfuncpy
source libfuncpy/bin/activate
If you are using Anaconda go for:
conda create --name libfuncpy python=3.7
conda activate libfuncpy
Where libfuncpy is the name you wish to give to the environment
dedicated to this project.
Either under pip or conda, install the package in develop
mode, and also tox. Note, here I
assume our project has no dependencies.
python setup.py develop
pip install tox
This configuration, together with the use of the src folder layer,
guarantee that you will always run the code after installation. Also,
thanks to the develop flag, any changes in the code will be
automatically reflected in the installed version.
Make a new branch¶
From the main branch create a new branch where to develop the new code.
git checkout main
git checkout -b new_branch
Develop the feature and keep regular pushes to your fork with comprehensible commit messages.
git status
git add (the files you want)
git commit -m (add a nice commit message)
git push origin new_branch
While you are developing, you can execute tox as needed to run your
unittests or inspect lint, etc. See the last section of this page.
Update CHANGELOG¶
Update the changelog file under docs/CHANGELOG.rst with an
explanatory bullet list of your contribution. Add that list right after
the main title and before the last version subtitle:
Changelog
=========
* here goes my new additions
* explain them shortly and well
vX.X.X (1900-01-01)
-------------------
Also add your name to the authors list at docs/AUTHORS.rst.
Pull Request¶
Once you are finished, you can Pull Request you additions to the main
repository, and engage with the community. Please read the
PULLREQUEST.rst guidelines first, you will see them when you open a
PR.
Before submitting a Pull Request, verify your development branch passes all tests as described bellow . If you are developing new code you should also implement new test cases.
Uniformed Tests with tox¶
Thanks to Tox we can have a unified testing platform where all developers are forced to follow the same rules and, above all, all tests occur in a controlled Python environment.
With Tox, the testing setup can be defined in a configuration file,
the tox.ini, which contains all the operations that are performed
during the test phase. Therefore, to run the unified test suite,
developers just need to execute tox, provided tox is installed in
the Python environment in use.
pip install tox
# or
conda install tox -c conda-forge
Before creating a Pull Request from your branch, certify that all the tests pass correctly by running:
tox
These are exactly the same tests that will be performed online in the Github Actions.
Also, you can run individual environments if you wish to test only specific functionalities, for example:
tox -e lint # code style
tox -e build # packaging
tox -e docs # only builds the documentation
tox -e prreqs # special requirements before Pull Request
tox -e py37 # performs pytest in Python 3.7 environment (it should
be installed)
libfuncpy API¶
Contain functions.
- libfuncpy.lib.ITE(iflogic, assertion, elselogic)¶
Apply ternary operator logic executing functions.
Functions should be preconfigured and accept no arguments.
Better if you see the code:
return iflogic() if assertion() else elselogic()
- libfuncpy.lib.ITEX(x, iflogic, assertion, elselogic)¶
Apply ternary operator logic executing functions.
Functions should receive a single value: x.
Better if you see the code:
return iflogic(x) if assertion(x) else elselogic(x)
- Parameters
x – The value to pass to each function.
- libfuncpy.lib.chainf(init, *funcs)[source]¶
Run functions in sequence starting from an initial value.
Example
>>> chainf(2, [str, int, float]) 2.0
- libfuncpy.lib.chainfs(*funcs)[source]¶
Store functions be executed on a value.
Example
>>> do = chainfs(str, int, float) >>> do(2) 2.0
- libfuncpy.lib.consume(gen)[source]¶
Consume generator in a single statement.
Example
>>> consume(generator)
- libfuncpy.lib.context_engine(func, exceptions, doerror, doelse, dofinally, *args, **kwargs)[source]¶
Make a context engine.
- libfuncpy.lib.f1f2(f1, f2, *a, **k)[source]¶
Apply one function after the other.
Call f1 on the return value of f2.
Args and kwargs apply to f2.
Example
>>> f1f2(str, int, 2) "2"
- libfuncpy.lib.f2f1(f1, f2, *a, **k)[source]¶
Apply the second function after the first.
Call f2 on the return value of f1.
Args and kwargs apply to f1.
Example
>>> f2f1(str, int, 2) 2
- libfuncpy.lib.give(value)[source]¶
Preare a function to return a value when called.
Ignore *args and **kwargs.
Example
>>> true = give(True) >>> true() True
>>> five = give(5) >>> five(4, 6, 7, 8, some_args='some string') 5
- libfuncpy.lib.if_elif_else(value, condition_function_pair)[source]¶
Apply logic if condition is True.
- Parameters
value (anything) – The initial value
condition_function_pair (tuple) – First element is the assertion function, second element is the logic function to execute if assertion is true.
- Returns
The result of the first function for which assertion is true.
- libfuncpy.lib.ite(iflogic, assertion, elselogic)¶
Apply ternary operator logic executing functions.
Functions should be preconfigured and accept no arguments.
Better if you see the code:
return iflogic() if assertion() else elselogic()
- libfuncpy.lib.itev(x, iflogic, assertion, elselogic)¶
Apply ternary operator logic executing functions.
Functions should receive a single value: x.
Better if you see the code:
return iflogic(x) if assertion(x) else elselogic(x)
- Parameters
x – The value to pass to each function.
- libfuncpy.lib.make_iterable(value)[source]¶
Transform into an iterable.
Transforms a given value into an iterable if it is not. Else, return the value itself.
Example
>>> make_iterable(1) [1]
>>> make_iterable([1]) [1]
- libfuncpy.lib.mapc(f, *iterables)[source]¶
Consume map function.
Like map() but it is not a generator; map is consumed immediately.
- libfuncpy.lib.reduce_helper(value, f, *a, **k)[source]¶
Help in reduce.
Helper function when applying reduce to a list of functions.
- Parameters
value (anything)
f (callable) – The function to call. This function receives value as first positional argument.
*a, **k – Args and kwargs passed to f.
- libfuncpy.lib.ternary_operator(iflogic, assertion, elselogic)[source]¶
Apply ternary operator logic executing functions.
Functions should be preconfigured and accept no arguments.
Better if you see the code:
return iflogic() if assertion() else elselogic()
- libfuncpy.lib.ternary_operator_v(x, iflogic, assertion, elselogic)[source]¶
Apply ternary operator logic executing functions.
Functions should receive a single value: x.
Better if you see the code:
return iflogic(x) if assertion(x) else elselogic(x)
- Parameters
x – The value to pass to each function.
- libfuncpy.lib.ternary_operator_x(x, iflogic, assertion, elselogic)¶
Apply ternary operator logic executing functions.
Functions should receive a single value: x.
Better if you see the code:
return iflogic(x) if assertion(x) else elselogic(x)
- Parameters
x – The value to pass to each function.
- libfuncpy.lib.vartial(func, *args, **keywords)[source]¶
Prepare a function with args and kwargs except for the first arg.
Functions like functools.partial except that the resulting preprepared function expects the first positional argument.
Example
>>> pow2 = vartial(math.pow, 2) >>> pow2(3) 9 >>> pow2(4) 16
This is different from: >>> pow_base_3 = partial(math.pow, 3) >>> pow_base_3(2) 9 >>> pow_base_3(4) 81
- libfuncpy.lib.whileloop(cond, func, do_stopiteration=<function give.<locals>.newfunc>, do_exhaust=<function give.<locals>.newfunc>)[source]¶
Execute while loop.
All function accept no arguments. If state needs to be evaluated, cond and func need to be synchronized.
- Parameters
cond (callable) – The while loop condition.
func (callable) – The function to call on each while loop iteration.
do_stopiteration (callable) – The function to execute when func raises StopIteration error.
do_exhaust (callable) – The function to execute when while loop exhausts.
- Returns
None