iterproxy

.. .. image:: https://readthedocs.org/projects/iterproxy/badge/?version=latest :target: https://iterproxy.readthedocs.io/index.html :alt: Documentation Status

.. image:: https://github.com/MacHu-GWU/iterproxy-project/workflows/CI/badge.svg :target: https://github.com/MacHu-GWU/iterproxy-project/actions?query=workflow:CI

.. image:: https://codecov.io/gh/MacHu-GWU/iterproxy-project/branch/main/graph/badge.svg :target: https://codecov.io/gh/MacHu-GWU/iterproxy-project

.. image:: https://img.shields.io/pypi/v/iterproxy.svg :target: https://pypi.python.org/pypi/iterproxy

.. image:: https://img.shields.io/pypi/l/iterproxy.svg :target: https://pypi.python.org/pypi/iterproxy

.. image:: https://img.shields.io/pypi/pyversions/iterproxy.svg :target: https://pypi.python.org/pypi/iterproxy

.. image:: https://img.shields.io/badge/Release_History!--None.svg?style=social :target: https://github.com/MacHu-GWU/iterproxy-project/blob/main/release-history.rst

.. image:: https://img.shields.io/badge/STAR_Me_on_GitHub!--None.svg?style=social :target: https://github.com/MacHu-GWU/iterproxy-project

---

.. .. image:: https://img.shields.io/badge/Link-Document-blue.svg :target: https://iterproxy.readthedocs.io/index.html

.. .. image:: https://img.shields.io/badge/Link-API-blue.svg :target: https://iterproxy.readthedocs.io/py-modindex.html

.. .. image:: https://img.shields.io/badge/Link-Source_Code-blue.svg :target: https://iterproxy.readthedocs.io/py-modindex.html

.. image:: https://img.shields.io/badge/Link-Install-blue.svg :target: install_

.. image:: https://img.shields.io/badge/Link-GitHub-blue.svg :target: https://github.com/MacHu-GWU/iterproxy-project

.. image:: https://img.shields.io/badge/Link-Submit_Issue-blue.svg :target: https://github.com/MacHu-GWU/iterproxy-project/issues

.. image:: https://img.shields.io/badge/Link-Request_Feature-blue.svg :target: https://github.com/MacHu-GWU/iterproxy-project/issues

.. image:: https://img.shields.io/badge/Link-Download-blue.svg :target: https://pypi.org/pypi/iterproxy#files

⏩ Welcome to iterproxy Documentation

You may seen the following code style in many ORM framework, this pattern provides a user friendly API to access items from the iterator object:

.. code-block:: python

query(...).one()
query(...).one_or_none()
query(...).many(3)
query(...).all(5)
query(...).skip(5).many(3)

iterproxy <https://github.com/MacHu-GWU/iterproxy-project>_ library can give any iterable object similar capabilities.

Usage Example

Convert any iterable object to a IterProxy object:

.. code-block:: python

from iterproxy import IterProxy

# Suppose you have an iterable object
iterator = range(10)

# Convert it to a IterProxy object
proxy = IterProxy(iterator)

Access items from the IterProxy object:

.. code-block:: python

proxy = IterProxy(range(10))

proxy.one() # it will return 0
proxy.many(3) # it will return [1, 2, 3]
proxy.skip(2).many(2) # it will skip [4, 5] and return [6, 7]
proxy.all() # it will return the rest [8, 9]
proxy.one_or_none() # it will return None

IterProxy.iter_chunks can group items into chunks having K items, the last chunk may have less items than K:

.. code-block:: python

proxy = IterProxy(range(3))
list(proxy.iter_chunks(2)) # it will return [[0, 1], [2]]

Another example:

.. code-block:: python

proxy = IterProxy(range(10))
proxy.all() # it will return [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Of course the IterProxy itself is a iterator:

.. code-block:: python

for i in IterProxy(range(10)): # 0, 1, 2, ...
    ...

You can use custom filter function to filter the result. Other than the nesting style in built-in filter function, it use chain pattern.

.. code-block:: python

def is_odd(x):
    return x % 2 == 1

def gte_5(x):
    return x >= 5

# with IterProxy, you can chain them
# it returns you [5, 7, 9]
for i in IterProxy(range(10)).filter(is_odd).filter(gte_5):
    print(i)

# or put them together, by default, it is logic and
for i in IterProxy(range(10)).filter(is_odd, gte_5):
    print(i)

# with the built-in filter, this is not that intuitive
for i in filter(gte_5, filter(is_odd, range(10))):
    ...

You can also use compound logic and_, or_, not_:

.. code-block:: python

def is_odd(i):
    return i % 2

def is_even(i):
    return not (i % 2)

def lte_3(i):
    return i <= 3

def gte_4(i):
    return i >= 4

def lte_6(i):
    return i <= 6

def gte_7(i):
    return i >= 7

IterProxy(range(10)).filter(and_(gte_4, lte_6)).all() # [4, 5, 6]
IterProxy(range(10)).filter(or_(lte_3, gte_7)).all() # [0, 1, 2, 3, 7, 8, 9]
IterProxy(range(10)).filter(not_(is_odd)).all() # [0, 2, 4, 6, 8]

# of course you can nest and_, or_, not_
IterProxy(range(10)).filter(not_(and_(is_odd, or_(lte_3, gte_7)))).all() # [0, 2, 4, 5, 6, 8]

(Advanced) In order to enable type hint, you can do:

.. code-block:: python

from iterproxy import IterProxy

class Dog:
    def bark(self):
        pass

class DogIterProxy(IterProxy[Dog]): # subclass from IterProxy[${YourTypeHint}]
    pass

many_dogs = [Dog(),]*10

proxy = DogIterProxy(many_dogs)

proxy.one_or_none().bark()
for dog in proxy.many(2):
    dog.bark()
for dog in proxy.skip(1).many(2):
    dog.bark()
for dog in proxy.all():
    dog.bark()

filtered_proxy = DogIterProxy(many_dogs).filter(lambda dog: True)
filtered_proxy.one_or_none().bark()
for dog in filtered_proxy.many(2):
    dog.bark()
for dog in filtered_proxy.skip(1).many(2):
    dog.bark()
for dog in filtered_proxy.all():
    dog.bark()

.. _install:

Install

iterproxy is released on PyPI, so all you need is:

.. code-block:: console

$ pip install iterproxy

To upgrade to latest version:

.. code-block:: console

$ pip install --upgrade iterproxy