paprika 1.1.0

Paprika is a python library that reduces boilerplate. Heavily inspired by Project Lombok.

Paprika is a python library that reduces boilerplate. Heavily inspired by Project Lombok.

Installation

paprika is available on PyPi.

$ pip install paprika

Usage

paprika is a decorator-only library and all decorators are exposed at the top-level of the module. If you want to use shorthand notation (i.e. @data), you can import all decorators as follows:

from paprika import *

Alternatively, you can opt to use the longhand notation (i.e. @paprika.data) by importing paprika as follows:

import paprika

Features & Examples

@to_string

The @to_string decorator automatically overrides __str__

Python

class Person:
    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age

    def __str__(self):
        return f"{self.__name__}@[name={self.name}, age={self.age}]"

Python with paprika

@to_string
class Person:
    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age

---

@equals_and_hashcode

The @equals_and_hashcode decorator automatically overrides __eq__ and __hash__

Python

class Person:
    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age

    def __eq__(self, other):
        return (self.__class__ == other.__class__
                and
                self.__dict__ == other.__dict__)

    def __hash__(self):
        return hash((self.name, self.age))

Python with paprika

@equals_and_hashcode
class Person:
    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age

---

@data

The @data decorator creates a dataclass by combining @to_string and @equals_and_hashcode and automatically creating a constructor!

Python

class Person:
    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age

    def __str__(self):
        return f"{self.__name__}@[name={self.name}, age={self.age}]"

    def __eq__(self, other):
        return (self.__class__ == other.__class__
                and
                self.__dict__ == other.__dict__)

    def __hash__(self):
        return hash((self.name, self.age))

Python with paprika

@data
class Person:
    name: str
    age: int

Footnote on @data and NonNull

paprika exposes a NonNull generic type that can be used in conjunction with the @data decorator to enforce that certain arguments passed to the constructor are not null. The following snippet will raise a ValueError:

@data
class Person:
    name: NonNull[str]
    age: int


p = Person(name=None, age=42)  # ValueError ❌

---

@singleton

The @singleton decorator can be used to enforce that a class only gets instantiated once within the lifetime of a program. Any subsequent instantiation will return the original instance.

@singleton
class Person:
    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age


p1 = Person(name="Rayan", age=19)
p2 = Person()
print(p1 == p2 and p1 is p2)  # True ✅

@singleton can be seamlessly combined with @data!

@singleton
@data
class Person:
    name: str
    age: int


p1 = Person(name="Rayan", age=19)
p2 = Person()
print(p1 == p2 and p1 is p2)  # True ✅

☠️ Important note on combining @data and @singleton ☠️

When combining @singleton with @data, @singleton should come before @data. Combining them the other way around will work in most cases but is not thoroughly tested and relies on assumptions that might not hold.

---

@threaded

The @threaded decorator will run the decorated function in a thread by submitting it to a ThreadPoolExecutor. When the decorated function is called, it will immediately return a Future object. The result can be extracted by calling .result() on that Future

@threaded
def waste_time(sleep_time):
    thread_name = threading.current_thread().name
    time.sleep(sleep_time)
    print(f"{thread_name} woke up after {sleep_time}s!")
    return 42


t1 = waste_time(5)
t2 = waste_time(2)
print(t1)  # <Future at 0x104130a90 state=running>
print(t1.result())  # 42

ThreadPoolExecutor-0_1 woke up after 2s!
ThreadPoolExecutor-0_0 woke up after 5s!

---

@repeat

The @repeat decorator will run the decorated function consecutively, as many times as specified.

@repeat(n=5)
def hello_world():
    print("Hello world!")


hello_world()

Hello world!
Hello world!
Hello world!
Hello world!
Hello world!

---

@timeit

The @timeit decorator times the total execution time of the decorated function. It uses a timer::perf_timer by default but that can be replaced by any object of type Callable[None, int].

def time_waster1():
    time.sleep(2)

def time_waster2():
    time.sleep(5)

@timeit
def test_timeit():
    time_waster1()
    time_waster2()

test_timeit executed in 7.002189894999999 seconds

Here's how you can replace the default timer:

@timeit(timer: lambda: 0) # Or something actually useful like time.time()
def test_timeit():
    time_waster1()
    time_waster2()

test_timeit executed in 0 seconds

---

@access_counter

The @access_counter displays a summary of how many times each of the structures that are passed to the decorated function are accessed (number of reads and number of writes).

@access_counter
def test_access_counter(list, dict, person, tuple):
    for i in range(500):
        list[0] = dict["key"]
        dict["key"] = person.age
        person.age = tuple[0]


test_access_counter([1, 2, 3, 4, 5], {"key": 0}, Person(name="Rayan", age=19),
                    (0, 0))

data access summary for function: test
+------------+----------+-----------+
| Arg Name   |   nReads |   nWrites |
+============+==========+===========+
| list       |        0 |       500 |
+------------+----------+-----------+
| dict       |      500 |       500 |
+------------+----------+-----------+
| person     |      500 |       500 |
+------------+----------+-----------+
| tuple      |      500 |         0 |
+------------+----------+-----------+

---

@hotspots

The @hotspots automatically runs cProfiler on the decorated function and display the top_n (default = 10) most expensive function calls sorted by cumulative time taken (this metric will be customisable in the future). The sample error can be reduced by using a higher n_runs (default = 1) parameter.

def time_waster1():
    time.sleep(2)


def time_waster2():
    time.sleep(5)


@hotspots(top_n=5, n_runs=2)  # You can also do just @hotspots
def test_hotspots():
    time_waster1()
    time_waster2()


test_hotspots()

   11 function calls in 14.007 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        2    0.000    0.000   14.007    7.004 main.py:27(test_hot)
        4   14.007    3.502   14.007    3.502 {built-in method time.sleep}
        2    0.000    0.000   10.004    5.002 main.py:23(time_waster2)
        2    0.000    0.000    4.003    2.002 main.py:19(time_waster1)
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

---

@profile

The @profile decorator is simply syntatic sugar that allows to perform both hotspot analysis and data access analysis. Under the hood, it simply uses @access_counter followed by @hotspots.

---

Contributing

Encountered a bug? Have an idea for a new feature? This project is open to all sorts of contribution! Feel free to head to the Issues tab and describe your request!

Authors

• Rayan Hatout - GitHub | Twitter | LinkedIn

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details