jacktrade 0.4.0

A collection of commonly used Python utilities.

jacktrade

Jack of all trades, master of none - a collection of commonly used Python utilities. Install using:

pip install jacktrade

The package consists of the following submodules:

• Benchmark
• Buffers
• Collections
• Files
• Multicore
• Pickler
• Systenv

Benchmark

Contains a CodeTimer class which is used to elegantly and precisely time a piece of code:

from jacktrade import CodeTimer
from time import sleep

with CodeTimer() as ct:
    # Enter code to time here
    sleep(0.1)  # Simulates a piece of code

# Prints: "Code execution took 100 ms."
(ct.ns, ct.us, ct.ms. ct.s)  # Access code duration in nano/micro/milli/seconds.

Buffers

Contains a StringBuffers class, whose purpose is to reduce the number of I/O operations when writing to files. By speficying buffer_size parameter, the contents of the buffer are automatically flushed to disk when the buffer fills up. The class handles any number of simultaneously "open" files.

from jacktrade import StringBuffers

output_file = "out.txt"
buffers = StringBuffers(output_dir="text", buffer_size=3)
buffers.add(output_file, "Hello")   # Nothing is written out
buffers.add(output_file, " world")  # Nothing is written out
buffers.add(output_file, "!")  # "Hello world!" is written to ./text/out.txt

Collections

Contains utility functions for working with collections, namely dictionaries and iterables. Usage examples include:

from jacktrade import *

# Dict utilities
dict_data = {"a": 1, "b": {"c": 2}}
flatten_dict(dict_data)             # Returns: [1, 2]
get_first_dict_item(dict_data)      # Returns: ("a", 1)
get_first_dict_key(dict_data)       # Returns: "a"
get_first_dict_value(dict_data)     # Returns: 1

# Iterable utilities
list_data = [1, 2, [3, 4], 5, 6]
flatten_list(list_data)             # Returns: [1, 2, 3, 4, 5, 6]
chunkify(list_data, chunk_size=2)   # Yields: [1, 2], [[3, 4], 5], [6]
limit_iterator(list_data, limit=3)  # Yields: 1, 2, [3, 4]

Files

Provides utilities for working with files. Currently it contains only a single function for merging CSV files.

from jacktrade import merge_csv_files

# Merges A.csv and B.csv into AB.csv without duplicating headers
merge_csv_files(["A.csv", "B.csv"], "AB.csv")

# Merges A.csv and B.csv into AB.csv verbatim, treating headers as data
merge_csv_files(["A.csv", "B.csv"], "AB.csv", has_headers=False)

Multicore

Provides an elegant and memory-efficient way to process data using multiple cores. The main advantage of using do_multicore_work function over manually using concurrent.futures or multiprocessing modules is that new jobs are only submitted for execution when a CPU core is available. This optimises CPU and RAM usage. Using the aforementioned modules directly, it is all too easy to inadvarently cause memory leaks and crash the interpreter (if not the whole system).

Usage example (does not work in the interactive interpreter):

from jacktrade import do_multicore_work

def worker(first, second) -> tuple:
    """Receives two arguments and returns them as a tuple."""
    return (first, second)

def worker_done_callback(future):
    """Called whenever a worker process terminates and returns a result."""
    print(future.result())

if __name__ == "__main__":
    do_multicore_work(
        worker, args=[(1, 2), (3, 4), (5, 6)], worker_done_callback=worker_done_callback
    )    # Prints: (1, 2)\n(3, 4)\n(5, 6)\n

Pickler

This tiny module contains two convenience functions for pickling and unpickling Python objects, making it possible to do so with a single function call (a feature missing from pickle module):

from jacktrade import pickle_object, unpickle_object

pickle_object(obj := [1, 2, 3], filename := "obj.pickle")   # Pickles obj to obj.pickle file
assert unpickle_object(filename) == obj     # Unpickle obj.pickle and test equality with obj

Sysenv

Contains utilities for interacting with the operating system and the environment.

from jacktrade import in_virtual_environment

in_virtual_environment()  # True if called inside venv, else False