Convenient matrices and other improvements Python 3.5 brought us | Opensource.com

Convenient matrices and other improvements Python 3.5 brought us

Explore some of the underutilized but still useful Python features.

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This is the sixth in a series of articles about features that first appeared in a version of Python 3.x. Python 3.5 was first released in 2015, and even though it has been out for a long time, many of the features it introduced are underused and pretty cool. Here are three of them.

The @ operator

The @ operator is unique in Python in that there are no objects in the standard library that implement it! It was added for use in mathematical packages that have matrices.

Matrices have two concepts of multiplication; point-wise multiplication is done with the * operator. But matrix composition (also considered multiplication) needed its own symbol. It is done using @.

For example, composing an "eighth-turn" matrix (rotating the axis by 45 degrees) with itself results in a quarter-turn matrix:

import numpy

hrt2 = 2**0.5 / 2
eighth_turn = numpy.array([
    [hrt2, hrt2],
    [-hrt2, hrt2]
])
eighth_turn @ eighth_turn
    array([[ 4.26642159e-17,  1.00000000e+00],
           [-1.00000000e+00, -4.26642159e-17]])

Floating-point numbers being imprecise, this is harder to see. It is easier to check by subtracting the quarter-turn matrix from the result, summing the squares, and taking the square root.

This is one advantage of the new operator: especially in complex formulas, the code looks more like the underlying math:

almost_zero = ((eighth_turn @ eighth_turn) - numpy.array([[0, 1], [-1, 0]]))**2
round(numpy.sum(almost_zero) ** 0.5, 10)
    0.0

Multiple keyword dictionaries in arguments

Python 3.5 made it possible to call functions with multiple keyword-argument dictionaries. This means multiple sources of defaults can "co-operate" with clearer code.

For example, here is a function with a ridiculous amount of keyword arguments:

def show_status(
    *,
    the_good=None,
    the_bad=None,
    the_ugly=None,
    fistful=None,
    dollars=None,
    more=None
):
    if the_good:
        print("Good", the_good)
    if the_bad:
        print("Bad", the_bad)
    if the_ugly:
        print("Ugly", the_ugly)
    if fistful:
        print("Fist", fistful)
    if dollars:
        print("Dollars", dollars)
    if more:
        print("More", more)

When you call this function in the application, some arguments are hardcoded:

defaults = dict(
    the_good="You dig",
    the_bad="I have to have respect",
    the_ugly="Shoot, don't talk",
)

More arguments are read from a configuration file:

import json

others = json.loads("""
{
"fistful": "Get three coffins ready",
"dollars": "Remember me?",
"more": "It's a small world"
}
"""
)

You can call the function from both sources together without having to construct an intermediate dictionary:

show_status(**defaults, **others)
    Good You dig
    Bad I have to have respect
    Ugly Shoot, don't talk
    Fist Get three coffins ready
    Dollars Remember me?
    More It's a small world

os.scandir

The os.scandir function is a new way to iterate through directories' contents. It returns a generator that yields rich data about each object. For example, here is a way to print a directory listing with a trailing / at the end of directories:

for entry in os.scandir(".git"):
    print(entry.name + ("/" if entry.is_dir() else ""))
    refs/
    HEAD
    logs/
    index
    branches/
    config
    objects/
    description
    COMMIT_EDITMSG
    info/
    hooks/

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About the author

Moshe sitting down, head slightly to the side. His t-shirt has Guardians of the Galaxy silhoutes against a background of sound visualization bars.
Moshe Zadka - Moshe has been involved in the Linux community since 1998, helping in Linux "installation parties". He has been programming Python since 1999, and has contributed to the core Python interpreter. Moshe has been a DevOps/SRE since before those terms existed, caring deeply about software reliability, build reproducibility and other such things. He has worked in companies as small as three people and as big as tens of thousands -- usually some place around where software meets system administration...