Boost Your Python Workflow: Sublime Build System, iTerm2 Image Viewer, and Typing Tricks

This article presents three useful utilities that can improve your development efficiency: enabling Python execution in Sublime Text, viewing images directly in iTerm2 on macOS, and an overview of Python's typing module.

Enable Python execution in Sublime Text

To run Python scripts from Sublime Text, open Tools → Build System → New Build System... and replace the default content with the following JSON configuration:

{
    "cmd": ["D:/Anaconda3/python3.7.exe", "-u", "$file"]
}

Save the file, then open a Python script (e.g., test.py) and press Ctrl+B or select Tools → Build to execute it. The output will appear in the Sublime console.

View images directly in iTerm2 on macOS

The iTerm2 terminal supports inline image display via the imgcat utility. Create a shell script named imgcat.sh following the instructions at iTerm2 imgcat , make it executable with chmod u+x imgcat.sh, and then run the script to display images in the terminal.

Python typing module overview

The typing module provides optional type hints for Python code, enabling static type checking, improving documentation, and offering non‑intrusive runtime warnings.

Type checking to prevent mismatched parameter and return types.

Documentation aid for developers.

Does not affect program execution; only provides hints.

Example: a function that sums the digits of a numeric string.

from typing import *

def digits_sum(num: str) -> int:
    digits_arr = map(lambda x: int(x), num)
    return sum(digits_arr)

num = "352"
result = digits_sum(num=num)
print(result)  # 10

Another example: a function that multiplies numeric values in a dictionary by two.

from typing import Dict, Any

def dict_multipy(d: Dict[str, Any]) -> Dict[str, float or int]:
    new_dict = {}
    for k, v in d.items():
        if isinstance(v, (float, int)):
            new_dict[k] = v * 2
    return new_dict

d = {"no": "100", "age": 12, "work_year": 3, "name": "JC"}
new_d = dict_multipy(d=d)
print(new_d)  # {'age': 24, 'work_year': 6}

Creating type aliases, e.g., defining Vector as List[float] and a scaling function:

from typing import List

Vector = List[float]

def scale(scalar: float, vector: Vector) -> Vector:
    return [scalar * num for num in vector]

new_vector = scale(2.0, [1.0, -4.2, 5.4])