startrace 1.0.1

A lightweight string pattern tool

Star Trace

A program originally created by William Coker for ACU Atom Smashers

Installation

Pypi (pip)

This project is on the python install index, meaning you may install it via pip:

pip install startrace

Release

There are pre-built .whl files in the releases section for this project on github available for download. Once downloaded, call

pip install /location/of/your/file/startrace-x-x-x-py3-none-any.whl

Build from source

Alternatively you may also clone this repository using

git clone https://github.com/wdc756/StarTrace.git

Then you may make any changes you'd like, and call

python -m build

Note: This is assuming you already have build, setuptools, and wheel installed. If this is not the case run pip install build setuptools wheel first. Additionally there is already a file with unit tests in the test/ dir. If you wish to run unit tests before building, run pytest (Assuming it's already installed)

Usage

StarTrace is a lightweight python module aimed to help manage similarly-named files or objects. Main usage generally follows this flow:

# To cut down on verbosity, it's easier to just import everything 
from startrace import *
import numpy as np

# Create pattern via Token list
file_pattern = Pattern([
    Token('syn ', 1, Iter(1, 3, 1)),
    Token('.npy')
])

# While the pattern can increment, load each numpy file
while True:
    print(file_pattern.get_pattern())
    values = np.load(file_pattern.get_pattern())

    if not values.increment():
        break

This example code will create a file pattern that will load the following files:

syn 1.npy
syn 2.npy
syn 3.npy

Iters

Iters are the most basic dataclass included in StarTrace. They hold three Numbers: start, end, and step. These values are then used when Pattern.increment() is called to either increment the number or through a list of strings

Tokens

Tokens are the main dataclass you will interact with when creating Patterns, and they can be created using several different arguments.

from startrace import *
import numpy as np

# This will create a token that iterates over numbers 1-10
Token('', 1, Iter(1, 10, 1))
# This will also create a token iterating over 1-10
Token('', 1, (1, 10, 1))
# This creates a Token iterating over 1-9, counting by 3 -> (1, 3, 9)
Token('', 1, (1, 9, 3))

# This creates a Token that iterates over the string list -> 'test', 'hello', 'world'
Token(['test', 'hello', 'world'])
# This creates a Token that iterates over the int values in the list -> 1, 3, 4, 2, 5
list = [1, 3, 4, 2, 5]
Token(list)
# There's also numpy support, so you can enter 1D np.ndarrays for phrases and Token will interpret it
npList = np.arange(1, 10, 1)
Token("I'm a numpy value:", npList)

Patterns

Patterns are the high-level dataclass you'll interact with most once created. It has two only two functions: get_pattern() and increment(). Get Pattern returns a string stitching all Tokens together with their current number/str values, and Increment moves all numbers up by one value, one Token at a time. For example

from startrace import *

pat = Pattern([
    Token('syn', 1, (1, 2, 1)),
    Token('_percent', 1, (1, 3, 1))
])

while True:
    print(pat.get_pattern())
    if not pat.increment():
        break

will print

syn1_percent1
syn1_percent2
syn1_percent3
syn2_percent1
syn2_percent2
syn2_percent3

License

This project is under the GNU General Public License, feel free to modify or distribute this code however you see fit