LOS — Language for Optimization Specification

LOS is a Language for Optimization Specification. It compiles human-readable model definitions into executable Python code (currently using PuLP as the primary engine), keeping your business logic clean and your data pipeline separate.

"Write Math, Run Python."

Installation

pip install los-lang

Or install from source:

git clone https://github.com/jowpereira/los.git
cd los
pip install -e .

Quick Start

1. Write a Model (`production.los`)

import "products.csv"
import "factories.csv"

set Products
set Factories

param Cost[Products]
param Capacity[Factories]

var qty[Products, Factories] >= 0

minimize:
    sum(qty[p,f] * Cost[p] for p in Products, f in Factories)

subject to:
    capacity_limit:
        sum(qty[p,f] for p in Products) <= Capacity[f]
        for f in Factories

2. Prepare Data

products.csv

Products,Cost
WidgetA,10
WidgetB,15

factories.csv

Factories,Capacity
Factory1,1000
Factory2,2000

3. Solve (`solve.py`)

import los

result = los.solve("production.los")

if result.is_optimal:
    print(f"Optimal Cost: {result.objective}")
    print(result.get_variable("qty", as_df=True))

Why LOS?

Feature	LOS	Raw PuLP/Pyomo
Readability	Whiteboard-like syntax	Python boilerplate
Data Binding	Native CSV imports	Manual DataFrame wrangling
Security	Sandboxed execution	Full Python access
Debug	Inspect generated code (`model.code()`)	Black box
Solver	CBC, GLPK, Gurobi, CPLEX (via PuLP)	Same
Backends	PuLP (Pyomo planned)	N/A

Advanced: Manual Data Binding

For dynamic data (APIs, databases), inject DataFrames directly:

import los
import pandas as pd

df = pd.DataFrame({"Products": ["A", "B"], "Cost": [10, 20]})

result = los.solve("model.los", data={"Products": df})

Documentation

Document	Description
User Manual	Full syntax reference and API guide
Security Policy	Sandbox details and threat model
Changelog	Version history
Backlog	Roadmap and future features
Contributing	How to contribute

License

MIT © Jonathan Pereira

Metadata

Release files for los-lang 3.3.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for los-lang 3.3.7
File	Size	Uploaded
los_lang-3.3.7.tar.gz	280.2 kB	Feb 20, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for los-lang 3.3.7
File	Interpreter	ABI	Platform	Reset
los_lang-3.3.7-py3-none-any.whl 78.7 kB Feb 20, 2026	Python 3	none	any	Details

Total release size: 358.9 kB

Release files / los_lang-3.3.7.tar.gz

Download URL	los_lang-3.3.7.tar.gz
Size	280.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`102ef55433f2ac2104dafed9f2008479177953e10a7723d5ff6572c170ff0d08`
BLAKE2b-256 checksum How to use checksums	`4994cedf2e91ecec9f9a0d01cd9ff6f0ed62f6f30a9f82af274d12e14d606937`
Upload date	Feb 20, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.7.9`

Release files / los_lang-3.3.7-py3-none-any.whl

Download URL	los_lang-3.3.7-py3-none-any.whl
Size	78.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`59800c82845fc199f9608826f408c5aa59f7e51f7353dac06b3d256ffc3706e3`
BLAKE2b-256 checksum How to use checksums	`16f48120d434bdb4e1db1d315b18403e7cab542567d22d1ef720eef2210de236`
Upload date	Feb 20, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.7.9`