# Time to BOTEC

## About

This repository contains example of very simple code to manipulate samples in various programming languages. It implements this estimate:

```
p_a = 0.8
p_b = 0.5
p_c = p_a * p_b

dists = [0, 1, 1 to 3, 2 to 10] # each dist represented as 1M samples
weights = [(1 - p_c), p_c/2, p_c/4, p_c/4 ]

result = mixture(dists, weights)
mean(result)
```

As of now, it may be useful for checking the validity of simple estimations. The title of this repository is a pun on two meanings of "time to": "how much time does it take to do x", and "let's do x".

## Current languages

- [x] C
- [x] Javascript (NodeJS)
- [x] Squiggle 
- [x] R
- [x] Python
- [x] Nim 

## Performance table

With the [time](https://man7.org/linux/man-pages/man1/time.1.html) tool, using 1M samples:

| Language             | Time      |
|----------------------|-----------|
| Nim                  | 0m0.153s  |
| C                    | 0m0,442s  |
| Node                 | 0m0,732s  |
| Squiggle             | 0m1,536s  |
| R                    | 0m7,000s  |
| Python (CPython)     | 0m16,641s |

I was very surprised that Node/Squiggle code was almost as fast as the raw C code. For the Python code, it's possible that the lack of speed is more a function of me not being as familiar with Python. It's also very possible that the code would run faster with [PyPy](https://doc.pypy.org).

I was also really happy with trying [Nim](https://nim-lang.org/). The version which beats all others is just the fastest "danger" compilation of Nim (the "release" compilation is 0m0.183s instead). The Nim version has the particularity that I define the normal function from scratch, using the [Box–Muller transform](https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform#Basic_form). For Nim I also have a version of the code which takes around 4 seconds, where I define some very inefficient sine & logarithm functions to feed into the Box-Muller method, because it felt like fun to really write a botec tool really from scratch.

## Languages I may add later

- [ ] Julia (TuringML) 
- [ ] Rust
- [ ] Lisp
- [ ] Stan
- [ ] Go 
- [ ] Zig
- [ ] Forth
- ... and suggestions welcome

## Roadmap

The future of this project is uncertain. In most words, I simply forget about this repository.

To do:
- [ ] Check whether the Squiggle code is producing 1M samples. Still not too sure.
- Differentiate between initial startup time (e.g., compiling, loading environment) and runtime. This matters because startup time could be ~constant, so for larger projects only the runtime matters.

## Other similar projects

- Squigglepy: <https://github.com/rethinkpriorities/squigglepy>
- Simple Squiggle: <https://github.com/quantified-uncertainty/simple-squiggle>