semvec/pypi-packages huggingface dataset:
proq 0.0.4
Process Queue
Requires Python: >=3.9

# Process Queue

Simplify data processing using pipelines.

- Inline

```python
# Create the numbers 1-10, square them, then sum the results
result = proq.create(range(11)).map(lambda x: x ** 2).reduce(lambda x, y: x + y).next()
```

- Procedurally

```python
# Create a square numbers proq and split into two
data1, data2 = proq.create(range(11)).map(lambda x: x ** 2).tee()

# negate one of the streams
data1_neg = data1.map(lambda x: -x)

# add 1 to the other
data2_plus_1 = data2.map(lambda x: x + 1)

# Iterate over the results
for d1, d2 in zip(data1_neg, data2_plus_1):
    print(d1, d2, d1 + d2)
    assert d1 + d2 == 1
```

- In parallel

```python
# Get prime numbers under 1,000,000 - calculation happens concurrently
primes = (
    proq.create(range(1000001))
        .par_map(lambda x: x, is_prime(x))
        .filter(lambda x, is_prime: is_prime)
        .map(lambda x, is_prime: x)
        .collect()
)
```

# Installation

```bash
pip install proq
```

# Development

- Download source
- Install development dependencies: `flit install -s --deps develop`
- Format code: `black .`
- Run tests: `pytest`
- Bump version in `src/proq/__init__.py`
- Build package: `flit build`
- Deploy: `flit publish`
whila 0.1.0
WhiLa (Whisper-to-LaTeX) connects tools to convert spoken mathematics into LaTeX code. It includes a Speech-To-Text (STT) layer using OpenAI's whisper model and a Math-To-LaTeX (MTL) layer to render mathematics in LaTeX. The MTL layer is a Large-Language Model (LLM) for converting spoken math to legible LaTeX code. WhiLa aims to bridge the gap between writing math and the digital world, particularly for education and those unable to use conventional math writing techniques.
Requires Python: <4.0,>=3.10

# WhiLa
A Speech-to-Math software using AI