semvec/pypi-packages huggingface dataset:
atmose 0.0.38
ATMOSE- Automatic Topic Modeling Over Semantic Embeddings
License: MIT
Requires Python: <4.0,>=3.10

# ATMOSE
ATMOSE- Automatic Topic Modeling Over Semantic Embeddings


## What it does
This package is a tool for quick EDA of emergent topics among your semantic embeddings.
### Problem
- I have all these embedding vectors. What are the general topics that emerge from them?
### Solution
- ATMOSE will cluster your embeddings and then label the clusters using an LLM.
- Instead of throwing a random sample of embeddings from each cluster at an LLM, ATMOSE uses stratified sampling and refinement to ensure that the topic labels balance generalization and specificity.

## Algorithms (more coming soon)
- UMAP
- HDBSCAN
- TOPSIS

## LLM agnostic (coming soon)
- All LLMs are supported via LiteLLM

## Experiment tracking (coming soon)
- MLflow
- Dim reduction and clustering Model serialization and versioning
- Helicone tracking (optional)

## C++ compiler required

Before installing ATMOSE, ensure you have:

- Windows: Microsoft Visual C++ Build Tools

- Linux: GCC/G++ compiler (`sudo apt-get install build-essential` on Ubuntu)

- macOS: Xcode Command Line Tools (`xcode-select --install`)

## Tip for building in Docker
Add this to your dockerfile:
```Dockerfile
RUN apt-get update && apt-get install -y \
    curl \
    build-essential \
    gcc \
    g++ \
    libpq-dev \
    libx11-dev \
    libxrandr-dev \
    libxext-dev \
    libxi-dev \
    libgl1-mesa-dev \
    && rm -rf /var/lib/apt/lists/*

ENV POETRY_VERSION=1.8.2
RUN curl -sSL https://install.python-poetry.org | python3 -
ENV PATH="/root/.local/bin:$PATH"
```

## Notebook demo


## Quickstart


## Number of LLM calls
- 2 LLM calls per cluster


## Instructions for use
Assume df has columns: 
- content_str
- embedding_vector

Gets additional columns after applying .label(df, data_description)
- cluster_id
- topic_label
- membership_score
- outlier_score
- silhouette_score
- reduced_vector
python-boiler-controller 0.5.0
A sample cli app to control boiler state.
License: MIT
Requires Python: >=3.9

# Boiler Controller

A comprehensive boiler startup controller using Python. The application will simulate the operations of a boiler system, reflecting real-world scenarios with detailed state transitions, timers, and logging capabilities.

## Application Flow

Initially, the boiler will be in lockout state with interlock switch being open. To start the boiler, first toggle the interlock switch and start the boiler.

Boiler will enter into 3 stages for start sequence. The first stage will be pre-purge phase which will happen for 10 seconds, and then initiate phase for 10 seconds. After that, boiler will be in operational state.

We can also stop the sequence in the middle by pressing Ctrl-C during the stages.