semvec/pypi-packages huggingface dataset:
makeitaquote 1.1.0
DiscordBOT用のmake it a quote生成パッケージです
Requires Python: >=3.7
Keywords: discord, makeitaquote, image, generator, bot

# makeitaquote
Discord用Make it a Quote生成パッケージです。
使用しているAPIが死んだら終わりです()

## Installation

```bash
pip install makeitaquote
```

## 使い方
```python
from makeitaquote import MiQ
# ...
miq = (
    MiQ()
    .set_from_message(referenced_message, format_text=True)
    .set_color(True)
    .set_watermark(bot.user.name)
)
image_bytes = miq.generate_beta()
file = discord.File(io.BytesIO(image_bytes), filename="quote.png")
```
netseer 0.1.1
Predicting graph structure from a time series of graphs
Requires Python: >=3.12

# Netseer

## Predicting graph structure from a time series of graphs

This is a Python implementation of [*netseer*](https://arxiv.org/abs/2507.05806).  
Netseer is a tool that outputs a predicted graph based on a time series graph sequence

## Purpose

The goal of netseer is to predict the graph structure including new nodes and edges from a time series of graphs.  
The methodology is explained in the preprint (Kandanaarachchi et al. 2025).

## Authors

Stefan Westerlund: Created netseer code.
Brodie Oldfield: Packaging and docs.

## Installation

This package is available on PyPI, and can be installed with PIP or with a Package Manager:

``` Bash
pip install netseer # or uv add netseer
```

## Quick Example

Generating an example graph list:

``` Python
from netseer import generate_graph

graph_list = generate_graph.generate_graph_list()
```

The `generate_graph_list()` function has parameters for templating what types of graphs to generate. Information about these can be found in the reference docs.

Predicting on that graph:

``` Python
from netseer import prediction

predict = prediction.predict_graph(graph_list, h=1)
```

Increasing the `h` parameter increases how many steps into the future the prediction is, with `h=1` being 1 step in the graph sequence.

## References

Kandanaarachchi, Sevvandi, Ziqi Xu, Stefan Westerlund, and Conrad Sanderson. 2025.
  “Predicting Graph Structure via Adapted Flux Balance Analysis.” <https://arxiv.org/abs/2507.05806>.
dbt-databricks-runner 0.1.1
Add your description here
Requires Python: >=3.12

## Overview

This repository contains the dbt-runner to run dbt-spark on Databricks through:
- VSCode extension for local development
- Deployed with Databricks Job Clusters using spark session.

---

## Prerequisites

Before setting up, ensure you have the following tools installed:

- **Databricks CLI:** [Installation Guide](https://docs.databricks.com/aws/en/dev-tools/cli/install)
- **VSCode Databricks Extension:** Available via VSCode marketplace.
- **uv:** [Installation Guide](https://docs.astral.sh/uv/getting-started/installation/)

---
## Set-Up Instructions

1. **Install this repository with pip/uv**

3. **Configure Databricks CLI:**
    - Run `databricks configure --token` and follow prompts to set up authentication.
4. **Set up DBT profiles:**
    - Create or update your `~/.dbt/profiles.yml` with your Databricks connection details.
5. **Run feature preparation pipelines:**
    - Use `uv run dbt run` to execute pipelines.
    - Use `uv run dbt test` to validate models.

---