semvec/pypi-packages huggingface dataset:
trenchcoat 0.0.2
Small example for security research
Requires Python: >=3.8

# Trenchcoat

This is a super simple package, created following the [packaging projects tutorial](https://packaging.python.org/en/latest/tutorials/packaging-projects/).

It just contains a function to add 1 to an input number.

The objective of this package is to be used in security research, trying to create situations where it is added to projects unbeknownst to their developers.

Check [github.com/vicenteherrera/starter-ml](https://github.com/vicenteherrera/starter-ml) for more information.

Visit my webpage [vicenteherrera.com](https://vicenteherrera.com) to learn more about my work on cybersecurity, Kubernetes and AI.
juicy-serato 0.0.2
Write song Cue data from the DJuced database to audio files in a Serato-compatible fromat
Requires Python: >=3.8

## JuicedSerato

> Developed by [Davis_Software](https://pages.software-city.org/d) © 2024

This Project aims to enable users of the DJuiced DJ Software to write their configured track cues into the ID3 tags of the track files
with the Serato GEOB format. This will essentially transfer all cues into Serato.

---
### Installation
You can Install JuicySerato through multiple sources
* Via Python Package Index
  * `pip install juicy_serato`
* Download an executable
  * Go to GitHubs Release section and download the correct executable file for your os.

---
### Usage
* Inject Serato ID3 tags in-place:
  * `juicy_serato -b <path to djuced db> inject`
* Copy all Library files to a target folder and inject the ID3 tags into these:
  * `juicy_serato -b <path to djuced db> copy <target path>`

---
#### used resourced / open-source projects
* https://github.com/Holzhaus/serato-tags
* https://github.com/Holzhaus/serato-tags/blob/main/docs/fileformats.md
* https://github.com/DJUCED/DJUCED_DJ/blob/main/doc/meta-tags.md

---
### Development
* Generate virtual env `python -m venv .venv`
* Activate the venv
* Install packages `pip install -r requirements.txt`
* Install the package in edit mode `pip install -e .`
* Build with `python -m build`
dbt-postgres-python 1.7.17
Run python scripts from any dbt project. This project is based on the project https://github.com/fal-ai/fal initially authored by FAL.AI.
Requires Python: <4.0,>=3.8
Keywords: dbt, pandas, fal, runtime

# Welcome to dbt-postgres-python 👋 do more with dbt

dbt-postgres-python adapter is the ✨easiest✨ way to run your [dbt Python models](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/python-models).

Starting with dbt v1.3, you can now build your dbt models in Python. This leads to some cool use cases that was once difficult to build with SQL alone. Some examples are:

- Using Python stats libraries to calculate stats
- Building forecasts
- Building other predictive models such as classification and clustering

This is fantastic! BUT, there is still one issue though! There is no Python support for Postgres. dbt-postgres-python provides the best environment to run your Python models that works with Postgres!

## Project background

This project is based off the [dbt-fal](https://github.com/fal-ai/dbt-fal) project. Thank you [FAL-AI](https://github.com/fal-ai) for starting that project.
Priorities change and FAL-AI in April, 2024 chose to archive that project and focus their efforts elsewhere.

My team at work used dbt-fal, so that we could run python code on postgres. As dbt-fal was not being kept up-to-date with DBT,
I chose to pick up that project and bring it up-to-date. As a result, this project will only support Postgres going forward.
Support for the FAL CLI has also been dropped.

## Getting Started

### 1. Install dbt-postgres-python
`pip install dbt-postgres-python`

### 2. Update your `profiles.yml` and add the fal adapter

```yaml
jaffle_shop:
  target: dev_with_fal
  outputs:
    dev_with_fal:
      type: fal  # "fal" type is kept for backwards compatibility
      db_profile: dev_postgres # This points to your main adapter
    dev_postgres:
      type: postgres
      ...
```

Don't forget to point to your main adapter with the `db_profile` attribute. This is how the fal adapter knows how to connect to your data warehouse.

### 3. Add your python models

Add in your python models to your project just as you would your SQL models. Follow DBT's [instructions](https://docs.getdbt.com/docs/build/python-models) 
on how to do so.

```python
# a_python_model.py

import ...

def model(dbt, session):

    my_sql_model_df = dbt.ref("my_sql_model")

    final_df = ...  # stuff you can't write in SQL!

    return final_df
```

### 4. `dbt run`!
That is it! It is really that simple 😊