semvec/pypi-packages huggingface dataset:
picarlo 0.4.5
Calculate Pi via the Monte Carlo method
Requires Python: >=3.9

# Calculating Pi with Modern Tooling

[![image](https://img.shields.io/pypi/v/picarlo)](https://pypi.org/project/picarlo/)
[![image](https://img.shields.io/pypi/l/picarlo)](https://pypi.org/project/picarlo/)
[![image](https://img.shields.io/pypi/pyversions/picarlo)](https://pypi.org/project/picarlo/)

We need a few things:
1. a CLI tool to specify the number of iterations and the number of cores it runs on:
`picarlo --cores 4 --iterations 10000`
2. a library that can be imported in a notebook

> [!CAUTION]
> The CLI requires a few more dependencies like `typer`

## Tooling setup (runs on each commit)
* [x] `uv self update` & `uv sync --group dev`
* [x] linting/format checking: `uv run ruff check`
* [x] auto-formatting: `uv run ruff format`
* [x] type checking: `uv run pyrefly check`
* [x] testing: `uv run pytest`
* [x] integrate into pre-commit `pre-commit run --all-files`
* [ ] use maturin framework to run MC via Rust
* [ ] build a GitHub actions pipeline to run prior to merge into `main` (and post to publish to PyPi)
* [ ] run tests in parallel
* [x] split between dev and prod dependencies: `uv add --group dev`
* [x] add a build system, hatchling in [pyproject.toml](pyproject.toml)
* [x] run a build `uv build`

## Checks
1. Check that the package can be installed: `uv run --with picarlo --no-project -- python -c "import picarlo"`

## Goal
1. [x] run from command-line (uvx)
2. [x] import lib into notebook (either via PyPi or from local dist)
3. [x] published module

## Useful stuff
1. Create docstrings via LLM
2. Create docs from docstrings
3. Calculate test coverage
4. Tracing
5. [server-sent event via starlette](https://github.com/sysid/sse-starlette)

## More interesting stuff
* parallelism, e.g. [concurrent.futures](https://docs.python.org/3/library/concurrent.futures.html) (see prime number example)
* bulk python version testing through tox

## Release preparation
1. **Generate Changelog:** `make changelog` (requires [Conventional Commits](https://www.conventionalcommits.org/))
2. **Bump Version:** Update `pyproject.toml` and `Cargo.toml`
3. **Build:** `make build`
4. **Publish:** `make publish`

## Commit Convention
This project uses Conventional Commits to automate changelog generation.
* `feat: ...` for new features
* `fix: ...` for bug fixes
* `docs: ...` for documentation
* `perf: ...` for performance improvements

Example: `git commit -m "feat: add parallel processing support"`

# [vscode extensions](.vscode/extensions.json)
1. Ruff
2. TOML syntax highlighting
total-perspective-vortex 3.1.3
A library for routing entities (jobs, users or groups) to destinations in Galaxy
Requires Python: >=3.9

[![Tests](https://github.com/galaxyproject/total-perspective-vortex/actions/workflows/tests.yaml/badge.svg)](https://github.com/galaxyproject/total-perspective-vortex/actions/workflows/tests.yaml)
[![Coverage Status](https://coveralls.io/repos/github/galaxyproject/total-perspective-vortex/badge.svg?branch=main)](https://coveralls.io/github/galaxyproject/total-perspective-vortex?branch=main)
[![Documentation Status](https://readthedocs.org/projects/total-perspective-vortex/badge/?version=latest)](http://total-perspective-vortex.readthedocs.org/en/latest/?badge=latest)

## <img src="https://raw.githubusercontent.com/galaxyproject/total-perspective-vortex/main/docs/images/tpv-logo-wide.png" width="800" height="100">

Total Perspective Vortex (TPV) provides an installable set of dynamic rules for
the [Galaxy application](https://galaxyproject.org/) that can route entities
(Tools, Users, Roles) to appropriate job destinations based on a configurable
YAML file.


### Documentation

Documentation on how to enable TPV in your Galaxy instance and configure the
relevant routing rules is available on [Read the
Docs](http://total-perspective-vortex.readthedocs.org/).

For a guided tutorial, also check out the [TPV tutorial on
GTN](https://training.galaxyproject.org/training-material/topics/admin/tutorials/job-destinations/tutorial.html).


### Installing latest released version

Once configured in the Galaxy application, TPV will be automatically installed
in the Galaxy environment. If you would like to manually install it instead, you
can do so from [PyPI](https://pypi.org/project/total-perspective-vortex/) with
the following command:

```python
pip install total-perspective-vortex
```


### Shared default resource database for tools

A shared database of reusable default resource requirements and scheduling rules
for TPV is maintained in: https://github.com/galaxyproject/tpv-shared-database/.
(This is likely something you want to take a look at - it will save a lot of
time.)
dragohan-grimoire 7.0.0
💀 AI Automation Grimoire v2.1.0 - PRODUCTION with Fixed Monarch System
Requires Python: >=3.8
Keywords: json, files, automation, api, ai, agents, langchain, llm, deepseek, shadow-monarch, lead-enrichment, data-processing

# DragoHan's Grimoire 🔥

My personal AI automation library - built by an automation god, for automation gods.

## Installation
```bash
pip install git+https://github.com/farhanistopG1/my_grimoire.git --break-system-packages
```

## Libraries

### JSON Mage
Master any JSON structure without bullshit.
```python
from json_mage import modify

# Convert any JSON to powerful object
response = api.get_data()
data = modify(response)

# Use it
print(data.first)
print(data.last)
print(data.get('email'))
print(data.all('name'))
```

### Simple File
File handling made stupid simple.
```python
import simple_file

# Save anything
simple_file.save('config', {'key': 'value'})

# Load anything
config = simple_file.load('config')

# Delete, rewrite, append
simple_file.delete('old_file')
simple_file.rewrite('note', 'New content')
simple_file.add('log', 'Error message')

# Check, list, backup
simple_file.exists('config')
simple_file.list_files()
simple_file.backup('important.json')
```

## Author

DragoHan - AI Automation God
mscore3 1.17.1
A python library for opening/inspecting/modifying MuseScore3 files.

# mscore3

A python library for opening/inspecting/modifying MuseScore3 files.

## Importing

The import name is "mscore"

## Simple usage

	from mscore import Score

	score = Score(argv[1])
	for part in score.parts():
		if part.name == "Harp":
			for channel in part.instrument().channels():
				channel.port = 4

## Formats

Reads/writes both .mscx and .mscz files.