semvec/pypi-packages huggingface dataset:
repolyze 0.1.0
Repo statistics
Requires Python: >=3.9
Keywords: repo, repo statistics, repolyze, statistics, stats

# Repo Stats

A Python CLI tool for analyzing and reporting statistics about code repositories.

## Features
- Scans local directories for repository statistics
- Provides human-readable and tree-formatted outputs
- Modular core for filesystem, formatting, and stats
- Easily extensible for new analysis types


## Installation
Clone the repository and install dependencies:
```bash
pip install repolyze
```

## Usage
Run the CLI from the repo directory to analyze a repository:
```bash
repolyze
```
memory-allocator 0.2.0
An extension class to allocate memory easily with cython
Requires Python: >=3.12

# MemoryAllocator
An extension class to allocate memory easily with cython.

This extension class started as part of the [Sage](https://sagemath.org) software.

It provides a single extension class `MemoryAllocator` with `cdef` methods

- `malloc`,
- `calloc`,
- `allocarray`,
- `realloc`,
- `reallocarray`,
- `aligned_malloc`,
- `aligned_calloc`,
- `aligned_allocarray`.

Memory is freed when the instance of `MemoryAllocator` is deallocated.
On failure to allocate the memory, a proper error is raised.

## Building from source

This project uses [meson-python](https://mesonbuild.com/meson-python/) for
its build backend. A typical editable install looks like:

```shell
  python -m pip install -e .
```

# Changelog

## 0.1.5
- Migrate build system to Meson.
- Require Python >= 3.12.


## 0.1.4

- Modernize Python metadata, require Python >= 3.8.
  [#9](https://github.com/sagemath/memory_allocator/pull/9) by @mkoeppe
- Add missing `noexcept` clauses for Cython 3
  [#8](https://github.com/sagemath/memory_allocator/pull/8) by @tornaria

## 0.1.3

- Python 3.11 compatible.

## 0.1.2

- Provide wheels build by github actions.

## 0.1.1

- Fixed doctests on 32bit systems.
pinkfish777 0.1.6
Add your description here
Requires Python: >=3.13

pinkfish
======     
A backtester and spreadsheet library for security analysis.

Why another python backtesting library?  How is pinkfish different?
Simple, I couldn't find a python backtesting library that allowed me to backtest intraday strategies with daily data.  Even simple strategies like 'buying on the close' on the SAME day a 'new 20 day high is set' were not allowed.  Additionally, I was put off by the complexity of some of the libraries available, and wanted something simple, that doesn't get in the way, and just allows me to test my trading ideas.  One user commented that Pinkfish is "very lightweight and to the point".  I didn't set out to write a new backtesting library, but I had to.  Daily data is free; minute and tick data are typically not.  Using minute and tick data can require hours to run vs seconds for daily data.

Some of the key features of pinkfish:
 - leverages pandas for dataframe, spreadsheet like features
 - comes batteries included with the most common technical indicators
 - also integrates with ta-lib or pandas_ta for more advanced indicators
 - leverages matplotlib for making financial graphs
 - uses daily data (vs minute or tick data) for intraday trading
 - uses free daily data from yahoo finance
 - simple to use python API
 - backtest single stock/ETF strategy or a portfolio (basket of stocks/ETFs)
 - backtest short selling strategies and simulate trading with margin
 - write optimizers to select the best parameters
 - create spreadsheets within Jupyter Notebook by utilizing pandas dataframes and itable formatting

## Installation
For basic installation of the pinkfish library (not including ta-lib):  
```
# Note: It's recomended to create a new python virtual environment.

$ git clone https://github.com/fja05680/pinkfish.git
$ cd pinkfish
$ python -m venv venv
$ pip install setuptools
$ . venv/bin/activate
$ python setup.py install (or develop)
```

--OR--  

For developer installation with ta-lib, follow the instructions located at:  
https://fja05680.github.io/pinkfish/  

## Examples
 - [buy-and-hold](https://fja05680.github.io/pinkfish/examples/buy-and-hold.html) - basic buy and hold strategy
 - [golden-cross](http://fja05680.github.io/pinkfish/examples/golden-cross.html) - classic long term trading algorithm
 - [spreadsheet](https://fja05680.github.io/pinkfish/examples/spreadsheet.html) - read only spreadsheet within jupyter notebook

Many more examples in `examples` folder.  They are roughly ordered by complexity beginning with the easiest.
 
## Documentation
The pinkfish API documentation:
https://fja05680.github.io/pinkfish/docs/html/pinkfish/index.html

## Pinkfish on youtube
https://www.youtube.com/channel/UCsPHH2UBn8Fz0g0MGrZ2Ihw
metarace-tagreg 1.0.6
Transponder allocation tool
Requires Python: >=3.11
Keywords: transponder

# metarace-tagreg

Read transponder ids from a connected decoder.

![tagreg screenshot](screenshot.png "tagreg")

## Requirements

   - Python >= 3.11
   - Gtk >= 3.0
   - metarace >= 2.1.14


## Installation

Use shared installer from metarace to collect requirements
and install with roadmeet and trackmeet:

	$ wget https://github.com/ndf-zz/metarace/raw/refs/heads/main/metarace-install.sh
	$ sh metarace-install.sh

Alternatively, install system requirements and use pip:

	$ sudo apt-get install python3-venv python3-pip python3-cairo \
	python3-gi python3-gi-cairo gir1.2-gtk-3.0 gir1.2-rsvg-2.0 \
	gir1.2-pango-1.0
	$ mkdir -p ~/Documents/metarace
	$ python3 -m venv --system-site-packages ~/Documents/metarace/venv
	$ ~/Documents/metarace/venv/bin/pip install metarace-tagreg
	$ ~/Documents/metarace/venv/bin/tagreg