semvec/pypi-packages huggingface dataset:
moneyline 0.6.0
Python package meant to ease the use of the LiveOdds API to find arbitrage opportunities
Requires Python: >=3.6

# Moneyline 
Moneyline is a python package meant to help detect arbitrage opportunities in sports (only for moneyline bets). 


FOR MONEYLINE BETS, run these functions sequentially to get the final dataframe that is structured to detect arbitrage and non-arbitrage opportunities

fetch_and_process_ml_data(api_key, sports, regions), where the api key is a string, and sports and regions are lists of strings; returns df

group_event_ml(df)

find_arb_ml(result_df)



FOR SPREADS AND TOTALS follow the exact same sequence of functions, but call:

fetch_and_process_spreads_totals_data(api_key, sports, regions)

group_event_spreads_totals(df)

find_arb_spreads_totals(df)



FOR PLAYER PROPS

fetch_player_props(api_key)

group_player_odds(df)

find_arb_player(df)


Player Prop Markets are built in and include:
SPORTS_MARKETS = {
        'basketball_nba': 'player_points,player_blocks,player_assists,player_steals,player_rebounds',
        'icehockey_nhl': 'player_points,player_goals,player_assists',
        'soccer_epl': 'player_shots,player_assists,player_shots_on_target',
        'soccer_france_ligue_one': 'player_shots,player_assists,player_shots_on_target',
        'soccer_germany_bundesliga': 'player_shots,player_assists,player_shots_on_target',
        'soccer_italy_serie_a': 'player_shots,player_assists,player_shots_on_target',
        'soccer_spain_la_liga': 'player_shots,player_assists,player_shots_on_target',
        'soccer_usa_mls': 'player_shots,player_assists,player_shots_on_target'
    }



FOR AUTOMATED EMAIL sending, use:

send_arb_email(email_body)


## Installation 
```bash 
pip install moneyline
uvrun 0.1.5
Run Python scripts with inline metadata directly from URLs
Requires Python: >=3.11

# uvrun

Run Python scripts with inline metadata directly from URLs. This simple tool integrates with [uv](https://github.com/astral-sh/uv) to provide a seamless experience for running Python scripts from GitHub repositories.

## Features

- Run Python scripts directly from GitHub repositories
- Manage multiple script repositories
- Smart script discovery with inline metadata
- Pass arguments directly to scripts and uv
- List available scripts with a nice directory structure

## Installation

```bash
pip install uvrun
```

## Usage

Add a repository:

```bash
uvrun --add https://github.com/username/repo
```

List available scripts:

```bash
uvrun --list
```

Run a script:

```bash
uvrun script_name arg1 arg2
```

(Note: both script_name and script_name.py are valid)

With specific Python version:

```bash
uvrun script_name --uv-args "--refresh"
```

## Script Metadata

To make a script discoverable by uvrun, add the following [inline metadata](https://peps.python.org/pep-0723/) at the top of the file:

```python
# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "flask>=2.0.0",
#     "requests>=2.31.0",
# ]
# ///
```

## License

[MIT License](LICENSE)
oct-tissuemasking 0.0.8
A PyTorch based package for automated OCT tissue masking.
Requires Python: ~=3.9

# oct_tissuemasking
This package contains a basic 3D UNet and patching scripts to create training data.

# 1 Installation

## 1.1 Create a new mamba environment

Create a new mamba environment called oct_tissuemasking with python 3.9.

```
mamba create -n oct_tissuemasking python=3.9
mamba activate oct_tissuemasking
```

## 1.2 Install dependencies

We will need to install synthspline for vasculature synthesis.

```
pip install git+https://github.com/balbasty/synthspline.git#f78ba23
```

## 1.3 Set cuda parameters

We need to identify and set our cuda version to make sure we install the right prebuilt wheel for cupy.

```
export CUDA_VERSION=<cuda-version>
```

## 1.4 Install oct_tissuemasking

This will take a while so we will set the timeout for a 20,000 seconds!

```
pip install oct_tissuemasking --default-timeout=20000
```