semvec/pypi-packages huggingface dataset:
poisson-odds 0.1.3
A Python library designed for developers and analysts to calculate betting odds with ease using the power of Poisson distribution based on team qualities.
Requires Python: >=3.6

# Odds by Poisson Distribution
This library enables the calculation of betting odds (moneyline, handicaps, totals) based on team qualities using the Poisson distribution.

It provides a simple interface for initializing calculations, requiring only the qualities of both teams. These qualities can be obtained using metrics such as expected goals (xG) or average number of goals.

## Installation
```
pip install poisson-odds
```

## Usage Example
```
from poisson_odds import *

test = Poisson(1.1, 2.1)

test.print_probability_table_goal_draws()
### Output:
```
![Probability Table](https://github.com/nikolaitolmachev/poisson_odds/blob/master/src/prob_table.jpg?raw=true)
```
print(test.moneyline)

### Output:
```
```
1-X-2: 5.291-4.878-1.669
```
```
handicaps = test.calculate_handicap_odds_by_Poisson()
print('\n'.join([str(items) for key, items in handicaps.items()]))
### Output:
```
```
...
0.75: 2.206 / 1.879
1.0: 1.918 / 2.089
1.25: 1.75 / 2.403
1.5: 1.582 / 2.717
...
```
```
totals = test.calculate_total_odds_by_Poisson()
print('\n'.join([str(items) for key, items in totals.items()]))
### Output:
```
```
...
Over/Under 2.75: 1.788 / 2.333
Over/Under 3: 1.961 / 2.04
Over/Under 3.25: 2.243 / 1.848
Over/Under 3.5: 2.525 / 1.656
...
```

## Demonstrating
A [real-world example](https://github.com/nikolaitolmachev/poisson_odds_demo) of using the 'poisson_odds' library to analyze actual NHL odds by comparing them to probabilities based on expected goals (xG).
skit-game 0.1.7
A tool for prototyping card games
Requires Python: >=3.11

# Skit

A tool for prototyping card games.

Inspired by [squib][squib], but I could never quite get my head around Ruby.

[squib]: https://github.com/andymeneely/squib

## Installation

PyPI didn't like the name `skit`, so it's called `skit-game`:

```ShellSession
pip install skit-game
# or
poetry add skit-game
```

But the package inside is just called `skit`.

## Usage

See the [examples](https://github.com/vtbassmatt/skit/blob/main/examples) for basic usage.

```python
from skit import Deck

d = Deck(3) # make a deck of 3 cards, default size
d.background('#cccccc') # set a gray background
d.render_png('basic_{index}.png') # render basic_0.png, basic_1.png, and basic_2.png
```

## Docs

API documentation is available at https://vtbassmatt.github.io/skit/.

## Credits

- Andy Meneely for the idea, and for the awesome Squib!
- game-icons.net for icons used in the examples
mlchatbotai 0.10
A simple AI chatbot package
Requires Python: >=3.6

# MLChatbotAI

MLChatbotAI is a Python package that provides tools for building, training, and deploying machine learning-based chatbots.

## Features

- Build and train chatbot models using custom or inbuilt intents data.
- Flexible preprocessing tools for text data.
- Integration support for various applications.

## Installation

You can install MLChatbotAI using pip:

```bash
pip install MLChatbotAI
```
# This code for use the pre exits model 
```bash
from MLChatbotAI import ChatBot

bot = ChatBot()

user_input = bot.take_command()

response = bot.get_response(user_input)

print(response)
```

# This portion use for models which is train on external data
```bash
custom_model_path = "path/to/custom_model.h5"
custom_words_path = "path/to/custom_words.pkl"
custom_classes_path = "path/to/custom_classes.pkl"

custom_model, custom_words, custom_classes = trainer.load_custom_model(custom_model_path, custom_words_path, custom_classes_path)
user_input_custom = "How are you?"
response_custom = trainer.get_response_using_model(custom_model, custom_words, custom_classes, user_input_custom)
print(response_custom)
```
# Train Chatbot with external data

# This for how can train model with pre built data
```bash
from MLChatbotAI import ChatBotTrainer

trainer = ChatBotTrainer()

trained_model_default, words_default, classes_default = trainer.train_default()
```
# This is example of train model on custom data wihtout giving no path
```bash
from MLChatbotAI import ChatBotTrainer

trainer = ChatBotTrainer()

custom_intents_data = {
    "intents": [
        {"tag": "greeting", "patterns": ["Hi", "Hello", "Hey"], "responses": ["Hello! How can I help you?"]},
        # Add more custom intents as needed
    ]
}
trained_model_custom, words_custom, classes_custom = trainer.train_custom(custom_intents_data)
```

# This is code for train model with giving path
```bash
from MLChatbotAI import ChatBotTrainer

trainer = ChatBotTrainer()

file_path = "path/to/custom_intents_file.json"
trained_model_file, words_file, classes_file = trainer.train_from_file(file_path)
```
glvrd 0.0.5
Клиент к сервису glvrd.ru
Requires Python: >=3.8

# glvrd - неофициальный клиент к сервису glvrd.ru

[![Downloads](https://static.pepy.tech/badge/glvrd/month)](https://pepy.tech/project/glvrd)
[![Downloads](https://static.pepy.tech/badge/glvrd)](https://pepy.tech/project/glvrd)
[![codecov](https://codecov.io/gh/pomponchik/glvrd/graph/badge.svg?token=LVGTo8aGM2)](https://codecov.io/gh/pomponchik/glvrd)
[![Hits-of-Code](https://hitsofcode.com/github/pomponchik/glvrd?branch=main)](https://hitsofcode.com/github/pomponchik/glvrd/view?branch=main)
[![Tests](https://github.com/pomponchik/glvrd/actions/workflows/tests_and_coverage.yml/badge.svg)](https://github.com/pomponchik/glvrd/actions/workflows/tests_and_coverage.yml)
[![Python versions](https://img.shields.io/pypi/pyversions/glvrd.svg)](https://pypi.python.org/pypi/glvrd)
[![PyPI version](https://badge.fury.io/py/glvrd.svg)](https://badge.fury.io/py/glvrd)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

Это неофициальный и не production-ready клиент для замечательного сервиса проверки текстов - glvrd.ru. Работает в обход API, так что на особую скорость советую не расчитывать.

Устанавливается так:

```bash
pip install glvrd
```

Пример кода:

```python
from glvrd import GlvrdClient

client = GlvrdClient()
text = 'Это неофициальный и не production-ready клиент для замечательного сервиса проверки текстов - glvrd.ru. Работает в обход API, так что на особую скорость советую не расчитывать.'

def print_estimate(estimate, what_is_it):
  print(f'{what_is_it}: {estimate.estimate}/10')
  for error_name, examples in estimate.errors.items():
    print(f'{error_name}:')
    for example in examples:
      print(f'\t{example}')
  print()

print_estimate(client.estimate_clarity(text), 'Чистота')
print_estimate(client.estimate_readability(text), 'Читаемость')
```

... выдаст что-то вроде:

```
Чистота: 8.1/10
Необъективная оценка:
	замечательного
Усилитель:
	особую

Читаемость: 8.8/10
В начале предложения нет глагола:
	Это неофициальный и не production-ready клиент для замечательного сервиса
Подозрение на парцелляцию:
	Работает в обход
```
fennel-invest-api 1.2.0
Unofficial Fennel.com Invest API written in Python Requests

# Unofficial Fennel Invest API

This is an unofficial API for Fennel.com. It is a simple Python wrapper around the Fennel.com GraphQL API. It is not affiliated with Fennel.com in any way.

Fennel does everything via GraphQL, so yes, this is very slow.

This is still a work in progress, so it will have bugs and missing features. Please feel free to contribute!

## Important!
Do not use any version of this library before 1.0.9. Earlier versions had a bug with placing certain orders that when executed excessively could lead to your account being locked. This has been fixed in 1.0.9. See [this issue](https://github.com/NelsonDane/fennel-invest-api/issues/14)

## Installation

```bash
pip install 'fennel-invest-api>=1.1.0'
```

## Usage: Logging In

```python
from fennel_invest_api import Fennel

fennel = Fennel()
fennel.login(
    email="your-email@email.com",
    wait_for_code=True # When logging in for the first time, you need to wait for email 2FA
)
```

If you'd like to handle the 2FA yourself programmatically instead of waiting for `input()`, you can call it with `wait_for_code=False`, catch the 2FA exception, then call it again with the 2FA code:

```python
fennel.login(
    email="your-email@email.com",
    wait_for_code=False
    code="123456" # Should be six-digit integer from email
)
```

## Usage: Get Stock Holdings
```python
account_ids = fennel.get_account_ids()
for account_id in account_ids:
    print(account_id)
    positions = fennel.get_stock_holdings(account_id)
    for position in positions:
        print(position)
```

## Usage: Get Portfolio
```python
# For all account IDs
portfolio = fennel.get_account_ids()
for account_id in account_ids:
    print(account_id)
    portfolio = fennel.get_portfolio_summary(account_id)
    print(portfolio)
# For a single account ID
portfolio = fennel.get_portfolio_summary(account_id)
print(portfolio)
```

## Usage: Placing Orders
```python
order = fennel.place_order(
    account_id=account_id,
    ticker="AAPL",
    quantity=1,
    side="buy", # Must be "buy" or "sell"
    price="market" # Only market orders are supported for now
    dry_run=False # If True, will not actually place the order
)
print(order)
```

## Contributing
Found or fixed a bug? Have a feature request? Feel free to open an issue or pull request!

Enjoying the project? Feel free to Sponsor me on GitHub or Ko-fi!

[![Sponsor](https://img.shields.io/badge/sponsor-30363D?style=for-the-badge&logo=GitHub-Sponsors&logoColor=#white)](https://github.com/sponsors/NelsonDane)
[![ko-fi](https://img.shields.io/badge/Ko--fi-F16061?style=for-the-badge&logo=ko-fi&logoColor=white
)](https://ko-fi.com/X8X6LFCI0)

## DISCLAIMER
DISCLAIMER: I am not a financial advisor and not affiliated with Fennel.com. Use this tool at your own risk. I am not responsible for any losses or damages you may incur by using this project. This tool is provided as-is with no warranty.
elerium 0.2.2
Improve your UFOs
Requires Python: >=3.12

<!--
SPDX-FileCopyrightText: 2025 Rose Davidson <rose@metaclassical.com>
SPDX-License-Identifier: CC-BY-ND-4.0
-->

# Elerium

Elerium is a library for working with [UFOs (Unified Font Objects)](https://unifiedfontobject.org/).

In the [X-COM video game series](https://en.wikipedia.org/wiki/XCOM) (MicroProse, Firaxis, _et al._), a substance known as [Elerium-115](https://www.ufopaedia.org/index.php/Elerium-115) is the power source for UFOs and other alien technology.

## MTI to FEA

Certain OFL-licensed fonts (particularly those from the Noto and Croscore projects) have OpenType features specified using [Monotype's format](https://monotype.github.io/OpenType_Table_Source/otl_source.html). The open font community seems to be standardized around [AFDKO syntax](https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html).

`elerium mti-to-fea` is capable of translating from Monotype syntax to AFDKO syntax.

## Licensing

The code in this library is licensed under the [MIT license](https://spdx.org/licenses/MIT.html). Some test data is sourced from upstream repositories and is licensed under their licenses; see `tests/data/mti/README.md` and `tests/data/nototools/README.md` for details.
flask-fixture 0.0.7
Start and stop the flask server for your tests
Requires Python: >=3.8

![logo](https://raw.githubusercontent.com/pomponchik/flask_fixture/main/docs/assets/logo_5.png)

[![Downloads](https://static.pepy.tech/badge/flask_fixture/month)](https://pepy.tech/project/flask_fixture)
[![Downloads](https://static.pepy.tech/badge/flask_fixture)](https://pepy.tech/project/flask_fixture)
[![codecov](https://codecov.io/gh/pomponchik/flask_fixture/graph/badge.svg?token=8iyMsUaLvN)](https://codecov.io/gh/pomponchik/flask_fixture)
[![Hits-of-Code](https://hitsofcode.com/github/pomponchik/flask_fixture?branch=main)](https://hitsofcode.com/github/pomponchik/flask_fixture/view?branch=main)
[![Tests](https://github.com/pomponchik/flask_fixture/actions/workflows/tests_and_coverage.yml/badge.svg)](https://github.com/pomponchik/flask_fixture/actions/workflows/tests_and_coverage.yml)
[![Python versions](https://img.shields.io/pypi/pyversions/flask_fixture.svg)](https://pypi.python.org/pypi/flask_fixture)
[![PyPI version](https://badge.fury.io/py/flask_fixture.svg)](https://badge.fury.io/py/flask_fixture)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)


A simple plugin for [Pytest](https://docs.pytest.org/) containing a [fixture](https://docs.pytest.org/explanation/fixtures.html) that can start and stop the [Flask server](https://flask.palletsprojects.com/server/) to run related tests. You just have to define the [routes](https://flask.palletsprojects.com/quickstart/#routing).

Install it by the command:

```bash
pip install flask_fixture
```

Define some routes in your `conftest.py` file:

```python
from flask_fixture import endpoint

@endpoint('/')
def root():
    return 'some text'
```

And use a URL of a server in your tests as a fixture `local_server_url`:

```python
import requests

def test_server(local_server_url):
    assert requests.get(local_server_url).text == 'some text'
```

The example uses the [Requests](https://requests.readthedocs.io/en/latest/) library.