semvec/pypi-packages huggingface dataset:
empyrikos 0.1.0
Python interface to Empirikos.jl for empirical Bayes methods
Requires Python: >=3.10

# Empyrikos Python Package

Python interface to [Empirikos.jl](https://github.com/nignatiadis/Empirikos.jl) for empirical Bayes methods.

## Installation

```bash
pip install git+https://github.com/nignatiadis/empyrikos.git
```

## Requirements

- Python 3.10+
- Julia 1.10+ (will be automatically installed via JuliaCall if not present)

## Quick Start

```python
import empyrikos as eb
import numpy as np

# Example: Empirical partially Bayes t-test
# Generate test data consistent with model assumptions
np.random.seed(42)
n_tests = 100
df = np.full(n_tests, 10)  # degrees of freedom

# True effect sizes: mix of nulls and non-nulls  
true_beta = np.zeros(n_tests)
true_beta[50:] = np.random.normal(0, 1, 50)  # 50 nulls, 50 non-nulls
# True variances from inverse gamma (conjugate prior)
true_sigma_sq = 1.0 / np.random.gamma(2, 1/0.5, n_tests)

# Generate observed data according to model
beta_hat = np.random.normal(true_beta, np.sqrt(true_sigma_sq))
se_hat_squared = true_sigma_sq * np.random.chisquare(df) / df

# Run empirical partially Bayes t-test
result = eb.epb_ttest(
    beta_hat=beta_hat,
    se_hat_squared=se_hat_squared, 
    df=df,
    alpha=0.05
)

print(f"Number of rejections: {result.n_rejected}")
print(f"Adjusted p-values: {result.adj_pvalues[:5]}...")  # first 5
```

### Additional Methods on Results

After running `epb_ttest()`, the returned `EPBTTestResult` object provides additional methods:

#### `pvalue_function(beta_hat, se_hat_squared, df)`

Compute empirical partially Bayes p-values for new data using the fitted prior from the original analysis.

```python
# Using fitted result to compute p-values for new data
new_pval = result.pvalue_function(beta_hat=1.5, se_hat_squared=0.25, df=10)
print(f"P-value: {new_pval}")

# Multiple new observations
new_pvals = result.pvalue_function(
    beta_hat=[1.5, -0.8, 2.1], 
    se_hat_squared=[0.25, 0.30, 0.18], 
    df=[10, 15, 12]
)
print(f"P-values: {new_pvals}")
```

#### `se_hat_squared_pdf(se_hat_squared, df)`

Compute the marginal probability density function of the variance estimates using the fitted prior.

```python
# Compute PDF at a single point
pdf_val = result.se_hat_squared_pdf(se_hat_squared=0.25, df=10)
print(f"PDF: {pdf_val}")

# Compute PDF at multiple points
pdf_vals = result.se_hat_squared_pdf(
    se_hat_squared=[0.25, 0.30, 0.18], 
    df=[10, 15, 12]
)
print(f"PDF values: {pdf_vals}")
```

## Documentation

For detailed documentation and examples, see [the documentation](docs/).

## License

MIT License - see LICENSE file for details.
moy-nalog-py 0.2.1
Python клиент для работы с API сервиса Мой налог
Requires Python: >=3.6

# MoyNalogPy

MoyNalogPy - библиотека на Python для работы с API сервиса "Мой налог" ФНС России, предназначенная для самозанятых.

## Установка

```bash
pip install moy-nalog
```

## Перед началом работы

Для начала работы с библиотекой необходимо пройти аутентификацию:

```bash
python authOLD.py
```

После выполнения этой команды будут сохранены необходимые учетные данные для дальнейшей работы с API.

## Возможности

- Получение данных о профиле пользователя
- Работа с доходами и чеками
- Взаимодействие с сервисами и клиентами

## Использование

### Инициализация

```python
from MoyNalogPy import MoyNalog

# Инициализация клиента
api = MoyNalog(inn="ваш_инн", password="ваш_пароль")
```

### Получение данных профиля

```python
# Получить профиль пользователя
profile = api.get_user_profile()
print(profile)
```

### Работа с доходами

```python
# Получить список доходов
incomes = api.get_incomes()
print(incomes)
```

### Работа с чеками

```python

# Создание чека
from MoyNalogPy.schemas import Service, Client
from MoyNalogPy import MoyNalog

my_nalog = MoyNalog()

# Создаем объект услуги
service = Service(
    name="Консультация по программированию",
    quantity=1,
    amount=2000.0,
    serviceNumber=1
)

# Создаем объект клиента
client = Client(
    displayName="Иванов Иван Иванович",
    inn="7712345678",
    type="LEGAL_ENTITY"
)

# Вызываем метод создания чека
approvedReceiptUuid = my_nalog.create_invoice(
    operationTime='2023-10-01T12:00:00+03:00',
    svs=service,
    client=client
)

# Получаем URL чека для печати
receipt_url = my_nalog.get_receipt_url(approvedReceiptUuid)
print(receipt_url)


```
Так же можно получить сразу ссылку на чек:

```python

receipt_url = my_nalog.create_invoice(
    operationTime='2023-10-01T12:00:00+03:00',
    svs=service,
    client=client,
    return_recipt_url=True
)

## Требования

- Python 3.8+
- requests
- pydantic

## Лицензия

MIT

## Автор

senylar (senyvlar@gmail.com)

## Исходный код

GitHub: [https://github.com/senylar/MoyNalogPy](https://github.com/senylar/MoyNalogPy)
nuvom 0.11.2
Nuvom gives developers a task engine they can trust: clean APIs, predictable behavior, and production-ready task scheduling for Python.
Requires Python: >=3.8

# nuvom

> Nuvom gives developers a task engine they can trust: clean APIs, zero magic, and predictable behavior from local dev to production scale.

![status](https://img.shields.io/badge/version-v0.11-blue)
![python](https://img.shields.io/badge/python-3.8%2B-yellow)
![license](https://img.shields.io/badge/license-Apache--2.0-green)

---

## Why Nuvom?

**Nuvom** is a modern task engine for Python that focuses on **clarity, reliability, and flexibility**.
It’s simple to start, easy to scale, and built for developers who value predictable, production-ready behavior.

### Core Principles

* **Developer-first design** — clean APIs, no magic, no surprises
* **Pluggable architecture** — use SQLite, PostgreSQL, Redis, or your own backend
* **Cross-platform** — consistent behavior on Linux, macOS, and Windows
* **Static task discovery** — AST-powered for speed and safety
* **Built-in scheduling** — one-off, interval, or cron-style recurring jobs
* **Predictable in production** — fault-tolerant workers and durable job persistence

---

## Installation

```bash
pip install nuvom
```

---

## Quickstart

### 1. Define a Task

```python
from nuvom.task import task

@task(retries=2, retry_delay_secs=5, timeout_secs=3, store_result=True)
def add(x, y):
    return x + y
```

### 2. Discover Tasks

```bash
nuvom discover tasks
```

Generates `.nuvom/manifest.json` for faster worker startup.

### 3. Queue a Job

```python
from tasks import add

job = add.delay(5, 7)
print(job.id)
```

### 4. Schedule a Job

```python
from datetime import timedelta, datetime, timezone
from tasks import add

# Run at a specific time (2038 iykyk)
add.schedule(5, 7, at=datetime(2038, 1, 19, 3, 14, 7, tzinfo=timezone.utc)) 

# Run after 30 seconds
add.schedule(5, 7, in_=timedelta(seconds=30))

# Repeat every 5 minutes
add.schedule(2, 3, interval=300)

# Daily at midnight UTC
add.schedule(1, 2, cron="0 0 * * *")
```

Start the scheduler service:

```bash
nuvom runscheduler
```

Workers will automatically pick up due jobs.

### 5. Run the Worker

```bash
nuvom runworker
```

### 6. Inspect Results

```bash
nuvom inspect job <job_id>
```

---

## Plugin Architecture

Nuvom is modular by design:

* Implement custom queue or result backends
* Extend scheduling with custom triggers
* Add metrics, persistence, or monitoring plugins

Configure via `.nuvom_plugins.toml`.

---

## Documentation

Explore:

* Advanced task & scheduling options
* Plugin development guides
* CLI usage and environment setup
* Architecture and internals

👉 **[Documentation](https://nuvom.netlify.app)**

---

## License

Apache 2.0 - open, reliable, and production-ready.
pasjonsfrukt 0.2.6
Scrape PodMe podcast streams to mp3 and host with RSS feed
Requires Python: <4.0,>=3.11

# 🍹 pasjonsfrukt

[![PyPI](https://img.shields.io/pypi/v/pasjonsfrukt)](https://pypi.org/project/pasjonsfrukt/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pasjonsfrukt)
[![PyPI - License](https://img.shields.io/pypi/l/pasjonsfrukt)](https://github.com/mathiazom/pasjonsfrukt/blob/main/LICENSE)

Scrape PodMe podcast streams to mp3 and host with RSS feed.

<i style="color:grey">Note: A valid PodMe subscription is required to access premium content</i>

### Setup

1. Install `pasjonsfrukt`

```
pip install pasjonsfrukt
```

2. Install [`ffmpeg`](https://ffmpeg.org/)

3. Define harvest and feed configurations by copying [`config.template.yaml`](config.template.yaml) to your own `config.yaml`.  
   Most importantly, you need to provide:

   - a `host` path (for links in the RSS feeds)
   - login credentials (`auth`) for your PodMe account
   - the `podcasts` you wish to harvest and serve

### Usage

##### Harvest episodes

Harvest episodes of all podcasts defined in config

```sh
pasjonsfrukt harvest
```

Harvest episodes of specific podcast(s)

```sh
pasjonsfrukt harvest [PODCAST_SLUG]...
```

##### Update feeds

Update all RSS feeds defined in config

```sh
pasjonsfrukt sync
```

Update RSS feed of specific podcast

```sh
pasjonsfrukt sync [PODCAST_SLUG]...
```

> The feeds are always updated after harvest, so manual feed syncing is only required if files are changed externally

##### Serve RSS feeds with episodes

Run web server

```sh
pasjonsfrukt serve
```

RSS feeds will be served at `<host>/<podcast_slug>`, while episode files are served
at `<host>/<podcast_slug>/<episode_id>`

> `host` must be defined in the config file.

##### Secret

If a `secret` has been defined in the config, a query parameter (`?secret=<secret-string>`) with matching secret string
is required to access the served podcast feeds and episode files. This is useful for making RSS feeds accessible on the
web, without making them fully public. Still, the confidentiality is provided as is, with no warranties 🙃

### Development

#### Formatting

```commandline
poe fmt
```
> uses [Black](https://black.readthedocs.io/en/stable/) code formatter