semvec/pypi-packages huggingface dataset:
python-eveauth 0.1.0b2
A library for authorizing desktop apps with the EVE online SSO.
Requires Python: >=3.10

# python-eveauth

An Python library for authorizing desktop apps with the EVE online SSO.

[![release](https://img.shields.io/pypi/v/python-eveauth?label=release)](https://pypi.org/project/python-eveauth/)
[![python](https://img.shields.io/pypi/pyversions/python-eveauth)](https://pypi.org/project/python-eveauth/)
[![CI/CD](https://github.com/ErikKalkoken/python-eveauth/actions/workflows/cicd.yaml/badge.svg)](https://github.com/ErikKalkoken/python-eveauth/actions/workflows/cicd.yaml)
[![codecov](https://codecov.io/gh/ErikKalkoken/python-eveauth/graph/badge.svg?token=NBGLASsNXq)](https://codecov.io/gh/ErikKalkoken/python-eveauth)
[![license](https://img.shields.io/badge/license-MIT-green)](https://gitlab.com/ErikKalkoken/python-eveauth/-/blob/master/LICENSE)

## Description

python-eveauth is a library for authorizing Python scripts on desktops with the EVE online SSO. This allows obtaining SSO tokens with any Python script, e.g. CLI tools, GUI apps or Jupiter notebooks.

## Installation

```sh
pip install python-eveauth
```

### Usage

First you need to create an EVE SSO app for your script on [Eve Online's developers site](https://developers.eveonline.com/). The default callback for your SSO app is: `http://127.0.0.1:8080/callback`

Then you can start authorizing your script with **eveauth**.

Below is an basic example that show how you can use **eveauth**. It first authorizes the script and obtains a token. Then fetches the wallet balance for the authorized character with the token. The token can later be refreshed as needed.

```py
import requests
from eveauth import Client

# Create an auth client
c = Client(client_id="YOUR-SSO-CLIENT-ID")

# Authorize the current script with the character wallet scope
token = c.authorize("esi-wallet.read_character_wallet.v1")

# Request the wallet balance for the authorized character
r = requests.get(
    url=f"https://esi.evetech.net/characters/{token.character_id}/wallet",
    headers={"Authorization": f"Bearer {token.access_token}"},
)
r.raise_for_status()

# Print the balance
print(r.text)


# Refresh the token
# c.refresh_token(token)
```
hebrewdate 2.2.1
A Python package for handling Hebrew dates
Requires Python: >=3.9

# hebrewdate
A comprehensive Python library for working with the Hebrew calendar, providing date conversions, calendar generation, and holiday information.

## Key Features
- **Conversion**: Seamlessly convert between Hebrew and Gregorian dates.
- **Holidays**: Built-in support for major holidays, festive days (Chol HaMoed, Hanukkah), and fasts.
- **Arithmetic**: Easily add or subtract days, months, or years from Hebrew dates.
- **Calendar**: Generate text or HTML calendars for any Hebrew month or year.
- **Leap Years**: Full handling of the 19-year Hebrew calendar cycle.
- **Traditional Formatting**: Support for traditional Hebrew year strings (e.g., ה'תשפ"ה).

## Installation
```bash
pip install hebrewdate
```

## Quick Start

### Basic Date Operations
```python
from hebrewdate import HebrewDate

# Get today's Hebrew date
today = HebrewDate.today()
print(today)  # יום חמישי יט כסלו ה'תשפ"ה

# Create a specific Hebrew date
date = HebrewDate(15, "ניסן", 5785)
print(date.to_gregorian())  # 2025-04-13

# Date arithmetic
next_week = date + 7
print(next_week)  # יום ראשון כב ניסן ה'תשפ"ה

# Check for holidays
if date.is_holiday:
    print(f"Today is {date.holiday}")  # Today is יו"ט ראשון של פסח
```

### Working with Years and Months
```python
from hebrewdate import HebrewYear, HebrewMonth

# Explore a Hebrew year
year = HebrewYear(5784)
print(year.is_leap)  # True
print(year.month_count)  # 13

# Explore a Hebrew month
month = HebrewMonth(year, "אדר א")
print(month.length)  # 30
```

### Generating HTML Calendars
```python
from hebrewdate import HTMLHebrewCalendar

# Create an HTML calendar for Tishrei 5785
cal = HTMLHebrewCalendar(with_gregorian=True, with_holidays=True)
html = cal.formatmonth(5785, 1)

# Save to file
with open("calendar.html", "w", encoding="utf-8") as f:
    f.write(html)
```

## Limitations
- Supported Python versions: 3.9 and later.
- Date conversions before 1752 CE may be slightly inaccurate due to historical calendar variations.
- Extremely ancient dates might not be convertible to the Gregorian system.

## License
This project is licensed under the MIT License.

## Author
Isaac Dovolsky
ddcd 0.0.1
Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives

# Causal Structure Learning via Diffusion Denoising Objectives

Understanding causal dependencies in observational data is critical for informing decision-making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acyclic Graphs (DAGs). Existing methods, such as NOTEARS and DAG-GNN, often face issues with scalability and stability in high-dimensional data, especially when there is a feature-sample imbalance. Here, we show that the denoising score matching objective of diffusion models could smooth the gradients for faster, more stable convergence. We also propose an adaptive k-hop acyclicity constraint that improves runtime over existing solutions that require matrix inversion. We name this framework Denoising Diffusion Causal Discovery (DDCD). Unlike generative diffusion models, DDCD utilizes the reverse denoising process to infer a parameterized causal structure rather than to generate data. We demonstrate the competitive performance of DDCDs on synthetic benchmarking data. We also show that our methods are practically useful by conducting qualitative analyses on two real-world examples.

# Get started

## Installation
```
pip install ddcd
```

## Example
```
import ddcd
from castle.datasets import IIDSimulation, DAG

# an unwanted behavior from castle
torch.set_default_dtype(torch.float)

# Generating synthetic data 
dag_adj = DAG.scale_free(
    n_nodes = 100, n_edges = 1000,
    weight_range = (0.5, 1.5), seed=42
)

X = IIDSimulation(
    W=dag_adj, 
    n=2000, method='linear', 
    sem_type='gauss', noise_scale=1
).X 

# Training
model = ddcd.DDCD_Linear_Trainer(X, device='cuda')
model.train(5000)

w = model.get_adj()
A = (np.abs(w) > 0.3) 
```