semvec/pypi-packages huggingface dataset:
cs-util 0.1.9
Utility library for CosmoStat
Requires Python: >=3.10

# cs_util package

Utility library for CosmoStat

| Usage | Development | Release |
| ----- | ----------- | ------- |
| [![docs](https://img.shields.io/badge/docs-Sphinx-blue)](https://martinkilbinger.github.io/cs_util/) | [![build](https://github.com/martinkilbinger/cs_util/workflows/CI/badge.svg)](https://github.com/martinkilbinger/cs_util/actions?query=workflow%3ACI) | [![release](https://img.shields.io/github/v/release/martinkilbinger/cs_util)](https://github.com/martinkilbinger/cs_util/releases/latest) |
| [![license](https://img.shields.io/github/license/martinkilbinger/cs_util)](https://github.com/martinkilbinger/cs_util/blob/master/LICENCE.txt) | [![deploy](https://github.com/martinkilbinger/cs_util/workflows/CD/badge.svg)](https://github.com/martinkilbinger/cs_util/actions?query=workflow%3ACD) | [![pypi](https://img.shields.io/pypi/v/cs_util)](https://pypi.org/project/cs_util/) |
| [![wemake-python-styleguide](https://img.shields.io/badge/style-wemake-000000.svg)](https://github.com/wemake-services/wemake-python-styleguide) | [![codecov](https://codecov.io/gh/martinkilbinger/cs_util/branch/master/graph/badge.svg?token=XHJIQXV7AX)](https://codecov.io/gh/martinkilbinger/cs_util) | [![python](https://img.shields.io/pypi/pyversions/cs_util)](https://www.python.org/downloads/source/) |
| [![contribute](https://img.shields.io/badge/contribute-read-lightgrey)](https://github.com/martinkilbinger/cs_util/blob/master/CONTRIBUTING.md) | [![CodeFactor](https://www.codefactor.io/repository/github/martinkilbinger/cs_util/badge)](https://www.codefactor.io/repository/github/martinkilbinger/cs_util) | |
| [![coc](https://img.shields.io/badge/conduct-read-lightgrey)](https://github.com/martinkilbinger/cs_util/blob/master/CODE_OF_CONDUCT.md) | [![Updates](https://pyup.io/repos/github/martinkilbinger/cs_util/shield.svg)](https://pyup.io/repos/github/martinkilbinger/cs_util/) | |

---
> Author: <a href="www.cosmostat.org/people/kilbinger/" target="_blank" style="text-decoration:none; color: #F08080">Martin Kilbinger</a>  
> Email: <a href="mailto:martin.kilbinger@cea.fr" style="text-decoration:none; color: #F08080">martin.kilbinger@cea.fr</a>  
> Year: 2022  
---



## Contents

### Library

- Galaxy catalogue handling
- Weak-lensing related cosmological quantities (e.g. surface mass density)
- VOS (Virtual Observatory Software)
- Command line logging
- Plotting
- Basic statistic calculations
- UNIONS/CFIS weak-lensing survey handling
mpay 0.0.1
Machine Payments Protocol (MPP) SDK - Implementation of the Payment HTTP Authentication Scheme
Requires Python: >=3.8
Keywords: 402, authentication, http, machine-payments, payments

# mpay

Python SDK for the Machine Payments Protocol (MPP) - an implementation of the ["Payment" HTTP Authentication Scheme](https://datatracker.ietf.org/doc/draft-ietf-httpauth-payment/).

## Status

This package is a placeholder. Full implementation coming soon.

See [mpay-rs](https://github.com/tempoxyz/mpay-rs) for the Rust implementation.

## License

MIT
google-cloud-helper 0.1.8
Add your description here
Requires Python: >=3.11

# google-cloud-helper

This repository contains common functions for easy access to Google Cloud Infrastructure, such as Big Query or Google buckets.


## Example Usage

```python
from google_cloud_helper.BigQueryHelper import BigQueryHelper
from google_cloud_helper.GoogleBucketHelper import GoogleBucketHelper
from google_cloud_helper.SecretManagerHelper import SecretManagerHelper

# BigQuery
bq_helper = BigQueryHelper("your-gcp-project-id")
exists = bq_helper.exists_table("your-gcp-project-id.dataset.table")
print(f"Table exists: {exists}")

# Google Cloud Storage
bucket_helper = GoogleBucketHelper("your-gcp-project-id")
bucket_exists = bucket_helper.exists_bucket("your-bucket-name")
print(f"Bucket exists: {bucket_exists}")

# Secret Manager
secret_helper = SecretManagerHelper()
my_secret = secret_helper.get_secret("your-gcp-project-id", "your-secret-id")
print("Successfully retrieved secret!")
```

## Available Functions

### BigQueryHelper

<!-- BIGQUERYHELPER START -->
- `create_dataset(self, dataset_id, location)`
- `create_table_from_df(self, table_id, df, partitioning, clustering, labels, specific_types)`
- `create_table_labels(self, cost_category, triggered_by, caller)`
- `delete_dataset(self, dataset_id)`
- `delete_table(self, table_id)`
- `exists_table(self, table_id)`
- `generate_bigquery_schema_from_df(self, df, specific_types)`
- `incremental_insert_with_deduplication(self, table_id, df, id_cols)`
- `read_table_as_df(self, table_id)`
- `sql2df(self, query)`
- `update_table(self, table_id, df, id_cols)`
- `upload_df_to_table(self, table_id, df)`

<!-- BIGQUERYHELPER END -->


### GoogleBucketHelper

<!-- GOOGLEBUCKETHELPER START -->
- `download_as_text(self, bucket_name, path)`
- `exists_bucket(self, bucket_name)`
- `list_all_files_in_bucket(self, bucket_name, folder_name, suffix)`
- `upload_content(self, content, bucket_name, filename, content_type)`

<!-- GOOGLEBUCKETHELPER END -->

### SecretManagerHelper

<!-- SECRETMANAGERHELPER START -->
- `get_secret(self, project_id, secret_id)`

<!-- SECRETMANAGERHELPER END -->

## Development

When new functions are added run `uv run update_readme.py` to update the README.md file.

## Testing

To run the tests, execute the following command:

```
uv run pytest
```

## Build and Publish

To build and publish the package to PyPI, execute the following command:

```
uv build
uv publish --token <pypi-token>
```
swingtrend 2.0.3
A mechanical approach to determine the trend of a stock along with breakout and reversal levels.
Requires Python: >=3.8
Keywords: finance, python3, stock-markets, technical-analysis, technical-indicators, trend-detection

# SwingTrend

SwingTrend provides a mechanical approach to determine the stock trend, with breakout and reversal levels.

Python version: >= 3.8

- Can track trends on historical as well as real-time OHLC data.
- Use it as a trend indicator or screener.
- Lightweight and serializable. The Class state can be saved/restored. Useful for day-to-day tracking of trends.
- Timeframe agnostic - pass data from any timeframe to establish the current trend.
- Linux, Windows, and Mac. No external dependencies. 90% test coverage of core functionality.

If you ❤️ my work so far, please 🌟 this repo.

## 👽 Documentation

[https://bennythadikaran.github.io/SwingTrend](https://bennythadikaran.github.io/SwingTrend)

## Installation

`pip install swingtrend`

## Basic Usage (As of v2.0.1`)

```py
from swingtrend import Swing`

# Initialise with default values
swing = Swing(
    retrace_threshold_pct=5,
    sideways_threshold=20,
    minimum_bar_count=40,
    debug=False,
)

swing = swing.run(sym="HDFCBANK", df.iloc[-60:])

swing.trend # UP or DOWN or None

swing.is_sideways # True or False.

swing.bars_since # Count of candles since last swing high or low.

swing.is_trend_stable # Is trend accurate, given the number candles supplied?

swing.leg_count # Number of swing legs, the trend has completed.

swing.sph # if trend is UP and SPH is confirmed else None

swing.spl # if trend in DOWN and SPL is confirmed else None

swing.coc # Reversal price for the current trend.

swing.high # the current highest high within a swing.

swing.low # the current lowest low within a swing.

swing.df # A reference to the dataframe passed to Swing.run()

swing.symbol # Symbol name passed to Swing.run()

# Below represent datetime of the respective candles.
swing.sph_dt
swing.spl_dt
swing.coc_dt
swing.high_dt
swing.low_dt
```

See the documentation for more details.

## Inspiration

This work was inspired by youtube channel **Matt Donlevey - Photon Trading**.

You can watch their video [How To Understand Market Structure](https://www.youtube.com/watch?v=Pd9ASRCHWmQ&t=251) to understand some of the concepts.

## How the class works

See [simple explanation of how the program works](https://bennythadikaran.github.io/SwingTrend/swing_algorithm.html)

To use the Photon method as explained in the video, instantiate the class as `Swing(retrace_threshold_pct=None)`

With the Photon method, both major or minor pivots can result in trend continuation or reversal. (including a single bar pullback).

I prefer avoiding the minor pivots by setting a minimum threshold percent. With an 8% threshold, the pullback must retrace atleast 8% or more to be considered a level for trend reversal or continuation.
tyba-client 0.5.4
A Python API client for the Tyba Public API
Requires Python: <4.0,>=3.10

<h1 align="center">
<img src="https://cdn.prod.website-files.com/669594441bac4d6528273301/669594441bac4d6528273329_Group%20180.svg" alt="TYBA" width="300">
</h1><br>


Python client to Tyba's project simulation API.

__Documentation:__ https://docs.tybaenergy.com/api
claim-master 1.0.0
CLI to manage custom claims of Firebase users
Requires Python: >=3.13

# Welcome to claim-master's source repository.

Firebase doesn't allow you to edit auth users' custom claims from the console.
The Admin SDK is the only way to edit custom claims. claim-master is a command
line tool that allows you to modify and view custom claims with simple commands.

## Usage

Since this tool uses the Firebase Admin SDK, you must have a
firebase-service-account.json file.

```shell
# View claims with an email or UID
# Must specify the firebase-service-account.json location
cm view myemail@gmail.com -k firebase-service-account.json
cm view qj82qM78GND72q98x -k firebase-service-account.json

# Set claims with an email or UID
cm set myemail@gmail.com '{"admin":true}' -k firebase-service-account.json
cm set qj82qM78GND72q98x '{"admin":true}' -k firebase-service-account.json
```

## License

GPL v3.0 or later; check the LICENSE file for details.
slidegen-pptx 0.1.0
AI-powered slide generation library for PowerPoint. Generate professional presentations from YAML/JSON schemas using LLMs.
Requires Python: >=3.9
Keywords: powerpoint, presentation, slides, ai, yaml, schema, llm, openai, automation

# SlideGen

AI-powered slide generation library for PowerPoint. Generate professional presentations from YAML/JSON schemas using LLMs.

## Installation

### From PyPI (Recommended)

```bash
pip install slidegen-pptx
```

### From Source

```bash
git clone https://github.com/nicolairobles/slidegen.git
cd slidegen
pip install -e .
```

## Quick Start

### 1. Create a Presentation Schema

Create a YAML file describing your presentation:

```yaml
presentation:
  title: "My Presentation"
  slides:
    - layout: title
      title: "Welcome"
      subtitle: "Introduction"
    
    - layout: bullet_list
      title: "Key Points"
      bullets:
        - "Point 1"
        - "Point 2"
```

### 2. Validate the Schema

```bash
slidegen validate presentation.yaml
```

### 3. Generate PowerPoint

```bash
slidegen build presentation.yaml -o output.pptx
```

## Features

- 🎨 **10 Layout Types**: Title, bullet lists, two-column, comparison, charts, tables, images, quotes, section headers, and blank slides
- 🤖 **AI-Powered**: Generate schemas from natural language using LLMs (OpenAI, DeepSeek)
- ✅ **Schema Validation**: Comprehensive validation with helpful error messages
- 🎨 **Theming**: Support for custom themes and styling
- 📊 **Data Binding**: Connect charts and tables to CSV/JSON data sources
- 🚀 **CLI Tool**: Simple command-line interface for quick generation

## Documentation

Full documentation is available at: https://nicolairobles.github.io/slidegen/

## Example Schemas

See the `examples/` directory for complete examples:
- Corporate quarterly results
- Client proposals
- Product roadmaps
- Technical analysis
- Educational content

## Development

```bash
# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run type checking
mypy slidegen/

# Run linting
ruff check slidegen/
```

## Contributing

Contributions are welcome! Please see our [Contributing Guide](https://nicolairobles.github.io/slidegen/contributing/guide/) for details.

## License

MIT License - see [LICENSE](LICENSE) file for details.

## Links

- **Documentation**: https://nicolairobles.github.io/slidegen/
- **GitHub**: https://github.com/nicolairobles/slidegen
- **Issues**: https://github.com/nicolairobles/slidegen/issues
gramps-webapi 3.7.1
A RESTful web API for the Gramps genealogical database.
Requires Python: >=3.10
Keywords: RESTful, web API, genealogy, Gramps

# Gramps Web API

This is the repository for **Gramps Web API**, a Python REST API for [Gramps](https://gramps-project.org).

It allows to query and manipulate a [Gramps](https://gramps-project.org) family tree database via the web.

Gramps Web API is the backend of [Gramps Web](https://www.grampsweb.org/), a genealogy web app based on Gramps, but can also be used as backend for other tools.

## More information

- API documentation for Gramps Web API: https://gramps-project.github.io/gramps-web-api/
- Developer documentation for Gramps Web API: https://www.grampsweb.org/dev-backend/
- Documentation for Gramps Web: https://www.grampsweb.org

## Related projects

- Gramps Web frontend repository: https://github.com/gramps-project/gramps-web