semvec/pypi-packages huggingface dataset:
vibehowmanyrs 0.1.0
String R-counting library that uses GPT to count occurrences of the letter R
Requires Python: >=3.10

# VibeHowManyRs

AI-powered letter R counting using GPT.

## Usage

Install the package:
```bash
pip install vibehowmanyrs
```

Set your OpenAI API key as an environment variable.
```bash
export OPENAI_API_KEY=your_key_here
```

```python
from vibehowmanyrs import vibehowmanyrs

result = vibehowmanyrs("Raspberry")
print(result)  # 3

result = vibehowmanyrs("ROAR")
print(result)  # 2
```

## Test

```bash
pytest tests/
```

## Dependencies

- openai
- pydantic
- typing-extensions

⚠️ Requires OpenAI API key. Experimental project - not for production use.

## Why?

Because strawberry has 2 r's

## DISCLAIMER
Will not work half the time :) Enjoy!
vibetrimleft 0.1.0
String left-trimming library that uses GPT to remove leading whitespace
Requires Python: >=3.10

# Vibetrimleft

AI-powered string left-trimming using GPT.

## Usage

Install the package:
```bash
pip install vibetrimleft
```

Set your OpenAI API key as an environment variable.
```bash
export OPENAI_API_KEY=your_key_here
```

```python
from vibetrimleft import vibetrimleft

result = vibetrimleft("   hello world")
print(repr(result))  # 'hello world'
```

## Test

```bash
pytest tests/
```

## Dependencies

- openai
- pydantic
- typing-extensions

⚠️ Requires OpenAI API key. Experimental project - not for production use.

## DISCLAIMER
Will not work half the time :) Enjoy!
zwergetl-engine 0.1.11
ZwergETL Engine
Requires Python: >=3.6

ZwergETL Engine
===================

ETL engine for ZwergETL framework.

A lightweight ETL engine that implements the same technique for building ETL pipelines
as implemented in CloverDX engine (former CloverETL).

ZwergETL framework uses modular design. This particular module
implements the ETL engine itself without any additional components that can actually
extract transform or load data. The engine defines base classes and implements
classes that are responsible for execution of an ETL *graph*.

Other ETL components (readers, writers, connections etc.) are implemented in separate packages
named `zwergetl-components-*`.
vibetrimright 0.1.1
String right-trimming library that uses GPT to remove trailing whitespace
Requires Python: >=3.10

# vibetrimright

AI-powered string right-trimming using GPT.

## Usage

Install the package:
```bash
pip install vibetrimright
```

Set your OpenAI API key as an environment variable.
```bash
export OPENAI_API_KEY=your_key_here
```

```python
from vibetrimright import vibetrimright

result = vibetrimright("hello world   ")
print(repr(result))  # 'hello world'
```

## Test

```bash
pytest tests/
```

## Dependencies

- openai
- pydantic
- typing-extensions

⚠️ Requires OpenAI API key. Experimental project - not for production use.

## DISCLAIMER
Will not work half the time :) Enjoy!
vibesum 0.1.1
Addition library that uses GPT to add two numbers
Requires Python: >=3.10

# vibesum

AI-powered number addition using GPT.

## Usage

Install the package:
```bash
pip install vibesum
```

Set your OpenAI API key as an environment variable.
```bash
export OPENAI_API_KEY=your_key_here
```

```python
from vibesum import vibesum

result = vibesum(5, 3)
print(result)  # 8
```

## Test

```bash
pytest tests/
```

## Dependencies

- openai
- pydantic
- typing-extensions

⚠️ Requires OpenAI API key. Experimental project - not for production use.
streetview 0.0.16
Retrieve current and historical photos from Google Street View
Requires Python: >=3.10

# streetview

[![PyPI version](https://badge.fury.io/py/streetview.svg)](https://badge.fury.io/py/streetview)
[![PyPI Downloads](https://static.pepy.tech/badge/streetview)](https://pepy.tech/projects/streetview)

This is a light module for downloading photos from Google street view. The
functions allow you to retrieve current and **old** photos. Google does have an
API for accessing Street View. However, it does not allow you to access old
photos. Their javascript API allows you to download segments of current photos.
This API also allows you to download each full panorama as you see it on Google
Street View.

*Please note, Google does not maintain the access points used by this API for
public use. Therefore, this hack may break if Google makes changes to how
Street View works.*

# Install

Install from pip with:

	pip install streetview

# Quick start

## Search for Panoramas

The photos on Google street view are panoramas. Each parnorama has its own
unique ID. Retrieving photos is a two step process. First, you must translate GPS
coordinates into panorama IDs. The following code retrieves a list of
the closest panoramas:

```python
from streetview import search_panoramas

panos = search_panoramas(lat=41.8982208, lon=12.4764804)
first = panos[0]

print(first)
# pano_id='_R1mwpMkiqa2p0zp48EBJg' lat=41.89820676786453 lon=12.47644220919742 heading=0.8815613985061646 pitch=89.001953125 roll=0.1744659692049026 date='2019-08'
```

## Get Metadata

Not all panoramas will have a `date` field in the search results. You can fetch a date for any valid panorama from the metadata api:

```python
from streetview import get_panorama_meta

meta = get_panorama_meta(pano_id='_R1mwpMkiqa2p0zp48EBJg', api_key=GOOGLE_MAPS_API_KEY)

print(meta)
# date='2019-08' location=Location(lat=41.89820659475458, lng=12.47644649615282) pano_id='_R1mwpMkiqa2p0zp48EBJg'
```
## Download streetview image

You can then use the panorama ids to download streetview images:
```python
from streetview import get_streetview

image = get_streetview(
    pano_id="z80QZ1_QgCbYwj7RrmlS0Q",
    api_key=GOOGLE_MAPS_API_KEY,
)

image.save("image.jpg", "jpeg")
```

To download the panorama in an asynchronous context:

```python
from streetview import get_panorama_async

image = await get_panorama_async(pano_id="z80QZ1_QgCbYwj7RrmlS0Q")

image.save("image.jpg", "jpeg")
```

## Download panorama

You can download a full panorama like this:

```python
from streetview import get_panorama

image = get_panorama(pano_id="z80QZ1_QgCbYwj7RrmlS0Q")

image.save("image.jpg", "jpeg")
```

# Development

Run tests with:
```bash
make test
```
this will install mamba and the required Python packages into a local env.

If you want to rebuild VCR cassettes, you will need to copy `.env-example` to `.env` and add your Google Maps API Key.
spinmobile 0.2.0
SpinMobile Python SDK
Requires Python: >=3.12

# Spinmobile API Client

This project provides an asynchronous Python client for interacting with the Spinmobile API. It includes services for KYC verification, credit scoring, and financial statement analysis.

## Features

- **KYC Verification**: Verify user identity using national ID and name.
- **Credit Scoring**: Retrieve credit scores for users.
- **Financial Statement Analysis**: Submit and analyze financial statements.

## Installation

1. Clone the repository:
   ```bash
   gh repo clone collinmutembei/spinmobile
   ```

2. Install dependencies:
   ```bash
   uv sync --dev
   ```

## Usage

### Initialization

```python
from spinmobile.client import SpinmobileClient

# Initialize the client
client = SpinmobileClient(
    base_url="https://sanbox-api.spinmobile.co/",
    consumer_key="sandbox_consumer_key",
    consumer_secret="sandbox_consumer_secret",
)
```

### KYC Verification

```python
from spinmobile.kyc import KYCService
kyc_service = KYCService(client)
response = await kyc_service.verify_identity("12345678", "John", "Doe")
print(response)
```

### Credit Scoring

```python
from spinmobile.credit import CreditService
credit_service = CreditService(client)
response = await credit_service.get_credit_score("12345678")
print(response)
```

### Financial Statement Analysis

```python
from spinmobile.files import FileAnalysisService
file_service = FileAnalysisService(client)
payload = {"document_type": "BANK", "other_metadata": "value"}
response = await file_service.submit_statements(["/path/to/file1.pdf", "/path/to/file2.pdf"], payload)
print(response)
```

## Contributing

Contributions are welcome! Please submit a pull request or open an issue for any improvements or bug fixes.

## License

This project is licensed under the MIT License.
orange3-salesforce 0.0.7
Read and manage data in Salesforce
Requires Python: >=3.11
Keywords: orange3 add-on

# Orange3-Salesforce

This allows users to interact and manage data in Salesforce directly within Orange3. This is only possible because of the fantastic library [simple-salesforce](https://github.com/simple-salesforce/simple-salesforce).

## Installation

### Option 1: Install via Orange3 Add-ons Manager (Recommended)
1. Open Orange3
2. Go to Options → Add-ons
3. Click "Add more..." 
4. Search for "orange3-salesforce"
5. Click Install

## Features

### Current (MVP)
- **Salesforce Authentication Widget**: Connect to Salesforce orgs using username/password + security token
- **Salesforce Query Widget**: Execute SOQL queries and retrieve data as Orange3 tables
- Support for Contacts and Opportunities objects
- Simple dropdown selection for common objects

### Planned
- Bulk create, update, and delete operations
- Data validation and error handling
- Support for custom objects
- Advanced SOQL query builder

## Usage

### Basic Workflow
1. **Connect**: Use the Salesforce Authentication widget to connect to your org
2. **Query**: Use the Salesforce Query widget to retrieve data
3. **Analyze**: Use Orange3's built-in data analysis tools on your Salesforce data



## Requirements

- Orange3
- Python 3.11+



## License

MIT License - see LICENSE file for details



## TO BE DELETED

Local testing

```
uv build 
uv publish
```

Above assumes to environment variables form pypi

UV_PUBLISH_USERNAME which is a token
UV_PUBLISH_PASSWORD which is the PYPI token from the web
netbox-better-templates 1.0.2
Adds some functionality to netbox templates and config render.
Requires Python: >=3.11
Keywords: python, netbox, provisioning, template, jinja, jinja2

# Netbox Better Templates Plugin
Adds some functionality to netbox templates and config render.
The plugin uses `Monkey-patching` and injects extensions into the netbox render method.

## Installation

1. Install plugin with pip

```bash
cd /opt/netbox
source venv/bin/activate
pip install netbox_better_templates
```

2. Add `netbox_better_templates` to `local_requirements.txt` file

3. Edit the `PLUGINS` in `configuration.py`
```python
PLUGINS = [
    'netbox_better_templates'
]
```


## Added Functions

- **datetime**: adds datetime to config templates.
```jinja3
{{ datetime.now() }}
```

- **now**: standard now function of datetime.
```jinja3
{{ now() }}
```

contributors are welcome. fork for any changes you want to make
minisynth 0.1.3
A Python-based modular synthesizer framework for generating audio and datasets
Requires Python: >=3.8

### MiniSynth
A Python-based modular synthesizer framework for generating audio and datasets with controllable parameters.

## Overview
MiniSynth provides a flexible framework for creating different types of synthesizers with time-varying parameters. The project includes:

# Components
- Subtractive synthesis (MiniSynthSubtractive) - Traditional synthesis with oscillators and filters
- FM synthesis (MiniSynthFM) - Frequency modulation synthesis using wavetables
- Wavetable generation (WavetableGenerator) - Create custom waveforms and wavetables
- Parameter modulation (ModGenerator) - Generate time-varying control signals
- Dataset management (DatasetManager) - Generate and manage synthesis datasets


# Features
- Multiple synthesis methods (subtractive, FM)
- Wavetable synthesis with interpolation
- Time-varying parameter modulation with customizable envelopes
- Built-in waveforms: sine, triangle, square, sawtooth
- Anti-aliasing filters
- Dataset generation for machine learning applications
- Linear and logarithmic parameter scaling
gpx2mesh 0.1.4
Add your description here
Requires Python: >=3.13

# gpx2mesh

This project uses [uv](https://docs.astral.sh/uv/getting-started/installation/)
to manage its dependencies. Run the following to install a matching Python
version and project dependencies :

```bash
# Install a version of Python matching the one declared in .python-version
uv python install

# Install project dependencies
uv sync --all-groups
```
CityLearn 2.3.1
An open source Farama Foundation Gymnasium environment for benchmarking distributed energy resource control algorithms to provide energy flexibility in a district of buildings.
Requires Python: >=3.7.7

# CityLearn
CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. A major challenge for RL in demand response is the ability to compare algorithm performance. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.

![Demand-response](https://github.com/intelligent-environments-lab/CityLearn/blob/master/assets/images/dr.jpg)

## Environment Overview

CityLearn includes energy models of buildings and distributed energy resources (DER) including air-to-water heat pumps, electric heaters and batteries. A collection of building energy models makes up a virtual district (a.k.a neighborhood or community). In each building, space cooling, space heating and domestic hot water end-use loads may be independently satisfied through air-to-water heat pumps. Alternatively, space heating and domestic hot water loads can be satisfied through electric heaters.

![Citylearn](https://github.com/intelligent-environments-lab/CityLearn/blob/master/assets/images/environment.jpg)

## Installation
Install latest release in PyPi with `pip`:
```console
pip install CityLearn
```

## Documentation
Refer to the [docs](https://intelligent-environments-lab.github.io/CityLearn/).
math-build02 0.1.0
Add your description here
Requires Python: >=3.11

# math_build02
math_build02提供了两种方法：
|方法|描述|
|:--:|:--:|
|math_tool|用于计算两个数的减法|
|math_tool2|用于计算三个数的运算|

## 使用讲解
### 通过pip安装
`pip install math_build02`
#### 1.使用方法(直接在代码中引用)
<pre>
math_build02 import math_tool
from math_build02 import math_tool02

print(math_tool.new_numpy(1, 2))
print(math_tool02.new_numpy02(1, 2, 3))
</pre>
#### 2.使用方法（通过命令行调用）
<pre>
PS C:\Users\14976\Desktop\app_build\math_build02> uv run math-build-two --a 1 --b 2        
a + b = 12
PS C:\Users\14976\Desktop\app_build\math_build02> uv run math-build-three --a 1 --b 2 --c 4
a - b + c = 3
</pre>
py-Xtranslator 0.1.0
Simple Python translation library using Google Translate unofficial API.
Requires Python: >=3.6

# py-Xtranslator

Simple Python translation library using Google Translate unofficial API.

## Installation

```bash
pip install py-Xtranslator
```

- - -

## Usage
```python
from py-Xtranslator import translate

print(translate("سلام دنیا", to_lang="en"))
# Output: Hello World

print(translate(["سلام", "دنیا"], to_lang="en"))
# Output: ['Hello', 'World']
```

- - -

## Features
- Translate a text or a list of texts.
- Auto-detect source languages.
- No Api-Key Requested.

- - -
## LICENSE
The project is published with MIT License.
utstd 0.1.8
Add your description here
Requires Python: >=3.9.13

# utstd

# Github Actions
https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/

https://docs.astral.sh/uv/guides/integration/github/

# Publish Package
https://docs.astral.sh/uv/guides/package/#publishing-your-package
```sh

uv lock
uv sync

uv version --bump patch

uv build
uv run --with utstd --no-project -- python -c "import utstd; print(utstd.__version__)"

git add .
git commit -m ""
git push
```
linsecure 0.1.3
A lightweight Linux vulnerability scanner
Requires Python: >=3.7

# linsecure

**linsecure** is a lightweight CLI tool that scans a Linux system for basic security misconfigurations like SSH settings, open ports, outdated packages, and more.


## Features
- Detects weak SSH configurations
- Checks for firewall status
- Scans world-writable files and sudo misconfigs
- Now supports **dynamic SSH check options**
- Generates clean `.txt` reports
- Easy to run: one command, zero setup
- Easily extensible for future modules

## ✅ Requirements

- Python **3.7+**
- **Linux** environment (Ubuntu, Debian, WSL, etc.)
## Installation

```bash
pip install linsecure

## Usage
python3 -m linsecure.cli  --ssh-check config ports --output ~/linsecure_report.txt