semvec/pypi-packages huggingface dataset:
xfox 0.1.9.6
Asynchronous Low-code compiler for python
Requires Python: <4.0,>=3.10

### Asynchronous Low-code compiler for python
[github](https://github.com/play-go/xfox-code)

# How to install

```bash
pip install xfox
```

## Optional

```bash
pip install termcolor
```

# Example

```python
import asyncio
import xfox
#Custom Function

@xfox.addfunc(xfox.funcs)
async def test_func(item:str, *args, **kwargs):
    print("-----",kwargs)

@xfox.addfunc(xfox.funcs)
async def somef(item:str,*args, **kwargs):
    return kwargs["ctx"]

@xfox.addfunc(xfox.funcs)
async def function4(*args, **kwargs):
    return "FOX"

@xfox.addfunc(xfox.funcs)
async def function1(item:int, type:bool,*args, **kwargs):
    return type, args

@xfox.addfunc(xfox.funcs)
async def function2(item:str, *args, **kwargs):
    await xfox.isempty(item)
    return eval(item)

@xfox.addfunc(xfox.funcs)
async def inside(item:str, type:bool=False,*args, **kwargs):
    return type

@xfox.addfunc(xfox.funcs, 'usefunc')
async def functest(func:xfox.AnonFunction,*args, **kwargs):
    return (await func.compile(), func.name)



# Sync Parsing

print(asyncio.run(xfox.parse("""
/* TRASH (xd) */
$test_func[$test_func[sfsaf]]
$function1[1;$inside[true];$sadsadsdsd[]] $onlyif[1<2;ONLYIF]
$function2[1+2;dsadasd] sometext
$function4[]
$function1[1;$inside[fsfdfd;true];$sadsadsdsd[]]
$sdasdasd[sadsadsad]
sdasdsa $exec[true;print(1+231321, end='')] $exec[true;print(1+231321, end='')] $exec[true;print(1+231321, end='')]

/* Let/Get */
$let[test;good]
$get[test]

/* Internal Functions Test */
$usefunc[$def[$print[hello!]]]
$Test[]
$def[$print[hello!];Test]
$Test[] 
                             
/* While Test */
$let[a;0]
$while[$eval[$get[a]<=5];$let[a;$eval[$get[a]+1]]]
$get[a]
123 $while[True;$if[$get[a]<15;$let[a;$eval[$get[a]+1]];$break[]] $get[a]]
123 $while[True;$break[]]
$break[]

/* DoWhile Test */
$dowhile[1>2;Test]
                             
/* For Test */
$for[1..5;$get[i]]
$for[0..100..10;$get[i]]
$for[5;$if[$get[i]==2;$break[];$get[i]]]

/* Try Test */
$try[ERROR $get[_];$function1[]] 
$try[$print[$get[_]];$function2[]]
$try[$get[_];$raise[name;text]]

/* Import usage
$import[ttest]
$functiomm[] <- (custom function from ttest) */

/* Exit - $exit[] */
                                                                              
/* a Comment */ <-- comment here
""", ctx="asdsdsdasds",del_empty_lines=True)))
```

Output:
```
----- {'ctx': 'asdsdsdasds'}
----- {'ctx': 'asdsdsdasds'}
[LOG] hello!
[LOG] hello!
[LOG] Mising var item in function2
None
(False, ('$sadsadsdsd[]',))
3 sometext
FOX
(True, ('$sadsadsdsd[]',))
$sdasdasd[sadsadsad]
sdasdsa 231322 231322 231322
good
('', 'a547f0')
$Test[]
6
123 789101112131415
123
Test
12345
0102030405060708090100
01
ERROR Mising var type in function1
[ERROR] name: text
 <-- comment here
```

# Interactive Xfox Interpreter

```bash
python -m xfox
```

# Start xfox files

```bash
python -m xfox (filename).xfox
```

# Examples

Soon...
ovcirrusapi 10.4.4
OmniVista Cirrus RestFul API library for Python
Requires Python: >=3.8


# OmniVista Cirrus API Library for Python - **OVCirrusApi**

**OVCirrusApi** is a lightweight python library that enables developers to interact seamlessly with the OmniVista Cirrus RESTful API.

---

## ✨ Supported APIs

The full API documentation is available at: [OmniVista Cirrus API Docs](https://eu.manage.ovcirrus.com/apidoc/apidoc.html)

- Authentication API
- User API
- Organization API

---

## 🛠️ Built With

- **python 3.8**

```python
    dependencies = [
        "httpx>=0.24.1",
        "requests>=2.31.0",
        "pydantic>=2.0",
        "pybreaker>=0.7.0",
        "backoff>=2.2.1"
    ]
```

## 🚀 Installation


1. pip install ovcirrusapi

---

## 📦 Usage Guide

### Step 1: Create a global instance


```python
    # auth_instance.py
    from api_client.auth import Authenticator
    auth = (
        Authenticator()
        .setURL("https://api.example.com/auth")
        .setCredentials("your@email.com", "your_password")
        .setApp("your_app_id", "your_app_secret")
        .build()
    )
```

### Step 2: Import the instance and use the apiClient in the .py file

```python

    from auth_instance import auth
    
    apiClient = OVCirrusApiClient(base_url=API_BASE_URL, auth=auth)
    userProfile = await apiClient.getUserProfile()
    if userProfile is None:
        print("Failed to retrieve profile.")
        return

    print(userProfile.firstname)

```
## 🧪 Give it a try - example_usage.py

1. Fill in the API credentials in the .env file

```
API_BASE_URL = "https://<base_url>/"
AUTH_URL = API_BASE_URL + "api/ov/v1/applications/authenticate"

EMAIL = "<email>"
PASSWORD = "<password>"
APP_ID = "<appId>"
APP_SECRET = "<appSecret>"
```

2. Run the script, python example_usage.py

---

## 📚 Available Methods

User API:
- getUserProfile
- UpdateUserProfile

Organization API:
- createAnOrganization
- getAllUserOrganizations
- getOrganizationBasicSettings
- getOrganization
- updateOrganization
- deleteOrganization

---

## 📦 Releases

| Version          | Date       | Notes           |
|------------------|------------|-----------------|
| v10.4.3          | 2025-04-16 | Initial release |

Visit the [Releases](https://github.com/Samuelyip74/OVCirrusApiPython/releases/tag/10.4.3) page to download `.zip` packages.

---

## 📄 License

```
Copyright (c) Samuel Yip Kah Yean 2025

This software is licensed for personal, non-commercial use only.

You are NOT permitted to:
- Use this software for any commercial purposes.
- Modify, adapt, reverse-engineer, or create derivative works.
- Distribute, sublicense, or share this software.

All rights are reserved by the author.

For commercial licensing or permission inquiries, please contact:
kahyean.yip@gmail.com
```
llm-sdk 0.3.7
SDK to get all the available LLM models per Provider
Requires Python: >=3.6

# LLM SDK (AI MODELS)

### What is LLM SDK ?
LLM SDK is an API wrapper designed to access and manage all available LLM models, including LLM, Multimodal, and Embedding models. It employs agents to automate the process of collecting information about available models, ensuring a comprehensive list of models is available. Despite the automation, human verification is required to ensure accuracy.

### Installation
`pip install llm-sdk`

### Usage
The LLM SDK provides a simple interface to interact with the API and retrieve information about models, providers, and model creators. Below is an example of how to use the SDK.

```python
from llm_sdk import ai_models

# Get a list of available models
available_models = ai_models.get_available_models(model_type_name="LLM", provider_name="OpenAI", is_verified=True)
for model in available_models:
    print(model.name)
```

```python
# Get a list of providers
providers = ai_models.get_providers(model_type_name="LLM")
for provider in providers:
    print(provider.name)
```
```python
# Get a list of model creators
model_creators = ai_models.get_model_creators(model_type_name="LLM")
for creator in model_creators:
    print(creator.name)
```
```python
# Get details of a specific model creator by ID
model_creator = ai_models.get_model_creator(creator_id=1)
print(model_creator.name)
```


### API Methods


* `get_available_models`: Retrieve a list of available models based on optional filters.

  ##### Filters:
  * `model_type_name` (Optional[str]): Filter by model type name.
  * `provider_name` (Optional[str]): Filter by provider name.
  * `is_verified` (Optional[bool]): Filter by verification status.


* `get_providers`: Retrieve a list of providers based on an optional model type filter.
  
  ##### Filters:
  * `model_type_name` (Optional[str]): Filter by model type name.


* `get_model_creators`: Retrieve a list of model creators based on an optional model type filter.

  ##### Filters:
    * `model_type_name` (Optional[str]): Filter by model type name.
 

* `get_model_creator`: Retrieve details of a specific model creator by their ID.

  ##### Parameters:
  * `creator_id` (int): The ID of the model creator.
vencopy 1.0.6
Vehicle Energy Consumption in Python: A tool to simulate load flexibility of electric vehicle fleets.
Requires Python: >=3.9

# Welcome to venco.py!

- Authors: Niklas Wulff, Fabia Miorelli
- Contact: vencopy@dlr.de, fabia.miorelli@dlr.de

# Contents

- [Description](#description)
- [Installation](#installation)
- [Codestyle](#codestyle)
- [Documentation](#documentation)
- [Useful Links](#useful-links)
- [Want to contribute?](#want-to-contribute)

## Description

A data processing tool estimating hourly electric demand and flexibility profiles for future 
electric vehicle fleets. Profiles are targeted to be scalable for the use in large-scale
energy system models. 

## Installation

Depending on if you want to use venco.py or if you want to contribute, there are
two different installation procedures described in venco.py's documentation:

[I want to apply the tool](https://dlr-ve.gitlab.io/esy/vencopy/vencopy/gettingstarted/installation.html#installation-for-users)

[I want to contribute to the codebase, the documentation or the tutorials](https://dlr-ve.gitlab.io/esy/vencopy/vencopy/gettingstarted/installation.html#installation-for-developers)

In order to start using venco.py, check out our [tutorials](https://dlr-ve.gitlab.io/esy/vencopy/vencopy/gettingstarted/start.html). For this you won't need any additional data.

To run venco.py in full mode, you will need the data set Mobilität in Deutschland (German for "mobility in Germany"). You
can request it here from the clearingboard transport: https://daten.clearingstelle-verkehr.de/order-form.html 
Alternatively you can use venco.py with any National Travel Survey or mobility pattern dataset.


## Codestyle

We use PEP-8, with the exception of UpperCamelCase for class names.

## Documentation

The documentation can be found here: https://dlr-ve.gitlab.io/esy/vencopy/vencopy/
To be able to build the documentation locally on your machine you should additionally install the following three packages in your vencopy environment : sphinx, sphinx_rtd_theme and rst2pdf.
After that you can build the documentation locally from a conda bash with the following command:

```python
sphinx-build -b html ./docs/ ./build/
```

## Useful Links

- Documentation: https://dlr-ve.gitlab.io/esy/vencopy/vencopy/
- Source code: https://gitlab.com/dlr-ve/esy/vencopy/vencopy
- PyPI release: https://pypi.org/project/vencopy/
- Licence: https://opensource.org/licenses/BSD-3-Clause

## Want to contribute?

Please read our contribute section in the documentation and reach out to Fabia
(fabia.miorelli@dlr.de). If you experience difficulties on set up or have other technical questions, join our
[gitter community](https://gitter.im/vencopy/community)
ja-htCondor-log-utilities 0.0.4
Utilities to analize, summarize, and visualize condor log files
Requires Python: <3.12,>=3.10

# htCondor log utilities

The problem we are trint to solve is how to find which log file out
of sveral thousand jobs to examne manually.

## Programs

- scalng-log-analyzer - Coverts one or more htCondor job logs to
  a CSV file for import into summary and visualization programs.