semvec/pypi-packages huggingface dataset:
SymbSyntDec 1.0.9
This package takes a symbolic DFA and produces an AIGER file of type aag
Requires Python: >=3.8

# SymbSyntDec

## Description

SymbSyntDec is a tool designed to convert a DECLARE specification of a system into an AIGER file (.aag). This conversion is achieved through a novel transformation into a symbolic deterministic finite automaton (SDFA), which is then used as input for the `SymDFA2AIGER` tool. 
The main function provided is `SymbSyntDec`. 
An example input file is included in this directory to demonstrate the expected format for the function.

#### Personal Note

This tool was created by Daniel Bachmann Aisen as part of his master’s thesis. 
In 2024, amidst challenging times, this project is dedicated to the safe return of all hostages, to the soldiers safeguarding my friends, family, and me, and in memory of those who have fallen. 
May we look forward to better days.

## Inputs

The tool requires four inputs corresponding to full DECLARE specification of a system:

- `sigma_controlled_str`: `set[str]`
- `sigma_environment_str`: `set[str]`
- `specification_env_phiE_str`: `set[str]`
- `specification_con_phiC_str`: `set[str]`

### Optional Inputs

- `file_name`: `str` (default: `"SymbSyntDec_master_thesis.aag"`)


### Example Usage

```python
sigma_environment_str = {"set", "pay", "cancel"}
sigma_controlled_str = {"ship", "refund"}

specification_env_phiE_str = {"absence2(pay)", "absence2(cancel)", "resp-existence(pay,set)", "neg-succession(ship,cancel)", "neg-succession(ship,set)"}
specification_con_phiC_str = {"precedence(set, ship)", "precedence(pay, ship)", "precedence(pay, refund)", "response(pay, ship | refund)", "neg-succession(cancel, ship)"
}

symbolicDFA = SymbSyntDec(sigma_controlled_str, sigma_environment_str, specification_env_phiE_str, specification_con_phiC_str, "SymbSyntDec_master_thesis.aag")
```

## Installation and Dependencies

This tool requires three external libraries: [pylogics_modalities](https://github.com/danielaisen/pylogics_modalities), [SymDFA2Aiger](https://github.com/danielaisen/SymDFA2Aiger) and `multipledispatch`.

- **pylogics_modalities**: This library extends the pylogics library by whitemech. To install it, use the `.whl` file provided in the `dependencies` folder.

`pip install dependencies/pylogics_modalities-0.2.2-py3-none-any.whl`
`pip install SymDFA2Aiger`
`pip install multipledispatch`

```bash
pip install SymbSyntDec
Cut4Url 1.3
A simple library for shortening links
Requires Python: >=3.6

# My library for shortening links

A simple library for shortening links

## Installation

**You can install this library using pip**:
```
pip install Cut4Url
```
## How to Short
```
from Cut4Url import Cut4Url

shortener = Cut4Url.Short()

shortened_url = shortener.clck("https://google.com")
print("Clck:", shortened_url)

shortened_url = shortener.ulvis("https://google.com")
print("Ulvis:", shortened_url)

shortened_url = shortener.isgd("https://google.com")
print("Is.gd:", shortened_url)

shortened_url = shortener.cleanuri("https://google.com")
print("CleanURI:", shortened_url)

shortened_url = shortener.abre("https://google.com")
print("Abre:", shortened_url)

shortened_url = shortener.shorturl("https://google.com")
print("ShortUrl:", shortened_url)

shortened_url = shortener.de("https://google.com")
print("De:", shortened_url)

```
## How To Check Short Url
```
from Cut4Url import Cut4Url

shortener = Cut4Url.Short()
shortened_url = shortener.check("https://in.mt/6ER")
print("Short Url: "+shortened_url[0])
print("Long Url: "+shortened_url[1])
curies-rs 0.1.3
Python bindings for the curies library
Requires Python: >=3.8
Keywords: semantic-web, linked-data, linked-open-data

<h1 align="center">
  🦀 curies.rs
</h1>

<p align="center">
    <a href="https://crates.io/crates/curies">
        <img alt="crates.io" src="https://img.shields.io/crates/v/curies.svg" />
    </a>
    <a href="https://www.npmjs.com/package/@biopragmatics/curies">
        <img alt="npm" src="https://img.shields.io/npm/v/@biopragmatics/curies" />
    </a>
    <a href="https://pypi.org/project/curies-rs">
        <img alt="PyPI" src="https://img.shields.io/pypi/v/curies-rs" />
    </a>
</p>

<p align="center">
    <a href="https://github.com/biopragmatics/curies.rs/actions/workflows/test.yml">
        <img alt="Test" src="https://github.com/biopragmatics/curies.rs/actions/workflows/test.yml/badge.svg" />
    </a>
    <a href="https://github.com/biopragmatics/curies.rs/actions/workflows/build.yml">
        <img alt="Build" src="https://github.com/biopragmatics/curies.rs/actions/workflows/build.yml/badge.svg" />
    </a>
    <a href="https://docs.rs/curies">
        <img alt="crates" src="https://docs.rs/curies/badge.svg" />
    </a>
    <a href="https://deps.rs/repo/github/biopragmatics/curies.rs">
        <img src="https://deps.rs/repo/github/biopragmatics/curies.rs/status.svg" alt="Dependency status" />
    </a>
    <a href="https://github.com/biopragmatics/curies.rs/blob/main/LICENSE">
        <img alt="MIT license" src="https://img.shields.io/badge/License-MIT-brightgreen.svg" />
    </a>
    <a href="https://codecov.io/gh/biopragmatics/curies.rs/branch/main">
        <img src="https://codecov.io/gh/biopragmatics/curies.rs/branch/main/graph/badge.svg" alt="Codecov status" />
    </a>
</p>
Idiomatic conversion between URIs and compact URIs (CURIEs) in Rust, with bindings to Python, and JavaScript compiled to WebAssembly.

## 📖 Documentation

Checkout the **[biopragmatics.github.io/curies.rs](https://biopragmatics.github.io/curies.rs)** for more details on how to install and use it.
demingfit 0.1.0
A package for performing Deming regression
Requires Python: >=3.7
Keywords: deming regression statistics data-analysis

# Deming Regression

This package provides a simple implementation of Deming regression, an errors-in-variables model which tries to find the line of best fit for a two-dimensional dataset when there are errors in both the x and y variables.

## Installation

You can install the package using pip:

```
pip install deming_regression
```

## Usage

Here's a simple example of how to use the `deming_regression` function:

```python
from deming_regression import deming_regression
import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = 2 * x + 1 + np.random.normal(0, 0.1, 5)

intercept, slope = deming_regression(x, y, 0.1, 0.1)
print(f"Intercept: {intercept}, Slope: {slope}")
```

## Running Tests

To run the unit tests, navigate to the package directory and run:

```
python -m unittest discover tests
```

## License

This project is licensed under the MIT License.
dotrouter 1.0.0
The official Python library for the dotrouter API
Requires Python: >=3.7.1

# Dotrouter Python API library

The Dotrouter Python library provides convenient access to the Dotrouter REST API from any Python 3.7+
application.

## Installation

```sh
pip install dotrouter
```

## Usage

```python
from dotrouter import OpenAI

client = OpenAI(
    api_key="My Dotrouter Key",  # defaults to os.environ.get("DOTROUTER_API_KEY")
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ]
    # No need to pass model, dotrouter will decide the best model for your query!
)
```
SymDFA2AIGER 1.1.6
This package takes a symbolic DFA and produces an AIGER file of type aag
Requires Python: >=3.8

# SymDFA2Aiger

## Description

SymDFA2Aiger is a tool for converting a symbolic deterministic finite automaton (SDFA) into an AIGER file (.aag). The main function provided is `SymDFA2Aiger`. 
An example input file is included in this directory to illustrate the expected format for the function.

#### Personal Note
This tool was created by Daniel Bachmann Aisen as part of his master’s thesis. 
In 2024, amidst challenging times, this project is dedicated to the safe return of all hostages, to the soldiers safeguarding my friends, family, and me, and in memory of those who have fallen. 
May we look forward to better days.

## Inputs

The tool requires five inputs corresponding to SDFA properties:

- `sigma_controlled`: `set[Formula]`
- `sigma_environment`: `set[Formula]`
- `state_variables`: `set[Formula]`
- `initial_state`: `PLTLAnd`
- `transition_system`: `dict`

### Optional Inputs

- `file_name`: `str` (default: `"SymbDFA_AIGER.aag"`)
- `state_variables_return_dict`: `dict` (default: `None`)

### Example Usage

```python
from SymDFA2AIGER import SymDFA2AIGER

a = parse_pltl("a")
b = parse_pltl("b")
c = parse_pltl("c")
d = parse_pltl("d")
_x1 = parse_pltl("x_var1")
_x2 = parse_pltl("x_var2")
_x3 = parse_pltl("x_var3")
_x4 = parse_pltl("x_var4")

sigma_controlled = {a, c}
sigma_environment = {b, d}

initial_state_input = PLTLAnd(_x1, PLTLNot(_x2), _x3, _x4)
final_state_variable = PLTLAnd(_x1, _x2, PLTLNot(_x3), PLTLNot(_x4))
state_variables_input = [_x1, _x2, _x3, _x4]

transition_system_input = {}
transition_system_input["x_var1_prime"] = parse_pltl("a")
transition_system_input["x_var2_prime"] = parse_pltl("b")
transition_system_input["x_var3_prime"] = parse_pltl("(false|a|(b&d)|!c)")
transition_system_input["x_var4_prime"] = parse_pltl(" ((! a) & ! c )")

SymDFA2AIGER(sigma_controlled, sigma_environment, state_variables_input, initial_state_input, transition_system_input, final_state_variable, "experiment_aiger.aag")
```


## Installation and Dependencies

This tool requires two external libraries: [pylogics_modalities](https://github.com/danielaisen/pylogics_modalities) and `multipledispatch`.

- **pylogics_modalities**: This library extends the pylogics library by whitemech. To install it, use the `.whl` file provided in the `dependencies` folder:

`pip install dependencies/pylogics_modalities-0.2.2-py3-none-any.whl`

```bash
pip install SymDFA2Aiger
cognite-robotics-sdk 0.1.19
Cognite Robotics SDK
Requires Python: <4.0,>=3.8

# Cognite Robotics SDK

[![PyPI version](https://badge.fury.io/py/cognite-robotics-sdk.svg)](https://pypi.org/project/cognite-robotics-sdk/)
[![mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org)
[![ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v1.json)](https://github.com/charliermarsh/ruff)

This is the Cognite Robotics SDK for Python. It is a Python package that provides a client library for interacting with the Cognite Robotics APIs of Cognite Data Fusions.

## Installation

The SDK is available on PyPI and can be installed using `pip`:
```sh
pip install cognite-robotics-sdk
```

### Environment Variables
Set the following environment variables in a .env file:
- `COGNITE_PROJECT`
- `COGNITE_CLUSTER`
- `COGNITE_TENANT_ID`
- `COGNITE_CLIENT_ID`
- `COGNITE_CLIENT_SECRET`

### Reference documentation
The reference documentation will be added soon. Please reach out to the Cognite Robotics team for documentation and example code.

### Getting started

Please reach out to the Cognite Robotics team for documentation and example code.

### Changelog

Wondering about upcoming or previous changes to the SDK? Check out the [changelog](CHANGELOG.md).
BackupTree 0.0.1
This project is to create the directory tree to upload your project to Pypi
Requires Python: >=3.6
Keywords: directory tree backup

<h1 align="center">
<img src="https://img.shields.io/static/v1?label=backupTree%20POR&message=MAYCON%20BATESTIN&color=7159c1&style=flat-square&logo=ghost"/>


<h3> <p align="center">Backup Tree </p> </h3>
<h3> <p align="center"> ================= </p> </h3>

>> <h3> Resume </h3>

<p> This is a program that starts a software called Backup Tree, capable of backing up any directory (including external hard drives) into txt files, so that you can later search for where the desired information is located.</p>

>> <h3> How install </h3>
```
pip install backupTree

```

>> <h3> How Works </h3>

```
from backupTree import execute

execute()

```