semvec/pypi-packages huggingface dataset:
pyonfireapi 0.0.4
Unofficial API client for the FireAPI from 24fire GmbH.
Requires Python: >=3.8

# Python FireAPI Client
Unofficial API client for the FireAPI from 24fire GmbH. <br>

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

## Installation
````shell
pip install pyonfireapi 
````

## Examples
### 1. List all VMs
````python
from pyonfireapi import FireAPIClient

# Set the client and authenticate
client = FireAPIClient("api-key")

# Perform the Request
r = client.vm.list_all_vms()

# Print statuscode
print("Status Code: ", r.status_code)

# Print json
print(r.json())
````

Output:
```Status Code: 200```

````json
{
    "status": "success",
    "requestID": "a24effc3-b1d6-46a3-a49a-fe07734717ad",
    "message": "All vm data are listed below",
    "data": {
        "total_vms": 2,
        "total_stats": {
            "cores": 12,
            "mem": 3072,
            "disk": 20
        },
        "list": [
            {
                "vmid": 30071,
                "createDate": "2023-05-31T00:38:40.000Z",
                "node": "node09",
                "config": {
                    "cores": 10,
                    "mem": 1024,
                    "disk": 10
                }
            },
            {
                "vmid": 30074,
                "createDate": "2023-06-10T11:10:34.000Z",
                "node": "node09",
                "config": {
                    "cores": 2,
                    "mem": 2048,
                    "disk": 10
                }
            }
        ]
    }
}
````
datadivecsv 0.1.2
Herramienta de Análisis Exploratorio de Datos para archivos CSV

# datadivecsv: AnÃ¡lisis Exploratorio de Datos para CSV

El mÃ³dulo datadivecsv proporciona una herramienta sencilla y eficaz para realizar un anÃ¡lisis exploratorio bÃ¡sico de datos contenidos en archivos CSV. Este mÃ³dulo es ideal para obtener una rÃ¡pida comprensiÃ³n de los datos, especialmente Ãºtil en las etapas iniciales de cualquier proyecto de anÃ¡lisis de datos.

## CaracterÃ­sticas

- Lectura de Archivos CSV: Capacidad para leer archivos CSV desde el directorio actual o subdirectorios especÃ­ficos (datasets, notebooks).
- NormalizaciÃ³n de Nombres de Columnas: OpciÃ³n para convertir nombres de columnas a snake_case, facilitando el manejo de los datos.
- VerificaciÃ³n y EliminaciÃ³n de Duplicados: Chequea y elimina filas duplicadas en el conjunto de datos para asegurar la precisiÃ³n del anÃ¡lisis.
- Resumen del DataFrame: Proporciona un resumen exhaustivo del DataFrame, incluyendo las primeras y Ãºltimas filas, una muestra aleatoria de filas, informaciÃ³n del DataFrame, estadÃ­sticas descriptivas, valores faltantes, y mÃ¡s.
- VisualizaciÃ³n de Datos: Genera histogramas para variables numÃ©ricas y mapas de calor de correlaciÃ³n, ademÃ¡s de anÃ¡lisis de variables categÃ³ricas.

## Uso

Para utilizar datadivecsv, simplemente importa la funciÃ³n execute_analysis y pÃ¡sale el nombre del archivo CSV que deseas analizar. AquÃ­ tienes un ejemplo bÃ¡sico de uso:

```py
from datadivecsv import execute_analysis

df = execute_analysis('tu_archivo.csv', normalize_cols=True, check_duplicates=True, show_summary_flag=True)
```

**ParÃ¡metros de execute_analysis**

- `file_name`: Nombre del archivo CSV a analizar.
- `normalize_cols` (opcional): Booleano para normalizar los nombres de las columnas a snake_case (por defecto True).
- `check_duplicates` (opcional): Booleano para verificar y eliminar duplicados (por defecto True).
- `show_summary_flag` (opcional): Booleano para mostrar un resumen del DataFrame (por defecto True).

## InstalaciÃ³n

Puedes instalar este paquete usando pip:

```bash
pip install datadivecsv
```

## Contribuir

Las contribuciones son bienvenidas. Por favor, revisa las `issues` en GitHub para ver cÃ³mo puedes contribuir.

## Licencia

Este proyecto estÃ¡ bajo la Licencia MIT.
symqc 1.1.1
Symbolic Quantum Computing Simulator
Requires Python: >=3.8

# SymQC：符号化量子计算模拟器

该文档已过时，待更新。

### &#x1F308; 介绍
`SymQC`是一款符号化的量子计算模拟器，同时提供了符号化量子运算的相关API，旨在帮助用户学习量子计算以及量子算法相关知识。

作为`Quingo`项目的一部分，支持使用 `QCIS` 指令集作为输入, 并集成为**Qunigo 量子-经典异构编程框架**中的量子模拟后端。

### &#x1F31F; 安装
- 运行依赖

![](https://img.shields.io/badge/Python-3.10-green.svg)
![](https://img.shields.io/badge/Sympy-1.5.1-green.svg)
![](https://img.shields.io/badge/ply-3.11-green.svg)

- 安装
```sh
pip install symqc
```
或者
```sh
git clone https://gitee.com/quingo/SymQC.git
cd SymQC
pip install -e .
```

### 使用

- 安装后可以通过如下方式使用SymQC
```sh
python -m symqc input -o output
```

#### 一个简单的示例
输入文件`test.qcis`
```qcis
H       Q1
CNOT    Q1  Q2
M       Q1
M       Q2
```
> 关于输入文件格式可以参考[QCIS指令集](https://quantumcomputer.ac.cn/UserBook.html)

然后运行`SymQC`
```sh
python -m symqc test.qcis -o result.md
```
可以得到运行结果`result.md`

#### The ans of test.qcis

**Init state** is:
$$
\left[\begin{matrix}a_{0} & a_{1} & a_{2} & a_{3}\end{matrix}\right]
$$

**Final state** is:
$$
\left[\begin{matrix}0 & 0 & 0 & \frac{\sqrt{2} a_{0}}{2} - \frac{\sqrt{2} a_{1}}{2}\end{matrix}\right]
$$

- 您也可以通过`--help`选项来了解各个选项的含义

### 测试
> use `pytest` to run the unittest in SymQC.

### &#x1F975; 文档
[SymQC API Rference](doc/API.md)
rospy-nonlocalstubs 0.1.3
A hack to get your editor to recognise rospy functions without installing ros on your system.
Requires Python: >=3.8

# rospy-nonlocalstubs

A hack to get your editor to recognize rospy functions without installing ros or rospy on your system. This is for people like me, who run their ROS nodes inside a docker container and therefore don't have ROS installed on the environment they run their editor in.

This makes use of the fact that most editors/LSPs will also check for the PEP 561 -stubs package, even when the base package is not installed, and the fact that type stubs are still valid, even when all arguments are `Any` and `Incomplete`. To reiterate: this is an ugly hack that just happens to work awfully conveniently.

Please do not install this package in any environment that also has ros installed. That might break stuff.

To install, simply run `pip install rospy-nonlocalstubs` in the same environment as the one your editor lives in.

This package can be automatically updated to the latest version of rospy by anyone who has mypy (more specifically, its submodule stubgen) installed:
```sh
# Update the git submodule in which rospy lives
git submodule init && git submodule update

# Generate the stubs with stubgen
./build.sh
```
telfhk0 2.2.1
Telegram for Hackers
Keywords: ..:::EsFeLuRm:::..

Hello dear friend who uses our library

this module is at your service with speed and security!

What is Telegram FHK module?

It means "Telegram for hackers".

Why is this module trusted by hackers?

Because this module is written with simple codes and has high speed and is safe!

To read the module document, refer to the channel below : https://t.me/Telfhk

What are the features of this module?

- High speed
- safe
- Send message without filter for Iranians
- Download the file from the site through a robot without filters for Iranians
- Has practical methods
- Updated every month + new options

To be able to use the module, be sure to subscribe to our channel!
ultr-bias-toolkit 0.0.5
Toolkit for bias estimation in unbiased learning to rank
Requires Python: >=3.9,<4.0

# Personal toolkit for bias estimation in unbiased learning to rank

## Installation
```
pip install ultr-bias-toolkit
```

## Offline bias estimation methods
We implement multiple offline position bias estimation methods, including [three intervention harvesting](https://arxiv.org/abs/1812.05161) approaches:

```
from ultr_bias_toolkit.bias.naive import NaiveCtrEstimator
from ultr_bias_toolkit.bias.intervention_harvesting import PivotEstimator, AdjacentChainEstimator, AllPairsEstimator

estimators = {
    "CTR Rate": NaiveCtrEstimator(),
    "Pivot One": PivotEstimator(pivot_rank=1),
    "Adjacent Chain": AdjacentChainEstimator(),
    "Global All Pairs": AllPairsEstimator(),
}
examination_dfs = []

for name, estimator in estimators.items():
    examination_df = estimator(df)
    examination_df["estimator"] = name
    examination_dfs.append(examination_df)

examination_df = pd.concat(examination_dfs)
examination_df.head()
```