semvec/pypi-packages huggingface dataset:
apimatic-calculator-97-sdk 1.0.1
This is a long description
Requires Python: >=3.7
Keywords: demo,test


# Getting Started with APIMATIC Calculator

## Introduction

Simple calculator API hosted on APIMATIC

## Install the Package

The package is compatible with Python versions `3 >=3.7, <= 3.11`.
Install the package from PyPi using the following pip command:

```python
pip install apimatic-calculator-97-sdk==1.0.1
```

You can also view the package at:
https://pypi.python.org/pypi/apimatic-calculator-97-sdk/1.0.1

## Test the SDK

You can test the generated SDK and the server with test cases. `unittest` is used as the testing framework and `pytest` is used as the test runner. You can run the tests as follows:

Navigate to the root directory of the SDK and run the following commands

```
pip install -r test-requirements.txt
pytest
```

## Initialize the API Client

**_Note:_** Documentation for the client can be found [here.](https://www.github.com/shield-jaguar/apimatic-calculator-97-python-sdk/tree/1.0.1/doc/client.md)

The following parameters are configurable for the API Client:

| Parameter | Type | Description |
|  --- | --- | --- |
| `environment` | Environment | The API environment. <br> **Default: `Environment.PRODUCTION`** |
| `http_client_instance` | `HttpClient` | The Http Client passed from the sdk user for making requests |
| `override_http_client_configuration` | `bool` | The value which determines to override properties of the passed Http Client from the sdk user |
| `http_call_back` | `HttpCallBack` | The callback value that is invoked before and after an HTTP call is made to an endpoint |
| `timeout` | `float` | The value to use for connection timeout. <br> **Default: 60** |
| `max_retries` | `int` | The number of times to retry an endpoint call if it fails. <br> **Default: 0** |
| `backoff_factor` | `float` | A backoff factor to apply between attempts after the second try. <br> **Default: 2** |
| `retry_statuses` | `Array of int` | The http statuses on which retry is to be done. <br> **Default: [408, 413, 429, 500, 502, 503, 504, 521, 522, 524]** |
| `retry_methods` | `Array of string` | The http methods on which retry is to be done. <br> **Default: ['GET', 'PUT']** |

The API client can be initialized as follows:

```python
from apimaticcalculator.apimaticcalculator_client import ApimaticcalculatorClient
from apimaticcalculator.configuration import Environment

client = ApimaticcalculatorClient()
```

## List of APIs

* [Simple Calculator](https://www.github.com/shield-jaguar/apimatic-calculator-97-python-sdk/tree/1.0.1/doc/controllers/simple-calculator.md)

## Classes Documentation

* [Utility Classes](https://www.github.com/shield-jaguar/apimatic-calculator-97-python-sdk/tree/1.0.1/doc/utility-classes.md)
* [HttpResponse](https://www.github.com/shield-jaguar/apimatic-calculator-97-python-sdk/tree/1.0.1/doc/http-response.md)
* [HttpRequest](https://www.github.com/shield-jaguar/apimatic-calculator-97-python-sdk/tree/1.0.1/doc/http-request.md)
promptengine-ai 0.0.2
PromptEngine is a Python library for conducting interview sessions using OpenAI's ChatGPT model.
Keywords: python,interview,chatbot,AI


# PromptEngine

PromptEngine is a Python library that provides an interface for conducting interview sessions using OpenAI's ChatGPT model. It allows you to interact with the AI assistant to simulate interview conversations and generate responses based on candidate input.

## Installation

You can install PromptEngine using pip:
pip install promptengine


## Prerequisites

Before using PromptEngine, make sure you have the following:

- Python 3.6 or higher
- OpenAI API key
- .env file having following info:
  OPENAI_API_KEY = "Your-key"
  LOG_MODE=DEBUG

## Usage

Here's an example of how to use PromptEngine:

import promptengine

# Create a PromptEngine instance
prompt_engine = promptengine.PromptEngine(redis_instance)

# Store user data for an interview session
interview_id = "interview-1"
username = "John Doe"
position = "Software Engineer"
skills = "Python, JavaScript"
job_description = "A software engineer responsible for developing web applications."
experience = "5 years of experience in software development."

prompt_engine.store_user_data(
    interview_id,
    username,
    position,
    skills,
    job_description,
    experience
)

# Conduct an interview conversation
state = "ONGOING"
candidate_input = "Tell me about your team management experience."

system_message = prompt_engine.chatAI(interview_id, state, candidate_input)

# Convert voice to text
audio_file = "interview_audio.wav"
transcription = prompt_engine.voiceToText(audio_file)

For detailed API documentation and usage examples, please refer to the documentation.

# Contributing
Contributions to PromptEngine are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

# License
This project is licensed under the MIT License - see the LICENSE file for details.

# Acknowledgments
PromptEngine was inspired by the need to automate interview simulations and make the process more efficient and interactive.

# Contact
For any questions or inquiries, please contact akybharat02@gmail.com.

We hope you find PromptEngine useful! Enjoy conducting interview sessions with AI assistance.
DigiTokens 1.0.0
Получение токена для API DIGISELLER
Requires Python: >=3.10
Keywords: Digiseller token python

# Модуль получения токена для Digiseller API

Этот модуль предоставляет простой способ получения токена для работы с API продавца на сервисе Digiseller.com.

Срок действия полученного токена ограничен. Каждый новый полученный токен обнуляет предыдущий.
Полученный токен и его срок действия сохраняются. Если срок действия токена истекает, происходит его автоматическое обновление.

Этот модуль был создан для личного использования и удобен при одновременном использовании в нескольких проектах в глобальной среде.
При одновременном использовании в локальной среде нескольких проектов может возникать конфликт токенов, где новый токен обнуляет предыдущий.

## Использование

Для использования модуля импортируйте его и вызовите функцию `get_token`, передавая параметры `seller_id` и `api_key`.

Чтобы получить API-ключ и идентификатор продавца, обратитесь к документации в вашей учетной записи Digiseller.
pyswisseph 2.10.3.2
Python extension to the Swiss Ephemeris
Requires Python: >=3.5
Keywords: Astrology Ephemeris Swisseph

Python extension to AstroDienst Swiss Ephemeris library.

The Swiss Ephemeris is the de-facto standard library for astrological
calculations. It is a high-precision ephemeris, based upon the DE431
ephemerides from NASA's JPL, and covering the time range 13201 BC to AD 17191.

::

    >>> import swisseph as swe
    >>> # first set path to ephemeris files
    >>> swe.set_ephe_path('/usr/share/sweph/ephe')
    >>> # find time of next lunar eclipse
    >>> jd = swe.julday(2007, 3, 3) # julian day
    >>> res = swe.lun_eclipse_when(jd)
    >>> ecltime = swe.revjul(res[1][0])
    >>> print(ecltime)
    (2007, 3, 3, 23.347926892340183)
    >>> # get ecliptic position of asteroid 13681 "Monty Python"
    >>> jd = swe.julday(2008, 3, 21)
    >>> xx, rflags = swe.calc_ut(jd, swe.AST_OFFSET+13681)
    >>> # print longitude
    >>> print(xx[0])
    0.09843983166646618

:Documentation: https://astrorigin.com/pyswisseph
:Repository: https://github.com/astrorigin/pyswisseph
gerbil-api-wrapper 1.0.3
A package that provides wrapper functionality for the Gerbil Benchmark Service
Requires Python: >=3.6

# GerbilExperimentApiWrapper

This API wrapper is meant to automate the testing of QA results with a standardized process
using [Gerbil for QA](http://gerbil-qa.aksw.org/gerbil/) 
(see also [Paper](https://dl.acm.org/doi/pdf/10.1145/2736277.2741626) and [GitHub repository](https://github.com/dice-group/gerbil)).

## initialization

The `Gerbil` object is initialized with a *Gold standard dataset*, 
a *test dataset* or alternatively a *live annotator*, as well as the used *language*. 

As part of the initialization, the files are uploaded to the Gerbil service and an experiment
is started. 
If upload of the experiment configuration fails or the received results page URL is not valid, 
then an exception is thrown and the api wrapper is *not* initialized.

**Note:** File names for the upload to the Gerbil API must not exceed 100 characters.

## Examples

Running an expeiment with a local test results file: 

```python
from gerbil_api_wrapper.gerbil import Gerbil

wrapper = Gerbil(
    gold_standard_file="original_data/qald_9_plus_test_dbpedia.json",
    test_results_file="original_data/qald_9_plus_test_dbpedia-test.json",
    test_results_name="MyTestResults",
    gold_standard_name="MyGoldStandard",
    language="de"
)
```

Running an experiment with a live annotator: 

```python
from gerbil_api_wrapper.gerbil import Gerbil

wrapper = Gerbil(
    gold_standard_file="results_gold.json",
    live_annotator_name="QAnswer",
    live_annotator_url="http://qanswer-core1.univ-st-etienne.fr/api/gerbil",
    language="en")
```

After successful initialization the results can be accessed with the following functions: 

* `get_results_url()` returns the URL of the Gerilb website for the started experiment.
* `get_results()` returns a dict containing the JSON-LD data for the started experiment.
ArverRubika 2.0.1
Coding the robot in Rubika in the easiest way !
Keywords: python,rubel,Rubika,ArverRubika,robika,robot,ArseinRubika,arsein,arver,Arver,pyrubi

a simple module for creating & coding a robika platform bot

easy to work

install :
       1 open cmd 
       2 write  git install ArverRubika
       3 use in project by import

creator : Aboli Coder 
devops : Ilia.Django
sqlite-rw 0.0.2
sqlite读写分离库

<!--
 * @Author       : xupingmao
 * @email        : 578749341@qq.com
 * @Date         : 2023-03-11 13:22:54
 * @LastEditors  : xupingmao
 * @LastEditTime : 2023-03-11 13:42:41
 * @FilePath     : /sqlite-rw/README.md
 * @Description  : 描述
-->
# sqlite-rw

为sqlite提供读写分离能力，提高并发度。

# 解决什么问题?

sqlite是一个很好用的嵌入式数据库，但是由于sqlite采用了库级别的文件锁，所以并发能力不强，当存在慢SQL，比如在大表上`select count(*) from large_table`的时候，会导致其他任务无法写入。

`sqlite-rw`就是为了解决这个问题的，思路如下
- 采用读写分离的存储模式，每次写入会持久化写库，并且记录日志到binlog表，然后异步写入读库。
- 读取的时候可以选择从读库或者写库读取，默认从读库读取。

# 提高并发度的原理
- sqlite本身读取是支持并发的，但是写入是不能和读取并发的。所以读库本身是支持并发读取的，同步的写操作是通过id主键写入的，耗时会比较短，基本上不会阻塞读操作。
- 由于是读写分离的，在读库存在慢SQL的情况下，写库也能够正常工作，在读库慢SQL执行完成之后，系统会把写库的数据同步到读库中，使两者保持一致。

# 使用限制和影响
- 存储量相比于单独的sqlite会翻倍
- 写入性能会有所下降，待详细的基准测试
- 依赖`web.py`的`db`接口进行操作