semvec/pypi-packages huggingface dataset:
pinterest-crawler 0.2.0
Pinterest Crawler: Download as many images as you want about the searched words

# Pinterest Crawler

[![Upload Python Package](https://github.com/SajjadAemmi/Pinterest-Crawler/actions/workflows/python-publish.yml/badge.svg)](https://github.com/SajjadAemmi/Pinterest-Crawler/actions/workflows/python-publish.yml)
[![Python package](https://github.com/SajjadAemmi/Pinterest-Crawler/actions/workflows/python-package.yml/badge.svg)](https://github.com/SajjadAemmi/Pinterest-Crawler/actions/workflows/python-package.yml)
[![Python application](https://github.com/SajjadAemmi/Pinterest-Crawler/actions/workflows/python-app.yml/badge.svg)](https://github.com/SajjadAemmi/Pinterest-Crawler/actions/workflows/python-app.yml)
[![Downloads](https://static.pepy.tech/badge/pinterest-crawler)](https://pepy.tech/project/pinterest-crawler)

<img src="https://raw.githubusercontent.com/SajjadAemmi/Pinterest-Crawler/main/Pinterest-Logo.png" width="400px">

Downloads HD images from pinterest by your favorite keywords. A useful tool to create a dataset for machine learning projects.

## Install

Install the package with pip in a Python>=3.8 environment:

```bash
pip install pinterest-crawler
```

## Usage

### CLI

Pinterest Crawler may be used directly in the Command Line Interface (CLI):

```bash
pinterest-crawler --keywords lion tiger bear
```

Also you can write your favorite keywords in a file for example `my_keywords.txt` and set path of file in `--keywords` argument:

```bash
pinterest-crawler --keywords my_keywords.txt
```

### Python

Pinterest Crawler may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:

```python
from pinterest_crawler import PinterestCrawler


pinterest_crawler = PinterestCrawler()
pinterest_crawler(keywords=['lion', 'programmer'])
```

<!-- Due to some limitations of Pinterest, you can download 100 images per keyword. If you want to download more images, you can run following command for infinite execution:

```
python loop.py
``` -->

## TODO
- [ ] download images in a loop
- [ ] download images in a specific size
- [ ] download images in a specific format
DataSynthesis131 0.0.2
A package for generating synthetic tabular data and evaluating the quality.
Requires Python: >=3.8

# DataSynthesis131
Library to generate synthetic tabular data and evaluate quality of generated data.

# QualityEvaluator
A tool to evaluate sythetic data quality.

# Synthesizer
A tool to generate sythetic tabular data.


For more information and detailed instructions on how to use the QualityEvaluator or Synthesizer, please refer to the [README](https://github.com/MaratElagin/DataSynthesis131/blob/main/README.md) in our GitHub repository.

If you notice any inaccuracies, add issue [here](https://github.com/MaratElagin/DataSynthesis131/issues).
slurpkit 0.9.19
A robust Python SDK for slurpit
Requires Python: <4.0,>=3.10

# Slurpit SDK

The Slurpit SDK is a Python package for interacting with the Slurpit API, enabling developers to easily manage devices and planning resources. It is designed for simplicity and flexibility, offering methods for listing devices, retrieving planning data, and exporting information to CSV format.

## Installation

You can install the Slurpit SDK using pip with the following command:

```bash
pip install slurpkit
```

Alternatively, if you prefer to install from source, clone the repository and run the setup script:

```bash
git clone https://gitlab.com/slurpit.io/python-sdk.git
cd slurpit
python setup.py install
```

## Quick Start

To use the SDK, start by importing the package and setting up the API client:

```python
import slurpit
api = slurpit.api(
    host="http://localhost:8000", 
    api_key="1234567890abcdefghijklmnopqrstuvwxqz"
)
```

Replace the `host` and `api_key` with the URL of your Slurpit instance and your API key respectively.

## Working with Devices

Retrieve and print the hostnames of all devices:

```python
devices = api.device.get_devices()
for device in devices:
    print(device.hostname)
```

## Exporting Data to CSV

To export planning data to a CSV file:

```python
plannings_csvdata = api.planning.get_plannings(export_csv=True)
result = api.device.save_csv_bytes(plannings_csvdata, "csv/plannings.csv")
```
## Exporting Data as Pandas DataFrame

To export planning data as a pandas dataframe

```python
plannings_df = api.planning.get_plannings(export_df=True)
```

## Pagination

Handle large sets of devices with pagination:

```python
devices = api.device.get_devices(offset=100, limit=1000)
for device in devices:
    print(device.hostname)
```
tc-core-analyzer-lib 1.3.1
TogetherCrew core analyzer library.

# tc-core-analyzer

[![Maintainability](https://api.codeclimate.com/v1/badges/116d55363d2feda928d5/maintainability)](https://codeclimate.com/github/TogetherCrew/core-analyzer-lib/maintainability)
[![Test Coverage](https://api.codeclimate.com/v1/badges/116d55363d2feda928d5/test_coverage)](https://codeclimate.com/github/TogetherCrew/core-analyzer-lib/test_coverage)

This repository is made for togethercrew core-analyzer scripts that could be used across different platforms. To install this library

```bash
pip install tc-core-analyzer-lib
```

Usage:

```Python
from tc_core_analyzer_lib.assess_engagement import EngagementAssessment
```
pmb-py 0.4.5.0
pmb restful api python wrap
Requires Python: >=3.6

## 就是個簡單的 Pmb Restful Api Wrap

需要二個環境變數
- AUTH_URL

用來指定公司統一登入(ad)的 api 網址
- PMB_API_URL

用來指定 pmb 服務的網址

#### 除錯專用

可以追加設定 DEBUG 環境變數

有設定 DEBUG 的話就可以用 PMB_API_URL_DEBUG 來指定 Pmb 服務的網址
### 例
```env
DEBUG=True
PMB_API_URL_DEBUG=http://127.0.0.1:5000
```
### 使用範例

```python
import pmb_py as pmb
from pmb_py import log_in, log_out

log_in(YOUR_USERNAME, YOUR_PASSWORD)
# log_in(YOUR_USERNAME, YOUR_PASSWORD, mode='pmb')
# mode 指定為 'pmb' 時會切換成 pmb 自己的登錄方式, 'ad' 為預設值

quert_result = pmb.api.Projects.list()
print(quert_result.result)
# list 會回傳 QueryResult 物件

project = pmb.api.Projects.get(id=100)
print(project)
# 未找到的話會引發 PmbError 例外

log_out()  # 記得登出
```

`目前未對權限部份做太多著墨，所以想用的同事，請記得問一下專用的登入帳號密碼`

不然可能會因為權限的問題使得功能無法操作

## 查詢
### list() 
list() 接受以 keyword 引數的方式查詢，可以接受 `limit` 引數，預設為 1000，最大為 4000

### 使用範例
```python
import pmb_py as pmb
from pmb_py import log_in, log_out

q_result = pmb.api.GanttItem.list(project_id=1311)
print(q_result.results)

q_result = pmb.api.Blocks.list(project_id=1311, type_id=2)
print(q_result.results)
```

### get()
get() 固定接收 id 做為查詢依據，查無物件會引發異常


#### QueryResult 物件

##### 屬性說明

 - limit = limit
    
    本次查詢的數量限制
 
 - next = next
    
    查詢的下一頁
 - previous = previous
    
    查詢的上一頁
 - results = results
    
    查詢的結果
 - start = start
    
    查詢的啟始數
 - total_count = total_count
    
    全部的數量
 - query_params():
    把查詢參數字典化


此外還有一個 first() 函式方便取得第一個元素，取不到時回傳 None


## 建立

可接受所有可用欄位當做 keyword 建立物件，不要給 id

### 例
```python
gantt_item = pmb_py.api.GanttItems.create(
    name='test',
    task_type=4,
    project_id=5,
    sg_task_id=15000,
    read_only=True,
    status='',
)
```

### 更新
可接受所有可用欄位當做 keyword 更新物件，固定以 id 為第一位引數

```python
pmb_py.api.GanttItems.update(5, legend='test update')
```

### 刪除
固定以 id 為第一位引數, 回傳 `True` 或 `False`, 

```python
pmb_py.api.GanttItems.remove(5)
```


## Projects
### list
- 當給予 `member_id` 引數時其他引數將會無效，會回傳指定的 `member_id` 所有專案

### 歷史更新紀錄

V 0.4.5
增加對 project  欄位 meta(誤植為 mata)的支援

V 0.4.4
增加對 is_evaluation 的支援

V 0.4.3.0
增加對應 Project 的 total extra cost 欄位

V 0.4.2.1
修正 cookie 的更名, 修正 header

V 0.4.2
修正對 pmb 2.6.0 之後的登入機制支援(只剩 pmb 自己的 login api 登錄)

V 0.4.1
增加對 pmb 的新登入機制支援(ad 跟 pmb 自己的登錄)

V 0.4.0
增加對 pmb session 支援

v 0.3.4
增加對 gantt task api (api.anttTasks) 的支援

v 0.3.3
增加欄位 (對應 pmb 0.2.0 版的 api)

V 0.3.2
add Project new column "hour_type"

V 0.3.1
add query_params method for QueryResult

V 0.3.0
add blocks_between_date function
google-spreadsheets-fdw 1.0.3
Multicorn-based PostgreSQL foreign data wrapper for Google Spreadsheets

# Google Spreadsheets FDW

Multicorn based PostgreSQL foreign data wrapper for Google Spreadsheets

## Installation

### Requirements

PostgreSQL 9.1+ with [Multicorn](http://multicorn.org/) extension installed.

If you haven't used Multicorn yet, enable it with:

```postgresql
create extension multicorn;
```

From source:

```bash
git clone https://github.com/TheDeadJoe/google_spreadsheets_fdw
cd google_spreadsheets_fdw
python setup.py install
```

## Usage

```postgresql
create server multicorn_srv foreign data wrapper multicorn options (
    wrapper 'google_spreadsheets_fdw.GoogleSpreadsheetFDW'
);

create foreign table my_table (
    id int default nextval('my_seq'),
    foo varchar,
    bar int,
    baz float
) server multicorn_srv options (
    gskey 'zVshdGDuaQKKaQoXqNOwjeTWcxcUtOlSJDZoLeIMUsYx',
    keyfile '/path/to/credentials.json',
    sheet '0',
    row_id 'id'
);
```

### Options

- `gskey` - "ID" of a spreadsheet (the 44 char length part between `/spreadsheets/d/` and `/edit/` from spreadsheet URL)
- `keyfile` - path to Google Cloud Services credentials json file
- `sheet` - index of a sheet
- `row_id` - name of the column which value will be treated as an ID of the whole row

## Example

We start with an empty spreadsheet:

![1](https://user-images.githubusercontent.com/8329442/87254938-d9629600-c486-11ea-8ed2-b5ccb342868b.png)

Let's insert some data into it:

```postgresql
insert into my_table(foo, bar, baz) values ('a', 1, 0.1);
insert into my_table(foo, bar, baz) values ('b', 2, 0.2);
insert into my_table(foo, bar, baz) values ('c', 3, 0.3);
insert into my_table(baz, bar, foo) values (0.4, 4, 'd') returning *;
```

Spreadsheet contains our data.

![2](https://user-images.githubusercontent.com/8329442/87254971-2a728a00-c487-11ea-88ac-9916b84af62f.png)

Now let's try retrieve the data:

```postgresql
select * from my_table;
```

The result:

![3](https://user-images.githubusercontent.com/8329442/87254972-2ba3b700-c487-11ea-8a44-4d993aeeeccd.png)


Of course, we can also perform other SQL operations e.g.: 

```postgresql
update my_table set bar = 9, baz = 0.9 where bar = 2;

delete from my_table where bar > 5;
```
jpegtran 0.0.3
A Python wrapper for jpegtran.
Requires Python: >=3.8

# JPEG: A Python wrapper for jpegtran

JPEG is a Python wrapper for jpegtran, designed to make it easier to interact with and manipulate JPEG files directly from Python. It is much faster than Pillow and OpenCV, as it operates directly on the JPEG file headers. This also means, as you might expect, only the JPEG format is supported.

## Dependancies
You must have [jpegtran](https://jpegclub.org/jpegtran/) installed on your system to use this library.

## Installation
To install the JPEG Python wrapper, you can use pip:

```python
pip install jpegtran
```

## Usage

Here's how to use the JPEG wrapper to open a JPEG file, optimize it, rotate it by 90 degrees, and save the result:

```python
import jpeg

jpeg \
    .open('example.jpg') \
    .optimize() \
    .rotate(90) \
    .save('example-rotated.jpg')
```
sqs-client 0.1.7
SQS client
Requires Python: >=3.8

<p align="center">
  <a href="https://www.digitalfortress.dev/">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="https://okela-bucket-s3.s3.ap-southeast-1.amazonaws.com/logo/Digital+Fortress+-+Logo.png">
      <img alt="Digital Fortress logo" src="https://okela-bucket-s3.s3.ap-southeast-1.amazonaws.com/logo/Digital+Fortress+-+Logo.png">
    </picture>    
  </a>
</p>

---

# Python SQS client

## Getting Started

Install package
```commandline
pip install sqs-client
```

## Example

#### Subscribe

```python
from sqs_client.client import SQSClient

sqs_client = SQSClient()


# Subscribe to a SQS
@sqs_client.task(
    queue_name="sqs-queue-name",
    wait_time_seconds=20,
    visibility_timeout=300,
    daemon=False,
)
def test_task(message):
    print("test_task received:", message)
```

#### Publish
```python
from sqs_client.client import SQSClient
from sqs_client.publisher import Publisher

sqs_client = SQSClient()

sqs_client.publish(
    queue_name="sqs-queue-name",
    message="test message",
)

# or

publisher = Publisher(
    sqs_client=sqs_client,
    queue_name="sqs-queue-name",
)

publisher.publish("test message")
```

### Lazy mode

Faster to subscribe and publish a message to SQS

```python
from sqs_client.client import SQSClient

sqs_client = SQSClient()


# Subscribe to a SQS
@sqs_client.task(
    queue_name="sqs-queue-name",
    lazy=True,
    daemon=False,
    wait_time_seconds=20,
    visibility_timeout=300,
)
def test_task(message, abc):
    print("test_task received message:", message)
    print("test_task received abc:", abc)


# Publish a message
test_task.trigger("Test message", abc=1)
```

Publish a lazy mode message without subscribe

```python
from sqs_client.client import SQSClient
from sqs_client.publisher import Publisher

sqs_client = SQSClient()

publisher = Publisher(
    sqs_client=sqs_client,
    queue_name="sqs-queue-name",
)

publisher.publish_lazy("Test lazy message", abc=1)
```

## License

This project is Copyright (c) 2023 and onwards Digital Fortress. It is free software and may be redistributed under the terms specified in the [LICENSE] file.

[LICENSE]: /LICENSE

## About
<a href="https://www.digitalfortress.dev/">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://okela-bucket-s3.s3.ap-southeast-1.amazonaws.com/logo/Digital+Fortress+-+Logo.png">
    <img alt="Digital Fortress logo" src="https://okela-bucket-s3.s3.ap-southeast-1.amazonaws.com/logo/Digital+Fortress+-+Logo.png" width="160">
  </picture>
</a>

This project is made and maintained by Digital Fortress.

We are an experienced team in R&D, software, hardware, cross-platform mobile and DevOps.

See more of [our projects][projects] or do you need to complete one?

-> [Let’s connect with us][website]

[projects]: https://github.com/digitalfortress-dev
[website]: https://www.digitalfortress.dev