semvec/pypi-packages huggingface dataset:
pdf2images 0.0.6
Convert PDF file to image files ROBUSTLY.
Requires Python: >=3.5

# pdf2images
Convert PDF file to image files **ROBUSTLY**.

# Example
```
$ pdf2images -h
usage: pdf2images [-h] [--max-size MAX_SIZE] pdf_file output_dir

positional arguments:
  pdf_file
  output_dir

optional arguments:
  -h, --help           show this help message and exit
  --max-size MAX_SIZE  max size of either side of the image
```

# Why another "pdf-to-image" package
Once in a while, I need to convert a pdf file (usually slides or academic
paper) into image files (thumbnails) in order to get a fast glance to the
readers without downloading the pdf file.

However, I found all the pdf2image solutions cannot robustly process all the
pdf files, since many pdf files are in non-standard format or come up with
extensions. They are always broken in some cases.

But to look them on the bright side, for any plausible case, there is almost
one of them can process it successfully.

So I combined (a.k.a. *ensemble*) them together to make it work across most cases.

# Installation
As mentioned above, we combined multiple pdf manipulation libraries. Here are
the list of the libraries used:
- [wand](http://docs.wand-py.org), an ImageMagick python wrapper.
- `pdftotext` command line tool provided by [xpdf](http://www.xpdfreader.com/)
- [preview-generator](https://github.com/algoo/preview-generator)
- [qpdf](https://github.com/qpdf/qpdf)

where wand and preview-generator are python packages that can be automatically
installed along with pdf2images. However, you have to install xpdf and qpdf
manually.

On Ubuntu:
```
sudo apt install -y qpdf xpdf libimage-exiftool-perl
```

On Arch Linux:
```
sudo pacman -S --noconfirm qpdf xpdf perl-image-exiftool
```

On macOS:
```
brew install freetype imagemagick qpdf xpdf exiftool libmagic ghostscript
```

The installation of pdf2images is quite simple:
```
pip install pdf2images
```

# Robustness
This package has successfully processed hundreds of thousands of arxiv papers
(for generating thumbnails).


# Gallary
The following images are converted from a [slide](https://www.deeplearningbook.org/slides/02_linear_algebra.pdf) from [Deep Learning Book](https://www.deeplearningbook.org/lecture_slides.html)

![page-0](assets/0.png)
![page-1](assets/1.png)
![page-2](assets/2.png)
![page-3](assets/3.png)

# Development
```
pip3 install -r requirements.dev.txt
pre-commit install
```
meme-manager 0.3.1
A meme manager with web UI
Requires Python: >=3.6
Keywords: application web

# 简介
Web UI 表情包管理器。

功能：
- 浏览图片。
- 添加、删除图片。
- 给图片打标签，并可以按标签搜索。
- 图片分组。

# 安装：
要求：Python 3.6 及以上。

本软件使用 PyPI 进行分发，所以你可以使用 pip 来安装。但是更建议使用 [pipx](https://github.com/pipxproject/pipx) 将其安装到一个单独的虚拟环境中，避免污染全局系统依赖，如下：
```
$ pipx install meme-manager
```

验证安装成功：
```bash
$ meme-manager --version
# 或
$ meme-manager --help
```

# 使用：
```bash
# 初始化 sqlite 数据库：
$ meme-manager initdb foo.sqlite

# 运行：
$ meme-manager run foo.sqlite

# URL：http://localhost:5000/index.html
```

# 开发：
说明：
后端使用 Python Flask + waitress，前端使用 React + Antd 开发。

后端：
```bash
$ git clone https://github.com/valleygtc/meme-manager.git
$ cd meme-manager

# create venv
$ python3 -m venv .venv
$ source .venv/bin/activate
# install meme-manager and its dependencies.
$ pip install --editable .

# run unittest
$ python -m unittest discover

# ENV
$ cp env.sh.example env.sh
$ vi env.sh # 填写好程序运行所需环境变量。
$ source env.sh # 读入环境变量。

# init db memes.sqlite
$ meme-manager initdb

# run on db memes.sqlite
$ flask run
```

前端：

见：https://github.com/valleygtc/meme-manager-frontend

# 构建与发布：
prerequsite：
```
$ pip install --user --upgrade setuptools wheel

$ pipx install twine
```

build and upload to pypi:
```bash
# 首先要 build 前端。
# 然后把前端 build 出来的 build/ 目录复制到 src/meme-manager 目录下，并改名为 frontend。

# build
$ python3 setup.py sdist bdist_wheel

# upload to pypi
$ twine upload dist/*
```
WTDpy 0.0.3
Basic calls to the World Trading Data API with Python.
Keywords: wtdpy,world trading data,API

[![CircleCI](https://circleci.com/gh/uijl/wtdpy.svg?style=svg)](https://circleci.com/gh/uijl/wtdpy)
[![Documentation Status](https://readthedocs.org/projects/wtdpy/badge/?version=latest)](https://wtdpy.readthedocs.io/en/latest/?badge=latest)
[![PyPI Latest Release](https://img.shields.io/pypi/v/wtdpy.svg)](https://pypi.org/project/wtdpy/)

# WTDpy

Basic calls to the World Trading Data API with Python. 
You can find the documentation over [here](https://wtdpy.readthedocs.io).

## Installation

Installing WTDpy via [pip](https://pip.pypa.io) is the preferred method, as it will always install the most recent stable release. If you do not have
[pip](https://pip.pypa.io) installed, this [Python installation guide](http://docs.python-guide.org/en/latest/starting/installation/) can guide you through the process.

To install WTDpy, run this command in your terminal:

``` bash
# Use pip to install wtdpy
pip install wtdpy
```

Downloading and installing WTDpy from source is also possible, follow the code below.

``` bash
# Download the package
git clone https://github.com/uijl/wtdpy

# Go to the correct folder
cd wtdpy

# Install package
pip install -e .
```

## Starting up

The code snip below shows how you can initialise the WTDpy class. As soon as you have the `WTDpy` class initialised you can start calling the various functions.

```python
# Import wtdpy library
from wtdpy import WTDpy

# Specifiy your api key
api_token = "Your_API_Token"

# Initialise class
wtdpy = WTDpy(api_token=api_token)

# Search for a stock or index
MSFT = wtdpy.search("Microsoft")
```