semvec/pypi-packages huggingface dataset:
dlercloud 0.0.8
A Python Wrapper for DlerCloud API
Keywords: DlerCloud

# Python DlerCloud API



## 这是什么

这是一个可用于调用 [DlerCloud](https://dlercloud.co/auth/register?affid=29731) API 的非官方 Python 模块。

你可以用它实现自动节点筛选和配置更新等操作。

> 支持 Python 3.x，尚未验证 Python 2.x 中的兼容性。

## 怎么安装

使用 pip 安装:

```bash
pip3 install -U dlercloud
```

## 如何使用

DlerCloud 在其 [Telegram 频道](https://t.me/dlercloud_news/1113) 中提供了 API 的简要说明。

本 Python 模块按照该说明进行构建。

| API                       | URL                      | `DlerCloudAPI()`            |
| ------------------------- | ------------------------ | ------------------------- |
| 登陆                      | /api/v1/login            | `.login(email, password)` |
| SS 节点列表               | /api/v1/nodes/ss         | `.nodes.ss()`             |
| V2ray 节点列表            | /api/v1/nodes/v2ray      | `.nodes.v2ray()`          |
| Clash SS 托管配置         | /api/v1/managed/clash_ss | `.managed.clash_ss()`     |
| Clash V2ray 托管配置      | /api/v1/managed/clash_v2 | `.managed.clash_v2()`     |
| SS 订阅地址               | /api/v1/subscribe/ss     | `.subscribe.ss()`         |
| SSD 订阅地址              | /api/v1/subscribe/ssd    | `.subscribe.ssd()`        |
| SSR 订阅地址              | /api/v1/subscribe/ssr    | `.subscribe.ssr()`        |
| V2RayN 的订阅地址         | /api/v1/subscribe/av2    | `.subscribe.av2()`        |
| Quantumult V2ray 订阅地址 | /api/v1/subscribe/qv2    | `.subscribe.qv2()`        |
| 使当前 access token 失效 | /api/v1/logout | `.logout()` |

## 示例代码

这些示例代码可以帮助你更好的理解：

```python
from dlercloud import DlerCloudAPI

api = DlerCloudAPI()

# 使用邮箱和密码登录
api.login('your@email.com', 'YoUr*PasSwoRD')

# 若不想每次重复登陆，可将获得的 access_token 保存下来，
# 并在下次创建 DlerCloudAPI 实例时作为初始化参数传入，如:
# api = DlerCloudAPI('mYtOkEnAbCdEf0123456789')
# 目前 access_token 的有效期为 24 小时

# 例: 使用 SSNode 节点信息，创建一条 Surge 节点配置

# 获得 ss 节点列表
nodes = api.nodes.ss()
# [<SSNode: 节点 1>, <SSNode: 节点 2>, <SSNode: 节点 3>, ...]

node = nodes[0]
# <SSNode: 节点 1>

surge_node_conf = '''
{n.name} = ss, {n.server}, {n.port}, encrypt-method={n.cipher}, password={n.password}{o}{u}
'''.format(
    n=node,
    o=', obfs={}'.format(node.advanced['obfs'][12:])
    if node.advanced.get('obfs') not in ('plain', None) else '',
    u=', udp-relay=true' if node.udp else ''
).strip()

print(surge_node_conf)
# 节点 1 = ss, 1.2.3.4, 1234, encrypt-method=aes-256-gcm, password=abc123, obfs=tls, udp-relay=true
```

## 免责声明

本 Python 模块的作者并非 DlerCloud 网站或公司的人员，不参与 DlerCloud 的经营和建设，也并非其投资者。
cellannotation 0.1.0
Package for annotating the data (e.g. cell data).
Keywords: cells,annotation

# cell-annotation

This package provides functionalities to annotate data with labels based on the 
Mann-Whitney U test and Hypergeometric test. Currently, we provide examples of 
the fast annotation of gene expression data with the cell types based on marker 
genes.

## Build the documentation

For building the documentation one needs to have sphinx python package 
installed.

    cd docs
    make hmtl

After build the documentation is available in the _build directory.
TP2Unicode 0.0.9
共Taiwanese package拍出來的羅馬字轉做其他環境mā會當顯示的編碼

[![Build Status](https://travis-ci.org/i3thuan5/Taiwanese-serif-to-Unicode.svg?branch=master)](https://travis-ci.org/i3thuan5/Taiwanese-serif-to-Unicode)

[![Coverage Status](https://coveralls.io/repos/github/i3thuan5/Taiwanese-serif-to-Unicode/badge.svg?branch=master)](https://coveralls.io/github/i3thuan5/Taiwanese-serif-to-Unicode?branch=master)

# Taiwanese-serif-unicode

### 作業流程
1. 先看出現亂碼的文字，臆Taiwanese serif編碼的對應
2. 用fontforge開字型檔
3. 確定逐字的unicode位置，整理去文件
parpar 0.0.19
Parallel parser for large files

# About
ParPar (Parallel Parser) is a light tool which makes it easy to distribute a function across a large file.

ParPar is meant to work on serialized data where some values are highly repeated across records for a given field. e.g.

```
a 1 1
a 1 2
a 2 3
a 2 4
a 3 5
a 3 6
b 1 7
b 1 8
b 2 9
b 2 10
b 3 11
b 3 12
```

although we have 12 records, for column 1 there are only 2 unique values. Likewise, for column 2 there are only 3 unique values. We could break this file up into smaller files under a directory:

```
<out-dir>/<col-1-value>/<col-2-value>
```
or vis versa.

# How to use.

1. Start by importing the ParPar class:

```python
from parpar import ParPar
```

2. Initialize an instance:

```python
ppf = ParPar()
```

3. Shard a large file into sub-files:

```python
# shard by columns
ppf.shard(
  <input-file>, <output-directory>,
  <columns>, <delim>, <newline>
)

# shard by lines
ppf.shard_by_lines(
  <input-file>, <output-directory>,
  <number_of_lines>, <delim>, <newline>
)
```

4. Check to make sure the number of records are the same:

```python
files = ppf.shard_files(<output-directory>)
records = ppf.sharded_records(files)

from parpar.utils import filelines

print(records == filelines(<input-file>))
```


5. Map an arbitrary function across each _line_ of all shared files:

```python
def foo(line, *args, **kwargs):
    pass

args = [1, 2, 3]
kwargs = {'a': 'x', 'b': 'y'}

ppf.shard_line_apply(<output-directory>,
  foo, args, kwargs,
  processes=<number-of-processes>
)
```


6. Map an arbitrary function across all shared files:

```python
def bar(file, *args, **kwargs):
    pass

args = [1, 2, 3]
kwargs = {'a': 'x', 'b': 'y'}

ppf.shard_file_apply(<output-directory>,
  bar, args, kwargs,
  processes=<number-of-processes>
)
```