semvec/pypi-packages huggingface dataset:
pyfetchh 1.5.3
Stylish and simple fetch for your terminal that is customizable, and fast.
Requires Python: >=3.5

<p align="left">
<img src="./preview/pyfetch.png">
</p>

# pyfetch
Stylish and simple fetch for your terminal that is customizable and fast.

# Dependencies
* [Python 3](https://python.org)
* [fontawesome](https://fontawesome.com/) on your system
* [distro](https://pypi.org/project/distro/) package on PyPi (only for linux systems)
* [psutil](https://pypi.org/project/psutil/) package on PyPi
* [colorama](https://pypi.org/project/colorama/) package on PyPi

# Installation

## Installation through the binary
* Get the latest release through [here](https://github.com/kreat0/pyfetch/releases)
* Copy the binary to /usr/bin 
* Make it an executable by running `chmod +x /usr/bin/pyfetch`

## Installation from source
* Clone the repo
* Type `pip install distro colorama psutil` to install the dependencies (for Windows, it's only `pip install colorama psutil` but for Gentoo its `pip install distro colorama psutil --user`. The binary may also be accessable at `pip3`, `python3 -m pip`, `python -m pip`, `py3 -m pip`, or `py -m pip`.)
* Type `make install` as root
* Enjoy!

## Installation through the AUR package
```
git clone https://aur.archlinux.org/packages/pyfetch-git.git
cd pyfetch-git
makepkg -si
```
Or use an AUR helper like `yay`:
`yay -S pyfetch-git`

# Credits
* Yellowsink
* mugman
BaseScary 0.1.2
简单的爬虫解析框架


## 根据规则解析 JSON 数据，返回对应内容数据
```JSON
from base_scary import BaseScary

scary = BaseScary("http://httpbin.org/ip",[{"tag": 'IP',"rule": "origin","rules": []}])

rules = [
    {"tag": 'IP', "rule": "origin", "rules": []},

]
scary = BaseScary("https://httpbin.org/ip", rules)
result, err = scary.active()
if err:
    print(err)
else:
    print(result)
```
## 根据规则解析 HTML 数据，返回对应内容数据
```html
from base_scary import BaseScary

rules = [
    {"tag": 'links',
        "rule": "//div[contains(@class,'nav-mod-1')]/ul/li/a", "rules": [
            {"tag": "text", "rule": "text()","rules": []},
            {"tag": "url", "rule": "@href","rules": []}
        ]}
]
scary = BaseScary(
    "https://www.sina.com.cn/", rules)
result, err = scary.active()
if err:
    print(err)
else:
    print(result)

```

## 继承BaseScary 对Url 请求使用 代理
```
from base_scary import BaseScary
class SinaScary(BaseScary):
    def _get_proxies(self):
        return {"https": "http://14.18.109.42:8081"}
```
hub-rest-api-client 0.1.2.post1
A client library for accessing Hub REST API
Requires Python: >=3.6,<4.0

# hub-rest-api-client `0.1.2.post1`
A client library for accessing Hub REST API

⚠️This SDK generated using `2021.1.13439` [OpenAPI](https://hub.jetbrains.com/api/rest/openapi.json) version by [openapi-python-client](https://github.com/mtovts/openapi-python-client/tree/fix/overriding-parameters) which is under development.
 It may have some bugs. Use with caution.⚠️

If you find an bug or want to request a new feature, please create issue in [YouTrack](https://youtrack.jetbrains.com/newIssue?project=JT&c=State%20Open&c=Subsystem%20Python%20client%20library).

### It will be fixed\added soon:
- [ ] Generators for endpoints with query parameters `skip`, `top`.
- [ ] Several data model not generating now. It leads to generating only `*_detailed` APIs which return raw [`Response`](\hub_rest_api_client\types.py) object.


## Usage
First, create a client:

```python
from hub_rest_api_client import Client

client = Client(hub_base_url="https://hub.jetbrains.com/api/rest")
```

If the endpoints you're going to hit require authentication, use `AuthenticatedClient` instead:

```python
import os

from hub_rest_api_client import AuthenticatedClient


client = AuthenticatedClient(hub_base_url="https://hub.jetbrains.com/api/rest", token=os.getenv("JB_HUB_TOKEN"))
```

Now call your endpoint and use your models:

```python
from hub_rest_api_client.models import MyDataModel
from hub_rest_api_client.api.my_tag import get_my_data_model
from hub_rest_api_client.types import Response

my_data: MyDataModel = get_my_data_model.sync(client=client)
# or if you need more info (e.g. status_code)
response: Response[MyDataModel] = get_my_data_model.sync_detailed(client=client)
```

Or do the same thing with an async version:

```python
from hub_rest_api_client.models import MyDataModel
from hub_rest_api_client.api.my_tag import get_my_data_model
from hub_rest_api_client.types import Response

my_data: MyDataModel = await get_my_data_model.asyncio(client=client)
response: Response[MyDataModel] = await get_my_data_model.asyncio_detailed(client=client)
```

Things to know:
1. Every path/method combo becomes a Python module with four functions:
    1. `sync`: Blocking request that returns parsed data (if successful) or `None`
    1. `sync_detailed`: Blocking request that always returns a `Request`, optionally with `parsed` set if the request was successful.
    1. `asyncio`: Like `sync` but the async instead of blocking
    1. `asyncio_detailed`: Like `sync_detailed` by async instead of blocking
     
1. All path/query params, and bodies become method arguments.
1. If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)
1. Any endpoint which did not have a tag will be in `hub_rest_api_client.api.default`