semvec/pypi-packages huggingface dataset:
WeatherData 0.0.21
Get weather data
Requires Python: >=3.6
Keywords: weather,forecast,observation,taiwan

<p align="center">
  <img src="https://raw.githubusercontent.com/cloudybay/weatherdata-sdk/main/Logo.png">
</p>



## 這是什麼?


**WeatherData** 是收集了氣象局的開放氣象資料以及安吉氣象決策的預報資料。

所有資料都整合在 WeatherData 的後台伺服器中，使用這個套件就不需要再自行開發爬蟲爬取氣象局，只要輸入位置(經緯度或縣市鄉鎮名)，即可取得天氣資料。

## 安裝

```shell

$ pip install WeatherData

```


## 如何開始

```python
>>> import WeatherData as wd
>>> wd.obs.get(citytown='台北市中正區')
# 回傳最新一次觀測時間的資料
{
    'lat': 25.046058,
    'lon': 121.516565,
    'dtime': '2022-03-17T08:00+00:00',
    'ws': 2.7,
    'tx': 26.7,
    'rh': 58.3,
    'pres': 1004.1,
    'wd': 307.3039482779834,
    'precp_hour': 0.0,
    'cloud': 0.28
}

>>> from datetime import datetime
>>> wd.obs.get(lat=21., lon=124., dtime=datetime(2021,12,31))
# 回傳指定觀測時間的資料
{
    'lat': 21.0,
    'lon': 124.0,
    'dtime': '2021-12-31T00:00+00:00',
    'ws': 0.0,
    'tx': 0.0,
    'rh': 0.0,
    'pres': 0.0,
    'wd': 270.0,
    'precp_hour': 0.0,
    'cloud': 0
}

>>> wd.fcst.get(lat=21., lon=124.)
# 回傳最新一次預報的所有資料，總共資料長度 14 天，每小時一筆，共 336 筆資料
[{
    'dtime': '2022-04-13T00:00:00+00:00',
    'wd': 97.5,
    'ws': 6.1,
    'wg': 7.3,
    'precp': -999.0,
    'cloud': 50.9,
    'tx': 25.5,
    'rh': 73.5,
    'pres': 1008.5,
    'lat': 20.0,
    'lon': 120.0
}, {
    'dtime': '2022-04-13T01:00:00+00:00',
    'wd': 99.1,
    'ws': 5.5,
    'wg': 6.5,
    'precp': 0.0,
    'cloud': 47.8,
    'tx': 25.6,
    'rh': 75.0,
    'pres': 1008.4,
    'lat': 20.0,
    'lon': 120.0
}, {
    'dtime': '2022-04-13T02:00:00+00:00',
    'wd': 104.2,
    'ws': 5.1,
    'wg': 6.0,
    'precp': 0.0,
    'cloud': 49.4,
    'tx': 25.6,
    'rh': 74.6,
    'pres': 1008.3,
    'lat': 20.0,
    'lon': 120.0
}...]
```

### 參數說明

#### 輸入參數
- dtime: 資料時間(UTC)
- citytown: 縣市+鄉鎮
- lat: 緯度
- lon: 經度

#### 輸出參數
- dtime: 資料時間(UTC)
- wd: 風向(方位角 0~360度 北向為0)
- ws: 風速(m/s)
- wg: 最大陣風風速(m/s)
- precp: 預報小時雨量(mm)
- precp_hour: 觀測小時雨量(mm)
- cloud: 雲量 0~100
- tx: 溫度(攝氏溫度)
- rh: 相對濕度
- pres: 大氣壓力(hPa)
- lat: 緯度(度)
- lon: 經度(度)

### 錯誤排除


```
requests.exceptions.HTTPError: 503 Server Error: Service Temporarily Unavailable
```

如果出現以上 503 Server Error ， 可以等個 10 秒鐘再試試看。

## 資料來源

- [氣象資料開放平台](https://opendata.cwb.gov.tw/index)
- [安吉氣象決策](https://www.weatherangel.com.tw/company/services2.php)

所有預報資料均來自中央氣象局及安吉氣象決策公司等機構提供，本公司並未進行任何天氣預報。


## License

- [License Detail](./LICENSE)

## 免責聲明

本專案提供的所有內容均用於教育、非商業用途。資料僅供參考，對於資料內容任何錯誤、更新延誤或傳輸中斷，本公司均不負任何責任。
pquiz 1.0
Ports quiz

# PQuiz

`pip install pquiz`

```
usage: pquiz [-h] [-b [1-65536]] [-e [1-65536]] [--all] -p PORT [PORT ...]
             [-m {pretty,printable}] [-t TIMEOUT]

A simple utility to check what ports are available for outgoing tcp connection
(ports 22 and 25 are not testable). Gianluca Caronte 2019-2022 (c)

optional arguments:
  -h, --help            show this help message and exit
  -b [1-65536], --range-begin [1-65536]
                        the begin of the ports range
  -e [1-65536], --range-end [1-65536]
                        the end of the ports range
  --all                 test all the ports
  -p PORT [PORT ...], --port PORT [PORT ...]
                        the port(s) to test
  -m {pretty,printable}, --print-mode {pretty,printable}
  -t TIMEOUT, --timeout TIMEOUT
                        timeout in seconds
```

## Examples

*Testing ports from 79 to 83:*
```
> python -m pquiz -b 79 -e 83
79      KO
80      OK
81      KO
82      KO
83      KO
```

*Testing ports from 79,80 and 83:*
```
> python -m pquiz -p 79 80 83
79      KO
80      OK
83      KO
```

*Testing ports from 79 to 83, with printable output:*
```
> python -m pquiz -b 79 -e 83 -m printable
80
```

*Testing ports from 79 to 83, with a half second timeout:*
```
> pquiz -b 79 -e 83 -t 0.5
79      KO
80      OK
81      KO
82      KO
83      KO
```
easysrv 0.0.3
Easy to use State Reversible Vampnet with fit, transform and fittransform methods
Requires Python: >=3.6
Keywords: machine learning,molecular dynamics,tensorflow

# Easy State Reversible Vampnet 

This is an package providign State Reversible Vampnet from the J. Chem. Phys. 150, 214114 (2019). This is a deep dimensionality reduction used for time series to extract low dimensional slow process description. Usually it is used as an pre-processing tool to build Markov State Model to reduce number of dimensions in the system.

## Installation
At the moment installation is available through github and pypi

### github:
```
git clone https://github.com/DanielWicz/easysrv
cd easysrv
pip install .
```

### PyPi
```
pip install easysrv
```

## Usage
To use it you need two things
1. Your data as an list of numpy arrays. Where each element of the list is an numpy array of shape (time_series_length, number_of_dims).
2. Tensorflow 2 Sequence Model

```
import tensorflow as tf


# use smooth activation, to have smooth slow process assigment
# use non-smooth activation (relu, lerelu etc.) to have non-smooth slow process assigment
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(100, kernel_regularizer=tf.keras.regularizers.L2(0.0001), kernel_initializer='lecun_normal'),
    tf.keras.layers.Activation("swish"),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dense(100, kernel_regularizer=tf.keras.regularizers.L2(0.0001), kernel_initializer='lecun_normal'),
    tf.keras.layers.Activation("swish"),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dense(2, kernel_regularizer=tf.keras.regularizers.L2(0.0001)),
    tf.keras.layers.GaussianNoise(1)])
# assume that list with data are described as a variable features
# pass single datapoint to set shapes of the matrices
model(features[0])

# initialize optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

# initialize SRV, epochs - number of training iterations, split - train:validation split
srv = SRV(model=model, optimizer=optimizer, epochs=20, lagtime=1, split=0.05)

# fit SRV model
history = srv.fit(features)

# depict training process
plt.plot(history['Training loss'])
plt.title("Training loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")

plt.plot(history['Val Training loss'])
plt.title("Validation Training loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")

plt.plot(history['VAMP2 valid score'])
plt.title("Validation VAMP2")
plt.xlabel("Epochs")
plt.ylabel("VAMP2 score")

modes = ["IC{}".format(i+1) for i in range(len(history['eigenvalues']))]
plt.bar(modes, history['eigenvalues'])
plt.title("Slow processses eigenvalues")

# transform relevant features onto slow processes
# remove_modes allows for removing not interesting slow processes from slowest to the fastest
model_out = svr.transform(features, remove_modes=[])
```

## References

- [Chen, Wei, Hythem Sidky, and Andrew L. Ferguson. "Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets." The Journal of chemical physics 150.21 (2019): 214114.](https://aip.scitation.org/doi/full/10.1063/1.5092521)
Scull 1.0.0
A package that allows you to create skeleton animations
Requires Python: >=3.6

# Scull

Scull is a package that allows you to create skeleton animations.

## Creating a skeleton

To start making animation you need to create a skeleton.
You can load a model:
```python
from scull.models.<model_name> import *

simple_model = simple("<skeleton_name>")
complex_model = complex("<skeleton_name>")
```
Or create custom:
```python
from scull import *

skeleton = Skeleton("<skeleton_name>")
backbone = Backbone(0, 0, 7, 90)
skeleton["right_leg"] = Bone(backbone, 0, 10, -45)
skeleton["right_arm"] = Bone(backbone, 90, 10, -90)
skeleton["backbone"] = backbone
skeleton["head"] = Bone(backbone, 100, 3, 180)
skeleton["left_leg"] = Bone(backbone, 0, 10, 45)
skeleton["left_arm"] = Bone(backbone, 90, 10, 90)
```

## Applying poses

When you have a skeleton you can apply a pose to it:
```python
from scull.models.<model_name> import *

<pose_name>.apply(skeleton)
```
You can also create poses:
```python
from scull import *

<pose_name> = Pose(
    left_upper_leg=-22.5,
    left_lower_leg=135,
    right_upper_leg=-56.25,
    right_lower_leg=157.5,
    left_upper_arm=22.5,
    left_lower_arm=-45,
    right_upper_arm=-22.5,
    right_lower_arm=-22.5,
    head=191.25
)
```

## Animating the skeleton

Now you should animate the skeleton:
```python
from scull.models.<model_name> import *

<animation_name>.play(skeleton)
```

## Render

```python
from scull.render.<render_type> import *

renderer = <renderer_name>()
renderer.render(skeleton, {
    "head": {
        "image": "head.png"
    },
    "left_upper_arm": {
        "image": "base.png"
    },
    "left_lower_arm": {
        "image": "lower_arm.png"
    },
    "left_upper_leg": {
        "image": "base.png"
    },
    "left_lower_leg": {
        "image": "lower_leg.png"
    },
    "backbone": {
        "image": "base.png",
        "effects": {
            "shadow": 15
        }
    },
    "right_upper_arm": {
        "image": "base.png",
        "effects": {
            "shadow": 25
        }
    },
    "right_lower_arm": {
        "image": "lower_arm.png",
        "effects": {
            "shadow": 25
        }
    },
    "right_upper_leg": {
        "image": "base.png",
        "effects": {
            "shadow": 25
        }
    },
    "right_lower_leg": {
        "image": "lower_leg.png",
        "effects": {
            "shadow": 25
        }
    }
})
```
feios 0.4.0.dev4
The feios main module
Requires Python: >=3.5

### FEIOS
* An os still indev.
* Developed by fyc,it is remade.

### Changelog
#### 0.4.0dev4
* Even *more* and *more* msgs.
#### 0.4.0dev3
* More error msgs.
#### 0.4.0dev2
* Requires at least setuptools 47 to build.
#### 0.4.0dev1
* Removed "load_cmd" from main module.Instead,using load_cmd from feios_utils.funcs.
#### 0.3.2
* *MUCH SORRY* Because the author cannot afford a Mac,so from now on small releases wont be supporting MacOS.
* We recommend using linux or Windows.
#### 0.3.1
* Deleted useless spaces in err msg.
#### 0.3.0
* From now on,we will provide windows built-dist for major releases 
#### 0.3.0b1
* Fixed CVE-001:One Line Bug
#### 0.3.0a2
* Added outl.
#### Changelog @ 0.3.0a1
* Fixed Unfixed IndexError in the main module.
#### Changelog @ 0.3.0dev3
* Uploaded README.md
#### Changelog @ 0.3.0dev2
* Fixed IndexError(requires feios_utils >= 0.2.0b3)(README.md not uploaded)
#### Changelog @ 0.3.0dev1
* Added support back to py3.5.
* Fixed some IndexError.
pywowgen 0.0.27
Generate and configure Workadventure worlds, maps and resources
Requires Python: >=3.6

# pywowgen

Create Workadventure maps and worlds (set of interconnected maps) suitable for "rc3" or "vanilla" Workadventure
instances.

- Generate maps from templates and generator scripts.
- Check maps for errors and missing resources and display this in an interactive graph view.
- Fix map errors using the graph view.
- Apply some overwrites and fixes to generate maps which will run in default workadventure and / or the community / rc3
  edition.
- Merge resources metadata (licenses, ...) into a single file info to be used in maps.

## Use the CLI

    # Optionally set the env WOWGEN_EXTRA prior execution to define where all the resources go. If not set, its will be set to the operating systems temp dir
    # E.g. WOWGEN_EXTRA=/tmp/wowgen_extra

    # Load external resources from git and verify installation
    pywowgen test

    # Run the api
    pywowgen api --res ${WOWGEN_EXTRA}\wowgen.git\res --tmp ${WOWGEN_EXTRA}\TMP --fin ${WOWGEN_EXTRA}\sites --force True
    
    # Check a resource folder
    pywowgen overview --path ${WOWGEN_EXTRA}\wowgen.git\res 
    
    # Run a world build
    pywowgen world --file . ${WOWGEN_EXTRA}\wowgen.git\examples\default_pipeline.json --res ${WOWGEN_EXTRA}\wowgen.git\res --tmp .${WOWGEN_EXTRA}\wowgen.git\sites\default_pipeline --fin ${WOWGEN_EXTRA}
    
    # Analyze a world build
    pywowgen check --path  ${WOWGEN_EXTRA}\wowgen.git\sites\default_pipeline
ttkbootstrap-adv 0.1.1
ttkbootstrap现代化工具包
Requires Python: >=3.6

# ttkbootstrap-adv

---
# 概述
### 我从`flutter`的组件找到了灵感，开发了一个工具库，有`AdvAppBa`r应用栏，可以在上面自定义想要的效果，`AdvListBox`是可以添加一些组件成为列表。

# 提示
### 作者告诉你一个小秘密，其实我是从我废弃的tkinterAdvanced复制过来的😁😁😁。我就是重构了一些导入和参数，不过把TaskWindow给做好了（之前的项目没做完荒废的东西）

# 快速入手

```python
from ttkbootstrapAdv import AdvWindow, AdvHeaderBar, AdvAppBar


Window = AdvWindow()
Window.geometry("500x500+10+10")
Frame = AdvHeaderBar(Window, "Demo", True)
Frame.show()
Window.titlebar(Frame)
Window.creator_taskwindow()
AppBar = AdvAppBar(Window, default=False)
AppBar.show()

Window.mainloop()
```

# 目标
### 我决定尝试做出MDI界面（作者头发已经做好了掉光的准备）

# 版本概述
## 0.1 原始发布
####  没什么好介绍的
OneClick 3.0.1
OneClik
Keywords: OneClick


<h1 align="center">OneClick</h1>

![](https://img.shields.io/badge/SidraELEzz-orange?style=for-the-badge&logo=python.svg) 
<p align="center">
<a href="#"><img title="Made in UAE" src="https://img.shields.io/badge/MADE%20IN-UAE-red.svg?style=for-the-badge&logo=github"></a>
 
</p>
<p align="center">
<img alt="SidraELEzz' Github Stats" src="https://github-readme-stats.vercel.app/api?username=SidraELEzz&show_icons=true&include_all_commits=true&hide_border=true" />

</p>
<p align="center">
<a href="#"><img title="telegram Num" src="https://img.shields.io/badge/telegram%20Num-SidtaTools-red.svg?style=for-the-badge&logo=telegram"></a>
</p>
<p align="center">
<a href="https://github.com/SidraELEzz/followers"><img title="Followers" src="https://img.shields.io/github/followers/SidraELEzz?color=blue&style=flat-square"></a>
</p>

## Installation :
```
pip install OneClick

```