semvec/pypi-packages huggingface dataset:
syncsign 0.1.0
SyncSign SDK package
Requires Python: >=3.6

# SyncSign Python SDK

This client library is designed to support the [SyncSign API](https://dev.sync-sign.com/webapi/) for SyncSign IoT system. You can read more about the API by accessing its official documentation.

## Install

```
python3 -m pip install --upgrade syncsign
```

## Usage

- Modify one of the examples with your own API KEY, e.g. `devices_list.py`
- Run with `python devices_list.py`
ScreenshotFormat 1
Python package to help create screenshot to upload on stores

# ScreenshotFormat
Python package to help create screenshot to upload on stores

### Instantiate the helper with your screenshot, and the desired store image size
```
from ScreenshotFormat import ScreenshotFormat, StoreSizeName, BackgroundType, Position

# 1. your screenshot
# - pass the path of your screenshot with "screenshot_path"
# - Or directly a PIL image with "screenshot_img"

2. screenshot size
# - By using the Enum StoreSizeName in "store_size_name" param
# - Or by passing a tuple (with, height) in "store_size"
helper = ScreenshotFormat(
                screenshot_path=PATH_SCREENSHOT, 
                store_size_name=StoreSizeName.iphone_6_5)
```

### Retrieve sizes
```
# get your screenshot size with :
width, height = helper.screenshot_size

# get your store size with :
width, height = helper.store_size
```

### Crop your screenshot
```
helper.crop_screenshot(left=None, top=100, right=None, bottom=None)
```

### Resize your screenshot
A ratio will multply width & height to keep
```  
helper.resize_screenshot(zoom_ratio=1.3)
```
Pass the desired size
```  
helper.resize_screenshot(size=(1080, 1920))
```

### Create a background
the image created will have the size of the store you pass the constructor.
All the background types are inside the Enum "BackgroundType":
- plain : A background with only one color
- vertical_gradient : A gradient from top to bottom
- horizontal_gradient : A gradient from left to right
- diagonal_gradient_right : A gradient from the top right corner to the bottom left corner
- diagonal_gradient_left : A gradient from the top left corner to the bottom right corner

You can pass as many color as you want in the "color_palette" argument
```
background = helper.create_background(type_=BackgroundType.diagonal_gradient_right, color_palette=[
    (255, 0, 0),
    (255, 255, 0),
    (255, 255, 255),
])
```

### Adding text
```
text_position = (Position.center, 150)
text = "My Caption"
background = helper.add_text_with_halo(background,
                                       text=text,
                                       position=text_position,
                                       color=(0, 0, 0),
                                       halo_col=(255, 255, 255),
                                       font_path=PATH_TO_YOUR_FONT,
                                       font_size=120)
```

### Pasting the screenshot on the background
```
position = (Position.center, helper.store_height - helper.screenshot_height - 100)
final_image = helper.apply_screenshot_on_background(background, position)
```

### Save image
```
ScreenshotFormat.save(img=final_image, path=OUTPUT_PATH, file_name="MyScreenshot.jpg")
```
pyfreedompro 1.1.0
Freedompro API
Requires Python: >=3.6

# pyfreedompro

Python library for Freedompro API.

Installation
------------

You can install pyfreedompro with `pip install pyfreedompro`.

Example
-------

```python
import asyncio
import pyfreedompro
import aiohttp

async def main():
    httpsession = aiohttp.ClientSession(connector=aiohttp.TCPConnector(ssl=False));
    response = await pyfreedompro.get_list(httpsession, APY_KEY);
    print(response);
    await httpsession.close();
loop = asyncio.get_event_loop()
loop.run_until_complete(main())

```


See the complete documentation of the Fredompro API <https://api.freedompro.eu/>.
mspca 0.0.4
Multiscale Principal Component Analysis Algorithm

# mspca (MSPCA)
Multiscale Principal Component Analysis.

Multiscale PCA (MSPCA) combines the ability of PCA to extract the crosscorrelation or relationship
between the variables, with that of orthonormal wavelets to separate deterministic features
from stochastic processes and approximately decorrelate the autocorrelation among the measurements[1].

![mspca_model](https://user-images.githubusercontent.com/28721422/111423028-0280a800-8733-11eb-8a68-4726130eb542.PNG)

*Fig 1. Schematic illustration of MSPCA model[2].*


![mspca_signal](https://user-images.githubusercontent.com/28721422/111423035-04e30200-8733-11eb-92b7-bf08f452ef56.PNG)

*Fig 2. Schematic diagram for multiscale representation of data[2].*


*******
#### References
[1] Bhavik R. Bakshi, Multiscale PCA with Application to Multivariate Statistical Process Monitoring, The Ohio State University, 1998.

[2] M. Ziyan Sheriff, Majdi Mansouri, M. Nazmul Karim, Hazem Nounou, Fault detection using multiscale PCA-based moving window GLRT, Journal of Process Control, 2017.

# Installation
#### Dependencies
mspca requires:

+ Python >= 3.7
+ PyWavelets == 1.0.3
+ numpy == 1.19.5
+ pandas == 0.25.1


#### Pip
The easiest way to install mspca is using 'pip'

    pip install mspca

# Example

    from mspca import mspca

    mymodel = mspca.MultiscalePCA()
    x_pred = mymodel.fit_transform(X, wavelet_func='db4', threshold=0.3)

![example1](https://user-images.githubusercontent.com/28721422/111422652-5939b200-8732-11eb-9d92-e966191e2b72.PNG)
![example2](https://user-images.githubusercontent.com/28721422/111422673-62c31a00-8732-11eb-9ff2-b74824fc62cb.PNG)
![example3](https://user-images.githubusercontent.com/28721422/111423017-feed2100-8732-11eb-8c11-acf498dffef0.PNG)

# Contact us
Heeyu Kim / khudd@naver.com

Kyuhan Seok / asdm159@naver.com