semvec/pypi-packages huggingface dataset:
text-content-generator 0.0.3
To scrape or collect text based content from media
Requires Python: >=3.9

# TextContentGeneration
Generates Text Content from various sources such as YouTube Videos, Local Audio/Video Files, Webpages and PDF's.

##  Imports

Use the **_tcg.generate_content()_** function to generate content from
four sources:
1. PDFS
2. Web Pages
3. YouTube Video Transcripts
4. Local Audio/Video Files

You can also individual modules if you need only specific functionality.
1. audio 
   1. audio_conv - converts files to wav format
   2. transcribe - whisper model to transcribe audio files
2. youtube
   1. yt_donwload - Download the yt video
   2. search_yt - Returns search titles based on search query
   3. search_and_download
   4. youtube_script - Gets transcript/captions content (EN)
3. summarize
   1. summarize_chunks - Summarizes content using BART
4. webpage
   1. read_pages - Scrapes a page
5. pdf
   1. pdf_read - Reads pdfs
pyneato 0.0.8
Package to control a neato vacuum robot
Requires Python: >=3.7

# pyneato

Python package to control a Neato vacuum robot 
managed via MyNeato app (starting from D8).

This package was mostly developed in order to control
a vacuum robot through [Home Assistant](https://www.home-assistant.io/).

This package is currently far from being finished. Contributions are welcome. 

## Usage

Use the sample script for a short introduction on
how to use this package.

Create a `.passwd` file containing your MyNeato password and
execute this command on the cli:

```bash
MYNEATO_USER=<YOUR_USERNAME> MYNEATO_PASSWORD=`cat .passwd` python pyneato/sample/sample.py
```

## Thanks

Thanks to @stianaske for his work on [pybotvac](https://github.com/stianaske/pybotvac). This
package is highly inspired by pybotvac because I don't really know much about python but wanted
to give it a shot.
quadcube 1.0.0
Python bindings for `pix2vec` for converting between quadcube res15 pixel number to ecliptic unit vectors.
Requires Python: >=3.7

# quadcube

Python library (written in rust) that currently provide one method `pix2vec` for converting between quadcube res15 pixel number to ecliptic unit vectors.

The code is a reimplementation of parts of the COBE DIRBE map binning routines provided by A.J. Banday. See the [COBE Explanatory Supplement](https://lambda.gsfc.nasa.gov/product/cobe/dirbe_exsup.html) for more information.

## Install
`pip install quadcube`

## Example
```python
import quadcube

quadcube.pix2vec(98477345)
# >>> array([[0.37147827],
#           [0.62248052],
#           [0.6888555 ]])

pixels = [315879751, 238749305, 408302824, 290970621, 427780527]
quadcube.pix2vec(pixels)
# >>> array([[ 0.90477257, -0.51544922,  0.91755844,  0.74030402,  0.98561299],
#            [-0.40535959,  0.10338045, -0.39665074, -0.00232418, -0.00949373],
#            [-0.13065298,  0.85066127,  0.02747185, -0.67226822,  0.16875101]])

```
cmo-databaseutils 0.0.2
Connect to databases from Python
Requires Python: >=3.6

# Cmotions DatabaseUtils

cmo_databaseutils is a Python library created by [The Analytics Lab](https://www.theanalyticslab.nl/), which is powered by [Cmotions](https://cmotions.nl/). This library combines our most used functionality when it comes to connecting to databases from Python. Nothing fancy, but very useful for our consultants. Mostly since this package is used in other packages, which makes our workflow simpler and more efficient.

Since we love to share what we do, why not also do that with our packages, that is why we've decided to make (almost) all of our packages open source, this way we hope to give back to the community that brings us so much. Enjoy!

## Installation
Install cmo_databaseutils using pip
```bash
pip install cmo-databaseutils
```

If you want to be able to use the tutorial notebook or the code below, you also need to install pydataset and dotenv using pip
```bash
pip install python-dotenv
pip install pydataset
```

## Usage
```python
import os
from pydataset import data
from dotenv import load_dotenv
from cmo_databaseutils import db_utils

load_dotenv()

df = data("iris")

# create a database connection to SQL Server using the credentials provided in a .env file
db_conn = db_utils(server=os.getenv("DB1_SERVER"), db=os.getenv("DB1_DATABASE"), server_name=os.getenv("DB1_SERVER_NAME"), usr=os.getenv("DB1_USERNAME"), pwd=os.getenv("DB1_PASSWORD"), driver=os.getenv("DB1_DRIVER"), port=os.getenv("DB1_PORT"))

# create a database connection to SQL Server using Windows credentials
db_conn = db_utils(server="localhost", db="localtemp")

# get an overview of all active schemas in the database
db_conn.list_all_active_schemas()

# get an overview of all tables in the database within the specified schemas
db_conn.list_all_tables(schema=['dbo'])

# load the data from an entire table
cars = db_conn.retrieve_table_from_sql(table="cars", schema="dbo")

# write a table to the SQL database, when possible the package will use BCP to optimize the efficiency of the process
db_conn.write_dataframe_to_sql(data=df, table="mynewtable", schema="dbo", replace_if_exists=False, index=False, dtype=None)
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

## License
[GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/)

## Contributors
Jeanine Schoonemann<br>
[Contact us](mailto:info@theanalyticslab.nl)
cvisp 0.1.3
A small example package of demosaic-raw and cvisp, for our team
Requires Python: >=3.6


"""
This is a demo for cvisp, 开发板、相机等拍摄设备取到的RAW with metadata，可以直接用该工具包进行demosaic，得到RGB图像，以便于展示。
    - pip install cvisp
    - from cvisp.utils import Demosaic_RAW
    
"""

"""
三种模式，分别为：预览raw图，推理GDIP，预览demosaic（自用）
    run_demosaic(): return the demosaiced image, (HWC, u16, bmp)
    run_isp_with_meta(): return the rgb image , (HWC, u8, bmp)
    run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0'): return the output of       your          model, (HWC, u8, bmp) 
    
    完整传参说明：
    - Demosaic_RAW(mode : str = None, input_data_path : str = None, show_meta_data : bool = True, 
    show_image : bool = False, save_image : bool = True, result_output_dir : str = None):
        mode: defatult = 'simple_dm', choices = ['simple_dm', 'opencv_dm']
        input_data_path: raw图片路径，支持单张图片，如：'./test.dng'
        show_meta_data: show the meta data of the input data, default = True，你可以选择是否展示meta data
        show_image: show the output image, default = False，你可以选择是否展示输出图像
        save_image: save the output image, default = True，你可以选择是否保存输出图像，保存路径为result_output_dir
        result_output_dir: the output dir of the result image, default = './demosaic/'，你可以选择保存路径
"""

if __name__ == '__main__':
    for im in glob.glob(image_path_list + '*.dng'):
        demosaicd_raw = Demosaic_RAW(input_data_path = im).run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0')
        # rgb = Demosaic_RAW(input_data_path = im).run_isp_with_meta()
        # gdip_out = Demosaic_RAW(input_data_path = im).run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0')