semvec/pypi-packages huggingface dataset:
explainable-boosting-machine-pipeline 0.0.1
Generic pipeline for inporting a set of labelled data from AWS, training and evaluating an Explainable Boosting Machine using the data and then producing visualisations of feature important and dependance.
Requires Python: >=3.8

# explainable_boosting_machine_pipeline
Generic pipeline for inporting a set of labelled data from AWS, training and evaluating an Explainable Boosting Machine using the data and then producing visualisations of feature important and dependance.
dashit 1.2.0
Custom Command Submitit Launcher for Hydra apps

## Installation:

`pip install git+https://github.com/sede-x/dashit.git --upgrade`


## Run:

`python examples/run.py -m`

tested with `hydra-core==1.3.2, omegaconf==2.3.0, submitit==1.5.1, Python 3.11.3`

## Tutorial:

https://github.com/sede-x/playbooks/blob/main/submitit/tutorial.ipynb

## ToDo:

- code checking with black
- writeup/examples to README
TNet-Segmentation 1.0.2
pytorch module for T-Net image segmentation model with DiceLoss and code for visualization of model
Requires Python: >=3.8,<4.0

# TNet-Segmentation

- It is a pytorch based Segmentation model inspired by the research paper ```T. M. Khan, A. Robles-Kelly and S. S. Naqvi, "T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation," 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2022, pp. 1799-1808, doi: 10.1109/WACV51458.2022.00186.``` published in CVPR 2022


# How to install

* run ```pip install TNet-Segmentation``` to install the package

# How to use

## Creating Model 
* The model takes three parameters: -  
    - input_channnel (defaults to 3)
    - emb_size (size of the output channel after first downsampling conv layer.Defaults to 512)
    - num_classes (the num of segmentation classes, defaults to 3)
* Usage is as simple as below:- 
    ```
    >>> from TNet_Segmentation import TNet                                
    >>> net = TNet(input_channel=3, emb_size=256, num_classes=3) 
    ```

## Visualizing 
* After creating the model call visualize function as follows:-
    ```
    >>> from TNet_Segmentation import visualize 
    >>> visualize(net)
    =================================================================
    Layer (type:depth-idx)                   Param #
    =================================================================
    TNet                                     --
    ├─Conv2d: 1-1                            7,168
    ├─TNetConvBlock: 1-2                     --
    │    └─ModuleList: 2-1                   --
    │    │    └─Conv2d: 3-1                  590,080
    │    │    └─BatchNorm2d: 3-2             512
    │    │    └─ReLU: 3-3                    --
    │    │    └─Conv2d: 3-4                  65,792
    │    │    └─BatchNorm2d: 3-5             512
    │    │    └─Conv2d: 3-6                  2,560
    │    │    └─BatchNorm2d: 3-7             512
    │    │    └─ReLU: 3-8                    --
    │    │    └─MaxPool2d: 3-9               --
    ```

## DiceLoss function

* Dice Loss is implemented in this library and can be used as follows:
    ```
    >>> from TNet_Segmentation import DiceLoss
    >>> loss = DiceLoss()


    ```
TNet-Seg 1.0.0
pytorch module for T-Net image segmentation model with DiceLoss and code for visualization of model
Requires Python: >=3.8,<4.0

# TNet_Seg

- It is a pytorch based Segmentation model inspired by the research paper ```T. M. Khan, A. Robles-Kelly and S. S. Naqvi, "T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation," 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2022, pp. 1799-1808, doi: 10.1109/WACV51458.2022.00186.``` published in CVPR 2022


# How to install

* run ```pip install TNet-Seg``` to install the package

# How to use

## Creating Model 
* The model takes three parameters: -  
    - input_channnel (defaults to 3)
    - emb_size (size of the output channel after first downsampling conv layer.Defaults to 512)
    - num_classes (the num of segmentation classes, defaults to 3)
* Usage is as simple as below:- 
    ```
    >>> from TNet_Seg import TNet                                
    >>> net = TNet(input_channel=3, emb_size=256, num_classes=3) 
    ```

## Visualizing 
* After creating the model call visualize function as follows:-
    ```
    >>> from TNet_Seg import visualize 
    >>> visualize(net)
    =================================================================
    Layer (type:depth-idx)                   Param #
    =================================================================
    TNet                                     --
    ├─Conv2d: 1-1                            7,168
    ├─TNetConvBlock: 1-2                     --
    │    └─ModuleList: 2-1                   --
    │    │    └─Conv2d: 3-1                  590,080
    │    │    └─BatchNorm2d: 3-2             512
    │    │    └─ReLU: 3-3                    --
    │    │    └─Conv2d: 3-4                  65,792
    │    │    └─BatchNorm2d: 3-5             512
    │    │    └─Conv2d: 3-6                  2,560
    │    │    └─BatchNorm2d: 3-7             512
    │    │    └─ReLU: 3-8                    --
    │    │    └─MaxPool2d: 3-9               --
    ```

## DiceLoss function

* Dice Loss is implemented in this library and can be used as follows:
    ```
    >>> from TNet_Seg import DiceLoss
    >>> loss = DiceLoss()


    ```
cmo-dataloader 0.1.0
Load a bulk of data from flat files or databases at once
Requires Python: >=3.6

# Cmotions Dataloader
cmo_dataloader 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 loading flat files or database tables into Python in bulk. Regarding flat files it also tries to take into account which separator, decimal sign and encoding to use when loading a file, this way the data is loaded automatically the best way possible, so you don't have to waste time fixing difficult bugs and errors. 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_dataloader using pip
```bash
pip install cmo-dataloader
```

If you want to be able to use the tutorial notebook or the code below, you also need to install dotenv using pip
```bash
pip install python-dotenv
```

## Usage
```python
import os
from dotenv import load_dotenv
from cmo_dataloader.resources import get_data_path
import cmo_dataloader as dl

load_dotenv()

# load flat files from a directory
dlc = dl.load_flat_file(get_data_path('formula_1'))
datafiles_dict = dlc.load()

# load tables from a SQL Server database
# using credentials stored in a .env file
ddu = dl.load_database(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")
                       , tables=['dbo.mytable', 'dbo.anothertable'])
datafiles_dict = ddu.load()

# load tables from a SQL Server database
# using your Windows credentials
ddu = dl.load_database(server="localhost"
                       , db="mylocaldb"
                       , tables=['dbo.mytable', 'dbo.anothertable'])
datafiles_dict = ddu.load()
```

## 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)
spiros 0.0.13
Streaming video data via networks
Keywords: python,video,stream,video stream,camera stream,sockets


vidstream
Under construction! Not ready for use yet! Currently experimenting and planning!

Developed by Florian Dedov from NeuralNine (c) 2020

Examples of How To Use (Buggy Alpha Version)
Creating A Server

from vidstream import StreamingServer

server = StreamingServer('127.0.0.1', 9999)
server.start_server()

# Other Code

# When You Are Done
server.stop_server()
Creating A Client

from vidstream import CameraClient
from vidstream import VideoClient
from vidstream import ScreenShareClient

# Choose One
client1 = CameraClient('127.0.0.1', 9999)
client2 = VideoClient('127.0.0.1', 9999, 'video.mp4')
client3 = ScreenShareClient('127.0.0.1', 9999)

client1.start_stream()
client2.start_stream()
client3.start_stream()