semvec/pypi-packages huggingface dataset:
SERD 0.1.2
A Python package to detect solvent-exposed residues of a target biomolecule.
Requires Python: >=3.8, <4
Keywords: structural biology,proteins,biomolecular surface,solvent-exposed residues

####
SERD
####

.. image:: https://img.shields.io/pypi/v/SERD
    :target: https://pypi.org/project/SERD/

.. image:: https://img.shields.io/pypi/pyversions/SERD
    :target: https://pypi.org/project/SERD/

.. image:: https://img.shields.io/pypi/dm/SERD
    :target: https://pypi.org/project/SERD/

A Python package to detect solvent-exposed residues of a target biomolecule.

* `GitHub repository <https://github.com/jvsguerra/SERD>`_
* `Documentation and tutorial <https://jvsguerra.github.io/SERD/>`_

************
Installation
************

The prerequisites for installing SERD is SWIG, Python v3 and PyMOL v2.

To install the latest release on `PyPI <https://pypi.org/project/SERD>`_, 
run:

::

  pip install SERD

Or to install the latest developmental version, run:

::

  git clone https://github.com/jvsguerra/SERD.git
  pip install SERD

*******
License
*******

The software is licensed under the terms of the GNU General Public License version 3 (GPL3) and is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
cam-tool 1.3.8
Cloud Assignment Manager Tool
Keywords: Cloud Assignment Manager

# Cloud Assignment Manager Tool
CAM-Tool is a cloud assignment manager tool that helps you to manage your tasks across different machines. You can start several workers across different machines and upload the command to cam-tool. Cam-tool will then distribute the tasks to workers automatically.

## Install
```
pip install cam-tool
conda install redis # only required for server machine.
conda install -c conda-forge nodejs # only required if you want to use a web-based gui
```

## Config
The config file is located at `~/.cam.conf`. You can set the server address, port, and password for redis. You can simply run `cam config` to edit the yaml file.

## Start Server
On the server machine, simply run the following command to start the server.
```
cam server
```

## Start Worker
On a worker machine, please run the following command to start a worker. You can start many worker on the same machine.
```
cam worker 
``` 
You may also want to add more parameters as resource calculation, prefix or suffix.
```
cam worker "ngpu()" "sbash"
``` 
where `ngpu()` calculate how many GPU is idle and "sbash" is a prefix to all the command running on this server.


## Add new task
Please run the following command to add a new task
```
cam add "ls -lah"
```

## Status
You can see the status of eash task with the `ls` command:
```
> cam ls

ID  Time                 Command    Host
----  -------------------  ---------  -------
   3  2022-03-07 06:39:33  ls -lah    Pending
```

## Kill tasks
You can kill task with its task id.
```
cam kill 3
```

## Get the log of a worker
You can run the following command to get the log of a running or finished worker:
```
cam log 3
```

## Web Console
Cam tool provides a simple web console to add, kill, and watch tasks. Please install nodejs before you use this function. Just run the following code to start a web server. Never run this command on any public server.
```
cam web
```
Then open the link http://localhost:8257/ and you can see the following user interface:
<img width="1349" alt="gui" src="https://user-images.githubusercontent.com/1419566/161306901-b5e417cb-1f55-4534-b272-636b29a0f754.png">
rtg 0.7.2
Reader Translator Generator(RTG), a Neural Machine Translator(NMT) toolkit based on Pytorch
Requires Python: >=3.7

# Reader-Translator-Generator (RTG)  
[![image](http://img.shields.io/pypi/v/rtg.svg)](https://pypi.python.org/pypi/rtg/)


Reader-Translator-Generator (RTG) is a Neural Machine Translation toolkit based on pytorch. 

Documentation: https://isi-nlp.github.io/rtg/
> for editing/improving docs go to [docs/](docs/index.adoc) directory.

---------

### Questions or Issues 

Please use github issues to ask a question or report an issue :
1. https://github.com/isi-nlp/rtg/issues   (public/ external repo)
2. https://github.com/isi-nlp/rtg-in/issues (an internal fork, for ISI NLP)


### ACKNOWLEDGEMENTS

* The research is based upon work supported by the Office of the Director of
National Intelligence (ODNI), Intelligence Advanced Research Projects
Activity (IARPA), via AFRL Contract #FA8650-17-C-9116.
The views and conclusions contained herein are those of the authors and
should not be interpreted as necessarily representing the official policies or
endorsements, either expressed or implied, of the ODNI, IARPA, or the
U.S. Government. The U.S. Government is authorized to reproduce and
distribute reprints for Governmental purposes notwithstanding any
copyright annotation thereon.

* This material is based on research sponsored by
 Air Force Research Laboratory (AFRL) under agreement number FA8750-19-1-1000.
The U.S. Government is authorized to reproduce and distribute reprints for
Government purposes notwithstanding any copyright notation therein.
texture-replicator 0.0.1
A package to replicate textures

# Texture Replicator

Texture replicator is an app that takes a base image and a folder of images, and replicates the folder while replacing all the images with the base image.

## Description

One day, my friends and I were using Dolphin to emulate a popular GameCube game, and my friend Ned suggested that we dump the textures and replace them with pictures of bread. After an hour of tediously copying, rescaling, and renaming one bread image over and over, I decided there must be a better way. So, the Texture Replicator was born. 

I decided to use Python to create this app because it is a powerful language that I am unfamiliar with. I also want to showcase my ability to learn new things and to develop using TDD.


## 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
MIT
BCM3D 2.0.1
BCM3D 2.0: Accurate segmentation of single bacterialcells in dense biofilms using computationallygenerated intermediate image representations
Requires Python: >=3.8

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<br />


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# BCM3D 2.0

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## About The Project

 BCM3D 2.0 is entirely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images.The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time. This capability opens the door to investigating time38 dependent phenomena in bacterial biofilms at the cellular level. 


<div align="left">

## Getting Started

This package was tested on a high-performace computing cluster at University of Virginia with CUDA-enabled GPU and Tensorflow 2.x

### Prerequisites

This package uses CSBdeep for training deep leanirng modules. Tensorflow 2.x with its dependencies (CUDA, cuDNN) is required. Please refer to CSBdeep for further instructions (http://csbdeep.bioimagecomputing.com/doc/install.html).

### Installation
1. create and activate a conda enviroment 
   ```sh
   conda create -n BCM3D2.0 python=3.8
   conda activate BCM3D2.0
   ```
2. pip install
   ```sh
   pip install BCM3D2.0
   ```

<!-- USAGE EXAMPLES -->
## Usage

#### Model training

1. Use **src/IntermediateImageGenerate.py** to generate image representations from ground truth cell arrangement.

2. Use **dategen.ipynb** to generate training pairs (raw data and the corresponding image representations).

3. Use **train.ipynb** to train CNNs that will train a model.

#### Prediction

1. Use **src/preprocess.py** to apply background substraction to raw data. (optional)

2. Use **predict.ipynb** to generate segmentations


<!-- LICENSE -->



## License

Distributed under the MIT License. See `LICENSE.txt` for more information.

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<!-- CONTACT -->
## Contact

 yw9et@virginia.com; jz3nc@virginia.edu; ag5vu@virginia.edu

Project Link: https://github.com/GahlmannLab/BCM3D-2.0

Data Link: https://osf.io/m4637

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