semvec/pypi-packages huggingface dataset:
npsn 0.1
Nuclear Power Surrogate Network
Requires Python: >=3.6

# NPSN: Nuclear Power Surrogate Network

NPSN is a package that allows easy training and optimization of neural networks that provide multidimensional regression of a nuclear reactor's power distribution based on control blade position(s).
The package is developed using the [tensorflow](https://github.com/tensorflow/tensorflow) backend and [keras](https://keras.io) API. 
The package is written to abstract the process of importing/pre-processing data, optimizing neural network architecture, and providing performance metrics.
The aim of this project is to facilitate development of surrogate models that are needed in autonomous reactor control systems.
Format for training data is detailed at the top of the [data generation](npsn/dg.py) script.

## Example
```python
import npsn
t
# Define dataset directory
data_dir = '~/some/data_location'
# Define model name
proj_nm = 'npsn_surrogate'

# Define number of control blades
n_x = 4
# Define nodalization of power distribution
n_y = (15, 20)  #(axial_nodes, fuel_locations)

# Train neural network without optimization
npsn.train(proj_nm, data_dir, n_x, n_y)
# Or with optimization
npsn.train(proj_nm, data_dir, n_x, n_y, max_evals=100)
# Post-process to quantify error
npsn.post(proj_nm)
```

The output will be a `keras` model, in the current working directory (_/cwd_), that can be loaded using `keras.models.load_model`.
Error metrics will be output to the _/cwd/csv_ directory and consist of mean and standard deviation of MAP error against test and training data.
If optimization studies are conducted, the data on each permutation will be output ot the _/cwd/mat_ directory and consist of a .mat file that can be loaded into MATLAB or with `scipy.io.loadmat`.

## Installation

To install with pip:
```
pip install npsn
```
The dependency requirements will be satisfied by pip. A full list of the environment used is in [requirements](requirements.txt). 
The package was developed on Ubuntu 18.04, but is written to also work on Mac and Windows OS.

## Paper using NPSN


## Cite 

If you use NPSN in your work, please cite as:
> A. J. Dave, J. Wilson, K. Sun, "Deep Surrogate Models for Multi-dimensional Regression of Reactor Power," arXiv:, 2020.

## Contact
If you have any questions, comments, or suggestions feel free to [email](mailto:akshayjd@mit.edu) me!
bytesbufio 1.0.3
io.BytesIO that preserves bytes after close
Requires Python: >=3.6

# Python package bytesbufio
[![Sources](https://img.shields.io/badge/sources-github-blue)](https://github.com/cbuschka/python-bytesbufio) ![Written in Python](https://img.shields.io/badge/python-3.6,%203.7,%203.8-blue.svg) [![PyPI](https://img.shields.io/pypi/v/bytesbufio)](https://pypi.org/project/bytesbufio/) [![Build Status](https://travis-ci.com/cbuschka/python-bytesbufio.svg?branch=master)](https://travis-ci.com/cbuschka/python-bytesbufio) [![codecov](https://codecov.io/gh/cbuschka/python-bytesbufio/branch/master/graph/badge.svg)](https://codecov.io/gh/cbuschka/python-bytesbufio) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/cbuschka/python-bytesbufio/blob/master/license.txt)

### bytesbufio provides BytesBufferIO - an io.BytesIO implementation whose value can be accessed after it has been closed

* [Test that shows the problem](https://github.com/cbuschka/python-bytesbufio/blob/v1.0.3/tests/bytesio_test.py)
* ["Fixed" implementation - BytesBufferIO](https://github.com/cbuschka/python-bytesbufio/blob/v1.0.3/bytesbufio/bytes_buffer_io.py)

## Installation
```
pip install bytesbufio
```

## Usage

```python
import io

from bytesbufio import BytesBufferIO

bytesbuf = BytesBufferIO()
with io.TextIOWrapper(bytesbuf, encoding='utf-8') as textout:
    textout.write("Hello world.")

text = bytesbuf.getvalue().decode('utf-8') # BytesIO would have raised an ValueError here 
print(text)
```

## Related
* [Python Issue 22003 - BytesIO and shared bufferes](https://bugs.python.org/issue22003)
* [Python Issue 23099 - value not available after close](https://bugs.python.org/issue23099)

## License
Copyright (c) 2020 by [Cornelius Buschka](https://github.com/cbuschka).

[Apache License, Version 2.0](https://github.com/cbuschka/python-bytesbufio/blob/v1.0.3/license.txt)
global-benchmark-database-tool 2.8.8
Maintenance of Benchmark Instances and their Attributes

# gbd (Global Benchmark Database)

## Programming Language
Python 3

## Installation
- Setup python3 and pip3
- Install GBD via ```pip3 install global-benchmark-database-tool```

## Configuration
- Download a database, e.g., [http://gbd.iti.kit.edu/getdatabase/](http://gbd.iti.kit.edu/getdatabase/), and safe it as [path/file]
- ```export GBD_DB=[path/file]``` (and put it in your .bashrc)
- If no database path is given via --db, then gbd uses path in ```GBD_DB```

## Initialize local paths to benchmark instances
> ```gbd init /path/to/cnf```

## GBD Command Line Interface
> ```gbd --help```

### Help on specific command
>	```gbd [command] --help```

### Using GBD Server and Microservices
> ```gbd-server --help```.
bg-prob 0.4
Gaussian distributions

# bg_prob package

This package calculates Gaussian and Binomial distributions

### Files

- Binomialdistribution.py
- Generaldistribution.py
- Binomialdistribution.py

- license.txt
- README.md

### Installation
pip install bg_prob

### Run

python

from bg_prob import Gaussian
from bg_prob import Binomial