Metadata-Version: 2.1
Name: spacopt
Version: 0.2.1
Summary: spacopt is a package for bringing optimization techniques to spacal-simulation application
Home-page: https://github.com/PrinceRichfather/spacopt
Author: Shakhzod Dadabaev Urazalievich
Author-email: misis.dsu@gmail.com
License: MIT license
Keywords: spacopt
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: hyperactive (>=4.1.1)
Requires-Dist: matplotlib (>=3.1)
Requires-Dist: numpy (>=1.20)
Requires-Dist: pandas (>=0.24.2)
Requires-Dist: root-numpy (>=4.8.0)

# spacopt

[![image](https://img.shields.io/pypi/v/spacopt.svg)](https://pypi.python.org/pypi/spacopt)

[![image](https://img.shields.io/travis/PrinceRichfather/spacopt.svg)](https://travis-ci.com/PrinceRichfather/spacopt)

[![Documentation Status](https://readthedocs.org/projects/spacopt/badge/?version=latest)](https://spacopt.readthedocs.io/en/latest/?version=latest)

`spacopt` - short for `spacal-optimization`

## Description

spacopt is a package for bringing optimization techniques to spacal-simulation application, created for LHCb ECAL studies for different types of calorimeters, such as spacal and shashlik.
Should be considered as complementary to [spacal-simulation](https://gitlab.cern.ch/spacal-rd/spacal-simulation), hosted at gitlab under CERN domain.
Up to this point, the main package for optimization is considered [Hyperactive](https://github.com/SimonBlanke/Hyperactive).

## Features

* Create config files, with user defined parameters of the module.
* Run a MC simulation, using pyton script
* Run Optimization for finding best user-defined parameters of module, to minimize the loss function: $
\dfrac{a}{\sqrt{E}}+b,$
where $a$ - sampling term,
$b$ - constant term.

In fact, both $a$ and $b$ could be considered as independent subjects to minimize, as well as other functions of one or both of them.

## Installation

```{.shell}
pip install spacopt
```

## Usage

```
import spacopt

# Run Simulation
```

## Contributing

## License

* Free software: MIT license
* Documentation: <https://spacopt.readthedocs.io>.


# History

## 0.2.0 (2022-05-18)

### New scipts

* New scipt: Optimization_run.py

### Caveats

* For now works only with hard-coded paths, so one needs copy-paste supplementary folders and scipts

## 0.1.3 (2022-05-15)

* Minor changes to package wrappers

## 0.1.2 (2022-05-15)

* Minor changes to package wrappers

## 0.1.0 (2022-05-15)

* First release on PyPI.


