semvec/pypi-packages huggingface dataset:
binalyzer-core 1.0.5
Binary Data Analyzer

# Binalyzer Core

This repository implements Binalyzer's core features. Functionality that is not
central to Binalyzer's concept is added through its extension mechanism.

Core Features:

* Template Engine
* Template Providers
* Template Transformations
* Data Binding System
* Data Providers
* Value Providers
* Extension Mechanism

The core features are part of the overall [Binalyzer] project and come with its
installation.

[Binalyzer]: https://github.com/denisvasilik/binalyzer

## Installation

Binalyzer is published on [PyPI] and can be installed from there:

```sh
~$ pip3 install --upgrade binalyzer
```

If you wish to install [Binalyzer] for development purposes, refer to [the
developer's guide].

[PyPI]: https://pypi.org/project/Binalyzer/
[Binalyzer]: https://pypi.org/project/Binalyzer/
[the developer's guide]: DEVELOPMENT.md

## Documentation

Documentation is available from [binalyzer.readthedocs.io].

[binalyzer.readthedocs.io]: https://binalyzer.readthedocs.io/en/latest/

## Get in touch

- Report bugs, suggest features or view the source code [on GitHub].
- For contributions refer to [the contributor's guide].

[on GitHub]: https://github.com/denisvasilik/binalyzer
[the contributor's guide]: CONTRIBUTING.md

# License

Licensed under the MIT license ([LICENSE-MIT] or http://opensource.org/licenses/MIT).

[LICENSE-MIT]: LICENSE.rst
fermentools 0.1.5
Package to teach chemometrics in the context of fermentation processes
Requires Python: >=3.10

# dtu.prosys

## About this project 🚀
This project intends to be a didactic tool to teach spectroscopy and chemometrics in the context of fermentation technology. During my studies, I often felt that many courses were theory-based only due to the limited access to real-world data. For this reason, I have decided to distribute the data I generated during my studies hoping to improve the learning experience of future students. 

This module contains:
 - Training data (spectra of different samples and the glucose concentration). 
 - Fermentation spectra (spectra measured in _real-time_ every minute).
 - Fermentation HPLC data (measured off-line every hour).
 - Common preprocessing operations used in chemometrics.
 - Workflow to train partial least squares (PLS) models.
 - Plotting functions for time-series and spectral data.

These functions can be used as a starting point for the, but more advanced users are encouradged to explore other packages to play with this data (e.g., ```scikit-learn```, or ```scipy```). 

## About the data 📈
This project provides two datasets (a training and a validation set). Both data sets were recorded at the Technical University of Denmark, at the PROSYS research center (department of Chemical and Biochemical engineering) during 2019. More information about the dataset can be found in the following article [Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation](https://onlinelibrary.wiley.com/doi/10.1002/bit.27586)

### The training set
The training set contains the spectra of 20 semi-synthetic samples and their reference glucose concentration measured with high performance liquid chromatography (HPLC). The spectra were measured using attenuated total refractance mid infrared (ATR-MIR) spectroscopy.

### Validation set
The validation contains spectra measured every minute during a lignocellulose to ethanol fermentation. These spectra were collected in _real-time_ using the same ATR-MIR instrument, connected to a flow-cell. Moreover, the extracellular concentrations of glucose, xylose, ethanol, furfural and acetic acid were also measured every hour using HPLC.  

## Installation 💻
### Dependencies
This project is build targetting Python >= 3.7 to ensure compatibility with Google Colab.

### User installation ```pip```

```
pip install -U dtuprosys
```

## Quick start 🏁
A complete example of can be found in the [```example.ipynb```](https://github.com/paucablop/dtu.prosys/blob/main/example.ipynb). The raw data can be conviniently accessed using as the following commands:

```
from dtuprosys.datasets import load_train_data, load_fermentation_data
```
to access the training data

```
train_spectra, train_hplc = load_train_data()
```
to access the validation data:
```
fermentation_spectra, fermentation_hplc = load_fermentation_data()
```
school-choice 1.0.1
This Python implementation tries to model school choice and resulting school segregation based on the work of Schelling (1971) and Stoica & Flache (2014).
Requires Python: >=3.10

# Computational Modelling of Primary School Segregation (COMPASS)

[![DOI](https://zenodo.org/badge/483226861.svg)](https://zenodo.org/badge/latestdoi/483226861)

This Python implementation tries to model school choice and resulting school segregation based on the work of Schelling (1971) and Stoica & Flache (2014).

**NB: This repo is folked from and improved based on the [original GitLab repo](https://gitlab.computationalscience.nl/edignum/school-choice---understanding-segregation), where the dataset used can be found [here](https://surfdrive.surf.nl/files/index.php/s/MN7DfAWklDgtoYG).**

## Usage

### Install and Run
In a terminal, navigate to the project folder, install all the dependencies by running

```shell
pip install -r requirements
```

and run the program within a GUI by running

```shell
python run.py
```

This will start a local server to automatically open your web browser and show the program as a webpage. If not seeing the webpage open, manually input `http://localhost:5004/` in the link bar.

### Update Documentation
Install pdoc3 if you haven't already done so. Browse to the compassproject folder in your terminal and run `pdoc3 --html --force --output-dir docs compass`. The documentation should be updated now.

### Overview
The repository consists of:
* **run.py:** a script that runs the model interactively with a visualisation (browser)
* **testrun.py:** a test script (work in progress)
* **household.py:** the household class
* **student.py:** the student class
* **neighbourhood.py:** the neighbourhood class
* **school.py** the school class
* **allocator.py:** allocates the students to their school of choice
* **agents_base.py:** overarching agent used for inheritance
* **model.py:** initialises the entire system and all of its components
* **parameters.py:** contains all the parameter values for the simulation
* **scheduler.py:** takes care of the activation, sequence and placement of all agents
* **visualisation.py:** browser based visualisation
* **utils.py:** containing all measurements
* **functions.py:** containing some math functions to be used by the classes

### Simulations
Information on how to run the code here.

### Testing and development

Setup a virtualenv with the required dependencies.
```bash
$ python -m venv env
$ . env/bin/activate
$ pip install -r requirements.txt
```

Install the package locally (in developement, or editing mode):
```bash
$ pip install -e .
```

Then run the tests with:
```bash
$ pytest
```

### Profiling

Some profiling result can be found in this [notebook](https://github.com/ODISSEI-School-Choice/school-choice/blob/jisk-v2/profile.ipynb). Also, some scaling graphs can be found in this [notebook](https://github.com/ODISSEI-School-Choice/school-choice/blob/jisk-v2/scaling_graph.ipynb).