semvec/pypi-packages huggingface dataset:
neslab-find 0.0.4
FIND model
License: MIT

# FIND model

This directory provides a Python implementation of the FIND model and protocol.

The provided code allows to calculate the probability for each node in a clique to become active in any slot, the probability for any individual link to be discovered in any slot as well as the network discovery latency.

## Installation

Depending on your setup, install the python package together with the requirements for the examples using

```
pipenv install neslab.find[examples]
```

or

```
pip install neslab.find[examples]
```

## Usage

Instantiate the model with a uniform distribution with scale 10 and three nodes with charging times of 100, 125 and 200 that become active for the first time without an offset:

```python
import matplotlib.pyplot as plt
from neslab.find import Model

m = Model(10, "Uniform", [100, 125, 200], offset=0)
```

Compute for every node the probability of being active in a slot and plot the results:

```python
act = m.activity()
plt.plot(act[:2000])
plt.show()
```

Compute the cumulative distribution function (cdf) of the slot in which each of the three links between the three nodes is discovered and plot the results:

```python
cdfs = m.cdf()
plt.plot(cdfs)
plt.show()
```

Compute the fraction of discovered links and plot the result:

```python
dff = m.disco_frac()
plt.plot(dff)
plt.show()
```

Compute the discovery latency:

```python
lat = m.disco_latency())
print(f"Discovery latency: {lat} slots")
```

## Examples

We provide more involved example scripts in the [examples](./examples) directory:

To compare the probability of activity and the cdf of the corresponding discovery latency of two nodes with a charging time of 30 and 60 slots, using a uniform delay distribution, call

```
python examples/plot_example.py
```

To compare the discovery latency of two nodes when using different distributions across a range of their scale parameters, run

```
python examples/compare_dists.py
```

To plot the discovery latency of a clique of nodes over the node density, run

```
python examples/plot_density.py
```

To optimize the scale parameter of the geometric distribution to a range of charging times, run

```
python examples/optimize_scale.py
```


## Reproduce key results

We used the code provided in this directory to gain the insights presented in section 2 of [our paper](https://nes-lab.org/pubs/2021-Geissdoerfer-Find.pdf).
We invite you to reproduce our results with the scripts provided in the [reproducibility](./reproducibility) directory.
Some of the calculations take significant computing resources and require a high performance computing system with a large number of CPUs.
trashy-poetry 1.0.3
Poetry for everyday use
License: MIT
Requires Python: >=3.6,<4.0

# Prose

An unpoetic version of [Poetry](https://python-poetry.org/).

While the Poetry package is elegant and reduces a Python developers burden, every day usage has some small annoyances. Some of these things could be fixed but have been considered out of scope and unpoetic.

Prose is made to be the everyday version of Poetry and little bit more trashy. But the trashiness serves those well in the dirty jobs of life. Prose is a wrapper around Poetry so all commands and APIs should function the same with the addition of more features.

## Installation

```
pip install trashy-poetry
```

## Usage

Substitute all `poetry` commands for `prose`.

See [Poetry Docs](https://python-poetry.org/docs/). All commands and APIs function the same except for the additional features documented.

For Example:

```
poetry init -> prose init

poetry shell -> prose shell
```

## Features

### Task Runner

Prose includes [Poe the Poet](https://github.com/nat-n/poethepoet) by default. Poe the Poet lets you create shortcuts to common tasks such as shell scripts and Python functions. The usage and API functions the same as documented except for the addition of the `poe` shortcut.

`prose run poe [options] task [task_args]`

can also be run as

`prose poe [options] task [task_args]`

### Hardcoded Environmental Variables

Inject environmental variables into the `run` and `shell` commands by hard coding them into you `pyproject.toml` file.

**Example:**

```
[tool.prose.env]
TEST_ENV = "hello world"
TEST_PATH = "${PATH}:/narf"
```

Any variables set in the `tool.prose.env` section of your toml file will be injected into the environment for you.

### Load DotEnv Files

### Default DotEnv Files

Prose loads `.env` files if it finds it in the current working directory. See [python-dotenv](https://pypi.org/project/python-dotenv/) for usage documentation.

## Custom DotEnv Files
Prose allows you to add custom `.env` files with a command line switch for the `poe`, `run`, and `shell` commands.

```
OPTIONS
  -e (--env)             Dotenv file to load (multiple values allowed)
```

**Examples:**

```
prose shell -e path/custom.env
prose run -e path/custom.env printenv NARF
prose poe -e path/custom.env mycommand
```

*Note: When using the `run` and `poe` commands, the `-e` and `--env` options must be used before your command arguments*
ColoreRegioni 0.2.0
Web-scraping del sito del governo italiano per la classificazione delle misure restrittive regionali
License: MIT


# ColoreRegioni
[![Build Status](https://travis-ci.com/MCilento93/ColoreRegioni.svg)](https://travis-ci.com/MCilento93/ColoreRegioni)
[![Coverage Status](https://coveralls.io/repos/github/MCilento93/ColoreRegioni/badge.svg)](https://coveralls.io/github/MCilento93/ColoreRegioni)
[![PyPI](https://img.shields.io/pypi/v/ColoreRegioni)](https://pypi.org/project/ColoreRegioni/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/ColoreRegioni)](https://pypistats.org/packages/coloreregioni)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ColoreRegioni)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/ColoreRegioni)
![GitHub contributors](https://img.shields.io/github/contributors/MCilento93/ColoreRegioni)
![GitHub issues](https://img.shields.io/github/issues-raw/MCilento93/ColoreRegioni)
![GitHub](https://img.shields.io/github/license/MCilento93/ColoreRegioni)

Web-scraping del **[sito del governo italiano](http://www.governo.it/it/articolo/domande-frequenti-sulle-misure-adottate-dal-governo/15638?gclid=CjwKCAiAwrf-BRA9EiwAUWwKXicC1bzopYynHP9pvRxHUza7Ar4dte9hWHi55Uj4xfuAHanOCf7a1BoCTggQAvD_BwE)** per la classificazione delle misure restrittive regionali per COVID-19.
Denominazione regione in accordo a [nomenclatura istat](https://www.istat.it/it/archivio/6789).

## Installazione
```
pip install ColoreRegioni
```

## Esempio di utilizzo
La classe ColoreRegioni() restituisce il dizionario con il colore e la corrispondente emoji:
```
from ColoreRegioni import ColoreRegioni

colore_regioni=ColoreRegioni()
full_dict=colore_regioni.colori_emoji
```
![full_dict](https://github.com/MCilento93/ColoreRegioni/blob/main/images/full_dict.png?raw=true)</br>
Disponibili anche i risultati parziali:
```
denominazioni=colore_regioni.denominazioni
dict_only_colors=colore_regioni.colori
dict_only_emoji=colore_regioni.emoji
```

## Licenza
Il software Ã¨ liceziato con licenza **[MIT](LICENSE)** (c) 2020 GitHub, Inc.

## Progetti che utilizzano ColoreRegioni
* [checoloresono.info](https://checoloresono.info/#/) di **[Azzeccagarbugli](https://github.com/Azzeccagarbugli/checoloresono)**
* [coronavirusitaliamap](https://coronavirusitaliamap.altervista.org/) di **Stefano Quaggio**