semvec/pypi-packages huggingface dataset:
tvbk 0.5
do virtual brains eat computational popcorn?
Requires Python: >=3.8

# tvbk

Computational kernels for tvb.

## setup

My local dev setup is
in VS Code w/ Python, C/C++ extensions, and a venv setup for incremental rebuilds like so
```bash
rm -rf build env
uv venv env
source env/bin/activate
uv pip install nanobind 'scikit-build-core[pyproject]' pytest pytest-benchmark numpy cibuildwheel scipy 
uv pip install --no-build-isolation -Ceditable.rebuild=true -ve .
```
following https://nanobind.readthedocs.io/en/latest/packaging.html#step-5-incremental-rebuilds.
This enables editing and running the tests directly, with changes to the C++ automatically
taken into account, just running
```
pytest
```
will rebuild the C++ if required.  This also occurs on import in e.g. a Jupyter kernel.

## next

- make first release to start integrating w/ TVB
- all the neural mass models
- add bold

- refactor buffers
- rm scipy dep for sparsity
- cuda/hip/webgpu or something
popcast 2.0.0
Long-term forecasts for pathogen populations
Requires Python: >=3.6

# popcast: Long-term forecasts for pathogen populations

See the methods of [Huddleston et al. 2020](https://doi.org/10.7554/eLife.60067) for more details or to cite this tool.

## Install

For a full installation with the OpenCV package that's required for model fitting, specify the "full" package dependencies.

``` bash
python -m pip install 'popcast[full]'
```

For a smaller installation that only supports forecasting with an existing model, omit the optional package dependency.

``` bash
python -m pip install popcast
```

## Usage

Download seasonal influenza A/H3N2 data for model fitting.

``` bash
curl -LO "https://github.com/blab/flu-forecasting/raw/master/results/builds/natural/natural_sample_1_with_90_vpm_sliding/tip_attributes_with_weighted_distances.tsv"
```

Fit a model using default 6 year training windows and 12-month forecasts.

``` bash
popcast fit \
  --tip-attributes tip_attributes_with_weighted_distances.tsv \
  --output lbi_model.json \
  --predictors lbi
```

## Development

### Install locally

``` bash
python -m pip install '.[full,test]'
```

### Lint and run tests

Lint code.

``` bash
flake8 . --count --show-source --statistics
```

Run tests.

``` bash
cram --shell=/bin/bash tests/
```

### Publish

Install or upgrade publishing tools.

``` bash
python -m pip install --upgrade build twine
```

Build the distribution packages.

``` bash
python -m build
```

Upload the distribution packages.

``` bash
python -m twine upload dist/*
```
geometria 0.5
Biblioteca para formulas de geometria
Requires Python: >=3.8

# Geometria (Em construção)

**Geometria** é uma biblioteca Python para cálculos geométricos, como áreas, perímetros, e outras propriedades de figuras geométricas.

## Funcionalidades

- Cálculo da área de triângulos (incluindo equiláteros) e retângulos.
- Cálculo do perímetro de triângulos e retângulos.
- Verificação da existência de triângulos com base nos lados.
- Cálculo de propriedades avançadas de triângulos:
  - Fórmula de Heron.
  - Altura do triângulo.
  - Raio do círculo inscrito e circunscrito.
  - Ângulos internos.
  - Tipo do triângulo (Equilátero, Isósceles, Escaleno).

---

## Instalação

Você pode instalar a biblioteca diretamente do PyPI:

```bash
pip install geometria
```

## Como Usar
Exemplo de Uso
```
from geometria import area_triangulo, area_triangulo_equilatero, tipo_triangulo

# Área de um triângulo
print(area_triangulo(10, 5))  # Saída: 25.0

# Área de um triângulo equilátero
print(area_triangulo_equilatero(12))  # Saída: 62.35

# Tipo do triângulo
print(tipo_triangulo(3, 4, 5))  # Saída: Escaleno
```

### Desenvolvimento
Pré-requisitos
Certifique-se de ter o Python 3.9 ou superior instalado. Instale as dependências do projeto:
```
pip install -r [requirements.txt](http://_vscodecontentref_/0)
```

Rodando os Testes
Para executar os testes unitários, utilize o comando:
```
pytest tests/
```

Cobertura de Testes
Para verificar a cobertura de testes, execute:
```
coverage run -m pytest
coverage report
coverage html
```

O relatório HTML será gerado na pasta htmlcov.

### Contribuindo
Contribuições são bem-vindas! Siga os passos abaixo para contribuir:

Faça um fork do repositório.
```
git checkout -b minha-feature
```

Faça as alterações e adicione os commits.
Envie um pull request para o repositório principal.

Licença
Este projeto está licenciado sob a licença MIT. Consulte o arquivo LICENSE para mais detalhes.

Contato
Criado por Mayumi Shingaki.
E-mail: contato.mayulabs@gmail.com
openrte 1.0.1
Python wrapper for RTE API requests
Requires Python: >=3.10

# OpenRTE

![openrte-logo](https://raw.githubusercontent.com/henriupton99/openrte/main/images/openrte.png)

Python wrapper for [RTE API](https://data.rte-france.com/) requests. 

## 1. Usage

#### 1.1. Get RTE API credentials

You need to follow these first steps in order to setup your wrapper :  

* [create an account](https://data.rte-france.com/create_account) on the RTE platform
* [create an application](https://data.rte-france.com/group/guest/apps) associated to your account (the name and description of the app is not relevant)
* collect your app IDs (**ID Client** and **ID Secret**) available in your application dashboard

#### 1.2. Generate a data retrieval

To retrieve data using the wrapper, follow this pipeline :

```python
from openrte import Client
client = Client(client_id="XXX", client_secret="XXX")
dfs = client.retrieve_data(start_date="2024-01-01 00:00:00", end_date="2024-01-02 23:59:00", data_type="actual_generations_per_unit", output_dir="./output")
```

where :
* **start_date** is the first date of the data retrieval (format *YYYY-MM-DD HH:MM:SS*)
* **end_date** is the last date of the data retrieval (format *YYYY-MM-DD HH:MM:SS*)
* **data_type** is the desired data to collect (a keyword list is given in the next section). It can be a single keyword *"XXX"* or a list of keyword separated by a comma *"XXX,YYY,ZZZ"*
* **output_dir** (*optionnal*): the output directory to store the results

The generic output format is a pandas dataframe / **.csv** file containing the data for all dates between **start_date** and **end_date**. It will generate one file per desired **data_type** and will store all of them in a **./results** folder with the generic name *"<data_type>_<start_date>_<end_date>.csv"*.

## 2. Available *data_type* options

The following table is an exhaustive list of all possible (currently handled) options for the **data_type** argument for the retrieval, and the description of the associated data :

| **data_type** | Description | Documentation |
| --- | --- | --- |
| *actual_generations_per_production_type* | Actual production data aggregated by sector (in MW) in intraday | [Link](https://data.rte-france.com/catalog/-/api/doc/user-guide/Actual+Generation/1.1) |
| *actual_generations_per_unit* | Actual production data aggregated by unit (in MW) in intraday | [Link](https://data.rte-france.com/catalog/-/api/doc/user-guide/Actual+Generation/1.1) |