semvec/pypi-packages huggingface dataset:
ecodata 0.1.4
Extracción de series de tiempo de las cinco principales instituciones económicas para el Perú
Requires Python: >=3.6

# Ecodata
Extracción de series de tiempo de las cinco principales instituciones económicas para el Perú:
1. BCRP
2. YahooFinance
3. FRED
4. WorldBank
5. OCDE

```
# Instalación mediante PyPI
!pip install ecodata
```


## Versión 0.1.4
* Para el Banco Central de Reserva del Perú (`BCRP`), `Yahoo Finance`, Federal Reserve Economic Data (`FRED`) y el `World Bank`, se cuenta con ambas métodos: `get_data()` y `search()`.
* Para la OCDE, se cuenta únicamente con el método `get_data()`.


## Métodos
Para cada institución se tienen dos métodos comunes. El primero sirve para extraer las series dado un rango de peridos:
```
get_data()
```

La segunda sirve para conseguir la metadata (códigos de las series, base de datos, etc.) asociada a una consulta: 
```
search()
```
El resultado incluye los nombres de las series, códigos y fechas que servirán como complemento con la función anterior `get_data()`.


## Test !
Para cada una de las instituciones, el código guía para usar las funciones está disponible [aquí](https://github.com/mauricioalvaradoo/ecodata/blob/master/test.py).


## Créditos
* [Mauricio Alvarado](https://github.com/mauricioalvaradoo)
* [Andrei Romero](https://github.com/Ixtalia)
StableDiffusionAPI 0.0.5
A package to interface with the StableDiffusionAPI.com API
Requires Python: >=3.7

# StableDiffuionAPI
```
import StableDiffusionAPI

StableDiffusionAPI.Key("StableDiffusionAPI_KEY")
StableDiffusionAPI.Text2Img("Tractor", 512, 512, "Tractor.png")
```

# StableDiffuionAPI.Community
```
StableDiffusionAPI.Community as Community

Community.Text2Img("dreamlike", "Cat", "dreamlike-cat.png")
Community.Text2Img("midjourney", "Cat", "midjourney-cat.png")
```
# Requires API Key
[StableDiffusionAPI.com](https://stablediffusionapi.com/)
newa-downloader 0.0.2
A package to download data from the New European Wind Atlas
Requires Python: >=3.7

# NEWA Downloader

This is a small package to download data from the new european wind atlas.
It is by no means complete or very robust, but should do its job.

## Installation

Install via 
``pip install NEWA_downloader`` or 
```
git clone https://github.com/Hkorb/newa_downloader
cd newa_downloader && pip install .
```


## Usage


```python 
time_span = TimeSpan(datetime(2008, 1, 1), datetime(2008,1,2))
point = DataPoint(57.18, 18.3)
file = Path("visby_2008.nc")
variables = ["WS"]
heights = [50, 100]
download_mesoscale_timeseries(file, variables, point, time_span, heights)
```
pyolaf 0.2.1
3D reconstruction framework for light field microscopy

# pyolaf - A Python-based 3D reconstruction framework for light field microscopy

pyolaf is a Python port of the [oLaF](https://gitlab.lrz.de/IP/olaf/) 3D reconstruction framework for light field microscopy (LFM). 

## Overview
  
The light field microscope (LFM) allows for 3D imaging of fluorescent specimens using an array of micro-lenses (MLA) that capture both spatial and directional light field information in a single shot. oLaF is a Matlab framework for 3D reconstruction of LFM data with a deconvolution algorithm that reduces aliasing artifacts.

pyolaf brings these same features to the Python ecosystem, using GPU acceleration and some further code optimizations to **speed up deconvolution by 20x**. 

## Limitations

pyolaf only supports regular grids and single-focus conventional light-field microscopes.
In particular Fourier LFM, hexagonal grids, and multi-focus lenslets are currently not supported.
Pull requests to add these are welcome!

## Copyright

Copyright (c) 2017-2020 Anca Stefanoiu, Josue Page, and Tobias Lasser -- original oLaF code  
Copyright (c) 2023 Lili Karashchuk -- pyolaf

## Citation

When using pyolaf in academic publications, please reference the following citation:

- A. Stefanoiu et. al., "Artifact-free deconvolution in light field microscopy", Opt. Express, 27(22):31644, (2019).