semvec/pypi-packages huggingface dataset:
GeoNLPlify 0.1.0
A NLP library for data augmentation focusing on spatial information contained in text.
Requires Python: >=3.7

<h1 align="center"> GeoNLPlify
:earth_africa: :book:
</h1>

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

A NLP library for data augmentation focusing on spatial information contained in text.

<p align="center">
  <img src="./readme_ressources/geonlplify_example_schema.png" />
</p>


## Usage
```python
import geonlplify

my_text = "My name is Clara and I live in Berkeley."
geonlplify.geonlplify(my_text)
```
```bash
'My name is Clara and I live in Bristol'
```

## Installation
You can install GeoNLPlify in three ways: in a [virtual_env](#virtual-environment), in a [conda environment](#conda-environment), or using :construction: [pip](#pip-install)
### Virtual Environment
1. Git clone this repository
  ```bash
  git clone https://github.com/remydecoupes/GeoNLPlify.git
  ```
2. Create a virtual env
  ```bash
  python -m venv geonlplify_venv
  source geonlplify_venv/bin/activate
  pip install --upgrade pip
  ```
3. Install dependencies
  ```bash
  cd GeoNLPlify
  pip install -r virtual_env_requirements.txt
  ```

5. Donwload [world-cities from simple maps](https://simplemaps.com/data/world-cities)
  ```bash
  wget -qO- https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.75.zip  | bsdtar -xvf- -C ./geonlplify/simplemaps/
  ```
### Conda environment
1. Git clone this repository
  ```bash
  git clone https://github.com/remydecoupes/GeoNLPlify.git
  ```
2. Create a conda with all the required dependencies
  ```bash
  cd GeoNLPlify
  conda env create -n geonlplify_conda --file conda_environment.yml python==3.10.6
  conda activate geonlplify_conda
  ```
3. Install spacy models
  ```bash 
  python -m spacy download en_core_web_trf
  ```
4. Donwload [world-cities from simple maps](https://simplemaps.com/data/world-cities)
  ```bash
  wget -qO- https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.75.zip  | bsdtar -xvf- -C ./geonlplify/simplemaps/
  ```
### Pip install
:construction: **Packaging in progress** :construction:

## Acknowledgement
:pray: This library use those terrific tools/libraries/data :muscle::
+ [Spacy](https://spacy.io/)
+ [The Komoot geocoder Photon](https://photon.komoot.io/)
+ [OpenStreeMap](https://www.openstreetmap.org/copyright)
+ [Simplemaps data](https://simplemaps.com/data/world-cities)
gww-service 0.2.0
Backend API for synchronous operations within Global Water Watch
Requires Python: >=3.8

# Global Water Watch Service
Backend API for synchronous operations within Global Water Watch

## Testing
```bash
docker-compose up
pytest
```

## Local deployment

Currently, I have 2 ways of deploying locally

### Debugging
For debugging, it is best to debug the main.py file in python, and use `docker-compose` to get the database running:
```docker-compose up```

Get earthengine credentials:
```make get_secrets```

And add the following environment variables (to your launch.json in VSCode) and .env:
```json
{
    "EE_SA": "dagster-workloads@global-water-watch.iam.gserviceaccount.com",
    "EE_PK": "${workspaceFolder}/gcloud_dist/privatekey.json"
}
```

We can then use `psql` to create the postgis extension:
```psql -U testuser -h localhost -d testdb```
using the `testpassword`.

Then use (for example) VSCode to debug main.py.

### Local kubernetes

To test kubernetes (later for some extra functionality like workflows), you can deploy a local cluster.
This is simplified in the `Makefile`. 
- Install [kind](https://kind.sigs.k8s.io/).
- Install [kubectl](https://kubernetes.io/docs/tasks/tools/)
- Install [helm](https://helm.sh/)

```
make build-docker
make deploy-local
```

Check out the database:
in one terminal:
```
export PGMASTER=$(kubectl get pods -o jsonpath={.items..metadata.name} -l application=spilo,cluster-name=gwwuser-gww,spilo-role=master -n gww)
kubectl port-forward $PGMASTER 6432:5432 -n gww
```
in another:
```
export PGPASSWORD=$(kubectl get secret -n gww gwwuser.gwwuser-gww.credentials.postgresql.acid.zalan.do -o 'jsonpath={.data.password}' | base64 -d)
export PGSSLMODE=require
psql -U gwwuser -h localhost -p 6432 -d gwwdb
```

Check out the api:
```
kubectl port-forward -n gww svc/gww-api 8080:80
```

After you are done:
```
kind delete clusters waterwatch
```

> Note: This is to test working with other software, for debugging, using VSCode together with
> docker-compose and FastAPI's `--reload` option helps developing quickly.

## Deploy on kubernetes

This repository contains a helm chart that deploys the API to kubernetes.
For example values, check out [the values file](helm/gww-api/values.yaml).
obkreator 0.4.1
Observing block creator for ESO/2.2m telescope
Requires Python: >=3.9

## Brief intro

### Purpose

Create ESO observing blocks from a list of targets either specified in the command line or in a file. Instrument supported: WFI, FEROS, GROND at 2.2m telescope.

### Installation

It is packaged as [obkreator](https://pypi.org/project/obkreator/).

Site-wide installation will be performed with `sudo -H pip3 install obkreator` on unix-like systems. At a user level, within a [virtual environment](https://docs.python.org/3/library/venv.html "venv package"), `pip3 install obkreator`. 

### Short examples

Create FEROS OBs with 2 × 10 min integrations for a few SIMBAD targets.

```sh

> mkfrsob --target "HD 10" "HD 20" "HD 30" --objcal --dit 600 --nint 2 --pid '0107.A-9032(A)'
0107.A-9032(A): 2.2/FEROS observations by GREDEL, R
Coordinates have been queried from SIMBAD
OB #1 created: OB/FEROS-2.2/HD10.obd
OB #2 created: OB/FEROS-2.2/HD20.obd
OB #3 created: OB/FEROS-2.2/HD30.obd
 
```

Create WFI OBs from information in a table
```sh

> cat <<EOF >WFI.dat
heredoc> target   ag    dit             nint filter       acq       acqdit offsets
heredoc> ------ ------  --------------- ---- ---------- ----------- ------ -------------       
heredoc> HD 1   auto    100,50,50,20,10    4 U,B,V,Rc,I movetopixel    200
heredoc> HD 2           50 300          10 2 V,Halpha
heredoc> HD 3   manual  20              2    Rc                            10,20 900,900
heredoc> EOF
> mkwfob --pid '0107.A-9031(A)' --table WFI.dat
0107.A-9031(A): 2.2/WFI observations by GREDEL, R
Coordinates have been queried from SIMBAD
3 OBs to be generated using table
Columns are: target, coo, pm, ag, dit, nint, filter, acq, acqdit, offsets
OB #1 created: OB/WFI-2.2/HD1.obd
OB #2 created: OB/WFI-2.2/HD2.obd
OB #3 created: OB/WFI-2.2/HD3.obd
>
```

Create GROND OBs with several templates

```sh
> mkgrndob --target 'HD 1' 'HD 2' 'HD 3' --tpltype 4m4td 8m4td 20m4td --pid '0107.A-9033(A)' --pi 'Doe, J.'
0107.A-9033(A): 2.2/GROND observations by Doe, J. under GREDEL, R PID
Coordinates have been queried from SIMBAD
OB #1 created: OB/GROND-2.2/HD1.obd
OB #2 created: OB/GROND-2.2/HD2.obd
OB #3 created: OB/GROND-2.2/HD3.obd
```

### Changes
 
#### 0.3.2

* fix a bug in GROND IR DITs.
* modify WFI OBs according to new `WFI_img_acq_MoveToPixel` template

#### 0.4

* fix a bug in obx creation
* add entry points
* migration to pyproject.toml 

#### 0.4.1

* fix a bug in low-precision coordinates in obx
* use requirements.txt
package-image-processing-DiO 0.0.2
Test version Image processing package using skimage. This project belongs to Karina Tiemi Kato.
Requires Python: >=3.5

# Projeto: Pacote de Processamento de Imagens
## Autora do Projeto: Karina Kato
### Projeto: Descomplicando a criaÃ§Ã£o de pacotes de processamento de imagens em Python (DiO)
#### Tecnologia: Python
#### Data: 25/10/2022
-----------------------------------------
### DescriÃ§Ã£o
O pacote "package_image_processing_DiO" Ã© usado para:

- MÃ³dulo "Processing":
  - CorrespondÃªncia de histograma;
  - Similaridade estrutural;
  - Redimensionar imagem;

- MÃ³dulo "Utils":
  - Ler imagem;
  - Salvar imagem;
  - Plotar imagem;
  - Resultado do grÃ¡fico;
  - Plotar histograma;
---------------------------------------------
## Passo a passo da configuraÃ§Ã£o para hospedar um pacote em Python no ambiente de testes Test Pypi

- [x] InstalaÃ§Ã£o das Ãºltimas versÃµes de "setuptools" e "wheel"

```
py -m pip install --user --upgrade setuptools wheel
```
- [x] Tenha certeza que o diretÃ³rio no terminal seja o mesmo do arquivo "setup.py"

```
C:\User\user-name\image-processing-package> py setup.py sdist bdist_wheel
```

- [x] ApÃ³s completar a instalaÃ§Ã£o, verifique se as pastas abaixo foram adicionadas ao projeto:
  - [x] build;
  - [x] dist;
  - [x] image_processing_test.egg-info.

- [x] Basta subir os arquivos, usando o Twine, para o Test Pypi:

```
py -m twine upload --repository testpypi dist/*
```

- [x] ApÃ³s rodar o comando acima no terminal, serÃ¡ pedido para inserir o usuÃ¡rio e senha. Feito isso, o projeto estarÃ¡ hospedado no Test Pypi.hospedÃ¡-lo no Pypi diretamente.

### O objetivo este projeto foi realizar um simples teste com o projeto da Karina com hospedagem no Test Pypi em um projeto do bootcamp Unimed BH da DiO.

----------------------------------------------------
## InstalaÃ§Ã£o local, apÃ³s hospedagem no Test Pypi

- [x] InstalaÃ§Ã£o de dependÃªncias
```
pip install -r requirements.txt
```

- [x] InstalÃ§Ã£o do Pacote

Use o gerenciador de pacotes ```pip install -i https://test.pypi.org/simple/ package-image-processing-DiO ```para instalar image_processing-test

```bash
pip install image-processing-test
```
-------------------------------------------------
## Como usar em qualquer projeto

```python
from image-processing-test.processing import combination
combination.find_difference(image1, image2)
```
![alt text](image.png)

## Autor (quem hospedou o projeto no Test Pypi)
jonathansilva13



## License
[MIT](https://choosealicense.com/licenses/mit/)