semvec/pypi-packages huggingface dataset:
jump2odps 0.0.1
This library provides convenient access to the Citybrain Platform API
Requires Python: >=3.7

# Citybrain Platform Python Library

The Citybrain Platform Python Library provides convenient access to the Citybrain Platform API from applications written in the Python language.

## installation

If you just want to use the package, just run:

```sh
pip install --upgrade citybrain-platform
```

## Usage

The library needs to be configured with your account's secret key which is available on [www.citybrain.org](https://www.citybrain.org/#/settings). Either set it as the `CITYBRAIN_APIKEY` environment variable before using the library:

```sh
export CITYBRAIN_APIKEY='...'
```

Or set `citybrain_platform.api_key` to its value:

```python
import citybrain_platform

citybrain_platform.api_key = "..."
```

## Example Code

Examples of how to use this Python library to accomplish various tasks.

### Table Operations

#### Create Table

```python
import citybrain_platform
from citybrain_platform.computing.data_types import Column, ColumnType

columns = [
    Column("col_str", ColumnType.STRING, "this is a comment"),
    Column("col_id", ColumnType.BIGINT),
]

partition_columns = [
    Column("col_pt", ColumnType.INT, "a column for partitioning")
]

ok = citybrain_platform.Computing.create_table(name="test_tbl", columns=columns, partition_columns=partition_columns)
print(ok)
```

#### Get Table Schema

```python
import citybrain_platform

schema = citybrain_platform.Computing.get_table_schema(name="test_tbl")
print(schema)
```

#### Upload Table Data

```python
import citybrain_platform

result = citybrain_platform.Computing.upload_table_data(name="test_tbl", append=True, csv_filepath="aa.csv", partition_key={"col_pt": "19"})
print(result)
```

#### Truncate Table

```python
import citybrain_platform

result = citybrain_platform.Computing.truncate_table(name="test_tbl", partition_key={"col_pt": "19"})
print(result)
```

#### Drop Table

```python
import citybrain_platform

result = citybrain_platform.Computing.drop_table(name="test_tbl")
print(result)
```

#### Make Table Public To Others

```python
import citybrain_platform

public_table_name = citybrain_platform.Computing.public_table(name="test_tbl")
print(public_table_name)
```

#### List Available Tables

```python
import citybrain_platform

tables = citybrain_platform.Computing.list_tables()
print(tables)
```

### Job Operations

#### Create Job

```python
import citybrain_platform

job_id = citybrain_platform.Computing.create_job(sql="select col_str from test_tbl limit 12;")
print(job_id)
```

#### Get Job Status

```python
import citybrain_platform

job_status = citybrain_platform.Computing.get_job_status(job_id="...")
print(job_status)
```

#### Stop Running Job

```python
import citybrain_platform

result = citybrain_platform.Computing.stop_job(job_id="...")
print(result)
```


#### Download Terminated Job Results

```python
import citybrain_platform

citybrain_platform.Computing.get_job_results(job_id="...", filepath="results.csv")
```
packagedraft 0.0.0
A small example package
Requires Python: >=3.7

# Example Package

This is a simple example package. You can use [Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/) to write your content.

## test and build package 

```
python3 -m pip install --upgrade pip
python3 -m pip install --upgrade build
python3 -m pip install --upgrade twine
```

create pyproject.toml
```
cd ../deployments/package/

python3 -m build
```

```
```

```
```

```
```
HPrIm 0.1
Herramientas para el Procesamiento de Imágenes
Keywords: file,files,text

# HPrIm
---------------

Esta es una Heramienta para el Procesamiento de Imágnes (HPrIm) provee funciones que son utilizadas en el procesamiento de imágenes, como la ecualización o expansión de un histograma para mejorar el contraste y tecnicás algo más avanzadas como la aplicación de filtros laplaciono, sobel y mediana.

## Uso
Esta librería proporciona filtrado para agudizamiento y el suavizado de imágenes. Para esto se puede usar la clase `Filtro` que implementa todos los métodos para cualquier filtrado.

```python
from HPrIm import Filtro, MascaraLaplaciana

matriz = [
    [4, 4, 3, 2],
    [7, 7, 6, 6],
    [4, 1, 2, 3],
    [2, 5, 1, 2],
]

filtro = Filtro(matriz)
filtro.laplaciano(MascaraLaplaciana.F4)
print(filtro)
```
Por otra parte puedes hacer uso de la clase individual si quieres un poco más de control

```python
from HPrIm import FLaplaciano, MascaraLaplaciana
matriz = [
    [4, 4, 3, 2],
    [7, 7, 6, 6],
    [4, 1, 2, 3],
    [2, 5, 1, 2],
]

filtro = FLaplaciano(matriz)
matriz_filtrada = filtro.ejecutar(MascaraLaplaciana.F4)
print(matriz_filtrada)
```

Si deseas aplicar otros filtros o hacer cosass mucho más a "bajo nivel" puedes optar por la clase `FEspacial` que provee métodos como:

- `get_vecinos`: Sirve para obtener los vecinos de un elemento en la matriz
- `aplicar_filtro`: Aplica una máscara que recibe como parámetro
- `reescalar`: Reescala la matriz según un rango que recibe como parámetro 

```python
from HPrIm import FEspacial
matriz = [
    [4, 4, 3, 2],
    [7, 7, 6, 6],
    [4, 1, 2, 3],
    [2, 5, 1, 2],
]

mascara = [
    [1, 1, 1],
    [1, -8, 1],
    [1, 1, 1]
]

filtro = FEspacial(matriz)
res = filtro.aplicar_filtro(mascara)
print(res)
```
signature-package 0.0.0
A small example package
Requires Python: >=3.7

# Example Package

This is a simple example package. You can use [Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/) to write your content.

## test and build package 

```
python3 -m pip install --upgrade pip
python3 -m pip install --upgrade build
python3 -m pip install --upgrade twine
```

create pyproject.toml
```
cd ../deployments/package/

python3 -m build
```

```
```

```
```

```
```