semvec/pypi-packages huggingface dataset:
json-linter 1.3.0
Lint your JSON files!
Requires Python: >=3.10

# json-linter

Lint your JSON files!

## Features

* Check if keys are sorted alphabetically...
* Check if your keys are either in camelCase, snake\_case or kebab-case...
* ...and automatically fix (some) of these issues!

## Install

You need to have Python 3.10+ installed.

```bash
$ pip install json-linter
```

## Usage

```bash
# Lint a single file...
$ json-linter my-file.json

# Lint multiple files...
$ json-linter a.json b.json

# Lint all files in a directory
$ json-linter files

# Lint all files in a directory recursively
$ json-linter files --recursive

# Lint all files in a directory recursively with .config and .cfg ending
$ json-linter files -r --extensions cfg config

# Fix files (will overwrite the file with a fixed version and then lint)
$ json-linter my-file.json --fix

# Overwrite configuration values
$ json-linter my-file.json --config-set naming_style=KEBAB_CASE indent=2

# Return results as json
$ json-linter my-file.json --json
```

### Use as a package

```python
from pathlib import Path

from json_linter import lint_file


results = lint_file(Path("./my-file.json"))

for result in results:
    if not result.was_successful:
        print(result.path, "failed!")

```

## License

GNU General Public License v3

![](https://www.gnu.org/graphics/gplv3-127x51.png)
infixpy 0.4.0
Infix data structures for Python

# infixpy 
See blog post, 
[Introducing infixpy: Scala-inspired data structures for Python](https://medium.com/@matthagy/introducing-infixpy-scala-inspired-data-structures-for-python-53f3afc8696)
to learn about using this library.
> A functional, object-oriented approach for working with sequences and collections. Also similar to Spark RDDs and Java Streams. Hope you find they simplify your code by providing a plethora of common algorithms for working with sequences and collections.

## Stephen's Direct from Scala example

Scala version

```scala
val a = ((1 to 50)
  .map(_ * 4)
  .filter( _ <= 170)
  .filter(_.toString.length == 2)
  .filter (_ % 20 == 0)
  .zipWithIndex
  .map{ case(x,n) => s"Result[$n]=$x"}
  .mkString("  .. "))

  a: String = Result[0]=20  .. Result[1]=40  .. Result[2]=60  .. Result[3]=80
```
Version using the infixpy library with python

```python
from infixpy import * 
a = (Seq(range(1,51))
     .map(lambda x: x * 4)
     .filter(lambda x: x <= 170)
     .filter(lambda x: len(str(x)) == 2)
     .filter( lambda x: x % 20 ==0)
     .enumerate()
     .map(lambda x: 'Result[%d]=%s' %(x[0],x[1]))
     .mkstring(' .. '))
print(a)
  
  # Result[0]=20  .. Result[1]=40  .. Result[2]=60  .. Result[3]=80
```


## Original Example
```python
from infixpy import Seq

(Seq(range(10))
 .map(lambda x: x+3)
 .filter(lambda x: x%2==0)
 .groupby(lambda x: x%3)
 .keys()
 .foreach(print))
```

#### Output
```
1
0
2
```

## Examples
See examples/ directory for additional examples of using infixpy. 

Also see example usages in
[career_village_entities](https://github.com/matthagy/career_village_entities).
DeepPhysX.Torch 22.12
A PyTorch compatibility layer for DeepPhysX framework.

# DeepPhysX.Torch

![logo](docs/source/_static/image/logo.png)

This python package is part of the [DeepPhysX](https://github.com/mimesis-inria/DeepPhysX) project.
It contains adaptations of some Core components that are compatible with the [PyTorch](https://pytorch.org/)
framework.

### Quick install

The package requires [DeepPhysX](https://github.com/mimesis-inria/DeepPhysX) and [PyTorch](https://pytorch.org/) to be 
installed.

The easiest way to install is using `pip`, but there are a several way to install and configure a **DeepPhysX**
environment (refer to the [**documentation**](https://deepphysx.readthedocs.io) for further instructions).

```bash
$ pip install DeepPhysX.Torch
```

If cloning sources, clone it in the same repository as other `DeepPhysX` packages.
It must be cloned in a directory with the corresponding name as shown below:

``` bash
$ mkdir DeepPhysX
$ cd DeepPhysX
$ git clone https://github.com/mimesis-inria/DeepPhysX.git Core             # Clone default package
$ git clone https://github.com/mimesis-inria/DeepPhysX.Torch.git Torch        # Clone AI package
$ ls
Core Torch
```
triton-requests 0.0.3
A high level package for Nvidia Triton requests
Requires Python: >=3.7

Python package to interact with Nvidia Triton Server


```python

from triton_requests import TritonEndPoint

tep = TritonEndPoint(model_name="emotion-english-distilroberta-base_onnx_inference", url="localhost:9000")

tep.encode("Harry is really happy after his present")
 
```

Will result a numpy array 

	array([[-1.1953125 , -2.4921875 , -2.8964844 ,  4.9921875 ,  0.35498047, 0.70458984,  1.9570312 ]], dtype=float32)