semvec/pypi-packages huggingface dataset:
httpj 0.28.1
HTTPX fork with custom JSON serializer support
Requires Python: >=3.8

<p align="center">
  <a href="https://www.python-httpx.org/"><img width="350" height="208" src="https://raw.githubusercontent.com/gtors/httpj/master/docs/img/butterfly.png" alt='HTTPJ'></a>
</p>

<p align="center"><strong>HTTPJ</strong> <em>HTTPX fork with custom JSON serializer support.</em></p>

<p align="center">
<a href="https://github.com/gtors/httpj/actions">
    <img src="https://github.com/gtors/httpj/workflows/Test%20Suite/badge.svg" alt="Test Suite">
</a>
<a href="https://pypi.org/project/httpj/">
    <img src="https://badge.fury.io/py/httpj.svg" alt="Package version">
</a>
</p>

- HTTPJ is nearly identical to HTTPX. The main difference lies in two extra parameters, `json_serialize` and `json_deserialize`, which afford you precise control over the serialization and deserialization of your objects in JSON format.
- HTTPJ will remain synchronized with the mainstream HTTPX until similar functionality emerges.

---

Install HTTPJ using pip:

```shell
pip install httpj
```

Now, let's get started:

```python
import datetime
import pprint

import httpj
import orjson


resp = httpj.post(
    "https://postman-echo.com/post",
    json={"dt": datetime.datetime.utcnow()},
    json_serialize=lambda j: orjson.dumps(j, option=orjson.OPT_NAIVE_UTC),  # optional
    json_deserialize=orjson.loads,  # optional
)
pprint.pprint(resp.json(), indent=4)
```

## Release Information

### Fixed

* Reintroduced supposedly-private `URLTypes` shortcut. (#2673)


---

[Full changelog](https://github.com/gtors/httpj/blob/master/CHANGELOG.md)
mmetrics 1.1.0
A library to perform image quality and classification assessment.
Requires Python: >=3.12

# Metrics Library

This library provides utilities for calculating various metrics in image quality assessment and classification.

## Features
- Image Quality Metrics: PSNR, SSIM, MSE, FID, NIQE
- Classification Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1 Score

## Installation
```bash
pip install mmetrics
```

## Usage
```python
'''Image Quality Assessment'''
from metrics.image_quality import psnr, ssim, mse, niqe, fid

#Load your images to compare using any tool to have them as numpy arrays
psnr(img1, img2)
ssim(img1, img2)
mse(img1, img2)
niqe(img1)

#Put the path of your images
fid(real_images_path, fake_images_path)

'''Classification Assessment'''
from metrics.classification import confusion_matrix, accuracy, precision, recall, f1_score, jaccard_index

#Load your ground truth (y_true) and your predictions (y_pred) as numpy arrays 
confusion_matrix(y_true, y_pred)
accuracy(y_true, y_pred)
precision(y_true, y_pred)
recall(y_true, y_pred)
f1_score(y_true, y_pred)
jaccard_index(y_true, y_pred)

```