semvec/pypi-packages huggingface dataset:
rectiou 0.0.1
rectiou: vectorized 2d rectangle intersection with pytorch
Requires Python: >=3.8
Keywords: bounding box,efficient,fast,intersection,pytorch,rectangles,vectorized

# rectiou

Rectiou is a pytorch implementation of the intersection over union (IoU) between two rotated rectangles.

Supports:
- ✅ Works with rotated rectangles: can handle rectangles with any angle.
- ✅ Batched computation: No for loops, if statements, or other control flow. Can have as many batch dimensions as you want.
- ✅ Fast: faster than OpenCV's implementation.
- ✅ Differentiable: Can be used in a pytorch model and backpropagated through.
- ✅ GPU: can be run on the GPU.
- ✅ No weird stuff: everything is implemented in plain pytorch, no weird custom CUDA kernels or anything like that.

## Example

Usage is simple:
```python

import torch
import rectiou

# Rectangles: [x, y, w, h, radians]
rect_a = torch.tensor([0.0, 0.0, 1.0, 1.0, 0.0])
rect_b = torch.tensor([0.2, 0.1, 1.1, 0.8, torch.pi / 4])

# Intersection over union
iou = rectiou.compute_iou(rect_a, rect_b)
```

## Installation

```bash
pip install rectiou
```

To test that everything is working correctly, run:
```bash
pip install rectiou[tests]
pytest
```
best-csv 1.2
Smarter Faster way from CSV to dict.
Requires Python: >=3.7

# best_csv

This library converts a csv file to a Python dictionary. "So what!?!?" you may ask :). This library does it better, more efficienty and greatly improves your querry times over the built in csv Python library. The built in csv Python library has a lookup complexity 
of O(n) where runtime grows directly in proportion to n; best_csv has a lookup complexity of O(1) which means that it takes a constant time to lookup regardless of the dataset size.

Now, this library might not be ideal for all csv datasets but is great for datasets that have a column that we can use as a unique identifier for any given row of information. For example, if we have a dataset of employees and each row is an employee record we can use the employee IDs as the key for any given employee in the dictionary. 

The standard Python csv library will create a list of dicts so if you want to search for specific employee IDs you have to potentialy loop though all of the dataset to find the employee you want; whereas with best_csv you simply call the employee ID you need without having to loop through the dataset. This is great if you have massive datasets and you are running multiple querries.




## Installation

Use the package manager pip to install dnac_device_list.

```bash
  pip install best_csv
```
    
## Usage/Examples


```python
import best_csv

# Headers in employee_data.csv
# User Id,First Name,Last Name,Email,Phone,Date of birth,Job Title

# Convert your csv to dict with column 1 as the unique key for any given row.
result = best_csv.csv_to_dict("employee_data.csv", 1)


print(result['AB123']) # Print info of Employee ID AB123

```


## result['AB123'] print simulation

```
{'First Name': 'Wesley', 'Last Name': 'Chung', 'Email': 'wcab123s@acme.org', 'Phone': '(280)277-4903', 'Date of birth': '1953-04-21', 'Job Title': 'Security Engineer'}

```


## Authors

- [@Alexios Nersessian](https://github.com/alekos3)