semvec/pypi-packages huggingface dataset:
csv-tabulator 1.0.1
Convert the csv to html table
Requires Python: >=3.7

# csv-tabulator

The `csv-tabulator` is a Python package that simplifies the process of converting CSV files into styled HTML tables. The resulting HTML tables are designed to be responsive and visually appealing.

## Installation

You can install the package using `pip`:

```bash
pip install csv-tabulator
```

## Usage
```bash
>>> from csv_tabulator import converter
>>> converter.convert('/Users/amiaynarayan/Projects/utilities/tabulator/tests/csv_file', 'table.html')
File Reading... Done!
Converting to HTML...
Writing the HTML file to table.html
Done...!
```
python-okx-async 0.1.1
Python SDK with async support for the OKX v5 API

# About
```python-okx-async``` is an unofficial Python wrapper for the [OKX exchange v5 API](https://www.okx.com/okx-api) that comes with async support.
The wrapper is an extension of the [```python-okx```](https://github.com/okxapi/python-okx) package, which supports synchronous REST requests and websocket streams.

# Installation
To install the package, run
```
pip install python-okx-async
```

# Quick start

### Create an OKX account
- If you don't already have an OKX account, register for one on https://www.okx.com/account/register

### Create API credentials
- Log into your OK account and select ```API keys``` in the user menu
- Click the ```+ Create V5 API key``` button
- Follow the instructions to create API credentials (key, passhprase, secret)

### Save API credentials
- If there isn't a ```.env``` file in your home directory, create one and make sure it can only be read and written to by you.
```
touch ~/.env
chmod 600 ~/.env
```
- Add the following lines to the ```.env``` file, replacing the text to the right of the equal signs with the credentials created above. Note that the credentials should NOT be enclosed in quotation marks.
```
OKX_API_KEY=<key>
OKX_API_PASSPHRASE=<passphrase>
OKX_API_SECRET=<secret>
```
API credentials are stored in a ```.env``` file for security reasons. It is not advisable to include API credentials directly in source code or to supply them as command line arguments.

### Run examples
Import and instantiate the API wrapper class ```AsyncTradeAPI```, which is used for order placement, as shown below. You may first need to run ```pip install python-dotenv``` to install the ```dotenv``` package on your system.
```
import os
from dotenv import load_dotenv
from okx_async.AsyncTrade import AsyncTradeAPI

load_dotenv()

tradeAPI = AsyncTradeAPI(os.getenv("OKX_API_KEY"), os.getenv("OKX_API_SECRET"), os.getenv("OKX_API_PASSPHRASE"))
```
The other REST API classes can be instantiated similarly. Note that async classes are initialized with default arguments ```flag='1'``` and ```debug=True```, i.e. async classes are by default in the OKX production environment and not in debug mode.

For a complete example see the ```example_order_book.py``` file included in the Github repository. The script prints the order book for the XCH-USDT spot market to a depth of 20 levels.

Also make sure to check out the documentation of ```python-okx```, and the additional [examples](https://github.com/okxapi/python-okx/tree/master/example) included in that repository.
texere 1.0.2
A Python package for text removal and edge marking from images

# Texere: This is the start of a new Module
Currently it has two function 1.Text Removal from Images and generate mask 2.Marking Edges in Images

Texere is a Python package that provides functionality for removing text from images and marking edges, along with the option to generate a mask for further processing.

## Installation

You can install Texere using pip:

```
pip install texere
```

## Usage: 
# Removing Text from Images
```
from texere.purge import txt

modified_image, mask = txt('image_path.jpg',pixels,threshold)

# 'modified_image' contains the image with text removed
# 'mask' is a binary image indicating the regions where text was removed
# Further processing using the 'mask' image can be done here
```
# Marking Edges in the image
```
from texere.edges import mark

# Replace 'image_path.jpg' with the path to your input image
marked_image = mark('image_path.jpg',threshold)

# 'marked_image' contains the image with marked edges
# You can further process or visualize the edges here
```
## Example:
# Removing Text from Images
```
from texere.purge import txt

# Replace 'input_image.jpg' with the path to your input image
modified_image, mask = txt('input_image.jpg',7,10)

#threshold value is from 0-100
#pixels value >0
#depending on these two value the image will turn out Good 
# Save the modified image
cv2.imwrite('output_image.jpg', modified_image)

# Save the mask as a binary image its a grayscaled image so it can be used for impainting the text area
cv2.imwrite('mask_image.jpg', mask)
```

# Marking Edges in the image
```
from texere.edges import mark

# Replace 'input_image.jpg' with the path to your input image
marked_image = mark('input_image.jpg',threshold)
#threshold value can be between 0-255 also images i tried on works best with 70 
# Save the marked image
marked_image.save('marked_image.jpg')
```

# Contributing:
Contributions to Texere are welcome! If you have ideas for improvements, bug fixes, or new features, feel free to open an issue or submit a pull request on GitHub.

# License:
This project is licensed under the MIT License - see the LICENSE file for details.