semvec/pypi-packages huggingface dataset:
sneklib 0.0.1
Library with various libs, with type-safety and async support
Requires Python: >=3.12


# TODOs:
- sqlite orm:
  - enforce strict tables (for inflexible datatypes)
  - enforce foreign keys validation
  - add not null constraint on primary key (even though it's not necessary if you have STRICT and WITHOUT_ROWIDS features)
  - sqlite does not do unicode case folding, so better to disallow upper/lower entirely
    - what does that mean for case insensitive search?
  - make string literals use single quotes
  - columns with INTEGER PRIMARY KEY should also allow an AUTOINCREMENT option (maybe you expect results to be chronologically sorted by id idk)
PrivacySherlock 0.0.1
A Python package for PII detection and classification
Requires Python: >=3.6
Keywords: python, pii detection, privacy, data privacy, pii, data classification


# Privacy Sherlock - PII Detection and Classification

This Python package helps detect Personally Identifiable Information (PII) in various data sources (e.g., MySQL, Amazon S3, MongoDB) and classifies them into categories (financial, personal, etc.). It supports PII risk assessment and visualization of the data.

## Features
- Detects PII using regular expressions.
- Classifies PII into categories like financial and personal.
- Supports data ingestion from MySQL, Amazon S3, and MongoDB.
- Provides a risk score based on the detected PII.
- Visualizes PII distribution using Plotly.
tf-image-processing 0.0.1
modules for transforming images and its colors through a histogram reference
Requires Python: >=3.9.0

# TF Image Processing

## Description
The package package_name is used to:  
- Processing:
	- Histogram matching
	- Structural similarity
	- Resize image  
- Utils:
	- Read image
	- Save image
	- Plot image
	- Plot result
	- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install tf_image_processing

```bash
pip install tf-image-processing
```

## Usage
### For Histogram Matching
1. Import the modules to open, read, plot, and save the imagens you need to combine as well as the combination module:  
```python
from tf_image_processing.utils import io, plot
from tf_image_processing.processing import combination
```
2. Get both images' paths:
	- a base image which is going to be modified
	- the reference image to get the histogram to be applied on the base image  

	It is easier if you store their paths in two variables, like the example below:
```python
image_1_path = io.read_image(image_1_path)
image_2_path = io.read_image(image_2_path)
```
3. Combine the images using the transfer_histogram passing as its arguments the base image (image_1), and the histogram reference image(image_2), storing it in a variable (result_image):
```python
result_image = combination.transfer_histogram(image_1, image_2)
```  
4. You can plot the results in two diferent ways:
	- Comparing the three images using the plot_result and passing the images as the arguments:
		```python
		plot.plot_result(image_1, image_2, result_image)
		```
		![alt text](image.png)  
	
	- Ploting only the result image with the plot_image:
		```python
		plot.plot_image(result_image)
		``` 
		![alt text](image-1.png)  

5. Finally you can save the result_image using the io.save_image, passing the result image and the path to where you want to save it.
	```python
	result_img_path = "./image-processing-package/result_image.jpg"
	io.save_image(result_image, result_image)
	```

## Author
Thiago Ferrari

## License
[MIT](https://choosealicense.com/licenses/mit/)
ctdcal 0.1.5b1.dev0
CTD and bottle data processing package from UCSD ODF
Requires Python: >=3.8
Keywords: oceanography, seawater, TEOS-10, ctd, calibration

[![PyPI Latest Release](https://img.shields.io/pypi/v/ctdcal.svg)](https://pypi.org/project/ctdcal/)
[![PyPI Python Versions](https://img.shields.io/pypi/pyversions/ctdcal.svg)](https://pypi.org/project/ctdcal/)
[![Package Status](https://img.shields.io/pypi/status/ctdcal.svg)](https://pypi.org/project/ctdcal/)
[![License](https://img.shields.io/pypi/l/ctdcal.svg)](https://github.com/cchdo/ctdcal/blob/master/LICENSE.md)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)


![GH Testing](https://github.com/cchdo/ctdcal/actions/workflows/run-tests.yml/badge.svg)
[![Documentation Status](https://readthedocs.org/projects/ctdcal/badge/?version=latest)](https://ctdcal.readthedocs.io/en/latest/?badge=latest)

# ctdcal project

The ctdcal project is a library primarily designed to process data from CTD casts and calibrate
them against Niskin bottle samples. This project is currently a work in progress and not yet
in public production.

A final public release is planned for 2025.

To contribute or try using ctdcal for yourself, feel free to install it and reach a member of the [SIO-ODF group](https://github.com/orgs/SIO-ODF/people)!

---

## Installation
ctdcal can be installed using pip:

```
$ pip install ctdcal
```

---

## CLI usage
### Initialize data folders
Initialize default `/data/` folders by running:

```
$ ctdcal init
```

(Future versions of ctdcal are planned have more robust init options/flags/etc.)

### Import and process data
To process data, copy over raw `.hex` and `.xmlcon` files into `/data/raw/` and reference data to their appropriate folder (`oxygen`, `reft`, `salt`).

Users can process their data with individual ctdcal functions or try:

```
$ ctdcal process [--group ODF]
```

to process using ODF procedures.

---

## Package usage
### Explore user settings
Most ctdcal functions get settings from `user_settings.yaml` and subsequently `config.py`. Call the configuration loader to explore default settings:

```py
from ctdcal import get_ctdcal_config
cfg = get_ctdcal_config()

# directories for I/O purposes
print(cfg.dirs)
print(cfg.fig_dirs)

# experiment-specific settings (e.g., expocode, CTD serial number) from user_settings.yaml
print(cfg.settings)

# dictionary mapping of short/long column names
print(cfg.columns)
```

As ctdcal continues to be developed, more robust [tutorials](https://ctdcal.readthedocs.io/en/latest/tutorials.html) will be added to [our documentation](https://ctdcal.readthedocs.io/en/latest/).

---

## LICENSING
BSD 3-clause
quantgrid-cli 0.1.4
A CLI for managing QuantGrid projects and workflows
Requires Python: >=3.6

# Quantgrid CLI

Quantgrid CLI is a command-line interface tool for managing data science and machine learning workflows. It is designed to be the quickest way to deploy a hosted MLFlow tracking server with included boilerplate code for time series model development, tracking, and deployment.

## Installation

To install Quantgrid CLI, use pip:

```bash
pip install quantgrid-cli
```

## Usage

To use Quantgrid CLI, run the following command:

```bash
quantgrid <command> [options]
```

### Commands

#### `deploy`

Deploy a new MLFlow tracking server and setup boilerplate code for a new project.

```bash
quantgrid deploy <project_name>
```