semvec/pypi-packages huggingface dataset:
configwizard 1.0.5
A package for handling configuration files
Requires Python: >=3.11.3

# configwizard

configwizard is a Python package for handling configuration files. It provides a `Config` class that allows you to easily create, read, update, and delete configuration files in different formats such as JSON and TOML.

# [Changelog](https://github.com/LapisPhoenix/ConfigWizard/blob/main/Changelog.md)

## Installation

You can install configwizard using pip:

```bash
pip install configwizard
```

## Usage
Here's an example of how to use configwizard:

```python
from configwizard import ConfigHandler

# Create a new configuration file
config = ConfigHandler.Config('config', 'json')

# Add content to the configuration file
config.update_content({'key': 'value'})

# Get the contents of the configuration file
contents = config.get_content()
print(contents)  # Output: {'key': 'value'}

# Update the contents of the configuration file
config.update_content({'key2': 'value2'})

# Get the updated contents
contents = config.get_content()
print(contents)  # Output: {'key': 'value', 'key2': 'value2'}

# Destroy the configuration file
config.destroy_config()
```

In the example above, we create a Config instance with a file name of 'config' and a file type of 'json'. We then add content to the configuration file using the add_content method and retrieve the contents using the get_content method. We update the contents with new values using the update_content method and finally destroy the configuration file with the destroy_config method.

## Supported File Types

configwizard supports the following file types for configuration files:

- JSON (.json)
- TOML (.toml)

When creating a Config instance, make sure to provide the appropriate file type.

## License

This project is licensed under the MIT License. See the __LICENSE__ file for more information.
nupyosc 0.0.3
NuPy: A new way to numerically compute neutrino oscillations
Keywords: probability,neutrino,oscillations

# NuPy

We use the recently discovered Eigenvalue-Eigenvector and Adjugate Identities to compute oscillation probabilities with minimal algorithmic steps.

# Usage


## Imports
```Python
import numpy as np
np.set_printoptions(precision=3)

from NuPy.oscprobs import OscProbIdentities
```

## Define your oscillation parameters

```Python
a = 0.2
theta12 = np.radians(33.2)
theta13 = np.radians(4.4)
theta23 = np.radians(46.1)
alpha = 0.026
delta = 50
deltacp = np.pi/6
```

## Compute probabilities
```Python
prob = OscProbIdentities(alpha, a, delta, deltacp, theta12, theta13, theta23)

print(prob.mat_angles_phase()) # prints mixing angles and phase in matter
# (29.988882340423384, 84.48628984963665, 3.5930259417910695, 44.188561816129464)

print(prob.PMNS()) # prints values of PMNS matrix elements
"""
[[ 0.849+0.000e+00j -0.297+0.000e+00j -0.062-4.318e-01j]
 [-0.335+8.346e-02j -0.92 -2.921e-02j -0.184+1.342e-18j]
 [ 0.01 +3.989e-01j  0.214-1.396e-01j -0.881-7.870e-19j]]
"""

print(prob.PMNS() @ prob.PMNS().H) # unitarity check
"""
[[1.000e+00-1.696e-22j 0.000e+00+1.561e-17j 6.939e-18+5.551e-17j]
 [0.000e+00-1.561e-17j 1.000e+00-4.276e-19j 5.551e-17-1.327e-18j]
 [1.388e-17-4.857e-17j 5.551e-17+0.000e+00j 1.000e+00-8.097e-19j]]
"""

print(prob.probabilities()) # prints all 9 probabilities
"""
[[0.97  0.015 0.014]
 [0.014 0.236 0.75 ]
 [0.015 0.749 0.236]]
"""
```
reddit-photo-aggregator 1.0.3
A library to scrape subreddits for images
Requires Python: >=3.11

# Reddit-Photo-Aggregator
A library to scrape subreddits for images. Useful for wall-papers, lockscreens, raspberry pi display projects, etc.

Read the Documentation [Here](https://hamolicious-reddit-photo-aggregator.readthedocs.io/en/latest/)
dq-ai-module 1.1.0
Create data quality rules and apply them to datasets.
Requires Python: >=3.8

# DQAI (Data Quality Artificial Intelligence)

This code provides a Python class called `DQAI` that utilizes the OpenAI Chat API to analyze a dataset and generate data quality rules specific to the data.

## Usage

1. Install the necessary dependencies.
2. Set up your OpenAI API key or use the provided default key.
3. Prepare your dataset in a suitable format (e.g., CSV).
4. Instantiate the `DQAI` class.
5. Invoke the `invoke_from_dataset` method, passing the dataset as input.
6. The code will generate Python code based on the dataset and execute it.
7. The generated rules and the results will be saved in the current directory as "generated_code.py" and "rulesapplication.csv," respectively.
8. The generated rules can be obtained by calling the `_get_rules_from_file` method.


## Example:

    import pandas as pd
    from dqai import DQAI

# Read the dataset from a CSV file
    path = "path/to/your/dataset.csv"
    data = pd.read_csv(path)

# Instantiate DQAI and generate data quality rules
    key = OPEN_AI_KEY
    dqai = DQAI(key)
    result = dqai.invoke_from_dataset(data)

# Access the generated rules and results
    rules = result["0"]
    results_df = result["1"]
make-room 0.0.1
Scripts to convert pictures and videos using more space efficient encodings.
Requires Python: >=3.10

# Development

Clone this repository.

Create a virtual environment, for example, directory `venv` using Python's `venv` module.
```
python -m venv venv
```

Activate the new environment with:
```
source ./venv/bin/activate
```

Make sure the latest pip version is in your virtual environment:
```
pip install --upgrade pip
```

Install all dependencies:
```
pip install -e ".[test,dev]"
```

Test run cli tool:
```
make-room --help
```

# Usage

To run the `make_room.py` script:

```sh
# at project root
hatch run make-room ~/ec-keep/photos-videos/2023/
```

This finds video within the `~/ec-keep/photos-videos/2023/` directory and encodes them using H265 with a constant rate factor of 28.