semvec/pypi-packages huggingface dataset:
gps-anomaly 0.1.14
Detecet and Remove GPS anomalies points
Requires Python: >=3.7,<4.0

# GPS anomaly detection

```bash
pip install gps-anomaly
``` 

### Usage
```python
from gps_anomaly.detector import Anomaly

anomaly = Anomaly()
points = [{
        "latitude": 22.32689719997222,
        "longitude": 11.49237269997222,
        "laptureTime": "2021_11_18_15_22_52_000",
        "altitude": 7.386,
        "sequenceUuid": "1490d87b-d5ba-4df3-b354-c01e7acaae29",
        "heading": 195.9560290711052,
        "orientation": 3,
        "deviceMake": "GoPro Max",
        "deviceModel": "GOPRO",
        "imageSize": "2704x2028",
        "fov": 100.4,
        "photoUuid": "21887915-e624-4246-b7e9-695b44fb6442",
        "filename": "GPAG8025.JPG",
        "path": ""
    },
    {
        "latitude": 22.32654149997222,
        "longitude": 11.4922393,
        "captureTime": "2021_11_18_15_22_53_000",
        "altitude": 6.029,
        "sequenceUuid": "1490d87b-d5ba-4df3-b354-c01e7acaae29",
        "orientation": 3,
        "deviceMake": "GoPro Max",
        "deviceModel": "GOPRO",
        "imageSize": "2704x2028",
        "fov": 100.4,
        "photoUuid": "ff612ec5-9479-473a-925b-8336af0b1e1f",
        "filename": "GPAG8026.JPG",
        "path": ""
    },
    {
        "Information": {
            "total_images": 2,
            "processed_images": 2,
            "failed_images": 0,
            "duplicated_images": 0,
            "id": "8323ff0a01fe49d1b55e610279f62828"
        }
    }
]
print(anomaly.anomaly_detector(frames=points))
```

### Output
```bash
[
    {
        "Information": {
            "total_images": 2,
            "processed_images": 2,
            "failed_images": 0,
            "duplicated_images": 0,
            "id": "8323ff0a01fe49d1b55e610279f62828"
        }
    }
]
```
StreamHelper 0.3.0
A package of RL algorithms

# streamlit-tutorial
[Doc](https://docs.streamlit.io/)

# Usage
1. install:
  ```
  pip install -r requirements.txt
  ```
2. run:
  ```
  streamlit run xxx.py
  ```

An example:
```
streamlit run multi-page_apps/home.py
```

# Deployment
After you’ve built a Streamlit app, it's time to share it! To show it off to the world you can use Streamlit Cloud to deploy, manage, and share your app for free.

It works in 3 simple steps:

Put your app in a public GitHub repo (and make sure it has a requirements.txt!)

Sign into share.streamlit.io

Click 'Deploy an app' and then paste in your GitHub URL

That's it! 🎈 You now have a publicly deployed app that you can share with the world. Click to learn more about how to use Streamlit Cloud.
vibeAI24 1.0.3
A library for predicting and blocking suspicious traffic
Requires Python: >=3.6
Keywords: cyberAI,AIcyber,vibeAI24

# Secure Application With AI

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                 

[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)   [![Python 3.8](https://img.shields.io/badge/python-3.8-yellow.svg)](https://www.python.org/downloads/release/python-380/)   


## Functionality of the vibeAI2

- Better Looking With AI  
- Block unauthorized  user 
- Authorized user can Allow

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.
 ```
  pip install vibeAI24
  ```
## Example

 ```
# test.py
from src.vibeAI24 import data_breache, predict_breach

  ```

## Run the following Script.
 ```
  python test.py
 ```



## Note 
- I have tried to implement all the functionality, it might have some bugs also. Ignore that or please try to solve that bug.