semvec/pypi-packages huggingface dataset:
overdose 0.0.6
Overdose of drugs in the US
Requires Python: >=3.7

# How to install overdose
$ pip install overdose

# How to run overdose
$ overdose

## Graph

<img src='https://github.com/i-inose/overdose/blob/main/Drug%20Overdose%20by%20indicator.png?raw=true' height=480, width=640>


"Cocaine (T40.5)," "Heroin (T40.1)," and "Opioids (T40.0-T40.4,T40.6)" are up since 2019.
"Opioids (T40.0-T40.4,T40.6)" are particularly prolific.
ZEMO 1.0.0
Zernike moments: Digital images
Requires Python: >=3.8

# Zernike moments: Digital images

The Zernike moment's package program is developed for square digital images mimicking some part of Matlab code provided by Christian Wolf. Zernike moments are unique due to orthogonality and a complete set of Zernike polynomials. Zernike moments are used in image analysis to characterize the shape and structure of objects. The following articles and their references give a detailed description of the Zernike polynomials and Zernike moments.

## Description

The code includes the following functions:

**zernike_order_list**: Generates a list of Zernike polynomial orders.

**robust_fact_quot**: Calculates the robust factor quotient between two lists of values.

**zernike_bf**: Generates Zernike basis functions for a given size and order.

**zernike_mom**: Calculates the Zernike moments of an input image using precomputed Zernike basis functions.

**zernike_rec**: Reconstructs an image from Zernike moments(Inverse trasformation).

## Installation
Install the ZEMO library using pip:
```bash
pip install ZEMO
```
## Usage

1. The standard way to import ZEMO library:
```python
import ZEMO 
from ZEMO import zemo
```
2. Generates Zernike basis functions for a given `size`, `order`, and optional `withneg` parameter for a **square images** of size **SZ**.
   If withneg is 1, then the basis functions with negative repetition are included.
```python
ZBFSTR = zemo.zernike_bf(SZ,order,withneg)
```

3. Calculates Zernike moments of an input image (images) using precomputed Zernike basis functions. **I** is the input image.
```python
Z = zemo.zernike_mom(I,ZBFSTR)
```

4. Reconstructs an image from Zernike moments using precomputed Zernike basis functions.
```python
I = zemo.zernike_rec(Z,SZ,ZBFSTR)
```

Here is an example of face (Hossein Safari) image:
```python
from ZEMO import dataset
dataset.Face_Data()
```
Due to the slightly large size of the "dataset", the command "dataset.Face_Data()" will take a few moments.

You can see some examples of the ZEMO code in https://github.com/hmddev1/ZEMO

## How to Cite
[Raboonik, A., Safari, H., Alipour, N., & Wheatland, M. S. 2017, ApJ, 834, 11](https://iopscience.iop.org/article/10.3847/1538-4357/834/1/11/meta)

[Alipour, N., Mohammadi, F., Safari, H. 2019, ApJS, 243, 20](https://iopscience.iop.org/article/10.3847/1538-4365/ab289b/meta)

## Authors

[Hossein Safari](https://scholar.google.com/citations?user=nCc1FV8AAAAJ&hl=en) (https://orcid.org/0000-0003-2326-3201), [Nasibe Alipour](https://scholar.google.com/citations?user=PfzZOI0AAAAJ&hl=en) (https://orcid.org/0000-0003-3643-5121), [Hamed Ghaderi](https://github.com/hmddev1) (https://orcid.org/0009-0005-7934-2752), [Pardis Garavand](https://www.linkedin.com/in/pardis-garavand-25b30527b/)

## License
[MIT](https://choosealicense.com/licenses/mit/)