semvec/pypi-packages huggingface dataset:
VIIRS-tiled-granules 1.1.1
Visible Infrared Imaging Radiometer Suite (VIIRS) Sinusoidal Tile Product Search and Download Utility
Requires Python: >=3.10

# Visible Infrared Imaging Radiometer Suite (VIIRS) Sinusoidal Tile Product Search and Download Utility

This tool uses the `earthaccess` library to search and download tiled remote sensing data products from the Visible Infrared Imaging Radiometer Suite (VIIRS).

[Gregory H. Halverson](https://github.com/gregory-halverson-jpl) (they/them)<br>
[gregory.h.halverson@jpl.nasa.gov](mailto:gregory.h.halverson@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 329G

[Evan W. Davis](https://github.com/evandjpl) (he/him)<br>
[evan.w.davis@jpl.nasa.gov](mailto:evan.w.davis@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 397K

## Installation

Install the `VIIRS-Tiled-Granules` package with dashes in the name from PyPi using pip:

```
pip install VIIRS-Tiled-Granules
```

## Usage

Import the `VIIRS_tiled_granules` package with underscores in the name:

```
import VIIRS_tiled_granules
```

See the [example notebook](Search%20and%20Download.ipynb) for usage.
elbowK 1.0
Automatically find the optimal number of clusters (k) for KMeans using the Elbow Method and visualize the result.
Requires Python: >=3.7

# elbowK

This package provides tools for determining the optimal number of clusters in K-Means clustering using the **Elbow Method**.
It automatically calculates the sum of squared errors (SSE) for different values of *k*, detects the optimal k, and visualizes the results with an elbow plot.

---

## Installation

### Install from requirements.txt

```bash
pip install -r requirements.txt
```

### Install in development mode (local)

```bash
pip install -e .
```

### Install from PyPI

```bash
pip install elbowK
```

---

## Usage

To use the package, import the main function and pass your scaled data:

```python
import pandas as pd
from sklearn.preprocessing import StandardScaler
from elbowK.elbow import find_best_k

# Example dataset
data = pd.DataFrame({
    'Income_$': [15, 16, 17, 18, 19, 20],
    'SpendingScore': [39, 81, 6, 77, 40, 76]
})

# Scale the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(data)

# Find the best k and save the elbow plot
best_k = find_best_k(X_scaled, max_k=10, save_plot=True)
print(f"Optimal k: {best_k}")
# The elbow plot will be saved as 'elbow_plot.png' in your working directory.
```

---

## Package Structure

```
elbowK/
    __init__.py       # Initializes the package
    elbow.py          # Core functionality for determining optimal clusters
setup.py              # Package metadata and setup configuration
requirements.txt      # Dependencies required
tests/
    elbow_test.py     # Unit tests
```

---

## License

This project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.