jq-sdk 0.1.0

A Python package for visualizing 1x1024 matrices as 32x32 heatmaps

JQ-SDK

A Python package for visualizing 1x1024 matrices as beautiful 32x32 heatmaps with multiple color schemes.

Features

• Convert 1D arrays (1024 elements) into 32x32 heatmap visualizations
• Interactive Plotly-based heatmaps
• 10 beautiful pre-configured color schemes
• Simple and intuitive API
• Python 3.7+ support

Installation

Install from PyPI:

pip install jq-sdk

Or install from source:

git clone https://github.com/yourusername/JQ-SDK.git
cd JQ-SDK
pip install -e .

Quick Start

import jq_sdk

# Create sample data (1024 elements)
data = list(range(1, 1025))

# Plot with default color scheme (viridis)
fig = jq_sdk.plot_heatmap(data)
fig.show()

# Use a different color scheme
fig = jq_sdk.plot_heatmap(data, colorscheme='plasma')
fig.show()

# Customize the plot
fig = jq_sdk.plot_heatmap(
    data,
    colorscheme='hot',
    title='My Custom Heatmap',
    width=1000,
    height=1000
)
fig.show()

Available Color Schemes

JQ-SDK provides 10 beautiful color schemes:

• viridis (default) - Purple to yellow gradient
• plasma - Dark purple to yellow gradient
• hot - Black to red to yellow
• blues - White to dark blue
• reds - White to dark red
• greens - White to dark green
• rainbow - Full spectrum rainbow
• inferno - Black to purple to yellow
• magma - Black to purple to white
• cividis - Colorblind-friendly blue to yellow

You can get the list programmatically:

import jq_sdk

schemes = jq_sdk.get_available_colorschemes()
print(schemes)

API Reference

plot_heatmap(data, colorscheme='viridis', title='Heatmap Visualization', show_colorbar=True, width=800, height=800)

Plot a 1x1024 matrix as a 32x32 heatmap.

Parameters:

• data (list or numpy.ndarray): Input data with exactly 1024 elements
• colorscheme (str, optional): Color scheme name. Default is 'viridis'
• title (str, optional): Title of the heatmap. Default is 'Heatmap Visualization'
• show_colorbar (bool, optional): Whether to show the colorbar. Default is True
• width (int, optional): Width of the figure in pixels. Default is 800
• height (int, optional): Height of the figure in pixels. Default is 800

Returns:

• plotly.graph_objects.Figure: Plotly figure object. Call .show() to display.

Raises:

• ValueError: If input data does not contain exactly 1024 elements
• KeyError: If an invalid colorscheme is specified

get_available_colorschemes()

Get a list of available color schemes.

Returns:

• list: List of available colorscheme names

Examples

Basic Usage

import jq_sdk
import numpy as np

# Using a list
data = list(range(1024))
fig = jq_sdk.plot_heatmap(data)
fig.show()

# Using numpy array
data = np.random.rand(1024)
fig = jq_sdk.plot_heatmap(data, colorscheme='plasma')
fig.show()

Comparing Different Color Schemes

import jq_sdk
import numpy as np

# Generate sample data
data = np.sin(np.linspace(0, 4*np.pi, 1024))

# Try different color schemes
for scheme in ['viridis', 'plasma', 'hot', 'rainbow']:
    fig = jq_sdk.plot_heatmap(
        data,
        colorscheme=scheme,
        title=f'Heatmap with {scheme} colorscheme'
    )
    fig.show()

Saving to File

import jq_sdk

data = list(range(1, 1025))
fig = jq_sdk.plot_heatmap(data, colorscheme='viridis')

# Save as HTML
fig.write_html('heatmap.html')

# Save as PNG (requires kaleido)
# pip install kaleido
fig.write_image('heatmap.png')

Requirements

• Python >= 3.7
• numpy >= 1.19.0
• plotly >= 5.0.0

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

If you encounter any issues or have questions, please file an issue on the GitHub issue tracker.

Changelog

0.1.0 (Initial Release)

• Initial release with basic heatmap visualization
• Support for 10 color schemes
• Interactive Plotly-based visualizations
• Python 3.7+ support