image-slide 1.0.1

An interactive image viewer for multidimensional numpy arrays

image-slide

A comprehensive Python image viewer for multidimensional numpy arrays with interactive controls for navigation, contrast adjustment, and complex data visualization.

Features

Core Functionality

• Multi-dimensional array support: View numpy arrays of arbitrary dimensions and shapes
• Image navigation: Slider-based navigation through image stacks
• Complex array support: Display magnitude, phase, real, or imaginary parts
• Interactive controls: Contrast, brightness, and colormap adjustment
• Keyboard shortcuts: Fast navigation with arrow keys
• Professional interface: Compact layout with all controls accessible

Display Controls

• Contrast adjustment: 0.1x to 3.0x multiplication
• Brightness adjustment: -1.0 to +1.0 offset
• Colormap selection: 11 built-in colormaps (gray, viridis, plasma, etc.)
• Auto-contrast: Automatic contrast adjustment based on percentiles
• Reset function: Restore all settings to defaults

Navigation

• Mouse: Use the slider to navigate between images
• Keyboard shortcuts:
  • ←/→: Previous/next image
  • Page Up/Down: Jump by 10 images
  • Home/End: Go to first/last image

Installation

From PyPI (when published)

pip install image-slide

From Source

git clone https://github.com/GyroTools/image-slide.git
cd image-slide
pip install -e .

Dependencies

• Python 3.8+
• numpy >= 1.19.0
• matplotlib >= 3.3.0

Quick Start

Basic Usage

import numpy as np
from image_slide import ImageSlideViewer

# Create or load your numpy array of arbitrary shape
image_stack = np.random.random((10, 100, 100))

# Create and run viewer
viewer = ImageSlideViewer(image_stack, "My Images")
viewer.run()

Convenience Function

from image_slide import image_slide

# Even simpler - direct function call
image_slide(image_stack, "My Images")

Complex Arrays

# Complex array example
n_images, height, width = 5, 100, 100
complex_stack = np.random.random((n_images, height, width)) + 1j * np.random.random((n_images, height, width))

viewer = ImageSlideViewer(complex_stack, "Complex Data")
viewer.run()

Loading from Files

# Load from numpy file
data = np.load('your_data.npy')
image_slide(data, "Loaded Data")

# Load multiple images into stack
import glob
from PIL import Image

files = glob.glob("*.png")
images = [np.array(Image.open(f)) for f in files]
image_stack = np.array(images)
image_slide(image_stack, "Image Stack")

Input Requirements

Data Types

• Real arrays: Any numeric numpy dtype (int, float, etc.)
• Complex arrays: complex64, complex128 with magnitude/phase/real/imaginary display options
• Normalized: Data is automatically normalized for display

Advanced Usage

Complex Array Display Modes

When working with complex arrays, you can choose what to display:

1. Magnitude: |z| - Shows the amplitude
2. Phase: arg(z) - Shows the phase angle
3. Real: Re(z) - Shows the real part
4. Imaginary: Im(z) - Shows the imaginary part

Programmatic Control

# Create viewer but don't show immediately
viewer = ImageSlideViewer(image_stack, "My Images")

# Set initial display parameters
viewer.contrast = 1.5
viewer.brightness = 0.2
viewer.current_complex_display = "phase"  # for complex arrays

# Update display and show
viewer.update_display()
viewer.run()

Keyboard Shortcuts Reference

Key	Action
←	Previous image
→	Next image
Page Up	Jump back 10 images
Page Down	Jump forward 10 images
Home	Go to first image
End	Go to last image

Interface Components

• Top Controls: Contrast, brightness, colormap, and action buttons arranged horizontally
• Image Display: Central area with no borders, maximum space utilization
• Bottom Navigation: Image slider and index display
• Status Bar: Min/max values and data type information

Examples

Scientific Data Visualization

import numpy as np
from image_slide import image_slide

# Simulate time series data
t = np.linspace(0, 4*np.pi, 50)
x, y = np.meshgrid(np.linspace(0, 2*np.pi, 100), np.linspace(0, 2*np.pi, 100))
time_series = np.array([np.sin(x + t[i]) * np.cos(y + t[i]) for i in range(len(t))])

image_slide(time_series, "Time Series Visualization")

Medical Imaging

# Simulate medical image stack (e.g., CT slices)
slices = np.random.random((30, 256, 256))
# Add some structure
for i, slice_img in enumerate(slices):
    slices[i] = gaussian_filter(slice_img, sigma=2)

image_slide(slices, "Medical Image Stack")

Fourier Transform Analysis

# Create test pattern and its FFT
pattern = np.sin(2*np.pi*x*3) * np.cos(2*np.pi*y*2)
fft_data = np.fft.fftshift(np.fft.fft2(pattern))

# Stack original and FFT for comparison
combined = np.array([pattern, np.abs(fft_data), np.angle(fft_data)])
image_slide(combined, "FFT Analysis")

Command Line Usage

The package is designed to be used as a Python library. Import and use the viewer in your Python scripts as shown in the examples above.

Development

Setting up Development Environment

git clone https://github.com/GyroTools/image-slide.git
cd image-slide
pip install -e ".[dev]"

Running Tests

pytest

Code Formatting

black image_slide/

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

Version 1.0.0

• Initial release
• Basic image navigation and display
• Complex array support
• Contrast and brightness controls
• Multiple colormap options
• Keyboard shortcuts
• Professional compact interface

Acknowledgments

• Built with matplotlib for image display
• Uses tkinter for GUI
• Inspired by scientific image analysis workflows

Support

• Create an issue for bug reports
• Start a discussion for questions
• Check the documentation for usage examples