ndslice 0.1.4

Interactive N-dimensional numpy array viewer with FFT support

ndslice

Quick interactive visualization for N-dimensional NumPy arrays

A python package for browsing slices, applying FFTs, and inspecting data.

Quickly checking multi-dimensional data usually means writing the same matplotlib boilerplate over and over. This tool lets you just call ndslice(data) and interactively explore what you've got.

Usage

from ndslice import ndslice
import numpy as np

# Create some data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
z = np.linspace(-5, 5, 50)
X, Y, Z = np.meshgrid(x, y, z, indexing='ij')

mag = np.exp(-(X**2 + Y**2 + Z**2) / 10)
pha = np.pi/4 * (X + Y + Z)
complex_data = mag * np.exp(1j * pha)

ndslice(complex_data, title='3D Complex Gaussian')

Features

Data slicing and dimension selection should be intuitive: click the two dimensions you want to show and slice using the spinboxes.

Centered FFT - Click dimension labels to apply centered 1D FFT transforms. Useful for checking k-space data in MRI reconstructions or analyzing frequency content.

Line plot - See 1D slices through your data. Shift+scroll for Y zoom, Ctrl+scroll for X zoom:

Scaling

Log scaling is often good for k-space visualization. Symmetric log scaling is an extension of the log scale which supports negative values.

Colormap Change colormap:

• Ctrl+1 Grayscale
• Ctrl+2 Viridis
• Ctrl+3 Plasma
• Ctrl+4 Rainbow

Non-blocking windows

By default, windows open in separate processes, allowing multiple simultaneous views:

ndslice(data1)
ndslice(data2) # Both windows appear

Use block=True to wait for the window to close before continuing:

ndslice(data1, block=True)  # Script pauses here
ndslice(data2)  # Shown after first closes

Installation

From PyPI

pip install ndslice

From source

git clone https://github.com/henricryden/ndslice.git
cd ndslice
pip install -e .

Requirements

• Python >= 3.8
• NumPy >= 1.20.0
• PyQtGraph >= 0.12.0
• PyQt5 >= 5.15.0

License

MIT License - see LICENSE file for details.

Contributing

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

Acknowledgments

Built with PyQtGraph for high-performance visualization.

---

Henric Rydén

Karolinska University Hospital

Stockholm, Sweden