ndslice 0.8.0

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')
# Optional dimension labels:
ndslice(data, dim_labels=["X", "Y", "Z"])

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:

Video export Right-clicking a dimension button to export a video or PNG frames along that dimension. The video export functionality is optional, and can be installed with

pip install ndslice[video_export]

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: Gray
• Ctrl+2: Viridis
• Ctrl+3: Plasma
• Ctrl+4: Cyclic rainbow, hides phase wraps
• Ctrl+5: Cividis
• Ctrl+6: Cubehelix
• Ctrl+7: Cool
• Ctrl+8: Warm

Axis flipping Click arrow icons (⬇️/⬆️ and ⬅️/➡️) next to dimension labels to flip axes. Default orientation is image-style (origin lower-left). Flip the primary axis for matrix-style (origin upper-left).

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

If Qt is already initialized in the current process, ndslice(..., block=False) cannot safely fork a child process. In that case:

• In IPython/Jupyter with %gui qt, ndslice opens inline in the current process (still non-blocking).
• Without an active Qt event loop, ndslice falls back to blocking mode and emits a warning.

Command Line

ndslice data.npy # Numpy file
ndslice image.nii.gz
ndslice image.dcm
ndslice some_dicom_dir/ # Automatically attemps to form an nd-array from DICOM the files
ndslice --help   # Show all options

File support ndslice has CLI support and can conveniently display:

Format	File suffix	Requirement
NumPy	.npy, .npz	NumPy
MATLAB	.mat	scipy
HDF5	.h5, .hdf5	h5py
BART	.cfl + .hdr	—
Philips REC	.REC + .xml	—
NIfTI	.nii, .nii.gz	nibabel
DICOM file	.dcm	pydicom
DICOM directory	directory containing .dcm files	pydicom, nibabel, dcm2niix on PATH

HDF5 files can be compound complex dtype, or real/imag fields.

For DICOM directories, ndslice does not infer series dimensions itself. It runs dcm2niix, then loads the produced NIfTI volume.

If there are multiple datasets in the file, a selection GUI appears which highlights arrays supported by ndslice (essentially numeric). Double click to open.

Installation

From PyPI

pip install ndslice
# Or, if you want just the package without pulling in dependencies:
pip install --no-deps ndslice

For DICOM directories you also need the external dcm2niix binary available on PATH. A practical install route is:

conda install -c conda-forge dcm2niix

If dcm2niix is missing or conversion fails, ndslice reports a clear error instead of trying to infer the series layout itself.

From source

git clone https://github.com/henricryden/ndslice.git
cd ndslice

# Use directly without installing
python -m ndslice data.npy

pip install -e .

Requirements

• Python >= 3.8
• NumPy >= 1.20.0
• PyQtGraph >= 0.12.0
• PyQt5 >= 5.15.0
• h5py >= 3.0.0
• scipy >= 1.7.0
• pydicom >= 2.4.0
• nibabel >= 4.0.0
• imageio >= 2.9.0
• imageio-ffmpeg >= 0.4.2
• Pillow >= 8.0.0

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