ialdev-vis

Visualization utilities for the iad toolbox, published as ialdev-vis and imported as iad.vis.

Use this package for quick image grids, histograms, Matplotlib helpers, custom colormaps, interactive annotation widgets, Jupyter logging, and optional Qt/3D visualization tools.

Install

pip install ialdev-vis

Optional extras:

pip install "ialdev-vis[qt]"
pip install "ialdev-vis[jupyter]"
pip install "ialdev-vis[notebook]"   # anywidget interactive image grid (imview)
pip install "ialdev-vis[marimo]"
pip install "ialdev-vis[3d]"
pip install "ialdev-vis[all]"

Requires Python >=3.10.

Highlights

• imgrid and imhist for fast inspection of images and distributions.
• iad.vis.imview.imgrid — interactive notebook image grid (marimo / Jupyter) via anywidget; static oversampling, figsize layout, client-side colormap/clim/zoom, mosaic layouts, data-budget readout, and cross-panel inspection. See IMVIEW.md.
• Matplotlib helpers for figure capture and conversion to arrays/PIL images.
• Built-in custom colormaps.
• Polygon-to-mask helpers and interactive ROI tools.
• Optional Qt image viewer and optional ipyvolume/Open3D 3D views.

Examples

from iad.vis.insight import imgrid, imhist

imgrid(left_image, right_image, titles=["left", "right"], clim="auto")
imhist(left_image, right_image, titles=["left", "right"])

from iad.vis.mpl_utils import fig2img

pil_image = fig2img(figure)

Interactive image grid in marimo / Jupyter (imview)

See IMVIEW.md for architecture, parameters (figsize, max_zoom, downsample), module layout, and marimo stacking patterns.

from iad.vis.imview import imgrid
import marimo as mo

grid = imgrid(left, right, diff, titles=["left", "right", "diff"], figsize=(900, None), max_zoom=2)
grid.ui          # use in mo.vstack / mo.hstack
grid.value       # reactive cursor, selection, view state
grid.update_data({"left": new_left})  # in-place buffer refresh

In marimo, iad.vis.mutils.mogrid(..., backend='wgt') routes to the same anywidget backend.

Rebuild the frontend after editing TypeScript (widget developers only):

cd vis/js && npm install && npm run build
# or: pixi run build-imview-js   (requires pixi feature build-js / nodejs)

Roadmap: WebGL renderer (large images), ROI / line-profile tools, live histogram panel, linked selections into pandas/marimo cells.

Note: iad.vis.plgrid remains a lightweight Plotly overview option; desktop/Qt inspection still uses iad.vis.insight.imgrid (matplotlib).