iterlab

You are a scientist, not a software developer, focus on what matters to you! Science and research are iterative processes and they require a lot of graphs, plots and inputs, data processing and algorithm revisions. IterLab is the tool that lets you focus on your work, not on the visualization and GUI code - those are made for you seamlessly.

What this means in practice

• Layout is drawn, never programmed. Drag, drop, resize. No GUI code, ever.
• Add code? Change layout? No problem! — We know science is iterative and constantly changes. Start with one plot and go from there. The magic happens because we create two separate files, a .yaml and a .py sharing a name. Rearranging the interface cannot disturb your code, and editing your code cannot disturb the interface. The only link between them is an element's tag.
• No need to re-run the script when you change code! Yes you heard correctly, change your algorithm, add axis labels, no need to re-run the app, no need to reload from scratch! save hours and hours without losing anything already in memory — loaded data, computed results, and plots already drawn all survive.
• Made a bug? Just fix it without re-running heavy code Code bugs and typos are part of life, that doesn’t mean you should waste time re-running heavy code. Everything survives and stays in memory until you fix the code and continue from where you left off. Restarting is the cost the tool exists to remove.

Usage

pip install iterlab
iterlab demo

One command. A new interface opens in editor mode: a blank canvas, a palette of element types, and a properties panel. Drag out an axes and a button, tag them, then click the toggle in the top-left corner to switch to GUI mode and use what you drew.

demo.py is yours. iterlab appends a handler stub for the elements that need one, and never touches anything else. Assume an axes tagged spectrum and a button tagged run_fit:

import numpy as np

def on_startup(ev):
    ev.x = np.linspace(0, 10, 500)      # loaded once per session, accessible from anywhere in your code
    ev.spectrum.plot(ev.x, np.sin(ev.x))

def on_clicked_run_fit(ev, event):
    ev.spectrum.clear()
    ev.spectrum.plot(ev.x, np.sin(2 * ev.x))

Edit that file while the window is open, click the button, and the new code runs — no restart, and ev.x is never reloaded. Make a typo and the session survives, tells you what broke, and lets you fix it and carry on.

ev is yours to fill: anything you assign to it lives for the session. Each element is reachable on it under the tag you gave it in the designer, and an axes is a matplotlib Axes — not a wrapper around one — so every call you already know works, and any library taking an ax= argument accepts it.

On Linux, tkinter is packaged separately: apt install python3-tk or dnf install python3-tkinter.

New here? GUIDE.md walks through installing, drawing your first interface, and the code API for every element.

Status

Early, and honest about it. The end-to-end loop is implemented and tested, with five element types: axes, button, label, text box, number box. Checkboxes, radio buttons, dropdowns, lists and sliders are next.

Built on

Tk and matplotlib, both used deliberately: Tk for every control, matplotlib for plots only.

License

Apache License 2.0. You may use, modify, and redistribute this code, including in closed-source and commercial work. If you do, you must retain the copyright notice, the license, and the NOTICE file, and state which files you changed.