Launching today
Autoplot
Unify data analysis and plotting in one powerful workspace
73 followers
Unify data analysis and plotting in one powerful workspace
73 followers
AutoPlot is a Mac native scientific workspace for plotting and exploratory data analysis. Import tabular datasets, create variables, build X&Y plots, histograms, heat maps, correlations, 3D plots, and charts, run fits, then compose figures with vector export. Metal-backed GPU rendering keeps 3D scenes and correlation networks interactive at full dataset size — rotate, zoom, and re-fit without waiting on a redraw. Raw data values never leave your Mac.
Autoplot
Unifying analysis and plotting in one place is a real pain point — too many tools force you to jump between a notebook and a separate charting tool. Does it support live data sources, or is it mainly for static datasets right now?
Autoplot
@ark_y_k Hello. Support for live data is in our roadmap. Thanks for your interest.
as someone who's bounced between a notebook for the analysis and a separate app for publication-quality figures, the vector export at the end of the pipeline is the part that stands out to me. what format does that export to - straight SVG/PDF, or does it keep layers editable in something like Illustrator? and does "raw data never leaves your Mac" mean fits run fully on-device too, or just the raw import step?
Autoplot
@galdayan Hello Gal, all the algorithms, pre-constructed or new ones, run on device. The AI doesn’t see your data, only headers, and returns python code that runs on device. So AI has been used to replace coding stage with python. It can also be used to manipulate data files inside the working folder to replace AWK type of command line tools. Vectorial output keeps layers editable in Pixelmator or similar apps.
@lts_sw that's a clean answer, thanks. replacing AWK-style munging with described intent is the part I hadn't considered - that's a bigger workflow win than the plotting itself for a lot of messy lab data. keeping the layers editable in Pixelmator after the fact is the detail that'll actually get this into people's publication pipeline. good luck with the launch.
Would love a Jupyter notebook style export so I can pair each plot with the exact code or transformation steps that produced it. Also makes it way easier to share reproducible figures with collaborators who don't have AutoPlot.
Autoplot
@ekremtaag9834 Great news: this already exists today. AutoPlot has a Reproducible Python export that captures your whole session as an execution trace — every plot, fit, and transformation becomes its own section with the exact code that produced it, plus metadata comments (source, inputs, filters, row counts, and the prompt if the AI assistant generated it).
And it's closer to a notebook than you'd expect: each section is emitted as a # %% cell, which is the standard Jupytext "percent" format. That means you can open the exported .py directly as a notebook in VS Code, PyCharm, or Spyder, or run jupytext --to notebook autoplot_trace.py to get a real .ipynb — no conversion work on your end.
On the collaborator point: the script is standalone and only needs numpy, and scipy (plus powerlaw if you used heavy-tail fits), so anyone can run it without AutoPlot installed.
Native .ipynb export so you skip the jupytext step entirely is a fair ask and is on the roadmap — appreciate you pushing on it. 🙏
the Metal-backed 3D plots are genuinely smooth, way better than fighting with matplotlib on a big dataset. kind of nice that everything stays local too
Autoplot
@mertcan815955 thanks. The smoothness in 3D plots was a must-have so we spent a lot time to improve this feature.
The reproducible-Python export is the right call, because with AI-written analysis the plot that renders cleanly is the one you stop checking. Every bad LLM-generated analysis we hit was silent, never a crash: NaN rows quietly dropped before a mean, or a default bin width smearing a real bimodal peak into one hump. The chart looks fine so nobody re-reads the code. Do you surface the assumptions the model made, rows dropped, null handling, aggregation choice, somewhere near the figure itself, so a wrong-but-pretty plot is catchable without opening the export every time?
Would love a built-in scripting pane so I can automate repetitive fits and re-run an analysis when the underlying CSV gets updated. Right now I have to re-click through the same steps every time the dataset refreshes.
Autoplot
@hediye237189 thanks for the feedback. This is a feature we will be looking into.