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Quickstart

This walks through the fastest path from a fresh install to a browsable set of results: no data preparation of your own required, using the small example dataset bundled with MEA-NAP.

:class: tip
Complete [Installation](installation.md) first (`uv sync`), so `uv run
meanap-gui` launches the desktop app.

1. Launch the GUI

uv run meanap-gui

A tabbed desktop window opens. Each tab configures one part of the pipeline — see the GUI guide for what every field does. For this quickstart you don't need to touch any of them.

2. Run the test pipeline

Go to the Pipeline tab and click 🧪 Test pipeline.

This single button:

  1. Downloads the bundled example dataset (two short recordings) if it isn't already cached locally.
  2. Points the Data tab at it automatically.
  3. Runs all four pipeline steps — spike detection, neuronal activity, functional connectivity, and network metrics — end to end.

Progress streams into the Status log at the bottom of the Run tab. On a typical laptop this takes a few minutes; functional connectivity thresholding (step 3) and the network-metrics null models (step 4) are the slowest parts, by design — see MATLAB vs. Python if you're curious why.

:class: note
Set **Start at step** / **Stop at step** to `1`–`2` on the Run tab before
clicking **Test pipeline** — spike detection and firing-rate analysis alone
finish in well under a minute, and already produce plots worth looking at.

3. Browse the results

Once the run finishes, go to the Results tab and click 🌐 View report. This generates report.html inside the output folder and opens it in your default browser — no server, no extra install, works entirely offline.

You'll see:

  • A folder tree on the left, matching the same output structure MATLAB's pipeline produces (1_SpikeDetection, 2_NeuronalActivity, ...).
  • A captioned image gallery on the right for whichever folder is selected — every plot the pipeline produced, with a plain-language caption.

See Output report for more on how this viewer works, including deep links you can share to a specific plot.

4. Where to go next

::::{grid} 2 :gutter: 2

:::{grid-item-card} Explore every GUI tab :link: gui-guide :link-type: doc Field-by-field reference for Data, Spike detection, Connectivity, CAT-NAP, Run, and Results. :::

:::{grid-item-card} Script against the Python API directly :link: notebooks/network-plotting-tutorial :link-type: doc Skip the GUI entirely and drive meanap.network_plot from a notebook or script. :::

:::{grid-item-card} Run on your own recordings :link: /setting-up-meanap :link-type: doc The MATLAB "preparing your data" guide (spreadsheet format) applies equally to the Python port — except that the Python port also reads Multi Channel Systems .h5 files directly, so you can skip the .mat conversion step. :::

:::{grid-item-card} Check what's implemented :link: matlab-vs-python :link-type: doc Read this before trusting the Python port's numbers for a publication figure. :::

::::