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Interactive GUI

A no-code interface for running CBAS analyses. Load data, configure parameters, run the pipeline, and explore results visually.

Install and launch

pip install 'pycbas[gui]'
pycbas gui

To use a different port:

pycbas gui --port 5008

Development install

git clone https://github.com/droumis/pycbas.git
cd pycbas
pixi run gui          # option 1: pixi
pip install -e '.[gui]' && pycbas gui  # option 2: pip

Workflow

The GUI walks you through the CBAS pipeline in five steps.

Step 1: Load data

Load data step

Three options for loading data:

Local folder (default) — navigate to a folder on disk. The loader auto-detects:

  • An *Info.txt file (subject_id, label_or_score per line), matching subject data files by trailing ID number
  • Or group membership from filename prefixes (e.g. control0.txt, lesion0.txt). Recognized keywords: control/ctrl/sham/wt (group 0), lesion/exp/ko/mutant (group 1)

CSV per subject — upload individual subject files plus a labels/scores file.

Single spreadsheet — upload one table with subject, choice, and group/score columns.

Step 2: Analysis mode

Mode selection

Mode is auto-detected from the data:

  • Binary labels (0/1) → Comparative
  • Continuous values → Correlative

You can override the detection if needed.

Step 3: Configure parameters

Parameters

Parameters are auto-configured from the loaded data:

Parameter Auto-detected from
Number of arms Max choice value in data
Encode reward Whether reward column has non-zero values
Contingency filter Distinct contingency values present
Criterion Min trial count per subject (filtered by contingency)
Block aware Multiple sessions/blocks detected in data

Max sequence length and bootstrap resamples must be set manually as they depend on the research question.

The resource estimate shows the actual number of observed sequences (not the worst-case theoretical space), estimated memory, runtime, and a verdict based on your system's available RAM.

Step 4: Run analysis

Click "Run CBAS Analysis" to execute the full pipeline. Progress is shown in real time.

Step 5: View results

Results

Results are presented across several tabs:

Summary — subject count, sequences tested, significant count, k-FWER value, mode.

Manhattan Plot — sequences ranked by length on a log x-axis, colored by sequence length. Significant sequences appear above the threshold line.

Manhattan plot

Top Sequences — horizontal bar chart of the most significant sequences, colored by direction. Shows the test statistic magnitude and which direction the effect goes.

Top sequences

k-FWER Convergence — how k and the number of rejections evolve across iterations until convergence.

k convergence

Significant Sequences — sortable, paginated table of all significant sequences with their g-values and directions.

Export — download full results or significant-only as CSV.

Technical notes

  • The GUI is a locally-served Panel application. The browser is just a display layer; all computation runs server-side with full access to your system's CPU and RAM.
  • The app detects available system memory and CPU cores to inform resource estimates.
  • Loading a new dataset clears previous results and resets parameter detection.
  • Demo data is available via the "Load demo data" button for testing the interface without real data.