mr-dapa — Multi-Robot Data Animation & Plotting Assistance

Rapid visualization of multi-agent time-series data. Researchers have N robots' timestamped data and need to quickly generate static plots and animations.

Quick Start

pip install mr-dapa
python examples/minimal/generate_data.py
python examples/minimal/main.py

API Overview

import mr_dapa as mrdp

components = {
    'x': {'title': 'X Position', 'class': 'LinesComponent', 'keys': ['x']},
    'map': {'title': 'Map', 'class': 'MapComponent', 'limits': {"x": [-3, 7], "y": [-3, 7]}},
}

# Static plot — returns figure (no file saved)
fig = mrdp.StaticGlobalPlotDrawer(files=['data.json'], components=components).draw(['x', 'map'])

# Chain API for filtering
mrdp.StaticGlobalPlotDrawer(files=['data.json'], components=components) \
    .set_id_list([1, 3]) \
    .set_time_range((0.2, 0.5)) \
    .draw(['x'], save=True)                # save=True writes to file

# Animation
mrdp.AnimationDrawer(files=['data.json'], components=components) \
    .draw(['x', 'map'], time_ratio=2, save=True)

Draw Modes

Components

Adapters

from mr_dapa import CSVAdapter, MultiFileAdapter, NumPyAdapter, SimulationLogAdapter, ParametricStudyAdapter

loader = mrdp.StaticGlobalPlotDrawer(files=['data.csv'], components=components, adapter=CSVAdapter())
loader = mrdp.StaticGlobalPlotDrawer(files=['r1.json', 'r2.json'], components=components, adapter=MultiFileAdapter())
loader = mrdp.StaticGlobalPlotDrawer(files=['sim/data.json'], components=components, adapter=SimulationLogAdapter())
loader = mrdp.StaticGlobalPlotDrawer(files=['summary.json'], components=components, adapter=ParametricStudyAdapter())

SimulationLogAdapter is for frame-based simulation logs with state[*].runtime and state[*].robots[*]. It extracts robot state, control inputs, CBF-like metric dictionaries, link-denial metrics, and global search coverage so existing line/map components can plot real simulation runs quickly.

ParametricStudyAdapter is for parameter sweep summaries with a parametric_study object. It maps parameter values onto the canonical x-axis and exposes metrics such as final_coverage and duration for comparison plots.

Agent-Friendly Inspection

data = mrdp.SimulationLogAdapter().load('sim/data.json')
summary = mrdp.inspect_data(data)
components = mrdp.suggest_components(data)

print(summary['robot_ids'])
mrdp.StaticGlobalPlotDrawer(
    files=['sim/data.json'],
    components=components,
    adapter=mrdp.SimulationLogAdapter(),
).draw(list(components), save=True)

Interactive Menu

Install with [menu] extra and use run_interactive_session() for quick interactive visualization:

from mr_dapa import run_interactive_session

run_interactive_session(data_folder="data", file_pattern="*.json")

Data Format

Canonical JSON format (each robot has its own timestamp array — supports async data):

[
  {
    "id": 1,
    "timestamp": [0.0, 0.02, 0.04],
    "values": [
      {"name": "X Position", "alias": "x", "unit": "m", "value": [1.0, 1.1, 1.2]},
      {"name": "Y Position", "alias": "y", "unit": "m", "value": [2.0, 2.1, 2.2]}
    ]
  }
]

Requirements

• Python >= 3.9
• numpy, matplotlib, tqdm
• ffmpeg (for animation export)

Installation

pip install mr-dapa
pip install mr-dapa[menu]   # includes basic-interactive-menu for interactive CLI
pip install -e ".[dev]"    # development: pytest, ruff

Testing

pytest tests/               # 168 tests
pytest tests/ -v             # verbose
pytest tests/ -k "adapter"   # filter by name

Test structure: conftest.py provides shared fixtures. Each test_*.py covers one module.

License

MIT