madsci.experiment_manager 0.4.7

The Modular Autonomous Discovery for Science (MADSci) Experiment Manager.

MADSci Experiment Manager

Manages experimental runs and campaigns across a MADSci-powered lab, providing experiment design, tracking, and lifecycle management.

Features

• Experiment Campaigns: Organize related experiments under a common research goal
• Experiment Designs: Define experimental parameters, conditions, and metadata
• Experiment Runs: Track individual experiment executions with status and results
• Lifecycle Management: Monitor experiment progress from design to completion
• Integration: Works with all MADSci managers for comprehensive lab coordination

Installation

See the main README for installation options. This package is available as:

• PyPI: pip install madsci.experiment_manager
• Docker: Included in ghcr.io/ad-sdl/madsci
• Example configuration: See example_lab/managers/example_experiment.manager.yaml

Dependencies: MongoDB database (see example_lab)

Usage

Quick Start

Use the example_lab as a starting point:

# Start with working example
docker compose up  # From repo root
# Experiment Manager available at http://localhost:8002/docs

# Or run standalone
python -m madsci.experiment_manager.experiment_server

Manager Setup

For custom deployments, see example_experiment.manager.yaml for configuration options.

Experiment Client

Use ExperimentClient to manage experiments programmatically:

from madsci.client.experiment_client import ExperimentClient
from madsci.common.types.experiment_types import (
    ExperimentDesign,
    ExperimentRegistration,
    ExperimentalCampaign
)

client = ExperimentClient("http://localhost:8002")

# Create an experiment campaign
campaign = ExperimentalCampaign(
    name="Drug Discovery Campaign",
    description="Testing compound effectiveness",
    principal_investigator="Dr. Smith"
)
created_campaign = client.create_campaign(campaign)

# Design an experiment
design = ExperimentDesign(
    name="Compound Screen Experiment",
    description="Screen compounds for activity",
    campaign_id=created_campaign.campaign_id,
    parameters={"compounds": ["A", "B", "C"], "concentrations": [1, 10, 100]}
)
created_design = client.create_experiment_design(design)

# Register and run an experiment
registration = ExperimentRegistration(
    experiment_design_id=created_design.design_id,
    parameters={"compound": "A", "concentration": 10}
)
experiment = client.register_experiment(registration)

# Track experiment status
status = client.get_experiment_status(experiment.experiment_id)

Core Concepts

Experiment Campaigns

Group related experiments under a research theme or project:

• Campaign management: Track multiple related experiments
• Principal investigator: Associate experiments with researchers
• Metadata: Store campaign-level information and goals

Experiment Designs

Templates defining experimental parameters and structure:

• Parameter definitions: Specify experiment variables and ranges
• Conditions: Define prerequisites and constraints
• Metadata: Store design rationale and protocols

Experiment Runs

Individual executions of an experiment design:

• Status tracking: Monitor progress from registration to completion
• Results storage: Capture experimental outcomes and data
• Lineage: Link runs to their designs and campaigns

Experiment Application

The ExperimentApplication class provides scaffolding for custom experiment logic:

from madsci.experiment_application import ExperimentApplication

class MyExperiment(ExperimentApplication):
    def run_experiment(self, experiment_id: str) -> dict:
        # Custom experimental logic
        # Use other MADSci clients (workcell, data, etc.)
        return {"result": "success"}

app = MyExperiment(experiment_server_url="http://localhost:8002")
app.start()

Integration with MADSci Ecosystem

The Experiment Manager coordinates with other MADSci components:

• Workcell Manager: Execute workflows as part of experiments
• Data Manager: Store experimental results and files
• Event Manager: Log experimental events and milestones
• Resource Manager: Track samples and consumables used

Example: See example_lab/ for complete integration examples with all managers working together.