madsci.common 0.4.7

The Modular Autonomous Discovery for Science (MADSci) Common Definitions and Utilities.

MADSci Common

Shared types, utilities, validators, base classes and other common code used across the MADSci toolkit.

Installation

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

• PyPI: pip install madsci.common
• Docker: Included in ghcr.io/ad-sdl/madsci
• Dependency: Required by all other MADSci packages

Core Components

Types System

Pydantic-based data models for the entire MADSci ecosystem:

# Import types organized by subsystem
from madsci.common.types.workflow_types import WorkflowDefinition
from madsci.common.types.node_types import NodeDefinition
from madsci.common.types.experiment_types import ExperimentDesign
from madsci.common.types.datapoint_types import ValueDataPoint

Available type modules:

• action_types: Action definitions and results
• experiment_types: Experiment campaigns, designs, runs
• workflow_types: Workflow and step definitions
• node_types: Node configurations and status
• datapoint_types: Data storage and retrieval
• event_types: Event logging and querying
• resource_types: Resource management and tracking
• auth_types: Ownership and authentication
• base_types: Foundation classes and utilities

Utilities

Common helper functions and validators:

from madsci.common.utils import utcnow, new_ulid_str
from madsci.common.validators import ulid_validator
from madsci.common.serializers import serialize_to_yaml

# Generate unique IDs
experiment_id = new_ulid_str()

# UTC timestamps
timestamp = utcnow()

# YAML serialization
yaml_content = serialize_to_yaml(my_pydantic_model)

Settings Framework

Hierarchical configuration system using Pydantic Settings:

from madsci.common.types.base_types import MadsciBaseSettings

class MyManagerSettings(MadsciBaseSettings):
    server_url: str = "http://localhost:8000"
    database_url: str = "mongodb://localhost:27017"
    # Supports env vars, CLI args, config files

settings = MyManagerSettings()

Configuration sources (in precedence order):

1. Command line arguments
2. Environment variables
3. Subsystem-specific files (workcell.env, event.yaml)
4. Generic files (.env, settings.yaml)
5. Default values

Configuration options: See Configuration.md and example_lab/managers/ for examples.

Usage Patterns

Creating Custom Types

from madsci.common.types.base_types import MadsciBaseModel
from pydantic import Field

class MyCustomType(MadsciBaseModel):
    name: str = Field(description="Object name")
    value: float = Field(gt=0, description="Positive value")
    metadata: dict = Field(default_factory=dict)

# Automatic validation, serialization to JSON/YAML
obj = MyCustomType(name="test", value=42.0)
json_str = obj.model_dump_json()

Extending Base Settings

from madsci.common.types.base_types import MadsciBaseSettings

class CustomSettings(MadsciBaseSettings, env_prefix="CUSTOM_"):
    api_key: str = Field(description="API authentication key")
    timeout: int = Field(default=30, description="Request timeout")

# Reads from CUSTOM_API_KEY, CUSTOM_TIMEOUT environment variables
settings = CustomSettings()