madsci.client 0.7.0

The Modular Autonomous Discovery for Science (MADSci) Python Clients.

MADSci Clients

Provides a collection of clients for interacting with the different components of a MADSci interface.

Installation

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

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

Node Clients

Node clients provide a robust interface for interacting with MADSci Nodes:

• Action execution: Send actions with automatic parameter serialization and result handling
• Node introspection: Get detailed node information, capabilities, and schemas
• State monitoring: Monitor current state and status with real-time updates
• Administrative control: Send commands (safety stop, pause, resume, etc.)
• Error handling: Comprehensive error reporting and retry mechanisms
• File operations: Seamless file upload/download support

Multiple communication protocols are supported through a common interface. The AbstractNodeClient base class enables custom protocol implementations.

REST Client

Communicate with MADSci Nodes via REST API with enhanced argument handling:

from madsci.client.node.rest_node_client import RestNodeClient
from madsci.common.types.action_types import ActionRequest
from pathlib import Path

client = RestNodeClient(url="http://example:2000")

# Simple action execution
action_request = ActionRequest(action_name="get_temperature", args={}, files={})
result = client.send_action(action_request)

# Action with parameters (automatically serialized)
action_request = ActionRequest(
    action_name="analyze_sample",
    args={"sample_id": "sample_001", "duration": 60, "temperature": 25.0},
    files={}
)
result = client.send_action(action_request)

# File upload handling
action_request = ActionRequest(
    action_name="process_file",
    args={"output_dir": "./results"},
    files={"input_file": Path("./data.csv")}
)
result = client.send_action(action_request)

# Get comprehensive node info
info = client.get_info()
status = client.get_status()

Key Features:

• Automatic parameter validation and serialization
• File upload/download handling with progress tracking
• Comprehensive error messages and debugging information
• Support for complex return types (JSON, files, datapoint IDs)
• Node capability checking and schema introspection

Examples: See node_notebook.ipynb for detailed usage.

Event Client

Allows a user or system to interface with a MADSci EventManager, or log events locally if one isn't available/configured. Can be used to both log new events and query logged events.

For detailed documentation on usage, see the EventManager Documentation.

Logging with Context

MADSci clients automatically participate in the logging context system, enabling hierarchical logging across components:

from madsci.common.context import event_client_context, get_event_client
from madsci.client.resource_client import ResourceClient
from madsci.client.workcell_client import WorkcellClient

# All clients created within this context share logging context
with event_client_context(name="my_app", app_id="app-123") as logger:
    logger.info("Starting application")

    # Clients automatically use the shared context
    resource_client = ResourceClient()
    workcell_client = WorkcellClient()

    # Logs from these clients will include app_id="app-123"
    resource_client.logger.info("Using resource client")
    workcell_client.logger.info("Using workcell client")

For library code or utility functions, use get_event_client() to inherit context:

from madsci.common.context import get_event_client

def utility_function():
    # Uses context if available, creates new client if not
    logger = get_event_client()
    logger.info("Utility function running")

See the Migration Guide for detailed migration patterns, and the Logging Guide for comprehensive documentation on structured logging and context management.

Experiment Application

The ExperimentApplication class is a helper class designed to act as scaffolding for a user's own python experiment. It provides helpful tooling around tracking and responding to changes in Experiment status, marshalling the clients needed to leverage different parts of a MADSci-enabled lab, and implementing your own custom experimental logic.

Experiment Client

Allows the user or an automated system/agent to inerface with a MADSci ExperimentManager to capture Experiment Designs and track status and metadata related to specific Experimental Runs and whole Experimental Campaigns.

For detailed documentation on usage, see the ExperimentManager Documentation

Data Client

Allows the user or an automated system/agent to interface with a MADSci DataManager to upload, query, and fetch DataPoints. Currently supports ValueDataPoints (which can include any JSON-serializable data) and FileDataPoints (which directly stores the files).

Enhanced Datapoint Operations

The Data Client provides comprehensive methods for working with datapoints in workflows:

from madsci.client.data_client import DataClient

client = DataClient()

# Upload value datapoints
datapoint_id = client.submit_datapoint({
    "label": "experiment_result",
    "value": {"temperature": 25.0, "pressure": 1.2}
})

# Upload file datapoints
file_datapoint_id = client.submit_file_datapoint(
    file_path=Path("./results.csv"),
    label="analysis_results"
)

# Batch fetch multiple datapoints efficiently
datapoints = client.get_datapoints_by_ids(["id1", "id2", "id3"])

# Query datapoints with filters
results = client.query_datapoints(
    label_pattern="experiment_*",
    limit=10
)

# Get lightweight metadata without loading full data
metadata = client.get_datapoint_metadata("datapoint_id")

The Data Client integrates seamlessly with the workflow system, storing only ULID strings in workflows for optimal performance while providing easy access to full datapoint objects when needed.

Integration with Workflows:

# Workflow helper methods
from madsci.client.workcell_client import WorkcellClient

workcell = WorkcellClient()
workflow = workcell.submit_workflow("analysis.yaml")

# Get datapoint from workflow step
datapoint_id = workflow.get_datapoint_id("analysis_step")
datapoint = workflow.get_datapoint("analysis_step")

For detailed documentation on usage, see the DataManager Documentation.

Resource Client

Allows the user or an automated system/agent to interface with a MADSci ResourceManager to initialize, manage, track, query, update, and remove physical resources (including samples, consumables, containers, labware, etc.).

For detailed documentation on usage, see the ResourceManager Documentation.

Workcell Client

Allows the user or an automated system/agent to interface with a MADSci WorkcellManager. Includes support for submitting, querying, and controlling Workflows, sending admin commands to the Workcell, and interacting with Workcell Locations.

For detailed documentation on usage, see the WorkcellManager Documentation.