panoseti-grpc 0.4.12

gRPC for the PANOSETI project.

PANOSETI gRPC Services

This repository contains the microservice architecture for the PANOSETI observatory. It provides gRPC interfaces for real-time data access, observatory control, and general telemetry logging. See here for the main software repo.

Service Directory

Each service operates independently. Click the links below for detailed API documentation and configuration guides.

Service	Description	Status	Documentation
DAQ Data	Streams real-time science data from Hashpipe via headnode gateway.	🟢 Production	Read Docs
DAQ Control	Manages Hashpipe lifecycle on DAQ nodes (start/stop/status).	🟢 Production	Read Docs
Telemetry	Device status → Redis/InfluxDB; log shipping → Grafana Alloy → Loki.	🟡 Beta	Read Docs
ML Inference	Real-time cloud-detection scores from the streaming pipeline; pub-sub for predictions and alerts.	🟡 Beta	Read Docs
U-blox Control	Controls and configures GNSS chips (F9T/F9P).	🔴 Deprecated	Read Docs

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📜 Changelog

Keep track of the latest changes, modernization efforts, and breaking changes in our Changelog.

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📦 Installation (Client Mode)

If you only need to write scripts to control the observatory or analyze data, install the package from PyPI:

pip install panoseti-grpc

Example Usage:

# Stream real-time science images from the headnode gateway
import asyncio
from panoseti_grpc.daq_data.client import AioDaqDataClient

async def main():
    async with AioDaqDataClient(host="headnode", port=50051) as client:
        async for image in client.stream_images(stream_movie_data=True):
            print(f"Module {image['module_id']}  {image['type']}")

asyncio.run(main())

# Upload device telemetry
from panoseti_grpc.telemetry.client import TelemetryClient
client = TelemetryClient("headnode", 50051)
client.log_flexible("DEV_weather", "station-01", {"status": "Online", "is-raining": True})

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🛠️ Development & Contribution

Environment Setup

If you are deploying the servers on the head node or contributing to the codebase, we recommend installing miniconda (link), then following these steps to setup your environment:

# 0. Clone this repo and go to the repo root
git clone https://github.com/panoseti/panoseti_grpc.git
cd panoseti_grpc

# Option 1. Install with pip
conda create -n grpc-py314 python=3.14
conda activate grpc-py314
pip install -e ".[dev]"

# Option 2. Install with uv
uv tool install -e .

🚦 Unified Server

All three active services (daq_data, daq_control, telemetry) can run on a single port under a unified process. gRPC routes RPCs by proto package name automatically — no collision between services.

Quick Start

# See the options
pseti-grpc -h

# DAQ node: daq_data + daq_control only
pseti-grpc server --profile daq_node

# Head node: telemetry only
pseti-grpc server --profile headnode

# Custom config file
pseti-grpc server --config /etc/panoseti/server.toml

# List all registered services
pseti-grpc server --list-services

# Check gRPC service health, Alloy liveness, and log disk usage
pseti-grpc daqnode --log-dir /var/log/panoseti

Deployment Profiles

Profile	Services	Machine
default	telemetry + daq_data + daq_control	Single-machine dev / test
daq_node	daq_data + daq_control	Each DAQ compute node
headnode	telemetry	Observatory head node

On DAQ nodes (telemetry = false), services configured with grpc_logging = true automatically forward logs to the head node's telemetry endpoint via the HEADNODE_IP / HEADNODE_GRPC_PORT environment variables.

Config File Structure

Bundled profiles live in src/panoseti_grpc/config/. A custom server.toml follows this structure:

[server]
port = 50051
shutdown_grace_period = 5.0
log_dir = "/var/log/panoseti"
grpc_logging = true

[server.services]
telemetry   = true
daq_data    = true
daq_control = true

[telemetry]
redis_host = "localhost"
redis_port = 6379

[daq_data]
# ... DaqDataServerConfig fields ...

[daq_control]
log_dir = "/var/log/panoseti"

🧪 Testing

We use a comprehensive CI pipeline (GitHub Actions) to verify every commit. You can—and should—run these same tests locally before pushing code.

Unified QA Runner (Recommended)

The most efficient way to run quality checks and tests is via the unified QA runner:

# Run all linters and test suites
python tests/qa.py all

# Run specific tasks
python tests/qa.py lint
python tests/qa.py telemetry

Run CI Tests Locally via Bash Scripts

Alternatively, you can use the individual scripts in scripts/run-ci-tests/. Each service has an associated script which builds the Docker containers and runs the appropriate test suites.

Examples:

# Run DAQ Data Service tests
./scripts/run-ci-tests/run-daq-data-ci-test.sh

# Run U-blox Control Service tests
./scripts/run-ci-tests/run-ublox-control-ci-test.sh

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🚀 Adding New Services

The PANOSETI gRPC architecture is designed to be extensible. New services slot into the unified server without modifying PanosetiServer itself — only server.py registration and config additions are needed.

0. Branching Strategy

Always create a new feature branch off the development branch:

git checkout dev
git checkout -b feature/daq-control-service

1. Define the Interface (.proto)

Create a new Protocol Buffer definition file in the protos/ directory. This defines the contract between your client and server.

• File: protos/daq_control.proto
• Example:

syntax = "proto3";
package panoseti.daq_control;

service DaqControl {
  rpc SetHighVoltage (VoltageRequest) returns (StatusResponse) {}
}

message VoltageRequest { float voltage = 1; }
message StatusResponse { bool success = 1; }

2. Compile the Protos

Run the compilation script to generate the Python gRPC code.

python scripts/compile_protos.py

This will automatically generate two files in src/panoseti_grpc/generated/:

• daq_control_pb2.py (Message definitions)
• daq_control_pb2_grpc.py (Client/Server stubs)

3. Create the Service Module

Create a new directory for your service source code. You must include an __init__.py file for Python to recognize it as a package.

mkdir -p src/panoseti_grpc/daq_control
touch src/panoseti_grpc/daq_control/__init__.py

4. Implement Client & Server

Develop your application logic. You can now import your generated protobuf code using the package path.

Example src/panoseti_grpc/daq_control/server.py:

import grpc
from panoseti_grpc.generated import daq_control_pb2, daq_control_pb2_grpc

class DaqControlServicer(daq_control_pb2_grpc.DaqControlServicer):
    def SetHighVoltage(self, request, context):
        print(f"Setting voltage to {request.voltage}")
        return daq_control_pb2.StatusResponse(success=True)

5. Register with the Unified Server

To make the service available via pseti-grpc server, add it to src/panoseti_grpc/server.py:

1. Write async def _make_<name>_servicer(cfg, shutdown_event) that returns (servicer, [post_start_coros])
2. Add <name>: NewServiceConfig = Field(default_factory=NewServiceConfig) to PanosetiServerConfig
3. Add <name>: bool = False to ServiceToggles
4. Call ServiceRegistry.register(ServiceDescriptor("<name>", ...)) at module level
5. Add a [<name>] section to the relevant server*.toml profile files

6. Add CI Tests

Finally, ensure your new service is robust by adding a test suite.

1. Create a test directory: tests/<name>/
2. Add a BuildKit stage to the root Dockerfile.ci for your test environment.
3. Add a runner script in scripts/run-ci-tests/run-<name>-ci-test.sh.
4. Create unit and integration tests with pytest.