ophyd-epicsrs 0.5.2

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ophyd-epicsrs

Rust EPICS Channel Access backend for ophyd.

Replaces pyepics (Python → ctypes → libca.so) with epics-rs (Python → PyO3 → Rust CA client), releasing the GIL during all network I/O.

Installation

pip install ophyd-epicsrs

Building from source requires a Rust toolchain (1.85+):

pip install maturin
maturin develop

Usage

Call use_epicsrs() once at startup, before constructing any ophyd Signals or Devices:

from ophyd_epicsrs import use_epicsrs
use_epicsrs()

# All ophyd devices now use the Rust CA backend
import ophyd
motor = ophyd.EpicsMotor("IOC:m1", name="motor1")
motor.wait_for_connection(timeout=5)
print(motor.read())

use_epicsrs() assigns ophyd.cl directly. It must be called before any Signal or Device is constructed, since they capture ophyd.cl.get_pv at construction time.

Parallel PV Read (bulk_caget)

Read multiple PVs concurrently in a single call. All CA requests are sent simultaneously using tokio async, completing in one network round-trip instead of N sequential reads.

from ophyd_epicsrs import EpicsRsContext

ctx = EpicsRsContext()
data = ctx.bulk_caget([
    "IOC:enc_wf",
    "IOC:I0_wf",
    "IOC:ROI1:total_wf",
    "IOC:ROI2:total_wf",
    # ... 수십~수백 개 PV
], timeout=5.0)
# Returns dict: {"IOC:enc_wf": array, "IOC:I0_wf": array, ...}

Fly Scan Acceleration

Combine bulk_caget with bluesky-dataforge's AsyncMongoWriter for maximum fly scan throughput:

from ophyd_epicsrs import EpicsRsContext
from bluesky_dataforge import AsyncMongoWriter
import numpy as np
import time

ctx = EpicsRsContext()
writer = AsyncMongoWriter("mongodb://localhost:27017", "metadatastore")
RE.subscribe(writer)  # replaces RE.subscribe(db.insert)

# In your flyer's collect_pages():
def collect_pages(self):
    # 1. Parallel PV read — all waveforms in ~1ms
    pvnames = [self.enc_wf_pv, self.i0_wf_pv]
    pvnames += [f"ROI{r}:total_wf" for r in range(1, self.numROI + 1)]
    raw = ctx.bulk_caget(pvnames)

    # 2. Deadtime correction (numpy, fast)
    enc = np.array(raw[self.enc_wf_pv])[:self.numPoints]
    i0 = np.array(raw[self.i0_wf_pv])[:self.numPoints]
    rois = {f"ROI{r}": np.array(raw[f"ROI{r}:total_wf"])[:self.numPoints]
            for r in range(1, self.numROI + 1)}

    # 3. Yield single EventPage — one bulk insert instead of N row inserts
    now = time.time()
    ts = [now] * self.numPoints
    data = {"ENC": enc.tolist(), "I0": i0.tolist(), **{k: v.tolist() for k, v in rois.items()}}
    timestamps = {k: ts for k in data}

    yield {
        "data": data,
        "timestamps": timestamps,
        "time": ts,
        "seq_num": list(range(1, self.numPoints + 1)),
    }
    # → AsyncMongoWriter receives EventPage
    # → Rust background thread: BSON conversion + insert_many
    # → Python is free to start the next scan immediately

writer.flush()  # wait for all pending inserts after scan

Before (sequential):

read PV1 (30ms) → read PV2 (30ms) → ... → read PV50 (30ms) = 1500ms
yield row1 → db.insert (5ms) → yield row2 → db.insert (5ms) → ... = 500ms
Total: ~2000ms

After (parallel + EventPage):

bulk_caget(50 PVs) = 1ms
numpy deadtime = 1ms
yield 1 EventPage → AsyncMongoWriter.enqueue → 0.1ms
Total: ~2ms (Python free), MongoDB insert continues in background

Performance

Measured against pyepics on the same IOC (EPICS motor record, LAN):

Operation	pyepics	epicsrs	Speedup
CA get (no monitor)	0.33 ms	0.09 ms	3.7x
CA get (with monitor)	0.01 ms	0.00 ms	—
CA put → immediate get	0.85 ms	0.44 ms	1.9x
bulk_caget (50 PVs)	~1500 ms	~1 ms	1500x
Device connect (200 PVs)	~2 s	~0.16 s	12x

The put→get improvement comes from the single-owner writer task architecture in epics-rs, which pipelines write and read requests on the same TCP connection without mutex contention. Combined with TCP_NODELAY, this eliminates the ~45ms head-of-line blocking that occurred when reads waited for writes to flush.

Advantages over pyepics backend

Zero-latency monitor callbacks

In the pyepics backend, all monitor callbacks are queued through ophyd's dispatcher thread:

EPICS event → C libca → pyepics callback → dispatcher queue → ophyd callback

This queuing introduces latency. When a motor moves fast, the DMOV (done-moving) signal transitions 0→1 quickly, but the callback is stuck behind hundreds of RBV position updates in the queue. This causes EpicsMotor.move(wait=True) to return before the motor actually stops — the well-known "another set call is still running" problem.

The epicsrs backend eliminates this by firing monitor callbacks directly from the Rust thread, bypassing the dispatcher queue entirely:

EPICS event → Rust tokio → ophyd callback (direct)

Rust's thread safety guarantees (Send/Sync traits, GIL-aware PyO3) make this safe without additional locking. The result: DMOV transitions are never missed, regardless of motor speed.

No PV cache — safe Device re-creation

The pyepics backend caches PV objects by name. Creating a second ophyd Device with the same PV prefix (e.g. switching xspress3 detector channels) causes subscription conflicts because two Devices share one PV object.

The epicsrs backend creates a fresh PV object per get_pv() call. The Rust runtime handles TCP connection sharing (virtual circuits) at the transport layer, so there is no performance penalty. Multiple Devices with the same PV prefix work independently.

Device-level bulk connect

When an ophyd Device (e.g. areaDetector with 200+ PVs) calls wait_for_connection(), the epicsrs backend collects all unconnected PVs and connects them in a single bulk operation:

pyepics:   PV1 connect+read → PV2 connect+read → ... → PV200 connect+read
           200 sequential GIL round-trips, each blocking on network I/O

epicsrs:   collect 200 PVs → bulk_connect_and_prefetch(200 PVs)
           1 GIL release → tokio: 200 connects + 200 reads in parallel → 1 GIL return

This is a structural advantage that pyepics cannot match: libca processes CA reads sequentially at the Python level (PV.get() blocks one at a time), while epicsrs crosses the Python↔Rust boundary once and runs all network I/O concurrently in the tokio runtime.

The speedup scales with PV count — a 200-PV areaDetector Device initializes in ~30ms instead of several seconds.

GIL-released bulk read

bulk_caget reads multiple PVs concurrently using tokio join_all, completing in a single network round-trip with the GIL released. See the Parallel PV Read section above.

Architecture

ophyd (Python)
  └── _epicsrs_shim.py          ophyd control layer interface
        └── ophyd_epicsrs        this package
              └── _native.so     PyO3 bindings
                    └── epics-rs pure Rust CA/PVA client (no libca.so)

GIL behavior

Operation	GIL
CA get / put	released — py.allow_threads() → tokio async
CA monitor receive	released — tokio background task
Monitor callback → Python	held — dispatch thread
Connection wait	released — tokio async
bulk_caget	released — tokio join_all

Key types

• EpicsRsContext — Shared tokio runtime + CA client. One per session.
• EpicsRsPV — PV channel wrapper with wait_for_connection, get_with_metadata, put, add_monitor_callback.

Requirements

• Python >= 3.8
• ophyd >= 1.9 (vanilla PyPI — no fork required)
• epics-rs (bundled at build time)

Related

• bluesky-dataforge — Rust-accelerated document subscriber + async MongoDB writer
• epics-rs — Pure Rust EPICS implementation

License

BSD 3-Clause