ophyd-epicsrs 0.6.5

No project description provided

ophyd-epicsrs

Rust EPICS backend for ophyd and ophyd-async — supports both Channel Access (CA) and pvAccess (PVA).

Replaces pyepics (Python → ctypes → libca.so) with epics-rs (Python → PyO3 → Rust client), releasing the GIL during all network I/O. CA and PVA share a single tokio runtime — no separate aioca + p4p binding stacks. Sync (legacy ophyd) and async (ophyd-async, asyncio) call paths share the same runtime, channel cache, and monitor subscriptions.

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.

PVA support

PVs are dispatched by name prefix (pvxs / ophyd-async convention):

import ophyd
from ophyd_epicsrs import use_epicsrs
use_epicsrs()

# CA (default — preserves existing ophyd code)
sig_ca = ophyd.EpicsSignal("IOC:foo")
sig_ca = ophyd.EpicsSignal("ca://IOC:foo")    # explicit prefix also works

# PVA (NTScalar / NTScalarArray / NTEnum)
sig_pva = ophyd.EpicsSignal("pva://IOC:bar")

The PVA backend supports the standard NT (Normative Type) shapes: NTScalar, NTScalarArray, NTEnum, and NTTable (with typed PvField columns derived from Table.__annotations__ so dtype information is preserved through the wire format). The NTScalar value, alarm.severity, alarm.status, timeStamp.{secondsPastEpoch, nanoseconds}, and display.{units, precision, limitLow, limitHigh} fields are projected onto the ophyd metadata dict so existing Signals/Devices receive the same keys they expect from CA.

NTNDArray (the raw image-carrying PV) is not decoded into a numpy array on the Python side. This matches how ophyd-async's standard StandardDetector pattern uses areaDetector PVs — image bytes go from the camera's HDF5 plugin straight to disk, and bluesky receives Resource/Datum events rather than ndarrays. The companion control PVs (ArrayCounter_RBV, Capture_RBV, FilePath, AcquireTime, etc.) are NTScalar / NTEnum / string and work today. Live-preview or alignment paths that do want frames in Python are not yet covered.

ophyd-async support (ophyd_epicsrs.ophyd_async)

For ophyd-async-based devices, the package ships factory functions that return standard ophyd-async SignalR / SignalRW / SignalW / SignalX instances backed by epics-rs. No fork required — they drop straight into StandardDetector, StandardReadable, plan stubs, etc.

from ophyd_epicsrs.ophyd_async import (
    epicsrs_signal_r,
    epicsrs_signal_rw,
    epicsrs_signal_rw_rbv,
    epicsrs_signal_w,
    epicsrs_signal_x,
)

# Bare name and ca://… → CA backend; pva://… → PVA backend.
sig_ca  = epicsrs_signal_rw(float, "IOC:motor.RBV", "IOC:motor.VAL")
sig_pva = epicsrs_signal_rw(float, "pva://IOC:nt:scalar")

await sig_pva.connect()
await sig_pva.set(0.5)
print(await sig_pva.get_value())

Under the hood, EpicsRsSignalBackend implements ophyd-async's SignalBackend[T] ABC and routes by URL prefix to the appropriate native client. The package includes datatype-aware converters covering the full ophyd-async type surface: bool, int, float, str, Enum / StrictEnum / SubsetEnum / SupersetEnum, npt.NDArray, Sequence, and Table. IOC schema is validated against the requested datatype at connect time using PVA pvinfo, so type mismatches surface as a clear error during connect() rather than a silent corruption at first read.

Async surface

Both EpicsRsPV (CA) and EpicsRsPvaPV (PVA) expose *_async methods that return Python awaitables, in addition to the sync methods used by ophyd. The async path goes through pyo3-async-runtimes and shares the same tokio runtime as the sync path — no runtime fragmentation, same channel cache, mixed use against the same PV is safe.

from ophyd_epicsrs._native import EpicsRsContext, EpicsRsPvaContext
import asyncio

ctx_ca = EpicsRsContext()
ctx_pva = EpicsRsPvaContext()

async def main():
    pv_ca = ctx_ca.create_pv("IOC:motor.RBV")
    pv_pva = ctx_pva.create_pv("IOC:nt:scalar")

    # Wait for connection in parallel
    ok_ca, ok_pva = await asyncio.gather(
        pv_ca.connect_async(timeout=5.0),
        pv_pva.connect_async(timeout=5.0),
    )

    # Read value (scalar) or full reading (value + alarm + timestamp + display)
    val = await pv_ca.get_value_async()
    reading = await pv_pva.get_reading_async()

    # Write — returns True on success
    ok = await pv_ca.put_async(0.5)

asyncio.run(main())

Available async methods on both CA and PVA wrappers:

• connect_async(timeout) -> bool
• get_value_async(timeout) -> Any
• get_reading_async(timeout, form) -> dict | None
• put_async(value, timeout) -> bool
• connect_and_prefetch_async(timeout) -> None — single round-trip connect + metadata fetch
• get_field_desc_async(timeout) -> dict | None — PVA pvinfo introspection (CA: returns None)

The sync surface (wait_for_connection, get_with_metadata, put, etc.) remains unchanged — existing ophyd code works exactly as before.

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

Versus pyepics — honest like-for-like benchmark

Run on the same machine, same mini-beamline IOC, same PV pool, same connection state (warm). Reproducible from tests/integration/bench_vs_pyepics.py:

Operation	pyepics	epicsrs	Comment
Single PV cached get (PV.get() after monitor)	p50 2 µs	p50 61 µs	pyepics returns the cached monitor value with no network round-trip; EpicsRsPV.get_with_metadata always issues a fresh CA read. Different semantics.
Single PV fresh CA read	~100 µs avg (epics.caget())	p50 61 µs	Same network round-trip; epicsrs releases the GIL during the read.
Sequential fresh reads, 48 PVs (caget loop)	4.71 ms	2.28 ms	2.1× — fewer C↔Python crossings + per-call GIL releases.
bulk_caget(48)	n/a (no bulk primitive)	2.60 ms	Same wall time as the sequential loop above because the IOC + LAN are fast enough that the per-PV GIL drops dominate; the real bulk_caget win is at higher latencies (see flyer scenario below).

Where the much larger speedups show up:

• Sluggish IOC / WAN link, 50 PVs. Sequential pyepics adds N round-trip latencies; bulk_caget adds one. At 30 ms RTT this is the difference between ~1.5 s and ~30 ms — the original "1500×" number in earlier README revisions came from this regime, but it was not labelled honestly.
• Device connect with many PVs. The legacy ophyd path issues per-PV wait_for_connection calls serialised by the GIL. bulk_connect_and_prefetch collects all unconnected PVs and connects them concurrently in a single tokio call — see Device-level bulk connect below.
• Mixed sync + async usage in the same process. With pyepics + aioca/p4p, you pay for two separate EPICS stacks (separate channels, separate threads). With epicsrs both surfaces share one backend.

Where pyepics wins: the cached-monitor PV.get() path. If your hot loop is reading a value that already has an active monitor and you don't need fresh metadata, pyepics's in-process cache is hard to beat. EpicsRsShimPV.get (the legacy-ophyd surface) does cache monitor values too, so the gap mostly closes when you go through the ophyd Signal layer rather than calling _native.get_with_metadata directly.

The put→get improvement (single-owner writer task + TCP_NODELAY) remains unchanged from earlier releases — it eliminates the ~45 ms head-of-line blocking that occurred when reads waited for writes to flush.

Reproducible mini-beamline measurements

The integration suite (tests/integration/test_performance.py) measures a fixed set of operations against the mini-beamline IOC from epics-rs/examples/mini-beamline. Anyone can reproduce these numbers — just run the integration suite locally with the IOC up, or trigger the nightly CI workflow. Numbers below are local Apple Silicon, IOC and tests on the same host:

Operation	Result
Single get_with_metadata latency (200 samples)	p50 54 µs · p95 81 µs · p99 135 µs
bulk_caget(10)	0.63 ms (63 µs/PV)
bulk_caget(25)	1.32 ms (53 µs/PV)
bulk_caget(48)	2.63 ms (55 µs/PV)
ophyd-async parallel connect (30 PVs)	4.1 ms
ophyd-async parallel connect (3 × StandardReadable, 9 PVs)	3.4 ms
ophyd sync Device connect (4 components)	55 ms
ophyd sync Device connect (DCM, 9 PVs incl. 3 motors)	51 ms
ophyd sync Device connect (areaDetector cam, 11 PVs)	52 ms

Note the ~15× gap between ophyd-async parallel connect (4 ms for 30 PVs) and ophyd sync Device.wait_for_connection (~50 ms for 9–11 PVs): both go through the same _native backend, but the sync path issues per-PV wait_for_connection calls serialised by the GIL, while the async path's asyncio.gather(...) overlaps every PV's connect inside the single tokio runtime. Mixed sync + async usage works (same backend, same circuit per IOC), so the recommended migration path is "new device classes in ophyd-async, legacy classes left as-is".

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.

Reliability

Spawned tokio tasks (monitor delivery, connection-event watchers, pyo3-log forwarding) may execute callbacks into Python while the interpreter is being finalized — typically during pytest fixture teardown or normal process exit. A Python::with_gil call in that window panics; in a spawned task that panic would normally crash the process.

Every such call site is wrapped with safe_warn! / safe_call! / safe_call_or! macros that catch_unwind the panic, increment a process-wide counter, and write a one-line stderr notice on the first caught panic. The counter is exposed for telemetry:

from ophyd_epicsrs import caught_panic_count
print(caught_panic_count())  # 0 in normal operation

panic = "unwind" is enforced at compile time via a #[cfg(panic = "abort")] compile_error! so a downstream Cargo.toml cannot silently disarm the guards.

Architecture

ophyd (sync)              ophyd-async (asyncio)
  │                         │
  └── ophyd.cl              └── ophyd_epicsrs.ophyd_async
        │                         │ (EpicsRsSignalBackend)
        └── ophyd_epicsrs._shim   │
              │                   │
              └─→ ophyd_epicsrs._native (PyO3 bindings) ←─┘
                    │
                    ├── EpicsRsContext / EpicsRsPV       (CA)
                    └── EpicsRsPvaContext / EpicsRsPvaPV (PVA)
                                │
                                └── epics-rs (pure Rust, no libca.so)
                                      └── shared tokio runtime

GIL behavior

Operation	GIL
CA / PVA get / put	released — py.allow_threads() → tokio async
Monitor receive	released — tokio background task
Monitor callback → Python	held — dispatch thread
Connection wait	released — tokio async
bulk_caget	released — tokio join_all
*_async methods	released — pyo3-async-runtimes future

Key types

• EpicsRsContext / EpicsRsPvaContext — Shared tokio runtime + CA / PVA client. One of each per session.
• EpicsRsPV / EpicsRsPvaPV — PV channel wrappers. Sync surface (wait_for_connection, get_with_metadata, put, add_monitor_callback) plus *_async siblings.
• ophyd_epicsrs.ophyd_async.EpicsRsSignalBackend — ophyd-async SignalBackend implementation; routes pva:// / ca:// / bare names to the appropriate native client and applies the datatype-aware converter for the requested ophyd-async type. The factory functions (epicsrs_signal_rw etc.) wrap this and are the recommended entry point.

Logging

Rust-side tracing events are bridged to Python's logging module via pyo3-log. Standard configuration applies:

import logging
logging.getLogger("ophyd_epicsrs.ca").setLevel(logging.WARN)
logging.getLogger("ophyd_epicsrs.pva").setLevel(logging.DEBUG)

pyo3-log caches the level lookup for ~30 s. Call ophyd_epicsrs.reset_log_cache() after changing levels at runtime to force re-check on the next event.

Requirements

• Python >= 3.10
• ophyd >= 1.9 (vanilla PyPI — no fork required)
• ophyd-async >= 0.16 (only required if you use ophyd_epicsrs.ophyd_async)
• bluesky >= 1.13
• epics-rs >= 0.13 (bundled at build time)
• Rust toolchain >= 1.85 (build-time only)

Related

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

License

BSD 3-Clause