diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d92962b..8a3315c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -7,6 +7,9 @@ on:
     branches:
       - main
 
+permissions:
+  contents: read
+
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
@@ -80,3 +83,27 @@ jobs:
               except ImportError as e:
                   print(f'⚠ {module} import failed: {e}')
           "
+
+  tests:
+    name: Tests (Python ${{ matrix.python-version }})
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - uses: astral-sh/setup-uv@v7
+
+      - name: Run test suite
+        run: |
+          uv sync --locked --group dev --python "${{ matrix.python-version }}"
+          uv run --no-sync pytest tests -v
diff --git a/pyproject.toml b/pyproject.toml
index 33c3294..ae13c17 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,35 +9,35 @@ description = "NSLS-II Hard X-ray Nanoprobe data acquisition tools"
 readme = "README.md"
 requires-python = ">=3.11"
 license = "BSD-3-Clause"
-authors = [
-    {name = "Brookhaven National Laboratory", email = "DAMA@bnl.gov"}
-]
+authors = [{ name = "Brookhaven National Laboratory", email = "DAMA@bnl.gov" }]
 classifiers = [
-    "Development Status :: 5 - Production/Stable",
-    "Intended Audience :: Science/Research",
-    "Intended Audience :: Developers",
-    "Natural Language :: English",
-    "Operating System :: OS Independent",
-    "Programming Language :: Python",
-    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
-    "Topic :: Scientific/Engineering",
+  "Development Status :: 5 - Production/Stable",
+  "Intended Audience :: Science/Research",
+  "Intended Audience :: Developers",
+  "Natural Language :: English",
+  "Operating System :: OS Independent",
+  "Programming Language :: Python",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3 :: Only",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Topic :: Scientific/Engineering",
 ]
 dependencies = [
-    "bluesky",
-    "boltons",
-    "cycler",
-    "databroker",
-    "h5py",
-    "matplotlib",
-    "nslsii",
-    "numpy",
-    "ophyd",
-    "pandas",
-    "pyepics",
-    "scipy",
+  "bluesky",
+  "boltons",
+  "cycler",
+  "databroker",
+  "doct",
+  "h5py",
+  "matplotlib",
+  "nslsii",
+  "numpy",
+  "ophyd",
+  "pandas",
+  "pims",
+  "pyepics",
+  "scipy",
 ]
 
 [project.urls]
@@ -48,7 +48,10 @@ Changelog = "https://github.com/NSLS-II-HXN/hxntools/releases"
 
 [dependency-groups]
 dev = [
-    "pre-commit>=4.3",
+  "pre-commit>=4.3",
+  "pytest>=8.0",
+  "bluesky-tiled-plugins>=2.0.11",
+  "tiled[server]>=0.2.18",
 ]
 
 [tool.hatch.version]
diff --git a/src/hxntools/panda_flyer.py b/src/hxntools/panda_flyer.py
new file mode 100644
index 0000000..961cb2e
--- /dev/null
+++ b/src/hxntools/panda_flyer.py
@@ -0,0 +1,592 @@
+import itertools
+import os
+import time
+import time as ttime
+import uuid
+from collections import OrderedDict, deque
+from datetime import datetime
+
+import h5py
+import numpy as np
+from bluesky.utils import short_uid
+from event_model import compose_resource
+from ophyd import Device
+from ophyd.areadetector.filestore_mixins import resource_factory
+from ophyd.sim import NullStatus
+
+from hxntools.handlers.rasmi2 import SISHDF5Handler
+
+
+class ExportSISDataPanda:
+    def __init__(self):
+        self._fp = None
+        self._filepath = None
+
+    def open(self, filepath, mca_names, ion, panda):
+        self.close()
+        self._filepath = filepath
+        self._fp = h5py.File(filepath, "w", libver="latest")
+
+        print(f'{filepath = }')
+
+        self._fp.swmr_mode = True
+
+        self._ion = ion
+        self._panda = panda
+        self._mca_names = mca_names
+
+        def create_ds(ds_name):
+            ds = self._fp.create_dataset(ds_name, data=np.array([], dtype="f"), maxshape=(None,), dtype="f")
+
+        for ds_name in self._mca_names:
+            create_ds(ds_name)
+
+        self._fp.flush()
+
+    def close(self):
+        if self._fp:
+            self._fp.close()
+            self._fp = None
+
+    def __del__(self):
+        self.close()
+
+    def export(self):
+
+        n_mcas = len(self._mca_names)
+
+        mca_data = []
+        for n in range(1, n_mcas + 1):
+            mca = self._ion.mca_by_index[n].spectrum.get(timeout=5.0)
+            mca_data.append(mca)
+        
+        #some problem here ROI data is saved correctly but correct len is 0:  AP 04/24/2026
+        correct_length = int(self._panda.data.num_captured.get()/self._panda.position_supersample)
+        # correct_length = int(self._panda.data.num_capture.get()/self._panda.position_supersample) trial on 4/26/2026
+        print (f"{'='*30}")
+        print(f' {correct_length = }')
+        print (f"{'='*30}")
+
+        for n in range(len(mca_data)):
+            mca = mca_data[n]
+            # print(f"Number of mca points: {len(mca)}")
+            # mca = mca[1::2]
+            if len(mca) != correct_length:
+                print(f"Incorrect number of points ({len(mca)}) loaded from MCA{n + 1}: {correct_length} points are expected")
+                if len(mca > correct_length):
+                    mca = mca[:correct_length]
+                else:
+                    mca = np.append(mca, [1e10] * (correct_length - len(mca)))
+            mca_data[n] = mca
+
+        j = 0
+        while self._panda.data.capture.get() == 1:
+            print("Waiting for pandabox data...")
+            ttime.sleep(0.1)
+            j += 1
+            if j > 10:
+                print("PANDABOX IS BEHAVING BADLY CARRYING ON")
+                break
+
+        def add_data(ds_name, data):
+            ds = self._fp[ds_name]
+            n_ds = ds.shape[0]
+            ds.resize((n_ds + len(data),))
+            ds[n_ds:] = np.array(data)
+
+        for n, name in enumerate(self._mca_names):
+            add_data(name, np.asarray(mca_data[n]))
+
+        self._fp.flush()
+
+
+class ExportXpsROI:
+    def __init__(self):
+        self._fp = None
+        self._filepath = None
+
+    def open(self, filepath, xspress3):
+        self.close()
+        self._filepath = filepath
+        self._fp = h5py.File(filepath, "w", libver="latest")
+
+        self._fp.swmr_mode = True
+
+        self._xsp = xspress3
+
+        def create_ds(det_name):
+            if not det_name in self._fp:
+                self._fp.create_dataset(det_name, data=np.array([], dtype="f"), maxshape=(None,), dtype="f")
+
+        for det_name in [roi.name for roi in self._xsp.enabled_rois]:
+            create_ds(det_name)
+
+        self._fp.flush()
+
+    def close(self):
+        if self._fp:
+            self._fp.close()
+            self._fp = None
+
+    def __del__(self):
+        self.close()
+
+    def export(self, npoints):
+        def add_data(det_name, data):
+            ds = self._fp[det_name]
+            if ds.size == 0:
+                ds.resize((npoints,))
+                ds[:len(data)] = np.array(data)
+                ds[len(data):] = ds[len(data)-1]
+
+        for roi in self._xsp.enabled_rois:
+            if hasattr(roi,'settings'):
+                add_data(roi.name, roi.settings.array_data.get())
+            else:
+                add_data(roi.name, roi.ts_total.get())
+        self._fp.flush()
+
+
+class HXNFlyerPanda(Device):
+    """
+    This is the PandaBox panda1 and panda2
+    """
+
+    @property
+    def detectors(self):
+        return tuple(self._dets)
+
+    @detectors.setter
+    def detectors(self, value):
+        dets = tuple(value)
+        # if not all([d.name in self.KNOWN_DETS for d in dets]):
+        #     raise ValueError(
+        #         f"One or more of {[d.name for d in dets]}"
+        #         f"is not known to the panda. "
+        #         f"The known detectors are {self.KNOWN_DETS})"
+        #     )
+        self._dets = dets
+
+    @property
+    def sclr(self):
+        return self._sis
+
+    def __init__(
+        self,
+        panda,
+        dets,
+        sclr,
+        *,
+        large_file_directory_root,
+        large_file_directory_write_path,
+        large_file_directory_read_path,
+        motor=None,
+        root_dir=None,
+        n_scaler_mca=16,
+        **kwargs,
+    ):
+        super().__init__("", parent=None, **kwargs)
+        self.name = "PandaFlyer"
+        self.LARGE_FILE_DIRECTORY_ROOT = large_file_directory_root
+        self.LARGE_FILE_DIRECTORY_WRITE_PATH = large_file_directory_write_path
+        self.LARGE_FILE_DIRECTORY_READ_PATH = large_file_directory_read_path
+        if root_dir is None:
+            root_dir = self.LARGE_FILE_DIRECTORY_ROOT
+        self._mode = "idle"
+        self._dets = dets
+        self._sis = sclr
+        self._root_dir = root_dir
+        self._resource_document, self._datum_factory = None, None
+        self._document_cache = deque()
+        self._last_bulk = None
+
+        self._point_counter = None
+        self.frame_per_point = None
+        # Number of raw PandA gates captured per *scan point*. Real callers
+        # (flyscan_pd et al.) always set this before kickoff(); default to 1
+        # (no oversampling) so the flyer is safe to use standalone.
+        self.position_supersample = 1
+        # Optional hook invoked as `callback(finished: bool)` from complete();
+        # hxntools has no beamline live-plotting dependency of its own, so the
+        # profile wires this to its plot-update function after construction.
+        self.live_plot_callback = None
+        self._n_scaler_mca = n_scaler_mca
+
+        self.panda = panda
+        self.motor = motor
+
+        self._document_cache = []
+        self._resource_document = None
+        self._datum_factory = None
+
+        if self._sis is not None:
+            self._data_sis_exporter = ExportSISDataPanda()
+
+        self._xsp_roi_exporter = None
+
+        type_map = {"int32": "<i4", "float32": "<f4", "float64": "<f8"}
+
+        self.fields = {
+            "inenc1_val": {
+                "value": "INENC1.VAL.Value",
+                "dtype_str": type_map["int32"],
+            },
+            "inenc2_val": {
+                "value": "INENC2.VAL.Value",
+                "dtype_str": type_map["int32"],
+            },
+            "inenc3_val": {
+                "value": "INENC3.VAL.Value",
+                "dtype_str": type_map["int32"],
+            },
+            "inenc4_val": {
+                "value": "INENC4.VAL.Value",
+                "dtype_str": type_map["int32"],
+            },
+            "pcap_ts_trig": {
+                "value": "PCAP.TS_TRIG.Value",
+                "dtype_str": type_map["float64"],
+            },
+        }
+        self.panda.data.hdf_directory.put_complete = True
+        self.panda.data.hdf_file_name.put_complete = True
+
+    def stage(self):
+        super().stage()
+
+    def unstage(self):
+        self._point_counter = None
+        if self._sis is not None:
+            self._data_sis_exporter.close()
+        if self._xsp_roi_exporter is not None:
+            self._xsp_roi_exporter.close()
+        super().unstage()
+
+    def kickoff(self, *, num):
+        """Kickoff the acquisition process."""
+        # Prepare parameters:
+        self._document_cache = deque()
+        self._datum_docs = {}
+        self._counter = itertools.count()
+        self._point_counter = 0
+
+        self.frame_per_point = int(num)
+        # Prepare 'resource' factory.
+        now = datetime.now()
+        self.fl_path = self.LARGE_FILE_DIRECTORY_WRITE_PATH
+        self.fl_name = f"panda_rbdata_{now.strftime('%Y%m%d_%H%M%S')}_{short_uid()}.h5"
+
+        resource_path = self.fl_name
+        self._resource_document, self._datum_factory, _ = compose_resource(
+            start={"uid": "needed for compose_resource() but will be discarded"},
+            spec="PANDA",
+            root=self.fl_path,
+            resource_path=resource_path,
+            resource_kwargs={},
+        )
+        # now discard the start uid, a real one will be added later
+        self._resource_document.pop("run_start")
+        self._document_cache.append(("resource", self._resource_document))
+
+        for key, value in self.fields.items():
+            datum_document = self._datum_factory(datum_kwargs={"field": value["value"]})
+            self._document_cache.append(("datum", datum_document))
+            self._datum_docs[key] = datum_document
+
+
+        ## Scaler
+        if self._sis is not None:
+            # Stop the SIS3820
+            self._sis.stop_all.put(1)
+
+        self.__filename_sis = "{}.h5".format(uuid.uuid4())
+        self.__read_filepath_sis = os.path.join(
+            self.LARGE_FILE_DIRECTORY_READ_PATH, self.__filename_sis
+        )
+        self.__write_filepath_sis = os.path.join(
+            self.LARGE_FILE_DIRECTORY_WRITE_PATH, self.__filename_sis
+        )
+
+        self.__filestore_resource_sis, self._datum_factory_sis = resource_factory(
+            SISHDF5Handler.HANDLER_NAME,
+            root=self.LARGE_FILE_DIRECTORY_ROOT,
+            resource_path=self.__read_filepath_sis,
+            # `frame_per_point` here is the number of samples represented by a single
+            # Datum, i.e. the true per-row length (see describe_collect() for the
+            # matching descriptor shape) -- not the raw, supersampled PandA gate
+            # count in `self.frame_per_point`.
+            resource_kwargs={
+                "frame_per_point": self.frame_per_point // self.position_supersample
+            },
+            path_semantics="posix",
+        )
+
+        resources = [self.__filestore_resource_sis]
+
+        self._xsp_roi_exporter = None
+        ## Xspress3 ROIs
+        for d in self._dets:
+            #print(f"{d = }")
+            if d.name == 'xspress3' or d.name == 'xspress3_det2':
+
+                self.xsp = d
+
+                self.__filename_xsp_roi = "ROI0_{}.h5".format(uuid.uuid4())
+                self.__read_filepath_xsp_roi = os.path.join(
+                    self.LARGE_FILE_DIRECTORY_READ_PATH, self.__filename_xsp_roi
+                )
+                self.__write_filepath_xsp_roi = os.path.join(
+                    self.LARGE_FILE_DIRECTORY_WRITE_PATH, self.__filename_xsp_roi
+                )
+
+                self._xsp_roi_exporter = ExportXpsROI()
+                self._xsp_roi_exporter.open(
+                    self. __write_filepath_xsp_roi, d
+                )
+
+                self.__filestore_resource_xsp_roi, self._datum_factory_xsp_roi = resource_factory(
+                    'ROI_HDF5_FLY',
+                    root=self.LARGE_FILE_DIRECTORY_ROOT,
+                    resource_path=self.__read_filepath_xsp_roi,
+                    # Same true per-row length as the SIS resource above (and
+                    # describe_collect()'s declared shape) -- required so that
+                    # BlueskyRunV3.validate() can reconcile the on-disk,
+                    # single-column-per-ROI dataset shape with the declared
+                    # [1, num_scan_points] structure.
+                    resource_kwargs={
+                        "frame_per_point": self.frame_per_point // self.position_supersample
+                    },
+                    path_semantics="posix",
+                )
+
+
+                resources.append(self.__filestore_resource_xsp_roi)
+
+        # if self._sis:
+        #     resources.append(self.__filestore_resource_sis)
+
+        self._document_cache.extend(("resource", _) for _ in resources)
+
+        if self._sis is not None:
+            sis_mca_names = self._sis_mca_names()
+            self._data_sis_exporter.open(
+                self.__write_filepath_sis, mca_names=sis_mca_names, ion=self._sis, panda=self.panda
+            )
+
+
+        # Kickoff panda process:
+        print(f"[Panda]Starting acquisition ...")
+
+        self.panda.position_supersample = self.position_supersample
+
+        self.panda.data.hdf_directory.set(self.fl_path).wait()
+        self.panda.data.hdf_file_name.set(self.fl_name).wait()
+        #self.panda.data.flush_period.set(1).wait()
+
+        self.panda.data.capture_mode.set("FIRST_N").wait()
+        self.panda.data.num_capture.set(self.frame_per_point).wait()
+
+        self.panda.pcap.arm.set(1).wait()
+
+        self.panda.data.capture.set(1).wait()
+
+        print(f"[Panda]Panda kickoff complete ...")
+
+        return NullStatus()
+
+    def complete(self):
+        print("[Panda]complete")
+        """Wait for the acquisition process started in kickoff to complete."""
+        # Wait until done
+        timeout = 60
+        counter = 0
+        while (self.panda.data.capture.get() == 1) and (counter<timeout):
+            time.sleep(0.1)
+            counter+=1
+
+        self.panda.pcap.arm.set(0).wait()
+        self.panda.data.capture.put(0)
+
+        for d in self._dets:
+            d.stop(success=True)
+
+        now = ttime.time()  # TODO: figure out how to get it from PandABox (maybe?)
+
+        data_dict = {
+            key: datum_doc["datum_id"] for key, datum_doc in self._datum_docs.items()
+        }
+
+        self._last_bulk = {
+            "data": data_dict,
+            "timestamps": {key: now for key in self._datum_docs},
+            "time": now,
+            "filled": {key: False for key in self._datum_docs},
+        }
+
+        if self._sis:
+            sis_mca_names = self._sis_mca_names()
+            sis_datum = []
+            for name in sis_mca_names:
+                sis_datum.append(self._datum_factory_sis({"column": name, "point_number": self._point_counter}))
+            self._document_cache.extend(("datum", d) for d in sis_datum)
+
+        # @timer_wrapper
+        def get_sis_data():
+            if self._sis is None:
+                return
+            self._data_sis_exporter.export()
+
+        get_sis_data()
+
+        if self._xsp_roi_exporter is not None:
+            self._xsp_roi_exporter.export(int(self.frame_per_point/self.position_supersample))
+
+            if self.live_plot_callback is not None:
+                self.live_plot_callback(True)
+
+            roi_datum = []
+            for roi in self.xsp.enabled_rois:
+                roi_datum.append(self._datum_factory_xsp_roi({"det_elem": roi.name}))
+            self._last_bulk["data"].update({k: v["datum_id"] for k, v in zip([roi.name for roi in self.xsp.enabled_rois], roi_datum)})
+            self._last_bulk["timestamps"].update({k: v["datum_id"] for k, v in zip([roi.name for roi in self.xsp.enabled_rois], roi_datum)})
+            self._document_cache.extend(("datum", d) for d in roi_datum)
+
+        for d in self._dets:
+            if d.name != 'fs' and d.name != 'bshutter' and d.name != 'xspress3':
+                #print (f"{'='*30}")
+                #print (d)
+                self._document_cache.extend(d.collect_asset_docs())
+            if d.name == 'xspress3':
+                doc_cnt = 0
+                for doc in d.collect_asset_docs():
+                    self._document_cache.append(doc)
+                    doc_cnt += 1
+                    if doc_cnt == 4:
+                        break
+
+        print("[Panda]collect data")
+
+        if self._sis:
+            self._last_bulk["data"].update({k: v["datum_id"] for k, v in zip(sis_mca_names, sis_datum)})
+            self._last_bulk["timestamps"].update({k: v["datum_id"] for k, v in zip(sis_mca_names, sis_datum)})
+
+        for d in self._dets:
+            if d.name == 'merlin1' or d.name == 'merlin2':
+                reading = d.read()
+                self._last_bulk["data"].update(
+                    {k: v["value"] for k, v in reading.items()}
+                    )
+                self._last_bulk["timestamps"].update(
+                    {k: v["timestamp"] for k, v in reading.items()}
+                    )
+            if d.name.startswith('eiger'):
+                reading = d.read()
+                self._last_bulk["data"].update(
+                    {k: v["value"] for k, v in reading.items()}
+                    )
+                self._last_bulk["timestamps"].update(
+                    {k: v["timestamp"] for k, v in reading.items()}
+                    )
+            if d.name == 'xspress3':
+                reading = d.read()
+                self._last_bulk["data"].update(
+                    {k: v["value"] for k, v in reading.items() if k.startswith('xspress3')}
+                )
+                self._last_bulk["timestamps"].update(
+                    {k: v["timestamp"] for k, v in reading.items() if k.startswith('xspress3')}
+                )
+            if d.name == 'xspress3_det2':
+                reading = d.read()
+                self._last_bulk["data"].update(
+                    {k: v["value"] for k, v in reading.items()}
+                )
+                self._last_bulk["timestamps"].update(
+                    {k: time.time() for k, v in reading.items()}
+                )
+
+        return NullStatus()
+
+    def describe_collect(self):
+        """Describe the data structure."""
+        return_dict = {"primary": OrderedDict()}
+        desc = return_dict["primary"]
+
+        ext_spec = "FileStore:"
+
+        # `frame_per_point` (== `self._npts` previously) is the raw number of
+        # PandA gates captured, which is `position_supersample` gates per
+        # real scan point (see kickoff()/flyscan_pd()). The SIS scaler and
+        # Xspress3 ROI exporters both collapse that oversampling before
+        # writing their HDF5 files (ExportSISDataPanda.export() and
+        # ExportXpsROI.export(), called with frame_per_point /
+        # position_supersample in complete()), so the declared per-row
+        # length for those keys must match the reduced, on-disk point count
+        # -- not the raw supersampled gate count.
+        num_scan_points = self.frame_per_point // self.position_supersample
+
+        def _spec(source):
+            return {
+                "external": ext_spec,
+                "dtype": "array",
+                # ExportSISDataPanda/ExportXpsROI both hardcode a 32-bit float
+                # HDF5 dataset (dtype="f") for every scaler/ROI channel; declare
+                # it here so downstream consumers don't have to guess/default.
+                "dtype_str": "<f4",
+                "shape": [num_scan_points],
+                "source": source,
+            }
+
+        for key, value in self.fields.items():
+            desc.update(
+                {
+                    key: {
+                        "source": "PANDA",
+                        "dtype": "array",
+                        "dtype_str": value["dtype_str"],
+                        "shape": [
+                            self.frame_per_point
+                        ],  # TODO: figure out variable shape
+                        "external": "FILESTORE:",
+                    }
+                }
+            )
+
+        for d in self._dets:
+            if d.name == 'merlin1' or d.name == 'merlin2':
+                desc.update(d.describe())
+            if d.name.startswith('eiger'):
+                desc.update(d.describe())
+            if d.name == 'xspress3':
+                desc.update([(k, v) for k,v in d.describe().items() if k.startswith('xspress3')])
+            if d.name == 'xspress3_det2':
+                desc.update(d.describe())
+
+        if self._sis is not None:
+            sis_mca_names = self._sis_mca_names()
+            for n, name in enumerate(sis_mca_names):
+                desc[name] = _spec(self._sis.mca_by_index[n + 1].spectrum.pvname)
+
+        if self._xsp_roi_exporter is not None:
+            for roi in self.xsp.enabled_rois:
+                if hasattr(roi, 'settings'):
+                    source = roi.settings.array_data.pvname
+                else:
+                    source = roi.ts_total.pvname
+                desc[roi.name] = _spec(source)
+
+
+        return return_dict
+
+    def collect(self):
+        yield self._last_bulk
+        self._point_counter += 1
+
+    def collect_asset_docs(self):
+        """The method to collect resource/datum documents."""
+        items = list(self._document_cache)
+        self._document_cache.clear()
+        yield from items
+
+    def _sis_mca_names(self):
+        n_mcas = self._n_scaler_mca
+        return [getattr(self._sis.channels, f"chan{_}").name for _ in range(1, n_mcas + 1)]
diff --git a/tests/test_panda_flyer.py b/tests/test_panda_flyer.py
new file mode 100644
index 0000000..a33c907
--- /dev/null
+++ b/tests/test_panda_flyer.py
@@ -0,0 +1,257 @@
+"""Integration tests for HXNFlyerPanda."""
+
+import os
+from types import SimpleNamespace
+
+import bluesky.plan_stubs as bps
+import h5py
+import numpy as np
+import pytest
+from bluesky import RunEngine
+from bluesky_tiled_plugins import TiledWriter
+from bluesky_tiled_plugins.writing.tiled_writer import RunNormalizer
+
+from hxntools.panda_flyer import HXNFlyerPanda
+
+# Literal used by HXNFlyerPanda.kickoff() for the ROI resource spec.
+ROI_SPEC = "ROI_HDF5_FLY"
+
+
+def _alias_dataset(doc):
+    """RunNormalizer patch: map the legacy `column`/`det_elem`/`field` datum_kwargs
+    to `dataset`.
+
+    The real SIS/ROI HDF5 files hold one flat dataset per channel/ROI name at
+    the file root (see `ExportSISDataPanda.export()` / `ExportXpsROI.export()`),
+    selected at read time via `column`/`det_elem`; the raw PandA capture file
+    (spec "PANDA") is likewise one flat dataset per field, selected via `field`
+    (see `kickoff()`'s `datum_kwargs={"field": value["value"]}`).
+    `bluesky_tiled_plugins`'s generic HDF5 consolidator expects that selector
+    under the `dataset` parameter; this mirrors the kind of normalizer patch a
+    Tiled deployment supplies for these HXN-specific legacy handler specs.
+    """
+    dk = dict(doc.get("datum_kwargs", {}))
+    for legacy_key in ("column", "det_elem", "field"):
+        if legacy_key in dk:
+            dk["dataset"] = dk.pop(legacy_key)
+    doc["datum_kwargs"] = dk
+    return doc
+
+
+class _FakeSignal:
+    """Minimal stand-in for an ophyd/EPICS-style signal: get/put/set(...).wait()."""
+
+    def __init__(self, value=None, pvname=""):
+        self.value = value
+        self.pvname = pvname
+
+    def get(self, **kwargs):
+        return self.value
+
+    def put(self, value, **kwargs):
+        self.value = value
+
+    def set(self, value, **kwargs):
+        self.value = value
+        return self
+
+    def wait(self, **kwargs):
+        return None
+
+
+# PandA field PVs declared in HXNFlyerPanda.fields, with their true on-disk
+# dtype (see the `type_map` used to build that dict in __init__).
+PANDA_FIELD_DTYPES = {
+    "INENC1.VAL.Value": "<i4",
+    "INENC2.VAL.Value": "<i4",
+    "INENC3.VAL.Value": "<i4",
+    "INENC4.VAL.Value": "<i4",
+    "PCAP.TS_TRIG.Value": "<f8",
+}
+
+
+class _FakeCaptureSignal(_FakeSignal):
+    """Stand-in for the PandA `data.capture` PV.
+
+    `get()` reports the capture as already finished, so `complete()`'s
+    (blocking) wait loop exits at once. `set(1)` simulates the PandA
+    hardware/IOC itself writing its raw capture HDF5 file -- one flat
+    dataset per field, `frame_per_point` raw gates long -- at the
+    directory/filename `kickoff()` configures on the sibling `hdf_directory`/
+    `hdf_file_name`/`num_capture` signals just before arming.
+    """
+
+    def __init__(self, data_ns):
+        super().__init__(value=0)
+        self._data_ns = data_ns
+
+    def get(self, **kwargs):
+        return 0
+
+    def set(self, value, **kwargs):
+        self.value = value
+        if value == 1:
+            raw_count = int(self._data_ns.num_capture.value)
+            path = os.path.join(
+                self._data_ns.hdf_directory.value, self._data_ns.hdf_file_name.value
+            )
+            with h5py.File(path, "w") as fp:
+                for field_name, dtype in PANDA_FIELD_DTYPES.items():
+                    # HXNFlyerPanda.collect() yields exactly one Event for the
+                    # whole flyer scan, so RunNormalizer declares each PandA
+                    # field's structure with a leading singleton Event
+                    # dimension: shape (1, frame_per_point). The PandA
+                    # resource's compose_resource() call (unlike the SIS/ROI
+                    # resource_factory() calls) does not set a `frame_per_point`
+                    # resource_kwarg, so Tiled's validator does not reshape a
+                    # flat on-disk array to match -- write the dataset already
+                    # in the declared shape.
+                    fp.create_dataset(
+                        field_name, data=np.zeros((1, raw_count), dtype=dtype)
+                    )
+        return self
+
+
+def _fake_panda():
+    data = SimpleNamespace(
+        hdf_directory=_FakeSignal(),
+        hdf_file_name=_FakeSignal(),
+        capture_mode=_FakeSignal(),
+        num_capture=_FakeSignal(),
+        num_captured=_FakeSignal(),
+    )
+    data.capture = _FakeCaptureSignal(data)
+    return SimpleNamespace(data=data, pcap=SimpleNamespace(arm=_FakeSignal()))
+
+
+def _fake_scaler(channel_names, real_points):
+    channels = SimpleNamespace(
+        **{
+            f"chan{i}": SimpleNamespace(name=name)
+            for i, name in enumerate(channel_names, start=1)
+        }
+    )
+    mca_by_index = {
+        i: SimpleNamespace(
+            spectrum=_FakeSignal(
+                np.arange(real_points, dtype="f8") + i * 1000, pvname=f"SCLR:mca{i}"
+            )
+        )
+        for i in range(1, len(channel_names) + 1)
+    }
+    return SimpleNamespace(
+        channels=channels, stop_all=_FakeSignal(), mca_by_index=mca_by_index
+    )
+
+
+def _fake_xspress3(roi_names, real_points):
+    rois = [
+        SimpleNamespace(
+            name=name,
+            settings=SimpleNamespace(
+                array_data=_FakeSignal(
+                    np.arange(real_points, dtype="f8") + i * 10, pvname=f"XSP:{name}"
+                )
+            ),
+        )
+        for i, name in enumerate(roi_names)
+    ]
+    return SimpleNamespace(
+        name="xspress3",
+        enabled_rois=rois,
+        stop=lambda success=True: None,
+        collect_asset_docs=lambda: iter(()),
+        describe=lambda: {},
+        read=lambda: {},
+    )
+
+
+@pytest.fixture(scope="module")
+def tiled_context(tmp_path_factory):
+    tiled_catalog = pytest.importorskip("tiled.catalog")
+    tsa = pytest.importorskip("tiled.server.app")
+    tc = pytest.importorskip("tiled.client")
+
+    tmp_path = tmp_path_factory.mktemp("tiled_catalog")
+    catalog = tiled_catalog.in_memory(
+        writable_storage={
+            "filesystem": str(tmp_path),
+            "sql": f"duckdb:///{tmp_path}/test.db",
+        },
+        readable_storage=[str(tmp_path.parent)],
+    )
+    app = tsa.build_app(catalog)
+    with tc.Context.from_app(app) as context:
+        yield context
+
+
+@pytest.fixture
+def tiled_client(tiled_context):
+    tc = pytest.importorskip("tiled.client")
+    return tc.from_context(tiled_context)
+
+
+def _primary_stream(run):
+    node = run["streams"] if "streams" in run.keys() else run
+    return node["primary"]
+
+
+SCLR_CHANNELS = ["sclr1_ch1", "sclr1_ch2"]
+ROI_NAMES = ["Det1_Fe", "Det1_Ni"]
+REAL_POINTS = 20  # e.g. a 4x5 map
+
+
+@pytest.mark.parametrize("position_supersample", [1, 10])
+def test_flyer_declares_true_point_count(tiled_client, tmp_path, position_supersample):
+    fake_panda = _fake_panda()
+    fake_scaler = _fake_scaler(SCLR_CHANNELS, REAL_POINTS)
+    fake_xspress3 = _fake_xspress3(ROI_NAMES, REAL_POINTS)
+
+    flyer = HXNFlyerPanda(
+        fake_panda,
+        [fake_xspress3],
+        fake_scaler,
+        name="test_panda_flyer",
+        # HXNFlyerPanda builds the SIS/ROI `resource_path` as an absolute path
+        # already rooted at `large_file_directory_read_path` (see kickoff()'s
+        # `os.path.join(self.LARGE_FILE_DIRECTORY_READ_PATH, filename)`), so
+        # `root` must stay empty here -- otherwise bluesky_tiled_plugins's
+        # root+resource_path URI composition would prefix the already-rooted
+        # path a second time.
+        large_file_directory_root="",
+        large_file_directory_write_path=str(tmp_path),
+        large_file_directory_read_path=str(tmp_path),
+        n_scaler_mca=len(SCLR_CHANNELS),
+    )
+    flyer.position_supersample = position_supersample
+    raw_count = REAL_POINTS * position_supersample
+
+    writer = TiledWriter(
+        tiled_client,
+        normalizer=RunNormalizer,
+        patches={"datum": _alias_dataset},
+    )
+    RE = RunEngine({})
+    RE.subscribe(writer)
+
+    def plan():
+        yield from bps.open_run()
+        yield from bps.kickoff(flyer, num=raw_count, wait=True)
+        # Simulate the PandA hardware having captured exactly the requested
+        # number of raw gates (ExportSISDataPanda.export() reads this back).
+        fake_panda.data.num_captured.value = raw_count
+        yield from bps.complete(flyer, wait=True)
+        yield from bps.collect(flyer)
+        yield from bps.close_run()
+
+    (uid,) = RE(plan())
+    primary = _primary_stream(tiled_client[uid])
+
+    for key in SCLR_CHANNELS + ROI_NAMES:
+        node = primary[key]
+        assert node.structure().shape == (1, REAL_POINTS)
+        assert node.read().shape == (1, REAL_POINTS)
+
+    assert tiled_client[uid].validate(
+        fix_errors=False, raise_on_error=True, write_notes=False
+    )
diff --git a/uv.lock b/uv.lock
index 4f8d5da..7279bfd 100644
--- a/uv.lock
+++ b/uv.lock
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 name = "hxntools"
 source = { editable = "." }
@@ -833,6 +1754,7 @@ dependencies = [
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     { name = "cycler" },
     { name = "databroker" },
+    { name = "doct" },
     { name = "h5py" },
     { name = "matplotlib" },
     { name = "nslsii" },
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     { name = "ophyd" },
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@@ -847,7 +1770,10 @@ dependencies = [
 
 [package.dev-dependencies]
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     { name = "pre-commit" },
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     { name = "matplotlib" },
     { name = "nslsii" },
     { name = "numpy" },
     { name = "ophyd" },
     { name = "pandas" },
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     { name = "scipy" },
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