[essimaging] Odin image normalization wf

packages/essimaging/docs/odin/index.md

@@ -6,5 +6,6 @@ maxdepth: 1
 ---
 
 odin-data-reduction
+odin-orca-image-normalization
 odin-make-wavelength-lookup-table
 ```

packages/essimaging/docs/odin/odin-orca-image-normalization.ipynb

@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Odin: Orca image normalization workflow\n",
+    "\n",
+    "This notebook shows how to use the workflow to compute normalized images recorded by the Orca detector on the ODIN instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ess.odin.data  # noqa: F401\n",
+    "from ess import odin\n",
+    "from ess.imaging.types import *\n",
+    "import scipp as sc\n",
+    "import plopp as pp\n",
+    "\n",
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Workflow setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf = odin.OdinOrcaWorkflow()\n",
+    "\n",
+    "wf[Filename[AllRuns]] = odin.data.odin_lego_images()\n",
+    "\n",
+    "wf[NeXusDetectorName] = 'histogram_mode_detectors/orca'\n",
+    "\n",
+    "wf[MaskingRules] = {}  # No masks to begin with\n",
+    "wf[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf.visualize(NormalizedImage, compact=True, graph_attr={\"rankdir\": \"LR\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "## Run the workflow\n",
+    "\n",
+    "We compute the final normalized image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = wf.compute(NormalizedImage)\n",
+    "image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp.slicer(image, autoscale=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "## Adding masks\n",
+    "\n",
+    "If we want to mask some part of the image, we update the masking rules.\n",
+    "For example, here we mask the upper part of the image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf[MaskingRules] = {'y_pixel_offset': lambda x: x > sc.scalar(0.082, unit='m')}\n",
+    "\n",
+    "pp.slicer(wf.compute(NormalizedImage), autoscale=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "## Intermediate results\n",
+    "\n",
+    "We can also inspect intermediate results, which is useful for debugging:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = wf.compute(\n",
+    "    [\n",
+    "        FluxNormalizedDetector[SampleRun],\n",
+    "        FluxNormalizedDetector[OpenBeamRun],\n",
+    "        BackgroundSubtractedDetector[SampleRun],\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "fig = pp.tiled(2, 2, hspace=0.3, wspace=0.3)\n",
+    "fig[0, 0] = results[FluxNormalizedDetector[SampleRun]]['time', 0].plot(\n",
+    "    title='Sample (proton-charge normalized)'\n",
+    ")\n",
+    "fig[0, 1] = results[FluxNormalizedDetector[OpenBeamRun]]['time', 0].plot(\n",
+    "    title='Open beam (proton-charge normalized)'\n",
+    ")\n",
+    "fig[1, 0] = results[BackgroundSubtractedDetector[SampleRun]]['time', 0].plot(\n",
+    "    title='Background subtracted sample'\n",
+    ")\n",
+    "fig[1, 1] = image['time', 0].plot(title='Final image')\n",
+    "fig"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

packages/essimaging/src/ess/tbl/orca.py → packages/essimaging/src/ess/imaging/orca.py

@@ -8,10 +8,10 @@
 import scipp as sc
 
 from ess.reduce.nexus import GenericNeXusWorkflow
-from ess.reduce.nexus.types import NeXusDetectorName
 
 from .. import imaging
 from ..imaging.types import (
+    AllRuns,
     BackgroundSubtractedDetector,
     DarkBackgroundRun,
     FluxNormalizedDetector,
@@ -57,20 +57,13 @@ def normalize_by_proton_charge_orca(
 orca_providers = (normalize_by_proton_charge_orca,)
 
 
-def default_parameters() -> dict:
-    """Return the default NeXus names and detector name for the ORCA workflow."""
-    return {
-        NeXusDetectorName: 'orca_detector',
-    }
-
-
 def OrcaNormalizedImagesWorkflow(**kwargs) -> sl.Pipeline:
     """
     Workflow with default parameters for ORCA image normalization.
     """
 
     wf = GenericNeXusWorkflow(
-        run_types=[SampleRun, OpenBeamRun, DarkBackgroundRun],
+        run_types=[SampleRun, OpenBeamRun, DarkBackgroundRun, AllRuns],
         monitor_types=[],
         **kwargs,
     )
@@ -81,6 +74,4 @@ def OrcaNormalizedImagesWorkflow(**kwargs) -> sl.Pipeline:
         *orca_providers,
     ):
         wf.insert(provider)
-    for key, param in default_parameters().items():
-        wf[key] = param
     return wf

packages/essimaging/src/ess/imaging/types.py

@@ -36,7 +36,6 @@
 
 UncertaintyBroadcastMode = _UncertaintyBroadcastMode
 
-
 SampleRun = NewType("SampleRun", int)
 """Sample run; a run with a sample in the beam."""
 
@@ -48,6 +47,9 @@
 """Open beam run; a run with no sample in the beam, and the shutter open, to measure the
 beam profile."""
 
+AllRuns = NewType("AllRuns", int)
+"""A type to represent all runs: sample, open beam, and dark background runs."""
+
 BeamMonitor1 = NewType("BeamMonitor1", int)
 """Beam monitor number 1"""
 
@@ -96,4 +98,12 @@ class BackgroundSubtractedDetector(sciline.Scope[RunType, sc.DataArray], sc.Data
 beam run."""
 
 
+ImageKey = NewType("ImageKey", sc.DataArray)
+"""Key mapping image type to the time axis of the recording:
+    - 0: sample run
+    - 1: open beam run
+    - 2: dark background run
+    """
+
+
 del sc, sciline, NewType

packages/essimaging/src/ess/odin/__init__.py

@@ -4,7 +4,7 @@
 import importlib.metadata
 
 from . import beamline
-from .workflows import OdinBraggEdgeWorkflow, OdinWorkflow
+from .workflows import OdinBraggEdgeWorkflow, OdinOrcaWorkflow, OdinWorkflow
 
 try:
     __version__ = importlib.metadata.version("esstbl")
@@ -15,6 +15,7 @@
 
 __all__ = [
     "OdinBraggEdgeWorkflow",
+    "OdinOrcaWorkflow",
     "OdinWorkflow",
     "beamline",
 ]

packages/essimaging/src/ess/odin/data.py

@@ -15,6 +15,7 @@
         "iron_simulation_sample_large.nxs": "md5:c162b6abeccb51984880d8d5002bae95",
         "iron_simulation_sample_small.nxs": "md5:dda6fb30aa88780c5a3d4cef6ea05278",
         "ODIN-wavelength-lookup-table-5m-65m.h5": "md5:44eef2a2e826cec688aeb1b985eb9f9e",  # noqa: E501
+        "ymir_lego_odin.hdf": "md5:8e8708891e2574046b6f372e5e3516a5",
     },
 )
 
@@ -69,3 +70,13 @@ def odin_wavelength_lookup_table() -> pathlib.Path:
     with ``NumberOfSimulatedNeutrons = 5_000_000``.
     """
     return _registry.get_path("ODIN-wavelength-lookup-table-5m-65m.h5")
+
+
+def odin_lego_images() -> pathlib.Path:
+    """
+    Return the path to the ODIN LEGO HDF5 file, created from the YMIR data.
+    This file was created using the tools/make-odin-images-from-ymir.ipynb notebook.
+    A ODIN file (coda_odin_999999_00011093.hdf) was used as a template for the NeXus
+    structure. The images were extracted from the YMIR LEGO run.
+    """
+    return _registry.get_path("ymir_lego_odin.hdf")