Merge pull request #90 from scipp/refactor-visualizations

Refactor visualizations

Author: SimonHeybrock

docs/api-reference/index.md

@@ -52,4 +52,6 @@
    orso
    resolution
    types
+   utils
+   figures
 ```

docs/user-guide/amor/amor-reduction.ipynb

@@ -160,13 +160,22 @@
     "    '611': sc.scalar(5.05, unit='deg'),\n",
     "}\n",
     "\n",
-    "results = {}\n",
+    "reflectivity = {}\n",
     "for file, angle in runs.items():\n",
     "    workflow[Filename[SampleRun]] = amor.data.amor_sample_run(file)\n",
     "    workflow[SampleRotation[SampleRun]] = angle\n",
-    "    results[file] = workflow.compute(ReflectivityOverQ).hist()\n",
+    "    reflectivity[file] = workflow.compute(ReflectivityOverQ).hist()\n",
     "\n",
-    "sc.plot(results, norm='log', vmin=1e-4)"
+    "sc.plot(reflectivity, norm='log', vmin=1e-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scaling the reflectivity curves to overlap\n",
+    "\n",
+    "In case we know the curves are have been scaled by different factors (that are constant in Q) it can be useful to scale them so they overlap:"
    ]
   },
   {
@@ -176,12 +185,16 @@
    "outputs": [],
    "source": [
     "from ess.reflectometry.tools import scale_reflectivity_curves_to_overlap\n",
-    "results_scaled = dict(zip(\n",
-    "    results.keys(),\n",
-    "    scale_reflectivity_curves_to_overlap(results.values())[0],\n",
-    "    strict=True\n",
-    "))\n",
-    "sc.plot(results_scaled, norm='log', vmin=1e-5)"
+    "\n",
+    "scaled_reflectivity_curves, scale_factors = scale_reflectivity_curves_to_overlap(reflectivity.values())\n",
+    "sc.plot(dict(zip(reflectivity.keys(), scaled_reflectivity_curves, strict=True)), norm='log', vmin=1e-5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Curves obtained from measurements at different angles can be combined to one common reflectivity curve:"
    ]
   },
   {
@@ -191,15 +204,28 @@
    "outputs": [],
    "source": [
     "from ess.reflectometry.tools import combine_curves\n",
-    "c = combine_curves(results_scaled.values(), workflow.compute(QBins))\n",
-    "c.plot(norm='log')"
+    "combined = combine_curves(scaled_reflectivity_curves, workflow.compute(QBins))\n",
+    "combined.plot(norm='log')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Additional diagnostics plots"
+    "## Diagnostic figures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some useful visualizations that can be used to troubleshoot the instrument.\n",
+    "They typically operate on the `ReflectivityData`.\n",
+    "\n",
+    "The difference between `ReflectivityData` and `ReflectivityOverQ` is that `ReflectivityData` is not binned in $Q$, but instead has the same shape as the reference.\n",
+    "\n",
+    "Essentially it represents a \"reflectivity\" computed in every wavelength-detector coordinate (`z_index`) bin.\n",
+    "This makes it easier to spot inhomogeneities and diagnose problems."
    ]
   },
   {
@@ -208,16 +234,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "workflow[Filename[SampleRun]] = amor.data.amor_sample_run(608)\n",
-    "workflow[SampleRotation[SampleRun]] = sc.scalar(0.85, unit='deg')\n",
-    "workflow.compute(ReflectivityDiagnosticsView)"
+    "# Start by computing the `ReflectivityData` for each of the files\n",
+    "diagnostics = {}\n",
+    "for file, angle in runs.items():\n",
+    "    workflow[Filename[SampleRun]] = amor.data.amor_sample_run(file)\n",
+    "    workflow[SampleRotation[SampleRun]] = angle\n",
+    "    diagnostics[file] = workflow.compute((ReflectivityData, ThetaBins[SampleRun]))\n",
+    "\n",
+    "# Scale the results using the scale factors computed earlier\n",
+    "for key, scale_factor in zip(reflectivity.keys(), scale_factors, strict=True):\n",
+    "    diagnostics[key][ReflectivityData] *= scale_factor"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Make a $(\\lambda, \\theta)$ map\n",
+    "### Make a $(\\lambda, \\theta)$ map\n",
     "A good sanity check is to create a two-dimensional map of the counts in $\\lambda$ and $\\theta$ bins and make sure the triangles converge at the origin."
    ]
   },
@@ -227,7 +260,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "workflow.compute(WavelengthThetaFigure)"
+    "from ess.amor.figures import wavelength_theta_figure\n",
+    "\n",
+    "wavelength_theta_figure(\n",
+    "    [res[ReflectivityData] for res in diagnostics.values()],\n",
+    "    theta_bins=[res[ThetaBins[SampleRun]] for res in diagnostics.values()],\n",
+    "    q_edges_to_display=(sc.scalar(0.018, unit='1/angstrom'), sc.scalar(0.113, unit='1/angstrom'))\n",
+    ")"
    ]
   },
   {
@@ -237,6 +276,60 @@
     "This plot can be used to check if the value of the sample rotation angle $\\omega$ is correct. The bright triangles should be pointing back to the origin $\\lambda = \\theta = 0$. In the figure above the black lines are all passing through the origin."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make a $(Q, \\theta)$ map\n",
+    "Another good sanity check is to create a two-dimensional map of the counts in $\\lambda$ and $Q$ and make sure the stripes are vertical. If they are not that could indicate that the `ChopperPhase` setting is incorrect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ess.amor.figures import q_theta_figure\n",
+    "\n",
+    "q_theta_figure(\n",
+    "    [res[ReflectivityData] for res in diagnostics.values()],\n",
+    "    theta_bins=[res[ThetaBins[SampleRun]] for res in diagnostics.values()],\n",
+    "    q_bins=workflow.compute(QBins)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the sample measurement to the reference on the detector\n",
+    "\n",
+    "Here we compare the raw number of counts on the detector for the sample measurement and the reference respectively.\n",
+    "\n",
+    "`z_index` is the $z-$detector coordinate, that is, the detector coordinate in the direction of the scattering angle $\\theta$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ess.amor.figures import wavelength_z_figure\n",
+    "\n",
+    "workflow[Filename[SampleRun]] = amor.data.amor_sample_run('608')\n",
+    "workflow[SampleRotation[SampleRun]] = runs['608']\n",
+    "wavelength_z_figure(\n",
+    "    workflow.compute(FootprintCorrectedData[SampleRun]),\n",
+    "    wavelength_bins=workflow.compute(WavelengthBins),\n",
+    "    grid=False\n",
+    ") + wavelength_z_figure(\n",
+    "    reference_result,\n",
+    "    grid=False\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

src/ess/amor/__init__.py

@@ -16,7 +16,7 @@
     SamplePosition,
     BeamDivergenceLimits,
 )
-from . import conversions, load, orso, resolution, utils
+from . import conversions, load, orso, resolution, utils, figures
 from .instrument_view import instrument_view
 from .types import (
     AngularResolution,
@@ -44,6 +44,7 @@
     *conversions.providers,
     *resolution.providers,
     *utils.providers,
+    *figures.providers,
     *orso.providers,
 )
 """