update notebook and example config

OpenCOMPES · Mar 8, 2024 · 810dea0 · 810dea0
1 parent 99ea5e4
commit 810dea0
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diff --git a/sed/config/flash_example_config.yaml b/sed/config/flash_example_config.yaml
@@ -5,8 +5,6 @@ core:
   loader: flash
   # the beamline where experiment took place
   beamline: pg2
-  # the instrument used
-  instrument: hextof
   # the ID number of the beamtimme
   beamtime_id: 11013410
   # the year of the beamtime

diff --git a/tutorial/4_hextof_workflow.ipynb b/tutorial/4_hextof_workflow.ipynb
@@ -66,7 +66,7 @@
     "\n",
     "    os.system(f\"mkdir {data_path}/flash_data\")\n",
     "    os.system(f\"mv {data_path}/analysis_data/*/*.h5 {data_path}/flash_data\")\n",
-    "    os.system(f\"mv {data_path}/calibration_data/*/*.h5 {data_path}/flash_data\")\n"
+    "    os.system(f\"mv {data_path}/calibration_data/*/*.h5 {data_path}/flash_data\")"
    ]
   },
   {
@@ -102,7 +102,7 @@
     "            \"data_parquet_dir\": \"/asap3/flash/gpfs/pg2/2023/data/11019101/parquet\"\n",
     "        },\n",
     "    },\n",
-    "}\n"
+    "}"
    ]
   },
   {
@@ -219,6 +219,23 @@
     "sp.dataframe.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing event histograms\n",
+    "For getting a first impression of the data, and to determine binning ranges, the method sp.`view_even_histogram()` allows visualizing the events in one dataframe partition as histograms. Default axes and ranges are defined in the config, and show the dldPosX, dldPosY, and dldTimeStep columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp.view_event_histogram(dfpid=0)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -310,7 +327,7 @@
     "fig,ax = plt.subplots(1,3,figsize=(8,2), layout='tight')\n",
     "res_chessy.plot(ax=ax[0], robust=True)\n",
     "res_t05.plot(ax=ax[1], robust=True)\n",
-    "res_t10.plot(ax=ax[2], robust=True)\n"
+    "res_t10.plot(ax=ax[2], robust=True)"
    ]
   },
   {
@@ -338,6 +355,22 @@
     "sp.add_jitter()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the `view_event_histogram()` function also to e.g. visualize the events per microbunch along the train, or hit multiplicity per microbunch:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp.view_event_histogram(dfpid=0, axes=[\"pulseId\", \"electronId\"], ranges=[[0, 600], [0,10]], bins=[100, 10])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -383,6 +416,22 @@
     "sp.append_tof_ns_axis()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, to determine propper binning ranges, let's have again a look at the event histograms:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp.view_event_histogram(dfpid=0, axes=[\"sampleBias\", \"dldTime\"], ranges=[[27, 33], [650,1050]], bins=[50, 100])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -459,8 +508,8 @@
    "outputs": [],
    "source": [
     "sp.calibrate_energy_axis(\n",
-    "    ref_id=2,\n",
-    "    ref_energy=-1,\n",
+    "    ref_id=4,\n",
+    "    ref_energy=-.55,\n",
     "    method=\"lmfit\",\n",
     "    energy_scale='kinetic',\n",
     "    d={'value':1.0,'min': .2, 'max':1.0, 'vary':False},\n",
@@ -549,10 +598,10 @@
    "outputs": [],
    "source": [
     "sp.add_energy_offset(\n",
-    "    constant=0,\n",
+    "    constant=-32, # Sample bias used as reference for energy calibration\n",
     "    columns=['sampleBias','monochromatorPhotonEnergy','tofVoltage'],\n",
     "    weights=[1,-1,-1],\n",
-    "    preserve_mean=True,\n",
+    "    preserve_mean=[False, True, True],\n",
     ")"
    ]
   },
@@ -701,7 +750,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig,ax = plt.subplots(1,2,figsize=(6,3), layout='constrained')\n",
+    "fig,ax = plt.subplots(1,2,figsize=(8,3), layout='constrained')\n",
     "res.plot(robust=True, ax=ax[0])\n",
     "bg = res.isel(delayStage=slice(0,10)).mean('delayStage')\n",
     "(res-bg).plot(robust=True, ax=ax[1])"
@@ -755,7 +804,7 @@
     "axes = ['energy','delayStage']\n",
     "bins = [100,150]\n",
     "delay_start,delay_stop=1462.00,1464.85\n",
-    "ranges = [[-5,2], [-1.15, 1.7]]\n",
+    "ranges = [[-3,2], [-1.15, 1.7]]\n",
     "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
    ]
   },
@@ -769,9 +818,30 @@
    },
    "outputs": [],
    "source": [
-    "fig,ax = plt.subplots(1,2,figsize=(12,5))\n",
+    "fig,ax = plt.subplots(1,2,figsize=(8,3))\n",
     "res.plot(robust=True, ax=ax[0])\n",
-    "bg = res.isel(delayStage=slice(0,10)).mean('delayStage')\n",
+    "bg = res.sel(delayStage=slice(-1,-0.2)).mean('delayStage')\n",
+    "(res-bg).plot(robust=True, ax=ax[1])\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may note some intensity variation along the delay axis. This comes mainly from inhomogenous speed of the delay stage, and thus inquivalently time spend on every delay point. This can be corrected for by normalizing the data to the aquisition time per delay point:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time=\"delayStage\")\n",
+    "fig,ax = plt.subplots(1,2,figsize=(8,3), layout='constrained')\n",
+    "res.plot(robust=True, ax=ax[0])\n",
+    "bg = res.sel(delayStage=slice(-1,-.2)).mean('delayStage')\n",
     "(res-bg).plot(robust=True, ax=ax[1])"
    ]
   },
@@ -857,7 +927,7 @@
     "bins = [100,150]\n",
     "delay_start,delay_stop=1462.00,1464.85\n",
     "ranges = [[-5,2], [-1.15, 1.7]]\n",
-    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
+    "res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time=\"delayStage\")"
    ]
   },
   {
@@ -866,9 +936,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig,ax = plt.subplots(1,2,figsize=(12,5))\n",
+    "fig,ax = plt.subplots(1,2,figsize=(8,3), layout='constrained')\n",
     "res.plot(robust=True, ax=ax[0])\n",
-    "bg = res.isel(delayStage=slice(0,10)).mean('delayStage')\n",
+    "bg = res.sel(delayStage=slice(-1,-.2)).mean('delayStage')\n",
     "(res-bg).plot(robust=True, ax=ax[1])"
    ]
   },

diff --git a/tutorial/hextof_config.yaml b/tutorial/hextof_config.yaml