Refactor match_peaks (#100)

* trying to refactormatch_peaks * docstr * load_peaks * fraction options * fraction options * debug * debug * debug * redo example events * removed outputs * scatter debug * updated range * get_available_runs * debug * debug * debug * sort runs] * blanck line * daq cuts for peaks * updated example_peaks
XENONnT · Apr 6, 2024 · cf00f05 · cf00f05
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diff --git a/notebooks/README.md b/notebooks/README.md
@@ -5,4 +5,4 @@ These are notebooks for hopefully pedagogical purpose. Due to the sensitive info
 An example of using this framework to sprinkle events into real data, with followed analysis functions defined in this package.
 
 ## `example_peaks.ipynb`
-An example of using this framework to sprinkle single electron peaks into real data, with followed analysis functions defined in this package.
+An example of using this framework to sprinkle single electron peaks (with realistic timestamps and XY information passing SE selection, based on bootstrapping data SEs) into real data, with followed analysis functions defined in this package.
diff --git a/notebooks/example_events.ipynb b/notebooks/example_events.ipynb
@@ -22,9 +22,7 @@
   {
    "cell_type": "markdown",
    "id": "e034079b-cae9-4586-9ba4-0122e2ef4c8d",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "# Preparation"
    ]
@@ -123,9 +121,7 @@
   {
    "cell_type": "markdown",
    "id": "06bc9312-c74d-4d2d-9ea8-6a97d4b7cb9d",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "# Overview of simulation only"
    ]
@@ -137,13 +133,14 @@
    "source": [
     "This dataset should be considered without any ambience interference. The cuts considered are\n",
     "```python\n",
-    "AmBe_CUTS_EXCEPT_S2PatternS1Width = np.array([\n",
+    "AmBe_CUTS_EXCEPT_S2Pattern = np.array([\n",
     "            'cut_daq_veto',\n",
     "            'cut_interaction_exists',\n",
     "            'cut_main_is_valid_triggering_peak',\n",
     "            'cut_run_boundaries',\n",
     "            'cut_s1_area_fraction_top',\n",
     "            'cut_s1_max_pmt',\n",
+    "            'cut_s1_width'\n",
     "            'cut_s1_pattern_bottom',\n",
     "            'cut_s1_pattern_top',\n",
     "            'cut_s1_single_scatter',\n",
@@ -166,7 +163,7 @@
    "source": [
     "mask_simu_only_cut = apply_cut_lists(\n",
     "    events_simu,\n",
-    "    AmBe_CUTS_EXCEPT_S2PatternS1Width # Compromise based on waveform simulation quality\n",
+    "    AmBe_CUTS_EXCEPT_S2Pattern # Compromise based on waveform simulation quality\n",
     ")\n",
     "mask_simu_only_min = apply_cut_lists(\n",
     "    events_simu,\n",
@@ -187,7 +184,7 @@
     "plt.scatter(\n",
     "    events_simu[mask_simu_only_cut]['cs1'], events_simu[mask_simu_only_cut]['cs2'], \n",
     "    color='tab:red', s=2, \n",
-    "    label='After AmBe_CUTS_EXCEPT_S2PatternS1Width'\n",
+    "    label='After AmBe_CUTS_EXCEPT_S2Pattern'\n",
     ")\n",
     "plt.xlim(0,100)\n",
     "plt.ylim(0,6500)\n",
@@ -219,7 +216,7 @@
     "plt.scatter(\n",
     "    events_simu[mask_simu_only_cut]['x'], events_simu[mask_simu_only_cut]['y'], \n",
     "    color='tab:red', s=2, \n",
-    "    label='After AmBe_CUTS_EXCEPT_S2PatternS1Width'\n",
+    "    label='After AmBe_CUTS_EXCEPT_S2Pattern'\n",
     ")\n",
     "plt.xlim(-70,70)\n",
     "plt.ylim(-70,70)\n",
@@ -252,7 +249,7 @@
     "    events_simu[mask_simu_only_cut]['x']**2+events_simu[mask_simu_only_cut]['y']**2, \n",
     "    events_simu[mask_simu_only_cut]['z'],\n",
     "    color='tab:red', s=2, \n",
-    "    label='After AmBe_CUTS_EXCEPT_S2PatternS1Width'\n",
+    "    label='After AmBe_CUTS_EXCEPT_S2Pattern'\n",
     ")\n",
     "plt.xlim(0,4900)\n",
     "plt.ylim(-150,0)\n",
@@ -273,9 +270,7 @@
   {
    "cell_type": "markdown",
    "id": "ce788c14-de84-4b01-8dc6-945ea5a2f991",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Analysis wrapper"
    ]
@@ -429,19 +424,21 @@
     "        events_salt_matched_to_simu[mask_salt_cut&mask_simu_cut], \n",
     "        events_simu_matched_to_salt[mask_salt_cut&mask_simu_cut], \n",
     "        title='After Cuts Ambience Interference in SR1 AmBe HEV', \n",
+    "        fraction=True,\n",
     "        coord='s1_area', \n",
-    "        s1s2='S1', \n",
+    "        s1s2='s1', \n",
     "        n_bins=16, \n",
-    "        ylim=(-2,10)\n",
+    "        ylim=(-10,30)\n",
     "    )\n",
     "    show_area_bias(\n",
     "        events_salt_matched_to_simu[mask_salt_cut&mask_simu_cut], \n",
     "        events_simu_matched_to_salt[mask_salt_cut&mask_simu_cut], \n",
     "        title='After Cuts Ambience Interference in SR1 AmBe HEV', \n",
+    "        fraction=True,\n",
     "        coord='s2_area', \n",
-    "        s1s2='S2', \n",
+    "        s1s2='s2', \n",
     "        n_bins=16, \n",
-    "        ylim=(-2,500)\n",
+    "        ylim=(-10,30)\n",
     "    )\n",
     "    #show_area_bias(\n",
     "    #    events_salt_matched_to_simu[mask_salt_cut&mask_simu_cut], \n",
@@ -457,9 +454,7 @@
   {
    "cell_type": "markdown",
    "id": "506840f2-af84-4d1c-8c75-db5e07e1a9cd",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Event building efficiency"
    ]
@@ -490,6 +485,23 @@
     "    'cut_main_is_valid_triggering_peak', \n",
     "    'cut_run_boundaries',\n",
     "])\n",
+    "AmBe_CUTS_EXCEPT_S2Pattern = np.array([\n",
+    "            'cut_daq_veto',\n",
+    "            'cut_interaction_exists',\n",
+    "            'cut_main_is_valid_triggering_peak',\n",
+    "            'cut_run_boundaries',\n",
+    "            'cut_s1_area_fraction_top',\n",
+    "            'cut_s1_max_pmt',\n",
+    "            'cut_s1_pattern_bottom',\n",
+    "            'cut_s1_pattern_top',\n",
+    "            'cut_s1_single_scatter',\n",
+    "            'cut_s1_tightcoin_3fold',\n",
+    "            'cut_s1_width',\n",
+    "            'cut_s2_recon_pos_diff',\n",
+    "            'cut_s2_single_scatter',\n",
+    "            'cut_s2_width',\n",
+    "            'cut_cs2_area_fraction_top',\n",
+    "])\n",
     "```"
    ]
   },
@@ -845,9 +857,7 @@
   {
    "cell_type": "markdown",
    "id": "bf0b4653-485a-4f7d-95df-432d9610e6c8",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Physical S1/S2 downgraded to alt_S1/alt_S2"
    ]
@@ -906,9 +916,7 @@
   {
    "cell_type": "markdown",
    "id": "88cdab16-eb1f-438e-9fcf-20ed876cb7f6",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Lost or split"
    ]
@@ -982,9 +990,7 @@
   {
    "cell_type": "markdown",
    "id": "cc6e87e5-ff7e-4a49-bf54-01b0f97b92d3",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Z Error"
    ]
@@ -1029,9 +1035,7 @@
   {
    "cell_type": "markdown",
    "id": "529e4883-1a72-4fe6-9626-259b47a11061",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Match events time"
    ]
@@ -1066,7 +1070,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "00806ed9-be8a-40af-8d97-dc237aef737b",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "super_mega_analysis(events_salt_matched_to_simu, events_simu_matched_to_salt)"
@@ -1075,9 +1081,7 @@
   {
    "cell_type": "markdown",
    "id": "5d63e958-ad28-434a-b19f-14db212d7d58",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Match S1 time"
    ]
@@ -1119,7 +1123,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "47e7914f-4be9-40c5-894f-03f79686fe92",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "# Mimic what happen if we only use minimal cuts (no 3-fold tc though) + NV tagging\n",
@@ -1240,9 +1246,7 @@
   {
    "cell_type": "markdown",
    "id": "f3198748-3aa3-44e9-9565-9f089dab9fa9",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "# Waveform Viewer"
    ]