LM recon notebook

ETSIhackers · Nov 14, 2023 · 5a68175 · 5a68175
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diff --git a/python/data.bin b/python/data.bin
diff --git a/python/paraproj_data.bin b/python/paraproj_data.bin
diff --git a/python/prd_test.ipynb b/python/prd_test.ipynb
@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../PETSIRD/python')\n",
+    "import numpy as np\n",
+    "import prd\n",
+    "from io import BytesIO\n",
+    "from prd.types import TimeBlock, CoincidenceEvent"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate fake data to convert to PETSIRD"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Part 1: Python dtypes to PETSIRD format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N_events = 7 # Number of detected events\n",
+    "N_detectors = 10 # Number of detectors\n",
+    "counts = np.random.poisson(np.ones(N_events)*3)\n",
+    "detectors1 = np.random.choice(np.arange(N_detectors), N_events)\n",
+    "detectors2 = np.random.choice(np.arange(N_detectors), N_events)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events = []\n",
+    "for count, detector1, detector2 in zip(counts, detectors1, detectors2):\n",
+    "    for _ in range(count):\n",
+    "        events.append(CoincidenceEvent(detector_1_id=int(detector1), detector_2_id=int(detector2)))\n",
+    "time_block = TimeBlock(id=0, prompt_events=events)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with prd.BinaryPrdExperimentWriter('data.bin') as writer:\n",
+    "    writer.write_header(prd.Header())\n",
+    "    writer.write_time_blocks(\n",
+    "        (time_block,)\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('data.bin', 'rb') as f:\n",
+    "    data = f.read()\n",
+    "    with prd.BinaryPrdExperimentReader(BytesIO(data)) as reader:\n",
+    "        header = reader.read_header()\n",
+    "        time_blocks = [t for t in reader.read_time_blocks()]\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Part 2: PETSIRD to python dtypes for recon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('data.bin', 'rb') as f:\n",
+    "    data = f.read()\n",
+    "    with prd.BinaryPrdExperimentReader(BytesIO(data)) as reader:\n",
+    "        header = reader.read_header()\n",
+    "        time_blocks = [t for t in reader.read_time_blocks()]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[TimeBlock(id=0, promptEvents=[CoincidenceEvent(detector1Id=5, detector2Id=2, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=5, detector2Id=2, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=5, detector2Id=2, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=2, detector2Id=4, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=2, detector2Id=4, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=2, detector2Id=4, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=7, detector2Id=5, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=7, detector2Id=5, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=2, detector2Id=3, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=4, detector2Id=8, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=4, detector2Id=8, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=4, detector2Id=8, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=4, detector2Id=8, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=4, detector2Id=8, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=4, detector2Id=8, tofIdx=0, energy1Idx=0, energy2Idx=0), CoincidenceEvent(detector1Id=4, detector2Id=8, tofIdx=0, energy1Idx=0, energy2Idx=0)], delayedEvents=None)]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_blocks\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "prd",
+   "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": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}