06-read_clusters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b8f61d2b",
   "metadata": {},
   "source": [
    "# Read clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b8d429d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Example run on: **2025-01-15 08:18** with aare: **dev**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#add the dev version of aare\n",
    "import sys; sys.path.append('/home/l_msdetect/erik/aare/build')\n",
    "from helpers import nb_aare_version\n",
    "nb_aare_version()\n",
    "\n",
    "#Our normal python imports\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec7ad75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f44684be0c0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from aare import ClusterFile\n",
    "import boost_histogram as bh\n",
    "\n",
    "\n",
    "base = Path('/mnt/sls_det_storage/matterhorn_data/aare_test_data/ci/aare_test_data/clusters/')\n",
    "fname = 'beam_En700eV_-40deg_300V_10us_d0_f0_100.clust'\n",
    "\n",
    "\n",
    "# Create the histogram\n",
    "n_bins = 100\n",
    "xmin = -100\n",
    "xmax = 1e4\n",
    "hist1 = bh.Histogram(bh.axis.Regular(n_bins, xmin, xmax))\n",
    "\n",
    "#Read clusters and fill the histogram with pixel values\n",
    "with ClusterFile(base/fname, chunk_size = 10000) as f:\n",
    "    for clusters in f:\n",
    "        arr = np.array(clusters, copy = False)\n",
    "        hist1.fill(arr['data'].flat)\n",
    "\n",
    "\n",
    "\n",
    "#Plotting\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(hist1.axes[0].centers, hist1.values(), width=hist1.axes[0].widths, label = 'data')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('ADU [1]')\n",
    "ax.set_ylabel('Intensity [a.u.]')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04187305",
   "metadata": {},
   "source": [
    "## Sanity check using pure python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46d56266",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f445f92c1a0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins = 100\n",
    "xmin = -100\n",
    "xmax = 1e4\n",
    "hist1 = bh.Histogram(bh.axis.Regular(n_bins, xmin, xmax))\n",
    "with open(base/fname, 'rb') as f:\n",
    "    for i in range(100):\n",
    "        frame_number = np.fromfile(f, count=1, dtype = np.int32)\n",
    "        n_clusters = np.fromfile(f, count=1, dtype = np.uint32)[0]\n",
    "#         print(frame_number, n_clusters)\n",
    "        for i in range(n_clusters):\n",
    "            xy = np.fromfile(f, count=2, dtype = np.uint16)\n",
    "            data = np.fromfile(f, count=9, dtype = np.int32)\n",
    "            hist1.fill(data)\n",
    "    \n",
    "#Plotting\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(hist1.axes[0].centers, hist1.values(), width=hist1.axes[0].widths, label = 'data')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('ADU [1]')\n",
    "ax.set_ylabel('Intensity [a.u.]')\n",
    "ax.legend()"
   ]
  }
 ],
 "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}