{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc37377b",
   "metadata": {},
   "source": [
    "# Using ClusterFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5dcb9f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import boost_histogram as bh\n",
    "import time\n",
    "\n",
    "#Python cluster reader\n",
    "from creader import ClusterFile\n",
    "path = Path('/mnt/sls_det_storage/moench_data/cluster_reader_test/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f27bec",
   "metadata": {},
   "source": [
    "## Read clusters from file and plot a histogram\n",
    "\n",
    "- Passing chunk, to the cluster reader to set the default amount of clusters to be read\n",
    "- Using the context manager to manage the file closing\n",
    "- Looping over chunks in the file (reads chunksize until a zero read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45482025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4b6c136d10>"
      ]
     },
     "execution_count": 6,
     "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": [
    "fname = 'beam_En700eV_-40deg_300V_10us_d0_f0_100.clust'\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",
    "with ClusterFile(path/fname, chunk = 10000) as f:\n",
    "    for clusters in f:\n",
    "        hist1.fill(clusters['data'].flat)\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": "code",
   "execution_count": null,
   "id": "a2973368",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}