sfdata_test_scripts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "%load_ext line_profiler\n",
    "import jungfrau_utils as ju\n",
    "import numpy as np\n",
    "import json, h5py\n",
    "import os, glob\n",
    "import time\n",
    "import csv\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm\n",
    "from scipy.optimize import curve_fit\n",
    "from IPython.display import clear_output, display\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.ticker as ticker\n",
    "from ipyfilechooser import FileChooser\n",
    "from scipy.stats.stats import pearsonr\n",
    "\n",
    "from collections import namedtuple\n",
    "\n",
    "from sfdata import SFDataFile, SFDataFiles\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from alvra_tools.load_data import *\n",
    "from alvra_tools.channels import *\n",
    "from alvra_tools.utils import *\n",
    "from alvra_tools.XAS_functions import *\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test static data (BSREAD, BSDATA, CAMERA and JF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events = f['/SAR-CVME-TIFALL4:EvtSet/data'][:]\n",
    "        back = f['/SARES11-SPEC125-M2.roi_background_x_profile/data'][:]\n",
    "        sig = f['/SARES11-SPEC125-M2.roi_signal_x_profile/data'][:]\n",
    "        pulse_ids = f['/SARES11-SPEC125-M2.roi_signal_x_profile/pulse_id'][:]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SARES11-SPEC125-M2.roi_background_x_profile'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "\u001b[31mSAR-CVME-TIFALL4:EvtSet                     186 / 201 -> 7% loss ▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSARES11-SPEC125-M2.roi_background_x_profile 201 / 201 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSARES11-SPEC125-M2.roi_signal_x_profile     201 / 201 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\n",
      "\u001b[31mover the whole data set: 186 / 201 -> 7% loss\u001b[39m\n",
      "\u001b[31mcomplete channels: 2 / 3 -> 33% incomplete\u001b[39m\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "FEL rep rate is 100.0 Hz\n",
      "(186, 2047)\n",
      "(186, 2047)\n",
      "(186, 256)\n"
     ]
    }
   ],
   "source": [
    "ff = \"/sf/alvra/data/p19634/raw/run0073-Water_timescan_T9000_grating_7146eV/data/acq0001.BSDATA.h5\"\n",
    "\n",
    "fnames = SFDataFiles(ff)\n",
    "\n",
    "channels = [channel_Events, channel_PSEN119_signal, channel_PSEN119_bkg]\n",
    "\n",
    "results,pids = load_data_compact(channels, fnames)\n",
    "\n",
    "print (results[channel_PSEN119_bkg].shape)\n",
    "print (results[channel_PSEN119_signal].shape)\n",
    "print (results[channel_Events].shape)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15160281085, 15160281086, 15160281087, 15160281088, 15160281089,\n",
       "       15160281090, 15160281091, 15160281092, 15160281093, 15160281094,\n",
       "       15160281095, 15160281096, 15160281098, 15160281099, 15160281100,\n",
       "       15160281101, 15160281102, 15160281103, 15160281104, 15160281105,\n",
       "       15160281106, 15160281107, 15160281108, 15160281109, 15160281111,\n",
       "       15160281112, 15160281113, 15160281114, 15160281115, 15160281116,\n",
       "       15160281117, 15160281118, 15160281119, 15160281120, 15160281121,\n",
       "       15160281122, 15160281124, 15160281125, 15160281126, 15160281127,\n",
       "       15160281128, 15160281129, 15160281130, 15160281131, 15160281132,\n",
       "       15160281133, 15160281134, 15160281135, 15160281137, 15160281138,\n",
       "       15160281139, 15160281140, 15160281141, 15160281142, 15160281143,\n",
       "       15160281144, 15160281145, 15160281146, 15160281147, 15160281148,\n",
       "       15160281150, 15160281151, 15160281152, 15160281153, 15160281154,\n",
       "       15160281155, 15160281156, 15160281157, 15160281158, 15160281159,\n",
       "       15160281160, 15160281161, 15160281163, 15160281164, 15160281165,\n",
       "       15160281166, 15160281167, 15160281168, 15160281169, 15160281170,\n",
       "       15160281171, 15160281172, 15160281173, 15160281174, 15160281176,\n",
       "       15160281177, 15160281178, 15160281179, 15160281180, 15160281181,\n",
       "       15160281182, 15160281183, 15160281184, 15160281185, 15160281186,\n",
       "       15160281187, 15160281189, 15160281190, 15160281191, 15160281192,\n",
       "       15160281193, 15160281194, 15160281195, 15160281196, 15160281197,\n",
       "       15160281198, 15160281199, 15160281200, 15160281202, 15160281203,\n",
       "       15160281204, 15160281205, 15160281206, 15160281207, 15160281208,\n",
       "       15160281209, 15160281210, 15160281211, 15160281212, 15160281213,\n",
       "       15160281215, 15160281216, 15160281217, 15160281218, 15160281219,\n",
       "       15160281220, 15160281221, 15160281222, 15160281223, 15160281224,\n",
       "       15160281225, 15160281226, 15160281228, 15160281229, 15160281230,\n",
       "       15160281231, 15160281232, 15160281233, 15160281234, 15160281235,\n",
       "       15160281236, 15160281237, 15160281238, 15160281239, 15160281241,\n",
       "       15160281242, 15160281243, 15160281244, 15160281245, 15160281246,\n",
       "       15160281247, 15160281248, 15160281249, 15160281250, 15160281251,\n",
       "       15160281252, 15160281254, 15160281255, 15160281256, 15160281257,\n",
       "       15160281258, 15160281259, 15160281260, 15160281261, 15160281262,\n",
       "       15160281263, 15160281264, 15160281265, 15160281267, 15160281268,\n",
       "       15160281269, 15160281270, 15160281271, 15160281272, 15160281273,\n",
       "       15160281274, 15160281275, 15160281276, 15160281277, 15160281278,\n",
       "       15160281280, 15160281281, 15160281282, 15160281283, 15160281284,\n",
       "       15160281285])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2410ae5d8b94d37a72ae325ed1587cd",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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+fbouueQSNTU1admyZWppaVFeXp48Ho/y8/NVWFiopKQkJSUlqbCwUDExMZo5c6bTpQMAAISVawPgv//9b33zm9/Uxx9/rIsuukjXX3+9tm3bpjFjxkiSFi1apKNHj2ru3Lk6dOiQUlNTtWnTJsXGxjpcOQAAQHi5NgCuW7fujPs9Ho8CgYACgUDfFATX627lKwAA/ZE19wACAADgFAIgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAlolyugAA4TF28St9cg4AIPIwAwgAAGAZAiAAAIBlCIAAAACWIQACAABYhgAIAABgGVYBI2J1XrG6r3iaQ5UAABBZmAEEAACwDAEQAADAMgRAAAAAyxAAAQAALMMiEFiPx5/1DRbtAED/wQwgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACW4VFwsAqPfQMAgBlAAAAA6xAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMuwChjA58JKagCIfFbMABYVFcnj8Sg/Pz/YZoxRIBBQQkKCoqOjlZmZqdraWueKBAAA6COuD4DV1dVavXq1JkyYENK+fPlylZSUqKysTNXV1fL7/crKylJra6tDlQIAAPQNVwfATz/9VPfcc49+8YtfaOjQocF2Y4xKS0u1ZMkS5ebmKjk5WeXl5Tpy5IjWrl3rYMUAAADh5+oAOG/ePE2bNk233HJLSHtdXZ0aGxuVnZ0dbPN6vcrIyFBVVVWP12tra1NLS0vIBgAAEGlcuwhk3bp1evvtt1VdXd1lX2NjoyTJ5/OFtPt8PtXX1/d4zaKiIi1durR3CwUAAOhjrpwBbGho0MMPP6znnntOAwcO7PE4j8cT8toY06XtdAUFBWpubg5uDQ0NvVYzAABAX3HlDOCOHTvU1NSkSZMmBdtOnDihrVu3qqysTHv27JF0aiYwPj4+eExTU1OXWcHTeb1eeb3e8BUOAADQB1w5Azh16lTV1NRo165dwS0lJUX33HOPdu3apUsvvVR+v18VFRXBc9rb21VZWan09HQHKwcAAAg/V84AxsbGKjk5OaRt0KBBGj58eLA9Pz9fhYWFSkpKUlJSkgoLCxUTE6OZM2c6UTIAAECfcWUAPBuLFi3S0aNHNXfuXB06dEipqanatGmTYmNjnS4NAAAgrKwJgFu2bAl57fF4FAgEFAgEHKkHAADAKa68BxAAAAA9IwACAABYhgAIAABgGQIgAACAZaxZBAKg/xu7+JWQ1/uKpzlUCQC4GzOAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGWinC4ACKexi19xugSch7MZv33F0/qgEgBwF2YAAQAALEMABAAAsAwBEAAAwDIEQAAAAMuwCASAI3prgU5312FhCACcGTOAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWIQACAABYhkfBwTV669FiAAC4HTOAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJZhFTAA1+m8Inxf8TSHKgGA/sm1M4CrVq3ShAkTFBcXp7i4OKWlpWnjxo3B/cYYBQIBJSQkKDo6WpmZmaqtrXWwYgAAgL7h2gA4atQoFRcXa/v27dq+fbtuvvlm3X777cGQt3z5cpWUlKisrEzV1dXy+/3KyspSa2urw5UDAACEl2sD4PTp03Xrrbdq3LhxGjdunJ544gkNHjxY27ZtkzFGpaWlWrJkiXJzc5WcnKzy8nIdOXJEa9eudbp0AACAsHJtADzdiRMntG7dOh0+fFhpaWmqq6tTY2OjsrOzg8d4vV5lZGSoqqqqx+u0tbWppaUlZAMAAIg0rl4EUlNTo7S0NB07dkyDBw/W+vXrddVVVwVDns/nCzne5/Opvr6+x+sVFRVp6dKlYa0ZgDO6e5Qgi0cAuJWrZwCvuOIK7dq1S9u2bdOcOXOUl5en3bt3B/d7PJ6Q440xXdpOV1BQoObm5uDW0NAQttoBAADCxdUzgAMGDNDll18uSUpJSVF1dbVWrFihH/zgB5KkxsZGxcfHB49vamrqMit4Oq/XK6/XG96iAQAAwszVM4CdGWPU1tamxMRE+f1+VVRUBPe1t7ersrJS6enpDlYIAAAQfq6dAXzssceUk5Oj0aNHq7W1VevWrdOWLVv02muvyePxKD8/X4WFhUpKSlJSUpIKCwsVExOjmTNnOl06AABAWLk2AH700Ue67777dODAAQ0ZMkQTJkzQa6+9pqysLEnSokWLdPToUc2dO1eHDh1SamqqNm3apNjYWIcrBwAACC/XBsBnn332jPs9Ho8CgYACgUDfFIQesfoSAIC+ZdU9gAAAACAAAgAAWIcACAAAYBkCIAAAgGVcuwgEADqw0AgAQjEDCAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZVgEDwOfQeUUxq4kBRCJmAAEAACxDAAQAALAMARAAAMAyBEAAAADLsAgEgJW6ezwcANiCGUAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAy7AKGH2O1ZcAADiLGUAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwTJTTBQDdGbv4lZDX+4qnOVQJAADuwwwgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJZxbQAsKirSddddp9jYWI0cOVJf+9rXtGfPnpBjjDEKBAJKSEhQdHS0MjMzVVtb61DFAAAAfcO1AbCyslLz5s3Ttm3bVFFRoePHjys7O1uHDx8OHrN8+XKVlJSorKxM1dXV8vv9ysrKUmtrq4OVAwAAhJdrPwj6tddeC3m9Zs0ajRw5Ujt27NCNN94oY4xKS0u1ZMkS5ebmSpLKy8vl8/m0du1azZ4924myAQAAws61M4CdNTc3S5KGDRsmSaqrq1NjY6Oys7ODx3i9XmVkZKiqqqrba7S1tamlpSVkAwAAiDSunQE8nTFGCxcu1Fe+8hUlJydLkhobGyVJPp8v5Fifz6f6+vpur1NUVKSlS5eGt9gIxyPcYJvO3/MS3/cA+j8rZgDnz5+vd955R7/97W+77PN4PCGvjTFd2joUFBSoubk5uDU0NISlXgAAgHBy/QzgggULtGHDBm3dulWjRo0Ktvv9fkmnZgLj4+OD7U1NTV1mBTt4vV55vd7wFgwAABBmrp0BNMZo/vz5evHFF/WnP/1JiYmJIfsTExPl9/tVUVERbGtvb1dlZaXS09P7ulwAAIA+49oZwHnz5mnt2rV6+eWXFRsbG7znb8iQIYqOjpbH41F+fr4KCwuVlJSkpKQkFRYWKiYmRjNnznS4egAAgPBxbQBctWqVJCkzMzOkfc2aNZo1a5YkadGiRTp69Kjmzp2rQ4cOKTU1VZs2bVJsbGwfVwsAANB3XBsAjTGfeYzH41EgEFAgEAh/QQAAAP2Ea+8BBAAAQPcIgAAAAJYhAAIAAFiGAAgAAGAZ1y4CQe9z8jFv3T1uCwg3vu8AuBUzgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlopwuAO42dvErTpcA9LnO3/f7iqc5VAkAdI8ZQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALMOj4HDOeMwbAACRiRlAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIsAkG3WOAB9J6z+XnaVzytDyoBgFOYAQQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMq4NgFu3btX06dOVkJAgj8ejl156KWS/MUaBQEAJCQmKjo5WZmamamtrnSkWAACgD7k2AB4+fFgTJ05UWVlZt/uXL1+ukpISlZWVqbq6Wn6/X1lZWWptbe3jSgEAAPqWaz8IOicnRzk5Od3uM8aotLRUS5YsUW5uriSpvLxcPp9Pa9eu1ezZs/uyVAAAgD7l2hnAM6mrq1NjY6Oys7ODbV6vVxkZGaqqqurxvLa2NrW0tIRsAAAAkca1M4Bn0tjYKEny+Xwh7T6fT/X19T2eV1RUpKVLl4a1tr7Q3WOpeAwV4Kxw/lyey6Po+D0BuJuVM4AdPB5PyGtjTJe20xUUFKi5uTm4NTQ0hLtEAACAXmflDKDf75d0aiYwPj4+2N7U1NRlVvB0Xq9XXq837PUBAACEk5UzgImJifL7/aqoqAi2tbe3q7KyUunp6Q5WBgAAEH6unQH89NNP9a9//Sv4uq6uTrt27dKwYcN0ySWXKD8/X4WFhUpKSlJSUpIKCwsVExOjmTNnOlg1AABA+Lk2AG7fvl033XRT8PXChQslSXl5efrVr36lRYsW6ejRo5o7d64OHTqk1NRUbdq0SbGxsU6VDAAA0Cc8xhjjdBGRqqWlRUOGDFFzc7Pi4uKcLuesnc2KQAD9T3ercHvr55lVwLBJpP773ZusvAcQAADAZgRAAAAAyxAAAQAALEMABAAAsIxrVwHjf1j0AQAATscMIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlWAUMABEinCv6w3VtHikH9E/MAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWYRFIhOt8gzU3VwMIl778fcPvNiC8mAEEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAyrgAEAvSacj6sD0HuYAQQAALAMARAAAMAyBEAAAADLEAABAAAswyIQl+EGbAD9HY95A5zHDCAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZVgFHEFY4QsA9unudz8rp3G+mAEEAACwDAEQAADAMgRAAAAAyxAAAQAALMMikH6MRR8AbHA2v+vO5phzXRhxNo+mO5evz+KNM+Pvx1nMAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWIQACAABYhlXAAIBzEgmfVHA2K3zD9bXO5phzXXF8Nuecy6rkcK7M7a2/H/QO62cAV65cqcTERA0cOFCTJk3Sm2++6XRJAAAAYWV1AHz++eeVn5+vJUuWaOfOnbrhhhuUk5Oj/fv3O10aAABA2FgdAEtKSvTAAw/oO9/5jq688kqVlpZq9OjRWrVqldOlAQAAhI219wC2t7drx44dWrx4cUh7dna2qqqquj2nra1NbW1twdfNzc2SpJaWlrDUeLLtSFiuCwBu1N3v4s6/R3vrmN5yrl+r83ndnROuY87Vufwdhuvf147rGmPCcv1I4DGW9v7DDz/UxRdfrL/85S9KT08PthcWFqq8vFx79uzpck4gENDSpUv7skwAABAmDQ0NGjVqlNNlOMLaGcAOHo8n5LUxpktbh4KCAi1cuDD4+uTJk/rvf/+r4cOHd3tOS0uLRo8erYaGBsXFxfVu4f0EfXQH+ugONvRRsqOf9DG8jDFqbW1VQkJCn37d/sTaADhixAhdcMEFamxsDGlvamqSz+fr9hyv1yuv1xvS9sUvfvEzv1ZcXJxrf4A70Ed3oI/uYEMfJTv6SR/DZ8iQIX3+NfsTaxeBDBgwQJMmTVJFRUVIe0VFRchbwgAAAG5j7QygJC1cuFD33XefUlJSlJaWptWrV2v//v166KGHnC4NAAAgbKwOgHfddZcOHjyoH/3oRzpw4ICSk5P16quvasyYMb1yfa/Xq8cff7zL28ZuQh/dgT66gw19lOzoJ31EuFm7ChgAAMBW1t4DCAAAYCsCIAAAgGUIgAAAAJYhAAIAAFiGABgmK1euVGJiogYOHKhJkybpzTffdLqkXhUIBOTxeEI2v9/vdFnnZevWrZo+fboSEhLk8Xj00ksvhew3xigQCCghIUHR0dHKzMxUbW2tM8Weo8/q46xZs7qM6/XXX+9MseeoqKhI1113nWJjYzVy5Eh97Wtf6/Jox0gfy7PpY6SP5apVqzRhwoTghwSnpaVp48aNwf2RPobSZ/cx0sewO0VFRfJ4PMrPzw+2uWEsIxEBMAyef/555efna8mSJdq5c6duuOEG5eTkaP/+/U6X1quuvvpqHThwILjV1NQ4XdJ5OXz4sCZOnKiysrJu9y9fvlwlJSUqKytTdXW1/H6/srKy1Nra2seVnrvP6qMkffWrXw0Z11dffbUPKzx/lZWVmjdvnrZt26aKigodP35c2dnZOnz4cPCYSB/Ls+mjFNljOWrUKBUXF2v79u3avn27br75Zt1+++3BYBDpYyh9dh+lyB7Dzqqrq7V69WpNmDAhpN0NYxmRDHrd5MmTzUMPPRTS9qUvfcksXrzYoYp63+OPP24mTpzodBlhI8msX78++PrkyZPG7/eb4uLiYNuxY8fMkCFDzM9+9jMHKjx/nftojDF5eXnm9ttvd6SecGlqajKSTGVlpTHGnWPZuY/GuHMshw4dap555hlXjmGHjj4a464xbG1tNUlJSaaiosJkZGSYhx9+2Bjjzp/HSMEMYC9rb2/Xjh07lJ2dHdKenZ2tqqoqh6oKj/fff18JCQlKTEzU3Xffrb179zpdUtjU1dWpsbExZFy9Xq8yMjJcN65btmzRyJEjNW7cOD344INqampyuqTz0tzcLEkaNmyYJHeOZec+dnDLWJ44cULr1q3T4cOHlZaW5sox7NzHDm4Zw3nz5mnatGm65ZZbQtrdOJaRwuongYTDxx9/rBMnTsjn84W0+3w+NTY2OlRV70tNTdWvf/1rjRs3Th999JGWLVum9PR01dbWavjw4U6X1+s6xq67ca2vr3eipLDIycnRjBkzNGbMGNXV1emHP/yhbr75Zu3YsSMiP63fGKOFCxfqK1/5ipKTkyW5byy766PkjrGsqalRWlqajh07psGDB2v9+vW66qqrgsHADWPYUx8ld4yhJK1bt05vv/22qquru+xz289jJCEAhonH4wl5bYzp0hbJcnJygn8eP3680tLSdNlll6m8vFwLFy50sLLwcvu43nXXXcE/JycnKyUlRWPGjNErr7yi3NxcBys7N/Pnz9c777yjP//5z132uWUse+qjG8byiiuu0K5du/TJJ5/o97//vfLy8lRZWRnc74Yx7KmPV111lSvGsKGhQQ8//LA2bdqkgQMH9nicG8Yy0vAWcC8bMWKELrjggi6zfU1NTV3+D8dNBg0apPHjx+v99993upSw6FjhbNu4xsfHa8yYMRE5rgsWLNCGDRv0xhtvaNSoUcF2N41lT33sTiSO5YABA3T55ZcrJSVFRUVFmjhxolasWOGqMeypj92JxDHcsWOHmpqaNGnSJEVFRSkqKkqVlZX66U9/qqioqOB4uWEsIw0BsJcNGDBAkyZNUkVFRUh7RUWF0tPTHaoq/Nra2vTuu+8qPj7e6VLCIjExUX6/P2Rc29vbVVlZ6epxPXjwoBoaGiJqXI0xmj9/vl588UX96U9/UmJiYsh+N4zlZ/WxO5E4lp0ZY9TW1uaKMexJRx+7E4ljOHXqVNXU1GjXrl3BLSUlRffcc4927dqlSy+91LVj2e85tPjE1datW2cuvPBC8+yzz5rdu3eb/Px8M2jQILNv3z6nS+s1jzzyiNmyZYvZu3ev2bZtm7nttttMbGxsRPextbXV7Ny50+zcudNIMiUlJWbnzp2mvr7eGGNMcXGxGTJkiHnxxRdNTU2N+eY3v2ni4+NNS0uLw5WfvTP1sbW11TzyyCOmqqrK1NXVmTfeeMOkpaWZiy++OKL6OGfOHDNkyBCzZcsWc+DAgeB25MiR4DGRPpaf1Uc3jGVBQYHZunWrqaurM++884557LHHzBe+8AWzadMmY0zkj6ExZ+6jG8awJ6evAjbGHWMZiQiAYfL000+bMWPGmAEDBphrr7025OMZ3OCuu+4y8fHx5sILLzQJCQkmNzfX1NbWOl3WeXnjjTeMpC5bXl6eMebUxxU8/vjjxu/3G6/Xa2688UZTU1PjbNGf05n6eOTIEZOdnW0uuugic+GFF5pLLrnE5OXlmf379ztd9ufSXf8kmTVr1gSPifSx/Kw+umEs77///uDv0IsuushMnTo1GP6MifwxNObMfXTDGPakcwB0w1hGIo8xxvTdfCMAAACcxj2AAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJYhAAIAAFiGAAgAAGCZ/wdLFEM0mfbkJQAAAABJRU5ErkJggg==",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg fluo is 14.358692797660854\n"
     ]
    }
   ],
   "source": [
    "fluo = results[channel_diode_1]\n",
    "plt.figure()\n",
    "plt.title(ff.split('/')[-3])\n",
    "plt.hist(fluo, bins = 100)\n",
    "plt.show()\n",
    "print ('Avg fluo is {}'.format(np.mean(fluo)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test FEL pump / laser probe (YAG scans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probe rep rate (laser) is 100.0 Hz\n",
      "Pump rep rate (FEL) is 50.0 Hz\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "\u001b[32mSAR-CVME-TIFALL5:EvtSet       10001 / 10001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[31mSAROP11-PALMK118:CH1_BUFFER    5000 / 10001 -> 50% loss ▇▇▇▇▇\u001b[39m\n",
      "\u001b[31mSAROP11-PALMK118:CH2_BUFFER    5000 / 10001 -> 50% loss ▇▇▇▇▇\u001b[39m\n",
      "\u001b[31mSAROP11-PBPS122:INTENSITY      5000 / 10001 -> 50% loss ▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSLAAR11-LMOT-M453:ENC_1_BS    10001 / 10001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[39mSLAAR11-LSCP1-FNS:CH0:VAL_GET 10000 / 10001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[39mSLAAR11-LSCP1-FNS:CH2:VAL_GET 10000 / 10001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\n",
      "\u001b[31mover the whole data set: 5000 / 10001 -> 50% loss\u001b[39m\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "(4999,) (4999,) (4999,) (4999, 2000) (4999,)\n"
     ]
    }
   ],
   "source": [
    "bsread_file = \"/sf/alvra/data/p17589/raw/2020-07-02_YAG_timing_9/run_000451.BSREAD.h5\"\n",
    "\n",
    "channel_list_pp = [channel_Events, channel_LaserDiode, channel_Laser_refDiode]\n",
    "channel_list_all = channel_list_pp + [channel_Izero122, channel_delay_harm]\n",
    "\n",
    "results_pp, results_FEL, FEL_reprate, ratio = load_data_compact_FEL_pump(channel_list_pp, channel_list_all, bsread_file)\n",
    "\n",
    "print (results_pp[channel_LaserDiode].pump.shape,\n",
    "       results_pp[channel_LaserDiode].unpump.shape,\n",
    "       results_FEL[channel_Izero122].shape,\n",
    "       results_FEL[channel_delay_harm].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test laser pump / FEL probe (different pumping schemes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "\u001b[32mSAR-CVME-TIFALL5:EvtSet    1000 / 1000 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSARES11-GES1:CH1_VAL_GET   1000 / 1000 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[31mSAROP11-PBPS117:INTENSITY   978 / 1000 -> 2% loss ▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSLAAR11-LMOT-M453:ENC_1_BS 1000 / 1000 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\n",
      "\u001b[31mover the whole data set: 978 / 1000 -> 2% loss\u001b[39m\n",
      "\u001b[31mcomplete channels: 3 / 4 -> 25% incomplete\u001b[39m\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Probe rep rate (FEL) is 100.0 Hz\n",
      "Pump scheme is 6:1\n",
      "Loaded 839 pump and 139 unpump shots\n",
      "(839,) (139,) (839,) (139,)\n"
     ]
    }
   ],
   "source": [
    "#bsread_file = \"/sf/alvra/data/p17589/raw/laserPump4t1/run_001197.BSREAD.h5\"\n",
    "#bsread_file = \"/sf/alvra/data/p17589/raw/laserPump11t1/run_001195.BSREAD.h5\"run_034923\n",
    "ff = \"/sf/alvra/data/p19198/raw/s4_Au_50nm_2/run_000896.BSDATA.h5\"\n",
    "#bsread_file = \"/sf/alvra/data/p17589/raw/laserPump1t1/run_001202.BSREAD.h5\"\n",
    "\n",
    "fnames = SFDataFiles(ff)\n",
    "\n",
    "channel_list_pp = [channel_Events, channel_Izero117, channel_APD_Ch1]\n",
    "channel_list_all = channel_list_pp + [channel_delay_harm]\n",
    "\n",
    "\n",
    "results_pp, results_FEL, _, _ = load_data_compact_laser_pump(channel_list_pp, channel_list_all, fnames)\n",
    "\n",
    "print (#results_pp['SAROP11-CVME-PBPS2:Lnk9Ch13-DATA-SUM/data'].pump.shape,\n",
    "       #results_pp['SAROP11-CVME-PBPS2:Lnk9Ch13-DATA-SUM/data'].unpump.shape,\n",
    "       results_pp[channel_Izero117].pump.shape,\n",
    "       results_pp[channel_Izero117].unpump.shape,\n",
    "       results_pp[channel_APD_Ch1].pump.shape,\n",
    "       results_pp[channel_APD_Ch1].unpump.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function for FEL pump / laser probe (as in alvra_tools/load_data.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "\u001b[32mSAR-CVME-TIFALL5:EvtSet       3001 / 3001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[39mSAROP11-PALMK118:CH2_BUFFER   1501 / 3001 -> 50% loss ▇▇▇▇▇\u001b[39m\n",
      "\u001b[39mSAROP11-PBPS122:INTENSITY     1500 / 3001 -> 50% loss ▇▇▇▇▇\u001b[39m\n",
      "\u001b[39mSLAAR11-LMOT-M453:ENC_1_BS    2999 / 3001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSLAAR11-LSCP1-FNS:CH0:VAL_GET 3001 / 3001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSLAAR11-LSCP1-FNS:CH2:VAL_GET 3001 / 3001 ->  0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\n",
      "\u001b[31mover the whole data set: 1498 / 3001 -> 50% loss\u001b[39m\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "(1497,) (1497,) (1497, 2000) (1497,) (1497,)\n"
     ]
    }
   ],
   "source": [
    "#bsread_file = \"/sf/alvra/data/p17589/raw/scan_data/scan0010_2020-07-03-SiN4_5/scan0010_2020-07-03-SiN4_5_step0047/run_001149.BSREAD.h5\"\n",
    "#bsread_file = \"/sf/alvra/data/p17589/raw/scan_data/scan0010_2020-07-03-SiN4_5/scan0010_2020-07-03-SiN4_5_step0000/run_001102.BSREAD.h5\"\n",
    "bsread_file = \"/sf/alvra/data/p17589/raw/scan_data/scan0010_2020-07-03-SiN4_5/scan0010_2020-07-03-SiN4_5_step0019/run_001121.BSREAD.h5\"\n",
    "\n",
    "channels_always = [channel_Events, channel_LaserDiode, channel_Laser_refDiode]\n",
    "channels_pump   = channels_always + [channel_Izero122, channel_delay_harm, channel_PALM_streak]\n",
    "channels_unpump = channels_always\n",
    "\n",
    "with SFDataFiles(bsread_file) as data:\n",
    "    subset_unpump = data[channels_unpump]\n",
    "    #subset_unpump.print_stats(show_complete=True)\n",
    "    subset_unpump.drop_missing()\n",
    "\n",
    "    event_code = subset_unpump[channel_Events].data\n",
    "\n",
    "    ec_FEL      = event_code[:, 12] # Event 12: BAM bunch 1\n",
    "    ec_Laser    = event_code[:, 18]\n",
    "    ec_Darkshot = event_code[:, 21]\n",
    "\n",
    "    index_dark_before = np.append([True], np.logical_not(ec_Darkshot))[:-1]\n",
    "    index_light = np.logical_and.reduce((ec_FEL, ec_Laser, np.logical_not(ec_Darkshot), index_dark_before))\n",
    "    index_dark  = np.logical_and.reduce((np.logical_not(ec_FEL), ec_Laser, np.logical_not(ec_Darkshot), index_dark_before))\n",
    "\n",
    "    result_unpump = {}\n",
    "    for ch in channels_unpump:\n",
    "        try:\n",
    "            result_unpump[ch] = subset_unpump[ch].data[index_dark] # these are still all shots, thus we ignore the pump shots here!\n",
    "        except Exception as e:\n",
    "            print(\"channel missing:\", ch, e)\n",
    "\n",
    "    actual_pids_unpump = subset_unpump[channel_Events].pids[index_dark]\n",
    "\n",
    "with SFDataFiles(bsread_file) as data:\n",
    "        \n",
    "    subset_pump = data[channels_pump]\n",
    "    subset_pump.print_stats(show_complete=True)\n",
    "    subset_pump.drop_missing()\n",
    "        \n",
    "    result_pump = {}\n",
    "    for ch in channels_pump:\n",
    "        try:\n",
    "            result_pump[ch] = subset_pump[ch].data\n",
    "        except Exception as e:\n",
    "            print (\"Channel missing:\", ch, e)\n",
    "        \n",
    "    actual_pids_pump = subset_pump[channel_Events].pids\n",
    "\n",
    "#wanted_pids_pump  = actual_pids_unpump + 1\n",
    "#final_pids, ind_pump, ind_unpump = np.intersect1d(actual_pids_pump, wanted_pids_pump, return_indices=True)\n",
    "#_, _, ind_unpump = np.intersect1d(final_pids, actual_pids_unpump, return_indices=True)\n",
    "\n",
    "wanted_pids_pump  = actual_pids_unpump + 1\n",
    "final_pids_pump, ind_pump, _ind_unpump = np.intersect1d(actual_pids_pump, wanted_pids_pump, return_indices=True)\n",
    "final_pids_unpump = final_pids_pump - 1\n",
    "_, _, ind_unpump = np.intersect1d(final_pids_unpump, actual_pids_unpump, return_indices=True)\n",
    "\n",
    "for ch in channels_pump:\n",
    "    result_pump[ch] = result_pump[ch][ind_pump]\n",
    "\n",
    "for ch in channels_unpump:\n",
    "    result_unpump[ch] = result_unpump[ch][ind_unpump]\n",
    "\n",
    "\n",
    "ppdata = namedtuple(\"PPData\", [\"pump\", \"unpump\"])\n",
    "\n",
    "result_pp = {}\n",
    "shared_channels = set(channels_pump).intersection(channels_unpump)\n",
    "for ch in shared_channels:\n",
    "     result_pp[ch] = ppdata(pump=result_pump[ch], unpump= result_unpump[ch])\n",
    "\n",
    "print (result_pp[channel_LaserDiode].pump.shape,\n",
    "       result_pp[channel_LaserDiode].unpump.shape,\n",
    "       result_pump[channel_PALM_streak].shape,\n",
    "       result_pump[channel_Izero122].shape,\n",
    "       result_pump[channel_delay_harm].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function for laser pump / FEL probe (as in alvra_tools/load_data.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "\u001b[32mSAR-CVME-TIFALL5:EvtSet    1000 / 1000 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSARES11-GES1:CH1_VAL_GET   1000 / 1000 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[31mSAROP11-PBPS117:INTENSITY   978 / 1000 -> 2% loss ▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSLAAR11-LMOT-M453:ENC_1_BS 1000 / 1000 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\n",
      "\u001b[31mover the whole data set: 978 / 1000 -> 2% loss\u001b[39m\n",
      "\u001b[31mcomplete channels: 3 / 4 -> 25% incomplete\u001b[39m\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Probe rep rate (FEL) is 100.0 Hz\n",
      "Pump scheme is 6:1\n",
      "Pump rep rate (laser) is 16.666666666666668 Hz\n",
      "(839,) (139,) (839,) (139,)\n"
     ]
    }
   ],
   "source": [
    "#bsread_file = \"/sf/alvra/data/p18741/raw/Co_YIG_monoscan_Fe_002/run_001733.BSREAD.h5\"\n",
    "#bsread_file = \"/sf/alvra/data/p17589/raw/test_laserPump11/run_001174.BSREAD.h5\"\n",
    "#bsread_file = \"/sf/alvra/data/p18442/raw/scan_data/RuBpy_RIXS_1/run_001495.BSREAD.h5\"\n",
    "#bsread_file = \"/sf/alvra/data/p18741/raw/Co_YIG_delayscan_Fe_014/run_002756*.h5\"\n",
    "ff = \"/sf/alvra/data/p19198/raw/s4_Au_50nm_2/run_000896.BSDATA.h5\"\n",
    "\n",
    "channel_list_pp = [channel_Events, channel_APD_Ch1, channel_Izero117]#, 'JF04T01V01']\n",
    "channel_list_FEL = channel_list_pp + [channel_delay_harm]\n",
    "\n",
    "with SFDataFiles(ff) as data:\n",
    "    \n",
    "    subset_FEL = data[channel_list_FEL]\n",
    "    subset_FEL.print_stats(show_complete=True)\n",
    "    \n",
    "    Event_code = subset_FEL[channel_Events].data\n",
    "    FEL      = Event_code[:,12] #Event 12: BAM bunch 1\n",
    "    \n",
    "    Deltap_FEL = (1 / FEL.mean()).round().astype(int) #Get the FEL rep rate from the Event code\n",
    "    FEL_reprate = 100 / Deltap_FEL\n",
    "    print ('Probe rep rate (FEL) is {} Hz'.format(FEL_reprate))\n",
    "    \n",
    "    subset_FEL.drop_missing()\n",
    "\n",
    "    Event_code = subset_FEL[channel_Events].data\n",
    "\n",
    "    FEL      = Event_code[:,12] #Event 12: BAM bunch 1\n",
    "    Laser    = Event_code[:,18]\n",
    "    Darkshot = Event_code[:,21]\n",
    "\n",
    "    if Darkshot.mean()==0:\n",
    "        laser_reprate = (1 / Laser.mean() - 1).round().astype(int)\n",
    "        index_light = np.logical_and.reduce((FEL, Laser))\n",
    "        index_dark  = np.logical_and.reduce((FEL, np.logical_not(Laser)))\n",
    "    else:\n",
    "        laser_reprate = (Laser.mean() / Darkshot.mean() - 1).round().astype(int)\n",
    "\n",
    "        index_light = np.logical_and.reduce((FEL, Laser, np.logical_not(Darkshot)))\n",
    "        index_dark = np.logical_and.reduce((FEL, Laser, Darkshot))\n",
    "\n",
    "    #index_probe = np.logical_and.reduce((Laser, np.logical_not(Darkshot)))\n",
    "\n",
    "    #Deltap_FEL = (1 / FEL.mean()).round().astype(int) #Get the FEL rep rate from the Event code\n",
    "    #FEL_reprate = 100 / Deltap_FEL\n",
    "    #print ('Probe rep rate (FEL) is {} Hz'.format(FEL_reprate))\n",
    "\n",
    "    print ('Pump scheme is {}:1'.format(laser_reprate))\n",
    "    \n",
    "    if Darkshot.mean()==0:\n",
    "        laser_reprate = Laser.mean().round().astype(int)\n",
    "    else:\n",
    "        laser_reprate = (Laser.mean() / Darkshot.mean() - 1).round().astype(int)\n",
    "    \n",
    "    print ('Pump rep rate (laser) is {} Hz'.format(100 / laser_reprate))\n",
    "    \n",
    "    result_pp = {}\n",
    "    for ch in channel_list_pp:\n",
    "        ch_pump   = subset_FEL[ch].data[index_light]\n",
    "        pids_pump   = subset_FEL[ch].pids[index_light]\n",
    "\n",
    "        ch_unpump = subset_FEL[ch].data[index_dark]\n",
    "        pids_unpump = subset_FEL[ch].pids[index_dark]\n",
    "\n",
    "        correct_pids_pump   = pids_unpump + Deltap_FEL\n",
    "        final_pids, indPump, indUnPump = np.intersect1d(pids_pump, correct_pids_pump, return_indices=True)\n",
    "\n",
    "        if ((100 / laser_reprate) == FEL_reprate):\n",
    "            ch_pump   = ch_pump[indPump]\n",
    "            ch_unpump = ch_unpump[indUnPump] \n",
    "\n",
    "        ppdata = namedtuple(\"PPData\", [\"pump\", \"unpump\"])\n",
    "        result_pp[ch] = ppdata(pump=ch_pump, unpump=ch_unpump)\n",
    "\n",
    "\n",
    "    result_FEL = {}\n",
    "    for ch in channel_list_FEL:\n",
    "        result_FEL[ch] = subset_FEL[ch].data\n",
    "\n",
    "print (result_pp[channel_Izero117].pump.shape,\n",
    "       result_pp[channel_Izero117].unpump.shape,\n",
    "       result_pp[channel_APD_Ch1].pump.shape,\n",
    "       result_pp[channel_APD_Ch1].unpump.shape,)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Deltap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cell to test ipyfilechooser library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc = FileChooser('/sf/alvra/data/p17589/raw/')\n",
    "fc.use_dir_icons = True\n",
    "display(fc)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:alvra-analysis-p20774]",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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