PSEN_Jitter_Extractor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from scipy import optimize\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy import signal\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from IPython.display import clear_output\n",
    "from sfdata import SFDataFile, SFDataFiles\n",
    "from alvra_tools.channels import *\n",
    "from alvra_tools.load_data import *\n",
    "%matplotlib widget\n",
    "#from alvra_tools.psen import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    with h5py.File(Specfile, 'r') as f:\n",
    "        peakAmp = f['arrival_times_amplitude'][:]\n",
    "        arrTimes = f['arrival_times'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/sf/alvra/anaconda/alvra-analysis/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<KeysViewHDF5 ['S10BC01-DBAM070:EOM1_T1', 'S10BC01-DBAM070:EOM1_T1-VALID', 'S10CB01-DBPM220:Q1', 'S10CB01-DBPM220:Q1-VALID', 'S10CB01-DBPM220:X1', 'S10CB01-DBPM220:X1-VALID', 'S10CB01-DBPM220:Y1', 'S10CB01-DBPM220:Y1-VALID', 'S10CB01-DBPM420:Q1', 'S10CB01-DBPM420:Q1-VALID', 'S10CB01-DBPM420:X1', 'S10CB01-DBPM420:X1-VALID', 'S10CB01-DBPM420:Y1', 'S10CB01-DBPM420:Y1-VALID', 'S10CB02-DBPM220:Q1', 'S10CB02-DBPM220:Q1-VALID', 'S10CB02-DBPM220:X1', 'S10CB02-DBPM220:X1-VALID', 'S10CB02-DBPM220:Y1', 'S10CB02-DBPM220:Y1-VALID', 'S10CB02-DBPM420:Q1', 'S10CB02-DBPM420:Q1-VALID', 'S10CB02-DBPM420:X1', 'S10CB02-DBPM420:X1-VALID', 'S10CB02-DBPM420:Y1', 'S10CB02-DBPM420:Y1-VALID', 'S10CB03-DBPM220:Q1', 'S10CB03-DBPM220:Q1-VALID', 'S10CB03-DBPM220:X1', 'S10CB03-DBPM220:X1-VALID', 'S10CB03-DBPM220:Y1', 'S10CB03-DBPM220:Y1-VALID', 'S10CB03-DBPM420:Q1', 'S10CB03-DBPM420:Q1-VALID', 'S10CB03-DBPM420:X1', 'S10CB03-DBPM420:X1-VALID', 'S10CB03-DBPM420:Y1', 'S10CB03-DBPM420:Y1-VALID', 'S10CB04-DBPM220:Q1', 'S10CB04-DBPM220:Q1-VALID', 'S10CB04-DBPM220:X1', 'S10CB04-DBPM220:X1-VALID', 'S10CB04-DBPM220:Y1', 'S10CB04-DBPM220:Y1-VALID', 'S10CB04-DBPM420:Q1', 'S10CB04-DBPM420:Q1-VALID', 'S10CB04-DBPM420:X1', 'S10CB04-DBPM420:X1-VALID', 'S10CB04-DBPM420:Y1', 'S10CB04-DBPM420:Y1-VALID', 'S10CB05-DBPM220:Q1', 'S10CB05-DBPM220:Q1-VALID', 'S10CB05-DBPM220:X1', 'S10CB05-DBPM220:X1-VALID', 'S10CB05-DBPM220:Y1', 'S10CB05-DBPM220:Y1-VALID', 'S10CB05-DBPM420:Q1', 'S10CB05-DBPM420:Q1-VALID', 'S10CB05-DBPM420:X1', 'S10CB05-DBPM420:X1-VALID', 'S10CB05-DBPM420:Y1', 'S10CB05-DBPM420:Y1-VALID', 'S10CB06-DBPM220:Q1', 'S10CB06-DBPM220:Q1-VALID', 'S10CB06-DBPM220:X1', 'S10CB06-DBPM220:X1-VALID', 'S10CB06-DBPM220:Y1', 'S10CB06-DBPM220:Y1-VALID', 'S10CB06-DBPM420:Q1', 'S10CB06-DBPM420:Q1-VALID', 'S10CB06-DBPM420:X1', 'S10CB06-DBPM420:X1-VALID', 'S10CB06-DBPM420:Y1', 'S10CB06-DBPM420:Y1-VALID', 'S10CB07-DBPM220:Q1', 'S10CB07-DBPM220:Q1-VALID', 'S10CB07-DBPM220:X1', 'S10CB07-DBPM220:X1-VALID', 'S10CB07-DBPM220:Y1', 'S10CB07-DBPM220:Y1-VALID', 'S10CB07-DBPM420:Q1', 'S10CB07-DBPM420:Q1-VALID', 'S10CB07-DBPM420:X1', 'S10CB07-DBPM420:X1-VALID', 'S10CB07-DBPM420:Y1', 'S10CB07-DBPM420:Y1-VALID', 'S10CB08-DBPM220:Q1', 'S10CB08-DBPM220:Q1-VALID', 'S10CB08-DBPM220:X1', 'S10CB08-DBPM220:X1-VALID', 'S10CB08-DBPM220:Y1', 'S10CB08-DBPM220:Y1-VALID', 'S10CB08-DBPM420:Q1', 'S10CB08-DBPM420:Q1-VALID', 'S10CB08-DBPM420:X1', 'S10CB08-DBPM420:X1-VALID', 'S10CB08-DBPM420:Y1', 'S10CB08-DBPM420:Y1-VALID', 'S10CB09-DBPM220:Q1', 'S10CB09-DBPM220:Q1-VALID', 'S10CB09-DBPM220:X1', 'S10CB09-DBPM220:X1-VALID', 'S10CB09-DBPM220:Y1', 'S10CB09-DBPM220:Y1-VALID', 'S10MA01-DCDR080:AL1-B1', 'S10MA01-DCDR080:AL1-B1-ERROR', 'S10MA01-DCDR080:AL1-B1-VALID', 'S10MA01-DCDR080:AL1-B2', 'S10MA01-DCDR080:AL1-B2-ERROR', 'S10MA01-DCDR080:AL1-B2-VALID', 'S10MA01-DCDR080:AL2-B1', 'S10MA01-DCDR080:AL2-B1-ERROR', 'S10MA01-DCDR080:AL2-B1-VALID', 'S10MA01-DCDR080:AL2-B2', 'S10MA01-DCDR080:AL2-B2-ERROR', 'S10MA01-DCDR080:AL2-B2-VALID', 'SAR-CVME-TIFALL4:EvtSet', 'SAR-CVME-TIFALL5:EvtSet', 'SARCL01-DBAM110:EOM1_T1', 'SARCL01-DBPM010:Q1', 'SARCL01-DBPM010:Q1-VALID', 'SARCL01-DBPM010:X1', 'SARCL01-DBPM010:X1-VALID', 'SARCL01-DBPM010:Y1', 'SARCL01-DBPM010:Y1-VALID', 'SARCL01-DBPM060:Q1', 'SARCL01-DBPM060:Q1-VALID', 'SARCL01-DBPM060:X1', 'SARCL01-DBPM060:X1-VALID', 'SARCL01-DBPM060:Y1', 'SARCL01-DBPM060:Y1-VALID', 'SARCL01-DBPM120:Q1', 'SARCL01-DBPM120:Q1-VALID', 'SARCL01-DBPM120:X1', 'SARCL01-DBPM120:X1-VALID', 'SARCL01-DBPM120:Y1', 'SARCL01-DBPM120:Y1-VALID', 'SARCL01-DBPM150:Q1', 'SARCL01-DBPM150:Q1-VALID', 'SARCL01-DBPM150:X1', 'SARCL01-DBPM150:X1-VALID', 'SARCL01-DBPM150:Y1', 'SARCL01-DBPM150:Y1-VALID', 'SARCL02-DBPM110:Q1', 'SARCL02-DBPM110:Q1-VALID', 'SARCL02-DBPM110:X1', 'SARCL02-DBPM110:X1-VALID', 'SARCL02-DBPM110:Y1', 'SARCL02-DBPM110:Y1-VALID', 'SARCL02-DBPM220:Q1', 'SARCL02-DBPM220:Q1-VALID', 'SARCL02-DBPM220:X1', 'SARCL02-DBPM220:X1-VALID', 'SARCL02-DBPM220:Y1', 'SARCL02-DBPM220:Y1-VALID', 'SARCL02-DBPM260:Q1', 'SARCL02-DBPM260:Q1-VALID', 'SARCL02-DBPM260:X1', 'SARCL02-DBPM260:X1-VALID', 'SARCL02-DBPM260:Y1', 'SARCL02-DBPM260:Y1-VALID', 'SARCL02-DBPM330:Q1', 'SARCL02-DBPM330:Q1-VALID', 'SARCL02-DBPM330:X1', 'SARCL02-DBPM330:X1-VALID', 'SARCL02-DBPM330:Y1', 'SARCL02-DBPM330:Y1-VALID', 'SARCL02-DBPM470:Q1', 'SARCL02-DBPM470:Q1-VALID', 'SARCL02-DBPM470:X1', 'SARCL02-DBPM470:X1-VALID', 'SARCL02-DBPM470:Y1', 'SARCL02-DBPM470:Y1-VALID', 'SARES11-CVME-EVR0:DUMMY_PV10_NBS', 'SARES11-CVME-EVR0:DUMMY_PV1_NBS', 'SARES11-CVME-EVR0:DUMMY_PV2_NBS', 'SARES11-CVME-EVR0:DUMMY_PV3_NBS', 'SARES11-CVME-EVR0:DUMMY_PV4_NBS', 'SARES11-CVME-EVR0:DUMMY_PV5_NBS', 'SARES11-CVME-EVR0:DUMMY_PV6_NBS', 'SARES11-CVME-EVR0:DUMMY_PV7_NBS', 'SARES11-CVME-EVR0:DUMMY_PV8_NBS', 'SARES11-CVME-EVR0:DUMMY_PV9_NBS', 'SARES11-GES1:BUFF_BS_INT', 'SARES11-GES1:BUFF_WFM_CTR', 'SARES11-GES1:CH1_BUFFER', 'SARES11-GES1:CH1_VAL_GET', 'SARES11-GES1:CH2_BUFFER', 'SARES11-GES1:CH2_VAL_GET', 'SARES11-LSCP10-FNS:CH0:VAL_GET', 'SARES11-LSCP10-FNS:CH1:VAL_GET', 'SARES11-LSCP10-FNS:CH2:VAL_GET', 'SARES11-LSCP10-FNS:CH3:VAL_GET', 'SARES11-LSCP10-FNS:CH4:VAL_GET', 'SARES11-LSCP10-FNS:CH5:VAL_GET', 'SARES11-LSCP10-FNS:CH6:VAL_GET', 'SARES11-LSCP10-FNS:CH7:VAL_GET', 'SARES11-SPEC125-M1.edge_amplitude', 'SARES11-SPEC125-M1.edge_derivative', 'SARES11-SPEC125-M1.edge_position', 'SARES11-SPEC125-M1.edge_raw', 'SARES11-SPEC125-M1.processing_parameters', 'SARES11-SPEC125-M1.projection_background', 'SARES11-SPEC125-M1.projection_signal', 'SARES11-XSAM125:ENC_X1_BS', 'SARES11-XSAM125:ENC_Y1_BS', 'SARES11-XSAM125:ENC_Z1_BS', 'SARES12-CVME-EVR0:DUMMY_PV10_NBS', 'SARES12-CVME-EVR0:DUMMY_PV1_NBS', 'SARES12-CVME-EVR0:DUMMY_PV2_NBS', 'SARES12-CVME-EVR0:DUMMY_PV3_NBS', 'SARES12-CVME-EVR0:DUMMY_PV4_NBS', 'SARES12-CVME-EVR0:DUMMY_PV5_NBS', 'SARES12-CVME-EVR0:DUMMY_PV6_NBS', 'SARES12-CVME-EVR0:DUMMY_PV7_NBS', 'SARES12-CVME-EVR0:DUMMY_PV8_NBS', 'SARES12-CVME-EVR0:DUMMY_PV9_NBS', 'SARES12-LSCP11-FNS:CH0:VAL_GET', 'SARES12-LSCP11-FNS:CH1:VAL_GET', 'SARES12-LSCP11-FNS:CH2:VAL_GET', 'SARES12-LSCP11-FNS:CH3:VAL_GET', 'SARES12-LSCP11-FNS:CH4:VAL_GET', 'SARES12-LSCP11-FNS:CH5:VAL_GET', 'SARES12-LSCP11-FNS:CH6:VAL_GET', 'SARES12-LSCP11-FNS:CH7:VAL_GET', 'SARFE10-PBIG050-EVR0:CALCI', 'SARFE10-PBPG050:FELPHOTENE', 'SARFE10-PBPG050:HAMP-INTENSITY-CAL', 'SARFE10-PBPG050:PHOTON-ENERGY-PER-PULSE-AVG', 'SARFE10-PBPG050:XBPM-INTENSITY', 'SARFE10-PBPG050:XBPM-INTENSITY-X', 'SARFE10-PBPG050:XBPM-INTENSITY-Y', 'SARFE10-PBPG050:XBPM-XPOS', 'SARFE10-PBPG050:XBPM-YPOS', 'SARFE10-PBPS053:INTENSITY', 'SARFE10-PBPS053:XPOS', 'SARFE10-PBPS053:YPOS', 'SARFE10-PSSS059:SPECTRUM_CENTER', 'SARFE10-PSSS059:SPECTRUM_FWHM', 'SARFE10-PSSS059:SPECTRUM_X', 'SARFE10-PSSS059:SPECTRUM_Y', 'SARFE10-PSSS059:processing_parameters', 'SAROP11-CVME-PBPS1:Lnk9Ch3-DATA-CALIBRATED', 'SAROP11-CVME-PBPS1:Lnk9Ch3-DATA-SUM', 'SAROP11-CVME-PBPS1:Lnk9Ch4-DATA-CALIBRATED', 'SAROP11-CVME-PBPS1:Lnk9Ch4-DATA-SUM', 'SAROP11-CVME-PBPS1:Lnk9Ch5-DATA-CALIBRATED', 'SAROP11-CVME-PBPS1:Lnk9Ch5-DATA-SUM', 'SAROP11-CVME-PBPS1:Lnk9Ch7-DATA-CALIBRATED', 'SAROP11-CVME-PBPS1:Lnk9Ch7-DATA-SUM', 'SAROP11-CVME-PBPS2:Lnk9Ch11-DATA-CALIBRATED', 'SAROP11-CVME-PBPS2:Lnk9Ch11-DATA-SUM', 'SAROP11-CVME-PBPS2:Lnk9Ch13-DATA-CALIBRATED', 'SAROP11-CVME-PBPS2:Lnk9Ch13-DATA-SUM', 'SAROP11-CVME-PBPS2:Lnk9Ch14-DATA-CALIBRATED', 'SAROP11-CVME-PBPS2:Lnk9Ch14-DATA-SUM', 'SAROP11-CVME-PBPS2:Lnk9Ch15-DATA-CALIBRATED', 'SAROP11-CVME-PBPS2:Lnk9Ch15-DATA-SUM', 'SAROP11-PALMK118:BUFF_BS_INT', 'SAROP11-PALMK118:BUFF_WFM_CTR', 'SAROP11-PALMK118:CH1_BUFFER', 'SAROP11-PALMK118:CH1_VAL_GET', 'SAROP11-PALMK118:CH2_BUFFER', 'SAROP11-PALMK118:CH2_VAL_GET', 'SAROP11-PBPS110:INTENSITY', 'SAROP11-PBPS110:XPOS', 'SAROP11-PBPS110:YPOS', 'SAROP11-PBPS122:INTENSITY', 'SAROP11-PBPS122:XPOS', 'SAROP11-PBPS122:YPOS', 'SARUN01-DBPM070:Q1', 'SARUN01-DBPM070:Q1-VALID', 'SARUN01-DBPM070:X1', 'SARUN01-DBPM070:X1-VALID', 'SARUN01-DBPM070:Y1', 'SARUN01-DBPM070:Y1-VALID', 'SARUN02-DBPM070:Q1', 'SARUN02-DBPM070:Q1-VALID', 'SARUN02-DBPM070:X1', 'SARUN02-DBPM070:X1-VALID', 'SARUN02-DBPM070:Y1', 'SARUN02-DBPM070:Y1-VALID', 'SARUN03-DBPM070:Q1', 'SARUN03-DBPM070:Q1-VALID', 'SARUN03-DBPM070:X1', 'SARUN03-DBPM070:X1-VALID', 'SARUN03-DBPM070:Y1', 'SARUN03-DBPM070:Y1-VALID', 'SARUN04-DBPM070:Q1', 'SARUN04-DBPM070:Q1-VALID', 'SARUN04-DBPM070:X1', 'SARUN04-DBPM070:X1-VALID', 'SARUN04-DBPM070:Y1', 'SARUN04-DBPM070:Y1-VALID', 'SARUN05-DBPM070:Q1', 'SARUN05-DBPM070:Q1-VALID', 'SARUN05-DBPM070:X1', 'SARUN05-DBPM070:X1-VALID', 'SARUN05-DBPM070:Y1', 'SARUN05-DBPM070:Y1-VALID', 'SARUN06-DBPM070:Q1', 'SARUN06-DBPM070:Q1-VALID', 'SARUN06-DBPM070:X1', 'SARUN06-DBPM070:X1-VALID', 'SARUN06-DBPM070:Y1', 'SARUN06-DBPM070:Y1-VALID', 'SARUN07-DBPM070:Q1', 'SARUN07-DBPM070:Q1-VALID', 'SARUN07-DBPM070:X1', 'SARUN07-DBPM070:X1-VALID', 'SARUN07-DBPM070:Y1', 'SARUN07-DBPM070:Y1-VALID', 'SARUN08-DBPM070:Q1', 'SARUN08-DBPM070:Q1-VALID', 'SARUN08-DBPM070:X1', 'SARUN08-DBPM070:X1-VALID', 'SARUN08-DBPM070:Y1', 'SARUN08-DBPM070:Y1-VALID', 'SARUN09-DBPM070:Q1', 'SARUN09-DBPM070:Q1-VALID', 'SARUN09-DBPM070:X1', 'SARUN09-DBPM070:X1-VALID', 'SARUN09-DBPM070:Y1', 'SARUN09-DBPM070:Y1-VALID', 'SARUN10-DBPM070:Q1', 'SARUN10-DBPM070:Q1-VALID', 'SARUN10-DBPM070:X1', 'SARUN10-DBPM070:X1-VALID', 'SARUN10-DBPM070:Y1', 'SARUN10-DBPM070:Y1-VALID', 'SARUN11-DBPM070:Q1', 'SARUN11-DBPM070:Q1-VALID', 'SARUN11-DBPM070:X1', 'SARUN11-DBPM070:X1-VALID', 'SARUN11-DBPM070:Y1', 'SARUN11-DBPM070:Y1-VALID', 'SARUN12-DBPM070:Q1', 'SARUN12-DBPM070:Q1-VALID', 'SARUN12-DBPM070:X1', 'SARUN12-DBPM070:X1-VALID', 'SARUN12-DBPM070:Y1', 'SARUN12-DBPM070:Y1-VALID', 'SARUN13-DBPM070:Q1', 'SARUN13-DBPM070:Q1-VALID', 'SARUN13-DBPM070:X1', 'SARUN13-DBPM070:X1-VALID', 'SARUN13-DBPM070:Y1', 'SARUN13-DBPM070:Y1-VALID', 'SARUN14-DBPM070:Q1', 'SARUN14-DBPM070:Q1-VALID', 'SARUN14-DBPM070:X1', 'SARUN14-DBPM070:X1-VALID', 'SARUN14-DBPM070:Y1', 'SARUN14-DBPM070:Y1-VALID', 'SARUN15-DBPM070:Q1', 'SARUN15-DBPM070:Q1-VALID', 'SARUN15-DBPM070:X1', 'SARUN15-DBPM070:X1-VALID', 'SARUN15-DBPM070:Y1', 'SARUN15-DBPM070:Y1-VALID', 'SARUN16-DBPM070:Q1', 'SARUN16-DBPM070:Q1-VALID', 'SARUN16-DBPM070:X1', 'SARUN16-DBPM070:X1-VALID', 'SARUN16-DBPM070:Y1', 'SARUN16-DBPM070:Y1-VALID', 'SARUN17-DBPM070:Q1', 'SARUN17-DBPM070:Q1-VALID', 'SARUN17-DBPM070:X1', 'SARUN17-DBPM070:X1-VALID', 'SARUN17-DBPM070:Y1', 'SARUN17-DBPM070:Y1-VALID', 'SARUN18-DBPM070:Q1', 'SARUN18-DBPM070:Q1-VALID', 'SARUN18-DBPM070:X1', 'SARUN18-DBPM070:X1-VALID', 'SARUN18-DBPM070:Y1', 'SARUN18-DBPM070:Y1-VALID', 'SARUN19-DBPM070:Q1', 'SARUN19-DBPM070:Q1-VALID', 'SARUN19-DBPM070:X1', 'SARUN19-DBPM070:X1-VALID', 'SARUN19-DBPM070:Y1', 'SARUN19-DBPM070:Y1-VALID', 'SARUN20-DBPM070:Q1', 'SARUN20-DBPM070:Q1-VALID', 'SARUN20-DBPM070:X1', 'SARUN20-DBPM070:X1-VALID', 'SARUN20-DBPM070:Y1', 'SARUN20-DBPM070:Y1-VALID', 'SIN-CVME-TIFGUN-EVR0:BUNCH-1-OK', 'SINLH01-DBAM010:EOM1_T1', 'SLAAR11-LMOT-M451:ENC_1_BS', 'SLAAR11-LMOT-M452:ENC_1_BS', 'SLAAR11-LMOT-M453:ENC_1_BS', 'SLAAR11-LSCP1-FNS:CH0:VAL_GET', 'SLAAR11-LSCP1-FNS:CH1:VAL_GET', 'SLAAR11-LSCP1-FNS:CH2:VAL_GET', 'SLAAR11-LSCP1-FNS:CH3:VAL_GET', 'SLAAR11-LSCP1-FNS:CH4:VAL_GET', 'SLAAR11-LSCP1-FNS:CH5:VAL_GET', 'SLAAR11-LSCP1-FNS:CH6:VAL_GET', 'SLAAR11-LSCP1-FNS:CH7:VAL_GET', 'SLAAR11-LTIM01-EVR0:CALCI', 'SLAAR11-LTIM01-EVR0:CALCS', 'SLAAR11-LTIM01-EVR0:CALCX', 'SLAAR11-LTIM01-EVR0:CALCY', 'SLAAR11-LTIM01-EVR0:DUMMY_PV10_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV1_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV2_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV3_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV4_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV5_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV6_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV7_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV8_NBS', 'SLAAR11-LTIM01-EVR0:DUMMY_PV9_NBS']>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf = h5py.File('/sf/alvra/data/p19638/raw/run0001-Knife_edgeX/data/acq0002.BSDATA.h5', 'r')\n",
    "hf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/sf/alvra/anaconda/alvra-analysis/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "pgroup = 'p19634'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdas = 467.55 + 0.07219*np.arange(0,2047)      #calibration new spec 23-9-2020\n",
    "nus = 299792458 / (lambdas * 10**-9)\n",
    "nus_new = np.linspace(nus[0], nus[-1], num=2047, endpoint=True)\n",
    "\n",
    "def goodshots(events, *arrays):\n",
    "    fel = events[:, 13]\n",
    "    laser = events[:, 18]\n",
    "    darkShot = events[:, 21]\n",
    "    good_shots = np.logical_and.reduce((fel, laser, np.logical_not(darkShot)))\n",
    "    return [a[good_shots] for a in arrays]\n",
    "\n",
    "\n",
    "def backgroundshots(events, *arrays):\n",
    "    laser = events[:, 18]\n",
    "    darkShot = events[:, 21]\n",
    "    background_shots = np.logical_and.reduce((laser, darkShot))\n",
    "    return [a[background_shots] for a in arrays]\n",
    "\n",
    "\n",
    "def polyfxn(x, a, b, c, d, e, f, g, h):\n",
    "    return a*x**7 + b*x**6 + c*x**5 + d*x**4 + e*x**3 + f*x**2 + g*x + h\n",
    "\n",
    "\n",
    "def interpolate(xold, xnew, vals):\n",
    "    \"\"\"\n",
    "    Interpolate vals from xold to xnew\n",
    "    \"\"\"\n",
    "    interp = interp1d(xold, vals, kind='cubic')\n",
    "    return interp(xnew)\n",
    "\n",
    "\n",
    "def fourier_filter(vals, filt):\n",
    "    \"\"\"\n",
    "    Fourier transform, filter, inverse fourier transform, take the real part\n",
    "    \"\"\"\n",
    "    vals = np.hstack((vals, np.zeros_like(vals))) # pad\n",
    "    transformed = np.fft.fft(vals)\n",
    "    filtered = transformed * filt\n",
    "    inverse = np.fft.ifft(filtered)\n",
    "    invreal = 2 * np.real(inverse)\n",
    "    return invreal\n",
    "\n",
    "\n",
    "def edge(filter_name, backgrounds, signals, background_from_fit, peakback):\n",
    "    \"\"\"\n",
    "    returns:\n",
    "    edge positions determined from argmax of peak traces, converted to fs\n",
    "    edge amplitudes determined from the amax of the peak traces\n",
    "    the actual peak traces, which are the derivative of signal traces (below)\n",
    "    the raw signal traces, without any processing or smoothing except for the Fourier filter applied to remove the etalon\n",
    "    \"\"\"\n",
    "    ffilter = filters[filter_name]\n",
    "\n",
    "    # background normalization\n",
    "    sig_norm = (signals / backgrounds / background_from_fit) - 1\n",
    "    # interpolate to get evenly sampled in frequency space\n",
    "    sig_interp = interpolate(nus, nus_new, sig_norm)\n",
    "    # Fourier filter\n",
    "    sig_filtered = fourier_filter(sig_interp, ffilter)\n",
    "    # interpolate to get unevenly sampled in frequency space (back to original wavelength space)\n",
    "    sig_uninterp = interpolate(nus_new, nus, sig_filtered[..., 0:2047]) + 1\n",
    "    # peak via the derivative\n",
    "    sig_deriv = 17500*gaussian_filter1d(sig_uninterp, 50, order=1)\n",
    "    sig_deriv -= peakback\n",
    "    peak_pos = 1024 - (np.argmax(sig_deriv, axis=-1))\n",
    "    peak_pos = peak_pos*px2fs\n",
    "    peak_amp = np.amax(sig_deriv, axis=-1)\n",
    "\n",
    "    return peak_pos, peak_amp, sig_deriv, sig_uninterp\n",
    "\n",
    "filters = {\n",
    "    \"YAG\": np.concatenate((np.ones(50),signal.tukey(40)[20:40], np.zeros(1977), np.zeros(2047))), # old timetool YAG\n",
    "    \"YAGbaby\": np.concatenate((np.ones(20),signal.tukey(40)[20:40], np.zeros(2008), np.zeros(2048))) # back timetool YAG\n",
    "}\n",
    "\n",
    "#background_from_fit = np.loadtxt('/sf/alvra/data/p18954/scratch/2021-03-28-19:30-psen-background.txt')\n",
    "#peakback = np.loadtxt('/sf/alvra/data/p18954/scratch/2021-03-28-19:30-psen-peak-background.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run next cell only once to make the directories for the SPECENC files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/sf/alvra/data/' + pgroup + '/raw/'\n",
    "\n",
    "folderlist = []\n",
    "for file in glob(path + '*run0116*'):\n",
    "    folderlist.append(file)\n",
    "folderlist = sorted(folderlist)\n",
    "\n",
    "#for folder in folderlist:\n",
    "#    print (folder.split('/')[-1])\n",
    "#    os.mkdir('/sf/alvra/data/' + pgroup + '/scratch/timetool/' + folder.split('/')[-1])\n",
    "\n",
    "datafiles = []\n",
    "for file in glob(folderlist[0] + '/data/*.BSDATA.h5'):\n",
    "    datafiles.append(file)\n",
    "datafiles = sorted(datafiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/sf/alvra/data/p19634/raw/run0116-Ferroxilat_200mM_timescan_7.13keV_mono/data/acq0136.BSDATA.h5\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "\u001b[32mSAR-CVME-TIFALL4:EvtSet                     1001 / 1001 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSARES11-SPEC125-M2.roi_background_x_profile 1001 / 1001 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\u001b[32mSARES11-SPEC125-M2.roi_signal_x_profile     1001 / 1001 -> 0% loss ▇▇▇▇▇▇▇▇▇▇\u001b[39m\n",
      "\n",
      "\u001b[32mover the whole data set: 1001 / 1001 -> 0% loss\u001b[39m\n",
      "\u001b[32mcomplete channels: 3 / 3 -> 0% incomplete\u001b[39m\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "FEL rep rate is 100.0 Hz\n",
      "(2047,)\n",
      "(1001, 2047)\n",
      "/sf/alvra/data/p19634/scratch/timetool/run0116-Ferroxilat_200mM_timescan_7.13keV_mono/run0116-Ferroxilat_200mM_timescan_7.SPECENC.h5\n"
     ]
    }
   ],
   "source": [
    "px2fs = 1.91 # new spectrometer, it is ~ 2 px to fs\n",
    "channels = [channel_Events, channel_PSEN119_signal, channel_PSEN119_bkg]\n",
    "\n",
    "for file in datafiles:\n",
    "    clear_output(wait=True)\n",
    "    print(file)\n",
    "\n",
    "    fnames = SFDataFiles(file)\n",
    "    \n",
    "    results, pulse_ids = load_data_compact(channels, fnames)\n",
    "    events = results[channel_Events]\n",
    "    back = np.nan_to_num(results[channel_PSEN119_bkg])\n",
    "    sig = results[channel_PSEN119_signal]\n",
    "     \n",
    "    sig_bkg, back_bkg = backgroundshots(events, sig, back)\n",
    "    sig_bkg_mean = np.mean(sig_bkg, axis=0)\n",
    "    back_bkg_mean = np.mean(back_bkg, axis=0)\n",
    "    fitBackground, _ = optimize.curve_fit(polyfxn, lambdas, sig_bkg_mean/np.nan_to_num(back_bkg_mean), p0=[1, 1, 1, 1, 1, 1, 1, 1])\n",
    "    background_from_fit = polyfxn(lambdas, *fitBackground)\n",
    "    _, _, peakback, _ = edge('YAG', back_bkg_mean, sig_bkg_mean, background_from_fit, 0)\n",
    "    \n",
    "    peak_pos, peak_amp, sig_deriv, sig_uninterp = edge('YAG', back, sig, background_from_fit, peakback)\n",
    "    \n",
    "    folderName = file.split('/')[-3]\n",
    "    runNumber = file.split('.')[0].split('/')[-1]\n",
    "    \n",
    "    savefile = '/sf/alvra/data/' + pgroup + '/scratch/timetool/' + folderName + '/' + runNumber + '.SPECENC.h5'\n",
    "          \n",
    "    hf = h5py.File(savefile, 'w')\n",
    "    hf.create_dataset('pulse_id', data=pulse_ids)\n",
    "    hf.create_dataset('arrival_time', data=peak_pos)\n",
    "    hf.create_dataset('arrival_time_amplitude', data=peak_amp)\n",
    "    hf.close()\n",
    "    print (savefile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/sf/alvra/anaconda/alvra-analysis/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "/sf/alvra/anaconda/alvra-analysis/lib/python3.8/site-packages/ipywidgets/widgets/widget.py:412: DeprecationWarning: Passing unrecognized arguments to super(Toolbar).__init__().\n",
      "__init__() missing 1 required positional argument: 'canvas'\n",
      "This is deprecated in traitlets 4.2.This error will be raised in a future release of traitlets.\n",
      "  super(Widget, self).__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2fdc4cceda44870bf7e7f26b9be9fc8",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "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"
    }
   ],
   "source": [
    "plt.figure()\n",
    "#plt.plot(back_bkg_mean, 'o')\n",
    "#plt.plot(peak_pos[peak_amp > 10], '-')\n",
    "plt.plot(peak_amp, 'o')\n",
    "#plt.hist(peak_pos[peak_amp>10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run0107-Feroxilate_200mMinWater_timescan_7116eV_ATT0p02\n",
      "acq0001\n"
     ]
    }
   ],
   "source": [
    "folderName = file.split('/')[-3]\n",
    "print(folderName)\n",
    "runNumber = file.split('.')[0].split('/')[-1]\n",
    "print(runNumber)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "dataSpecEnc = []\n",
    "for file in glob.glob('/sf/alvra/data/p18954/scratch/ocp_mut_1500fs_32nJ_100Hz_36/*.SPECENC.h5'):\n",
    "    #print (file)\n",
    "    dataSpecEnc.append(file)\n",
    "dataSpecEnc = sorted(dataSpecEnc)\n",
    "dataSpecEnc\n",
    "print (len(dataSpecEnc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/sf/alvra/data/p18954/scratch/ocp_mut_1500fs_32nJ_100Hz_36/run_003265.SPECENC.h5\n",
      "4339 4339\n",
      "Total loaded shots = 304329 from 61 runs\n"
     ]
    }
   ],
   "source": [
    "all_arrTimes = []\n",
    "\n",
    "import itertools\n",
    "\n",
    "for Specfile in dataSpecEnc:\n",
    "    clear_output(wait=True)\n",
    "    print (Specfile)\n",
    "    with h5py.File(Specfile, 'r') as f:\n",
    "        peakAmp = f['arrival_times_amplitude'][:]\n",
    "        arrTimes = f['arrival_times'][:]\n",
    "#        delay_fs = f['nominal_delay_from_stage'][:]\n",
    "        \n",
    "        light_shots = peakAmp * 10000 > 10\n",
    "        \n",
    "        arrTimes_light = arrTimes[light_shots] + 300\n",
    "        \n",
    "        print (len(arrTimes), len(arrTimes_light))\n",
    "        \n",
    "    all_arrTimes.append(arrTimes_light)\n",
    "        \n",
    "all_arrTimes = list(itertools.chain.from_iterable(all_arrTimes))\n",
    "print ('Total loaded shots = {} from {} runs'.format(len(all_arrTimes), len(dataSpecEnc)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(all_arrTimes, bins = 25)\n",
    "plt.xlim(0,500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b12841922e8>]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(all_arrTimes, 'o')\n",
    "#plt.xlim(0,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    with h5py.File(file, 'r') as f:\n",
    "#        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'][:]        \n",
    "#        stage_delay = f['/SLAAR11-LTIM01-EVR0:DUMMY_PV5_NBS/data'][0]  ## NBS PV, set to delay in fs from Delay Panel"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:alvra-analysis]",
   "language": "python",
   "name": "conda-env-alvra-analysis-py"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}