{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# t3pa2scls\n",
    "basic script for CdTe  \n",
    "  \n",
    ".t3pa file example:  \n",
    ">Index $\\quad$ Matrix $\\quad$ Index\t$\\quad$ ToA\t$\\quad$ ToT $\\quad$\tFToA $\\quad$ Overflow  \n",
    "0  $\\quad$ 4574 $\\quad$ 832 $\\quad$ 29 $\\quad$ 6 $\\quad$ 0  \n",
    "1 $\\quad$ 4831 $\\quad$ 832 $\\quad$ 35 $\\quad$ 7 $\\quad$ 0  \n",
    "2 $\\quad$ 4575 $\\quad$ 832 $\\quad$ 100 $\\quad$ 8 $\\quad$ 0  \n",
    "3 $\\quad$ 31031 $\\quad$ 1745 $\\quad$ 22 $\\quad$ 11 $\\quad$ 0  \n",
    ".  \n",
    ".  \n",
    ".  \n",
    "  \n",
    ".t3pa_cls file example:\n",
    ">% Index $\\quad$ Matrix Index $\\quad$ [ RowNo, ClmNo ] $\\quad$ ToA $\\quad$ FToA $\\quad$ ( ToA_in_ns ) $\\quad$ ToT ( ToT_in_keV ) $\\quad$ Overflow  \n",
    ">  \n",
    ">\\# 1, $\\quad$ Nunmasked = 3, $\\quad$ Nmasked = 0, $\\quad$ Ntot = 3\n",
    "\\# Tfirst = 2.0787500000000000e+04 ns, $\\quad$ Tlast = 2.0790625000000000e+04 ns, $\\quad$ dT = 3.125000 ns, $\\quad$ Etot = 64.428148 keV  \n",
    "2 $\\quad$ 4575 $\\quad$ [ 17, 223 ] $\\quad$ 832 $\\quad$ 8 $\\quad$ ( 2.0787500000000000e+04 ns ) $\\quad$ 100 $\\quad$ ( 37.867914 keV ) $\\quad$ 0  \n",
    "1 $\\quad$ 4831 $\\quad$ [ 18, 223 ] $\\quad$ 832 $\\quad$ 7 $\\quad$ ( 2.0789062500000000e+04 ns ) $\\quad$ 35 $\\quad$ ( 14.733453 keV ) $\\quad$ 0  \n",
    "0 $\\quad$ 4574 $\\quad$ [ 17, 222 ] $\\quad$ 832 $\\quad$ 6 $\\quad$ ( 2.0790625000000000e+04 ns ) $\\quad$ 29 $\\quad$ ( 11.826781 keV ) $\\quad$ 0  \n",
    ">  \n",
    ">\\# 2, $\\quad$ Nunmasked = 3, $\\quad$ Nmasked = 0, $\\quad$ Ntot = 3\n",
    "\\# Tfirst = 4.3601562500000000e+04 ns, $\\quad$ Tlast = 4.3607812500000000e+04 ns, $\\quad$ dT = 6.250000 ns, $\\quad$ Etot = 63.577435 keV  \n",
    "5 $\\quad$ 30775\t$\\quad$ [ 120, 55 ]\t$\\quad$ 1745 $\\quad$ 15 $\\quad$ ( 4.3601562500000000e+04 ns ) $\\quad$ 99 $\\quad$ ( 37.617059 keV ) $\\quad$ 0  \n",
    "4 $\\quad$ 30776 $\\quad$ [ 120, 56 ] $\\quad$ 1745 $\\quad$ 13 $\\quad$ ( 4.3604687500000000e+04 ns ) $\\quad$ 44 $\\quad$ ( 14.715446 keV ) $\\quad$ 0  \n",
    "3 $\\quad$ 31031 $\\quad$ [ 121, 55 ] $\\quad$ 1745 $\\quad$ 11 $\\quad$ ( 4.3607812500000000e+04 ns ) $\\quad$2 2 $\\quad$ ( 11.244929 keV ) $\\quad$ 0  \n",
    ">  \n",
    ".  \n",
    ".  \n",
    ".  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "#import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "#from matplotlib.mlab import griddata\n",
    "\n",
    "from urllib.error import HTTPError # recognise the error stemming from missing data\n",
    "#import urllib\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "t3pa2cls_XII  - upravena fce energy(a, b, c, t, ToT, pocet_udalosti, RowNo, ClmNo) - nyni je se pocita i s pripadem \"nan\"\n",
    "t3pa2cls_XV_pc - zkousim vyzobat vysoke energie (jednotlive interakce) - funkce single_interaction. Dale delam prumernou velikost stopy stopy interakce pro danou energii - funkce size_of_interactions_average. Dale delam spektra 2 casti vyboje podle zadaneho casu - primarne pro double breakdown, tj. funkce energy_spectra_doublebreakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define an exception which will be raised if the data is missing and stop the notebook execution\n",
    "class StopExecution(Exception):\n",
    "    def _render_traceback_(self):\n",
    "        pass\n",
    "    \n",
    "#shot_no = 45559  #test discharge for which the notebook will definitely work\n",
    "shot_no = 47310\n",
    "shot = shot_no\n",
    "identifier='F10-W0049_shot_'+str(shot)+'_-450V'\n",
    "detector = 'F10-W0049'\n",
    "\n",
    "ds = np.DataSource('/tmp')  # temporary storage for downloaded files\n",
    "scalars_URL = 'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/PlasmaDetection/Results/{name}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scalar(shot_no, name):\n",
    "    return float(ds.open(scalars_URL.format(shot_no=shot_no, name=name)).read())\n",
    "t_plasma_start = get_scalar(shot_no, 't_plasma_start')\n",
    "t_plasma_end = get_scalar(shot_no, 't_plasma_end')\n",
    "is_plasma = get_scalar(shot_no, 'b_plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_file(shot, identifier):\n",
    "    #Pick the discharge to analyse \n",
    "    URL = 'http://golem.fjfi.cvut.cz/shots/{shot}/Diagnostics/TimepixDetector/F10/{identifier}.t3pa'\n",
    "    \n",
    "    url = URL.format(shot=shot, identifier=identifier)\n",
    "    try:\n",
    "        file_name_t3pa=url\n",
    "        with urllib.request.urlopen(file_name_t3pa) as ft3pa:\n",
    "            line = ft3pa.readline()\n",
    "            line = line.decode('utf‐8')\n",
    "        ft3pa.close\n",
    "    except HTTPError:\n",
    "        print('File not found at %s. Aborting notebook execution.' % url)\n",
    "        raise StopExecution\n",
    "    \n",
    "    return file_name_t3pa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_file_calib(name_calib):\n",
    "    #Pick the discharge to analyse \n",
    "    URL = 'http://golem.fjfi.cvut.cz/shots/{shot}/Diagnostics/TimepixDetector/calib_matrix_F10/{name_calib}.txt'\n",
    "    \n",
    "    url = URL.format(shot=shot, name_calib=name_calib)\n",
    "    #print(url)\n",
    "    try:\n",
    "        file_calib=url\n",
    "        with urllib.request.urlopen(file_calib) as calib:\n",
    "            line = calib.readline()\n",
    "            line = line.decode('utf‐8')\n",
    "        calib.close\n",
    "    except HTTPError:\n",
    "        print('File not found at %s. Aborting notebook execution.' % url)\n",
    "        raise StopExecution\n",
    "    \n",
    "    return file_calib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_calib(file_calib):\n",
    "    with urllib.request.urlopen(file_calib) as fc:\n",
    "        calib=[] #vytvoreni 1D pole\n",
    "        for i in range(0,256): #tj. rozsah 0-255\n",
    "            temp = [] # docasne pole\n",
    "            for j in range(0,256):\n",
    "                temp.append(0) #naplneni docasneho pole 0\n",
    "            calib.append(temp)  #naplneni pole a[] docasnym polem temp\n",
    "            \n",
    "        for i in range(0,256): #nacteni calib matice do pole calib\n",
    "            line = fc.readline()\n",
    "            line = line.decode('utf‐8')\n",
    "            word=line.strip().split(' ')\n",
    "            for j in range(0,256):\n",
    "                #calib[i][j]=float(word[j]) #i = radek, j = sloupec0\n",
    "                calib[j][i]=float(word[j]) #j = radek, i = sloupec0 - pouze pro stavajici kalibraci - verze XV\n",
    "    fc.close       \n",
    "    return calib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_t3pa_file(file_t3pa):\n",
    "    index=[]\n",
    "    matrix_index=[]\n",
    "    ToA=[]\n",
    "    ToT=[]\n",
    "    FToA=[]\n",
    "    overflow=[]\n",
    "    pocet_udalosti = 0\n",
    "    with urllib.request.urlopen(file_t3pa) as ft3pa:\n",
    "        line = ft3pa.readline()\n",
    "        line = line.decode('utf‐8')\n",
    "        while True:\n",
    "            line = ft3pa.readline()\n",
    "            line = line.decode('utf‐8')\n",
    "            word=line.strip().split('\\t') #v t3pa souboru je oddelovac \\t\n",
    "            if line == '':\n",
    "                break\n",
    "            \n",
    "            index.append(word[0])\n",
    "            matrix_index.append(word[1])\n",
    "            ToA.append(float(word[2]))\n",
    "            ToT.append(float(word[3]))\n",
    "            FToA.append(float(word[4]))\n",
    "            overflow.append(float(word[5]))\n",
    "            pocet_udalosti = pocet_udalosti + 1\n",
    "    ft3pa.close\n",
    "    return index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noise(index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti): #tuto fci nemus9m explicitn2 volat - volam ji v fci load_t3pa\n",
    "    pocet=int(0) #pocet sumicich pixelu\n",
    "    konst=int(len(index)/1000)+1\n",
    "    noise_matrix_index=[]\n",
    "    for i in range(0,konst):      \n",
    "        pom = [] # pomocne pole\n",
    "        k=0 #pomocna promenna - udava, kolik je v czklu ve skutecnosti udalosti - aby nebyla chyba 'list index out of range'\n",
    "        for j in range(0,1001):\n",
    "            if i*1000+j>=len(index):\n",
    "                break\n",
    "            pom.append(matrix_index[i*1000+j])\n",
    "            k=k+1\n",
    "        for m in range(0,k):\n",
    "                count=int(0) #pocet vvyskytu stejneho matrix index behem 1000 udalosti\n",
    "                index_=int(-1) #budu testovat, jestli pixel na ktery koukam je sumici (abych ho nezapocital 2x)\n",
    "                \n",
    "                for p in range(0,pocet):\n",
    "                    #index=int(p)\n",
    "                    if pom[m]==noise_matrix_index[p]:\n",
    "                        index_=p #pixel na ktery jsem uz koukal a byl sumici\n",
    "                        break\n",
    "                \n",
    "                if index_ >=0 and pom[m]==noise_matrix_index[index_]:\n",
    "                    continue\n",
    "                                 \n",
    "                for l in range(0,k):\n",
    "                    if pom[m]==pom[l]:\n",
    "                        count=count+1\n",
    "                ####podminka na sumici pixely\n",
    "                if count>=50: #kdyz se pixel vyskytne behem tisice udalosti vicekrat nez toto cislo, je sumici \n",
    "                    noise_matrix_index.append(pom[m])\n",
    "                    #noise_matrix_index[pocet]=pom[i]\n",
    "                    pocet=pocet+1\n",
    "        pom.clear()           \n",
    "        \n",
    "    pocet_udalosti=len(index)\n",
    "    \n",
    "    for n in range (0,pocet_udalosti):\n",
    "        for o in range(0,len(noise_matrix_index)):\n",
    "            if n >=pocet_udalosti:\n",
    "                    break\n",
    "            if(matrix_index[n]==noise_matrix_index[o]):\n",
    "                del matrix_index[n]\n",
    "                del index[n]\n",
    "                del ToA[n]\n",
    "                del ToT[n]\n",
    "                del FToA[n]\n",
    "                del overflow[n]\n",
    "                pocet_udalosti=pocet_udalosti-1\n",
    "                continue\n",
    "    return pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def t3pa_data(pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow):\n",
    "    #rovnou vyhodim sumici pixely\n",
    "    pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow=noise(index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti)\n",
    "    RowNo=[]\n",
    "    ClmNo=[]\n",
    "    for i in range(0,len(matrix_index)):\n",
    "        #RowNo.append(int(int(matrix_index[i]))//int(256))\n",
    "        #ClmNo.append(int(int(matrix_index[i]))%int(256))\n",
    "        ClmNo.append(int(int(matrix_index[i]))//int(256)) #ver XV - bude pro novou kalibraci - nyn9 to bere 3patnou kalibraci jednotlivych pixelu (cislovani radku a sloupcu stejne jako v pixetu)\n",
    "        RowNo.append(int(int(matrix_index[i]))%int(256)) ##ver XV - bude super pro novou kalibraci - odpovida radkum a sloupcum v pixetu. Nyni bere spatne kalibrace pixelu (viz fce load_calib)\n",
    "    return index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti, RowNo, ClmNo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hit_map(detector,hit_map_fig,RowNo,ClmNo):\n",
    "    plt.hist2d(RowNo,ClmNo,bins=(256,256),cmap='Blues')\n",
    "    cb=plt.colorbar()\n",
    "    cb.set_label('Counts in pixel')\n",
    "    plt.xlabel('x [pixel]')\n",
    "    plt.ylabel('y [pixel]')\n",
    "    plt.title(detector)\n",
    "    plt.savefig(hit_map_fig, dpi = 1000)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def energy(a, b, c, t, ToT, pocet_udalosti, RowNo, ClmNo):\n",
    "    E=[] #energy in keV\n",
    "    #for i in range (0,pocet_udalosti):\n",
    "    pom=0\n",
    "    for i in range (0,len(ToT)):\n",
    "        Sqrt=float(0.0)\n",
    "        e1=float(0.0)\n",
    "        e2=float(0.0)\n",
    "        \n",
    "        # promenna sqrt je vnitrek odmocniny\n",
    "        Sqrt =  (((float(b[RowNo[i]][ClmNo[i]])+float(a[RowNo[i]][ClmNo[i]])*float(t[RowNo[i]][ClmNo[i]])-float(ToT[i])))*(((float(b[RowNo[i]][ClmNo[i]])+float(a[RowNo[i]][ClmNo[i]])*float(t[RowNo[i]][ClmNo[i]])-float(ToT[i])))) + (float(4)*float(a[RowNo[i]][ClmNo[i]])*float(c[RowNo[i]][ClmNo[i]]))) #zmena oproti verzi VI\n",
    "        if float(Sqrt)<float(0):\n",
    "            E.append(float(0))\n",
    "            \n",
    "            \n",
    "        else:\n",
    "            '''\n",
    "            V kalibracni matici a se obcas vyskytne 0 -> ve vypoctu energie\n",
    "            je tim padem deleni nulou -> energie diverguje. Jak to vyresit?\n",
    "            zatim polozim energii = 0 (kdyz a=0), pak se uvidi\n",
    "            \n",
    "            nakonec udelam limitu vyrazu energie pro a->0 (L'hopital)\n",
    "            '''\n",
    "            if a[RowNo[i]][ClmNo[i]]==0:\n",
    "                e1=((float(t[RowNo[i]][ClmNo[i]]))/float(2)) + ((((float(b[RowNo[i]][ClmNo[i]])+float(a[RowNo[i]][ClmNo[i]])*float(t[RowNo[i]][ClmNo[i]])-float(ToT[i]))*(float(t[RowNo[i]][ClmNo[i]]))) - 2*(float(c[RowNo[i]][ClmNo[i]])))/(float(2)*np.sqrt(float(Sqrt))))\n",
    "                e2=((float(t[RowNo[i]][ClmNo[i]]))/float(2)) - ((((float(b[RowNo[i]][ClmNo[i]])+float(a[RowNo[i]][ClmNo[i]])*float(t[RowNo[i]][ClmNo[i]])-float(ToT[i]))*(float(t[RowNo[i]][ClmNo[i]]))) - 2*(float(c[RowNo[i]][ClmNo[i]])))/(float(2)*np.sqrt(float(Sqrt))))\n",
    "                \n",
    "            \n",
    "            else:\n",
    "                e1=((-(float(b[RowNo[i]][ClmNo[i]]) - (float(a[RowNo[i]][ClmNo[i]])*float(t[RowNo[i]][ClmNo[i]]))-float(ToT[i])))+np.sqrt(float(Sqrt)))/(float(2)*float(a[RowNo[i]][ClmNo[i]]))\n",
    "                e2=((-(float(b[RowNo[i]][ClmNo[i]]) - (float(a[RowNo[i]][ClmNo[i]])*float(t[RowNo[i]][ClmNo[i]]))-float(ToT[i])))-np.sqrt(float(Sqrt)))/(float(2)*float(a[RowNo[i]][ClmNo[i]]))\n",
    "                \n",
    "            if a[RowNo[i]][ClmNo[i]]<0:\n",
    "                e1=-1\n",
    "                e2=-1\n",
    "                \n",
    "            if math.isnan(e1):\n",
    "                e1=-1 \n",
    "                \n",
    "            if math.isnan(e2):\n",
    "                e2=-1 \n",
    "                \n",
    "            if e1<0 and e2<0:\n",
    "                E.append(float(0))\n",
    "                \n",
    "            if e1>=0 and e1>e2:\n",
    "                E.append(float(e1))\n",
    "                    \n",
    "            if e2>=0 and e2>e1:\n",
    "                E.append(float(e2))\n",
    "                \n",
    "            if e1>=0 and e2==e1:\n",
    "                E.append(float(e1)) \n",
    "            \n",
    "    return E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Time(ToA, FToA, pocet_udalosti, RowNo, ClmNo):\n",
    "    T=[] #time in ns\n",
    "    for i in range (0,pocet_udalosti):\n",
    "        Time=float(0.0)\n",
    "        Time=(float(ToA[i])-((float(FToA[i])/float(16))))*float(25)\n",
    "        T.append(float(Time))\n",
    "    return T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Timewalk_parameters_Si():\n",
    "    #SI - korekce na TimeWalk - parametry\n",
    "    A = -1.21988\n",
    "    B = 4.33638\n",
    "    C = 29.5075\n",
    "    D = 1\n",
    "    sigma_A=0.7013\n",
    "    sigma_B=0.1366\n",
    "    sigma_C=4.753\n",
    "    sigma_D=0\n",
    "    return A, B, C, D, sigma_A, sigma_B, sigma_C, sigma_D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Timewalk(E,T):\n",
    "    i=0\n",
    "    A, B, C, D, sigma_A, sigma_B, sigma_C, sigma_D = Timewalk_parameters_Si()\n",
    "    while i < len(T):\n",
    "        timewalk=float(0)\n",
    "        timewalk = (C / (E[i] - B)) + A \n",
    "        if(timewalk<0):\n",
    "            timewalk=0\n",
    "        T[i]=T[i]-timewalk\n",
    "        i=i+1\n",
    "        \n",
    "    return E,T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_interactions_with_zero_energy(index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T):\n",
    "    i=0\n",
    "    treshold=5.015347\n",
    "    while i < len(T):\n",
    "        if E[i]<treshold: #E[i] < energy treshold\n",
    "            index.pop(i)\n",
    "            matrix_index.pop(i)\n",
    "            ToA.pop(i)\n",
    "            ToT.pop(i)\n",
    "            FToA.pop(i)\n",
    "            overflow.pop(i)\n",
    "            RowNo.pop(i)\n",
    "            ClmNo.pop(i)\n",
    "            E.pop(i)\n",
    "            T.pop(i)\n",
    "            continue\n",
    "        i=i+1\n",
    "    return index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clustering_new(index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T):\n",
    "    dT=float(50)\n",
    "    \n",
    "    indexCl, TCl,ECl, matrix_indexCl, ToACl,ToTCl,FToACl,RowNoCl,ClmNoCl,overflowCl=[],[],[],[],[],[],[],[],[],[]\n",
    "    StartLastElem=len(T)-1\n",
    "    \n",
    "    indexCl.append(int(index[StartLastElem]))\n",
    "    TCl.append(float(T[StartLastElem]))\n",
    "    ECl.append(float(E[StartLastElem]))\n",
    "    matrix_indexCl.append(int(matrix_index[StartLastElem]))\n",
    "    RowNoCl.append(int(RowNo[StartLastElem]))\n",
    "    ClmNoCl.append(int(ClmNo[StartLastElem]))\n",
    "    ToACl.append(float(ToA[StartLastElem]))\n",
    "    ToTCl.append(float(ToT[StartLastElem]))\n",
    "    FToACl.append(float(FToA[StartLastElem]))\n",
    "    overflowCl.append(float(overflow[StartLastElem]))\n",
    "    \n",
    "    del index[StartLastElem]\n",
    "    del T[StartLastElem]\n",
    "    del E[StartLastElem]\n",
    "    del matrix_index[StartLastElem]\n",
    "    del RowNo[StartLastElem]\n",
    "    del ClmNo[StartLastElem]\n",
    "    del ToA[StartLastElem]\n",
    "    del ToT[StartLastElem]\n",
    "    del FToA[StartLastElem]\n",
    "    del overflow[StartLastElem]\n",
    "    \n",
    "    j=1\n",
    "    pom=float(TCl[0]+dT)\n",
    "    while(j >0):\n",
    "        if(len(T) == 0):\n",
    "           break\n",
    "        k=0\n",
    "        j=0\n",
    "        while (k<=(len(TCl)-1)):\n",
    "            i=len(T)-1\n",
    "            if(len(T) == 0):\n",
    "                break\n",
    "            pocet_sousedu=0 #pocet sousednich pixelu - mohou byt maximalne 4\n",
    "            delka=0\n",
    "            # verze X\n",
    "            count=0 #pomocna promanna, kterou urcuji, ze se ma nasledujici cyklus while projit jeste jednou, pokud je i = -1\n",
    "            while(float(T[i])<=(pom)):\n",
    "                delka=delka+1\n",
    "                if(((((int(RowNoCl[k]))==(int(RowNo[i])+1))or((int(RowNoCl[k]))==(int(RowNo[i])-1))) and ((int(ClmNoCl[k]))==(int(ClmNo[i])))) or (((int(RowNoCl[k]))==(int(RowNo[i]))) and (((int(ClmNoCl[k]))==(int(ClmNo[i])+1))or((int(ClmNoCl[k]))==(int(ClmNo[i])-1))))):\n",
    "                    #beru jen pixely, které mají společnou jednu stranu.\n",
    "                    #pixely, kter0 spolu sousedí přes roh neuvažuji\n",
    "                    indexCl.append(int(index[i]))\n",
    "                    TCl.append(float(T[i]))\n",
    "                    ECl.append(float(E[i]))\n",
    "                    matrix_indexCl.append(int(matrix_index[i]))\n",
    "                    RowNoCl.append(int(RowNo[i]))\n",
    "                    ClmNoCl.append(int(ClmNo[i]))\n",
    "                    ToACl.append(float(ToA[i]))\n",
    "                    ToTCl.append(float(ToT[i]))\n",
    "                    FToACl.append(float(FToA[i]))\n",
    "                    overflowCl.append(float(overflow[i]))\n",
    "                    \n",
    "                    # Removes i-th Row\n",
    "                    del index[i]\n",
    "                    del T[i]\n",
    "                    del E[i]\n",
    "                    del matrix_index[i]\n",
    "                    del RowNo[i]\n",
    "                    del ClmNo[i]\n",
    "                    del ToA[i]\n",
    "                    del ToT[i]\n",
    "                    del FToA[i]\n",
    "                    del overflow[i]\n",
    "                    \n",
    "                    j=j+1\n",
    "                    i=len(T)-1\n",
    "                    pocet_sousedu=pocet_sousedu+1\n",
    "                    \n",
    "                    if(len(T) == 0):\n",
    "                        break\n",
    "                    \n",
    "                    if(pocet_sousedu==4):\n",
    "                        break\n",
    "                    \n",
    "                    continue\n",
    "                \n",
    "                i=i-1\n",
    "                if(i==-1): # verze X\n",
    "                    count=count+1\n",
    "                    \n",
    "                if(i<0 and len(T)>0): # verze X\n",
    "                    i=0\n",
    "                    if(count>1):\n",
    "                        break\n",
    "                    \n",
    "                if(i>=len(T)):\n",
    "                    break\n",
    "            k=k+1\n",
    "            \n",
    "        if(len(TCl)>2):\n",
    "            indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl = insertionSort(indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl)\n",
    "            \n",
    "    return T, indexCl,TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insertionSort(indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl):\n",
    "    # Function to do insertion sort\n",
    "    # Traverse through 1 to len(arr)\n",
    "    for i in range(1, len(TCl)):\n",
    "        key = TCl[i]\n",
    "        # Move elements of arr[0..i-1], that are\n",
    "        # greater than key, to one position ahead\n",
    "        # of their current position\n",
    "        \n",
    "        #ostatni\n",
    "        key1 = indexCl[i]\n",
    "        key2 = ECl[i]\n",
    "        key3 = matrix_indexCl[i]\n",
    "        key4 = RowNoCl[i]\n",
    "        key5 = ClmNoCl[i]\n",
    "        key6 = ToACl[i]\n",
    "        key7 = ToTCl[i]\n",
    "        key8 = FToACl[i]\n",
    "        key9 = overflowCl[i]\n",
    "        \n",
    "        j = i-1\n",
    "        while j >= 0 and key < TCl[j] :\n",
    "                TCl[j + 1] = TCl[j]\n",
    "                #ostatni\n",
    "                indexCl[j + 1] = indexCl[j]\n",
    "                ECl[j + 1] = ECl[j]\n",
    "                matrix_indexCl[j + 1] = matrix_indexCl[j]\n",
    "                RowNoCl[j + 1] = RowNoCl[j]\n",
    "                ClmNoCl[j + 1] = ClmNoCl[j]\n",
    "                ToACl[j + 1] = ToACl[j]\n",
    "                ToTCl[j + 1] = ToTCl[j]\n",
    "                FToACl[j + 1] = FToACl[j]\n",
    "                overflowCl[j + 1] = overflowCl[j]\n",
    "                j -= 1\n",
    "                \n",
    "        TCl[j + 1] = key\n",
    "        #ostatni\n",
    "        indexCl[j + 1] = key1\n",
    "        ECl[j + 1] = key2\n",
    "        matrix_indexCl[j + 1] = key3\n",
    "        RowNoCl[j + 1] =key4\n",
    "        ClmNoCl[j + 1] = key5\n",
    "        ToACl[j + 1] = key6\n",
    "        ToTCl[j + 1] = key7\n",
    "        FToACl[j + 1] = key8\n",
    "        overflowCl [j + 1] = key9 \n",
    "    return indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_t3pa_cls_new(file_t3pa_cls,T):\n",
    "    with open(file_t3pa_cls, \"w\", encoding=\"utf-8\") as t3pa_cls:\n",
    "        t3pa_cls.write('%\\n')\n",
    "        t3pa_cls.write('% Index\tMatrix Index\t[ RowNo, ClmNo ]\tToA\tFToA\t( ToA_in_ns )\tToT\t( ToT_in_keV )\tOverflow\\n')\n",
    "        t3pa_cls.write('\\n')\n",
    "        i=1\n",
    "        T_first=[]\n",
    "        E_tot=[]\n",
    "        N_tot=[]\n",
    "        eventNo=0 #for high_energy_event function\n",
    "        while(len(T) > 0):\n",
    "            T, indexCl,TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl = clustering_new(index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T) \n",
    "            Tfirst=float(TCl[0])\n",
    "            Tlast=float(TCl[len(TCl)-1])\n",
    "            dT=Tlast-Tfirst\n",
    "            Etot=float(0)\n",
    "            for k in range(0,len(TCl)):\n",
    "                Etot=Etot+float(ECl[k])\n",
    "            T_first.append(float(Tfirst))\n",
    "            dT=Tlast-Tfirst\n",
    "            E_tot.append(float(Etot))\n",
    "            N_tot.append(len(TCl)) #new in ver. XV\n",
    "            \n",
    "            t3pa_cls.write('# '+str(i)+', Nunmasked = '+str(len(TCl))+', Nmasked = 0, Ntot = '+str(len(TCl))+'\\n')\n",
    "            t3pa_cls.write('# Tfirst = '+str(Tfirst)+' ns, Tlast = '+str(Tlast)+' ns, dT = '+str(dT)+' ns, Etot = '+str(Etot)+' keV\\n')\n",
    "            for j in range(0,len(TCl)):\n",
    "                t3pa_cls.write(str(indexCl[j])+'\t'+str(matrix_indexCl[j])+'\t[ '+str(RowNoCl[j])+',\t'+str(ClmNoCl[j])+' ]\t'+str(ToACl[j])+'\t'+str(FToACl[j])+'\t( '+str(TCl[j])+' ns )\t'+str(ToTCl[j])+'\t( '+str(ECl[j])+' keV )\t'+str(overflowCl[j])+'\\n')\n",
    "            t3pa_cls.write('\\n') \n",
    "            #eventNo=single_interaction(eventNo,Etot, Tfirst, ECl, TCl, RowNoCl, ClmNoCl,path)\n",
    "            i=i+1\n",
    "    t3pa_cls.close\n",
    "    return T_first, E_tot, N_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def energy_spectrum_in_time(Tfirst, Etot): #dela histogram - energie zaznamenana v case \n",
    "    pom = 0\n",
    "    dt=100 #(ns) time width of 1 bin\n",
    "    \n",
    "    T_first=0 #cas, kdy prisel trigger a yacalo mereni\n",
    "    T_last=(max(Tfirst)) #posledni z Tfirst\n",
    "       \n",
    "    Delta_T = T_last - T_first\n",
    "    poc = int(int(Delta_T) / float(dt)) + 1 #pocet casovych oken\n",
    "    \n",
    "    T_int_first=[] #cas\n",
    "    E=[] #energie\n",
    "    \n",
    "    for i in range(0,poc):\n",
    "        T_int_first.append((i*dt) + dt/2)\n",
    "        E.append(0)\n",
    "    \n",
    "    #XII\n",
    "    for j in range(0,len(Tfirst)):\n",
    "        time_index=0\n",
    "        time_index=int(((Tfirst[j]-T_first)/dt))\n",
    "        if float(Tfirst[j]-T_first) >= (T_int_first[time_index] - dt / 2) and float(Tfirst[j]-T_first) < (T_int_first[time_index] + dt / 2):\n",
    "            E[time_index]=float(E[time_index])+float(Etot[j])\n",
    "            pom=pom+1\n",
    "            \n",
    "    for l in range(0,len(T_int_first)):\n",
    "        T_int_first[l]=T_int_first[l]+T_first\n",
    "        \n",
    "    caption, T_int_first = energy_in_time_hist(T_int_first, E, figure_E_in_time_hist, t_plasma_start, t_plasma_end, is_plasma, dt)\n",
    "    return dt, caption, T_int_first, E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def energy_in_time_hist(T_int_first, E,figure_E_in_time_hist, t_plasma_start, t_plasma_end, is_plasma, dt):    \n",
    "    plt.rcParams.update({'font.size': 20})\n",
    "    fig, ax = plt.subplots(figsize =(10, 7))\n",
    "    for k in range(0,len(T_int_first)):\n",
    "                T_int_first[k] = T_int_first[k] / 1000000\n",
    "    plt.plot(T_int_first, E, drawstyle='steps-mid')\n",
    "    plt.title(detector+', #'+str(shot_no))\n",
    "    plt.xlabel('Time [ms]')\n",
    "    plt.ylabel('Energy [keV]')\n",
    "    if is_plasma == 1:\n",
    "        for t in (t_plasma_start, t_plasma_end):\n",
    "                plt.axvline(t, color='k', linestyle='--')\n",
    "                plt.xlim([0, (t_plasma_start + t_plasma_end)])\n",
    "    else:\n",
    "        plt.xlim(0,)\n",
    "        \n",
    "    plt.ylim(0,) #10 000 keV\n",
    "    plt.savefig(figure_E_in_time_hist, dpi = 1000)\n",
    "    caption = '# x = time in ms, count = energy in keV, dT= '+str(dt)+' ns'\n",
    "    return caption, T_int_first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hits_in_time_hist_new(T, dt, t_plasma_start, t_plasma_end, is_plasma,figure_count_in_time_hist):\n",
    "    pom = 0\n",
    "    \n",
    "    T_first=0 #cas, kdy prisel trigger a yacalo mereni\n",
    "    T_last=(max(T)) #posledni z Tfirst\n",
    "       \n",
    "    Delta_T = T_last - T_first\n",
    "    poc = int(int(Delta_T) / float(dt)) + 1 #pocet casovych oken\n",
    "    \n",
    "    T_hit=[] #cas\n",
    "    count=[] #energie\n",
    "    for i in range(0,poc):\n",
    "        T_hit.append((i*dt) + dt/2)\n",
    "        count.append(0)\n",
    "        \n",
    "    for j in range(0,len(T)):\n",
    "        time_index=0\n",
    "        time_index=int(((T[j]-T_first)/dt))\n",
    "        k=time_index\n",
    "        \n",
    "    for j in range(0,len(T)):\n",
    "        time_index=0\n",
    "        time_index=int(((T[j]-T_first)/dt))\n",
    "        if float(T[j]-T_first) >= (T_hit[time_index] - dt / 2) and float(T[j]-T_first) < (T_hit[time_index] + dt / 2):\n",
    "                count[time_index] = count[time_index] + 1\n",
    "                pom=pom+1\n",
    "      \n",
    "    for l in range(0,len(T_hit)):\n",
    "        T_hit[l]=T_hit[l]+T_first\n",
    "    \n",
    "    plt.rcParams.update({'font.size': 20})\n",
    "    fig, ax = plt.subplots(figsize =(10, 7))\n",
    "    for k in range(0,len(T_hit)):\n",
    "                T_hit[k] = T_hit[k] / 1000000\n",
    "    #plt.plot(T_hit, count)\n",
    "    plt.plot(T_hit, count, drawstyle='steps-mid')\n",
    "    plt.title(detector+', #'+str(shot_no))\n",
    "    plt.xlabel('Time [ms]')\n",
    "    plt.ylabel('Count')\n",
    "    if is_plasma == 1:\n",
    "        for t in (t_plasma_start, t_plasma_end):\n",
    "                plt.axvline(t, color='k', linestyle='--')\n",
    "                plt.xlim([0, (t_plasma_start + t_plasma_end)])\n",
    "    else:\n",
    "        plt.xlim(0,)\n",
    "        \n",
    "    plt.ylim(0,) #10 000 keV\n",
    "    plt.savefig(figure_count_in_time_hist, dpi = 1000)\n",
    "    caption = '# x = time in ms, dT= '+str(dt)+' ns'\n",
    "    return caption, T_hit,count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def energy_spectrum(Etot):\n",
    "    E_min=0\n",
    "    dE=5 #keV\n",
    "    E_max=max(Etot)\n",
    "    pocet=(E_max//dE) + 3\n",
    "    pocet=int(pocet)\n",
    "    E_max=float(dE*pocet)\n",
    "    \n",
    "    xle=[]\n",
    "    xre=[]\n",
    "    xmean=[]\n",
    "    \n",
    "    for p in range (0,pocet):\n",
    "        xle.append(E_min + (p * (E_max - E_min)) / pocet)\n",
    "        xre.append(xle[p]+dE)\n",
    "        xmean.append((xle[p] + xre[p]) / 2)\n",
    "    \n",
    "    count=[]\n",
    "    for l in range(0,pocet):\n",
    "        count.append(0)\n",
    "    #XII\n",
    "    for i in range(0,len(Etot)):\n",
    "        E_index=int(((Etot[i]-E_min)/dE))\n",
    "        if ((xle[E_index] <= Etot[i]) and (Etot[i] < xre[E_index])):\n",
    "            count[E_index]=count[E_index]+1\n",
    "    \n",
    "    plt.rcParams.update({'font.size': 20})\n",
    "    fig, ax = plt.subplots(figsize =(10, 7))\n",
    "    #ax.hist(Etot, bins = xle)\n",
    "    plt.fill_between(xmean, count,step='mid') #pre mid post\n",
    "    plt.plot(xmean, count, drawstyle='steps-mid')\n",
    "    plt.title(detector+', #'+str(shot_no))\n",
    "    plt.xlabel('Energy [keV]')\n",
    "    plt.ylabel('Count')\n",
    "    plt.xlim(0,)\n",
    "    ax.set_yscale('log') #log scale y\n",
    "    caption = '# x = energy in keV, dE= '+str(dE)+' keV'\n",
    "    plt.savefig(figure_E_hist, dpi = 1000)\n",
    "    return caption, xmean,count, xle, Etot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hist_file(file_hist, xmean, count, caption ):\n",
    "    with open(file_hist, \"w\", encoding=\"utf-8\") as hist:\n",
    "        hist.write('#\\n')\n",
    "        hist.write('#'+str(caption)+'\\n')\n",
    "        hist.write('# x_mean     count\\n')\n",
    "        hist.write('\\n')\n",
    "        for m in range(0,len(xmean)):\n",
    "                hist.write(str(xmean[m])+'     '+str(count[m])+'\\n')\n",
    "        \n",
    "    hist.close\n",
    "    return #T_first, E_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiplot(icon_fig, x1,y1,x2,y2):\n",
    "    plt.rcParams.update({'font.size': 20})\n",
    "    fig, ax = plt.subplots(nrows=2,figsize =(10, 7))\n",
    "    \n",
    "    ax[0].plot(x1, y1, drawstyle='steps-mid')\n",
    "    ax[0].set_xlabel('Time [ms]')\n",
    "    ax[0].set_ylabel('Energy [keV]')\n",
    "    if is_plasma == 1:\n",
    "        for t in (t_plasma_start, t_plasma_end):\n",
    "                ax[0].axvline(t, color='k', linestyle='--')\n",
    "                ax[0].set_xlim([0, (t_plasma_start + t_plasma_end)])\n",
    "    else:\n",
    "        ax[0].set_xlim(0,)\n",
    "    ax[0].set_ylim(0,) #keV\n",
    "    \n",
    "    #ax[1].hist(y2, bins = x2)\n",
    "    ax[1].fill_between(x2, y2,step='mid') #pre mid post\n",
    "    ax[1].plot(x2, y2, drawstyle='steps-mid')\n",
    "    ax[1].set_xlabel('Energy [keV]')\n",
    "    ax[1].set_ylabel('Count')\n",
    "    ax[1].set_xlim(0,)\n",
    "    #ax[1].set_ylim(0,)\n",
    "    ax[1].set_yscale('log') #log scale y\n",
    "    \n",
    "    fig.subplots_adjust(hspace=0.3)\n",
    "    \n",
    "    plt.savefig(icon_fig, dpi = 1000)\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#soubory, ktere ctu\n",
    "#read files\n",
    "t3pa=get_file(shot, identifier)\n",
    "'''\n",
    "name_calib='caliba'\n",
    "caliba=get_file_calib(name_calib)\n",
    "name_calib='calibb'\n",
    "calibb=get_file_calib(name_calib)\n",
    "name_calib='calibc'\n",
    "calibc=get_file_calib(name_calib)\n",
    "name_calib='calibt'\n",
    "calibt=get_file_calib(name_calib)\n",
    "'''\n",
    "#vytvorene soubory:\n",
    "#created files\n",
    "t3pa_cls= 'F10-W0049_shot_'+str(shot)+'_-450V.t3pa_cls'\n",
    "E_hist= 'F10-W0049_shot_'+str(shot)+'_-450V_E_hist.txt'\n",
    "E_in_time_hist= 'F10-W0049_shot_'+str(shot)+'_-450V_discharge_energy.txt'\n",
    "count_in_time_hist= 'F10-W0049_shot_'+str(shot)+'_-450V_discharge_hits.txt'\n",
    "size_interaction= 'F10-W0049_shot_'+str(shot)+'size_interaction.txt'\n",
    "file_energy_spectra_doublebreakdown= 'F10-W0049_shot_'+str(shot)+'energy_spectra_doublebreakdown.txt'\n",
    "\n",
    "#created figures\n",
    "icon_fig='icon-fig_CdTe'\n",
    "figure_E_in_time_hist='discharge_energy'\n",
    "figure_count_in_time_hist='discharge_hits_CdTe'\n",
    "figure_E_hist='Energy_spectrum'\n",
    "hit_map_fig='hit-map_CdTe'\n",
    "figure_size_interaction= 'F10-W0049_shot_'+str(shot)+'size_interaction'\n",
    "figure_energy_spectra_doublebreakdown= 'F10-W0049_shot_'+str(shot)+'energy_spectra_doublebreakdown'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calling functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nactu jednotlive kalibracni matice - abych to nemusel delat v kazde funkci\n",
    "'''\n",
    "a=load_calib(caliba)\n",
    "b=load_calib(calibb)\n",
    "c=load_calib(calibc)\n",
    "t=load_calib(calibt)\n",
    "'''\n",
    "#nactu a urcim jednotlive hodnoty - abych to nemusel delat v kazde funkci\n",
    "index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti = load_t3pa_file(t3pa)\n",
    "index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti, RowNo, ClmNo = t3pa_data(pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hit map \n",
    "raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hit map\n",
    "hit_map(detector,hit_map_fig,RowNo,ClmNo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy and time calculation\n",
    "Energy and time calculation from raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#E=energy(a, b, c, t, ToT, pocet_udalosti, RowNo, ClmNo)\n",
    "T=Time(ToA, FToA, pocet_udalosti, RowNo, ClmNo)\n",
    "\n",
    "#index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T = remove_interactions_with_zero_energy(index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T)\n",
    "#E,T=Timewalk(E,T)\n",
    "\n",
    "#sort by time\n",
    "#T, index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E = (list(t) for t in zip(*sorted(zip(T, index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E), reverse=True))) #serazeni od nejvetsiho po nejmensi\n",
    "\n",
    "T_pom=T.copy()\n",
    "\n",
    "#save to file\n",
    "#T_first, E_tot, Ntot = file_t3pa_cls_new(t3pa_cls,T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detected energies during the discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dt, caption, T_int_first, E = energy_spectrum_in_time(T_first, E_tot)\n",
    "#hist_file(E_in_time_hist, T_int_first, E, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#caption, xmean,count, xle, Etot = energy_spectrum(E_tot)\n",
    "#hist_file(E_hist, xmean, count, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count of hits during the discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#caption, T_hit,count1 = hits_in_time_hist_new(T_pom, dt, t_plasma_start, t_plasma_end, is_plasma, figure_count_in_time_hist)\n",
    "dt=100 #(ns) time width of 1 bin\n",
    "caption, T_hit,count1 = hits_in_time_hist_new(T_pom, dt, t_plasma_start, t_plasma_end, is_plasma, icon_fig)\n",
    "hist_file(count_in_time_hist, T_hit, count1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph for home page \n",
    "Detected energies during the discharge + Energy spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#multiplot(icon_fig, T_int_first,E,xmean,count)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}