
import numpy as np
import math
#import pandas as pd
import matplotlib.pyplot as plt

import matplotlib.cm as cm
#from matplotlib.mlab import griddata

from urllib.error import HTTPError # recognise the error stemming from missing data
#import urllib
import urllib.request


#Define an exception which will be raised if the data is missing and stop the notebook execution
class StopExecution(Exception):
    def _render_traceback_(self):
        pass
    
#shot_no = 44395  #test discharge for which the notebook will definitely work
shot_no = 44820
shot = shot_no
identifier='H03-W0051_shot_'+str(shot)+'_450V'
detector = 'H03-W0051'

ds = np.DataSource('/tmp')  # temporary storage for downloaded files
scalars_URL = 'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/PlasmaDetection/Results/{name}'


def get_scalar(shot_no, name):
    return float(ds.open(scalars_URL.format(shot_no=shot_no, name=name)).read())
t_plasma_start = get_scalar(shot_no, 't_plasma_start')
t_plasma_end = get_scalar(shot_no, 't_plasma_end')
is_plasma = get_scalar(shot_no, 'b_plasma')


def get_file(shot, identifier):
    #Pick the discharge to analyse 
    URL = 'http://golem.fjfi.cvut.cz/shots/{shot}/Diagnostics/TimepixDetector/H03/{identifier}.t3pa'
    
    url = URL.format(shot=shot, identifier=identifier)
    try:
        file_name_t3pa=url
        with urllib.request.urlopen(file_name_t3pa) as ft3pa:
            line = ft3pa.readline()
            line = line.decode('utf‐8')
        ft3pa.close
    except HTTPError:
        print('File not found at %s. Aborting notebook execution.' % url)
        raise StopExecution
    
    return file_name_t3pa


def get_file_calib(name_calib):
    #Pick the discharge to analyse 
    URL = 'http://golem.fjfi.cvut.cz/shots/{shot}/Diagnostics/TimepixDetector/calib_matrix_H03/{name_calib}.txt'
    
    url = URL.format(shot=shot, name_calib=name_calib)
    #print(url)
    try:
        file_calib=url
        with urllib.request.urlopen(file_calib) as calib:
            line = calib.readline()
            line = line.decode('utf‐8')
        calib.close
    except HTTPError:
        print('File not found at %s. Aborting notebook execution.' % url)
        raise StopExecution
    
    return file_calib


def load_calib(file_calib):
    with urllib.request.urlopen(file_calib) as fc:
        calib=[] #vytvoreni 1D pole
        for i in range(0,256): #tj. rozsah 0-255
            temp = [] # docasne pole
            for j in range(0,256):
                temp.append(0) #naplneni docasneho pole 0
            calib.append(temp)  #naplneni pole a[] docasnym polem temp
            
        for i in range(0,256): #nacteni calib matice do pole calib
            line = fc.readline()
            line = line.decode('utf‐8')
            word=line.strip().split(' ')
            for j in range(0,256):
                calib[i][j]=float(word[j]) #i = radek, j = sloupec0
    fc.close       
    return calib


def load_t3pa_file(file_t3pa):
    index=[]
    matrix_index=[]
    ToA=[]
    ToT=[]
    FToA=[]
    overflow=[]
    pocet_udalosti = 0
    with urllib.request.urlopen(file_t3pa) as ft3pa:
        line = ft3pa.readline()
        line = line.decode('utf‐8')
        while True:
            line = ft3pa.readline()
            line = line.decode('utf‐8')
            word=line.strip().split('\t') #v t3pa souboru je oddelovac \t
            if line == '':
                break
            
            index.append(word[0])
            matrix_index.append(word[1])
            ToA.append(float(word[2]))
            ToT.append(float(word[3]))
            FToA.append(float(word[4]))
            overflow.append(float(word[5]))
            pocet_udalosti = pocet_udalosti + 1
    ft3pa.close
    return index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti


def noise(index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti): #tuto fci nemus9m explicitn2 volat - volam ji v fci load_t3pa
    pocet=int(0) #pocet sumicich pixelu
    konst=int(len(index)/1000)+1
    noise_matrix_index=[]
    for i in range(0,konst):      
        pom = [] # pomocne pole
        k=0 #pomocna promenna - udava, kolik je v czklu ve skutecnosti udalosti - aby nebyla chyba 'list index out of range'
        for j in range(0,1001):
            if i*1000+j>=len(index):
                break
            pom.append(matrix_index[i*1000+j])
            k=k+1
        for m in range(0,k):
                count=int(0) #pocet vvyskytu stejneho matrix index behem 1000 udalosti
                index_=int(-1) #budu testovat, jestli pixel na ktery koukam je sumici (abych ho nezapocital 2x)
                
                for p in range(0,pocet):
                    #index=int(p)
                    if pom[m]==noise_matrix_index[p]:
                        index_=p #pixel na ktery jsem uz koukal a byl sumici
                        break
                
                if index_ >=0 and pom[m]==noise_matrix_index[index_]:
                    continue
                                 
                for l in range(0,k):
                    if pom[m]==pom[l]:
                        count=count+1
                ####podminka na sumici pixely
                if count>=50: #kdyz se pixel vyskytne behem tisice udalosti vicekrat nez toto cislo, je sumici 
                    noise_matrix_index.append(pom[m])
                    #noise_matrix_index[pocet]=pom[i]
                    pocet=pocet+1
        pom.clear()           
        
    pocet_udalosti=len(index)
    
    for n in range (0,pocet_udalosti):
        for o in range(0,len(noise_matrix_index)):
            if n >=pocet_udalosti:
                    break
            if(matrix_index[n]==noise_matrix_index[o]):
                del matrix_index[n]
                del index[n]
                del ToA[n]
                del ToT[n]
                del FToA[n]
                del overflow[n]
                pocet_udalosti=pocet_udalosti-1
                continue
    return pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow


def t3pa_data(pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow):
    #rovnou vyhodim sumici pixely
    pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow=noise(index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti)
    RowNo=[]
    ClmNo=[]
    for i in range(0,len(matrix_index)):
        RowNo.append(int(int(matrix_index[i]))//int(256))
        ClmNo.append(int(int(matrix_index[i]))%int(256))
        #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)
        #RowNo.append(int(int(matrix_index[i]))%int(256)) #ver XV
    return index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti, RowNo, ClmNo


def hit_map(detector,hit_map_fig,RowNo,ClmNo):
    plt.hist2d(RowNo,ClmNo,bins=(256,256),cmap='Blues')
    cb=plt.colorbar()
    cb.set_label('Counts in pixel')
    plt.xlabel('x [pixel]')
    plt.ylabel('y [pixel]')
    plt.title(detector)
    plt.savefig(hit_map_fig, dpi = 1000)
    return


def energy(a, b, c, t, ToT, pocet_udalosti, RowNo, ClmNo):
    E=[] #energy in keV
    #for i in range (0,pocet_udalosti):
    pom=0
    for i in range (0,len(ToT)):
        Sqrt=float(0.0)
        e1=float(0.0)
        e2=float(0.0)
        
        # promenna sqrt je vnitrek odmocniny
        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
        if float(Sqrt)<float(0):
            E.append(float(0))
            
            
        else:
            '''
            V kalibracni matici a se obcas vyskytne 0 -> ve vypoctu energie
            je tim padem deleni nulou -> energie diverguje. Jak to vyresit?
            zatim polozim energii = 0 (kdyz a=0), pak se uvidi
            
            nakonec udelam limitu vyrazu energie pro a->0 (L'hopital)
            '''
            if a[RowNo[i]][ClmNo[i]]==0:
                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))))
                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))))
                
            
            else:
                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]]))
                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]]))
                
            if a[RowNo[i]][ClmNo[i]]<0:
                e1=-1
                e2=-1
                
            if math.isnan(e1):
                e1=-1 
                
            if math.isnan(e2):
                e2=-1 
                
            if e1<0 and e2<0:
                E.append(float(0))
                
            if e1>=0 and e1>e2:
                E.append(float(e1))
                    
            if e2>=0 and e2>e1:
                E.append(float(e2))
                
            if e1>=0 and e2==e1:
                E.append(float(e1)) 
            
    return E


def Time(ToA, FToA, pocet_udalosti, RowNo, ClmNo):
    T=[] #time in ns
    for i in range (0,pocet_udalosti):
        Time=float(0.0)
        Time=(float(ToA[i])-((float(FToA[i])/float(16))))*float(25)
        T.append(float(Time))
    return T


def Timewalk_parameters_Si():
    #SI - korekce na TimeWalk - parametry
    A = -1.21988
    B = 4.33638
    C = 29.5075
    D = 1
    sigma_A=0.7013
    sigma_B=0.1366
    sigma_C=4.753
    sigma_D=0
    return A, B, C, D, sigma_A, sigma_B, sigma_C, sigma_D


def Timewalk(E,T):
    i=0
    A, B, C, D, sigma_A, sigma_B, sigma_C, sigma_D = Timewalk_parameters_Si()
    while i < len(T):
        timewalk=float(0)
        timewalk = (C / (E[i] - B)) + A 
        if(timewalk<0):
            timewalk=0
        T[i]=T[i]-timewalk
        i=i+1
        
    return E,T


def remove_interactions_with_zero_energy(index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T):
    i=0
    treshold=5.015347
    while i < len(T):
        if E[i]<treshold: #E[i] < energy treshold
            index.pop(i)
            matrix_index.pop(i)
            ToA.pop(i)
            ToT.pop(i)
            FToA.pop(i)
            overflow.pop(i)
            RowNo.pop(i)
            ClmNo.pop(i)
            E.pop(i)
            T.pop(i)
            continue
        i=i+1
    return index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T


def clustering_new(index, matrix_index, ToA, ToT, FToA, overflow, RowNo, ClmNo, E, T):
    dT=float(50)
    
    indexCl, TCl,ECl, matrix_indexCl, ToACl,ToTCl,FToACl,RowNoCl,ClmNoCl,overflowCl=[],[],[],[],[],[],[],[],[],[]
    StartLastElem=len(T)-1
    
    indexCl.append(int(index[StartLastElem]))
    TCl.append(float(T[StartLastElem]))
    ECl.append(float(E[StartLastElem]))
    matrix_indexCl.append(int(matrix_index[StartLastElem]))
    RowNoCl.append(int(RowNo[StartLastElem]))
    ClmNoCl.append(int(ClmNo[StartLastElem]))
    ToACl.append(float(ToA[StartLastElem]))
    ToTCl.append(float(ToT[StartLastElem]))
    FToACl.append(float(FToA[StartLastElem]))
    overflowCl.append(float(overflow[StartLastElem]))
    
    del index[StartLastElem]
    del T[StartLastElem]
    del E[StartLastElem]
    del matrix_index[StartLastElem]
    del RowNo[StartLastElem]
    del ClmNo[StartLastElem]
    del ToA[StartLastElem]
    del ToT[StartLastElem]
    del FToA[StartLastElem]
    del overflow[StartLastElem]
    
    j=1
    pom=float(TCl[0]+dT)
    while(j >0):
        if(len(T) == 0):
           break
        k=0
        j=0
        while (k<=(len(TCl)-1)):
            i=len(T)-1
            if(len(T) == 0):
                break
            pocet_sousedu=0 #pocet sousednich pixelu - mohou byt maximalne 4
            delka=0
            # verze X
            count=0 #pomocna promanna, kterou urcuji, ze se ma nasledujici cyklus while projit jeste jednou, pokud je i = -1
            while(float(T[i])<=(pom)):
                delka=delka+1
                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))))):
                    #beru jen pixely, které mají společnou jednu stranu.
                    #pixely, kter0 spolu sousedí přes roh neuvažuji
                    indexCl.append(int(index[i]))
                    TCl.append(float(T[i]))
                    ECl.append(float(E[i]))
                    matrix_indexCl.append(int(matrix_index[i]))
                    RowNoCl.append(int(RowNo[i]))
                    ClmNoCl.append(int(ClmNo[i]))
                    ToACl.append(float(ToA[i]))
                    ToTCl.append(float(ToT[i]))
                    FToACl.append(float(FToA[i]))
                    overflowCl.append(float(overflow[i]))
                    
                    # Removes i-th Row
                    del index[i]
                    del T[i]
                    del E[i]
                    del matrix_index[i]
                    del RowNo[i]
                    del ClmNo[i]
                    del ToA[i]
                    del ToT[i]
                    del FToA[i]
                    del overflow[i]
                    
                    j=j+1
                    i=len(T)-1
                    pocet_sousedu=pocet_sousedu+1
                    
                    if(len(T) == 0):
                        break
                    
                    if(pocet_sousedu==4):
                        break
                    
                    continue
                
                i=i-1
                if(i==-1): # verze X
                    count=count+1
                    
                if(i<0 and len(T)>0): # verze X
                    i=0
                    if(count>1):
                        break
                    
                if(i>=len(T)):
                    break
            k=k+1
            
        if(len(TCl)>2):
            indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl = insertionSort(indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl)
            
    return T, indexCl,TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl


def insertionSort(indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl):
    # Function to do insertion sort
    # Traverse through 1 to len(arr)
    for i in range(1, len(TCl)):
        key = TCl[i]
        # Move elements of arr[0..i-1], that are
        # greater than key, to one position ahead
        # of their current position
        
        #ostatni
        key1 = indexCl[i]
        key2 = ECl[i]
        key3 = matrix_indexCl[i]
        key4 = RowNoCl[i]
        key5 = ClmNoCl[i]
        key6 = ToACl[i]
        key7 = ToTCl[i]
        key8 = FToACl[i]
        key9 = overflowCl[i]
        
        j = i-1
        while j >= 0 and key < TCl[j] :
                TCl[j + 1] = TCl[j]
                #ostatni
                indexCl[j + 1] = indexCl[j]
                ECl[j + 1] = ECl[j]
                matrix_indexCl[j + 1] = matrix_indexCl[j]
                RowNoCl[j + 1] = RowNoCl[j]
                ClmNoCl[j + 1] = ClmNoCl[j]
                ToACl[j + 1] = ToACl[j]
                ToTCl[j + 1] = ToTCl[j]
                FToACl[j + 1] = FToACl[j]
                overflowCl[j + 1] = overflowCl[j]
                j -= 1
                
        TCl[j + 1] = key
        #ostatni
        indexCl[j + 1] = key1
        ECl[j + 1] = key2
        matrix_indexCl[j + 1] = key3
        RowNoCl[j + 1] =key4
        ClmNoCl[j + 1] = key5
        ToACl[j + 1] = key6
        ToTCl[j + 1] = key7
        FToACl[j + 1] = key8
        overflowCl [j + 1] = key9 
    return indexCl, TCl, ECl, matrix_indexCl, RowNoCl, ClmNoCl, ToACl, ToTCl, FToACl, overflowCl


def file_t3pa_cls_new(file_t3pa_cls,T):
    with open(file_t3pa_cls, "w", encoding="utf-8") as t3pa_cls:
        t3pa_cls.write('%\n')
        t3pa_cls.write('% Index	Matrix Index	[ RowNo, ClmNo ]	ToA	FToA	( ToA_in_ns )	ToT	( ToT_in_keV )	Overflow\n')
        t3pa_cls.write('\n')
        i=1
        T_first=[]
        E_tot=[]
        N_tot=[]
        eventNo=0 #for high_energy_event function
        while(len(T) > 0):
            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) 
            Tfirst=float(TCl[0])
            Tlast=float(TCl[len(TCl)-1])
            dT=Tlast-Tfirst
            Etot=float(0)
            for k in range(0,len(TCl)):
                Etot=Etot+float(ECl[k])
            T_first.append(float(Tfirst))
            dT=Tlast-Tfirst
            E_tot.append(float(Etot))
            N_tot.append(len(TCl)) #new in ver. XV
            
            t3pa_cls.write('# '+str(i)+', Nunmasked = '+str(len(TCl))+', Nmasked = 0, Ntot = '+str(len(TCl))+'\n')
            t3pa_cls.write('# Tfirst = '+str(Tfirst)+' ns, Tlast = '+str(Tlast)+' ns, dT = '+str(dT)+' ns, Etot = '+str(Etot)+' keV\n')
            for j in range(0,len(TCl)):
                t3pa_cls.write(str(indexCl[j])+'	'+str(matrix_indexCl[j])+'	[ '+str(RowNoCl[j])+',	'+str(ClmNoCl[j])+' ]	'+str(ToACl[j])+'	'+str(FToACl[j])+'	( '+str(TCl[j])+' ns )	'+str(ToTCl[j])+'	( '+str(ECl[j])+' keV )	'+str(overflowCl[j])+'\n')
            t3pa_cls.write('\n') 
            #eventNo=single_interaction(eventNo,Etot, Tfirst, ECl, TCl, RowNoCl, ClmNoCl,path)
            i=i+1
    t3pa_cls.close
    return T_first, E_tot, N_tot


def energy_spectrum_in_time(Tfirst, Etot): #dela histogram - energie zaznamenana v case 
    pom = 0
    dt=100 #(ns) time width of 1 bin
    
    T_first=0 #cas, kdy prisel trigger a yacalo mereni
    T_last=(max(Tfirst)) #posledni z Tfirst
       
    Delta_T = T_last - T_first
    poc = int(int(Delta_T) / float(dt)) + 1 #pocet casovych oken
    
    T_int_first=[] #cas
    E=[] #energie
    
    for i in range(0,poc):
        T_int_first.append((i*dt) + dt/2)
        E.append(0)
    
    #XII
    for j in range(0,len(Tfirst)):
        time_index=0
        time_index=int(((Tfirst[j]-T_first)/dt))
        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):
            E[time_index]=float(E[time_index])+float(Etot[j])
            pom=pom+1
            
    for l in range(0,len(T_int_first)):
        T_int_first[l]=T_int_first[l]+T_first
        
    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)
    return dt, caption, T_int_first, E


def energy_in_time_hist(T_int_first, E,figure_E_in_time_hist, t_plasma_start, t_plasma_end, is_plasma, dt):    
    plt.rcParams.update({'font.size': 20})
    fig, ax = plt.subplots(figsize =(10, 7))
    for k in range(0,len(T_int_first)):
                T_int_first[k] = T_int_first[k] / 1000000
    plt.plot(T_int_first, E, drawstyle='steps-mid')
    plt.title(detector+', #'+str(shot_no))
    plt.xlabel('Time [ms]')
    plt.ylabel('Energy [keV]')
    if is_plasma == 1:
        for t in (t_plasma_start, t_plasma_end):
                plt.axvline(t, color='k', linestyle='--')
                plt.xlim([0, (t_plasma_start + t_plasma_end)])
    else:
        plt.xlim(0,)
        
    plt.ylim(0,) #10 000 keV
    plt.savefig(figure_E_in_time_hist, dpi = 1000)
    caption = '# x = time in ms, count = energy in keV, dT= '+str(dt)+' ns'
    return caption, T_int_first


def hits_in_time_hist_new(T, dt, t_plasma_start, t_plasma_end, is_plasma,figure_count_in_time_hist):
    pom = 0
    
    T_first=0 #cas, kdy prisel trigger a yacalo mereni
    T_last=(max(T)) #posledni z Tfirst
       
    Delta_T = T_last - T_first
    poc = int(int(Delta_T) / float(dt)) + 1 #pocet casovych oken
    
    T_hit=[] #cas
    count=[] #energie
    for i in range(0,poc):
        T_hit.append((i*dt) + dt/2)
        count.append(0)
        
    for j in range(0,len(T)):
        time_index=0
        time_index=int(((T[j]-T_first)/dt))
        k=time_index
        
    for j in range(0,len(T)):
        time_index=0
        time_index=int(((T[j]-T_first)/dt))
        if float(T[j]-T_first) >= (T_hit[time_index] - dt / 2) and float(T[j]-T_first) < (T_hit[time_index] + dt / 2):
                count[time_index] = count[time_index] + 1
                pom=pom+1
      
    for l in range(0,len(T_hit)):
        T_hit[l]=T_hit[l]+T_first
    
    plt.rcParams.update({'font.size': 20})
    fig, ax = plt.subplots(figsize =(10, 7))
    for k in range(0,len(T_hit)):
                T_hit[k] = T_hit[k] / 1000000
    #plt.plot(T_hit, count)
    plt.plot(T_hit, count, drawstyle='steps-mid')
    plt.title(detector+', #'+str(shot_no))
    plt.xlabel('Time [ms]')
    plt.ylabel('Count')
    if is_plasma == 1:
        for t in (t_plasma_start, t_plasma_end):
                plt.axvline(t, color='k', linestyle='--')
                plt.xlim([0, (t_plasma_start + t_plasma_end)])
    else:
        plt.xlim(0,)
        
    plt.ylim(0,) #10 000 keV
    plt.savefig(figure_count_in_time_hist, dpi = 1000)
    caption = '# x = time in ms, dT= '+str(dt)+' ns'
    return caption, T_hit,count


def energy_spectrum(Etot):
    E_min=0
    dE=5 #keV
    E_max=max(Etot)
    pocet=(E_max//dE) + 3
    pocet=int(pocet)
    E_max=float(dE*pocet)
    
    xle=[]
    xre=[]
    xmean=[]
    
    for p in range (0,pocet):
        xle.append(E_min + (p * (E_max - E_min)) / pocet)
        xre.append(xle[p]+dE)
        xmean.append((xle[p] + xre[p]) / 2)
    
    count=[]
    for l in range(0,pocet):
        count.append(0)
    #XII
    for i in range(0,len(Etot)):
        E_index=int(((Etot[i]-E_min)/dE))
        if ((xle[E_index] <= Etot[i]) and (Etot[i] < xre[E_index])):
            count[E_index]=count[E_index]+1
    
    plt.rcParams.update({'font.size': 20})
    fig, ax = plt.subplots(figsize =(10, 7))
    #ax.hist(Etot, bins = xle)
    plt.fill_between(xmean, count,step='mid') #pre mid post
    plt.plot(xmean, count, drawstyle='steps-mid')
    plt.title(detector+', #'+str(shot_no))
    plt.xlabel('Energy [keV]')
    plt.ylabel('Count')
    plt.xlim(0,)
    ax.set_yscale('log') #log scale y
    caption = '# x = energy in keV, dE= '+str(dE)+' keV'
    plt.savefig(figure_E_hist, dpi = 1000)
    return caption, xmean,count, xle, Etot


def hist_file(file_hist, xmean, count, caption ):
    with open(file_hist, "w", encoding="utf-8") as hist:
        hist.write('#\n')
        hist.write('#'+str(caption)+'\n')
        hist.write('# x_mean     count\n')
        hist.write('\n')
        for m in range(0,len(xmean)):
                hist.write(str(xmean[m])+'     '+str(count[m])+'\n')
        
    hist.close
    return T_first, E_tot


def multiplot(icon_fig, x1,y1,x2,y2):
    plt.rcParams.update({'font.size': 20})
    fig, ax = plt.subplots(nrows=2,figsize =(10, 7))
    
    ax[0].plot(x1, y1, drawstyle='steps-mid')
    ax[0].set_xlabel('Time [ms]')
    ax[0].set_ylabel('Energy [keV]')
    if is_plasma == 1:
        for t in (t_plasma_start, t_plasma_end):
                ax[0].axvline(t, color='k', linestyle='--')
                ax[0].set_xlim([0, (t_plasma_start + t_plasma_end)])
    else:
        ax[0].set_xlim(0,)
    ax[0].set_ylim(0,) #keV
    
    #ax[1].hist(y2, bins = x2)
    ax[1].fill_between(x2, y2,step='mid') #pre mid post
    ax[1].plot(x2, y2, drawstyle='steps-mid')
    ax[1].set_xlabel('Energy [keV]')
    ax[1].set_ylabel('Count')
    ax[1].set_xlim(0,)
    #ax[1].set_ylim(0,)
    ax[1].set_yscale('log') #log scale y
    
    fig.subplots_adjust(hspace=0.3)
    
    plt.savefig(icon_fig, dpi = 1000)
    return


def single_interaction(eventNo,Etot, Tfirst, ECl, TCl, RowNoCl, ClmNoCl,path): # ver XV
    #tuto funkci volam ve funkci file_t3pa_cls_new(file_t3pa_cls,T):
    #This function is calling in function file_t3pa_cls_new(file_t3pa_cls,T)
    
    high_E = 1500 #[keV] - zadani, jaka energie je pro me vysoka

    if(Etot>=high_E or len(ECl)>15):
    #if (len(ECl)>15): #len(ECl) = pocet pixelu dane interakce
        print("Total energy of track: "+str(Etot)+" keV\n")
        A, B, C, D, sigma_A, sigma_B, sigma_C, sigma_D = Timewalk_parameters_Si()
        
        ######################
        #SI - rekonstrukce z(t) - parametry
        a=0.0193947
        b=-0.0273934
        sigma_a=0.0004163
        sigma_b=0.009832
        #######################
        
        time=[]
        sigma_time=[]
        time_min = []
        time_max=[]
        z_rec=[]
        z_rec_min=[]
        z_rec_max=[]
        sigma_z_rec=[]
        
        for i in range(0, len(TCl)):
            time.append(TCl[i] - Tfirst)
            #chyba urceni casu = chyba timewalk ->
            sigma_time.append(np.sqrt(((sigma_C / (pow((ECl[i] - B), D)))*(sigma_C / (pow((ECl[i] - B), D)))) + (((sigma_B*C*D) / (pow((ECl[i] - B), (D + 1))))*((sigma_B*C*D) / (pow((ECl[i] - B), (D + 1))))) + (((sigma_D*C*math.log(ECl[i] - B)) / (pow((ECl[i] - B), D)))* ((sigma_D*C*math.log(ECl[i] - B)) / (pow((ECl[i] - B), D)))) + (sigma_A*sigma_A)))
            time_min.append(time[i] - sigma_time[i])
            time_max.append(time[i] + sigma_time[i])

            #//
            #//rekonstrukce hloubky interakce
            z_rec.append(a * time[i] + (b))
            sigma_z_rec.append(np.sqrt(((time[i]*time[i])*(sigma_a*sigma_a)) + (sigma_b*sigma_b) + (a*a)*(sigma_time[i]*sigma_time[i])))
            z_rec_min.append(z_rec[i] - sigma_z_rec[i])
            z_rec_max.append(z_rec[i] + sigma_z_rec[i])
            #//
            
        single_interaction_file=str(path)+'event'+str(eventNo)+'.txt'
        single_interaction_2D_fig=str(path)+'2D_event'+str(eventNo)+'.png'
        single_interaction_3D_fig=str(path)+'3D_event'+str(eventNo)+'.png'
        
        with open(single_interaction_file, "w", encoding="utf-8") as f:
            f.write('#\n')
            f.write('# x_mean   y_mean   z_rec   z_rec_min   z_rec_max   sigma_z_rec\n')
            f.write('#E_tot='+str(Etot)+'\n')
            f.write('\n')
            for m in range(0,len(TCl)):
                    #print(str(RowNoCl[m])+'     '+str(ClmNoCl[m])+'     '+str(z_rec[m])+'     '+str(z_rec_min[m])+'     '+str(z_rec_max[m])+'     '+str(sigma_z_rec[m])+'     '+str(ECl[m])+'\n')
                    f.write(str(RowNoCl[m])+'     '+str(ClmNoCl[m])+'     '+str(z_rec[m])+'     '+str(z_rec_min[m])+'     '+str(z_rec_max[m])+'     '+str(sigma_z_rec[m])+'     '+str(ECl[m])+'\n')

        f.close
        
        ############################### plot
        #binwidth=1
        if ((max(RowNoCl)-min(RowNoCl))>=(max(ClmNoCl)-min(ClmNoCl))):
            rang=max(RowNoCl)-min(RowNoCl)
            #bins=(range((min(RowNoCl)-1), (max(RowNoCl)+3), binwidth), range((min(ClmNoCl)-1), (min(ClmNoCl)+rang+3), binwidth))
            x_min=min(RowNoCl)-1
            x_max=max(RowNoCl)+2
            y_min=min(ClmNoCl)-1
            y_max=min(ClmNoCl)+rang+2
        else:
            rang = max(ClmNoCl)-min(ClmNoCl)
            #bins=(range((min(RowNoCl)-1), (min(RowNoCl)+rang+3), binwidth), range((min(ClmNoCl)-1), (max(ClmNoCl)+3), binwidth))
            x_min=min(RowNoCl)-1
            x_max=min(RowNoCl)+rang+2
            y_min=min(ClmNoCl)-1
            y_max=max(ClmNoCl)+2
            
        ###############$$$$$$$$$$$$$$$$$$$$
        rows, cols = (x_max-x_min, x_max-x_min)
        #arrECl = [[0]*cols]*rows
        arrECl= [[0] * cols for _ in range(rows)]
        for i in range (x_min,x_max):
            for j in range (y_min,y_max):
                for k in range(0,len(RowNoCl)):
                        if ((i==RowNoCl[k]) and (j==ClmNoCl[k])):
                            #print(str(i)+"   "+str(j)+"    "+str(RowNoCl[k])+"    "+str(ClmNoCl[k])+"\n")
                            arrECl[i-x_min][j-y_min]=ECl[k]
        
        arrECl=np.transpose(arrECl)
        arrECl[arrECl == 0] = np.nan
        
        #2D plot
        plt.imshow(arrECl,cmap='jet',  interpolation='none', origin=[(x_min-0.2),(y_min-0.2)], aspect='equal', extent=[x_min-0.2,x_max,y_min-0.2,y_max])
       
        ###############$$$$$$$$$$$$$$$$$$$$
        cb=plt.colorbar(pad=0.005)
        plt.tick_params(axis="x",direction="in")
        plt.tick_params(axis="y",direction="in")
        cb.ax.tick_params(axis='y', direction='in')
        cb.set_label('Energy in pixel [keV]')
        plt.xlabel('x [pixel]')
        plt.ylabel('y [pixel]')
        plt.title('$E_{tot}$ = '+str(round(Etot, 2))+' keV, $N_{tot}$ = '+str(len(ECl)))
        plt.savefig(single_interaction_2D_fig, dpi = 1000)
        plt.show()
        plt.close()
        ############################### plot
        
        
        ############## 3D plot
        # Creating figure
        fig = plt.figure(figsize = (16, 9))
        ax = plt.axes(projection ="3d")

        # Add x, y gridlines 
        ax.grid(b = True, color ='grey', # 
                linestyle ='-.', linewidth = 0.3, 
                alpha = 0.2) 

        # Creating plot
        sctt = ax.scatter3D(RowNoCl, ClmNoCl, z_rec, 
                            alpha = 0.8,
                            c = ECl, 
                            cmap = 'jet', 
                            s = 4000, #size of bubbles in plot
                            marker ='o')
        plt.title('$E_{tot}$ = '+str(round(Etot, 2))+' keV, $N_{tot}$ = '+str(len(ECl)))
        ax.set_xlabel('x [pixel]', fontweight ='bold') 
        ax.set_ylabel('y [pixel]', fontweight ='bold') 
        ax.set_zlabel('z [mm]', fontweight ='bold')
        cb=fig.colorbar(sctt, ax = ax, shrink = 0.5,aspect = 10,pad=0.00005) #
        cb.ax.tick_params(axis='y', direction='in')
        cb.set_label('Energy in pixel [keV]', fontweight ='bold')
        plt.savefig(single_interaction_3D_fig, dpi = 1000)
        # show plot
        plt.show()
        plt.close()
        ############## 3D plot
        
        eventNo=eventNo+1
    return eventNo


def size_of_interactions_average(Etot, Ntot,file_hist):  #ver XV
    E_min=0
    dE=5 #keV
    E_max=max(Etot)
    pocet=(E_max//dE) + 3
    pocet=int(pocet)
    E_max=float(dE*pocet)
    
    xle=[]
    xre=[]
    xmean=[]
    count=[]
    nop=[] #number of pixels
    nop_mean=[] #mean number of pixels in dE
    sigma=[] #root mean square error of the mean - stredni kvadraticka chyba prumeru
    
    for p in range (0,pocet):
        xle.append(E_min + (p * (E_max - E_min)) / pocet)
        xre.append(xle[p]+dE)
        xmean.append((xle[p] + xre[p]) / 2)
        count.append(0)
        nop.append(0) 
        nop_mean.append(0) 
        sigma.append(0)
    
    #XII
    for i in range(0,len(Etot)):
        E_index=int(((Etot[i]-E_min)/dE))
        if ((xle[E_index] <= Etot[i]) and (Etot[i] < xre[E_index])):
            count[E_index]=count[E_index]+1
            nop[E_index]=nop[E_index]+Ntot[i] #ver XV
    
    for l in range(0,pocet):
        if nop[l]!=0:
            nop_mean[l]=float(nop[l])/float(count[l])
    
    #####Computing sigma
    for i in range(0,len(Etot)):
        E_index=int(((Etot[i]-E_min)/dE))
        if ((xle[E_index] <= Etot[i]) and (Etot[i] < xre[E_index])):
            if nop_mean[E_index]!=0:
                sigma[E_index]=sigma[E_index]+((float(Ntot[i])-float(nop_mean[E_index]))*(float(Ntot[i])-float(nop_mean[E_index])))
            
    for l in range(0,pocet):
        if nop[l]!=0 and count[l]>1:
            sigma[l]=np.sqrt(sigma[l]/(float(count[l])*(float(count[l])-1)))
        if nop[l]!=0 and count[l]==1:
            sigma[l]=10
    #####Computing sigma
    
    #####write to file
    with open(file_hist, "w", encoding="utf-8") as hist:
        hist.write('# x_mean [keV]     mean_size_of_interaction [pixel]     sigma_mean_size_of_interaction\n')
        for m in range(0,len(xmean)):
                hist.write(str(xmean[m])+'     '+str(nop_mean[m])+'     '+str(sigma[m])+'\n')
        
    hist.close
    #####write to file
    
    plt.rcParams.update({'font.size': 20})
    fig, ax = plt.subplots(figsize =(10, 7))
    #plt.fill_between(xmean, nop,step='mid',color='blue', alpha=0.3) #pre mid post
    #plt.fill_between(xmean, nop,step='mid') #pre mid post
    plt.plot(xmean, nop_mean, drawstyle='steps-mid')
    plt.errorbar(xmean, nop_mean, yerr = sigma,fmt ='.', linewidth=0.7)
    plt.title(detector+', #'+str(shot_no))
    plt.xlabel('Energy [keV]')
    plt.ylabel('Average size of interaction [pixel]')
    plt.xlim(0,500)
    plt.ylim(0,17)
    #ax.set_yscale('log') #log scale y
    caption = '# x = energy in keV, dE= '+str(dE)+' keV'
    plt.savefig(figure_size_interaction, dpi = 1000)
    return


def energy_spectra_doublebreakdown(Etot, Tfirst, file_hist):
    #this function separates discharge into two parts acording to time Tsep and creates energy spectrum for each part.
    #This is for discharges with double brakdown
    
    Tsep=14636000 # [ns] 
    
    E_min=0
    dE=5 #keV
    E_max=max(Etot)
    pocet=(E_max//dE) + 3
    pocet=int(pocet)
    E_max=float(dE*pocet)
    
    xle=[]
    xre=[]
    xmean=[]
    
    for p in range (0,pocet):
        xle.append(E_min + (p * (E_max - E_min)) / pocet)
        xre.append(xle[p]+dE)
        xmean.append((xle[p] + xre[p]) / 2)
    
    count_first=[]
    count_second=[]
    for l in range(0,pocet):
        count_first.append(0)
        count_second.append(0)
    #XII
    for i in range(0,len(Etot)):
        E_index=int(((Etot[i]-E_min)/dE))
        if ((xle[E_index] <= Etot[i]) and (Etot[i] < xre[E_index])):
            if(Tfirst[i]<Tsep):
                count_first[E_index]=count_first[E_index]+1
            else:
                count_second[E_index]=count_second[E_index]+1
    
    caption = '# x = energy in keV, dE= '+str(dE)+' keV'
    with open(file_hist, "w", encoding="utf-8") as hist:
        hist.write('#\n')
        hist.write('#'+str(caption)+'\n')
        hist.write('# x_mean     count_before_2nd_breakdown     count_2nd_breakdown\n')
        hist.write('\n')
        for m in range(0,len(xmean)):
                hist.write(str(xmean[m])+'     '+str(count_first[m])+'     '+str(count_second[m])+'\n')
        
    hist.close
    
    plt.rcParams.update({'font.size': 20})
    fig, ax = plt.subplots(figsize =(10, 7))
    #plt.fill_between(xmean, count_first,step='mid') #pre mid post
    plt.plot(xmean, count_first, label='Before 2nd breakdown', drawstyle='steps-mid')
    plt.plot(xmean, count_second, label='2nd breakdown', drawstyle='steps-mid')
    plt.legend(loc='best')
    plt.title(detector+', #'+str(shot_no))
    plt.xlabel('Energy [keV]')
    plt.ylabel('Count')
    plt.xlim(0,500)
    ax.set_yscale('log') #log scale y
    plt.savefig(figure_energy_spectra_doublebreakdown, dpi = 1000)
    
    return


#soubory, ktere ctu
#read files
t3pa=get_file(shot, identifier)
name_calib='caliba'
caliba=get_file_calib(name_calib)
name_calib='calibb'
calibb=get_file_calib(name_calib)
name_calib='calibc'
calibc=get_file_calib(name_calib)
name_calib='calibt'
calibt=get_file_calib(name_calib)

#vytvorene soubory:
#created files
t3pa_cls= 'H03-W0051_shot_'+str(shot)+'_450V.t3pa_cls'
E_hist= 'H03-W0051_shot_'+str(shot)+'_450V_E_hist.txt'
E_in_time_hist= 'H03-W0051_shot_'+str(shot)+'_450V_discharge_energy.txt'
count_in_time_hist= 'H03-W0051_shot_'+str(shot)+'_450V_discharge_hits.txt'
size_interaction= 'H03-W0051_shot_'+str(shot)+'size_interaction.txt'
file_energy_spectra_doublebreakdown= 'H03-W0051_shot_'+str(shot)+'energy_spectra_doublebreakdown.txt'

#created figures
icon_fig='icon-fig'
figure_E_in_time_hist='discharge_energy'
figure_count_in_time_hist='discharge_hits'
figure_E_hist='Energy_spectrum'
hit_map_fig='hit-map'
figure_size_interaction= 'H03-W0051_shot_'+str(shot)+'size_interaction'
figure_energy_spectra_doublebreakdown= 'H03-W0051_shot_'+str(shot)+'energy_spectra_doublebreakdown'


#nactu jednotlive kalibracni matice - abych to nemusel delat v kazde funkci
a=load_calib(caliba)
b=load_calib(calibb)
c=load_calib(calibc)
t=load_calib(calibt)

#nactu a urcim jednotlive hodnoty - abych to nemusel delat v kazde funkci
index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti = load_t3pa_file(t3pa)
index,  matrix_index, ToA, ToT, FToA, overflow, pocet_udalosti, RowNo, ClmNo = t3pa_data(pocet_udalosti,index,  matrix_index, ToA, ToT, FToA, overflow)


#hit map
hit_map(detector,hit_map_fig,RowNo,ClmNo)


E=energy(a, b, c, t, ToT, pocet_udalosti, RowNo, ClmNo)
T=Time(ToA, FToA, pocet_udalosti, RowNo, ClmNo)

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)
E,T=Timewalk(E,T)

#sort by time
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

T_pom=T.copy()

#save to file
T_first, E_tot, Ntot = file_t3pa_cls_new(t3pa_cls,T)


dt, caption, T_int_first, E = energy_spectrum_in_time(T_first, E_tot)
hist_file(E_in_time_hist, T_int_first, E, caption);

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caption, xmean,count, xle, Etot = energy_spectrum(E_tot)
hist_file(E_hist, xmean, count, caption);

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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)
hist_file(count_in_time_hist, T_hit, count1, caption);

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multiplot(icon_fig, T_int_first,E,xmean,count)


size_of_interactions_average(Etot, Ntot,size_interaction)


#energy_spectra_doublebreakdown(Etot, T_first, file_energy_spectra_doublebreakdown)

t3pa2cls¶

Functions¶

File names¶

Calling functions¶

Hit map¶

Energy and time calculation¶

Detected energies during the discharge¶

Energy spectrum¶

Count of hits during the discharge¶

Graph for home page¶

Average size of interactions¶

Energy spectra for parts of doublebreadown discharges¶