import numpy as np
import sys, os, re, glob
import uproot
from matplotlib import pyplot as plt
import matplotlib as mpl

File da caricare

path = "/eos/project/i/insulab-como/testBeam/TB_2023_10_T9_OREO/H2ROOT_NEW"
numRun = 720258

nameFile = f"run{numRun}_00001.root"
# nameFile = f"run{numRun}_00003.root"
nameFile = f"run{numRun}_00009.root"

fileToLoad = os.path.join(path, nameFile)

Primo digi

Carico i dati del primo 1742

with uproot.open(fileToLoad)["h3"] as f:
    f.show()
    for k in f:
        print(k)
    Nwdigi_742a = f["Nwdigi_742a"].arrays(library = "np")["Nwdigi_742a"]
    Idigi_742a = f["Idigi_742a"].arrays(library = "np")["Idigi_742a"]
    
    print(Nwdigi_742a.shape, Idigi_742a.shape)
    
# Condizione da soddisfare, (direi che è sempre vera)
conda = (Nwdigi_742a == 32992)#32992)
print(f"Good events: {conda.sum()}\nTotal: {conda.shape}")

#Nwdigi_742a = Nwdigi_742a[conda]
Idigi_742a = Idigi_742a[conda]

name                 | typename                 | interpretation                
---------------------+--------------------------+-------------------------------
Idigi3               | int32_t                  | AsDtype('>i4')
Idtime3              | int32_t                  | AsDtype('>i4')
Idatime3             | int32_t                  | AsDtype('>i4')
Nwdigi_742a          | uint16_t                 | AsDtype('>u2')
Idigi_742a           | uint16_t[40000]          | AsDtype("('>u2', (40000,))")
Nwfast_742a          | uint16_t                 | AsDtype('>u2')
Idigifast_742a       | uint16_t[4112]           | AsDtype("('>u2', (4112,))")
Ifast_base742a       | int32_t[32]              | AsDtype("('>i4', (32,))")
Ifast_time742a       | int32_t[32]              | AsDtype("('>i4', (32,))")
Ifast_time50_742a    | int32_t[32]              | AsDtype("('>i4', (32,))")
Ifast_ph742a         | int32_t[32]              | AsDtype("('>i4', (32,))")
Iev_wri_a            | int32_t                  | AsDtype('>i4')
Iev_rd_a             | int32_t                  | AsDtype('>i4')
<TBranch 'Idigi3' at 0x7fb9c0514af0>
<TBranch 'Idtime3' at 0x7fb9ab9517c0>
<TBranch 'Idatime3' at 0x7fb9abd12b20>
<TBranch 'Nwdigi_742a' at 0x7fba42f260a0>
<TBranch 'Idigi_742a' at 0x7fba403f2af0>
<TBranch 'Nwfast_742a' at 0x7fba42a9dca0>
<TBranch 'Idigifast_742a' at 0x7fba42f1e760>
<TBranch 'Ifast_base742a' at 0x7fb9abce4c70>
<TBranch 'Ifast_time742a' at 0x7fba42452460>
<TBranch 'Ifast_time50_742a' at 0x7fba42f3b2e0>
<TBranch 'Ifast_ph742a' at 0x7fba4046d640>
<TBranch 'Iev_wri_a' at 0x7fba42aa4160>
<TBranch 'Iev_rd_a' at 0x7fb9ab954b50>
(57,) (57, 40000)
Good events: 57
Total: (57,)

print(type(Idigi_742a))
print(Idigi_742a.shape, Idigi_742a.dtype)

<class 'numpy.ndarray'>
(57, 40000) uint16

# Parametri caratteristici che ho appena descritto sopra

nPtsDigi = 1024
nWordSingleChannel = 5+nPtsDigi+2
nChannels = 32


def getWaveform(matrice, canale, evento):
    """
    matrice: matrice dei dati
    canale: numero di canale, contando da 0
    evento: numero di evento (riga), contando da 0
    """
    i = evento
    j = canale
    return matrice[i, (5 + nWordSingleChannel*j) : (5 + nWordSingleChannel*j + nPtsDigi)]

xVect = np.arange(0, nPtsDigi) * 0.2 #ns, sampling 5 GHz chanToPlot = 1 for i in range(10): fig, ax = plt.subplots() fig.set_size_inches(12, 5) wf = getWaveform(Idigi_742a, chanToPlot, i) ax.plot(xVect, wf, ls = ":", c = "tab:green") ax.grid() ax.set_xlabel("Time [ns]", fontsize = 14) ax.set_ylabel("[ADC]", fontsize = 14) ax.set_title(f"Chan. {chanToPlot} -- Ev. {i}", fontsize = 16) plt.show()

La persistenza

Vero plot interessante: sovrappongo tutte le waveform

Idigi_742a.shape

(57, 40000)

Idigipack_742a = Idigi_742a[:, :nChannels*nWordSingleChannel].reshape(Idigi_742a.shape[0], nChannels, nWordSingleChannel)[:,:,5:5+nPtsDigi ]

iChan = 2

fig, ax = plt.subplots(8,4)
fig.set_size_inches(60, 40)
ax = ax.flatten()

for i in range(32):
    iChan = i

    # Ripeto 0..512 per il numero di eventi (Idigipack.shape[0])
    hh = ax[i].hist2d(np.tile(np.arange(nPtsDigi),Idigipack_742a.shape[0]), 
                      Idigipack_742a[:,iChan,:].flatten(), 
                      bins=(nPtsDigi,50), norm=mpl.colors.LogNorm(), cmap = "jet")
    ax[i].set_title(f"Chan {iChan}", fontsize = 40)
    fig.colorbar(hh[3], ax = ax[i])
plt.show()

Secondo digi

Attenzione: leggo solo 16 canali

with uproot.open(fileToLoad)["h4"] as f:
    f.show()
    for k in f:
        print(k)
    Nwdigi_742b = f["Nwdigi_742b"].arrays(library = "np")["Nwdigi_742b"]
    Idigi_742b = f["Idigi_742b"].arrays(library = "np")["Idigi_742b"]
    
    print(Nwdigi_742b.shape, Idigi_742b.shape)
    
# Condizione da soddisfare, (direi che è sempre vera)
conda = (Nwdigi_742b == 32992/2)#32992)
print(f"Good events: {conda.sum()}\nTotal: {conda.shape}")

#Nwdigi_742a = Nwdigi_742a[conda]
Idigi_742b = Idigi_742b[conda]

name                 | typename                 | interpretation                
---------------------+--------------------------+-------------------------------
Idigi4               | int32_t                  | AsDtype('>i4')
Idtime4              | int32_t                  | AsDtype('>i4')
Idatime4             | int32_t                  | AsDtype('>i4')
Nwdigi_742b          | uint16_t                 | AsDtype('>u2')
Idigi_742b           | uint16_t[40000]          | AsDtype("('>u2', (40000,))")
Nwfast_742b          | uint16_t                 | AsDtype('>u2')
Idigifast_742b       | uint16_t[4112]           | AsDtype("('>u2', (4112,))")
Ifast_base742b       | int32_t[32]              | AsDtype("('>i4', (32,))")
Ifast_time742b       | int32_t[32]              | AsDtype("('>i4', (32,))")
Ifast_time50_742b    | int32_t[32]              | AsDtype("('>i4', (32,))")
Ifast_ph742b         | int32_t[32]              | AsDtype("('>i4', (32,))")
Iev_wri_b            | int32_t                  | AsDtype('>i4')
Iev_rd_b             | int32_t                  | AsDtype('>i4')
<TBranch 'Idigi4' at 0x7fb9b8216ac0>
<TBranch 'Idtime4' at 0x7fb9c07f8f10>
<TBranch 'Idatime4' at 0x7fb9c07c8640>
<TBranch 'Nwdigi_742b' at 0x7fb9c07c0250>
<TBranch 'Idigi_742b' at 0x7fb9c07c3e80>
<TBranch 'Nwfast_742b' at 0x7fb9c07c0940>
<TBranch 'Idigifast_742b' at 0x7fb9c07c6130>
<TBranch 'Ifast_base742b' at 0x7fb9c07c68e0>
<TBranch 'Ifast_time742b' at 0x7fb9c07c40d0>
<TBranch 'Ifast_time50_742b' at 0x7fb9c07c4880>
<TBranch 'Ifast_ph742b' at 0x7fb9c07c4fa0>
<TBranch 'Iev_wri_b' at 0x7fb9c07ea820>
<TBranch 'Iev_rd_b' at 0x7fb9c07eafd0>
(57,) (57, 40000)
Good events: 57
Total: (57,)

print(type(Idigi_742b))
print(Idigi_742b.shape, Idigi_742b.dtype)

<class 'numpy.ndarray'>
(57, 40000) uint16

# Parametri caratteristici che ho appena descritto sopra

nPtsDigi_b = 1024
nWordSingleChannel_b = 5+nPtsDigi_b+2
nChannels_b = 16


def getWaveform(matrice, canale, evento):
    """
    matrice: matrice dei dati
    canale: numero di canale, contando da 0
    evento: numero di evento (riga), contando da 0
    """
    i = evento
    j = canale
    return matrice[i, (5 + nWordSingleChannel_b*j) : (5 + nWordSingleChannel_b*j + nPtsDigi_b)]

xVect = np.arange(0, nPtsDigi) * 0.2 #ns, sampling 5 GHz chanToPlot = 1 for i in range(10): fig, ax = plt.subplots() fig.set_size_inches(12, 5) wf = getWaveform(Idigi_742b, chanToPlot, i) ax.plot(xVect, wf, ls = ":", c = "tab:green") ax.grid() ax.set_xlabel("Time [ns]", fontsize = 14) ax.set_ylabel("[ADC]", fontsize = 14) ax.set_title(f"Chan. {chanToPlot} -- Ev. {i}", fontsize = 16) plt.show()

Idigipack_742b = Idigi_742b[:, :nChannels_b*nWordSingleChannel_b].reshape(Idigi_742b.shape[0], nChannels_b, nWordSingleChannel_b)[:,:,5:5+nPtsDigi_b ]

iChan = 2

fig, ax = plt.subplots(8,4)
fig.set_size_inches(60, 40)
ax = ax.flatten()

for i in range(16):
    iChan = i

    # Ripeto 0..512 per il numero di eventi (Idigipack.shape[0])
    hh = ax[i].hist2d(np.tile(np.arange(nPtsDigi_b),Idigipack_742b.shape[0]), 
                      Idigipack_742b[:,iChan,:].flatten(), 
                      bins=(nPtsDigi_b,50), norm=mpl.colors.LogNorm(), cmap = "jet")
    ax[i].set_title(f"Chan {iChan}", fontsize = 40)
    fig.colorbar(hh[3], ax = ax[i])
plt.show()