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diff --git a/python/__pycache__/noisy.cpython-38.pyc b/python/__pycache__/noisy.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..27091080e209d0d4822fa1f3a8d5dad2e6f3ebed
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diff --git a/python/__pycache__/rmf.cpython-38.pyc b/python/__pycache__/rmf.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6421dbd07ce8b73b7a50995a34a212d7d9418a4d
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diff --git a/python/a.out b/python/a.out
deleted file mode 100755
index 67b98a4d229102453ed94ee2a9591fbce78cb5a2..0000000000000000000000000000000000000000
Binary files a/python/a.out and /dev/null differ
diff --git a/python/aeFF.py b/python/aeFF.py
index 2f800aeaa5ae76f1b003477770f586146d214bb2..c5ea7bbdd01c926f8b947e3a00a08a48d82fe8cc 100644
--- a/python/aeFF.py
+++ b/python/aeFF.py
@@ -12,6 +12,7 @@ def main():
# 1 Estract data from fits file and give these output file_dat
file_dat = "areaEfficace.dat"
data_estraction(file_dat)
+
#2 Filter data
data = np.loadtxt(file_dat, dtype=str)
@@ -38,6 +39,7 @@ def main():
# Maschera booleana per filtrare i dati per la combinazione corrente di theta e phi
# confronta i dati del file con i dati dell'array creato
mask = (theta == theta_val) & (phi == phi_val)
+
energy_filtered = energy[mask]
ratio_X_filtered = ratio_X[mask]
ratio_S_filtered = ratio_S[mask]
@@ -49,11 +51,11 @@ def main():
#plot_data_S.append((energy_filtered, ratio_S_filtered, theta_val, phi_val))
# 3 ARF
- #arf_file(energy_filtered, ratio_X_filtered, ratio_S_filtered, theta_val, phi_val)
+ arf_file(energy_filtered, ratio_X_filtered, ratio_S_filtered, theta_val, phi_val)
# 4 RMF
- #rmf_file(energy_filtered, 'X', X_en_dep_file_filtered, theta_val, phi_val)
- #rmf_file(energy_filtered, 'S', S_en_dep_file_filtered, theta_val, phi_val)
+ rmf_file('X', energy_filtered, X_en_dep_file_filtered, theta_val, phi_val)
+ rmf_file('S', energy_filtered, S_en_dep_file_filtered, theta_val, phi_val)
# approcci diversi per arf e rmf
# in arf essendo più semplice la chiamo una volta e dentro scrivo due volte le cose che deve fare
diff --git a/python/arf.py b/python/arf.py
index d74d73efe1884723e9548e0728aa6f0c2e808d86..2f4759871c4a685cfa5a2efdf2ade59c94f566f2 100644
--- a/python/arf.py
+++ b/python/arf.py
@@ -16,6 +16,7 @@ def create_header(extension):
extension.header['ARFVERSN'] = '1992A'
extension.header['HDUVERS1'] = '1.0.0'
extension.header['HDUVERS2'] = '1.1.0'
+ extension.header['COMMENT'] = "Alfonso Pisapia"
def arf_file(energy_filt, ratio_X_filt, ratio_S_filt, theta, phi):
try:
diff --git a/python/convertFits2Ascii.py b/python/convertFits2Ascii.py
index 0d33c62affe30998ecde6bab9c598c5204456005..c871b9f622da101407e933270fc71567829cc0b8 100644
--- a/python/convertFits2Ascii.py
+++ b/python/convertFits2Ascii.py
@@ -1,13 +1,14 @@
-import astropy.io.fits as fits
+import astropy.io.fits as pyfits
import numpy as np
# ! posso anche lanciare questo script dal bash iniziale e gli do come argomento il nome del file
-mask_file_fits = "msk_55_4752313.fits"
+mask_file_fits = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/msk_55_4752313.fits"
+mask_file_ascii = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/mask.dat"
def fits_to_ascii(mask_file_fits, mask_file_ascii):
- with fits.open(mask_file_fits) as hdul:
+ with pyfits.open(mask_file_fits) as hdul:
data = hdul[0].data
# Estrarre l'HDU primaria (dati e header)
@@ -27,4 +28,4 @@ def fits_to_ascii(mask_file_fits, mask_file_ascii):
np.savetxt(f, data, fmt="%d", delimiter=" ")
# Esegui la funzione specificando il file FITS di input e il file ASCII di output
-fits_to_ascii('/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/msk_55_4752313.fits', 'mask.dat')
\ No newline at end of file
+fits_to_ascii(mask_file_fits, mask_file_ascii)
\ No newline at end of file
diff --git a/python/convertRoot2Fits.py b/python/convertRoot2Fits.py
index ac5a2ae8ac5d198ae187f7334056d7c36f1a1b6b..aa2e3b001c1ffff80f8e86358c7b98146e471740 100644
--- a/python/convertRoot2Fits.py
+++ b/python/convertRoot2Fits.py
@@ -85,12 +85,11 @@ def generate_fits(energy, theta_deg, phi_deg):
pyfits.Column(name='Event_time', format='1D', unit='ns', array=t_TI[sorting])
])
- tbhdu.header.set('EXTNAME', 'EVENTS', 'Name of this binary table extension')
+ tbhdu.header.set('EXTNAME', 'EVENTS', 'Alfonso Pisapia')
hdulist = pyfits.HDUList([prhdu, tbhdu])
output_dir = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/fits"
-
if not os.path.exists(output_dir):
os.makedirs(output_dir)
diff --git a/python/data.py b/python/data.py
index 3f0c28217a745c24fc8bf2ba209b0ef05b371710..49c871b9485e68d4a060c0e3abf0064cc6b5de92 100644
--- a/python/data.py
+++ b/python/data.py
@@ -1,11 +1,48 @@
import os
import numpy as np
from multiprocessing import Pool
-import astropy.io.fits as fits
+import astropy.io.fits as pyfits
from noisy import add_noise_X, add_noise_S
+def write_fits_dep(energy, theta, phi, type, en_dep_noisy, en_dep):
+
+ # Write FITS file
+ # "Null" primary array
+ prhdu = pyfits.PrimaryHDU()
+
+ # Extension
+ tbhdu = pyfits.BinTableHDU.from_columns([
+ pyfits.Column(name='En_dep_Noise', format='1D', unit='keV', array = en_dep_noisy),
+ pyfits.Column(name='En_dep', format='1D', unit='keV', array = en_dep)
+ ])
+
+ tbhdu.header['EXTNAME'] = 'EVENTS'
+ tbhdu.header['TELESCOP'] = 'THESEUS'
+ tbhdu.header['INSTRUME'] = 'XGIS'
+ tbhdu.header['COMMENT'] = "Simulated energy deposits with noise"
+ tbhdu.header['TUNIT1'] = "keV"
+ tbhdu.header['TYPE'] = type
+ tbhdu.header['ENERGY'] = energy
+ tbhdu.header['THETA'] = theta
+ tbhdu.header['PHI'] = phi
+ tbhdu.header['COMMENT'] = "Alfonso Pisapia"
+
+ hdulist = pyfits.HDUList([prhdu, tbhdu])
+
+ fits_dep_dir = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/fits_dep"
+ if not os.path.exists(fits_dep_dir):
+ os.makedirs(fits_dep_dir)
+
+ file_name = f"{type}_en_dep_{energy:.1f}_{theta}_{phi}.fits"
+
+ output_file = os.path.join(fits_dep_dir, file_name)
+ hdulist.writeto(output_file, overwrite=True)
+
+ return file_name
+
+
# Funzione che prende in input un singolo file FITS da processare.
def get_data(files):
# Estrazione di energia, theta e phi dal nome del file
@@ -17,7 +54,7 @@ def get_data(files):
phi = float(name_parts[3])
# Apertura del file fits e lettura dei dati
- with fits.open(files) as hdul:
+ with pyfits.open(files) as hdul:
data = hdul[1].data
tot_events = len(data)
scint_id_col = data['Scint_ID']
@@ -29,42 +66,25 @@ def get_data(files):
eventi_S = tot_events - eventi_X
ratio_X = eventi_X/tot_events
ratio_S = eventi_S/tot_events
-
- X_en_dep_noisy = [add_noise_X(X_dep) for X_dep in X_en_dep]
+
+ X_en_dep_noisy = [add_noise_X(X_dep) for X_dep in X_en_dep] # list comprehension di Python, una struttura compatta che consente di applicare una funzione a ciascun elemento di un array o di una lista, creando direttamente un nuovo array con i risultati.
S_en_dep_noisy = [add_noise_S(S_dep) for S_dep in S_en_dep]
-
- return energy, ratio_X, ratio_S, theta, phi, X_en_dep, S_en_dep
+
+ return energy, ratio_X, ratio_S, theta, phi, X_en_dep_noisy, S_en_dep_noisy, X_en_dep, S_en_dep
+
def write_results(file_dat, results):
-
- npy_dir = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/npy"
- npy_noise_dir = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/npy_noise"
- if not os.path.exists(npy_dir):
- os.makedirs(npy_dir)
- if not os.path.exists(npy_noise_dir):
- os.makedirs(npy_noise_dir)
-
+
with open(file_dat, "w") as f:
- for energy, ratio_X, ratio_S, theta, phi, X_en_dep, S_en_dep in results:
- # Salva l'array en_dep in un file separato, e salva tutto in una cartella, Smisto i dati tra X ed S
- X_npy_file = f"X_en_dep_{energy:.1f}_{theta}_{phi}.npy"
- S_npy_file = f"S_en_dep_{energy:.1f}_{theta}_{phi}.npy"
+ for energy, ratio_X, ratio_S, theta, phi, X_en_dep_noisy, S_en_dep_noisy, X_en_dep, S_en_dep in results:
+
+ X_fits = write_fits_dep(energy, theta, phi, 'X', X_en_dep_noisy, X_en_dep)
+ S_fits = write_fits_dep(energy, theta, phi, 'S', S_en_dep_noisy, S_en_dep)
- X_en_dep_file = os.path.join(npy_dir, X_npy_file)
- S_en_dep_file = os.path.join(npy_dir, S_npy_file)
-
- np.save(X_en_dep_file, X_en_dep)
- np.save(S_en_dep_file, S_en_dep)
-
- f.write(f"{energy:.1f}\t{ratio_X:.3f}\t{ratio_S:.3f}\t{theta}\t{phi}\t{X_npy_file}\t{S_npy_file}\n")
+ f.write(f"{energy:.1f}\t{ratio_X:.3f}\t{ratio_S:.3f}\t{theta}\t{phi}\t{X_fits}\t{S_fits}\n")
+
+
- # Add noise to Energy Depoists Files and save in another folder
- npy_noise_dir = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/npy_noise"
- if not os.path.exists(npy_noise_dir):
- os.makedirs(npy_noise_dir)
-
- add_noise_X()
- add_noise_S()
# Estrae i dati, processa in parallelo tutti i file .fits presenti nella folder, usa le due funzioni già definite
def data_estraction(file_dat):
diff --git a/python/noisy.py b/python/noisy.py
index 7e70bfe1f8f96ff870ff52b4dc4f93058445380c..733844ec873c7baec280a9cc9990236ec6d5b0c0 100644
--- a/python/noisy.py
+++ b/python/noisy.py
@@ -2,72 +2,39 @@ import os
import numpy as np
import matplotlib.pyplot as plt
-
+# ! check riccardo
def add_noise_X(input):
-
-
- w = 3.66 # eV/e-
- fano_factor = 0.125 # for semiconductor detectors
+ w = 3.66 # eV/e-
+ fano_factor = 0.125 # for semiconductor detectors
sigma_fano = np.sqrt(fano_factor*input/w) # keV*(e-)/eV -> e-rms
- sigma_el = 30.0 # e- rms in termini di devizaioni standard
+ sigma_el = 30.0 # e-rms in termini di devizaioni standard
- variance = sigma_fano**2 + sigma_el**2 # Varianza della gaussiana
+ variance = sigma_fano**2 + sigma_el**2 # Varianza della gaussiana
std_row = np.sqrt(variance)
- std = (std_row*3.66)/1000 # (e-rms)*eV/(e-rms) --> keV
+ std = (std_row*3.66)/1000 # (e-rms)*eV/(e-rms) --> keV
# Estrai un valore campionato dalla distribuzione gaussiana
output = np.random.normal(loc=input, scale=std)
- #print(std)
- #print("Valore estratto dalla distribuzione gaussiana:", output)
-
-
- os.chdir("/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/npy")
- array = np.load("X_en_dep_2.0_0.0_0.0.npy")
- #print(array)
-
- dep = [add_noise(input) for input in array] # list comprehension di Python, una struttura compatta che consente di applicare una funzione a ciascun elemento di un array o di una lista, creando direttamente un nuovo array con i risultati.
-
-
-
-
- X_npy_noise_file = "X_en_dep_noy_2.0_0.0_0.0.npy"
- X_en_dep_noise_file = os.path.join(npy_noise_dir, X_npy_noise_file)
-
- new = np.save(X_en_dep_noise_file, dep)
-
- loaded_dep = np.load(X_en_dep_noise_file)
- print("Contenuto del nuovo file .npy con rumore aggiunto:")
- print(loaded_dep)
-
-
-
-
+ return output
def add_noise_S(input):
- light_efficiency = 25 # e-/keV
+ light_efficiency = 25 # e-/keV
sigma_stat = input*light_efficiency
- sigma_el = 30.0 # conto due volte perche ho due sdd
+ sigma_el = 30.0 # conto due volte perche ho due sdd
variance = sigma_stat**2 + 2*sigma_el**2
-
- std = np.sqrt(variance)
+ std = np.sqrt(variance)/1000
# Estrai un valore campionato dalla distribuzione gaussiana
output = np.random.normal(loc=input, scale=std)
- #print(std)
- #print("Valore estratto dalla distribuzione gaussiana:", output)
-
-
+
return output
-
-
-
# # PLOT
# # Genera i valori per la curva gaussiana
# x = np.linspace(input - 4*std, input + 4*std, 1000)
diff --git a/python/rmf.py b/python/rmf.py
index 810bee27c03d7dda71e8e432fdc5647436f1460e..502770ea06ea085bec1319c7ac96f27e47d712ab 100644
--- a/python/rmf.py
+++ b/python/rmf.py
@@ -26,6 +26,7 @@ def create_matrix_header(extension):
extension.header['RMFVERSN'] = '1992A'
extension.header['HDUVERS1'] = '1.0.0'
extension.header['HDUVERS2'] = '1.3.0'
+ extension.header['COMMENT'] = "Alfonso Pisapia"
def create_ebounds_header(extension):
@@ -49,48 +50,51 @@ def create_ebounds_header(extension):
def estract_row(en_dep_file, bins):
- en_dep = np.load(en_dep_file)
- lenght = en_dep.size
-
- counts, bin_edges = np.histogram(en_dep, bins=bins)
- counts_norm = counts/lenght
-
- print(counts)
- print(counts_norm)
-
- n_grp = 0
- f_chan = []
- n_chan = []
+ with pyfits.open(en_dep_file) as hdul:
+ data = hdul[1].data
+ en_dep_noise = data['En_dep_Noise']
+ lenght = len(data)
+
+ counts, bin_edges = np.histogram(en_dep_noise, bins=bins)
+ counts_norm = counts/lenght
+
+ print(counts)
+ print(counts_norm)
+
+ n_grp = 0
+ f_chan = []
+ n_chan = []
- # Controlla i gruppi di valori consecutivi non nulli
- in_group = False # Stato per controllare se siamo all'interno di un gruppo, booleano
- group_start = None # Per memorizzare l'inizio del gruppo, intero, noen= senza valore
+ # Controlla i gruppi di valori consecutivi non nulli
+ in_group = False # Stato per controllare se siamo all'interno di un gruppo, booleano
+ group_start = None # Per memorizzare l'inizio del gruppo, intero, noen= senza valore
- # Il ciclo scorre ogni valore dell'array counts_norm e controlla se è maggiore di zero (cioè non nullo).
- for i in range(len(counts_norm)):
- if counts_norm[i] > 0:
- if not in_group: # se non sono gia in un gruppo svolgi i seguenti # ! questi comandi li esegue solo per i valori iniziali di ogni gruppo
- in_group = True # apro un nuovo gruppo
- group_start = i # Memorizza l'indice di inizio del gruppo
- f_chan.append(i) # Aggiungi l'indice di inizio al vettore f_chan
- n_grp += 1 # Incrementa il numero di gruppi
- else:
- if in_group: # Termina il gruppo corrente # ! qui esegue solo al primo zero che trova, se ne trova uno consecutivo non esegue perche trova in_group falso
- in_group = False
- group_length = i - group_start # Calcola la lunghezza del gruppo
- n_chan.append(group_length) # Aggiungi la lunghezza del gruppo a n_chan
+ # Il ciclo scorre ogni valore dell'array counts_norm e controlla se è maggiore di zero (cioè non nullo).
+ for i in range(len(counts_norm)):
+ if counts_norm[i] > 0:
+ if not in_group: # se non sono gia in un gruppo svolgi i seguenti # ! questi comandi li esegue solo per i valori iniziali di ogni gruppo
+ in_group = True # apro un nuovo gruppo
+ group_start = i # Memorizza l'indice di inizio del gruppo
+ f_chan.append(i) # Aggiungi l'indice di inizio al vettore f_chan
+ n_grp += 1 # Incrementa il numero di gruppi
+ else:
+ if in_group: # Termina il gruppo corrente # ! qui esegue solo al primo zero che trova, se ne trova uno consecutivo non esegue perche trova in_group falso
+ in_group = False
+ group_length = i - group_start # Calcola la lunghezza del gruppo
+ n_chan.append(group_length) # Aggiungi la lunghezza del gruppo a n_chan
- # Controlla se l'ultimo gruppo arriva fino alla fine dell'array
- if in_group: # se è true esegue
- n_chan.append(len(counts_norm) - group_start)
-
- return n_grp, f_chan, n_chan, counts_norm # row_matrix = counts_norm
+ # Controlla se l'ultimo gruppo arriva fino alla fine dell'array
+ if in_group: # se è true esegue
+ n_chan.append(len(counts_norm) - group_start)
+
+ return n_grp, f_chan, n_chan, counts_norm # row_matrix = counts_norm
-def rmf_file(energy, type, en_dep_file_filt, theta, phi):
+def rmf_file(type, energy, en_dep_file_filt, theta, phi):
+
+ os.chdir('/home/alfonso/Scrivania/THESEUS/xgis_m7-main/fits_dep')
+
try:
- os.chdir("/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/npy")
-
n_grp_col = []
f_chan_col = []
n_chan_col = []
@@ -111,10 +115,10 @@ def rmf_file(energy, type, en_dep_file_filt, theta, phi):
n_chan_col.append(n_chan)
row_matrix_col.append(row_matrix)
- print(f"n_grp: {n_grp_col}")
- print(f"f_chan: {f_chan_col}")
- print(f"n_chan: {n_chan_col}")
- print(f"row_matrix: {row_matrix_col}")
+ # print(f"n_grp: {n_grp_col}")
+ # print(f"f_chan: {f_chan_col}")
+ # print(f"n_chan: {n_chan_col}")
+ # print(f"row_matrix: {row_matrix_col}")
# ! *************************************************************************************************************
diff --git a/python/zero.CANCELLA.py b/python/zero.CANCELLA.py
deleted file mode 100644
index abd69086dae30b04a6a88310c486a1aa0efa50ed..0000000000000000000000000000000000000000
--- a/python/zero.CANCELLA.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Nome del file da leggere
-file_path = "mask.dat"
-
-# Apri il file in modalità lettura
-with open(file_path, 'r') as file:
- # Itera su ogni riga del file
- for line_num, line in enumerate(file, start=1):
- # Rimuovi eventuali spazi vuoti ai bordi e dividi la riga in elementi
- elements = line.strip().split()
-
- # Conta il numero di '0' nella riga corrente
- zero_count = elements.count('0')
-
- # Stampa il numero di zeri per la riga corrente
- print(f"Riga {line_num}: {zero_count} zeri")
-
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