diff --git a/python/aeFF.py b/python/aeFF.py
index 01b8aa4e5ae2082e844f6efb430b42e43fcc3a95..aa858852415e91a9a4d961cfce578cdbba48e66b 100644
--- a/python/aeFF.py
+++ b/python/aeFF.py
@@ -12,7 +12,7 @@ from rmf import rmf_file
def main():
# 1 Estract data from fits file and give this output file_dat
- file_dat = "areaEfficace.dat"
+ file_dat = "areaEfficace.dat" # è solo un file di testo che mi lista i dati, serve per scrivere rmf e arf, passarci sempre anche se non faccio rmf e arf
data_estraction(file_dat)
#2 Filter data
diff --git a/python/convertRoot2Fits.py b/python/convertRoot2Fits.py
index abe02c5b51fbee0120d31b7bad6dee5403f7af33..090dbff732a1e834d771e5f546182b30de394f29 100644
--- a/python/convertRoot2Fits.py
+++ b/python/convertRoot2Fits.py
@@ -65,7 +65,7 @@ def generate_fits(energy, theta_deg, phi_deg):
pyfits.Column(name='Pixel_ID', format='1K', array=t_SL[sorting]),
pyfits.Column(name='X_Pixel', format='1D', unit='cm', array=t_XH[sorting]),
-
+
pyfits.Column(name='Y_Pixel', format='1D', unit='cm', array=t_YH[sorting]),
pyfits.Column(name='Z_Pixel', format='1D', unit='cm', array=t_ZH[sorting]),
diff --git a/python/data.py b/python/data.py
index a403951b9e25ed4006514434ec7e95631658ebd4..af7f0a61d383207ae9887290e277a0fa2fcb6cab 100644
--- a/python/data.py
+++ b/python/data.py
@@ -6,7 +6,9 @@ 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, name_id, pos_id):
+def write_fits_dep(energy, en_primary, theta, phi, type, en_dep_noisy, en_dep, name_id, pos_id, x_pixel, y_pixel): # ! è qui che decido quali informazioni sulla simulazione voglio nei file fits_dep
+
+
# Write FITS file
# "Null" primary array
@@ -14,18 +16,19 @@ def write_fits_dep(energy, theta, phi, type, en_dep_noisy, en_dep, name_id, pos_
# Extension
tbhdu = pyfits.BinTableHDU.from_columns([
- pyfits.Column(name='En_dep_Noise', format='1D', unit='keV', array = en_dep_noisy),
- pyfits.Column(name=name_id, format='1I', array = pos_id)
+ #pyfits.Column(name='En_dep_Noise', format='1D', unit='keV', array = en_dep_noisy),
+ pyfits.Column(name='En_Primary', format='1D', unit='keV', array = en_primary),
+ #pyfits.Column(name='(X,Y)', format='2I', array = (x_pixel,y_pixel))
#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['COMMENT'] = "Simulated GRB spectrum, Band funcition alpha1 = -1, alpha2 = -3, Ebreak = 100 "
tbhdu.header['TUNIT1'] = "keV"
tbhdu.header['TYPE'] = type
- tbhdu.header['ENERGY'] = energy
+ #tbhdu.header['ENERGY'] = energy
tbhdu.header['THETA'] = theta
tbhdu.header['PHI'] = phi
tbhdu.header['COMMENT'] = "Alfonso Pisapia"
@@ -36,13 +39,12 @@ def write_fits_dep(energy, theta, phi, type, en_dep_noisy, en_dep, name_id, pos_
if not os.path.exists(fits_dep_dir):
os.makedirs(fits_dep_dir)
- file_name = f"{type}_en_dep_{energy:.1f}_{theta}_{phi}.fits"
+ file_name = f"{type}_grb_{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):
@@ -54,7 +56,7 @@ def get_data(files):
theta = float(name_parts[2])
phi = float(name_parts[3])
- # Apertura del file fits e lettura dei dati
+ # Apertura del file fits e lettura dei dati # ! in base alle info che voglio nel fits_dep qui devo richiamare le info
with pyfits.open(files) as hdul:
data = hdul[1].data
tot_events = len(data)
@@ -63,7 +65,10 @@ def get_data(files):
scint_id_col = data['Scint_ID']
scint_id = scint_id_col[scint_id_col != -1000]
energy = data['En_Primary'][0]
+ en_primary = data['En_Primary']
en_dep = data['En_dep']
+ x_pixel = data['X_Pixel']
+ y_pixel = data['Y_Pixel']
X_en_dep = en_dep[scint_id_col == -1000]
S_en_dep = en_dep[scint_id_col != -1000]
eventi_X = (scint_id_col == -1000).sum()
@@ -74,16 +79,16 @@ def get_data(files):
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_noisy, S_en_dep_noisy, X_en_dep, S_en_dep, pixel_id, scint_id
-
+ return energy, en_primary, ratio_X, ratio_S, theta, phi, X_en_dep_noisy, S_en_dep_noisy, X_en_dep, S_en_dep, pixel_id, scint_id, x_pixel, y_pixel
+# scrive sia il file di testo che i file fits
def write_results(file_dat, results):
with open(file_dat, "w") as f:
- for energy, ratio_X, ratio_S, theta, phi, X_en_dep_noisy, S_en_dep_noisy, X_en_dep, S_en_dep, pixel_id, scint_id in results:
+ for energy, en_primary, ratio_X, ratio_S, theta, phi, X_en_dep_noisy, S_en_dep_noisy, X_en_dep, S_en_dep, pixel_id, scint_id in results:
- X_fits = write_fits_dep(energy, theta, phi, 'X', X_en_dep_noisy, X_en_dep, 'Pixel_ID', pixel_id)
- S_fits = write_fits_dep(energy, theta, phi, 'S', S_en_dep_noisy, S_en_dep, 'Scint_ID', scint_id)
+ X_fits = write_fits_dep(energy, en_primary, theta, phi, 'X', X_en_dep_noisy, X_en_dep, 'Pixel_ID', pixel_id)
+ S_fits = write_fits_dep(energy, en_primary, theta, phi, 'S', S_en_dep_noisy, S_en_dep, 'Scint_ID', scint_id)
f.write(f"{energy:.1f}\t{ratio_X:.3f}\t{ratio_S:.3f}\t{theta}\t{phi}\t{X_fits}\t{S_fits}\n")
@@ -103,4 +108,4 @@ def data_estraction(file_dat):
with Pool() as pool:
results = pool.map(get_data, files) # qui files è l'argomento che passi a get_data
- write_results(file_dat, results)
\ No newline at end of file
+ write_results(file_dat, results) # per tutti i file fits che trova, in parallelo, ottiene i dati e li scrive in file_dat
\ No newline at end of file