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Add acoustics monitoring

Merged Add acoustics monitoring
cguidi/km3mon:undefined into master
Merged Carlo Guidi requested to merge cguidi/km3mon:undefined into master
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scripts/acoustics.py
+ 67
− 113
@@ -8,7 +8,8 @@ Usage:
acoustics.py (-h | --help)
Options:
-d DET_ID Detector ID
-d DET_ID Detector ID
-ndu N_DUS Number of DUs
-o PLOT_DIR The directory to save the plot
-h --help Show this screen.
@@ -24,45 +25,49 @@ from datetime import datetime
import km3pipe as kp
parser = argparse.ArgumentParser(description='Online_monitoring')
parser.add_argument('-d', dest='det_id', type=str, nargs=1, required=True,
help='The detector ID (e.g. D_ORCA005)')
parser.add_argument('-o', dest='plot_dir', type=str, nargs=1, required=True,
help='Directory in which the plot is saved')
def diff(first, second):
second = set(second)
return [item for item in first if item not in second]
parser = argparse.ArgumentParser(description = 'Online_monitoring')
parser.add_argument('-d', dest = 'det_id', type = str, nargs = 1, required = True,
help = 'The detector ID (e.g. D_ORCA005)')
parser.add_argument('-ndu', dest = 'n_dus', type = int, nargs = 1, required = True,
help = 'Number of DUs (e.g. 5)')
parser.add_argument('-o', dest = 'plot_dir', type = str, nargs = 1, required = True,
help = 'Directory in which the plot is saved')
args = parser.parse_args()
detid = args.det_id[0]
directory = args.plot_dir[0]
N_DUS = args.n_dus[0]
def diff(first, second):
second = set(second)
return [item for item in first if item not in second]
db = kp.db.DBManager()
sds = kp.db.StreamDS()
table=db.run_table(detid)
ACOUSTIC_BEACONS = [12, 14, 16]
N_DOMS = 18
N_ABS=3
DOMS = range(N_DOMS + 1)
N_DUS = 5
DUS = range(1, N_DUS + 1)
TIT = 600 # Time Interval between Trains of acoustic pulses)
SSW = 160 # Signal Security Window (Window size with signal)
check = True
while check == True:
while check:
table = db.run_table(detid)
minrun = table["RUN"][len(table["RUN"]) - 1]
ind, = np.where((table["RUN"] == minrun))
mintime1 = table['UNIXSTARTTIME'][ind]
mintime = mintime1.values
maxrun = table["RUN"][len(table["RUN"]) - 1]
now = time.time()
if (now-mintime/1000) < TIT:
if (now - mintime/1000) < TIT:
minrun = table["RUN"][len(table["RUN"]) - 1] - 1
print(now)
@@ -73,7 +78,7 @@ while check == True:
for dom in DOMS:
try:
domID = db.doms.via_omkey((du, dom), detid).dom_id
toas_all = sds.toashort(detid=detid, minrun=minrun, maxrun=maxrun, domid=domID, emitterid=ab)
toas_all = sds.toashort(detid = detid, minrun = minrun, maxrun = maxrun, domid = domID, emitterid = ab)
QF_abdom = toas_all["QUALITYFACTOR"]
UTB_abdom = toas_all["UNIXTIMEBASE"]
@@ -124,7 +129,7 @@ while check == True:
if len(QF_second) > 11:
QF_second = np.array(QF_second)
QF_third = [k for k in QF_second if (np.where(QF_second == k)[0][0]<11)]
QF_third = [k for k in QF_second if (np.where(QF_second == k)[0][0] < 11)]
else:
QF_third = QF_second
@@ -139,11 +144,11 @@ while check == True:
Q.append(np.where(SIGNAL_OLD == QF_fourth[q])[0][0])
UTB_fourth = np.array(UTB_SIGNAL.tolist())[Q]
UTB_fourth_l = UTB_fourth.tolist()
D=[]
D = []
for g in np.arange(len(UTB_fourth_l)):
if ((np.mod((UTB_fourth_l[g] - UTB_fourth_l[0]), 5) > 0.5 and np.mod((UTB_fourth_l[g] - UTB_fourth_l[0]), 5) < 4.5) or (np.mod((UTB_fourth_l[g] - UTB_fourth_l[0]), 5)>5)):
D.append(g)
for d in sorted(D, reverse=True):
for d in sorted(D, reverse = True):
del QF_fourth[d]
QF_fifth = QF_fourth
QF_OK = QF_fifth
@@ -160,124 +165,73 @@ while check == True:
N_Pulses_Indicator_DU.append(-1.5)
except:
stop = []
except (TypeError, ValueError): # TypeError if no data found for a certain piezo, ValueError if there are zero data for a certain piezo
N_Pulses_Indicator_DU.append(-1.5)
N_Pulses_Indicator.append(N_Pulses_Indicator_DU)
fig = plt.figure()
ax = fig.add_subplot(111)
dom = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ,11, 12, 13, 14, 15, 16, 17, 18]
l = len(dom)
du1AB1 = 0.9*np.ones(l)
du1AB2 = 1*np.ones(l)
du1AB3 = 1.1*np.ones(l)
du2AB1 = 1.9*np.ones(l)
du2AB2 = 2*np.ones(l)
du2AB3 = 2.1*np.ones(l)
du3AB1 = 2.9*np.ones(l)
du3AB2 = 3*np.ones(l)
du3AB3 = 3.1*np.ones(l)
du4AB1 = 3.9*np.ones(l)
du4AB2 = 4*np.ones(l)
du4AB3 = 4.1*np.ones(l)
du5AB1 = 4.9*np.ones(l)
du5AB2 = 5*np.ones(l)
du5AB3 = 5.1*np.ones(l)
DU1 = np.array(N_Pulses_Indicator[0])
DU2 = np.array(N_Pulses_Indicator[1])
DU3 = np.array(N_Pulses_Indicator[2])
DU4 = np.array(N_Pulses_Indicator[3])
DU5 = np.array(N_Pulses_Indicator[4])
ind = np.where(DU2 < 1000)
iAB1 = np.where(ind[0] < l)
iAB2_up = np.where(ind[0] > (l - 1))
iAB2_down = np.where(ind[0] < 2*l)
N_doms = 18
doms = range(N_doms + 1)
L = len(doms)
duab = []
DUs = []
for du in range(N_DUS):
duabdu = []
duab1 = (du+0.9)*np.ones(L)
duab2 = (du+1)*np.ones(L)
duab3 = (du+1.1)*np.ones(L)
duabdu.append(duab1)
duabdu.append(duab2)
duabdu.append(duab3)
duab.append(duabdu)
DUs.append(np.array(N_Pulses_Indicator[du]))
ind = np.where(DUs[1] < 1000)
iAB1 = np.where(ind[0] < L)
iAB2_up = np.where(ind[0] > (L - 1))
iAB2_down = np.where(ind[0] < 2*L)
iAB2 = np.intersect1d(iAB2_up, iAB2_down)
iAB3 = np.where(ind[0] > (2*l - 1))
iAB3 = np.where(ind[0] > (2*L - 1))
colorsList = [(0, 0, 0),(1, 0.3, 0),(1, 1, 0),(0.2, 0.9, 0)]
CustomCmap = matplotlib.colors.ListedColormap(colorsList)
bounds=[-2, -1, 0, 1, 2]
norma = colors.BoundaryNorm(bounds, CustomCmap.N)
color = ax.scatter(du1AB1, dom, s=20, c = DU1[iAB1], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du1AB2, dom, s=20, c = DU1[iAB2], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du1AB3, dom, s=20, c = DU1[iAB3], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du2AB1, dom, s=20, c = DU2[iAB1], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du2AB2, dom, s=20, c = DU2[iAB2], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du2AB3, dom, s=20, c = DU2[iAB3], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du3AB1, dom, s=20, c = DU3[iAB1], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du3AB2, dom, s=20, c = DU3[iAB2], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du3AB3, dom, s=20, c = DU3[iAB3], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du4AB1, dom, s=20, c = DU4[iAB1], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du4AB2, dom, s=20, c = DU4[iAB2], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du4AB3, dom, s=20, c = DU4[iAB3], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du5AB1, dom, s=20, c = DU5[iAB1], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du5AB2, dom, s=20, c = DU5[iAB2], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(du5AB3, dom, s=20, c = DU5[iAB3], norm=norma, marker = 's', cmap = CustomCmap);
for du in range(N_DUS):
for ab in range(N_ABS):
color = ax.scatter(duab[du][ab], doms, s = 20, c = DUs[du][iAB1], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(duab[du][ab], doms, s = 20, c = DUs[du][iAB2], norm=norma, marker = 's', cmap = CustomCmap);
color = ax.scatter(duab[du][ab], doms, s = 20, c = DUs[du][iAB3], norm=norma, marker = 's', cmap = CustomCmap);
cbar = plt.colorbar(color)
cbar.ax.get_yaxis().set_ticks([])
for j, lab in enumerate(['$0. pings$', '$1-3 pings$', '$4-7 pings$', '$>7. pings$']):
cbar.ax.text(3.5, (2 * j + 1)/8.0, lab, ha='center', va='center')
cbar.ax.text(3.5, (2*j + 1)/8.0, lab, ha = 'center', va = 'center')
cbar.ax.get_yaxis().labelpad = 18
matplotlib.pyplot.xticks(np.arange(1, 6, step=1))
matplotlib.pyplot.yticks(np.arange(0, 19, step=1))
matplotlib.pyplot.grid(color='k', linestyle='-', linewidth=0.2)
ax.set_xlabel('DUs', fontsize=18)
ax.set_ylabel('Floors', fontsize=18)
ts = now+3600
matplotlib.pyplot.xticks(np.arange(1, N_DUS + 1, step = 1))
matplotlib.pyplot.yticks(np.arange(0, 19, step = 1))
matplotlib.pyplot.grid(color = 'k', linestyle = '-', linewidth = 0.2)
ax.set_xlabel('DUs', fontsize = 18)
ax.set_ylabel('Floors', fontsize = 18)
ts = now + 3600
DATE = datetime.utcfromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
ax.set_title(r' %.16s Detection of the pings emitted by autonomous beacons' %DATE, fontsize=10)
# plt.show()
ax.set_title(r' %.16s Detection of the pings emitted by autonomous beacons' %DATE, fontsize = 10)
my_path = os.path.abspath(directory)
my_file = 'Online_Acoustic_Monitoring.png'
fig.savefig(os.path.join(my_path, my_file))
print(time.time())
check=False
check = False
check_time=time.time() - now
print(check_time)
time.sleep(abs(TIT - check_time))
check=True
check = True