diff --git a/scripts/acoustics.py b/scripts/acoustics.py
deleted file mode 100644
index 58d56f6647fe77dffaca920694be34314d7d4c17..0000000000000000000000000000000000000000
--- a/scripts/acoustics.py
+++ /dev/null
@@ -1,229 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-"""
-Online Acoustic Monitoring
-
-Usage:
- acoustics.py [options]
- acoustics.py (-h | --help)
-
-Options:
- -d DET_ID Detector ID
- -n N_DUS Number of DUs
- -o PLOT_DIR The directory to save the plot
- -h --help Show this screen.
-
-"""
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-import os
-import matplotlib
-from matplotlib import colors
-from datetime import datetime
-from docopt import docopt
-import km3pipe as kp
-
-
-def diff(first, second):
- second = set(second)
- return [item for item in first if item not in second]
-
-
-args = docopt(__doc__)
-
-detid = args['-d']
-directory = args['-o']
-N_DUS = args['-n']
-N_DUS = int(N_DUS)
-
-db = kp.db.DBManager()
-sds = kp.db.StreamDS()
-
-ACOUSTIC_BEACONS = [12, 14, 16]
-N_DOMS = 18
-N_ABS=3
-DOMS = range(N_DOMS + 1)
-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:
-
- 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:
- minrun = table["RUN"][len(table["RUN"]) - 1] - 1
- print(now)
-
- N_Pulses_Indicator = [] # Matrix indicating how many pulses each piezo reveals
- for du in DUS:
- N_Pulses_Indicator_DU = [] # Array indicating for each DU how many pulses each piezo reveals.
- for ab in ACOUSTIC_BEACONS:
- 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)
-
- QF_abdom = toas_all["QUALITYFACTOR"]
- UTB_abdom = toas_all["UNIXTIMEBASE"]
- TOAS_abdom = toas_all["TOA_S"]
- UTB_abdom = UTB_abdom.values
- up = np.where(UTB_abdom>(now-TIT))
- down = np.where(UTB_abdom<(now))
- intr = np.intersect1d(up,down)
- UTB_abdom = UTB_abdom[intr]
- QF_abdom = QF_abdom[intr]
- QF_abdom = QF_abdom.values
- QFlist = QF_abdom.tolist()
- QFlist.sort(reverse = True)
- QF_max = max(QF_abdom)
- QF_max_index = np.where(QF_abdom == QF_max)
- UTB_signal_min = UTB_abdom[QF_max_index] - SSW/2
- UTB_signal_max = UTB_abdom[QF_max_index] + SSW/2
- temp1 = np.where(UTB_abdom > (UTB_signal_min[0]))
- temp2 = np.where(UTB_abdom < (UTB_signal_max[0]))
- inter = np.intersect1d(temp1, temp2)
- inter = inter.tolist()
- signal_index = inter
- QF_abdom_index = np.where(QF_abdom)
- all_data_index = QF_abdom_index[0].tolist()
- noise_index = diff(all_data_index, signal_index)
- SIGNAL = QF_abdom[signal_index]
- UTB_SIGNAL = UTB_abdom[signal_index]
- NOISE = QF_abdom[noise_index]
- NOISElist = NOISE.tolist()
- NOISElist.sort(reverse = True)
- noise_threshold = max(NOISE)
-
- # First filter: 22 greatest
- Security_Number = 22 # To be sure to take all the pulses
-
- SIGNAL = SIGNAL.tolist()
- SIGNAL_OLD = np.array(SIGNAL)
- SIGNAL.sort(reverse = True)
- QF_first = SIGNAL[0:Security_Number]
-
- # Second filter: delete duplicates
-
- QF_second = np.unique(QF_first)
- QF_second = QF_second.tolist()
- QF_second.sort(reverse = True)
-
- # Third filter: If there are more than 11 elements I will eliminate the worst
-
- 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)]
- else:
- QF_third = QF_second
-
- # Fourth filter: I remove the data if it is below the maximum noise
-
- QF_fourth = [k for k in QF_third if k > (noise_threshold + (5*np.std(NOISE)))]
-
- # Fifth filter: Check if the clicks are interspersed in the right way
-
- Q = []
- for q in np.arange(len(QF_fourth)):
- 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 = []
- 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):
- del QF_fourth[d]
- QF_fifth = QF_fourth
- QF_OK = QF_fifth
- NUM = len(QF_OK) # Number of pulses
- print(NUM)
-
- if (NUM > 7):
- N_Pulses_Indicator_DU.append(1.5)
- elif (NUM < 8 and NUM > 3):
- N_Pulses_Indicator_DU.append(0.5)
- elif (NUM < 4 and NUM > 0):
- N_Pulses_Indicator_DU.append(-0.5)
- elif (NUM == 0):
- N_Pulses_Indicator_DU.append(-1.5)
-
-
- 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)
- 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))
-
- 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)
- 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.get_yaxis().labelpad = 18
-
- 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)
-
- 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_time = time.time() - now
- print(check_time)
-
- time.sleep(abs(TIT - check_time))
- check = True