if(debug==True): print "Reading thermal noise data"
time,a_uncor_therm,b_uncor_therm,c_uncor_therm,d_uncor_therm,e_uncor_therm,f_uncor_therm,a_therm,b_therm,c_therm,d_therm,e_therm,f_therm = ttb.read_data_from_data_file(100.00,samples_to_read,0)
if(debug==True): print "Finished reading first thermal noise data"
time,d_uncor_therm,e_uncor_therm,f_uncor_therm,g_uncor_therm,h_uncor_therm,i_uncor_therm,d_therm,e_therm,f_therm,g_therm,h_therm,i_therm = ttb.read_data_from_data_file(100.00,samples_to_read,1)
if(debug==True): print "Finished reading second thermal noise data"
num_thermal_samples = len(a_therm)
num_trials = 100#int(num_thermal_samples/TISC_sample_length)
for timestep in range(1,num_trials+1):
if(debug==True and timestep % 100==0):
print "\nStarting trial: "+str(timestep)
start = timestep*num_samples_per_window
end = start+num_samples_per_window
if(end>=num_thermal_samples):
break
d1,d2,d3,abc_correlation_mean,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,a_therm[start:end],b_therm[start:end],c_therm[start:end],100,abc_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=abc_correlation_mean,trial_run_number=timestep)
d1,d2,d3,def_correlation_mean,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,d_therm[start:end],e_therm[start:end],f_therm[start:end],100,def_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=def_correlation_mean,trial_run_number=timestep)
d1,d2,d3,ghi_correlation_mean,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,g_therm[start:end],h_therm[start:end],i_therm[start:end],100,ghi_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=ghi_correlation_mean,trial_run_number=timestep)
d1,d2,d3,abc_correlation_mean_uncor,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,a_uncor_therm[start:end],b_uncor_therm[start:end],c_uncor_therm[start:end],100,abc_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=abc_correlation_mean_uncor,trial_run_number=timestep)
d1,d2,d3,def_correlation_mean_uncor,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,d_uncor_therm[start:end],e_uncor_therm[start:end],f_uncor_therm[start:end],100,def_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=def_correlation_mean_uncor,trial_run_number=timestep)
d1,d2,d3,ghi_correlation_mean_uncor,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,g_uncor_therm[start:end],h_uncor_therm[start:end],i_uncor_therm[start:end],100,ghi_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=ghi_correlation_mean_uncor,trial_run_number=timestep)
# Set offsets for plots
max_offset = 100
#print abc_correlation_mean
####################################################################
# Setup output files
data_filename = open(output_dir+"/data_output.dat","w")
if(debug==True): print "Reading thermal noise data"
time,a_uncor_therm,b_uncor_therm,c_uncor_therm,d_uncor_therm,e_uncor_therm,f_uncor_therm,a_therm,b_therm,c_therm,d_therm,e_therm,f_therm = ttb.read_data_from_data_file(100.00,samples_to_read,0)
if(debug==True): print "Finished reading first thermal noise data"
time,d_uncor_therm,e_uncor_therm,f_uncor_therm,g_uncor_therm,h_uncor_therm,i_uncor_therm,d_therm,e_therm,f_therm,g_therm,h_therm,i_therm = ttb.read_data_from_data_file(100.00,samples_to_read,1)
if(debug==True): print "Finished reading second thermal noise data"
num_thermal_samples = len(a_therm)
num_trials = 100#int(num_thermal_samples/TISC_sample_length)
for timestep in range(1,num_trials+1):
if(debug==True and timestep % 100==0):
print "\nStarting trial: "+str(timestep)
start = timestep*num_samples_per_window
end = start+num_samples_per_window
if(end>=num_thermal_samples):
break
d1,d2,d3,abc_correlation_mean,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,a_therm[start:end],b_therm[start:end],c_therm[start:end],100,abc_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=abc_correlation_mean,trial_run_number=timestep)
d1,d2,d3,def_correlation_mean,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,d_therm[start:end],e_therm[start:end],f_therm[start:end],100,def_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=def_correlation_mean,trial_run_number=timestep)
d1,d2,d3,ghi_correlation_mean,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,g_therm[start:end],h_therm[start:end],i_therm[start:end],100,ghi_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=ghi_correlation_mean,trial_run_number=timestep)
d1,d2,d3,abc_correlation_mean_uncor,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,a_uncor_therm[start:end],b_uncor_therm[start:end],c_uncor_therm[start:end],100,abc_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=abc_correlation_mean_uncor,trial_run_number=timestep)
d1,d2,d3,def_correlation_mean_uncor,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,d_uncor_therm[start:end],e_uncor_therm[start:end],f_uncor_therm[start:end],100,def_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=def_correlation_mean_uncor,trial_run_number=timestep)
d1,d2,d3,ghi_correlation_mean_uncor,d4,d5,d6,d7,d8,d9 = sum_correlate(num_samples_per_window,g_uncor_therm[start:end],h_uncor_therm[start:end],i_uncor_therm[start:end],100,ghi_baseline,TISC_sample_length=16,delay_type_flag=1,average_subtract_flag=1,correlation_mean=ghi_correlation_mean_uncor,trial_run_number=timestep)
# Set offsets for plots
max_offset = 100
#print abc_correlation_mean
####################################################################
# Setup output files
data_filename = open(output_dir+"/data_output.dat","w")
def TISC_sim(SNR,threshold,
b_input_delay,c_input_delay,num_bits=3,
noise_sigma=32.0,
sample_freq=2600000000.0,TISC_sample_length=16,
num_samples=80,upsample=10,cw_flag=0,
cw_amplitude=20.0,carrier_frequency=260000000.0,modulation_frequency=1.0,
seed=5522684,draw_flag=0,digitization_factor=32.0,
delay_type_flag=1,
output_dir="output/",average_subtract_flag=0,abc_correlation_mean=np.zeros(46),
def_correlation_mean=np.zeros(44),ghi_correlation_mean=np.zeros(46),trial_run_number=1,boresight=0,baseline=0,six_phi_sector_add=False,window_length=0,window_weight=0.5, debug=False):
# Setup
save_output_flag = 0
#if(save_output_flag):
#outfile = str(output_dir+"/test.root")
trigger_flag = 0
#num_bits = 3 # Number of bits available to the digitizer
#num_samples = num_samples*upsample
filter_flag = False
#sample_frequency = 2800000000.0
def_max_sum = 0.0
def_as_max_sum = 0.0
ghi_max_sum = 0.0
ghi_as_max_sum = 0.0
timestep = 1.0/sample_freq
# Phi sectors have alternating baselines
if(boresight==0):
abc_impulse_amp = 1.000
def_impulse_amp = 0.835
ghi_impulse_amp = 0.776
if(baseline==0):
abc_baseline = 0
def_baseline = 1
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
elif(boresight==1):
abc_impulse_amp = 0.962
def_impulse_amp = 0.885
ghi_impulse_amp = 0.650
if(baseline==0):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
# Fill numpy arrays with zeros
a_input_noise = np.zeros(num_samples)
b_input_noise = np.zeros(num_samples)
c_input_noise = np.zeros(num_samples)
d_input_noise = np.zeros(num_samples)
e_input_noise = np.zeros(num_samples)
f_input_noise = np.zeros(num_samples)
g_input_noise = np.zeros(num_samples)
h_input_noise = np.zeros(num_samples)
i_input_noise = np.zeros(num_samples)
time = np.zeros(num_samples)
upsampled_time = np.zeros(num_samples)
a_input_signal = np.zeros(num_samples)
b_input_signal = np.zeros(num_samples)
c_input_signal = np.zeros(num_samples)
d_input_signal = np.zeros(num_samples)
e_input_signal = np.zeros(num_samples)
f_input_signal = np.zeros(num_samples)
g_input_signal = np.zeros(num_samples)
h_input_signal = np.zeros(num_samples)
i_input_signal = np.zeros(num_samples)
a_input_signal_noise = np.zeros(num_samples)
b_input_signal_noise = np.zeros(num_samples)
c_input_signal_noise = np.zeros(num_samples)
d_input_signal_noise = np.zeros(num_samples)
e_input_signal_noise = np.zeros(num_samples)
f_input_signal_noise = np.zeros(num_samples)
g_input_signal_noise = np.zeros(num_samples)
h_input_signal_noise = np.zeros(num_samples)
i_input_signal_noise = np.zeros(num_samples)
a_dig_waveform = np.zeros(num_samples)
b_dig_waveform = np.zeros(num_samples)
c_dig_waveform = np.zeros(num_samples)
d_dig_waveform = np.zeros(num_samples)
e_dig_waveform = np.zeros(num_samples)
f_dig_waveform = np.zeros(num_samples)
g_dig_waveform = np.zeros(num_samples)
h_dig_waveform = np.zeros(num_samples)
i_dig_waveform = np.zeros(num_samples)
empty_list = np.zeros(num_samples)
###################################
# Generate Thermal Noise
a_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed)
b_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+1)
c_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+2)
d_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+3)
e_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+4)
f_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+5)
g_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+6)
h_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+7)
i_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+8)
###################################
#####################################
# Determine RMS of noise and signal amplitude
#noise_rms = np.sqrt(np.mean((a_input_noise-noise_mean)**2,))
signal_amp = SNR*2*noise_sigma
#####################################
#################################
#Generate CW noise if desired
if cw_flag:
cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,cw_amplitude,filter_flag)
a_input_noise = np.add(a_input_noise,cw_noise)#generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
#a_input_noise += a_input_cw_noise
# b_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
b_input_noise = np.add(cw_noise,b_input_noise)
#c_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
c_input_noise = np.add(cw_noise,c_input_noise)
#d_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
d_input_noise = np.add(cw_noise,d_input_noise)
#e_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
e_input_noise = np.add(cw_noise,e_input_noise)
#f_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
f_input_noise = np.add(cw_noise,f_input_noise)
#g_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
g_input_noise = np.add(cw_noise,g_input_noise)
#h_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
h_input_noise = np.add(cw_noise,h_input_noise)
#i_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
i_input_noise = np.add(cw_noise,i_input_noise)
#####################################
a_input_noise = butter_bandpass_filter(a_input_noise)
b_input_noise = butter_bandpass_filter(b_input_noise)
c_input_noise = butter_bandpass_filter(c_input_noise)
d_input_noise = butter_bandpass_filter(d_input_noise)
e_input_noise = butter_bandpass_filter(e_input_noise)
f_input_noise = butter_bandpass_filter(f_input_noise)
g_input_noise = butter_bandpass_filter(g_input_noise)
h_input_noise = butter_bandpass_filter(h_input_noise)
i_input_noise = butter_bandpass_filter(i_input_noise)
#####################################
# Generate impulse
if (SNR != 0):
# Generate Signal and Amplify
a_input_signal = impulse_gen(num_samples,upsample,draw_flag=draw_flag,output_dir=output_dir)
difference=np.amax(a_input_signal)-np.amin(a_input_signal) # Get peak to peak voltage
a_input_signal *= (1/difference) # Normalize input
a_input_signal *= signal_amp # Amplify
#b_input_signal = np.concatenate([a_input_signal[:num_samples+b_input_delay],empty_list[:(-1)*b_input_delay]])
#c_input_signal = np.concatenate([a_input_signal[:num_samples+c_input_delay],empty_list[:(-1)*c_input_delay]])
b_input_signal = np.concatenate([a_input_signal[-b_input_delay:],empty_list[:(-1)*b_input_delay]])
c_input_signal = np.concatenate([a_input_signal[-c_input_delay:],empty_list[:(-1)*c_input_delay]])
a_input_signal =a_input_signal*abc_impulse_amp
b_input_signal =b_input_signal*abc_impulse_amp
c_input_signal =c_input_signal*abc_impulse_amp
d_input_signal =a_input_signal*def_impulse_amp
e_input_signal =b_input_signal*def_impulse_amp
f_input_signal =c_input_signal*def_impulse_amp
g_input_signal =a_input_signal*ghi_impulse_amp
h_input_signal =b_input_signal*ghi_impulse_amp
i_input_signal =c_input_signal*ghi_impulse_amp
"""
if(boresight==0):
d_input_signal = a_input_signal*0.776 # Average dB loss at -22.5 degrees
e_input_signal = b_input_signal*0.776 # from Seavey measurements
f_input_signal = c_input_signal*0.776
g_input_signal = a_input_signal*0.835 # Average dB loss at +22.5 degrees
h_input_signal = b_input_signal*0.835 # from Seavey measurements
i_input_signal = c_input_signal*0.835
elif(boresight==1):
# For event between two phi sectors, the two antennas are down by about -0.5dB at
a_input_signal = a_input_signal*0.885 # Average dB los at +11.25 degrees
b_input_signal = b_input_signal*0.885 # from Seavey measurements
c_input_signal = c_input_signal*0.885
d_input_signal = a_input_signal*0.962 # Average dB loss at -11.25 degrees
e_input_signal = b_input_signal*0.962 # from Seavey measurements
f_input_signal = c_input_signal*0.962
g_input_signal = a_input_signal*0.650 # Average dB loss at +-33.75 degrees
h_input_signal = b_input_signal*0.650 # from Seavey measurements
i_input_signal = c_input_signal*0.650
"""
if(debug):
time = np.linspace(0.0,timestep*num_samples,num_samples)
plt.plot(time,a_input_signal,time,b_input_signal,time,c_input_signal)
plt.plot(time,d_input_signal,time,e_input_signal,time,f_input_signal)
plt.plot(time,g_input_signal,time,h_input_signal,time,i_input_signal)
plt.title("impulse")
plt.show()
# Add the signal to the noise
a_input_signal_noise = np.add(a_input_noise, a_input_signal)
b_input_signal_noise = np.add(b_input_noise, b_input_signal)
c_input_signal_noise = np.add(c_input_noise, c_input_signal)
d_input_signal_noise = np.add(d_input_noise, d_input_signal)
e_input_signal_noise = np.add(e_input_noise, e_input_signal)
f_input_signal_noise = np.add(f_input_noise, f_input_signal)
g_input_signal_noise = np.add(g_input_noise, g_input_signal)
h_input_signal_noise = np.add(h_input_noise, h_input_signal)
i_input_signal_noise = np.add(i_input_noise, i_input_signal)
if(debug):
time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_input_signal_noise,time,b_input_signal_noise,time,c_input_signal_noise)
#plt.plot(time,d_input_signal_noise,time,e_input_signal_noise,time,f_input_signal_noise)
plt.plot(time,g_input_signal_noise,time,h_input_signal_noise,time,i_input_signal_noise)
plt.title("impulse+noise")
plt.show()
else:
a_input_signal_noise = a_input_noise
b_input_signal_noise = b_input_noise
c_input_signal_noise = c_input_noise
d_input_signal_noise = d_input_noise
e_input_signal_noise = e_input_noise
f_input_signal_noise = f_input_noise
g_input_signal_noise = g_input_noise
h_input_signal_noise = h_input_noise
i_input_signal_noise = i_input_noise
##########################################
#time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_input_noise,time,b_input_noise,time,c_input_noise)
#plt.title("noise")
#plt.show()
##########################################
# Digitized the incoming signal and noise (RITC)
a_dig_waveform = digitize(a_input_signal_noise,num_samples,num_bits,digitization_factor)
b_dig_waveform = digitize(b_input_signal_noise,num_samples,num_bits,digitization_factor)
c_dig_waveform = digitize(c_input_signal_noise,num_samples,num_bits,digitization_factor)
d_dig_waveform = digitize(d_input_signal_noise,num_samples,num_bits,digitization_factor)
e_dig_waveform = digitize(e_input_signal_noise,num_samples,num_bits,digitization_factor)
f_dig_waveform = digitize(f_input_signal_noise,num_samples,num_bits,digitization_factor)
g_dig_waveform = digitize(g_input_signal_noise,num_samples,num_bits,digitization_factor)
h_dig_waveform = digitize(h_input_signal_noise,num_samples,num_bits,digitization_factor)
i_dig_waveform = digitize(i_input_signal_noise,num_samples,num_bits,digitization_factor)
##########################################
if(debug):
time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_dig_waveform,time,b_dig_waveform,time,c_dig_waveform)
#plt.plot(time,d_dig_waveform,time,e_dig_waveform,time,f_dig_waveform)
plt.plot(time,g_dig_waveform,time,h_dig_waveform,time,i_dig_waveform)
plt.title("Digitized")
plt.show()
##########################################
# Run the signal through the GLITC module to get trigger
if(average_subtract_flag):
abc_trigger_flag, abc_max_sum , abc_as_max_sum, abc_correlation_mean, abc_test_sum, abc_as_test_sum,as_abc_angle,abc_angle,d1,d2 = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,threshold,abc_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=abc_correlation_mean,trial_run_number=trial_run_number)
def_trigger_flag, def_max_sum , def_as_max_sum, def_correlation_mean, def_test_sum, def_as_test_sum,as_def_angle,def_angle,d1,d2 = sum_correlate(num_samples,d_dig_waveform,e_dig_waveform,f_dig_waveform,threshold,def_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=def_correlation_mean,trial_run_number=trial_run_number)
ghi_trigger_flag, ghi_max_sum , ghi_as_max_sum, ghi_correlation_mean, ghi_test_sum, ghi_as_test_sum,as_ghi_angle,ghi_angle,d1,d2 = sum_correlate(num_samples,g_dig_waveform,h_dig_waveform,i_dig_waveform,threshold,ghi_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=ghi_correlation_mean,trial_run_number=trial_run_number)
#abc_max_sum
#print len(a_dig_waveform)
else:
abc_trigger_flag, abc_max_sum,abc_angle,d1 = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,threshold,abc_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=abc_correlation_mean,trial_run_number=trial_run_number,window_length=window_length,window_weight=window_weight)
def_trigger_flag, def_max_sum,def_angle,d1 = sum_correlate(num_samples,d_dig_waveform,e_dig_waveform,f_dig_waveform,threshold,def_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=def_correlation_mean,trial_run_number=trial_run_number,window_length=window_length,window_weight=window_weight)
ghi_trigger_flag, ghi_max_sum,ghi_angle,d1 = sum_correlate(num_samples,g_dig_waveform,h_dig_waveform,i_dig_waveform,threshold,ghi_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=ghi_correlation_mean,trial_run_number=trial_run_number,window_length=window_length,window_weight=window_weight)
#print abc_max_sum
#print def_max_sum
#print ghi_max_sum
#if(abc_trigger_flag & def_trigger_flag & ghi_trigger_flag):
#trigger_flag = True
#else:
#trigger_flag = False
#########################################
#dummy = raw_input('Press any key to close')
if (average_subtract_flag):
return abc_max_sum,abc_as_max_sum,def_max_sum,def_as_max_sum,ghi_max_sum,ghi_as_max_sum,abc_correlation_mean, def_correlation_mean, ghi_correlation_mean,as_abc_angle,abc_angle,as_def_angle,def_angle,as_ghi_angle,ghi_angle
else:
return abc_max_sum,def_max_sum,ghi_max_sum,abc_angle,def_angle,ghi_angle
def TISC_sim(SNR,threshold,
b_input_delay,c_input_delay,num_bits=3,
noise_sigma=32.0,
sample_freq=2600000000.0,TISC_sample_length=16,
num_samples=74,upsample=10,cw_flag=0,
cw_rms=25.0,carrier_frequency=260000000.0,modulation_frequency=1.0,
seed=5522684,draw_flag=0,digitization_factor=32.0,
delay_type_flag=1,
output_dir="output/",average_subtract_flag=0,correlation_mean=np.zeros(44),trial_run_number=1):
#print b_input_delay
# Setup
save_output_flag = 0
#if(save_output_flag):
#outfile = str(output_dir+"/test.root")
trigger_flag = 0
#num_bits = 3 # Number of bits available to the digitizer
filter_flag = False
#print SNR
# Fill numpy arrays with zeros
a_input_noise = np.zeros(num_samples)
b_input_noise = np.zeros(num_samples)
c_input_noise = np.zeros(num_samples)
a_input_noise_test = np.zeros(num_samples)
b_input_noise_test = np.zeros(num_samples)
c_input_noise_test = np.zeros(num_samples)
a_input_signal = np.zeros(num_samples)
b_input_signal = np.zeros(num_samples)
c_input_signal = np.zeros(num_samples)
a_dig_waveform = np.zeros(num_samples)
b_dig_waveform = np.zeros(num_samples)
c_dig_waveform = np.zeros(num_samples)
cw_noise = np.zeros(num_samples)
empty_list = np.zeros(num_samples)
###################################
# Generate Thermal Noise
a_input_noise = generate_noise(num_samples,noise_sigma,filter_flag)
b_input_noise = generate_noise(num_samples,noise_sigma,filter_flag)
c_input_noise = generate_noise(num_samples,noise_sigma,filter_flag)
#a_input_noise_test = a_input_noise
#b_input_noise_test = b_input_noise
#c_input_noise_test = c_input_noise
###################################
#print a_input_noise[0]
#print b_input_noise[0]
#print c_input_noise[0]
#####################################
# Determine signal amplitude
signal_amp = SNR*2*noise_sigma
#####################################
#################################
#Generate CW & thermal noise
if cw_flag:
cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,cw_rms,filter_flag)
a_input_noise = np.add(a_input_noise,cw_noise)
#cw_noise = generate_cw(num_samples,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
b_input_noise = np.add(b_input_noise,cw_noise)
#cw_noise = generate_cw(num_samples,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
c_input_noise = np.add(c_input_noise,cw_noise)
#####################################
# Filter the noise
a_input_noise = butter_bandpass_filter(a_input_noise)
b_input_noise = butter_bandpass_filter(b_input_noise)
c_input_noise = butter_bandpass_filter(c_input_noise)
#print "Filter Took: " +str(datetime.now()-start_filter)
#start_signal = datetime.now()
#####################################
# Generate impulse
if (SNR != 0):
# Generate Signal and Amplify
a_input_signal = impulse_gen(num_samples,upsample,draw_flag=draw_flag,output_dir=output_dir)
difference=np.amax(a_input_signal)-np.amin(a_input_signal) # Get peak to peak voltage
a_input_signal *= (1/difference) # Normalize input
a_input_signal *= signal_amp # Amplify
b_input_signal = np.concatenate([a_input_signal[:num_samples+b_input_delay],empty_list[:(-1)*b_input_delay]])
c_input_signal = np.concatenate([a_input_signal[:num_samples+c_input_delay],empty_list[:(-1)*c_input_delay]])
# Add the signal to the noise
a_input_signal = np.add(a_input_noise, a_input_signal)
b_input_signal = np.add(b_input_noise, b_input_signal)
c_input_signal = np.add(c_input_noise, c_input_signal)
else:
a_input_signal = a_input_noise
b_input_signal = b_input_noise
c_input_signal = c_input_noise
##########################################
##########################################
# Digitized the incoming signal and noise (RITC)
a_dig_waveform = digitize(a_input_signal,num_samples,num_bits,digitization_factor)
b_dig_waveform = digitize(b_input_signal,num_samples,num_bits,digitization_factor)
c_dig_waveform = digitize(c_input_signal,num_samples,num_bits,digitization_factor)
##########################################
#print a_dig_waveform
##########################################
# Run the signal through the GLITC module to get trigger
if (average_subtract_flag):
trigger_flag, max_sum, as_max_sum, correlation_mean, test_sum, as_test_sum = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,
threshold,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=correlation_mean,trial_run_number=trial_run_number)
else:
trigger_flag, max_sum, test_sum = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,
threshold,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=correlation_mean)
"""
if (max_sum>800):
import matplotlib.pyplot as plt
#print a_dig_waveform
#print b_dig_waveform
#print c_dig_waveform
#print np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform)
#print (np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))**2
#print np.sum((np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))**2)
time = np.linspace(0.0,((num_samples*(10**9))/sample_freq), num_samples)
plt.figure(1)
plt.clf()
plt.plot(time,a_dig_waveform)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch A")
plt.savefig(output_dir+"/ch_A_large_correlation.png")
plt.figure(2)
plt.clf()
plt.plot(time,b_dig_waveform)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch B")
plt.savefig(output_dir+"/ch_B_large_correlation.png")
plt.figure(3)
plt.clf()
plt.plot(time,c_dig_waveform)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch C")
plt.savefig(output_dir+"/ch_C_large_correlation.png")
plt.figure(4)
plt.clf()
plt.plot(time,np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("ABC Added")
plt.savefig(output_dir+"/ABC_large_correlation.png")
plt.figure(5)
plt.clf()
plt.plot(time,(np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))**2)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("ABC Square Added")
plt.savefig(output_dir+"/ABC_square_large_correlation.png")
plt.figure(6)
plt.clf()
plt.plot(time,a_input_noise_test)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch A Thermal Noise")
plt.savefig(output_dir+"/ch_A_thermal_large_correlation.png")
plt.figure(7)
plt.clf()
plt.plot(time,b_input_noise_test)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch B Thermal Noise")
plt.savefig(output_dir+"/ch_B_thermal_large_correlation.png")
plt.figure(8)
plt.clf()
plt.plot(time,c_input_noise_test)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch C Thermal Noise")
plt.savefig(output_dir+"/ch_C_thermal_large_correlation.png")
plt.figure(9)
plt.clf()
plt.plot(time,cw_noise)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("CW Noise")
plt.savefig(output_dir+"/cw_large_correlation.png")
plt.show()
"""
#########################################
#########################################
# Output data
#if(save_output_flag):
# Now to more ROOT stuff
#rf_tree.Fill()
#f.Write()
#f.Close()
if draw_flag:
dummy = raw_input('Press any key to close')
#print "Everything took: " +str(datetime.now()-start_time)
if (average_subtract_flag):
return trigger_flag, max_sum, as_max_sum, correlation_mean, test_sum, as_test_sum
else:
return trigger_flag, max_sum
def TISC_sim(SNR,threshold,
b_input_delay,c_input_delay,num_bits=3,
noise_sigma=32.0,
sample_freq=2600000000.0,TISC_sample_length=16,
num_samples=80,upsample=10,cw_flag=0,
cw_amplitude=20.0,carrier_frequency=260000000.0,modulation_frequency=1.0,
seed=5522684,draw_flag=0,digitization_factor=32.0,
delay_type_flag=1,
output_dir="output/",average_subtract_flag=0,abc_correlation_mean=np.zeros(46),
def_correlation_mean=np.zeros(44),ghi_correlation_mean=np.zeros(46),trial_run_number=1,boresight=0,baseline=0,six_phi_sector_add=False):
# Setup
save_output_flag = 0
#if(save_output_flag):
#outfile = str(output_dir+"/test.root")
trigger_flag = 0
#num_bits = 3 # Number of bits available to the digitizer
#num_samples = num_samples*upsample
filter_flag = False
#sample_frequency = 2800000000.0
def_max_sum = 0.0
def_as_max_sum = 0.0
ghi_max_sum = 0.0
ghi_as_max_sum = 0.0
timestep = 1.0/sample_freq
# Phi sectors have alternating baselines
if(boresight==0):
abc_impulse_amp = 1.000
def_impulse_amp = 0.835
ghi_impulse_amp = 0.776
if(baseline==0):
abc_baseline = 0
def_baseline = 1
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
elif(boresight==1):
abc_impulse_amp = 0.962
def_impulse_amp = 0.885
ghi_impulse_amp = 0.650
if(baseline==0):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 1
elif(baseline==1):
abc_baseline = 1
def_baseline = 0
ghi_baseline = 0
# Fill numpy arrays with zeros
a_input_noise = np.zeros(num_samples)
b_input_noise = np.zeros(num_samples)
c_input_noise = np.zeros(num_samples)
d_input_noise = np.zeros(num_samples)
e_input_noise = np.zeros(num_samples)
f_input_noise = np.zeros(num_samples)
g_input_noise = np.zeros(num_samples)
h_input_noise = np.zeros(num_samples)
i_input_noise = np.zeros(num_samples)
time = np.zeros(num_samples)
upsampled_time = np.zeros(num_samples)
a_input_signal = np.zeros(num_samples)
b_input_signal = np.zeros(num_samples)
c_input_signal = np.zeros(num_samples)
d_input_signal = np.zeros(num_samples)
e_input_signal = np.zeros(num_samples)
f_input_signal = np.zeros(num_samples)
g_input_signal = np.zeros(num_samples)
h_input_signal = np.zeros(num_samples)
i_input_signal = np.zeros(num_samples)
a_input_signal_noise = np.zeros(num_samples)
b_input_signal_noise = np.zeros(num_samples)
c_input_signal_noise = np.zeros(num_samples)
d_input_signal_noise = np.zeros(num_samples)
e_input_signal_noise = np.zeros(num_samples)
f_input_signal_noise = np.zeros(num_samples)
g_input_signal_noise = np.zeros(num_samples)
h_input_signal_noise = np.zeros(num_samples)
i_input_signal_noise = np.zeros(num_samples)
a_dig_waveform = np.zeros(num_samples)
b_dig_waveform = np.zeros(num_samples)
c_dig_waveform = np.zeros(num_samples)
d_dig_waveform = np.zeros(num_samples)
e_dig_waveform = np.zeros(num_samples)
f_dig_waveform = np.zeros(num_samples)
g_dig_waveform = np.zeros(num_samples)
h_dig_waveform = np.zeros(num_samples)
i_dig_waveform = np.zeros(num_samples)
empty_list = np.zeros(num_samples)
###################################
# Generate Thermal Noise
a_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed)
b_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+1)
c_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+2)
d_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+3)
e_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+4)
f_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+5)
g_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+6)
h_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+7)
i_input_noise = generate_noise(num_samples,noise_sigma,filter_flag,seed=seed+8)
###################################
#####################################
# Determine RMS of noise and signal amplitude
#noise_rms = np.sqrt(np.mean((a_input_noise-noise_mean)**2,))
signal_amp = SNR*2*noise_sigma
#####################################
#################################
#Generate CW noise if desired
if cw_flag:
cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,cw_amplitude,filter_flag)
a_input_noise = np.add(a_input_noise,cw_noise)#generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
#a_input_noise += a_input_cw_noise
# b_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
b_input_noise = np.add(cw_noise,b_input_noise)
#c_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
c_input_noise = np.add(cw_noise,c_input_noise)
#d_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
d_input_noise = np.add(cw_noise,d_input_noise)
#e_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
e_input_noise = np.add(cw_noise,e_input_noise)
#f_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
f_input_noise = np.add(cw_noise,f_input_noise)
#g_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
g_input_noise = np.add(cw_noise,g_input_noise)
#h_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
h_input_noise = np.add(cw_noise,h_input_noise)
#i_input_cw_noise = generate_cw(num_samples,upsample,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
i_input_noise = np.add(cw_noise,i_input_noise)
#####################################
a_input_noise = butter_bandpass_filter(a_input_noise)
b_input_noise = butter_bandpass_filter(b_input_noise)
c_input_noise = butter_bandpass_filter(c_input_noise)
d_input_noise = butter_bandpass_filter(d_input_noise)
e_input_noise = butter_bandpass_filter(e_input_noise)
f_input_noise = butter_bandpass_filter(f_input_noise)
g_input_noise = butter_bandpass_filter(g_input_noise)
h_input_noise = butter_bandpass_filter(h_input_noise)
i_input_noise = butter_bandpass_filter(i_input_noise)
#####################################
# Generate impulse
if (SNR != 0):
# Generate Signal and Amplify
a_input_signal = impulse_gen(num_samples,upsample,draw_flag=draw_flag,output_dir=output_dir)
difference=np.amax(a_input_signal)-np.amin(a_input_signal) # Get peak to peak voltage
a_input_signal *= (1/difference) # Normalize input
a_input_signal *= signal_amp # Amplify
#b_input_signal = np.concatenate([a_input_signal[:num_samples+b_input_delay],empty_list[:(-1)*b_input_delay]])
#c_input_signal = np.concatenate([a_input_signal[:num_samples+c_input_delay],empty_list[:(-1)*c_input_delay]])
b_input_signal = np.concatenate([a_input_signal[-b_input_delay:],empty_list[:(-1)*b_input_delay]])
c_input_signal = np.concatenate([a_input_signal[-c_input_delay:],empty_list[:(-1)*c_input_delay]])
a_input_signal =a_input_signal*abc_impulse_amp
b_input_signal =b_input_signal*abc_impulse_amp
c_input_signal =c_input_signal*abc_impulse_amp
d_input_signal =a_input_signal*def_impulse_amp
e_input_signal =b_input_signal*def_impulse_amp
f_input_signal =c_input_signal*def_impulse_amp
g_input_signal =a_input_signal*ghi_impulse_amp
h_input_signal =b_input_signal*ghi_impulse_amp
i_input_signal =c_input_signal*ghi_impulse_amp
"""
if(boresight==0):
d_input_signal = a_input_signal*0.776 # Average dB loss at -22.5 degrees
e_input_signal = b_input_signal*0.776 # from Seavey measurements
f_input_signal = c_input_signal*0.776
g_input_signal = a_input_signal*0.835 # Average dB loss at +22.5 degrees
h_input_signal = b_input_signal*0.835 # from Seavey measurements
i_input_signal = c_input_signal*0.835
elif(boresight==1):
# For event between two phi sectors, the two antennas are down by about -0.5dB at
a_input_signal = a_input_signal*0.885 # Average dB los at +11.25 degrees
b_input_signal = b_input_signal*0.885 # from Seavey measurements
c_input_signal = c_input_signal*0.885
d_input_signal = a_input_signal*0.962 # Average dB loss at -11.25 degrees
e_input_signal = b_input_signal*0.962 # from Seavey measurements
f_input_signal = c_input_signal*0.962
g_input_signal = a_input_signal*0.650 # Average dB loss at +-33.75 degrees
h_input_signal = b_input_signal*0.650 # from Seavey measurements
i_input_signal = c_input_signal*0.650
"""
"""
time = np.linspace(0.0,timestep*num_samples,num_samples)
plt.plot(time,a_input_signal,time,b_input_signal,time,c_input_signal)
plt.plot(time,d_input_signal,time,e_input_signal,time,f_input_signal)
plt.plot(time,g_input_signal,time,h_input_signal,time,i_input_signal)
plt.title("impulse")
plt.show()
"""
# Add the signal to the noise
a_input_signal_noise = np.add(a_input_noise, a_input_signal)
b_input_signal_noise = np.add(b_input_noise, b_input_signal)
c_input_signal_noise = np.add(c_input_noise, c_input_signal)
d_input_signal_noise = np.add(d_input_noise, d_input_signal)
e_input_signal_noise = np.add(e_input_noise, e_input_signal)
f_input_signal_noise = np.add(f_input_noise, f_input_signal)
g_input_signal_noise = np.add(g_input_noise, g_input_signal)
h_input_signal_noise = np.add(h_input_noise, h_input_signal)
i_input_signal_noise = np.add(i_input_noise, i_input_signal)
"""
time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_input_signal_noise,time,b_input_signal_noise,time,c_input_signal_noise)
#plt.plot(time,d_input_signal_noise,time,e_input_signal_noise,time,f_input_signal_noise)
plt.plot(time,g_input_signal_noise,time,h_input_signal_noise,time,i_input_signal_noise)
plt.title("impulse+noise")
plt.show()
"""
else:
a_input_signal_noise = a_input_noise
b_input_signal_noise = b_input_noise
c_input_signal_noise = c_input_noise
d_input_signal_noise = d_input_noise
e_input_signal_noise = e_input_noise
f_input_signal_noise = f_input_noise
g_input_signal_noise = g_input_noise
h_input_signal_noise = h_input_noise
i_input_signal_noise = i_input_noise
##########################################
#time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_input_noise,time,b_input_noise,time,c_input_noise)
#plt.title("noise")
#plt.show()
##########################################
# Digitized the incoming signal and noise (RITC)
a_dig_waveform = digitize(a_input_signal_noise,num_samples,num_bits,digitization_factor)
b_dig_waveform = digitize(b_input_signal_noise,num_samples,num_bits,digitization_factor)
c_dig_waveform = digitize(c_input_signal_noise,num_samples,num_bits,digitization_factor)
d_dig_waveform = digitize(d_input_signal_noise,num_samples,num_bits,digitization_factor)
e_dig_waveform = digitize(e_input_signal_noise,num_samples,num_bits,digitization_factor)
f_dig_waveform = digitize(f_input_signal_noise,num_samples,num_bits,digitization_factor)
g_dig_waveform = digitize(g_input_signal_noise,num_samples,num_bits,digitization_factor)
h_dig_waveform = digitize(h_input_signal_noise,num_samples,num_bits,digitization_factor)
i_dig_waveform = digitize(i_input_signal_noise,num_samples,num_bits,digitization_factor)
##########################################
"""
time = np.linspace(0.0,timestep*num_samples,num_samples)
#plt.plot(time,a_dig_waveform,time,b_dig_waveform,time,c_dig_waveform)
#plt.plot(time,d_dig_waveform,time,e_dig_waveform,time,f_dig_waveform)
plt.plot(time,g_dig_waveform,time,h_dig_waveform,time,i_dig_waveform)
plt.title("Digitized")
plt.show()
"""
##########################################
# Run the signal through the GLITC module to get trigger
if(average_subtract_flag):
abc_trigger_flag, abc_max_sum , abc_as_max_sum, abc_correlation_mean, abc_test_sum, abc_as_test_sum,as_abc_angle,abc_angle,d1,d2 = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,threshold,abc_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=abc_correlation_mean,trial_run_number=trial_run_number)
def_trigger_flag, def_max_sum , def_as_max_sum, def_correlation_mean, def_test_sum, def_as_test_sum,as_def_angle,def_angle,d1,d2 = sum_correlate(num_samples,d_dig_waveform,e_dig_waveform,f_dig_waveform,threshold,def_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=def_correlation_mean,trial_run_number=trial_run_number)
ghi_trigger_flag, ghi_max_sum , ghi_as_max_sum, ghi_correlation_mean, ghi_test_sum, ghi_as_test_sum,as_ghi_angle,ghi_angle,d1,d2 = sum_correlate(num_samples,g_dig_waveform,h_dig_waveform,i_dig_waveform,threshold,ghi_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=ghi_correlation_mean,trial_run_number=trial_run_number)
#abc_max_sum
#print len(a_dig_waveform)
else:
abc_trigger_flag, abc_max_sum,abc_andle,d1 = sum_correlate(num_samples,a_dig_waveform,b_dig_waveform,c_dig_waveform,threshold,abc_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=abc_correlation_mean,trial_run_number=trial_run_number)
def_trigger_flag, def_max_sum,def_angle,d1 = sum_correlate(num_samples,d_dig_waveform,e_dig_waveform,f_dig_waveform,threshold,def_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=def_correlation_mean,trial_run_number=trial_run_number)
ghi_trigger_flag, ghi_max_sum,ghi_angle,d1 = sum_correlate(num_samples,g_dig_waveform,h_dig_waveform,i_dig_waveform,threshold,ghi_baseline,TISC_sample_length,delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,correlation_mean=ghi_correlation_mean,trial_run_number=trial_run_number)
#print abc_max_sum
#print def_max_sum
#print ghi_max_sum
#if(abc_trigger_flag & def_trigger_flag & ghi_trigger_flag):
#trigger_flag = True
#else:
#trigger_flag = False
#########################################
#dummy = raw_input('Press any key to close')
if (average_subtract_flag):
return abc_max_sum,abc_as_max_sum,def_max_sum,def_as_max_sum,ghi_max_sum,ghi_as_max_sum,abc_correlation_mean, def_correlation_mean, ghi_correlation_mean,as_abc_angle,abc_angle,as_def_angle,def_angle,as_ghi_angle,ghi_angle
else:
return abc_max_sum,def_max_sum,ghi_max_sum,abc_angle,def_angle,ghi_angle
def TISC_sim(SNR,
threshold,
b_input_delay,
c_input_delay,
num_bits=3,
noise_sigma=32.0,
sample_freq=2600000000.0,
TISC_sample_length=16,
num_samples=74,
upsample=10,
cw_flag=0,
cw_rms=25.0,
carrier_frequency=260000000.0,
modulation_frequency=1.0,
seed=5522684,
draw_flag=0,
digitization_factor=32.0,
delay_type_flag=1,
output_dir="output/",
average_subtract_flag=0,
correlation_mean=np.zeros(44),
trial_run_number=1):
#print b_input_delay
# Setup
save_output_flag = 0
#if(save_output_flag):
#outfile = str(output_dir+"/test.root")
trigger_flag = 0
#num_bits = 3 # Number of bits available to the digitizer
filter_flag = False
#print SNR
# Fill numpy arrays with zeros
a_input_noise = np.zeros(num_samples)
b_input_noise = np.zeros(num_samples)
c_input_noise = np.zeros(num_samples)
a_input_noise_test = np.zeros(num_samples)
b_input_noise_test = np.zeros(num_samples)
c_input_noise_test = np.zeros(num_samples)
a_input_signal = np.zeros(num_samples)
b_input_signal = np.zeros(num_samples)
c_input_signal = np.zeros(num_samples)
a_dig_waveform = np.zeros(num_samples)
b_dig_waveform = np.zeros(num_samples)
c_dig_waveform = np.zeros(num_samples)
cw_noise = np.zeros(num_samples)
empty_list = np.zeros(num_samples)
###################################
# Generate Thermal Noise
a_input_noise = generate_noise(num_samples, noise_sigma, filter_flag)
b_input_noise = generate_noise(num_samples, noise_sigma, filter_flag)
c_input_noise = generate_noise(num_samples, noise_sigma, filter_flag)
#a_input_noise_test = a_input_noise
#b_input_noise_test = b_input_noise
#c_input_noise_test = c_input_noise
###################################
#print a_input_noise[0]
#print b_input_noise[0]
#print c_input_noise[0]
#####################################
# Determine signal amplitude
signal_amp = SNR * 2 * noise_sigma
#####################################
#################################
#Generate CW & thermal noise
if cw_flag:
cw_noise = generate_cw(num_samples, upsample, sample_freq,
carrier_frequency, modulation_frequency, cw_rms,
filter_flag)
a_input_noise = np.add(a_input_noise, cw_noise)
#cw_noise = generate_cw(num_samples,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
b_input_noise = np.add(b_input_noise, cw_noise)
#cw_noise = generate_cw(num_samples,sample_freq,carrier_frequency,modulation_frequency,peak_amplitude,filter_flag)
c_input_noise = np.add(c_input_noise, cw_noise)
#####################################
# Filter the noise
a_input_noise = butter_bandpass_filter(a_input_noise)
b_input_noise = butter_bandpass_filter(b_input_noise)
c_input_noise = butter_bandpass_filter(c_input_noise)
#print "Filter Took: " +str(datetime.now()-start_filter)
#start_signal = datetime.now()
#####################################
# Generate impulse
if (SNR != 0):
# Generate Signal and Amplify
a_input_signal = impulse_gen(num_samples,
upsample,
draw_flag=draw_flag,
output_dir=output_dir)
difference = np.amax(a_input_signal) - np.amin(
a_input_signal) # Get peak to peak voltage
a_input_signal *= (1 / difference) # Normalize input
a_input_signal *= signal_amp # Amplify
b_input_signal = np.concatenate([
a_input_signal[:num_samples + b_input_delay],
empty_list[:(-1) * b_input_delay]
])
c_input_signal = np.concatenate([
a_input_signal[:num_samples + c_input_delay],
empty_list[:(-1) * c_input_delay]
])
# Add the signal to the noise
a_input_signal = np.add(a_input_noise, a_input_signal)
b_input_signal = np.add(b_input_noise, b_input_signal)
c_input_signal = np.add(c_input_noise, c_input_signal)
else:
a_input_signal = a_input_noise
b_input_signal = b_input_noise
c_input_signal = c_input_noise
##########################################
##########################################
# Digitized the incoming signal and noise (RITC)
a_dig_waveform = digitize(a_input_signal, num_samples, num_bits,
digitization_factor)
b_dig_waveform = digitize(b_input_signal, num_samples, num_bits,
digitization_factor)
c_dig_waveform = digitize(c_input_signal, num_samples, num_bits,
digitization_factor)
##########################################
#print a_dig_waveform
##########################################
# Run the signal through the GLITC module to get trigger
if (average_subtract_flag):
trigger_flag, max_sum, as_max_sum, correlation_mean, test_sum, as_test_sum = sum_correlate(
num_samples,
a_dig_waveform,
b_dig_waveform,
c_dig_waveform,
threshold,
TISC_sample_length,
delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,
correlation_mean=correlation_mean,
trial_run_number=trial_run_number)
else:
trigger_flag, max_sum, test_sum = sum_correlate(
num_samples,
a_dig_waveform,
b_dig_waveform,
c_dig_waveform,
threshold,
TISC_sample_length,
delay_type_flag=delay_type_flag,
average_subtract_flag=average_subtract_flag,
correlation_mean=correlation_mean)
"""
if (max_sum>800):
import matplotlib.pyplot as plt
#print a_dig_waveform
#print b_dig_waveform
#print c_dig_waveform
#print np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform)
#print (np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))**2
#print np.sum((np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))**2)
time = np.linspace(0.0,((num_samples*(10**9))/sample_freq), num_samples)
plt.figure(1)
plt.clf()
plt.plot(time,a_dig_waveform)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch A")
plt.savefig(output_dir+"/ch_A_large_correlation.png")
plt.figure(2)
plt.clf()
plt.plot(time,b_dig_waveform)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch B")
plt.savefig(output_dir+"/ch_B_large_correlation.png")
plt.figure(3)
plt.clf()
plt.plot(time,c_dig_waveform)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch C")
plt.savefig(output_dir+"/ch_C_large_correlation.png")
plt.figure(4)
plt.clf()
plt.plot(time,np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("ABC Added")
plt.savefig(output_dir+"/ABC_large_correlation.png")
plt.figure(5)
plt.clf()
plt.plot(time,(np.add(np.add(a_dig_waveform,b_dig_waveform),c_dig_waveform))**2)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("ABC Square Added")
plt.savefig(output_dir+"/ABC_square_large_correlation.png")
plt.figure(6)
plt.clf()
plt.plot(time,a_input_noise_test)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch A Thermal Noise")
plt.savefig(output_dir+"/ch_A_thermal_large_correlation.png")
plt.figure(7)
plt.clf()
plt.plot(time,b_input_noise_test)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch B Thermal Noise")
plt.savefig(output_dir+"/ch_B_thermal_large_correlation.png")
plt.figure(8)
plt.clf()
plt.plot(time,c_input_noise_test)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("Ch C Thermal Noise")
plt.savefig(output_dir+"/ch_C_thermal_large_correlation.png")
plt.figure(9)
plt.clf()
plt.plot(time,cw_noise)
plt.xlabel("Time [ns]")
plt.ylabel("Amplitude [unitless]")
plt.title("CW Noise")
plt.savefig(output_dir+"/cw_large_correlation.png")
plt.show()
"""
#########################################
#########################################
# Output data
#if(save_output_flag):
# Now to more ROOT stuff
#rf_tree.Fill()
#f.Write()
#f.Close()
if draw_flag:
dummy = raw_input('Press any key to close')
#print "Everything took: " +str(datetime.now()-start_time)
if (average_subtract_flag):
return trigger_flag, max_sum, as_max_sum, correlation_mean, test_sum, as_test_sum
else:
return trigger_flag, max_sum
