current_dir = os.path.dirname(os.path.abspath(filename))
local_path="data" # this folder should containt the files in file_list
for fname in file_list:
fullfile=os.path.join(current_dir,local_path,fname)
T=import_data(fullfile) # get astro-data from file
t0=time.time() # benchmark performance
O_period,p_period,m_opt,S,w,w_peak,w_mean,w_conf=compute_GL(T,parallel=px) # run GL, parallel execution
t1=time.time()
print "total time used = %f with parallel execution\n" % (t1-t0)
n=compute_bin(T,m=m_opt,w=w_peak,p=0) # compute resulting bin histogram
print ('File:%s - Likelihood of periodic process =%3.2f %% most likely frequency %e mean frequency %e 95 %% confidence interval = [%e %e]\n') % (fname,p_period*100,w_peak,w_mean,w_conf[0],w_conf[1])
# serial execution
print "serial execution\n"
px=False
for fname in file_list:
fullfile=os.path.join(current_dir,local_path,fname)
T=import_data(fullfile) # get astro-data from file
t0=time.time() # benchmark performance
import matplotlib.pyplot as plt
if __name__ == '__main__':
psx=False # parallel execution
w0=0.0004 # simulate slow frequency signal
phase0=1.0 # with phase offset 1.0
# simulate constant rate process as test of Null - hypothesis
T1=simulate_arrival_times()
# simulate periodic rate process as positive test
T2=simulate_arrival_times(w=w0,phase=phase0,noise_rate=0.0000)
#make bin histograms
n1=compute_bin(T1,m=7,w=w0,p=phase0)
n2=compute_bin(T2,m=7,w=w0,p=phase0)
# plot the bin histograms
ind=np.arange(7) # x-axis for the bin histogram
width = 0.35 # the width of the bars
# constant rate process
fig, ax = plt.subplots()
rects1 = ax.bar(ind, n1, width, color='b')
ax.set_ylabel('bin count')
ax.set_title('phase bin histogramm for constant rate process')
# periodic process
fig, ax = plt.subplots()
rects2 = ax.bar(ind, n2, width, color='b')