print "parallel execution\n"
# find the data directory
filename = inspect.getframeinfo(inspect.currentframe()).filename
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:
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')
ax.set_ylabel('bin count')
ax.set_title('phase bin histogramm for periodic rate process')
# test GL algorithm for constant rate process
O_period1,p_period1,m_opt1,S1,w1,w_peak1,w_mean1,w_conf1=compute_GL(T1,parallel=psx)
fig, ax = plt.subplots()
ax.plot(w1,S1)
ax.set_ylabel('probability')
ax.set_xlabel('f [rad/s]')
ax.set_title('spectrum for constant rate process')
print ('Likelihood of periodic process =%3.2f %% most likely frequency %e mean frequency %e 95 %% confidence interval = [%e %e]\n') % (p_period1*100,w_peak1,w_mean1,w_conf1[0],w_conf1[1])
O_period2,p_period2,m_opt2,S2,w2,w_peak2,w_mean2,w_conf2=compute_GL(T2,parallel=psx)
fig, ax = plt.subplots()
ax.plot(w2,S2)
