def dih_target_tester(filename,num1,num2):
list = pickle.load(open(filename+'.txt','rb'))
target = list[num1][1]
domain = list[num1][0]
target = dd.dih_spike_picker(target)
target = dd.dih_dip_picker(target)
window = 11
recursions = 11
endrange = dih_boxavg_recurs(target,window,recursions)
targetlist = []
targetlist.append(domain)
targetlist.append(endrange)
peak = max(endrange[(window-1)/2:len(target)-(window-1)/2])
subpeaklist = [i for i, j in enumerate(endrange) if j== peak]
peaklist = argrelextrema(endrange,np.greater)
print peaklist
plt.figure()
plt.plot(domain,endrange)
plt.plot(domain,list[num1][1])
crestlist = []
cresttimelist = []
extrapeaks = []
for member in peaklist[0]:
if member < window or member > (len(endrange)-window):
extrapeaks.append(member)
continue
else:
crestlist.append(endrange[member])
cresttimelist.append(domain[member])
plt.plot(domain[member],endrange[member],'yD')
continue
for member in subpeaklist:
plt.plot(domain[member],endrange[member],'rD')
print crestlist
observed = np.array(list[num1][1])
expected = np.array(endrange)*np.sum(observed)
chi = chisquare(observed,expected)
plt.xlabel('Seconds since first data point')
plt.ylabel('Arbitrary Flux Units')
plt.title('Lightcurve for '+filename+str(num1)+' '+'chi = '+str(chi[0])+'\n r='+str(recursions)+' w='+str(window) ,y = 1.03)
plt.savefig('test'+str(num2)+'.ps')
return targetlist
def dih_target_tester(filename,num1,num2):
list = pickle.load(open(filename+'.txt','rb'))
target = list[num1][1]
domain = list[num1][0]
target = dd.dih_spike_picker(target)
target = dd.dih_dip_picker(target)
window = 5
endrange = dih_boxavg_recurs(target,window,50)
targetlist = []
targetlist.append(domain)
targetlist.append(endrange)
diplist = argrelextrema(endrange[window:len(target)-window],np.less)
print diplist
peaklist = argrelextrema(endrange[window:len(target)-window], np.greater)
x_short = domain[window:len(target)-window]
y_short = target[window:len(target)-window]
sub_y_list = np.split(y_short,diplist[0])
sub_x_list = np.split(x_short,diplist[0])
plt.figure()
plt.plot(domain,target,color = 'b')
plt.plot(domain,endrange,color = 'g')
diff = max(target)-min(target)
for idx,member in enumerate(sub_y_list):
member = dd.dih_spike_picker3(member)
subpeak = max(member)
if (subpeak-min(target))>.1*diff:
subpeaklist = [i for i, j in enumerate(member) if j== subpeak]
for num in subpeaklist:
plt.plot(sub_x_list[idx][num],member[num],'rD')
else:
print 'Bad Peak!'
plt.xlabel('Seconds since first data point')
plt.ylabel('Arbitrary Flux Units')
plt.title('Lightcurve for '+filename+str(num1))
plt.savefig('test'+str(num2)+'.ps')
return targetlist
def dih_sun_scratch_plot(dirname,savename,num,newname):
#directory_lists = finder.dih_dir_finder(dirname)#gets fits files and ivo files
#fits_list = directory_lists[0]
#ivo_list = directory_lists[1]
datalist = pickle.load(open('/Volumes/Scratch/Users/dherman/sundata/rawdata' + savename + str(num) + '.txt','rb'))
meta_datalist = pickle.load(open('/Volumes/Scratch/Users/dherman/sundata/metadata' + savename + '_meta' + str(num) + '.txt','rb'))
fits_date = meta_datalist[0]#datetime for first fits file in dirpath
fits_channel = meta_datalist[1]#channel for first fits
ivo = meta_datalist[5]
fits_ev_peak = meta_datalist[10][0]
fits_ev_peak = datetime.strptime(fits_ev_peak,'%Y-%m-%dT%H:%M:%S')
print str(fits_channel)
fits_center = meta_datalist[2]#center of first fits
fits_box = meta_datalist[-4]
colors = iter(cm.rainbow(np.linspace(0,1,len(datalist[0])))) #creates color table
#x,y coordinate data sorting
testsort = zip(*[datalist[0],datalist[1]])
sorted_data = sorted(testsort, key=getKey)
x = np.array(zip(*sorted_data)[0])
y = np.array(zip(*sorted_data)[1])
xcopy = x
x = x - x[0]
ycopy = y
yspikeless = spike.dih_spike_picker(y)#removes ultra noisy peaks
yspikeless = spike.dih_dip_picker(yspikeless)#removes ultra noisy dips
#channel-selective smoothing
if fits_channel == 131:
ysmooth = box.dih_boxavg_recurs(yspikeless,11,2)
window = 11
elif fits_channel == 171:
ysmooth = box.dih_boxavg_recurs(yspikeless,7,2)
window = 7
elif fits_channel == 211:
ysmooth = box.dih_boxavg_recurs(yspikeless,7,3)
window = 7
elif fits_channel == 193:
ysmooth = box.dih_boxavg_recurs(yspikeless,7,2)
window = 7
elif fits_channel == 304:
ysmooth = box.dih_boxavg_recurs(yspikeless,7,2)
window = 7
elif fits_channel == 335:
ysmooth = box.dih_boxavg_recurs(yspikeless,7,2)
window = 7
elif fits_channel == 94:
ysmooth = box.dih_boxavg_recurs(yspikeless,7,2)
window = 7
else:
print "Bad Channel!"
fits_time = datetime.strptime(fits_date,'%Y-%m-%dT%H:%M:%S.%f')
fits_first = timedelta(seconds = xcopy[0])
fits_last = timedelta(seconds = xcopy[-1])
fits_begin = fits_time+fits_first
fits_end = fits_time+fits_last
fits_begin = datetime.strftime(fits_begin,'%Y-%m-%dT%H:%M:%S.%f')
fits_end = datetime.strftime(fits_end,'%Y-%m-%dT%H:%M:%S.%f')
peaklist = argrelextrema(ysmooth,np.greater)#relative max
peak = max(ysmooth[(window-1)/2:len(ysmooth)-(window-1)/2])#absolute max ignoring the very ends of the data set
maxpeaklist = [i for i, j in enumerate(ysmooth) if j == peak]
plt.figure()
relpeaktimelist = []
for member in peaklist[0]:
if member < window or member > (len(ysmooth)-window):
continue
else:
plt.plot(x[member],ysmooth[member],'yD',markersize =8)
#recreating peak times from time difference data
first_time = datetime.strptime(fits_begin,'%Y-%m-%dT%H:%M:%S.%f')
timediff = timedelta(seconds = x[member])
peaktime = first_time+timediff
relpeaktimelist.append(peaktime.strftime('%Y/%m/%d %H:%M:%S.%f'))
continue
maxpeaktimelist = []
flagged_peaktimelist=[]
for member in maxpeaklist:
first_time = datetime.strptime(fits_begin,'%Y-%m-%dT%H:%M:%S.%f')
timediff = timedelta(seconds = x[member])
print 'timediff'
print timediff
peaktime = first_time+timediff
if x[member] > 250 and x[member] < x[-1]-250:
maxpeaktimelist.append(peaktime.strftime('%Y/%m/%d %H:%M:%S.%f'))
else:
flagged_peaktimelist.append(peaktime.strftime('%Y/%m/%d %H:%M:%S.%f'))
real_peaklist = [j for j, j in enumerate(maxpeaklist) if x[j] > 250 and x[j] < x[-1]-250]
print maxpeaklist
print real_peaklist
flagged_peaklist = [j for j, j in enumerate(maxpeaklist) if x[j] < 250 or x[j] > x[-1]-250]
print flagged_peaklist
for member in real_peaklist:
plt.plot(x[member],ysmooth[member],'gD',markersize = 8)
for member in flagged_peaklist:
plt.plot(x[member],ysmooth[member],'rD',markersize = 8)
#creating chi-squared value
observed = np.array(ycopy)
expected = np.array(ysmooth)*np.sum(observed)
chi = chisquare(observed,expected)
metadatalist = []
metadatalist.append(fits_begin)
metadatalist.append(fits_channel)
metadatalist.append(fits_center)
metadatalist.append(relpeaktimelist)
if len(maxpeaktimelist) == 1:
max_peak_dt = datetime.strptime(maxpeaktimelist[0],'%Y/%m/%d %H:%M:%S.%f')
peakdiff = fits_ev_peak-max_peak_dt
peakdiff = peakdiff.total_seconds()
if abs(peakdiff) < 900:
metadatalist.append(maxpeaktimelist)
else:
metadatalist.append([])
else:
metadatalist.append([])
metadatalist.append(ivo)
metadatalist.append(flagged_peaktimelist)
smooth_range = max(ysmooth)-min(ysmooth)
#consider raising .75 to .9
if smooth_range > max(ysmooth)*1.0:
metadatalist.append('flag')
else:
metadatalist.append('clear')
if len(flagged_peaktimelist) > 0:
metadatalist.append('peakflag')
#file_peakflag = open('/data/george/dherman/metadata/' + 'all_peakflag_meta.txt','a')
#simplejson.dump(metadatalist,file_peakflag)
#file_peakflag.write('\n')
#file_peakflag.close()
else:
metadatalist.append('no_peakflag')
metadatalist.append(fits_box)
metadatalist.append(list(x))
metadatalist.append(list(ysmooth))
metadatalist.append(fits_date)
metadatalist.append(fits_end)
#pickling of metadata
#with open(savename+'_'+newname+'_meta'+str(num)+'.txt','wb') as fff:
#pickle.dump(metadatalist,fff)
#shutil.move(savename+'_'+newname+'_meta'+str(num)+'.txt','/data/george/dherman/metadata')
#with open(savename+'_'+newname+'_chi'+str(num)+'.txt','wb') as fff:
#pickle.dump(chi,fff)
#shutil.move(savename+'_'+newname+'_chi'+str(num)+'.txt','/data/george/dherman/metadata')
#Saving all relavant metadata/peakdata to human readable text file
#file = open(savename+'_human_'+newname+'_meta'+str(num)+'.txt','wb')
#simplejson.dump(metadatalist,file)
#file.close()
#shutil.move(savename+'_human_'+newname+'_meta'+str(num)+'.txt','/data/george/dherman/metadata')
#finish up plot characteristics
plt.plot(x,y,'b',linewidth = 1.0)
plt.plot(x,ysmooth,'r',linewidth = 1.5)
plt.title('Lightcurve at'+' '+fits_begin+ ' '+ str(fits_channel)+'$\AA$',y=1.07)
plt.xlabel('Seconds Since'+' '+fits_begin)
plt.ylabel('Arbitrary Flux Units')
plt.savefig('/Volumes/Scratch/Users/dherman/sundata/sun_plots/'+newname+'_'+fits_begin+'_'+savename+str(num)+'.ps')#saves postscript file
return metadatalist