def process_accumulated_data(self,
accumulated_data,
save=True,
rawfile=False):
if rawfile:
for idx, raw_npz_file in enumerate(os.listdir(self.save_folder)):
d = np.load(os.path.join(self.save_folder, raw_npz_file))
rawdata = d['data']
accumulated_data = hht4.filter_raw_antibunch_data(
rawdata, 0, 700)
dts = hht4.get_dts(accumulated_data, start_ch=0, stop_ch=1)
if save:
np.savez(os.path.join(self.save_folder,
'Dts_%.3d.npz' % idx),
dts=dts,
data=accumulated_data)
else:
dts = hht4.get_dts(accumulated_data, start_ch=0, stop_ch=1)
if save:
np.savez(os.path.join(self.save_folder, 'Dts.npz'),
dts=dts,
data=accumulated_data)
return dts
def process_accumulated_data(self, accumulated_data, save = True, rawfile = False):
if rawfile:
for idx, raw_npz_file in enumerate(os.listdir(self.save_folder)):
d = np.load(os.path.join(self.save_folder, raw_npz_file))
rawdata = d['data']
accumulated_data = hht4.filter_raw_antibunch_data(rawdata, 0, 700)
dts = hht4.get_dts(accumulated_data, start_ch = 0, stop_ch = 1)
if save:
np.savez(os.path.join(self.save_folder, 'Dts_%.3d.npz'%idx),
dts = dts, data = accumulated_data)
else:
dts = hht4.get_dts(accumulated_data, start_ch = 0, stop_ch = 1)
if save:
np.savez(os.path.join(self.save_folder, 'Dts.npz'),
dts = dts, data = accumulated_data)
return dts
def get_T3_pulsed_events(self,
sync_period,
start_ch0=0,
start_ch1=0,
save_raw=True,
raw_max_len=1E6,
sleep_time=10E-3):
"""
Get ch0 and ch1 events in T3 mode.
Author: Wolfgang, July 1, 2012.
Adapted by Gerwin, June 20, 2013
Filtering options:
start_ch0, start_ch1:
minimum event time (in bins) after the sync
max_pulses:
how many syncs after a marker on channel 1 are taken into
account
returns:
(ch0, ch1, markers)
ch0: array with two columns: col1 = absolute sync no, col2 = time
ch1: ditto
markers: array with three columns:
col1 = sync no, col2 = time, col3 = marker channel
"""
if .001 * hharp.get_ResolutionPS() * 2**15 < sync_period:
print('Warning: resolution is too high to cover entire sync \
period in T3 mode, events might get lost.')
#prepare for saving the data; create directory etc...
if save_raw:
rawdir = self.save_folder
if not os.path.isdir(rawdir):
os.makedirs(rawdir)
rawidx = 0
lengths = np.array([])
times = np.array([])
accumulated_data = np.empty((0, 4), dtype=np.uintc)
else:
#initialize the overflow offset number to 0. This will be updated in
#the loop everytime the function is called later on. In this way the
#sync number increases linearly with time and doesn't fall back
#everytime the function is called.
syncnr_offset = 0
accumulated_data = np.empty((0, 4), dtype=np.uintc)
rawidx = 0
#here comes the actual data acquisition
while hharp.get_MeasRunning() == True:
length, data = hharp.get_TTTR_Data()
#if we don't want to process the data at all use this
if save_raw:
accumulated_data = np.append(accumulated_data, data[:length])
lengths = np.append(lengths, length)
times = np.append(times, int(time.strftime('%M%S')))
if len(accumulated_data) > raw_max_len:
np.savez(os.path.join(rawdir, 'LDE_rawdata-%.3d' % rawidx),
length=len(accumulated_data),
data=accumulated_data)
accumulated_data = np.array([])
rawidx += 1
#if we first want to filter the data before saving use this
else:
print "I registered... %d events" % length
#analyze the entire array or just the new data?
prefiltered, syncnr_offset = hht4.filter_raw_antibunch_data(
data[:length], syncnr_offset,
int(self.sync_rep_rate / 0.256),
int(self.sync_rep_rate / 0.256))
accumulated_data = np.append(accumulated_data,
prefiltered,
axis=0)
#if the accumulated_data is large enough, save it to a file
if len(accumulated_data) > raw_max_len:
print "Saving accumulated data..."
np.savez(os.path.join(rawdir,
'LDE_prefiltered-%.3d' % rawidx),
length=len(accumulated_data),
data=accumulated_data)
rawidx += 1
accumulated_data = np.array((0, 4), dtype=np.uintc)
#Sleeping might give a bit more stable data acquisition
#But be careful not to make it too big
qt.msleep(sleep_time, exact=True)
if (msvcrt.kbhit() and msvcrt.getch() == 'x'):
print 'x pressed, quitting current run'
hharp.StopMeas()
return accumulated_data
def get_T3_pulsed_events(self, sync_period,
start_ch0=0, start_ch1=0, save_raw=True, raw_max_len=1E6,
sleep_time = 10E-3):
"""
Get ch0 and ch1 events in T3 mode.
Author: Wolfgang, July 1, 2012.
Adapted by Gerwin, June 20, 2013
Filtering options:
start_ch0, start_ch1:
minimum event time (in bins) after the sync
max_pulses:
how many syncs after a marker on channel 1 are taken into
account
returns:
(ch0, ch1, markers)
ch0: array with two columns: col1 = absolute sync no, col2 = time
ch1: ditto
markers: array with three columns:
col1 = sync no, col2 = time, col3 = marker channel
"""
if .001*hharp.get_ResolutionPS() * 2**15 < sync_period:
print('Warning: resolution is too high to cover entire sync \
period in T3 mode, events might get lost.')
#prepare for saving the data; create directory etc...
if save_raw:
rawdir = self.save_folder
if not os.path.isdir(rawdir):
os.makedirs(rawdir)
rawidx = 0
lengths = np.array([])
times = np.array([])
accumulated_data = np.empty((0,4), dtype = np.uintc)
else:
#initialize the overflow offset number to 0. This will be updated in
#the loop everytime the function is called later on. In this way the
#sync number increases linearly with time and doesn't fall back
#everytime the function is called.
syncnr_offset = 0
accumulated_data = np.empty((0,4), dtype = np.uintc)
rawidx = 0
#here comes the actual data acquisition
while hharp.get_MeasRunning() == True:
length, data = hharp.get_TTTR_Data()
#if we don't want to process the data at all use this
if save_raw:
accumulated_data = np.append(accumulated_data, data[:length])
lengths = np.append(lengths, length)
times = np.append(times, int(time.strftime('%M%S')))
if len(accumulated_data) > raw_max_len:
np.savez(os.path.join(rawdir, 'LDE_rawdata-%.3d'%rawidx),
length=len(accumulated_data), data=accumulated_data)
accumulated_data = np.array([])
rawidx += 1
#if we first want to filter the data before saving use this
else:
print "I registered... %d events"%length
#analyze the entire array or just the new data?
prefiltered, syncnr_offset = hht4.filter_raw_antibunch_data(
data[:length], syncnr_offset, int(self.sync_rep_rate/0.256),
int(self.sync_rep_rate/0.256))
accumulated_data = np.append(accumulated_data, prefiltered, axis = 0)
#if the accumulated_data is large enough, save it to a file
if len(accumulated_data) > raw_max_len:
print "Saving accumulated data..."
np.savez(os.path.join(rawdir, 'LDE_prefiltered-%.3d'%rawidx),
length=len(accumulated_data), data=accumulated_data)
rawidx += 1
accumulated_data = np.array((0,4), dtype = np.uintc)
#Sleeping might give a bit more stable data acquisition
#But be careful not to make it too big
qt.msleep(sleep_time, exact = True)
if(msvcrt.kbhit() and msvcrt.getch()=='x'):
print 'x pressed, quitting current run'
hharp.StopMeas()
return accumulated_data
