def char_fresp_access():
wmoment = int(request.args.get('wmoment'))
M_fresp[session['user_name']].selectmoment(wmoment)
M_fresp[session['user_name']].accesstructure()
data_progress = M_fresp[session['user_name']].data_progress
# Measurement time:
filetime = getmtime(M_fresp[session['user_name']].pqfile) # in seconds
startmeasure = mktime(
strptime(
M_fresp[session['user_name']].day + " " +
M_fresp[session['user_name']].startime(),
"%Y-%m-%d(%a) %H:%M")) # made into seconds
measureacheta = str(
timedelta(seconds=(filetime - startmeasure) / data_progress *
(trunc(data_progress / 100 + 1) * 100 - data_progress)))
try:
cfluxbias = waveform(M_fresp[session['user_name']].corder['Flux-Bias'])
except (KeyError):
cfluxbias = waveform('opt,')
csparam = waveform(M_fresp[session['user_name']].corder['S-Parameter'])
cifb = waveform(M_fresp[session['user_name']].corder['IF-Bandwidth'])
cpowa = waveform(M_fresp[session['user_name']].corder['Power'])
cfreq = waveform(M_fresp[session['user_name']].corder['Frequency'])
session['c_fresp_structure'] = [
cfluxbias.count, csparam.count, cifb.count, cpowa.count,
cfreq.count * M_fresp[session['user_name']].datadensity
]
session['c_fresp_address'] = cdatasearch(
M_fresp[session['user_name']].resumepoint - 1,
session['c_fresp_structure'])
# list each parameter range based on data-progress:
cfluxbias_data = cfluxbias.data[0:session['c_fresp_address'][0] + 1]
csparam_data = csparam.data[0:session['c_fresp_address'][1] + 1]
cifb_data = cifb.data[0:session['c_fresp_address'][2] + 1]
cpowa_data = cpowa.data[0:session['c_fresp_address'][3] + 1]
cfreq_data = cfreq.data # within buffer
return jsonify(data_progress=data_progress,
measureacheta=measureacheta,
corder=M_fresp[session['user_name']].corder,
comment=M_fresp[session['user_name']].comment,
cfluxbias_data=cfluxbias_data,
csparam_data=csparam_data,
cifb_data=cifb_data,
cpowa_data=cpowa_data,
cfreq_data=cfreq_data)
def TESTC(
corders={
'C1': [0, 0, 0],
'C2': [0, 0, 0],
'C3': [0, 0, 0],
'C4': [0, 0, 0],
'C5': [0, 0, 0]
},
comment='',
dayindex='',
taskentry=0,
resumepoint=0):
'''Serve as a template for other real tasks to come
dayindex: {string:access data, -1:new data 0-:manage data}
'''
x = 0
C1 = waveform('%s to %s * %s' % tuple(corders['C1']))
C2 = waveform('%s to %s * %s' % tuple(corders['C2']))
C3 = waveform('%s to %s * %s' % tuple(corders['C3']))
C4 = waveform('%s to %s * %s' % tuple(corders['C4']))
C5 = waveform('%s to %s * %s' % tuple(corders['C5']))
datasize = C1.count * C2.count * C3.count * C4.count * C5.count
# setting how often the data is logged along the way:
buffersize = C5.count
yield corders, comment, dayindex, taskentry, buffersize, resumepoint
# running measurement:
data = []
for i in range(resumepoint, datasize):
# print("%s of %s"%(i+1,datasize))
caddress = cdatasearch(
i, [C1.count, C2.count, C3.count, C4.count, C5.count])
# Use Cj.data[caddress[j]] for parameters' value here
x += 1
# x = C1.data[caddress[0]] + C5.data[caddress[4]]*C2.data[caddress[1]]*sin(pi/2*C3.data[caddress[2]]) + C4.data[caddress[3]]
data.append(x)
# saving chunck by chunck improves speed a lot!
if not (i + 1) % buffersize: #multiples of buffersize
yield data
data = []
def F_Response(user,
tag="",
corder={},
comment='',
dayindex='',
taskentry=0,
resumepoint=0,
instr=['YOKO', 'ENA'],
testeach=False):
'''Characterizing Frequency Response:
C-Order: Flux-Bias, S-Parameter, IF-Bandwidth, Power, Frequency
'''
sample = get_status("MSSN")['sample']
# pushing pre-measurement parameters to settings:
yield user, sample, tag, instr, corder, comment, dayindex, taskentry, testeach
# User-defined Controlling-PARAMETER(s) ======================================================================================
fluxbias = waveform(corder['Flux-Bias'])
Sparam = waveform(corder['S-Parameter'])
ifb = waveform(corder['IF-Bandwidth'])
powa = waveform(corder['Power'])
freq = waveform(corder['Frequency'])
# Total data points:
datasize = prod([waveform(x).count for x in corder.values()
]) * 2 #data density of 2 due to IQ
# Pre-loop settings:
# ENA:
bench = ENA.Initiate(True)
ENA.dataform(bench, action=['Set', 'REAL'])
ENA.sweep(bench, action=['Set', 'ON', freq.count])
fstart, fstop = freq.data[0] * 1e9, freq.data[-1] * 1e9
ENA.linfreq(bench, action=['Set', fstart,
fstop]) # Linear Freq-sweep-range
# YOKO:
if "opt" not in fluxbias.data: # check if it is in optional-state
yokog = YOKO.Initiate(
current=True
) # PENDING option: choose between Voltage / Current output
YOKO.output(yokog, 1)
# Buffer setting(s) for certain loop(s):
buffersize_1 = freq.count * 2 #data density of 2 due to IQ
# User-defined Measurement-FLOW ==============================================================================================
if testeach: # measure-time contribution from each measure-loop
loopcount, loop_dur = [], []
stage, prev = clocker(0) # Marking starting point of time
# Registerring parameter(s)-structure
cstructure = [fluxbias.count, Sparam.count, ifb.count, powa.count]
# set previous parameters based on resumepoint:
if resumepoint > 0:
caddress = cdatasearch(resumepoint // buffersize_1, cstructure)
# Only those involved in virtual for-loop need to be pre-set here:
if "opt" not in fluxbias.data: # check if it is in optional-state
YOKO.sweep(
yokog,
str(fluxbias.data[caddress[0]]),
pulsewidth=77 * 1e-3,
sweeprate=0.0007
) # A-mode: sweeprate=0.0007 A/s ; V-mode: sweeprate=0.07 V/s
ENA.setrace(bench, Mparam=[Sparam.data[caddress[1]]], window='D1')
ENA.ifbw(bench, action=['Set', ifb.data[caddress[2]]])
measure_loop_1 = range(
resumepoint // buffersize_1, datasize //
buffersize_1) # saving chunck by chunck improves speed a lot!
while True:
for i in measure_loop_1:
# Registerring parameter(s)
caddress = cdatasearch(i, cstructure)
# setting each c-order (From High to Low level of execution):
if not i % prod(cstructure[
1::]): # virtual for-loop using exact-multiples condition
if "opt" not in fluxbias.data: # check if it is in optional-state
if testeach: # test each measure-loop:
loopcount += [fluxbias.count]
if fluxbias.count > 1:
loop_dur += [
abs(fluxbias.data[0] - fluxbias.data[1]) / 0.2
+ 35 * 1e-3
]
else:
loop_dur += [0]
stage, prev = clocker(stage, prev) # Marking time
else:
YOKO.sweep(
yokog,
str(fluxbias.data[caddress[0]]),
pulsewidth=77 * 1e-3,
sweeprate=0.0007
) # A-mode: sweeprate=0.0007 A/s ; V-mode: sweeprate=0.07 V/s
if not i % prod(cstructure[
2::]): # virtual for-loop using exact-multiples condition
ENA.setrace(bench,
Mparam=[Sparam.data[caddress[1]]],
window='D1')
if not i % prod(cstructure[
3::]): # virtual for-loop using exact-multiples condition
ENA.ifbw(bench, action=['Set', ifb.data[caddress[2]]])
ENA.power(bench, action=['Set', powa.data[caddress[3]]
]) # same as the whole measure-loop
# start sweeping:
stat = ENA.sweep(bench) #getting the estimated sweeping time
print("Time-taken for this loop would be: %s (%spts)" %
(stat[1]['TIME'], stat[1]['POINTS']))
print("Operation Complete: %s" % bool(ENA.measure(bench)))
# adjusting display on ENA:
ENA.autoscal(bench)
ENA.selectrace(bench, action=['Set', 'para 1 calc 1'])
data = ENA.sdata(bench)
# print(Fore.YELLOW + "\rProgress: %.3f%% [%s]" %((i+1)/datasize*100, data), end='\r', flush=True)
print(Fore.YELLOW + "\rProgress: %.3f%%" %
((i + 1) / datasize * buffersize_1 * 100),
end='\r',
flush=True)
# test for the last loop if there is
if testeach: # test each measure-loop:
loopcount += [len(measure_loop_1)]
loop_dur += [time() - prev]
stage, prev = clocker(stage, prev) # Marking time
ENA.close(bench)
if "opt" not in fluxbias.data: # check if it is in optional-state
YOKO.close(yokog, False)
yield loopcount, loop_dur
else:
if get_status("F_Response")['pause']:
break
else:
yield data
if not get_status("F_Response")['repeat']:
set_status("F_Response", dict(pause=True))
ENA.close(bench)
if "opt" not in fluxbias.data: # check if it is in optional-state
YOKO.close(yokog, True)
return
def CW_Sweep(user,
tag="",
corder={},
comment='',
dayindex='',
taskentry=0,
resumepoint=0,
instr=['PSG', 'YOKO', 'ENA'],
testeach=False):
'''Continuous Wave Sweeping:
C-Order: Flux-Bias, XY-Frequency, XY-Power, S-Parameter, IF-Bandwidth, Frequency, Power
'''
sample = get_status("MSSN")['sample']
# pushing pre-measurement parameters to settings:
yield user, sample, tag, instr, corder, comment, dayindex, taskentry, testeach
# User-defined Controlling-PARAMETER(s) ======================================================================================
fluxbias = waveform(corder['Flux-Bias'])
xyfreq = waveform(corder['XY-Frequency'])
xypowa = waveform(corder['XY-Power'])
Sparam = waveform(corder['S-Parameter'])
ifb = waveform(corder['IF-Bandwidth'])
freq = waveform(corder['Frequency'])
# special treatment to power in this CW-Mode Sweeping:
powa = waveform(corder['Power'])
powa_repeat = powa.inner_repeat
print("power sequence: %s, length: %s, inner-repeat-counts: %s" %
(powa.command, powa.count, powa_repeat))
# input("continue?")
# Total data points:
datasize = int(
prod([
waveform(x).count * waveform(x).inner_repeat
for x in corder.values()
],
dtype='uint64')) * 2 #data density of 2 due to IQ
print("data size: %s" % datasize)
# Pre-loop settings:
# ENA:
bench = ENA.Initiate(True)
ENA.dataform(bench, action=['Set', 'REAL'])
if powa_repeat == 1:
# collect swept power-data every measure-loop
ENA.sweep(bench, action=['Set', 'ON', powa.count])
ENA.power(bench, action=['Set', '', powa.data[0], powa.data[-1]
]) # for power sweep (set pstart & pstop)
buffersize_1 = powa.count * 2 # (buffer) data density of 2 due to IQ
else:
# collect repetitive power-data every measure-loop
ENA.sweep(bench, action=['Set', 'ON', powa_repeat])
buffersize_1 = powa_repeat * 2 # (buffer) data density of 2 due to IQ
# YOKO:
if "opt" not in fluxbias.data: # check if it is in optional-state / serious-state
yokog = YOKO.Initiate(current=True) # pending option
YOKO.output(yokog, 1)
# PSG:
if "opt" not in xyfreq.data: # check if it is in optional-state / serious-state
sogo = PSG0.Initiate() # pending option
PSG0.rfoutput(sogo, action=['Set', 1])
# User-defined Measurement-FLOW ==============================================================================================
if testeach: # measure-time contribution from each measure-loop
loopcount, loop_dur = [], []
stage, prev = clocker(0) # Marking starting point of time
# Registerring parameter(s)-structure
if powa_repeat == 1:
cstructure = [
fluxbias.count, xyfreq.count, xypowa.count, Sparam.count,
ifb.count, freq.count, 1
] # just single CW
else:
cstructure = [
fluxbias.count, xyfreq.count, xypowa.count, Sparam.count,
ifb.count, freq.count, powa.count
] # take CW average by repeating
# set previous parameters based on resumepoint:
if resumepoint // buffersize_1 > 0:
caddress = cdatasearch(resumepoint // buffersize_1, cstructure)
# Only those involved in virtual for-loop need to be pre-set here:
# Optionals:
if "opt" not in fluxbias.data: # check if it is in optional-state / serious-state
YOKO.sweep(
yokog,
str(fluxbias.data[caddress[0]]),
pulsewidth=77 * 1e-3,
sweeprate=0.0007
) # A-mode: sweeprate=0.0007 A/s ; V-mode: sweeprate=0.07 V/s
if "opt" not in xyfreq.data: # check if it is in optional-state / serious-state
PSG0.frequency(
sogo, action=['Set',
str(xyfreq.data[caddress[1]]) + "GHz"])
PSG0.power(sogo,
action=['Set',
str(xypowa.data[caddress[2]]) + "dBm"])
# Basics:
ENA.setrace(bench, Mparam=[Sparam.data[caddress[3]]], window='D1')
ENA.ifbw(bench, action=['Set', ifb.data[caddress[4]]])
ENA.cwfreq(bench, action=['Set', freq.data[caddress[5]] * 1e9])
measure_loop_1 = range(
resumepoint // buffersize_1, datasize //
buffersize_1) # saving chunck by chunck improves speed a lot!
while True:
for i in measure_loop_1:
# determining the index-locations for each parameters, i.e. the address at any instance
caddress = cdatasearch(i, cstructure)
# setting each c-order (From High to Low level of execution):
# ***************************************************************
# Optionals:
if not i % prod(cstructure[
1::]): # virtual for-loop using exact-multiples condition
if "opt" not in fluxbias.data: # check if it is in optional-state
if testeach: # adding instrument transition-time between set-values:
loopcount += [fluxbias.count]
if fluxbias.count > 1:
loop_dur += [
abs(fluxbias.data[0] - fluxbias.data[1]) / 0.2
+ 35 * 1e-3
]
else:
loop_dur += [0]
stage, prev = clocker(stage, prev) # Marking time
else:
YOKO.sweep(
yokog,
str(fluxbias.data[caddress[0]]),
pulsewidth=77 * 1e-3,
sweeprate=0.0007
) # A-mode: sweeprate=0.0007 A/s ; V-mode: sweeprate=0.07 V/s
if not i % prod(cstructure[
2::]): # virtual for-loop using exact-multiples condition
if "opt" not in xyfreq.data: # check if it is in optional-state
PSG0.frequency(
sogo,
action=['Set',
str(xyfreq.data[caddress[1]]) + "GHz"])
if not i % prod(cstructure[
3::]): # virtual for-loop using exact-multiples condition
if "opt" not in xypowa.data: # check if it is in optional-state
PSG0.power(
sogo,
action=['Set',
str(xypowa.data[caddress[2]]) + "dBm"])
# Basics:
if not i % prod(cstructure[
4::]): # virtual for-loop using exact-multiples condition
ENA.setrace(bench,
Mparam=[Sparam.data[caddress[3]]],
window='D1')
if not i % prod(cstructure[
5::]): # virtual for-loop using exact-multiples condition
ENA.ifbw(bench, action=['Set', ifb.data[caddress[4]]])
if not i % prod(cstructure[
6::]): # virtual for-loop using exact-multiples condition
ENA.cwfreq(bench, action=['Set', freq.data[caddress[5]] * 1e9])
if powa_repeat > 1:
ENA.power(bench,
action=[
'Set', '', powa.data[caddress[6]],
powa.data[caddress[6]]
]) # same as the whole measure-loop
# start sweeping:
stat = ENA.sweep(bench) #getting the estimated sweeping time
print("Time-taken for this loop would be: %s (%spts)" %
(stat[1]['TIME'], stat[1]['POINTS']))
print("Operation Complete: %s" % bool(ENA.measure(bench)))
# adjusting display on ENA:
ENA.autoscal(bench)
ENA.selectrace(bench, action=['Set', 'para 1 calc 1'])
data = ENA.sdata(bench)
print(Fore.YELLOW + "\rProgress: %.3f%%" %
((i + 1) / datasize * buffersize_1 * 100),
end='\r',
flush=True)
# test for the last loop if there is
if testeach: # test each measure-loop:
loopcount += [len(measure_loop_1)]
loop_dur += [time() - prev]
stage, prev = clocker(stage, prev) # Marking time
ENA.close(bench)
if "opt" not in xyfreq.data: # check if it is in optional-state
PSG0.close(sogo, False)
if "opt" not in fluxbias.data: # check if it is in optional-state
YOKO.close(yokog, False)
yield loopcount, loop_dur
else:
if get_status("CW_Sweep")['pause']:
break
else:
yield data
if not get_status("CW_Sweep")['repeat']:
set_status("CW_Sweep", dict(pause=True))
ENA.close(bench)
if "opt" not in xyfreq.data: # check if it is in optional-state
PSG0.rfoutput(sogo, action=['Set', 0])
PSG0.close(sogo, False)
if "opt" not in fluxbias.data: # check if it is in optional-state
YOKO.output(yokog, 0)
YOKO.close(yokog, False)
return
def SQE_Pulse(user,
tag="",
corder={},
comment='',
dayindex='',
taskentry=0,
resumepoint=0,
instr=['YOKO', 'PSGV', 'PSGA', 'AWG', 'VSA'],
testeach=False):
'''Time-domain Square-wave measurement:
C-Structure: ['Flux-Bias',
'Average', 'Pulse-Period', 'ADC-delay',
'LO-Frequency', 'LO-Power', 'RO-Frequency', 'RO-Power', 'RO-ifLevel', 'RO-Pulse-Delay', 'RO-Pulse-Width',
'XY-Frequency', 'XY-Power', 'XY-ifLevel', 'XY-Pulse-Delay', 'XY-Pulse-Width',
'Sampling-Time'] (IQ-Bandwidth (250MHz or its HALFlings) + Acquisition-Time (dt must be multiples of 2ns))
'''
# Loading sample:
sample = get_status("MSSN")[session['user_name']]['sample']
# sample = get_status("MSSN")['abc']['sample'] # by-pass HTTP-request before interface is ready
# pushing pre-measurement parameters to settings:
yield user, sample, tag, instr, corder, comment, dayindex, taskentry, testeach
# ***USER_DEFINED*** Controlling-PARAMETER(s) ======================================================================================
structure = corder['C-Structure']
fluxbias = waveform(corder['Flux-Bias'])
averaging = waveform(corder['Average'])
pperiod = waveform(corder['Pulse-Period'])
adcdelay = waveform(corder['ADC-delay'])
lofreq = waveform(corder['LO-Frequency'])
lopowa = waveform(corder['LO-Power'])
rofreq = waveform(corder['RO-Frequency'])
ropowa = waveform(corder['RO-Power'])
roiflevel = waveform(corder['RO-ifLevel'])
ropdelay = waveform(corder['RO-Pulse-Delay'])
ropwidth = waveform(corder['RO-Pulse-Width'])
xyfreq = waveform(corder['XY-Frequency'])
xypowa = waveform(corder['XY-Power'])
xyiflevel = waveform(corder['XY-ifLevel'])
xypdelay = waveform(corder['XY-Pulse-Delay'])
xypwidth = waveform(corder['XY-Pulse-Width'])
samptime = waveform(corder['Sampling-Time'])
# Total data points:
datasize = int(
prod([waveform(corder[param]).count for param in structure],
dtype='uint64')) * 2 #data density of 2 due to IQ
print("data size: %s" % datasize)
# Pre-loop settings:
# Optionals:
# YOKO:
if "opt" not in fluxbias.data: # check if it is in optional-state / serious-state
yokog = YOKO.Initiate(current=True) # pending option
YOKO.output(yokog, 1)
# PSGV:
if "opt" not in xyfreq.data: # check if it is in optional-state / serious-state
sogo = PSG0.Initiate() # pending option
PSG0.rfoutput(sogo, action=['Set', 1])
# Basics:
# PSGA for LO:
saga = PSG1.Initiate() # pending option
PSG1.rfoutput(saga, action=['Set', 1])
# AWG for Control:
awgsess = AWG.InitWithOptions()
AWG.Abort_Gen(awgsess)
AWG.ref_clock_source(awgsess,
action=['Set',
int(1)]) # External 10MHz clock-reference
AWG.predistortion_enabled(awgsess, action=['Set', True])
AWG.output_mode_adv(awgsess, action=['Set',
int(2)]) # Sequence output mode
AWG.arb_sample_rate(awgsess,
action=['Set',
float(1250000000)]) # maximum sampling rate
AWG.active_marker(awgsess, action=['Set', '1']) # master
AWG.marker_delay(awgsess, action=['Set', float(0)])
AWG.marker_pulse_width(awgsess, action=['Set', float(1e-7)])
AWG.marker_source(awgsess, action=['Set', int(7)])
# PRESET Output:
for ch in range(2):
channel = str(ch + 1)
AWG.output_config(awgsess, RepCap=channel, action=["Set",
0]) # Single-ended
AWG.output_filter_bandwidth(awgsess, RepCap=channel, action=["Set", 0])
AWG.arb_gain(awgsess, RepCap=channel, action=["Set", 0.5])
AWG.output_impedance(awgsess, RepCap=channel, action=["Set", 50])
# output settings:
for ch in range(2):
channel = str(ch + 1)
AWG.output_enabled(awgsess, RepCap=channel, action=["Set",
int(1)]) # ON
AWG.output_filter_enabled(awgsess,
RepCap=channel,
action=["Set", True])
AWG.output_config(awgsess, RepCap=channel,
action=["Set", int(2)]) # Amplified 1:2
AWG.output_filter_bandwidth(awgsess, RepCap=channel, action=["Set", 0])
AWG.arb_gain(awgsess, RepCap=channel, action=["Set", 0.5])
AWG.output_impedance(awgsess, RepCap=channel, action=["Set", 50])
# VSA for Readout
vsasess = VSA.InitWithOptions()
# Buffer-size for lowest-bound data-collecting instrument:
buffersize_1 = samptime.count * 2 #data density of 2 due to IQ
print("Buffer-size: %s" % buffersize_1)
# User-defined Measurement-FLOW ==============================================================================================
if testeach: # measure-time contribution from each measure-loop
loopcount, loop_dur = [], []
stage, prev = clocker(0) # Marking starting point of time
# Registerring parameter(s)-structure
cstructure = [waveform(corder[param]).count for param in structure
][:-1] # The last one will become a buffer
print('cstructure: %s' % cstructure)
measure_loop_1 = range(
resumepoint // buffersize_1, datasize //
buffersize_1) # saving chunck by chunck improves speed a lot!
while True:
for i in measure_loop_1:
print(Back.BLUE + Fore.WHITE + 'measure %s/%s' %
(i, datasize // buffersize_1))
# determining the index-locations for each parameters, i.e. the address at any instance
caddress = cdatasearch(i, cstructure)
# setting each c-order (From High to Low level of execution):
# ***************************************************************
for j in range(
len(cstructure) -
1): # the last one will be run for every i (common sense!)
if (
not i % prod(cstructure[j + 1::])
) or i == resumepoint // buffersize_1: # virtual for-loop using exact-multiples condition
# print("entering %s-stage" %j)
# Optionals:
# YOKO
if structure[j] == 'Flux-Bias':
if "opt" not in fluxbias.data: # check if it is in optional-state
if testeach: # adding instrument transition-time between set-values:
loopcount += [fluxbias.count]
if fluxbias.count > 1:
loop_dur += [
abs(fluxbias.data[0] -
fluxbias.data[1]) / 0.2 + 35 * 1e-3
] # manually calculating time without really setting parameter on the instrument
else:
loop_dur += [0]
stage, prev = clocker(stage,
prev) # Marking time
else:
YOKO.sweep(
yokog,
str(fluxbias.data[caddress[structure.index(
'Flux-Bias')]]),
pulsewidth=77 * 1e-3,
sweeprate=0.0007
) # A-mode: sweeprate=0.0007 A/s ; V-mode: sweeprate=0.07 V/s
# PSG
if structure[j] == 'XY-Frequency':
if "opt" not in xyfreq.data: # check if it is in optional-state
PSG0.frequency(
sogo,
action=[
'Set',
str(xyfreq.data[caddress[structure.index(
'XY-Frequency')]]) + "GHz"
])
if structure[j] == 'XY-Power':
if "opt" not in xypowa.data: # check if it is in optional-state
PSG0.power(sogo,
action=[
'Set',
str(xypowa.data[caddress[
structure.index('XY-Power')]]) +
"dBm"
])
if structure[j] == 'RO-Frequency':
if "opt" not in rofreq.data: # check if it is in optional-state
PSG1.frequency(
saga,
action=[
'Set',
str(rofreq.data[caddress[structure.index(
'RO-Frequency')]]) + "GHz"
])
if structure[j] == 'RO-Power':
if "opt" not in ropowa.data: # check if it is in optional-state
PSG1.power(saga,
action=[
'Set',
str(ropowa.data[caddress[
structure.index('RO-Power')]]) +
"dBm"
])
# AWG (Every-loop)
if "opt" not in pperiod.data: # check if it is in optional-state
AWG.Clear_ArbMemory(awgsess)
WAVE = []
# construct waveform:
ifperiod = pperiod.data[caddress[structure.index(
'Pulse-Period')]]
ifscale = float(
xyiflevel.data[caddress[structure.index('XY-ifLevel')]]
), float(
roiflevel.data[caddress[structure.index('RO-ifLevel')]])
if "lockxypwd" in str(ropdelay.data[0]):
if '+' in str(ropdelay.data[0]):
rooffset = float(ropdelay.data[0].split('+')[1])
else:
rooffset = 0 # default value
ifdelay = float(xypdelay.data[caddress[structure.index(
'XY-Pulse-Delay')]]), float(xypwidth.data[caddress[
structure.index('XY-Pulse-Width')]]) + rooffset
print("RO-Pulse Delays behind XY-Pulse for %sns" %
(ifdelay[1] - ifdelay[0]))
else:
ifdelay = float(xypdelay.data[caddress[structure.index(
'XY-Pulse-Delay')]]), float(ropdelay.data[caddress[
structure.index('RO-Pulse-Delay')]])
ifontime = float(
xypwidth.data[caddress[structure.index('XY-Pulse-Width')]]
), float(
ropwidth.data[caddress[structure.index('RO-Pulse-Width')]])
for ch in range(2):
channel = str(ch + 1)
wavefom = squarewave(ifperiod, ifontime[ch], ifdelay[ch],
ifscale[ch]) # in ns
stat, wave = AWG.CreateArbWaveform(awgsess, wavefom)
print('Waveform channel %s: %s <%s>' %
(channel, wave, status_code(stat)))
WAVE.append(wave)
# Building Sequences:
for ch in range(2):
channel = str(ch + 1)
status, seqhandl = AWG.CreateArbSequence(
awgsess, [WAVE[ch]], [1]
) # loop# canbe >1 if longer sequence is needed in the future!
# print('Sequence channel %s: %s <%s>' %(channel, seqhandl, status_code(status)))
# Channel Assignment:
stat = AWG.arb_sequence_handle(awgsess,
RepCap=channel,
action=["Set", seqhandl])
# print('Sequence channel %s embeded: %s <%s>' %(channel, stat[1], status_code(stat[0])))
# Trigger Settings:
for ch in range(2):
channel = str(ch + 1)
AWG.operation_mode(awgsess,
RepCap=channel,
action=["Set", 0])
AWG.trigger_source_adv(awgsess,
RepCap=channel,
action=["Set", 0])
AWG.Init_Gen(awgsess)
AWG.Send_Pulse(awgsess, 1)
# Basic / Buffer:
# VSA (Every-loop)
VSA.acquisition_time(vsasess,
action=['Set',
float(samptime.count * 2e-9)
]) # minimum time resolution
VSA.preselector_enabled(vsasess, action=[
'Set', False
]) # disable preselector to allow the highest bandwidth of 250MHz
if "lockro" in str(lofreq.data[0]):
if '+' in str(lofreq.data[0]):
lof_offset = float(lofreq.data[0].split('+')[1])
elif '-' in str(lofreq.data[0]):
lof_offset = -float(lofreq.data[0].split('-')[1])
else:
lof_offset = 0 # default value
VSA.frequency(vsasess,
action=[
'Set',
float(rofreq.data[caddress[structure.index(
'RO-Frequency')]]) * 1e9 + lof_offset
]) # freq offset / correction in Hz
print("Locking on RO at %sGHz" %
(VSA.frequency(vsasess)[1] / 1e9))
else:
VSA.frequency(vsasess,
action=[
'Set',
float(lofreq.data[caddress[structure.index(
'LO-Frequency')]]) * 1e9
])
VSA.power(
vsasess,
action=[
'Set',
float(lopowa.data[caddress[structure.index('LO-Power')]])
])
VSA.bandwidth(
vsasess, action=['Set', 250e6]
) # maximum LO bandwidth of 250MHz (500MHz Sampling-rate gives 2ns of time resolution)
VSA.trigger_source(vsasess,
action=['Set',
int(1)]) # External Trigger (slave)
# Delay for Readout
if "lockxypwd" in str(ropdelay.data[0]):
# trigger-delay sync with xy-pulse-width for Rabi measurement:
VSA.trigger_delay(vsasess, action=['Set', float(adcdelay.data[caddress[structure.index('ADC-delay')]]) + \
float(xypwidth.data[caddress[structure.index('XY-Pulse-Width')]])*1e-9 + rooffset*1e-9])
print("ACQ delays with XY-Pulse for %sns" %
int(VSA.trigger_delay(vsasess)[1] / 1e-9))
elif "lockropdelay" in str(adcdelay.data[0]):
# trigger-delay sync with ro-pulse-delay for T1 measurement:
VSA.trigger_delay(
vsasess,
action=[
'Set',
float(ropdelay.data[caddress[structure.index(
'RO-Pulse-Delay')]]) * 1e-9
])
print("ACQ delays with RO-Pulse for %sns" %
int(VSA.trigger_delay(vsasess)[1] / 1e-9))
else:
VSA.trigger_delay(vsasess,
action=[
'Set',
float(adcdelay.data[caddress[
structure.index('ADC-delay')]])
])
VSA.external_trigger_level(vsasess, action=['Set', float(0.3)])
VSA.external_trigger_slope(vsasess,
action=['Set',
int(1)]) # Positive slope
VSA.trigger_timeout(vsasess, action=['Set',
int(1000)]) # 1s of timeout
stat = VSA.Init_Measure(vsasess) # Initiate Measurement
# Start Quantum machine:
# Start Averaging Loop:
avenum = int(averaging.data[caddress[structure.index('Average')]])
vsasn = VSA.samples_number(vsasess)[1]
iqdata = zeros((avenum, 2 * vsasn))
for ave in range(avenum):
VSA.Arm_Measure(vsasess)
gd = VSA.Get_Data(vsasess, 2 * vsasn)
iqdata[ave, :] = array(gd[1]['ComplexData'])
iqdata = mean(iqdata, axis=0)
print("Operation Complete")
print(Fore.YELLOW + "\rProgress: %.3f%%" %
((i + 1) / datasize * buffersize_1 * 100),
end='\r',
flush=True)
# test for the last loop if there is
if testeach: # test each measure-loop:
loopcount += [len(measure_loop_1)]
loop_dur += [time() - prev]
stage, prev = clocker(stage, prev) # Marking time
VSA.close(vsasess)
if "opt" not in pperiod.data: # check if it is in optional-state
AWG.close(awgsess)
if "opt" not in xyfreq.data: # check if it is in optional-state
PSG0.close(sogo, False)
if "opt" not in rofreq.data: # check if it is in optional-state
PSG1.close(saga, False)
if "opt" not in fluxbias.data: # check if it is in optional-state
YOKO.close(yokog, False)
yield loopcount, loop_dur
else:
if get_status("SQE_Pulse")['pause']:
break
else:
yield list(iqdata)
if not get_status("SQE_Pulse")['repeat']:
set_status("SQE_Pulse", dict(pause=True))
VSA.close(vsasess)
if "opt" not in pperiod.data: # check if it is in optional-state
AWG.Abort_Gen(awgsess)
AWG.close(awgsess)
if "opt" not in xyfreq.data: # check if it is in optional-state
PSG0.rfoutput(sogo, action=['Set', 0])
PSG0.close(sogo, False)
if "opt" not in rofreq.data: # check if it is in optional-state
PSG1.rfoutput(saga, action=['Set', 0])
PSG1.close(saga, False)
if "opt" not in fluxbias.data: # check if it is in optional-state
YOKO.output(yokog, 0)
YOKO.close(yokog, False)
return
def char_cwsweep_access():
wmoment = int(request.args.get('wmoment'))
M_cwsweep[session['user_name']].selectmoment(wmoment)
M_cwsweep[session['user_name']].accesstructure()
data_progress = M_cwsweep[session['user_name']].data_progress
data_repeat = data_progress // 100 + int(bool(data_progress % 100))
# Measurement time:
filetime = getmtime(M_cwsweep[session['user_name']].pqfile) # in seconds
startmeasure = mktime(
strptime(
M_cwsweep[session['user_name']].day + " " +
M_cwsweep[session['user_name']].startime(),
"%Y-%m-%d(%a) %H:%M")) # made into seconds
measureacheta = str(
timedelta(seconds=(filetime - startmeasure) / data_progress *
(trunc(data_progress / 100 + 1) * 100 - data_progress)))
# Scale-up optional parameters:
try:
cfluxbias = waveform(
M_cwsweep[session['user_name']].corder['Flux-Bias'])
except (KeyError):
cfluxbias = waveform(
'opt,'
) # create virtual list for the absence of this in older file
try:
cxyfreq = waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency'])
except (KeyError):
cxyfreq = waveform(
'opt,'
) # create virtual list for the absence of this in older file
try:
cxypowa = waveform(M_cwsweep[session['user_name']].corder['XY-Power'])
except (KeyError):
cxypowa = waveform(
'opt,'
) # create virtual list for the absence of this in older file
csparam = waveform(M_cwsweep[session['user_name']].corder['S-Parameter'])
cifb = waveform(M_cwsweep[session['user_name']].corder['IF-Bandwidth'])
cfreq = waveform(M_cwsweep[session['user_name']].corder['Frequency'])
cpowa = waveform(M_cwsweep[session['user_name']].corder['Power'])
cpowa_repeat = cpowa.inner_repeat
session['c_cwsweep_structure'] = [
data_repeat, cfluxbias.count, cxyfreq.count, cxypowa.count,
csparam.count, cifb.count, cfreq.count, cpowa.count * cpowa_repeat *
M_cwsweep[session['user_name']].datadensity
]
session['c_cwsweep_address'] = cdatasearch(
M_cwsweep[session['user_name']].resumepoint - 1,
session['c_cwsweep_structure'])
# list each parameter range based on data-progress:
cfluxbias_data = cfluxbias.data[0:session['c_cwsweep_address'][1] + 1]
cxyfreq_data = cxyfreq.data[0:session['c_cwsweep_address'][2] + 1]
cxypowa_data = cxypowa.data[0:session['c_cwsweep_address'][3] + 1]
csparam_data = csparam.data[0:session['c_cwsweep_address'][4] + 1]
cifb_data = cifb.data[0:session['c_cwsweep_address'][5] + 1]
cfreq_data = cfreq.data[0:session['c_cwsweep_address'][6] + 1]
cpowa_data = cpowa.data[0:(session['c_cwsweep_address'][7] + 1) //
cpowa_repeat // 2] # (to be adjusted ***)
# print("cpowa_data: %s" %cpowa_data)
return jsonify(data_progress=data_progress,
measureacheta=measureacheta,
corder=M_cwsweep[session['user_name']].corder,
comment=M_cwsweep[session['user_name']].comment,
data_repeat=data_repeat,
cfluxbias_data=cfluxbias_data,
cxyfreq_data=cxyfreq_data,
cxypowa_data=cxypowa_data,
csparam_data=csparam_data,
cifb_data=cifb_data,
cfreq_data=cfreq_data,
cpowa_data=cpowa_data)