def main():
stage, prev = clocker(0)
results = [cube(x) for x in range(1, 10000)]
print("Finished processing %s results" % len(results))
stage, prev = clocker(stage, prev) # Marking time
# input("Continue?")
# calling from outside
multiprocessor.worker_fresp(1000, 10000)
stage, prev = clocker(stage, prev) # Marking time
def test():
points = 70
C = eval('[1,70,%s]' % points)
# access-only mode:
M = TESTC()
k = M.whichday()
if k < 0:
stage, prev = clocker(0) # Marking starting point of time
i = prev
# Creating New Data:
M = TESTC(
{
'C1': [0, 0, 0],
'C2': [0.1, 0.1, 0],
'C3': [1, 1, 0],
'C4': [0, 12, 3],
'C5': C
}, '', -1)
print("For %s points:" % points)
stage, prev = clocker(stage, prev) # Marking time lapsed
print("Hence this pc can write %ss per point" % ((prev - i) / points))
else:
M.selectday(k)
M.listime()
m = M.whichmoment()
# reading Data
print("Before compensating:\n")
M.accesstructure()
M.loadata()
M.buildata()
print(M.datacontainer)
# Manage Data
M = TESTC(dayindex=k, taskentry=m)
# reading Data
print("After compensating:\n")
M.buildata()
print(M.datacontainer)
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
'network'
] #tools
print("These modules are available for test:")
print(Fore.GREEN + str(MDL))
while True:
selectedmdl = str(
input("Pick those you'd like to test with comma between them:\n"))
selectedmdl = selectedmdl.strip().split(',')
for i, val in enumerate(selectedmdl):
val = val.strip()
if len(val) < 5: #limit up to 4 capital-letters for instruments
selectedmdl[i] = val.upper()
else:
selectedmdl[i] = val.lower(
) #tools have to be more than 5 letters in lower-case
if set(MDL) & set(selectedmdl):
break
for i in list(set(selectedmdl) - set(MDL)): #pop those not in the list
selectedmdl.remove(i)
print("The following modules will be tested:")
print(Fore.YELLOW + str(selectedmdl))
# Marking starting point of time:
stage, prev = logger.clocker(0)
# Testing each module individually
for mdl in selectedmdl:
eval(mdl + ".test()")
stage, prev = logger.clocker(stage, prev) # Marking time
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_fresp_2ddata():
# M_fresp[session['user_name']].loadata()
# selectedata = M_fresp[session['user_name']].selectedata
ifluxbias = request.args.get('ifluxbias')
# if ifluxbias == "o": ifluxbias = '0' # for backward compatibility
isparam = request.args.get('isparam')
iifb = request.args.get('iifb')
ipowa = request.args.get('ipowa')
ifreq = request.args.get('ifreq')
x, y, ZZ = [], [], []
dict_for_MPW = {
"pqfile": str(M_fresp[session['user_name']].pqfile),
"datalocation": M_fresp[session['user_name']].datalocation,
"writtensize": M_fresp[session['user_name']].writtensize,
"c_fresp_structure": session['c_fresp_structure'],
"ifluxbias": ifluxbias,
"isparam": isparam,
"iifb": iifb,
"ipowa": ipowa,
"ifreq": ifreq,
}
set_status("MPW", dict_for_MPW)
if ifluxbias == "x" and ifreq == "y":
print("X: Flux-Bias, Y: Frequency")
xtitle, ytitle = "<b>Flux-Bias(V/A)</b>", "<b>Frequency(GHz)</b>"
x, y = waveform(
M_fresp[session['user_name']].corder['Flux-Bias']
).data[0:session['c_fresp_address'][0] + 1], waveform(
M_fresp[session['user_name']].corder['Frequency']).data
x_count, y_count = session['c_fresp_address'][0] + 1, waveform(
M_fresp[session['user_name']].corder['Frequency']).count
stage, prev = clocker(0)
CMD = [
"python", "-c",
"from pyqum.directive import MP_fresp as mp; print(mp.worker(%s,%s))"
% (y_count, x_count)
]
with Popen(CMD, stdout=PIPE, shell=True) as proc:
output = json.loads(proc.stdout.read().decode("utf-8").replace(
"\'", "\""))
# try: os.kill(os.getppid(), signal.SIGTERM) # terminate parent process
# except: pass
stage, prev = clocker(stage, prev) # Marking time
# slow iteration method:
# Amp, Pha = [], []
# for j in range(y_count):
# I = [selectedata[gotocdata([x, int(session['isparam']), int(session['iifb']), int(session['ipowa']), 2*j], session['c_fresp_structure'])] for x in range(x_count)]
# Q = [selectedata[gotocdata([x, int(session['isparam']), int(session['iifb']), int(session['ipowa']), 2*j+1], session['c_fresp_structure'])] for x in range(x_count)]
# amp, pha = [], []
# for i,q in zip(I,Q):
# a,p = IQAP(i,q)
# amp.append(a); pha.append(p)
# Amp += [amp]; Pha += [pha]
print("x is of length %s and of type %s" % (len(x), type(x)))
print("y is of length %s and of type %s" % (len(y), type(y)))
Amp = output['rA']
Pha = output['rP']
print("Amp of shape %s" % str(array(Amp).shape))
ZZA, ZZP = Amp, Pha
elif ipowa == "x" and ifreq == "y":
print("X: Power, Y: Frequency")
xtitle, ytitle = "<b>Power(dBm)</b>", "<b>Frequency(GHz)</b>"
x, y = waveform(
M_fresp[session['user_name']].corder['Power']
).data[0:session['c_fresp_address'][3] + 1], waveform(
M_fresp[session['user_name']].corder['Frequency']).data
x_count, y_count = session['c_fresp_address'][3] + 1, waveform(
M_fresp[session['user_name']].corder['Frequency']).count
stage, prev = clocker(0)
CMD = [
"python", "-c",
"from pyqum.directive import MP_fresp as mp; print(mp.worker(%s,%s,%s,%s))"
% (y_count, x_count, '"freq"', '"powa"')
]
with Popen(CMD, stdout=PIPE, shell=True) as proc:
output = json.loads(proc.stdout.read().decode("utf-8").replace(
"\'", "\""))
# try: os.kill(os.getppid(), signal.SIGTERM) # terminate parent process
# except: pass
stage, prev = clocker(stage, prev) # Marking time
print("x is of length %s and of type %s" % (len(x), type(x)))
print("y is of length %s and of type %s" % (len(y), type(y)))
Amp = output['rA']
Pha = output['rP']
print("Amp of shape %s" % str(array(Amp).shape))
ZZA, ZZP = Amp, Pha
elif iifb == "x":
pass
# x = list(range(len(x))) # for repetitive data
return jsonify(x=x, y=y, ZZA=ZZA, ZZP=ZZP, xtitle=xtitle, ytitle=ytitle)
def char_cwsweep_2ddata():
irepeat = request.args.get('irepeat')
ifluxbias = request.args.get('ifluxbias')
ixyfreq = request.args.get('ixyfreq')
ixypowa = request.args.get('ixypowa')
isparam = request.args.get('isparam')
iifb = request.args.get('iifb')
ipowa = request.args.get('ipowa')
ifreq = request.args.get('ifreq')
# pre-transform ipowa:
powa_order = M_cwsweep[session['user_name']].corder['Power']
# preparing MPW dictionary
dict_for_MPW = {
"pqfile": str(M_cwsweep[session['user_name']].pqfile),
"datalocation": M_cwsweep[session['user_name']].datalocation,
"writtensize": M_cwsweep[session['user_name']].writtensize,
"c_cwsweep_structure": session['c_cwsweep_structure'],
"irepeat": irepeat,
"ifluxbias": ifluxbias,
"ixyfreq": ixyfreq,
"ixypowa": ixypowa,
"isparam": isparam,
"iifb": iifb,
"ifreq": ifreq,
"ipowa": ipowa,
"powa_order": powa_order
}
set_status("MPW", dict_for_MPW)
# Check progress:
if not M_cwsweep[session['user_name']].data_progress % 100:
offset = 1
print(Fore.GREEN +
"The data is complete: we can see the whole picture now")
else:
offset = 0 # to avoid incomplete array error
print(Back.RED + "The data is NOT YET complete!")
# Selecting 2D options:
# REPEAT:
if irepeat == "x" and ifluxbias == "y":
x_name, y_name = "repeat", "fluxbias"
print("X: REPEAT#, Y: Flux-Bias")
xtitle, ytitle = "<b>REPEAT#</b>", "<b>Flux-Bias(V/A)</b>"
x, y = list(range(session['c_cwsweep_address'][0] + offset)), waveform(
M_cwsweep[session['user_name']].corder['Flux-Bias']).data
x_count, y_count = session['c_cwsweep_address'][0] + offset, waveform(
M_cwsweep[session['user_name']].corder['Flux-Bias']).count
if irepeat == "x" and ixyfreq == "y":
x_name, y_name = "repeat", "xyfreq"
print("X: REPEAT#, Y: XY-Frequency")
xtitle, ytitle = "<b>REPEAT#</b>", "<b>XY-Frequency(GHz)</b>"
x, y = list(range(session['c_cwsweep_address'][0] + offset)), waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency']).data
x_count, y_count = session['c_cwsweep_address'][0] + offset, waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency']).count
# ONCE:
elif ifluxbias == "x" and ixyfreq == "y":
x_name, y_name = "fluxbias", "xyfreq"
print("X: Flux-Bias, Y: XY-Frequency")
xtitle, ytitle = "<b>Flux-Bias(V/A)</b>", "<b>XY-Frequency(GHz)</b>"
x, y = waveform(
M_cwsweep[session['user_name']].corder['Flux-Bias']
).data[0:session['c_cwsweep_address'][1] + offset], waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency']).data
x_count, y_count = session['c_cwsweep_address'][1] + offset, waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency']).count
elif ixyfreq == "x" and ixypowa == "y":
x_name, y_name = "xyfreq", "xypowa"
print("X: XY-Frequency, Y: XY-Power")
xtitle, ytitle = "<b>XY-Frequency(GHz)</b>", "<b>XY-Power(dBm)</b>"
x, y = waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency']
).data[0:session['c_cwsweep_address'][2] + offset], waveform(
M_cwsweep[session['user_name']].corder['XY-Power']).data
x_count, y_count = session['c_cwsweep_address'][2] + offset, waveform(
M_cwsweep[session['user_name']].corder['XY-Power']).count
elif ixyfreq == "x" and ifreq == "y":
x_name, y_name = "xyfreq", "freq"
print("X: XY-Frequency, Y: Frequency")
xtitle, ytitle = "<b>XY-Frequency(GHz)</b>", "<b>Probe-Frequency(GHz)</b>"
x, y = waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency']
).data[0:session['c_cwsweep_address'][2] + offset], waveform(
M_cwsweep[session['user_name']].corder['Frequency']).data
x_count, y_count = session['c_cwsweep_address'][2] + offset, waveform(
M_cwsweep[session['user_name']].corder['Frequency']).count
elif ixyfreq == "x" and ipowa == "y":
x_name, y_name = "xyfreq", "powa"
print("X: XY-Frequency, Y: Power")
xtitle, ytitle = "<b>XY-Frequency(GHz)</b>", "<b>Probing-Power(dBm)</b>"
x, y = waveform(
M_cwsweep[session['user_name']].corder['XY-Frequency']
).data[0:session['c_cwsweep_address'][2] + offset], waveform(
M_cwsweep[session['user_name']].corder['Power']).data
x_count, y_count = session['c_cwsweep_address'][2] + offset, waveform(
M_cwsweep[session['user_name']].corder['Power']).count
# fast iteration method (parallel computing):
stage, prev = clocker(0)
CMD = [
"python", "-c",
"from pyqum.directive import MP_cwsweep as mp; print(mp.worker(%s,%s,'%s','%s'))"
% (y_count, x_count, y_name, x_name)
]
with Popen(CMD, stdout=PIPE, shell=True) as proc:
doutput = proc.stdout.read().decode("utf-8")
output = json.loads(doutput.replace("\'", "\""))
# try: os.kill(os.getppid(), signal.SIGTERM) # terminate parent process
# except: pass
Amp = output['rA']
Pha = output['rP']
stage, prev = clocker(stage, prev) # Marking time
print("x is of length %s and of type %s" % (len(x), type(x)))
print("y is of length %s and of type %s" % (len(y), type(y)))
print("Amp of shape %s" % str(array(Amp).shape))
ZZA, ZZP = Amp, Pha
# x = list(range(len(x))) # for repetitive data
return jsonify(x=x, y=y, ZZA=ZZA, ZZP=ZZP, xtitle=xtitle, ytitle=ytitle)