kmc.electrodes[cf.Q] = Q[k] * V_high
kmc.update_V()
kmc.simulate_discrete(prehops=500, hops=5000)
output[k] = kmc.current[cf.output]
outputArray[i, j] = output
fitness += cf.Fitness(output, cf.target)
genePool.fitness[j] = fitness / avg
fitnessArray[i, j] = genePool.fitness[j]
# Status print
print("Generation nr. " + str(i + 1) + " completed")
print("Highest fitness: " + str(max(genePool.fitness)))
# Evolve to the next generation
genePool.NextGen()
# Save experiment
filepath = SaveLib.createSaveDirectory(cf.filepath, cf.name)
SaveLib.saveExperiment(filepath,
geneArray=geneArray,
fitnessArray=fitnessArray,
outputArray=outputArray,
target=cf.target,
P=P,
Q=Q)
domain = kmc_dn_utils.visualize_basic(kmc)
plt.figure()
plt.plot(output)
plt.show()
outputArray[i, :, :] = outputTemp
fitnessArray[i, :] = genePool.fitness
# Update main figure
PlotBuilder.updateMainFigEvolution(mainFig,
geneArray,
fitnessArray,
outputArray,
i + 1,
t,
cf.amplification*target,
output,
w)
# Save generation
SaveLib.saveExperiment(saveDirectory,
geneArray = geneArray,
outputArray = outputArray,
fitnessArray = fitnessArray,
t = t,
x = x,
amplified_target = cf.amplification*target)
# Evolve to the next generation
genePool.NextGen()
PlotBuilder.finalMain(mainFig)
InstrumentImporter.reset(0, 0)
print("Generation nr. " + str(i + 1) + " completed; took " +
str(end - start) + " sec.")
print("Highest fitness: " + str(max(genePool.fitness)))
# Save generation data
geneArray[i, :, :] = genePool.pool
outputArray[i, :, :] = outputTemp
fitnessArray[i, :] = genePool.fitness
# # Update main figure
# PlotBuilder.updateMainFigEvolution(mainFig,
# geneArray,
# fitnessArray,
# outputArray,
# i + 1,
# t,
# cf.amplification*target,
# output,
# w)
# Save generation
SaveLib.saveExperiment(saveDirectory,
genes=geneArray,
output=outputArray,
fitness=fitnessArray)
# Evolve to the next generation
genePool.NextGen()
PlotBuilder.finalMain(mainFig)
P = [0, 1, 0, 1]
Q = [0, 0, 1, 1]
w = [1, 1, 1, 1]
kmc.electrodes[cf.output, 3] = 0
# Initialize arrays to save experiment
geneArray = np.zeros((cf.generations, cf.genomes, cf.genes))
currentArray = np.zeros((cf.generations, cf.genomes, 8, 4 * cf.avg))
voltageArray = np.zeros((cf.generations, cf.genomes, 8, 4 * cf.avg))
fitnessArray = np.zeros((cf.generations, cf.genomes))
filepath = SaveLib.createSaveDirectory(cf.filepath, cf.name)
# Save experiment (also copies files)
SaveLib.saveExperiment(filepath,
geneArray=geneArray,
fitnessArray=fitnessArray,
currentArray=currentArray,
voltageArray=voltageArray,
target=cf.target,
P=P,
Q=Q)
for i in range(cf.generations):
geneArray[i] = genePool.pool
for j in range(cf.genomes):
if (i == 0 and j == 1):
tic = time.time()
if (i == 0 and j == 2):
print(
f'Estimated time: {(time.time()-tic)*cf.generations*cf.genomes/3600} h'
)
# Set the Voltages
for k in cf.controls:
# Initialize instruments
ivvi = InstrumentImporter.IVVIrack.initInstrument(dac_step=500,
dac_delay=0.001)
nr_blocks = len(cf.input1) * len(cf.input2)
blockSize = int(len(voltages) / nr_blocks)
assert len(
voltages
) == blockSize * nr_blocks, 'Nr of gridpoints not divisible by nr_blocks!!'
#main acquisition loop
for j in range(nr_blocks):
print('Getting Data for block ' + str(j) + '...')
start_block = time.time()
InstrumentImporter.IVVIrack.setControlVoltages(ivvi,
voltages[j * blockSize, :])
time.sleep(1) #extra delay to account for changing the input voltages
for i in range(blockSize):
IVVIrack.setControlVoltages(ivvi, voltages[j * blockSize + i, :])
time.sleep(0.01) #tune this to avoid transients
data[j * blockSize + i, -cf.samples:] = InstrumentImporter.nidaqIO.IO(
np.zeros(cf.samples), cf.samples / cf.acqTime)
end_block = time.time()
print('CV-sweep over one input state took ' +
str(end_block - start_block) + ' sec.')
#SAVE DATA following conventions for NN training
SaveLib.saveExperiment(saveDirectory, data=data, filename='training_NN_data')
InstrumentImporter.reset(0, 0)
for index, control in enumerate(cf.controls):
kmc.electrodes[control, 3] = (1-cf.gene[index])*cf.controlrange[0] \
+ cf.gene[index]*cf.controlrange[1]
# Obtain device response
output = np.zeros((cf.n_voltage, cf.n_voltage, 8, cf.avg))
for ii in range(cf.n_voltage):
for jj in range(cf.n_voltage):
# Time estimate
if (ii == 0 and jj == 0):
starttime = time.time()
if (ii == 0 and jj == 1):
eta = (time.time() - starttime) * cf.avg * cf.n_voltage**2
print(f'Estimated time remaining: {eta} s, or {eta/3600} h')
# Set input voltages
kmc.electrodes[cf.P] = cf.inputaxis[ii]
kmc.electrodes[cf.Q] = cf.inputaxis[jj]
kmc.update_V()
# Prestabilize the system
kmc.python_simulation(prehops=cf.prehops)
for kk in range(cf.avg):
kmc.python_simulation(hops=cf.hops)
output[ii, jj, :, kk] = kmc.current
SaveLib.saveExperiment(saveDirectory, output=output, inputaxis=cf.inputaxis)
print('All done!')
kmc.static_electrodes[0, 3] = P[k] * (cf.inputrange[0] *
(1 - cf.gene[-1]) +
cf.gene[-1] * cf.inputrange[1])
kmc.static_electrodes[1, 3] = Q[k] * (cf.inputrange[0] *
(1 - cf.gene[-1]) +
cf.gene[-1] * cf.inputrange[1])
else:
kmc.static_electrodes[0, 3] = P[k] * cf.inputrange
kmc.static_electrodes[1, 3] = Q[k] * cf.inputrange
kmc.update_V()
if (cf.use_go):
kmc.go_simulation(hops=0,
prehops=cf.prehops,
goSpecificFunction='wrapperSimulateRecord')
else:
kmc.python_simulation(prehops=cf.prehops)
for l in range(cf.avg):
if (cf.use_go):
kmc.go_simulation(hops=cf.hops,
goSpecificFunction='wrapperSimulateRecord')
else:
kmc.python_simulation(hops=cf.hops)
output[:, k * cf.avg + l] = kmc.current
# Save experiment
SaveLib.saveExperiment(saveDirectory, output=output)
print('All done!')
# Load the information from the config class.
config = config.experiment_config()
# Initialize save directory.
saveDirectory = SaveLib.createSaveDirectory(config.filepath, config.name)
# Define the device input using the function in the config class.
Input = config.Sweepgen(config.v_high, config.v_low, config.n_points,
config.direction)
# Measure using the device specified in the config class.
if config.device == 'nidaq':
Output = InstrumentImporter.nidaqIO.IO(Input, config.fs)
elif config.device == 'adwin':
adwin = InstrumentImporter.adwinIO.InitInstrument()
Output = InstrumentImporter.adwinIO.IO(adwin, Input, config.fs)
else:
print('specify measurement device')
# Save the Input and Output
SaveLib.saveExperiment(saveDirectory, input=Input, output=Output)
# Plot the IV curve.
plt.figure()
plt.plot(Input[0:len(Output)], Output)
plt.show()
# Final reset
InstrumentImporter.reset(0, 0)
if (ii == 1):
dt = time.time() - starttime
print(f'Estimated remaining time: {dt*len(cf.kT)} s, or \
{dt*len(cf.kT)/3600} h')
# Set the temperature
kmc.kT = cf.kT[ii]
# Update constant quantities that depend on I_0 and ab
kmc.calc_E_constant()
kmc.calc_transitions_constant()
for jj in range(len(cf.voltages)):
# Set voltage
kmc.electrodes[0] = cf.voltages[jj]
kmc.update_V()
# Prestabilize the system
kmc.python_simulation(prehops=cf.prehops)
# Get current
for kk in range(cf.avg):
kmc.python_simulation(hops=cf.hops)
output[ii, :, jj, kk] = kmc.current
SaveLib.saveExperiment(saveDirectory,
kT=cf.kT,
output=output,
voltages=cf.voltages)
print('All done!')
P = [0, 1, 0, 1]
Q = [0, 0, 1, 1]
w = [1, 1, 1, 1]
kmc.electrodes[cf.output] = 0
# Set control voltages and electrode 0 to high
for index, control in enumerate(cf.controls):
kmc.electrodes[control, 3] = (1-cf.gene[index])*cf.controlrange[0] \
+ cf.gene[index]*cf.controlrange[1]
kmc.electrodes[cf.P, 3] = cf.inputrange
if cf.allzero:
kmc.electrodes[:, 3] = 0
kmc.update_V()
# Obtain IV curve by sweeping electrode 1
currentlist = np.zeros((cf.avg, 8, cf.n_voltage))
for i in range(cf.avg):
currentlist[i] = kmc_dn_utils.IV(kmc,
cf.sweep_electrode,
cf.voltagelist,
hops=cf.hops,
prehops=cf.prehops)
SaveLib.saveExperiment(saveDirectory, V=cf.voltagelist, I=currentlist)
plt.figure()
plt.plot(cf.voltagelist, currentlist[0, cf.output])
plt.show()
# Obtain device response
electrode_occupation = np.zeros((4, cf.avg, 8, cf.hops))
time = np.zeros((4, cf.avg, cf.hops))
traffic = np.zeros(
(4, cf.avg, kmc.transitions.shape[0], kmc.transitions.shape[0]))
for k in range(4):
if (k == 0):
starttime = timelib.time()
if (k == 1):
print(f'Time remaining: {(timelib.time()-starttime)*3}')
# Set input voltages
kmc.electrodes[cf.P] = P[k] * cf.inputrange
kmc.electrodes[cf.Q] = Q[k] * cf.inputrange
kmc.update_V()
# Prestabilize the system
kmc.simulate_discrete(hops=cf.prehops)
for l in range(cf.avg):
kmc.simulate_discrete(hops=cf.hops, record_current=True)
electrode_occupation[k, l] = kmc.electrode_occupation
time[k, l] = kmc.time
traffic[k, l] = kmc.traffic
SaveLib.saveExperiment(saveDirectory,
electrode_occupation=electrode_occupation,
time=time,
traffic=traffic)
# Update constant quantities that depend on I_0 and ab
kmc.calc_E_constant()
kmc.calc_transitions_constant()
# Initialize starting occupation
kmc.place_charges_random()
start_occupation = kmc.occupation.copy()
# Obtain device response
currents = kmc_dn_utils.IV(kmc,
cf.bias_electrode,
voltagelist,
prehops=prehops,
hops=hops,
printETA=True)
output = currents[1].copy()
# Save output and kmc object
outputs[ii] = output
kmc.saveSelf(filepath + '/kmc_objects/' + '{0:03}'.format(ii) + '.kmc')
# Save experiment
SaveLib.saveExperiment(
filepath,
outputs=outputs,
voltagelist=voltagelist,
)
print('All done!')
kT = cf.kT
prehops = cf.prehops
hops = cf.hops
kmc.kT = kT
# Initialize arrays to save experiment
geneArray = np.zeros((cf.generations, cf.genomes, cf.genes))
outputArray = np.zeros((cf.generations, cf.genomes, len(voltagelist)))
fitnessArray = np.zeros((cf.generations, cf.genomes))
filepath = SaveLib.createSaveDirectory(cf.filepath, cf.name)
# Save experiment (also copies files)
SaveLib.saveExperiment(
filepath,
geneArray=geneArray,
fitnessArray=fitnessArray,
outputArray=outputArray,
voltagelist=voltagelist,
target=target,
)
for i in range(cf.generations):
geneArray[i] = genePool.pool
for j in range(cf.genomes):
# Time estimation
if (i == 0 and j == 1):
tic = time.time()
if (i == 0 and j == 2):
print(
f'Estimated time: {(time.time()-tic)*cf.generations*cf.genomes/3600} h'
)