def set_data_location():
"""
Sets location for qcodes to save data based on the
qcodes.config.user.data_location value, the
qc.config.user.data_format and the sample name and sets
data_loc_fmt in global_settings.files_setup dictionary to True.
"""
sample_name = get_sample_name()
try:
dat_loc = qc.config.user.data_location.format(sample_name=sample_name)
dat_fmt = qc.config.user.data_format
dat_loc_fmt = dat_loc + dat_fmt
except KeyError:
raise KeyError('data_location or data_format not set in config, see '
'"https://github.com/QCoDeS/Qcodes/blob/master'
'/docs/examples/Configuring_QCoDeS.ipynb"')
if exp.files_setup['data_loc_fmt']:
print('Data location already set at {}.\n'
'Data file format already set as {}.'.format(dat_loc, dat_fmt))
print('-------------------------')
else:
if not os.path.exists(dat_loc):
os.makedirs(dat_loc)
loc_provider = qc.FormatLocation(fmt=dat_loc_fmt)
qc.data.data_set.DataSet.location_provider = loc_provider
exp.files_setup['data_loc_fmt'] = True
logging.info('Set data location: {}'.format(dat_loc))
print('Set data location: {}'.format(dat_loc))
print('-------------------------')
logging.info('Set data file format: {}'.format(dat_fmt))
print('Set data file format: {}'.format(dat_fmt))
print('-------------------------')
def _set_up_exp_file(sample_name: str, mainfolder: str= None):
"""
Makes
Args:
mainfolder: base location for the data
sample_name: name of the sample
"""
if sep in sample_name:
raise TypeError("Use Relative names. That is without {}".format(sep))
if mainfolder is None:
try:
mainfolder = qc.config.user.mainfolder
except KeyError:
raise KeyError('mainfolder not set in qc.config, see '
'"https://github.com/QCoDeS/Qcodes/blob/master'
'/docs/examples/Configuring_QCoDeS.ipynb"')
# always remove trailing sep in the main folder
if mainfolder[-1] == sep:
mainfolder = mainfolder[:-1]
mainfolder = abspath(mainfolder)
CURRENT_EXPERIMENT["mainfolder"] = mainfolder
CURRENT_EXPERIMENT["sample_name"] = sample_name
CURRENT_EXPERIMENT['init'] = True
path_to_experiment_folder = sep.join([mainfolder, sample_name, ""])
CURRENT_EXPERIMENT["exp_folder"] = path_to_experiment_folder
try:
makedirs(path_to_experiment_folder)
except FileExistsError:
pass
loc_provider = qc.FormatLocation(
fmt=path_to_experiment_folder + '{counter}')
qc.data.data_set.DataSet.location_provider = loc_provider
CURRENT_EXPERIMENT["provider"] = loc_provider
log.info("experiment started at {}".format(path_to_experiment_folder))
def init(mainfolder: str,
sample_name: str,
station,
plot_x_position=0.66,
annotate_image=True,
display_pdf=True,
display_individual_pdf=False):
"""
Args:
mainfolder: base location for the data
sample_name: name of the sample
plot_x_position: fractional of screen position to put QT plots.
0 is all the way to the left and
1 is all the way to the right.
"""
pdfdisplay['individual'] = display_individual_pdf
pdfdisplay['combined'] = display_pdf
if sep in sample_name:
raise TypeError("Use Relative names. That is without {}".format(sep))
# always remove trailing sep in the main folder
if mainfolder[-1] == sep:
mainfolder = mainfolder[:-1]
mainfolder = abspath(mainfolder)
CURRENT_EXPERIMENT["mainfolder"] = mainfolder
CURRENT_EXPERIMENT["sample_name"] = sample_name
CURRENT_EXPERIMENT['init'] = True
CURRENT_EXPERIMENT['plot_x_position'] = plot_x_position
path_to_experiment_folder = sep.join([mainfolder, sample_name, ""])
CURRENT_EXPERIMENT["exp_folder"] = path_to_experiment_folder
CURRENT_EXPERIMENT['pdf_subfolder'] = 'pdf'
try:
makedirs(path_to_experiment_folder)
except FileExistsError:
pass
try:
makedirs(
sep.join(
[mainfolder, sample_name,
CURRENT_EXPERIMENT['pdf_subfolder']]))
except FileExistsError:
pass
log.info("experiment started at {}".format(path_to_experiment_folder))
loc_provider = qc.FormatLocation(fmt=path_to_experiment_folder +
'{counter}')
qc.data.data_set.DataSet.location_provider = loc_provider
CURRENT_EXPERIMENT["provider"] = loc_provider
CURRENT_EXPERIMENT['station'] = station
ipython = get_ipython()
# turn on logging only if in ipython
# else crash and burn
if ipython is None:
raise RuntimeWarning("History can't be saved. "
"-Refusing to proceed (use IPython/jupyter)")
else:
logfile = "{}{}".format(path_to_experiment_folder, "commands.log")
CURRENT_EXPERIMENT['logfile'] = logfile
if not CURRENT_EXPERIMENT["logging_enabled"]:
log.debug("Logging commands to: t{}".format(logfile))
ipython.magic("%logstart -t -o {} {}".format(logfile, "append"))
CURRENT_EXPERIMENT["logging_enabled"] = True
else:
log.debug("Logging already started at {}".format(logfile))
# Annotate image if wanted and necessary
if annotate_image:
_init_device_image(station)
def fft_noise(dev_name: str, channel: Dict[str, int], unit: str,
prefactor: Any, samplerate: int,
sampleduration: Union[float, int], navg: int, fmax: Union[float,
int]):
"""Noise measurement of a single channel.
Args:
dev_name: DAQ device name (e.g. 'Dev1').
channel: Dict of {channel_name: analog_input} (e.g. {'MAG': 0}).
unit: Physical unit of the channel (e.g. 'Phi0').
prefactor: Pint Quantity with dimenions of unit/V, from microscope.get_prefactors().
samplerate: DAQ sampling rate in Hz.
sampleduration: Sampling time in seconds.
navg: Number of times to average the spectrum.
fmax: Maximum frequency up to which the spectrum will be saved.
Returns:
Dict: mdict
"""
loc_provider = qc.FormatLocation(
fmt='./data/{date}/#{counter}_{name}_{time}')
loc = loc_provider(DiskIO('.'), record={'name': 'fft_noise'})
pathlib.Path(loc).mkdir(parents=True, exist_ok=True)
prefactor_str = {}
prefactor.ito('{}/V'.format(unit))
prefactor_str.update({
list(channel.keys())[0]:
'{} {}'.format(prefactor.magnitude, prefactor.units)
})
mdict = {
'metadata': {
'channel': channel,
'unit': unit,
'prefactor': prefactor_str,
'samplerate': samplerate,
'sampleduration': sampleduration,
'navg': navg,
'fmax': fmax,
'location': loc
}
}
nsamples = int(samplerate * sampleduration)
v_fft_avg = np.zeros((nsamples // 2, ))
with nidaqmx.Task('fft_noise_ai_task') as ai_task:
for inst in DAQAnalogInputs.instances():
inst.close()
daq_ai = DAQAnalogInputs('daq_ai',
dev_name,
samplerate,
channel,
ai_task,
samples_to_read=nsamples,
timeout=sampleduration + 10)
for i in range(navg):
data_v = daq_ai.voltage()[0].T
Fs = nsamples / sampleduration
v_fft = np.fft.fft(data_v) / (nsamples /
np.sqrt(2 * sampleduration))
v_fft_abs = np.abs(v_fft[:nsamples // 2])
freqs = np.fft.fftfreq(nsamples, d=1 / Fs)[:nsamples // 2]
v_fft_avg += v_fft_abs
daq_ai.close()
v_fft_avg = v_fft_avg / navg
sig_fft_avg = prefactor.magnitude * v_fft_avg
mdict.update({
'v_fft_avg': v_fft_avg[freqs < fmax],
'sig_fft_avg': sig_fft_avg[freqs < fmax],
'freqs': freqs[freqs < fmax]
})
fig, ax = plt.subplots(1, 2, figsize=(8, 4), tight_layout=True)
ax[0].loglog(freqs, v_fft_avg, lw=1)
ax[0].set_ylabel('V/$\\sqrt{Hz}$')
ax[1].loglog(freqs, sig_fft_avg, lw=1)
ax[1].set_ylabel('{}/$\\sqrt{Hz}$'.format(unit))
fig.suptitle(loc, x=0.5, y=1)
for i in [0, 1]:
ax[i].set_xlabel('Frequency [Hz]')
ax[i].grid()
plt.savefig(loc + '/fft_noise.png')
io.savemat(loc + '/fft_noise.mat', mdict)
return mdict
def time_trace(dev_name: str, channels: Dict[str, int], units: Dict[str, str],
prefactors: Dict[str, Any], samplerate: int,
sampleduration: Union[float, int]):
"""Records a time trace of data from DAQ analog input channels, converts data to desired units.
Args:
dev_name: DAQ device name (e.g. 'Dev1').
channel: Dict of {channel_name: analog_input} (e.g. {'MAG': 0, 'SUSCX': 1}).
unit: Physical unit of the channel (e.g. {'MAG': 'Phi0', 'SUSCX': 'Phi0/A'}).
prefactor: Dict of {channel_name: Pint Quantity} from microscope.get_prefactors().
samplerate: DAQ sampling rate (for each channel) in Hz.
sampleduration: Sampling time in seconds.
Returns:
Dict: mdict
"""
loc_provider = qc.FormatLocation(
fmt='./data/{date}/#{counter}_{name}_{time}')
loc = loc_provider(DiskIO('.'), record={'name': 'time_trace'})
pathlib.Path(loc).mkdir(parents=True, exist_ok=True)
prefactor_strs = {}
for ch in channels:
unit = units[ch]
prefactors[ch].ito('{}/V'.format(unit))
prefactor_strs.update({
ch:
'{} {}'.format(prefactors[ch].magnitude, prefactors[ch].units)
})
nsamples = int(samplerate * sampleduration)
time = np.linspace(0, sampleduration, nsamples)
mdict = {
'time': {
'array': time,
'unit': 's'
},
'metadata': {
#'channels': channels,
#'units': units,
#'prefactors': prefactor_strs,
'samplerate': samplerate,
'sampleduration': sampleduration,
'location': loc
}
}
nsamples = int(samplerate * sampleduration)
time = np.linspace(0, sampleduration, nsamples)
with nidaqmx.Task('time_trace_ai_task') as ai_task:
for inst in DAQAnalogInputs.instances():
inst.close()
daq_ai = DAQAnalogInputs('daq_ai',
dev_name,
samplerate,
channels,
ai_task,
samples_to_read=nsamples,
timeout=sampleduration + 10)
data_v = daq_ai.voltage()
daq_ai.close()
for i, ch in enumerate(channels):
mdict.update({
ch: {
'array': data_v[i] * prefactors[ch].magnitude,
'unit': units[ch],
'prefactor': prefactor_strs[ch]
}
})
io.savemat(loc + '/time_trace.mat', mdict)
return mdict
def get_surface(self, x_vec: np.ndarray, y_vec: np.ndarray,
tdc_params: Dict[str, Any]) -> None:
"""Performs touchdowns on a grid and fits a plane to the resulting surface.
Args:
x_vec: 1D array of x positions (must be same length as y_vec).
y_vec: 1D array of y positions (must be same length as x_vec).
tdc_params: Dict of capacitive touchdown parameters as defined
in measurement configuration file.
"""
old_pos = self.scanner.position()
#: True if touchdown doesn't occur for any point in the grid
out_of_range = False
#: True if the loop is exited before finishing
premature_exit = False
self.scanner.break_loop = False
self.scanner.td_has_occurred = False
self.snapshot(update=True)
x_grid, y_grid = np.meshgrid(x_vec, y_vec, indexing='ij')
td_grid = np.full((len(x_vec), len(y_vec)), np.nan, dtype=np.double)
log.info('Aqcuiring a plane.')
v_retract = self.scanner.voltage_retract[self.temp].to('V').magnitude
fig = plt.figure(figsize=(8, 3))
ax0 = fig.add_subplot(121, projection='3d')
ax1 = fig.add_subplot(122, projection='3d')
for ax in [ax0, ax1]:
ax.set_xlabel('x position [V]')
ax.set_ylabel('y position [V]')
ax.set_zlabel('z position [V]')
ax0.set_title('Sample Plane')
ax1.set_title('Sample Surface')
fig.canvas.draw()
fig.show()
for i in range(len(x_vec)):
for j in range(len(y_vec)):
#: If any of the safety limits in td_cap() are exceeded,
#: or the loop is interrupted by the user.
if self.scanner.break_loop and not self.scanner.td_has_occurred:
log.warning('Aborting get_surface().')
premature_exit = True
break #: goes to outer break statement
else:
self.scanner.goto([x_grid[i, j], y_grid[i, j], v_retract])
data, tdc_plot = self.td_cap(tdc_params, update_snap=False)
td_grid[i, j] = self.scanner.td_height
clear_output(wait=True)
if self.scanner.td_height is None:
out_of_range = True
premature_exit = True
log.warning(
'Touchdown out of range. Stopping get_surface().')
self.scanner.goto(old_pos)
break #: goes to outer break statement
plt.close(fig)
fig = plt.figure(figsize=(8, 3))
ax0 = fig.add_subplot(121, projection='3d')
ax1 = fig.add_subplot(122, projection='3d')
for ax in [ax0, ax1]:
ax.scatter(x_grid[np.isfinite(td_grid)],
y_grid[np.isfinite(td_grid)],
td_grid[np.isfinite(td_grid)],
cmap='viridis')
ax.set_xlabel('x position [V]')
ax.set_ylabel('y position [V]')
ax.set_zlabel('z position [V]')
ax0.set_title('Sample Plane')
ax1.set_title('Sample Surface')
fig.canvas.draw()
fig.show()
plt.close(tdc_plot.fig)
continue #: skips outer break statement
break #: occurs only if out_of_range or loop is broken
self.scanner.goto(old_pos)
if not out_of_range and not premature_exit:
self.scanner.metadata.update(
{'td_grid': {
'x': x_grid,
'y': y_grid,
'z': td_grid
}})
# Create spline function to interpolate over surface:
self.scanner.surface_interp = Rbf(x_grid,
y_grid,
td_grid,
function='cubic')
#: Fit a plane to the td_grid
x = np.reshape(x_grid, (-1, 1))
y = np.reshape(y_grid, (-1, 1))
td = np.reshape(td_grid, (-1, 1))
z = np.column_stack((x, y, np.ones_like(x)))
plane, res, _, _ = lstsq(z, td)
log.info('New plane : {}.'.format([plane[i][0] for i in range(3)]))
ax0.plot_surface(x_grid,
y_grid,
plane[0] * x_grid + plane[1] * y_grid + plane[2],
cmap='viridis',
alpha=0.5)
ax1.plot_surface(x_grid,
y_grid,
self.scanner.surface_interp(x_grid, y_grid),
cmap='viridis',
alpha=0.5)
for i, axis in enumerate(['x', 'y', 'z']):
self.scanner.metadata['plane'].update({axis: plane[i][0]})
self.atto.surface_is_current = True
loc_provider = qc.FormatLocation(
fmt='./data/{date}/#{counter}_{name}_{time}')
loc = loc_provider(DiskIO('.'), record={'name': 'surface'})
pathlib.Path(loc).mkdir(parents=True, exist_ok=True)
fig.suptitle(loc)
fig.canvas.draw()
fig.show()
plt.savefig(loc + '/surface.png')
mdict = {
'plane': {
ax: self.scanner.metadata['plane'][ax]
for ax in ['x', 'y', 'z']
},
'td_grid': {
ax: self.scanner.metadata['td_grid'][ax]
for ax in ['x', 'y', 'z']
}
}
io.savemat(loc + '/surface.mat', mdict)
def iv_tek_mod_daq(self, ivm_params: Dict[str, Any]) -> None:
"""Performs digital feedback on mod coil to measure flux vs. delay.
AFG ch1 is used for pulse generator bias.
AFG ch2 is used for comparator bias.
DG ch1 is used for pulse generator trigger.
Args:
ivm_params: Dict of measurement parameters as definted in config_measurements json file.
Returns:
Tuple[Dict]: data_dict, metadict
Dictionaries containing data arrays and instrument metadata.
"""
data_dict = {}
meta_dict = {}
daq_config = self.config['instruments']['daq']
ai_channels = daq_config['channels']['analog_inputs']
meas_channels = ivm_params['channels']
channels = {}
for ch in meas_channels:
channels.update({ch: ai_channels[ch]})
vmod = self.Q_(ivm_params['vmod_initial']).to('V').magnitude
vcomp_set = self.Q_(ivm_params['vcomp_set']).to('V').magnitude
vmod_low = self.Q_(ivm_params['vmod_low']).to('V').magnitude
vmod_high = self.Q_(ivm_params['vmod_high']).to('V').magnitude
tsettle = self.Q_(ivm_params['tsettle']).to('s').magnitude
tavg = self.Q_(ivm_params['tavg']).to('s').magnitude
time_constant = self.Q_(ivm_params['time_constant'])
period = self.Q_(ivm_params['afg']['ch1']['period'])
delay0, delay1 = [self.Q_(val).to('s').magnitude for val in ivm_params['dg']['range']]
delay_vec = np.linspace(delay0, delay1, ivm_params['dg']['nsteps'])
vmod_vec = np.full_like(delay_vec, np.nan, dtype=np.double)
#: Set AFG pulse parameters
for ch in [1, 2]:
p = ivm_params['afg']['ch{}'.format(ch)]
getattr(self.afg, 'voltage_high{}'.format(ch))('{}V'.format(self.Q_(p['high']).to('V').magnitude))
getattr(self.afg, 'voltage_low{}'.format(ch))('{}V'.format(self.Q_(p['low']).to('V').magnitude))
getattr(self.afg, 'pulse_period{}'.format(ch))('{}us'.format(self.Q_(p['period']).to('us').magnitude))
getattr(self.afg, 'pulse_width{}'.format(ch))('{}us'.format(self.Q_(p['width']).to('us').magnitude))
getattr(self.afg, 'pulse_trans_lead{}'.format(ch))('{}us'.format(self.Q_(p['lead']).to('us').magnitude))
getattr(self.afg, 'pulse_trans_trail{}'.format(ch))('{}us'.format(self.Q_(p['trail']).to('us').magnitude))
getattr(self.afg, 'pulse_delay{}'.format(ch))('{}us'.format(self.Q_(p['delay']).to('us').magnitude))
#: Set delay generator parameters
p = ivm_params['dg']
self.dg.delay_B('A, {:e}'.format(delay0))
self.dg.delay_C('T0, {:e}'.format(self.Q_(p['ch2']['delay']).to('s').magnitude))
self.dg.delay_D('C, {:e}'.format(self.Q_(p['ch2']['width']).to('s').magnitude))
self.dg.amp_out_AB(self.Q_(p['ch1']['voltage']).to('V').magnitude)
self.dg.offset_out_AB(self.Q_(p['ch1']['offset']).to('V').magnitude)
self.dg.amp_out_CD(self.Q_(p['ch2']['voltage']).to('V').magnitude)
self.dg.offset_out_CD(self.Q_(p['ch2']['offset']).to('V').magnitude)
self.MAG_lockin.time_constant(self.Q_(ivm_params['time_constant']).to('s').magnitude)
#: Get instrument metadata and prefactors
lockin_snap = self.MAG_lockin.snapshot(update=True)
lockin_meta = {}
for param in ['time_constant', 'sensitivity', 'phase', 'reserve', 'filter_slope']:
lockin_meta.update({param: lockin_snap['parameters'][param]})
meta_dict.update({'metadata':
{'lockin': lockin_meta,
'afg': self.afg.snapshot(update=True),
'dg': self.dg.snapshot(update=True),
'ivm_params': ivm_params}
})
#prefactor = 1 / (10 / lockin_snap['parameters']['sensitivity']['value'])
#prefactor /= ivm_params['channels']['lockinX']['gain']
with nidaqmx.Task('ai_task') as ai_task, nidaqmx.Task('ao_task') as ao_task:
ao = '{}/ao{}'.format(daq_config['name'], daq_config['channels']['analog_outputs']['mod'])
ao_task.ao_channels.add_ao_voltage_chan(ao, 'mod')
for ch, idx in channels.items():
channel = '{}/ai{}'.format(daq_config['name'], idx)
ai_task.ai_channels.add_ai_voltage_chan(channel, ch)
figM = plt.figure(figsize=(4,3))
axM = plt.gca()
plt.xlim(min(delay_vec), max(delay_vec))
plt.xlabel(r'Delay time [$\mu$s]')
plt.ylabel('Modulation Voltage [V]')
figT = plt.figure(figsize=(4,3))
axT = plt.gca()
plt.xlabel('Iteration number')
plt.ylabel('Modulation Voltage [V]')
log.info('Starting iv_tek_mod_daq.')
try:
#: Sweep delay time
for j in range(len(delay_vec)):
self.dg.delay_A('T0, {:e}'.format(delay_vec[j]))
self.dg.delay_B('A, {:e}'.format(period.to('s').magnitude - delay_vec[j]))
elapsed_time = 0
nsamples = 0
vmod_time = np.array([])
t0 = time.time()
time.sleep(0.01)
#: Do digital PID control
while elapsed_time < tsettle + tavg:
ai_data = ai_task.read()
vcomp = ai_data[0]
err = vcomp - vcomp_set
vmod += P * err
vmod = np.mod(vmod, vmod_high)
#vmod = max(vmod, vmod_low) if vmod < 0 else min(vmod, vmod_high)
ao_task.write(vmod)
elapsed_time = time.time() - t0
nsamples += 1
vmod_time = np.append(vmod_time, vmod)
avg_start_pt = int(nsamples * tavg // (tsettle + tavg))
vmod_vec[j] = np.mean(vmod_time[avg_start_pt:])
clear_artists(axM)
axM.plot(delay_vec, vmod_vec, 'bo-')
plt.tight_layout()
figM.canvas.draw()
clear_artists(axT)
axT.plot(vmod_time, 'bo')
plt.tight_layout()
figT.canvas.draw()
time.sleep(0.05)
except KeyboardInterrupt:
log.warning('Measurement aborted by user.')
data_dict.update({
'delay_vec': {'array': delay_vec, 'unit': 's'},
'vmod_vec': {'array': vmod_vec, 'unit': 'V'}
})
if ivm_params['save']:
#: Get/create data location
loc_provider = qc.FormatLocation(fmt='{date}/#{counter}_{name}_{time}')
loc = loc_provider(DiskIO('.'), record={'name': ivm_params['fname']})
pathlib.Path(loc).mkdir(parents=True, exist_ok=True)
#: Save arrays to mat
io.savemat('{}/{}'.format(loc, ivm_params['fname']), data_dict)
#: Save metadata to json
with open(loc + '/metadata.json', 'w') as f:
try:
json.dump(meta_dict, f, sort_keys=True, indent=4, skipkeys=True)
except TypeError:
pass
#: Save figures to png
figM.suptitle(loc)
figM.tight_layout(rect=[0, 0.03, 1, 0.95])
figM.savefig('{}/{}mod_d.png'.format(loc, ivm_params['fname']))
figT.suptitle(loc)
figT.tight_layout(rect=[0, 0.03, 1, 0.95])
figT.savefig('{}/{}mod_t.png'.format(loc, ivm_params['fname']))
log.info('Data saved to {}.'.format(loc))
return data_dict, meta_dict
def iv_mod_tek(self, ivm_params: Dict[str, Any]) -> Tuple[Dict[str, Any]]:
"""Measures IV characteristic at different mod coil voltages.
Args:
ivm_params: Dict of measurement parameters as definted in config_measurements json file.
Returns:
Tuple[Dict]: data_dict, metadict
Dictionaries containing data arrays and instrument metadata.
"""
data_dict = {}
meta_dict = {}
mod_range = [self.Q_(value).to('V').magnitude for value in ivm_params['mod_range']]
bias_range = [self.Q_(value).to('V').magnitude for value in ivm_params['bias_range']]
mod_vec = np.linspace(mod_range[0], mod_range[1], ivm_params['ntek2'])
bias_vec = np.linspace(bias_range[0], bias_range[1], ivm_params['ntek1'])
mod_grid, bias_grid = np.meshgrid(mod_vec, bias_vec)
data_dict.update({
'mod_vec': {'array': mod_vec, 'unit': 'V'},
'bias_vec': {'array': bias_vec, 'unit': 'V'},
'mod_grid': {'array': mod_grid, 'unit': 'V'},
'bias_grid': {'array': bias_grid, 'unit': 'V'}
})
ivmX = np.full_like(mod_grid, np.nan, dtype=np.double)
ivmY = np.full_like(mod_grid, np.nan, dtype=np.double)
#: Set AFG output channels
self.afg.voltage_low1('0V')
self.afg.voltage_high1('{}V'.format(bias_range[0]))
self.afg.voltage_low2('0V')
self.afg.voltage_high2('{}V'.format(mod_range[0]))
self.afg.voltage_offset2('0V')
#: Set pulse parameters
for ch in [1, 2]:
p = ivm_params['afg']['ch{}'.format(ch)]
getattr(self.afg, 'pulse_period{}'.format(ch))('{}us'.format(self.Q_(p['period']).to('us').magnitude))
getattr(self.afg, 'pulse_width{}'.format(ch))('{}us'.format(self.Q_(p['width']).to('us').magnitude))
getattr(self.afg, 'pulse_trans_lead{}'.format(ch))('{}us'.format(self.Q_(p['lead']).to('us').magnitude))
getattr(self.afg, 'pulse_trans_trail{}'.format(ch))('{}us'.format(self.Q_(p['trail']).to('us').magnitude))
getattr(self.afg, 'pulse_delay{}'.format(ch))('{}us'.format(self.Q_(p['delay']).to('us').magnitude))
#: Get instrument metadata and prefactors
lockin_snap = self.MAG_lockin.snapshot(update=True)
lockin_meta = {}
for param in ['time_constant', 'sensitivity', 'phase', 'reserve', 'filter_slope']:
lockin_meta.update({param: lockin_snap['parameters'][param]})
meta_dict.update({'metadata':
{'lockin': lockin_meta,
'afg': self.afg.snapshot(update=True),
'ivm_params': ivm_params}
})
prefactor = 1 / (10 / lockin_snap['parameters']['sensitivity']['value'])
prefactor /= ivm_params['channels']['lockinX']['gain']
delay = ivm_params['delay_factor'] * lockin_snap['parameters']['time_constant']['value']
fig, ax = plt.subplots(1, figsize=(4,3))
ax.set_xlim(min(bias_range), max(bias_range))
ax.set_xlabel('Bias [V]')
ax.set_ylabel('Voltage [V]')
ax.set_title(ivm_params['channels']['lockinX']['label'])
log.info('Starting iv_mod_tek.')
try:
for j in range(len(mod_vec)):
dataX, dataY, dataX_avg, dataY_avg = (np.zeros(len(mod_vec)), ) * 4
self.afg.voltage_offset2('{}V'.format(mod_vec[j]))
for _ in range(ivm_params['navg']):
for i in range(len(bias_vec)):
self.afg.voltage_high1('{}V'.format(bias_vec[i]))
time.sleep(delay)
dataX[i] = self.MAG_lockin.X()
dataY[i] = self.MAG_lockin.Y()
dataX_avg += dataX
dataY_avg += dataY
dataX_avg /= ivm_params['navg']
dataY_avg /= ivm_params['navg']
ivmX[:,j] = prefactor * dataX_avg
ivmY[:,j] = prefactor * dataY_avg
clear_artists(ax)
ax.plot(bias_vec, prefactor * dataX_avg, 'bo-')
plt.tight_layout()
fig.canvas.draw()
fig.show()
except KeyboardInterrupt:
log.warning('Measurement aborted by user.')
figX = plt.figure(figsize=(4,3))
plt.pcolormesh(mod_grid, bias_grid, ivmX)
plt.xlabel('Modulation [V]')
plt.ylabel('Bias [V]')
plt.title(ivm_params['channels']['lockinX']['label'])
figX.tight_layout(rect=[0, 0.03, 1, 0.95])
cbarX = plt.colorbar()
cbarX.set_label('Voltage [V]')
figY = plt.figure(figsize=(4,3))
plt.pcolormesh(mod_grid, bias_grid, ivmY)
plt.xlabel('Modulation [V]')
plt.ylabel('Bias [V]')
plt.title(ivm_params['channels']['lockinY']['label'])
figY.tight_layout(rect=[0, 0.03, 1, 0.95])
cbarY = plt.colorbar()
cbarY.set_label('Voltage [V]')
data_dict.update({
'lockinX': {'array': ivmX, 'unit': 'V'},
'lockinY': {'array': ivmY, 'unit': 'V'}
})
if ivm_params['save']:
#: Get/create data location
loc_provider = qc.FormatLocation(fmt='{date}/#{counter}_{name}_{time}')
loc = loc_provider(DiskIO('.'), record={'name': ivm_params['fname']})
pathlib.Path(loc).mkdir(parents=True, exist_ok=True)
#: Save arrays to mat
io.savemat('{}/{}'.format(loc, ivm_params['fname']), data_dict)
#: Save metadata to json
with open(loc + '/metadata.json', 'w') as f:
try:
json.dump(meta_dict, f, sort_keys=True, indent=4, skipkeys=True)
except TypeError:
pass
#: Save figures to png
figX.suptitle(loc)
figX.savefig('{}/{}X.png'.format(loc, ivm_params['fname']))
figY.suptitle(loc)
figY.savefig('{}/{}Y.png'.format(loc, ivm_params['fname']))
log.info('Data saved to {}.'.format(loc))
return data_dict, meta_dict