def process_file(filename):
print filename
try:
rnc = kid_readout.measurement.io.readoutnc.ReadoutNetCDF(filename)
num_timestreams = len(rnc.timestreams)
num_sweeps = len(rnc.sweeps)
if num_timestreams == num_sweeps:
has_source_off_timestream = False
num_power_steps = num_timestreams - 1 # last time stream is modulated measurement
elif num_timestreams == num_sweeps + 1:
has_source_off_timestream = True
num_power_steps = num_timestreams - 2
else:
raise Exception(
"Found unexpected number of timestreams %d and number of sweeps %d for file %s"
% (num_timestreams, num_sweeps, filename)
)
resonator_ids = np.unique(rnc.sweeps[0].index)
noise_on_measurements = []
noise_modulated_measurements = []
noise_off_sweep_params = []
for resonator_id in resonator_ids:
power_steps_mmw_on = []
for idx in range(num_power_steps):
noise_on_measurement = SweepNoiseMeasurement(
sweep_filename=filename,
sweep_group_index=idx,
timestream_group_index=idx,
resonator_index=resonator_id,
)
power_steps_mmw_on.append(noise_on_measurement)
noise_on_measurement._close_files()
noise_modulated_measurement = SweepNoiseMeasurement(
sweep_filename=filename,
sweep_group_index=0,
timestream_group_index=num_power_steps,
resonator_index=resonator_id,
deglitch_threshold=None,
)
if has_source_off_timestream:
noise_off_measurement = SweepNoiseMeasurement(
sweep_filename=filename,
sweep_group_index=num_sweeps - 1,
timestream_group_index=num_timestreams - 1,
resonator_index=resonator_id,
)
# all the zbd_voltages are the same, so we can grab any of them
noise_modulated_measurement.zbd_voltage = rnc.timestreams[num_power_steps].zbd_voltage[0]
for noise_on_measurement in power_steps_mmw_on:
noise_on_measurement.zbd_voltage = rnc.timestreams[num_power_steps].zbd_voltage[0]
if noise_modulated_measurement.timestream_modulation_period_samples != 0:
noise_modulated_measurement.folded_projected_timeseries = noise_modulated_measurement.projected_timeseries.reshape(
(-1, noise_modulated_measurement.timestream_modulation_period_samples)
)
folded = noise_modulated_measurement.folded_projected_timeseries.mean(0)
high, low, rising_edge = kid_readout.analysis.fit_pulses.find_high_low(folded)
noise_modulated_measurement.folded_projected_timeseries = np.roll(
noise_modulated_measurement.folded_projected_timeseries, -rising_edge, axis=1
)
noise_modulated_measurement.folded_normalized_timeseries = np.roll(
noise_modulated_measurement.normalized_timeseries.reshape(
(-1, noise_modulated_measurement.timestream_modulation_period_samples)
),
-rising_edge,
axis=1,
)
else:
noise_modulated_measurement.folded_projected_timeseries = None
if not has_source_off_timestream:
fr, s21, err = rnc.sweeps[-1].select_by_index(resonator_id)
noise_off_sweep = kid_readout.analysis.resonator.fit_best_resonator(fr, s21, errors=err)
noise_off_sweep_params.append(noise_off_sweep.result.params)
else:
noise_off_measurement.zbd_voltage = rnc.timestreams[num_power_steps].zbd_voltage[0]
noise_off_sweep_params.append(noise_off_measurement)
noise_on_measurements.extend(power_steps_mmw_on)
noise_modulated_measurements.append(noise_modulated_measurement)
noise_modulated_measurement._close_files()
rnc.close()
data = dict(
noise_on_measurements=noise_on_measurements,
noise_modulated_measurements=noise_modulated_measurements,
noise_off_sweeps=noise_off_sweep_params,
)
blah, fbase = os.path.split(filename)
fbase, ext = os.path.splitext(fbase)
pklname = os.path.join("/home/data/pkl", fbase + ".pkl")
save_noise_pkl(pklname, data)
return data
except KeyboardInterrupt:
return None
def extract_and_pickle(nc_filename, deglitch_threshold=5):
basedir = os.path.split(nc_filename)[0] # should make this more robust, currently assumes all nc files are in top
# level of /data/<machine>/*.nc
machine = os.path.split(basedir)[1]
cryostat = cryostats[machine]
print cryostat
try:
print("Processing {}".format(nc_filename))
snms = []
rnc = ReadoutNetCDF(nc_filename)
for timestream_index,timestream in enumerate(rnc.timestreams):
if timestream.epoch.shape[0] == 0:
print "no timestreams in", nc_filename
return
start_epoch = timestream.epoch.min()
sweep_index = find_closest_sweep(timestream,rnc.sweeps)
sweep = rnc.sweeps[sweep_index]
sweep_epoch = sweep.end_epoch
modulation_state,modulation_frequency = rnc.get_modulation_state_at(start_epoch)
try:
manual_modulation_frequency = timestream.mmw_source_modulation_frequency[0]
except AttributeError:
manual_modulation_frequency = 0
if modulation_state == 2 or manual_modulation_frequency > 0:
this_deglitch_threshold = None
else:
this_deglitch_threshold = deglitch_threshold
resonator_indexes = np.array(list(set(sweep.index)))
resonator_indexes.sort()
print "%s: timestream[%d] at %s, associated sweep[%d] at %s, %d resonators" % (nc_filename,timestream_index,
time.ctime(start_epoch),
sweep_index,
time.ctime(sweep_epoch),
len(resonator_indexes))
if this_deglitch_threshold is None:
print "Found modulation, not deglitching"
for resonator_index in resonator_indexes:
tic = time.time()
snm = SweepNoiseMeasurement(rnc, sweep_group_index=sweep_index,
timestream_group_index=timestream_index,
resonator_index=resonator_index, cryostat=cryostat,
deglitch_threshold=this_deglitch_threshold,
)
print "created snm for",rnc.filename,timestream_index,resonator_index,deglitch_threshold,"in",\
(time.time()-tic)
try:
snm.zbd_voltage = timestream.zbd_voltage[0]
except AttributeError:
pass
tic = time.time()
if snm.timestream_modulation_period_samples != 0:
snm.folded_projected_timeseries = snm.projected_timeseries.reshape((-1, snm.timestream_modulation_period_samples))
folded = snm.folded_projected_timeseries.mean(0)
high, low, rising_edge = kid_readout.analysis.fit_pulses.find_high_low(folded)
snm.folded_projected_timeseries = np.roll(snm.folded_projected_timeseries,-rising_edge,
axis=1).mean(0)
snm.folded_normalized_timeseries = np.roll(
snm.normalized_timeseries.reshape((-1,snm.timestream_modulation_period_samples)),
-rising_edge, axis=1).mean(0)
print "folded time series in ",(time.time()-tic)
pkld = cPickle.dumps(snm,cPickle.HIGHEST_PROTOCOL)
del snm
snm = cPickle.loads(pkld)
snms.append(snm)
rnc.close()
pkl_filename = os.path.join(basedir,'pkl', os.path.splitext(os.path.split(nc_filename)[1])[0] + '.pkl')
save_noise_pkl(pkl_filename, snms)
print("Saved {}".format(pkl_filename))
except KeyboardInterrupt:
pass
except Exception as e:
print "failed on",nc_filename,e
def process_file(filename):
print filename
try:
rnc = kid_readout.measurement.io.readoutnc.ReadoutNetCDF(filename)
num_timestreams = len(rnc.timestreams)
num_sweeps = len(rnc.sweeps)
if num_timestreams == num_sweeps:
has_source_off_timestream = False
num_power_steps = num_timestreams - 1 # last time stream is modulated measurement
elif num_timestreams == num_sweeps + 1:
has_source_off_timestream = True
num_power_steps = num_timestreams - 2
else:
raise Exception(
"Found unexpected number of timestreams %d and number of sweeps %d for file %s"
% (num_timestreams, num_sweeps, filename))
resonator_ids = np.unique(rnc.sweeps[0].index)
noise_on_measurements = []
noise_modulated_measurements = []
noise_off_sweep_params = []
for resonator_id in resonator_ids:
power_steps_mmw_on = []
for idx in range(num_power_steps):
noise_on_measurement = SweepNoiseMeasurement(
sweep_filename=filename,
sweep_group_index=idx,
timestream_group_index=idx,
resonator_index=resonator_id,
)
power_steps_mmw_on.append(noise_on_measurement)
noise_on_measurement._close_files()
noise_modulated_measurement = SweepNoiseMeasurement(
sweep_filename=filename,
sweep_group_index=0,
timestream_group_index=num_power_steps,
resonator_index=resonator_id,
deglitch_threshold=None,
)
if has_source_off_timestream:
noise_off_measurement = SweepNoiseMeasurement(
sweep_filename=filename,
sweep_group_index=num_sweeps - 1,
timestream_group_index=num_timestreams - 1,
resonator_index=resonator_id)
#all the zbd_voltages are the same, so we can grab any of them
noise_modulated_measurement.zbd_voltage = rnc.timestreams[
num_power_steps].zbd_voltage[0]
for noise_on_measurement in power_steps_mmw_on:
noise_on_measurement.zbd_voltage = rnc.timestreams[
num_power_steps].zbd_voltage[0]
if noise_modulated_measurement.timestream_modulation_period_samples != 0:
noise_modulated_measurement.folded_projected_timeseries = noise_modulated_measurement.projected_timeseries.reshape(
(-1, noise_modulated_measurement.
timestream_modulation_period_samples))
folded = noise_modulated_measurement.folded_projected_timeseries.mean(
0)
high, low, rising_edge = kid_readout.analysis.fit_pulses.find_high_low(
folded)
noise_modulated_measurement.folded_projected_timeseries = np.roll(
noise_modulated_measurement.folded_projected_timeseries,
-rising_edge,
axis=1)
noise_modulated_measurement.folded_normalized_timeseries = np.roll(
noise_modulated_measurement.normalized_timeseries.reshape(
(-1, noise_modulated_measurement.
timestream_modulation_period_samples)),
-rising_edge,
axis=1)
else:
noise_modulated_measurement.folded_projected_timeseries = None
if not has_source_off_timestream:
fr, s21, err = rnc.sweeps[-1].select_by_index(resonator_id)
noise_off_sweep = kid_readout.analysis.resonator.fit_best_resonator(
fr, s21, errors=err)
noise_off_sweep_params.append(noise_off_sweep.result.params)
else:
noise_off_measurement.zbd_voltage = rnc.timestreams[
num_power_steps].zbd_voltage[0]
noise_off_sweep_params.append(noise_off_measurement)
noise_on_measurements.extend(power_steps_mmw_on)
noise_modulated_measurements.append(noise_modulated_measurement)
noise_modulated_measurement._close_files()
rnc.close()
data = dict(noise_on_measurements=noise_on_measurements,
noise_modulated_measurements=noise_modulated_measurements,
noise_off_sweeps=noise_off_sweep_params)
blah, fbase = os.path.split(filename)
fbase, ext = os.path.splitext(fbase)
pklname = os.path.join('/home/data/pkl', fbase + '.pkl')
save_noise_pkl(pklname, data)
return data
except KeyboardInterrupt:
return None
def extract_and_pickle(nc_filename):
"""
The format is that the file contains equal numbers of sweeps and timestreams. The first sweep is used to locate
the resonances and is taken with the source off at the lowest power level, i.e. the maximum attenuation. The
first timestream is taken under the same conditions except that the source is modulated. Subsequent sweeps and
timestreams are paired.
:param nc_filename: the file name of the netCDF4 file with the above format.
:return: a dictionary
"""
try:
all_noise_on = []
all_noise_off = []
all_noise_modulated = []
all_coarse_sweep_params = []
coarse_sweep_index = 0
modulated_timestream_index = 0
print("Processing {}".format(nc_filename))
rnc = ReadoutNetCDF(nc_filename)
resonator_indices = sorted(set(rnc.sweeps[0].index))
n_attenuations = len(rnc.sweeps) - 1
for resonator_index in resonator_indices:
noise_on = []
for on_index in range(1, n_attenuations, 2):
noise_on.append(SweepNoiseMeasurement(nc_filename, resonator_index=resonator_index,
sweep_group_index=on_index, timestream_group_index=on_index))
all_noise_on.extend(noise_on)
noise_off = []
for off_index in range(2, n_attenuations + 1, 2):
noise_off.append(SweepNoiseMeasurement(nc_filename, resonator_index=resonator_index,
sweep_group_index=off_index, timestream_group_index=off_index))
all_noise_off.extend(noise_off)
# Create the modulated measurement from the modulated timestream and the noise off sweep at the same power.
# Skip deglitching.
attenuations = [snm.atten for snm in noise_off]
off_max_attenuation_index = 1 + 2 * attenuations.index(max(attenuations)) + 1
noise_modulated = SweepNoiseMeasurement(nc_filename, resonator_index=resonator_index,
sweep_group_index=off_max_attenuation_index,
timestream_group_index=modulated_timestream_index,
deglitch_threshold=None)
noise_modulated.folded_projected_timeseries = noise_modulated.projected_timeseries.reshape(
(-1, noise_modulated.timestream_modulation_period_samples))
folded = noise_modulated.folded_projected_timeseries.mean(0)
high, low, rising_edge = find_high_low(folded)
noise_modulated.folded_projected_timeseries = np.roll(noise_modulated.folded_projected_timeseries,
-rising_edge, axis=1)
noise_modulated.folded_normalized_timeseries = np.roll(
noise_modulated.normalized_timeseries.reshape((-1,
noise_modulated.timestream_modulation_period_samples)),
-rising_edge, axis=1)
all_noise_modulated.append(noise_modulated)
# Add the ZBD voltage from the modulated timestream to the modulated and static on measurements:
zbd_voltage = rnc.timestreams[modulated_timestream_index].zbd_voltage[0]
noise_modulated.zbd_voltage = zbd_voltage
for snm in noise_on:
snm.zbd_voltage = zbd_voltage
# Save only the Parameters object from a fit to the coarse sweep.
freq, s21, err = rnc.sweeps[coarse_sweep_index].select_by_index(resonator_index)
coarse_resonator = fit_best_resonator(freq, s21, errors=err)
all_coarse_sweep_params.append(coarse_resonator.result.params)
rnc.close()
data = {'noise_on_measurements': all_noise_on,
'noise_off_measurements': all_noise_off,
'noise_modulated_measurements': all_noise_modulated,
'coarse_sweep_params': all_coarse_sweep_params}
# We decided to keep the .pkl files in /home/data regardless of origin.
pkl_filename = os.path.join('/home/data/pkl', os.path.splitext(os.path.split(nc_filename)[1])[0] + '.pkl')
save_noise_pkl(pkl_filename, data)
print("Saved {}".format(pkl_filename))
except KeyboardInterrupt:
print("Aborting {}".format(nc_filename))
def extract_and_pickle(nc_filename, deglitch_threshold=5):
basedir = os.path.split(
nc_filename
)[0] # should make this more robust, currently assumes all nc files are in top
# level of /data/<machine>/*.nc
machine = os.path.split(basedir)[1]
cryostat = cryostats[machine]
print cryostat
try:
print("Processing {}".format(nc_filename))
snms = []
rnc = ReadoutNetCDF(nc_filename)
for timestream_index, timestream in enumerate(rnc.timestreams):
if timestream.epoch.shape[0] == 0:
print "no timestreams in", nc_filename
return
start_epoch = timestream.epoch.min()
sweep_index = find_closest_sweep(timestream, rnc.sweeps)
sweep = rnc.sweeps[sweep_index]
sweep_epoch = sweep.end_epoch
modulation_state, modulation_frequency = rnc.get_modulation_state_at(
start_epoch)
try:
manual_modulation_frequency = timestream.mmw_source_modulation_frequency[
0]
except AttributeError:
manual_modulation_frequency = 0
if modulation_state == 2 or manual_modulation_frequency > 0:
this_deglitch_threshold = None
else:
this_deglitch_threshold = deglitch_threshold
resonator_indexes = np.array(list(set(sweep.index)))
resonator_indexes.sort()
print "%s: timestream[%d] at %s, associated sweep[%d] at %s, %d resonators" % (
nc_filename, timestream_index, time.ctime(start_epoch),
sweep_index, time.ctime(sweep_epoch), len(resonator_indexes))
if this_deglitch_threshold is None:
print "Found modulation, not deglitching"
for resonator_index in resonator_indexes:
tic = time.time()
snm = SweepNoiseMeasurement(
rnc,
sweep_group_index=sweep_index,
timestream_group_index=timestream_index,
resonator_index=resonator_index,
cryostat=cryostat,
deglitch_threshold=this_deglitch_threshold,
)
print "created snm for",rnc.filename,timestream_index,resonator_index,deglitch_threshold,"in",\
(time.time()-tic)
try:
snm.zbd_voltage = timestream.zbd_voltage[0]
except AttributeError:
pass
tic = time.time()
if snm.timestream_modulation_period_samples != 0:
snm.folded_projected_timeseries = snm.projected_timeseries.reshape(
(-1, snm.timestream_modulation_period_samples))
folded = snm.folded_projected_timeseries.mean(0)
high, low, rising_edge = kid_readout.analysis.fit_pulses.find_high_low(
folded)
snm.folded_projected_timeseries = np.roll(
snm.folded_projected_timeseries, -rising_edge,
axis=1).mean(0)
snm.folded_normalized_timeseries = np.roll(
snm.normalized_timeseries.reshape(
(-1, snm.timestream_modulation_period_samples)),
-rising_edge,
axis=1).mean(0)
print "folded time series in ", (time.time() - tic)
pkld = cPickle.dumps(snm, cPickle.HIGHEST_PROTOCOL)
del snm
snm = cPickle.loads(pkld)
snms.append(snm)
rnc.close()
pkl_filename = os.path.join(
basedir, 'pkl',
os.path.splitext(os.path.split(nc_filename)[1])[0] + '.pkl')
save_noise_pkl(pkl_filename, snms)
print("Saved {}".format(pkl_filename))
except KeyboardInterrupt:
pass
except Exception as e:
print "failed on", nc_filename, e
def extract_and_pickle(nc_filename):
"""
The format is that the file contains equal numbers of sweeps and timestreams. The first sweep is used to locate
the resonances and is taken with the source off at the lowest power level, i.e. the maximum attenuation. The
first timestream is taken under the same conditions except that the source is modulated. Subsequent sweeps and
timestreams are paired.
:param nc_filename: the file name of the netCDF4 file with the above format.
:return: a dictionary
"""
try:
all_noise_on = []
all_noise_off = []
all_noise_modulated = []
all_coarse_sweep_params = []
coarse_sweep_index = 0
modulated_timestream_index = 0
print("Processing {}".format(nc_filename))
rnc = ReadoutNetCDF(nc_filename)
resonator_indices = sorted(set(rnc.sweeps[0].index))
n_attenuations = len(rnc.sweeps) - 1
for resonator_index in resonator_indices:
noise_on = []
for on_index in range(1, n_attenuations, 2):
noise_on.append(
SweepNoiseMeasurement(nc_filename,
resonator_index=resonator_index,
sweep_group_index=on_index,
timestream_group_index=on_index))
all_noise_on.extend(noise_on)
noise_off = []
for off_index in range(2, n_attenuations + 1, 2):
noise_off.append(
SweepNoiseMeasurement(nc_filename,
resonator_index=resonator_index,
sweep_group_index=off_index,
timestream_group_index=off_index))
all_noise_off.extend(noise_off)
# Create the modulated measurement from the modulated timestream and the noise off sweep at the same power.
# Skip deglitching.
attenuations = [snm.atten for snm in noise_off]
off_max_attenuation_index = 1 + 2 * attenuations.index(
max(attenuations)) + 1
noise_modulated = SweepNoiseMeasurement(
nc_filename,
resonator_index=resonator_index,
sweep_group_index=off_max_attenuation_index,
timestream_group_index=modulated_timestream_index,
deglitch_threshold=None)
noise_modulated.folded_projected_timeseries = noise_modulated.projected_timeseries.reshape(
(-1, noise_modulated.timestream_modulation_period_samples))
folded = noise_modulated.folded_projected_timeseries.mean(0)
high, low, rising_edge = find_high_low(folded)
noise_modulated.folded_projected_timeseries = np.roll(
noise_modulated.folded_projected_timeseries,
-rising_edge,
axis=1)
noise_modulated.folded_normalized_timeseries = np.roll(
noise_modulated.normalized_timeseries.reshape(
(-1,
noise_modulated.timestream_modulation_period_samples)),
-rising_edge,
axis=1)
all_noise_modulated.append(noise_modulated)
# Add the ZBD voltage from the modulated timestream to the modulated and static on measurements:
zbd_voltage = rnc.timestreams[
modulated_timestream_index].zbd_voltage[0]
noise_modulated.zbd_voltage = zbd_voltage
for snm in noise_on:
snm.zbd_voltage = zbd_voltage
# Save only the Parameters object from a fit to the coarse sweep.
freq, s21, err = rnc.sweeps[coarse_sweep_index].select_by_index(
resonator_index)
coarse_resonator = fit_best_resonator(freq, s21, errors=err)
all_coarse_sweep_params.append(coarse_resonator.result.params)
rnc.close()
data = {
'noise_on_measurements': all_noise_on,
'noise_off_measurements': all_noise_off,
'noise_modulated_measurements': all_noise_modulated,
'coarse_sweep_params': all_coarse_sweep_params
}
# We decided to keep the .pkl files in /home/data regardless of origin.
pkl_filename = os.path.join(
'/home/data/pkl',
os.path.splitext(os.path.split(nc_filename)[1])[0] + '.pkl')
save_noise_pkl(pkl_filename, data)
print("Saved {}".format(pkl_filename))
except KeyboardInterrupt:
print("Aborting {}".format(nc_filename))