## other settings ##
plot_fit = True
show_guess = True
fit_results = []
cum_pts = 0
cum_sweep_pts = np.empty(0)
cum_p0 = np.empty(0)
cum_u_p0 = np.empty(0)
cum_normalized_ssro = np.empty(0)
#for k in range(0,len(measurement_name)):
for kk in range(len(timestamp)):
folder = toolbox.data_from_time(timestamp[kk])
a = mbi.MBIAnalysis(folder)
a.get_sweep_pts()
a.get_readout_results(name='adwindata')
a.get_electron_ROC()
cum_pts += a.pts
if kk == 0:
cum_sweep_pts = a.sweep_pts
cum_p0 = a.p0
cum_u_p0 = a.u_p0
else:
cum_sweep_pts = np.concatenate((cum_sweep_pts, a.sweep_pts))
cum_p0 = np.concatenate((cum_p0, a.p0))
cum_u_p0 = np.concatenate((cum_u_p0, a.u_p0))
import numpy as np
from matplotlib import pyplot as plt
from analysis.lib.m2.ssro import mbi
from measurement.lib.tools import toolbox
# settings
msmnt_folders = [
'20130207175652',
'20130207180726',
]
fig = plt.figure(figsize=(12,4))
ax = fig.add_subplot(111)
for f in msmnt_folders:
a = mbi.ConditionalPrecessionAnalysis(toolbox.data_from_time(f))
a.get_sweep_pts()
a.get_readout_results()
of = min(a.normalized_ssro[:,0])
ax.errorbar(a.sweep_pts, a.normalized_ssro[:,0], fmt='o-',
yerr=a.u_normalized_ssro[:,0], label=a.name + ' (%s)' % f)
ax.legend()
ax.set_ylim(0.5,0.8)
ax.set_xlim(0,500)
ax.set_title('conditional precession')
ax.set_xlabel('delay ($\mu$s)')
ax.set_ylabel(r'readout results $|0\rangle$')
import h5py
import logging
from matplotlib import pyplot as plt
from analysis.lib.fitting import fit, common
from measurement.lib.tools import toolbox
from analysis.lib.tools import plot
from analysis.lib.m2.ssro import ssro
folder = None
timestamp = None
if folder == None:
if timestamp != None:
folder = toolbox.data_from_time(timestamp)
else:
folder = toolbox.latest_data('AdwinSSRO')
a = ssro.SSROAnalysis(folder)
pwrs = []
taus = []
u_taus = []
for g in a.g.items():
gn = g[0]
if 'instrument_settings' in gn:
continue
a.get_run(gn)
E_y = np.array(
[51.08, 51.53, 51.96, 52.33, 52.61, 53.03, 53.36, 53.58, 53.5, 54.84])
E_x = np.array(
[53.75, 53.7, 53.73, 53.79, 54.15, 54.57, 55.04, 56.01, 57.04, 59.09])
ssro_data_E_y = np.array([
'20140513124215', '20140513125308', '20140513125914', '20140513130340',
'20140513130712', '20140513131304', '20140513131824', '20140513132605',
'20140513134100', '20140513134217'
])
cpsh = []
fid = []
fid_err = []
for i, d in enumerate(ssro_data_E_y):
folder = toolbox.data_from_time(d)
a = ssro.SSROAnalysis(folder)
a.get_run('ms0')
cpsh_run = np.sum(a.ro_counts[:, 0:-1], axis=1)
cpsh.append(sum(cpsh_run) / float(reps))
#f = h5py.File(folder, 'r')
#cts_m0 =
#times = f['fidelity/time'].value
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(gate_V, cpsh, 'bo', ms=8)
ax.set_xlabel('strain splitting [GHz]')
ax.set_ylabel('counts per shot $E_y$')
