def perform_phenom_fit(self,key,plotting = False, ax = None): """performs a fit of time trace data to a gaussian function""" if len(key) == 1: data = self.pickle_dict[key[0]] else: data = self.pickle_dict[key[0]][key[1]] evotime = data[timetrace_keylist[0]] fid = data[timetrace_keylist[1]] fid_u = data[timetrace_keylist[2]] ii = self.find_index(key[0]) p0, fitfunc, fitfunc_str = common.fit_gauss(self.fit_offset, 0.43, 0., 8.25) fixed =[1] if self.use_fixed: if int(key[0]) % 2 ==0: ### key[0] is always the number of measurements. fixed = [0,1] else: fixed = [1] fit_result = fit.fit1d(evotime,fid, None, p0=p0, fitfunc=fitfunc, do_print=False, ret=True,fixed=fixed) if plotting: plot.plot_fit1d(fit_result, np.linspace(0,200,201), ax=ax, plot_data=False,color = color_list[ii],add_txt = False, lw = fit_lw) p1 = abs(round(fit_result['params'][3-len(fixed)],3)) p1_u = round(fit_result['error'][3-len(fixed)],3) self.results.append(key[0] + ' : T = ' + str(round(p1,2)) + ' +- ' + str(round(p1_u,2))) # print 'uncertainty ', p1_u; if len(key) == 1: self.pickle_dict[key[0]]['fit'] = fit_result else: self.pickle_dict[key[0]][key[1]]['fit'] = fit_result
def average_repump_time(contains='', do_fit=False, **kw):
'''
gets data from a folder whose name contains the contains variable.
Does or does not fit the data with a gaussian function
'''
### kw for fitting
fit_offset = kw.pop('fit_offset', 0)
fit_amplitude = kw.pop('fit_amplitude', 1)
fit_x0 = kw.pop('fit_x0', None)
fit_sigma = kw.pop('fit_sigma', 0.2)
fixed = kw.pop('fixed', [])
show_guess = kw.pop('show_guess', False)
### folder choice
if contains == '':
contains = 'Sweep_Repump_time'
elif len(contains) == 2:
contains = 'Sweep_Repump_time' + contains
elif len(contains) == 1:
contains = 'Sweep_Repump_time_' + contains
# older_than = kw.get('older_than',None) automatically handled by kws
### acquire data
f = toolbox.latest_data(contains, **kw)
a = mbi.MBIAnalysis(f)
if '_Z' in f:
x, y, y_u = get_pos_neg_data(a, adwindata_str='Z_', **kw)
ylabel = 'Z'
else:
x, y1, y1_u = get_pos_neg_data(a, adwindata_str='X_', **kw)
x2, y2, y2_u = get_pos_neg_data(a, adwindata_str='Y_', **kw)
y, y_u = quadratic_addition(y1, y2, y1_u, y2_u)
# y=y1
# y_u = y1_u
ylabel = 'Bloch vector length'
### create a plot
xlabel = a.g.attrs['sweep_name']
x = a.g.attrs['sweep_pts'] # could potentially be commented out?
title = kw.pop("title", "avg repump time")
fig, ax = create_plot(f, xlabel=xlabel, ylabel=ylabel, title=title)
if fit_x0 is None:
max_idx = np.argmax(y)
fit_x0 = x[max_idx]
## plot data
plot_data(x, y, y_u=y_u)
### fitting if you feel like it
if do_fit:
p0, fitfunc, fitfunc_str = common.fit_gauss(fit_offset, fit_amplitude,
fit_x0, fit_sigma)
if show_guess:
# print decay
ax.plot(np.linspace(x[0], x[-1], 201),
fitfunc(np.linspace(x[0], x[-1], 201)),
':',
lw=2)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=True,
fixed=fixed,
ret=True)
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], 100),
ax=ax,
plot_data=False)
try:
fit_result['carbon_id'] = a.g.attrs['carbons'][0]
except:
pass
## save and close plot. We are done.
save_and_close_plot(f)
if kw.get('ret_data_fit', False):
return x, y, y_u, fit_result
if kw.get('ret', False):
return fit_result
def perform_semi_pheno_model_fit(self,key, plotting = False, ax = None, xlim = None): if len(key) == 1: data = self.pickle_dict[key[0]] elif self.use_averaged: data = self.pickle_dict[key[0]]['state_dict_averaged'][key[1]] else: data = self.pickle_dict[key[0]][key[1]] evotime = data[timetrace_keylist[0]] fid = data[timetrace_keylist[1]] fid_u = data[timetrace_keylist[2]] if xlim != None: last_plot_point = xlim[-1] else: last_plot_point = evotime[-1] ### find the position in the data dictionary i = self.find_index(key[0]) # print 'this is the ', key if key[0] == '0' or key[0] == 'single_qubit' or key[0] == '00': t = 8.25 p0, fitfunc, fitfunc_str = common.fit_gauss(self.fit_offset, 0.43, 0., t) fixed = [0,1] if self.process_fidelities: fixed = [1] if self.contrast: p0, fitfunc, fitfunc_str = common.fit_gauss(0, 0.43, 0., t) fit_result = fit.fit1d(evotime,fid, None, p0=p0, fitfunc=fitfunc, do_print=True, ret=True,fixed=fixed) if plotting: plot.plot_fit1d(fit_result, np.linspace(0.0,200,1001), ax=ax,color = color_list[i], plot_data=False,add_txt = False, lw = fit_lw) if key[0] == 'single_qubit': label_begin = '1Q' else: label_begin = key[0] self.results.append(label_begin + ' : T = ' + str(round(abs(fit_result['params'][-1]),2)) + ' +- ' + str(round(abs(fit_result['error'][-1]),2))) if key[0] == '0' or key[0] == '00': self.t = fit_result['params_dict']['sigma'] ### use t for the following fits with multiple measurements. self.A = fit_result['params_dict']['A'] if self.process_fidelities: self.fit_offset = fit_result['params_dict']['a'] else: if '0' not in self.pickle_dict.keys(): ### in exceptional cases there might be no 0 measurements reference self.t = 8.25 if not self.process_fidelities and not self.contrast: ### logical decision for state fidelities. fit_result, result_string = ZAnalysis.fit_pheno_State_decay(key[0],ax,self.A*2,evotime,fid, t = self.t) elif self.process_fidelities: fit_result, result_string = ZAnalysis.fit_pheno_process_decay(key[0],ax,self.A,evotime,fid,'2',offset0 = self.fit_offset, t = self.t) elif self.contrast: fit_result, result_string = ZAnalysis.fit_pheno_State_decay(key[0],ax,self.A,evotime,fid,t = self.t,contrast = self.contrast) if plotting: plot.plot_fit1d(fit_result, np.linspace(0.0,200,1001), ax=ax, plot_data=False,color = color_list[i],add_txt = False, lw = fit_lw) # print fit_result['params_dict'] result_string = str(round(abs(fit_result['params_dict']['t']),2)) + ' +- ' + str(round(fit_result['error_dict']['t'],2)) self.results.append(key[0]+' : T = ' + result_string) # print 'uncertainty ', p1_u; if len(key) == 1: self.pickle_dict[key[0]]['fit'] = fit_result elif self.use_averaged: self.pickle_dict[key[0]]['state_dict_averaged'][key[1]]['fit'] = fit_result else: self.pickle_dict[key[0]][key[1]]['fit'] = fit_result
def fit_gaussian(self, ax=None, ret_ax=False, **kw):
"""
function that fits a gaussian, and returns the FWHM (not sigma).
x and y can be given as input, ad well as all the parameter guesses.
"""
y = kw.pop('y', self.y_data)
x = kw.pop('x', self.frq_data)
g_sigma = kw.pop('g_sigma', 10)
plot_title = kw.pop('plot_title', '')
plot_fit = kw.pop('plot_fit', False)
save_plot = kw.pop('save_plot', True)
fixed = kw.pop('fixed', [])
label = kw.pop('label', '')
g_a = np.nanmean(y)
g_A = max(y) - g_a
g_x0 = x[np.argmax(y)]
p0, fitfunc, fitfunc_str = common.fit_gauss(g_a, g_A, g_x0, g_sigma)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=False,
ret=True,
fixed=fixed)
if 'sigma' not in fit_result['params_dict']:
print 'WARNING: COULD NOT FIT sigma'
return 0, 10, 0
sigma = np.abs(fit_result['params_dict']['sigma'])
u_sigma = np.abs(fit_result['error_dict']['sigma'])
x0 = fit_result['params_dict']['x0']
FWHM = 2 * math.sqrt(2 * math.log(2)) * sigma
u_FWHM = 2 * math.sqrt(2 * math.log(2)) * u_sigma
if plot_fit:
if ax == None:
fig, ax = plt.subplots(figsize=(10, 4))
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1],
len(x) * 10),
ax=ax,
label=label,
show_guess=False,
plot_data=True,
print_info=False)
ax.set_title('FWHM = %.1f +- %.1f GHz %s' %
(FWHM, u_FWHM, plot_title))
if save_plot:
fig = ax.get_figure()
fig.savefig(self.folder + '/gaussian_fit_%s.png' %
(plot_title))
if ret_ax:
return FWHM, u_FWHM, ax
plt.show(ax.get_figure())
plt.close(ax.get_figure())
return FWHM, u_FWHM, x0
# V = V[0:len(V):10]
# y = y[0:len(y):10]
'''
Start fitting
'''
offset = 0.00
amplitude = max_value
x0 = V[max_index]
exponent = 1
decay_constant = 3
sigma = 0.0004
gamma = 0.5
if fitting_method == 'gauss':
p0, fitfunc, fitfunc_str = common.fit_gauss(
offset, amplitude, x0, sigma)
elif fitting_method == 'exp':
p0, fitfunc, fitfunc_str = common.fit_general_exponential(
offset, amplitude, x0, decay_constant, exponent)
elif fitting_method == 'lorentz':
p0, fitfunc, fitfunc_str = common.fit_lorentz(
offset, amplitude, x0, gamma)
fit_result = fit.fit1d(V,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=True,
ret=True,
fixed=fixed)
def return_phase_stab(contains, start_rep_no=3,mode='only_meas',**kw):
base_folder_lt4 = analysis_params.data_settings['base_folder_lt4']
folder = os.path.join(base_folder_lt4,contains)
filename_str = kw.pop('filename_str','XsweepY')
measfiles=tb.latest_data(contains = filename_str,folder =folder,return_all=True)
x0s = []
sigmas = []
for measfile in measfiles:
a = ppq.purifyPQAnalysis(measfile, hdf5_mode='r')
# general params
delay_stable = np.float(a.g.attrs['count_int_time_stab'])
delay_meas = np.float(a.g.attrs['count_int_time_meas'])
if mode == 'only_meas':
sample_counts_1 = a.g['adwindata']['sampling_counts_1'].value
sample_counts_2 = a.g['adwindata']['sampling_counts_2'].value
sample_counts_1 = sample_counts_1[start_rep_no:]
sample_counts_2 = sample_counts_2[start_rep_no:]
delay = delay_meas
v_1 = sample_counts_1
v_2 = sample_counts_2
t = np.arange(0, (len(v_1)*delay/1000), (float(delay)/1000))
elif mode == 'only_stab':
pid_counts_1 = a.g['adwindata']['pid_counts_1'].value
pid_counts_2 = a.g['adwindata']['pid_counts_2'].value
pid_counts_1 = pid_counts_1[start_rep_no:]
pid_counts_2 = pid_counts_2[start_rep_no:]
delay = delay_stable
v_1 = pid_counts_1
v_2 = pid_counts_2
t = np.arange(0, (len(v_1)*delay/1000), (float(delay)/1000))
g_0 = a.g.attrs['Phase_Msmt_g_0']
visibility = a.g.attrs['Phase_Msmt_Vis']
cosvals = [2*(float(n0)/(float(n0)+float(n1)*g_0)-0.5)*visibility for n0,n1 in zip(v_1,v_2)]
cosvals = [cosval if np.abs(cosval) < 1 else (1.0 * np.sign(cosval)) for cosval in cosvals]
angle = 180*np.arccos(cosvals)/np.pi
hist, bins = np.histogram(angle,bins= 100,normed = True)
width = np.diff(bins)
center = (bins[:-1] + bins[1:]) / 2
g_a = 0.0
g_x0 = 90
g_sigma = 45
g_A = 1/(np.sqrt(2 * np.pi) * g_sigma)
p0, fitfunc,fitfunc_str = common.fit_gauss(g_a, g_A, g_x0, g_sigma)
fit_result = fit.fit1d(center,hist, None, p0=p0, fitfunc=fitfunc,
ret=True,fixed=[])
x0s.append(fit_result['params_dict']['x0'])
sigmas.append(fit_result['params_dict']['sigma'])
return x0s, sigmas
def analyze_phase(contains, mode, plot_zoomed = [], start_rep_no = 1,**kw):
# Import
lt3_analysis = kw.pop('lt3_analysis', False)
if not(lt3_analysis):
measfile= tb.latest_data(contains)
else:
base_folder_lt4 = analysis_params.data_settings['base_folder_lt4']
folder = os.path.join(base_folder_lt4,contains)
filename_str = kw.pop('filename_str','XsweepY')
measfile=tb.latest_data(contains = filename_str,folder =folder,return_all=False)
a = ppq.purifyPQAnalysis(measfile, hdf5_mode='r')
# general params
delay_stable = np.float(a.g.attrs['count_int_time_stab'])
delay_meas = np.float(a.g.attrs['count_int_time_meas'])
pid_cycles = a.g.attrs['pid_points_to_store']
if a.g.attrs['do_post_ent_phase_msmt'] and mode == 'do_only_meas':
sample_cycles = 1
max_repetitions = a.g['adwindata']['completed_reps'].value
else:
sample_cycles = a.g.attrs['sample_points']
max_repetitions = a.g['adwindata']['store_index_stab'].value/a.g.attrs['pid_points_to_store']
g_0 = a.g.attrs['Phase_Msmt_g_0']
visibility = a.g.attrs['Phase_Msmt_Vis']
if mode == 'only_meas':
sample_counts_1 = a.g['adwindata']['sampling_counts_1'].value
sample_counts_2 = a.g['adwindata']['sampling_counts_2'].value
sample_counts_1 = sample_counts_1[(start_rep_no - 1)*sample_cycles:]
sample_counts_2 = sample_counts_2[(start_rep_no - 1)*sample_cycles:]
delay = delay_meas
total_cycles = sample_cycles
v_1 = sample_counts_1
v_2 = sample_counts_2
t = np.arange(0, (len(v_1)*delay/1000), (float(delay)/1000))
elif mode == 'only_stab':
pid_counts_1 = a.g['adwindata']['pid_counts_1'].value
pid_counts_2 = a.g['adwindata']['pid_counts_2'].value
pid_counts_1 = pid_counts_1[(start_rep_no - 1)*pid_cycles:]
pid_counts_2 = pid_counts_2[(start_rep_no - 1)*pid_cycles:]
delay = delay_stable
total_cycles = pid_cycles
v_1 = pid_counts_1
v_2 = pid_counts_2
t = np.arange(0, (len(v_1)*delay/1000), (float(delay)/1000))
else:
pid_counts_1 = a.g['adwindata']['pid_counts_1'].value
pid_counts_2 = a.g['adwindata']['pid_counts_2'].value
sample_counts_1 = a.g['adwindata']['sampling_counts_1'].value
sample_counts_2 = a.g['adwindata']['sampling_counts_2'].value
sample_counts_1 = sample_counts_1[(start_rep_no - 1)*sample_cycles:]
sample_counts_2 = sample_counts_2[(start_rep_no - 1)*sample_cycles:]
pid_counts_1 = pid_counts_1[(start_rep_no - 1)*pid_cycles:]
pid_counts_2 = pid_counts_2[(start_rep_no - 1)*pid_cycles:]
v_1 = []
v_2 = []
t = []
angle = []
for i in xrange(len(pid_counts_1)/pid_cycles):
v_1.extend(pid_counts_1[i*pid_cycles:((i+1)*pid_cycles)])
v_2.extend(pid_counts_2[i*pid_cycles:((i+1)*pid_cycles)])
if len(t) == 0:
t.extend(delay_stable * (1 + np.arange(pid_cycles)))
else:
t.extend(t[-1] + delay_stable * (1 + np.arange(pid_cycles)))
v_1.extend(sample_counts_1[i*sample_cycles:((i+1)*sample_cycles)])
v_2.extend(sample_counts_2[i*sample_cycles:((i+1)*sample_cycles)])
if len(t) == 0:
t.extend(delay_meas * (1 + np.arange(sample_cycles)))
else:
t.extend(t[-1] + delay_meas * (1 + np.arange(sample_cycles)))
t = np.array(t)
total_cycles = pid_cycles + sample_cycles
cosvals = [2*(float(n0)/(float(n0)+float(n1)*g_0)-0.5)*visibility for n0,n1 in zip(v_1,v_2)]
cosvals = [cosval if np.abs(cosval) < 1 else (1.0 * np.sign(cosval)) for cosval in cosvals]
angle = 180*np.arccos(cosvals)/np.pi
# counts
fig = plt.figure(figsize=(17,6))
ax = plt.subplot(211)
plt.plot(t, v_1, 'b')
plt.title('Counts ZPL detector 1 {0}'.format(a.folder))
ax.set_xlabel('elapsed time (ms)')
ax.set_ylabel('counts')
ax2 = plt.subplot(212)
plt.plot(t, v_2, 'b')
plt.title('Counts ZPL detector 2 {0}'.format(a.folder))
ax2.set_xlabel('elapsed time (ms)')
ax2.set_ylabel('counts')
plt.tight_layout()
fig.savefig(os.path.join(a.folder, 'trace_counts.png'))
if len(plot_zoomed):
fig = plt.figure(figsize=(17,6))
ax = plt.subplot(111)
plt.plot(t[total_cycles*plot_zoomed[0]:total_cycles*plot_zoomed[1]], angle[total_cycles*plot_zoomed[0]:total_cycles*plot_zoomed[1]])
plt.title('Zoomed trace {0}'.format(a.folder))
ax.set_xlabel('elapsed time (milliseconds)')
ax.set_ylabel('Phase')
plt.tight_layout()
fig.savefig(os.path.join(a.folder, 'trace_zoomed.png'))
# phase
fig = plt.figure(figsize=(17,6))
ax = plt.subplot(111)
plt.plot(t, angle, 'r')
plt.title('Phase of ZPL photons {0}'.format(a.folder))
plt.ylim([0,180])
ax.set_xlabel('elapsed time (ms)')
ax.set_ylabel('angle ($^o$)')
fig.savefig(os.path.join(a.folder, 'trace_angle.png'))
# fft
if mode == 'only_meas' or mode == 'only_stab':
yf = np.abs(scipy.fftpack.fft(angle))
xf = np.linspace(0, 1.0/(2*delay*1e-6), len(angle)/2)
fig, ax = plt.subplots()
ymax = 1.2*np.max(yf[15:-15])
plt.ylim([0,ymax])
ax.plot(xf[:len(yf)], yf[:len(yf)/2])
xlim = plt.xlim()
if (xlim[1]>1000):
plt.xlim(0,1000)
plt.title('FFT {0}'.format(a.folder))
ax.set_xlabel('frequency (Hz)')
ax.set_ylabel('Amplitude (a.u.)')
fig.savefig(os.path.join(a.folder, 'fft.png'))
# histogram
fig = plt.figure()
ax = plt.subplot(111)
hist, bins = np.histogram(angle,bins= 100,normed = True)
width = np.diff(bins)
center = (bins[:-1] + bins[1:]) / 2
ax.bar(center, hist, align='center', width=width)
g_a = 0.0
g_x0 = 90
g_sigma = 45
g_A = 1/(np.sqrt(2 * np.pi) * g_sigma)
p0, fitfunc,fitfunc_str = common.fit_gauss(g_a, g_A, g_x0, g_sigma)
fit_result = fit.fit1d(center,hist, None, p0=p0, fitfunc=fitfunc,
ret=True,fixed=[])
plot.plot_fit1d(fit_result, np.linspace(center[0],center[-1],201), ax=ax,
plot_data=False,print_info = True)
ax.set_xlabel('Phase')
fig.savefig(os.path.join(a.folder, 'histogram.png'))
if len(fit_result['params_dict']):
print 'x0, sigma ', fit_result['params_dict']['x0'] , fit_result['params_dict']['sigma']
# standard dev
fig = plt.figure()
ax = plt.subplot(111)
angle_reshape = (np.reshape(angle,[total_cycles,-1]))
binsize = 1.0
var_array = np.zeros([int(np.floor(total_cycles/binsize)),1])
for x in range(int(np.floor(total_cycles/binsize))):
var_array[x] = np.sqrt(np.var(angle_reshape[binsize*x:binsize*(x+1),:]))
plt.plot(binsize*t[0:(int(np.floor(total_cycles/binsize)))], var_array)
plt.title('Standard deviation of Phase {0}'.format(a.folder))
ax.set_xlabel('time (ms)')
ax.set_ylabel('std dev ($^o$)')