def workup_adiabatic_w_control_correct_phase_bnc(fh, T_before, T_after, T_bf, T_af,
fp, fc, fs_dec):
tps = fh['tp tip [s]'][:] # ms to s
groups = fh['data'].keys()
df = pd.DataFrame(index=pd.MultiIndex.from_product(
(['data', 'control'], tp_groups), names=['expt', 'ds']))
lis = {}
locks = {}
i = 0
for control_or_data in ('control', 'data'):
lis[control_or_data] = []
locks[control_or_data] = []
for tp_group in tqdm(groups):
gr = fh[control_or_data][tp_group]
print_response = i == 0
try:
N2even = gr.attrs['Calc BNC565 CantClk.N2 (even)']
t1 = gr.attrs['Abrupt BNC565 CantClk.t1 [s]']
t2 = np.sum(gr["half periods [s]"][:N2even+1])
tp = np.sum(gr.attrs["Abrupt BNC565 CantClk.tp tip [s]"])
t0 = -(t1 + t2)
x = gr['cantilever-nm'][:]
dt = fh['data/0000/dt [s]'].value
fs = 1. / dt
lock = lockin.adiabatic2lockin(gr, t0=t0)
lock.lock2(fp=fp, fc=fc, print_response=False)
lock.phase(tf=-t2)
f0_V0 = lock.f0corr
lock.phase(ti=-t2, tf=0)
f0_V1 = lock.f0corr
dphi, li = lockin.delta_phi_group(
gr, tp, T_before, T_after,
T_bf, T_af, fp, fc, fs_dec, print_response=print_response,
t0=t0)
phi_at_tp, dA, dphi_tp_end = measure_dA_dphi(lock, li, tp)
lis[control_or_data].append(li)
locks[control_or_data].append(lock)
i += 1
curr_index = (control_or_data, tp_group)
df.loc[curr_index, 'tp'] = tp
df.loc[curr_index, 'dphi [cyc]'] = dphi/(2*np.pi)
df.loc[curr_index, 'f0 [Hz]'] = f0_V0
df.loc[curr_index, 'df_dV [Hz]'] = f0_V1 - f0_V0
df.loc[curr_index, 'dA [nm]'] = dA
df.loc[curr_index, 'dphi_tp_end [cyc]'] = dphi_tp_end
df.loc[curr_index, 'phi_at_tp [rad]'] = phi_at_tp
df.loc[curr_index, 'relative time [s]'] = gr['relative time [s]'].value
except Exception as e:
print(e)
pass
try:
df.sort_index(inplace=True)
print(df)
df_clean = df.dropna()
control = df_clean.xs('control')
data = df_clean.xs('data')
popt_phi, pcov_phi = optimize.curve_fit(offset_cos,
control['phi_at_tp [rad]'], control['dphi_tp_end [cyc]'])
popt_A, pcov_A = optimize.curve_fit(offset_cos,
control['phi_at_tp [rad]'], control['dA [nm]'])
df['dphi_corrected [cyc]'] = (df['dphi [cyc]']
- offset_cos(df['phi_at_tp [rad]'], *popt_phi))
df_clean = df.dropna()
control = df_clean.xs('control')
data = df_clean.xs('data')
# Issues here; not properly sorted, etc.
popt_phase_corr, pcov_phase_corr = optimize.curve_fit(phase_step, data['tp'], data['dphi_corrected [cyc]'])
popt_phase, pcov_phase = optimize.curve_fit(phase_step, data['tp'], data['dphi [cyc]'])
# Extra information
extras = {'popt_phi': popt_phi,
'pcov_phi': pcov_phi,
'pdiag_phi': np.diagonal(pcov_phi)**0.5,
'popt_A': popt_A,
'pcov_A': pcov_A,
'pdiag_A': np.diagonal(pcov_A)**0.5,
'popt_phase_corr': popt_phase_corr,
'pcov_phase_corr': pcov_phase_corr,
'pdiag_phase_corr': np.diagonal(pcov_phase_corr)**0.5,
'popt_phase': popt_phase,
'pcov_phase': pcov_phase,
'pdiag_phase': np.diagonal(pcov_phase)**0.5,
'params': {
'T_before': T_before,
'T_after': T_after,
'T_bf': T_bf,
'T_af': T_af,
'fp': fp,
'fc': fc,
'fs_dec': fs_dec,
'filename': fh.filename
},
'file_attrs_str': prnDict(dict(fh.attrs.items()), braces=False),
'dataset_attrs_str': prnDict(dict(fh['data/0000'].attrs.items()), braces=False),
'lis': lis,
'locks': locks}
return df, extras
except Exception as e:
print(e)
raise
def workup_adiabatic_w_control_correct_phase(fh, T_before, T_after, T_bf, T_af,
fp, fc, fs_dec):
tps = fh['tp'][:] * 0.001 # ms to s
groups = fh['data'].keys()
df = pd.DataFrame(index=pd.MultiIndex.from_product(
(['data', 'control'], groups), names=['expt', 'ds']))
lis = {}
locks = {}
i = 0
for control_or_data in ('control', 'data'):
lis[control_or_data] = []
locks[control_or_data] = []
for tp_group in tqdm(groups):
gr = fh[control_or_data][tp_group]
tp = gr.attrs['Adiabatic Parameters.tp [ms]'] * 0.001
print_response = i == 0
t1 = gr.attrs['Adiabatic Parameters.t1 [ms]'] * 0.001
t2 = gr.attrs['Adiabatic Parameters.t2 [ms]'] * 0.001
lock = lockin.adiabatic2lockin(gr)
lock.lock2(fp=fp, fc=fc, print_response=False)
lock.phase(tf=-t2)
f0_V0 = lock.f0corr
lock.phase(ti=-t2, tf=0)
f0_V1 = lock.f0corr
dphi, li = lockin.delta_phi_group(
gr, tp, T_before, T_after,
T_bf, T_af, fp, fc, fs_dec, print_response=print_response)
phi_at_tp, dA, dphi_tp_end = measure_dA_dphi(lock, li, tp)
lis[control_or_data].append(li)
locks[control_or_data].append(lock)
i += 1
curr_index = (control_or_data, tp_group)
df.loc[curr_index, 'tp'] = tp
df.loc[curr_index, 'dphi [cyc]'] = dphi/(2*np.pi)
df.loc[curr_index, 'f0 [Hz]'] = f0_V0
df.loc[curr_index, 'df_dV [Hz]'] = f0_V1 - f0_V0
df.loc[curr_index, 'dA [nm]'] = dA
df.loc[curr_index, 'dphi_tp_end [cyc]'] = dphi_tp_end
df.loc[curr_index, 'phi_at_tp [rad]'] = phi_at_tp
df.loc[curr_index, 'relative time [s]'] = gr['relative time [s]'].value
sys.stdout.write('\n')
df.sort_index(inplace=True)
control = df.xs('control')
data = df.xs('data')
popt_phi, pcov_phi = optimize.curve_fit(offset_cos,
control['phi_at_tp [rad]'], control['dphi_tp_end [cyc]'])
popt_A, pcov_A = optimize.curve_fit(offset_cos,
control['phi_at_tp [rad]'], control['dA [nm]'])
df['dphi_corrected [cyc]'] = (df['dphi [cyc]']
- offset_cos(df['phi_at_tp [rad]'], *popt_phi))
control = df.xs('control')
data = df.xs('data')
popt_phase_corr, pcov_phase_corr = optimize.curve_fit(phase_step, data['tp'], data['dphi_corrected [cyc]'])
popt_phase, pcov_phase = optimize.curve_fit(phase_step, data['tp'], data['dphi [cyc]'])
# Extra information
extras = {'popt_phi': popt_phi,
'pcov_phi': pcov_phi,
'pdiag_phi': np.diagonal(pcov_phi)**0.5,
'popt_A': popt_A,
'pcov_A': pcov_A,
'pdiag_A': np.diagonal(pcov_A)**0.5,
'popt_phase_corr': popt_phase_corr,
'pcov_phase_corr': pcov_phase_corr,
'pdiag_phase_corr': np.diagonal(pcov_phase_corr)**0.5,
'popt_phase': popt_phase,
'pcov_phase': pcov_phase,
'pdiag_phase': np.diagonal(pcov_phase)**0.5,
'params': {
'T_before': T_before,
'T_after': T_after,
'T_bf': T_bf,
'T_af': T_af,
'fp': fp,
'fc': fc,
'fs_dec': fs_dec,
'filename': fh.filename
},
'file_attrs_str': prnDict(dict(fh.attrs.items()), braces=False),
'dataset_attrs_str': prnDict(dict(fh['data/0000'].attrs.items()), braces=False),
'lis': lis,
'locks': locks}
# Do a fit to the corrected phase data here.
return df, extras
