def fit(f,
xdata,
ydata,
p0=None,
bounds=(-np.inf, np.inf),
tfit=None,
name=''):
fit_data = munch.Munch()
fit_data.popt, fit_data.pcov = optimize.curve_fit(f,
xdata,
ydata,
p0=p0,
bounds=bounds)
fit_data.x = f(xdata, *fit_data.popt)
fit_data.resid = ydata - fit_data.x
if tfit is None:
tfit = xdata
fs = 1.0 / np.mean(np.diff(tfit))
li = lockin.LockIn(tfit, fit_data.x, fs)
li.lock2()
li.phase(tf=0)
fit_data.li = li
fit_data.name = name
fit_data.li.name = name
return fit_data
def signal_average_parab_list(gr_list,
ti,
tf,
invert=True,
align_voltage=False):
"""Utility function to signal average a group from an HDF5 file."""
b = munch.Munch()
xs = []
ts = []
if invert:
scale = -1
else:
scale = 1
for ds in tqdm.tqdm(gr_list):
# Move the zero in time to the initial voltage pulse
x = ds['cantilever-nm'][:]
dt = ds['dt [s]'].value
if align_voltage:
t1 = ds.attrs['Abrupt BNC565 CantClk.t1 [s]']
t0 = -t1 - ds["half periods [s]"][0]
t = np.arange(x.size) * dt + t0
else:
t = pk.gr2t(ds)
m = (t > ti) & (t < tf)
ts.append(t[m])
x = ds['cantilever-nm'][:]
xs.append(scale * x[m])
ts = np.array(ts)
b.t = np.mean(ts, axis=0)
xs = np.array(xs)
# Do proper signal averaging, fitting data to a parabola at each point
x_err = np.zeros(xs.shape[1])
x_bf = np.zeros(xs.shape[1])
for i, (tr, xr) in tqdm.tqdm(enumerate(zip(ts.T, xs.T))):
x, err = avg_xt(tr, xr)
x_err[i] = err
x_bf[i] = x
b.x = x_bf
b.x_err = x_err
m2 = b.t < 0.0
b.x0neg = b.x[m2].mean()
b.x = b.x - b.x0neg
b.t_ms = b.t * 1e3
b.t_us = b.t * 1e6
b.t5 = b.t[500:]
b.x5 = b.x[500:]
b.t5ms = b.t5 * 1e3
b.t5us = b.t5 * 1e6
b.li = lockin.LockIn(b.t, b.x, 1e6)
b.li.lock2()
b.li.phase(tf=0)
b.li.name = 'data'
return b
def signal_average_gr(gr, ti, tf):
"""Utility function to signal average a group from an HDF5 file."""
b = munch.Munch()
xs = []
ts = []
for ds in tqdm.tqdm(gr.values()):
t = pk.gr2t(ds)
t1 = ds.attrs['Abrupt BNC565 CantClk.t1 [s]']
t0 = -t1 - ds["half periods [s]"][0]
m = (t > ti) & (t < tf)
ts.append(t[m])
x = ds['cantilever-nm'][:]
xs.append(x[m])
ts = np.array(ts)
b.t = np.mean(ts, axis=0)
x_array = np.array(xs)
b.x = np.mean(x_array, axis=0)
m2 = b.t < 0.0
b.x = b.x - b.x[m2].mean()
b.t_ms = b.t * 1e3
b.t_us = b.t * 1e6
b.t5 = b.t[500:]
b.x5 = b.x[500:]
b.t5ms = b.t5 * 1e3
b.t5us = b.t5 * 1e6
b.li = lockin.LockIn(b.t, b.x, 1e6)
b.li.lock2()
b.li.phase(tf=0)
b.li.name = 'data'
return b
def gr2locks(top_gr):
locks = []
for gr in tqdm(top_gr.values()):
half_periods = gr["half periods [s]"][:]
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])
t0 = -(t1 + t2)
x = gr['cantilever-nm'][:]
dt = gr['dt [s]'].value
t = np.arange(x.size) * dt + t0
lock = lockin.LockIn(t, x, 1 / dt)
lock.lock2(fp=5000, fc=15000, print_response=False)
lock.phase(ti=3e-3, tf=7e-3)
locks.append(lock)
return locks
def delay_dA_dphi(top_gr, tp, tLi, tLf, tRi, tRf):
dAs = []
dPhis = []
delays = []
for ds_name, gr in tqdm(top_gr.items()):
half_periods = gr["half periods [s]"][:]
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])
t0 = -(t1 + t2)
x = gr['cantilever-nm'][:]
dt = gr['dt [s]'].value
t = np.arange(x.size) * dt + t0
li = lockin.LockIn(t, x, 1 / dt)
li.lock2(fp=5000, fc=15000, print_response=False)
li.phase(ti=3e-3, tf=7e-3)
dA, dphi = measure_dA_dphi(li, tp, tLi, tLf, tRi, tRf)
dAs.append(dA)
dPhis.append(dphi)
delays.append(gr.attrs['Abrupt BNC565 CantClk.D tip [s]'])
return delays, dAs, dPhis
df.loc[idx[200, :], 'dPhi'] = dPhis
# In[4]:
locks = []
fir = lockin.lock2(62000, 5000, 15000, 1e6)
for gr in tqdm(fh800['data'].values()):
half_periods = gr["half periods [s]"][:]
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])
t0 = -(t1 + t2)
x = gr['cantilever-nm'][:]
dt = gr['dt [s]'].value
t = np.arange(x.size) * dt + t0
lock = lockin.LockIn(t, x, 1 / dt)
lock.lock2(fp=5000, fc=15000, print_response=False)
lock.phase(ti=3e-3, tf=7e-3)
locks.append(lock)
m0 = phasekick.masklh(t, -5e-6, 5e-6)
# In[5]:
with mpl.rc_context(rcParams):
fig, ax2 = plt.subplots(figsize=(1.4, 0.9))
lock = locks[48]
m2 = phasekick.masklh(lock.t, -1e-6, 8e-6)
m = phasekick.masklh(lock.t, -3e-3, 4.6e-3)
t_many = np.linspace(lock.t[m2][0], lock.t[m2][-1], 3001) * 1e6
ti = 16 / 4.0