def plot_two_level_threshold(results, fig=100, plot_initial_estimate=False):
plt.figure(fig)
plt.clf()
bin_centres = results['histogram']['bin_centres']
counts = results['histogram']['counts']
plt.bar(bin_centres,
counts,
width=bin_centres[1] - bin_centres[0],
label='histogram')
plt.ylabel('Counts')
plt.xlabel('Signal [a.u.]')
plot_vertical_line(results['signal_threshold'], label='threshold')
plot_double_gaussian_fit(results['double_gaussian_fit'], bin_centres)
plt.title('Result of two level threshold processing')
if plot_initial_estimate:
xdata = np.linspace(bin_centres[0], bin_centres[-1], 300)
initial_estimate = results['double_gaussian_fit'][
'parameters initial guess']
left0 = initial_estimate[::2][::-1]
right0 = initial_estimate[1::2][::-1]
plt.plot(xdata,
gaussian(xdata, *left0),
':g',
label='initial estimate left')
plt.plot(xdata,
gaussian(xdata, *right0),
':r',
label='initial estimate right')
def estimate_dominant_frequency(signal, sample_rate=1, remove_dc=True, fig=None):
""" Estimate dominant frequency in a signal
Args:
signal (array): Input data
sample_rate (float): Sample rate of the data
remove_dc (bool): If True, then do not estimate the DC component
fig (int or None): Optionally plot the estimated frequency
Returns:
Estimated dominant frequency
"""
w = np.fft.fft(signal)
freqs = np.fft.fftfreq(len(signal), d=1. / sample_rate)
if remove_dc:
w[0] = 0
w[freqs < 0] = 0
dominant_idx = np.argmax(np.abs(w))
dominant_frequency = freqs[dominant_idx]
if 0 < dominant_idx < freqs.size / 2 - 1:
dominant_idx_subpixel, _ = subpixelmax(np.abs(w), [dominant_idx])
dominant_frequency = np.interp(dominant_idx_subpixel, np.arange(freqs.size), freqs)[0]
if fig:
plt.figure(fig)
plt.clf()
plt.plot(freqs, np.abs(w), '.b')
plt.xlabel('Frequency')
plt.ylabel('Abs of fft')
plot_vertical_line(dominant_frequency, label='Dominant frequency')
return dominant_frequency
def plot_allxy(dataset: DataSet,
result: Dict[str, Any],
fig: int = 1,
plot_initial_estimate: bool = False):
""" Plot the results of an AllXY fit
Args:
dataset: Dataset containing the measurement data
result: Fitting result of fit_allxy
int: Figure handle
plot_initial_guess: If True, then plot the initial estimate of the model
"""
allxy_data = _default_measurement_array(dataset)
xy_pairs = generate_allxy_combinations()
x_data = np.arange(21)
plt.figure(fig)
plt.clf()
fitted_parameters = result['fitted_parameters']
xfine = np.arange(0, 21, 1e-3)
plt.plot(xfine,
allxy_model(xfine, *fitted_parameters),
'm',
label='fitted allxy',
alpha=.5)
plt.plot(x_data, allxy_data, '.b', label='allxy data')
if plot_initial_estimate:
p = [0, 0, .5, 0, 1, 0]
plt.plot(xfine,
allxy_model(xfine, *p),
'c',
label='baseline',
alpha=.5)
initial_params = _estimate_allxy_parameters(allxy_data)
plt.plot(xfine,
allxy_model(xfine, *initial_params),
'g',
label='initial estimate',
alpha=.35)
initial_parameters = result['initial_parameters']
plt.plot(xfine,
allxy_model(xfine, *initial_parameters),
':g',
label='initial estimate',
alpha=.35)
plt.xticks(x_data, [v[0] + "," + v[1] for v in xy_pairs],
rotation='vertical')
vl = plot_vertical_line(4.5)
vl.set_linestyle(':')
vl = plot_vertical_line(16.5)
vl.set_linestyle(':')
plt.title('AllXY')
plt.legend()
def plot_allxy(dataset: DataSet, result: Dict[str, Any], fig: Union[int, Axes, None] = 1, plot_initial_estimate: bool = False):
""" Plot the results of an AllXY fit
Args:
dataset: Dataset containing the measurement data
result: Fitting result of fit_allxy
fig: Figure or axis handle. Is passed on to `get_axis`
plot_initial_guess: If True, then plot the initial estimate of the model
"""
allxy_data = _default_measurement_array(dataset)
xy_pairs = generate_allxy_combinations()
x_data = np.arange(21)
ax = get_axis(fig)
fitted_parameters = result['fitted_parameters']
xfine = np.arange(0, 21, 1e-3)
ax.plot(xfine, allxy_model(xfine, *fitted_parameters), 'm', label='fitted allxy', alpha=.5)
ax.plot(x_data, allxy_data, '.b', label='allxy data')
if plot_initial_estimate:
p = [0, 0, .5, 0, 1, 0]
ax.plot(xfine, allxy_model(xfine, *p), 'c', label='baseline', alpha=.5)
initial_parameters = result['initial_parameters']
ax.plot(xfine, allxy_model(xfine, *initial_parameters), ':g', label='initial estimate', alpha=.35)
ax.set_xticks(x_data)
ax.set_xticklabels([v[0] + "," + v[1] for v in xy_pairs], rotation='vertical')
vl = plot_vertical_line(4.5)
vl.set_linestyle(':')
vl = plot_vertical_line(16.5)
vl.set_linestyle(':')
ax.set_title(f'AllXY: {dataset.location}')
ax.legend()
def plot_two_level_threshold(results: dict,
fig: int = 100,
plot_initial_estimate: bool = False):
separation = results['separation']
threshold = results['signal_threshold']
ax = get_axis(fig)
bin_centres = results['histogram']['bin_centres']
counts = results['histogram']['counts']
ax.bar(bin_centres,
counts,
width=bin_centres[1] - bin_centres[0],
label='histogram')
ax.set_ylabel('Counts')
ax.set_xlabel('Signal [a.u.]')
plot_vertical_line(threshold, label='threshold')
plot_double_gaussian_fit(results['double_gaussian_fit'], bin_centres)
ax.set_title(
f'Two-level signal: separation {separation:.3f}, threshold {threshold:.3g}'
)
if plot_initial_estimate:
xdata = np.linspace(bin_centres[0], bin_centres[-1], 300)
initial_estimate = results['double_gaussian_fit'][
'parameters initial guess']
left0 = initial_estimate[::2][::-1]
right0 = initial_estimate[1::2][::-1]
ax.plot(xdata,
gaussian(xdata, *left0),
':g',
label='initial estimate left')
ax.plot(xdata,
gaussian(xdata, *right0),
':r',
label='initial estimate right')