def _update_plot(self, axes_list):
"""
plotting function for esr
Args:
axes_list: list of axes objects on which to plot plots the esr on the first axes object
data: data (dictionary that contains keys frequency, data and fit_params) if not provided use self.data
Returns:
"""
data = self.data
plot_esr(axes_list[0], data['frequency'], data['data'], data['fit_params'])
def _plot(self, axes_list, data=None):
"""
plotting function for esr
Args:
axes_list: list of axes objects on which to plot plots the esr on the first axes object
data: data (dictionary that contains keys frequency, data and fit_params) if not provided use self.data
Returns:
"""
if data is None:
data = self.data
mean_counts = self.current_avg_cnts
plot_esr(axes_list[0],
data['frequency'],
data['data'],
data['fit_params'],
avg_counts=mean_counts)
def display_data(lower_peak_widget = None, upper_peak_widget = None, display_fit = True):
# find the NV index
pt_id = int(os.path.basename(esr_folder).split('pt_')[-1])
findnv_folder = glob.glob(folder + '\\data_subscripts\\*find_nv*pt_*{:d}'.format(pt_id))[0]
f.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax = [ax0, ax1]
plt.suptitle('NV #{:d}'.format(pt_id), fontsize=16)
# load data
data = Script.load_data(esr_folder)
if lower_fit_widget.value == 0 and upper_fit_widget.value == 10e9:
freq = data['frequency']
ampl = data['data']
else:
freq = data['frequency'][np.logical_and(data['frequency'] > lower_fit_widget.value, data['frequency'] < upper_fit_widget.value)]
ampl = data['data'][np.logical_and(data['frequency'] > lower_fit_widget.value, data['frequency'] < upper_fit_widget.value)]
if lower_peak_widget is None:
fit_params = fit_data_set_array[pt_id, 2:8]
else:
lower_peak = lower_peak_widget.value
upper_peak = upper_peak_widget.value
if upper_peak == 0:
start_vals = get_lorentzian_fit_starting_values(freq, ampl)
start_vals[2] = lower_peak
start_vals[1] = ampl[np.argmin(np.abs(freq - lower_peak))] - start_vals[0]
try:
fit_params = fit_lorentzian(freq, ampl, starting_params=start_vals,
bounds=[(0, -np.inf, 0, 0), (np.inf, 0, np.inf, np.inf)])
except:
# ESR fit failed!
fit_params = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
else:
center_freq = np.mean(freq)
start_vals = []
start_vals.append(
get_lorentzian_fit_starting_values(freq[freq < center_freq], ampl[freq < center_freq]))
start_vals.append(
get_lorentzian_fit_starting_values(freq[freq > center_freq], ampl[freq > center_freq]))
start_vals = [
np.mean([start_vals[0][0], start_vals[1][0]]), # offset
np.sum([start_vals[0][3], start_vals[1][3]]), # FWHM
ampl[np.argmin(np.abs(freq-lower_peak))] - start_vals[0][0], ampl[np.argmin(np.abs(freq-upper_peak))]- start_vals[1][0], # amplitudes
lower_peak, upper_peak # centers
]
try:
fit_params = fit_double_lorentzian(freq, ampl, starting_params=start_vals, bounds=
[(0, 0, -np.inf, -np.inf, min(freq), min(freq)), (np.inf, np.inf, 0, 0, max(freq), max(freq))])
except:
fit_params = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
if len(fit_params) == 4 or np.isnan(fit_params[4]):
fit_params = fit_params[0:4]
# get nv positions
# data_pos = {'initial_point': [fit_data_set['xo'].values[pt_id]]}
data_pos = Script.load_data(findnv_folder)
# pos = data_pos['maximum_point']
# pos_init = data_pos['initial_point']
# plot NV image
FindNV.plot_data([ax[1]], data_pos)
# plot data and fits
# print("fit_params: ", fit_params)
sys.stdout.flush()
if display_fit:
plot_esr(ax[0], data['frequency'], data['data'], fit_params=fit_params)
else:
plot_esr(ax[0], data['frequency'], data['data'], fit_params=[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan])
plt.tight_layout()
plt.subplots_adjust(top=0.85) # Makes room at top of plot for figure suptitle
plt.draw()
plt.show()
return fit_params, pt_id