def _plot(self, axes_list):
if len(self.data['plant_output']) > 0:
time_step = 1. / self.settings['sample rate']
axes1, axes2 = axes_list
# plot time domain signals
axes1.hold(False)
signal = self.data['plant_output']
control_value = self.data['control_output']
t = np.linspace(0, len(signal) * time_step, len(signal))
axes1.plot(t, signal, '-o')
axes1.hold(True)
axes1.plot(t, control_value, '-o')
axes1.set_title('time signal')
axes1.set_xlabel('time (s)')
# only plot spectra if there is a sufficiently long signal and only refresh after 5 seconds
if (len(signal) > 2 and
(datetime.datetime.now() - self.last_plot).total_seconds() > 5
) or self.is_running is False:
# plot freq domain signals
axes2.hold(False)
f, psd = power_spectral_density(signal, time_step)
axes2.loglog(f, psd, '-o')
axes2.hold(True)
f, psd = power_spectral_density(control_value, time_step)
axes2.loglog(f, psd, '-o')
axes2.set_title('spectra')
axes1.set_xlabel('frequency (Hz)')
self.last_plot = datetime.datetime.now()
def _plot(self, axes_list, data = None):
'''
Plots the galvo scan image
Args:
axes_list: list of axes objects on which to plot the keyseight spectrun on the first axes object
data: data (dictionary that contains keys amplitudes, frequencies) if not provided use self.data
'''
if data is None:
data = self.data
dt = self.data['meta_data']['xincrement']
data = self.data['voltage']
time = dt*np.arange(len(data))
axes_list[0].plot(time, data, '-')
axes_list[0].set_xlabel('time (s)')
axes_list[0].set_ylabel('signal (arb.)')
F, P = power_spectral_density(data, dt)
print(('JG adasd', data, dt))
axes_list[1].plot(F, P, '-')
axes_list[1].set_xlabel('freq (Hz)')
axes_list[1].set_ylabel('signal (arb.)')
axes_list[1].set_xscale("log")
# JG: try to display on a log scale, this doesn't work if the psd is negative or zero (which might happen if the oscilloscope is out of range)
if np.mean(data) > 0:
axes_list[1].set_yscale("log")
def _plot(self, axes_list, data=None):
'''
Plots the galvo scan image
Args:
axes_list: list of axes objects on which to plot the keyseight spectrun on the first axes object
data: data (dictionary that contains keys amplitudes, frequencies) if not provided use self.data
'''
if data is None:
data = self.data
dt = self.data['meta_data']['xincrement']
data = self.data['voltage']
time = dt * np.arange(len(data))
axes_list[0].plot(time, data, '-')
axes_list[0].set_xlabel('time (s)')
axes_list[0].set_ylabel('signal (arb.)')
F, P = power_spectral_density(data, dt)
print(('JG adasd', data, dt))
axes_list[1].plot(F, P, '-')
axes_list[1].set_xlabel('freq (Hz)')
axes_list[1].set_ylabel('signal (arb.)')
axes_list[1].set_xscale("log")
# JG: try to display on a log scale, this doesn't work if the psd is negative or zero (which might happen if the oscilloscope is out of range)
if np.mean(data) > 0:
axes_list[1].set_yscale("log")
def _plot(self, axes_list, data=None):
# COMMENT_ME
if data is None:
data = self.data
if len(data['counts']) > 0:
plot_counts(axes_list[0], data['counts'])
freq, psd = power_spectral_density(
data['counts'], self.settings['integration_time'])
plot_psd(freq, psd, axes_list[1], y_scaling='log', x_scaling='log')
def plot_2d_fft_rabi_vs_powers(RABI_FOLDER,
xlog=False,
ylog=False,
freq_range=None):
'''
Args:
RABI_FOLDER: folder with the rabi data to use
Returns:
rabi_data: rabi contrast APD output 2 / APD output 1 versus tau
taus: tau values in the rabi sweep
powers: power values used in the 2D sweep, in dBm
'''
rabi_data = []
taus = []
powers = []
freqs = []
for f in sorted(glob.glob('{:s}/data_subscripts/*'.format(RABI_FOLDER))):
data = Script.load_data(f)
if 'tau' not in data or data is None: # not a Rabi folder (e.g., find_nv instead)
continue
cnts = np.transpose(data['counts'])
cnts1 = cnts[1]
cnts0 = cnts[0]
single_rabi_data = cnts1 / cnts0
taus = data['tau']
single_rabi_data -= np.mean(single_rabi_data)
freqs, single_rabi_data = power_spectral_density(
single_rabi_data, (taus[1] - taus[0]) * 1e-9,
freq_range=freq_range)
if ylog:
single_rabi_data = np.log10(single_rabi_data)
rabi_data.append(single_rabi_data)
power = float(f.split('_')[-1])
powers.append(power)
if xlog:
freqs += 1
freqs = np.log10(freqs)
print(freqs)
rabi_data = np.array(rabi_data)[np.argsort(powers)]
powers = np.sort(np.array(powers))
plt.pcolormesh(freqs, powers, rabi_data)
plt.xlabel('frequencies (Hz)')
plt.ylabel('input power (dBm)')
def _update_plot(self, axes_list):
"""
updates the galvo scan image
Args:
axes_list: list of axes objects on which to plot plots the esr on the first axes object
"""
print(('axes list lenght update', len(axes_list)))
update_fluorescence(self.data['image_data'], axes_list[0])
last_data = self.data['point_data'][-1]
dt = self.data['meta_data']['xincrement']
freq, psd = power_spectral_density(last_data, dt)
plot_psd(freq, psd, axes_list[1], y_scaling='log', x_scaling='log')
axes_list[1].hold(False)
def _update_plot(self, axes_list):
"""
updates the galvo scan image
Args:
axes_list: list of axes objects on which to plot plots the esr on the first axes object
"""
print(('axes list lenght update', len(axes_list)))
update_fluorescence(self.data['image_data'], axes_list[0])
last_data = self.data['point_data'][-1]
dt = self.data['meta_data']['xincrement']
freq, psd = power_spectral_density(last_data, dt)
plot_psd(freq, psd, axes_list[1], y_scaling='log', x_scaling='log')
axes_list[1].hold(False)
def plot_2d_fft_rabi_vs_powers(RABI_FOLDER, xlog=False, ylog = False, freq_range = None):
'''
Args:
RABI_FOLDER: folder with the rabi data to use
Returns:
rabi_data: rabi contrast APD output 2 / APD output 1 versus tau
taus: tau values in the rabi sweep
powers: power values used in the 2D sweep, in dBm
'''
rabi_data = []
taus = []
powers = []
freqs = []
for f in sorted(glob.glob('{:s}/data_subscripts/*'.format(RABI_FOLDER))):
data = Script.load_data(f)
if 'tau' not in data or data is None: # not a Rabi folder (e.g., find_nv instead)
continue
cnts = np.transpose(data['counts'])
cnts1 = cnts[1]
cnts0 = cnts[0]
single_rabi_data = cnts1/cnts0
taus = data['tau']
single_rabi_data -= np.mean(single_rabi_data)
freqs, single_rabi_data = power_spectral_density(single_rabi_data, (taus[1]-taus[0])*1e-9, freq_range=freq_range)
if ylog:
single_rabi_data = np.log10(single_rabi_data)
rabi_data.append(single_rabi_data)
power = float(f.split('_')[-1])
powers.append(power)
if xlog:
freqs += 1
freqs = np.log10(freqs)
print(freqs)
rabi_data = np.array(rabi_data)[np.argsort(powers)]
powers = np.sort(np.array(powers))
plt.pcolormesh(freqs, powers, rabi_data)
plt.xlabel('frequencies (Hz)')
plt.ylabel('input power (dBm)')
def _plot(self, axes_list, data=None):
"""
Plots the galvo scan image
Args:
axes_list: list of axes objects on which to plot the galvo scan on the first axes object
data: data (dictionary that contains keys image_data, extent) if not provided use self.data
"""
print(('axes list lenght plot', len(axes_list)))
if data is None:
data = self.data
plot_fluorescence_new(data['image_data'], data['extent'], axes_list[0])
last_data = self.data['point_data'][-1]
dt = self.data['meta_data']['xincrement']
freq, psd = power_spectral_density(last_data, dt)
plot_psd(freq, psd, axes_list[1], y_scaling='log', x_scaling='log')
def _plot(self, axes_list, data=None):
"""
Plots the galvo scan image
Args:
axes_list: list of axes objects on which to plot the galvo scan on the first axes object
data: data (dictionary that contains keys image_data, extent) if not provided use self.data
"""
print(('axes list lenght plot', len(axes_list)))
if data is None:
data = self.data
plot_fluorescence_new(data['image_data'], data['extent'], axes_list[0])
last_data = self.data['point_data'][-1]
dt = self.data['meta_data']['xincrement']
freq, psd = power_spectral_density(last_data, dt)
plot_psd(freq, psd, axes_list[1], y_scaling='log', x_scaling='log')
def _plot(self, axes_list, data=None):
# COMMENT_ME
if data is None:
data = self.data
for signal in [data['counts']]: #, data['ai']]: ER 20190130
if len(signal) > 0:
plot_counts(axes_list[0], signal / np.mean(signal))
freq, psd = power_spectral_density(
signal / np.mean(signal),
self.settings['integration_time'])
print('freqs: ', freq) # ER 20190129
print('psd: ', psd) # ER 20190129
print('freq[-1:]: ', freq[-1:])
# plot_psd(freq, psd, axes_list[1], y_scaling='log', x_scaling='log')
plot_psd(freq[1:],
psd[1:],
axes_list[1],
y_scaling='log',
x_scaling='lin') # remove dc component ER 20190129