def main():
xs = np.linspace(0, 1, 20)
plt.close('all')
fig, (ax1, ax2, ax3) = plt.subplots(1,
3,
figsize=(cm_to_inch(15),
cm_to_inch(5)),
sharey=True)
gamma = 1
upwards = xs * gamma
downwards = xs * -gamma
color = tum_color(0)
axis = ax1
plot_the_shit(axis, color, downwards, upwards, xs, legend=u'$^{13}$C')
axis.set_ylabel(u'$E_Z$ (arb. u.)')
gamma = 0.6
upwards = xs * gamma
downwards = xs * -gamma
color = tum_color(5)
axis = ax2
plot_the_shit(axis, color, downwards, upwards, xs, legend=u'$^{14}$N')
axis.plot_for_thesis([0, 1], [0, 0], color=color)
gamma = -0.4
upwards = xs * gamma
downwards = xs * -gamma
color = tum_color(2)
axis = ax3
plot_the_shit(axis, color, downwards, upwards, xs, legend=u'$^{15}$N')
plt.tight_layout()
plt.savefig('zeeman_splitting.png', dpi=300)
def main():
ks = np.arange(0, 11, 1)
plt.close('all')
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(cm_to_inch(15), cm_to_inch(6)))
polarizations = calc_polarization(ks)
ax1.plot_for_thesis(ks, polarizations, '.', color=tum_color(0))
ax1.set_xlabel(r'$n$')
ax1.set_xticks(ks[::2])
ax1.set_ylabel(r'$ P_n $')
ax1.__set_ticks(np.arange(0, 1.1, 0.25))
ax2.plot_for_thesis(ks,
percentage_of_polarized_states(polarizations),
'.',
color=tum_color(0))
ax2.set_xlabel(r'$n$')
ax2.set_xticks(ks[::2])
ax2.set_ylabel(r'$ \dfrac{N_0}{N_0 + N_{\pm 1}} $')
ax2.__set_ticks(np.arange(0.5, 1.01, 0.1))
plt.tight_layout()
plt.savefig('polarization.png', dpi=300)
def plot_and_save_odmr_spectrum(cts_broad, cts_narrow, freqs_broad,
freqs_narrow):
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_figheight(cm_to_inch(5.5))
fig.set_figwidth(cm_to_inch(15))
plot_one_axis(ax1, cts_broad, freqs_broad)
plot_one_axis(ax2, cts_narrow, freqs_narrow)
plt.tight_layout()
plt.savefig('pulsed_odmr_spectrum.png', dpi=500)
def plot_and_save_odmr_spectrum(cts, freqs):
plt.figure(figsize=(inches.cm_to_inch(15), inches.cm_to_inch(5.5)))
plt.plot(
freqs * 1e-9,
graph_transformation.smooth_array_by_rolling_average(cts, 2) * 1e-3,
'.')
plt.xlabel('mw frequency (GHz)')
plt.ylabel('luminescence (kcts/s)')
plt.tight_layout()
plt.savefig('cw_odmr_spectrum.png', dpi=500)
def plot(self):
plt.figure(figsize=(cm_to_inch(15), cm_to_inch(5.5)))
luminescences = self.__load_luminescences()
average = np.average(luminescences[:20])
plt.ylabel('luminescence (normalized)')
plt.xlabel('time (h)')
plt.plot(1.5 * np.array(list(range(len(luminescences)))) / 600,
luminescences / average,
color=tum_color(0))
plt.tight_layout()
plt.savefig('luminescence_fluctuation.png', dpi=500)
def main():
file_data = load_file_data()
plt.close('all')
plt.figure(figsize=(cm_to_inch(7.5), cm_to_inch(5)))
plt.imshow(file_data['result'], vmax=8e4, cmap=tum_jet, interpolation='bilinear', origin='lower',
extent=[0, x_extent(file_data), 0, y_extent(file_data)])
plt.xlabel(r'$x$ ($\si{\micro \meter}$)')
plt.ylabel(r'$y$ ($\si{\micro \meter}$)')
plt.yticks([0, 10, 20])
cbar = plt.colorbar(aspect=10)
cbar.ax.set_yticklabels(['20', '40', '60', '80'])
cbar.set_label(r'luminescence (kcts/s)')
plt.tight_layout()
plt.savefig('confocal_scan.png', dpi=500)
def plot_t1():
xs = np.linspace(0, 3, 100)
ys = np.exp(-xs)
plt.close('all')
plt.figure(figsize=(inch.cm_to_inch(7.5), inch.cm_to_inch(5)))
plt.plot(xs, ys, color=tum.tum_color(0))
plt.ylim((-0.1, 1.1))
plt.yticks([0, 1], [
r'$\braket{\boldsymbol{\mu}_0}_z$',
r'$\left| \braket{\boldsymbol{\mu}_S} \right|$'
])
plt.ylabel(r'$z$-component')
plt.xlabel('time (arb.u.)')
plt.tight_layout()
plt.savefig('t1_decay.png', dpi=300)
def plot_t2():
xs = np.linspace(0, 3, 1000)
damping = 0.4
ys = np.exp(-xs * damping) * np.cos(10 * xs)
upper_envelope = np.exp(-xs * damping)
lower_envelope = -np.exp(-xs * damping)
plt.close('all')
plt.figure(figsize=(inch.cm_to_inch(7.5), inch.cm_to_inch(5)))
plt.plot(xs, ys, color=tum.tum_color(0))
alpha = 0.4
plt.plot(xs, upper_envelope, '--', color=tum.tum_color(0), alpha=alpha)
plt.plot(xs, lower_envelope, '--', color=tum.tum_color(0), alpha=alpha)
plt.ylim((-1.1, 1.1))
plt.yticks([-1, 0, 1], [
r'$-\left| \braket{\boldsymbol{\mu}_S} \right|$', 0,
r'$\left| \braket{\boldsymbol{\mu}_S} \right|$'
])
plt.ylabel(r'$y$-component')
plt.xlabel('time (arb.u.)')
plt.tight_layout()
plt.savefig('t2_decay.png', dpi=300)
def __create_and_scale_figure_and_axes(self):
self.fig, self.axes = plt.subplots(nrows=2, ncols=2)
self.fig.set_figheight(cm_to_inch(15))
self.fig.set_figwidth(cm_to_inch(15))
def __init__(self):
plt.rcParams['text.latex.preamble'] = [r"\usepackage{siunitx}"]
self.data = load_echo_data()
self.fig = plt.figure(figsize=(cm_to_inch(15), cm_to_inch(6.5)))
def create_figure_with_two_axes():
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_figheight(cm_to_inch(5.5))
fig.set_figwidth(cm_to_inch(15))
return fig, ax1, ax2
def __init__(self):
self.path = "//file/e24/Projects/ReinhardLab/data_setup_nv1/180704_hf_setup_deer_tests"
self.fig = plt.figure(figsize=(cm_to_inch(15), cm_to_inch(6.5)))
self.slow_rabis = self.load_file('pulsed.000.mat')
self.fast_rabis = self.load_file('pulsed.001.mat')