def fit_linewidth(folder=None, threshold=0.01, delta=0.03, **kw):
print folder
show_plots = kw.pop(
'show_plots', True
) #if the keyword show_plots exists, set to the given value, otherwise default is True
a = cga.cavity_analysis(folder)
print a.f
a.get_x_pts() #the x points of the laser of voltage scan
a.get_data()
#x_datas contains nr_repetitions copies of a.x_pts
x_datas = np.full((a.nr_scans, len(a.x_pts)), a.x_pts)
single_scan_lw = np.zeros(a.nr_scans)
u_single_scan_lw = np.zeros(a.nr_scans)
#new_delays = np.zeros(len(a.sweep_pts)*len(a.nr_scans))
name = '' #reps_0_1_10_11_20_21_30_31_40_41'
print a.nr_scans
print x_datas
print a.data
for i in np.arange(a.nr_scans):
single_scan_lw[i], u_single_scan_lw[i] = cf.fit_piezo_plots(
folder,
x_datas[i],
a.data[i],
show_plots=True,
threshold=threshold,
averaging=False)
return single_scan_lw, u_single_scan_lw #SvD: you want to return not the index i, but the whole array
def lengthscan_analysis(folder, **kw):
do_get_peaks = kw.pop('do_get_peaks', False)
a = cga.cavity_analysis(folder)
x, y = a.get_lengthscan_data()
fig, ax = plt.subplots()
ax.set_title(folder)
ax.set_xlabel('length tuning voltage (V)')
ax.set_ylabel('PD signal (V)')
plt.plot(x, y)
if do_get_peaks:
minimum_peak_height = kw.pop('minimum_peak_height',
0.4 * max(a.y_data))
minimum_peak_distance = kw.pop('minimum_peak_distance', 60)
a.peak_xs = np.array([])
a.peak_ys = np.array([])
indices = peakdetect.detect_peaks(a.y_data,
mph=minimum_peak_height,
mpd=minimum_peak_distance)
for ii in indices:
a.peak_xs = np.append(a.peak_xs, a.x_data[ii])
a.peak_ys = np.append(a.peak_ys, a.y_data[ii])
ax.plot(a.peak_xs, a.peak_ys, '+', mfc=None, mec='r', mew=2, ms=8)
fig.savefig(a.folder + '/PDsignal_vs_length_tuning_voltage.png')
plt.show()
a.finish()
if do_get_peaks:
return a.peak_xs
def fit_3_lorentzians(indexmax,
date,
g_a1,
g_A1,
g_x01,
g_gamma1,
g_dx,
g_A2,
folder=None,
fixed=[],
threshold=0.01,
delta=0.03,
**kw):
print folder
show_plots = kw.pop(
'show_plots', True
) #if the keyword show_plots exists, set to the given value, otherwise default is True
a = cga.cavity_analysis(folder)
print a.f
a.get_x_pts() #the x points of the laser of voltage scan
a.get_data()
#x_datas contains nr_repetitions copies of a.x_pts
#x_datas = np.full((a.nr_scans,len(a.x_pts)),a.x_pts)
single_scan_lw = np.zeros(a.nr_scans)
u_single_scan_lw = np.zeros(a.nr_scans)
#new_delays = np.zeros(len(a.sweep_pts)*len(a.nr_scans))
name = '' #reps_0_1_10_11_20_21_30_31_40_41'
#Now we check to see that we have the correct voltage and amplitude points.
# print a.x_pts
# print a.data
#We want to analyze only one of the peaks that pops up for the linewidth. So we select a portion of the data and do the analysis on this
#First we have to find the length of the data
print('Length of Data')
print len(a.x_pts)
#The entire range of the data
x = a.x_pts
y = a.data[0]
print len(x)
print len(y)
#Plot the entire range of the data
fig, ax = plt.subplots(figsize=(14, 8))
ax.plot(x, y)
ax.set_xlabel("Voltage (V)]", fontsize=14)
ax.set_ylabel("Intensity (a.u.)", fontsize=14)
ax.set_title('Raw' + filename + date)
plt.savefig(os.path.join(folder, filename + '_' + date + '.png'))
#Selecting one peak of the data for the proper fit
#indexmax=y.argmax(axis=0)
#print indexmax
#indexmax=30
dataleft = indexmax - (0.05 * len(x))
dataright = indexmax + (0.05 * len(x))
x = x[dataleft:dataright]
y = y[dataleft:dataright]
print len(x)
print len(y)
#x = 1.e3*np.a.x_pts[0]#[3000:] # multiplied by factor 1.e3 to get ms
#y = np.a#[3000:]
#Plot just one of the peaks
fig, ax = plt.subplots(figsize=(8, 4))
ax.plot(x, y)
ax.set_xlabel("Voltage (V)]", fontsize=14)
ax.set_ylabel("Intensity (a.u.)", fontsize=14)
#ax.set_xlim(x[0],x[-1])
#X_min_freq = g_x01-ax.get_xlim()[0]
#X_max_freq = g_x01+ax.get_xlim()[-1]
#xticklabels = np.linspace(X_min_freq,X_max_freq,n_xticks)
#ax.set_xticks(xticks)
ax.set_title('Select Data' + filename + date)
plt.savefig(
os.path.join(folder, filename + 'Select Data' + '_' + date + '.png'))
print 'fitting data to 3 lorentzians'
p0, fitfunc, fitfunc_str = common.fit_3lorentz_symmetric(
g_a1, g_A1, g_x01, g_gamma1, g_dx, g_A2)
fit_result = fit.fit1d(x,
y,
None,
p0=p0,
fitfunc=fitfunc,
do_print=True,
ret=True,
fixed=fixed)
# # x01 = fit_result['params_dict']['x01']
dx = fit_result['params_dict']['dx']
gamma1 = fit_result['params_dict']['gamma1']
# # gamma2 = fit_result['params_dict']['gamma2']
u_gamma1 = fit_result['error_dict']['gamma1']
# # u_gamma2 = fit_result['error_dict']['gamma2']
scaling = EOM_freq / dx #scale the known EOM freq with the separation here.
linewidth = gamma1 * scaling #scale the linewidth to get linewidht in frequency
u_linewidth = u_gamma1 * scaling
linewidth_string = 'gamma = ' + str(round(linewidth, 2)) + '+-' + str(
round(u_linewidth, 3)) + 'GHz'
print linewidth_string
#Plotting
fig, ax = plt.subplots(figsize=(8, 4))
plot.plot_fit1d(fit_result,
np.linspace(x[0], x[-1], len(x)),
ax=ax,
label='Fit',
show_guess=True,
plot_data=True,
color='red',
data_linestyle='-',
print_info=False)
ax.set_xlabel("Frequency [GHz]", fontsize=14)
ax.set_ylabel("Intensity (a.u.)", fontsize=14)
ax.set_xlim(x[0], x[-1])
print ax.set_xlim(x[0], x[-1])
xticks = np.linspace(ax.get_xlim()[0], ax.get_xlim()[-1], n_xticks)
#rescaling for x-axis in GHz
X_min_freq = ax.get_xlim()[0] * scaling
X_max_freq = ax.get_xlim()[-1] * scaling
print g_x01 * scaling
print X_min_freq
print X_max_freq
print scaling
xticklabels = np.linspace(X_min_freq, X_max_freq, n_xticks)
xticklabels_round = []
for j in xticklabels:
round_ = round(j, 3)
xticklabels_round = np.append(xticklabels_round, round_)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels_round)
ax.set_title('Fitted' + filename + date + '\n' + linewidth_string)
plt.savefig(os.path.join(folder, filename + '_' + date + '_fit.png'))
def fit_sweep_msync_delays(folder = tb.latest_data('141702'),threshold=0.01,delta=0.05):
a = cga.cavity_analysis(folder)
print a.f
a.get_x_pts() #the x points of the laser of voltage scan
a.get_sweep_pts() #the sweep points (here: delays)
a.get_sweep_data()
#x_datas contains nr_repetitions copies of a.x_pts
x_datas = np.full((a.nr_repetitions,len(a.x_pts)),a.x_pts)
selected_reps = np.arange(a.nr_repetitions)#np.array([0,5,10,15])#np.arange(a.nr_repetitions)#np.array([0,1,10,11,20,21,30,31,40,41]) #
avg_lws = np.zeros(len(a.sweep_pts))
u_avg_lws = np.zeros(len(a.sweep_pts))
peak_range=np.zeros([len(a.sweep_pts),len(selected_reps)])
nr_peaks=np.zeros([len(a.sweep_pts),len(selected_reps)])
#new_delays = np.zeros(len(a.sweep_pts)*len(a.nr_scans))
name = ''#reps_0_1_10_11_20_21_30_31_40_41'
for i,pt in enumerate(a.sweep_pts):
for j,rep in enumerate(selected_reps):
peak_range[i,j],nr_peaks[i,j]=cf.determine_peak_range(folder,a.sweep_data[i,j],a.x_pts,delta,tag=str(pt)+'_'+str(rep),show_plot=False)
avg_peak_range = np.average(peak_range,axis=1)
u_avg_peak_range = np.std(peak_range,axis=1)
avg_nr_peaks = np.average(nr_peaks,axis=1)
u_avg_nr_peaks = np.std(nr_peaks,axis=1)
print avg_peak_range
print u_avg_peak_range
print avg_nr_peaks
print u_avg_nr_peaks
#plot and save peak range vs sync delay
fig,ax = plt.subplots(figsize=(6,4.7))
ax.errorbar(a.sweep_pts, avg_peak_range, fmt='o', yerr=u_avg_peak_range)
# ax.legend()
ax.set_xlabel('sync delay (ms)',fontsize=14)
ax.set_ylabel('average peak range (V)',fontsize=14)
ax.tick_params(axis = 'both', which = 'major',labelsize = 14)
ax.set_xlim(a.sweep_pts[0],a.sweep_pts[-1])
ax.set_ylim(0,max(avg_peak_range)+max(u_avg_peak_range))
ax.set_title(folder +'\n'+'peakrange_vs_delays'+name )
fig.savefig(folder +'/'+'peakrange_vs_delays'+name+'.png')
plt.show()
plt.close()
#plot and save nr peaks vs sync delay
fig,ax = plt.subplots(figsize=(6,4.7))
ax.errorbar(a.sweep_pts, avg_nr_peaks, fmt='o', yerr=u_avg_nr_peaks)
# ax.legend()
ax.set_xlabel('sync delay (ms)',fontsize=14)
ax.set_ylabel('average nr peaks',fontsize=14)
ax.tick_params(axis = 'both', which = 'major',labelsize = 14)
ax.set_xlim(a.sweep_pts[0],a.sweep_pts[-1])
ax.set_ylim(0,max(avg_nr_peaks)+max(u_avg_nr_peaks))
ax.set_title(folder +'\n'+'nrpeaks_vs_delays'+name)
fig.savefig(folder +'/'+'nrpeaks_vs_delays'+name+'.png')
plt.show()
plt.close()
#closed the file
a.finish()
return avg_peak_range, u_avg_peak_range, avg_nr_peaks, u_avg_nr_peaks