def plot_single(contains='', ind=0, fit=False, plot_range=None, **kw):
title = kw.pop('title', None)
folder = toolbox.latest_data(contains)
filename = toolbox.measurement_filename(folder, ext='dat')
V, f, c, i, st = np.loadtxt(filename, unpack=True)
fig = plt.figure()
ax = plt.subplot(111)
fltr = i == ind #(dd[:,2]<(y+0.05)) & (dd[:,2]>(j-0.05)) #
xx = f[np.where(fltr)]
zz = c[np.where(fltr)]
ax.plot(xx, zz)
if fit:
X = np.arange(np.min(xx), np.max(xx), 0.005)
fitfunc = fit_laserscan(xx, zz)
ax.plot(X, fitfunc['fitfunc'](X))
if plot_range != None:
ax.set_xlim(plot_range)
ax.set_ylabel('Counts')
ax.set_xlabel('Laser frequency [GHz]')
if title != None:
plt.title(title + ' ' + os.path.split(filename)[1])
else:
plt.title(os.path.split(filename)[1])
def load_hdf5data(self):
self.h5filepath = toolbox.measurement_filename(self.folder)
self.f = h5py.File(self.h5filepath, 'r')
self.name = self.f.keys()[0]
self.g = self.f[self.name]
self.measurementstring = os.path.split(self.folder)[1]
self.timestamp = os.path.split(os.path.split(self.folder)[0])[1] \
+ '/' + self.measurementstring[:6]
self.measurementstring = self.measurementstring[7:]
self.default_plot_title = self.timestamp+'\n'+self.measurementstring
def __init__(self, folder, **kw):
sequence.SequenceAnalysis.__init__(self,folder, **kw)
pq_folder=kw.pop('pq_folder', None)
if pq_folder != None:
if pq_folder =='bs_remote':
h5filepath = 'X:' + self.g.attrs['bs_data_path'][12:]
else:
h5filepath = toolbox.measurement_filename(pq_folder)
print 'h5filepath:',h5filepath
h5mode=kw.get('hdf5_mode', 'r')
self.pqf = h5py.File(h5filepath,h5mode)
else:
self.pqf=self.f
def get_temperature(older_than, newer_than):
x = np.array([])
y = np.array([])
while toolbox.latest_data(contains='temp_monitor',
older_than=older_than,
newer_than=newer_than,
raise_exc=False) != False:
## Find the data folder
timestamp, folder = toolbox.latest_data(contains='temp_monitor',
older_than=older_than,
newer_than=newer_than,
return_timestamp=True)
## convert timestamp to Epoch time (absolute time)
Time_temp1 = datetime.datetime(int(timestamp[:4]), int(timestamp[4:6]),
int(timestamp[6:8]),
int(timestamp[8:10]),
int(timestamp[10:12]),
int(timestamp[12:14]), 0)
Time_temp1 = datetime.datetime.timetuple(Time_temp1)
Epoch_timestamp = calendar.timegm(Time_temp1) # in seconds
## get the data
filename = toolbox.measurement_filename(folder, ext='dat')
data = np.loadtxt(filename, skiprows=16)
# convert time data to Epoch time
time_data = data[:, 0] * 3600 + Epoch_timestamp
temperature_data = (data[:, 1] - 100) / 0.385
# Filter out fluke measurements
indices_flukes = [i for i, j in enumerate(temperature_data) if j < 10]
time_data = np.delete(time_data, indices_flukes)
temperature_data = np.delete(temperature_data, indices_flukes)
x = np.concatenate(
(x, time_data)) # Time after beginning of measurement [sec]
y = np.concatenate((y, temperature_data))
older_than = str(int(timestamp) - 1)
## Ordering in time:
sort_indices = np.argsort(x)
x = np.array(x)[sort_indices]
y = np.array(y)[sort_indices]
print 'folder = ' + folder
return x, y, folder
def load_hdf5data(self, **kw):
self.h5filepath = toolbox.measurement_filename(self.folder)
h5mode=kw.pop('hdf5_mode', 'r')
self.f = h5py.File(self.h5filepath,h5mode)
for k in self.f.keys():
if type(self.f[k])==h5py.Group:
self.name = k
# print k
self.g = self.f[self.name]
self.measurementstring = os.path.split(self.folder)[1]
self.timestamp = os.path.split(os.path.split(self.folder)[0])[1] \
+ '/' + self.measurementstring[:6]
self.measurementstring = self.measurementstring[7:]
self.default_plot_title = self.timestamp+'\n'+self.measurementstring
def load_hdf5data(self, **kw):
self.h5filepath = toolbox.measurement_filename(self.folder)
h5mode = kw.pop('hdf5_mode', 'r')
self.f = h5py.File(self.h5filepath, h5mode)
for k in self.f.keys():
if type(self.f[k]) == h5py.Group and k in os.path.split(
self.h5filepath
)[1]: # PH added this check because sometimes additional files added to hdf5 file (06/16)
self.name = k
self.g = self.f[self.name]
break
self.measurementstring = os.path.split(self.folder)[1]
self.timestamp = os.path.split(os.path.split(self.folder)[0])[1] \
+ '/' + self.measurementstring[:6]
self.measurementstring = self.measurementstring[7:]
self.default_plot_title = self.timestamp + '\n' + self.measurementstring
def __init__(self, folder, **kw):
sequence.SequenceAnalysis.__init__(self, folder, **kw)
pq_folder = kw.pop('pq_folder', None)
pq_device = kw.pop('pq_device', '')
self.pq_device = pq_device
if pq_folder != None:
if pq_folder == 'bs_remote':
h5filepath = 'X:' + self.g.attrs['bs_data_path'][12:]
else:
h5filepath = toolbox.measurement_filename(pq_folder)
print 'h5filepath:', h5filepath
h5mode = kw.get('hdf5_mode', 'r')
self.pqf = h5py.File(h5filepath, h5mode)
else:
self.pqf = self.f
def plot_freq_against_index(contains='',
plot_range=None,
interp_res=0.01,
**kw):
title = kw.pop('title', None)
folder = toolbox.latest_data(contains)
filename = toolbox.measurement_filename(folder, ext='dat')
V, f, c, i, st = np.loadtxt(filename, unpack=True)
Y = np.unique(i)
if plot_range == None:
X = np.arange(np.min(f), np.max(f), interp_res)
else:
X = np.arange(plot_range[0], plot_range[1], interp_res)
Z = np.zeros((len(X), len(Y)))
for j, y in enumerate(Y):
fltr = i == y #(dd[:,2]<(y+0.05)) & (dd[:,2]>(j-0.05)) #
xx = f[np.where(fltr)]
zz = c[np.where(fltr)]
interp_func = interp1d(xx, zz, bounds_error=False, fill_value=0.)
Z[:, j] = np.nan_to_num(interp_func(X))
fig = plt.figure()
ax = plt.subplot(111)
ax.imshow(Z,
aspect='auto',
origin='lower',
vmin=0,
vmax=np.max(Z),
extent=[min(Y), max(Y), X[0], X[-1]],
cmap='binary')
ax.set_xlabel('Scan nr')
ax.set_ylabel('Laser frequency [GHz]')
if title != None:
plt.title(title + ' ' + os.path.split(filename)[1])
else:
plt.title(os.path.split(filename)[1])
save_and_close_plot(folder, name='Laserscan_2d')
def fix_hdf5_file(contains='', **kw):
folder = toolbox.latest_data(contains, **kw)
h5filepath = toolbox.measurement_filename(folder)
filename = os.path.split(folder)[1]
h5mode = kw.pop('hdf5_mode', 'r+')
f = h5py.File(h5filepath, h5mode)
groups = 0
for k in f.keys():
if type(f[k]) == h5py.Group:
groups += 1
group_name = k
print k
if groups != 1:
print 'Ambiguous hdf5 group! Cannot automatically rename!'
return
f[filename] = f[k]
def load_grouped_pulses(contains="", filepath=""):
filepath = filepath if filepath != "" else toolbox.measurement_filename(
toolbox.latest_data(contains=contains))
print filepath
with h5py.File(filepath, 'r') as hf:
num_pulses = np.max(
np.array(hf['pulse_sim/pulses'].keys()).astype(int))
overview_keys = [str(j) for j in (hf['pulse_sim/seq_overview'].keys())]
overview_data = {}
for key in overview_keys:
overview_data[key] = np.array(
hf.get('pulse_sim/seq_overview/' + key))
grouped_seq = []
for i in range(num_pulses):
overview = {}
for key in overview_keys:
overview[key] = overview_data[key][i]
pulses = np.array(hf.get('pulse_sim/pulses/' + str(i)))
grouped_seq.append([overview, pulses])
return grouped_seq
def result(self, folder):
data = toolbox.measurement_filename(directory = folder, ext = 'npz')
result = np.load(data)
return result
def spectral_diffusion(contains='',
print_avg_counts=False,
count_filter=None,
plot_range=None,
**kw):
title = kw.pop('title', None)
folder = toolbox.latest_data(contains)
filename = toolbox.measurement_filename(folder, ext='dat')
V, f, c, i, st = np.loadtxt(filename, unpack=True)
Y = np.unique(i)
centres = np.array([])
errors = np.array([])
diff = np.array([])
overlaps = np.array([])
avg_counts = 0.
for j, y in enumerate(Y):
fltr = i == y #(dd[:,2]<(y+0.05)) & (dd[:,2]>(j-0.05)) #
xx = f[np.where(fltr)]
zz = c[np.where(fltr)]
fitfunc = fit_laserscan(xx, zz, do_print=False)
if count_filter == None or np.sum(zz) > count_filter:
centres = np.append(centres, fitfunc['params'][2])
errors = np.append(errors, fitfunc['params'][3])
if centres.size != 1:
diff = np.append(diff, centres[-1] - centres[-2])
overlaps = np.append(overlaps, overlap(diff[-1], errors[-1]))
avg_counts = (avg_counts * j + np.sum(zz)) / (j + 1)
#print j,fitfunc['params'][3]
ys = range(len(centres))
fig = plt.figure()
ax = plt.subplot(111)
ax.errorbar(ys, centres, yerr=errors)
ax.set_ylabel('Centre')
ax.set_xlabel('Scan ind')
if print_avg_counts:
print 'Avg counts ', avg_counts
if plot_range != None:
ax.set_ylim(plot_range)
if title != None:
plt.title(title + ' ' + os.path.split(filename)[1])
else:
plt.title(os.path.split(filename)[1])
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(ys[:-1], 1e3 * diff)
ax.set_ylabel('Difference (MHz)')
ax.set_xlabel('Scan ind')
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(ys[:-1], overlaps)
ax.set_ylabel('Overlap with previous')
ax.set_xlabel('Scan ind')
print "Mean overlap ", np.mean(overlaps)
def plot_overlaid(contains='',
show_indiv=False,
fit=False,
compensate_drift=False,
plot_range=None,
interp_res=0.005,
**kw):
title = kw.pop('title', None)
folder = toolbox.latest_data(contains)
filename = toolbox.measurement_filename(folder, ext='dat')
V, f, c, i, st = np.loadtxt(filename, unpack=True)
if not show_indiv:
if plot_range == None:
if compensate_drift:
X = np.arange(
np.min(f) - np.mean(f),
np.max(f) - np.mean(f), interp_res)
else:
X = np.arange(np.min(f), np.max(f), interp_res)
else:
X = np.arange(plot_range[0], plot_range[1], interp_res)
Z = np.zeros(len(X))
Y = np.unique(i)
fig = plt.figure()
ax = plt.subplot(111)
for j, y in enumerate(Y):
fltr = i == y #(dd[:,2]<(y+0.05)) & (dd[:,2]>(j-0.05)) #
xx = f[np.where(fltr)]
zz = c[np.where(fltr)]
if compensate_drift:
avg_pos = np.sum(xx * zz) / np.sum(zz)
xx = xx - avg_pos
if show_indiv:
ax.plot(xx, zz)
else:
interp_func = interp1d(xx, zz, bounds_error=False, fill_value=0.)
Z = Z + np.nan_to_num(interp_func(X))
if not show_indiv:
ax.plot(X, Z)
if fit:
fitfunc = fit_laserscan(X, Z)
ax.plot(X, fitfunc['fitfunc'](X))
if plot_range != None:
ax.set_xlim(plot_range)
ax.set_ylabel('Counts')
ax.set_xlabel('Laser frequency [GHz]')
if title != None:
plt.title(title + ' ' + os.path.split(filename)[1])
else:
plt.title(os.path.split(filename)[1])
