def save(self, HH_data=None):
reps = self.adwin_var('adwin_lt1', 'completed_reps')
self.save_adwin_data(
'adwin_lt1',
'data',
[
'CR_preselect',
'CR_probe',
'completed_reps',
'noof_starts',
('CR_hist_time_out', ADWINLT1_MAX_RED_HIST_CTS),
('CR_hist_all', ADWINLT1_MAX_RED_HIST_CTS),
('repump_hist_time_out', ADWINLT1_MAX_REPUMP_HIST_CTS),
('repump_hist_all', ADWINLT1_MAX_REPUMP_HIST_CTS),
('CR_after', reps),
('statistics', ADWINLT1_MAX_STAT),
('SSRO1_results', reps),
('SSRO2_results', reps),
# ('PLU_Bell_states', reps), we took that out for now (oct 7, 2013)
('CR_before', reps),
('CR_probe_timer', reps),
])
reps = self.adwin_var('adwin_lt1', 'completed_reps')
self.save_adwin_data('adwin_lt2', 'data', [
'completed_reps', 'total_CR_counts', ('CR_before', reps),
('CR_after', reps), ('CR_hist', ADWINLT2_MAX_CR_HIST_CTS),
('CR_hist_time_out', ADWINLT2_MAX_CR_HIST_CTS),
('repump_hist_time_out', ADWINLT2_MAX_REPUMP_HIST_CTS),
('repump_hist_all', ADWINLT2_MAX_REPUMP_HIST_CTS),
('SSRO_lt2_data', reps), ('statistics', ADWINLT2_MAX_STAT)
])
params_lt1 = self.params_lt1.to_dict()
lt1_grp = h5.DataGroup("lt1_params", self.h5data, base=self.h5base)
for k in params_lt1:
lt1_grp.group.attrs[k] = self.params_lt1[k]
self.h5data.flush()
params_lt2 = self.params_lt2.to_dict()
lt2_grp = h5.DataGroup("lt2_params", self.h5data, base=self.h5base)
for k in params_lt2:
lt2_grp.group.attrs[k] = self.params_lt2[k]
self.h5data.flush()
if HH_data != None:
self.h5data['HH_data'] = HH_data
self.h5data.flush()
self.save_params()
def save_adwin_data(self, name, variables):
grp = h5.DataGroup(name, self.h5data, base=self.h5base)
for v in variables:
name = v if type(v) == str else v[0]
data = self.adwin_var(v)
if data != None:
grp.add(name, data=data)
# save all parameters in each group (could change per run!)
self.save_params(grp=grp.group)
# then save all specific adwin params, overwriting other params
# if double
adwinparams = self.adwin_process_params.to_dict()
for k in adwinparams:
grp.group.attrs[k] = adwinparams[k]
self.h5data.flush()
def measure(self):
x = np.linspace(0, self.params['xmax'], self.params['xpts'])
y = np.linspace(0, self.params['ymax'], self.params['ypts'])
z = np.zeros((self.params['xpts'], self.params['ypts']))
for i, xval in enumerate(x):
print 'linesweep %d / %d ...' % (i + 1, self.params['xpts'])
for j, yval in enumerate(y):
qt.msleep(0.01)
z[i, j] = xval * yval
# save the data into the pre-created group.
# note the passed meta-data (optional).
# you can have a look at the data with HDFView
# (you can get it from hdfgroup.com)
grp = h5.DataGroup('xy-scan', self.h5data, base=self.h5base)
grp.add('x', data=x, unit='um', note='somewhat inaccurate')
grp.add('y', data=y, unit='um')
grp.add('z', data=z, unit='counts per second', dimensions='1=x, 2=y')
return
# fits into numpy arrays can be stuffed in there.
dat['/my first group'].attrs['description'] = 'an utterly pointless group'
dat['/my first group'].attrs['yo mama'] = 'probably fat'
dat['/my first group/my first subgroup/an array'].\
attrs['unit'] = 'TT'
dat['/my first group/my first subgroup/an array'].\
attrs['ridiculously large magnetic fields'] = True
# don't forget closing! (ends up unreadable otherwise)
dat.close()
### A simple approach to group several data sets (e.g., for a N-d matrix plus
### the N axes) into a group somewhat autmated (not too many features yet)
dat = h5.HDF5Data(name='data_number_two')
grp = h5.DataGroup('my_data',
dat,
description='pretty useless',
taken_by='some student') # arbitrary metadata as kw
# register some data dimensions
grp.add_coordinate('lab temperature', unit='deg C') # arbitrary metadata as kw
grp.add_coordinate('lab air pressure', unit='hPa')
grp.add_value('overnight lab volume increase', unit='l')
# set data (setting requires the dimensions to be set up)
# with this group class we can re-set exisiting arrays; normally hdf5 requires
# deletion and re-creation; like this we can keep the meta data.
grp['lab temperature'] = np.arange(25)
grp['lab air pressure'] = np.arange(25) + 1000
grp['overnight lab volume increase'] = np.random.rand(25, 25)
print dat['/my_data/lab temperature']
def save_2D_data(self, timename='t (ns)', sweepname='sweep_value', ret=False):
grp=h5.DataGroup('p7889_raw_data',self.h5data,base=self.h5base)
self.save_params(grp=grp.group)
x,yint,z = self._get_p7889_data()
#### convert x to bins.... because this is what we see on the
y=self.params['sweep_pts']
xx, yy = np.meshgrid(x,y)
#dat=h5.HDF5Data(name=self.name)
grp.add_coordinate(name='t (ns)',data=x, dtype='f8')
grp.add_coordinate(name=self.params['sweep_name'],data=y)
grp.add_value(name='Counts',data=z)
# m2.save_instrument_settings_file(grp.group)
#do post-processing
grp1=h5.DataGroup('processed data',self.h5data,base=self.h5base)
if self.params['Eval_ROI_start']<np.amin(x):
startind=[0]
else:
startind=np.where(x==self.params['Eval_ROI_start'])
if self.params['Eval_ROI_end']>np.amax(x):
endind=[x.size-1]
else:
endind=np.where(x==self.params['Eval_ROI_end'])
#strip the count array of points in time that lie outside of the specified ROIs
# print x
# print startind,endind
sliced=z[:,startind[0]:endind[0]]
# print sliced
summation=np.array(range(self.params['pts']))
for i,row in enumerate(sliced):
summation[i]=np.sum(row)
grp1.add_coordinate(name=self.params['sweep_name'],data=y)
grp1.add_value(name='Counts in ROI',data=summation)
plt=qt.plot(name=self.mprefix+self.name,clear=True)
plt.add(y,summation,'rO',yerr=np.sqrt(summation))
plt.add(y,summation,'r-')
plt.set_plottitle(self.name)
plt.set_legend(False)
plt.set_xlabel(self.params['sweep_name'])
plt.set_ylabel('counts')
plt.save_png(self.datafolder+'\\'+self.name+'.png')
self.h5data.flush()
if ret:
return x,y,z
else:
self.h5data.flush()