def scan_peak(id):
aname = 's2'
try:
if DEBUGGING :
aname = 'dummy_motor'
except:
pass
pos = eval('peak_' + str(id + 1) + '.value')
if id < 3:
exec('axis_min.value=' + 'peak_' + str(id + 1) + '.value-2')
exec('axis_max.value=' + 'peak_' + str(id + 1) + '.value+2')
elif id == 3:
exec('axis_min.value=' + 'peak_' + str(id + 1) + '.value-3.5')
exec('axis_max.value=' + 'peak_' + str(id + 1) + '.value+2')
elif id == 4:
exec('axis_min.value=' + 'peak_' + str(id + 1) + '.value-3')
exec('axis_max.value=' + 'peak_' + str(id + 1) + '.value+3')
sicsext.call_back = __load_experiment_data__
print 'scan on ' + aname
print 'scan_' + str(id + 1) + '.value'
spara = eval('scan_' + str(id + 1) + '.value')
print spara
exec('sicsext.runbmonscan(\'' + aname + '\', ' + spara + ', 0, \'call_back()\')')
time.sleep(2)
slog('Nexus file: ' + str(sicsext.getBaseFilename()))
pos_index.value = id + 1
fit_curve(axis_min.value, axis_max.value)
if id == 3:
fit_2nd_peak(peak_4.value + 2, peak_4.value + 6)
def find_a2():
aname = 'a2'
try:
if DEBUGGING :
aname = 'dummy_motor'
except:
pass
axis_name.value = aname
sicsext.call_back = __load_experiment_data__
slog('bmonscan ' + aname + ' ' + scan.value)
exec('sicsext.runbmonscan(\'' + aname + '\', ' + scan.value + ', 0, \'call_back()\')')
time.sleep(2)
slog('Nexus file: ' + str(sicsext.getBaseFilename()))
fit_curve()
def load_experiment_data():
basename = sicsext.getBaseFilename()
fullname = str(System.getProperty('sics.data.path') + '/' + basename)
df.datasets.clear()
ds = df[fullname]
dname = str(data_name.value)
data = SimpleData(ds[dname])
# data = ds[str(data_name.value)]
axis = SimpleData(ds[str(axis_name.value)])
if data.size > axis.size:
data = data[:axis.size]
if normalise.value :
if dname == 'bm1_counts':
tname = 'bm1_time'
elif dname == 'bm2_counts':
tname = 'bm2_time'
else:
tname = 'detector_time'
norm = ds[tname]
if norm != None and hasattr(norm, '__len__'):
avg = norm.sum() / len(norm)
niter = norm.item_iter()
if niter.next() <= 0:
niter.set_curr(1)
data = data / norm * avg
ds2 = Dataset(data, axes=[axis])
ds2.title = ds.id
ds2.location = fullname
fit_min.value = axis.min()
fit_max.value = axis.max()
Plot1.set_dataset(ds2)
Plot1.x_label = axis_name.value
Plot1.y_label = str(data_name.value)
Plot1.title = str(data_name.value) + ' vs ' + axis_name.value
Plot1.pv.getPlot().setMarkerEnabled(True)
def load_experiment_data():
basename = sicsext.getBaseFilename()
fullname = str(System.getProperty('sics.data.path') + '/' + basename)
df.datasets.clear()
ds = df[fullname]
dname = str(data_name.value)
data = SimpleData(ds[dname])
# data = ds[str(data_name.value)]
axis = SimpleData(ds[str(axis_name.value)])
if data.size > axis.size:
data = data[:axis.size]
if normalise.value:
if dname == 'bm1_counts':
tname = 'bm1_time'
elif dname == 'bm2_counts':
tname = 'bm2_time'
else:
tname = 'detector_time'
norm = ds[tname]
if norm != None and hasattr(norm, '__len__'):
avg = norm.sum() / len(norm)
niter = norm.item_iter()
if niter.next() <= 0:
niter.set_curr(1)
data = data / norm * avg
ds2 = Dataset(data, axes=[axis])
ds2.title = ds.id
ds2.location = fullname
fit_min.value = axis.min()
fit_max.value = axis.max()
Plot1.set_dataset(ds2)
Plot1.x_label = axis_name.value
Plot1.y_label = str(data_name.value)
Plot1.title = str(data_name.value) + ' vs ' + axis_name.value
Plot1.pv.getPlot().setMarkerEnabled(True)
