def get_data(run=127, threshold=0.05, force=False, ishotref=0, ishotsam=1):
fname = "../data/fig_focusing_run%04d.h5" % run
if not os.path.isfile(fname) or force:
# this functions splits the data based on FOM < 0.5 --> p1 is the ref, p2 is the sample
data = xppl37_spectra.calcSpectraForRun(run, force)
idx = data.run.results[0].fom < 0.5
E = data.run.E
ref = ds.DataStorage(E=E, p1=data.p1[0], p2=data.p2[0])
sample = ds.DataStorage(E=E, p1=data.p1[1], p2=data.p2[1])
_, _, Abs = xppl37_spectra.calcAbs(ref, ref, threshold=threshold)
ref.Abs = Abs
_, _, Abs = xppl37_spectra.calcAbs(ref, sample, threshold=threshold)
sample.Abs = Abs
temp = ds.DataStorage(ref=ref, sample=sample)
temp.info = "Abs calculated with threshold = %.3f" % threshold
temp.save(fname)
print('maybe forced to calculate again the Spectra to get idx_fom')
data = ds.read(fname)
ref = data.ref
sample = data.sample
_, _, Abs = xppl37_spectra.calcAbs(ref, ref, threshold=threshold)
ref.Abs = Abs
_, _, Abs = xppl37_spectra.calcAbs(ref, sample, threshold=threshold)
sample.Abs = Abs
data = ds.DataStorage(ref=ref, sample=sample)
data["threshold"] = threshold
return data, idx
def setup_filename(self, runno, name, overwrite):
fname = '{}run_{}.h5'.format(self.dir_save, runno)
if name:
fname = '{}run_{}_{}.h5'.format(self.dir_save, runno, name)
if os.path.isfile(fname):
print('The file {} already exists'.format(fname))
if overwrite == 'question':
mes = input('Do you want to remove (y,n) or rename (type new name):')
if mes == 'y':
mes = True
elif mes == 'n':
mes = False
else:
mes = overwrite
print(mes)
if mes:
os.remove(fname)
ds = self.setup_ds(fname)
ds.save()
elif not mes:
ds = dsg.DataStorage(filename = fname)
else:
fname = '{}run_{}_{}.h5'.format(self.dir_save, runno, mes)
ds = self.setup_ds(fname)
ds.save()
else:
ds = self.setup_ds(fname)
ds.save()
print(fname)
return ds, fname
def setup_ds(self, fname):
ds = dsg.DataStorage(filename = fname)
print('Setting up datastorage file')
ds['config'] = self.cfg
ds['channels'] = {}
self.ds = ds
ds.save()
return ds
def saveAndRead(obj,fname="/tmp/test.h5",link_copy=False):
obj = datastorage.DataStorage(obj)
t0 = time.time()
obj.save(fname,link_copy=link_copy,raiseError=True)
t1 = time.time()
obj1 = datastorage.read(fname)
# for k in obj:
# print(" %s %s %s"%(k,str(obj[k])[:10],str(obj1[k])[:10]))
t2 = time.time()
return t2-t1,t1-t0
def get_data(run=82,calib=1,threshold=0.02,force=False):
fname = "../data/fig_fe_xas_run%04d.h5" % run
if not os.path.isfile(fname) or force:
E,p1,p2,Abs=xppl37_spectra.calcAbsForRun(run,merge_calibs=True,threshold=threshold)
temp = ds.DataStorage( E=E,p1=p1,p2=p2,Abs=Abs)
temp.info="Abs calculated with threshold = %.3f" % threshold
temp.save(fname)
data = ds.read(fname)
# nan is saved as -1 for masked arrays
for k in data.keys():
try:
data[k][data[k]==-1] =np.nan
except TypeError:
pass
return data
def get_data(runs=(155,156),threshold=0.02,force=False):
run_hash = "_".join(map(str,runs))
fname = "../data/fig_fe_xas_runs_%s.h5" % run_hash
if not os.path.isfile(fname) or force:
E,p1,p2,Abs=xppl37_spectra.calcAbsForRun(runs,merge_calibs=True,threshold=threshold)
temp = ds.DataStorage( E=E,p1=p1,p2=p2,Abs=Abs)
temp.info="Abs calculated with threshold = %.3f" % threshold
temp.save(fname)
data = ds.read(fname)
# nan is saved as -1 for masked arrays
for k in data.keys():
try:
data[k][data[k]==-1] =np.nan
except TypeError:
pass
# for runs 155,156 the vernier stopped working after shots ~ 2000
if runs == (155,156):
data.p1=data.p1[:2000]
data.p2=data.p2[:2000]
data.Abs=data.Abs[:2000]
return data
def get_data(run, threshold=0.02, force=False):
if run == 80:
refCalibs = slice(1, None, 2)
elif run == 84:
refCalibs = slice(None, None, 2)
elif run == 60:
refCalibs = slice(None, None, 2)
elif run == 76:
refCalibs = slice(None, None)
else:
refCalibs = slice(None, None, 2)
fname = "../data/fig_fel_modes_run%04d.h5" % run
if not os.path.isfile(fname) or force:
r = xanes_analyzeRun.AnalyzeRun(run=run)
r.load()
E = r.E
calibs = list(r.results.keys())
calibs.sort()
calibs = calibs[refCalibs]
p1 = np.vstack([r.results[c].p1 for c in calibs])
p2 = np.vstack([r.results[c].p2 for c in calibs])
#temp = ds.DataStorage( E=E,p1=p1.astype(np.float16),p2=p2.astype(np.float16))
temp = ds.DataStorage(E=E, p1=p1, p2=p2)
_, _, Abs = xppl37_spectra.calcAbs(temp, threshold=0.02)
temp.Abs = Abs #.astype(np.float16)
temp.info = "Abs calculated with threshold = 0.02"
temp.save(fname)
data = ds.read(fname)
# nan is saved as -1 for masked arrays
for k in data.keys():
try:
data[k][data[k] == -1] = np.nan
except (TypeError, AttributeError):
pass
print("Run %d → nshots = %d" % (run, len(data.p1)))
p1, p2, Abs = xppl37_spectra.calcAbs(data, threshold=threshold)
data.Abs = Abs
return data
if bins is None:
bin_center = motor_pos
else:
bin_center = bins
bin_size = min(np.diff(bin_center))
bin_edges = np.append(bin_center[0] - 0.5 * bin_size,
bin_center + 0.5 * bin_size)
bin_target = np.digitize(motor_pos_tot, bin_edges)
""" Loop through all bins """
print('Start bin loop. \n')
for bin_count, step_pos in enumerate(bin_center):
if (not np.in1d(bin_count, step)) & (not (step == None)):
continue
start = time.time()
results = datastorage.DataStorage()
print('\n\nAnalyze bin {} of {}'.format(bin_count,
len(bin_center) - 1))
""" Find index for laser on/off shots in the bin """
idx_bin = bin_target == (bin_count + 1)
eventIds_bin = eventIds[idx_bin]
if config.laserReprate:
laser_off = np.mod(eventIds_bin, int(
100 / config.laserReprate)) > 0
laser_on = np.logical_not(laser_off)
else:
laser_off = np.ones(eventIds_bin.shape)
laser_on = np.zeros(eventIds_bin.shape)
JFdata = dict()
""" Analyze I0 detector """
def get_1b():
E,data=np.loadtxt("../data/Fe_1bunch.txt",unpack=True)
return ds.DataStorage(E=E*1e3,data=data/2.05+0.07)