def analyze(self):
h5_filename = self.output_filename +'.h5'
with tb.open_file(h5_filename, 'r+') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
meta_data = in_file_h5.root.meta_data[:]
hit_data = self.dut.interpret_raw_data(raw_data, meta_data)
in_file_h5.createTable(in_file_h5.root, 'hit_data', hit_data, filters=self.filter_tables)
status_plot = plotting.plot_status(h5_filename)
occ_plot, H = plotting.plot_occupancy(h5_filename)
tot_plot,_ = plotting.plot_tot_dist(h5_filename)
lv1id_plot, _ = plotting.plot_lv1id_dist(h5_filename)
scan_pix_hist, _ = plotting.scan_pix_hist(h5_filename)
output_file(self.output_filename + '.html', title=self.run_name)
save(vplot(hplot(occ_plot, tot_plot, lv1id_plot), scan_pix_hist, status_plot))
def tdc_table(self, scanrange):
h5_filename = self.output_filename + '.h5'
with tb.open_file(h5_filename, 'r+') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
meta_data = in_file_h5.root.meta_data[:]
if (meta_data.shape[0] == 0): return
repeat_command = in_file_h5.root.meta_data.attrs.kwargs
a = repeat_command.rfind("repeat_command: ")
repeat_command = repeat_command[a + len("repeat_command: "):a + len("repeat_command: ") + 7]
a = repeat_command.rfind("\n")
repeat_command = int(repeat_command[0:a])
param, index = np.unique(meta_data['scan_param_id'], return_index=True)
pxl_list = []
for p in param:
pix_no = int(p) / int(len(self.inj_charge))
pxl_list.append(self.pixel_list[pix_no][0] * 64 + self.pixel_list[pix_no][1])
index = index[1:]
index = np.append(index, meta_data.shape[0])
index = index - 1
stops = meta_data['index_stop'][index]
split = np.split(raw_data, stops)
avg_tdc = []
avg_tdc_err = []
avg_del = []
avg_del_err = []
hits = []
deletelist = ()
for i in range(len(split[:-1])): # loop on pulses
rwa_data_param = split[i]
tdc_data = rwa_data_param & 0xFFF # take last 12 bit
tdc_delay = (rwa_data_param & 0x0FF00000) >> 20
counter = 0.0
TOT_sum = 0.0
DEL_sum = 0.0
if (tdc_data.shape[0] == 0 or tdc_data.shape[0] == 1):
counter = 1.0
for j in range(tdc_data.shape[0]): # loop on repeats
if (j > 0):
counter += 1
TOT_sum += tdc_data[j]
DEL_sum += tdc_delay[j]
if (counter > 1):
hits.append(counter)
avg_tdc.append((float(TOT_sum) / float(counter)) * 1.5625)
avg_tdc_err.append(1.5625 / (np.sqrt(12.0 * counter)))
avg_del.append((float(DEL_sum) / float(counter)) * 1.5625)
avg_del_err.append(1.5625 / (np.sqrt(12.0 * counter)))
else:
deletelist = np.append(deletelist, i)
pxl_list = np.delete(pxl_list, deletelist)
newpix = [0]
pix_no_old = pxl_list[0]
runparam = 0
for p in pxl_list:
if p != pix_no_old:
newpix = np.append(newpix, runparam)
pix_no_old = p
runparam = runparam + 1
addedvalues = 0
for pixels in range(len(newpix)):
missingvalues = 0
if newpix[pixels] == newpix[-1]:
missingvalues = scanrange - abs(newpix[pixels] + addedvalues - len(hits))
else:
if abs(newpix[pixels] - newpix[pixels + 1]) < scanrange:
missingvalues = scanrange - abs(newpix[pixels] - newpix[pixels + 1])
if missingvalues != 0:
hits = np.insert(hits, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_tdc = np.insert(avg_tdc, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_tdc_err = np.insert(avg_tdc_err, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_del = np.insert(avg_del, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_del_err = np.insert(avg_del_err, newpix[pixels] + addedvalues, np.zeros(missingvalues))
pxl_list = np.insert(pxl_list, newpix[pixels] + addedvalues,
(pxl_list[newpix[pixels] + addedvalues]) * np.ones(missingvalues))
addedvalues = addedvalues + missingvalues
injections = []
for pixels in range(int(len(pxl_list) / len(self.inj_charge))):
for i in range(len(self.inj_charge)):
injections = np.append(injections, self.inj_charge[i])
pix, stop = np.unique(pxl_list, return_index=True)
stop = np.sort(stop)
stop = list(stop)
stop.append(len(avg_tdc))
repeat_command_dic={}
repeat_command_dic['repeat_command']=repeat_command
avg_tab = np.rec.fromarrays([injections, pxl_list, hits, avg_tdc, avg_tdc_err, avg_del, avg_del_err],
dtype=[('charge', float), ('pixel_no', int), ('hits', int),
('tot_ns', float), ('err_tot_ns', float), ('delay_ns', float),
('err_delay_ns', float)])
tdc_table=in_file_h5.createTable(in_file_h5.root, 'tdc_data', avg_tab, filters=self.filter_tables)
tdc_table.attrs.repeat_command = repeat_command_dic
thresholds = ()
expfit0 = ()
expfit1 = ()
expfit2 = ()
expfit3 = ()
pixels = ()
for i in range(len(stop) - 1):
s1 = int(stop[i])
s2 = int(stop[i + 1])
A, mu, sigma = analysis.fit_scurve(hits[s1:s2], injections[s1:s2],repeat_command)
if np.max(hits[s1:s2]) > (repeat_command + 200): # or mu > 3000:
thresholds = np.append(thresholds, 0)
expfit0 = np.append(expfit0, 0)
expfit1 = np.append(expfit1, 0)
expfit2 = np.append(expfit2, 0)
expfit3 = np.append(expfit3, 0)
pixels = np.append(pixels, pxl_list[s1])
continue
for values in range(s1, s2):
if injections[values] >= 5 / 4 * mu:
s1 = values
break
numberer = 0
hitvaluesold = hits[-1]
for hitvalues in hits[s1:s2]:
if abs(hitvalues - hitvaluesold) <= 1 and hitvalues != 0:
break
numberer = numberer + 1
hitvaluesold = hitvalues
if numberer == len(avg_del[s1:s2]):
numberer = 0
expfit = analysis.fit_exp(injections[s1:s2], avg_del[s1:s2], mu, abs(numberer))
startexp = -expfit[0] * np.log((25.0 + np.min(avg_del[s1:s2]) - expfit[3]) / expfit[2]) - expfit[1]
if np.isnan(startexp) or startexp >= 2000:
startexp = 0
thresholds = np.append(thresholds, startexp)
expfit0 = np.append(expfit0, expfit[0])
expfit1 = np.append(expfit1, expfit[1])
expfit2 = np.append(expfit2, expfit[2])
expfit3 = np.append(expfit3, expfit[3])
pixels = np.append(pixels, pxl_list[s1])
thresh = np.rec.fromarrays([pixels, thresholds, expfit0, expfit1, expfit2, expfit3],
dtype=[('pixel_no', int), ('td_threshold', float),
('expfit0', float), ('expfit1', float), ('expfit2', float),
('expfit3', float)])
in_file_h5.createTable(in_file_h5.root, 'td_threshold', thresh, filters=self.filter_tables)
p1, p2, single_scan = plotting.plot_timewalk(h5_filename)
output_file(self.output_filename + '.html', title=self.run_name)
status = plotting.plot_status(h5_filename)
save(hplot(vplot(p1, p2, status), single_scan))
def tdc_table(self, scanrange):
h5_filename = self.output_filename + '.h5'
with tb.open_file(h5_filename, 'r+') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
meta_data = in_file_h5.root.meta_data[:]
if (meta_data.shape[0] == 0): return
repeat_command = in_file_h5.root.meta_data.attrs.kwargs
a = repeat_command.rfind("repeat_command: ")
repeat_command = repeat_command[a + len("repeat_command: "):a +
len("repeat_command: ") + 7]
a = repeat_command.rfind("\n")
repeat_command = int(repeat_command[0:a])
param, index = np.unique(meta_data['scan_param_id'],
return_index=True)
pxl_list = []
for p in param:
pix_no = int(p) / int(len(self.inj_charge))
pxl_list.append(self.pixel_list[pix_no][0] * 64 +
self.pixel_list[pix_no][1])
index = index[1:]
index = np.append(index, meta_data.shape[0])
index = index - 1
stops = meta_data['index_stop'][index]
split = np.split(raw_data, stops)
avg_tdc = []
avg_tdc_err = []
avg_del = []
avg_del_err = []
hits = []
deletelist = ()
for i in range(len(split[:-1])): # loop on pulses
rwa_data_param = split[i]
tdc_data = rwa_data_param & 0xFFF # take last 12 bit
tdc_delay = (rwa_data_param & 0x0FF00000) >> 20
counter = 0.0
TOT_sum = 0.0
DEL_sum = 0.0
if (tdc_data.shape[0] == 0 or tdc_data.shape[0] == 1):
counter = 1.0
for j in range(tdc_data.shape[0]): # loop on repeats
if (j > 0):
counter += 1
TOT_sum += tdc_data[j]
DEL_sum += tdc_delay[j]
if (counter > 1):
hits.append(counter)
avg_tdc.append((float(TOT_sum) / float(counter)) * 1.5625)
avg_tdc_err.append(1.5625 / (np.sqrt(12.0 * counter)))
avg_del.append((float(DEL_sum) / float(counter)) * 1.5625)
avg_del_err.append(1.5625 / (np.sqrt(12.0 * counter)))
else:
deletelist = np.append(deletelist, i)
pxl_list = np.delete(pxl_list, deletelist)
newpix = [0]
pix_no_old = pxl_list[0]
runparam = 0
for p in pxl_list:
if p != pix_no_old:
newpix = np.append(newpix, runparam)
pix_no_old = p
runparam = runparam + 1
addedvalues = 0
for pixels in range(len(newpix)):
missingvalues = 0
if newpix[pixels] == newpix[-1]:
missingvalues = scanrange - abs(newpix[pixels] +
addedvalues - len(hits))
else:
if abs(newpix[pixels] - newpix[pixels + 1]) < scanrange:
missingvalues = scanrange - abs(newpix[pixels] -
newpix[pixels + 1])
if missingvalues != 0:
hits = np.insert(hits, newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_tdc = np.insert(avg_tdc, newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_tdc_err = np.insert(avg_tdc_err,
newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_del = np.insert(avg_del, newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_del_err = np.insert(avg_del_err,
newpix[pixels] + addedvalues,
np.zeros(missingvalues))
pxl_list = np.insert(
pxl_list, newpix[pixels] + addedvalues,
(pxl_list[newpix[pixels] + addedvalues]) *
np.ones(missingvalues))
addedvalues = addedvalues + missingvalues
injections = []
for pixels in range(int(len(pxl_list) / len(self.inj_charge))):
for i in range(len(self.inj_charge)):
injections = np.append(injections, self.inj_charge[i])
pix, stop = np.unique(pxl_list, return_index=True)
stop = np.sort(stop)
stop = list(stop)
stop.append(len(avg_tdc))
repeat_command_dic = {}
repeat_command_dic['repeat_command'] = repeat_command
avg_tab = np.rec.fromarrays([
injections, pxl_list, hits, avg_tdc, avg_tdc_err, avg_del,
avg_del_err
],
dtype=[('charge', float),
('pixel_no', int),
('hits', int),
('tot_ns', float),
('err_tot_ns', float),
('delay_ns', float),
('err_delay_ns', float)])
tdc_table = in_file_h5.createTable(in_file_h5.root,
'tdc_data',
avg_tab,
filters=self.filter_tables)
tdc_table.attrs.repeat_command = repeat_command_dic
thresholds = ()
expfit0 = ()
expfit1 = ()
expfit2 = ()
expfit3 = ()
pixels = ()
for i in range(len(stop) - 1):
s1 = int(stop[i])
s2 = int(stop[i + 1])
A, mu, sigma = analysis.fit_scurve(hits[s1:s2],
injections[s1:s2],
repeat_command)
if np.max(
hits[s1:s2]) > (repeat_command + 200): # or mu > 3000:
thresholds = np.append(thresholds, 0)
expfit0 = np.append(expfit0, 0)
expfit1 = np.append(expfit1, 0)
expfit2 = np.append(expfit2, 0)
expfit3 = np.append(expfit3, 0)
pixels = np.append(pixels, pxl_list[s1])
continue
for values in range(s1, s2):
if injections[values] >= 5 / 4 * mu:
s1 = values
break
numberer = 0
hitvaluesold = hits[-1]
for hitvalues in hits[s1:s2]:
if abs(hitvalues - hitvaluesold) <= 1 and hitvalues != 0:
break
numberer = numberer + 1
hitvaluesold = hitvalues
if numberer == len(avg_del[s1:s2]):
numberer = 0
expfit = analysis.fit_exp(injections[s1:s2], avg_del[s1:s2],
mu, abs(numberer))
startexp = -expfit[0] * np.log(
(25.0 + np.min(avg_del[s1:s2]) - expfit[3]) /
expfit[2]) - expfit[1]
if np.isnan(startexp) or startexp >= 2000:
startexp = 0
thresholds = np.append(thresholds, startexp)
expfit0 = np.append(expfit0, expfit[0])
expfit1 = np.append(expfit1, expfit[1])
expfit2 = np.append(expfit2, expfit[2])
expfit3 = np.append(expfit3, expfit[3])
pixels = np.append(pixels, pxl_list[s1])
thresh = np.rec.fromarrays(
[pixels, thresholds, expfit0, expfit1, expfit2, expfit3],
dtype=[('pixel_no', int), ('td_threshold', float),
('expfit0', float), ('expfit1', float),
('expfit2', float), ('expfit3', float)])
in_file_h5.createTable(in_file_h5.root,
'td_threshold',
thresh,
filters=self.filter_tables)
p1, p2, single_scan = plotting.plot_timewalk(h5_filename)
output_file(self.output_filename + '.html', title=self.run_name)
status = plotting.plot_status(h5_filename)
save(hplot(vplot(p1, p2, status), single_scan))