def __init__(self, filename, label_type):
""" This class yields events from pregenerated MC file.
Parameters:
filename : str; filename to read
table_name : str; name of the table to read
currently available BinClassHits and SegClassHits
"""
self.filename = filename
if not isinstance(label_type, LabelType):
raise ValueError(f'{label_type} not recognized!')
self.label_type = label_type
self.events = load_dst(filename, 'DATASET', 'EventsInfo')
self.bininfo = load_dst(filename, 'DATASET', 'BinsInfo')
def test_cut_time_evolution_multievents(folder_test_dst,
test_dst_file ):
dst = load_dst(folder_test_dst+test_dst_file, "DST", "Events").iloc[:1000]
dst_uniquevs = dst.drop_duplicates('event')
#make sure that the dataframe repeated 3x gives the same results as the original one
mask_S1_ue = np.random.choice([True, False], len(dst_uniquevs))
dst_multievs = dst_uniquevs.append([dst_uniquevs]*2, ignore_index=True)
mask_S1_me = np.tile(mask_S1_ue, 3)
mask_cut_ue = masks_container(s1 = mask_S1_ue,
s2 = mask_S1_ue,
band = mask_S1_ue)
mask_cut_me = masks_container(s1 = mask_S1_me,
s2 = mask_S1_me,
band = mask_S1_me)
min_time = dst_uniquevs.time.min()
max_time = dst_uniquevs.time.max()
ts_ue, masks_ue = get_time_series_df(time_bins = 1,
time_range = (min_time, max_time),
dst = dst_uniquevs)
ts_me, masks_me = get_time_series_df(time_bins = 1,
time_range = (min_time, max_time),
dst = dst_multievs)
pars_ue = pd.DataFrame({'ts' : ts_ue})
pars_me = pd.DataFrame({'ts' : ts_me})
pars_out_ue = cut_time_evolution(masks_ue, dst_uniquevs, mask_cut_ue, pars_ue)
pars_out_me = cut_time_evolution(masks_me, dst_multievs, mask_cut_me, pars_me)
assert_dataframes_close(pars_out_ue, pars_out_me)
def runfo_from_hdf(filename):
""" read the runinfo from a h5 file
inputs:
filename : (str) the filename of the h5 data
output:
pmaps : (DFPmap) the pmaps (s1, s2, s2i) dataframes
"""
#try:
# hdf = pd.HDFStore(filename)
# # dat = [hdf['s1'], hdf['s2'], hdf['s2si'], hdf['s1pmt'], hdf['s2pmt']]
# dat = (hdf['s1'], hdf['s2'], hdf['s2si'])
# return DFPmap(*dat)
#except:
try:
runinfo = load_dst(filename, 'Run', 'events')
return runinfo
except:
try:
hd = pd.HDFStore(filename)
keys = hd.keys()
ok = ('/runinfo' in keys)
if (not ok):
raise IOError
runinfo = hd['runinfo']
return runinfo
except:
raise IOError
def load_hits_skipping_NN(DST_file_name, evt_number):
"""Return the Hits as PD DataFrames."""
dst = load_dst(DST_file_name,'RECO','Events')
dst_size = len(dst)
all_events = {}
event = dst.event.values
time = dst.time .values
npeak = dst.npeak.values
X = dst.X .values
Y = dst.Y .values
Z = dst.Z .values
Q = dst.Q .values
E = dst.E .values
for i in range(dst_size):
if event[i] == evt_number:
current_event = all_events.setdefault(event[i],
HitCollection(event[i], time[i] * 1e-3))
hit = Hit(npeak[i],
Cluster(Q[i], xy(X[i], Y[i]), xy(0, 0),
0, Z[i], E[i]), Z[i], E[i], xy(0, 0))
if(hit.Q != NN):
current_event.hits.append(hit)
good_events = {}
for event, hitc in all_events.items():
if len(hitc.hits) > 0:
good_events[event] = hitc
return good_events
def test_cut_time_evolution_different_time_bins(folder_test_dst,
test_dst_file ):
dst = load_dst(folder_test_dst+test_dst_file, "DST", "Events")
dst = dst.drop_duplicates('event')
min_time = dst.time.min()
max_time = dst.time.max()
ts, masks_time = get_time_series_df(time_bins = 3,
time_range = (min_time, max_time),
dst = dst)
#set different probability of passing for different time bins, only for first mask
probs_s1 = [0.9, 0.5, 0.1]
mask_s1 = np.zeros(len(dst), dtype=bool)
mask_s1[masks_time[0]] = np.random.choice([True, False], sum(masks_time[0]), p=(probs_s1[0], 1-probs_s1[0]))
mask_s1[masks_time[1]] = np.random.choice([True, False], sum(masks_time[1]), p=(probs_s1[1], 1-probs_s1[1]))
mask_s1[masks_time[2]] = np.random.choice([True, False], sum(masks_time[2]), p=(probs_s1[2], 1-probs_s1[2]))
mask_cut = masks_container(s1 = mask_s1,
s2 = mask_s1,
band = mask_s1)
#the expected relative efficiencies for other cuts are 1 since the mask is repeated
pars_expected = pd.DataFrame({'ts':ts,
'S1eff' : probs_s1,
'S2eff': [1.]*3,
'Bandeff':[1.]*3})
pars = pd.DataFrame({'ts' : ts})
pars_out = cut_time_evolution(masks_time, dst, mask_cut, pars)
assert_dataframes_close(pars_expected, pars_out, atol=5e-2)
def load_map(filename,
group = "Radius",
node = "f100bins",
x_name = 'PhiRms',
y_name = 'Rpos',
u_name = 'RposUncertainty'):
dst = load_dst(filename, group, node)
return Map((dst[x_name].values,),
dst[y_name].values,
dst[u_name].values)
def data(input_filename):
try:
hits = load_dst(input_filename, 'RECO', 'Events')
except:
print('Not able to load file : ', input_filename)
raise IOError
print('processing ', input_filename)
return hits
def create_labels_data(list_filenames, en_range=(1.4,1.9)):
full_csv = pd.DataFrame(columns=['filename', 'event', 'evt_energy', 'evt_ntrks', 'evt_r_max', 'evt_z_min', 'evt_z_max'])
for filename in list_filenames:
summary_df = load_dst(filename, 'Summary', 'Events')
if summary_df is None:
print('skipping empty {}'.format(filename))
continue
summary_df = summary_df[['event', 'evt_energy', 'evt_ntrks', 'evt_r_max', 'evt_z_min', 'evt_z_max']][(summary_df.evt_energy>=en_range[0]) & (summary_df.evt_energy<=en_range[1])]
summary_df.loc[:, 'filename'] = filename
full_csv = pd.concat([full_csv, summary_df], ignore_index=True)
#full_csv.to_csv(csv_files_name, index=False)
return full_csv
def test_correct_map_with_unsorted_dst(folder_test_dst, test_dst_file,
output_maps_tmdir):
"""
This test shuffles the input dst, and checks that the map is the same
as the one created with the same sorted dst.
"""
map_file_sort = os.path.join(output_maps_tmdir, 'test_out_map.h5')
map_file_unsort = os.path.join(output_maps_tmdir, 'test_out_unsort.h5')
histo_file_out = os.path.join(output_maps_tmdir, 'test_out_histo.h5')
dst = load_dst(folder_test_dst + test_dst_file, 'DST', 'Events')
if "index" in dst: del dst["index"]
dst = dst.sort_values(by=['S2e'])
tmp_unsorted_dst = 'unsorted_dst.h5'
dst.to_hdf(output_maps_tmdir + tmp_unsorted_dst,
key="DST",
mode="w",
format="table",
data_columns=True,
complib="zlib",
complevel=4)
with tb.open_file(output_maps_tmdir + tmp_unsorted_dst, "r+") as file:
file.rename_node(file.root.DST.table, "Events")
file.root.DST.Events.title = "Events"
default_n_bins = 15
run_number = 7517
config = configure(
'maps $ICARO/krcal/map_builder/config_LBphys.conf'.split())
map_params_new = config.as_namespace.map_params
map_params_new['nmin'] = 100
map_params_new['z_range'] = (0, 10000)
config.update(
dict(folder=output_maps_tmdir,
file_in=tmp_unsorted_dst,
file_out_map=map_file_unsort,
file_out_hists=histo_file_out,
default_n_bins=default_n_bins,
run_number=run_number,
map_params=map_params_new))
map_builder(config.as_namespace)
unsorted_maps = read_maps(map_file_unsort)
sorted_maps = read_maps(map_file_sort)
assert_dataframes_close(unsorted_maps.e0, sorted_maps.e0, rtol=1e-5)
assert_dataframes_close(unsorted_maps.e0u, sorted_maps.e0u, rtol=1e-5)
assert_dataframes_close(unsorted_maps.lt, sorted_maps.lt, rtol=1e-5)
assert_dataframes_close(unsorted_maps.ltu, sorted_maps.ltu, rtol=1e-5)
def test_map_writer(config_tmpdir, map_toy_data, bins, pos):
output_file = os.path.join(config_tmpdir, "test_map.h5")
_, (xs, ys, us) = map_toy_data
group = "Radius"
name = f"f{bins}bins"
with tb.open_file(output_file, 'w') as h5out:
write = map_writer(h5out, group=group, table_name=name)
for x, y, u in zip(xs[pos], ys[pos], us[pos]):
write(x, y, u)
dst = load_dst(output_file, group=group, node=name)
assert_allclose(xs[pos], dst.PhiRms.values)
assert_allclose(ys[pos], dst.Rpos.values)
assert_allclose(us[pos], dst.RposUncertainty.values)
def load_corrections(filename, *,
group = "Corrections",
node = "XYcorrections",
x_name = 'X',
y_name = 'Y',
f_name = 'Factor',
u_name = 'Uncertainty',
n_name = 'NEvt',
**kwargs):
dst = load_dst(filename, group, node)
x, y = np.unique(dst[x_name].values), np.unique(dst[y_name].values)
f, u = dst[f_name].values, dst[u_name].values
n = dst[n_name].values
return Map((x, y),
f.reshape(x.size, y.size),
u.reshape(x.size, y.size),
**kwargs)
def fill_kdst_histos(in_path, config_dict):
"""
Creates and returns an HistoManager object with the kdst histograms.
Arguments:
in_path = String with the path to the file(s) to be monitored.
config_dict = Dictionary with the configuration parameters (bins, labels)
"""
var_bins, var_labels = kdst_bins(config_dict)
histo_manager = histf.create_histomanager_from_dicts(var_bins, var_labels)
for in_file in glob.glob(in_path):
var = defaultdict(list)
kdst = load_dst(in_file, 'DST', 'Events')
fill_kdst_var_1d(kdst, var)
fill_kdst_var_2d(var)
histo_manager.fill_histograms(var)
return histo_manager
def test_correction_writer(config_tmpdir, corr_toy_data):
output_file = os.path.join(config_tmpdir, "test_corr.h5")
_, (x, y, F, U, N) = corr_toy_data
group = "Corrections"
name = "XYcorrections"
with tb.open_file(output_file, 'w') as h5out:
write = correction_writer(h5out, group=group, table_name=name)
write(x, y, F, U, N)
x, y = np.repeat(x, y.size), np.tile(y, x.size)
F, U, N = F.flatten(), U.flatten(), N.flatten()
dst = load_dst(output_file, group=group, node=name)
assert_allclose(x, dst.X.values)
assert_allclose(y, dst.Y.values)
assert_allclose(F, dst.Factor.values)
assert_allclose(U, dst.Uncertainty.values)
assert_allclose(N, dst.NEvt.values)
def load_and_sel_data(fname):
kdst = load_dst(fname, 'DST', 'Events')
kdst = kdst[(kdst.nS1 == 1) & (kdst.nS2 == 1) & (kdst.R <= 198)]
maxZ = np.quantile(kdst.Z, 0.99)
kdst = kdst[kdst.Z < maxZ]
file_bootstrap_map = '$ICDIR/database/test_data/kr_emap_xy_100_100_r_6573_time.h5'
file_bootstrap_map = os.path.expandvars(file_bootstrap_map)
boot_map = read_maps(file_bootstrap_map)
emaps = e0_xy_correction(boot_map, norm_strat=norm_strategy.max)
E0 = kdst.S2e.values * emaps(kdst.X.values, kdst.Y.values)
sel_krband, _, _, _, _ = selection_in_band(kdst.Z,
E0,
range_z=(10, maxZ),
range_e=(10.0e+3, 14e+3),
nbins_z=50,
nbins_e=50,
nsigma=3.5)
kdst = kdst[sel_krband].reset_index(drop=True)
return kdst
def test_cut_time_evolution_different_cuts(folder_test_dst,
test_dst_file ):
dst = load_dst(folder_test_dst + test_dst_file, "DST", "Events")
dst = dst.drop_duplicates('event')
mask_S1 = np.random.choice([True, False], len(dst ))
mask_S2 = np.zeros_like(mask_S1, dtype=bool)
mask_band = np.zeros_like(mask_S1, dtype=bool)
mask_S2[mask_S1] = np.random.choice([True, False], sum(mask_S1))
mask_band[mask_S2] = np.random.choice([True, False], sum(mask_S2))
mask_cut = masks_container(s1 = mask_S1,
s2 = mask_S2,
band = mask_band)
min_time = dst.time.min()
max_time = dst.time.max()
ts, masks_time = get_time_series_df(time_bins = 1,
time_range = (min_time, max_time),
dst = dst)
pars = pd.DataFrame({'ts':ts})
pars_out = cut_time_evolution(masks_time, dst, mask_cut, pars)
pars_expected = pd.DataFrame({'ts':ts, 'S1eff':0.5, 'S2eff':0.5, 'Bandeff':0.5})
assert_dataframes_close(pars_expected, pars_out, atol=5e-2)
def KrMC_kdst(ICDATADIR):
test_file = "Kr83_nexus_v5_03_00_ACTIVE_7bar_10evts_KDST.h5"
test_file = os.path.join(ICDATADIR, test_file)
wrong_file = "kdst_5881_map_lt.h5"
wrong_file = os.path.join(ICDATADIR, wrong_file)
group = "DST"
node = "Events"
configuration = dict(run_number=-4734,
drift_v=2 * units.mm / units.mus,
s1_nmin=1,
s1_nmax=1,
s1_emin=0 * units.pes,
s1_emax=30 * units.pes,
s1_wmin=100 * units.ns,
s1_wmax=500 * units.ns,
s1_hmin=0.5 * units.pes,
s1_hmax=10 * units.pes,
s1_ethr=0.37 * units.pes,
s2_nmin=1,
s2_nmax=2,
s2_emin=1e3 * units.pes,
s2_emax=1e8 * units.pes,
s2_wmin=1 * units.mus,
s2_wmax=20 * units.mus,
s2_hmin=500 * units.pes,
s2_hmax=1e5 * units.pes,
s2_ethr=1 * units.pes,
s2_nsipmmin=2,
s2_nsipmmax=1000,
global_reco_params=dict(Qthr=1 * units.pes,
Qlm=0 * units.pes,
lm_radius=-1 * units.mm,
new_lm_radius=-1 * units.mm,
msipm=1))
event = [0, 1, 2, 3, 4, 5, 6, 7, 9]
time = [0, 0, 0, 0, 0, 0, 0, 0, 0]
s1_peak = [0, 0, 0, 0, 0, 0, 0, 0, 0]
nS1 = [1, 1, 1, 1, 1, 1, 1, 1, 1]
s2_peak = [0, 0, 0, 0, 0, 0, 0, 0, 0]
nS2 = [1, 1, 1, 1, 1, 1, 1, 1, 1]
S1w = [200., 200., 175., 175., 200., 175., 175., 250., 200.]
S1h = [
2.20967579, 4.76641989, 2.19646263, 2.28649235, 2.10444999, 2.80842876,
1.66933489, 2.51325226, 1.45906901
]
S1e = [
11.79215431, 24.58425140, 11.20991993, 10.81333828, 10.09474468,
15.06069851, 7.42862463, 18.16711235, 9.39478493
]
S1t = [
100225, 100100, 100150, 100125, 100100, 100150, 100150, 100175, 100150
]
S2w = [8.300, 8.775, 9.575, 8.550, 11.175, 8.175, 6.800, 11.025, 6.600]
S2h = [
709.73730469, 746.38555908, 717.25158691, 1004.44152832, 972.86987305,
1064.57861328, 1101.23962402, 532.64318848, 1389.43591309
]
S2e = [
3333.79321289, 3684.11181641, 3815.17578125, 4563.97412109,
4995.36083984, 4556.64892578, 4572.18945313, 3301.98510742,
5057.96923828
]
S2q = [
502.42642212, 520.74383545, 507.32211304, 607.92211914, 585.70526123,
578.10437012, 595.45251465, 479.56573486, 633.57226563
]
S2t = [
424476.3, 495500.0, 542483.1, 392466.5, 277481.8, 373512.6, 322489.5,
562502.9, 263483.5
]
Nsipm = [13, 14, 15, 13, 12, 14, 14, 16, 12]
DT = [
324.25134277, 395.40005493, 442.33322144, 292.34155273, 177.38185120,
273.36264038, 222.33953857, 462.32797241, 163.33348083
]
Z = [
648.50268555, 790.80010986, 884.66644287, 584.68310547, 354.76370239,
546.72528076, 444.67907715, 924.65594482, 326.66696167
]
Zrms = [
1.71384448, 1.79561206, 1.95215662, 1.69040076, 2.67407129, 1.57506763,
1.39490784, 2.27981592, 1.25179053
]
X = [
137.83250525, -137.61807146, 98.89289503, 45.59115903, -55.56241288,
108.79125729, -65.40441501, 146.19707698, 111.82481626
]
Y = [
124.29040384, -99.36910382, 97.30389804, -136.05131238, -91.45151520,
56.89100575, 130.07815715, 85.99513413, -43.18583024
]
R = [
185.59607752, 169.74378453, 138.73663273, 143.48697984, 107.00729581,
122.76857985, 145.59555099, 169.61352662, 119.87412342
]
Phi = [
0.733780880, -2.51621137, 0.77729934, -1.24745421, -2.11675716,
0.481828610, 2.03668828, 0.53170808, -0.36854640
]
Xrms = [
7.46121721, 8.06076669, 7.81822080, 7.57937056, 6.37266772,
15.45983120, 7.42686347, 8.19314573, 7.17110860
]
Yrms = [
7.32118281, 7.63813330, 8.35199371, 6.93334565, 7.22325362, 7.49551110,
7.32949621, 8.06191793, 6.26888356
]
df_true = DataFrame({
"event": event,
"time": time,
"nS1": nS1,
"s1_peak": s1_peak,
"nS2": nS2,
"s2_peak": s2_peak,
"S1w": S1w,
"S1h": S1h,
"S1e": S1e,
"S1t": S1t,
"S2w": S2w,
"S2h": S2h,
"S2e": S2e,
"S2q": S2q,
"S2t": S2t,
"Nsipm": Nsipm,
"DT": DT,
"Z": Z,
"Zrms": Zrms,
"X": X,
"Y": Y,
"R": R,
"Phi": Phi,
"Xrms": Xrms,
"Yrms": Yrms
})
df_read = load_dst(test_file, group=group, node=node)
return dst_data(tbl_data(test_file, group, node), configuration, df_read,
df_true), tbl_data(wrong_file, group, node)
def load_rpos(filename, group="Radius", node="f100bins"):
dst = load_dst(filename, group, node)
return Correction((dst.RmsPhi.values, ), dst.Rpos.values,
dst.Uncertainty.values)
evt_number = int(sys.argv[3])
mc = int(sys.argv[4])
radius = int(sys.argv[5])
corrections = "/Users/paola/Software/ic_data/corrections/corrections_run6352.h5"
time_evolution = "/Users/paola/Software/ic_data/corrections/Time_evolution_6352.h5"
LTcorrection = dstf.load_lifetime_xy_corrections(corrections,
group="XYcorrections",
node="Elifetime")
XYcorrection = dstf.load_xy_corrections(corrections,
group = "XYcorrections",
node = f"Egeometry",
norm_strategy = "index",
norm_opts = {"index": (40,40)})
dv_dst = load_dst(time_evolution, group="parameters", node="test")
dvs = dv_dst.dv.values
drift_velocity = dvs.mean()
if mc:
correctionsLT = "/Users/paola/Software/ic_data/corrections/corrections_run6198.h5"
correctionsXY = "/Users/paola/Software/ic_data/corrections/corrections_MC_4734.h5"
LTcorrection = dstf.load_lifetime_xy_corrections(correctionsLT,
group="XYcorrections",
node="Elifetime")
XYcorrection = dstf.load_xy_corrections(correctionsXY,
group="XYcorrections",
node="GeometryE_6.7mm",
norm_strategy = "index",
norm_opts = {"index": (40, 40)})
get_file_number = lambda filename: int(
filename.split("/")[-1].split("_")[1].split(".")[0])
def get_pdata(filenames: list,
nevents: int,
/,
background: str = None,
region: str = None):
np.random.shuffle(filenames)
dsts = []
selected = 0
for filename in filenames:
dst = load_dst(filename, "tracks", "events")
in_file = dst.event.nunique()
dst.loc[:, "nfile"] = get_file_number(filename)
if background:
dst.loc[:, "background"] = background
if region:
dst.loc[:, "region"] = region
if (selected + in_file) < nevents:
dsts.append(dst)
selected += in_file
elif (selected + in_file) >= nevents:
n = nevents - selected
events = np.random.choice(dst["event"].unique(),
def test_get_time_series_df_pandas(folder_test_dst, test_dst_file, n):
kdst = load_dst(folder_test_dst + test_dst_file, 'DST', 'Events')
time_indx = pd.cut(kdst.time, n, labels=np.arange(n))
ts, masks = get_time_series_df(n, (kdst.time.min(), kdst.time.max()), kdst)
for i, mask in enumerate(masks):
assert_dataframes_equal(kdst[mask], kdst[time_indx == i])
def KrMC_hdst(ICDATADIR):
test_file = "Kr83_nexus_v5_03_00_ACTIVE_7bar_10evts_HDST.h5"
test_file = os.path.join(ICDATADIR, test_file)
ZANODE = -9.425 * units.mm
group = "RECO"
node = "Events"
configuration = dict(run_number=-4734,
rebin=2,
drift_v=2 * units.mm / units.mus,
s1_nmin=1,
s1_nmax=1,
s1_emin=0 * units.pes,
s1_emax=30 * units.pes,
s1_wmin=100 * units.ns,
s1_wmax=500 * units.ns,
s1_hmin=0.5 * units.pes,
s1_hmax=10 * units.pes,
s1_ethr=0.37 * units.pes,
s2_nmin=1,
s2_nmax=2,
s2_emin=1e3 * units.pes,
s2_emax=1e8 * units.pes,
s2_wmin=1 * units.mus,
s2_wmax=20 * units.mus,
s2_hmin=500 * units.pes,
s2_hmax=1e5 * units.pes,
s2_ethr=1 * units.pes,
s2_nsipmmin=2,
s2_nsipmmax=1000,
global_reco_params=dict(Qthr=1 * units.pes,
Qlm=0 * units.pes,
lm_radius=-1 * units.mm,
new_lm_radius=-1 * units.mm,
msipm=1),
slice_reco_params=dict(Qthr=2 * units.pes,
Qlm=5 * units.pes,
lm_radius=0 * units.mm,
new_lm_radius=15 * units.mm,
msipm=1))
event = [0] * 7 + [1] * 10 + [2] * 8 + [3] * 5 + [4] * 8 + [5] * 9 + [
6
] * 7 + [7] * 7 + [9] * 6
time = [0] * 67
peak = [0] * 67
nsipm = [
8, 9, 2, 9, 7, 1, 0, 7, 2, 8, 2, 9, 1, 2, 8, 1, 0, 5, 9, 2, 2, 9, 2, 8,
4, 8, 9, 9, 7, 0, 7, 9, 3, 7, 7, 2, 9, 0, 5, 9, 2, 8, 3, 7, 2, 1, 0, 8,
2, 9, 3, 1, 9, 3, 6, 9, 9, 8, 2, 8, 0, 7, 9, 2, 7, 2, 4
]
X = [
137.02335083, 136.05361214, 155, 137.50739563, 139.44340629, 135, 0.,
-136.91523372, -138.31812254, -137.40236334, -139.36096871,
-136.614188, -135., -155., -137.13894421, -135., 0., 96.69981723,
97.48205345, 96.84759934, 115., 96.87320369, 115., 101.92102321,
97.29559249, 43.04750178, 46.37438754, 45.18567591, 45.30916066, 0,
-56.51582522, -56.16605278, -54.00613888, -55.77326839, -54.43059006,
-52.08436199, -54.76790039, 0., 106.9129152, 108.02149081, 125.,
111.23591207, 95., 109.36687514, 125., -35., 0, -66.81829731,
-60.99443543, -65.21616666, -56.82709265, -85., -66.38902019,
-67.16981495, 146.62612941, 145.98052086, 144.56275403, 146.57670316,
165., 143.75471749, 0., 112.68567761, 113.05236723, 95., 112.18326138,
95., 111.13948562
]
Y = [
123.3554201, 124.21305414, 130.32028088, 125.13468301, 125.723091,
105., 0., -102.20454509, -85., -101.19527484, -85., -98.18372292,
-115., -97.50736874, -97.65435292, -115., 0., 97.83535504, 96.71162732,
115., 99.94110169, 96.96828871, 91.9964654, 96.47442012, 97.41483565,
-135.46032693, -135.78741482, -135.45706672, -136.84192389, 0,
-91.93474203, -92.22587474, -75., -91.79293263, -92.05845326, -75.,
-93.17777252, 0, 60.37973152, 55.49979777, 58.7016448, 56.05515802,
52.82336998, 57.04430991, 61.02136179, 65., 0., 131.35172348, 115.,
131.55083652, 116.93600131, 125., 131.38450477, 127.55032531,
86.4185202, 85.50276576, 85.08681108, 85.63406367, 92.12569852,
87.37079899, 0., -41.26775923, -43.95642973, -42.01248503, -43.6128179,
-39.43319563, -41.83429664
]
Xrms = [
6.82943353, 6.54561049, 0., 6.57516493, 6.10884172, 0., 0., 6.34641453,
4.70863974, 6.99252219, 4.95899577, 6.99286177, 0., 0., 7.31434091, 0.,
0., 5.46065169, 6.95289236, 3.88102693, 0., 7.08872666, 0., 6.12004979,
4.20549403, 6.50762165, 6.53200551, 6.72634463, 6.63232452, 0.,
6.01927117, 6.15010511, 7.21567172, 6.18867493, 6.68684705, 4.54482509,
6.49772479, 0, 6.93471034, 6.73472262, 0., 6.23230238, 0., 6.60554616,
0., 0., 0., 6.5619163, 4.90011206, 6.62430967, 7.32479932, 0.,
6.44742043, 4.12189915, 5.39988073, 6.60345367, 6.72834925, 6.74313437,
0., 7.29624974, 0., 6.91160248, 6.11943089, 0., 5.61844221, 0.,
4.86842608
]
Yrms = [
7.53561556, 6.7267195, 4.98973147, 6.66971537, 6.72639992, 0., 0,
7.6755039, 0., 6.32848615, 0., 6.5346365, 0., 4.33437301, 6.08928588,
0., 0, 4.50714013, 7.16164783, 0., 4.99965309, 6.85733991, 4.5841167,
6.9783623, 4.27982772, 6.75410279, 6.79005446, 6.72968451, 6.11981085,
0, 5.70842513, 6.38557803, 0., 5.52636093, 5.17674174, 0., 6.9095478,
0., 4.98555954, 6.83197289, 4.82848566, 6.69411227, 6.65765325,
7.0682494, 4.89457047, 0., 0, 6.26325683, 0., 6.33195824, 3.95119122,
0., 6.41624414, 4.35879501, 5.88461762, 7.49736328, 6.59322568,
6.52422553, 4.52563872, 6.98159176, 0., 5.49357201, 6.10943873,
4.57710651, 5.55130713, 4.9677694, 4.65138214
]
Z = [
642.291, 645.7665, 645.7665, 649.3726875, 653.05675, 653.05675,
656.1485, 784.297, 784.297, 788.0405, 788.0405, 791.746, 791.746,
791.746, 795.323, 795.323, 798.587875, 876.165125, 879.974125,
879.974125, 879.974125, 883.695, 883.695, 887.402625, 891.02675,
578.47125, 582.0140625, 585.5928125, 589.239125, 592.4990625, 350.6575,
353.8748125, 353.8748125, 357.335875, 361.9025, 361.9025, 365.6785,
368.9885, 540.8020625, 544.093625, 544.093625, 547.667375, 547.667375,
551.231625, 551.231625, 551.231625, 554.582, 440.6051875, 440.6051875,
443.814375, 443.814375, 443.814375, 447.4028125, 450.6166875, 916.4715,
919.979, 923.716, 927.529125, 927.529125, 931.291125, 935.062625,
320.8508125, 324.0374375, 324.0374375, 327.4530625, 327.4530625,
330.653625
]
Q = [
6.21135249e+01, 1.93207452e+02, 1.17878785e+01, 1.52947767e+02,
5.92023814e+01, 5.98997307e+00, NN, 5.11176143e+01, 7.79754877e+00,
1.74522912e+02, 1.52560458e+01, 1.51785870e+02, 9.12431908e+00,
1.20135555e+01, 6.86140332e+01, 5.19579268e+00, NN, 38.49134254,
130.57549524, 12.6504097, 12.36747885, 158.67850065, 13.2809248,
80.46449661, 18.56184649, 7.04285767e+01, 2.03529454e+02,
2.23206003e+02, 7.24489250e+01, NN, 8.47140183e+01, 2.18426581e+02,
1.07217135e+01, 8.33113148e+01, 6.86556058e+01, 8.63394284e+00,
7.88299217e+01, NN, 2.79961238e+01, 1.58822554e+02, 1.55958052e+01,
2.37206266e+02, 1.47270021e+01, 7.56110237e+01, 1.20107818e+01,
5.01965523e+00, NN, 119.14831066, 13.13961554, 247.54202271,
26.03989458, 6.0433917, 145.98711324, 21.32093287, 3.22954066e+01,
7.83973203e+01, 1.35020741e+02, 1.22896467e+02, 7.75498104e+00,
4.41479754e+01, NN, 61.85911345, 297.65047169, 14.66699696,
208.5009613, 15.51440573, 14.46035504
]
Ql = [-1] * len(Q)
Qc = [-1] * len(Q)
E = [
300.67897224, 1031.45098773, 62.93038282, 1264.9021225, 574.87652458,
58.16480385, 40.7894727, 261.72873756, 39.92444923, 1007.64861959,
88.08432872, 1275.08688025, 76.64942446, 100.92063957, 663.7729013,
50.26415467, 120.03185987, 255.05263805, 751.72643276, 72.82872899,
71.19988891, 1293.0319564, 108.22297982, 1000.51422119, 262.59910393,
358.72694397, 1477.87328339, 1833.91390991, 799.69952011, 93.7603054,
435.34708595, 1719.97730608, 84.42701369, 1209.82822418, 439.89744612,
55.32031015, 815.31221771, 235.25139713, 129.01727104, 988.40456541,
97.05778352, 1980.85363294, 122.98172472, 876.96921053, 139.30621875,
58.22012279, 163.83844948, 434.5464781, 47.92156618, 1774.64676229,
186.6818979, 43.32551461, 1761.74893188, 323.31822968, 127.10046768,
485.85840988, 1011.98860931, 919.25156787, 58.00637433, 525.53125763,
174.24835968, 230.65227795, 1978.67551363, 97.50103056, 2193.99010015,
163.2532165, 393.89710426
]
El = [-1] * len(E)
Ec = [-1] * len(E)
Zc = [ZANODE] * len(E)
Xpeak = [137.83250525] * 7 + [-137.61807157] * 10 + [98.89289499] * 8 + [
45.59115902
] * 5 + [-55.56241282] * 8 + [108.79125728] * 9 + [-65.40441505] * 7 + [
146.19707713
] * 7 + [111.82481627] * 6
Ypeak = [124.29040397] * 7 + [-99.36910381] * 10 + [97.3038981] * 8 + [
-136.05131239
] * 5 + [-91.45151521] * 8 + [56.89100565] * 9 + [130.07815717] * 7 + [
85.995134
] * 7 + [-43.18583022] * 6
df_true = DataFrame({
"event": event,
"time": time,
"npeak": peak,
"Xpeak": Xpeak,
"Ypeak": Ypeak,
"nsipm": nsipm,
"X": X,
"Y": Y,
"Xrms": Xrms,
"Yrms": Yrms,
"Z": Z,
"Q": Q,
"E": E,
"Ql": Ql,
"El": El,
"Qc": Qc,
"Ec": Ec,
"Zc": Zc
})
df_read = load_dst(test_file, group=group, node=node)
return dst_data(tbl_data(test_file, group, node), configuration, df_read,
df_true)
