class Crossover(tb.IsDescription):
sat = tb.StringCol(32, pos=1)
region = tb.Int16Col(pos=2)
lon = tb.Float64Col(pos=3)
lat = tb.Float64Col(pos=4)
time_1 = tb.StringCol(64, pos=5)
time_2 = tb.StringCol(64, pos=6)
year_1 = tb.Int16Col(pos=7)
year_2 = tb.Int16Col(pos=8)
month_1 = tb.Int16Col(pos=9)
month_2 = tb.Int16Col(pos=10)
season_1 = tb.StringCol(32, pos=11)
season_2 = tb.StringCol(32, pos=12)
orbit_1 = tb.Int32Col(pos=13)
orbit_2 = tb.Int32Col(pos=14)
utc85_1 = tb.Float64Col(pos=15)
utc85_2 = tb.Float64Col(pos=16)
elev_1 = tb.Float64Col(pos=17)
elev_2 = tb.Float64Col(pos=18)
agc_1 = tb.Float64Col(pos=19)
agc_2 = tb.Float64Col(pos=20)
fmode_1 = tb.Int8Col(pos=21)
fmode_2 = tb.Int8Col(pos=22)
fret_1 = tb.Int8Col(pos=23)
fret_2 = tb.Int8Col(pos=24)
fprob_1 = tb.Int8Col(pos=25)
fprob_2 = tb.Int8Col(pos=26)
fmask_1 = tb.Int8Col(pos=27)
fmask_2 = tb.Int8Col(pos=28)
fbord_1 = tb.Int8Col(pos=29)
fbord_2 = tb.Int8Col(pos=30)
ftrack_1 = tb.Int8Col(pos=31)
ftrack_2 = tb.Int8Col(pos=32)
inc_1 = tb.Float64Col(pos=33)
inc_2 = tb.Float64Col(pos=34)
class LightningEvent(tables.IsDescription):
event_id = tables.UInt32Col(pos=0)
timestamp = tables.Time32Col(pos=1)
corr_distance = tables.Int16Col(pos=2)
uncorr_distance = tables.Int16Col(pos=3)
uncorr_angle = tables.Float32Col(pos=4)
corr_angle = tables.Float32Col(pos=5)
class HMMInfoParticle(tables.IsDescription):
# HMM_ID, taste, din_channel, n_cells, time_start, time_end, thresh,
# unit_type, n_repeats, dt, n_states, n_iters, BIC, cost, converged,
# area
hmm_id = tables.Int16Col()
taste = tables.StringCol(45)
channel = tables.Int16Col()
n_cells = tables.Int32Col()
unit_type = tables.StringCol(15)
n_trials = tables.Int32Col()
dt = tables.Float64Col()
max_iter = tables.Int32Col()
threshold = tables.Float64Col()
time_start = tables.Int32Col()
time_end = tables.Int32Col()
n_repeats = tables.Int16Col()
n_states = tables.Int32Col()
n_iterations = tables.Int32Col()
BIC = tables.Float64Col()
cost = tables.Float64Col()
converged = tables.BoolCol()
fitted = tables.BoolCol()
max_log_prob = tables.Float64Col()
log_likelihood = tables.Float64Col()
area = tables.StringCol(15)
hmm_class = tables.StringCol(20)
notes = tables.StringCol(40)
class AnonHMMInfoParticle(tables.IsDescription):
# HMM_ID, taste, din_channel, n_cells, time_start, time_end, thresh,
# unit_type, n_repeats, dt, n_states, n_iters, BIC, cost, converged,
# area
# info particle for anonymous hmm data, so if hdf5 store isn't tied to a
# single recording, adds column for rec_dir
hmm_id = tables.Int16Col()
taste = tables.StringCol(45)
channel = tables.Int32Col()
n_cells = tables.Int32Col()
unit_type = tables.StringCol(15)
n_trials = tables.Int32Col()
dt = tables.Float64Col()
max_iter = tables.Int32Col()
threshold = tables.Float64Col()
time_start = tables.Int32Col()
time_end = tables.Int32Col()
n_repeats = tables.Int16Col()
n_states = tables.Int32Col()
n_iterations = tables.Int32Col()
BIC = tables.Float64Col()
cost = tables.Float64Col()
converged = tables.BoolCol()
fitted = tables.BoolCol()
max_log_prob = tables.Float64Col()
log_likelihood = tables.Float64Col()
area = tables.StringCol(15)
hmm_class = tables.StringCol(20)
notes = tables.StringCol(30)
rec_dir = tables.StringCol(150)
class Data(tables.IsDescription):
''' Description of data table, each row refers to an event/trace '''
receiver_table_n_i = tables.Int32Col()
response_table_n_i = tables.Int32Col()
time_table_n_i = tables.Int32Col()
#
#start_time = Time () # Start time of trace
class time(tables.IsDescription):
''' Time, either epoch or human readable '''
type_s = tables.StringCol(8) # 'EPOCH', 'ASCII', or 'BOTH'
epoch_l = tables.Int64Col() # Seconds since January 1, 1970
ascii_s = tables.StringCol(32) # WWW MMM DD HH:MM:SS YYYY
micro_seconds_i = tables.Int32Col()
#
event_number_i = tables.Int32Col() # Event number
channel_number_i = tables.Int8Col() # Channel number
sample_rate_i = tables.Int16Col() # Trace sample rate
sample_rate_multiplier_i = tables.Int16Col(
) # This will be needed for sample rates < 1 sps
sample_count_i = tables.Int32Col() # Version 2007.191a bleeding
stream_number_i = tables.Int8Col() # Stream
raw_file_name_s = tables.StringCol(32) # Original file name
array_name_data_a = tables.StringCol(
16) # Name of array that contains trace
array_name_SOH_a = tables.StringCol(16) # The SOH array name
array_name_event_a = tables.StringCol(16) # The event table array
array_name_log_a = tables.StringCol(16) # The log array
class run_g2(tb.IsDescription):
""" Row template for CAETÊ output data"""
# ID
row_id = tb.Int64Col(dflt=0, pos=0)
date = tb.StringCol(itemsize=8, dflt="yyyymmdd", pos=1)
grid_y = tb.Int16Col(dflt=0, pos=2)
grid_x = tb.Int16Col(dflt=0, pos=3)
csoil1 = tb.Float32Col(dflt=0.0, pos=4)
csoil2 = tb.Float32Col(dflt=0.0, pos=5)
csoil3 = tb.Float32Col(dflt=0.0, pos=6)
csoil4 = tb.Float32Col(dflt=0.0, pos=7)
sncN1 = tb.Float32Col(dflt=0.0, pos=8)
sncN2 = tb.Float32Col(dflt=0.0, pos=8)
sncN3 = tb.Float32Col(dflt=0.0, pos=10)
sncN4 = tb.Float32Col(dflt=0.0, pos=11)
sncP1 = tb.Float32Col(dflt=0.0, pos=12)
sncP2 = tb.Float32Col(dflt=0.0, pos=13)
sncP3 = tb.Float32Col(dflt=0.0, pos=14)
sncP4 = tb.Float32Col(dflt=0.0, pos=15)
inorg_n = tb.Float32Col(dflt=0.0, pos=16)
inorg_p = tb.Float32Col(dflt=0.0, pos=17)
sorbed_n = tb.Float32Col(dflt=0.0, pos=18)
sorbed_p = tb.Float32Col(dflt=0.0, pos=19)
hresp = tb.Float32Col(dflt=0.0, pos=20)
nmin = tb.Float32Col(dflt=0.0, pos=21)
pmin = tb.Float32Col(dflt=0.0, pos=22)
class WeatherConfig(tables.IsDescription):
event_id = tables.UInt32Col(pos=0)
timestamp = tables.Time32Col(pos=1)
com_port = tables.UInt8Col(pos=2)
baud_rate = tables.Int16Col(pos=3)
station_id = tables.UInt32Col(pos=4)
database_name = tables.Int32Col(dflt=-1, pos=5)
help_url = tables.Int32Col(dflt=-1, pos=6)
daq_mode = tables.BoolCol(pos=7)
latitude = tables.Float64Col(pos=8)
longitude = tables.Float64Col(pos=9)
altitude = tables.Float64Col(pos=10)
temperature_inside = tables.BoolCol(pos=11)
temperature_outside = tables.BoolCol(pos=12)
humidity_inside = tables.BoolCol(pos=13)
humidity_outside = tables.BoolCol(pos=14)
barometer = tables.BoolCol(pos=15)
wind_direction = tables.BoolCol(pos=16)
wind_speed = tables.BoolCol(pos=17)
solar_radiation = tables.BoolCol(pos=18)
uv_index = tables.BoolCol(pos=19)
evapotranspiration = tables.BoolCol(pos=20)
rain_rate = tables.BoolCol(pos=21)
heat_index = tables.BoolCol(pos=22)
dew_point = tables.BoolCol(pos=23)
wind_chill = tables.BoolCol(pos=24)
offset_inside_temperature = tables.Float32Col(pos=25)
offset_outside_temperature = tables.Float32Col(pos=26)
offset_inside_humidity = tables.Int16Col(pos=27)
offset_outside_humidity = tables.Int16Col(pos=28)
offset_wind_direction = tables.Int16Col(pos=29)
offset_station_altitude = tables.Float32Col(pos=30)
offset_bar_sea_level = tables.Float32Col(pos=31)
class run_g1(tb.IsDescription):
""" Row template for CAETÊ output data"""
# ID
row_id = tb.Int64Col(dflt=0, pos=0)
date = tb.StringCol(itemsize=8, dflt="yyyymmdd", pos=1)
grid_y = tb.Int16Col(dflt=0, pos=2)
grid_x = tb.Int16Col(dflt=0, pos=3)
# Fluxees
emaxm = tb.Float32Col(dflt=0.0, pos=4)
tsoil = tb.Float32Col(dflt=0.0, pos=5)
photo = tb.Float32Col(dflt=0.0, pos=6)
aresp = tb.Float32Col(dflt=0.0, pos=7)
npp = tb.Float32Col(dflt=0.0, pos=8)
lai = tb.Float32Col(dflt=0.0, pos=9)
f5 = tb.Float32Col(dflt=0.0, pos=10)
rm = tb.Float32Col(dflt=0.0, pos=11)
rg = tb.Float32Col(dflt=0.0, pos=12)
wue = tb.Float32Col(dflt=0.0, pos=13)
cue = tb.Float32Col(dflt=0.0, pos=14)
cdef = tb.Float32Col(dflt=0.0, pos=15)
vcmax = tb.Float32Col(dflt=0.0, pos=16)
specific_la = tb.Float32Col(dflt=0.0, pos=17)
nupt1 = tb.Float32Col(dflt=0.0, pos=18)
nupt2 = tb.Float32Col(dflt=0.0, pos=19)
pupt1 = tb.Float32Col(dflt=0.0, pos=20)
pupt2 = tb.Float32Col(dflt=0.0, pos=21)
pupt3 = tb.Float32Col(dflt=0.0, pos=22)
class run_g3(tb.IsDescription):
""" Row template for CAETÊ output data"""
# ID
row_id = tb.Int64Col(dflt=0, pos=0)
date = tb.StringCol(itemsize=8, dflt="yyyymmdd", pos=1)
grid_y = tb.Int16Col(dflt=0, pos=2)
grid_x = tb.Int16Col(dflt=0, pos=3)
rcm = tb.Float32Col(dflt=0.0, pos=4)
runom = tb.Float32Col(dflt=0.0, pos=5)
evapm = tb.Float32Col(dflt=0.0, pos=6)
wsoil = tb.Float32Col(dflt=0.0, pos=7)
swsoil = tb.Float32Col(dflt=0.0, pos=8)
cleaf = tb.Float32Col(dflt=0.0, pos=9)
cawood = tb.Float32Col(dflt=0.0, pos=10)
cfroot = tb.Float32Col(dflt=0.0, pos=11)
litter_l = tb.Float32Col(dflt=0.0, pos=12)
cwd = tb.Float32Col(dflt=0.0, pos=13)
litter_fr = tb.Float32Col(dflt=0.0, pos=14)
lnc1 = tb.Float32Col(dflt=0.0, pos=15)
lnc2 = tb.Float32Col(dflt=0.0, pos=16)
lnc3 = tb.Float32Col(dflt=0.0, pos=17)
lnc4 = tb.Float32Col(dflt=0.0, pos=18)
lnc5 = tb.Float32Col(dflt=0.0, pos=19)
lnc6 = tb.Float32Col(dflt=0.0, pos=20)
sto1 = tb.Float32Col(dflt=0.0, pos=21)
sto2 = tb.Float32Col(dflt=0.0, pos=22)
sto3 = tb.Float32Col(dflt=0.0, pos=23)
c_cost = tb.Float32Col(dflt=0.0, pos=24)
class FEE(tb.IsDescription):
"""
Stores the parameters used by the EP simulation as metadata
"""
OFFSET = tb.Int16Col(pos=1) # displaces the baseline (e.g, 700)
CEILING = tb.Int16Col(pos=2) # adc top count (4096)
PMT_GAIN = tb.Float32Col(pos=3) # Gain of PMT (4.5e6)
FEE_GAIN = tb.Float32Col(pos=4) # FE gain (250*ohm)
R1 = tb.Float32Col(pos=5) # resistor in Ohms (2350*ohm)
C1 = tb.Float32Col(pos=6) # Capacitor C1 in nF
C2 = tb.Float32Col(pos=7) # Capacitor C2 in nF
ZIN = tb.Float32Col(pos=8) # equivalent impedence
DAQ_GAIN = tb.Float32Col(pos=9)
NBITS = tb.Float32Col(pos=10) # number of bits ADC
LSB = tb.Float32Col(pos=11) # LSB (adc count)
NOISE_I = tb.Float32Col(pos=12) # Noise at the input
NOISE_DAQ = tb.Float32Col(pos=13) # Noise at DAQ
t_sample = tb.Float32Col(pos=14) # sampling time
f_sample = tb.Float32Col(pos=15) # sampling frequency
f_mc = tb.Float32Col(pos=16) # sampling frequency in MC (1ns)
f_LPF1 = tb.Float32Col(pos=17) # LPF
f_LPF2 = tb.Float32Col(pos=18) # LPF
coeff_c = tb.Float64Col(shape=12, pos=19) # cleaning coeff
coeff_blr = tb.Float64Col(shape=12, pos=20) # COEFF BLR
adc_to_pes = tb.Float32Col(shape=12, pos=21) # CALIB CONST
pmt_noise_rms = tb.Float32Col(shape=12, pos=22) # rms noise
class Info(tables.IsDescription):
year = tables.Int16Col()
station_id = tables.Int32Col()
wban_id = tables.Int32Col()
call_id = tables.StringCol(6)
lat = tables.Float32Col()
lon = tables.Float32Col()
elevation = tables.Int16Col()
class DirectionReconstruction(tb.IsDescription):
MC_Energy = tb.FloatCol(dflt=1, pos=1)
N_LST = tb.Int16Col(dflt=1, pos=2)
N_MST = tb.Int16Col(dflt=1, pos=3)
N_SST = tb.Int16Col(dflt=1, pos=4)
multiplicity = tb.Int64Col(dflt=1, pos=5)
off_angle = tb.FloatCol(dflt=1, pos=6)
offset = tb.FloatCol(dflt=1, pos=7)
class LightningStatus(tables.IsDescription):
event_id = tables.UInt32Col(pos=0)
timestamp = tables.Time32Col(pos=1)
close_rate = tables.Int16Col(pos=2)
total_rate = tables.Int16Col(pos=3)
close_alarm = tables.BoolCol(pos=4)
sever_alarm = tables.BoolCol(pos=5)
current_heading = tables.Float32Col(pos=6)
class AMASS_ROW(pytables.IsDescription):
fid = pytables.Int16Col(1) # 1-character String
fname = pytables.Int32Col(1) # 1-character String
gender = pytables.Int16Col(1) # 1-character String
pose = pytables.Float32Col(52 * 3) # float (single-precision)
dmpl = pytables.Float32Col(8) # float (single-precision)
pose_matrot = pytables.Float32Col(52 * 9) # float (single-precision)
betas = pytables.Float32Col(16) # float (single-precision)
trans = pytables.Float32Col(3) # float (single-precision)
class MCHitInfo(tb.IsDescription):
"""Stores the simulated hits as metadata using Pytables.
"""
hit_position = tb.Float32Col(pos=0, shape=3)
hit_time = tb.Float64Col(pos=1)
hit_energy = tb.Float32Col(pos=2)
label = tb.StringCol(20, pos=3)
particle_indx = tb.Int16Col(pos=4)
hit_indx = tb.Int16Col(pos=5)
class RecoEvent(tb.IsDescription):
NTels_trigg = tb.Int16Col(dflt=1, pos=0)
NTels_clean = tb.Int16Col(dflt=1, pos=1)
EnMC = tb.Float32Col(dflt=1, pos=2)
xi = tb.Float32Col(dflt=1, pos=3)
DeltaR = tb.Float32Col(dflt=1, pos=4)
ErrEstPos = tb.Float32Col(dflt=1, pos=5)
ErrEstDir = tb.Float32Col(dflt=1, pos=6)
h_max = tb.Float32Col(dflt=1, pos=7)
class ScanInfo(tables.IsDescription):
rt = tables.Float32Col(pos=0)
polarity = tables.Int16Col(pos=1)
ms_level = tables.Int16Col(pos=2)
max_mz = tables.Float32Col(pos=3)
min_mz = tables.Float32Col(pos=4)
# the rest are only relevant for ms2 spectra
precursor_MZ = tables.Float32Col(pos=5)
precursor_intensity = tables.Float32Col(pos=6)
collision_energy = tables.Float32Col(pos=7)
class AMASS_ROW(pytables.IsDescription):
gender = pytables.Int16Col(1) # 1-character String
idx = pytables.Int16Col(1) # 1-character String
frame = pytables.Int16Col(1) # 1-character String
pose = pytables.Float32Col(24 * 3) # float (single-precision)
pose_matrot = pytables.Float32Col(24 * 9) # float (single-precision)
betas = pytables.Float32Col(10) # float (single-precision)
trans = pytables.Float32Col(3) # float (single-precision)
tightness = pytables.Float32Col(2)
outfit = pytables.Int16Col(6)
class TimeSeries(tb.IsDescription):
id = tb.Int16Col(pos=1)
year = tb.Int16Col(pos=2)
month = tb.Int16Col(pos=3)
dh = tb.Float64Col(pos=4)
se_dh = tb.Float64Col(pos=5)
dg = tb.Float64Col(pos=6)
se_dg = tb.Float64Col(pos=7)
n_ad = tb.Int16Col(pos=8)
n_da = tb.Int16Col(pos=9)
class digital_mapping_particle(tables.IsDescription):
'''Pytables particle for storing digital input/output mappings
'''
channel = tables.Int16Col()
name = tables.StringCol(20)
palatability_rank = tables.Int16Col()
laser = tables.BoolCol()
spike_array = tables.BoolCol()
exclude = tables.BoolCol()
laser_channels = tables.BoolCol()
class electrode_map_particle(tables.IsDescription):
'''PyTables particle for storing electrode mapping
'''
Electrode = tables.Int16Col()
Port = tables.StringCol(5)
Channel = tables.Int16Col()
area = tables.StringCol(20)
CAR_group = tables.Int16Col()
dead = tables.BoolCol()
cutoff_time = tables.Float32Col()
clustering_result = tables.Int16Col()
class Reco(tables.IsDescription):
"""
Stores reconstructed hits
"""
event_indx = tables.Int32Col(pos=0)
nof_hits = tables.Int16Col(pos=1)
hit_indx = tables.Int16Col(pos=2)
hit_position = tables.Float32Col(shape=3, pos=3)
hit_energy = tables.Float32Col(pos=4)
hit_geocorr = tables.Float32Col(pos=5)
hit_ltcorr = tables.Float32Col(pos=6)
class EaarlGps(tables.IsDescription):
sod = tables.Float32Col(pos=3)
lon = tables.Float64Col(pos=0)
lat = tables.Float64Col(pos=1)
alt = tables.Float32Col(pos=2)
pdop = tables.Float32Col(pos=4)
xrms = tables.Float32Col(pos=5)
veast = tables.Float32Col(pos=6)
vnorth = tables.Float32Col(pos=7)
vup = tables.Float32Col(pos=8)
sv = tables.Int16Col(pos=9)
flag = tables.Int16Col(pos=10)
class S2Si(tb.IsDescription):
"""
Store for a S2Si
The table maps a S2Si
peak is the same than the S2 peak
nsipm gives the SiPM number
only energies are stored (times are defined in S2)
"""
event = tb.Int32Col(pos=0)
peak = tb.UInt8Col(pos=1) # peak number
nsipm = tb.Int16Col(pos=2) # sipm number
nsample = tb.Int16Col(pos=3) # sample number
ene = tb.Float32Col(pos=4) # energy in pes
class MCParticleInfo(tb.IsDescription):
"""Stores the simulated particles as metadata using Pytables.
"""
particle_indx = tb.Int16Col(pos=0)
particle_name = tb.StringCol(20, pos=1)
primary = tb.Int16Col(pos=2)
mother_indx = tb.Int16Col(pos=3)
initial_vertex = tb.Float32Col(pos=4, shape=4)
final_vertex = tb.Float32Col(pos=5, shape=4)
initial_volume = tb.StringCol(20, pos=6)
final_volume = tb.StringCol(20, pos=7)
momentum = tb.Float32Col(pos=8, shape=3)
kin_energy = tb.Float32Col(pos=9)
creator_proc = tb.StringCol(20, pos=10)
class HisparcEvent(tables.IsDescription):
# DISCUSS: use of signed (dflt -1) vs unsigned (labview code)
event_id = tables.UInt32Col(pos=0)
timestamp = tables.Time32Col(pos=2)
nanoseconds = tables.UInt32Col(pos=3)
ext_timestamp = tables.UInt64Col(pos=4)
data_reduction = tables.BoolCol(pos=5)
trigger_pattern = tables.UInt32Col(pos=6)
baseline = tables.Int16Col(shape=4, dflt=-1, pos=7)
std_dev = tables.Int16Col(shape=4, dflt=-1, pos=8)
n_peaks = tables.Int16Col(shape=4, dflt=-1, pos=9)
pulseheights = tables.Int16Col(shape=4, dflt=-1, pos=10)
integrals = tables.Int32Col(shape=4, dflt=-1, pos=11)
traces = tables.Int32Col(shape=4, dflt=-1, pos=12)
event_rate = tables.Float32Col(pos=13)
class FontGlyphData(tb.IsDescription):
index = tb.IntCol(pos=1)
charcode = tb.IntCol(pos=2)
unichar = tb.StringCol(8, pos=3)
offset_x = tb.Int16Col(pos=4)
offset_y = tb.Int16Col(pos=5)
width = tb.UInt8Col(pos=6)
height = tb.UInt8Col(pos=7)
atlas_x = tb.UInt16Col(pos=8)
atlas_y = tb.UInt16Col(pos=9)
atlas_w = tb.UInt16Col(pos=10)
atlas_h = tb.UInt16Col(pos=11)
tex_x1 = tb.Float32Col(pos=12)
tex_y1 = tb.Float32Col(pos=13)
tex_x2 = tb.Float32Col(pos=14)
tex_y2 = tb.Float32Col(pos=15)
class AMASS_Params_Row(tables.IsDescription):
subject = tables.Int16Col(pos=1)
gender = tables.Int8Col(pos=2)
shape = tables.Float32Col(16, pos=3)
pose = tables.Float32Col(52 * 3, pos=4)
dmpl = tables.Float32Col(8, pos=5)
trans = tables.Float32Col(3, pos=6)
class Time(tables.IsDescription):
''' Time correction table '''
class das(tables.IsDescription):
manufacturer_s = tables.StringCol(64, pos=3)
model_s = tables.StringCol(64, pos=2)
serial_number_s = tables.StringCol(64, pos=1)
notes_s = tables.StringCol(1024, pos=4)
# Time of first lock
class start_time(tables.IsDescription):
type_s = tables.StringCol(8)
epoch_l = tables.Int64Col()
ascii_s = tables.StringCol(32)
micro_seconds_i = tables.Int32Col()
# Time of ending lock
class end_time(tables.IsDescription):
type_s = tables.StringCol(8)
epoch_l = tables.Int64Col()
ascii_s = tables.StringCol(32)
micro_seconds_i = tables.Int32Col()
slope_d = tables.Float64Col() # Slope
offset_d = tables.Float64Col() # Offset at end time
description_s = tables.StringCol(1024)
corrected_i = tables.Int16Col()
def _create_events_table(file, group):
"""Create event table in PyTables file
Create an event table containing the ESD data columns which are
available in the TSV download.
:param file: PyTables file.
:param group: the group to contain the events table, which need not
exist.
"""
description = {
'event_id': tables.UInt32Col(pos=0),
'timestamp': tables.Time32Col(pos=1),
'nanoseconds': tables.UInt32Col(pos=2),
'ext_timestamp': tables.UInt64Col(pos=3),
'pulseheights': tables.Int16Col(pos=4, shape=4),
'integrals': tables.Int32Col(pos=5, shape=4),
'n1': tables.Float32Col(pos=6),
'n2': tables.Float32Col(pos=7),
'n3': tables.Float32Col(pos=8),
'n4': tables.Float32Col(pos=9),
't1': tables.Float32Col(pos=10),
't2': tables.Float32Col(pos=11),
't3': tables.Float32Col(pos=12),
't4': tables.Float32Col(pos=13),
't_trigger': tables.Float32Col(pos=14)
}
return file.create_table(group, 'events', description, createparents=True)
