class SessionMetaData(tables.IsDescription):
session_id = UInt32Col(pos=1)
experiment_id = UInt32Col(pos=2)
code = StringCol(24, pos=3)
name = StringCol(48, pos=4)
comments = StringCol(256, pos=5)
user_variables = StringCol(2048, pos=6) # will hold json encoded version of user variable dict for session
def setup(self):
self.x_blob = self.file_.create_earray(self.node, "ts_x_values",
Atom.from_dtype(np.dtype("int64")), (0,),
filters=filters)
self.y_blob = self.file_.create_earray(self.node, "ts_y_values",
Atom.from_dtype(np.dtype("float64")), (0,),
filters=filters)
self.bp = self.file_.create_earray(self.node, "bp",
Atom.from_dtype(np.dtype("int32")), (0,),
filters=filters)
description = {}
description["unique_id"] = StringCol(itemsize=64, pos=0)
description["index"] = UInt32Col(pos=1)
description["blank_flags_is_none"] = BoolCol(pos=2)
description["label"] = StringCol(itemsize=32, pos=3)
description["start"] = UInt32Col(pos=4)
description["size"] = UInt32Col(pos=5)
description["bp_start"] = UInt32Col(pos=6)
description["bp_size"] = UInt32Col(pos=7)
self.ts_index = self.file_.create_table(self.node, "ts_index", description,
filters=None)
# every colums which appears in a where method call should/must be indexed !
# this is not only for performance but for correct lookup as well (I had strange bugs
# else)
self.ts_index.cols.unique_id.create_index()
self.ts_index.cols.index.create_index()
class EventTableRow(IsDescription):
"""Describe row format for event table.
Contains event-level information, mostly from Monte Carlo simulation
parameters. NOTE: Additional columns are added dynamically to some tables,
see the github wiki page for the full table/data format descriptions.
Attributes
----------
event_id : tables.UInt32Col
Shower event id.
obs_id : tables.UInt32Col
Shower observation (run) id. Replaces old "run_id" in ctapipe r0
container.
shower_primary_id : tables.UInt8Col
Particle type id for the shower primary particle. From Monte Carlo
simulation parameters.
core_x : tables.Float32Col
Shower core position x coordinate. From Monte Carlo simulation
parameters.
core_y : tables.Float32Col
Shower core position y coordinate. From Monte Carlo simulation
parameters.
h_first_int : tables.Float32Col
Height of shower primary particle first interaction. From Monte Carlo
simulation parameters.
mc_energy : tables.Float32Col
Energy of the shower primary particle in TeV. From Monte Carlo simulation
parameters.
log_mc_energy : tables.Float32Col
Energy of the shower primary particle in log(TeV). From Monte Carlo simulation
parameters.
az : tables.Float32Col
Shower azimuth angle. From Monte Carlo simulation parameters.
alt : tables.Float32Col
Shower altitude (zenith) angle. From Monte Carlo simulation parameters.
array_pointing_az : tables.Float32Col
Array pointing azimuth angle.
array_pointing_alt : tables.Float32Col
Array pointing altitude (zenith) angle.
delta_direction : tables.Float32Col(2)
Angular distance of the shower azimuth and the array pointing.
"""
event_id = UInt32Col()
obs_id = UInt32Col()
shower_primary_id = UInt8Col()
core_x = Float32Col()
core_y = Float32Col()
h_first_int = Float32Col()
x_max = Float32Col()
mc_energy = Float32Col()
log_mc_energy = Float32Col()
az = Float32Col()
alt = Float32Col()
array_pointing_az = Float32Col()
array_pointing_alt = Float32Col()
delta_direction = Float32Col(2)
class Post(IsDescription):
id = Int32Col()
post_type_id = UInt8Col()
parent_id = Int32Col()
accepted_answer_id = Int32Col()
creation_date = Time32Col()
score = UInt32Col()
viewcount = UInt32Col()
owner_user_id = Int32Col()
answer_count = UInt16Col()
favorite_count = UInt16Col()
tags = StringCol(itemsize=25, shape=(5, ))
class Record(tables.IsDescription):
var1 = StringCol(itemsize=4, dflt=b"abcd", pos=0)
var2 = StringCol(itemsize=1, dflt=b"a", pos=1)
var3 = BoolCol(dflt=1)
var4 = Int8Col(dflt=1)
var5 = UInt8Col(dflt=1)
var6 = Int16Col(dflt=1)
var7 = UInt16Col(dflt=1)
var8 = Int32Col(dflt=1)
var9 = UInt32Col(dflt=1)
var10 = Int64Col(dflt=1)
var11 = Float32Col(dflt=1.0)
var12 = Float64Col(dflt=1.0)
var13 = ComplexCol(itemsize=8, dflt=(1. + 0.j))
var14 = ComplexCol(itemsize=16, dflt=(1. + 0.j))
if hasattr(tables, 'Float16Col'):
var15 = tables.Float16Col(dflt=1.0)
if hasattr(tables, 'Float96Col'):
var16 = tables.Float96Col(dflt=1.0)
if hasattr(tables, 'Float128Col'):
var17 = tables.Float128Col(dflt=1.0)
if hasattr(tables, 'Complex196Col'):
var18 = tables.ComplexCol(itemsize=24, dflt=(1. + 0.j))
if hasattr(tables, 'Complex256Col'):
var19 = tables.ComplexCol(itemsize=32, dflt=(1. + 0.j))
class ExperimentMetaData(tables.IsDescription):
experiment_id = UInt32Col(pos=1)
code = StringCol(24, pos=2)
title = StringCol(48, pos=3)
description = StringCol(256, pos=4)
version = StringCol(6, pos=5)
total_sessions_to_run = UInt16Col(pos=9)
class TelTableRow(IsDescription):
"""Describe row format for telescope type table.
Contains parameter information for each telescope type in the data. NOTE:
Additional columns are added dynamically to some tables,
see the github wiki page for the full table/data format descriptions.
Attributes
----------
type : tables.StringCol
Telescope type name (i.e. 'LST:LSTCam')
optics : tables.StringCol
Telescope optics type name (i.e. 'LST').
camera : tables.StringCol
Telescope camera type name (i.e. 'LSTCam').
num_pixels: tables.UInt32Col
Number of pixels in the telescope camera.
pix_rotation: tables.Float32Col
Rotation angle in deg.
cam_rotation: tables.Float32Col
Overall camera rotation in deg.
"""
type = StringCol(20)
optics = StringCol(20)
camera = StringCol(20)
num_pixels = UInt32Col()
pix_rotation = Float32Col()
cam_rotation = Float32Col()
class TwoGenomeWideAssociationLocusMapTable(tables.IsDescription):
"""
2013.1.26 further attributes associated with this table:
input1_fname, input2_fname,
gw_association_locus1 (id, call-method, analysis-method-ls), gw_association_locus2 (id, cm, am)
if "pos=.." is not added, they are sorted alphabetically by their names.
"""
id = UInt64Col(pos=0)
#2013.2.24 overall position of the locus
chromosome = StringCol(64, pos=1) #64 byte-long
start = UInt64Col(pos=2)
stop = UInt64Col(pos=3)
input1_locus_id = UInt64Col(pos=4)
input1_chromosome = StringCol(64, pos=5)
input1_start = UInt64Col(pos=6)
input1_stop = UInt64Col(pos=7)
input2_locus_id = UInt64Col(pos=8)
input2_chromosome = StringCol(64, pos=9)
input2_start = UInt64Col(pos=10)
input2_stop = UInt64Col(pos=11)
locusOverlapFraction = Float64Col(pos=12)
no_of_total_phenotypes = UInt32Col(pos=13) #all significant phenotypes
total_phenotype_ls_in_str = StringCol(1000, pos=14)
fraction_of_total_phenotypes = Float32Col(
pos=15) #divided by all phenotypes with association
no_of_overlap_phenotypes = UInt32Col(pos=16)
overlap_phenotype_ls_in_str = StringCol(1000, pos=17)
fraction_of_overlap_phenotypes = Float32Col(
pos=18
) #divided by the no_of_total_phenotypes (3 cols above, with significant hits)
no_of_input1_only_phenotypes = UInt32Col(pos=19)
input1_only_phenotype_ls_in_str = StringCol(1000, pos=20)
fraction_of_input1_only_phenotypes = Float32Col(pos=21)
no_of_input2_only_phenotypes = UInt32Col(pos=22)
input2_only_phenotype_ls_in_str = StringCol(1000, pos=23)
fraction_of_input2_only_phenotypes = Float32Col(pos=24)
def setup_spec_table(self):
if not hasattr(self.node, "spec_table"):
description = {}
description["pm_index"] = UInt32Col(pos=0)
description["rt"] = Float32Col(pos=1)
description["ms_level"] = UInt8Col(pos=2)
description["start"] = UInt64Col(pos=3)
description["size"] = UInt32Col(pos=4)
spec_table = self.file_.create_table(self.node,
'spec_table',
description,
filters=filters)
# every colums which appears in a where method call should/must be indexed !
# this is not only for performance but for correct lookup as well (I had strange bugs
# else)
spec_table.cols.pm_index.create_index()
spec_table.cols.rt.create_index()
class Event(IsDescription):
"""Row descriptor class for Pytables event table.
Contains event-level parameters, mostly from Monte Carlo simulation
parameters.
Attributes
----------
event_number : UInt32Col
UInt32 placeholder type for the shower event id
run_number : UInt32Col
UInt32 placeholder type for the run number
particle_id : UInt8Col
UInt8 placeholder type for the CORSIKA-simulated primary particle type
code.
core_x : Float64Col
Float64 placeholder type for the Monte Carlo shower core
position x coordinate.
core_y : Float64Col
Float64 placeholder type for the Monte Carlo shower core
position y coordinate.
h_first_int : Float64Col
Float64 placeholder type for the Monte Carlo height of first
interaction of the primary particle.
mc_energy : Float64Col
Float64 placeholder type for the Monte Carlo energy of the
primary particle.
az : Float32Col
Float32 placeholder type for the shower azimuth angle.
alt : Float32Col
Float32 placeholder type for the shower altitude (zenith) angle
"""
event_number = UInt32Col()
run_number = UInt32Col()
particle_id = UInt8Col()
core_x = Float32Col()
core_y = Float32Col()
h_first_int = Float32Col()
mc_energy = Float32Col()
az = Float32Col()
alt = Float32Col()
def setup(self, node, block_size, blob_name, index_name):
if not hasattr(node, blob_name):
self.file_.create_earray(node,
blob_name,
StringAtom(itemsize=block_size), (0, ),
filters=filters)
description = {}
description["index"] = Int64Col(pos=0)
description["start"] = UInt32Col(pos=1)
description["size"] = UInt32Col(pos=2)
# every colums which appears in a where method call should/must be indexed !
# this is not only for performance but for correct lookup as well (I had strange bugs
# else)
string_index = self.file_.create_table(node,
index_name,
description,
filters=None)
string_index.cols.index.create_index()
def setup_spec_table(self):
if not hasattr(self.node, "ms1_spec_table"):
for level in (1, 2):
description = {}
description["pm_index"] = UInt32Col(pos=0)
description["rt"] = Float32Col(pos=1)
description["scan_number"] = Int32Col(pos=2)
description["start"] = UInt64Col(pos=3)
description["end"] = UInt64Col(pos=4)
t = self.file_.create_table(self.node, 'ms%d_spec_table' % level, description,
filters=filters)
# every colums which appears in a where method call should/must be indexed !
# this is not only for performance but for correct lookup as well (I had strange bugs
# else)
t.cols.pm_index.create_index()
t.cols.rt.create_index()
class Tel(IsDescription):
"""Row descriptor class for Pytables telescope data table.
Contains parameter information for each selected telescope
in the data.
Attributes
----------
tel_type : StringCol
String placeholder type for the telescope type name (i.e. 'LST')
num_pixels: UInt32Col
UInt32 placeholder type for telescope's number of pixels
pixel_pos: Float32Col
Float32Col placeholder type for pixel's coordinates
"""
tel_type = StringCol(8)
num_pixels = UInt32Col()
pixel_pos = Float32Col(2)
def setup_peakmap_table(self):
if not hasattr(self.node, "pm_table"):
description = {}
description["unique_id"] = StringCol(itemsize=64, pos=0)
description["index"] = UInt32Col(pos=1)
description["ms_levels"] = StringCol(
itemsize=self.MSLEVEL_FIELD_SIZE, pos=2)
description["rtmin"] = Float32Col(pos=3)
description["rtmax"] = Float32Col(pos=4)
description["mzmin"] = Float32Col(pos=5)
description["mzmax"] = Float32Col(pos=6)
pm_table = self.file_.create_table(self.node,
'pm_table',
description,
filters=filters)
# every colums which appears in a where method call should/must be indexed !
# this is not only for performance but for correct lookup as well (I had strange bugs
# else)
pm_table.cols.unique_id.create_index()
pm_table.cols.index.create_index()
class _Links(IsDescription):
source = UInt32Col()
target = UInt32Col()
class _Nodes(IsDescription):
seqid = UInt8Col()
start = UInt32Col()
stop = UInt32Col()
class ClassTableMappings(tables.IsDescription):
class_id = UInt32Col(pos=1)
class_type_id = UInt32Col(pos=2) # Device or Event etc.
class_name = StringCol(32, pos=3)
table_path = StringCol(128, pos=4)
class ExperimentMetaData(tables.IsDescription):
experiment_id = UInt32Col(pos=1)
code = StringCol(256, pos=2)
title = StringCol(256, pos=3)
description = StringCol(4096, pos=4)
version = StringCol(32, pos=5)
class TimestampedZeoDesc(IsDescription):
timestamp = UInt32Col()
timestamp_subsec = Float32Col()
version = UInt8Col()
class Photon(IsDescription):
hits = Float32Col([len(template.hits), 4])
first_hit_index = UInt32Col()
nb_true_hits = UInt32Col()
class Photon(IsDescription):
hits = Float32Col([len(template.hits), 4])
crystal_index = UInt32Col([len(template.hits)])