class EventTable(tables.IsDescription):
n_triggers = tables.Int64Col(shape=self.n_superpixels)
true_charge = tables.Int64Col(shape=self.n_pixels)
measured_charge = tables.Float64Col(shape=self.n_pixels)
# Event metadata
illumination = tables.Float64Col()
class Persons_Leaving_Id_R(t.IsDescription):
tripid = t.Int32Col()
houseid = t.Int64Col()
personid = t.Int32Col()
starttime = t.Int32Col()
fromzone = t.Int32Col()
tripcount = t.Int64Col()
class Education_Forecast_R(t.IsDescription):
houseid = t.Int64Col()
personid = t.Int64Col()
enroll_f = t.Int32Col()
grade_disagg_f = t.Int32Col()
educ_disagg_f = t.Int32Col()
educ_in_yrs_f = t.Int32Col()
class Education_R(t.IsDescription):
houseid = t.Int64Col()
personid = t.Int64Col()
enroll = t.Int32Col()
grade_disagg = t.Int32Col()
educ_disagg = t.Int32Col()
educ_in_yrs = t.Int32Col()
class Qdata(tables.IsDescription):
event_id = tables.Int32Col()
timestamp = tables.Int64Col()
chargestamp = tables.Int64Col()
channel = tables.Int32Col()
hv = tables.Int32Col()
threshold = tables.Int32Col()
class ReconData(tables.IsDescription):
EventID = tables.Int64Col(pos=0) # EventNo
# inner recon
E_sph_in = tables.Float16Col(pos=1) # Energy
x_sph_in = tables.Float16Col(pos=2) # x position
y_sph_in = tables.Float16Col(pos=3) # y position
z_sph_in = tables.Float16Col(pos=4) # z position
t0_in = tables.Float16Col(pos=5) # time offset
success_in = tables.Int64Col(pos=6) # recon failure
Likelihood_in = tables.Float16Col(pos=7)
# outer recon
E_sph_out = tables.Float16Col(pos=8) # Energy
x_sph_out = tables.Float16Col(pos=9) # x position
y_sph_out = tables.Float16Col(pos=10) # y position
z_sph_out = tables.Float16Col(pos=11) # z position
t0_out = tables.Float16Col(pos=12) # time offset
success_out = tables.Int64Col(pos=13) # recon failure
Likelihood_out = tables.Float16Col(pos=14)
# truth info
x_truth = tables.Float16Col(pos=15) # x position
y_truth = tables.Float16Col(pos=16) # y position
z_truth = tables.Float16Col(pos=17) # z position
E_truth = tables.Float16Col(pos=18) # z position
# unfinished
tau_d = tables.Float16Col(pos=18) # decay time constant
class GroundTruthData(tables.IsDescription):
EventID = tables.Int64Col(pos=0)
ChannelID = tables.Int64Col(pos=1)
PETime = tables.Float64Col(pos=2)
photonTime = tables.Float64Col(pos=3)
PulseTime = tables.Float64Col(pos=4)
dETime = tables.Float64Col(pos=5)
class Persons_Location_R(t.IsDescription):
houseid = t.Int64Col()
personid = t.Int32Col()
personuniqueid = t.Int32Col()
time = t.Int32Col()
location = t.Int32Col()
lasttripcount = t.Int64Col()
class Occupancy_R(t.IsDescription):
houseid = t.Int64Col()
personid = t.Int32Col()
tripid = t.Int32Col()
occupancy = t.Int32Col()
dependentpersonid = t.Int64Col()
tripcount = t.Int64Col()
class Volunteer(tables.IsDescription):
idnumber = tables.Int64Col(pos=0)
name = tables.StringCol(128, pos=1)
remarqs = tables.StringCol(128, pos=2) #
time_start = tables.Time32Col(pos=4, shape=MAX_DAYS)
time_end = tables.Time32Col(pos=5, shape=MAX_DAYS)
affected_tasks = tables.Int64Col(pos=3, shape=50, dflt=-1)
class Order2(Order):
traded_price = tables.Float64Col()
traded_max_price = tables.Float64Col()
traded_min_price = tables.Float64Col()
traded_weighted_price = tables.Float64Col()
traded_quantity = tables.Int64Col()
aggr_quantity = tables.Int64Col()
trades = tables.Int64Col()
class Tick(tables.IsDescription):
timestamp = tables.Int64Col(pos=0)
last_price = tables.Float64Col(pos=1)
qty = tables.Int64Col(pos=2)
bid_price1 = tables.Float64Col(pos=3)
bid_qty1 = tables.Int64Col(pos=4)
ask_price1 = tables.Float64Col(pos=5)
ask_qty1 = tables.Int64Col(pos=6)
class ReconData(tables.IsDescription):
EventID = tables.Int64Col(pos=0)
x = tables.Float16Col(pos=1)
y = tables.Float16Col(pos=2)
z = tables.Float16Col(pos=3)
t0 = tables.Float16Col(pos=4)
E = tables.Float16Col(pos=5)
tau_d = tables.Float16Col(pos=6)
success = tables.Int64Col(pos=7)
class ReconData(tables.IsDescription):
EventID = tables.Int64Col(pos=0) # EventNo
x = tables.Float16Col(pos=1) # x position
y = tables.Float16Col(pos=2) # y position
z = tables.Float16Col(pos=3) # z position
t0 = tables.Float16Col(pos=4) # time offset
E = tables.Float16Col(pos=5) # energy
tau_d = tables.Float16Col(pos=6) # decay time constant
success = tables.Int64Col(pos=7) # recon failure
def create_input_group(h5f,
title="input data at transmitter",
rolloff_dflt=np.nan,
attrs={},
arrays=["symbols", "bits"],
**kwargs):
"""
Create the table for saving the input symbols and bits
Parameters
----------
h5f : string or h5filehandle
The file to use, if a string create or open new file
title: string, optional
The title description of the group
attrs: dict, optional
attributes on the table
arrays: list, optional
name of arrays referenced in the table
**kwargs:
keyword arguments passed to create_table/array, it is highly recommended to set expectedrows
Returns
-------
h5f : h5filehandle
Pytables handle to the hdf file
"""
try:
gr = h5f.create_group("/", "input", title=title)
except AttributeError:
h5f = tb.open_file(h5f, "a")
gr = h5f.create_group("/", "input", title=title)
# if no shape for input syms or bits is given use scalar
t_in = h5f.create_table(gr,
"signal", {
"id": tb.Int64Col(),
"idx_symbols": tb.Int64Col(dflt=0),
"idx_bits": tb.Int64Col(dflt=0),
"rolloff": tb.Float64Col(dflt=rolloff_dflt)
},
title="parameters of input signal",
**kwargs)
setattr(t_in.attrs, "arrays", arrays)
arr_syms = h5f.create_mdvlarray(gr,
"symbols",
tb.ComplexAtom(itemsize=16, dflt=np.nan),
title="sent symbols",
**kwargs)
arr_bits = h5f.create_mdvlarray(gr,
"bits",
tb.BoolAtom(),
title="sent bits",
**kwargs)
for k, v in attrs:
setattr(t_in.attrs, k, v)
return h5f
class TSeries(tb.IsDescription):
year = tb.Int64Col(pos=1)
month = tb.Int64Col(pos=2)
lon = tb.Float64Col(pos=3)
lat = tb.Float64Col(pos=4)
dh = tb.Float64Col(pos=5)
dagc = tb.Float64Col(pos=6)
std = tb.Float64Col(pos=7)
mode = tb.Int64Col(pos=8)
class Schedule_R(t.IsDescription):
houseid = t.Int64Col()
personid = t.Int32Col()
activitytype = t.Int32Col()
locationid = t.Int32Col()
starttime = t.Int32Col()
endtime = t.Int32Col()
duration = t.Int32Col()
dependentpersonid = t.Int64Col()
class ReconData(tables.IsDescription):
EventID = tables.Int64Col(pos=0) # EventNo
t0 = tables.Float16Col(pos=4) # time offset
tau_d = tables.Float16Col(pos=6) # decay time constant
x_sph = tables.Float16Col(pos=8) # x position
y_sph = tables.Float16Col(pos=9) # y position
z_sph = tables.Float16Col(pos=10) # z position
l0_sph = tables.Float16Col(pos=11) # energy
success_sph = tables.Int64Col(pos=12) # recon failure
class Persons_Arrived_Id_R(t.IsDescription):
houseid = t.Int64Col()
personid = t.Int32Col()
actualarrivaltime = t.Int32Col()
expectedarrivaltime = t.Int32Col()
tripdependentpersonid = t.Int64Col()
tozone = t.Int32Col()
personuniqueid = t.Int64Col()
tripcount = t.Int64Col()
class RegionDescription(t.IsDescription):
"""
Description of a genomic region for PyTables Table
"""
ix = t.Int32Col(pos=0)
chromosome = t.StringCol(100, pos=1)
start = t.Int64Col(pos=2)
end = t.Int64Col(pos=3)
strand = t.Int8Col(pos=4)
_mask_ix = t.Int32Col(pos=5)
class Ev_and_PN(tables.IsDescription):
model_name = tables.StringCol(100)
date = tables.StringCol(20)
number_of_points = tables.Int64Col()
bandwidth = tables.Int64Col()
dis_param = tables.Float64Col()
eig_vals = tables.Float64Col(_number_of_points)
PN = tables.Float64Col(_number_of_points)
class Trades(tables.IsDescription):
time = tables.Int64Col()
trader_id = tables.Int64Col()
trade_id = tables.Int64Col()
sequence_id = tables.Int64Col()
side = tables.Int8Col()
price = tables.Float64Col()
quantity = tables.Int64Col()
origin_id = tables.Int8Col()
is_auction = tables.BoolCol()
is_aggressor = tables.BoolCol()
class SourceDescriptor(tb.IsDescription):
id = tb.Int64Col(pos=0)
tile_id = tb.Int64Col(pos=1)
order = tb.Int64Col(pos=2)
ra_j = tb.FloatCol(pos=3)
ra_k = tb.FloatCol(pos=4)
ra_h = tb.FloatCol(pos=5)
dec_h = tb.FloatCol(pos=6)
dec_j = tb.FloatCol(pos=7)
dec_k = tb.FloatCol(pos=8)
obs_number = tb.Int64Col(pos=9)
class Bars(tables.IsDescription):
session_date = tables.Int64Col()
first_time = tables.Int64Col()
last_time = tables.Int64Col()
first_price = tables.Float64Col()
last_price = tables.Float64Col()
high_price = tables.Float64Col()
low_price = tables.Float64Col()
volume = tables.Int64Col()
trade_nb = tables.Int64Col()
class CKG(tables.IsDescription):
model_name = tables.StringCol(100)
date = tables.StringCol(20)
number_of_points = tables.Int64Col()
bandwidth = tables.Int64Col()
dis_param = tables.Float64Col()
c = tables.Float64Col()
k = tables.Float64Col()
g = tables.ComplexCol(16)
seed = tables.Int64Col()
class Schedule_Allocation_R1(t.IsDescription):
scheduleid = t.Int64Col()
houseid = t.Int64Col()
personid = t.Int32Col()
activitytype = t.Int32Col()
locationid = t.Int32Col()
starttime = t.Int32Col()
endtime = t.Int32Col()
duration = t.Int32Col()
dependentpersonid = t.Int64Col()
tripcount = t.Int64Col()
def create_meas_group(h5f,
title="measurement data",
description=None,
attrs=MEAS_UNITS,
arrays=["data"],
**kwargs):
"""
Create the table for saving oscilloscope measurements
Parameters
----------
h5f : string or h5filehandle
The file to use, if a string create or open new file
title: string, optional
The title description of the group
data_shape: int
Number of modes/polarizations
description: dict or tables.IsDescription (optional)
If given use to create the table
arrays: list, optional
name of arrays referenced in the table
attrs: dict, optional
attributes on the table
**kwargs:
keyword arguments passed to create_table/array, it is highly recommended to set expectedrows
Returns
-------
h5f : h5filehandle
Pytables handle to the hdf file
"""
try:
gr_meas = h5f.create_group("/", "measurements", title=title)
except AttributeError:
h5f = tb.open_file(h5f, "a")
gr_meas = h5f.create_group("/", "measurements", title=title)
gr_osc = h5f.create_group(gr_meas,
"oscilloscope",
title="Data from Realtime oscilloscope")
if description is None:
description = {
"id": tb.Int64Col(),
"samplingrate": tb.Float64Col(),
"idx_data": tb.Int64Col()
}
t_meas = h5f.create_table(gr_osc, "signal", description, "sampled signal",
**kwargs)
setattr(t_meas.attrs, "arrays", arrays)
arr = h5f.create_mdvlarray(gr_osc, "data", tb.ComplexAtom(itemsize=16),
**kwargs)
for k, v in attrs.items():
setattr(t_meas.attrs, k, v)
return h5f
class Trips_Final_R(t.IsDescription):
tripid = t.Int64Col()
houseid = t.Int64Col()
personid = t.Int32Col()
vehid = t.Int32Col()
tripmode = t.Int32Col()
fromzone = t.Int32Col()
tozone = t.Int32Col()
starttime = t.Int32Col()
endtime = t.Int32Col()
trippurpose = t.Int32Col()
def create_schema(fp_length: int) -> Any:
class Particle(tb.IsDescription):
pass
columns = {}
pos = 1
columns["fp_id"] = tb.Int64Col(pos=pos)
for i in range(1, math.ceil(fp_length / 64) + 1):
pos += 1
columns["f" + str(i)] = tb.UInt64Col(pos=pos)
columns["popcnt"] = tb.Int64Col(pos=pos + 1)
Particle.columns = columns
return Particle
class Household_Forecast_Population_R(t.IsDescription):
houseid = t.Int64Col()
bldgsz = t.Int32Col()
hht = t.Int32Col()
hinc = t.Int32Col()
noc = t.Int32Col()
persons = t.Int32Col()
unittype = t.Int32Col()
vehicl = t.Int32Col()
wif = t.Int32Col()
yrmoved = t.Int32Col()
old_houseid = t.Int64Col()
