Data model

The ROOT schema and the layer that reads and writes it. See The data model for the conventions these classes assume.

`grand.dataio.run_trees`
`grand.dataio.event_trees`
`grand.dataio.data_tree`
`grand.dataio.descriptors`
`grand.dataio.root_files`	Manage ROOT file of type event : Efield, voltage The main functionnalities of this module is * to synchronize the relevant TTree (tshower , trun) on the same event * provide a numpy container of traces by event, with method get_obj_handling3dtraces()
`grand.dataio.data_handling`
`grand.dataio.protocol`
`grand.dataio.xmax_frame`	Which vertical frame a file's `xmax_pos_shc` is in, read from the file.

`grand.dataio.run_trees`

class grand.dataio.run_trees.MotherRunTree(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = '', _type: str = '', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>)[source]

Bases: DataTree

A mother class for classes with Run values

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number, which identifies the entry

run_number: TTreeScalarDesc: Run number, which identifies the entry

fill()[source]: Adds the current variable values as a new event to the tree

add_proper_friends()[source]: Add proper friends to this tree

print_list_of_runs()[source]: List runs in the tree

get_list_of_runs()[source]

Gets list of runs in the tree together

Returns:: Every run number in the tree.
Return type:: list of int

get_run(run_no)[source]

Readout the TTree entry corresponding to the run

Parameters:: run_no (int) – Run number.
Returns:: Bytes read.
Return type:: int
Raises:: LookupError – When the tree has no such run.

has_run(run_no)[source]

Check if the TTree has an entry with the given run number

Parameters:: run_no (int) – Run number.
Returns:: True when the tree holds that run.
Return type:: bool

build_index(run_id)[source]

Build the tree index (necessary for working with friends)

Parameters:: run_id (str, optional) – Branch holding the run number.

fill_entry_list()[source]: Fills the entry list from the tree

is_unique_event()[source]

Check if specified run_number/event_number already exist in the tree

Returns:: True when no run number appears twice.
Return type:: bool

class grand.dataio.run_trees.TRun(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trun', _type: str = 'run', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, run_mode: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, first_event: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, first_event_time: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, last_event: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, last_event_time: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, data_source: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, data_generator: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, data_generator_version: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, event_type: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_version: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, site: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, site_layout: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, origin_geoid: TTreeArrayDesc = <grand.dataio.descriptors.TTreeArrayDesc object>, du_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_geoid: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_xyz: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_type: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_tilt: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_ground_tilt: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_nut: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_feb: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, t_bin_size: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherRunTree

What stays the same over a run: the site, the layout and the detection units.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number, which identifies the entry
run_mode (uint32) – Run mode (calibration, test or physics). The values are not yet defined.
first_event (uint32) – Number of the run’s first event.
first_event_time (uint32) – Time of the first event, in Unix seconds
last_event (uint32) – Number of the run’s last event.
last_event_time (uint32) – Time of the last event, in Unix seconds
data_source (str) – Where the data come from: detector, sim or other.
data_generator (str) – Name of the program that produced the data.
data_generator_version (str) – Version of the program that produced the data.
event_type (uint32) – Trigger type: 0x1000 for the 10-second trigger, 0x8000 for a random trigger; any other value is a shower trigger.
event_version (uint32) – Version of the DAQ’s event format.
site (str) – Name of the site, such as Dunhuang or Xiaodushan.
site_layout (str) – Name of the antenna layout, such as GP300.
origin_geoid (array of shape 3, float32) – Origin of the array frame: (latitude in degrees, longitude in degrees, height in meters). du_xyz is relative to it
du_id (std::vector<int>) – Identifier of each detection unit.
du_geoid (std::vector<vector<float>>) – Detector unit (antenna) position: (latitude in degrees, longitude in degrees, height in meters) per DU
du_xyz (std::vector<vector<float>>) – Position of each unit in the array frame, in meters: x north, y west and z up, relative to origin_geoid.
du_type (std::vector<string>) – Antenna type of each unit.
du_tilt (std::vector<vector<float>>) – Tilt of each antenna, as two angles.
du_ground_tilt (std::vector<vector<float>>) – Tilt of the ground at each antenna, as two angles.
du_nut (std::vector<int>) – Identifier of each unit’s nut, the antenna head that holds the low-noise amplifier.
du_feb (std::vector<int>) – Identifier of each unit’s front-end board.
t_bin_size (std::vector<float>, in ns) – Sampling interval of each unit’s traces, in ns: the inverse of the sampling frequency.

See also

grand.geo.coordinates.GRANDCS: The array frame of du_xyz.
grand.dataio.event_trees.TEfield: The traces of the units it lists.

Examples

Read the site and the antenna layout of a run:

from pathlib import Path

import numpy as np
import grand
from grand.dataio import TRun

sample = (Path(grand.__file__).parents[1]
          / "sim2root/Common/sim_Xiaodushan_20221026_000000_RUN1_CD_ZHAireS_0000")
with TRun(str(sample / "run_1_L0_0000.root")) as trun:
    trun.get_entry(0)
    print("site %s, origin (lat, lon, height) %s" % (trun.site, np.asarray(trun.origin_geoid)))
    print("%d antennas; the first at %s m (x north, y west, z up)"
          % (len(trun.du_id), np.round(np.asarray(trun.du_xyz)[0], 1)))

site Xiaodushan, origin (lat, lon, height) [  40.99   93.94 1264.  ]
49 antennas; the first at [  866.  -5500.    -26.8] m (x north, y west, z up)

run_mode: TTreeScalarDesc: Run mode (calibration, test or physics). The values are not yet defined.

first_event: TTreeScalarDesc: Number of the run’s first event.

first_event_time: TTreeScalarDesc: Time of the first event, in Unix seconds

last_event: TTreeScalarDesc: Number of the run’s last event.

last_event_time: TTreeScalarDesc: Time of the last event, in Unix seconds

data_source: StdStringDesc

detector, sim or other.

Type:: Where the data come from

data_generator: StdStringDesc: Name of the program that produced the data.

data_generator_version: StdStringDesc: Version of the program that produced the data.

event_type: TTreeScalarDesc

0x1000 for the 10-second trigger, 0x8000 for a random trigger; any other value is a shower trigger.

Type:: Trigger type

event_version: TTreeScalarDesc: Version of the DAQ’s event format.

site: StdStringDesc: Name of the site, such as Dunhuang or Xiaodushan.

site_layout: StdStringDesc: Name of the antenna layout, such as GP300.

origin_geoid: TTreeArrayDesc

(latitude in degrees, longitude in degrees, height in meters). du_xyz is relative to it

Type:: Origin of the array frame

du_id: StdVectorListDesc: Identifier of each detection unit.

du_geoid: StdVectorListDesc

(latitude in degrees, longitude in degrees, height in meters) per DU

Type:: Detector unit (antenna) position

du_xyz: StdVectorListDesc

x north, y west and z up, relative to origin_geoid.

Type:: Position of each unit in the array frame, in meters

du_type: StdVectorListDesc: Antenna type of each unit.

du_tilt: StdVectorListDesc: Tilt of each antenna, as two angles.

du_ground_tilt: StdVectorListDesc: Tilt of the ground at each antenna, as two angles.

du_nut: StdVectorListDesc: Identifier of each unit’s nut, the antenna head that holds the low-noise amplifier.

du_feb: StdVectorListDesc: Identifier of each unit’s front-end board.

t_bin_size: StdVectorListDesc

the inverse of the sampling frequency.

Type:: Sampling interval of each unit’s traces, in ns

class grand.dataio.run_trees.TRunVoltage(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trunvoltage', _type: str = 'runvoltage', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, digi_ctrl: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, firmware_version: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trace_length: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_sampling_frequency: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_sampling_resolution: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_input_channels: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_enabled_channels: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gain: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_conversion: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, digi_prepost_trig_windows: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, channel_properties_x: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, channel_properties_y: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, channel_properties_z: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, channel_trig_settings_x: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, channel_trig_settings_y: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, channel_trig_settings_z: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherRunTree

Settings of the voltage traces, common to all events of a run.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number, which identifies the entry
digi_ctrl (std::vector<vector<unsigned short>>) – Control parameters - the list of general parameters that can set the mode of operation, select trigger sources and preset the common coincidence read out time window (Digitizer mode parameters in the manual).
firmware_version (std::vector<unsigned short>) – Firmware version
trace_length (std::vector<vector<int>>) – Nominal trace length in units of samples
adc_sampling_frequency (std::vector<unsigned short>) – ADC sampling frequency in MHz
adc_sampling_resolution (std::vector<unsigned short>) – ADC sampling resolution in bits
adc_input_channels (std::vector<unsigned short>) – ADC input channels - > 16 BIT WORD (4*4 BITS) LOWEST IS CHANNEL 1, HIGHEST CHANNEL 4. FOR EACH CHANNEL IN THE EVENT WE HAVE: 0: ADC1, 1: ADC2, 2:ADC3, 3:ADC4 4:FILTERED ADC1, 5:FILTERED ADC 2, 6:FILTERED ADC3, 7:FILTERED ADC4. ToDo: decode this?
adc_enabled_channels (std::vector<unsigned short>) – ADC enabled channels - LOWEST 4 BITS STATE WHICH CHANNEL IS READ OUT ToDo: Decode this?
gain (std::vector<vector<int>>) – Value of the Variable gain amplification on the board
adc_conversion (std::vector<vector<float>>) – Conversion factor from bits to V for ADC
digi_prepost_trig_windows (std::vector<vector<unsigned short>>) – Window parameters - describe Pre Coincidence, Coincidence and Post Coincidence readout windows (Digitizer window parameters in the manual). ToDo: Decode?
channel_properties_x (std::vector<vector<unsigned short>>) – Channel x properties - described in Channel property parameters in the manual. ToDo: Decode?
channel_properties_y (std::vector<vector<unsigned short>>) – Channel y properties - described in Channel property parameters in the manual. ToDo: Decode?
channel_properties_z (std::vector<vector<unsigned short>>) – Channel z properties - described in Channel property parameters in the manual. ToDo: Decode?
channel_trig_settings_x (std::vector<vector<unsigned short>>) – Channel x trigger settings - described in Channel trigger parameters in the manual. ToDo: Decode?
channel_trig_settings_y (std::vector<vector<unsigned short>>) – Channel y trigger settings - described in Channel trigger parameters in the manual. ToDo: Decode?
channel_trig_settings_z (std::vector<vector<unsigned short>>) – Channel z trigger settings - described in Channel trigger parameters in the manual. ToDo: Decode?

digi_ctrl: StdVectorListDesc: Control parameters - the list of general parameters that can set the mode of operation, select trigger sources and preset the common coincidence read out time window (Digitizer mode parameters in the manual).

firmware_version: StdVectorListDesc: Firmware version

trace_length: StdVectorListDesc: Nominal trace length in units of samples

adc_sampling_frequency: StdVectorListDesc: ADC sampling frequency in MHz

adc_sampling_resolution: StdVectorListDesc: ADC sampling resolution in bits

adc_input_channels: StdVectorListDesc

ADC1, 1: ADC2, 2:ADC3, 3:ADC4 4:FILTERED ADC1, 5:FILTERED ADC 2, 6:FILTERED ADC3, 7:FILTERED ADC4. ToDo: decode this?

Type:: ADC input channels - > 16 BIT WORD (4*4 BITS) LOWEST IS CHANNEL 1, HIGHEST CHANNEL 4. FOR EACH CHANNEL IN THE EVENT WE HAVE
Type:: 0

adc_enabled_channels: StdVectorListDesc

Decode this?

Type:: ADC enabled channels - LOWEST 4 BITS STATE WHICH CHANNEL IS READ OUT ToDo

gain: StdVectorListDesc: Value of the Variable gain amplification on the board

adc_conversion: StdVectorListDesc: Conversion factor from bits to V for ADC

digi_prepost_trig_windows: StdVectorListDesc

Decode?

Type:: Window parameters - describe Pre Coincidence, Coincidence and Post Coincidence readout windows (Digitizer window parameters in the manual). ToDo

channel_properties_x: StdVectorListDesc

Decode?

Type:: Channel x properties - described in Channel property parameters in the manual. ToDo

channel_properties_y: StdVectorListDesc

Decode?

Type:: Channel y properties - described in Channel property parameters in the manual. ToDo

channel_properties_z: StdVectorListDesc

Decode?

Type:: Channel z properties - described in Channel property parameters in the manual. ToDo

channel_trig_settings_x: StdVectorListDesc

Decode?

Type:: Channel x trigger settings - described in Channel trigger parameters in the manual. ToDo

channel_trig_settings_y: StdVectorListDesc

Decode?

Type:: Channel y trigger settings - described in Channel trigger parameters in the manual. ToDo

channel_trig_settings_z: StdVectorListDesc

Decode?

Type:: Channel z trigger settings - described in Channel trigger parameters in the manual. ToDo

class grand.dataio.run_trees.TRunRawVoltage(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trunrawvoltage', _type: str = 'runrawvoltage', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, trigger_position: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, firmware_version: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_sampling_frequency: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_sampling_resolution: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_input_channels_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_enabled_channels_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, pre_coincidence_window_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, post_coincidence_window_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gain_correction_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, integration_time_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, offset_correction_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, base_maximum_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, base_minimum_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, signal_threshold_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, noise_threshold_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, tper_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, tprev_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, ncmax_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, tcmax_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, qmax_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, ncmin_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, qmin_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, notch_filters_no_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trace_length: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_conversion: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherRunTree

Settings of the raw voltage traces, common to all events of a run.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number, which identifies the entry
trigger_position (std::vector<unsigned short>) – Trigger position in the trace (trigger start = nanoseconds - 2*sample number)
firmware_version (std::vector<unsigned short>) – Firmware version
adc_sampling_frequency (std::vector<unsigned short>) – ADC sampling frequency in MHz
adc_sampling_resolution (std::vector<unsigned short>) – ADC sampling resolution in bits
adc_input_channels_ch (std::vector<vector<unsigned char>>) – ADC input channels for fv2: 0: ADC1, 1: ADC2, 2:ADC3, 3:ADC4, 4:FILTERED ADC1, 5:FILTERED ADC2, 6:FILTERED ADC3, 7:FILTERED ADC4, 15: off
adc_enabled_channels_ch (std::vector<vector<bool>>) – ADC enabled channels - LOWEST 4 BITS STATE WHICH CHANNEL IS READ OUT
pre_coincidence_window_ch (std::vector<vector<unsigned short>>) – Digitizer window parameters - describe Pre Coincidence, Coincidence and Post Coincidence readout windows (Digitizer window parameters in the manual). The unit is samples (to get time, multiply by 2).
post_coincidence_window_ch (std::vector<vector<unsigned short>>) – Digitizer window parameters - describe Pre Coincidence, Coincidence and Post Coincidence readout windows (Digitizer window parameters in the manual). The unit is samples (to get time, multiply by 2).
gain_correction_ch (std::vector<vector<unsigned short>>) – Channel property parameters
integration_time_ch (std::vector<vector<unsigned short>>)
offset_correction_ch (std::vector<vector<unsigned char>>)
base_maximum_ch (std::vector<vector<unsigned short>>)
base_minimum_ch (std::vector<vector<unsigned short>>)
signal_threshold_ch (std::vector<vector<unsigned short>>) – Channel trigger parameters
noise_threshold_ch (std::vector<vector<unsigned short>>)
tper_ch (std::vector<vector<unsigned short>>) – The unit is samples (to get time, multiply by 2)
tprev_ch (std::vector<vector<unsigned short>>)
ncmax_ch (std::vector<vector<unsigned short>>)
tcmax_ch (std::vector<vector<unsigned short>>)
qmax_ch (std::vector<vector<unsigned short>>)
ncmin_ch (std::vector<vector<unsigned short>>)
qmin_ch (std::vector<vector<unsigned short>>)
notch_filters_no_ch (std::vector<vector<unsigned char>>)
trace_length (std::vector<unsigned int>) – Trace length
adc_conversion (std::vector<float>) – ADC to voltage conversion factor

trigger_position: StdVectorListDesc: Trigger position in the trace (trigger start = nanoseconds - 2*sample number)

firmware_version: StdVectorListDesc: Firmware version

adc_sampling_frequency: StdVectorListDesc: ADC sampling frequency in MHz

adc_sampling_resolution: StdVectorListDesc: ADC sampling resolution in bits

adc_input_channels_ch: StdVectorListDesc

ADC1, 1: ADC2, 2:ADC3, 3:ADC4, 4:FILTERED ADC1, 5:FILTERED ADC2, 6:FILTERED ADC3, 7:FILTERED ADC4, 15: off

Type:: ADC input channels for fv2
Type:: 0

adc_enabled_channels_ch: StdVectorListDesc: ADC enabled channels - LOWEST 4 BITS STATE WHICH CHANNEL IS READ OUT

pre_coincidence_window_ch: StdVectorListDesc: Digitizer window parameters - describe Pre Coincidence, Coincidence and Post Coincidence readout windows (Digitizer window parameters in the manual). The unit is samples (to get time, multiply by 2).

post_coincidence_window_ch: StdVectorListDesc: Digitizer window parameters - describe Pre Coincidence, Coincidence and Post Coincidence readout windows (Digitizer window parameters in the manual). The unit is samples (to get time, multiply by 2).

gain_correction_ch: StdVectorListDesc: Channel property parameters

integration_time_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

offset_correction_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

base_maximum_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

base_minimum_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

signal_threshold_ch: StdVectorListDesc: Channel trigger parameters

noise_threshold_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

tper_ch: StdVectorListDesc: The unit is samples (to get time, multiply by 2)

tprev_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

ncmax_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

tcmax_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

qmax_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

ncmin_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

qmin_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

notch_filters_no_ch: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

trace_length: StdVectorListDesc: Trace length

adc_conversion: StdVectorListDesc: ADC to voltage conversion factor

class grand.dataio.run_trees.TRunEfieldSim(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trunefieldsim', _type: str = 'runefieldsim', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, refractivity_model: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, refractivity_model_parameters: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, t_pre: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, t_post: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, site: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, sim_name: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, sim_version: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>)[source]

Bases: MotherRunTree

Simulation settings of the electric-field traces, common to all events of a run.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number, which identifies the entry
refractivity_model (str) – Name of the atmospheric index of refraction model
refractivity_model_parameters (std::vector<double>) – Parameters of refractivity_model; their meaning depends on the model (the CoREAS converter writes the placeholder [1, 1, 1])
t_pre (float32) – Start of the antenna data window before the trigger, in ns (it can be shorter than the voltage trace, so this can differ from the voltage’s)
t_post (float32) – End of the antenna data window after the trigger, in ns
site (str) – Site for which the efield simulation was done
sim_name (str) – Simulator name (aires/corsika, etc.)
sim_version (str) – Simulator version string

refractivity_model: StdStringDesc: Name of the atmospheric index of refraction model

refractivity_model_parameters: StdVectorListDesc: Parameters of refractivity_model; their meaning depends on the model (the CoREAS converter writes the placeholder [1, 1, 1])

t_pre: TTreeScalarDesc: Start of the antenna data window before the trigger, in ns (it can be shorter than the voltage trace, so this can differ from the voltage’s)

t_post: TTreeScalarDesc: End of the antenna data window after the trigger, in ns

site: StdStringDesc: Site for which the efield simulation was done

sim_name: StdStringDesc: Simulator name (aires/corsika, etc.)

sim_version: StdStringDesc: Simulator version string

class grand.dataio.run_trees.TRunShowerSim(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trunshowersim', _type: str = 'runshowersim', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, rel_thin: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, maximum_weight: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, hadronic_thinning: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, hadronic_thinning_weight: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, lowe_cut_e: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, lowe_cut_gamma: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, lowe_cut_mu: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, lowe_cut_meson: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, lowe_cut_nucleon: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, site: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, sim_name: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, sim_version: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>)[source]

Bases: MotherRunTree

Simulation settings of the showers, common to all events of a run.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number, which identifies the entry
rel_thin (float32) – relative thinning energy
maximum_weight (float32) – the maximum weight, computed in zhaires as PrimaryEnergy*RelativeThinning*WeightFactor/14.0 (see aires manual section 3.3.6 and 2.3.2) to make it mean the same as Corsika Wmax
hadronic_thinning (float32) – the ratio of energy at wich thining starts in hadrons and electromagnetic particles
hadronic_thinning_weight (float32) – the ratio of electromagnetic to hadronic maximum weights
lowe_cut_e (float32) – low energy cut for electrons (GeV)
lowe_cut_gamma (float32) – low energy cut for gammas (GeV)
lowe_cut_mu (float32) – low energy cut for muons (GeV)
lowe_cut_meson (float32) – low energy cut for mesons (GeV)
lowe_cut_nucleon (float32) – low energy cut for nucleons (GeV)
site (str) – Site for which the shower simulation was done
sim_name (str) – Simulator name (aires/corsika, etc.)
sim_version (str) – Simulator version string

rel_thin: TTreeScalarDesc: relative thinning energy

maximum_weight: TTreeScalarDesc: the maximum weight, computed in zhaires as PrimaryEnergy*RelativeThinning*WeightFactor/14.0 (see aires manual section 3.3.6 and 2.3.2) to make it mean the same as Corsika Wmax

hadronic_thinning: TTreeScalarDesc: the ratio of energy at wich thining starts in hadrons and electromagnetic particles

hadronic_thinning_weight: TTreeScalarDesc: the ratio of electromagnetic to hadronic maximum weights

lowe_cut_e: TTreeScalarDesc: low energy cut for electrons (GeV)

lowe_cut_gamma: TTreeScalarDesc: low energy cut for gammas (GeV)

lowe_cut_mu: TTreeScalarDesc: low energy cut for muons (GeV)

lowe_cut_meson: TTreeScalarDesc: low energy cut for mesons (GeV)

lowe_cut_nucleon: TTreeScalarDesc: low energy cut for nucleons (GeV)

site: StdStringDesc: Site for which the shower simulation was done

sim_name: StdStringDesc: Simulator name (aires/corsika, etc.)

sim_version: StdStringDesc: Simulator version string

class grand.dataio.run_trees.TRunNoise(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trunnoise', _type: str = 'runnoise', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, gal_noise_map: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, gal_noise_LST: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, gal_noise_sigma: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherRunTree

Settings of the Galactic-noise generation for a run.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number, which identifies the entry
gal_noise_map (str) – Info to retrieve the map of galactic noise
gal_noise_LST (float32) – Local sidereal time, in hours (0 to 24), at which the galactic noise was generated
gal_noise_sigma (std::vector<vector<float>>) – Galactic-noise standard deviation for each arm of each antenna, in µV

gal_noise_map: StdStringDesc: Info to retrieve the map of galactic noise

gal_noise_LST: TTreeScalarDesc: Local sidereal time, in hours (0 to 24), at which the galactic noise was generated

gal_noise_sigma: StdVectorListDesc: Galactic-noise standard deviation for each arm of each antenna, in µV

`grand.dataio.event_trees`

class grand.dataio.event_trees.MotherEventTree(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = '', _type: str = '', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>)[source]

Bases: DataTree

A mother class for classes with Event values

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run

run_number: TTreeScalarDesc: Run number; with the event number it identifies the entry

event_number: TTreeScalarDesc: Event number, unique within its run

fill()[source]: Adds the current variable values as a new event to the tree

add_proper_friends()[source]

Add proper friends to this tree

Returns:: Attaches the run and simulation trees this one needs.
Return type:: None

print_list_of_events()[source]: List events in the tree together with runs

get_list_of_events()[source]

Gets list of events in the tree together with runs

Returns:: Every (event number, run number) in the tree.
Return type:: list of tuple

get_event(ev_no, run_no=0)[source]

Readout the TTree entry corresponding to the event and run

Parameters:

ev_no (int) – Event number.
run_no (int, optional) – Run number; the pair is unique.

Returns:

Bytes read.

Return type:

int

Raises:

LookupError – When the tree has no such event.

has_event(ev_no, run_no=0)[source]

Check if the TTree has an entry with the given event and run number

Parameters:

ev_no (int) – Event number.
run_no (int, optional) – Run number; the pair is unique.

Returns:

True when the tree holds that event.

Return type:

bool

build_index(run_id, evt_id)[source]

Builds index based on run_id and evt_id for the TTree

Parameters:

run_id (str, optional) – Branch holding the run number.
evt_id (str, optional) – Branch holding the event number.

fill_entry_list(tree=None)[source]

Fills the entry list from the tree

Parameters:: tree (ROOT.TTree, optional) – Tree to index; this one by default.

is_unique_event()[source]

Check if specified run_number/event_number already exist in the tree

Returns:: True when no (event, run) pair appears twice.
Return type:: bool

get_traces_lengths()[source]

Gets the trace lengths of the current entry

Returns:: For each detection unit of the loaded entry, the length of each of its channels; None if this tree holds no traces. (It looked for branches named trace_x or trace_0, which no tree has, and always returned None.)
Return type:: list of list of int or None

get_list_of_dus()[source]

Gets the detector units of the current entry

Returns:: Detection units in the loaded entry, in its order; None if this tree has no du_id. For the units of the whole tree, use get_list_of_all_used_dus.
Return type:: list of int or None

get_list_of_all_used_dus()[source]

Compiles the list of all detector units used in the events of the tree

Returns:: Every detection unit appearing anywhere in the tree, sorted; None if this tree has no du_id.
Return type:: list of int or None

get_dus_indices_in_run(trun)[source]

Gets an array of the indices of DUs of the current event in the TRun tree

Parameters:: trun (TRun) – The run tree to look the units up in.
Returns:: Index of each unit of this event within the run unit list, in the event’s order, so that run_array[indices] lines up with this event’s traces.
Return type:: ndarray
Raises:: ValueError – If a unit of this event is not in the run.

class grand.dataio.event_trees.TADC(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'tadc', _type: str = 'adc', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_size: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, t3_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, first_du: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_seconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_nanoseconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_type: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_version: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, du_count: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_seconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_nanoseconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_position: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_flag: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atm_temperature: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atm_pressure: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atm_humidity: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, acceleration_x: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, acceleration_y: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, acceleration_z: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, battery_level: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, firmware_version: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_sampling_frequency: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_sampling_resolution: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_input_channels_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_enabled_channels_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_samples_count_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_ch0_ch1: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_notch0_ch1: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_redch0_ch1: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_ch2_ch3: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_calibration: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_10s: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_external_test_pulse: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_rate: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, clock_tick: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, clock_ticks_per_second: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_offset: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_leap_second: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_status: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_alarms: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_warnings: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_time: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_long: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_lat: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_alt: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_temp: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_auto_reset_timeout: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, force_firmware_reset: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_filter_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_1PPS: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_DAQ: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_ch0_ch1: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_notch0_ch1: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_redch0_ch1: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_ch2_ch3: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_calibration: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_10s: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_trigger_external_test_pulse: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, enable_readout_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, fire_single_test_pulse: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, test_pulse_rate_divider: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, common_coincidence_time: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, selector_readout_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, pre_coincidence_window_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, post_coincidence_window_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gain_correction_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, integration_time_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, offset_correction_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, base_maximum_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, base_minimum_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, signal_threshold_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, noise_threshold_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, tper_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, tprev_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, ncmax_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, tcmax_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, qmax_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, ncmin_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, qmin_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, ioff: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trace_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, pps_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, fpga_temp: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_temp: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, hardware_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_status: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_ddr_storage: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, data_format_version: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adaq_version: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, dudaq_version: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_ch0_ch1_ch2: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_ch0_ch1_notch2: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern_20Hz: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_external_test_pulse_period: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_sec_sun: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_week_num: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_receiver_mode: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_disciplining_mode: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_self_survey: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_minor_alarms: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_gnss_decoding: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_disciplining_activity: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, notch_filters_no_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, nutrig_rhox: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, nutrig_rhoy: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherEventTree

Digitized traces of each event, in ADC counts, with each detection unit’s status and firmware settings.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run
event_size (uint32) – Size of the event as recorded by the DAQ.
t3_number (uint32) – Number of the T3 (array-level) trigger that recorded the event.
first_du (uint32) – Identifier of the first unit that triggered.
time_seconds (uint32) – Time of the event’s trigger, in Unix seconds, converted from GPS time.
time_nanoseconds (uint32) – Nanoseconds to add to time_seconds, for the first unit that triggered.
event_type (uint32) – Trigger type: 0x1000 for the 10-second trigger, 0x8000 for a random trigger; any other value is a shower trigger.
event_version (uint32) – Version of the DAQ’s event format.
du_count (uint32) – Number of detection units in the event.
event_id (std::vector<unsigned short>) – Number of the T3 trigger, for each unit.
du_id (std::vector<unsigned short>) – Identifier of each detection unit, in the order of the traces.
du_seconds (std::vector<unsigned int>) – Trigger time of each unit, in Unix seconds.
du_nanoseconds (std::vector<unsigned int>, in ns) – Nanoseconds to add to du_seconds, for each unit.
trigger_position (std::vector<unsigned short>) – Sample of each unit’s trace at which it triggered.
trigger_flag (std::vector<unsigned short>) – Trigger type of each unit, with the codes of event_type; for simulations with the offline T1 trigger, 1 if the unit passed and 0 if not.
atm_temperature (std::vector<unsigned short>) – Atmospheric temperature (read via I2C)
atm_pressure (std::vector<unsigned short>) – Atmospheric pressure
atm_humidity (std::vector<unsigned short>) – Atmospheric humidity
acceleration_x (std::vector<unsigned short>) – Acceleration of the antenna in X
acceleration_y (std::vector<unsigned short>) – Acceleration of the antenna in Y
acceleration_z (std::vector<unsigned short>) – Acceleration of the antenna in Z
battery_level (std::vector<unsigned short>) – Battery voltage
firmware_version (std::vector<unsigned short>) – Firmware version
adc_sampling_frequency (std::vector<unsigned short>) – ADC sampling frequency in MHz
adc_sampling_resolution (std::vector<unsigned short>) – ADC sampling resolution in bits
adc_input_channels_ch (std::vector<vector<unsigned char>>) – ADC input channels
adc_enabled_channels_ch (std::vector<vector<bool>>) – ADC enabled channels
adc_samples_count_ch (std::vector<vector<unsigned short>>) – Number of samples recorded on each channel, for each unit.
trigger_pattern_ch (std::vector<vector<bool>>) – Which channels triggered, for each unit.
trigger_pattern_ch0_ch1 (std::vector<bool>) – Whether the coincidence of channels 0 and 1 triggered the unit.
trigger_pattern_notch0_ch1 (std::vector<bool>) – Whether channel 1 without channel 0 triggered the unit.
trigger_pattern_redch0_ch1 (std::vector<bool>) – Whether the reduced coincidence of channels 0 and 1 triggered the unit.
trigger_pattern_ch2_ch3 (std::vector<bool>) – Whether the coincidence of channels 2 and 3 triggered the unit.
trigger_pattern_calibration (std::vector<bool>) – Whether a calibration trigger recorded the unit.
trigger_pattern_10s (std::vector<bool>) – Whether the 10-second trigger recorded the unit.
trigger_pattern_external_test_pulse (std::vector<bool>) – Whether an external test pulse triggered the unit.
trigger_rate (std::vector<unsigned short>) – Number of triggers each unit recorded in the second before the event.
clock_tick (std::vector<unsigned int>) – Clock tick at which the event was triggered (used to calculate the trigger time)
clock_ticks_per_second (std::vector<unsigned int>) – Clock ticks per second
gps_offset (std::vector<float>) – Offset between the GPS one-pulse-per-second signal and the true second.
gps_leap_second (std::vector<unsigned short>) – GPS leap second
gps_status (std::vector<unsigned short>) – GPS status
gps_alarms (std::vector<unsigned short>) – GPS alarms
gps_warnings (std::vector<unsigned short>) – GPS warnings
gps_time (std::vector<unsigned int>) – GPS time
gps_long (std::vector<unsigned long long>) – Longitude of each unit, as reported by its GPS receiver.
gps_lat (std::vector<unsigned long long>) – Latitude of each unit, as reported by its GPS receiver.
gps_alt (std::vector<unsigned long long>) – Altitude of each unit, as reported by its GPS receiver.
gps_temp (std::vector<unsigned int>) – GPS temperature
enable_auto_reset_timeout (std::vector<bool>) – Digital control register
force_firmware_reset (std::vector<bool>) – Firmware control register: force a reset.
enable_filter_ch (std::vector<vector<bool>>) – Whether each channel’s digital filter is on, for each unit.
enable_1PPS (std::vector<bool>) – Firmware control register: the GPS one-pulse-per-second signal is on.
enable_DAQ (std::vector<bool>) – Firmware control register: data taking is on.
enable_trigger_ch (std::vector<vector<bool>>) – Trigger enable mask register
enable_trigger_ch0_ch1 (std::vector<bool>) – Whether the coincidence of channels 0 and 1 may trigger the unit.
enable_trigger_notch0_ch1 (std::vector<bool>) – Whether channel 1 without channel 0 may trigger the unit.
enable_trigger_redch0_ch1 (std::vector<bool>) – Whether the reduced coincidence of channels 0 and 1 may trigger the unit.
enable_trigger_ch2_ch3 (std::vector<bool>) – Whether the coincidence of channels 2 and 3 may trigger the unit.
enable_trigger_calibration (std::vector<bool>) – Whether calibration triggers are on.
enable_trigger_10s (std::vector<bool>) – Whether the 10-second trigger is on.
enable_trigger_external_test_pulse (std::vector<bool>) – Whether an external test pulse may trigger the unit.
enable_readout_ch (std::vector<vector<bool>>) – Test pulse rate divider and channel readout enable
fire_single_test_pulse (std::vector<bool>) – Firmware control register: fire one test pulse.
test_pulse_rate_divider (std::vector<unsigned char>) – Divider that sets the test-pulse rate.
common_coincidence_time (std::vector<unsigned short>) – Common coincidence readout time window
selector_readout_ch (std::vector<vector<unsigned char>>) – Input selector for readout channel
pre_coincidence_window_ch (std::vector<vector<unsigned short>>) – Readout window before the coincidence, per channel, as set in the unit’s firmware.
post_coincidence_window_ch (std::vector<vector<unsigned short>>) – Readout window after the coincidence, per channel, as set in the unit’s firmware.
gain_correction_ch (std::vector<vector<unsigned short>>) – Gain correction, per channel, as set in the unit’s firmware.
integration_time_ch (std::vector<vector<unsigned short>>) – Integration time, per channel, as set in the unit’s firmware.
offset_correction_ch (std::vector<vector<unsigned char>>) – Offset correction, per channel, as set in the unit’s firmware.
base_maximum_ch (std::vector<vector<unsigned short>>) – Upper limit of the baseline, per channel, as set in the unit’s firmware.
base_minimum_ch (std::vector<vector<unsigned short>>) – Lower limit of the baseline, per channel, as set in the unit’s firmware.
signal_threshold_ch (std::vector<vector<unsigned short>>) – T1 threshold (th1 in grand.sim.detector.trigger), per channel, as set in the unit’s firmware.
noise_threshold_ch (std::vector<vector<unsigned short>>) – T2 threshold (th2), per channel, as set in the unit’s firmware.
tper_ch (std::vector<vector<unsigned short>>) – T1 window length (t_period), per channel, as set in the unit’s firmware.
tprev_ch (std::vector<vector<unsigned short>>) – Quiet time before the T1 crossing (t_quiet), per channel, as set in the unit’s firmware.
ncmax_ch (std::vector<vector<unsigned short>>) – Largest number of T2 crossings (nc_max), per channel, as set in the unit’s firmware.
tcmax_ch (std::vector<vector<unsigned short>>) – Largest separation between T2 crossings (t_sepmax), per channel, as set in the unit’s firmware.
qmax_ch (std::vector<vector<unsigned char>>) – Upper limit of the charge ratio (q_max), per channel, as set in the unit’s firmware.
ncmin_ch (std::vector<vector<unsigned short>>) – Smallest number of T2 crossings (nc_min), per channel, as set in the unit’s firmware.
qmin_ch (std::vector<vector<unsigned char>>) – Lower limit of the charge ratio (q_min), per channel, as set in the unit’s firmware.
ioff (std::vector<unsigned short>) – Offset of the ADC trace, for each unit; its exact meaning is not documented.
trace_ch (std::vector<vector<vector<short>>>) – ADC counts of each unit’s channels 0 to 3: one row per unit, one trace per channel.
pps_id (std::vector<unsigned int>) – PPS-ID
fpga_temp (std::vector<unsigned int>) – FPGA temperature
adc_temp (std::vector<unsigned int>) – ADC temperature
hardware_id (std::vector<unsigned int>) – Hardware ID
trigger_status (std::vector<unsigned short>) – Trigger status
trigger_ddr_storage (std::vector<unsigned short>) – Trigger DDR storage
data_format_version (std::vector<unsigned short>) – Data format version
adaq_version (std::vector<unsigned short>) – ADAQ version
dudaq_version (std::vector<unsigned short>) – DUDAQ version
trigger_pattern_ch0_ch1_ch2 (std::vector<bool>) – Trigger selection: ch0&ch1&ch2
trigger_pattern_ch0_ch1_notch2 (std::vector<bool>) – Trigger selection: ch0&ch1&~ch2
trigger_pattern_20Hz (std::vector<bool>) – Trigger selection: 20 Hz
trigger_external_test_pulse_period (std::vector<int>) – External pulse trigger period
gps_sec_sun (std::vector<unsigned int>) – GPS seconds since Sunday 00:00
gps_week_num (std::vector<unsigned short>) – GPS week number
gps_receiver_mode (std::vector<unsigned char>) – GPS receiver mode
gps_disciplining_mode (std::vector<unsigned char>) – GPS disciplining mode
gps_self_survey (std::vector<unsigned char>) – GPS self-survey progress
gps_minor_alarms (std::vector<unsigned short>) – GPS minor alarms
gps_gnss_decoding (std::vector<unsigned char>) – GPS GNSS decoding
gps_disciplining_activity (std::vector<unsigned short>) – GPS disciplining activity
notch_filters_no_ch (std::vector<vector<unsigned char>>) – Notch filter number
nutrig_rhox (std::vector<unsigned short>) – NUTRIG correlation with X
nutrig_rhoy (std::vector<unsigned short>) – NUTRIG correlation with Y

See also

grand.sim.detector.adc.ADC: Digitizes simulated voltages into these traces.
grand.sim.detector.trigger.t1_du_triggers: The T1 trigger on these traces.

event_size: TTreeScalarDesc: Size of the event as recorded by the DAQ.

t3_number: TTreeScalarDesc: Number of the T3 (array-level) trigger that recorded the event.

first_du: TTreeScalarDesc: Identifier of the first unit that triggered.

time_seconds: TTreeScalarDesc: Time of the event’s trigger, in Unix seconds, converted from GPS time.

time_nanoseconds: TTreeScalarDesc: Nanoseconds to add to time_seconds, for the first unit that triggered.

event_type: TTreeScalarDesc

0x1000 for the 10-second trigger, 0x8000 for a random trigger; any other value is a shower trigger.

Type:: Trigger type

event_version: TTreeScalarDesc: Version of the DAQ’s event format.

du_count: TTreeScalarDesc: Number of detection units in the event.

event_id: StdVectorListDesc: Number of the T3 trigger, for each unit.

du_id: StdVectorListDesc: Identifier of each detection unit, in the order of the traces.

du_seconds: StdVectorListDesc: Trigger time of each unit, in Unix seconds.

du_nanoseconds: StdVectorListDesc: Nanoseconds to add to du_seconds, for each unit.

trigger_position: StdVectorListDesc: Sample of each unit’s trace at which it triggered.

trigger_flag: StdVectorListDesc: Trigger type of each unit, with the codes of event_type; for simulations with the offline T1 trigger, 1 if the unit passed and 0 if not.

atm_temperature: StdVectorListDesc: Atmospheric temperature (read via I2C)

atm_pressure: StdVectorListDesc: Atmospheric pressure

atm_humidity: StdVectorListDesc: Atmospheric humidity

acceleration_x: StdVectorListDesc: Acceleration of the antenna in X

acceleration_y: StdVectorListDesc: Acceleration of the antenna in Y

acceleration_z: StdVectorListDesc: Acceleration of the antenna in Z

battery_level: StdVectorListDesc: Battery voltage

firmware_version: StdVectorListDesc: Firmware version

adc_sampling_frequency: StdVectorListDesc: ADC sampling frequency in MHz

adc_sampling_resolution: StdVectorListDesc: ADC sampling resolution in bits

adc_input_channels_ch: StdVectorListDesc: ADC input channels

adc_enabled_channels_ch: StdVectorListDesc: ADC enabled channels

adc_samples_count_ch: StdVectorListDesc: Number of samples recorded on each channel, for each unit.

trigger_pattern_ch: StdVectorListDesc: Which channels triggered, for each unit.

trigger_pattern_ch0_ch1: StdVectorListDesc: Whether the coincidence of channels 0 and 1 triggered the unit.

trigger_pattern_notch0_ch1: StdVectorListDesc: Whether channel 1 without channel 0 triggered the unit.

trigger_pattern_redch0_ch1: StdVectorListDesc: Whether the reduced coincidence of channels 0 and 1 triggered the unit.

trigger_pattern_ch2_ch3: StdVectorListDesc: Whether the coincidence of channels 2 and 3 triggered the unit.

trigger_pattern_calibration: StdVectorListDesc: Whether a calibration trigger recorded the unit.

trigger_pattern_10s: StdVectorListDesc: Whether the 10-second trigger recorded the unit.

trigger_pattern_external_test_pulse: StdVectorListDesc: Whether an external test pulse triggered the unit.

trigger_rate: StdVectorListDesc: Number of triggers each unit recorded in the second before the event.

clock_tick: StdVectorListDesc: Clock tick at which the event was triggered (used to calculate the trigger time)

clock_ticks_per_second: StdVectorListDesc: Clock ticks per second

gps_offset: StdVectorListDesc: Offset between the GPS one-pulse-per-second signal and the true second.

gps_leap_second: StdVectorListDesc: GPS leap second

gps_status: StdVectorListDesc: GPS status

gps_alarms: StdVectorListDesc: GPS alarms

gps_warnings: StdVectorListDesc: GPS warnings

gps_time: StdVectorListDesc: GPS time

gps_long: StdVectorListDesc: Longitude of each unit, as reported by its GPS receiver.

gps_lat: StdVectorListDesc: Latitude of each unit, as reported by its GPS receiver.

gps_alt: StdVectorListDesc: Altitude of each unit, as reported by its GPS receiver.

gps_temp: StdVectorListDesc: GPS temperature

enable_auto_reset_timeout: StdVectorListDesc: Digital control register

force_firmware_reset: StdVectorListDesc

force a reset.

Type:: Firmware control register

enable_filter_ch: StdVectorListDesc: Whether each channel’s digital filter is on, for each unit.

enable_1PPS: StdVectorListDesc

the GPS one-pulse-per-second signal is on.

Type:: Firmware control register

enable_DAQ: StdVectorListDesc

data taking is on.

Type:: Firmware control register

enable_trigger_ch: StdVectorListDesc: Trigger enable mask register

enable_trigger_ch0_ch1: StdVectorListDesc: Whether the coincidence of channels 0 and 1 may trigger the unit.

enable_trigger_notch0_ch1: StdVectorListDesc: Whether channel 1 without channel 0 may trigger the unit.

enable_trigger_redch0_ch1: StdVectorListDesc: Whether the reduced coincidence of channels 0 and 1 may trigger the unit.

enable_trigger_ch2_ch3: StdVectorListDesc: Whether the coincidence of channels 2 and 3 may trigger the unit.

enable_trigger_calibration: StdVectorListDesc: Whether calibration triggers are on.

enable_trigger_10s: StdVectorListDesc: Whether the 10-second trigger is on.

enable_trigger_external_test_pulse: StdVectorListDesc: Whether an external test pulse may trigger the unit.

enable_readout_ch: StdVectorListDesc: Test pulse rate divider and channel readout enable

fire_single_test_pulse: StdVectorListDesc

fire one test pulse.

Type:: Firmware control register

test_pulse_rate_divider: StdVectorListDesc: Divider that sets the test-pulse rate.

common_coincidence_time: StdVectorListDesc: Common coincidence readout time window

selector_readout_ch: StdVectorListDesc: Input selector for readout channel

pre_coincidence_window_ch: StdVectorListDesc: Readout window before the coincidence, per channel, as set in the unit’s firmware.

post_coincidence_window_ch: StdVectorListDesc: Readout window after the coincidence, per channel, as set in the unit’s firmware.

gain_correction_ch: StdVectorListDesc: Gain correction, per channel, as set in the unit’s firmware.

integration_time_ch: StdVectorListDesc: Integration time, per channel, as set in the unit’s firmware.

offset_correction_ch: StdVectorListDesc: Offset correction, per channel, as set in the unit’s firmware.

base_maximum_ch: StdVectorListDesc: Upper limit of the baseline, per channel, as set in the unit’s firmware.

base_minimum_ch: StdVectorListDesc: Lower limit of the baseline, per channel, as set in the unit’s firmware.

signal_threshold_ch: StdVectorListDesc: T1 threshold (th1 in grand.sim.detector.trigger), per channel, as set in the unit’s firmware.

noise_threshold_ch: StdVectorListDesc: T2 threshold (th2), per channel, as set in the unit’s firmware.

tper_ch: StdVectorListDesc: T1 window length (t_period), per channel, as set in the unit’s firmware.

tprev_ch: StdVectorListDesc: Quiet time before the T1 crossing (t_quiet), per channel, as set in the unit’s firmware.

ncmax_ch: StdVectorListDesc: Largest number of T2 crossings (nc_max), per channel, as set in the unit’s firmware.

tcmax_ch: StdVectorListDesc: Largest separation between T2 crossings (t_sepmax), per channel, as set in the unit’s firmware.

qmax_ch: StdVectorListDesc: Upper limit of the charge ratio (q_max), per channel, as set in the unit’s firmware.

ncmin_ch: StdVectorListDesc: Smallest number of T2 crossings (nc_min), per channel, as set in the unit’s firmware.

qmin_ch: StdVectorListDesc: Lower limit of the charge ratio (q_min), per channel, as set in the unit’s firmware.

ioff: StdVectorListDesc: Offset of the ADC trace, for each unit; its exact meaning is not documented.

trace_ch: StdVectorListDesc

one row per unit, one trace per channel.

Type:: ADC counts of each unit’s channels 0 to 3

pps_id: StdVectorListDesc: PPS-ID

fpga_temp: StdVectorListDesc: FPGA temperature

adc_temp: StdVectorListDesc: ADC temperature

hardware_id: StdVectorListDesc: Hardware ID

trigger_status: StdVectorListDesc: Trigger status

trigger_ddr_storage: StdVectorListDesc: Trigger DDR storage

data_format_version: StdVectorListDesc: Data format version

adaq_version: StdVectorListDesc: ADAQ version

dudaq_version: StdVectorListDesc: DUDAQ version

trigger_pattern_ch0_ch1_ch2: StdVectorListDesc

ch0&ch1&ch2

Type:: Trigger selection

trigger_pattern_ch0_ch1_notch2: StdVectorListDesc

ch0&ch1&~ch2

Type:: Trigger selection

trigger_pattern_20Hz: StdVectorListDesc

20 Hz

Type:: Trigger selection

trigger_external_test_pulse_period: StdVectorListDesc: External pulse trigger period

gps_sec_sun: StdVectorListDesc

00

Type:: GPS seconds since Sunday 00

gps_week_num: StdVectorListDesc: GPS week number

gps_receiver_mode: StdVectorListDesc: GPS receiver mode

gps_disciplining_mode: StdVectorListDesc: GPS disciplining mode

gps_self_survey: StdVectorListDesc: GPS self-survey progress

gps_minor_alarms: StdVectorListDesc: GPS minor alarms

gps_gnss_decoding: StdVectorListDesc: GPS GNSS decoding

gps_disciplining_activity: StdVectorListDesc: GPS disciplining activity

notch_filters_no_ch: StdVectorListDesc: Notch filter number

nutrig_rhox: StdVectorListDesc: NUTRIG correlation with X

nutrig_rhoy: StdVectorListDesc: NUTRIG correlation with Y

class grand.dataio.event_trees.TRawVoltage(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trawvoltage', _type: str = 'rawvoltage', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_size: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, first_du: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_seconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_nanoseconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, du_count: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, du_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_seconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_nanoseconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_flag: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_position: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atm_temperature: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atm_pressure: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atm_humidity: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_acceleration: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, battery_level: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adc_samples_count_channel: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_pattern: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_rate: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, clock_tick: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, clock_ticks_per_second: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_offset: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_leap_second: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_status: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_alarms: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_warnings: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_time: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_long: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_lat: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_alt: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, gps_temp: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, ioff: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trace_ch: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, nutrig_rhox: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, nutrig_rhoy: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherEventTree

Voltage at the ADC input of each event, converted from TADC to physical units.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run
event_size (uint32) – Common for the whole event Event size
first_du (uint32) – First detector unit that triggered in the event
time_seconds (uint32) – Unix time corresponding to the GPS seconds of the trigger
time_nanoseconds (uint32) – GPS nanoseconds corresponding to the trigger of the first triggered station
du_count (uint32) – Number of detector units in the event - basically the antennas count
du_id (std::vector<unsigned short>) – Detector unit (antenna) ID
du_seconds (std::vector<unsigned int>) – Unix time of the trigger for this DU
du_nanoseconds (std::vector<unsigned int>, in ns) – Nanoseconds of the trigger for this DU
trigger_flag (std::vector<unsigned short>) – Same as event_type, but event_type could consist of different triggered DUs
trigger_position (std::vector<unsigned short>) – Trigger position in the trace, in samples
atm_temperature (std::vector<float>) – Atmospheric temperature (read via I2C)
atm_pressure (std::vector<float>) – Atmospheric pressure
atm_humidity (std::vector<float>) – Atmospheric humidity
du_acceleration (std::vector<vector<float>>) – Acceleration of the antenna in (x,y,z) in m/s2
battery_level (std::vector<float>) – Battery voltage
adc_samples_count_channel (std::vector<vector<unsigned short>>) – ADC samples callected in channels (0,1,2,3)
trigger_pattern (std::vector<unsigned short>) – Trigger pattern - which of the trigger sources (more than one may be present) fired to actually the trigger the digitizer - explained in the docs. ToDo: Decode this?
trigger_rate (std::vector<unsigned short>) – Trigger rate - the number of triggers recorded in the second preceding the event
clock_tick (std::vector<unsigned int>) – Clock tick at which the event was triggered (used to calculate the trigger time)
clock_ticks_per_second (std::vector<unsigned int>) – Clock ticks per second
gps_offset (std::vector<float>) – GPS offset - offset between the PPS and the real second (in GPS). ToDo: is it already included in the time calculations?
gps_leap_second (std::vector<unsigned short>) – GPS leap second
gps_status (std::vector<unsigned short>) – GPS status
gps_alarms (std::vector<unsigned short>) – GPS alarms
gps_warnings (std::vector<unsigned short>) – GPS warnings
gps_time (std::vector<unsigned int>) – GPS time
gps_long (std::vector<double>) – Longitude
gps_lat (std::vector<double>) – Latitude
gps_alt (std::vector<double>) – Altitude
gps_temp (std::vector<float>) – GPS temperature
ioff (std::vector<unsigned short>) – ?? What is it? Some kind of the adc trace offset?
trace_ch (std::vector<vector<vector<float>>>) – Voltage traces for channels 1,2,3,4 in muV
nutrig_rhox (std::vector<float>) – NUTRIG correlation with X
nutrig_rhoy (std::vector<float>) – NUTRIG correlation with Y

event_size: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

first_du: TTreeScalarDesc: First detector unit that triggered in the event

time_seconds: TTreeScalarDesc: Unix time corresponding to the GPS seconds of the trigger

time_nanoseconds: TTreeScalarDesc: GPS nanoseconds corresponding to the trigger of the first triggered station

du_count: TTreeScalarDesc: Number of detector units in the event - basically the antennas count

du_id: StdVectorListDesc: Detector unit (antenna) ID

du_seconds: StdVectorListDesc: Unix time of the trigger for this DU

du_nanoseconds: StdVectorListDesc: Nanoseconds of the trigger for this DU

trigger_flag: StdVectorListDesc: Same as event_type, but event_type could consist of different triggered DUs

trigger_position: StdVectorListDesc: Trigger position in the trace, in samples

atm_temperature: StdVectorListDesc: Atmospheric temperature (read via I2C)

atm_pressure: StdVectorListDesc: Atmospheric pressure

atm_humidity: StdVectorListDesc: Atmospheric humidity

du_acceleration: StdVectorListDesc: Acceleration of the antenna in (x,y,z) in m/s2

battery_level: StdVectorListDesc: Battery voltage

adc_samples_count_channel: StdVectorListDesc: ADC samples callected in channels (0,1,2,3)

trigger_pattern: StdVectorListDesc

Decode this?

Type:: Trigger pattern - which of the trigger sources (more than one may be present) fired to actually the trigger the digitizer - explained in the docs. ToDo

trigger_rate: StdVectorListDesc: Trigger rate - the number of triggers recorded in the second preceding the event

clock_tick: StdVectorListDesc: Clock tick at which the event was triggered (used to calculate the trigger time)

clock_ticks_per_second: StdVectorListDesc: Clock ticks per second

gps_offset: StdVectorListDesc

is it already included in the time calculations?

Type:: GPS offset - offset between the PPS and the real second (in GPS). ToDo

gps_leap_second: StdVectorListDesc: GPS leap second

gps_status: StdVectorListDesc: GPS status

gps_alarms: StdVectorListDesc: GPS alarms

gps_warnings: StdVectorListDesc: GPS warnings

gps_time: StdVectorListDesc: GPS time

gps_long: StdVectorListDesc: Longitude

gps_lat: StdVectorListDesc: Latitude

gps_alt: StdVectorListDesc: Altitude

gps_temp: StdVectorListDesc: GPS temperature

ioff: StdVectorListDesc: ?? What is it? Some kind of the adc trace offset?

trace_ch: StdVectorListDesc: Voltage traces for channels 1,2,3,4 in muV

nutrig_rhox: StdVectorListDesc: NUTRIG correlation with X

nutrig_rhoy: StdVectorListDesc: NUTRIG correlation with Y

class grand.dataio.event_trees.TVoltage(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'tvoltage', _type: str = 'voltage', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, first_du: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_seconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_nanoseconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, du_count: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, du_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_seconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_nanoseconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_flag: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_position: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_acceleration: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_rate: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trace: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, p2p: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, time_max: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, grandlib_version: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>)[source]

Bases: MotherEventTree

Voltage traces of each event, in µV, as simulated by Efield2Voltage.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run
first_du (uint32) – Identifier of the first unit that triggered.
time_seconds (uint32) – Time of the event’s trigger, in Unix seconds, converted from GPS time.
time_nanoseconds (uint32) – Nanoseconds to add to time_seconds, for the first unit that triggered.
du_count (uint32) – Number of detection units in the event.
du_id (std::vector<unsigned short>) – Identifier of each detection unit, in the order of the traces.
du_seconds (std::vector<unsigned int>) – Trigger time of each unit, in Unix seconds.
du_nanoseconds (std::vector<unsigned int>, in ns) – Nanoseconds to add to du_seconds, for each unit.
trigger_flag (std::vector<unsigned short>) – Trigger type of each unit, with the codes of event_type.
trigger_position (std::vector<unsigned short>) – Sample of each unit’s trace at which it triggered.
du_acceleration (std::vector<vector<float>>) – Acceleration measured at each unit, as (x, y, z), in m/s².
trigger_rate (std::vector<unsigned short>) – Number of triggers each unit recorded in the second before the event.
trace (std::vector<vector<vector<float>>>) – Voltage at each unit, in µV: one row per unit, holding the X (south-north), Y (east-west) and Z (vertical) arms. A simulated voltage is at the ADC input, or at the antenna terminals when the RF chain is left out.
p2p (std::vector<vector<float>>) – Peak-to-peak amplitude of each arm, for each unit, in µV.
time_max (std::vector<vector<float>>) – Time of the peak of each component, for each unit, in ns.
grandlib_version (str) –
Version of GRANDlib that produced this file.

Written by Efield2Voltage. The simulated voltage depends on the code as well as on the input: the Galactic-noise level, for example, changed by a factor of \(\sqrt2\) on 7 September 2026. Files written before that date carry no version. It sat at 0.1.0.dev0 across twenty-seven milestone tags, which would have made the stamp useless; see the note in pyproject.toml.

See also

grand.sim.efield2voltage.Efield2Voltage: Writes this tree.
grand.dataio.event_trees.TADC: The digitized traces.

first_du: TTreeScalarDesc: Identifier of the first unit that triggered.

time_seconds: TTreeScalarDesc: Time of the event’s trigger, in Unix seconds, converted from GPS time.

time_nanoseconds: TTreeScalarDesc: Nanoseconds to add to time_seconds, for the first unit that triggered.

du_count: TTreeScalarDesc: Number of detection units in the event.

du_id: StdVectorListDesc: Identifier of each detection unit, in the order of the traces.

du_seconds: StdVectorListDesc: Trigger time of each unit, in Unix seconds.

du_nanoseconds: StdVectorListDesc: Nanoseconds to add to du_seconds, for each unit.

trigger_flag: StdVectorListDesc: Trigger type of each unit, with the codes of event_type.

trigger_position: StdVectorListDesc: Sample of each unit’s trace at which it triggered.

du_acceleration: StdVectorListDesc: Acceleration measured at each unit, as (x, y, z), in m/s².

trigger_rate: StdVectorListDesc: Number of triggers each unit recorded in the second before the event.

trace: StdVectorListDesc

one row per unit, holding the X (south-north), Y (east-west) and Z (vertical) arms. A simulated voltage is at the ADC input, or at the antenna terminals when the RF chain is left out.

Type:: Voltage at each unit, in µV

p2p: StdVectorListDesc: Peak-to-peak amplitude of each arm, for each unit, in µV.

time_max: StdVectorListDesc: Time of the peak of each component, for each unit, in ns.

grandlib_version: StdStringDesc

Version of GRANDlib that produced this file.

Written by Efield2Voltage. The simulated voltage depends on the code as well as on the input: the Galactic-noise level, for example, changed by a factor of \(\sqrt2\) on 7 September 2026. Files written before that date carry no version. It sat at 0.1.0.dev0 across twenty-seven milestone tags, which would have made the stamp useless; see the note in pyproject.toml.

class grand.dataio.event_trees.TEfield(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'tefield', _type: str = 'efield', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_seconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, time_nanoseconds: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_type: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, du_count: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, du_id: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_seconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_nanoseconds: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trigger_position: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, trace: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, fft_mag: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, fft_phase: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, p2p: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, pol: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, time_max: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherEventTree

Electric-field traces of each event at each detection unit, in µV/m.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run
time_seconds (uint32) – Time of the event’s trigger, in Unix seconds, converted from GPS time.
time_nanoseconds (uint32) – Nanoseconds to add to time_seconds, for the first unit that triggered.
event_type (uint32) – Trigger type: 0x1000 for the 10-second trigger, 0x8000 for a random trigger; any other value is a shower trigger.
du_count (uint32) – Number of detection units in the event.
du_id (std::vector<unsigned short>) – Identifier of each detection unit, in the order of the traces.
du_seconds (std::vector<unsigned int>) – Trigger time of each unit, in Unix seconds.
du_nanoseconds (std::vector<unsigned int>, in ns) – Nanoseconds to add to du_seconds, for each unit.
trigger_position (std::vector<unsigned short>) – Sample of each unit’s trace at which it triggered.
trace (std::vector<vector<vector<float>>>) – Electric field at each unit, in µV/m: one row per unit, holding the x, y and z components.
fft_mag (std::vector<vector<vector<float>>>) – Magnitude of the Fourier transform of each component, for each unit.
fft_phase (std::vector<vector<vector<float>>>) – Phase of the Fourier transform of each component, for each unit.
p2p (std::vector<vector<float>>) – Peak-to-peak amplitude of each component, for each unit, in µV/m.
pol (std::vector<vector<float>>) – Polarization of the field at each unit.
time_max (std::vector<vector<float>>) – Time of the peak of each component, for each unit, in ns.

See also

grand.dataio.run_trees.TRun: The positions of the units, by du_id.
grand.sim.efield2voltage.Efield2Voltage: Turns these traces into voltages.

Examples

Read the electric-field traces of one event, one row per detection unit:

from pathlib import Path

import numpy as np
import grand
from grand.dataio import TEfield

sample = (Path(grand.__file__).parents[1]
          / "sim2root/Common/sim_Xiaodushan_20221026_000000_RUN1_CD_ZHAireS_0000")
with TEfield(str(sample / "efield_1618-13790_L0_0000.root")) as tefield:
    tefield.get_event(13790, 1)
    traces = np.asarray(tefield.trace)               # (units, 3, samples), µV/m
    print("%d units, peak %.0f µV/m" % (len(tefield.du_id), np.abs(traces).max()))

44 units, peak 6652 µV/m

time_seconds: TTreeScalarDesc: Time of the event’s trigger, in Unix seconds, converted from GPS time.

time_nanoseconds: TTreeScalarDesc: Nanoseconds to add to time_seconds, for the first unit that triggered.

event_type: TTreeScalarDesc

0x1000 for the 10-second trigger, 0x8000 for a random trigger; any other value is a shower trigger.

Type:: Trigger type

du_count: TTreeScalarDesc: Number of detection units in the event.

du_id: StdVectorListDesc: Identifier of each detection unit, in the order of the traces.

du_seconds: StdVectorListDesc: Trigger time of each unit, in Unix seconds.

du_nanoseconds: StdVectorListDesc: Nanoseconds to add to du_seconds, for each unit.

trigger_position: StdVectorListDesc: Sample of each unit’s trace at which it triggered.

trace: StdVectorListDesc

one row per unit, holding the x, y and z components.

Type:: Electric field at each unit, in µV/m

fft_mag: StdVectorListDesc: Magnitude of the Fourier transform of each component, for each unit.

fft_phase: StdVectorListDesc: Phase of the Fourier transform of each component, for each unit.

p2p: StdVectorListDesc: Peak-to-peak amplitude of each component, for each unit, in µV/m.

pol: StdVectorListDesc: Polarization of the field at each unit.

time_max: StdVectorListDesc: Time of the peak of each component, for each unit, in ns.

class grand.dataio.event_trees.TShower(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'tshower', _type: str = 'shower', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, primary_type: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, energy_em: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, energy_primary: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, azimuth: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, zenith: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, direction: TTreeArrayDesc = <grand.dataio.descriptors.TTreeArrayDesc object>, shower_core_pos: TTreeArrayDesc = <grand.dataio.descriptors.TTreeArrayDesc object>, atmos_model: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, atmos_model_param: TTreeArrayDesc = <grand.dataio.descriptors.TTreeArrayDesc object>, magnetic_field: TTreeArrayDesc = <grand.dataio.descriptors.TTreeArrayDesc object>, core_alt: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, xmax_grams: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, xmax_pos: TTreeArrayDesc = <grand.dataio.descriptors.TTreeArrayDesc object>, xmax_pos_shc: TTreeArrayDesc = <grand.dataio.descriptors.TTreeArrayDesc object>, core_time_s: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, core_time_ns: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>)[source]

Bases: MotherEventTree

The shower of each event: primary, energy, direction, core and shower maximum.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run
primary_type (str) – Primary particle, as a PDG code written as text, such as "2212" for a proton.
energy_em (float32, in GeV) – Energy in the electromagnetic component of the shower (electrons and positrons), which produces the radio emission, in GeV.
energy_primary (float32, in GeV) – Energy of the primary particle, in GeV.
azimuth (float32, in degrees) – Azimuth of the direction the shower comes from, in degrees, measured from north toward west in the array frame (x north, y west, z up).
zenith (float32, in degrees) – Zenith angle of the direction the shower comes from, in degrees: 0 for a vertical shower, above 90 for an upgoing one.
direction (array of shape 3, float32) – Unit vector along which the shower travels, in the array frame (x north, y west, z up): minus the direction that zenith and azimuth name, so its z component is negative for a downgoing shower.
shower_core_pos (array of shape 3, float32) – Position of the shower core, where the shower axis meets the ground, in meters in the array frame. Not defined for an upgoing shower.
atmos_model (str) – Name of the atmospheric model the simulation used.
atmos_model_param (array of shape 3, float32) – Parameters of the atmospheric model; their meaning depends on the model and the simulation code.
magnetic_field (array of shape 3, float32) – Geomagnetic field the simulation used: inclination and declination in degrees, then the strength in µT. Files from older CoREAS conversions hold the strength in mT or in gauss.
core_alt (float32) – Altitude of the ground at the core, in meters above sea level.
xmax_grams (float32, in g/cm2) – Atmospheric depth of the shower maximum, measured along the shower axis, in g/cm².
xmax_pos (array of shape 3, float32) – Position of the shower maximum, in meters in the array frame, the frame of du_xyz and shower_core_pos. NaN if unknown.
xmax_pos_shc (array of shape 3, float32) – Position of the shower maximum relative to the core, as the simulation gives it.
core_time_s (float64) – Time at which the shower reached the core, in Unix seconds.
core_time_ns (float64) – Nanoseconds to add to core_time_s.

See also

grand.dataio.event_trees.TShowerSim: What a simulation adds about the shower.
grand.dataio.event_trees.TEfield: The electric field of the same event.

Examples

Read the shower of one event:

from pathlib import Path

import grand
from grand.dataio import TShower

sample = (Path(grand.__file__).parents[1]
          / "sim2root/Common/sim_Xiaodushan_20221026_000000_RUN1_CD_ZHAireS_0000")
with TShower(str(sample / "shower_1618-13790_L0_0000.root")) as tshower:
    print("events (event, run):", tshower.get_list_of_events())
    tshower.get_event(13790, 1)
    print("zenith %.2f deg, azimuth %.2f deg, energy %.3g GeV"
          % (tshower.zenith, tshower.azimuth, tshower.energy_primary))

events (event, run): [(13790, 1), (1618, 1)]
zenith 79.43 deg, azimuth 310.00 deg, energy 3.87e+09 GeV

primary_type: StdStringDesc: Primary particle, as a PDG code written as text, such as "2212" for a proton.

energy_em: TTreeScalarDesc: Energy in the electromagnetic component of the shower (electrons and positrons), which produces the radio emission, in GeV.

energy_primary: TTreeScalarDesc: Energy of the primary particle, in GeV.

azimuth: TTreeScalarDesc: Azimuth of the direction the shower comes from, in degrees, measured from north toward west in the array frame (x north, y west, z up).

zenith: TTreeScalarDesc

0 for a vertical shower, above 90 for an upgoing one.

Type:: Zenith angle of the direction the shower comes from, in degrees

direction: TTreeArrayDesc

minus the direction that zenith and azimuth name, so its z component is negative for a downgoing shower.

Type:: Unit vector along which the shower travels, in the array frame (x north, y west, z up)

shower_core_pos: TTreeArrayDesc: Position of the shower core, where the shower axis meets the ground, in meters in the array frame. Not defined for an upgoing shower.

atmos_model: StdStringDesc: Name of the atmospheric model the simulation used.

atmos_model_param: TTreeArrayDesc: Parameters of the atmospheric model; their meaning depends on the model and the simulation code.

magnetic_field: TTreeArrayDesc

inclination and declination in degrees, then the strength in µT. Files from older CoREAS conversions hold the strength in mT or in gauss.

Type:: Geomagnetic field the simulation used

core_alt: TTreeScalarDesc: Altitude of the ground at the core, in meters above sea level.

xmax_grams: TTreeScalarDesc: Atmospheric depth of the shower maximum, measured along the shower axis, in g/cm².

xmax_pos: TTreeArrayDesc: Position of the shower maximum, in meters in the array frame, the frame of du_xyz and shower_core_pos. NaN if unknown.

xmax_pos_shc: TTreeArrayDesc: Position of the shower maximum relative to the core, as the simulation gives it.

core_time_s: TTreeScalarDesc: Time at which the shower reached the core, in Unix seconds.

core_time_ns: TTreeScalarDesc: Nanoseconds to add to core_time_s.

class grand.dataio.event_trees.TShowerSim(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'tshowersim', _type: str = 'showersim', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, input_name: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, event_date: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, rnd_seed: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, primary_inj_point_shc: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, primary_inj_alt_shc: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, primary_inj_dir_shc: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atmos_altitude: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atmos_density: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, atmos_depth: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, hadronic_model: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, low_energy_model: StdStringDesc = <grand.dataio.descriptors.StdStringDesc object>, cpu_time: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, long_depth: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_pd_depth: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_pd_eminus: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_pd_eplus: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_pd_muminus: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_pd_muplus: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_pd_gamma: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_pd_hadron: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_gamma_elow: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_e_elow: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_e_edep: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_mu_elow: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_mu_edep: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_hadron_elow: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_hadron_edep: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, long_neutrino: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, tested_core_positions: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, event_weight: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, tested_cores: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>)[source]

Bases: MotherEventTree

What a simulation records about the shower of each event, beyond TShower.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run
input_name (str) – File name in the simulator
event_date (uint32) – The date for which we simulate the event (epoch)
rnd_seed (float64) – Random seed
primary_inj_point_shc (std::vector<vector<float>>) – Primary injection point in Shower Coordinates
primary_inj_alt_shc (std::vector<float>) – Primary injection altitude in Shower Coordinates
primary_inj_dir_shc (std::vector<vector<float>>) – Primary injection direction in Shower Coordinates
atmos_altitude (std::vector<vector<float>>) – Table of air density [g/cm3] and vertical depth [g/cm2] versus altitude [m]
atmos_density (std::vector<vector<float>>) – Air density at each altitude of atmos_altitude, in g/cm3
atmos_depth (std::vector<vector<float>>) – Vertical depth at each altitude of atmos_altitude, in g/cm2
hadronic_model (str) – High energy hadronic model (and version) used
low_energy_model (str) – Energy model (and version) used
cpu_time (float32) – Time it took for the sim
long_depth (std::vector<float>) – Slant depth of the observing levels for longitudinal development tables
long_pd_depth (std::vector<float>) – Slant depth of the levels of the particle-number profiles (long_pd_*), in g/cm2
long_pd_eminus (std::vector<float>) – Number of electrons
long_pd_eplus (std::vector<float>) – Number of positrons
long_pd_muminus (std::vector<float>) – Number of muons-
long_pd_muplus (std::vector<float>) – Number of muons+
long_pd_gamma (std::vector<float>) – Number of gammas
long_pd_hadron (std::vector<float>) – Number of pions, kaons, etc.
long_gamma_elow (std::vector<float>) – Energy in low energy gammas
long_e_elow (std::vector<float>) – Energy in low energy e+/e-
long_e_edep (std::vector<float>) – Energy deposited by e+/e-
long_mu_elow (std::vector<float>) – Energy in low energy mu+/mu-
long_mu_edep (std::vector<float>) – Energy deposited by mu+/mu-
long_hadron_elow (std::vector<float>) – Energy in low energy hadrons
long_hadron_edep (std::vector<float>) – Energy deposited by hadrons
long_neutrino (std::vector<float>) – Energy in created neutrinos
tested_core_positions (std::vector<vector<float>>) – Core positions tested for that shower to generate the event (effective area study)
event_weight (uint32) – statistical weight given to the event
tested_cores (std::vector<vector<float>>) – tested core positions

input_name: StdStringDesc: File name in the simulator

event_date: TTreeScalarDesc: The date for which we simulate the event (epoch)

rnd_seed: TTreeScalarDesc: Random seed

primary_inj_point_shc: StdVectorListDesc: Primary injection point in Shower Coordinates

primary_inj_alt_shc: StdVectorListDesc: Primary injection altitude in Shower Coordinates

primary_inj_dir_shc: StdVectorListDesc: Primary injection direction in Shower Coordinates

atmos_altitude: StdVectorListDesc: Table of air density [g/cm3] and vertical depth [g/cm2] versus altitude [m]

atmos_density: StdVectorListDesc: Air density at each altitude of atmos_altitude, in g/cm3

atmos_depth: StdVectorListDesc: Vertical depth at each altitude of atmos_altitude, in g/cm2

hadronic_model: StdStringDesc: High energy hadronic model (and version) used

low_energy_model: StdStringDesc: Energy model (and version) used

cpu_time: TTreeScalarDesc: Time it took for the sim

long_depth: StdVectorListDesc: Slant depth of the observing levels for longitudinal development tables

long_pd_depth: StdVectorListDesc: Slant depth of the levels of the particle-number profiles (long_pd_*), in g/cm2

long_pd_eminus: StdVectorListDesc: Number of electrons

long_pd_eplus: StdVectorListDesc: Number of positrons

long_pd_muminus: StdVectorListDesc: Number of muons-

long_pd_muplus: StdVectorListDesc: Number of muons+

long_pd_gamma: StdVectorListDesc: Number of gammas

long_pd_hadron: StdVectorListDesc: Number of pions, kaons, etc.

long_gamma_elow: StdVectorListDesc: Energy in low energy gammas

long_e_elow: StdVectorListDesc: Energy in low energy e+/e-

long_e_edep: StdVectorListDesc: Energy deposited by e+/e-

long_mu_elow: StdVectorListDesc: Energy in low energy mu+/mu-

long_mu_edep: StdVectorListDesc: Energy deposited by mu+/mu-

long_hadron_elow: StdVectorListDesc: Energy in low energy hadrons

long_hadron_edep: StdVectorListDesc: Energy deposited by hadrons

long_neutrino: StdVectorListDesc: Energy in created neutrinos

tested_core_positions: StdVectorListDesc: Core positions tested for that shower to generate the event (effective area study)

event_weight: TTreeScalarDesc: statistical weight given to the event

tested_cores: StdVectorListDesc: tested core positions

class grand.dataio.event_trees.TRecons(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = 'trecons', _type: str = 'recons', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False, run_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, event_number: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, peak_amps: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, peak_time: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, Xants: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, du_count: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, zenith_pwf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, azimuth_pwf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, chi2_pwf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, zenith_swf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, azimuth_swf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, r_xmax: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, t_s: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, Xsource: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, chi2_swf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, distance_source_antenna: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, zenith_adf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, azimuth_adf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, width: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, scaling_factor: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, chi2_adf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, eta: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, omega: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, omega_cr: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, adf_amplitude: StdVectorListDesc = <grand.dataio.descriptors.StdVectorListDesc object>, energy_elm_voltage: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_zenith_adf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_azimuth_adf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_scaling_factor: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_width: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_zenith_swf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_azimuth_swf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_r_xmax: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_t_s: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_zenith_pwf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>, crb_azimuth_pwf: TTreeScalarDesc = <grand.dataio.descriptors.TTreeScalarDesc object>)[source]

Bases: MotherEventTree

Reconstruction results of each event: the fits of grand.analysis.

Parameters:

is_tchain (bool) – True when the tree reads a chain of files (TChain) rather than one file.
run_number (uint32) – Run number; with the event number it identifies the entry
event_number (uint32) – Event number, unique within its run
peak_amps (std::vector<float>) – Event processing Maximum amplitude of the Hilbert envelope (in ADC counts or µV/m depending on the input)
peak_time (std::vector<float>) – Time corresponding to the peak amplitude (in seconds)
Xants (std::vector<vector<float>>) – Antenna positions in the GRAND detector reference frame (in meters)
du_count (uint32) – Number of triggered antennas
zenith_pwf (float32, in radians) – Plane Wave Fits (PWF) reconstruction outputs The angles and their bounds are in radians: a value in degrees by mistake (85.0) warns, as it lies outside 0 to pi (#206) Shower zenith angle from PWF (in radians) Coordinate system: NWU, origin at layout center, “coming from”
azimuth_pwf (float32, in radians) – Shower azimuth angle from PWF (in radians) Coordinate system: NWU, origin at layout center, “coming from”
chi2_pwf (float32) – Non-reduced (raw) chi² from PWF; NaN if not filled Divide by du_count - 2, the degrees of freedom, to obtain the reduced chi²
zenith_swf (float32, in radians) – Spherical Wave Fits (SWF) Reconstruction outputs polar zenith from SWF (in rad)
azimuth_swf (float32, in radians) – polar azimuth from SWF (in rad)
r_xmax (float32) – Distance between the reconstructed Xsource and the origin = layout center (in meters)
t_s (float32) – Emission time from SWF (in seconds)
Xsource (std::vector<vector<float>>) – Reconstructed shower emission point (Xsource) in GRAND detector frame (in meters) Coordinate system: NWU, origin at layout center
chi2_swf (float32) – Non-reduced (raw) chi² from SWF; NaN if not filled Divide by du_count - 4, the degrees of freedom, to obtain the reduced chi²
distance_source_antenna (std::vector<float>) – Distance between the reconstructed Xsource and each antenna (in meters)
zenith_adf (float32, in radians) – Angular Distribution Function (ADF) Shower zenith angle from ADF (in radians) Coordinate system: NWU, origin at layout center, “coming from”
azimuth_adf (float32, in radians) – Shower azimuth angle from ADF (in radians) Coordinate system: NWU, origin at layout center, “coming from”
width (float32) – Width parameter from ADF fit (delta_omega)
scaling_factor (float32) – Scaling factor A from ADF fit
chi2_adf (float32) – Non-reduced (raw) chi² from ADF; NaN if not filled Divide by du_count - 4, the degrees of freedom, to obtain the reduced chi²
eta (std::vector<float>) – Azimuth angle in the shower plane (in radians)
omega (std::vector<float>) – Angular distance to the shower axis (in radians)
omega_cr (std::vector<float>) – Cherenkov angle (computed using a two emission points toy model) (in radians)
adf_amplitude (std::vector<float>) – Amplitude predicted by the ADF model at each antenna (in ADC counts or µV/m)
energy_elm_voltage (float32) – (Electromagnetic energy in eV, obtained directly from voltage data)
crb_zenith_adf (float32, in radians) – Cramér-Rao (lower) bound (CRB); NaN if not filled (main_DOI.py fills them, main_AOI.py does not) CRB of shower zenith angle from ADF (in radians)
crb_azimuth_adf (float32, in radians) – CRB of shower azimuth angle from ADF (in radians)
crb_scaling_factor (float32) – CRB of scaling factor A from ADF fit
crb_width (float32) – CRB of width parameter from ADF fit (delta_omega)
crb_zenith_swf (float32, in radians) – CRB of shower zenith from SWF (in rad)
crb_azimuth_swf (float32, in radians) – CRB of shower azimuth from SWF (in rad)
crb_r_xmax (float32) – CRB of distance between the reconstructed Xsource and the origin = layout center (in meters)
crb_t_s (float32) – CRB of emission time from SWF (in seconds)
crb_zenith_pwf (float32, in radians) – CRB of shower zenith from PWF (in radians)
crb_azimuth_pwf (float32, in radians) – CRB of shower azimuth from PWF (in radians)

run_number: TTreeScalarDesc: Run number; with the event number it identifies the entry

event_number: TTreeScalarDesc: Event number, unique within its run

peak_amps: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

peak_time: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

Xants: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

du_count: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

zenith_pwf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

azimuth_pwf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

chi2_pwf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

zenith_swf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

azimuth_swf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

r_xmax: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

t_s: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

Xsource: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

chi2_swf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

distance_source_antenna: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

zenith_adf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

azimuth_adf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

width: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

scaling_factor: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

chi2_adf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

eta: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

omega: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

omega_cr: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

adf_amplitude: StdVectorListDesc: A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

energy_elm_voltage: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_zenith_adf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_azimuth_adf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_scaling_factor: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_width: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_zenith_swf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_azimuth_swf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_r_xmax: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_t_s: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_zenith_pwf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

crb_azimuth_pwf: TTreeScalarDesc: A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

`grand.dataio.data_tree`

grand.dataio.data_tree.grand_tree_list = <grand.dataio.data_tree._TreeRegistry object>: Internal registry of the live tree objects (weak references)

grand.dataio.data_tree.partial_name(final)[source]

The temporary name an output is written under before it is complete.

Hidden (leading dot), so a DataDirectory scan of the folder never sees it.

Parameters:: final (str) – The output’s final path.
Returns:: The temporary path, in the same directory.
Return type:: str

grand.dataio.data_tree.replace_output(partial, final)[source]

Moves a completed output into place, replacing an earlier one.

The earlier file may be open in this process – an input DataDirectory opens every file of its folder – and is closed first, so that nothing reads or writes the replaced file afterwards.

Parameters:

partial (str) – The completed file, written under partial_name().
final (str) – Where it goes.

class grand.dataio.data_tree.DataTree(_file: <_ROOTMock name = 'mock.TFile' id='140595425037296'> = None, _file_name: str = None, _tree: <_ROOTMock name = 'mock.TTree' id='140595425041376'> = None, _tree_name: str = '', _type: str = '', _entry_list: list = <factory>, _comment: str = '', _creation_datetime: datetime = None, _modification_history: str = '', _source_datetime: datetime = None, _modification_software: str = '', _modification_software_version: str = '', _analysis_level: int = 0, is_tchain: bool = False)[source]

Bases: object

Mother class for GRAND Tree data classes

Every instance is kept in grand_tree_list until stop_using() is called, so a loop over many files must release each tree, either with tree.stop_using() or by using the tree as a context manager:

for path in paths:
    with TADC(path) as tadc:
        ...

is_tchain: bool = False: True when the tree reads a chain of files (TChain) rather than one file.

mod_setattr(key, value)[source]

Sets an attribute, recording the change in the modification history.

Parameters:

key (str) – Attribute name.
value (object) – New value.

property tree

The ROOT TTree in which the variables’ values are stored

Returns:: The underlying ROOT tree.
Return type:: ROOT.TTree

property type

The type of the tree

Returns:: The tree type, as stored in its metadata.
Return type:: str

property file

The ROOT TFile in which the tree is stored

Returns:: The file this tree lives in.
Return type:: ROOT.TFile

property tree_name

The name of the TTree

Returns:: Name of the tree in the file.
Return type:: str
Parameters:: val (str) – Name to give the tree.

property file_name

The file in which the TTree is stored

Returns:: Path of the file this tree lives in.
Return type:: str

property comment

Comment - if needed, added by user

Returns:: Free-text comment stored with the tree.
Return type:: str

property creation_datetime

Returns the time the tree was created.

Returns:: Creation time, as recorded in the file.
Return type:: datetime
Parameters:: val (datetime or int) – Creation time to record.

property modification_history

Modification_history - if needed, added by user

Returns:: Record of the modifications made to this tree.
Return type:: str

property source_datetime

Unix creation datetime of the source tree; 0 s means no source

Returns:: Timestamp of the data this tree derives from.
Return type:: datetime

property modification_software

The tool used to generate this tree’s values from another tree

Returns:: Name of the software that last modified the tree.
Return type:: str

property modification_software_version

The tool used to generate this tree’s values from another tree

Returns:: Version of that software.
Return type:: str

property analysis_level

The analysis level of this tree

Returns:: How far through the processing chain this data has been taken.
Return type:: int

classmethod get_default_tree_name()[source]

Gets the default name of the tree of the class

Returns:: The conventional name for this tree class, such as "trun".
Return type:: str

fill()[source]: Adds the current variable values as a new event to the tree

write(*args, close_file=True, overwrite=False, force_close_file=False, **kwargs)[source]

Write the tree to the file

Parameters:

close_file (bool, optional) – Close the file after writing.
overwrite (bool, optional) – Replace a tree of the same name that the file already holds. Without it, writing into a file that already holds such a tree (other than this one) is refused: to add events to it, open it with this class first, TShower("file.root"), then fill and write. Other trees in the file are kept either way.
force_close_file (bool, optional) – Close the file even if other trees reference it.

Raises:

FileExistsError – If the file already holds another tree of this name and overwrite is False.

fill_entry_list()[source]: Fills the entry list from the tree

is_unique_event()[source]: Check if specified run_number/event_number already exist in the tree

add_proper_friends()[source]: Add the proper friend trees to this tree (reimplemented in daughter classes)

scan(*args)[source]: Print out the values of the specified members of the tree (TTree::Scan() interface)

get_entry(ev_no)[source]

Read into memory the ev_no entry of the tree

Parameters:: ev_no (int) – Entry index to load.
Returns:: Bytes read.
Return type:: int
Raises:: IndexError – When the tree has no entry ev_no.

draw(varexp, selection, option='', nentries=<_ROOTMock name='mock.TTree.kMaxEntries' id='140595425045408'>, firstentry=0, delete_temp_histogram=True)[source]

An interface to TTree::Draw(). Allows for drawing specific TTree columns or getting their values with get_vX().

Parameters:

varexp (str) – Expression to plot, in ROOT syntax.
selection (str, optional) – Cut applied before plotting.
option (str, optional) – ROOT draw option.
nentries (int, optional) – Maximum entries to use.
firstentry (int, optional) – First entry to use.
delete_temp_histogram (bool, optional) – Remove the temporary histogram ROOT creates.

Returns:

Number of entries drawn.

Return type:

int

get_v1()[source]

Get first vector of results from draw()

Returns:: First variable of the last draw().
Return type:: ndarray

get_v2()[source]

Get second vector of results from draw()

Returns:: Second variable of the last draw().
Return type:: ndarray

get_v3()[source]

Get third vector of results from draw()

Returns:: Third variable of the last draw().
Return type:: ndarray

get_v4()[source]

Get fourth vector of results from draw()

Returns:: Fourth variable of the last draw().
Return type:: ndarray

get_entries()[source]

Return the number of events in the tree

Returns:: Number of entries in the tree.
Return type:: int

get_number_of_entries()[source]

Return the number of events in the tree

Returns:: Number of entries in the tree.
Return type:: int

get_number_of_events()[source]

Return the number of events in the tree

Returns:: Number of entries. Every tree holds one entry per event (or per run), so this is also the number of events.
Return type:: int

add_friend(value, filename='')[source]

Attaches another tree as a ROOT friend, so its branches are readable here.

Parameters:

value (DataTree or str) – The tree to attach, or its name.
filename (str, optional) – File holding it, when it is not in this one.

Returns:

The friend that was attached.

Return type:

ROOT.TTree

remove_friend(value)[source]

Remove a friend from the tree

Parameters:: value (DataTree or str) – Friend to detach.

set_tree_index(value)[source]

Set the tree index (necessary for working with friends)

Parameters:: value (str) – Index expression, typically run and event number.

get_current_file()[source]

Get’s the current TFile the TTree is in

Returns:: The file currently being read, which differs from file when the tree is a chain spanning several.
Return type:: ROOT.TFile

create_branches(set_if_exists=True)[source]

Create branches of the TTree based on the class fields

Parameters:: set_if_exists (bool, optional) – Attach to existing branches rather than failing.

create_branch_from_field(value, set_branches=False, value_name='')[source]

Create a specific branch of a TTree computing its type from the corresponding class field

Parameters:

value (object) – Dataclass field to create a branch for.
set_branches (bool, optional) – Bind the branch immediately.
value_name (str, optional) – Branch name; the field name by default.

assign_branches()[source]: Assign branches to the instance - without calling it, the instance does not show the values read to the TTree

create_metadata()[source]: Create metadata for the tree

assign_metadata()[source]: Assign metadata to the instance - without calling it, the instance does not show the metadata stored in the TTree

get_entry_with_index(*args, run_no=0, evt_no=0)[source]

Get the event with run_no and evt_no

The arguments are keyword-only: given by position, the run came first, the reverse of get_event(ev_no, run_no) and of the tuples get_list_of_events() returns, so a tuple passed through looked up another event.

Parameters:

run_no (int) – Run number.
evt_no (int) – Event number.

Returns:

Bytes read.

Return type:

int

Raises:

LookupError – When no entry has that run and event number.

print()[source]

Print out the tree scheme

Returns:: Prints a summary to standard output.
Return type:: None

print_metadata()[source]: Print the meta information

static get_metadata_as_dict(tree)[source]

Get the meta information as a dictionary

Parameters:: tree (DataTree, optional) – Tree to read; this one by default.
Returns:: The metadata fields and their values.
Return type:: dict

copy_contents(source)[source]

Copy contents of another dataclass instance of similar type to this instance The source has to have some field the same as this tree. For example EventEfieldTree and EventVoltageTree

Parameters:: source (DataTree) – Tree to copy entries from.

get_tree_size()[source]

Get the tree size in memory and on disk, similar to what comes from the Print()

Returns:: Size of the tree on disk, in bytes.
Return type:: int

close_file()[source]

Close the file associated to the tree

The file is closed even if other tree instances still read from it. Every tree object stored in it, this one included, can no longer be used: calling it raises a clear error rather than crashing. To release a tree when you are done with it, prefer stop_using() or the with form, which close the file only when no other tree uses it.

stop_using(close_file=True)[source]

Stop using this instance and release the memory it holds

Every tree instance is kept in the module-level grand_tree_list, so it is never garbage collected on its own. When reading many files in a loop, call stop_using() on each tree at the end of the loop, or use the tree as a context manager, which calls it on exit:

with TADC("file.root") as tadc:
    for event, run in tadc.get_list_of_events():
        tadc.get_event(event, run)

This removes the instance from grand_tree_list and, if the tree opened its ROOT file itself from a file name, closes that file once no other tree in grand_tree_list uses it. A ROOT.TFile passed in by the caller (for example by DataFile) is left open. Calling it again does nothing.

Do not use the instance after this call: if its file was closed, the underlying TTree is gone and tree is None. A tree that was filled but not written is not saved; call write() first.

The first fill() reopens an existing file for update, as ROOT writes full baskets while filling. Closing it then rewrites ROOT’s bookkeeping in the file even when nothing is written: its trees are unchanged, but its checksum is not.

Parameters:: close_file (bool, optional) – Close the ROOT file the tree opened, if no other tree uses it. Pass False to only remove the instance from grand_tree_list.

`grand.dataio.descriptors`

class grand.dataio.descriptors.StdVectorList(vec_type, value=[], sec_vec_type=None)[source]

Bases: MutableSequence

A python list interface to ROOT’s std::vector

vec_type: C++ type for the std::vector (eg. “float”, “string”, etc.)

tolist()[source]

Returns the contents as (nested) Python lists.

Returns:: The contents; char elements as ints, strings as str.
Return type:: list

insert(index, value)[source]

Insert the value to the vector at index

Parameters:

index (int) – Position to insert at, as for a list.
value (object) – Value to insert.

append(value)[source]

Append the value to the list

Parameters:: value (object) – Value to append.

clear()[source]: Remove all the values from the vector

switch_to_sec_vec_type()[source]: Change the vector type to sec_vec_type. Expected to be ran only on branch creation.

asnumpy()[source]

Simple conversion to numpy array. If the initial shape is correct is up to the user

Returns:: The contents as a NumPy array, without a copy where possible.
Return type:: ndarray

class grand.dataio.descriptors.StdVectorListDesc(vec_type, sec_vec_type=None, inner_length=None, minimum=None, maximum=None, positive=False, unit='')[source]

Bases: object

A descriptor for StdVectorList - makes use of it possible in dataclasses without setting property and setter

create_default(obj)[source]

Creates the backing std::vector on obj, holding the declared default.

Parameters:: obj (object) – The tree instance to install it on.

class grand.dataio.descriptors.TTreeScalarDesc(dtype, minimum=None, maximum=None, positive=False, unit='')[source]

Bases: object

A descriptor for scalars assigned to TTrees as numpy arrays of size 1 - makes use of it possible in dataclasses without setting property and setter

create_default(obj)[source]

Creates the backing one-element array on obj, holding the declared default.

Parameters:: obj (object) – The tree instance to install it on.

class grand.dataio.descriptors.TTreeArrayDesc(shape, dtype, component_limits=None)[source]

Bases: object

A descriptor for numpy arrays stored in TTrees. Ensures the type and converts to array (in case of for eg. list). Makes use of it possible in dataclasses without setting property and setter

create_default(obj)[source]

Creates the backing array on obj, holding the declared default.

Parameters:: obj (object) – The tree instance to install it on.

class grand.dataio.descriptors.StdString(value)[source]

Bases: object

A python string interface to ROOT’s std::string

class grand.dataio.descriptors.StdStringDesc(value='')[source]

Bases: object

A descriptor for strings assigned to TTrees as python strings - makes use of it possible in dataclasses without setting property and setter

create_default(obj)[source]

Creates the backing std::string on obj, holding the declared default.

Parameters:: obj (object) – The tree instance to install it on.

grand.dataio.descriptors.chars_to_uint8_array(nested_chars)[source]

Returns nested character vectors as an array of unsigned bytes.

ROOT returns vector<vector<char>> branches as nested character sequences; this flattens them into the numeric array callers expect.

Parameters:: nested_chars (sequence of sequence) – The nested character data read from a branch.
Returns:: The same data as uint8.
Return type:: ndarray

grand.dataio.descriptors.convert_deepest_lists_to_arrays(data)[source]

Recursively traverse a nested list structure and convert only the deepest lists (which contain non-lists) into numpy arrays.

Parameters:: data (sequence) – Possibly nested data.
Returns:: The same structure with its innermost lists replaced by arrays.
Return type:: list

grand.dataio.descriptors.split_2d_arrays_to_rows(data)[source]

Traverse data, and for every 2D numpy array found, replace it with a list of its 1D row arrays.

Parameters:: data (ndarray) – Two-dimensional array.
Returns:: One entry per row.
Return type:: list of ndarray

grand.dataio.descriptors.fill_stdvectorlist_with_array(target, value)[source]

Fills target from value, converting as the element type requires.

Parameters:

target (StdVectorList)
replaced. (Vector to fill. Its existing contents are)
value (array_like)
write. (Data to)

exception grand.dataio.descriptors.NotUniqueEvent[source]

Bases: Exception

Exception raised when an already existing event/run is added to a tree

`grand.dataio.root_files`

Manage ROOT file of type event : Efield, voltage The main functionnalities of this module is * to synchronize the relevant TTree (tshower , trun) on the same event * provide a numpy container of traces by event, with method get_obj_handling3dtraces()

grand.dataio.root_files.get_file_event(f_name)[source]

Event factory.

Returns a reader for an event ROOT file, with trun and tshower synchronized on the same event. Which reader depends on the trees the file holds, and there are three, tried in this order: tefield gives a FileEfield, tvoltage a FileVoltage, and tadc a FileAdc. A file holding none of them raises.

Parameters:: f_name (str) – File to open.
Returns:: A reader of the appropriate kind for the trees the file holds.
Return type:: _FileEventBase

grand.dataio.root_files.get_handling3dtraces(f_name, idx_evt=0)[source]

Return a traces containers from ROOT file <f_name> for event with index <idx_evt>

Handling3dTraces class is the traces containers

Parameters:

f_name (str) – File to open.
idx_evt (int, optional) – Event index.

Returns:

The traces of the current event.

Return type:

Handling3dTraces

grand.dataio.root_files.get_simu_parameters(f_name, idx_evt=0)[source]

Return dictionary of simulation parameters

The keys of _FileEventBase.get_simu_parameters(): values from TShower (and origin_geoid from TRun), plus the derived FIX_xmax_pos_grandlib, FIX_xmax_pos and xmax_frame.

Parameters:

f_name (str) – File to open.
idx_evt (int, optional) – Event index.

Returns:

Simulation parameters of that event.

Return type:

dict

class grand.dataio.root_files.FileEfield(f_name)[source]

Bases: _FileEventBase

Goals of the class:

Event type is Efield

get_obj_handling3dtraces()[source]

Returns the traces as a Handling3dTraces.

Returns:: The event’s traces, wrapped in the object that provides the plotting and peak-finding helpers.
Return type:: Handling3dTraces

class grand.dataio.root_files.FileVoltage(f_name)[source]

Bases: _FileEventBase

Goals of the class:

Event type is voltage

get_obj_handling3dtraces()[source]

Returns the traces as a Handling3dTraces.

Returns:: The event’s traces, wrapped in the object that provides the plotting and peak-finding helpers.
Return type:: Handling3dTraces

class grand.dataio.root_files.FileAdc(f_name)[source]

Bases: _FileEventBase

Reader for an ADC event file (tadc), with its run and shower trees.

get_obj_handling3dtraces()[source]

Returns the traces as a Handling3dTraces.

Returns:: The event’s traces, wrapped in the object that provides the plotting and peak-finding helpers.
Return type:: Handling3dTraces

`grand.dataio.data_handling`

class grand.dataio.data_handling.DataDirectory(dir_name: str, recursive: bool = False, analysis_level: int = -1, sim2root_structure: bool = True)[source]

Bases: object

Class holding the information about GRAND data in a directory

See also

grand.aoi.event_list.EventList: Events joined from these trees.
grand.dataio.event_trees.TEfield: One of the trees it opens.

Examples

Open every tree of a folder, and pick one by type and analysis level:

from pathlib import Path

import grand
from grand.dataio import DataDirectory

sample = (Path(grand.__file__).parents[1]
          / "sim2root/Common/sim_Xiaodushan_20221026_000000_RUN1_CD_ZHAireS_0000")
folder = DataDirectory(str(sample))
tshower = folder.tshower_l0
tshower.get_entry(0)
print("primary %s, Xmax %.1f g/cm2" % (tshower.primary_type, tshower.xmax_grams))

primary 2212, Xmax 794.6 g/cm2

get_list_of_files(recursive: bool = False)[source]

Gets list of files in the directory

Parameters:: recursive (bool, optional) – Descend into subdirectories.
Returns:: Paths of the ROOT files found.
Return type:: list of str

get_list_of_files_handles()[source]

Go through the list of files in the directory and open all of them

Returns:: One handle per group of files that belong together.
Return type:: list of DataFile

init_structure()[source]: Detects the directory layout and indexes the files it holds.

init_sim2root_structure()[source]: Indexes a directory written by the sim2root converters.

init_exp_structure()[source]: Indexes a directory of experimental data.

print(verbose=True)[source]

Prints all the information about all the data

Parameters:: verbose (bool, optional) – Include per-tree detail.

get_list_of_chains()[source]: Gets list of TTree chains of specific type from the directory

print_list_of_chains()[source]: Prints list of TTree chains of specific type from the directory

close()[source]: Close all the files of the directory

get_max_list_of_events()[source]

Gets the max list of event,run from all the trees

Returns:: The events of the file holding the most of them.
Return type:: list

class grand.dataio.data_handling.DataFile(filename)[source]

Bases: object

Class holding the information about GRAND TTrees in the specified file

dict_of_trees: Holds all the trees in the file, by tree name

list_of_trees: Holds the list of trees in the file, but just with maximal level

tree_instances: List of tree instances

tree_types: Holds dict of tree types, each containing a dict of tree names with tree meta-data as values

is_tchain: Does this instace hold a chain of files

flist: File list in case this is a chain

print()[source]: Prints the information about the TTrees in the file

get_tree_info(tree)[source]

Gets the information about the tree

Parameters:: tree (DataTree or str) – Tree to describe.
Returns:: Its metadata, entry count and trace lengths.
Return type:: dict

print_tree_info(tree)[source]

Prints the information about the tree

Parameters:: tree (DataTree or str, optional) – Tree to describe; all of them by default.

close()[source]: Close the file and the belonging trees

get_max_list_of_events()[source]

Gets the max list of event,run from all the trees

Returns:: The events of the tree holding the most of them.
Return type:: list

`grand.dataio.protocol`

exception grand.dataio.protocol.InvalidBLOB[source]

Bases: OSError

Wrapper for store errors.

grand.dataio.protocol.get(name: str, tag: str = '101') → bytes[source]

Get a BLOB from the store.

Parameters:

name (str) – File to fetch.
tag (str, optional) – Release tag to fetch it from.

Returns:

The downloaded contents.

Return type:

bytes

`grand.dataio.xmax_frame`

Which vertical frame a file’s xmax_pos_shc is in, read from the file.

xmax_pos_shc is Xmax in shower-core coordinates, so its z should be a height above the ground. Files written before the ZHAireS converter subtracted the ground altitude – including every sample committed under sim2root/Common/ – carry the height above sea level instead. Nothing in a file says which it is.

The geometry does. Xmax lies on the shower axis, so the vector from the core to Xmax points where the shower comes from, which the same tree stores as zenith and azimuth. In the right frame the two agree; with the ground altitude still in z they do not. On all twelve committed ZHAireS events the ground-relative vector matches the stored direction to 0.001 degrees and the raw one misses by 0.49 to 7.0 degrees.

Readers detect the frame, so files written under either convention, including the DC2 data, read correctly.

grand.dataio.xmax_frame.TOLERANCE_DEG = 0.05: How closely the core-to-Xmax vector must follow the stored direction, in degrees. Measured agreement is 0.001; the smallest miss is 0.49.

grand.dataio.xmax_frame.arrival_direction(zenith, azimuth)[source]

Unit vector pointing to where the shower comes from.

Parameters:

zenith (float) – The shower’s direction in degrees, “comes from”, as stored in tshower (see tests/geo/test_angle_convention.py).
azimuth (float) – The shower’s direction in degrees, “comes from”, as stored in tshower (see tests/geo/test_angle_convention.py).

Returns:

Shape (3,), x North, y West, z Up. Upwards for a downgoing shower.

Return type:

numpy.ndarray

grand.dataio.xmax_frame.propagation_direction(zenith, azimuth)[source]

Unit vector along which the shower travels: -arrival_direction.

This is what tshower.direction stores, and the same vector the ZHAireS converter writes as primary_inj_dir_shc.

Parameters:

zenith (float) – The shower’s direction in degrees, “comes from”.
azimuth (float) – The shower’s direction in degrees, “comes from”.

Returns:

Shape (3,), x North, y West, z Up. Downwards for a downgoing shower.

Return type:

numpy.ndarray

grand.dataio.xmax_frame.xmax_above_ground(xmax_pos_shc, zenith, azimuth, ground_altitude)[source]

Returns Xmax in the shower-core frame, whichever frame the file used.

Parameters:

xmax_pos_shc (array-like, shape (3,)) – As stored, in meters, x North, y West, z Up.
zenith (float) – The shower’s stored direction, in degrees, “comes from”.
azimuth (float) – The shower’s stored direction, in degrees, “comes from”.
ground_altitude (float) – The site’s ground altitude in meters, origin_geoid[2].

Returns:

numpy.ndarray – Xmax with z above the ground, shape (3,).
str – "ground" if the file already stored it so, "sea-level" if the ground altitude was subtracted here, "undetermined" if neither reading follows the stored direction – then the value is returned as stored, with a warning.

Notes

A shower straight down cannot be told apart: both readings point up. Then, and whenever both match, the stored value is kept as it is.

grand.dataio.xmax_frame.xmax_in_site_frame(xmax_pos_shc, zenith, azimuth, ground_altitude, shower_core_pos)[source]

Returns Xmax in the site frame, the one tshower.xmax_pos is in.

The site frame (the “GRAND detector ref” of the data format) is the frame of du_xyz and shower_core_pos: x North, y West, z Up, origin at origin_geoid. Xmax there is its ground-relative shower-core position plus the core position – the point get_simu_parameters returns as FIX_xmax_pos.

Parameters:

xmax_pos_shc (array-like, shape (3,)) – As stored, in meters, in either vertical frame.
zenith (float) – The shower’s stored direction, in degrees, “comes from”.
azimuth (float) – The shower’s stored direction, in degrees, “comes from”.
ground_altitude (float) – The ground altitude in meters that a sea-level z would carry.
shower_core_pos (array-like, shape (3,)) – The core in the site frame, in meters.

Returns:

numpy.ndarray – Xmax in the site frame, shape (3,). All NaN if xmax_pos_shc is not finite: a simulation that does not know Xmax says so.
str – The frame xmax_pos_shc was found in, as xmax_above_ground() reports it; "undetermined" for a non-finite input.

Data model

grand.dataio.run_trees

grand.dataio.event_trees

grand.dataio.data_tree

grand.dataio.descriptors

grand.dataio.root_files

grand.dataio.data_handling

grand.dataio.protocol

grand.dataio.xmax_frame

`grand.dataio.run_trees`

`grand.dataio.event_trees`

`grand.dataio.data_tree`

`grand.dataio.descriptors`

`grand.dataio.root_files`

`grand.dataio.data_handling`

`grand.dataio.protocol`

`grand.dataio.xmax_frame`