From 578de782d8217e630b5efa362926e21c2b709ffe Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 21 Oct 2024 20:13:29 +0100 Subject: [PATCH 01/81] basic template for hpge post processing --- pyproject.toml | 1 + src/reboost/__init__.py | 4 +- src/reboost/hpge/cli.py | 68 ++++++++++++++++++++++++++++++++++ src/reboost/hpge/hit.py | 10 +++++ src/reboost/hpge/processors.py | 19 ++++++++++ src/reboost/hpge/utils.py | 60 ++++++++++++++++++++++++++++++ 6 files changed, 160 insertions(+), 2 deletions(-) create mode 100644 src/reboost/hpge/cli.py create mode 100644 src/reboost/hpge/hit.py create mode 100644 src/reboost/hpge/processors.py create mode 100644 src/reboost/hpge/utils.py diff --git a/pyproject.toml b/pyproject.toml index 75976e7..3cde88d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,6 +72,7 @@ test = [ [project.scripts] reboost-optical = "reboost.optical.cli:optical_cli" +reboost-hpge = "reboost.hpge.cli:hpge_cli" [tool.setuptools] include-package-data = true diff --git a/src/reboost/__init__.py b/src/reboost/__init__.py index 8e49755..c0d8185 100644 --- a/src/reboost/__init__.py +++ b/src/reboost/__init__.py @@ -1,6 +1,6 @@ from __future__ import annotations -from reboost import optical +from reboost import hpge, optical from reboost._version import version as __version__ -__all__ = ["__version__", "optical"] +__all__ = ["__version__", "optical", "hpge"] diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py new file mode 100644 index 0000000..3938fee --- /dev/null +++ b/src/reboost/hpge/cli.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import argparse +import json +import logging +from collections.abc import Iterable +from pathlib import Path + +import colorlog + + +def hpge_cli() -> None: + parser = argparse.ArgumentParser( + prog="reboost-hpge", + description="%(prog)s command line interface", + ) + + + parser.add_argument( + "--verbose", + "-v", + action="store_true", + help="""Increase the program verbosity""", + ) + + parser.add_argument( + "--bufsize", + action="store", + type=int, + default=int(5e6), + help="""Row count for input table buffering (only used if applicable). default: %(default)e""", + ) + + # step 1: hit tier + subparsers = parser.add_subparsers(dest="command", required=True) + hit_parser = subparsers.add_parser("hit", help="build hit file from remage raw file") + + hit_parser.add_argument( + "--detectors", + help="file that contains a list of detector ids that are part of the input file", + required=True, + ) + hit_parser.add_argument("input", help="input hit LH5 file", metavar="INPUT_HIT") + hit_parser.add_argument("output", help="output evt LH5 file", metavar="OUTPUT_EVT") + + # parse arguments + args = parser.parse_args() + + + handler = colorlog.StreamHandler() + handler.setFormatter( + colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") + ) + logger = logging.getLogger("reboost.hpge") + logger.addHandler(handler) + if args.verbose: + logger.setLevel(logging.DEBUG) + + if args.command=="hit": + # is the import here a good idea? + from reboost.hpge.hit import build_hit + + with Path.open(Path(args.detectors)) as detectors_f: + detectors = json.load(detectors_f) + + build_hit(args.input, args.output, detectors, args.bufsize) + + diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py new file mode 100644 index 0000000..7bb0bf8 --- /dev/null +++ b/src/reboost/hpge/hit.py @@ -0,0 +1,10 @@ +from collections.abc import Iterable +import reboost.hpge.utils as utils +import reboost.hpge.processors as processors + +def build_hit(lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6))->None: + + for idx,d in enumerate(detectors): + delete_input = True if (idx==0) else False + utils.read_write_incremental(lh5_out_file,f"hit/{d}",processors.group_by_event,f"hit/{d}", + lh5_in_file,buffer_len,delete_input=delete_input) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py new file mode 100644 index 0000000..f604ac4 --- /dev/null +++ b/src/reboost/hpge/processors.py @@ -0,0 +1,19 @@ +import awkward as ak +import numpy as np + + + +def group_by_event(data): + counts = ak.run_lengths(data['evtid']) + + grouped = ak.unflatten(data, counts) + sum_energy = ak.sum(grouped.edep,axis=-1) + + t0 = ak.fill_none(ak.firsts(grouped.time,axis=-1),np.nan) + index = ak.fill_none(ak.firsts(grouped.evtid,axis=-1),np.nan) + + + return ak.zip({"sum_energy":sum_energy,"t0":t0,"evtid":index}) + +def smear_energy(energies,reso_func): + return np.random.normal(energies,reso_func(energies)) \ No newline at end of file diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py new file mode 100644 index 0000000..4bc4649 --- /dev/null +++ b/src/reboost/hpge/utils.py @@ -0,0 +1,60 @@ + +from lgdo import lh5 +import matplotlib.pyplot as plt +import awkward as ak +import numpy as np +from lgdo.lh5 import LH5Iterator +from lgdo.types import Table +from tqdm import tqdm +import copy +from typing import Callable + +def read_write_incremental(file_out:str,name_out:str,func:Callable,field:str,file:str,buffer:int=1000000,delete_input=False)->None: + """ + Read incrementally the files compute something and then write + Parameters + ---------- + file_out (str): output file path + out_name (str): lh5 group name for output + func : function converting into to output + field (str): lh5 field name to read + file (str): file name to read + buffer (int): length of buffer + + """ + + entries = LH5Iterator(file, field, buffer_len=buffer)._get_file_cumentries(0) + + # number of blocks is ceil of entries/buffer, + # shift by 1 since idx starts at 0 + # this is maybe too high if buffer exactly divides idx + max_idx = int(np.ceil(entries/buffer))-1 + buffer_rows=None + + for idx,(lh5_obj, entry, n_rows) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): + + ak_obj = lh5_obj.view_as("ak") + counts = ak.run_lengths(ak_obj.evtid) + rows= ak.num(ak_obj,axis=-1) + end_rows= counts[-1] + + if (idx==0): + mode="of" if (delete_input==True) else "append" + obj = ak_obj[0:rows-end_rows] + buffer_rows= copy.deepcopy(ak_obj[rows-end_rows:]) + elif (idx!=max_idx): + mode="append" + obj = ak.concatenate((buffer_rows,ak_obj[0:rows-end_rows])) + buffer_rows= copy.deepcopy(ak_obj[rows-end_rows:]) + else: + mode="append" + obj = ak.concatenate((buffer_rows,ak_obj)) + + # do stuff + out = func(obj) + + # convert to a table + out_lh5 =Table(out) + + # write lh5 file + lh5.write(out_lh5,name_out,file_out,wo_mode=mode) From a6d3d12b842568b1b3c8175ae669a26baedb1c10 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 21 Oct 2024 19:15:26 +0000 Subject: [PATCH 02/81] style: pre-commit fixes --- src/reboost/hpge/cli.py | 9 ++--- src/reboost/hpge/hit.py | 25 +++++++++---- src/reboost/hpge/processors.py | 21 +++++------ src/reboost/hpge/utils.py | 66 +++++++++++++++++++--------------- 4 files changed, 68 insertions(+), 53 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index 3938fee..4ca6909 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -3,7 +3,6 @@ import argparse import json import logging -from collections.abc import Iterable from pathlib import Path import colorlog @@ -15,7 +14,6 @@ def hpge_cli() -> None: description="%(prog)s command line interface", ) - parser.add_argument( "--verbose", "-v", @@ -46,7 +44,6 @@ def hpge_cli() -> None: # parse arguments args = parser.parse_args() - handler = colorlog.StreamHandler() handler.setFormatter( colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") @@ -55,8 +52,8 @@ def hpge_cli() -> None: logger.addHandler(handler) if args.verbose: logger.setLevel(logging.DEBUG) - - if args.command=="hit": + + if args.command == "hit": # is the import here a good idea? from reboost.hpge.hit import build_hit @@ -64,5 +61,3 @@ def hpge_cli() -> None: detectors = json.load(detectors_f) build_hit(args.input, args.output, detectors, args.bufsize) - - diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 7bb0bf8..f30259c 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,10 +1,21 @@ +from __future__ import annotations + from collections.abc import Iterable -import reboost.hpge.utils as utils -import reboost.hpge.processors as processors -def build_hit(lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6))->None: +from reboost.hpge import processors, utils + - for idx,d in enumerate(detectors): - delete_input = True if (idx==0) else False - utils.read_write_incremental(lh5_out_file,f"hit/{d}",processors.group_by_event,f"hit/{d}", - lh5_in_file,buffer_len,delete_input=delete_input) +def build_hit( + lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6) +) -> None: + for idx, d in enumerate(detectors): + delete_input = True if (idx == 0) else False + utils.read_write_incremental( + lh5_out_file, + f"hit/{d}", + processors.group_by_event, + f"hit/{d}", + lh5_in_file, + buffer_len, + delete_input=delete_input, + ) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index f604ac4..86c180f 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -1,19 +1,20 @@ +from __future__ import annotations + import awkward as ak import numpy as np - def group_by_event(data): - counts = ak.run_lengths(data['evtid']) + counts = ak.run_lengths(data["evtid"]) grouped = ak.unflatten(data, counts) - sum_energy = ak.sum(grouped.edep,axis=-1) - - t0 = ak.fill_none(ak.firsts(grouped.time,axis=-1),np.nan) - index = ak.fill_none(ak.firsts(grouped.evtid,axis=-1),np.nan) - + sum_energy = ak.sum(grouped.edep, axis=-1) + + t0 = ak.fill_none(ak.firsts(grouped.time, axis=-1), np.nan) + index = ak.fill_none(ak.firsts(grouped.evtid, axis=-1), np.nan) + + return ak.zip({"sum_energy": sum_energy, "t0": t0, "evtid": index}) - return ak.zip({"sum_energy":sum_energy,"t0":t0,"evtid":index}) -def smear_energy(energies,reso_func): - return np.random.normal(energies,reso_func(energies)) \ No newline at end of file +def smear_energy(energies, reso_func): + return np.random.normal(energies, reso_func(energies)) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 4bc4649..b35be25 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -1,60 +1,68 @@ +from __future__ import annotations + +import copy +from typing import Callable -from lgdo import lh5 -import matplotlib.pyplot as plt import awkward as ak import numpy as np +from lgdo import lh5 from lgdo.lh5 import LH5Iterator from lgdo.types import Table -from tqdm import tqdm -import copy -from typing import Callable -def read_write_incremental(file_out:str,name_out:str,func:Callable,field:str,file:str,buffer:int=1000000,delete_input=False)->None: + +def read_write_incremental( + file_out: str, + name_out: str, + func: Callable, + field: str, + file: str, + buffer: int = 1000000, + delete_input=False, +) -> None: """ Read incrementally the files compute something and then write Parameters ---------- file_out (str): output file path out_name (str): lh5 group name for output - func : function converting into to output + func : function converting into to output field (str): lh5 field name to read file (str): file name to read buffer (int): length of buffer - + """ - + entries = LH5Iterator(file, field, buffer_len=buffer)._get_file_cumentries(0) # number of blocks is ceil of entries/buffer, # shift by 1 since idx starts at 0 # this is maybe too high if buffer exactly divides idx - max_idx = int(np.ceil(entries/buffer))-1 - buffer_rows=None - - for idx,(lh5_obj, entry, n_rows) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): - + max_idx = int(np.ceil(entries / buffer)) - 1 + buffer_rows = None + + for idx, (lh5_obj, entry, n_rows) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): ak_obj = lh5_obj.view_as("ak") counts = ak.run_lengths(ak_obj.evtid) - rows= ak.num(ak_obj,axis=-1) - end_rows= counts[-1] - - if (idx==0): - mode="of" if (delete_input==True) else "append" - obj = ak_obj[0:rows-end_rows] - buffer_rows= copy.deepcopy(ak_obj[rows-end_rows:]) - elif (idx!=max_idx): - mode="append" - obj = ak.concatenate((buffer_rows,ak_obj[0:rows-end_rows])) - buffer_rows= copy.deepcopy(ak_obj[rows-end_rows:]) + rows = ak.num(ak_obj, axis=-1) + end_rows = counts[-1] + + if idx == 0: + mode = "of" if (delete_input == True) else "append" + obj = ak_obj[0 : rows - end_rows] + buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) + elif idx != max_idx: + mode = "append" + obj = ak.concatenate((buffer_rows, ak_obj[0 : rows - end_rows])) + buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) else: - mode="append" - obj = ak.concatenate((buffer_rows,ak_obj)) + mode = "append" + obj = ak.concatenate((buffer_rows, ak_obj)) # do stuff out = func(obj) # convert to a table - out_lh5 =Table(out) + out_lh5 = Table(out) # write lh5 file - lh5.write(out_lh5,name_out,file_out,wo_mode=mode) + lh5.write(out_lh5, name_out, file_out, wo_mode=mode) From 05b42e6c645773017b30b4eefbf40a32cbf1f664 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 21 Oct 2024 20:15:48 +0100 Subject: [PATCH 03/81] pc fixes --- src/reboost/hpge/cli.py | 9 ++--- src/reboost/hpge/hit.py | 25 +++++++++---- src/reboost/hpge/processors.py | 21 +++++------ src/reboost/hpge/utils.py | 66 +++++++++++++++++++--------------- 4 files changed, 68 insertions(+), 53 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index 3938fee..4ca6909 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -3,7 +3,6 @@ import argparse import json import logging -from collections.abc import Iterable from pathlib import Path import colorlog @@ -15,7 +14,6 @@ def hpge_cli() -> None: description="%(prog)s command line interface", ) - parser.add_argument( "--verbose", "-v", @@ -46,7 +44,6 @@ def hpge_cli() -> None: # parse arguments args = parser.parse_args() - handler = colorlog.StreamHandler() handler.setFormatter( colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") @@ -55,8 +52,8 @@ def hpge_cli() -> None: logger.addHandler(handler) if args.verbose: logger.setLevel(logging.DEBUG) - - if args.command=="hit": + + if args.command == "hit": # is the import here a good idea? from reboost.hpge.hit import build_hit @@ -64,5 +61,3 @@ def hpge_cli() -> None: detectors = json.load(detectors_f) build_hit(args.input, args.output, detectors, args.bufsize) - - diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 7bb0bf8..f30259c 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,10 +1,21 @@ +from __future__ import annotations + from collections.abc import Iterable -import reboost.hpge.utils as utils -import reboost.hpge.processors as processors -def build_hit(lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6))->None: +from reboost.hpge import processors, utils + - for idx,d in enumerate(detectors): - delete_input = True if (idx==0) else False - utils.read_write_incremental(lh5_out_file,f"hit/{d}",processors.group_by_event,f"hit/{d}", - lh5_in_file,buffer_len,delete_input=delete_input) +def build_hit( + lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6) +) -> None: + for idx, d in enumerate(detectors): + delete_input = True if (idx == 0) else False + utils.read_write_incremental( + lh5_out_file, + f"hit/{d}", + processors.group_by_event, + f"hit/{d}", + lh5_in_file, + buffer_len, + delete_input=delete_input, + ) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index f604ac4..86c180f 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -1,19 +1,20 @@ +from __future__ import annotations + import awkward as ak import numpy as np - def group_by_event(data): - counts = ak.run_lengths(data['evtid']) + counts = ak.run_lengths(data["evtid"]) grouped = ak.unflatten(data, counts) - sum_energy = ak.sum(grouped.edep,axis=-1) - - t0 = ak.fill_none(ak.firsts(grouped.time,axis=-1),np.nan) - index = ak.fill_none(ak.firsts(grouped.evtid,axis=-1),np.nan) - + sum_energy = ak.sum(grouped.edep, axis=-1) + + t0 = ak.fill_none(ak.firsts(grouped.time, axis=-1), np.nan) + index = ak.fill_none(ak.firsts(grouped.evtid, axis=-1), np.nan) + + return ak.zip({"sum_energy": sum_energy, "t0": t0, "evtid": index}) - return ak.zip({"sum_energy":sum_energy,"t0":t0,"evtid":index}) -def smear_energy(energies,reso_func): - return np.random.normal(energies,reso_func(energies)) \ No newline at end of file +def smear_energy(energies, reso_func): + return np.random.normal(energies, reso_func(energies)) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 4bc4649..b35be25 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -1,60 +1,68 @@ +from __future__ import annotations + +import copy +from typing import Callable -from lgdo import lh5 -import matplotlib.pyplot as plt import awkward as ak import numpy as np +from lgdo import lh5 from lgdo.lh5 import LH5Iterator from lgdo.types import Table -from tqdm import tqdm -import copy -from typing import Callable -def read_write_incremental(file_out:str,name_out:str,func:Callable,field:str,file:str,buffer:int=1000000,delete_input=False)->None: + +def read_write_incremental( + file_out: str, + name_out: str, + func: Callable, + field: str, + file: str, + buffer: int = 1000000, + delete_input=False, +) -> None: """ Read incrementally the files compute something and then write Parameters ---------- file_out (str): output file path out_name (str): lh5 group name for output - func : function converting into to output + func : function converting into to output field (str): lh5 field name to read file (str): file name to read buffer (int): length of buffer - + """ - + entries = LH5Iterator(file, field, buffer_len=buffer)._get_file_cumentries(0) # number of blocks is ceil of entries/buffer, # shift by 1 since idx starts at 0 # this is maybe too high if buffer exactly divides idx - max_idx = int(np.ceil(entries/buffer))-1 - buffer_rows=None - - for idx,(lh5_obj, entry, n_rows) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): - + max_idx = int(np.ceil(entries / buffer)) - 1 + buffer_rows = None + + for idx, (lh5_obj, entry, n_rows) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): ak_obj = lh5_obj.view_as("ak") counts = ak.run_lengths(ak_obj.evtid) - rows= ak.num(ak_obj,axis=-1) - end_rows= counts[-1] - - if (idx==0): - mode="of" if (delete_input==True) else "append" - obj = ak_obj[0:rows-end_rows] - buffer_rows= copy.deepcopy(ak_obj[rows-end_rows:]) - elif (idx!=max_idx): - mode="append" - obj = ak.concatenate((buffer_rows,ak_obj[0:rows-end_rows])) - buffer_rows= copy.deepcopy(ak_obj[rows-end_rows:]) + rows = ak.num(ak_obj, axis=-1) + end_rows = counts[-1] + + if idx == 0: + mode = "of" if (delete_input == True) else "append" + obj = ak_obj[0 : rows - end_rows] + buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) + elif idx != max_idx: + mode = "append" + obj = ak.concatenate((buffer_rows, ak_obj[0 : rows - end_rows])) + buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) else: - mode="append" - obj = ak.concatenate((buffer_rows,ak_obj)) + mode = "append" + obj = ak.concatenate((buffer_rows, ak_obj)) # do stuff out = func(obj) # convert to a table - out_lh5 =Table(out) + out_lh5 = Table(out) # write lh5 file - lh5.write(out_lh5,name_out,file_out,wo_mode=mode) + lh5.write(out_lh5, name_out, file_out, wo_mode=mode) From 3dfbf23c979ea4854a6e0d0156b33028f407a3aa Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 21 Oct 2024 20:19:55 +0100 Subject: [PATCH 04/81] style improvements --- src/reboost/hpge/hit.py | 2 +- src/reboost/hpge/processors.py | 2 +- src/reboost/hpge/utils.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index f30259c..44c5d09 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -9,7 +9,7 @@ def build_hit( lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6) ) -> None: for idx, d in enumerate(detectors): - delete_input = True if (idx == 0) else False + delete_input = bool(idx == 0) utils.read_write_incremental( lh5_out_file, f"hit/{d}", diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 86c180f..5ba6a8e 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -17,4 +17,4 @@ def group_by_event(data): def smear_energy(energies, reso_func): - return np.random.normal(energies, reso_func(energies)) + return np.random.Generator.normal(energies, reso_func(energies)) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index b35be25..09c216e 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -40,14 +40,14 @@ def read_write_incremental( max_idx = int(np.ceil(entries / buffer)) - 1 buffer_rows = None - for idx, (lh5_obj, entry, n_rows) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): + for idx, (lh5_obj, _, _) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): ak_obj = lh5_obj.view_as("ak") counts = ak.run_lengths(ak_obj.evtid) rows = ak.num(ak_obj, axis=-1) end_rows = counts[-1] if idx == 0: - mode = "of" if (delete_input == True) else "append" + mode = "of" if (delete_input) else "append" obj = ak_obj[0 : rows - end_rows] buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) elif idx != max_idx: From 8b1fd87d5e8fb606dcbc3a28e1a5a15d65998cc4 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 22 Oct 2024 18:00:00 +0100 Subject: [PATCH 05/81] basic functionality for time-windowing --- src/reboost/hpge/cli.py | 3 +++ src/reboost/hpge/hit.py | 13 ++++++++++- src/reboost/hpge/processors.py | 40 +++++++++++++++++++++++++++++----- src/reboost/hpge/utils.py | 10 +++++++++ 4 files changed, 59 insertions(+), 7 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index 4ca6909..5379034 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -52,9 +52,12 @@ def hpge_cli() -> None: logger.addHandler(handler) if args.verbose: logger.setLevel(logging.DEBUG) + else: + logger.setLevel(logging.INFO) if args.command == "hit": # is the import here a good idea? + logger.info("...running raw->hit tier") from reboost.hpge.hit import build_hit with Path.open(Path(args.detectors)) as detectors_f: diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 44c5d09..e235d20 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,19 +1,30 @@ from __future__ import annotations +import logging from collections.abc import Iterable from reboost.hpge import processors, utils +log = logging.getLogger(__name__) + def build_hit( lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6) ) -> None: + # build the processing chain + proc = processors.def_chain( + [processors.group_by_time, processors.sum_energy, processors.smear_energy], + [{"window": 10}, {}, {"reso": 2, "energy_name": "summed_energy"}], + ) + for idx, d in enumerate(detectors): + msg = f"...running event grouping for {d}" + log.debug(msg) delete_input = bool(idx == 0) utils.read_write_incremental( lh5_out_file, f"hit/{d}", - processors.group_by_event, + proc, f"hit/{d}", lh5_in_file, buffer_len, diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 5ba6a8e..7d4472e 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -4,17 +4,45 @@ import numpy as np -def group_by_event(data): - counts = ak.run_lengths(data["evtid"]) +def def_chain(funcs, kwargs_list): + def func(data): + tmp = data + for f, kw in zip(funcs, kwargs_list): + tmp = f(tmp, **kw) - grouped = ak.unflatten(data, counts) - sum_energy = ak.sum(grouped.edep, axis=-1) + return tmp + + return func + + +def group_by_evtid(data): + counts = ak.run_lengths(data.evtid) + return ak.unflatten(data, counts) + + +def group_by_time(obj, window=10): + runs = np.array(np.cumsum(ak.run_lengths(obj.evtid))) + counts = ak.run_lengths(obj.evtid) + time_diffs = np.diff(obj.time) + index_diffs = np.diff(obj.evtid) + + change_points = np.array(np.where((time_diffs > window * 1000) & (index_diffs == 0)))[0] + total_change = np.sort(np.concatenate(([0], change_points, runs), axis=0)) + + counts = ak.Array(np.diff(total_change)) + return ak.unflatten(obj, counts) + + +def sum_energy(grouped): + sum_energy = ak.sum(grouped.edep, axis=-1) t0 = ak.fill_none(ak.firsts(grouped.time, axis=-1), np.nan) index = ak.fill_none(ak.firsts(grouped.evtid, axis=-1), np.nan) return ak.zip({"sum_energy": sum_energy, "t0": t0, "evtid": index}) -def smear_energy(energies, reso_func): - return np.random.Generator.normal(energies, reso_func(energies)) +def smear_energy(data, reso=2, energy_name="sum_energy"): + return ak.with_field( + data, np.random.Generator.normal(data[energy_name], reso), "energy_smeared" + ) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 09c216e..65fdd22 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -1,6 +1,7 @@ from __future__ import annotations import copy +import logging from typing import Callable import awkward as ak @@ -9,6 +10,8 @@ from lgdo.lh5 import LH5Iterator from lgdo.types import Table +log = logging.getLogger(__name__) + def read_write_incremental( file_out: str, @@ -32,6 +35,9 @@ def read_write_incremental( """ + msg = f"...begin processing with {file} to {file_out}" + log.info(msg) + entries = LH5Iterator(file, field, buffer_len=buffer)._get_file_cumentries(0) # number of blocks is ceil of entries/buffer, @@ -41,6 +47,9 @@ def read_write_incremental( buffer_rows = None for idx, (lh5_obj, _, _) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): + msg = f"... processed {idx} files out of {max_idx}" + log.debug(msg) + ak_obj = lh5_obj.view_as("ak") counts = ak.run_lengths(ak_obj.evtid) rows = ak.num(ak_obj, axis=-1) @@ -65,4 +74,5 @@ def read_write_incremental( out_lh5 = Table(out) # write lh5 file + log.info("...finished processing and save file") lh5.write(out_lh5, name_out, file_out, wo_mode=mode) From 223a7d0a3f4eab5d7ead17b77d78668270687b23 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 22 Oct 2024 18:40:36 +0100 Subject: [PATCH 06/81] detectors file to config --- src/reboost/hpge/cli.py | 11 +++++------ src/reboost/hpge/hit.py | 20 +++++++++++--------- src/reboost/hpge/processors.py | 4 +++- 3 files changed, 19 insertions(+), 16 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index 5379034..d7766d1 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -34,14 +34,13 @@ def hpge_cli() -> None: hit_parser = subparsers.add_parser("hit", help="build hit file from remage raw file") hit_parser.add_argument( - "--detectors", - help="file that contains a list of detector ids that are part of the input file", + "--config", + help="file that contains the configuration", required=True, ) hit_parser.add_argument("input", help="input hit LH5 file", metavar="INPUT_HIT") hit_parser.add_argument("output", help="output evt LH5 file", metavar="OUTPUT_EVT") - # parse arguments args = parser.parse_args() handler = colorlog.StreamHandler() @@ -60,7 +59,7 @@ def hpge_cli() -> None: logger.info("...running raw->hit tier") from reboost.hpge.hit import build_hit - with Path.open(Path(args.detectors)) as detectors_f: - detectors = json.load(detectors_f) + with Path.open(Path(args.config)) as config_f: + config = json.load(config_f) - build_hit(args.input, args.output, detectors, args.bufsize) + build_hit(args.input, args.output, config, args.bufsize) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index e235d20..14f3144 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,7 +1,6 @@ from __future__ import annotations import logging -from collections.abc import Iterable from reboost.hpge import processors, utils @@ -9,18 +8,21 @@ def build_hit( - lh5_in_file: str, lh5_out_file: str, detectors: Iterable[str | int], buffer_len: int = int(5e6) + lh5_in_file: str, lh5_out_file: str, config: dict, buffer_len: int = int(5e6) ) -> None: - # build the processing chain - proc = processors.def_chain( - [processors.group_by_time, processors.sum_energy, processors.smear_energy], - [{"window": 10}, {}, {"reso": 2, "energy_name": "summed_energy"}], - ) - - for idx, d in enumerate(detectors): + for idx, d in enumerate(config.keys()): msg = f"...running event grouping for {d}" log.debug(msg) delete_input = bool(idx == 0) + + # build the processing chain + # TODO replace this with a config file similar to pygama.build_evt + + proc = processors.def_chain( + [processors.group_by_time, processors.sum_energy, processors.smear_energy], + [{"window": 10}, {}, {"reso": config[d]["reso"], "energy_name": "sum_energy"}], + ) + utils.read_write_incremental( lh5_out_file, f"hit/{d}", diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 7d4472e..26e8183 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -43,6 +43,8 @@ def sum_energy(grouped): def smear_energy(data, reso=2, energy_name="sum_energy"): + flat_energy = data[energy_name].to_numpy() + rng = np.random.default_rng() return ak.with_field( - data, np.random.Generator.normal(data[energy_name], reso), "energy_smeared" + data, rng.normal(loc=flat_energy, scale=np.ones_like(flat_energy) * reso), "energy_smeared" ) From 5ded4c95d41fdbf911fb291069106fd1cc63f4e9 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 22 Oct 2024 18:57:43 +0100 Subject: [PATCH 07/81] [fix] sort by time --- src/reboost/hpge/hit.py | 9 +++++++-- src/reboost/hpge/processors.py | 7 +++++++ 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 14f3144..9c74798 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -19,8 +19,13 @@ def build_hit( # TODO replace this with a config file similar to pygama.build_evt proc = processors.def_chain( - [processors.group_by_time, processors.sum_energy, processors.smear_energy], - [{"window": 10}, {}, {"reso": config[d]["reso"], "energy_name": "sum_energy"}], + [ + processors.sort_data, + processors.group_by_time, + processors.sum_energy, + processors.smear_energy, + ], + [{}, {"window": 10}, {}, {"reso": config[d]["reso"], "energy_name": "sum_energy"}], ) utils.read_write_incremental( diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 26e8183..9c76cec 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -15,6 +15,13 @@ def func(data): return func +def sort_data(obj): + indexs = np.lexsort((obj.time, obj.evtid)) + obj = obj[indexs] + + return obj + + def group_by_evtid(data): counts = ak.run_lengths(data.evtid) return ak.unflatten(data, counts) From 426134857d9e48e2aea6627e8b7bf9abb120a66d Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 24 Oct 2024 11:50:52 +0100 Subject: [PATCH 08/81] [wip] adding functions for DL calculations --- src/reboost/hpge/utils.py | 49 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 65fdd22..024b9cd 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -13,6 +13,55 @@ log = logging.getLogger(__name__) +def distance_3d(v1, v2): + return np.sqrt(np.power(v2.x - v1.x, 2) + np.power(v2.y - v1.y, 2) + np.power(v2.z - v1.z, 2)) + + +def distance_2d(v1, v2): + return np.sqrt(np.power(v2.x - v1.x, 2) + np.power(v2.y - v1.y, 2)) + + +def add_ak_3d(a, b): + return ak.zip({"x": a.x + b.x, "y": a.y + b.y, "z": a.z + b.z}) + + +def add_ak_2d(a, b): + return ak.zip({"x": a.x + b.x, "y": a.y + b.y}) + + +def sub_ak_2d(a, b): + return ak.zip({"x": a.x - b.x, "y": a.y - b.y}) + + +def sub_ak_3d(a, b): + return ak.zip({"x": a.x - b.x, "y": a.y - b.y, "z": a.z - b.z}) + + +def prod_ak_2d(a, b): + return a.x * b.x + a.y * b.y + + +def prod_ak_3d(a, b): + return a.x * b.x + a.y * b.y + a.z * b.z + + +def prod(a, b): + return ak.zip({"x": a.x * b, "y": a.y * b}) + + +def proj(s1, s2, v): + dist_one = prod_ak_2d(sub_ak_2d(v, s1), sub_ak_2d(v, s2)) / distance_2d(s1, s2) + dist_one = np.where(dist_one > 1, dist_one, 1) + dist_one = np.where(dist_one > 0, dist_one, 0) + return prod(add_ak_2d(s1, sub_ak_2d(s2, s1)), dist_one) + + +def dist(s1, s2, v): + return distance_2d(proj(s1, s2, v), v) + +def get_detector_origin(name): + raise NotImplementedError + def read_write_incremental( file_out: str, name_out: str, From 2f888fb170eb354a91d2eebd4d4bb566cd08c453 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 24 Oct 2024 11:52:05 +0100 Subject: [PATCH 09/81] [wip] adding functions for DL calc and __init__ for hpge subpackage --- src/reboost/hpge/__init__.py | 0 src/reboost/hpge/processors.py | 31 ++++++++++++++++++++++++++----- 2 files changed, 26 insertions(+), 5 deletions(-) create mode 100644 src/reboost/hpge/__init__.py diff --git a/src/reboost/hpge/__init__.py b/src/reboost/hpge/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 9c76cec..8154e9f 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -2,7 +2,7 @@ import awkward as ak import numpy as np - +import utils def def_chain(funcs, kwargs_list): def func(data): @@ -16,10 +16,8 @@ def func(data): def sort_data(obj): - indexs = np.lexsort((obj.time, obj.evtid)) - obj = obj[indexs] - - return obj + indices = np.lexsort((obj.time, obj.evtid)) + return obj[indices] def group_by_evtid(data): @@ -55,3 +53,26 @@ def smear_energy(data, reso=2, energy_name="sum_energy"): return ak.with_field( data, rng.normal(loc=flat_energy, scale=np.ones_like(flat_energy) * reso), "energy_smeared" ) + +def distance_to_surface(data,detector="det001"): + + # get detector origin + x,y,z = utils.get_detector_origin(detector) + + # get the r-z points to produce the detector (G4GenericPolyCone) + r,z = utils.get_detector_corners(detector) + + # loop over pairs + dists=[] + for (rtmp,ztmp,rnext_tmp,znext_tmp) in zip(r[:-1],z[:-1],r[1:],z[1:]): + + s1 = ak.zip({"x":rtmp,"y":ztmp}) + s2 = ak.zip({"x":rnext_tmp,"y":znext_tmp}) + v_3d=ak.zip({"x":data.xloc-x,"y":data.yloc-y,"z":data.zloc-z}) + v = ak.zip({"x":np.sqrt(np.power(v_3d.x,2)+np.power(v_3d.y,2)),"y":v_3d.z}) + + dist = utils.dist(s1,s2,v) + dists.append(dist) + + + raise np.min(dists) From ddea2d5aa265e9b27ced219a65eb1387bb420421 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 24 Oct 2024 10:54:03 +0000 Subject: [PATCH 10/81] style: pre-commit fixes --- src/reboost/hpge/processors.py | 27 +++++++++++++-------------- src/reboost/hpge/utils.py | 2 ++ 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 8154e9f..b523187 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -4,6 +4,7 @@ import numpy as np import utils + def def_chain(funcs, kwargs_list): def func(data): tmp = data @@ -54,25 +55,23 @@ def smear_energy(data, reso=2, energy_name="sum_energy"): data, rng.normal(loc=flat_energy, scale=np.ones_like(flat_energy) * reso), "energy_smeared" ) -def distance_to_surface(data,detector="det001"): +def distance_to_surface(data, detector="det001"): # get detector origin - x,y,z = utils.get_detector_origin(detector) - + x, y, z = utils.get_detector_origin(detector) + # get the r-z points to produce the detector (G4GenericPolyCone) - r,z = utils.get_detector_corners(detector) + r, z = utils.get_detector_corners(detector) # loop over pairs - dists=[] - for (rtmp,ztmp,rnext_tmp,znext_tmp) in zip(r[:-1],z[:-1],r[1:],z[1:]): - - s1 = ak.zip({"x":rtmp,"y":ztmp}) - s2 = ak.zip({"x":rnext_tmp,"y":znext_tmp}) - v_3d=ak.zip({"x":data.xloc-x,"y":data.yloc-y,"z":data.zloc-z}) - v = ak.zip({"x":np.sqrt(np.power(v_3d.x,2)+np.power(v_3d.y,2)),"y":v_3d.z}) - - dist = utils.dist(s1,s2,v) + dists = [] + for rtmp, ztmp, rnext_tmp, znext_tmp in zip(r[:-1], z[:-1], r[1:], z[1:]): + s1 = ak.zip({"x": rtmp, "y": ztmp}) + s2 = ak.zip({"x": rnext_tmp, "y": znext_tmp}) + v_3d = ak.zip({"x": data.xloc - x, "y": data.yloc - y, "z": data.zloc - z}) + v = ak.zip({"x": np.sqrt(np.power(v_3d.x, 2) + np.power(v_3d.y, 2)), "y": v_3d.z}) + + dist = utils.dist(s1, s2, v) dists.append(dist) - raise np.min(dists) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 024b9cd..bb8ae1e 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -59,9 +59,11 @@ def proj(s1, s2, v): def dist(s1, s2, v): return distance_2d(proj(s1, s2, v), v) + def get_detector_origin(name): raise NotImplementedError + def read_write_incremental( file_out: str, name_out: str, From ca94f10ce60c369b3bd8880728b8563ed2382198 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 24 Oct 2024 12:08:08 +0100 Subject: [PATCH 11/81] Update __init__.py --- src/reboost/hpge/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/reboost/hpge/__init__.py b/src/reboost/hpge/__init__.py index e69de29..d3f5a12 100644 --- a/src/reboost/hpge/__init__.py +++ b/src/reboost/hpge/__init__.py @@ -0,0 +1 @@ + From adb1f6e9c4fa149fd43b33ca6bd6ff6c73d66494 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 24 Oct 2024 11:08:19 +0000 Subject: [PATCH 12/81] style: pre-commit fixes --- src/reboost/hpge/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/reboost/hpge/__init__.py b/src/reboost/hpge/__init__.py index d3f5a12..e69de29 100644 --- a/src/reboost/hpge/__init__.py +++ b/src/reboost/hpge/__init__.py @@ -1 +0,0 @@ - From 534882c73a9b50c90706c71b9c2feec282f3b787 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 28 Oct 2024 09:45:05 +0000 Subject: [PATCH 13/81] Update src/reboost/hpge/processors.py Co-authored-by: Manuel Huber --- src/reboost/hpge/processors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index b523187..518a3c9 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -2,7 +2,7 @@ import awkward as ak import numpy as np -import utils +from . import utils def def_chain(funcs, kwargs_list): From 3d5a535a796c3f6a0747172b5062b3d9713ea4a5 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 28 Oct 2024 09:45:12 +0000 Subject: [PATCH 14/81] Update src/reboost/hpge/hit.py Co-authored-by: Manuel Huber --- src/reboost/hpge/hit.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 9c74798..e9d4317 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -2,7 +2,7 @@ import logging -from reboost.hpge import processors, utils +from . import processors, utils log = logging.getLogger(__name__) From 39c587c3a4f705757208ba0618092da7c7ae019a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 28 Oct 2024 09:45:15 +0000 Subject: [PATCH 15/81] style: pre-commit fixes --- src/reboost/hpge/processors.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 518a3c9..5634b16 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -2,6 +2,7 @@ import awkward as ak import numpy as np + from . import utils From 78e130a730557e53e560223ad642b92910911662 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 28 Oct 2024 10:12:26 +0000 Subject: [PATCH 16/81] [wip] starting functionality for DLs --- src/reboost/hpge/cli.py | 12 ++++++- src/reboost/hpge/hit.py | 44 +++++++++++++++++++++++- src/reboost/hpge/processors.py | 27 +++++++-------- src/reboost/hpge/utils.py | 63 ++++++++++++++++++++++++++-------- 4 files changed, 116 insertions(+), 30 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index d7766d1..a7f14b9 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -38,6 +38,16 @@ def hpge_cli() -> None: help="file that contains the configuration", required=True, ) + hit_parser.add_argument( + "--gdml", + help="GDML file used for Geant4", + required=False, + ) + hit_parser.add_argument( + "--macro", + help="Gean4 macro file used to generate raw tier", + required=False, + ) hit_parser.add_argument("input", help="input hit LH5 file", metavar="INPUT_HIT") hit_parser.add_argument("output", help="output evt LH5 file", metavar="OUTPUT_EVT") @@ -62,4 +72,4 @@ def hpge_cli() -> None: with Path.open(Path(args.config)) as config_f: config = json.load(config_f) - build_hit(args.input, args.output, config, args.bufsize) + build_hit(args.input, args.output, config, args.bufsize, gdml=args.gdml, macro=args.macro) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 9c74798..4eda023 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -8,8 +8,50 @@ def build_hit( - lh5_in_file: str, lh5_out_file: str, config: dict, buffer_len: int = int(5e6) + lh5_in_file: str, + lh5_out_file: str, + config: dict, + buffer_len: int = int(5e6), + gdml: str | None = None, + macro: str | None = None, ) -> None: + """ + Build the hit tier from the raw Geant4 output + + Parameters + ---------- + lh5_in_file + input file containing the raw tier + lh5_out_file + output file + config + dictonary containg the configuration / parameters + should contain one sub-dictonary per detector with a format like: + + .. code-block:: json + + "det001": { + "reso": 1, + ... + } + + This can contain any parameters needed in the processing chain. + buffer_len + number of rows to read at once + gdml + path to the gdml file of the geometry + macro + path to the Geant4 macro used to generate the raw tier + """ + + # get the geant4 gdml and macro + + if gdml is not None: + pass + + if macro is not None: + pass + for idx, d in enumerate(config.keys()): msg = f"...running event grouping for {d}" log.debug(msg) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 8154e9f..b523187 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -4,6 +4,7 @@ import numpy as np import utils + def def_chain(funcs, kwargs_list): def func(data): tmp = data @@ -54,25 +55,23 @@ def smear_energy(data, reso=2, energy_name="sum_energy"): data, rng.normal(loc=flat_energy, scale=np.ones_like(flat_energy) * reso), "energy_smeared" ) -def distance_to_surface(data,detector="det001"): +def distance_to_surface(data, detector="det001"): # get detector origin - x,y,z = utils.get_detector_origin(detector) - + x, y, z = utils.get_detector_origin(detector) + # get the r-z points to produce the detector (G4GenericPolyCone) - r,z = utils.get_detector_corners(detector) + r, z = utils.get_detector_corners(detector) # loop over pairs - dists=[] - for (rtmp,ztmp,rnext_tmp,znext_tmp) in zip(r[:-1],z[:-1],r[1:],z[1:]): - - s1 = ak.zip({"x":rtmp,"y":ztmp}) - s2 = ak.zip({"x":rnext_tmp,"y":znext_tmp}) - v_3d=ak.zip({"x":data.xloc-x,"y":data.yloc-y,"z":data.zloc-z}) - v = ak.zip({"x":np.sqrt(np.power(v_3d.x,2)+np.power(v_3d.y,2)),"y":v_3d.z}) - - dist = utils.dist(s1,s2,v) + dists = [] + for rtmp, ztmp, rnext_tmp, znext_tmp in zip(r[:-1], z[:-1], r[1:], z[1:]): + s1 = ak.zip({"x": rtmp, "y": ztmp}) + s2 = ak.zip({"x": rnext_tmp, "y": znext_tmp}) + v_3d = ak.zip({"x": data.xloc - x, "y": data.yloc - y, "z": data.zloc - z}) + v = ak.zip({"x": np.sqrt(np.power(v_3d.x, 2) + np.power(v_3d.y, 2)), "y": v_3d.z}) + + dist = utils.dist(s1, s2, v) dists.append(dist) - raise np.min(dists) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 024b9cd..fdf8d20 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -13,55 +13,90 @@ log = logging.getLogger(__name__) -def distance_3d(v1, v2): +def _distance_3d(v1, v2): return np.sqrt(np.power(v2.x - v1.x, 2) + np.power(v2.y - v1.y, 2) + np.power(v2.z - v1.z, 2)) -def distance_2d(v1, v2): +def _distance_2d(v1, v2): return np.sqrt(np.power(v2.x - v1.x, 2) + np.power(v2.y - v1.y, 2)) -def add_ak_3d(a, b): +def _add_ak_3d(a, b): return ak.zip({"x": a.x + b.x, "y": a.y + b.y, "z": a.z + b.z}) -def add_ak_2d(a, b): +def _add_ak_2d(a, b): return ak.zip({"x": a.x + b.x, "y": a.y + b.y}) -def sub_ak_2d(a, b): +def _sub_ak_2d(a, b): return ak.zip({"x": a.x - b.x, "y": a.y - b.y}) -def sub_ak_3d(a, b): +def _sub_ak_3d(a, b): return ak.zip({"x": a.x - b.x, "y": a.y - b.y, "z": a.z - b.z}) -def prod_ak_2d(a, b): +def _prod_ak_2d(a, b): return a.x * b.x + a.y * b.y -def prod_ak_3d(a, b): +def _prod_ak_3d(a, b): return a.x * b.x + a.y * b.y + a.z * b.z -def prod(a, b): +def _prod(a, b): return ak.zip({"x": a.x * b, "y": a.y * b}) -def proj(s1, s2, v): - dist_one = prod_ak_2d(sub_ak_2d(v, s1), sub_ak_2d(v, s2)) / distance_2d(s1, s2) +def proj(s1: ak.Array, s2: ak.Array, v: ak.Array) -> ak.Array: + """ + Projection v on the line segment s1 to s2. + All of s1,s2 and v are `ak.Array` of records with two fields `x` and `y` + I.e. they represent lists of 2D cartesian vectors. + Parameters + ---------- + s1 + first points in the line segment + s2 + second points in the line segment + v + points to project onto s1-s2 + Returns + ------- + the coordinates of the projection onto s1-s2 + """ + + dist_one = _prod_ak_2d(_sub_ak_2d(v, s1), _sub_ak_2d(v, s2)) / _distance_2d(s1, s2) dist_one = np.where(dist_one > 1, dist_one, 1) dist_one = np.where(dist_one > 0, dist_one, 0) - return prod(add_ak_2d(s1, sub_ak_2d(s2, s1)), dist_one) + return _prod(_add_ak_2d(s1, _sub_ak_2d(s2, s1)), dist_one) -def dist(s1, s2, v): - return distance_2d(proj(s1, s2, v), v) +def dist(s1: ak.Array, s2: ak.Array, v: ak.Array) -> float: + """ + Shortest distance between a point v and the line segment defined by s1-s2. + All of s1,s2 and v are `ak.Array` of records with two fields `x` and `y` + I.e. they represent lists of 2D cartesian vectors. + Parameters + ---------- + s1 + first points in the line segment + s2 + second points in the line segment + v + points to project onto s1-s2 + Returns + ------- + the shortest distance from the point to the line + """ + return _distance_2d(proj(s1, s2, v), v) + def get_detector_origin(name): raise NotImplementedError + def read_write_incremental( file_out: str, name_out: str, From a2e25990fa85ea33e2e57011f81dc5c6b3e668fc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 28 Oct 2024 10:12:45 +0000 Subject: [PATCH 17/81] style: pre-commit fixes --- src/reboost/hpge/hit.py | 8 ++++---- src/reboost/hpge/processors.py | 1 - src/reboost/hpge/utils.py | 1 - 3 files changed, 4 insertions(+), 6 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 23d2b3f..c34ec20 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -23,13 +23,13 @@ def build_hit( lh5_in_file input file containing the raw tier lh5_out_file - output file + output file config dictonary containg the configuration / parameters should contain one sub-dictonary per detector with a format like: .. code-block:: json - + "det001": { "reso": 1, ... @@ -42,13 +42,13 @@ def build_hit( path to the gdml file of the geometry macro path to the Geant4 macro used to generate the raw tier - """ + """ # get the geant4 gdml and macro if gdml is not None: pass - + if macro is not None: pass diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 0d53ac1..5634b16 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -6,7 +6,6 @@ from . import utils - def def_chain(funcs, kwargs_list): def func(data): tmp = data diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index f49079c..fdf8d20 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -93,7 +93,6 @@ def dist(s1: ak.Array, s2: ak.Array, v: ak.Array) -> float: return _distance_2d(proj(s1, s2, v), v) - def get_detector_origin(name): raise NotImplementedError From 16550ae49b77e145d47a9e9e79098cf0c1f8d54a Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 28 Oct 2024 10:37:59 +0000 Subject: [PATCH 18/81] fix some parts of the docs --- src/reboost/hpge/hit.py | 5 ++--- src/reboost/hpge/processors.py | 29 ++++++++++++++++++++++++++++- src/reboost/hpge/utils.py | 31 ++++++++++++++++++++++--------- 3 files changed, 52 insertions(+), 13 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index c34ec20..bf062ad 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -25,14 +25,13 @@ def build_hit( lh5_out_file output file config - dictonary containg the configuration / parameters + dictionary containing the configuration / parameters should contain one sub-dictonary per detector with a format like: .. code-block:: json "det001": { - "reso": 1, - ... + "reso": 1 } This can contain any parameters needed in the processing chain. diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 5634b16..c2725d9 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -7,6 +7,10 @@ def def_chain(funcs, kwargs_list): + """ + Builds the processing chain function from a list of functions + """ + def func(data): tmp = data for f, kw in zip(funcs, kwargs_list): @@ -18,16 +22,27 @@ def func(data): def sort_data(obj): + """ + Sort the data by evtid then time + """ indices = np.lexsort((obj.time, obj.evtid)) return obj[indices] def group_by_evtid(data): + """ + Simple grouping by evtid + """ + counts = ak.run_lengths(data.evtid) return ak.unflatten(data, counts) def group_by_time(obj, window=10): + """ + Grouping by evtid and by time + """ + runs = np.array(np.cumsum(ak.run_lengths(obj.evtid))) counts = ak.run_lengths(obj.evtid) @@ -42,7 +57,11 @@ def group_by_time(obj, window=10): def sum_energy(grouped): - sum_energy = ak.sum(grouped.edep, axis=-1) + """ + Sum the energy deposits per hit + """ + + sum_energy = ak.sum(grouped.ed6ep, axis=-1) t0 = ak.fill_none(ak.firsts(grouped.time, axis=-1), np.nan) index = ak.fill_none(ak.firsts(grouped.evtid, axis=-1), np.nan) @@ -50,6 +69,10 @@ def sum_energy(grouped): def smear_energy(data, reso=2, energy_name="sum_energy"): + """ + Smearing og energies + """ + flat_energy = data[energy_name].to_numpy() rng = np.random.default_rng() return ak.with_field( @@ -58,6 +81,10 @@ def smear_energy(data, reso=2, energy_name="sum_energy"): def distance_to_surface(data, detector="det001"): + """ + [WIP] computes the distance of a point to the detector surface + """ + # get detector origin x, y, z = utils.get_detector_origin(detector) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index fdf8d20..cb31b5e 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -52,8 +52,10 @@ def _prod(a, b): def proj(s1: ak.Array, s2: ak.Array, v: ak.Array) -> ak.Array: """ Projection v on the line segment s1 to s2. + All of s1,s2 and v are `ak.Array` of records with two fields `x` and `y` I.e. they represent lists of 2D cartesian vectors. + Parameters ---------- s1 @@ -76,8 +78,11 @@ def proj(s1: ak.Array, s2: ak.Array, v: ak.Array) -> ak.Array: def dist(s1: ak.Array, s2: ak.Array, v: ak.Array) -> float: """ Shortest distance between a point v and the line segment defined by s1-s2. - All of s1,s2 and v are `ak.Array` of records with two fields `x` and `y` + + All of s1,s2 and v are `ak.Array` of records with two fields `x` and `y`. + I.e. they represent lists of 2D cartesian vectors. + Parameters ---------- s1 @@ -86,10 +91,12 @@ def dist(s1: ak.Array, s2: ak.Array, v: ak.Array) -> float: second points in the line segment v points to project onto s1-s2 + Returns ------- the shortest distance from the point to the line """ + return _distance_2d(proj(s1, s2, v), v) @@ -107,16 +114,22 @@ def read_write_incremental( delete_input=False, ) -> None: """ - Read incrementally the files compute something and then write + Read incrementally the files compute something and then write output + Parameters ---------- - file_out (str): output file path - out_name (str): lh5 group name for output - func : function converting into to output - field (str): lh5 field name to read - file (str): file name to read - buffer (int): length of buffer - + file_out + output file path + name_out + lh5 group name for output + func + function converting into to output + field + lh5 field name to read + file + file name to read + buffer + length of buffer """ msg = f"...begin processing with {file} to {file_out}" From 4c4f60328eff9b3cd2b31aac24e1012d45f31a05 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 28 Oct 2024 10:38:35 +0000 Subject: [PATCH 19/81] style: pre-commit fixes --- src/reboost/hpge/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index cb31b5e..489c35f 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -118,7 +118,7 @@ def read_write_incremental( Parameters ---------- - file_out + file_out output file path name_out lh5 group name for output From f8778cc5770d733dc5e2bf36b64838ff0c07e409 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 7 Nov 2024 16:59:32 +0100 Subject: [PATCH 20/81] generate proccesing chain from config file --- src/reboost/hpge/cli.py | 34 ++++- src/reboost/hpge/hit.py | 239 ++++++++++++++++++++++++++++++--- src/reboost/hpge/processors.py | 160 ++++++++++++++-------- src/reboost/hpge/utils.py | 202 ++++++++-------------------- 4 files changed, 407 insertions(+), 228 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index a7f14b9..a581470 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -1,11 +1,11 @@ from __future__ import annotations import argparse -import json import logging from pathlib import Path import colorlog +import yaml def hpge_cli() -> None: @@ -34,8 +34,13 @@ def hpge_cli() -> None: hit_parser = subparsers.add_parser("hit", help="build hit file from remage raw file") hit_parser.add_argument( - "--config", - help="file that contains the configuration", + "--proc_chain", + help="YAML file that contains the processing chain", + required=True, + ) + hit_parser.add_argument( + "--pars", + help="YAML file that contains the pars", required=True, ) hit_parser.add_argument( @@ -69,7 +74,24 @@ def hpge_cli() -> None: logger.info("...running raw->hit tier") from reboost.hpge.hit import build_hit - with Path.open(Path(args.config)) as config_f: - config = json.load(config_f) + with Path.open(Path(args.pars)) as config_f: + pars = yaml.safe_load(config_f) + + with Path.open(Path(args.proc_chain)) as config_f: + proc_config = yaml.safe_load(config_f) + + # check the processing chain + for req_field in ["channels", "outputs", "step_group", "operations"]: + if req_field not in proc_config: + msg = f"error proc chain config must contain the field {req_field}" + raise ValueError(msg) - build_hit(args.input, args.output, config, args.bufsize, gdml=args.gdml, macro=args.macro) + build_hit( + args.input, + args.output, + proc_config, + pars, + args.bufsize, + gdml=args.gdml, + macro=args.macro, + ) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index bf062ad..87221bb 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -2,15 +2,226 @@ import logging -from . import processors, utils +import numpy as np +from lgdo import Array, ArrayOfEqualSizedArrays, LH5Iterator, Table, VectorOfVectors, lh5 + +from . import utils log = logging.getLogger(__name__) +def eval_expression( + table: Table, info: dict, pars: dict +) -> Array | ArrayOfEqualSizedArrays | VectorOfVectors: + """Evaluate an expression returning an LGDO object. + + Parameters + ---------- + table + hit table, with columns possibly used in the operations. + info + dict containing the information on the expression. Must contain `mode` and `expressions` keys + For example: + + .. code-block:: json + + { + "mode": "eval", + "expression":"ak.sum(hit.edep,axis=-1)" + } + + variables preceded by `hit` will be taken from the supplied table. Mode can be either `eval`, + in which case a simple expression is based (only involving numpy, awkward or inbuilt python functions), + or `function` in which case an arbitrary function is passed (for example defined in processors). + + pars + dict of parameters, can contain any fields passed to the expression prefixed by `pars.`. + + + Returns + ------- + a new column for the hit table either :class:`Array`,:class:`ArrayOfEqualSizedArrays` or :class:` VectorOfVectors`. + """ + + pars_tuple = utils.dict2tuple(pars) + local_dict = {"pars": pars_tuple} + + if info["mode"] == "eval": + # replace hit. + expr = info["expression"].replace("hit.", "") + + msg = f"evaluating table with command {expr} and local_dict {local_dict.keys()}" + log.debug(msg) + + col = table.eval(expr, local_dict) + + elif info["mode"] == "function": + proc_func, globs = utils.get_function_string(info["expression"]) + + # add hit table to locals + local_dict = {"hit": table} | local_dict + + msg = f"evaluating table with command {info['expression']} and local_dict {local_dict.keys()} and global dict {globs.keys()}" + log.debug(msg) + col = eval(proc_func, globs, local_dict) + + else: + msg = "mode is not recognised." + raise ValueError(msg) + return col + + +def read_write_incremental( + file_out: str, + name_out: str, + proc_config: dict, + pars: dict, + field: str, + file: str, + buffer: int = 1000000, + delete_input: bool = False, +) -> None: + """ + Read incrementally the files compute something and then write output + + Parameters + ---------- + file_out + output file path + name_out + lh5 group name for output + proc_config + the configuration file for the processing. Must contain the fields `channels`, `outputs`, `step_group` and operations`. + For example: + + .. code-block:: json + + { + "channels": [ + "det000", + "det001", + "det002", + "det003" + ], + "outputs": [ + "t0", + "truth_energy_sum", + "smeared_energy_sum", + "evtid" + ], + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)" + }, + "smeared_energy_sum": { + "description": "summed energy after convolution with energy response.", + "mode": "function", + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + } + } + } + + pars + a dictionary of parameters, must have a field per channel consisting of a `dict` of parameters. For example: + + .. code-block:: json + { + "det000": { + "reso": 1., + "fccd": 0.1 + } + } + + field + lh5 field name to read. + file + file name to read + buffer + length of buffer + delete_input + flag to delete the input file. + + Note + ---- + The operations can depend on the outputs of previous steps, so operations order is important. + """ + + msg = f"...begin processing with {file} to {file_out}" + log.info(msg) + + entries = LH5Iterator(file, field, buffer_len=buffer)._get_file_cumentries(0) + + # number of blocks is ceil of entries/buffer, + # shift by 1 since idx starts at 0 + # this is maybe too high if buffer exactly divides idx + max_idx = int(np.ceil(entries / buffer)) - 1 + buffer_rows = None + + for idx, (lh5_obj, _, _) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): + msg = f"... processed {idx} files out of {max_idx}" + log.debug(msg) + + # convert to awkward + ak_obj = lh5_obj.view_as("ak") + + # handle the buffers + obj, buffer_rows, mode = utils._merge_arrays( + ak_obj, buffer_rows, idx=idx, max_idx=max_idx, delete_input=delete_input + ) + + # convert back to a table, should work + data = Table(obj) + + # define glob and local dicts for the func cal + group_func, globs = utils.get_function_string( + proc_config["step_group"]["expression"], + ) + locs = {"stp": data} + + msg = f"running step grouping with {group_func} and globals {globs.keys()} and locals {locs.keys()}" + log.debug(msg) + + # group to create hit table + grouped = eval(group_func, globs, locs) + + # processors + for name, info in proc_config["operations"].items(): + msg = f"adding column {name}" + log.debug(msg) + + col = eval_expression(grouped, info, pars) + grouped.add_field(name, col) + + # remove unwanted columns + log.debug("removing unwanted columns") + + existing_cols = list(grouped.keys()) + for col in existing_cols: + if col not in proc_config["outputs"]: + grouped.remove_column(col, delete=True) + + # write lh5 file + msg = f"...finished processing and save file with wo_mode {mode}" + log.debug(msg) + lh5.write(grouped, name_out, file_out, wo_mode=mode) + + def build_hit( lh5_in_file: str, lh5_out_file: str, - config: dict, + proc_config: dict, + pars: dict, buffer_len: int = int(5e6), gdml: str | None = None, macro: str | None = None, @@ -51,28 +262,16 @@ def build_hit( if macro is not None: pass - for idx, d in enumerate(config.keys()): - msg = f"...running event grouping for {d}" - log.debug(msg) + for idx, d in enumerate(proc_config["channels"]): + msg = f"...running hit tier for {d}" + log.info(msg) delete_input = bool(idx == 0) - # build the processing chain - # TODO replace this with a config file similar to pygama.build_evt - - proc = processors.def_chain( - [ - processors.sort_data, - processors.group_by_time, - processors.sum_energy, - processors.smear_energy, - ], - [{}, {"window": 10}, {}, {"reso": config[d]["reso"], "energy_name": "sum_energy"}], - ) - - utils.read_write_incremental( + read_write_incremental( lh5_out_file, f"hit/{d}", - proc, + proc_config, + pars[d], f"hit/{d}", lh5_in_file, buffer_len, diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index c2725d9..1282511 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -1,9 +1,9 @@ from __future__ import annotations import awkward as ak +import lgdo import numpy as np - -from . import utils +from lgdo import Array, Table, VectorOfVectors def def_chain(funcs, kwargs_list): @@ -21,85 +21,133 @@ def func(data): return func -def sort_data(obj): - """ - Sort the data by evtid then time +def sort_data(obj: ak.Array) -> ak.Array: + """Sort the data by evtid then time. + + Parameters + ---------- + obj + array of records containing fields `time` and `evtid` + + Returns + ------- + sorted awkward array """ indices = np.lexsort((obj.time, obj.evtid)) return obj[indices] -def group_by_evtid(data): - """ - Simple grouping by evtid - """ +def group_by_evtid(data: Table) -> Table: + """Simple grouping by evtid. - counts = ak.run_lengths(data.evtid) - return ak.unflatten(data, counts) + Takes the input `stp` :class:`LGOD.Table` from remage and defines groupings of steps (i.e the + `cumulative_length` for a vector of vectors). This then defines the output table (also :class:`LGDO.Table`), + on which processors can add fields. + Parameters + ---------- + data + LGDO Table which must contain the `evtid` field. -def group_by_time(obj, window=10): - """ - Grouping by evtid and by time + Returns + ------- + LGDO table of :class:`VectorOfVector` for each field. + + Note + ---- + The input table must be sorted (by `evtid`). """ - runs = np.array(np.cumsum(ak.run_lengths(obj.evtid))) - counts = ak.run_lengths(obj.evtid) + # convert to awkward + obj_ak = data.view_as("ak") - time_diffs = np.diff(obj.time) - index_diffs = np.diff(obj.evtid) + # sort input + obj_ak = sort_data(obj_ak) - change_points = np.array(np.where((time_diffs > window * 1000) & (index_diffs == 0)))[0] - total_change = np.sort(np.concatenate(([0], change_points, runs), axis=0)) + # extract cumulative lengths + counts = ak.run_lengths(obj_ak.evtid) + cumulative_length = np.cumsum(counts) - counts = ak.Array(np.diff(total_change)) - return ak.unflatten(obj, counts) + # build output table + out_tbl = Table(size=len(cumulative_length)) + for f in obj_ak.fields: + out_tbl.add_field( + f, VectorOfVectors(cumulative_length=cumulative_length, flattened_data=obj_ak[f]) + ) + return out_tbl -def sum_energy(grouped): - """ - Sum the energy deposits per hit - """ - sum_energy = ak.sum(grouped.ed6ep, axis=-1) - t0 = ak.fill_none(ak.firsts(grouped.time, axis=-1), np.nan) - index = ak.fill_none(ak.firsts(grouped.evtid, axis=-1), np.nan) +def group_by_time(data: Table, window: float = 10) -> lgdo.Table: + """Grouping of steps by `evtid` and `time`. - return ak.zip({"sum_energy": sum_energy, "t0": t0, "evtid": index}) + Takes the input `stp` :class:`LGOD.Table` from remage and defines groupings of steps (i.e the + `cumulative_length` for a vector of vectors). This then defines the output table (also :class:`LGDO.Table`), + on which processors can add fields. + The windowing is based on defining a new group when the `evtid` changes or when the time increases by `> window`, + which is in units of us. -def smear_energy(data, reso=2, energy_name="sum_energy"): - """ - Smearing og energies - """ - flat_energy = data[energy_name].to_numpy() - rng = np.random.default_rng() - return ak.with_field( - data, rng.normal(loc=flat_energy, scale=np.ones_like(flat_energy) * reso), "energy_smeared" - ) + Parameters + ---------- + data + LGDO Table which must contain the `evtid`, `time` field. + Returns + ------- + LGDO table of :class:`VectorOfVector` for each field. -def distance_to_surface(data, detector="det001"): - """ - [WIP] computes the distance of a point to the detector surface + Note + ---- + The input table must be sorted (first by `evtid` then `time`). """ - # get detector origin - x, y, z = utils.get_detector_origin(detector) + obj = data.view_as("ak") + + # get difference + time_diffs = np.diff(obj.time) + index_diffs = np.diff(obj.evtid) + + # index of thhe last element in each run + time_change = (time_diffs > window * 1000) & (index_diffs == 0) + index_change = index_diffs > 0 + + # cumulative length is just the index of changes plus 1 + cumulative_length = np.array(np.where(time_change | index_change))[0] + 1 + + # add the las grouping + cumulative_length = np.append(cumulative_length, len(obj.time)) + + # build output table + out_tbl = Table(size=len(cumulative_length)) - # get the r-z points to produce the detector (G4GenericPolyCone) - r, z = utils.get_detector_corners(detector) + for f in obj.fields: + out_tbl.add_field( + f, VectorOfVectors(cumulative_length=cumulative_length, flattened_data=obj[f]) + ) - # loop over pairs - dists = [] - for rtmp, ztmp, rnext_tmp, znext_tmp in zip(r[:-1], z[:-1], r[1:], z[1:]): - s1 = ak.zip({"x": rtmp, "y": ztmp}) - s2 = ak.zip({"x": rnext_tmp, "y": znext_tmp}) - v_3d = ak.zip({"x": data.xloc - x, "y": data.yloc - y, "z": data.zloc - z}) - v = ak.zip({"x": np.sqrt(np.power(v_3d.x, 2) + np.power(v_3d.y, 2)), "y": v_3d.z}) + return out_tbl - dist = utils.dist(s1, s2, v) - dists.append(dist) - raise np.min(dists) +def smear_energies(truth_energy: Array, reso: float = 2) -> Array: + """Smearing of energies. + + Parameters + ---------- + truth_energy + Array of energies to be smeared. + reso + energy resolution (sigma). + + Returns + ------- + New array after sampling from a Gaussian with mean :math:`energy_i` and sigma `reso` for every element of + `truth_array`. + + """ + + flat_energy = truth_energy + rng = np.random.default_rng() + + return Array(rng.normal(loc=flat_energy, scale=np.ones_like(flat_energy) * reso)) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 489c35f..32fc318 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -1,175 +1,85 @@ from __future__ import annotations import copy +import importlib import logging -from typing import Callable +import re +from collections import namedtuple import awkward as ak -import numpy as np -from lgdo import lh5 -from lgdo.lh5 import LH5Iterator -from lgdo.types import Table log = logging.getLogger(__name__) -def _distance_3d(v1, v2): - return np.sqrt(np.power(v2.x - v1.x, 2) + np.power(v2.y - v1.y, 2) + np.power(v2.z - v1.z, 2)) - - -def _distance_2d(v1, v2): - return np.sqrt(np.power(v2.x - v1.x, 2) + np.power(v2.y - v1.y, 2)) - - -def _add_ak_3d(a, b): - return ak.zip({"x": a.x + b.x, "y": a.y + b.y, "z": a.z + b.z}) - - -def _add_ak_2d(a, b): - return ak.zip({"x": a.x + b.x, "y": a.y + b.y}) - +def get_detector_origin(name): + raise NotImplementedError -def _sub_ak_2d(a, b): - return ak.zip({"x": a.x - b.x, "y": a.y - b.y}) +def dict2tuple(dictionary: dict) -> namedtuple: + return namedtuple("parameters", dictionary.keys())(**dictionary) -def _sub_ak_3d(a, b): - return ak.zip({"x": a.x - b.x, "y": a.y - b.y, "z": a.z - b.z}) +def get_function_string(expr: str) -> tuple[str, dict]: + """Get a function call to evaluate.""" -def _prod_ak_2d(a, b): - return a.x * b.x + a.y * b.y + args_str = re.search(r"\((.*)\)$", expr.strip()).group(1) + # get module and function names + func_call = expr.strip().split("(")[0] + subpackage, func = func_call.rsplit(".", 1) + package = subpackage.split(".")[0] + importlib.import_module(subpackage, package=__package__) -def _prod_ak_3d(a, b): - return a.x * b.x + a.y * b.y + a.z * b.z + # declare imported package as globals (see eval() call later) + globs = { + package: importlib.import_module(package), + } + call_str = f"{func_call}({args_str})" -def _prod(a, b): - return ak.zip({"x": a.x * b, "y": a.y * b}) + return call_str, globs -def proj(s1: ak.Array, s2: ak.Array, v: ak.Array) -> ak.Array: - """ - Projection v on the line segment s1 to s2. +def _merge_arrays( + ak_obj: ak.Array, buffer_rows: ak.Array, idx: int, max_idx: int, delete_input: bool = False +) -> tuple[ak.Array, ak.Array, str]: + """Merge awkward arrays with a buffer and simultaneously remove the last rows. - All of s1,s2 and v are `ak.Array` of records with two fields `x` and `y` - I.e. they represent lists of 2D cartesian vectors. + This function is used since when iterating through the rows of an Array it will + sometimes happen to split some events. This concatenates rows in the buffer onto the start of the data. + This also removes the last rows of each chunk and saves them into a buffer. Parameters ---------- - s1 - first points in the line segment - s2 - second points in the line segment - v - points to project onto s1-s2 - Returns - ------- - the coordinates of the projection onto s1-s2 - """ - - dist_one = _prod_ak_2d(_sub_ak_2d(v, s1), _sub_ak_2d(v, s2)) / _distance_2d(s1, s2) - dist_one = np.where(dist_one > 1, dist_one, 1) - dist_one = np.where(dist_one > 0, dist_one, 0) - return _prod(_add_ak_2d(s1, _sub_ak_2d(s2, s1)), dist_one) - - -def dist(s1: ak.Array, s2: ak.Array, v: ak.Array) -> float: - """ - Shortest distance between a point v and the line segment defined by s1-s2. - - All of s1,s2 and v are `ak.Array` of records with two fields `x` and `y`. - - I.e. they represent lists of 2D cartesian vectors. - - Parameters - ---------- - s1 - first points in the line segment - s2 - second points in the line segment - v - points to project onto s1-s2 + obj + array of data + buffer_rows + buffer to concatenate with. + idx + integer index used control the behaviour for the first and last chunk. + max_idx + largest index. + delete_input + flag to delete the input files. Returns ------- - the shortest distance from the point to the line + (obj,buffer,mode) tuple of the data, the buffer for the next chunk and the IO mode for file writing. """ - - return _distance_2d(proj(s1, s2, v), v) - - -def get_detector_origin(name): - raise NotImplementedError - - -def read_write_incremental( - file_out: str, - name_out: str, - func: Callable, - field: str, - file: str, - buffer: int = 1000000, - delete_input=False, -) -> None: - """ - Read incrementally the files compute something and then write output - - Parameters - ---------- - file_out - output file path - name_out - lh5 group name for output - func - function converting into to output - field - lh5 field name to read - file - file name to read - buffer - length of buffer - """ - - msg = f"...begin processing with {file} to {file_out}" - log.info(msg) - - entries = LH5Iterator(file, field, buffer_len=buffer)._get_file_cumentries(0) - - # number of blocks is ceil of entries/buffer, - # shift by 1 since idx starts at 0 - # this is maybe too high if buffer exactly divides idx - max_idx = int(np.ceil(entries / buffer)) - 1 - buffer_rows = None - - for idx, (lh5_obj, _, _) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): - msg = f"... processed {idx} files out of {max_idx}" - log.debug(msg) - - ak_obj = lh5_obj.view_as("ak") - counts = ak.run_lengths(ak_obj.evtid) - rows = ak.num(ak_obj, axis=-1) - end_rows = counts[-1] - - if idx == 0: - mode = "of" if (delete_input) else "append" - obj = ak_obj[0 : rows - end_rows] - buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) - elif idx != max_idx: - mode = "append" - obj = ak.concatenate((buffer_rows, ak_obj[0 : rows - end_rows])) - buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) - else: - mode = "append" - obj = ak.concatenate((buffer_rows, ak_obj)) - - # do stuff - out = func(obj) - - # convert to a table - out_lh5 = Table(out) - - # write lh5 file - log.info("...finished processing and save file") - lh5.write(out_lh5, name_out, file_out, wo_mode=mode) + counts = ak.run_lengths(ak_obj.evtid) + rows = ak.num(ak_obj, axis=-1) + end_rows = counts[-1] + + if idx == 0: + mode = "of" if (delete_input) else "append" + obj = ak_obj[0 : rows - end_rows] + buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) + elif idx != max_idx: + mode = "append" + obj = ak.concatenate((buffer_rows, ak_obj[0 : rows - end_rows])) + buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) + else: + mode = "append" + obj = ak.concatenate((buffer_rows, ak_obj)) + + return obj, buffer_rows, mode From 0e7aaf5877048cfd3a1c33eb54633742150a7f56 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 7 Nov 2024 17:39:13 +0100 Subject: [PATCH 21/81] bit of clean up / improved docs --- src/reboost/hpge/cli.py | 16 +- src/reboost/hpge/hit.py | 282 +++++++++++++++------------------ src/reboost/hpge/processors.py | 3 +- 3 files changed, 144 insertions(+), 157 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index a581470..d22be02 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -53,6 +53,12 @@ def hpge_cli() -> None: help="Gean4 macro file used to generate raw tier", required=False, ) + + hit_parser.add_argument("--infield", help="input LH5 field", required=False, default="hit") + hit_parser.add_argument( + "--outfield", help="output LH5 field name", required=False, default="hit" + ) + hit_parser.add_argument("input", help="input hit LH5 file", metavar="INPUT_HIT") hit_parser.add_argument("output", help="output evt LH5 file", metavar="OUTPUT_EVT") @@ -87,11 +93,13 @@ def hpge_cli() -> None: raise ValueError(msg) build_hit( - args.input, args.output, - proc_config, - pars, - args.bufsize, + args.input, + out_field=args.outfield, + in_field=args.infield, + proc_config=proc_config, + pars=pars, + buffer=args.bufsize, gdml=args.gdml, macro=args.macro, ) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 87221bb..9629bb9 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -10,6 +10,40 @@ log = logging.getLogger(__name__) +def step_group(data: Table, group_config: dict) -> Table: + """Performs a grouping of geant4 steps to build the `hit` table. + + Parameters + ---------- + data + `stp` table from remage. + group_config + dict with the configuration describing the step grouping. + For example + + .. code-block:: json + + { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" + } + + this will then evaluate the function chosen by `expression` resulting in a new Table. + + """ + + # group to create hit table + + group_func, globs = utils.get_function_string( + group_config["expression"], + ) + locs = {"stp": data} + + msg = f"running step grouping with {group_func} and globals {globs.keys()} and locals {locs.keys()}" + log.debug(msg) + return eval(group_func, globs, locs) + + def eval_expression( table: Table, info: dict, pars: dict ) -> Array | ArrayOfEqualSizedArrays | VectorOfVectors: @@ -20,7 +54,7 @@ def eval_expression( table hit table, with columns possibly used in the operations. info - dict containing the information on the expression. Must contain `mode` and `expressions` keys + `dict` containing the information on the expression. Must contain `mode` and `expressions` keys For example: .. code-block:: json @@ -40,7 +74,7 @@ def eval_expression( Returns ------- - a new column for the hit table either :class:`Array`,:class:`ArrayOfEqualSizedArrays` or :class:` VectorOfVectors`. + a new column for the hit table either :class:`Array`, :class:`ArrayOfEqualSizedArrays` or :class:`VectorOfVectors`. """ pars_tuple = utils.dict2tuple(pars) @@ -71,15 +105,16 @@ def eval_expression( return col -def read_write_incremental( +def build_hit( file_out: str, - name_out: str, + file_in: str, + out_field: str, + in_field: str, proc_config: dict, pars: dict, - field: str, - file: str, buffer: int = 1000000, - delete_input: bool = False, + gdml: str | None = None, + macro: str | None = None, ) -> None: """ Read incrementally the files compute something and then write output @@ -88,8 +123,12 @@ def read_write_incremental( ---------- file_out output file path - name_out + file_in + input_file_path + out_field lh5 group name for output + in_field + lh5 group name for input proc_config the configuration file for the processing. Must contain the fields `channels`, `outputs`, `step_group` and operations`. For example: @@ -97,183 +136,124 @@ def read_write_incremental( .. code-block:: json { - "channels": [ - "det000", - "det001", - "det002", - "det003" - ], - "outputs": [ - "t0", - "truth_energy_sum", - "smeared_energy_sum", - "evtid" - ], - "step_group": { - "description": "group steps by time and evtid.", - "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" - }, - "operations": { - "t0": { - "description": "first time in the hit.", - "mode": "eval", - "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" - }, - "truth_energy_sum": { - "description": "truth summed energy in the hit.", - "mode": "eval", - "expression": "ak.sum(hit.edep,axis=-1)" + "channels": [ + "det000", + "det001", + "det002", + "det003" + ], + "outputs": [ + "t0", + "truth_energy_sum", + "smeared_energy_sum", + "evtid" + ], + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" }, - "smeared_energy_sum": { - "description": "summed energy after convolution with energy response.", - "mode": "function", - "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)" + }, + "smeared_energy_sum": { + "description": "summed energy after convolution with energy response.", + "mode": "function", + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + } + } } - } pars a dictionary of parameters, must have a field per channel consisting of a `dict` of parameters. For example: .. code-block:: json + { "det000": { - "reso": 1., + "reso": 1, "fccd": 0.1 } } - field - lh5 field name to read. - file - file name to read buffer length of buffer - delete_input - flag to delete the input file. + gdml + path to the input gdml file. + macro + path to the macro file. Note ---- The operations can depend on the outputs of previous steps, so operations order is important. """ + if gdml is not None: + pass - msg = f"...begin processing with {file} to {file_out}" - log.info(msg) - - entries = LH5Iterator(file, field, buffer_len=buffer)._get_file_cumentries(0) - - # number of blocks is ceil of entries/buffer, - # shift by 1 since idx starts at 0 - # this is maybe too high if buffer exactly divides idx - max_idx = int(np.ceil(entries / buffer)) - 1 - buffer_rows = None - - for idx, (lh5_obj, _, _) in enumerate(LH5Iterator(file, field, buffer_len=buffer)): - msg = f"... processed {idx} files out of {max_idx}" - log.debug(msg) - - # convert to awkward - ak_obj = lh5_obj.view_as("ak") - - # handle the buffers - obj, buffer_rows, mode = utils._merge_arrays( - ak_obj, buffer_rows, idx=idx, max_idx=max_idx, delete_input=delete_input - ) + if macro is not None: + pass - # convert back to a table, should work - data = Table(obj) + for ch_idx, d in enumerate(proc_config["channels"]): + msg = f"...running hit tier for {d}" + log.info(msg) + delete_input = bool(ch_idx == 0) - # define glob and local dicts for the func cal - group_func, globs = utils.get_function_string( - proc_config["step_group"]["expression"], - ) - locs = {"stp": data} + msg = f"...begin processing with {file_in} to {file_out}" + log.info(msg) - msg = f"running step grouping with {group_func} and globals {globs.keys()} and locals {locs.keys()}" - log.debug(msg) + entries = LH5Iterator(file_in, f"{in_field}/{d}", buffer_len=buffer)._get_file_cumentries(0) - # group to create hit table - grouped = eval(group_func, globs, locs) + # number of blocks is ceil of entries/buffer, + # shift by 1 since idx starts at 0 + # this is maybe too high if buffer exactly divides idx + max_idx = int(np.ceil(entries / buffer)) - 1 + buffer_rows = None - # processors - for name, info in proc_config["operations"].items(): - msg = f"adding column {name}" + for idx, (lh5_obj, _, _) in enumerate( + LH5Iterator(file_in, f"{in_field}/{d}", buffer_len=buffer) + ): + msg = f"... processed {idx} files out of {max_idx}" log.debug(msg) - col = eval_expression(grouped, info, pars) - grouped.add_field(name, col) + # convert to awkward + ak_obj = lh5_obj.view_as("ak") - # remove unwanted columns - log.debug("removing unwanted columns") + # handle the buffers + obj, buffer_rows, mode = utils._merge_arrays( + ak_obj, buffer_rows, idx=idx, max_idx=max_idx, delete_input=delete_input + ) - existing_cols = list(grouped.keys()) - for col in existing_cols: - if col not in proc_config["outputs"]: - grouped.remove_column(col, delete=True) + # convert back to a table, should work + data = Table(obj) - # write lh5 file - msg = f"...finished processing and save file with wo_mode {mode}" - log.debug(msg) - lh5.write(grouped, name_out, file_out, wo_mode=mode) + # group steps into hits + grouped = step_group(data, proc_config["step_group"]) + # processors + for name, info in proc_config["operations"].items(): + msg = f"adding column {name}" + log.debug(msg) -def build_hit( - lh5_in_file: str, - lh5_out_file: str, - proc_config: dict, - pars: dict, - buffer_len: int = int(5e6), - gdml: str | None = None, - macro: str | None = None, -) -> None: - """ - Build the hit tier from the raw Geant4 output + col = eval_expression(grouped, info, pars) + grouped.add_field(name, col) - Parameters - ---------- - lh5_in_file - input file containing the raw tier - lh5_out_file - output file - config - dictionary containing the configuration / parameters - should contain one sub-dictonary per detector with a format like: + # remove unwanted columns + log.debug("removing unwanted columns") - .. code-block:: json - - "det001": { - "reso": 1 - } - - This can contain any parameters needed in the processing chain. - buffer_len - number of rows to read at once - gdml - path to the gdml file of the geometry - macro - path to the Geant4 macro used to generate the raw tier - """ - - # get the geant4 gdml and macro + existing_cols = list(grouped.keys()) + for col in existing_cols: + if col not in proc_config["outputs"]: + grouped.remove_column(col, delete=True) - if gdml is not None: - pass - - if macro is not None: - pass - - for idx, d in enumerate(proc_config["channels"]): - msg = f"...running hit tier for {d}" - log.info(msg) - delete_input = bool(idx == 0) - - read_write_incremental( - lh5_out_file, - f"hit/{d}", - proc_config, - pars[d], - f"hit/{d}", - lh5_in_file, - buffer_len, - delete_input=delete_input, - ) + # write lh5 file + msg = f"...finished processing and save file with wo_mode {mode}" + log.debug(msg) + lh5.write(grouped, f"{out_field}/{d}", file_out, wo_mode=mode) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 1282511..97dfc27 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -142,8 +142,7 @@ def smear_energies(truth_energy: Array, reso: float = 2) -> Array: Returns ------- - New array after sampling from a Gaussian with mean :math:`energy_i` and sigma `reso` for every element of - `truth_array`. + New array after sampling from a Gaussian with mean :math:`energy_i` and sigma `reso` for every element of `truth_array`. """ From 5a9595bab43e49da85e7927a83dffcc45033a8b8 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 8 Nov 2024 00:11:47 +0100 Subject: [PATCH 22/81] [tests] add test for the windowing --- src/reboost/hpge/processors.py | 1 + tests/test_hpge_step_group.py | 38 ++++++++++++++++++++++++++++++++++ 2 files changed, 39 insertions(+) create mode 100644 tests/test_hpge_step_group.py diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 97dfc27..15d01eb 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -104,6 +104,7 @@ def group_by_time(data: Table, window: float = 10) -> lgdo.Table: """ obj = data.view_as("ak") + obj = sort_data(obj) # get difference time_diffs = np.diff(obj.time) diff --git a/tests/test_hpge_step_group.py b/tests/test_hpge_step_group.py new file mode 100644 index 0000000..d88ee22 --- /dev/null +++ b/tests/test_hpge_step_group.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +from reboost.hpge import processors +from lgdo import Array,VectorOfVectors,Table +import numpy as np +import awkward as ak + +def test_evtid_group(): + + in_arr_evtid = ak.Array({"evtid":[1,1,1,2,2,10,10,11,12,12,12],"time":np.zeros(11)}) + + in_tab = Table(in_arr_evtid) + + + out = processors.group_by_evtid(in_tab) + out_ak =out.view_as("ak") + assert ak.all(out_ak.evtid==[[1,1,1],[2,2],[10,10],[11],[12,12,12]]) + assert ak.all(out_ak.time==[[0,0,0],[0,0],[0,0],[0],[0,0,0]]) + + + +def test_time_group(): + + # time units are ns + in_arr_evtid = ak.Array({"evtid":[1, 1, 1,2, 2, 2, 2, 2,11,12,12,12,15,15,15,15,15], + "time":[0,-2000,3000,0,100,1200,17000,17010,0,0,0,-5000,150,151,152,3000,3100]}) + + + in_tab = Table(in_arr_evtid) + + + # 1us =1000ns + out = processors.group_by_time(in_tab,window=1) + out_ak =out.view_as("ak") + assert ak.all(out_ak.evtid==[[1],[1],[1],[2,2],[2],[2,2],[11],[12],[12,12],[15,15,15],[15,15]]) + assert ak.all(out_ak.time==[[-2000],[0],[3000],[0,100],[1200],[17000,17010],[0],[-5000],[0,0],[150,151,152],[3000,3100]]) + + From 63ca42b6d2124c212d89f0184cf190ccb601b5e9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 7 Nov 2024 23:12:01 +0000 Subject: [PATCH 23/81] style: pre-commit fixes --- tests/test_hpge_step_group.py | 78 +++++++++++++++++++++++++---------- 1 file changed, 57 insertions(+), 21 deletions(-) diff --git a/tests/test_hpge_step_group.py b/tests/test_hpge_step_group.py index d88ee22..61829b2 100644 --- a/tests/test_hpge_step_group.py +++ b/tests/test_hpge_step_group.py @@ -1,38 +1,74 @@ from __future__ import annotations -from reboost.hpge import processors -from lgdo import Array,VectorOfVectors,Table -import numpy as np import awkward as ak +import numpy as np +from lgdo import Table -def test_evtid_group(): +from reboost.hpge import processors - in_arr_evtid = ak.Array({"evtid":[1,1,1,2,2,10,10,11,12,12,12],"time":np.zeros(11)}) - in_tab = Table(in_arr_evtid) +def test_evtid_group(): + in_arr_evtid = ak.Array( + {"evtid": [1, 1, 1, 2, 2, 10, 10, 11, 12, 12, 12], "time": np.zeros(11)} + ) + in_tab = Table(in_arr_evtid) out = processors.group_by_evtid(in_tab) - out_ak =out.view_as("ak") - assert ak.all(out_ak.evtid==[[1,1,1],[2,2],[10,10],[11],[12,12,12]]) - assert ak.all(out_ak.time==[[0,0,0],[0,0],[0,0],[0],[0,0,0]]) - + out_ak = out.view_as("ak") + assert ak.all(out_ak.evtid == [[1, 1, 1], [2, 2], [10, 10], [11], [12, 12, 12]]) + assert ak.all(out_ak.time == [[0, 0, 0], [0, 0], [0, 0], [0], [0, 0, 0]]) def test_time_group(): - # time units are ns - in_arr_evtid = ak.Array({"evtid":[1, 1, 1,2, 2, 2, 2, 2,11,12,12,12,15,15,15,15,15], - "time":[0,-2000,3000,0,100,1200,17000,17010,0,0,0,-5000,150,151,152,3000,3100]}) - + in_arr_evtid = ak.Array( + { + "evtid": [1, 1, 1, 2, 2, 2, 2, 2, 11, 12, 12, 12, 15, 15, 15, 15, 15], + "time": [ + 0, + -2000, + 3000, + 0, + 100, + 1200, + 17000, + 17010, + 0, + 0, + 0, + -5000, + 150, + 151, + 152, + 3000, + 3100, + ], + } + ) in_tab = Table(in_arr_evtid) - # 1us =1000ns - out = processors.group_by_time(in_tab,window=1) - out_ak =out.view_as("ak") - assert ak.all(out_ak.evtid==[[1],[1],[1],[2,2],[2],[2,2],[11],[12],[12,12],[15,15,15],[15,15]]) - assert ak.all(out_ak.time==[[-2000],[0],[3000],[0,100],[1200],[17000,17010],[0],[-5000],[0,0],[150,151,152],[3000,3100]]) - - + out = processors.group_by_time(in_tab, window=1) + out_ak = out.view_as("ak") + assert ak.all( + out_ak.evtid + == [[1], [1], [1], [2, 2], [2], [2, 2], [11], [12], [12, 12], [15, 15, 15], [15, 15]] + ) + assert ak.all( + out_ak.time + == [ + [-2000], + [0], + [3000], + [0, 100], + [1200], + [17000, 17010], + [0], + [-5000], + [0, 0], + [150, 151, 152], + [3000, 3100], + ] + ) From d65ffade253719e7f5a70e346582b3b9a64df95d Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 8 Nov 2024 11:27:14 +0100 Subject: [PATCH 24/81] [tests] adding more tests --- src/reboost/hpge/processors.py | 14 ------------- tests/test_hpge_hit.py | 38 ++++++++++++++++++++++++++++++++++ tests/test_hpge_processors.py | 12 +++++++++++ tests/test_hpge_step_group.py | 17 ++++++++++++++- 4 files changed, 66 insertions(+), 15 deletions(-) create mode 100644 tests/test_hpge_hit.py create mode 100644 tests/test_hpge_processors.py diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 15d01eb..6a27306 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -6,20 +6,6 @@ from lgdo import Array, Table, VectorOfVectors -def def_chain(funcs, kwargs_list): - """ - Builds the processing chain function from a list of functions - """ - - def func(data): - tmp = data - for f, kw in zip(funcs, kwargs_list): - tmp = f(tmp, **kw) - - return tmp - - return func - def sort_data(obj: ak.Array) -> ak.Array: """Sort the data by evtid then time. diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py new file mode 100644 index 0000000..9c910e1 --- /dev/null +++ b/tests/test_hpge_hit.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +import awkward as ak +import numpy as np +import pytest +from lgdo import Table + +from reboost.hpge import hit + + +def test_eval(): + in_arr = ak.Array( + { + "evtid": [[1, 1, 1], [2, 2], [10, 10], [11], [12, 12, 12]], + "time": [[0, 0, 0], [0, 0], [0, 0], [0], [0, 0, 0]], + "edep": [[100, 150, 300], [0, 2000], [10, 20], [19], [100, 200, 300]], + } + ) + tab = Table(in_arr) + basic_eval = {"mode": "eval", "expression": "ak.sum(hit.edep,axis=-1)"} + + assert ak.all( + hit.eval_expression(tab, basic_eval, {}).view_as("ak") == [550, 2000, 30, 19, 600] + ) + + tab.add_field("e_sum", hit.eval_expression(tab, basic_eval, {})) + bad_eval = {"mode": "sum", "expression": "ak.sum(hit.edep,axis=-1)"} + + with pytest.raises(ValueError): + hit.eval_expression(tab, bad_eval, {}) + + func_eval = { + "mode": "function", + "expression": "reboost.hpge.processors.smear_energies(hit.e_sum,reso=pars.reso)", + } + pars = {"reso": 2} + + assert np.size(hit.eval_expression(tab, func_eval, pars).view_as("np")) == 5 diff --git a/tests/test_hpge_processors.py b/tests/test_hpge_processors.py new file mode 100644 index 0000000..167912f --- /dev/null +++ b/tests/test_hpge_processors.py @@ -0,0 +1,12 @@ +from __future__ import annotations + +import numpy as np + +from reboost.hpge import processors + + +def test_smear(): + truth = np.array([1, 2, 3, 4, 5]) + smeared = processors.smear_energies(truth, reso=0.01) + + assert np.size(smeared) == 5 diff --git a/tests/test_hpge_step_group.py b/tests/test_hpge_step_group.py index 61829b2..52d9bab 100644 --- a/tests/test_hpge_step_group.py +++ b/tests/test_hpge_step_group.py @@ -4,7 +4,7 @@ import numpy as np from lgdo import Table -from reboost.hpge import processors +from reboost.hpge import hit, processors def test_evtid_group(): @@ -19,6 +19,21 @@ def test_evtid_group(): assert ak.all(out_ak.evtid == [[1, 1, 1], [2, 2], [10, 10], [11], [12, 12, 12]]) assert ak.all(out_ak.time == [[0, 0, 0], [0, 0], [0, 0], [0], [0, 0, 0]]) + # test the eval in build hit also + + out_eval = hit.step_group( + in_tab, + { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_evtid(stp)", + }, + ) + + out_eval_ak = out_eval.view_as("ak") + + assert ak.all(out_ak.evtid == out_eval_ak.evtid) + assert ak.all(out_ak.time == out_eval_ak.time) + def test_time_group(): # time units are ns From bfe9782c1f6e5ddaee1cd1e92d877238a3c6f173 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 8 Nov 2024 10:28:39 +0000 Subject: [PATCH 25/81] style: pre-commit fixes --- src/reboost/hpge/processors.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 6a27306..8c68388 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -6,7 +6,6 @@ from lgdo import Array, Table, VectorOfVectors - def sort_data(obj: ak.Array) -> ak.Array: """Sort the data by evtid then time. From 670f5dfca57fc40ff60085e7a689445d0fdb25ba Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 8 Nov 2024 11:54:23 +0100 Subject: [PATCH 26/81] [tests] test merging arrays --- src/reboost/hpge/processors.py | 1 - src/reboost/hpge/utils.py | 7 ++++- tests/test_hpge_utils.py | 52 ++++++++++++++++++++++++++++++++++ 3 files changed, 58 insertions(+), 2 deletions(-) create mode 100644 tests/test_hpge_utils.py diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 6a27306..8c68388 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -6,7 +6,6 @@ from lgdo import Array, Table, VectorOfVectors - def sort_data(obj: ak.Array) -> ak.Array: """Sort the data by evtid then time. diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 32fc318..f9048da 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -41,7 +41,11 @@ def get_function_string(expr: str) -> tuple[str, dict]: def _merge_arrays( - ak_obj: ak.Array, buffer_rows: ak.Array, idx: int, max_idx: int, delete_input: bool = False + ak_obj: ak.Array, + buffer_rows: ak.Array | None, + idx: int, + max_idx: int, + delete_input: bool = False, ) -> tuple[ak.Array, ak.Array, str]: """Merge awkward arrays with a buffer and simultaneously remove the last rows. @@ -81,5 +85,6 @@ def _merge_arrays( else: mode = "append" obj = ak.concatenate((buffer_rows, ak_obj)) + buffer_rows = None return obj, buffer_rows, mode diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py new file mode 100644 index 0000000..1e14fcb --- /dev/null +++ b/tests/test_hpge_utils.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import awkward as ak +import numpy as np +import pytest +from lgdo import Table + +from reboost.hpge.utils import _merge_arrays + + +def test_merge(): + + ak_obj =ak.Array({"evtid":[1,1,1,1,2,2,3],"edep":[100,50,1000,20,100,200,10]}) + bufer_rows = ak.Array({"evtid":[1,1],"edep":[60,50]}) + + + # should only remove te last element + merged_idx_0, buffer_0,mode= _merge_arrays(ak_obj,None,0,100 ,True) + + assert ak.all(merged_idx_0.evtid ==[1,1,1,1,2,2]) + assert ak.all(merged_idx_0.edep ==[100,50,1000,20,100,200] ) + + assert ak.all(buffer_0.evtid ==[3]) + assert ak.all(buffer_0.edep ==[10] ) + + # delete input file + assert mode=="of" + + # if delete input is false it should be appended + _,_,mode= _merge_arrays(ak_obj,None,0,100 ,False) + assert mode=="append" + + # now if idx isnt 0 or the max_idx should add the buffer and remove the end + + merged_idx, buffer,mode= _merge_arrays(ak_obj,bufer_rows,2,100 ,True) + + assert ak.all(merged_idx.evtid ==[1,1,1,1,1,1,2,2]) + assert ak.all(merged_idx.edep ==[60,50,100,50,1000,20,100,200] ) + + assert ak.all(buffer.evtid ==[3]) + assert ak.all(buffer.edep ==[10] ) + + assert mode =="append" + + # now for the final index just adds the buffer + + merged_idx_end, buffer_end,mode= _merge_arrays(ak_obj,bufer_rows,100,100 ,True) + + assert ak.all(merged_idx_end.evtid ==[1,1,1,1,1,1,2,2,3]) + assert ak.all(merged_idx_end.edep ==[60,50,100,50,1000,20,100,200,10] ) + + assert buffer_end is None \ No newline at end of file From cdc8767d069778c932684f2cca855e15a3159f37 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 8 Nov 2024 10:56:20 +0000 Subject: [PATCH 27/81] style: pre-commit fixes --- tests/test_hpge_utils.py | 45 ++++++++++++++++++---------------------- 1 file changed, 20 insertions(+), 25 deletions(-) diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 1e14fcb..93ada87 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -1,52 +1,47 @@ from __future__ import annotations import awkward as ak -import numpy as np -import pytest -from lgdo import Table from reboost.hpge.utils import _merge_arrays def test_merge(): - - ak_obj =ak.Array({"evtid":[1,1,1,1,2,2,3],"edep":[100,50,1000,20,100,200,10]}) - bufer_rows = ak.Array({"evtid":[1,1],"edep":[60,50]}) - + ak_obj = ak.Array({"evtid": [1, 1, 1, 1, 2, 2, 3], "edep": [100, 50, 1000, 20, 100, 200, 10]}) + bufer_rows = ak.Array({"evtid": [1, 1], "edep": [60, 50]}) # should only remove te last element - merged_idx_0, buffer_0,mode= _merge_arrays(ak_obj,None,0,100 ,True) + merged_idx_0, buffer_0, mode = _merge_arrays(ak_obj, None, 0, 100, True) - assert ak.all(merged_idx_0.evtid ==[1,1,1,1,2,2]) - assert ak.all(merged_idx_0.edep ==[100,50,1000,20,100,200] ) + assert ak.all(merged_idx_0.evtid == [1, 1, 1, 1, 2, 2]) + assert ak.all(merged_idx_0.edep == [100, 50, 1000, 20, 100, 200]) - assert ak.all(buffer_0.evtid ==[3]) - assert ak.all(buffer_0.edep ==[10] ) + assert ak.all(buffer_0.evtid == [3]) + assert ak.all(buffer_0.edep == [10]) # delete input file - assert mode=="of" + assert mode == "of" # if delete input is false it should be appended - _,_,mode= _merge_arrays(ak_obj,None,0,100 ,False) - assert mode=="append" + _, _, mode = _merge_arrays(ak_obj, None, 0, 100, False) + assert mode == "append" # now if idx isnt 0 or the max_idx should add the buffer and remove the end - merged_idx, buffer,mode= _merge_arrays(ak_obj,bufer_rows,2,100 ,True) + merged_idx, buffer, mode = _merge_arrays(ak_obj, bufer_rows, 2, 100, True) - assert ak.all(merged_idx.evtid ==[1,1,1,1,1,1,2,2]) - assert ak.all(merged_idx.edep ==[60,50,100,50,1000,20,100,200] ) + assert ak.all(merged_idx.evtid == [1, 1, 1, 1, 1, 1, 2, 2]) + assert ak.all(merged_idx.edep == [60, 50, 100, 50, 1000, 20, 100, 200]) - assert ak.all(buffer.evtid ==[3]) - assert ak.all(buffer.edep ==[10] ) + assert ak.all(buffer.evtid == [3]) + assert ak.all(buffer.edep == [10]) - assert mode =="append" + assert mode == "append" # now for the final index just adds the buffer - merged_idx_end, buffer_end,mode= _merge_arrays(ak_obj,bufer_rows,100,100 ,True) + merged_idx_end, buffer_end, mode = _merge_arrays(ak_obj, bufer_rows, 100, 100, True) - assert ak.all(merged_idx_end.evtid ==[1,1,1,1,1,1,2,2,3]) - assert ak.all(merged_idx_end.edep ==[60,50,100,50,1000,20,100,200,10] ) + assert ak.all(merged_idx_end.evtid == [1, 1, 1, 1, 1, 1, 2, 2, 3]) + assert ak.all(merged_idx_end.edep == [60, 50, 100, 50, 1000, 20, 100, 200, 10]) - assert buffer_end is None \ No newline at end of file + assert buffer_end is None From 8e8de77b55c7b86a4a427ab4949fb3e4029590c2 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 8 Nov 2024 16:14:09 +0100 Subject: [PATCH 28/81] update to be able to read json or yaml --- src/reboost/hpge/cli.py | 17 ++++---- src/reboost/hpge/utils.py | 29 +++++++++++++ tests/test_hpge_utils.py | 86 +++++++++++++++++++++++++++------------ 3 files changed, 95 insertions(+), 37 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index d22be02..001534e 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -2,10 +2,10 @@ import argparse import logging -from pathlib import Path import colorlog -import yaml + +from . import utils def hpge_cli() -> None: @@ -35,12 +35,12 @@ def hpge_cli() -> None: hit_parser.add_argument( "--proc_chain", - help="YAML file that contains the processing chain", + help="JSON or YAML file that contains the processing chain", required=True, ) hit_parser.add_argument( "--pars", - help="YAML file that contains the pars", + help="JSON or YAML file that contains the pars", required=True, ) hit_parser.add_argument( @@ -50,7 +50,7 @@ def hpge_cli() -> None: ) hit_parser.add_argument( "--macro", - help="Gean4 macro file used to generate raw tier", + help="Geant4 macro file used to generate raw tier", required=False, ) @@ -80,11 +80,8 @@ def hpge_cli() -> None: logger.info("...running raw->hit tier") from reboost.hpge.hit import build_hit - with Path.open(Path(args.pars)) as config_f: - pars = yaml.safe_load(config_f) - - with Path.open(Path(args.proc_chain)) as config_f: - proc_config = yaml.safe_load(config_f) + pars = utils.load_dict(args.pars, None) + proc_config = utils.load_dict(args.proc_config, None) # check the processing chain for req_field in ["channels", "outputs", "step_group", "operations"]: diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index f9048da..cbffc35 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -2,11 +2,14 @@ import copy import importlib +import json import logging import re from collections import namedtuple +from pathlib import Path import awkward as ak +import yaml log = logging.getLogger(__name__) @@ -88,3 +91,29 @@ def _merge_arrays( buffer_rows = None return obj, buffer_rows, mode + + +__file_extensions__ = {"json": [".json"], "yaml": [".yaml", ".yml"]} + + +def load_dict(fname: str, ftype: str | None = None) -> dict: + """Load a text file as a Python dict.""" + fname = Path(fname) + + # determine file type from extension + if ftype is None: + for _ftype, exts in __file_extensions__.items(): + if fname.suffix in exts: + ftype = _ftype + + msg = f"loading {ftype} dict from: {fname}" + log.debug(msg) + + with fname.open() as f: + if ftype == "json": + return json.load(f) + if ftype == "yaml": + return yaml.safe_load(f) + + msg = f"unsupported file format {ftype}" + raise NotImplementedError(msg) diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 1e14fcb..190884f 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -1,52 +1,84 @@ from __future__ import annotations +import json +from pathlib import Path + import awkward as ak -import numpy as np import pytest -from lgdo import Table +import yaml -from reboost.hpge.utils import _merge_arrays +from reboost.hpge.utils import _merge_arrays, load_dict def test_merge(): - - ak_obj =ak.Array({"evtid":[1,1,1,1,2,2,3],"edep":[100,50,1000,20,100,200,10]}) - bufer_rows = ak.Array({"evtid":[1,1],"edep":[60,50]}) - + ak_obj = ak.Array({"evtid": [1, 1, 1, 1, 2, 2, 3], "edep": [100, 50, 1000, 20, 100, 200, 10]}) + bufer_rows = ak.Array({"evtid": [1, 1], "edep": [60, 50]}) - # should only remove te last element - merged_idx_0, buffer_0,mode= _merge_arrays(ak_obj,None,0,100 ,True) + # should only remove the last element + merged_idx_0, buffer_0, mode = _merge_arrays(ak_obj, None, 0, 100, True) - assert ak.all(merged_idx_0.evtid ==[1,1,1,1,2,2]) - assert ak.all(merged_idx_0.edep ==[100,50,1000,20,100,200] ) + assert ak.all(merged_idx_0.evtid == [1, 1, 1, 1, 2, 2]) + assert ak.all(merged_idx_0.edep == [100, 50, 1000, 20, 100, 200]) - assert ak.all(buffer_0.evtid ==[3]) - assert ak.all(buffer_0.edep ==[10] ) + assert ak.all(buffer_0.evtid == [3]) + assert ak.all(buffer_0.edep == [10]) # delete input file - assert mode=="of" + assert mode == "of" # if delete input is false it should be appended - _,_,mode= _merge_arrays(ak_obj,None,0,100 ,False) - assert mode=="append" + _, _, mode = _merge_arrays(ak_obj, None, 0, 100, False) + assert mode == "append" - # now if idx isnt 0 or the max_idx should add the buffer and remove the end + # now if idx isn't 0 or the max_idx should add the buffer and remove the end - merged_idx, buffer,mode= _merge_arrays(ak_obj,bufer_rows,2,100 ,True) + merged_idx, buffer, mode = _merge_arrays(ak_obj, bufer_rows, 2, 100, True) - assert ak.all(merged_idx.evtid ==[1,1,1,1,1,1,2,2]) - assert ak.all(merged_idx.edep ==[60,50,100,50,1000,20,100,200] ) + assert ak.all(merged_idx.evtid == [1, 1, 1, 1, 1, 1, 2, 2]) + assert ak.all(merged_idx.edep == [60, 50, 100, 50, 1000, 20, 100, 200]) - assert ak.all(buffer.evtid ==[3]) - assert ak.all(buffer.edep ==[10] ) + assert ak.all(buffer.evtid == [3]) + assert ak.all(buffer.edep == [10]) - assert mode =="append" + assert mode == "append" # now for the final index just adds the buffer - merged_idx_end, buffer_end,mode= _merge_arrays(ak_obj,bufer_rows,100,100 ,True) + merged_idx_end, buffer_end, mode = _merge_arrays(ak_obj, bufer_rows, 100, 100, True) + + assert ak.all(merged_idx_end.evtid == [1, 1, 1, 1, 1, 1, 2, 2, 3]) + assert ak.all(merged_idx_end.edep == [60, 50, 100, 50, 1000, 20, 100, 200, 10]) + + assert buffer_end is None + + +@pytest.fixture +def file_fixture(tmp_path): + # Create a simple YAML file + data = {"det": 1} + yaml_file = tmp_path / "data.yaml" + with Path.open(yaml_file, "w") as yf: + yaml.dump(data, yf) + + json_file = tmp_path / "data.json" + with Path.open(json_file, "w") as jf: + json.dump(data, jf) + + # Create a simple TXT file + txt_file = tmp_path / "data.txt" + with Path.open(txt_file, "w") as tf: + tf.write("Some text.\n") + + # Return paths for the test functions + return {"yaml_file": yaml_file, "json_file": json_file, "txt_file": txt_file} + + +def test_read(file_fixture): + json_dict = load_dict(file_fixture["json_file"], None) + assert json_dict["det"] == 1 - assert ak.all(merged_idx_end.evtid ==[1,1,1,1,1,1,2,2,3]) - assert ak.all(merged_idx_end.edep ==[60,50,100,50,1000,20,100,200,10] ) + yaml_dict = load_dict(file_fixture["yaml_file"], None) + assert yaml_dict["det"] == 1 - assert buffer_end is None \ No newline at end of file + with pytest.raises(NotImplementedError): + load_dict(file_fixture["txt_file"], None) From 3dcc0f65535bb8854355894251224f7bae8c40a6 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sun, 10 Nov 2024 21:36:26 +0100 Subject: [PATCH 29/81] processor for distance to surface --- src/reboost/hpge/processors.py | 63 ++++++++++++++++++++++++++ tests/test_hpge_distance_to_surface.py | 34 ++++++++++++++ 2 files changed, 97 insertions(+) create mode 100644 tests/test_hpge_distance_to_surface.py diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 8c68388..d6a25d6 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -1,9 +1,11 @@ from __future__ import annotations import awkward as ak +import legendhpges import lgdo import numpy as np from lgdo import Array, Table, VectorOfVectors +from numpy.typing import ArrayLike def sort_data(obj: ak.Array) -> ak.Array: @@ -136,3 +138,64 @@ def smear_energies(truth_energy: Array, reso: float = 2) -> Array: rng = np.random.default_rng() return Array(rng.normal(loc=flat_energy, scale=np.ones_like(flat_energy) * reso)) + + +def distance_to_surface( + positions_x: VectorOfVectors, + positions_y: VectorOfVectors, + positions_z: VectorOfVectors, + hpge: legendhpges.base.HPGe, + det_pos: ArrayLike, + surface_type: str | None = None, +) -> Array: + """Computes the distance from each step to the detector surface. + + Parameters + ---------- + positions_x + Global x positions for each step. + positions_y + Global y positions for each step. + positions_z + Global z positions for each step. + hpge + HPGe object. + det_pos + position of the detector origin, must be a 3 component array corresponding to `(x,y,z)`. + surface_type + string of which surface to use, can be `nplus`, `pplus` `passive` or None (in which case the distance to any surface is calculated). + + Returns + ------- + VectorOfVectors with the same shape as `positions_x/y/z` of the distance to the surface. + + Note + ---- + `positions_x/positions_y/positions_z` must all have the same shape. + + """ + + # compute local positions + local_positions_x = positions_x - det_pos[0] + local_positions_y = positions_y - det_pos[1] + local_positions_z = positions_z - det_pos[2] + + # sizes for unflattening the ak.Array + sizes = ak.num(local_positions_x, axis=1) + + local_position_x_flat = ak.flatten(local_positions_x).to_numpy() + local_position_y_flat = ak.flatten(local_positions_y).to_numpy() + local_position_z_flat = ak.flatten(local_positions_z).to_numpy() + + # restructure the positions + local_positions = np.vstack( + [local_position_x_flat, local_position_y_flat, local_position_z_flat] + ).T + + # get indices + surface_indices = np.where(hpge.surfaces == surface_type) if surface_type is not None else None + + # distance calc itself + distances = hpge.distance_to_surface(local_positions, surface_indices=surface_indices) + + return ak.unflatten(distances, sizes) diff --git a/tests/test_hpge_distance_to_surface.py b/tests/test_hpge_distance_to_surface.py new file mode 100644 index 0000000..7b1789c --- /dev/null +++ b/tests/test_hpge_distance_to_surface.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import awkward as ak +import pytest +from legendhpges import make_hpge +from legendtestdata import LegendTestData + +from reboost.hpge.processors import distance_to_surface + + +@pytest.fixture(scope="session") +def test_data_configs(): + ldata = LegendTestData() + ldata.checkout("5f9b368") + return ldata.get_path("legend/metadata/hardware/detectors/germanium/diodes") + + +def test_distance_to_surface(test_data_configs): + gedet = make_hpge(test_data_configs + "/V99000A.json") + dist = [100, 0, 0] + + pos = ak.Array( + { + "xloc": [[0, 100, 200], [100], [700, 500, 200]], + "yloc": [[100, 0, 0], [200], [100, 300, 200]], + "zloc": [[700, 10, 20], [100], [300, 100, 0]], + } + ) + + # check just the shape + assert ak.all( + ak.num(distance_to_surface(pos.xloc, pos.yloc, pos.zloc, gedet, dist, None), axis=1) + == [3, 1, 3] + ) From 0d22b951591dcef2c9bcc45f792b68631331ca85 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 12 Nov 2024 16:07:47 +0100 Subject: [PATCH 30/81] [docs] improved documentation --- docs/source/index.rst | 43 ++++++++++- docs/source/manual/hpge.rst | 4 + docs/source/manual/index.rst | 8 ++ docs/source/manual/optical.rst | 4 + src/reboost/hpge/hit.py | 68 +++++++++++++---- src/reboost/hpge/processors.py | 30 ++++---- src/reboost/hpge/utils.py | 52 ++++++++++++- tests/configs/geom.gdml | 132 +++++++++++++++++++++++++++++++++ tests/test_hpge_hit.py | 71 +++++++++++++++++- tests/test_hpge_utils.py | 47 ++++++++++-- 10 files changed, 418 insertions(+), 41 deletions(-) create mode 100644 docs/source/manual/hpge.rst create mode 100644 docs/source/manual/index.rst create mode 100644 docs/source/manual/optical.rst create mode 100644 tests/configs/geom.gdml diff --git a/docs/source/index.rst b/docs/source/index.rst index 7885f27..5981c0f 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,10 +1,49 @@ Welcome to reboost's documentation! ========================================== -Table of Contents ------------------ +*reboost* is a python package for the post-processing of `remage `_ monte-carlo Simulations. + +Getting started +--------------- + +*reboost* can be installed with *pip*: + +.. code-block:: console + + $ git clone git@github.com:legend-exp/reboost.git + $ cd reboost + $ pip install . + +*reboost* is currently divided into two programs: + - *reboost-optical* for processing optical simulations, + - *reboost-hpge* for processing HPGe detector simulations. + +Both can be run on the command line with: + +.. code-block:: console + + $ reboost-optical -h + $ reboost-hpge -h + +Next steps +---------- + +.. toctree:: + :maxdepth: 2 + + User Manual .. toctree:: :maxdepth: 1 Package API reference + + +See also +-------- + - `remage `_: Modern *Geant4* application for HPGe and LAr experiments, + - `legend-pygeom-hpges `_: Package for handling HPGe detector geometry in python, + - `pyg4ometry `_: Package to create simulation geometry in python, + - `legend-pygeom-optics `_: Package to handle optical properties in python, + - `legend-pygeom-l200 `_: Implementation of the LEGEND-200 experiment (**private**), + - `pyvertexgen `_: Generation of vertices for simulations. diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst new file mode 100644 index 0000000..6bfe936 --- /dev/null +++ b/docs/source/manual/hpge.rst @@ -0,0 +1,4 @@ +hpge simulations +================ + +Come back later for more complete documentation. diff --git a/docs/source/manual/index.rst b/docs/source/manual/index.rst new file mode 100644 index 0000000..99ecefb --- /dev/null +++ b/docs/source/manual/index.rst @@ -0,0 +1,8 @@ +User Manual +=========== + +.. toctree:: + :maxdepth: 2 + + optical + hpge diff --git a/docs/source/manual/optical.rst b/docs/source/manual/optical.rst new file mode 100644 index 0000000..2e3ad69 --- /dev/null +++ b/docs/source/manual/optical.rst @@ -0,0 +1,4 @@ +optical simulations +=================== + +Come back later for more complete documentation. diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 9629bb9..60357ca 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -2,7 +2,9 @@ import logging +import legendhpges import numpy as np +import pyg4ometry from lgdo import Array, ArrayOfEqualSizedArrays, LH5Iterator, Table, VectorOfVectors, lh5 from . import utils @@ -45,7 +47,11 @@ def step_group(data: Table, group_config: dict) -> Table: def eval_expression( - table: Table, info: dict, pars: dict + table: Table, + info: dict, + pars: dict | None = None, + hpge: legendhpges.HPGe | None = None, + phy_vol: pyg4ometry.geant4.PhysicalVolume | None = None, ) -> Array | ArrayOfEqualSizedArrays | VectorOfVectors: """Evaluate an expression returning an LGDO object. @@ -54,8 +60,7 @@ def eval_expression( table hit table, with columns possibly used in the operations. info - `dict` containing the information on the expression. Must contain `mode` and `expressions` keys - For example: + `dict` containing the information on the expression. Must contain `mode` and `expressions` keys. For example: .. code-block:: json @@ -64,21 +69,39 @@ def eval_expression( "expression":"ak.sum(hit.edep,axis=-1)" } - variables preceded by `hit` will be taken from the supplied table. Mode can be either `eval`, + Variables preceded by `hit` will be taken from the supplied table. Mode can be either `eval`, in which case a simple expression is based (only involving numpy, awkward or inbuilt python functions), or `function` in which case an arbitrary function is passed (for example defined in processors). + There are several objects passed to the evaluation as 'locals' which can be references by the expression. + - `pars`: dictionary of parameters (converted to namedtuple) (see `pars` argument), + - `hpge`: the legendhpges object for this detector (see `hpge` argument), + - `phy_vol`: the physical volume of the detector (see `phy` argument). + pars dict of parameters, can contain any fields passed to the expression prefixed by `pars.`. - + hpge + `legendhpges` object with the information on the HPGe detector. + phy_vol + `pyg4ometry.geant4.PhysicalVolume` object from GDML, Returns ------- a new column for the hit table either :class:`Array`, :class:`ArrayOfEqualSizedArrays` or :class:`VectorOfVectors`. + + Note + ---- + In future the passing of local variables (pars,hpge,reg) to the evaluation should be make more generic. """ + local_dict = {} - pars_tuple = utils.dict2tuple(pars) - local_dict = {"pars": pars_tuple} + if pars is not None: + pars_tuple = utils.dict2tuple(pars) + local_dict = {"pars": pars_tuple} + if phy_vol is not None: + local_dict |= {"phy_vol": phy_vol} + if hpge is not None: + local_dict |= {"hpge": hpge} if info["mode"] == "eval": # replace hit. @@ -114,7 +137,7 @@ def build_hit( pars: dict, buffer: int = 1000000, gdml: str | None = None, - macro: str | None = None, + metadata_path: str | None = None, ) -> None: """ Read incrementally the files compute something and then write output @@ -180,30 +203,43 @@ def build_hit( { "det000": { "reso": 1, - "fccd": 0.1 + "fccd": 0.1, + "phy_vol_name":"det_phy", + "meta_name": "icpc.json" } } + this should also contain the channel mappings needed by reboost. These are: + - `phy_vol_name`: is the name of the physical volume, + - `meta_name` : is the name of the JSON file with the metadata. + + If these keys are not present both will be set to the remage output table name. + buffer length of buffer gdml path to the input gdml file. macro path to the macro file. + metadata_path + path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) Note ---- - The operations can depend on the outputs of previous steps, so operations order is important. + - The operations can depend on the outputs of previous steps, so operations order is important. + - It would be better to have a cleaner way to supply metadata and detector maps. """ - if gdml is not None: - pass - - if macro is not None: - pass + # get the gdml file + reg = pyg4ometry.gdml.Reader(gdml).getRegistry() if gdml is not None else None for ch_idx, d in enumerate(proc_config["channels"]): msg = f"...running hit tier for {d}" log.info(msg) + + # get HPGe and phy_vol object to pass to build_hit + hpge = utils.get_hpge(metadata_path, pars=pars, detector=d) + phy_vol = utils.get_phy_vol(reg, pars=pars, detector=d) + delete_input = bool(ch_idx == 0) msg = f"...begin processing with {file_in} to {file_out}" @@ -242,7 +278,7 @@ def build_hit( msg = f"adding column {name}" log.debug(msg) - col = eval_expression(grouped, info, pars) + col = eval_expression(grouped, info, pars=pars, phy=phy_vol, hpge=hpge) grouped.add_field(name, col) # remove unwanted columns diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index d6a25d6..bdc17ea 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -176,21 +176,21 @@ def distance_to_surface( """ # compute local positions - local_positions_x = positions_x - det_pos[0] - local_positions_y = positions_y - det_pos[1] - local_positions_z = positions_z - det_pos[2] - - # sizes for unflattening the ak.Array - sizes = ak.num(local_positions_x, axis=1) - - local_position_x_flat = ak.flatten(local_positions_x).to_numpy() - local_position_y_flat = ak.flatten(local_positions_y).to_numpy() - local_position_z_flat = ak.flatten(local_positions_z).to_numpy() - + pos = [] + sizes = [] + for idx, pos_tmp in enumerate([positions_x, positions_y, positions_z]): + local_pos_tmp = ak.Array(pos_tmp) - det_pos[idx] + local_pos_flat_tmp = ak.flatten(local_pos_tmp).to_numpy() + pos.append(local_pos_flat_tmp) + sizes.append(ak.num(local_pos_tmp, axis=1)) + + if not ak.all(sizes[0] == sizes[1]) or not ak.all(sizes[0] == sizes[2]): + msg = "all position vector of vector must have the same shape" + raise ValueError(msg) + + size = sizes[0] # restructure the positions - local_positions = np.vstack( - [local_position_x_flat, local_position_y_flat, local_position_z_flat] - ).T + local_positions = np.vstack(pos).T # get indices surface_indices = np.where(hpge.surfaces == surface_type) if surface_type is not None else None @@ -198,4 +198,4 @@ def distance_to_surface( # distance calc itself distances = hpge.distance_to_surface(local_positions, surface_indices=surface_indices) - return ak.unflatten(distances, sizes) + return ak.unflatten(distances, size) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index cbffc35..d80df9a 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -9,13 +9,61 @@ from pathlib import Path import awkward as ak +import legendhpges +import pyg4ometry import yaml log = logging.getLogger(__name__) +reg = pyg4ometry.geant4.Registry() -def get_detector_origin(name): - raise NotImplementedError + +def get_hpge(meta_path: str, pars: dict, detector: str) -> legendhpges.HPGe: + """Extract the :class:`legendhpges.HPGe` object from metadata. + + Parameters + ---------- + meta_path + path to the folder with the `diodes` metadata. + pars + dictionary of parameters. + detector + remage output name for the detector + + Returns + ------- + hpge + the `legendhpges` object for the detector. + """ + + meta_name = pars.get("meta_name", f"{detector}.json") + meta_dict = Path(meta_path) / Path(meta_name) + return legendhpges.make_hpge(meta_dict, registry=reg) + + +def get_phy_vol( + reg: pyg4ometry.geant4.Registry | None, pars: dict, detector: str +) -> pyg4ometry.geant4.PhysicalVolume: + """Extract the :class:`pyg4ometry.geant4.PhysicalVolume` object from GDML + + Parameters + ---------- + reg + Geant4 registry from GDML + pars + dictionary of parameters. + detector + remage output name for the detector. + + Returns + ------- + phy_vol + the `pyg4ometry.geant4.PhysicalVolume` object for the detector + """ + if reg is not None: + phy_name = pars.get("phy_vol_name", f"{detector}") + return reg.physicalVolumeDict[phy_name] + return None def dict2tuple(dictionary: dict) -> namedtuple: diff --git a/tests/configs/geom.gdml b/tests/configs/geom.gdml new file mode 100644 index 0000000..62ebc8d --- /dev/null +++ b/tests/configs/geom.gdml @@ -0,0 +1,132 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index 9c910e1..5dff6c4 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -1,11 +1,26 @@ from __future__ import annotations +import pathlib + import awkward as ak import numpy as np +import pyg4ometry import pytest +from legendhpges import make_hpge +from legendtestdata import LegendTestData from lgdo import Table +from pyg4ometry import geant4 + +from reboost.hpge import hit, utils + +configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") + -from reboost.hpge import hit +@pytest.fixture(scope="session") +def test_data_configs(): + ldata = LegendTestData() + ldata.checkout("5f9b368") + return ldata.get_path("legend/metadata/hardware/detectors/germanium/diodes") def test_eval(): @@ -36,3 +51,57 @@ def test_eval(): pars = {"reso": 2} assert np.size(hit.eval_expression(tab, func_eval, pars).view_as("np")) == 5 + + +def test_eval_with_hpge(test_data_configs): + reg = geant4.Registry() + gedet = make_hpge(test_data_configs + "/V99000A.json", registry=reg) + + pos = ak.Array( + { + "xloc": [[0, 100, 200], [100], [700, 500, 200]], + "yloc": [[100, 0, 0], [200], [100, 300, 200]], + "zloc": [[700, 10, 20], [100], [300, 100, 0]], + } + ) + tab = Table(pos) + + func_eval = { + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, [0,0,0], None)", + } + + assert ak.all( + ak.num(hit.eval_expression(tab, func_eval, {}, hpge=gedet, phy_vol=None), axis=1) + == [3, 1, 3] + ) + + +def test_eval_with_hpge_and_phy_vol(test_data_configs): + reg = geant4.Registry() + gedet = make_hpge(test_data_configs + "/V99000A.json", registry=reg) + + pos = ak.Array( + { + "xloc": [[0, 100, 200], [100], [700, 500, 200]], + "yloc": [[100, 0, 0], [200], [100, 300, 200]], + "zloc": [[700, 10, 20], [100], [300, 100, 0]], + } + ) + tab = Table(pos) + + func_eval = { + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)", + } + gdml_path = configs / pathlib.Path("geom.gdml") + + gdml = pyg4ometry.gdml.Reader(gdml_path).getRegistry() + + # read with the det_phy_vol_name + phy = utils.get_phy_vol(gdml, {"phy_vol_name": "det_phy_1"}, "det001") + + assert ak.all( + ak.num(hit.eval_expression(tab, func_eval, {}, hpge=gedet, phy_vol=phy), axis=1) + == [3, 1, 3] + ) diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 190884f..4200a50 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -1,13 +1,18 @@ from __future__ import annotations import json -from pathlib import Path +import pathlib import awkward as ak +import pyg4ometry import pytest import yaml +from legendhpges.base import HPGe +from legendtestdata import LegendTestData -from reboost.hpge.utils import _merge_arrays, load_dict +from reboost.hpge.utils import _merge_arrays, get_hpge, get_phy_vol, load_dict + +configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") def test_merge(): @@ -57,16 +62,16 @@ def file_fixture(tmp_path): # Create a simple YAML file data = {"det": 1} yaml_file = tmp_path / "data.yaml" - with Path.open(yaml_file, "w") as yf: + with pathlib.Path.open(yaml_file, "w") as yf: yaml.dump(data, yf) json_file = tmp_path / "data.json" - with Path.open(json_file, "w") as jf: + with pathlib.Path.open(json_file, "w") as jf: json.dump(data, jf) # Create a simple TXT file txt_file = tmp_path / "data.txt" - with Path.open(txt_file, "w") as tf: + with pathlib.Path.open(txt_file, "w") as tf: tf.write("Some text.\n") # Return paths for the test functions @@ -82,3 +87,35 @@ def test_read(file_fixture): with pytest.raises(NotImplementedError): load_dict(file_fixture["txt_file"], None) + + +@pytest.fixture(scope="session") +def test_data_configs(): + ldata = LegendTestData() + ldata.checkout("5f9b368") + return ldata.get_path("legend/metadata/hardware/detectors/germanium/diodes") + + +def test_get_hpge(test_data_configs): + # specify name in pars + hpge = get_hpge(str(test_data_configs), {"meta_name": "C99000A.json"}, "det001") + assert isinstance(hpge, HPGe) + + # now read without metaname + hpge_ic = get_hpge(str(test_data_configs), {}, "V99000A") + assert isinstance(hpge_ic, HPGe) + + +def test_get_phy_vol(): + gdml_path = configs / pathlib.Path("geom.gdml") + + gdml = pyg4ometry.gdml.Reader(gdml_path).getRegistry() + + # read with the det_phy_vol_name + phy = get_phy_vol(gdml, {"phy_vol_name": "det_phy_1"}, "det001") + + assert isinstance(phy, pyg4ometry.geant4.PhysicalVolume) + + # read without + phy = get_phy_vol(gdml, {}, "det_phy_0") + assert isinstance(phy, pyg4ometry.geant4.PhysicalVolume) From 83be05d203bb169d721d88f51ca5d1c9f7743b44 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 12 Nov 2024 16:26:38 +0100 Subject: [PATCH 31/81] add hpges and pyg4ometry to the dependencies --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 3cde88d..1697de4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ dependencies = [ "numba", "legend-pydataobj>=1.9.0", "legend-pygeom-optics>=0.6.5", + "legend-pygeom-hpges", "hist", "particle", "pandas", From a2e8d4ac4227e62d973805fd60ebff97e8857059 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 12 Nov 2024 16:26:46 +0100 Subject: [PATCH 32/81] add pyg4ometry --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 1697de4..4b979ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,7 @@ dependencies = [ "scipy", "numba", "legend-pydataobj>=1.9.0", + "pyg4ometry", "legend-pygeom-optics>=0.6.5", "legend-pygeom-hpges", "hist", From 58e6f36b94e5052d20781ff0b44990d9e09d84dc Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 12 Nov 2024 16:30:01 +0100 Subject: [PATCH 33/81] [docs] add legendtestdata to deps --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 4b979ac..196246f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ dependencies = [ "numba", "legend-pydataobj>=1.9.0", "pyg4ometry", + "legendtestdata", "legend-pygeom-optics>=0.6.5", "legend-pygeom-hpges", "hist", From 8ec0df60d45d836e30cb011003a78c86b3407817 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 13 Nov 2024 00:03:29 +0100 Subject: [PATCH 34/81] [tests] test on the whole of build_hit (IO) --- src/reboost/hpge/cli.py | 58 +++++++++++++++-- src/reboost/hpge/hit.py | 133 ++++++++++++++++++++++++-------------- src/reboost/hpge/utils.py | 53 +++++++++++++++ tests/test_hpge_hit.py | 119 +++++++++++++++++++++++++++++++++- tests/test_hpge_utils.py | 85 +++++++++++++++++++++++- 5 files changed, 390 insertions(+), 58 deletions(-) diff --git a/src/reboost/hpge/cli.py b/src/reboost/hpge/cli.py index 001534e..03ba0c0 100644 --- a/src/reboost/hpge/cli.py +++ b/src/reboost/hpge/cli.py @@ -33,6 +33,24 @@ def hpge_cli() -> None: subparsers = parser.add_subparsers(dest="command", required=True) hit_parser = subparsers.add_parser("hit", help="build hit file from remage raw file") + hit_parser.add_argument( + "--num", + "-n", + help="Number of events to process, if not set process all", + default=None, + type=int, + required=False, + ) + + hit_parser.add_argument( + "--start", + "-s", + help="First event to process (default 0)", + default=0, + type=int, + required=False, + ) + hit_parser.add_argument( "--proc_chain", help="JSON or YAML file that contains the processing chain", @@ -46,20 +64,30 @@ def hpge_cli() -> None: hit_parser.add_argument( "--gdml", help="GDML file used for Geant4", + default=None, required=False, ) + hit_parser.add_argument( - "--macro", - help="Geant4 macro file used to generate raw tier", + "--meta_path", + help="Path to metadata (diodes folder)", + default=None, required=False, ) - hit_parser.add_argument("--infield", help="input LH5 field", required=False, default="hit") hit_parser.add_argument( "--outfield", help="output LH5 field name", required=False, default="hit" ) - - hit_parser.add_argument("input", help="input hit LH5 file", metavar="INPUT_HIT") + parser.add_argument( + "--merge_input", + "-m", + action="store_true", + default=True, + help="""Merge input lh5 files into a single output""", + ) + hit_parser.add_argument( + "input", help="input hit LH5 files (can include wildcars) ", nargs="+", metavar="INPUT_HIT" + ) hit_parser.add_argument("output", help="output evt LH5 file", metavar="OUTPUT_EVT") args = parser.parse_args() @@ -70,6 +98,7 @@ def hpge_cli() -> None: ) logger = logging.getLogger("reboost.hpge") logger.addHandler(handler) + if args.verbose: logger.setLevel(logging.DEBUG) else: @@ -89,6 +118,22 @@ def hpge_cli() -> None: msg = f"error proc chain config must contain the field {req_field}" raise ValueError(msg) + msg = f""" + Running build_hit with: + - output file :{args.output} + - input_file(s) :{args.input} + - output field :{args.outfield} + - input field :{args.infield} + - proc_config :{args.proc_config} + - pars :{args.pars} + - buffer :{args.bufsize} + - gdml file :{args.gdml} + - metadata_path :{args.meta_path} + - merge input :{args.merge_input} + """ + + logger.info(msg) + build_hit( args.output, args.input, @@ -98,5 +143,6 @@ def hpge_cli() -> None: pars=pars, buffer=args.bufsize, gdml=args.gdml, - macro=args.macro, + metadata_path=args.meta_path, + merge_input=args.merge_input, ) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 60357ca..f501a24 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -5,7 +5,8 @@ import legendhpges import numpy as np import pyg4ometry -from lgdo import Array, ArrayOfEqualSizedArrays, LH5Iterator, Table, VectorOfVectors, lh5 +from lgdo import Array, ArrayOfEqualSizedArrays, Table, VectorOfVectors, lh5 +from lgdo.lh5 import LH5Iterator from . import utils @@ -130,7 +131,7 @@ def eval_expression( def build_hit( file_out: str, - file_in: str, + list_file_in: list, out_field: str, in_field: str, proc_config: dict, @@ -138,6 +139,7 @@ def build_hit( buffer: int = 1000000, gdml: str | None = None, metadata_path: str | None = None, + merge_input_files: bool = True, ) -> None: """ Read incrementally the files compute something and then write output @@ -146,8 +148,8 @@ def build_hit( ---------- file_out output file path - file_in - input_file_path + list_file_in + list of input files out_field lh5 group name for output in_field @@ -223,73 +225,104 @@ def build_hit( path to the macro file. metadata_path path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) + merge_input_files + boolean flag to merge all input files into a single output. Note ---- - The operations can depend on the outputs of previous steps, so operations order is important. - It would be better to have a cleaner way to supply metadata and detector maps. """ + # expand wildcards + expanded_list_file_in = utils.get_file_list(list_file_in) + # get the gdml file reg = pyg4ometry.gdml.Reader(gdml).getRegistry() if gdml is not None else None - for ch_idx, d in enumerate(proc_config["channels"]): - msg = f"...running hit tier for {d}" - log.info(msg) + # loop over input files + for file_idx, file_in in enumerate(expanded_list_file_in): + for ch_idx, d in enumerate(proc_config["channels"]): + msg = f"...running hit tier for {d}" + log.info(msg) - # get HPGe and phy_vol object to pass to build_hit - hpge = utils.get_hpge(metadata_path, pars=pars, detector=d) - phy_vol = utils.get_phy_vol(reg, pars=pars, detector=d) + # get HPGe and phy_vol object to pass to build_hit - delete_input = bool(ch_idx == 0) + hpge = ( + utils.get_hpge(metadata_path, pars=pars, detector=d) + if (metadata_path is not None) + else None + ) + phy_vol = utils.get_phy_vol(reg, pars=pars, detector=d) - msg = f"...begin processing with {file_in} to {file_out}" - log.info(msg) + is_first_chan = bool(ch_idx == 0) + is_first_file = bool(file_idx == 0) - entries = LH5Iterator(file_in, f"{in_field}/{d}", buffer_len=buffer)._get_file_cumentries(0) + # flag to overwrite input file + delete_input = (is_first_chan & merge_input_files is False) | ( + is_first_chan & is_first_file + ) - # number of blocks is ceil of entries/buffer, - # shift by 1 since idx starts at 0 - # this is maybe too high if buffer exactly divides idx - max_idx = int(np.ceil(entries / buffer)) - 1 - buffer_rows = None + msg = f"...begin processing with {file_in} to {file_out}" + log.info(msg) - for idx, (lh5_obj, _, _) in enumerate( - LH5Iterator(file_in, f"{in_field}/{d}", buffer_len=buffer) - ): - msg = f"... processed {idx} files out of {max_idx}" - log.debug(msg) + entries = LH5Iterator( + file_in, f"{in_field}/{d}", buffer_len=buffer + )._get_file_cumentries(0) - # convert to awkward - ak_obj = lh5_obj.view_as("ak") + # number of blocks is ceil of entries/buffer, + # shift by 1 since idx starts at 0 - # handle the buffers - obj, buffer_rows, mode = utils._merge_arrays( - ak_obj, buffer_rows, idx=idx, max_idx=max_idx, delete_input=delete_input - ) + max_idx = int(np.ceil(entries / buffer)) - 1 + remainder = entries % buffer + buffer_rows = None - # convert back to a table, should work - data = Table(obj) + for idx, (lh5_obj, _, _) in enumerate( + LH5Iterator(file_in, f"{in_field}/{d}", buffer_len=buffer) + ): + msg = f"... processed {idx} chunks out of {max_idx}" + log.debug(msg) - # group steps into hits - grouped = step_group(data, proc_config["step_group"]) + # convert to awkward + ak_obj = lh5_obj.view_as("ak") - # processors - for name, info in proc_config["operations"].items(): - msg = f"adding column {name}" - log.debug(msg) + # fix for a bug in lh5 iterator + if idx == max_idx & remainder != 0: + ak_obj = ak_obj[:remainder] - col = eval_expression(grouped, info, pars=pars, phy=phy_vol, hpge=hpge) - grouped.add_field(name, col) + # handle the buffers + obj, buffer_rows, mode = utils._merge_arrays( + ak_obj, buffer_rows, idx=idx, max_idx=max_idx, delete_input=delete_input + ) - # remove unwanted columns - log.debug("removing unwanted columns") + # convert back to a table, should work + data = Table(obj) - existing_cols = list(grouped.keys()) - for col in existing_cols: - if col not in proc_config["outputs"]: - grouped.remove_column(col, delete=True) + # group steps into hits + grouped = step_group(data, proc_config["step_group"]) + + # processors + for name, info in proc_config["operations"].items(): + msg = f"adding column {name}" + log.debug(msg) + + col = eval_expression(grouped, info, pars=pars, phy_vol=phy_vol, hpge=hpge) + grouped.add_field(name, col) + + # remove unwanted columns + log.debug("removing unwanted columns") + + existing_cols = list(grouped.keys()) + for col in existing_cols: + if col not in proc_config["outputs"]: + grouped.remove_column(col, delete=True) + + # write lh5 file + msg = f"...finished processing and save file with wo_mode {mode}" + log.debug(msg) - # write lh5 file - msg = f"...finished processing and save file with wo_mode {mode}" - log.debug(msg) - lh5.write(grouped, f"{out_field}/{d}", file_out, wo_mode=mode) + file_out_tmp = ( + f"{file_out.split('.')[0]}_{file_idx}.lh5" + if (merge_input_files is False) + else file_out + ) + lh5.write(grouped, f"{out_field}/{d}", file_out_tmp, wo_mode=mode) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index d80df9a..b7c2044 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -10,14 +10,67 @@ import awkward as ak import legendhpges +import numpy as np import pyg4ometry import yaml +from lgdo import lh5 log = logging.getLogger(__name__) reg = pyg4ometry.geant4.Registry() +def get_num_simulated(file_list: list, table: str = "hit") -> int: + """Loop over a list of files and extract the number of simulated events. + + Based on the size of the `vertices` tables. + + Parameters + ---------- + file_list + list of input files (each must contain the vertices table) + table + name of the lh5 input table. + """ + n = 0 + for file in file_list: + it = lh5.LH5Iterator(file, f"{table}/vertices", buffer_len=int(5e6)) + n += it._get_file_cumlen(0) + + msg = f"files contain {n} events" + log.info(msg) + return n + + +def get_file_list(path: str | list[str]) -> list[str]: + """Get list of files to read. + + Parameters + ---------- + path + either a string or a list of strings containing files paths to process. May contain wildcards which are expanded. + + Returns + ------- + sorted list of files, after expanding wildcards, removing duplicates and sorting. + + """ + + if isinstance(path, str): + path = [path] + + path_out_list = [] + + for ptmp in path: + ptmp_path = Path(ptmp) + dir_tmp = ptmp_path.parent + pattern_tmp = ptmp_path.name + + path_out_list.extend(dir_tmp.glob(pattern_tmp)) + path_out_list_str = [str(ptmp) for ptmp in path_out_list] + return list(np.sort(np.unique(path_out_list_str))) + + def get_hpge(meta_path: str, pars: dict, detector: str) -> legendhpges.HPGe: """Extract the :class:`legendhpges.HPGe` object from metadata. diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index 5dff6c4..2589793 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -8,7 +8,7 @@ import pytest from legendhpges import make_hpge from legendtestdata import LegendTestData -from lgdo import Table +from lgdo import Table, lh5 from pyg4ometry import geant4 from reboost.hpge import hit, utils @@ -105,3 +105,120 @@ def test_eval_with_hpge_and_phy_vol(test_data_configs): ak.num(hit.eval_expression(tab, func_eval, {}, hpge=gedet, phy_vol=phy), axis=1) == [3, 1, 3] ) + + +# test the full processing chain +@pytest.fixture +def test_reboost_input_file(tmp_path): + # make it large enough to be multiple groups + rng = np.random.default_rng() + evtid_1 = np.sort(rng.integers(int(1e5), size=(int(1e6)))) + time_1 = rng.uniform(low=0, high=1, size=(int(1e6))) + edep_1 = rng.uniform(low=0, high=1000, size=(int(1e6))) + + # make it not divide by the buffer len + evtid_2 = np.sort(rng.integers(int(1e5), size=(30040))) + time_2 = rng.uniform(low=0, high=1, size=(30040)) + edep_2 = rng.uniform(low=0, high=1000, size=(30040)) + + vertices_1 = ak.Array({"evtid": np.arange(int(1e5))}) + vertices_2 = ak.Array({"evtid": np.arange(30040)}) + + arr_1 = ak.Array({"evtid": evtid_1, "time": time_1, "edep": edep_1}) + arr_2 = ak.Array({"evtid": evtid_2, "time": time_2, "edep": edep_2}) + + lh5.write(Table(vertices_1), "hit/vertices", tmp_path / "file1.lh5", wo_mode="of") + lh5.write(Table(vertices_2), "hit/vertices", tmp_path / "file2.lh5", wo_mode="of") + + lh5.write(Table(arr_1), "hit/det001", tmp_path / "file1.lh5", wo_mode="append") + lh5.write(Table(arr_2), "hit/det001", tmp_path / "file2.lh5", wo_mode="append") + + return tmp_path + + +def test_build_hit(test_reboost_input_file): + # first just one file no pars + + proc_config = { + "channels": [ + "det001", + ], + "outputs": ["t0", "evtid"], + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)", + }, + }, + } + + hit.build_hit( + str(test_reboost_input_file / "out.lh5"), + [str(test_reboost_input_file / "file1.lh5")], + "hit", + "hit", + proc_config, + {}, + buffer=100000, + ) + hit.build_hit( + str(test_reboost_input_file / "out_rem.lh5"), + [str(test_reboost_input_file / "file2.lh5")], + "hit", + "hit", + proc_config, + {}, + buffer=10000, + ) + + # now with wildcard + hit.build_hit( + str(test_reboost_input_file / "out_merge.lh5"), + [str(test_reboost_input_file / "file*.lh5")], + "hit", + "hit", + proc_config, + {}, + buffer=100000, + merge_input_files=True, + ) + hit.build_hit( + str(test_reboost_input_file / "out.lh5"), + [str(test_reboost_input_file / "file*.lh5")], + "hit", + "hit", + proc_config, + {}, + buffer=100000, + merge_input_files=False, + ) + + # read back in the data and check this works (no errors) + + tab = lh5.read("hit/det001",str(test_reboost_input_file / "out.lh5")).view_as("ak") + tab_merge = lh5.read("hit/det001",str(test_reboost_input_file / "out_merge.lh5")).view_as("ak") + tab_0 = lh5.read("hit/det001",str(test_reboost_input_file / "out_0.lh5")).view_as("ak") + tab_1 = lh5.read("hit/det001",str(test_reboost_input_file / "out_1.lh5")).view_as("ak") + + # check size of the output + assert len(ak.flatten(tab.evtid,axis=-1))==int(1e6) + assert len(ak.flatten(tab_merge.evtid,axis=-1))==int(1e6+30040) + assert len(ak.flatten(tab_0.evtid,axis=-1))==int(1e6) + assert len(ak.flatten(tab_1.evtid,axis=-1))==int(30040) + + # check on evtid + + assert ak.all(ak.all(tab.evtid == ak.firsts(tab.evtid,axis=-1), axis=1)) + assert ak.all(ak.all(tab_merge.evtid == ak.firsts(tab_merge.evtid,axis=-1), axis=1)) + assert ak.all(ak.all(tab_0.evtid == ak.firsts(tab_0.evtid,axis=-1), axis=1)) + assert ak.all(ak.all(tab_1.evtid == ak.firsts(tab_1.evtid,axis=-1), axis=1)) \ No newline at end of file diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 4200a50..7051456 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -4,13 +4,22 @@ import pathlib import awkward as ak +import numpy as np import pyg4ometry import pytest import yaml from legendhpges.base import HPGe from legendtestdata import LegendTestData +from lgdo import Array, Table, lh5 -from reboost.hpge.utils import _merge_arrays, get_hpge, get_phy_vol, load_dict +from reboost.hpge.utils import ( + _merge_arrays, + get_file_list, + get_hpge, + get_num_simulated, + get_phy_vol, + load_dict, +) configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") @@ -89,6 +98,38 @@ def test_read(file_fixture): load_dict(file_fixture["txt_file"], None) +@pytest.fixture +def file_list(tmp_path): + # make a list of files + for i in range(5): + data = {"det": i} + + # make a json file + json_file = tmp_path / f"data_{i}.json" + with pathlib.Path.open(json_file, "w") as jf: + json.dump(data, jf) + + # and a text file + txt_file = tmp_path / f"data_{i}.txt" + with pathlib.Path.open(txt_file, "w") as tf: + tf.write("Some text.\n") + return pathlib.Path(tmp_path) + + +def test_get_file_list(file_list): + first_file_list = get_file_list(str(pathlib.Path(file_list) / "data_0.json")) + assert len(first_file_list) == 1 + + json_file_list = get_file_list(str(pathlib.Path(file_list) / "data*.json")) + assert len(json_file_list) == 5 + json_file_list_repeat = get_file_list( + [str(pathlib.Path(file_list) / "data*.json"), str(pathlib.Path(file_list) / "data*.json")] + ) + assert len(json_file_list_repeat) == 5 + all_file_list = get_file_list([str(pathlib.Path(file_list) / "data*")]) + assert len(all_file_list) == 10 + + @pytest.fixture(scope="session") def test_data_configs(): ldata = LegendTestData() @@ -119,3 +160,45 @@ def test_get_phy_vol(): # read without phy = get_phy_vol(gdml, {}, "det_phy_0") assert isinstance(phy, pyg4ometry.geant4.PhysicalVolume) + + +@pytest.fixture +def test_lh5_files(tmp_path): + n1 = 14002 + tab1 = Table(size=n1) + tab1.add_field("a", Array(np.ones(n1))) + lh5.write(tab1, "hit/vertices", tmp_path / "file1.lh5", wo_mode="of") + + n2 = 25156 + tab2 = Table(size=n2) + tab2.add_field("a", Array(np.ones(n2))) + + lh5.write(tab2, "hit/vertices", tmp_path / "file2.lh5", wo_mode="of") + + n3 = int(1e7) + tab3 = Table(size=n3) + tab3.add_field("a", Array(np.ones(n3))) + + lh5.write(tab3, "hit/vertices", tmp_path / "file3.lh5", wo_mode="of") + + return tmp_path + + +def test_get_n_sim(test_lh5_files): + # single file + n1 = get_num_simulated([str(test_lh5_files / "file1.lh5")]) + assert n1 == 14002 + + # two files + n12 = get_num_simulated([str(test_lh5_files / "file1.lh5"), str(test_lh5_files / "file2.lh5")]) + assert n12 == 14002 + 25156 + + # length > buffer + n123 = get_num_simulated( + [ + str(test_lh5_files / "file1.lh5"), + str(test_lh5_files / "file2.lh5"), + str(test_lh5_files / "file3.lh5"), + ] + ) + assert n123 == 14002 + 25156 + int(1e7) From ee048fada05f3e2b7f2660052d7dc8b3d25dddce Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 13 Nov 2024 00:03:50 +0100 Subject: [PATCH 35/81] precommit --- tests/test_hpge_hit.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index 2589793..21d5e5b 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -205,20 +205,20 @@ def test_build_hit(test_reboost_input_file): # read back in the data and check this works (no errors) - tab = lh5.read("hit/det001",str(test_reboost_input_file / "out.lh5")).view_as("ak") - tab_merge = lh5.read("hit/det001",str(test_reboost_input_file / "out_merge.lh5")).view_as("ak") - tab_0 = lh5.read("hit/det001",str(test_reboost_input_file / "out_0.lh5")).view_as("ak") - tab_1 = lh5.read("hit/det001",str(test_reboost_input_file / "out_1.lh5")).view_as("ak") + tab = lh5.read("hit/det001", str(test_reboost_input_file / "out.lh5")).view_as("ak") + tab_merge = lh5.read("hit/det001", str(test_reboost_input_file / "out_merge.lh5")).view_as("ak") + tab_0 = lh5.read("hit/det001", str(test_reboost_input_file / "out_0.lh5")).view_as("ak") + tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") # check size of the output - assert len(ak.flatten(tab.evtid,axis=-1))==int(1e6) - assert len(ak.flatten(tab_merge.evtid,axis=-1))==int(1e6+30040) - assert len(ak.flatten(tab_0.evtid,axis=-1))==int(1e6) - assert len(ak.flatten(tab_1.evtid,axis=-1))==int(30040) + assert len(ak.flatten(tab.evtid, axis=-1)) == int(1e6) + assert len(ak.flatten(tab_merge.evtid, axis=-1)) == int(1e6 + 30040) + assert len(ak.flatten(tab_0.evtid, axis=-1)) == int(1e6) + assert len(ak.flatten(tab_1.evtid, axis=-1)) == 30040 # check on evtid - assert ak.all(ak.all(tab.evtid == ak.firsts(tab.evtid,axis=-1), axis=1)) - assert ak.all(ak.all(tab_merge.evtid == ak.firsts(tab_merge.evtid,axis=-1), axis=1)) - assert ak.all(ak.all(tab_0.evtid == ak.firsts(tab_0.evtid,axis=-1), axis=1)) - assert ak.all(ak.all(tab_1.evtid == ak.firsts(tab_1.evtid,axis=-1), axis=1)) \ No newline at end of file + assert ak.all(ak.all(tab.evtid == ak.firsts(tab.evtid, axis=-1), axis=1)) + assert ak.all(ak.all(tab_merge.evtid == ak.firsts(tab_merge.evtid, axis=-1), axis=1)) + assert ak.all(ak.all(tab_0.evtid == ak.firsts(tab_0.evtid, axis=-1), axis=1)) + assert ak.all(ak.all(tab_1.evtid == ak.firsts(tab_1.evtid, axis=-1), axis=1)) From 410bb227e38c9eb9cc64703bc8919c1896324a74 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 13 Nov 2024 00:06:55 +0100 Subject: [PATCH 36/81] remove dependency --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 196246f..4b979ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,6 @@ dependencies = [ "numba", "legend-pydataobj>=1.9.0", "pyg4ometry", - "legendtestdata", "legend-pygeom-optics>=0.6.5", "legend-pygeom-hpges", "hist", From a4140bfc217febe5a30caca2c6d29760b9b8d3ab Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 13 Nov 2024 00:16:27 +0100 Subject: [PATCH 37/81] [tests] fix the test data --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 4b979ac..03d2463 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ dependencies = [ "numba", "legend-pydataobj>=1.9.0", "pyg4ometry", + "pylegendtestdata", "legend-pygeom-optics>=0.6.5", "legend-pygeom-hpges", "hist", From b615949058b8707fa3fc69504f68b55cb17348ce Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 14 Nov 2024 18:54:20 +0100 Subject: [PATCH 38/81] add the option to just read n evtid starting at a particular index. --- src/reboost/hpge/hit.py | 40 +++++++++-- src/reboost/hpge/utils.py | 145 ++++++++++++++++++++++++++++++++++++-- tests/test_hpge_hit.py | 136 ++++++++++++++++++++++++++++++----- tests/test_hpge_utils.py | 52 +++++++++++++- 4 files changed, 344 insertions(+), 29 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index f501a24..f772394 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -2,6 +2,7 @@ import logging +import awkward as ak import legendhpges import numpy as np import pyg4ometry @@ -136,6 +137,8 @@ def build_hit( in_field: str, proc_config: dict, pars: dict, + n_evtid: int | None = None, + start_evtid: int = 0, buffer: int = 1000000, gdml: str | None = None, metadata_path: str | None = None, @@ -217,6 +220,10 @@ def build_hit( If these keys are not present both will be set to the remage output table name. + start_evtid + first `evtid` to read, defaults to 0. + n_evtid + number of `evtid` to read, if `None` all steps are read (the default). buffer length of buffer gdml @@ -233,20 +240,27 @@ def build_hit( - The operations can depend on the outputs of previous steps, so operations order is important. - It would be better to have a cleaner way to supply metadata and detector maps. """ - # expand wildcards - expanded_list_file_in = utils.get_file_list(list_file_in) # get the gdml file reg = pyg4ometry.gdml.Reader(gdml).getRegistry() if gdml is not None else None + # get info on the files to read in a nice named tuple + file_info = utils.get_selected_files( + table=in_field, + file_list=list_file_in, + n_evtid=n_evtid, + start_evtid=start_evtid, + ) + # loop over input files - for file_idx, file_in in enumerate(expanded_list_file_in): + for first_evtid, file_idx, file_in in zip( + file_info.file_start_global_evtids, file_info.file_indices, file_info.file_list + ): for ch_idx, d in enumerate(proc_config["channels"]): msg = f"...running hit tier for {d}" log.info(msg) # get HPGe and phy_vol object to pass to build_hit - hpge = ( utils.get_hpge(metadata_path, pars=pars, detector=d) if (metadata_path is not None) @@ -294,6 +308,24 @@ def build_hit( ak_obj, buffer_rows, idx=idx, max_idx=max_idx, delete_input=delete_input ) + # add global evtid + obj = ak.with_field(obj, first_evtid + obj.evtid, "global_evtid") + + # check if the chunk can be skipped + + if not utils.get_include_chunk( + obj.global_evtid, + start_glob_evtid=file_info.first_global_evtid, + end_glob_evtid=file_info.last_global_evtid, + ): + continue + + # select just the correct global evtid objects + obj = obj[ + (obj.global_evtid >= file_info.first_global_evtid) + & (obj.global_evtid <= file_info.last_global_evtid) + ] + # convert back to a table, should work data = Table(obj) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index b7c2044..ce5a75e 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -14,12 +14,80 @@ import pyg4ometry import yaml from lgdo import lh5 +from numpy.typing import ArrayLike, NDArray log = logging.getLogger(__name__) reg = pyg4ometry.geant4.Registry() +FileInfo = namedtuple( + "FileInfo", + [ + "file_list", + "file_indices", + "file_start_global_evtids", + "first_global_evtid", + "last_global_evtid", + ], +) + + +def get_selected_files( + file_list: list[str], table: str, n_evtid: int | None, start_evtid: int +) -> tuple[list, list, list]: + """Get the files to read based on removing those with global evtid out of the selected range. + + - expands wildcards, + - extracts number of evtid per file, + - removes files outside of the range `low_evtid` to `high_evtid`. + + Parameters + ---------- + file_list + list of files to process (can include wildcards) + table + lh5 input field + n_evtid + number of simulation events to process. + high_global_evtid + start_evtid: first (global) simulation event to process. + + Returns + ------- + `FileInfo` named tuple with fields: + - `file_list`:list of files. + - `file_indices`: indices of selected files. + - `file_start_global_evtids`: first global `evtid` in each file. + - `first_global_evtid`: first global evtid to process + - `last_global_evtid`: last global evtid to process. + """ + # expand wildcards + expanded_list_file_in = get_file_list(path=file_list) + + n_sim = get_num_simulated(expanded_list_file_in, table=table) + + # first global index of each file + cum_nsim = np.concatenate([[0], np.cumsum(n_sim)]) + + low_global_evtid, high_global_evtid = get_global_evtid_range( + start_evtid=start_evtid, n_evtid=n_evtid, n_tot_sim=cum_nsim[-1] + ) + file_indices = np.array( + get_files_to_read( + cum_nsim, start_glob_evtid=low_global_evtid, end_glob_evtid=high_global_evtid + ) + ) + + # select just the necessary files + file_list_sel = np.array(expanded_list_file_in)[file_indices] + start_evtid_sel = cum_nsim[file_indices] + + return FileInfo( + file_list_sel, file_indices, start_evtid_sel, low_global_evtid, high_global_evtid + ) + + def get_num_simulated(file_list: list, table: str = "hit") -> int: """Loop over a list of files and extract the number of simulated events. @@ -32,17 +100,17 @@ def get_num_simulated(file_list: list, table: str = "hit") -> int: table name of the lh5 input table. """ - n = 0 + n = [] for file in file_list: it = lh5.LH5Iterator(file, f"{table}/vertices", buffer_len=int(5e6)) - n += it._get_file_cumlen(0) + n.append(it._get_file_cumlen(0)) msg = f"files contain {n} events" log.info(msg) return n -def get_file_list(path: str | list[str]) -> list[str]: +def get_file_list(path: str | list[str]) -> NDArray: """Get list of files to read. Parameters @@ -52,7 +120,7 @@ def get_file_list(path: str | list[str]) -> list[str]: Returns ------- - sorted list of files, after expanding wildcards, removing duplicates and sorting. + sorted array of files, after expanding wildcards, removing duplicates and sorting. """ @@ -68,7 +136,74 @@ def get_file_list(path: str | list[str]) -> list[str]: path_out_list.extend(dir_tmp.glob(pattern_tmp)) path_out_list_str = [str(ptmp) for ptmp in path_out_list] - return list(np.sort(np.unique(path_out_list_str))) + return np.array(np.sort(np.unique(path_out_list_str))) + + +def get_global_evtid_range( + start_evtid: int, n_evtid: int | None, n_tot_sim: int +) -> tuple[int, int]: + """Get the global evtid range""" + + # some checks + if (n_evtid is not None) and (start_evtid + n_evtid > n_tot_sim): + msg = "Index are out of the range of the simulation." + raise ValueError(msg) + + start_glob_index = start_evtid + end_glob_index = start_evtid + n_evtid - 1 if (n_evtid is not None) else n_tot_sim - 1 + return start_glob_index, end_glob_index + + +def get_files_to_read(cum_n_sim: ArrayLike, start_glob_evtid: int, end_glob_evtid: int) -> NDArray: + """Get the index of files to read based on the number of evtid to read and the start evtid. + + Parameters + ---------- + cum_n_sim + cumulative list of the number of evtid per file. + start_glob_evtid + first global evtid to include. + end_glob_evtid + last global evtid to process. + + Returns + ------- + array of the indices of files to process. + """ + # find which files to read + + file_indices = [] + cum_n_sim = np.array(cum_n_sim) + + for i, (low, high) in enumerate(zip(cum_n_sim, cum_n_sim[1:] - 1)): + if (high >= start_glob_evtid) & (low <= end_glob_evtid): + file_indices.append(i) + return np.array(file_indices) + + +def get_include_chunk( + global_evtid: ak.Array, + start_glob_evtid: int, + end_glob_evtid: int, +) -> bool: + """Check if a chunk can be skipped based on evtid range. + + Parameters + ---------- + global_evtid + awkward array of the (local) evtid in the chunk + start_glob_evtid + first global evtid to include. + end_glob_evtid + last global evtid to process. + Returns + ------- + boolean flag of whether to include in the chunk. + + """ + low = ak.min(global_evtid) + high = ak.max(global_evtid) + return (high >= start_glob_evtid) & (low <= end_glob_evtid) def get_hpge(meta_path: str, pars: dict, detector: str) -> legendhpges.HPGe: diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index 21d5e5b..401b6c4 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -122,7 +122,7 @@ def test_reboost_input_file(tmp_path): edep_2 = rng.uniform(low=0, high=1000, size=(30040)) vertices_1 = ak.Array({"evtid": np.arange(int(1e5))}) - vertices_2 = ak.Array({"evtid": np.arange(30040)}) + vertices_2 = ak.Array({"evtid": np.arange(int(1e5))}) arr_1 = ak.Array({"evtid": evtid_1, "time": time_1, "edep": edep_1}) arr_2 = ak.Array({"evtid": evtid_2, "time": time_2, "edep": edep_2}) @@ -165,19 +165,19 @@ def test_build_hit(test_reboost_input_file): hit.build_hit( str(test_reboost_input_file / "out.lh5"), [str(test_reboost_input_file / "file1.lh5")], - "hit", - "hit", - proc_config, - {}, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, buffer=100000, ) hit.build_hit( str(test_reboost_input_file / "out_rem.lh5"), [str(test_reboost_input_file / "file2.lh5")], - "hit", - "hit", - proc_config, - {}, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, buffer=10000, ) @@ -185,20 +185,20 @@ def test_build_hit(test_reboost_input_file): hit.build_hit( str(test_reboost_input_file / "out_merge.lh5"), [str(test_reboost_input_file / "file*.lh5")], - "hit", - "hit", - proc_config, - {}, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, buffer=100000, merge_input_files=True, ) hit.build_hit( str(test_reboost_input_file / "out.lh5"), [str(test_reboost_input_file / "file*.lh5")], - "hit", - "hit", - proc_config, - {}, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, buffer=100000, merge_input_files=False, ) @@ -222,3 +222,105 @@ def test_build_hit(test_reboost_input_file): assert ak.all(ak.all(tab_merge.evtid == ak.firsts(tab_merge.evtid, axis=-1), axis=1)) assert ak.all(ak.all(tab_0.evtid == ak.firsts(tab_0.evtid, axis=-1), axis=1)) assert ak.all(ak.all(tab_1.evtid == ak.firsts(tab_1.evtid, axis=-1), axis=1)) + + +def test_build_hit_some_row(test_reboost_input_file): + proc_config = { + "channels": [ + "det001", + ], + "outputs": ["t0", "evtid"], + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)", + }, + }, + } + + # test asking to read too many rows + with pytest.raises(ValueError): + hit.build_hit( + str(test_reboost_input_file / "out.lh5"), + [str(test_reboost_input_file / "file1.lh5")], + n_evtid=int(1e7), + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=100000, + ) + + # test read only some events + hit.build_hit( + str(test_reboost_input_file / "out_some_rows.lh5"), + [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], + n_evtid=int(1e4), + start_evtid=0, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=100000, + ) + + hit.build_hit( + str(test_reboost_input_file / "out_rest_rows.lh5"), + [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], + n_evtid=int(1e5 - 1e4), + start_evtid=int(1e4), + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=100000, + ) + # read all of file 1 + hit.build_hit( + str(test_reboost_input_file / "out_all_file_one.lh5"), + [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], + n_evtid=int(1e5), + start_evtid=0, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=100000, + ) + + # read a mix of the two files + hit.build_hit( + str(test_reboost_input_file / "out_mix.lh5"), + [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], + n_evtid=int(1e5), + start_evtid=1000, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=100000, + ) + + tab_some = lh5.read("hit/det001", str(test_reboost_input_file / "out_some_rows.lh5")).view_as( + "ak" + ) + tab_rest = lh5.read("hit/det001", str(test_reboost_input_file / "out_rest_rows.lh5")).view_as( + "ak" + ) + + tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_all_file_one.lh5")).view_as( + "ak" + ) + + tab_merge = ak.concatenate((tab_some, tab_rest)) + assert ak.all(ak.all(tab_merge.evtid == tab_1.evtid, axis=-1)) diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 7051456..3855b4f 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -15,7 +15,10 @@ from reboost.hpge.utils import ( _merge_arrays, get_file_list, + get_files_to_read, + get_global_evtid_range, get_hpge, + get_include_chunk, get_num_simulated, get_phy_vol, load_dict, @@ -187,11 +190,11 @@ def test_lh5_files(tmp_path): def test_get_n_sim(test_lh5_files): # single file n1 = get_num_simulated([str(test_lh5_files / "file1.lh5")]) - assert n1 == 14002 + assert n1 == [14002] # two files n12 = get_num_simulated([str(test_lh5_files / "file1.lh5"), str(test_lh5_files / "file2.lh5")]) - assert n12 == 14002 + 25156 + assert n12 == [14002, 25156] # length > buffer n123 = get_num_simulated( @@ -201,4 +204,47 @@ def test_get_n_sim(test_lh5_files): str(test_lh5_files / "file3.lh5"), ] ) - assert n123 == 14002 + 25156 + int(1e7) + assert n123 == [14002, 25156, int(1e7)] + + +def test_global_evtid_range(): + # raise exception if n_evtid is too large + with pytest.raises(ValueError): + get_global_evtid_range(100, 1000, 200) + + # test that we get the right ranges + assert get_global_evtid_range(200, 5, 2000) == (200, 204) + assert get_global_evtid_range(200, None, 2000) == (200, 1999) + + +def test_get_files_to_read(): + n_sim = [1000, 1200, 200, 5000] + n_sim = np.concatenate([[0], np.cumsum(n_sim)]) + + # read all evtid and thus all files + assert np.all(get_files_to_read(n_sim, start_glob_evtid=0, end_glob_evtid=7399) == [0, 1, 2, 3]) + + # all of file 0 + assert np.all(get_files_to_read(n_sim, start_glob_evtid=0, end_glob_evtid=999) == [0]) + + # and some of file 1 + assert np.all(get_files_to_read(n_sim, start_glob_evtid=0, end_glob_evtid=1000) == [0, 1]) + + # some of file 0, 1 and 2 + assert np.all(get_files_to_read(n_sim, start_glob_evtid=0, end_glob_evtid=2200) == [0, 1, 2]) + + # only file 1 and 2 + assert np.all(get_files_to_read(n_sim, start_glob_evtid=1000, end_glob_evtid=2300) == [1, 2]) + + # only file 3 + assert np.all(get_files_to_read(n_sim, start_glob_evtid=2400, end_glob_evtid=5000) == [3]) + + +def test_skip_chunk(): + evtid = ak.Array([4, 5, 10, 30]) + + assert get_include_chunk(evtid, start_glob_evtid=0, end_glob_evtid=35) + assert get_include_chunk(evtid, start_glob_evtid=0, end_glob_evtid=4) + assert get_include_chunk(evtid, start_glob_evtid=30, end_glob_evtid=33) + assert not get_include_chunk(evtid, start_glob_evtid=0, end_glob_evtid=3) + assert not get_include_chunk(evtid, start_glob_evtid=31, end_glob_evtid=100) From 42390b8cc4681a88e0c4e17fdfbb3f3189a0ac30 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 14 Nov 2024 19:07:20 +0100 Subject: [PATCH 39/81] trying to fix tests --- src/reboost/hpge/utils.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index ce5a75e..4218de9 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -31,6 +31,14 @@ "last_global_evtid", ], ) +FileInfo.__doc__ = "NamedTuple storing the information on the input files" +FileInfo.file_list.__doc__ = "list of strings of the selected files." +FileInfo.file_indices.__doc__ = "list of integers of the indices of the files." +FileInfo.file_start_global_evtids.__doc__ = ( + "list of integers of the first global evtid for each file." +) +FileInfo.first_global_evtid.__doc__ = "first global evtid to process." +FileInfo.last_global_evtid.__doc__ = "Last global evtid to process." def get_selected_files( @@ -201,8 +209,8 @@ def get_include_chunk( boolean flag of whether to include in the chunk. """ - low = ak.min(global_evtid) - high = ak.max(global_evtid) + low = ak.min(global_evtid,axis=0) + high = ak.max(global_evtid,axis=0) return (high >= start_glob_evtid) & (low <= end_glob_evtid) From 40329f51dd8d698543e310c67d81aaea225e7d0d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 14 Nov 2024 18:07:42 +0000 Subject: [PATCH 40/81] style: pre-commit fixes --- src/reboost/hpge/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 4218de9..c941602 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -209,8 +209,8 @@ def get_include_chunk( boolean flag of whether to include in the chunk. """ - low = ak.min(global_evtid,axis=0) - high = ak.max(global_evtid,axis=0) + low = ak.min(global_evtid, axis=0) + high = ak.max(global_evtid, axis=0) return (high >= start_glob_evtid) & (low <= end_glob_evtid) From 432bd702908dd990524fd7f7ac9e792b8d494cbb Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 14 Nov 2024 19:11:59 +0100 Subject: [PATCH 41/81] add awkward to dependencies --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 03d2463..d2dccf9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,7 @@ classifiers = [ ] requires-python = ">=3.9" dependencies = [ + "awkward", "colorlog", "numpy", "scipy", From dea22dda5e6e06313ebe13de4dadb8cb799a6dd9 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 00:43:49 +0100 Subject: [PATCH 42/81] update main.yaml --- .github/workflows/main.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 2c81d69..acb382e 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -31,6 +31,10 @@ jobs: uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} + - name: Install non-python (homebrew) dependencies + if: ${{ matrix.os == 'macOS-latest' }} + run: | + brew install opencascade cgal gmp mpfr boost - name: Get dependencies and install reboost run: | python -m pip install --upgrade pip wheel setuptools From 623768194247534463fbfce923904f4f4f7251cc Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 00:44:08 +0100 Subject: [PATCH 43/81] improving documentation --- docs/source/manual/hpge.rst | 216 ++++++++++++++++++++++++++++++++- docs/source/manual/optical.rst | 4 +- 2 files changed, 215 insertions(+), 5 deletions(-) diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index 6bfe936..4198fac 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -1,4 +1,214 @@ -hpge simulations -================ +HPGe detector simulations +========================= -Come back later for more complete documentation. +*reboost-hpge* is a sub-package for post-processing the high purity Germanium detector (HPGe) part of *remage* simulations. +It provides a flexible framework to implement a user customised post-processing. + +Command line interface +---------------------- + +A command line tool *reboost-hpge* is created to run the processing. + +.. code-block:: console + + $ reboost-hpge -h + +Different modes are implemented to run the tiers. For example to run the **hit** tier processing (more details in the next section). + +.. code-block:: console + + $ reboost-hpge hit -h + + +*remage* lh5 output format +-------------------------- + +The output simulations from *remage* are described in `remage-docs `_. +By default two ``lgdo.Table`` `docs `_ are stored with the +following format: + +.. code-block:: console + + / + └── hit · HDF5 group + ├── det000 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} + | .... + | .... + └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} + ├── evtid · array<1>{real} + ├── n_part · array<1>{real} + ├── time · array<1>{real} + ├── xloc · array<1>{real} + ├── yloc · array<1>{real} + └── zloc · array<1>{real} + + + +One table is stored per sensitive Germanium detector and a Table of the vertices is also stored. +All the data is stored as (flat) 1D arrays. + +- *edep*: energy deposited in Germanium (in keV). +- *evtid*: index of the simulated event, +- *particle*: Geant4 code for the particle type, +- *time*: time of the event relative to the start of the event, +- *xloc/yloc/xzloc*: Position of the interaction / vertex, +- *n_part*: Number of particles emitted. + +However, this format is not directly comparable to experimental data. + + +Data tiers +---------- + +The processing is defined in terms of several *tiers*: + +- **stp** or "step" the raw *remage* outputs corresponding to Geant4 steps, +- **hit** the data from each channel independently after grouping in discrete physical interactions in the detector. +- **evt** or "event" the data combining the information from various detectors. + +The processing is divided into two steps :func:`build_hit` ``build_evt`` [WIP]. + +Hit tier processing +------------------- + +The processing is based on a YAML or JSON configuration file. For example: + +.. code-block:: json + + { + "channels": [ + "det000", + "det001", + "det002", + "det003" + ], + "outputs": [ + "t0", + "truth_energy_sum", + "smeared_energy_sum", + "evtid" + ], + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" + }, + "locals": { + "hpge": "reboost.hpge.utils.get_hpge()" + + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)" + }, + "smeared_energy_sum": { + "description": "summed energy after convolution with energy response.", + "mode": "function", + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + } + + } + } + +It is necessary to provide several sub-dictionaries: + +- **channels**: list of HPGe channels to process. +- **outputs**: list of fields for the output file. +- **locals**: get objects used by the processors (passed as ``locals`` to ``LGDO.Table.eval``) +- **step_group**: this should describe the function that groups the Geant4 steps into physical *hits*. +- **operations**: further computations / manipulations to apply. + +The **step_group** block sets the structure of the output file, this function reformats the flat input table into a table +with a jagged structure where each row corresponds to a physical hit in the detector. For example: + +.. code-block:: console + + evtid: [0 , 0, 1, ... ] + edep: [101.2, 201.2, 303.7, ... ] + time: [0 , 0.1 , 0, ... ] + .... + +Becomes a Table of ``VectorOfVectors`` with a jagged structure. For example: + +.. code-block:: console + + evtid: [[0 , 0], [ 1],[...],... ] + edep: [[101.2, 201.2], [303.7],[...],... ] + time: [[0 , 0.1], [ 0],[...],... ] + .... + +The recommended tool to manipulate jagged arrays is awkward `[docs] `_ and much of *reboost* is based on this. + + +It is necessary to chose a function to perform this step grouping, this function must take in the *remage* output table and return +a table where all the input arrays are converted to ``LGDO.VectorOfVectors`` with a jagged structure. In the expression of the function *stp* is an alias +for the input *remage* Table. This then must return the original LH5 table with the same fields as above restructured so each field is a ``VectorOfVectors``. +In addition a ``global_evtid`` field is adding which represents the index of the event over all input files. + +Next a set of operations can be specified, these can perform any operation that doesn't change the length of the data. They can be either basic numerical operations +(including awkward or numpy) or be specified by a function. The functions can reference several variables: + +- **hit** the output table of step grouping (note that the table is constantly updated so the order of operations is important), +- **pars** a named tuple of parameters (more details later) for this detector, +- **hpge** the ``legendhpges.HPGe`` object for this detector, +- **phy_vol** the ``pygometry`` physical volume for the detector. + +Finally the outputs field specifies the columns of the Table to include in the output table. + +lh5 i/o operations +------------------ + +:func:`build_hit` contains several options to handle i/o of lh5 files. + +Typically raw geant4 output files can be very large (many GB) so it is not desirable or feasible to read the full file into memory. +Instead the :class:`lgdo.lh5.LH5Ierator` is used to handle iteration over chunks of files keeping memory use reasonable. The *buffer* keyword argument +to :func:`build_hit` controls the size of the buffer. + +It is possible to specify a list of files of use wildcards, the *merge_input_files* argument controls whether the outputs are merged or kept as separate files. + +Finally, it is sometimes desirable to process a subset of the simulated events, for example to split the simulation by run or period. The *n_evtid* and *start_evtid* +keywords arguments control the first simulation index to process and the number of events. Note that the indices refer to the *global* evtid when multiple files are used. + +parameters and other *local* variables +-------------------------------------- + +Often it is neccesary to include processors that depend on parameters (which) may vary by detector. To enable this the user can specify a dictonary of +parameters with the *pars* keyword, this should contain a sub-dictionary per detector for example: + +.. code-block:: json + + { + "det000": { + "reso": 1, + "fccd": 0.1, + "phy_vol_name":"det_phy", + "meta_name": "icpc.json" + } + } + +This dictionary is internally converted into a python ``NamedTuple`` to make cleaner syntax. The named tuple for each detector is then passed as a +``local`` dictonary to the evaulation of the operations with name "pars". + +In addition, for many post-processing applications it is neccesary for the processor functions to know the geometry. This is made possible +by passing the path to the GDML file and the path to the metadata ("diodes" folder) with the *gdml* and *meta_path* arguments to build_hit. + + + +adding new processors +--------------------- + +Any python function diff --git a/docs/source/manual/optical.rst b/docs/source/manual/optical.rst index 2e3ad69..caf38b7 100644 --- a/docs/source/manual/optical.rst +++ b/docs/source/manual/optical.rst @@ -1,4 +1,4 @@ -optical simulations -=================== +Optical (SiPM) simulations processing +===================================== Come back later for more complete documentation. From 1370087a87b1087c7109a3c0f67844377b03a7f7 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 00:44:25 +0100 Subject: [PATCH 44/81] change FileInfo into class (cleaner) --- src/reboost/hpge/utils.py | 56 ++++++++++++++++++--------------------- 1 file changed, 26 insertions(+), 30 deletions(-) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index c941602..d51559c 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -7,6 +7,7 @@ import re from collections import namedtuple from pathlib import Path +from typing import NamedTuple import awkward as ak import legendhpges @@ -21,29 +22,28 @@ reg = pyg4ometry.geant4.Registry() -FileInfo = namedtuple( - "FileInfo", - [ - "file_list", - "file_indices", - "file_start_global_evtids", - "first_global_evtid", - "last_global_evtid", - ], -) -FileInfo.__doc__ = "NamedTuple storing the information on the input files" -FileInfo.file_list.__doc__ = "list of strings of the selected files." -FileInfo.file_indices.__doc__ = "list of integers of the indices of the files." -FileInfo.file_start_global_evtids.__doc__ = ( - "list of integers of the first global evtid for each file." -) -FileInfo.first_global_evtid.__doc__ = "first global evtid to process." -FileInfo.last_global_evtid.__doc__ = "Last global evtid to process." +class FileInfo(NamedTuple): + """NamedTuple storing the information on the input files""" + + file_list: list[str] + """list of strings of the selected files.""" + + file_indices: list[int] + """list of integers of the indices of the files.""" + + file_start_global_evtids: list[int] + """list of integers of the first global evtid for each file.""" + + first_global_evtid: int + """first global evtid to process.""" + + last_global_evtid: int + """Last global evtid to process.""" def get_selected_files( file_list: list[str], table: str, n_evtid: int | None, start_evtid: int -) -> tuple[list, list, list]: +) -> FileInfo: """Get the files to read based on removing those with global evtid out of the selected range. - expands wildcards, @@ -63,12 +63,7 @@ def get_selected_files( Returns ------- - `FileInfo` named tuple with fields: - - `file_list`:list of files. - - `file_indices`: indices of selected files. - - `file_start_global_evtids`: first global `evtid` in each file. - - `first_global_evtid`: first global evtid to process - - `last_global_evtid`: last global evtid to process. + `FileInfo` object with information on the files. """ # expand wildcards expanded_list_file_in = get_file_list(path=file_list) @@ -214,7 +209,7 @@ def get_include_chunk( return (high >= start_glob_evtid) & (low <= end_glob_evtid) -def get_hpge(meta_path: str, pars: dict, detector: str) -> legendhpges.HPGe: +def get_hpge(meta_path: str | None, pars: dict, detector: str) -> legendhpges.HPGe: """Extract the :class:`legendhpges.HPGe` object from metadata. Parameters @@ -231,10 +226,11 @@ def get_hpge(meta_path: str, pars: dict, detector: str) -> legendhpges.HPGe: hpge the `legendhpges` object for the detector. """ - - meta_name = pars.get("meta_name", f"{detector}.json") - meta_dict = Path(meta_path) / Path(meta_name) - return legendhpges.make_hpge(meta_dict, registry=reg) + if metadata_path is not None: + meta_name = pars.get("meta_name", f"{detector}.json") + meta_dict = Path(meta_path) / Path(meta_name) + return legendhpges.make_hpge(meta_dict, registry=reg) + None def get_phy_vol( From 54663e07735dbcaeb5bceb66559bfd24b46c786c Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 00:49:44 +0100 Subject: [PATCH 45/81] [docs] improve documentation and start working on locals option --- docs/source/manual/hpge.rst | 8 ++++---- src/reboost/hpge/hit.py | 13 ++++++++----- src/reboost/hpge/utils.py | 4 ++-- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index 4198fac..b49d183 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -186,7 +186,7 @@ keywords arguments control the first simulation index to process and the number parameters and other *local* variables -------------------------------------- -Often it is neccesary to include processors that depend on parameters (which) may vary by detector. To enable this the user can specify a dictonary of +Often it is necessary to include processors that depend on parameters (which) may vary by detector. To enable this the user can specify a dictionary of parameters with the *pars* keyword, this should contain a sub-dictionary per detector for example: .. code-block:: json @@ -200,10 +200,10 @@ parameters with the *pars* keyword, this should contain a sub-dictionary per det } } -This dictionary is internally converted into a python ``NamedTuple`` to make cleaner syntax. The named tuple for each detector is then passed as a -``local`` dictonary to the evaulation of the operations with name "pars". +This dictionary is internally converted into a python ``NamedTuple`` to make cleaner syntax. The named tuple for each detector is then passed as a +``local`` dictionary to the evaluation of the operations with name "pars". -In addition, for many post-processing applications it is neccesary for the processor functions to know the geometry. This is made possible +In addition, for many post-processing applications it is necessary for the processor functions to know the geometry. This is made possible by passing the path to the GDML file and the path to the metadata ("diodes" folder) with the *gdml* and *meta_path* arguments to build_hit. diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index f772394..c2ac7af 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,6 +1,7 @@ from __future__ import annotations import logging +from typing import NamedTuple import awkward as ak import legendhpges @@ -48,6 +49,11 @@ def step_group(data: Table, group_config: dict) -> Table: return eval(group_func, globs, locs) +def get_locals(local_dict: dict, pars: NamedTuple, detector: str, meta_path: str) -> dict: + """Compute any local variables needed for processing""" + raise NotImplementedError + + def eval_expression( table: Table, info: dict, @@ -261,11 +267,8 @@ def build_hit( log.info(msg) # get HPGe and phy_vol object to pass to build_hit - hpge = ( - utils.get_hpge(metadata_path, pars=pars, detector=d) - if (metadata_path is not None) - else None - ) + hpge = utils.get_hpge(metadata_path, pars=pars, detector=d) + phy_vol = utils.get_phy_vol(reg, pars=pars, detector=d) is_first_chan = bool(ch_idx == 0) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index d51559c..5f4e0cf 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -226,11 +226,11 @@ def get_hpge(meta_path: str | None, pars: dict, detector: str) -> legendhpges.HP hpge the `legendhpges` object for the detector. """ - if metadata_path is not None: + if meta_path is not None: meta_name = pars.get("meta_name", f"{detector}.json") meta_dict = Path(meta_path) / Path(meta_name) return legendhpges.make_hpge(meta_dict, registry=reg) - None + return None def get_phy_vol( From 097b9e6c9cc0d21849275a173ed808356db9281f Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 15:32:15 +0100 Subject: [PATCH 46/81] add option to specify local objects in config --- docs/source/manual/hpge.rst | 32 +++- src/reboost/hpge/hit.py | 98 ++++++----- src/reboost/hpge/processors.py | 2 +- src/reboost/hpge/utils.py | 13 +- tests/test_hpge_distance_to_surface.py | 6 +- tests/test_hpge_hit.py | 215 ++++++++++++++----------- tests/test_hpge_utils.py | 9 +- 7 files changed, 228 insertions(+), 147 deletions(-) diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index b49d183..207f709 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -13,7 +13,7 @@ A command line tool *reboost-hpge* is created to run the processing. $ reboost-hpge -h -Different modes are implemented to run the tiers. For example to run the **hit** tier processing (more details in the next section). +Different modes are implemented to run the tiers. For example to run the *hit* tier processing (more details in the next section). .. code-block:: console @@ -101,8 +101,7 @@ The processing is based on a YAML or JSON configuration file. For example: "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" }, "locals": { - "hpge": "reboost.hpge.utils.get_hpge()" - + "hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)" }, "operations": { "t0": { @@ -205,10 +204,35 @@ This dictionary is internally converted into a python ``NamedTuple`` to make cle In addition, for many post-processing applications it is necessary for the processor functions to know the geometry. This is made possible by passing the path to the GDML file and the path to the metadata ("diodes" folder) with the *gdml* and *meta_path* arguments to build_hit. +From the GDML file the ``pyg4ometry.geant4.Registry`` is extracted. + +To allow the flexibility to write processors depending on arbitrary (more complicated python objects), it is possible to add the *locals* dictionary +to the config file. The code will then evaluate the supplied expression for each sub-dictionary. These expressions can depend on: + +- the *remage* detector name: "detector", +- the path to the metadata: "meta", +- the geant4 registry: "reg", +- the parameters for this detector: "pars". + +These expressions are then evaluated (once per detector) and added to the *locals* dictionary of ``Table.eval``, so can be references in the expressions. + +For example one useful object for post-processing is the `legendhpges.base.HPGe `_ object for the detector. +This can be constructed from the metadata using. + +.. code-block:: json + + {"hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)"} + +This will then create the hpge object for each detector and add it to the "locals" mapping of "eval" so it can be used. +Possible intended use case of this functionality are: + - extracting detector mappings (eg drift time maps), + - extracting the kernel of a machine learning model. + - any more complicated (non-JSON serialisable objects). adding new processors --------------------- -Any python function +Any python function can be a ``reboost.hit`` processor. The only requirement is that it should return a :class:`VectorOfVectors`, :class:`Array`` or :class:`ArrayOfEqualSizedArrays` +with the same length as the hit table. This means processors can act on subarrays (``axis=-1`` in awkward syntax) but should not combine multiple rows of the hit table. diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index c2ac7af..62e982a 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,10 +1,8 @@ from __future__ import annotations import logging -from typing import NamedTuple import awkward as ak -import legendhpges import numpy as np import pyg4ometry from lgdo import Array, ArrayOfEqualSizedArrays, Table, VectorOfVectors, lh5 @@ -49,17 +47,58 @@ def step_group(data: Table, group_config: dict) -> Table: return eval(group_func, globs, locs) -def get_locals(local_dict: dict, pars: NamedTuple, detector: str, meta_path: str) -> dict: - """Compute any local variables needed for processing""" - raise NotImplementedError +def get_locals( + local_info: dict, + pars_dict: dict | None, + detector: str|None=None, + meta_path: str|None=None, + reg: pyg4ometry.geant4.Registry|None =None, +) -> dict: + """Compute any local variables needed for processing. + + Parameters + ---------- + local_info + config file block of the local objects to compute. For example: + + ..code-block:: + + {"hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)"} + + pars_dict + dictionary of parameters + detector + remage name for the dete + meta_path + path to thhe diodes folder of the metadata + reg + geant4 registry of the experiment + + Returns + ------- + dictionary of local variables to pass to eval + """ + local_dict = {} + + # get parameters named tuple + if pars_dict is not None: + pars_tuple = utils.dict2tuple(pars_dict) + local_dict = {"pars": pars_tuple} + + objs = {"pars": pars_tuple, "detector": detector, "meta": meta_path, "reg": reg} + + for name, eval_str in local_info.items(): + proc_func, globs = utils.get_function_string(eval_str) + + msg = f"extracting local object with {eval_str} " + log.debug(msg) + obj = eval(proc_func, globs, objs) + local_dict = local_dict | {name: obj} + return local_dict def eval_expression( - table: Table, - info: dict, - pars: dict | None = None, - hpge: legendhpges.HPGe | None = None, - phy_vol: pyg4ometry.geant4.PhysicalVolume | None = None, + table: Table, info: dict, local_dict: dict | None ) -> Array | ArrayOfEqualSizedArrays | VectorOfVectors: """Evaluate an expression returning an LGDO object. @@ -81,17 +120,10 @@ def eval_expression( in which case a simple expression is based (only involving numpy, awkward or inbuilt python functions), or `function` in which case an arbitrary function is passed (for example defined in processors). - There are several objects passed to the evaluation as 'locals' which can be references by the expression. - - `pars`: dictionary of parameters (converted to namedtuple) (see `pars` argument), - - `hpge`: the legendhpges object for this detector (see `hpge` argument), - - `phy_vol`: the physical volume of the detector (see `phy` argument). + There are several objects passed to the evaluation as 'locals' determined by the `local_dict` - pars - dict of parameters, can contain any fields passed to the expression prefixed by `pars.`. - hpge - `legendhpges` object with the information on the HPGe detector. - phy_vol - `pyg4ometry.geant4.PhysicalVolume` object from GDML, + local_dict + mapping of local variables for the evaluation Returns ------- @@ -101,15 +133,8 @@ def eval_expression( ---- In future the passing of local variables (pars,hpge,reg) to the evaluation should be make more generic. """ - local_dict = {} - - if pars is not None: - pars_tuple = utils.dict2tuple(pars) - local_dict = {"pars": pars_tuple} - if phy_vol is not None: - local_dict |= {"phy_vol": phy_vol} - if hpge is not None: - local_dict |= {"hpge": hpge} + if local_dict is None: + local_dict = {} if info["mode"] == "eval": # replace hit. @@ -165,6 +190,7 @@ def build_hit( lh5 group name for input proc_config the configuration file for the processing. Must contain the fields `channels`, `outputs`, `step_group` and operations`. + Optionally can also contain the `locals` field to extract other non-JSON serializable objects used by the processors. For example: .. code-block:: json @@ -186,6 +212,9 @@ def build_hit( "description": "group steps by time and evtid.", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" }, + "locals": { + "hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)" + }, "operations": { "t0": { "description": "first time in the hit.", @@ -234,8 +263,6 @@ def build_hit( length of buffer gdml path to the input gdml file. - macro - path to the macro file. metadata_path path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) merge_input_files @@ -266,10 +293,9 @@ def build_hit( msg = f"...running hit tier for {d}" log.info(msg) - # get HPGe and phy_vol object to pass to build_hit - hpge = utils.get_hpge(metadata_path, pars=pars, detector=d) - - phy_vol = utils.get_phy_vol(reg, pars=pars, detector=d) + # get local variables + local_info = proc_config.get("locals",{}) + local_dict = get_locals(local_info,pars_dict=pars.get(d,{}), meta_path=metadata_path, detector=d, reg=reg) is_first_chan = bool(ch_idx == 0) is_first_file = bool(file_idx == 0) @@ -340,7 +366,7 @@ def build_hit( msg = f"adding column {name}" log.debug(msg) - col = eval_expression(grouped, info, pars=pars, phy_vol=phy_vol, hpge=hpge) + col = eval_expression(grouped, info, local_dict=local_dict) grouped.add_field(name, col) # remove unwanted columns diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index bdc17ea..f0c9e1d 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -198,4 +198,4 @@ def distance_to_surface( # distance calc itself distances = hpge.distance_to_surface(local_positions, surface_indices=surface_indices) - return ak.unflatten(distances, size) + return VectorOfVectors(ak.unflatten(distances, size)) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 5f4e0cf..df35c76 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -209,7 +209,7 @@ def get_include_chunk( return (high >= start_glob_evtid) & (low <= end_glob_evtid) -def get_hpge(meta_path: str | None, pars: dict, detector: str) -> legendhpges.HPGe: +def get_hpge(meta_path: str | None, pars: NamedTuple, detector: str) -> legendhpges.HPGe: """Extract the :class:`legendhpges.HPGe` object from metadata. Parameters @@ -217,7 +217,7 @@ def get_hpge(meta_path: str | None, pars: dict, detector: str) -> legendhpges.HP meta_path path to the folder with the `diodes` metadata. pars - dictionary of parameters. + named tuple of parameters. detector remage output name for the detector @@ -226,15 +226,16 @@ def get_hpge(meta_path: str | None, pars: dict, detector: str) -> legendhpges.HP hpge the `legendhpges` object for the detector. """ + reg = pyg4ometry.geant4.Registry() if meta_path is not None: - meta_name = pars.get("meta_name", f"{detector}.json") + meta_name = pars.meta_name if ("meta_name" in pars._fields) else f"{detector}.json" meta_dict = Path(meta_path) / Path(meta_name) return legendhpges.make_hpge(meta_dict, registry=reg) return None def get_phy_vol( - reg: pyg4ometry.geant4.Registry | None, pars: dict, detector: str + reg: pyg4ometry.geant4.Registry | None, pars: NamedTuple, detector: str ) -> pyg4ometry.geant4.PhysicalVolume: """Extract the :class:`pyg4ometry.geant4.PhysicalVolume` object from GDML @@ -243,7 +244,7 @@ def get_phy_vol( reg Geant4 registry from GDML pars - dictionary of parameters. + named tuple of parameters. detector remage output name for the detector. @@ -253,7 +254,7 @@ def get_phy_vol( the `pyg4ometry.geant4.PhysicalVolume` object for the detector """ if reg is not None: - phy_name = pars.get("phy_vol_name", f"{detector}") + phy_name = pars.phy_vol_name if ("phy_vol_name" in pars._fields) else f"{detector}" return reg.physicalVolumeDict[phy_name] return None diff --git a/tests/test_hpge_distance_to_surface.py b/tests/test_hpge_distance_to_surface.py index 7b1789c..3e77b86 100644 --- a/tests/test_hpge_distance_to_surface.py +++ b/tests/test_hpge_distance_to_surface.py @@ -4,7 +4,7 @@ import pytest from legendhpges import make_hpge from legendtestdata import LegendTestData - +from lgdo import types from reboost.hpge.processors import distance_to_surface @@ -32,3 +32,7 @@ def test_distance_to_surface(test_data_configs): ak.num(distance_to_surface(pos.xloc, pos.yloc, pos.zloc, gedet, dist, None), axis=1) == [3, 1, 3] ) + + # check it can be written + + assert isinstance(distance_to_surface(pos.xloc, pos.yloc, pos.zloc, gedet, dist, None),types.LGDO) \ No newline at end of file diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index 401b6c4..6701ba5 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -7,6 +7,7 @@ import pyg4ometry import pytest from legendhpges import make_hpge +from legendhpges.base import HPGe from legendtestdata import LegendTestData from lgdo import Table, lh5 from pyg4ometry import geant4 @@ -22,6 +23,13 @@ def test_data_configs(): ldata.checkout("5f9b368") return ldata.get_path("legend/metadata/hardware/detectors/germanium/diodes") +def test_get_locals(test_data_configs): + + local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)"} + + local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json"},detector="det001",meta_path=test_data_configs) + + assert isinstance(local_dict["hpge"],HPGe) def test_eval(): in_arr = ak.Array( @@ -49,13 +57,15 @@ def test_eval(): "expression": "reboost.hpge.processors.smear_energies(hit.e_sum,reso=pars.reso)", } pars = {"reso": 2} - - assert np.size(hit.eval_expression(tab, func_eval, pars).view_as("np")) == 5 + # test get locals + local_dict = hit.get_locals({},pars_dict=pars) + assert np.size(hit.eval_expression(tab, func_eval, local_dict).view_as("np")) == 5 def test_eval_with_hpge(test_data_configs): - reg = geant4.Registry() - gedet = make_hpge(test_data_configs + "/V99000A.json", registry=reg) + + local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)"} + local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json"},detector="det001",meta_path=test_data_configs) pos = ak.Array( { @@ -72,14 +82,20 @@ def test_eval_with_hpge(test_data_configs): } assert ak.all( - ak.num(hit.eval_expression(tab, func_eval, {}, hpge=gedet, phy_vol=None), axis=1) + ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) == [3, 1, 3] ) def test_eval_with_hpge_and_phy_vol(test_data_configs): - reg = geant4.Registry() - gedet = make_hpge(test_data_configs + "/V99000A.json", registry=reg) + gdml_path = configs / pathlib.Path("geom.gdml") + + gdml = pyg4ometry.gdml.Reader(gdml_path).getRegistry() + + local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol":"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)" + } + local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json","phy_vol_name":"det_phy_1"},detector="det001",meta_path=test_data_configs,reg=gdml) pos = ak.Array( { @@ -94,15 +110,9 @@ def test_eval_with_hpge_and_phy_vol(test_data_configs): "mode": "function", "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)", } - gdml_path = configs / pathlib.Path("geom.gdml") - - gdml = pyg4ometry.gdml.Reader(gdml_path).getRegistry() - - # read with the det_phy_vol_name - phy = utils.get_phy_vol(gdml, {"phy_vol_name": "det_phy_1"}, "det001") assert ak.all( - ak.num(hit.eval_expression(tab, func_eval, {}, hpge=gedet, phy_vol=phy), axis=1) + ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) == [3, 1, 3] ) @@ -115,17 +125,24 @@ def test_reboost_input_file(tmp_path): evtid_1 = np.sort(rng.integers(int(1e5), size=(int(1e6)))) time_1 = rng.uniform(low=0, high=1, size=(int(1e6))) edep_1 = rng.uniform(low=0, high=1000, size=(int(1e6))) + pos_x_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) + pos_y_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) + pos_z_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) + # make it not divide by the buffer len evtid_2 = np.sort(rng.integers(int(1e5), size=(30040))) time_2 = rng.uniform(low=0, high=1, size=(30040)) edep_2 = rng.uniform(low=0, high=1000, size=(30040)) + pos_x_2 = rng.uniform(low=-50,high=50,size=(30040)) + pos_y_2 = rng.uniform(low=-50,high=50,size=(30040)) + pos_z_2 = rng.uniform(low=-50,high=50,size=(30040)) vertices_1 = ak.Array({"evtid": np.arange(int(1e5))}) vertices_2 = ak.Array({"evtid": np.arange(int(1e5))}) - arr_1 = ak.Array({"evtid": evtid_1, "time": time_1, "edep": edep_1}) - arr_2 = ak.Array({"evtid": evtid_2, "time": time_2, "edep": edep_2}) + arr_1 = ak.Array({"evtid": evtid_1, "time": time_1, "edep": edep_1, "xloc":pos_x_1,"yloc":pos_y_1,"zloc":pos_z_1}) + arr_2 = ak.Array({"evtid": evtid_2, "time": time_2, "edep": edep_2, "xloc":pos_x_2,"yloc":pos_y_2,"zloc":pos_z_2}) lh5.write(Table(vertices_1), "hit/vertices", tmp_path / "file1.lh5", wo_mode="of") lh5.write(Table(vertices_2), "hit/vertices", tmp_path / "file2.lh5", wo_mode="of") @@ -162,36 +179,18 @@ def test_build_hit(test_reboost_input_file): }, } - hit.build_hit( - str(test_reboost_input_file / "out.lh5"), - [str(test_reboost_input_file / "file1.lh5")], - in_field="hit", - out_field="hit", - proc_config=proc_config, - pars={}, - buffer=100000, - ) - hit.build_hit( - str(test_reboost_input_file / "out_rem.lh5"), - [str(test_reboost_input_file / "file2.lh5")], - in_field="hit", - out_field="hit", - proc_config=proc_config, - pars={}, - buffer=10000, - ) - - # now with wildcard - hit.build_hit( - str(test_reboost_input_file / "out_merge.lh5"), - [str(test_reboost_input_file / "file*.lh5")], - in_field="hit", - out_field="hit", - proc_config=proc_config, - pars={}, - buffer=100000, - merge_input_files=True, - ) + for output_file,input_file in zip(["out.lh5","out_rem.lh5","out_merge.lh5"], + ["file1.lh5","file2.lh5","file*.lh5"]): + hit.build_hit( + str(test_reboost_input_file / output_file), + [str(test_reboost_input_file / input_file)], + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=100000, + ) + hit.build_hit( str(test_reboost_input_file / "out.lh5"), [str(test_reboost_input_file / "file*.lh5")], @@ -261,56 +260,23 @@ def test_build_hit_some_row(test_reboost_input_file): buffer=100000, ) - # test read only some events - hit.build_hit( - str(test_reboost_input_file / "out_some_rows.lh5"), - [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], - n_evtid=int(1e4), - start_evtid=0, - in_field="hit", - out_field="hit", - proc_config=proc_config, - pars={}, - buffer=100000, - ) - - hit.build_hit( - str(test_reboost_input_file / "out_rest_rows.lh5"), - [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], - n_evtid=int(1e5 - 1e4), - start_evtid=int(1e4), - in_field="hit", - out_field="hit", - proc_config=proc_config, - pars={}, - buffer=100000, - ) - # read all of file 1 - hit.build_hit( - str(test_reboost_input_file / "out_all_file_one.lh5"), - [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], - n_evtid=int(1e5), - start_evtid=0, - in_field="hit", - out_field="hit", - proc_config=proc_config, - pars={}, - buffer=100000, - ) - - # read a mix of the two files - hit.build_hit( - str(test_reboost_input_file / "out_mix.lh5"), - [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], - n_evtid=int(1e5), - start_evtid=1000, - in_field="hit", - out_field="hit", - proc_config=proc_config, - pars={}, - buffer=100000, - ) + for n_ev,s_ev,out in zip([int(1e4),int(1e5-1e4),int(1e5),int(1e5)], + [0,int(1e4),0,1000],["out_some_rows.lh5","out_rest_rows.lh5","out_all_file_one.lh5","out_mix.lh5"]): + + # test read only some events + hit.build_hit( + str(test_reboost_input_file / out), + [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], + n_evtid=n_ev, + start_evtid=s_ev, + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=100000, + ) + tab_some = lh5.read("hit/det001", str(test_reboost_input_file / "out_some_rows.lh5")).view_as( "ak" ) @@ -324,3 +290,62 @@ def test_build_hit_some_row(test_reboost_input_file): tab_merge = ak.concatenate((tab_some, tab_rest)) assert ak.all(ak.all(tab_merge.evtid == tab_1.evtid, axis=-1)) + + + +def test_build_hit_with_locals(test_reboost_input_file,test_data_configs): + + + proc_config = { + "channels": [ + "det001", + ], + "outputs": ["t0", "evtid","distance_to_surface"], + + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + }, + "locals":{ + "hpge":"reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol":"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)" + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)", + }, + "smear_energy_sum":{ + "description": "summed energy after convolution with energy response.", + "mode": "function", + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + }, + "distance_to_surface":{ + "description":"distance to the nplus surface", + "mode":"function", + "expression":"reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)" + } + } + } + gdml_path = configs / pathlib.Path("geom.gdml") + meta_path = test_data_configs + # complete check on the processing chain including parameters / local variables + + hit.build_hit( + str(test_reboost_input_file / "out.lh5"), + [str(test_reboost_input_file / "file*.lh5")], + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={"det001":{"reso":1,"meta_name":"V99000A.json","phy_vol_name":"det_phy_1"}}, + buffer=100000, + merge_input_files=False, + metadata_path = meta_path, + gdml =gdml_path + ) \ No newline at end of file diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 3855b4f..6f4a04c 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -22,6 +22,7 @@ get_num_simulated, get_phy_vol, load_dict, + dict2tuple ) configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") @@ -142,11 +143,11 @@ def test_data_configs(): def test_get_hpge(test_data_configs): # specify name in pars - hpge = get_hpge(str(test_data_configs), {"meta_name": "C99000A.json"}, "det001") + hpge = get_hpge(str(test_data_configs), dict2tuple({"meta_name": "C99000A.json"}), "det001") assert isinstance(hpge, HPGe) # now read without metaname - hpge_ic = get_hpge(str(test_data_configs), {}, "V99000A") + hpge_ic = get_hpge(str(test_data_configs), dict2tuple({}), "V99000A") assert isinstance(hpge_ic, HPGe) @@ -156,12 +157,12 @@ def test_get_phy_vol(): gdml = pyg4ometry.gdml.Reader(gdml_path).getRegistry() # read with the det_phy_vol_name - phy = get_phy_vol(gdml, {"phy_vol_name": "det_phy_1"}, "det001") + phy = get_phy_vol(gdml, dict2tuple({"phy_vol_name": "det_phy_1"}), "det001") assert isinstance(phy, pyg4ometry.geant4.PhysicalVolume) # read without - phy = get_phy_vol(gdml, {}, "det_phy_0") + phy = get_phy_vol(gdml, dict2tuple({}), "det_phy_0") assert isinstance(phy, pyg4ometry.geant4.PhysicalVolume) From 133da5ef76ea3bbfc225bf14b502e743cf3f6f6e Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 15:33:43 +0100 Subject: [PATCH 47/81] ak.min to np.min for 1D array --- src/reboost/hpge/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index df35c76..087a227 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -204,8 +204,8 @@ def get_include_chunk( boolean flag of whether to include in the chunk. """ - low = ak.min(global_evtid, axis=0) - high = ak.max(global_evtid, axis=0) + low = np.min(global_evtid) + high = np.max(global_evtid) return (high >= start_glob_evtid) & (low <= end_glob_evtid) From 9f0a9ba8f419521661749f27d9e16c91a8b2d738 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 15 Nov 2024 14:34:34 +0000 Subject: [PATCH 48/81] style: pre-commit fixes --- src/reboost/hpge/hit.py | 14 +-- tests/test_hpge_distance_to_surface.py | 5 +- tests/test_hpge_hit.py | 143 +++++++++++++++---------- tests/test_hpge_utils.py | 2 +- 4 files changed, 98 insertions(+), 66 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 62e982a..c7f0de6 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -50,9 +50,9 @@ def step_group(data: Table, group_config: dict) -> Table: def get_locals( local_info: dict, pars_dict: dict | None, - detector: str|None=None, - meta_path: str|None=None, - reg: pyg4ometry.geant4.Registry|None =None, + detector: str | None = None, + meta_path: str | None = None, + reg: pyg4ometry.geant4.Registry | None = None, ) -> dict: """Compute any local variables needed for processing. @@ -60,7 +60,7 @@ def get_locals( ---------- local_info config file block of the local objects to compute. For example: - + ..code-block:: {"hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)"} @@ -294,8 +294,10 @@ def build_hit( log.info(msg) # get local variables - local_info = proc_config.get("locals",{}) - local_dict = get_locals(local_info,pars_dict=pars.get(d,{}), meta_path=metadata_path, detector=d, reg=reg) + local_info = proc_config.get("locals", {}) + local_dict = get_locals( + local_info, pars_dict=pars.get(d, {}), meta_path=metadata_path, detector=d, reg=reg + ) is_first_chan = bool(ch_idx == 0) is_first_file = bool(file_idx == 0) diff --git a/tests/test_hpge_distance_to_surface.py b/tests/test_hpge_distance_to_surface.py index 3e77b86..1114903 100644 --- a/tests/test_hpge_distance_to_surface.py +++ b/tests/test_hpge_distance_to_surface.py @@ -5,6 +5,7 @@ from legendhpges import make_hpge from legendtestdata import LegendTestData from lgdo import types + from reboost.hpge.processors import distance_to_surface @@ -35,4 +36,6 @@ def test_distance_to_surface(test_data_configs): # check it can be written - assert isinstance(distance_to_surface(pos.xloc, pos.yloc, pos.zloc, gedet, dist, None),types.LGDO) \ No newline at end of file + assert isinstance( + distance_to_surface(pos.xloc, pos.yloc, pos.zloc, gedet, dist, None), types.LGDO + ) diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index 6701ba5..f0b4e8e 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -6,13 +6,11 @@ import numpy as np import pyg4ometry import pytest -from legendhpges import make_hpge from legendhpges.base import HPGe from legendtestdata import LegendTestData from lgdo import Table, lh5 -from pyg4ometry import geant4 -from reboost.hpge import hit, utils +from reboost.hpge import hit configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") @@ -23,13 +21,19 @@ def test_data_configs(): ldata.checkout("5f9b368") return ldata.get_path("legend/metadata/hardware/detectors/germanium/diodes") -def test_get_locals(test_data_configs): +def test_get_locals(test_data_configs): local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)"} - local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json"},detector="det001",meta_path=test_data_configs) + local_dict = hit.get_locals( + local_info, + pars_dict={"meta_name": "V99000A.json"}, + detector="det001", + meta_path=test_data_configs, + ) + + assert isinstance(local_dict["hpge"], HPGe) - assert isinstance(local_dict["hpge"],HPGe) def test_eval(): in_arr = ak.Array( @@ -58,14 +62,18 @@ def test_eval(): } pars = {"reso": 2} # test get locals - local_dict = hit.get_locals({},pars_dict=pars) + local_dict = hit.get_locals({}, pars_dict=pars) assert np.size(hit.eval_expression(tab, func_eval, local_dict).view_as("np")) == 5 def test_eval_with_hpge(test_data_configs): - local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)"} - local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json"},detector="det001",meta_path=test_data_configs) + local_dict = hit.get_locals( + local_info, + pars_dict={"meta_name": "V99000A.json"}, + detector="det001", + meta_path=test_data_configs, + ) pos = ak.Array( { @@ -81,10 +89,7 @@ def test_eval_with_hpge(test_data_configs): "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, [0,0,0], None)", } - assert ak.all( - ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) - == [3, 1, 3] - ) + assert ak.all(ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) == [3, 1, 3]) def test_eval_with_hpge_and_phy_vol(test_data_configs): @@ -92,10 +97,17 @@ def test_eval_with_hpge_and_phy_vol(test_data_configs): gdml = pyg4ometry.gdml.Reader(gdml_path).getRegistry() - local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", - "phy_vol":"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)" + local_info = { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", } - local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json","phy_vol_name":"det_phy_1"},detector="det001",meta_path=test_data_configs,reg=gdml) + local_dict = hit.get_locals( + local_info, + pars_dict={"meta_name": "V99000A.json", "phy_vol_name": "det_phy_1"}, + detector="det001", + meta_path=test_data_configs, + reg=gdml, + ) pos = ak.Array( { @@ -111,10 +123,7 @@ def test_eval_with_hpge_and_phy_vol(test_data_configs): "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)", } - assert ak.all( - ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) - == [3, 1, 3] - ) + assert ak.all(ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) == [3, 1, 3]) # test the full processing chain @@ -125,24 +134,41 @@ def test_reboost_input_file(tmp_path): evtid_1 = np.sort(rng.integers(int(1e5), size=(int(1e6)))) time_1 = rng.uniform(low=0, high=1, size=(int(1e6))) edep_1 = rng.uniform(low=0, high=1000, size=(int(1e6))) - pos_x_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) - pos_y_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) - pos_z_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) - + pos_x_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) + pos_y_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) + pos_z_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) # make it not divide by the buffer len evtid_2 = np.sort(rng.integers(int(1e5), size=(30040))) time_2 = rng.uniform(low=0, high=1, size=(30040)) edep_2 = rng.uniform(low=0, high=1000, size=(30040)) - pos_x_2 = rng.uniform(low=-50,high=50,size=(30040)) - pos_y_2 = rng.uniform(low=-50,high=50,size=(30040)) - pos_z_2 = rng.uniform(low=-50,high=50,size=(30040)) + pos_x_2 = rng.uniform(low=-50, high=50, size=(30040)) + pos_y_2 = rng.uniform(low=-50, high=50, size=(30040)) + pos_z_2 = rng.uniform(low=-50, high=50, size=(30040)) vertices_1 = ak.Array({"evtid": np.arange(int(1e5))}) vertices_2 = ak.Array({"evtid": np.arange(int(1e5))}) - arr_1 = ak.Array({"evtid": evtid_1, "time": time_1, "edep": edep_1, "xloc":pos_x_1,"yloc":pos_y_1,"zloc":pos_z_1}) - arr_2 = ak.Array({"evtid": evtid_2, "time": time_2, "edep": edep_2, "xloc":pos_x_2,"yloc":pos_y_2,"zloc":pos_z_2}) + arr_1 = ak.Array( + { + "evtid": evtid_1, + "time": time_1, + "edep": edep_1, + "xloc": pos_x_1, + "yloc": pos_y_1, + "zloc": pos_z_1, + } + ) + arr_2 = ak.Array( + { + "evtid": evtid_2, + "time": time_2, + "edep": edep_2, + "xloc": pos_x_2, + "yloc": pos_y_2, + "zloc": pos_z_2, + } + ) lh5.write(Table(vertices_1), "hit/vertices", tmp_path / "file1.lh5", wo_mode="of") lh5.write(Table(vertices_2), "hit/vertices", tmp_path / "file2.lh5", wo_mode="of") @@ -179,8 +205,9 @@ def test_build_hit(test_reboost_input_file): }, } - for output_file,input_file in zip(["out.lh5","out_rem.lh5","out_merge.lh5"], - ["file1.lh5","file2.lh5","file*.lh5"]): + for output_file, input_file in zip( + ["out.lh5", "out_rem.lh5", "out_merge.lh5"], ["file1.lh5", "file2.lh5", "file*.lh5"] + ): hit.build_hit( str(test_reboost_input_file / output_file), [str(test_reboost_input_file / input_file)], @@ -190,7 +217,7 @@ def test_build_hit(test_reboost_input_file): pars={}, buffer=100000, ) - + hit.build_hit( str(test_reboost_input_file / "out.lh5"), [str(test_reboost_input_file / "file*.lh5")], @@ -260,13 +287,18 @@ def test_build_hit_some_row(test_reboost_input_file): buffer=100000, ) - for n_ev,s_ev,out in zip([int(1e4),int(1e5-1e4),int(1e5),int(1e5)], - [0,int(1e4),0,1000],["out_some_rows.lh5","out_rest_rows.lh5","out_all_file_one.lh5","out_mix.lh5"]): - + for n_ev, s_ev, out in zip( + [int(1e4), int(1e5 - 1e4), int(1e5), int(1e5)], + [0, int(1e4), 0, 1000], + ["out_some_rows.lh5", "out_rest_rows.lh5", "out_all_file_one.lh5", "out_mix.lh5"], + ): # test read only some events hit.build_hit( str(test_reboost_input_file / out), - [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], + [ + str(test_reboost_input_file / "file1.lh5"), + str(test_reboost_input_file / "file2.lh5"), + ], n_evtid=n_ev, start_evtid=s_ev, in_field="hit", @@ -276,7 +308,6 @@ def test_build_hit_some_row(test_reboost_input_file): buffer=100000, ) - tab_some = lh5.read("hit/det001", str(test_reboost_input_file / "out_some_rows.lh5")).view_as( "ak" ) @@ -292,23 +323,19 @@ def test_build_hit_some_row(test_reboost_input_file): assert ak.all(ak.all(tab_merge.evtid == tab_1.evtid, axis=-1)) - -def test_build_hit_with_locals(test_reboost_input_file,test_data_configs): - - +def test_build_hit_with_locals(test_reboost_input_file, test_data_configs): proc_config = { "channels": [ "det001", ], - "outputs": ["t0", "evtid","distance_to_surface"], - + "outputs": ["t0", "evtid", "distance_to_surface"], "step_group": { "description": "group steps by time and evtid.", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", }, - "locals":{ - "hpge":"reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", - "phy_vol":"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)" + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", }, "operations": { "t0": { @@ -321,17 +348,17 @@ def test_build_hit_with_locals(test_reboost_input_file,test_data_configs): "mode": "eval", "expression": "ak.sum(hit.edep,axis=-1)", }, - "smear_energy_sum":{ + "smear_energy_sum": { "description": "summed energy after convolution with energy response.", "mode": "function", - "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)", }, - "distance_to_surface":{ - "description":"distance to the nplus surface", - "mode":"function", - "expression":"reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)" - } - } + "distance_to_surface": { + "description": "distance to the nplus surface", + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)", + }, + }, } gdml_path = configs / pathlib.Path("geom.gdml") meta_path = test_data_configs @@ -343,9 +370,9 @@ def test_build_hit_with_locals(test_reboost_input_file,test_data_configs): in_field="hit", out_field="hit", proc_config=proc_config, - pars={"det001":{"reso":1,"meta_name":"V99000A.json","phy_vol_name":"det_phy_1"}}, + pars={"det001": {"reso": 1, "meta_name": "V99000A.json", "phy_vol_name": "det_phy_1"}}, buffer=100000, merge_input_files=False, - metadata_path = meta_path, - gdml =gdml_path - ) \ No newline at end of file + metadata_path=meta_path, + gdml=gdml_path, + ) diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 6f4a04c..5126277 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -14,6 +14,7 @@ from reboost.hpge.utils import ( _merge_arrays, + dict2tuple, get_file_list, get_files_to_read, get_global_evtid_range, @@ -22,7 +23,6 @@ get_num_simulated, get_phy_vol, load_dict, - dict2tuple ) configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") From f07559448ec41c6cda0cc688ac0820d063486bfd Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 15:34:55 +0100 Subject: [PATCH 49/81] style fixes --- src/reboost/hpge/hit.py | 14 +-- tests/test_hpge_distance_to_surface.py | 5 +- tests/test_hpge_hit.py | 143 +++++++++++++++---------- tests/test_hpge_utils.py | 2 +- 4 files changed, 98 insertions(+), 66 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 62e982a..c7f0de6 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -50,9 +50,9 @@ def step_group(data: Table, group_config: dict) -> Table: def get_locals( local_info: dict, pars_dict: dict | None, - detector: str|None=None, - meta_path: str|None=None, - reg: pyg4ometry.geant4.Registry|None =None, + detector: str | None = None, + meta_path: str | None = None, + reg: pyg4ometry.geant4.Registry | None = None, ) -> dict: """Compute any local variables needed for processing. @@ -60,7 +60,7 @@ def get_locals( ---------- local_info config file block of the local objects to compute. For example: - + ..code-block:: {"hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)"} @@ -294,8 +294,10 @@ def build_hit( log.info(msg) # get local variables - local_info = proc_config.get("locals",{}) - local_dict = get_locals(local_info,pars_dict=pars.get(d,{}), meta_path=metadata_path, detector=d, reg=reg) + local_info = proc_config.get("locals", {}) + local_dict = get_locals( + local_info, pars_dict=pars.get(d, {}), meta_path=metadata_path, detector=d, reg=reg + ) is_first_chan = bool(ch_idx == 0) is_first_file = bool(file_idx == 0) diff --git a/tests/test_hpge_distance_to_surface.py b/tests/test_hpge_distance_to_surface.py index 3e77b86..1114903 100644 --- a/tests/test_hpge_distance_to_surface.py +++ b/tests/test_hpge_distance_to_surface.py @@ -5,6 +5,7 @@ from legendhpges import make_hpge from legendtestdata import LegendTestData from lgdo import types + from reboost.hpge.processors import distance_to_surface @@ -35,4 +36,6 @@ def test_distance_to_surface(test_data_configs): # check it can be written - assert isinstance(distance_to_surface(pos.xloc, pos.yloc, pos.zloc, gedet, dist, None),types.LGDO) \ No newline at end of file + assert isinstance( + distance_to_surface(pos.xloc, pos.yloc, pos.zloc, gedet, dist, None), types.LGDO + ) diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index 6701ba5..f0b4e8e 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -6,13 +6,11 @@ import numpy as np import pyg4ometry import pytest -from legendhpges import make_hpge from legendhpges.base import HPGe from legendtestdata import LegendTestData from lgdo import Table, lh5 -from pyg4ometry import geant4 -from reboost.hpge import hit, utils +from reboost.hpge import hit configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") @@ -23,13 +21,19 @@ def test_data_configs(): ldata.checkout("5f9b368") return ldata.get_path("legend/metadata/hardware/detectors/germanium/diodes") -def test_get_locals(test_data_configs): +def test_get_locals(test_data_configs): local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)"} - local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json"},detector="det001",meta_path=test_data_configs) + local_dict = hit.get_locals( + local_info, + pars_dict={"meta_name": "V99000A.json"}, + detector="det001", + meta_path=test_data_configs, + ) + + assert isinstance(local_dict["hpge"], HPGe) - assert isinstance(local_dict["hpge"],HPGe) def test_eval(): in_arr = ak.Array( @@ -58,14 +62,18 @@ def test_eval(): } pars = {"reso": 2} # test get locals - local_dict = hit.get_locals({},pars_dict=pars) + local_dict = hit.get_locals({}, pars_dict=pars) assert np.size(hit.eval_expression(tab, func_eval, local_dict).view_as("np")) == 5 def test_eval_with_hpge(test_data_configs): - local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)"} - local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json"},detector="det001",meta_path=test_data_configs) + local_dict = hit.get_locals( + local_info, + pars_dict={"meta_name": "V99000A.json"}, + detector="det001", + meta_path=test_data_configs, + ) pos = ak.Array( { @@ -81,10 +89,7 @@ def test_eval_with_hpge(test_data_configs): "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, [0,0,0], None)", } - assert ak.all( - ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) - == [3, 1, 3] - ) + assert ak.all(ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) == [3, 1, 3]) def test_eval_with_hpge_and_phy_vol(test_data_configs): @@ -92,10 +97,17 @@ def test_eval_with_hpge_and_phy_vol(test_data_configs): gdml = pyg4ometry.gdml.Reader(gdml_path).getRegistry() - local_info = {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", - "phy_vol":"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)" + local_info = { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", } - local_dict = hit.get_locals(local_info,pars_dict={"meta_name":"V99000A.json","phy_vol_name":"det_phy_1"},detector="det001",meta_path=test_data_configs,reg=gdml) + local_dict = hit.get_locals( + local_info, + pars_dict={"meta_name": "V99000A.json", "phy_vol_name": "det_phy_1"}, + detector="det001", + meta_path=test_data_configs, + reg=gdml, + ) pos = ak.Array( { @@ -111,10 +123,7 @@ def test_eval_with_hpge_and_phy_vol(test_data_configs): "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)", } - assert ak.all( - ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) - == [3, 1, 3] - ) + assert ak.all(ak.num(hit.eval_expression(tab, func_eval, local_dict), axis=1) == [3, 1, 3]) # test the full processing chain @@ -125,24 +134,41 @@ def test_reboost_input_file(tmp_path): evtid_1 = np.sort(rng.integers(int(1e5), size=(int(1e6)))) time_1 = rng.uniform(low=0, high=1, size=(int(1e6))) edep_1 = rng.uniform(low=0, high=1000, size=(int(1e6))) - pos_x_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) - pos_y_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) - pos_z_1 = rng.uniform(low=-50,high=50,size=(int(1e6))) - + pos_x_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) + pos_y_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) + pos_z_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) # make it not divide by the buffer len evtid_2 = np.sort(rng.integers(int(1e5), size=(30040))) time_2 = rng.uniform(low=0, high=1, size=(30040)) edep_2 = rng.uniform(low=0, high=1000, size=(30040)) - pos_x_2 = rng.uniform(low=-50,high=50,size=(30040)) - pos_y_2 = rng.uniform(low=-50,high=50,size=(30040)) - pos_z_2 = rng.uniform(low=-50,high=50,size=(30040)) + pos_x_2 = rng.uniform(low=-50, high=50, size=(30040)) + pos_y_2 = rng.uniform(low=-50, high=50, size=(30040)) + pos_z_2 = rng.uniform(low=-50, high=50, size=(30040)) vertices_1 = ak.Array({"evtid": np.arange(int(1e5))}) vertices_2 = ak.Array({"evtid": np.arange(int(1e5))}) - arr_1 = ak.Array({"evtid": evtid_1, "time": time_1, "edep": edep_1, "xloc":pos_x_1,"yloc":pos_y_1,"zloc":pos_z_1}) - arr_2 = ak.Array({"evtid": evtid_2, "time": time_2, "edep": edep_2, "xloc":pos_x_2,"yloc":pos_y_2,"zloc":pos_z_2}) + arr_1 = ak.Array( + { + "evtid": evtid_1, + "time": time_1, + "edep": edep_1, + "xloc": pos_x_1, + "yloc": pos_y_1, + "zloc": pos_z_1, + } + ) + arr_2 = ak.Array( + { + "evtid": evtid_2, + "time": time_2, + "edep": edep_2, + "xloc": pos_x_2, + "yloc": pos_y_2, + "zloc": pos_z_2, + } + ) lh5.write(Table(vertices_1), "hit/vertices", tmp_path / "file1.lh5", wo_mode="of") lh5.write(Table(vertices_2), "hit/vertices", tmp_path / "file2.lh5", wo_mode="of") @@ -179,8 +205,9 @@ def test_build_hit(test_reboost_input_file): }, } - for output_file,input_file in zip(["out.lh5","out_rem.lh5","out_merge.lh5"], - ["file1.lh5","file2.lh5","file*.lh5"]): + for output_file, input_file in zip( + ["out.lh5", "out_rem.lh5", "out_merge.lh5"], ["file1.lh5", "file2.lh5", "file*.lh5"] + ): hit.build_hit( str(test_reboost_input_file / output_file), [str(test_reboost_input_file / input_file)], @@ -190,7 +217,7 @@ def test_build_hit(test_reboost_input_file): pars={}, buffer=100000, ) - + hit.build_hit( str(test_reboost_input_file / "out.lh5"), [str(test_reboost_input_file / "file*.lh5")], @@ -260,13 +287,18 @@ def test_build_hit_some_row(test_reboost_input_file): buffer=100000, ) - for n_ev,s_ev,out in zip([int(1e4),int(1e5-1e4),int(1e5),int(1e5)], - [0,int(1e4),0,1000],["out_some_rows.lh5","out_rest_rows.lh5","out_all_file_one.lh5","out_mix.lh5"]): - + for n_ev, s_ev, out in zip( + [int(1e4), int(1e5 - 1e4), int(1e5), int(1e5)], + [0, int(1e4), 0, 1000], + ["out_some_rows.lh5", "out_rest_rows.lh5", "out_all_file_one.lh5", "out_mix.lh5"], + ): # test read only some events hit.build_hit( str(test_reboost_input_file / out), - [str(test_reboost_input_file / "file1.lh5"), str(test_reboost_input_file / "file2.lh5")], + [ + str(test_reboost_input_file / "file1.lh5"), + str(test_reboost_input_file / "file2.lh5"), + ], n_evtid=n_ev, start_evtid=s_ev, in_field="hit", @@ -276,7 +308,6 @@ def test_build_hit_some_row(test_reboost_input_file): buffer=100000, ) - tab_some = lh5.read("hit/det001", str(test_reboost_input_file / "out_some_rows.lh5")).view_as( "ak" ) @@ -292,23 +323,19 @@ def test_build_hit_some_row(test_reboost_input_file): assert ak.all(ak.all(tab_merge.evtid == tab_1.evtid, axis=-1)) - -def test_build_hit_with_locals(test_reboost_input_file,test_data_configs): - - +def test_build_hit_with_locals(test_reboost_input_file, test_data_configs): proc_config = { "channels": [ "det001", ], - "outputs": ["t0", "evtid","distance_to_surface"], - + "outputs": ["t0", "evtid", "distance_to_surface"], "step_group": { "description": "group steps by time and evtid.", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", }, - "locals":{ - "hpge":"reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", - "phy_vol":"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)" + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", }, "operations": { "t0": { @@ -321,17 +348,17 @@ def test_build_hit_with_locals(test_reboost_input_file,test_data_configs): "mode": "eval", "expression": "ak.sum(hit.edep,axis=-1)", }, - "smear_energy_sum":{ + "smear_energy_sum": { "description": "summed energy after convolution with energy response.", "mode": "function", - "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)", }, - "distance_to_surface":{ - "description":"distance to the nplus surface", - "mode":"function", - "expression":"reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)" - } - } + "distance_to_surface": { + "description": "distance to the nplus surface", + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), None)", + }, + }, } gdml_path = configs / pathlib.Path("geom.gdml") meta_path = test_data_configs @@ -343,9 +370,9 @@ def test_build_hit_with_locals(test_reboost_input_file,test_data_configs): in_field="hit", out_field="hit", proc_config=proc_config, - pars={"det001":{"reso":1,"meta_name":"V99000A.json","phy_vol_name":"det_phy_1"}}, + pars={"det001": {"reso": 1, "meta_name": "V99000A.json", "phy_vol_name": "det_phy_1"}}, buffer=100000, merge_input_files=False, - metadata_path = meta_path, - gdml =gdml_path - ) \ No newline at end of file + metadata_path=meta_path, + gdml=gdml_path, + ) diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 6f4a04c..5126277 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -14,6 +14,7 @@ from reboost.hpge.utils import ( _merge_arrays, + dict2tuple, get_file_list, get_files_to_read, get_global_evtid_range, @@ -22,7 +23,6 @@ get_num_simulated, get_phy_vol, load_dict, - dict2tuple ) configs = pathlib.Path(__file__).parent.resolve() / pathlib.Path("configs") From cd16454a4386c9cdb768665883c7b8f349c6ce51 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 15 Nov 2024 16:33:41 +0100 Subject: [PATCH 50/81] ak -> np to fix CI failures --- docs/source/manual/hpge.rst | 63 ++++++++++++++++++++++++++++++------- src/reboost/hpge/utils.py | 4 +-- 2 files changed, 54 insertions(+), 13 deletions(-) diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index 207f709..3925a62 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -68,7 +68,7 @@ However, this format is not directly comparable to experimental data. Data tiers ---------- -The processing is defined in terms of several *tiers*: +The processing is defined in terms of several *tiers*, mirroring the logic of the `pygama `_ data processing software used for LEGEND. - **stp** or "step" the raw *remage* outputs corresponding to Geant4 steps, - **hit** the data from each channel independently after grouping in discrete physical interactions in the detector. @@ -79,6 +79,8 @@ The processing is divided into two steps :func:`build_hit` ``build_evt`` [WIP]. Hit tier processing ------------------- +The hit tier converts the raw remage file based on Geant4 steps to a file corresponding to the physical interactions in the detectors. +Only steps corresponding to individual detectors are performed in this step. The processing is based on a YAML or JSON configuration file. For example: .. code-block:: json @@ -127,7 +129,7 @@ It is necessary to provide several sub-dictionaries: - **channels**: list of HPGe channels to process. - **outputs**: list of fields for the output file. -- **locals**: get objects used by the processors (passed as ``locals`` to ``LGDO.Table.eval``) +- **locals**: get objects used by the processors (passed as ``locals`` to ``LGDO.Table.eval``), more details below. - **step_group**: this should describe the function that groups the Geant4 steps into physical *hits*. - **operations**: further computations / manipulations to apply. @@ -169,7 +171,7 @@ Next a set of operations can be specified, these can perform any operation that Finally the outputs field specifies the columns of the Table to include in the output table. lh5 i/o operations ------------------- +^^^^^^^^^^^^^^^^^^ :func:`build_hit` contains several options to handle i/o of lh5 files. @@ -183,7 +185,7 @@ Finally, it is sometimes desirable to process a subset of the simulated events, keywords arguments control the first simulation index to process and the number of events. Note that the indices refer to the *global* evtid when multiple files are used. parameters and other *local* variables --------------------------------------- +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Often it is necessary to include processors that depend on parameters (which) may vary by detector. To enable this the user can specify a dictionary of parameters with the *pars* keyword, this should contain a sub-dictionary per detector for example: @@ -209,10 +211,10 @@ From the GDML file the ``pyg4ometry.geant4.Registry`` is extracted. To allow the flexibility to write processors depending on arbitrary (more complicated python objects), it is possible to add the *locals* dictionary to the config file. The code will then evaluate the supplied expression for each sub-dictionary. These expressions can depend on: -- the *remage* detector name: "detector", -- the path to the metadata: "meta", -- the geant4 registry: "reg", -- the parameters for this detector: "pars". +- **detector**: the *remage* detector name, +- **meta**: the path to the metadata, +- **reg**: the geant4 registry, +- **pars**: the parameters for this detector. These expressions are then evaluated (once per detector) and added to the *locals* dictionary of ``Table.eval``, so can be references in the expressions. @@ -231,8 +233,47 @@ Possible intended use case of this functionality are: - extracting the kernel of a machine learning model. - any more complicated (non-JSON serialisable objects). -adding new processors ---------------------- +Adding new processors +^^^^^^^^^^^^^^^^^^^^^ + +Any python function can be a ``reboost.hit`` processor. The only requirement is that it should return a: + +- :class:`VectorOfVectors`, +- :class:`Array`` or +- :class:`ArrayOfEqualSizedArrays` -Any python function can be a ``reboost.hit`` processor. The only requirement is that it should return a :class:`VectorOfVectors`, :class:`Array`` or :class:`ArrayOfEqualSizedArrays` with the same length as the hit table. This means processors can act on subarrays (``axis=-1`` in awkward syntax) but should not combine multiple rows of the hit table. + +Event tier processing (work in progress) +---------------------------------------- + +The event tier combines the information from various detector systems. Including in future the optical detector channels. This step is thus only necessary for experiments with +many output channels. + +The processing is again based on a YAML or JSON configuration file. For example: + +.. code-block:: json + + { + + "channels":{ + "geds_usable":[ + "det000", + "det001", + "det002" + ], + "geds_ac":[ + "det003" + ] + }, + "outputs": [ + "energy", + "detector", + "is_good_hit", + "multiplicity" + ], + "event_group": { + "description": "group hits by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" + } + } diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 087a227..1750b3b 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -204,8 +204,8 @@ def get_include_chunk( boolean flag of whether to include in the chunk. """ - low = np.min(global_evtid) - high = np.max(global_evtid) + low = np.min(np.array(global_evtid)) + high = np.max(np.array(global_evtid)) return (high >= start_glob_evtid) & (low <= end_glob_evtid) From a28d1a4f6ff05f2a131657ede7f1091ae0c710c5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 15 Nov 2024 15:33:57 +0000 Subject: [PATCH 51/81] style: pre-commit fixes --- docs/source/manual/hpge.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index 3925a62..df91b98 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -255,7 +255,7 @@ The processing is again based on a YAML or JSON configuration file. For example: .. code-block:: json { - + "channels":{ "geds_usable":[ "det000", From b9bc3a4fb66fe71f74a800d2af65497efb316531 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sat, 16 Nov 2024 23:41:48 +0100 Subject: [PATCH 52/81] [docs] adding a basic tutorial --- docs/source/conf.py | 3 + docs/source/index.rst | 5 + docs/source/manual/hpge.rst | 2 +- docs/source/notebooks/images/output_20_1.png | Bin 0 -> 19028 bytes docs/source/notebooks/images/output_24_0.png | Bin 0 -> 18905 bytes docs/source/notebooks/images/output_27_1.png | Bin 0 -> 93413 bytes docs/source/notebooks/images/output_28_1.png | Bin 0 -> 76691 bytes docs/source/notebooks/images/output_31_0.png | Bin 0 -> 17749 bytes docs/source/notebooks/images/output_34_0.png | Bin 0 -> 32931 bytes docs/source/notebooks/images/output_35_0.png | Bin 0 -> 33451 bytes docs/source/notebooks/images/output_37_0.png | Bin 0 -> 20548 bytes .../notebooks/reboost_hpge_tutorial.rst | 680 ++++++++++++++++++ docs/source/tutorial.rst | 9 + src/reboost/hpge/hit.py | 98 ++- src/reboost/hpge/processors.py | 11 +- src/reboost/hpge/utils.py | 36 +- 16 files changed, 818 insertions(+), 26 deletions(-) create mode 100644 docs/source/notebooks/images/output_20_1.png create mode 100644 docs/source/notebooks/images/output_24_0.png create mode 100644 docs/source/notebooks/images/output_27_1.png create mode 100644 docs/source/notebooks/images/output_28_1.png create mode 100644 docs/source/notebooks/images/output_31_0.png create mode 100644 docs/source/notebooks/images/output_34_0.png create mode 100644 docs/source/notebooks/images/output_35_0.png create mode 100644 docs/source/notebooks/images/output_37_0.png create mode 100644 docs/source/notebooks/reboost_hpge_tutorial.rst create mode 100644 docs/source/tutorial.rst diff --git a/docs/source/conf.py b/docs/source/conf.py index e8e0ac8..f3fbd16 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,9 +19,12 @@ "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx_copybutton", + "nbsphinx", + "IPython.sphinxext.ipython_console_highlighting", "myst_parser", ] + source_suffix = { ".rst": "restructuredtext", ".md": "markdown", diff --git a/docs/source/index.rst b/docs/source/index.rst index 5981c0f..9965e25 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -33,6 +33,11 @@ Next steps User Manual +.. toctree:: + :maxdepth: 1 + + tutorial + .. toctree:: :maxdepth: 1 diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index 3925a62..df91b98 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -255,7 +255,7 @@ The processing is again based on a YAML or JSON configuration file. 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{edep: [0.0826, 0.00863, 0.0171, 0.0175, 0.224, ..., 34.1, 28.9, 34.1, 32.3],
+     time: [1.32e+15, 1.32e+15, 1.32e+15, ..., 1.32e+15, 1.32e+15, 1.32e+15],
+     t0: 1.32e+15,
+     hit_evtid: 9.49e+03,
+     hit_global_evtid: 9.49e+03,
+     distance_to_nplus_surface_mm: [1e-11, 0.654, 0.654, ..., 0.617, 0.614, 0.614],
+     activeness: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+     rpos_loc: [21, 36.3, 36.3, 36.3, 36.3, ..., 36.3, 36.4, 36.4, 36.4, 36.4],
+     zpos_loc: [29.5, 21.1, 21.1, 21.1, 21.1, ..., 21.1, 21.1, 21.1, 21.1, 21.1],
+     energy_sum: 203,
+     energy_sum_deadlayer: 0,
+     energy_sum_smeared: -0.44}
+    -------------------------------------------------------------------------------
+    type: {
+        edep: var * float64,
+        time: var * float64,
+        t0: float64,
+        hit_evtid: float64,
+        hit_global_evtid: float64,
+        distance_to_nplus_surface_mm: var * float64,
+        activeness: var * int64,
+        rpos_loc: var * float64,
+        zpos_loc: var * float64,
+        energy_sum: float64,
+        energy_sum_deadlayer: float64,
+        energy_sum_smeared: float64
+    }
+ + + +Part 4) Steps in a standard processing chain +-------------------------------------------- + +The next part of the tutorial gives more details on each step of the +processing chain. + +4.1) Windowing +~~~~~~~~~~~~~~ + +We can compare the decay index (“evtid” in the “stp” file) to the index +of the “hit”, the row of the hit table. We see that only some decays +correspond to “hits” in the detector, as we expect. We also see that a +single decay does not often produce multiple hits. This is also expected +since the probability of detection is fairly low. + +.. code:: ipython3 + + plt.scatter(np.sort(data_det001.hit_global_evtid),np.arange(len(data_det001)),marker=".",alpha=1) + plt.xlabel("Decay index (evtid)") + plt.ylabel("Hit Index") + plt.grid() + plt.xlim(0,1000) + plt.ylim(0,100) + + + + +.. parsed-literal:: + + (0.0, 100.0) + + + + +.. image:: images/output_20_1.png + + +However, we can use some array manipulation to extract decay index with +multiple hits, by plotting the times we see the effect of the windowing. + +.. code:: ipython3 + + def plot_times(times:ak.Array,xrange=None,sub_zero=False,**kwargs): + fig,ax = plt.subplots() + for idx,_time in enumerate(times): + if (sub_zero): + _time=_time-ak.min(_time) + h=hist.new.Reg(100,(ak.min(times)/1e9),(ak.max(times)/1e9)+1, name="Time since event start [s]").Double() + h.fill(_time/1e9) + h.plot(**kwargs,label=f"Hit {idx}") + ax.legend() + ax.set_yscale("log") + if xrange is not None: + ax.set_xlim(*xrange) + + +.. code:: ipython3 + + unique,counts = np.unique(data_det001.hit_global_evtid,return_counts=True) + +.. code:: ipython3 + + plot_times(data_det001[data_det001.hit_global_evtid==unique[counts>1][1]].time,histtype="step",yerr=False) + + + + +.. image:: images/output_24_0.png + + +4.2) Distance to surface and dead layer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +One of the important step in the post-processing of HPGe detector +simulations is the detector activeness mapping. Energy deposited close +to the surface of the Germanium detector will result in incomplete +charge collection and a degraded signal. To account for this we added a +processor to compute the distance to the detector surface (based on +``legendhpges.base.HPGe.distance_to_surface()``) + +For the steps in the detector we extracted in the processing chain the +local r and z coordinates and we can plot maps of the distance to the +detector surface and the activeness for each step. We select only events +within 5 mm of the surface for the first plots. We can see that the +processor works as expected. + +.. code:: ipython3 + + def plot_map(field,scale="BuPu",clab="Distance [mm]"): + fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) + n=100000 + for idx, (data,config) in enumerate(zip([data_det001,data_det002],["cfg/metadata/BEGe.json","cfg/metadata/Coax.json"])): + + reg=pg4.geant4.Registry() + hpge = legendhpges.make_hpge(config,registry=reg) + + legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx]) + r = np.random.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc) + z = ak.flatten(data.zpos_loc) + c=ak.flatten(data[field]) + cut = c<5 + + s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=".", label="gen. points",cmap=scale) + #axs[idx].axis("equal") + + if idx == 0: + axs[idx].set_ylabel("Height [mm]") + c=plt.colorbar(s) + c.set_label(clab) + + axs[idx].set_xlabel("Radius [mm]") + + +.. code:: ipython3 + + plot_map("distance_to_nplus_surface_mm") + + +.. image:: images/output_27_1.png + + +.. code:: ipython3 + + plot_map("activeness",clab="Activeness",scale="viridis") + + +.. image:: images/output_28_1.png + + +We can also plot a histogram of the distance to the surface. + +.. code:: ipython3 + + def plot_distances(axes,distances,xrange=None,label=" ",**kwargs): + + h=hist.new.Reg(100,*xrange, name="Distance to n+ surface [mm]").Double() + h.fill(distances) + h.plot(**kwargs,label=label) + ax.legend() + ax.set_yscale("log") + if xrange is not None: + ax.set_xlim(*xrange) + + +.. code:: ipython3 + + fig,ax = plt.subplots() + plot_distances(ax,ak.flatten(data_det001.distance_to_nplus_surface_mm),xrange=(0,35),label="BEGe",histtype="step",yerr=False) + plot_distances(ax,ak.flatten(data_det002.distance_to_nplus_surface_mm),xrange=(0,35),label="Coax",histtype="step",yerr=False) + + + + +.. image:: images/output_31_0.png + + +4.3) Summed energies +~~~~~~~~~~~~~~~~~~~~ + +Our processing chain also sums the energies of the hits, both before and +after weighting by the activeness. + +.. code:: ipython3 + + def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): + + h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double() + h.fill(energy) + h.plot(**kwargs,label=label) + axes.legend() + if (log_y): + axes.set_yscale("log") + if xrange is not None: + axes.set_xlim(*xrange) + +.. code:: ipython3 + + fig, ax = plt.subplots() + ax.set_title("BEGe energy spectrum") + plot_energy(ax,data_det001.energy_sum,yerr=False,label="True energy",xrange=(0,4000)) + plot_energy(ax,data_det001.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000)) + + + +.. image:: images/output_34_0.png + + +.. code:: ipython3 + + fig, ax = plt.subplots() + ax.set_title("COAX energy spectrum") + plot_energy(ax,data_det002.energy_sum,yerr=False,label="True energy",xrange=(0,4000)) + plot_energy(ax,data_det002.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000)) + + + +.. image:: images/output_35_0.png + + +4.4) Smearing +~~~~~~~~~~~~~ + +The final step in the processing chain smeared the energies by the +energy resolution. This represents a general class of processors based +on ‘’heuristic’’ models. Other similar processors could be implemented +in a similar way. It would also be simple to use insted an energy +dependent resolution curve. To see the effect we have to zoom into the +2615 keV peak. + +.. code:: ipython3 + + fig, axs = plt.subplots() + plot_energy(axs,data_det001.energy_sum_smeared,yerr=False,label="BEGe",xrange=(2600,2630),log_y=False,bins=150,density=True) + plot_energy(axs,data_det002.energy_sum_smeared,yerr=False,label="COAX",xrange=(2600,2630),log_y=False,bins=150,density=True) + + + +.. image:: images/output_37_0.png + + +We see clearly the worse energy resolution for the COAX detector. > **To +Do**: add a gaussian fit of this. + +Part 5) Adding a new processor +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The next part of the tutorial describes how to add a new processor to +the chain. We use as an example spatial *clustering* of steps. This will +be added later. + diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst new file mode 100644 index 0000000..cc078dd --- /dev/null +++ b/docs/source/tutorial.rst @@ -0,0 +1,9 @@ +Basic Tutorial +============== + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + notebooks/reboost_hpge_tutorial \ No newline at end of file diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index c7f0de6..7956fe2 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,12 +1,14 @@ from __future__ import annotations import logging +import time import awkward as ak import numpy as np import pyg4ometry from lgdo import Array, ArrayOfEqualSizedArrays, Table, VectorOfVectors, lh5 from lgdo.lh5 import LH5Iterator +from tqdm import tqdm from . import utils @@ -174,6 +176,7 @@ def build_hit( gdml: str | None = None, metadata_path: str | None = None, merge_input_files: bool = True, + has_global_evtid: bool = False, ) -> None: """ Read incrementally the files compute something and then write output @@ -267,6 +270,8 @@ def build_hit( path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) merge_input_files boolean flag to merge all input files into a single output. + has_global_evtid + boolean flag to indicate the evtid are already global Note ---- @@ -274,8 +279,21 @@ def build_hit( - It would be better to have a cleaner way to supply metadata and detector maps. """ + time_dict = { + "setup": 0, + "read": 0, + "write": 0, + "locals": 0, + "step_group": 0, + "proc": {}, + } + + time_start = time.time() + # get the gdml file - reg = pyg4ometry.gdml.Reader(gdml).getRegistry() if gdml is not None else None + + with utils.debug_logging(logging.CRITICAL): + reg = pyg4ometry.gdml.Reader(gdml).getRegistry() if gdml is not None else None # get info on the files to read in a nice named tuple file_info = utils.get_selected_files( @@ -285,30 +303,46 @@ def build_hit( start_evtid=start_evtid, ) + time_end = time.time() + + time_dict["setup"] += time_end - time_start + + # initialise timing object + time_start = time.time() + time_end = time.time() + # loop over input files - for first_evtid, file_idx, file_in in zip( - file_info.file_start_global_evtids, file_info.file_indices, file_info.file_list + for first_evtid, file_idx, file_in in tqdm( + zip(file_info.file_start_global_evtids, file_info.file_indices, file_info.file_list) ): for ch_idx, d in enumerate(proc_config["channels"]): msg = f"...running hit tier for {d}" - log.info(msg) + log.debug(msg) # get local variables + time_start = time.time() + local_info = proc_config.get("locals", {}) local_dict = get_locals( local_info, pars_dict=pars.get(d, {}), meta_path=metadata_path, detector=d, reg=reg ) + time_end = time.time() + + time_dict["locals"] += time_end - time_start + is_first_chan = bool(ch_idx == 0) is_first_file = bool(file_idx == 0) # flag to overwrite input file - delete_input = (is_first_chan & merge_input_files is False) | ( + delete_input = (is_first_chan & (merge_input_files is False)) | ( is_first_chan & is_first_file ) - msg = f"...begin processing with {file_in} to {file_out}" - log.info(msg) + msg = ( + f"...begin processing with {file_in} to {file_out} and delete input {delete_input}" + ) + log.debug(msg) entries = LH5Iterator( file_in, f"{in_field}/{d}", buffer_len=buffer @@ -340,9 +374,15 @@ def build_hit( ) # add global evtid - obj = ak.with_field(obj, first_evtid + obj.evtid, "global_evtid") + if has_global_evtid is False: + obj = ak.with_field(obj, first_evtid + obj.evtid, "global_evtid") + else: + obj = ak.with_field(obj, obj.evtid, "global_evtid") + + time_end = time.time() + time_dict["read"] += time_end - time_start - # check if the chunk can be skipped + # check if the chunk can be skipped, does lack of sorting break this? if not utils.get_include_chunk( obj.global_evtid, @@ -361,18 +401,32 @@ def build_hit( data = Table(obj) # group steps into hits + time_start = time.time() grouped = step_group(data, proc_config["step_group"]) + time_end = time.time() + + time_dict["step_group"] += time_end - time_start # processors for name, info in proc_config["operations"].items(): - msg = f"adding column {name}" + msg = f"... adding column {name}" log.debug(msg) + time_start = time.time() + col = eval_expression(grouped, info, local_dict=local_dict) grouped.add_field(name, col) + time_end = time.time() + + if name in time_dict["proc"]: + time_dict["proc"][name] += time_end - time_start + else: + time_dict["proc"][name] = 0 + # remove unwanted columns - log.debug("removing unwanted columns") + log.debug("... removing unwanted columns") + time_start = time.time() existing_cols = list(grouped.keys()) for col in existing_cols: @@ -380,8 +434,6 @@ def build_hit( grouped.remove_column(col, delete=True) # write lh5 file - msg = f"...finished processing and save file with wo_mode {mode}" - log.debug(msg) file_out_tmp = ( f"{file_out.split('.')[0]}_{file_idx}.lh5" @@ -389,3 +441,23 @@ def build_hit( else file_out ) lh5.write(grouped, f"{out_field}/{d}", file_out_tmp, wo_mode=mode) + time_end = time.time() + + time_dict["write"] += time_end - time_start + + # start timing read + time_start = time.time() + + # print timing info + + for step, time_val in time_dict.items(): + if isinstance(time_val, dict): + msg = "Time for processors:" + log.info(msg) + + for name, t in time_val.items(): + msg = f" {name} elapsed time: {t:.1f} s" + log.info(msg) + else: + msg = f"{step} elapsed time: {time_val:.1f} s" + log.info(msg) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index f0c9e1d..fca94b4 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -147,6 +147,7 @@ def distance_to_surface( hpge: legendhpges.base.HPGe, det_pos: ArrayLike, surface_type: str | None = None, + unit: str = "mm", ) -> Array: """Computes the distance from each step to the detector surface. @@ -164,6 +165,8 @@ def distance_to_surface( position of the detector origin, must be a 3 component array corresponding to `(x,y,z)`. surface_type string of which surface to use, can be `nplus`, `pplus` `passive` or None (in which case the distance to any surface is calculated). + unit + unit for the hit tier positions table. Returns ------- @@ -174,12 +177,13 @@ def distance_to_surface( `positions_x/positions_y/positions_z` must all have the same shape. """ + factor = np.array([1, 100, 1000])[unit == np.array(["mm", "cm", "m"])][0] # compute local positions pos = [] sizes = [] for idx, pos_tmp in enumerate([positions_x, positions_y, positions_z]): - local_pos_tmp = ak.Array(pos_tmp) - det_pos[idx] + local_pos_tmp = ak.Array(pos_tmp) * factor - det_pos[idx] local_pos_flat_tmp = ak.flatten(local_pos_tmp).to_numpy() pos.append(local_pos_flat_tmp) sizes.append(ak.num(local_pos_tmp, axis=1)) @@ -193,7 +197,10 @@ def distance_to_surface( local_positions = np.vstack(pos).T # get indices - surface_indices = np.where(hpge.surfaces == surface_type) if surface_type is not None else None + + surface_indices = ( + np.where(np.array(hpge.surfaces) == surface_type) if surface_type is not None else None + ) # distance calc itself distances = hpge.distance_to_surface(local_positions, surface_indices=surface_indices) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 1750b3b..414fad0 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -6,6 +6,7 @@ import logging import re from collections import namedtuple +from contextlib import contextmanager from pathlib import Path from typing import NamedTuple @@ -226,12 +227,13 @@ def get_hpge(meta_path: str | None, pars: NamedTuple, detector: str) -> legendhp hpge the `legendhpges` object for the detector. """ - reg = pyg4ometry.geant4.Registry() - if meta_path is not None: - meta_name = pars.meta_name if ("meta_name" in pars._fields) else f"{detector}.json" - meta_dict = Path(meta_path) / Path(meta_name) - return legendhpges.make_hpge(meta_dict, registry=reg) - return None + with debug_logging(logging.CRITICAL): + reg = pyg4ometry.geant4.Registry() + if meta_path is not None: + meta_name = pars.meta_name if ("meta_name" in pars._fields) else f"{detector}.json" + meta_dict = Path(meta_path) / Path(meta_name) + return legendhpges.make_hpge(meta_dict, registry=reg) + return None def get_phy_vol( @@ -253,10 +255,24 @@ def get_phy_vol( phy_vol the `pyg4ometry.geant4.PhysicalVolume` object for the detector """ - if reg is not None: - phy_name = pars.phy_vol_name if ("phy_vol_name" in pars._fields) else f"{detector}" - return reg.physicalVolumeDict[phy_name] - return None + + with debug_logging(logging.CRITICAL): + if reg is not None: + phy_name = pars.phy_vol_name if ("phy_vol_name" in pars._fields) else f"{detector}" + return reg.physicalVolumeDict[phy_name] + + return None + + +@contextmanager +def debug_logging(level): + logger = logging.getLogger("root") + old_level = logger.getEffectiveLevel() + logger.setLevel(level) + try: + yield + finally: + logger.setLevel(old_level) def dict2tuple(dictionary: dict) -> namedtuple: From 50017cc3baf93a04aff165d23f81044fba26871b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 16 Nov 2024 22:42:04 +0000 Subject: [PATCH 53/81] style: pre-commit fixes --- .../notebooks/reboost_hpge_tutorial.rst | 231 +++++++++--------- docs/source/tutorial.rst | 2 +- 2 files changed, 116 insertions(+), 117 deletions(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index 8ca1605..23d44ea 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -13,13 +13,13 @@ tutorial directory structure to organise the outputs and config inputs. - ├── cfg - │   └── metadata - ├── output - │   - ├── stp - │   - └── hit + ├── cfg + │   └── metadata + ├── output + │   + ├── stp + │   + └── hit └── reboost_hpge_tutorial.ipynb .. @@ -83,43 +83,43 @@ We can use ``lh5.show()`` to check the output files. .. code:: text / - └── stp · struct{det001,det002,det003,vertices} - ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── v_post · array<1>{real} - │ ├── v_pre · array<1>{real} - │ ├── xloc_post · array<1>{real} - │ ├── xloc_pre · array<1>{real} - │ ├── yloc_post · array<1>{real} - │ ├── yloc_pre · array<1>{real} - │ ├── zloc_post · array<1>{real} - │ └── zloc_pre · array<1>{real} - └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} - ├── evtid · array<1>{real} - ├── n_part · array<1>{real} - ├── time · array<1>{real} - ├── xloc · array<1>{real} - ├── yloc · array<1>{real} - └── zloc · array<1>{real} + └── stp · struct{det001,det002,det003,vertices} + ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── v_post · array<1>{real} + │ ├── v_pre · array<1>{real} + │ ├── xloc_post · array<1>{real} + │ ├── xloc_pre · array<1>{real} + │ ├── yloc_post · array<1>{real} + │ ├── yloc_pre · array<1>{real} + │ ├── zloc_post · array<1>{real} + │ └── zloc_pre · array<1>{real} + └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} + ├── evtid · array<1>{real} + ├── n_part · array<1>{real} + ├── time · array<1>{real} + ├── xloc · array<1>{real} + ├── yloc · array<1>{real} + └── zloc · array<1>{real} Part 2) reboost config files ---------------------------- @@ -144,14 +144,14 @@ First we set up the python enviroment. import colorlog import hist import numpy as np - - + + plt.rcParams['figure.figsize'] = [12, 4] plt.rcParams['axes.titlesize'] =12 plt.rcParams['axes.labelsize'] = 12 plt.rcParams['legend.fontsize'] = 12 - - + + handler = colorlog.StreamHandler() handler.setFormatter( colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") @@ -161,7 +161,7 @@ First we set up the python enviroment. logger.addHandler(handler) logger.setLevel(logging.INFO) logger.info("test") - + @@ -203,7 +203,7 @@ effect of the processors. "energy_sum_deadlayer", # energy sum after dead layers "energy_sum_smeared" # energy sum after smearing with resolution ], - "step_group": { + "step_group": { "description": "group steps by time and evtid with 10us window", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", }, @@ -262,7 +262,7 @@ effect of the processors. "mode": "function", "expression": "reboost.hpge.processors.smear_energies(hit.energy_sum_deadlayer,reso=pars.fwhm_in_keV/2.355)" } - + } } @@ -274,16 +274,16 @@ We also create our parameters file. "det001": { "meta_name":"BEGe.json", "phy_vol_name":"BEGe", - "fwhm_in_keV":2.69, - "fccd_in_mm":1.42, # dead layer in mm + "fwhm_in_keV":2.69, + "fccd_in_mm":1.42, # dead layer in mm }, "det002": { "meta_name":"Coax.json", "phy_vol_name":"Coax", - "fwhm_in_keV":4.42, - "fccd_in_mm":2.19, + "fwhm_in_keV":4.42, + "fccd_in_mm":2.19, } - + } Part 3) Running the processing @@ -345,57 +345,57 @@ distance to the detector surface. .. parsed-literal:: / - └── hit · HDF5 group - ├── det001 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} - │ ├── activeness · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── edep · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── energy_sum · array<1>{real} - │ ├── energy_sum_deadlayer · array<1>{real} - │ ├── energy_sum_smeared · array<1>{real} - │ ├── hit_evtid · array<1>{real} - │ ├── hit_global_evtid · array<1>{real} - │ ├── rpos_loc · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── t0 · array<1>{real} - │ ├── time · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ └── zpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── det002 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} - ├── activeness · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── edep · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── energy_sum · array<1>{real} - ├── energy_sum_deadlayer · array<1>{real} - ├── energy_sum_smeared · array<1>{real} - ├── hit_evtid · array<1>{real} - ├── hit_global_evtid · array<1>{real} - ├── rpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── t0 · array<1>{real} - ├── time · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── zpos_loc · array<1>{array<1>{real}} - ├── cumulative_length · array<1>{real} - └── flattened_data · array<1>{real} + └── hit · HDF5 group + ├── det001 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + │ ├── activeness · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── edep · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── energy_sum · array<1>{real} + │ ├── energy_sum_deadlayer · array<1>{real} + │ ├── energy_sum_smeared · array<1>{real} + │ ├── hit_evtid · array<1>{real} + │ ├── hit_global_evtid · array<1>{real} + │ ├── rpos_loc · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── t0 · array<1>{real} + │ ├── time · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ └── zpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── det002 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + ├── activeness · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── edep · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── energy_sum · array<1>{real} + ├── energy_sum_deadlayer · array<1>{real} + ├── energy_sum_smeared · array<1>{real} + ├── hit_evtid · array<1>{real} + ├── hit_global_evtid · array<1>{real} + ├── rpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── t0 · array<1>{real} + ├── time · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── zpos_loc · array<1>{array<1>{real}} + ├── cumulative_length · array<1>{real} + └── flattened_data · array<1>{real} The new format is a factor of x17 times smaller than the input file due @@ -540,24 +540,24 @@ processor works as expected. fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) n=100000 for idx, (data,config) in enumerate(zip([data_det001,data_det002],["cfg/metadata/BEGe.json","cfg/metadata/Coax.json"])): - + reg=pg4.geant4.Registry() hpge = legendhpges.make_hpge(config,registry=reg) - + legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx]) r = np.random.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc) z = ak.flatten(data.zpos_loc) c=ak.flatten(data[field]) cut = c<5 - + s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=".", label="gen. points",cmap=scale) #axs[idx].axis("equal") - + if idx == 0: axs[idx].set_ylabel("Height [mm]") c=plt.colorbar(s) c.set_label(clab) - + axs[idx].set_xlabel("Radius [mm]") @@ -582,7 +582,7 @@ We can also plot a histogram of the distance to the surface. .. code:: ipython3 def plot_distances(axes,distances,xrange=None,label=" ",**kwargs): - + h=hist.new.Reg(100,*xrange, name="Distance to n+ surface [mm]").Double() h.fill(distances) h.plot(**kwargs,label=label) @@ -590,7 +590,7 @@ We can also plot a histogram of the distance to the surface. ax.set_yscale("log") if xrange is not None: ax.set_xlim(*xrange) - + .. code:: ipython3 @@ -613,7 +613,7 @@ after weighting by the activeness. .. code:: ipython3 def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): - + h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double() h.fill(energy) h.plot(**kwargs,label=label) @@ -677,4 +677,3 @@ Part 5) Adding a new processor The next part of the tutorial describes how to add a new processor to the chain. We use as an example spatial *clustering* of steps. This will be added later. - diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index cc078dd..85e317a 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -6,4 +6,4 @@ Basic Tutorial :maxdepth: 2 :caption: Contents: - notebooks/reboost_hpge_tutorial \ No newline at end of file + notebooks/reboost_hpge_tutorial From e301bd7f6477102d1e8dbda2856457e29a392c69 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sat, 16 Nov 2024 23:44:20 +0100 Subject: [PATCH 54/81] [docs] fix spelling --- .../notebooks/reboost_hpge_tutorial.rst | 245 +++++++++--------- docs/source/tutorial.rst | 2 +- 2 files changed, 123 insertions(+), 124 deletions(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index 8ca1605..b312c0b 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -2,7 +2,7 @@ Basic HPGe simulation processing ================================ This tutorial describes how to process the HPGe detector simulations -from **remage** with **reboost**. It buils on the offical **remage** +from **remage** with **reboost**. It buils on the official **remage** tutorial `[link] `__ @@ -13,13 +13,13 @@ tutorial directory structure to organise the outputs and config inputs. - ├── cfg - │   └── metadata - ├── output - │   - ├── stp - │   - └── hit + ├── cfg + │   └── metadata + ├── output + │   + ├── stp + │   + └── hit └── reboost_hpge_tutorial.ipynb .. @@ -30,7 +30,7 @@ Part 1) Running the remage simulation Before we can run any post-processing we need to run the Geant4 simulation. For this we follow the remage tutorial to generate the GDML geometry. We save this into the GDML file *cfg/geom.gdml* for use by -remage. We also need to save the metadata dictonaries into json files +remage. We also need to save the metadata dictionaries into json files (in the *cfg/metadata* folder as *BEGe.json* and *Coax.json* We use a slightly modified Geant4 macro to demonstrate some features of @@ -59,7 +59,7 @@ command line). /run/beamOn 10000000 -We then use the remage exectuable (see +We then use the remage executable (see `[remage-docs] `__ for installation instructions) to run the simulation: > #### *Note* > Both of *cfg/th228.mac* and *cfg/geometry.gdml* are needed to run remage @@ -83,43 +83,43 @@ We can use ``lh5.show()`` to check the output files. .. code:: text / - └── stp · struct{det001,det002,det003,vertices} - ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── v_post · array<1>{real} - │ ├── v_pre · array<1>{real} - │ ├── xloc_post · array<1>{real} - │ ├── xloc_pre · array<1>{real} - │ ├── yloc_post · array<1>{real} - │ ├── yloc_pre · array<1>{real} - │ ├── zloc_post · array<1>{real} - │ └── zloc_pre · array<1>{real} - └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} - ├── evtid · array<1>{real} - ├── n_part · array<1>{real} - ├── time · array<1>{real} - ├── xloc · array<1>{real} - ├── yloc · array<1>{real} - └── zloc · array<1>{real} + └── stp · struct{det001,det002,det003,vertices} + ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── v_post · array<1>{real} + │ ├── v_pre · array<1>{real} + │ ├── xloc_post · array<1>{real} + │ ├── xloc_pre · array<1>{real} + │ ├── yloc_post · array<1>{real} + │ ├── yloc_pre · array<1>{real} + │ ├── zloc_post · array<1>{real} + │ └── zloc_pre · array<1>{real} + └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} + ├── evtid · array<1>{real} + ├── n_part · array<1>{real} + ├── time · array<1>{real} + ├── xloc · array<1>{real} + ├── yloc · array<1>{real} + └── zloc · array<1>{real} Part 2) reboost config files ---------------------------- @@ -127,10 +127,10 @@ Part 2) reboost config files For this tutorial we perform a basic post-processing of the *hit* tier for the two Germanium channels. -2.1) Setup the enviroment +2.1) Setup the environment ~~~~~~~~~~~~~~~~~~~~~~~~~ -First we set up the python enviroment. +First we set up the python environment. .. code:: ipython3 @@ -144,14 +144,14 @@ First we set up the python enviroment. import colorlog import hist import numpy as np - - + + plt.rcParams['figure.figsize'] = [12, 4] plt.rcParams['axes.titlesize'] =12 plt.rcParams['axes.labelsize'] = 12 plt.rcParams['legend.fontsize'] = 12 - - + + handler = colorlog.StreamHandler() handler.setFormatter( colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") @@ -161,7 +161,7 @@ First we set up the python enviroment. logger.addHandler(handler) logger.setLevel(logging.INFO) logger.info("test") - + @@ -203,7 +203,7 @@ effect of the processors. "energy_sum_deadlayer", # energy sum after dead layers "energy_sum_smeared" # energy sum after smearing with resolution ], - "step_group": { + "step_group": { "description": "group steps by time and evtid with 10us window", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", }, @@ -262,7 +262,7 @@ effect of the processors. "mode": "function", "expression": "reboost.hpge.processors.smear_energies(hit.energy_sum_deadlayer,reso=pars.fwhm_in_keV/2.355)" } - + } } @@ -274,16 +274,16 @@ We also create our parameters file. "det001": { "meta_name":"BEGe.json", "phy_vol_name":"BEGe", - "fwhm_in_keV":2.69, - "fccd_in_mm":1.42, # dead layer in mm + "fwhm_in_keV":2.69, + "fccd_in_mm":1.42, # dead layer in mm }, "det002": { "meta_name":"Coax.json", "phy_vol_name":"Coax", - "fwhm_in_keV":4.42, - "fccd_in_mm":2.19, + "fwhm_in_keV":4.42, + "fccd_in_mm":2.19, } - + } Part 3) Running the processing @@ -345,61 +345,61 @@ distance to the detector surface. .. parsed-literal:: / - └── hit · HDF5 group - ├── det001 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} - │ ├── activeness · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── edep · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── energy_sum · array<1>{real} - │ ├── energy_sum_deadlayer · array<1>{real} - │ ├── energy_sum_smeared · array<1>{real} - │ ├── hit_evtid · array<1>{real} - │ ├── hit_global_evtid · array<1>{real} - │ ├── rpos_loc · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── t0 · array<1>{real} - │ ├── time · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ └── zpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── det002 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} - ├── activeness · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── edep · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── energy_sum · array<1>{real} - ├── energy_sum_deadlayer · array<1>{real} - ├── energy_sum_smeared · array<1>{real} - ├── hit_evtid · array<1>{real} - ├── hit_global_evtid · array<1>{real} - ├── rpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── t0 · array<1>{real} - ├── time · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── zpos_loc · array<1>{array<1>{real}} - ├── cumulative_length · array<1>{real} - └── flattened_data · array<1>{real} + └── hit · HDF5 group + ├── det001 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + │ ├── activeness · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── edep · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── energy_sum · array<1>{real} + │ ├── energy_sum_deadlayer · array<1>{real} + │ ├── energy_sum_smeared · array<1>{real} + │ ├── hit_evtid · array<1>{real} + │ ├── hit_global_evtid · array<1>{real} + │ ├── rpos_loc · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── t0 · array<1>{real} + │ ├── time · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ └── zpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── det002 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + ├── activeness · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── edep · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── energy_sum · array<1>{real} + ├── energy_sum_deadlayer · array<1>{real} + ├── energy_sum_smeared · array<1>{real} + ├── hit_evtid · array<1>{real} + ├── hit_global_evtid · array<1>{real} + ├── rpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── t0 · array<1>{real} + ├── time · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── zpos_loc · array<1>{array<1>{real}} + ├── cumulative_length · array<1>{real} + └── flattened_data · array<1>{real} The new format is a factor of x17 times smaller than the input file due -to the removal of many *step* based fields which use alot of memory and +to the removal of many *step* based fields which use a lot of memory and due to the removal of the *vertices* table and the LAr hits. So we can easily read the whole file into memory. We use *awkward* to analyse the output files. @@ -540,24 +540,24 @@ processor works as expected. fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) n=100000 for idx, (data,config) in enumerate(zip([data_det001,data_det002],["cfg/metadata/BEGe.json","cfg/metadata/Coax.json"])): - + reg=pg4.geant4.Registry() hpge = legendhpges.make_hpge(config,registry=reg) - + legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx]) r = np.random.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc) z = ak.flatten(data.zpos_loc) c=ak.flatten(data[field]) cut = c<5 - + s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=".", label="gen. points",cmap=scale) #axs[idx].axis("equal") - + if idx == 0: axs[idx].set_ylabel("Height [mm]") c=plt.colorbar(s) c.set_label(clab) - + axs[idx].set_xlabel("Radius [mm]") @@ -582,7 +582,7 @@ We can also plot a histogram of the distance to the surface. .. code:: ipython3 def plot_distances(axes,distances,xrange=None,label=" ",**kwargs): - + h=hist.new.Reg(100,*xrange, name="Distance to n+ surface [mm]").Double() h.fill(distances) h.plot(**kwargs,label=label) @@ -590,7 +590,7 @@ We can also plot a histogram of the distance to the surface. ax.set_yscale("log") if xrange is not None: ax.set_xlim(*xrange) - + .. code:: ipython3 @@ -613,7 +613,7 @@ after weighting by the activeness. .. code:: ipython3 def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): - + h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double() h.fill(energy) h.plot(**kwargs,label=label) @@ -653,7 +653,7 @@ after weighting by the activeness. The final step in the processing chain smeared the energies by the energy resolution. This represents a general class of processors based on ‘’heuristic’’ models. Other similar processors could be implemented -in a similar way. It would also be simple to use insted an energy +in a similar way. It would also be simple to use instead an energy dependent resolution curve. To see the effect we have to zoom into the 2615 keV peak. @@ -677,4 +677,3 @@ Part 5) Adding a new processor The next part of the tutorial describes how to add a new processor to the chain. We use as an example spatial *clustering* of steps. This will be added later. - diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index cc078dd..85e317a 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -6,4 +6,4 @@ Basic Tutorial :maxdepth: 2 :caption: Contents: - notebooks/reboost_hpge_tutorial \ No newline at end of file + notebooks/reboost_hpge_tutorial From 03e60889b9009457602ee9c4dc4035a33a7f89c9 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sat, 16 Nov 2024 23:52:04 +0100 Subject: [PATCH 55/81] [docs] fix --- docs/source/manual/hpge.rst | 6 ++++++ docs/source/notebooks/reboost_hpge_tutorial.rst | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index df91b98..395319a 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -244,6 +244,12 @@ Any python function can be a ``reboost.hit`` processor. The only requirement is with the same length as the hit table. This means processors can act on subarrays (``axis=-1`` in awkward syntax) but should not combine multiple rows of the hit table. +It is simple to accommodate most of the current and future envisiged post-processing in this framework. For example: + +- clustering hits would result in a new VectorOfVectors with the same number of rows but fewer entries per vector, +- pulse shape simulations to produce waveforms (or ML emmulation of this) would give an ArrayOfEqualSizedArrays, +- processing in parallel many parameters (eg for systematic) studies would give a nested VectorOfVectors. + Event tier processing (work in progress) ---------------------------------------- diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index b312c0b..8ecc0bf 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -128,7 +128,7 @@ For this tutorial we perform a basic post-processing of the *hit* tier for the two Germanium channels. 2.1) Setup the environment -~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ First we set up the python environment. From 90112a24c2bc661be2b14881b2ec1a5f535f7d5f Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sat, 16 Nov 2024 23:57:30 +0100 Subject: [PATCH 56/81] [docs] remove nbspinx --- docs/source/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index f3fbd16..4a4730d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,6 @@ "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx_copybutton", - "nbsphinx", "IPython.sphinxext.ipython_console_highlighting", "myst_parser", ] From e4feb8f3150c9232814351fb2b0cc0b60e29ef33 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sun, 17 Nov 2024 00:00:15 +0100 Subject: [PATCH 57/81] [docs] update conf.p --- docs/source/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 4a4730d..239cfe9 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,6 @@ "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx_copybutton", - "IPython.sphinxext.ipython_console_highlighting", "myst_parser", ] From 66c63fc2614d159823fdb055612817d182db70d0 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sun, 17 Nov 2024 14:16:28 +0100 Subject: [PATCH 58/81] Update pyproject.toml --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index d2dccf9..25f75e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,6 +32,7 @@ requires-python = ">=3.9" dependencies = [ "awkward", "colorlog", + "tqdm", "numpy", "scipy", "numba", From 22c4b78fd66d84b4bc949674767f770ad53f9539 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sun, 17 Nov 2024 14:27:15 +0100 Subject: [PATCH 59/81] [docs] fix tutorial --- docs/source/notebooks/reboost_hpge_tutorial.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index 8ecc0bf..7d06c6a 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -14,11 +14,11 @@ tutorial ├── cfg - │   └── metadata + │ └── metadata ├── output - │   + │ ├── stp - │   + │ └── hit └── reboost_hpge_tutorial.ipynb @@ -268,7 +268,7 @@ effect of the processors. We also create our parameters file. -.. code:: ipython3 +.. code:: python pars = { "det001": { From c2bc9634e1945bc4cc38f4432f1f3fb00696566b Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sun, 17 Nov 2024 14:27:26 +0100 Subject: [PATCH 60/81] [docs] fix --- .../notebooks/reboost_hpge_tutorial.rst | 42 +++++++++---------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index 7d06c6a..06d90a5 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -72,11 +72,11 @@ You can lower the number of simulated events to speed up the simulation. We can use ``lh5.show()`` to check the output files. -.. code:: ipython3 +.. code:: python from lgdo import lh5 -.. code:: ipython3 +.. code:: python lh5.show("output/stp/output_t0.lh5") @@ -132,7 +132,7 @@ for the two Germanium channels. First we set up the python environment. -.. code:: ipython3 +.. code:: python from reboost.hpge import hit import matplotlib.pyplot as plt @@ -182,7 +182,7 @@ response model (gaussian energy resolution). We also include some step based quantities in the output to show the effect of the processors. -.. code:: ipython3 +.. code:: python chain = { "channels": [ @@ -291,7 +291,7 @@ Part 3) Running the processing Now we can run our post-processing -.. code:: ipython3 +.. code:: python %%time hit.build_hit(file_out="output/hit/output.lh5",list_file_in="output/stp/*.lh5", out_field="hit",in_field="stp", @@ -337,7 +337,7 @@ fraction of the time spent on the geant4 simulation (around 30 mins). The most time consuming steps are writing the file and computing the distance to the detector surface. -.. code:: ipython3 +.. code:: python lh5.show("output/hit/output.lh5") @@ -404,12 +404,12 @@ due to the removal of the *vertices* table and the LAr hits. So we can easily read the whole file into memory. We use *awkward* to analyse the output files. -.. code:: ipython3 +.. code:: python data_det001 = lh5.read_as("hit/det001","output/hit/output.lh5","ak") data_det002 = lh5.read_as("hit/det002","output/hit/output.lh5","ak") -.. code:: ipython3 +.. code:: python data_det001[0] @@ -463,7 +463,7 @@ correspond to “hits” in the detector, as we expect. We also see that a single decay does not often produce multiple hits. This is also expected since the probability of detection is fairly low. -.. code:: ipython3 +.. code:: python plt.scatter(np.sort(data_det001.hit_global_evtid),np.arange(len(data_det001)),marker=".",alpha=1) plt.xlabel("Decay index (evtid)") @@ -488,7 +488,7 @@ since the probability of detection is fairly low. However, we can use some array manipulation to extract decay index with multiple hits, by plotting the times we see the effect of the windowing. -.. code:: ipython3 +.. code:: python def plot_times(times:ak.Array,xrange=None,sub_zero=False,**kwargs): fig,ax = plt.subplots() @@ -504,11 +504,11 @@ multiple hits, by plotting the times we see the effect of the windowing. ax.set_xlim(*xrange) -.. code:: ipython3 +.. code:: python unique,counts = np.unique(data_det001.hit_global_evtid,return_counts=True) -.. code:: ipython3 +.. code:: python plot_times(data_det001[data_det001.hit_global_evtid==unique[counts>1][1]].time,histtype="step",yerr=False) @@ -534,7 +534,7 @@ detector surface and the activeness for each step. We select only events within 5 mm of the surface for the first plots. We can see that the processor works as expected. -.. code:: ipython3 +.. code:: python def plot_map(field,scale="BuPu",clab="Distance [mm]"): fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) @@ -561,7 +561,7 @@ processor works as expected. axs[idx].set_xlabel("Radius [mm]") -.. code:: ipython3 +.. code:: python plot_map("distance_to_nplus_surface_mm") @@ -569,7 +569,7 @@ processor works as expected. .. image:: images/output_27_1.png -.. code:: ipython3 +.. code:: python plot_map("activeness",clab="Activeness",scale="viridis") @@ -579,7 +579,7 @@ processor works as expected. We can also plot a histogram of the distance to the surface. -.. code:: ipython3 +.. code:: python def plot_distances(axes,distances,xrange=None,label=" ",**kwargs): @@ -592,7 +592,7 @@ We can also plot a histogram of the distance to the surface. ax.set_xlim(*xrange) -.. code:: ipython3 +.. code:: python fig,ax = plt.subplots() plot_distances(ax,ak.flatten(data_det001.distance_to_nplus_surface_mm),xrange=(0,35),label="BEGe",histtype="step",yerr=False) @@ -610,7 +610,7 @@ We can also plot a histogram of the distance to the surface. Our processing chain also sums the energies of the hits, both before and after weighting by the activeness. -.. code:: ipython3 +.. code:: python def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): @@ -623,7 +623,7 @@ after weighting by the activeness. if xrange is not None: axes.set_xlim(*xrange) -.. code:: ipython3 +.. code:: python fig, ax = plt.subplots() ax.set_title("BEGe energy spectrum") @@ -635,7 +635,7 @@ after weighting by the activeness. .. image:: images/output_34_0.png -.. code:: ipython3 +.. code:: python fig, ax = plt.subplots() ax.set_title("COAX energy spectrum") @@ -657,7 +657,7 @@ in a similar way. It would also be simple to use instead an energy dependent resolution curve. To see the effect we have to zoom into the 2615 keV peak. -.. code:: ipython3 +.. code:: python fig, axs = plt.subplots() plot_energy(axs,data_det001.energy_sum_smeared,yerr=False,label="BEGe",xrange=(2600,2630),log_y=False,bins=150,density=True) From 387ac50e002c9a1104288e3a573e913be8fdcf72 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Sun, 17 Nov 2024 14:30:12 +0100 Subject: [PATCH 61/81] [docs] more format fixes --- docs/source/notebooks/reboost_hpge_tutorial.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index 06d90a5..796a4d7 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -12,6 +12,7 @@ tutorial To run this tutorial it is recommended to create the following directory structure to organise the outputs and config inputs. +.. code:: text ├── cfg │ └── metadata @@ -22,6 +23,7 @@ tutorial └── hit └── reboost_hpge_tutorial.ipynb + .. Part 1) Running the remage simulation From e78e90e5485a7d2ec1a5c57771f2cbb33939276a Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 18 Nov 2024 19:47:43 +0100 Subject: [PATCH 62/81] clean up build hit --- src/reboost/hpge/hit.py | 255 ++++++++++++++++----------------- src/reboost/hpge/processors.py | 26 ++-- src/reboost/hpge/utils.py | 50 ++++++- tests/test_hpge_hit.py | 49 ++++--- tests/test_hpge_utils.py | 18 +++ 5 files changed, 237 insertions(+), 161 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 7956fe2..8b49c9c 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -3,12 +3,8 @@ import logging import time -import awkward as ak -import numpy as np import pyg4ometry from lgdo import Array, ArrayOfEqualSizedArrays, Table, VectorOfVectors, lh5 -from lgdo.lh5 import LH5Iterator -from tqdm import tqdm from . import utils @@ -65,7 +61,7 @@ def get_locals( ..code-block:: - {"hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)"} + {"hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)"} pars_dict dictionary of parameters @@ -176,107 +172,102 @@ def build_hit( gdml: str | None = None, metadata_path: str | None = None, merge_input_files: bool = True, - has_global_evtid: bool = False, ) -> None: """ - Read incrementally the files compute something and then write output - - Parameters - ---------- - file_out - output file path - list_file_in - list of input files - out_field - lh5 group name for output - in_field - lh5 group name for input - proc_config - the configuration file for the processing. Must contain the fields `channels`, `outputs`, `step_group` and operations`. - Optionally can also contain the `locals` field to extract other non-JSON serializable objects used by the processors. - For example: - - .. code-block:: json - - { - "channels": [ - "det000", - "det001", - "det002", - "det003" - ], - "outputs": [ - "t0", - "truth_energy_sum", - "smeared_energy_sum", - "evtid" - ], - "step_group": { - "description": "group steps by time and evtid.", - "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" - }, - "locals": { - "hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)" - }, - "operations": { - "t0": { - "description": "first time in the hit.", - "mode": "eval", - "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" - }, - "truth_energy_sum": { - "description": "truth summed energy in the hit.", - "mode": "eval", - "expression": "ak.sum(hit.edep,axis=-1)" - }, - "smeared_energy_sum": { - "description": "summed energy after convolution with energy response.", - "mode": "function", - "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" - } - - } - } - - pars - a dictionary of parameters, must have a field per channel consisting of a `dict` of parameters. For example: - - .. code-block:: json - - { - "det000": { - "reso": 1, - "fccd": 0.1, - "phy_vol_name":"det_phy", - "meta_name": "icpc.json" - } - } - - this should also contain the channel mappings needed by reboost. These are: - - `phy_vol_name`: is the name of the physical volume, - - `meta_name` : is the name of the JSON file with the metadata. - - If these keys are not present both will be set to the remage output table name. - - start_evtid - first `evtid` to read, defaults to 0. - n_evtid - number of `evtid` to read, if `None` all steps are read (the default). - buffer - length of buffer - gdml - path to the input gdml file. - metadata_path - path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) - merge_input_files - boolean flag to merge all input files into a single output. - has_global_evtid - boolean flag to indicate the evtid are already global - - Note - ---- - - The operations can depend on the outputs of previous steps, so operations order is important. - - It would be better to have a cleaner way to supply metadata and detector maps. + Read incrementally the files compute something and then write output + + Parameters + ---------- + file_out + output file path + list_file_in + list of input files + out_field + lh5 group name for output + in_field + lh5 group name for input + proc_config + the configuration file for the processing. Must contain the fields `channels`, `outputs`, `step_group` and operations`. + Optionally can also contain the `locals` field to extract other non-JSON serializable objects used by the processors. + For example: + + .. code-block:: json + + { + "channels": [ + "det000", + has_global_evtid: bool = False, + ], + "outputs": [ + "t0", + "truth_energy_sum", + "smeared_energy_sum", + "evtid" + ], + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" + }, + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)" + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)" + }, + "smeared_energy_sum": { + "description": "summed energy after convolution with energy response.", + "mode": "function", + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + } + + } + } + + pars + a dictionary of parameters, must have a field per channel consisting of a `dict` of parameters. For example: + + .. code-block:: json + + { + "det000": { + "reso": 1, + "fccd": 0.1, + "phy_vol_name":"det_phy", + "meta_name": "icpc.json" + } + } + + this should also contain the channel mappings needed by reboost. These are: + - `phy_vol_name`: is the name of the physical volume, + - `meta_name` : is the name of the JSON file with the metadata. + + If these keys are not present both will be set to the remage output table name. + + start_evtid + first `evtid` to read, defaults to 0. + n_evtid + number of `evtid` to read, if `None` all steps are read (the default). + buffer + length of buffer + gdml + path to the input gdml file. + metadata_path + path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) + merge_input_files + boolean flag to merge all input files into a single output. + + Note + ---- + - The operations can depend on the outputs of previous steps, so operations order is important. + - It would be better to have a cleaner way to supply metadata and detector maps. """ time_dict = { @@ -291,12 +282,11 @@ def build_hit( time_start = time.time() # get the gdml file - with utils.debug_logging(logging.CRITICAL): reg = pyg4ometry.gdml.Reader(gdml).getRegistry() if gdml is not None else None # get info on the files to read in a nice named tuple - file_info = utils.get_selected_files( + finfo = utils.get_selected_files( table=in_field, file_list=list_file_in, n_evtid=n_evtid, @@ -304,7 +294,6 @@ def build_hit( ) time_end = time.time() - time_dict["setup"] += time_end - time_start # initialise timing object @@ -312,9 +301,12 @@ def build_hit( time_end = time.time() # loop over input files - for first_evtid, file_idx, file_in in tqdm( - zip(file_info.file_start_global_evtids, file_info.file_indices, file_info.file_list) + for first_evtid, file_idx, file_in in zip( + finfo.file_start_global_evtids, finfo.file_indices, finfo.file_list ): + # get the evtids in the file: may use a lot of memory + vertices = lh5.read_as(f"{in_field}/vertices/evtid", file_in, "np") + for ch_idx, d in enumerate(proc_config["channels"]): msg = f"...running hit tier for {d}" log.debug(msg) @@ -322,13 +314,15 @@ def build_hit( # get local variables time_start = time.time() - local_info = proc_config.get("locals", {}) local_dict = get_locals( - local_info, pars_dict=pars.get(d, {}), meta_path=metadata_path, detector=d, reg=reg + proc_config.get("locals", {}), + pars_dict=pars.get(d, {}), + meta_path=metadata_path, + detector=d, + reg=reg, ) time_end = time.time() - time_dict["locals"] += time_end - time_start is_first_chan = bool(ch_idx == 0) @@ -344,29 +338,23 @@ def build_hit( ) log.debug(msg) - entries = LH5Iterator( - file_in, f"{in_field}/{d}", buffer_len=buffer - )._get_file_cumentries(0) - - # number of blocks is ceil of entries/buffer, - # shift by 1 since idx starts at 0 - - max_idx = int(np.ceil(entries / buffer)) - 1 - remainder = entries % buffer + # number of iterations (used to handle last iteration) + it, entries, max_idx = utils.get_iterator( + file=file_in, field=in_field, detector=d, buffer=buffer + ) buffer_rows = None - for idx, (lh5_obj, _, _) in enumerate( - LH5Iterator(file_in, f"{in_field}/{d}", buffer_len=buffer) - ): + # iterate over the LH5 file + for idx, (lh5_obj, _, n_rows) in enumerate(it): msg = f"... processed {idx} chunks out of {max_idx}" - log.debug(msg) + log.info(msg) # convert to awkward ak_obj = lh5_obj.view_as("ak") # fix for a bug in lh5 iterator - if idx == max_idx & remainder != 0: - ak_obj = ak_obj[:remainder] + if idx == max_idx: + ak_obj = ak_obj[:n_rows] # handle the buffers obj, buffer_rows, mode = utils._merge_arrays( @@ -374,10 +362,7 @@ def build_hit( ) # add global evtid - if has_global_evtid is False: - obj = ak.with_field(obj, first_evtid + obj.evtid, "global_evtid") - else: - obj = ak.with_field(obj, obj.evtid, "global_evtid") + obj = utils.get_global_evtid(first_evtid, obj, vertices) time_end = time.time() time_dict["read"] += time_end - time_start @@ -386,15 +371,15 @@ def build_hit( if not utils.get_include_chunk( obj.global_evtid, - start_glob_evtid=file_info.first_global_evtid, - end_glob_evtid=file_info.last_global_evtid, + start_glob_evtid=finfo.first_global_evtid, + end_glob_evtid=finfo.last_global_evtid, ): continue # select just the correct global evtid objects obj = obj[ - (obj.global_evtid >= file_info.first_global_evtid) - & (obj.global_evtid <= file_info.last_global_evtid) + (obj.global_evtid >= finfo.first_global_evtid) + & (obj.global_evtid <= finfo.last_global_evtid) ] # convert back to a table, should work diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index fca94b4..79f01b9 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -8,7 +8,7 @@ from numpy.typing import ArrayLike -def sort_data(obj: ak.Array) -> ak.Array: +def sort_data(obj: ak.Array, *, time_name: str = "time", evtid_name: str = "evtid") -> ak.Array: """Sort the data by evtid then time. Parameters @@ -20,7 +20,7 @@ def sort_data(obj: ak.Array) -> ak.Array: ------- sorted awkward array """ - indices = np.lexsort((obj.time, obj.evtid)) + indices = np.lexsort((obj[time_name], obj[evtid_name])) return obj[indices] @@ -65,7 +65,9 @@ def group_by_evtid(data: Table) -> Table: return out_tbl -def group_by_time(data: Table, window: float = 10) -> lgdo.Table: +def group_by_time( + data: Table | ak.Array, window: float = 10, time_name: str = "time", evtid_name: str = "evtid" +) -> lgdo.Table: """Grouping of steps by `evtid` and `time`. Takes the input `stp` :class:`LGOD.Table` from remage and defines groupings of steps (i.e the @@ -79,7 +81,13 @@ def group_by_time(data: Table, window: float = 10) -> lgdo.Table: Parameters ---------- data - LGDO Table which must contain the `evtid`, `time` field. + LGDO Table or ak.Array which must contain the time_name and evtid_name fields + window + time window in us used to search for coincident hits + time_name + name of the timing field + evtid_name + name of the evtid field Returns ------- @@ -90,12 +98,12 @@ def group_by_time(data: Table, window: float = 10) -> lgdo.Table: The input table must be sorted (first by `evtid` then `time`). """ - obj = data.view_as("ak") - obj = sort_data(obj) + obj = data.view_as("ak") if isinstance(data, Table) else data + obj = sort_data(obj, time_name=time_name, evtid_name=evtid_name) # get difference - time_diffs = np.diff(obj.time) - index_diffs = np.diff(obj.evtid) + time_diffs = np.diff(obj[time_name]) + index_diffs = np.diff(obj[evtid_name]) # index of thhe last element in each run time_change = (time_diffs > window * 1000) & (index_diffs == 0) @@ -105,7 +113,7 @@ def group_by_time(data: Table, window: float = 10) -> lgdo.Table: cumulative_length = np.array(np.where(time_change | index_change))[0] + 1 # add the las grouping - cumulative_length = np.append(cumulative_length, len(obj.time)) + cumulative_length = np.append(cumulative_length, len(obj[time_name])) # build output table out_tbl = Table(size=len(cumulative_length)) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 414fad0..09fb666 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -16,6 +16,7 @@ import pyg4ometry import yaml from lgdo import lh5 +from lgdo.lh5 import LH5Iterator from numpy.typing import ArrayLike, NDArray log = logging.getLogger(__name__) @@ -185,6 +186,37 @@ def get_files_to_read(cum_n_sim: ArrayLike, start_glob_evtid: int, end_glob_evti return np.array(file_indices) +def get_global_evtid(first_evtid: int, obj: ak.Array, vertices: ArrayLike) -> ak.Array: + """Adds a global evtid field to the array. + + The global evtid is the index of the decay in the order the files are read in. This is obtained + from the first_evtid, defined as the number of decays in the previous files, plus the row of the + vertices array corresponding to this evtid. In this way we obtain an index both sorted and unique + over all the input files, this enables easy selection of some chunks. + + Parameters + ---------- + first_evtid + number of decays in the previous files + obj + awkward array of the input data + vertices + array of the vertex evtids + + Returns + ------- + the obj with the global_evtid field added + """ + vertices = np.array(vertices) + indices = np.searchsorted(vertices, np.array(obj.evtid)) + + if np.any(vertices[indices] != np.array(obj.evtid)): + msg = "Some of the evtids in the obj do not correspond to rows in the input" + raise ValueError(msg) + + return ak.with_field(obj, first_evtid + indices, "global_evtid") + + def get_include_chunk( global_evtid: ak.Array, start_glob_evtid: int, @@ -334,7 +366,10 @@ def _merge_arrays( rows = ak.num(ak_obj, axis=-1) end_rows = counts[-1] - if idx == 0: + if max_idx == 0: + mode = "of" if (delete_input) else "append" + obj = ak_obj + elif idx == 0: mode = "of" if (delete_input) else "append" obj = ak_obj[0 : rows - end_rows] buffer_rows = copy.deepcopy(ak_obj[rows - end_rows :]) @@ -350,6 +385,19 @@ def _merge_arrays( return obj, buffer_rows, mode +def get_iterator(file: str, field: str, detector: str, buffer: int): + """Get information on the iterator (number of index and entries).""" + + it = LH5Iterator(file, f"{field}/{detector}", buffer_len=buffer) + entries = it._get_file_cumentries(0) + + # number of blocks is ceil of entries/buffer, + # shift by 1 since idx starts at 0 + max_idx = int(np.ceil(entries / buffer)) - 1 + + return it, entries, max_idx + + __file_extensions__ = {"json": [".json"], "yaml": [".yaml", ".yml"]} diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index f0b4e8e..da557ce 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -205,8 +205,9 @@ def test_build_hit(test_reboost_input_file): }, } + # build hit for file 1 and 2 separately and then also merging them for output_file, input_file in zip( - ["out.lh5", "out_rem.lh5", "out_merge.lh5"], ["file1.lh5", "file2.lh5", "file*.lh5"] + ["out_1.lh5", "out_2.lh5", "out_merge.lh5"], ["file1.lh5", "file2.lh5", "file*.lh5"] ): hit.build_hit( str(test_reboost_input_file / output_file), @@ -218,6 +219,16 @@ def test_build_hit(test_reboost_input_file): buffer=100000, ) + tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") + tab_2 = lh5.read("hit/det001", str(test_reboost_input_file / "out_2.lh5")).view_as("ak") + tab_merge = lh5.read("hit/det001", str(test_reboost_input_file / "out_merge.lh5")).view_as("ak") + + # check lengths + assert len(ak.flatten(tab_1.evtid, axis=-1)) == int(1e6) + assert len(ak.flatten(tab_2.evtid, axis=-1)) == 30040 + assert len(ak.flatten(tab_merge.evtid, axis=-1)) == 30040 + int(1e6) + + # one call but write both files hit.build_hit( str(test_reboost_input_file / "out.lh5"), [str(test_reboost_input_file / "file*.lh5")], @@ -229,25 +240,30 @@ def test_build_hit(test_reboost_input_file): merge_input_files=False, ) - # read back in the data and check this works (no errors) + tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_0.lh5")).view_as("ak") + tab_2 = lh5.read("hit/det001", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") - tab = lh5.read("hit/det001", str(test_reboost_input_file / "out.lh5")).view_as("ak") - tab_merge = lh5.read("hit/det001", str(test_reboost_input_file / "out_merge.lh5")).view_as("ak") - tab_0 = lh5.read("hit/det001", str(test_reboost_input_file / "out_0.lh5")).view_as("ak") - tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") + # check lengths + assert len(ak.flatten(tab_1.evtid, axis=-1)) == int(1e6) + assert len(ak.flatten(tab_2.evtid, axis=-1)) == 30040 - # check size of the output - assert len(ak.flatten(tab.evtid, axis=-1)) == int(1e6) - assert len(ak.flatten(tab_merge.evtid, axis=-1)) == int(1e6 + 30040) - assert len(ak.flatten(tab_0.evtid, axis=-1)) == int(1e6) - assert len(ak.flatten(tab_1.evtid, axis=-1)) == 30040 + # test with a smaller buffer + hit.build_hit( + str(test_reboost_input_file / "out_small_buffer.lh5"), + [str(test_reboost_input_file / "file*.lh5")], + in_field="hit", + out_field="hit", + proc_config=proc_config, + pars={}, + buffer=10000, + ) - # check on evtid + tab_small = lh5.read( + "hit/det001", str(test_reboost_input_file / "out_small_buffer.lh5") + ).view_as("ak") - assert ak.all(ak.all(tab.evtid == ak.firsts(tab.evtid, axis=-1), axis=1)) - assert ak.all(ak.all(tab_merge.evtid == ak.firsts(tab_merge.evtid, axis=-1), axis=1)) - assert ak.all(ak.all(tab_0.evtid == ak.firsts(tab_0.evtid, axis=-1), axis=1)) - assert ak.all(ak.all(tab_1.evtid == ak.firsts(tab_1.evtid, axis=-1), axis=1)) + # buffer does not affect results + assert ak.all(ak.flatten(tab_merge.evtid) == ak.flatten(tab_small.evtid)) def test_build_hit_some_row(test_reboost_input_file): @@ -362,6 +378,7 @@ def test_build_hit_with_locals(test_reboost_input_file, test_data_configs): } gdml_path = configs / pathlib.Path("geom.gdml") meta_path = test_data_configs + # complete check on the processing chain including parameters / local variables hit.build_hit( diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 5126277..850bd90 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -17,6 +17,7 @@ dict2tuple, get_file_list, get_files_to_read, + get_global_evtid, get_global_evtid_range, get_hpge, get_include_chunk, @@ -218,6 +219,23 @@ def test_global_evtid_range(): assert get_global_evtid_range(200, None, 2000) == (200, 1999) +def test_get_global_evtid(): + # single file + first_evtid = 0 + vertices = [0, 1, 2, 3, 4, 5] + input_evtid = [2, 3, 4, 5, 5, 5] + obj = ak.Array({"evtid": input_evtid}) + assert np.all(get_global_evtid(first_evtid, obj, vertices).global_evtid == input_evtid) + + # now if we only have some vertices + vertices = [0, 2, 4, 6, 8, 10] + input_evtid = [4, 6, 8, 10, 10, 10] + obj = ak.Array({"evtid": input_evtid}) + assert np.all( + get_global_evtid(first_evtid, obj, vertices).global_evtid == np.array(input_evtid) / 2.0 + ) + + def test_get_files_to_read(): n_sim = [1000, 1200, 200, 5000] n_sim = np.concatenate([[0], np.cumsum(n_sim)]) From 9530b6502f91df5ae82e7dab7109776611f321cf Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 18 Nov 2024 19:47:57 +0100 Subject: [PATCH 63/81] first version of building tcm --- src/reboost/hpge/evt.py | 61 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 src/reboost/hpge/evt.py diff --git a/src/reboost/hpge/evt.py b/src/reboost/hpge/evt.py new file mode 100644 index 0000000..2ccb3ef --- /dev/null +++ b/src/reboost/hpge/evt.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import logging + +import awkward as ak +from lgdo import Table + +from . import processors + +log = logging.getLogger(__name__) + + +def build_tcm( + hit_data: list[ak.Array], + channels: list[int], + *, + window: float = 10, + time_name: str = "t0", + idx_name: str = "hit_global_evtid", +) -> Table: + """Builds a time-coincidence map from a hit of hit data Tables. + + - build an ak.Array of the data merging channels with fields base on "time_name", and "idx_name" and adding a field `rawid` from the channel idx, also add the row (`hit_idx`) + - sorts this array by "idx_name" then "time_name" fields + - group by "idx_name" and "time_name" based on the window parameter + + Parameters + ---------- + hit_data + list of hit tier data for each channel + channels + list of channel indices + window + time window for selecting coincidences (in us) + time_name + name of the field for time information + idx_name + name of the decay index field + + Returns + ------- + an LGDO.VectorOfVectors containing the time-coincidence map + """ + # build ak_obj for sorting + sort_objs = [] + for ch_idx, data_tmp in zip(channels, hit_data): + obj_tmp = ak.copy(data_tmp) + obj_tmp = obj_tmp[[time_name, idx_name]] + hit_idx = ak.local_index(obj_tmp) + + obj_tmp = ak.with_field(obj_tmp, hit_idx, "hit_idx") + + obj_tmp["rawid"] = ch_idx + + sort_objs.append(obj_tmp) + + obj_tot = ak.concatenate(sort_objs) + + return processors.group_by_time( + obj_tot, time_name=time_name, evtid_name=idx_name, window=window + ) From 78116a438d134f3ac5aef3c73f34e97420005c00 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 18 Nov 2024 19:51:05 +0100 Subject: [PATCH 64/81] remove timing debug (cleanup) --- src/reboost/hpge/hit.py | 65 +++-------------------------------------- tests/test_hpge_hit.py | 1 + 2 files changed, 5 insertions(+), 61 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 8b49c9c..c83a4c6 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -270,16 +270,6 @@ def build_hit( - It would be better to have a cleaner way to supply metadata and detector maps. """ - time_dict = { - "setup": 0, - "read": 0, - "write": 0, - "locals": 0, - "step_group": 0, - "proc": {}, - } - - time_start = time.time() # get the gdml file with utils.debug_logging(logging.CRITICAL): @@ -293,13 +283,6 @@ def build_hit( start_evtid=start_evtid, ) - time_end = time.time() - time_dict["setup"] += time_end - time_start - - # initialise timing object - time_start = time.time() - time_end = time.time() - # loop over input files for first_evtid, file_idx, file_in in zip( finfo.file_start_global_evtids, finfo.file_indices, finfo.file_list @@ -312,8 +295,7 @@ def build_hit( log.debug(msg) # get local variables - time_start = time.time() - + local_dict = get_locals( proc_config.get("locals", {}), pars_dict=pars.get(d, {}), @@ -322,9 +304,6 @@ def build_hit( reg=reg, ) - time_end = time.time() - time_dict["locals"] += time_end - time_start - is_first_chan = bool(ch_idx == 0) is_first_file = bool(file_idx == 0) @@ -364,9 +343,6 @@ def build_hit( # add global evtid obj = utils.get_global_evtid(first_evtid, obj, vertices) - time_end = time.time() - time_dict["read"] += time_end - time_start - # check if the chunk can be skipped, does lack of sorting break this? if not utils.get_include_chunk( @@ -386,33 +362,19 @@ def build_hit( data = Table(obj) # group steps into hits - time_start = time.time() grouped = step_group(data, proc_config["step_group"]) - time_end = time.time() - - time_dict["step_group"] += time_end - time_start - + # processors for name, info in proc_config["operations"].items(): msg = f"... adding column {name}" log.debug(msg) - time_start = time.time() - col = eval_expression(grouped, info, local_dict=local_dict) grouped.add_field(name, col) - time_end = time.time() - - if name in time_dict["proc"]: - time_dict["proc"][name] += time_end - time_start - else: - time_dict["proc"][name] = 0 - # remove unwanted columns log.debug("... removing unwanted columns") - time_start = time.time() - + existing_cols = list(grouped.keys()) for col in existing_cols: if col not in proc_config["outputs"]: @@ -426,23 +388,4 @@ def build_hit( else file_out ) lh5.write(grouped, f"{out_field}/{d}", file_out_tmp, wo_mode=mode) - time_end = time.time() - - time_dict["write"] += time_end - time_start - - # start timing read - time_start = time.time() - - # print timing info - - for step, time_val in time_dict.items(): - if isinstance(time_val, dict): - msg = "Time for processors:" - log.info(msg) - - for name, t in time_val.items(): - msg = f" {name} elapsed time: {t:.1f} s" - log.info(msg) - else: - msg = f"{step} elapsed time: {time_val:.1f} s" - log.info(msg) + \ No newline at end of file diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index da557ce..c0517bf 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -303,6 +303,7 @@ def test_build_hit_some_row(test_reboost_input_file): buffer=100000, ) + # test reading the data in two goes gives the same result for n_ev, s_ev, out in zip( [int(1e4), int(1e5 - 1e4), int(1e5), int(1e5)], [0, int(1e4), 0, 1000], From 0abd90fe1fc261e9c34cac3d5a428283a97d8d9c Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 18 Nov 2024 19:58:33 +0100 Subject: [PATCH 65/81] [evt] first version of build_tcm code --- src/reboost/hpge/evt.py | 6 +++++- src/reboost/hpge/hit.py | 9 +++------ src/reboost/hpge/processors.py | 9 ++++++++- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/src/reboost/hpge/evt.py b/src/reboost/hpge/evt.py index 2ccb3ef..aed3694 100644 --- a/src/reboost/hpge/evt.py +++ b/src/reboost/hpge/evt.py @@ -57,5 +57,9 @@ def build_tcm( obj_tot = ak.concatenate(sort_objs) return processors.group_by_time( - obj_tot, time_name=time_name, evtid_name=idx_name, window=window + obj_tot, + time_name=time_name, + evtid_name=idx_name, + window=window, + fields=["rawid", "hit_idx"], ) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index c83a4c6..f16f736 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -1,7 +1,6 @@ from __future__ import annotations import logging -import time import pyg4ometry from lgdo import Array, ArrayOfEqualSizedArrays, Table, VectorOfVectors, lh5 @@ -270,7 +269,6 @@ def build_hit( - It would be better to have a cleaner way to supply metadata and detector maps. """ - # get the gdml file with utils.debug_logging(logging.CRITICAL): reg = pyg4ometry.gdml.Reader(gdml).getRegistry() if gdml is not None else None @@ -295,7 +293,7 @@ def build_hit( log.debug(msg) # get local variables - + local_dict = get_locals( proc_config.get("locals", {}), pars_dict=pars.get(d, {}), @@ -363,7 +361,7 @@ def build_hit( # group steps into hits grouped = step_group(data, proc_config["step_group"]) - + # processors for name, info in proc_config["operations"].items(): msg = f"... adding column {name}" @@ -374,7 +372,7 @@ def build_hit( # remove unwanted columns log.debug("... removing unwanted columns") - + existing_cols = list(grouped.keys()) for col in existing_cols: if col not in proc_config["outputs"]: @@ -388,4 +386,3 @@ def build_hit( else file_out ) lh5.write(grouped, f"{out_field}/{d}", file_out_tmp, wo_mode=mode) - \ No newline at end of file diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index 79f01b9..aba0704 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -66,7 +66,11 @@ def group_by_evtid(data: Table) -> Table: def group_by_time( - data: Table | ak.Array, window: float = 10, time_name: str = "time", evtid_name: str = "evtid" + data: Table | ak.Array, + window: float = 10, + time_name: str = "time", + evtid_name: str = "evtid", + fields: list | None = None, ) -> lgdo.Table: """Grouping of steps by `evtid` and `time`. @@ -88,6 +92,8 @@ def group_by_time( name of the timing field evtid_name name of the evtid field + fields + names of fields to include in the output table, if None includes all Returns ------- @@ -118,6 +124,7 @@ def group_by_time( # build output table out_tbl = Table(size=len(cumulative_length)) + fields = obj.fields if fields is None else fields for f in obj.fields: out_tbl.add_field( f, VectorOfVectors(cumulative_length=cumulative_length, flattened_data=obj[f]) From d9509bc5707afd5b8d03729076dc922d11a4b130 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 18 Nov 2024 19:59:22 +0100 Subject: [PATCH 66/81] [docs] small fix --- docs/source/notebooks/reboost_hpge_tutorial.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index 796a4d7..7a71e6b 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -295,7 +295,6 @@ Now we can run our post-processing .. code:: python - %%time hit.build_hit(file_out="output/hit/output.lh5",list_file_in="output/stp/*.lh5", out_field="hit",in_field="stp", proc_config=chain,pars=pars,gdml="cfg/geom.gdml",metadata_path="cfg/metadata/",merge_input_files=True,has_global_evtid=True) From abd94507dbbe34f1eb9943f7d23064091aacba1d Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Mon, 18 Nov 2024 19:59:39 +0100 Subject: [PATCH 67/81] pre-commit --- .../notebooks/reboost_hpge_tutorial.rst | 362 +++++++++++------- 1 file changed, 220 insertions(+), 142 deletions(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_tutorial.rst index 7a71e6b..012c5e9 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial.rst @@ -148,10 +148,10 @@ First we set up the python environment. import numpy as np - plt.rcParams['figure.figsize'] = [12, 4] - plt.rcParams['axes.titlesize'] =12 - plt.rcParams['axes.labelsize'] = 12 - plt.rcParams['legend.fontsize'] = 12 + plt.rcParams["figure.figsize"] = [12, 4] + plt.rcParams["axes.titlesize"] = 12 + plt.rcParams["axes.labelsize"] = 12 + plt.rcParams["legend.fontsize"] = 12 handler = colorlog.StreamHandler() @@ -187,85 +187,81 @@ effect of the processors. .. code:: python chain = { - "channels": [ - "det001", - "det002" - ], - "outputs": [ - "t0", # first timestamp - "time", # time of each step - "edep", # energy deposited in each step - "hit_evtid", # id of the hit - "hit_global_evtid", # global id of the hit - "distance_to_nplus_surface_mm", # distance to detector nplus surface - "activeness", # activeness for the step - "rpos_loc", # radius of step - "zpos_loc", # z position - "energy_sum", # true summed energy before dead layer or smearing - "energy_sum_deadlayer", # energy sum after dead layers - "energy_sum_smeared" # energy sum after smearing with resolution - ], - "step_group": { - "description": "group steps by time and evtid with 10us window", - "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + "channels": ["det001", "det002"], + "outputs": [ + "t0", # first timestamp + "time", # time of each step + "edep", # energy deposited in each step + "hit_evtid", # id of the hit + "hit_global_evtid", # global id of the hit + "distance_to_nplus_surface_mm", # distance to detector nplus surface + "activeness", # activeness for the step + "rpos_loc", # radius of step + "zpos_loc", # z position + "energy_sum", # true summed energy before dead layer or smearing + "energy_sum_deadlayer", # energy sum after dead layers + "energy_sum_smeared", # energy sum after smearing with resolution + ], + "step_group": { + "description": "group steps by time and evtid with 10us window", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + }, + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", + }, + "hit_evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.evtid,axis=-1),np.nan)", + }, + "hit_global_evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.global_evtid,axis=-1),np.nan)", + }, + "distance_to_nplus_surface_mm": { + "description": "distance to the nplus surface in mm", + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')", + }, + "activeness": { + "description": "activness based on FCCD (no TL)", + "mode": "eval", + "expression": "ak.where(hit.distance_to_nplus_surface_mm1][1]].time,histtype="step",yerr=False) + plot_times( + data_det001[data_det001.hit_global_evtid == unique[counts > 1][1]].time, + histtype="step", + yerr=False, + ) @@ -537,26 +556,39 @@ processor works as expected. .. code:: python - def plot_map(field,scale="BuPu",clab="Distance [mm]"): + def plot_map(field, scale="BuPu", clab="Distance [mm]"): fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) - n=100000 - for idx, (data,config) in enumerate(zip([data_det001,data_det002],["cfg/metadata/BEGe.json","cfg/metadata/Coax.json"])): - - reg=pg4.geant4.Registry() - hpge = legendhpges.make_hpge(config,registry=reg) - - legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx]) - r = np.random.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc) + n = 100000 + for idx, (data, config) in enumerate( + zip( + [data_det001, data_det002], + ["cfg/metadata/BEGe.json", "cfg/metadata/Coax.json"], + ) + ): + reg = pg4.geant4.Registry() + hpge = legendhpges.make_hpge(config, registry=reg) + + legendhpges.draw.plot_profile(hpge, split_by_type=True, axes=axs[idx]) + r = np.random.choice( + [-1, 1], p=[0.5, 0.5], size=len(ak.flatten(data.rpos_loc)) + ) * ak.flatten(data.rpos_loc) z = ak.flatten(data.zpos_loc) - c=ak.flatten(data[field]) - cut = c<5 - - s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=".", label="gen. points",cmap=scale) - #axs[idx].axis("equal") + c = ak.flatten(data[field]) + cut = c < 5 + + s = axs[idx].scatter( + r[cut][0:n], + z[cut][0:n], + c=c[cut][0:n], + marker=".", + label="gen. points", + cmap=scale, + ) + # axs[idx].axis("equal") if idx == 0: axs[idx].set_ylabel("Height [mm]") - c=plt.colorbar(s) + c = plt.colorbar(s) c.set_label(clab) axs[idx].set_xlabel("Radius [mm]") @@ -572,7 +604,7 @@ processor works as expected. .. code:: python - plot_map("activeness",clab="Activeness",scale="viridis") + plot_map("activeness", clab="Activeness", scale="viridis") .. image:: images/output_28_1.png @@ -582,11 +614,10 @@ We can also plot a histogram of the distance to the surface. .. code:: python - def plot_distances(axes,distances,xrange=None,label=" ",**kwargs): - - h=hist.new.Reg(100,*xrange, name="Distance to n+ surface [mm]").Double() + def plot_distances(axes, distances, xrange=None, label=" ", **kwargs): + h = hist.new.Reg(100, *xrange, name="Distance to n+ surface [mm]").Double() h.fill(distances) - h.plot(**kwargs,label=label) + h.plot(**kwargs, label=label) ax.legend() ax.set_yscale("log") if xrange is not None: @@ -595,9 +626,23 @@ We can also plot a histogram of the distance to the surface. .. code:: python - fig,ax = plt.subplots() - plot_distances(ax,ak.flatten(data_det001.distance_to_nplus_surface_mm),xrange=(0,35),label="BEGe",histtype="step",yerr=False) - plot_distances(ax,ak.flatten(data_det002.distance_to_nplus_surface_mm),xrange=(0,35),label="Coax",histtype="step",yerr=False) + fig, ax = plt.subplots() + plot_distances( + ax, + ak.flatten(data_det001.distance_to_nplus_surface_mm), + xrange=(0, 35), + label="BEGe", + histtype="step", + yerr=False, + ) + plot_distances( + ax, + ak.flatten(data_det002.distance_to_nplus_surface_mm), + xrange=(0, 35), + label="Coax", + histtype="step", + yerr=False, + ) @@ -613,13 +658,12 @@ after weighting by the activeness. .. code:: python - def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): - - h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double() + def plot_energy(axes, energy, bins=400, xrange=None, label=" ", log_y=True, **kwargs): + h = hist.new.Reg(bins, *xrange, name="energy [keV]").Double() h.fill(energy) - h.plot(**kwargs,label=label) + h.plot(**kwargs, label=label) axes.legend() - if (log_y): + if log_y: axes.set_yscale("log") if xrange is not None: axes.set_xlim(*xrange) @@ -628,8 +672,16 @@ after weighting by the activeness. fig, ax = plt.subplots() ax.set_title("BEGe energy spectrum") - plot_energy(ax,data_det001.energy_sum,yerr=False,label="True energy",xrange=(0,4000)) - plot_energy(ax,data_det001.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000)) + plot_energy( + ax, data_det001.energy_sum, yerr=False, label="True energy", xrange=(0, 4000) + ) + plot_energy( + ax, + data_det001.energy_sum_deadlayer, + yerr=False, + label="Energy after dead layer", + xrange=(0, 4000), + ) @@ -640,8 +692,16 @@ after weighting by the activeness. fig, ax = plt.subplots() ax.set_title("COAX energy spectrum") - plot_energy(ax,data_det002.energy_sum,yerr=False,label="True energy",xrange=(0,4000)) - plot_energy(ax,data_det002.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000)) + plot_energy( + ax, data_det002.energy_sum, yerr=False, label="True energy", xrange=(0, 4000) + ) + plot_energy( + ax, + data_det002.energy_sum_deadlayer, + yerr=False, + label="Energy after dead layer", + xrange=(0, 4000), + ) @@ -661,8 +721,26 @@ dependent resolution curve. To see the effect we have to zoom into the .. code:: python fig, axs = plt.subplots() - plot_energy(axs,data_det001.energy_sum_smeared,yerr=False,label="BEGe",xrange=(2600,2630),log_y=False,bins=150,density=True) - plot_energy(axs,data_det002.energy_sum_smeared,yerr=False,label="COAX",xrange=(2600,2630),log_y=False,bins=150,density=True) + plot_energy( + axs, + data_det001.energy_sum_smeared, + yerr=False, + label="BEGe", + xrange=(2600, 2630), + log_y=False, + bins=150, + density=True, + ) + plot_energy( + axs, + data_det002.energy_sum_smeared, + yerr=False, + label="COAX", + xrange=(2600, 2630), + log_y=False, + bins=150, + density=True, + ) From 01cc0007e5e30aba52dbb16d191172e0c2520acd Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Tue, 19 Nov 2024 12:42:52 +0100 Subject: [PATCH 68/81] [docs] fix build-hit docsring --- src/reboost/hpge/hit.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index f16f736..1aee644 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -195,8 +195,8 @@ def build_hit( { "channels": [ "det000", - has_global_evtid: bool = False, - ], + "det001" + ], "outputs": [ "t0", "truth_energy_sum", From f0d13cbae12e687fb7ec4c4bc0ff000a721180d2 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 20 Nov 2024 16:10:16 +0100 Subject: [PATCH 69/81] change evtid to _evtid and global_evtid to _global_evtid since its not intended to be outputed --- src/reboost/hpge/evt.py | 65 ---------- src/reboost/hpge/hit.py | 213 +++++++++++++++++---------------- src/reboost/hpge/processors.py | 8 +- src/reboost/hpge/tcm.py | 139 +++++++++++++++++++++ src/reboost/hpge/utils.py | 115 +++++++++++++++++- tests/test_hpge_hit.py | 78 ++++++------ tests/test_hpge_step_group.py | 10 +- tests/test_hpge_utils.py | 32 ++++- 8 files changed, 440 insertions(+), 220 deletions(-) delete mode 100644 src/reboost/hpge/evt.py create mode 100644 src/reboost/hpge/tcm.py diff --git a/src/reboost/hpge/evt.py b/src/reboost/hpge/evt.py deleted file mode 100644 index aed3694..0000000 --- a/src/reboost/hpge/evt.py +++ /dev/null @@ -1,65 +0,0 @@ -from __future__ import annotations - -import logging - -import awkward as ak -from lgdo import Table - -from . import processors - -log = logging.getLogger(__name__) - - -def build_tcm( - hit_data: list[ak.Array], - channels: list[int], - *, - window: float = 10, - time_name: str = "t0", - idx_name: str = "hit_global_evtid", -) -> Table: - """Builds a time-coincidence map from a hit of hit data Tables. - - - build an ak.Array of the data merging channels with fields base on "time_name", and "idx_name" and adding a field `rawid` from the channel idx, also add the row (`hit_idx`) - - sorts this array by "idx_name" then "time_name" fields - - group by "idx_name" and "time_name" based on the window parameter - - Parameters - ---------- - hit_data - list of hit tier data for each channel - channels - list of channel indices - window - time window for selecting coincidences (in us) - time_name - name of the field for time information - idx_name - name of the decay index field - - Returns - ------- - an LGDO.VectorOfVectors containing the time-coincidence map - """ - # build ak_obj for sorting - sort_objs = [] - for ch_idx, data_tmp in zip(channels, hit_data): - obj_tmp = ak.copy(data_tmp) - obj_tmp = obj_tmp[[time_name, idx_name]] - hit_idx = ak.local_index(obj_tmp) - - obj_tmp = ak.with_field(obj_tmp, hit_idx, "hit_idx") - - obj_tmp["rawid"] = ch_idx - - sort_objs.append(obj_tmp) - - obj_tot = ak.concatenate(sort_objs) - - return processors.group_by_time( - obj_tot, - time_name=time_name, - evtid_name=idx_name, - window=window, - fields=["rawid", "hit_idx"], - ) diff --git a/src/reboost/hpge/hit.py b/src/reboost/hpge/hit.py index 1aee644..b17104b 100644 --- a/src/reboost/hpge/hit.py +++ b/src/reboost/hpge/hit.py @@ -2,6 +2,7 @@ import logging +import awkward as ak import pyg4ometry from lgdo import Array, ArrayOfEqualSizedArrays, Table, VectorOfVectors, lh5 @@ -173,100 +174,100 @@ def build_hit( merge_input_files: bool = True, ) -> None: """ - Read incrementally the files compute something and then write output - - Parameters - ---------- - file_out - output file path - list_file_in - list of input files - out_field - lh5 group name for output - in_field - lh5 group name for input - proc_config - the configuration file for the processing. Must contain the fields `channels`, `outputs`, `step_group` and operations`. - Optionally can also contain the `locals` field to extract other non-JSON serializable objects used by the processors. - For example: - - .. code-block:: json - - { - "channels": [ - "det000", - "det001" - ], - "outputs": [ - "t0", - "truth_energy_sum", - "smeared_energy_sum", - "evtid" - ], - "step_group": { - "description": "group steps by time and evtid.", - "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" - }, - "locals": { - "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)" - }, - "operations": { - "t0": { - "description": "first time in the hit.", - "mode": "eval", - "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" - }, - "truth_energy_sum": { - "description": "truth summed energy in the hit.", - "mode": "eval", - "expression": "ak.sum(hit.edep,axis=-1)" - }, - "smeared_energy_sum": { - "description": "summed energy after convolution with energy response.", - "mode": "function", - "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" - } - - } - } - - pars - a dictionary of parameters, must have a field per channel consisting of a `dict` of parameters. For example: - - .. code-block:: json - - { - "det000": { - "reso": 1, - "fccd": 0.1, - "phy_vol_name":"det_phy", - "meta_name": "icpc.json" - } - } - - this should also contain the channel mappings needed by reboost. These are: - - `phy_vol_name`: is the name of the physical volume, - - `meta_name` : is the name of the JSON file with the metadata. - - If these keys are not present both will be set to the remage output table name. - - start_evtid - first `evtid` to read, defaults to 0. - n_evtid - number of `evtid` to read, if `None` all steps are read (the default). - buffer - length of buffer - gdml - path to the input gdml file. - metadata_path - path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) - merge_input_files - boolean flag to merge all input files into a single output. - - Note - ---- - - The operations can depend on the outputs of previous steps, so operations order is important. - - It would be better to have a cleaner way to supply metadata and detector maps. + Read incrementally the files compute something and then write output + + Parameters + ---------- + file_out + output file path + list_file_in + list of input files + out_field + lh5 group name for output + in_field + lh5 group name for input + proc_config + the configuration file for the processing. Must contain the fields `channels`, `outputs`, `step_group` and operations`. + Optionally can also contain the `locals` field to extract other non-JSON serializable objects used by the processors. + For example: + + .. code-block:: json + + { + "channels": [ + "det000", + "det001" + ], + "outputs": [ + "t0", + "truth_energy_sum", + "smeared_energy_sum", + "evtid" + ], + "step_group": { + "description": "group steps by time and evtid.", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" + }, + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)" + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" + }, + "truth_energy_sum": { + "description": "truth summed energy in the hit.", + "mode": "eval", + "expression": "ak.sum(hit.edep,axis=-1)" + }, + "smeared_energy_sum": { + "description": "summed energy after convolution with energy response.", + "mode": "function", + "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" + } + + } + } + + pars + a dictionary of parameters, must have a field per channel consisting of a `dict` of parameters. For example: + + .. code-block:: json + + { + "det000": { + "reso": 1, + "fccd": 0.1, + "phy_vol_name":"det_phy", + "meta_name": "icpc.json" + } + } + + this should also contain the channel mappings needed by reboost. These are: + - `phy_vol_name`: is the name of the physical volume, + - `meta_name` : is the name of the JSON file with the metadata. + + If these keys are not present both will be set to the remage output table name. + + start_evtid + first `evtid` to read, defaults to 0. + n_evtid + number of `evtid` to read, if `None` all steps are read (the default). + buffer + length of buffer + gdml + path to the input gdml file. + metadata_path + path to the folder with the metadata (i.e. the `hardware.detectors.germanium.diodes` folder of `legend-metadata`) + merge_input_files + boolean flag to merge all input files into a single output. + + Note + ---- + - The operations can depend on the outputs of previous steps, so operations order is important. + - It would be better to have a cleaner way to supply metadata and detector maps. """ # get the gdml file @@ -289,8 +290,8 @@ def build_hit( vertices = lh5.read_as(f"{in_field}/vertices/evtid", file_in, "np") for ch_idx, d in enumerate(proc_config["channels"]): - msg = f"...running hit tier for {d}" - log.debug(msg) + msg = f"...running hit tier for {file_in} and {d}" + log.info(msg) # get local variables @@ -317,14 +318,14 @@ def build_hit( # number of iterations (used to handle last iteration) it, entries, max_idx = utils.get_iterator( - file=file_in, field=in_field, detector=d, buffer=buffer + file=file_in, lh5_table=f"{in_field}/{d}", buffer=buffer ) buffer_rows = None # iterate over the LH5 file for idx, (lh5_obj, _, n_rows) in enumerate(it): msg = f"... processed {idx} chunks out of {max_idx}" - log.info(msg) + log.debug(msg) # convert to awkward ak_obj = lh5_obj.view_as("ak") @@ -344,7 +345,7 @@ def build_hit( # check if the chunk can be skipped, does lack of sorting break this? if not utils.get_include_chunk( - obj.global_evtid, + obj._global_evtid, start_glob_evtid=finfo.first_global_evtid, end_glob_evtid=finfo.last_global_evtid, ): @@ -352,10 +353,18 @@ def build_hit( # select just the correct global evtid objects obj = obj[ - (obj.global_evtid >= finfo.first_global_evtid) - & (obj.global_evtid <= finfo.last_global_evtid) + (obj._global_evtid >= finfo.first_global_evtid) + & (obj._global_evtid <= finfo.last_global_evtid) ] + # rename the fields + obj = ak.with_field( + obj, + obj["evtid"], + "_evtid", + ) + obj = ak.without_field(obj, "evtid") + # convert back to a table, should work data = Table(obj) @@ -385,4 +394,4 @@ def build_hit( if (merge_input_files is False) else file_out ) - lh5.write(grouped, f"{out_field}/{d}", file_out_tmp, wo_mode=mode) + lh5.write(grouped, f"{d}/{out_field}", file_out_tmp, wo_mode=mode) diff --git a/src/reboost/hpge/processors.py b/src/reboost/hpge/processors.py index aba0704..d390364 100644 --- a/src/reboost/hpge/processors.py +++ b/src/reboost/hpge/processors.py @@ -8,7 +8,7 @@ from numpy.typing import ArrayLike -def sort_data(obj: ak.Array, *, time_name: str = "time", evtid_name: str = "evtid") -> ak.Array: +def sort_data(obj: ak.Array, *, time_name: str = "time", evtid_name: str = "_evtid") -> ak.Array: """Sort the data by evtid then time. Parameters @@ -52,7 +52,7 @@ def group_by_evtid(data: Table) -> Table: obj_ak = sort_data(obj_ak) # extract cumulative lengths - counts = ak.run_lengths(obj_ak.evtid) + counts = ak.run_lengths(obj_ak._evtid) cumulative_length = np.cumsum(counts) # build output table @@ -69,7 +69,7 @@ def group_by_time( data: Table | ak.Array, window: float = 10, time_name: str = "time", - evtid_name: str = "evtid", + evtid_name: str = "_evtid", fields: list | None = None, ) -> lgdo.Table: """Grouping of steps by `evtid` and `time`. @@ -125,7 +125,7 @@ def group_by_time( out_tbl = Table(size=len(cumulative_length)) fields = obj.fields if fields is None else fields - for f in obj.fields: + for f in fields: out_tbl.add_field( f, VectorOfVectors(cumulative_length=cumulative_length, flattened_data=obj[f]) ) diff --git a/src/reboost/hpge/tcm.py b/src/reboost/hpge/tcm.py new file mode 100644 index 0000000..c14ef11 --- /dev/null +++ b/src/reboost/hpge/tcm.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +import logging +import re + +import awkward as ak +from lgdo import Table, lh5 + +from . import processors, utils + +log = logging.getLogger(__name__) + + +def build_tcm( + hit_file: str, + out_file: str, + channels: list[str], + time_name: str = "t0", + idx_name: str = "hit_global_evtid", + time_window_in_us: float = 10, + idx_buffer: int = int(1e7), +) -> None: + """Build the (Time Coincidence Map) TCM from the hit tier. + + Parameters + ---------- + hit_file + path to hit tier file. + out_file + output path for tcm. + channels + list of channel names to include. + time_name + name of the hit tier field used for time grouping. + idx_name + name of the hit tier field used for index grouping. + time_window_in_us + time window used to define the grouping. + idx_buffer + number of evtid to read in simultaneously. + + + Notes + ----- + This function avoids excessive memory usage by iterating over the files select sets of evtid, + as such it may be a bit wasteful of IO, since the same block may need to be read multiple times. + Since only a few fields are read a high value of idx_buffer can be used. + + """ + + hash_func = r"\d+" + + # get number of evtids + n_evtid = utils.get_num_evtid_hit_tier(hit_file, channels, idx_name) + + # not iterate over evtid + n_evtid_read = 0 + mode = "of" + msg = "start building time-coincidence map" + log.info(msg) + + chan_ids = [re.search(hash_func, channel).group() for channel in channels] + + while n_evtid_read < n_evtid: + # object for the data + + msg = f"... iterating: selecting evtid {n_evtid_read} to {n_evtid_read+idx_buffer}" + log.debug(msg) + + hit_data, n_evtid_read = utils.read_some_idx_as_ak( + channels=channels, + file=hit_file, + n_idx_read=n_evtid_read, + idx_buffer=idx_buffer, + idx_name=idx_name, + field_mask=[idx_name, time_name], + ) + + tcm = get_tcm_from_ak( + hit_data, chan_ids, window=time_window_in_us, time_name=time_name, idx_name=idx_name + ) + if tcm is not None: + lh5.write(tcm, "tcm", out_file, wo_mode=mode) + mode = "append" + + +def get_tcm_from_ak( + hit_data: list[ak.Array], + channels: list[int], + *, + window: float = 10, + time_name: str = "t0", + idx_name: str = "hit_global_evtid", +) -> Table: + """Builds a time-coincidence map from a hit of hit data Tables. + + - build an ak.Array of the data merging channels with fields base on "time_name", and "idx_name" and adding a field `rawid` from the channel idx, also add the row (`hit_idx`) + - sorts this array by "idx_name" then "time_name" fields + - group by "idx_name" and "time_name" based on the window parameter + + Parameters + ---------- + hit_data + list of hit tier data for each channel + channels + list of channel indices + window + time window for selecting coincidences (in us) + time_name + name of the field for time information + idx_name + name of the decay index field + + Returns + ------- + an LGDO.VectorOfVectors containing the time-coincidence map + """ + # build ak_obj for sorting + sort_objs = [] + + for ch_idx, data_tmp in zip(channels, hit_data): + obj_tmp = ak.copy(data_tmp) + obj_tmp = obj_tmp[[time_name, idx_name]] + hit_idx = ak.local_index(obj_tmp) + + obj_tmp = ak.with_field(obj_tmp, hit_idx, "hit_idx") + + obj_tmp["channel_id"] = int(ch_idx) + sort_objs.append(obj_tmp) + + obj_tot = ak.concatenate(sort_objs) + + return processors.group_by_time( + obj_tot, + time_name=time_name, + evtid_name=idx_name, + window=window, + fields=["channel_id", "hit_idx"], + ) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index 09fb666..bc71f8c 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -115,6 +115,42 @@ def get_num_simulated(file_list: list, table: str = "hit") -> int: return n +def get_num_evtid_hit_tier(hit_file: str, channels: list[str], idx_name: str) -> int: + """Get the number of evtid in the hit file. + + Parameters + ---------- + hit_file + path to the hit file + channels + list of channel names + idx_name + name of the index field + + Returns + ------- + largest evtid in the files. + + Note + ---- + To avoid reading the full file into memory we assume the hit tier files are sorted by "idx_name". This should always be the case. + """ + + n_max = 0 + for channel in channels: + # use the iterator to get the file size + it = lh5.LH5Iterator(hit_file, f"{channel}/hit/", buffer_len=int(5e6)) + entries = it._get_file_cumentries(0) + + ob_ak = lh5.read(f"{channel}/hit/", hit_file, idx=[entries - 1]).view_as("ak") + + max_evtid = ob_ak[idx_name][0] + + n_max = max_evtid if (max_evtid > n_max) else n_max + + return n_max + + def get_file_list(path: str | list[str]) -> NDArray: """Get list of files to read. @@ -214,7 +250,7 @@ def get_global_evtid(first_evtid: int, obj: ak.Array, vertices: ArrayLike) -> ak msg = "Some of the evtids in the obj do not correspond to rows in the input" raise ValueError(msg) - return ak.with_field(obj, first_evtid + indices, "global_evtid") + return ak.with_field(obj, first_evtid + indices, "_global_evtid") def get_include_chunk( @@ -385,10 +421,10 @@ def _merge_arrays( return obj, buffer_rows, mode -def get_iterator(file: str, field: str, detector: str, buffer: int): +def get_iterator(file: str, buffer: int, lh5_table: str, **kwargs) -> tuple[LH5Iterator, int, int]: """Get information on the iterator (number of index and entries).""" - it = LH5Iterator(file, f"{field}/{detector}", buffer_len=buffer) + it = LH5Iterator(file, lh5_table, buffer_len=buffer, **kwargs) entries = it._get_file_cumentries(0) # number of blocks is ceil of entries/buffer, @@ -398,6 +434,79 @@ def get_iterator(file: str, field: str, detector: str, buffer: int): return it, entries, max_idx +def read_some_idx_as_ak( + channels: list, + file: str, + n_idx_read: int, + idx_buffer: int, + idx_name: str, + field_mask: list[str], + it_buffer: int = 100000, +) -> tuple[list[ak.Array], int]: + """Read just rows corresponding to a idx_name field in the range n_idx_read to n_idx_read + idx_buffer. + + Works by iterating over the file only selecting entries in the desired range, also converting to awkward + + Parameters + ---------- + channels + list of channels to read + file + path to hit tier file to read + n_idx_read + first index to read + idx_buffer + number of indices to read. + idx_name + name of the field containing the index. + field_mask + fields to read + it_buffer + buffer for the iterator + + Returns + ------- + tuple of a list of the data per channel and the updated number of index read. + """ + + hit_data = [] + + # loop over channels + for channel in channels: + data_tmp = [] + # iteration over the file + it, entries, max_idx = get_iterator( + file=file, buffer=it_buffer, lh5_table=f"{channel}/hit", field_mask=field_mask + ) + + for idx, (lh5_obj, _, n_rows) in enumerate(it): + ak_obj = lh5_obj.view_as("ak") + + if idx == max_idx: + ak_obj = ak_obj[:n_rows] + + # assumes index are sorted + low_idx = ak_obj[idx_name][0] + + # check if the chunk contains evtid low_evtid + if not ((low_idx >= n_idx_read) & (low_idx < n_idx_read + idx_buffer)): + continue + + # slice and select just the needed cols + condition = (ak_obj[idx_name] >= n_idx_read) & ( + ak_obj[idx_name] < n_idx_read + idx_buffer + ) + ak_obj_sel = ak_obj[condition] + + data_tmp.append(ak_obj_sel) + + hit_data.append(ak.concatenate(data_tmp)) + + n_idx_read += idx_buffer + + return hit_data, n_idx_read + + __file_extensions__ = {"json": [".json"], "yaml": [".yaml", ".yml"]} diff --git a/tests/test_hpge_hit.py b/tests/test_hpge_hit.py index c0517bf..806c6da 100644 --- a/tests/test_hpge_hit.py +++ b/tests/test_hpge_hit.py @@ -131,23 +131,23 @@ def test_eval_with_hpge_and_phy_vol(test_data_configs): def test_reboost_input_file(tmp_path): # make it large enough to be multiple groups rng = np.random.default_rng() - evtid_1 = np.sort(rng.integers(int(1e5), size=(int(1e6)))) - time_1 = rng.uniform(low=0, high=1, size=(int(1e6))) - edep_1 = rng.uniform(low=0, high=1000, size=(int(1e6))) - pos_x_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) - pos_y_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) - pos_z_1 = rng.uniform(low=-50, high=50, size=(int(1e6))) + evtid_1 = np.sort(rng.integers(int(1e4), size=(int(1e5)))) + time_1 = rng.uniform(low=0, high=1, size=(int(1e5))) + edep_1 = rng.uniform(low=0, high=1000, size=(int(1e5))) + pos_x_1 = rng.uniform(low=-50, high=50, size=(int(1e5))) + pos_y_1 = rng.uniform(low=-50, high=50, size=(int(1e5))) + pos_z_1 = rng.uniform(low=-50, high=50, size=(int(1e5))) # make it not divide by the buffer len - evtid_2 = np.sort(rng.integers(int(1e5), size=(30040))) - time_2 = rng.uniform(low=0, high=1, size=(30040)) - edep_2 = rng.uniform(low=0, high=1000, size=(30040)) - pos_x_2 = rng.uniform(low=-50, high=50, size=(30040)) - pos_y_2 = rng.uniform(low=-50, high=50, size=(30040)) - pos_z_2 = rng.uniform(low=-50, high=50, size=(30040)) + evtid_2 = np.sort(rng.integers(int(1e4), size=(3004))) + time_2 = rng.uniform(low=0, high=1, size=(3004)) + edep_2 = rng.uniform(low=0, high=1000, size=(3004)) + pos_x_2 = rng.uniform(low=-50, high=50, size=(3004)) + pos_y_2 = rng.uniform(low=-50, high=50, size=(3004)) + pos_z_2 = rng.uniform(low=-50, high=50, size=(3004)) - vertices_1 = ak.Array({"evtid": np.arange(int(1e5))}) - vertices_2 = ak.Array({"evtid": np.arange(int(1e5))}) + vertices_1 = ak.Array({"evtid": np.arange(int(1e4))}) + vertices_2 = ak.Array({"evtid": np.arange(int(1e4))}) arr_1 = ak.Array( { @@ -186,7 +186,7 @@ def test_build_hit(test_reboost_input_file): "channels": [ "det001", ], - "outputs": ["t0", "evtid"], + "outputs": ["t0", "_evtid"], "step_group": { "description": "group steps by time and evtid.", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", @@ -216,17 +216,17 @@ def test_build_hit(test_reboost_input_file): out_field="hit", proc_config=proc_config, pars={}, - buffer=100000, + buffer=10000, ) - tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") - tab_2 = lh5.read("hit/det001", str(test_reboost_input_file / "out_2.lh5")).view_as("ak") - tab_merge = lh5.read("hit/det001", str(test_reboost_input_file / "out_merge.lh5")).view_as("ak") + tab_1 = lh5.read("det001/hit", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") + tab_2 = lh5.read("det001/hit", str(test_reboost_input_file / "out_2.lh5")).view_as("ak") + tab_merge = lh5.read("det001/hit", str(test_reboost_input_file / "out_merge.lh5")).view_as("ak") # check lengths - assert len(ak.flatten(tab_1.evtid, axis=-1)) == int(1e6) - assert len(ak.flatten(tab_2.evtid, axis=-1)) == 30040 - assert len(ak.flatten(tab_merge.evtid, axis=-1)) == 30040 + int(1e6) + assert len(ak.flatten(tab_1._evtid, axis=-1)) == int(1e5) + assert len(ak.flatten(tab_2._evtid, axis=-1)) == 3004 + assert len(ak.flatten(tab_merge._evtid, axis=-1)) == 3004 + int(1e5) # one call but write both files hit.build_hit( @@ -236,16 +236,16 @@ def test_build_hit(test_reboost_input_file): out_field="hit", proc_config=proc_config, pars={}, - buffer=100000, + buffer=10000, merge_input_files=False, ) - tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_0.lh5")).view_as("ak") - tab_2 = lh5.read("hit/det001", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") + tab_1 = lh5.read("det001/hit", str(test_reboost_input_file / "out_0.lh5")).view_as("ak") + tab_2 = lh5.read("det001/hit", str(test_reboost_input_file / "out_1.lh5")).view_as("ak") # check lengths - assert len(ak.flatten(tab_1.evtid, axis=-1)) == int(1e6) - assert len(ak.flatten(tab_2.evtid, axis=-1)) == 30040 + assert len(ak.flatten(tab_1._evtid, axis=-1)) == int(1e5) + assert len(ak.flatten(tab_2._evtid, axis=-1)) == 3004 # test with a smaller buffer hit.build_hit( @@ -255,15 +255,15 @@ def test_build_hit(test_reboost_input_file): out_field="hit", proc_config=proc_config, pars={}, - buffer=10000, + buffer=1000, ) tab_small = lh5.read( - "hit/det001", str(test_reboost_input_file / "out_small_buffer.lh5") + "det001/hit", str(test_reboost_input_file / "out_small_buffer.lh5") ).view_as("ak") # buffer does not affect results - assert ak.all(ak.flatten(tab_merge.evtid) == ak.flatten(tab_small.evtid)) + assert ak.all(ak.flatten(tab_merge._evtid) == ak.flatten(tab_small._evtid)) def test_build_hit_some_row(test_reboost_input_file): @@ -271,7 +271,7 @@ def test_build_hit_some_row(test_reboost_input_file): "channels": [ "det001", ], - "outputs": ["t0", "evtid"], + "outputs": ["t0", "_evtid"], "step_group": { "description": "group steps by time and evtid.", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", @@ -300,13 +300,13 @@ def test_build_hit_some_row(test_reboost_input_file): out_field="hit", proc_config=proc_config, pars={}, - buffer=100000, + buffer=10000, ) # test reading the data in two goes gives the same result for n_ev, s_ev, out in zip( - [int(1e4), int(1e5 - 1e4), int(1e5), int(1e5)], - [0, int(1e4), 0, 1000], + [int(1e3), int(1e4 - 1e3), int(1e4), int(1e4)], + [0, int(1e3), 0, 100], ["out_some_rows.lh5", "out_rest_rows.lh5", "out_all_file_one.lh5", "out_mix.lh5"], ): # test read only some events @@ -325,19 +325,19 @@ def test_build_hit_some_row(test_reboost_input_file): buffer=100000, ) - tab_some = lh5.read("hit/det001", str(test_reboost_input_file / "out_some_rows.lh5")).view_as( + tab_some = lh5.read("det001/hit", str(test_reboost_input_file / "out_some_rows.lh5")).view_as( "ak" ) - tab_rest = lh5.read("hit/det001", str(test_reboost_input_file / "out_rest_rows.lh5")).view_as( + tab_rest = lh5.read("det001/hit", str(test_reboost_input_file / "out_rest_rows.lh5")).view_as( "ak" ) - tab_1 = lh5.read("hit/det001", str(test_reboost_input_file / "out_all_file_one.lh5")).view_as( + tab_1 = lh5.read("det001/hit", str(test_reboost_input_file / "out_all_file_one.lh5")).view_as( "ak" ) tab_merge = ak.concatenate((tab_some, tab_rest)) - assert ak.all(ak.all(tab_merge.evtid == tab_1.evtid, axis=-1)) + assert ak.all(ak.all(tab_merge._evtid == tab_1._evtid, axis=-1)) def test_build_hit_with_locals(test_reboost_input_file, test_data_configs): @@ -345,7 +345,7 @@ def test_build_hit_with_locals(test_reboost_input_file, test_data_configs): "channels": [ "det001", ], - "outputs": ["t0", "evtid", "distance_to_surface"], + "outputs": ["t0", "_evtid", "distance_to_surface"], "step_group": { "description": "group steps by time and evtid.", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", diff --git a/tests/test_hpge_step_group.py b/tests/test_hpge_step_group.py index 52d9bab..69141cd 100644 --- a/tests/test_hpge_step_group.py +++ b/tests/test_hpge_step_group.py @@ -9,14 +9,14 @@ def test_evtid_group(): in_arr_evtid = ak.Array( - {"evtid": [1, 1, 1, 2, 2, 10, 10, 11, 12, 12, 12], "time": np.zeros(11)} + {"_evtid": [1, 1, 1, 2, 2, 10, 10, 11, 12, 12, 12], "time": np.zeros(11)} ) in_tab = Table(in_arr_evtid) out = processors.group_by_evtid(in_tab) out_ak = out.view_as("ak") - assert ak.all(out_ak.evtid == [[1, 1, 1], [2, 2], [10, 10], [11], [12, 12, 12]]) + assert ak.all(out_ak._evtid == [[1, 1, 1], [2, 2], [10, 10], [11], [12, 12, 12]]) assert ak.all(out_ak.time == [[0, 0, 0], [0, 0], [0, 0], [0], [0, 0, 0]]) # test the eval in build hit also @@ -31,7 +31,7 @@ def test_evtid_group(): out_eval_ak = out_eval.view_as("ak") - assert ak.all(out_ak.evtid == out_eval_ak.evtid) + assert ak.all(out_ak._evtid == out_eval_ak._evtid) assert ak.all(out_ak.time == out_eval_ak.time) @@ -39,7 +39,7 @@ def test_time_group(): # time units are ns in_arr_evtid = ak.Array( { - "evtid": [1, 1, 1, 2, 2, 2, 2, 2, 11, 12, 12, 12, 15, 15, 15, 15, 15], + "_evtid": [1, 1, 1, 2, 2, 2, 2, 2, 11, 12, 12, 12, 15, 15, 15, 15, 15], "time": [ 0, -2000, @@ -68,7 +68,7 @@ def test_time_group(): out = processors.group_by_time(in_tab, window=1) out_ak = out.view_as("ak") assert ak.all( - out_ak.evtid + out_ak._evtid == [[1], [1], [1], [2, 2], [2], [2, 2], [11], [12], [12, 12], [15, 15, 15], [15, 15]] ) assert ak.all( diff --git a/tests/test_hpge_utils.py b/tests/test_hpge_utils.py index 850bd90..cd1f2fd 100644 --- a/tests/test_hpge_utils.py +++ b/tests/test_hpge_utils.py @@ -21,6 +21,7 @@ get_global_evtid_range, get_hpge, get_include_chunk, + get_num_evtid_hit_tier, get_num_simulated, get_phy_vol, load_dict, @@ -209,6 +210,33 @@ def test_get_n_sim(test_lh5_files): assert n123 == [14002, 25156, int(1e7)] +@pytest.fixture +def test_hit_file(tmp_path): + n1 = 10 + tab_ch1 = Table(size=n1) + tab_ch1.add_field("global_evtid", Array(np.array([1, 4, 5, 6, 7, 9, 11, 12, 15, 17]))) + lh5.write(tab_ch1, "det001/hit", tmp_path / "file1.lh5", wo_mode="of") + + n2 = 5 + tab_ch2 = Table(size=n2) + tab_ch2.add_field("global_evtid", Array(np.array([7, 8, 12, 13, 14]))) + lh5.write(tab_ch2, "det002/hit", tmp_path / "file1.lh5", wo_mode="append") + return tmp_path / "file1.lh5" + + +def test_num_evtid_hit_tier(test_hit_file): + # both channels + assert get_num_evtid_hit_tier(str(test_hit_file), ["det001", "det002"], "global_evtid") == 17 + + # one at a time + assert get_num_evtid_hit_tier(str(test_hit_file), ["det001"], "global_evtid") == 17 + assert get_num_evtid_hit_tier(str(test_hit_file), ["det002"], "global_evtid") == 14 + + +def test_read_some_index(test_hit_file): + pass + + def test_global_evtid_range(): # raise exception if n_evtid is too large with pytest.raises(ValueError): @@ -225,14 +253,14 @@ def test_get_global_evtid(): vertices = [0, 1, 2, 3, 4, 5] input_evtid = [2, 3, 4, 5, 5, 5] obj = ak.Array({"evtid": input_evtid}) - assert np.all(get_global_evtid(first_evtid, obj, vertices).global_evtid == input_evtid) + assert np.all(get_global_evtid(first_evtid, obj, vertices)._global_evtid == input_evtid) # now if we only have some vertices vertices = [0, 2, 4, 6, 8, 10] input_evtid = [4, 6, 8, 10, 10, 10] obj = ak.Array({"evtid": input_evtid}) assert np.all( - get_global_evtid(first_evtid, obj, vertices).global_evtid == np.array(input_evtid) / 2.0 + get_global_evtid(first_evtid, obj, vertices)._global_evtid == np.array(input_evtid) / 2.0 ) From f59423198aa728a97c1ce9e26634a83543f4b821 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 20 Nov 2024 16:10:27 +0100 Subject: [PATCH 70/81] additions to documentation --- docs/source/conf.py | 2 + docs/source/manual/hpge.rst | 217 +++++++++++------- .../notebooks/reboost_hpge_evt_tutorial.rst | 2 + ...rial.rst => reboost_hpge_hit_tutorial.rst} | 4 +- docs/source/tutorial.rst | 3 +- 5 files changed, 137 insertions(+), 91 deletions(-) create mode 100644 docs/source/notebooks/reboost_hpge_evt_tutorial.rst rename docs/source/notebooks/{reboost_hpge_tutorial.rst => reboost_hpge_hit_tutorial.rst} (99%) diff --git a/docs/source/conf.py b/docs/source/conf.py index 239cfe9..b422399 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -54,6 +54,8 @@ "scipy": ("http://docs.scipy.org/doc/scipy/reference", None), "pandas": ("https://pandas.pydata.org/docs", None), "matplotlib": ("http://matplotlib.org/stable", None), + "pygama": ("https://pygama.readthedocs.io/en/stable/", None), + "legendhpges": ("https://legend-pygeom-hpges.readthedocs.io/en/latest/", None), } # add new intersphinx mappings here # sphinx-autodoc diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index 395319a..681aef5 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -72,7 +72,7 @@ The processing is defined in terms of several *tiers*, mirroring the logic of th - **stp** or "step" the raw *remage* outputs corresponding to Geant4 steps, - **hit** the data from each channel independently after grouping in discrete physical interactions in the detector. -- **evt** or "event" the data combining the information from various detectors. +- **evt** or "event" the data combining the information from various detectors (includes generating the **tcm** or time-coincidence map). The processing is divided into two steps :func:`build_hit` ``build_evt`` [WIP]. @@ -83,47 +83,40 @@ The hit tier converts the raw remage file based on Geant4 steps to a file corres Only steps corresponding to individual detectors are performed in this step. The processing is based on a YAML or JSON configuration file. For example: -.. code-block:: json - - { - "channels": [ - "det000", - "det001", - "det002", - "det003" - ], - "outputs": [ - "t0", - "truth_energy_sum", - "smeared_energy_sum", - "evtid" - ], - "step_group": { - "description": "group steps by time and evtid.", - "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" - }, - "locals": { - "hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)" - }, - "operations": { - "t0": { - "description": "first time in the hit.", - "mode": "eval", - "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)" - }, - "truth_energy_sum": { - "description": "truth summed energy in the hit.", - "mode": "eval", - "expression": "ak.sum(hit.edep,axis=-1)" - }, - "smeared_energy_sum": { - "description": "summed energy after convolution with energy response.", - "mode": "function", - "expression": "reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso)" - } - - } - } +.. code-block:: yaml + + channels: + - det000 + - det001 + - det002 + - det003 + + outputs: + - t0 + - truth_energy_sum + - smeared_energy_sum + - evtid + + step_group: + description: group steps by time and evtid. + expression: 'reboost.hpge.processors.group_by_time(stp,window=10)' + locals: + hpge: 'reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)' + + operations: + t0: + description: first time in the hit. + mode: eval + expression: 'ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)' + truth_energy_sum: + description: truth summed energy in the hit. + mode: eval + expression: 'ak.sum(hit.edep,axis=-1)' + smeared_energy_sum: + description: summed energy after convolution with energy response. + mode: function + expression: | + reboost.hpge.processors.smear_energies(hit.truth_energy_sum,reso=pars.reso) It is necessary to provide several sub-dictionaries: @@ -143,7 +136,7 @@ with a jagged structure where each row corresponds to a physical hit in the dete time: [0 , 0.1 , 0, ... ] .... -Becomes a Table of ``VectorOfVectors`` with a jagged structure. For example: +Becomes a :class:`Table`` of :class:`VectorOfVectors` with a jagged structure. For example: .. code-block:: console @@ -156,8 +149,8 @@ The recommended tool to manipulate jagged arrays is awkward `[docs] `_ object for the detector. +For example one useful object for post-processing is the :class:`legendhpges.base.HPGe` object for the detector. This can be constructed from the metadata using. -.. code-block:: json +.. code-block:: yaml - {"hpge": "reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)"} + hpge: 'reboost.hpge.utils(meta_path=meta,pars=pars,detector=detector)' This will then create the hpge object for each detector and add it to the "locals" mapping of "eval" so it can be used. @@ -246,40 +237,90 @@ with the same length as the hit table. This means processors can act on subarray It is simple to accommodate most of the current and future envisiged post-processing in this framework. For example: -- clustering hits would result in a new VectorOfVectors with the same number of rows but fewer entries per vector, -- pulse shape simulations to produce waveforms (or ML emmulation of this) would give an ArrayOfEqualSizedArrays, -- processing in parallel many parameters (eg for systematic) studies would give a nested VectorOfVectors. +- clustering hits would result in a new :class:`VectorOfVectors` with the same number of rows but fewer entries per vector, +- pulse shape simulations to produce waveforms (or ML emmulation of this) would give an :class:`ArrayOfEqualSizedArrays`, +- processing in parallel many parameters (eg. for systematic) studies would give a nested :class:`VectorOfVectors`. + +Time coincidence map (TCM) +-------------------------- + +The next step in the processing chain is the **event** tier, this combines the information from the various sub-systems to produce detector wide events. +However, before we can generate the *evt* tier we need to generate the "time-coincidence-map". This determines which of the hits in the various detectors +are occurring *simultaneously* (actually within some coincidence time window) and should be part of the same event. +Some information on the TCM in data is given in `[pygama-evt-docs] `_. The *reboost* TCM is fairly similar. + +The generation of the TCM is performed by :func:`reboost.hpge.tcm.build_tcm` which generates and stores the TCM on disk. + +.. warning:: + The generation of the TCM from the times of hits is slightly different to the "hardware-tcm" used for LEGEND physics data. In the experimental data, a signal on one channel, triggers + the readout of the full array. Care should be taken for deecays or interactions with ~ :math:`10-100 \mu s` time differences between hits. + However, in practice for most cases the time differences are very small and the two TCM should be equivalent after removing hits below threshold. + +Before explaining how the TCM is constructed we make a detour to explain the different indices present in the reboost and remage files. + +- **stp.evtid**: in the remage output files we have a variable called evtid. This is the index of the decay, so as explained earlier a single evtid can result in multiple hits in the detector. +- **hit.global_evtid**: However, when multiple files are read the evtid are now no longer necessarily sorted or unique and so we define a new index in the hit tier. This is extracted from the vertices table as + the sum of the number of evtid in the previous files plus the row in the vertex table of the evtid. A vector of vectors called "hit._global_evtid" is added to the hit table. We can also extract + a flat array of indices (for easier use in the evt tier) with a simple processor: + + .. code:: yaml + + global_evtid: + description: global evtid of the hit. + mode: eval + expression: 'ak.fill_none(,axis=-1),np.nan)' + + + This field is mandatory to generate the TCM, and the name of the field is an argument to "build_tcm". +- **hit idx**: Multiple rows in the hit table may contain the same "global_evtid" while many "global_evtid" do not result in a hit. The hit idx is just the row of the hit table a hit corresponds to. +- **channel_id**: When we combine multiple channels we assign them an index, this is set in the original remage macro file. + +:func:`build_tcm` saves two VectorOfVectors (with the same shape) to the output file, corresponding to the **channel_id** and the **hit_idx** of each event. + +.. note:: + - This storage is slightly different to the TCM in data, but is chosen to allow easy iteration through the TCM. + - We do not currently support merging multiple **hit** tier files, this is since then the TCM would need to know which file each hit corresponded to. Event tier processing (work in progress) ---------------------------------------- + + The event tier combines the information from various detector systems. Including in future the optical detector channels. This step is thus only necessary for experiments with many output channels. -The processing is again based on a YAML or JSON configuration file. For example: - -.. code-block:: json - - { - - "channels":{ - "geds_usable":[ - "det000", - "det001", - "det002" - ], - "geds_ac":[ - "det003" - ] - }, - "outputs": [ - "energy", - "detector", - "is_good_hit", - "multiplicity" - ], - "event_group": { - "description": "group hits by time and evtid.", - "expression": "reboost.hpge.processors.group_by_time(stp,window=10)" - } - } +The processing is again based on a YAML or JSON configuration file. Most of the work to evaluate each expression is done by the :func:`pygama.evt.build_evt.evaluate_expression`. + +The input configuration file is identical to a pygama evt tier configuration file (see an example in :func:`pygama.evt.build_evt.build_evt`). + +For example: + +.. code-block:: yaml + + channels: + geds_on: + - det000 + - det001 + - det002 + geds_ac: + - det003 + + outputs: + - energy + - multiplicity + + energy_id: + channels: geds_on + aggregation_mode: gather + query: hit.energy > 25 + expression: tcm.channel_id + energy: + aggregation_mode: 'keep_at_ch:evt.energy_id' + expression: hit.energy > 25 + multiplicity: + channels: + - geds_on + - geds_ac + aggregation_mode: sum + expression: hit.energy > 25 + initial: 0 diff --git a/docs/source/notebooks/reboost_hpge_evt_tutorial.rst b/docs/source/notebooks/reboost_hpge_evt_tutorial.rst new file mode 100644 index 0000000..efe47ab --- /dev/null +++ b/docs/source/notebooks/reboost_hpge_evt_tutorial.rst @@ -0,0 +1,2 @@ +Event tier simulation processing +================================ diff --git a/docs/source/notebooks/reboost_hpge_tutorial.rst b/docs/source/notebooks/reboost_hpge_hit_tutorial.rst similarity index 99% rename from docs/source/notebooks/reboost_hpge_tutorial.rst rename to docs/source/notebooks/reboost_hpge_hit_tutorial.rst index 012c5e9..c0d1427 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.rst +++ b/docs/source/notebooks/reboost_hpge_hit_tutorial.rst @@ -1,5 +1,5 @@ -Basic HPGe simulation processing -================================ +Hit tier simulation processing +============================== This tutorial describes how to process the HPGe detector simulations from **remage** with **reboost**. It buils on the official **remage** diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 85e317a..bf2ffdb 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -6,4 +6,5 @@ Basic Tutorial :maxdepth: 2 :caption: Contents: - notebooks/reboost_hpge_tutorial + notebooks/reboost_hpge_hit_tutorial + notebooks/reboost_hpge_evt_tutorial From 9509c2a25bbf7343b3a7ad9bc4383c57aff84dbb Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 20 Nov 2024 17:06:59 +0100 Subject: [PATCH 71/81] [docs] switch tutorials from rst to ipynb (easier to mantain) --- docs/source/conf.py | 11 +- .../notebooks/reboost_hpge_hit_tutorial.rst | 758 -------------- .../notebooks/reboost_hpge_tutorial.ipynb | 952 ++++++++++++++++++ docs/source/tutorial.rst | 2 +- 4 files changed, 963 insertions(+), 760 deletions(-) delete mode 100644 docs/source/notebooks/reboost_hpge_hit_tutorial.rst create mode 100644 docs/source/notebooks/reboost_hpge_tutorial.ipynb diff --git a/docs/source/conf.py b/docs/source/conf.py index b422399..f93dc26 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -20,6 +20,8 @@ "sphinx.ext.intersphinx", "sphinx_copybutton", "myst_parser", + "nbsphinx", + "IPython.sphinxext.ipython_console_highlighting", ] @@ -28,7 +30,7 @@ ".md": "markdown", } master_doc = "index" -language = "python" +# language = "python" # Furo theme html_theme = "furo" @@ -58,6 +60,13 @@ "legendhpges": ("https://legend-pygeom-hpges.readthedocs.io/en/latest/", None), } # add new intersphinx mappings here + +suppress_warnings = [ + # "histogram" is defined both in pygama.dsp.processors.histogram.histogram + # and in pygama.math.histogram, leading to a Sphinx cross-referencing + # warning. I don't know how to fix this properly + "ref.python", +] # sphinx-autodoc # Include __init__() docstring in class docstring autoclass_content = "both" diff --git a/docs/source/notebooks/reboost_hpge_hit_tutorial.rst b/docs/source/notebooks/reboost_hpge_hit_tutorial.rst deleted file mode 100644 index c0d1427..0000000 --- a/docs/source/notebooks/reboost_hpge_hit_tutorial.rst +++ /dev/null @@ -1,758 +0,0 @@ -Hit tier simulation processing -============================== - -This tutorial describes how to process the HPGe detector simulations -from **remage** with **reboost**. It buils on the official **remage** -tutorial -`[link] `__ - - .. rubric:: *Note* - :name: note - - To run this tutorial it is recommended to create the following - directory structure to organise the outputs and config inputs. - -.. code:: text - - ├── cfg - │ └── metadata - ├── output - │ - ├── stp - │ - └── hit - └── reboost_hpge_tutorial.ipynb - - -.. - -Part 1) Running the remage simulation -------------------------------------- - -Before we can run any post-processing we need to run the Geant4 -simulation. For this we follow the remage tutorial to generate the GDML -geometry. We save this into the GDML file *cfg/geom.gdml* for use by -remage. We also need to save the metadata dictionaries into json files -(in the *cfg/metadata* folder as *BEGe.json* and *Coax.json* - -We use a slightly modified Geant4 macro to demonstrate some features of -reboost (this should be saved as *cfg/th228.mac* to run remage on the -command line). - -.. code:: text - - /RMG/Manager/Logging/LogLevel detail - - /RMG/Geometry/RegisterDetector Germanium BEGe 001 - /RMG/Geometry/RegisterDetector Germanium Coax 002 - /RMG/Geometry/RegisterDetector Scintillator LAr 003 - - /run/initialize - - /RMG/Generator/Confine Volume - /RMG/Generator/Confinement/Physical/AddVolume Source - - /RMG/Generator/Select GPS - /gps/particle ion - /gps/energy 0 eV - /gps/ion 88 224 # 224-Ra - /process/had/rdm/nucleusLimits 208 224 81 88 #Ra-224 to 208-Pb - - - /run/beamOn 10000000 - -We then use the remage executable (see -`[remage-docs] `__ for -installation instructions) to run the simulation: > #### *Note* > Both -of *cfg/th228.mac* and *cfg/geometry.gdml* are needed to run remage - -.. code:: console - - $ remage --threads 8 --gdml-files cfg/geom.gdml --output-file output/stp/output.lh5 -- cfg/th228.mac - -You can lower the number of simulated events to speed up the simulation. - -We can use ``lh5.show()`` to check the output files. - -.. code:: python - - from lgdo import lh5 - -.. code:: python - - lh5.show("output/stp/output_t0.lh5") - -.. code:: text - - / - └── stp · struct{det001,det002,det003,vertices} - ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── v_post · array<1>{real} - │ ├── v_pre · array<1>{real} - │ ├── xloc_post · array<1>{real} - │ ├── xloc_pre · array<1>{real} - │ ├── yloc_post · array<1>{real} - │ ├── yloc_pre · array<1>{real} - │ ├── zloc_post · array<1>{real} - │ └── zloc_pre · array<1>{real} - └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} - ├── evtid · array<1>{real} - ├── n_part · array<1>{real} - ├── time · array<1>{real} - ├── xloc · array<1>{real} - ├── yloc · array<1>{real} - └── zloc · array<1>{real} - -Part 2) reboost config files ----------------------------- - -For this tutorial we perform a basic post-processing of the *hit* tier -for the two Germanium channels. - -2.1) Setup the environment -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -First we set up the python environment. - -.. code:: python - - from reboost.hpge import hit - import matplotlib.pyplot as plt - import pyg4ometry as pg4 - import legendhpges - from legendhpges import draw - import awkward as ak - import logging - import colorlog - import hist - import numpy as np - - - plt.rcParams["figure.figsize"] = [12, 4] - plt.rcParams["axes.titlesize"] = 12 - plt.rcParams["axes.labelsize"] = 12 - plt.rcParams["legend.fontsize"] = 12 - - - handler = colorlog.StreamHandler() - handler.setFormatter( - colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") - ) - logger = logging.getLogger() - logger.handlers.clear() - logger.addHandler(handler) - logger.setLevel(logging.INFO) - logger.info("test") - - - - -2.2) Processing chain and parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Next we need to make the processing chain config file. - -The processing chain below gives a standard set of steps for a HPGe -simulation. 1. first the steps are windowed into hits, 2. the first -timestamp and index of each hit is computed (for use in event building), -3. the distance to the detector n+ surface is computed and from this the -activeness is calculated (based on the FCCD) 4. the energy in each step -is summed to extract the deposited energy (both with and without -deadlayer correction), 5. the energy is convolved with the detector -response model (gaussian energy resolution). - -We also include some step based quantities in the output to show the -effect of the processors. - -.. code:: python - - chain = { - "channels": ["det001", "det002"], - "outputs": [ - "t0", # first timestamp - "time", # time of each step - "edep", # energy deposited in each step - "hit_evtid", # id of the hit - "hit_global_evtid", # global id of the hit - "distance_to_nplus_surface_mm", # distance to detector nplus surface - "activeness", # activeness for the step - "rpos_loc", # radius of step - "zpos_loc", # z position - "energy_sum", # true summed energy before dead layer or smearing - "energy_sum_deadlayer", # energy sum after dead layers - "energy_sum_smeared", # energy sum after smearing with resolution - ], - "step_group": { - "description": "group steps by time and evtid with 10us window", - "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", - }, - "locals": { - "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", - "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", - }, - "operations": { - "t0": { - "description": "first time in the hit.", - "mode": "eval", - "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", - }, - "hit_evtid": { - "description": "global evtid of the hit.", - "mode": "eval", - "expression": "ak.fill_none(ak.firsts(hit.evtid,axis=-1),np.nan)", - }, - "hit_global_evtid": { - "description": "global evtid of the hit.", - "mode": "eval", - "expression": "ak.fill_none(ak.firsts(hit.global_evtid,axis=-1),np.nan)", - }, - "distance_to_nplus_surface_mm": { - "description": "distance to the nplus surface in mm", - "mode": "function", - "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')", - }, - "activeness": { - "description": "activness based on FCCD (no TL)", - "mode": "eval", - "expression": "ak.where(hit.distance_to_nplus_surface_mm{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── edep · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── energy_sum · array<1>{real} - │ ├── energy_sum_deadlayer · array<1>{real} - │ ├── energy_sum_smeared · array<1>{real} - │ ├── hit_evtid · array<1>{real} - │ ├── hit_global_evtid · array<1>{real} - │ ├── rpos_loc · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── t0 · array<1>{real} - │ ├── time · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ └── zpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── det002 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} - ├── activeness · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── edep · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── energy_sum · array<1>{real} - ├── energy_sum_deadlayer · array<1>{real} - ├── energy_sum_smeared · array<1>{real} - ├── hit_evtid · array<1>{real} - ├── hit_global_evtid · array<1>{real} - ├── rpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── t0 · array<1>{real} - ├── time · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── zpos_loc · array<1>{array<1>{real}} - ├── cumulative_length · array<1>{real} - └── flattened_data · array<1>{real} - - -The new format is a factor of x17 times smaller than the input file due -to the removal of many *step* based fields which use a lot of memory and -due to the removal of the *vertices* table and the LAr hits. So we can -easily read the whole file into memory. We use *awkward* to analyse the -output files. - -.. code:: python - - data_det001 = lh5.read_as("hit/det001", "output/hit/output.lh5", "ak") - data_det002 = lh5.read_as("hit/det002", "output/hit/output.lh5", "ak") - -.. code:: python - - data_det001[0] - - - - -.. raw:: html - - 
{edep: [0.0826, 0.00863, 0.0171, 0.0175, 0.224, ..., 34.1, 28.9, 34.1, 32.3],
-     time: [1.32e+15, 1.32e+15, 1.32e+15, ..., 1.32e+15, 1.32e+15, 1.32e+15],
-     t0: 1.32e+15,
-     hit_evtid: 9.49e+03,
-     hit_global_evtid: 9.49e+03,
-     distance_to_nplus_surface_mm: [1e-11, 0.654, 0.654, ..., 0.617, 0.614, 0.614],
-     activeness: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-     rpos_loc: [21, 36.3, 36.3, 36.3, 36.3, ..., 36.3, 36.4, 36.4, 36.4, 36.4],
-     zpos_loc: [29.5, 21.1, 21.1, 21.1, 21.1, ..., 21.1, 21.1, 21.1, 21.1, 21.1],
-     energy_sum: 203,
-     energy_sum_deadlayer: 0,
-     energy_sum_smeared: -0.44}
-    -------------------------------------------------------------------------------
-    type: {
-        edep: var * float64,
-        time: var * float64,
-        t0: float64,
-        hit_evtid: float64,
-        hit_global_evtid: float64,
-        distance_to_nplus_surface_mm: var * float64,
-        activeness: var * int64,
-        rpos_loc: var * float64,
-        zpos_loc: var * float64,
-        energy_sum: float64,
-        energy_sum_deadlayer: float64,
-        energy_sum_smeared: float64
-    }
- - - -Part 4) Steps in a standard processing chain --------------------------------------------- - -The next part of the tutorial gives more details on each step of the -processing chain. - -4.1) Windowing -~~~~~~~~~~~~~~ - -We can compare the decay index (“evtid” in the “stp” file) to the index -of the “hit”, the row of the hit table. We see that only some decays -correspond to “hits” in the detector, as we expect. We also see that a -single decay does not often produce multiple hits. This is also expected -since the probability of detection is fairly low. - -.. code:: python - - plt.scatter( - np.sort(data_det001.hit_global_evtid), - np.arange(len(data_det001)), - marker=".", - alpha=1, - ) - plt.xlabel("Decay index (evtid)") - plt.ylabel("Hit Index") - plt.grid() - plt.xlim(0, 1000) - plt.ylim(0, 100) - - - - -.. parsed-literal:: - - (0.0, 100.0) - - - - -.. image:: images/output_20_1.png - - -However, we can use some array manipulation to extract decay index with -multiple hits, by plotting the times we see the effect of the windowing. - -.. code:: python - - def plot_times(times: ak.Array, xrange=None, sub_zero=False, **kwargs): - fig, ax = plt.subplots() - for idx, _time in enumerate(times): - if sub_zero: - _time = _time - ak.min(_time) - h = hist.new.Reg( - 100, - (ak.min(times) / 1e9), - (ak.max(times) / 1e9) + 1, - name="Time since event start [s]", - ).Double() - h.fill(_time / 1e9) - h.plot(**kwargs, label=f"Hit {idx}") - ax.legend() - ax.set_yscale("log") - if xrange is not None: - ax.set_xlim(*xrange) - - -.. code:: python - - unique, counts = np.unique(data_det001.hit_global_evtid, return_counts=True) - -.. code:: python - - plot_times( - data_det001[data_det001.hit_global_evtid == unique[counts > 1][1]].time, - histtype="step", - yerr=False, - ) - - - - -.. image:: images/output_24_0.png - - -4.2) Distance to surface and dead layer -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -One of the important step in the post-processing of HPGe detector -simulations is the detector activeness mapping. Energy deposited close -to the surface of the Germanium detector will result in incomplete -charge collection and a degraded signal. To account for this we added a -processor to compute the distance to the detector surface (based on -``legendhpges.base.HPGe.distance_to_surface()``) - -For the steps in the detector we extracted in the processing chain the -local r and z coordinates and we can plot maps of the distance to the -detector surface and the activeness for each step. We select only events -within 5 mm of the surface for the first plots. We can see that the -processor works as expected. - -.. code:: python - - def plot_map(field, scale="BuPu", clab="Distance [mm]"): - fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) - n = 100000 - for idx, (data, config) in enumerate( - zip( - [data_det001, data_det002], - ["cfg/metadata/BEGe.json", "cfg/metadata/Coax.json"], - ) - ): - reg = pg4.geant4.Registry() - hpge = legendhpges.make_hpge(config, registry=reg) - - legendhpges.draw.plot_profile(hpge, split_by_type=True, axes=axs[idx]) - r = np.random.choice( - [-1, 1], p=[0.5, 0.5], size=len(ak.flatten(data.rpos_loc)) - ) * ak.flatten(data.rpos_loc) - z = ak.flatten(data.zpos_loc) - c = ak.flatten(data[field]) - cut = c < 5 - - s = axs[idx].scatter( - r[cut][0:n], - z[cut][0:n], - c=c[cut][0:n], - marker=".", - label="gen. points", - cmap=scale, - ) - # axs[idx].axis("equal") - - if idx == 0: - axs[idx].set_ylabel("Height [mm]") - c = plt.colorbar(s) - c.set_label(clab) - - axs[idx].set_xlabel("Radius [mm]") - - -.. code:: python - - plot_map("distance_to_nplus_surface_mm") - - -.. image:: images/output_27_1.png - - -.. code:: python - - plot_map("activeness", clab="Activeness", scale="viridis") - - -.. image:: images/output_28_1.png - - -We can also plot a histogram of the distance to the surface. - -.. code:: python - - def plot_distances(axes, distances, xrange=None, label=" ", **kwargs): - h = hist.new.Reg(100, *xrange, name="Distance to n+ surface [mm]").Double() - h.fill(distances) - h.plot(**kwargs, label=label) - ax.legend() - ax.set_yscale("log") - if xrange is not None: - ax.set_xlim(*xrange) - - -.. code:: python - - fig, ax = plt.subplots() - plot_distances( - ax, - ak.flatten(data_det001.distance_to_nplus_surface_mm), - xrange=(0, 35), - label="BEGe", - histtype="step", - yerr=False, - ) - plot_distances( - ax, - ak.flatten(data_det002.distance_to_nplus_surface_mm), - xrange=(0, 35), - label="Coax", - histtype="step", - yerr=False, - ) - - - - -.. image:: images/output_31_0.png - - -4.3) Summed energies -~~~~~~~~~~~~~~~~~~~~ - -Our processing chain also sums the energies of the hits, both before and -after weighting by the activeness. - -.. code:: python - - def plot_energy(axes, energy, bins=400, xrange=None, label=" ", log_y=True, **kwargs): - h = hist.new.Reg(bins, *xrange, name="energy [keV]").Double() - h.fill(energy) - h.plot(**kwargs, label=label) - axes.legend() - if log_y: - axes.set_yscale("log") - if xrange is not None: - axes.set_xlim(*xrange) - -.. code:: python - - fig, ax = plt.subplots() - ax.set_title("BEGe energy spectrum") - plot_energy( - ax, data_det001.energy_sum, yerr=False, label="True energy", xrange=(0, 4000) - ) - plot_energy( - ax, - data_det001.energy_sum_deadlayer, - yerr=False, - label="Energy after dead layer", - xrange=(0, 4000), - ) - - - -.. image:: images/output_34_0.png - - -.. code:: python - - fig, ax = plt.subplots() - ax.set_title("COAX energy spectrum") - plot_energy( - ax, data_det002.energy_sum, yerr=False, label="True energy", xrange=(0, 4000) - ) - plot_energy( - ax, - data_det002.energy_sum_deadlayer, - yerr=False, - label="Energy after dead layer", - xrange=(0, 4000), - ) - - - -.. image:: images/output_35_0.png - - -4.4) Smearing -~~~~~~~~~~~~~ - -The final step in the processing chain smeared the energies by the -energy resolution. This represents a general class of processors based -on ‘’heuristic’’ models. Other similar processors could be implemented -in a similar way. It would also be simple to use instead an energy -dependent resolution curve. To see the effect we have to zoom into the -2615 keV peak. - -.. code:: python - - fig, axs = plt.subplots() - plot_energy( - axs, - data_det001.energy_sum_smeared, - yerr=False, - label="BEGe", - xrange=(2600, 2630), - log_y=False, - bins=150, - density=True, - ) - plot_energy( - axs, - data_det002.energy_sum_smeared, - yerr=False, - label="COAX", - xrange=(2600, 2630), - log_y=False, - bins=150, - density=True, - ) - - - -.. image:: images/output_37_0.png - - -We see clearly the worse energy resolution for the COAX detector. > **To -Do**: add a gaussian fit of this. - -Part 5) Adding a new processor -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The next part of the tutorial describes how to add a new processor to -the chain. We use as an example spatial *clustering* of steps. This will -be added later. diff --git a/docs/source/notebooks/reboost_hpge_tutorial.ipynb b/docs/source/notebooks/reboost_hpge_tutorial.ipynb new file mode 100644 index 0000000..25b6db8 --- /dev/null +++ b/docs/source/notebooks/reboost_hpge_tutorial.ipynb @@ -0,0 +1,952 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hit Tier HPGe simulation processing\n", + "This tutorial describes how to process the HPGe detector simulations from **remage** with **reboost**. It buils on the offical **remage** tutorial [[link]](https://remage.readthedocs.io/en/stable/tutorial.html)\n", + "\n", + "> #### *Note*\n", + ">\n", + "> To run this tutorial it is recommended to create the following directory structure to organise the outputs and config inputs.\n", + "> \n", + "> \n", + "\n", + "\n", + "```text\n", + "├── cfg\n", + "│   └── metadata\n", + "├── output\n", + "│   ├── stp\n", + "│   └── hit\n", + "└── reboost_hpge_tutorial.ipynb\n", + "\n", + "> " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1) Running the remage simulation\n", + "Before we can run any post-processing we need to run the Geant4 simulation. For this we follow the remage tutorial to generate the GDML geometry. We save this into the GDML file *cfg/geom.gdml* for use by remage. We also need to save the metadata dictonaries into json files (in the *cfg/metadata* folder as *BEGe.json* and *Coax.json*\n", + "\n", + "We use a slightly modified Geant4 macro to demonstrate some features of reboost (this should be saved as *cfg/th228.mac* to run remage on the command line).\n", + "\n", + "```text\n", + "/RMG/Manager/Logging/LogLevel detail\n", + "\n", + "/RMG/Geometry/RegisterDetector Germanium BEGe 001\n", + "/RMG/Geometry/RegisterDetector Germanium Coax 002\n", + "/RMG/Geometry/RegisterDetector Scintillator LAr 003\n", + "\n", + "/run/initialize\n", + "\n", + "/RMG/Generator/Confine Volume\n", + "/RMG/Generator/Confinement/Physical/AddVolume Source\n", + "\n", + "/RMG/Generator/Select GPS\n", + "/gps/particle ion\n", + "/gps/energy 0 eV\n", + "/gps/ion 88 224 # 224-Ra\n", + "/process/had/rdm/nucleusLimits 208 224 81 88 #Ra-224 to 208-Pb\n", + "\n", + "\n", + "/run/beamOn 1000000\n", + "```\n", + "\n", + "We then use the remage exectuable (see [[remage-docs]](https://remage.readthedocs.io/en/stable/) for installation instructions) to run the simulation:\n", + "> #### *Note*\n", + "> Both of *cfg/th228.mac* and *cfg/geometry.gdml* are needed to run remage\n", + "\n", + "```console\n", + "$ remage --threads 8 --gdml-files cfg/geom.gdml --output-file output/stp/output.lh5 -- cfg/th228.mac\n", + "```\n", + "\n", + "You can lower the number of simulated events to speed up the simulation.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use `lh5.show()` to check the output files." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from lgdo import lh5" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m/\u001b[0m\n", + "└── \u001b[1mstp\u001b[0m · struct{det001,det002,det003,vertices} \n", + " ├── \u001b[1mdet001\u001b[0m · table{evtid,particle,edep,time,xloc,yloc,zloc} \n", + " │ ├── \u001b[1medep\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mparticle\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mxloc\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1myloc\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mzloc\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mdet002\u001b[0m · table{evtid,particle,edep,time,xloc,yloc,zloc} \n", + " │ ├── \u001b[1medep\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mparticle\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mxloc\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1myloc\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mzloc\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mdet003\u001b[0m · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} \n", + " │ ├── \u001b[1medep\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mparticle\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mv_post\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mv_pre\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mxloc_post\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mxloc_pre\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1myloc_post\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1myloc_pre\u001b[0m · array<1>{real} \n", + " │ ├── \u001b[1mzloc_post\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mzloc_pre\u001b[0m · array<1>{real} \n", + " └── \u001b[1mvertices\u001b[0m · table{evtid,time,xloc,yloc,zloc,n_part} \n", + " ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mn_part\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mxloc\u001b[0m · array<1>{real} \n", + " ├── \u001b[1myloc\u001b[0m · array<1>{real} \n", + " └── \u001b[1mzloc\u001b[0m · array<1>{real} \n" + ] + } + ], + "source": [ + "lh5.show(\"output/stp/output_t0.lh5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2) reboost config files\n", + "For this tutorial we perform a basic post-processing of the *hit* tier for the two Germanium channels.\n", + "\n", + "### 2.1) Setup the enviroment\n", + "First we set up the python enviroment." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mroot [INFO] test\u001b[0m\n" + ] + } + ], + "source": [ + "from reboost.hpge import hit\n", + "import matplotlib.pyplot as plt\n", + "import pyg4ometry as pg4\n", + "import legendhpges\n", + "from legendhpges import draw\n", + "import awkward as ak\n", + "import logging\n", + "import colorlog\n", + "import hist\n", + "import numpy as np\n", + "\n", + "\n", + "plt.rcParams['figure.figsize'] = [12, 4]\n", + "plt.rcParams['axes.titlesize'] =12\n", + "plt.rcParams['axes.labelsize'] = 12\n", + "plt.rcParams['legend.fontsize'] = 12\n", + "\n", + "\n", + "handler = colorlog.StreamHandler()\n", + "handler.setFormatter(\n", + " colorlog.ColoredFormatter(\"%(log_color)s%(name)s [%(levelname)s] %(message)s\")\n", + ")\n", + "logger = logging.getLogger()\n", + "logger.handlers.clear()\n", + "logger.addHandler(handler)\n", + "logger.setLevel(logging.INFO)\n", + "logger.info(\"test\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2) Processing chain and parameters\n", + "Next we need to make the processing chain config file.\n", + "\n", + "The processing chain below gives a standard set of steps for a HPGe simulation.\n", + "1. first the steps are windowed into hits,\n", + "2. the first timestamp and index of each hit is computed (for use in event building),\n", + "3. the distance to the detector n+ surface is computed and from this the activeness is calculated (based on the FCCD)\n", + "4. the energy in each step is summed to extract the deposited energy (both with and without deadlayer correction),\n", + "5. the energy is convolved with the detector response model (gaussian energy resolution).\n", + "\n", + "We also include some step based quantities in the output to show the effect of the processors.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "chain = {\n", + " \"channels\": [\n", + " \"det001\",\n", + " \"det002\"\n", + " ],\n", + " \"outputs\": [\n", + " \"t0\", # first timestamp\n", + " \"time\", # time of each step\n", + " \"edep\", # energy deposited in each step\n", + " \"evtid\", # id of the hit\n", + " \"global_evtid\", # global id of the hit\n", + " \"distance_to_nplus_surface_mm\", # distance to detector nplus surface\n", + " \"activeness\", # activeness for the step\n", + " \"rpos_loc\", # radius of step\n", + " \"zpos_loc\", # z position\n", + " \"energy_sum\", # true summed energy before dead layer or smearing\n", + " \"energy_sum_deadlayer\", # energy sum after dead layers\n", + " \"energy_sum_smeared\" # energy sum after smearing with resolution\n", + " ],\n", + " \"step_group\": { \n", + " \"description\": \"group steps by time and evtid with 10us window\",\n", + " \"expression\": \"reboost.hpge.processors.group_by_time(stp,window=10)\",\n", + " },\n", + " \"locals\": {\n", + " \"hpge\": \"reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)\",\n", + " \"phy_vol\": \"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)\",\n", + " },\n", + " \"operations\": {\n", + " \"t0\": {\n", + " \"description\": \"first time in the hit.\",\n", + " \"mode\": \"eval\",\n", + " \"expression\": \"ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)\",\n", + " },\n", + " \"evtid\": {\n", + " \"description\": \"global evtid of the hit.\",\n", + " \"mode\": \"eval\",\n", + " \"expression\": \"ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)\",\n", + " },\n", + " \"global_evtid\": {\n", + " \"description\": \"global evtid of the hit.\",\n", + " \"mode\": \"eval\",\n", + " \"expression\": \"ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)\",\n", + " },\n", + " \"distance_to_nplus_surface_mm\": {\n", + " \"description\": \"distance to the nplus surface in mm\",\n", + " \"mode\": \"function\",\n", + " \"expression\": \"reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')\",\n", + " },\n", + " \"activeness\": {\n", + " \"description\": \"activness based on FCCD (no TL)\",\n", + " \"mode\": \"eval\",\n", + " \"expression\": \"ak.where(hit.distance_to_nplus_surface_mm{array<1>{real}} \n", + "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1mdistance_to_nplus_surface_mm\u001b[0m · array<1>{array<1>{real}} \n", + "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1medep\u001b[0m · array<1>{array<1>{real}} \n", + "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1menergy_sum\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1menergy_sum_deadlayer\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1menergy_sum_smeared\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1mglobal_evtid\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1mrpos_loc\u001b[0m · array<1>{array<1>{real}} \n", + "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1mt0\u001b[0m · array<1>{real} \n", + "│ ├── \u001b[1mtime\u001b[0m · array<1>{array<1>{real}} \n", + "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + "│ └── \u001b[1mzpos_loc\u001b[0m · array<1>{array<1>{real}} \n", + "│ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + "│ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + "└── \u001b[1mdet002\u001b[0m · HDF5 group \n", + " └── \u001b[1mhit\u001b[0m · table{edep,time,t0,evtid,global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} \n", + " ├── \u001b[1mactiveness\u001b[0m · array<1>{array<1>{real}} \n", + " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mdistance_to_nplus_surface_mm\u001b[0m · array<1>{array<1>{real}} \n", + " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + " ├── \u001b[1medep\u001b[0m · array<1>{array<1>{real}} \n", + " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + " ├── \u001b[1menergy_sum\u001b[0m · array<1>{real} \n", + " ├── \u001b[1menergy_sum_deadlayer\u001b[0m · array<1>{real} \n", + " ├── \u001b[1menergy_sum_smeared\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mglobal_evtid\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mrpos_loc\u001b[0m · array<1>{array<1>{real}} \n", + " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mt0\u001b[0m · array<1>{real} \n", + " ├── \u001b[1mtime\u001b[0m · array<1>{array<1>{real}} \n", + " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", + " └── \u001b[1mzpos_loc\u001b[0m · array<1>{array<1>{real}} \n", + " ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", + " └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n" + ] + } + ], + "source": [ + "lh5.show(\"output/hit/output.lh5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new format is a factor of x17 times smaller than the input file due to the removal of many *step* based fields which use alot of memory and due to the removal of the *vertices* table and the LAr hits. So we can easily read the whole file into memory.\n", + "We use *awkward* to analyse the output files." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "data_det001 = lh5.read_as(\"det001/hit\",\"output/hit/output.lh5\",\"ak\")\n", + "data_det002 = lh5.read_as(\"det002/hit\",\"output/hit/output.lh5\",\"ak\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_det001" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 4) Steps in a standard processing chain" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next part of the tutorial gives more details on each step of the processing chain.\n", + "\n", + "### 4.1) Windowing\n", + "We can compare the decay index (\"evtid\" in the \"stp\" file) to the index of the \"hit\", the row of the hit table.\n", + "We see that only some decays correspond to \"hits\" in the detector, as we expect. We also see that a single decay does not often produce multiple hits. This is also expected since the probability of detection is fairly low." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(np.sort(data_det001.global_evtid),np.arange(len(data_det001)),marker=\".\",alpha=1)\n", + "plt.xlabel(\"Decay index (evtid)\")\n", + "plt.ylabel(\"Hit Index\")\n", + "plt.grid()\n", + "plt.xlim(0,1000)\n", + "plt.ylim(0,100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, we can use some array manipulation to extract decay index with multiple hits, by plotting the times we see the effect of the windowing." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_times(times:ak.Array,xrange=None,sub_zero=False,**kwargs):\n", + " fig,ax = plt.subplots()\n", + " for idx,_time in enumerate(times):\n", + " if (sub_zero):\n", + " _time=_time-ak.min(_time)\n", + " h=hist.new.Reg(100,(ak.min(times)/1e9),(ak.max(times)/1e9)+1, name=\"Time since event start [s]\").Double()\n", + " h.fill(_time/1e9)\n", + " h.plot(**kwargs,label=f\"Hit {idx}\")\n", + " ax.legend()\n", + " ax.set_yscale(\"log\")\n", + " if xrange is not None:\n", + " ax.set_xlim(*xrange)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "unique,counts = np.unique(data_det001.global_evtid,return_counts=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_times(data_det001[data_det001.global_evtid==unique[counts>1][1]].time,histtype=\"step\",yerr=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2) Distance to surface and dead layer\n", + "One of the important step in the post-processing of HPGe detector simulations is the detector activeness mapping. Energy deposited close to the surface of the Germanium detector will result in incomplete charge collection and a degraded signal.\n", + "To account for this we added a processor to compute the distance to the detector surface (based on `legendhpges.base.HPGe.distance_to_surface()`)\n", + "\n", + "For the steps in the detector we extracted in the processing chain the local r and z coordinates and we can plot maps of the distance to the detector surface and the activeness for each step. We select only events within 5 mm of the surface for the first plots. We can see that the processor works as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_map(field,scale=\"BuPu\",clab=\"Distance [mm]\"):\n", + " fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n", + " n=100000\n", + " for idx, (data,config) in enumerate(zip([data_det001,data_det002],[\"cfg/metadata/BEGe.json\",\"cfg/metadata/Coax.json\"])):\n", + "\n", + " reg=pg4.geant4.Registry()\n", + " hpge = legendhpges.make_hpge(config,registry=reg)\n", + "\n", + " legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx])\n", + " r = np.random.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc)\n", + " z = ak.flatten(data.zpos_loc)\n", + " c=ak.flatten(data[field])\n", + " cut = c<5\n", + "\n", + " s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=\".\", label=\"gen. points\",cmap=scale)\n", + " #axs[idx].axis(\"equal\")\n", + "\n", + " if idx == 0:\n", + " axs[idx].set_ylabel(\"Height [mm]\")\n", + " c=plt.colorbar(s)\n", + " c.set_label(clab)\n", + "\n", + " axs[idx].set_xlabel(\"Radius [mm]\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-36.98, -36.98, 0.0] [36.98, 36.98, 29.46]\u001b[0m\n", + "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n", + "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-38.25, -38.25, 0.0] [38.25, 38.25, 84.0]\u001b[0m\n", + "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_map(\"distance_to_nplus_surface_mm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-36.98, -36.98, 0.0] [36.98, 36.98, 29.46]\u001b[0m\n", + "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n", + "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", + "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-38.25, -38.25, 0.0] [38.25, 38.25, 84.0]\u001b[0m\n", + "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_map(\"activeness\",clab=\"Activeness\",scale=\"viridis\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot a histogram of the distance to the surface.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_distances(axes,distances,xrange=None,label=\" \",**kwargs):\n", + " \n", + " h=hist.new.Reg(100,*xrange, name=\"Distance to n+ surface [mm]\").Double()\n", + " h.fill(distances)\n", + " h.plot(**kwargs,label=label)\n", + " ax.legend()\n", + " ax.set_yscale(\"log\")\n", + " if xrange is not None:\n", + " ax.set_xlim(*xrange)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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L0sMPP6ynn35aGzZssIXw8wP4Dz/8oNLSUg0fPlx/+MMfdODAAUVEREg6N/05IyNDERERio+P165du/Too48qMjLSqeNwJ5eGcF9fXw0ZMkSbN2+23basoqJCmzdvVlJSkivfynQ8NQfdkWHtle9NWG8kmlPPuyMYRg8AANBoBQUFSToXlGtz5MgReXl5qXdv+98Tw8PDFRISoiNHjti27du3T48++qi2bNlSZXHugoICu8e9evXS/Pnz9cgjj2jAgAF67LHH6no4buF0CC8qKtKhQ4dsj7Ozs5WZmanQ0FB17dpVycnJSkhIUGRkpIYOHar09HQVFxfbVkuvq8Y6HN2VGmIOOovFNXONvefdEQyjBwAATZUrfw9zBR/nf58KCgpSx44dtXfvXodfU1umyM/PV0xMjIKCgrRw4UL16tVLLVu21J49e/Tb3/5WFRUVVV7zzjvvSJKOHTumU6dO1XskdkNyOvHt2rXLNkRA+mkoQUJCgjIyMjRp0iSdPHlSc+fOVU5OjgYPHqyNGzdWWazNWY11OLq7NcSwdonF4po1d/e8O6KxD6MnqAMAAFdw5e9hHnT99dfrxRdf1Pbt2zVs2LCLtuvWrZsqKiqUlZWlyy67zLY9NzdX+fn56tatmyTpvffe06lTp/SPf/xDI0aMsLXLzs6udr/Lly/Xpk2b9MQTTyg1NVX33XefXn/9dRcdnes5HcJHjhwpwzBqbJOUlOTy4efNoSfcEa4a1i41zGJxDH9v5lz1g6SxD6On5x0AAMDmN7/5jf785z9r+vTp2rJlS5UO2MOHD+tf//qXxo0bpzlz5ig9PV1/+MMfbM8vXbpUknTddddJku0WZOfnzrKyMv3+97+v8t7Z2dl65JFHdPPNN2vOnDm65JJLNHPmTP3pT3/SlClTXH6srmAxakvUjUxlT/jxb79WeEf33KqsqTIMQz+eqfmPGnXpVXcEveqoN1euJtpQgd4RBHoAAJq806dPKzs7Wz169FDLli09XU6D+Oc//6lJkybJ399fU6ZM0YABA1RWVqZ///vfWrdunaZOnao//OEPmjp1qlatWqXbbrtNMTEx2rFjh1atWqUJEybYblF26tQpXXrppQoKCtL9998vi8Wi1atXq6KiQp9++qm2bt1q6xwePXq09u3bp3379qldu3aSpGuuuUY7d+7Uvn371LGjYwvf1fYZVebQgoIC2zz4uiKEo0YEdTQLjgR6TwZ1RzGUHgCARqk5hHBJysrKUlpamjZt2qRjx47Jz89PgwYN0uTJkzVjxgz5+fnp7NmzWrx4sTIyMvTNN98oPDxcd955p+bNm2d3S7F///vfevjhh/Xpp5+qTZs2uvPOOzVmzBjFxcXZQvhzzz2nBx54QH//+981ceJE22uPHj2qAQMG6Oqrr9aGDRscqp0QXo3zh6MfPHiQEN6IOBLUpYYJ6wx/h9vQ826PMA8AgMOaSwg3M0J4DegJNy961YH/aQqB3tEwT1gHAIAQbgLuDOGuvR8WUIOGuFe6o1j9HY1KU7gXvKP7Y4g8AACAHUI4GpWmsPo7QR1u5c57wdclzLtqtXmCOgAAaCJME8K5RRkqORLUpYbpVec2bTAld946TmqY+7yzijwAAGgimBOOZo156kADaOyrzdPzDgBwM+aEN37MCQfchHnqQANw1RD5hgrq9LwDAAAPIoQDtWgK89QlwjoaGXfOZZcaZoi8o+h5BwD8j8kGITcr7vxsCOGACzT2eeoSveowoaawirzE4nQAALVoce73xLNnz3q4ElzMmTNnJEne3rV3btWXaeaEn78w28GDB5kTjiaLeeqAhzT2+7dzb3YAMC3DMJSVlaVWrVqpU6dOni4HFzAMQ998843OnDmjnj17VtvGlXPCTRPCK7EwG+BYUJcaJqwz/B1wgCcXp6NXHQAapfz8fB0/flzt2rVTq1at+D2pETAMQ2fOnFFBQYGKiorUqVOniwZsQjghHHAIvepAI9bYg7qjCPQA4BDDMJSTk6OCggLmhjcyfn5+atu2bY3hmhBOCAdchqAONGKODpHnlm8AYBrl5eW2+cfwPG9vb/n4+NTajhBOCAfcqrEPfyeoo9nj3uwAADQoQjghHGiUPNWrvn9hnEOr0wPNWlNZeM4RBHoAgIu5MoSb5rfW81dHB9A4ueqe6s4G9ZIy110X6FVHk9XYb/nGvdkBAM0EPeEAGh1PzlOnVx1wEYbIAwCakGbZEw6g+XCkR72hONKrTm854ABHet4d6VF3VEP0vHNvdgBAA6AnHIApObpYnCOc7VXnXulAI9TYb/lGUAcAU6MnHECz58neckcDOyu7A27kqp73ugR1V/aqO4JADwCmRk84gGaPe6UDsOHe7ACAanCLMkI4ADdr7PdKlwjrgFux8BwANCuEcEI4gEaKld0B2DSVe7MT1gGgec4J5z7hAMzAU/dKr3xNbegtB9yoqdybnV51AHApesIBoBFqqOHvzEEHmjBWiAeABtMse8IBoDlpqNXfHQnsBHXApFx5b3ZP3XedoA6gGaAnHABMzFNz0Jl/DjRxnupVZ546gEaKnnAAgCTPzUF3ZP555XvTYw6YkKfuu848dQDNAD3hAIAG61FnaDvQzDFPHUATQU84AMClmIMOoEE0hXnqEmEdgEvREw4AcEhDrdjuCOagA6BXHYAnmb4nvHv37goKCpKXl5fatGmjrVu3eqIMAIATHO0tZw46gAbhqXnqEqu/A3Apj/SEd+/eXXv37lXr1q2dfi094QBgfg01B50ecwC1cqRHXWL1dwB2TN8TDgBo3hpqDjoA1MqRHnWJ1d8BNBinfwPatm2b0tLStHv3bh0/flzr16/XhAkT7NpYrValpaUpJydHERERev755zV06FDb8xaLRTExMfLy8tKDDz6oX/ziF/U+EABA0+LIsHapYeagA0CjH/4+55hjf0wA0Og4HcKLi4sVERGhu+++WxMnTqzy/Nq1a5WcnKzly5crOjpa6enpiouL05dffqn27dtLkj788EN16tRJx48fV2xsrAYOHKhBgwbV/2gAAE0GveUAGj1Prv5e5sCQenrLgUbJ6d9u4uPjFR8ff9Hnly5dqhkzZmjatGmSpOXLl2vDhg1asWKFZs+eLUnq1KmTJKlDhw4aN26c9uzZc9EQXlpaqtLSUtvjwsJCZ0sGAAAAPKOhhr+zWBxgWi7tYigrK9Pu3buVkpJi2+bl5aXY2Fht375d0rme9IqKCgUGBqqoqEhbtmzRbbfddtF9pqamasGCBa4sEwAAAGhcHA3rzuBe6UCj5NIQnpeXp/LycoWFhdltDwsL04EDByRJubm5uummmyRJ5eXlmjFjhqKioi66z5SUFCUnJ9seFxYWqksXVkUHAABAM8NicUCT4PbJdj179tSnn37qcHs/Pz/5+fnJarXKarWqvNyxe8UCAAAATQqLxQFNgktDeNu2beXt7a3c3Fy77bm5uQoPD6/XvhMTE5WYmGi7PxsAAACAC7BYHNDouTSE+/r6asiQIdq8ebPttmUVFRXavHmzkpKS6rVvesIBAAAAF2jsi8VVvjdhHU2U0yG8qKhIhw4dsj3Ozs5WZmamQkND1bVrVyUnJyshIUGRkZEaOnSo0tPTVVxcbFstva7oCQcAAADcyFOLxUkMbUeT5nQI37Vrl0aNGmV7XLloWkJCgjIyMjRp0iSdPHlSc+fOVU5OjgYPHqyNGzdWWawNAAAAgMk11Bx0hrajCbMYhmF4ughHnD8c/eDBgzr+7dcK78gq6QDQ3JWUnVX/uW9LkvYvjFOAr9vXHAUA1MQwpDMOhGpnwzortsONKkdkFxQUKCgoqF77Ms1vKgxHBwAAAEyoIYa1S01zDrqjf7Co1FjqhlNME8IBAAAANGHcB/1cAF/c0fH2zJ03JdOEcFZHBwAAAJow7oOOZsI0IZzh6AAAAEAz15zug36x3vm6/JEBjYppQjgAAAAA1MqT90F3ZW+5bwA9700UIRwAAABA89MQC8ZxazU4wDQhnDnhAAAAANyqIXrLa1oEzpEQD9MzTQhnTjgAAAAAt2qI3nLmczd7pgnhAAAAANDoNNSK7WiyCOEAAAAAUFeuXLH9wtegSSKEAwAAAEBDaohh7TAtL08X4Cir1ar+/fsrKirK06UAAAAAAFAnpgnhiYmJ2r9/v3bu3OnpUgAAAAAAqBPThHAAAAAAAMyOEA4AAAAAgJsQwgEAAAAAcBPThHAWZgMAAAAAmJ1pQjgLswEAAAAAzI77hAMAAACAGZWV1N7GJ+DcfcrRaBDCAQAAAMCMnu5de5s5xyTfVg1fCxxmmuHoAAAAAACYHT3hAIAmo6SsvNY2/j7esjAsDwBgVj4B53q3a1JW4lgvOTyCEA4AaDIiH3+31jb7F8YpwJcffwAAk7JYGF5ucgxHBwAAAADATegKAACYmr+Pt/YvjKuxTUlZuUO95AAAAA3NNCHcarXKarWqvLz2+X4AgObDYrEwvBwAAJiGaYajJyYmav/+/dq5c6enSwEAAAAAoE5ME8IBAAAAADA7QjgAAAAAAG5CCAcAAAAAwE0I4QAAAAAAuAkhHAAAAAAANyGEAwAAAADgJh4L4SUlJerWrZtmzZrlqRIAAAAAAHArj4XwJ554Qj/72c889fYAAAAAALidR0J4VlaWDhw4oPj4eE+8PQAAAAAAHuF0CN+2bZvGjx+vjh07ymKx6LXXXqvSxmq1qnv37mrZsqWio6O1Y8cOu+dnzZql1NTUOhcNAAAAAIAZOR3Ci4uLFRERIavVWu3za9euVXJysubNm6c9e/YoIiJCcXFxOnHihCTp9ddfV58+fdSnT5/6VQ4AAAAAgMm0cPYF8fHxNQ4jX7p0qWbMmKFp06ZJkpYvX64NGzZoxYoVmj17tj7++GP95S9/0bp161RUVKQzZ84oKChIc+fOrXZ/paWlKi0ttT0uLCx0tmQAAAAAABoFl84JLysr0+7duxUbG/vTG3h5KTY2Vtu3b5ckpaam6ujRo/rqq6/09NNPa8aMGRcN4JXtg4ODbV9dunRxZckAAAAAALiNS0N4Xl6eysvLFRYWZrc9LCxMOTk5ddpnSkqKCgoKbF9Hjx51RakAAAAAALid08PRXWnq1Km1tvHz85Ofn5+sVqusVqvKy8sbvjAAAAAAABqAS3vC27ZtK29vb+Xm5tptz83NVXh4eL32nZiYqP3792vnzp312g8AAAAAAJ7i0hDu6+urIUOGaPPmzbZtFRUV2rx5s4YNG1avfVutVvXv319RUVH1LRMAAAAAAI9wejh6UVGRDh06ZHucnZ2tzMxMhYaGqmvXrkpOTlZCQoIiIyM1dOhQpaenq7i42LZael0lJiYqMTFRhYWFCg4Orte+AAAAAADwBKdD+K5duzRq1Cjb4+TkZElSQkKCMjIyNGnSJJ08eVJz585VTk6OBg8erI0bN1ZZrA0AAAAAgObG6RA+cuRIGYZRY5ukpCQlJSXVuajqsDAbAAAAAMDsXDonvCGxMBsAAAAAwOw8eosyAADcraSs9hFV/j7eslgsbqgGAAA0N6YJ4QxHBwC4QuTj79baZv/COAX4muZHJAAAMBGGowMAAAAA4Cb8mR8A0OT5+3hr/8K4GtuUlJU71EsOAABQH4RwAECTZ7FYGF4OAAAaBdMMR7darerfv7+ioqI8XQoAAAAAAHVimhDOnHAAAAAAgNmZJoQDAAAAAGB2hHAAAAAAANzENCGcOeEAAAAAALMzTQhnTjgAAAAAwOxME8IBAAAAADA7QjgAAAAAAG5CCAcAAAAAwE0I4QAAAAAAuEkLTxfgKKvVKqvVqvLyck+XAgBo4krKHPtZ4+/jLYvF0sDVAACApsQ0ITwxMVGJiYkqLCxUcHCwp8sBADRhkY+/61C7/QvjFOBrmh+lAACgEWA4OgAAAAAAbsKf7wEA0Lmh5fsXxtXarqSs3OGecgAAgAsRwgEAkGSxWBhaDgAAGhzD0QEAAAAAcBNCOAAAAAAAbkIIBwAAAADATQjhAAAAAAC4iWlCuNVqVf/+/RUVFeXpUgAAAAAAqBPThPDExETt379fO3fu9HQpAAAAAADUiWlCOAAAAAAAZkcIBwAAAADATQjhAAAAAAC4CSEcAAAAAAA3IYQDAAAAAOAmhHAAAAAAANzE7SE8Pz9fkZGRGjx4sAYMGKA//vGP7i4BAAAAAACPaOHuNwwMDNS2bdsUEBCg4uJiDRgwQBMnTtQll1zi7lIAAAAAAHArt4dwb29vBQQESJJKS0tlGIYMw3B3GQAA1FtJWXmtbfx9vGWxWNxQDQAAMAOnQ/i2bduUlpam3bt36/jx41q/fr0mTJhg18ZqtSotLU05OTmKiIjQ888/r6FDh9qez8/PV0xMjLKyspSWlqa2bdvW+0AAAHC3yMffrbXN/oVxCvB1+9+8AQBAI+X0nPDi4mJFRETIarVW+/zatWuVnJysefPmac+ePYqIiFBcXJxOnDhhaxMSEqJPP/1U2dnZWrNmjXJzc+t+BAAAAAAAmITTf5qPj49XfHz8RZ9funSpZsyYoWnTpkmSli9frg0bNmjFihWaPXu2XduwsDBFRETogw8+0C233FLt/kpLS1VaWmp7XFhY6GzJAAC4jL+Pt/YvjKuxTUlZuUO95AAAoPlx6eroZWVl2r17t2JjY396Ay8vxcbGavv27ZKk3Nxc/fDDD5KkgoICbdu2TX379r3oPlNTUxUcHGz76tKliytLBgDAKRaLRQG+LWr58vZ0mQAAoJFyaQjPy8tTeXm5wsLC7LaHhYUpJydHknTkyBENHz5cERERGj58uH79619r4MCBF91nSkqKCgoKbF9Hjx51ZckAAAAAALiN21eKGTp0qDIzMx1u7+fnJz8/P1mtVlmtVpWX174SLQAAAAAAjZFLe8Lbtm0rb2/vKgut5ebmKjw8vF77TkxM1P79+7Vz58567QcAAAAAAE9xaQj39fXVkCFDtHnzZtu2iooKbd68WcOGDavXvq1Wq/r376+oqKj6lgkAAAAAgEc4PRy9qKhIhw4dsj3Ozs5WZmamQkND1bVrVyUnJyshIUGRkZEaOnSo0tPTVVxcbFstva4SExOVmJiowsJCBQcH12tfAAAAAAB4gtMhfNeuXRo1apTtcXJysiQpISFBGRkZmjRpkk6ePKm5c+cqJydHgwcP1saNG6ss1gYAAAAAQHPjdAgfOXKkDMOosU1SUpKSkpLqXFR1WJgNAAAAAGB2Lp0T3pBYmA0AAAAAYHamCeEAAAAAAJidaUI4q6MDAAAAAMzONCGc4egAAAAAALMzTQgHAAAAAMDsCOEAAAAAALiJaUI4c8IBAAAAAGZnmhDOnHAAAAAAgNmZJoQDAAAAAGB2hHAAAAAAANzENCGcOeEAAAAAALMzTQhnTjgAAAAAwOxME8IBAAAAADA7QjgAAAAAAG5CCAcAAAAAwE0I4QAAAAAAuIlpQjirowMAAAAAzM40IZzV0QEAAAAAZmeaEA4AAAAAgNkRwgEAAAAAcBNCOAAAAAAAbtLC0wUAANCUlZSV19rG38dbFovFDdUAAABPI4QDANCAIh9/t9Y2+xfGKcCXH8kAADQHDEcHAAAAAMBN+LM7AAAu5u/jrf0L42psU1JW7lAvOQAAaFpME8KtVqusVqvKy2ufWwcAgCdZLBaGlwMAgGqZZjh6YmKi9u/fr507d3q6FAAAAAAA6sQ0IRwAAAAAALMjhAMAAAAA4CaEcAAAAAAA3IQQDgAAAACAmxDCAQAAAABwE0I4AAAAAABu4vYQfvToUY0cOVL9+/fXoEGDtG7dOneXAAAAAACAR7Rw+xu2aKH09HQNHjxYOTk5GjJkiMaNG6dWrVq5uxQAAAAAANzK7SG8Q4cO6tChgyQpPDxcbdu21XfffUcIBwAAAAA0eU4PR9+2bZvGjx+vjh07ymKx6LXXXqvSxmq1qnv37mrZsqWio6O1Y8eOave1e/dulZeXq0uXLk4XDgAAAACA2TgdwouLixURESGr1Vrt82vXrlVycrLmzZunPXv2KCIiQnFxcTpx4oRdu++++05TpkzRiy++WLfKAQAAAAAwGaeHo8fHxys+Pv6izy9dulQzZszQtGnTJEnLly/Xhg0btGLFCs2ePVuSVFpaqgkTJmj27Nn6+c9/XuP7lZaWqrS01Pa4sLDQ2ZIBAAAAAGgUXLo6ellZmXbv3q3Y2Nif3sDLS7Gxsdq+fbskyTAMTZ06VaNHj9Zdd91V6z5TU1MVHBxs+2LoOgAAAADArFwawvPy8lReXq6wsDC77WFhYcrJyZEkffTRR1q7dq1ee+01DR48WIMHD9bnn39+0X2mpKSooKDA9nX06FFXlgwAAAAAgNu4fXX0q6++WhUVFQ639/Pzk5+fn6xWq6xWq8rLyxuwOgAAAAAAGo5Le8Lbtm0rb29v5ebm2m3Pzc1VeHh4vfadmJio/fv3a+fOnfXaDwAAAAAAnuLSEO7r66shQ4Zo8+bNtm0VFRXavHmzhg0bVq99W61W9e/fX1FRUfUtEwAAAAAAj3B6OHpRUZEOHTpke5ydna3MzEyFhoaqa9euSk5OVkJCgiIjIzV06FClp6eruLjYtlp6XSUmJioxMVGFhYUKDg6u174AAAAAAPAEp0P4rl27NGrUKNvj5ORkSVJCQoIyMjI0adIknTx5UnPnzlVOTo4GDx6sjRs3VlmsDQAAAACA5sbpED5y5EgZhlFjm6SkJCUlJdW5qOqwMBsAAAAAwOxcOie8IbEwGwAAAADA7EwTwgEAAAAAMDvThHBWRwcAAAAAmJ1pQjjD0QEAAAAAZmeaEA4AAAAAgNkRwgEAAAAAcBPThHDmhAMAAAAAzM40IZw54QAAAAAAszNNCAcAAAAAwOwI4QAAAAAAuIlpQjhzwgEAAAAAZmeaEM6ccAAAAACA2ZkmhAMAAAAAYHaEcAAAAAAA3IQQDgAAAACAmxDCAQAAAABwE9OEcFZHBwAAAACYnWlCOKujAwAAAADMzjQhHAAAAAAAsyOEAwAAAADgJoRwAAAAAADchBAOAAAAAICbEMIBAAAAAHATQjgAAAAAAG5imhDOfcIBAAAAAGZnmhDOfcIBAAAAAGZnmhAOAAAAAIDZEcIBAAAAAHATQjgAAAAAAG5CCAcAAAAAwE0I4QAAAAAAuAkhHAAAAAAANyGEAwAAAADgJh4J4TfddJPatGmjW265xRNvDwAAAACAR3gkhD/wwAP605/+5Im3BgAAAADAYzwSwkeOHKnAwEBPvDUAAAAAAB7jdAjftm2bxo8fr44dO8pisei1116r0sZqtap79+5q2bKloqOjtWPHDlfUCgAAAACAqTkdwouLixURESGr1Vrt82vXrlVycrLmzZunPXv2KCIiQnFxcTpx4kS9iwUAAAAAwMxaOPuC+Ph4xcfHX/T5pUuXasaMGZo2bZokafny5dqwYYNWrFih2bNnO11gaWmpSktLbY8LCwud3gcAAAAAAI2BS+eEl5WVaffu3YqNjf3pDby8FBsbq+3bt9dpn6mpqQoODrZ9denSxVXlAgAAAADgVi4N4Xl5eSovL1dYWJjd9rCwMOXk5Ngex8bG6tZbb9Wbb76pzp071xjQU1JSVFBQYPs6evSoK0sGAAAAAMBtnB6O7grvvvuuw239/Pzk5+cnq9Uqq9Wq8vLyBqwMAAAAAICG49Ke8LZt28rb21u5ubl223NzcxUeHl6vfScmJmr//v3auXNnvfYDAAAAAICnuDSE+/r6asiQIdq8ebNtW0VFhTZv3qxhw4bVa99Wq1X9+/dXVFRUfcsEAAAAAMAjnB6OXlRUpEOHDtkeZ2dnKzMzU6GhoeratauSk5OVkJCgyMhIDR06VOnp6SouLratll5XiYmJSkxMVGFhoYKDg+u1LwAAAAAAPMHpEL5r1y6NGjXK9jg5OVmSlJCQoIyMDE2aNEknT57U3LlzlZOTo8GDB2vjxo1VFmsDAAAAAKC5cTqEjxw5UoZh1NgmKSlJSUlJdS6qOizMBgAAAAAwO5fOCW9ILMwGAAAAADA704RwAAAAAADMzjQhnNXRAQAAAABmZ5oQznB0AAAAAIDZOb0wGwAAcK2SstoXHfX38ZbFYnFDNQAAoCERwgEA8LDIx9+ttc3+hXEK8OXHNgAAZmea4ejMCQcAAAAAmJ1p/qSemJioxMREFRYWKjg42NPlAABQL/4+3tq/MK7GNiVl5Q71kgMAAPMwTQgHAKApsVgsDC8HAKAZMs1wdAAAAAAAzM40IZw54QAAAAAAszNNCOc+4QAAAAAAszNNCAcAAAAAwOwI4QAAAAAAuAkhHAAAAAAANyGEAwAAAADgJqYJ4ayODgAAAAAwO9OEcFZHBwAAAACYnWlCOAAAAAAAZtfC0wU4yzAMSdIPP/yggMJCD1cDAEDDKSk7q4rSEklSYWGhzvqa7sc2AMATyoql0nO5SYWFkm+5Z+tpAgr/lz0r82h9WAxX7MWN/vvf/6pXr16eLgMAAAAA0MwcPnxYPXv2rNc+TPcn9dDQUEnS119/reDgYA9Xg8agsLBQXbp00dGjRxUUFOTpctAIcE7gQpwTuBDnBM7H+YALcU7gQgUFBeratastj9aH6UK4l9e5aezBwcH8DwE7QUFBnBOwwzmBC3FO4EKcEzgf5wMuxDmBC1Xm0XrtwwV1AAAAAAAABxDCAQAAAABwE9OFcD8/P82bN09+fn6eLgWNBOcELsQ5gQtxTuBCnBM4H+cDLsQ5gQu58pww3eroAAAAAACYlel6wgEAAAAAMCtCOAAAAAAAbkIIBwAAAADATQjhAAAAAAC4ielCuNVqVffu3dWyZUtFR0drx44dni4JHjJ//nxZLBa7r379+nm6LLjRtm3bNH78eHXs2FEWi0Wvvfaa3fOGYWju3Lnq0KGD/P39FRsbq6ysLM8UiwZX2/kwderUKteMa6+91jPFwi1SU1MVFRWlwMBAtW/fXhMmTNCXX35p1+b06dNKTEzUJZdcotatW+vmm29Wbm6uhypGQ3PknBg5cmSVa8XMmTM9VDEa2gsvvKBBgwYpKChIQUFBGjZsmN566y3b81wjmpfazgdXXR9MFcLXrl2r5ORkzZs3T3v27FFERITi4uJ04sQJT5cGD7n88st1/Phx29eHH37o6ZLgRsXFxYqIiJDVaq32+aeeekrPPfecli9frk8++UStWrVSXFycTp8+7eZK4Q61nQ+SdO2119pdM1599VU3Vgh3e//995WYmKiPP/5YmzZt0pkzZ3TNNdeouLjY1uahhx7SG2+8oXXr1un999/XsWPHNHHiRA9WjYbkyDkhSTNmzLC7Vjz11FMeqhgNrXPnzlqyZIl2796tXbt2afTo0brxxhu1b98+SVwjmpvazgfJRdcHw0SGDh1qJCYm2h6Xl5cbHTt2NFJTUz1YFTxl3rx5RkREhKfLQCMhyVi/fr3tcUVFhREeHm6kpaXZtuXn5xt+fn7Gq6++6oEK4U4Xng+GYRgJCQnGjTfe6JF60DicOHHCkGS8//77hmGcuyb4+PgY69ats7X54osvDEnG9u3bPVUm3OjCc8IwDCMmJsZ44IEHPFcUPK5NmzbGSy+9xDUChmH8dD4YhuuuD6bpCS8rK9Pu3bsVGxtr2+bl5aXY2Fht377dg5XBk7KystSxY0f17NlTv/jFL/T11197uiQ0EtnZ2crJybG7ZgQHBys6OpprRjP23nvvqX379urbt69++ctf6tSpU54uCW5UUFAgSQoNDZUk7d69W2fOnLG7TvTr109du3blOtFMXHhOVPrzn/+stm3basCAAUpJSVFJSYknyoOblZeX6y9/+YuKi4s1bNgwrhHN3IXnQyVXXB9auLLQhpSXl6fy8nKFhYXZbQ8LC9OBAwc8VBU8KTo6WhkZGerbt6+OHz+uBQsWaPjw4dq7d68CAwM9XR48LCcnR5KqvWZUPofm5dprr9XEiRPVo0cPHT58WHPmzFF8fLy2b98ub29vT5eHBlZRUaEHH3xQV111lQYMGCDp3HXC19dXISEhdm25TjQP1Z0TknTHHXeoW7du6tixoz777DP99re/1Zdffql//OMfHqwWDenzzz/XsGHDdPr0abVu3Vrr169X//79lZmZyTWiGbrY+SC57vpgmhAOXCg+Pt7270GDBik6OlrdunXTX//6V91zzz0erAxAYzR58mTbvwcOHKhBgwapV69eeu+99zRmzBgPVgZ3SExM1N69e1k7BDYXOyfuvfde278HDhyoDh06aMyYMTp8+LB69erl7jLhBn379lVmZqYKCgr0t7/9TQkJCXr//fc9XRY85GLnQ//+/V12fTDNcPS2bdvK29u7ymqEubm5Cg8P91BVaExCQkLUp08fHTp0yNOloBGovC5wzcDF9OzZU23btuWa0QwkJSXpX//6l7Zu3arOnTvbtoeHh6usrEz5+fl27blONH0XOyeqEx0dLUlcK5owX19f9e7dW0OGDFFqaqoiIiL07LPPco1opi52PlSnrtcH04RwX19fDRkyRJs3b7Ztq6io0ObNm+3G6KP5Kioq0uHDh9WhQwdPl4JGoEePHgoPD7e7ZhQWFuqTTz7hmgFJ0jfffKNTp05xzWjCDMNQUlKS1q9fry1btqhHjx52zw8ZMkQ+Pj5214kvv/xSX3/9NdeJJqq2c6I6mZmZksS1ohmpqKhQaWkp1whI+ul8qE5drw+mGo6enJyshIQERUZGaujQoUpPT1dxcbGmTZvm6dLgAbNmzdL48ePVrVs3HTt2TPPmzZO3t7duv/12T5cGNykqKrL7y2N2drYyMzMVGhqqrl276sEHH9Tjjz+uSy+9VD169NBjjz2mjh07asKECZ4rGg2mpvMhNDRUCxYs0M0336zw8HAdPnxYv/nNb9S7d2/FxcV5sGo0pMTERK1Zs0avv/66AgMDbXM4g4OD5e/vr+DgYN1zzz1KTk5WaGiogoKC9Otf/1rDhg3Tz372Mw9Xj4ZQ2zlx+PBhrVmzRuPGjdMll1yizz77TA899JBGjBihQYMGebh6NISUlBTFx8era9eu+uGHH7RmzRq99957evvtt7lGNEM1nQ8uvT7Ue311N3v++eeNrl27Gr6+vsbQoUONjz/+2NMlwUMmTZpkdOjQwfD19TU6depkTJo0yTh06JCny4Ibbd261ZBU5SshIcEwjHO3KXvssceMsLAww8/PzxgzZozx5ZdferZoNJiazoeSkhLjmmuuMdq1a2f4+PgY3bp1M2bMmGHk5OR4umw0oOrOB0nGypUrbW1+/PFH41e/+pXRpk0bIyAgwLjpppuM48ePe65oNKjazomvv/7aGDFihBEaGmr4+fkZvXv3Nh555BGjoKDAs4Wjwdx9991Gt27dDF9fX6Ndu3bGmDFjjHfeecf2PNeI5qWm88GV1weLYRhGff9iAAAAAAAAameaOeEAAAAAAJgdIRwAAAAAADchhAMAAAAA4CaEcAAAAAAA3IQQDgAAAACAmxDCAQAAAABwE0I4AAAAAABuQggHAAAAAMBNCOEAgHqbP3++LBaLp8uASaWlpalnz57y9vbW4MGDPV2OnYyMDFksFttXXl6ep0uyyczMtKvtb3/7m6dLAgA4gBAOALBzYeho2bKlOnbsqLi4OD333HP64YcfXPI+x44d0/z585WZmemS/Xnav//9b82fP1/5+fmeLsVU3nnnHf3mN7/RVVddpZUrV2rx4sWeLqlay5Yt0+rVqxUYGOjpUmy6deum1atXa86cOZ4uBQDgBEI4AKBaCxcu1OrVq/XCCy/o17/+tSTpwQcf1MCBA/XZZ5/ZtX300Uf1448/OrX/Y8eOacGCBU0qhC9YsIAQ7qQtW7bIy8tLL7/8sqZMmaJx48Z5uqRqTZgwQXfeeaf8/Pw8XYpNmzZtdOedd2rs2LGeLgUA4IQWni4AANA4xcfHKzIy0vY4JSVFW7Zs0fXXX68bbrhBX3zxhfz9/SVJLVq0UIsW/EhpbDIyMjRt2jQZhuHpUqooKSlRQECATpw4IX9/f/n6+nq6JAAA3IKecACAw0aPHq3HHntMR44c0SuvvGLbXt2c8E2bNunqq69WSEiIWrdurb59+9qGzb733nuKioqSJE2bNs029D0jI0OS9MEHH+jWW29V165d5efnpy5duuihhx6q0ts+depUtW7dWt9++60mTJig1q1bq127dpo1a5bKy8vt2lZUVOjZZ5/VwIED1bJlS7Vr107XXnutdu3aZdfulVde0ZAhQ+Tv76/Q0FBNnjxZR48erfH7Mn/+fD3yyCOSpB49etiO56uvvpIknT17VosWLVKvXr3k5+en7t27a86cOSotLa31e+7MMbpSTZ+f9NO0hcpjrPTee+/JYrHovffes20bOXKkBgwYoN27d2vEiBEKCAjQnDlzZLFYtHLlShUXF1c5B1auXKnRo0erffv28vPzU//+/fXCCy9UW+tbb72lmJgYBQYGKigoSFFRUVqzZo1dm08++UTXXnutgoODFRAQoJiYGH300Uf1+h5VHtdnn32mmJgYBQQEqHfv3ra52e+//76io6Pl7++vvn376t1337V7feX/NwcPHtSdd96p4OBgtWvXTo899pgMw9DRo0d14403KigoSOHh4XrmmWfqVS8AoHEghAMAnHLXXXdJOjeX92L27dun66+/XqWlpVq4cKGeeeYZ3XDDDbbQc9lll2nhwoWSpHvvvVerV6/W6tWrNWLECEnSunXrVFJSol/+8pd6/vnnFRcXp+eff15Tpkyp8l7l5eWKi4vTJZdcoqeffloxMTF65pln9OKLL9q1u+eee/Tggw+qS5cuevLJJzV79my1bNlSH3/8sa3NE088oSlTpujSSy/V0qVL9eCDD2rz5s0aMWJEjcPMJ06cqNtvv13ST3OHV69erXbt2kmSpk+frrlz5+rKK6/UsmXLFBMTo9TUVE2ePLm2b7dTx+gqtX1+dXHq1CnFx8dr8ODBSk9P16hRo7R69WoNHz5cfn5+Vc6BF154Qd26ddOcOXP0zDPPqEuXLvrVr34lq9Vqt9+MjAxdd911+u6775SSkqIlS5Zo8ODB2rhxo63Nli1bNGLECBUWFmrevHlavHix8vPzNXr0aO3YsaPOxyRJ33//va6//npFR0frqaeekp+fnyZPnqy1a9dq8uTJGjdunJYsWaLi4mLdcsst1a6pMGnSJFVUVGjJkiWKjo7W448/rvT0dI0dO1adOnXSk08+qd69e2vWrFnatm1bveoFADQCBgAA51m5cqUhydi5c+dF2wQHBxtXXHGF7fG8efOM83+kLFu2zJBknDx58qL72LlzpyHJWLlyZZXnSkpKqmxLTU01LBaLceTIEdu2hIQEQ5KxcOFCu7ZXXHGFMWTIENvjLVu2GJKM+++/v8p+KyoqDMMwjK+++srw9vY2nnjiCbvnP//8c6NFixZVtl8oLS3NkGRkZ2fbbc/MzDQkGdOnT7fbPmvWLEOSsWXLlhr36+gxVqfys3SWI59f5b4vPN6tW7cakoytW7fatsXExBiSjOXLl1fZT0JCgtGqVasq26s7B+Li4oyePXvaHufn5xuBgYFGdHS08eOPP9q1rfxcKyoqjEsvvdSIi4uzbavcf48ePYyxY8de9BhrOs7zj2vNmjW2bQcOHDAkGV5eXsbHH39s2/72229XOd8r/7+59957bdvOnj1rdO7c2bBYLMaSJUts27///nvD39/fSEhIqFJH5fd83bp1NR4LAKBxoCccAOC01q1b17hKekhIiCTp9ddfV0VFhdP7r5xrLknFxcXKy8vTz3/+cxmGof/85z9V2s+cOdPu8fDhw/Xf//7X9vjvf/+7LBaL5s2bV+W1lcPo//GPf6iiokK33Xab8vLybF/h4eG69NJLtXXrVqePQ5LefPNNSVJycrLd9ocffliStGHDBof2U9sxSud6Zc+vvaioSJLstuXl5amkpKTG96rv51cdPz8/TZs2zeH2558DBQUFysvLU0xMjP773/+qoKBA0rkh8z/88INtVMP5Kj/XzMxMZWVl6Y477tCpU6ds34Pi4mKNGTNG27Ztq9cxtm7d2m5EQ9++fRUSEqLLLrtM0dHRtu2V/77wM5POjZSo5O3trcjISBmGoXvuuce2PSQkRH379q329QAAcyGEAwCcVlRUVOOtmiZNmqSrrrpK06dPV1hYmCZPnqy//vWvDoedr7/+WlOnTlVoaKhtDnRMTIwk2QJYpcr53edr06aNvv/+e9vjw4cPq2PHjgoNDb3oe2ZlZckwDF166aVq166d3dcXX3yhEydOOFT7hY4cOSIvLy/17t3bbnt4eLhCQkJ05MiRWvfhyDFK0hVXXGFXd+Wq9hcez1NPPVXj+9X386tOp06dnFp87aOPPlJsbKxatWqlkJAQtWvXzjYnvfIcOHz4sCRpwIABF91PVlaWJCkhIaHK9+Gll15SaWlplXPKGZ07d66yHkJwcLC6dOlSZZukKp+ZJHXt2rVK25YtW6pt27ZVtlf3egCAubCULQDAKd98840KCgqqhMrz+fv7a9u2bdq6das2bNigjRs3au3atRo9erTeeecdeXt7X/S15eXlGjt2rL777jv99re/Vb9+/dSqVSt9++23mjp1apUgWNO+nFFRUSGLxaK33nqr2n22bt26Xvu/MKg5w9Fj/POf/2y3eN0777yjtLQ0bdq0ya5dz549a9yPI5/fxY7nYovFnd+zXZvDhw9rzJgx6tevn5YuXaouXbrI19dXb775ppYtW+bUHwMq26alpWnw4MHVtqnPZ3uxz+Zi241qVqqvrq0zrwcAmAshHADglNWrV0uS4uLiamzn5eWlMWPGaMyYMVq6dKkWL16s//u//9PWrVsVGxt70RD3+eef6+DBg1q1apXdQmwXBkln9OrVS2+//ba+++67i/aG9+rVS4ZhqEePHurTp4/T73Gx4+nWrZsqKiqUlZWlyy67zLY9NzdX+fn56tatm9PvdTFXXXWV3eNvvvlGkhQbG+v0vmr7/Nq0aSNJVRasc6RnvzZvvPGGSktL9c9//tOul/jCKQG9evWSJO3du/eifxSqbBMUFFSn7wMAAK7GcHQAgMO2bNmiRYsWqUePHvrFL35x0XbfffddlW2VvZCVt+Vq1aqVpKohrrIH8PweP8Mw9Oyzz9a57ptvvlmGYWjBggVVnqt8n4kTJ8rb21sLFiyo0ttoGIZOnTpV43tc7HjGjRsnSUpPT7fbvnTpUknSdddd5/BxuIsjn19luD1/te7y8nKXrNhe3TlQUFCglStX2rW75pprFBgYqNTUVJ0+fdruucrXDhkyRL169dLTTz9tmyN/vpMnT9a7XgAAnEFPOACgWm+99ZYOHDigs2fPKjc3V1u2bNGmTZvUrVs3/fOf/6yyENb5Fi5cqG3btum6665Tt27ddOLECf3+979X586ddfXVV0s6F+JCQkK0fPlyBQYGqlWrVoqOjla/fv3Uq1cvzZo1S99++62CgoL097//vV5zYUeNGqW77rpLzz33nLKysnTttdeqoqJCH3zwgUaNGqWkpCT16tVLjz/+uFJSUvTVV19pwoQJCgwMVHZ2ttavX697771Xs2bNuuh7DBkyRJL0f//3f5o8ebJ8fHw0fvx4RUREKCEhQS+++KLy8/MVExOjHTt2aNWqVZowYYJGjRpV5+NqKI58fpdffrl+9rOfKSUlxTbC4C9/+YvOnj1b7/e/5ppr5Ovrq/Hjx+u+++5TUVGR/vjHP6p9+/Y6fvy4rV1QUJCWLVum6dOnKyoqSnfccYfatGmjTz/9VCUlJVq1apW8vLz00ksvKT4+XpdffrmmTZumTp066dtvv9XWrVsVFBSkN954o941AwDgKEI4AKBac+fOlST5+voqNDRUAwcOVHp6uqZNm1bjomySdMMNN+irr77SihUrlJeXp7Zt2yomJkYLFiywLVDl4+OjVatWKSUlRTNnztTZs2e1cuVKTZ06VW+88Ybuv/9+paamqmXLlrrpppuUlJSkiIiIOh/PypUrNWjQIL388st65JFHFBwcrMjISP385z+3tZk9e7b69OmjZcuW2XrNu3TpomuuuUY33HBDjfuPiorSokWLtHz5cm3cuFEVFRXKzs5Wq1at9NJLL6lnz57KyMjQ+vXrFR4erpSUlGpXa28MHPn8pHNz0O+77z4tWbJEISEhuueeezRq1CiNHTu2Xu/ft29f/e1vf9Ojjz6qWbNmKTw8XL/85S/Vrl073X333XZt77nnHrVv315LlizRokWL5OPjo379+umhhx6ytRk5cqS2b9+uRYsW6Xe/+52KiooUHh6u6Oho3XffffWqFQAAZ1kMVvgAAACoVkZGhqZNm6Y9e/aoS5cuuuSSS+q1yJ4rlZeX6/vvv9dHH32kCRMmaN26dbrllls8XRYAoBb0hAMAANTiyiuvlHRuDvmFtw7zlM8//1xXXHGFp8sAADiJnnAAAICLOH78uPbt22d7HBMTIx8fHw9W9JOioiJ9/PHHtseDBg1S+/btPVgRAMARhHAAAAAAANyEW5QBAAAAAOAmhHAAAAAAANyEEA4AAAAAgJsQwgEAAAAAcBNCOAAAAAAAbkIIBwAAAADATQjhAAAAAAC4CSEcAAAAAAA3IYQDAAAAAOAm/w+2U1d/CbGGogAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots()\n", + "plot_distances(ax,ak.flatten(data_det001.distance_to_nplus_surface_mm),xrange=(0,35),label=\"BEGe\",histtype=\"step\",yerr=False)\n", + "plot_distances(ax,ak.flatten(data_det002.distance_to_nplus_surface_mm),xrange=(0,35),label=\"Coax\",histtype=\"step\",yerr=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3) Summed energies\n", + "Our processing chain also sums the energies of the hits, both before and after weighting by the activeness.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_energy(axes,energy,bins=400,xrange=None,label=\" \",log_y=True,**kwargs):\n", + " \n", + " h=hist.new.Reg(bins,*xrange, name=\"energy [keV]\").Double()\n", + " h.fill(energy)\n", + " h.plot(**kwargs,label=label)\n", + " axes.legend()\n", + " if (log_y):\n", + " axes.set_yscale(\"log\")\n", + " if xrange is not None:\n", + " axes.set_xlim(*xrange)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.set_title(\"BEGe energy spectrum\")\n", + "plot_energy(ax,data_det001.energy_sum,yerr=False,label=\"True energy\",xrange=(0,4000))\n", + "plot_energy(ax,data_det001.energy_sum_deadlayer,yerr=False,label=\"Energy after dead layer\",xrange=(0,4000))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.set_title(\"COAX energy spectrum\")\n", + "plot_energy(ax,data_det002.energy_sum,yerr=False,label=\"True energy\",xrange=(0,4000))\n", + "plot_energy(ax,data_det002.energy_sum_deadlayer,yerr=False,label=\"Energy after dead layer\",xrange=(0,4000))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4) Smearing\n", + "The final step in the processing chain smeared the energies by the energy resolution. This represents a general class of processors based on ''heuristic'' models.\n", + "Other similar processors could be implemented in a similar way. It would also be simple to use insted an energy dependent resolution curve.\n", + "To see the effect we have to zoom into the 2615 keV peak." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots()\n", + "plot_energy(axs,data_det001.energy_sum_smeared,yerr=False,label=\"BEGe\",xrange=(2600,2630),log_y=False,bins=150,density=True)\n", + "plot_energy(axs,data_det002.energy_sum_smeared,yerr=False,label=\"COAX\",xrange=(2600,2630),log_y=False,bins=150,density=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see clearly the worse energy resolution for the COAX detector.\n", + "> **To Do**: add a gaussian fit of this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 5) Adding a new processor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next part of the tutorial describes how to add a new processor to the chain. We use as an example spatial *clustering* of steps.\n", + "This will be added later. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "sims", + "language": "python", + "name": "sims" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index bf2ffdb..0ed19e2 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -6,5 +6,5 @@ Basic Tutorial :maxdepth: 2 :caption: Contents: - notebooks/reboost_hpge_hit_tutorial + notebooks/reboost_hpge_tutorial.ipynb notebooks/reboost_hpge_evt_tutorial From df7e6d909848911c709cb84f57579afb6377729a Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 20 Nov 2024 17:38:14 +0100 Subject: [PATCH 72/81] change to notebook for docs --- .../notebooks/reboost_hpge_tutorial.ipynb | 402 ++++++++++-------- 1 file changed, 228 insertions(+), 174 deletions(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial.ipynb b/docs/source/notebooks/reboost_hpge_tutorial.ipynb index 25b6db8..616c6ec 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial.ipynb +++ b/docs/source/notebooks/reboost_hpge_tutorial.ipynb @@ -4,8 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Hit Tier HPGe simulation processing\n", - "This tutorial describes how to process the HPGe detector simulations from **remage** with **reboost**. It buils on the offical **remage** tutorial [[link]](https://remage.readthedocs.io/en/stable/tutorial.html)\n", + "# Hit tier hpge simulation processing\n", + "This tutorial describes how to process the HPGe detector simulations from **remage** with **reboost**. It buils on the official **remage** tutorial [[link]](https://remage.readthedocs.io/en/stable/tutorial.html)\n", "\n", "> #### *Note*\n", ">\n", @@ -22,7 +22,7 @@ "│   └── hit\n", "└── reboost_hpge_tutorial.ipynb\n", "\n", - "> " + "```" ] }, { @@ -30,7 +30,7 @@ "metadata": {}, "source": [ "## Part 1) Running the remage simulation\n", - "Before we can run any post-processing we need to run the Geant4 simulation. For this we follow the remage tutorial to generate the GDML geometry. We save this into the GDML file *cfg/geom.gdml* for use by remage. We also need to save the metadata dictonaries into json files (in the *cfg/metadata* folder as *BEGe.json* and *Coax.json*\n", + "Before we can run any post-processing we need to run the Geant4 simulation. For this we follow the remage tutorial to generate the GDML geometry. We save this into the GDML file *cfg/geom.gdml* for use by remage. We also need to save the metadata dictionaries into json files (in the *cfg/metadata* folder as *BEGe.json* and *Coax.json*\n", "\n", "We use a slightly modified Geant4 macro to demonstrate some features of reboost (this should be saved as *cfg/th228.mac* to run remage on the command line).\n", "\n", @@ -56,7 +56,7 @@ "/run/beamOn 1000000\n", "```\n", "\n", - "We then use the remage exectuable (see [[remage-docs]](https://remage.readthedocs.io/en/stable/) for installation instructions) to run the simulation:\n", + "We then use the remage executable (see [[remage-docs]](https://remage.readthedocs.io/en/stable/) for installation instructions) to run the simulation:\n", "> #### *Note*\n", "> Both of *cfg/th228.mac* and *cfg/geometry.gdml* are needed to run remage\n", "\n", @@ -81,6 +81,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "from lgdo import lh5" ] }, @@ -145,8 +147,8 @@ "## Part 2) reboost config files\n", "For this tutorial we perform a basic post-processing of the *hit* tier for the two Germanium channels.\n", "\n", - "### 2.1) Setup the enviroment\n", - "First we set up the python enviroment." + "### 2.1) Setup the environment\n", + "First we set up the python environment." ] }, { @@ -163,34 +165,31 @@ } ], "source": [ - "from reboost.hpge import hit\n", - "import matplotlib.pyplot as plt\n", - "import pyg4ometry as pg4\n", - "import legendhpges\n", - "from legendhpges import draw\n", - "import awkward as ak\n", "import logging\n", + "\n", + "import awkward as ak\n", "import colorlog\n", "import hist\n", + "import legendhpges\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import pyg4ometry as pg4\n", "\n", + "from reboost.hpge import hit\n", "\n", - "plt.rcParams['figure.figsize'] = [12, 4]\n", - "plt.rcParams['axes.titlesize'] =12\n", - "plt.rcParams['axes.labelsize'] = 12\n", - "plt.rcParams['legend.fontsize'] = 12\n", + "plt.rcParams[\"figure.figsize\"] = [12, 4]\n", + "plt.rcParams[\"axes.titlesize\"] = 12\n", + "plt.rcParams[\"axes.labelsize\"] = 12\n", + "plt.rcParams[\"legend.fontsize\"] = 12\n", "\n", "\n", "handler = colorlog.StreamHandler()\n", - "handler.setFormatter(\n", - " colorlog.ColoredFormatter(\"%(log_color)s%(name)s [%(levelname)s] %(message)s\")\n", - ")\n", + "handler.setFormatter(colorlog.ColoredFormatter(\"%(log_color)s%(name)s [%(levelname)s] %(message)s\"))\n", "logger = logging.getLogger()\n", "logger.handlers.clear()\n", "logger.addHandler(handler)\n", "logger.setLevel(logging.INFO)\n", - "logger.info(\"test\")\n", - "\n" + "logger.info(\"test\")" ] }, { @@ -218,85 +217,81 @@ "outputs": [], "source": [ "chain = {\n", - " \"channels\": [\n", - " \"det001\",\n", - " \"det002\"\n", - " ],\n", - " \"outputs\": [\n", - " \"t0\", # first timestamp\n", - " \"time\", # time of each step\n", - " \"edep\", # energy deposited in each step\n", - " \"evtid\", # id of the hit\n", - " \"global_evtid\", # global id of the hit\n", - " \"distance_to_nplus_surface_mm\", # distance to detector nplus surface\n", - " \"activeness\", # activeness for the step\n", - " \"rpos_loc\", # radius of step\n", - " \"zpos_loc\", # z position\n", - " \"energy_sum\", # true summed energy before dead layer or smearing\n", - " \"energy_sum_deadlayer\", # energy sum after dead layers\n", - " \"energy_sum_smeared\" # energy sum after smearing with resolution\n", - " ],\n", - " \"step_group\": { \n", - " \"description\": \"group steps by time and evtid with 10us window\",\n", - " \"expression\": \"reboost.hpge.processors.group_by_time(stp,window=10)\",\n", + " \"channels\": [\"det001\", \"det002\"],\n", + " \"outputs\": [\n", + " \"t0\", # first timestamp\n", + " \"time\", # time of each step\n", + " \"edep\", # energy deposited in each step\n", + " \"evtid\", # id of the hit\n", + " \"global_evtid\", # global id of the hit\n", + " \"distance_to_nplus_surface_mm\", # distance to detector nplus surface\n", + " \"activeness\", # activeness for the step\n", + " \"rpos_loc\", # radius of step\n", + " \"zpos_loc\", # z position\n", + " \"energy_sum\", # true summed energy before dead layer or smearing\n", + " \"energy_sum_deadlayer\", # energy sum after dead layers\n", + " \"energy_sum_smeared\", # energy sum after smearing with resolution\n", + " ],\n", + " \"step_group\": {\n", + " \"description\": \"group steps by time and evtid with 10us window\",\n", + " \"expression\": \"reboost.hpge.processors.group_by_time(stp,window=10)\",\n", + " },\n", + " \"locals\": {\n", + " \"hpge\": \"reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)\",\n", + " \"phy_vol\": \"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)\",\n", + " },\n", + " \"operations\": {\n", + " \"t0\": {\n", + " \"description\": \"first time in the hit.\",\n", + " \"mode\": \"eval\",\n", + " \"expression\": \"ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)\",\n", " },\n", - " \"locals\": {\n", - " \"hpge\": \"reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)\",\n", - " \"phy_vol\": \"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)\",\n", + " \"evtid\": {\n", + " \"description\": \"global evtid of the hit.\",\n", + " \"mode\": \"eval\",\n", + " \"expression\": \"ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)\",\n", " },\n", - " \"operations\": {\n", - " \"t0\": {\n", - " \"description\": \"first time in the hit.\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)\",\n", - " },\n", - " \"evtid\": {\n", - " \"description\": \"global evtid of the hit.\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)\",\n", - " },\n", - " \"global_evtid\": {\n", - " \"description\": \"global evtid of the hit.\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)\",\n", - " },\n", - " \"distance_to_nplus_surface_mm\": {\n", - " \"description\": \"distance to the nplus surface in mm\",\n", - " \"mode\": \"function\",\n", - " \"expression\": \"reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')\",\n", - " },\n", - " \"activeness\": {\n", - " \"description\": \"activness based on FCCD (no TL)\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.where(hit.distance_to_nplus_surface_mm1][1]].time,histtype=\"step\",yerr=False)\n" + "plot_times(\n", + " data_det001[data_det001.global_evtid == unique[counts > 1][1]].time, histtype=\"step\", yerr=False\n", + ")" ] }, { @@ -656,38 +663,44 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "def plot_map(field,scale=\"BuPu\",clab=\"Distance [mm]\"):\n", + "def plot_map(field, scale=\"BuPu\", clab=\"Distance [mm]\"):\n", " fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n", - " n=100000\n", - " for idx, (data,config) in enumerate(zip([data_det001,data_det002],[\"cfg/metadata/BEGe.json\",\"cfg/metadata/Coax.json\"])):\n", + " n = 100000\n", + " for idx, (data, config) in enumerate(\n", + " zip([data_det001, data_det002], [\"cfg/metadata/BEGe.json\", \"cfg/metadata/Coax.json\"])\n", + " ):\n", + " reg = pg4.geant4.Registry()\n", + " hpge = legendhpges.make_hpge(config, registry=reg)\n", "\n", - " reg=pg4.geant4.Registry()\n", - " hpge = legendhpges.make_hpge(config,registry=reg)\n", - "\n", - " legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx])\n", - " r = np.random.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc)\n", + " legendhpges.draw.plot_profile(hpge, split_by_type=True, axes=axs[idx])\n", + " rng = np.random.default_rng()\n", + " r = rng.choice([-1, 1], p=[0.5, 0.5], size=len(ak.flatten(data.rpos_loc))) * ak.flatten(\n", + " data.rpos_loc\n", + " )\n", " z = ak.flatten(data.zpos_loc)\n", - " c=ak.flatten(data[field])\n", - " cut = c<5\n", + " c = ak.flatten(data[field])\n", + " cut = c < 5\n", "\n", - " s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=\".\", label=\"gen. points\",cmap=scale)\n", - " #axs[idx].axis(\"equal\")\n", + " s = axs[idx].scatter(\n", + " r[cut][0:n], z[cut][0:n], c=c[cut][0:n], marker=\".\", label=\"gen. points\", cmap=scale\n", + " )\n", + " # axs[idx].axis(\"equal\")\n", "\n", " if idx == 0:\n", " axs[idx].set_ylabel(\"Height [mm]\")\n", - " c=plt.colorbar(s)\n", + " c = plt.colorbar(s)\n", " c.set_label(clab)\n", "\n", - " axs[idx].set_xlabel(\"Radius [mm]\")\n" + " axs[idx].set_xlabel(\"Radius [mm]\")" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -706,7 +719,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -721,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -740,7 +753,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -750,7 +763,7 @@ } ], "source": [ - "plot_map(\"activeness\",clab=\"Activeness\",scale=\"viridis\")" + "plot_map(\"activeness\", clab=\"Activeness\", scale=\"viridis\")" ] }, { @@ -762,25 +775,23 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ - "def plot_distances(axes,distances,xrange=None,label=\" \",**kwargs):\n", - " \n", - " h=hist.new.Reg(100,*xrange, name=\"Distance to n+ surface [mm]\").Double()\n", + "def plot_distances(axes, distances, xrange=None, label=\" \", **kwargs):\n", + " h = hist.new.Reg(100, *xrange, name=\"Distance to n+ surface [mm]\").Double()\n", " h.fill(distances)\n", - " h.plot(**kwargs,label=label)\n", - " ax.legend()\n", - " ax.set_yscale(\"log\")\n", + " h.plot(**kwargs, label=label)\n", + " axes.legend()\n", + " axes.set_yscale(\"log\")\n", " if xrange is not None:\n", - " ax.set_xlim(*xrange)\n", - " " + " ax.set_xlim(*xrange)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -795,9 +806,23 @@ } ], "source": [ - "fig,ax = plt.subplots()\n", - "plot_distances(ax,ak.flatten(data_det001.distance_to_nplus_surface_mm),xrange=(0,35),label=\"BEGe\",histtype=\"step\",yerr=False)\n", - "plot_distances(ax,ak.flatten(data_det002.distance_to_nplus_surface_mm),xrange=(0,35),label=\"Coax\",histtype=\"step\",yerr=False)\n" + "fig, ax = plt.subplots()\n", + "plot_distances(\n", + " ax,\n", + " ak.flatten(data_det001.distance_to_nplus_surface_mm),\n", + " xrange=(0, 35),\n", + " label=\"BEGe\",\n", + " histtype=\"step\",\n", + " yerr=False,\n", + ")\n", + "plot_distances(\n", + " ax,\n", + " ak.flatten(data_det002.distance_to_nplus_surface_mm),\n", + " xrange=(0, 35),\n", + " label=\"Coax\",\n", + " histtype=\"step\",\n", + " yerr=False,\n", + ")" ] }, { @@ -810,17 +835,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ - "def plot_energy(axes,energy,bins=400,xrange=None,label=\" \",log_y=True,**kwargs):\n", - " \n", - " h=hist.new.Reg(bins,*xrange, name=\"energy [keV]\").Double()\n", + "def plot_energy(axes, energy, bins=400, xrange=None, label=\" \", log_y=True, **kwargs):\n", + " h = hist.new.Reg(bins, *xrange, name=\"energy [keV]\").Double()\n", " h.fill(energy)\n", - " h.plot(**kwargs,label=label)\n", + " h.plot(**kwargs, label=label)\n", " axes.legend()\n", - " if (log_y):\n", + " if log_y:\n", " axes.set_yscale(\"log\")\n", " if xrange is not None:\n", " axes.set_xlim(*xrange)" @@ -828,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -845,8 +869,14 @@ "source": [ "fig, ax = plt.subplots()\n", "ax.set_title(\"BEGe energy spectrum\")\n", - "plot_energy(ax,data_det001.energy_sum,yerr=False,label=\"True energy\",xrange=(0,4000))\n", - "plot_energy(ax,data_det001.energy_sum_deadlayer,yerr=False,label=\"Energy after dead layer\",xrange=(0,4000))" + "plot_energy(ax, data_det001.energy_sum, yerr=False, label=\"True energy\", xrange=(0, 4000))\n", + "plot_energy(\n", + " ax,\n", + " data_det001.energy_sum_deadlayer,\n", + " yerr=False,\n", + " label=\"Energy after dead layer\",\n", + " xrange=(0, 4000),\n", + ")" ] }, { @@ -868,8 +898,14 @@ "source": [ "fig, ax = plt.subplots()\n", "ax.set_title(\"COAX energy spectrum\")\n", - "plot_energy(ax,data_det002.energy_sum,yerr=False,label=\"True energy\",xrange=(0,4000))\n", - "plot_energy(ax,data_det002.energy_sum_deadlayer,yerr=False,label=\"Energy after dead layer\",xrange=(0,4000))" + "plot_energy(ax, data_det002.energy_sum, yerr=False, label=\"True energy\", xrange=(0, 4000))\n", + "plot_energy(\n", + " ax,\n", + " data_det002.energy_sum_deadlayer,\n", + " yerr=False,\n", + " label=\"Energy after dead layer\",\n", + " xrange=(0, 4000),\n", + ")" ] }, { @@ -900,8 +936,26 @@ ], "source": [ "fig, axs = plt.subplots()\n", - "plot_energy(axs,data_det001.energy_sum_smeared,yerr=False,label=\"BEGe\",xrange=(2600,2630),log_y=False,bins=150,density=True)\n", - "plot_energy(axs,data_det002.energy_sum_smeared,yerr=False,label=\"COAX\",xrange=(2600,2630),log_y=False,bins=150,density=True)" + "plot_energy(\n", + " axs,\n", + " data_det001.energy_sum_smeared,\n", + " yerr=False,\n", + " label=\"BEGe\",\n", + " xrange=(2600, 2630),\n", + " log_y=False,\n", + " bins=150,\n", + " density=True,\n", + ")\n", + "plot_energy(\n", + " axs,\n", + " data_det002.energy_sum_smeared,\n", + " yerr=False,\n", + " label=\"COAX\",\n", + " xrange=(2600, 2630),\n", + " log_y=False,\n", + " bins=150,\n", + " density=True,\n", + ")" ] }, { From ae1cea597e252f6383b1f49134874e55431131d2 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Wed, 20 Nov 2024 17:55:27 +0100 Subject: [PATCH 73/81] [docs] switch back to rst (dont want to run the notebooks) --- .pre-commit-config.yaml | 11 + .../notebooks/reboost_hpge_tutorial.ipynb | 1006 ----------------- .../notebooks/reboost_hpge_tutorial_hit.rst | 712 ++++++++++++ docs/source/tutorial.rst | 2 +- 4 files changed, 724 insertions(+), 1007 deletions(-) delete mode 100644 docs/source/notebooks/reboost_hpge_tutorial.ipynb create mode 100644 docs/source/notebooks/reboost_hpge_tutorial_hit.rst diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 498eb1c..17754b4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -57,6 +57,17 @@ repos: additional_dependencies: - pytest + - repo: https://github.com/kynan/nbstripout + rev: "0.7.1" + hooks: + - id: nbstripout + args: + [ + "--drop-empty-cells", + "--extra-keys", + "metadata.kernelspec metadata.language_info", + ] + - repo: https://github.com/codespell-project/codespell rev: "v2.3.0" hooks: diff --git a/docs/source/notebooks/reboost_hpge_tutorial.ipynb b/docs/source/notebooks/reboost_hpge_tutorial.ipynb deleted file mode 100644 index 616c6ec..0000000 --- a/docs/source/notebooks/reboost_hpge_tutorial.ipynb +++ /dev/null @@ -1,1006 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hit tier hpge simulation processing\n", - "This tutorial describes how to process the HPGe detector simulations from **remage** with **reboost**. It buils on the official **remage** tutorial [[link]](https://remage.readthedocs.io/en/stable/tutorial.html)\n", - "\n", - "> #### *Note*\n", - ">\n", - "> To run this tutorial it is recommended to create the following directory structure to organise the outputs and config inputs.\n", - "> \n", - "> \n", - "\n", - "\n", - "```text\n", - "├── cfg\n", - "│   └── metadata\n", - "├── output\n", - "│   ├── stp\n", - "│   └── hit\n", - "└── reboost_hpge_tutorial.ipynb\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Part 1) Running the remage simulation\n", - "Before we can run any post-processing we need to run the Geant4 simulation. For this we follow the remage tutorial to generate the GDML geometry. We save this into the GDML file *cfg/geom.gdml* for use by remage. We also need to save the metadata dictionaries into json files (in the *cfg/metadata* folder as *BEGe.json* and *Coax.json*\n", - "\n", - "We use a slightly modified Geant4 macro to demonstrate some features of reboost (this should be saved as *cfg/th228.mac* to run remage on the command line).\n", - "\n", - "```text\n", - "/RMG/Manager/Logging/LogLevel detail\n", - "\n", - "/RMG/Geometry/RegisterDetector Germanium BEGe 001\n", - "/RMG/Geometry/RegisterDetector Germanium Coax 002\n", - "/RMG/Geometry/RegisterDetector Scintillator LAr 003\n", - "\n", - "/run/initialize\n", - "\n", - "/RMG/Generator/Confine Volume\n", - "/RMG/Generator/Confinement/Physical/AddVolume Source\n", - "\n", - "/RMG/Generator/Select GPS\n", - "/gps/particle ion\n", - "/gps/energy 0 eV\n", - "/gps/ion 88 224 # 224-Ra\n", - "/process/had/rdm/nucleusLimits 208 224 81 88 #Ra-224 to 208-Pb\n", - "\n", - "\n", - "/run/beamOn 1000000\n", - "```\n", - "\n", - "We then use the remage executable (see [[remage-docs]](https://remage.readthedocs.io/en/stable/) for installation instructions) to run the simulation:\n", - "> #### *Note*\n", - "> Both of *cfg/th228.mac* and *cfg/geometry.gdml* are needed to run remage\n", - "\n", - "```console\n", - "$ remage --threads 8 --gdml-files cfg/geom.gdml --output-file output/stp/output.lh5 -- cfg/th228.mac\n", - "```\n", - "\n", - "You can lower the number of simulated events to speed up the simulation.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use `lh5.show()` to check the output files." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from __future__ import annotations\n", - "\n", - "from lgdo import lh5" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m/\u001b[0m\n", - "└── \u001b[1mstp\u001b[0m · struct{det001,det002,det003,vertices} \n", - " ├── \u001b[1mdet001\u001b[0m · table{evtid,particle,edep,time,xloc,yloc,zloc} \n", - " │ ├── \u001b[1medep\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mparticle\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mxloc\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1myloc\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mzloc\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mdet002\u001b[0m · table{evtid,particle,edep,time,xloc,yloc,zloc} \n", - " │ ├── \u001b[1medep\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mparticle\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mxloc\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1myloc\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mzloc\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mdet003\u001b[0m · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} \n", - " │ ├── \u001b[1medep\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mparticle\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mv_post\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mv_pre\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mxloc_post\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mxloc_pre\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1myloc_post\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1myloc_pre\u001b[0m · array<1>{real} \n", - " │ ├── \u001b[1mzloc_post\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mzloc_pre\u001b[0m · array<1>{real} \n", - " └── \u001b[1mvertices\u001b[0m · table{evtid,time,xloc,yloc,zloc,n_part} \n", - " ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mn_part\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mtime\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mxloc\u001b[0m · array<1>{real} \n", - " ├── \u001b[1myloc\u001b[0m · array<1>{real} \n", - " └── \u001b[1mzloc\u001b[0m · array<1>{real} \n" - ] - } - ], - "source": [ - "lh5.show(\"output/stp/output_t0.lh5\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Part 2) reboost config files\n", - "For this tutorial we perform a basic post-processing of the *hit* tier for the two Germanium channels.\n", - "\n", - "### 2.1) Setup the environment\n", - "First we set up the python environment." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mroot [INFO] test\u001b[0m\n" - ] - } - ], - "source": [ - "import logging\n", - "\n", - "import awkward as ak\n", - "import colorlog\n", - "import hist\n", - "import legendhpges\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pyg4ometry as pg4\n", - "\n", - "from reboost.hpge import hit\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = [12, 4]\n", - "plt.rcParams[\"axes.titlesize\"] = 12\n", - "plt.rcParams[\"axes.labelsize\"] = 12\n", - "plt.rcParams[\"legend.fontsize\"] = 12\n", - "\n", - "\n", - "handler = colorlog.StreamHandler()\n", - "handler.setFormatter(colorlog.ColoredFormatter(\"%(log_color)s%(name)s [%(levelname)s] %(message)s\"))\n", - "logger = logging.getLogger()\n", - "logger.handlers.clear()\n", - "logger.addHandler(handler)\n", - "logger.setLevel(logging.INFO)\n", - "logger.info(\"test\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2) Processing chain and parameters\n", - "Next we need to make the processing chain config file.\n", - "\n", - "The processing chain below gives a standard set of steps for a HPGe simulation.\n", - "1. first the steps are windowed into hits,\n", - "2. the first timestamp and index of each hit is computed (for use in event building),\n", - "3. the distance to the detector n+ surface is computed and from this the activeness is calculated (based on the FCCD)\n", - "4. the energy in each step is summed to extract the deposited energy (both with and without deadlayer correction),\n", - "5. the energy is convolved with the detector response model (gaussian energy resolution).\n", - "\n", - "We also include some step based quantities in the output to show the effect of the processors.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "chain = {\n", - " \"channels\": [\"det001\", \"det002\"],\n", - " \"outputs\": [\n", - " \"t0\", # first timestamp\n", - " \"time\", # time of each step\n", - " \"edep\", # energy deposited in each step\n", - " \"evtid\", # id of the hit\n", - " \"global_evtid\", # global id of the hit\n", - " \"distance_to_nplus_surface_mm\", # distance to detector nplus surface\n", - " \"activeness\", # activeness for the step\n", - " \"rpos_loc\", # radius of step\n", - " \"zpos_loc\", # z position\n", - " \"energy_sum\", # true summed energy before dead layer or smearing\n", - " \"energy_sum_deadlayer\", # energy sum after dead layers\n", - " \"energy_sum_smeared\", # energy sum after smearing with resolution\n", - " ],\n", - " \"step_group\": {\n", - " \"description\": \"group steps by time and evtid with 10us window\",\n", - " \"expression\": \"reboost.hpge.processors.group_by_time(stp,window=10)\",\n", - " },\n", - " \"locals\": {\n", - " \"hpge\": \"reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)\",\n", - " \"phy_vol\": \"reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)\",\n", - " },\n", - " \"operations\": {\n", - " \"t0\": {\n", - " \"description\": \"first time in the hit.\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)\",\n", - " },\n", - " \"evtid\": {\n", - " \"description\": \"global evtid of the hit.\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)\",\n", - " },\n", - " \"global_evtid\": {\n", - " \"description\": \"global evtid of the hit.\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)\",\n", - " },\n", - " \"distance_to_nplus_surface_mm\": {\n", - " \"description\": \"distance to the nplus surface in mm\",\n", - " \"mode\": \"function\",\n", - " \"expression\": \"reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')\",\n", - " },\n", - " \"activeness\": {\n", - " \"description\": \"activness based on FCCD (no TL)\",\n", - " \"mode\": \"eval\",\n", - " \"expression\": \"ak.where(hit.distance_to_nplus_surface_mm{array<1>{real}} \n", - "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1mdistance_to_nplus_surface_mm\u001b[0m · array<1>{array<1>{real}} \n", - "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1medep\u001b[0m · array<1>{array<1>{real}} \n", - "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1menergy_sum\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1menergy_sum_deadlayer\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1menergy_sum_smeared\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1mglobal_evtid\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1mrpos_loc\u001b[0m · array<1>{array<1>{real}} \n", - "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1mt0\u001b[0m · array<1>{real} \n", - "│ ├── \u001b[1mtime\u001b[0m · array<1>{array<1>{real}} \n", - "│ │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - "│ │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - "│ └── \u001b[1mzpos_loc\u001b[0m · array<1>{array<1>{real}} \n", - "│ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - "│ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - "└── \u001b[1mdet002\u001b[0m · HDF5 group \n", - " └── \u001b[1mhit\u001b[0m · table{edep,time,t0,evtid,global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} \n", - " ├── \u001b[1mactiveness\u001b[0m · array<1>{array<1>{real}} \n", - " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mdistance_to_nplus_surface_mm\u001b[0m · array<1>{array<1>{real}} \n", - " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - " ├── \u001b[1medep\u001b[0m · array<1>{array<1>{real}} \n", - " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - " ├── \u001b[1menergy_sum\u001b[0m · array<1>{real} \n", - " ├── \u001b[1menergy_sum_deadlayer\u001b[0m · array<1>{real} \n", - " ├── \u001b[1menergy_sum_smeared\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mevtid\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mglobal_evtid\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mrpos_loc\u001b[0m · array<1>{array<1>{real}} \n", - " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mt0\u001b[0m · array<1>{real} \n", - " ├── \u001b[1mtime\u001b[0m · array<1>{array<1>{real}} \n", - " │ ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - " │ └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n", - " └── \u001b[1mzpos_loc\u001b[0m · array<1>{array<1>{real}} \n", - " ├── \u001b[1mcumulative_length\u001b[0m · array<1>{real} \n", - " └── \u001b[1mflattened_data\u001b[0m · array<1>{real} \n" - ] - } - ], - "source": [ - "lh5.show(\"output/hit/output.lh5\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The new format is a factor of x17 times smaller than the input file due to the removal of many *step* based fields which use a lot of memory and due to the removal of the *vertices* table and the LAr hits. So we can easily read the whole file into memory.\n", - "We use *awkward* to analyse the output files." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "data_det001 = lh5.read_as(\"det001/hit\", \"output/hit/output.lh5\", \"ak\")\n", - "data_det002 = lh5.read_as(\"det002/hit\", \"output/hit/output.lh5\", \"ak\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       " {edep: [0.118, 0.0254, ..., 41.2, 38.6], time: [...], t0: 9.67e+14, ...},\n",
-       " {edep: [0.0824, 0.0254, ..., 34.6, 18.9], time: [...], t0: 6.64e+14, ...},\n",
-       " {edep: [0.148, 0.0802, ..., 40.9, 24], time: [...], t0: 5.56e+14, ...},\n",
-       " {edep: [0.022, 0.0148, ..., 34.8, 11.9], time: [...], t0: 6.52e+14, ...},\n",
-       " {edep: [0.0155, 0.118, ..., 0.458, 9.65], time: [...], t0: 3.97e+14, ...},\n",
-       " {edep: [0.0065, 0.00615, ..., 13.7], time: [3.98e+14, ...], t0: 3.98e+14, ...}]\n",
-       "--------------------------------------------------------------------------------\n",
-       "type: 835793 * {\n",
-       "    edep: var * float64,\n",
-       "    time: var * float64,\n",
-       "    t0: float64,\n",
-       "    evtid: float64,\n",
-       "    global_evtid: float64,\n",
-       "    distance_to_nplus_surface_mm: var * float64,\n",
-       "    activeness: var * int64,\n",
-       "    rpos_loc: var * float64,\n",
-       "    zpos_loc: var * float64,\n",
-       "    energy_sum: float64,\n",
-       "    energy_sum_deadlayer: float64,\n",
-       "    energy_sum_smeared: float64\n",
-       "}
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_det001" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Part 4) Steps in a standard processing chain" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next part of the tutorial gives more details on each step of the processing chain.\n", - "\n", - "### 4.1) Windowing\n", - "We can compare the decay index (\"evtid\" in the \"stp\" file) to the index of the \"hit\", the row of the hit table.\n", - "We see that only some decays correspond to \"hits\" in the detector, as we expect. We also see that a single decay does not often produce multiple hits. This is also expected since the probability of detection is fairly low." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 100.0)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(np.sort(data_det001.global_evtid), np.arange(len(data_det001)), marker=\".\", alpha=1)\n", - "plt.xlabel(\"Decay index (evtid)\")\n", - "plt.ylabel(\"Hit Index\")\n", - "plt.grid()\n", - "plt.xlim(0, 1000)\n", - "plt.ylim(0, 100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, we can use some array manipulation to extract decay index with multiple hits, by plotting the times we see the effect of the windowing." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_times(times: ak.Array, xrange=None, sub_zero=False, **kwargs):\n", - " fig, ax = plt.subplots()\n", - " for idx, _time in enumerate(times):\n", - " if sub_zero:\n", - " _time = _time - ak.min(_time)\n", - " h = hist.new.Reg(\n", - " 100, (ak.min(times) / 1e9), (ak.max(times) / 1e9) + 1, name=\"Time since event start [s]\"\n", - " ).Double()\n", - " h.fill(_time / 1e9)\n", - " h.plot(**kwargs, label=f\"Hit {idx}\")\n", - " ax.legend()\n", - " ax.set_yscale(\"log\")\n", - " if xrange is not None:\n", - " ax.set_xlim(*xrange)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "unique, counts = np.unique(data_det001.global_evtid, return_counts=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_times(\n", - " data_det001[data_det001.global_evtid == unique[counts > 1][1]].time, histtype=\"step\", yerr=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.2) Distance to surface and dead layer\n", - "One of the important step in the post-processing of HPGe detector simulations is the detector activeness mapping. Energy deposited close to the surface of the Germanium detector will result in incomplete charge collection and a degraded signal.\n", - "To account for this we added a processor to compute the distance to the detector surface (based on `legendhpges.base.HPGe.distance_to_surface()`)\n", - "\n", - "For the steps in the detector we extracted in the processing chain the local r and z coordinates and we can plot maps of the distance to the detector surface and the activeness for each step. We select only events within 5 mm of the surface for the first plots. We can see that the processor works as expected." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_map(field, scale=\"BuPu\", clab=\"Distance [mm]\"):\n", - " fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n", - " n = 100000\n", - " for idx, (data, config) in enumerate(\n", - " zip([data_det001, data_det002], [\"cfg/metadata/BEGe.json\", \"cfg/metadata/Coax.json\"])\n", - " ):\n", - " reg = pg4.geant4.Registry()\n", - " hpge = legendhpges.make_hpge(config, registry=reg)\n", - "\n", - " legendhpges.draw.plot_profile(hpge, split_by_type=True, axes=axs[idx])\n", - " rng = np.random.default_rng()\n", - " r = rng.choice([-1, 1], p=[0.5, 0.5], size=len(ak.flatten(data.rpos_loc))) * ak.flatten(\n", - " data.rpos_loc\n", - " )\n", - " z = ak.flatten(data.zpos_loc)\n", - " c = ak.flatten(data[field])\n", - " cut = c < 5\n", - "\n", - " s = axs[idx].scatter(\n", - " r[cut][0:n], z[cut][0:n], c=c[cut][0:n], marker=\".\", label=\"gen. points\", cmap=scale\n", - " )\n", - " # axs[idx].axis(\"equal\")\n", - "\n", - " if idx == 0:\n", - " axs[idx].set_ylabel(\"Height [mm]\")\n", - " c = plt.colorbar(s)\n", - " c.set_label(clab)\n", - "\n", - " axs[idx].set_xlabel(\"Radius [mm]\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-36.98, -36.98, 0.0] [36.98, 36.98, 29.46]\u001b[0m\n", - "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n", - "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-38.25, -38.25, 0.0] [38.25, 38.25, 84.0]\u001b[0m\n", - "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_map(\"distance_to_nplus_surface_mm\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-36.98, -36.98, 0.0] [36.98, 36.98, 29.46]\u001b[0m\n", - "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n", - "\u001b[32mroot [INFO] genericpolycone.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] genericpolyhedra.antlr>\u001b[0m\n", - "\u001b[32mroot [INFO] visualisation.Mesh.getBoundingBox> [-38.25, -38.25, 0.0] [38.25, 38.25, 84.0]\u001b[0m\n", - "\u001b[32mroot [INFO] box.pycsgmesh> getBoundingBoxMesh\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_map(\"activeness\", clab=\"Activeness\", scale=\"viridis\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also plot a histogram of the distance to the surface.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_distances(axes, distances, xrange=None, label=\" \", **kwargs):\n", - " h = hist.new.Reg(100, *xrange, name=\"Distance to n+ surface [mm]\").Double()\n", - " h.fill(distances)\n", - " h.plot(**kwargs, label=label)\n", - " axes.legend()\n", - " axes.set_yscale(\"log\")\n", - " if xrange is not None:\n", - " ax.set_xlim(*xrange)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "plot_distances(\n", - " ax,\n", - " ak.flatten(data_det001.distance_to_nplus_surface_mm),\n", - " xrange=(0, 35),\n", - " label=\"BEGe\",\n", - " histtype=\"step\",\n", - " yerr=False,\n", - ")\n", - "plot_distances(\n", - " ax,\n", - " ak.flatten(data_det002.distance_to_nplus_surface_mm),\n", - " xrange=(0, 35),\n", - " label=\"Coax\",\n", - " histtype=\"step\",\n", - " yerr=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.3) Summed energies\n", - "Our processing chain also sums the energies of the hits, both before and after weighting by the activeness.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_energy(axes, energy, bins=400, xrange=None, label=\" \", log_y=True, **kwargs):\n", - " h = hist.new.Reg(bins, *xrange, name=\"energy [keV]\").Double()\n", - " h.fill(energy)\n", - " h.plot(**kwargs, label=label)\n", - " axes.legend()\n", - " if log_y:\n", - " axes.set_yscale(\"log\")\n", - " if xrange is not None:\n", - " axes.set_xlim(*xrange)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ElStXHI7p7nU4duyYGDVqlKhbt64ICwsTHTp0EPPmzZPVeeaZZ0RsbKwICgqSLdUGQKSkpCi+psuXL4u77rpL6HQ6Ua9ePTFlyhRx7NgxxeXZwsPDHfbv16+f6Nixo0N5y5YtxbBhw5y+l0RERPZUQjC7CRERUU1XUlKChg0b4s4778S//vUvh+2jRo3C0aNHcfr0aa+dMy0tDZMnT8bBgwdl2f2JiIjINc5RJyIiqmEKCwsd5uP/+9//RnZ2Nvr37+9Q//z58/jyyy8xfvz4amohERERucI56kRERDXMDz/8gOnTp+Oee+5BgwYN8OOPP+Ktt95Cp06dZOujZ2Zm4rvvvsObb76J0NBQTJkyxYetJiIiIisG6kRERDVMq1at0Lx5c6xatQrZ2dmoX78+JkyYgBdeeAFqtVqqt2fPHkyePBktWrTAO++8o7iGOREREVU/zlEnIiIiIiIi8iOco05ERERERETkRxioExEREREREfmRgJujXlJSgr/++gt16tSBSqXydXOIiIiIiIiohhNC4Nq1a2jatCmCgqq+vzvgAvW//voLzZs393UziIiIiIiIqJb53//+h2bNmlX5eQIuUK9Tpw4AyxsUGRnp49YQERERERFRTZebm4vmzZtL8WhVC7hA3TrcPTIykoE6ERERERERVZvqmn7NZHJEREREREREfoSBOhEREREREZEfCZhAXa/XIy4uDj169PB1U4iIiIiIiIiqjEoIIXzdCE/k5uYiKioKOTk5nKNOREREREREVa6649CASyZHRERERFSVhBAwm80oLi72dVOIqIqFhIQgODi42pLEuYuBOhERERERLAH61atXcfnyZZjNZl83h4iqSXBwMBo1aoSoqCi/CdgZqBMRERERAbhw4QKuXr0qLQMcEhLiNz/aicj7hBAoLi5Gbm4uzp8/j4KCAjRp0sTXzQLAQJ2IiIiICGazGTk5OWjYsCGio6N93RwiqkZ16tSBRqNBVlYWGjVqhODgYF83KXCyvhMRERERVRWTyQQhBMLDw33dFCLygfDwcAghYDKZfN0UAIEcqBvzLY/ASlpPRERERH6MQ92Jaid/++wHbqD+Ugfg+aaAyeDrlhARERERERF5TcAE6nq9HnFxcejRo4evm0JERERERERUZQImUE9JSUFGRgYOHjzo66YQERERERERVZmACdSJiIiIiMhzKpXKrcc333zj66YSUSkuz0ZEREREVIO9++67suf//ve/sWPHDofyG264oTqbRUQuMFAnIiIiIqrB7r//ftnzH374ATt27HAot2cwGKDT6aqyaX5LCIHCwkJotVpfN4VqKQ59JyIiIiKq5fr3749OnTohPT0dt9xyC3Q6HZ566ikAlqHzCxcudNinVatWmDRpkqzs6tWrePzxx9G8eXNoNBq0a9cOL774IkpKStxqx9atW5GQkIDw8HDUqVMHw4YNw/Hjx2V1Jk2ahIiICJw7dw4jR45EREQEGjZsiJkzZ8JsNsvqlpSUYMWKFejYsSPCwsIQExODKVOm4O+//3Z4Lbfffju2b9+O+Ph4aLVarF27FgDwxx9/YMSIEQgPD0ejRo0wffp0bN++XTZdYMGCBQgNDcXly5cdXtNDDz2EunXrorCw0K33gAhgoE5ERERERACuXLmCoUOHolu3blixYgUGDBjg0f4GgwH9+vXDe++9hwkTJmDVqlW4+eabMWfOHMyYMaPc/d99910MGzYMERERePHFFzFv3jxkZGSgb9++OHPmjKyu2WxGUlISGjRogOXLl6Nfv3546aWX8MYbb8jqTZkyBU888QRuvvlmrFy5EpMnT8b69euRlJQEk8kkq/vLL79g7NixGDx4MFauXIlu3bohPz8fAwcOxM6dO/HYY4/h6aefxvfff48nn3xStu/48eNRXFyMDz74QFZuNBrx0Ucf4a677kJYWJgH7ybVdhz6TkRERETkhBACBSZz+RWriTY0GCqVqkqOfeHCBaxZswZTpkyp0P4vv/wyfvvtN/z0009o3749AEug3LRpUyxbtgz/93//h+bNmyvum5eXh8ceewwPPvigLNieOHEiOnTogOeff15WXlhYiDFjxmDevHkAgIcffhg33XQT3nrrLUydOhUA8O233+LNN9/E+vXrcd9990n7DhgwAEOGDMGmTZtk5adPn8a2bduQlJQke02///47tmzZgjvuuEN6TTfeeKOs/e3atUPv3r3x3nvvITU1VSr/8ssv8ffff2P8+PGevZlU6zFQJyIiIiJyosBkRtz87b5uhiRjcRJ06qr5Ca/RaDB58uQK779p0yYkJCSgXr16yMrKksoTExPxwgsvYO/evRg3bpzivjt27MDVq1cxduxY2b7BwcHo1asXdu/e7bDPww8/LHuekJAgS5C3adMmREVFYfDgwbJjdu/eHREREdi9e7csUG/durUsSAeAbdu2ITY2FiNGjJDKwsLCkJycjP/7v/+T1Z0wYQKmTp2K3377DW3btgUArF+/Hs2bN0e/fv0UXzeRMwzUiYiIiIgIsbGxUKvVFd7/1KlT+Pnnn9GwYUPF7ZcuXXK5LwAMHDhQcXtkZKTseVhYmMN56tWrJ5t7furUKeTk5KBRo0Zutad169YOdf744w+0bdvWYRRDu3btHOqOGTMGjz/+ONavX4/58+cjJycHX3zxBaZPn15loyCo5vJJoN6qVStERkYiKCgI9erVU7xDRkRERETka9rQYGQsTiq/YjXRhgZX3bE9zHCulLht8ODBmDVrlmL96667zumxrMnm3n33XTRu3Nhhe0iIPGwJDi7/fSgpKUGjRo2wfv16xe32gX5lM7zXq1cPt99+uxSof/TRRygqKio3uz6REp/1qH///feIiIjw1emJiIiIiMqlUqmqbKh5oKhXrx6uXr0qKzMajTh//rysrG3btsjLy0NiYqLH57AOFW/UqFGF9nd2zJ07d+Lmm2+ucBDesmVLZGRkQAgh6xU/ffq0Yv0JEybgjjvuwMGDB7F+/XrceOON6NixY4XOTbUbs74TEREREZFTbdu2xd69e2Vlb7zxhkOP+ujRo7F//35s3+44p//q1asoLi52eo6kpCRERkbi+eefd8jGDkBx2bPyjB49GmazGc8884zDtuLiYoebD87ade7cOXz22WdSWWFhIf71r38p1h86dCiio6Px4osvYs+ePexNpwrzOFDfu3cvhg8fjqZNm0KlUmHLli0OdfR6PVq1aoWwsDD06tULBw4ckG1XqVTo168fevTo4XQoChERERER+d6DDz6In376CXfddRfWrFmDqVOn4uWXX0Z0dLSs3hNPPIGbbroJt99+O5KTk7FmzRq89NJLmDRpEpo1a+YyMI6MjMTq1auxb98+3HTTTXjuuefwxhtvYO7cubjxxhuxaNEij9vdr18/TJkyBUuWLMFtt92GFStWQK/X4/HHH0fLli2xc+fOco8xZcoUtGrVCmPHjsWcOXOwatUq9OvXT1pqzX7ueWhoKO699158/PHHUKlUGDt2rMftJgIqMPQ9Pz8fXbt2xQMPPIA777zTYfsHH3yAGTNmYM2aNejVqxdWrFiBpKQk/PLLL1Iih2+//RaxsbE4f/48EhMT0blzZ3Tp0qXyr4aIiIiIiLwqOTkZmZmZeOutt7Bt2zYkJCRgx44dGDRokKyeTqfDnj178Pzzz2PTpk3497//jcjISFx33XVYtGgRoqKiXJ7nvvvuQ9OmTfHCCy9g2bJlKCoqQmxsLBISEiqcjX7NmjXo3r071q5di6eeegohISFo1aoV7r//ftx8883l7h8REYGvv/4ajz76KFauXImIiAhMmDABffr0cbo2+oQJE/Daa69h0KBBaNKkSYXaTaQSQogK76xSYfPmzRg5cqRU1qtXL/To0QOvvfYaAEsSh+bNm+PRRx/F7NmzHY7xxBNPoGPHjpg0aZLiOYqKilBUVCQ9z83NRfPmzZEzuw4iNSrgqb8AdXhFXwIREREREQoLC5GZmYnWrVsrBl9EtlasWIHp06fjzz//RGxsrGzbkSNH0K1bN/z73//m+ukBpLzvgNzcXERFRSEnJ8dhFYKq4NU56kajEenp6bIEEEFBQUhMTMT+/fsBWHrkr127BgDIy8vD119/7TLBwpIlSxAVFSU9mjdv7s0mExEREREROVVQUCB7XlhYiLVr16J9+/YOQToA/Otf/0JERITi6GMid3k1hWVWVhbMZjNiYmJk5TExMTh58iQA4OLFixg1ahQAy5IOycnJ6NGjh9NjzpkzBzNmzJCeW3vUiYiIiIiIqtqdd96JFi1aoFu3bsjJycF7772HkydPOuTa+vzzz5GRkYE33ngDqampCA/nqF+quGpfa6JNmzY4cuSI2/U1Gg00Gg30ej30er1DdsmaQAiBApP8dWlDgx2SUxARERERUfVKSkrCm2++ifXr18NsNiMuLg7vv/8+xowZI6v36KOP4uLFi7jtttsqlPyOyJZXA/Xo6GgEBwfj4sWLsvKLFy+icePGlTp2SkoKUlJSpLkBNUmByYy4+fJlLDIWJ9X6NTuJiIiIiHzt8ccfx+OPP15uvTNnzlR5W6j28OocdbVaje7du2PXrl1SWUlJCXbt2oXevXt781RERERERERENZLHXbZ5eXk4ffq09DwzMxOHDx9G/fr10aJFC8yYMQMTJ05EfHw8evbsiRUrViA/P7/CSypY1eSh70RERERERERWHgfqhw4dwoABA6Tn1kRvEydORFpaGsaMGYPLly9j/vz5uHDhArp164Zt27Y5JJjzVE0e+k5ERERERERk5XGg3r9/f5S39HpqaipSU1Mr3CgiIiIiIiKi2sqrc9Srkl6vR1xcnMul3IiIiIiIiIgCXcAE6ikpKcjIyMDBgwd93RTvEwJaFMoeKGfUAhEREREREdVMXP/LH5gMOBH2gKzIYDoLaDgXn4iIiIiIqLYJmB51IiIiIiIiotogYAJ1zlEnIiIiIqKqVFxcjFmzZqF58+YICgrCyJEjfd0kmYULF0KlUvnk3JMmTUKrVq3Krde/f3/079+/yttT0wVMoF6j56gTEREREVWhtLQ0qFQqp48ffvjB1030C+vWrcOyZctw991345133sH06dORkZGBhQsX4syZM75uHtUinKNORERERFRLLF68GK1bt3Yob9eunQ9a43++/vprxMbG4pVXXpHKPvroIyxatAj9+/d3q0eZyBsYqBMRERER1RJDhw5FfHy8r5uB/Px8hIeH+7oZDi5duoS6detWy7n89T2oyUpKSmA0GhEWFubrppQrYIa+c446EREREVHVOnPmDFQqFZYvX4433ngDbdu2hUajQY8ePRSnoJ48eRJ333036tevj7CwMMTHx+Ozzz6T1bEOu9+zZw8eeeQRNGrUCM2aNZO26/V6tGnTBlqtFj179sS+fftk85zz8vIQHh6OadOmOZz/zz//RHBwMJYsWeLydS1fvhx9+vRBgwYNoNVq0b17d3z00UcOr3v37t04fvy4NCUgLS0N99xzDwBgwIABUvk333wj7bt161YkJCQgPDwcderUwbBhw3D8+HHZ+SdNmoSIiAj89ttvuO2221CnTh2MGzfOZZu//fZb9OjRA2FhYWjbti3Wrl3rtO57772H7t27Q6vVon79+rj33nvxv//9T1Zn3759uOeee9CiRQtoNBo0b94c06dPR0FBgcPxtmzZgk6dOiEsLAydOnXC5s2bXbbVFaPRiPnz56N79+6IiopCeHg4EhISsHv3bqmOEAKtWrXCHXfc4bB/YWEhoqKiMGXKFKmsqKgICxYsQLt27aTXMmvWLBQVFcn2ValUSE1Nxfr169GxY0doNBps27atwq+lOgVMj3pKSgpSUlKQm5uLqCguW0ZERERE1UAIwGTwdSvKhOqASiQTy8nJQVZWlqxMpVKhQYMGsrINGzbg2rVrmDJlClQqFZYuXYo777wTv//+O0JDQwEAx48fx80334zY2FjMnj0b4eHh+PDDDzFy5Eh8/PHHGDVqlOyYjzzyCBo2bIj58+cjPz8fALB69WqkpqYiISEB06dPx5kzZzBy5EjUq1dPCuYjIiIwatQofPDBB3j55ZcRHBwsHXPjxo0QQpQb9K5cuRIjRozAuHHjYDQa8f777+Oee+7BF198gWHDhqFhw4Z499138dxzzyEvL08K/Nu3b4/HHnsMq1atwlNPPYUbbrgBAKT/vvvuu5g4cSKSkpLw4osvwmAwYPXq1ejbty9++ukn2VD54uJiJCUloW/fvli+fDl0Op3T9h49ehS33norGjZsiIULF6K4uBgLFixATEyMQ93nnnsO8+bNw+jRo/Hggw/i8uXLePXVV3HLLbfgp59+kkYIbNq0CQaDAVOnTkWDBg1w4MABvPrqq/jzzz+xadMm6XhfffUV7rrrLsTFxWHJkiW4cuUKJk+eLLu54onc3Fy8+eabGDt2LJKTk3Ht2jW89dZbSEpKwoEDB9CtWzeoVCrcf//9WLp0KbKzs1G/fn1p/88//xy5ubm4//77AVh6xUeMGIFvv/0WDz30EG644QYcPXoUr7zyCn799Vds2bJFdv6vv/4aH374IVJTUxEdHR040xdEgMnJyREARM7sOkIsiBSiKM/XTaq0/GtXLa/F5pF/7aqvm0VERERUaxQUFIiMjAxRUFAg31CU5/A7zaePCv72ffvttwUAxYdGo5HqZWZmCgCiQYMGIjs7Wyr/9NNPBQDx+eefS2WDBg0SnTt3FoWFhVJZSUmJ6NOnj2jfvr3Dufv27SuKi4vL3tqiItGgQQPRo0cPYTKZpPK0tDQBQPTr108q2759uwAgtm7dKntdXbp0kdVzxmAwyJ4bjUbRqVMnMXDgQFl5v379RMeOHWVlmzZtEgDE7t27ZeXXrl0TdevWFcnJybLyCxcuiKioKFn5xIkTBQAxe/bsctsqhBAjR44UYWFh4o8//pDKMjIyRHBwsLAN4c6cOSOCg4PFc889J9v/6NGjIiQkRFZu/x4IIcSSJUuESqWSnadbt26iSZMm4urVsnjkq6++EgBEy5Yty217v379ZNekuLhYFBUVyer8/fffIiYmRjzwwANS2S+//CIAiNWrV8vqjhgxQrRq1UqUlJQIIYR49913RVBQkNi3b5+s3po1awQA8d1330llAERQUJA4fvx4ue12+h1QSopDc3LKPZY3BMzQdyIiIiIiqhy9Xo8dO3bIHlu3bnWoN2bMGNSrV096npCQAAD4/fffAQDZ2dn4+uuvMXr0aFy7dg1ZWVnIysrClStXkJSUhFOnTuHcuXOyYyYnJ8t6ww8dOoQrV64gOTkZISFlA33HjRsnOzcAJCYmomnTpli/fr1UduzYMfz8889ST6srWq1W+vvvv/9GTk4OEhIS8OOPP5a7rzM7duzA1atXMXbsWOn1Z2VlITg4GL169ZIN7baaOnVqucc1m83Yvn07Ro4ciRYtWkjlN9xwA5KSkmR1P/nkE5SUlGD06NGyNjRu3Bjt27eXtcH2PcjPz0dWVhb69OkDIQR++uknAMD58+dx+PBhTJw4UTaKefDgwYiLi3P/zbERHBwMtVoNwNIbnp2djeLiYsTHx8ve/+uuuw69evWSXePs7Gxs3boV48aNk5al27RpE2644QZcf/31stc8cOBAAHB43/v161fhtvtSwAx9JyIiIiKqdqE64Km/fN2KMqHOh0u7o2fPnm4lk7MNEAFIgfPff/8NADh9+jSEEJg3bx7mzZuneIxLly4hNjZWem6fbf6PP/4A4JhxPiQkxGF4clBQEMaNG4fVq1fDYDBAp9Nh/fr1CAsLk+aQu/LFF1/g2WefxeHDh2XzmCuzJvmpU6cAQAoQ7UVGRsqeh4SEuDV8/PLlyygoKED79u0dtnXo0AH/+c9/ZG0QQijWBSBNUwCAs2fPYv78+fjss8+k62iVk5MDoOyaODt3RW9svPPOO3jppZdw8uRJmEwmqdz+38SECROQmpqKP/74Ay1btsSmTZtgMpkwfvx4qc6pU6dw4sQJNGzYUPFcly5dkj1XWuUgEARMoK7X66HX62E2m33dFCIiIiKqLVQqQF37MnPb9nzbEkIAsPSMAsDMmTMdenmt7ANw2x7dipgwYQKWLVuGLVu2YOzYsdiwYQNuv/32cvNX7du3DyNGjMAtt9yC119/HU2aNEFoaCjefvttbNiwocLtsb4H7777Lho3buyw3XaUAABoNBoEBXl3QHNJSQlUKhW2bt2qeM0iIiIAWHrpBw8ejOzsbDz55JO4/vrrER4ejnPnzmHSpEnSa6kK7733HiZNmoSRI0fiiSeeQKNGjaQEgL/99pus7r333ovp06dj/fr1eOqpp/Dee+8hPj4eHTp0kL3mzp074+WXX1Y8X/PmzWXPK/vvzlcCJlD3RTI5IQQKTM5vDGhDgyt1F46IiIiIKBC1adMGgKXHNjExsULHaNmyJQBL7/yAAQOk8uLiYpw5cwZdunSR1e/UqRNuvPFGrF+/Hs2aNcPZs2fx6quvlnuejz/+GGFhYdi+fTs0Go1U/vbbb7vVTme/99u2bQsAaNSoUYXfAyUNGzaEVquVeuxt/fLLLw5tEEKgdevWuO6665we8+jRo/j111/xzjvvYMKECVL5jh07ZPWs18Sdc7vro48+Qps2bfDJJ5/I3ssFCxY41K1fvz6GDRuG9evXY9y4cfjuu++wYsUKWZ22bdviyJEjGDRoUI2OxThHXYEQAgZjMa7kGxE3f7vTh6sgnoiIiIiopmrUqBH69++PtWvX4vz58w7bL1++XO4x4uPj0aBBA/zrX/9CcXGxVL5+/XqHodlW48ePx1dffYUVK1agQYMGGDp0aLnnCQ62dK7Zjsw9c+aMQ3ZwZ6xrnV+9elVWnpSUhMjISDz//POy4dxW7rwHztqblJSELVu24OzZs1L5iRMnsH37dlndO++8E8HBwVi0aJE02sFKCIErV65Ix7SW2W5fuXKlbJ8mTZqgW7dueOedd6Th8IAloM/IyKjw67E/93//+1/s379fsf748eORkZGBJ554AsHBwbj33ntl20ePHo1z587hX//6l8O+BQUF0ooCgS5getSrmm3vucFoRvyzO33cIiIiIiIi79q6dStOnjzpUN6nTx+pl9xder0effv2RefOnZGcnIw2bdrg4sWL2L9/P/78808cOXLE5f5qtRoLFy7Eo48+ioEDB2L06NE4c+YM0tLS0LZtW8Xe0vvuuw+zZs3C5s2bMXXqVNkcbGeGDRuGl19+GUOGDMF9992HS5cuQa/Xo127dvj555/L3b9bt24IDg7Giy++iJycHGg0GgwcOBCNGjXC6tWrMX78eNx0002499570bBhQ5w9exZffvklbr75Zrz22mvlHl/JokWLsG3bNiQkJOCRRx5BcXExXn31VXTs2FHW5rZt2+LZZ5/FnDlzpKXt6tSpg8zMTGzevBkPPfQQZs6cieuvvx5t27bFzJkzce7cOURGRuLjjz9WvCGyZMkSDBs2DH379sUDDzyA7Oxs6dx5eXkev5bbb78dn3zyCUaNGoVhw4YhMzMTa9asQVxcnOLxhg0bhgYNGmDTpk0YOnQoGjVqJNs+fvx4fPjhh3j44Yexe/du3HzzzTCbzTh58iQ+/PBDbN++3a08DP6u1gfq1gC9vOD80NxE6NTBDOKJiIiIKGDNnz9fsfztt9/2OFCPi4vDoUOHsGjRIqSlpeHKlSto1KgRbrzxRqfnsZeamgohBF566SXMnDkTXbt2xWeffYbHHnsMYWFhDvVjYmJw66234j//+Y8swZgrAwcOxFtvvYUXXngBjz/+OFq3bo0XX3wRZ86ccStQb9y4MdasWYMlS5bgn//8J8xmM3bv3o1GjRrhvvvuQ9OmTfHCCy9g2bJlKCoqQmxsLBISEjB58mS32qekS5cu2L59O2bMmIH58+ejWbNmWLRoEc6fP+/Q5tmzZ+O6667DK6+8gkWLFgGwzNO+9dZbMWLECACWKQqff/45HnvsMSxZsgRhYWEYNWoUUlNT0bVrV9nxhgwZgk2bNmHu3LmYM2cO2rZti7fffhuffvopvvnmG49fy6RJk3DhwgWsXbsW27dvR1xcHN577z1s2rRJ8XhqtRpjxozB66+/rniNg4KCsGXLFrzyyiv497//jc2bN0On06FNmzaYNm2ayykAgUQl7MdI+DnrHPWc2XUQqVFZsnBWIsGHwViMuPnbnW63BujW+ei29e23VbgNeTnQLZdn1jTMPAtdRPXMxSciIiKq7QoLC5GZmYnWrVsrBohUfUpKStCwYUPceeedisObR40ahaNHj+L06dM+aB1Vh+nTp+Ott97ChQsXoNNVbqUDd5X3HSDFoTk5Dhn9q0Kt71FXYg3AAdcJ46w96xmLk6BT860kIiIiIvJEYWEhNBqN7Pf2v//9b2RnZ6N///4O9c+fP48vv/wSTz/9dDW2kqpTYWEh3nvvPdx1113VFqT7o4CJLqtjeTZv9ZATEREREVH5fvjhB0yfPh333HMPGjRogB9//BFvvfUWOnXqJFsfPTMzE9999x3efPNNhIaGYsqUKT5sNVWFS5cuYefOnfjoo49w5coVTJs2zddN8qmACdS9vTyb7dx0K506WLlnXAjAZAAAaIVAxty+lrnqS78HwICeiIiIiKgiWrVqhebNm2PVqlXIzs5G/fr1MWHCBLzwwgtQq9VSvT179mDy5Mlo0aIF3nnnHcV1yymwZWRkYNy4cWjUqBFWrVqFbt26+bpJPhUwgbq3FZjMLuemAygL0I0GYHk7AJawXFf60GIdChAmBfvsifcdpTXveT2IiIiI/FurVq3w2WeflVtv0qRJmDRpUtU3iHymf//+DkvM1Wa1NlB3SSFAd4Vz1X1P6cYLrwcREREREQUiRjGQz00HYAnSn2+qXHnaEWClZQkDHYoAAAXQgEPgiYiIiAIfe/SIaid/++wzUEfp3PTQYGkeOowGx0ozTwNqnaW3vVR62FQAwA2F8iHwAIddExEREQWS4GBLh43JZIJWq/Vxa4iouplMJgBl3wW+VqsCddt5zLZBNQDnvejWAD1UB6hUgDHf6fGtQ+ABZpAnIiIiCiShoaHQaDTIyclBnTp1+PuNqBYRQiAnJwcajQahoaG+bg6AWhaou5VAzp5aB6jDy56H6oCn/ip3/ro1aGfATkRERBQYoqOjce7cOfz555+IiopCaGgof78R1WBCCJhMJuTk5CAvLw+xsbG+bpKkVgXqykRpL7mxrMjaiw5YAnNbKpU8cAeQ/mQfIFQHg9Ag/rldsm1MNFdNhIAWhQ5lRERERO6KjIwEAGRlZeHcuXM+bg0RVReNRoPY2FjpO8AfBEzkqNfrodfrYTaby6/sBmtPN4z50C1vId9o34teDt3K6wEA2jnnkLE4ybLGus0weACcv17VTAacCHtAVmQwnQU0UT5qEBEREQWiyMhIREZGwmQyee13JxH5r+DgYL8Z7m4rYAL1lJQUpKSkIDc3F1FRlQ++dOrg0h5u770FKlMBdGoVtKECGYtuhcFUIgXsnL9OREREFDhCQ0P98sc7EdUOAROoVwvbxHHlUZqrXvpfFQDdzNMA1AAE7Jdu43B4IiIiIiIicoZRoi1PhrwrzFWXWd4OOgAZT550On/dOhyeU6mJiIiIiIjIqlYE6tZl2WRLsonSJHJKa6Z7wtqzDihmgpfmr888hYy5fS3z15d+D0BVlhl+Vm+4PyOeiIiIiIiIarJaEagrLstmMgD2SeQqwrZn3cXSbarl7aEDoAOgxToUIEzaVmgqcTiswWgGjMWcx05ERERERFTLBPm6ATWKNWgPj7YE7DNPK1ZLf7IPDs3qDcv8dWDwK3sc6iQs3Y24+dtRYGK2USIiIiIiotqkVvSo25IyrgubNbc9SSLnDmvA7mRYvG7l9dABaIDVMEAD9pcTERERERGRVY0O1JXmpkvLshltwmMP1013WzkJ59LDpgIADNNOAiu9f3oiIiIiIiIKPAEfqAshUGAslpVZ53U7zk0vTSCHkMonkfOUi/nrOhRVb1uIiIiIiIjIb/ksUDcYDLjhhhtwzz33YPny5RU+zpV8I+KXyud4W4e3y7K8A9CiCDpvJJCrCPvh8LYB+8quvmkTERERERER+R2fBerPPfcc/vGPf1T6OAlLdwM2GdQBSMue2To0NxE6FAIVvyfgHeWtv15K6mXnIutERERERES1ik8C9VOnTuHkyZMYPnw4jh07Vi3n1KmDobN9udYEcoD3ksh5wjbRnJVNL7s0f910FtBEVXfriIiIiIiIyEc8DtT37t2LZcuWIT09HefPn8fmzZsxcuRIWR29Xo9ly5bhwoUL6Nq1K1599VX07NlT2j5z5kwsW7YM33//faVfwL5ZA6CLiJQ6np0tOa4NDQZMNgVVlUDOXW72rBMREREREVHt4vE66vn5+ejatSv0er3i9g8++AAzZszAggUL8OOPP6Jr165ISkrCpUuXAACffvoprrvuOlx33XWVa3kpnToIOhQhXGV56GDzCLVkeNeFBkNlMlR/AjlPlfayG6ad9Gg3IQQMxmLZQwTYkPma8BqIiIiIiIi8weMe9aFDh2Lo0KFOt7/88stITk7G5MmTAQBr1qzBl19+iXXr1mH27Nn44Ycf8P7772PTpk3Iy8uDyWRCZGQk5s+fr3i8oqIiFBWVZUXPzc2VbVcZrgDLb1JujHV4u0Kmdb9k7WW3y2IPlC01Z8t5dnsgY3GSZRm6AFETXgMREREREZE3eDUKMhqNSE9Px5w5c6SyoKAgJCYmYv/+/QCAJUuWYMmSJQCAtLQ0HDt2zGmQbq2/aNEip9u1rzsJ0oHACM7dxECWiIiIqpurjgIiIqo6Xo3ysrKyYDabERMTIyuPiYnByZOeDee2mjNnDmbMmCE9z83NRfPmzZUrzzxt+a+rAN3ay+6LBHJEREREAYQdBUREvuHTb9lJkyaVW0ej0UCj0UCv10Ov18NsttzVNTx8CJFNYuWVrcG3NZu60pB3XyeRIyIiIiIiInLBq4F6dHQ0goODcfHiRVn5xYsX0bhx40odOyUlBSkpKcjNzUVUVBQQqnUecFvLlZZAY086ERERERER+TGPs767olar0b17d+zatUsqKykpwa5du9C7d29vnso91uRsto9AnVMlBLQolD1Qk7Ki1/TXR0RERERE5CaPe9Tz8vJw+vRp6XlmZiYOHz6M+vXro0WLFpgxYwYmTpyI+Ph49OzZEytWrEB+fr6UBb6i7Ie+1xYGoxkwFsOQfw0nwh6QbzOdBTRRPmqZl5kMNfv1ERERERERucnjQP3QoUMYMGCA9Nya6G3ixIlIS0vDmDFjcPnyZcyfPx8XLlxAt27dsG3bNocEc55yGPpeSyQs3Y0ChEGLQpwI83VriIiIqFYpHfFmX0ZERFXL40C9f//+EOV8QaempiI1NbXCjSIP8X+iREREVBU44o2IyCcCZm2N2jT0XRsaLP29b9YAxC/dr1iv1gyLJyIiIiIiqkUCJlCvTUPfVTYJ73SqImhRCB2KHOpxWDwREREREVHNEzCBem2lW3k9g3AiIiIiIqJaxKvLs1UlvV6PuLg49OjRw9dN8Rv7ZvV32ttOREREREREgSlgetRr09B3hOqAp/6y/G00AMvbKVYLL77qMDedfEMIgQKTY/4EbWiwbCoDERERERFReQImUK9VVCpAHW752zZoB2SBu/b1m5wewppozhaDxqpTYDIjbv52h/KMxUnQqfkxIyIiIiIi9zGC8He2QbsLhmknUWA0o8HqjgDKEs3ZYtBIRERERETk/xi1BRr7HvZSulAdkJ/rcleD0TI0mz3rVWvfrAFIWLrb180gIiIiIqIAxWRygcbaw27/cBJ475s1QPo7/tmdiJu/HVfyjTAYiyGEqK5W1ypadbCvm0BERERERAEsYHrUa1UyuQrShpYFiOlP9gFCdRDCBC0KAQAF0ABQIf7ZnQCAQ3MTobMLKtnbTkRERERE5FsBE6hT+WwDbN3K66W/reuw31C4TjZv3Rqw22LwXkFCSDdEYMwv+5ujFoiIiIiIyEMM1GuR9Cf7wCA0iF/6PQDlwNtV8M6A3QWToWypvFVlN0cMprOAhiNAiIiIiIjIfQzUa7ppR4CVXQFYetl1ADKePGlJSheqA1QqGIxmxQDdyrqNWeOJiIiIiIiqXsBEXXq9Hnq9Hmaz2ddN8V9KGeEVhl5Lw+JnngbUOmhDBTLm9pXVMRjNUuZy69x2Zo13T/aDB1H/zVqe9JCIiIiIiCosYAJ1JpNzg9Ka60JYgnejAVjeTr6t9LkKgM7uUDqUDd/uXrgaBmgQ/+wOACr2rJdDhNq/m0RERERERO4LmOXZqIKswXt4tCVgn3na40Okh03FibAHoEVRFTSQiIiIiIiIbLFbtLawBuxKw+OVKPXAk18TQqDAJJ8awmkKRERERESBh4F6baM0PF6JNaC3Cdh1pT3qhqJiqRoDQf9RYDIjbv52WRmnKRARERERBR7+gidlCgF9ethUAMANz5Wtx85AkIiIiIiIyLsCZo66Xq9HXFwcevRgNm1/YjCaYTAWQyhklyciIiIiIiLPBUygnpKSgoyMDBw8eNDXTaldSofAi5mnpKJ9swZIf8c/uxNx87fjSr6RATsREREREZEXcMwyuVY6BN52FrpOVQQtCgGUrbEe/+xOAMDxRUmwn7LOeexERERERETuY6BOHtOtvF5aY/2GwrL56gCQnW9EwtLdsvqcx05EREREROQ+Rk9UKelP9oFBaBC/9HsAKocgnaqRENJIB9syIiIiIiIKLAzUyT2266/bLtm28nroADTAahigkYbC2zIYLWt7cwh8FTMZcCLsAVmRwXQW0ET5qEFERERERFQRDNTJPeWsv25dus0w8yygDofBaJbmrVv/e2huInTqYAAM2omIiIiIiJxhoE6es/au2/SsW+lghOWfVbHThHNAWdDOgJ2IiIiIiEiOgTp5ztq7rhSwW4fEA04TzgGOvewGoxm66mo/ERERERGRH2OgThVXznB4q/Qn+wChOkvSued2ybZZA3YtCqXAnoiIiIiIqDYLmEBdr9dDr9fDbDb7uilkzzbRnJVdwjkA0M45h4zFSbL5664YjGbAWMzh8UREfk4IgQKT4/+f+f1NRERUMQETqKekpCAlJQW5ubmIimIWa7/iZs+6ylQAnVoFbahAxty+lsJQnWV/Yz6wXF4/YeluFCCMSeiIiPxcgcmMuPnbHcozFidBpw6YnxpERER+g//3pKrhYv66Ciibjz7zNKDWATA6PRST0BERERERUW3CQJ2qhpu97PZZ48tjDdrZS0NE5J/2zRqAhKW7fd0MIiKigMZIh6pWOfPXXXGVhI6IiPyTtnSqEhEREVUcA3WqWko960rBu5WbSegMRkvSIg6BJwosSknH+DmuAYSAFoWWv435ZX8L4bs2ERERBTAG6lT93B0Wb61emoQOKAYgAKg4BJ4oQCklHePnuAYwGXAi7AHL36sgLbdpMJ0FNEwAS0RE5Cn+MiL/4iIJnQ5AA6yGARoUQANAJfWsG4zmsgR1ROS/bHtebcqIiIiIqAwDdfIv5fS2p4dNBQDcULgOBQiTeta1KJR6cIjIj9n2vJZir2vNkv3gQdR/s4evm0FERBTQGKiTf7Kdx66QfE6HIgCQetaJiMg/iFCObyIiIqqsag/Ur169isTERBQXF6O4uBjTpk1DcnJydTeD/J1tz7rCcHhrz7ph5llLPWM+sNxXjSUiIiIiIvKeag/U69Spg71790Kn0yE/Px+dOnXCnXfeiQYNGlR3UyhQuBgOr4MRln/GxmptkrcoZcAGmAWbiIiIiKg2q/ZAPTg4GDqdZVhcUVERhBAQTCRE7nCRaC5QKWXABpgFm4iIiIioNgvydIe9e/di+PDhaNq0KVQqFbZs2eJQR6/Xo1WrVggLC0OvXr1w4MAB2farV6+ia9euaNasGZ544glER0dX+AVQLWLtWVdz/iMREREREdVcHgfq+fn56Nq1K/R6veL2Dz74ADNmzMCCBQvw448/omvXrkhKSsKlS5ekOnXr1sWRI0eQmZmJDRs24OLFi07PV1RUhNzcXNmDajlrz/pTfwEzTzutZjCaYTAWw2AsDohRG/tmDfB1E4iIiIiIyA94PLZ26NChGDp0qNPtL7/8MpKTkzF58mQAwJo1a/Dll19i3bp1mD17tqxuTEwMunbtin379uHuu+9WPN6SJUuwaNEiT5tJNZlSorlShvxc6FZeDwBIWLobBbCs2XZobiJ06mCpnq/mgNvPSbeuAw8AWpv2ERERERFR7eXVSbBGoxHp6emYM2eOVBYUFITExETs378fAHDx4kXodDrUqVMHOTk52Lt3L6ZOner0mHPmzMGMGTOk57m5uWjevLk3m02BzD7RnLFYsZp1vXUr+zng1ZXUzXFOuoC2dKk5GPOhRaG1QV47J1GgUPocMrEiERER1UZeDdSzsrJgNpsRExMjK4+JicHJkycBAH/88QceeughKYnco48+is6dOzs9pkajgUaj8WYzqQbThpb1Su+bNQDxS/cr1rPtybY+tw/mgapP6qZFEU6EPWB5sgo4EVbaHtNZQBNVZecl8kdKyRWZWJH8AW8iERFRdav2Xz89e/bE4cOHPd5Pr9dDr9fDbHbs9SSysv3R1EBdjIy5fS1PQnUwmEqkYFwpKCciIlLCm0hERFTdPE4m50p0dDSCg4MdksNdvHgRjRs3rtSxU1JSkJGRgYMHD1bqOFR7qJa3h255C8tDZZTNUXfFV0ndsh/kv20iokAhBKSEpfaPQEhgSkRE/s2rt4LVajW6d++OXbt2YeTIkQCAkpIS7Nq1C6mpqd48FZFnjAZoQ4Wshx0qleKQd9ukbtYh8tUxxFGEctk5IiJfUxrmbj9dCgCy841IWLpb8RjsbSciosry+P8ieXl5OH26bEmszMxMHD58GPXr10eLFi0wY8YMTJw4EfHx8ejZsydWrFiB/Px8KQt8RXHoO7nFNgu80QAsb2f5e3k7qABIofCccwBU8uC9lBCFUlK3+Gd3AFB570eXEGUJ4wDorInkiMjh82EtI6pKSqtxuDM9ylmQTkRE5A0eRx6HDh3CgAFlQ4OtGdknTpyItLQ0jBkzBpcvX8b8+fNx4cIFdOvWDdu2bXNIMOeplJQUpKSkIDc3F1FRTLJFTthngXfGkAWs7CoP3m1Yk7rdULgOBQhT7E2pUC+7yVCWPI6I5BQ+H0ysSFVNaf65I5sVOqz7QSOVWadMxS/9HgATzBERUeV5HKj379+/3LlXqampHOpOvme3xrqsh31lV7cOYe3xtvas27KuzW6baZ6IiAKDtSdd6UaslfV7HsZ86Ja3kG0zTDsJ3crrLU9WWf6jheXmLhERUWUFzAQqDn0nj7nbuz7zNKC26Ve3CejTw6YCKOtZt2UdGnlobiJgNCv2zBMRke8pzTsXAui4QN6TLgXmpbQhQVAVFwAwOhxTaeqStcxQVFx2DC7jRkREFRAwgTqHvlOl2few25aX8yMqfW4ioA5XnLsY/+xOaFEoDZcnIqLq52qtc6Xh7WUrfJQNa9ehEDpRGqirVIDBZiSWPYWRWdLN3efKbu4ysRwREVUE/89BtYe7PezWgN6mZ10HI4AQaHVaZCxOcjvZkL9w9QOWiMgfKX1vAZCmGyllZrf/Xrb2kCsNb7cmg9OiqCw3wnJvtJyIiKjyAiZQ59B3qjZKAX1pwK6aeRo6tc6SLX7RrWU98cZ86QdeWKjd8m7GYp8HxUq9Sf7Yy8MbCkRk5SzJW8biJABwIwEcvHtDdeZpIFRr+dv2O6k0b48wGaBa3h6Apbc+ful+ANW7zCcREdUc/vUr3QUOfSe/YA3YAeisc9tDdbD9KNn+DktYuhsFCJN6dbjSlGuBckOBiKpOeUneXCV/c4c0D10IwGSwPFY6qWybw6ScaVK2QbhOVeSwzKdtAlIG7EREVB7++iVyRmEIvIy1bObpcg8lJZ6b1RtuDL4nIqoRnI0qqsh8cutQdftectttpUfHoVl9ZEnhAOtdUhW0oSaoUGwps8vk7pBc1I0cJkp0K693WObT2m7efCQiInfw/xREzliHwNsmoVMK2u2eu1qurdBU4u1WEhH5LftRRa6CcVfzybVq++/VsgRwWhRCi0JpAU0tihC9qnX5jZt2xLFMrXMvl0kl2L4+9q4TEZEzAROoc446+YztnPXyetkBqGzWW09/sg8MQoP4pd8DUGHwK3t8kx1eCGkYpm0ZEVF1sF3O0lkwrjSf3Nm0IVkCuFWo2PeqbdZ22VSmCnJyUzd9biIMNj3qtq/TYTk4Bu5ERFQqYAJ1zlEnv2Dfy+4iYAcswx91ALRwXIe9WpkMZT9qSxlMZwGN689StSd3U7ihIEoEDMZiWRl/zFJN5yzjuVVVfQZcZVp393xhLkYVuZfczW65NIRAQCBjbl/L5lCdZV65Oxnapx1RXEbNgTd60p2sLKKT1mAXAOTvobMs9QC/54iIaruACdSJ/IrSsHgrhZ7q9Cf7lPbUCLd+XFrndVppQ4N9kg292pO7KdxQyDJkStmTq6UNROVw5wZWRW9y2SZScxXUOuuJdXZeZ22xr+/svPbnK2uv5b8FRjOsfdG2L9G+N9lmTykY3zdrgOXY1gDcZIBu5fWWaqXflypAOr5DXhClYNy2h9z2O9raYPvrUJme9PIsbwcdgIy5ZwF1uMtra1vO7zkiotqN/wcgqgylHhQhHNdht/7odJF4zrYXyjqv08oxWRKYQZiqDZfNk3PnBparOq56y8sL0K1crRdefrK1srY4WwKtvPPZ06JQcfi5Th0CbUhoWW+4lW0wvqq0zN3eb/tRTLpoxxumtkngqnjOubssPeshAIqlkUMF0MC+l52IiAhgoE7kfU6GP7qzmzP2P7IB5TmfVdgnRLVYbVk2zxvDvt3hbnBc2iopg7nBaJa+C+wDPFeBtNL3h3WOuKulzpQCfI8ZDVAZDdC5mCIkUQrSrT3jrqYZVfA7t0opTY+y3rhF2Zx6w8yzEKGWtqtU8hs1XH+diKh2C5hfWUwmRwFH6YeaKd9pddts8fZJ6Fyx/qhT6tGyDqGvcBDPJHTkp7wxh7uiw76Vglv7Mld13FsHvDRAVxVBt9KSwdw2wOteuBoGaKTanvbMlr3OsiHosDmCgGM2dYGyYeqONw3KyFa+cCdAV2I7dF2lUp5mZFWVw9Yrys2bBzoYAZXNT7FQtfSn/c1YBuxERLVLwATqTCZHAUfph5qLYZ222eKtSegynjwp+xEqBIBQHQqKS9waHmsdQq8UxAsBhyRtVtIPwgomofMu3iyozezzNQBlc65d9Uo7G2ZuO0W5osPM7YNbS50dcPemmu1xnK73bSqQAnQl6WFTZc8N006W5cEAIISlLSoVpLnfSj3ysuzp9pSyqZcOU7fvFQZgM5/cxfvgaj65Lfv1y/2x19wdSjcYFHrZJdNOOgyL5/rrRES1E7/xifyYNIfT1szT0IVq5RmQVSrAmO9eFuRSfxtM6PuccqDjznD66pq3rDIV+MHNAteqa8i0z1TxyApXIz/s8zUAln+fbh0T5QXjjgG3NThyNexbKbi9odB+ZQfnx7Zu07m73reVizncit8Vdvsp9ch/O70XsNr9JiieW8pqXipU67z3W+nfTTWsXe4zHt5g0K28XrpGjv+miIioNmGgTlTVXGWGtw3i3FzyDcvbyTMgzzkHy49/o0PVfbMGOGRMtxr8yh7AyY9Ad4fT2wdAVdHjU2gq8erxqoKz3l1vvR+uhnnbZ/R2ltS6UjcNqnhkhauRH0rcSaTmTk+5UsBtDWCtw76BsqHdUu+3yQCslB9LWtlBKXN5KanXW2GbS9Ye54reHFEI7qUeeVdBuqveb1e9wq6CU6Pz6T+1hv3/E5x859tnzLedMlFjbgISEZFTDNSJqponPSrOlnxzFbwbspz3siksp6TE2kPpyXB6xaYozL2t7A/KEa99i3Q3O5U87eWvymW2vNlOV8O8D81NdOu6eXLTQGnutq9nATv2cDuutQ0UQ2mtarmy/ZR6k6UA1mbYt2HaSct5XAxFlwLvSvZ6y9jP07auKCG9FJu7MuXd4KuI8rKpV4TS95s/zjGvSvb/T7B9T2xXC7Fbf51LtxER1S78lifyN0qBvaseGBfz3rWhwchYnKQ4LD597iBYAxptqAkApOH0BqFB/HO7PG66q0Rc7qzl7G5AaO3Vtw9uPc1O7s4SWkojB1wlF7MNKO1vXFjbq3Te44uSnMY/3hhl7l4Cs7LzdVxQ1j53e7rL9q/YzY19swaULdVlp3642vJv2cqYD93yFpa/S/9t6wA0gKVH3DrM/NDTg6BTFUnzs3Uocq832YZHvd/uLC9my9l630Dl52nbz/1WCuZdZVV3db6KJncL1LnmVcnZe1K6/roWHAJPRFQbMVAnCgTu/ri1rtNe+oNbZSqATq08LF4Hk8MPc+nntTU5lIKy4fSu5uCWsc1c7GyovDVwVQoI9z05wGGYsbVX351s3FZKwaNSAOzO3GZXPdham/a4GqJtLzvf6HROdNmcbEvyMcBmyS0XUbztTQN3et0dz1cxFV3OzdUIEBWE3Vxox3/TQFmPuHUIu674byk5oyc3G1yu6W0/FN1UoNybPfO0Zb424LzX23Z+trcDWPu5365uBnja082Au9qkz00E1OFuJz8kIqKaIWACdS7PRmTDWW+WdX6sVUWHwpoM0KIQOrtAHAAaWHs2bXs0SxlmnnX6g7KggstV6UKdf02586PVVeC9b9aACh3Tkc1NC2O+Q9Zmd47tar1q63untUk+Zg06/zT85rQtSvOs7W+uKLXT9lodmpsIHQqdJir09AaI0jbr/G6ltknbTH8Dy9srN0IhqJZ6zVcq1Ldy1ZscqhCE2g9Ft1KHO/88KuWhsK/jDe4c21VwzcDbPyjkKrHcoAqRLd3GNdaJiGo+lRCBtc6RdXm28+fOonHT5r5uDpH/MeYDzzctv55tcGPXE++Sq+CmdJsQJaXDxktky0GV1wNv6VG3WypKIQiz9pY69uC718vvrL7tetG29j09DNrS3mDb38SGomIkPPelfDi1DcO0k5ZpBEu/L7ed9ueDSuUQ2Cu9P3GF62TtddaWGwrXAYDiUlyGmWdlSatsZSxOstywsfs3lfVYJnQRkU6nBji7KfHt9F5otrqt4rYKm3POvXna9kPBbVdMsP/MWI9pq7Lzs4ncpfBv0jDtJLq/+D2Asu+16pirbsjLUbwpq4vwn5U3iIiqgzUOzcnJQWRkZJWfL2B61InITa7mjjqb226/hrEr5WSkByBlpbcdbty3aAW+1Twuq+7W8kMuMlZbA/Z9Tw+DThOi2Mtfdg7H4FgFIMPZGtI2hOoUVCrH3katKt/5GtSANOQ648mT8t7NcrJ+C/W50hsb8iXRlEY4aJ0E5g5tcXFjwPJDX2nYuSjN0u04zNxVUsGyHnnH9/zWV/Ygo7LTbZ0F3K7+7dvWUyr3dtI0Ii/j0m1ERLULA3WimqaiQ1idZB72FvsgHbBZ0spKYdkrV6wBqlCfg0odBqWvNOv8TqUgvuCRH4HXyz+PysmQa8UwTmEEgEfJyGDJLaCF45JoSr6d0cvpaxCPHYZqVTcAcAzmbdtpNEAbKqRkgrIlxuzeM6mNgMONBGsvX8LSr6V1wu3P272wLIObYdpJ6MIV7kg7W2POylkQXdF/+xz2Tf7G3eU6iYioxmKgTlSbuJrbXl6w4saQd09/VLoMYO17TV0k7lKZCsqGPtufwzq/U6FXWPv6Tc7P7yqZmKv2evoD28m61O725bp6DSp1hPMdbedgl56vLJng6XLbfujJPg7XzzrCwdVIBdsbC1pdHQbIREqs38dVfAOViIj8FwN1otqkMj2H9hmklbbZ3whQ6hl198ems/NZE3fZBbZOVfSHratkYu5k71a6KVLefs64s8yW4mvQVqxXzpSv3AagLMGVQpI/a++5q5EKtjcWmASLqBxOvrOtU1kMRcUAmFSOiKgmYqBORJ5xlV26ojcC7ANR22Pa88YwZXcCX2uQa98md38Me7Od7mQPd7UOtrNrZlvmLH+BbRtsVxRQCuZLyXr5PUlUSERusd4Uu+E5y1z16kgqR0RE1Yvf6kTkmcoGoN5K3OUqcZinc5w9bVNFl9lytZ8nbVC6BhW5geLOdXTW428fzAPKwbjtdAB73lqajIiIiKiGYaBORNXLW4m7vJkAzNNjVUXSssq+nqq4gWItL095wTiTtRFVXulnTBjzHRJcGqSVFjgMnoiopgiYQF2v10Ov18NsNpdfmYgqp6I9xhS43AmmGYwT+U7pZ0wpBI9/dqf0N4fBExHVDEG+boC7UlJSkJGRgYMHD/q6KUQ1nzXosn2wh4aU/l3w3wYRERGR1/GWKxEREVEASp+bCKjDYTCaZb3qREQU+BioExEREQUgnToEsBvmbp2vzrnqRESBjYE6ERERUSAyli6ZKNRSkbVnnXPViYgCG7/BiYiIiAKRdUnEmWd92w4iIvK6gEkmR0RERESOtKHByFichENzE33dFCIi8hIG6kRERESBwrpM4szTUpHKVAAdiqBDIQDhu7YREZHXcOg7ERERUaCwLpNoq3QIvA6AFutQgDAmlSMiCnAM1ImIiIhqCB2KAADxz+4AoMLxRUmwj9MZvBMR+T8G6kRERESBxjoEHrBkfy/tVU8PmwoAuKHQ0rNeYHJcY50Z4YmI/F+1z1H/3//+h/79+yMuLg5dunTBpk2bqrsJRERERIHNOgReHQ6odb5uDREReVm1304NCQnBihUr0K1bN1y4cAHdu3fHbbfdhvDw8PJ3JiIiIiI5a++6Tc86EREFtmoP1Js0aYImTZoAABo3bozo6GhkZ2czUCciIiKqCKUEc0REFNA8Hvq+d+9eDB8+HE2bNoVKpcKWLVsc6uj1erRq1QphYWHo1asXDhw4oHis9PR0mM1mNG/e3OOGExEREZEyHYqgRSEKioodthmMZhiMxRCCS7kREfkrjwP1/Px8dO3aFXq9XnH7Bx98gBkzZmDBggX48ccf0bVrVyQlJeHSpUuyetnZ2ZgwYQLeeOMNl+crKipCbm6u7EFEREREzqWHTcWJsAdw67JtDtvin92JuPnbUWAy+6BlRETkDo8D9aFDh+LZZ5/FqFGjFLe//PLLSE5OxuTJkxEXF4c1a9ZAp9Nh3bp1Up2ioiKMHDkSs2fPRp8+fVyeb8mSJYiKipIe7H0nIiIico8KgBaFsgfAnnQiIn/n1azvRqMR6enpSExMLDtBUBASExOxf/9+AIAQApMmTcLAgQMxfvz4co85Z84c5OTkSI///e9/3mwyERERUc1QmlROzDwlFaXP6IYTYQ/IHtrStdaJiMh/eTVQz8rKgtlsRkxMjKw8JiYGFy5cAAB89913+OCDD7BlyxZ069YN3bp1w9GjR50eU6PRIDIyUvYgIiIiIjulSeVUNonltK/f5MMGERFRRVV71ve+ffuipKTE4/30ej30ej3MZs6nIiIiIiIioprLq4F6dHQ0goODcfHiRVn5xYsX0bhx40odOyUlBSkpKcjNzUVUVFSljkVERERUY1nXVbelsMa6wVjW+aENDYZKpaqO1hERkRu8OvRdrVaje/fu2LVrl1RWUlKCXbt2oXfv3pU6tl6vR1xcHHr06FHZZhIRERHVXNZ11WUPnUM1a/b3uPnbcSXfyCXbiIj8iMc96nl5eTh9+rT0PDMzE4cPH0b9+vXRokULzJgxAxMnTkR8fDx69uyJFStWID8/H5MnT65UQ9mjTkRERFR5utJkcgXQwJIX3hK0A0DG4iTo1NU+M5KIiOx4/E186NAhDBgwQHo+Y8YMAMDEiRORlpaGMWPG4PLly5g/fz4uXLiAbt26Ydu2bQ4J5oiIiIio+qWHTQUAZD2Wifil+33cGiIiUuJxoN6/f/9yh0WlpqYiNTW1wo1SwmRyRERERN7TIFyNjMVJMBjNUo86ERH5B6/OUa9KKSkpyMjIwMGDB33dFCIiIqLAYk0wN7Ns+qJKpYJOHQKdOtiHDSMiIiWchERERERU01kTzNkSAjDmA8ZiaFFYVuYmIQQKTPKRjsweT0TkHQzUiYiIiGojUwGwvB10AE6EWYoMprOAxr2kvQUmM+Lmb5eVMRkdEZF3BMw3KeeoExEREXmRKd/XLXDAXvrAwWtFVLUCJlDn8mxEREREXrSyq9NN1iDMYDTDcQX2qsNe+sDBa0VUtfhJIiIiIiIAgMFoBozFEALouGA7tCiUhsX7M/buElFNw0CdiIiIqLawZn+3YcjPhW7l9QCAhKW7UYAw7Js1wBetqzD27hJRTRMw316co05ERERUSUrZ343FDtUSlu6upgZVnG0vusHI34dEVLMETKDOOepERERE3qcNLVtHXYci2Tb7555SGpJuPWdlh6Ur9aITEdUUAROoExEREZH32QbM6WFTy61vnceu1IttW6YNDXYaTGcsTpK22/JgGXciohqNgToRERERuc06j11J/LM7pb8PzU10eRylIF5pH6UbArajAKz76dTBMBjNsjYQEQUqBupEREREJDftiMvl29xhHzDvmzWgQnPflQJv+4Bepw52mjiOGeGJKBAFTKDOZHJEREREVUAhE7yrMej7Zg1A/NL9sjJ3erS16mDF8orwpNfcWxnhq3K+PRGRvYAJ1JlMjoiIiKgKKGaCz3daXScF3ALa0mRzOhRChxBodVpkLE4qdwi60yztQkCLQllRATQAVB73yFvP4a2M8K7m23MZOCLyNn6rEBEREZHHtCjCibAHLE+WW/6jeuov6NTh0IYGI2NxkqVn3mQAAAhRKAXh8c/uAKDQC20ylB2zlGHmWcDmmIo3AYSw3FwwFpd/DgUcHk9E/oaBOhERERF5lUqlsvQyG/OB5S2k8hOlOehuKFznNCGdPZ06BCjtsXbac20yAMtbQFfBc3g6PL6i8+1rFIXRD0zbT+Q9DNSJiIiIyCesSeE8mXMu9dbblolCJ7WrJiO8N+fbByyl0Q+ms4CGU1SJvIGBOhERERHJ2SeYMxqA5e0qdciCR36E9vWbZGW6CgS8Um+9LaPzIequMsITEfkrfmsRERERkZxSgrlSWhQhY25fy3Dzle4fUoTqyq1TaCpxLDQa7A4kytpoX+ZFtkno7NdtJyKqagETqHN5NiIiIiLfUy1vj/JD7ooZ/MoeaY65xJ2e/JmnHYrS5yZKSegqwnaYvP1QeyKiqhYwgTqXZyMiIiIKXLYBs20QrbQ2uTfYJqEjIgo0/PYiIiIiItds56xXcL66ymapNNsgWpYczpgvLfXmYNoRYGVXj8/rqapIPkdE5CkG6kRERETkmos56xVWusa6CigbSu9qmHqo985vnX+utFY6k88RkT/gtxARERERuc9VRnhr4jdrcjdTgfPjmAzA803lZQpzzcvOq3V+XlO+e20vZe0pd7VWurcIIRSH9yvdJCAismKgTkRERETuc9W77u6QePtM7pU9bxUPibfNAO9sm7PAu8BkRtz87Q7l1XGTgIgCF78diIiIiKh6uZvJXW2TX96N5d0UlQ6x1wqBjLl9LXPPl34PQAUhAIOx2GUgDsDlXPXq7J33FvbyE/m/wPg2ISIiIiL/VF6iOWvA7ela52qd63nx9kPwnZ2/dIi9dS68DoAW61CAMBSYqjdh3L5ZA5CwdDeA8nviqxJ7+Yn8Hz+JRERERFRxtkPSlYLnUJ2ljlFhHrl1TnoFssh7N8GdgBZFlj+N+QBCAKF2qGXNCG/NVO8qM7y119q2t16rLkuWZ93P9pjszSYiq4AJ1PV6PfR6Pczmqllrk4iIiIgqydPgWV3B4ezusM6DdzEfvsDaq40inAh7wFJoXR5u5lm72gI6FEKHEMBkzVRfDEAANkvP2Qbo7vTW+3rovG0vPxH5j4AJ1FNSUpCSkoLc3FxERUX5ujlERERE5M/c6KX3JEDVogi65S1kZbbD6K2cDSsH4FZPfHWz7eUnIv8RMIE6EREREdVwHi6zFijsh7f7Y8BORP6FgToRERER+YeqWGbNmszOJtFc+txE+RB9Y37ZkHcrkwFaFAIACqCRb5t2xK22Opt/rlKpmLSNiFziNwQRERER1VwK2eN16hBAFig7/iTWrbweJ0pHtN9QuE6+MdS9efg6dTADciKqEH5zEBEREVHVc5YRHnAst93mB9Kf7GP5Y2XVn0tpjXNmhCeqfRioExEREVHVc5UR3mvLrLnJmgneunScLYWh8rqV15d7SOsybGXLsYmypd6slM5nRykZHdc3J6p9+IknIiIiotrFmhH+qb8cbxIoDJV3h31SOKUs8YrnIyJSwECdiIiIiGoGV8PrvcHa2y5E5Y4jBGAyAMZiKWGdq2OW9dJzGDxRbcFAnYiIiIhqBlfD661BvM2Qdo9Ze9uNZcvIOWSQt1LKJG9lMgDPN4UOkBLWGUxnAU2UYnXb3noOgyeqHYJ8cdJRo0ahXr16uPvuu31xeiIiIiKqbaxBvNq7Sep06hCnD08IARiMxbLecyKqvXxyO27atGl44IEH8M477/ji9EREREREfqXAZEb8c/IkctZ12A1Gs8MceCKq2XzSo96/f3/UqVPHF6cmIiIiIrIwGixD1K1Z4P2MdR12nTrY100homrmcaC+d+9eDB8+HE2bNoVKpcKWLVsc6uj1erRq1QphYWHo1asXDhw44I22EhERERF5z/J2wPNNKz5nvYocmpuIjMVJ0IYyQCeqrTwO1PPz89G1a1fo9XrF7R988AFmzJiBBQsW4Mcff0TXrl2RlJSES5cuVbqxRERERER+R5SumS57KPTSS/UctxWaSqS/rT3pStndDUYzDMZiiMpknhcCWhRaMs4b88v+rmw2eyLyGo/nqA8dOhRDhw51uv3ll19GcnIyJk+eDABYs2YNvvzyS6xbtw6zZ8/2uIFFRUUoKiqSnufm5np8DCIiIiIiAMpLuNlv91RpFvfy6xU47b0f/MoeAGHlHsI6V71S2d9NBpwIe8Dy9yr3Ms8TUfXy6hx1o9GI9PR0JCYmlp0gKAiJiYnYv39/hY65ZMkSREVFSY/mzZt7q7lEREREVNtI2d+dPDxdo9xo8MocdxUg6+WGMZ893ES1mFezvmdlZcFsNiMmJkZWHhMTg5MnT0rPExMTceTIEeTn56NZs2bYtGkTevfurXjMOXPmYMaMGdLz3NxcButERERE5B/se8hnnpYvAeds3faZpyEgoFreHgBw6Mk+0K28vvSYpXWe+gtQh0MbGoyMxUlVkv09+8GDqP9mD68ek4gqzyfLs+3c6f4XjEajgUajgV6vh16vh9nMtSWJiIiIyE+pdZaeeTfq2fbd60Kd/yxXqVQVH+ZeDlGRof5EVOW8OvQ9OjoawcHBuHjxoqz84sWLaNy4caWOnZKSgoyMDBw8eLBSxyEiIiIiqhTrPPeZpz3bz5RfNe0hohrHq4G6Wq1G9+7dsWvXLqmspKQEu3btcjq0nYiIiIgooEjz3D3sjV7ZtWraQ0Q1jsdjaPLy8nD6dNndw8zMTBw+fBj169dHixYtMGPGDEycOBHx8fHo2bMnVqxYgfz8fCkLfEVx6DsRERERERHVBh4H6ocOHcKAAQOk59ZEbxMnTkRaWhrGjBmDy5cvY/78+bhw4QK6deuGbdu2OSSY81RKSgpSUlKQm5uLqCguG0FEREREVUhpGTf7+dwVrWMtN5WTLd4+m7yxGEBpJnhjPmx/ygshUACNLGu9NjRYcS128g4hBApM8k5EvufkLR4H6v3794coZ6mI1NRUpKamVrhRREREREQ+ZR3eXh11nLHLFq8DoMU6y9/LW8hPA6B74ToU2KzFXqm11qlcBSYz4uZvl5XxPSdv8eoc9aqk1+sRFxeHHj24fAQRERERERHVXAETqDPrOxERERHVStOOVGwbEQWsgAnUiYiIiIhqLFdLt4U6HzrvyTroQggYjMUwGJmcmcjfcQIFEREREZGvVXDptgKTGe6G6tY51VoU4kRY+fWJyHcCpkedc9SJiIiIiIioNgiYHnUuz0ZERERENYrS0m3W1ZVsl/gqZ8Ulq32zBgDqcMQ/u9PtJoSFBrtdl4iqT8AE6kRERERENYq7S7cZnc9ft43hdepgQO1Z4M0lv4n8U8AMfSciIiIiIrlCExPDEdVEAROoc446ERERERER1QYBE6hzHXUiIiIiqu3S5yZa5qITUY3GOepERERERAFCp+bPd6LagJ90IiIiIqJAYTQApmLpaaGppGybyQAA0KLQ8tzNbPFeYzIARrvwIlTHjHVEFcBAnYiIiIgoUCxvB53N0xGvfYv0MMvfupXXAwBOlD43mM4Cmupb1th6fpmn/nIvsz0RyQTMHHUmkyMiIiIiIqLaIGB61FNSUpCSkoLc3FxERVXfnUEiIiIiIp8K1Vl6pksJIVBQuizbPghgua8a5sS0I8DKrr5uBVFAC5hAnYiIiIioVlKpZMPHVQB0mtInxnyfNMmlUA51J6qsgBn6TkRERERERFQbMFAnIiIiIiIi8iMc+k5EREREVAMZjMVAXg4AQBsaDJjMAJwv2WYwmgFj2dJv2pAgqIoL5JXcWG7NYCqWMtMLIeDp4my2c/CltoQGQ+Vvy7wJUbYUnk0ZkTcwUCciIiIiqoGSlm5HethU6bkOgBbrnNZPWLobBQiTnmfM7Qvd8hbySm4st5bw4m5pybgCk7lsPr2bCkxmxM3fLivLWJwEndrPQheTASfCHpAVVfeSeFRzBczQdy7PRkRERERERLVBwATqKSkpyMjIwMGDB33dFCIiIiIi/2Bduq30IWaekjZ9Nf0WnzTps9S+PjkvUU3iZ+NHiIiIiIjIbQpLt1lp1cHV3x4AYaEB0xdI5Lf4KSIiIiIiIiLyIwzUiYiIiIiIiPwIA3UiIiIiIiIiP8JAnYiIiIiIiMiPMJkcEREREVENpDIZKrW/EEplQpawrjwGoxkwFkvPtaHBKDCZZXW0ocFQqWyOKgS0KJSft0TAYHMc+/3LPaanhADs379QnSV5H1E1YKBORERERFQDaV+/qVL7F5jMCFco02ncP0bC0t0oQJj0/NDcRMQ/u1NWJ2NxEnRqm7DEZMCJsAdkdbIMmYhful/xHG4d01MmA/B8U3nZU3/JMuwTVaWAGfqu1+sRFxeHHj16+LopRERERERERFUmYHrUU1JSkJKSgtzcXERFRfm6OURERERE/idUZ+n5hWWYeoHJDJgM0K28HgCQPjfRUm+55T/a0LK11tPnJsKAMIfe6fJ0L1wNA8q62feFaivxAsp3qPQ1eNpOokASMIE6ERERERGVQ6WShmerAMswdWPZT3774eAqmxnnlm3B8JQBGtnwdng0i91zOrXnbSQKNAEz9J2IiIiIiIioNmCgTkRERERERORHGKgTERERERER+REG6kRERERERER+hIE6ERERERERkR9hoE5ERERERETkRxioExEREREREfkRnwTqX3zxBTp06ID27dvjzTff9EUTiIiIiIiIiPxSSHWfsLi4GDNmzMDu3bsRFRWF7t27Y9SoUWjQoEF1N4WIiIiIiIjI71R7j/qBAwfQsWNHxMbGIiIiAkOHDsVXX31V3c0gIiIiIiIi8kseB+p79+7F8OHD0bRpU6hUKmzZssWhjl6vR6tWrRAWFoZevXrhwIED0ra//voLsbGx0vPY2FicO3euYq0nIiIiIiIiqmE8DtTz8/PRtWtX6PV6xe0ffPABZsyYgQULFuDHH39E165dkZSUhEuXLlW6sUREREREVAlGg+XhdFs+tCiEFoUoMuQ5VCnIvwZDXg4K8q+5dTodiqTjaVEICCF7rkUhDEXFMBhtH2bPXpPCMSGEZVNJCQx5ObKHKCnx7Pil5G2sQDu9TAih0KZiiNLXToHN4znqQ4cOxdChQ51uf/nll5GcnIzJkycDANasWYMvv/wS69atw+zZs9G0aVNZD/q5c+fQs2dPp8crKipCUVGR9Dw3N9fTJhMREREREQAsb+dymw7AibDS5wo5nxus7ggA0Ll5uvSwqbLnVwzHcSLsAVnZDc+tQwHCpOdaFJa1wR0mg8MxDaazgCYKBYZr0C1vId828yx0EVEenMCi+7M7K9dOLyswmRE3f7tDecbiJOjU1Z6KjLzMq3PUjUYj0tPTkZiYWHaCoCAkJiZi//79AICePXvi2LFjOHfuHPLy8rB161YkJSU5PeaSJUsQFRUlPZo3b+7NJhMRERERERH5Fa/easnKyoLZbEZMTIysPCYmBidPnrScMCQEL730EgYMGICSkhLMmjXLZcb3OXPmYMaMGdLz3NxcButERERERO4K1QFP/eXWNiEECkzyId3WodQqlcphdyGAQ6E6qILKtomifI+alz43EVCHw2A0I/7ZnR7t6wv7Zg1AwtLdvm6GjD+2iSrHJ2MiRowYgREjRrhVV6PRQKPRQK/XQ6/Xw2z27VwQIiIiIqKAolIB6nDn2222qQDoNJU7ncHoWX2dOgQIoKHaWnWwr5vgwB/bRJXj1aHv0dHRCA4OxsWLF2XlFy9eROPGjSt17JSUFGRkZODgwYOVOg4RERERERGRP/NqoK5Wq9G9e3fs2rVLKispKcGuXbvQu3dvb56KiIiIiIiIqEbyeIxJXl4eTp8+LT3PzMzE4cOHUb9+fbRo0QIzZszAxIkTER8fj549e2LFihXIz8+XssBXFIe+ExERERERUW3gcaB+6NAhDBgwQHpuTfQ2ceJEpKWlYcyYMbh8+TLmz5+PCxcuoFu3bti2bZtDgjlPpaSkICUlBbm5uYiK8nw5BSIiIiIiIqJA4HGg3r9/fynzozOpqalITU2tcKOIiIiIiIiIaiuvzlGvSnq9HnFxcejRo4evm0JERERERERUZQImUGfWdyIiIiIiIqoNAiZQJyIiIiIiIqoNAiZQ59B3IiIiIiIiqg0CJlDn0HciIiIiIiKqDTzO+u5r1ozz165dgy4318etISIiIiIiW4a8XBQXKa8Sde3aNYTab8vNBdRmGIzFKCkywIxC5Krkda7l5qKkyFBa3RIDWJ9fy82F2u6YhtxcFJeoFNti3eaSMR+w289cZEAJSqS2KLXTrWN7ifX9Ahzfn2J1wIV5fs/67668FdC8RSWq60xe8vvvv6Nt27a+bgYRERERERHVMr/99hvatGlT5ecJuFst9evXBwCcPXsWUVFRPm4NVVZubi6aN2+O//3vf4iMjPR1c8gLeE1rFl7PmoXXs2bh9ax5eE1rFl7PmiUnJwctWrSQ4tGqFnCBelCQZVp9VFQU/8HXIJGRkbyeNQyvac3C61mz8HrWLLyeNQ+vac3C61mzWOPRKj9PtZyFiIiIiIiIiNzCQJ2IiIiIiIjIjwRcoK7RaLBgwQJoNBpfN4W8gNez5uE1rVl4PWsWXs+ahdez5uE1rVl4PWuW6r6eAZf1nYiIiIiIiKgmC7gedSIiIiIiIqKajIE6ERERERERkR9hoE5ERERERETkRxioExEREREREfmRgAvU9Xo9WrVqhbCwMPTq1QsHDhzwdZPIzsKFC6FSqWSP66+/XtpeWFiIlJQUNGjQABEREbjrrrtw8eJF2THOnj2LYcOGQafToVGjRnjiiSdQXFxc3S+l1tq7dy+GDx+Opk2bQqVSYcuWLbLtQgjMnz8fTZo0gVarRWJiIk6dOiWrk52djXHjxiEyMhJ169bFP//5T+Tl5cnq/Pzzz0hISEBYWBiaN2+OpUuXVvVLq5XKu56TJk1y+MwOGTJEVofX038sWbIEPXr0QJ06ddCoUSOMHDkSv/zyi6yOt75nv/nmG9x0003QaDRo164d0tLSqvrl1TruXM/+/fs7fEYffvhhWR1eT/+wevVqdOnSBZGRkYiMjETv3r2xdetWaTs/m4GlvOvJz2Zge+GFF6BSqfD4449LZX71GRUB5P333xdqtVqsW7dOHD9+XCQnJ4u6deuKixcv+rppZGPBggWiY8eO4vz589Lj8uXL0vaHH35YNG/eXOzatUscOnRI/OMf/xB9+vSRthcXF4tOnTqJxMRE8dNPP4n//Oc/Ijo6WsyZM8cXL6dW+s9//iOefvpp8cknnwgAYvPmzbLtL7zwgoiKihJbtmwRR44cESNGjBCtW7cWBQUFUp0hQ4aIrl27ih9++EHs27dPtGvXTowdO1banpOTI2JiYsS4cePEsWPHxMaNG4VWqxVr166trpdZa5R3PSdOnCiGDBki+8xmZ2fL6vB6+o+kpCTx9ttvi2PHjonDhw+L2267TbRo0ULk5eVJdbzxPfv7778LnU4nZsyYITIyMsSrr74qgoODxbZt26r19dZ07lzPfv36ieTkZNlnNCcnR9rO6+k/PvvsM/Hll1+KX3/9Vfzyyy/iqaeeEqGhoeLYsWNCCH42A01515OfzcB14MAB0apVK9GlSxcxbdo0qdyfPqMBFaj37NlTpKSkSM/NZrNo2rSpWLJkiQ9bRfYWLFggunbtqrjt6tWrIjQ0VGzatEkqO3HihAAg9u/fL4SwBBVBQUHiwoULUp3Vq1eLyMhIUVRUVKVtJ0f2gV1JSYlo3LixWLZsmVR29epVodFoxMaNG4UQQmRkZAgA4uDBg1KdrVu3CpVKJc6dOyeEEOL1118X9erVk13TJ598UnTo0KGKX1Ht5ixQv+OOO5zuw+vp3y5duiQAiD179gghvPc9O2vWLNGxY0fZucaMGSOSkpKq+iXVavbXUwhLMGD7Q9Ier6d/q1evnnjzzTf52awhrNdTCH42A9W1a9dE+/btxY4dO2TX0N8+owEz9N1oNCI9PR2JiYlSWVBQEBITE7F//34ftoyUnDp1Ck2bNkWbNm0wbtw4nD17FgCQnp4Ok8kku47XX389WrRoIV3H/fv3o3PnzoiJiZHqJCUlITc3F8ePH6/eF0IOMjMzceHCBdk1jIqKQq9evWTXsG7duoiPj5fqJCYmIigoCP/973+lOrfccgvUarVUJykpCb/88gv+/vvvano1ZPXNN9+gUaNG6NChA6ZOnYorV65I23g9/VtOTg4AoH79+gC89z27f/9+2TGsdfj/3Kplfz2t1q9fj+joaHTq1Alz5syBwWCQtvF6+iez2Yz3338f+fn56N27Nz+bAc7+elrxsxl4UlJSMGzYMIf33d8+oyEe1fahrKwsmM1m2ZsCADExMTh58qSPWkVKevXqhbS0NHTo0AHnz5/HokWLkJCQgGPHjuHChQtQq9WoW7eubJ+YmBhcuHABAHDhwgXF62zdRr5lvQZK18j2GjZq1Ei2PSQkBPXr15fVad26tcMxrNvq1atXJe0nR0OGDMGdd96J1q1b47fffsNTTz2FoUOHYv/+/QgODub19GMlJSV4/PHHcfPNN6NTp04A4LXvWWd1cnNzUVBQAK1WWxUvqVZTup4AcN9996Fly5Zo2rQpfv75Zzz55JP45Zdf8MknnwDg9fQ3R48eRe/evVFYWIiIiAhs3rwZcXFxOHz4MD+bAcjZ9QT42QxE77//Pn788UccPHjQYZu//f8zYAJ1ChxDhw6V/u7SpQt69eqFli1b4sMPP+SXDZEfuvfee6W/O3fujC5duqBt27b45ptvMGjQIB+2jMqTkpKCY8eO4dtvv/V1U8gLnF3Phx56SPq7c+fOaNKkCQYNGoTffvsNbdu2re5mUjk6dOiAw4cPIycnBx999BEmTpyIPXv2+LpZVEHOrmdcXBw/mwHmf//7H6ZNm4YdO3YgLCzM180pV8AMfY+OjkZwcLBD1r2LFy+icePGPmoVuaNu3bq47rrrcPr0aTRu3BhGoxFXr16V1bG9jo0bN1a8ztZt5FvWa+Dqs9i4cWNcunRJtr24uBjZ2dm8zgGgTZs2iI6OxunTpwHwevqr1NRUfPHFF9i9ezeaNWsmlXvre9ZZncjISN50rQLOrqeSXr16AYDsM8rr6T/UajXatWuH7t27Y8mSJejatStWrlzJz2aAcnY9lfCz6d/S09Nx6dIl3HTTTQgJCUFISAj27NmDVatWISQkBDExMX71GQ2YQF2tVqN79+7YtWuXVFZSUoJdu3bJ5omQ/8nLy8Nvv/2GJk2aoHv37ggNDZVdx19++QVnz56VrmPv3r1x9OhRWWCwY8cOREZGSkONyHdat26Nxo0by65hbm4u/vvf/8qu4dWrV5Geni7V+frrr1FSUiL9T6x3797Yu3cvTCaTVGfHjh3o0KEDh0n72J9//okrV66gSZMmAHg9/Y0QAqmpqdi8eTO+/vprhykH3vqe7d27t+wY1jr8f653lXc9lRw+fBgAZJ9RXk//VVJSgqKiIn42awjr9VTCz6Z/GzRoEI4ePYrDhw9Lj/j4eIwbN076268+o57nyfOd999/X2g0GpGWliYyMjLEQw89JOrWrSvLuke+93//93/im2++EZmZmeK7774TiYmJIjo6Wly6dEkIYVn2oEWLFuLrr78Whw4dEr179xa9e/eW9rcue3DrrbeKw4cPi23btomGDRtyebZqdO3aNfHTTz+Jn376SQAQL7/8svjpp5/EH3/8IYSwLM9Wt25d8emnn4qff/5Z3HHHHYrLs914443iv//9r/j2229F+/btZct5Xb16VcTExIjx48eLY8eOiffff1/odDou51UFXF3Pa9euiZkzZ4r9+/eLzMxMsXPnTnHTTTeJ9u3bi8LCQukYvJ7+Y+rUqSIqKkp88803siWBDAaDVMcb37PW5WWeeOIJceLECaHX67lkUBUo73qePn1aLF68WBw6dEhkZmaKTz/9VLRp00bccsst0jF4Pf3H7NmzxZ49e0RmZqb4+eefxezZs4VKpRJfffWVEIKfzUDj6nrys1kz2Gfu96fPaEAF6kII8eqrr4oWLVoItVotevbsKX744QdfN4nsjBkzRjRp0kSo1WoRGxsrxowZI06fPi1tLygoEI888oioV6+e0Ol0YtSoUeL8+fOyY5w5c0YMHTpUaLVaER0dLf7v//5PmEym6n4ptdbu3bsFAIfHxIkThRCWJdrmzZsnYmJihEajEYMGDRK//PKL7BhXrlwRY8eOFRERESIyMlJMnjxZXLt2TVbnyJEjom/fvkKj0YjY2FjxwgsvVNdLrFVcXU+DwSBuvfVW0bBhQxEaGipatmwpkpOTHW6A8nr6D6VrCUC8/fbbUh1vfc/u3r1bdOvWTajVatGmTRvZOcg7yrueZ8+eFbfccouoX7++0Gg0ol27duKJJ56QrdUsBK+nv3jggQdEy5YthVqtFg0bNhSDBg2SgnQh+NkMNK6uJz+bNYN9oO5Pn1GVEEJ41gdPRERERERERFUlYOaoExEREREREdUGDNSJiIiIiIiI/AgDdSIiIiIiIiI/wkCdiIiIiIiIyI8wUCciIiIiIiLyIwzUiYiIiIiIiPwIA3UiIiIiIiIiP8JAnYiIiIiIiMiPMFAnIiIiRWlpaVCpVNIjKytL2ta/f3906tTJJ+2qW7eu1KbU1FSftIGIiKgqMVAnIiIil1555RW8++67qFOnjteP/dhjj0GlUuH06dNO6zz99NNQqVT4+eefAQBvvPEG3n33Xa+3hYiIyF8wUCciIiKXRo4cifvvvx8ajcbrxx43bhwAYMOGDU7rbNy4EZ07d0aXLl0AAKNHj8b999/v9bYQERH5CwbqREREASQ/P9/XTfCqXr16oV27dti4caPi9v379yMzM1MK6ImIiGoDBupERETlOHfuHB544AHExMRAo9GgY8eOWLdunazON998A5VKhQ8//BDPPfccmjVrhrCwMAwaNEhxWPd///tfDBkyBFFRUdDpdOjXrx++++47WZ2FCxdCpVIhIyMD9913H+rVq4e+ffsCAEpKSrBw4UI0bdoUOp0OAwYMQEZGBlq1aoVJkyYBAH7//XeoVCq88sorDuf//vvvoVKpnAbIFfHVV19Bp9Nh7NixKC4uBgCcPHkSd999N+rXr4+wsDDEx8fjs88+k+03btw4nDx5Ej/++KPDMTds2ACVSoWxY8d6rZ1ERET+joE6ERGRCxcvXsQ//vEP7Ny5E6mpqVi5ciXatWuHf/7zn1ixYoVD/RdeeAGbN2/GzJkzMWfOHPzwww8OvcFff/01brnlFuTm5mLBggV4/vnncfXqVQwcOBAHDhxwOOY999wDg8GA559/HsnJyQCAOXPmYNGiRYiPj8eyZcvQvn17JCUlyXrc27Rpg5tvvhnr1693OOb69etRp04d3HHHHZV8hyy++OILjBgxAvfccw/ee+89hISE4Pjx4/jHP/6BEydOYPbs2XjppZcQHh6OkSNHYvPmzdK+zoa/m81mfPjhh0hISECLFi280k4iIqKAIIiIiMipf/7zn6JJkyYiKytLVn7vvfeKqKgoYTAYhBBC7N69WwAQN9xwgygqKpLqrVy5UgAQR48eFUIIUVJSItq3by+SkpJESUmJVM9gMIjWrVuLwYMHS2ULFiwQAMTYsWNl575w4YIICQkRI0eOlJUvXLhQABATJ06UytauXSsAiBMnTkhlRqNRREdHy+opefvttwUAkZmZ6bCtX79+omPHjkIIIT7++GMRGhoqkpOThdlsluoMGjRIdO7cWRQWFkplJSUlok+fPqJ9+/ay4/Xo0UM0a9ZMtv+2bdsEALF27VrF9gEQKSkpLl8DERFRIGKPOhERkRNCCHz88ccYPnw4hBDIysqSHklJScjJyXEYrj158mSo1WrpeUJCAgDLMHQAOHz4ME6dOoX77rsPV65ckY6Xn5+PQYMGYe/evSgpKZEd8+GHH5Y937VrF4qLi/HII4/Iyh999FGH1zB69GiEhYXJetW3b9+OrKwsryRk27hxI8aMGYMpU6Zg7dq1CAqy/LTIzs7G119/jdGjR+PatWvS67xy5QqSkpJw6tQpnDt3TjrO/fffjz///BN79+6VyjZs2AC1Wo177rmn0u0kIiIKJCG+bgAREZG/unz5Mq5evYo33ngDb7zxhmKdS5cuyZ7bD9GuV68eAODvv/8GAJw6dQoAMHHiRKfnzcnJkfYDgNatW8u2//HHHwCAdu3aycrr168v2w+wrDk+fPhwbNiwAc888wwAy7D32NhYDBw40Gkb3JGZmYn7778f99xzD1599VXZttOnT0MIgXnz5mHevHmK+1+6dAmxsbEAgHvvvRczZszAhg0b0L9/fxQWFmLz5s0YOnSow2siIiKq6RioExEROWHt2b7//vudBtbWJcOsgoODFesJIWTHXLZsGbp166ZYNyIiQvZcq9W63WYlEyZMwKZNm/D999+jc+fO+Oyzz/DII49Ivd8V1aRJEzRp0gT/+c9/cOjQIcTHx0vbrK9z5syZSEpKUtzf9kZDo0aNMHjwYHz88cfQ6/X4/PPPce3aNWZ7JyKiWomBOhERkRMNGzZEnTp1YDabkZiY6JVjtm3bFgAQGRlZ4WO2bNkSgKXX2ra3/cqVK1LPva0hQ4agYcOGWL9+PXr16gWDwYDx48dX6Ny2wsLC8MUXX2DgwIEYMmQI9uzZg44dOwKwJLIDgNDQULdf57hx47Bt2zZs3boVGzZsQGRkJIYPH17pdhIREQUazlEnIiJyIjg4GHfddRc+/vhjHDt2zGH75cuXPT5m9+7d0bZtWyxfvhx5eXkVOuagQYMQEhKC1atXy8pfe+01xfohISEYO3YsPvzwQ6SlpaFz584OIwEqKioqCtu3b5d6xH/77TcAlh7y/v37Y+3atTh//rzDfkqvc+TIkdDpdHj99dexdetW3HnnnQgLC/NKO4mIiAIJe9SJiIhceOGFF7B792706tULycnJiIuLQ3Z2Nn788Ufs3LkT2dnZHh0vKCgIb775JoYOHYqOHTti8uTJiI2Nxblz57B7925ERkbi888/d3mMmJgYTJs2DS+99BJGjBiBIUOG4MiRI9i6dSuio6OhUqkc9pkwYQJWrVqF3bt348UXX/SozeWJjo7Gjh070LdvXyQmJuLbb79FbGws9Ho9+vbti86dOyM5ORlt2rTBxYsXsX//fvz55584cuSI7DgREREYOXKktEwbh70TEVFtxR51IiIiF2JiYnDgwAFMnjwZn3zyibSWenZ2doUD3v79+2P//v2Ij4/Ha6+9hkcffRRpaWlo3Lgxpk+f7tYxXnzxRcybNw8HDx7EzJkzcfr0aXz11VcQQij2Qnfv3h0dO3ZEUFBQlQTAsbGx2LlzJ4qKijB48GBkZWUhLi4Ohw4dwrBhw5CWloaUlBSsWbMGQUFBmD9/vuJxrG1r0qRJpZPdERERBSqVsGa3ISIiooB29epV1KtXD88++yyefvpph+033ngj6tevj127drl1vLS0NEyePBk//vgjmjdvjgYNGij21le37OxslJSUoGHDhkhJSXE65J+IiChQsUediIgoABUUFDiUrVixAoClx97eoUOHcPjwYUyYMMHjc910001o2LAhrly54vG+VaFNmzZo2LChr5tBRERUZdijTkREFIDS0tKQlpaG2267DREREfj222+xceNG3Hrrrdi+fbtU79ixY0hPT8dLL72ErKws/P77724naDt//jyOHz8uPe/Xrx9CQ0O9/lo8tWfPHphMJgBA8+bN0aFDBx+3iIiIyLuYTI6IiCgAdenSBSEhIVi6dClyc3OlBHPPPvusrN5HH32ExYsXo0OHDti4caNHWdSt66T7m379+vm6CURERFWKPepEREREREREfoRz1ImIiIiIiIj8CAN1IiIiIiIiIj/CQJ2IiIiIiIjIjzBQJyIiIiIiIvIjDNSJiIiIiIiI/AgDdSIiIiIiIiI/wkCdiIiIiIiIyI8wUCciIiIiIiLyI/8P1cSqIx/0Q2wAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.set_title(\"BEGe energy spectrum\")\n", - "plot_energy(ax, data_det001.energy_sum, yerr=False, label=\"True energy\", xrange=(0, 4000))\n", - "plot_energy(\n", - " ax,\n", - " data_det001.energy_sum_deadlayer,\n", - " yerr=False,\n", - " label=\"Energy after dead layer\",\n", - " xrange=(0, 4000),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.set_title(\"COAX energy spectrum\")\n", - "plot_energy(ax, data_det002.energy_sum, yerr=False, label=\"True energy\", xrange=(0, 4000))\n", - "plot_energy(\n", - " ax,\n", - " data_det002.energy_sum_deadlayer,\n", - " yerr=False,\n", - " label=\"Energy after dead layer\",\n", - " xrange=(0, 4000),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.4) Smearing\n", - "The final step in the processing chain smeared the energies by the energy resolution. This represents a general class of processors based on ''heuristic'' models.\n", - "Other similar processors could be implemented in a similar way. It would also be simple to use insted an energy dependent resolution curve.\n", - "To see the effect we have to zoom into the 2615 keV peak." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots()\n", - "plot_energy(\n", - " axs,\n", - " data_det001.energy_sum_smeared,\n", - " yerr=False,\n", - " label=\"BEGe\",\n", - " xrange=(2600, 2630),\n", - " log_y=False,\n", - " bins=150,\n", - " density=True,\n", - ")\n", - "plot_energy(\n", - " axs,\n", - " data_det002.energy_sum_smeared,\n", - " yerr=False,\n", - " label=\"COAX\",\n", - " xrange=(2600, 2630),\n", - " log_y=False,\n", - " bins=150,\n", - " density=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see clearly the worse energy resolution for the COAX detector.\n", - "> **To Do**: add a gaussian fit of this." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part 5) Adding a new processor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next part of the tutorial describes how to add a new processor to the chain. We use as an example spatial *clustering* of steps.\n", - "This will be added later. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sims", - "language": "python", - "name": "sims" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/source/notebooks/reboost_hpge_tutorial_hit.rst b/docs/source/notebooks/reboost_hpge_tutorial_hit.rst new file mode 100644 index 0000000..36d71fa --- /dev/null +++ b/docs/source/notebooks/reboost_hpge_tutorial_hit.rst @@ -0,0 +1,712 @@ +Hit tier hpge simulation processing +=================================== + +This tutorial describes how to process the HPGe detector simulations +from **remage** with **reboost**. It buils on the official **remage** +tutorial +`[link] `__ + + .. rubric:: *Note* + :name: note + + To run this tutorial it is recommended to create the following + directory structure to organise the outputs and config inputs. + +.. code:: text + + ├── cfg + │   └── metadata + ├── output + │   ├── stp + │   └── hit + └── reboost_hpge_tutorial.ipynb + +Part 1) Running the remage simulation +------------------------------------- + +Before we can run any post-processing we need to run the Geant4 +simulation. For this we follow the remage tutorial to generate the GDML +geometry. We save this into the GDML file *cfg/geom.gdml* for use by +remage. We also need to save the metadata dictionaries into json files +(in the *cfg/metadata* folder as *BEGe.json* and *Coax.json* + +We use a slightly modified Geant4 macro to demonstrate some features of +reboost (this should be saved as *cfg/th228.mac* to run remage on the +command line). + +.. code:: text + + /RMG/Manager/Logging/LogLevel detail + + /RMG/Geometry/RegisterDetector Germanium BEGe 001 + /RMG/Geometry/RegisterDetector Germanium Coax 002 + /RMG/Geometry/RegisterDetector Scintillator LAr 003 + + /run/initialize + + /RMG/Generator/Confine Volume + /RMG/Generator/Confinement/Physical/AddVolume Source + + /RMG/Generator/Select GPS + /gps/particle ion + /gps/energy 0 eV + /gps/ion 88 224 # 224-Ra + /process/had/rdm/nucleusLimits 208 224 81 88 #Ra-224 to 208-Pb + + + /run/beamOn 1000000 + +We then use the remage executable (see +`[remage-docs] `__ for +installation instructions) to run the simulation: > #### *Note* > Both +of *cfg/th228.mac* and *cfg/geometry.gdml* are needed to run remage + +.. code:: console + + $ remage --threads 8 --gdml-files cfg/geom.gdml --output-file output/stp/output.lh5 -- cfg/th228.mac + +You can lower the number of simulated events to speed up the simulation. + +We can use ``lh5.show()`` to check the output files. + +.. code:: ipython3 + + from lgdo import lh5 + +.. code:: ipython3 + + lh5.show("output/stp/output_t0.lh5") + + +.. parsed-literal:: + + / + └── stp · struct{det001,det002,det003,vertices} + ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── v_post · array<1>{real} + │ ├── v_pre · array<1>{real} + │ ├── xloc_post · array<1>{real} + │ ├── xloc_pre · array<1>{real} + │ ├── yloc_post · array<1>{real} + │ ├── yloc_pre · array<1>{real} + │ ├── zloc_post · array<1>{real} + │ └── zloc_pre · array<1>{real} + └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} + ├── evtid · array<1>{real} + ├── n_part · array<1>{real} + ├── time · array<1>{real} + ├── xloc · array<1>{real} + ├── yloc · array<1>{real} + └── zloc · array<1>{real} + + +Part 2) reboost config files +---------------------------- + +For this tutorial we perform a basic post-processing of the *hit* tier +for the two Germanium channels. + +2.1) Setup the environment +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First we set up the python environment. + +.. code:: ipython3 + + from reboost.hpge import hit + import matplotlib.pyplot as plt + import pyg4ometry as pg4 + import legendhpges + from legendhpges import draw + import awkward as ak + import logging + import colorlog + import hist + import numpy as np + + + plt.rcParams['figure.figsize'] = [12, 4] + plt.rcParams['axes.titlesize'] =12 + plt.rcParams['axes.labelsize'] = 12 + plt.rcParams['legend.fontsize'] = 12 + + + handler = colorlog.StreamHandler() + handler.setFormatter( + colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") + ) + logger = logging.getLogger() + logger.handlers.clear() + logger.addHandler(handler) + logger.setLevel(logging.INFO) + logger.info("test") + + + + +.. parsed-literal:: + + root [INFO] test + + +2.2) Processing chain and parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Next we need to make the processing chain config file. + +The processing chain below gives a standard set of steps for a HPGe +simulation. 1. first the steps are windowed into hits, 2. the first +timestamp and index of each hit is computed (for use in event building), +3. the distance to the detector n+ surface is computed and from this the +activeness is calculated (based on the FCCD) 4. the energy in each step +is summed to extract the deposited energy (both with and without +deadlayer correction), 5. the energy is convolved with the detector +response model (gaussian energy resolution). + +We also include some step based quantities in the output to show the +effect of the processors. + +.. code:: ipython3 + + chain = { + "channels": [ + "det001", + "det002" + ], + "outputs": [ + "t0", # first timestamp + "time", # time of each step + "edep", # energy deposited in each step + "evtid", # id of the hit + "global_evtid", # global id of the hit + "distance_to_nplus_surface_mm", # distance to detector nplus surface + "activeness", # activeness for the step + "rpos_loc", # radius of step + "zpos_loc", # z position + "energy_sum", # true summed energy before dead layer or smearing + "energy_sum_deadlayer", # energy sum after dead layers + "energy_sum_smeared" # energy sum after smearing with resolution + ], + "step_group": { + "description": "group steps by time and evtid with 10us window", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + }, + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", + }, + "evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)", + }, + "global_evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)", + }, + "distance_to_nplus_surface_mm": { + "description": "distance to the nplus surface in mm", + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')", + }, + "activeness": { + "description": "activness based on FCCD (no TL)", + "mode": "eval", + "expression": "ak.where(hit.distance_to_nplus_surface_mm{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── edep · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── energy_sum · array<1>{real} + │ ├── energy_sum_deadlayer · array<1>{real} + │ ├── energy_sum_smeared · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── global_evtid · array<1>{real} + │ ├── rpos_loc · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── t0 · array<1>{real} + │ ├── time · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ └── zpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── det002 · HDF5 group + └── hit · table{edep,time,t0,evtid,global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + ├── activeness · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── edep · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── energy_sum · array<1>{real} + ├── energy_sum_deadlayer · array<1>{real} + ├── energy_sum_smeared · array<1>{real} + ├── evtid · array<1>{real} + ├── global_evtid · array<1>{real} + ├── rpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── t0 · array<1>{real} + ├── time · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── zpos_loc · array<1>{array<1>{real}} + ├── cumulative_length · array<1>{real} + └── flattened_data · array<1>{real} + + +The new format is a factor of x17 times smaller than the input file due +to the removal of many *step* based fields which use a lot of memory and +due to the removal of the *vertices* table and the LAr hits. So we can +easily read the whole file into memory. We use *awkward* to analyse the +output files. + +.. code:: ipython3 + + data_det001 = lh5.read_as("det001/hit","output/hit/output.lh5","ak") + data_det002 = lh5.read_as("det002/hit","output/hit/output.lh5","ak") + +.. code:: ipython3 + + data_det001 + + + + +.. raw:: html + + 
[{edep: [0.0826, 0.00863, ..., 32.3], time: [1.32e+15, ...], t0: 1.32e+15, ...},
+     {edep: [0.103, 0.0256, ..., 37.6, 6.44], time: [...], t0: 1.24e+15, ...},
+     {edep: [0.0824, 0.00863, ..., 16.8], time: [2.21e+14, ...], t0: 2.21e+14, ...},
+     {edep: [0.101, 0.0802, ..., 20.9], time: [9.09e+14, ...], t0: 9.09e+14, ...},
+     {edep: [0.00332, 0.0171, ..., 45, 27.7], time: [...], t0: 4.26e+13, ...},
+     {edep: [0.0845, 0.00863, ..., 27.9], time: [6.86e+14, ...], t0: 6.86e+14, ...},
+     {edep: [0.0065, 0.255, ..., 41.5, 2.69], time: [...], t0: 1.24e+14, ...},
+     {edep: [0.0388, 0.188, ..., 1.1, 41.5], time: [...], t0: 6.48e+14, ...},
+     {edep: [0.00332, 0.116, ..., 16.2], time: [4.39e+14, ...], t0: 4.39e+14, ...},
+     {edep: [0.00615, 0.0204, ..., 22.1], time: [7.11e+14, ...], t0: 7.11e+14, ...},
+     ...,
+     {edep: [0.19, 0.0171, ..., 42.2, 10.9], time: [...], t0: 1.1e+15, ...},
+     {edep: [0.0118, 0.0303, ..., 34.5, 21.4], time: [...], t0: 1.51e+15, ...},
+     {edep: [0.0204, 0.152, ..., 2.79, 51.9], time: [...], t0: 9.73e+14, ...},
+     {edep: [0.118, 0.0254, ..., 41.2, 38.6], time: [...], t0: 9.67e+14, ...},
+     {edep: [0.0824, 0.0254, ..., 34.6, 18.9], time: [...], t0: 6.64e+14, ...},
+     {edep: [0.148, 0.0802, ..., 40.9, 24], time: [...], t0: 5.56e+14, ...},
+     {edep: [0.022, 0.0148, ..., 34.8, 11.9], time: [...], t0: 6.52e+14, ...},
+     {edep: [0.0155, 0.118, ..., 0.458, 9.65], time: [...], t0: 3.97e+14, ...},
+     {edep: [0.0065, 0.00615, ..., 13.7], time: [3.98e+14, ...], t0: 3.98e+14, ...}]
+    --------------------------------------------------------------------------------
+    type: 835793 * {
+        edep: var * float64,
+        time: var * float64,
+        t0: float64,
+        evtid: float64,
+        global_evtid: float64,
+        distance_to_nplus_surface_mm: var * float64,
+        activeness: var * int64,
+        rpos_loc: var * float64,
+        zpos_loc: var * float64,
+        energy_sum: float64,
+        energy_sum_deadlayer: float64,
+        energy_sum_smeared: float64
+    }
+ + + +Part 4) Steps in a standard processing chain +-------------------------------------------- + +The next part of the tutorial gives more details on each step of the +processing chain. + +4.1) Windowing +~~~~~~~~~~~~~~ + +We can compare the decay index (“evtid” in the “stp” file) to the index +of the “hit”, the row of the hit table. We see that only some decays +correspond to “hits” in the detector, as we expect. We also see that a +single decay does not often produce multiple hits. This is also expected +since the probability of detection is fairly low. + +.. code:: ipython3 + + plt.scatter(np.sort(data_det001.global_evtid),np.arange(len(data_det001)),marker=".",alpha=1) + plt.xlabel("Decay index (evtid)") + plt.ylabel("Hit Index") + plt.grid() + plt.xlim(0,1000) + plt.ylim(0,100) + + + + +.. parsed-literal:: + + (0.0, 100.0) + + + + +.. image:: images/output_20_1.png + + +However, we can use some array manipulation to extract decay index with +multiple hits, by plotting the times we see the effect of the windowing. + +.. code:: ipython3 + + def plot_times(times:ak.Array,xrange=None,sub_zero=False,**kwargs): + fig,ax = plt.subplots() + for idx,_time in enumerate(times): + if (sub_zero): + _time=_time-ak.min(_time) + h=hist.new.Reg(100,(ak.min(times)/1e9),(ak.max(times)/1e9)+1, name="Time since event start [s]").Double() + h.fill(_time/1e9) + h.plot(**kwargs,label=f"Hit {idx}") + ax.legend() + ax.set_yscale("log") + if xrange is not None: + ax.set_xlim(*xrange) + + +.. code:: ipython3 + + unique,counts = np.unique(data_det001.global_evtid,return_counts=True) + +.. code:: ipython3 + + plot_times(data_det001[data_det001.global_evtid==unique[counts>1][1]].time,histtype="step",yerr=False) + + + + +.. image:: images/output_24_0.png + + +4.2) Distance to surface and dead layer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +One of the important step in the post-processing of HPGe detector +simulations is the detector activeness mapping. Energy deposited close +to the surface of the Germanium detector will result in incomplete +charge collection and a degraded signal. To account for this we added a +processor to compute the distance to the detector surface (based on +``legendhpges.base.HPGe.distance_to_surface()``) + +For the steps in the detector we extracted in the processing chain the +local r and z coordinates and we can plot maps of the distance to the +detector surface and the activeness for each step. We select only events +within 5 mm of the surface for the first plots. We can see that the +processor works as expected. + +.. code:: ipython3 + + def plot_map(field,scale="BuPu",clab="Distance [mm]"): + fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) + n=100000 + for idx, (data,config) in enumerate(zip([data_det001,data_det002],["cfg/metadata/BEGe.json","cfg/metadata/Coax.json"])): + + reg=pg4.geant4.Registry() + hpge = legendhpges.make_hpge(config,registry=reg) + + legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx]) + rng = np.random.default_rng() + r = rng.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc) + z = ak.flatten(data.zpos_loc) + c=ak.flatten(data[field]) + cut = c<5 + + s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=".", label="gen. points",cmap=scale) + #axs[idx].axis("equal") + + if idx == 0: + axs[idx].set_ylabel("Height [mm]") + c=plt.colorbar(s) + c.set_label(clab) + + axs[idx].set_xlabel("Radius [mm]") + + +.. code:: ipython3 + + plot_map("distance_to_nplus_surface_mm") + + +.. parsed-literal:: + + root [INFO] genericpolycone.antlr> + root [INFO] genericpolyhedra.antlr> + root [INFO] visualisation.Mesh.getBoundingBox> [-36.98, -36.98, 0.0] [36.98, 36.98, 29.46] + root [INFO] box.pycsgmesh> getBoundingBoxMesh + root [INFO] genericpolycone.antlr> + root [INFO] genericpolyhedra.antlr> + root [INFO] visualisation.Mesh.getBoundingBox> [-38.25, -38.25, 0.0] [38.25, 38.25, 84.0] + root [INFO] box.pycsgmesh> getBoundingBoxMesh + + + +.. image:: images/output_27_1.png + + +.. code:: ipython3 + + plot_map("activeness",clab="Activeness",scale="viridis") + + +.. parsed-literal:: + + root [INFO] genericpolycone.antlr> + root [INFO] genericpolyhedra.antlr> + root [INFO] visualisation.Mesh.getBoundingBox> [-36.98, -36.98, 0.0] [36.98, 36.98, 29.46] + root [INFO] box.pycsgmesh> getBoundingBoxMesh + root [INFO] genericpolycone.antlr> + root [INFO] genericpolyhedra.antlr> + root [INFO] visualisation.Mesh.getBoundingBox> [-38.25, -38.25, 0.0] [38.25, 38.25, 84.0] + root [INFO] box.pycsgmesh> getBoundingBoxMesh + + + +.. image:: images/output_28_1.png + + +We can also plot a histogram of the distance to the surface. + +.. code:: ipython3 + + def plot_distances(axes,distances,xrange=None,label=" ",**kwargs): + + h=hist.new.Reg(100,*xrange, name="Distance to n+ surface [mm]").Double() + h.fill(distances) + h.plot(**kwargs,label=label) + axes.legend() + axes.set_yscale("log") + if xrange is not None: + ax.set_xlim(*xrange) + + +.. code:: ipython3 + + fig,ax = plt.subplots() + plot_distances(ax,ak.flatten(data_det001.distance_to_nplus_surface_mm),xrange=(0,35),label="BEGe",histtype="step",yerr=False) + plot_distances(ax,ak.flatten(data_det002.distance_to_nplus_surface_mm),xrange=(0,35),label="Coax",histtype="step",yerr=False) + + + + +.. image:: images/output_31_0.png + + +4.3) Summed energies +~~~~~~~~~~~~~~~~~~~~ + +Our processing chain also sums the energies of the hits, both before and +after weighting by the activeness. + +.. code:: ipython3 + + def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): + + h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double() + h.fill(energy) + h.plot(**kwargs,label=label) + axes.legend() + if (log_y): + axes.set_yscale("log") + if xrange is not None: + axes.set_xlim(*xrange) + +.. code:: ipython3 + + fig, ax = plt.subplots() + ax.set_title("BEGe energy spectrum") + plot_energy(ax,data_det001.energy_sum,yerr=False,label="True energy",xrange=(0,4000)) + plot_energy(ax,data_det001.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000)) + + + +.. image:: images/output_34_0.png + + +.. code:: ipython3 + + fig, ax = plt.subplots() + ax.set_title("COAX energy spectrum") + plot_energy(ax,data_det002.energy_sum,yerr=False,label="True energy",xrange=(0,4000)) + plot_energy(ax,data_det002.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000)) + + + +.. image:: images/output_35_0.png + + +4.4) Smearing +~~~~~~~~~~~~~ + +The final step in the processing chain smeared the energies by the +energy resolution. This represents a general class of processors based +on ‘’heuristic’’ models. Other similar processors could be implemented +in a similar way. It would also be simple to use insted an energy +dependent resolution curve. To see the effect we have to zoom into the +2615 keV peak. + +.. code:: ipython3 + + fig, axs = plt.subplots() + plot_energy(axs,data_det001.energy_sum_smeared,yerr=False,label="BEGe",xrange=(2600,2630),log_y=False,bins=150,density=True) + plot_energy(axs,data_det002.energy_sum_smeared,yerr=False,label="COAX",xrange=(2600,2630),log_y=False,bins=150,density=True) + + + +.. image:: images/output_37_0.png + + +We see clearly the worse energy resolution for the COAX detector. > **To +Do**: add a gaussian fit of this. + +Part 5) Adding a new processor +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The next part of the tutorial describes how to add a new processor to +the chain. We use as an example spatial *clustering* of steps. This will +be added later. diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 0ed19e2..a535497 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -6,5 +6,5 @@ Basic Tutorial :maxdepth: 2 :caption: Contents: - notebooks/reboost_hpge_tutorial.ipynb + notebooks/reboost_hpge_tutorial_hit notebooks/reboost_hpge_evt_tutorial From ecd90e724f85e330df6ae87d5dcc3f5f6255e6a7 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Thu, 21 Nov 2024 23:59:16 +0100 Subject: [PATCH 74/81] [evt] adding build_tcm functionality --- .../notebooks/reboost_hpge_tutorial_hit.rst | 220 +++++++++--------- src/reboost/hpge/evt.py | 152 ++++++++++++ src/reboost/hpge/tcm.py | 56 ++--- src/reboost/hpge/utils.py | 73 ------ 4 files changed, 277 insertions(+), 224 deletions(-) create mode 100644 src/reboost/hpge/evt.py diff --git a/docs/source/notebooks/reboost_hpge_tutorial_hit.rst b/docs/source/notebooks/reboost_hpge_tutorial_hit.rst index 36d71fa..91d4836 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial_hit.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial_hit.rst @@ -81,43 +81,43 @@ We can use ``lh5.show()`` to check the output files. .. parsed-literal:: / - └── stp · struct{det001,det002,det003,vertices} - ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── xloc · array<1>{real} - │ ├── yloc · array<1>{real} - │ └── zloc · array<1>{real} - ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} - │ ├── edep · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── particle · array<1>{real} - │ ├── time · array<1>{real} - │ ├── v_post · array<1>{real} - │ ├── v_pre · array<1>{real} - │ ├── xloc_post · array<1>{real} - │ ├── xloc_pre · array<1>{real} - │ ├── yloc_post · array<1>{real} - │ ├── yloc_pre · array<1>{real} - │ ├── zloc_post · array<1>{real} - │ └── zloc_pre · array<1>{real} - └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} - ├── evtid · array<1>{real} - ├── n_part · array<1>{real} - ├── time · array<1>{real} - ├── xloc · array<1>{real} - ├── yloc · array<1>{real} - └── zloc · array<1>{real} + └── stp · struct{det001,det002,det003,vertices} + ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── v_post · array<1>{real} + │ ├── v_pre · array<1>{real} + │ ├── xloc_post · array<1>{real} + │ ├── xloc_pre · array<1>{real} + │ ├── yloc_post · array<1>{real} + │ ├── yloc_pre · array<1>{real} + │ ├── zloc_post · array<1>{real} + │ └── zloc_pre · array<1>{real} + └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} + ├── evtid · array<1>{real} + ├── n_part · array<1>{real} + ├── time · array<1>{real} + ├── xloc · array<1>{real} + ├── yloc · array<1>{real} + └── zloc · array<1>{real} Part 2) reboost config files @@ -143,14 +143,14 @@ First we set up the python environment. import colorlog import hist import numpy as np - - + + plt.rcParams['figure.figsize'] = [12, 4] plt.rcParams['axes.titlesize'] =12 plt.rcParams['axes.labelsize'] = 12 plt.rcParams['legend.fontsize'] = 12 - - + + handler = colorlog.StreamHandler() handler.setFormatter( colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") @@ -160,7 +160,7 @@ First we set up the python environment. logger.addHandler(handler) logger.setLevel(logging.INFO) logger.info("test") - + @@ -207,7 +207,7 @@ effect of the processors. "energy_sum_deadlayer", # energy sum after dead layers "energy_sum_smeared" # energy sum after smearing with resolution ], - "step_group": { + "step_group": { "description": "group steps by time and evtid with 10us window", "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", }, @@ -266,7 +266,7 @@ effect of the processors. "mode": "function", "expression": "reboost.hpge.processors.smear_energies(hit.energy_sum_deadlayer,reso=pars.fwhm_in_keV/2.355)" } - + } } @@ -278,16 +278,16 @@ We also create our parameters file. "det001": { "meta_name":"BEGe.json", "phy_vol_name":"BEGe", - "fwhm_in_keV":2.69, - "fccd_in_mm":1.420, # dead layer in mm + "fwhm_in_keV":2.69, + "fccd_in_mm":1.420, # dead layer in mm }, "det002": { "meta_name":"Coax.json", "phy_vol_name":"Coax", - "fwhm_in_keV":4.420, - "fccd_in_mm":2.69, + "fwhm_in_keV":4.420, + "fccd_in_mm":2.69, } - + } Part 3) Running the processing @@ -342,58 +342,58 @@ file structure showing the new *hit* oriented data format. .. parsed-literal:: / - ├── det001 · HDF5 group - │ └── hit · table{edep,time,t0,evtid,global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} - │ ├── activeness · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── edep · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── energy_sum · array<1>{real} - │ ├── energy_sum_deadlayer · array<1>{real} - │ ├── energy_sum_smeared · array<1>{real} - │ ├── evtid · array<1>{real} - │ ├── global_evtid · array<1>{real} - │ ├── rpos_loc · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ ├── t0 · array<1>{real} - │ ├── time · array<1>{array<1>{real}} - │ │ ├── cumulative_length · array<1>{real} - │ │ └── flattened_data · array<1>{real} - │ └── zpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── det002 · HDF5 group - └── hit · table{edep,time,t0,evtid,global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} - ├── activeness · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── edep · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── energy_sum · array<1>{real} - ├── energy_sum_deadlayer · array<1>{real} - ├── energy_sum_smeared · array<1>{real} - ├── evtid · array<1>{real} - ├── global_evtid · array<1>{real} - ├── rpos_loc · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - ├── t0 · array<1>{real} - ├── time · array<1>{array<1>{real}} - │ ├── cumulative_length · array<1>{real} - │ └── flattened_data · array<1>{real} - └── zpos_loc · array<1>{array<1>{real}} - ├── cumulative_length · array<1>{real} - └── flattened_data · array<1>{real} + ├── det001 · HDF5 group + │ └── hit · table{edep,time,t0,evtid,global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + │ ├── activeness · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── edep · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── energy_sum · array<1>{real} + │ ├── energy_sum_deadlayer · array<1>{real} + │ ├── energy_sum_smeared · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── global_evtid · array<1>{real} + │ ├── rpos_loc · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── t0 · array<1>{real} + │ ├── time · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ └── zpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── det002 · HDF5 group + └── hit · table{edep,time,t0,evtid,global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + ├── activeness · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── edep · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── energy_sum · array<1>{real} + ├── energy_sum_deadlayer · array<1>{real} + ├── energy_sum_smeared · array<1>{real} + ├── evtid · array<1>{real} + ├── global_evtid · array<1>{real} + ├── rpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── t0 · array<1>{real} + ├── time · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── zpos_loc · array<1>{array<1>{real}} + ├── cumulative_length · array<1>{real} + └── flattened_data · array<1>{real} The new format is a factor of x17 times smaller than the input file due @@ -546,25 +546,25 @@ processor works as expected. fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True) n=100000 for idx, (data,config) in enumerate(zip([data_det001,data_det002],["cfg/metadata/BEGe.json","cfg/metadata/Coax.json"])): - + reg=pg4.geant4.Registry() hpge = legendhpges.make_hpge(config,registry=reg) - + legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx]) rng = np.random.default_rng() r = rng.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc) z = ak.flatten(data.zpos_loc) c=ak.flatten(data[field]) cut = c<5 - + s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=".", label="gen. points",cmap=scale) #axs[idx].axis("equal") - + if idx == 0: axs[idx].set_ylabel("Height [mm]") c=plt.colorbar(s) c.set_label(clab) - + axs[idx].set_xlabel("Radius [mm]") @@ -615,7 +615,7 @@ We can also plot a histogram of the distance to the surface. .. code:: ipython3 def plot_distances(axes,distances,xrange=None,label=" ",**kwargs): - + h=hist.new.Reg(100,*xrange, name="Distance to n+ surface [mm]").Double() h.fill(distances) h.plot(**kwargs,label=label) @@ -623,7 +623,7 @@ We can also plot a histogram of the distance to the surface. axes.set_yscale("log") if xrange is not None: ax.set_xlim(*xrange) - + .. code:: ipython3 @@ -646,7 +646,7 @@ after weighting by the activeness. .. code:: ipython3 def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): - + h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double() h.fill(energy) h.plot(**kwargs,label=label) @@ -686,7 +686,7 @@ after weighting by the activeness. The final step in the processing chain smeared the energies by the energy resolution. This represents a general class of processors based on ‘’heuristic’’ models. Other similar processors could be implemented -in a similar way. It would also be simple to use insted an energy +in a similar way. It would also be simple to use instead an energy dependent resolution curve. To see the effect we have to zoom into the 2615 keV peak. diff --git a/src/reboost/hpge/evt.py b/src/reboost/hpge/evt.py new file mode 100644 index 0000000..f0ef72e --- /dev/null +++ b/src/reboost/hpge/evt.py @@ -0,0 +1,152 @@ +from __future__ import annotations + +import logging + +import awkward as ak +import numpy as np +from lgdo import Table +from lgdo.lh5 import LH5Iterator, write +from pygama.evt.build_evt import evaluate_expression +from pygama.evt.utils import TCMData + +log = logging.getLogger(__name__) + + +def build_evt( + hit_file: str, tcm_file: str, evt_file: str | None, config: dict, buffer: int = int(5e6) +) -> ak.Array | None: + """Generates the event tier from the hit and tcm. + + + Parameters + ---------- + hit_file + path to the hit tier file + tcm_file + path to the tcm tier file + evt_file + path to the evt tier (output) file, if `None` the :class:`Table` is returned in memory + config + dictionary of the configuration. For example: + + .. code-block:: json + + { + "channels": [ "det001", "det002"] + }, + "outputs": [ + "energy", + "multiplicity" + ], + "operations": { + "energy_id": { + "channels": "geds_on", + "aggregation_mode": "gather", + "query": "hit.energy > 25", + "expression": "tcm.channel_id" + }, + "energy": { + "aggregation_mode": "keep_at_ch:evt.energy_id", + "expression": "hit.energy > 25" + }, + "multiplicity": { + "channels": [ + "geds_on", + "geds_ac" + ], + "aggregation_mode": "sum", + "expression": "hit.energy > 25", + "initial": 0 + } + } + } + + Must contain: + - "channels": list of channel groupings + - "outputs": fields for the output file + - "operations": operations to perform see :func:`pygama.evt.build_evt.evaluate_expression` for more details. + + buffer + number of events to process simultaneously + + Returns + ------- + ak.Array of the evt tier data (if the data is not saved to disk) + """ + + msg = "... beginning the evt tier processing" + log.info(msg) + + # create the objects needed for evaluate expression + + file_info = { + "hit": (hit_file, "hit", "det{:03}"), + "tcm": (tcm_file, "tcm", "tcm"), + "evt": (evt_file, "evt"), + } + + # iterate through the TCM + + out_ak = ak.Array([]) + mode = "of" + + for tcm_lh5, _, n_rows_read in LH5Iterator(tcm_file, "tcm", buffer_len=buffer): + tcm_lh5_sel = tcm_lh5 + tcm_ak = tcm_lh5_sel.view_as("ak")[:n_rows_read] + + tcm = TCMData( + id=np.array(ak.flatten(tcm_ak.array_id)), + idx=np.array(ak.flatten(tcm_ak.array_idx)), + cumulative_length=np.array(np.cumsum(ak.num(tcm_ak.array_id, axis=-1))), + ) + + n_rows = len(tcm.cumulative_length) + out_tab = Table(size=n_rows) + + for name, info in config["operations"].items(): + msg = f"computing field {name}" + log.debug(msg) + + defaultv = info.get("initial", np.nan) + if isinstance(defaultv, str) and (defaultv in ["np.nan", "np.inf", "-np.inf"]): + defaultv = eval(defaultv) + + channels = info["channels"] if ("channels" in info) else config["channels"] + + if "aggregation_mode" not in info: + field = out_tab.eval( + info["expression"].replace("evt.", ""), info.get("parameters", {}) + ) + else: + + field = evaluate_expression( + file_info, + tcm, + channels, + table=out_tab, + mode=info["aggregation_mode"], + expr=info["expression"], + query=info.get("query", None), + sorter=info.get("sort", None), + channels_skip=info.get("exclude_channels", []), + default_value=defaultv, + n_rows=n_rows, + ) + out_tab.add_field(name, field) + + # remove fields if necessary + existing_cols = list(out_tab.keys()) + for col in existing_cols: + if col not in config["outputs"]: + out_tab.remove_column(col, delete=True) + + # write + if evt_file is not None: + write(out_tab, "evt", evt_file, wo_mode=mode) + mode = "append" + else: + out_ak = ak.concatenate((out_ak, out_tab.view_as("ak"))) + + if evt_file is None: + return out_ak + return None diff --git a/src/reboost/hpge/tcm.py b/src/reboost/hpge/tcm.py index c14ef11..99b951c 100644 --- a/src/reboost/hpge/tcm.py +++ b/src/reboost/hpge/tcm.py @@ -6,7 +6,7 @@ import awkward as ak from lgdo import Table, lh5 -from . import processors, utils +from . import processors log = logging.getLogger(__name__) @@ -16,9 +16,8 @@ def build_tcm( out_file: str, channels: list[str], time_name: str = "t0", - idx_name: str = "hit_global_evtid", + idx_name: str = "global_evtid", time_window_in_us: float = 10, - idx_buffer: int = int(1e7), ) -> None: """Build the (Time Coincidence Map) TCM from the hit tier. @@ -36,52 +35,27 @@ def build_tcm( name of the hit tier field used for index grouping. time_window_in_us time window used to define the grouping. - idx_buffer - number of evtid to read in simultaneously. - - - Notes - ----- - This function avoids excessive memory usage by iterating over the files select sets of evtid, - as such it may be a bit wasteful of IO, since the same block may need to be read multiple times. - Since only a few fields are read a high value of idx_buffer can be used. """ hash_func = r"\d+" - # get number of evtids - n_evtid = utils.get_num_evtid_hit_tier(hit_file, channels, idx_name) - - # not iterate over evtid - n_evtid_read = 0 - mode = "of" msg = "start building time-coincidence map" log.info(msg) chan_ids = [re.search(hash_func, channel).group() for channel in channels] - while n_evtid_read < n_evtid: - # object for the data - - msg = f"... iterating: selecting evtid {n_evtid_read} to {n_evtid_read+idx_buffer}" - log.debug(msg) - - hit_data, n_evtid_read = utils.read_some_idx_as_ak( - channels=channels, - file=hit_file, - n_idx_read=n_evtid_read, - idx_buffer=idx_buffer, - idx_name=idx_name, - field_mask=[idx_name, time_name], + hit_data = [] + for channel in channels: + hit_data.append( + lh5.read(f"{channel}/hit", hit_file, field_mask=[idx_name, time_name]).view_as("ak") ) + tcm = get_tcm_from_ak( + hit_data, chan_ids, window=time_window_in_us, time_name=time_name, idx_name=idx_name + ) - tcm = get_tcm_from_ak( - hit_data, chan_ids, window=time_window_in_us, time_name=time_name, idx_name=idx_name - ) - if tcm is not None: - lh5.write(tcm, "tcm", out_file, wo_mode=mode) - mode = "append" + if tcm is not None: + lh5.write(tcm, "tcm", out_file, wo_mode="of") def get_tcm_from_ak( @@ -90,7 +64,7 @@ def get_tcm_from_ak( *, window: float = 10, time_name: str = "t0", - idx_name: str = "hit_global_evtid", + idx_name: str = "global_evtid", ) -> Table: """Builds a time-coincidence map from a hit of hit data Tables. @@ -123,9 +97,9 @@ def get_tcm_from_ak( obj_tmp = obj_tmp[[time_name, idx_name]] hit_idx = ak.local_index(obj_tmp) - obj_tmp = ak.with_field(obj_tmp, hit_idx, "hit_idx") + obj_tmp = ak.with_field(obj_tmp, hit_idx, "array_idx") - obj_tmp["channel_id"] = int(ch_idx) + obj_tmp["array_id"] = int(ch_idx) sort_objs.append(obj_tmp) obj_tot = ak.concatenate(sort_objs) @@ -135,5 +109,5 @@ def get_tcm_from_ak( time_name=time_name, evtid_name=idx_name, window=window, - fields=["channel_id", "hit_idx"], + fields=["array_id", "array_idx"], ) diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index bc71f8c..c1c4835 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -434,79 +434,6 @@ def get_iterator(file: str, buffer: int, lh5_table: str, **kwargs) -> tuple[LH5I return it, entries, max_idx -def read_some_idx_as_ak( - channels: list, - file: str, - n_idx_read: int, - idx_buffer: int, - idx_name: str, - field_mask: list[str], - it_buffer: int = 100000, -) -> tuple[list[ak.Array], int]: - """Read just rows corresponding to a idx_name field in the range n_idx_read to n_idx_read + idx_buffer. - - Works by iterating over the file only selecting entries in the desired range, also converting to awkward - - Parameters - ---------- - channels - list of channels to read - file - path to hit tier file to read - n_idx_read - first index to read - idx_buffer - number of indices to read. - idx_name - name of the field containing the index. - field_mask - fields to read - it_buffer - buffer for the iterator - - Returns - ------- - tuple of a list of the data per channel and the updated number of index read. - """ - - hit_data = [] - - # loop over channels - for channel in channels: - data_tmp = [] - # iteration over the file - it, entries, max_idx = get_iterator( - file=file, buffer=it_buffer, lh5_table=f"{channel}/hit", field_mask=field_mask - ) - - for idx, (lh5_obj, _, n_rows) in enumerate(it): - ak_obj = lh5_obj.view_as("ak") - - if idx == max_idx: - ak_obj = ak_obj[:n_rows] - - # assumes index are sorted - low_idx = ak_obj[idx_name][0] - - # check if the chunk contains evtid low_evtid - if not ((low_idx >= n_idx_read) & (low_idx < n_idx_read + idx_buffer)): - continue - - # slice and select just the needed cols - condition = (ak_obj[idx_name] >= n_idx_read) & ( - ak_obj[idx_name] < n_idx_read + idx_buffer - ) - ak_obj_sel = ak_obj[condition] - - data_tmp.append(ak_obj_sel) - - hit_data.append(ak.concatenate(data_tmp)) - - n_idx_read += idx_buffer - - return hit_data, n_idx_read - - __file_extensions__ = {"json": [".json"], "yaml": [".yaml", ".yml"]} From d6b6d27c9376ae964a9cc11832f54f247a9708c8 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 22 Nov 2024 18:58:09 +0100 Subject: [PATCH 75/81] [docs] documentation for event tier --- docs/source/manual/hpge.rst | 120 ++- docs/source/notebooks/images/output_17_1.png | Bin 0 -> 53031 bytes docs/source/notebooks/images/output_30_1.png | Bin 0 -> 12961 bytes docs/source/notebooks/images/output_54_1.png | Bin 0 -> 29182 bytes docs/source/notebooks/images/output_58_1.png | Bin 0 -> 47718 bytes .../notebooks/reboost_hpge_evt_tutorial.rst | 2 - .../notebooks/reboost_hpge_tutorial_evt.rst | 834 ++++++++++++++++++ docs/source/tutorial.rst | 2 +- src/reboost/hpge/evt.py | 89 +- src/reboost/hpge/utils.py | 17 + 10 files changed, 1003 insertions(+), 61 deletions(-) create mode 100644 docs/source/notebooks/images/output_17_1.png create mode 100644 docs/source/notebooks/images/output_30_1.png create mode 100644 docs/source/notebooks/images/output_54_1.png create mode 100644 docs/source/notebooks/images/output_58_1.png delete mode 100644 docs/source/notebooks/reboost_hpge_evt_tutorial.rst create mode 100644 docs/source/notebooks/reboost_hpge_tutorial_evt.rst diff --git a/docs/source/manual/hpge.rst b/docs/source/manual/hpge.rst index 681aef5..d0012b8 100644 --- a/docs/source/manual/hpge.rst +++ b/docs/source/manual/hpge.rst @@ -230,7 +230,7 @@ Adding new processors Any python function can be a ``reboost.hit`` processor. The only requirement is that it should return a: - :class:`VectorOfVectors`, -- :class:`Array`` or +- :class:`Array` or - :class:`ArrayOfEqualSizedArrays` with the same length as the hit table. This means processors can act on subarrays (``axis=-1`` in awkward syntax) but should not combine multiple rows of the hit table. @@ -268,7 +268,7 @@ Before explaining how the TCM is constructed we make a detour to explain the dif global_evtid: description: global evtid of the hit. mode: eval - expression: 'ak.fill_none(,axis=-1),np.nan)' + expression: 'ak.fill_none(hit._global_evtid,axis=-1),np.nan)' This field is mandatory to generate the TCM, and the name of the field is an argument to "build_tcm". @@ -281,16 +281,14 @@ Before explaining how the TCM is constructed we make a detour to explain the dif - This storage is slightly different to the TCM in data, but is chosen to allow easy iteration through the TCM. - We do not currently support merging multiple **hit** tier files, this is since then the TCM would need to know which file each hit corresponded to. -Event tier processing (work in progress) ----------------------------------------- - - +Event tier processing +--------------------- The event tier combines the information from various detector systems. Including in future the optical detector channels. This step is thus only necessary for experiments with many output channels. -The processing is again based on a YAML or JSON configuration file. Most of the work to evaluate each expression is done by the :func:`pygama.evt.build_evt.evaluate_expression`. - +The processing is again based on a YAML or JSON configuration file. Most of the work to evaluate each expression is done by the :func:`pygama.evt.build_evt.evaluate_expression` and our conventions for processors +follow those for pygama. The input configuration file is identical to a pygama evt tier configuration file (see an example in :func:`pygama.evt.build_evt.build_evt`). For example: @@ -306,21 +304,101 @@ For example: - det003 outputs: - - energy - - multiplicity + - energy + - multiplicity + + operations: + energy_id: + channels: geds_on + aggregation_mode: gather + query: hit.energy > 25 + expression: tcm.channel_id + energy: + aggregation_mode: 'keep_at_ch:evt.energy_id' + channels: geds_on + expression: hit.energy + multiplicity: + channels: + - geds_on + - geds_ac + aggregation_mode: sum + expression: hit.energy > 25 + initial: 0 + +- **channels** : defines a set of groups of channel names which the operations will be applied to. +- **outputs** : defines the fields to include in the output file. +- **operations**: a list of operations to perform. + +The type of operations is based on the "evaluation modes of" :func:`pygama.evt.build_evt.build_evt`. +Each operation is defined by a configuration block which can have the following keys: + +- **channels**: list of channels to perform the operation on, +- **exlude_channels**: channels to set to the default value, +- **initial**: initial value of the aggregator, +- **aggregation_mode**: how to combine the channels (more information below), +- **expression**: expression to evaluate, +- **query**: logical statement to only select some channels, +- **sort**: expression used for sorting the output, format of "ascend_by:field" or "descend_by:field". + + - energy_id: +Aggregation modes +^^^^^^^^^^^^^^^^^ + +There are several different ways to aggregate the data from different detectors / channels. + +- *"no aggregator supplied"* : then the code will perform a simple evaluation of quantities in the ``evt`` tier data for example: + + .. code-block:: yaml + + energy_sum: + expression: ak.sum(evt.energies,axis=-1) + +- *"first_at:sorter"* picks the value corresponding to the channel (TCM ID) with the lowest value of the "sorter" field. For example: + + .. code-block:: yaml + + first_time: + channels: geds_on + aggregation_mode: first_at:hit.timestamp + expression: hit.timestamp + +- *"last_at:sorter"* similar for the highest value, +- *"gather"*: combines the fields into a :class:`VectorOfVectors`, sorted by the "sort" keys. For example the the following processor is used to extract the channel id (`tcm.array_id`) for every hit above a 25 keV energy threshold. + + .. code-block:: yaml + + channel_id: channels: geds_on aggregation_mode: gather query: hit.energy > 25 - expression: tcm.channel_id + expression: tcm.array_id + sort: descend_by:hit.energy + +- *"keep_at_channel:channel_id_field"*: similarly combines into a :class:`VectorOfVectors`, however uses only the ids from the "channel_id_field" and preserves the order of the subvectors. + For example we can make a processor to extract the energy of each hit from the previous part. + + .. code-block:: yaml + energy: - aggregation_mode: 'keep_at_ch:evt.energy_id' - expression: hit.energy > 25 - multiplicity: - channels: - - geds_on - - geds_ac - aggregation_mode: sum - expression: hit.energy > 25 - initial: 0 + channels: geds_on + aggregation_mode: keep_at_channel:evt.array_id + expression: hit.energy + +- *"keep_at_idx:tcm_index_field"* similar but instead preserves the shape of the tcm, first we need to generate a tcm index field. + + .. code-block:: yaml + + tcm_idx: + channels: geds_on + aggregation_mode: gather + query: hit.energy > 25 + expression: tcm.index + sort: descend_by:hit.energy + + this says for every element in the :class:`VectorOfVectors` which index in the flattened data of the tcm to extract the value from. 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0000000..c84d65e --- /dev/null +++ b/docs/source/notebooks/reboost_hpge_tutorial_evt.rst @@ -0,0 +1,834 @@ +Event tier simulation processing +================================ + +The next tutorial describes how to combine the information from multiple +detectors, i.e. to create detector-wide “events”, stored in the **evt** +tier. + +To demonstrate the power of multi-detector event building we need a +detector system with a large number of detectors. We chose an array of +20 detectors emersed in liquid argon + +0) Setting up the python environment +------------------------------------ + +.. code:: ipython3 + + from lgdo import lh5 + from reboost.hpge import hit, tcm + %load_ext memory_profiler + import matplotlib.pyplot as plt + import pyg4ometry as pg4 + import legendhpges + from legendhpges import draw, make_hpge + import awkward as ak + import logging + import colorlog + import hist + import numpy as np + + + plt.rcParams['figure.figsize'] = [12, 4] + plt.rcParams['axes.titlesize'] =12 + plt.rcParams['axes.labelsize'] = 12 + plt.rcParams['legend.fontsize'] = 12 + + + handler = colorlog.StreamHandler() + handler.setFormatter( + colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") + ) + logger = logging.getLogger() + logger.handlers.clear() + logger.addHandler(handler) + logger.setLevel(logging.INFO) + logger.info("test") + + + + +.. parsed-literal:: + + The memory_profiler extension is already loaded. To reload it, use: + %reload_ext memory_profiler + + +1) Running the simulation +------------------------- + +First we generate a geometry and run the simulation. We use similar BeGe +detectors as in the part before + +.. code:: ipython3 + + + reg = pg4.geant4.Registry() + + bege_meta = { + "name": "B00000B", + "type": "bege", + "production": { + "enrichment": {"val": 0.874, "unc": 0.003}, + "mass_in_g": 697.0, + }, + "geometry": { + "height_in_mm": 29.46, + "radius_in_mm": 36.98, + "groove": {"depth_in_mm": 2.0, "radius_in_mm": {"outer": 10.5, "inner": 7.5}}, + "pp_contact": {"radius_in_mm": 7.5, "depth_in_mm": 0}, + "taper": { + "top": {"angle_in_deg": 0.0, "height_in_mm": 0.0}, + "bottom": {"angle_in_deg": 0.0, "height_in_mm": 0.0}, + }, + }, + } + + # make a detector + bege_l = make_hpge(bege_meta, name="BEGe_L", registry=reg) + + + # create a world volume + world_s = pg4.geant4.solid.Tubs("World_s", 0,60,100,0,2*np.pi, registry=reg, lunit="cm") + world_l = pg4.geant4.LogicalVolume(world_s, "G4_Galactic", "World", registry=reg) + reg.setWorld(world_l) + + # let's make a liquid argon balloon + lar_s = pg4.geant4.solid.Tubs("LAr_s", 0, 20, 40, 0, 2*np.pi, registry=reg, lunit="cm") + lar_l = pg4.geant4.LogicalVolume(lar_s, "G4_lAr", "LAr_l", registry=reg) + pg4.geant4.PhysicalVolume([0, 0, 0], [0, 0, 0], lar_l, "LAr", world_l, registry=reg) + + # lets make 4 strings of 5 detectors + + det_count = 0 + for x in [-50,50]: + for y in [-50,50]: + for z in [-100,-50,0,50,100]: + + pg4.geant4.PhysicalVolume([0, 0, 0], [x, y,z, "mm"], bege_l, f"BEGe_{det_count}", lar_l, registry=reg) + det_count+=1 + + w = pg4.gdml.Writer() + w.addDetector(reg) + w.write("cfg/geom.gdml") + + +Uncomment the next block to visualise +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: ipython3 + + + viewer = pg4.visualisation.VtkViewerColoured(materialVisOptions={"G4_lAr": [0, 0, 1, 0.1]}) + viewer.addLogicalVolume(reg.getWorldVolume()) + viewer.view() + +.. figure:: images/det.png + :alt: image.png + + image.png + +We create our remage macro, this time for K42 in the LAr, a critical +background source in LEGEND! + +.. code:: text + + /RMG/Manager/Logging/LogLevel detail + + /RMG/Geometry/RegisterDetector Germanium BEGe_0 000 + /RMG/Geometry/RegisterDetector Germanium BEGe_1 001 + /RMG/Geometry/RegisterDetector Germanium BEGe_2 002 + /RMG/Geometry/RegisterDetector Germanium BEGe_3 003 + /RMG/Geometry/RegisterDetector Germanium BEGe_4 004 + /RMG/Geometry/RegisterDetector Germanium BEGe_5 005 + /RMG/Geometry/RegisterDetector Germanium BEGe_6 006 + /RMG/Geometry/RegisterDetector Germanium BEGe_7 007 + /RMG/Geometry/RegisterDetector Germanium BEGe_8 008 + /RMG/Geometry/RegisterDetector Germanium BEGe_9 009 + /RMG/Geometry/RegisterDetector Germanium BEGe_10 010 + /RMG/Geometry/RegisterDetector Germanium BEGe_11 011 + /RMG/Geometry/RegisterDetector Germanium BEGe_12 012 + /RMG/Geometry/RegisterDetector Germanium BEGe_13 013 + /RMG/Geometry/RegisterDetector Germanium BEGe_14 014 + /RMG/Geometry/RegisterDetector Germanium BEGe_15 015 + /RMG/Geometry/RegisterDetector Germanium BEGe_16 016 + /RMG/Geometry/RegisterDetector Germanium BEGe_17 017 + /RMG/Geometry/RegisterDetector Germanium BEGe_18 018 + /RMG/Geometry/RegisterDetector Germanium BEGe_19 019 + + + /run/initialize + + /RMG/Generator/Confine Volume + /RMG/Generator/Confinement/Physical/AddVolume LAr + + /RMG/Generator/Select GPS + /gps/particle ion + /gps/energy 0 eV + /gps/ion 19 42 # 42-K + + + /run/beamOn 10000000 + +2) Remage + hit tier files +-------------------------- + +For this tutorial we use the same remage simulation as for the hit tier +simulation. See the previous tutorial for how to generate them. + +We can check these files. + +We run a simplified reboost hit tier processing, unlike that in the +previous tutorial which deliberately included many outputs to show the +effect of processors. + +First we define the config file and parameters. + +.. code:: ipython3 + + chans = [f"det{num:03}" for num in range(20)] + + +.. code:: ipython3 + + chain = { + "channels": chans, + "outputs": [ + "t0", # first timestamp + "evtid", # id of the hit + "global_evtid", # global id of the hit + "energy_sum_no_deadlyer", + "energy_sum" # true summed energy before dead layer or smearing + ], + "step_group": { + "description": "group steps by time and evtid with 10us window", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + }, + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", + }, + "evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)", + }, + "global_evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)", + }, + "distance_to_nplus_surface_mm": { + "description": "distance to the nplus surface in mm", + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')", + }, + "activeness": { + "description": "activness based on FCCD (no TL)", + "mode": "eval", + "expression": "ak.where(hit.distance_to_nplus_surface_mm + + + + +.. image:: images/output_17_1.png + + +We see that the local index varies between 0 and 1e7 per file while the +global index increases constantly. We can even check this (as it must +from our hit tier processing). + +.. code:: ipython3 + + evtid_change = np.diff(data_det001.global_evtid) + +.. code:: ipython3 + + print(f"evtid change = {evtid_change}, increasing? {np.all(evtid_change>=0)}") + + +.. parsed-literal:: + + evtid change = [843. 153. 313. ... 27. 500. 345.], increasing? True + + +Now we can build the time-coincidence map and save to a new file. + +.. code:: ipython3 + + %%memit + tcm.build_tcm("output/hit/output.lh5","output/tcm/test_tcm.lh5",chans,time_name="t0",idx_name="global_evtid") + + + +.. parsed-literal:: + + peak memory: 690.61 MiB, increment: 0.27 MiB + + +We see that building the TCM was fast only taking around 1s. However +since building the TCM requires reading all the files simultaneously we +should be careful about the memory usage. Here using around 1 GB is +still quite significant. + + **Technical note**: - The code reads all the hit files in + simultaneously, this could cause a memory issue if the file is very + large - In this case the user must break the hit tier file into more + chunks (see build_hit) options + +Now we can look at our TCM. + +.. code:: ipython3 + + tcm_ak = lh5.read("tcm","output/tcm/test_tcm.lh5").view_as("ak") + +.. code:: ipython3 + + lh5.show("output/tcm/test_tcm.lh5") + + +.. code:: ipython3 + + tcm_ak + + + + +.. raw:: html + + 

[{array_id: [11], array_idx: [0]},
+     {array_id: [2], array_idx: [0]},
+     {array_id: [11], array_idx: [1]},
+     {array_id: [8], array_idx: [0]},
+     {array_id: [3], array_idx: [0]},
+     {array_id: [5, 16], array_idx: [0, 0]},
+     {array_id: [0], array_idx: [0]},
+     {array_id: [13], array_idx: [0]},
+     {array_id: [18], array_idx: [0]},
+     {array_id: [7], array_idx: [0]},
+     ...,
+     {array_id: [9, 8], array_idx: [25998, 26398]},
+     {array_id: [17], array_idx: [27265]},
+     {array_id: [17], array_idx: [27266]},
+     {array_id: [3, 4], array_idx: [26889, 26009]},
+     {array_id: [15], array_idx: [27763]},
+     {array_id: [12], array_idx: [27111]},
+     {array_id: [13], array_idx: [26832]},
+     {array_id: [17], array_idx: [27267]},
+     {array_id: [19], array_idx: [25884]}]
+    -----------------------------------------------
+    type: 458647 * {
+        array_id: var * int64,
+        array_idx: var * int64
+    }
+ + + +We see that almost all of the events have only trigger in a single +channel. We can also extract easily the multiplicity of the events or +the number of triggers in the TCM. + +More sophisticated calcations can be performed by also grabbing +information from the hit tier files. This is done by ``build_evt``. + +.. code:: ipython3 + + plt.hist(ak.num(tcm_ak.array_id,axis=-1),range=(.5,20.5),bins=20,alpha=0.3,density=True) + plt.yscale("log") + plt.xlim(0.5,10) + plt.xlabel("Multiplicity") + plt.ylabel("Probability ") + + + + +.. parsed-literal:: + + Text(0, 0.5, 'Probability ') + + + + +.. image:: images/output_30_1.png + + +3.1) TCM ``id``,\ ``idx`` and ``index`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Internally we have three different TCM attributes. \* ``tcm.idx`` : the +row of the hit table \* ``tcm.id`` : the channel number \* +``tcm.index``: the row of the flattened tcm the hit corresponds to + +This names are surely very confusing but are inherited from pygama and +will be updated in future. + +These are computed with the following block of code: + +.. code:: python + + idx_ch = tcm.idx[tcm.id == table_id] + outsize = len(idx_ch) + + if expr == "tcm.array_id": + res = np.full(outsize, table_id, dtype=int) + elif expr == "tcm.array_idx": + res = idx_ch + elif expr == "tcm.index": + res = np.where(tcm.id == table_id)[0] + +4) Generating the event tier +---------------------------- + +Next we can use our time-coincidence-map to generate files containing +information on each event. Similar to the ``hit`` tier the processing is +based on a YAML or JSON configuration file. + +This config file must contain a key with lists of different groups of +channels. It must define the output fields and then finally a block of +operations to perform. Similar to our hit tier processing the idea is +that each operation / processor computes an LGDO object +(``VectorOfVectors``, ``Array`` or ``ArrayOfEqualSizedArrays``) with the +same length as the TCM. + +We can define some groups of channels for our processing chain. Lets set +some channels off and some to ac (this means the channel is used for +anticoincidence but is not fully usable). + +.. code:: ipython3 + + chans_off = ["det003","det007"] + chans_ac = ["det013","det016"] + chans_on = [chan for chan in chans if (chan not in chans_off) and (chan not in chans_ac) ] + chans_on + + + + +.. parsed-literal:: + + ['det000', + 'det001', + 'det002', + 'det004', + 'det005', + 'det006', + 'det008', + 'det009', + 'det010', + 'det011', + 'det012', + 'det014', + 'det015', + 'det017', + 'det018', + 'det019'] + + + +We create a simple event tier processing config demonstrating the +different possible aggregation modes. + + **Note**: this processing chain deliberately includes some additional + operations to show the effect of each aggregation mode. It: 1. finds + the channel ids with energy above threshold 2. finds the TCM + “index’s” or the index of the TCM VoV 3. computes a vector of vector + of the energies (ordered by channel) 4. computes a boolean flag of + whether each channel is ON (and not AC) 5. computes another flag + checking if each hit is above threshold 6. checks if the event + contains any AC hits 7. computes the summed energy, the first + timestamp and the multiplicity + +.. code:: ipython3 + + evt_config = { + "channels": { + "geds_on":chans_on, + "geds_ac":chans_ac + }, + "outputs": [ + "channel_id", + "all_channel_id", + "tcm_index", + "is_good_channels", + "energy_sum", + "energy_vector", + "energy_no_threshold", + "is_all_above_threshold", + "t0", + "is_good_event", + "multiplicity" + ], + "operations": { + "channel_id": { + "channels":["geds_on","geds_ac"], + "aggregation_mode": "gather", + "query": "hit.energy_sum > 25", + "expression": "tcm.array_id", + "sort":"descend_by:hit.energy_sum" + }, + "all_channel_id": { + "channels":["geds_on","geds_ac"], + "aggregation_mode": "gather", + "expression": "tcm.array_id", + "sort":"descend_by:hit.energy_sum" + }, + "tcm_index": { + "channels":["geds_on","geds_ac"], + "aggregation_mode": "gather", + "query": "hit.energy_sum > 25", + "expression": "tcm.index" + }, + "energy_no_threshold": { + "channels":["geds_on"], + "aggregation_mode": "keep_at_ch:evt.all_channel_id", + "expression": "hit.energy_sum" + }, + "energy_vector": { + "channels":["geds_on"], + "aggregation_mode": "keep_at_ch:evt.channel_id", + "expression": "hit.energy_sum" + }, + "is_good_channels":{ + "channels":["geds_on","geds_ac"], + "aggregation_mode":"keep_at_idx:evt.tcm_index", + "expression":"True", + "initial":False, + "exclude_channels":"geds_ac" + }, + "is_all_above_threshold" :{ + "channels":["geds_on","geds_ac"], + "aggregation_mode":"all", + "expression":"hit.energy_sum>25", + "initial":True + }, + "is_good_event" :{ + "expression":"ak.all(evt.is_good_channels,axis=-1)" + }, + + "energy_sum": { + "channels":["geds_on"], + "aggregation_mode": "sum", + "query":"hit.energy_sum > 25", + "expression": "hit.energy_sum", + "initial":0 + }, + "t0": { + "channels":["geds_on"], + "aggregation_mode": "first_at:hit.t0", + "expression": "hit.t0" + }, + "multiplicity": { + "channels": ["geds_on"], + "aggregation_mode": "sum", + "expression": "hit.energy_sum > 25", + "initial": 0 + } + } + } + +.. code:: ipython3 + + from reboost.hpge import evt + logger.setLevel(logging.INFO) + +.. code:: ipython3 + + %%time + evt_ak = evt.build_evt(hit_file="output/hit/output.lh5",tcm_file = "output/tcm/test_tcm.lh5",evt_file=None,config = evt_config) + + + +.. parsed-literal:: + + CPU times: user 5.21 s, sys: 530 ms, total: 5.74 s + Wall time: 5.74 s + + +4.1) “Gather” aggregation mode +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The gather mode is used by the ``channel_id`` and ``tcm_index`` +processors. It returns a VectorOfVector of the expression evaluated for +each hit in the event. \* the “query” removes some hits below the energy +threshold. \* “channels” defines the channels to include \* “sort”, +controls which order the values are in. + +For example we see that the “channel_id” is just a subset of the +original ``tcm_id`` removing some hits (below 25 keV) and changing the +order in some cases. These fields are then useful to extract values from +other fields keeping the correspondence with channel or tcm index. + +.. code:: ipython3 + + print("tcm.array_id ",tcm_ak.array_id) + print("evt.all_channel_id ",evt_ak.all_channel_id) + print("evt.channel_id ",evt_ak.channel_id) + print("evt.tcm_index ",evt_ak.tcm_index) + + +.. parsed-literal:: + + tcm.array_id [[11], [2], [11], [8], [3], [5, 16], ..., [...], [15], [12], [13], [17], [19]] + evt.all_channel_id [[11], [2], [11], [8], [], [16, 5], [0], ..., [4], [15], [12], [13], [17], [19]] + evt.channel_id [[11], [], [11], [], [], [16, 5], [0], ..., [4], [15], [], [13], [17], [19]] + evt.tcm_index [[0], [], [2], [], [], [...], ..., [540269], [], [540271], [540272], [540273]] + + +4.2) “keep_at_ch” and “keep_at_idx” +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + +These modes are similar but order the results according to either the +TCM index or the channel id fields. In our processing chain we used this +to extract the energies preserving the order with respect to the +channel_id. We see in both cases there are some “nan” values +(corresponding to the “geds_ac” channels) and some elements are removed +by the energy threshold. + +.. code:: ipython3 + + print("evt.energy_vector ",evt_ak.energy_vector) + print("evt.energy_no_threshold ",evt_ak.energy_no_threshold) + + +.. parsed-literal:: + + evt.energy_vector [[894], [], [125], [], [], [nan, ...], ..., [784], [], [nan], [1.36e+03], [258]] + evt.energy_no_threshold [[894], [0.761], [125], [-1.65], [], ..., [-0.79], [nan], [1.36e+03], [258]] + + +Or we can check if each channel is in AC mode. + +.. code:: ipython3 + + print("evt.is_good_channels ",evt_ak.is_good_channels) + + +.. parsed-literal:: + + evt.is_good_channels [[True], [], [True], [], [], [...], ..., [True], [], [False], [True], [True]] + + +4.3) “all” or “sum” mode +^^^^^^^^^^^^^^^^^^^^^^^^ + + +These modes aggregate the data from each channel by either summation or +all. Thus we get out a 1D vector. + +For example we checked if every hit is above threshold (compare to the +energy vectors), compute the total energy and check the number of +channels above threshold (multiplicity). + +.. code:: ipython3 + + print("evt_ak.is_all_above_threshold ",evt_ak.is_all_above_threshold) + print("evt_ak.energy_sum ",evt_ak.energy_sum) + print("evt_ak.energy_sum ",evt_ak.energy_sum) + print("evt_ak.multiplicity ",evt_ak.multiplicity) + + +.. parsed-literal:: + + evt_ak.is_all_above_threshold [True, False, True, False, True, True, ..., True, True, False, True, True, True] + evt_ak.energy_sum [894, 0, 125, 0, 0, 437, 147, 0, ..., 1.22e+03, 218, 784, 0, 0, 1.36e+03, 258] + evt_ak.energy_sum [894, 0, 125, 0, 0, 437, 147, 0, ..., 1.22e+03, 218, 784, 0, 0, 1.36e+03, 258] + evt_ak.multiplicity [1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, ..., 1, 1, 0, 2, 0, 1, 1, 1, 0, 0, 1, 1] + + +4.4) No aggregation mode +^^^^^^^^^^^^^^^^^^^^^^^^ + +Finally, we can perform some basic operations on evt tier variables, eg. +first checking if any above threshold channel is in AC mode. + +.. code:: ipython3 + + print("evt_ak.is_good_event ",evt_ak.is_good_event) + + +.. parsed-literal:: + + evt_ak.is_good_event [True, True, True, True, True, False, ..., True, True, True, False, True, True] + + +5) Analysis on the event tier data +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + +Finally with our new evt files we can make some plots of experiment wide +quantities. + +.. code:: ipython3 + + def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs): + + h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double() + h.fill(energy) + h.plot(**kwargs,label=label) + axes.legend() + if (log_y): + axes.set_yscale("log") + if xrange is not None: + axes.set_xlim(*xrange) + +.. code:: ipython3 + + fig, ax = plt.subplots() + plot_energy(ax,evt_ak.energy_sum[evt_ak.multiplicity>0],yerr=False,label="Summed energies",xrange=(0,4000)) + plot_energy(ax,ak.flatten(evt_ak[evt_ak.multiplicity==1].energy_vector),yerr=False,label="M1 energies",xrange=(0,4000)) + + ax.set_title("summed event energy") + + + + +.. parsed-literal:: + + Text(0.5, 1.0, 'summed event energy') + + + + +.. image:: images/output_54_1.png + + +Or we can select multiplicity two (M2) events and plot the 2D energy +spectra. + +.. code:: ipython3 + + import matplotlib as mpl + +.. code:: ipython3 + + def plot_energy_2D(axes,energy_1,energy_2,bins=400,xrange=None,yrange=None,label=" ",**kwargs): + + x_axis = hist.axis.Regular(bins, *xrange, name="Energy 2 [keV]") + y_axis = hist.axis.Regular(bins, *yrange, name="Energy 1 [keV]") + h = hist.Hist(x_axis, y_axis) + h.fill(energy_2,energy_1) + cmap = mpl.colormaps["BuPu"].copy() + cmap.set_under(color="white") + + + w, x, y = h.to_numpy() + mesh = ax.pcolormesh(x, y, w.T, cmap=cmap,vmin=0.5) + ax.set_xlabel("Energy 2 [keV]") + ax.set_ylabel("Energy 1 [keV]") + fig.colorbar(mesh) + + if xrange is not None: + axes.set_xlim(*xrange) + +.. code:: ipython3 + + fig, ax = plt.subplots() + + energy_1 = np.array(evt_ak[(evt_ak.multiplicity==2)&(evt_ak.is_good_event)].energy_vector[:,1]) + energy_2 = np.array(evt_ak[(evt_ak.multiplicity==2)&(evt_ak.is_good_event)].energy_vector[:,0]) + plot_energy_2D(ax,energy_2,energy_1,label=None,xrange=(0,1000),yrange=(0,1600),bins=200,vmin=0.5) + ax.set_title("summed event energy") + + + + +.. parsed-literal:: + + Text(0.5, 1.0, 'summed event energy') + + + + +.. image:: images/output_58_1.png + + +Many more things are possible via manipulation of our event tier files, +the recommended tool is awkward (see +`[docs] `__). diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index a535497..45c8065 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -7,4 +7,4 @@ Basic Tutorial :caption: Contents: notebooks/reboost_hpge_tutorial_hit - notebooks/reboost_hpge_evt_tutorial + notebooks/reboost_hpge_tutorial_evt diff --git a/src/reboost/hpge/evt.py b/src/reboost/hpge/evt.py index f0ef72e..17ebbc4 100644 --- a/src/reboost/hpge/evt.py +++ b/src/reboost/hpge/evt.py @@ -9,6 +9,8 @@ from pygama.evt.build_evt import evaluate_expression from pygama.evt.utils import TCMData +from . import utils + log = logging.getLogger(__name__) @@ -29,42 +31,42 @@ def build_evt( config dictionary of the configuration. For example: - .. code-block:: json - - { - "channels": [ "det001", "det002"] - }, - "outputs": [ - "energy", - "multiplicity" - ], - "operations": { - "energy_id": { - "channels": "geds_on", - "aggregation_mode": "gather", - "query": "hit.energy > 25", - "expression": "tcm.channel_id" - }, - "energy": { - "aggregation_mode": "keep_at_ch:evt.energy_id", - "expression": "hit.energy > 25" + .. code-block:: json + + { + "channels": { + "geds_on":[ "det001", "det002"], + "geds_ac":["det003"] }, - "multiplicity": { - "channels": [ - "geds_on", - "geds_ac" - ], - "aggregation_mode": "sum", - "expression": "hit.energy > 25", - "initial": 0 + "outputs": [ + "energy", + "multiplicity" + ], + "operations": { + "energy_id": { + "channels": "geds_on", + "aggregation_mode": "gather", + "query": "hit.energy > 25", + "expression": "tcm.channel_id" + }, + "energy": { + "aggregation_mode": "keep_at_ch:evt.energy_id", + "expression": "hit.energy > 25", + "channels": "geds_on" + }, + "multiplicity": { + "channels": "geds_on", + "aggregation_mode": "sum", + "expression": "hit.energy > 25", + "initial": 0 + } } } - } - Must contain: - - "channels": list of channel groupings - - "outputs": fields for the output file - - "operations": operations to perform see :func:`pygama.evt.build_evt.evaluate_expression` for more details. + Must contain: + - "channels": dictionary of channel groupings + - "outputs": fields for the output file + - "operations": operations to perform see :func:`pygama.evt.build_evt.evaluate_expression` for more details. buffer number of events to process simultaneously @@ -81,7 +83,6 @@ def build_evt( file_info = { "hit": (hit_file, "hit", "det{:03}"), - "tcm": (tcm_file, "tcm", "tcm"), "evt": (evt_file, "evt"), } @@ -90,6 +91,15 @@ def build_evt( out_ak = ak.Array([]) mode = "of" + # get channel groupings + channels = {} + for group, info in config["channels"].items(): + if isinstance(info, str): + channels[group] = [info] + + elif isinstance(info, list): + channels[group] = info + for tcm_lh5, _, n_rows_read in LH5Iterator(tcm_file, "tcm", buffer_len=buffer): tcm_lh5_sel = tcm_lh5 tcm_ak = tcm_lh5_sel.view_as("ak")[:n_rows_read] @@ -111,27 +121,32 @@ def build_evt( if isinstance(defaultv, str) and (defaultv in ["np.nan", "np.inf", "-np.inf"]): defaultv = eval(defaultv) - channels = info["channels"] if ("channels" in info) else config["channels"] + channels_use = utils.get_channels_from_groups(info.get("channels", []), channels) + channels_exclude = utils.get_channels_from_groups( + info.get("exclude_channels", []), channels + ) if "aggregation_mode" not in info: field = out_tab.eval( info["expression"].replace("evt.", ""), info.get("parameters", {}) ) else: - field = evaluate_expression( file_info, tcm, - channels, + channels_use, table=out_tab, mode=info["aggregation_mode"], expr=info["expression"], query=info.get("query", None), sorter=info.get("sort", None), - channels_skip=info.get("exclude_channels", []), + channels_skip=channels_exclude, default_value=defaultv, n_rows=n_rows, ) + + msg = f"field {field}" + log.debug(msg) out_tab.add_field(name, field) # remove fields if necessary diff --git a/src/reboost/hpge/utils.py b/src/reboost/hpge/utils.py index c1c4835..4cb4265 100644 --- a/src/reboost/hpge/utils.py +++ b/src/reboost/hpge/utils.py @@ -2,6 +2,7 @@ import copy import importlib +import itertools import json import logging import re @@ -43,6 +44,22 @@ class FileInfo(NamedTuple): """Last global evtid to process.""" +def get_channels_from_groups(names: list | str | None, groupings: dict | None = None) -> list: + """Get a list of channels from a list of groups""" + + if names is None: + channels_e = [] + elif isinstance(names, str): + channels_e = groupings[names] + elif isinstance(names, list): + channels_e = list(itertools.chain.from_iterable([groupings[e] for e in names])) + else: + msg = f"names {names} must be list or str or `None`" + raise ValueError(msg) + + return channels_e + + 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zS%B*pQ3R1|mq}3l`6a~&*NSb~{Ob8DnJm;ofnUOp^b%3$)A7a(K|dO6ph55%L5He* zMb4l#!sv3tM+<)5dO?VcR(VBM$>aWXXWGYt`0w`*aE+tH(9DeU;+u$`_AIQ07_N5T zD_85fm*@o=pUh2acZh^(liI=IAD6bGA6XK~WDVTs)d&L#4q~u=Ma*nO^>??~0+h(#)~+CnuDF2THR0lP@oC z0DhQGFI5!Tl@KV&rJ}O3%Wo!y@6eLX4y$k=Jw0oSEi#L!crSw;yYYlM??Z`tU1a6u zb0~Vj74bI}hRr;uQd&+X96lDy)9c zrQphlGOF;1`om%^jrF!or&g*r Date: Fri, 22 Nov 2024 19:00:36 +0100 Subject: [PATCH 77/81] [docs] remove nbsphinx --- docs/source/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index f93dc26..19c7625 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -20,7 +20,6 @@ "sphinx.ext.intersphinx", "sphinx_copybutton", "myst_parser", - "nbsphinx", "IPython.sphinxext.ipython_console_highlighting", ] From 2d8c6348c1e50f323f5936435324b9c86d28c5b2 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 22 Nov 2024 19:06:50 +0100 Subject: [PATCH 78/81] [docs] fix --- docs/source/conf.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 19c7625..3059b38 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,9 +19,8 @@ "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx_copybutton", - "myst_parser", - "IPython.sphinxext.ipython_console_highlighting", -] + "myst_parser" + ] source_suffix = { From 0a97068b669367f92f2f4795a3af6c94b9953020 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 22 Nov 2024 18:07:03 +0000 Subject: [PATCH 79/81] style: pre-commit fixes --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 3059b38..3cf5f89 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,8 +19,8 @@ "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx_copybutton", - "myst_parser" - ] + "myst_parser", +] source_suffix = { From 5790828f743449c2a2db056a68e8f643f7fea581 Mon Sep 17 00:00:00 2001 From: Toby Dixon Date: Fri, 22 Nov 2024 19:14:16 +0100 Subject: [PATCH 80/81] [docs] ipython --> python --- .../notebooks/reboost_hpge_tutorial_evt.rst | 62 +++++++++---------- 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/docs/source/notebooks/reboost_hpge_tutorial_evt.rst b/docs/source/notebooks/reboost_hpge_tutorial_evt.rst index c84d65e..d917589 100644 --- a/docs/source/notebooks/reboost_hpge_tutorial_evt.rst +++ b/docs/source/notebooks/reboost_hpge_tutorial_evt.rst @@ -12,7 +12,7 @@ detector system with a large number of detectors. We chose an array of 0) Setting up the python environment ------------------------------------ -.. code:: ipython3 +.. code:: python from lgdo import lh5 from reboost.hpge import hit, tcm @@ -59,7 +59,7 @@ detector system with a large number of detectors. We chose an array of First we generate a geometry and run the simulation. We use similar BeGe detectors as in the part before -.. code:: ipython3 +.. code:: python reg = pg4.geant4.Registry() @@ -115,7 +115,7 @@ detectors as in the part before Uncomment the next block to visualise ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. code:: ipython3 +.. code:: python viewer = pg4.visualisation.VtkViewerColoured(materialVisOptions={"G4_lAr": [0, 0, 1, 0.1]}) @@ -183,12 +183,12 @@ effect of processors. First we define the config file and parameters. -.. code:: ipython3 +.. code:: python chans = [f"det{num:03}" for num in range(20)] -.. code:: ipython3 +.. code:: python chain = { "channels": chans, @@ -252,7 +252,7 @@ First we define the config file and parameters. } } -.. code:: ipython3 +.. code:: python ## all detectors have the same performance pars = { @@ -266,7 +266,7 @@ First we define the config file and parameters. } -.. code:: ipython3 +.. code:: python %%time logger.setLevel(logging.CRITICAL) @@ -289,12 +289,12 @@ In our processing chain we saved both the “local” and “global” evtid, and we can extract the “hit_idx” as the row of the output table. We can compare these indices. -.. code:: ipython3 +.. code:: python data_det001 = lh5.read("det001/hit","output/hit/output.lh5") -.. code:: ipython3 +.. code:: python fig,ax = plt.subplots() ax.plot(data_det001.evtid,np.arange(len(data_det001.evtid)),label="Local") @@ -324,11 +324,11 @@ We see that the local index varies between 0 and 1e7 per file while the global index increases constantly. We can even check this (as it must from our hit tier processing). -.. code:: ipython3 +.. code:: python evtid_change = np.diff(data_det001.global_evtid) -.. code:: ipython3 +.. code:: python print(f"evtid change = {evtid_change}, increasing? {np.all(evtid_change>=0)}") @@ -340,7 +340,7 @@ from our hit tier processing). Now we can build the time-coincidence map and save to a new file. -.. code:: ipython3 +.. code:: python %%memit tcm.build_tcm("output/hit/output.lh5","output/tcm/test_tcm.lh5",chans,time_name="t0",idx_name="global_evtid") @@ -364,16 +364,16 @@ still quite significant. Now we can look at our TCM. -.. code:: ipython3 +.. code:: python tcm_ak = lh5.read("tcm","output/tcm/test_tcm.lh5").view_as("ak") -.. code:: ipython3 +.. code:: python lh5.show("output/tcm/test_tcm.lh5") -.. code:: ipython3 +.. code:: python tcm_ak @@ -382,7 +382,7 @@ Now we can look at our TCM. .. raw:: html - 

[{array_id: [11], array_idx: [0]},
+    0],yerr=False,label="Summed energies",xrange=(0,4000))
@@ -782,11 +782,11 @@ quantities.
 Or we can select multiplicity two (M2) events and plot the 2D energy
 spectra.
 
-.. code:: ipython3
+.. code:: python
 
     import matplotlib as mpl
 
-.. code:: ipython3
+.. code:: python
 
     def plot_energy_2D(axes,energy_1,energy_2,bins=400,xrange=None,yrange=None,label=" ",**kwargs):
 
@@ -807,7 +807,7 @@ spectra.
         if xrange is not None:
             axes.set_xlim(*xrange)
 
-.. code:: ipython3
+.. code:: python
 
     fig, ax = plt.subplots()
 

From b779c4d66f7a5b3c8fa4de6e0e933889d0890c1c Mon Sep 17 00:00:00 2001
From: Toby Dixon 
Date: Fri, 22 Nov 2024 19:49:32 +0100
Subject: [PATCH 81/81] [docs] small fixes

---
 .../notebooks/reboost_hpge_tutorial_evt.rst   | 371 +++++++++-------
 .../notebooks/reboost_hpge_tutorial_hit.rst   | 407 +++++++++++-------
 2 files changed, 453 insertions(+), 325 deletions(-)

diff --git a/docs/source/notebooks/reboost_hpge_tutorial_evt.rst b/docs/source/notebooks/reboost_hpge_tutorial_evt.rst
index d917589..055f279 100644
--- a/docs/source/notebooks/reboost_hpge_tutorial_evt.rst
+++ b/docs/source/notebooks/reboost_hpge_tutorial_evt.rst
@@ -16,7 +16,6 @@ detector system with a large number of detectors. We chose an array of
 
     from lgdo import lh5
     from reboost.hpge import hit, tcm
-    %load_ext memory_profiler
     import matplotlib.pyplot as plt
     import pyg4ometry as pg4
     import legendhpges
@@ -28,10 +27,10 @@ detector system with a large number of detectors. We chose an array of
     import numpy as np
 
 
-    plt.rcParams['figure.figsize'] = [12, 4]
-    plt.rcParams['axes.titlesize'] =12
-    plt.rcParams['axes.labelsize'] = 12
-    plt.rcParams['legend.fontsize'] = 12
+    plt.rcParams["figure.figsize"] = [12, 4]
+    plt.rcParams["axes.titlesize"] = 12
+    plt.rcParams["axes.labelsize"] = 12
+    plt.rcParams["legend.fontsize"] = 12
 
 
     handler = colorlog.StreamHandler()
@@ -88,24 +87,34 @@ detectors as in the part before
 
 
     # create a world volume
-    world_s = pg4.geant4.solid.Tubs("World_s", 0,60,100,0,2*np.pi, registry=reg, lunit="cm")
+    world_s = pg4.geant4.solid.Tubs(
+        "World_s", 0, 60, 100, 0, 2 * np.pi, registry=reg, lunit="cm"
+    )
     world_l = pg4.geant4.LogicalVolume(world_s, "G4_Galactic", "World", registry=reg)
     reg.setWorld(world_l)
 
     # let's make a liquid argon balloon
-    lar_s = pg4.geant4.solid.Tubs("LAr_s",  0, 20, 40, 0, 2*np.pi, registry=reg, lunit="cm")
+    lar_s = pg4.geant4.solid.Tubs(
+        "LAr_s", 0, 20, 40, 0, 2 * np.pi, registry=reg, lunit="cm"
+    )
     lar_l = pg4.geant4.LogicalVolume(lar_s, "G4_lAr", "LAr_l", registry=reg)
     pg4.geant4.PhysicalVolume([0, 0, 0], [0, 0, 0], lar_l, "LAr", world_l, registry=reg)
 
     # lets make 4 strings of 5 detectors
 
     det_count = 0
-    for x in [-50,50]:
-        for y in [-50,50]:
-            for z in [-100,-50,0,50,100]:
-
-                pg4.geant4.PhysicalVolume([0, 0, 0], [x, y,z, "mm"], bege_l, f"BEGe_{det_count}", lar_l, registry=reg)
-                det_count+=1
+    for x in [-50, 50]:
+        for y in [-50, 50]:
+            for z in [-100, -50, 0, 50, 100]:
+                pg4.geant4.PhysicalVolume(
+                    [0, 0, 0],
+                    [x, y, z, "mm"],
+                    bege_l,
+                    f"BEGe_{det_count}",
+                    lar_l,
+                    registry=reg,
+                )
+                det_count += 1
 
     w = pg4.gdml.Writer()
     w.addDetector(reg)
@@ -118,7 +127,9 @@ Uncomment the next block to visualise
 .. code:: python
 
 
-    viewer = pg4.visualisation.VtkViewerColoured(materialVisOptions={"G4_lAr": [0, 0, 1, 0.1]})
+    viewer = pg4.visualisation.VtkViewerColoured(
+        materialVisOptions={"G4_lAr": [0, 0, 1, 0.1]}
+    )
     viewer.addLogicalVolume(reg.getWorldVolume())
     viewer.view()
 
@@ -191,65 +202,64 @@ First we define the config file and parameters.
 .. code:: python
 
     chain = {
-            "channels": chans,
-            "outputs": [
-                "t0",                           # first timestamp
-                "evtid",                        # id of the hit
-                "global_evtid",                 # global id of the hit
-                "energy_sum_no_deadlyer",
-                "energy_sum"                   # true summed energy before dead layer or smearing
-            ],
-            "step_group": {
-                "description": "group steps by time and evtid with 10us window",
-                "expression": "reboost.hpge.processors.group_by_time(stp,window=10)",
+        "channels": chans,
+        "outputs": [
+            "t0",  # first timestamp
+            "evtid",  # id of the hit
+            "global_evtid",  # global id of the hit
+            "energy_sum_no_deadlyer",
+            "energy_sum",  # true summed energy before dead layer or smearing
+        ],
+        "step_group": {
+            "description": "group steps by time and evtid with 10us window",
+            "expression": "reboost.hpge.processors.group_by_time(stp,window=10)",
+        },
+        "locals": {
+            "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)",
+            "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)",
+        },
+        "operations": {
+            "t0": {
+                "description": "first time in the hit.",
+                "mode": "eval",
+                "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)",
+            },
+            "evtid": {
+                "description": "global evtid of the hit.",
+                "mode": "eval",
+                "expression": "ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)",
+            },
+            "global_evtid": {
+                "description": "global evtid of the hit.",
+                "mode": "eval",
+                "expression": "ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)",
             },
-            "locals": {
-                "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)",
-                "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)",
+            "distance_to_nplus_surface_mm": {
+                "description": "distance to the nplus surface in mm",
+                "mode": "function",
+                "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')",
             },
-            "operations": {
-                "t0": {
-                    "description": "first time in the hit.",
-                    "mode": "eval",
-                    "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)",
-                },
-                "evtid": {
-                    "description": "global evtid of the hit.",
-                    "mode": "eval",
-                    "expression": "ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)",
-                },
-                "global_evtid": {
-                    "description": "global evtid of the hit.",
-                    "mode": "eval",
-                    "expression": "ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)",
-                },
-                "distance_to_nplus_surface_mm": {
-                    "description": "distance to the nplus surface in mm",
-                    "mode": "function",
-                    "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')",
-                },
-                "activeness": {
-                    "description": "activness based on FCCD (no TL)",
-                    "mode": "eval",
-                    "expression": "ak.where(hit.distance_to_nplus_surface_mm 25",
                 "expression": "tcm.array_id",
-                "sort":"descend_by:hit.energy_sum"
+                "sort": "descend_by:hit.energy_sum",
             },
             "all_channel_id": {
-                "channels":["geds_on","geds_ac"],
+                "channels": ["geds_on", "geds_ac"],
                 "aggregation_mode": "gather",
                 "expression": "tcm.array_id",
-                "sort":"descend_by:hit.energy_sum"
+                "sort": "descend_by:hit.energy_sum",
             },
             "tcm_index": {
-                "channels":["geds_on","geds_ac"],
+                "channels": ["geds_on", "geds_ac"],
                 "aggregation_mode": "gather",
                 "query": "hit.energy_sum > 25",
-                "expression": "tcm.index"
+                "expression": "tcm.index",
             },
             "energy_no_threshold": {
-                "channels":["geds_on"],
+                "channels": ["geds_on"],
                 "aggregation_mode": "keep_at_ch:evt.all_channel_id",
-                "expression": "hit.energy_sum"
+                "expression": "hit.energy_sum",
             },
             "energy_vector": {
-                "channels":["geds_on"],
+                "channels": ["geds_on"],
                 "aggregation_mode": "keep_at_ch:evt.channel_id",
-                "expression": "hit.energy_sum"
-            },
-            "is_good_channels":{
-                "channels":["geds_on","geds_ac"],
-                "aggregation_mode":"keep_at_idx:evt.tcm_index",
-                "expression":"True",
-                "initial":False,
-                "exclude_channels":"geds_ac"
+                "expression": "hit.energy_sum",
             },
-            "is_all_above_threshold" :{
-                "channels":["geds_on","geds_ac"],
-                "aggregation_mode":"all",
-                "expression":"hit.energy_sum>25",
-                "initial":True
+            "is_good_channels": {
+                "channels": ["geds_on", "geds_ac"],
+                "aggregation_mode": "keep_at_idx:evt.tcm_index",
+                "expression": "True",
+                "initial": False,
+                "exclude_channels": "geds_ac",
             },
-            "is_good_event" :{
-                "expression":"ak.all(evt.is_good_channels,axis=-1)"
+            "is_all_above_threshold": {
+                "channels": ["geds_on", "geds_ac"],
+                "aggregation_mode": "all",
+                "expression": "hit.energy_sum>25",
+                "initial": True,
             },
-
+            "is_good_event": {"expression": "ak.all(evt.is_good_channels,axis=-1)"},
             "energy_sum": {
-                "channels":["geds_on"],
+                "channels": ["geds_on"],
                 "aggregation_mode": "sum",
-                "query":"hit.energy_sum > 25",
+                "query": "hit.energy_sum > 25",
                 "expression": "hit.energy_sum",
-                "initial":0
+                "initial": 0,
             },
             "t0": {
-                "channels":["geds_on"],
+                "channels": ["geds_on"],
                 "aggregation_mode": "first_at:hit.t0",
-                "expression": "hit.t0"
+                "expression": "hit.t0",
             },
             "multiplicity": {
                 "channels": ["geds_on"],
                 "aggregation_mode": "sum",
                 "expression": "hit.energy_sum > 25",
-                "initial": 0
-            }
-        }
+                "initial": 0,
+            },
+        },
     }
 
 .. code:: python
 
     from reboost.hpge import evt
+
     logger.setLevel(logging.INFO)
 
 .. code:: python
 
-    %%time
-    evt_ak = evt.build_evt(hit_file="output/hit/output.lh5",tcm_file = "output/tcm/test_tcm.lh5",evt_file=None,config = evt_config)
+    evt_ak = evt.build_evt(
+        hit_file="output/hit/output.lh5",
+        tcm_file="output/tcm/test_tcm.lh5",
+        evt_file=None,
+        config=evt_config,
+    )
 
 
 
@@ -646,10 +677,10 @@ other fields keeping the correspondence with channel or tcm index.
 
 .. code:: python
 
-    print("tcm.array_id       ",tcm_ak.array_id)
-    print("evt.all_channel_id ",evt_ak.all_channel_id)
-    print("evt.channel_id     ",evt_ak.channel_id)
-    print("evt.tcm_index      ",evt_ak.tcm_index)
+    print("tcm.array_id       ", tcm_ak.array_id)
+    print("evt.all_channel_id ", evt_ak.all_channel_id)
+    print("evt.channel_id     ", evt_ak.channel_id)
+    print("evt.tcm_index      ", evt_ak.tcm_index)
 
 
 .. parsed-literal::
@@ -673,8 +704,8 @@ by the energy threshold.
 
 .. code:: python
 
-    print("evt.energy_vector        ",evt_ak.energy_vector)
-    print("evt.energy_no_threshold  ",evt_ak.energy_no_threshold)
+    print("evt.energy_vector        ", evt_ak.energy_vector)
+    print("evt.energy_no_threshold  ", evt_ak.energy_no_threshold)
 
 
 .. parsed-literal::
@@ -687,7 +718,7 @@ Or we can check if each channel is in AC mode.
 
 .. code:: python
 
-    print("evt.is_good_channels ",evt_ak.is_good_channels)
+    print("evt.is_good_channels ", evt_ak.is_good_channels)
 
 
 .. parsed-literal::
@@ -708,10 +739,10 @@ channels above threshold (multiplicity).
 
 .. code:: python
 
-    print("evt_ak.is_all_above_threshold ",evt_ak.is_all_above_threshold)
-    print("evt_ak.energy_sum             ",evt_ak.energy_sum)
-    print("evt_ak.energy_sum             ",evt_ak.energy_sum)
-    print("evt_ak.multiplicity           ",evt_ak.multiplicity)
+    print("evt_ak.is_all_above_threshold ", evt_ak.is_all_above_threshold)
+    print("evt_ak.energy_sum             ", evt_ak.energy_sum)
+    print("evt_ak.energy_sum             ", evt_ak.energy_sum)
+    print("evt_ak.multiplicity           ", evt_ak.multiplicity)
 
 
 .. parsed-literal::
@@ -730,7 +761,7 @@ first checking if any above threshold channel is in AC mode.
 
 .. code:: python
 
-    print("evt_ak.is_good_event           ",evt_ak.is_good_event)
+    print("evt_ak.is_good_event           ", evt_ak.is_good_event)
 
 
 .. parsed-literal::
@@ -747,13 +778,12 @@ quantities.
 
 .. code:: python
 
-    def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs):
-
-        h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double()
+    def plot_energy(axes, energy, bins=400, xrange=None, label=" ", log_y=True, **kwargs):
+        h = hist.new.Reg(bins, *xrange, name="energy [keV]").Double()
         h.fill(energy)
-        h.plot(**kwargs,label=label)
+        h.plot(**kwargs, label=label)
         axes.legend()
-        if (log_y):
+        if log_y:
             axes.set_yscale("log")
         if xrange is not None:
             axes.set_xlim(*xrange)
@@ -761,8 +791,20 @@ quantities.
 .. code:: python
 
     fig, ax = plt.subplots()
-    plot_energy(ax,evt_ak.energy_sum[evt_ak.multiplicity>0],yerr=False,label="Summed energies",xrange=(0,4000))
-    plot_energy(ax,ak.flatten(evt_ak[evt_ak.multiplicity==1].energy_vector),yerr=False,label="M1 energies",xrange=(0,4000))
+    plot_energy(
+        ax,
+        evt_ak.energy_sum[evt_ak.multiplicity > 0],
+        yerr=False,
+        label="Summed energies",
+        xrange=(0, 4000),
+    )
+    plot_energy(
+        ax,
+        ak.flatten(evt_ak[evt_ak.multiplicity == 1].energy_vector),
+        yerr=False,
+        label="M1 energies",
+        xrange=(0, 4000),
+    )
 
     ax.set_title("summed event energy")
 
@@ -788,18 +830,18 @@ spectra.
 
 .. code:: python
 
-    def plot_energy_2D(axes,energy_1,energy_2,bins=400,xrange=None,yrange=None,label=" ",**kwargs):
-
+    def plot_energy_2D(
+        axes, energy_1, energy_2, bins=400, xrange=None, yrange=None, label=" ", **kwargs
+    ):
         x_axis = hist.axis.Regular(bins, *xrange, name="Energy 2 [keV]")
         y_axis = hist.axis.Regular(bins, *yrange, name="Energy 1 [keV]")
         h = hist.Hist(x_axis, y_axis)
-        h.fill(energy_2,energy_1)
+        h.fill(energy_2, energy_1)
         cmap = mpl.colormaps["BuPu"].copy()
         cmap.set_under(color="white")
 
-
         w, x, y = h.to_numpy()
-        mesh = ax.pcolormesh(x, y, w.T, cmap=cmap,vmin=0.5)
+        mesh = ax.pcolormesh(x, y, w.T, cmap=cmap, vmin=0.5)
         ax.set_xlabel("Energy 2 [keV]")
         ax.set_ylabel("Energy 1 [keV]")
         fig.colorbar(mesh)
@@ -811,9 +853,22 @@ spectra.
 
     fig, ax = plt.subplots()
 
-    energy_1 = np.array(evt_ak[(evt_ak.multiplicity==2)&(evt_ak.is_good_event)].energy_vector[:,1])
-    energy_2 = np.array(evt_ak[(evt_ak.multiplicity==2)&(evt_ak.is_good_event)].energy_vector[:,0])
-    plot_energy_2D(ax,energy_2,energy_1,label=None,xrange=(0,1000),yrange=(0,1600),bins=200,vmin=0.5)
+    energy_1 = np.array(
+        evt_ak[(evt_ak.multiplicity == 2) & (evt_ak.is_good_event)].energy_vector[:, 1]
+    )
+    energy_2 = np.array(
+        evt_ak[(evt_ak.multiplicity == 2) & (evt_ak.is_good_event)].energy_vector[:, 0]
+    )
+    plot_energy_2D(
+        ax,
+        energy_2,
+        energy_1,
+        label=None,
+        xrange=(0, 1000),
+        yrange=(0, 1600),
+        bins=200,
+        vmin=0.5,
+    )
     ax.set_title("summed event energy")
 
 
diff --git a/docs/source/notebooks/reboost_hpge_tutorial_hit.rst b/docs/source/notebooks/reboost_hpge_tutorial_hit.rst
index 91d4836..2b7d096 100644
--- a/docs/source/notebooks/reboost_hpge_tutorial_hit.rst
+++ b/docs/source/notebooks/reboost_hpge_tutorial_hit.rst
@@ -69,11 +69,11 @@ You can lower the number of simulated events to speed up the simulation.
 
 We can use ``lh5.show()`` to check the output files.
 
-.. code:: ipython3
+.. code:: python
 
     from lgdo import lh5
 
-.. code:: ipython3
+.. code:: python
 
     lh5.show("output/stp/output_t0.lh5")
 
@@ -131,7 +131,7 @@ for the two Germanium channels.
 
 First we set up the python environment.
 
-.. code:: ipython3
+.. code:: python
 
     from reboost.hpge import hit
     import matplotlib.pyplot as plt
@@ -145,10 +145,10 @@ First we set up the python environment.
     import numpy as np
 
 
-    plt.rcParams['figure.figsize'] = [12, 4]
-    plt.rcParams['axes.titlesize'] =12
-    plt.rcParams['axes.labelsize'] = 12
-    plt.rcParams['legend.fontsize'] = 12
+    plt.rcParams["figure.figsize"] = [12, 4]
+    plt.rcParams["axes.titlesize"] = 12
+    plt.rcParams["axes.labelsize"] = 12
+    plt.rcParams["legend.fontsize"] = 12
 
 
     handler = colorlog.StreamHandler()
@@ -186,108 +186,103 @@ response model (gaussian energy resolution).
 We also include some step based quantities in the output to show the
 effect of the processors.
 
-.. code:: ipython3
+.. code:: python
 
     chain = {
-            "channels": [
-                "det001",
-                "det002"
-            ],
-            "outputs": [
-                "t0",                           # first timestamp
-                "time",                         # time of each step
-                "edep",                         # energy deposited in each step
-                "evtid",                    # id of the hit
-                "global_evtid",             # global id of the hit
-                "distance_to_nplus_surface_mm", # distance to detector nplus surface
-                "activeness",                   # activeness for the step
-                "rpos_loc",                     # radius of step
-                "zpos_loc",                     # z position
-                "energy_sum",                   # true summed energy before dead layer or smearing
-                "energy_sum_deadlayer",         # energy sum after dead layers
-                "energy_sum_smeared"            # energy sum after smearing with resolution
-            ],
-            "step_group": {
-                "description": "group steps by time and evtid with 10us window",
-                "expression": "reboost.hpge.processors.group_by_time(stp,window=10)",
+        "channels": ["det001", "det002"],
+        "outputs": [
+            "t0",  # first timestamp
+            "time",  # time of each step
+            "edep",  # energy deposited in each step
+            "evtid",  # id of the hit
+            "global_evtid",  # global id of the hit
+            "distance_to_nplus_surface_mm",  # distance to detector nplus surface
+            "activeness",  # activeness for the step
+            "rpos_loc",  # radius of step
+            "zpos_loc",  # z position
+            "energy_sum",  # true summed energy before dead layer or smearing
+            "energy_sum_deadlayer",  # energy sum after dead layers
+            "energy_sum_smeared",  # energy sum after smearing with resolution
+        ],
+        "step_group": {
+            "description": "group steps by time and evtid with 10us window",
+            "expression": "reboost.hpge.processors.group_by_time(stp,window=10)",
+        },
+        "locals": {
+            "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)",
+            "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)",
+        },
+        "operations": {
+            "t0": {
+                "description": "first time in the hit.",
+                "mode": "eval",
+                "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)",
+            },
+            "evtid": {
+                "description": "global evtid of the hit.",
+                "mode": "eval",
+                "expression": "ak.fill_none(ak.firsts(hit._evtid,axis=-1),np.nan)",
+            },
+            "global_evtid": {
+                "description": "global evtid of the hit.",
+                "mode": "eval",
+                "expression": "ak.fill_none(ak.firsts(hit._global_evtid,axis=-1),np.nan)",
+            },
+            "distance_to_nplus_surface_mm": {
+                "description": "distance to the nplus surface in mm",
+                "mode": "function",
+                "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')",
+            },
+            "activeness": {
+                "description": "activness based on FCCD (no TL)",
+                "mode": "eval",
+                "expression": "ak.where(hit.distance_to_nplus_surface_mm1][1]].time,histtype="step",yerr=False)
+    plot_times(
+        data_det001[data_det001.global_evtid == unique[counts > 1][1]].time,
+        histtype="step",
+        yerr=False,
+    )
 
 
 
@@ -540,35 +554,48 @@ detector surface and the activeness for each step. We select only events
 within 5 mm of the surface for the first plots. We can see that the
 processor works as expected.
 
-.. code:: ipython3
+.. code:: python
 
-    def plot_map(field,scale="BuPu",clab="Distance [mm]"):
+    def plot_map(field, scale="BuPu", clab="Distance [mm]"):
         fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharey=True)
-        n=100000
-        for idx, (data,config) in enumerate(zip([data_det001,data_det002],["cfg/metadata/BEGe.json","cfg/metadata/Coax.json"])):
-
-            reg=pg4.geant4.Registry()
-            hpge = legendhpges.make_hpge(config,registry=reg)
-
-            legendhpges.draw.plot_profile(hpge, split_by_type=True,axes=axs[idx])
+        n = 100000
+        for idx, (data, config) in enumerate(
+            zip(
+                [data_det001, data_det002],
+                ["cfg/metadata/BEGe.json", "cfg/metadata/Coax.json"],
+            )
+        ):
+            reg = pg4.geant4.Registry()
+            hpge = legendhpges.make_hpge(config, registry=reg)
+
+            legendhpges.draw.plot_profile(hpge, split_by_type=True, axes=axs[idx])
             rng = np.random.default_rng()
-            r = rng.choice([-1,1],p=[0.5,0.5],size=len(ak.flatten(data.rpos_loc)))*ak.flatten(data.rpos_loc)
+            r = rng.choice(
+                [-1, 1], p=[0.5, 0.5], size=len(ak.flatten(data.rpos_loc))
+            ) * ak.flatten(data.rpos_loc)
             z = ak.flatten(data.zpos_loc)
-            c=ak.flatten(data[field])
-            cut = c<5
-
-            s=axs[idx].scatter(r[cut][0:n],z[cut][0:n], c= c[cut][0:n],marker=".", label="gen. points",cmap=scale)
-            #axs[idx].axis("equal")
+            c = ak.flatten(data[field])
+            cut = c < 5
+
+            s = axs[idx].scatter(
+                r[cut][0:n],
+                z[cut][0:n],
+                c=c[cut][0:n],
+                marker=".",
+                label="gen. points",
+                cmap=scale,
+            )
+            # axs[idx].axis("equal")
 
             if idx == 0:
                 axs[idx].set_ylabel("Height [mm]")
-            c=plt.colorbar(s)
+            c = plt.colorbar(s)
             c.set_label(clab)
 
             axs[idx].set_xlabel("Radius [mm]")
 
 
-.. code:: ipython3
+.. code:: python
 
     plot_map("distance_to_nplus_surface_mm")
 
@@ -589,9 +616,9 @@ processor works as expected.
 .. image:: images/output_27_1.png
 
 
-.. code:: ipython3
+.. code:: python
 
-    plot_map("activeness",clab="Activeness",scale="viridis")
+    plot_map("activeness", clab="Activeness", scale="viridis")
 
 
 .. parsed-literal::
@@ -612,24 +639,37 @@ processor works as expected.
 
 We can also plot a histogram of the distance to the surface.
 
-.. code:: ipython3
-
-    def plot_distances(axes,distances,xrange=None,label=" ",**kwargs):
+.. code:: python
 
-        h=hist.new.Reg(100,*xrange, name="Distance to n+ surface [mm]").Double()
+    def plot_distances(axes, distances, xrange=None, label=" ", **kwargs):
+        h = hist.new.Reg(100, *xrange, name="Distance to n+ surface [mm]").Double()
         h.fill(distances)
-        h.plot(**kwargs,label=label)
+        h.plot(**kwargs, label=label)
         axes.legend()
         axes.set_yscale("log")
         if xrange is not None:
             ax.set_xlim(*xrange)
 
 
-.. code:: ipython3
+.. code:: python
 
-    fig,ax = plt.subplots()
-    plot_distances(ax,ak.flatten(data_det001.distance_to_nplus_surface_mm),xrange=(0,35),label="BEGe",histtype="step",yerr=False)
-    plot_distances(ax,ak.flatten(data_det002.distance_to_nplus_surface_mm),xrange=(0,35),label="Coax",histtype="step",yerr=False)
+    fig, ax = plt.subplots()
+    plot_distances(
+        ax,
+        ak.flatten(data_det001.distance_to_nplus_surface_mm),
+        xrange=(0, 35),
+        label="BEGe",
+        histtype="step",
+        yerr=False,
+    )
+    plot_distances(
+        ax,
+        ak.flatten(data_det002.distance_to_nplus_surface_mm),
+        xrange=(0, 35),
+        label="Coax",
+        histtype="step",
+        yerr=False,
+    )
 
 
 
@@ -643,37 +683,52 @@ We can also plot a histogram of the distance to the surface.
 Our processing chain also sums the energies of the hits, both before and
 after weighting by the activeness.
 
-.. code:: ipython3
-
-    def plot_energy(axes,energy,bins=400,xrange=None,label=" ",log_y=True,**kwargs):
+.. code:: python
 
-        h=hist.new.Reg(bins,*xrange, name="energy [keV]").Double()
+    def plot_energy(axes, energy, bins=400, xrange=None, label=" ", log_y=True, **kwargs):
+        h = hist.new.Reg(bins, *xrange, name="energy [keV]").Double()
         h.fill(energy)
-        h.plot(**kwargs,label=label)
+        h.plot(**kwargs, label=label)
         axes.legend()
-        if (log_y):
+        if log_y:
             axes.set_yscale("log")
         if xrange is not None:
             axes.set_xlim(*xrange)
 
-.. code:: ipython3
+.. code:: python
 
     fig, ax = plt.subplots()
     ax.set_title("BEGe energy spectrum")
-    plot_energy(ax,data_det001.energy_sum,yerr=False,label="True energy",xrange=(0,4000))
-    plot_energy(ax,data_det001.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000))
+    plot_energy(
+        ax, data_det001.energy_sum, yerr=False, label="True energy", xrange=(0, 4000)
+    )
+    plot_energy(
+        ax,
+        data_det001.energy_sum_deadlayer,
+        yerr=False,
+        label="Energy after dead layer",
+        xrange=(0, 4000),
+    )
 
 
 
 .. image:: images/output_34_0.png
 
 
-.. code:: ipython3
+.. code:: python
 
     fig, ax = plt.subplots()
     ax.set_title("COAX energy spectrum")
-    plot_energy(ax,data_det002.energy_sum,yerr=False,label="True energy",xrange=(0,4000))
-    plot_energy(ax,data_det002.energy_sum_deadlayer,yerr=False,label="Energy after dead layer",xrange=(0,4000))
+    plot_energy(
+        ax, data_det002.energy_sum, yerr=False, label="True energy", xrange=(0, 4000)
+    )
+    plot_energy(
+        ax,
+        data_det002.energy_sum_deadlayer,
+        yerr=False,
+        label="Energy after dead layer",
+        xrange=(0, 4000),
+    )
 
 
 
@@ -690,11 +745,29 @@ in a similar way. It would also be simple to use instead an energy
 dependent resolution curve. To see the effect we have to zoom into the
 2615 keV peak.
 
-.. code:: ipython3
+.. code:: python
 
     fig, axs = plt.subplots()
-    plot_energy(axs,data_det001.energy_sum_smeared,yerr=False,label="BEGe",xrange=(2600,2630),log_y=False,bins=150,density=True)
-    plot_energy(axs,data_det002.energy_sum_smeared,yerr=False,label="COAX",xrange=(2600,2630),log_y=False,bins=150,density=True)
+    plot_energy(
+        axs,
+        data_det001.energy_sum_smeared,
+        yerr=False,
+        label="BEGe",
+        xrange=(2600, 2630),
+        log_y=False,
+        bins=150,
+        density=True,
+    )
+    plot_energy(
+        axs,
+        data_det002.energy_sum_smeared,
+        yerr=False,
+        label="COAX",
+        xrange=(2600, 2630),
+        log_y=False,
+        bins=150,
+        density=True,
+    )

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It buils on the offical **remage** +tutorial +`[link] `__ + + .. rubric:: *Note* + :name: note + + To run this tutorial it is recommended to create the following + directory structure to organise the outputs and config inputs. + + + ├── cfg + │   └── metadata + ├── output + │   + ├── stp + │   + └── hit + └── reboost_hpge_tutorial.ipynb + +.. + +Part 1) Running the remage simulation +------------------------------------- + +Before we can run any post-processing we need to run the Geant4 +simulation. For this we follow the remage tutorial to generate the GDML +geometry. We save this into the GDML file *cfg/geom.gdml* for use by +remage. We also need to save the metadata dictonaries into json files +(in the *cfg/metadata* folder as *BEGe.json* and *Coax.json* + +We use a slightly modified Geant4 macro to demonstrate some features of +reboost (this should be saved as *cfg/th228.mac* to run remage on the +command line). + +.. code:: text + + /RMG/Manager/Logging/LogLevel detail + + /RMG/Geometry/RegisterDetector Germanium BEGe 001 + /RMG/Geometry/RegisterDetector Germanium Coax 002 + /RMG/Geometry/RegisterDetector Scintillator LAr 003 + + /run/initialize + + /RMG/Generator/Confine Volume + /RMG/Generator/Confinement/Physical/AddVolume Source + + /RMG/Generator/Select GPS + /gps/particle ion + /gps/energy 0 eV + /gps/ion 88 224 # 224-Ra + /process/had/rdm/nucleusLimits 208 224 81 88 #Ra-224 to 208-Pb + + + /run/beamOn 10000000 + +We then use the remage exectuable (see +`[remage-docs] `__ for +installation instructions) to run the simulation: > #### *Note* > Both +of *cfg/th228.mac* and *cfg/geometry.gdml* are needed to run remage + +.. code:: console + + $ remage --threads 8 --gdml-files cfg/geom.gdml --output-file output/stp/output.lh5 -- cfg/th228.mac + +You can lower the number of simulated events to speed up the simulation. + +We can use ``lh5.show()`` to check the output files. + +.. code:: ipython3 + + from lgdo import lh5 + +.. code:: ipython3 + + lh5.show("output/stp/output_t0.lh5") + +.. code:: text + + / + └── stp · struct{det001,det002,det003,vertices} + ├── det001 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det002 · table{evtid,particle,edep,time,xloc,yloc,zloc} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── xloc · array<1>{real} + │ ├── yloc · array<1>{real} + │ └── zloc · array<1>{real} + ├── det003 · table{evtid,particle,edep,time,xloc_pre,yloc_pre,zloc_pre,xloc_post,yloc_post,zloc_post,v_pre,v_post} + │ ├── edep · array<1>{real} + │ ├── evtid · array<1>{real} + │ ├── particle · array<1>{real} + │ ├── time · array<1>{real} + │ ├── v_post · array<1>{real} + │ ├── v_pre · array<1>{real} + │ ├── xloc_post · array<1>{real} + │ ├── xloc_pre · array<1>{real} + │ ├── yloc_post · array<1>{real} + │ ├── yloc_pre · array<1>{real} + │ ├── zloc_post · array<1>{real} + │ └── zloc_pre · array<1>{real} + └── vertices · table{evtid,time,xloc,yloc,zloc,n_part} + ├── evtid · array<1>{real} + ├── n_part · array<1>{real} + ├── time · array<1>{real} + ├── xloc · array<1>{real} + ├── yloc · array<1>{real} + └── zloc · array<1>{real} + +Part 2) reboost config files +---------------------------- + +For this tutorial we perform a basic post-processing of the *hit* tier +for the two Germanium channels. + +2.1) Setup the enviroment +~~~~~~~~~~~~~~~~~~~~~~~~~ + +First we set up the python enviroment. + +.. code:: ipython3 + + from reboost.hpge import hit + import matplotlib.pyplot as plt + import pyg4ometry as pg4 + import legendhpges + from legendhpges import draw + import awkward as ak + import logging + import colorlog + import hist + import numpy as np + + + plt.rcParams['figure.figsize'] = [12, 4] + plt.rcParams['axes.titlesize'] =12 + plt.rcParams['axes.labelsize'] = 12 + plt.rcParams['legend.fontsize'] = 12 + + + handler = colorlog.StreamHandler() + handler.setFormatter( + colorlog.ColoredFormatter("%(log_color)s%(name)s [%(levelname)s] %(message)s") + ) + logger = logging.getLogger() + logger.handlers.clear() + logger.addHandler(handler) + logger.setLevel(logging.INFO) + logger.info("test") + + + + +2.2) Processing chain and parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Next we need to make the processing chain config file. + +The processing chain below gives a standard set of steps for a HPGe +simulation. 1. first the steps are windowed into hits, 2. the first +timestamp and index of each hit is computed (for use in event building), +3. the distance to the detector n+ surface is computed and from this the +activeness is calculated (based on the FCCD) 4. the energy in each step +is summed to extract the deposited energy (both with and without +deadlayer correction), 5. the energy is convolved with the detector +response model (gaussian energy resolution). + +We also include some step based quantities in the output to show the +effect of the processors. + +.. code:: ipython3 + + chain = { + "channels": [ + "det001", + "det002" + ], + "outputs": [ + "t0", # first timestamp + "time", # time of each step + "edep", # energy deposited in each step + "hit_evtid", # id of the hit + "hit_global_evtid", # global id of the hit + "distance_to_nplus_surface_mm", # distance to detector nplus surface + "activeness", # activeness for the step + "rpos_loc", # radius of step + "zpos_loc", # z position + "energy_sum", # true summed energy before dead layer or smearing + "energy_sum_deadlayer", # energy sum after dead layers + "energy_sum_smeared" # energy sum after smearing with resolution + ], + "step_group": { + "description": "group steps by time and evtid with 10us window", + "expression": "reboost.hpge.processors.group_by_time(stp,window=10)", + }, + "locals": { + "hpge": "reboost.hpge.utils.get_hpge(meta_path=meta,pars=pars,detector=detector)", + "phy_vol": "reboost.hpge.utils.get_phy_vol(reg=reg,pars=pars,detector=detector)", + }, + "operations": { + "t0": { + "description": "first time in the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.time,axis=-1),np.nan)", + }, + "hit_evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.evtid,axis=-1),np.nan)", + }, + "hit_global_evtid": { + "description": "global evtid of the hit.", + "mode": "eval", + "expression": "ak.fill_none(ak.firsts(hit.global_evtid,axis=-1),np.nan)", + }, + "distance_to_nplus_surface_mm": { + "description": "distance to the nplus surface in mm", + "mode": "function", + "expression": "reboost.hpge.processors.distance_to_surface(hit.xloc, hit.yloc, hit.zloc, hpge, phy_vol.position.eval(), surface_type='nplus',unit='m')", + }, + "activeness": { + "description": "activness based on FCCD (no TL)", + "mode": "eval", + "expression": "ak.where(hit.distance_to_nplus_surface_mm{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── edep · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── energy_sum · array<1>{real} + │ ├── energy_sum_deadlayer · array<1>{real} + │ ├── energy_sum_smeared · array<1>{real} + │ ├── hit_evtid · array<1>{real} + │ ├── hit_global_evtid · array<1>{real} + │ ├── rpos_loc · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ ├── t0 · array<1>{real} + │ ├── time · array<1>{array<1>{real}} + │ │ ├── cumulative_length · array<1>{real} + │ │ └── flattened_data · array<1>{real} + │ └── zpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── det002 · table{edep,time,t0,hit_evtid,hit_global_evtid,distance_to_nplus_surface_mm,activeness,rpos_loc,zpos_loc,energy_sum,energy_sum_deadlayer,energy_sum_smeared} + ├── activeness · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── distance_to_nplus_surface_mm · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── edep · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── energy_sum · array<1>{real} + ├── energy_sum_deadlayer · array<1>{real} + ├── energy_sum_smeared · array<1>{real} + ├── hit_evtid · array<1>{real} + ├── hit_global_evtid · array<1>{real} + ├── rpos_loc · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + ├── t0 · array<1>{real} + ├── time · array<1>{array<1>{real}} + │ ├── cumulative_length · array<1>{real} + │ └── flattened_data · array<1>{real} + └── zpos_loc · array<1>{array<1>{real}} + ├── cumulative_length · array<1>{real} + └── flattened_data · array<1>{real} + + +The new format is a factor of x17 times smaller than the input file due +to the removal of many *step* based fields which use alot of memory and +due to the removal of the *vertices* table and the LAr hits. So we can +easily read the whole file into memory. We use *awkward* to analyse the +output files. + +.. code:: ipython3 + + data_det001 = lh5.read_as("hit/det001","output/hit/output.lh5","ak") + data_det002 = lh5.read_as("hit/det002","output/hit/output.lh5","ak") + +.. code:: ipython3 + + data_det001[0] + + + + +.. raw:: html + +