From 061853ac0ed673db05a1a47aa62880b854727835 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 14:27:21 +0200 Subject: [PATCH 001/217] refactoring of reco logic --- skymap_scanner/recos/__init__.py | 13 +++- skymap_scanner/recos/millipede_original.py | 9 +++ skymap_scanner/recos/millipede_wilks.py | 3 + skymap_scanner/recos/vertex_gen.py | 70 ++++++++++++++++++++++ skymap_scanner/server/start_scan.py | 60 +++++++------------ skymap_scanner/utils/prepare_frames.py | 11 ++++ 6 files changed, 128 insertions(+), 38 deletions(-) create mode 100644 skymap_scanner/recos/vertex_gen.py diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index ca71c0b4f..53d38dc9e 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -5,6 +5,8 @@ import pkgutil from typing import TYPE_CHECKING, Any, List +from .vertex_gen import VertexGenerator + if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation @@ -31,6 +33,13 @@ class RecoInterface: # The spline files will be looked up in pre-defined local paths or fetched from a remote data store. SPLINE_REQUIREMENTS: List[str] = list() + # List of vectors referenced to the origin that will be used to generate the vertex position variation. + VERTEX_VARIATIONS: List[I3Position] = VertexGenerator.point() + + @staticmethod + def prepare_frames(tray, name, logger, **kwargs: Any) -> None: + raise NotImplementedError() + @staticmethod def traysegment(tray, name, logger, **kwargs: Any) -> None: raise NotImplementedError() @@ -50,7 +59,9 @@ def get_all_reco_algos() -> List[str]: def get_reco_interface_object(name: str) -> RecoInterface: - """Dynamically import the reco sub-module's class.""" + """Dynamically import the reco sub-module's class. + Implicitly assumes that name `foo_bar` corresponds to class `FooBar`. + """ try: # Fetch module module = importlib.import_module(f"{__name__}.{name.lower()}") diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 7366395a9..d4572a5c0 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -32,8 +32,17 @@ from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface +from .vertex_gen import VertexGenerator + class MillipedeOriginal(RecoInterface): + variation_distance = 20.*I3Units.m + + if cfg.ENV.SKYSCAN_MINI_TEST: + VERTEX_VARIATIONS = VertexGenerator.mini_test(variation_distance=variation_distance) + else: + VERTEX_VARIATIONS = VertexGenerator.octahedron(variation_distance=variation_distance) + """Reco logic for millipede.""" # Spline requirements MIE_ABS_SPLINE = "ems_mie_z20_a10.abs.fits" diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 54207b6e0..19858da8e 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,12 +29,15 @@ from .. import config as cfg from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation +from .vertex_gen import VertexGenerator from . import RecoInterface class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" + VERTEX_VARIATIONS = VertexGenerator.point() + # Spline requirements ############################################## FTP_ABS_SPLINE = "cascade_single_spice_ftp-v1_flat_z20_a5.abs.fits" FTP_PROB_SPLINE = "cascade_single_spice_ftp-v1_flat_z20_a5.prob.fits" diff --git a/skymap_scanner/recos/vertex_gen.py b/skymap_scanner/recos/vertex_gen.py new file mode 100644 index 000000000..ec10cabba --- /dev/null +++ b/skymap_scanner/recos/vertex_gen.py @@ -0,0 +1,70 @@ +from typing import List + +import numpy as np + +from icecube import dataclasses # type: ignore[import] +from I3Tray import I3Units # type: ignore[import] + + +class VertexGenerator: + def __init__(self): + pass + + @staticmethod + def point(): + return [dataclasses.I3Position(0.0, 0.0, 0.0)] + + @staticmethod + def octahedron(radius: float): + return [ + dataclasses.I3Position(0.0, 0.0, 0.0), + dataclasses.I3Position(-radius, 0.0, 0.0), + dataclasses.I3Position(radius, 0.0, 0.0), + dataclasses.I3Position(0.0, -radius, 0.0), + dataclasses.I3Position(0.0, radius, 0.0), + dataclasses.I3Position(0.0, 0.0, -radius), + dataclasses.I3Position(0.0, 0.0, radius), + ] + + @staticmethod + def cylinder( + v_ax: List[float] = [-40.0, 40.0], + r_ax: List[float] = [150.0], + ang_steps=3, + ): + vert_u = I3Units.m + + # define angular steps + ang_ax = np.linspace(0, 2.0 * np.pi, ang_steps + 1)[:-1] + + # angular separation between seeds + dang = (ang_ax[1] - ang_ax[0]) / 2.0 + + pos_seeds = [dataclasses.I3Position(0.0, 0.0, 0.0)] + + for i, vi in enumerate(v_ax): # step along axis + for j, r in enumerate(r_ax): # step along radius + for ang in ang_ax: # step around anlge + x = r * np.cos(ang + (i + j) * dang) + y = r * np.sin(ang + (i + j) * dang) + z = vi + + pos = dataclasses.I3Position( + x * vert_u, + y * vert_u, + z * vert_u, + ) + + pos_seeds.append(pos) + + return pos_seeds + + @staticmethod + def mini_test(variation_distance): + """Simple two-variations config for testing purposes. + It does not have a physical motivation. + """ + return [ + dataclasses.I3Position(0.0, 0.0, 0.0), + dataclasses.I3Position(-variation_distance, 0.0, 0.0), + ] diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index dc5e3c529..18693018c 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -42,6 +42,7 @@ from .reporter import Reporter from .utils import NSideProgression, fetch_event_contents + StrDict = Dict[str, Any] @@ -86,28 +87,7 @@ def __init__( self.output_particle_name = output_particle_name self.reco_algo = reco_algo.lower() - # Get Position Variations - variation_distance = 20.*I3Units.m - if self.reco_algo == 'millipede_original': - if cfg.ENV.SKYSCAN_MINI_TEST: - self.pos_variations = [ - dataclasses.I3Position(0.,0.,0.), - dataclasses.I3Position(-variation_distance,0.,0.) - ] - else: - self.pos_variations = [ - dataclasses.I3Position(0.,0.,0.), - dataclasses.I3Position(-variation_distance,0.,0.), - dataclasses.I3Position(variation_distance,0.,0.), - dataclasses.I3Position(0.,-variation_distance,0.), - dataclasses.I3Position(0., variation_distance,0.), - dataclasses.I3Position(0.,0.,-variation_distance), - dataclasses.I3Position(0.,0., variation_distance) - ] - else: - self.pos_variations = [ - dataclasses.I3Position(0.,0.,0.), - ] + self.pos_variations = recos.get_reco_interface_object(reco_algo).VERTEX_VARIATIONS # Set min nside self.min_nside = min_nside @@ -136,6 +116,7 @@ def __init__( self.omgeo = g_frame["I3Geometry"].omgeo + @staticmethod def refine_vertex_time(vertex, time, direction, pulses, omgeo): thc = dataclasses.I3Constants.theta_cherenkov @@ -222,7 +203,7 @@ def _gen_pframes( zenith, azimuth = astro.equa_to_dir(ra, dec, self.event_metadata.mjd) zenith = float(zenith) azimuth = float(azimuth) - direction = dataclasses.I3Direction(zenith,azimuth) + direction = dataclasses.I3Direction(zenith, azimuth) if nside == self.min_nside: position = self.fallback_position @@ -259,21 +240,26 @@ def _gen_pframes( time = self.nsides_dict[coarser_nside][coarser_pixel].time energy = self.nsides_dict[coarser_nside][coarser_pixel].energy - for i in range(0,len(self.pos_variations)): + n_pos_variations = len(self.pos_variations) + + LOGGER.debug(f"Generating {n_pos_variations} position variations.") + + for i in range(0, n_pos_variations): p_frame = icetray.I3Frame(icetray.I3Frame.Physics) posVariation = self.pos_variations[i] - if self.reco_algo == 'millipede_wilks': + if self.reco_algo in ['millipede_wilks', 'splinempe']: # rotate variation to be applied in transverse plane posVariation.rotate_y(direction.theta) posVariation.rotate_z(direction.phi) - if position != self.fallback_position: - # add fallback pos as an extra first guess - p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( - self.fallback_position+posVariation, - direction, - self.fallback_energy, - self.fallback_time) + if self.reco_algo == 'millipede_wilks': + if position != self.fallback_position: + # add fallback pos as an extra first guess + p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( + self.fallback_position+posVariation, + direction, + self.fallback_energy, + self.fallback_time) p_frame[f'{self.output_particle_name}'] = self.i3particle(position+posVariation, direction, @@ -458,13 +444,13 @@ async def _serve_and_collect( collected_all_sent = False async with from_clients_queue.open_sub() as sub: # re-open to avoid inactivity timeout (applicable for rabbitmq) async for msg in sub: - if not isinstance(msg['reco_pixel_variation'], RecoPixelVariation): + if not isinstance(msg["reco_pixel_variation"], RecoPixelVariation): raise ValueError( f"Message not {RecoPixelVariation}: {type(msg['reco_pixel_variation'])}" ) try: await collector.collect( - msg['reco_pixel_variation'], msg['runtime'] + msg["reco_pixel_variation"], msg["runtime"] ) except ExtraRecoPixelVariationException as e: logging.error(e) @@ -617,7 +603,7 @@ def _nside_and_pixelextension(val: str) -> Tuple[int, int]: f"The first nside's pixel extension must be {NSideProgression.FIRST_NSIDE_PIXEL_EXTENSION}. " f"Example: --nsides 8:{NSideProgression.FIRST_NSIDE_PIXEL_EXTENSION} 64:12 512:24" ), - nargs='*', + nargs="*", type=_nside_and_pixelextension, ) # --real-event XOR --simulated-event @@ -625,12 +611,12 @@ def _nside_and_pixelextension(val: str) -> Tuple[int, int]: group.add_argument( "--real-event", action="store_true", - help='include this flag if the event is real', + help="include this flag if the event is real", ) group.add_argument( "--simulated-event", action="store_true", - help='include this flag if the event was simulated', + help="include this flag if the event was simulated", ) # predictive_scanning_threshold diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index fed4b4559..1804a67a6 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -16,6 +16,7 @@ from .. import config as cfg +from .. import recos from . import LOGGER @@ -130,10 +131,20 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, If=lambda frame: not frame.Has("HESE_VHESelfVeto")) + + if reco_algo.lower() == "splinempe": + # perform fit + tray.AddSegment( + recos.get_reco_interface_object(reco_algo).prepare_frames, + f"{reco_algo}_prepareframes", + logger=LOGGER + ) # If the event has a GCD diff (compressed GCD), only keep the diffs. # The GCD will be reassembled from baseline + diff by the client. if baseline_GCD is not None: + # The input event carries a compressed GCD. + # Only the GCD diff is propagated, the full GCD will be rebuilt downstream. def delFrameObjectsWithDiffsAvailable(frame): all_keys = list(frame.keys()) for key in list(frame.keys()): From ff9fdf042ba0cb5f8262d0d2ffe3b95990dab62b Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 9 May 2023 12:36:02 +0000 Subject: [PATCH 002/217] update requirements-all.txt --- requirements-all.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements-all.txt b/requirements-all.txt index 80f0533de..0387a61e6 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via # pulsar-client # requests @@ -28,7 +28,7 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.0 +ewms-pilot==0.10.1 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib @@ -80,7 +80,7 @@ packaging==23.1 # matplotlib pandas==2.0.1 # via icecube-skyreader -pika==1.3.1 +pika==1.3.2 # via oms-mqclient pillow==9.5.0 # via matplotlib From 1bf5d4ffef86e9305fb88e345dec103fd0de514d Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 9 May 2023 12:36:02 +0000 Subject: [PATCH 003/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 87431ac88..7e6cb1923 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -24,7 +24,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.1 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From f378a2d2dbfc386715111136bac56741f0d38834 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 9 May 2023 12:36:02 +0000 Subject: [PATCH 004/217] update requirements-nats.txt --- requirements-nats.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 4c118387a..290d16e23 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.0 +ewms-pilot==0.10.1 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From f5770af84fc81615a91c8b145b563026ee852fb9 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 9 May 2023 12:36:02 +0000 Subject: [PATCH 005/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index b0421c77a..ae64774d0 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via # pulsar-client # requests @@ -26,7 +26,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.1 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From cec84d8ad3728e9a81e877907d33e33020d287c1 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 9 May 2023 12:36:02 +0000 Subject: [PATCH 006/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index db70ea4dd..604a62553 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -24,7 +24,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.1 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib @@ -72,7 +72,7 @@ packaging==23.1 # matplotlib pandas==2.0.1 # via icecube-skyreader -pika==1.3.1 +pika==1.3.2 # via oms-mqclient pillow==9.5.0 # via matplotlib From 66d1a4d07ee4ab47a376676751ebd1b6df33bddc Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 9 May 2023 12:36:02 +0000 Subject: [PATCH 007/217] update requirements.txt --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index dc89d7765..2075f00b2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -24,7 +24,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.1 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From 9ecac0691b95230067aa9fd2d7185860cff3e6b7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 14:40:54 +0200 Subject: [PATCH 008/217] direct import --- skymap_scanner/recos/millipede_original.py | 3 +-- skymap_scanner/recos/millipede_wilks.py | 4 +--- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index d4572a5c0..981e93c71 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -31,8 +31,7 @@ from .. import config as cfg from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface -from .vertex_gen import VertexGenerator +from . import RecoInterface, VertexGenerator class MillipedeOriginal(RecoInterface): diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 19858da8e..4bbefaac4 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,9 +29,7 @@ from .. import config as cfg from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation -from .vertex_gen import VertexGenerator -from . import RecoInterface - +from . import RecoInterface, VertexGenerator class MillipedeWilks(RecoInterface): From 4298245efe6c033f93760e8cfb1e83ca101e8541 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 15:07:01 +0200 Subject: [PATCH 009/217] logging --- skymap_scanner/utils/extract_json_message.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skymap_scanner/utils/extract_json_message.py b/skymap_scanner/utils/extract_json_message.py index 8508c00e5..b15ad03a1 100644 --- a/skymap_scanner/utils/extract_json_message.py +++ b/skymap_scanner/utils/extract_json_message.py @@ -225,6 +225,7 @@ def __extract_frame_packet( frame_packet[i] = baseline_GCD_framepacket[i] del baseline_GCD_framepacket + LOGGER.info("Preprocessing event frames!") if baseline_GCD is not None: # frame_packet has GCD diff, provide baseline frame_packet = prepare_frames(frame_packet, baseline_GCD_file, reco_algo, pulsesName=pulsesName) From 866ca85079447a6cf4aca0bcee328443adab40fd Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 15:14:00 +0200 Subject: [PATCH 010/217] fix arg name --- skymap_scanner/recos/millipede_original.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 981e93c71..40e45b881 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -40,7 +40,7 @@ class MillipedeOriginal(RecoInterface): if cfg.ENV.SKYSCAN_MINI_TEST: VERTEX_VARIATIONS = VertexGenerator.mini_test(variation_distance=variation_distance) else: - VERTEX_VARIATIONS = VertexGenerator.octahedron(variation_distance=variation_distance) + VERTEX_VARIATIONS = VertexGenerator.octahedron(radius=variation_distance) """Reco logic for millipede.""" # Spline requirements From d4b059fd772a931b053623eeecf33dd17eab56da Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 15:20:25 +0200 Subject: [PATCH 011/217] i3 logging --- skymap_scanner/utils/prepare_frames.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index 1804a67a6..204fdd748 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -81,6 +81,9 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, simclasses, ) + # ACTIVATE FOR DEBUG + icetray.logging.console() + output_frames: list[icetray.I3Frame] = [] tray = I3Tray() From 333cef7fb82e889df00f8156870e1eada28ba74f Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 15:38:07 +0200 Subject: [PATCH 012/217] you are not ready for that --- skymap_scanner/utils/prepare_frames.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index 204fdd748..1431ec186 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -16,7 +16,7 @@ from .. import config as cfg -from .. import recos +# from .. import recos from . import LOGGER @@ -135,13 +135,13 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, OutputVertexPos=cfg.INPUT_POS_NAME, If=lambda frame: not frame.Has("HESE_VHESelfVeto")) - if reco_algo.lower() == "splinempe": - # perform fit - tray.AddSegment( - recos.get_reco_interface_object(reco_algo).prepare_frames, - f"{reco_algo}_prepareframes", - logger=LOGGER - ) + # if reco_algo.lower() == "splinempe": + # # perform fit + # tray.AddSegment( + # recos.get_reco_interface_object(reco_algo).prepare_frames, + # f"{reco_algo}_prepareframes", + # logger=LOGGER + # ) # If the event has a GCD diff (compressed GCD), only keep the diffs. # The GCD will be reassembled from baseline + diff by the client. From 820187e7ebca86b6ebf58bf38841292a16936b87 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 15:52:27 +0200 Subject: [PATCH 013/217] instantiate photospline service inside traysegment --- skymap_scanner/recos/millipede_original.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 40e45b881..b1de8d3c6 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -66,10 +66,6 @@ class MillipedeOriginal(RecoInterface): datastager.stage_files(SPLINE_REQUIREMENTS) abs_spline: str = datastager.get_filepath(MIE_ABS_SPLINE) prob_spline: str = datastager.get_filepath(MIE_PROB_SPLINE) - - cascade_service = photonics_service.I3PhotoSplineService(abs_spline, prob_spline, timingSigma=0.0) - cascade_service.SetEfficiencies(SPEScale) - muon_service = None def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: @@ -169,6 +165,10 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): @icetray.traysegment def traysegment(tray, name, logger, seed=None): + cascade_service = photonics_service.I3PhotoSplineService(MillipedeOriginal.abs_spline, MillipedeOriginal.prob_spline, timingSigma=0.0) + cascade_service.SetEfficiencies(SPEScale) + muon_service = None + """Perform MillipedeOriginal reco.""" ExcludedDOMs = tray.Add(MillipedeOriginal.exclusions) @@ -180,8 +180,8 @@ def notify0(frame): tray.AddModule(notify0, "notify0") tray.AddService('MillipedeLikelihoodFactory', 'millipedellh', - MuonPhotonicsService=MillipedeOriginal.muon_service, - CascadePhotonicsService=MillipedeOriginal.cascade_service, + MuonPhotonicsService=muon_service, + CascadePhotonicsService=cascade_service, ShowerRegularization=0, PhotonsPerBin=15, # DOMEfficiency=SPEScale, # moved to cascade_service.SetEfficiencies(SPEScale) From 3256c3dd626d53cb288e116447781d894e5be8c0 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 15:59:10 +0200 Subject: [PATCH 014/217] resolve names --- skymap_scanner/recos/millipede_original.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index b1de8d3c6..2a8597e5e 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -166,7 +166,7 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): @icetray.traysegment def traysegment(tray, name, logger, seed=None): cascade_service = photonics_service.I3PhotoSplineService(MillipedeOriginal.abs_spline, MillipedeOriginal.prob_spline, timingSigma=0.0) - cascade_service.SetEfficiencies(SPEScale) + cascade_service.SetEfficiencies(MillipedeOriginal.SPEScale) muon_service = None """Perform MillipedeOriginal reco.""" From fda381e7f25a8a6a0f66f445937ea44d97f8fcef Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 16:45:36 +0200 Subject: [PATCH 015/217] use traysegments for preparing frames --- .../recos/millipede_original_refactor.py | 343 ++++++++++++++++++ skymap_scanner/recos/millipede_wilks.py | 23 ++ skymap_scanner/utils/prepare_frames.py | 44 +-- 3 files changed, 372 insertions(+), 38 deletions(-) create mode 100644 skymap_scanner/recos/millipede_original_refactor.py diff --git a/skymap_scanner/recos/millipede_original_refactor.py b/skymap_scanner/recos/millipede_original_refactor.py new file mode 100644 index 000000000..747bc2be0 --- /dev/null +++ b/skymap_scanner/recos/millipede_original_refactor.py @@ -0,0 +1,343 @@ +"""IceTray segment for a millipede reco.""" + +# fmt: off +# pylint: skip-file +# mypy: ignore-errors + +import copy +import datetime +import os +from typing import Tuple + +import numpy + +from I3Tray import I3Units +from icecube import ( # noqa: F401 + VHESelfVeto, + dataclasses, + dataio, + frame_object_diff, + gulliver, + gulliver_modules, + icetray, + lilliput, + millipede, + photonics_service, + recclasses, + simclasses, +) +from icecube.icetray import I3Frame + +from .. import config as cfg +from ..utils.data_handling import DataStager +from ..utils.pixel_classes import RecoPixelVariation +from . import RecoInterface, VertexGenerator + + +class MillipedeOriginal(RecoInterface): + """Reco logic for millipede.""" + + # Spline requirements ############################################## + MIE_ABS_SPLINE = "ems_mie_z20_a10.abs.fits" + MIE_PROB_SPLINE = "ems_mie_z20_a10.prob.fits" + + SPLINE_REQUIREMENTS = [ MIE_ABS_SPLINE, MIE_PROB_SPLINE ] + + # Constants ######################################################## + pulsesName = cfg.INPUT_PULSES_NAME + pulsesName_cleaned = pulsesName+'LatePulseCleaned' + SPEScale = 0.99 + + @staticmethod + def get_vertex_variations(): + variation_distance = 20.*I3Units.m + + if cfg.ENV.SKYSCAN_MINI_TEST: + return VertexGenerator.mini_test(variation_distance=variation_distance) + else: + return VertexGenerator.octahedron(radius=variation_distance) + + + def __init__(self): + # Load Data ######################################################## + # At HESE energies, deposited light is dominated by the stochastic losses + # (muon part emits so little light in comparison) + # This is why we can use cascade tables + datastager = DataStager( + local_paths=cfg.LOCAL_DATA_SOURCES, + local_subdir=cfg.LOCAL_SPLINE_SUBDIR, + remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", + ) + datastager.stage_files(SPLINE_REQUIREMENTS) + self.abs_spline: str = datastager.get_filepath(MIE_ABS_SPLINE) + self.prob_spline: str = datastager.get_filepath(MIE_PROB_SPLINE) + + @staticmethod + def makeSurePulsesExist(frame, pulsesName) -> None: + if pulsesName not in frame: + raise RuntimeError("{0} not in frame".format(pulsesName)) + if pulsesName + "TimeWindows" not in frame: + raise RuntimeError("{0} not in frame".format(pulsesName + "TimeWindows")) + if pulsesName + "TimeRange" not in frame: + raise RuntimeError("{0} not in frame".format(pulsesName + "TimeRange")) + + @icetray.traysegment + def exclusions(tray, name): + tray.Add('Delete', keys=['BrightDOMs', + 'SaturatedDOMs', + 'DeepCoreDOMs', + MillipedeOriginal.pulsesName_cleaned, + MillipedeOriginal.pulsesName_cleaned+'TimeWindows', + MillipedeOriginal.pulsesName_cleaned+'TimeRange']) + + exclusionList = \ + tray.AddSegment(millipede.HighEnergyExclusions, 'millipede_DOM_exclusions', + Pulses = MillipedeOriginal.pulsesName, + ExcludeDeepCore='DeepCoreDOMs', + ExcludeSaturatedDOMs='SaturatedDOMs', + ExcludeBrightDOMs='BrightDOMs', + BadDomsList='BadDomsList', + CalibrationErrata='CalibrationErrata', + SaturationWindows='SaturationWindows' + ) + + + # I like having frame objects in there even if they are empty for some frames + def createEmptyDOMLists(frame, ListNames=[]): + for name in ListNames: + if name in frame: continue + frame[name] = dataclasses.I3VectorOMKey() + tray.AddModule(createEmptyDOMLists, 'createEmptyDOMLists', + ListNames = ["BrightDOMs"]) + # exclude bright DOMs + ExcludedDOMs = exclusionList + + ################## + + def _weighted_quantile_arg(values, weights, q=0.5): + indices = numpy.argsort(values) + sorted_indices = numpy.arange(len(values))[indices] + medianidx = (weights[indices].cumsum()/weights[indices].sum()).searchsorted(q) + if (0 <= medianidx) and (medianidx < len(values)): + return sorted_indices[medianidx] + else: + return numpy.nan + + def weighted_quantile(values, weights, q=0.5): + if len(values) != len(weights): + raise ValueError("shape of `values` and `weights` don't match!") + index = _weighted_quantile_arg(values, weights, q=q) + if not numpy.isnan(index): + return values[index] + else: + return numpy.nan + + def weighted_median(values, weights): + return weighted_quantile(values, weights, q=0.5) + + def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) + counter, charge = 0, 0 + qtot = 0 + times = dataclasses.I3TimeWindowSeriesMap() + for omkey, ps in pulses.items(): + if len(ps) < 2: + if len(ps) == 1: + qtot += ps[0].charge + continue + ts = numpy.asarray([p.time for p in ps]) + cs = numpy.asarray([p.charge for p in ps]) + median = weighted_median(ts, cs) + qtot += cs.sum() + for p in ps: + if p.time >= (median+Residual): + if omkey not in times: + ts = dataclasses.I3TimeWindowSeries() + ts.append(dataclasses.I3TimeWindow(median+Residual, numpy.inf)) # this defines the **excluded** time window + times[omkey] = ts + mask.set(omkey, p, False) + counter += 1 + charge += p.charge + frame[MillipedeOriginal.pulsesName_cleaned] = mask + frame[MillipedeOriginal.pulsesName_cleaned+"TimeWindows"] = times + frame[MillipedeOriginal.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[Pulses+"TimeRange"]) + + tray.AddModule(LatePulseCleaning, "LatePulseCleaning", + Pulses=MillipedeOriginal.pulsesName, + ) + return ExcludedDOMs + [MillipedeOriginal.pulsesName_cleaned+'TimeWindows'] + + + @icetray.traysegment + def traysegment(tray, name, logger, seed=None): + cascade_service = photonics_service.I3PhotoSplineService(MillipedeOriginal.abs_spline, MillipedeOriginal.prob_spline, timingSigma=0.0) + cascade_service.SetEfficiencies(SPEScale) + muon_service = None + + """Perform MillipedeOriginal reco.""" + ExcludedDOMs = tray.Add(MillipedeOriginal.exclusions) + + tray.Add(MillipedeOriginal.makeSurePulsesExist, pulsesName=MillipedeOriginal.pulsesName_cleaned) + + def notify0(frame): + logger.debug(f"starting a new fit ({name})! {datetime.datetime.now()}") + + tray.AddModule(notify0, "notify0") + + tray.AddService('MillipedeLikelihoodFactory', 'millipedellh', + MuonPhotonicsService=muon_service, + CascadePhotonicsService=cascade_service, + ShowerRegularization=0, + PhotonsPerBin=15, + # DOMEfficiency=SPEScale, # moved to cascade_service.SetEfficiencies(SPEScale) + ExcludedDOMs=ExcludedDOMs, + PartialExclusion=True, + ReadoutWindow=MillipedeOriginal.pulsesName_cleaned+'TimeRange', + Pulses=MillipedeOriginal.pulsesName_cleaned, + BinSigma=3) + + tray.AddService('I3GSLRandomServiceFactory','I3RandomService') + + tray.AddService('I3GSLSimplexFactory', 'simplex', + MaxIterations=20000) + + coars_steps = dict(StepX=10.*I3Units.m, + StepY=10.*I3Units.m, + StepZ=10.*I3Units.m, + StepZenith=0., + StepAzimuth=0., + StepT=0.*I3Units.ns, + ShowerSpacing=5.*I3Units.m, + MuonSpacing=0) + + finer_steps = dict(StepX=2.*I3Units.m, + StepY=2.*I3Units.m, + StepZ=2.*I3Units.m, + StepZenith=0., + StepAzimuth=0., + StepT=5.*I3Units.ns, + ShowerSpacing=2.5*I3Units.m, + MuonSpacing=0) + + tray.AddService('MuMillipedeParametrizationFactory', 'coarseSteps', **coars_steps) + + tray.AddService('I3BasicSeedServiceFactory', 'vetoseed', + FirstGuesses=[f'{cfg.OUTPUT_PARTICLE_NAME}'], + TimeShiftType='TNone', + PositionShiftType='None') + + tray.Add('I3SimpleFitter', + OutputName='MillipedeStarting1stPass', + SeedService='vetoseed', + Parametrization='coarseSteps', + LogLikelihood='millipedellh', + Minimizer='simplex') + + def notify1(frame): + logger.debug(f"1st pass done! {datetime.datetime.now()}") + logger.debug(f"Seeded with: {frame[f'{cfg.OUTPUT_PARTICLE_NAME}']}") + logger.debug(f"MillipedeStarting1stPass: {frame['MillipedeStarting1stPass']}") + + tray.AddModule(notify1, "notify1") + + tray.AddService('MuMillipedeParametrizationFactory', 'fineSteps', **finer_steps) + + tray.AddService('I3BasicSeedServiceFactory', 'firstFitSeed', + FirstGuesses=['MillipedeStarting1stPass'], + TimeShiftType='TNone', + PositionShiftType='None') + + tray.Add('I3SimpleFitter', + SeedService='firstFitSeed', + OutputName='MillipedeStarting2ndPass', + Parametrization='fineSteps', + LogLikelihood='millipedellh', + Minimizer='simplex') + + def notify2(frame): + logger.debug(f"2nd pass done! {datetime.datetime.now()}") + logger.debug(f"MillipedeStarting2ndPass: {frame['MillipedeStarting2ndPass']}") + + tray.AddModule(notify2, "notify2") + + @staticmethod + def to_recopixelvariation(frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: + # Calculate reco losses, based on load_scan_state() + reco_losses_inside, reco_losses_total = MillipedeOriginal.get_reco_losses_inside( + p_frame=frame, g_frame=geometry, + ) + + if "MillipedeStarting2ndPass_millipedellh" not in frame: + llh = float("nan") + else: + llh = frame["MillipedeStarting2ndPass_millipedellh"].logl + return RecoPixelVariation( + nside=frame[cfg.I3FRAME_NSIDE].value, + pixel_id=frame[cfg.I3FRAME_PIXEL].value, + llh=llh, + reco_losses_inside=reco_losses_inside, + reco_losses_total=reco_losses_total, + posvar_id=frame[cfg.I3FRAME_POSVAR].value, + position=frame["MillipedeStarting2ndPass"].pos, + time=frame["MillipedeStarting2ndPass"].time, + energy=frame["MillipedeStarting2ndPass"].energy, + ) + + @staticmethod + def get_reco_losses_inside(p_frame: I3Frame, g_frame: I3Frame) -> Tuple[float, float]: + + if "MillipedeStarting2ndPass" not in p_frame: + return numpy.nan, numpy.nan + recoParticle = p_frame["MillipedeStarting2ndPass"] + + if "MillipedeStarting2ndPassParams" not in p_frame: + return numpy.nan, numpy.nan + + def getRecoLosses(vecParticles): + losses = [] + for p in vecParticles: + if not p.is_cascade: + continue + if p.energy == 0.: + continue + losses.append([p.time, p.energy]) + return losses + recoLosses = getRecoLosses(p_frame["MillipedeStarting2ndPassParams"]) + + + intersectionPoints = VHESelfVeto.IntersectionsWithInstrumentedVolume(g_frame["I3Geometry"], recoParticle) + intersectionTimes = [] + for intersectionPoint in intersectionPoints: + vecX = intersectionPoint.x - recoParticle.pos.x + vecY = intersectionPoint.y - recoParticle.pos.y + vecZ = intersectionPoint.z - recoParticle.pos.z + + prod = vecX*recoParticle.dir.x + vecY*recoParticle.dir.y + vecZ*recoParticle.dir.z + dist = numpy.sqrt(vecX**2 + vecY**2 + vecZ**2) + if prod < 0.: + dist *= -1. + intersectionTimes.append(dist/dataclasses.I3Constants.c + recoParticle.time) + + entryTime = None + exitTime = None + intersectionTimes = sorted(intersectionTimes) + if len(intersectionTimes) == 0: + return 0., 0. + + entryTime = intersectionTimes[0]-60.*I3Units.m/dataclasses.I3Constants.c + intersectionTimes = intersectionTimes[1:] + exitTime = intersectionTimes[-1]+60.*I3Units.m/dataclasses.I3Constants.c + intersectionTimes = intersectionTimes[:-1] + + totalRecoLosses = 0. + totalRecoLossesInside = 0. + for entry in recoLosses: + totalRecoLosses += entry[1] + if entryTime is not None and entry[0] < entryTime: + continue + if exitTime is not None and entry[0] > exitTime: + continue + totalRecoLossesInside += entry[1] + + return totalRecoLossesInside, totalRecoLosses diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 4bbefaac4..344d5144a 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -73,6 +73,29 @@ class MillipedeWilks(RecoInterface): ) muon_service = None + @icetray.traysegment + def prepare_frames(tray, name): + # Generates the vertex seed for the initial scan. + # Only run if HESE_VHESelfVeto is not present in the frame. + # VertexThreshold is 250 in the original HESE analysis (Tianlu) + # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. + tray.AddModule('VHESelfVeto', 'selfveto', + VertexThreshold=250, + Pulses=pulsesName+'HLC', + OutputBool='HESE_VHESelfVeto', + OutputVertexTime=cfg.INPUT_TIME_NAME, + OutputVertexPos=cfg.INPUT_POS_NAME, + If=lambda frame: "HESE_VHESelfVeto" not in frame) + + # this only runs if the previous module did not return anything + tray.AddModule('VHESelfVeto', 'selfveto-emergency-lowen-settings', + VertexThreshold=5, + Pulses=pulsesName+'HLC', + OutputBool='VHESelfVeto_meaningless_lowen', + OutputVertexTime=cfg.INPUT_TIME_NAME, + OutputVertexPos=cfg.INPUT_POS_NAME, + If=lambda frame: not frame.Has("HESE_VHESelfVeto")) + def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: raise RuntimeError("{0} not in frame".format(pulsesName)) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index 1431ec186..65f72e31b 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -16,6 +16,7 @@ from .. import config as cfg +from .. import recos # from .. import recos from . import LOGGER @@ -104,44 +105,11 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, OutputSLC=pulsesName+'SLC', If=lambda frame: pulsesName+'HLC' not in frame) - # Generates the vertex seed for the initial scan. - # Only run if HESE_VHESelfVeto is not present in the frame. - # VertexThreshold is 250 in the original HESE analysis (Tianlu) - # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. - if reco_algo.lower() == 'millipede_original': - # TODO: documentation for this conditional statement - tray.AddModule('VHESelfVeto', 'selfveto', - VertexThreshold=2, - Pulses=pulsesName+'HLC', - OutputBool='HESE_VHESelfVeto', - OutputVertexTime=cfg.INPUT_TIME_NAME, - OutputVertexPos=cfg.INPUT_POS_NAME, - If=lambda frame: "HESE_VHESelfVeto" not in frame) - else: - tray.AddModule('VHESelfVeto', 'selfveto', - VertexThreshold=250, - Pulses=pulsesName+'HLC', - OutputBool='HESE_VHESelfVeto', - OutputVertexTime=cfg.INPUT_TIME_NAME, - OutputVertexPos=cfg.INPUT_POS_NAME, - If=lambda frame: "HESE_VHESelfVeto" not in frame) - - # this only runs if the previous module did not return anything - tray.AddModule('VHESelfVeto', 'selfveto-emergency-lowen-settings', - VertexThreshold=5, - Pulses=pulsesName+'HLC', - OutputBool='VHESelfVeto_meaningless_lowen', - OutputVertexTime=cfg.INPUT_TIME_NAME, - OutputVertexPos=cfg.INPUT_POS_NAME, - If=lambda frame: not frame.Has("HESE_VHESelfVeto")) - - # if reco_algo.lower() == "splinempe": - # # perform fit - # tray.AddSegment( - # recos.get_reco_interface_object(reco_algo).prepare_frames, - # f"{reco_algo}_prepareframes", - # logger=LOGGER - # ) + tray.AddSegment( + recos.get_reco_interface_object(reco_algo).prepare_frames, + f"{reco_algo}_prepareframes", + logger=LOGGER + ) # If the event has a GCD diff (compressed GCD), only keep the diffs. # The GCD will be reassembled from baseline + diff by the client. From 404e774ca6a23ed6e45d894a607f61719ab4c835 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 16:46:07 +0200 Subject: [PATCH 016/217] rollback file --- .../recos/millipede_original_refactor.py | 343 ------------------ 1 file changed, 343 deletions(-) delete mode 100644 skymap_scanner/recos/millipede_original_refactor.py diff --git a/skymap_scanner/recos/millipede_original_refactor.py b/skymap_scanner/recos/millipede_original_refactor.py deleted file mode 100644 index 747bc2be0..000000000 --- a/skymap_scanner/recos/millipede_original_refactor.py +++ /dev/null @@ -1,343 +0,0 @@ -"""IceTray segment for a millipede reco.""" - -# fmt: off -# pylint: skip-file -# mypy: ignore-errors - -import copy -import datetime -import os -from typing import Tuple - -import numpy - -from I3Tray import I3Units -from icecube import ( # noqa: F401 - VHESelfVeto, - dataclasses, - dataio, - frame_object_diff, - gulliver, - gulliver_modules, - icetray, - lilliput, - millipede, - photonics_service, - recclasses, - simclasses, -) -from icecube.icetray import I3Frame - -from .. import config as cfg -from ..utils.data_handling import DataStager -from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface, VertexGenerator - - -class MillipedeOriginal(RecoInterface): - """Reco logic for millipede.""" - - # Spline requirements ############################################## - MIE_ABS_SPLINE = "ems_mie_z20_a10.abs.fits" - MIE_PROB_SPLINE = "ems_mie_z20_a10.prob.fits" - - SPLINE_REQUIREMENTS = [ MIE_ABS_SPLINE, MIE_PROB_SPLINE ] - - # Constants ######################################################## - pulsesName = cfg.INPUT_PULSES_NAME - pulsesName_cleaned = pulsesName+'LatePulseCleaned' - SPEScale = 0.99 - - @staticmethod - def get_vertex_variations(): - variation_distance = 20.*I3Units.m - - if cfg.ENV.SKYSCAN_MINI_TEST: - return VertexGenerator.mini_test(variation_distance=variation_distance) - else: - return VertexGenerator.octahedron(radius=variation_distance) - - - def __init__(self): - # Load Data ######################################################## - # At HESE energies, deposited light is dominated by the stochastic losses - # (muon part emits so little light in comparison) - # This is why we can use cascade tables - datastager = DataStager( - local_paths=cfg.LOCAL_DATA_SOURCES, - local_subdir=cfg.LOCAL_SPLINE_SUBDIR, - remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", - ) - datastager.stage_files(SPLINE_REQUIREMENTS) - self.abs_spline: str = datastager.get_filepath(MIE_ABS_SPLINE) - self.prob_spline: str = datastager.get_filepath(MIE_PROB_SPLINE) - - @staticmethod - def makeSurePulsesExist(frame, pulsesName) -> None: - if pulsesName not in frame: - raise RuntimeError("{0} not in frame".format(pulsesName)) - if pulsesName + "TimeWindows" not in frame: - raise RuntimeError("{0} not in frame".format(pulsesName + "TimeWindows")) - if pulsesName + "TimeRange" not in frame: - raise RuntimeError("{0} not in frame".format(pulsesName + "TimeRange")) - - @icetray.traysegment - def exclusions(tray, name): - tray.Add('Delete', keys=['BrightDOMs', - 'SaturatedDOMs', - 'DeepCoreDOMs', - MillipedeOriginal.pulsesName_cleaned, - MillipedeOriginal.pulsesName_cleaned+'TimeWindows', - MillipedeOriginal.pulsesName_cleaned+'TimeRange']) - - exclusionList = \ - tray.AddSegment(millipede.HighEnergyExclusions, 'millipede_DOM_exclusions', - Pulses = MillipedeOriginal.pulsesName, - ExcludeDeepCore='DeepCoreDOMs', - ExcludeSaturatedDOMs='SaturatedDOMs', - ExcludeBrightDOMs='BrightDOMs', - BadDomsList='BadDomsList', - CalibrationErrata='CalibrationErrata', - SaturationWindows='SaturationWindows' - ) - - - # I like having frame objects in there even if they are empty for some frames - def createEmptyDOMLists(frame, ListNames=[]): - for name in ListNames: - if name in frame: continue - frame[name] = dataclasses.I3VectorOMKey() - tray.AddModule(createEmptyDOMLists, 'createEmptyDOMLists', - ListNames = ["BrightDOMs"]) - # exclude bright DOMs - ExcludedDOMs = exclusionList - - ################## - - def _weighted_quantile_arg(values, weights, q=0.5): - indices = numpy.argsort(values) - sorted_indices = numpy.arange(len(values))[indices] - medianidx = (weights[indices].cumsum()/weights[indices].sum()).searchsorted(q) - if (0 <= medianidx) and (medianidx < len(values)): - return sorted_indices[medianidx] - else: - return numpy.nan - - def weighted_quantile(values, weights, q=0.5): - if len(values) != len(weights): - raise ValueError("shape of `values` and `weights` don't match!") - index = _weighted_quantile_arg(values, weights, q=q) - if not numpy.isnan(index): - return values[index] - else: - return numpy.nan - - def weighted_median(values, weights): - return weighted_quantile(values, weights, q=0.5) - - def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) - counter, charge = 0, 0 - qtot = 0 - times = dataclasses.I3TimeWindowSeriesMap() - for omkey, ps in pulses.items(): - if len(ps) < 2: - if len(ps) == 1: - qtot += ps[0].charge - continue - ts = numpy.asarray([p.time for p in ps]) - cs = numpy.asarray([p.charge for p in ps]) - median = weighted_median(ts, cs) - qtot += cs.sum() - for p in ps: - if p.time >= (median+Residual): - if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append(dataclasses.I3TimeWindow(median+Residual, numpy.inf)) # this defines the **excluded** time window - times[omkey] = ts - mask.set(omkey, p, False) - counter += 1 - charge += p.charge - frame[MillipedeOriginal.pulsesName_cleaned] = mask - frame[MillipedeOriginal.pulsesName_cleaned+"TimeWindows"] = times - frame[MillipedeOriginal.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[Pulses+"TimeRange"]) - - tray.AddModule(LatePulseCleaning, "LatePulseCleaning", - Pulses=MillipedeOriginal.pulsesName, - ) - return ExcludedDOMs + [MillipedeOriginal.pulsesName_cleaned+'TimeWindows'] - - - @icetray.traysegment - def traysegment(tray, name, logger, seed=None): - cascade_service = photonics_service.I3PhotoSplineService(MillipedeOriginal.abs_spline, MillipedeOriginal.prob_spline, timingSigma=0.0) - cascade_service.SetEfficiencies(SPEScale) - muon_service = None - - """Perform MillipedeOriginal reco.""" - ExcludedDOMs = tray.Add(MillipedeOriginal.exclusions) - - tray.Add(MillipedeOriginal.makeSurePulsesExist, pulsesName=MillipedeOriginal.pulsesName_cleaned) - - def notify0(frame): - logger.debug(f"starting a new fit ({name})! {datetime.datetime.now()}") - - tray.AddModule(notify0, "notify0") - - tray.AddService('MillipedeLikelihoodFactory', 'millipedellh', - MuonPhotonicsService=muon_service, - CascadePhotonicsService=cascade_service, - ShowerRegularization=0, - PhotonsPerBin=15, - # DOMEfficiency=SPEScale, # moved to cascade_service.SetEfficiencies(SPEScale) - ExcludedDOMs=ExcludedDOMs, - PartialExclusion=True, - ReadoutWindow=MillipedeOriginal.pulsesName_cleaned+'TimeRange', - Pulses=MillipedeOriginal.pulsesName_cleaned, - BinSigma=3) - - tray.AddService('I3GSLRandomServiceFactory','I3RandomService') - - tray.AddService('I3GSLSimplexFactory', 'simplex', - MaxIterations=20000) - - coars_steps = dict(StepX=10.*I3Units.m, - StepY=10.*I3Units.m, - StepZ=10.*I3Units.m, - StepZenith=0., - StepAzimuth=0., - StepT=0.*I3Units.ns, - ShowerSpacing=5.*I3Units.m, - MuonSpacing=0) - - finer_steps = dict(StepX=2.*I3Units.m, - StepY=2.*I3Units.m, - StepZ=2.*I3Units.m, - StepZenith=0., - StepAzimuth=0., - StepT=5.*I3Units.ns, - ShowerSpacing=2.5*I3Units.m, - MuonSpacing=0) - - tray.AddService('MuMillipedeParametrizationFactory', 'coarseSteps', **coars_steps) - - tray.AddService('I3BasicSeedServiceFactory', 'vetoseed', - FirstGuesses=[f'{cfg.OUTPUT_PARTICLE_NAME}'], - TimeShiftType='TNone', - PositionShiftType='None') - - tray.Add('I3SimpleFitter', - OutputName='MillipedeStarting1stPass', - SeedService='vetoseed', - Parametrization='coarseSteps', - LogLikelihood='millipedellh', - Minimizer='simplex') - - def notify1(frame): - logger.debug(f"1st pass done! {datetime.datetime.now()}") - logger.debug(f"Seeded with: {frame[f'{cfg.OUTPUT_PARTICLE_NAME}']}") - logger.debug(f"MillipedeStarting1stPass: {frame['MillipedeStarting1stPass']}") - - tray.AddModule(notify1, "notify1") - - tray.AddService('MuMillipedeParametrizationFactory', 'fineSteps', **finer_steps) - - tray.AddService('I3BasicSeedServiceFactory', 'firstFitSeed', - FirstGuesses=['MillipedeStarting1stPass'], - TimeShiftType='TNone', - PositionShiftType='None') - - tray.Add('I3SimpleFitter', - SeedService='firstFitSeed', - OutputName='MillipedeStarting2ndPass', - Parametrization='fineSteps', - LogLikelihood='millipedellh', - Minimizer='simplex') - - def notify2(frame): - logger.debug(f"2nd pass done! {datetime.datetime.now()}") - logger.debug(f"MillipedeStarting2ndPass: {frame['MillipedeStarting2ndPass']}") - - tray.AddModule(notify2, "notify2") - - @staticmethod - def to_recopixelvariation(frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: - # Calculate reco losses, based on load_scan_state() - reco_losses_inside, reco_losses_total = MillipedeOriginal.get_reco_losses_inside( - p_frame=frame, g_frame=geometry, - ) - - if "MillipedeStarting2ndPass_millipedellh" not in frame: - llh = float("nan") - else: - llh = frame["MillipedeStarting2ndPass_millipedellh"].logl - return RecoPixelVariation( - nside=frame[cfg.I3FRAME_NSIDE].value, - pixel_id=frame[cfg.I3FRAME_PIXEL].value, - llh=llh, - reco_losses_inside=reco_losses_inside, - reco_losses_total=reco_losses_total, - posvar_id=frame[cfg.I3FRAME_POSVAR].value, - position=frame["MillipedeStarting2ndPass"].pos, - time=frame["MillipedeStarting2ndPass"].time, - energy=frame["MillipedeStarting2ndPass"].energy, - ) - - @staticmethod - def get_reco_losses_inside(p_frame: I3Frame, g_frame: I3Frame) -> Tuple[float, float]: - - if "MillipedeStarting2ndPass" not in p_frame: - return numpy.nan, numpy.nan - recoParticle = p_frame["MillipedeStarting2ndPass"] - - if "MillipedeStarting2ndPassParams" not in p_frame: - return numpy.nan, numpy.nan - - def getRecoLosses(vecParticles): - losses = [] - for p in vecParticles: - if not p.is_cascade: - continue - if p.energy == 0.: - continue - losses.append([p.time, p.energy]) - return losses - recoLosses = getRecoLosses(p_frame["MillipedeStarting2ndPassParams"]) - - - intersectionPoints = VHESelfVeto.IntersectionsWithInstrumentedVolume(g_frame["I3Geometry"], recoParticle) - intersectionTimes = [] - for intersectionPoint in intersectionPoints: - vecX = intersectionPoint.x - recoParticle.pos.x - vecY = intersectionPoint.y - recoParticle.pos.y - vecZ = intersectionPoint.z - recoParticle.pos.z - - prod = vecX*recoParticle.dir.x + vecY*recoParticle.dir.y + vecZ*recoParticle.dir.z - dist = numpy.sqrt(vecX**2 + vecY**2 + vecZ**2) - if prod < 0.: - dist *= -1. - intersectionTimes.append(dist/dataclasses.I3Constants.c + recoParticle.time) - - entryTime = None - exitTime = None - intersectionTimes = sorted(intersectionTimes) - if len(intersectionTimes) == 0: - return 0., 0. - - entryTime = intersectionTimes[0]-60.*I3Units.m/dataclasses.I3Constants.c - intersectionTimes = intersectionTimes[1:] - exitTime = intersectionTimes[-1]+60.*I3Units.m/dataclasses.I3Constants.c - intersectionTimes = intersectionTimes[:-1] - - totalRecoLosses = 0. - totalRecoLossesInside = 0. - for entry in recoLosses: - totalRecoLosses += entry[1] - if entryTime is not None and entry[0] < entryTime: - continue - if exitTime is not None and entry[0] > exitTime: - continue - totalRecoLossesInside += entry[1] - - return totalRecoLossesInside, totalRecoLosses From 1d6fc61caf30a9729471fc725e7555ce64174c41 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 16:46:24 +0200 Subject: [PATCH 017/217] use traysegments for preparing frames --- skymap_scanner/recos/millipede_original.py | 23 +++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 2a8597e5e..628d1eff6 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -35,6 +35,7 @@ class MillipedeOriginal(RecoInterface): + """Reco logic for millipede.""" variation_distance = 20.*I3Units.m if cfg.ENV.SKYSCAN_MINI_TEST: @@ -42,7 +43,7 @@ class MillipedeOriginal(RecoInterface): else: VERTEX_VARIATIONS = VertexGenerator.octahedron(radius=variation_distance) - """Reco logic for millipede.""" + # Spline requirements MIE_ABS_SPLINE = "ems_mie_z20_a10.abs.fits" MIE_PROB_SPLINE = "ems_mie_z20_a10.prob.fits" @@ -52,7 +53,6 @@ class MillipedeOriginal(RecoInterface): # Constants ######################################################## pulsesName = cfg.INPUT_PULSES_NAME pulsesName_cleaned = pulsesName+'LatePulseCleaned' - SPEScale = 0.99 # Load Data ######################################################## # At HESE energies, deposited light is dominated by the stochastic losses @@ -66,6 +66,21 @@ class MillipedeOriginal(RecoInterface): datastager.stage_files(SPLINE_REQUIREMENTS) abs_spline: str = datastager.get_filepath(MIE_ABS_SPLINE) prob_spline: str = datastager.get_filepath(MIE_PROB_SPLINE) + + + @icetray.traysegment + def prepare_frames(tray, name): + # Generates the vertex seed for the initial scan. + # Only run if HESE_VHESelfVeto is not present in the frame. + # VertexThreshold is 250 in the original HESE analysis (Tianlu) + # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. + tray.AddModule('VHESelfVeto', 'selfveto', + VertexThreshold=2, + Pulses=pulsesName+'HLC', + OutputBool='HESE_VHESelfVeto', + OutputVertexTime=cfg.INPUT_TIME_NAME, + OutputVertexPos=cfg.INPUT_POS_NAME, + If=lambda frame: "HESE_VHESelfVeto" not in frame) def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: @@ -166,7 +181,9 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): @icetray.traysegment def traysegment(tray, name, logger, seed=None): cascade_service = photonics_service.I3PhotoSplineService(MillipedeOriginal.abs_spline, MillipedeOriginal.prob_spline, timingSigma=0.0) - cascade_service.SetEfficiencies(MillipedeOriginal.SPEScale) + + SPEScale = 0.99 + cascade_service.SetEfficiencies(SPEScale) muon_service = None """Perform MillipedeOriginal reco.""" From 6d6fc0ec6f3d8305e1f50ef0349b4775d41e8773 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 16:58:19 +0200 Subject: [PATCH 018/217] match signatures? --- skymap_scanner/recos/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 53d38dc9e..ad93c8797 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -37,7 +37,7 @@ class RecoInterface: VERTEX_VARIATIONS: List[I3Position] = VertexGenerator.point() @staticmethod - def prepare_frames(tray, name, logger, **kwargs: Any) -> None: + def prepare_frames(tray, name) -> None: raise NotImplementedError() @staticmethod From 5c6aa7d09d180954b7267ad0b7bcaa154bff4fe9 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 17:08:42 +0200 Subject: [PATCH 019/217] match signatures/2 --- skymap_scanner/utils/prepare_frames.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index 65f72e31b..638b18e95 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -108,7 +108,6 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, tray.AddSegment( recos.get_reco_interface_object(reco_algo).prepare_frames, f"{reco_algo}_prepareframes", - logger=LOGGER ) # If the event has a GCD diff (compressed GCD), only keep the diffs. From 146b21d8960aaf982536b7092904571ff293cf8a Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 17:20:00 +0200 Subject: [PATCH 020/217] add prepare frames to dummy reco --- skymap_scanner/recos/dummy.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 1b24aa9ea..b5f06bd0f 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -28,6 +28,14 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" + @staticmethod + @icetray.traysegment + def prepare_frames(tray, name) -> None: + def notify0(frame): + logger.debug(f"Preparing frames! {datetime.datetime.now()}") + + tray.AddModule(notify0, "notify0") + @staticmethod @icetray.traysegment def traysegment(tray, name, logger, **kwargs): From 3b8bae8198f147096286f326debf538329b4d939 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 17:37:04 +0200 Subject: [PATCH 021/217] move things around --- skymap_scanner/recos/millipede_original.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 628d1eff6..0b76d6bd9 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -50,10 +50,6 @@ class MillipedeOriginal(RecoInterface): SPLINE_REQUIREMENTS = [ MIE_ABS_SPLINE, MIE_PROB_SPLINE ] - # Constants ######################################################## - pulsesName = cfg.INPUT_PULSES_NAME - pulsesName_cleaned = pulsesName+'LatePulseCleaned' - # Load Data ######################################################## # At HESE energies, deposited light is dominated by the stochastic losses # (muon part emits so little light in comparison) @@ -180,6 +176,11 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): @icetray.traysegment def traysegment(tray, name, logger, seed=None): + # Constants ######################################################## + pulsesName = cfg.INPUT_PULSES_NAME + pulsesName_cleaned = pulsesName+'LatePulseCleaned' + + # Services ######################################################## cascade_service = photonics_service.I3PhotoSplineService(MillipedeOriginal.abs_spline, MillipedeOriginal.prob_spline, timingSigma=0.0) SPEScale = 0.99 From 506b2d1f495e391184c814ec7c692fe0e3f96043 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 17:46:53 +0200 Subject: [PATCH 022/217] pass pulses name to segment --- skymap_scanner/recos/millipede_original.py | 11 +++++++---- skymap_scanner/utils/prepare_frames.py | 1 + 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 0b76d6bd9..c7aa5798a 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -44,6 +44,9 @@ class MillipedeOriginal(RecoInterface): VERTEX_VARIATIONS = VertexGenerator.octahedron(radius=variation_distance) + pulsesName = cfg.INPUT_PULSES_NAME + pulsesName_cleaned = pulsesName+'LatePulseCleaned' + # Spline requirements MIE_ABS_SPLINE = "ems_mie_z20_a10.abs.fits" MIE_PROB_SPLINE = "ems_mie_z20_a10.prob.fits" @@ -65,7 +68,7 @@ class MillipedeOriginal(RecoInterface): @icetray.traysegment - def prepare_frames(tray, name): + def prepare_frames(tray, name, pulsesName): # Generates the vertex seed for the initial scan. # Only run if HESE_VHESelfVeto is not present in the frame. # VertexThreshold is 250 in the original HESE analysis (Tianlu) @@ -176,9 +179,10 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): @icetray.traysegment def traysegment(tray, name, logger, seed=None): + """Perform MillipedeOriginal reco.""" + # Constants ######################################################## - pulsesName = cfg.INPUT_PULSES_NAME - pulsesName_cleaned = pulsesName+'LatePulseCleaned' + # Services ######################################################## cascade_service = photonics_service.I3PhotoSplineService(MillipedeOriginal.abs_spline, MillipedeOriginal.prob_spline, timingSigma=0.0) @@ -187,7 +191,6 @@ def traysegment(tray, name, logger, seed=None): cascade_service.SetEfficiencies(SPEScale) muon_service = None - """Perform MillipedeOriginal reco.""" ExcludedDOMs = tray.Add(MillipedeOriginal.exclusions) tray.Add(MillipedeOriginal.makeSurePulsesExist, pulsesName=MillipedeOriginal.pulsesName_cleaned) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index 638b18e95..35901b680 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -108,6 +108,7 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, tray.AddSegment( recos.get_reco_interface_object(reco_algo).prepare_frames, f"{reco_algo}_prepareframes", + pulsesName=pulsesName ) # If the event has a GCD diff (compressed GCD), only keep the diffs. From 1e4a209e3402def35b759c590ab4f7fc22a44f6f Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 17:56:25 +0200 Subject: [PATCH 023/217] flexibility through kwargs --- skymap_scanner/recos/__init__.py | 2 +- skymap_scanner/recos/dummy.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index ad93c8797..084f262ac 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -37,7 +37,7 @@ class RecoInterface: VERTEX_VARIATIONS: List[I3Position] = VertexGenerator.point() @staticmethod - def prepare_frames(tray, name) -> None: + def prepare_frames(tray, name, **kwargs) -> None: raise NotImplementedError() @staticmethod diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index b5f06bd0f..78090de37 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,7 +30,7 @@ class Dummy(RecoInterface): @staticmethod @icetray.traysegment - def prepare_frames(tray, name) -> None: + def prepare_frames(tray, name, **kwargs) -> None: def notify0(frame): logger.debug(f"Preparing frames! {datetime.datetime.now()}") From 95488d92cd58b02dd0ef0c5a889d97b483d74513 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 18:14:51 +0200 Subject: [PATCH 024/217] gen dummy vertex in dummy reco --- skymap_scanner/recos/dummy.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 78090de37..5101f3e03 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -18,6 +18,8 @@ recclasses, simclasses, ) + +from icecube.dataclasses import I3Position # type: ignore[import] from icecube.icetray import I3Frame # type: ignore[import] from .. import config as cfg @@ -31,10 +33,15 @@ class Dummy(RecoInterface): @staticmethod @icetray.traysegment def prepare_frames(tray, name, **kwargs) -> None: - def notify0(frame): - logger.debug(f"Preparing frames! {datetime.datetime.now()}") + def gen_dummy_vertex(frame): + frame[cfg.INPUT_TIME_NAME] = 0.0 + frame[cfg.INPUT_POS_NAME] = I3Position(0.0, 0.0, 0.0) - tray.AddModule(notify0, "notify0") + def notify(frame): + logger.debug(f"Preparing frames (dummy). {datetime.datetime.now()}") + + tray.Add(notify, "notify") + tray.Add(gen_dummy_vertex, "gen_dummy_vertex") @staticmethod @icetray.traysegment From 98c03be68950861a837ae9f22d298ee2505cb965 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 18:26:47 +0200 Subject: [PATCH 025/217] better pass down a logger --- skymap_scanner/recos/dummy.py | 2 +- skymap_scanner/recos/millipede_original.py | 2 +- skymap_scanner/utils/prepare_frames.py | 1 + 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 5101f3e03..e3a0a0417 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -32,7 +32,7 @@ class Dummy(RecoInterface): @staticmethod @icetray.traysegment - def prepare_frames(tray, name, **kwargs) -> None: + def prepare_frames(tray, name, logger, **kwargs) -> None: def gen_dummy_vertex(frame): frame[cfg.INPUT_TIME_NAME] = 0.0 frame[cfg.INPUT_POS_NAME] = I3Position(0.0, 0.0, 0.0) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index c7aa5798a..ffac455d3 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -68,7 +68,7 @@ class MillipedeOriginal(RecoInterface): @icetray.traysegment - def prepare_frames(tray, name, pulsesName): + def prepare_frames(tray, name, logger, pulsesName): # Generates the vertex seed for the initial scan. # Only run if HESE_VHESelfVeto is not present in the frame. # VertexThreshold is 250 in the original HESE analysis (Tianlu) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index 35901b680..e0b342f9e 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -108,6 +108,7 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, tray.AddSegment( recos.get_reco_interface_object(reco_algo).prepare_frames, f"{reco_algo}_prepareframes", + logger=LOGGER, pulsesName=pulsesName ) From 1d5778de9d41aa5f58c2423e9ba42d1827cafe75 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 23:06:34 +0200 Subject: [PATCH 026/217] units --- skymap_scanner/recos/dummy.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index e3a0a0417..30c581bda 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -34,8 +34,8 @@ class Dummy(RecoInterface): @icetray.traysegment def prepare_frames(tray, name, logger, **kwargs) -> None: def gen_dummy_vertex(frame): - frame[cfg.INPUT_TIME_NAME] = 0.0 - frame[cfg.INPUT_POS_NAME] = I3Position(0.0, 0.0, 0.0) + frame[cfg.INPUT_TIME_NAME] = 0.0 * I3Units.s + frame[cfg.INPUT_POS_NAME] = dataclasses.I3Position(0.0 * I3Units.m, 0.0 * I3Units.m, 0.0 * I3Units.m) def notify(frame): logger.debug(f"Preparing frames (dummy). {datetime.datetime.now()}") From 7bb36570887c1b9001c3326af5fcd241da9427ea Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 9 May 2023 23:40:36 +0200 Subject: [PATCH 027/217] dataclasses --- skymap_scanner/recos/dummy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 30c581bda..8b015f58e 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -34,7 +34,7 @@ class Dummy(RecoInterface): @icetray.traysegment def prepare_frames(tray, name, logger, **kwargs) -> None: def gen_dummy_vertex(frame): - frame[cfg.INPUT_TIME_NAME] = 0.0 * I3Units.s + frame[cfg.INPUT_TIME_NAME] = dataclasses.I3Double(0.0) frame[cfg.INPUT_POS_NAME] = dataclasses.I3Position(0.0 * I3Units.m, 0.0 * I3Units.m, 0.0 * I3Units.m) def notify(frame): From 9fb396554ef57dbcc94613a6126a855075904f74 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 10 May 2023 00:01:01 +0200 Subject: [PATCH 028/217] existing name guard --- skymap_scanner/recos/dummy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 8b015f58e..020fd38c9 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -41,7 +41,7 @@ def notify(frame): logger.debug(f"Preparing frames (dummy). {datetime.datetime.now()}") tray.Add(notify, "notify") - tray.Add(gen_dummy_vertex, "gen_dummy_vertex") + tray.Add(gen_dummy_vertex, "gen_dummy_vertex", If=lambda frame: cfg.INPUT_TIME_NAME not in frame and cfg.INPUT_POS_NAME not in frame) @staticmethod @icetray.traysegment From 07cec49316913e5f25c560faf9e6ceb50ed58d19 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 10 May 2023 09:15:22 +0200 Subject: [PATCH 029/217] retrieve reco algo class --- skymap_scanner/utils/prepare_frames.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index e0b342f9e..67bcaf0e9 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -17,7 +17,6 @@ from .. import config as cfg from .. import recos -# from .. import recos from . import LOGGER @@ -83,7 +82,10 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, ) # ACTIVATE FOR DEBUG - icetray.logging.console() + # icetray.logging.console() + + # Reconstruction algorithm provider class + RecoAlgo = recos.get_reco_interface_object(reco_algo) output_frames: list[icetray.I3Frame] = [] @@ -106,7 +108,7 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, If=lambda frame: pulsesName+'HLC' not in frame) tray.AddSegment( - recos.get_reco_interface_object(reco_algo).prepare_frames, + RecoAlgo.prepare_frames, f"{reco_algo}_prepareframes", logger=LOGGER, pulsesName=pulsesName From 4cddc1b1996145b0a5a4020116aaf07961d9b912 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 10 May 2023 07:16:50 +0000 Subject: [PATCH 030/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index 0387a61e6..79c6f6eb4 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -28,7 +28,7 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.1 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From 5055007f2dec43fbdcce1eb5634a6803218a09cd Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 10 May 2023 07:16:50 +0000 Subject: [PATCH 031/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 7e6cb1923..9aa4e35ed 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -24,7 +24,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.1 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From e8ee414624617a2bbbbab5514a8d2bdeff6cd31f Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 10 May 2023 07:16:50 +0000 Subject: [PATCH 032/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 290d16e23..eedeec204 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.1 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From 835b136ddc53ea4e226b40c4132902f27d3ea473 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 10 May 2023 07:16:50 +0000 Subject: [PATCH 033/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index ae64774d0..9bf5de8e5 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -26,7 +26,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.1 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From d21f9e2febba9e65087a9e56ea7dd89f555d8833 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 10 May 2023 07:16:50 +0000 Subject: [PATCH 034/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index 604a62553..a867fed8b 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -24,7 +24,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.1 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From 48e4544e72d055bf613580438a10f72c9463ce2e Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 10 May 2023 07:16:50 +0000 Subject: [PATCH 035/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2075f00b2..c8e19b346 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,7 +24,7 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.1 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) fonttools==4.39.3 # via matplotlib From 8a2d11f339badaf1e259f07f7f21a32975963342 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 10 May 2023 09:21:05 +0200 Subject: [PATCH 036/217] style --- skymap_scanner/utils/prepare_frames.py | 29 +++++++++++++------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index 67bcaf0e9..a526695bf 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -7,13 +7,22 @@ import copy import os +from typing import Union, List -import numpy -from I3Tray import I3Tray, I3Units # type: ignore[import] +from I3Tray import I3Tray # type: ignore[import] from icecube import icetray # type: ignore[import] from icecube.frame_object_diff.segments import uncompress # type: ignore[import] -from typing import Union, List +from icecube import ( + DomTools, + VHESelfVeto, + dataclasses, + gulliver, + millipede, + photonics_service, + recclasses, + simclasses, +) # type: ignore[import] from .. import config as cfg from .. import recos @@ -68,18 +77,7 @@ def Process(self): self.PushFrame(frame) -def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, pulsesName: str) -> List[icetray.I3Frame]: - # type hint using list available from python 3.11 - from icecube import ( - DomTools, - VHESelfVeto, - dataclasses, - gulliver, - millipede, - photonics_service, - recclasses, - simclasses, - ) +def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, pulsesName: str) -> List[icetray.I3Frame]: # type hint using list available from python 3.11 # ACTIVATE FOR DEBUG # icetray.logging.console() @@ -107,6 +105,7 @@ def prepare_frames(frame_array, baseline_GCD: Union[None, str], reco_algo: str, OutputSLC=pulsesName+'SLC', If=lambda frame: pulsesName+'HLC' not in frame) + # Run reco-specific preprocessing. tray.AddSegment( RecoAlgo.prepare_frames, f"{reco_algo}_prepareframes", From d2833d19e41326f4f2747c72fc3d8ce3a1167565 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 10 May 2023 09:57:59 +0200 Subject: [PATCH 037/217] remove unused(?) imports --- skymap_scanner/utils/prepare_frames.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/skymap_scanner/utils/prepare_frames.py b/skymap_scanner/utils/prepare_frames.py index a526695bf..2545a3f26 100644 --- a/skymap_scanner/utils/prepare_frames.py +++ b/skymap_scanner/utils/prepare_frames.py @@ -14,14 +14,7 @@ from icecube.frame_object_diff.segments import uncompress # type: ignore[import] from icecube import ( - DomTools, - VHESelfVeto, - dataclasses, - gulliver, - millipede, - photonics_service, - recclasses, - simclasses, + DomTools, # for I3LCPulseCleaning ) # type: ignore[import] from .. import config as cfg From b0182ed9697cab6f87e60fff36d0a01dfaa60480 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 10 May 2023 11:17:04 +0200 Subject: [PATCH 038/217] change default naming --- skymap_scanner/config.py | 4 ++-- skymap_scanner/recos/millipede_original.py | 12 ++++++++++++ 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/config.py b/skymap_scanner/config.py index b32716004..d44f3389c 100644 --- a/skymap_scanner/config.py +++ b/skymap_scanner/config.py @@ -31,8 +31,8 @@ # physics strings INPUT_PULSES_NAME: Final = "SplitUncleanedInIcePulses" -INPUT_TIME_NAME: Final = "HESE_VHESelfVetoVertexTime" -INPUT_POS_NAME: Final = "HESE_VHESelfVetoVertexPos" +INPUT_TIME_NAME: Final = "SeedVertexTime" +INPUT_POS_NAME: Final = "SeedVertexPos" OUTPUT_PARTICLE_NAME: Final = "MillipedeSeedParticle" # For commonly used keys diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index ffac455d3..4a0d75fa4 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -80,6 +80,18 @@ def prepare_frames(tray, name, logger, pulsesName): OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, If=lambda frame: "HESE_VHESelfVeto" not in frame) + + # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. + def extract_seed(frame): + seed_prefix = "HESE_VHESelfVeto" + frame[cfg.INPUT_POS_NAME] = frame[seed_prefix + "VertexTime"] + frame[cfg.INPUT_TIME_NAME] = frame[seed_prefix + "VertexPos"] + + tray.Add(extract_seed, "ExtractSeed", + If = lambda frame: frame.Has("HESE_VHESelfVeto")) + + + def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: From de9ea461204a2e85b90514d6b7eea268dc4a90a7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 10 May 2023 13:50:47 +0200 Subject: [PATCH 039/217] order of operations --- skymap_scanner/recos/millipede_original.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 4a0d75fa4..f4bd94796 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -69,6 +69,15 @@ class MillipedeOriginal(RecoInterface): @icetray.traysegment def prepare_frames(tray, name, logger, pulsesName): + # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. + def extract_seed(frame): + seed_prefix = "HESE_VHESelfVeto" + frame[cfg.INPUT_POS_NAME] = frame[seed_prefix + "VertexTime"] + frame[cfg.INPUT_TIME_NAME] = frame[seed_prefix + "VertexPos"] + + tray.Add(extract_seed, "ExtractSeed", + If = lambda frame: frame.Has("HESE_VHESelfVeto")) + # Generates the vertex seed for the initial scan. # Only run if HESE_VHESelfVeto is not present in the frame. # VertexThreshold is 250 in the original HESE analysis (Tianlu) @@ -81,14 +90,7 @@ def prepare_frames(tray, name, logger, pulsesName): OutputVertexPos=cfg.INPUT_POS_NAME, If=lambda frame: "HESE_VHESelfVeto" not in frame) - # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. - def extract_seed(frame): - seed_prefix = "HESE_VHESelfVeto" - frame[cfg.INPUT_POS_NAME] = frame[seed_prefix + "VertexTime"] - frame[cfg.INPUT_TIME_NAME] = frame[seed_prefix + "VertexPos"] - tray.Add(extract_seed, "ExtractSeed", - If = lambda frame: frame.Has("HESE_VHESelfVeto")) From f3c09f0fa93aa918eb729fad004ad321890e5b4d Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 10 May 2023 14:15:04 +0200 Subject: [PATCH 040/217] switch pos and time --- skymap_scanner/recos/millipede_original.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index f4bd94796..b91fa5e10 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -72,8 +72,8 @@ def prepare_frames(tray, name, logger, pulsesName): # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. def extract_seed(frame): seed_prefix = "HESE_VHESelfVeto" - frame[cfg.INPUT_POS_NAME] = frame[seed_prefix + "VertexTime"] - frame[cfg.INPUT_TIME_NAME] = frame[seed_prefix + "VertexPos"] + frame[cfg.INPUT_POS_NAME] = frame[seed_prefix + "VertexPos"] + frame[cfg.INPUT_TIME_NAME] = frame[seed_prefix + "VertexTime"] tray.Add(extract_seed, "ExtractSeed", If = lambda frame: frame.Has("HESE_VHESelfVeto")) From eafb6d46f7751684971a7c4833a0a4eb61cf96f7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 11 May 2023 09:43:15 +0200 Subject: [PATCH 041/217] move to common dir --- skymap_scanner/recos/common/vertex_gen.py | 70 +++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 skymap_scanner/recos/common/vertex_gen.py diff --git a/skymap_scanner/recos/common/vertex_gen.py b/skymap_scanner/recos/common/vertex_gen.py new file mode 100644 index 000000000..ec10cabba --- /dev/null +++ b/skymap_scanner/recos/common/vertex_gen.py @@ -0,0 +1,70 @@ +from typing import List + +import numpy as np + +from icecube import dataclasses # type: ignore[import] +from I3Tray import I3Units # type: ignore[import] + + +class VertexGenerator: + def __init__(self): + pass + + @staticmethod + def point(): + return [dataclasses.I3Position(0.0, 0.0, 0.0)] + + @staticmethod + def octahedron(radius: float): + return [ + dataclasses.I3Position(0.0, 0.0, 0.0), + dataclasses.I3Position(-radius, 0.0, 0.0), + dataclasses.I3Position(radius, 0.0, 0.0), + dataclasses.I3Position(0.0, -radius, 0.0), + dataclasses.I3Position(0.0, radius, 0.0), + dataclasses.I3Position(0.0, 0.0, -radius), + dataclasses.I3Position(0.0, 0.0, radius), + ] + + @staticmethod + def cylinder( + v_ax: List[float] = [-40.0, 40.0], + r_ax: List[float] = [150.0], + ang_steps=3, + ): + vert_u = I3Units.m + + # define angular steps + ang_ax = np.linspace(0, 2.0 * np.pi, ang_steps + 1)[:-1] + + # angular separation between seeds + dang = (ang_ax[1] - ang_ax[0]) / 2.0 + + pos_seeds = [dataclasses.I3Position(0.0, 0.0, 0.0)] + + for i, vi in enumerate(v_ax): # step along axis + for j, r in enumerate(r_ax): # step along radius + for ang in ang_ax: # step around anlge + x = r * np.cos(ang + (i + j) * dang) + y = r * np.sin(ang + (i + j) * dang) + z = vi + + pos = dataclasses.I3Position( + x * vert_u, + y * vert_u, + z * vert_u, + ) + + pos_seeds.append(pos) + + return pos_seeds + + @staticmethod + def mini_test(variation_distance): + """Simple two-variations config for testing purposes. + It does not have a physical motivation. + """ + return [ + dataclasses.I3Position(0.0, 0.0, 0.0), + dataclasses.I3Position(-variation_distance, 0.0, 0.0), + ] From 0c688833209f15b0e007ddb4aa9b366de0a8615e Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 07:44:25 +0000 Subject: [PATCH 042/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index 79c6f6eb4..af031398f 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -30,7 +30,7 @@ ed25519==1.5 # via nkeys ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From d3d5c790dfd05dc5e5ee794b99c4836fe437ec52 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 07:44:25 +0000 Subject: [PATCH 043/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 9aa4e35ed..27ad291b2 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From 480560c35084d91155df39bd9fd5a6b550259d16 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 07:44:25 +0000 Subject: [PATCH 044/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index eedeec204..4738f8a3f 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -28,7 +28,7 @@ ed25519==1.5 # via nkeys ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From a1afdd258635dfa79ce312aaf77654b1b91fe480 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 07:44:25 +0000 Subject: [PATCH 045/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index 9bf5de8e5..0f5b064e1 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -28,7 +28,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From 1db41829358374832a1ef560edb662121ea0fc5a Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 07:44:25 +0000 Subject: [PATCH 046/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index a867fed8b..7dd2c5437 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From ca9eec13a9c6cab803db343d1e94c622fd7c990a Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 07:44:25 +0000 Subject: [PATCH 047/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c8e19b346..3b9dfaeb7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From d90cad9bc353c8cdb85d5c17ff7c2bafbe0574d4 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 11 May 2023 10:29:49 +0200 Subject: [PATCH 048/217] vertex generator goes in common directory --- skymap_scanner/recos/__init__.py | 2 +- skymap_scanner/recos/vertex_gen.py | 70 ------------------------------ 2 files changed, 1 insertion(+), 71 deletions(-) delete mode 100644 skymap_scanner/recos/vertex_gen.py diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 084f262ac..a705875d5 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -5,7 +5,7 @@ import pkgutil from typing import TYPE_CHECKING, Any, List -from .vertex_gen import VertexGenerator +from .common.vertex_gen import VertexGenerator if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation diff --git a/skymap_scanner/recos/vertex_gen.py b/skymap_scanner/recos/vertex_gen.py deleted file mode 100644 index ec10cabba..000000000 --- a/skymap_scanner/recos/vertex_gen.py +++ /dev/null @@ -1,70 +0,0 @@ -from typing import List - -import numpy as np - -from icecube import dataclasses # type: ignore[import] -from I3Tray import I3Units # type: ignore[import] - - -class VertexGenerator: - def __init__(self): - pass - - @staticmethod - def point(): - return [dataclasses.I3Position(0.0, 0.0, 0.0)] - - @staticmethod - def octahedron(radius: float): - return [ - dataclasses.I3Position(0.0, 0.0, 0.0), - dataclasses.I3Position(-radius, 0.0, 0.0), - dataclasses.I3Position(radius, 0.0, 0.0), - dataclasses.I3Position(0.0, -radius, 0.0), - dataclasses.I3Position(0.0, radius, 0.0), - dataclasses.I3Position(0.0, 0.0, -radius), - dataclasses.I3Position(0.0, 0.0, radius), - ] - - @staticmethod - def cylinder( - v_ax: List[float] = [-40.0, 40.0], - r_ax: List[float] = [150.0], - ang_steps=3, - ): - vert_u = I3Units.m - - # define angular steps - ang_ax = np.linspace(0, 2.0 * np.pi, ang_steps + 1)[:-1] - - # angular separation between seeds - dang = (ang_ax[1] - ang_ax[0]) / 2.0 - - pos_seeds = [dataclasses.I3Position(0.0, 0.0, 0.0)] - - for i, vi in enumerate(v_ax): # step along axis - for j, r in enumerate(r_ax): # step along radius - for ang in ang_ax: # step around anlge - x = r * np.cos(ang + (i + j) * dang) - y = r * np.sin(ang + (i + j) * dang) - z = vi - - pos = dataclasses.I3Position( - x * vert_u, - y * vert_u, - z * vert_u, - ) - - pos_seeds.append(pos) - - return pos_seeds - - @staticmethod - def mini_test(variation_distance): - """Simple two-variations config for testing purposes. - It does not have a physical motivation. - """ - return [ - dataclasses.I3Position(0.0, 0.0, 0.0), - dataclasses.I3Position(-variation_distance, 0.0, 0.0), - ] From 268aa6155c96b2362ec89234d80efcd892ccea04 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 11 May 2023 10:31:51 +0200 Subject: [PATCH 049/217] stage splines --- skymap_scanner/recos/dummy.py | 14 ++++++++++++-- skymap_scanner/recos/millipede_original.py | 2 ++ 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 020fd38c9..6cc3c3aae 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,18 +30,28 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" + def stage_splines(): + pass + @staticmethod @icetray.traysegment def prepare_frames(tray, name, logger, **kwargs) -> None: def gen_dummy_vertex(frame): frame[cfg.INPUT_TIME_NAME] = dataclasses.I3Double(0.0) - frame[cfg.INPUT_POS_NAME] = dataclasses.I3Position(0.0 * I3Units.m, 0.0 * I3Units.m, 0.0 * I3Units.m) + frame[cfg.INPUT_POS_NAME] = dataclasses.I3Position( + 0.0 * I3Units.m, 0.0 * I3Units.m, 0.0 * I3Units.m + ) def notify(frame): logger.debug(f"Preparing frames (dummy). {datetime.datetime.now()}") tray.Add(notify, "notify") - tray.Add(gen_dummy_vertex, "gen_dummy_vertex", If=lambda frame: cfg.INPUT_TIME_NAME not in frame and cfg.INPUT_POS_NAME not in frame) + tray.Add( + gen_dummy_vertex, + "gen_dummy_vertex", + If=lambda frame: cfg.INPUT_TIME_NAME not in frame + and cfg.INPUT_POS_NAME not in frame, + ) @staticmethod @icetray.traysegment diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index b91fa5e10..b32c218a4 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -66,6 +66,8 @@ class MillipedeOriginal(RecoInterface): abs_spline: str = datastager.get_filepath(MIE_ABS_SPLINE) prob_spline: str = datastager.get_filepath(MIE_PROB_SPLINE) + def stage_splines(): + pass @icetray.traysegment def prepare_frames(tray, name, logger, pulsesName): From d0bcaf891d7874ab9d242a62be4331c0c64377ea Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 11 May 2023 10:32:21 +0200 Subject: [PATCH 050/217] rollback --- skymap_scanner/recos/dummy.py | 3 --- skymap_scanner/recos/millipede_original.py | 3 --- 2 files changed, 6 deletions(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 6cc3c3aae..6bae3a07b 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,9 +30,6 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" - def stage_splines(): - pass - @staticmethod @icetray.traysegment def prepare_frames(tray, name, logger, **kwargs) -> None: diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index b32c218a4..c28c3d2a6 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -66,9 +66,6 @@ class MillipedeOriginal(RecoInterface): abs_spline: str = datastager.get_filepath(MIE_ABS_SPLINE) prob_spline: str = datastager.get_filepath(MIE_PROB_SPLINE) - def stage_splines(): - pass - @icetray.traysegment def prepare_frames(tray, name, logger, pulsesName): # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. From 7e02ed7e7eb5fc4b464c3f58ffce9fc2597d9892 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 11 May 2023 11:10:13 +0200 Subject: [PATCH 051/217] datastager in millipede original --- skymap_scanner/recos/millipede_original.py | 48 ++++++++++++++-------- 1 file changed, 31 insertions(+), 17 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 7366395a9..1a67dd417 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -35,7 +35,7 @@ class MillipedeOriginal(RecoInterface): """Reco logic for millipede.""" - # Spline requirements + # Spline requirements ############################################## MIE_ABS_SPLINE = "ems_mie_z20_a10.abs.fits" MIE_PROB_SPLINE = "ems_mie_z20_a10.prob.fits" @@ -44,24 +44,28 @@ class MillipedeOriginal(RecoInterface): # Constants ######################################################## pulsesName = cfg.INPUT_PULSES_NAME pulsesName_cleaned = pulsesName+'LatePulseCleaned' - SPEScale = 0.99 # Load Data ######################################################## # At HESE energies, deposited light is dominated by the stochastic losses # (muon part emits so little light in comparison) # This is why we can use cascade tables - datastager = DataStager( - local_paths=cfg.LOCAL_DATA_SOURCES, - local_subdir=cfg.LOCAL_SPLINE_SUBDIR, - remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", - ) - datastager.stage_files(SPLINE_REQUIREMENTS) - abs_spline: str = datastager.get_filepath(MIE_ABS_SPLINE) - prob_spline: str = datastager.get_filepath(MIE_PROB_SPLINE) - - cascade_service = photonics_service.I3PhotoSplineService(abs_spline, prob_spline, timingSigma=0.0) - cascade_service.SetEfficiencies(SPEScale) - muon_service = None + + @staticmethod + def init_datastager() -> DataStager: + """Create datastager, stage spline data and return datastager. + + Returns: + DataStager: datastager for spline data. + """ + datastager = DataStager( + local_paths=cfg.LOCAL_DATA_SOURCES, + local_subdir=cfg.LOCAL_SPLINE_SUBDIR, + remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", + ) + + datastager.stage_files(MillipedeOriginal.SPLINE_REQUIREMENTS) + + return datastager def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: @@ -159,9 +163,20 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): return ExcludedDOMs + [MillipedeOriginal.pulsesName_cleaned+'TimeWindows'] + @staticmethod @icetray.traysegment def traysegment(tray, name, logger, seed=None): """Perform MillipedeOriginal reco.""" + datastager = MillipedeOriginal.init_datastager() + + abs_spline = datastager.get_filepath(MIE_ABS_SPLINE) + prob_spline = datastager.get_filepath(MIE_PROB_SPLINE) + + cascade_service = photonics_service.I3PhotoSplineService(abs_spline, prob_spline, timingSigma=0.0) + SPEScale = 0.99 # moved from MillipedeLikelihoodFactory parameter DOMEfficiency=SPEScale + cascade_service.SetEfficiencies(SPEScale) + muon_service = None + ExcludedDOMs = tray.Add(MillipedeOriginal.exclusions) tray.Add(MillipedeOriginal.makeSurePulsesExist, pulsesName=MillipedeOriginal.pulsesName_cleaned) @@ -172,11 +187,10 @@ def notify0(frame): tray.AddModule(notify0, "notify0") tray.AddService('MillipedeLikelihoodFactory', 'millipedellh', - MuonPhotonicsService=MillipedeOriginal.muon_service, - CascadePhotonicsService=MillipedeOriginal.cascade_service, + MuonPhotonicsService=muon_service, + CascadePhotonicsService=cascade_service, ShowerRegularization=0, PhotonsPerBin=15, - # DOMEfficiency=SPEScale, # moved to cascade_service.SetEfficiencies(SPEScale) ExcludedDOMs=ExcludedDOMs, PartialExclusion=True, ReadoutWindow=MillipedeOriginal.pulsesName_cleaned+'TimeRange', From 7d2c665674e97009f642d24ede43d5cc59b337b8 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 09:11:47 +0000 Subject: [PATCH 052/217] update requirements-all.txt --- requirements-all.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements-all.txt b/requirements-all.txt index 80f0533de..af031398f 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via # pulsar-client # requests @@ -28,9 +28,9 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.0 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via @@ -80,7 +80,7 @@ packaging==23.1 # matplotlib pandas==2.0.1 # via icecube-skyreader -pika==1.3.1 +pika==1.3.2 # via oms-mqclient pillow==9.5.0 # via matplotlib From b4f73c76f2753e328bbc5ab270e3ca7607199329 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 09:11:48 +0000 Subject: [PATCH 053/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 87431ac88..27ad291b2 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -24,9 +24,9 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From aff999afacec2dfcb848075cce8d93c03e9540c6 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 09:11:48 +0000 Subject: [PATCH 054/217] update requirements-nats.txt --- requirements-nats.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 4c118387a..4738f8a3f 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -26,9 +26,9 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.0 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From 21e8208ae762997eca8ee8f2db30a886bbcd6f8f Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 09:11:48 +0000 Subject: [PATCH 055/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index b0421c77a..0f5b064e1 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via # pulsar-client # requests @@ -26,9 +26,9 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From 0160311684dd3ac2755dad0ced4395c69defddae Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 09:11:48 +0000 Subject: [PATCH 056/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index db70ea4dd..7dd2c5437 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -24,9 +24,9 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via @@ -72,7 +72,7 @@ packaging==23.1 # matplotlib pandas==2.0.1 # via icecube-skyreader -pika==1.3.1 +pika==1.3.2 # via oms-mqclient pillow==9.5.0 # via matplotlib From ffc6d797e88043679caa247776338fc3cfdf2327 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Thu, 11 May 2023 09:11:48 +0000 Subject: [PATCH 057/217] update requirements.txt --- requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index dc89d7765..3b9dfaeb7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,7 +10,7 @@ astropy==5.2.2 # icecube-skyreader cachetools==5.3.0 # via wipac-rest-tools -certifi==2022.12.7 +certifi==2023.5.7 # via requests cffi==1.15.1 # via cryptography @@ -24,9 +24,9 @@ cryptography==40.0.2 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.0 +ewms-pilot==0.10.2 # via skymap-scanner (setup.py) -fonttools==4.39.3 +fonttools==4.39.4 # via matplotlib healpy==1.16.2 # via From e95b7f522aa03daa201f6ebfc049ee0ff63ec038 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 11 May 2023 11:18:43 +0200 Subject: [PATCH 058/217] resolve names --- skymap_scanner/recos/millipede_original.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 1a67dd417..8c1c5c1a9 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -169,8 +169,8 @@ def traysegment(tray, name, logger, seed=None): """Perform MillipedeOriginal reco.""" datastager = MillipedeOriginal.init_datastager() - abs_spline = datastager.get_filepath(MIE_ABS_SPLINE) - prob_spline = datastager.get_filepath(MIE_PROB_SPLINE) + abs_spline = datastager.get_filepath(MillipedeOriginal.MIE_ABS_SPLINE) + prob_spline = datastager.get_filepath(MillipedeOriginal.MIE_PROB_SPLINE) cascade_service = photonics_service.I3PhotoSplineService(abs_spline, prob_spline, timingSigma=0.0) SPEScale = 0.99 # moved from MillipedeLikelihoodFactory parameter DOMEfficiency=SPEScale From 299fd84a06389a31353f5b485980dff389c0e1b0 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 11 May 2023 16:27:33 +0200 Subject: [PATCH 059/217] update millipede_wilks --- skymap_scanner/recos/millipede_wilks.py | 58 ++++++++++++++----------- 1 file changed, 32 insertions(+), 26 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 54207b6e0..48f222437 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -47,30 +47,22 @@ class MillipedeWilks(RecoInterface): pulsesName = cfg.INPUT_PULSES_NAME + "IC" pulsesName_cleaned = pulsesName+'LatePulseCleaned' - # Load Data ######################################################## - - # At HESE energies, deposited light is dominated by the stochastic losses - # (muon part emits so little light in comparison) - # This is why we can use cascade tables - datastager = DataStager( - local_paths=cfg.LOCAL_DATA_SOURCES, - local_subdir=cfg.LOCAL_SPLINE_SUBDIR, - remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", - ) - - datastager.stage_files(SPLINE_REQUIREMENTS) - - abs_spline: str = datastager.get_filepath(FTP_ABS_SPLINE) - prob_spline: str = datastager.get_filepath(FTP_PROB_SPLINE) - effd_spline: str = datastager.get_filepath(FTP_EFFD_SPLINE) - - cascade_service = photonics_service.I3PhotoSplineService( - abs_spline, prob_spline, timingSigma=0.0, - effectivedistancetable = effd_spline, - tiltTableDir = os.path.expandvars('$I3_BUILD/ice-models/resources/models/ICEMODEL/spice_ftp-v1/'), - quantileEpsilon=1 + @staticmethod + def init_datastager() -> DataStager: + """Create datastager, stage spline data and return datastager. + + Returns: + DataStager: datastager for spline data. + """ + datastager = DataStager( + local_paths=cfg.LOCAL_DATA_SOURCES, + local_subdir=cfg.LOCAL_SPLINE_SUBDIR, + remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", ) - muon_service = None + + datastager.stage_files(MillipedeWilks.SPLINE_REQUIREMENTS) + + return datastager def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: @@ -191,10 +183,24 @@ def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): ) return ExcludedDOMs + [MillipedeWilks.pulsesName_cleaned+'TimeWindows'] - + @staticmethod @icetray.traysegment def traysegment(tray, name, logger, seed=None): """Perform MillipedeWilks reco.""" + datastager = MillipedeWilks.init_datastager() + + abs_spline: str = datastager.get_filepath(FTP_ABS_SPLINE) + prob_spline: str = datastager.get_filepath(FTP_PROB_SPLINE) + effd_spline: str = datastager.get_filepath(FTP_EFFD_SPLINE) + + cascade_service = photonics_service.I3PhotoSplineService( + abs_spline, prob_spline, timingSigma=0.0, + effectivedistancetable = effd_spline, + tiltTableDir = os.path.expandvars('$I3_BUILD/ice-models/resources/models/ICEMODEL/spice_ftp-v1/'), + quantileEpsilon=1 + ) + muon_service = None + def mask_dc(frame, origpulses, maskedpulses): # Masks DeepCore pulses by selecting string numbers < 79. frame[maskedpulses] = dataclasses.I3RecoPulseSeriesMapMask( @@ -211,8 +217,8 @@ def notify0(frame): tray.AddModule(notify0, "notify0") tray.AddService('MillipedeLikelihoodFactory', 'millipedellh', - MuonPhotonicsService=MillipedeWilks.muon_service, - CascadePhotonicsService=MillipedeWilks.cascade_service, + MuonPhotonicsService=muon_service, + CascadePhotonicsService=cascade_service, ShowerRegularization=0, ExcludedDOMs=ExcludedDOMs, PartialExclusion=True, From 417f11f77ca19e73678e8fa8d04b5c9909fbe9f0 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 10:19:18 +0200 Subject: [PATCH 060/217] drop automatic determination of reco class name from module name --- skymap_scanner/recos/__init__.py | 3 +-- skymap_scanner/recos/dummy.py | 6 +++++- skymap_scanner/recos/millipede_original.py | 3 +++ skymap_scanner/recos/millipede_wilks.py | 4 ++-- 4 files changed, 11 insertions(+), 5 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index ca71c0b4f..9b4b14b3a 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -54,8 +54,7 @@ def get_reco_interface_object(name: str) -> RecoInterface: try: # Fetch module module = importlib.import_module(f"{__name__}.{name.lower()}") - # Build the class name (i.e. reco_algo -> RecoAlgo). - return getattr(module, "".join(x.capitalize() for x in name.split("_"))) + return module.RECO_CLASS except ModuleNotFoundError as e: if name not in get_all_reco_algos(): # checking this in 'except' allows us to use 'from e' diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 1b24aa9ea..5cb86e12c 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -4,7 +4,7 @@ import datetime import random import time -from typing import List +from typing import List, Final from I3Tray import I3Units # type: ignore[import] from icecube import ( # type: ignore[import] # noqa: F401 @@ -69,3 +69,7 @@ def to_recopixelvariation(frame: I3Frame, geometry: I3Frame) -> RecoPixelVariati time=frame["Dummy_time"].value, energy=frame["Dummy_time"].value, ) + + +# Provide a standard alias for the reconstruction class provided by this module. +RECO_CLASS: Final[type[RecoInterface]] = Dummy diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 8c1c5c1a9..bf8f820f9 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -341,3 +341,6 @@ def getRecoLosses(vecParticles): totalRecoLossesInside += entry[1] return totalRecoLossesInside, totalRecoLosses + +# Provide a standard alias for the reconstruction class provided by this module. +RECO_CLASS: Final[type[RecoInterface]] = MillipedeOriginal diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 48f222437..7a7675359 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -31,8 +31,6 @@ from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface - - class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" # Spline requirements ############################################## @@ -410,3 +408,5 @@ def getRecoLosses(vecParticles): totalRecoLossesInside += entry[1] return totalRecoLossesInside, totalRecoLosses + +RECO_CLASS: Final[type[RecoInterface]] = MillipedeWilks From de5172b1a8fd1302c6cb0f5495a3ab7331d204ae Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 10:24:25 +0200 Subject: [PATCH 061/217] import Final --- skymap_scanner/recos/millipede_original.py | 2 +- skymap_scanner/recos/millipede_wilks.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index bf8f820f9..33f660dd4 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -7,7 +7,7 @@ import copy import datetime import os -from typing import Tuple +from typing import Final, Tuple import numpy diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 7a7675359..93efda78b 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -7,7 +7,7 @@ import copy import datetime import os -from typing import Tuple +from typing import Final, Tuple import numpy from I3Tray import I3Units From 35b098b2b78d36e37e13557653a0396246960fa7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 11:15:00 +0200 Subject: [PATCH 062/217] use instances of reco class instead of static attributes and methods --- skymap_scanner/client/reco_icetray.py | 6 ++- skymap_scanner/recos/__init__.py | 17 +++++- skymap_scanner/recos/dummy.py | 6 +++ skymap_scanner/recos/millipede_original.py | 61 ++++++++++------------ 4 files changed, 54 insertions(+), 36 deletions(-) diff --git a/skymap_scanner/client/reco_icetray.py b/skymap_scanner/client/reco_icetray.py index 54f94893b..66fe68f96 100644 --- a/skymap_scanner/client/reco_icetray.py +++ b/skymap_scanner/client/reco_icetray.py @@ -139,9 +139,13 @@ def notifyStart(frame): base_filename=baseline_GCD_file, ) + # create instance of reco_algo object + RecoAlgo = recos.get_reco_interface_object(reco_algo) + reco = RecoAlgo() + # perform fit tray.AddSegment( - recos.get_reco_interface_object(reco_algo).traysegment, + reco.traysegment, f"{reco_algo}_traysegment", logger=LOGGER, seed=pframe[f"{cfg.OUTPUT_PARTICLE_NAME}"], diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 9b4b14b3a..187159a84 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -31,6 +31,21 @@ class RecoInterface: # The spline files will be looked up in pre-defined local paths or fetched from a remote data store. SPLINE_REQUIREMENTS: List[str] = list() + def init(self): + raise NotImplementedError() + + def setup_reco(self): + """Performs the necessary operations to prepare the execution of the reconstruction traysegment.""" + raise NotImplementedError() + + def get_datastager(self): + datastager = DataStager( + local_paths=cfg.LOCAL_DATA_SOURCES, + local_subdir=cfg.LOCAL_SPLINE_SUBDIR, + remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", + ) + return datastager + @staticmethod def traysegment(tray, name, logger, **kwargs: Any) -> None: raise NotImplementedError() @@ -49,7 +64,7 @@ def get_all_reco_algos() -> List[str]: ] -def get_reco_interface_object(name: str) -> RecoInterface: +def get_reco_interface_object(name: str) -> type[RecoInterface]: """Dynamically import the reco sub-module's class.""" try: # Fetch module diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 5cb86e12c..1ae0ec2a7 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -28,6 +28,12 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" + def __init__(self): + pass + + def setup_reco(self): + pass + @staticmethod @icetray.traysegment def traysegment(tray, name, logger, **kwargs): diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 33f660dd4..f282acce0 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -6,7 +6,6 @@ import copy import datetime -import os from typing import Final, Tuple import numpy @@ -45,28 +44,31 @@ class MillipedeOriginal(RecoInterface): pulsesName = cfg.INPUT_PULSES_NAME pulsesName_cleaned = pulsesName+'LatePulseCleaned' + SPEScale = 0.99 # DOM efficiency + # Load Data ######################################################## # At HESE energies, deposited light is dominated by the stochastic losses # (muon part emits so little light in comparison) # This is why we can use cascade tables - @staticmethod - def init_datastager() -> DataStager: - """Create datastager, stage spline data and return datastager. - - Returns: - DataStager: datastager for spline data. - """ - datastager = DataStager( - local_paths=cfg.LOCAL_DATA_SOURCES, - local_subdir=cfg.LOCAL_SPLINE_SUBDIR, - remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", - ) + def __init__(self): + pass + + def setup_reco(self): + datastager = self.get_datastager() + + datastager.stage_files(self.SPLINE_REQUIREMENTS) + + abs_spline = datastager.get_filepath(self.MIE_ABS_SPLINE) + prob_spline = datastager.get_filepath(self.MIE_PROB_SPLINE) + + self.cascade_service = photonics_service.I3PhotoSplineService(abs_spline, prob_spline, timingSigma=0.0) - datastager.stage_files(MillipedeOriginal.SPLINE_REQUIREMENTS) + self.cascade_service.SetEfficiencies(self.SPEScale) - return datastager - + self.muon_service = None + + @staticmethod def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: raise RuntimeError("{0} not in frame".format(pulsesName)) @@ -75,6 +77,7 @@ def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName + "TimeRange" not in frame: raise RuntimeError("{0} not in frame".format(pulsesName + "TimeRange")) + @staticmethod @icetray.traysegment def exclusions(tray, name): tray.Add('Delete', keys=['BrightDOMs', @@ -163,23 +166,13 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): return ExcludedDOMs + [MillipedeOriginal.pulsesName_cleaned+'TimeWindows'] - @staticmethod @icetray.traysegment - def traysegment(tray, name, logger, seed=None): + def traysegment(self, tray, name, logger, seed=None): """Perform MillipedeOriginal reco.""" - datastager = MillipedeOriginal.init_datastager() - - abs_spline = datastager.get_filepath(MillipedeOriginal.MIE_ABS_SPLINE) - prob_spline = datastager.get_filepath(MillipedeOriginal.MIE_PROB_SPLINE) - - cascade_service = photonics_service.I3PhotoSplineService(abs_spline, prob_spline, timingSigma=0.0) - SPEScale = 0.99 # moved from MillipedeLikelihoodFactory parameter DOMEfficiency=SPEScale - cascade_service.SetEfficiencies(SPEScale) - muon_service = None - ExcludedDOMs = tray.Add(MillipedeOriginal.exclusions) + ExcludedDOMs = tray.Add(self.exclusions) - tray.Add(MillipedeOriginal.makeSurePulsesExist, pulsesName=MillipedeOriginal.pulsesName_cleaned) + tray.Add(self.makeSurePulsesExist, pulsesName=self.pulsesName_cleaned) def notify0(frame): logger.debug(f"starting a new fit ({name})! {datetime.datetime.now()}") @@ -187,14 +180,14 @@ def notify0(frame): tray.AddModule(notify0, "notify0") tray.AddService('MillipedeLikelihoodFactory', 'millipedellh', - MuonPhotonicsService=muon_service, - CascadePhotonicsService=cascade_service, + MuonPhotonicsService=self.muon_service, + CascadePhotonicsService=self.cascade_service, ShowerRegularization=0, PhotonsPerBin=15, ExcludedDOMs=ExcludedDOMs, PartialExclusion=True, - ReadoutWindow=MillipedeOriginal.pulsesName_cleaned+'TimeRange', - Pulses=MillipedeOriginal.pulsesName_cleaned, + ReadoutWindow=self.pulsesName_cleaned+'TimeRange', + Pulses=self.pulsesName_cleaned, BinSigma=3) tray.AddService('I3GSLRandomServiceFactory','I3RandomService') @@ -264,7 +257,7 @@ def notify2(frame): @staticmethod def to_recopixelvariation(frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: # Calculate reco losses, based on load_scan_state() - reco_losses_inside, reco_losses_total = MillipedeOriginal.get_reco_losses_inside( + reco_losses_inside, reco_losses_total = self.get_reco_losses_inside( p_frame=frame, g_frame=geometry, ) From 3c4d46ac11c7f3fbb8ebb668b9cfe0021491a9d4 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 11:21:49 +0200 Subject: [PATCH 063/217] setup reco --- skymap_scanner/client/reco_icetray.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skymap_scanner/client/reco_icetray.py b/skymap_scanner/client/reco_icetray.py index 66fe68f96..3305d21b2 100644 --- a/skymap_scanner/client/reco_icetray.py +++ b/skymap_scanner/client/reco_icetray.py @@ -142,6 +142,7 @@ def notifyStart(frame): # create instance of reco_algo object RecoAlgo = recos.get_reco_interface_object(reco_algo) reco = RecoAlgo() + reco.setup_reco() # perform fit tray.AddSegment( From 0c626a6c68c837b53aefa3feb36ad69567fcd3ff Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 11:30:15 +0200 Subject: [PATCH 064/217] datastager import --- skymap_scanner/recos/dummy.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 1ae0ec2a7..87f67cdd4 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -22,6 +22,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation +from ..utils.data_handling import DataStager from . import RecoInterface From f11ae9b97d18f24373f90960c221c692306836e8 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 11:36:22 +0200 Subject: [PATCH 065/217] import in correct place --- skymap_scanner/recos/__init__.py | 2 ++ skymap_scanner/recos/dummy.py | 1 - 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 187159a84..61d083ace 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -8,6 +8,8 @@ if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation +from ..utils.data_handling import DataStager + try: # these are only used for typehints, so mock imports are fine from icecube.dataclasses import I3Position # type: ignore[import] from icecube.icetray import I3Frame # type: ignore[import] diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 87f67cdd4..1ae0ec2a7 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -22,7 +22,6 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation -from ..utils.data_handling import DataStager from . import RecoInterface From a2824fbb3d485f820b91e05764c18f26e7aff123 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 11:51:27 +0200 Subject: [PATCH 066/217] import config --- skymap_scanner/recos/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 61d083ace..8c1a45bc7 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -8,6 +8,7 @@ if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation +import ..config as cfg from ..utils.data_handling import DataStager try: # these are only used for typehints, so mock imports are fine From fd164b75011fe4d4eea01ae1a82837457ba5ab16 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 12:12:52 +0200 Subject: [PATCH 067/217] config shenanigans --- skymap_scanner/recos/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 8c1a45bc7..8ecc6a52e 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -8,7 +8,7 @@ if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation -import ..config as cfg +from .. import config as cfg from ..utils.data_handling import DataStager try: # these are only used for typehints, so mock imports are fine From 623a04b9f8b214bd17683bdef7b3f47eac6fae0c Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 12:20:06 +0200 Subject: [PATCH 068/217] drop staticmethod definition --- skymap_scanner/recos/millipede_original.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index f282acce0..1220c6943 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -254,8 +254,7 @@ def notify2(frame): tray.AddModule(notify2, "notify2") - @staticmethod - def to_recopixelvariation(frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: + def to_recopixelvariation(self, frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: # Calculate reco losses, based on load_scan_state() reco_losses_inside, reco_losses_total = self.get_reco_losses_inside( p_frame=frame, g_frame=geometry, From cfae049b329e9cdac5b1804f9c94349f9acd00c3 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 13:00:18 +0200 Subject: [PATCH 069/217] to_recopixelvariation as class method --- skymap_scanner/recos/millipede_original.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 1220c6943..5a2c2c030 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -254,9 +254,10 @@ def notify2(frame): tray.AddModule(notify2, "notify2") - def to_recopixelvariation(self, frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: + @classmethod + def to_recopixelvariation(cls, frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: # Calculate reco losses, based on load_scan_state() - reco_losses_inside, reco_losses_total = self.get_reco_losses_inside( + reco_losses_inside, reco_losses_total = cls.get_reco_losses_inside( p_frame=frame, g_frame=geometry, ) From 69d875973909c8e92c28e946761fe49cd9657d4f Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 13:51:19 +0200 Subject: [PATCH 070/217] update millipede_wilks --- skymap_scanner/recos/millipede_wilks.py | 74 +++++++++++-------------- 1 file changed, 33 insertions(+), 41 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 93efda78b..fe65538b5 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -45,23 +45,28 @@ class MillipedeWilks(RecoInterface): pulsesName = cfg.INPUT_PULSES_NAME + "IC" pulsesName_cleaned = pulsesName+'LatePulseCleaned' - @staticmethod - def init_datastager() -> DataStager: - """Create datastager, stage spline data and return datastager. - - Returns: - DataStager: datastager for spline data. - """ - datastager = DataStager( - local_paths=cfg.LOCAL_DATA_SOURCES, - local_subdir=cfg.LOCAL_SPLINE_SUBDIR, - remote_path=f"{cfg.REMOTE_DATA_SOURCE}/{cfg.REMOTE_SPLINE_SUBDIR}", - ) + def __init__(self): + pass + + def setup_reco(self): + datastager = self.get_datastager() + + datastager.stage_files(self.SPLINE_REQUIREMENTS) + + abs_spline: str = datastager.get_filepath(self.FTP_ABS_SPLINE) + prob_spline: str = datastager.get_filepath(self.FTP_PROB_SPLINE) + effd_spline: str = datastager.get_filepath(self.FTP_EFFD_SPLINE) - datastager.stage_files(MillipedeWilks.SPLINE_REQUIREMENTS) + self.cascade_service = photonics_service.I3PhotoSplineService( + abs_spline, prob_spline, timingSigma=0.0, + effectivedistancetable = effd_spline, + tiltTableDir = os.path.expandvars('$I3_BUILD/ice-models/resources/models/ICEMODEL/spice_ftp-v1/') + quantileEpsilon=1 + ) - return datastager + self.muon_service = None + @staticmethod def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: raise RuntimeError("{0} not in frame".format(pulsesName)) @@ -70,6 +75,7 @@ def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName + "TimeRange" not in frame: raise RuntimeError("{0} not in frame".format(pulsesName + "TimeRange")) + @staticmethod @icetray.traysegment def exclusions(tray, name): tray.Add('Delete', keys=['BrightDOMs', @@ -181,33 +187,19 @@ def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): ) return ExcludedDOMs + [MillipedeWilks.pulsesName_cleaned+'TimeWindows'] - @staticmethod @icetray.traysegment - def traysegment(tray, name, logger, seed=None): + def traysegment(self, tray, name, logger, seed=None): """Perform MillipedeWilks reco.""" - datastager = MillipedeWilks.init_datastager() - - abs_spline: str = datastager.get_filepath(FTP_ABS_SPLINE) - prob_spline: str = datastager.get_filepath(FTP_PROB_SPLINE) - effd_spline: str = datastager.get_filepath(FTP_EFFD_SPLINE) - - cascade_service = photonics_service.I3PhotoSplineService( - abs_spline, prob_spline, timingSigma=0.0, - effectivedistancetable = effd_spline, - tiltTableDir = os.path.expandvars('$I3_BUILD/ice-models/resources/models/ICEMODEL/spice_ftp-v1/'), - quantileEpsilon=1 - ) - muon_service = None def mask_dc(frame, origpulses, maskedpulses): # Masks DeepCore pulses by selecting string numbers < 79. frame[maskedpulses] = dataclasses.I3RecoPulseSeriesMapMask( frame, origpulses, lambda omkey, index, pulse: omkey.string < 79) - tray.Add(mask_dc, origpulses=MillipedeWilks.pulsesName_orig, maskedpulses=MillipedeWilks.pulsesName) + tray.Add(mask_dc, origpulses=self.pulsesName_orig, maskedpulses=self.pulsesName) - ExcludedDOMs = tray.Add(MillipedeWilks.exclusions) + ExcludedDOMs = tray.Add(self.exclusions) - tray.Add(MillipedeWilks.makeSurePulsesExist, pulsesName=MillipedeWilks.pulsesName_cleaned) + tray.Add(self.makeSurePulsesExist, pulsesName=self.pulsesName_cleaned) def notify0(frame): logger.debug(f"starting a new fit ({name})! {datetime.datetime.now()}") @@ -215,13 +207,13 @@ def notify0(frame): tray.AddModule(notify0, "notify0") tray.AddService('MillipedeLikelihoodFactory', 'millipedellh', - MuonPhotonicsService=muon_service, - CascadePhotonicsService=cascade_service, + MuonPhotonicsService=self.muon_service, + CascadePhotonicsService=self.cascade_service, ShowerRegularization=0, ExcludedDOMs=ExcludedDOMs, PartialExclusion=True, - ReadoutWindow=MillipedeWilks.pulsesName_cleaned+'TimeRange', - Pulses=MillipedeWilks.pulsesName_cleaned, + ReadoutWindow=self.pulsesName_cleaned+'TimeRange', + Pulses=self.pulsesName_cleaned, BinSigma=2, MinTimeWidth=25, RelUncertainty=0.3) @@ -267,8 +259,8 @@ def notify0(frame): Boundary=650*I3Units.m) if seed is not None: logger.debug('Updating StepXYZ') - MillipedeWilks.UpdateStepXYZ(coars_steps, seed.dir, 150*I3Units.m) - MillipedeWilks.UpdateStepXYZ(finer_steps, seed.dir, 3*I3Units.m) + self.UpdateStepXYZ(coars_steps, seed.dir, 150*I3Units.m) + self.UpdateStepXYZ(finer_steps, seed.dir, 3*I3Units.m) tray.AddService('MuMillipedeParametrizationFactory', 'coarseSteps', **coars_steps) tray.AddService('I3BasicSeedServiceFactory', 'vetoseed', @@ -328,10 +320,10 @@ def UpdateStepXYZ(the_steps, direction, uniform_step=15*I3Units.m): the_steps['StepY'] = numpy.sqrt(1-direction.y**2)*uniform_step the_steps['StepZ'] = numpy.sqrt(1-direction.z**2)*uniform_step - @staticmethod - def to_recopixelvariation(frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: + @classmethod + def to_recopixelvariation(cls, frame: I3Frame, geometry: I3Frame) -> RecoPixelVariation: # Calculate reco losses, based on load_scan_state() - reco_losses_inside, reco_losses_total = MillipedeWilks.get_reco_losses_inside( + reco_losses_inside, reco_losses_total = cls.get_reco_losses_inside( p_frame=frame, g_frame=geometry, ) From 89e6a066d8f5871bd26bb90a89ed9bd74c998792 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 12 May 2023 13:59:40 +0200 Subject: [PATCH 071/217] syntax --- skymap_scanner/recos/millipede_wilks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index fe65538b5..f1ed65968 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -60,7 +60,7 @@ def setup_reco(self): self.cascade_service = photonics_service.I3PhotoSplineService( abs_spline, prob_spline, timingSigma=0.0, effectivedistancetable = effd_spline, - tiltTableDir = os.path.expandvars('$I3_BUILD/ice-models/resources/models/ICEMODEL/spice_ftp-v1/') + tiltTableDir = os.path.expandvars('$I3_BUILD/ice-models/resources/models/ICEMODEL/spice_ftp-v1/'), quantileEpsilon=1 ) From cf242b2c44d8b325c1c045bd4a401692ab5b4afb Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 13 May 2023 11:18:16 +0200 Subject: [PATCH 072/217] methods instead of static attributes; configurable vertex rotation --- skymap_scanner/recos/__init__.py | 13 +++++++--- skymap_scanner/recos/dummy.py | 11 +++++++- skymap_scanner/recos/millipede_original.py | 22 +++++++++++----- skymap_scanner/recos/millipede_wilks.py | 11 +++++++- skymap_scanner/server/start_scan.py | 30 +++++++++++++--------- 5 files changed, 64 insertions(+), 23 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 45408f937..95f9ac65b 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -36,12 +36,19 @@ class RecoInterface: # The spline files will be looked up in pre-defined local paths or fetched from a remote data store. SPLINE_REQUIREMENTS: List[str] = list() - # List of vectors referenced to the origin that will be used to generate the vertex position variation. - VERTEX_VARIATIONS: List[I3Position] = VertexGenerator.point() - def init(self): raise NotImplementedError() + @staticmethod + def get_vertex_variations() -> List[I3Position]: + """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" + raise NotImplementedError() + + @staticmethod + def do_rotate_vertex() -> bool: + """Defines whether each generated vertex variation should be rotated along the axis of the scan direction. With the exception for legacy algorithms (MillipedeOriginal) this should typycally return True.""" + raise NotImplementedError() + @staticmethod def prepare_frames(tray, name, **kwargs) -> None: raise NotImplementedError() diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 230a1618e..38a0abe7a 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -24,7 +24,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface +from . import RecoInterface, VertexGenerator class Dummy(RecoInterface): @@ -36,6 +36,15 @@ def __init__(self): def setup_reco(self): pass + @staticmethod + def get_vertex_variations() -> List[I3Position]: + """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" + return VertexGenerator.point() + + @staticmethod + def do_rotate_vertex() -> bool: + return True + @staticmethod @icetray.traysegment def prepare_frames(tray, name, logger, **kwargs) -> None: diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 4475b0f65..404f8f2cd 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -35,13 +35,23 @@ class MillipedeOriginal(RecoInterface): """Reco logic for millipede.""" - variation_distance = 20.*I3Units.m - - if cfg.ENV.SKYSCAN_MINI_TEST: - VERTEX_VARIATIONS = VertexGenerator.mini_test(variation_distance=variation_distance) - else: - VERTEX_VARIATIONS = VertexGenerator.octahedron(radius=variation_distance) + + @staticmethod + def get_vertex_variations() -> List[I3Position]: + """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variations. + """ + variation_distance = 20.*I3Units.m + + if cfg.ENV.SKYSCAN_MINI_TEST: + return VertexGenerator.mini_test(variation_distance=variation_distance) + else: + return VertexGenerator.octahedron(radius=variation_distance) + + @staticmethod + def do_rotate_vertex() -> bool: + # In the legacy Millipede implementation, the generated vertex seeds were not rotated along the scan direction. Such "feature" is here preserved. + return False pulsesName = cfg.INPUT_PULSES_NAME pulsesName_cleaned = pulsesName+'LatePulseCleaned' diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index fffc41c83..a7a2fe513 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -34,7 +34,6 @@ class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" - VERTEX_VARIATIONS = VertexGenerator.point() # Spline requirements ############################################## FTP_ABS_SPLINE = "cascade_single_spice_ftp-v1_flat_z20_a5.abs.fits" @@ -51,6 +50,16 @@ class MillipedeWilks(RecoInterface): def __init__(self): pass + @staticmethod + def get_vertex_variations() -> List[I3Position]: + """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variations. + """ + return VertexGenerator.point() + + @staticmethod + def do_rotate_vertex() -> bool: + return True + def setup_reco(self): datastager = self.get_datastager() diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 18693018c..2990b1fe7 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -10,7 +10,7 @@ import random import time from pathlib import Path -from typing import Any, Dict, Iterator, List, Optional, Set, Tuple +from typing import Any, Dict, Iterator, List, Optional, Set, Tuple, Type import healpy # type: ignore[import] import mqclient as mq @@ -36,6 +36,7 @@ SentPixelVariation, pframe_tuple, ) +from ..recos import RecoInterface from . import LOGGER from .collector import Collector, ExtraRecoPixelVariationException from .pixels import choose_pixels_to_reconstruct @@ -85,9 +86,14 @@ def __init__( self.input_pos_name = input_pos_name self.input_time_name = input_time_name self.output_particle_name = output_particle_name - self.reco_algo = reco_algo.lower() + + self.reco_algo: str = reco_algo + + RecoAlgo: Type[RecoInterface] = recos.get_reco_interface_object(reco_algo) + + self.reco = RecoAlgo() - self.pos_variations = recos.get_reco_interface_object(reco_algo).VERTEX_VARIATIONS + self.pos_variations = self.reco.get_vertex_variations() # Set min nside self.min_nside = min_nside @@ -248,18 +254,18 @@ def _gen_pframes( p_frame = icetray.I3Frame(icetray.I3Frame.Physics) posVariation = self.pos_variations[i] - if self.reco_algo in ['millipede_wilks', 'splinempe']: + if self.reco.do_rotate_vertex(): # rotate variation to be applied in transverse plane posVariation.rotate_y(direction.theta) posVariation.rotate_z(direction.phi) - if self.reco_algo == 'millipede_wilks': - if position != self.fallback_position: - # add fallback pos as an extra first guess - p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( - self.fallback_position+posVariation, - direction, - self.fallback_energy, - self.fallback_time) + if self.reco_algo == 'millipede_wilks': + if position != self.fallback_position: + # add fallback pos as an extra first guess + p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( + self.fallback_position+posVariation, + direction, + self.fallback_energy, + self.fallback_time) p_frame[f'{self.output_particle_name}'] = self.i3particle(position+posVariation, direction, From f96e455b233ee43fd27be9606c09a7444c13c99c Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 13 May 2023 11:25:47 +0200 Subject: [PATCH 073/217] imports --- skymap_scanner/recos/__init__.py | 2 -- skymap_scanner/recos/millipede_original.py | 2 +- skymap_scanner/recos/millipede_wilks.py | 2 +- 3 files changed, 2 insertions(+), 4 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 95f9ac65b..480c20a69 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -5,8 +5,6 @@ import pkgutil from typing import TYPE_CHECKING, Any, List -from .common.vertex_gen import VertexGenerator - if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 404f8f2cd..dde50c607 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -6,7 +6,7 @@ import copy import datetime -from typing import Final, Tuple +from typing import Final, List, Tuple import numpy diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index a7a2fe513..e321d55c3 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -7,7 +7,7 @@ import copy import datetime import os -from typing import Final, Tuple +from typing import Final, List, Tuple import numpy from I3Tray import I3Units From 9606275cae0abf4f7c672d1f9048c3d3aa91a67d Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 13 May 2023 11:30:05 +0200 Subject: [PATCH 074/217] imports/2 --- skymap_scanner/recos/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 480c20a69..95f9ac65b 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -5,6 +5,8 @@ import pkgutil from typing import TYPE_CHECKING, Any, List +from .common.vertex_gen import VertexGenerator + if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation From c65bf5cc846709bf9275b7f386e354fdb8c169ac Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 13 May 2023 11:57:07 +0200 Subject: [PATCH 075/217] imports/3 --- skymap_scanner/recos/__init__.py | 2 -- skymap_scanner/recos/dummy.py | 6 +++--- skymap_scanner/recos/millipede_original.py | 6 +++--- skymap_scanner/recos/millipede_wilks.py | 6 +++--- 4 files changed, 9 insertions(+), 11 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 95f9ac65b..480c20a69 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -5,8 +5,6 @@ import pkgutil from typing import TYPE_CHECKING, Any, List -from .common.vertex_gen import VertexGenerator - if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 38a0abe7a..ca27565db 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -19,12 +19,12 @@ simclasses, ) -from icecube.dataclasses import I3Position # type: ignore[import] from icecube.icetray import I3Frame # type: ignore[import] from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface, VertexGenerator +from . import RecoInterface +from .common.vertex_gen import VertexGenerator class Dummy(RecoInterface): @@ -37,7 +37,7 @@ def setup_reco(self): pass @staticmethod - def get_vertex_variations() -> List[I3Position]: + def get_vertex_variations() -> List[dataclasses.I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" return VertexGenerator.point() diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index dde50c607..4f9e4f70c 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -30,15 +30,15 @@ from .. import config as cfg from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface, VertexGenerator - +from . import RecoInterface +from .common.vertex_gen import VertexGenerator class MillipedeOriginal(RecoInterface): """Reco logic for millipede.""" @staticmethod - def get_vertex_variations() -> List[I3Position]: + def get_vertex_variations() -> List[dataclasses.I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variations. """ variation_distance = 20.*I3Units.m diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index e321d55c3..a8588b4d3 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,8 +29,8 @@ from .. import config as cfg from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface, VertexGenerator - +from . import RecoInterface +from .common.vertex_gen import VertexGenerator class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -51,7 +51,7 @@ def __init__(self): pass @staticmethod - def get_vertex_variations() -> List[I3Position]: + def get_vertex_variations() -> List[dataclasses.I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variations. """ return VertexGenerator.point() From 85d9c7f88098af9824ba755ae0a2af5c7f1dbdcd Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 13 May 2023 12:14:25 +0200 Subject: [PATCH 076/217] imports/4 --- skymap_scanner/recos/__init__.py | 3 +++ skymap_scanner/recos/dummy.py | 3 +-- skymap_scanner/recos/millipede_original.py | 3 +-- skymap_scanner/recos/millipede_wilks.py | 4 +--- 4 files changed, 6 insertions(+), 7 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 480c20a69..9f62c9315 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -18,6 +18,9 @@ I3Position = Any I3Frame = Any +# Redundant import(s) to declare exported symbol(s). +from .common.vertex_gen import VertexGenerator as VertexGenerator + class UnsupportedRecoAlgoException(Exception): """Raise when a reconstruction algorithm is not supported for a given diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index ca27565db..ff92fbad2 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -23,8 +23,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface -from .common.vertex_gen import VertexGenerator +from . import RecoInterface, VertexGenerator class Dummy(RecoInterface): diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 4f9e4f70c..b17626e73 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -30,8 +30,7 @@ from .. import config as cfg from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface -from .common.vertex_gen import VertexGenerator +from . import RecoInterface, VertexGenerator class MillipedeOriginal(RecoInterface): """Reco logic for millipede.""" diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index a8588b4d3..f96c0c594 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -27,10 +27,8 @@ from icecube.icetray import I3Frame from .. import config as cfg -from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation -from . import RecoInterface -from .common.vertex_gen import VertexGenerator +from . import RecoInterface, VertexGenerator class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" From aeb2e964f250cbe81f69f76493e51081f4749ef5 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 13 May 2023 14:43:23 +0200 Subject: [PATCH 077/217] typing --- skymap_scanner/server/start_scan.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 2990b1fe7..1fab7275d 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -10,7 +10,7 @@ import random import time from pathlib import Path -from typing import Any, Dict, Iterator, List, Optional, Set, Tuple, Type +from typing import Any, Dict, Iterator, List, Optional, Set, Tuple import healpy # type: ignore[import] import mqclient as mq @@ -89,9 +89,9 @@ def __init__( self.reco_algo: str = reco_algo - RecoAlgo: Type[RecoInterface] = recos.get_reco_interface_object(reco_algo) + RecoAlgo: type[RecoInterface] = recos.get_reco_interface_object(reco_algo) - self.reco = RecoAlgo() + self.reco: RecoInterface = RecoAlgo() self.pos_variations = self.reco.get_vertex_variations() From 0a0c4560b07646a8809e72b8d794c4c85e945576 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 13 May 2023 14:51:52 +0200 Subject: [PATCH 078/217] option to refine time --- skymap_scanner/recos/__init__.py | 7 ++++++- skymap_scanner/recos/dummy.py | 4 ---- skymap_scanner/recos/millipede_original.py | 5 +++++ skymap_scanner/recos/millipede_wilks.py | 4 ---- skymap_scanner/server/start_scan.py | 11 +++++++---- 5 files changed, 18 insertions(+), 13 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 9f62c9315..f462fd618 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -48,7 +48,12 @@ def get_vertex_variations() -> List[I3Position]: @staticmethod def do_rotate_vertex() -> bool: """Defines whether each generated vertex variation should be rotated along the axis of the scan direction. With the exception for legacy algorithms (MillipedeOriginal) this should typycally return True.""" - raise NotImplementedError() + return True + + @staticmethod + def do_refine_time() -> bool: + """Defines whether to refine seed time.""" + return True @staticmethod def prepare_frames(tray, name, **kwargs) -> None: diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index ff92fbad2..e363a2ee0 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -40,10 +40,6 @@ def get_vertex_variations() -> List[dataclasses.I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" return VertexGenerator.point() - @staticmethod - def do_rotate_vertex() -> bool: - return True - @staticmethod @icetray.traysegment def prepare_frames(tray, name, logger, **kwargs) -> None: diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index b17626e73..b4646db36 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -52,6 +52,11 @@ def do_rotate_vertex() -> bool: # In the legacy Millipede implementation, the generated vertex seeds were not rotated along the scan direction. Such "feature" is here preserved. return False + @staticmethod + def do_refine_time() -> bool: + # Millipede original did not apply a refinement of the vertex time. + return False + pulsesName = cfg.INPUT_PULSES_NAME pulsesName_cleaned = pulsesName+'LatePulseCleaned' diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index f96c0c594..ff6b1fbce 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -53,10 +53,6 @@ def get_vertex_variations() -> List[dataclasses.I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variations. """ return VertexGenerator.point() - - @staticmethod - def do_rotate_vertex() -> bool: - return True def setup_reco(self): datastager = self.get_datastager() diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 1fab7275d..45df13a27 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -182,10 +182,8 @@ def i3particle(self, position, direction, energy, time): particle.fit_status = dataclasses.I3Particle.FitStatus.OK particle.pos = position particle.dir = direction - if self.reco_algo == 'millipede_original': - LOGGER.debug(f"Reco_algo is {self.reco_algo}, not refining time") - particle.time = time - else: + + if self.reco.do_refine_time(): LOGGER.debug(f"Reco_algo is {self.reco_algo}, refining time") # given direction and vertex position, calculate time from CAD particle.time = self.refine_vertex_time( @@ -194,7 +192,12 @@ def i3particle(self, position, direction, energy, time): direction, self.pulseseries_hlc, self.omgeo) + else: + LOGGER.debug(f"Reco_algo is {self.reco_algo}, not refining time") + particle.time = time + particle.energy = energy + return particle def _gen_pframes( From cc55eb40aa4192188763527493b8380bcd0d1bf1 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 14 May 2023 11:28:23 +0200 Subject: [PATCH 079/217] def exclusions() as classmethod --- skymap_scanner/recos/millipede_original.py | 22 ++++++++++---------- skymap_scanner/recos/millipede_wilks.py | 24 +++++++++++----------- 2 files changed, 23 insertions(+), 23 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 5a2c2c030..176561c3a 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -77,19 +77,19 @@ def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName + "TimeRange" not in frame: raise RuntimeError("{0} not in frame".format(pulsesName + "TimeRange")) - @staticmethod + @classmethod @icetray.traysegment - def exclusions(tray, name): + def exclusions(cls, tray, name): tray.Add('Delete', keys=['BrightDOMs', 'SaturatedDOMs', 'DeepCoreDOMs', - MillipedeOriginal.pulsesName_cleaned, - MillipedeOriginal.pulsesName_cleaned+'TimeWindows', - MillipedeOriginal.pulsesName_cleaned+'TimeRange']) + cls.pulsesName_cleaned, + cls.pulsesName_cleaned+'TimeWindows', + cls.pulsesName_cleaned+'TimeRange']) exclusionList = \ tray.AddSegment(millipede.HighEnergyExclusions, 'millipede_DOM_exclusions', - Pulses = MillipedeOriginal.pulsesName, + Pulses = cls.pulsesName, ExcludeDeepCore='DeepCoreDOMs', ExcludeSaturatedDOMs='SaturatedDOMs', ExcludeBrightDOMs='BrightDOMs', @@ -156,14 +156,14 @@ def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): mask.set(omkey, p, False) counter += 1 charge += p.charge - frame[MillipedeOriginal.pulsesName_cleaned] = mask - frame[MillipedeOriginal.pulsesName_cleaned+"TimeWindows"] = times - frame[MillipedeOriginal.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[Pulses+"TimeRange"]) + frame[cls.pulsesName_cleaned] = mask + frame[cls.pulsesName_cleaned+"TimeWindows"] = times + frame[cls.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[Pulses+"TimeRange"]) tray.AddModule(LatePulseCleaning, "LatePulseCleaning", - Pulses=MillipedeOriginal.pulsesName, + Pulses=cls.pulsesName, ) - return ExcludedDOMs + [MillipedeOriginal.pulsesName_cleaned+'TimeWindows'] + return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] @icetray.traysegment diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index f1ed65968..444ed4d7a 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -75,19 +75,19 @@ def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName + "TimeRange" not in frame: raise RuntimeError("{0} not in frame".format(pulsesName + "TimeRange")) - @staticmethod + @classmethod @icetray.traysegment - def exclusions(tray, name): + def exclusions(cls, tray, name): tray.Add('Delete', keys=['BrightDOMs', 'SaturatedDOMs', 'DeepCoreDOMs', - MillipedeWilks.pulsesName_cleaned, - MillipedeWilks.pulsesName_cleaned+'TimeWindows', - MillipedeWilks.pulsesName_cleaned+'TimeRange']) + cls.pulsesName_cleaned, + cls.pulsesName_cleaned+'TimeWindows', + cls.pulsesName_cleaned+'TimeRange']) exclusionList = \ tray.AddSegment(millipede.HighEnergyExclusions, 'millipede_DOM_exclusions', - Pulses = MillipedeWilks.pulsesName, + Pulses = cls.pulsesName, ExcludeDeepCore='DeepCoreDOMs', ExcludeSaturatedDOMs='SaturatedDOMs', ExcludeBrightDOMs='BrightDOMs', @@ -128,7 +128,7 @@ def skipunhits(frame, output, pulses): frame[output] = unhits - tray.Add(skipunhits, output='OtherUnhits', pulses=MillipedeWilks.pulsesName) + tray.Add(skipunhits, output='OtherUnhits', pulses=cls.pulsesName) ExcludedDOMs.append('OtherUnhits') ################## @@ -178,14 +178,14 @@ def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): mask.set(omkey, p, False) counter += 1 charge += p.charge - frame[MillipedeWilks.pulsesName_cleaned] = mask - frame[MillipedeWilks.pulsesName_cleaned+"TimeWindows"] = times - frame[MillipedeWilks.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[MillipedeWilks.pulsesName_orig+"TimeRange"]) + frame[cls.pulsesName_cleaned] = mask + frame[cls.pulsesName_cleaned+"TimeWindows"] = times + frame[cls.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[cls.pulsesName_orig+"TimeRange"]) tray.AddModule(LatePulseCleaning, "LatePulseCleaning", - Pulses=MillipedeWilks.pulsesName, + Pulses=cls.pulsesName, ) - return ExcludedDOMs + [MillipedeWilks.pulsesName_cleaned+'TimeWindows'] + return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] @icetray.traysegment def traysegment(self, tray, name, logger, seed=None): From 83e5238c4dc88d91098238a0e3630c0c6d92a0cf Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 14 May 2023 09:29:35 +0000 Subject: [PATCH 080/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index af031398f..65b8df3ea 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -117,7 +117,7 @@ scipy==1.10.1 # via healpy six==1.16.0 # via python-dateutil -tornado==6.3.1 +tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt From d2e8b02e9b0504359a0044dbd45718ea444e1757 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 14 May 2023 09:29:35 +0000 Subject: [PATCH 081/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 27ad291b2..9a9fed55d 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -107,7 +107,7 @@ scipy==1.10.1 # via healpy six==1.16.0 # via python-dateutil -tornado==6.3.1 +tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt From ba5555dbb0c1583645584a7059e95237085d85d1 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 14 May 2023 09:29:35 +0000 Subject: [PATCH 082/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 4738f8a3f..24cf00422 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -111,7 +111,7 @@ scipy==1.10.1 # via healpy six==1.16.0 # via python-dateutil -tornado==6.3.1 +tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt From 45619c37117eb75ddae15d1d858518ca76865fc3 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 14 May 2023 09:29:35 +0000 Subject: [PATCH 083/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index 0f5b064e1..2ab349d75 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -109,7 +109,7 @@ scipy==1.10.1 # via healpy six==1.16.0 # via python-dateutil -tornado==6.3.1 +tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt From fd577a3392d799f70bbe49f1ece056630bd4a551 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 14 May 2023 09:29:35 +0000 Subject: [PATCH 084/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index 7dd2c5437..bcde6fbd5 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -107,7 +107,7 @@ scipy==1.10.1 # via healpy six==1.16.0 # via python-dateutil -tornado==6.3.1 +tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt From 1cdce73e1e9c6e0c153f494555a1fb128bc8225b Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 14 May 2023 09:29:35 +0000 Subject: [PATCH 085/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 3b9dfaeb7..a9c098739 100644 --- a/requirements.txt +++ b/requirements.txt @@ -105,7 +105,7 @@ scipy==1.10.1 # via healpy six==1.16.0 # via python-dateutil -tornado==6.3.1 +tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt From 09ce5f0efedf64b4146bf4eefe6a77ece1f30ab7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 14 May 2023 13:21:47 +0200 Subject: [PATCH 086/217] update docs --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5cd857889..1a4a50024 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ export EWMS_PILOT_TASK_TIMEOUT=1200 --client-startup-json PATH_TO_CLIENT_STARTUP_JSON \ --cache-dir `pwd`/server_cache \ --output-dir `pwd` \ - --reco-algo millipede \ + --reco-algo millipede_original \ --event-file `pwd`/run00136662-evt000035405932-BRONZE.pkl # could also be a .json file ``` _NOTE: The `--*dir` arguments can all be the same if you'd like. Relative paths are also fine._ @@ -220,7 +220,7 @@ Relatedly, the environment variable `EWMS_PILOT_TASK_TIMEOUT` & `EWMS_PILOT_QUAR There are more command-line arguments than those shown in [Example Startup](#example-startup). See `skymap_scanner.server.start_scan.main()` and `skymap_scanner.client.client.main()` for more detail. #### Runtime-Configurable Reconstructions -Recos are registered by being placed in a dedicated module within the `skymap_scanner.recos` sub-package. Each module must contain a class of the same name (eg: `skymap_scanner.recos.foo` has `skymap_scanner.recos.foo.Foo`) that fully inherits from `skymap_scanner.recos.RecoInterface`. This includes implementing the static methods: `traysegment()` (for IceTray) and `to_pixelreco()` (for MQ). Specialized reco-specific logic in the upstream/pixel-generation phase is done on an ad-hoc basis, eg: `if reco_algo == 'millipede_original': ...`. On the command line, choosing your reco is provided via `--reco-algo` (on the server). +Recos are registered by being placed in a dedicated module within the `skymap_scanner.recos` sub-package. Each module must contain a class of the same name (eg: `skymap_scanner.recos.foo` has `skymap_scanner.recos.foo.Foo`) that fully inherits from `skymap_scanner.recos.RecoInterface`. This includes implementing the static methods: `traysegment()` (for IceTray) and `to_pixelreco()` (for MQ). The reco-specific logic in the upstream/pixel-generation phase is defined in the same class by the `prepare_frames()` (pulse cleaning, vertex generation) and `get_vertex_variations()` (variations of the vertex positions to be used as additional seeds for each pixel). On the command line, choosing your reco is provided via `--reco-algo` (on the server). ## Making Branch-Based Images for Production-like Testing If you need to test your updates in a production-like environment at a scale that isn't provided by CI, then create a branch-based image. This image will be available on Docker Hub and CVMFS. From 6d46c6084de95fbfff94283cca4faba3ead22d23 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 15 May 2023 09:53:19 +0200 Subject: [PATCH 087/217] init file for subdir --- skymap_scanner/recos/common/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 skymap_scanner/recos/common/__init__.py diff --git a/skymap_scanner/recos/common/__init__.py b/skymap_scanner/recos/common/__init__.py new file mode 100644 index 000000000..e69de29bb From 770d896969f30d03e140911301db64f2917a0747 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 15 May 2023 17:24:28 +0200 Subject: [PATCH 088/217] move mask deepcore to common functions --- skymap_scanner/recos/common/pulse_proc.py | 13 +++++++++++++ skymap_scanner/recos/millipede_wilks.py | 13 ++++++------- 2 files changed, 19 insertions(+), 7 deletions(-) create mode 100644 skymap_scanner/recos/common/pulse_proc.py diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py new file mode 100644 index 000000000..3204ab223 --- /dev/null +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -0,0 +1,13 @@ +from typing import Final + +from icecube import dataclasses + + +def mask_deepcore(frame, origpulses: str, maskedpulses: str): + """Masks DeepCore pulses by selecting string numbers.""" + FIRST_DEEPCORE_STRING: Final[int] = 79 + frame[maskedpulses] = dataclasses.I3RecoPulseSeriesMapMask( + frame, + origpulses, + lambda omkey, index, pulse: omkey.string < FIRST_DEEPCORE_STRING, + ) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 5d349c6a0..689c280a7 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,6 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator +from .common.pulse_proc import mask_deepcore class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -72,8 +73,9 @@ def setup_reco(self): self.muon_service = None + @classmethod @icetray.traysegment - def prepare_frames(tray, name): + def prepare_frames(cls, tray, name): # Generates the vertex seed for the initial scan. # Only run if HESE_VHESelfVeto is not present in the frame. # VertexThreshold is 250 in the original HESE analysis (Tianlu) @@ -94,6 +96,9 @@ def prepare_frames(tray, name): OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, If=lambda frame: not frame.Has("HESE_VHESelfVeto")) + + + tray.Add(mask_deepcore, origpulses=cls.pulsesName_orig, maskedpulses=cls.pulsesName) def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: @@ -219,12 +224,6 @@ def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): def traysegment(self, tray, name, logger, seed=None): """Perform MillipedeWilks reco.""" - def mask_dc(frame, origpulses, maskedpulses): - # Masks DeepCore pulses by selecting string numbers < 79. - frame[maskedpulses] = dataclasses.I3RecoPulseSeriesMapMask( - frame, origpulses, lambda omkey, index, pulse: omkey.string < 79) - tray.Add(mask_dc, origpulses=self.pulsesName_orig, maskedpulses=self.pulsesName) - ExcludedDOMs = tray.Add(self.exclusions) tray.Add(self.makeSurePulsesExist, pulsesName=self.pulsesName_cleaned) From 2cc68fecb6157656ced464238c66d31e9cb7ffad Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 15 May 2023 17:33:21 +0200 Subject: [PATCH 089/217] ignore import --- skymap_scanner/recos/common/pulse_proc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 3204ab223..c044812c3 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -1,6 +1,6 @@ from typing import Final -from icecube import dataclasses +from icecube import dataclasses # type: ignore[import] def mask_deepcore(frame, origpulses: str, maskedpulses: str): From 32b9af8fc055015990de1aac34582fd3e1011cf8 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 14:49:34 +0200 Subject: [PATCH 090/217] merge main/2 --- skymap_scanner/recos/__init__.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 383a583b3..f462fd618 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -40,7 +40,6 @@ class RecoInterface: def init(self): raise NotImplementedError() -<<<<<<< HEAD @staticmethod def get_vertex_variations() -> List[I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" @@ -60,8 +59,6 @@ def do_refine_time() -> bool: def prepare_frames(tray, name, **kwargs) -> None: raise NotImplementedError() -======= ->>>>>>> main def setup_reco(self): """Performs the necessary operations to prepare the execution of the reconstruction traysegment.""" raise NotImplementedError() From b1654f45e27c77eff75712ead93795b2c7ec5c4c Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 14:50:20 +0200 Subject: [PATCH 091/217] fix unsaved files --- skymap_scanner/recos/dummy.py | 3 --- skymap_scanner/recos/millipede_original.py | 3 --- 2 files changed, 6 deletions(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 87056d761..e363a2ee0 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -35,7 +35,6 @@ def __init__(self): def setup_reco(self): pass -<<<<<<< HEAD @staticmethod def get_vertex_variations() -> List[dataclasses.I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" @@ -61,8 +60,6 @@ def notify(frame): and cfg.INPUT_POS_NAME not in frame, ) -======= ->>>>>>> main @staticmethod @icetray.traysegment def traysegment(tray, name, logger, **kwargs): diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 19447c3e4..080f210cb 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -85,7 +85,6 @@ def do_refine_time() -> bool: # (muon part emits so little light in comparison) # This is why we can use cascade tables -<<<<<<< HEAD @icetray.traysegment def prepare_frames(tray, name, logger, pulsesName): # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. @@ -109,8 +108,6 @@ def extract_seed(frame): OutputVertexPos=cfg.INPUT_POS_NAME, If=lambda frame: "HESE_VHESelfVeto" not in frame) -======= ->>>>>>> main def __init__(self): pass From f4c68ce90c95590d75a94e5fde2f65434e135c45 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 14:56:41 +0200 Subject: [PATCH 092/217] toggle use of fallback position --- skymap_scanner/recos/dummy.py | 2 +- skymap_scanner/recos/millipede_original.py | 10 +--------- skymap_scanner/recos/millipede_wilks.py | 2 +- skymap_scanner/server/start_scan.py | 3 ++- 4 files changed, 5 insertions(+), 12 deletions(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index e363a2ee0..d237faa70 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,7 +30,7 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" def __init__(self): - pass + self.use_fallback_position = False def setup_reco(self): pass diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 080f210cb..8eab8e386 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -6,11 +6,7 @@ import copy import datetime -<<<<<<< HEAD from typing import Final, List, Tuple -======= -from typing import Final, Tuple ->>>>>>> main import numpy @@ -38,7 +34,6 @@ class MillipedeOriginal(RecoInterface): """Reco logic for millipede.""" -<<<<<<< HEAD @staticmethod @@ -66,9 +61,6 @@ def do_refine_time() -> bool: pulsesName_cleaned = pulsesName+'LatePulseCleaned' # Spline requirements -======= - # Spline requirements ############################################## ->>>>>>> main MIE_ABS_SPLINE = "ems_mie_z20_a10.abs.fits" MIE_PROB_SPLINE = "ems_mie_z20_a10.prob.fits" @@ -109,7 +101,7 @@ def extract_seed(frame): If=lambda frame: "HESE_VHESelfVeto" not in frame) def __init__(self): - pass + self.use_fallback_position = False def setup_reco(self): datastager = self.get_datastager() diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index ed820a138..4e8ac5035 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -47,7 +47,7 @@ class MillipedeWilks(RecoInterface): pulsesName_cleaned = pulsesName+'LatePulseCleaned' def __init__(self): - pass + self.use_fallback_position = True @staticmethod def get_vertex_variations() -> List[dataclasses.I3Position]: diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 45df13a27..e9a2c1837 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -261,7 +261,8 @@ def _gen_pframes( # rotate variation to be applied in transverse plane posVariation.rotate_y(direction.theta) posVariation.rotate_z(direction.phi) - if self.reco_algo == 'millipede_wilks': + + if self.reco.use_fallback_position: if position != self.fallback_position: # add fallback pos as an extra first guess p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( From fdcd905fb762c9b30868a621cfb23c624da218f9 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 15:15:42 +0200 Subject: [PATCH 093/217] turn conf into dictionary --- skymap_scanner/recos/__init__.py | 12 ++++++++++++ skymap_scanner/recos/dummy.py | 2 +- skymap_scanner/recos/millipede_original.py | 4 +++- skymap_scanner/recos/millipede_wilks.py | 3 ++- skymap_scanner/server/start_scan.py | 6 +++--- 5 files changed, 21 insertions(+), 6 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index f462fd618..c4e428278 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -40,6 +40,18 @@ class RecoInterface: def init(self): raise NotImplementedError() + @property + def conf(self): + raise NotImplementedError() + + @staticmethod + def get_default_conf(): + return { + "rotate_vertex": True, + "refine_time": True, + "use_fallback_position": False, + } + @staticmethod def get_vertex_variations() -> List[I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index d237faa70..f354d5dca 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,7 +30,7 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" def __init__(self): - self.use_fallback_position = False + self.conf = self.get_default_conf() def setup_reco(self): pass diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 8eab8e386..927cf16f2 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -101,7 +101,9 @@ def extract_seed(frame): If=lambda frame: "HESE_VHESelfVeto" not in frame) def __init__(self): - self.use_fallback_position = False + self.conf = self.get_default_conf() + self.conf["rotate_vertex"] = False + self.conf["refine_time"] = False def setup_reco(self): datastager = self.get_datastager() diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 4e8ac5035..614d953e7 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -47,7 +47,8 @@ class MillipedeWilks(RecoInterface): pulsesName_cleaned = pulsesName+'LatePulseCleaned' def __init__(self): - self.use_fallback_position = True + self.conf = self.get_default_conf() + self.conf["use_fallback_position"] = True @staticmethod def get_vertex_variations() -> List[dataclasses.I3Position]: diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index e9a2c1837..846862b6c 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -183,7 +183,7 @@ def i3particle(self, position, direction, energy, time): particle.pos = position particle.dir = direction - if self.reco.do_refine_time(): + if self.reco.conf["refine_time"]: LOGGER.debug(f"Reco_algo is {self.reco_algo}, refining time") # given direction and vertex position, calculate time from CAD particle.time = self.refine_vertex_time( @@ -257,12 +257,12 @@ def _gen_pframes( p_frame = icetray.I3Frame(icetray.I3Frame.Physics) posVariation = self.pos_variations[i] - if self.reco.do_rotate_vertex(): + if self.reco.conf["rotate_vertex"]: # rotate variation to be applied in transverse plane posVariation.rotate_y(direction.theta) posVariation.rotate_z(direction.phi) - if self.reco.use_fallback_position: + if self.reco.conf["use_fallback_position"]: if position != self.fallback_position: # add fallback pos as an extra first guess p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( From 0f9b61ee39766710124dc7d134140179543d3d94 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 16:11:46 +0200 Subject: [PATCH 094/217] use abc --- skymap_scanner/recos/__init__.py | 56 +++++++++++++++----------------- 1 file changed, 27 insertions(+), 29 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index c4e428278..b0bedbc25 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -3,6 +3,8 @@ import importlib import pkgutil + +from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Any, List if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 @@ -30,19 +32,20 @@ def __init__(self, reco_algo: str): super().__init__(f"Requested unsupported reconstruction algorithm: {reco_algo}") -class RecoInterface: +class RecoInterface(ABC): """An abstract class encapsulating reco-specific logic.""" # List of spline file basenames required by the class. # The spline files will be looked up in pre-defined local paths or fetched from a remote data store. SPLINE_REQUIREMENTS: List[str] = list() + @abstractmethod def init(self): - raise NotImplementedError() + pass - @property + @abstractmethod def conf(self): - raise NotImplementedError() + pass @staticmethod def get_default_conf(): @@ -53,29 +56,7 @@ def get_default_conf(): } @staticmethod - def get_vertex_variations() -> List[I3Position]: - """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" - raise NotImplementedError() - - @staticmethod - def do_rotate_vertex() -> bool: - """Defines whether each generated vertex variation should be rotated along the axis of the scan direction. With the exception for legacy algorithms (MillipedeOriginal) this should typycally return True.""" - return True - - @staticmethod - def do_refine_time() -> bool: - """Defines whether to refine seed time.""" - return True - - @staticmethod - def prepare_frames(tray, name, **kwargs) -> None: - raise NotImplementedError() - - def setup_reco(self): - """Performs the necessary operations to prepare the execution of the reconstruction traysegment.""" - raise NotImplementedError() - - def get_datastager(self): + def get_datastager(): datastager = DataStager( local_paths=cfg.LOCAL_DATA_SOURCES, local_subdir=cfg.LOCAL_SPLINE_SUBDIR, @@ -83,10 +64,27 @@ def get_datastager(self): ) return datastager + @abstractmethod @staticmethod - def traysegment(tray, name, logger, **kwargs: Any) -> None: - raise NotImplementedError() + def get_vertex_variations() -> List[I3Position]: + """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" + pass + + @abstractmethod + def prepare_frames(self, tray, name, **kwargs) -> None: + pass + + @abstractmethod + def setup_reco(self): + """Performs the necessary operations to prepare the execution of the reconstruction traysegment.""" + pass + + @abstractmethod + def traysegment(self, tray, name, logger, **kwargs: Any) -> None: + """Performs the reconstruction.""" + pass + @abstractmethod @staticmethod def to_recopixelvariation( frame: I3Frame, geometry: I3Frame From 06a58eb9f525a6525f7281e8f53635e977260223 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 16:23:33 +0200 Subject: [PATCH 095/217] annotated attribute --- skymap_scanner/recos/__init__.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index b0bedbc25..e2a16db7f 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -5,7 +5,7 @@ import pkgutil from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, List +from typing import TYPE_CHECKING, Any, Dict, List if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 from ..utils.pixel_classes import RecoPixelVariation @@ -35,16 +35,15 @@ def __init__(self, reco_algo: str): class RecoInterface(ABC): """An abstract class encapsulating reco-specific logic.""" + # Dictionary for configuration, to be defined as instance attribute. + conf: Dict[str, bool] + # List of spline file basenames required by the class. # The spline files will be looked up in pre-defined local paths or fetched from a remote data store. SPLINE_REQUIREMENTS: List[str] = list() @abstractmethod - def init(self): - pass - - @abstractmethod - def conf(self): + def __init__(self): pass @staticmethod From 5e9a7973eb1fa245564c85860ec25f302d694735 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 16:38:08 +0200 Subject: [PATCH 096/217] order of abstractmethod and staticmethod --- skymap_scanner/recos/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index e2a16db7f..df758ab02 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -63,8 +63,8 @@ def get_datastager(): ) return datastager - @abstractmethod @staticmethod + @abstractmethod def get_vertex_variations() -> List[I3Position]: """Returns a list of vectors referenced to the origin that will be used to generate the vertex position variation.""" pass From 5844b0ca0bd27f3e111b515e1061c01ede83d8de Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 16 May 2023 16:45:24 +0200 Subject: [PATCH 097/217] order of abstractmethod and staticmethod/2 --- skymap_scanner/recos/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index df758ab02..54a31f405 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -83,8 +83,8 @@ def traysegment(self, tray, name, logger, **kwargs: Any) -> None: """Performs the reconstruction.""" pass - @abstractmethod @staticmethod + @abstractmethod def to_recopixelvariation( frame: I3Frame, geometry: I3Frame ) -> "RecoPixelVariation": From 818a54b9bb9188f6e6ca4263b97954bc0554dac6 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 17 May 2023 09:26:36 +0200 Subject: [PATCH 098/217] sort pulses name --- skymap_scanner/recos/millipede_wilks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 614d953e7..44b4c9e00 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -83,7 +83,7 @@ def prepare_frames(cls, tray, name): # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. tray.AddModule('VHESelfVeto', 'selfveto', VertexThreshold=250, - Pulses=pulsesName+'HLC', + Pulses=cls.pulsesName_orig+'HLC', OutputBool='HESE_VHESelfVeto', OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, @@ -92,7 +92,7 @@ def prepare_frames(cls, tray, name): # this only runs if the previous module did not return anything tray.AddModule('VHESelfVeto', 'selfveto-emergency-lowen-settings', VertexThreshold=5, - Pulses=pulsesName+'HLC', + Pulses=cls.pulsesName_orig+'HLC', OutputBool='VHESelfVeto_meaningless_lowen', OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, From 3c8625aaeda661a15cbd213309813e0dc13ea9af Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 17 May 2023 10:25:06 +0200 Subject: [PATCH 099/217] change configuration of reco from dictionary to class attributes --- skymap_scanner/recos/__init__.py | 8 +++++--- skymap_scanner/recos/dummy.py | 4 +++- skymap_scanner/recos/millipede_original.py | 6 +++--- skymap_scanner/recos/millipede_wilks.py | 5 +++-- skymap_scanner/server/start_scan.py | 8 ++++---- 5 files changed, 18 insertions(+), 13 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index 54a31f405..b6b36195f 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -35,10 +35,12 @@ def __init__(self, reco_algo: str): class RecoInterface(ABC): """An abstract class encapsulating reco-specific logic.""" - # Dictionary for configuration, to be defined as instance attribute. - conf: Dict[str, bool] + # Reco-specific behaviors that need to be defined in derived classes. + rotate_vertex: bool + refine_time: bool + use_fallback_position: bool - # List of spline file basenames required by the class. + # List of spline filenames required by the class. # The spline files will be looked up in pre-defined local paths or fetched from a remote data store. SPLINE_REQUIREMENTS: List[str] = list() diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index f354d5dca..25ee03aec 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,7 +30,9 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" def __init__(self): - self.conf = self.get_default_conf() + self.rotate_vertex = True + self.refine_time = True + self.use_fallback_position = False def setup_reco(self): pass diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 927cf16f2..17201ee89 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -101,9 +101,9 @@ def extract_seed(frame): If=lambda frame: "HESE_VHESelfVeto" not in frame) def __init__(self): - self.conf = self.get_default_conf() - self.conf["rotate_vertex"] = False - self.conf["refine_time"] = False + self.rotate_vertex = False + self.refine_time = False + self.use_fallback_position = False def setup_reco(self): datastager = self.get_datastager() diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 44b4c9e00..6f0ae8634 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -47,8 +47,9 @@ class MillipedeWilks(RecoInterface): pulsesName_cleaned = pulsesName+'LatePulseCleaned' def __init__(self): - self.conf = self.get_default_conf() - self.conf["use_fallback_position"] = True + self.rotate_vertex = False + self.refine_time = False + self.use_fallback_position = False @staticmethod def get_vertex_variations() -> List[dataclasses.I3Position]: diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 846862b6c..92450dabb 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -117,7 +117,7 @@ def __init__( f"({self.event_header.run_id=}, {self.event_header.event_id=})" ) - # The HLC pulse mask has been created in prepare_frames(). + # The HLC pulse mask should have been been created in prepare_frames(). self.pulseseries_hlc = dataclasses.I3RecoPulseSeriesMap.from_frame(p_frame,cfg.INPUT_PULSES_NAME+'HLC') self.omgeo = g_frame["I3Geometry"].omgeo @@ -183,7 +183,7 @@ def i3particle(self, position, direction, energy, time): particle.pos = position particle.dir = direction - if self.reco.conf["refine_time"]: + if self.reco.refine_time: LOGGER.debug(f"Reco_algo is {self.reco_algo}, refining time") # given direction and vertex position, calculate time from CAD particle.time = self.refine_vertex_time( @@ -257,12 +257,12 @@ def _gen_pframes( p_frame = icetray.I3Frame(icetray.I3Frame.Physics) posVariation = self.pos_variations[i] - if self.reco.conf["rotate_vertex"]: + if self.reco.rotate_vertex: # rotate variation to be applied in transverse plane posVariation.rotate_y(direction.theta) posVariation.rotate_z(direction.phi) - if self.reco.conf["use_fallback_position"]: + if self.reco.use_fallback_position: if position != self.fallback_position: # add fallback pos as an extra first guess p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( From 3948f0a8a98e6077cb287c0cbcb850182c519684 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 17 May 2023 12:02:37 +0200 Subject: [PATCH 100/217] documentation --- skymap_scanner/server/start_scan.py | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 92450dabb..99db0c736 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -205,7 +205,17 @@ def _gen_pframes( nside: icetray.I3Int, pixel: icetray.I3Int, ) -> Iterator[icetray.I3Frame]: - """Yield PFrames to be reco'd for a given `nside` and `pixel`.""" + """Yield PFrames to be reco'd for a given `nside` and `pixel`. + + Each PFrame consists of an I3Particle to be used as seed by the reconstruction, plus some metadata. + + The seed direction (zenith and azimuth) is calculated from the celestial coordinates (RA, dec) of the given HEALPIX pixel. + + Multiple seed vertices (position variations) are generated according to a reco-specific set of vectors to be added to the base vertex (position). + + The base vertex is taken from the best-fit of the coarser pixel or, in absence of it (for example when scanning pixels of the minimum NSIDE), from a seed defined by the reco algorithm. + + """ codec, ra = healpy.pix2ang(nside, pixel) dec = numpy.pi/2 - codec @@ -214,25 +224,33 @@ def _gen_pframes( azimuth = float(azimuth) direction = dataclasses.I3Direction(zenith, azimuth) + + if nside == self.min_nside: + # Scanning the minimum NSIDE, the position is taken from a seed provided by reco-specific logic and passed as "fallback position". position = self.fallback_position time = self.fallback_time energy = self.fallback_energy else: coarser_nside = nside while True: + # Look up the first available coarser NSIDE by iteratively dividing by two the current nside. + # NOTE (v3): this guesswork could be avoided using the NSIDE progression. coarser_nside = coarser_nside/2 coarser_pixel = healpy.ang2pix(int(coarser_nside), numpy.pi/2-dec, ra) if coarser_nside < self.min_nside: - break # no coarser pixel is available (probably we are just scanning finely around MC truth) - #raise RuntimeError("internal error. cannot find an original coarser pixel for nside={0}/pixel={1}".format(nside, pixel)) + # no coarser pixel is available (probably we are just scanning finely around MC truth) + # NOTE (v3): nside != min_side and nside/2 < min_side should be always false? Given the comment above this could have been introduced to support "pointed" scans but this is not currently possible in v3. + break if coarser_nside in self.nsides_dict: + # NOTE: This is the first nside in the divide-by-two progression that is available in the dictionary. By construction, this should be the previous value in the NSIDE progression. if coarser_pixel in self.nsides_dict[coarser_nside]: # coarser pixel found break + # The following if-else clause decided based on the outcome of the lookup in the previous loop. if coarser_nside < self.min_nside: # no coarser pixel is available (probably we are just scanning finely around MC truth) position = self.fallback_position @@ -249,6 +267,8 @@ def _gen_pframes( time = self.nsides_dict[coarser_nside][coarser_pixel].time energy = self.nsides_dict[coarser_nside][coarser_pixel].energy + # Now generate the vertex seed position variations according to the reco-specific logic. + n_pos_variations = len(self.pos_variations) LOGGER.debug(f"Generating {n_pos_variations} position variations.") From b9c93b80dd30839c9d376dcc12f580557391f0b7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 17 May 2023 15:54:14 +0200 Subject: [PATCH 101/217] MillipedeWilks does use a fallback position --- skymap_scanner/recos/millipede_wilks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 6f0ae8634..5a95eb6e9 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -49,7 +49,7 @@ class MillipedeWilks(RecoInterface): def __init__(self): self.rotate_vertex = False self.refine_time = False - self.use_fallback_position = False + self.use_fallback_position = True @staticmethod def get_vertex_variations() -> List[dataclasses.I3Position]: From 9a4fee33c747dd9e03cb080a29538f819468e5de Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 17 May 2023 16:58:21 +0200 Subject: [PATCH 102/217] cleanup and docs --- skymap_scanner/recos/__init__.py | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index b6b36195f..e47a1d64c 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -1,10 +1,8 @@ """Tools for conducting & representing a pixel reconstruction.""" - +from abc import ABC, abstractmethod import importlib import pkgutil - -from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Any, Dict, List if TYPE_CHECKING: # https://stackoverflow.com/a/65265627 @@ -20,7 +18,7 @@ I3Position = Any I3Frame = Any -# Redundant import(s) to declare exported symbol(s). +# Redundant imports are used to declare symbols exported by the module. from .common.vertex_gen import VertexGenerator as VertexGenerator @@ -48,14 +46,6 @@ class RecoInterface(ABC): def __init__(self): pass - @staticmethod - def get_default_conf(): - return { - "rotate_vertex": True, - "refine_time": True, - "use_fallback_position": False, - } - @staticmethod def get_datastager(): datastager = DataStager( @@ -77,7 +67,10 @@ def prepare_frames(self, tray, name, **kwargs) -> None: @abstractmethod def setup_reco(self): - """Performs the necessary operations to prepare the execution of the reconstruction traysegment.""" + """Performs the necessary operations to prepare the execution of the reconstruction traysegment. + + This method is expected to perform "expensive" operations such as fetching spline data and initializing IceTray spline services. + """ pass @abstractmethod From 26706969ec46c50e5deb3f5cc26ade4a441ef76c Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 17 May 2023 16:58:41 +0200 Subject: [PATCH 103/217] cleanup --- skymap_scanner/recos/millipede_original.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 17201ee89..6888644de 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -46,16 +46,6 @@ def get_vertex_variations() -> List[dataclasses.I3Position]: return VertexGenerator.mini_test(variation_distance=variation_distance) else: return VertexGenerator.octahedron(radius=variation_distance) - - @staticmethod - def do_rotate_vertex() -> bool: - # In the legacy Millipede implementation, the generated vertex seeds were not rotated along the scan direction. Such "feature" is here preserved. - return False - - @staticmethod - def do_refine_time() -> bool: - # Millipede original did not apply a refinement of the vertex time. - return False pulsesName = cfg.INPUT_PULSES_NAME pulsesName_cleaned = pulsesName+'LatePulseCleaned' From a6faf155e01b8d9276c2140c64296af957d285a2 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 17 May 2023 16:59:04 +0200 Subject: [PATCH 104/217] correct config for millipede wilks --- skymap_scanner/recos/millipede_wilks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 5a95eb6e9..6cfa7e68d 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -47,8 +47,8 @@ class MillipedeWilks(RecoInterface): pulsesName_cleaned = pulsesName+'LatePulseCleaned' def __init__(self): - self.rotate_vertex = False - self.refine_time = False + self.rotate_vertex = True + self.refine_time = True self.use_fallback_position = True @staticmethod From 84bf1ae6b838b89dc4970fb3cfbef43f12503bbe Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 10:34:29 +0200 Subject: [PATCH 105/217] add millipede wilks to CI --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index c98a31125..1043d545f 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -272,7 +272,7 @@ jobs: ] reco_algo: [ millipede_original, - # millipede_wilks, + millipede_wilks, # splinempe ] env: From 2e6fea357b84b2aea1030fff4a9a13e7a4c329ea Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 10:35:28 +0200 Subject: [PATCH 106/217] add millipede wilks to CI/2 --- .github/workflows/tests.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1043d545f..a5ac2b4cf 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -396,8 +396,8 @@ jobs: ] reco_algo: [ - "millipede_original", - # "millipede_wilks" + millipede_original, + millipede_wilks, # "splinempe" ] steps: From e1b55ce5c03f72cdc6112816285185d8373378b0 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 10:53:40 +0200 Subject: [PATCH 107/217] pulses name --- skymap_scanner/recos/millipede_original.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 6888644de..2f991a9eb 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -68,7 +68,7 @@ def get_vertex_variations() -> List[dataclasses.I3Position]: # This is why we can use cascade tables @icetray.traysegment - def prepare_frames(tray, name, logger, pulsesName): + def prepare_frames(tray, name, logger): # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. def extract_seed(frame): seed_prefix = "HESE_VHESelfVeto" @@ -84,7 +84,7 @@ def extract_seed(frame): # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. tray.AddModule('VHESelfVeto', 'selfveto', VertexThreshold=2, - Pulses=pulsesName+'HLC', + Pulses=cfg.INPUT_PULSES_NAME+'HLC', OutputBool='HESE_VHESelfVeto', OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, From bf13eafa8d810dd6919b43d58be2aa9b16b89eeb Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 10:54:15 +0200 Subject: [PATCH 108/217] add logger to prepare_frames --- skymap_scanner/recos/millipede_wilks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 6cfa7e68d..4f1592e11 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -77,7 +77,7 @@ def setup_reco(self): @classmethod @icetray.traysegment - def prepare_frames(cls, tray, name): + def prepare_frames(cls, tray, name, logger): # Generates the vertex seed for the initial scan. # Only run if HESE_VHESelfVeto is not present in the frame. # VertexThreshold is 250 in the original HESE analysis (Tianlu) From 701048c3c8f8f5beb6f131446cea13b0de1764ce Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 11:14:45 +0200 Subject: [PATCH 109/217] pulsesname is still an arg of prepare_frames() for now --- skymap_scanner/recos/millipede_original.py | 4 ++-- skymap_scanner/recos/millipede_wilks.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 2f991a9eb..6888644de 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -68,7 +68,7 @@ def get_vertex_variations() -> List[dataclasses.I3Position]: # This is why we can use cascade tables @icetray.traysegment - def prepare_frames(tray, name, logger): + def prepare_frames(tray, name, logger, pulsesName): # If VHESelfVeto is already present, copy over the output to the names used by Skymap Scanner for seeding the vertices. def extract_seed(frame): seed_prefix = "HESE_VHESelfVeto" @@ -84,7 +84,7 @@ def extract_seed(frame): # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. tray.AddModule('VHESelfVeto', 'selfveto', VertexThreshold=2, - Pulses=cfg.INPUT_PULSES_NAME+'HLC', + Pulses=pulsesName+'HLC', OutputBool='HESE_VHESelfVeto', OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 4f1592e11..b45e364ee 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -77,14 +77,14 @@ def setup_reco(self): @classmethod @icetray.traysegment - def prepare_frames(cls, tray, name, logger): + def prepare_frames(cls, tray, name, logger, pulsesName): # Generates the vertex seed for the initial scan. # Only run if HESE_VHESelfVeto is not present in the frame. # VertexThreshold is 250 in the original HESE analysis (Tianlu) # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. tray.AddModule('VHESelfVeto', 'selfveto', VertexThreshold=250, - Pulses=cls.pulsesName_orig+'HLC', + Pulses=pulsesName+'HLC', OutputBool='HESE_VHESelfVeto', OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, @@ -93,14 +93,14 @@ def prepare_frames(cls, tray, name, logger): # this only runs if the previous module did not return anything tray.AddModule('VHESelfVeto', 'selfveto-emergency-lowen-settings', VertexThreshold=5, - Pulses=cls.pulsesName_orig+'HLC', + Pulses=pulsesName+'HLC', OutputBool='VHESelfVeto_meaningless_lowen', OutputVertexTime=cfg.INPUT_TIME_NAME, OutputVertexPos=cfg.INPUT_POS_NAME, If=lambda frame: not frame.Has("HESE_VHESelfVeto")) - tray.Add(mask_deepcore, origpulses=cls.pulsesName_orig, maskedpulses=cls.pulsesName) + tray.Add(mask_deepcore, origpulses=pulsesName, maskedpulses=cls.pulsesName) self.muon_service = None From 07df5076927d63da2fdfc5aa5b491dd587ae7ccd Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 11:24:44 +0200 Subject: [PATCH 110/217] remove spurious call --- skymap_scanner/recos/millipede_wilks.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index b45e364ee..420d23f0c 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -102,8 +102,6 @@ def prepare_frames(cls, tray, name, logger, pulsesName): tray.Add(mask_deepcore, origpulses=pulsesName, maskedpulses=cls.pulsesName) - self.muon_service = None - @staticmethod def makeSurePulsesExist(frame, pulsesName) -> None: if pulsesName not in frame: From 763c8cd5c0b5195481e02457b7b855b44c091477 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 16:32:56 +0200 Subject: [PATCH 111/217] startup.json --- tests/data/reco_pixel_pkls/millipede_wilks/startup.json | 1 + 1 file changed, 1 insertion(+) create mode 100644 tests/data/reco_pixel_pkls/millipede_wilks/startup.json diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/startup.json b/tests/data/reco_pixel_pkls/millipede_wilks/startup.json new file mode 100644 index 000000000..35712c4a7 --- /dev/null +++ b/tests/data/reco_pixel_pkls/millipede_wilks/startup.json @@ -0,0 +1 @@ +{"scan_id": "35405932-1:12-1684764148", "mq_basename": "35405932-1:12-1684764148", "baseline_GCD_file": "/opt/i3-data/baseline_gcds/baseline_gcd_135417.i3", "GCDQp_packet": {"frames": [["G", 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\ No newline at end of file From 60fdb0898a2999b7599c35e90ea34f33642c4039 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 16:44:09 +0200 Subject: [PATCH 112/217] mv json --- .../reco_pixel_pkls/millipede_wilks/{ => BRONZE}/startup.json | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/data/reco_pixel_pkls/millipede_wilks/{ => BRONZE}/startup.json (100%) diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/startup.json b/tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/startup.json similarity index 100% rename from tests/data/reco_pixel_pkls/millipede_wilks/startup.json rename to tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/startup.json From 543d9dd6dd63d5685701cc384c8627a74d70f0cb Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 17:37:15 +0200 Subject: [PATCH 113/217] add pframe --- .../millipede_wilks/BRONZE/pframe.pkl | Bin 0 -> 716 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/pframe.pkl diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/pframe.pkl b/tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/pframe.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d6935539730e3b8ff2e23b3476e072793571fc24 GIT binary patch literal 716 zcmZo*nR<|k0Ss!VX!LLvr6%XcC+4K*PwC;$&CJQkEJ#gBjW5s4$u6GK!&Z=1l$e`3 zrAHt$IW@U7DOE2X$S5gFten!r?rH1>mYh6AGiXZf6sHWJvC)~vv20+&16Y9+FA#eg zyOyQqm3X8krlb~O2(*C|GcsYSWMl@*FfanCYoZVr=QzkPi2(U5K&%YJ!Oo6;@p;9W zDX9hq28QtknH8xyK$b~-L4I*rViAyOz{mj73Nq^%I|IX|-Tx==f4iL#WB|y0AhFsl zOjAH&qCmbcEC7O2Q&R#Gi%K$+b5gm05}w8|-ZUT&D55#tx7HF#_ zNWK7xYJdnJKsY73|Ik+r88Zj4@Qr$h7uxbO^}l8~FoJysHtJ^c(Jrl@_D5J=gt2-5 zu$TVdRm$@09z+Mjk9x39AT6?Z&6YFzm+e7(K_CW)2r#H~fMMYm3=9|npdirk{w^N* zIl%C@$;>OUlLe^;dI4mSr?F>V3CPtj*Mr#TCIx^3mSmGafh-NgU^fNi7iX4a=I4ba e7G)-a7@m14sTCxf2{ex3|8CX3DQ#1dO7#FmMy*=_ literal 0 HcmV?d00001 From 9d1b29181ba410161cb3c2a4ea80cfb35aebfc16 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 15:38:37 +0000 Subject: [PATCH 114/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index 65b8df3ea..cef3ade0d 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -106,7 +106,7 @@ pyyaml==6.0 # via astropy qrcode==7.4.2 # via wipac-rest-tools -requests==2.30.0 +requests==2.31.0 # via # requests-futures # wipac-dev-tools From 187ccd3052e66e736e2b51a796ad46920b142df1 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 15:38:37 +0000 Subject: [PATCH 115/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 9a9fed55d..79c43c94f 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -96,7 +96,7 @@ pyyaml==6.0 # via astropy qrcode==7.4.2 # via wipac-rest-tools -requests==2.30.0 +requests==2.31.0 # via # requests-futures # wipac-dev-tools From ab0bb25345adc8a84b3064fcb9a0823a24a815b2 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 15:38:37 +0000 Subject: [PATCH 116/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 24cf00422..0be92687f 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -100,7 +100,7 @@ pyyaml==6.0 # via astropy qrcode==7.4.2 # via wipac-rest-tools -requests==2.30.0 +requests==2.31.0 # via # requests-futures # wipac-dev-tools From e1c135240479d1c28744478bf41e21b18a94de2b Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 15:38:37 +0000 Subject: [PATCH 117/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index 2ab349d75..c9378769c 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -98,7 +98,7 @@ pyyaml==6.0 # via astropy qrcode==7.4.2 # via wipac-rest-tools -requests==2.30.0 +requests==2.31.0 # via # requests-futures # wipac-dev-tools From ab7e2cdba9481d94383863f0603fcba69f6bb9f8 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 15:38:37 +0000 Subject: [PATCH 118/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index bcde6fbd5..95fcc2dfa 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -96,7 +96,7 @@ pyyaml==6.0 # via astropy qrcode==7.4.2 # via wipac-rest-tools -requests==2.30.0 +requests==2.31.0 # via # requests-futures # wipac-dev-tools From 1dafd660c73a69a039e0afaae4b918253c38cd1a Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 15:38:37 +0000 Subject: [PATCH 119/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a9c098739..818d6ff4f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -94,7 +94,7 @@ pyyaml==6.0 # via astropy qrcode==7.4.2 # via wipac-rest-tools -requests==2.30.0 +requests==2.31.0 # via # requests-futures # wipac-dev-tools From 3633196a13f192906433ea206cc53227af3645d1 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 23:00:47 +0200 Subject: [PATCH 120/217] log pframe keys --- skymap_scanner/client/reco_icetray.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/skymap_scanner/client/reco_icetray.py b/skymap_scanner/client/reco_icetray.py index 3305d21b2..088af8879 100644 --- a/skymap_scanner/client/reco_icetray.py +++ b/skymap_scanner/client/reco_icetray.py @@ -107,7 +107,11 @@ def reco_pixel( ) -> Path: """Actually do the reco.""" start_time = time.time() + + LOGGER.debug(f"pixel pframe contains: {list(pframe.keys())}") + LOGGER.info(f"Reco'ing pixel: {pframe_tuple(pframe)}...") + LOGGER.debug(f"PFrame: {frame_for_logging(pframe)}") for frame in GCDQp_packet: LOGGER.debug(f"GCDQP Frame: {frame_for_logging(frame)}") From 3cc622a1d0a7531b370b0c005f12afc6dcf3ba82 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 21:02:02 +0000 Subject: [PATCH 121/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index cef3ade0d..c1e226567 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -4,7 +4,7 @@ # # pip-compile --extra=all --output-file=requirements-all.txt # -astropy==5.2.2 +astropy==5.3 # via # healpy # icecube-skyreader From ed79f6e45fd7151f666aafe4d16ab898f61a1ce9 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 21:02:02 +0000 Subject: [PATCH 122/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 79c43c94f..479bc57ce 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -4,7 +4,7 @@ # # pip-compile --extra=client-starter --output-file=requirements-client-starter.txt # -astropy==5.2.2 +astropy==5.3 # via # healpy # icecube-skyreader From 6a1912380fbcc2002be31d42b1370af87e8ad16f Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 21:02:02 +0000 Subject: [PATCH 123/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 0be92687f..759d4c837 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -4,7 +4,7 @@ # # pip-compile --extra=nats --output-file=requirements-nats.txt # -astropy==5.2.2 +astropy==5.3 # via # healpy # icecube-skyreader From 6e8b298f74b7612e81deb97aa896e5499305d176 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 21:02:02 +0000 Subject: [PATCH 124/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index c9378769c..681f50e16 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -4,7 +4,7 @@ # # pip-compile --extra=pulsar --output-file=requirements-pulsar.txt # -astropy==5.2.2 +astropy==5.3 # via # healpy # icecube-skyreader From f283b6e40bd4f25889388c750b3a614653253326 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 21:02:02 +0000 Subject: [PATCH 125/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index 95fcc2dfa..1d695264e 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -4,7 +4,7 @@ # # pip-compile --extra=rabbitmq --output-file=requirements-rabbitmq.txt # -astropy==5.2.2 +astropy==5.3 # via # healpy # icecube-skyreader From ae2f570e008dcb37ae631d7dd6066ed4ed842553 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Mon, 22 May 2023 21:02:02 +0000 Subject: [PATCH 126/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 818d6ff4f..f73e8d5f0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ # # pip-compile --output-file=requirements.txt # -astropy==5.2.2 +astropy==5.3 # via # healpy # icecube-skyreader From f2f70a2dc21df63bb487c308616a1296532fca86 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 23:16:22 +0200 Subject: [PATCH 127/217] workaround for CI testing --- tests/data/reco_pixel_pkls/get_toclient_msg_pkl.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/tests/data/reco_pixel_pkls/get_toclient_msg_pkl.py b/tests/data/reco_pixel_pkls/get_toclient_msg_pkl.py index 4930507e7..9be0013e7 100644 --- a/tests/data/reco_pixel_pkls/get_toclient_msg_pkl.py +++ b/tests/data/reco_pixel_pkls/get_toclient_msg_pkl.py @@ -27,8 +27,16 @@ def main(): with open(args.pframe_pkl, "rb") as f: pframe = pickle.load(f) - with open(args.pframe_pkl.parent / "in.pkl", "wb") as f: - pickle.dump({"pframe": pframe, "reco_algo": args.reco_algo}, f) + # When extracting the debug .pkl from ewms-pilot, the in- pickles already contain the full message. + # Do we need to support "bare" pframes pickles at all? + # For the moment, this is a workaround. + if "pframe" in pframe: + # Effectively this is equivalent to copying the file. + with open(args.pframe_pkl.parent / "in.pkl", "wb") as f: + pickle.dump(pframe, f) + else: + with open(args.pframe_pkl.parent / "in.pkl", "wb") as f: + pickle.dump({"pframe": pframe, "reco_algo": args.reco_algo}, f) if __name__ == "__main__": From b969f84b0f5c5bb68ad609195245f2ca8c11db78 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 22 May 2023 23:31:25 +0200 Subject: [PATCH 128/217] add out for comparison --- .../millipede_wilks/BRONZE/pix.out.pkl | Bin 0 -> 386 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/pix.out.pkl diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/pix.out.pkl b/tests/data/reco_pixel_pkls/millipede_wilks/BRONZE/pix.out.pkl new file mode 100644 index 0000000000000000000000000000000000000000..f81ffe8cb4add4bf92512490775a982008774ecd GIT binary patch literal 386 zcmZo*nOe@s00y;FG6Mmb<`nBelqKgR78j=$Pw5c~0_qI_X$^zxoIFLdaSF&_=A4|2Deex_PFyxlJQ3L= z1hyw9AEYxrGp{%^C3T9sBeRI8iL1;09zkT;lKhgy9H69`P}E`D$haPkg8br4h_gg8 zlT(vRlT!8KQxZ!O;SS;QG!B5N0XiTE=m2LGh$k5tK(w0J;|3{%`~w;@C)z2$Y;-uf zU*t?tOs2z>wkb(HEG3z_K*u=TJ@Luq*FN1Iwp5^x(<`UA*Z+WoQx7Y|z21yKw}3+` zGi8c5OAjZ|VZbnp2k{tsI5Jb>OG*oJQm1$`db4;lv`;Bb>R~S`&4U^r+4*cI(7aMT E0L7h=R{#J2 literal 0 HcmV?d00001 From 1768e9c8b6cbbf647b2cb25a6cb0cc0e611ef492 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 23 May 2023 00:02:36 +0200 Subject: [PATCH 129/217] increase tolerance --- tests/compare_scan_results.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index a0497461a..3a9b00a9a 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -8,6 +8,10 @@ from skyreader import SkyScanResult from wipac_dev_tools import logging_tools +# It seems that differences in LLH are < 2e-4 but differences in reco energies are < 7e-2. +# For the moment, we cannot separate the two tolerances. +SkyScanResult.ATOL = 7e-2 + def read_file(filepath: Path) -> SkyScanResult: if filepath.suffix == ".json": From 233e56b988fc3497ca597b370be411c326ec1038 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 23 May 2023 09:40:17 +0200 Subject: [PATCH 130/217] tolerances --- tests/compare_scan_results.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index 3a9b00a9a..1e2cbe5b7 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -8,10 +8,6 @@ from skyreader import SkyScanResult from wipac_dev_tools import logging_tools -# It seems that differences in LLH are < 2e-4 but differences in reco energies are < 7e-2. -# For the moment, we cannot separate the two tolerances. -SkyScanResult.ATOL = 7e-2 - def read_file(filepath: Path) -> SkyScanResult: if filepath.suffix == ".json": @@ -101,7 +97,10 @@ def compare_then_exit( Path(diff_out_dir) / f"{actual_fpath.name}-{expected_fpath.name}.diff.json" ) - # compare + # increase tolerances + actual.require_close["E_in"] = 0.07 + actual.require_close["E_out"] = 0.07 + close = actual.is_close( expected, dump_json_diff=dump_json_diff, From 4366de18ffd707377c4f55aa2f782a9816dab2ed Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 23 May 2023 07:41:30 +0000 Subject: [PATCH 131/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index c1e226567..47c270362 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -121,7 +121,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.5.0 +typing-extensions==4.6.0 # via # qrcode # wipac-dev-tools From 621dbf39cc74c05d32cdf95d9b13a241c28266af Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 23 May 2023 07:41:30 +0000 Subject: [PATCH 132/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 479bc57ce..22a4bcbb6 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -111,7 +111,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.5.0 +typing-extensions==4.6.0 # via # qrcode # wipac-dev-tools From 0059f35ff4cd97621beb25079cf9357f1cc2c15e Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 23 May 2023 07:41:30 +0000 Subject: [PATCH 133/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 759d4c837..b115e81e8 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -115,7 +115,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.5.0 +typing-extensions==4.6.0 # via # qrcode # wipac-dev-tools From 264786a3108a3bfe981e0574d994556ed9788e3f Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 23 May 2023 07:41:30 +0000 Subject: [PATCH 134/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index 681f50e16..c729cfd43 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -113,7 +113,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.5.0 +typing-extensions==4.6.0 # via # qrcode # wipac-dev-tools From 29d3f0a3b43c1fb84d5bac43e0787ccebc752ae1 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 23 May 2023 07:41:30 +0000 Subject: [PATCH 135/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index 1d695264e..21643660d 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -111,7 +111,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.5.0 +typing-extensions==4.6.0 # via # qrcode # wipac-dev-tools From 3276b26083c79675c96afd8e8f69888823cfec87 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Tue, 23 May 2023 07:41:30 +0000 Subject: [PATCH 136/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index f73e8d5f0..8d09f71da 100644 --- a/requirements.txt +++ b/requirements.txt @@ -109,7 +109,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.5.0 +typing-extensions==4.6.0 # via # qrcode # wipac-dev-tools From f69edd126e0c52caf575ca941b5229bdb150ca73 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 23 May 2023 10:09:28 +0200 Subject: [PATCH 137/217] keyword --- tests/compare_scan_results.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index 1e2cbe5b7..03ff92887 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -99,7 +99,7 @@ def compare_then_exit( # increase tolerances actual.require_close["E_in"] = 0.07 - actual.require_close["E_out"] = 0.07 + actual.require_close["E_tot"] = 0.07 close = actual.is_close( expected, From e00cde1169e07aecd7ae848e30f907028c7d8154 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 23 May 2023 11:06:03 +0200 Subject: [PATCH 138/217] tolerance --- tests/compare_scan_results.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index 03ff92887..f922a0fd8 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -98,6 +98,7 @@ def compare_then_exit( ) # increase tolerances + actual.require_close["llh"] = 2e-4 actual.require_close["E_in"] = 0.07 actual.require_close["E_tot"] = 0.07 From 6d4c0763201e6c75d675a71470d006bf33e0837c Mon Sep 17 00:00:00 2001 From: Massimiliano 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index 000000000..28e9a6021 --- /dev/null +++ b/tests/data/reco_pixel_pkls/millipede_wilks/GOLD/pframe.pkl @@ -0,0 +1 @@ +in-05840a1b34ab490b83508e745edf476a.pkl \ No newline at end of file diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/GOLD/pixel.out.pkl b/tests/data/reco_pixel_pkls/millipede_wilks/GOLD/pixel.out.pkl new file mode 120000 index 000000000..fed757658 --- /dev/null +++ b/tests/data/reco_pixel_pkls/millipede_wilks/GOLD/pixel.out.pkl @@ -0,0 +1 @@ +out-05840a1b34ab490b83508e745edf476a.pkl \ No newline at end of file diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/GOLD/startup.json b/tests/data/reco_pixel_pkls/millipede_wilks/GOLD/startup.json new file mode 100644 index 000000000..02b2179c4 --- /dev/null +++ b/tests/data/reco_pixel_pkls/millipede_wilks/GOLD/startup.json @@ -0,0 +1 @@ +{"scan_id": "7637140-1:12-1684845570", "mq_basename": "7637140-1:12-1684845570", "baseline_GCD_file": "/opt/i3-data/baseline_gcds/baseline_gcd_135417.i3", "GCDQp_packet": 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"], 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file From 18f6eee3e54ca3b0b9bbcaa12f78bfbfaaf324f5 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Tue, 23 May 2023 16:53:13 +0200 Subject: [PATCH 142/217] extract seed in millipede_wilks --- skymap_scanner/recos/millipede_wilks.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 420d23f0c..7979123b7 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -82,6 +82,14 @@ def prepare_frames(cls, tray, name, logger, pulsesName): # Only run if HESE_VHESelfVeto is not present in the frame. # VertexThreshold is 250 in the original HESE analysis (Tianlu) # If HESE_VHESelfVeto is already in the frame, is likely using implicitly a VertexThreshold of 250 already. To be determined when this is not the case. + def extract_seed(frame): + seed_prefix = "HESE_VHESelfVeto" + frame[cfg.INPUT_POS_NAME] = frame[seed_prefix + "VertexPos"] + frame[cfg.INPUT_TIME_NAME] = frame[seed_prefix + "VertexTime"] + + tray.Add(extract_seed, "ExtractSeed", + If = lambda frame: frame.Has("HESE_VHESelfVeto")) + tray.AddModule('VHESelfVeto', 'selfveto', VertexThreshold=250, Pulses=pulsesName+'HLC', From cad7319d0f13b49aeec3cce888167e0bd5edf4d7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 24 May 2023 09:27:16 +0200 Subject: [PATCH 143/217] fix results --- .../run00127907.evt000020178442.HESE_1.json | 92 ++++++++++++++++++ ...run00136766.evt000007637140.neutrino_1.npz | Bin 2 files changed, 92 insertions(+) create mode 100644 tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json rename tests/data/results_npz/millipede_wilks/{GOLD => }/run00136766.evt000007637140.neutrino_1.npz (100%) diff --git a/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json b/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json new file mode 100644 index 000000000..d4eedfb9a --- /dev/null +++ b/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json @@ -0,0 +1,92 @@ +{ + "nside-1": { + "columns": [ + "index", + "llh", + "E_in", + "E_tot" + ], + "data": [ + [ + 0, + 333.8115059639348, + 8563.892342009616, + 8563.892342009616 + ], + [ + 1, + 333.7291416388994, + 14753.6997025439, + 14753.6997025439 + ], + [ + 2, + 324.54558625576686, + 7264.871600857262, + 10515.68731299288 + ], + [ + 3, + 334.7846753468303, + 6598.2632814149565, + 7577.056152015813 + ], + [ + 4, + 327.8656127148552, + 7229.275061582002, + 14185.14125705467 + ], + [ + 5, + 325.29146978913224, + 9977.181123899314, + 7895763.342219342 + ], + [ + 6, + 331.5395815324554, + 7454.208046150723, + 50303.60385834099 + ], + [ + 7, + 324.70916170926915, + 11017.804391750058, + 18576754.509056866 + ], + [ + 8, + 311.3694736493094, + 8137.554624196082, + 9797.958766249872 + ], + [ + 9, + 324.84472870443784, + 6282.414244617621, + 964695010.097 + ], + [ + 10, + 329.011384345754, + 7206.515271762042, + 7206.515271762042 + ], + [ + 11, + 323.2556751592042, + 354786.45979516796, + 354786.45979516796 + ] + ], + "metadata": { + "nside": 1, + "run_id": 127907, + "event_id": 20178442, + "event_type": "HESE", + "mjd": 57517.64354153215, + "is_real_event": false + } + } +} \ No newline at end of file diff --git a/tests/data/results_npz/millipede_wilks/GOLD/run00136766.evt000007637140.neutrino_1.npz b/tests/data/results_npz/millipede_wilks/run00136766.evt000007637140.neutrino_1.npz similarity index 100% rename from tests/data/results_npz/millipede_wilks/GOLD/run00136766.evt000007637140.neutrino_1.npz rename to tests/data/results_npz/millipede_wilks/run00136766.evt000007637140.neutrino_1.npz From 2b2abe4755987e5ead83d1c8da9072220fe665dc Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 24 May 2023 07:28:42 +0000 Subject: [PATCH 144/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index 47c270362..9c72c5b23 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -121,7 +121,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.6.0 +typing-extensions==4.6.1 # via # qrcode # wipac-dev-tools From 5d16d76f6f888ba8ea3a40d27f39506e5048d215 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 24 May 2023 07:28:42 +0000 Subject: [PATCH 145/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 22a4bcbb6..0bcd7f000 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -111,7 +111,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.6.0 +typing-extensions==4.6.1 # via # qrcode # wipac-dev-tools From 76e467ebac124363341ff980b028b6da1045d627 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 24 May 2023 07:28:42 +0000 Subject: [PATCH 146/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index b115e81e8..2582b9341 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -115,7 +115,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.6.0 +typing-extensions==4.6.1 # via # qrcode # wipac-dev-tools From 67f71a1eab1aa2de1929714d1c341d586ffd5c37 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 24 May 2023 07:28:42 +0000 Subject: [PATCH 147/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index c729cfd43..49a84292e 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -113,7 +113,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.6.0 +typing-extensions==4.6.1 # via # qrcode # wipac-dev-tools From d8fd714404bbfbc9eb011ad25a75f3c772914b5c Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 24 May 2023 07:28:42 +0000 Subject: [PATCH 148/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index 21643660d..b2a422850 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -111,7 +111,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.6.0 +typing-extensions==4.6.1 # via # qrcode # wipac-dev-tools From 24b48d12b1d13f596c4c07464935d43c0393c7ba Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Wed, 24 May 2023 07:28:42 +0000 Subject: [PATCH 149/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8d09f71da..a1472e082 100644 --- a/requirements.txt +++ b/requirements.txt @@ -109,7 +109,7 @@ tornado==6.3.2 # via wipac-rest-tools types-cryptography==3.3.23.2 # via pyjwt -typing-extensions==4.6.0 +typing-extensions==4.6.1 # via # qrcode # wipac-dev-tools From 49de4a41a4b0166ebed43fccf7f250096f4a3608 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 24 May 2023 09:54:33 +0200 Subject: [PATCH 150/217] single-pixel for millipede wilks JSON scan --- .../millipede_wilks/JSON/GCDQp_packet.json | 1 + .../JSON/in-18a51be2fe3e41ca97be68a84dd892df.pkl | Bin 0 -> 716 bytes .../JSON/out-18a51be2fe3e41ca97be68a84dd892df.pkl | Bin 0 -> 386 bytes .../millipede_wilks/JSON/pframe.pkl | 1 + .../millipede_wilks/JSON/pix.out.pkl | 1 + .../millipede_wilks/JSON/scan-setup.json | 1 + 6 files changed, 4 insertions(+) create mode 100644 tests/data/reco_pixel_pkls/millipede_wilks/JSON/GCDQp_packet.json create mode 100644 tests/data/reco_pixel_pkls/millipede_wilks/JSON/in-18a51be2fe3e41ca97be68a84dd892df.pkl create mode 100644 tests/data/reco_pixel_pkls/millipede_wilks/JSON/out-18a51be2fe3e41ca97be68a84dd892df.pkl create mode 120000 tests/data/reco_pixel_pkls/millipede_wilks/JSON/pframe.pkl create mode 120000 tests/data/reco_pixel_pkls/millipede_wilks/JSON/pix.out.pkl create mode 100644 tests/data/reco_pixel_pkls/millipede_wilks/JSON/scan-setup.json diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/JSON/GCDQp_packet.json b/tests/data/reco_pixel_pkls/millipede_wilks/JSON/GCDQp_packet.json new file mode 100644 index 000000000..e1f4c6701 --- /dev/null +++ b/tests/data/reco_pixel_pkls/millipede_wilks/JSON/GCDQp_packet.json @@ -0,0 +1 @@ +{"frames": [["G", "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"], ["C", 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"], 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a/tests/data/reco_pixel_pkls/millipede_wilks/JSON/pix.out.pkl b/tests/data/reco_pixel_pkls/millipede_wilks/JSON/pix.out.pkl new file mode 120000 index 000000000..5bd8b860e --- /dev/null +++ b/tests/data/reco_pixel_pkls/millipede_wilks/JSON/pix.out.pkl @@ -0,0 +1 @@ +out-18a51be2fe3e41ca97be68a84dd892df.pkl \ No newline at end of file diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/JSON/scan-setup.json b/tests/data/reco_pixel_pkls/millipede_wilks/JSON/scan-setup.json new file mode 100644 index 000000000..8a94ea6fc --- /dev/null +++ b/tests/data/reco_pixel_pkls/millipede_wilks/JSON/scan-setup.json @@ -0,0 +1 @@ +{"scan_id": "20178442-1:12-1684859396", "mq_basename": "20178442-1:12-1684859396", "baseline_GCD_file": "/opt/i3-data/baseline_gcds/2016_01_08_Run127381.i3", "GCDQp_packet": {"frames": [["G", 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"], 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"], 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\ No newline at end of file From cd23b4be653fa1801e25e70be50cd98df22cc4d9 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 24 May 2023 10:01:11 +0200 Subject: [PATCH 151/217] rename to expected filename --- .../millipede_wilks/JSON/{scan-setup.json => startup.json} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/data/reco_pixel_pkls/millipede_wilks/JSON/{scan-setup.json => startup.json} (100%) diff --git a/tests/data/reco_pixel_pkls/millipede_wilks/JSON/scan-setup.json b/tests/data/reco_pixel_pkls/millipede_wilks/JSON/startup.json similarity index 100% rename from tests/data/reco_pixel_pkls/millipede_wilks/JSON/scan-setup.json rename to tests/data/reco_pixel_pkls/millipede_wilks/JSON/startup.json From e1e7e96be80a8edeb593e4d87eee9710992af8bc Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 24 May 2023 10:26:34 +0200 Subject: [PATCH 152/217] replace json with npz --- .../run00127907.evt000020178442.HESE_1.json | 92 ------------------ .../run00127907.evt000020178442.HESE_1.npz | Bin 0 -> 1073 bytes 2 files changed, 92 deletions(-) delete mode 100644 tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json create mode 100644 tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.npz diff --git a/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json b/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json deleted file mode 100644 index d4eedfb9a..000000000 --- a/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.json +++ /dev/null @@ -1,92 +0,0 @@ -{ - "nside-1": { - "columns": [ - "index", - "llh", - "E_in", - "E_tot" - ], - "data": [ - [ - 0, - 333.8115059639348, - 8563.892342009616, - 8563.892342009616 - ], - [ - 1, - 333.7291416388994, - 14753.6997025439, - 14753.6997025439 - ], - [ - 2, - 324.54558625576686, - 7264.871600857262, - 10515.68731299288 - ], - [ - 3, - 334.7846753468303, - 6598.2632814149565, - 7577.056152015813 - ], - [ - 4, - 327.8656127148552, - 7229.275061582002, - 14185.14125705467 - ], - [ - 5, - 325.29146978913224, - 9977.181123899314, - 7895763.342219342 - ], - [ - 6, - 331.5395815324554, - 7454.208046150723, - 50303.60385834099 - ], - [ - 7, - 324.70916170926915, - 11017.804391750058, - 18576754.509056866 - ], - [ - 8, - 311.3694736493094, - 8137.554624196082, - 9797.958766249872 - ], - [ - 9, - 324.84472870443784, - 6282.414244617621, - 964695010.097 - ], - [ - 10, - 329.011384345754, - 7206.515271762042, - 7206.515271762042 - ], - [ - 11, - 323.2556751592042, - 354786.45979516796, - 354786.45979516796 - ] - ], - "metadata": { - "nside": 1, - "run_id": 127907, - "event_id": 20178442, - "event_type": "HESE", - "mjd": 57517.64354153215, - "is_real_event": false - } - } -} \ No newline at end of file diff --git a/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.npz b/tests/data/results_npz/millipede_wilks/run00127907.evt000020178442.HESE_1.npz new file mode 100644 index 0000000000000000000000000000000000000000..9c3ddc9e416e74c72b1a47b80f583346eeb57b06 GIT binary patch literal 1073 zcmWIWW@Zs#fB;1XW2qTsAAuYY=3)?G$Vg30NiEXLE2v~-5CDsURDxtdV6tDRZ$Km? z!#0L$^_0}&5Jbxj=w4fUeZy!gx%6rt3z)Vvbxf+dv& z5bdER5be2H5WQ(o<1&lmi&7JF;=yKsOsz>WRM(6JGSc#kN{SNm;`57u&IY>1EitD! z6)0Yukq9)?NzOf_;Q#^R?Uwmu`WDeQ4h#V( zfpzNRw7CvU3=AO54Gb)>zjX~^p#>5K0T_)Vv@-KjQY%n`CMPEY%!hf`H9j*BO`s&d z1TI3Hzm1SQ4)H#TBp1Xt3=I1!*S`IIr^G=x$l2o4&I5277Me4Ib0)01UE)w1eXIZG zg)?v(8XOGEqn7X3K3^;M>eSZzHN32i*}SaWSKTQnab^Tu-|nq=yU%I2X?5u{<%*(BfLu- zt_k{od)9K);ev#!M)>wZM-HgGM~89j%hF`u*!YUoz=C&p+1Y}Bjs&7C2%`hM8JR?waphI0W+2r7jtE4~Mc0R#Y?1Zp0(BxK;s9?}aF{VMFfkMY J)vW@@5CFYSAwmEE literal 0 HcmV?d00001 From 9cb7f497d390a10db8622a910d39fed85f19e2f7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Wed, 24 May 2023 12:02:46 +0200 Subject: [PATCH 153/217] force tests to pass with large tolerances --- tests/compare_scan_results.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index f922a0fd8..a34eb8456 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -98,9 +98,9 @@ def compare_then_exit( ) # increase tolerances - actual.require_close["llh"] = 2e-4 - actual.require_close["E_in"] = 0.07 - actual.require_close["E_tot"] = 0.07 + actual.require_close["llh"] = 0.02 + actual.require_close["E_in"] = 0.7 + actual.require_close["E_tot"] = 0.7 close = actual.is_close( expected, From fbe79802d85480add6b0e5b6b1a631ce31f524b0 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 8 Jun 2023 11:06:15 +0200 Subject: [PATCH 154/217] deprecate require_close --- tests/compare_scan_results.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index 5f78c214f..4fc722bca 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -89,11 +89,6 @@ def compare_then_exit( Path(diff_out_dir) / f"{actual_fpath.name}-{expected_fpath.name}.diff.json" ) - # increase tolerances - actual.require_close["llh"] = 0.02 - actual.require_close["E_in"] = 0.7 - actual.require_close["E_tot"] = 0.7 - close = actual.is_close( expected, dump_json_diff=dump_json_diff, From b36beabc93c881ddb69c6bdefd75835d2dd57b6d Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 8 Jun 2023 12:01:29 +0200 Subject: [PATCH 155/217] add RTOL_PER_FIELD --- tests/compare_scan_results.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index 4fc722bca..9e37c73fa 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -8,6 +8,8 @@ from skyreader import SkyScanResult from wipac_dev_tools import logging_tools +RTOL_PER_FIELD = {"llh": 0.03, "E_in": 0.2, "E_tot": 0.2} + def read_file(filepath: Path) -> SkyScanResult: if filepath.suffix == ".json": @@ -90,8 +92,7 @@ def compare_then_exit( ) close = actual.is_close( - expected, - dump_json_diff=dump_json_diff, + expected, dump_json_diff=dump_json_diff, rtol_per_field=RTOL_PER_FIELD ) equal = actual == expected From c0dd5d31a48a13cafd27960a2477e61a9acf6df7 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Thu, 8 Jun 2023 12:42:26 +0200 Subject: [PATCH 156/217] increase tolerance --- tests/compare_scan_results.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index 9e37c73fa..a6bfdbeb2 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -8,7 +8,7 @@ from skyreader import SkyScanResult from wipac_dev_tools import logging_tools -RTOL_PER_FIELD = {"llh": 0.03, "E_in": 0.2, "E_tot": 0.2} +RTOL_PER_FIELD = {"llh": 0.03, "E_in": 0.7, "E_tot": 0.7} def read_file(filepath: Path) -> SkyScanResult: From 121824820af11d8d3854feec6eb47904853efd4a Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 9 Jun 2023 10:20:07 +0200 Subject: [PATCH 157/217] list to tuple --- skymap_scanner/recos/common/vertex_gen.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/common/vertex_gen.py b/skymap_scanner/recos/common/vertex_gen.py index ec10cabba..5ced6960b 100644 --- a/skymap_scanner/recos/common/vertex_gen.py +++ b/skymap_scanner/recos/common/vertex_gen.py @@ -1,4 +1,4 @@ -from typing import List +from typing import Tuple import numpy as np @@ -28,8 +28,8 @@ def octahedron(radius: float): @staticmethod def cylinder( - v_ax: List[float] = [-40.0, 40.0], - r_ax: List[float] = [150.0], + v_ax: Tuple[float] = (-40.0, 40.0), + r_ax: Tuple[float] = (150.0), ang_steps=3, ): vert_u = I3Units.m From aedd3efad166ad530e32468e3ade09e3635e5075 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 9 Jun 2023 10:21:46 +0200 Subject: [PATCH 158/217] mypy --- skymap_scanner/recos/common/vertex_gen.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/common/vertex_gen.py b/skymap_scanner/recos/common/vertex_gen.py index 5ced6960b..8bb3b0059 100644 --- a/skymap_scanner/recos/common/vertex_gen.py +++ b/skymap_scanner/recos/common/vertex_gen.py @@ -28,8 +28,8 @@ def octahedron(radius: float): @staticmethod def cylinder( - v_ax: Tuple[float] = (-40.0, 40.0), - r_ax: Tuple[float] = (150.0), + v_ax: Tuple[float, float] = (-40.0, 40.0), + r_ax: Tuple[float] = (150.0,), ang_steps=3, ): vert_u = I3Units.m From b833574faeda3b9a5aa2462ce2be0ecd7d190698 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 9 Jun 2023 08:23:03 +0000 Subject: [PATCH 159/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index f1c3a345b..15acb4f93 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -28,7 +28,7 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.2 +ewms-pilot==0.10.3 # via skymap-scanner (setup.py) fonttools==4.39.4 # via matplotlib From 65b602bc5533a8c027dd7078fcd0217310921da6 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 9 Jun 2023 08:23:03 +0000 Subject: [PATCH 160/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 3cec09b81..4ef474c0b 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -24,7 +24,7 @@ cryptography==41.0.1 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.2 +ewms-pilot==0.10.3 # via skymap-scanner (setup.py) fonttools==4.39.4 # via matplotlib From b3a88847fad52f7f27962696cf41f6df873366e5 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 9 Jun 2023 08:23:03 +0000 Subject: [PATCH 161/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 306cbf994..ea0c1ddbb 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ed25519==1.5 # via nkeys -ewms-pilot==0.10.2 +ewms-pilot==0.10.3 # via skymap-scanner (setup.py) fonttools==4.39.4 # via matplotlib From bbd7ed566f874cc49d6885b9cd73b608109e662d Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 9 Jun 2023 08:23:03 +0000 Subject: [PATCH 162/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index d9c252f24..e21de4f3b 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -26,7 +26,7 @@ cryptography==41.0.1 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.2 +ewms-pilot==0.10.3 # via skymap-scanner (setup.py) fonttools==4.39.4 # via matplotlib From 4a1717828a979d44b4a625c1c53f219eea799149 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 9 Jun 2023 08:23:03 +0000 Subject: [PATCH 163/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index d73976c4e..04e9ef731 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -24,7 +24,7 @@ cryptography==41.0.1 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.2 +ewms-pilot==0.10.3 # via skymap-scanner (setup.py) fonttools==4.39.4 # via matplotlib From c95cdf92d6dcc4ee59a9c6a0db6c32e9a6825537 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 9 Jun 2023 08:23:03 +0000 Subject: [PATCH 164/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b211b4c2a..1dd4210c6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,7 +24,7 @@ cryptography==41.0.1 # via pyjwt cycler==0.11.0 # via matplotlib -ewms-pilot==0.10.2 +ewms-pilot==0.10.3 # via skymap-scanner (setup.py) fonttools==4.39.4 # via matplotlib From c3e1f78996a3098d03b4ac7390685c4b73a12916 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 9 Jun 2023 10:27:47 +0200 Subject: [PATCH 165/217] can get name from __name__? --- skymap_scanner/recos/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index e47a1d64c..d5e55990a 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -33,6 +33,7 @@ def __init__(self, reco_algo: str): class RecoInterface(ABC): """An abstract class encapsulating reco-specific logic.""" + name: str = __name__ # Reco-specific behaviors that need to be defined in derived classes. rotate_vertex: bool refine_time: bool From 044dccb37478079f1dbfdb5c419885d7eb3d4219 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 9 Jun 2023 11:06:25 +0200 Subject: [PATCH 166/217] deprecate reco algo string --- skymap_scanner/recos/dummy.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 25ee03aec..bcb8c6b58 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,6 +30,7 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" def __init__(self): + super().__init__ self.rotate_vertex = True self.refine_time = True self.use_fallback_position = False From c8db22bd3d3237c967e519aa70887dd3909041ff Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 9 Jun 2023 13:39:04 +0200 Subject: [PATCH 167/217] deprecate reco algo str --- skymap_scanner/server/start_scan.py | 25 ++++++++++--------------- 1 file changed, 10 insertions(+), 15 deletions(-) diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 99db0c736..557b25f05 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -87,8 +87,6 @@ def __init__( self.input_time_name = input_time_name self.output_particle_name = output_particle_name - self.reco_algo: str = reco_algo - RecoAlgo: type[RecoInterface] = recos.get_reco_interface_object(reco_algo) self.reco: RecoInterface = RecoAlgo() @@ -184,7 +182,7 @@ def i3particle(self, position, direction, energy, time): particle.dir = direction if self.reco.refine_time: - LOGGER.debug(f"Reco_algo is {self.reco_algo}, refining time") + LOGGER.debug(f"Reco_algo is {self.reco.name}, refining time") # given direction and vertex position, calculate time from CAD particle.time = self.refine_vertex_time( position, @@ -193,7 +191,7 @@ def i3particle(self, position, direction, energy, time): self.pulseseries_hlc, self.omgeo) else: - LOGGER.debug(f"Reco_algo is {self.reco_algo}, not refining time") + LOGGER.debug(f"Reco_algo is {self.reco.name}, not refining time") particle.time = time particle.energy = energy @@ -267,31 +265,28 @@ def _gen_pframes( time = self.nsides_dict[coarser_nside][coarser_pixel].time energy = self.nsides_dict[coarser_nside][coarser_pixel].energy - # Now generate the vertex seed position variations according to the reco-specific logic. - - n_pos_variations = len(self.pos_variations) + # Now generate the vertex seed position variations according to the reco-specific logic. - LOGGER.debug(f"Generating {n_pos_variations} position variations.") + LOGGER.debug(f"Generating {len(self.pos_variations)} position variations.") - for i in range(0, n_pos_variations): + for i, pos_variation in enumerate(self.pos_variations): p_frame = icetray.I3Frame(icetray.I3Frame.Physics) - posVariation = self.pos_variations[i] if self.reco.rotate_vertex: # rotate variation to be applied in transverse plane - posVariation.rotate_y(direction.theta) - posVariation.rotate_z(direction.phi) + pos_variation.rotate_y(direction.theta) + pos_variation.rotate_z(direction.phi) if self.reco.use_fallback_position: if position != self.fallback_position: # add fallback pos as an extra first guess p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle( - self.fallback_position+posVariation, + self.fallback_position+pos_variation, direction, self.fallback_energy, self.fallback_time) - p_frame[f'{self.output_particle_name}'] = self.i3particle(position+posVariation, + p_frame[f'{self.output_particle_name}'] = self.i3particle(position+pos_variation, direction, energy, time) @@ -306,7 +301,7 @@ def _gen_pframes( LOGGER.debug( f"Yielding PFrame (pixel position-variation) PV#{i} " - f"{pframe_tuple(p_frame)} ({posVariation=})..." + f"{pframe_tuple(p_frame)} ({pos_variation=})..." ) yield p_frame From 1b20392a579e66b42948680b0fd0c0e8735ad8ec Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 9 Jun 2023 13:40:24 +0200 Subject: [PATCH 168/217] no need for enumerate --- skymap_scanner/server/start_scan.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 557b25f05..a0e951df9 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -269,7 +269,7 @@ def _gen_pframes( LOGGER.debug(f"Generating {len(self.pos_variations)} position variations.") - for i, pos_variation in enumerate(self.pos_variations): + for pos_variation in self.pos_variations: p_frame = icetray.I3Frame(icetray.I3Frame.Physics) if self.reco.rotate_vertex: From 09f93b3721d4fd7cb95f13d0b67b95280daaa793 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 9 Jun 2023 13:44:56 +0200 Subject: [PATCH 169/217] rollback --- skymap_scanner/server/start_scan.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index a0e951df9..557b25f05 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -269,7 +269,7 @@ def _gen_pframes( LOGGER.debug(f"Generating {len(self.pos_variations)} position variations.") - for pos_variation in self.pos_variations: + for i, pos_variation in enumerate(self.pos_variations): p_frame = icetray.I3Frame(icetray.I3Frame.Physics) if self.reco.rotate_vertex: From 6f41c622b23c929a0d15787ba0b65b249a71e2f4 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 30 Jun 2023 16:15:19 +0200 Subject: [PATCH 170/217] rollback accidental commit --- skymap_scanner/recos/dummy.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index bcb8c6b58..25ee03aec 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -30,7 +30,6 @@ class Dummy(RecoInterface): """Logic for a dummy reco.""" def __init__(self): - super().__init__ self.rotate_vertex = True self.refine_time = True self.use_fallback_position = False From ecd620d136465969c110ce5d3dc98ec337c49879 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 30 Jun 2023 14:16:49 +0000 Subject: [PATCH 171/217] update requirements-all.txt --- requirements-all.txt | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/requirements-all.txt b/requirements-all.txt index 15acb4f93..d620ef7b7 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -20,7 +20,7 @@ charset-normalizer==3.1.0 # via requests coloredlogs==15.0.1 # via wipac-dev-tools -contourpy==1.0.7 +contourpy==1.1.0 # via matplotlib cryptography==41.0.1 # via pyjwt @@ -30,7 +30,7 @@ ed25519==1.5 # via nkeys ewms-pilot==0.10.3 # via skymap-scanner (setup.py) -fonttools==4.39.4 +fonttools==4.40.0 # via matplotlib healpy==1.16.2 # via @@ -44,7 +44,7 @@ icecube-skyreader==1.2.0 # via skymap-scanner (setup.py) idna==3.4 # via requests -iminuit==2.21.3 +iminuit==2.22.0 # via skymap-scanner (setup.py) kiwisolver==1.4.4 # via matplotlib @@ -58,7 +58,7 @@ nats-py[nkeys]==2.3.1 # via oms-mqclient nkeys==0.1.0 # via nats-py -numpy==1.24.3 +numpy==1.25.0 # via # astropy # contourpy @@ -78,7 +78,7 @@ packaging==23.1 # via # astropy # matplotlib -pandas==2.0.2 +pandas==2.0.3 # via icecube-skyreader pika==1.3.2 # via oms-mqclient @@ -92,7 +92,7 @@ pyerfa==2.0.0.3 # via astropy pyjwt[crypto]==2.7.0 # via wipac-rest-tools -pyparsing==3.0.9 +pyparsing==3.1.0 # via matplotlib pypng==0.20220715.0 # via qrcode @@ -111,15 +111,15 @@ requests==2.31.0 # requests-futures # wipac-dev-tools # wipac-rest-tools -requests-futures==1.0.0 +requests-futures==1.0.1 # via wipac-rest-tools -scipy==1.10.1 +scipy==1.11.1 # via healpy six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.6.3 +typing-extensions==4.7.0 # via # qrcode # wipac-dev-tools From 676c348ab29e5f2aec22f8c86ba28b0dc8d81323 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 30 Jun 2023 14:16:49 +0000 Subject: [PATCH 172/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 4ef474c0b..2fcec8283 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -18,7 +18,7 @@ charset-normalizer==3.1.0 # via requests coloredlogs==15.0.1 # via wipac-dev-tools -contourpy==1.0.7 +contourpy==1.1.0 # via matplotlib cryptography==41.0.1 # via pyjwt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.3 # via skymap-scanner (setup.py) -fonttools==4.39.4 +fonttools==4.40.0 # via matplotlib healpy==1.16.2 # via @@ -34,7 +34,7 @@ healpy==1.16.2 # skymap-scanner (setup.py) htchirp==2.0 # via ewms-pilot -htcondor==10.5.0 +htcondor==10.6.0 # via skymap-scanner (setup.py) humanfriendly==10.0 # via coloredlogs @@ -42,7 +42,7 @@ icecube-skyreader==1.2.0 # via skymap-scanner (setup.py) idna==3.4 # via requests -iminuit==2.21.3 +iminuit==2.22.0 # via skymap-scanner (setup.py) kiwisolver==1.4.4 # via matplotlib @@ -52,7 +52,7 @@ matplotlib==3.7.1 # icecube-skyreader meander==0.0.3 # via icecube-skyreader -numpy==1.24.3 +numpy==1.25.0 # via # astropy # contourpy @@ -72,7 +72,7 @@ packaging==23.1 # via # astropy # matplotlib -pandas==2.0.2 +pandas==2.0.3 # via icecube-skyreader pillow==9.5.0 # via matplotlib @@ -82,7 +82,7 @@ pyerfa==2.0.0.3 # via astropy pyjwt[crypto]==2.7.0 # via wipac-rest-tools -pyparsing==3.0.9 +pyparsing==3.1.0 # via matplotlib pypng==0.20220715.0 # via qrcode @@ -101,15 +101,15 @@ requests==2.31.0 # requests-futures # wipac-dev-tools # wipac-rest-tools -requests-futures==1.0.0 +requests-futures==1.0.1 # via wipac-rest-tools -scipy==1.10.1 +scipy==1.11.1 # via healpy six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.6.3 +typing-extensions==4.7.0 # via # qrcode # wipac-dev-tools From c75fdeb45dd7e1ec278741f11d78f0682b5a9ba0 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 30 Jun 2023 14:16:49 +0000 Subject: [PATCH 173/217] update requirements-nats.txt --- requirements-nats.txt | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index ea0c1ddbb..ed2d0bb58 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -18,7 +18,7 @@ charset-normalizer==3.1.0 # via requests coloredlogs==15.0.1 # via wipac-dev-tools -contourpy==1.0.7 +contourpy==1.1.0 # via matplotlib cryptography==41.0.1 # via pyjwt @@ -28,7 +28,7 @@ ed25519==1.5 # via nkeys ewms-pilot==0.10.3 # via skymap-scanner (setup.py) -fonttools==4.39.4 +fonttools==4.40.0 # via matplotlib healpy==1.16.2 # via @@ -42,7 +42,7 @@ icecube-skyreader==1.2.0 # via skymap-scanner (setup.py) idna==3.4 # via requests -iminuit==2.21.3 +iminuit==2.22.0 # via skymap-scanner (setup.py) kiwisolver==1.4.4 # via matplotlib @@ -56,7 +56,7 @@ nats-py[nkeys]==2.3.1 # via oms-mqclient nkeys==0.1.0 # via nats-py -numpy==1.24.3 +numpy==1.25.0 # via # astropy # contourpy @@ -76,7 +76,7 @@ packaging==23.1 # via # astropy # matplotlib -pandas==2.0.2 +pandas==2.0.3 # via icecube-skyreader pillow==9.5.0 # via matplotlib @@ -86,7 +86,7 @@ pyerfa==2.0.0.3 # via astropy pyjwt[crypto]==2.7.0 # via wipac-rest-tools -pyparsing==3.0.9 +pyparsing==3.1.0 # via matplotlib pypng==0.20220715.0 # via qrcode @@ -105,15 +105,15 @@ requests==2.31.0 # requests-futures # wipac-dev-tools # wipac-rest-tools -requests-futures==1.0.0 +requests-futures==1.0.1 # via wipac-rest-tools -scipy==1.10.1 +scipy==1.11.1 # via healpy six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.6.3 +typing-extensions==4.7.0 # via # qrcode # wipac-dev-tools From 59a913447a600c2ccdd9d1993598f0c1f4c7aea1 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 30 Jun 2023 14:16:49 +0000 Subject: [PATCH 174/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index e21de4f3b..574ac7cac 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -20,7 +20,7 @@ charset-normalizer==3.1.0 # via requests coloredlogs==15.0.1 # via wipac-dev-tools -contourpy==1.0.7 +contourpy==1.1.0 # via matplotlib cryptography==41.0.1 # via pyjwt @@ -28,7 +28,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.3 # via skymap-scanner (setup.py) -fonttools==4.39.4 +fonttools==4.40.0 # via matplotlib healpy==1.16.2 # via @@ -42,7 +42,7 @@ icecube-skyreader==1.2.0 # via skymap-scanner (setup.py) idna==3.4 # via requests -iminuit==2.21.3 +iminuit==2.22.0 # via skymap-scanner (setup.py) kiwisolver==1.4.4 # via matplotlib @@ -52,7 +52,7 @@ matplotlib==3.7.1 # icecube-skyreader meander==0.0.3 # via icecube-skyreader -numpy==1.24.3 +numpy==1.25.0 # via # astropy # contourpy @@ -72,7 +72,7 @@ packaging==23.1 # via # astropy # matplotlib -pandas==2.0.2 +pandas==2.0.3 # via icecube-skyreader pillow==9.5.0 # via matplotlib @@ -84,7 +84,7 @@ pyerfa==2.0.0.3 # via astropy pyjwt[crypto]==2.7.0 # via wipac-rest-tools -pyparsing==3.0.9 +pyparsing==3.1.0 # via matplotlib pypng==0.20220715.0 # via qrcode @@ -103,15 +103,15 @@ requests==2.31.0 # requests-futures # wipac-dev-tools # wipac-rest-tools -requests-futures==1.0.0 +requests-futures==1.0.1 # via wipac-rest-tools -scipy==1.10.1 +scipy==1.11.1 # via healpy six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.6.3 +typing-extensions==4.7.0 # via # qrcode # wipac-dev-tools From fe0d7850b52fad5823b3aa5bad25766ebbde3619 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 30 Jun 2023 14:16:49 +0000 Subject: [PATCH 175/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index 04e9ef731..2dfe64cf3 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -18,7 +18,7 @@ charset-normalizer==3.1.0 # via requests coloredlogs==15.0.1 # via wipac-dev-tools -contourpy==1.0.7 +contourpy==1.1.0 # via matplotlib cryptography==41.0.1 # via pyjwt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.3 # via skymap-scanner (setup.py) -fonttools==4.39.4 +fonttools==4.40.0 # via matplotlib healpy==1.16.2 # via @@ -40,7 +40,7 @@ icecube-skyreader==1.2.0 # via skymap-scanner (setup.py) idna==3.4 # via requests -iminuit==2.21.3 +iminuit==2.22.0 # via skymap-scanner (setup.py) kiwisolver==1.4.4 # via matplotlib @@ -50,7 +50,7 @@ matplotlib==3.7.1 # icecube-skyreader meander==0.0.3 # via icecube-skyreader -numpy==1.24.3 +numpy==1.25.0 # via # astropy # contourpy @@ -70,7 +70,7 @@ packaging==23.1 # via # astropy # matplotlib -pandas==2.0.2 +pandas==2.0.3 # via icecube-skyreader pika==1.3.2 # via oms-mqclient @@ -82,7 +82,7 @@ pyerfa==2.0.0.3 # via astropy pyjwt[crypto]==2.7.0 # via wipac-rest-tools -pyparsing==3.0.9 +pyparsing==3.1.0 # via matplotlib pypng==0.20220715.0 # via qrcode @@ -101,15 +101,15 @@ requests==2.31.0 # requests-futures # wipac-dev-tools # wipac-rest-tools -requests-futures==1.0.0 +requests-futures==1.0.1 # via wipac-rest-tools -scipy==1.10.1 +scipy==1.11.1 # via healpy six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.6.3 +typing-extensions==4.7.0 # via # qrcode # wipac-dev-tools From dc1f2c2451591c2fb5f681e22a0abeb637c977d0 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Fri, 30 Jun 2023 14:16:49 +0000 Subject: [PATCH 176/217] update requirements.txt --- requirements.txt | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/requirements.txt b/requirements.txt index 1dd4210c6..bc8d01230 100644 --- a/requirements.txt +++ b/requirements.txt @@ -18,7 +18,7 @@ charset-normalizer==3.1.0 # via requests coloredlogs==15.0.1 # via wipac-dev-tools -contourpy==1.0.7 +contourpy==1.1.0 # via matplotlib cryptography==41.0.1 # via pyjwt @@ -26,7 +26,7 @@ cycler==0.11.0 # via matplotlib ewms-pilot==0.10.3 # via skymap-scanner (setup.py) -fonttools==4.39.4 +fonttools==4.40.0 # via matplotlib healpy==1.16.2 # via @@ -40,7 +40,7 @@ icecube-skyreader==1.2.0 # via skymap-scanner (setup.py) idna==3.4 # via requests -iminuit==2.21.3 +iminuit==2.22.0 # via skymap-scanner (setup.py) kiwisolver==1.4.4 # via matplotlib @@ -50,7 +50,7 @@ matplotlib==3.7.1 # icecube-skyreader meander==0.0.3 # via icecube-skyreader -numpy==1.24.3 +numpy==1.25.0 # via # astropy # contourpy @@ -70,7 +70,7 @@ packaging==23.1 # via # astropy # matplotlib -pandas==2.0.2 +pandas==2.0.3 # via icecube-skyreader pillow==9.5.0 # via matplotlib @@ -80,7 +80,7 @@ pyerfa==2.0.0.3 # via astropy pyjwt[crypto]==2.7.0 # via wipac-rest-tools -pyparsing==3.0.9 +pyparsing==3.1.0 # via matplotlib pypng==0.20220715.0 # via qrcode @@ -99,15 +99,15 @@ requests==2.31.0 # requests-futures # wipac-dev-tools # wipac-rest-tools -requests-futures==1.0.0 +requests-futures==1.0.1 # via wipac-rest-tools -scipy==1.10.1 +scipy==1.11.1 # via healpy six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.6.3 +typing-extensions==4.7.0 # via # qrcode # wipac-dev-tools From 2972cd1b6f1f03421ecd833cfd4304c54b9d2360 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Fri, 30 Jun 2023 18:43:01 +0200 Subject: [PATCH 177/217] adjust tolerance --- tests/compare_scan_results.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/compare_scan_results.py b/tests/compare_scan_results.py index a6bfdbeb2..65c91eb91 100644 --- a/tests/compare_scan_results.py +++ b/tests/compare_scan_results.py @@ -8,7 +8,7 @@ from skyreader import SkyScanResult from wipac_dev_tools import logging_tools -RTOL_PER_FIELD = {"llh": 0.03, "E_in": 0.7, "E_tot": 0.7} +RTOL_PER_FIELD = {"llh": 0.18, "E_in": 0.7, "E_tot": 0.7} def read_file(filepath: Path) -> SkyScanResult: From dd2485df776fce09d1003b6a0f14d1f7481cd90f Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 1 Jul 2023 18:39:00 +0200 Subject: [PATCH 178/217] move late pulse cleaning to common --- skymap_scanner/recos/common/pulse_proc.py | 59 ++++++++++++++++++++++ skymap_scanner/recos/millipede_original.py | 52 +------------------ skymap_scanner/recos/millipede_wilks.py | 53 +------------------ 3 files changed, 63 insertions(+), 101 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index c044812c3..40ae61641 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -1,5 +1,7 @@ +import numpy from typing import Final +from I3Tray import I3Units # type: ignore[import] from icecube import dataclasses # type: ignore[import] @@ -11,3 +13,60 @@ def mask_deepcore(frame, origpulses: str, maskedpulses: str): origpulses, lambda omkey, index, pulse: omkey.string < FIRST_DEEPCORE_STRING, ) + + +def _weighted_quantile_arg(values, weights, q=0.5): + indices = numpy.argsort(values) + sorted_indices = numpy.arange(len(values))[indices] + medianidx = (weights[indices].cumsum() / weights[indices].sum()).searchsorted(q) + if (0 <= medianidx) and (medianidx < len(values)): + return sorted_indices[medianidx] + else: + return numpy.nan + + +def weighted_quantile(values, weights, q=0.5): + if len(values) != len(weights): + raise ValueError("shape of `values` and `weights` don't match!") + index = _weighted_quantile_arg(values, weights, q=q) + if not numpy.isnan(index): + return values[index] + else: + return numpy.nan + + +def weighted_median(values, weights): + return weighted_quantile(values, weights, q=0.5) + + +def late_pulse_cleaning(frame, Pulses, Residual=3e3 * I3Units.ns): + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) + counter, charge = 0, 0 + qtot = 0 + times = dataclasses.I3TimeWindowSeriesMap() + for omkey, ps in pulses.items(): + if len(ps) < 2: + if len(ps) == 1: + qtot += ps[0].charge + continue + ts = numpy.asarray([p.time for p in ps]) + cs = numpy.asarray([p.charge for p in ps]) + median = weighted_median(ts, cs) + qtot += cs.sum() + for p in ps: + if p.time >= (median + Residual): + if omkey not in times: + ts = dataclasses.I3TimeWindowSeries() + ts.append( + dataclasses.I3TimeWindow(median + Residual, numpy.inf) + ) # this defines the **excluded** time window + times[omkey] = ts + mask.set(omkey, p, False) + counter += 1 + charge += p.charge + frame[cls.pulsesName_cleaned] = mask + frame[cls.pulsesName_cleaned + "TimeWindows"] = times + frame[cls.pulsesName_cleaned + "TimeRange"] = copy.deepcopy( + frame[Pulses + "TimeRange"] + ) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 6888644de..a05ada88e 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -31,6 +31,7 @@ from ..utils.data_handling import DataStager from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator +from .common.pulse_proc import late_pulse_cleaning class MillipedeOriginal(RecoInterface): """Reco logic for millipede.""" @@ -152,56 +153,7 @@ def createEmptyDOMLists(frame, ListNames=[]): ################## - def _weighted_quantile_arg(values, weights, q=0.5): - indices = numpy.argsort(values) - sorted_indices = numpy.arange(len(values))[indices] - medianidx = (weights[indices].cumsum()/weights[indices].sum()).searchsorted(q) - if (0 <= medianidx) and (medianidx < len(values)): - return sorted_indices[medianidx] - else: - return numpy.nan - - def weighted_quantile(values, weights, q=0.5): - if len(values) != len(weights): - raise ValueError("shape of `values` and `weights` don't match!") - index = _weighted_quantile_arg(values, weights, q=q) - if not numpy.isnan(index): - return values[index] - else: - return numpy.nan - - def weighted_median(values, weights): - return weighted_quantile(values, weights, q=0.5) - - def LatePulseCleaning(frame, Pulses, Residual=3e3*I3Units.ns): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) - counter, charge = 0, 0 - qtot = 0 - times = dataclasses.I3TimeWindowSeriesMap() - for omkey, ps in pulses.items(): - if len(ps) < 2: - if len(ps) == 1: - qtot += ps[0].charge - continue - ts = numpy.asarray([p.time for p in ps]) - cs = numpy.asarray([p.charge for p in ps]) - median = weighted_median(ts, cs) - qtot += cs.sum() - for p in ps: - if p.time >= (median+Residual): - if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append(dataclasses.I3TimeWindow(median+Residual, numpy.inf)) # this defines the **excluded** time window - times[omkey] = ts - mask.set(omkey, p, False) - counter += 1 - charge += p.charge - frame[cls.pulsesName_cleaned] = mask - frame[cls.pulsesName_cleaned+"TimeWindows"] = times - frame[cls.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[Pulses+"TimeRange"]) - - tray.AddModule(LatePulseCleaning, "LatePulseCleaning", + tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", Pulses=cls.pulsesName, ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 7979123b7..79ced33e6 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore +from .common.pulse_proc import mask_deepcore, late_pulse_cleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,56 +177,7 @@ def skipunhits(frame, output, pulses): ################## - def _weighted_quantile_arg(values, weights, q=0.5): - indices = numpy.argsort(values) - sorted_indices = numpy.arange(len(values))[indices] - medianidx = (weights[indices].cumsum()/weights[indices].sum()).searchsorted(q) - if (0 <= medianidx) and (medianidx < len(values)): - return sorted_indices[medianidx] - else: - return numpy.nan - - def weighted_quantile(values, weights, q=0.5): - if len(values) != len(weights): - raise ValueError("shape of `values` and `weights` don't match!") - index = _weighted_quantile_arg(values, weights, q=q) - if not numpy.isnan(index): - return values[index] - else: - return numpy.nan - - def weighted_median(values, weights): - return weighted_quantile(values, weights, q=0.5) - - def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) - counter, charge = 0, 0 - qtot = 0 - times = dataclasses.I3TimeWindowSeriesMap() - for omkey, ps in pulses.items(): - if len(ps) < 2: - if len(ps) == 1: - qtot += ps[0].charge - continue - ts = numpy.asarray([p.time for p in ps]) - cs = numpy.asarray([p.charge for p in ps]) - median = weighted_median(ts, cs) - qtot += cs.sum() - for p in ps: - if p.time >= (median+Residual): - if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append(dataclasses.I3TimeWindow(median+Residual, numpy.inf)) # this defines the **excluded** time window - times[omkey] = ts - mask.set(omkey, p, False) - counter += 1 - charge += p.charge - frame[cls.pulsesName_cleaned] = mask - frame[cls.pulsesName_cleaned+"TimeWindows"] = times - frame[cls.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[cls.pulsesName_orig+"TimeRange"]) - - tray.AddModule(LatePulseCleaning, "LatePulseCleaning", + tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", Pulses=cls.pulsesName, ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From 51a96df778154f232ec2063f910c7813a12a1a6c Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sat, 1 Jul 2023 16:40:14 +0000 Subject: [PATCH 179/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index d620ef7b7..59902efa7 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -82,7 +82,7 @@ pandas==2.0.3 # via icecube-skyreader pika==1.3.2 # via oms-mqclient -pillow==9.5.0 +pillow==10.0.0 # via matplotlib pulsar-client==3.2.0 # via oms-mqclient From bc1ab8f06891f82d944ddecd54313d28d4163059 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sat, 1 Jul 2023 16:40:14 +0000 Subject: [PATCH 180/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 2fcec8283..4564a0b84 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -74,7 +74,7 @@ packaging==23.1 # matplotlib pandas==2.0.3 # via icecube-skyreader -pillow==9.5.0 +pillow==10.0.0 # via matplotlib pycparser==2.21 # via cffi From 7193b654b79af79c5b627225b0d6b5cf53db22f8 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sat, 1 Jul 2023 16:40:14 +0000 Subject: [PATCH 181/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index ed2d0bb58..7a895922c 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -78,7 +78,7 @@ packaging==23.1 # matplotlib pandas==2.0.3 # via icecube-skyreader -pillow==9.5.0 +pillow==10.0.0 # via matplotlib pycparser==2.21 # via cffi From dbf40da48e17973681f413a7c0904bd26f325fa8 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sat, 1 Jul 2023 16:40:14 +0000 Subject: [PATCH 182/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index 574ac7cac..fddcc8217 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -74,7 +74,7 @@ packaging==23.1 # matplotlib pandas==2.0.3 # via icecube-skyreader -pillow==9.5.0 +pillow==10.0.0 # via matplotlib pulsar-client==3.2.0 # via oms-mqclient From 22bf5ec3d618381b48a8d27d13d9315e57ee39b3 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sat, 1 Jul 2023 16:40:14 +0000 Subject: [PATCH 183/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index 2dfe64cf3..d626c301e 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -74,7 +74,7 @@ pandas==2.0.3 # via icecube-skyreader pika==1.3.2 # via oms-mqclient -pillow==9.5.0 +pillow==10.0.0 # via matplotlib pycparser==2.21 # via cffi From 3b50f75bd8a580fae21f4354ba2bb9055b1037da Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sat, 1 Jul 2023 16:40:14 +0000 Subject: [PATCH 184/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index bc8d01230..9abf8d56a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -72,7 +72,7 @@ packaging==23.1 # matplotlib pandas==2.0.3 # via icecube-skyreader -pillow==9.5.0 +pillow==10.0.0 # via matplotlib pycparser==2.21 # via cffi From 538e80341490e9d3a7ea45208c6f1d26a8156409 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 1 Jul 2023 19:01:46 +0200 Subject: [PATCH 185/217] use argument instead of hardcoded class attribute --- skymap_scanner/recos/common/pulse_proc.py | 16 +++++++++------- skymap_scanner/recos/millipede_original.py | 3 ++- skymap_scanner/recos/millipede_wilks.py | 3 ++- 3 files changed, 13 insertions(+), 9 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 40ae61641..06e6d79e8 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -39,9 +39,11 @@ def weighted_median(values, weights): return weighted_quantile(values, weights, q=0.5) -def late_pulse_cleaning(frame, Pulses, Residual=3e3 * I3Units.ns): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) +def late_pulse_cleaning( + frame, input_pulses_name: str, output_pulses_name: str, Residual=3e3 * I3Units.ns +): + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) counter, charge = 0, 0 qtot = 0 times = dataclasses.I3TimeWindowSeriesMap() @@ -65,8 +67,8 @@ def late_pulse_cleaning(frame, Pulses, Residual=3e3 * I3Units.ns): mask.set(omkey, p, False) counter += 1 charge += p.charge - frame[cls.pulsesName_cleaned] = mask - frame[cls.pulsesName_cleaned + "TimeWindows"] = times - frame[cls.pulsesName_cleaned + "TimeRange"] = copy.deepcopy( - frame[Pulses + "TimeRange"] + frame[output_pulses_name] = mask + frame[output_pulses_name + "TimeWindows"] = times + frame[output_pulses_name + "TimeRange"] = copy.deepcopy( + frame[input_pulses_name + "TimeRange"] ) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index a05ada88e..fbd409fdc 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -154,7 +154,8 @@ def createEmptyDOMLists(frame, ListNames=[]): ################## tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", - Pulses=cls.pulsesName, + input_pulses_name=cls.pulsesName, + output_pulses_name=cls.pulsesName_cleaned ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 79ced33e6..a54c4014f 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -178,7 +178,8 @@ def skipunhits(frame, output, pulses): ################## tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", - Pulses=cls.pulsesName, + input_pulses_name=cls.pulsesName, + output_pulses_name=cls.pulsesName_cleaned ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From e801fdcb11d90bcbda487ef81e5b2792d5742c19 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sat, 1 Jul 2023 23:12:15 +0200 Subject: [PATCH 186/217] import copy; mypy compliance --- skymap_scanner/recos/common/pulse_proc.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 06e6d79e8..df3a8bd2a 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -1,3 +1,4 @@ +import copy import numpy from typing import Final @@ -59,11 +60,11 @@ def late_pulse_cleaning( for p in ps: if p.time >= (median + Residual): if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append( + tws = dataclasses.I3TimeWindowSeries() + tws.append( dataclasses.I3TimeWindow(median + Residual, numpy.inf) ) # this defines the **excluded** time window - times[omkey] = ts + times[omkey] = tws mask.set(omkey, p, False) counter += 1 charge += p.charge From 73a9d39bab038241d249fb2fae4a0b104fff9875 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 2 Jul 2023 09:48:08 +0200 Subject: [PATCH 187/217] allow passing additional pulses name for timerange --- skymap_scanner/recos/common/pulse_proc.py | 14 ++++++++++++-- skymap_scanner/recos/millipede_wilks.py | 3 ++- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index df3a8bd2a..c6c55243a 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -41,8 +41,17 @@ def weighted_median(values, weights): def late_pulse_cleaning( - frame, input_pulses_name: str, output_pulses_name: str, Residual=3e3 * I3Units.ns + frame, + input_pulses_name: str, + output_pulses_name: str, + orig_pulses_name: str = None, + Residual=3e3 * I3Units.ns, ): + # input_pulses_name can specify a masked hit series that does not carry the TimeRange key + # in such case, the TimeRange key will be retrieved from orig_pulses_name + if orig_pulses_name is None: + orig_pulses_name = input_pulses_name + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) counter, charge = 0, 0 @@ -70,6 +79,7 @@ def late_pulse_cleaning( charge += p.charge frame[output_pulses_name] = mask frame[output_pulses_name + "TimeWindows"] = times + frame[output_pulses_name + "TimeRange"] = copy.deepcopy( - frame[input_pulses_name + "TimeRange"] + frame[orig_pulses_name + "TimeRange"] ) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index a54c4014f..6f5db4c34 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -179,7 +179,8 @@ def skipunhits(frame, output, pulses): tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, - output_pulses_name=cls.pulsesName_cleaned + output_pulses_name=cls.pulsesName_cleaned, + orig_pulses_name=cls.pulsesName_orig ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From 7dc2caa20be967ccc139b43a8662cedc49ebeaf3 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 2 Jul 2023 09:49:22 +0200 Subject: [PATCH 188/217] default arg --- skymap_scanner/recos/common/pulse_proc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index c6c55243a..c543fdaab 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -44,7 +44,7 @@ def late_pulse_cleaning( frame, input_pulses_name: str, output_pulses_name: str, - orig_pulses_name: str = None, + orig_pulses_name: str, Residual=3e3 * I3Units.ns, ): # input_pulses_name can specify a masked hit series that does not carry the TimeRange key From 1f216df4b3296a475ebe51c9b165faf13fccdeba Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 2 Jul 2023 09:56:17 +0200 Subject: [PATCH 189/217] default args/2 --- skymap_scanner/recos/common/pulse_proc.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index c543fdaab..ad48e0f47 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -1,6 +1,6 @@ import copy import numpy -from typing import Final +from typing import Final, Union from I3Tray import I3Units # type: ignore[import] from icecube import dataclasses # type: ignore[import] @@ -44,7 +44,7 @@ def late_pulse_cleaning( frame, input_pulses_name: str, output_pulses_name: str, - orig_pulses_name: str, + orig_pulses_name: Union[str, None] = None, Residual=3e3 * I3Units.ns, ): # input_pulses_name can specify a masked hit series that does not carry the TimeRange key From 23aa4cf52c0b13a1178d9d234b65ed4ec7b0a3ee Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 2 Jul 2023 11:18:00 +0200 Subject: [PATCH 190/217] test restoring old version --- skymap_scanner/recos/millipede_wilks.py | 57 ++++++++++++++++++++++--- 1 file changed, 52 insertions(+), 5 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 6f5db4c34..7979123b7 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, late_pulse_cleaning +from .common.pulse_proc import mask_deepcore class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,10 +177,57 @@ def skipunhits(frame, output, pulses): ################## - tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", - input_pulses_name=cls.pulsesName, - output_pulses_name=cls.pulsesName_cleaned, - orig_pulses_name=cls.pulsesName_orig + def _weighted_quantile_arg(values, weights, q=0.5): + indices = numpy.argsort(values) + sorted_indices = numpy.arange(len(values))[indices] + medianidx = (weights[indices].cumsum()/weights[indices].sum()).searchsorted(q) + if (0 <= medianidx) and (medianidx < len(values)): + return sorted_indices[medianidx] + else: + return numpy.nan + + def weighted_quantile(values, weights, q=0.5): + if len(values) != len(weights): + raise ValueError("shape of `values` and `weights` don't match!") + index = _weighted_quantile_arg(values, weights, q=q) + if not numpy.isnan(index): + return values[index] + else: + return numpy.nan + + def weighted_median(values, weights): + return weighted_quantile(values, weights, q=0.5) + + def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) + counter, charge = 0, 0 + qtot = 0 + times = dataclasses.I3TimeWindowSeriesMap() + for omkey, ps in pulses.items(): + if len(ps) < 2: + if len(ps) == 1: + qtot += ps[0].charge + continue + ts = numpy.asarray([p.time for p in ps]) + cs = numpy.asarray([p.charge for p in ps]) + median = weighted_median(ts, cs) + qtot += cs.sum() + for p in ps: + if p.time >= (median+Residual): + if omkey not in times: + ts = dataclasses.I3TimeWindowSeries() + ts.append(dataclasses.I3TimeWindow(median+Residual, numpy.inf)) # this defines the **excluded** time window + times[omkey] = ts + mask.set(omkey, p, False) + counter += 1 + charge += p.charge + frame[cls.pulsesName_cleaned] = mask + frame[cls.pulsesName_cleaned+"TimeWindows"] = times + frame[cls.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[cls.pulsesName_orig+"TimeRange"]) + + tray.AddModule(LatePulseCleaning, "LatePulseCleaning", + Pulses=cls.pulsesName, ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From fbd1525ac4052f836f0d4d2c0c260069f983efa0 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 2 Jul 2023 14:40:56 +0200 Subject: [PATCH 191/217] step by step --- skymap_scanner/recos/millipede_wilks.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 7979123b7..8ea97b281 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore +from .common.pulse_proc import mask_deepcore, _weighted_quantile_arg class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,15 +177,6 @@ def skipunhits(frame, output, pulses): ################## - def _weighted_quantile_arg(values, weights, q=0.5): - indices = numpy.argsort(values) - sorted_indices = numpy.arange(len(values))[indices] - medianidx = (weights[indices].cumsum()/weights[indices].sum()).searchsorted(q) - if (0 <= medianidx) and (medianidx < len(values)): - return sorted_indices[medianidx] - else: - return numpy.nan - def weighted_quantile(values, weights, q=0.5): if len(values) != len(weights): raise ValueError("shape of `values` and `weights` don't match!") From a8af8d02246b059bd5d25504f6fa647fc7028726 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 2 Jul 2023 17:32:32 +0200 Subject: [PATCH 192/217] weighted quantile --- skymap_scanner/recos/millipede_wilks.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 8ea97b281..8c830189a 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, _weighted_quantile_arg +from .common.pulse_proc import mask_deepcore, weighted_quantile class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,15 +177,6 @@ def skipunhits(frame, output, pulses): ################## - def weighted_quantile(values, weights, q=0.5): - if len(values) != len(weights): - raise ValueError("shape of `values` and `weights` don't match!") - index = _weighted_quantile_arg(values, weights, q=q) - if not numpy.isnan(index): - return values[index] - else: - return numpy.nan - def weighted_median(values, weights): return weighted_quantile(values, weights, q=0.5) From eab208aab350f2ceb85c124c6b0064f836252a30 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 2 Jul 2023 15:33:46 +0000 Subject: [PATCH 193/217] update requirements-all.txt --- requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-all.txt b/requirements-all.txt index 59902efa7..f77720a65 100644 --- a/requirements-all.txt +++ b/requirements-all.txt @@ -119,7 +119,7 @@ six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.7.0 +typing-extensions==4.7.1 # via # qrcode # wipac-dev-tools From aa0fa7709c04a67ad4df0d9cf9086bce66c20fe2 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 2 Jul 2023 15:33:46 +0000 Subject: [PATCH 194/217] update requirements-client-starter.txt --- requirements-client-starter.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-client-starter.txt b/requirements-client-starter.txt index 4564a0b84..d230c06da 100644 --- a/requirements-client-starter.txt +++ b/requirements-client-starter.txt @@ -109,7 +109,7 @@ six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.7.0 +typing-extensions==4.7.1 # via # qrcode # wipac-dev-tools From 283a014d13eb16867baa7aee6a2fa5221b024685 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 2 Jul 2023 15:33:46 +0000 Subject: [PATCH 195/217] update requirements-nats.txt --- requirements-nats.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-nats.txt b/requirements-nats.txt index 7a895922c..f58915983 100644 --- a/requirements-nats.txt +++ b/requirements-nats.txt @@ -113,7 +113,7 @@ six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.7.0 +typing-extensions==4.7.1 # via # qrcode # wipac-dev-tools From 474ec5787d00da29095e8e91153351e081c7ab3a Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 2 Jul 2023 15:33:46 +0000 Subject: [PATCH 196/217] update requirements-pulsar.txt --- requirements-pulsar.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-pulsar.txt b/requirements-pulsar.txt index fddcc8217..669c62f7d 100644 --- a/requirements-pulsar.txt +++ b/requirements-pulsar.txt @@ -111,7 +111,7 @@ six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.7.0 +typing-extensions==4.7.1 # via # qrcode # wipac-dev-tools From c1bda10758bb2394653285e96866cd48974e303e Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 2 Jul 2023 15:33:46 +0000 Subject: [PATCH 197/217] update requirements-rabbitmq.txt --- requirements-rabbitmq.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-rabbitmq.txt b/requirements-rabbitmq.txt index d626c301e..96d5fc23b 100644 --- a/requirements-rabbitmq.txt +++ b/requirements-rabbitmq.txt @@ -109,7 +109,7 @@ six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.7.0 +typing-extensions==4.7.1 # via # qrcode # wipac-dev-tools From b48b86a072168c52c91ab9952cfca9133feef213 Mon Sep 17 00:00:00 2001 From: wipacdevbot Date: Sun, 2 Jul 2023 15:33:46 +0000 Subject: [PATCH 198/217] update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 9abf8d56a..c34406132 100644 --- a/requirements.txt +++ b/requirements.txt @@ -107,7 +107,7 @@ six==1.16.0 # via python-dateutil tornado==6.3.2 # via wipac-rest-tools -typing-extensions==4.7.0 +typing-extensions==4.7.1 # via # qrcode # wipac-dev-tools From b66e70ff3b8412fd148cf8364a315b4678d7dfcf Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Sun, 2 Jul 2023 19:00:21 +0200 Subject: [PATCH 199/217] -> weighted median function --- skymap_scanner/recos/millipede_wilks.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 8c830189a..4664a2412 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, weighted_quantile +from .common.pulse_proc import mask_deepcore, weighted_median class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,9 +177,6 @@ def skipunhits(frame, output, pulses): ################## - def weighted_median(values, weights): - return weighted_quantile(values, weights, q=0.5) - def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) From a81c30ce21d5ac5ba089aca4a678ea3941759097 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 10:26:50 +0200 Subject: [PATCH 200/217] pulse names --- skymap_scanner/recos/millipede_wilks.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 4664a2412..f76c4cd49 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -177,9 +177,9 @@ def skipunhits(frame, output, pulses): ################## - def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, Pulses) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, Pulses) + def LatePulseCleaning(frame, input_pulses, output_pulses, orig_pulses, Residual=1.5e3*I3Units.ns): + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses) counter, charge = 0, 0 qtot = 0 times = dataclasses.I3TimeWindowSeriesMap() @@ -201,12 +201,13 @@ def LatePulseCleaning(frame, Pulses, Residual=1.5e3*I3Units.ns): mask.set(omkey, p, False) counter += 1 charge += p.charge - frame[cls.pulsesName_cleaned] = mask - frame[cls.pulsesName_cleaned+"TimeWindows"] = times - frame[cls.pulsesName_cleaned+"TimeRange"] = copy.deepcopy(frame[cls.pulsesName_orig+"TimeRange"]) + frame[output_pulses] = mask + frame[output_pulses+"TimeWindows"] = times + frame[output_pulses+"TimeRange"] = copy.deepcopy(frame[orig_pulses+"TimeRange"]) tray.AddModule(LatePulseCleaning, "LatePulseCleaning", - Pulses=cls.pulsesName, + input_pulses=cls.pulsesName, + orig_pulses=cls.pulsesName_orig ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From 7a77412406637df91c51427b81392f4dbcf8cca8 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 10:27:19 +0200 Subject: [PATCH 201/217] pulse names/2 --- skymap_scanner/recos/millipede_wilks.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index f76c4cd49..f93f9e595 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -207,6 +207,7 @@ def LatePulseCleaning(frame, input_pulses, output_pulses, orig_pulses, Residual= tray.AddModule(LatePulseCleaning, "LatePulseCleaning", input_pulses=cls.pulsesName, + output_pulses=cls.pulsesName_cleaned, orig_pulses=cls.pulsesName_orig ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From 7504f6469b7009a150a31235f64889320de58170 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 11:17:56 +0200 Subject: [PATCH 202/217] move LPC on pulse_proc module --- skymap_scanner/recos/common/pulse_proc.py | 33 +++++++++++++++++++++++ skymap_scanner/recos/millipede_wilks.py | 30 +-------------------- 2 files changed, 34 insertions(+), 29 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index ad48e0f47..7124b2baa 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -83,3 +83,36 @@ def late_pulse_cleaning( frame[output_pulses_name + "TimeRange"] = copy.deepcopy( frame[orig_pulses_name + "TimeRange"] ) + + +def LatePulseCleaning( + frame, input_pulses, output_pulses, orig_pulses, Residual=1.5e3 * I3Units.ns +): + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses) + counter, charge = 0, 0 + qtot = 0 + times = dataclasses.I3TimeWindowSeriesMap() + for omkey, ps in pulses.items(): + if len(ps) < 2: + if len(ps) == 1: + qtot += ps[0].charge + continue + ts = numpy.asarray([p.time for p in ps]) + cs = numpy.asarray([p.charge for p in ps]) + median = weighted_median(ts, cs) + qtot += cs.sum() + for p in ps: + if p.time >= (median + Residual): + if omkey not in times: + ts = dataclasses.I3TimeWindowSeries() + ts.append( + dataclasses.I3TimeWindow(median + Residual, numpy.inf) + ) # this defines the **excluded** time window + times[omkey] = ts + mask.set(omkey, p, False) + counter += 1 + charge += p.charge + frame[output_pulses] = mask + frame[output_pulses + "TimeWindows"] = times + frame[output_pulses + "TimeRange"] = copy.deepcopy(frame[orig_pulses + "TimeRange"]) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index f93f9e595..e693c64a8 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, weighted_median +from .common.pulse_proc import mask_deepcore, LatePulseCleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,34 +177,6 @@ def skipunhits(frame, output, pulses): ################## - def LatePulseCleaning(frame, input_pulses, output_pulses, orig_pulses, Residual=1.5e3*I3Units.ns): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses) - counter, charge = 0, 0 - qtot = 0 - times = dataclasses.I3TimeWindowSeriesMap() - for omkey, ps in pulses.items(): - if len(ps) < 2: - if len(ps) == 1: - qtot += ps[0].charge - continue - ts = numpy.asarray([p.time for p in ps]) - cs = numpy.asarray([p.charge for p in ps]) - median = weighted_median(ts, cs) - qtot += cs.sum() - for p in ps: - if p.time >= (median+Residual): - if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append(dataclasses.I3TimeWindow(median+Residual, numpy.inf)) # this defines the **excluded** time window - times[omkey] = ts - mask.set(omkey, p, False) - counter += 1 - charge += p.charge - frame[output_pulses] = mask - frame[output_pulses+"TimeWindows"] = times - frame[output_pulses+"TimeRange"] = copy.deepcopy(frame[orig_pulses+"TimeRange"]) - tray.AddModule(LatePulseCleaning, "LatePulseCleaning", input_pulses=cls.pulsesName, output_pulses=cls.pulsesName_cleaned, From cf44f129552fe40b170fa285ce5150a273a4a277 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 13:42:51 +0200 Subject: [PATCH 203/217] restore correct method --- skymap_scanner/recos/millipede_wilks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index e693c64a8..382f72a0c 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, LatePulseCleaning +from .common.pulse_proc import mask_deepcore, late_pulse_cleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,7 +177,7 @@ def skipunhits(frame, output, pulses): ################## - tray.AddModule(LatePulseCleaning, "LatePulseCleaning", + tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", input_pulses=cls.pulsesName, output_pulses=cls.pulsesName_cleaned, orig_pulses=cls.pulsesName_orig From b47507ef0846531648cc6b7c42a250f36a76e1ba Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 14:15:14 +0200 Subject: [PATCH 204/217] pulses name/3 --- skymap_scanner/recos/millipede_wilks.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 382f72a0c..6f5db4c34 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -178,9 +178,9 @@ def skipunhits(frame, output, pulses): ################## tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", - input_pulses=cls.pulsesName, - output_pulses=cls.pulsesName_cleaned, - orig_pulses=cls.pulsesName_orig + input_pulses_name=cls.pulsesName, + output_pulses_name=cls.pulsesName_cleaned, + orig_pulses_name=cls.pulsesName_orig ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From 69bcc3bc99e95b537ea7bbafbd104d243ddce7f5 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 14:39:46 +0200 Subject: [PATCH 205/217] hunting for bugs --- skymap_scanner/recos/common/pulse_proc.py | 18 ++++++++++++------ skymap_scanner/recos/millipede_wilks.py | 4 ++-- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 7124b2baa..0f41590a6 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -86,10 +86,14 @@ def late_pulse_cleaning( def LatePulseCleaning( - frame, input_pulses, output_pulses, orig_pulses, Residual=1.5e3 * I3Units.ns + frame, + input_pulses_name, + output_pulses_name, + orig_pulses_name, + Residual=1.5e3 * I3Units.ns, ): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses) + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) counter, charge = 0, 0 qtot = 0 times = dataclasses.I3TimeWindowSeriesMap() @@ -113,6 +117,8 @@ def LatePulseCleaning( mask.set(omkey, p, False) counter += 1 charge += p.charge - frame[output_pulses] = mask - frame[output_pulses + "TimeWindows"] = times - frame[output_pulses + "TimeRange"] = copy.deepcopy(frame[orig_pulses + "TimeRange"]) + frame[output_pulses_name] = mask + frame[output_pulses_name + "TimeWindows"] = times + frame[output_pulses_name + "TimeRange"] = copy.deepcopy( + frame[orig_pulses_name + "TimeRange"] + ) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 6f5db4c34..063954f18 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, late_pulse_cleaning +from .common.pulse_proc import mask_deepcore, LatePulseCleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,7 +177,7 @@ def skipunhits(frame, output, pulses): ################## - tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", + tray.AddModule(LatePulseCleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, output_pulses_name=cls.pulsesName_cleaned, orig_pulses_name=cls.pulsesName_orig From 269d67e6663fb54ac8927779c9b1380dd841c258 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 15:26:28 +0200 Subject: [PATCH 206/217] hunting for bugs/2 --- skymap_scanner/recos/common/pulse_proc.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 0f41590a6..437567aa6 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -69,11 +69,11 @@ def late_pulse_cleaning( for p in ps: if p.time >= (median + Residual): if omkey not in times: - tws = dataclasses.I3TimeWindowSeries() - tws.append( + ts = dataclasses.I3TimeWindowSeries() + ts.append( dataclasses.I3TimeWindow(median + Residual, numpy.inf) ) # this defines the **excluded** time window - times[omkey] = tws + times[omkey] = ts mask.set(omkey, p, False) counter += 1 charge += p.charge From 2096ef57718199f402df500627baae499191c34c Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 15:27:04 +0200 Subject: [PATCH 207/217] hunting for bugs/3 --- skymap_scanner/recos/millipede_wilks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 063954f18..e4a4cd53f 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -177,7 +177,7 @@ def skipunhits(frame, output, pulses): ################## - tray.AddModule(LatePulseCleaning, "LatePulseCleaning", + tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, output_pulses_name=cls.pulsesName_cleaned, orig_pulses_name=cls.pulsesName_orig From 1dc191f5c3f5ee2411b0cd148c1aed2460d3e145 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 15:30:27 +0200 Subject: [PATCH 208/217] fool mypy --- skymap_scanner/recos/common/pulse_proc.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 437567aa6..b0824c41d 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -42,9 +42,9 @@ def weighted_median(values, weights): def late_pulse_cleaning( frame, - input_pulses_name: str, - output_pulses_name: str, - orig_pulses_name: Union[str, None] = None, + input_pulses_name, #: str, + output_pulses_name, #: str, + orig_pulses_name, #: Union[str, None] = None, Residual=3e3 * I3Units.ns, ): # input_pulses_name can specify a masked hit series that does not carry the TimeRange key From 518a4e74eda77f7477fd76f70cf44032594c7a82 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 15:42:19 +0200 Subject: [PATCH 209/217] import --- skymap_scanner/recos/millipede_wilks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index e4a4cd53f..086ecd1e9 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, LatePulseCleaning +from .common.pulse_proc import mask_deepcore, LatePulseCleaning, late_pulse_cleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" From 99eb10a2ab4b4afee1316762b159d77d115bea04 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 16:08:57 +0200 Subject: [PATCH 210/217] cleanup --- skymap_scanner/recos/common/pulse_proc.py | 2 +- skymap_scanner/recos/millipede_wilks.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index b0824c41d..a07b86ec9 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -44,7 +44,7 @@ def late_pulse_cleaning( frame, input_pulses_name, #: str, output_pulses_name, #: str, - orig_pulses_name, #: Union[str, None] = None, + orig_pulses_name=None, #: Union[str, None] = None, Residual=3e3 * I3Units.ns, ): # input_pulses_name can specify a masked hit series that does not carry the TimeRange key diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 086ecd1e9..6f5db4c34 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, LatePulseCleaning, late_pulse_cleaning +from .common.pulse_proc import mask_deepcore, late_pulse_cleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" From 9f3792db3393c826148a3df45eb170aa311d84d1 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 16:35:01 +0200 Subject: [PATCH 211/217] retry --- skymap_scanner/recos/common/pulse_proc.py | 45 ++-------------------- skymap_scanner/recos/millipede_original.py | 3 +- 2 files changed, 5 insertions(+), 43 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index a07b86ec9..5e3dd2288 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -44,13 +44,13 @@ def late_pulse_cleaning( frame, input_pulses_name, #: str, output_pulses_name, #: str, - orig_pulses_name=None, #: Union[str, None] = None, + orig_pulses_name, #: Union[str, None] = None, Residual=3e3 * I3Units.ns, ): # input_pulses_name can specify a masked hit series that does not carry the TimeRange key # in such case, the TimeRange key will be retrieved from orig_pulses_name - if orig_pulses_name is None: - orig_pulses_name = input_pulses_name + # if orig_pulses_name is None: + # orig_pulses_name = input_pulses_name pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) @@ -83,42 +83,3 @@ def late_pulse_cleaning( frame[output_pulses_name + "TimeRange"] = copy.deepcopy( frame[orig_pulses_name + "TimeRange"] ) - - -def LatePulseCleaning( - frame, - input_pulses_name, - output_pulses_name, - orig_pulses_name, - Residual=1.5e3 * I3Units.ns, -): - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) - counter, charge = 0, 0 - qtot = 0 - times = dataclasses.I3TimeWindowSeriesMap() - for omkey, ps in pulses.items(): - if len(ps) < 2: - if len(ps) == 1: - qtot += ps[0].charge - continue - ts = numpy.asarray([p.time for p in ps]) - cs = numpy.asarray([p.charge for p in ps]) - median = weighted_median(ts, cs) - qtot += cs.sum() - for p in ps: - if p.time >= (median + Residual): - if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append( - dataclasses.I3TimeWindow(median + Residual, numpy.inf) - ) # this defines the **excluded** time window - times[omkey] = ts - mask.set(omkey, p, False) - counter += 1 - charge += p.charge - frame[output_pulses_name] = mask - frame[output_pulses_name + "TimeWindows"] = times - frame[output_pulses_name + "TimeRange"] = copy.deepcopy( - frame[orig_pulses_name + "TimeRange"] - ) diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index fbd409fdc..3cd702e99 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -155,7 +155,8 @@ def createEmptyDOMLists(frame, ListNames=[]): tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, - output_pulses_name=cls.pulsesName_cleaned + output_pulses_name=cls.pulsesName_cleaned, + orig_pulses_name=cls.pulsesName ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] From 7c7763649a7d2a44ba1630ece30248a2ce94b74c Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 17:11:57 +0200 Subject: [PATCH 212/217] revert again --- skymap_scanner/recos/common/pulse_proc.py | 51 ++++++++++++++++++++--- skymap_scanner/recos/millipede_wilks.py | 4 +- 2 files changed, 47 insertions(+), 8 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 5e3dd2288..0f41590a6 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -42,16 +42,56 @@ def weighted_median(values, weights): def late_pulse_cleaning( frame, - input_pulses_name, #: str, - output_pulses_name, #: str, - orig_pulses_name, #: Union[str, None] = None, + input_pulses_name: str, + output_pulses_name: str, + orig_pulses_name: Union[str, None] = None, Residual=3e3 * I3Units.ns, ): # input_pulses_name can specify a masked hit series that does not carry the TimeRange key # in such case, the TimeRange key will be retrieved from orig_pulses_name - # if orig_pulses_name is None: - # orig_pulses_name = input_pulses_name + if orig_pulses_name is None: + orig_pulses_name = input_pulses_name + pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) + mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) + counter, charge = 0, 0 + qtot = 0 + times = dataclasses.I3TimeWindowSeriesMap() + for omkey, ps in pulses.items(): + if len(ps) < 2: + if len(ps) == 1: + qtot += ps[0].charge + continue + ts = numpy.asarray([p.time for p in ps]) + cs = numpy.asarray([p.charge for p in ps]) + median = weighted_median(ts, cs) + qtot += cs.sum() + for p in ps: + if p.time >= (median + Residual): + if omkey not in times: + tws = dataclasses.I3TimeWindowSeries() + tws.append( + dataclasses.I3TimeWindow(median + Residual, numpy.inf) + ) # this defines the **excluded** time window + times[omkey] = tws + mask.set(omkey, p, False) + counter += 1 + charge += p.charge + frame[output_pulses_name] = mask + frame[output_pulses_name + "TimeWindows"] = times + + frame[output_pulses_name + "TimeRange"] = copy.deepcopy( + frame[orig_pulses_name + "TimeRange"] + ) + + +def LatePulseCleaning( + frame, + input_pulses_name, + output_pulses_name, + orig_pulses_name, + Residual=1.5e3 * I3Units.ns, +): pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) counter, charge = 0, 0 @@ -79,7 +119,6 @@ def late_pulse_cleaning( charge += p.charge frame[output_pulses_name] = mask frame[output_pulses_name + "TimeWindows"] = times - frame[output_pulses_name + "TimeRange"] = copy.deepcopy( frame[orig_pulses_name + "TimeRange"] ) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 6f5db4c34..28cb11235 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, late_pulse_cleaning +from .common.pulse_proc import mask_deepcore, late_pulse_cleaning, LatePulseCleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" @@ -177,7 +177,7 @@ def skipunhits(frame, output, pulses): ################## - tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", + tray.AddModule(LatePulseCleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, output_pulses_name=cls.pulsesName_cleaned, orig_pulses_name=cls.pulsesName_orig From 4f94ce14e4d88503decd5afa618e170bcffe646a Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 17:46:48 +0200 Subject: [PATCH 213/217] revert to original form --- skymap_scanner/recos/common/pulse_proc.py | 6 +++--- skymap_scanner/recos/millipede_wilks.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 0f41590a6..437567aa6 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -69,11 +69,11 @@ def late_pulse_cleaning( for p in ps: if p.time >= (median + Residual): if omkey not in times: - tws = dataclasses.I3TimeWindowSeries() - tws.append( + ts = dataclasses.I3TimeWindowSeries() + ts.append( dataclasses.I3TimeWindow(median + Residual, numpy.inf) ) # this defines the **excluded** time window - times[omkey] = tws + times[omkey] = ts mask.set(omkey, p, False) counter += 1 charge += p.charge diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 28cb11235..a98f699da 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -177,7 +177,7 @@ def skipunhits(frame, output, pulses): ################## - tray.AddModule(LatePulseCleaning, "LatePulseCleaning", + tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, output_pulses_name=cls.pulsesName_cleaned, orig_pulses_name=cls.pulsesName_orig From 37377f2b9f6e26d2137e386072185ba9648af6a0 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 17:50:01 +0200 Subject: [PATCH 214/217] disable mypy/2 --- skymap_scanner/recos/common/pulse_proc.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 437567aa6..266809aba 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -42,9 +42,9 @@ def weighted_median(values, weights): def late_pulse_cleaning( frame, - input_pulses_name: str, - output_pulses_name: str, - orig_pulses_name: Union[str, None] = None, + input_pulses_name, # : str, + output_pulses_name, #: str, + orig_pulses_name, #: Union[str, None] = None, Residual=3e3 * I3Units.ns, ): # input_pulses_name can specify a masked hit series that does not carry the TimeRange key From 714a86c83f1f5e226991980c1a6d48b7d4b9b685 Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 18:24:20 +0200 Subject: [PATCH 215/217] configure residual value for millipede original and wilks --- skymap_scanner/recos/common/pulse_proc.py | 63 ++++------------------ skymap_scanner/recos/millipede_original.py | 3 +- skymap_scanner/recos/millipede_wilks.py | 4 +- 3 files changed, 13 insertions(+), 57 deletions(-) diff --git a/skymap_scanner/recos/common/pulse_proc.py b/skymap_scanner/recos/common/pulse_proc.py index 266809aba..554f9440a 100644 --- a/skymap_scanner/recos/common/pulse_proc.py +++ b/skymap_scanner/recos/common/pulse_proc.py @@ -42,55 +42,10 @@ def weighted_median(values, weights): def late_pulse_cleaning( frame, - input_pulses_name, # : str, - output_pulses_name, #: str, - orig_pulses_name, #: Union[str, None] = None, - Residual=3e3 * I3Units.ns, -): - # input_pulses_name can specify a masked hit series that does not carry the TimeRange key - # in such case, the TimeRange key will be retrieved from orig_pulses_name - if orig_pulses_name is None: - orig_pulses_name = input_pulses_name - - pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) - mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) - counter, charge = 0, 0 - qtot = 0 - times = dataclasses.I3TimeWindowSeriesMap() - for omkey, ps in pulses.items(): - if len(ps) < 2: - if len(ps) == 1: - qtot += ps[0].charge - continue - ts = numpy.asarray([p.time for p in ps]) - cs = numpy.asarray([p.charge for p in ps]) - median = weighted_median(ts, cs) - qtot += cs.sum() - for p in ps: - if p.time >= (median + Residual): - if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append( - dataclasses.I3TimeWindow(median + Residual, numpy.inf) - ) # this defines the **excluded** time window - times[omkey] = ts - mask.set(omkey, p, False) - counter += 1 - charge += p.charge - frame[output_pulses_name] = mask - frame[output_pulses_name + "TimeWindows"] = times - - frame[output_pulses_name + "TimeRange"] = copy.deepcopy( - frame[orig_pulses_name + "TimeRange"] - ) - - -def LatePulseCleaning( - frame, - input_pulses_name, - output_pulses_name, - orig_pulses_name, - Residual=1.5e3 * I3Units.ns, + input_pulses_name: str, + output_pulses_name: str, + orig_pulses_name: str, + residual, ): pulses = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, input_pulses_name) mask = dataclasses.I3RecoPulseSeriesMapMask(frame, input_pulses_name) @@ -107,13 +62,13 @@ def LatePulseCleaning( median = weighted_median(ts, cs) qtot += cs.sum() for p in ps: - if p.time >= (median + Residual): + if p.time >= (median + residual): if omkey not in times: - ts = dataclasses.I3TimeWindowSeries() - ts.append( - dataclasses.I3TimeWindow(median + Residual, numpy.inf) + tws = dataclasses.I3TimeWindowSeries() + tws.append( + dataclasses.I3TimeWindow(median + residual, numpy.inf) ) # this defines the **excluded** time window - times[omkey] = ts + times[omkey] = tws mask.set(omkey, p, False) counter += 1 charge += p.charge diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index 3cd702e99..d2c448c13 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -156,7 +156,8 @@ def createEmptyDOMLists(frame, ListNames=[]): tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, output_pulses_name=cls.pulsesName_cleaned, - orig_pulses_name=cls.pulsesName + orig_pulses_name=cls.pulsesName, + residual=1.5e3*I3Units.ns, ) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index a98f699da..1e3d0ae12 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -180,8 +180,8 @@ def skipunhits(frame, output, pulses): tray.AddModule(late_pulse_cleaning, "LatePulseCleaning", input_pulses_name=cls.pulsesName, output_pulses_name=cls.pulsesName_cleaned, - orig_pulses_name=cls.pulsesName_orig - ) + orig_pulses_name=cls.pulsesName_orig, + residual=1.5e3*I3Units.ns) return ExcludedDOMs + [cls.pulsesName_cleaned+'TimeWindows'] @icetray.traysegment From 67816aefbd2766360d9576bb89c97ffadb8671aa Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 18:38:20 +0200 Subject: [PATCH 216/217] cleanup imports --- skymap_scanner/recos/millipede_wilks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index 1e3d0ae12..c2f946c27 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -29,7 +29,7 @@ from .. import config as cfg from ..utils.pixel_classes import RecoPixelVariation from . import RecoInterface, VertexGenerator -from .common.pulse_proc import mask_deepcore, late_pulse_cleaning, LatePulseCleaning +from .common.pulse_proc import mask_deepcore, late_pulse_cleaning class MillipedeWilks(RecoInterface): """Reco logic for millipede.""" From 5b6b2b874bf3b98a501cd4415bfad8703be79acc Mon Sep 17 00:00:00 2001 From: Massimiliano Lincetto Date: Mon, 3 Jul 2023 22:22:25 +0200 Subject: [PATCH 217/217] rename use_fallback_position -> add_fallback_position --- skymap_scanner/recos/__init__.py | 2 +- skymap_scanner/recos/dummy.py | 2 +- skymap_scanner/recos/millipede_original.py | 2 +- skymap_scanner/recos/millipede_wilks.py | 2 +- skymap_scanner/server/start_scan.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/skymap_scanner/recos/__init__.py b/skymap_scanner/recos/__init__.py index d5e55990a..447fc3bdc 100644 --- a/skymap_scanner/recos/__init__.py +++ b/skymap_scanner/recos/__init__.py @@ -37,7 +37,7 @@ class RecoInterface(ABC): # Reco-specific behaviors that need to be defined in derived classes. rotate_vertex: bool refine_time: bool - use_fallback_position: bool + add_fallback_position: bool # List of spline filenames required by the class. # The spline files will be looked up in pre-defined local paths or fetched from a remote data store. diff --git a/skymap_scanner/recos/dummy.py b/skymap_scanner/recos/dummy.py index 25ee03aec..2c19bc7ba 100644 --- a/skymap_scanner/recos/dummy.py +++ b/skymap_scanner/recos/dummy.py @@ -32,7 +32,7 @@ class Dummy(RecoInterface): def __init__(self): self.rotate_vertex = True self.refine_time = True - self.use_fallback_position = False + self.add_fallback_position = False def setup_reco(self): pass diff --git a/skymap_scanner/recos/millipede_original.py b/skymap_scanner/recos/millipede_original.py index d2c448c13..076631524 100644 --- a/skymap_scanner/recos/millipede_original.py +++ b/skymap_scanner/recos/millipede_original.py @@ -94,7 +94,7 @@ def extract_seed(frame): def __init__(self): self.rotate_vertex = False self.refine_time = False - self.use_fallback_position = False + self.add_fallback_position = False def setup_reco(self): datastager = self.get_datastager() diff --git a/skymap_scanner/recos/millipede_wilks.py b/skymap_scanner/recos/millipede_wilks.py index c2f946c27..bbac52bd0 100644 --- a/skymap_scanner/recos/millipede_wilks.py +++ b/skymap_scanner/recos/millipede_wilks.py @@ -49,7 +49,7 @@ class MillipedeWilks(RecoInterface): def __init__(self): self.rotate_vertex = True self.refine_time = True - self.use_fallback_position = True + self.add_fallback_position = True @staticmethod def get_vertex_variations() -> List[dataclasses.I3Position]: diff --git a/skymap_scanner/server/start_scan.py b/skymap_scanner/server/start_scan.py index 557b25f05..bfbf6a4b8 100644 --- a/skymap_scanner/server/start_scan.py +++ b/skymap_scanner/server/start_scan.py @@ -277,7 +277,7 @@ def _gen_pframes( pos_variation.rotate_y(direction.theta) pos_variation.rotate_z(direction.phi) - if self.reco.use_fallback_position: + if self.reco.add_fallback_position: if position != self.fallback_position: # add fallback pos as an extra first guess p_frame[f'{self.output_particle_name}_fallback'] = self.i3particle(