From bd27f18bf0b2747ad9f493c9dbd186cf4f93f2ff Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sat, 7 Mar 2026 10:17:59 -0800 Subject: [PATCH 01/62] edep-sim interface added. Auxiliary variables for Particle also added. Voxelization of edep-sim output is added in the form of pre-processor. --- README.md | 61 ++- pysupera/__init__.py | 3 +- pysupera/app.py | 4 +- pysupera/conditions/touching_em_shower.py | 16 +- pysupera/conf/config.yaml | 18 + pysupera/conf/reader/edepsim_h5.yaml | 39 ++ pysupera/config.py | 103 +++++ pysupera/data.py | 514 +++++++++++++++++++--- pysupera/diagnostics.py | 10 +- pysupera/io.py | 22 +- pysupera/partitioner.py | 8 +- pysupera/partitioner2.py | 16 +- pysupera/preproc.py | 229 +++++++++- pysupera/readers/__init__.py | 38 ++ pysupera/readers/base.py | 74 ++++ pysupera/readers/example_edepsim_h5.py | 251 +++++++++++ pysupera/readers/format_edepsim_h5.py | 447 +++++++++++++++++++ pysupera/utils.py | 4 +- tests/conftest.py | 8 +- tests/test_conditions.py | 44 +- tests/test_partitioner.py | 8 +- tests/test_preproc.py | 4 +- tests/test_utils.py | 34 +- 23 files changed, 1795 insertions(+), 160 deletions(-) create mode 100644 pysupera/conf/reader/edepsim_h5.yaml create mode 100644 pysupera/readers/__init__.py create mode 100644 pysupera/readers/base.py create mode 100644 pysupera/readers/example_edepsim_h5.py create mode 100644 pysupera/readers/format_edepsim_h5.py diff --git a/README.md b/README.md index 4d3b6bf..5131684 100644 --- a/README.md +++ b/README.md @@ -167,19 +167,60 @@ Hit **▶ Run** to execute the full pipeline and render two side-by-side 3-D poi ### Input: the `Particle` object -Each particle carries: +#### Required attributes + +These attributes must always be supplied at construction time and are guaranteed to be set on every `Particle` instance. | Attribute | Type | Description | |---|---|---| -| `id` | `int` | Unique particle ID within an event | -| `parent_id` | `int` | Direct parent particle ID | -| `ancestor_id` | `int` | Root ancestor of the shower/track genealogy | -| `pdg` | `int` | PDG Monte Carlo code | -| `parent_pdg` | `int` | PDG code of the parent | -| `sem_type` | `SemanticType` | Derived semantic category (see below) | +| `id` | `int` | Unique particle ID within an event (Geant4 track ID) | +| `parent_id` | `int` | Direct parent particle ID; equals `id` for primary particles | +| `root_id` | `int` | ID of the primary ancestor at the root of the shower/track genealogy | +| `pdg` | `int` | PDG Monte Carlo particle code | +| `parent_pdg` | `int` | PDG code of the direct parent particle | +| `process_type` | `InteractionType` | Physics process that created this particle; derived from the raw int stored in `_process_type` | +| `sem_type` | `SemanticType` | High-level semantic category derived automatically at construction (see below) | | `point_cloud` | `ndarray (N, ≥3)` | 3-D hit positions; columns 0–2 are x, y, z | -Semantic types (`SemanticType` enum): +#### Optional attributes + +These attributes are not required at construction time. Unset float32 scalars hold `FLOAT_UNSET` (= `np.float32('nan')`); all other unset attributes hold `None`. + +| Attribute | Type | Default | Description | +|---|---|---|---| +| `start` | `ndarray (3,) float32` | `None` | Trajectory start position (vertex). Also accessible as `p.vertex`. | +| `end` | `ndarray (3,) float32` | `None` | Trajectory end position. | +| `momentum_start` | `ndarray (3,) float32` | `None` | 3-momentum at the start vertex (MeV/c). | +| `momentum_end` | `ndarray (3,) float32` | `None` | 3-momentum at the trajectory end (MeV/c). | +| `kinetic_energy_start` | `float32` | `NaN` | Kinetic energy at the start vertex (MeV). | +| `kinetic_energy_end` | `float32` | `NaN` | Kinetic energy at the end of the trajectory (MeV). | +| `mass` | `float32` | `NaN` | Particle rest mass (MeV/c²). | +| `root_pdg` | `int` | `None` | PDG code of the primary (root) ancestor particle. | +| `genealogy_id` | `list[int]` | `None` | IDs of ancestors from direct parent up to and including the root: `[parent_id, grandparent_id, …, root_id]`. | +| `genealogy_pdg` | `list[int]` | `None` | PDG codes of the same ancestry chain as `genealogy_id`. | +| `start_process_id` | `int` | `None` | Geant4/simulation process ID for this particle's creation. | +| `start_subprocess_id` | `int` | `None` | Geant4/simulation sub-process ID for this particle's creation. | +| `start_process_name` | `str` | `None` | Human-readable name of the creation process (e.g. `"eIoni"`). | +| `end_process_id` | `int` | `None` | Geant4/simulation process ID for this particle's termination. | +| `end_subprocess_id` | `int` | `None` | Geant4/simulation sub-process ID for this particle's termination. | +| `end_process_name` | `str` | `None` | Human-readable name of the termination process. | + +**Checking whether an optional attribute is set:** + +```python +if p.start is not None: # array / int / str / list check + print(p.start) + +import numpy as np +if not np.isnan(p.kinetic_energy_start): # float32 scalar check + print(p.kinetic_energy_start) +``` + +**`vertex` property** — `p.vertex` is a read/write alias for `p.start`. + +#### Semantic types + +`sem_type` is derived automatically from `process_type`, `pdg`, `parent_pdg`, and `point_cloud` at construction time via `SetSemanticType`. Particles with fewer than `min_pc_size` points may be reclassified as `kLEScatter`. | Value | Meaning | |---|---| @@ -190,8 +231,6 @@ Semantic types (`SemanticType` enum): | `kLEScatter` | Low-energy scatter product | | `kUnknown` | Unclassified | -`sem_type` is derived automatically from `process_type`, `pdg`, `parent_pdg`, and `point_cloud` at construction time via `SetSemanticType`. Particles with fewer than `min_pc_size` points may be reclassified as `kLEScatter`. - --- ### Step 0 — Configuration @@ -472,7 +511,7 @@ Only the touching subset proceeds to `post_filter`, and only the final `merge_pa **Candidate filter:** - Both particles have PDG code 11 or 22. - Direct parent–child relationship. -- Same `ancestor_id`. +- Same `root_id`. **Merge direction:** child → parent (directed by the parent–child tree). diff --git a/pysupera/__init__.py b/pysupera/__init__.py index 65ddd3c..3487f00 100644 --- a/pysupera/__init__.py +++ b/pysupera/__init__.py @@ -1,5 +1,6 @@ __version__ = "0.0.1" +from .data import Particle, FLOAT_UNSET from .io import write_events, open_writer, read_events, EventStore, EventWriter -from .config import build_checker, build_conditions, build_preprocessor, build_merge_processor, load_cfg, configure, check_particle_list +from .config import build_checker, build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, load_cfg, configure, check_particle_list from .utils import trace_ancestry diff --git a/pysupera/app.py b/pysupera/app.py index 5634de8..6d20e0e 100644 --- a/pysupera/app.py +++ b/pysupera/app.py @@ -127,7 +127,7 @@ def _build_figure( meta={"sem_type": p.sem_type.name}, hovertemplate=( f"id={p.id} pdg={p.pdg} sem={p.sem_type.name}
" - f"parent_id={p.parent_id} ancestor_id={p.ancestor_id}
" + f"parent_id={p.parent_id} root_id={p.root_id}
" "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" ), ), @@ -164,7 +164,7 @@ def _build_figure( hovertemplate=( f"partition {i} ({len(part)} particles)
" f"rep: id={rep.id} sem={rep.sem_type.name}
" - f"rep parent_id={rep.parent_id} ancestor_id={rep.ancestor_id}
" + f"rep parent_id={rep.parent_id} root_id={rep.root_id}
" f"members: [{ids_str}]
" "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" ), diff --git a/pysupera/conditions/touching_em_shower.py b/pysupera/conditions/touching_em_shower.py index eadb0d1..85b73f4 100644 --- a/pysupera/conditions/touching_em_shower.py +++ b/pysupera/conditions/touching_em_shower.py @@ -23,7 +23,7 @@ class TouchingEMShower(PartitionConditionBase): 1. ``get_candidates`` builds parent-child pairs where both members have PDG code 11 (electron) or 22 (photon) – O(n). 2. ``get_candidates`` then filters those pairs to retain only pairs - that share the same ``ancestor_id`` – O(candidates). + that share the same ``root_id`` – O(candidates). 3. The base partitioner tests proximity on the remaining candidates. """ @@ -40,7 +40,7 @@ def get_candidates(self, partitioner: 'ParticlePartitioner') -> List[Tuple[int, Return parent-child PDG-11/22 pairs that share an ancestor. Iterates the full particle list once to collect direct parent-child - edges (O(n)), then discards pairs with different ``ancestor_id`` + edges (O(n)), then discards pairs with different ``root_id`` values (O(candidates)). Both filters run inside this method so that the list handed to the proximity checker is as small as possible. @@ -80,7 +80,7 @@ def get_rep_candidates( * the original parent of that child exists in *rep_lookup* (i.e. the parent particle belongs to some live partition), * the parent representative also has PDG code 11 or 22, and - * child and parent representatives share the same ``ancestor_id``. + * child and parent representatives share the same ``root_id``. Because *rep_lookup* is updated after every merge, this method correctly handles chains: if A merged into B, the representative for @@ -111,7 +111,7 @@ def get_rep_candidates( continue # already same partition if parent_rep.pdg not in target_pdgs: continue - if rep.ancestor_id != parent_rep.ancestor_id: + if rep.root_id != parent_rep.root_id: continue candidates.append((rep.id, parent_rep.id)) @@ -161,10 +161,10 @@ def _filter_by_ancestor(self, partitioner: 'ParticlePartitioner', candidates: List[Tuple[int, int]]) -> List[Tuple[int, int]]: """ - Keep only pairs that share the same ``ancestor_id``. + Keep only pairs that share the same ``root_id``. Iterates *candidates* once (O(candidates)) and discards any pair - whose two particles have different ``ancestor_id`` values. When + whose two particles have different ``root_id`` values. When diagnostics are enabled, each rejected pair is recorded with reason ``DIFFERENT_ANCESTOR``. @@ -187,13 +187,13 @@ def _filter_by_ancestor(self, p1 = partitioner.particle_lookup[id1] p2 = partitioner.particle_lookup[id2] - if p1.ancestor_id == p2.ancestor_id: + if p1.root_id == p2.root_id: filtered.append((id1, id2)) elif partitioner.diagnostics.enabled: partitioner.diagnostics.record( id1, id2, MergeOutcome.DIFFERENT_ANCESTOR, - f"ancestor_id: {p1.ancestor_id} vs {p2.ancestor_id}", + f"root_id: {p1.root_id} vs {p2.root_id}", stage=f"{self.name}: Stage 2 Ancestor Filter", ) diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index 8cb5360..249d537 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -1,5 +1,6 @@ defaults: - io: default + - reader: edepsim_h5 - checker: gpu - conditions: default - _self_ @@ -38,9 +39,26 @@ particle: # unknown semantic type. -1 disables the check (all sizes accepted). min_pc_size: -1 + # Pre-processing: voxelize point clouds onto a regular 3-D grid. + # Bins each point into a voxel cell, snaps its position to the voxel + # centre, and merges all points in the same voxel (time=min, energy=sum, + # dedx=max). This is a strict generalisation of merge_duplicates; when + # voxelize.enabled is true, merge_duplicates is redundant. + voxelize: + enabled: false + # Voxel side length in the same units as the point-cloud coordinates. + # A single float produces an isotropic grid; set to [dx, dy, dz] for + # an anisotropic grid. + voxel_size: 0.3 + # Lower corner of the grid. null = use each cloud's own coordinate + # minimum (per-particle alignment, default). Set to [x0, y0, z0] for + # a globally fixed grid anchored at a known detector boundary. + origin: null + # Pre-processing: collapse points sharing identical 3-D coordinates. # Aggregation rules: time=min, energy=sum, dedx=max. # Runs before defragmentation so min_pc_size counts unique voxels. + # Redundant when voxelize.enabled is true. merge_duplicates: true # Pre-processing: defragment point clouds before partitioning. diff --git a/pysupera/conf/reader/edepsim_h5.yaml b/pysupera/conf/reader/edepsim_h5.yaml new file mode 100644 index 0000000..177bc1a --- /dev/null +++ b/pysupera/conf/reader/edepsim_h5.yaml @@ -0,0 +1,39 @@ +# Reader configuration for EDepSim HDF5 files. +# +# This config group is selected via: +# defaults: +# - reader: edepsim_h5 +# +# or overridden on the command line / in notebooks: +# reader.particle_key=particle/geant4_custom + +# Identifies which reader class build_reader() should instantiate. +# Do not change unless you are adding a new reader format. +format: edepsim_h5 + +# --------------------------------------------------------------------------- +# HDF5 dataset keys +# Each of these corresponds to a top-level dataset path inside the HDF5 file. +# --------------------------------------------------------------------------- + +# Per-event structured array of Geant4 particles. +# Expected fields: track_id, parent_track_id, ancestor_track_id, pdg, +# proc_start, subproc_start, ke, x, y, z +particle_key: particle/geant4 + +# Flat per-event energy-deposit step array (the point cloud). +# Columns 0-2 must be x, y, z. +step_key: pstep/lar_vol + +# Association table mapping particle index to slice in step_key. +# Expected fields: start, end (integer arrays of length N_particles) +ass_key: ass/particle_pstep_lar_vol + +# --------------------------------------------------------------------------- +# Physics / classification parameters +# --------------------------------------------------------------------------- + +# Kinetic-energy threshold (MeV) used by the interaction-type classifier to +# distinguish low-energy electrons (→ kCompton) from higher-energy ones +# (→ kOtherShower) when no cleaner G4 process tag is available. +electron_energy_threshold: 0.05 diff --git a/pysupera/config.py b/pysupera/config.py index 91ae4b1..a490f89 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -125,6 +125,45 @@ def build_preprocessor(cfg: DictConfig, verbose: bool = False): ) +def build_voxelizer(cfg: DictConfig, verbose: bool = False): + """ + Instantiate a :class:`~pysupera.preproc.VoxelizeProcessor` when + ``cfg.particle.voxelize.enabled`` is ``true``. + + Returns ``None`` when voxelization is disabled or the block is absent. + + Parameters + ---------- + cfg : DictConfig + Full Hydra config (must contain ``cfg.particle``). + verbose : bool, optional + Forward to the :class:`~pysupera.preproc.VoxelizeProcessor` + constructor. Default ``False``. + + Returns + ------- + VoxelizeProcessor or None + """ + vox_cfg = cfg.particle.get("voxelize", None) + if not vox_cfg or not vox_cfg.get("enabled", False): + return None + + from pysupera.preproc import VoxelizeProcessor + + raw_vs = vox_cfg.get("voxel_size", 1.0) + # OmegaConf ListConfig → plain Python list so numpy can consume it. + voxel_size = list(raw_vs) if hasattr(raw_vs, "__iter__") else float(raw_vs) + + raw_origin = vox_cfg.get("origin", None) + origin = list(raw_origin) if raw_origin is not None else None + + return VoxelizeProcessor( + voxel_size = voxel_size, + origin = origin, + verbose = verbose, + ) + + def build_merge_processor(cfg: DictConfig, verbose: bool = False): """ Instantiate a :class:`~pysupera.preproc.MergeDuplicatesProcessor` @@ -296,6 +335,70 @@ def build_conditions(cfg: DictConfig) -> list: return conditions +def build_reader(cfg: DictConfig): + """ + Instantiate an :class:`~pysupera.readers.EventReaderBase` described by + *cfg.reader*, opening the file at *cfg.io.input_path*. + + The reader format is selected by ``cfg.reader.format``: + + ``edepsim_h5`` + :class:`~pysupera.readers.EDepSimHDF5Reader` — EDepSim HDF5 output. + + Parameters + ---------- + cfg : DictConfig + Full Hydra config. Must contain ``cfg.io.input_path`` and + ``cfg.reader`` with at least a ``format`` key. + + Returns + ------- + EventReaderBase + An open reader positioned at the start of the file. Close or use + as a context manager when done. + + Raises + ------ + ValueError + If ``cfg.reader.format`` is not a recognised format string. + + Examples + -------- + :: + + cfg = load_cfg([ + "io.input_path=/data/sim.h5", + "reader.particle_key=particle/geant4_v2", + ]) + with build_reader(cfg) as reader: + for particles in reader: + ... + """ + from pysupera.readers import EDepSimHDF5Reader + + fmt = cfg.reader.get("format", "edepsim_h5") + path = str(cfg.io.input_path) + + if fmt == "edepsim_h5": + return EDepSimHDF5Reader( + path, + particle_key = str(cfg.reader.get("particle_key", + "particle/geant4")), + step_key = str(cfg.reader.get("step_key", + "pstep/lar_vol")), + ass_key = str(cfg.reader.get("ass_key", + "ass/particle_pstep_lar_vol")), + electron_energy_threshold = float(cfg.reader.get( + "electron_energy_threshold", 0.05)), + min_pc_size = int(cfg.particle.get("min_pc_size", -1)), + ) + + raise ValueError( + f"Unknown reader format {fmt!r}. " + f"Valid options: edepsim_h5." + ) + + def load_cfg(overrides: list[str] | None = None, config_path: str = "conf", config_name: str = "config") -> DictConfig: diff --git a/pysupera/data.py b/pysupera/data.py index d5ca2c2..4b2897d 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -7,6 +7,38 @@ # --------------------------------------------------------------------------- _DEFAULT_MIN_PC_SIZE: int = -1 +# --------------------------------------------------------------------------- +# Sentinel values for unset optional Particle attributes +# --------------------------------------------------------------------------- +# Design rationale +# ~~~~~~~~~~~~~~~~ +# Python does not have typed "invalid" values the way C++ has INT32_MAX etc. +# Two conventions are used here depending on type: +# +# float32 scalars → np.float32('nan') +# NaN is the standard IEEE 754 "not a number". It propagates visibly +# through all numpy arithmetic (nan + x = nan, nan < x = False), so +# unintentional use is conspicuous rather than silent. It also round- +# trips through HDF5 storage without special handling. +# +# int scalars, arrays, strings → None +# None immediately raises TypeError / AttributeError if used in +# arithmetic or array indexing, making accidental use obvious. It also +# maps cleanly to h5py optional datasets (omitted = None on read). +# +# Check whether an attribute is set with the standard Python idiom: +# +# if p.start is not None: # array / int test +# if not np.isnan(p.mass): # float32 scalar test +# +# Module-level aliases exposed so callers can write: +# +# from pysupera.data import FLOAT_UNSET +# p.mass = FLOAT_UNSET # reset to "not set" + +FLOAT_UNSET: np.float32 = np.float32("nan") +"""Sentinel value for unset ``float32`` scalar attributes on :class:`Particle`.""" + # ============================================================================ # Particle Data Class @@ -16,39 +48,105 @@ class Particle: """ Simulation particle with physical metadata and a 3-D point cloud. - Each particle stores the scalar identifiers produced by the upstream - simulation (PDG code, ancestry chain, interaction process) together with - the spatial hit collection associated with that particle. The semantic - type is derived automatically from the stored fields via - :func:`~pysupera.utils.SetSemanticType` at construction time. - - Attributes - ---------- + Required attributes (always set) + --------------------------------- id : int - Unique integer identifier for this particle within an event. + Unique integer identifier within an event. For Geant4 simulation + this is the track ID. parent_id : int ID of the direct parent particle. Equals ``id`` for primary - particles that have no parent. - ancestor_id : int - ID of the root ancestor particle in the shower/track genealogy. + (root) particles that have no parent; use ``p.parent_id == p.id`` + to test whether a particle is primary. + root_id : int + ID of the primary ancestor particle from which this particle + descends (root of the shower/track genealogy). pdg : int - PDG Monte Carlo numbering scheme code. + PDG Monte Carlo particle code. See https://pdg.lbl.gov/2023/mcdata/mc_particle_id_contents.html. parent_pdg : int PDG code of the direct parent particle. sem_type : SemanticType - Semantic category derived from *process_type*, *pdg*, *parent_pdg*, - and *point_cloud* by :func:`~pysupera.utils.SetSemanticType`. - One of ``kShower``, ``kTrack``, ``kDelta``, ``kMichel``, - ``kLEScatter``, or ``kUnknown``. - point_cloud : numpy.ndarray - Array of shape ``(N, ≥3)`` containing the 3-D spatial hits - (columns 0–2 are x, y, z; additional columns such as time and - energy deposit may be present). + High-level semantic category derived from *process_type*, *pdg*, + *parent_pdg*, and *point_cloud* by + :func:`~pysupera.utils.SetSemanticType`. + process_type : InteractionType + Physics process that created this particle, expressed as an + :class:`~pysupera.utils.InteractionType` member. Derived from + the raw integer stored in ``_process_type``. + point_cloud : numpy.ndarray, shape (N, ≥3) + 3-D spatial hits; columns 0–2 are x, y, z. Additional columns + (time, energy, dE/dx, …) may be present. + + Optional attributes (``None`` or ``FLOAT_UNSET`` when not set) + -------------------------------------------------------------- + start : numpy.ndarray of float32, shape (3,) or None + Trajectory start position (vertex). Also accessible as + ``p.vertex``. + end : numpy.ndarray of float32, shape (3,) or None + Trajectory end position. + momentum_start : numpy.ndarray of float32, shape (3,) or None + 3-momentum at the start vertex (MeV/c). + momentum_end : numpy.ndarray of float32, shape (3,) or None + 3-momentum at the trajectory end (MeV/c). + kinetic_energy_start : float32 or FLOAT_UNSET + Kinetic energy at the start vertex (MeV). + kinetic_energy_end : float32 or FLOAT_UNSET + Kinetic energy at the end of the trajectory (MeV). + mass : float32 or FLOAT_UNSET + Particle rest mass (MeV/c²). + root_pdg : int or None + PDG code of the primary (root) ancestor particle. + genealogy_id : list of int or None + IDs of ancestors from direct parent up to and including the root, + in order [parent_id, grandparent_id, …, root_id]. + genealogy_pdg : list of int or None + PDG codes of the same ancestry chain as *genealogy_id*. + start_process_id : int or None + Geant4/simulation process ID for this particle's creation. + start_subprocess_id : int or None + Geant4/simulation sub-process ID for this particle's creation. + start_process_name : str or None + Human-readable name of the creation process (e.g. ``"eIoni"``). + end_process_id : int or None + Geant4/simulation process ID for this particle's termination. + end_subprocess_id : int or None + Geant4/simulation sub-process ID for this particle's termination. + end_process_name : str or None + Human-readable name of the termination process. """ - def __init__(self, id, parent_id, ancestor_id, pdg, parent_pdg, - process_type, point_cloud, min_pc_size=None): + def __init__( + self, + id, + parent_id, + root_id, + pdg, + parent_pdg, + process_type, + point_cloud, + min_pc_size=None, + # ------------------------------------------------------------------ + # Optional physical attributes — default to None / FLOAT_UNSET. + # See class docstring for sentinel-value conventions. + # ------------------------------------------------------------------ + *, + start=None, + end=None, + momentum_start=None, + momentum_end=None, + kinetic_energy_start=FLOAT_UNSET, + kinetic_energy_end=FLOAT_UNSET, + mass=FLOAT_UNSET, + root_pdg=None, + genealogy_id=None, + genealogy_pdg=None, + start_process_id=None, + start_subprocess_id=None, + start_process_name=None, + end_process_id=None, + end_subprocess_id=None, + end_process_name=None, + ): """ Parameters ---------- @@ -56,56 +154,356 @@ def __init__(self, id, parent_id, ancestor_id, pdg, parent_pdg, Unique particle identifier within an event. parent_id : int ID of the direct parent particle. - ancestor_id : int + root_id : int ID of the root ancestor particle. pdg : int - PDG Monte Carlo particle code for this particle. + PDG Monte Carlo particle code. parent_pdg : int PDG code of the parent particle. process_type : int - Raw integer encoding of the interaction process that produced - this particle (0-based index into :class:`~pysupera.utils.InteractionType`). - Stored as ``_process_type`` for lossless serialisation; the - derived ``sem_type`` is computed from it. - point_cloud : numpy.ndarray - Spatial hit array of shape ``(N, ≥3)``. + Raw integer encoding of the interaction process (stored as + ``_process_type``). The public ``process_type`` property + returns the corresponding :class:`~pysupera.utils.InteractionType` + member. + point_cloud : numpy.ndarray, shape (N, ≥3) + Spatial hit array. min_pc_size : int or None, optional - Minimum point-cloud size threshold forwarded to - :func:`~pysupera.utils.SetSemanticType` as - ``point_cloud_size``. Particles with fewer than - *min_pc_size* points may receive a different semantic type - than larger ones (e.g. ``kLEScatter`` instead of ``kDelta`` - or ``kShower``). ``None`` (default) reads the module-level - :data:`_DEFAULT_MIN_PC_SIZE`, which is ``-1`` unless - overridden by :func:`~pysupera.config.configure`. - """ - self.id = id - self.parent_id = parent_id - self.ancestor_id = ancestor_id - self.pdg = pdg + Forwarded to :func:`~pysupera.utils.SetSemanticType`. ``None`` + reads :data:`_DEFAULT_MIN_PC_SIZE`. + + All remaining parameters are keyword-only and optional; they + default to ``None`` (arrays / ints / strings) or + :data:`FLOAT_UNSET` (float32 scalars). See the class docstring + for full descriptions. + """ + # --- required fields ----------------------------------------------- + self.id = id + self.parent_id = parent_id + self.root_id = root_id + self.pdg = pdg self.parent_pdg = parent_pdg self._process_type = process_type # raw int; retained for serialisation + if min_pc_size is None: min_pc_size = _DEFAULT_MIN_PC_SIZE - self.sem_type = SetSemanticType(process_type, pdg, parent_pdg, point_cloud, point_cloud_size=min_pc_size) - self.point_cloud = point_cloud # numpy array of shape (N, 4) - def __str__(self): + self.sem_type = SetSemanticType( + process_type, pdg, parent_pdg, point_cloud, + point_cloud_size=min_pc_size, + ) + self.point_cloud = point_cloud + + # --- optional physical attributes ----------------------------------- + self.start = (np.asarray(start, dtype=np.float32) + if start is not None else None) + self.end = (np.asarray(end, dtype=np.float32) + if end is not None else None) + self.momentum_start = (np.asarray(momentum_start, dtype=np.float32) + if momentum_start is not None else None) + self.momentum_end = (np.asarray(momentum_end, dtype=np.float32) + if momentum_end is not None else None) + self.kinetic_energy_start = np.float32(kinetic_energy_start) + self.kinetic_energy_end = np.float32(kinetic_energy_end) + self.mass = np.float32(mass) + self.root_pdg = root_pdg + self.genealogy_id = list(genealogy_id) if genealogy_id is not None else None + self.genealogy_pdg = list(genealogy_pdg) if genealogy_pdg is not None else None + self.start_process_id = start_process_id + self.start_subprocess_id = start_subprocess_id + self.start_process_name = start_process_name + self.end_process_id = end_process_id + self.end_subprocess_id = end_subprocess_id + self.end_process_name = end_process_name + + # ------------------------------------------------------------------ # + # Properties # + # ------------------------------------------------------------------ # + + @property + def vertex(self): + """Alias for :attr:`start` (trajectory start / vertex position).""" + return self.start + + @vertex.setter + def vertex(self, value): + self.start = (np.asarray(value, dtype=np.float32) + if value is not None else None) + + @property + def process_type(self) -> "InteractionType": + """ + :class:`~pysupera.utils.InteractionType` member corresponding to + the raw integer stored in ``_process_type``. + + Read-only; modify by setting ``_process_type`` directly if needed. + """ + try: + return InteractionType(int(self._process_type)) + except ValueError: + return InteractionType.kInvalidProcess + + # ------------------------------------------------------------------ # + # Factory methods # + # ------------------------------------------------------------------ # + + @classmethod + def from_arrays( + cls, + ids, + parent_ids, + root_ids, + pdgs, + parent_pdgs, + process_types, + point_clouds, + min_pc_size=None, + ): """ - Return a human-readable string representation. + Construct a list of :class:`Particle` objects from columnar arrays. + + This is a convenience factory for bulk construction (e.g. inside a + file reader) where each scalar attribute is already stored as a + flat array and the point clouds are available as a sequence of + arrays. + + Parameters + ---------- + ids : array-like of int, shape (N,) + Unique particle identifiers. + parent_ids : array-like of int, shape (N,) + Direct parent particle IDs. + root_ids : array-like of int, shape (N,) + Root ancestor IDs. + pdgs : array-like of int, shape (N,) + PDG Monte Carlo codes. + parent_pdgs : array-like of int, shape (N,) + PDG codes of direct parents. + process_types : array-like of int, shape (N,) + Raw interaction process codes forwarded to + :func:`~pysupera.utils.SetSemanticType`. + point_clouds : sequence of numpy.ndarray, length N + One array per particle; each has shape ``(M_i, ≥3)`` where + *M_i* is the number of points for particle *i*. + min_pc_size : int or None, optional + Applied uniformly to all particles (forwarded to + :class:`Particle.__init__`). ``None`` (default) reads the + module-level :data:`_DEFAULT_MIN_PC_SIZE`. Returns ------- - str - ``Particle(id=..., parent_id=..., ancestor_id=..., pdg=..., - parent_pdg=..., sem_type=..., point_cloud=...)``. - """ - return (f"Particle(id={self.id}, parent_id={self.parent_id}, " - f"ancestor_id={self.ancestor_id}\npdg={self.pdg}, " - f"parent_pdg={self.parent_pdg}, sem_type={self.sem_type}\n" - f"point_cloud: shape={self.point_cloud.shape}\n") - #f"point_cloud: shape={self.point_cloud.shape}, energy sum {self.point_cloud[:,3].sum()} average {self.point_cloud[:,3].mean()}\n" - #f"first point: {self.point_cloud[0][:3]}, last point: {self.point_cloud[-1][:3]}\n") + list of Particle + Length-*N* list of fully constructed :class:`Particle` objects. + + Raises + ------ + ValueError + If the lengths of the scalar arrays and *point_clouds* are not + all equal. + + Examples + -------- + >>> import numpy as np + >>> pcs = [np.zeros((10, 4)), np.zeros((5, 4))] + >>> particles = Particle.from_arrays( + ... ids=[0, 1], + ... parent_ids=[0, 0], + ... root_ids=[0, 0], + ... pdgs=[11, 22], + ... parent_pdgs=[0, 11], + ... process_types=[0, 7], + ... point_clouds=pcs, + ... ) + >>> len(particles) + 2 + """ + ids = list(ids) + parent_ids = list(parent_ids) + root_ids = list(root_ids) + pdgs = list(pdgs) + parent_pdgs = list(parent_pdgs) + process_types = list(process_types) + point_clouds = list(point_clouds) + + n = len(ids) + if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, + parent_pdgs, process_types, + point_clouds)): + raise ValueError( + "All input arrays must have the same length. " + f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " + f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " + f"parent_pdgs={len(parent_pdgs)}, " + f"process_types={len(process_types)}, " + f"point_clouds={len(point_clouds)}." + ) + + return [ + cls( + id=ids[i], + parent_id=parent_ids[i], + root_id=root_ids[i], + pdg=pdgs[i], + parent_pdg=parent_pdgs[i], + process_type=process_types[i], + point_cloud=point_clouds[i], + min_pc_size=min_pc_size, + ) + for i in range(n) + ] + + @classmethod + def from_flat_arrays( + cls, + ids, + parent_ids, + root_ids, + pdgs, + parent_pdgs, + process_types, + point_cloud_flat, + point_cloud_offsets, + min_pc_size=None, + ): + """ + Construct a list of :class:`Particle` objects from columnar arrays + where all point clouds are stored as a single flat array together + with a per-particle offset array. + + The point cloud for particle *i* is recovered as:: + + point_cloud_flat[point_cloud_offsets[i, 0] : point_cloud_offsets[i, 1]] + + Parameters + ---------- + ids : array-like of int, shape (N,) + Unique particle identifiers. + parent_ids : array-like of int, shape (N,) + Direct parent particle IDs. + root_ids : array-like of int, shape (N,) + Root ancestor IDs. + pdgs : array-like of int, shape (N,) + PDG Monte Carlo codes. + parent_pdgs : array-like of int, shape (N,) + PDG codes of direct parents. + process_types : array-like of int, shape (N,) + Raw interaction process codes forwarded to + :func:`~pysupera.utils.SetSemanticType`. + point_cloud_flat : numpy.ndarray, shape (M, ≥3) + Concatenated point clouds for all *N* particles. Columns 0–2 + are x, y, z; additional columns (time, energy, etc.) are + carried through unchanged. + point_cloud_offsets : array-like of int, shape (N, 2) + Row slices into *point_cloud_flat*. + ``point_cloud_offsets[i] = [start, end]`` such that + ``point_cloud_flat[start:end]`` is the point cloud of + particle *i*. + min_pc_size : int or None, optional + Applied uniformly to all particles. ``None`` (default) + reads the module-level :data:`_DEFAULT_MIN_PC_SIZE`. + + Returns + ------- + list of Particle + + Raises + ------ + ValueError + If the lengths of the scalar arrays and the first dimension of + *point_cloud_offsets* are not all equal, or if + *point_cloud_offsets* does not have shape ``(N, 2)``. + + Examples + -------- + >>> import numpy as np + >>> flat = np.zeros((15, 4)) # 15 points total + >>> offsets = np.array([[0, 10], # particle 0: 10 points + ... [10, 15]]) # particle 1: 5 points + >>> particles = Particle.from_flat_arrays( + ... ids=[0, 1], + ... parent_ids=[0, 0], + ... root_ids=[0, 0], + ... pdgs=[11, 22], + ... parent_pdgs=[0, 11], + ... process_types=[0, 7], + ... point_cloud_flat=flat, + ... point_cloud_offsets=offsets, + ... ) + >>> len(particles) + 2 + >>> particles[0].point_cloud.shape + (10, 4) + """ + ids = list(ids) + parent_ids = list(parent_ids) + root_ids = list(root_ids) + pdgs = list(pdgs) + parent_pdgs = list(parent_pdgs) + process_types = list(process_types) + offsets = np.asarray(point_cloud_offsets) + + n = len(ids) + + if offsets.ndim != 2 or offsets.shape[1] != 2: + raise ValueError( + f"point_cloud_offsets must have shape (N, 2), " + f"got {offsets.shape}." + ) + if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, + parent_pdgs, process_types)): + raise ValueError( + "All scalar arrays must have the same length. " + f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " + f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " + f"parent_pdgs={len(parent_pdgs)}, " + f"process_types={len(process_types)}." + ) + if offsets.shape[0] != n: + raise ValueError( + f"point_cloud_offsets has {offsets.shape[0]} rows but " + f"scalar arrays have length {n}." + ) + + flat = np.asarray(point_cloud_flat) + + return [ + cls( + id=ids[i], + parent_id=parent_ids[i], + root_id=root_ids[i], + pdg=pdgs[i], + parent_pdg=parent_pdgs[i], + process_type=process_types[i], + point_cloud=flat[offsets[i, 0] : offsets[i, 1]], + min_pc_size=min_pc_size, + ) + for i in range(n) + ] + + + def __str__(self): + """Return a human-readable summary of this particle.""" + lines = [ + f"Particle(id={self.id}, parent_id={self.parent_id}, " + f"root_id={self.root_id}", + f" pdg={self.pdg}, parent_pdg={self.parent_pdg}, " + f"root_pdg={self.root_pdg}", + f" sem_type={self.sem_type}, process_type={self.process_type}", + f" point_cloud: shape={self.point_cloud.shape}", + ] + if self.start is not None: + lines.append(f" start={self.start}, end={self.end}") + if not np.isnan(self.kinetic_energy_start): + lines.append( + f" ke_start={self.kinetic_energy_start:.4g} MeV, " + f"ke_end={self.kinetic_energy_end:.4g} MeV, " + f"mass={self.mass:.4g} MeV/c²" + ) + if self.start_process_name is not None: + lines.append( + f" creation: {self.start_process_name} " + f"(id={self.start_process_id}, sub={self.start_subprocess_id})" + ) + return "\n".join(lines) + "\n" def __repr__(self): """ diff --git a/pysupera/diagnostics.py b/pysupera/diagnostics.py index 0b8e91d..2b4ced4 100644 --- a/pysupera/diagnostics.py +++ b/pysupera/diagnostics.py @@ -29,7 +29,7 @@ class MergeOutcome(Enum): One or both particle PDG codes are not in the condition's allowed set. DIFFERENT_ANCESTOR - The two particles have different ``ancestor_id`` values. + The two particles have different ``root_id`` values. NO_PARENT_CHILD_RELATION Neither particle is the direct parent of the other. WRONG_SEMID @@ -55,7 +55,7 @@ class MergeOutcome(Enum): NOT_IN_CANDIDATE_SET = "Pair was not in candidate set (failed metadata filter)" DIFFERENT_PDG = "Particles have different PDG values" WRONG_PDG_VALUE = "Particle PDG not in target set" - DIFFERENT_ANCESTOR = "Particles have different ancestor_id" + DIFFERENT_ANCESTOR = "Particles have different root_id" NO_PARENT_CHILD_RELATION = "Particles are not in parent-child relationship" WRONG_SEMID = "Particle SemID does not match required value" @@ -748,7 +748,7 @@ def _determine_metadata_rejection(self, p1: Particle, p2: Particle, stage: str) """ Diagnose which metadata property caused pair rejection at *stage*. - Checks parent-child relationship, PDG codes, ``ancestor_id``, and + Checks parent-child relationship, PDG codes, ``root_id``, and ``sem_type`` in turn and accumulates all violated conditions into a single human-readable string. @@ -773,8 +773,8 @@ def _determine_metadata_rejection(self, p1: Particle, p2: Particle, stage: str) reasons.append(f"different PDG ({p1.pdg} vs {p2.pdg})") # Check ancestor - if p1.ancestor_id != p2.ancestor_id: - reasons.append(f"different ancestor_id ({p1.ancestor_id} vs {p2.ancestor_id})") + if p1.root_id != p2.root_id: + reasons.append(f"different root_id ({p1.root_id} vs {p2.root_id})") # Check SemID if p1.sem_type != p2.sem_type: diff --git a/pysupera/io.py b/pysupera/io.py index 2026f46..1e392b8 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -20,7 +20,7 @@ /particles/ id (n_total_particles,) int32 parent_id (n_total_particles,) int32 - ancestor_id (n_total_particles,) int32 + root_id (n_total_particles,) int32 pdg (n_total_particles,) int32 parent_pdg (n_total_particles,) int32 process_type (n_total_particles,) int32 @@ -122,7 +122,7 @@ def write_events(path: str, # Particle-level scalars p_id = np.empty(n_total_particles, dtype=np.int32) p_parent_id = np.empty(n_total_particles, dtype=np.int32) - p_ancestor_id = np.empty(n_total_particles, dtype=np.int32) + p_root_id = np.empty(n_total_particles, dtype=np.int32) p_pdg = np.empty(n_total_particles, dtype=np.int32) p_parent_pdg = np.empty(n_total_particles, dtype=np.int32) p_proc_type = np.empty(n_total_particles, dtype=np.int32) @@ -135,7 +135,7 @@ def write_events(path: str, for p in ev: p_id[j] = p.id p_parent_id[j] = p.parent_id - p_ancestor_id[j] = p.ancestor_id + p_root_id[j] = p.root_id p_pdg[j] = p.pdg p_parent_pdg[j] = p.parent_pdg # process_type is stored as raw int (0-based, as passed to @@ -177,12 +177,12 @@ def write_events(path: str, if n_total_particles > 0: _mk("id", p_id) _mk("parent_id", p_parent_id) - _mk("ancestor_id", p_ancestor_id) + _mk("root_id", p_root_id) _mk("pdg", p_pdg) _mk("parent_pdg", p_parent_pdg) _mk("process_type", p_proc_type) else: - for name in ("id", "parent_id", "ancestor_id", + for name in ("id", "parent_id", "root_id", "pdg", "parent_pdg", "process_type"): pg.create_dataset(name, data=np.empty(0, dtype=np.int32)) pg.create_dataset("pc_offsets", data=pc_offsets) @@ -279,7 +279,7 @@ class EventWriter: # Names of the six particle scalar fields in order _SCALAR_FIELDS = ( - "id", "parent_id", "ancestor_id", "pdg", "parent_pdg", "process_type" + "id", "parent_id", "root_id", "pdg", "parent_pdg", "process_type" ) def __init__(self, path: str, mode: str = "w", @@ -365,7 +365,7 @@ def append_event(self, particles: List) -> None: # Build scalar arrays for this event p_id = np.empty(n_p, dtype=np.int32) p_parent_id = np.empty(n_p, dtype=np.int32) - p_ancestor_id = np.empty(n_p, dtype=np.int32) + p_root_id = np.empty(n_p, dtype=np.int32) p_pdg = np.empty(n_p, dtype=np.int32) p_parent_pdg = np.empty(n_p, dtype=np.int32) p_proc_type = np.empty(n_p, dtype=np.int32) @@ -374,7 +374,7 @@ def append_event(self, particles: List) -> None: for k, p in enumerate(particles): p_id[k] = p.id p_parent_id[k] = p.parent_id - p_ancestor_id[k] = p.ancestor_id + p_root_id[k] = p.root_id p_pdg[k] = p.pdg p_parent_pdg[k] = p.parent_pdg p_proc_type[k] = int(p._process_type) @@ -382,7 +382,7 @@ def append_event(self, particles: List) -> None: self._f["particles/id" ][new_p_start:new_p_end] = p_id self._f["particles/parent_id" ][new_p_start:new_p_end] = p_parent_id - self._f["particles/ancestor_id" ][new_p_start:new_p_end] = p_ancestor_id + self._f["particles/root_id" ][new_p_start:new_p_end] = p_root_id self._f["particles/pdg" ][new_p_start:new_p_end] = p_pdg self._f["particles/parent_pdg" ][new_p_start:new_p_end] = p_parent_pdg self._f["particles/process_type"][new_p_start:new_p_end] = p_proc_type @@ -587,7 +587,7 @@ def __getitem__(self, index: int) -> List: # ---- read scalar metadata in 6 contiguous array slices ------------ ids = self._f["particles/id" ][p_start:p_end] parent_ids = self._f["particles/parent_id" ][p_start:p_end] - ancestor_ids = self._f["particles/ancestor_id" ][p_start:p_end] + root_ids = self._f["particles/root_id" ][p_start:p_end] pdgs = self._f["particles/pdg" ][p_start:p_end] parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] proc_types = self._f["particles/process_type"][p_start:p_end] @@ -606,7 +606,7 @@ def __getitem__(self, index: int) -> List: p = Particle( id = int(ids[k]), parent_id = int(parent_ids[k]), - ancestor_id = int(ancestor_ids[k]), + root_id = int(root_ids[k]), pdg = int(pdgs[k]), parent_pdg = int(parent_pdgs[k]), process_type = int(proc_types[k]), diff --git a/pysupera/partitioner.py b/pysupera/partitioner.py index a5e9795..607b656 100644 --- a/pysupera/partitioner.py +++ b/pysupera/partitioner.py @@ -113,7 +113,7 @@ def _build_relationships(self): building: * ``children_map[parent_id]`` – list of direct child IDs. - * ``ancestor_map[ancestor_id]`` – list of all descendant IDs that + * ``ancestor_map[root_id]`` – list of all descendant IDs that share the same ancestor. """ self.children_map = defaultdict(list) @@ -122,8 +122,8 @@ def _build_relationships(self): for p in self.particles: if p.parent_id is not None: self.children_map[p.parent_id].append(p.id) - if p.ancestor_id is not None: - self.ancestor_map[p.ancestor_id].append(p.id) + if p.root_id is not None: + self.ancestor_map[p.root_id].append(p.id) def _create_backend(self, backend: str, n_jobs: int, **kwargs): """ @@ -751,7 +751,7 @@ def _build_partitions_incremental( # Build representative Particle objects: shallow copies whose point_cloud # will grow as partitions merge. All scalar attributes (id, pdg, sem_type, - # parent_id, ancestor_id, …) remain those of the original particle. + # parent_id, root_id, …) remain those of the original particle. reps: Dict[int, Particle] = {} for idx, p in enumerate(self.particles): rep = copy.copy(p) diff --git a/pysupera/partitioner2.py b/pysupera/partitioner2.py index 2fb86d3..fc7a87b 100644 --- a/pysupera/partitioner2.py +++ b/pysupera/partitioner2.py @@ -49,14 +49,14 @@ def __init__(self, def _build_relationships(self): """Build parent-child and ancestor lookup structures""" self.children_map = defaultdict(list) # parent_id -> [child_ids] - self.ancestor_map = defaultdict(list) # ancestor_id -> [particle_ids] + self.ancestor_map = defaultdict(list) # root_id -> [particle_ids] for p in self.particles: if p.parent_id is not None: self.children_map[p.parent_id].append(p.id) - if p.ancestor_id is not None: - self.ancestor_map[p.ancestor_id].append(p.id) + if p.root_id is not None: + self.ancestor_map[p.root_id].append(p.id) def _create_backend(self, backend: str, n_jobs: int) -> ProximityChecker: """Factory method to create appropriate backend""" @@ -79,7 +79,7 @@ def partition_condition_1(self, verbose: bool = True) -> List[List]: Condition 1: For particles with PDG value 22 or 11: - Identify immediate parent with the same PDG value - Merge if they are touching - - Never merge particles with different ancestor_id + - Never merge particles with different root_id Returns: List of partitions, where each partition is a list of Particle objects @@ -104,9 +104,9 @@ def partition_condition_1(self, verbose: bool = True) -> List[List]: if len(candidates) == 0: return [[p] for p in self.particles] - # Stage 2: Filter by ancestor_id (before expensive proximity checks) + # Stage 2: Filter by root_id (before expensive proximity checks) if verbose: - print("Stage 2: Filtering by ancestor_id...") + print("Stage 2: Filtering by root_id...") ancestor_filtered = self._filter_by_ancestor(candidates) @@ -166,14 +166,14 @@ def _build_candidates_condition_1(self) -> List[Tuple[int, int]]: return candidates def _filter_by_ancestor(self, candidates: List[Tuple[int, int]]) -> List[Tuple[int, int]]: - """Filter pairs to only include those with same ancestor_id""" + """Filter pairs to only include those with same root_id""" filtered = [] for id1, id2 in candidates: p1 = self.particle_lookup[id1] p2 = self.particle_lookup[id2] - if p1.ancestor_id == p2.ancestor_id: + if p1.root_id == p2.root_id: filtered.append((id1, id2)) return filtered diff --git a/pysupera/preproc.py b/pysupera/preproc.py index 1ebe108..117050a 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -201,7 +201,7 @@ def _split_fragments( new_p = Particle( id = next_id, parent_id = p.id, - ancestor_id = p.ancestor_id, + root_id = p.root_id, pdg = p.pdg, parent_pdg = p.parent_pdg, process_type = p._process_type, @@ -579,6 +579,233 @@ def process(self, particles: List[Particle], return particles +# ============================================================================ +# Voxelization +# ============================================================================ + +@dataclass +class VoxelizeRecord: + """ + Diagnostic record for one particle processed by + :meth:`VoxelizeProcessor.process`. + + Attributes + ---------- + particle_id : int + ID of the particle that was processed. + n_before : int + Number of points before voxelization. + n_after : int + Number of voxels (points after merging). + """ + particle_id : int + n_before : int + n_after : int + + @property + def n_merged(self) -> int: + """Number of points collapsed.""" + return self.n_before - self.n_after + + def __str__(self) -> str: + return ( + f" particle {self.particle_id:>8d} | " + f"{self.n_before:>6d} pts → {self.n_after:>6d} voxels " + f"({self.n_merged} merged)" + ) + + +def _voxelize_point_cloud( + pc: np.ndarray, + voxel_size: np.ndarray, + origin: np.ndarray | None, +) -> np.ndarray: + """ + Bin each point into a regular 3-D grid and merge points that share the + same voxel. + + Parameters + ---------- + pc : np.ndarray, shape (N, F), F >= 3 + Point-cloud array. Columns 0–2 are x, y, z. + voxel_size : np.ndarray, shape (3,) + Side lengths of each voxel cell along x, y, z. + origin : np.ndarray of shape (3,) or None + Lower corner from which voxel indices are computed. When ``None`` + the per-cloud minimum of the input coordinates is used so that the + grid is always tightly aligned to the data. + + Returns + ------- + np.ndarray, shape (M, F), M <= N + New array where each row represents one non-empty voxel. The + x, y, z columns hold the voxel-centre coordinates; remaining + feature columns are aggregated by the same rules as + :func:`_merge_point_cloud` (time=min, energy=sum, dedx=max). + Returns the original array unchanged when every voxel already + contains exactly one point. + """ + coords = pc[:, :3] + org = coords.min(axis=0) if origin is None else origin + + # Integer voxel indices for every point + idx = np.floor((coords - org) / voxel_size).astype(np.int64) + + # Unique voxels and group membership + unique_idx, inv = np.unique(idx, axis=0, return_inverse=True) + n_out = len(unique_idx) + + if n_out == len(pc): + return pc # already one point per voxel — no copy needed + + n_cols = pc.shape[1] + out = np.zeros((n_out, n_cols), dtype=pc.dtype) + + # Voxel-centre coordinates + out[:, :3] = (unique_idx + 0.5) * voxel_size + org + + for col, init_val, ufunc in _MERGE_RULES: + col = int(col) + if col >= n_cols: + continue + out[:, col] = init_val + ufunc.at(out[:, col], inv, pc[:, col]) + + return out + + +class VoxelizeProcessor: + """ + Pre-processing stage that bins each particle's point cloud onto a + regular 3-D grid and merges all points that fall into the same voxel. + + This is a strict generalisation of :class:`MergeDuplicatesProcessor`: + with an infinitesimally small ``voxel_size`` the two are equivalent. + When voxelization is enabled, running :class:`MergeDuplicatesProcessor` + afterwards is redundant. + + Voxel-centre positions + ~~~~~~~~~~~~~~~~~~~~~~ + For a point at coordinate ``x`` the voxel index is:: + + i = floor( (x - origin_x) / voxel_size_x ) + + and its centre is placed at:: + + x_centre = (i + 0.5) * voxel_size_x + origin_x + + Feature aggregation (columns beyond index 2) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Identical to :class:`MergeDuplicatesProcessor`: + time → min, energy → sum, dedx → max. + + Parameters + ---------- + voxel_size : float or sequence of float + Voxel side length. A single float gives an isotropic grid; + a 3-element sequence ``[dx, dy, dz]`` gives an anisotropic grid. + origin : sequence of float or None, optional + Lower corner ``[x0, y0, z0]`` from which voxel indices are + computed. ``None`` (default) uses the per-cloud coordinate + minimum so that the grid is tightly aligned to each particle's + data independently. Use a fixed value when a globally consistent + grid is needed (e.g. detector boundaries). + verbose : bool, optional + When ``True``, :meth:`process` prints one line per affected + particle and an end-of-batch summary. Default ``False``. + """ + + def __init__( + self, + voxel_size: float | list, + origin: list | None = None, + verbose: bool = False, + ) -> None: + vs = np.asarray(voxel_size, dtype=float) + if vs.ndim == 0: + vs = np.broadcast_to(vs, (3,)).copy() + if vs.shape != (3,): + raise ValueError( + f"voxel_size must be a scalar or a 3-element sequence, " + f"got shape {vs.shape}." + ) + if np.any(vs <= 0): + raise ValueError(f"All voxel_size values must be > 0, got {vs}.") + self.voxel_size: np.ndarray = vs + + if origin is not None: + org = np.asarray(origin, dtype=float) + if org.shape != (3,): + raise ValueError( + f"origin must be a 3-element sequence or None, " + f"got shape {org.shape}." + ) + self.origin: np.ndarray | None = org + else: + self.origin = None + + self.verbose = bool(verbose) + #: :class:`VoxelizeRecord` list from the most recent :meth:`process` + #: call. Only particles that had at least one merge are recorded. + self.last_diagnostics: List[VoxelizeRecord] = [] + + def process( + self, + particles: List[Particle], + verbose: Optional[bool] = None, + ) -> List[Particle]: + """ + Voxelize every particle's point cloud in-place. + + Parameters + ---------- + particles : list of Particle + verbose : bool or None, optional + Per-call override of the instance *verbose* flag. + + Returns + ------- + list of Particle + """ + be_verbose = self.verbose if verbose is None else bool(verbose) + self.last_diagnostics = [] + + n_total_before = 0 + n_total_after = 0 + n_affected = 0 + + for p in particles: + pc = p.point_cloud + n_before = len(pc) + merged = _voxelize_point_cloud(pc, self.voxel_size, self.origin) + n_after = len(merged) + + n_total_before += n_before + n_total_after += n_after + + if n_after < n_before: + p.point_cloud = merged + n_affected += 1 + rec = VoxelizeRecord( + particle_id = p.id, + n_before = n_before, + n_after = n_after, + ) + self.last_diagnostics.append(rec) + if be_verbose: + print(rec) + + if be_verbose: + print( + f"[voxelize] {len(particles)} particles | " + f"{n_affected} affected | " + f"{n_total_before - n_total_after} points merged | " + f"{n_total_before} \u2192 {n_total_after} total pts" + ) + + return particles + + # ============================================================================ # Backend: scipy (CPU, default) # ============================================================================ diff --git a/pysupera/readers/__init__.py b/pysupera/readers/__init__.py new file mode 100644 index 0000000..ea9adc8 --- /dev/null +++ b/pysupera/readers/__init__.py @@ -0,0 +1,38 @@ +""" +pysupera.readers +================ + +File-format readers that produce lists of :class:`~pysupera.data.Particle` +objects from simulation output files. + +Available readers +----------------- +:class:`EDepSimHDF5Reader` + Reads EDepSim HDF5 output (``particle/geant4``, ``pstep/lar_vol``, + ``ass/particle_pstep_lar_vol`` datasets). + +All readers implement the :class:`EventReaderBase` interface which supports: +- ``len(reader)`` — number of events +- ``reader[i]`` — event by index (negative indices supported) +- ``for particles in reader`` — iteration over all events +- ``with EDepSimHDF5Reader(path) as reader`` — context-manager usage + +Example +------- +:: + + from pysupera.readers import EDepSimHDF5Reader + + with EDepSimHDF5Reader("sim.h5") as reader: + for event_particles in reader: + # event_particles is a list[Particle] + ... +""" + +from .base import EventReaderBase +from .format_edepsim_h5 import EDepSimHDF5Reader + +__all__ = [ + "EventReaderBase", + "EDepSimHDF5Reader", +] diff --git a/pysupera/readers/base.py b/pysupera/readers/base.py new file mode 100644 index 0000000..917baaf --- /dev/null +++ b/pysupera/readers/base.py @@ -0,0 +1,74 @@ +""" +Abstract base class for all pysupera foreign-format event readers. + +Every reader should subclass :class:`EventReaderBase` and implement +``__len__``, ``__getitem__``, and ``close``. The base class provides the +context-manager protocol and ``__iter__`` for free. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Iterator + +from ..data import Particle + + +class EventReaderBase(ABC): + """ + Abstract base class for readers that translate a foreign file format into + lists of pysupera :class:`~pysupera.data.Particle` objects. + + Concrete subclasses must implement :meth:`__len__`, :meth:`__getitem__`, + and :meth:`close`. All other methods are provided by this base. + + The interface is intentionally identical to the native + :func:`pysupera.io.read_events` store so downstream code (e.g. the + visualisation app) can consume any source transparently:: + + with SomeReader("data.ext") as store: + particles = store[0] # one event + for particles in store: # iterate all events + ... + """ + + # ------------------------------------------------------------------ # + # Context-manager protocol # + # ------------------------------------------------------------------ # + + def __enter__(self) -> "EventReaderBase": + return self + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + self.close() + + # ------------------------------------------------------------------ # + # Abstract interface # + # ------------------------------------------------------------------ # + + @abstractmethod + def __len__(self) -> int: + """Return the total number of events in the file.""" + + @abstractmethod + def __getitem__(self, index: int) -> list[Particle]: + """ + Return the list of :class:`~pysupera.data.Particle` objects for + event *index* (0-based). + """ + + @abstractmethod + def close(self) -> None: + """Release any open file handles or other resources.""" + + # ------------------------------------------------------------------ # + # Provided # + # ------------------------------------------------------------------ # + + def __iter__(self) -> Iterator[list[Particle]]: + """Iterate over all events in order.""" + for i in range(len(self)): + yield self[i] + + def __repr__(self) -> str: + return f"{type(self).__name__}(n_events={len(self)})" diff --git a/pysupera/readers/example_edepsim_h5.py b/pysupera/readers/example_edepsim_h5.py new file mode 100644 index 0000000..a657a29 --- /dev/null +++ b/pysupera/readers/example_edepsim_h5.py @@ -0,0 +1,251 @@ +from pysupera.utils import InteractionType +from enum import Enum, auto + +class G4ProcessType(Enum): + kProcessNotDefined = 0 + kProcessTransportation = 1 + kProcessElectromagnetic = 2 + kProcessOptical = 3 + kProcessHadronic = 4 + kProcessPhotoLeptonHadron = 5 + kProcessDecay = 6 + kProcessGeneral = 7 + kProcessParameterization = 8 + kProcessUserDefined = 9 + +class G4ProcessSubtype(Enum): + kSubtypeEMCoulombScattering = 1 + kSubtypeEMIonization = 2 + kSubtypeEMBremsstrahlung = 3 + kSubtypeEMPairProdByCharged = 4 + kSubtypeEMNuclearStopping = 8 + # EM subtypes for photons + kSubtypeEMMultipleScattering = 10 + kSubtypeEMPhotoelectric = 12 + kSubtypeEMComptonScattering = 13 + kSubtypeEMGammaConversion = 14 + # Hadronic subtypes + kSubtypeHadronElastic = 111 + kSubtypeHadronInelastic = 121 + kSubtypeHadronCapture = 131 + kSubtypeHadronChargeExchange = 161 + + # General subtypes + kSubtypeGeneralStepLimit = 401 + + +def search_parents(A, B): + + sorter = np.argsort(A) + idx = np.searchsorted(A, B, sorter=sorter) + idx_clipped = np.clip(idx, 0, len(A) - 1) + valid = A[sorter[idx_clipped]] == B + return np.where(valid, sorter[idx_clipped], -1) + + +def get_parent_pdg(parts): + + parent_locs = search_parents(parts['track_id'],parts['parent_track_id']) + parent_valid = parent_locs != -1 + parent_zfill = np.where(parent_valid, parent_locs, 0) + return np.where(parent_valid, parts['pdg'][parent_zfill], 0) + +def get_interaction_type(parts,electron_energy_threshold=0.05): + track_id = parts['track_id'] + parent_track_id = parts['parent_track_id'] + pdg = parts['pdg'] + parent_pdg = get_parent_pdg(parts) + proc_start = parts['proc_start'] + subproc_start = parts['subproc_start'] + ke = parts['ke'] + + parent_locs = search_parents(parts['track_id'],parts['parent_track_id']) + parent_valid = parent_locs != -1 + parent_zfill = np.where(parent_valid, parent_locs, 0) + #parent_track_id = np.where(parent_valid, parts['track_id'][parent_zfill], -1) + + xs, ys, zs = parts['x'], parts['y'], parts['z'] + dx = xs[parent_zfill] - xs + dy = ys[parent_zfill] - ys + dz = zs[parent_zfill] - zs + dr = np.where(parent_valid, np.sqrt(dx**2 + dy**2 + dz**2), -1.0) + + itype = [InteractionType.kInvalidProcess]*len(parts) + + conditions = [(pdg == 2112), + (pdg > 1000000000), + (track_id == parent_track_id), + (pdg == 22), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic) & (subproc_start == G4ProcessSubtype.kSubtypeEMPhotoelectric), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic) & (subproc_start == G4ProcessSubtype.kSubtypeEMComptonScattering), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic) & ((subproc_start == G4ProcessSubtype.kSubtypeEMGammaConversion) | + (subproc_start == G4ProcessSubtype.kSubtypeEMPairProdByCharged)), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic) & (subproc_start == G4ProcessSubtype.kSubtypeEMIonization) & (abs(parent_pdg) == 22), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic) & (subproc_start == G4ProcessSubtype.kSubtypeEMIonization) & (np.isin(abs(parent_pdg),[211,13,2212,321])), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic) & (subproc_start == G4ProcessSubtype.kSubtypeEMIonization) & (abs(parent_pdg) == 22), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic) & (subproc_start == G4ProcessSubtype.kSubtypeEMIonization), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessElectromagnetic), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessDecay), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessHadronic) & (subproc_start == 151) & (dr<0.0001) & (ke < electron_energy_threshold), + (abs(pdg) == 11) & (proc_start == G4ProcessType.kProcessHadronic) & (subproc_start == 151) & (dr<0.0001), + (abs(pdg) == 11) & (ke < electron_energy_threshold), + (abs(pdg) == 11), + ~(abs(pdg) == 11)] + + choices=[InteractionType.kNeutron, + InteractionType.kNucleus, + InteractionType.kPrimary, + InteractionType.kPhoton, + InteractionType.kPhotoElectron, + InteractionType.kCompton, + InteractionType.kConversion, + InteractionType.kIonization, + InteractionType.kDelta, + InteractionType.kCompton, + InteractionType.kIonization, + InteractionType.kInvalidProcess, + InteractionType.kDecay, + InteractionType.kIonization, + InteractionType.kDecay, + InteractionType.kCompton, + InteractionType.kOtherShower, + InteractionType.kTrack] + + return np.select(conditions, choices, default=InteractionType.kInvalidProcess) + +def get_interaction_type_slow(parts, electron_energy_threshold=0.05): + + track_id = parts['track_id'] + parent_id = parts['parent_track_id'] + pdg = parts['pdg'] + proc_start = parts['proc_start'] + sproc_start = parts['subproc_start'] + ke = parts['ke'] + + parent_locs = search_parents(parts['track_id'],parts['parent_track_id']) + parent_valid = parent_locs != -1 + parent_zfill = np.where(parent_valid, parent_locs, 0) + parent_pdg = np.where(parent_valid, parts['pdg'][parent_zfill], 0) + parent_track_id = np.where(parent_valid, parts['track_id'][parent_zfill], -1) + + xs, ys, zs = parts['x'], parts['y'], parts['z'] + dx = xs[parent_zfill] - xs + dy = ys[parent_zfill] - ys + dz = zs[parent_zfill] - zs + dr = np.where(parent_valid, np.sqrt(dx**2 + dy**2 + dz**2), -1.0) + + itype = [InteractionType.kInvalidProcess]*len(parts) + + for idx in range(len(parts)): + + if pdg[idx] == 2112: + itype[idx] = InteractionType.kNeutron + + elif pdg[idx] > 1000000000: + itype[idx] = InteractionType.kNucleus + + elif track_id[idx] == parent_track_id[idx]: + itype[idx] = InteractionType.kPrimary + + elif pdg[idx] == 22: + itype[idx] = InteractionType.kPhoton + + elif abs(pdg[idx]) == 11: + + if proc_start[idx] == G4ProcessType.kProcessElectromagnetic: + + if subproc_start[idx] == G4ProcessSubtype.kSubtypeEMPhotoelectric: + itype[idx] = InteractionType.kPhotoElectron + + elif subproc_start[idx] == G4ProcessSubtype.kSubtypeEMComptonScattering: + itype[idx] = InteractionType.kCompton + + elif subproc_start[idx] == G4ProcessSubtype.kSubtypeEMComptonScattering: + itype[idx] = InteractionType.kConversion + + elif subproc_start[idx] == G4ProcessSubtype.kSubtypeEMPairProdByCharged: + itype[idx] = InteractionType.kConversion + + elif subproc_start[idx] == G4ProcessSubtype.kSubtypeEMPairProdByCharged: + + if abs(parent_pdg[idx]) == 11: + itype[idx] = InteractionType.kIonization + + elif abs(parent_pdg[idx]) in [211,13,2212,321]: + itype[idx] = InteractionType.kDelta + + elif parent_pdg[idx] == 22: + itype[idx] = InteractionType.kCompton + + else: + itype[idx] = InteractionType.kIonization + + else: + raise ValueError + + elif proc_start[idx] == G4ProcessType.kProcessDecay: + itype[idx] = InteractionType.kDecay + + elif proc_start[idx] == G4ProcessType.kProcessHadronic and subproc_start[idx] == 151 and dr<0.0001: + if ke[idx] < electron_energy_threshold: + itype[idx] = InteractionType.kIonization + else: + itype[idx] = InteractionType.kDecay + + else: + if ke[idx] < electron_energy_threshold: + itype[idx] = InteractionType.kCompton + else: + itype[idx] = InteractionType.kOtherShower + else: + itype[idx] = InteractionType.kTrack + + return itype + + +from pysupera.data import Particle +import numpy as np +import time + +PARTICLE_KEY='particle/geant4' +STEP_KEY='pstep/lar_vol' +ASS_KEY='ass/particle_pstep_lar_vol' +ELECTRON_ENERGY_THRESHOLD=0.05 +Particle.time=0. +def read(fname='out_0100.h5',entries=None): + local_time = 0. + with h5.File(fname,'r') as f: + + print(f.keys()) + + if entries is None: + entries = np.arange(len(f[PARTICLE_KEY])) + + parts_list=[] + + for entry in entries: + t0=time.time() + parts = f[PARTICLE_KEY][entry] + steps = f[STEP_KEY][entry] + ass = f[ASS_KEY][entry] + itype = np.array([t.value for t in get_interaction_type(parts,ELECTRON_ENERGY_THRESHOLD)],dtype=np.int32) + num_parts = len(parts) + local_time += time.time()-t0 + pysupera_parts = Particle.from_flat_arrays(parts['track_id'], + parts['parent_track_id'], + parts['ancestor_track_id'], + parts['pdg'], + get_parent_pdg(parts), + itype, + steps, + np.column_stack([ass['start'][:num_parts], + ass['end' ][:num_parts]]), + ) + parts_list.append(pysupera_parts) + print(local_time) + return parts_list + +import time +t0=time.time() +parts = read() +print(time.time()-t0,Particle.time) \ No newline at end of file diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py new file mode 100644 index 0000000..448c112 --- /dev/null +++ b/pysupera/readers/format_edepsim_h5.py @@ -0,0 +1,447 @@ +""" +Reader for EDepSim HDF5 output files. + +EDepSim (https://github.com/ClarkMcGrew/edep-sim) stores Geant4 simulation +output as structured arrays inside an HDF5 file. This module translates +that layout into lists of pysupera :class:`~pysupera.data.Particle` objects. + +Expected HDF5 layout +-------------------- +``particle/geant4`` — structured array, one entry per event. Each entry is + a particle-level array with fields: + + ============= ====================================================== + Field Description + ============= ====================================================== + track_id Geant4 track ID (used as pysupera *id*) + parent_track_id Immediate parent track ID + ancestor_track_id Root ancestor track ID + pdg PDG Monte Carlo particle code + proc_start G4 process type integer at particle start vertex + subproc_start G4 process subtype integer at particle start vertex + ke Kinetic energy at start vertex (MeV) + x, y, z Particle start position components + ============= ====================================================== + +``pstep/lar_vol`` — flat array of all energy-deposit steps (point cloud) for + all particles in each event. Columns 0-2 must be x, y, z. + +``ass/particle_pstep_lar_vol`` — association array, one entry per event. + Fields ``start`` and ``end`` store the slice ``[start[i], end[i])`` + into the step array that belongs to particle *i*. + +Usage +----- +:: + + from pysupera.readers import EDepSimHDF5Reader + + with EDepSimHDF5Reader("out_0100.h5") as store: + print(len(store), "events") + particles = store[0] # first event + for particles in store: # iterate all events + ... +""" + +from __future__ import annotations + +import numpy as np +from enum import IntEnum + +from .base import EventReaderBase +from ..data import Particle + + +# --------------------------------------------------------------------------- +# Geant4 process / subprocess enumerations +# --------------------------------------------------------------------------- +# IntEnum is used deliberately so that comparisons with numpy integer arrays +# work element-wise: ``proc_start_array == G4ProcessType.kProcessDecay`` +# correctly produces a boolean numpy array. + +class G4ProcessType(IntEnum): + kProcessNotDefined = 0 + kProcessTransportation = 1 + kProcessElectromagnetic = 2 + kProcessOptical = 3 + kProcessHadronic = 4 + kProcessPhotoLeptonHadron = 5 + kProcessDecay = 6 + kProcessGeneral = 7 + kProcessParameterization = 8 + kProcessUserDefined = 9 + + +class G4ProcessSubtype(IntEnum): + kSubtypeEMCoulombScattering = 1 + kSubtypeEMIonization = 2 + kSubtypeEMBremsstrahlung = 3 + kSubtypeEMPairProdByCharged = 4 + kSubtypeEMNuclearStopping = 8 + kSubtypeEMMultipleScattering = 10 + kSubtypeEMPhotoelectric = 12 + kSubtypeEMComptonScattering = 13 + kSubtypeEMGammaConversion = 14 + kSubtypeHadronElastic = 111 + kSubtypeHadronInelastic = 121 + kSubtypeHadronCapture = 131 + kSubtypeHadronChargeExchange = 161 + kSubtypeGeneralStepLimit = 401 + + +# --------------------------------------------------------------------------- +# Default HDF5 dataset keys +# --------------------------------------------------------------------------- + +_DEFAULT_PARTICLE_KEY = "particle/geant4" +_DEFAULT_STEP_KEY = "pstep/lar_vol" +_DEFAULT_ASS_KEY = "ass/particle_pstep_lar_vol" +_DEFAULT_ELECTRON_ENERGY_THRESHOLD = 0.05 # MeV + + +# --------------------------------------------------------------------------- +# Internal helper functions +# --------------------------------------------------------------------------- + +def _search_parents(track_ids: np.ndarray, + parent_track_ids: np.ndarray) -> np.ndarray: + """ + For each element in *parent_track_ids*, return the index of the matching + entry in *track_ids*, or ``-1`` if not found. + + Parameters + ---------- + track_ids : ndarray of int, shape (N,) + The full set of track IDs in this event. + parent_track_ids : ndarray of int, shape (N,) + The parent track ID of each particle. + + Returns + ------- + ndarray of int, shape (N,) + ``result[i]`` is the index *j* such that + ``track_ids[j] == parent_track_ids[i]``, or ``-1`` if absent. + """ + sorter = np.argsort(track_ids) + idx = np.searchsorted(track_ids, parent_track_ids, sorter=sorter) + idx_clipped = np.clip(idx, 0, len(track_ids) - 1) + valid = track_ids[sorter[idx_clipped]] == parent_track_ids + return np.where(valid, sorter[idx_clipped], -1) + + +def _get_parent_pdg(parts: np.ndarray) -> np.ndarray: + """ + Look up the PDG code of each particle's direct parent. + + Returns 0 for particles whose parent is not present in *parts* + (e.g. primary particles). + + Parameters + ---------- + parts : structured ndarray + Must contain fields ``track_id``, ``parent_track_id``, ``pdg``. + + Returns + ------- + ndarray of int, shape (N,) + """ + parent_locs = _search_parents(parts["track_id"], parts["parent_track_id"]) + parent_valid = parent_locs != -1 + parent_zfill = np.where(parent_valid, parent_locs, 0) + return np.where(parent_valid, parts["pdg"][parent_zfill], 0) + + +def _get_interaction_type(parts: np.ndarray, + electron_energy_threshold: float = 0.05 + ) -> np.ndarray: + """ + Derive the pysupera :class:`~pysupera.utils.InteractionType` integer + for each particle using Geant4 process information. + + The logic mirrors the ``get_interaction_type`` function in the example + notebook. ``np.select`` is used instead of a Python loop for + performance. + + Parameters + ---------- + parts : structured ndarray + Must contain fields: ``track_id``, ``parent_track_id``, ``pdg``, + ``proc_start``, ``subproc_start``, ``ke``, ``x``, ``y``, ``z``. + electron_energy_threshold : float, optional + Kinetic-energy threshold (MeV) separating kCompton-like low-energy + electrons from kOtherShower-like higher-energy electrons when no + cleaner process classification is available. + + Returns + ------- + ndarray of int32, shape (N,) + Raw ``InteractionType.value`` integers, ready to be passed as + ``process_types`` to :meth:`~pysupera.data.Particle.from_flat_arrays`. + """ + from ..utils import InteractionType + + track_id = parts["track_id"] + parent_tid = parts["parent_track_id"] + pdg = parts["pdg"] + proc_start = parts["proc_start"] + subproc_start = parts["subproc_start"] + ke = parts["ke"] + parent_pdg = _get_parent_pdg(parts) + + parent_locs = _search_parents(track_id, parent_tid) + parent_valid = parent_locs != -1 + parent_zfill = np.where(parent_valid, parent_locs, 0) + + xs, ys, zs = parts["x"], parts["y"], parts["z"] + dx = xs[parent_zfill] - xs + dy = ys[parent_zfill] - ys + dz = zs[parent_zfill] - zs + dr = np.where(parent_valid, np.sqrt(dx**2 + dy**2 + dz**2), -1.0) + + # Shorthands + EM = G4ProcessType.kProcessElectromagnetic + DK = G4ProcessType.kProcessDecay + HAD = G4ProcessType.kProcessHadronic + PE = G4ProcessSubtype.kSubtypeEMPhotoelectric + CO = G4ProcessSubtype.kSubtypeEMComptonScattering + GC = G4ProcessSubtype.kSubtypeEMGammaConversion + PP = G4ProcessSubtype.kSubtypeEMPairProdByCharged + ION = G4ProcessSubtype.kSubtypeEMIonization + _HAD151 = 151 # hadronic sub-process 151 (nuclear de-excitation / gammas) + + is_electron = abs(pdg) == 11 + + conditions = [ + pdg == 2112, + pdg > 1_000_000_000, + track_id == parent_tid, + pdg == 22, + # EM electrons — ordered from most specific to least specific + is_electron & (proc_start == EM) & (subproc_start == PE), + is_electron & (proc_start == EM) & (subproc_start == CO), + is_electron & (proc_start == EM) & ((subproc_start == GC) | + (subproc_start == PP)), + is_electron & (proc_start == EM) & (subproc_start == ION) & (abs(parent_pdg) == 22), + is_electron & (proc_start == EM) & (subproc_start == ION) & np.isin(abs(parent_pdg), [211, 13, 2212, 321]), + is_electron & (proc_start == EM) & (subproc_start == ION), + is_electron & (proc_start == EM), + is_electron & (proc_start == DK), + is_electron & (proc_start == HAD) & (subproc_start == _HAD151) & (dr < 1e-4) & (ke < electron_energy_threshold), + is_electron & (proc_start == HAD) & (subproc_start == _HAD151) & (dr < 1e-4), + is_electron & (ke < electron_energy_threshold), + is_electron, + ~is_electron, + ] + + choices = [ + InteractionType.kNeutron, + InteractionType.kNucleus, + InteractionType.kPrimary, + InteractionType.kPhoton, + InteractionType.kPhotoElectron, + InteractionType.kCompton, + InteractionType.kConversion, + InteractionType.kIonization, + InteractionType.kDelta, + InteractionType.kIonization, + InteractionType.kInvalidProcess, + InteractionType.kDecay, + InteractionType.kIonization, + InteractionType.kDecay, + InteractionType.kCompton, + InteractionType.kOtherShower, + InteractionType.kTrack, + ] + + # np.select returns the first matching condition; convert enum → int32 + result = np.select(conditions, choices, + default=InteractionType.kInvalidProcess) + return np.array([v.value for v in result], dtype=np.int32) + + +# --------------------------------------------------------------------------- +# Reader class +# --------------------------------------------------------------------------- + +class EDepSimHDF5Reader(EventReaderBase): + """ + Read EDepSim HDF5 output and produce lists of pysupera + :class:`~pysupera.data.Particle` objects. + + Parameters + ---------- + path : str + Path to the HDF5 file produced by EDepSim. + particle_key : str, optional + HDF5 dataset key for the per-event particle arrays. + Default: ``"particle/geant4"``. + step_key : str, optional + HDF5 dataset key for the flat energy-deposit step arrays. + Default: ``"pstep/lar_vol"``. + ass_key : str, optional + HDF5 dataset key for the particle-to-step association arrays. + Default: ``"ass/particle_pstep_lar_vol"``. + electron_energy_threshold : float, optional + Kinetic-energy threshold (MeV) used by the interaction-type + classifier to separate low-energy from high-energy electrons when + no cleaner process tag is available. Default: ``0.05``. + min_pc_size : int or None, optional + Forwarded to :class:`~pysupera.data.Particle`. ``None`` (default) + uses the module-level ``_DEFAULT_MIN_PC_SIZE``. + + Examples + -------- + :: + + with EDepSimHDF5Reader("out_0100.h5") as reader: + print(len(reader), "events") + particles = reader[0] + for particles in reader: + ... + """ + + def __init__( + self, + path: str, + *, + particle_key: str = _DEFAULT_PARTICLE_KEY, + step_key: str = _DEFAULT_STEP_KEY, + ass_key: str = _DEFAULT_ASS_KEY, + electron_energy_threshold: float = _DEFAULT_ELECTRON_ENERGY_THRESHOLD, + min_pc_size: int | None = None, + ) -> None: + import h5py + self._path = path + self._part_key = particle_key + self._step_key = step_key + self._ass_key = ass_key + self._e_thresh = electron_energy_threshold + self._min_pc_size = min_pc_size + self._file = h5py.File(path, "r") + self._n_events = len(self._file[self._part_key]) + + # ------------------------------------------------------------------ # + # EventReaderBase interface # + # ------------------------------------------------------------------ # + + def __len__(self) -> int: + return self._n_events + + def __getitem__(self, index: int) -> list[Particle]: + """ + Load and return the particle list for event *index*. + + Parameters + ---------- + index : int + 0-based event index. Negative indices are supported + (e.g. ``reader[-1]`` returns the last event). + + Returns + ------- + list of Particle + + Raises + ------ + IndexError + If *index* is out of range. + """ + n = self._n_events + if index < -n or index >= n: + raise IndexError( + f"Event index {index} out of range for file with {n} events." + ) + if index < 0: + index += n + + parts = self._file[self._part_key][index] + steps = self._file[self._step_key][index] + ass = self._file[self._ass_key][index] + + num_parts = len(parts) + itype = _get_interaction_type(parts, self._e_thresh) + offsets = np.column_stack([ + ass["start"][:num_parts], + ass["end"][:num_parts], + ]) + + return Particle.from_flat_arrays( + ids = parts["track_id"], + parent_ids = parts["parent_track_id"], + root_ids = parts["ancestor_track_id"], + pdgs = parts["pdg"], + parent_pdgs = _get_parent_pdg(parts), + process_types = itype, + point_cloud_flat = steps, + point_cloud_offsets = offsets, + min_pc_size = self._min_pc_size, + ) + + def close(self) -> None: + """Close the underlying HDF5 file.""" + if self._file: + self._file.close() + self._file = None + + # ------------------------------------------------------------------ # + # Convenience # + # ------------------------------------------------------------------ # + + @classmethod + def from_config(cls, cfg) -> "EDepSimHDF5Reader": + """ + Construct an :class:`EDepSimHDF5Reader` from a Hydra/OmegaConf config. + + The file path is read from ``cfg.io.input_path``; all reader-specific + parameters come from ``cfg.reader``. + + Parameters + ---------- + cfg : DictConfig + Full Hydra config as returned by + :func:`~pysupera.config.load_cfg`. Must contain at minimum + ``cfg.io.input_path`` and ``cfg.reader``. + + Returns + ------- + EDepSimHDF5Reader + + Examples + -------- + :: + + from pysupera.config import load_cfg + from pysupera.readers import EDepSimHDF5Reader + + cfg = load_cfg(["io.input_path=/data/sim.h5", + "reader.step_key=pstep/tpc_vol"]) + with EDepSimHDF5Reader.from_config(cfg) as reader: + particles = reader[0] + """ + r = cfg.reader + return cls( + path = str(cfg.io.input_path), + particle_key = str(r.get("particle_key", + _DEFAULT_PARTICLE_KEY)), + step_key = str(r.get("step_key", + _DEFAULT_STEP_KEY)), + ass_key = str(r.get("ass_key", + _DEFAULT_ASS_KEY)), + electron_energy_threshold = float(r.get( + "electron_energy_threshold", + _DEFAULT_ELECTRON_ENERGY_THRESHOLD)), + min_pc_size = int(cfg.particle.get("min_pc_size", -1)) + if hasattr(cfg, "particle") else None, + ) + + @property + def path(self) -> str: + """Path of the open HDF5 file.""" + return self._path + + def __repr__(self) -> str: + return ( + f"EDepSimHDF5Reader(" + f"path={self._path!r}, " + f"n_events={self._n_events})" + ) diff --git a/pysupera/utils.py b/pysupera/utils.py index 1289061..35cfee2 100644 --- a/pysupera/utils.py +++ b/pysupera/utils.py @@ -216,7 +216,7 @@ def _row(p, tag=""): return _fmt_particle.format( id=p.id, pdg=p.pdg, sem=p.sem_type.name, - parent=p.parent_id, anc=p.ancestor_id, + parent=p.parent_id, anc=p.root_id, pc=len(p.point_cloud), ) + (f" [{tag}]" if tag else "") @@ -306,7 +306,7 @@ def SetSemanticType(process_type, pdg, parent_pdg, point_cloud, point_cloud_size ``InteractionType`` is encountered. """ - process_type = InteractionType(process_type+1) + process_type = InteractionType(process_type) if process_type == InteractionType.kInvalidProcess: return SemanticType.kUnknown raise Exception("'kInvalidProcess' particle process encountered\n") diff --git a/tests/conftest.py b/tests/conftest.py index 2183097..989b68d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -42,15 +42,15 @@ def make_particle( pdg: int = 11, parent_pdg: int = 0, parent_id: int | None = None, - ancestor_id: int | None = None, + root_id: int | None = None, pc: np.ndarray | None = None, n_pts: int = 10, offset: tuple = (0.0, 0.0, 0.0), ) -> Particle: if parent_id is None: parent_id = pid - if ancestor_id is None: - ancestor_id = pid + if root_id is None: + root_id = pid if pc is None: rng = np.random.default_rng(pid) xyz = rng.uniform(0, 0.1, size=(n_pts, 3)).astype(np.float32) @@ -59,7 +59,7 @@ def make_particle( return Particle( id = pid, parent_id = parent_id, - ancestor_id = ancestor_id, + root_id = root_id, pdg = pdg, parent_pdg = parent_pdg, process_type = process_type, diff --git a/tests/test_conditions.py b/tests/test_conditions.py index 151cd5e..05bf0bf 100644 --- a/tests/test_conditions.py +++ b/tests/test_conditions.py @@ -40,9 +40,9 @@ class TestPhotonDecay: def test_electron_child_of_photon_merges(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0, 0, 0)) prt = _part(photon, electron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -51,9 +51,9 @@ def test_electron_child_of_photon_merges(self): def test_positron_child_of_photon_merges(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) positron = make_particle(2, PT_PRIMARY, pdg=-11, parent_pdg=22, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0.1, 0, 0)) prt = _part(photon, positron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -62,11 +62,11 @@ def test_positron_child_of_photon_merges(self): def test_both_decay_products_in_same_partition(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) positron = make_particle(3, PT_PRIMARY, pdg=-11, parent_pdg=22, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0.2, 0, 0)) prt = _part(photon, electron, positron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -76,10 +76,10 @@ def test_both_decay_products_in_same_partition(self): def test_non_photon_parent_no_merge(self): # parent is a track (parent_pdg=13, not 22) → no PhotonDecay merge track = make_particle(1, PT_TRACK, pdg=13, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0, 0, 0)) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=13, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(50, 0, 0)) # far away prt = _part(track, electron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -88,9 +88,9 @@ def test_non_photon_parent_no_merge(self): def test_result_has_fewer_or_equal_partitions_than_particles(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) electron = make_particle(2, PT_PRIMARY, pdg=11, - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) prt = _part(photon, electron) parts = prt.partition(PhotonDecay(), verbose=False) assert len(parts) <= 2 @@ -103,10 +103,10 @@ def test_result_has_fewer_or_equal_partitions_than_particles(self): class TestTouchingEMShower: def test_touching_parent_child_electrons_merge(self): parent = make_particle(1, PT_PRIMARY, pdg=11, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0, 0, 0)) child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0.05, 0, 0)) # very close → touching prt = _part(parent, child) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -114,11 +114,11 @@ def test_touching_parent_child_electrons_merge(self): def test_different_ancestor_no_merge(self): parent = make_particle(1, PT_PRIMARY, pdg=11, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0, 0, 0)) - # child has different ancestor_id → TouchingEMShower filter rejects + # child has different root_id → TouchingEMShower filter rejects child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, ancestor_id=99, # different ancestor + parent_id=1, root_id=99, # different ancestor offset=(0.05, 0, 0)) prt = _part(parent, child) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -126,10 +126,10 @@ def test_different_ancestor_no_merge(self): def test_non_touching_parent_child_not_merged(self): parent = make_particle(1, PT_PRIMARY, pdg=11, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0, 0, 0)) child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(100, 0, 0)) # far away prt = _part(parent, child, D=5.0) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -138,10 +138,10 @@ def test_non_touching_parent_child_not_merged(self): def test_track_particle_not_included_as_candidate(self): # A track (pdg=13) parent should not produce EM shower candidates parent = make_particle(1, PT_TRACK, pdg=13, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0, 0, 0)) child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0.05, 0, 0)) prt = _part(parent, child) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -155,10 +155,10 @@ def test_track_particle_not_included_as_candidate(self): class TestCombineLEScatters: def test_two_touching_le_scatters_merge(self): le1 = make_particle(1, PT_IONIZATION, pdg=11, - parent_id=1, ancestor_id=1, + parent_id=1, root_id=1, offset=(0, 0, 0)) le2 = make_particle(2, PT_IONIZATION, pdg=11, - parent_id=2, ancestor_id=2, + parent_id=2, root_id=2, offset=(0.05, 0, 0)) # touching assert le1.sem_type == SemanticType.kLEScatter assert le2.sem_type == SemanticType.kLEScatter diff --git a/tests/test_partitioner.py b/tests/test_partitioner.py index cf389c3..ed7dfa8 100644 --- a/tests/test_partitioner.py +++ b/tests/test_partitioner.py @@ -180,9 +180,9 @@ def test_all_particles_preserved(self): particles = [ make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, ancestor_id=1), + parent_id=1, root_id=1), make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, ancestor_id=1), + parent_id=1, root_id=1), make_particle(3, PT_IONIZATION, pdg=11, offset=(50, 0, 0)), ] _, parts = self._run_pipeline(particles) @@ -192,9 +192,9 @@ def test_all_particles_preserved(self): def test_photon_and_electron_merged_in_pipeline(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, ancestor_id=1) + parent_id=1, root_id=1) _, parts = self._run_pipeline([photon, electron]) # After PhotonDecay, they should be in 1 partition all_ids_per_part = [frozenset(p.id for p in part) for part in parts] diff --git a/tests/test_preproc.py b/tests/test_preproc.py index be5acd4..63d7362 100644 --- a/tests/test_preproc.py +++ b/tests/test_preproc.py @@ -167,11 +167,11 @@ def test_spawned_particle_has_correct_parentage(self): large = _cluster_pts(10, (0, 0, 0)) small = _cluster_pts(2, (100, 0, 0)) p = make_particle(42, PT_PRIMARY, pdg=11, pc=np.vstack([large, small]), - ancestor_id=99) + root_id=99) result = self.defrag().process([p]) spawned = next(r for r in result if r.id != 42) assert spawned.parent_id == 42 - assert spawned.ancestor_id == 99 + assert spawned.root_id == 99 # ── sem_types filter ───────────────────────────────────────────────── diff --git a/tests/test_utils.py b/tests/test_utils.py index 8ba27ab..5c58d94 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -14,13 +14,13 @@ class TestTraceAncestry: # ── basic cases ────────────────────────────────────────────────────────── def test_root_particle_chain_length_one(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) chain, children = trace_ancestry(1, [root], print_result=False) assert len(chain) == 1 assert chain[0].id == 1 def test_root_has_no_children(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) chain, children = trace_ancestry(1, [root], print_result=False) assert children == [] @@ -33,32 +33,32 @@ def test_unknown_id_raises_key_error(self): def test_three_level_chain_root_first(self): # root (1) → middle (2) → leaf (3) - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) - middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, ancestor_id=1) - leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, ancestor_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) + middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, root_id=1) + leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, root_id=1) particles = [root, middle, leaf] chain, children = trace_ancestry(3, particles, print_result=False) assert [p.id for p in chain] == [1, 2, 3] def test_children_of_middle_node_listed(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) - middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, ancestor_id=1) - leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, ancestor_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) + middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, root_id=1) + leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, root_id=1) chain, children = trace_ancestry(2, [root, middle, leaf], print_result=False) assert len(children) == 1 assert children[0].id == 3 def test_leaf_has_no_children(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) - leaf = make_particle(2, PT_DECAY, pdg=11, parent_id=1, ancestor_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) + leaf = make_particle(2, PT_DECAY, pdg=11, parent_id=1, root_id=1) _, children = trace_ancestry(2, [root, leaf], print_result=False) assert children == [] def test_multiple_children_all_listed(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) - ch1 = make_particle(2, PT_DECAY, pdg=11, parent_id=1, ancestor_id=1) - ch2 = make_particle(3, PT_DECAY, pdg=-11, parent_id=1, ancestor_id=1) - ch3 = make_particle(4, PT_DECAY, pdg=13, parent_id=1, ancestor_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) + ch1 = make_particle(2, PT_DECAY, pdg=11, parent_id=1, root_id=1) + ch2 = make_particle(3, PT_DECAY, pdg=-11, parent_id=1, root_id=1) + ch3 = make_particle(4, PT_DECAY, pdg=13, parent_id=1, root_id=1) _, children = trace_ancestry(1, [root, ch1, ch2, ch3], print_result=False) child_ids = {c.id for c in children} assert child_ids == {2, 3, 4} @@ -89,15 +89,15 @@ def test_print_result_true_produces_output(self, capsys): def test_orphan_chain_terminates_at_orphan(self): # Particle's parent is missing from the list → chain stops at orphan - orphan = make_particle(2, PT_DECAY, pdg=11, parent_id=99, ancestor_id=99) + orphan = make_particle(2, PT_DECAY, pdg=11, parent_id=99, root_id=99) chain, _ = trace_ancestry(2, [orphan], print_result=False) assert chain[-1].id == 2 # ── chain direction ────────────────────────────────────────────────────── def test_chain_ordered_root_to_target(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) - child = make_particle(2, PT_DECAY, pdg=11, parent_id=1, ancestor_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) + child = make_particle(2, PT_DECAY, pdg=11, parent_id=1, root_id=1) chain, _ = trace_ancestry(2, [root, child], print_result=False) assert chain[0].id == 1 assert chain[-1].id == 2 From f2b55b4b96596f30e00e50da8d48eb6000c2fa8c Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 10 Mar 2026 09:03:59 -0700 Subject: [PATCH 02/62] ready to interface edep-sim H5 files. visualization options tweaked to handle very busy event topologies (which we plan to use for FM data generation). Algorithms (LEScatter merging ones) optimized for speed for similarly complex dataset (180s=>2s process time per event) --- README.md | 2 - pysupera/__init__.py | 4 +- pysupera/_run.py | 31 +- pysupera/app.py | 501 ++++++++++++++++----- pysupera/conditions/absorb_le_scatter.py | 55 ++- pysupera/conditions/combine_le_scatters.py | 69 ++- pysupera/conf/config.yaml | 3 + pysupera/conf/reader/edepsim_h5.yaml | 2 +- pysupera/config.py | 494 +++++++++++++++++++- pysupera/data.py | 28 +- pysupera/partitioner.py | 75 ++- pysupera/preproc.py | 329 +++++++++++--- pysupera/proxck.py | 80 ++++ pysupera/readers/example_edepsim_h5.py | 2 +- pysupera/readers/format_edepsim_h5.py | 69 ++- tests/test_conditions.py | 10 - tests/test_partitioner.py | 21 - tests/test_preproc.py | 228 +++++++++- 18 files changed, 1733 insertions(+), 270 deletions(-) diff --git a/README.md b/README.md index 5131684..b91b88d 100644 --- a/README.md +++ b/README.md @@ -196,8 +196,6 @@ These attributes are not required at construction time. Unset float32 scalars h | `kinetic_energy_end` | `float32` | `NaN` | Kinetic energy at the end of the trajectory (MeV). | | `mass` | `float32` | `NaN` | Particle rest mass (MeV/c²). | | `root_pdg` | `int` | `None` | PDG code of the primary (root) ancestor particle. | -| `genealogy_id` | `list[int]` | `None` | IDs of ancestors from direct parent up to and including the root: `[parent_id, grandparent_id, …, root_id]`. | -| `genealogy_pdg` | `list[int]` | `None` | PDG codes of the same ancestry chain as `genealogy_id`. | | `start_process_id` | `int` | `None` | Geant4/simulation process ID for this particle's creation. | | `start_subprocess_id` | `int` | `None` | Geant4/simulation sub-process ID for this particle's creation. | | `start_process_name` | `str` | `None` | Human-readable name of the creation process (e.g. `"eIoni"`). | diff --git a/pysupera/__init__.py b/pysupera/__init__.py index 3487f00..c6f61ff 100644 --- a/pysupera/__init__.py +++ b/pysupera/__init__.py @@ -2,5 +2,7 @@ from .data import Particle, FLOAT_UNSET from .io import write_events, open_writer, read_events, EventStore, EventWriter -from .config import build_checker, build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, load_cfg, configure, check_particle_list +from .config import (build_checker, build_conditions, build_preprocessor, + build_merge_processor, build_voxelizer, build_pipeline, + load_cfg, configure, check_particle_list, Pipeline) from .utils import trace_ancestry diff --git a/pysupera/_run.py b/pysupera/_run.py index f97e634..1b3f8cb 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -32,16 +32,20 @@ def main(cfg: DictConfig) -> None: from collections import defaultdict from pysupera import read_events, open_writer from pysupera.partitioner import ParticlePartitioner - from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, configure + from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, configure configure(cfg) # set module-level defaults (e.g. min_pc_size) before any Particle is created merge_processor = build_merge_processor(cfg) # None when merge_duplicates: false + voxelizer = build_voxelizer(cfg) # None when voxelize.enabled: false preprocessor = build_preprocessor(cfg) # None when defragment: false + _vox_info = (f"enabled (voxel_size={cfg.particle.voxelize.voxel_size})" + if voxelizer is not None else "disabled") print(f"[run] checker : {cfg.checker.name}") print(f"[run] distance : {cfg.distance_threshold}") print(f"[run] merge_duplicates: {'enabled' if merge_processor is not None else 'disabled'}") + print(f"[run] voxelize : {_vox_info}") print(f"[run] preprocessor : {cfg.particle.get('preprocessor', {}).get('name', 'scipy') if cfg.particle.get('defragment', False) else 'disabled'}") print(f"[run] input : {cfg.io.input_path}") print(f"[run] output : {cfg.io.output_path}") @@ -56,7 +60,7 @@ def main(cfg: DictConfig) -> None: 'pc_size_min': [], # int : smallest non-zero PC in event 'pc_size_max': [], # int : largest non-zero PC in event 'pc_size_mean': [], # float: mean non-zero PC size in event - 'n_partitions': defaultdict(list), # condition.name → [int per event] + 'n_partitions': [], # int: final partition count after partition_combined } n_events = 0 @@ -71,11 +75,17 @@ def main(cfg: DictConfig) -> None: t0 = time.perf_counter() n_events += 1 - if merge_processor is not None: + # skip merge_duplicates when voxelizer subsumes it + if merge_processor is not None and not (voxelizer and voxelizer.merge_duplicates): _t = time.perf_counter() particles = merge_processor.process(particles) profile['merge_duplicates'] += time.perf_counter() - _t + if voxelizer is not None: + _t = time.perf_counter() + particles = voxelizer.process(particles) + profile['voxelize'] += time.perf_counter() - _t + if preprocessor is not None: _t = time.perf_counter() particles = preprocessor.process(particles) @@ -106,11 +116,10 @@ def main(cfg: DictConfig) -> None: ) profile['partitioner_init'] += time.perf_counter() - _t - for condition in conditions: - _t = time.perf_counter() - partitions = partitioner.partition(condition, verbose=cfg.verbose) - profile[condition.name] += time.perf_counter() - _t - stats['n_partitions'][condition.name].append(len(partitions)) + _t = time.perf_counter() + partitions = partitioner.partition_combined(conditions, verbose=cfg.verbose) + profile['partition_combined'] += time.perf_counter() - _t + stats['n_partitions'].append(len(partitions)) partitioner.checker.cleanup() @@ -151,10 +160,8 @@ def _agg(vals): print(f" {'PC size mean':<{_W}} {lo:>10.2f} {mu:>10.2f} {hi:>10.2f}") print(f" {_SEP}") - for cname, counts in stats['n_partitions'].items(): - lo, mu, hi = _agg(counts) - label = f"Partitions [{cname}]" - print(f" {label:<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") + lo, mu, hi = _agg(stats['n_partitions']) + print(f" {'Partitions (final)':<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") # ── Time profile ────────────────────────────────────────────────── total = sum(profile.values()) diff --git a/pysupera/app.py b/pysupera/app.py index 6d20e0e..b2a07f6 100644 --- a/pysupera/app.py +++ b/pysupera/app.py @@ -29,11 +29,13 @@ import argparse import contextlib +import hashlib import io import os import shutil import time import traceback +from collections import OrderedDict, defaultdict import numpy as np import dash @@ -73,6 +75,63 @@ # Checkers that accept an n_jobs override in their Hydra schema _MULTI_CHECKERS = {"cpu-multi", "cell-hash-cpu-multi"} +# --------------------------------------------------------------------------- +# Fixed colours per SemanticType +# --------------------------------------------------------------------------- + +_SEM_COLORS: dict[str, str] = { + "kShower": "#e74c3c", + "kTrack": "#3498db", + "kDelta": "#2ecc71", + "kMichel": "#f39c12", + "kLEScatter": "#9b59b6", + "kUnknown": "#95a5a6", +} + +# --------------------------------------------------------------------------- +# Preprocessing cache (LRU, keyed by all params that affect load + preproc) +# --------------------------------------------------------------------------- + +_MAX_CACHE_ENTRIES = 4 +# Maps cache-key → (particles_for_display, log_lines_from_load_and_preproc) +_PARTICLE_CACHE: OrderedDict[str, tuple[list, list[str]]] = OrderedDict() + + +def _preproc_cache_key( + file_path, event_idx, format_val, + step_key, particle_key, elec_thresh, + preproc_flags, min_pc_size, preproc_backend, + sem_types, voxel_size, +) -> str: + key_data = ( + file_path, + int(event_idx), + format_val, + step_key, + particle_key, + float(elec_thresh), + tuple(sorted(preproc_flags)), + int(min_pc_size), + preproc_backend, + tuple(sorted(sem_types)), + float(voxel_size), + ) + return hashlib.md5(str(key_data).encode()).hexdigest() + + +# --------------------------------------------------------------------------- +# Fixed colours per SemanticType (used in the merged left subplot) +# --------------------------------------------------------------------------- + +_SEM_COLORS: dict[str, str] = { + "kShower": "#e74c3c", + "kTrack": "#3498db", + "kDelta": "#2ecc71", + "kMichel": "#f39c12", + "kLEScatter": "#9b59b6", + "kUnknown": "#95a5a6", +} + # --------------------------------------------------------------------------- # Colour helper # --------------------------------------------------------------------------- @@ -89,89 +148,146 @@ def _build_figure( *, marker_size: int = 2, show_legend: bool = True, + draw_mode: str = "by_instance", # "by_instance" | "by_sem_type" ) -> go.Figure: """ Construct the side-by-side Plotly figure. - Left subplot – one trace per original particle, coloured by index. - Right subplot – one trace per partition, coloured by partition index. - Empty when *partitions* is None. + draw_mode="by_instance": + Left – one trace per particle, coloured by particle index. + Right – one trace per partition, coloured by partition index. + draw_mode="by_sem_type": + Left – one trace per SemanticType with fixed colours. + Right – one trace per SemanticType (all-partition points merged by type). """ - cols = 2 fig = make_subplots( - rows=1, cols=cols, + rows=1, cols=2, specs=[[{"type": "scene"}, {"type": "scene"}]], subplot_titles=["Original particles", "Partitions"], ) - N_raw = len(particles_raw) + N_raw = len(particles_raw) N_parts = len(partitions) if partitions else 0 - colours_raw = _make_colorscale_array(N_raw) colours_parts = _make_colorscale_array(N_parts) - # ── Left subplot: original particles ───────────────────────────────── - for i, p in enumerate(particles_raw): - pc = p.point_cloud - if pc is None or len(pc) == 0: - continue - fig.add_trace( - go.Scatter3d( - x=pc[:, 0], y=pc[:, 1], z=pc[:, 2], - mode="markers", - marker=dict(size=marker_size, color=colours_raw[i]), - name=f"p{p.id} {p.sem_type.name}", - legendgroup=f"raw_{i}", - legend="legend", - showlegend=show_legend, - meta={"sem_type": p.sem_type.name}, - hovertemplate=( - f"id={p.id} pdg={p.pdg} sem={p.sem_type.name}
" - f"parent_id={p.parent_id} root_id={p.root_id}
" - "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" - ), - ), - row=1, col=1, - ) - - # ── Right subplot: partitions ────────────────────────────────────────── - if partitions: - for i, part in enumerate(partitions): - chunks = [p.point_cloud for p in part - if p.point_cloud is not None and len(p.point_cloud) > 0] - if not chunks: + def _collect_sem_groups(particle_iter): + """Bucket points by sem_type name; returns dict[name -> (xs, ys, zs)].""" + groups: dict[str, tuple[list, list, list]] = defaultdict(lambda: ([], [], [])) + for p in particle_iter: + pc = p.point_cloud + if pc is None or len(pc) == 0: continue - pts = np.concatenate(chunks, axis=0) - # Representative particle = largest point cloud in the partition - rep = max( - (p for p in part if p.point_cloud is not None and len(p.point_cloud) > 0), - key=lambda p: len(p.point_cloud), - default=part[0], + xs, ys, zs = groups[p.sem_type.name] + xs.append(pc[:, 0]) + ys.append(pc[:, 1]) + zs.append(pc[:, 2]) + return groups + + # ── Left subplot ─────────────────────────────────────────────────────── + if draw_mode == "by_sem_type": + for sem_name, (xs, ys, zs) in _collect_sem_groups(particles_raw).items(): + fig.add_trace( + go.Scatter3d( + x=np.concatenate(xs), y=np.concatenate(ys), z=np.concatenate(zs), + mode="markers", + marker=dict(size=marker_size, + color=_SEM_COLORS.get(sem_name, "#e0e0e0")), + name=sem_name, + legendgroup=f"raw_{sem_name}", + legend="legend", + showlegend=show_legend, + meta={"sem_type": sem_name}, + hovertemplate=( + f"sem={sem_name}
" + "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" + ), + ), + row=1, col=1, ) - ids_str = ",".join(str(p.id) for p in part[:8]) - if len(part) > 8: - ids_str += f"+{len(part)-8}" + else: # by_instance + for i, p in enumerate(particles_raw): + pc = p.point_cloud + if pc is None or len(pc) == 0: + continue fig.add_trace( go.Scatter3d( - x=pts[:, 0], y=pts[:, 1], z=pts[:, 2], + x=pc[:, 0], y=pc[:, 1], z=pc[:, 2], mode="markers", - marker=dict(size=marker_size, color=colours_parts[i]), - name=f"part {i} ({len(part)}p)", - legendgroup=f"part_{i}", - legend="legend2", + marker=dict(size=marker_size, color=colours_raw[i]), + name=f"p{p.id} {p.sem_type.name}", + legendgroup=f"raw_{i}", + legend="legend", showlegend=show_legend, - meta={"sem_type": rep.sem_type.name}, + meta={"sem_type": p.sem_type.name}, hovertemplate=( - f"partition {i} ({len(part)} particles)
" - f"rep: id={rep.id} sem={rep.sem_type.name}
" - f"rep parent_id={rep.parent_id} root_id={rep.root_id}
" - f"members: [{ids_str}]
" + f"id={p.id} pdg={p.pdg} sem={p.sem_type.name}
" + f"parent_id={p.parent_id} root_id={p.root_id}
" "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" ), ), - row=1, col=2, + row=1, col=1, ) + # ── Right subplot ────────────────────────────────────────────────────── + if partitions: + if draw_mode == "by_sem_type": + all_part_particles = (p for part in partitions for p in part) + for sem_name, (xs, ys, zs) in _collect_sem_groups(all_part_particles).items(): + fig.add_trace( + go.Scatter3d( + x=np.concatenate(xs), y=np.concatenate(ys), z=np.concatenate(zs), + mode="markers", + marker=dict(size=marker_size, + color=_SEM_COLORS.get(sem_name, "#e0e0e0")), + name=sem_name, + legendgroup=f"part_sem_{sem_name}", + legend="legend2", + showlegend=show_legend, + meta={"sem_type": sem_name}, + hovertemplate=( + f"sem={sem_name}
" + "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" + ), + ), + row=1, col=2, + ) + else: # by_instance + for i, part in enumerate(partitions): + chunks = [p.point_cloud for p in part + if p.point_cloud is not None and len(p.point_cloud) > 0] + if not chunks: + continue + pts = np.concatenate(chunks, axis=0) + rep = max( + (p for p in part if p.point_cloud is not None and len(p.point_cloud) > 0), + key=lambda p: len(p.point_cloud), + default=part[0], + ) + ids_str = ",".join(str(p.id) for p in part[:8]) + if len(part) > 8: + ids_str += f"+{len(part)-8}" + fig.add_trace( + go.Scatter3d( + x=pts[:, 0], y=pts[:, 1], z=pts[:, 2], + mode="markers", + marker=dict(size=marker_size, color=colours_parts[i]), + name=f"part {i} ({len(part)}p)", + legendgroup=f"part_{i}", + legend="legend2", + showlegend=show_legend, + meta={"sem_type": rep.sem_type.name}, + hovertemplate=( + f"partition {i} ({len(part)} particles)
" + f"rep: id={rep.id} sem={rep.sem_type.name}
" + f"rep parent_id={rep.parent_id} root_id={rep.root_id}
" + f"members: [{ids_str}]
" + "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" + ), + ), + row=1, col=2, + ) + fig.update_layout( scene=dict(aspectmode="data"), scene2=dict(aspectmode="data"), @@ -307,6 +423,25 @@ def build_layout() -> html.Div: _labelled("Event index", _input("event-idx", "number", 0, min=0, step=1)), ), + # ── Input format ────────────────────────────────────────────────── + _section( + "INPUT FORMAT", + _labelled("Format", + _dropdown("format-selector", [ + {"label": "native (pysupera HDF5)", "value": "native"}, + {"label": "EDepSim HDF5", "value": "edepsim_h5"}, + ], "native")), + html.Div([ + _labelled("step_key (HDF5 dataset)", + _input("step-key", value="pstep/lar_vol")), + _labelled("particle_key (HDF5 dataset)", + _input("particle-key", value="particle/geant4")), + _labelled("electron_energy_threshold", + _input("elec-threshold", "number", 0.05, min=0.0, step="any")), + ], id="edepsim-options", style={"display": "none"}), + collapsed=True, + ), + # ── Preprocessing (collapsed by default) ─────────────────────────────────────────── _section( "PREPROCESSING", @@ -314,12 +449,17 @@ def build_layout() -> html.Div: id="preproc-flags", options=[ {"label": " merge duplicates", "value": "merge_duplicates"}, + {"label": " voxelize", "value": "voxelize"}, {"label": " defragment", "value": "defragment"}, ], value=["merge_duplicates", "defragment"], inputStyle={"marginRight": "6px"}, labelStyle={"display": "block", "marginBottom": "4px"}, ), + html.Div([ + _labelled("voxel_size", + _input("voxel-size", "number", 0.3, min=0.01, step="any")), + ], id="voxelize-options", style={"display": "none"}), html.Div([ _labelled("min_pc_size", _input("min-pc-size", "number", 10, min=1, step=1)), @@ -395,16 +535,18 @@ def build_layout() -> html.Div: dcc.Checklist( id="view-options", options=[ - {"label": " show legend", "value": "show_legend"}, - {"label": " sync cameras", "value": "sync_cameras"}, + {"label": " show legend", "value": "show_legend"}, + {"label": " sync cameras", "value": "sync_cameras"}, + {"label": " colour by sem type", "value": "draw_by_sem"}, ], value=["show_legend", "sync_cameras"], inputStyle={"marginRight": "6px"}, labelStyle={"display": "block", "marginBottom": "4px"}, + persistence=True, persistence_type="session", ), ), - # ── Particle filter (by sem_type) ──────────────────────────────── + # ── Particle filter (by sem_type + min PC size) ────────────────── _section( "PARTICLE FILTER", html.Div("Uncheck to hide particles of that type:", @@ -424,6 +566,13 @@ def build_layout() -> html.Div: labelStyle={"display": "block", "marginBottom": "2px"}, persistence=True, persistence_type="session", ), + html.Div( + _labelled( + "Min points to display (display-only filter)", + _input("min-display-pc", "number", 0, min=0, step=1), + ), + style={"marginTop": "8px"}, + ), ), # ── Run ──────────────────────────────────────────────────────── @@ -562,6 +711,28 @@ def toggle_defrag(flags): return visible if "defragment" in (flags or []) else hidden +# ── Show/hide voxelize options ──────────────────────────────────────────────── +@app.callback( + Output("voxelize-options", "style"), + Input("preproc-flags", "value"), +) +def toggle_voxelize(flags): + visible = {"display": "block", "marginTop": "6px"} + hidden = {"display": "none"} + return visible if "voxelize" in (flags or []) else hidden + + +# ── Show/hide EDepSim reader options ───────────────────────────────────────── +@app.callback( + Output("edepsim-options", "style"), + Input("format-selector", "value"), +) +def toggle_edepsim_options(fmt): + visible = {"display": "block", "marginTop": "6px"} + hidden = {"display": "none"} + return visible if fmt == "edepsim_h5" else hidden + + # ── Show/hide n_jobs ───────────────────────────────────────────────────────── @app.callback( Output("n-jobs-div", "style"), @@ -607,35 +778,59 @@ def toggle_log(show_val): State("dist-thresh", "value"), State("checker-type", "value"), State("n-jobs", "value"), + State("voxel-size", "value"), State("conditions", "value"), State("verbose-toggle", "value"), State("view-options", "value"), + State("format-selector", "value"), + State("step-key", "value"), + State("particle-key", "value"), + State("elec-threshold", "value"), + State("min-display-pc", "value"), prevent_initial_call=True, ) def run_pipeline( n_clicks, file_path, event_idx, preproc_flags, min_pc_size, preproc_backend, sem_types_input, - dist_thresh, checker_type, n_jobs, + dist_thresh, checker_type, n_jobs, voxel_size_input, condition_keys, verbose_flags, view_options, + format_val, step_key_val, particle_key_val, elec_thresh_val, + min_display_pc_val, ): preproc_flags = preproc_flags or [] condition_keys = condition_keys or [] sem_types_input = sem_types_input or [] verbose_flags = verbose_flags or [] view_options = view_options or [] - show_legend = "show_legend" in view_options - event_idx = int(event_idx if event_idx is not None else 0) + show_legend = "show_legend" in view_options + draw_mode = "by_sem_type" if "draw_by_sem" in view_options else "by_instance" + min_display_pc = int(min_display_pc_val) if min_display_pc_val is not None else 0 + event_idx = int(event_idx if event_idx is not None else 0) dist_thresh = float(dist_thresh if dist_thresh is not None else 0.8) n_jobs = int(n_jobs if n_jobs is not None else -1) min_pc_size = int(min_pc_size if min_pc_size is not None else 10) checker_type = checker_type or "cpu-single" preproc_backend = preproc_backend or "scipy" verbose = "verbose" in verbose_flags + voxel_size_val = float(voxel_size_input if voxel_size_input is not None else 0.3) + format_val = format_val or "native" + step_key_val = step_key_val or "pstep/lar_vol" + particle_key_val = particle_key_val or "particle/geant4" + elec_thresh_val = float(elec_thresh_val) if elec_thresh_val is not None else 0.05 lines: list[str] = [] t_total = time.perf_counter() + # ── Check preprocessing cache ───────────────────────────────────────── + _preproc_key = _preproc_cache_key( + file_path, event_idx, format_val, + step_key_val, particle_key_val, elec_thresh_val, + preproc_flags, min_pc_size, preproc_backend, + sem_types_input, voxel_size_val, + ) + _cached = _PARTICLE_CACHE.get(_preproc_key) + # ── Build config via load_cfg (same path as the notebook) ──────────── # load_cfg composes the full Hydra config with all defaults, then calls # configure(cfg) internally — which sets _DEFAULT_MIN_PC_SIZE before @@ -650,6 +845,8 @@ def run_pipeline( f"particle.min_pc_size={min_pc_size}", f"particle.merge_duplicates={'true' if 'merge_duplicates' in preproc_flags else 'false'}", f"particle.defragment={'true' if 'defragment' in preproc_flags else 'false'}", + f"particle.voxelize.enabled={'true' if 'voxelize' in preproc_flags else 'false'}", + f"particle.voxelize.voxel_size={voxel_size_val}", f"particle.preprocessor.name={preproc_backend}", f"particle.preprocessor.sem_types={sem_types_str}", f"checker={_CHECKER_CFG.get(checker_type, 'cpu_single')}", @@ -688,65 +885,114 @@ def run_pipeline( err = f"✘ Config error:\n{traceback.format_exc(limit=3)}" return dash.no_update, "✘", err - # ── Load event ─────────────────────────────────────────────────────── - try: - from pysupera import read_events - from collections import Counter - - with read_events(file_path) as store: - n_events = len(store) - particles = store[min(event_idx, n_events - 1)] - - lines.append(f"✔ Loaded {file_path} ({n_events} events)") - lines.append(f" Event {event_idx}: {len(particles)} particles") - if verbose: - _counts = Counter(str(p.sem_type) for p in particles) - for sem, cnt in sorted(_counts.items()): - lines.append(f" {sem}: {cnt}") - - except Exception: - err = f"✘ Load error:\n{traceback.format_exc(limit=3)}" - return dash.no_update, "✘", err - - # ── Preprocessing ──────────────────────────────────────────────────── - try: - from pysupera.config import build_merge_processor, build_preprocessor - - if "merge_duplicates" in preproc_flags: - _t = time.perf_counter() - merger = build_merge_processor(cfg, verbose=verbose) - if merger: - _buf = io.StringIO() - with contextlib.redirect_stdout(_buf), contextlib.redirect_stderr(_buf): - particles = merger.process(particles) - _cap = _buf.getvalue() - if _cap: - lines.append(_cap.rstrip("\n")) - lines.append(f" merge_duplicates: {time.perf_counter()-_t:.3f}s" - f" → {len(particles)} particles") - if verbose: - _counts = Counter(str(p.sem_type) for p in particles) - for sem, cnt in sorted(_counts.items()): - lines.append(f" {sem}: {cnt}") - - if "defragment" in preproc_flags: - _t = time.perf_counter() - preprocessor = build_preprocessor(cfg) - if preprocessor: - particles = preprocessor.process(particles) - sem_label = f" sem_types={sem_types_input}" if sem_types_input else " (all types)" - lines.append(f" defragment ({preproc_backend}{sem_label}): " - f"{time.perf_counter()-_t:.3f}s → {len(particles)} particles") + # ── Load event + preprocessing (skipped on cache hit) ────────────────── + if _cached is not None: + _PARTICLE_CACHE.move_to_end(_preproc_key) + particles_for_display, _cache_log = _cached + lines.extend(_cache_log) + lines.append(" ↩ load+preproc result from cache") + else: + _load_preproc_lines: list[str] = [] + try: + from collections import Counter + + if format_val == "edepsim_h5": + from pysupera.readers import EDepSimHDF5Reader + with EDepSimHDF5Reader( + file_path, + particle_key=particle_key_val, + step_key=step_key_val, + electron_energy_threshold=elec_thresh_val, + min_pc_size=min_pc_size, + ) as reader: + n_events = len(reader) + particles = reader[min(event_idx, n_events - 1)] + _load_preproc_lines.append( + f"✔ Loaded {file_path} ({n_events} events) [edepsim_h5]" + ) + else: + from pysupera import read_events + with read_events(file_path) as store: + n_events = len(store) + particles = store[min(event_idx, n_events - 1)] + _load_preproc_lines.append( + f"✔ Loaded {file_path} ({n_events} events) [native pysupera]" + ) + + _load_preproc_lines.append( + f" Event {event_idx}: {len(particles)} particles" + ) if verbose: _counts = Counter(str(p.sem_type) for p in particles) for sem, cnt in sorted(_counts.items()): - lines.append(f" {sem}: {cnt}") - except Exception: - err = f"✘ Preprocessing error:\n{traceback.format_exc(limit=3)}" - return dash.no_update, "✘", err - - # Keep a copy of original particles for the left subplot - particles_for_display = list(particles) + _load_preproc_lines.append(f" {sem}: {cnt}") + + except Exception: + err = f"✘ Load error:\n{traceback.format_exc(limit=3)}" + return dash.no_update, "✘", err + + # ── Preprocessing ──────────────────────────────────────────────── + try: + from pysupera.config import build_merge_processor, build_preprocessor, build_voxelizer + + if "merge_duplicates" in preproc_flags: + _t = time.perf_counter() + merger = build_merge_processor(cfg, verbose=verbose) + if merger: + _buf = io.StringIO() + with contextlib.redirect_stdout(_buf), contextlib.redirect_stderr(_buf): + particles = merger.process(particles) + _cap = _buf.getvalue() + if _cap: + _load_preproc_lines.append(_cap.rstrip("\n")) + _load_preproc_lines.append( + f" merge_duplicates: {time.perf_counter()-_t:.3f}s" + f" → {len(particles)} particles" + ) + if verbose: + _counts = Counter(str(p.sem_type) for p in particles) + for sem, cnt in sorted(_counts.items()): + _load_preproc_lines.append(f" {sem}: {cnt}") + + if "voxelize" in preproc_flags: + _t = time.perf_counter() + voxelizer = build_voxelizer(cfg) + if voxelizer: + particles = voxelizer.process(particles) + _load_preproc_lines.append( + f" voxelize (voxel_size={voxel_size_val}): " + f"{time.perf_counter()-_t:.3f}s \u2192 {len(particles)} particles" + ) + + if "defragment" in preproc_flags: + _t = time.perf_counter() + preprocessor = build_preprocessor(cfg) + if preprocessor: + particles = preprocessor.process(particles) + sem_label = (f" sem_types={sem_types_input}" + if sem_types_input else " (all types)") + _load_preproc_lines.append( + f" defragment ({preproc_backend}{sem_label}): " + f"{time.perf_counter()-_t:.3f}s → {len(particles)} particles" + ) + if verbose: + _counts = Counter(str(p.sem_type) for p in particles) + for sem, cnt in sorted(_counts.items()): + _load_preproc_lines.append(f" {sem}: {cnt}") + + except Exception: + err = f"✘ Preprocessing error:\n{traceback.format_exc(limit=3)}" + return dash.no_update, "✘", err + + # Store preprocessed particles in cache (evict oldest if full) + particles_for_display = list(particles) + _PARTICLE_CACHE[_preproc_key] = (particles_for_display, list(_load_preproc_lines)) + if len(_PARTICLE_CACHE) > _MAX_CACHE_ENTRIES: + _PARTICLE_CACHE.popitem(last=False) + lines.extend(_load_preproc_lines) + + # particles alias for the partition step (works on both cache-hit and miss) + particles = particles_for_display # ── Partition ──────────────────────────────────────────────────────── try: @@ -815,10 +1061,25 @@ def run_pipeline( total_time = time.perf_counter() - t_total lines.append(f"\ntotal: {total_time:.3f}s") + # ── Apply display-only point-cloud size filter ──────────────────────── + if min_display_pc > 0: + particles_for_display = [ + p for p in particles_for_display if len(p.point_cloud) >= min_display_pc + ] + partitions = [ + [p for p in part if len(p.point_cloud) >= min_display_pc] + for part in partitions + ] + partitions = [part for part in partitions if part] + lines.append( + f" display filter: pc ≥ {min_display_pc} pts →" + f" {len(particles_for_display)} particles, {len(partitions)} partitions shown" + ) + # ── Build figure ───────────────────────────────────────────────────── try: fig = _build_figure(particles_for_display, partitions, - show_legend=show_legend) + show_legend=show_legend, draw_mode=draw_mode) except Exception: err = f"✘ Figure error:\n{traceback.format_exc(limit=3)}" return dash.no_update, "✘", err diff --git a/pysupera/conditions/absorb_le_scatter.py b/pysupera/conditions/absorb_le_scatter.py index 2496059..43e6115 100644 --- a/pysupera/conditions/absorb_le_scatter.py +++ b/pysupera/conditions/absorb_le_scatter.py @@ -1,6 +1,8 @@ from collections import defaultdict from typing import Dict, List, Tuple, TYPE_CHECKING +import numpy as np + from .base import PartitionConditionBase from ..diagnostics import MergeOutcome from ..utils import SemanticType @@ -121,10 +123,57 @@ def get_rep_candidates( le_reps = [r for r in unique_reps if r.sem_type == SemanticType.kLEScatter] other_reps = [r for r in unique_reps if r.sem_type != SemanticType.kLEScatter] + if not le_reps or not other_reps: + partitioner.diagnostics.record_candidates( + f"{self.name}: Rep Candidate Building", [] + ) + return [] + + D = partitioner.checker.D + + # Global point-cloud KDTree: build separate LE and other point arrays + # labelled by rep index, then call query_ball_tree(D) once to find all + # (LE_point, other_point) pairs within D in O(N log N). + # LE scatter clouds are always small (few hits/voxels), so the output + # size is bounded by n_le_pts × local_density near touching boundaries. + def _stack(reps): + clouds = [] + rep_tags = [] + for k, rep in enumerate(reps): + parts = rep._cloud_parts + c = parts[0] if len(parts) == 1 else np.concatenate(parts, axis=0) + if len(c) == 0: + continue + clouds.append(c[:, :3].astype(np.float32)) + rep_tags.append(np.full(len(c), k, dtype=np.int32)) + if not clouds: + return None, None + return np.concatenate(rep_tags), np.concatenate(clouds) + + le_tags, le_pts = _stack(le_reps) + ot_tags, ot_pts = _stack(other_reps) + + if le_tags is None or ot_tags is None: + partitioner.diagnostics.record_candidates( + f"{self.name}: Rep Candidate Building", [] + ) + return [] + + from scipy.spatial import KDTree + # query_ball_tree returns for each LE point the list of other-point + # indices within D. Map to rep indices and deduplicate. + hits = KDTree(le_pts).query_ball_tree(KDTree(ot_pts), r=D) + + touching: set = set() + for i, js in enumerate(hits): + if js: + lk = int(le_tags[i]) + for j in js: + touching.add((lk, int(ot_tags[j]))) + candidates = [ - (le.id, other.id) - for le in le_reps - for other in other_reps + (le_reps[i].id, other_reps[j].id) + for i, j in touching ] partitioner.diagnostics.record_candidates( f"{self.name}: Rep Candidate Building", candidates diff --git a/pysupera/conditions/combine_le_scatters.py b/pysupera/conditions/combine_le_scatters.py index b9b6081..291c54e 100644 --- a/pysupera/conditions/combine_le_scatters.py +++ b/pysupera/conditions/combine_le_scatters.py @@ -201,19 +201,72 @@ def get_rep_candidates( # ensures that already-absorbed particles are never re-inspected: # an absorbed particle's rep_lookup entry points to its absorber, which # is deduplicated away by unique_reps(), leaving only active reps. - seen: Dict[int, 'Particle'] = { - rep.id: rep + unique_le = [ + rep for rep in self.unique_reps(rep_lookup) if rep.sem_type == SemanticType.kLEScatter - } + ] - unique_reps = list(seen.values()) + if len(unique_le) < 2: + partitioner.diagnostics.record_candidates( + f"{self.name}: Rep Candidate Building", [] + ) + return [] + + D = partitioner.checker.D + + # Global point-cloud KDTree: concatenate all LE rep clouds into one + # array labelled by rep index, then call query_pairs(D) once. This + # finds all touching (rep_i, rep_j) pairs in O(N log N) — strictly + # better than O(n_reps²) centroid pre-filter followed by per-pair + # cloud checks, regardless of whether point clouds are voxelized. + def _xyz(rep): + parts = rep._cloud_parts + return parts[0] if len(parts) == 1 else np.concatenate(parts, axis=0) + + clouds = [_xyz(r) for r in unique_le] + nonempty = [(k, c) for k, c in enumerate(clouds) if len(c) > 0] + + if len(nonempty) < 2: + partitioner.diagnostics.record_candidates( + f"{self.name}: Rep Candidate Building", [] + ) + return [] + + rep_tags = np.repeat( + [k for k, _ in nonempty], + [len(c) for _, c in nonempty], + ).astype(np.int32) + pts = np.concatenate([c[:, :3] for _, c in nonempty], axis=0).astype(np.float32) + + from scipy.spatial import KDTree + pt_pairs = KDTree(pts).query_pairs(r=D, output_type='ndarray') + + # Map point-pairs → rep-pairs, drop same-rep and deduplicate. + if len(pt_pairs) == 0: + partitioner.diagnostics.record_candidates( + f"{self.name}: Rep Candidate Building", [] + ) + return [] + + ri = rep_tags[pt_pairs[:, 0]] + rj = rep_tags[pt_pairs[:, 1]] + mask = ri != rj + if not mask.any(): + partitioner.diagnostics.record_candidates( + f"{self.name}: Rep Candidate Building", [] + ) + return [] + + # Canonical (min, max) ordering → unique set of rep-pairs. + rep_pairs = np.unique( + np.sort(np.stack([ri[mask], rj[mask]], axis=1), axis=1), axis=0 + ) candidates: List[Tuple[int, int]] = [] - for i in range(len(unique_reps)): - for j in range(i + 1, len(unique_reps)): - child, parent = _le_direction(unique_reps[i], unique_reps[j]) - candidates.append((child.id, parent.id)) + for i, j in rep_pairs: + child, parent = _le_direction(unique_le[i], unique_le[j]) + candidates.append((child.id, parent.id)) partitioner.diagnostics.record_candidates( f"{self.name}: Rep Candidate Building", candidates diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index 249d537..fa07f1c 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -54,6 +54,9 @@ particle: # minimum (per-particle alignment, default). Set to [x0, y0, z0] for # a globally fixed grid anchored at a known detector boundary. origin: null + # When true, skips the separate merge_duplicates preprocessing step. + # Safe because exact-coordinate duplicates always fall in the same voxel. + merge_duplicates: false # Pre-processing: collapse points sharing identical 3-D coordinates. # Aggregation rules: time=min, energy=sum, dedx=max. diff --git a/pysupera/conf/reader/edepsim_h5.yaml b/pysupera/conf/reader/edepsim_h5.yaml index 177bc1a..a4a1d2f 100644 --- a/pysupera/conf/reader/edepsim_h5.yaml +++ b/pysupera/conf/reader/edepsim_h5.yaml @@ -17,7 +17,7 @@ format: edepsim_h5 # --------------------------------------------------------------------------- # Per-event structured array of Geant4 particles. -# Expected fields: track_id, parent_track_id, ancestor_track_id, pdg, +# Expected fields: track_id, parent_track_id, root_track_id, pdg, # proc_start, subproc_start, ke, x, y, z particle_key: particle/geant4 diff --git a/pysupera/config.py b/pysupera/config.py index a490f89..8d77fdc 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -28,6 +28,85 @@ from omegaconf import DictConfig +# --------------------------------------------------------------------------- +# Override-key auditing infrastructure +# --------------------------------------------------------------------------- + +# Complete set of dotted config paths that build_pipeline consumes. +# Any user override whose key is NOT in this set is flagged as unused. +_PIPELINE_KEYS: frozenset[str] = frozenset({ + # I/O + "io.input_path", + "io.output_path", + "io.compression", + # Reader (config-group switch + individual keys) + "reader", + "reader.format", + "reader.particle_key", + "reader.step_key", + "reader.ass_key", + "reader.electron_energy_threshold", + # Particle construction + "particle.min_pc_size", + # Merge-duplicates preprocessor + "particle.merge_duplicates", + # Voxelizer + "particle.voxelize.enabled", + "particle.voxelize.voxel_size", + "particle.voxelize.origin", + # Defragmentation preprocessor + "particle.defragment", + "particle.preprocessor.name", + "particle.preprocessor.min_pts_for_gpu", + "particle.preprocessor.sem_types", + # Proximity checker / partitioner (config-group switch + per-backend keys) + "checker", + "checker.name", + "checker.n_jobs", + "checker.chunk_size", + "checker.block_size", + "distance_threshold", + # Partitioner control + "verbose", + "report", + "enable_diagnostics", + "check_particle_tree", + # Conditions (config-group switch + individual toggles) + "conditions", + "conditions.photon_decay", + "conditions.touching_em_shower", + "conditions.combine_le_scatters", + "conditions.absorb_le_scatter", +}) + +# Registry: id(cfg) → frozenset of override key strings. +# Populated by load_cfg; consumed by build_pipeline. +_cfg_override_keys: dict[int, frozenset[str]] = {} + + +def _parse_override_keys(overrides: list[str]) -> frozenset[str]: + """ + Extract the dotted key portion from a list of Hydra override strings. + + Strips leading sigils (``+``, ``++``, ``~``) and returns only the key + part (everything before the first ``=``). + + Examples + -------- + ``"distance_threshold=0.8"`` → ``"distance_threshold"`` + ``"checker=cpu_single"`` → ``"checker"`` + ``"+extra.key=1"`` → ``"extra.key"`` + ``"~remove.key"`` → ``"remove.key"`` + """ + keys: set[str] = set() + for s in overrides: + s = s.lstrip("+~") # remove Hydra append / force / delete sigils + key = s.split("=")[0] # key=value or key (group switch) + if key: + keys.add(key) + return frozenset(keys) + + # --------------------------------------------------------------------------- # Public factory functions # --------------------------------------------------------------------------- @@ -105,19 +184,27 @@ def build_preprocessor(cfg: DictConfig, verbose: bool = False): ) sem_types = [SemanticType[s] for s in raw_sem_types] if raw_sem_types else None + # n_jobs: prefer preprocessor-specific override, fall back to checker.n_jobs + n_jobs = int( + preproc_cfg.get('n_jobs', cfg.checker.get('n_jobs', 1)) + if preproc_cfg else cfg.checker.get('n_jobs', 1) + ) + if name == 'scipy': return ScipyDefragmenter(D, min_pc_size, verbose=verbose, - sem_types=sem_types) + sem_types=sem_types, n_jobs=n_jobs) if name == 'gpu': min_pts = int(preproc_cfg.get('min_pts_for_gpu', 64)) return GPUDefragmenter(D, min_pc_size, min_pts_for_gpu=min_pts, - verbose=verbose, sem_types=sem_types) + verbose=verbose, sem_types=sem_types, + n_jobs=n_jobs) if name == 'rapids': min_pts = int(preproc_cfg.get('min_pts_for_gpu', 64)) return RAPIDSDefragmenter(D, min_pc_size, min_pts_for_gpu=min_pts, - verbose=verbose, sem_types=sem_types) + verbose=verbose, sem_types=sem_types, + n_jobs=n_jobs) raise ValueError( f"Unknown preprocessor name {name!r}. " @@ -158,9 +245,10 @@ def build_voxelizer(cfg: DictConfig, verbose: bool = False): origin = list(raw_origin) if raw_origin is not None else None return VoxelizeProcessor( - voxel_size = voxel_size, - origin = origin, - verbose = verbose, + voxel_size = voxel_size, + origin = origin, + verbose = verbose, + merge_duplicates = bool(vox_cfg.get('merge_duplicates', False)), ) @@ -216,42 +304,39 @@ def build_checker(cfg: DictConfig): ValueError If ``cfg.checker.name`` is not a recognised backend. """ - from pysupera.proxck import ( - CPUSingleThreadChecker, - CPUMultiThreadChecker, - GPUChecker, - BulkGPUChecker, - NumbaKernelChecker, - CellHashCPUSingleThreadChecker, - CellHashCPUMultiThreadChecker, - CellHashGPUChecker, - ) - D = float(cfg.distance_threshold) name = cfg.checker.name if name == "cpu-single": + from pysupera.proxck import CPUSingleThreadChecker return CPUSingleThreadChecker(D) if name == "cpu-multi": + from pysupera.proxck import CPUMultiThreadChecker return CPUMultiThreadChecker(D, n_jobs=int(cfg.checker.get("n_jobs", -1))) if name == "gpu": + from pysupera.proxck import GPUChecker return GPUChecker(D) if name == "bulk-gpu": + from pysupera.proxck import BulkGPUChecker return BulkGPUChecker(D, chunk_size=int(cfg.checker.get("chunk_size", 512))) if name == "numba": + from pysupera.proxck import NumbaKernelChecker return NumbaKernelChecker(D, block_size=int(cfg.checker.get("block_size", 128))) if name == "cell-hash-cpu-single": + from pysupera.proxck import CellHashCPUSingleThreadChecker return CellHashCPUSingleThreadChecker(D) if name == "cell-hash-cpu-multi": + from pysupera.proxck import CellHashCPUMultiThreadChecker return CellHashCPUMultiThreadChecker(D, n_jobs=int(cfg.checker.get("n_jobs", -1))) if name == "cell-hash-gpu": + from pysupera.proxck import CellHashGPUChecker return CellHashGPUChecker(D) raise ValueError( @@ -444,4 +529,379 @@ def load_cfg(overrides: list[str] | None = None, cfg = compose(config_name, overrides=overrides or []) configure(cfg) + _cfg_override_keys[id(cfg)] = _parse_override_keys(overrides or []) return cfg + + +# --------------------------------------------------------------------------- +# Pipeline +# --------------------------------------------------------------------------- + +class Pipeline: + """ + A fully-configured pysupera processing pipeline. + + Holds pre-built reader, preprocessors, and conditions so that per-event + work is reduced to a single :meth:`partition` call. Construct via + :func:`build_pipeline` rather than directly. + + Parameters + ---------- + reader : EventReaderBase + Open file reader. Owned by this object; closed on :meth:`close`. + merger : MergeDuplicatesProcessor or None + Merge-duplicates step, or ``None`` if disabled. + voxelizer : VoxelizeProcessor or None + Voxelization step, or ``None`` if disabled. + preprocessor : DefragmentBase or None + Defragmentation step, or ``None`` if disabled. + conditions : list of PartitionConditionBase + Merging conditions in canonical application order. + cfg : DictConfig + Full Hydra config retained for partitioner construction. + + Examples + -------- + :: + + pipeline = build_pipeline(cfg) + with pipeline: + for event_idx, particles, partitions in pipeline: + print(event_idx, len(partitions)) + """ + + def __init__(self, reader, merger, voxelizer, preprocessor, conditions, cfg): + self.reader = reader + self.merger = merger + self.voxelizer = voxelizer + self.preprocessor = preprocessor + self.conditions = conditions + self.cfg = cfg + + # ------------------------------------------------------------------ # + + def preprocess(self, particles: list) -> list: + """ + Apply all enabled preprocessing steps to *particles* in order: + merge-duplicates → voxelize → defragment. + + Parameters + ---------- + particles : list of Particle + + Returns + ------- + list of Particle + The (possibly modified / extended) particle list. + """ + if self.merger and not ( + self.voxelizer and self.voxelizer.merge_duplicates + ): + particles = self.merger.process(particles) + if self.voxelizer: + particles = self.voxelizer.process(particles) + if self.preprocessor: + particles = self.preprocessor.process(particles) + return particles + + def partition(self, particles: list, verbose: bool = True) -> list: + """ + Preprocess *particles* and run the full partitioning algorithm. + + Timing for each stage is recorded and accessible via + :meth:`print_timing` after this call returns. The underlying + :class:`~pysupera.partitioner.ParticlePartitioner` is kept as + :attr:`last_partitioner` for post-hoc diagnostics. + + Parameters + ---------- + particles : list of Particle + Raw particle list (typically one event from the reader). + verbose : bool, optional + Forwarded to + :meth:`~pysupera.partitioner.ParticlePartitioner.partition_combined`. + When ``True`` (default) the partitioner prints per-pass progress + and a partition-size histogram to stdout. Set ``False`` to + suppress all partitioner output and inspect results solely via + :meth:`print_timing`. + + Returns + ------- + list of list of Particle + Each inner list is one partition. + """ + import time + from pysupera.partitioner import ParticlePartitioner + + n_in = len(particles) + + # Time each preprocessing step individually for print_timing detail. + _preproc_t: dict = {} + t0 = time.perf_counter() + if self.merger and not ( + self.voxelizer and self.voxelizer.merge_duplicates + ): + _ts = time.perf_counter() + particles = self.merger.process(particles) + _preproc_t['merge_duplicates'] = time.perf_counter() - _ts + if self.voxelizer: + _ts = time.perf_counter() + particles = self.voxelizer.process(particles) + label = 'voxelize (+merge_duplicates)' if self.voxelizer.merge_duplicates else 'voxelize' + _preproc_t[label] = time.perf_counter() - _ts + if self.preprocessor: + _ts = time.perf_counter() + particles = self.preprocessor.process(particles) + _preproc_t['defragment'] = time.perf_counter() - _ts + self._t_preprocess = time.perf_counter() - t0 + self._t_preprocess_steps = _preproc_t + self._n_after_preprocess = len(particles) + + partitioner = ParticlePartitioner( + particles, + distance_threshold = float(self.cfg.distance_threshold), + backend = self.cfg.checker.name, + n_jobs = int(self.cfg.checker.get("n_jobs", -1)), + enable_diagnostics = bool(self.cfg.enable_diagnostics), + check_completeness = bool(self.cfg.check_particle_tree), + ) + self.last_partitioner = partitioner + + t0 = time.perf_counter() + partitions = partitioner.partition_combined(self.conditions, verbose=verbose) + self._t_partition = time.perf_counter() - t0 + + self._last_n_in = n_in + self._last_n_partitions = len(partitions) + return partitions + + def print_timing(self) -> None: + """ + Print a summary table of the most recent :meth:`partition` call. + + Shows wall-clock time for preprocessing and partitioning, particle + counts before/after preprocessing, and the resulting partition count. + + Raises + ------ + RuntimeError + If :meth:`partition` has not yet been called. + """ + if not hasattr(self, "_t_partition"): + raise RuntimeError("No timing data available — call partition() first.") + + total = self._t_preprocess + self._t_partition + W = 60 # table width + + print("=" * W) + print(f"{'Pipeline timing summary':^{W}}") + print("=" * W) + print(f" {'Stage':<36} {'Time (s)':>8} {'%':>5}") + print("-" * W) + + # ---- Preprocessing section ---- + preproc_steps = getattr(self, "_t_preprocess_steps", {}) + if preproc_steps: + print(f" {'Preprocessing':<36} {self._t_preprocess:>8.4f}" + f" {100*self._t_preprocess/total:>4.1f}%") + for step_name, step_t in preproc_steps.items(): + pct = 100 * step_t / total + label = f" {step_name}" + print(f" {label:<36} {step_t:>8.4f} {pct:>4.1f}%") + # Sub-row: per-step statistics from last_stats + if step_name == 'merge_duplicates': + proc = self.merger + elif step_name.startswith('voxelize'): + proc = self.voxelizer + elif step_name == 'defragment': + proc = self.preprocessor + else: + proc = None + stats = getattr(proc, 'last_stats', {}) if proc else {} + if stats: + if step_name == 'defragment': + parts = [ + f"sem_skip={stats['n_skipped']}", + f"early_exit={stats['n_early_exit']}", + f"cc_checked={stats['n_cc_checked']}", + f"fragmented={stats['n_fragmented']}", + f"spawned={stats['n_spawned']}", + f"n_jobs={stats.get('n_jobs', '?')}", + f"cc_pts(med/max/tot)={stats.get('cc_pts_median','?')}/{stats.get('cc_pts_max','?')}/{stats.get('cc_pts_total','?')}", + ] + else: + removed = stats['pts_before'] - stats['pts_after'] + parts = [ + f"affected={stats['n_affected']}", + f"pts {stats['pts_before']}→{stats['pts_after']}", + f"removed={removed}", + ] + print(f" {'':36} ({', '.join(parts)})") + else: + preproc_label = "Preprocessing (none)" + print(f" {preproc_label:<36} {self._t_preprocess:>8.4f}" + f" {100*self._t_preprocess/total:>4.1f}%") + + # ---- Partitioning section ---- + print(f" {'Partitioning':<36} {self._t_partition:>8.4f}" + f" {100*self._t_partition/total:>4.1f}%") + + # Per-condition breakdown (available when incremental path was used) + cond_timing = getattr(self.last_partitioner, "_condition_timing", None) + if cond_timing: + for cname, ct in cond_timing.items(): + pct = 100 * ct['time_s'] / total + label = f" {cname}" + print(f" {label:<36} {ct['time_s']:>8.4f} {pct:>4.1f}%") + # Sub-row: candidate → resolved → touching → merged + parts = [] + if ct['n_unconditional']: + parts.append(f"unconditional={ct['n_unconditional']}") + parts.append(f"candidates={ct['n_candidates']}") + parts.append(f"resolved={ct['n_resolved']}") + parts.append(f"touching={ct['n_touching']}") + parts.append(f"merged={ct['n_merged']}") + print(f" {'':36} ({', '.join(parts)})") + + print("-" * W) + print(f" {'Total':<36} {total:>8.4f} 100.0%") + print("=" * W) + print(f" Particles in : {self._last_n_in}") + print(f" Particles after preproc: {self._n_after_preprocess}" + + (f" (+{self._n_after_preprocess - self._last_n_in} from defrag)" + if self._n_after_preprocess != self._last_n_in else "")) + print(f" Partitions out : {self._last_n_partitions}") + print("=" * W) + + def __len__(self) -> int: + """Number of events in the attached reader.""" + return len(self.reader) + + def __iter__(self): + """ + Iterate over all events, yielding ``(event_idx, particles, partitions)`` + tuples. Preprocessing and partitioning are applied to each event. + The ``verbose`` setting from the config (``cfg.verbose``) is forwarded + to the partitioner. + """ + verbose = bool(self.cfg.get("verbose", True)) + for idx, particles in enumerate(self.reader): + partitions = self.partition(particles, verbose=verbose) + yield idx, particles, partitions + + def close(self) -> None: + """Close the underlying file reader.""" + if self.reader is not None: + self.reader.close() + self.reader = None + + def __enter__(self): + return self + + def __exit__(self, *args): + self.close() + + def __repr__(self) -> str: + n = len(self.reader) if self.reader is not None else 0 + steps = [] + if self.merger: steps.append("merge_duplicates") + if self.voxelizer: steps.append("voxelize") + if self.preprocessor: steps.append("defragment") + steps.append(f"{len(self.conditions)} conditions") + return ( + f"Pipeline(events={n}, backend={self.cfg.checker.name!r}, " + f"D={self.cfg.distance_threshold}, steps=[{', '.join(steps)}])" + ) + + +def build_pipeline(cfg: DictConfig, strict: bool = False) -> "Pipeline": + """ + Build a fully-configured :class:`Pipeline` from a Hydra config. + + This is the **recommended entry point** for running the pysupera + workflow. It constructs every pipeline component in the correct order + (reader, merge-duplicates, voxelizer, defragmenter, conditions) and + audits the config for user overrides that were not consumed by any + component — catching typos and accidentally ignored settings before + data processing begins. + + .. note:: + Only overrides that the user explicitly passed to :func:`load_cfg` + are checked. Default values baked into ``conf/*.yaml`` are never + flagged. + + Parameters + ---------- + cfg : DictConfig + Full Hydra config as returned by :func:`load_cfg`. If *cfg* was + not produced by :func:`load_cfg` (e.g. loaded via ``hydra.compose`` + directly), no unused-override check is performed. + strict : bool, optional + When ``False`` (default), unused overrides are reported as a + :class:`UserWarning`. When ``True``, a :exc:`ValueError` is + raised instead — useful in automated pipelines or CI where a + misconfigured run should hard-fail. + + Returns + ------- + Pipeline + + Raises + ------ + ValueError + If *strict* is ``True`` and unused override keys are detected. + + Examples + -------- + :: + + from pysupera.config import load_cfg, build_pipeline + + cfg = load_cfg([ + "io.input_path=/data/sim.h5", + "checker=cpu_single", + "distance_threshold=0.8", + "particle.defragment=true", + ]) + + with build_pipeline(cfg) as pipeline: + print(pipeline) # Pipeline(events=100, backend='cpu-single', ...) + for idx, particles, partitions in pipeline: + print(idx, len(partitions)) + + # Or process a single pre-loaded particle list: + pipeline = build_pipeline(cfg) + partitions = pipeline.partition(my_particles) + """ + import warnings + + # ── build all components ────────────────────────────────────────────── + reader = build_reader(cfg) + merger = build_merge_processor(cfg) + voxelizer = build_voxelizer(cfg) + preprocessor = build_preprocessor(cfg) + conditions = build_conditions(cfg) + + # ── audit unused user overrides ─────────────────────────────────────── + override_keys = _cfg_override_keys.get(id(cfg)) + if override_keys is not None: # None = cfg not from load_cfg; skip + unused = override_keys - _PIPELINE_KEYS + if unused: + msg = ( + "build_pipeline: the following override key(s) were not " + "consumed by any pipeline step and will have no effect:\n" + + "\n".join(f" {k}" for k in sorted(unused)) + ) + if strict: + reader.close() + raise ValueError(msg) + warnings.warn(msg, UserWarning, stacklevel=2) + + return Pipeline( + reader = reader, + merger = merger, + voxelizer = voxelizer, + preprocessor = preprocessor, + conditions = conditions, + cfg = cfg, + ) diff --git a/pysupera/data.py b/pysupera/data.py index 4b2897d..dbe385f 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -96,11 +96,6 @@ class Particle: Particle rest mass (MeV/c²). root_pdg : int or None PDG code of the primary (root) ancestor particle. - genealogy_id : list of int or None - IDs of ancestors from direct parent up to and including the root, - in order [parent_id, grandparent_id, …, root_id]. - genealogy_pdg : list of int or None - PDG codes of the same ancestry chain as *genealogy_id*. start_process_id : int or None Geant4/simulation process ID for this particle's creation. start_subprocess_id : int or None @@ -138,8 +133,6 @@ def __init__( kinetic_energy_end=FLOAT_UNSET, mass=FLOAT_UNSET, root_pdg=None, - genealogy_id=None, - genealogy_pdg=None, start_process_id=None, start_subprocess_id=None, start_process_name=None, @@ -206,8 +199,6 @@ def __init__( self.kinetic_energy_end = np.float32(kinetic_energy_end) self.mass = np.float32(mass) self.root_pdg = root_pdg - self.genealogy_id = list(genealogy_id) if genealogy_id is not None else None - self.genealogy_pdg = list(genealogy_pdg) if genealogy_pdg is not None else None self.start_process_id = start_process_id self.start_subprocess_id = start_subprocess_id self.start_process_name = start_process_name @@ -389,9 +380,16 @@ def from_flat_arrays( Raw interaction process codes forwarded to :func:`~pysupera.utils.SetSemanticType`. point_cloud_flat : numpy.ndarray, shape (M, ≥3) - Concatenated point clouds for all *N* particles. Columns 0–2 - are x, y, z; additional columns (time, energy, etc.) are - carried through unchanged. + Concatenated point clouds for all *N* particles. Must be a + plain (non-structured) 2-D ``float32`` array. Columns 0–2 + are x, y, z; additional columns (time, energy, dEdx, …) + are carried through unchanged. + + .. note:: + File-format-specific readers (e.g. + :class:`~pysupera.readers.EDepSimHDF5Reader`) are + responsible for converting their native structured arrays + into this layout before calling :meth:`from_flat_arrays`. point_cloud_offsets : array-like of int, shape (N, 2) Row slices into *point_cloud_flat*. ``point_cloud_offsets[i] = [start, end]`` such that @@ -464,6 +462,12 @@ def from_flat_arrays( ) flat = np.asarray(point_cloud_flat) + if flat.dtype.names is not None: + raise TypeError( + "point_cloud_flat must be a plain 2-D array, not a structured " + "array. Convert named fields to columns before calling " + "from_flat_arrays (e.g. in the file-format reader)." + ) return [ cls( diff --git a/pysupera/partitioner.py b/pysupera/partitioner.py index 607b656..678693e 100644 --- a/pysupera/partitioner.py +++ b/pysupera/partitioner.py @@ -781,17 +781,40 @@ def _get_cloud(rep: Particle) -> np.ndarray: rep.point_cloud = cloud return cloud + # Bbox cache: keyed by UF root index. Updated in O(1) on every merge + # as the merged bbox = (min of mins, max of maxes). Used to skip + # parent KDTree builds for pairs that are provably not touching. + self._rep_bmin: Dict[int, np.ndarray] = {} + self._rep_bmax: Dict[int, np.ndarray] = {} + for idx, rep in reps.items(): + c = rep.point_cloud # already xyz‑only float32 + if len(c) > 0: + self._rep_bmin[idx] = c.min(axis=0) + self._rep_bmax[idx] = c.max(axis=0) + else: + self._rep_bmin[idx] = np.array([ np.inf, np.inf, np.inf], dtype=np.float32) + self._rep_bmax[idx] = np.array([-np.inf, -np.inf, -np.inf], dtype=np.float32) + self._D_sq = float(self.checker.D_squared) + if verbose: print(f" Partitioning {n} particles with {len(conditions)} condition(s)") total_merges = 0 + # Per-condition timing and merge-count accumulators (cumulative across passes). + _cond_t: Dict[str, dict] = { + cond.name: dict(time_s=0.0, n_unconditional=0, n_candidates=0, + n_resolved=0, n_touching=0, n_merged=0) + for cond in conditions + } for pass_num in range(1, max_passes + 1): pass_merges = 0 for condition in conditions: + _t_cond0 = time.perf_counter() # per-condition wall-clock start # ---- Unconditional merges (e.g. photon decay: no proximity needed) ---- unconditional = condition.get_unconditional_merges(self, rep_lookup) + _n_unc = len(unconditional) for child_id, parent_id in unconditional: child_idx = self.id_to_idx[child_id] parent_idx = self.id_to_idx[parent_id] @@ -826,12 +849,16 @@ def _get_cloud(rep: Particle) -> np.ndarray: else: reps[new_root] = parent_rep del reps[r_parent] + # Update bbox cache (O(1): element-wise min/max of the two bboxes). + self._rep_bmin[new_root] = np.minimum(self._rep_bmin.pop(r_child), self._rep_bmin.pop(r_parent)) + self._rep_bmax[new_root] = np.maximum(self._rep_bmax.pop(r_child), self._rep_bmax.pop(r_parent)) pass_merges += 1 # ---- Proximity-based candidates ---- # Ask the condition which partition pairs are candidates this pass. # Returns (child_rep_id, parent_rep_id): child merges INTO parent. candidates = condition.get_rep_candidates(self, rep_lookup) + _n_cands = len(candidates) touching_pairs: List[Tuple[int, int]] = [] # Resolve to UF roots and deduplicate stale/duplicate rep-pairs. @@ -851,19 +878,34 @@ def _get_cloud(rep: Particle) -> np.ndarray: resolved.append((child_id, parent_id, r_child, r_parent)) # Group by parent rep so each parent KDTree is built only once. + # Bbox pre-filter: pairs whose bboxes are >D apart skip the KDTree + # entirely (no cloud materialization for that parent group). by_parent: Dict[int, List[int]] = defaultdict(list) - for i, (_, _, _, r_parent) in enumerate(resolved): + for i, (_, _, r_child, r_parent) in enumerate(resolved): + if r_child in self._rep_bmin and r_parent in self._rep_bmin: + delta = np.maximum(0.0, np.maximum( + self._rep_bmin[r_child] - self._rep_bmax[r_parent], + self._rep_bmin[r_parent] - self._rep_bmax[r_child])) + if float(np.dot(delta, delta)) > self._D_sq: + continue # provably not touching; skip KDTree by_parent[r_parent].append(i) hit = [False] * len(resolved) stage = f"{condition.name}: Partition Proximity (pass {pass_num})" + # Collect groups, then dispatch all parents in parallel. + # Multi-thread backends build each parent's KDTree in its own + # thread; single-thread backend falls back to sequential. + _groups: List = [] + _group_idxs: List = [] for r_parent, idxs in by_parent.items(): - parent_rep = reps[r_parent] - parent_cloud = _get_cloud(parent_rep) - child_clouds = [_get_cloud(reps[resolved[i][2]]) for i in idxs] - batch_results = self.checker.batch_check_cloud_proximity( - child_clouds, parent_cloud - ) + parent_cloud = _get_cloud(reps[r_parent]) + child_clouds = [_get_cloud(reps[resolved[i][2]]) for i in idxs] + _groups.append((child_clouds, parent_cloud)) + _group_idxs.append(idxs) + for idxs, batch_results in zip( + _group_idxs, + self.checker.batch_check_multi_cloud_proximity(_groups), + ): for i, touching in zip(idxs, batch_results): hit[i] = touching @@ -887,6 +929,10 @@ def _get_cloud(rep: Particle) -> np.ndarray: # Post-filter (e.g. one-merge-per-kLEScatter) merge_pairs = condition.post_filter(self, touching_pairs) + # Snapshot counts for print_timing (resolved / touching / merged after post-filter) + _n_res = len(resolved) + _n_touch = len(touching_pairs) + _n_merge = len(merge_pairs) # Apply accepted merges for child_id, parent_id in merge_pairs: @@ -923,9 +969,21 @@ def _get_cloud(rep: Particle) -> np.ndarray: # UF elected r_child as new root due to rank; remap. reps[new_root] = parent_rep # reps[r_child] = parent_rep del reps[r_parent] + # Update bbox cache (O(1)). + self._rep_bmin[new_root] = np.minimum(self._rep_bmin.pop(r_child), self._rep_bmin.pop(r_parent)) + self._rep_bmax[new_root] = np.maximum(self._rep_bmax.pop(r_child), self._rep_bmax.pop(r_parent)) pass_merges += 1 + # Accumulate per-condition stats for Pipeline.print_timing + _ct = _cond_t[condition.name] + _ct['time_s'] += time.perf_counter() - _t_cond0 + _ct['n_unconditional'] += _n_unc + _ct['n_candidates'] += _n_cands + _ct['n_resolved'] += _n_res + _ct['n_touching'] += _n_touch + _ct['n_merged'] += _n_merge + if verbose: print(f" Incremental pass {pass_num}: {pass_merges} merge(s)") @@ -936,6 +994,9 @@ def _get_cloud(rep: Particle) -> np.ndarray: if verbose: print(f" Converged after {pass_num} pass(es), {total_merges} total merge(s)") + # Expose per-condition stats to Pipeline.print_timing + self._condition_timing = _cond_t + partitions_dict: Dict = defaultdict(list) for idx, p in enumerate(self.particles): root = uf.find(idx) diff --git a/pysupera/preproc.py b/pysupera/preproc.py index 117050a..cdafe5d 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -246,11 +246,13 @@ class DefragmentBase(ABC): def __init__(self, distance_threshold: float, min_pc_size: int, verbose: bool = False, - sem_types=None): + sem_types=None, + n_jobs: int = 1): self.eps = float(distance_threshold) self.eps2 = self.eps ** 2 self.min_pc_size = int(min_pc_size) self.verbose = bool(verbose) + self.n_jobs = int(n_jobs) # sem_types: frozenset of SemanticType values to process, or None for all. # Particles whose sem_type is not in the set are passed through unchanged. if sem_types is None or len(sem_types) == 0: @@ -260,6 +262,8 @@ def __init__(self, distance_threshold: float, min_pc_size: int, #: List of :class:`FragmentRecord` objects from the most recent #: :meth:`process` call. Always populated regardless of *verbose*. self.last_diagnostics: List[FragmentRecord] = [] + #: Summary counts from the most recent :meth:`process` call. + self.last_stats: dict = {} # ------------------------------------------------------------------ # Subclass contract @@ -317,6 +321,11 @@ def process(self, particles: List[Particle], """ if not particles: self.last_diagnostics = [] + self.last_stats = {'n_particles': 0, 'n_skipped': 0, + 'n_early_exit': 0, 'n_cc_checked': 0, + 'n_fragmented': 0, 'n_spawned': 0, + 'n_jobs': self.n_jobs, + 'cc_pts_median': 0, 'cc_pts_max': 0, 'cc_pts_total': 0} return particles be_verbose = self.verbose if verbose is None else bool(verbose) @@ -324,7 +333,6 @@ def process(self, particles: List[Particle], max_id = max(p.id for p in particles) next_id = max_id + 1 - result: list[Particle] = [] spawned: list[Particle] = [] n_skipped = 0 @@ -333,20 +341,30 @@ def process(self, particles: List[Particle], n_fragmented = 0 n_spawned_total = 0 - for p in particles: + # --------------------------------------------------------------- + # First pass: fast early-exit checks; collect particles needing CC + # --------------------------------------------------------------- + # result_slots preserves input order; None = to be filled after CC. + result_slots: list = [None] * len(particles) + # needs_cc: (original_index, particle, xyz) + # precomp: original_index → pre-computed labels (2-pt case) + needs_cc: list = [] + precomp: dict = {} + + for i, p in enumerate(particles): # Skip particles whose semantic type is not in the allowed set. if self.sem_types is not None and p.sem_type not in self.sem_types: - result.append(p) + result_slots[i] = p n_skipped += 1 continue pc = p.point_cloud - # ---- fast early exits (cheapest first) ----------------------- + # ---- fast early exits (cheapest first) --------------------- # 1. Single point: trivially one cluster if len(pc) <= 1: - result.append(p) + result_slots[i] = p n_early_exit += 1 continue @@ -355,32 +373,77 @@ def process(self, particles: List[Particle], # 2. Two points: O(1) distance comparison if len(xyz) == 2: if ((xyz[0] - xyz[1]) ** 2).sum() <= self.eps2: - result.append(p) - n_early_exit += 1 - continue - # Two isolated points — labels are [0, 1] by definition - labels = np.array([0, 1], dtype=np.intp) - exit_reason = None # did not early-exit; fell through to CC logic - else: - # 3. Bounding-box diagonal: if the whole cloud fits inside a - # ball of radius eps, all pairs are within eps → 1 cluster - span = xyz.max(axis=0) - xyz.min(axis=0) - if (span ** 2).sum() <= self.eps2: - result.append(p) + result_slots[i] = p n_early_exit += 1 continue + # Two isolated points — labels known without CC + precomp[i] = np.array([0, 1], dtype=np.intp) + needs_cc.append((i, p, xyz)) + n_cc_checked += 1 + continue - # 4. Subclass-specific connected-components algorithm - exit_reason = None - labels = self._get_labels(xyz) + # 3. Bounding-box diagonal: if the whole cloud fits inside a + # ball of radius eps, all pairs are within eps → 1 cluster + span = xyz.max(axis=0) - xyz.min(axis=0) + if (span ** 2).sum() <= self.eps2: + result_slots[i] = p + n_early_exit += 1 + continue - # ---- reached CC — build diagnostic record ------------------- + # 4. Needs full CC + needs_cc.append((i, p, xyz)) n_cc_checked += 1 + + # --------------------------------------------------------------- + # Parallel (or sequential) CC computation + # --------------------------------------------------------------- + to_compute = [(idx, p, xyz) for idx, p, xyz in needs_cc + if idx not in precomp] + + if to_compute: + if self.n_jobs != 1 and len(to_compute) > 1: + import math + from joblib import Parallel, delayed + # Determine effective worker count (joblib uses cpu_count for -1) + import os + n_workers = ( + os.cpu_count() or 1 + if self.n_jobs < 0 + else self.n_jobs + ) + # Chunk into n_workers groups to eliminate per-task overhead. + # Each worker processes its chunk serially — joblib dispatches + # only n_workers tasks instead of len(to_compute). + chunk_size = max(1, math.ceil(len(to_compute) / n_workers)) + chunks = [ + to_compute[i : i + chunk_size] + for i in range(0, len(to_compute), chunk_size) + ] + + def _run_chunk(chunk): + return [self._get_labels(xyz) for _, _, xyz in chunk] + + nested = Parallel(n_jobs=self.n_jobs, prefer='threads')( + delayed(_run_chunk)(chunk) for chunk in chunks + ) + labels_list = [lbl for group in nested for lbl in group] + else: + labels_list = [self._get_labels(xyz) for _, _, xyz in to_compute] + + for (idx, _, _), labels in zip(to_compute, labels_list): + precomp[idx] = labels + + # --------------------------------------------------------------- + # Second pass: split fragments (sequential — needs next_id order) + # --------------------------------------------------------------- + for idx, p, xyz in needs_cc: + pc = p.point_cloud + labels = precomp[idx] + unique_labels, counts = np.unique(labels, return_counts=True) cluster_sizes = sorted(counts.tolist(), reverse=True) n_clusters = len(unique_labels) - # ---- split large vs. small fragments ------------------------- kept, new_particles, next_id = _split_fragments( p, labels, self.min_pc_size, next_id ) @@ -390,7 +453,7 @@ def process(self, particles: List[Particle], rec = FragmentRecord( particle_id = p.id, n_points = len(pc), - early_exit = exit_reason, + early_exit = None, n_clusters = n_clusters, cluster_sizes = cluster_sizes, kept_pts = kept_pts, @@ -402,13 +465,31 @@ def process(self, particles: List[Particle], print(rec) if kept is not None: - result.append(kept) + result_slots[idx] = kept + # else: result_slots[idx] remains None → excluded from output + spawned.extend(new_particles) if rec.fragmented: n_fragmented += 1 n_spawned_total += len(new_particles) + result = [p for p in result_slots if p is not None] + + cc_sizes = [len(xyz) for _, _, xyz in needs_cc] + self.last_stats = { + 'n_particles' : len(particles), + 'n_skipped' : n_skipped, + 'n_early_exit' : n_early_exit, + 'n_cc_checked' : n_cc_checked, + 'n_fragmented' : n_fragmented, + 'n_spawned' : n_spawned_total, + 'n_jobs' : self.n_jobs, + 'cc_pts_median' : int(np.median(cc_sizes)) if cc_sizes else 0, + 'cc_pts_max' : int(np.max(cc_sizes)) if cc_sizes else 0, + 'cc_pts_total' : int(np.sum(cc_sizes)) if cc_sizes else 0, + } + if be_verbose: sem_label = ( "all sem_types" @@ -520,6 +601,8 @@ def __init__(self, verbose: bool = False): #: :meth:`process` call (only particles *with* duplicates are #: recorded, since duplicate-free particles carry no information). self.last_diagnostics: List[MergeRecord] = [] + #: Summary counts from the most recent :meth:`process` call. + self.last_stats: dict = {} def process(self, particles: List[Particle], verbose: Optional[bool] = None) -> List[Particle]: @@ -543,21 +626,72 @@ def process(self, particles: List[Particle], be_verbose = self.verbose if verbose is None else bool(verbose) self.last_diagnostics = [] - n_total_before = 0 - n_total_after = 0 - n_affected = 0 + n_affected = 0 - for p in particles: - pc = p.point_cloud - n_before = len(pc) - merged = _merge_point_cloud(pc) - n_after = len(merged) + # --- batch: one np.unique call over all particles ---------------- + all_pcs = [p.point_cloud for p in particles] + lengths = [len(pc) for pc in all_pcs] + total = sum(lengths) + n_cols = next((pc.shape[1] for pc in all_pcs if len(pc) > 0), None) - n_total_before += n_before - n_total_after += n_after + if n_cols is None or total == 0: + self.last_stats = {'n_particles': len(particles), 'n_affected': 0, + 'pts_before': 0, 'pts_after': 0} + if be_verbose: + print( + f"[merge_duplicates] {len(particles)} particles | " + f"0 had duplicates | 0 points removed | 0 \u2192 0 total pts" + ) + return particles + + n_total_before = total + all_pts = np.concatenate(all_pcs) # (total, n_cols) + + # key: (float64-pid, x, y, z) — respects exact float equality for xyz + pids = np.repeat(np.arange(len(particles), dtype=np.float64), lengths) + keys = np.empty((total, 4), dtype=np.float64) + keys[:, 0] = pids + keys[:, 1:] = all_pts[:, :3] + + unique_keys, inv = np.unique(keys, axis=0, return_inverse=True) + n_out = len(unique_keys) + + # Fast path: no duplicates at all — skip aggregation entirely + if n_out == total: + self.last_stats = { + 'n_particles' : len(particles), + 'n_affected' : 0, + 'pts_before' : total, + 'pts_after' : total, + } + if be_verbose: + print( + f"[merge_duplicates] {len(particles)} particles | " + f"0 had duplicates | 0 points removed | " + f"{total} \u2192 {total} total pts" + ) + return particles + out = np.zeros((n_out, n_cols), dtype=all_pts.dtype) + out[:, :3] = unique_keys[:, 1:4].astype(all_pts.dtype) + for col, init_val, ufunc in _MERGE_RULES: + col = int(col) + if col >= n_cols: + continue + out[:, col] = init_val + ufunc.at(out[:, col], inv, all_pts[:, col]) + + # split back per particle — unique_keys is sorted so pid is monotone + pid_out = unique_keys[:, 0].astype(np.int64) + boundaries = np.searchsorted(pid_out, np.arange(len(particles) + 1)) + n_total_after = int(n_out) + + for i, p in enumerate(particles): + start, end = int(boundaries[i]), int(boundaries[i + 1]) + n_before = lengths[i] + n_after = end - start if n_after < n_before: - p.point_cloud = merged + p.point_cloud = out[start:end] n_affected += 1 rec = MergeRecord( particle_id = p.id, @@ -568,6 +702,13 @@ def process(self, particles: List[Particle], if be_verbose: print(rec) + self.last_stats = { + 'n_particles' : len(particles), + 'n_affected' : n_affected, + 'pts_before' : n_total_before, + 'pts_after' : n_total_after, + } + if be_verbose: print( f"[merge_duplicates] {len(particles)} particles | " @@ -646,6 +787,8 @@ def _voxelize_point_cloud( contains exactly one point. """ coords = pc[:, :3] + if len(coords) == 0: + return pc # nothing to voxelize org = coords.min(axis=0) if origin is None else origin # Integer voxel indices for every point @@ -684,6 +827,13 @@ class VoxelizeProcessor: When voxelization is enabled, running :class:`MergeDuplicatesProcessor` afterwards is redundant. + By setting ``merge_duplicates=True`` the voxelizer signals to the + :class:`~pysupera.config.Pipeline` that it already covers duplicate- + coordinate merging and the separate :class:`MergeDuplicatesProcessor` + step should be skipped. This is always correct: two points that share + exactly the same ``(x, y, z)`` always fall into the same voxel cell, + so they are guaranteed to be collapsed by the voxelization pass. + Voxel-centre positions ~~~~~~~~~~~~~~~~~~~~~~ For a point at coordinate ``x`` the voxel index is:: @@ -713,6 +863,13 @@ class VoxelizeProcessor: verbose : bool, optional When ``True``, :meth:`process` prints one line per affected particle and an end-of-batch summary. Default ``False``. + merge_duplicates : bool, optional + When ``True``, signals that this step also subsumes a preceding + :class:`MergeDuplicatesProcessor`. The voxelization algorithm is + unchanged (exact-coordinate duplicates already land in the same + voxel). Setting this to ``True`` lets the + :class:`~pysupera.config.Pipeline` skip a redundant separate + merge-duplicates pass. Default ``False``. """ def __init__( @@ -720,6 +877,7 @@ def __init__( voxel_size: float | list, origin: list | None = None, verbose: bool = False, + merge_duplicates: bool = False, ) -> None: vs = np.asarray(voxel_size, dtype=float) if vs.ndim == 0: @@ -745,9 +903,13 @@ def __init__( self.origin = None self.verbose = bool(verbose) + #: When ``True`` the Pipeline skips the separate MergeDuplicatesProcessor. + self.merge_duplicates: bool = bool(merge_duplicates) #: :class:`VoxelizeRecord` list from the most recent :meth:`process` #: call. Only particles that had at least one merge are recorded. self.last_diagnostics: List[VoxelizeRecord] = [] + #: Summary counts from the most recent :meth:`process` call. + self.last_stats: dict = {} def process( self, @@ -770,21 +932,79 @@ def process( be_verbose = self.verbose if verbose is None else bool(verbose) self.last_diagnostics = [] - n_total_before = 0 - n_total_after = 0 - n_affected = 0 + n_affected = 0 - for p in particles: - pc = p.point_cloud - n_before = len(pc) - merged = _voxelize_point_cloud(pc, self.voxel_size, self.origin) - n_after = len(merged) + # --- batch: one np.unique call over all particles ---------------- + all_pcs = [p.point_cloud for p in particles] + lengths = [len(pc) for pc in all_pcs] + total = sum(lengths) + n_cols = next((pc.shape[1] for pc in all_pcs if len(pc) > 0), None) - n_total_before += n_before - n_total_after += n_after + if n_cols is None or total == 0: + self.last_stats = {'n_particles': len(particles), 'n_affected': 0, + 'pts_before': 0, 'pts_after': 0} + if be_verbose: + print( + f"[voxelize] {len(particles)} particles | " + f"0 affected | 0 points merged | 0 \u2192 0 total pts" + ) + return particles + + n_total_before = total + all_pts = np.concatenate(all_pcs) # (total, n_cols) + coords = all_pts[:, :3] + + # Per-particle origins (preserves per-particle grid alignment) + if self.origin is None: + # compute per-particle min; empty particles get zeros + starts = np.cumsum([0] + lengths[:-1]) + orgs = np.array([ + coords[s:s + l].min(axis=0) if l > 0 else np.zeros(3) + for s, l in zip(starts, lengths) + ]) # (n_parts, 3) + else: + orgs = np.broadcast_to(self.origin, (len(particles), 3)) + + # Per-point origin (replicated from particle) + pt_orgs = np.repeat(orgs, lengths, axis=0) # (total, 3) + + # Integer voxel indices per point + vox_idx = np.floor( + (coords - pt_orgs) / self.voxel_size + ).astype(np.int64) # (total, 3) + + # Key: (pid, vx, vy, vz) as int64 + pids = np.repeat(np.arange(len(particles), dtype=np.int64), lengths) + keys = np.concatenate([pids[:, None], vox_idx], axis=1) # (total, 4) + + unique_keys, inv = np.unique(keys, axis=0, return_inverse=True) + n_out = len(unique_keys) + + # Voxel-centre coordinates per output row + out_pid = unique_keys[:, 0] # (n_out,) + out_orgs = orgs[out_pid] # (n_out, 3) + out = np.zeros((n_out, n_cols), dtype=all_pts.dtype) + out[:, :3] = ( + (unique_keys[:, 1:4] + 0.5) * self.voxel_size + out_orgs + ).astype(all_pts.dtype) + + for col, init_val, ufunc in _MERGE_RULES: + col = int(col) + if col >= n_cols: + continue + out[:, col] = init_val + ufunc.at(out[:, col], inv, all_pts[:, col]) + # split back per particle — unique_keys sorted so pid is monotone + boundaries = np.searchsorted(out_pid, np.arange(len(particles) + 1)) + n_total_after = int(n_out) + + for i, p in enumerate(particles): + start, end = int(boundaries[i]), int(boundaries[i + 1]) + n_before = lengths[i] + n_after = end - start if n_after < n_before: - p.point_cloud = merged + p.point_cloud = out[start:end] n_affected += 1 rec = VoxelizeRecord( particle_id = p.id, @@ -795,6 +1015,13 @@ def process( if be_verbose: print(rec) + self.last_stats = { + 'n_particles' : len(particles), + 'n_affected' : n_affected, + 'pts_before' : n_total_before, + 'pts_after' : n_total_after, + } + if be_verbose: print( f"[voxelize] {len(particles)} particles | " @@ -875,9 +1102,9 @@ class GPUDefragmenter(DefragmentBase): def __init__(self, distance_threshold: float, min_pc_size: int, min_pts_for_gpu: int = 64, verbose: bool = False, - sem_types=None): + sem_types=None, n_jobs: int = 1): super().__init__(distance_threshold, min_pc_size, verbose=verbose, - sem_types=sem_types) + sem_types=sem_types, n_jobs=n_jobs) self.min_pts_for_gpu = int(min_pts_for_gpu) self._scipy = ScipyDefragmenter(distance_threshold, min_pc_size) @@ -948,9 +1175,9 @@ class RAPIDSDefragmenter(DefragmentBase): def __init__(self, distance_threshold: float, min_pc_size: int, min_pts_for_gpu: int = 64, verbose: bool = False, - sem_types=None): + sem_types=None, n_jobs: int = 1): super().__init__(distance_threshold, min_pc_size, verbose=verbose, - sem_types=sem_types) + sem_types=sem_types, n_jobs=n_jobs) self.min_pts_for_gpu = int(min_pts_for_gpu) self._scipy = ScipyDefragmenter(distance_threshold, min_pc_size) diff --git a/pysupera/proxck.py b/pysupera/proxck.py index 8ef106b..13cec20 100644 --- a/pysupera/proxck.py +++ b/pysupera/proxck.py @@ -300,6 +300,35 @@ def batch_check_cloud_proximity( """ return [self.check_cloud_proximity(c, parent_cloud) for c in child_clouds] + def batch_check_multi_cloud_proximity( + self, + groups: List[Tuple[List[np.ndarray], np.ndarray]], + ) -> List[List[bool]]: + """ + Test each ``(child_clouds, parent_cloud)`` group and return results. + + Each entry in *groups* is a tuple ``(child_clouds, parent_cloud)`` + identical to the arguments of :meth:`batch_check_cloud_proximity`. + The default implementation processes groups sequentially. Multi- + threaded subclasses override this to run groups in parallel, which + is the primary source of speedup for the partition-level proximity + phase: each group's parent KDTree (or hash map) is built and queried + independently, so they can be dispatched to different threads. + + Parameters + ---------- + groups : list of (child_clouds, parent_cloud) tuples + + Returns + ------- + list of list of bool + Element *i* is the result list for ``groups[i]``. + """ + return [ + self.batch_check_cloud_proximity(child_clouds, parent_cloud) + for child_clouds, parent_cloud in groups + ] + # ============================================================================ # CPU Single-Threaded Implementation @@ -522,6 +551,21 @@ def batch_check_cloud_proximity( results.append(bool(np.any(dists <= self.D))) return results + def batch_check_multi_cloud_proximity( + self, + groups: List[Tuple[List[np.ndarray], np.ndarray]], + ) -> List[List[bool]]: + """Dispatch each (child_clouds, parent_cloud) group to a thread. + + Each thread builds its own parent KDTree independently, giving + near-linear scaling with the number of unique parent partitions. + """ + from joblib import Parallel, delayed + return Parallel(n_jobs=self.n_jobs, backend='threading')( + delayed(self.batch_check_cloud_proximity)(child_clouds, parent_cloud) + for child_clouds, parent_cloud in groups + ) + def cleanup(self): """Clear data structures""" self.kdtrees.clear() @@ -1042,6 +1086,42 @@ def check_cloud_proximity(self, cloud_a: np.ndarray, hmap = self._build_hashmap(b, self._inv_D) return self._query(a, b, hmap, self.D) + def batch_check_cloud_proximity( + self, + child_clouds: List[np.ndarray], + parent_cloud: np.ndarray, + ) -> List[bool]: + """Build the parent hash map once and query all children against it.""" + if len(parent_cloud) == 0: + return [False] * len(child_clouds) + b = parent_cloud[:, :3].astype(np.float32) + min_b, max_b = b.min(axis=0), b.max(axis=0) + parent_hmap = self._build_hashmap(b, self._inv_D) + results: List[bool] = [] + for child_cloud in child_clouds: + if len(child_cloud) == 0: + results.append(False) + continue + a = child_cloud[:, :3].astype(np.float32) + min_a, max_a = a.min(axis=0), a.max(axis=0) + delta = np.maximum(0.0, np.maximum(min_a - max_b, min_b - max_a)) + if float(np.dot(delta, delta)) > self.D_squared: + results.append(False) + continue + results.append(self._query(a, b, parent_hmap, self.D)) + return results + + def batch_check_multi_cloud_proximity( + self, + groups: List[Tuple[List[np.ndarray], np.ndarray]], + ) -> List[List[bool]]: + """Dispatch each (child_clouds, parent_cloud) group to a thread.""" + from joblib import Parallel, delayed + return Parallel(n_jobs=self.n_jobs, backend='threading')( + delayed(self.batch_check_cloud_proximity)(child_clouds, parent_cloud) + for child_clouds, parent_cloud in groups + ) + class CellHashGPUChecker(_CellHashMixin, ProximityChecker): """ diff --git a/pysupera/readers/example_edepsim_h5.py b/pysupera/readers/example_edepsim_h5.py index a657a29..6c95e21 100644 --- a/pysupera/readers/example_edepsim_h5.py +++ b/pysupera/readers/example_edepsim_h5.py @@ -233,7 +233,7 @@ def read(fname='out_0100.h5',entries=None): local_time += time.time()-t0 pysupera_parts = Particle.from_flat_arrays(parts['track_id'], parts['parent_track_id'], - parts['ancestor_track_id'], + parts['root_track_id'], parts['pdg'], get_parent_pdg(parts), itype, diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 448c112..1da65c6 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -15,7 +15,7 @@ ============= ====================================================== track_id Geant4 track ID (used as pysupera *id*) parent_track_id Immediate parent track ID - ancestor_track_id Root ancestor track ID + root_track_id Root ancestor track ID pdg PDG Monte Carlo particle code proc_start G4 process type integer at particle start vertex subproc_start G4 process subtype integer at particle start vertex @@ -50,6 +50,69 @@ from .base import EventReaderBase from ..data import Particle +from ..utils import PointFeature + + +# --------------------------------------------------------------------------- +# Step-array conversion +# --------------------------------------------------------------------------- +# The EDepSim HDF5 step datasets are structured arrays with named fields. +# pysupera's Particle expects a plain float32 2-D point cloud with columns +# ordered by PointFeature (x=0, y=1, z=2, time=3, energy=4, dedx=5). +# This mapping is EDepSim-format-specific and lives here rather than in +# Particle.from_flat_arrays so that the core data class stays format-agnostic. + +# Maps PointFeature column → candidate HDF5 field name(s) to try, in order. +_STEP_FIELD_MAP: list[tuple[int, tuple[str, ...]]] = [ + (PointFeature.x, ("x",)), + (PointFeature.y, ("y",)), + (PointFeature.z, ("z",)), + (PointFeature.time, ("t", "time")), + (PointFeature.energy, ("energy", "e")), + (PointFeature.dedx, ("dedx", "dEdx", "dE_dx")), +] + + +def _steps_to_plain_array(steps: np.ndarray) -> np.ndarray: + """ + Convert a structured EDepSim step array to a plain float32 2-D array. + + The output columns follow :class:`~pysupera.utils.PointFeature` order: + ``x=0, y=1, z=2, time=3, energy=4, dedx=5``. x, y, z are mandatory; + any optional field (time, energy, dedx) that is absent from the dtype + is left as zero in the output. + + Parameters + ---------- + steps : numpy.ndarray + 1-D structured array read from the HDF5 step dataset. + + Returns + ------- + numpy.ndarray, shape (N, 6), dtype float32 + """ + if steps.dtype.names is None: + # Already a plain (non-structured) array — nothing to do. + return steps.astype(np.float32, copy=False) + + n = len(steps) + out = np.zeros((n, len(_STEP_FIELD_MAP)), dtype=np.float32) + available = set(steps.dtype.names) + + for col, candidates in _STEP_FIELD_MAP: + for fname in candidates: + if fname in available: + out[:, col] = steps[fname] + break + else: + if col < 3: # x, y, z are required + raise ValueError( + f"EDepSim step array is missing a required coordinate field. " + f"Tried {candidates}; available fields: {sorted(available)}" + ) + # optional columns default to zero — already set + + return out # --------------------------------------------------------------------------- @@ -368,11 +431,11 @@ def __getitem__(self, index: int) -> list[Particle]: return Particle.from_flat_arrays( ids = parts["track_id"], parent_ids = parts["parent_track_id"], - root_ids = parts["ancestor_track_id"], + root_ids = parts["root_track_id"], pdgs = parts["pdg"], parent_pdgs = _get_parent_pdg(parts), process_types = itype, - point_cloud_flat = steps, + point_cloud_flat = _steps_to_plain_array(steps), point_cloud_offsets = offsets, min_pc_size = self._min_pc_size, ) diff --git a/tests/test_conditions.py b/tests/test_conditions.py index 05bf0bf..40c6efc 100644 --- a/tests/test_conditions.py +++ b/tests/test_conditions.py @@ -85,16 +85,6 @@ def test_non_photon_parent_no_merge(self): parts = prt.partition(PhotonDecay(), verbose=False) assert not _same_partition(parts, 1, 2) - def test_result_has_fewer_or_equal_partitions_than_particles(self): - photon = make_particle(1, PT_PHOTON, pdg=22, - pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, root_id=1) - electron = make_particle(2, PT_PRIMARY, pdg=11, - parent_id=1, root_id=1) - prt = _part(photon, electron) - parts = prt.partition(PhotonDecay(), verbose=False) - assert len(parts) <= 2 - # ============================================================================ # TouchingEMShower diff --git a/tests/test_partitioner.py b/tests/test_partitioner.py index ed7dfa8..26ba6ae 100644 --- a/tests/test_partitioner.py +++ b/tests/test_partitioner.py @@ -42,12 +42,6 @@ def test_all_particles_appear_in_result(self): all_ids = {p.id for part in parts for p in part} assert all_ids == {p.id for p in particles} - def test_partition_count_never_exceeds_particle_count(self): - particles = [make_particle(i, PT_IONIZATION, pdg=11) for i in range(6)] - prt = make_partitioner(particles, D=5.0) - parts = prt.partition(CombineLEScatters(), verbose=False) - assert len(parts) <= len(particles) - def test_each_particle_in_exactly_one_partition(self): particles = [ make_particle(i, PT_IONIZATION, pdg=11, offset=(i * 0.05, 0, 0)) @@ -114,21 +108,6 @@ def test_singleton_representative_is_itself(self): # ============================================================================ class TestPartitionBySemType: - def test_returns_dict(self): - p = make_particle(1, PT_PRIMARY, pdg=11) - prt = make_partitioner([p]) - parts = prt.partition(AbsorbLEScatter(), verbose=False) - result = prt.partition_by_sem_type(parts) - assert isinstance(result, dict) - - def test_keys_are_semantic_types(self): - p = make_particle(1, PT_PRIMARY, pdg=11) - prt = make_partitioner([p]) - parts = prt.partition(AbsorbLEScatter(), verbose=False) - result = prt.partition_by_sem_type(parts) - for key in result: - assert isinstance(key, SemanticType) - def test_total_partition_count_preserved(self): particles = [ make_particle(1, PT_PRIMARY, pdg=11, offset=(0, 0, 0)), diff --git a/tests/test_preproc.py b/tests/test_preproc.py index 63d7362..8d28883 100644 --- a/tests/test_preproc.py +++ b/tests/test_preproc.py @@ -2,7 +2,7 @@ import numpy as np import pytest -from pysupera.preproc import MergeDuplicatesProcessor, ScipyDefragmenter +from pysupera.preproc import MergeDuplicatesProcessor, ScipyDefragmenter, VoxelizeProcessor from pysupera.utils import SemanticType from tests.conftest import make_particle, cloud, PT_PRIMARY, PT_TRACK, PT_NEUTRON @@ -82,6 +82,26 @@ def test_multiple_particles_processed_independently(self): assert len(result[0].point_cloud) == 1 # p1 had duplicate assert len(result[1].point_cloud) == 2 # p2 had no duplicate + def test_last_stats_populated_no_duplicates(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0, 0, 0, 1, 2, 0.5], + [1, 0, 0, 2, 3, 0.3])) + self.proc.process([p]) + s = self.proc.last_stats + assert {'n_particles', 'n_affected', 'pts_before', 'pts_after'} <= s.keys() + assert s['n_affected'] == 0 + assert s['pts_before'] == s['pts_after'] == 2 + + def test_last_stats_populated_with_duplicates(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0, 0, 0, 0, 1, 0.5], + [0, 0, 0, 1, 2, 0.3])) # duplicate xyz + self.proc.process([p]) + s = self.proc.last_stats + assert s['n_affected'] == 1 + assert s['pts_before'] == 2 + assert s['pts_after'] == 1 + def test_diagnostic_records_only_duplicates(self): p_dup = make_particle(1, PT_PRIMARY, pdg=11, pc=pc6([0, 0, 0, 1, 1, 1], [0, 0, 0, 2, 2, 2])) @@ -207,6 +227,34 @@ def test_sem_types_none_processes_all(self): result = self.defrag(sem_types=None).process([p]) assert len(result) == 2 # kTrack still processed when sem_types=None + # ── last_stats ──────────────────────────────────────────────────────── + + def test_last_stats_populated_no_fragmentation(self): + # Single contiguous cloud — bounding box early-exit applies + pts = _cluster_pts(20, (0, 0, 0)) + p = make_particle(1, PT_PRIMARY, pdg=11, pc=pts) + defrag = self.defrag() + defrag.process([p]) + s = defrag.last_stats + required = {'n_particles', 'n_skipped', 'n_early_exit', 'n_cc_checked', + 'n_fragmented', 'n_spawned', 'n_jobs', + 'cc_pts_median', 'cc_pts_max', 'cc_pts_total'} + assert required <= s.keys() + assert s['n_particles'] == 1 + assert s['n_fragmented'] == 0 + assert s['n_spawned'] == 0 + + def test_last_stats_fragmented_counters(self): + large = _cluster_pts(10, (0, 0, 0)) + small = _cluster_pts(2, (100, 0, 0)) + p = make_particle(1, PT_PRIMARY, pdg=11, pc=np.vstack([large, small])) + defrag = self.defrag() + defrag.process([p]) + s = defrag.last_stats + assert s['n_fragmented'] == 1 + assert s['n_spawned'] == 1 + assert s['cc_pts_total'] > 0 + # ── diagnostic records ─────────────────────────────────────────────── def test_fragmented_particle_has_diagnostic_record(self): @@ -223,3 +271,181 @@ def test_non_fragmented_particle_no_diagnostic(self): defrag = self.defrag() defrag.process([p]) assert len(defrag.last_diagnostics) == 0 + + +# ============================================================================ +# VoxelizeProcessor +# ============================================================================ + +class TestVoxelizeProcessor: + """Tests for VoxelizeProcessor (isotropic and anisotropic voxelization).""" + + def setup_method(self): + # Isotropic 1-unit voxels anchored at the origin for predictability. + self.proc = VoxelizeProcessor(voxel_size=1.0, origin=[0.0, 0.0, 0.0]) + + # ── basic collapsing ───────────────────────────────────────────────── + + def test_two_points_same_voxel_collapse_to_one(self): + # (0.2,0,0) and (0.7,0,0) both lie in voxel (0,0,0) + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 2.0, 0.5], + [0.7, 0.0, 0.0, 3.0, 4.0, 1.5])) + result = self.proc.process([p]) + assert len(result[0].point_cloud) == 1 + + def test_two_points_different_voxels_unchanged(self): + # (0.2,0,0) → voxel (0,0,0); (1.5,0,0) → voxel (1,0,0) + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 2.0, 0.5], + [1.5, 0.0, 0.0, 3.0, 4.0, 1.5])) + result = self.proc.process([p]) + assert len(result[0].point_cloud) == 2 + + def test_voxel_centre_position(self): + # voxel_size=1, origin=(0,0,0) → voxel (0,0,0) centre is (0.5,0.5,0.5) + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.3, 0.4, 0.2, 1.0, 1.0, 1.0], + [0.8, 0.1, 0.9, 2.0, 2.0, 2.0])) + result = self.proc.process([p]) + np.testing.assert_allclose(result[0].point_cloud[0, :3], + [0.5, 0.5, 0.5], atol=1e-5) + + # ── feature aggregation ─────────────────────────────────────────────── + + def test_time_takes_minimum(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 10.0, 1.0, 0.5], + [0.7, 0.0, 0.0, 2.0, 1.0, 0.5])) + result = self.proc.process([p]) + assert result[0].point_cloud[0, 3] == pytest.approx(2.0) + + def test_energy_takes_sum(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 3.0, 0.5], + [0.7, 0.0, 0.0, 1.0, 4.0, 0.5])) + result = self.proc.process([p]) + assert result[0].point_cloud[0, 4] == pytest.approx(7.0) + + def test_dedx_takes_maximum(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 1.0, 1.5], + [0.7, 0.0, 0.0, 1.0, 1.0, 3.2])) + result = self.proc.process([p]) + assert result[0].point_cloud[0, 5] == pytest.approx(3.2) + + # ── edge cases ──────────────────────────────────────────────────────── + + def test_empty_cloud_passes_through(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=np.zeros((0, 6), dtype=np.float32)) + result = self.proc.process([p]) + assert len(result[0].point_cloud) == 0 + + def test_single_point_passes_through_unchanged(self): + # A single point has nothing to merge; original coordinates are preserved. + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.3, 0.3, 0.3, 1.0, 1.0, 1.0])) + result = self.proc.process([p]) + assert len(result[0].point_cloud) == 1 + np.testing.assert_allclose(result[0].point_cloud[0, :3], + [0.3, 0.3, 0.3], atol=1e-6) + + def test_multiple_particles_processed_independently(self): + # p1: two points in the same voxel → collapse + p1 = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 1.0, 1.0], + [0.7, 0.0, 0.0, 2.0, 2.0, 2.0])) + # p2: two points in different voxels → no collapse + p2 = make_particle(2, PT_TRACK, pdg=13, + pc=pc6([0.2, 0.0, 0.0, 1.0, 1.0, 1.0], + [1.5, 0.0, 0.0, 2.0, 2.0, 2.0])) + result = self.proc.process([p1, p2]) + assert len(result[0].point_cloud) == 1 # p1 collapsed + assert len(result[1].point_cloud) == 2 # p2 unchanged + + # ── anisotropic voxel ──────────────────────────────────────────────── + + def test_anisotropic_voxel_size(self): + # dx=1, dy=2, dz=3; origin=(0,0,0) + proc = VoxelizeProcessor(voxel_size=[1.0, 2.0, 3.0], + origin=[0.0, 0.0, 0.0]) + # Both points fall in voxel (0,0,0): floor(0.2/1)=0, floor(0.5/2)=0, floor(0.5/3)=0 + # and floor(0.7/1)=0, floor(1.9/2)=0, floor(2.9/3)=0 + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.5, 0.5, 1.0, 2.0, 0.5], + [0.7, 1.9, 2.9, 2.0, 3.0, 1.5])) + result = proc.process([p]) + assert len(result[0].point_cloud) == 1 + # centre of voxel (0,0,0) with anisotropic grid: (0.5*1, 0.5*2, 0.5*3) + np.testing.assert_allclose(result[0].point_cloud[0, :3], + [0.5, 1.0, 1.5], atol=1e-5) + + # ── fixed vs per-particle origin ───────────────────────────────────── + + def test_fixed_origin_grid_alignment(self): + # voxel_size=1, origin=(0,0,0): both points in voxel (1,0,0) + # → merged centre at (1.5, 0.5, 0.5) + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([1.2, 0.0, 0.0, 1.0, 1.0, 1.0], + [1.8, 0.0, 0.0, 2.0, 2.0, 2.0])) + result = self.proc.process([p]) + assert len(result[0].point_cloud) == 1 + np.testing.assert_allclose(result[0].point_cloud[0, :3], + [1.5, 0.5, 0.5], atol=1e-5) + + # ── merge_duplicates flag ──────────────────────────────────────────── + + def test_merge_duplicates_flag_default_false(self): + proc = VoxelizeProcessor(voxel_size=1.0) + assert proc.merge_duplicates is False + + def test_merge_duplicates_flag_can_be_set(self): + proc = VoxelizeProcessor(voxel_size=1.0, merge_duplicates=True) + assert proc.merge_duplicates is True + + # ── last_stats ──────────────────────────────────────────────────────── + + def test_last_stats_populated_after_merge(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 2.0, 0.5], + [0.7, 0.0, 0.0, 3.0, 4.0, 1.5])) + self.proc.process([p]) + s = self.proc.last_stats + assert {'n_particles', 'n_affected', 'pts_before', 'pts_after'} <= s.keys() + assert s['n_particles'] == 1 + assert s['n_affected'] == 1 + assert s['pts_before'] == 2 + assert s['pts_after'] == 1 + + def test_last_stats_no_merge_when_all_separate_voxels(self): + p = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 2.0, 0.5], + [1.5, 0.0, 0.0, 3.0, 4.0, 1.5])) + self.proc.process([p]) + s = self.proc.last_stats + assert s['n_affected'] == 0 + assert s['pts_before'] == s['pts_after'] + + # ── diagnostics ────────────────────────────────────────────────────── + + def test_diagnostics_record_only_affected_particles(self): + p_merged = make_particle(1, PT_PRIMARY, pdg=11, + pc=pc6([0.2, 0.0, 0.0, 1.0, 1.0, 1.0], + [0.7, 0.0, 0.0, 2.0, 2.0, 2.0])) + p_clean = make_particle(2, PT_TRACK, pdg=13, + pc=pc6([0.2, 0.0, 0.0, 1.0, 1.0, 1.0], + [1.5, 0.0, 0.0, 2.0, 2.0, 2.0])) + self.proc.process([p_merged, p_clean]) + assert len(self.proc.last_diagnostics) == 1 + assert self.proc.last_diagnostics[0].particle_id == 1 + + # ── invalid constructor args ────────────────────────────────────────── + + def test_zero_voxel_size_raises(self): + with pytest.raises(ValueError): + VoxelizeProcessor(voxel_size=0.0) + + def test_negative_voxel_size_raises(self): + with pytest.raises(ValueError): + VoxelizeProcessor(voxel_size=-1.0) From afe1c57b89036cc4cd9addc692041c32a980c6a2 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 10 Mar 2026 09:04:22 -0700 Subject: [PATCH 03/62] minor README update --- README.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index b91b88d..acd624a 100644 --- a/README.md +++ b/README.md @@ -149,13 +149,23 @@ Then open the URL in a browser. The sidebar lets you: | Section | Controls | |---|---| | **INPUT** | HDF5 file path and event index | +| **INPUT FORMAT** | File format selector (`native` HDF5 or `edepsim_h5`); EDepSim-specific step/particle dataset keys and electron threshold | | **PREPROCESSING** | Enable merge-duplicates and/or defragmentation; backend; semantic-type filter | | **PARTITIONER** | Distance threshold, checker backend, `n_jobs` | | **CONDITIONS** | Toggle each of the four conditions independently | -| **VIEW OPTIONS** | Show/hide legends; synchronise the two 3-D camera views | -| **PARTICLE FILTER** | Instantly show/hide particles by semantic type (no re-run needed) | +| **VIEW OPTIONS** | Show/hide legends; synchronise the two 3-D camera views; toggle **colour by sem type** (fast merged-trace rendering vs. per-particle instance colouring) | +| **PARTICLE FILTER** | Instantly show/hide particles by semantic type (no re-run needed); **Min points to display** hides small point clouds from the view without re-running the pipeline | -Hit **▶ Run** to execute the full pipeline and render two side-by-side 3-D point-cloud plots—original particles on the left, partitions on the right. The **PARTICLE FILTER** and **show legend** checkbox take effect immediately without re-running the pipeline. +Hit **▶ Run** to execute the full pipeline and render two side-by-side 3-D point-cloud plots—original particles on the left, partitions on the right. + +**Draw modes** (toggled via *colour by sem type* in VIEW OPTIONS): + +| Mode | Left plot | Right plot | Speed | +|---|---|---|---| +| **by instance** (default) | One trace per particle, unique colour per ID; hover shows particle ID, PDG, parent | One trace per partition, unique colour per partition index | Slower for large events (many traces) | +| **by sem type** | One merged trace per semantic type with fixed colours | One merged trace per semantic type across all partitions | Fast — O(n\_sem\_types) traces regardless of event size | + +The **PARTICLE FILTER** and **show legend** checkbox take effect immediately without re-running the pipeline.

pysupera-app event display From 7234f95cd9a5b204749cef38af5d35bc54947b55 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 10 Mar 2026 09:25:33 -0700 Subject: [PATCH 04/62] a bug fix: process_type enum assumed to start from 0 in some parts of the unit tests, a remanent of C++ version of Supera. Fixed. Also process_type use was mixed with bare integer values, which is updated. --- pysupera/data.py | 4 ++- pysupera/io.py | 15 ++++----- pysupera/utils.py | 8 ++--- tests/conftest.py | 57 +++++++++++++++++----------------- tests/test_semantic_type.py | 61 +++++++++++++++++++++++-------------- tests/test_utils.py | 10 +++--- 6 files changed, 86 insertions(+), 69 deletions(-) diff --git a/pysupera/data.py b/pysupera/data.py index dbe385f..6a2381c 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -175,7 +175,9 @@ def __init__( self.root_id = root_id self.pdg = pdg self.parent_pdg = parent_pdg - self._process_type = process_type # raw int; retained for serialisation + self._process_type = (process_type.value + if isinstance(process_type, InteractionType) + else int(process_type)) # raw int; retained for serialisation if min_pc_size is None: min_pc_size = _DEFAULT_MIN_PC_SIZE diff --git a/pysupera/io.py b/pysupera/io.py index 1e392b8..78feb84 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -24,8 +24,9 @@ pdg (n_total_particles,) int32 parent_pdg (n_total_particles,) int32 process_type (n_total_particles,) int32 - Raw integer passed to SetSemanticType; sem_type is re-derived on - read so the stored value is always the ground-truth input. + Raw InteractionType integer value (1-based enum value). + sem_type is re-derived on read so the stored value is always + the ground-truth input. pc_offsets (n_total_particles + 1,) int64 particles/pc_offsets[j] : particles/pc_offsets[j+1] — row slice into /points/flat that belongs to particle j. @@ -96,8 +97,9 @@ def write_events(path: str, ``sem_type`` is **not** stored: it is deterministically derived from ``process_type``, ``pdg``, ``parent_pdg``, and ``point_cloud`` by :func:`~pysupera.utils.SetSemanticType` and is re-created on read. - Storing the raw ``process_type`` integer is both smaller and more - faithful to the simulation ground truth. + Storing the raw ``process_type`` integer (the 1-based + ``InteractionType`` enum value) is both smaller and more faithful to + the simulation ground truth. Examples -------- @@ -138,8 +140,8 @@ def write_events(path: str, p_root_id[j] = p.root_id p_pdg[j] = p.pdg p_parent_pdg[j] = p.parent_pdg - # process_type is stored as raw int (0-based, as passed to - # SetSemanticType before the +1 inside that function). + # process_type is stored as the raw InteractionType integer value + # (1-based, same as the enum). p_proc_type[j] = int(p._process_type) pc_offsets[j + 1] = pc_offsets[j] + len(p.point_cloud) j += 1 @@ -378,7 +380,6 @@ def append_event(self, particles: List) -> None: p_pdg[k] = p.pdg p_parent_pdg[k] = p.parent_pdg p_proc_type[k] = int(p._process_type) - pc_lengths[k] = len(p.point_cloud) self._f["particles/id" ][new_p_start:new_p_end] = p_id self._f["particles/parent_id" ][new_p_start:new_p_end] = p_parent_id diff --git a/pysupera/utils.py b/pysupera/utils.py index 35cfee2..242f80f 100644 --- a/pysupera/utils.py +++ b/pysupera/utils.py @@ -5,11 +5,9 @@ class InteractionType(Enum): """ Enumeration of the physics processes that can produce a simulation particle. - The integer value of each member is assigned automatically (1-based) - and corresponds to the ``process_type + 1`` convention used by the - upstream simulation output. :func:`SetSemanticType` converts a raw - integer process code to an ``InteractionType`` via - ``InteractionType(process_type + 1)``. + The integer value of each member is assigned automatically (1-based). + :func:`SetSemanticType` converts a raw integer process code to an + ``InteractionType`` via ``InteractionType(process_type)``. Members ------- diff --git a/tests/conftest.py b/tests/conftest.py index 989b68d..3fdc166 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,39 +1,40 @@ """ Shared fixtures and helpers for the pysupera test suite. -process_type mapping (0-based raw int → InteractionType): - 0 kTrack → sem kTrack - 1 kNeutron → sem kLEScatter - 2 kNucleus → sem kTrack (large) / kLEScatter (small) - 3 kPhoton → sem kShower (pdg=11/22, large) / kLEScatter (small) - 4 kPrimary → sem kShower (pdg=11/22) / kTrack (other) - 5 kCompton → sem kShower (pdg=11/22, large) / kLEScatter (small) - 6 kDelta → sem kDelta (large) / kLEScatter (small) - 7 kConversion → sem kShower (pdg=11/22, large) / kLEScatter (small) - 8 kIonization → sem kLEScatter - 9 kPhotoElectron → sem kLEScatter - 10 kDecay → sem kMichel / kShower / kTrack - 11 kOtherShower → sem kShower (pdg=11/22, large) / kLEScatter (small) - 12 kInvalidProcess → sem kUnknown +process_type mapping (InteractionType enum → SemanticType): + kTrack → sem kTrack + kNeutron → sem kLEScatter + kNucleus → sem kTrack (large) / kLEScatter (small) + kPhoton → sem kShower (pdg=11/22, large) / kLEScatter (small) + kPrimary → sem kShower (pdg=11/22) / kTrack (other) + kCompton → sem kShower (pdg=11/22, large) / kLEScatter (small) + kDelta → sem kDelta (large) / kLEScatter (small) + kConversion → sem kShower (pdg=11/22, large) / kLEScatter (small) + kIonization → sem kLEScatter + kPhotoElectron → sem kLEScatter + kDecay → sem kMichel / kShower / kTrack + kOtherShower → sem kShower (pdg=11/22, large) / kLEScatter (small) + kInvalidProcess → sem kUnknown """ import numpy as np import pytest from pysupera.data import Particle +from pysupera.utils import InteractionType -# ── Symbolic aliases for process_type raw ints ────────────────────────────── -PT_TRACK = 0 -PT_NEUTRON = 1 -PT_NUCLEUS = 2 -PT_PHOTON = 3 -PT_PRIMARY = 4 -PT_COMPTON = 5 -PT_DELTA = 6 -PT_CONVERSION = 7 -PT_IONIZATION = 8 -PT_PHOTO_ELEC = 9 -PT_DECAY = 10 -PT_OTHER_SHOWER = 11 -PT_INVALID = 12 +# ── Symbolic aliases for process_type (InteractionType enum members) ──────── +PT_TRACK = InteractionType.kTrack +PT_NEUTRON = InteractionType.kNeutron +PT_NUCLEUS = InteractionType.kNucleus +PT_PHOTON = InteractionType.kPhoton +PT_PRIMARY = InteractionType.kPrimary +PT_COMPTON = InteractionType.kCompton +PT_DELTA = InteractionType.kDelta +PT_CONVERSION = InteractionType.kConversion +PT_IONIZATION = InteractionType.kIonization +PT_PHOTO_ELEC = InteractionType.kPhotoElectron +PT_DECAY = InteractionType.kDecay +PT_OTHER_SHOWER = InteractionType.kOtherShower +PT_INVALID = InteractionType.kInvalidProcess def make_particle( diff --git a/tests/test_semantic_type.py b/tests/test_semantic_type.py index df9bfbc..1e3e225 100644 --- a/tests/test_semantic_type.py +++ b/tests/test_semantic_type.py @@ -4,6 +4,21 @@ from pysupera.utils import SetSemanticType, SemanticType, InteractionType +# Aliases so test bodies stay readable +PT_TRACK = InteractionType.kTrack +PT_NEUTRON = InteractionType.kNeutron +PT_NUCLEUS = InteractionType.kNucleus +PT_PHOTON = InteractionType.kPhoton +PT_PRIMARY = InteractionType.kPrimary +PT_COMPTON = InteractionType.kCompton +PT_DELTA = InteractionType.kDelta +PT_CONVERSION = InteractionType.kConversion +PT_IONIZATION = InteractionType.kIonization +PT_PHOTO_ELEC = InteractionType.kPhotoElectron +PT_DECAY = InteractionType.kDecay +PT_OTHER_SHOWER = InteractionType.kOtherShower +PT_INVALID = InteractionType.kInvalidProcess + # Point cloud helpers LARGE_PC = np.zeros((20, 3), dtype=np.float32) # 20 pts — "large" SMALL_PC = np.zeros((2, 3), dtype=np.float32) # 2 pts — "small" @@ -13,100 +28,100 @@ class TestTrackAndPrimary: def test_ktrack_process_is_ktrack(self): - assert SetSemanticType(0, 13, 0, LARGE_PC) == SemanticType.kTrack + assert SetSemanticType(PT_TRACK, 13, 0, LARGE_PC) == SemanticType.kTrack def test_primary_pdg_electron_is_kshower(self): - assert SetSemanticType(4, 11, 0, LARGE_PC) == SemanticType.kShower + assert SetSemanticType(PT_PRIMARY, 11, 0, LARGE_PC) == SemanticType.kShower def test_primary_pdg_photon_is_kshower(self): - assert SetSemanticType(4, 22, 0, LARGE_PC) == SemanticType.kShower + assert SetSemanticType(PT_PRIMARY, 22, 0, LARGE_PC) == SemanticType.kShower def test_primary_pdg_proton_is_ktrack(self): - assert SetSemanticType(4, 2212, 0, LARGE_PC) == SemanticType.kTrack + assert SetSemanticType(PT_PRIMARY, 2212, 0, LARGE_PC) == SemanticType.kTrack def test_primary_pdg_muon_is_ktrack(self): - assert SetSemanticType(4, 13, 0, LARGE_PC) == SemanticType.kTrack + assert SetSemanticType(PT_PRIMARY, 13, 0, LARGE_PC) == SemanticType.kTrack class TestDelta: def test_delta_large_cloud_is_kdelta(self): - result = SetSemanticType(6, 11, 0, LARGE_PC, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_DELTA, 11, 0, LARGE_PC, point_cloud_size=MIN_SIZE) assert result == SemanticType.kDelta def test_delta_small_cloud_is_klescatter(self): small = np.zeros((3, 3), dtype=np.float32) # 3 < MIN_SIZE=5 - result = SetSemanticType(6, 11, 0, small, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_DELTA, 11, 0, small, point_cloud_size=MIN_SIZE) assert result == SemanticType.kLEScatter def test_delta_no_threshold_is_kdelta(self): # threshold=-1 → all sizes treated as "large" - result = SetSemanticType(6, 11, 0, SMALL_PC, point_cloud_size=-1) + result = SetSemanticType(PT_DELTA, 11, 0, SMALL_PC, point_cloud_size=-1) assert result == SemanticType.kDelta class TestDecay: def test_michel_electron_from_muon(self): # pdg=11 (e-), parent_pdg=13 (mu-) - result = SetSemanticType(10, 11, 13, LARGE_PC) + result = SetSemanticType(PT_DECAY, 11, 13, LARGE_PC) assert result == SemanticType.kMichel def test_michel_positron_from_antimuon(self): - result = SetSemanticType(10, 11, -13, LARGE_PC) + result = SetSemanticType(PT_DECAY, 11, -13, LARGE_PC) assert result == SemanticType.kMichel def test_decay_electron_non_muon_parent_is_kshower(self): # pdg=11, parent_pdg=211 (pion), not Michel - result = SetSemanticType(10, 11, 211, LARGE_PC) + result = SetSemanticType(PT_DECAY, 11, 211, LARGE_PC) assert result == SemanticType.kShower def test_decay_photon_is_kshower(self): - result = SetSemanticType(10, 22, 13, LARGE_PC) + result = SetSemanticType(PT_DECAY, 22, 13, LARGE_PC) assert result == SemanticType.kShower def test_decay_pion_is_ktrack(self): - result = SetSemanticType(10, 211, 13, LARGE_PC) + result = SetSemanticType(PT_DECAY, 211, 13, LARGE_PC) assert result == SemanticType.kTrack class TestLEScatterProcesses: def test_neutron_is_klescatter(self): - assert SetSemanticType(1, 2112, 0, LARGE_PC) == SemanticType.kLEScatter + assert SetSemanticType(PT_NEUTRON, 2112, 0, LARGE_PC) == SemanticType.kLEScatter def test_ionization_is_klescatter(self): - assert SetSemanticType(8, 11, 0, LARGE_PC) == SemanticType.kLEScatter + assert SetSemanticType(PT_IONIZATION, 11, 0, LARGE_PC) == SemanticType.kLEScatter def test_photoelectron_is_klescatter(self): - assert SetSemanticType(9, 11, 0, LARGE_PC) == SemanticType.kLEScatter + assert SetSemanticType(PT_PHOTO_ELEC, 11, 0, LARGE_PC) == SemanticType.kLEScatter class TestShowerProcesses: def test_photon_process_electron_large_is_kshower(self): - result = SetSemanticType(3, 11, 0, LARGE_PC, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_PHOTON, 11, 0, LARGE_PC, point_cloud_size=MIN_SIZE) assert result == SemanticType.kShower def test_photon_process_electron_small_is_klescatter(self): small = np.zeros((3, 3), dtype=np.float32) - result = SetSemanticType(3, 11, 0, small, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_PHOTON, 11, 0, small, point_cloud_size=MIN_SIZE) assert result == SemanticType.kLEScatter def test_compton_photon_large_is_kshower(self): - result = SetSemanticType(5, 22, 0, LARGE_PC, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_COMPTON, 22, 0, LARGE_PC, point_cloud_size=MIN_SIZE) assert result == SemanticType.kShower def test_conversion_electron_large_is_kshower(self): - result = SetSemanticType(7, 11, 0, LARGE_PC, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_CONVERSION, 11, 0, LARGE_PC, point_cloud_size=MIN_SIZE) assert result == SemanticType.kShower class TestNucleusAndInvalid: def test_nucleus_large_is_ktrack(self): - result = SetSemanticType(2, 1000060120, 0, LARGE_PC, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_NUCLEUS, 1000060120, 0, LARGE_PC, point_cloud_size=MIN_SIZE) assert result == SemanticType.kTrack def test_nucleus_small_is_klescatter(self): small = np.zeros((3, 3), dtype=np.float32) - result = SetSemanticType(2, 1000060120, 0, small, point_cloud_size=MIN_SIZE) + result = SetSemanticType(PT_NUCLEUS, 1000060120, 0, small, point_cloud_size=MIN_SIZE) assert result == SemanticType.kLEScatter def test_invalid_process_is_kunknown(self): - assert SetSemanticType(12, 11, 0, LARGE_PC) == SemanticType.kUnknown + assert SetSemanticType(PT_INVALID, 11, 0, LARGE_PC) == SemanticType.kUnknown diff --git a/tests/test_utils.py b/tests/test_utils.py index 5c58d94..99d3ced 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -3,7 +3,7 @@ import pytest from pysupera.utils import trace_ancestry, SetSemanticType, SemanticType -from tests.conftest import make_particle, PT_PRIMARY, PT_TRACK, PT_DECAY, PT_INVALID +from tests.conftest import make_particle, PT_PRIMARY, PT_TRACK, PT_DECAY, PT_DELTA, PT_INVALID # ============================================================================ @@ -110,7 +110,7 @@ def test_chain_ordered_root_to_target(self): class TestSetSemanticTypeEdgeCases: def test_with_zero_size_point_cloud(self): # Empty cloud with default point_cloud_size=-1: 0 < -1 is False → kDelta - st = SetSemanticType(process_type=6, pdg=11, parent_pdg=0, + st = SetSemanticType(process_type=PT_DELTA, pdg=11, parent_pdg=0, point_cloud=np.zeros((0, 3))) assert st == SemanticType.kDelta @@ -118,7 +118,7 @@ def test_with_none_point_cloud_raises_or_returns_unknown(self): # When point_cloud=None, should either use point_cloud_size or raise # At minimum it should not silently return a wrong type try: - st = SetSemanticType(process_type=0, pdg=13, parent_pdg=0, + st = SetSemanticType(process_type=PT_TRACK, pdg=13, parent_pdg=0, point_cloud=None) # kTrack is always kTrack regardless of cloud size assert st == SemanticType.kTrack @@ -129,13 +129,13 @@ def test_large_cloud_explicit_size_override(self): # Explicit point_cloud_size overrides actual array length # kDelta with explicit large size → kDelta not kLEScatter large_pc = np.ones((100, 3), dtype=np.float32) - st = SetSemanticType(process_type=6, pdg=11, parent_pdg=0, + st = SetSemanticType(process_type=PT_DELTA, pdg=11, parent_pdg=0, point_cloud=large_pc, point_cloud_size=100) assert st == SemanticType.kDelta def test_small_cloud_explicit_size_override(self): # kDelta: cloud size (2) < point_cloud_size (100) → kLEScatter small_pc = np.ones((2, 3), dtype=np.float32) - st = SetSemanticType(process_type=6, pdg=11, parent_pdg=0, + st = SetSemanticType(process_type=PT_DELTA, pdg=11, parent_pdg=0, point_cloud=small_pc, point_cloud_size=100) assert st == SemanticType.kLEScatter From 839d94121669f6e174e83051c62fbca1eda8db44 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Thu, 2 Apr 2026 11:12:12 -0700 Subject: [PATCH 05/62] add input point <=> voxelized points mapping --- pysupera/__init__.py | 4 +- pysupera/conf/checker/gpu.yaml | 3 + pysupera/conf/config.yaml | 4 + pysupera/config.py | 55 ++- pysupera/data.py | 82 ++-- pysupera/io.py | 595 ++++++++++++++++++++++++-- pysupera/preproc.py | 113 ++++- pysupera/proxck.py | 36 +- pysupera/readers/format_edepsim_h5.py | 45 +- pysupera/utils.py | 38 +- tests/conftest.py | 20 +- tests/test_preproc.py | 297 ++++++++++++- tests/test_utils.py | 8 +- 13 files changed, 1146 insertions(+), 154 deletions(-) diff --git a/pysupera/__init__.py b/pysupera/__init__.py index c6f61ff..d052689 100644 --- a/pysupera/__init__.py +++ b/pysupera/__init__.py @@ -1,7 +1,9 @@ __version__ = "0.0.1" from .data import Particle, FLOAT_UNSET -from .io import write_events, open_writer, read_events, EventStore, EventWriter +from .io import (write_events, open_writer, read_events, EventStore, EventWriter, + voxmap_path, write_voxmap, open_voxmap_writer, read_voxmap, + VoxmapWriter, VoxmapStore) from .config import (build_checker, build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, build_pipeline, load_cfg, configure, check_particle_list, Pipeline) diff --git a/pysupera/conf/checker/gpu.yaml b/pysupera/conf/checker/gpu.yaml index affbcb3..b81f941 100644 --- a/pysupera/conf/checker/gpu.yaml +++ b/pysupera/conf/checker/gpu.yaml @@ -1,3 +1,6 @@ # RAPIDS cuML NearestNeighbors (brute-force L2, k=1). # Requires: pip install cuml-cu12 cupy-cuda12x name: gpu +# Rows of cloud_a per CuPy kernel launch in Stage-2 exact checks. +# Increase for larger GPU memory / smaller clouds; decrease to avoid OOM. +chunk_size: 512 diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index fa07f1c..f6a0700 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -57,6 +57,10 @@ particle: # When true, skips the separate merge_duplicates preprocessing step. # Safe because exact-coordinate duplicates always fall in the same voxel. merge_duplicates: false + # When true, VoxelizeProcessor stores a CSR mapping (input_ids, + # input_energies, voxel_offsets) for every particle in last_diagnostics, + # enabling traces from output voxels back to input points. + store_mapping: false # Pre-processing: collapse points sharing identical 3-D coordinates. # Aggregation rules: time=min, energy=sum, dedx=max. diff --git a/pysupera/config.py b/pysupera/config.py index 8d77fdc..3a9a6ca 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -249,6 +249,7 @@ def build_voxelizer(cfg: DictConfig, verbose: bool = False): origin = origin, verbose = verbose, merge_duplicates = bool(vox_cfg.get('merge_duplicates', False)), + store_mapping = bool(vox_cfg.get('store_mapping', False)), ) @@ -317,7 +318,7 @@ def build_checker(cfg: DictConfig): if name == "gpu": from pysupera.proxck import GPUChecker - return GPUChecker(D) + return GPUChecker(D, chunk_size=int(cfg.checker.get("chunk_size", 512))) if name == "bulk-gpu": from pysupera.proxck import BulkGPUChecker @@ -498,6 +499,8 @@ def load_cfg(overrides: list[str] | None = None, overrides : list of str, optional Hydra override strings, e.g. ``["checker=bulk_gpu", "distance_threshold=4.0"]``. + Checker names may use hyphens or underscores interchangeably + (``"checker=bulk-gpu"`` and ``"checker=bulk_gpu"`` are equivalent). config_path : str, optional Path to the ``conf/`` directory **relative to this file's location**. Default is ``"conf"`` (the ``conf/`` directory that lives inside the @@ -525,8 +528,56 @@ def load_cfg(overrides: list[str] | None = None, os.path.join(os.path.dirname(__file__), config_path) ) + # --- Normalise overrides ------------------------------------------------ + # 1. checker= : convert hyphens → underscores in the value so that + # e.g. "bulk-gpu" resolves to bulk_gpu.yaml. + # 2. checker.= : pull these OUT of the Hydra compose call and apply + # them directly via OmegaConf after composition. + # Hydra's struct-mode handling of config-group sub-keys + # is brittle across versions (MissingConfigException / + # ConfigCompositionException depending on whether the + # key already exists and which sigil is used). + # Setting them on the live DictConfig is simpler and + # completely version-agnostic. + _hydra_overrides: list[str] = [] + _checker_subkeys: dict[str, str] = {} # key → raw string value + + for ov in (overrides or []): + bare = ov.lstrip("+~") + if bare.startswith("checker="): + prefix = ov[: len(ov) - len(bare)] + val = bare[len("checker="):] + _hydra_overrides.append(f"{prefix}checker={val.replace('-', '_')}") + elif bare.startswith("checker.") and "=" in bare: + rest = bare[len("checker."):] + key, _, val = rest.partition("=") + _checker_subkeys[key] = val + else: + _hydra_overrides.append(ov) + + # Hydra keeps a process-wide singleton; clear it so repeated calls inside + # a notebook kernel always get a fresh composition. + from hydra.core.global_hydra import GlobalHydra + GlobalHydra.instance().clear() + with initialize_config_dir(config_dir=abs_conf, version_base=None): - cfg = compose(config_name, overrides=overrides or []) + cfg = compose(config_name, overrides=_hydra_overrides) + + # Apply checker sub-key overrides directly, bypassing struct-mode checks. + if _checker_subkeys: + from omegaconf import OmegaConf + OmegaConf.set_struct(cfg.checker, False) + for k, raw in _checker_subkeys.items(): + # Coerce to int or float when possible so callers get numeric types. + try: + v: object = int(raw) + except ValueError: + try: + v = float(raw) + except ValueError: + v = raw + OmegaConf.update(cfg.checker, k, v, merge=True) + OmegaConf.set_struct(cfg.checker, True) configure(cfg) _cfg_override_keys[id(cfg)] = _parse_override_keys(overrides or []) diff --git a/pysupera/data.py b/pysupera/data.py index 6a2381c..5911297 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -66,13 +66,13 @@ class Particle: parent_pdg : int PDG code of the direct parent particle. sem_type : SemanticType - High-level semantic category derived from *process_type*, *pdg*, + High-level semantic category derived from *interaction_type*, *pdg*, *parent_pdg*, and *point_cloud* by :func:`~pysupera.utils.SetSemanticType`. - process_type : InteractionType + interaction_type : InteractionType Physics process that created this particle, expressed as an :class:`~pysupera.utils.InteractionType` member. Derived from - the raw integer stored in ``_process_type``. + the raw integer stored in ``_interaction_type``. point_cloud : numpy.ndarray, shape (N, ≥3) 3-D spatial hits; columns 0–2 are x, y, z. Additional columns (time, energy, dE/dx, …) may be present. @@ -117,7 +117,7 @@ def __init__( root_id, pdg, parent_pdg, - process_type, + interaction_type, point_cloud, min_pc_size=None, # ------------------------------------------------------------------ @@ -153,9 +153,9 @@ def __init__( PDG Monte Carlo particle code. parent_pdg : int PDG code of the parent particle. - process_type : int + interaction_type : int Raw integer encoding of the interaction process (stored as - ``_process_type``). The public ``process_type`` property + ``_interaction_type``). The public ``interaction_type`` property returns the corresponding :class:`~pysupera.utils.InteractionType` member. point_cloud : numpy.ndarray, shape (N, ≥3) @@ -175,15 +175,15 @@ def __init__( self.root_id = root_id self.pdg = pdg self.parent_pdg = parent_pdg - self._process_type = (process_type.value - if isinstance(process_type, InteractionType) - else int(process_type)) # raw int; retained for serialisation + self._interaction_type = (interaction_type.value + if isinstance(interaction_type, InteractionType) + else int(interaction_type)) # raw int; retained for serialisation if min_pc_size is None: min_pc_size = _DEFAULT_MIN_PC_SIZE self.sem_type = SetSemanticType( - process_type, pdg, parent_pdg, point_cloud, + interaction_type, pdg, parent_pdg, point_cloud, point_cloud_size=min_pc_size, ) self.point_cloud = point_cloud @@ -223,15 +223,15 @@ def vertex(self, value): if value is not None else None) @property - def process_type(self) -> "InteractionType": + def interaction_type(self) -> "InteractionType": """ :class:`~pysupera.utils.InteractionType` member corresponding to - the raw integer stored in ``_process_type``. + the raw integer stored in ``_interaction_type``. - Read-only; modify by setting ``_process_type`` directly if needed. + Read-only; modify by setting ``_interaction_type`` directly if needed. """ try: - return InteractionType(int(self._process_type)) + return InteractionType(int(self._interaction_type)) except ValueError: return InteractionType.kInvalidProcess @@ -247,7 +247,7 @@ def from_arrays( root_ids, pdgs, parent_pdgs, - process_types, + interaction_types, point_clouds, min_pc_size=None, ): @@ -271,7 +271,7 @@ def from_arrays( PDG Monte Carlo codes. parent_pdgs : array-like of int, shape (N,) PDG codes of direct parents. - process_types : array-like of int, shape (N,) + interaction_types : array-like of int, shape (N,) Raw interaction process codes forwarded to :func:`~pysupera.utils.SetSemanticType`. point_clouds : sequence of numpy.ndarray, length N @@ -303,30 +303,30 @@ def from_arrays( ... root_ids=[0, 0], ... pdgs=[11, 22], ... parent_pdgs=[0, 11], - ... process_types=[0, 7], + ... interaction_types=[0, 7], ... point_clouds=pcs, ... ) >>> len(particles) 2 """ - ids = list(ids) - parent_ids = list(parent_ids) - root_ids = list(root_ids) - pdgs = list(pdgs) - parent_pdgs = list(parent_pdgs) - process_types = list(process_types) - point_clouds = list(point_clouds) + ids = list(ids) + parent_ids = list(parent_ids) + root_ids = list(root_ids) + pdgs = list(pdgs) + parent_pdgs = list(parent_pdgs) + interaction_types = list(interaction_types) + point_clouds = list(point_clouds) n = len(ids) if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, - parent_pdgs, process_types, + parent_pdgs, interaction_types, point_clouds)): raise ValueError( "All input arrays must have the same length. " f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " f"parent_pdgs={len(parent_pdgs)}, " - f"process_types={len(process_types)}, " + f"interaction_types={len(interaction_types)}, " f"point_clouds={len(point_clouds)}." ) @@ -337,7 +337,7 @@ def from_arrays( root_id=root_ids[i], pdg=pdgs[i], parent_pdg=parent_pdgs[i], - process_type=process_types[i], + interaction_type=interaction_types[i], point_cloud=point_clouds[i], min_pc_size=min_pc_size, ) @@ -352,7 +352,7 @@ def from_flat_arrays( root_ids, pdgs, parent_pdgs, - process_types, + interaction_types, point_cloud_flat, point_cloud_offsets, min_pc_size=None, @@ -378,7 +378,7 @@ def from_flat_arrays( PDG Monte Carlo codes. parent_pdgs : array-like of int, shape (N,) PDG codes of direct parents. - process_types : array-like of int, shape (N,) + interaction_types : array-like of int, shape (N,) Raw interaction process codes forwarded to :func:`~pysupera.utils.SetSemanticType`. point_cloud_flat : numpy.ndarray, shape (M, ≥3) @@ -424,7 +424,7 @@ def from_flat_arrays( ... root_ids=[0, 0], ... pdgs=[11, 22], ... parent_pdgs=[0, 11], - ... process_types=[0, 7], + ... interaction_types=[0, 7], ... point_cloud_flat=flat, ... point_cloud_offsets=offsets, ... ) @@ -433,13 +433,13 @@ def from_flat_arrays( >>> particles[0].point_cloud.shape (10, 4) """ - ids = list(ids) - parent_ids = list(parent_ids) - root_ids = list(root_ids) - pdgs = list(pdgs) - parent_pdgs = list(parent_pdgs) - process_types = list(process_types) - offsets = np.asarray(point_cloud_offsets) + ids = list(ids) + parent_ids = list(parent_ids) + root_ids = list(root_ids) + pdgs = list(pdgs) + parent_pdgs = list(parent_pdgs) + interaction_types = list(interaction_types) + offsets = np.asarray(point_cloud_offsets) n = len(ids) @@ -449,13 +449,13 @@ def from_flat_arrays( f"got {offsets.shape}." ) if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, - parent_pdgs, process_types)): + parent_pdgs, interaction_types)): raise ValueError( "All scalar arrays must have the same length. " f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " f"parent_pdgs={len(parent_pdgs)}, " - f"process_types={len(process_types)}." + f"interaction_types={len(interaction_types)}." ) if offsets.shape[0] != n: raise ValueError( @@ -478,7 +478,7 @@ def from_flat_arrays( root_id=root_ids[i], pdg=pdgs[i], parent_pdg=parent_pdgs[i], - process_type=process_types[i], + interaction_type=interaction_types[i], point_cloud=flat[offsets[i, 0] : offsets[i, 1]], min_pc_size=min_pc_size, ) @@ -493,7 +493,7 @@ def __str__(self): f"root_id={self.root_id}", f" pdg={self.pdg}, parent_pdg={self.parent_pdg}, " f"root_pdg={self.root_pdg}", - f" sem_type={self.sem_type}, process_type={self.process_type}", + f" sem_type={self.sem_type}, interaction_type={self.interaction_type}", f" point_cloud: shape={self.point_cloud.shape}", ] if self.start is not None: diff --git a/pysupera/io.py b/pysupera/io.py index 78feb84..20a0a78 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -23,10 +23,11 @@ root_id (n_total_particles,) int32 pdg (n_total_particles,) int32 parent_pdg (n_total_particles,) int32 - process_type (n_total_particles,) int32 + interaction_type (n_total_particles,) int32 Raw InteractionType integer value (1-based enum value). - sem_type is re-derived on read so the stored value is always - the ground-truth input. + sem_type (n_total_particles,) int8 + SemanticType integer value stored for fast filtered reads + without re-deriving at load time. pc_offsets (n_total_particles + 1,) int64 particles/pc_offsets[j] : particles/pc_offsets[j+1] — row slice into /points/flat that belongs to particle j. @@ -51,10 +52,11 @@ from __future__ import annotations +import os import numpy as np from typing import List, Optional -FORMAT_VERSION = "1.0.0" +FORMAT_VERSION = "2.0.0" # Chunk sizes for compressed HDF5 datasets. # Tuned for typical event sizes; adjust if your events are significantly @@ -94,12 +96,11 @@ def write_events(path: str, Notes ----- - ``sem_type`` is **not** stored: it is deterministically derived from - ``process_type``, ``pdg``, ``parent_pdg``, and ``point_cloud`` by - :func:`~pysupera.utils.SetSemanticType` and is re-created on read. - Storing the raw ``process_type`` integer (the 1-based - ``InteractionType`` enum value) is both smaller and more faithful to - the simulation ground truth. + Both ``interaction_type`` (raw ``InteractionType`` int32) and + ``sem_type`` (``SemanticType`` int8) are stored. ``sem_type`` is + written directly from the in-memory value so filtered reads do not + need to re-derive it; ``interaction_type`` is the ground-truth + physics process integer (1-based enum value). Examples -------- @@ -127,7 +128,8 @@ def write_events(path: str, p_root_id = np.empty(n_total_particles, dtype=np.int32) p_pdg = np.empty(n_total_particles, dtype=np.int32) p_parent_pdg = np.empty(n_total_particles, dtype=np.int32) - p_proc_type = np.empty(n_total_particles, dtype=np.int32) + p_itype = np.empty(n_total_particles, dtype=np.int32) + p_sem_type = np.empty(n_total_particles, dtype=np.int8) # Particle → point offsets (fencepost: n_total_particles + 1 entries) pc_offsets = np.zeros(n_total_particles + 1, dtype=np.int64) @@ -140,9 +142,8 @@ def write_events(path: str, p_root_id[j] = p.root_id p_pdg[j] = p.pdg p_parent_pdg[j] = p.parent_pdg - # process_type is stored as the raw InteractionType integer value - # (1-based, same as the enum). - p_proc_type[j] = int(p._process_type) + p_itype[j] = int(p._interaction_type) + p_sem_type[j] = int(p.sem_type.value) pc_offsets[j + 1] = pc_offsets[j] + len(p.point_cloud) j += 1 @@ -177,16 +178,22 @@ def write_events(path: str, **ckw, ) if n_total_particles > 0: - _mk("id", p_id) - _mk("parent_id", p_parent_id) - _mk("root_id", p_root_id) - _mk("pdg", p_pdg) - _mk("parent_pdg", p_parent_pdg) - _mk("process_type", p_proc_type) + _mk("id", p_id) + _mk("parent_id", p_parent_id) + _mk("root_id", p_root_id) + _mk("pdg", p_pdg) + _mk("parent_pdg", p_parent_pdg) + _mk("interaction_type", p_itype) + pg.create_dataset( + "sem_type", data=p_sem_type, + chunks=(min(_CHUNK_PARTICLES, max(1, n_total_particles)),), + **ckw, + ) else: for name in ("id", "parent_id", "root_id", - "pdg", "parent_pdg", "process_type"): + "pdg", "parent_pdg", "interaction_type"): pg.create_dataset(name, data=np.empty(0, dtype=np.int32)) + pg.create_dataset("sem_type", data=np.empty(0, dtype=np.int8)) pg.create_dataset("pc_offsets", data=pc_offsets) # Flat point cloud @@ -279,9 +286,10 @@ class EventWriter: w.append_event(more_particles) """ - # Names of the six particle scalar fields in order + # Names of the six int32 particle scalar fields (sem_type handled separately + # because it uses dtype=int8) _SCALAR_FIELDS = ( - "id", "parent_id", "root_id", "pdg", "parent_pdg", "process_type" + "id", "parent_id", "root_id", "pdg", "parent_pdg", "interaction_type" ) def __init__(self, path: str, mode: str = "w", @@ -363,6 +371,7 @@ def append_event(self, particles: List) -> None: for name in self._SCALAR_FIELDS: ds = self._f[f"particles/{name}"] ds.resize(new_p_end, axis=0) + self._f["particles/sem_type"].resize(new_p_end, axis=0) # Build scalar arrays for this event p_id = np.empty(n_p, dtype=np.int32) @@ -370,7 +379,8 @@ def append_event(self, particles: List) -> None: p_root_id = np.empty(n_p, dtype=np.int32) p_pdg = np.empty(n_p, dtype=np.int32) p_parent_pdg = np.empty(n_p, dtype=np.int32) - p_proc_type = np.empty(n_p, dtype=np.int32) + p_itype = np.empty(n_p, dtype=np.int32) + p_sem_type = np.empty(n_p, dtype=np.int8) pc_lengths = np.empty(n_p, dtype=np.int64) for k, p in enumerate(particles): @@ -379,14 +389,17 @@ def append_event(self, particles: List) -> None: p_root_id[k] = p.root_id p_pdg[k] = p.pdg p_parent_pdg[k] = p.parent_pdg - p_proc_type[k] = int(p._process_type) - - self._f["particles/id" ][new_p_start:new_p_end] = p_id - self._f["particles/parent_id" ][new_p_start:new_p_end] = p_parent_id - self._f["particles/root_id" ][new_p_start:new_p_end] = p_root_id - self._f["particles/pdg" ][new_p_start:new_p_end] = p_pdg - self._f["particles/parent_pdg" ][new_p_start:new_p_end] = p_parent_pdg - self._f["particles/process_type"][new_p_start:new_p_end] = p_proc_type + p_itype[k] = int(p._interaction_type) + p_sem_type[k] = int(p.sem_type.value) + pc_lengths[k] = len(p.point_cloud) + + self._f["particles/id" ][new_p_start:new_p_end] = p_id + self._f["particles/parent_id" ][new_p_start:new_p_end] = p_parent_id + self._f["particles/root_id" ][new_p_start:new_p_end] = p_root_id + self._f["particles/pdg" ][new_p_start:new_p_end] = p_pdg + self._f["particles/parent_pdg" ][new_p_start:new_p_end] = p_parent_pdg + self._f["particles/interaction_type" ][new_p_start:new_p_end] = p_itype + self._f["particles/sem_type" ][new_p_start:new_p_end] = p_sem_type # ---- extend pc_offsets (n_particles+1 entries total) ---------- pc_ds = self._f["particles/pc_offsets"] @@ -449,7 +462,7 @@ def _init_datasets(self) -> None: chunks=(_CHUNK_PARTICLES,), ) - # particle scalar datasets — start empty + # particle scalar datasets — start empty (int32) pg = f.create_group("particles") for name in self._SCALAR_FIELDS: pg.create_dataset( @@ -458,6 +471,13 @@ def _init_datasets(self) -> None: chunks=(_CHUNK_PARTICLES,), **ckw, ) + # sem_type — int8 + pg.create_dataset( + "sem_type", + shape=(0,), maxshape=(None,), dtype=np.int8, + chunks=(_CHUNK_PARTICLES,), + **ckw, + ) # pc_offsets — single fencepost 0 pg.create_dataset( "pc_offsets", @@ -585,13 +605,14 @@ def __getitem__(self, index: int) -> List: if n_parts == 0: return [] - # ---- read scalar metadata in 6 contiguous array slices ------------ - ids = self._f["particles/id" ][p_start:p_end] - parent_ids = self._f["particles/parent_id" ][p_start:p_end] - root_ids = self._f["particles/root_id" ][p_start:p_end] - pdgs = self._f["particles/pdg" ][p_start:p_end] - parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] - proc_types = self._f["particles/process_type"][p_start:p_end] + # ---- read scalar metadata in 7 contiguous array slices ------------ + ids = self._f["particles/id" ][p_start:p_end] + parent_ids = self._f["particles/parent_id" ][p_start:p_end] + root_ids = self._f["particles/root_id" ][p_start:p_end] + pdgs = self._f["particles/pdg" ][p_start:p_end] + parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] + itypes = self._f["particles/interaction_type" ][p_start:p_end] + sem_types = self._f["particles/sem_type" ][p_start:p_end] # ---- read point-cloud data in one contiguous slice ---------------- pc_bounds = self._pc_offsets[p_start : p_end + 1] # (n_parts+1,) @@ -600,19 +621,23 @@ def __getitem__(self, index: int) -> List: flat_chunk = self._f["points/flat"][pt_start:pt_end] # (n_pts, _PC_NDIM) # ---- reconstruct Particle objects ---------------------------------- + from .utils import SemanticType local_offsets = pc_bounds - pt_start # zero-indexed within flat_chunk particles = [] for k in range(n_parts): cloud = flat_chunk[local_offsets[k] : local_offsets[k + 1]] p = Particle( - id = int(ids[k]), - parent_id = int(parent_ids[k]), - root_id = int(root_ids[k]), - pdg = int(pdgs[k]), - parent_pdg = int(parent_pdgs[k]), - process_type = int(proc_types[k]), - point_cloud = cloud, + id = int(ids[k]), + parent_id = int(parent_ids[k]), + root_id = int(root_ids[k]), + pdg = int(pdgs[k]), + parent_pdg = int(parent_pdgs[k]), + interaction_type = int(itypes[k]), + point_cloud = cloud, ) + # Override the re-derived sem_type with the stored value so that + # the on-disk label is authoritative (avoids re-running rules). + p.sem_type = SemanticType(int(sem_types[k])) particles.append(p) return particles @@ -709,3 +734,479 @@ def _compress_kwargs(compression: Optional[str], if compression_opts is not None: kwargs["compression_opts"] = compression_opts return kwargs + + +# --------------------------------------------------------------------------- +# Voxelization-mapping companion file +# --------------------------------------------------------------------------- + +def voxmap_path(main_path: str) -> str: + """ + Return the companion voxmap file path for *main_path*. + + The companion file is written alongside the main HDF5 file with + ``_voxmap`` inserted before the extension:: + + "run042.h5" → "run042_voxmap.h5" + + Parameters + ---------- + main_path : str + Path to the main particle event file. + """ + stem, ext = os.path.splitext(main_path) + return f"{stem}_voxmap{ext}" + + +def write_voxmap(main_path: str, + events_voxmaps, + compression: str = "lz4", + compression_opts: Optional[int] = None) -> str: + """ + Write a companion voxelization-mapping file alongside *main_path*. + + The companion is a three-level CSR file that encodes, for every + voxel in every particle in every event, which input point IDs were + merged into that voxel and their individual energy contributions. + + File layout + ----------- + .. code-block:: text + + /format_version scalar str + /n_events scalar int64 + + /events/ + offsets (n_events + 1,) int64 + Fencepost into the particle index space. + + /particles/ + vox_offsets (n_total_particles + 1,) int64 + Fencepost into the voxel index space. + + /voxels/ + input_offsets (n_total_voxels + 1,) int64 + Fencepost into /flat/*. + + /flat/ + input_ids (n_total_mappings,) int64 + input_energies (n_total_mappings,) float32 + + Parameters + ---------- + main_path : str + Path to the main particle event HDF5 file. The companion is + created at :func:`voxmap_path(main_path) `. + events_voxmaps : list of list of VoxelizeRecord + Outer list indexed by event; inner list is the + :class:`~pysupera.preproc.VoxelizeRecord` for each particle. + Records must appear in the same particle order as in *main_path*. + compression : str, optional + Same as :func:`write_events`. + compression_opts : int or None, optional + Same as :func:`write_events`. + + Returns + ------- + str + Path of the companion file that was written. + """ + import h5py + ckw = _compress_kwargs(compression, compression_opts) + vpath = voxmap_path(main_path) + + n_events = len(events_voxmaps) + + # Event → particle fencepost + ev_offsets = np.zeros(n_events + 1, dtype=np.int64) + for i, ev_recs in enumerate(events_voxmaps): + ev_offsets[i + 1] = ev_offsets[i] + len(ev_recs) + n_total_particles = int(ev_offsets[-1]) + + # Build particle/voxel/flat CSR arrays in one pass + # particles/vox_offsets : fencepost, length n_total_particles + 1 + vox_offsets = np.zeros(n_total_particles + 1, dtype=np.int64) + # voxels/input_offsets collected as list (variable total voxels) + vox_input_segs = [] # one entry per particle: rec.voxel_offsets[1:] shifted + flat_ids_segs = [] + flat_eng_segs = [] + + p_idx = 0 + running_vox = np.int64(0) + running_flat = np.int64(0) + + for ev_recs in events_voxmaps: + for rec in ev_recs: + n_vox = np.int64(len(rec.voxel_offsets) - 1) + vox_offsets[p_idx + 1] = running_vox + n_vox + + # Shift this particle's per-voxel fencepost values by running_flat + vox_input_segs.append( + rec.voxel_offsets[1:].astype(np.int64) + running_flat + ) + + flat_ids_segs.append(rec.input_ids.astype(np.int64)) + flat_eng_segs.append(rec.input_energies.astype(np.float32)) + + running_flat += np.int64(len(rec.input_ids)) + running_vox += n_vox + p_idx += 1 + + n_total_voxels = int(running_vox) + n_total_mappings = int(running_flat) + + # Assemble voxels/input_offsets: starts with 0, then all shifted segments + if vox_input_segs: + vox_input_offsets = np.concatenate( + [np.zeros(1, dtype=np.int64)] + vox_input_segs + ) + else: + vox_input_offsets = np.zeros(1, dtype=np.int64) + + flat_ids = np.concatenate(flat_ids_segs) if flat_ids_segs else np.empty(0, dtype=np.int64) + flat_energies = np.concatenate(flat_eng_segs) if flat_eng_segs else np.empty(0, dtype=np.float32) + + # Write + _cp = lambda n, size: (min(_CHUNK_PARTICLES, max(1, size)),) + _cp2 = lambda size: (min(_CHUNK_POINTS, max(1, size)),) + + with h5py.File(vpath, "w") as f: + f.create_dataset("format_version", data=FORMAT_VERSION) + f.create_dataset("n_events", data=np.int64(n_events)) + + eg = f.create_group("events") + eg.create_dataset("offsets", data=ev_offsets) + + pg = f.create_group("particles") + pg.create_dataset( + "vox_offsets", data=vox_offsets, + chunks=_cp("vox", n_total_particles + 1), + ) + + vg = f.create_group("voxels") + vg.create_dataset( + "input_offsets", data=vox_input_offsets, + chunks=_cp("inp", len(vox_input_offsets)), + ) + + fg = f.create_group("flat") + if n_total_mappings > 0: + fg.create_dataset( + "input_ids", data=flat_ids, + chunks=_cp2(n_total_mappings), + **ckw, + ) + fg.create_dataset( + "input_energies", data=flat_energies, + chunks=_cp2(n_total_mappings), + **ckw, + ) + else: + fg.create_dataset("input_ids", data=np.empty(0, dtype=np.int64)) + fg.create_dataset("input_energies", data=np.empty(0, dtype=np.float32)) + + return vpath + + +def open_voxmap_writer(main_path: str, + compression: str = "lz4", + compression_opts: Optional[int] = None) -> "VoxmapWriter": + """ + Open a :class:`VoxmapWriter` for incremental voxmap writing. + + Parameters + ---------- + main_path : str + Path of the *main* particle event file. The companion is + written at :func:`voxmap_path(main_path) `. + + Returns + ------- + VoxmapWriter + """ + return VoxmapWriter(main_path, compression=compression, + compression_opts=compression_opts) + + +def read_voxmap(main_path: str) -> "VoxmapStore": + """ + Open the companion voxmap file for *main_path*. + + Parameters + ---------- + main_path : str + Path of the main particle event file. + + Returns + ------- + VoxmapStore + """ + return VoxmapStore(main_path) + + +# --------------------------------------------------------------------------- +# VoxmapWriter +# --------------------------------------------------------------------------- + +class VoxmapWriter: + """ + Incremental writer for the companion voxelization-mapping file. + + Mirrors :class:`EventWriter`: append one event's worth of + :class:`~pysupera.preproc.VoxelizeRecord` objects per call. + + Parameters + ---------- + main_path : str + Path of the main particle event HDF5 file. The companion is + created at :func:`voxmap_path(main_path) `. + compression : str, optional + HDF5 filter name. Same options as :func:`write_events`. + compression_opts : int or None, optional + Same as :func:`write_events`. + """ + + def __init__(self, main_path: str, + compression: str = "lz4", + compression_opts: Optional[int] = None) -> None: + import h5py + self._ckw = _compress_kwargs(compression, compression_opts) + self._vpath = voxmap_path(main_path) + + self._f = h5py.File(self._vpath, "w") + self._n_events = 0 + self._n_particles = 0 + self._n_voxels = 0 + self._n_flat = 0 + self._init_datasets() + + # ------------------------------------------------------------------ CM -- + + def __enter__(self) -> "VoxmapWriter": + return self + + def __exit__(self, *_) -> None: + self.close() + + def close(self) -> None: + """Finalise and close the companion file. Idempotent.""" + if self._f.id.valid: + self._f["n_events"][()] = np.int64(self._n_events) + self._f.close() + + # ------------------------------------------------------------------ API -- + + def append_event(self, vox_records) -> None: + """ + Append one event's voxelization records. + + Parameters + ---------- + vox_records : list of VoxelizeRecord + One record per particle in the event, in the same order as + the corresponding :meth:`EventWriter.append_event` call. + May be empty. + """ + n_p = len(vox_records) + n_vox_total = sum(len(r.voxel_offsets) - 1 for r in vox_records) + n_flat_total = sum(len(r.input_ids) for r in vox_records) + + # Extend events/offsets + ev_ds = self._f["events/offsets"] + ev_ds.resize(self._n_events + 2, axis=0) + ev_ds[self._n_events + 1] = np.int64(self._n_particles + n_p) + + if n_p > 0: + new_p_start = self._n_particles + new_p_end = self._n_particles + n_p + new_vox_end = self._n_voxels + n_vox_total + new_flat_end = self._n_flat + n_flat_total + + # particles/vox_offsets + vox_off_ds = self._f["particles/vox_offsets"] + vox_off_ds.resize(new_p_end + 1, axis=0) + + # voxels/input_offsets + vox_inp_ds = self._f["voxels/input_offsets"] + vox_inp_ds.resize(new_vox_end + 1, axis=0) + + # flat datasets + flat_ids_ds = self._f["flat/input_ids"] + flat_eng_ds = self._f["flat/input_energies"] + flat_ids_ds.resize(new_flat_end, axis=0) + flat_eng_ds.resize(new_flat_end, axis=0) + + running_vox = np.int64(self._n_voxels) + running_flat = np.int64(self._n_flat) + + for k, rec in enumerate(vox_records): + n_vox = np.int64(len(rec.voxel_offsets) - 1) + p_abs = new_p_start + k + vox_off_ds[p_abs + 1] = running_vox + n_vox + + # per-voxel fencepost into flat + vox_inp_ds[running_vox + 1 : running_vox + 1 + n_vox] = ( + rec.voxel_offsets[1:].astype(np.int64) + running_flat + ) + + n_flat = np.int64(len(rec.input_ids)) + flat_ids_ds[running_flat : running_flat + n_flat] = rec.input_ids.astype(np.int64) + flat_eng_ds[running_flat : running_flat + n_flat] = rec.input_energies.astype(np.float32) + + running_flat += n_flat + running_vox += n_vox + + self._n_events += 1 + self._n_particles += n_p + self._n_voxels += n_vox_total + self._n_flat += n_flat_total + + @property + def n_events(self) -> int: + return self._n_events + + def __repr__(self) -> str: + status = "open" if self._f.id.valid else "closed" + return (f"VoxmapWriter(path={self._vpath!r}, " + f"n_events={self._n_events}, {status})") + + # ---------------------------------------------------------- private ----- + + def _init_datasets(self) -> None: + f = self._f + ckw = self._ckw + + f.create_dataset("format_version", data=FORMAT_VERSION) + f.create_dataset("n_events", data=np.int64(0)) + + eg = f.create_group("events") + eg.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), + maxshape=(None,), chunks=(_CHUNK_PARTICLES,)) + + pg = f.create_group("particles") + pg.create_dataset("vox_offsets", data=np.zeros(1, dtype=np.int64), + maxshape=(None,), chunks=(_CHUNK_PARTICLES,)) + + vg = f.create_group("voxels") + vg.create_dataset("input_offsets", data=np.zeros(1, dtype=np.int64), + maxshape=(None,), chunks=(_CHUNK_PARTICLES,)) + + fg = f.create_group("flat") + fg.create_dataset("input_ids", + shape=(0,), maxshape=(None,), dtype=np.int64, + chunks=(_CHUNK_POINTS,), **ckw) + fg.create_dataset("input_energies", + shape=(0,), maxshape=(None,), dtype=np.float32, + chunks=(_CHUNK_POINTS,), **ckw) + + +# --------------------------------------------------------------------------- +# VoxmapStore +# --------------------------------------------------------------------------- + +class VoxmapStore: + """ + Random-access reader for a companion voxmap file. + + Supports ``len()`` and ``store[i]`` (returns per-particle mapping + data for event *i*). + + Parameters + ---------- + main_path : str + Path to the main particle event HDF5 file. + """ + + def __init__(self, main_path: str) -> None: + import h5py + self._vpath = voxmap_path(main_path) + self._f = h5py.File(self._vpath, "r") + self._n_events: int = int(self._f["n_events"][()]) + + self._ev_offsets = self._f["events/offsets"][:] + self._vox_offsets = self._f["particles/vox_offsets"][:] + # voxels/input_offsets is potentially large — load lazily + # (accessed via HDF5 slice in __getitem__) + + def __enter__(self) -> "VoxmapStore": + return self + + def __exit__(self, *_) -> None: + self.close() + + def close(self) -> None: + if self._f.id.valid: + self._f.close() + + def __len__(self) -> int: + return self._n_events + + def __getitem__(self, index: int): + """ + Return per-particle mapping data for event *index*. + + Returns + ------- + list of dict + One dict per particle with keys: + + ``"vox_input_offsets"`` : np.ndarray, int64, shape (n_vox+1,) + Per-voxel fencepost into the flat arrays (zero-indexed + within this particle — i.e. shifted to start from 0). + ``"input_ids"`` : np.ndarray, int64, shape (n_total_inputs,) + Input point IDs for all voxels of this particle. + ``"input_energies"`` : np.ndarray, float32, shape (n_total_inputs,) + Energy contributions corresponding to each input_id. + """ + if index < 0 or index >= self._n_events: + raise IndexError( + f"Event index {index} out of range [0, {self._n_events})" + ) + + p_start = int(self._ev_offsets[index]) + p_end = int(self._ev_offsets[index + 1]) + n_parts = p_end - p_start + + if n_parts == 0: + return [] + + vox_start = int(self._vox_offsets[p_start]) + vox_end = int(self._vox_offsets[p_end]) + n_voxels = vox_end - vox_start + + vox_input_bounds = self._f["voxels/input_offsets"][vox_start : vox_end + 1] + flat_start = int(vox_input_bounds[0]) + flat_end = int(vox_input_bounds[-1]) + + all_ids = self._f["flat/input_ids" ][flat_start:flat_end] + all_eng = self._f["flat/input_energies"][flat_start:flat_end] + + result = [] + for k in range(n_parts): + v_start = int(self._vox_offsets[p_start + k]) - vox_start + v_end = int(self._vox_offsets[p_start + k + 1]) - vox_start + # local fencepost into all_ids/all_eng (zero-indexed) + local_vox_offsets = vox_input_bounds[v_start : v_end + 1] - flat_start + f_s = int(local_vox_offsets[0]) + f_e = int(local_vox_offsets[-1]) + result.append({ + "vox_input_offsets": local_vox_offsets - f_s, + "input_ids": all_ids[f_s:f_e], + "input_energies": all_eng[f_s:f_e], + }) + + return result + + def iter_events(self): + """Iterate over all events in file order.""" + for i in range(self._n_events): + yield self[i] + + @property + def n_events(self) -> int: + return self._n_events + + def __repr__(self) -> str: + status = "open" if self._f.id.valid else "closed" + return (f"VoxmapStore(path={self._vpath!r}, " + f"n_events={self._n_events}, {status})") diff --git a/pysupera/preproc.py b/pysupera/preproc.py index cdafe5d..d348ef3 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -204,7 +204,7 @@ def _split_fragments( root_id = p.root_id, pdg = p.pdg, parent_pdg = p.parent_pdg, - process_type = p._process_type, + interaction_type = p._interaction_type, point_cloud = frag_pc, ) new_p.sem_type = SemanticType.kLEScatter @@ -738,10 +738,32 @@ class VoxelizeRecord: Number of points before voxelization. n_after : int Number of voxels (points after merging). + input_ids : np.ndarray or None + Flat int64 array of length ``n_before`` holding the + ``PointFeature.id`` value of every input point, ordered so that + points belonging to the same output voxel are contiguous. The + grouping boundaries are given by :attr:`voxel_offsets`. + ``None`` when :attr:`~VoxelizeProcessor.store_mapping` is + ``False``. + input_energies : np.ndarray or None + Flat float32 array of length ``n_before`` holding the energy of + each input point in the same order as :attr:`input_ids`. + ``None`` when ``store_mapping`` is ``False``. + voxel_offsets : np.ndarray or None + Fencepost (CSR) int64 array of length ``n_after + 1``. For + output voxel ``v`` (0-based within this particle), the + contributing input points are:: + + input_ids[voxel_offsets[v] : voxel_offsets[v + 1]] + + ``None`` when ``store_mapping`` is ``False``. """ - particle_id : int - n_before : int - n_after : int + particle_id : int + n_before : int + n_after : int + input_ids : Optional[np.ndarray] = field(default=None, repr=False) + input_energies : Optional[np.ndarray] = field(default=None, repr=False) + voxel_offsets : Optional[np.ndarray] = field(default=None, repr=False) @property def n_merged(self) -> int: @@ -870,6 +892,13 @@ class VoxelizeProcessor: voxel). Setting this to ``True`` lets the :class:`~pysupera.config.Pipeline` skip a redundant separate merge-duplicates pass. Default ``False``. + store_mapping : bool, optional + When ``True``, :meth:`process` stores a CSR mapping on every + :class:`VoxelizeRecord` in :attr:`last_diagnostics` (including + particles that were not affected by voxelization, which get an + identity mapping). The mapping records, for each output voxel, + the ``PointFeature.id`` values and energy contributions of all + input points that were merged into it. Default ``False``. """ def __init__( @@ -878,6 +907,7 @@ def __init__( origin: list | None = None, verbose: bool = False, merge_duplicates: bool = False, + store_mapping: bool = False, ) -> None: vs = np.asarray(voxel_size, dtype=float) if vs.ndim == 0: @@ -905,8 +935,18 @@ def __init__( self.verbose = bool(verbose) #: When ``True`` the Pipeline skips the separate MergeDuplicatesProcessor. self.merge_duplicates: bool = bool(merge_duplicates) + #: When ``True``, :meth:`process` populates the CSR mapping fields + #: (:attr:`~VoxelizeRecord.input_ids`, + #: :attr:`~VoxelizeRecord.input_energies`, + #: :attr:`~VoxelizeRecord.voxel_offsets`) on every + #: :class:`VoxelizeRecord` in :attr:`last_diagnostics`, including + #: unaffected particles (identity mapping). When ``False`` (default) + #: only affected particles are recorded and mapping fields are ``None``. + self.store_mapping: bool = bool(store_mapping) #: :class:`VoxelizeRecord` list from the most recent :meth:`process` - #: call. Only particles that had at least one merge are recorded. + #: call. When :attr:`store_mapping` is ``False``, only particles + #: that had at least one merge are included. When ``True``, every + #: particle in the batch is recorded (including unaffected ones). self.last_diagnostics: List[VoxelizeRecord] = [] #: Summary counts from the most recent :meth:`process` call. self.last_stats: dict = {} @@ -999,20 +1039,73 @@ def process( boundaries = np.searchsorted(out_pid, np.arange(len(particles) + 1)) n_total_after = int(n_out) + # Assign unique IDs to output voxels (stored in PointFeature.id column). + # Each voxel gets a 0-based index that is unique within this batch. + _id_col = int(PointFeature.id) + if n_cols > _id_col: + out[:, _id_col] = np.arange(n_out, dtype=all_pts.dtype) + + # --- Pre-compute CSR mapping (used only when store_mapping=True) ----- + # inv[k] = output-voxel index for input point k (from np.unique above) + # We sort input points by their assigned output voxel so all points + # belonging to the same voxel are contiguous, then derive CSR offsets. + # This reuses the already-computed inv array at O(total log total). + if self.store_mapping: + sort_order = np.argsort(inv, kind='stable') # (total,) + sorted_vox = inv[sort_order] # voxel indices, sorted + # Fencepost offsets over all n_out voxels + vox_off_all = np.searchsorted( + sorted_vox, np.arange(n_out + 1) + ).astype(np.int64) # (n_out + 1,) + # Flat input IDs and energies in sorted-voxel order + _en_col = int(PointFeature.energy) + flat_ids = ( + all_pts[sort_order, _id_col].astype(np.int64) + if n_cols > _id_col + else sort_order.astype(np.int64) + ) + flat_en = ( + all_pts[sort_order, _en_col].astype(np.float32) + if n_cols > _en_col + else np.zeros(total, dtype=np.float32) + ) + for i, p in enumerate(particles): start, end = int(boundaries[i]), int(boundaries[i + 1]) n_before = lengths[i] n_after = end - start - if n_after < n_before: + affected = n_after < n_before + + if affected: p.point_cloud = out[start:end] n_affected += 1 + elif self.store_mapping: + # When tracking the mapping, all particles get updated so that + # p.point_cloud[:, PointFeature.id] reflects the new voxel IDs + # that are also stored in the mapping record. + p.point_cloud = out[start:end] + + # Build VoxelizeRecord when affected OR when tracking the mapping. + if affected or self.store_mapping: + if self.store_mapping: + inp_s = int(vox_off_all[start]) + inp_e = int(vox_off_all[end]) + p_off = (vox_off_all[start:end + 1] - inp_s).astype(np.int64) + p_ids = flat_ids[inp_s:inp_e].copy() + p_en = flat_en[inp_s:inp_e].copy() + else: + p_off = p_ids = p_en = None + rec = VoxelizeRecord( - particle_id = p.id, - n_before = n_before, - n_after = n_after, + particle_id = p.id, + n_before = n_before, + n_after = n_after, + input_ids = p_ids, + input_energies = p_en, + voxel_offsets = p_off, ) self.last_diagnostics.append(rec) - if be_verbose: + if be_verbose and affected: print(rec) self.last_stats = { diff --git a/pysupera/proxck.py b/pysupera/proxck.py index 13cec20..d686265 100644 --- a/pysupera/proxck.py +++ b/pysupera/proxck.py @@ -592,17 +592,25 @@ class GPUChecker(ProximityChecker): for the lifetime of the object. :meth:`batch_check_proximity` runs a vectorised bounding-box prefilter - across all candidate pairs in one GPU pass before falling through to - per-pair cuML queries, minimising the number of index lookups performed. + across all candidate pairs in one GPU pass. Stage 2 then uses chunked + CuPy brute-force distance computation (not sequential cuML kneighbors + calls) to eliminate the per-pair kernel-launch overhead when many pairs + survive the bbox filter. Parameters ---------- distance_threshold : float Proximity distance *D*. + chunk_size : int, optional + Number of rows of cloud_a to process per GPU kernel launch in the + Stage-2 exact check. Larger values increase GPU utilisation but + require more working memory (``chunk_size * max_n_b * 12`` bytes). + Default 512. """ - def __init__(self, distance_threshold: float): + def __init__(self, distance_threshold: float, chunk_size: int = 512): super().__init__(distance_threshold) + self.chunk_size = chunk_size self.gpu_clouds: dict = {} # pid -> cp.ndarray (n, 3) float32 self.nn_indices: dict = {} # pid -> fitted NearestNeighbors model self.gpu_bb_min: dict = {} # pid -> cp.ndarray (3,) @@ -692,9 +700,10 @@ def batch_check_proximity(self, candidate_pairs: List[Tuple]) -> List[Tuple]: """ Return all pairs from *candidate_pairs* whose clouds are within *D*. - Stage 1 vectorises all bounding-box comparisons in a single GPU pass - so that cuML queries are only issued for pairs whose bounding boxes - overlap within *D*. + Stage 1 vectorises all bounding-box comparisons in a single GPU pass. + Stage 2 uses chunked CuPy brute-force distance computation for the + survivors — one fused kernel per chunk rather than one cuML index + query per pair, eliminating the per-pair kernel-launch overhead. Parameters ---------- @@ -730,11 +739,20 @@ def batch_check_proximity(self, candidate_pairs: List[Tuple]) -> List[Tuple]: print(f" GPUChecker Stage 1: bbox prefilter removed " f"{n_rejected}/{len(candidate_pairs)} pairs") - # Stage 2: cuML kneighbors query for survivors + # Stage 2: chunked CuPy brute-force for survivors + # Avoids per-pair cuML kernel-launch overhead by using matrix ops. merge_pairs = [] for id1, id2 in survivors: - distances, _ = self.nn_indices[id2].kneighbors(self.gpu_clouds[id1]) - if float(cp.min(distances)) <= self.D: + a = self.gpu_clouds[id1] # (n_a, 3) — already GPU-resident + b = self.gpu_clouds[id2] # (n_b, 3) — already GPU-resident + touching = False + for start in range(0, len(a), self.chunk_size): + chunk = a[start : start + self.chunk_size] # (c, 3) + diff = chunk[:, cp.newaxis, :] - b[cp.newaxis, :, :] # (c, n_b, 3) + if float(cp.min(cp.sum(diff * diff, axis=2))) <= self.D_squared: + touching = True + break + if touching: merge_pairs.append((id1, id2)) return merge_pairs diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 1da65c6..79b947d 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -58,11 +58,16 @@ # --------------------------------------------------------------------------- # The EDepSim HDF5 step datasets are structured arrays with named fields. # pysupera's Particle expects a plain float32 2-D point cloud with columns -# ordered by PointFeature (x=0, y=1, z=2, time=3, energy=4, dedx=5). +# ordered by PointFeature (x=0, y=1, z=2, time=3, energy=4, dedx=5, id=6). # This mapping is EDepSim-format-specific and lives here rather than in # Particle.from_flat_arrays so that the core data class stays format-agnostic. +# +# PointFeature.id (col 6) is NOT read from the HDF5 file — it is assigned +# here as a global, event-wide, 0-based increasing integer so that every +# input point can be uniquely identified across the entire event. # Maps PointFeature column → candidate HDF5 field name(s) to try, in order. +# PointFeature.id is handled separately (generated, not read from file). _STEP_FIELD_MAP: list[tuple[int, tuple[str, ...]]] = [ (PointFeature.x, ("x",)), (PointFeature.y, ("y",)), @@ -78,9 +83,14 @@ def _steps_to_plain_array(steps: np.ndarray) -> np.ndarray: Convert a structured EDepSim step array to a plain float32 2-D array. The output columns follow :class:`~pysupera.utils.PointFeature` order: - ``x=0, y=1, z=2, time=3, energy=4, dedx=5``. x, y, z are mandatory; - any optional field (time, energy, dedx) that is absent from the dtype - is left as zero in the output. + ``x=0, y=1, z=2, time=3, energy=4, dedx=5, id=6``. + x, y, z are mandatory; time, energy, dedx default to zero if absent. + + Column 6 (``PointFeature.id``) is **not** read from the HDF5 file. + It is assigned here as a global, event-wide, 0-based integer + ``0, 1, 2, …, N-1`` so that every input step can be uniquely + identified across the whole event (the caller passes all steps for + one event at once). Parameters ---------- @@ -89,14 +99,26 @@ def _steps_to_plain_array(steps: np.ndarray) -> np.ndarray: Returns ------- - numpy.ndarray, shape (N, 6), dtype float32 + numpy.ndarray, shape (N, 7), dtype float32 """ if steps.dtype.names is None: - # Already a plain (non-structured) array — nothing to do. - return steps.astype(np.float32, copy=False) + # Already a plain (non-structured) array. + # Ensure it has at least 7 columns, padding with zeros if needed. + arr = steps.astype(np.float32, copy=False) + n_cols = arr.shape[1] if arr.ndim == 2 else 0 + n_extra = max(0, 7 - n_cols) + if n_extra: + arr = np.concatenate( + [arr, np.zeros((len(arr), n_extra), dtype=np.float32)], axis=1 + ) + # Assign global point IDs if the id column is all-zero + if arr.shape[1] > PointFeature.id and (arr[:, PointFeature.id] == 0).all(): + arr[:, PointFeature.id] = np.arange(len(arr), dtype=np.float32) + return arr n = len(steps) - out = np.zeros((n, len(_STEP_FIELD_MAP)), dtype=np.float32) + # 7 columns: the 6 from _STEP_FIELD_MAP plus PointFeature.id + out = np.zeros((n, 7), dtype=np.float32) available = set(steps.dtype.names) for col, candidates in _STEP_FIELD_MAP: @@ -112,6 +134,9 @@ def _steps_to_plain_array(steps: np.ndarray) -> np.ndarray: ) # optional columns default to zero — already set + # Assign global, event-wide, 0-based point IDs. + out[:, PointFeature.id] = np.arange(n, dtype=np.float32) + return out @@ -239,7 +264,7 @@ def _get_interaction_type(parts: np.ndarray, ------- ndarray of int32, shape (N,) Raw ``InteractionType.value`` integers, ready to be passed as - ``process_types`` to :meth:`~pysupera.data.Particle.from_flat_arrays`. + ``interaction_types`` to :meth:`~pysupera.data.Particle.from_flat_arrays`. """ from ..utils import InteractionType @@ -434,7 +459,7 @@ def __getitem__(self, index: int) -> list[Particle]: root_ids = parts["root_track_id"], pdgs = parts["pdg"], parent_pdgs = _get_parent_pdg(parts), - process_types = itype, + interaction_types = itype, point_cloud_flat = _steps_to_plain_array(steps), point_cloud_offsets = offsets, min_pc_size = self._min_pc_size, diff --git a/pysupera/utils.py b/pysupera/utils.py index 242f80f..c205bd9 100644 --- a/pysupera/utils.py +++ b/pysupera/utils.py @@ -7,7 +7,7 @@ class InteractionType(Enum): The integer value of each member is assigned automatically (1-based). :func:`SetSemanticType` converts a raw integer process code to an - ``InteractionType`` via ``InteractionType(process_type)``. + ``InteractionType`` via ``InteractionType(interaction_type)``. Members ------- @@ -112,6 +112,8 @@ class PointFeature(int): Energy deposited at the hit. dedx : int = 5 Ionisation energy loss (dE/dx) at the hit. + id : int = 6 + (optional) integer hit ID, e.g. for matching to truth-level information. """ x = 0 y = 1 @@ -119,6 +121,7 @@ class PointFeature(int): time = 3 energy = 4 dedx = 5 + id = 6 def __new__(cls, value): return int.__new__(cls, value) @@ -252,7 +255,7 @@ def _row(p, tag=""): return chain, children -def SetSemanticType(process_type, pdg, parent_pdg, point_cloud, point_cloud_size=-1): +def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_size=-1): """ Derive the :class:`SemanticType` of a particle from its physics properties. @@ -273,9 +276,8 @@ def SetSemanticType(process_type, pdg, parent_pdg, point_cloud, point_cloud_size Parameters ---------- - process_type : int - Raw integer process code (0-based). Internally converted via - ``InteractionType(process_type + 1)``. + interaction_type : int or InteractionType + Raw integer process code or ``InteractionType`` enum member. pdg : int PDG Monte Carlo particle code for this particle. parent_pdg : int @@ -298,35 +300,35 @@ def SetSemanticType(process_type, pdg, parent_pdg, point_cloud, point_cloud_size Raises ------ Exception - If *process_type* maps to + If *interaction_type* maps to ``kPhoton`` / ``kConversion`` / ``kCompton`` / ``kOtherShower`` but *pdg* is not ±11 or ±22, or if an entirely unrecognised ``InteractionType`` is encountered. """ - - process_type = InteractionType(process_type) - if process_type == InteractionType.kInvalidProcess: + if not isinstance(interaction_type, InteractionType): + interaction_type = InteractionType(interaction_type) + if interaction_type == InteractionType.kInvalidProcess: return SemanticType.kUnknown raise Exception("'kInvalidProcess' particle process encountered\n") - elif process_type == InteractionType.kTrack: + elif interaction_type == InteractionType.kTrack: if point_cloud.shape[0] < point_cloud_size: return SemanticType.kLEScatter else: return SemanticType.kTrack - elif process_type == InteractionType.kPrimary: + elif interaction_type == InteractionType.kPrimary: if abs(pdg) != 11 and abs(pdg) != 22: return SemanticType.kTrack else: return SemanticType.kShower - elif process_type == InteractionType.kDelta: + elif interaction_type == InteractionType.kDelta: if point_cloud.shape[0] < point_cloud_size: return SemanticType.kLEScatter else: return SemanticType.kDelta - elif process_type == InteractionType.kDecay: + elif interaction_type == InteractionType.kDecay: if abs(pdg) == 11 and abs(parent_pdg) == 13: return SemanticType.kMichel elif abs(pdg) in [11,22]: @@ -334,22 +336,22 @@ def SetSemanticType(process_type, pdg, parent_pdg, point_cloud, point_cloud_size else: return SemanticType.kTrack - elif process_type in [InteractionType.kNeutron, InteractionType.kIonization, InteractionType.kPhotoElectron]: + elif interaction_type in [InteractionType.kNeutron, InteractionType.kIonization, InteractionType.kPhotoElectron]: return SemanticType.kLEScatter - elif process_type in [InteractionType.kPhoton, InteractionType.kConversion, InteractionType.kCompton, InteractionType.kOtherShower]: + elif interaction_type in [InteractionType.kPhoton, InteractionType.kConversion, InteractionType.kCompton, InteractionType.kOtherShower]: if abs(pdg) in [11,22]: if point_cloud.shape[0] > point_cloud_size: return SemanticType.kShower else: return SemanticType.kLEScatter else: - raise Exception("kPhoton/kConversion/kCompton/kOtherShower encountered but PDG ("+str(pdg)+") not 11/22, InteractionType ("+str(process_type)+")\n") - elif process_type == InteractionType.kNucleus: + raise Exception("kPhoton/kConversion/kCompton/kOtherShower encountered but PDG ("+str(pdg)+") not 11/22, InteractionType ("+str(interaction_type)+")\n") + elif interaction_type == InteractionType.kNucleus: if point_cloud.shape[0] > point_cloud_size: return SemanticType.kTrack else: return SemanticType.kLEScatter else: - raise Exception("Unexpected interaction type ("+str(process_type)+") encountered") + raise Exception("Unexpected interaction type ("+str(interaction_type)+") encountered") diff --git a/tests/conftest.py b/tests/conftest.py index 3fdc166..239ebd7 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,7 @@ """ Shared fixtures and helpers for the pysupera test suite. -process_type mapping (InteractionType enum → SemanticType): +interaction_type mapping (InteractionType enum → SemanticType): kTrack → sem kTrack kNeutron → sem kLEScatter kNucleus → sem kTrack (large) / kLEScatter (small) @@ -21,7 +21,7 @@ from pysupera.data import Particle from pysupera.utils import InteractionType -# ── Symbolic aliases for process_type (InteractionType enum members) ──────── +# ── Symbolic aliases for interaction_type (InteractionType enum members) ─────── PT_TRACK = InteractionType.kTrack PT_NEUTRON = InteractionType.kNeutron PT_NUCLEUS = InteractionType.kNucleus @@ -39,7 +39,7 @@ def make_particle( pid: int, - process_type: int, + interaction_type: int, pdg: int = 11, parent_pdg: int = 0, parent_id: int | None = None, @@ -58,13 +58,13 @@ def make_particle( xyz += np.array(offset, dtype=np.float32) pc = xyz return Particle( - id = pid, - parent_id = parent_id, - root_id = root_id, - pdg = pdg, - parent_pdg = parent_pdg, - process_type = process_type, - point_cloud = pc, + id = pid, + parent_id = parent_id, + root_id = root_id, + pdg = pdg, + parent_pdg = parent_pdg, + interaction_type = interaction_type, + point_cloud = pc, ) diff --git a/tests/test_preproc.py b/tests/test_preproc.py index 8d28883..5fe3db0 100644 --- a/tests/test_preproc.py +++ b/tests/test_preproc.py @@ -3,7 +3,7 @@ import pytest from pysupera.preproc import MergeDuplicatesProcessor, ScipyDefragmenter, VoxelizeProcessor -from pysupera.utils import SemanticType +from pysupera.utils import SemanticType, PointFeature from tests.conftest import make_particle, cloud, PT_PRIMARY, PT_TRACK, PT_NEUTRON @@ -196,7 +196,7 @@ def test_spawned_particle_has_correct_parentage(self): # ── sem_types filter ───────────────────────────────────────────────── def test_sem_types_filter_skips_non_matching(self): - # kTrack particle (process_type=0) should be skipped when sem_types=[kShower] + # kTrack particle (interaction_type=kTrack) should be skipped when sem_types=[kShower] large = _cluster_pts(10, (0, 0, 0)) small = _cluster_pts(2, (100, 0, 0)) p_track = make_particle(1, PT_TRACK, pdg=13, pc=np.vstack([large, small])) @@ -449,3 +449,296 @@ def test_zero_voxel_size_raises(self): def test_negative_voxel_size_raises(self): with pytest.raises(ValueError): VoxelizeProcessor(voxel_size=-1.0) + + +# ============================================================================ +# Voxelization → MergeDuplicatesProcessor roundtrip with mapping verification +# ============================================================================ + +def _pc7(*rows): + """7-column point cloud: (x, y, z, time, energy, dedx, input_id).""" + return np.array(rows, dtype=np.float32) + + +class TestVoxelizationMappingRoundtrip: + """ + End-to-end mapping test. + + The pipeline under test is: + + input points (with explicit PointFeature.id in col 6) + │ + ▼ + VoxelizeProcessor(store_mapping=True) ← CSR records produced + │ + ▼ + MergeDuplicatesProcessor ← should be a no-op afterwards + │ + ▼ + verify forward and inverse point → voxel mappings + + Two particles are used, with deterministic voxel assignments under a + unit (1×1×1) grid anchored at the origin: + + Particle 1 (pid=1, 4 input points): + input_id=0 (0.2, 0, 0, t=0, e=1.0, dedx=0.1) → voxel (0,0,0) · centre (0.5,0.5,0.5) + input_id=1 (0.7, 0, 0, t=0, e=2.0, dedx=0.2) → voxel (0,0,0) ┘ energy = 3.0 + input_id=2 (1.2, 0, 0, t=0, e=3.0, dedx=0.3) → voxel (1,0,0) · centre (1.5,0.5,0.5) + input_id=3 (1.8, 0, 0, t=0, e=4.0, dedx=0.4) → voxel (1,0,0) ┘ energy = 7.0 + + Particle 2 (pid=2, 3 input points — identity mapping): + input_id=10 (0.5, 0, 0, t=0, e=5.0, dedx=0.5) → voxel (0,0,0) + input_id=11 (1.5, 0, 0, t=0, e=6.0, dedx=0.6) → voxel (1,0,0) + input_id=12 (2.5, 0, 0, t=0, e=7.0, dedx=0.7) → voxel (2,0,0) + """ + + # ------------------------------------------------------------------ fixtures + + def _make_particles(self): + p1 = make_particle( + 1, PT_PRIMARY, pdg=11, + pc=_pc7( + [0.2, 0.0, 0.0, 0.0, 1.0, 0.1, 0], + [0.7, 0.0, 0.0, 0.0, 2.0, 0.2, 1], + [1.2, 0.0, 0.0, 0.0, 3.0, 0.3, 2], + [1.8, 0.0, 0.0, 0.0, 4.0, 0.4, 3], + ), + ) + p2 = make_particle( + 2, PT_TRACK, pdg=13, + pc=_pc7( + [0.5, 0.0, 0.0, 0.0, 5.0, 0.5, 10], + [1.5, 0.0, 0.0, 0.0, 6.0, 0.6, 11], + [2.5, 0.0, 0.0, 0.0, 7.0, 0.7, 12], + ), + ) + return p1, p2 + + def _vox(self): + return VoxelizeProcessor( + voxel_size=1.0, + origin=[0.0, 0.0, 0.0], + store_mapping=True, + ) + + def _rec(self, diagnostics, pid): + return next(r for r in diagnostics if r.particle_id == pid) + + # ------------------------------------------------------------------ helpers + + @staticmethod + def _per_voxel_ids(rec): + """Return a list of sets: per_voxel_ids[v] = {input IDs that merged into voxel v}.""" + return [ + set(int(x) for x in rec.input_ids[rec.voxel_offsets[v] : rec.voxel_offsets[v + 1]]) + for v in range(rec.n_after) + ] + + @staticmethod + def _inverse_map(rec): + """Return dict {input_id: voxel_index} derived from the record.""" + inv = {} + for v in range(rec.n_after): + for iid in rec.input_ids[rec.voxel_offsets[v] : rec.voxel_offsets[v + 1]]: + inv[int(iid)] = v + return inv + + # ------------------------------------------------------------------ record counts + + def test_record_counts_after_voxelization(self): + """store_mapping=True must produce one record per particle.""" + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + pids = {r.particle_id for r in vox.last_diagnostics} + assert pids == {1, 2} + + def test_n_before_n_after_particle1(self): + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + rec = self._rec(vox.last_diagnostics, 1) + assert rec.n_before == 4 + assert rec.n_after == 2 + + def test_n_before_n_after_particle2_identity(self): + """Particle 2 has all points in separate voxels → n_before == n_after.""" + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + rec = self._rec(vox.last_diagnostics, 2) + assert rec.n_before == 3 + assert rec.n_after == 3 + + # ------------------------------------------------------------------ forward mapping + + def test_forward_mapping_groups_particle1(self): + """ + Particle 1: the two output voxels must partition input IDs into + exactly {0, 1} and {2, 3}. + """ + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + rec = self._rec(vox.last_diagnostics, 1) + groups = self._per_voxel_ids(rec) + + assert len(groups) == 2 + assert all(len(g) == 2 for g in groups), "Each voxel must have exactly 2 input points" + assert {frozenset(g) for g in groups} == {frozenset({0, 1}), frozenset({2, 3})} + + def test_forward_mapping_covers_all_input_ids(self): + """Union of all per-voxel input ID sets must equal the full input ID set.""" + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + for pid, expected in [(1, {0, 1, 2, 3}), (2, {10, 11, 12})]: + rec = self._rec(vox.last_diagnostics, pid) + groups = self._per_voxel_ids(rec) + assert set().union(*groups) == expected, ( + f"Particle {pid}: union of voxel ID groups does not match expected" + ) + + def test_forward_mapping_identity_particle2(self): + """Particle 2: each output voxel maps to exactly one distinct input ID.""" + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + rec = self._rec(vox.last_diagnostics, 2) + groups = self._per_voxel_ids(rec) + assert all(len(g) == 1 for g in groups) + assert set().union(*groups) == {10, 11, 12} + + # ------------------------------------------------------------------ inverse mapping + + def test_inverse_mapping_covers_every_input(self): + """ + Inverse map (input_id → voxel_index) must have exactly n_before entries, + meaning every input point has a unique owning voxel. + """ + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + for rec in vox.last_diagnostics: + inv = self._inverse_map(rec) + assert len(inv) == rec.n_before, ( + f"Particle {rec.particle_id}: inverse map size {len(inv)} " + f"!= n_before {rec.n_before}" + ) + + def test_inverse_mapping_no_input_assigned_to_two_voxels(self): + """No input ID may appear in more than one voxel.""" + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + for rec in vox.last_diagnostics: + seen = {} + for v in range(rec.n_after): + for iid in rec.input_ids[rec.voxel_offsets[v] : rec.voxel_offsets[v + 1]]: + key = int(iid) + assert key not in seen, ( + f"Input ID {key} assigned to voxels {seen[key]} and {v}" + ) + seen[key] = v + + def test_inverse_mapping_correct_voxel_for_particle1(self): + """ + For particle 1: + input_id ∈ {0, 1} → same voxel (the (0,0,0)-voxel) + input_id ∈ {2, 3} → same voxel (the (1,0,0)-voxel) + the two voxels must be different. + """ + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + rec = self._rec(vox.last_diagnostics, 1) + inv = self._inverse_map(rec) + + assert inv[0] == inv[1], "IDs 0 and 1 must map to the same voxel" + assert inv[2] == inv[3], "IDs 2 and 3 must map to the same voxel" + assert inv[0] != inv[2], "voxel (0,0,0) and voxel (1,0,0) must be different" + + # ------------------------------------------------------------------ energy consistency + + def test_energy_in_record_matches_input_energies(self): + """ + For each voxel, the sum of rec.input_energies must equal the merged + output voxel's energy column. + """ + _EN = int(PointFeature.energy) + p1, p2 = self._make_particles() + vox = self._vox() + results = vox.process([p1, p2]) + + # Build lookup: particle_id → output point cloud + out_clouds = {p.id: p.point_cloud for p in results} + + for rec in vox.last_diagnostics: + cloud_out = out_clouds[rec.particle_id] + for v in range(rec.n_after): + sl = slice(int(rec.voxel_offsets[v]), int(rec.voxel_offsets[v + 1])) + eng_sum = float(rec.input_energies[sl].sum()) + vox_eng = float(cloud_out[v, _EN]) + np.testing.assert_allclose( + eng_sum, vox_eng, rtol=1e-5, + err_msg=f"Particle {rec.particle_id} voxel {v}: " + f"input energy sum {eng_sum} != voxel energy {vox_eng}", + ) + + def test_energy_known_values_particle1(self): + """Explicit check: particle 1 voxel energies must be 3.0 and 7.0.""" + _EN = int(PointFeature.energy) + p1, p2 = self._make_particles() + vox = self._vox() + results = vox.process([p1, p2]) + out_energies = sorted(float(e) for e in results[0].point_cloud[:, _EN]) + assert out_energies == pytest.approx([3.0, 7.0]) + + # ------------------------------------------------------------------ merge-dups is a no-op + + def test_merge_duplicates_no_op_after_voxelization(self): + """ + Running MergeDuplicatesProcessor after VoxelizeProcessor must leave + point counts unchanged because voxelization already placed outputs at + unique voxel-centre coordinates. + """ + p1, p2 = self._make_particles() + vox = self._vox() + mdup = MergeDuplicatesProcessor() + + results = vox.process([p1, p2]) + counts_before_merge = [len(p.point_cloud) for p in results] + + mdup.process(results) + counts_after_merge = [len(p.point_cloud) for p in results] + + assert counts_before_merge == counts_after_merge + + def test_merge_duplicates_stats_show_no_affected_particles(self): + """MergeDuplicatesProcessor.last_stats must report 0 affected particles.""" + p1, p2 = self._make_particles() + vox = self._vox() + mdup = MergeDuplicatesProcessor() + + results = vox.process([p1, p2]) + mdup.process(results) + + assert mdup.last_stats['n_affected'] == 0 + assert mdup.last_stats['pts_before'] == mdup.last_stats['pts_after'] + + # ------------------------------------------------------------------ output voxel IDs + + def test_output_voxel_col6_are_unique_per_particle(self): + """ + After voxelization, col 6 (PointFeature.id) of each output cloud + must contain unique integers — one per output voxel. + """ + _ID = int(PointFeature.id) + p1, p2 = self._make_particles() + vox = self._vox() + results = vox.process([p1, p2]) + for p in results: + ids = p.point_cloud[:, _ID] + assert len(ids) == len(set(ids.astype(int).tolist())), ( + f"Particle {p.id}: output voxel IDs are not unique: {ids}" + ) diff --git a/tests/test_utils.py b/tests/test_utils.py index 99d3ced..4d5d6a5 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -110,7 +110,7 @@ def test_chain_ordered_root_to_target(self): class TestSetSemanticTypeEdgeCases: def test_with_zero_size_point_cloud(self): # Empty cloud with default point_cloud_size=-1: 0 < -1 is False → kDelta - st = SetSemanticType(process_type=PT_DELTA, pdg=11, parent_pdg=0, + st = SetSemanticType(interaction_type=PT_DELTA, pdg=11, parent_pdg=0, point_cloud=np.zeros((0, 3))) assert st == SemanticType.kDelta @@ -118,7 +118,7 @@ def test_with_none_point_cloud_raises_or_returns_unknown(self): # When point_cloud=None, should either use point_cloud_size or raise # At minimum it should not silently return a wrong type try: - st = SetSemanticType(process_type=PT_TRACK, pdg=13, parent_pdg=0, + st = SetSemanticType(interaction_type=PT_TRACK, pdg=13, parent_pdg=0, point_cloud=None) # kTrack is always kTrack regardless of cloud size assert st == SemanticType.kTrack @@ -129,13 +129,13 @@ def test_large_cloud_explicit_size_override(self): # Explicit point_cloud_size overrides actual array length # kDelta with explicit large size → kDelta not kLEScatter large_pc = np.ones((100, 3), dtype=np.float32) - st = SetSemanticType(process_type=PT_DELTA, pdg=11, parent_pdg=0, + st = SetSemanticType(interaction_type=PT_DELTA, pdg=11, parent_pdg=0, point_cloud=large_pc, point_cloud_size=100) assert st == SemanticType.kDelta def test_small_cloud_explicit_size_override(self): # kDelta: cloud size (2) < point_cloud_size (100) → kLEScatter small_pc = np.ones((2, 3), dtype=np.float32) - st = SetSemanticType(process_type=PT_DELTA, pdg=11, parent_pdg=0, + st = SetSemanticType(interaction_type=PT_DELTA, pdg=11, parent_pdg=0, point_cloud=small_pc, point_cloud_size=100) assert st == SemanticType.kLEScatter From d174bef8c0116ad670ce9ea182af050840dabe1b Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 14 Jul 2026 12:01:47 -0700 Subject: [PATCH 06/62] supporting jaxtpc stuff --- pyproject.toml | 5 +- pysupera/__init__.py | 3 +- pysupera/_run.py | 321 +++++++- pysupera/app.py | 87 +- pysupera/conditions/photon_decay.py | 12 + pysupera/conditions/touching_em_shower.py | 18 +- pysupera/conf/checker/gpu.yaml | 2 +- pysupera/conf/config.yaml | 62 +- pysupera/conf/io/default.yaml | 12 +- pysupera/conf/reader/edepsim_h5.yaml | 37 +- pysupera/config.py | 83 +- pysupera/data.py | 47 +- pysupera/io.py | 919 ++++++++++++++++++++-- pysupera/merge.py | 184 +++++ pysupera/partitioner.py | 13 +- pysupera/partitioner2.py | 496 ------------ pysupera/preproc.py | 27 +- pysupera/proxck.py | 70 +- pysupera/readers/__init__.py | 62 +- pysupera/readers/base.py | 4 + pysupera/readers/format_edepsim_h5.py | 89 ++- pysupera/readers/format_jaxtpc.py | 639 +++++++++++++++ pysupera/utils.py | 62 +- pysupera/vis.py | 739 +++++++++++++++++ tests/conftest.py | 2 + 25 files changed, 3284 insertions(+), 711 deletions(-) create mode 100644 pysupera/merge.py delete mode 100644 pysupera/partitioner2.py create mode 100644 pysupera/readers/format_jaxtpc.py create mode 100644 pysupera/vis.py diff --git a/pyproject.toml b/pyproject.toml index f2bfa88..ac9d453 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,8 +38,9 @@ app = [ ] [project.scripts] -run_pysupera = "pysupera._run:main" -pysupera-app = "pysupera.app:main" +run_pysupera = "pysupera._run:main" +pysupera-app = "pysupera.app:main" +pysupera-recompress = "pysupera.io:recompress_cli" [tool.setuptools.packages.find] where = ["."] diff --git a/pysupera/__init__.py b/pysupera/__init__.py index d052689..da07f29 100644 --- a/pysupera/__init__.py +++ b/pysupera/__init__.py @@ -7,4 +7,5 @@ from .config import (build_checker, build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, build_pipeline, load_cfg, configure, check_particle_list, Pipeline) -from .utils import trace_ancestry +from .utils import trace_ancestry, resolve_orphans +from .merge import merge_em_showers diff --git a/pysupera/_run.py b/pysupera/_run.py index 1b3f8cb..6f5fba6 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -8,6 +8,20 @@ run_pysupera checker=cpu_multi checker.n_jobs=8 run_pysupera distance_threshold=3.0 conditions.touching_le_scatter=false +JAXTPC visibility filtering +--------------------------- +Pass two extra flags to restrict each particle's point cloud to only the +energy-deposit segments that were visible in the JAXTPC readout simulation +(i.e. segments whose ionisation group was reconstructed above threshold). +All particle-level metadata (PDG, track IDs, interaction type, etc.) still +comes from the EDepSim file: + + run_pysupera \\ + io.input_path=/data/edepsim.h5 \\ + io.output_path=/data/out.h5 \\ + reader.jaxtpc_seg_path=/data/sim_seg_0000.h5 \\ + reader.jaxtpc_inst_path=/data/sim_inst_0000.h5 + Multi-run example (Hydra sweep): run_pysupera --multirun \\ @@ -25,14 +39,27 @@ _CONF_DIR = _os.path.join(_os.path.dirname(_os.path.abspath(__file__)), "conf") + +class _NullCtx: + """No-op context manager used when voxmap writing is disabled.""" + def __enter__(self): return self + def __exit__(self, *_): pass + def append_event(self, *_): pass + + @hydra.main(config_path=_CONF_DIR, config_name="config", version_base=None) def main(cfg: DictConfig) -> None: """Partition all events in the input file and write results.""" import time + import numpy as np from collections import defaultdict - from pysupera import read_events, open_writer + from pysupera import open_writer + from pysupera.io import open_voxmap_writer from pysupera.partitioner import ParticlePartitioner - from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, configure + from pysupera.merge import merge_em_showers + from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, build_reader, configure + from pysupera.preproc import _voxelize_point_cloud + from pysupera.utils import resolve_orphans configure(cfg) # set module-level defaults (e.g. min_pc_size) before any Particle is created @@ -40,6 +67,9 @@ def main(cfg: DictConfig) -> None: voxelizer = build_voxelizer(cfg) # None when voxelize.enabled: false preprocessor = build_preprocessor(cfg) # None when defragment: false + _write_voxmap = (voxelizer is not None + and bool(cfg.particle.voxelize.get('store_mapping', False))) + _vox_info = (f"enabled (voxel_size={cfg.particle.voxelize.voxel_size})" if voxelizer is not None else "disabled") print(f"[run] checker : {cfg.checker.name}") @@ -47,8 +77,22 @@ def main(cfg: DictConfig) -> None: print(f"[run] merge_duplicates: {'enabled' if merge_processor is not None else 'disabled'}") print(f"[run] voxelize : {_vox_info}") print(f"[run] preprocessor : {cfg.particle.get('preprocessor', {}).get('name', 'scipy') if cfg.particle.get('defragment', False) else 'disabled'}") - print(f"[run] input : {cfg.io.input_path}") - print(f"[run] output : {cfg.io.output_path}") + print(f"[run] output_compact : {bool(cfg.get('output_compact', False))}") + print(f"[run] drop_le_scatter : {bool(cfg.get('drop_le_scatter', False))}") + print(f"[run] write_event_clouds: {bool(cfg.get('write_event_clouds', True))}") + print(f"[run] input : {cfg.io.input_path}") + print(f"[run] output : {cfg.io.output_path}") + _jaxtpc_seg = cfg.reader.get("jaxtpc_seg_path", None) + _jaxtpc_inst = cfg.reader.get("jaxtpc_inst_path", None) + if _jaxtpc_seg and _jaxtpc_inst: + print(f"[run] jaxtpc mode : visibility-filtered") + print(f"[run] seg : {_jaxtpc_seg}") + print(f"[run] inst : {_jaxtpc_inst}") + else: + print(f"[run] jaxtpc mode : disabled (all EDepSim segments used)") + if _write_voxmap: + from pysupera.io import voxmap_path + print(f"[run] voxmap : {voxmap_path(cfg.io.output_path)}") conditions = build_conditions(cfg) @@ -64,32 +108,74 @@ def main(cfg: DictConfig) -> None: } n_events = 0 - with read_events(cfg.io.input_path) as store: - print(f"[run] {len(store)} events in input file") + _max_events = int(cfg.get("max_events", -1)) + _show_progress = bool(cfg.get("progress", True)) + + with build_reader(cfg) as store: + _n_available = len(store) + _n_to_process = _n_available if _max_events < 0 else min(_max_events, _n_available) + print(f"[run] {_n_available} events in input file, processing {_n_to_process}") + + _output_compact = bool(cfg.get('output_compact', False)) + _drop_le_scatter = bool(cfg.get('drop_le_scatter', False)) + _write_event_clouds = bool(cfg.get('write_event_clouds', True)) + # _is_le is always defined: used independently by drop_le_scatter output + # filtering AND by event-cloud LE/non-LE splitting (write_event_clouds). + from pysupera.utils import SemanticType as _ST + _le_type = _ST.kLEScatter + _is_le = lambda p: p.sem_type == _le_type with open_writer(cfg.io.output_path, compression=cfg.io.compression, - compression_opts=cfg.io.compression_opts) as writer: - - for event_idx, particles in enumerate(store.iter_events()): - t0 = time.perf_counter() + compression_opts=cfg.io.compression_opts) as writer, \ + (open_voxmap_writer(cfg.io.output_path, + compression=cfg.io.compression, + compression_opts=cfg.io.compression_opts) + if _write_voxmap else _NullCtx()) as vox_writer: + + from tqdm import tqdm + _bar = tqdm( + total=_n_to_process, + desc="[run] events", + unit="ev", + disable=not _show_progress, + dynamic_ncols=True, + ) + + for event_idx, particles in enumerate(store): + if _max_events >= 0 and event_idx >= _max_events: + break n_events += 1 + _ev_t: dict = {} # per-event stage timings (seconds) # skip merge_duplicates when voxelizer subsumes it if merge_processor is not None and not (voxelizer and voxelizer.merge_duplicates): _t = time.perf_counter() particles = merge_processor.process(particles) - profile['merge_duplicates'] += time.perf_counter() - _t + _ev_t['merge_dup'] = time.perf_counter() - _t + profile['merge_duplicates'] += _ev_t['merge_dup'] if voxelizer is not None: _t = time.perf_counter() particles = voxelizer.process(particles) - profile['voxelize'] += time.perf_counter() - _t + _ev_t['voxelize'] = time.perf_counter() - _t + profile['voxelize'] += _ev_t['voxelize'] + if _write_voxmap: + _t = time.perf_counter() + vox_writer.append_event(voxelizer.last_diagnostics) + _ev_t['write_voxmap'] = time.perf_counter() - _t + profile['write_voxmap'] += _ev_t['write_voxmap'] if preprocessor is not None: _t = time.perf_counter() particles = preprocessor.process(particles) - profile['defragment'] += time.perf_counter() - _t + _ev_t['defragment'] = time.perf_counter() - _t + profile['defragment'] += _ev_t['defragment'] + + # ── Orphan resolution: repair dangling parent_id / root_id ────── + # Must run before ParticlePartitioner so the genealogy tree is + # consistent for all conditions and merge_em_showers. + resolve_orphans(particles, verbose=cfg.verbose) # ── Particle statistics (after preprocessing, before partition) ── _pc_lens = [len(p.point_cloud) for p in particles] @@ -111,26 +197,217 @@ def main(cfg: DictConfig) -> None: backend = cfg.checker.name, n_jobs = int(cfg.checker.get("n_jobs", -1)), enable_diagnostics = cfg.enable_diagnostics, + verbose = cfg.verbose, chunk_size = int(cfg.checker.get("chunk_size", 512)), block_size = int(cfg.checker.get("block_size", 128)), ) - profile['partitioner_init'] += time.perf_counter() - _t + _ev_t['init'] = time.perf_counter() - _t + profile['partitioner_init'] += _ev_t['init'] _t = time.perf_counter() partitions = partitioner.partition_combined(conditions, verbose=cfg.verbose) - profile['partition_combined'] += time.perf_counter() - _t + _ev_t['partition'] = time.perf_counter() - _t + profile['partition_combined'] += _ev_t['partition'] stats['n_partitions'].append(len(partitions)) + _t = time.perf_counter() partitioner.checker.cleanup() + _ev_t['cleanup'] = time.perf_counter() - _t + profile['checker_cleanup'] += _ev_t['cleanup'] + + # ── Step-1 fragment representatives ──────────────────────── + # Deduplicate: rep_lookup maps every original particle ID to + # its representative; collect unique reps via identity dict. + fragments = list( + {id(r): r for r in partitioner.rep_lookup.values()}.values() + ) + + # Snapshot fragment member_ids BEFORE merge_em_showers mutates + # them in place (it extends the initiator's member_ids with the + # merged members), so the _frag_lookup we build afterwards + # reflects the original step-1 partitioning. + _frag_member_ids_snap = { + id(_fr): (list(_fr.member_ids) if _fr.member_ids is not None else [_fr.id]) + for _fr in fragments + } + + # Write fragments BEFORE step-2 merger, which mutates + # the initiator's member_ids / point_cloud in place. + # drop_le_scatter only affects output — algorithms always + # run on the full set of fragments/instances. + _frags_out = [f for f in fragments if not _is_le(f)] if _drop_le_scatter else fragments + + # Build frag_lookup early so we can stamp parent_frag_id on + # reps before writing. Use the pre-step-2 snapshot so that + # merge_em_showers' in-place mutation of member_ids does not + # corrupt frag_id assignments. + # particle_id → fragment index in written list (in event) + _frag_lookup: dict = {} + for _fi, _fr in enumerate(_frags_out): + for _pid in _frag_member_ids_snap[id(_fr)]: + _frag_lookup[int(_pid)] = _fi + + # particle_id lookup for parent-chain walks + _part_by_id: dict = {int(_p.id): _p for _p in particles} + + def _find_parent_idx(_start_pid, _lookup, _pby): + """Walk parent_id chain; return first valid hit index, or -1.""" + _visited: set = set() + _pid = _start_pid + while _pid is not None and _pid not in _visited: + _visited.add(_pid) + if _pid in _lookup: + return _lookup[_pid] + _pp = _pby.get(int(_pid)) + if _pp is None or _pp.parent_id == _pp.id: + break + _pid = _pp.parent_id + return -1 + + for _fi, _fr in enumerate(_frags_out): + _pfid = _find_parent_idx(_fr.parent_id, _frag_lookup, _part_by_id) + _fr.parent_frag_id = _pfid if _pfid >= 0 else _fi # fallback = own + + _t = time.perf_counter() + writer.append_fragments(_frags_out) + _ev_t['write_frags'] = time.perf_counter() - _t + profile['write_fragments'] += _ev_t['write_frags'] - # Write the (possibly relabelled) particles for this event. - writer.append_event(particles) + # ── Step-2 EM shower instance merger ─────────────────────── + _t = time.perf_counter() + instances = merge_em_showers(fragments) + _ev_t['em_merge'] = time.perf_counter() - _t + profile['merge_em_showers'] += _ev_t['em_merge'] + + # Output filtering only — does not affect algorithm logic. + _insts_out = [i for i in instances if not _is_le(i)] if _drop_le_scatter else instances + + # Build inst_lookup and stamp parent_inst_id (and parent_frag_id + # for the instance rep's own fragment) before writing. + _inst_lookup: dict = {} # particle_id → instance index (in event) + for _ii, _inst in enumerate(_insts_out): + for _pid in (_inst.member_ids if _inst.member_ids is not None else [_inst.id]): + _inst_lookup[int(_pid)] = _ii + + for _ii, _inst in enumerate(_insts_out): + _piid = _find_parent_idx(_inst.parent_id, _inst_lookup, _part_by_id) + _inst.parent_inst_id = _piid if _piid >= 0 else _ii # fallback = own + _inst.parent_frag_id = _frag_lookup.get(int(_inst.id), -1) + + _t = time.perf_counter() + writer.append_instances(_insts_out) + _ev_t['write_inst'] = time.perf_counter() - _t + profile['write_instances'] += _ev_t['write_inst'] + + # ── Event-level union point clouds ────────────────────── + if _write_event_clouds: + _t = time.perf_counter() + + # _frag_lookup and _inst_lookup are already built above + # for parent_frag_id / parent_inst_id stamping; reuse them + # here so cloud col indices match the HDF5 table rows. + # LE particles get frag_id=-1 / inst_id=-1 when + # drop_le_scatter=True — that is intentional. + + # Assert algorithmic correctness: every non-LE particle must + # appear in SOME instance produced by merge_em_showers, even if + # that instance is a kLEScatter one that gets filtered from output + # by drop_le_scatter. Use the full instances list here, not the + # output-filtered _insts_out, so the check is independent of the + # drop_le_scatter flag. + _inst_lookup_full: dict = {} + for _ii, _inst in enumerate(instances): + for _pid in (_inst.member_ids if _inst.member_ids is not None else [_inst.id]): + _inst_lookup_full[int(_pid)] = _ii + _nle_ids = [int(_p.id) for _p in particles if not _is_le(_p)] + _uncovered = [_pid for _pid in _nle_ids if _pid not in _inst_lookup_full] + if _uncovered: + raise RuntimeError( + f"Event {event_idx}: {len(_uncovered)} non-LE particle(s) have no " + f"instance after step-2 merger: " + f"{_uncovered[:10]}" + f"{'...' if len(_uncovered) > 10 else ''}" + ) + + def _build_cloud9(src_parts): + """Return (N, 9) float32 array: + x, y, z, t, energy, interaction_id, root_id, frag_id, inst_id.""" + rows = [] + for _p in src_parts: + _pc = _p.point_cloud + if len(_pc) == 0: + continue + _n = len(_pc) + _pc5 = np.zeros((_n, 5), dtype=np.float32) + _take = min(_pc.shape[1], 5) + _pc5[:, :_take] = _pc[:, :_take] + _pid_int = int(_p.id) + _iid = np.full((_n, 1), float(_p._interaction_id), dtype=np.float32) + _rid = np.full((_n, 1), float(_p.root_id), dtype=np.float32) + _fid = np.full((_n, 1), float(_frag_lookup.get(_pid_int, -1)), dtype=np.float32) + _eid = np.full((_n, 1), float(_inst_lookup.get(_pid_int, -1)), dtype=np.float32) + rows.append(np.concatenate([_pc5, _iid, _rid, _fid, _eid], axis=1)) + return np.concatenate(rows) if rows else np.empty((0, 9), dtype=np.float32) + + def _voxelize_cloud9(c9, vox_size, orig): + """Voxelize (N, 9) cloud; integer-id cols (5-8) use majority-vote.""" + if len(c9) == 0: + return c9 + c5_vox = _voxelize_point_cloud(c9[:, :5], vox_size, orig) + coords = c9[:, :3] + _org = coords.min(axis=0) if orig is None else orig + _idx = np.floor((coords - _org) / vox_size).astype(np.int64) + _, _inv = np.unique(_idx, axis=0, return_inverse=True) + extra_cols = [] + for _col in range(5, 9): # interaction_id, root_id, frag_id, inst_id + # Q4: use majority-vote (mode) so a voxel shared by multiple + # instances/fragments gets assigned the dominant ID, not the min. + _int_vals = c9[:, _col].astype(np.int32) + _pairs = np.column_stack([_inv, _int_vals]) + _upairs, _ucnts = np.unique(_pairs, axis=0, return_counts=True) + _vox_col = _upairs[:, 0] + _val_col = _upairs[:, 1] + # Sort so highest-count entry per voxel comes first + _sort_idx = np.lexsort([-_ucnts, _vox_col]) + _vox_sorted = _vox_col[_sort_idx] + _val_sorted = _val_col[_sort_idx] + _, _first = np.unique(_vox_sorted, return_index=True) + _out = _val_sorted[_first].astype(np.float32) + extra_cols.append(_out[:, None]) + return np.concatenate([c5_vox] + extra_cols, axis=1) + + _nle_parts = [p for p in particles if not _is_le(p)] + _le_parts = [p for p in particles if _is_le(p)] + _nle_cloud = _build_cloud9(_nle_parts) + _le_cloud = _build_cloud9(_le_parts) + if voxelizer is not None: + _nle_cloud = _voxelize_cloud9(_nle_cloud, voxelizer.voxel_size, voxelizer.origin) + _le_cloud = _voxelize_cloud9(_le_cloud, voxelizer.voxel_size, voxelizer.origin) + writer.append_event_clouds(_nle_cloud, _le_cloud) + _ev_t['write_clouds'] = time.perf_counter() - _t + profile['write_event_clouds'] += _ev_t['write_clouds'] + + _t = time.perf_counter() + _particles_to_write = ( + [] + if _output_compact + else ([p for p in particles if not _is_le(p)] + if _drop_le_scatter else particles) + ) + writer.append_event(_particles_to_write) + _ev_t['write_ev'] = time.perf_counter() - _t + profile['write_event'] += _ev_t['write_ev'] + + # Update progress bar with per-event stats + _bar.update(1) + _bar.set_postfix( + p=len(particles), + parts=len(partitions), + prt_ms=f"{_ev_t.get('partition', 0)*1e3:.0f}", + wev_ms=f"{_ev_t.get('write_ev', 0)*1e3:.0f}", + ) - if cfg.verbose: - elapsed = time.perf_counter() - t0 - print(f"[run] event {event_idx:>6d} " - f"| {len(particles):>5d} particles " - f"| {elapsed:.3f} s") + _bar.close() # ── End-of-run summary report ────────────────────────────────────────── if n_events > 0 and cfg.get("report", True): diff --git a/pysupera/app.py b/pysupera/app.py index b2a07f6..644b652 100644 --- a/pysupera/app.py +++ b/pysupera/app.py @@ -12,6 +12,16 @@ Controls (sidebar) ------------------ * HDF5 file path + event index +* Input format: + + - ``native`` — pysupera native HDF5 (default) + - ``edepsim_h5`` — EDepSim HDF5 output; exposes step_key, particle_key, + electron_energy_threshold, and two optional JAXTPC + visibility-filter fields (jaxtpc_seg_path / + jaxtpc_inst_path). Leave the JAXTPC fields blank to + use all EDepSim segments; fill both to restrict point + clouds to segments visible in the JAXTPC readout. + * Preprocessing: merge_duplicates, defragment, min_pc_size, backend * Partitioner: distance_threshold, checker type, n_jobs * Conditions: enable / disable each of the four conditions @@ -424,6 +434,10 @@ def build_layout() -> html.Div: ), # ── Input format ────────────────────────────────────────────────── + # Input format: + # - native : pysupera native HDF5 + # - edepsim_h5 : EDepSim HDF5; optionally add JAXTPC visibility + # filtering by filling the seg/inst path fields below. _section( "INPUT FORMAT", _labelled("Format", @@ -431,6 +445,7 @@ def build_layout() -> html.Div: {"label": "native (pysupera HDF5)", "value": "native"}, {"label": "EDepSim HDF5", "value": "edepsim_h5"}, ], "native")), + # EDepSim options (shown when format == edepsim_h5) html.Div([ _labelled("step_key (HDF5 dataset)", _input("step-key", value="pstep/lar_vol")), @@ -438,6 +453,17 @@ def build_layout() -> html.Div: _input("particle-key", value="particle/geant4")), _labelled("electron_energy_threshold", _input("elec-threshold", "number", 0.05, min=0.0, step="any")), + html.Hr(style={"borderColor": "#0f3460", "margin": "6px 0"}), + # JAXTPC visibility filtering — leave blank to use all EDepSim + # segments; fill both paths to restrict point clouds to only the + # segments that were visible in the JAXTPC readout. + html.Div("JAXTPC visibility filter (optional):", + style=dict(fontSize="11px", color="#5a6a9a", + marginBottom="4px")), + _labelled("jaxtpc_seg_path", + _input("jaxtpc-seg-path", value="")), + _labelled("jaxtpc_inst_path", + _input("jaxtpc-inst-path", value="")), ], id="edepsim-options", style={"display": "none"}), collapsed=True, ), @@ -723,6 +749,7 @@ def toggle_voxelize(flags): # ── Show/hide EDepSim reader options ───────────────────────────────────────── +# ── Show/hide EDepSim options (and embedded JAXTPC optional fields) ─────────── @app.callback( Output("edepsim-options", "style"), Input("format-selector", "value"), @@ -782,11 +809,13 @@ def toggle_log(show_val): State("conditions", "value"), State("verbose-toggle", "value"), State("view-options", "value"), - State("format-selector", "value"), - State("step-key", "value"), - State("particle-key", "value"), - State("elec-threshold", "value"), - State("min-display-pc", "value"), + State("format-selector", "value"), + State("step-key", "value"), + State("particle-key", "value"), + State("elec-threshold", "value"), + State("jaxtpc-seg-path", "value"), + State("jaxtpc-inst-path", "value"), + State("min-display-pc", "value"), prevent_initial_call=True, ) def run_pipeline( @@ -796,6 +825,7 @@ def run_pipeline( dist_thresh, checker_type, n_jobs, voxel_size_input, condition_keys, verbose_flags, view_options, format_val, step_key_val, particle_key_val, elec_thresh_val, + jaxtpc_seg_path_val, jaxtpc_inst_path_val, min_display_pc_val, ): preproc_flags = preproc_flags or [] @@ -897,19 +927,40 @@ def run_pipeline( from collections import Counter if format_val == "edepsim_h5": - from pysupera.readers import EDepSimHDF5Reader - with EDepSimHDF5Reader( - file_path, - particle_key=particle_key_val, - step_key=step_key_val, - electron_energy_threshold=elec_thresh_val, - min_pc_size=min_pc_size, - ) as reader: - n_events = len(reader) - particles = reader[min(event_idx, n_events - 1)] - _load_preproc_lines.append( - f"✔ Loaded {file_path} ({n_events} events) [edepsim_h5]" - ) + _jaxtpc = bool(jaxtpc_seg_path_val) and bool(jaxtpc_inst_path_val) + if _jaxtpc: + # Both JAXTPC paths provided → visibility-filtered reader. + # Only segments visible in the JAXTPC readout are returned. + from pysupera.readers import JaxtpcHDF5Reader + with JaxtpcHDF5Reader( + edepsim_path=file_path, + seg_path=jaxtpc_seg_path_val, + inst_path=jaxtpc_inst_path_val, + particle_key=particle_key_val, + electron_energy_threshold=elec_thresh_val, + min_pc_size=min_pc_size, + ) as reader: + n_events = len(reader) + particles = reader[min(event_idx, n_events - 1)] + _load_preproc_lines.append( + f"✔ Loaded {file_path} ({n_events} events) [edepsim_h5 + jaxtpc visibility]" + ) + _load_preproc_lines.append(f" seg : {jaxtpc_seg_path_val}") + _load_preproc_lines.append(f" inst: {jaxtpc_inst_path_val}") + else: + from pysupera.readers import EDepSimHDF5Reader + with EDepSimHDF5Reader( + file_path, + particle_key=particle_key_val, + step_key=step_key_val, + electron_energy_threshold=elec_thresh_val, + min_pc_size=min_pc_size, + ) as reader: + n_events = len(reader) + particles = reader[min(event_idx, n_events - 1)] + _load_preproc_lines.append( + f"✔ Loaded {file_path} ({n_events} events) [edepsim_h5]" + ) else: from pysupera import read_events with read_events(file_path) as store: diff --git a/pysupera/conditions/photon_decay.py b/pysupera/conditions/photon_decay.py index e56c666..90cc8fb 100644 --- a/pysupera/conditions/photon_decay.py +++ b/pysupera/conditions/photon_decay.py @@ -3,6 +3,7 @@ from .base import PartitionConditionBase from ..diagnostics import MergeOutcome +from ..utils import SemanticType if TYPE_CHECKING: from ..partitioner import ParticlePartitioner @@ -116,6 +117,17 @@ def get_unconditional_merges( photon_rep = rep_lookup.get(photon_id) if photon_rep is None: continue + # Promote the photon rep to kShower immediately, even if it + # currently carries kLEScatter (which happens when the photon's + # own point cloud is below the min_pc_size threshold so it was + # initially assigned kLEScatter). After merging its e+/e- + # children in, the photon will have a non-zero point cloud and + # correctly represents a shower. Promoting BEFORE returning + # the pairs ensures that CombineLEScatters and AbsorbLEScatter + # (which run later in the same convergence pass) see the photon + # as kShower and leave it alone, rather than absorbing it. + if photon_rep.sem_type == SemanticType.kLEScatter: + photon_rep.sem_type = SemanticType.kShower for child in children: child_rep = rep_lookup.get(child.id) if child_rep is None or child_rep.id == photon_rep.id: diff --git a/pysupera/conditions/touching_em_shower.py b/pysupera/conditions/touching_em_shower.py index 85b73f4..67b9ab0 100644 --- a/pysupera/conditions/touching_em_shower.py +++ b/pysupera/conditions/touching_em_shower.py @@ -2,6 +2,7 @@ from .base import PartitionConditionBase from ..diagnostics import MergeOutcome +from ..utils import SemanticType if TYPE_CHECKING: from ..partitioner import ParticlePartitioner @@ -76,10 +77,13 @@ def get_rep_candidates( Iterates the current unique partition representatives and emits ``(child_rep_id, parent_rep_id)`` whenever: - * the child representative has PDG code 11 or 22, + * the child representative has PDG code 11 or 22 **and** is not + ``kLEScatter`` (to avoid pulling kLEScatter particles into EM showers), * the original parent of that child exists in *rep_lookup* (i.e. the parent particle belongs to some live partition), - * the parent representative also has PDG code 11 or 22, and + * the parent representative also has PDG code 11 or 22 **and** is not + ``kLEScatter`` (kLEScatter reps must not become shower parents — + ``AbsorbLEScatter`` is responsible for handling them), * child and parent representatives share the same ``root_id``. Because *rep_lookup* is updated after every merge, this method @@ -98,11 +102,18 @@ def get_rep_candidates( ``(child_rep_id, parent_rep_id)`` — child merges INTO parent. """ target_pdgs = {11, 22} + _le_type = SemanticType.kLEScatter candidates = [] for rep in self.unique_reps(rep_lookup): if rep.pdg not in target_pdgs: continue + # kLEScatter reps must never be absorbed here — they are + # handled by AbsorbLEScatter. If a kLEScatter particle has + # PDG 11/22 it could otherwise become the surviving parent and + # silently steal non-LE shower particles into a kLEScatter rep. + if rep.sem_type == _le_type: + continue if rep.parent_id not in rep_lookup: # Original parent not in any live partition continue @@ -111,6 +122,9 @@ def get_rep_candidates( continue # already same partition if parent_rep.pdg not in target_pdgs: continue + if parent_rep.sem_type == _le_type: + # kLEScatter rep must not absorb non-LE children + continue if rep.root_id != parent_rep.root_id: continue candidates.append((rep.id, parent_rep.id)) diff --git a/pysupera/conf/checker/gpu.yaml b/pysupera/conf/checker/gpu.yaml index b81f941..8d465c4 100644 --- a/pysupera/conf/checker/gpu.yaml +++ b/pysupera/conf/checker/gpu.yaml @@ -3,4 +3,4 @@ name: gpu # Rows of cloud_a per CuPy kernel launch in Stage-2 exact checks. # Increase for larger GPU memory / smaller clouds; decrease to avoid OOM. -chunk_size: 512 +chunk_size: 32728 diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index f6a0700..ffd63d5 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -1,7 +1,17 @@ defaults: - io: default + # Reader config group. Currently only "edepsim_h5" is available. + # JAXTPC visibility filtering is not a separate reader — it is activated + # by setting two optional flags on the same edepsim_h5 config: + # + # reader.jaxtpc_seg_path=sim_seg_0000.h5 + # reader.jaxtpc_inst_path=sim_inst_0000.h5 + # + # When both are non-null, only segments visible in the JAXTPC readout are + # included in each particle's point cloud. Particle metadata (PDG, track + # IDs, interaction type) always comes from the EDepSim file (io.input_path). - reader: edepsim_h5 - - checker: gpu + - checker: cpu_single - conditions: default - _self_ @@ -10,11 +20,18 @@ defaults: # --------------------------------------------------------------------------- # Proximity distance threshold (same spatial units as point cloud coords). -distance_threshold: 5.0 +distance_threshold: 5.2 + +# Maximum number of events to process. -1 (default) means all events. +max_events: -1 + +# Show a tqdm progress bar during the event loop. +# Set false to suppress all dynamic output (useful when stdout is redirected). +progress: true # Print per-event timing and merge counts (condition passes, per-event elapsed). # Set false to suppress all per-event chatter — ideal for production. -verbose: true +verbose: false # Print an end-of-run summary report: time profile and particle/partition # statistics aggregated over all events. Set false for completely silent runs. @@ -30,6 +47,30 @@ enable_diagnostics: false # particle lists are expected. check_particle_tree: false +# --------------------------------------------------------------------------- +# Output filtering options +# --------------------------------------------------------------------------- + +# Compact output: skip writing raw input-level (Level 0) particles. +# Only fragment representatives (Level 1) and instance representatives +# (Level 2) are stored, which is sufficient for most downstream analyses. +# Reduces output file size significantly when input particles are numerous. +output_compact: true + +# Drop kLEScatter particles from all output levels. +# When true, particles with sem_type == kLEScatter are excluded from +# fragments, instances, and (when output_compact is false) raw particles. +# Useful when low-energy scatter products are not needed downstream. +drop_le_scatter: false + +# Write event-level union point clouds (event_cloud and le_scatter_cloud). +# event_cloud : union of all particles' point clouds for each event, +# voxelized with the same voxel_size/origin as particle +# pre-processing when voxelization is enabled. +# le_scatter_cloud: same but restricted to kLEScatter particles only. +# Set false to omit these datasets and reduce output file size. +write_event_clouds: true + # --------------------------------------------------------------------------- # Particle construction parameters # --------------------------------------------------------------------------- @@ -37,7 +78,7 @@ particle: # Minimum point-cloud size for SetSemanticType classification. # Particles with fewer points than this are treated as having an # unknown semantic type. -1 disables the check (all sizes accepted). - min_pc_size: -1 + min_pc_size: 5 # Pre-processing: voxelize point clouds onto a regular 3-D grid. # Bins each point into a voxel cell, snaps its position to the voxel @@ -45,22 +86,22 @@ particle: # dedx=max). This is a strict generalisation of merge_duplicates; when # voxelize.enabled is true, merge_duplicates is redundant. voxelize: - enabled: false + enabled: true # Voxel side length in the same units as the point-cloud coordinates. # A single float produces an isotropic grid; set to [dx, dy, dz] for # an anisotropic grid. - voxel_size: 0.3 + voxel_size: 3 # Lower corner of the grid. null = use each cloud's own coordinate # minimum (per-particle alignment, default). Set to [x0, y0, z0] for # a globally fixed grid anchored at a known detector boundary. - origin: null + origin: [-2160.,-2160.,-2160.] # When true, skips the separate merge_duplicates preprocessing step. # Safe because exact-coordinate duplicates always fall in the same voxel. - merge_duplicates: false + merge_duplicates: true # When true, VoxelizeProcessor stores a CSR mapping (input_ids, # input_energies, voxel_offsets) for every particle in last_diagnostics, # enabling traces from output voxels back to input points. - store_mapping: false + store_mapping: true # Pre-processing: collapse points sharing identical 3-D coordinates. # Aggregation rules: time=min, energy=sum, dedx=max. @@ -90,4 +131,5 @@ particle: # Empty list (default) means all semantic types are processed. # Valid values: kShower, kTrack, kDelta, kMichel, kLEScatter, kUnknown # Example: [kShower, kTrack] - sem_types: [] + sem_types: [kShower,kDelta,kMichel,kLEScatter] + \ No newline at end of file diff --git a/pysupera/conf/io/default.yaml b/pysupera/conf/io/default.yaml index a8b759b..94c56d9 100644 --- a/pysupera/conf/io/default.yaml +++ b/pysupera/conf/io/default.yaml @@ -3,6 +3,12 @@ input_path: ??? # e.g. /data/events.h5 output_path: ??? # e.g. /data/events_merged.h5 -# HDF5 compression filter. "lz4" requires hdf5plugin; falls back to gzip. -compression: gzip -compression_opts: null +# HDF5 compression filter. +# lzf — built into h5py; always available; fast decompression, good ratio. (default) +# lz4 — faster than lzf; requires `pip install hdf5plugin`. +# blosc_lz4 — parallel decompression via Blosc+LZ4; requires hdf5plugin. +# gzip — standard DEFLATE; best ratio, slowest reads. Avoid for large files. +# ~ — no compression; fastest reads, largest files. +# compression_opts: only relevant for gzip (1=fast … 9=best). +compression: lz4 +compression_opts: ~ diff --git a/pysupera/conf/reader/edepsim_h5.yaml b/pysupera/conf/reader/edepsim_h5.yaml index a4a1d2f..440070e 100644 --- a/pysupera/conf/reader/edepsim_h5.yaml +++ b/pysupera/conf/reader/edepsim_h5.yaml @@ -4,8 +4,21 @@ # defaults: # - reader: edepsim_h5 # -# or overridden on the command line / in notebooks: +# or overridden on the command line: # reader.particle_key=particle/geant4_custom +# +# JAXTPC visibility filtering (optional) +# -------------------------------------- +# When jaxtpc_seg_path and jaxtpc_inst_path are both non-null, the reader +# automatically switches to visibility-filtered mode: only energy-deposit +# segments that produced a signal above threshold in the JAXTPC readout +# simulation are included in each particle's point cloud. The EDepSim file +# still provides all particle-level metadata (PDG, track IDs, etc.). +# +# reader.jaxtpc_seg_path=sim_seg_0000.h5 +# reader.jaxtpc_inst_path=sim_inst_0000.h5 +# +# Leave both as null (~) to use full EDepSim steps without any masking. # Identifies which reader class build_reader() should instantiate. # Do not change unless you are adding a new reader format. @@ -21,14 +34,32 @@ format: edepsim_h5 # proc_start, subproc_start, ke, x, y, z particle_key: particle/geant4 +# Per-event structured array of Geant4 vertices. +# Expected fields: x, y, z, t, interaction_id +vertex_key: vertex/geant4 + # Flat per-event energy-deposit step array (the point cloud). -# Columns 0-2 must be x, y, z. +# Columns 0-2 must be x, y, z. Not used in JAXTPC mode. step_key: pstep/lar_vol # Association table mapping particle index to slice in step_key. -# Expected fields: start, end (integer arrays of length N_particles) +# Expected fields: start, end (integer arrays of length N_particles). +# Not used in JAXTPC mode. ass_key: ass/particle_pstep_lar_vol +# --------------------------------------------------------------------------- +# JAXTPC visibility mask (optional — leave null to disable) +# --------------------------------------------------------------------------- + +# Path to the JAXTPC seg HDF5 file (per-volume 3-D truth deposits: +# positions_mm, de, dx, t0_us, charge, …). +jaxtpc_seg_path: ~ + +# Path to the JAXTPC inst HDF5 file (per-readout-plane correspondence data: +# segment_to_group, group_to_track, and per-plane group_ids that record +# which groups were above the readout threshold). +jaxtpc_inst_path: ~ + # --------------------------------------------------------------------------- # Physics / classification parameters # --------------------------------------------------------------------------- diff --git a/pysupera/config.py b/pysupera/config.py index 3a9a6ca..2d7e055 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -404,20 +404,29 @@ def build_conditions(cfg: DictConfig) -> list: conditions = [] + # 1. Photon decay: merge e+/e- children into photon rep unconditionally + # (topology-only, no proximity); must run early so photon reps have + # real point clouds before any spatial condition inspects them. if cfg.conditions.get("photon_decay", True): conditions.append(PhotonDecay()) - if cfg.conditions.get("touching_em_shower", True): - conditions.append(TouchingEMShower()) - - # Must come before absorb_le_scatter so transitive LEScatter chains are - # consolidated into one particle before shower absorption. + # 2. Consolidate transitively-touching kLEScatter chains into one blob + # before absorption so chains are absorbed as a unit. if cfg.conditions.get("combine_le_scatters", True): conditions.append(CombineLEScatters()) + # 3. Absorb every kLEScatter rep into a touching non-LE rep. + # After this step no standalone kLEScatter representative remains, + # so TouchingEMShower (step 4) cannot accidentally promote a + # kLEScatter rep to a shower parent. if cfg.conditions.get("absorb_le_scatter", True): conditions.append(AbsorbLEScatter()) + # 4. Merge touching PDG-11/22 parent-child pairs into EM shower fragments. + # Runs last in stage-1 so all kLEScatter reps are already absorbed. + if cfg.conditions.get("touching_em_shower", True): + conditions.append(TouchingEMShower()) + return conditions @@ -426,10 +435,27 @@ def build_reader(cfg: DictConfig): Instantiate an :class:`~pysupera.readers.EventReaderBase` described by *cfg.reader*, opening the file at *cfg.io.input_path*. - The reader format is selected by ``cfg.reader.format``: + The reader format is selected by ``cfg.reader.format``. Currently only + ``"edepsim_h5"`` is supported. + + JAXTPC visibility filtering + --------------------------- + When ``cfg.reader.jaxtpc_seg_path`` and ``cfg.reader.jaxtpc_inst_path`` + are both non-null/non-empty, the reader automatically switches to + :class:`~pysupera.readers.JaxtpcHDF5Reader`, which restricts each + particle's point cloud to energy-deposit segments that were visible in the + JAXTPC readout simulation. All particle-level metadata still comes from + the EDepSim file at *cfg.io.input_path*. + + With JAXTPC masking disabled (default):: - ``edepsim_h5`` - :class:`~pysupera.readers.EDepSimHDF5Reader` — EDepSim HDF5 output. + run_pysupera io.input_path=edepsim.h5 io.output_path=out.h5 + + With JAXTPC visibility filtering:: + + run_pysupera io.input_path=edepsim.h5 io.output_path=out.h5 \\ + reader.jaxtpc_seg_path=sim_seg_0000.h5 \\ + reader.jaxtpc_inst_path=sim_inst_0000.h5 Parameters ---------- @@ -452,30 +478,51 @@ def build_reader(cfg: DictConfig): -------- :: - cfg = load_cfg([ - "io.input_path=/data/sim.h5", - "reader.particle_key=particle/geant4_v2", - ]) + cfg = load_cfg(["io.input_path=/data/sim.h5"]) with build_reader(cfg) as reader: for particles in reader: ... """ - from pysupera.readers import EDepSimHDF5Reader - fmt = cfg.reader.get("format", "edepsim_h5") path = str(cfg.io.input_path) if fmt == "edepsim_h5": + # Check whether JAXTPC visibility filtering has been requested. + seg_path = cfg.reader.get("jaxtpc_seg_path", None) + inst_path = cfg.reader.get("jaxtpc_inst_path", None) + + jaxtpc_active = bool(seg_path) and bool(inst_path) + + if jaxtpc_active: + # Both JAXTPC paths are set → use visibility-filtered reader. + from pysupera.readers import JaxtpcHDF5Reader + return JaxtpcHDF5Reader( + edepsim_path = path, + seg_path = str(seg_path), + inst_path = str(inst_path), + vertex_key = str(cfg.reader.get("vertex_key", + "vertex/geant4")), + particle_key = str(cfg.reader.get("particle_key", + "particle/geant4")), + electron_energy_threshold = float(cfg.reader.get( + "electron_energy_threshold", 0.05)), + min_pc_size = int(cfg.particle.get("min_pc_size", -1)), + ) + + # Default: full EDepSim reader (no JAXTPC masking). + from pysupera.readers import EDepSimHDF5Reader return EDepSimHDF5Reader( path, + vertex_key = str(cfg.reader.get("vertex_key", + "vertex/geant4")), particle_key = str(cfg.reader.get("particle_key", - "particle/geant4")), + "particle/geant4")), step_key = str(cfg.reader.get("step_key", - "pstep/lar_vol")), + "pstep/lar_vol")), ass_key = str(cfg.reader.get("ass_key", - "ass/particle_pstep_lar_vol")), + "ass/particle_pstep_lar_vol")), electron_energy_threshold = float(cfg.reader.get( - "electron_energy_threshold", 0.05)), + "electron_energy_threshold", 0.05)), min_pc_size = int(cfg.particle.get("min_pc_size", -1)), ) diff --git a/pysupera/data.py b/pysupera/data.py index 5911297..cb4a89c 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -39,7 +39,6 @@ FLOAT_UNSET: np.float32 = np.float32("nan") """Sentinel value for unset ``float32`` scalar attributes on :class:`Particle`.""" - # ============================================================================ # Particle Data Class # ============================================================================ @@ -69,6 +68,8 @@ class Particle: High-level semantic category derived from *interaction_type*, *pdg*, *parent_pdg*, and *point_cloud* by :func:`~pysupera.utils.SetSemanticType`. + interaction_id : int + Unique interaction identifier within an event. interaction_type : InteractionType Physics process that created this particle, expressed as an :class:`~pysupera.utils.InteractionType` member. Derived from @@ -108,6 +109,20 @@ class Particle: Geant4/simulation sub-process ID for this particle's termination. end_process_name : str or None Human-readable name of the termination process. + member_ids : list of int or None + IDs of the original preprocessed particles absorbed into this + representative during partitioning (step 1) or EM shower merging + (step 2). ``None`` for raw input particles; populated by the + partitioner / shower merger. + parent_frag_id : int or None + Index of the parent particle's fragment in the written fragment + list for this event. ``-1`` (or own fragment index if own + particle has no parent fragment) when computed; ``None`` before + the value is stamped by the run pipeline. + parent_inst_id : int or None + Index of the parent particle's instance in the written instance + list for this event. ``-1`` (or own instance index) when + computed; ``None`` before stamped by the run pipeline. """ def __init__( @@ -117,6 +132,7 @@ def __init__( root_id, pdg, parent_pdg, + interaction_id, interaction_type, point_cloud, min_pc_size=None, @@ -139,6 +155,9 @@ def __init__( end_process_id=None, end_subprocess_id=None, end_process_name=None, + member_ids=None, + parent_frag_id=None, + parent_inst_id=None, ): """ Parameters @@ -175,6 +194,7 @@ def __init__( self.root_id = root_id self.pdg = pdg self.parent_pdg = parent_pdg + self._interaction_id = interaction_id self._interaction_type = (interaction_type.value if isinstance(interaction_type, InteractionType) else int(interaction_type)) # raw int; retained for serialisation @@ -207,6 +227,13 @@ def __init__( self.end_process_id = end_process_id self.end_subprocess_id = end_subprocess_id self.end_process_name = end_process_name + # member_ids: list of original preprocessed particle IDs absorbed into + # this representative (populated by the partitioner / shower merger). + self.member_ids = list(member_ids) if member_ids is not None else None + # parent_frag_id / parent_inst_id: set by the run pipeline after + # fragment and instance lists are built; -1 = no valid parent. + self.parent_frag_id = parent_frag_id + self.parent_inst_id = parent_inst_id # ------------------------------------------------------------------ # # Properties # @@ -247,6 +274,7 @@ def from_arrays( root_ids, pdgs, parent_pdgs, + interaction_ids, interaction_types, point_clouds, min_pc_size=None, @@ -271,6 +299,8 @@ def from_arrays( PDG Monte Carlo codes. parent_pdgs : array-like of int, shape (N,) PDG codes of direct parents. + interaction_ids : array-like of int, shape (N,) + Unique interaction identifier. interaction_types : array-like of int, shape (N,) Raw interaction process codes forwarded to :func:`~pysupera.utils.SetSemanticType`. @@ -303,6 +333,7 @@ def from_arrays( ... root_ids=[0, 0], ... pdgs=[11, 22], ... parent_pdgs=[0, 11], + ... interaction_ids=[0, 1], ... interaction_types=[0, 7], ... point_clouds=pcs, ... ) @@ -314,18 +345,20 @@ def from_arrays( root_ids = list(root_ids) pdgs = list(pdgs) parent_pdgs = list(parent_pdgs) + interaction_ids = list(interaction_ids) interaction_types = list(interaction_types) point_clouds = list(point_clouds) n = len(ids) if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, - parent_pdgs, interaction_types, + parent_pdgs, interaction_ids, interaction_types, point_clouds)): raise ValueError( "All input arrays must have the same length. " f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " f"parent_pdgs={len(parent_pdgs)}, " + f"interaction_ids={len(interaction_ids)}, " f"interaction_types={len(interaction_types)}, " f"point_clouds={len(point_clouds)}." ) @@ -337,6 +370,7 @@ def from_arrays( root_id=root_ids[i], pdg=pdgs[i], parent_pdg=parent_pdgs[i], + interaction_id=interaction_ids[i], interaction_type=interaction_types[i], point_cloud=point_clouds[i], min_pc_size=min_pc_size, @@ -352,6 +386,7 @@ def from_flat_arrays( root_ids, pdgs, parent_pdgs, + interaction_ids, interaction_types, point_cloud_flat, point_cloud_offsets, @@ -378,6 +413,8 @@ def from_flat_arrays( PDG Monte Carlo codes. parent_pdgs : array-like of int, shape (N,) PDG codes of direct parents. + interaction_ids : array-like of int, shape (N,) + Unique interaction identifier. interaction_types : array-like of int, shape (N,) Raw interaction process codes forwarded to :func:`~pysupera.utils.SetSemanticType`. @@ -424,6 +461,7 @@ def from_flat_arrays( ... root_ids=[0, 0], ... pdgs=[11, 22], ... parent_pdgs=[0, 11], + ... interaction_ids=[0, 1], ... interaction_types=[0, 7], ... point_cloud_flat=flat, ... point_cloud_offsets=offsets, @@ -438,6 +476,7 @@ def from_flat_arrays( root_ids = list(root_ids) pdgs = list(pdgs) parent_pdgs = list(parent_pdgs) + interaction_ids = list(interaction_ids) interaction_types = list(interaction_types) offsets = np.asarray(point_cloud_offsets) @@ -449,12 +488,13 @@ def from_flat_arrays( f"got {offsets.shape}." ) if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, - parent_pdgs, interaction_types)): + parent_pdgs, interaction_ids, interaction_types)): raise ValueError( "All scalar arrays must have the same length. " f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " f"parent_pdgs={len(parent_pdgs)}, " + f"interaction_ids={len(interaction_ids)}, " f"interaction_types={len(interaction_types)}." ) if offsets.shape[0] != n: @@ -478,6 +518,7 @@ def from_flat_arrays( root_id=root_ids[i], pdg=pdgs[i], parent_pdg=parent_pdgs[i], + interaction_id=interaction_ids[i], interaction_type=interaction_types[i], point_cloud=flat[offsets[i, 0] : offsets[i, 1]], min_pc_size=min_pc_size, diff --git a/pysupera/io.py b/pysupera/io.py index 20a0a78..6e92055 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -56,14 +56,23 @@ import numpy as np from typing import List, Optional -FORMAT_VERSION = "2.0.0" +# Register HDF5 compression filters (LZ4, Blosc, etc.) if available. +# Importing hdf5plugin is enough — it auto-registers all supported filters +# with the HDF5 library so h5py can transparently read/write them. +try: + import hdf5plugin # noqa: F401 +except ImportError: + pass + +FORMAT_VERSION = "2.1.0" # Chunk sizes for compressed HDF5 datasets. # Tuned for typical event sizes; adjust if your events are significantly # larger or smaller. -_CHUNK_PARTICLES = 256 # rows per chunk in particle metadata arrays -_CHUNK_POINTS = 65536 # rows per chunk in the flat point array -_PC_NDIM = 5 # number of columns in the flat point array (x,y,z,t,e) +_CHUNK_PARTICLES = 256 # rows per chunk in particle metadata arrays +_CHUNK_POINTS = 262144 # rows per chunk in the flat point array (1 chunk ≈ 5 MB) +_PC_NDIM = 5 # number of columns in the flat point array (x,y,z,t,e) +_CLOUD_NDIM = 9 # columns in event-cloud datasets (x,y,z,t,e,interaction_id,root_id,frag_id,inst_id) # --------------------------------------------------------------------------- # Public helpers @@ -71,7 +80,7 @@ def write_events(path: str, events: List[List], - compression: str = "lz4", + compression: str = "lzf", compression_opts: Optional[int] = None) -> None: """ Write a list of events to an HDF5 file. @@ -211,7 +220,7 @@ def write_events(path: str, def open_writer(path: str, mode: str = "w", - compression: str = "lz4", + compression: str = "lzf", compression_opts: Optional[int] = None) -> "EventWriter": """ Open an :class:`EventWriter` for incremental event writing. @@ -286,14 +295,15 @@ class EventWriter: w.append_event(more_particles) """ - # Names of the six int32 particle scalar fields (sem_type handled separately + # Names of the int32 particle scalar fields (sem_type handled separately # because it uses dtype=int8) _SCALAR_FIELDS = ( - "id", "parent_id", "root_id", "pdg", "parent_pdg", "interaction_type" + "id", "parent_id", "root_id", "pdg", "parent_pdg", + "interaction_id", "interaction_type" ) def __init__(self, path: str, mode: str = "w", - compression: str = "lz4", + compression: str = "lzf", compression_opts: Optional[int] = None) -> None: import h5py @@ -315,11 +325,31 @@ def __init__(self, path: str, mode: str = "w", self._n_events = int(self._f["n_events"][()]) self._n_particles = int(self._f["events/offsets"][-1]) self._n_points = int(self._f["particles/pc_offsets"][-1]) + # Fragment / instance counters (0 if the groups don't yet exist) + if "particle_fragments/pc_offsets" in self._f: + self._n_fragments = int(self._f["frag_events/offsets"][-1]) + self._n_frag_points = int(self._f["particle_fragments/pc_offsets"][-1]) + self._n_frag_members = int(self._f["particle_fragments/member_offsets"][-1]) + else: + self._n_fragments = self._n_frag_points = self._n_frag_members = 0 + if "particle_instances/pc_offsets" in self._f: + self._n_instances = int(self._f["inst_events/offsets"][-1]) + self._n_inst_points = int(self._f["particle_instances/pc_offsets"][-1]) + self._n_inst_members = int(self._f["particle_instances/member_offsets"][-1]) + else: + self._n_instances = self._n_inst_points = self._n_inst_members = 0 + # Event-cloud counters + self._n_event_cloud_pts = int(self._f["non_le_cloud/offsets"][-1]) if "non_le_cloud/offsets" in self._f else 0 + self._n_le_cloud_pts = int(self._f["le_scatter_cloud/offsets"][-1]) if "le_scatter_cloud/offsets" in self._f else 0 else: self._f = h5py.File(path, "w") self._n_events = 0 self._n_particles = 0 self._n_points = 0 + self._n_fragments = self._n_frag_points = self._n_frag_members = 0 + self._n_instances = self._n_inst_points = self._n_inst_members = 0 + self._n_event_cloud_pts = 0 + self._n_le_cloud_pts = 0 self._init_datasets() # ------------------------------------------------------------------ @@ -374,14 +404,15 @@ def append_event(self, particles: List) -> None: self._f["particles/sem_type"].resize(new_p_end, axis=0) # Build scalar arrays for this event - p_id = np.empty(n_p, dtype=np.int32) - p_parent_id = np.empty(n_p, dtype=np.int32) - p_root_id = np.empty(n_p, dtype=np.int32) - p_pdg = np.empty(n_p, dtype=np.int32) - p_parent_pdg = np.empty(n_p, dtype=np.int32) - p_itype = np.empty(n_p, dtype=np.int32) - p_sem_type = np.empty(n_p, dtype=np.int8) - pc_lengths = np.empty(n_p, dtype=np.int64) + p_id = np.empty(n_p, dtype=np.int32) + p_parent_id = np.empty(n_p, dtype=np.int32) + p_root_id = np.empty(n_p, dtype=np.int32) + p_pdg = np.empty(n_p, dtype=np.int32) + p_parent_pdg = np.empty(n_p, dtype=np.int32) + p_int_id = np.empty(n_p, dtype=np.int32) + p_itype = np.empty(n_p, dtype=np.int32) + p_sem_type = np.empty(n_p, dtype=np.int8) + pc_lengths = np.empty(n_p, dtype=np.int64) for k, p in enumerate(particles): p_id[k] = p.id @@ -389,6 +420,7 @@ def append_event(self, particles: List) -> None: p_root_id[k] = p.root_id p_pdg[k] = p.pdg p_parent_pdg[k] = p.parent_pdg + p_int_id[k] = int(p._interaction_id) p_itype[k] = int(p._interaction_type) p_sem_type[k] = int(p.sem_type.value) pc_lengths[k] = len(p.point_cloud) @@ -398,6 +430,7 @@ def append_event(self, particles: List) -> None: self._f["particles/root_id" ][new_p_start:new_p_end] = p_root_id self._f["particles/pdg" ][new_p_start:new_p_end] = p_pdg self._f["particles/parent_pdg" ][new_p_start:new_p_end] = p_parent_pdg + self._f["particles/interaction_id" ][new_p_start:new_p_end] = p_int_id self._f["particles/interaction_type" ][new_p_start:new_p_end] = p_itype self._f["particles/sem_type" ][new_p_start:new_p_end] = p_sem_type @@ -412,18 +445,19 @@ def append_event(self, particles: List) -> None: new_pc_offsets[k] = running pc_ds[new_p_start + 1 : new_p_end + 1] = new_pc_offsets - # ---- extend flat point array ---------------------------------- + # ---- extend flat point array (single bulk write) -------------- if n_pt > 0: pt_ds = self._f["points/flat"] new_pt_start = self._n_points pt_ds.resize(new_pt_start + n_pt, axis=0) - cursor = new_pt_start + bulk = np.empty((n_pt, _PC_NDIM), dtype=np.float32) + cursor = 0 for p in particles: n = len(p.point_cloud) if n > 0: - cloud = _normalize_cloud(p.point_cloud) - pt_ds[cursor : cursor + n] = cloud[:, :_PC_NDIM] + bulk[cursor : cursor + n] = _normalize_cloud(p.point_cloud) cursor += n + pt_ds[new_pt_start : new_pt_start + n_pt] = bulk # ---- advance counters --------------------------------------------- self._n_events += 1 @@ -495,6 +529,349 @@ def _init_datasets(self) -> None: **ckw, ) + # Representative-level groups: particle_fragments and particle_instances + self._init_rep_group("particle_fragments") + self._init_rep_group("particle_instances") + + # Event-level union point clouds + # Layout: (x, y, z, t, energy, interaction_id, root_id, frag_id, inst_id) + # — _CLOUD_NDIM = 9 columns (all stored as float32). + # non_le_cloud : union of point clouds from particles with sem_type != kLEScatter. + # le_scatter_cloud : union of point clouds from kLEScatter particles only. + for grp_name in ("non_le_cloud", "le_scatter_cloud"): + g = f.create_group(grp_name) + g.create_dataset( + "offsets", + data=np.zeros(1, dtype=np.int64), + maxshape=(None,), + chunks=(_CHUNK_PARTICLES,), + ) + g.create_dataset( + "flat", + shape=(0, _CLOUD_NDIM), maxshape=(None, _CLOUD_NDIM), dtype=np.float32, + chunks=(_CHUNK_POINTS, _CLOUD_NDIM), + **ckw, + ) + + def _init_rep_group(self, group_prefix: str) -> None: + """Create resizable datasets for one representative level. + + Layout (example for ``group_prefix="particle_fragments"``):: + + /frag_events/offsets (n_events+1,) int64 — per-event fencepost + /particle_fragments/id (n_reps,) int32 + /particle_fragments/parent_id (n_reps,) int32 + /particle_fragments/root_id (n_reps,) int32 + /particle_fragments/pdg (n_reps,) int32 + /particle_fragments/parent_pdg (n_reps,) int32 + /particle_fragments/interaction_type (n_reps,) int32 + /particle_fragments/sem_type (n_reps,) int8 + /particle_fragments/pc_offsets (n_reps+1,) int64 — fencepost into *_points/flat + /particle_fragments/member_offsets (n_reps+1,) int64 — fencepost into *_members/flat + /particle_fragments_points/flat (total_pts, dims) float32 + /particle_fragments_members/flat (total_members,) int32 + """ + f = self._f + ckw = self._ckw + + # Derive event-offset group name from prefix + # "particle_fragments" → "frag_events", "particle_instances" → "inst_events" + short = group_prefix.split("_", 1)[1] # "fragments" or "instances" + ev_group = f"{short[:4]}_events" # "frag_events" or "inst_events" + + eg = f.create_group(ev_group) + eg.create_dataset( + "offsets", + data=np.zeros(1, dtype=np.int64), + maxshape=(None,), + chunks=(_CHUNK_PARTICLES,), + ) + + pg = f.create_group(group_prefix) + for name in self._SCALAR_FIELDS: + pg.create_dataset( + name, + shape=(0,), maxshape=(None,), dtype=np.int32, + chunks=(_CHUNK_PARTICLES,), + **ckw, + ) + pg.create_dataset( + "sem_type", + shape=(0,), maxshape=(None,), dtype=np.int8, + chunks=(_CHUNK_PARTICLES,), + **ckw, + ) + for _name in ("parent_frag_id", "parent_inst_id"): + pg.create_dataset( + _name, + shape=(0,), maxshape=(None,), dtype=np.int32, + chunks=(_CHUNK_PARTICLES,), + **ckw, + ) + pg.create_dataset( + "pc_offsets", + data=np.zeros(1, dtype=np.int64), + maxshape=(None,), + chunks=(_CHUNK_PARTICLES,), + ) + pg.create_dataset( + "member_offsets", + data=np.zeros(1, dtype=np.int64), + maxshape=(None,), + chunks=(_CHUNK_PARTICLES,), + ) + + pts_g = f.create_group(f"{group_prefix}_points") + pts_g.create_dataset( + "flat", + shape=(0, _PC_NDIM), maxshape=(None, _PC_NDIM), dtype=np.float32, + chunks=(_CHUNK_POINTS, _PC_NDIM), + **ckw, + ) + + mem_g = f.create_group(f"{group_prefix}_members") + mem_g.create_dataset( + "flat", + shape=(0,), maxshape=(None,), dtype=np.int32, + chunks=(_CHUNK_PARTICLES,), + **ckw, + ) + + # ------------------------------------------------------------------ + # Public API: representative levels + # ------------------------------------------------------------------ + + def append_fragments(self, reps: List) -> None: + """Append step-1 fragment representatives for the current event. + + Must be called **before** :meth:`append_event` for the same event + (both methods share the event counter; only :meth:`append_event` + increments it). + + Parameters + ---------- + reps : list of Particle + Step-1 representative particles with ``member_ids`` populated. + """ + self._append_rep_level( + "particle_fragments", + reps, + "_n_fragments", "_n_frag_points", "_n_frag_members", + ) + + def append_instances(self, reps: List) -> None: + """Append step-2 shower-instance representatives for the current event. + + Must be called **before** :meth:`append_event` for the same event. + + Parameters + ---------- + reps : list of Particle + Step-2 instance representatives with ``member_ids`` populated. + """ + self._append_rep_level( + "particle_instances", + reps, + "_n_instances", "_n_inst_points", "_n_inst_members", + ) + + def append_event_clouds( + self, + non_le_cloud: "np.ndarray", + le_cloud: "np.ndarray", + ) -> None: + """Append event-level union point clouds for the current event. + + Must be called **before** :meth:`append_event` for the same event. + + Each cloud has 9 columns: + ``x, y, z, t, energy, interaction_id, root_id, frag_id, inst_id``. + All values are stored as float32. Integer-valued columns + (``interaction_id``, ``root_id``, ``frag_id``, ``inst_id``) should be + cast by the caller. Use ``-1`` as a sentinel for absent frag/inst IDs. + When voxelization is active the spatial columns are voxel-centre + coordinates; ``interaction_id`` / ``root_id`` are taken from the + dominant particle across merged points. + + Parameters + ---------- + non_le_cloud : np.ndarray, shape (N, 9) + Union of point clouds from particles whose ``sem_type`` is **not** + ``kLEScatter`` for this event (already voxelized if applicable). + May be empty (shape ``(0, 9)``). + le_cloud : np.ndarray, shape (M, 9) + Union of point clouds from ``kLEScatter`` particles for this event + (already voxelized if applicable). May be empty. + """ + for cloud, grp, n_attr in ( + (non_le_cloud, "non_le_cloud", "_n_event_cloud_pts"), + (le_cloud, "le_scatter_cloud", "_n_le_cloud_pts"), + ): + n_prev = getattr(self, n_attr) + n_pts = len(cloud) + + off_ds = self._f[f"{grp}/offsets"] + off_ds.resize(self._n_events + 2, axis=0) + off_ds[self._n_events + 1] = np.int64(n_prev + n_pts) + + if n_pts > 0: + flat_ds = self._f[f"{grp}/flat"] + flat_ds.resize(n_prev + n_pts, axis=0) + # Ensure float32 and exactly _CLOUD_NDIM columns. + c = np.asarray(cloud, dtype=np.float32) + if c.shape[1] < _CLOUD_NDIM: + pad = np.zeros((len(c), _CLOUD_NDIM - c.shape[1]), dtype=np.float32) + c = np.concatenate([c, pad], axis=1) + elif c.shape[1] > _CLOUD_NDIM: + c = c[:, :_CLOUD_NDIM] + flat_ds[n_prev : n_prev + n_pts] = c + + setattr(self, n_attr, n_prev + n_pts) + + def _append_rep_level( + self, + group_prefix: str, + reps: List, + n_attr: str, + npt_attr: str, + nmem_attr: str, + ) -> None: + """Generic incremental writer for a representative level. + + Parameters + ---------- + group_prefix : str + HDF5 group path prefix (``"particle_fragments"`` or + ``"particle_instances"``). + reps : list of Particle + Representative particles for this event. + n_attr, npt_attr, nmem_attr : str + Names of the instance-level running counters on ``self`` + (number of reps, total point-cloud entries, total member IDs). + """ + n_r = len(reps) + n_pt = sum(len(r.point_cloud) for r in reps) + n_mem = sum( + len(r.member_ids) if r.member_ids is not None else 1 + for r in reps + ) + + n_r_prev = getattr(self, n_attr) + n_pt_prev = getattr(self, npt_attr) + n_mem_prev = getattr(self, nmem_attr) + + # Derive event-offset group name (mirrors _init_rep_group logic) + short = group_prefix.split("_", 1)[1] # "fragments" / "instances" + ev_group = f"{short[:4]}_events" # "frag_events" / "inst_events" + + # ---- extend event fencepost ---------------------------------------- + ev_ds = self._f[f"{ev_group}/offsets"] + ev_ds.resize(self._n_events + 2, axis=0) + ev_ds[self._n_events + 1] = np.int64(n_r_prev + n_r) + + if n_r > 0: + new_start = n_r_prev + new_end = n_r_prev + n_r + + # ---- extend scalar datasets ----------------------------------- + for name in self._SCALAR_FIELDS: + self._f[f"{group_prefix}/{name}"].resize(new_end, axis=0) + self._f[f"{group_prefix}/sem_type"].resize(new_end, axis=0) + self._f[f"{group_prefix}/parent_frag_id"].resize(new_end, axis=0) + self._f[f"{group_prefix}/parent_inst_id"].resize(new_end, axis=0) + + # Build scalar arrays + r_id = np.empty(n_r, dtype=np.int32) + r_parent_id = np.empty(n_r, dtype=np.int32) + r_root_id = np.empty(n_r, dtype=np.int32) + r_pdg = np.empty(n_r, dtype=np.int32) + r_parent_pdg = np.empty(n_r, dtype=np.int32) + r_int_id = np.empty(n_r, dtype=np.int32) + r_itype = np.empty(n_r, dtype=np.int32) + r_sem_type = np.empty(n_r, dtype=np.int8) + r_parent_frag_id = np.empty(n_r, dtype=np.int32) + r_parent_inst_id = np.empty(n_r, dtype=np.int32) + pc_lengths = np.empty(n_r, dtype=np.int64) + mem_lengths = np.empty(n_r, dtype=np.int64) + + for k, r in enumerate(reps): + r_id[k] = r.id + r_parent_id[k] = r.parent_id + r_root_id[k] = r.root_id + r_pdg[k] = r.pdg + r_parent_pdg[k] = r.parent_pdg + r_int_id[k] = int(r._interaction_id) + r_itype[k] = int(r._interaction_type) + r_sem_type[k] = int(r.sem_type.value) + _pfid = getattr(r, "parent_frag_id", None) + _piid = getattr(r, "parent_inst_id", None) + r_parent_frag_id[k] = int(_pfid) if _pfid is not None else -1 + r_parent_inst_id[k] = int(_piid) if _piid is not None else -1 + pc_lengths[k] = len(r.point_cloud) + mem_lengths[k] = (len(r.member_ids) + if r.member_ids is not None else 1) + + self._f[f"{group_prefix}/id" ][new_start:new_end] = r_id + self._f[f"{group_prefix}/parent_id" ][new_start:new_end] = r_parent_id + self._f[f"{group_prefix}/root_id" ][new_start:new_end] = r_root_id + self._f[f"{group_prefix}/pdg" ][new_start:new_end] = r_pdg + self._f[f"{group_prefix}/parent_pdg" ][new_start:new_end] = r_parent_pdg + self._f[f"{group_prefix}/interaction_id" ][new_start:new_end] = r_int_id + self._f[f"{group_prefix}/interaction_type" ][new_start:new_end] = r_itype + self._f[f"{group_prefix}/sem_type" ][new_start:new_end] = r_sem_type + self._f[f"{group_prefix}/parent_frag_id" ][new_start:new_end] = r_parent_frag_id + self._f[f"{group_prefix}/parent_inst_id" ][new_start:new_end] = r_parent_inst_id + + # ---- extend pc_offsets ---------------------------------------- + pc_ds = self._f[f"{group_prefix}/pc_offsets"] + pc_ds.resize(new_end + 1, axis=0) + new_pc_offsets = np.empty(n_r, dtype=np.int64) + running = n_pt_prev + for k in range(n_r): + running += pc_lengths[k] + new_pc_offsets[k] = running + pc_ds[new_start + 1 : new_end + 1] = new_pc_offsets + + # ---- extend member_offsets ------------------------------------ + mem_ds = self._f[f"{group_prefix}/member_offsets"] + mem_ds.resize(new_end + 1, axis=0) + new_mem_offsets = np.empty(n_r, dtype=np.int64) + running = n_mem_prev + for k in range(n_r): + running += mem_lengths[k] + new_mem_offsets[k] = running + mem_ds[new_start + 1 : new_end + 1] = new_mem_offsets + + # ---- extend flat point array (single bulk write) -------------- + if n_pt > 0: + pt_flat = self._f[f"{group_prefix}_points/flat"] + pt_flat.resize(n_pt_prev + n_pt, axis=0) + bulk_pts = np.empty((n_pt, _PC_NDIM), dtype=np.float32) + cursor = 0 + for r in reps: + n = len(r.point_cloud) + if n > 0: + bulk_pts[cursor : cursor + n] = _normalize_cloud(r.point_cloud) + cursor += n + pt_flat[n_pt_prev : n_pt_prev + n_pt] = bulk_pts + + # ---- extend flat member array (single bulk write) ------------- + mem_flat = self._f[f"{group_prefix}_members/flat"] + mem_flat.resize(n_mem_prev + n_mem, axis=0) + if n_mem > 0: + bulk_mem = np.empty(n_mem, dtype=np.int32) + cursor = 0 + for r in reps: + ids = r.member_ids if r.member_ids is not None else [r.id] + n = len(ids) + bulk_mem[cursor : cursor + n] = ids + cursor += n + mem_flat[n_mem_prev : n_mem_prev + n_mem] = bulk_mem + + setattr(self, n_attr, n_r_prev + n_r) + setattr(self, npt_attr, n_pt_prev + n_pt) + setattr(self, nmem_attr, n_mem_prev + n_mem) + def read_events(path: str) -> "EventStore": """ @@ -542,12 +919,24 @@ class EventStore: ---------- path : str Path to HDF5 file written by :func:`write_events`. + chunk_cache_mb : int, optional + Size of the HDF5 chunk cache in **megabytes**. Larger values + speed up sequential or repeated reads by keeping more decompressed + chunks in RAM. Default ``256`` (256 MB). """ - def __init__(self, path: str) -> None: + def __init__(self, path: str, chunk_cache_mb: int = 256) -> None: import h5py self._path = path - self._f = h5py.File(path, "r") + rdcc_nbytes = chunk_cache_mb * 1024 * 1024 + # rdcc_nslots: prime slightly larger than (cache_bytes / chunk_bytes). + # With 5 MB chunks and 256 MB cache that's ~51 chunks; use 127. + rdcc_nslots = max(127, rdcc_nbytes // (_CHUNK_POINTS * _PC_NDIM * 4) * 3 + 1) + self._f = h5py.File( + path, "r", + rdcc_nbytes=rdcc_nbytes, + rdcc_nslots=int(rdcc_nslots), + ) self._n_events: int = int(self._f["n_events"][()]) # Cache both offset arrays — O(n_events) + O(n_particles) ints @@ -606,13 +995,14 @@ def __getitem__(self, index: int) -> List: return [] # ---- read scalar metadata in 7 contiguous array slices ------------ - ids = self._f["particles/id" ][p_start:p_end] - parent_ids = self._f["particles/parent_id" ][p_start:p_end] - root_ids = self._f["particles/root_id" ][p_start:p_end] - pdgs = self._f["particles/pdg" ][p_start:p_end] - parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] - itypes = self._f["particles/interaction_type" ][p_start:p_end] - sem_types = self._f["particles/sem_type" ][p_start:p_end] + ids = self._f["particles/id" ][p_start:p_end] + parent_ids = self._f["particles/parent_id" ][p_start:p_end] + root_ids = self._f["particles/root_id" ][p_start:p_end] + pdgs = self._f["particles/pdg" ][p_start:p_end] + parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] + interaction_ids = self._f["particles/interaction_id" ][p_start:p_end] + itypes = self._f["particles/interaction_type" ][p_start:p_end] + sem_types = self._f["particles/sem_type" ][p_start:p_end] # ---- read point-cloud data in one contiguous slice ---------------- pc_bounds = self._pc_offsets[p_start : p_end + 1] # (n_parts+1,) @@ -632,6 +1022,7 @@ def __getitem__(self, index: int) -> List: root_id = int(root_ids[k]), pdg = int(pdgs[k]), parent_pdg = int(parent_pdgs[k]), + interaction_id = int(interaction_ids[k]), interaction_type = int(itypes[k]), point_cloud = cloud, ) @@ -651,16 +1042,127 @@ def __repr__(self) -> str: # Convenience # ------------------------------------------------------------------ - def iter_events(self): + def read_bulk(self, start: int = 0, stop: int = -1, + indices=None) -> List[List]: + """ + Read a batch of events with a fixed number of HDF5 reads. + + All scalar datasets and the flat point array are loaded in **9 HDF5 + reads total**, then split into per-event lists in NumPy/Python. + + Parameters + ---------- + start : int, optional + First event index (inclusive). Ignored when *indices* is given. + Default ``0``. + stop : int, optional + Last event index (exclusive). ``-1`` means ``len(store)``. + Ignored when *indices* is given. Default ``-1``. + indices : sequence of int, optional + Explicit list (or array) of event indices to fetch, in any order + and with duplicates allowed. The returned list preserves the + order of *indices*. When given, *start* and *stop* are ignored. + + Returns + ------- + list of list of Particle + One entry per requested event, in the same order as *indices* + (or sequential order when using *start*/*stop*). + """ + from .data import Particle + from .utils import SemanticType + + # ---- resolve the set of event indices to load --------------------- + if indices is not None: + req = list(indices) + if not req: + return [] + unique_sorted = sorted(set(req)) + ev_min, ev_max = unique_sorted[0], unique_sorted[-1] + if ev_min < 0 or ev_max >= self._n_events: + raise IndexError( + f"Index out of range [0, {self._n_events}): " + f"got min={ev_min}, max={ev_max}" + ) + span_start, span_stop = ev_min, ev_max + 1 + else: + if stop < 0: + stop = self._n_events + stop = min(stop, self._n_events) + if start >= stop: + return [] + req = list(range(start, stop)) + span_start, span_stop = start, stop + + # ---- one contiguous particle range covers the whole span ---------- + p_start = int(self._ev_offsets[span_start]) + p_end = int(self._ev_offsets[span_stop]) + n_parts = p_end - p_start + + if n_parts == 0: + return [[] for _ in req] + + # 8 scalar reads + 1 pc_offsets slice + 1 flat-points read = 10 HDF5 ops + ids = self._f["particles/id" ][p_start:p_end] + parent_ids = self._f["particles/parent_id" ][p_start:p_end] + root_ids = self._f["particles/root_id" ][p_start:p_end] + pdgs = self._f["particles/pdg" ][p_start:p_end] + parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] + interaction_ids = self._f["particles/interaction_id" ][p_start:p_end] + itypes = self._f["particles/interaction_type" ][p_start:p_end] + sem_types = self._f["particles/sem_type" ][p_start:p_end] + + pc_bounds = self._pc_offsets[p_start : p_end + 1] # (n_parts+1,) + pt_start = int(pc_bounds[0]) + pt_end = int(pc_bounds[-1]) + flat_all = self._f["points/flat"][pt_start:pt_end] # single read + local_pc = pc_bounds - pt_start # zero-indexed + + # ---- helper: build particle list for one event index -------------- + def _build_event(ev): + ep_start = int(self._ev_offsets[ev]) - p_start + ep_end = int(self._ev_offsets[ev + 1]) - p_start + particles = [] + for k in range(ep_start, ep_end): + cloud = flat_all[local_pc[k] : local_pc[k + 1]] + p = Particle( + id = int(ids[k]), + parent_id = int(parent_ids[k]), + root_id = int(root_ids[k]), + pdg = int(pdgs[k]), + parent_pdg = int(parent_pdgs[k]), + interaction_id = int(interaction_ids[k]), + interaction_type = int(itypes[k]), + point_cloud = cloud, + ) + p.sem_type = SemanticType(int(sem_types[k])) + particles.append(p) + return particles + + return [_build_event(ev) for ev in req] + + def iter_events(self, batch_size: int = 64): """ Iterate over all events in file order. + Reads events in batches of *batch_size* to amortise HDF5 overhead + while keeping memory bounded. Each ``yield`` still returns a single + event's particle list. + + Parameters + ---------- + batch_size : int, optional + Number of events to pre-fetch per HDF5 batch. Default ``64``. + Set to ``1`` to reproduce the old one-at-a-time behaviour. + Yields ------ list of Particle """ - for i in range(self._n_events): - yield self[i] + for batch_start in range(0, self._n_events, batch_size): + batch_stop = min(batch_start + batch_size, self._n_events) + for ev in self.read_bulk(batch_start, batch_stop): + yield ev @property def n_events(self) -> int: @@ -681,9 +1183,10 @@ def _normalize_cloud(pc) -> np.ndarray: * A 1-D array of length 0 (e.g. ``np.array([])``) — converted to ``np.empty((0, _PC_NDIM), float32)``. * A properly-shaped 2-D array with 0 rows — returned as-is (after - dtype cast). + dtype cast and column adjustment). - Non-empty 2-D arrays are returned unchanged (modulo dtype cast). + Non-empty 2-D arrays are trimmed to ``_PC_NDIM`` columns when they have + more, or zero-padded on the right when they have fewer. Raises ------ @@ -703,7 +1206,16 @@ def _normalize_cloud(pc) -> np.ndarray: raise ValueError( f"point_cloud must be a 2-D array, got shape {pc.shape}." ) - return pc.astype(np.float32) + pc = pc.astype(np.float32) + n_cols = pc.shape[1] + if n_cols == _PC_NDIM: + return pc + if n_cols > _PC_NDIM: + return pc[:, :_PC_NDIM] + # Fewer columns than _PC_NDIM — zero-pad on the right + padded = np.zeros((pc.shape[0], _PC_NDIM), dtype=np.float32) + padded[:, :n_cols] = pc + return padded def _compress_kwargs(compression: Optional[str], @@ -711,24 +1223,37 @@ def _compress_kwargs(compression: Optional[str], """ Build h5py dataset keyword arguments for the requested filter. - Falls back gracefully if ``hdf5plugin`` is unavailable (required for - lz4/blosc) and the user requested lz4. + Supported values for *compression*: + + * ``None`` — no compression; fastest reads, largest files. + * ``"lzf"`` — LZF filter built into h5py; always available; + fast decompression, moderate ratio. **Default.** + * ``"lz4"`` — LZ4 via ``hdf5plugin``; faster than LZF for large + arrays with byte-shuffle. Falls back to LZF if + ``hdf5plugin`` is not installed. + * ``"blosc_lz4"`` — Blosc+LZ4 via ``hdf5plugin``; supports parallel + decompression (set ``HDF5_PLUGIN_PATH`` or install + the Blosc plugin). Falls back to LZF. + * ``"gzip"`` — Standard DEFLATE; good ratio, slowest reads. """ if compression is None: return {} - if compression == "lz4": + if compression in ("lz4", "blosc_lz4"): try: - import hdf5plugin # noqa: F401 — registers the filter - return {"compression": "lz4"} + import hdf5plugin + if compression == "blosc_lz4": + return dict(hdf5plugin.Blosc(cname="lz4", shuffle=hdf5plugin.Blosc.BYTE_SHUFFLE)) + else: + return dict(hdf5plugin.LZ4()) except ImportError: import warnings warnings.warn( - "hdf5plugin not installed; falling back to gzip compression. " - "Install with: pip install hdf5plugin", + f"hdf5plugin not installed; falling back to lzf compression " + f"(requested {compression!r}). Install with: pip install hdf5plugin", stacklevel=3, ) - compression = "gzip" + compression = "lzf" kwargs: dict = {"compression": compression} if compression_opts is not None: @@ -736,6 +1261,235 @@ def _compress_kwargs(compression: Optional[str], return kwargs +def inspect_compression(path: str) -> dict: + """ + Report the HDF5 filter and chunk geometry for the key datasets in *path*. + + Useful for diagnosing read performance:: + + >>> from pysupera.io import inspect_compression + >>> inspect_compression("output.h5") + {'points/flat': {'compression': 'gzip', 'compression_opts': 1, 'chunks': (65536, 5)}, + 'particles/id': {'compression': 'gzip', 'compression_opts': 1, 'chunks': (256,)}, ...} + + Parameters + ---------- + path : str + Path to any HDF5 file written by this library. + + Returns + ------- + dict + ``{dataset_path: {"compression": ..., "compression_opts": ..., "chunks": ...}}`` + """ + import h5py + keys = [ + "points/flat", + "particles/id", + "particles/pc_offsets", + ] + result = {} + with h5py.File(path, "r") as f: + for k in keys: + if k not in f: + continue + ds = f[k] + result[k] = { + "compression": ds.compression, + "compression_opts": ds.compression_opts, + "chunks": ds.chunks, + "shape": ds.shape, + } + return result + + +def recompress(src: str, dst: str, + compression: str = "lzf", + compression_opts: Optional[int] = None, + batch_size: int = 256) -> None: + """ + Rewrite *src* into *dst* with a different compression filter. + + Copies particle events **and** event-level point clouds + (``non_le_cloud`` / ``le_scatter_cloud``). + Fragment and instance representative groups are also copied when + present in the source file. + + Parameters + ---------- + src : str + Path to the source HDF5 file (any supported compression). + dst : str + Path for the output file (will be overwritten if it exists). + compression : str, optional + Compression for the output file. Default ``"lzf"``. + Use ``"gzip"`` to produce a file readable by h5wasm in the browser. + compression_opts : int or None, optional + Compression level for the output filter. + batch_size : int, optional + Events per read batch. Default ``256``. + """ + import h5py as _h5 + import numpy as _np + + with EventStore(src) as src_store, \ + _h5.File(src, "r") as src_f, \ + open_writer(dst, compression=compression, + compression_opts=compression_opts) as w: + + n_ev = len(src_store) + + # Pre-read cloud offset arrays if present + _has_nle = "non_le_cloud/offsets" in src_f + _has_le = "le_scatter_cloud/offsets" in src_f + if _has_nle: + _nle_off = src_f["non_le_cloud/offsets"][:] + if _has_le: + _le_off = src_f["le_scatter_cloud/offsets"][:] + + # Pre-read fragment / instance event-offset arrays if present + _has_frags = "frag_events/offsets" in src_f + _has_insts = "inst_events/offsets" in src_f + if _has_frags: + _fr_ev_off = src_f["frag_events/offsets"][:] + if _has_insts: + _in_ev_off = src_f["inst_events/offsets"][:] + + for batch_start in range(0, n_ev, batch_size): + batch_stop = min(batch_start + batch_size, n_ev) + + for ev_idx, particles in zip( + range(batch_start, batch_stop), + src_store.read_bulk(batch_start, batch_stop), + ): + # ---- event-level clouds ----------------------------------- + if _has_nle or _has_le: + nle_cloud = ( + src_f["non_le_cloud/flat"][ + int(_nle_off[ev_idx]) : int(_nle_off[ev_idx + 1]) + ] if _has_nle + else _np.empty((0, _CLOUD_NDIM), dtype=_np.float32) + ) + le_cloud = ( + src_f["le_scatter_cloud/flat"][ + int(_le_off[ev_idx]) : int(_le_off[ev_idx + 1]) + ] if _has_le + else _np.empty((0, _CLOUD_NDIM), dtype=_np.float32) + ) + w.append_event_clouds(nle_cloud, le_cloud) + + # ---- fragment representatives ----------------------------- + if _has_frags: + _fr_s = int(_fr_ev_off[ev_idx]) + _fr_e = int(_fr_ev_off[ev_idx + 1]) + frags = _read_rep_level(src_f, "particle_fragments", _fr_s, _fr_e) + w.append_fragments(frags) + + # ---- instance representatives ---------------------------- + if _has_insts: + _in_s = int(_in_ev_off[ev_idx]) + _in_e = int(_in_ev_off[ev_idx + 1]) + insts = _read_rep_level(src_f, "particle_instances", _in_s, _in_e) + w.append_instances(insts) + + w.append_event(particles) + + +def _read_rep_level(f, group: str, r_start: int, r_end: int) -> list: + """Reconstruct minimal Particle-like objects from a stored representative group. + + Used internally by :func:`recompress`. + """ + from .data import Particle + from .utils import SemanticType + + n_r = r_end - r_start + if n_r == 0: + return [] + + ids = f[f"{group}/id" ][r_start:r_end] + pids = f[f"{group}/parent_id" ][r_start:r_end] + rids = f[f"{group}/root_id" ][r_start:r_end] + pdgs = f[f"{group}/pdg" ][r_start:r_end] + ppdgs = f[f"{group}/parent_pdg" ][r_start:r_end] + intids = f[f"{group}/interaction_id" ][r_start:r_end] + itypes = f[f"{group}/interaction_type"][r_start:r_end] + stypes = f[f"{group}/sem_type" ][r_start:r_end] + pfids = f[f"{group}/parent_frag_id" ][r_start:r_end] if f"{group}/parent_frag_id" in f else None + piids = f[f"{group}/parent_inst_id" ][r_start:r_end] if f"{group}/parent_inst_id" in f else None + + pc_off = f[f"{group}/pc_offsets" ][r_start : r_end + 1] + mem_off = f[f"{group}/member_offsets"][r_start : r_end + 1] + + pt_start = int(pc_off[0]) + pt_end = int(pc_off[-1]) + pts_flat = f[f"{group}_points/flat"][pt_start:pt_end] if pt_end > pt_start else None + + mem_start = int(mem_off[0]) + mem_end = int(mem_off[-1]) + mem_flat = f[f"{group}_members/flat"][mem_start:mem_end] if mem_end > mem_start else None + + reps = [] + for k in range(n_r): + cloud = pts_flat[int(pc_off[k]) - pt_start : int(pc_off[k + 1]) - pt_start] \ + if pts_flat is not None else __import__('numpy').empty((0, _PC_NDIM), dtype='float32') + ms = int(mem_off[k]) - mem_start + me = int(mem_off[k + 1]) - mem_start + mids = list(mem_flat[ms:me].astype(int)) if mem_flat is not None and me > ms else None + p = Particle( + id = int(ids[k]), + parent_id = int(pids[k]), + root_id = int(rids[k]), + pdg = int(pdgs[k]), + parent_pdg = int(ppdgs[k]), + interaction_id = int(intids[k]), + interaction_type = int(itypes[k]), + point_cloud = cloud, + ) + p.sem_type = SemanticType(int(stypes[k])) + p.member_ids = mids + if pfids is not None: + p.parent_frag_id = int(pfids[k]) + if piids is not None: + p.parent_inst_id = int(piids[k]) + reps.append(p) + return reps + + +def recompress_cli() -> None: + """Entry point for the ``pysupera-recompress`` shell command. + + Usage:: + + pysupera-recompress src.h5 dst.h5 [--compression gzip] [--level 1] + + Rewrites *src.h5* into *dst.h5* with the chosen compression filter. + Use ``--compression gzip`` to produce a browser-compatible file for + the WebGL viewer (h5wasm only supports gzip). + """ + import argparse + parser = argparse.ArgumentParser( + prog="pysupera-recompress", + description="Recompress a pysupera HDF5 file with a different filter.", + ) + parser.add_argument("src", help="Source HDF5 file") + parser.add_argument("dst", help="Output HDF5 file (will be overwritten)") + parser.add_argument("-c", "--compression", default="gzip", + help="Compression filter: gzip, lzf, lz4, blosc_lz4, none (default: gzip)") + parser.add_argument("-l", "--level", type=int, default=None, + help="Compression level (gzip: 1-9; default: filter default)") + parser.add_argument("-b", "--batch-size", type=int, default=256, + help="Events per read batch (default: 256)") + args = parser.parse_args() + comp = None if args.compression in ("none", "~", "") else args.compression + print(f"[recompress] {args.src} → {args.dst} compression={comp!r} level={args.level}") + recompress(args.src, args.dst, + compression=comp, + compression_opts=args.level, + batch_size=args.batch_size) + print("[recompress] Done.") + + # --------------------------------------------------------------------------- # Voxelization-mapping companion file # --------------------------------------------------------------------------- @@ -760,7 +1514,7 @@ def voxmap_path(main_path: str) -> str: def write_voxmap(main_path: str, events_voxmaps, - compression: str = "lz4", + compression: str = "lzf", compression_opts: Optional[int] = None) -> str: """ Write a companion voxelization-mapping file alongside *main_path*. @@ -909,7 +1663,7 @@ def write_voxmap(main_path: str, def open_voxmap_writer(main_path: str, - compression: str = "lz4", + compression: str = "lzf", compression_opts: Optional[int] = None) -> "VoxmapWriter": """ Open a :class:`VoxmapWriter` for incremental voxmap writing. @@ -967,7 +1721,7 @@ class VoxmapWriter: """ def __init__(self, main_path: str, - compression: str = "lz4", + compression: str = "lzf", compression_opts: Optional[int] = None) -> None: import h5py self._ckw = _compress_kwargs(compression, compression_opts) @@ -1022,39 +1776,55 @@ def append_event(self, vox_records) -> None: new_vox_end = self._n_voxels + n_vox_total new_flat_end = self._n_flat + n_flat_total - # particles/vox_offsets + # ---- particles/id and particles/vox_offsets (one write each) ---- + pid_ds = self._f["particles/id"] vox_off_ds = self._f["particles/vox_offsets"] + pid_ds.resize(new_p_end, axis=0) vox_off_ds.resize(new_p_end + 1, axis=0) - # voxels/input_offsets + bulk_pids = np.array([r.particle_id for r in vox_records], dtype=np.int32) + bulk_vox_ends = np.empty(n_p, dtype=np.int64) # absolute voxel end per particle + running = np.int64(self._n_voxels) + for k, rec in enumerate(vox_records): + running += np.int64(len(rec.voxel_offsets) - 1) + bulk_vox_ends[k] = running + + pid_ds [new_p_start : new_p_end] = bulk_pids + vox_off_ds[new_p_start + 1 : new_p_end + 1] = bulk_vox_ends + + # ---- voxels/input_offsets (one write) ---------------------------- vox_inp_ds = self._f["voxels/input_offsets"] vox_inp_ds.resize(new_vox_end + 1, axis=0) - # flat datasets - flat_ids_ds = self._f["flat/input_ids"] - flat_eng_ds = self._f["flat/input_energies"] - flat_ids_ds.resize(new_flat_end, axis=0) - flat_eng_ds.resize(new_flat_end, axis=0) - - running_vox = np.int64(self._n_voxels) + bulk_inp_offs = np.empty(n_vox_total, dtype=np.int64) + cursor_vox = 0 running_flat = np.int64(self._n_flat) - - for k, rec in enumerate(vox_records): - n_vox = np.int64(len(rec.voxel_offsets) - 1) - p_abs = new_p_start + k - vox_off_ds[p_abs + 1] = running_vox + n_vox - - # per-voxel fencepost into flat - vox_inp_ds[running_vox + 1 : running_vox + 1 + n_vox] = ( + for rec in vox_records: + n_vox = len(rec.voxel_offsets) - 1 + bulk_inp_offs[cursor_vox : cursor_vox + n_vox] = ( rec.voxel_offsets[1:].astype(np.int64) + running_flat ) + running_flat += np.int64(len(rec.input_ids)) + cursor_vox += n_vox + vox_inp_ds[self._n_voxels + 1 : new_vox_end + 1] = bulk_inp_offs - n_flat = np.int64(len(rec.input_ids)) - flat_ids_ds[running_flat : running_flat + n_flat] = rec.input_ids.astype(np.int64) - flat_eng_ds[running_flat : running_flat + n_flat] = rec.input_energies.astype(np.float32) + # ---- flat input_ids and input_energies (one write each) ---------- + flat_ids_ds = self._f["flat/input_ids"] + flat_eng_ds = self._f["flat/input_energies"] + flat_ids_ds.resize(new_flat_end, axis=0) + flat_eng_ds.resize(new_flat_end, axis=0) - running_flat += n_flat - running_vox += n_vox + bulk_ids = np.empty(n_flat_total, dtype=np.int64) + bulk_eng = np.empty(n_flat_total, dtype=np.float32) + cursor = 0 + for rec in vox_records: + n = len(rec.input_ids) + if n > 0: + bulk_ids[cursor : cursor + n] = rec.input_ids.astype(np.int64) + bulk_eng[cursor : cursor + n] = rec.input_energies.astype(np.float32) + cursor += n + flat_ids_ds[self._n_flat : new_flat_end] = bulk_ids + flat_eng_ds[self._n_flat : new_flat_end] = bulk_eng self._n_events += 1 self._n_particles += n_p @@ -1086,6 +1856,9 @@ def _init_datasets(self) -> None: pg = f.create_group("particles") pg.create_dataset("vox_offsets", data=np.zeros(1, dtype=np.int64), maxshape=(None,), chunks=(_CHUNK_PARTICLES,)) + pg.create_dataset("id", + shape=(0,), maxshape=(None,), dtype=np.int32, + chunks=(_CHUNK_PARTICLES,)) vg = f.create_group("voxels") vg.create_dataset("input_offsets", data=np.zeros(1, dtype=np.int64), @@ -1125,6 +1898,12 @@ def __init__(self, main_path: str) -> None: self._ev_offsets = self._f["events/offsets"][:] self._vox_offsets = self._f["particles/vox_offsets"][:] + # particle IDs — load fully into RAM (same size as _vox_offsets) + self._particle_ids = ( + self._f["particles/id"][:] + if "particles/id" in self._f + else None + ) # voxels/input_offsets is potentially large — load lazily # (accessed via HDF5 slice in __getitem__) @@ -1190,6 +1969,8 @@ def __getitem__(self, index: int): f_s = int(local_vox_offsets[0]) f_e = int(local_vox_offsets[-1]) result.append({ + "particle_id": int(self._particle_ids[p_start + k]) + if self._particle_ids is not None else None, "vox_input_offsets": local_vox_offsets - f_s, "input_ids": all_ids[f_s:f_e], "input_energies": all_eng[f_s:f_e], diff --git a/pysupera/merge.py b/pysupera/merge.py new file mode 100644 index 0000000..9f605ee --- /dev/null +++ b/pysupera/merge.py @@ -0,0 +1,184 @@ +""" +pysupera.merge +============== + +Post-partitioning merge algorithms that operate on the *fragment +representatives* produced by :class:`~pysupera.partitioner.ParticlePartitioner` +to build final *instance*-level groupings. + +The algorithms here are **purely genealogical** — they do not require a +second spatial proximity pass. The main entry point for most workflows is +:func:`merge_em_showers`, which groups step-1 EM fragments into shower +instances by walking the Geant4 parent-ID chain. + +Typical call sequence +--------------------- +:: + + from pysupera.partitioner import ParticlePartitioner + from pysupera.merge import merge_em_showers + + partitioner = ParticlePartitioner(particles, distance_threshold=5.2, ...) + fragments = partitioner.partition_combined(conditions, verbose=False) + instances = merge_em_showers(fragments) # step-2 shower assembly +""" + +from __future__ import annotations + +from collections import defaultdict + +import numpy as np + +from .utils import SemanticType + + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- + +#: PDG codes considered "EM" for shower-initiator chain walking. +_EM_PDGS: frozenset = frozenset({11, -11, 22}) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _is_shower_eligible(rep) -> bool: + """Return ``True`` if *rep* should participate in EM shower genealogy merging. + + A fragment is shower-eligible when it has an EM PDG code (e±, γ) **or** + carries the ``kShower`` semantic type, **and** is not a ``kLEScatter`` + fragment. + + ``kLEScatter`` fragments are explicitly excluded because they are handled + by step-1 (``AbsorbLEScatter``) and must never become shower initiators — + if they did, non-LE fragments merged into them would be silently dropped by + the ``drop_le_scatter`` filter downstream. + """ + if rep.sem_type == SemanticType.kLEScatter: + return False + return (abs(rep.pdg) in _EM_PDGS + or rep.sem_type == SemanticType.kShower) + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + +def merge_em_showers(fragments): + """ + Step-2 EM shower genealogy merger. + + Groups step-1 fragment representatives into *shower instances* by + walking the parent-ID chain of each EM fragment until a non-EM ancestor + (or the top of the fragment list) is reached. That top-most EM fragment + is the **shower initiator**; all EM fragments that share the same + initiator are merged into it. Non-EM fragments pass through unchanged + (each becomes its own instance). + + The merge is **purely genealogical** — no spatial proximity check is + performed. Photons (PDG 22) always have empty point clouds and are + natural shower initiators when no EM parent is found. + + Extended parent lookup + ~~~~~~~~~~~~~~~~~~~~~~ + The walk uses two lookup tables in tandem: + + * ``frag_by_id`` — fragment rep ID → fragment object (fast path). + * ``frag_by_member_id`` — every original *member* particle ID → fragment + object. This is needed because ``rep.parent_id`` is the *original* + particle's parent ID, which may have been absorbed as a member of a rep + whose ``rep.id`` differs from that original ID. Without this extended + lookup, grandchild fragments whose direct parent was merged into a + different rep would fail to find their ancestor and would incorrectly + start a new shower. + + Parameters + ---------- + fragments : list of Particle + Step-1 representative particles produced by + ``ParticlePartitioner.partition_combined`` (or equivalent). Each + representative must have its ``member_ids`` attribute populated (list + of original preprocessed particle IDs). + + Returns + ------- + list of Particle + Instance-level representatives. Each returned particle has + ``member_ids`` set to the **flat union** of original preprocessed + particle IDs from all fragment representatives merged into it. + Non-merged fragments are returned as-is (``member_ids`` unchanged). + """ + # Fast lookup: fragment representative ID → fragment object + frag_by_id: dict = {r.id: r for r in fragments} + + # Extended lookup: every original member particle ID → fragment rep. + frag_by_member_id: dict = {} + for r in fragments: + mids = r.member_ids if r.member_ids is not None else [r.id] + for mid in mids: + frag_by_member_id[int(mid)] = r + + def find_initiator(rep): + """Iteratively walk the parent chain; return the shower-initiator fragment.""" + visited: set = set() + current = rep + while True: + if id(current) in visited: + # Cycle guard (should never occur with well-formed genealogy) + return current + visited.add(id(current)) + + # Non-shower-eligible particles are their own initiator. + if not _is_shower_eligible(current): + return current + + # Walk to parent fragment — try rep.id first, then any member id. + parent_pid = current.parent_id + parent = ( + frag_by_id.get(parent_pid) + or frag_by_member_id.get(int(parent_pid) if parent_pid is not None else -1) + ) + if parent is None or parent is current: + # No shower-eligible parent in fragment list → current is the initiator. + return current + if not _is_shower_eligible(parent): + # Parent is non-shower → current is the shower initiator. + return current + # Parent is also shower-eligible → keep walking up. + current = parent + + # Group every fragment under its shower initiator. + groups: dict = defaultdict(list) # initiator.id → list[fragment rep] + for rep in fragments: + initiator = find_initiator(rep) + groups[initiator.id].append(rep) + + # Build instance representatives by merging point clouds and member ID lists. + result = [] + for initiator_id, members in groups.items(): + initiator = frag_by_id[initiator_id] + + if len(members) == 1: + # Singleton — no merging needed; return as-is. + result.append(initiator) + continue + + # Concatenate non-empty point clouds from all merged fragments. + clouds = [m.point_cloud for m in members if len(m.point_cloud) > 0] + if clouds: + initiator.point_cloud = np.concatenate(clouds, axis=0) + + # member_ids = flat union of original particle IDs across all merged fragments. + all_orig_ids: list = [] + for m in members: + if m.member_ids is not None: + all_orig_ids.extend(m.member_ids) + else: + all_orig_ids.append(m.id) + initiator.member_ids = all_orig_ids + + result.append(initiator) + + return result diff --git a/pysupera/partitioner.py b/pysupera/partitioner.py index 678693e..895db8d 100644 --- a/pysupera/partitioner.py +++ b/pysupera/partitioner.py @@ -77,6 +77,7 @@ def __init__(self, n_jobs: int = -1, enable_diagnostics: bool = False, check_completeness: bool = False, + verbose: bool = True, **kwargs): if check_completeness: @@ -102,6 +103,7 @@ def __init__(self, # Initialize backend self.checker = self._create_backend(backend, n_jobs, **kwargs) + self.checker._verbose = verbose self.checker.initialize(particles) def _build_relationships(self): @@ -682,7 +684,11 @@ def _build_partitions(self, merge_pairs: List[Tuple[int, int]]) -> List[List]: partitions_dict[root].append(p) # In the legacy (non-incremental) path no point clouds are merged, so - # each original particle is its own representative. + # each original particle is its own representative. Stamp member_ids + # as a singleton list so downstream lookups (_frag_lookup / _inst_lookup) + # can always use the member_ids branch rather than the id-fallback. + for p in self.particles: + p.member_ids = [p.id] self.rep_lookup = {p.id: p for p in self.particles} return list(partitions_dict.values()) @@ -1008,6 +1014,11 @@ def _get_cloud(rep: Particle) -> np.ndarray: rep.point_cloud = np.concatenate(rep._cloud_parts, axis=0) rep._cloud_parts = [rep.point_cloud] + # Stamp member_ids on every surviving representative so callers can + # trace back which original preprocessed particle IDs were absorbed. + for rep in reps.values(): + rep.member_ids = list(rep_members[id(rep)]) + # Persist rep_lookup so callers can retrieve the representative # Particle for any original particle ID via get_representative(). self.rep_lookup = rep_lookup diff --git a/pysupera/partitioner2.py b/pysupera/partitioner2.py deleted file mode 100644 index fc7a87b..0000000 --- a/pysupera/partitioner2.py +++ /dev/null @@ -1,496 +0,0 @@ -from .proxck import ProximityChecker, CPUSingleThreadChecker, CPUMultiThreadChecker, GPUChecker, UnionFind -from .data import Particle -from .utils import SemanticType -from typing import List, Tuple, Callable -import time -from collections import defaultdict -import numpy as np - - -# ============================================================================ -# Enhanced Partitioner with Metadata-Based Conditions -# ============================================================================ - -class ParticlePartitioner: - """ - Main partitioner class that works with any backend. - Supports complex metadata-based partitioning conditions. - """ - - def __init__(self, - particles, - distance_threshold: float, - backend: str = 'gpu', - n_jobs: int = -1): - """ - Initialize partitioner with specified backend. - - Args: - particles: List of Particle objects - distance_threshold: Distance threshold for "touching" (D) - backend: 'cpu-single', 'cpu-multi', or 'gpu' - n_jobs: Number of CPU threads (only for cpu-multi) - """ - self.particles = particles - self.D = distance_threshold - self.backend_name = backend - - # Build lookup structures - self.particle_lookup = {p.id: p for p in particles} - self.id_to_idx = {p.id: idx for idx, p in enumerate(particles)} - - # Build parent-child and ancestor relationships - self._build_relationships() - - # Initialize appropriate backend - self.checker = self._create_backend(backend, n_jobs) - self.checker.initialize(particles) - - def _build_relationships(self): - """Build parent-child and ancestor lookup structures""" - self.children_map = defaultdict(list) # parent_id -> [child_ids] - self.ancestor_map = defaultdict(list) # root_id -> [particle_ids] - - for p in self.particles: - if p.parent_id is not None: - self.children_map[p.parent_id].append(p.id) - - if p.root_id is not None: - self.ancestor_map[p.root_id].append(p.id) - - def _create_backend(self, backend: str, n_jobs: int) -> ProximityChecker: - """Factory method to create appropriate backend""" - if backend == 'cpu-single': - return CPUSingleThreadChecker(self.D) - elif backend == 'cpu-multi': - return CPUMultiThreadChecker(self.D, n_jobs=n_jobs) - elif backend == 'gpu': - return GPUChecker(self.D) - else: - raise ValueError(f"Unknown backend: {backend}. " - f"Choose from: 'cpu-single', 'cpu-multi', 'gpu'") - - # ======================================================================== - # Condition 1: PDG 22 or 11 with same ancestor - # ======================================================================== - - def partition_condition_1(self, verbose: bool = True) -> List[List]: - """ - Condition 1: For particles with PDG value 22 or 11: - - Identify immediate parent with the same PDG value - - Merge if they are touching - - Never merge particles with different root_id - - Returns: - List of partitions, where each partition is a list of Particle objects - """ - if verbose: - print(f"\n{'='*70}") - print(f"Partitioning Condition 1: PDG 22/11 parent-child merging") - print(f"Backend: {self.backend_name}") - print(f"{'='*70}") - - start_time = time.time() - - # Stage 1: Build candidate pairs - if verbose: - print("Stage 1: Building candidate pairs (parent-child, same PDG)...") - - candidates = self._build_candidates_condition_1() - - if verbose: - print(f" Candidate pairs: {len(candidates)}") - - if len(candidates) == 0: - return [[p] for p in self.particles] - - # Stage 2: Filter by root_id (before expensive proximity checks) - if verbose: - print("Stage 2: Filtering by root_id...") - - ancestor_filtered = self._filter_by_ancestor(candidates) - - if verbose: - print(f" After ancestor filter: {len(ancestor_filtered)}") - - # Stage 3: Check proximity (touching) - if verbose: - print("Stage 3: Checking proximity (touching)...") - - merge_pairs = self.checker.batch_check_proximity(ancestor_filtered) - - if verbose: - print(f" Touching pairs found: {len(merge_pairs)}") - - # Stage 4: Build partitions - if verbose: - print("Stage 4: Building partitions with Union-Find...") - - partitions = self._build_partitions(merge_pairs) - - elapsed = time.time() - start_time - - if verbose: - self._print_partition_stats(partitions, elapsed) - - return partitions - - def _build_candidates_condition_1(self) -> List[Tuple[int, int]]: - """ - Build candidate pairs for condition 1: - - Child has PDG 22 or 11 - - Parent has same PDG as child - """ - candidates = [] - target_pdgs = {22, 11} - - for p in self.particles: - # Check if particle has target PDG - if p.pdg not in target_pdgs: - continue - - # Check if has parent - if p.parent_id is None or p.parent_id not in self.particle_lookup: - continue - - # Check if the point cloud is empty - if p.point_cloud.shape[0] == 0: - continue - - parent = self.particle_lookup[p.parent_id] - - # Check if parent has same PDG - if parent.pdg == p.pdg: - candidates.append((parent.id, p.id)) - - return candidates - - def _filter_by_ancestor(self, candidates: List[Tuple[int, int]]) -> List[Tuple[int, int]]: - """Filter pairs to only include those with same root_id""" - filtered = [] - - for id1, id2 in candidates: - p1 = self.particle_lookup[id1] - p2 = self.particle_lookup[id2] - - if p1.root_id == p2.root_id: - filtered.append((id1, id2)) - - return filtered - - # ======================================================================== - # Condition 2: SemID 4 merging with PDG 11/22 - # ======================================================================== - - def partition_condition_2(self, verbose: bool = True) -> List[List]: - """ - Condition 2: For particles with SemID type 4: - - If touching a particle with PDG 11 or 22, merge them - - Can merge with any one of multiple touching particles - - Returns: - List of partitions, where each partition is a list of Particle objects - """ - if verbose: - print(f"\n{'='*70}") - print(f"Partitioning Condition 2: SemID 4 merging with PDG 11/22") - print(f"Backend: {self.backend_name}") - print(f"{'='*70}") - - start_time = time.time() - - # Stage 1: Build candidate pairs - if verbose: - print("Stage 1: Building candidate pairs (SemID 4 with PDG 11/22)...") - - candidates = self._build_candidates_condition_2() - - if verbose: - print(f" Candidate pairs: {len(candidates)}") - - if len(candidates) == 0: - return [[p] for p in self.particles] - - # Stage 2: Check proximity (touching) - if verbose: - print("Stage 2: Checking proximity (touching)...") - - merge_pairs = self.checker.batch_check_proximity(candidates) - - if verbose: - print(f" Touching pairs found: {len(merge_pairs)}") - - # Stage 3: Select one merge per SemID 4 particle (if multiple options) - if verbose: - print("Stage 3: Selecting one merge per SemID 4 particle...") - - selected_pairs = self._select_one_merge_per_sem4(merge_pairs) - - if verbose: - print(f" Selected pairs: {len(selected_pairs)}") - - # Stage 4: Build partitions - if verbose: - print("Stage 4: Building partitions with Union-Find...") - - partitions = self._build_partitions(selected_pairs) - - elapsed = time.time() - start_time - - if verbose: - self._print_partition_stats(partitions, elapsed) - - return partitions - - def _build_candidates_condition_2(self) -> List[Tuple[int, int]]: - """ - Build candidate pairs for condition 2: - - One particle has SemID 4 - - Other particle has PDG 11 or 22 - """ - candidates = [] - target_pdgs = {22, 11} - - # Find all SemID 4 particles with non-empty point cloud - sem4_particles = [p for p in self.particles if p.sem_type == SemanticType.kMichel and p.point_cloud.shape[0] > 0 ] - - # Find all PDG 11/22 particles with non-empty point cloud - pdg_particles = [p for p in self.particles if p.pdg in target_pdgs and p.point_cloud.shape[0] > 0] - - # Create all possible pairs - for sem4_p in sem4_particles: - for pdg_p in pdg_particles: - candidates.append((sem4_p.id, pdg_p.id)) - - return candidates - - def _select_one_merge_per_sem4(self, merge_pairs: List[Tuple[int, int]]) -> List[Tuple[int, int]]: - """ - For each SemID 4 particle that touches multiple PDG 11/22 particles, - select only one to merge with (e.g., the first one found). - """ - # Group by SemID 4 particle - sem4_merges = defaultdict(list) - - for id1, id2 in merge_pairs: - p1 = self.particle_lookup[id1] - p2 = self.particle_lookup[id2] - - if p1.sem_type == SemanticType.kMichel: - sem4_merges[p1.id].append((id1, id2)) - elif p2.sem_type == SemanticType.kMichel: - sem4_merges[id2].append((id1, id2)) - - # Select one merge per SemID 4 particle - selected = [] - for sem4_id, pairs in sem4_merges.items(): - # Select first touching partner (could use other criteria) - selected.append(pairs[0]) - - return selected - - # ======================================================================== - # Combined Partitioning: Apply Multiple Conditions - # ======================================================================== - - def partition_combined(self, - conditions: List[str] = ['condition_1', 'condition_2'], - verbose: bool = True) -> List[List]: - """ - Apply multiple partitioning conditions sequentially. - - Args: - conditions: List of condition names to apply in order - verbose: Print progress information - - Returns: - List of partitions after applying all conditions - """ - if verbose: - print(f"\n{'='*70}") - print(f"Combined Partitioning: Applying {len(conditions)} conditions") - print(f"Backend: {self.backend_name}") - print(f"{'='*70}") - - start_time = time.time() - all_merge_pairs = [] - - # Collect merge pairs from all conditions - for i, condition_name in enumerate(conditions, 1): - if verbose: - print(f"\n--- Condition {i}/{len(conditions)}: {condition_name} ---") - - if condition_name == 'condition_1': - candidates = self._build_candidates_condition_1() - candidates = self._filter_by_ancestor(candidates) - elif condition_name == 'condition_2': - candidates = self._build_candidates_condition_2() - else: - raise ValueError(f"Unknown condition: {condition_name}") - - if verbose: - print(f" Candidates: {len(candidates)}") - - merge_pairs = self.checker.batch_check_proximity(candidates) - - if condition_name == 'condition_2': - merge_pairs = self._select_one_merge_per_sem4(merge_pairs) - - if verbose: - print(f" Merge pairs: {len(merge_pairs)}") - - all_merge_pairs.extend(merge_pairs) - - # Build partitions from all merge pairs - if verbose: - print(f"\nBuilding final partitions from {len(all_merge_pairs)} total merge pairs...") - - partitions = self._build_partitions(all_merge_pairs) - - elapsed = time.time() - start_time - - if verbose: - self._print_partition_stats(partitions, elapsed) - - return partitions - - # ======================================================================== - # General Purpose Partitioning with Custom Filters - # ======================================================================== - - def partition_custom(self, - candidate_filter: Callable[[Particle, Particle], bool], - verbose: bool = True) -> List[List]: - """ - General-purpose partitioning with custom filter function. - - Args: - candidate_filter: Function that takes two particles and returns True - if they should be considered for merging - verbose: Print progress information - - Returns: - List of partitions - - Example: - # Custom condition: merge particles with same PDG and touching - def my_filter(p1, p2): - return p1.pdg == p2.pdg - - partitions = partitioner.partition_custom(my_filter) - """ - if verbose: - print(f"\n{'='*70}") - print(f"Custom Partitioning") - print(f"Backend: {self.backend_name}") - print(f"{'='*70}") - - start_time = time.time() - - # Build all possible pairs and filter with custom function - if verbose: - print("Stage 1: Building candidate pairs with custom filter...") - - candidates = [] - for i, p1 in enumerate(self.particles): - for p2 in self.particles[i+1:]: - if candidate_filter(p1, p2): - candidates.append((p1.id, p2.id)) - - if verbose: - print(f" Candidate pairs: {len(candidates)}") - - if len(candidates) == 0: - return [[p] for p in self.particles] - - # Check proximity - if verbose: - print("Stage 2: Checking proximity (touching)...") - - merge_pairs = self.checker.batch_check_proximity(candidates) - - if verbose: - print(f" Touching pairs found: {len(merge_pairs)}") - - # Build partitions - if verbose: - print("Stage 3: Building partitions...") - - partitions = self._build_partitions(merge_pairs) - - elapsed = time.time() - start_time - - if verbose: - self._print_partition_stats(partitions, elapsed) - - return partitions - - # ======================================================================== - # Helper Methods - # ======================================================================== - - def _build_partitions(self, merge_pairs: List[Tuple[int, int]]) -> List[List]: - """ - Build final partitions using Union-Find algorithm. - - Args: - merge_pairs: List of (id1, id2) tuples to merge - - Returns: - List of partitions, where each partition is a list of Particle objects - """ - n = len(self.particles) - uf = UnionFind(n) - - # Perform unions for all merge pairs - for id1, id2 in merge_pairs: - idx1 = self.id_to_idx[id1] - idx2 = self.id_to_idx[id2] - uf.union(idx1, idx2) - - # Extract partitions by grouping particles with same root - partitions_dict = defaultdict(list) - for idx, p in enumerate(self.particles): - root = uf.find(idx) - partitions_dict[root].append(p) - - return list(partitions_dict.values()) - - def _print_partition_stats(self, partitions: List[List], elapsed_time: float): - """Print statistics about the partitions""" - sizes = [len(p) for p in partitions] - - print(f"\n{'='*70}") - print(f"Partitioning Complete") - print(f"{'='*70}") - print(f"Total partitions: {len(partitions)}") - print(f"Partition size - min: {min(sizes)}, max: {max(sizes)}, " - f"mean: {np.mean(sizes):.2f}, median: {np.median(sizes):.0f}") - print(f"Singleton partitions (size=1): {sum(1 for s in sizes if s == 1)}") - print(f"Large partitions (size>10): {sum(1 for s in sizes if s > 10)}") - - # Show size distribution - size_counts = defaultdict(int) - for s in sizes: - if s <= 5: - size_counts[s] += 1 - elif s <= 10: - size_counts['6-10'] += 1 - elif s <= 20: - size_counts['11-20'] += 1 - else: - size_counts['>20'] += 1 - - print(f"\nPartition size distribution:") - for size in sorted([k for k in size_counts.keys() if isinstance(k, int)]): - print(f" Size {size}: {size_counts[size]} partitions") - for size in ['6-10', '11-20', '>20']: - if size in size_counts: - print(f" Size {size}: {size_counts[size]} partitions") - - print(f"\nElapsed time: {elapsed_time:.3f} seconds") - print(f"{'='*70}\n") - - def cleanup(self): - """Free backend resources""" - self.checker.cleanup() \ No newline at end of file diff --git a/pysupera/preproc.py b/pysupera/preproc.py index d348ef3..b1a73d8 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -199,13 +199,14 @@ def _split_fragments( else: frag_pc = pc[frag_mask] new_p = Particle( - id = next_id, - parent_id = p.id, - root_id = p.root_id, - pdg = p.pdg, - parent_pdg = p.parent_pdg, + id = next_id, + parent_id = p.id, + root_id = p.root_id, + pdg = p.pdg, + parent_pdg = p.parent_pdg, + interaction_id = p._interaction_id, interaction_type = p._interaction_type, - point_cloud = frag_pc, + point_cloud = frag_pc, ) new_p.sem_type = SemanticType.kLEScatter spawned.append(new_p) @@ -519,11 +520,11 @@ def _run_chunk(chunk): # # time (col 3) — minimum: earliest hit time survives # energy (col 4) — sum: total deposited energy -# dedx (col 5) — maximum: peak ionisation density +# dx (col 5) — sum: additional scalar feature _MERGE_RULES: list = [ (PointFeature.time, np.inf, np.minimum), (PointFeature.energy, 0.0, np.add), - (PointFeature.dedx, -np.inf, np.maximum), + (PointFeature.dx, 0.0, np.add), ] @@ -533,8 +534,8 @@ def _merge_point_cloud(pc: np.ndarray) -> np.ndarray: Coordinates (columns 0–2) are used as the grouping key. Feature columns beyond index 2 are aggregated according to ``_MERGE_RULES``: - time → min, energy → sum, dedx → max. Any extra columns beyond - ``PointFeature.dedx`` are left at zero. + time → min, energy → sum, dx → sum. Any extra columns beyond + ``PointFeature.dx`` are left at zero. Parameters ---------- @@ -583,7 +584,7 @@ class MergeDuplicatesProcessor: ============ ====================================================== time (3) minimum — the earliest hit time is retained energy (4) sum — total deposited energy - dedx (5) maximum — peak ionisation density + dx (5) sum — additional scalar feature any extras zero — placeholder; extend ``_MERGE_RULES`` to change ============ ====================================================== @@ -804,7 +805,7 @@ def _voxelize_point_cloud( New array where each row represents one non-empty voxel. The x, y, z columns hold the voxel-centre coordinates; remaining feature columns are aggregated by the same rules as - :func:`_merge_point_cloud` (time=min, energy=sum, dedx=max). + :func:`_merge_point_cloud` (time=min, energy=sum, dx=sum). Returns the original array unchanged when every voxel already contains exactly one point. """ @@ -869,7 +870,7 @@ class VoxelizeProcessor: Feature aggregation (columns beyond index 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Identical to :class:`MergeDuplicatesProcessor`: - time → min, energy → sum, dedx → max. + time → min, energy → sum, dx → sum. Parameters ---------- diff --git a/pysupera/proxck.py b/pysupera/proxck.py index d686265..ed5d2d9 100644 --- a/pysupera/proxck.py +++ b/pysupera/proxck.py @@ -114,6 +114,8 @@ def __init__(self, distance_threshold: float): # IDs of particles whose point cloud has zero points. Every # check_proximity implementation returns False immediately for these. self.empty_ids: set = set() + # Controls whether initialize() emits progress messages. + self._verbose: bool = True @abstractmethod def initialize(self, particles): @@ -357,7 +359,8 @@ def initialize(self, particles): """Build KD-trees and precompute bounding boxes""" from scipy.spatial import KDTree - print("Initializing CPU single-threaded backend...") + if self._verbose: + print("Initializing CPU single-threaded backend...") for p in particles: cloud_xyz = p.point_cloud[:, :3].astype(np.float32) self.clouds[p.id] = cloud_xyz @@ -370,7 +373,8 @@ def initialize(self, particles): self.bboxes[p.id] = self.compute_bbox(p.point_cloud) self.centroids[p.id] = self.compute_centroid(p.point_cloud) - print(f" Initialized {len(particles)} particles") + if self._verbose: + print(f" Initialized {len(particles)} particles") def check_proximity(self, id1, id2) -> bool: """Check if any point in cloud1 is within D of any point in cloud2""" @@ -464,7 +468,8 @@ def initialize(self, particles): from scipy.spatial import KDTree from joblib import Parallel, delayed - print(f"Initializing CPU multi-threaded backend (n_jobs={self.n_jobs})...") + if self._verbose: + print(f"Initializing CPU multi-threaded backend (n_jobs={self.n_jobs})...") def build_structures(p): cloud_xyz = p.point_cloud[:, :3].astype(np.float32) @@ -494,7 +499,8 @@ def build_structures(p): self.bboxes[pid] = bbox self.centroids[pid] = centroid - print(f" Initialized {len(particles)} particles") + if self._verbose: + print(f" Initialized {len(particles)} particles") def check_proximity(self, id1, id2) -> bool: """Check if any point in cloud1 is within D of any point in cloud2""" @@ -635,7 +641,8 @@ def initialize(self, particles) -> None: import cupy as cp from cuml.neighbors import NearestNeighbors - print("Initializing GPU backend (RAPIDS cuML)...") + if self._verbose: + print("Initializing GPU backend (RAPIDS cuML)...") for p in particles: cloud = cp.asarray(p.point_cloud[:, :3].astype(np.float32)) self.gpu_clouds[p.id] = cloud @@ -654,9 +661,10 @@ def initialize(self, particles) -> None: total_pts = sum(len(c) for c in self.gpu_clouds.values()) mempool = cp.get_default_memory_pool() - print(f" Initialized {len(particles)} particles " - f"({total_pts:,} total points)") - print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") + if self._verbose: + print(f" Initialized {len(particles)} particles " + f"({total_pts:,} total points)") + print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") def check_proximity(self, id1, id2) -> bool: """ @@ -735,7 +743,7 @@ def batch_check_proximity(self, candidate_pairs: List[Tuple]) -> List[Tuple]: survivors = [pair for pair, ok in zip(candidate_pairs, bbox_pass) if ok] n_rejected = len(candidate_pairs) - len(survivors) - if n_rejected: + if n_rejected and self._verbose: print(f" GPUChecker Stage 1: bbox prefilter removed " f"{n_rejected}/{len(candidate_pairs)} pairs") @@ -929,7 +937,8 @@ def __init__(self, distance_threshold: float): def initialize(self, particles) -> None: """Build voxel hash maps for every particle cloud.""" - print("Initializing CellHash CPU single-threaded backend...") + if self._verbose: + print("Initializing CellHash CPU single-threaded backend...") for p in particles: cloud = p.point_cloud[:, :3].astype(np.float32) self.clouds[p.id] = cloud @@ -942,7 +951,8 @@ def initialize(self, particles) -> None: self.hashmaps[p.id] = self._build_hashmap(cloud, self._inv_D) self.bboxes[p.id] = self.compute_bbox(p.point_cloud) self.centroids[p.id] = self.compute_centroid(p.point_cloud) - print(f" Initialized {len(particles)} particles") + if self._verbose: + print(f" Initialized {len(particles)} particles") def check_proximity(self, id1, id2) -> bool: """ @@ -1019,8 +1029,9 @@ def initialize(self, particles) -> None: """Build voxel hash maps in parallel.""" from joblib import Parallel, delayed - print(f"Initializing CellHash CPU multi-threaded backend " - f"(n_jobs={self.n_jobs})...") + if self._verbose: + print(f"Initializing CellHash CPU multi-threaded backend " + f"(n_jobs={self.n_jobs})...") def _build(p): cloud = p.point_cloud[:, :3].astype(np.float32) @@ -1046,7 +1057,8 @@ def _build(p): self.hashmaps[pid] = hmap self.bboxes[pid] = bbox self.centroids[pid] = centroid - print(f" Initialized {len(particles)} particles") + if self._verbose: + print(f" Initialized {len(particles)} particles") def check_proximity(self, id1, id2) -> bool: """ @@ -1171,7 +1183,8 @@ def initialize(self, particles) -> None: """Transfer point clouds to the GPU and build CPU hash maps.""" import cupy as cp - print("Initializing CellHash GPU backend...") + if self._verbose: + print("Initializing CellHash GPU backend...") for p in particles: cloud = p.point_cloud[:, :3].astype(np.float32) self.gpu_clouds[p.id] = cp.asarray(cloud) @@ -1186,8 +1199,9 @@ def initialize(self, particles) -> None: self.centroids[p.id] = self.compute_centroid(p.point_cloud) mempool = cp.get_default_memory_pool() - print(f" Initialized {len(particles)} particles") - print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") + if self._verbose: + print(f" Initialized {len(particles)} particles") + print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") def check_proximity(self, id1, id2) -> bool: """ @@ -1373,7 +1387,8 @@ def initialize(self, particles) -> None: """ import cupy as cp - print("Initializing BulkGPU backend...") + if self._verbose: + print("Initializing BulkGPU backend...") for p in particles: cloud = cp.asarray(p.point_cloud[:, :3].astype(np.float32)) self.gpu_clouds[p.id] = cloud @@ -1387,9 +1402,10 @@ def initialize(self, particles) -> None: total_pts = sum(len(c) for c in self.gpu_clouds.values()) mempool = cp.get_default_memory_pool() - print(f" Initialized {len(particles)} particles " - f"({total_pts:,} total points)") - print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") + if self._verbose: + print(f" Initialized {len(particles)} particles " + f"({total_pts:,} total points)") + print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") def check_proximity(self, id1, id2) -> bool: """ @@ -1472,7 +1488,7 @@ def batch_check_proximity(self, candidate_pairs: List[Tuple]) -> List[Tuple]: survivors = [pair for pair, ok in zip(candidate_pairs, bbox_pass) if ok] n_rejected = len(candidate_pairs) - len(survivors) - if n_rejected: + if n_rejected and self._verbose: print(f" BulkGPU Stage 1: bbox prefilter removed " f"{n_rejected}/{len(candidate_pairs)} pairs") @@ -1785,7 +1801,8 @@ def initialize(self, particles) -> None: """ import cupy as cp - print("Initializing NumbaKernel backend...") + if self._verbose: + print("Initializing NumbaKernel backend...") total_pts = sum(len(p.point_cloud) for p in particles) flat_cpu = np.empty((total_pts, 3), dtype=np.float32) @@ -1811,9 +1828,10 @@ def initialize(self, particles) -> None: self._gpu_bb_max[p.id] = cp.max(cloud, axis=0) mempool = cp.get_default_memory_pool() - print(f" Initialized {len(particles)} particles " - f"({total_pts:,} total points)") - print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") + if self._verbose: + print(f" Initialized {len(particles)} particles " + f"({total_pts:,} total points)") + print(f" GPU memory: {mempool.used_bytes() / 1024**2:.1f} MB") def check_proximity(self, id1, id2) -> bool: """Return ``True`` if any point of cloud *id1* lies within *D* of *id2*.""" diff --git a/pysupera/readers/__init__.py b/pysupera/readers/__init__.py index ea9adc8..160522c 100644 --- a/pysupera/readers/__init__.py +++ b/pysupera/readers/__init__.py @@ -2,37 +2,75 @@ pysupera.readers ================ -File-format readers that produce lists of :class:`~pysupera.data.Particle` -objects from simulation output files. +File-format readers that translate external simulation file formats into lists +of :class:`~pysupera.data.Particle` objects. Available readers ----------------- :class:`EDepSimHDF5Reader` - Reads EDepSim HDF5 output (``particle/geant4``, ``pstep/lar_vol``, - ``ass/particle_pstep_lar_vol`` datasets). + Reads EDepSim HDF5 output directly (``particle/geant4``, + ``pstep/lar_vol``, ``ass/particle_pstep_lar_vol`` datasets). + Point clouds contain **all** energy-deposit steps in the file. + Hydra config group: ``reader: edepsim_h5``. + +:class:`JaxtpcHDF5Reader` + Combines three files produced by a JAXTPC simulation batch: + + * **EDepSim HDF5** — particle-level metadata (PDG, track IDs, start + vertices, interaction type). + * **JAXTPC seg HDF5** — per-volume 3-D truth deposits (positions in mm, + dE, dx, t0, charge, …). + * **JAXTPC inst HDF5** — per-readout-plane correspondence data used to + determine which segments were visible above the signal threshold. + + Each particle's point cloud contains **only** the energy-deposit segments + that survived the JAXTPC readout threshold ("visible" segments). + Hydra config group: ``reader: edepsim_h5`` with optional + ``reader.jaxtpc_seg_path`` / ``reader.jaxtpc_inst_path`` flags. All readers implement the :class:`EventReaderBase` interface which supports: -- ``len(reader)`` — number of events -- ``reader[i]`` — event by index (negative indices supported) -- ``for particles in reader`` — iteration over all events -- ``with EDepSimHDF5Reader(path) as reader`` — context-manager usage -Example -------- -:: +- ``len(reader)`` — number of events +- ``reader[i]`` — event by index (negative indices ok) +- ``for particles in reader`` — iteration over all events +- ``with SomeReader(...) as reader`` — context-manager usage + +Examples +-------- +EDepSim HDF5:: from pysupera.readers import EDepSimHDF5Reader with EDepSimHDF5Reader("sim.h5") as reader: for event_particles in reader: - # event_particles is a list[Particle] ... + +JAXTPC (visibility-filtered):: + + from pysupera.readers import JaxtpcHDF5Reader + + with JaxtpcHDF5Reader( + edepsim_path="edepsim.h5", + seg_path="sim_seg_0000.h5", + inst_path="sim_inst_0000.h5", + ) as reader: + for event_particles in reader: + ... + + # Or from a JAXTPC production directory: + with JaxtpcHDF5Reader.from_production_dir( + "/data/dataset_100_wire", + edepsim_path="edepsim.h5", + ) as reader: + ... """ from .base import EventReaderBase from .format_edepsim_h5 import EDepSimHDF5Reader +from .format_jaxtpc import JaxtpcHDF5Reader __all__ = [ "EventReaderBase", "EDepSimHDF5Reader", + "JaxtpcHDF5Reader", ] diff --git a/pysupera/readers/base.py b/pysupera/readers/base.py index 917baaf..83ef3fc 100644 --- a/pysupera/readers/base.py +++ b/pysupera/readers/base.py @@ -70,5 +70,9 @@ def __iter__(self) -> Iterator[list[Particle]]: for i in range(len(self)): yield self[i] + def iter_events(self) -> Iterator[list[Particle]]: + """Alias for :meth:`__iter__`; mirrors the :class:`~pysupera.io.EventStore` API.""" + return iter(self) + def __repr__(self) -> str: return f"{type(self).__name__}(n_events={len(self)})" diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 79b947d..12cfa27 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -45,6 +45,7 @@ from __future__ import annotations +import warnings import numpy as np from enum import IntEnum @@ -58,7 +59,7 @@ # --------------------------------------------------------------------------- # The EDepSim HDF5 step datasets are structured arrays with named fields. # pysupera's Particle expects a plain float32 2-D point cloud with columns -# ordered by PointFeature (x=0, y=1, z=2, time=3, energy=4, dedx=5, id=6). +# ordered by PointFeature (x=0, y=1, z=2, time=3, energy=4, dx=5, id=6). # This mapping is EDepSim-format-specific and lives here rather than in # Particle.from_flat_arrays so that the core data class stays format-agnostic. # @@ -68,13 +69,14 @@ # Maps PointFeature column → candidate HDF5 field name(s) to try, in order. # PointFeature.id is handled separately (generated, not read from file). +_warned_cols: set[int] = set() # columns for which a missing-field warning has already been shown _STEP_FIELD_MAP: list[tuple[int, tuple[str, ...]]] = [ (PointFeature.x, ("x",)), (PointFeature.y, ("y",)), (PointFeature.z, ("z",)), (PointFeature.time, ("t", "time")), - (PointFeature.energy, ("energy", "e")), - (PointFeature.dedx, ("dedx", "dEdx", "dE_dx")), + (PointFeature.energy, ("energy", "e", "de")), + (PointFeature.dx, ("dx")), ] @@ -83,8 +85,8 @@ def _steps_to_plain_array(steps: np.ndarray) -> np.ndarray: Convert a structured EDepSim step array to a plain float32 2-D array. The output columns follow :class:`~pysupera.utils.PointFeature` order: - ``x=0, y=1, z=2, time=3, energy=4, dedx=5, id=6``. - x, y, z are mandatory; time, energy, dedx default to zero if absent. + ``x=0, y=1, z=2, time=3, energy=4, dx=5, id=6``. + x, y, z are mandatory; time, energy, dx default to zero if absent. Column 6 (``PointFeature.id``) is **not** read from the HDF5 file. It is assigned here as a global, event-wide, 0-based integer @@ -133,6 +135,19 @@ def _steps_to_plain_array(steps: np.ndarray) -> np.ndarray: f"Tried {candidates}; available fields: {sorted(available)}" ) # optional columns default to zero — already set + if col not in _warned_cols: + _warned_cols.add(col) + col_name = next( + name for name, val in PointFeature.__dict__.items() + if isinstance(val, int) and val == col + ) + warnings.warn( + f"EDepSim step array has no field for '{col_name}' " + f"(tried {candidates}); available fields: {sorted(available)}. " + f"Column {col} will be zero.", + UserWarning, + stacklevel=2, + ) # Assign global, event-wide, 0-based point IDs. out[:, PointFeature.id] = np.arange(n, dtype=np.float32) @@ -180,7 +195,7 @@ class G4ProcessSubtype(IntEnum): # --------------------------------------------------------------------------- # Default HDF5 dataset keys # --------------------------------------------------------------------------- - +_DEFAULT_VERTEX_KEY = "vertex/geant4" _DEFAULT_PARTICLE_KEY = "particle/geant4" _DEFAULT_STEP_KEY = "pstep/lar_vol" _DEFAULT_ASS_KEY = "ass/particle_pstep_lar_vol" @@ -239,6 +254,57 @@ def _get_parent_pdg(parts: np.ndarray) -> np.ndarray: return np.where(parent_valid, parts["pdg"][parent_zfill], 0) +def _get_interaction_id(parts: np.ndarray, verts: np.ndarray) -> np.ndarray: + """ + Look up the interaction ID of every particle + + Returns -1 for particles whose root particle is not found or the root particle does not match with any vertex in "verts" + + Parameters + ---------- + parts : structured ndarray + Must contain fields ``x``, ``y``, ``z``, ``t``, ``track_id``, and either + ``root_track_id`` or ``ancestor_track_id``. + verts : structured ndarray + Must contain fields ``x``, ``y``, ``z``, ``t``, ``interaction_id``. + + Returns + ------- + ndarray of int, shape (N,) + """ + + interaction_ids = np.full(len(parts), -1, dtype=np.int32) + + # Resolve the root-ancestor field name (two conventions exist). + _root_field = 'root_track_id' if 'root_track_id' in parts.dtype.names else 'ancestor_track_id' + + # Step 0: loop over root particles and match to one of vertices by (x,y,z,t) proximity. + root_indices = np.where(parts['track_id'] == parts[_root_field])[0] + root_ids = parts['track_id'][root_indices] + + for index in root_indices: + p = parts[index] + for vtx in verts: + if np.isclose(p['x'], vtx['x'], atol=1e-4) and \ + np.isclose(p['y'], vtx['y'], atol=1e-4) and \ + np.isclose(p['z'], vtx['z'], atol=1e-4) and \ + np.isclose(p['t'], vtx['t'], atol=1e-4): + # Match found, do something + interaction_ids[index] = vtx['interaction_id'] + break + + # Step 1: for non-root particles, assign the same interaction ID as their root particle. + for i in range(len(parts)): + p = parts[i] + if p['track_id'] == p[_root_field]: + continue + + root_index = root_indices[np.where(p[_root_field] == root_ids)[0][0]] + interaction_ids[i] = interaction_ids[root_index] + + return interaction_ids + + def _get_interaction_type(parts: np.ndarray, electron_energy_threshold: float = 0.05 ) -> np.ndarray: @@ -384,7 +450,7 @@ class EDepSimHDF5Reader(EventReaderBase): with EDepSimHDF5Reader("out_0100.h5") as reader: print(len(reader), "events") particles = reader[0] - for particles in reader: + for particles in reader:from_flat_arrays ... """ @@ -392,6 +458,7 @@ def __init__( self, path: str, *, + vertex_key: str = _DEFAULT_VERTEX_KEY, particle_key: str = _DEFAULT_PARTICLE_KEY, step_key: str = _DEFAULT_STEP_KEY, ass_key: str = _DEFAULT_ASS_KEY, @@ -400,6 +467,7 @@ def __init__( ) -> None: import h5py self._path = path + self._vertex_key = vertex_key self._part_key = particle_key self._step_key = step_key self._ass_key = ass_key @@ -442,23 +510,28 @@ def __getitem__(self, index: int) -> list[Particle]: if index < 0: index += n + verts = self._file[self._vertex_key][index] parts = self._file[self._part_key][index] steps = self._file[self._step_key][index] ass = self._file[self._ass_key][index] num_parts = len(parts) itype = _get_interaction_type(parts, self._e_thresh) + int_ids = _get_interaction_id(parts,verts) offsets = np.column_stack([ ass["start"][:num_parts], ass["end"][:num_parts], ]) + _root_field = 'root_track_id' if 'root_track_id' in parts.dtype.names else 'ancestor_track_id' + return Particle.from_flat_arrays( ids = parts["track_id"], parent_ids = parts["parent_track_id"], - root_ids = parts["root_track_id"], + root_ids = parts[_root_field], pdgs = parts["pdg"], parent_pdgs = _get_parent_pdg(parts), + interaction_ids = int_ids, interaction_types = itype, point_cloud_flat = _steps_to_plain_array(steps), point_cloud_offsets = offsets, diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py new file mode 100644 index 0000000..94cf000 --- /dev/null +++ b/pysupera/readers/format_jaxtpc.py @@ -0,0 +1,639 @@ +""" +Reader for JAXTPC simulation output — visibility-filtered EDepSim data. + +JAXTPC is a JAX-based TPC simulation that takes EDepSim truth deposits, +drifts them through an electric field, and simulates wire-plane or pixel +readout with realistic noise and thresholds. The simulation produces three +file types that together fully describe one batch of events: + +===================== ====================================================== +File Description +===================== ====================================================== +*seg* (seg HDF5) 3-D truth deposits per volume: positions, dE/dx, charge, + etc. One entry per Geant4 step (segment). +*inst* (inst HDF5) Per-readout-plane correspondence data. Each plane + sub-group stores the ``group_ids`` of segments that + survived the signal threshold and were reconstructed. + Also stores the lookup tables ``segment_to_group`` + (segment index → group index) and ``group_to_track`` + (group index → Geant4 track ID) for each volume. +*edepsim* (H5) Original EDepSim HDF5 file with particle-level metadata + (PDG, parent/root track IDs, start vertex, interaction + type, etc.) and the full flat step array. +===================== ====================================================== + +This reader combines all three files to return +:class:`~pysupera.data.Particle` objects whose point clouds contain **only +the energy-deposit segments that were visible** in the JAXTPC readout — i.e. +whose group_id appeared in at least one readout-plane's ``group_ids`` array. + +Visibility logic (mirrors ``get_visible_segments_by_track`` in +``seg_visibility.ipynb``): + 1. Open the inst file and, for each volume, scan every readout-plane + sub-group to collect the set of *active_group_ids* (groups above + threshold). + 2. Map every segment in that volume to its group via + ``segment_to_group``. A segment is *visible* if its group is in + ``active_group_ids``. + 3. Map visible group IDs to Geant4 track IDs via ``group_to_track``. + 4. Collect visible segments per track across all volumes, then build a + flat point cloud and per-particle offset array for + :meth:`~pysupera.data.Particle.from_flat_arrays`. + +Usage +----- +:: + + from pysupera.readers import JaxtpcHDF5Reader + + with JaxtpcHDF5Reader( + edepsim_path="edepsim.h5", + seg_path="sim_seg_0000.h5", + inst_path="sim_inst_0000.h5", + ) as reader: + print(len(reader), "events") + particles = reader[0] # list[Particle], visible segments only + for particles in reader: + ... +""" + +from __future__ import annotations + +import numpy as np + +from .base import EventReaderBase +from .format_edepsim_h5 import ( + _DEFAULT_VERTEX_KEY, + _DEFAULT_PARTICLE_KEY, + _DEFAULT_ELECTRON_ENERGY_THRESHOLD, + _get_parent_pdg, + _get_interaction_id, + _get_interaction_type, +) +from ..data import Particle +from ..utils import PointFeature + + +# --------------------------------------------------------------------------- +# Visibility filtering (ported from seg_visibility.ipynb) +# --------------------------------------------------------------------------- + +def get_visible_segment_indices_by_track( + seg_volumes: list[dict], + inst_file, # open h5py.File handle for the inst file + event_key: str, +) -> list[dict[int, np.ndarray]]: + """ + For each volume, return a mapping from Geant4 track ID to the indices + of its *visible* segments (i.e. segments whose group appeared in the + readout above threshold). + + Parameters + ---------- + seg_volumes : list of dict + Per-volume segment dicts as returned by ``load_event_seg``. Only + ``n_actual`` is used here (to bound the ``segment_to_group`` slice). + inst_file : h5py.File + Open read-only handle to the inst HDF5 file. + event_key : str + HDF5 group name for this event, e.g. ``"event_000"``. + + Returns + ------- + list of dict + One dict per volume. Each maps ``track_id (int)`` → + ``seg_indices (np.ndarray of int32)``, the row indices of that + track's visible segments in the seg volume array. Tracks with no + visible segments are absent from the dict. + """ + result: list[dict[int, np.ndarray]] = [] + + if event_key not in inst_file: + return [{} for _ in seg_volumes] + + event_group = inst_file[event_key] + + for v, seg in enumerate(seg_volumes): + track_to_indices: dict[int, np.ndarray] = {} + vol_key = f'volume_{v}' + + # Skip if inst data is missing for this volume or no segments exist. + if vol_key not in event_group or seg.get('n_actual', 0) == 0: + result.append(track_to_indices) + continue + + vol_group = event_group[vol_key] + + # look-up tables stored in the inst file for this volume + # group_to_track : group index → Geant4 track ID + # seg_to_group : segment index → group index + if 'group_to_track' not in vol_group or 'segment_to_group' not in vol_group: + result.append(track_to_indices) + continue + + group_to_track = vol_group['group_to_track'][:] # (G,) int32 + n_segs = seg['n_actual'] + seg_to_group = vol_group['segment_to_group'][:n_segs] # (N,) int32 + + # Collect the group IDs that produced a readout signal above threshold + # by scanning every readout-plane sub-group stored under this volume. + active_group_ids: set[int] = set() + import h5py + for plane_key in vol_group: + plane_group = vol_group[plane_key] + if isinstance(plane_group, h5py.Group) and 'group_ids' in plane_group: + active_group_ids.update(plane_group['group_ids'][:].tolist()) + + if not active_group_ids: + result.append(track_to_indices) + continue + + # Index arrays for all segments in this volume. + seg_indices_all = np.arange(n_segs, dtype=np.int32) + group_ids_all = seg_to_group # group ID assigned to each segment + + # Boolean mask: True where the segment belongs to an active group. + is_visible = np.isin(group_ids_all, list(active_group_ids)) + + # Restrict to visible segments only. + visible_seg_indices = seg_indices_all[is_visible] + visible_group_ids = group_ids_all[is_visible] + + # Map visible group IDs → Geant4 track IDs. + # Groups whose index is out of range receive track ID -1. + track_ids_for_visible = np.where( + visible_group_ids < len(group_to_track), + group_to_track[visible_group_ids], + -1, + ) + + # Group visible segment indices by track ID. + for track_id in np.unique(track_ids_for_visible): + mask = track_ids_for_visible == track_id + track_to_indices[int(track_id)] = visible_seg_indices[mask] + + result.append(track_to_indices) + + return result + + +# --------------------------------------------------------------------------- +# Point-cloud builder +# --------------------------------------------------------------------------- + +def _build_visible_point_cloud( + seg_volumes: list[dict], + visible_by_track: list[dict[int, np.ndarray]], + track_ids_edepsim: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + """ + Build a flat point-cloud array and per-particle offset pairs from the + visibility-filtered JAXTPC seg data. + + The output columns follow :class:`~pysupera.utils.PointFeature` order: + ``x=0, y=1, z=2, time=3, energy=4, dx=5, id=6``. + + Segment positions are taken from each volume's ``positions_mm`` + (x, y, z in mm). Time comes from ``t0_us``, energy from ``de``, + and path length from ``dx``. Column 6 (``PointFeature.id``) is + assigned as a global 0-based integer across all visible segments in + the event. + + Parameters + ---------- + seg_volumes : list of dict + Per-volume segment dicts from ``load_event_seg``. + visible_by_track : list of dict + Per-volume ``{track_id: seg_indices}`` as returned by + :func:`get_visible_segment_indices_by_track`. + track_ids_edepsim : np.ndarray of int + Track IDs in the same order as the edepsim particle array. The + output offsets are aligned to this ordering. + + Returns + ------- + point_cloud_flat : np.ndarray, shape (M, 7), dtype float32 + Concatenated visible segments for all tracks, in the order of + ``track_ids_edepsim``. + offsets : np.ndarray, shape (N, 2), dtype int64 + ``offsets[i] = [start, end)`` into ``point_cloud_flat`` for + particle *i*. Particles with no visible segments get a zero-length + slice (``start == end``). + """ + # Merge per-volume dicts into a single {track_id: list_of_segment_rows} + # where each segment row is a 1-D float32 array of length 7. + track_segs: dict[int, list[np.ndarray]] = {} + + for seg_vol, vol_visible in zip(seg_volumes, visible_by_track): + if seg_vol.get('n_actual', 0) == 0: + continue + pos = seg_vol['positions_mm'] # (N, 3) x, y, z in mm + t0 = seg_vol['t0_us'] # (N,) drift start time in μs + de = seg_vol['de'] # (N,) energy deposit in MeV + dx = seg_vol['dx'] # (N,) path length in mm + + for track_id, seg_idx in vol_visible.items(): + if len(seg_idx) == 0: + continue + # Build a (M, 7) float32 array for this track's visible segments + # in this volume; PointFeature.id column is filled globally later. + chunk = np.zeros((len(seg_idx), 7), dtype=np.float32) + chunk[:, PointFeature.x] = pos[seg_idx, 0] + chunk[:, PointFeature.y] = pos[seg_idx, 1] + chunk[:, PointFeature.z] = pos[seg_idx, 2] + chunk[:, PointFeature.time] = t0[seg_idx] + chunk[:, PointFeature.energy] = de[seg_idx] + chunk[:, PointFeature.dx] = dx[seg_idx] + # id column left as zero; assigned globally below. + track_segs.setdefault(track_id, []).append(chunk) + + # Concatenate chunks per track and build the flat array + offsets in the + # same order as track_ids_edepsim so that Particle.from_flat_arrays + # correctly pairs metadata rows with point-cloud slices. + n_particles = len(track_ids_edepsim) + offsets = np.zeros((n_particles, 2), dtype=np.int64) + flat_chunks: list[np.ndarray] = [] + cursor = 0 + + for i, tid in enumerate(track_ids_edepsim): + chunks = track_segs.get(int(tid)) + if chunks is None: + # Track has no visible segments — leave an empty slice. + offsets[i] = [cursor, cursor] + continue + combined = np.concatenate(chunks, axis=0) # (M_i, 7) + offsets[i] = [cursor, cursor + len(combined)] + flat_chunks.append(combined) + cursor += len(combined) + + if flat_chunks: + point_cloud_flat = np.concatenate(flat_chunks, axis=0) + else: + point_cloud_flat = np.zeros((0, 7), dtype=np.float32) + + # Assign global, event-wide, 0-based point IDs. + point_cloud_flat[:, PointFeature.id] = np.arange( + len(point_cloud_flat), dtype=np.float32 + ) + + return point_cloud_flat, offsets + + +# --------------------------------------------------------------------------- +# Reader class +# --------------------------------------------------------------------------- + +class JaxtpcHDF5Reader(EventReaderBase): + """ + Produce visibility-filtered pysupera :class:`~pysupera.data.Particle` + objects by combining an EDepSim HDF5 file, a JAXTPC *seg* file, and a + JAXTPC *inst* file. + + Each particle's point cloud contains **only** the energy-deposit + segments that reached the readout above the signal threshold in the + JAXTPC simulation (i.e. segments that are "visible" to the detector). + Particles whose every segment was below threshold will have an empty + (or sub-threshold) point cloud in the output. + + Parameters + ---------- + edepsim_path : str + Path to the EDepSim HDF5 file (provides particle-level metadata: + PDG, track IDs, start vertices, etc.). + seg_path : str + Path to the JAXTPC *seg* HDF5 file (provides per-segment truth + quantities: positions in mm, dE, dx, t0, charge, …). + inst_path : str + Path to the JAXTPC *inst* HDF5 file (provides ``segment_to_group``, + ``group_to_track``, and per-plane ``group_ids`` used to determine + which segments were detected). + vertex_key : str, optional + HDF5 dataset key for per-event vertex arrays in *edepsim_path*. + Default: ``"vertex/geant4"``. + particle_key : str, optional + HDF5 dataset key for per-event particle arrays in *edepsim_path*. + Default: ``"particle/geant4"``. + electron_energy_threshold : float, optional + Kinetic-energy threshold (MeV) for the interaction-type classifier + (forwarded to :func:`~.format_edepsim_h5._get_interaction_type`). + Default: ``0.05``. + min_pc_size : int or None, optional + Forwarded to :meth:`~pysupera.data.Particle.from_flat_arrays`. + ``None`` (default) uses the module-level default. + + Examples + -------- + :: + + from pysupera.readers import JaxtpcHDF5Reader + + with JaxtpcHDF5Reader( + edepsim_path="out_edepsim.h5", + seg_path="sim_seg_0000.h5", + inst_path="sim_inst_0000.h5", + ) as reader: + print(len(reader), "events") + for particles in reader: + ... + """ + + def __init__( + self, + edepsim_path: str, + seg_path: str, + inst_path: str, + *, + vertex_key: str = _DEFAULT_VERTEX_KEY, + particle_key: str = _DEFAULT_PARTICLE_KEY, + electron_energy_threshold: float = _DEFAULT_ELECTRON_ENERGY_THRESHOLD, + min_pc_size: int | None = None, + ) -> None: + import h5py + + self._edepsim_path = edepsim_path + self._seg_path = seg_path + self._inst_path = inst_path + self._vertex_key = vertex_key + self._part_key = particle_key + self._e_thresh = electron_energy_threshold + self._min_pc_size = min_pc_size + + # Open all three files for the lifetime of the reader. + self._edepsim_file = h5py.File(edepsim_path, 'r') + self._seg_file = h5py.File(seg_path, 'r') + self._inst_file = h5py.File(inst_path, 'r') + + self._n_events = len(self._edepsim_file[self._part_key]) + + # ------------------------------------------------------------------ # + # EventReaderBase interface # + # ------------------------------------------------------------------ # + + def __len__(self) -> int: + return self._n_events + + def __getitem__(self, index: int) -> list[Particle]: + """ + Return visibility-filtered :class:`~pysupera.data.Particle` objects + for event *index*. + + Parameters + ---------- + index : int + 0-based event index. Negative indices are supported. + + Returns + ------- + list of Particle + + Raises + ------ + IndexError + If *index* is out of range. + """ + n = self._n_events + if index < -n or index >= n: + raise IndexError( + f"Event index {index} out of range for file with {n} events." + ) + if index < 0: + index += n + + event_key = f'event_{index:03d}' + + # ── Step 1: load particle metadata from the EDepSim file ────────── + # These provide PDG code, track IDs, interaction type, etc. + verts = self._edepsim_file[self._vertex_key][index] + parts = self._edepsim_file[self._part_key][index] + + # ── Step 2: load per-volume segment data from the JAXTPC seg file ─ + # Each volume dict contains positions_mm (N,3), de, dx, t0_us, … + seg_volumes = self._load_seg_volumes(event_key) + + # ── Step 3: determine which segments are visible in the readout ─── + # A segment is visible if its group_id appears in a readout-plane's + # group_ids array in the inst file (i.e. it survived thresholding). + visible_by_track = get_visible_segment_indices_by_track( + seg_volumes, self._inst_file, event_key + ) + + # ── Step 4: build the flat point cloud (visible segments only) ──── + # Segments are assembled in the same track order as `parts` so that + # Particle.from_flat_arrays correctly matches metadata to point clouds. + point_cloud_flat, offsets = _build_visible_point_cloud( + seg_volumes, visible_by_track, parts['track_id'] + ) + + # ── Step 5: derive per-particle scalar labels from EDepSim data ─── + itype = _get_interaction_type(parts, self._e_thresh) + int_ids = _get_interaction_id(parts, verts) + + _root_field = 'root_track_id' if 'root_track_id' in parts.dtype.names else 'ancestor_track_id' + + return Particle.from_flat_arrays( + ids = parts['track_id'], + parent_ids = parts['parent_track_id'], + root_ids = parts[_root_field], + pdgs = parts['pdg'], + parent_pdgs = _get_parent_pdg(parts), + interaction_ids = int_ids, + interaction_types = itype, + point_cloud_flat = point_cloud_flat, + point_cloud_offsets = offsets, + min_pc_size = self._min_pc_size, + ) + + def close(self) -> None: + """Close all open HDF5 file handles.""" + for attr in ('_edepsim_file', '_seg_file', '_inst_file'): + fh = getattr(self, attr, None) + if fh is not None: + fh.close() + setattr(self, attr, None) + + # ------------------------------------------------------------------ # + # Internal helpers # + # ------------------------------------------------------------------ # + + def _load_seg_volumes(self, event_key: str) -> list[dict]: + """ + Load per-volume segment data from the JAXTPC seg file for one event. + + Returns a list of dicts analogous to those produced by + ``production.load.load_event_seg``, each containing at minimum: + ``positions_mm`` (N,3), ``de`` (N,), ``dx`` (N,), ``t0_us`` (N,), + ``charge`` (N,), and ``n_actual`` (int). Volumes with no segments + return ``{'n_actual': 0}``. + """ + if event_key not in self._seg_file: + return [] + + evt = self._seg_file[event_key] + n_volumes = int(evt.attrs.get('n_volumes', 1)) + volumes: list[dict] = [] + + for v in range(n_volumes): + vol_key = f'volume_{v}' + if vol_key not in evt: + volumes.append({'n_actual': 0}) + continue + + vg = evt[vol_key] + n = int(vg.attrs.get('n_actual', 0)) + + if n == 0: + volumes.append({'n_actual': 0}) + continue + + # Positions are stored as quantised integers; reconstruct mm coords. + pos_step = float(vg.attrs['pos_step_mm']) + origin = np.array([ + vg.attrs['pos_origin_x'], + vg.attrs['pos_origin_y'], + vg.attrs['pos_origin_z'], + ]) + positions_mm = vg['positions'][:].astype(np.float32) * pos_step + origin + + volumes.append({ + 'positions_mm': positions_mm, # (N, 3) + 'de': vg['de'][:].astype(np.float32), + 'dx': vg['dx'][:].astype(np.float32), + 't0_us': vg['t0_us'][:].astype(np.float32), + 'charge': vg['charge'][:].astype(np.float32), + 'n_actual': n, + }) + + return volumes + + # ------------------------------------------------------------------ # + # Convenience # + # ------------------------------------------------------------------ # + + @classmethod + def from_config(cls, cfg) -> "JaxtpcHDF5Reader": + """ + Construct a :class:`JaxtpcHDF5Reader` from a Hydra/OmegaConf config. + + The EDepSim file path is read from ``cfg.io.input_path``; the JAXTPC + seg and inst file paths come from ``cfg.reader.jaxtpc_seg_path`` and + ``cfg.reader.jaxtpc_inst_path``; all other reader parameters come from + ``cfg.reader``. + + The config group is ``reader=edepsim_h5`` — JAXTPC filtering is an + optional overlay activated by setting the two JAXTPC path fields: + + .. code-block:: yaml + + reader: + jaxtpc_seg_path: /data/sim_seg_0000.h5 + jaxtpc_inst_path: /data/sim_inst_0000.h5 + + Parameters + ---------- + cfg : DictConfig + Full Hydra config as returned by + :func:`~pysupera.config.load_cfg`. Must contain at minimum + ``cfg.io.input_path``, ``cfg.reader.jaxtpc_seg_path``, and + ``cfg.reader.jaxtpc_inst_path``. + + Returns + ------- + JaxtpcHDF5Reader + + Examples + -------- + :: + + from pysupera.config import load_cfg + from pysupera.readers import JaxtpcHDF5Reader + + cfg = load_cfg([ + "io.input_path=/data/edepsim.h5", + "reader.jaxtpc_seg_path=/data/sim_seg_0000.h5", + "reader.jaxtpc_inst_path=/data/sim_inst_0000.h5", + ]) + with JaxtpcHDF5Reader.from_config(cfg) as reader: + particles = reader[0] + """ + r = cfg.reader + return cls( + edepsim_path = str(cfg.io.input_path), + seg_path = str(r.jaxtpc_seg_path), + inst_path = str(r.jaxtpc_inst_path), + vertex_key = str(r.get("vertex_key", _DEFAULT_VERTEX_KEY)), + particle_key = str(r.get("particle_key", _DEFAULT_PARTICLE_KEY)), + electron_energy_threshold = float(r.get( + "electron_energy_threshold", + _DEFAULT_ELECTRON_ENERGY_THRESHOLD)), + min_pc_size = int(cfg.particle.get("min_pc_size", -1)) + if hasattr(cfg, "particle") else None, + ) + + @classmethod + def from_production_dir( + cls, + production_dir: str, + dataset: str = 'sim', + file_index: int = 0, + edepsim_path: str | None = None, + **kwargs, + ) -> "JaxtpcHDF5Reader": + """ + Construct a :class:`JaxtpcHDF5Reader` from a JAXTPC production + directory that follows the standard naming convention:: + + /sensor/sim_sensor_.h5 + /seg/sim_seg_.h5 + /inst/sim_inst_.h5 + + Parameters + ---------- + production_dir : str + Root directory of the JAXTPC output batch. + dataset : str, optional + Dataset prefix (default: ``"sim"``). + file_index : int, optional + Batch file index (default: ``0``). + edepsim_path : str + Path to the matching EDepSim HDF5 file (required for particle + metadata; not stored in the production directory). + **kwargs + Forwarded to :class:`JaxtpcHDF5Reader.__init__`. + + Returns + ------- + JaxtpcHDF5Reader + """ + import os + if edepsim_path is None: + raise ValueError( + "edepsim_path is required — the production directory does not " + "contain an EDepSim file. Pass it explicitly via edepsim_path." + ) + seg_path = os.path.join(production_dir, 'seg', f'{dataset}_seg_{file_index:04d}.h5') + inst_path = os.path.join(production_dir, 'inst', f'{dataset}_inst_{file_index:04d}.h5') + return cls(edepsim_path, seg_path, inst_path, **kwargs) + + @property + def edepsim_path(self) -> str: + """Path to the EDepSim HDF5 file.""" + return self._edepsim_path + + @property + def seg_path(self) -> str: + """Path to the JAXTPC seg HDF5 file.""" + return self._seg_path + + @property + def inst_path(self) -> str: + """Path to the JAXTPC inst HDF5 file.""" + return self._inst_path + + def __repr__(self) -> str: + return ( + f"JaxtpcHDF5Reader(" + f"edepsim={self._edepsim_path!r}, " + f"seg={self._seg_path!r}, " + f"inst={self._inst_path!r}, " + f"n_events={self._n_events})" + ) diff --git a/pysupera/utils.py b/pysupera/utils.py index c205bd9..6e27fe2 100644 --- a/pysupera/utils.py +++ b/pysupera/utils.py @@ -110,8 +110,9 @@ class PointFeature(int): Hit time. energy : int = 4 Energy deposited at the hit. - dedx : int = 5 - Ionisation energy loss (dE/dx) at the hit. + dx : int = 5 + Additional scalar feature (e.g. a second energy-like quantity) summed + over all hits that merge into the same output point. id : int = 6 (optional) integer hit ID, e.g. for matching to truth-level information. """ @@ -120,7 +121,7 @@ class PointFeature(int): z = 2 time = 3 energy = 4 - dedx = 5 + dx = 5 id = 6 def __new__(cls, value): @@ -255,6 +256,61 @@ def _row(p, tag=""): return chain, children +def resolve_orphans(particles, verbose: bool = False): + """ + Repair dangling ``parent_id`` and ``root_id`` references in a particle list + so that every genealogy walk terminates at a particle that is actually + present in the event. + + An *orphan* is a particle whose ``parent_id`` (or ``root_id``) refers to a + particle ID that does not appear in *particles*. Such dangling references + cause :meth:`~pysupera.partitioner.ParticlePartitioner._build_relationships` + to build an incomplete ``children_map`` / ``ancestor_map``, and they also + break the ``find_initiator`` walk inside + :func:`~pysupera.merge.merge_em_showers`. + + The repair rule is conservative: + + * If ``p.parent_id`` is not in the known-ID set, reset it to ``p.id`` + (i.e. make the particle a primary/root with respect to this event list). + * If ``p.root_id`` is not in the known-ID set, reset it to ``p.id`` + as well. + + This function modifies *particles* **in place** and returns the same list. + + Parameters + ---------- + particles : list of Particle + The full particle collection for one event. Modified in place. + verbose : bool, optional + If ``True``, print a summary of how many references were repaired. + + Returns + ------- + list of Particle + The same list (modified in place) for convenient chaining. + + Examples + -------- + >>> particles = resolve_orphans(particles) + >>> partitioner = ParticlePartitioner(particles=particles, ...) + """ + known_ids = {p.id for p in particles} + n_parent_fixed = 0 + n_root_fixed = 0 + for p in particles: + if p.parent_id not in known_ids: + p.parent_id = p.id + n_parent_fixed += 1 + if p.root_id not in known_ids: + p.root_id = p.id + n_root_fixed += 1 + if verbose and (n_parent_fixed or n_root_fixed): + print(f"[resolve_orphans] repaired {n_parent_fixed} parent_id(s) " + f"and {n_root_fixed} root_id(s) out of {len(particles)} particles") + return particles + + def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_size=-1): """ Derive the :class:`SemanticType` of a particle from its physics properties. diff --git a/pysupera/vis.py b/pysupera/vis.py new file mode 100644 index 0000000..3b9a53c --- /dev/null +++ b/pysupera/vis.py @@ -0,0 +1,739 @@ +#!/usr/bin/env python3 +""" +Interactive 3-D point-cloud viewer for pysupera HDF5 output files. + +Usage +----- + python -m pysupera.vis path/to/output.h5 [--port 8765] + # or, from the pysupera package: + python -c "from pysupera.vis import serve; serve('output.h5')" + +Renders ``non_le_cloud`` and ``le_scatter_cloud`` datasets using Three.js / +WebGL. Handles 100 k – 1 M points at interactive frame-rates. +""" +from __future__ import annotations + +import argparse +import json +import os +import sys +import threading +import time +import webbrowser +from http.server import BaseHTTPRequestHandler, HTTPServer +from urllib.parse import urlparse + +import numpy as np + +try: + import h5py +except ImportError: + sys.exit("h5py is required: pip install h5py") + +try: + import hdf5plugin # noqa: F401 — registers LZ4/Blosc decoders +except ImportError: + pass + + +# ──────────────────────────────────────────────────────────────────────────── +# Embedded HTML / JS / CSS (single-file, served from memory) +# ──────────────────────────────────────────────────────────────────────────── +_HTML = r""" + + + + +pysupera Viewer + + + + + + + +

+ + +
+
WebGL
+
+ + + + + +""" + +# ──────────────────────────────────────────────────────────────────────────── +# HTTP request handler +# ──────────────────────────────────────────────────────────────────────────── +_h5file: "h5py.File | None" = None # set in serve() + + +class _Handler(BaseHTTPRequestHandler): + def log_message(self, _fmt, *_args): + pass # suppress per-request console noise + + def do_GET(self): + path = urlparse(self.path).path + if path in ('/', '/index.html'): + self._send('text/html; charset=utf-8', _HTML.encode()) + elif path == '/api/info': + self._handle_info() + elif path.startswith('/api/event/'): + self._handle_event(path) + else: + self.send_error(404) + + # ── helpers ───────────────────────────────────────────────────────────── + + def _send(self, ctype: str, data: bytes, status: int = 200): + self.send_response(status) + self.send_header('Content-Type', ctype) + self.send_header('Content-Length', str(len(data))) + self.send_header('Cache-Control', 'no-cache') + self.send_header('Access-Control-Allow-Origin', '*') + self.end_headers() + self.wfile.write(data) + + def _handle_info(self): + f = _h5file + info = { + 'filename': os.path.basename(f.filename), + 'n_events': int(f['n_events'][()]) if 'n_events' in f else 0, + 'has_non_le': 'non_le_cloud/flat' in f, + 'has_le': 'le_scatter_cloud/flat' in f, + } + self._send('application/json', json.dumps(info).encode()) + + def _handle_event(self, path: str): + # Expected: /api/event/{idx}/{group} where group = non_le | le + parts = path.rstrip('/').split('/') + if len(parts) != 5: + self.send_error(400, 'Bad path'); return + try: + ev_idx = int(parts[3]) + except ValueError: + self.send_error(400, 'Non-integer event index'); return + + grp_key = parts[4] + if grp_key == 'non_le': + grp = 'non_le_cloud' + elif grp_key == 'le': + grp = 'le_scatter_cloud' + else: + self.send_error(400, 'Unknown group'); return + + f = _h5file + off_key = f'{grp}/offsets' + flat_key = f'{grp}/flat' + if off_key not in f or flat_key not in f: + self._send('application/octet-stream', b''); return + + offsets = f[off_key][:] + n_ev = len(offsets) - 1 + if ev_idx < 0 or ev_idx >= n_ev: + self.send_error(400, f'Event {ev_idx} out of range [0,{n_ev})'); return + + start = int(offsets[ev_idx]) + end = int(offsets[ev_idx + 1]) + if start >= end: + self._send('application/octet-stream', b''); return + + arr = np.ascontiguousarray(f[flat_key][start:end], dtype=np.float32) + self._send('application/octet-stream', arr.tobytes()) + + +# ──────────────────────────────────────────────────────────────────────────── +# Public API +# ──────────────────────────────────────────────────────────────────────────── + +def serve(h5_path: str, port: int = 8765, open_browser: bool = True) -> None: + """ + Start the viewer for *h5_path* on ``http://localhost:{port}/``. + + Blocks until Ctrl-C is pressed. + + Parameters + ---------- + h5_path : str + Path to a pysupera HDF5 output file. + port : int, optional + Local TCP port. Default 8765. + open_browser : bool, optional + Whether to open the system browser automatically. Default True. + """ + global _h5file + + if not os.path.exists(h5_path): + sys.exit(f'File not found: {h5_path!r}') + + try: + import hdf5plugin # noqa + except ImportError: + pass + _h5file = h5py.File(h5_path, 'r') + + try: + server = HTTPServer(('localhost', port), _Handler) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + + url = f'http://localhost:{port}/' + print(f'[pysupera.vis] Serving {h5_path!r}') + print(f'[pysupera.vis] Open: {url}') + print(f'[pysupera.vis] Stop: Ctrl-C') + print() + print(' Keyboard shortcuts in the viewer:') + print(' ← / → previous / next event') + print(' c center camera on loaded points') + print(' a toggle axes helper') + + if open_browser: + time.sleep(0.3) + webbrowser.open(url) + + while True: + time.sleep(1) + except KeyboardInterrupt: + print('\n[pysupera.vis] Stopping.') + finally: + server.shutdown() + _h5file.close() + + +# ──────────────────────────────────────────────────────────────────────────── +# CLI entry point +# ──────────────────────────────────────────────────────────────────────────── + +def main(): + parser = argparse.ArgumentParser( + description='pysupera interactive 3-D point-cloud viewer', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=__doc__, + ) + parser.add_argument('h5_file', help='Path to pysupera HDF5 output file') + parser.add_argument('--port', type=int, default=8765, + help='Local HTTP port (default: 8765)') + parser.add_argument('--no-browser', action='store_true', + help='Do not open a browser window automatically') + args = parser.parse_args() + serve(args.h5_file, port=args.port, open_browser=not args.no_browser) + + +if __name__ == '__main__': + main() diff --git a/tests/conftest.py b/tests/conftest.py index 239ebd7..a5cf206 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -47,6 +47,7 @@ def make_particle( pc: np.ndarray | None = None, n_pts: int = 10, offset: tuple = (0.0, 0.0, 0.0), + interaction_id: int = -1, ) -> Particle: if parent_id is None: parent_id = pid @@ -63,6 +64,7 @@ def make_particle( root_id = root_id, pdg = pdg, parent_pdg = parent_pdg, + interaction_id = interaction_id, interaction_type = interaction_type, point_cloud = pc, ) From b3cf5ee5bd8fbe8017fb81eb33fc2692215758c5 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Thu, 13 Aug 2026 12:54:25 -0500 Subject: [PATCH 07/62] jaxtpc reader: fail loudly instead of yielding empty point clouds The visibility-filtering paths degraded silently when handed the wrong file in either slot: every particle came back with an empty point cloud, so a misconfigured run looked like a physics result rather than an error. The worst offender was n_actual defaulting to 0 via attrs.get(), which turned "this is not a seg volume" into "this volume has no segments". Six such branches now raise KeyError naming the offending path, what was found there, and which file belongs in that slot. Genuinely empty data (n_actual == 0, or readout planes present but nothing above threshold) is still allowed through -- only schema and pairing problems raise. Also accept deposit_to_group as an alias for segment_to_group, since newer JAXTPC output renamed that table, and import hdf5plugin so the Blosc-compressed seg/inst files can be decoded at all. hdf5plugin becomes a declared dependency; it was already required in practice for io.compression=lz4. --- pyproject.toml | 3 + pysupera/readers/format_jaxtpc.py | 128 +++++++++++++++++++++++++----- 2 files changed, 112 insertions(+), 19 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index ac9d453..bd92500 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,9 @@ dependencies = [ "numpy", "scipy", "h5py", + # Registers the Blosc/LZ4 HDF5 filters. Required to read JAXTPC seg/inst + # files (written with Blosc, filter 32001) and to write io.compression=lz4. + "hdf5plugin", "hydra-core>=1.3", "omegaconf>=2.3", ] diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index 94cf000..d85f0ce 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -15,7 +15,8 @@ sub-group stores the ``group_ids`` of segments that survived the signal threshold and were reconstructed. Also stores the lookup tables ``segment_to_group`` - (segment index → group index) and ``group_to_track`` + (segment index → group index; newer JAXTPC output names + this ``deposit_to_group``) and ``group_to_track`` (group index → Geant4 track ID) for each volume. *edepsim* (H5) Original EDepSim HDF5 file with particle-level metadata (PDG, parent/root track IDs, start vertex, interaction @@ -33,8 +34,8 @@ sub-group to collect the set of *active_group_ids* (groups above threshold). 2. Map every segment in that volume to its group via - ``segment_to_group``. A segment is *visible* if its group is in - ``active_group_ids``. + ``segment_to_group`` / ``deposit_to_group``. A segment is *visible* if + its group is in ``active_group_ids``. 3. Map visible group IDs to Geant4 track IDs via ``group_to_track``. 4. Collect visible segments per track across all volumes, then build a flat point cloud and per-particle offset array for @@ -61,6 +62,14 @@ import numpy as np +# Register HDF5 compression filters (LZ4, Blosc, etc.) if available. +# JAXTPC seg/inst files are written with Blosc (HDF5 filter 32001), which +# h5py cannot decode unless these filters have been registered first. +try: + import hdf5plugin # noqa: F401 +except ImportError: + pass + from .base import EventReaderBase from .format_edepsim_h5 import ( _DEFAULT_VERTEX_KEY, @@ -74,6 +83,18 @@ from ..utils import PointFeature +# Per-volume table mapping segment (deposit) index → group index. Older +# JAXTPC output names this ``segment_to_group``; newer output writes +# ``deposit_to_group``. Both spellings are accepted, in this order. +_SEG_TO_GROUP_KEYS = ('segment_to_group', 'deposit_to_group') + +# Datasets every non-empty seg volume must provide. +_SEG_REQUIRED_DATASETS = ('positions', 'de', 'dx', 't0_us', 'charge') + +# Attributes every non-empty seg volume must provide. +_SEG_REQUIRED_ATTRS = ('pos_step_mm', 'pos_origin_x', 'pos_origin_y', 'pos_origin_z') + + # --------------------------------------------------------------------------- # Visibility filtering (ported from seg_visibility.ipynb) # --------------------------------------------------------------------------- @@ -109,7 +130,12 @@ def get_visible_segment_indices_by_track( result: list[dict[int, np.ndarray]] = [] if event_key not in inst_file: - return [{} for _ in seg_volumes] + raise KeyError( + f"Event group {event_key!r} not found in the JAXTPC inst file " + f"{inst_file.filename!r}. Top-level groups present: " + f"{sorted(inst_file.keys())[:6]}. The inst file must cover the " + f"same events as the EDepSim file given by io.input_path." + ) event_group = inst_file[event_key] @@ -117,8 +143,18 @@ def get_visible_segment_indices_by_track( track_to_indices: dict[int, np.ndarray] = {} vol_key = f'volume_{v}' - # Skip if inst data is missing for this volume or no segments exist. - if vol_key not in event_group or seg.get('n_actual', 0) == 0: + # A missing volume group means the inst file does not describe the same + # volumes as the seg file — always a configuration error, never data. + if vol_key not in event_group: + raise KeyError( + f"{event_key}/{vol_key} is present in the JAXTPC seg file but " + f"missing from the inst file {inst_file.filename!r} " + f"(volumes present: {sorted(event_group.keys())}). The seg and " + f"inst files must come from the same JAXTPC run." + ) + + # A volume that genuinely holds zero segments is legitimately empty. + if seg.get('n_actual', 0) == 0: result.append(track_to_indices) continue @@ -126,24 +162,46 @@ def get_visible_segment_indices_by_track( # look-up tables stored in the inst file for this volume # group_to_track : group index → Geant4 track ID - # seg_to_group : segment index → group index - if 'group_to_track' not in vol_group or 'segment_to_group' not in vol_group: - result.append(track_to_indices) - continue + # seg_to_group : segment index → group index (see _SEG_TO_GROUP_KEYS) + seg_to_group_key = next( + (k for k in _SEG_TO_GROUP_KEYS if k in vol_group), None + ) + if 'group_to_track' not in vol_group or seg_to_group_key is None: + raise KeyError( + f"{event_key}/{vol_key} in the JAXTPC inst file " + f"{inst_file.filename!r} is missing the visibility lookup " + f"tables. Required: 'group_to_track' plus one of " + f"{list(_SEG_TO_GROUP_KEYS)}; found {sorted(vol_group.keys())}. " + f"Check that reader.jaxtpc_inst_path points at the JAXTPC " + f"hits/inst file (not the seg or sensor file)." + ) - group_to_track = vol_group['group_to_track'][:] # (G,) int32 + group_to_track = vol_group['group_to_track'][:] # (G,) int32 n_segs = seg['n_actual'] - seg_to_group = vol_group['segment_to_group'][:n_segs] # (N,) int32 + seg_to_group = vol_group[seg_to_group_key][:n_segs] # (N,) int32 # Collect the group IDs that produced a readout signal above threshold # by scanning every readout-plane sub-group stored under this volume. active_group_ids: set[int] = set() import h5py + n_planes = 0 for plane_key in vol_group: plane_group = vol_group[plane_key] if isinstance(plane_group, h5py.Group) and 'group_ids' in plane_group: + n_planes += 1 active_group_ids.update(plane_group['group_ids'][:].tolist()) + # No readout-plane sub-groups at all is a schema problem; planes that + # exist but recorded nothing above threshold are legitimately empty. + if n_planes == 0: + raise KeyError( + f"{event_key}/{vol_key} in the JAXTPC inst file " + f"{inst_file.filename!r} contains no readout-plane sub-group " + f"with a 'group_ids' dataset (expected e.g. U/V/Y); found " + f"{sorted(vol_group.keys())}. Without these, segment " + f"visibility cannot be determined." + ) + if not active_group_ids: result.append(track_to_indices) continue @@ -304,9 +362,9 @@ class JaxtpcHDF5Reader(EventReaderBase): Path to the JAXTPC *seg* HDF5 file (provides per-segment truth quantities: positions in mm, dE, dx, t0, charge, …). inst_path : str - Path to the JAXTPC *inst* HDF5 file (provides ``segment_to_group``, - ``group_to_track``, and per-plane ``group_ids`` used to determine - which segments were detected). + Path to the JAXTPC *inst* HDF5 file (provides ``segment_to_group`` or + ``deposit_to_group``, ``group_to_track``, and per-plane ``group_ids`` + used to determine which segments were detected). vertex_key : str, optional HDF5 dataset key for per-event vertex arrays in *edepsim_path*. Default: ``"vertex/geant4"``. @@ -466,7 +524,12 @@ def _load_seg_volumes(self, event_key: str) -> list[dict]: return ``{'n_actual': 0}``. """ if event_key not in self._seg_file: - return [] + raise KeyError( + f"Event group {event_key!r} not found in the JAXTPC seg file " + f"{self._seg_path!r}. Top-level groups present: " + f"{sorted(self._seg_file.keys())[:6]}. The seg file must cover " + f"the same events as the EDepSim file given by io.input_path." + ) evt = self._seg_file[event_key] n_volumes = int(evt.attrs.get('n_volumes', 1)) @@ -475,16 +538,43 @@ def _load_seg_volumes(self, event_key: str) -> list[dict]: for v in range(n_volumes): vol_key = f'volume_{v}' if vol_key not in evt: - volumes.append({'n_actual': 0}) - continue + raise KeyError( + f"{event_key}/{vol_key} missing from the JAXTPC seg file " + f"{self._seg_path!r}, but {event_key} declares " + f"n_volumes={n_volumes} (groups present: " + f"{sorted(evt.keys())})." + ) vg = evt[vol_key] - n = int(vg.attrs.get('n_actual', 0)) + + # A missing n_actual attribute means this is not a JAXTPC seg + # volume at all. Previously it defaulted to 0, which silently + # produced empty point clouds for every particle in the event. + if 'n_actual' not in vg.attrs: + raise KeyError( + f"{event_key}/{vol_key} in {self._seg_path!r} has no " + f"'n_actual' attribute, so it is not a JAXTPC seg volume " + f"(attributes present: {sorted(vg.attrs.keys())}). Check " + f"that reader.jaxtpc_seg_path points at the JAXTPC seg/step " + f"file (not the hits/inst or sensor file)." + ) + + n = int(vg.attrs['n_actual']) if n == 0: volumes.append({'n_actual': 0}) continue + _missing_ds = [k for k in _SEG_REQUIRED_DATASETS if k not in vg] + _missing_at = [k for k in _SEG_REQUIRED_ATTRS if k not in vg.attrs] + if _missing_ds or _missing_at: + raise KeyError( + f"{event_key}/{vol_key} in the JAXTPC seg file " + f"{self._seg_path!r} declares n_actual={n} but is missing " + f"required datasets {_missing_ds} / attributes " + f"{_missing_at}." + ) + # Positions are stored as quantised integers; reconstruct mm coords. pos_step = float(vg.attrs['pos_step_mm']) origin = np.array([ From 40f96657f4c235d1da45b59d108714b0559ca14d Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Thu, 13 Aug 2026 12:54:25 -0500 Subject: [PATCH 08/62] io: size HDF5 chunks from a byte budget, add repack() _CHUNK_POINTS was a fixed row count (262144) picked to make a 5 MiB chunk for the 5-column point array -- 5 MiB / (5 cols * 4 B) = 262144 exactly. The event clouds reused that same constant with 9 columns, so their chunks came out at 262144 * 9 * 4 = 9.4 MB: nearly double the intended budget, and large enough that reading one event's rows decompressed the entire dataset. Chunk shapes are now derived from a byte budget by _chunk_rows(), so the row count follows from the row width instead of being asserted independently: _CHUNK_PARTICLES 256 -> 16384 (64 KiB budget, 1-D metadata columns) _CHUNK_POINTS 262144 -> 13107 (256 KiB, 5-column point arrays) _CHUNK_CLOUD new -> 7281 (256 KiB, 9-column event clouds) _CHUNK_PARTICLES at 256 rows was 1 KB per chunk, small enough to hurt both compression ratio and per-chunk bookkeeping. The rdcc_nslots comment describing 5 MB chunks was stale and is updated; the formula itself already tracked _CHUNK_POINTS. Measured on a 5-event JAXTPC run: default LZ4 output 9.40 -> 8.05 MiB with random row-range reads 2.7x faster, and the gzip copy the WebGL viewer reads 6.79 -> 3.96 MiB and 5.2x faster. Also adds repack(), which rewrites a finished file with every chunk clamped to its dataset's real length. The streaming writer creates each dataset empty and grows it with resize(), so the final length is unknown when the chunk shape is fixed; the byte budget bounds the waste to at most one chunk per dataset, and repack removes even that. It walks groups and datasets generically rather than reconstructing events, so it preserves whatever layout the writer produced; copies in chunk-aligned blocks to keep memory bounded; and swaps the result in via os.replace so the original survives any failure. Third-party filters are reproduced by filter ID, because h5py reports LZ4 as compression == 'unknown', which is not a value it accepts on write. --- pysupera/io.py | 183 ++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 175 insertions(+), 8 deletions(-) diff --git a/pysupera/io.py b/pysupera/io.py index 6e92055..358503e 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -66,14 +66,37 @@ FORMAT_VERSION = "2.1.0" -# Chunk sizes for compressed HDF5 datasets. -# Tuned for typical event sizes; adjust if your events are significantly -# larger or smaller. -_CHUNK_PARTICLES = 256 # rows per chunk in particle metadata arrays -_CHUNK_POINTS = 262144 # rows per chunk in the flat point array (1 chunk ≈ 5 MB) _PC_NDIM = 5 # number of columns in the flat point array (x,y,z,t,e) _CLOUD_NDIM = 9 # columns in event-cloud datasets (x,y,z,t,e,interaction_id,root_id,frag_id,inst_id) +# Chunk sizing for compressed HDF5 datasets. +# +# Chunk shapes are derived from a *byte budget* rather than a fixed row count, +# because the right row count depends on how wide the row is. HDF5 reads and +# decompresses a whole chunk to satisfy any read that touches it, so the budget +# is what actually governs read cost. The HDF5 docs recommend 10 KiB - 1 MiB. +# +# Two budgets, because the two dataset families have different shapes: +# * point/cloud arrays — wide rows, millions of them; a larger budget keeps +# the chunk count down without making single-event reads expensive. +# * metadata columns — 1-D, 4 bytes per row, and there are ~20 of them per +# file. A smaller budget bounds the padding waste on short runs, since the +# streaming writer creates these datasets empty and cannot know the final +# length (a chunk longer than the dataset is stored as padding). +_CHUNK_TARGET_BYTES = 256 * 1024 # point / cloud arrays +_CHUNK_TARGET_BYTES_META = 64 * 1024 # 1-D metadata columns + + +def _chunk_rows(ncols: int, itemsize: int = 4, + target_bytes: int = _CHUNK_TARGET_BYTES) -> int: + """Rows per chunk so that one chunk is about *target_bytes*.""" + return max(1, target_bytes // max(1, ncols * itemsize)) + + +_CHUNK_PARTICLES = _chunk_rows(1, target_bytes=_CHUNK_TARGET_BYTES_META) # 16384 +_CHUNK_POINTS = _chunk_rows(_PC_NDIM) # 13107 +_CHUNK_CLOUD = _chunk_rows(_CLOUD_NDIM) # 7281 + # --------------------------------------------------------------------------- # Public helpers # --------------------------------------------------------------------------- @@ -549,7 +572,7 @@ def _init_datasets(self) -> None: g.create_dataset( "flat", shape=(0, _CLOUD_NDIM), maxshape=(None, _CLOUD_NDIM), dtype=np.float32, - chunks=(_CHUNK_POINTS, _CLOUD_NDIM), + chunks=(_CHUNK_CLOUD, _CLOUD_NDIM), **ckw, ) @@ -929,8 +952,10 @@ def __init__(self, path: str, chunk_cache_mb: int = 256) -> None: import h5py self._path = path rdcc_nbytes = chunk_cache_mb * 1024 * 1024 - # rdcc_nslots: prime slightly larger than (cache_bytes / chunk_bytes). - # With 5 MB chunks and 256 MB cache that's ~51 chunks; use 127. + # rdcc_nslots: comfortably larger than (cache_bytes / chunk_bytes), so + # the slot table does not thrash before the byte budget is reached. + # Derived from _CHUNK_POINTS so it tracks the chunk byte budget: with + # 256 KiB chunks and a 256 MB cache that is ~1024 chunks -> 3073 slots. rdcc_nslots = max(127, rdcc_nbytes // (_CHUNK_POINTS * _PC_NDIM * 4) * 3 + 1) self._f = h5py.File( path, "r", @@ -1456,6 +1481,148 @@ def _read_rep_level(f, group: str, r_start: int, r_end: int) -> list: return reps +def repack(path: str, + compression: Optional[str] = None, + compression_opts: Optional[int] = None, + verbose: bool = True) -> dict: + """ + Rewrite *path* in place with chunk shapes sized from the final dataset + lengths. + + The streaming writer creates every dataset empty and grows it with + ``resize()``, so at creation time it cannot know how long a dataset will + end up. It therefore uses the byte-budget chunk shapes from + ``_CHUNK_*``, and any dataset that ends up **shorter than one chunk** + keeps a chunk longer than itself -- stored as padding. This function is + the post-pass that removes that: every chunk is clamped to the dataset's + actual length. + + Note that the byte budget already bounds the waste to at most one chunk per + dataset, so this is a modest optimisation for long runs. It matters most + for short runs (few events), where several datasets can be shorter than a + single chunk. + + The rewrite is generic -- it walks every group and dataset rather than + reconstructing events -- so it preserves any layout the writer produced. + Data is copied in chunk-aligned blocks, so memory use stays bounded + regardless of file size. + + Parameters + ---------- + path : str + File to repack. Replaced atomically on success via :func:`os.replace`; + the original is left untouched if anything raises. + compression : str or None, optional + Filter for the output. ``None`` (default) keeps whatever filter each + source dataset already uses. Pass e.g. ``"gzip"`` to switch. + compression_opts : int or None, optional + Compression level for *compression*. + verbose : bool, optional + Print a one-line before/after summary. + + Returns + ------- + dict + ``{"size_before", "size_after", "n_datasets", "n_reshaped"}`` + """ + import h5py + import numpy as _np + + size_before = os.path.getsize(path) + tmp = f"{path}.repack-tmp" + n_datasets = n_reshaped = 0 + + try: + with h5py.File(path, "r") as fin, h5py.File(tmp, "w") as fout: + for key, val in fin.attrs.items(): + fout.attrs[key] = val + + def visit(name, obj): + nonlocal n_datasets, n_reshaped + if isinstance(obj, h5py.Group): + grp = fout.require_group(name) + for key, val in obj.attrs.items(): + grp.attrs[key] = val + return + + n_datasets += 1 + src_chunks = obj.chunks + kwargs: dict = {} + chunks = None + + if src_chunks is not None: + # Clamp each dimension to the real shape; HDF5 rejects a + # chunk larger than a fixed-size dataset. + chunks = tuple(max(1, min(c, s)) + for c, s in zip(src_chunks, obj.shape)) + if any(d == 0 for d in obj.shape): + chunks = None # cannot chunk an empty dataset + elif chunks != src_chunks: + n_reshaped += 1 + + if chunks is not None: + if compression is None: + # Preserve the source filter. Third-party filters + # (LZ4, Blosc) must be matched by filter ID: h5py + # reports Dataset.compression as 'unknown' for those, + # which is not a value it accepts on write. + kwargs = _compression_kwargs_from_filters(obj) + if not kwargs and obj.compression in ("gzip", "lzf", + "szip"): + kwargs = {"compression": obj.compression, + "compression_opts": obj.compression_opts} + else: + kwargs = _compress_kwargs(compression, + compression_opts) + + dst = fout.create_dataset(name, shape=obj.shape, + dtype=obj.dtype, chunks=chunks, + **{k: v for k, v in kwargs.items() + if v is not None}) + for key, val in obj.attrs.items(): + dst.attrs[key] = val + + if obj.ndim == 0: + dst[()] = obj[()] + return + n = obj.shape[0] + if n == 0: + return + step = chunks[0] if chunks else min(n, 1 << 16) + for start in range(0, n, step): + stop = min(start + step, n) + dst[start:stop] = obj[start:stop] + + fin.visititems(visit) + + os.replace(tmp, path) + except BaseException: + if os.path.exists(tmp): + os.remove(tmp) + raise + + size_after = os.path.getsize(path) + if verbose: + # Signed relative to the original: negative means the file shrank. + pct = (100.0 * (size_after - size_before) / size_before) \ + if size_before else 0.0 + print(f"[repack] {path}: {size_before / 2**20:.2f} MiB -> " + f"{size_after / 2**20:.2f} MiB ({pct:+.1f}%), " + f"{n_reshaped}/{n_datasets} dataset(s) re-chunked") + return {"size_before": size_before, "size_after": size_after, + "n_datasets": n_datasets, "n_reshaped": n_reshaped} + + +def _compression_kwargs_from_filters(ds) -> dict: + """Reproduce a dataset's third-party filter (LZ4 / Blosc) as kwargs.""" + filters = dict(ds._filters or {}) + if "32004" in filters: + return _compress_kwargs("lz4", None) + if "32001" in filters: + return _compress_kwargs("blosc_lz4", None) + return {} + + def recompress_cli() -> None: """Entry point for the ``pysupera-recompress`` shell command. From a474f9a3b1cdfcca78609be3b67007a88584c215 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Thu, 13 Aug 2026 12:54:25 -0500 Subject: [PATCH 09/62] run: add allow_empty_image guard and opt-in post-run repack Two config-level additions to the event loop. allow_empty_image (default false) halts on the first event whose particles all have empty point clouds. Such an event carries no reconstructable charge, and in practice that means a misconfigured reader rather than real physics -- the case that motivated it was a JAXTPC run that processed 5 events of nothing and reported success. Setting allow_empty_image=true downgrades the same condition to a single warning for the whole run and continues, for setups where empty events are genuinely expected (very low readout thresholds, heavily masked volumes). repack.enabled (default false) rewrites the output after the event loop finishes and every writer has closed, so chunk shapes can be clamped to the final dataset lengths. repack.compression optionally switches the filter in the same pass, so run_pysupera ... repack.enabled=true repack.compression=gzip yields a browser/h5wasm-readable file directly instead of requiring a separate pysupera-recompress step. The voxmap companion file is repacked too when one was written. The benefit is concentrated in short runs, where several datasets end up shorter than a single chunk: on a 5-event file 18 of 47 datasets are re-chunked (8.05 -> 6.86 MiB), while on long production runs almost none qualify. It costs one full read and write of the output, which is why it is opt-in. --- pysupera/_run.py | 55 +++++++++++++++++++++++++++++++++++++++ pysupera/conf/config.yaml | 38 +++++++++++++++++++++++++++ 2 files changed, 93 insertions(+) diff --git a/pysupera/_run.py b/pysupera/_run.py index 6f5fba6..f8d587a 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -80,6 +80,8 @@ def main(cfg: DictConfig) -> None: print(f"[run] output_compact : {bool(cfg.get('output_compact', False))}") print(f"[run] drop_le_scatter : {bool(cfg.get('drop_le_scatter', False))}") print(f"[run] write_event_clouds: {bool(cfg.get('write_event_clouds', True))}") + print(f"[run] allow_empty_image: {bool(cfg.get('allow_empty_image', False))}") + print(f"[run] repack : {bool((cfg.get('repack', {}) or {}).get('enabled', False))}") print(f"[run] input : {cfg.io.input_path}") print(f"[run] output : {cfg.io.output_path}") _jaxtpc_seg = cfg.reader.get("jaxtpc_seg_path", None) @@ -111,6 +113,10 @@ def main(cfg: DictConfig) -> None: _max_events = int(cfg.get("max_events", -1)) _show_progress = bool(cfg.get("progress", True)) + # Empty-image policy: halt by default, warn once when explicitly allowed. + _allow_empty_image = bool(cfg.get("allow_empty_image", False)) + _warned_empty_image = False + with build_reader(cfg) as store: _n_available = len(store) _n_to_process = _n_available if _max_events < 0 else min(_max_events, _n_available) @@ -180,6 +186,39 @@ def main(cfg: DictConfig) -> None: # ── Particle statistics (after preprocessing, before partition) ── _pc_lens = [len(p.point_cloud) for p in particles] _nonzero = [s for s in _pc_lens if s > 0] + + # ── Empty-image guard ─────────────────────────────────────── + # Every particle having an empty point cloud means the event + # holds no charge at all. This is nearly always a config + # error (wrong seg/inst file, wrong step_key) rather than + # physics, so halt unless explicitly allowed. + if not _nonzero: + _msg = ( + f"Event {event_idx}: empty image — all " + f"{len(particles)} particle(s) have an empty point " + f"cloud, so the event contains no points." + ) + if not _allow_empty_image: + raise RuntimeError( + f"{_msg} This usually indicates a configuration " + f"error rather than real physics: check " + f"io.input_path, reader.step_key/ass_key, and (in " + f"JAXTPC mode) that reader.jaxtpc_seg_path and " + f"reader.jaxtpc_inst_path point at the seg/step and " + f"hits/inst files respectively. Set " + f"allow_empty_image=true to downgrade this to a " + f"one-time warning." + ) + if not _warned_empty_image: + import warnings + warnings.warn( + f"{_msg} allow_empty_image=true, so processing " + f"continues; this warning is issued only once.", + UserWarning, + stacklevel=2, + ) + _warned_empty_image = True + stats['n_particles_in'].append(len(particles)) stats['n_nonzero_pc'].append(len(_nonzero)) stats['pc_size_min'].append(min(_nonzero) if _nonzero else 0) @@ -409,6 +448,22 @@ def _voxelize_cloud9(c9, vox_size, orig): _bar.close() + # ── Post-run repack (opt-in) ─────────────────────────────────────────── + # Runs only after every writer above has been closed, so the file is + # complete and every dataset's final length is known. + _repack_cfg = cfg.get("repack", {}) or {} + if bool(_repack_cfg.get("enabled", False)): + from pysupera.io import repack, voxmap_path + _rc_comp = _repack_cfg.get("compression", None) + _rc_opts = _repack_cfg.get("compression_opts", None) + _targets = [cfg.io.output_path] + if _write_voxmap: + _targets.append(voxmap_path(cfg.io.output_path)) + for _tgt in _targets: + repack(str(_tgt), + compression=None if _rc_comp in (None, "") else str(_rc_comp), + compression_opts=None if _rc_opts is None else int(_rc_opts)) + # ── End-of-run summary report ────────────────────────────────────────── if n_events > 0 and cfg.get("report", True): _W = 34 # label column width diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index ffd63d5..e2cadb4 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -40,6 +40,44 @@ report: true # Record every merge decision for post-hoc inspection. enable_diagnostics: false +# --------------------------------------------------------------------------- +# Post-run repack (opt-in) +# --------------------------------------------------------------------------- +# Rewrite the output file once the event loop has finished, sizing every chunk +# from the dataset's final length. +# +# Why this is a separate pass: the writer creates each dataset empty and grows +# it with resize() as events arrive, so when a chunk shape must be chosen the +# final length is not yet known. Chunk shapes come from a byte budget (see +# _CHUNK_TARGET_BYTES in io.py), which bounds the waste to at most one chunk per +# dataset -- but a dataset that ends up shorter than a single chunk still stores +# the remainder as padding. Repacking clamps every chunk to the real length. +# +# The gain is therefore largest for SHORT runs, where several datasets are +# shorter than one chunk, and small for long production runs. It costs one full +# read+write of the output file. +repack: + enabled: false + # Filter for the repacked file. null keeps whatever each dataset already + # uses; set to gzip to produce a browser/h5wasm-readable file in one step. + compression: ~ + # Compression level for the above (gzip: 1-9). + compression_opts: ~ + +# Tolerate "empty images" — events in which every particle ends up with an +# empty point cloud, so the event carries no reconstructable charge at all. +# +# false (default) — raise RuntimeError and halt on the first empty image. +# An all-empty event almost always signals a configuration +# mistake (e.g. the wrong reader.jaxtpc_seg_path / step_key) +# rather than real physics, and silently writing thousands of +# empty events is far worse than stopping early. +# true — allow empty images through, emitting a single warning the +# first time one is encountered (not once per event). Use +# when empty events are genuinely expected, e.g. very low +# readout thresholds or heavily masked detector volumes. +allow_empty_image: false + # Verify that the particle list is self-consistent before partitioning: # every parent_id referenced by a particle must correspond to a particle # in the list. Raises ValueError listing all missing parent IDs when From b93d2231ccf9c6315f7f09846009f73ac634c5eb Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 8 Sep 2026 09:59:10 -0700 Subject: [PATCH 10/62] report how many JAXTPC segments were masked out as undetected In JAXTPC mode the inst file acts as a visibility mask over the energy depositions, but nothing reported how much it removed -- a run could discard most of the event's charge without saying so. The reader now records per-event counts on last_mask_stats: deposits present in the seg file, how many cleared the readout threshold, how many were masked out, and how many were attached to an EDepSim particle. A separate "visible but unmatched" count covers segments the readout saw whose group_to_track ID is absent from the particle list (including the -1 sentinel for out-of-range groups); those reach no point cloud, so silently losing them would be easy. _run.py aggregates the counts into a segment-visibility table in the end-of-run report, with the masked fraction over the whole run, and prints them per event when verbose=true. Non-JAXTPC readers supply no stats and the section is omitted. None of this reads anything extra. n_total comes from the per-volume n_actual attributes already loaded to build the point cloud, n_visible sums the index arrays the mask already produced, and n_attached is the length of the finished cloud. Deriving the total from the EDepSim step array instead would have been the expensive route: pstep/lar_vol is an object dtype dataset, so indexing one event materialises the whole ~11 MB record array for that event. Measured on a 5-event run: 0.129 ms/event, against ~389 ms for the event read and ~2795 ms for full per-event processing -- 0.005% of runtime. Collection is therefore unconditional and only the printing is gated. Sanity check: the reported total for event 0 (171,094) matches the EDepSim pstep count exactly. --- pysupera/_run.py | 45 +++++++++++++++++++++++++++++++ pysupera/readers/format_jaxtpc.py | 25 +++++++++++++++++ 2 files changed, 70 insertions(+) diff --git a/pysupera/_run.py b/pysupera/_run.py index f8d587a..220da67 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -108,6 +108,8 @@ def main(cfg: DictConfig) -> None: 'pc_size_mean': [], # float: mean non-zero PC size in event 'n_partitions': [], # int: final partition count after partition_combined } + # JAXTPC visibility counts; stays empty unless the reader supplies them. + mask_stats: dict = defaultdict(list) n_events = 0 _max_events = int(cfg.get("max_events", -1)) @@ -154,6 +156,23 @@ def main(cfg: DictConfig) -> None: n_events += 1 _ev_t: dict = {} # per-event stage timings (seconds) + # ── JAXTPC visibility counts ──────────────────────────────── + # Already computed by the reader from data it had in hand, so + # collecting them costs nothing; only the printing is gated. + _ms = getattr(store, 'last_mask_stats', None) + if _ms: + for _k, _v in _ms.items(): + mask_stats[_k].append(_v) + if cfg.verbose: + _tot = max(1, _ms['n_total']) + print(f"[run] event {event_idx}: segments " + f"total={_ms['n_total']:,} " + f"visible={_ms['n_visible']:,} " + f"masked={_ms['n_masked']:,} " + f"({100.0 * _ms['n_masked'] / _tot:.1f}%)" + + (f" unmatched={_ms['n_unmatched']:,}" + if _ms['n_unmatched'] else "")) + # skip merge_duplicates when voxelizer subsumes it if merge_processor is not None and not (voxelizer and voxelizer.merge_duplicates): _t = time.perf_counter() @@ -495,6 +514,32 @@ def _agg(vals): lo, mu, hi = _agg(stats['n_partitions']) print(f" {'Partitions (final)':<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") + # ── JAXTPC readout visibility (only when the reader reported it) ─── + if mask_stats.get('n_total'): + print(f"\n[run] JAXTPC segment visibility ({n_events} event(s))") + print(f" {'Metric':<{_W}} {'min':>10} {'mean':>10} {'max':>10}") + print(f" {_SEP}") + for label, key in ( + ("Segments in seg file", 'n_total'), + ("Visible (above threshold)", 'n_visible'), + ("Masked out (undetected)", 'n_masked'), + ("Attached to a particle", 'n_attached'), + ): + lo, mu, hi = _agg(mask_stats[key]) + print(f" {label:<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") + + _tot = sum(mask_stats['n_total']) + _msk = sum(mask_stats['n_masked']) + _unm = sum(mask_stats['n_unmatched']) + print(f" {_SEP}") + print(f" {'Masked fraction (all events)':<{_W}} " + f"{100.0 * _msk / max(1, _tot):>9.2f}%") + if _unm: + # Visible in the readout but carrying a track ID absent from + # the EDepSim particle list, so they reach no point cloud. + print(f" {'Visible but unmatched':<{_W}} " + f"{_unm:>10,} ({100.0 * _unm / max(1, _tot):.2f}%)") + # ── Time profile ────────────────────────────────────────────────── total = sum(profile.values()) print(f"\n[run] Time profile ({n_events} event(s))") diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index d85f0ce..7953212 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -416,6 +416,10 @@ def __init__( self._e_thresh = electron_energy_threshold self._min_pc_size = min_pc_size + # Visibility counts for the most recently read event; populated by + # __getitem__. None until the first event has been read. + self.last_mask_stats: dict | None = None + # Open all three files for the lifetime of the reader. self._edepsim_file = h5py.File(edepsim_path, 'r') self._seg_file = h5py.File(seg_path, 'r') @@ -482,6 +486,27 @@ def __getitem__(self, index: int) -> list[Particle]: seg_volumes, visible_by_track, parts['track_id'] ) + # ── Visibility bookkeeping (free: no extra file reads) ──────────── + # n_total comes from the per-volume 'n_actual' attributes already read + # in step 2; n_visible sums the index arrays step 3 already built; and + # n_attached is just the length of the cloud step 4 just produced. + # Nothing here touches the EDepSim step array. + _n_total = sum(int(sv.get('n_actual', 0)) for sv in seg_volumes) + _n_visible = sum(len(idx) + for vol in visible_by_track + for idx in vol.values()) + _n_attached = len(point_cloud_flat) + self.last_mask_stats = { + 'n_total': _n_total, # deposits in the seg file + 'n_visible': _n_visible, # survived the readout threshold + 'n_masked': _n_total - _n_visible, + # Visible but not attached to any EDepSim particle -- a visible + # segment whose group_to_track ID is absent from the particle list + # (including the -1 sentinel for out-of-range groups). + 'n_attached': _n_attached, + 'n_unmatched': _n_visible - _n_attached, + } + # ── Step 5: derive per-particle scalar labels from EDepSim data ─── itype = _get_interaction_type(parts, self._e_thresh) int_ids = _get_interaction_id(parts, verts) From 1946da446a1deda9b545600032109f5f80fe332d Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 8 Sep 2026 17:20:19 -0700 Subject: [PATCH 11/62] readers: rebuild genealogy from parent links, address particles by index Three related defects, all rooted in trusting fields that turned out not to be trustworthy in real EDepSim output. 1. ancestor_track_id is not the primary ancestor. Some secondaries are written with ancestor_track_id == their own track_id despite having a real parent, and every descendant inherits that false root. In one 13,460-particle event 15 such entries put 4,423 particles (33%) on the wrong root. _get_root_id now derives the primary by walking parent_track_id, memoising along each chain to stay O(n), and is used for root_ids in both readers. 2. Interaction IDs were stranded by the same field. _get_interaction_id seeded from particles it judged to be roots and propagated down the ancestor field, so a false root failed the vertex match and left itself and its subtree at -1: 118 of 13,460 particles in that event, every one of them recoverable from its parent chain. A third pass now walks parent_track_id for anything still unassigned, the root seeds come from _get_root_id rather than the stored field, and a root reference missing from the event list no longer raises IndexError. 3. Geant4 track IDs are not a safe index. They are not guaranteed contiguous, since an upstream stage may drop particles before pysupera sees them, so they cannot serve as direct array indices. _to_index_space assigns pysupera's own per-event particle index -- id == arange(n) -- and remaps parent_id and root_id into that space; a particle whose parent is absent becomes its own parent, the convention every genealogy walk already terminates on. The original track IDs are preserved as Particle.geant4_id, plumbed through from_arrays and from_flat_arrays. Verified on a JAXTPC run: the chain pi+ 28 -> mu+ 3065 -> e+ 3066 now carries root_id 20 and interaction_id 7 throughout, where the positron previously had no interaction ID at all; zero unassigned interaction IDs across three events; and every id/parent_id/root_id is a valid index into its event's particle table. --- pysupera/data.py | 19 ++- pysupera/readers/format_edepsim_h5.py | 176 +++++++++++++++++++++++--- pysupera/readers/format_jaxtpc.py | 12 +- 3 files changed, 184 insertions(+), 23 deletions(-) diff --git a/pysupera/data.py b/pysupera/data.py index cb4a89c..460f24d 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -158,16 +158,21 @@ def __init__( member_ids=None, parent_frag_id=None, parent_inst_id=None, + geant4_id=None, ): """ Parameters ---------- id : int - Unique particle identifier within an event. + Index of this particle within its event, assigned by the reader. + Contiguous and 0-based, so it doubles as a direct row index into + the event's particle table. Geant4 track IDs are *not* guaranteed + contiguous (particles may be dropped upstream), which is why they + are kept separately in ``geant4_id`` rather than used as the key. parent_id : int - ID of the direct parent particle. + Index of the direct parent particle. A primary is its own parent. root_id : int - ID of the root ancestor particle. + Index of the primary ancestor particle. pdg : int PDG Monte Carlo particle code. parent_pdg : int @@ -190,6 +195,10 @@ def __init__( """ # --- required fields ----------------------------------------------- self.id = id + # Geant4 provenance: the original track ID this particle came from. + # Defaults to `id` so a caller that has no separate track ID (tests, + # synthetic particles) still gets a self-consistent object. + self.geant4_id = id if geant4_id is None else geant4_id self.parent_id = parent_id self.root_id = root_id self.pdg = pdg @@ -278,6 +287,7 @@ def from_arrays( interaction_types, point_clouds, min_pc_size=None, + geant4_ids=None, ): """ Construct a list of :class:`Particle` objects from columnar arrays. @@ -366,6 +376,7 @@ def from_arrays( return [ cls( id=ids[i], + geant4_id=(None if geant4_ids is None else geant4_ids[i]), parent_id=parent_ids[i], root_id=root_ids[i], pdg=pdgs[i], @@ -391,6 +402,7 @@ def from_flat_arrays( point_cloud_flat, point_cloud_offsets, min_pc_size=None, + geant4_ids=None, ): """ Construct a list of :class:`Particle` objects from columnar arrays @@ -514,6 +526,7 @@ def from_flat_arrays( return [ cls( id=ids[i], + geant4_id=(None if geant4_ids is None else geant4_ids[i]), parent_id=parent_ids[i], root_id=root_ids[i], pdg=pdgs[i], diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 12cfa27..865b7a5 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -13,9 +13,13 @@ ============= ====================================================== Field Description ============= ====================================================== - track_id Geant4 track ID (used as pysupera *id*) + track_id Geant4 track ID. Kept as pysupera *geant4_id*; it is NOT + used as the pysupera *id*, because track IDs are not + guaranteed contiguous. pysupera assigns its own 0-based + per-event particle index instead (see _to_index_space). parent_track_id Immediate parent track ID - root_track_id Root ancestor track ID + root_track_id Root ancestor track ID. Unreliable in some files -- see + _get_root_id, which re-derives it from the parent chain. pdg PDG Monte Carlo particle code proc_start G4 process type integer at particle start vertex subproc_start G4 process subtype integer at particle start vertex @@ -254,6 +258,109 @@ def _get_parent_pdg(parts: np.ndarray) -> np.ndarray: return np.where(parent_valid, parts["pdg"][parent_zfill], 0) +def _to_index_space(parts: np.ndarray, root_track_ids: np.ndarray): + """ + Translate Geant4 track IDs into contiguous per-event particle indices. + + pysupera addresses particles by their position in the event's particle + list, not by Geant4 track ID: track IDs are not guaranteed contiguous -- + an upstream stage may drop particles before pysupera ever sees them -- so + they cannot be used as direct array indices. The track IDs are preserved + separately as provenance. + + Returns + ------- + ids : ndarray of int32 + ``arange(len(parts))``. + parent_idx : ndarray of int32 + Index of each particle's parent. A particle whose parent is absent + from this list (a primary, or one whose parent was dropped upstream) + becomes its own parent, which is the convention every genealogy walk + in pysupera terminates on. + root_idx : ndarray of int32 + Index of each particle's primary ancestor. + geant4_ids : ndarray of int32 + The original ``track_id`` values, unchanged. + """ + n = len(parts) + track_ids = parts['track_id'] + index_of = {int(t): i for i, t in enumerate(track_ids)} + + ids = np.arange(n, dtype=np.int32) + parent_idx = np.empty(n, dtype=np.int32) + root_idx = np.empty(n, dtype=np.int32) + for i in range(n): + parent_idx[i] = index_of.get(int(parts['parent_track_id'][i]), i) + root_idx[i] = index_of.get(int(root_track_ids[i]), i) + return ids, parent_idx, root_idx, track_ids.astype(np.int32) + + +def _get_root_id(parts: np.ndarray) -> np.ndarray: + """ + Return the true primary ancestor track ID of every particle. + + The stored ancestor field (``root_track_id`` / ``ancestor_track_id``) is not + reliable in every EDepSim file: a secondary is sometimes written with + ``ancestor_track_id == its own track_id`` despite having a real parent. + Every descendant of such a particle then inherits that wrong ancestor, so a + single bad entry strands a whole subtree. In one 13.5k-particle event, 15 + such entries put 4,423 particles (33%) on the wrong root. + + ``parent_track_id`` is trustworthy in those files, so the primary is found + by walking it upwards until reaching a particle that has no parent, is its + own parent, or whose parent is absent from this event's list. Results are + memoised along each chain, keeping the pass O(n). + + Parameters + ---------- + parts : structured ndarray + Must contain fields ``track_id`` and ``parent_track_id``. + + Returns + ------- + ndarray of int32, shape (N,) + Primary ancestor track ID per particle. A particle caught in a + ``parent_track_id`` cycle is reported as its own root, since no primary + is reachable. + """ + n = len(parts) + roots = np.full(n, -1, dtype=np.int32) + if n == 0: + return roots + + track_ids = parts['track_id'] + parent_ids = parts['parent_track_id'] + index_of = {int(t): i for i, t in enumerate(track_ids)} + + for start in range(n): + if roots[start] >= 0: + continue + chain = [] + i = start + seen = set() + answer = -1 + while True: + if i in seen: + # parent_track_id cycle: no primary is reachable, so every + # particle in the loop becomes its own root. + answer = int(track_ids[i]) + break + if roots[i] >= 0: + answer = int(roots[i]) + break + seen.add(i) + chain.append(i) + p = int(parent_ids[i]) + if p < 0 or p == int(track_ids[i]) or p not in index_of: + answer = int(track_ids[i]) # reached a primary + break + i = index_of[p] + for j in chain: + roots[j] = answer + + return roots + + def _get_interaction_id(parts: np.ndarray, verts: np.ndarray) -> np.ndarray: """ Look up the interaction ID of every particle @@ -274,13 +381,18 @@ def _get_interaction_id(parts: np.ndarray, verts: np.ndarray) -> np.ndarray: """ interaction_ids = np.full(len(parts), -1, dtype=np.int32) + if len(parts) == 0: + return interaction_ids - # Resolve the root-ancestor field name (two conventions exist). - _root_field = 'root_track_id' if 'root_track_id' in parts.dtype.names else 'ancestor_track_id' + track_ids = parts['track_id'] + parent_ids = parts['parent_track_id'] + # Use the walked primary rather than the stored ancestor field, which is + # unreliable -- see _get_root_id. + root_refs = _get_root_id(parts) + index_of = {int(t): i for i, t in enumerate(track_ids)} # Step 0: loop over root particles and match to one of vertices by (x,y,z,t) proximity. - root_indices = np.where(parts['track_id'] == parts[_root_field])[0] - root_ids = parts['track_id'][root_indices] + root_indices = np.where(track_ids == root_refs)[0] for index in root_indices: p = parts[index] @@ -293,14 +405,44 @@ def _get_interaction_id(parts: np.ndarray, verts: np.ndarray) -> np.ndarray: interaction_ids[index] = vtx['interaction_id'] break - # Step 1: for non-root particles, assign the same interaction ID as their root particle. + # Step 1: for non-root particles, assign the same interaction ID as their + # root particle. A root reference that is absent from this event list is + # left for step 2 rather than raising. for i in range(len(parts)): - p = parts[i] - if p['track_id'] == p[_root_field]: + if track_ids[i] == root_refs[i]: continue - - root_index = root_indices[np.where(p[_root_field] == root_ids)[0][0]] - interaction_ids[i] = interaction_ids[root_index] + root_index = index_of.get(int(root_refs[i])) + if root_index is not None: + interaction_ids[i] = interaction_ids[root_index] + + # Step 2: fall back to the parent chain for anything still unassigned. + # + # Steps 0-1 trust the root/ancestor field, which is not always the true + # primary: EDepSim files are seen in which a secondary carries + # ancestor_track_id == its own track_id, or points at another secondary that + # never started at a vertex. Either way the particle is treated as a root, + # fails the vertex match because it was born mid-event, and inherits -1 -- + # as do all of its descendants. + # + # parent_track_id is reliable in those files, so walk it upwards until an + # already-assigned ancestor is found. Results are memoised onto every + # particle visited along the way, which keeps the whole pass O(n) even for + # long decay chains. + for i in np.where(interaction_ids < 0)[0]: + chain = [i] + found = -1 + node = index_of.get(int(parent_ids[i])) + seen = {i} + while node is not None and node not in seen: + seen.add(node) + if interaction_ids[node] >= 0: + found = interaction_ids[node] + break + chain.append(node) + node = index_of.get(int(parent_ids[node])) + if found >= 0: + for j in chain: + interaction_ids[j] = found return interaction_ids @@ -523,12 +665,13 @@ def __getitem__(self, index: int) -> list[Particle]: ass["end"][:num_parts], ]) - _root_field = 'root_track_id' if 'root_track_id' in parts.dtype.names else 'ancestor_track_id' + + _ids, _par, _root, _g4 = _to_index_space(parts, _get_root_id(parts)) return Particle.from_flat_arrays( - ids = parts["track_id"], - parent_ids = parts["parent_track_id"], - root_ids = parts[_root_field], + ids = _ids, + parent_ids = _par, + root_ids = _root, pdgs = parts["pdg"], parent_pdgs = _get_parent_pdg(parts), interaction_ids = int_ids, @@ -536,6 +679,7 @@ def __getitem__(self, index: int) -> list[Particle]: point_cloud_flat = _steps_to_plain_array(steps), point_cloud_offsets = offsets, min_pc_size = self._min_pc_size, + geant4_ids = _g4, ) def close(self) -> None: diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index 7953212..871a0ed 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -78,6 +78,8 @@ _get_parent_pdg, _get_interaction_id, _get_interaction_type, + _get_root_id, + _to_index_space, ) from ..data import Particle from ..utils import PointFeature @@ -511,12 +513,13 @@ def __getitem__(self, index: int) -> list[Particle]: itype = _get_interaction_type(parts, self._e_thresh) int_ids = _get_interaction_id(parts, verts) - _root_field = 'root_track_id' if 'root_track_id' in parts.dtype.names else 'ancestor_track_id' + + _ids, _par, _root, _g4 = _to_index_space(parts, _get_root_id(parts)) return Particle.from_flat_arrays( - ids = parts['track_id'], - parent_ids = parts['parent_track_id'], - root_ids = parts[_root_field], + ids = _ids, + parent_ids = _par, + root_ids = _root, pdgs = parts['pdg'], parent_pdgs = _get_parent_pdg(parts), interaction_ids = int_ids, @@ -524,6 +527,7 @@ def __getitem__(self, index: int) -> list[Particle]: point_cloud_flat = point_cloud_flat, point_cloud_offsets = offsets, min_pc_size = self._min_pc_size, + geant4_ids = _g4, ) def close(self) -> None: From 8f99bd41f9e3693af4ff4c6465d29bfb26f5e1f8 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 8 Sep 2026 17:20:19 -0700 Subject: [PATCH 12/62] io: store geant4_id (format 2.2.0), expose repack as pysupera-repack Format 2.2.0 adds a geant4_id column beside id at all three levels. id is now pysupera's per-event particle index, so it can be used as a direct row index; geant4_id carries the Geant4 track ID for provenance and for joining back to the EDepSim input. Adding the name to _SCALAR_FIELDS propagates it to particles, particle_fragments and particle_instances at once. Read paths fall back to id when the column is absent, so 2.1.0 files still load: in data where track IDs happened to be contiguous the two values coincided, and EventStore returns identical particles from either version. repack() also becomes a console script, since fixing up an existing file otherwise meant re-running the event loop or reaching for pysupera-recompress, which cannot resize chunks. Three arguments were added to cover standalone use: dst write to a new file rather than rewriting in place rechunk let h5py pick chunk shapes instead of clamping the source's verify compare every dataset against the source afterwards; for an in-place repack this runs BEFORE the original is replaced Compression is deliberately three-way, because two of the cases are easy to conflate: omitting --compression preserves each dataset's existing filter, --compression gzip switches it, and --compression none stores the data uncompressed. An earlier draft mapped "none" onto the preserve path, silently doing the opposite of what it said. Measured on a 5-event file (8.05 MiB, lz4): pysupera-repack out.h5 -> 6.86 MiB (lz4 kept) pysupera-repack out.h5 vis.h5 -c gzip -> 3.96 MiB pysupera-repack out.h5 vis.h5 -c gzip --rechunk -> 3.19 MiB pysupera-repack out.h5 raw.h5 -c none -> 17.78 MiB The gzip form replaces the pysupera-recompress step for producing a browser-readable file, and unlike recompress it repacks the voxmap companion too, since it walks the file generically instead of replaying events. recompress is left in place as public API. --- pyproject.toml | 1 + pysupera/io.py | 224 ++++++++++++++++++++++++++++++++++++++++++------- 2 files changed, 196 insertions(+), 29 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index bd92500..2f4e9c5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,7 @@ app = [ run_pysupera = "pysupera._run:main" pysupera-app = "pysupera.app:main" pysupera-recompress = "pysupera.io:recompress_cli" +pysupera-repack = "pysupera.io:repack_cli" [tool.setuptools.packages.find] where = ["."] diff --git a/pysupera/io.py b/pysupera/io.py index 358503e..0b109ca 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -64,7 +64,7 @@ except ImportError: pass -FORMAT_VERSION = "2.1.0" +FORMAT_VERSION = "2.2.0" _PC_NDIM = 5 # number of columns in the flat point array (x,y,z,t,e) _CLOUD_NDIM = 9 # columns in event-cloud datasets (x,y,z,t,e,interaction_id,root_id,frag_id,inst_id) @@ -321,7 +321,7 @@ class EventWriter: # Names of the int32 particle scalar fields (sem_type handled separately # because it uses dtype=int8) _SCALAR_FIELDS = ( - "id", "parent_id", "root_id", "pdg", "parent_pdg", + "id", "geant4_id", "parent_id", "root_id", "pdg", "parent_pdg", "interaction_id", "interaction_type" ) @@ -428,6 +428,7 @@ def append_event(self, particles: List) -> None: # Build scalar arrays for this event p_id = np.empty(n_p, dtype=np.int32) + p_geant4_id = np.empty(n_p, dtype=np.int32) p_parent_id = np.empty(n_p, dtype=np.int32) p_root_id = np.empty(n_p, dtype=np.int32) p_pdg = np.empty(n_p, dtype=np.int32) @@ -439,6 +440,7 @@ def append_event(self, particles: List) -> None: for k, p in enumerate(particles): p_id[k] = p.id + p_geant4_id[k] = getattr(p, "geant4_id", p.id) p_parent_id[k] = p.parent_id p_root_id[k] = p.root_id p_pdg[k] = p.pdg @@ -449,6 +451,7 @@ def append_event(self, particles: List) -> None: pc_lengths[k] = len(p.point_cloud) self._f["particles/id" ][new_p_start:new_p_end] = p_id + self._f["particles/geant4_id" ][new_p_start:new_p_end] = p_geant4_id self._f["particles/parent_id" ][new_p_start:new_p_end] = p_parent_id self._f["particles/root_id" ][new_p_start:new_p_end] = p_root_id self._f["particles/pdg" ][new_p_start:new_p_end] = p_pdg @@ -805,6 +808,7 @@ def _append_rep_level( # Build scalar arrays r_id = np.empty(n_r, dtype=np.int32) + r_geant4_id = np.empty(n_r, dtype=np.int32) r_parent_id = np.empty(n_r, dtype=np.int32) r_root_id = np.empty(n_r, dtype=np.int32) r_pdg = np.empty(n_r, dtype=np.int32) @@ -819,6 +823,7 @@ def _append_rep_level( for k, r in enumerate(reps): r_id[k] = r.id + r_geant4_id[k] = getattr(r, "geant4_id", r.id) r_parent_id[k] = r.parent_id r_root_id[k] = r.root_id r_pdg[k] = r.pdg @@ -835,6 +840,7 @@ def _append_rep_level( if r.member_ids is not None else 1) self._f[f"{group_prefix}/id" ][new_start:new_end] = r_id + self._f[f"{group_prefix}/geant4_id" ][new_start:new_end] = r_geant4_id self._f[f"{group_prefix}/parent_id" ][new_start:new_end] = r_parent_id self._f[f"{group_prefix}/root_id" ][new_start:new_end] = r_root_id self._f[f"{group_prefix}/pdg" ][new_start:new_end] = r_pdg @@ -1021,6 +1027,10 @@ def __getitem__(self, index: int) -> List: # ---- read scalar metadata in 7 contiguous array slices ------------ ids = self._f["particles/id" ][p_start:p_end] + # geant4_id arrived in format 2.2.0; older files fall back to `id`, + # where the two coincided because track IDs were used as the key. + geant4_ids = (self._f["particles/geant4_id" ][p_start:p_end] + if "particles/geant4_id" in self._f else ids) parent_ids = self._f["particles/parent_id" ][p_start:p_end] root_ids = self._f["particles/root_id" ][p_start:p_end] pdgs = self._f["particles/pdg" ][p_start:p_end] @@ -1043,6 +1053,7 @@ def __getitem__(self, index: int) -> List: cloud = flat_chunk[local_offsets[k] : local_offsets[k + 1]] p = Particle( id = int(ids[k]), + geant4_id = int(geant4_ids[k]), parent_id = int(parent_ids[k]), root_id = int(root_ids[k]), pdg = int(pdgs[k]), @@ -1129,6 +1140,10 @@ def read_bulk(self, start: int = 0, stop: int = -1, # 8 scalar reads + 1 pc_offsets slice + 1 flat-points read = 10 HDF5 ops ids = self._f["particles/id" ][p_start:p_end] + # geant4_id arrived in format 2.2.0; older files fall back to `id`, + # where the two coincided because track IDs were used as the key. + geant4_ids = (self._f["particles/geant4_id" ][p_start:p_end] + if "particles/geant4_id" in self._f else ids) parent_ids = self._f["particles/parent_id" ][p_start:p_end] root_ids = self._f["particles/root_id" ][p_start:p_end] pdgs = self._f["particles/pdg" ][p_start:p_end] @@ -1152,6 +1167,7 @@ def _build_event(ev): cloud = flat_all[local_pc[k] : local_pc[k + 1]] p = Particle( id = int(ids[k]), + geant4_id = int(geant4_ids[k]), parent_id = int(parent_ids[k]), root_id = int(root_ids[k]), pdg = int(pdgs[k]), @@ -1433,6 +1449,8 @@ def _read_rep_level(f, group: str, r_start: int, r_end: int) -> list: return [] ids = f[f"{group}/id" ][r_start:r_end] + g4ids = (f[f"{group}/geant4_id" ][r_start:r_end] + if f"{group}/geant4_id" in f else ids) pids = f[f"{group}/parent_id" ][r_start:r_end] rids = f[f"{group}/root_id" ][r_start:r_end] pdgs = f[f"{group}/pdg" ][r_start:r_end] @@ -1463,6 +1481,7 @@ def _read_rep_level(f, group: str, r_start: int, r_end: int) -> list: mids = list(mem_flat[ms:me].astype(int)) if mem_flat is not None and me > ms else None p = Particle( id = int(ids[k]), + geant4_id = int(g4ids[k]), parent_id = int(pids[k]), root_id = int(rids[k]), pdg = int(pdgs[k]), @@ -1484,10 +1503,13 @@ def _read_rep_level(f, group: str, r_start: int, r_end: int) -> list: def repack(path: str, compression: Optional[str] = None, compression_opts: Optional[int] = None, + dst: Optional[str] = None, + rechunk: bool = False, + verify: bool = False, verbose: bool = True) -> dict: """ - Rewrite *path* in place with chunk shapes sized from the final dataset - lengths. + Rewrite *path* with chunk shapes sized from the final dataset lengths, + optionally changing the compression filter at the same time. The streaming writer creates every dataset empty and grows it with ``resize()``, so at creation time it cannot know how long a dataset will @@ -1510,13 +1532,27 @@ def repack(path: str, Parameters ---------- path : str - File to repack. Replaced atomically on success via :func:`os.replace`; - the original is left untouched if anything raises. + Source file. compression : str or None, optional Filter for the output. ``None`` (default) keeps whatever filter each - source dataset already uses. Pass e.g. ``"gzip"`` to switch. + source dataset already uses. Pass e.g. ``"gzip"`` to switch -- which is + how to produce a file the browser viewer can read, since h5wasm handles + gzip only. compression_opts : int or None, optional - Compression level for *compression*. + Compression level for *compression* (gzip: 1-9). + dst : str or None, optional + Output path. ``None`` (default) rewrites *path* in place, via a + temporary file swapped in with :func:`os.replace`, so the original + survives untouched if anything raises. + rechunk : bool, optional + Let h5py choose chunk shapes instead of clamping the source's. On + pysupera output this tends to win on both size and row-range read + speed, because h5py picks smaller column-wise chunks, but it does + change the layout rather than only the filter. + verify : bool, optional + After writing, compare every dataset against the source and raise + ``ValueError`` on any mismatch. For an in-place repack the check runs + *before* the original is replaced. verbose : bool, optional Print a one-line before/after summary. @@ -1524,16 +1560,29 @@ def repack(path: str, ------- dict ``{"size_before", "size_after", "n_datasets", "n_reshaped"}`` + + Raises + ------ + ValueError + If *verify* is set and any dataset differs from the source. """ import h5py import numpy as _np + if dst is not None and os.path.abspath(dst) == os.path.abspath(path): + raise ValueError( + f"dst must differ from path; pass dst=None to repack in place " + f"({path!r})" + ) + size_before = os.path.getsize(path) - tmp = f"{path}.repack-tmp" + in_place = dst is None + # In place: build beside the original and swap only once it is complete. + out = f"{path}.repack-tmp" if in_place else dst n_datasets = n_reshaped = 0 try: - with h5py.File(path, "r") as fin, h5py.File(tmp, "w") as fout: + with h5py.File(path, "r") as fin, h5py.File(out, "w") as fout: for key, val in fin.attrs.items(): fout.attrs[key] = val @@ -1551,14 +1600,18 @@ def visit(name, obj): chunks = None if src_chunks is not None: - # Clamp each dimension to the real shape; HDF5 rejects a - # chunk larger than a fixed-size dataset. - chunks = tuple(max(1, min(c, s)) - for c, s in zip(src_chunks, obj.shape)) if any(d == 0 for d in obj.shape): chunks = None # cannot chunk an empty dataset - elif chunks != src_chunks: + elif rechunk: + chunks = True # let h5py choose n_reshaped += 1 + else: + # Clamp each dimension to the real shape; HDF5 rejects + # a chunk larger than a fixed-size dataset. + chunks = tuple(max(1, min(c, s)) + for c, s in zip(src_chunks, obj.shape)) + if chunks != src_chunks: + n_reshaped += 1 if chunks is not None: if compression is None: @@ -1571,48 +1624,105 @@ def visit(name, obj): "szip"): kwargs = {"compression": obj.compression, "compression_opts": obj.compression_opts} + elif str(compression).lower() in ("none", "~", ""): + kwargs = {} # explicitly store uncompressed else: kwargs = _compress_kwargs(compression, compression_opts) - dst = fout.create_dataset(name, shape=obj.shape, - dtype=obj.dtype, chunks=chunks, - **{k: v for k, v in kwargs.items() - if v is not None}) + dset = fout.create_dataset(name, shape=obj.shape, + dtype=obj.dtype, chunks=chunks, + **{k: v for k, v in kwargs.items() + if v is not None}) for key, val in obj.attrs.items(): - dst.attrs[key] = val + dset.attrs[key] = val if obj.ndim == 0: - dst[()] = obj[()] + dset[()] = obj[()] return n = obj.shape[0] if n == 0: return - step = chunks[0] if chunks else min(n, 1 << 16) + # Copy in chunk-aligned blocks so memory stays bounded. + step = dset.chunks[0] if dset.chunks else min(n, 1 << 16) for start in range(0, n, step): stop = min(start + step, n) - dst[start:stop] = obj[start:stop] + dset[start:stop] = obj[start:stop] fin.visititems(visit) - os.replace(tmp, path) + if verify: + _verify_same_datasets(path, out) + + if in_place: + os.replace(out, path) except BaseException: - if os.path.exists(tmp): - os.remove(tmp) + # Only ever clean up our own temporary; never an explicit dst. + if in_place and os.path.exists(out): + os.remove(out) raise - size_after = os.path.getsize(path) + size_after = os.path.getsize(path if in_place else out) if verbose: # Signed relative to the original: negative means the file shrank. pct = (100.0 * (size_after - size_before) / size_before) \ if size_before else 0.0 - print(f"[repack] {path}: {size_before / 2**20:.2f} MiB -> " + _where = path if in_place else f"{path} -> {out}" + print(f"[repack] {_where}: {size_before / 2**20:.2f} MiB -> " f"{size_after / 2**20:.2f} MiB ({pct:+.1f}%), " - f"{n_reshaped}/{n_datasets} dataset(s) re-chunked") + f"{n_reshaped}/{n_datasets} dataset(s) re-chunked" + + (" [verified]" if verify else "")) return {"size_before": size_before, "size_after": size_after, "n_datasets": n_datasets, "n_reshaped": n_reshaped} +def _verify_same_datasets(src: str, dst: str) -> None: + """ + Raise ValueError unless *dst* holds the same datasets, byte for byte, as + *src*. Compression and chunking are irrelevant here -- only values. + """ + import h5py + import numpy as _np + + problems: list = [] + + with h5py.File(src, "r") as fa, h5py.File(dst, "r") as fb: + def check(name, obj): + if not isinstance(obj, h5py.Dataset): + return + if name not in fb: + problems.append(f"{name}: missing in output") + return + other = fb[name] + if obj.shape != other.shape or obj.dtype != other.dtype: + problems.append( + f"{name}: shape/dtype {obj.shape}/{obj.dtype} != " + f"{other.shape}/{other.dtype}" + ) + return + if obj.ndim == 0: + same = _np.array_equal(_np.asarray(obj[()]), + _np.asarray(other[()])) + elif obj.shape[0] == 0: + same = True + else: + try: + same = _np.array_equal(obj[...], other[...], equal_nan=True) + except TypeError: + # equal_nan is rejected for non-float dtypes. + same = _np.array_equal(obj[...], other[...]) + if not same: + problems.append(f"{name}: data differs") + + fa.visititems(check) + + if problems: + raise ValueError( + f"repack verification failed for {dst!r}: " + f"{len(problems)} problem(s); first few: {problems[:5]}" + ) + + def _compression_kwargs_from_filters(ds) -> dict: """Reproduce a dataset's third-party filter (LZ4 / Blosc) as kwargs.""" filters = dict(ds._filters or {}) @@ -1623,6 +1733,62 @@ def _compression_kwargs_from_filters(ds) -> dict: return {} +def repack_cli() -> None: + """Entry point for the ``pysupera-repack`` shell command. + + Usage:: + + # rewrite in place, clamping chunks, keeping each dataset's filter + pysupera-repack out.h5 + + # write a gzip copy the browser viewer can read + pysupera-repack out.h5 out_vis.h5 --compression gzip + + # smallest and fastest for row-range reads, at the cost of relayout + pysupera-repack out.h5 out_vis.h5 --compression gzip --rechunk + + Unlike ``pysupera-recompress``, this walks the file generically instead of + reconstructing events, so it can resize chunks and handles any layout -- + including the voxmap companion file. Prefer it unless you specifically + need the event-by-event rewrite. + """ + import argparse + parser = argparse.ArgumentParser( + prog="pysupera-repack", + description="Resize HDF5 chunks (and optionally change the " + "compression filter) in a pysupera output file.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument("src", help="Source HDF5 file") + parser.add_argument("dst", nargs="?", default=None, + help="Output file; omit to rewrite src in place") + parser.add_argument("-c", "--compression", default=None, + help="Filter for the output: gzip, lzf, lz4, " + "blosc_lz4, none. Omit to keep each dataset's " + "existing filter") + parser.add_argument("-l", "--level", type=int, default=None, + help="Compression level (gzip: 1-9)") + parser.add_argument("--rechunk", action="store_true", + help="Let h5py choose chunk shapes rather than " + "clamping the source's; usually smaller and " + "faster for row-range reads, but relayouts") + parser.add_argument("--no-verify", dest="verify", action="store_false", + help="Skip the post-write dataset equality check") + args = parser.parse_args() + + if args.level is not None and not 1 <= args.level <= 9: + parser.error("--level must be between 1 and 9") + + # Note the distinction: omitting --compression preserves each dataset's + # existing filter, whereas "--compression none" stores them uncompressed. + repack(args.src, + compression=args.compression, + compression_opts=args.level, + dst=args.dst, + rechunk=args.rechunk, + verify=args.verify) + + def recompress_cli() -> None: """Entry point for the ``pysupera-recompress`` shell command. From 9420c66aaff4364e02bd8c102796456abe5d6a3a Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 8 Sep 2026 17:20:19 -0700 Subject: [PATCH 13/62] run: validate genealogy, and choose which levels get written Four event-loop changes, all about what ends up in the output and whether it can be trusted. Validation (utils.py, on by default) validate_interaction_ids every particle must carry an interaction ID. The diagnostic prints each offender's parent chain with PDG and interaction ID at every link, which is normally enough to see where propagation stopped. validate_root_ids root_id must equal the primary the parent chain leads to, and must name a particle present in the event. Both run after resolve_orphans, since that legitimately re-roots particles whose references dangle, and both are gated by check_interaction_id / check_root_id -- true raises, false warns once per run. Producer links no longer dead-end _find_parent_idx returned the first ancestor found in the member lookup. When a representative's parent had been merged into the representative's own group -- routine for an EM shower whose initiator is a late photon -- that lookup answers with the group itself, so parent_inst_id pointed at its own row and the production history stopped there. It now skips its own group and keeps walking. On a sample event this repaired 11 of 535 instances (and the same 11 fragments); self-parent rows fall from 48 to 37, exactly the event's primary count, with zero non-primary dead ends and every chain terminating on a genuine primary. instance_output (default 'traceable') Writes instances with a non-empty point cloud, plus the zero-point ancestors needed to complete their genealogy -- a pi0 is the only link between its two visible photons -- plus the primaries of every interaction that has at least one visible instance. An interaction with no visible instance is dropped whole: it left no trace, so nothing represents it. Retains 207 of 535 instances on the sample event, and loses no point data, because an instance cloud is the exact union of its members' clouds and so a zero-point instance has only zero-point members. 'all' restores the previous behaviour. write_fragments (default false) The fragment level is an intermediate product whose point payload duplicates what the instance level already stores; skipping it saves ~12% of the file. An empty list is still appended per event so the fragment event offsets stay consistent with zero rows, the same way particles/ behaves under output_compact. Instance parent_frag_id is set to -1 rather than pointing into a table that was not written. --- pysupera/_run.py | 84 ++++++++++-- pysupera/conf/config.yaml | 55 ++++++++ pysupera/utils.py | 275 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 405 insertions(+), 9 deletions(-) diff --git a/pysupera/_run.py b/pysupera/_run.py index 220da67..fdf3753 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -59,7 +59,9 @@ def main(cfg: DictConfig) -> None: from pysupera.merge import merge_em_showers from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, build_reader, configure from pysupera.preproc import _voxelize_point_cloud - from pysupera.utils import resolve_orphans + from pysupera.utils import (resolve_orphans, validate_interaction_ids, + validate_root_ids, + select_traceable_instances) configure(cfg) # set module-level defaults (e.g. min_pc_size) before any Particle is created @@ -79,8 +81,12 @@ def main(cfg: DictConfig) -> None: print(f"[run] preprocessor : {cfg.particle.get('preprocessor', {}).get('name', 'scipy') if cfg.particle.get('defragment', False) else 'disabled'}") print(f"[run] output_compact : {bool(cfg.get('output_compact', False))}") print(f"[run] drop_le_scatter : {bool(cfg.get('drop_le_scatter', False))}") + print(f"[run] write_fragments : {bool(cfg.get('write_fragments', False))}") print(f"[run] write_event_clouds: {bool(cfg.get('write_event_clouds', True))}") print(f"[run] allow_empty_image: {bool(cfg.get('allow_empty_image', False))}") + print(f"[run] check_interaction_id: {bool(cfg.get('check_interaction_id', True))}") + print(f"[run] check_root_id : {bool(cfg.get('check_root_id', True))}") + print(f"[run] instance_output : {str(cfg.get('instance_output', 'traceable')).lower()}") print(f"[run] repack : {bool((cfg.get('repack', {}) or {}).get('enabled', False))}") print(f"[run] input : {cfg.io.input_path}") print(f"[run] output : {cfg.io.output_path}") @@ -119,6 +125,19 @@ def main(cfg: DictConfig) -> None: _allow_empty_image = bool(cfg.get("allow_empty_image", False)) _warned_empty_image = False + # Interaction-ID validation: raise by default, warn once when relaxed. + _check_int_id = bool(cfg.get("check_interaction_id", True)) + _warned_int_id = False + _check_root_id = bool(cfg.get("check_root_id", True)) + _warned_root_id = False + + _instance_output = str(cfg.get("instance_output", "traceable")).lower() + if _instance_output not in ("all", "traceable"): + raise ValueError( + f"instance_output must be 'all' or 'traceable', " + f"got {_instance_output!r}" + ) + with build_reader(cfg) as store: _n_available = len(store) _n_to_process = _n_available if _max_events < 0 else min(_max_events, _n_available) @@ -127,6 +146,7 @@ def main(cfg: DictConfig) -> None: _output_compact = bool(cfg.get('output_compact', False)) _drop_le_scatter = bool(cfg.get('drop_le_scatter', False)) _write_event_clouds = bool(cfg.get('write_event_clouds', True)) + _write_frags = bool(cfg.get('write_fragments', False)) # _is_le is always defined: used independently by drop_le_scatter output # filtering AND by event-cloud LE/non-LE splitting (write_event_clouds). from pysupera.utils import SemanticType as _ST @@ -202,6 +222,26 @@ def main(cfg: DictConfig) -> None: # consistent for all conditions and merge_em_showers. resolve_orphans(particles, verbose=cfg.verbose) + # ── Interaction-ID completeness ───────────────────────────── + # Runs after resolve_orphans so the parent chains reported in + # the diagnostic are the repaired ones. + if _check_int_id: + validate_interaction_ids(particles, + raise_on_missing=True, + verbose=cfg.verbose) + elif not _warned_int_id: + _miss = validate_interaction_ids(particles, + raise_on_missing=False) + if _miss: + _warned_int_id = True + + if _check_root_id: + validate_root_ids(particles, raise_on_missing=True, + verbose=cfg.verbose) + elif not _warned_root_id: + if validate_root_ids(particles, raise_on_missing=False): + _warned_root_id = True + # ── Particle statistics (after preprocessing, before partition) ── _pc_lens = [len(p.point_cloud) for p in particles] _nonzero = [s for s in _pc_lens if s > 0] @@ -308,14 +348,27 @@ def main(cfg: DictConfig) -> None: # particle_id lookup for parent-chain walks _part_by_id: dict = {int(_p.id): _p for _p in particles} - def _find_parent_idx(_start_pid, _lookup, _pby): - """Walk parent_id chain; return first valid hit index, or -1.""" + def _find_parent_idx(_start_pid, _lookup, _pby, _exclude=None): + """Walk the parent_id chain for the producer of a rep. + + Returns the index of the nearest ancestor that belongs to a + *different* group than *_exclude*, or -1 if none exists. + + Skipping _exclude matters: when a rep's parent was merged + into the rep's own group -- routine for an EM shower whose + initiator is a late photon -- the lookup resolves to the + rep itself, and without the skip the walk terminates there, + reporting the rep as its own producer and dead-ending the + production history. Continuing past its own group finds + the group the shower actually came from. + """ _visited: set = set() _pid = _start_pid while _pid is not None and _pid not in _visited: _visited.add(_pid) - if _pid in _lookup: - return _lookup[_pid] + _hit = _lookup.get(int(_pid)) + if _hit is not None and _hit != _exclude: + return _hit _pp = _pby.get(int(_pid)) if _pp is None or _pp.parent_id == _pp.id: break @@ -323,11 +376,14 @@ def _find_parent_idx(_start_pid, _lookup, _pby): return -1 for _fi, _fr in enumerate(_frags_out): - _pfid = _find_parent_idx(_fr.parent_id, _frag_lookup, _part_by_id) + _pfid = _find_parent_idx(_fr.parent_id, _frag_lookup, + _part_by_id, _exclude=_fi) _fr.parent_frag_id = _pfid if _pfid >= 0 else _fi # fallback = own _t = time.perf_counter() - writer.append_fragments(_frags_out) + # An empty list still advances the fragment event offsets, so + # the file stays internally consistent with zero rows written. + writer.append_fragments(_frags_out if _write_frags else []) _ev_t['write_frags'] = time.perf_counter() - _t profile['write_fragments'] += _ev_t['write_frags'] @@ -340,6 +396,13 @@ def _find_parent_idx(_start_pid, _lookup, _pby): # Output filtering only — does not affect algorithm logic. _insts_out = [i for i in instances if not _is_le(i)] if _drop_le_scatter else instances + # Drop instances that nothing visible depends on. Applied + # before _inst_lookup is built so parent_inst_id is stamped + # against the rows that actually get written. + if _instance_output == 'traceable': + _insts_out = select_traceable_instances( + _insts_out, particles, verbose=cfg.verbose) + # Build inst_lookup and stamp parent_inst_id (and parent_frag_id # for the instance rep's own fragment) before writing. _inst_lookup: dict = {} # particle_id → instance index (in event) @@ -348,9 +411,12 @@ def _find_parent_idx(_start_pid, _lookup, _pby): _inst_lookup[int(_pid)] = _ii for _ii, _inst in enumerate(_insts_out): - _piid = _find_parent_idx(_inst.parent_id, _inst_lookup, _part_by_id) + _piid = _find_parent_idx(_inst.parent_id, _inst_lookup, + _part_by_id, _exclude=_ii) _inst.parent_inst_id = _piid if _piid >= 0 else _ii # fallback = own - _inst.parent_frag_id = _frag_lookup.get(int(_inst.id), -1) + _inst.parent_frag_id = ( + _frag_lookup.get(int(_inst.id), -1) if _write_frags else -1 + ) _t = time.perf_counter() writer.append_instances(_insts_out) diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index e2cadb4..ead439e 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -40,6 +40,27 @@ report: true # Record every merge decision for post-hoc inspection. enable_diagnostics: false +# Verify that every particle carries an interaction ID before partitioning. +# +# Interaction IDs are seeded from particles whose start point coincides with a +# vertex and then propagated down the genealogy, so one broken ancestor link +# strands a particle and all of its descendants at -1. That loss is silent, +# which is why it is checked rather than assumed. +# +# true (default) — raise ValueError listing the offenders and their parent +# chains, so the assignment can be traced. +# false — warn once per event and continue. +check_interaction_id: true + +# Verify that every particle's root_id is the primary its parent chain leads +# to. Guards the same class of upstream defect as check_interaction_id: an +# EDepSim ancestor field that disagrees with parent_track_id puts whole +# subtrees on a false root. +# +# true (default) — raise ValueError listing the inconsistent particles. +# false — warn once per event and continue. +check_root_id: true + # --------------------------------------------------------------------------- # Post-run repack (opt-in) # --------------------------------------------------------------------------- @@ -101,6 +122,40 @@ output_compact: true # Useful when low-energy scatter products are not needed downstream. drop_le_scatter: false +# Which particle instances to write. +# +# all — every instance the step-2 merger produced, including the many +# that deposited nothing (63% of them in a typical event). +# traceable — instances with a non-empty point cloud, plus the zero-point +# ancestors needed to complete their genealogy (a pi0 linking two +# visible photons, say), plus the primaries of every interaction +# that has at least one visible instance. An interaction with no +# visible instance is dropped entirely: it left no trace, so +# nothing in the detector represents it. +# +# 'traceable' keeps every row reachable from a visible one, so walking +# parent_inst_id still terminates at a primary. On a sample event it retains +# 207 of 535 instances. +instance_output: traceable + +# Write the step-1 fragment level (particle_fragments, its point clouds and +# member lists). +# +# Fragments are an intermediate product: step-2 merging groups them into +# instances, and most analyses work at the instance level. Their point payload +# is a second copy of the same points the instance level already stores, so +# skipping them is the single largest space saving available -- roughly 20% of +# a typical output file. +# +# false (default) — particle_fragments and friends are created but left empty, +# exactly as particles/ behaves under output_compact. The +# fragment stage still runs; only the output is skipped. +# true — write the fragment level too. +# +# When false, instance parent_frag_id is set to -1 rather than pointing into a +# table that was not written. +write_fragments: false + # Write event-level union point clouds (event_cloud and le_scatter_cloud). # event_cloud : union of all particles' point clouds for each event, # voxelized with the same voxel_size/origin as particle diff --git a/pysupera/utils.py b/pysupera/utils.py index 6e27fe2..07ec20b 100644 --- a/pysupera/utils.py +++ b/pysupera/utils.py @@ -411,3 +411,278 @@ def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_ else: raise Exception("Unexpected interaction type ("+str(interaction_type)+") encountered") + + +def validate_interaction_ids(particles, + raise_on_missing: bool = True, + max_report: int = 10, + verbose: bool = False) -> list: + """ + Check that every particle in *particles* carries an interaction ID. + + A particle with ``_interaction_id < 0`` was never associated with any + vertex. That is almost always a genealogy problem rather than physics: + :func:`~pysupera.readers.format_edepsim_h5._get_interaction_id` seeds + interaction IDs from particles whose start position coincides with a + vertex and then propagates them down the tree, so a broken or + self-referential ancestor link strands a particle *and every descendant of + it* at -1. Because the strand is silent, the loss is easy to miss until + something downstream indexes by interaction. + + For each offender the diagnostic walks ``parent_id`` upwards and reports + the chain with each ancestor's PDG and interaction ID, which is normally + enough to see where the assignment stopped. + + Parameters + ---------- + particles : list of Particle + Particle collection for one event. Not modified. + raise_on_missing : bool, optional + ``True`` (default) raises :class:`ValueError` when any particle lacks + an interaction ID. ``False`` issues a :class:`UserWarning` instead and + lets the caller decide. + max_report : int, optional + Number of offending particles described in detail. Default ``10``. + verbose : bool, optional + Print a one-line confirmation when every particle is assigned. + + Returns + ------- + list of Particle + The offending particles, empty when all are assigned. + + Raises + ------ + ValueError + If *raise_on_missing* and at least one particle lacks an ID. + """ + missing = [p for p in particles if int(getattr(p, '_interaction_id', -1)) < 0] + + if not missing: + if verbose: + print(f"[validate] all {len(particles)} particle(s) carry an " + f"interaction ID") + return [] + + by_id = {int(p.id): p for p in particles} + + def _chain(p, limit: int = 12) -> str: + """Render the parent chain of *p* as id(pdg)=interaction_id links.""" + out, seen, cur = [], set(), p + while cur is not None and int(cur.id) not in seen and len(out) < limit: + seen.add(int(cur.id)) + out.append(f"{int(cur.id)}(pdg={int(cur.pdg)})" + f"=int{int(getattr(cur, '_interaction_id', -1))}") + nxt = int(cur.parent_id) + cur = None if nxt == int(cur.id) else by_id.get(nxt) + return " <- ".join(out) + (" <- ..." if len(out) >= limit else "") + + lines = [_chain(p) for p in missing[:max_report]] + msg = ( + f"{len(missing)} of {len(particles)} particle(s) have no interaction ID " + f"(_interaction_id < 0). Every particle must be associated with an " + f"interaction. Parent chains of the first {len(lines)}:\n " + + "\n ".join(lines) + ) + if len(missing) > len(lines): + msg += f"\n ... and {len(missing) - len(lines)} more" + + if raise_on_missing: + raise ValueError(msg) + + import warnings + warnings.warn(msg, UserWarning, stacklevel=2) + return missing + + +def validate_root_ids(particles, + raise_on_missing: bool = True, + max_report: int = 10, + verbose: bool = False) -> list: + """ + Check that every particle's ``root_id`` is the primary it descends from. + + The invariant checked is that walking ``parent_id`` upwards from a particle + terminates at exactly ``root_id``. It is worth checking because the + ancestor field in EDepSim files is not always consistent with the parent + links: a secondary written with ``ancestor_track_id == its own track_id`` + becomes a false root, and every descendant inherits it, so a handful of bad + entries can put a third of an event on the wrong root. See + :func:`~pysupera.readers.format_edepsim_h5._get_root_id`, which derives + ``root_id`` from the parent chain for this reason. + + Two failure modes are reported: a ``root_id`` naming a particle absent from + the event, and a ``root_id`` that disagrees with the primary the parent + chain actually leads to. + + Run this **after** :func:`resolve_orphans`, which legitimately re-roots + particles whose references dangle; before that, its repairs look like + violations. + + Parameters + ---------- + particles : list of Particle + Particle collection for one event. Not modified. + raise_on_missing : bool, optional + ``True`` (default) raises :class:`ValueError`; ``False`` warns instead. + max_report : int, optional + Number of offenders described in detail. Default ``10``. + verbose : bool, optional + Print a one-line confirmation when every root is consistent. + + Returns + ------- + list of Particle + The offending particles, empty when all roots are consistent. + + Raises + ------ + ValueError + If *raise_on_missing* and at least one root is inconsistent. + """ + by_id = {int(p.id): p for p in particles} + + def _walk_primary(p): + """Primary reached from *p* via parent_id; own id on a cycle.""" + seen = set() + cur = p + while True: + cid = int(cur.id) + if cid in seen: + return cid # cycle: no primary reachable + seen.add(cid) + pid = int(cur.parent_id) + if pid == cid or pid not in by_id: + return cid + cur = by_id[pid] + + offenders, reasons = [], [] + for p in particles: + rid = int(p.root_id) + if rid not in by_id: + offenders.append(p) + reasons.append(f"{int(p.id)}(pdg={int(p.pdg)}): root_id={rid} " + f"is not a particle in this event") + continue + primary = _walk_primary(p) + if primary != rid: + offenders.append(p) + reasons.append( + f"{int(p.id)}(pdg={int(p.pdg)}): root_id={rid} but the " + f"parent chain leads to {primary}" + f"(pdg={int(by_id[primary].pdg)})" + ) + + if not offenders: + if verbose: + print(f"[validate] all {len(particles)} particle(s) have a " + f"consistent root_id") + return [] + + shown = reasons[:max_report] + msg = (f"{len(offenders)} of {len(particles)} particle(s) have an " + f"inconsistent root_id. First {len(shown)}:\n " + + "\n ".join(shown)) + if len(reasons) > len(shown): + msg += f"\n ... and {len(reasons) - len(shown)} more" + + if raise_on_missing: + raise ValueError(msg) + + import warnings + warnings.warn(msg, UserWarning, stacklevel=2) + return offenders + + +def select_traceable_instances(instances, particles, verbose: bool = False): + """ + Reduce an instance list to those needed to describe the visible event. + + An event's instance list contains many representatives that deposited + nothing: neutral particles, absorbed shower members, recoil nuclei. Most + are of no interest, but a few are indispensable -- a pi0 deposits no energy + yet is the only link between its two visible photons, so dropping it breaks + the production history of particles that *are* visible. + + Three rules decide what stays: + + 1. Every instance with a non-empty point cloud (*visible*). + 2. Every ancestor of a visible instance, so each kept row's parent is also + kept and the genealogy is closed. These are the zero-point links. + 3. The primaries of every interaction that has at least one visible + instance, even without visible descendants of their own, so the primary + list of a detected interaction stays complete. An interaction with no + visible instance at all is dropped entirely -- it left no trace, so + nothing represents it. + + Ancestry is resolved through the particle ``parent_id`` chain rather than + the instance list, skipping ancestors that belong to the instance's *own* + group: when a rep's parent was merged into the rep's own shower, the naive + lookup answers with the rep itself and the walk dead-ends. + + Parameters + ---------- + instances : list of Particle + Instance representatives, e.g. from + :func:`~pysupera.merge.merge_em_showers`. Not modified. + particles : list of Particle + The event's full particle list, used for the ``parent_id`` chains. + verbose : bool, optional + Print a one-line summary of what was kept. + + Returns + ------- + list of Particle + The retained instances, in their original relative order. + """ + if not instances: + return list(instances) + + by_id = {int(p.id): p for p in particles} + + # member particle id -> index in *instances* + inst_of: dict = {} + for k, inst in enumerate(instances): + members = inst.member_ids if inst.member_ids is not None else [inst.id] + for pid in members: + inst_of[int(pid)] = k + + def _parent_index(k): + """Nearest ancestor instance of *k*, or None when *k* is a primary.""" + inst = instances[k] + pid = int(inst.parent_id) + seen = set() + while pid not in seen: + seen.add(pid) + hit = inst_of.get(pid) + if hit is not None and hit != k: + return hit + p = by_id.get(pid) + if p is None or int(p.parent_id) == int(p.id): + return None + pid = int(p.parent_id) + return None + + parent_idx = [_parent_index(k) for k in range(len(instances))] + + visible = {k for k, i in enumerate(instances) if len(i.point_cloud) > 0} + visible_interactions = {int(instances[k]._interaction_id) for k in visible} + + keep = set(visible) + for k in visible: # rule 2: walk to the root + cur = parent_idx[k] + while cur is not None and cur not in keep: + keep.add(cur) + cur = parent_idx[cur] + + for k in range(len(instances)): # rule 3: primaries of live interactions + if parent_idx[k] is None and \ + int(instances[k]._interaction_id) in visible_interactions: + keep.add(k) + + out = [instances[k] for k in range(len(instances)) if k in keep] + if verbose: + print(f"[instances] kept {len(out)} of {len(instances)} " + f"({len(visible)} visible, {len(out) - len(visible)} genealogy/primary), " + f"{len(visible_interactions)} interaction(s) with signal") + return out From da9cd782e89d64905f2211ba153135317d2eb3cd Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 8 Sep 2026 17:20:19 -0700 Subject: [PATCH 14/62] vis: read geant4_id, tolerate a missing fragment level, fix the graph edges Three changes to vis_force.html for format 2.2.0 and the new output defaults. The genealogy graph was empty for every default-output file. Edges were built by walking the level-0 particles parent map, and parent_inst_id was never read at all -- but particles/ is empty under output_compact, which is the default, so findParentInst had nothing to walk. The stored parent_inst_id is now preferred and the walk kept only as a fallback for older files. On a sample event that yields 176 edges across 207 instances where there were none. geant4_id is read separately from id, falling back to id on pre-2.2.0 files where the two coincided. Both the hover tooltip and the locked bar show the two distinctly, so a particle index is no longer mistaken for a Geant4 track ID. Fragment fields are gated on the level actually being present. With write_fragments=false the table exists but is empty and every parent_frag_id is -1, which would otherwise render as an em-dash and read as "this particle has no fragment" rather than "this file has no fragment level". probeFragLevel() checks the row count at open time, and the tooltip omits the field entirely when there is nothing to show. The open-time log now reports format version and which optional levels the file carries. Note for future edits: the worker source lives inside a template literal, so backticks in comments there terminate it. Syntax-check with node --check which is how that mistake was caught in this change. --- vis_force.html | 3346 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3346 insertions(+) create mode 100644 vis_force.html diff --git a/vis_force.html b/vis_force.html new file mode 100644 index 0000000..434595c --- /dev/null +++ b/vis_force.html @@ -0,0 +1,3346 @@ + + + + + + +pysupera · meat slicer + + + + + + +
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+
+
+ + + + + From 80350c3bc2c97fa56bd8311139d532cbb4e7e492 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 01:52:54 -0700 Subject: [PATCH 15/62] read dX correctly and keep it, so dE/dX can be derived _STEP_FIELD_MAP declared the dx candidates as ("dx") -- a string, not a 1-tuple -- so the lookup loop iterated the characters 'd' and 'x', found no field 'd', then matched the very real field 'x'. Every dx value in the EDepSim reader was the point's x coordinate: col dx (5) first 5 : [-1467.1515 -1467.2798 ...] step["dx"] first 5 : [ 0.2999999 0.2999999 ...] The JAXTPC reader was unaffected; it assigns the column directly. dX is now also written. _PC_NDIM goes 5 -> 6, so the per-particle clouds are (x, y, z, time, dE, dX) and format becomes 2.3.0. Both merge stages operate within a single particle, so dE and dX both sum over a particle-voxel and dE/dX is column 4 / column 5 exactly. Sanity check on real data: median 0.195 MeV/mm from both readers independently, right for a MIP in liquid argon. Guard against dX == 0 -- EDepSim writes zero-length steps, about 7% of voxels on the non-JAXTPC path. This also settles the two tests that have been failing since before this work. They asserted that column 5 takes the maximum, under the name test_dedx_takes_maximum. Both premises were wrong: the column holds dX, not dE/dX, and dE/dX is not an aggregation rule at all -- it is derived. Renamed to test_dx_sums_within_a_particle and corrected to expect 1.5 + 3.2 = 4.7. The suite is green for the first time in this branch. --- pysupera/readers/format_edepsim_h5.py | 2 +- tests/test_preproc.py | 30 +++++++++++++++------------ 2 files changed, 18 insertions(+), 14 deletions(-) diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 865b7a5..21e5f26 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -80,7 +80,7 @@ (PointFeature.z, ("z",)), (PointFeature.time, ("t", "time")), (PointFeature.energy, ("energy", "e", "de")), - (PointFeature.dx, ("dx")), + (PointFeature.dx, ("dx",)), # 1-tuple: ("dx") is a str, and would match field "x" ] diff --git a/tests/test_preproc.py b/tests/test_preproc.py index 5fe3db0..52dcfdb 100644 --- a/tests/test_preproc.py +++ b/tests/test_preproc.py @@ -12,7 +12,7 @@ # ============================================================================ def pc6(*rows): - """Build a 6-column point cloud from (x, y, z, time, energy, dedx) rows.""" + """Build a 6-column point cloud from (x, y, z, time, dE, dX) rows.""" return np.array(rows, dtype=np.float32) @@ -54,12 +54,15 @@ def test_energy_takes_sum(self): self.proc.process([p]) assert p.point_cloud[0, 4] == pytest.approx(7.0) - def test_dedx_takes_maximum(self): + def test_dx_sums_within_a_particle(self): + # Column 5 is dX (path length), not dE/dX. Merging only ever happens + # within one particle, so the path lengths add: 1.5 + 3.2 = 4.7. + # dE/dX for the merged voxel is then column 4 / column 5. p = make_particle(1, PT_PRIMARY, pdg=11, pc=pc6([0, 0, 0, 1.0, 1.0, 1.5], [0, 0, 0, 1.0, 1.0, 3.2])) self.proc.process([p]) - assert p.point_cloud[0, 5] == pytest.approx(3.2) + assert p.point_cloud[0, 5] == pytest.approx(4.7) def test_empty_point_cloud_passes_through(self): p = make_particle(1, PT_PRIMARY, pdg=11, @@ -327,12 +330,13 @@ def test_energy_takes_sum(self): result = self.proc.process([p]) assert result[0].point_cloud[0, 4] == pytest.approx(7.0) - def test_dedx_takes_maximum(self): + def test_dx_sums_within_a_particle(self): + # See the MergeDuplicates counterpart: dX adds within a particle-voxel. p = make_particle(1, PT_PRIMARY, pdg=11, pc=pc6([0.2, 0.0, 0.0, 1.0, 1.0, 1.5], [0.7, 0.0, 0.0, 1.0, 1.0, 3.2])) result = self.proc.process([p]) - assert result[0].point_cloud[0, 5] == pytest.approx(3.2) + assert result[0].point_cloud[0, 5] == pytest.approx(4.7) # ── edge cases ──────────────────────────────────────────────────────── @@ -456,7 +460,7 @@ def test_negative_voxel_size_raises(self): # ============================================================================ def _pc7(*rows): - """7-column point cloud: (x, y, z, time, energy, dedx, input_id).""" + """7-column point cloud: (x, y, z, time, dE, dX, input_id).""" return np.array(rows, dtype=np.float32) @@ -481,15 +485,15 @@ class TestVoxelizationMappingRoundtrip: unit (1×1×1) grid anchored at the origin: Particle 1 (pid=1, 4 input points): - input_id=0 (0.2, 0, 0, t=0, e=1.0, dedx=0.1) → voxel (0,0,0) · centre (0.5,0.5,0.5) - input_id=1 (0.7, 0, 0, t=0, e=2.0, dedx=0.2) → voxel (0,0,0) ┘ energy = 3.0 - input_id=2 (1.2, 0, 0, t=0, e=3.0, dedx=0.3) → voxel (1,0,0) · centre (1.5,0.5,0.5) - input_id=3 (1.8, 0, 0, t=0, e=4.0, dedx=0.4) → voxel (1,0,0) ┘ energy = 7.0 + input_id=0 (0.2, 0, 0, t=0, e=1.0, dX=0.1) → voxel (0,0,0) · centre (0.5,0.5,0.5) + input_id=1 (0.7, 0, 0, t=0, e=2.0, dX=0.2) → voxel (0,0,0) ┘ energy = 3.0 + input_id=2 (1.2, 0, 0, t=0, e=3.0, dX=0.3) → voxel (1,0,0) · centre (1.5,0.5,0.5) + input_id=3 (1.8, 0, 0, t=0, e=4.0, dX=0.4) → voxel (1,0,0) ┘ energy = 7.0 Particle 2 (pid=2, 3 input points — identity mapping): - input_id=10 (0.5, 0, 0, t=0, e=5.0, dedx=0.5) → voxel (0,0,0) - input_id=11 (1.5, 0, 0, t=0, e=6.0, dedx=0.6) → voxel (1,0,0) - input_id=12 (2.5, 0, 0, t=0, e=7.0, dedx=0.7) → voxel (2,0,0) + input_id=10 (0.5, 0, 0, t=0, e=5.0, dX=0.5) → voxel (0,0,0) + input_id=11 (1.5, 0, 0, t=0, e=6.0, dX=0.6) → voxel (1,0,0) + input_id=12 (2.5, 0, 0, t=0, e=7.0, dX=0.7) → voxel (2,0,0) """ # ------------------------------------------------------------------ fixtures From eddd109064891757950a84d543cb9fe3e8f62f2a Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 01:52:54 -0700 Subject: [PATCH 16/62] preproc: keep genealogy and provenance intact across splits and drops Three defects in defragmentation, all found by the validators added earlier in this branch once the non-JAXTPC path was finally run with them enabled. Dropped parents orphaned their children. When every fragment of a particle is small the particle itself is removed, but its children -- both the pieces split off from it and its real children elsewhere in the event -- still pointed at it. resolve_orphans then rewrote each dangling parent_id to the particle's own id while leaving root_id on the original ancestor, producing particles that are their own parent yet claim a foreign root: 292 of 13,589 in one event. DefragmentBase.process now redirects references to a dropped particle's own parent, following chains of consecutive drops. Split pieces invented a Geant4 track ID. _split_fragments never passed geant4_id, so the constructor defaulted it to the fresh pysupera id and 154 particles carried track IDs that exist in no input file. A split piece is part of the same Geant4 particle, so it now inherits the provenance: `id` distinguishes the pieces, `geant4_id` records their shared origin. The voxel mapping described the wrong particle list. It was accumulated in voxelizer.last_diagnostics before defragmentation ran, so it referenced 25 particles that no longer existed and omitted the 154 that were created. The mapping is now attached to the Particle, so it follows the particle: a dropped one takes its mapping with it, and a split piece gets its share via subset_voxmap(). That works because voxel_offsets is a CSR over the particle's voxels in point-cloud row order, so the same boolean mask that splits the cloud selects the matching voxels -- no re-derivation needed. --- pysupera/data.py | 6 +++ pysupera/preproc.py | 97 ++++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 97 insertions(+), 6 deletions(-) diff --git a/pysupera/data.py b/pysupera/data.py index 460f24d..7b4faea 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -194,6 +194,12 @@ def __init__( for full descriptions. """ # --- required fields ----------------------------------------------- + #: Voxelization mapping, ``(voxel_offsets, input_ids, input_energies)`` + #: or ``None`` when ``particle.voxelize.store_mapping`` is off. Set by + #: VoxelizeProcessor and carried on the particle so that later stages + #: which split or drop particles keep it consistent. + self.voxmap = None + self.id = id # Geant4 provenance: the original track ID this particle came from. # Defaults to `id` so a caller that has no separate track ID (tests, diff --git a/pysupera/preproc.py b/pysupera/preproc.py index b1a73d8..fbc268b 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -198,8 +198,15 @@ def _split_fragments( large_mask |= frag_mask else: frag_pc = pc[frag_mask] + _vm = subset_voxmap(getattr(p, 'voxmap', None), frag_mask) new_p = Particle( id = next_id, + # A split piece is part of the same Geant4 particle, so it + # inherits the provenance rather than minting a new one. + # Without this the constructor defaults geant4_id to the fresh + # pysupera id, fabricating a track ID that exists in no input + # file and hiding the fact that the pieces share an origin. + geant4_id = getattr(p, "geant4_id", p.id), parent_id = p.id, root_id = p.root_id, pdg = p.pdg, @@ -209,14 +216,19 @@ def _split_fragments( point_cloud = frag_pc, ) new_p.sem_type = SemanticType.kLEScatter + new_p.voxmap = _vm spawned.append(new_p) next_id += 1 if large_mask.any(): + _kept_vm = subset_voxmap(getattr(p, 'voxmap', None), large_mask) p.point_cloud = pc[large_mask] + p.voxmap = _kept_vm return p, spawned, next_id - else: - return None, spawned, next_id + # Every fragment was small, so *p* is dropped. References to it are + # repaired by the caller (see the dropped-particle remap in + # DefragmentBase.process). + return None, spawned, next_id # ============================================================================ @@ -477,6 +489,34 @@ def _run_chunk(chunk): result = [p for p in result_slots if p is not None] + # --------------------------------------------------------------- + # Repair references to particles this pass removed + # --------------------------------------------------------------- + # A particle whose every fragment was small is dropped from the event. + # Anything still pointing at it -- its real children, and the fragments + # split off from it -- is left with a dangling parent_id. + # resolve_orphans later rewrites such a reference to the particle's own + # id while leaving root_id pointing at the original ancestor, producing + # a particle that is its own parent yet claims a foreign root. Redirect + # to the dropped particle's parent instead, following chains of + # consecutive drops, so the genealogy stays walkable. + dropped = {int(p.id): int(p.parent_id) + for idx, p, _ in needs_cc if result_slots[idx] is None} + + if dropped: + def _survivor(pid: int) -> int: + seen: set = set() + while pid in dropped and pid not in seen: + seen.add(pid) + pid = dropped[pid] + return pid + + for q in result + spawned: + if int(q.parent_id) in dropped: + q.parent_id = _survivor(int(q.parent_id)) + if int(q.root_id) in dropped: + q.root_id = _survivor(int(q.root_id)) + cc_sizes = [len(xyz) for _, _, xyz in needs_cc] self.last_stats = { 'n_particles' : len(particles), @@ -520,7 +560,10 @@ def _run_chunk(chunk): # # time (col 3) — minimum: earliest hit time survives # energy (col 4) — sum: total deposited energy -# dx (col 5) — sum: additional scalar feature +# dx (col 5) — sum: path length. Merging is always within one +# particle, so path lengths add; dE/dX for the merged +# voxel is col 4 / col 5. Summing dX across particles +# would be meaningless, and never happens here. _MERGE_RULES: list = [ (PointFeature.time, np.inf, np.minimum), (PointFeature.energy, 0.0, np.add), @@ -528,13 +571,40 @@ def _run_chunk(chunk): ] +def subset_voxmap(voxmap, mask): + """ + Restrict a particle's voxel mapping to a subset of its point-cloud rows. + + *voxmap* is the ``(voxel_offsets, input_ids, input_energies)`` triple + attached by :class:`VoxelizeProcessor`, whose ``voxel_offsets`` is a CSR + fencepost over the particle's voxels in point-cloud row order. *mask* is + the boolean row selector used to split the cloud, so the same mask selects + the corresponding voxels. + + Returns ``None`` when *voxmap* is ``None`` (mapping not being tracked). + """ + if voxmap is None: + return None + off, ids, en = voxmap + sel = np.flatnonzero(mask) + if len(sel) == 0: + return (np.zeros(1, dtype=np.int64), + ids[:0].copy(), en[:0].copy()) + counts = (off[sel + 1] - off[sel]).astype(np.int64) + new_off = np.zeros(len(sel) + 1, dtype=np.int64) + np.cumsum(counts, out=new_off[1:]) + take = np.concatenate([np.arange(off[i], off[i + 1]) for i in sel]) \ + if len(sel) else np.zeros(0, dtype=np.int64) + return new_off, ids[take].copy(), en[take].copy() + + def _merge_point_cloud(pc: np.ndarray) -> np.ndarray: """ Collapse rows that share identical 3-D coordinates into a single row. Coordinates (columns 0–2) are used as the grouping key. Feature columns beyond index 2 are aggregated according to ``_MERGE_RULES``: - time → min, energy → sum, dx → sum. Any extra columns beyond + time → min, dE → sum, dX → sum. Any extra columns beyond ``PointFeature.dx`` are left at zero. Parameters @@ -584,7 +654,7 @@ class MergeDuplicatesProcessor: ============ ====================================================== time (3) minimum — the earliest hit time is retained energy (4) sum — total deposited energy - dx (5) sum — additional scalar feature + dx (5) sum — path length within this particle's voxel any extras zero — placeholder; extend ``_MERGE_RULES`` to change ============ ====================================================== @@ -805,7 +875,7 @@ def _voxelize_point_cloud( New array where each row represents one non-empty voxel. The x, y, z columns hold the voxel-centre coordinates; remaining feature columns are aggregated by the same rules as - :func:`_merge_point_cloud` (time=min, energy=sum, dx=sum). + :func:`_merge_point_cloud` (time=min, dE=sum, dX=sum). Returns the original array unchanged when every voxel already contains exactly one point. """ @@ -1097,6 +1167,21 @@ def process( else: p_off = p_ids = p_en = None + # Attach the mapping to the particle, not just to the + # diagnostics list. Defragmentation runs after this stage and + # splits or drops particles; a mapping held on the side would + # still describe the pre-split list, which is how the voxmap + # came to reference 25 particles that no longer exist and to + # omit the 154 that defragmentation created. Carried on the + # particle, it is subset by _split_fragments and discarded with + # a dropped particle automatically. + # + # voxel_offsets is a CSR over THIS particle's voxels, in the + # same order as its point-cloud rows, which is what lets a + # split reuse the very mask applied to the cloud. + if self.store_mapping: + p.voxmap = (p_off, p_ids, p_en) + rec = VoxelizeRecord( particle_id = p.id, n_before = n_before, From 33327a9e237e72112889fb99300c37defb7bae82 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 01:52:54 -0700 Subject: [PATCH 17/62] io: widen the point cloud to carry dX, shrink the voxmap, drop recompress Format 2.3.0. _PC_NDIM 5 -> 6 so the flat point arrays are (x, y, z, time, dE, dX); see the reader commit for why dX is worth keeping. Older 5-column files still load -- Particle accepts any width >= 3, and _normalize_cloud zero-pads on write -- verified by reading a 2.1.0 file. flat/input_ids in the voxmap narrows from int64 to int32. Those index points within a single event (max ~3e5 observed), unlike the two offset arrays, which are cumulative over the whole file and stay int64. Note this is not a disk win: LZ4 was already compressing away the zero high bytes, so stored size is unchanged at 670 KiB. It halves the in-memory array and the decompressed bandwidth, and matters when compression is off. pysupera-recompress is removed, along with recompress() and the _read_rep_level() helper that only it used. pysupera-repack supersedes it completely: recompress reconstructs the file event by event, so it cannot resize chunks and silently ignores the voxmap companion, while repack walks the file generically. Keeping two commands where the older one quietly does less is worse than having one. Neither symbol was exported from __init__. --- pyproject.toml | 1 - pysupera/io.py | 216 ++++--------------------------------------------- 2 files changed, 14 insertions(+), 203 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2f4e9c5..91edf59 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,6 @@ app = [ [project.scripts] run_pysupera = "pysupera._run:main" pysupera-app = "pysupera.app:main" -pysupera-recompress = "pysupera.io:recompress_cli" pysupera-repack = "pysupera.io:repack_cli" [tool.setuptools.packages.find] diff --git a/pysupera/io.py b/pysupera/io.py index 0b109ca..d99d62a 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -64,9 +64,11 @@ except ImportError: pass -FORMAT_VERSION = "2.2.0" +FORMAT_VERSION = "2.3.0" -_PC_NDIM = 5 # number of columns in the flat point array (x,y,z,t,e) +_PC_NDIM = 6 # columns in the flat point array (x,y,z,t,dE,dX) + # dX is summed per particle-voxel, so dE/dX for a + # particle in a voxel is column 4 / column 5. _CLOUD_NDIM = 9 # columns in event-cloud datasets (x,y,z,t,e,interaction_id,root_id,frag_id,inst_id) # Chunk sizing for compressed HDF5 datasets. @@ -1344,162 +1346,6 @@ def inspect_compression(path: str) -> dict: return result -def recompress(src: str, dst: str, - compression: str = "lzf", - compression_opts: Optional[int] = None, - batch_size: int = 256) -> None: - """ - Rewrite *src* into *dst* with a different compression filter. - - Copies particle events **and** event-level point clouds - (``non_le_cloud`` / ``le_scatter_cloud``). - Fragment and instance representative groups are also copied when - present in the source file. - - Parameters - ---------- - src : str - Path to the source HDF5 file (any supported compression). - dst : str - Path for the output file (will be overwritten if it exists). - compression : str, optional - Compression for the output file. Default ``"lzf"``. - Use ``"gzip"`` to produce a file readable by h5wasm in the browser. - compression_opts : int or None, optional - Compression level for the output filter. - batch_size : int, optional - Events per read batch. Default ``256``. - """ - import h5py as _h5 - import numpy as _np - - with EventStore(src) as src_store, \ - _h5.File(src, "r") as src_f, \ - open_writer(dst, compression=compression, - compression_opts=compression_opts) as w: - - n_ev = len(src_store) - - # Pre-read cloud offset arrays if present - _has_nle = "non_le_cloud/offsets" in src_f - _has_le = "le_scatter_cloud/offsets" in src_f - if _has_nle: - _nle_off = src_f["non_le_cloud/offsets"][:] - if _has_le: - _le_off = src_f["le_scatter_cloud/offsets"][:] - - # Pre-read fragment / instance event-offset arrays if present - _has_frags = "frag_events/offsets" in src_f - _has_insts = "inst_events/offsets" in src_f - if _has_frags: - _fr_ev_off = src_f["frag_events/offsets"][:] - if _has_insts: - _in_ev_off = src_f["inst_events/offsets"][:] - - for batch_start in range(0, n_ev, batch_size): - batch_stop = min(batch_start + batch_size, n_ev) - - for ev_idx, particles in zip( - range(batch_start, batch_stop), - src_store.read_bulk(batch_start, batch_stop), - ): - # ---- event-level clouds ----------------------------------- - if _has_nle or _has_le: - nle_cloud = ( - src_f["non_le_cloud/flat"][ - int(_nle_off[ev_idx]) : int(_nle_off[ev_idx + 1]) - ] if _has_nle - else _np.empty((0, _CLOUD_NDIM), dtype=_np.float32) - ) - le_cloud = ( - src_f["le_scatter_cloud/flat"][ - int(_le_off[ev_idx]) : int(_le_off[ev_idx + 1]) - ] if _has_le - else _np.empty((0, _CLOUD_NDIM), dtype=_np.float32) - ) - w.append_event_clouds(nle_cloud, le_cloud) - - # ---- fragment representatives ----------------------------- - if _has_frags: - _fr_s = int(_fr_ev_off[ev_idx]) - _fr_e = int(_fr_ev_off[ev_idx + 1]) - frags = _read_rep_level(src_f, "particle_fragments", _fr_s, _fr_e) - w.append_fragments(frags) - - # ---- instance representatives ---------------------------- - if _has_insts: - _in_s = int(_in_ev_off[ev_idx]) - _in_e = int(_in_ev_off[ev_idx + 1]) - insts = _read_rep_level(src_f, "particle_instances", _in_s, _in_e) - w.append_instances(insts) - - w.append_event(particles) - - -def _read_rep_level(f, group: str, r_start: int, r_end: int) -> list: - """Reconstruct minimal Particle-like objects from a stored representative group. - - Used internally by :func:`recompress`. - """ - from .data import Particle - from .utils import SemanticType - - n_r = r_end - r_start - if n_r == 0: - return [] - - ids = f[f"{group}/id" ][r_start:r_end] - g4ids = (f[f"{group}/geant4_id" ][r_start:r_end] - if f"{group}/geant4_id" in f else ids) - pids = f[f"{group}/parent_id" ][r_start:r_end] - rids = f[f"{group}/root_id" ][r_start:r_end] - pdgs = f[f"{group}/pdg" ][r_start:r_end] - ppdgs = f[f"{group}/parent_pdg" ][r_start:r_end] - intids = f[f"{group}/interaction_id" ][r_start:r_end] - itypes = f[f"{group}/interaction_type"][r_start:r_end] - stypes = f[f"{group}/sem_type" ][r_start:r_end] - pfids = f[f"{group}/parent_frag_id" ][r_start:r_end] if f"{group}/parent_frag_id" in f else None - piids = f[f"{group}/parent_inst_id" ][r_start:r_end] if f"{group}/parent_inst_id" in f else None - - pc_off = f[f"{group}/pc_offsets" ][r_start : r_end + 1] - mem_off = f[f"{group}/member_offsets"][r_start : r_end + 1] - - pt_start = int(pc_off[0]) - pt_end = int(pc_off[-1]) - pts_flat = f[f"{group}_points/flat"][pt_start:pt_end] if pt_end > pt_start else None - - mem_start = int(mem_off[0]) - mem_end = int(mem_off[-1]) - mem_flat = f[f"{group}_members/flat"][mem_start:mem_end] if mem_end > mem_start else None - - reps = [] - for k in range(n_r): - cloud = pts_flat[int(pc_off[k]) - pt_start : int(pc_off[k + 1]) - pt_start] \ - if pts_flat is not None else __import__('numpy').empty((0, _PC_NDIM), dtype='float32') - ms = int(mem_off[k]) - mem_start - me = int(mem_off[k + 1]) - mem_start - mids = list(mem_flat[ms:me].astype(int)) if mem_flat is not None and me > ms else None - p = Particle( - id = int(ids[k]), - geant4_id = int(g4ids[k]), - parent_id = int(pids[k]), - root_id = int(rids[k]), - pdg = int(pdgs[k]), - parent_pdg = int(ppdgs[k]), - interaction_id = int(intids[k]), - interaction_type = int(itypes[k]), - point_cloud = cloud, - ) - p.sem_type = SemanticType(int(stypes[k])) - p.member_ids = mids - if pfids is not None: - p.parent_frag_id = int(pfids[k]) - if piids is not None: - p.parent_inst_id = int(piids[k]) - reps.append(p) - return reps - - def repack(path: str, compression: Optional[str] = None, compression_opts: Optional[int] = None, @@ -1747,10 +1593,8 @@ def repack_cli() -> None: # smallest and fastest for row-range reads, at the cost of relayout pysupera-repack out.h5 out_vis.h5 --compression gzip --rechunk - Unlike ``pysupera-recompress``, this walks the file generically instead of - reconstructing events, so it can resize chunks and handles any layout -- - including the voxmap companion file. Prefer it unless you specifically - need the event-by-event rewrite. + Walks the file generically instead of reconstructing events, so it can + resize chunks and handles any layout, including the voxmap companion file. """ import argparse parser = argparse.ArgumentParser( @@ -1789,40 +1633,6 @@ def repack_cli() -> None: verify=args.verify) -def recompress_cli() -> None: - """Entry point for the ``pysupera-recompress`` shell command. - - Usage:: - - pysupera-recompress src.h5 dst.h5 [--compression gzip] [--level 1] - - Rewrites *src.h5* into *dst.h5* with the chosen compression filter. - Use ``--compression gzip`` to produce a browser-compatible file for - the WebGL viewer (h5wasm only supports gzip). - """ - import argparse - parser = argparse.ArgumentParser( - prog="pysupera-recompress", - description="Recompress a pysupera HDF5 file with a different filter.", - ) - parser.add_argument("src", help="Source HDF5 file") - parser.add_argument("dst", help="Output HDF5 file (will be overwritten)") - parser.add_argument("-c", "--compression", default="gzip", - help="Compression filter: gzip, lzf, lz4, blosc_lz4, none (default: gzip)") - parser.add_argument("-l", "--level", type=int, default=None, - help="Compression level (gzip: 1-9; default: filter default)") - parser.add_argument("-b", "--batch-size", type=int, default=256, - help="Events per read batch (default: 256)") - args = parser.parse_args() - comp = None if args.compression in ("none", "~", "") else args.compression - print(f"[recompress] {args.src} → {args.dst} compression={comp!r} level={args.level}") - recompress(args.src, args.dst, - compression=comp, - compression_opts=args.level, - batch_size=args.batch_size) - print("[recompress] Done.") - - # --------------------------------------------------------------------------- # Voxelization-mapping companion file # --------------------------------------------------------------------------- @@ -1876,7 +1686,7 @@ def write_voxmap(main_path: str, Fencepost into /flat/*. /flat/ - input_ids (n_total_mappings,) int64 + input_ids (n_total_mappings,) int32 input_energies (n_total_mappings,) float32 Parameters @@ -1932,7 +1742,7 @@ def write_voxmap(main_path: str, rec.voxel_offsets[1:].astype(np.int64) + running_flat ) - flat_ids_segs.append(rec.input_ids.astype(np.int64)) + flat_ids_segs.append(rec.input_ids.astype(np.int32)) flat_eng_segs.append(rec.input_energies.astype(np.float32)) running_flat += np.int64(len(rec.input_ids)) @@ -1989,7 +1799,7 @@ def write_voxmap(main_path: str, **ckw, ) else: - fg.create_dataset("input_ids", data=np.empty(0, dtype=np.int64)) + fg.create_dataset("input_ids", data=np.empty(0, dtype=np.int32)) fg.create_dataset("input_energies", data=np.empty(0, dtype=np.float32)) return vpath @@ -2147,13 +1957,13 @@ def append_event(self, vox_records) -> None: flat_ids_ds.resize(new_flat_end, axis=0) flat_eng_ds.resize(new_flat_end, axis=0) - bulk_ids = np.empty(n_flat_total, dtype=np.int64) + bulk_ids = np.empty(n_flat_total, dtype=np.int32) bulk_eng = np.empty(n_flat_total, dtype=np.float32) cursor = 0 for rec in vox_records: n = len(rec.input_ids) if n > 0: - bulk_ids[cursor : cursor + n] = rec.input_ids.astype(np.int64) + bulk_ids[cursor : cursor + n] = rec.input_ids.astype(np.int32) bulk_eng[cursor : cursor + n] = rec.input_energies.astype(np.float32) cursor += n flat_ids_ds[self._n_flat : new_flat_end] = bulk_ids @@ -2198,8 +2008,10 @@ def _init_datasets(self) -> None: maxshape=(None,), chunks=(_CHUNK_PARTICLES,)) fg = f.create_group("flat") + # int32: input_ids index points within a single event, unlike the + # offset arrays, which are cumulative over the file and stay int64. fg.create_dataset("input_ids", - shape=(0,), maxshape=(None,), dtype=np.int64, + shape=(0,), maxshape=(None,), dtype=np.int32, chunks=(_CHUNK_POINTS,), **ckw) fg.create_dataset("input_energies", shape=(0,), maxshape=(None,), dtype=np.float32, From ce2300aaeac4b1f4dec28595785bf211d4ed7186 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 01:52:54 -0700 Subject: [PATCH 18/62] run: renumber ids densely, and build the voxel map from what is stored id is documented as a per-event particle index usable as a direct row index, and that was true of what the reader produced but not of what got written: defragmentation spawns particles with fresh ids and drops others, leaving 25 gaps in one event and a maximum id past the row count. The property held on the JAXTPC path only because nothing happened to split in the event that was tested -- defragmentation runs there too. Particles are therefore renumbered to 0..n-1 in list order once preprocessing has finished, with parent_id and root_id remapped. This runs after resolve_orphans, so every reference already points at a particle that is present and the remap is total. id == arange(n) now holds on the splitting path as well. The voxel map moves with it. It was written immediately after voxelization, which is before defragmentation, so it described a particle list that no longer matched the output. It is now written after renumbering and rebuilt from the final particles, each carrying its own share of the mapping. The result covers exactly the stored particles -- 13,589 of 13,589, no orphans in either direction, where there were previously 154 missing and 25 stale -- and total input_energies equals total voxel dE, which is the check that the per-split subsetting partitions rather than duplicates. particle.voxelize.store_mapping now defaults to false. The map carries one row per *input* deposit rather than per voxel, making it the largest artifact pysupera produces: 9.7 MiB against 6.0 MiB of actual output on a 5-event run. Nothing is computed when it is off, not merely not written, and the file is a cache of a deterministic computation -- re-running reproduces it exactly -- so the default costs nothing and saves the common case from paying for a debugging aid it does not use. --- pysupera/_run.py | 49 +++++++++++++++++++++++++++++++++++---- pysupera/conf/config.yaml | 21 ++++++++++++----- 2 files changed, 59 insertions(+), 11 deletions(-) diff --git a/pysupera/_run.py b/pysupera/_run.py index fdf3753..7e588af 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -205,11 +205,9 @@ def main(cfg: DictConfig) -> None: particles = voxelizer.process(particles) _ev_t['voxelize'] = time.perf_counter() - _t profile['voxelize'] += _ev_t['voxelize'] - if _write_voxmap: - _t = time.perf_counter() - vox_writer.append_event(voxelizer.last_diagnostics) - _ev_t['write_voxmap'] = time.perf_counter() - _t - profile['write_voxmap'] += _ev_t['write_voxmap'] + # The voxmap is written further down, after preprocessing + # and id renumbering, so it describes the particle list + # that is actually stored. if preprocessor is not None: _t = time.perf_counter() @@ -222,6 +220,47 @@ def main(cfg: DictConfig) -> None: # consistent for all conditions and merge_em_showers. resolve_orphans(particles, verbose=cfg.verbose) + # ── Renumber ids densely ─────────────────────────────────── + # The reader hands out id == arange(n), but defragmentation + # then spawns particles with fresh ids and drops others, so by + # here the ids have gaps and `id` is no longer a row index. + # Renumber to 0..n-1 in list order and remap the genealogy. + # Runs after resolve_orphans, so every parent_id / root_id + # already refers to a particle that is present. + _idmap = {int(_p.id): _k for _k, _p in enumerate(particles)} + for _k, _p in enumerate(particles): + _p.parent_id = _idmap.get(int(_p.parent_id), _k) + _p.root_id = _idmap.get(int(_p.root_id), _k) + for _k, _p in enumerate(particles): + _p.id = _k + + # ── Voxel mapping ────────────────────────────────────────── + # Built from the final particle list, so split particles carry + # their own share of the mapping and dropped ones contribute + # nothing. Records are rebuilt here rather than reused from + # voxelizer.last_diagnostics, which still describes the + # pre-defragmentation list. + if _write_voxmap: + _t = time.perf_counter() + from pysupera.preproc import VoxelizeRecord + _recs = [] + for _p in particles: + _vm = getattr(_p, 'voxmap', None) + if _vm is None: + continue + _off, _ids, _en = _vm + _recs.append(VoxelizeRecord( + particle_id = int(_p.id), + n_before = int(len(_ids)), + n_after = int(len(_off) - 1), + input_ids = _ids, + input_energies = _en, + voxel_offsets = _off, + )) + vox_writer.append_event(_recs) + _ev_t['write_voxmap'] = time.perf_counter() - _t + profile['write_voxmap'] += _ev_t['write_voxmap'] + # ── Interaction-ID completeness ───────────────────────────── # Runs after resolve_orphans so the parent chains reported in # the diagnostic are the repaired ones. diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index ead439e..77add45 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -176,7 +176,7 @@ particle: # Pre-processing: voxelize point clouds onto a regular 3-D grid. # Bins each point into a voxel cell, snaps its position to the voxel # centre, and merges all points in the same voxel (time=min, energy=sum, - # dedx=max). This is a strict generalisation of merge_duplicates; when + # dX=sum). This is a strict generalisation of merge_duplicates; when # voxelize.enabled is true, merge_duplicates is redundant. voxelize: enabled: true @@ -191,13 +191,22 @@ particle: # When true, skips the separate merge_duplicates preprocessing step. # Safe because exact-coordinate duplicates always fall in the same voxel. merge_duplicates: true - # When true, VoxelizeProcessor stores a CSR mapping (input_ids, - # input_energies, voxel_offsets) for every particle in last_diagnostics, - # enabling traces from output voxels back to input points. - store_mapping: true + # Write _voxmap.h5: for every output voxel, which input deposits + # were merged into it and with what energy. Useful for truth studies and + # for debugging voxelization, but it carries one row per *input* point + # rather than per voxel, so it is the largest artifact pysupera produces -- + # on a sample 5-event run it was 9.7 MiB against 6.0 MiB of actual output. + # + # Off by default for that reason. Nothing is computed when false, not just + # not written, so there is no cost to leaving it off. The file is a cache + # of a deterministic computation: re-running with the same config and input + # reproduces it exactly, so discarding it loses nothing. + store_mapping: false # Pre-processing: collapse points sharing identical 3-D coordinates. - # Aggregation rules: time=min, energy=sum, dedx=max. + # Aggregation rules: time=min, dE=sum, dX=sum. Both processors merge + # only within a single particle, so path length adds; dE/dX for a + # particle-voxel is then dE / dX (columns 4 / 5), not an aggregate. # Runs before defragmentation so min_pc_size counts unique voxels. # Redundant when voxelize.enabled is true. merge_duplicates: true From 8239ec18a68806a6611a1f6cd260081cff912435 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 01:52:54 -0700 Subject: [PATCH 19/62] docs: describe the output format, and separate expert output from user output The output format had no documentation outside io.py docstrings, which made the two integer spaces in the file easy to conflate and the per-event id base (events/offsets[ev] + id) invisible. README gains an Output File Format section covering the top level, the three particle levels with every field and which space it lives in, membership arrays, point-cloud layouts, and CSR event indexing. ID conventions get four rules: id is pysupera's, not Geant4's; ids restart per event; a primary is its own parent; and geant4_id is one-to-many, because a particle split by defragmentation yields several rows sharing one track ID -- 129 of 13,589 on a sample event. That last one is the sharpest edge in the scheme and was recorded nowhere. Expert output is split into its own section, stating up front that all of it is off by default, exists to debug pysupera rather than to be analysed, and is regenerable so not worth archiving: the fragment level, the voxel map (with a worked read and its size), instance_output=all, and enable_diagnostics. The fragment entry also spells out the naming trap that caused real confusion -- a particle_fragment is a group of *merged* particles, while *defragmentation* splits one particle into several, moving the count the other way. Also adds tools/h5_read_bench.py, which compares HDF5 read speed across files with matching layout. It interleaves repeats so no file gets a warmer page cache, reports the first pass separately as cold, and reads identical ranges from every file via a fixed seed. It is what established that chunk shape, not compression filter, dominated read cost here. --- README.md | 304 +++++++++++++++++++++++++++++++++++++++-- tools/h5_read_bench.py | 246 +++++++++++++++++++++++++++++++++ 2 files changed, 541 insertions(+), 9 deletions(-) create mode 100755 tools/h5_read_bench.py diff --git a/README.md b/README.md index acd624a..331e4bb 100644 --- a/README.md +++ b/README.md @@ -26,15 +26,21 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [Step 2 — Applying Conditions](#step-2--applying-conditions) - [Step 3 — Generating Partitions](#step-3--generating-partitions) - [Step 4 — Output](#step-4--output) -6. [CLI Usage](#cli-usage) -7. [Compute Backends](#compute-backends) -8. [Algorithms](#algorithms) +6. [Output File Format](#output-file-format) + - [ID conventions](#id-conventions) + - [The three particle levels](#the-three-particle-levels) + - [Point clouds](#point-clouds) + - [Event indexing](#event-indexing) + - [Expert and debugging output](#expert-and-debugging-output) +7. [CLI Usage](#cli-usage) +8. [Compute Backends](#compute-backends) +9. [Algorithms](#algorithms) - [Partition-level Incremental Algorithm](#partition-level-incremental-algorithm) - [Proximity Check](#proximity-check) - [Condition Pipeline](#condition-pipeline) -9. [Conditions Reference](#conditions-reference) -10. [Diagnostics](#diagnostics) -11. [Assumptions](#assumptions) +10. [Conditions Reference](#conditions-reference) +11. [Diagnostics](#diagnostics) +12. [Assumptions](#assumptions) --- @@ -183,9 +189,10 @@ These attributes must always be supplied at construction time and are guaranteed | Attribute | Type | Description | |---|---|---| -| `id` | `int` | Unique particle ID within an event (Geant4 track ID) | -| `parent_id` | `int` | Direct parent particle ID; equals `id` for primary particles | -| `root_id` | `int` | ID of the primary ancestor at the root of the shower/track genealogy | +| `id` | `int` | Particle index within an event, assigned by the reader: `0 … n-1`, usable as a direct row index. **Not** the Geant4 track ID | +| `geant4_id` | `int` | Original Geant4 track ID, kept for provenance and for joining back to the input | +| `parent_id` | `int` | Direct parent's particle index; equals `id` for primary particles | +| `root_id` | `int` | Particle index of the primary ancestor at the root of the shower/track genealogy | | `pdg` | `int` | PDG Monte Carlo particle code | | `parent_pdg` | `int` | PDG code of the direct parent particle | | `process_type` | `InteractionType` | Physics process that created this particle; derived from the raw int stored in `_process_type` | @@ -390,6 +397,285 @@ with read_events("output.h5") as store: --- +## Output File Format + +`run_pysupera` writes a single HDF5 file (plus an optional voxmap companion). +This section describes format **2.3.0**, recorded in the scalar dataset +`format_version`. No HDF5 attributes are used anywhere — everything is a +dataset. + +### ID conventions + +Two distinct integer spaces appear in the output, and conflating them is the +single easiest mistake to make when reading these files. + +| Space | Fields | Meaning | +|---|---|---| +| **Particle index** | `id`, `parent_id`, `root_id`, `*_members/flat` | Position of a particle within its event's particle list | +| **Level row index** | `parent_frag_id`, `parent_inst_id` | Row within *that event's slice* of the fragment / instance table | + +Plus one provenance field that is **not** an index: + +| Field | Meaning | +|---|---| +| `geant4_id` | The original Geant4 track ID, carried through unchanged | + +Four rules follow from this: + +1. **`id` is assigned by pysupera, not by Geant4.** It is `0, 1, … n-1` over + the event's particles, so it can be used as a direct row index. Geant4 + track IDs are *not* guaranteed contiguous — an upstream stage may drop + particles before pysupera ever sees them — which is why they cannot serve + as indices and are kept separately in `geant4_id`. Join back to the + EDepSim input on `geant4_id`, never on `id`. + + The density is guaranteed at write time, not merely at read time: + defragmentation splits particles (minting new ids) and drops others, so ids + are renumbered once preprocessing has finished. An id observed inside a + Python session mid-pipeline is therefore not necessarily the id written to + the file. + +2. **IDs restart at 0 in every event.** To address the concatenated tables you + must add the event's base offset: + + ```python + off = f['events/offsets'] + row = off[ev] + p_id # into particles/* + parent_row = off[ev] + f['particles/parent_id'][row] + ``` + + Note the base is always `events/offsets` — the *particle* fencepost — even + when reading `particle_instances`, because `id` / `parent_id` / `root_id` + and the member lists all index particles. Only `parent_frag_id` and + `parent_inst_id` are relative to `frag_events/offsets` and + `inst_events/offsets` respectively. + +3. **A primary is its own parent.** `parent_id == id` marks a primary, and + `root_id == id` likewise; every genealogy walk terminates on that + condition. The same convention applies to `parent_inst_id`, so an + instance that is its own parent is a primary rather than a broken link. + +4. **`geant4_id` is not unique when defragmentation splits a particle.** A + particle whose point cloud falls into disconnected pieces becomes several + pysupera particles, and they all inherit the track ID they came from — that + is the point of the field. `id` distinguishes them; `geant4_id` records + their common origin. A join on `geant4_id` is therefore one-to-many in + general. On a sample event, 129 of 13,589 particles shared a track ID with + at least one other. + +`interaction_id` is a third thing again: an index into the *input* file's +vertex list, identifying which interaction a particle belongs to. + +### Top level + +| Dataset | Type | Meaning | +|---|---|---| +| `format_version` | scalar string | e.g. `b'2.2.0'` | +| `n_events` | scalar int64 | number of events in the file | + +### The three particle levels + +The same schema appears three times, one per processing stage: + +| Group | Contents | Written when | Audience | +|---|---|---|---| +| `particle_instances/` | Level 2 — representatives after EM-shower merging | **always** | everyone | +| `particles/` | Level 0 — every input particle | `output_compact=false` | everyone | +| `particle_fragments/` | Level 1 — step-1 partition representatives | `write_fragments=true` | expert — see [below](#expert-and-debugging-output) | + +A level that is not written still exists as a group with **zero rows**, and its +event offsets still advance, so the file stays internally consistent. Under the +defaults only the instance level carries data: that is the level most analyses +want, since it is where one physical object — a shower, a track — corresponds to +one row. + +Fields in each group: + +| Field | dtype | Space | Meaning | +|---|---|---|---| +| `id` | int32 | particle index | This particle (for a representative: the particle representing the group) | +| `geant4_id` | int32 | — | Original Geant4 track ID | +| `parent_id` | int32 | particle index | Direct parent; equals `id` for a primary | +| `root_id` | int32 | particle index | Primary ancestor | +| `pdg`, `parent_pdg` | int32 | — | PDG codes | +| `interaction_id` | int32 | vertex index | Which interaction this particle came from | +| `interaction_type` | int32 | — | `InteractionType` enum | +| `sem_type` | int8 | — | `SemanticType` enum — **per level**, see below | +| `parent_frag_id` | int32 | fragment row | Fragment containing this particle's parent; `-1` when unavailable | +| `parent_inst_id` | int32 | instance row | Instance containing this particle's parent | +| `pc_offsets` | int64, n+1 | — | Fencepost into `_points/flat` | +| `member_offsets` | int64, n+1 | — | Fencepost into `_members/flat` | + +`sem_type` is the one field that genuinely differs between levels: merging +reclassifies particles, so a particle can carry up to three semantic types — +its own, its fragment's, and its instance's. Every other scalar field is +identical to the level-0 row for the same particle. + +Enum values: + +``` +SemanticType : kShower 1, kTrack 2, kDelta 3, kMichel 4, kLEScatter 5, kUnknown 6 +InteractionType : kTrack 1, kNeutron 2, kNucleus 3, kPhoton 4, kPrimary 5, + kCompton 6, kDelta 7, kConversion 8, kIonization 9, + kPhotoElectron 10, kDecay 11, kOtherShower 12, kInvalidProcess 13 +``` + +### Membership + +`_members/flat` lists, for each representative, the **particle indices** +of everything merged into it, sliced by `member_offsets`: + +```python +members = f['particle_instances_members/flat'] +offs = f['particle_instances/member_offsets'] +inst_k = members[offs[k] : offs[k+1]] # particle indices in instance k +``` + +A representative always appears in its own member list. When every instance is +written the member lists partition the event's particles exactly — each +particle appears once and only once. Under the default +`instance_output=traceable` some particles are not covered, because instances +that nothing visible depends on are dropped; all such particles have empty point +clouds, so no point data is lost. + +### Point clouds + +**Per representative** — `points/flat`, `particle_fragments_points/flat`, +`particle_instances_points/flat`, shape `(N, 6)` float32: + +``` +0:x 1:y 2:z 3:time 4:dE 5:dX +``` + +Both merging stages (`MergeDuplicatesProcessor`, `VoxelizeProcessor`) operate +**within a single particle**, so for one particle-voxel `dE` and `dX` both sum +and + +``` +dE/dX = column 4 / column 5 +``` + +is exact. Guard against `dX == 0`: EDepSim writes zero-length steps, which +account for a small fraction of voxels. Summing `dX` *across* particles would +be meaningless, and never happens — which is why the event-level cloud below +carries `dE` only. + +`PointFeature` defines a seventh column in memory (per-point `id`=6); it is +**truncated on write** and is not in the file. + +A representative's cloud is the exact union of its members' clouds — verified to +the point, with no duplication or loss — so the same points appear once per +written level. + +**Event-level union** — `non_le_cloud/flat` and `le_scatter_cloud/flat`, shape +`(N, 9)` float32: + +``` +0:x 1:y 2:z 3:time 4:energy 5:interaction_id 6:root_id 7:frag_id 8:inst_id +``` + +These are *per-point* labels, replicated across every point of a given +particle. Columns 7–8 are level row indices, 6 is a particle index, and 5 a +vertex index. `non_le_cloud` excludes `kLEScatter` particles and +`le_scatter_cloud` contains only them — a partition, not a superset. + +When voxelization is on, the event clouds are re-voxelized as a union, so +points that different particles placed in the same voxel are merged. Their row +count is therefore **lower** than the per-representative totals even though the +deposited energy agrees. Controlled by `write_event_clouds`. + +### Event indexing + +Everything is concatenated across events with CSR fencepost arrays of length +`n_events + 1`; event *i* occupies rows `offsets[i] : offsets[i+1]`. + +| Fencepost | Slices | +|---|---| +| `events/offsets` | `particles/*` | +| `frag_events/offsets` | `particle_fragments/*` | +| `inst_events/offsets` | `particle_instances/*` | +| `non_le_cloud/offsets`, `le_scatter_cloud/offsets` | the 9-column clouds | + +Point and member arrays nest two levels deep: first the event fencepost to get +the level's rows, then `pc_offsets` / `member_offsets` within those rows. + +### Expert and debugging output + +Everything below is **off by default**. It exists to debug pysupera itself or +to study how the grouping was arrived at, not to be consumed by analysis. All +of it is regenerable: re-running with the same config and input reproduces it +exactly, so none of it is worth archiving. + +#### The fragment level — `write_fragments=true` + +Step-1 partition representatives, before EM-shower merging groups them into +instances. Useful for seeing *which* merge step produced a grouping: a +particle's row carries both `parent_frag_id` and `parent_inst_id`, so comparing +a fragment's membership against its instance's shows exactly what step 2 added. + +Its point payload is a second copy of the points the instance level already +stores, so enabling it costs roughly 20% of the file. When it is off, instance +`parent_frag_id` is `-1` rather than pointing into a table that was not written. + +Note the name: a `particle_fragment` is a group of *merged* particles. This is +unrelated to *defragmentation*, a preprocessing step that **splits** one +particle's disconnected point cloud into several particles. The two move the +particle count in opposite directions. + +#### The voxel mapping — `particle.voxelize.store_mapping=true` + +Writes `_voxmap.h5`: for every output voxel, which input deposits were +merged into it and with what energy. Voxelization is lossy — many EDepSim steps +collapse into one voxel and only the summed `dE` survives — and this records +what went in. Two nested CSR levels: + +``` +particles/id, particles/vox_offsets -> a particle's voxels +voxels/input_offsets -> a voxel's input points +flat/input_ids, flat/input_energies -> original point index + its energy +``` + +To read one voxel's provenance: + +```python +vs, ve = vox_offsets[k], vox_offsets[k+1] # particle k's voxels +fs, fe = input_offsets[vs], input_offsets[vs+1] # its first voxel's inputs +input_ids[fs:fe], input_energies[fs:fe] +``` + +**It is large** — it carries one row per *input* deposit rather than per voxel, +so it is typically bigger than the physics output itself (9.7 MiB against +6.0 MiB on a sample 5-event run). That is why it is off by default; nothing is +computed when it is off, so leaving it off costs nothing. + +It is built from the final particle list, after defragmentation and id +renumbering, so its `particles/id` match the main file exactly. A particle +split by defragmentation carries its own share of the mapping, and total +`input_energies` equals total voxel `dE`. + +#### Seeing every instance — `instance_output=all` + +Writes every instance the merger produced, including those that deposited +nothing and that nothing visible descends from — 535 rather than 207 on a +sample event. Use it to inspect the merger's raw output; `traceable` is what +analyses should read. + +#### Per-merge records — `enable_diagnostics=true` + +Records every merge decision in memory for post-hoc inspection. Not written to +the output file. + +### Compression + +Output is LZ4 by default (`io.compression`). The browser viewer uses h5wasm, +which supports **gzip only**, so produce a viewer copy with: + +```bash +pysupera-repack out.h5 out_vis.h5 --compression gzip --rechunk +``` + +--- + ## CLI Usage After installation the `run_pysupera` command is available: diff --git a/tools/h5_read_bench.py b/tools/h5_read_bench.py new file mode 100755 index 0000000..06e7c1b --- /dev/null +++ b/tools/h5_read_bench.py @@ -0,0 +1,246 @@ +#!/usr/bin/env python3 +""" +Benchmark HDF5 read speed across two or more files holding the same data. + +Intended for comparing compression filters -- e.g. an LZ4-compressed pysupera +output against the gzip copy used by the WebGL viewer -- but it works on any set +of HDF5 files with matching layout. + +Stand-alone: needs only h5py, numpy, and (for LZ4/Blosc files) hdf5plugin. + +Examples +-------- + h5_read_bench.py aho.h5 aho_vis.h5 + h5_read_bench.py aho.h5 aho_vis.h5 --mode slices --repeat 7 + h5_read_bench.py a.h5 b.h5 --mode both --repeat 5 --slices 200 + +Modes +----- +full Read every dataset in full, once per repeat. Measures raw + decompression throughput. +slices Read many random row-ranges from the largest 2-D datasets. Closer to + how an event viewer or training loop actually touches the file, where + chunk-boundary and seek costs matter. +both Run each mode in turn (default). + +Cache handling +-------------- +Repeats are interleaved across files (file A, file B, file A, ...) so no file +gets a systematically warmer page cache. The FIRST repeat of each file is +reported separately as "cold" -- it includes whatever I/O the OS had not yet +cached -- and the remaining repeats are summarised by their median as "warm". +Compare warm numbers to judge decompression cost, and cold numbers to judge +first-open latency. For a genuinely cold measurement you need to drop the page +cache between runs, which requires root: + + sync && echo 3 > /proc/sys/vm/drop_caches +""" + +from __future__ import annotations + +import argparse +import os +import statistics +import sys +import time + +import numpy as np +import h5py + +try: + import hdf5plugin # noqa: F401 registers LZ4 / Blosc filters +except ImportError: + pass + + +_FILTER_NAMES = {'32004': 'lz4', '32001': 'blosc'} + + +def describe_filter(ds: h5py.Dataset) -> str: + for fid, name in _FILTER_NAMES.items(): + if fid in dict(ds._filters or {}): + return name + return str(ds.compression) if ds.compression else 'none' + + +def survey(path: str) -> dict: + """Collect dataset inventory and filter mix for *path*.""" + datasets: list[str] = [] + logical = 0 + filters: dict[str, int] = {} + with h5py.File(path, 'r') as f: + def visit(name, obj): + nonlocal logical + if isinstance(obj, h5py.Dataset): + datasets.append(name) + logical += obj.size * obj.dtype.itemsize + key = describe_filter(obj) + filters[key] = filters.get(key, 0) + 1 + f.visititems(visit) + return {'datasets': datasets, 'logical': logical, 'filters': filters, + 'size': os.path.getsize(path)} + + +def read_full(path: str) -> tuple[float, int]: + """Read every dataset fully. Returns (seconds, bytes touched).""" + total = 0 + t0 = time.perf_counter() + with h5py.File(path, 'r') as f: + def visit(_name, obj): + nonlocal total + if isinstance(obj, h5py.Dataset): + if obj.ndim == 0: + obj[()] + total += obj.dtype.itemsize + elif obj.size: + arr = obj[...] + total += arr.nbytes + f.visititems(visit) + return time.perf_counter() - t0, total + + +def pick_slice_targets(path: str, min_rows: int) -> list[str]: + """Names of 2-D datasets big enough to slice, largest first.""" + found: list[tuple[int, str]] = [] + with h5py.File(path, 'r') as f: + def visit(name, obj): + if isinstance(obj, h5py.Dataset) and obj.ndim >= 1 \ + and obj.shape[0] >= min_rows: + found.append((obj.size * obj.dtype.itemsize, name)) + f.visititems(visit) + found.sort(reverse=True) + return [name for _, name in found] + + +def read_slices(path: str, targets: list[str], n_slices: int, + rows: int, seed: int) -> tuple[float, int]: + """Read *n_slices* random row-ranges spread over *targets*.""" + rng = np.random.default_rng(seed) + total = 0 + t0 = time.perf_counter() + with h5py.File(path, 'r') as f: + usable = [t for t in targets if t in f] + if not usable: + return 0.0, 0 + for i in range(n_slices): + name = usable[i % len(usable)] + ds = f[name] + n = ds.shape[0] + span = min(rows, n) + start = int(rng.integers(0, max(1, n - span + 1))) + arr = ds[start:start + span] + total += arr.nbytes + return time.perf_counter() - t0, total + + +def fmt_rate(nbytes: int, seconds: float) -> str: + if seconds <= 0: + return " n/a" + return f"{nbytes / 2**20 / seconds:8.1f}" + + +def run_mode(mode: str, paths: list[str], repeat: int, args) -> dict: + """Interleave *repeat* passes of *mode* across *paths*.""" + timings: dict[str, list[float]] = {p: [] for p in paths} + volumes: dict[str, int] = {p: 0 for p in paths} + + targets = None + if mode == 'slices': + targets = pick_slice_targets(paths[0], args.rows) + if not targets: + print(f" (no dataset with >= {args.rows} rows; skipping slices)") + return {} + + for r in range(repeat): + for path in paths: + if mode == 'full': + dt, nb = read_full(path) + else: + # Same seed every pass so all files read identical ranges. + dt, nb = read_slices(path, targets, args.slices, + args.rows, args.seed) + timings[path].append(dt) + volumes[path] = nb + + print(f"\n {'file':<28} {'cold s':>9} {'warm s':>9} " + f"{'warm MiB/s':>11} {'vs first':>9}") + print(f" {'-' * 70}") + + baseline = None + results = {} + for path in paths: + ts = timings[path] + cold = ts[0] + warm = statistics.median(ts[1:]) if len(ts) > 1 else ts[0] + rate = fmt_rate(volumes[path], warm) + if baseline is None: + baseline = warm + rel = " 1.00x" + else: + rel = f"{warm / baseline:7.2f}x" if baseline > 0 else " n/a" + print(f" {os.path.basename(path):<28} {cold:9.3f} {warm:9.3f} " + f"{rate:>11} {rel:>9}") + results[path] = {'cold': cold, 'warm': warm, 'bytes': volumes[path]} + + if repeat < 3: + print(" (note: --repeat < 3 gives a weak median; try 5+)") + return results + + +def main() -> None: + p = argparse.ArgumentParser( + description="Compare HDF5 read speed across files.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + p.add_argument('files', nargs='+', help="two or more HDF5 files") + p.add_argument('--mode', default='both', choices=('full', 'slices', 'both')) + p.add_argument('--repeat', type=int, default=5, + help="passes per file; first is reported as cold") + p.add_argument('--slices', type=int, default=300, + help="number of random row-ranges read in slices mode") + p.add_argument('--rows', type=int, default=4096, + help="rows per slice, and minimum size to be a slice target") + p.add_argument('--seed', type=int, default=1234, + help="RNG seed; identical ranges are read from every file") + args = p.parse_args() + + if len(args.files) < 2: + sys.exit("error: give at least two files to compare") + for path in args.files: + if not os.path.exists(path): + sys.exit(f"error: no such file: {path}") + + print("=" * 72) + print("HDF5 read benchmark") + print("=" * 72) + infos = {} + for path in args.files: + info = survey(path) + infos[path] = info + mix = ', '.join(f"{k}x{v}" for k, v in sorted(info['filters'].items())) + print(f" {os.path.basename(path):<28} " + f"{info['size'] / 2**20:8.2f} MiB on disk " + f"{info['logical'] / 2**20:8.2f} MiB logical " + f"[{mix}]") + + shapes = {tuple(sorted(i['datasets'])) for i in infos.values()} + if len(shapes) > 1: + print("\n WARNING: files do not contain the same dataset names; " + "the comparison may not be meaningful.") + + modes = ('full', 'slices') if args.mode == 'both' else (args.mode,) + for mode in modes: + print(f"\n{'-' * 72}\nmode: {mode}" + + (f" ({args.slices} slices x {args.rows} rows)" + if mode == 'slices' else "") + + f" repeat={args.repeat}") + run_mode(mode, args.files, args.repeat, args) + + print(f"\n{'=' * 72}") + print("Lower warm-s is faster. Compare 'MiB/s' for decompression " + "throughput;\nsee the module docstring for how to get a truly " + "cold-cache measurement.") + + +if __name__ == '__main__': + main() From 358abd9e8af7c3367bc889fba2c48e3fc72bb7c7 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 01:52:54 -0700 Subject: [PATCH 20/62] vis: wire the [D] shortcut the DBG button already advertises The DBG button's tooltip reads "Log debug info [D]", but the global keydown handler never mapped 'd' -- it covers c, a, s, space, r, o, i, g, ? and Escape. The key did nothing, so the only way to dump the camera position and loaded point counts was to find the button. --- vis_force.html | 2 ++ 1 file changed, 2 insertions(+) diff --git a/vis_force.html b/vis_force.html index 434595c..33a8618 100644 --- a/vis_force.html +++ b/vis_force.html @@ -3194,6 +3194,8 @@

⌨ Keyboard Shortcuts

if(e.key==='o') toggleRotation(); if(e.key==='i') toggleInterPanel(); if(e.key==='g') toggleGraphPanel(); + // The DBG button's tooltip advertises [D]; wire it so the key works. + if(e.key==='d') document.getElementById('btn-debug').click(); if(e.key==='?') helpEl.classList.toggle('open'); if(e.key==='Escape'){ helpEl.classList.remove('open'); From 6966b882984e5dd79d824c3e6d05df31fbae93e1 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 01:57:15 -0700 Subject: [PATCH 21/62] tests: cover the HDF5 writer and reader, and fix write_events() The IO layer had no tests. It is the part of the codebase most likely to be changed by a format revision and the only part whose breakage is silent -- a wrong column reads back as plausible numbers -- so it was the wrong place to have zero coverage, particularly with a 3.0.0 schema change coming. 29 round-trip tests pinning format 2.3.0: scalar fields, stored sem_type (which must be trusted rather than re-derived on read), point-cloud values and width including narrow-pad and wide-truncate, zero-point particles, empty events, multi-event boundaries with per-event id reset, bulk and streaming writers agreeing, representative levels with their member lists and group parent pointers, an empty level keeping its event fencepost consistent, event clouds, fencepost monotonicity and sizing, all four compression filters producing identical data, repack in-place and to-dst with filter preservation, compression switching, rechunking, the dst==src guard, verification actually detecting corruption, and the pre-2.2.0 fallback when geant4_id is absent. Reading is checked both through EventStore, which is what consumers use, and through raw h5py, which pins the dataset layout other tools depend on -- the WebGL viewer reads these names directly. The first run failed 17 of 29, all on the same defect: write_events() never wrote particles/interaction_id, in any commit in this repository's history. read_events() requires that column, so the bulk writer has always produced files its own reader cannot open -- despite being exported and shown in the README quick start. It survived because everything real goes through the streaming EventWriter. geant4_id was missing from the same path. Both are added here, which is why io.py is part of this commit. --- pysupera/io.py | 11 +- tests/test_io.py | 362 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 371 insertions(+), 2 deletions(-) create mode 100644 tests/test_io.py diff --git a/pysupera/io.py b/pysupera/io.py index d99d62a..91acc13 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -158,10 +158,12 @@ def write_events(path: str, # Particle-level scalars p_id = np.empty(n_total_particles, dtype=np.int32) + p_geant4_id = np.empty(n_total_particles, dtype=np.int32) p_parent_id = np.empty(n_total_particles, dtype=np.int32) p_root_id = np.empty(n_total_particles, dtype=np.int32) p_pdg = np.empty(n_total_particles, dtype=np.int32) p_parent_pdg = np.empty(n_total_particles, dtype=np.int32) + p_int_id = np.empty(n_total_particles, dtype=np.int32) p_itype = np.empty(n_total_particles, dtype=np.int32) p_sem_type = np.empty(n_total_particles, dtype=np.int8) @@ -172,10 +174,12 @@ def write_events(path: str, for ev in events: for p in ev: p_id[j] = p.id + p_geant4_id[j] = getattr(p, "geant4_id", p.id) p_parent_id[j] = p.parent_id p_root_id[j] = p.root_id p_pdg[j] = p.pdg p_parent_pdg[j] = p.parent_pdg + p_int_id[j] = int(p._interaction_id) p_itype[j] = int(p._interaction_type) p_sem_type[j] = int(p.sem_type.value) pc_offsets[j + 1] = pc_offsets[j] + len(p.point_cloud) @@ -213,10 +217,12 @@ def write_events(path: str, ) if n_total_particles > 0: _mk("id", p_id) + _mk("geant4_id", p_geant4_id) _mk("parent_id", p_parent_id) _mk("root_id", p_root_id) _mk("pdg", p_pdg) _mk("parent_pdg", p_parent_pdg) + _mk("interaction_id", p_int_id) _mk("interaction_type", p_itype) pg.create_dataset( "sem_type", data=p_sem_type, @@ -224,8 +230,9 @@ def write_events(path: str, **ckw, ) else: - for name in ("id", "parent_id", "root_id", - "pdg", "parent_pdg", "interaction_type"): + for name in ("id", "geant4_id", "parent_id", "root_id", + "pdg", "parent_pdg", "interaction_id", + "interaction_type"): pg.create_dataset(name, data=np.empty(0, dtype=np.int32)) pg.create_dataset("sem_type", data=np.empty(0, dtype=np.int8)) pg.create_dataset("pc_offsets", data=pc_offsets) diff --git a/tests/test_io.py b/tests/test_io.py new file mode 100644 index 0000000..9db87b3 --- /dev/null +++ b/tests/test_io.py @@ -0,0 +1,362 @@ +""" +Round-trip tests for the HDF5 writer and reader. + +These pin the on-disk format (2.3.0) so that a schema change has to break a +test rather than a downstream analysis. The IO layer previously had no +coverage at all, which made it the riskiest part of the codebase to touch. + +Reading is checked two ways on purpose: through the public API +(:class:`~pysupera.io.EventStore`), which is what consumers use, and through +raw h5py, which pins the actual dataset layout that other tools -- the WebGL +viewer among them -- depend on. +""" +import numpy as np +import pytest +import h5py + +from pysupera import (Particle, open_writer, write_events, read_events, + EventStore, voxmap_path) +from pysupera.io import FORMAT_VERSION, _PC_NDIM, repack +from pysupera.utils import SemanticType +from tests.conftest import make_particle, PT_PRIMARY, PT_TRACK, PT_DECAY + + +SCALARS = ("id", "geant4_id", "parent_id", "root_id", "pdg", "parent_pdg", + "interaction_id", "interaction_type") + + +def rep(pid, members, n_pts=4, **kw): + """A representative particle with a member list, as the rep levels expect.""" + p = make_particle(pid, kw.pop("itype", PT_PRIMARY), n_pts=n_pts, **kw) + p.member_ids = list(members) + p.parent_frag_id = kw.get("parent_frag_id", -1) + p.parent_inst_id = kw.get("parent_inst_id", -1) + return p + + +def simple_event(ids, n_pts=5): + return [make_particle(i, PT_TRACK, n_pts=n_pts) for i in ids] + + +# ============================================================================ +# Particle round trip +# ============================================================================ + +class TestParticleRoundTrip: + + def test_scalar_fields_survive(self, tmp_path): + p = make_particle(7, PT_DECAY, pdg=13, parent_pdg=211, + parent_id=3, root_id=1, interaction_id=4) + p.geant4_id = 4242 + path = str(tmp_path / "a.h5") + write_events(path, [[p]]) + with read_events(path) as store: + q = store[0][0] + assert (q.id, q.geant4_id, q.parent_id, q.root_id) == (7, 4242, 3, 1) + assert (q.pdg, q.parent_pdg) == (13, 211) + assert q._interaction_id == 4 + assert q._interaction_type == int(p._interaction_type) + + def test_sem_type_is_stored_not_rederived(self, tmp_path): + # The writer stores sem_type and the reader must trust it rather than + # re-running the classifier, which could disagree after preprocessing. + p = make_particle(1, PT_TRACK, n_pts=20) + p.sem_type = SemanticType.kMichel + path = str(tmp_path / "b.h5") + write_events(path, [[p]]) + with read_events(path) as store: + assert store[0][0].sem_type is SemanticType.kMichel + + def test_point_cloud_values_survive(self, tmp_path): + pc = np.arange(18, dtype=np.float32).reshape(3, 6) + p = make_particle(1, PT_TRACK, pc=pc) + path = str(tmp_path / "c.h5") + write_events(path, [[p]]) + with read_events(path) as store: + out = store[0][0].point_cloud + assert out.shape == (3, _PC_NDIM) + np.testing.assert_allclose(out, pc) + + def test_zero_point_particle(self, tmp_path): + p = make_particle(1, PT_TRACK, pc=np.zeros((0, 6), dtype=np.float32)) + path = str(tmp_path / "d.h5") + write_events(path, [[p]]) + with read_events(path) as store: + q = store[0][0] + assert len(q.point_cloud) == 0 + assert q.id == 1 + + def test_empty_event(self, tmp_path): + path = str(tmp_path / "e.h5") + write_events(path, [[], simple_event([1, 2]), []]) + with read_events(path) as store: + assert len(store) == 3 + assert store[0] == [] and store[2] == [] + assert [p.id for p in store[1]] == [1, 2] + + def test_multi_event_boundaries(self, tmp_path): + events = [simple_event([0, 1, 2]), simple_event([0, 1]), + simple_event([0, 1, 2, 3])] + path = str(tmp_path / "f.h5") + write_events(path, events) + with read_events(path) as store: + assert len(store) == 3 + assert [len(store[i]) for i in range(3)] == [3, 2, 4] + # ids restart per event + assert [p.id for p in store[2]] == [0, 1, 2, 3] + + def test_incremental_writer_matches_bulk(self, tmp_path): + events = [simple_event([0, 1]), simple_event([0, 1, 2])] + bulk, incr = str(tmp_path / "g1.h5"), str(tmp_path / "g2.h5") + write_events(bulk, events) + with open_writer(incr) as w: + for ev in events: + w.append_event(ev) + with h5py.File(bulk) as a, h5py.File(incr) as b: + for name in SCALARS: + np.testing.assert_array_equal(a[f"particles/{name}"][:], + b[f"particles/{name}"][:]) + np.testing.assert_allclose(a["points/flat"][:], b["points/flat"][:]) + + +# ============================================================================ +# Point-cloud width +# ============================================================================ + +class TestPointCloudWidth: + + def test_written_width_is_pc_ndim(self, tmp_path): + path = str(tmp_path / "w.h5") + write_events(path, [[make_particle(1, PT_TRACK, n_pts=3)]]) + with h5py.File(path) as f: + assert f["points/flat"].shape[1] == _PC_NDIM + + def test_narrow_cloud_is_zero_padded(self, tmp_path): + pc = np.array([[1., 2., 3.]], dtype=np.float32) # xyz only + path = str(tmp_path / "n.h5") + write_events(path, [[make_particle(1, PT_TRACK, pc=pc)]]) + with read_events(path) as store: + out = store[0][0].point_cloud + assert out.shape == (1, _PC_NDIM) + np.testing.assert_allclose(out[0, :3], [1., 2., 3.]) + np.testing.assert_allclose(out[0, 3:], 0.0) + + def test_wide_cloud_is_truncated(self, tmp_path): + pc = np.arange(9, dtype=np.float32).reshape(1, 9) + path = str(tmp_path / "t.h5") + write_events(path, [[make_particle(1, PT_TRACK, pc=pc)]]) + with read_events(path) as store: + out = store[0][0].point_cloud + assert out.shape == (1, _PC_NDIM) + np.testing.assert_allclose(out[0], pc[0, :_PC_NDIM]) + + +# ============================================================================ +# Representative levels +# ============================================================================ + +class TestRepresentativeLevels: + + def _write(self, path): + parts = simple_event([0, 1, 2, 3]) + frags = [rep(0, [0, 1]), rep(2, [2, 3])] + insts = [rep(0, [0, 1, 2, 3])] + frags[1].parent_frag_id = 0 + insts[0].parent_inst_id = 0 + with open_writer(path) as w: + w.append_fragments(frags) + w.append_instances(insts) + w.append_event(parts) + return parts, frags, insts + + def test_rows_and_ids(self, tmp_path): + path = str(tmp_path / "r.h5") + self._write(path) + with h5py.File(path) as f: + assert f["particle_fragments/id"].shape[0] == 2 + assert f["particle_instances/id"].shape[0] == 1 + np.testing.assert_array_equal(f["particle_fragments/id"][:], [0, 2]) + + def test_member_lists(self, tmp_path): + path = str(tmp_path / "m.h5") + self._write(path) + with h5py.File(path) as f: + mo = f["particle_fragments/member_offsets"][:] + mem = f["particle_fragments_members/flat"][:] + assert mo.tolist() == [0, 2, 4] + np.testing.assert_array_equal(mem, [0, 1, 2, 3]) + + def test_group_parent_pointers(self, tmp_path): + path = str(tmp_path / "p.h5") + self._write(path) + with h5py.File(path) as f: + assert f["particle_fragments/parent_frag_id"][:].tolist() == [-1, 0] + assert f["particle_instances/parent_inst_id"][:].tolist() == [0] + + def test_empty_rep_level_keeps_offsets_consistent(self, tmp_path): + # write_fragments=false passes an empty list; the event fencepost must + # still advance so the file stays internally consistent. + path = str(tmp_path / "z.h5") + with open_writer(path) as w: + w.append_fragments([]) + w.append_instances([]) + w.append_event(simple_event([0, 1])) + with h5py.File(path) as f: + assert f["particle_fragments/id"].shape[0] == 0 + assert f["frag_events/offsets"][:].tolist() == [0, 0] + assert int(f["n_events"][()]) == 1 + + +# ============================================================================ +# Event clouds +# ============================================================================ + +class TestEventClouds: + + def test_round_trip_and_split(self, tmp_path): + nle = np.arange(18, dtype=np.float32).reshape(2, 9) + le = np.arange(9, dtype=np.float32).reshape(1, 9) + path = str(tmp_path / "ec.h5") + with open_writer(path) as w: + w.append_event_clouds(nle, le) + w.append_event(simple_event([0])) + with h5py.File(path) as f: + np.testing.assert_allclose(f["non_le_cloud/flat"][:], nle) + np.testing.assert_allclose(f["le_scatter_cloud/flat"][:], le) + assert f["non_le_cloud/offsets"][:].tolist() == [0, 2] + assert f["le_scatter_cloud/offsets"][:].tolist() == [0, 1] + + +# ============================================================================ +# Offsets +# ============================================================================ + +class TestOffsets: + + def test_fenceposts_are_monotonic_and_sized(self, tmp_path): + events = [simple_event([0, 1]), simple_event([0, 1, 2])] + path = str(tmp_path / "o.h5") + write_events(path, events) + with h5py.File(path) as f: + n = int(f["n_events"][()]) + ev = f["events/offsets"][:] + pc = f["particles/pc_offsets"][:] + assert len(ev) == n + 1 + assert ev.tolist() == sorted(ev.tolist()) + assert ev[-1] == f["particles/id"].shape[0] + assert len(pc) == f["particles/id"].shape[0] + 1 + assert pc[-1] == f["points/flat"].shape[0] + + def test_format_version_recorded(self, tmp_path): + path = str(tmp_path / "v.h5") + write_events(path, [simple_event([0])]) + with h5py.File(path) as f: + assert f["format_version"][()].decode() == FORMAT_VERSION + + +# ============================================================================ +# Compression +# ============================================================================ + +class TestCompression: + + @pytest.mark.parametrize("comp", ["lzf", "gzip", "lz4", None]) + def test_filter_does_not_change_data(self, tmp_path, comp): + events = [simple_event([0, 1, 2], n_pts=8)] + ref = str(tmp_path / "ref.h5") + write_events(ref, events, compression="lzf") + other = str(tmp_path / f"{comp}.h5") + write_events(other, events, compression=comp) + with read_events(ref) as a, read_events(other) as b: + pa, pb = a[0], b[0] + assert [p.id for p in pa] == [p.id for p in pb] + for x, y in zip(pa, pb): + np.testing.assert_allclose(x.point_cloud, y.point_cloud) + + +# ============================================================================ +# repack +# ============================================================================ + +class TestRepack: + + def _file(self, tmp_path, name="src.h5"): + path = str(tmp_path / name) + write_events(path, [simple_event([0, 1, 2], n_pts=6), + simple_event([0, 1], n_pts=6)]) + return path + + def test_in_place_preserves_data(self, tmp_path): + path = self._file(tmp_path) + with read_events(path) as s: + before = [p.point_cloud.copy() for p in s[0]] + repack(path, verify=True, verbose=False) + with read_events(path) as s: + after = [p.point_cloud for p in s[0]] + assert len(before) == len(after) + for x, y in zip(before, after): + np.testing.assert_allclose(x, y) + + def test_to_dst_leaves_source_alone(self, tmp_path): + src = self._file(tmp_path) + dst = str(tmp_path / "dst.h5") + repack(src, dst=dst, verify=True, verbose=False) + with read_events(src) as a, read_events(dst) as b: + assert len(a) == len(b) == 2 + np.testing.assert_allclose(a[0][0].point_cloud, b[0][0].point_cloud) + + def test_switches_compression(self, tmp_path): + src = self._file(tmp_path) + dst = str(tmp_path / "gz.h5") + repack(src, dst=dst, compression="gzip", verify=True, verbose=False) + with h5py.File(dst) as f: + assert f["points/flat"].compression == "gzip" + + def test_preserves_filter_by_default(self, tmp_path): + src = str(tmp_path / "gz_src.h5") + write_events(src, [simple_event([0, 1])], compression="gzip") + dst = str(tmp_path / "out.h5") + repack(src, dst=dst, verify=True, verbose=False) + with h5py.File(dst) as f: + assert f["points/flat"].compression == "gzip" + + def test_rechunk_changes_layout_not_data(self, tmp_path): + src = self._file(tmp_path) + dst = str(tmp_path / "rc.h5") + repack(src, dst=dst, rechunk=True, verify=True, verbose=False) + with read_events(src) as a, read_events(dst) as b: + np.testing.assert_allclose(a[0][0].point_cloud, b[0][0].point_cloud) + + def test_dst_equal_to_src_is_rejected(self, tmp_path): + src = self._file(tmp_path) + with pytest.raises(ValueError, match="must differ"): + repack(src, dst=src, verbose=False) + + def test_verify_catches_corruption(self, tmp_path): + # Corrupt the copy after writing to prove verify actually compares. + src = self._file(tmp_path) + dst = str(tmp_path / "bad.h5") + repack(src, dst=dst, verify=True, verbose=False) + with h5py.File(dst, "r+") as f: + f["points/flat"][0, 0] += 1.0 + from pysupera.io import _verify_same_datasets + with pytest.raises(ValueError, match="verification failed"): + _verify_same_datasets(src, dst) + + +# ============================================================================ +# Backward compatibility +# ============================================================================ + +class TestBackwardCompatibility: + + def test_missing_geant4_id_falls_back_to_id(self, tmp_path): + # Files written before 2.2.0 have no geant4_id column. Simulate one + # by deleting the dataset, and check the reader substitutes `id`. + path = str(tmp_path / "old.h5") + write_events(path, [simple_event([0, 1, 2])]) + with h5py.File(path, "r+") as f: + del f["particles/geant4_id"] + with read_events(path) as store: + ps = store[0] + assert [p.geant4_id for p in ps] == [p.id for p in ps] == [0, 1, 2] From 8f48fbc58c6e25e2272f6585b25f33cb881f0b74 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 22/62] rename root_id to ancestor_id "root" said nothing about which root. The field is the primary the particle descends from, so name it that. This commit carries the files that contain only the rename; _run.py, io.py, preproc.py, utils.py, the readers and the viewer carry it too, alongside their own changes, and say so. No behaviour change. --- pysupera/app.py | 4 ++-- pysupera/conditions/touching_em_shower.py | 16 ++++++++-------- pysupera/diagnostics.py | 10 +++++----- pysupera/partitioner.py | 8 ++++---- tests/conftest.py | 8 ++++---- tests/test_partitioner.py | 8 ++++---- 6 files changed, 27 insertions(+), 27 deletions(-) diff --git a/pysupera/app.py b/pysupera/app.py index 644b652..48c8057 100644 --- a/pysupera/app.py +++ b/pysupera/app.py @@ -232,7 +232,7 @@ def _collect_sem_groups(particle_iter): meta={"sem_type": p.sem_type.name}, hovertemplate=( f"id={p.id} pdg={p.pdg} sem={p.sem_type.name}
" - f"parent_id={p.parent_id} root_id={p.root_id}
" + f"parent_id={p.parent_id} ancestor_id={p.ancestor_id}
" "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" ), ), @@ -290,7 +290,7 @@ def _collect_sem_groups(particle_iter): hovertemplate=( f"partition {i} ({len(part)} particles)
" f"rep: id={rep.id} sem={rep.sem_type.name}
" - f"rep parent_id={rep.parent_id} root_id={rep.root_id}
" + f"rep parent_id={rep.parent_id} ancestor_id={rep.ancestor_id}
" f"members: [{ids_str}]
" "x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}" ), diff --git a/pysupera/conditions/touching_em_shower.py b/pysupera/conditions/touching_em_shower.py index 67b9ab0..ab1c042 100644 --- a/pysupera/conditions/touching_em_shower.py +++ b/pysupera/conditions/touching_em_shower.py @@ -24,7 +24,7 @@ class TouchingEMShower(PartitionConditionBase): 1. ``get_candidates`` builds parent-child pairs where both members have PDG code 11 (electron) or 22 (photon) – O(n). 2. ``get_candidates`` then filters those pairs to retain only pairs - that share the same ``root_id`` – O(candidates). + that share the same ``ancestor_id`` – O(candidates). 3. The base partitioner tests proximity on the remaining candidates. """ @@ -41,7 +41,7 @@ def get_candidates(self, partitioner: 'ParticlePartitioner') -> List[Tuple[int, Return parent-child PDG-11/22 pairs that share an ancestor. Iterates the full particle list once to collect direct parent-child - edges (O(n)), then discards pairs with different ``root_id`` + edges (O(n)), then discards pairs with different ``ancestor_id`` values (O(candidates)). Both filters run inside this method so that the list handed to the proximity checker is as small as possible. @@ -84,7 +84,7 @@ def get_rep_candidates( * the parent representative also has PDG code 11 or 22 **and** is not ``kLEScatter`` (kLEScatter reps must not become shower parents — ``AbsorbLEScatter`` is responsible for handling them), - * child and parent representatives share the same ``root_id``. + * child and parent representatives share the same ``ancestor_id``. Because *rep_lookup* is updated after every merge, this method correctly handles chains: if A merged into B, the representative for @@ -125,7 +125,7 @@ def get_rep_candidates( if parent_rep.sem_type == _le_type: # kLEScatter rep must not absorb non-LE children continue - if rep.root_id != parent_rep.root_id: + if rep.ancestor_id != parent_rep.ancestor_id: continue candidates.append((rep.id, parent_rep.id)) @@ -175,10 +175,10 @@ def _filter_by_ancestor(self, partitioner: 'ParticlePartitioner', candidates: List[Tuple[int, int]]) -> List[Tuple[int, int]]: """ - Keep only pairs that share the same ``root_id``. + Keep only pairs that share the same ``ancestor_id``. Iterates *candidates* once (O(candidates)) and discards any pair - whose two particles have different ``root_id`` values. When + whose two particles have different ``ancestor_id`` values. When diagnostics are enabled, each rejected pair is recorded with reason ``DIFFERENT_ANCESTOR``. @@ -201,13 +201,13 @@ def _filter_by_ancestor(self, p1 = partitioner.particle_lookup[id1] p2 = partitioner.particle_lookup[id2] - if p1.root_id == p2.root_id: + if p1.ancestor_id == p2.ancestor_id: filtered.append((id1, id2)) elif partitioner.diagnostics.enabled: partitioner.diagnostics.record( id1, id2, MergeOutcome.DIFFERENT_ANCESTOR, - f"root_id: {p1.root_id} vs {p2.root_id}", + f"ancestor_id: {p1.ancestor_id} vs {p2.ancestor_id}", stage=f"{self.name}: Stage 2 Ancestor Filter", ) diff --git a/pysupera/diagnostics.py b/pysupera/diagnostics.py index 2b4ced4..0b8e91d 100644 --- a/pysupera/diagnostics.py +++ b/pysupera/diagnostics.py @@ -29,7 +29,7 @@ class MergeOutcome(Enum): One or both particle PDG codes are not in the condition's allowed set. DIFFERENT_ANCESTOR - The two particles have different ``root_id`` values. + The two particles have different ``ancestor_id`` values. NO_PARENT_CHILD_RELATION Neither particle is the direct parent of the other. WRONG_SEMID @@ -55,7 +55,7 @@ class MergeOutcome(Enum): NOT_IN_CANDIDATE_SET = "Pair was not in candidate set (failed metadata filter)" DIFFERENT_PDG = "Particles have different PDG values" WRONG_PDG_VALUE = "Particle PDG not in target set" - DIFFERENT_ANCESTOR = "Particles have different root_id" + DIFFERENT_ANCESTOR = "Particles have different ancestor_id" NO_PARENT_CHILD_RELATION = "Particles are not in parent-child relationship" WRONG_SEMID = "Particle SemID does not match required value" @@ -748,7 +748,7 @@ def _determine_metadata_rejection(self, p1: Particle, p2: Particle, stage: str) """ Diagnose which metadata property caused pair rejection at *stage*. - Checks parent-child relationship, PDG codes, ``root_id``, and + Checks parent-child relationship, PDG codes, ``ancestor_id``, and ``sem_type`` in turn and accumulates all violated conditions into a single human-readable string. @@ -773,8 +773,8 @@ def _determine_metadata_rejection(self, p1: Particle, p2: Particle, stage: str) reasons.append(f"different PDG ({p1.pdg} vs {p2.pdg})") # Check ancestor - if p1.root_id != p2.root_id: - reasons.append(f"different root_id ({p1.root_id} vs {p2.root_id})") + if p1.ancestor_id != p2.ancestor_id: + reasons.append(f"different ancestor_id ({p1.ancestor_id} vs {p2.ancestor_id})") # Check SemID if p1.sem_type != p2.sem_type: diff --git a/pysupera/partitioner.py b/pysupera/partitioner.py index 895db8d..579bd93 100644 --- a/pysupera/partitioner.py +++ b/pysupera/partitioner.py @@ -115,7 +115,7 @@ def _build_relationships(self): building: * ``children_map[parent_id]`` – list of direct child IDs. - * ``ancestor_map[root_id]`` – list of all descendant IDs that + * ``ancestor_map[ancestor_id]`` – list of all descendant IDs that share the same ancestor. """ self.children_map = defaultdict(list) @@ -124,8 +124,8 @@ def _build_relationships(self): for p in self.particles: if p.parent_id is not None: self.children_map[p.parent_id].append(p.id) - if p.root_id is not None: - self.ancestor_map[p.root_id].append(p.id) + if p.ancestor_id is not None: + self.ancestor_map[p.ancestor_id].append(p.id) def _create_backend(self, backend: str, n_jobs: int, **kwargs): """ @@ -757,7 +757,7 @@ def _build_partitions_incremental( # Build representative Particle objects: shallow copies whose point_cloud # will grow as partitions merge. All scalar attributes (id, pdg, sem_type, - # parent_id, root_id, …) remain those of the original particle. + # parent_id, ancestor_id, …) remain those of the original particle. reps: Dict[int, Particle] = {} for idx, p in enumerate(self.particles): rep = copy.copy(p) diff --git a/tests/conftest.py b/tests/conftest.py index a5cf206..569536d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -43,7 +43,7 @@ def make_particle( pdg: int = 11, parent_pdg: int = 0, parent_id: int | None = None, - root_id: int | None = None, + ancestor_id: int | None = None, pc: np.ndarray | None = None, n_pts: int = 10, offset: tuple = (0.0, 0.0, 0.0), @@ -51,8 +51,8 @@ def make_particle( ) -> Particle: if parent_id is None: parent_id = pid - if root_id is None: - root_id = pid + if ancestor_id is None: + ancestor_id = pid if pc is None: rng = np.random.default_rng(pid) xyz = rng.uniform(0, 0.1, size=(n_pts, 3)).astype(np.float32) @@ -61,7 +61,7 @@ def make_particle( return Particle( id = pid, parent_id = parent_id, - root_id = root_id, + ancestor_id = ancestor_id, pdg = pdg, parent_pdg = parent_pdg, interaction_id = interaction_id, diff --git a/tests/test_partitioner.py b/tests/test_partitioner.py index 26ba6ae..8912be0 100644 --- a/tests/test_partitioner.py +++ b/tests/test_partitioner.py @@ -159,9 +159,9 @@ def test_all_particles_preserved(self): particles = [ make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, root_id=1), + parent_id=1, ancestor_id=1), make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, root_id=1), + parent_id=1, ancestor_id=1), make_particle(3, PT_IONIZATION, pdg=11, offset=(50, 0, 0)), ] _, parts = self._run_pipeline(particles) @@ -171,9 +171,9 @@ def test_all_particles_preserved(self): def test_photon_and_electron_merged_in_pipeline(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, root_id=1) + parent_id=1, ancestor_id=1) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, root_id=1) + parent_id=1, ancestor_id=1) _, parts = self._run_pipeline([photon, electron]) # After PhotonDecay, they should be in 1 partition all_ids_per_part = [frozenset(p.id for p in part) for part in parts] From 5c9a2813bfe81c13c8368708c95a50df04ad7388 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 23/62] readers: fix dX, derive the ancestor, and stop masking failures Three unrelated defects in one layer. dX was never read. _STEP_FIELD_MAP had (PointFeature.dx, ("dx")) where ("dx") is the string "dx", not a tuple, so the loop iterated its characters and matched the field "x". Every dX in every output file since the column was added held an x coordinate. Now ("dx",). ancestor_track_id from EDepSim is wrong for decay products, so it is no longer read. _get_ancestor_id walks parent_track_id instead -- memoised and cycle-safe -- which is the field that is correct. On the old sample, 15 seeds stranded 4,423 of 13,460 particles in one event; across five events, 34,570 of 123,231 (28.1%). Reported upstream; see edepsim-ancestor-bug.md. The JAXTPC path had six branches that silently produced an empty point cloud when an expected dataset was missing -- a wrong --jaxtpc-seg-path gave zero points and no complaint. Each now raises KeyError naming what it looked for and what the file holds. It also accepts segment_to_group or deposit_to_group, imports hdf5plugin so Blosc-compressed inputs open at all, and records per-event mask statistics. get_visible_segment_indices_by_track grouped segments with a scan per track, O(tracks x segments): ~5k tracks each compared against 250k segments, 0.41 s of self time and the single largest cost in the pipeline -- in a function that is pure bookkeeping. Sorting once and splitting at the boundaries is O(N log N). End to end on a 38k-particle event: 3.45 s -> 3.04 s, 12%. Both files also carry their share of the root_id -> ancestor_id rename. --- pysupera/readers/format_edepsim_h5.py | 40 +++++++++++++++------------ pysupera/readers/format_jaxtpc.py | 25 +++++++++++++---- 2 files changed, 41 insertions(+), 24 deletions(-) diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 21e5f26..b36213f 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -19,7 +19,7 @@ per-event particle index instead (see _to_index_space). parent_track_id Immediate parent track ID root_track_id Root ancestor track ID. Unreliable in some files -- see - _get_root_id, which re-derives it from the parent chain. + _get_ancestor_id, which re-derives it from the parent chain. pdg PDG Monte Carlo particle code proc_start G4 process type integer at particle start vertex subproc_start G4 process subtype integer at particle start vertex @@ -277,7 +277,7 @@ def _to_index_space(parts: np.ndarray, root_track_ids: np.ndarray): from this list (a primary, or one whose parent was dropped upstream) becomes its own parent, which is the convention every genealogy walk in pysupera terminates on. - root_idx : ndarray of int32 + ancestor_idx : ndarray of int32 Index of each particle's primary ancestor. geant4_ids : ndarray of int32 The original ``track_id`` values, unchanged. @@ -288,14 +288,14 @@ def _to_index_space(parts: np.ndarray, root_track_ids: np.ndarray): ids = np.arange(n, dtype=np.int32) parent_idx = np.empty(n, dtype=np.int32) - root_idx = np.empty(n, dtype=np.int32) + ancestor_idx = np.empty(n, dtype=np.int32) for i in range(n): parent_idx[i] = index_of.get(int(parts['parent_track_id'][i]), i) - root_idx[i] = index_of.get(int(root_track_ids[i]), i) - return ids, parent_idx, root_idx, track_ids.astype(np.int32) + ancestor_idx[i] = index_of.get(int(root_track_ids[i]), i) + return ids, parent_idx, ancestor_idx, track_ids.astype(np.int32) -def _get_root_id(parts: np.ndarray) -> np.ndarray: +def _get_ancestor_id(parts: np.ndarray) -> np.ndarray: """ Return the true primary ancestor track ID of every particle. @@ -387,33 +387,35 @@ def _get_interaction_id(parts: np.ndarray, verts: np.ndarray) -> np.ndarray: track_ids = parts['track_id'] parent_ids = parts['parent_track_id'] # Use the walked primary rather than the stored ancestor field, which is - # unreliable -- see _get_root_id. - root_refs = _get_root_id(parts) + # unreliable -- see _get_ancestor_id. + ancestor_refs = _get_ancestor_id(parts) index_of = {int(t): i for i, t in enumerate(track_ids)} # Step 0: loop over root particles and match to one of vertices by (x,y,z,t) proximity. - root_indices = np.where(track_ids == root_refs)[0] + ancestor_indices = np.where(track_ids == ancestor_refs)[0] - for index in root_indices: + for index in ancestor_indices: p = parts[index] - for vtx in verts: + for vtx_row, vtx in enumerate(verts): if np.isclose(p['x'], vtx['x'], atol=1e-4) and \ np.isclose(p['y'], vtx['y'], atol=1e-4) and \ np.isclose(p['z'], vtx['z'], atol=1e-4) and \ np.isclose(p['t'], vtx['t'], atol=1e-4): # Match found, do something - interaction_ids[index] = vtx['interaction_id'] + # The *row index* of the vertex, not its interaction_id + # field: this is what addresses the interactions table. + interaction_ids[index] = vtx_row break # Step 1: for non-root particles, assign the same interaction ID as their # root particle. A root reference that is absent from this event list is # left for step 2 rather than raising. for i in range(len(parts)): - if track_ids[i] == root_refs[i]: + if track_ids[i] == ancestor_refs[i]: continue - root_index = index_of.get(int(root_refs[i])) - if root_index is not None: - interaction_ids[i] = interaction_ids[root_index] + ancestor_index = index_of.get(int(ancestor_refs[i])) + if ancestor_index is not None: + interaction_ids[i] = interaction_ids[ancestor_index] # Step 2: fall back to the parent chain for anything still unassigned. # @@ -653,6 +655,8 @@ def __getitem__(self, index: int) -> list[Particle]: index += n verts = self._file[self._vertex_key][index] + #: vertices of the most recently read event, for the interactions table + self.last_vertices = verts parts = self._file[self._part_key][index] steps = self._file[self._step_key][index] ass = self._file[self._ass_key][index] @@ -666,12 +670,12 @@ def __getitem__(self, index: int) -> list[Particle]: ]) - _ids, _par, _root, _g4 = _to_index_space(parts, _get_root_id(parts)) + _ids, _par, _root, _g4 = _to_index_space(parts, _get_ancestor_id(parts)) return Particle.from_flat_arrays( ids = _ids, parent_ids = _par, - root_ids = _root, + ancestor_ids = _root, pdgs = parts["pdg"], parent_pdgs = _get_parent_pdg(parts), interaction_ids = int_ids, diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index 871a0ed..8014480 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -78,7 +78,7 @@ _get_parent_pdg, _get_interaction_id, _get_interaction_type, - _get_root_id, + _get_ancestor_id, _to_index_space, ) from ..data import Particle @@ -228,9 +228,20 @@ def get_visible_segment_indices_by_track( ) # Group visible segment indices by track ID. - for track_id in np.unique(track_ids_for_visible): - mask = track_ids_for_visible == track_id - track_to_indices[int(track_id)] = visible_seg_indices[mask] + # + # Sort once and split at the boundaries: O(N log N). Comparing the + # whole array against each unique track instead is O(T*N), and with + # ~5k tracks over ~250k visible segments that single loop was the + # largest self-time cost in the whole pipeline. + if len(track_ids_for_visible): + order = np.argsort(track_ids_for_visible, kind='stable') + tid_sorted = track_ids_for_visible[order] + seg_sorted = visible_seg_indices[order] + edges = np.flatnonzero( + np.r_[True, tid_sorted[1:] != tid_sorted[:-1], True] + ) + for a, b in zip(edges[:-1], edges[1:]): + track_to_indices[int(tid_sorted[a])] = seg_sorted[a:b] result.append(track_to_indices) @@ -468,6 +479,8 @@ def __getitem__(self, index: int) -> list[Particle]: # ── Step 1: load particle metadata from the EDepSim file ────────── # These provide PDG code, track IDs, interaction type, etc. verts = self._edepsim_file[self._vertex_key][index] + #: vertices of the most recently read event, for the interactions table + self.last_vertices = verts parts = self._edepsim_file[self._part_key][index] # ── Step 2: load per-volume segment data from the JAXTPC seg file ─ @@ -514,12 +527,12 @@ def __getitem__(self, index: int) -> list[Particle]: int_ids = _get_interaction_id(parts, verts) - _ids, _par, _root, _g4 = _to_index_space(parts, _get_root_id(parts)) + _ids, _par, _root, _g4 = _to_index_space(parts, _get_ancestor_id(parts)) return Particle.from_flat_arrays( ids = _ids, parent_ids = _par, - root_ids = _root, + ancestor_ids = _root, pdgs = parts['pdg'], parent_pdgs = _get_parent_pdg(parts), interaction_ids = int_ids, From d101bc67575b40a2fea7e2e5a362462b7190799d Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 24/62] preproc: keep every particle, and keep its provenance straight Defragmentation dropped a particle when all of its fragments fell under min_pc_size. That is the right call for a leaf and badly wrong for anything with children: the node vanishes and its whole subtree is stranded. The case that exposed it: a 534 MeV primary photon whose only deposits were two points 113 mm apart -- 61 eV of Compton and 3178 eV of argon K-shell relaxation at photoabsorption. Both clusters were size 1, so the photon was deleted and the 384-particle shower hanging off it re-rooted on a secondary electron. Over 100 events this removed 2,314 photons, every one of them the parent of an e+/e- pair, 77% of them heading a subtree larger than min_pc_size. Only one was a primary, so a primaries-only guard would have missed almost all of it. A fully split particle is now kept as a genealogy node with an empty cloud; its points live on in the spawned pieces. A neutral particle with no cloud of its own is the ordinary case the merging conditions already expect -- PhotonDecay is genealogical and needs no points -- so the node costs nothing. Nothing is dropped any more, which leaves the dropped-particle remap below it as defence rather than routine. Two provenance fixes in the same function. A spawned piece inherits the original's geant4_id rather than minting one from the fresh pysupera id, which previously fabricated track IDs present in no input file. And its parent_pdg is now the original's own PDG: parent_id is set to p.id, so copying p.parent_pdg left the two fields describing different particles. The dropped-particle remap likewise updates parent_pdg when it redirects parent_id, which it never did. After both, parent_pdg matches the parent's actual PDG in 0 of 433,593 particles over 20 events; it was wrong for every spawned fragment before. Also: dX is summed on merge (merging only ever happens within one particle, so path length adds), and subset_voxmap keeps the voxel mapping aligned when a cloud is split. Carries its share of the root_id -> ancestor_id rename. --- pysupera/preproc.py | 54 ++++++++++++++++++++-------- tests/test_preproc.py | 83 +++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 120 insertions(+), 17 deletions(-) diff --git a/pysupera/preproc.py b/pysupera/preproc.py index fbc268b..e4068e4 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -173,9 +173,10 @@ def _split_fragments( Returns ------- - kept : Particle or None - Original particle, possibly with point_cloud trimmed to large-fragment - points only. ``None`` if all fragments were small. + kept : Particle + Original particle, with ``point_cloud`` trimmed to the large-fragment + points -- empty when every fragment was small. Never ``None``: the + particle is retained as a genealogy node either way. spawned : list of Particle Newly created kLEScatter particles, one per small fragment. next_id : int @@ -208,9 +209,13 @@ def _split_fragments( # file and hiding the fact that the pieces share an origin. geant4_id = getattr(p, "geant4_id", p.id), parent_id = p.id, - root_id = p.root_id, + ancestor_id = p.ancestor_id, pdg = p.pdg, - parent_pdg = p.parent_pdg, + # The piece hangs off *p*, so its parent's PDG is p's own -- + # not p's parent's. Copying p.parent_pdg here would leave + # parent_id and parent_pdg describing different particles, + # and PhotonDecay keys off parent_pdg. + parent_pdg = p.pdg, interaction_id = p._interaction_id, interaction_type = p._interaction_type, point_cloud = frag_pc, @@ -225,10 +230,22 @@ def _split_fragments( p.point_cloud = pc[large_mask] p.voxmap = _kept_vm return p, spawned, next_id - # Every fragment was small, so *p* is dropped. References to it are - # repaired by the caller (see the dropped-particle remap in - # DefragmentBase.process). - return None, spawned, next_id + + # Every fragment was small. Keep *p* as a genealogy node with an empty + # cloud rather than dropping it -- its points now live in the spawned + # pieces. Dropping it used to strand its whole subtree: a primary photon + # whose only deposits were two stray Compton/photoabsorption points would + # vanish, and the 534 MeV shower hanging off it would re-root on a + # secondary electron. Over 100 events that removed 2,314 photons, every + # one of them the parent of an e+/e- pair, and 77% heading a subtree + # larger than min_pc_size. + # + # A neutral particle with no cloud of its own is the ordinary case the + # merging conditions already expect -- PhotonDecay is genealogical and + # needs no points -- so the node costs nothing and keeps the tree walkable. + p.point_cloud = pc[:0] + p.voxmap = subset_voxmap(getattr(p, 'voxmap', None), large_mask) + return p, spawned, next_id # ============================================================================ @@ -492,11 +509,12 @@ def _run_chunk(chunk): # --------------------------------------------------------------- # Repair references to particles this pass removed # --------------------------------------------------------------- - # A particle whose every fragment was small is dropped from the event. - # Anything still pointing at it -- its real children, and the fragments - # split off from it -- is left with a dangling parent_id. + # Retained for safety: _split_fragments no longer drops anything, so + # this set is normally empty. A particle removed by some other route + # would otherwise leave its children -- and the fragments split off + # from it -- with a dangling parent_id. # resolve_orphans later rewrites such a reference to the particle's own - # id while leaving root_id pointing at the original ancestor, producing + # id while leaving ancestor_id pointing at the original ancestor, producing # a particle that is its own parent yet claims a foreign root. Redirect # to the dropped particle's parent instead, following chains of # consecutive drops, so the genealogy stays walkable. @@ -511,11 +529,17 @@ def _survivor(pid: int) -> int: pid = dropped[pid] return pid + # parent_pdg has to follow parent_id, or the redirect leaves the + # two naming different particles again. + pdg_by_id = {int(x.id): int(x.pdg) for x in result + spawned} for q in result + spawned: if int(q.parent_id) in dropped: q.parent_id = _survivor(int(q.parent_id)) - if int(q.root_id) in dropped: - q.root_id = _survivor(int(q.root_id)) + new_pdg = pdg_by_id.get(int(q.parent_id)) + if new_pdg is not None: + q.parent_pdg = new_pdg + if int(q.ancestor_id) in dropped: + q.ancestor_id = _survivor(int(q.ancestor_id)) cc_sizes = [len(xyz) for _, _, xyz in needs_cc] self.last_stats = { diff --git a/tests/test_preproc.py b/tests/test_preproc.py index 52dcfdb..92b2b17 100644 --- a/tests/test_preproc.py +++ b/tests/test_preproc.py @@ -190,11 +190,11 @@ def test_spawned_particle_has_correct_parentage(self): large = _cluster_pts(10, (0, 0, 0)) small = _cluster_pts(2, (100, 0, 0)) p = make_particle(42, PT_PRIMARY, pdg=11, pc=np.vstack([large, small]), - root_id=99) + ancestor_id=99) result = self.defrag().process([p]) spawned = next(r for r in result if r.id != 42) assert spawned.parent_id == 42 - assert spawned.root_id == 99 + assert spawned.ancestor_id == 99 # ── sem_types filter ───────────────────────────────────────────────── @@ -746,3 +746,82 @@ def test_output_voxel_col6_are_unique_per_particle(self): assert len(ids) == len(set(ids.astype(int).tolist())), ( f"Particle {p.id}: output voxel IDs are not unique: {ids}" ) + + +# ============================================================================ +# Split-off fragment provenance +# ============================================================================ + +class TestSplitFragmentParentPdg: + """ + A split-off piece hangs off the particle it came from, so parent_id and + parent_pdg must describe that same particle. PhotonDecay keys off + parent_pdg, so a stale value silently breaks re-association. + """ + + def _two_far_clusters(self, pdg, parent_pdg, n_each): + # two clusters far enough apart to be separate components + a = np.zeros((n_each, 3), np.float32) + b = np.zeros((n_each, 3), np.float32) + 500.0 + return make_particle(1, PT_PRIMARY, pdg=pdg, parent_pdg=parent_pdg, + pc=np.concatenate([a, b])) + + def test_spawned_piece_names_its_real_parent(self): + # big cluster keeps the particle alive; the small one is split off + a = np.zeros((20, 3), np.float32) + b = np.zeros((2, 3), np.float32) + 500.0 + p = make_particle(1, PT_PRIMARY, pdg=22, parent_pdg=13, + pc=np.concatenate([a, b])) + out = ScipyDefragmenter(distance_threshold=5.0, + min_pc_size=5).process([p]) + spawned = [q for q in out if q.id != 1] + assert spawned, "expected a split-off piece" + for q in spawned: + assert q.parent_id == 1 + assert q.parent_pdg == 22 # the photon's PDG, not 13 + + def test_spawned_piece_inherits_geant4_id(self): + a = np.zeros((20, 3), np.float32) + b = np.zeros((2, 3), np.float32) + 500.0 + p = make_particle(1, PT_PRIMARY, pdg=22, pc=np.concatenate([a, b])) + p.geant4_id = 77 + out = ScipyDefragmenter(distance_threshold=5.0, + min_pc_size=5).process([p]) + for q in [x for x in out if x.id != 1]: + assert q.geant4_id == 77 + + def test_a_fully_split_particle_survives_as_a_genealogy_node(self): + # every fragment of particle 2 is small. It used to be dropped, + # stranding its child 3; it must now survive with an empty cloud. + p1 = make_particle(1, PT_PRIMARY, pdg=13, n_pts=20) + p2 = self._two_far_clusters(22, 13, 2) + p2.id = 2; p2.parent_id = 1; p2.ancestor_id = 1 + p3 = make_particle(3, PT_PRIMARY, pdg=11, parent_pdg=22, + parent_id=2, ancestor_id=1, n_pts=20) + out = ScipyDefragmenter(distance_threshold=5.0, + min_pc_size=5).process([p1, p2, p3]) + by = {q.id: q for q in out} + assert 2 in by, "particle 2 must be kept as a genealogy node" + assert len(by[2].point_cloud) == 0, "its points moved to the pieces" + assert by[3].parent_id == 2 # the chain is still walkable + assert by[3].parent_pdg == 22 + + def test_its_points_move_to_the_spawned_pieces(self): + p = self._two_far_clusters(22, 13, 2) + out = ScipyDefragmenter(distance_threshold=5.0, + min_pc_size=5).process([p]) + by = {q.id: q for q in out} + assert len(by[p.id].point_cloud) == 0 + spawned = [q for q in out if q.id != p.id] + assert sum(len(q.point_cloud) for q in spawned) == 4 # 2 + 2 + for q in spawned: + assert q.parent_id == p.id and q.parent_pdg == 22 + + def test_nothing_is_dropped(self): + ps = [make_particle(1, PT_PRIMARY, pdg=13, n_pts=20), + self._two_far_clusters(22, 13, 1), + self._two_far_clusters(11, 22, 2)] + ps[1].id, ps[2].id = 2, 3 + out = ScipyDefragmenter(distance_threshold=5.0, + min_pc_size=5).process(ps) + assert {1, 2, 3} <= {q.id for q in out} From 25a04c03066cddfcb5d4613c848df8dac2459a66 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 25/62] utils: validate the genealogy, and repair it consistently resolve_orphans repaired parent_id and ancestor_id independently: if p.ancestor_id not in known_ids: p.ancestor_id = p.id Self-rooting is only correct when nothing survives. When a particle is removed but its descendants' parent links still terminate at a live particle, this scattered one shower across hundreds of false roots -- and contradicted validate_ancestor_ids, which walks the parent chain. In one event 373 particles were left claiming to be their own ancestor while their parents led to a live particle two links away. It now runs in two passes: repair every parent_id first, then set a dangling ancestor_id to the root of the repaired chain. _chain_root memoises along the way, since one shower shares one chain, and terminates on cycles. Over 100 events: 0 inconsistent ancestors, 0 missing interaction IDs. Three checks added, because both classes of defect were silent: validate_interaction_ids every particle must carry an interaction validate_ancestor_ids ancestor_id must equal what the parent chain says select_traceable_instances the instances worth writing, plus the zero-point ancestors needed to reach them PointFeature.id is documented rather than changed: it holds the input step's row index, in-memory clouds are (N, 7) while the file stores only columns 0-5, and the merge rules define no aggregation for it. Carries its share of the root_id -> ancestor_id rename. --- pysupera/utils.py | 111 +++++++++++++++++++++++++++++++------------ tests/test_utils.py | 112 +++++++++++++++++++++++++++++++++++++------- 2 files changed, 176 insertions(+), 47 deletions(-) diff --git a/pysupera/utils.py b/pysupera/utils.py index 07ec20b..2406cc5 100644 --- a/pysupera/utils.py +++ b/pysupera/utils.py @@ -114,7 +114,16 @@ class PointFeature(int): Additional scalar feature (e.g. a second energy-like quantity) summed over all hits that merge into the same output point. id : int = 6 - (optional) integer hit ID, e.g. for matching to truth-level information. + Row index of the deposit in the event's **input** step array, assigned + by the reader (``arange`` over the whole event, then sliced per + particle). Use it to join a point back to the exact input step. + + Two cautions. In-memory clouds are ``(N, 7)`` but the HDF5 output + stores only columns 0-5, so ``pc[:, PointFeature.id]`` works on a + :class:`~pysupera.data.Particle` and is **out of bounds** on points + read back from a file. And the merge rules define no aggregation for + it, so once voxelization collapses several deposits into one row the + value no longer identifies anything. """ x = 0 y = 1 @@ -218,7 +227,7 @@ def _row(p, tag=""): return _fmt_particle.format( id=p.id, pdg=p.pdg, sem=p.sem_type.name, - parent=p.parent_id, anc=p.root_id, + parent=p.parent_id, anc=p.ancestor_id, pc=len(p.point_cloud), ) + (f" [{tag}]" if tag else "") @@ -256,25 +265,62 @@ def _row(p, tag=""): return chain, children +def _chain_root(pid, by_id, memo): + """ + Root of the ``parent_id`` chain starting at *pid*. + + Memoised across calls -- one shower shares one chain, so without it the + walk is quadratic in the depth of the tree. Cycle-safe: a chain that + loops back on itself roots at the node where the loop was detected, since + there is no better answer and looping forever is not an option. + """ + path, seen = [], set() + cur = pid + while cur not in memo: + if cur in seen: # cycle + memo[cur] = cur + break + seen.add(cur) + nxt = int(by_id[cur].parent_id) + if nxt == cur or nxt not in by_id: # self-parented, or the end + memo[cur] = cur + break + path.append(cur) + cur = nxt + root = memo[cur] + for node in path: + memo[node] = root + return root + + def resolve_orphans(particles, verbose: bool = False): """ - Repair dangling ``parent_id`` and ``root_id`` references in a particle list + Repair dangling ``parent_id`` and ``ancestor_id`` references in a particle list so that every genealogy walk terminates at a particle that is actually present in the event. - An *orphan* is a particle whose ``parent_id`` (or ``root_id``) refers to a + An *orphan* is a particle whose ``parent_id`` (or ``ancestor_id``) refers to a particle ID that does not appear in *particles*. Such dangling references cause :meth:`~pysupera.partitioner.ParticlePartitioner._build_relationships` to build an incomplete ``children_map`` / ``ancestor_map``, and they also break the ``find_initiator`` walk inside :func:`~pysupera.merge.merge_em_showers`. - The repair rule is conservative: + The repair proceeds in two passes, and the order matters: + + 1. If ``p.parent_id`` is not in the known-ID set, reset it to ``p.id`` + (i.e. make the particle a primary/root with respect to this event list). + 2. If ``p.ancestor_id`` is not in the known-ID set, set it to the root of + the *repaired* parent chain. - * If ``p.parent_id`` is not in the known-ID set, reset it to ``p.id`` - (i.e. make the particle a primary/root with respect to this event list). - * If ``p.root_id`` is not in the known-ID set, reset it to ``p.id`` - as well. + Step 2 used to reset a dangling ancestor to ``p.id`` as well, which is + wrong whenever the parent chain still leads somewhere real. Dropping one + particle -- a primary photon whose only deposits were two stray Compton + scatters, say -- strands every descendant that named it as ancestor, even + though their ``parent_id`` links are intact and terminate at a surviving + particle. Self-rooting them then scatters one shower across hundreds of + false roots and contradicts :func:`validate_ancestor_ids`. Walking the + chain keeps the two fields telling the same story. This function modifies *particles* **in place** and returns the same list. @@ -295,19 +341,26 @@ def resolve_orphans(particles, verbose: bool = False): >>> particles = resolve_orphans(particles) >>> partitioner = ParticlePartitioner(particles=particles, ...) """ - known_ids = {p.id for p in particles} + by_id = {p.id: p for p in particles} n_parent_fixed = 0 - n_root_fixed = 0 + n_ancestor_fixed = 0 + + # Pass 1 -- parents. Must complete before any chain is walked, or a walk + # could follow a link that pass 1 is about to cut. for p in particles: - if p.parent_id not in known_ids: + if p.parent_id not in by_id: p.parent_id = p.id n_parent_fixed += 1 - if p.root_id not in known_ids: - p.root_id = p.id - n_root_fixed += 1 - if verbose and (n_parent_fixed or n_root_fixed): + + # Pass 2 -- ancestors, derived from the repaired chains. + memo: dict = {} + for p in particles: + if p.ancestor_id not in by_id: + p.ancestor_id = _chain_root(p.id, by_id, memo) + n_ancestor_fixed += 1 + if verbose and (n_parent_fixed or n_ancestor_fixed): print(f"[resolve_orphans] repaired {n_parent_fixed} parent_id(s) " - f"and {n_root_fixed} root_id(s) out of {len(particles)} particles") + f"and {n_ancestor_fixed} ancestor_id(s) out of {len(particles)} particles") return particles @@ -495,24 +548,24 @@ def _chain(p, limit: int = 12) -> str: return missing -def validate_root_ids(particles, +def validate_ancestor_ids(particles, raise_on_missing: bool = True, max_report: int = 10, verbose: bool = False) -> list: """ - Check that every particle's ``root_id`` is the primary it descends from. + Check that every particle's ``ancestor_id`` is the primary it descends from. The invariant checked is that walking ``parent_id`` upwards from a particle - terminates at exactly ``root_id``. It is worth checking because the + terminates at exactly ``ancestor_id``. It is worth checking because the ancestor field in EDepSim files is not always consistent with the parent links: a secondary written with ``ancestor_track_id == its own track_id`` becomes a false root, and every descendant inherits it, so a handful of bad entries can put a third of an event on the wrong root. See - :func:`~pysupera.readers.format_edepsim_h5._get_root_id`, which derives - ``root_id`` from the parent chain for this reason. + :func:`~pysupera.readers.format_edepsim_h5._get_ancestor_id`, which derives + ``ancestor_id`` from the parent chain for this reason. - Two failure modes are reported: a ``root_id`` naming a particle absent from - the event, and a ``root_id`` that disagrees with the primary the parent + Two failure modes are reported: a ``ancestor_id`` naming a particle absent from + the event, and a ``ancestor_id`` that disagrees with the primary the parent chain actually leads to. Run this **after** :func:`resolve_orphans`, which legitimately re-roots @@ -558,17 +611,17 @@ def _walk_primary(p): offenders, reasons = [], [] for p in particles: - rid = int(p.root_id) + rid = int(p.ancestor_id) if rid not in by_id: offenders.append(p) - reasons.append(f"{int(p.id)}(pdg={int(p.pdg)}): root_id={rid} " + reasons.append(f"{int(p.id)}(pdg={int(p.pdg)}): ancestor_id={rid} " f"is not a particle in this event") continue primary = _walk_primary(p) if primary != rid: offenders.append(p) reasons.append( - f"{int(p.id)}(pdg={int(p.pdg)}): root_id={rid} but the " + f"{int(p.id)}(pdg={int(p.pdg)}): ancestor_id={rid} but the " f"parent chain leads to {primary}" f"(pdg={int(by_id[primary].pdg)})" ) @@ -576,12 +629,12 @@ def _walk_primary(p): if not offenders: if verbose: print(f"[validate] all {len(particles)} particle(s) have a " - f"consistent root_id") + f"consistent ancestor_id") return [] shown = reasons[:max_report] msg = (f"{len(offenders)} of {len(particles)} particle(s) have an " - f"inconsistent root_id. First {len(shown)}:\n " + f"inconsistent ancestor_id. First {len(shown)}:\n " + "\n ".join(shown)) if len(reasons) > len(shown): msg += f"\n ... and {len(reasons) - len(shown)} more" diff --git a/tests/test_utils.py b/tests/test_utils.py index 4d5d6a5..f32d542 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,7 +2,8 @@ import numpy as np import pytest -from pysupera.utils import trace_ancestry, SetSemanticType, SemanticType +from pysupera.utils import (trace_ancestry, SetSemanticType, SemanticType, + resolve_orphans, validate_ancestor_ids) from tests.conftest import make_particle, PT_PRIMARY, PT_TRACK, PT_DECAY, PT_DELTA, PT_INVALID @@ -14,13 +15,13 @@ class TestTraceAncestry: # ── basic cases ────────────────────────────────────────────────────────── def test_root_particle_chain_length_one(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) chain, children = trace_ancestry(1, [root], print_result=False) assert len(chain) == 1 assert chain[0].id == 1 def test_root_has_no_children(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) chain, children = trace_ancestry(1, [root], print_result=False) assert children == [] @@ -33,32 +34,32 @@ def test_unknown_id_raises_key_error(self): def test_three_level_chain_root_first(self): # root (1) → middle (2) → leaf (3) - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) - middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, root_id=1) - leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, root_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) + middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, ancestor_id=1) + leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, ancestor_id=1) particles = [root, middle, leaf] chain, children = trace_ancestry(3, particles, print_result=False) assert [p.id for p in chain] == [1, 2, 3] def test_children_of_middle_node_listed(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) - middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, root_id=1) - leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, root_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) + middle = make_particle(2, PT_DECAY, pdg=13, parent_id=1, ancestor_id=1) + leaf = make_particle(3, PT_DECAY, pdg=11, parent_id=2, ancestor_id=1) chain, children = trace_ancestry(2, [root, middle, leaf], print_result=False) assert len(children) == 1 assert children[0].id == 3 def test_leaf_has_no_children(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) - leaf = make_particle(2, PT_DECAY, pdg=11, parent_id=1, root_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) + leaf = make_particle(2, PT_DECAY, pdg=11, parent_id=1, ancestor_id=1) _, children = trace_ancestry(2, [root, leaf], print_result=False) assert children == [] def test_multiple_children_all_listed(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) - ch1 = make_particle(2, PT_DECAY, pdg=11, parent_id=1, root_id=1) - ch2 = make_particle(3, PT_DECAY, pdg=-11, parent_id=1, root_id=1) - ch3 = make_particle(4, PT_DECAY, pdg=13, parent_id=1, root_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) + ch1 = make_particle(2, PT_DECAY, pdg=11, parent_id=1, ancestor_id=1) + ch2 = make_particle(3, PT_DECAY, pdg=-11, parent_id=1, ancestor_id=1) + ch3 = make_particle(4, PT_DECAY, pdg=13, parent_id=1, ancestor_id=1) _, children = trace_ancestry(1, [root, ch1, ch2, ch3], print_result=False) child_ids = {c.id for c in children} assert child_ids == {2, 3, 4} @@ -89,15 +90,15 @@ def test_print_result_true_produces_output(self, capsys): def test_orphan_chain_terminates_at_orphan(self): # Particle's parent is missing from the list → chain stops at orphan - orphan = make_particle(2, PT_DECAY, pdg=11, parent_id=99, root_id=99) + orphan = make_particle(2, PT_DECAY, pdg=11, parent_id=99, ancestor_id=99) chain, _ = trace_ancestry(2, [orphan], print_result=False) assert chain[-1].id == 2 # ── chain direction ────────────────────────────────────────────────────── def test_chain_ordered_root_to_target(self): - root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, root_id=1) - child = make_particle(2, PT_DECAY, pdg=11, parent_id=1, root_id=1) + root = make_particle(1, PT_PRIMARY, pdg=11, parent_id=1, ancestor_id=1) + child = make_particle(2, PT_DECAY, pdg=11, parent_id=1, ancestor_id=1) chain, _ = trace_ancestry(2, [root, child], print_result=False) assert chain[0].id == 1 assert chain[-1].id == 2 @@ -139,3 +140,78 @@ def test_small_cloud_explicit_size_override(self): st = SetSemanticType(interaction_type=PT_DELTA, pdg=11, parent_pdg=0, point_cloud=small_pc, point_cloud_size=100) assert st == SemanticType.kLEScatter + + +# ============================================================================ +# resolve_orphans +# ============================================================================ + +class TestResolveOrphans: + """ + Dropping one particle must not scatter its descendants across false + roots. When the parent links survive, the ancestor has to follow them. + """ + + def _family(self): + # 1 (primary) -> 2 -> 3, 4; all naming 1 as ancestor + return [make_particle(1, PT_PRIMARY, pdg=22, parent_id=1, ancestor_id=1), + make_particle(2, PT_TRACK, pdg=11, parent_id=1, ancestor_id=1), + make_particle(3, PT_TRACK, pdg=22, parent_id=2, ancestor_id=1), + make_particle(4, PT_TRACK, pdg=22, parent_id=2, ancestor_id=1)] + + def test_intact_family_is_untouched(self): + fam = self._family() + resolve_orphans(fam) + assert [p.ancestor_id for p in fam] == [1, 1, 1, 1] + + def test_dropped_primary_reroots_onto_the_surviving_chain(self): + # the primary (1) is dropped; 2 survives and becomes the root, so + # 3 and 4 must name 2 -- not themselves + fam = [p for p in self._family() if p.id != 1] + resolve_orphans(fam) + by = {p.id: p for p in fam} + assert by[2].parent_id == 2 and by[2].ancestor_id == 2 + assert by[3].ancestor_id == 2 + assert by[4].ancestor_id == 2 + + def test_the_repair_satisfies_the_validator(self): + fam = [p for p in self._family() if p.id != 1] + resolve_orphans(fam) + assert validate_ancestor_ids(fam, raise_on_missing=False) in (None, []) + + def test_self_rooting_only_when_nothing_survives(self): + # 3's parent 2 is gone too, so 3 genuinely has no chain left + fam = [p for p in self._family() if p.id == 3] + resolve_orphans(fam) + assert fam[0].parent_id == 3 + assert fam[0].ancestor_id == 3 + + def test_dangling_parent_is_reset(self): + p = make_particle(7, PT_TRACK, pdg=11, parent_id=99, ancestor_id=99) + resolve_orphans([p]) + assert p.parent_id == 7 and p.ancestor_id == 7 + + def test_a_live_ancestor_is_left_alone(self): + fam = self._family() + fam[2].ancestor_id = 2 # unusual but present: not repaired + resolve_orphans(fam) + assert fam[2].ancestor_id == 2 + + def test_deep_chain_shares_one_root(self): + n = 50 + ps = [make_particle(1, PT_PRIMARY, pdg=22, parent_id=1, ancestor_id=1)] + ps += [make_particle(i, PT_TRACK, pdg=11, parent_id=i - 1, + ancestor_id=999) for i in range(2, n + 1)] + resolve_orphans(ps) + assert {p.ancestor_id for p in ps} == {1} + + def test_a_cycle_terminates(self): + # 1 <-> 2 with a dangling ancestor: must not spin forever + a = make_particle(1, PT_TRACK, pdg=11, parent_id=2, ancestor_id=99) + b = make_particle(2, PT_TRACK, pdg=11, parent_id=1, ancestor_id=99) + resolve_orphans([a, b]) + assert a.ancestor_id in (1, 2) and b.ancestor_id in (1, 2) + + def test_returns_the_same_list(self): + fam = self._family() + assert resolve_orphans(fam) is fam From b6768d0bebfe73277a6f9f84505f47a78c09fb64 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 26/62] data: give Particle a Geant4 track ID and a voxel map geant4_id records where a particle came from, separately from the pysupera id, which is a dense per-event row index. It defaults to id so existing callers are unaffected, and it is what makes a join back to the input file possible -- the pysupera ids are assigned by the labeller and mean nothing upstream. voxmap carries the optional voxelization mapping so it can survive the splits and merges that follow. Carries its share of the root_id -> ancestor_id rename. --- pysupera/data.py | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/pysupera/data.py b/pysupera/data.py index 7b4faea..c6460e8 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -56,7 +56,7 @@ class Particle: ID of the direct parent particle. Equals ``id`` for primary (root) particles that have no parent; use ``p.parent_id == p.id`` to test whether a particle is primary. - root_id : int + ancestor_id : int ID of the primary ancestor particle from which this particle descends (root of the shower/track genealogy). pdg : int @@ -95,7 +95,7 @@ class Particle: Kinetic energy at the end of the trajectory (MeV). mass : float32 or FLOAT_UNSET Particle rest mass (MeV/c²). - root_pdg : int or None + ancestor_pdg : int or None PDG code of the primary (root) ancestor particle. start_process_id : int or None Geant4/simulation process ID for this particle's creation. @@ -129,7 +129,7 @@ def __init__( self, id, parent_id, - root_id, + ancestor_id, pdg, parent_pdg, interaction_id, @@ -148,7 +148,7 @@ def __init__( kinetic_energy_start=FLOAT_UNSET, kinetic_energy_end=FLOAT_UNSET, mass=FLOAT_UNSET, - root_pdg=None, + ancestor_pdg=None, start_process_id=None, start_subprocess_id=None, start_process_name=None, @@ -171,7 +171,7 @@ def __init__( are kept separately in ``geant4_id`` rather than used as the key. parent_id : int Index of the direct parent particle. A primary is its own parent. - root_id : int + ancestor_id : int Index of the primary ancestor particle. pdg : int PDG Monte Carlo particle code. @@ -206,7 +206,7 @@ def __init__( # synthetic particles) still gets a self-consistent object. self.geant4_id = id if geant4_id is None else geant4_id self.parent_id = parent_id - self.root_id = root_id + self.ancestor_id = ancestor_id self.pdg = pdg self.parent_pdg = parent_pdg self._interaction_id = interaction_id @@ -235,7 +235,7 @@ def __init__( self.kinetic_energy_start = np.float32(kinetic_energy_start) self.kinetic_energy_end = np.float32(kinetic_energy_end) self.mass = np.float32(mass) - self.root_pdg = root_pdg + self.ancestor_pdg = ancestor_pdg self.start_process_id = start_process_id self.start_subprocess_id = start_subprocess_id self.start_process_name = start_process_name @@ -286,7 +286,7 @@ def from_arrays( cls, ids, parent_ids, - root_ids, + ancestor_ids, pdgs, parent_pdgs, interaction_ids, @@ -309,7 +309,7 @@ def from_arrays( Unique particle identifiers. parent_ids : array-like of int, shape (N,) Direct parent particle IDs. - root_ids : array-like of int, shape (N,) + ancestor_ids : array-like of int, shape (N,) Root ancestor IDs. pdgs : array-like of int, shape (N,) PDG Monte Carlo codes. @@ -346,7 +346,7 @@ def from_arrays( >>> particles = Particle.from_arrays( ... ids=[0, 1], ... parent_ids=[0, 0], - ... root_ids=[0, 0], + ... ancestor_ids=[0, 0], ... pdgs=[11, 22], ... parent_pdgs=[0, 11], ... interaction_ids=[0, 1], @@ -358,7 +358,7 @@ def from_arrays( """ ids = list(ids) parent_ids = list(parent_ids) - root_ids = list(root_ids) + ancestor_ids = list(ancestor_ids) pdgs = list(pdgs) parent_pdgs = list(parent_pdgs) interaction_ids = list(interaction_ids) @@ -366,13 +366,13 @@ def from_arrays( point_clouds = list(point_clouds) n = len(ids) - if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, + if not all(len(a) == n for a in (parent_ids, ancestor_ids, pdgs, parent_pdgs, interaction_ids, interaction_types, point_clouds)): raise ValueError( "All input arrays must have the same length. " f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " - f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " + f"ancestor_ids={len(ancestor_ids)}, pdgs={len(pdgs)}, " f"parent_pdgs={len(parent_pdgs)}, " f"interaction_ids={len(interaction_ids)}, " f"interaction_types={len(interaction_types)}, " @@ -384,7 +384,7 @@ def from_arrays( id=ids[i], geant4_id=(None if geant4_ids is None else geant4_ids[i]), parent_id=parent_ids[i], - root_id=root_ids[i], + ancestor_id=ancestor_ids[i], pdg=pdgs[i], parent_pdg=parent_pdgs[i], interaction_id=interaction_ids[i], @@ -400,7 +400,7 @@ def from_flat_arrays( cls, ids, parent_ids, - root_ids, + ancestor_ids, pdgs, parent_pdgs, interaction_ids, @@ -425,7 +425,7 @@ def from_flat_arrays( Unique particle identifiers. parent_ids : array-like of int, shape (N,) Direct parent particle IDs. - root_ids : array-like of int, shape (N,) + ancestor_ids : array-like of int, shape (N,) Root ancestor IDs. pdgs : array-like of int, shape (N,) PDG Monte Carlo codes. @@ -476,7 +476,7 @@ def from_flat_arrays( >>> particles = Particle.from_flat_arrays( ... ids=[0, 1], ... parent_ids=[0, 0], - ... root_ids=[0, 0], + ... ancestor_ids=[0, 0], ... pdgs=[11, 22], ... parent_pdgs=[0, 11], ... interaction_ids=[0, 1], @@ -491,7 +491,7 @@ def from_flat_arrays( """ ids = list(ids) parent_ids = list(parent_ids) - root_ids = list(root_ids) + ancestor_ids = list(ancestor_ids) pdgs = list(pdgs) parent_pdgs = list(parent_pdgs) interaction_ids = list(interaction_ids) @@ -505,12 +505,12 @@ def from_flat_arrays( f"point_cloud_offsets must have shape (N, 2), " f"got {offsets.shape}." ) - if not all(len(a) == n for a in (parent_ids, root_ids, pdgs, + if not all(len(a) == n for a in (parent_ids, ancestor_ids, pdgs, parent_pdgs, interaction_ids, interaction_types)): raise ValueError( "All scalar arrays must have the same length. " f"Got lengths: ids={len(ids)}, parent_ids={len(parent_ids)}, " - f"root_ids={len(root_ids)}, pdgs={len(pdgs)}, " + f"ancestor_ids={len(ancestor_ids)}, pdgs={len(pdgs)}, " f"parent_pdgs={len(parent_pdgs)}, " f"interaction_ids={len(interaction_ids)}, " f"interaction_types={len(interaction_types)}." @@ -534,7 +534,7 @@ def from_flat_arrays( id=ids[i], geant4_id=(None if geant4_ids is None else geant4_ids[i]), parent_id=parent_ids[i], - root_id=root_ids[i], + ancestor_id=ancestor_ids[i], pdg=pdgs[i], parent_pdg=parent_pdgs[i], interaction_id=interaction_ids[i], @@ -550,9 +550,9 @@ def __str__(self): """Return a human-readable summary of this particle.""" lines = [ f"Particle(id={self.id}, parent_id={self.parent_id}, " - f"root_id={self.root_id}", + f"ancestor_id={self.ancestor_id}", f" pdg={self.pdg}, parent_pdg={self.parent_pdg}, " - f"root_pdg={self.root_pdg}", + f"ancestor_pdg={self.ancestor_pdg}", f" sem_type={self.sem_type}, interaction_type={self.interaction_type}", f" point_cloud: shape={self.point_cloud.shape}", ] From c1a0235361d05330cde4efece3a8e631c8f60436 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 27/62] io: size chunks by bytes, replace recompress with repack Chunk shapes were fixed row counts, so a chunk's size in bytes tracked the column count -- 262144 rows of a 9-wide float32 cloud is 9 MiB, far past anything useful, and HDF5 must read a whole chunk to return one row. Shapes now come from a byte budget: 256 KiB for bulk arrays, 64 KiB for metadata. recompress() only changed the filter and left chunk shapes alone, which is the half that matters least. repack() replaces it: it clamps every chunk to the dataset's real length -- the streaming writer cannot know that length when it creates the dataset empty -- and can change the filter in the same pass. It walks groups generically rather than reconstructing events, copies in chunk-aligned blocks so memory stays bounded, and verifies dataset equality before an in-place swap. Two options ported from the standalone h5_convert_compression.py, which this replaces: --compression auto flip the file's dominant filter between lz4 and gzip, so the round trip to a viewable copy needs no memory of which way round it currently is. Errors rather than guess on an uncompressed file. --scope all compress every chunked dataset, not only those already compressed (the default, "source"). On a 100-event file gzip is 65.1 MiB against LZ4's 88.8 MiB, and only gzip is readable by the browser viewer, which uses h5wasm. Also fixes write_events() never writing particles/interaction_id -- absent from every 2.x file ever produced -- and reads ancestor_id from a legacy root_id when present. Carries its share of the root_id -> ancestor_id rename. --- pysupera/io.py | 141 ++++++++++++++++++++++++++++++++++++++--------- tests/test_io.py | 101 ++++++++++++++++++++++++++++++++- 2 files changed, 213 insertions(+), 29 deletions(-) diff --git a/pysupera/io.py b/pysupera/io.py index 91acc13..12371a9 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -20,7 +20,7 @@ /particles/ id (n_total_particles,) int32 parent_id (n_total_particles,) int32 - root_id (n_total_particles,) int32 + ancestor_id (n_total_particles,) int32 pdg (n_total_particles,) int32 parent_pdg (n_total_particles,) int32 interaction_type (n_total_particles,) int32 @@ -69,7 +69,7 @@ _PC_NDIM = 6 # columns in the flat point array (x,y,z,t,dE,dX) # dX is summed per particle-voxel, so dE/dX for a # particle in a voxel is column 4 / column 5. -_CLOUD_NDIM = 9 # columns in event-cloud datasets (x,y,z,t,e,interaction_id,root_id,frag_id,inst_id) +_CLOUD_NDIM = 9 # columns in event-cloud datasets (x,y,z,t,e,interaction_id,ancestor_id,frag_id,inst_id) # Chunk sizing for compressed HDF5 datasets. # @@ -160,7 +160,7 @@ def write_events(path: str, p_id = np.empty(n_total_particles, dtype=np.int32) p_geant4_id = np.empty(n_total_particles, dtype=np.int32) p_parent_id = np.empty(n_total_particles, dtype=np.int32) - p_root_id = np.empty(n_total_particles, dtype=np.int32) + p_ancestor_id = np.empty(n_total_particles, dtype=np.int32) p_pdg = np.empty(n_total_particles, dtype=np.int32) p_parent_pdg = np.empty(n_total_particles, dtype=np.int32) p_int_id = np.empty(n_total_particles, dtype=np.int32) @@ -176,7 +176,7 @@ def write_events(path: str, p_id[j] = p.id p_geant4_id[j] = getattr(p, "geant4_id", p.id) p_parent_id[j] = p.parent_id - p_root_id[j] = p.root_id + p_ancestor_id[j] = p.ancestor_id p_pdg[j] = p.pdg p_parent_pdg[j] = p.parent_pdg p_int_id[j] = int(p._interaction_id) @@ -219,7 +219,7 @@ def write_events(path: str, _mk("id", p_id) _mk("geant4_id", p_geant4_id) _mk("parent_id", p_parent_id) - _mk("root_id", p_root_id) + _mk("ancestor_id", p_ancestor_id) _mk("pdg", p_pdg) _mk("parent_pdg", p_parent_pdg) _mk("interaction_id", p_int_id) @@ -230,7 +230,7 @@ def write_events(path: str, **ckw, ) else: - for name in ("id", "geant4_id", "parent_id", "root_id", + for name in ("id", "geant4_id", "parent_id", "ancestor_id", "pdg", "parent_pdg", "interaction_id", "interaction_type"): pg.create_dataset(name, data=np.empty(0, dtype=np.int32)) @@ -330,7 +330,7 @@ class EventWriter: # Names of the int32 particle scalar fields (sem_type handled separately # because it uses dtype=int8) _SCALAR_FIELDS = ( - "id", "geant4_id", "parent_id", "root_id", "pdg", "parent_pdg", + "id", "geant4_id", "parent_id", "ancestor_id", "pdg", "parent_pdg", "interaction_id", "interaction_type" ) @@ -439,7 +439,7 @@ def append_event(self, particles: List) -> None: p_id = np.empty(n_p, dtype=np.int32) p_geant4_id = np.empty(n_p, dtype=np.int32) p_parent_id = np.empty(n_p, dtype=np.int32) - p_root_id = np.empty(n_p, dtype=np.int32) + p_ancestor_id = np.empty(n_p, dtype=np.int32) p_pdg = np.empty(n_p, dtype=np.int32) p_parent_pdg = np.empty(n_p, dtype=np.int32) p_int_id = np.empty(n_p, dtype=np.int32) @@ -451,7 +451,7 @@ def append_event(self, particles: List) -> None: p_id[k] = p.id p_geant4_id[k] = getattr(p, "geant4_id", p.id) p_parent_id[k] = p.parent_id - p_root_id[k] = p.root_id + p_ancestor_id[k] = p.ancestor_id p_pdg[k] = p.pdg p_parent_pdg[k] = p.parent_pdg p_int_id[k] = int(p._interaction_id) @@ -462,7 +462,7 @@ def append_event(self, particles: List) -> None: self._f["particles/id" ][new_p_start:new_p_end] = p_id self._f["particles/geant4_id" ][new_p_start:new_p_end] = p_geant4_id self._f["particles/parent_id" ][new_p_start:new_p_end] = p_parent_id - self._f["particles/root_id" ][new_p_start:new_p_end] = p_root_id + self._f["particles/ancestor_id" ][new_p_start:new_p_end] = p_ancestor_id self._f["particles/pdg" ][new_p_start:new_p_end] = p_pdg self._f["particles/parent_pdg" ][new_p_start:new_p_end] = p_parent_pdg self._f["particles/interaction_id" ][new_p_start:new_p_end] = p_int_id @@ -569,7 +569,7 @@ def _init_datasets(self) -> None: self._init_rep_group("particle_instances") # Event-level union point clouds - # Layout: (x, y, z, t, energy, interaction_id, root_id, frag_id, inst_id) + # Layout: (x, y, z, t, energy, interaction_id, ancestor_id, frag_id, inst_id) # — _CLOUD_NDIM = 9 columns (all stored as float32). # non_le_cloud : union of point clouds from particles with sem_type != kLEScatter. # le_scatter_cloud : union of point clouds from kLEScatter particles only. @@ -596,7 +596,7 @@ def _init_rep_group(self, group_prefix: str) -> None: /frag_events/offsets (n_events+1,) int64 — per-event fencepost /particle_fragments/id (n_reps,) int32 /particle_fragments/parent_id (n_reps,) int32 - /particle_fragments/root_id (n_reps,) int32 + /particle_fragments/ancestor_id (n_reps,) int32 /particle_fragments/pdg (n_reps,) int32 /particle_fragments/parent_pdg (n_reps,) int32 /particle_fragments/interaction_type (n_reps,) int32 @@ -720,12 +720,12 @@ def append_event_clouds( Must be called **before** :meth:`append_event` for the same event. Each cloud has 9 columns: - ``x, y, z, t, energy, interaction_id, root_id, frag_id, inst_id``. + ``x, y, z, t, energy, interaction_id, ancestor_id, frag_id, inst_id``. All values are stored as float32. Integer-valued columns - (``interaction_id``, ``root_id``, ``frag_id``, ``inst_id``) should be + (``interaction_id``, ``ancestor_id``, ``frag_id``, ``inst_id``) should be cast by the caller. Use ``-1`` as a sentinel for absent frag/inst IDs. When voxelization is active the spatial columns are voxel-centre - coordinates; ``interaction_id`` / ``root_id`` are taken from the + coordinates; ``interaction_id`` / ``ancestor_id`` are taken from the dominant particle across merged points. Parameters @@ -819,7 +819,7 @@ def _append_rep_level( r_id = np.empty(n_r, dtype=np.int32) r_geant4_id = np.empty(n_r, dtype=np.int32) r_parent_id = np.empty(n_r, dtype=np.int32) - r_root_id = np.empty(n_r, dtype=np.int32) + r_ancestor_id = np.empty(n_r, dtype=np.int32) r_pdg = np.empty(n_r, dtype=np.int32) r_parent_pdg = np.empty(n_r, dtype=np.int32) r_int_id = np.empty(n_r, dtype=np.int32) @@ -834,7 +834,7 @@ def _append_rep_level( r_id[k] = r.id r_geant4_id[k] = getattr(r, "geant4_id", r.id) r_parent_id[k] = r.parent_id - r_root_id[k] = r.root_id + r_ancestor_id[k] = r.ancestor_id r_pdg[k] = r.pdg r_parent_pdg[k] = r.parent_pdg r_int_id[k] = int(r._interaction_id) @@ -851,7 +851,7 @@ def _append_rep_level( self._f[f"{group_prefix}/id" ][new_start:new_end] = r_id self._f[f"{group_prefix}/geant4_id" ][new_start:new_end] = r_geant4_id self._f[f"{group_prefix}/parent_id" ][new_start:new_end] = r_parent_id - self._f[f"{group_prefix}/root_id" ][new_start:new_end] = r_root_id + self._f[f"{group_prefix}/ancestor_id" ][new_start:new_end] = r_ancestor_id self._f[f"{group_prefix}/pdg" ][new_start:new_end] = r_pdg self._f[f"{group_prefix}/parent_pdg" ][new_start:new_end] = r_parent_pdg self._f[f"{group_prefix}/interaction_id" ][new_start:new_end] = r_int_id @@ -1041,7 +1041,11 @@ def __getitem__(self, index: int) -> List: geant4_ids = (self._f["particles/geant4_id" ][p_start:p_end] if "particles/geant4_id" in self._f else ids) parent_ids = self._f["particles/parent_id" ][p_start:p_end] - root_ids = self._f["particles/root_id" ][p_start:p_end] + # Renamed from root_id in 3.0.0; older files still carry the old + # name, so accept either. + ancestor_ids = (self._f["particles/ancestor_id"][p_start:p_end] + if "particles/ancestor_id" in self._f + else self._f["particles/root_id"][p_start:p_end]) pdgs = self._f["particles/pdg" ][p_start:p_end] parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] interaction_ids = self._f["particles/interaction_id" ][p_start:p_end] @@ -1064,7 +1068,7 @@ def __getitem__(self, index: int) -> List: id = int(ids[k]), geant4_id = int(geant4_ids[k]), parent_id = int(parent_ids[k]), - root_id = int(root_ids[k]), + ancestor_id = int(ancestor_ids[k]), pdg = int(pdgs[k]), parent_pdg = int(parent_pdgs[k]), interaction_id = int(interaction_ids[k]), @@ -1154,7 +1158,11 @@ def read_bulk(self, start: int = 0, stop: int = -1, geant4_ids = (self._f["particles/geant4_id" ][p_start:p_end] if "particles/geant4_id" in self._f else ids) parent_ids = self._f["particles/parent_id" ][p_start:p_end] - root_ids = self._f["particles/root_id" ][p_start:p_end] + # Renamed from root_id in 3.0.0; older files still carry the old + # name, so accept either. + ancestor_ids = (self._f["particles/ancestor_id"][p_start:p_end] + if "particles/ancestor_id" in self._f + else self._f["particles/root_id"][p_start:p_end]) pdgs = self._f["particles/pdg" ][p_start:p_end] parent_pdgs = self._f["particles/parent_pdg" ][p_start:p_end] interaction_ids = self._f["particles/interaction_id" ][p_start:p_end] @@ -1178,7 +1186,7 @@ def _build_event(ev): id = int(ids[k]), geant4_id = int(geant4_ids[k]), parent_id = int(parent_ids[k]), - root_id = int(root_ids[k]), + ancestor_id = int(ancestor_ids[k]), pdg = int(pdgs[k]), parent_pdg = int(parent_pdgs[k]), interaction_id = int(interaction_ids[k]), @@ -1353,12 +1361,67 @@ def inspect_compression(path: str) -> dict: return result +def _is_compressed(ds) -> bool: + """True when *ds* already carries any compression filter.""" + return ds.compression is not None or bool(ds._filters) + + +def dominant_filter(path: str) -> str: + """ + The filter most datasets in *path* use: ``gzip``, ``lzf``, ``lz4``, + ``blosc``, ``szip`` or ``none``. + + Files are mixed in principle -- every dataset carries its own filter -- + so "the file's compression" is a majority vote, which is what + ``compression="auto"`` flips. + """ + import h5py + counts: dict = {} + + def name_of(ds): + c = ds.compression + if c in ("gzip", "lzf", "szip"): + return c + # h5py reports third-party filters as 'unknown', so go by filter ID: + # 32004 is LZ4, 32001 Blosc (the same IDs _compression_kwargs_from_ + # filters matches on). + filters = dict(ds._filters or {}) + if "32004" in filters: + return "lz4" + if "32001" in filters: + return "blosc" + return "none" if not filters else "unknown" + + with h5py.File(path, "r") as f: + def visit(_name, obj): + if isinstance(obj, h5py.Dataset) and obj.chunks is not None: + n = name_of(obj) + counts[n] = counts.get(n, 0) + 1 + f.visititems(visit) + return max(counts, key=counts.get) if counts else "none" + + +def _resolve_auto(path: str, compression): + """Turn ``compression="auto"`` into a concrete filter by flipping.""" + if not isinstance(compression, str) or compression.lower() != "auto": + return compression + current = dominant_filter(path) + flipped = {"lz4": "gzip", "blosc": "gzip", "gzip": "lz4"}.get(current) + if flipped is None: + raise ValueError( + f"cannot auto-flip {path!r}: its dominant filter is {current!r}, " + f"which has no obvious counterpart. Pass an explicit filter." + ) + return flipped + + def repack(path: str, compression: Optional[str] = None, compression_opts: Optional[int] = None, dst: Optional[str] = None, rechunk: bool = False, verify: bool = False, + scope: str = "source", verbose: bool = True) -> dict: """ Rewrite *path* with chunk shapes sized from the final dataset lengths, @@ -1390,7 +1453,9 @@ def repack(path: str, Filter for the output. ``None`` (default) keeps whatever filter each source dataset already uses. Pass e.g. ``"gzip"`` to switch -- which is how to produce a file the browser viewer can read, since h5wasm handles - gzip only. + gzip only. ``"auto"`` flips the file's dominant filter between lz4 + and gzip, which covers the round trip between a fast working copy and + a viewable one without having to remember which way round it is. compression_opts : int or None, optional Compression level for *compression* (gzip: 1-9). dst : str or None, optional @@ -1402,6 +1467,11 @@ def repack(path: str, pysupera output this tends to win on both size and row-range read speed, because h5py picks smaller column-wise chunks, but it does change the layout rather than only the filter. + scope : {"source", "all"}, optional + Which datasets receive *compression*. ``"source"`` (default) only + compresses datasets that were already compressed, so uncompressed + ones stay that way. ``"all"`` compresses everything chunked. Only + meaningful when *compression* is given. verify : bool, optional After writing, compare every dataset against the source and raise ``ValueError`` on any mismatch. For an in-place repack the check runs @@ -1428,6 +1498,10 @@ def repack(path: str, f"({path!r})" ) + if scope not in ("source", "all"): + raise ValueError(f"scope must be 'source' or 'all', got {scope!r}") + compression = _resolve_auto(path, compression) + size_before = os.path.getsize(path) in_place = dst is None # In place: build beside the original and swap only once it is complete. @@ -1479,6 +1553,10 @@ def visit(name, obj): "compression_opts": obj.compression_opts} elif str(compression).lower() in ("none", "~", ""): kwargs = {} # explicitly store uncompressed + elif scope == "source" and not _is_compressed(obj): + # Leave an uncompressed dataset uncompressed; the + # offsets arrays are tiny and gain nothing. + kwargs = {} else: kwargs = _compress_kwargs(compression, compression_opts) @@ -1600,6 +1678,12 @@ def repack_cli() -> None: # smallest and fastest for row-range reads, at the cost of relayout pysupera-repack out.h5 out_vis.h5 --compression gzip --rechunk + # flip lz4 <-> gzip without having to remember which way round + pysupera-repack out.h5 out_flipped.h5 --compression auto + + # compress everything chunked, not just what was compressed already + pysupera-repack out.h5 out_small.h5 --compression gzip --scope all + Walks the file generically instead of reconstructing events, so it can resize chunks and handles any layout, including the voxmap companion file. """ @@ -1614,9 +1698,13 @@ def repack_cli() -> None: parser.add_argument("dst", nargs="?", default=None, help="Output file; omit to rewrite src in place") parser.add_argument("-c", "--compression", default=None, - help="Filter for the output: gzip, lzf, lz4, " - "blosc_lz4, none. Omit to keep each dataset's " - "existing filter") + help="Filter for the output: auto, gzip, lzf, lz4, " + "blosc_lz4, none. 'auto' flips the file's " + "dominant filter between lz4 and gzip. Omit to " + "keep each dataset's existing filter") + parser.add_argument("--scope", default="source", choices=("source", "all"), + help="Which datasets get the new filter: those already " + "compressed, or every chunked one") parser.add_argument("-l", "--level", type=int, default=None, help="Compression level (gzip: 1-9)") parser.add_argument("--rechunk", action="store_true", @@ -1637,6 +1725,7 @@ def repack_cli() -> None: compression_opts=args.level, dst=args.dst, rechunk=args.rechunk, + scope=args.scope, verify=args.verify) diff --git a/tests/test_io.py b/tests/test_io.py index 9db87b3..f510b17 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -21,7 +21,7 @@ from tests.conftest import make_particle, PT_PRIMARY, PT_TRACK, PT_DECAY -SCALARS = ("id", "geant4_id", "parent_id", "root_id", "pdg", "parent_pdg", +SCALARS = ("id", "geant4_id", "parent_id", "ancestor_id", "pdg", "parent_pdg", "interaction_id", "interaction_type") @@ -46,13 +46,13 @@ class TestParticleRoundTrip: def test_scalar_fields_survive(self, tmp_path): p = make_particle(7, PT_DECAY, pdg=13, parent_pdg=211, - parent_id=3, root_id=1, interaction_id=4) + parent_id=3, ancestor_id=1, interaction_id=4) p.geant4_id = 4242 path = str(tmp_path / "a.h5") write_events(path, [[p]]) with read_events(path) as store: q = store[0][0] - assert (q.id, q.geant4_id, q.parent_id, q.root_id) == (7, 4242, 3, 1) + assert (q.id, q.geant4_id, q.parent_id, q.ancestor_id) == (7, 4242, 3, 1) assert (q.pdg, q.parent_pdg) == (13, 211) assert q._interaction_id == 4 assert q._interaction_type == int(p._interaction_type) @@ -360,3 +360,98 @@ def test_missing_geant4_id_falls_back_to_id(self, tmp_path): with read_events(path) as store: ps = store[0] assert [p.geant4_id for p in ps] == [p.id for p in ps] == [0, 1, 2] + + +# ============================================================================ +# repack: auto-flip and scope +# ============================================================================ + +import pytest as _pytest # noqa: E402 +from pysupera.io import repack, dominant_filter # noqa: E402 + + +def _mk(path, compression, n=64): + import h5py + with h5py.File(path, "w") as f: + g = f.create_group("g") + kw = {} if compression is None else {"compression": compression} + g.create_dataset("a", data=np.arange(n, dtype=np.int64), + chunks=(16,), **kw) + g.create_dataset("b", data=np.arange(n, dtype=np.float32), + chunks=(16,), **kw) + f.create_dataset("scalar", data=np.int64(n)) + return str(path) + + +class TestDominantFilter: + + def test_reports_gzip(self, tmp_path): + assert dominant_filter(_mk(tmp_path / "g.h5", "gzip")) == "gzip" + + def test_reports_none(self, tmp_path): + assert dominant_filter(_mk(tmp_path / "n.h5", None)) == "none" + + def test_ignores_unchunked_scalars(self, tmp_path): + # the scalar dataset carries no filter and must not sway the vote + assert dominant_filter(_mk(tmp_path / "g.h5", "gzip")) == "gzip" + + +class TestRepackAuto: + """``auto`` exists so a round trip needs no memory of the current state.""" + + def test_gzip_flips_to_lz4(self, tmp_path): + src = _mk(tmp_path / "g.h5", "gzip") + dst = str(tmp_path / "out.h5") + repack(src, compression="auto", dst=dst, verbose=False, verify=True) + assert dominant_filter(dst) == "lz4" + + def test_the_flip_round_trips(self, tmp_path): + src = _mk(tmp_path / "g.h5", "gzip") + a = str(tmp_path / "a.h5"); b = str(tmp_path / "b.h5") + repack(src, compression="auto", dst=a, verbose=False) + repack(a, compression="auto", dst=b, verbose=False, verify=True) + assert dominant_filter(b) == "gzip" + + def test_uncompressed_cannot_be_flipped(self, tmp_path): + src = _mk(tmp_path / "n.h5", None) + with _pytest.raises(ValueError, match="auto-flip"): + repack(src, compression="auto", dst=str(tmp_path / "o.h5"), + verbose=False) + + def test_data_survives_the_flip(self, tmp_path): + import h5py + src = _mk(tmp_path / "g.h5", "gzip") + dst = str(tmp_path / "out.h5") + repack(src, compression="auto", dst=dst, verbose=False, verify=True) + with h5py.File(dst, "r") as f: + assert np.array_equal(f["g/a"][:], np.arange(64, dtype=np.int64)) + + +class TestRepackScope: + + def test_source_leaves_uncompressed_alone(self, tmp_path): + src = _mk(tmp_path / "n.h5", None) + dst = str(tmp_path / "out.h5") + repack(src, compression="gzip", dst=dst, verbose=False, verify=True) + assert dominant_filter(dst) == "none" + + def test_all_compresses_everything(self, tmp_path): + src = _mk(tmp_path / "n.h5", None) + dst = str(tmp_path / "out.h5") + repack(src, compression="gzip", dst=dst, scope="all", + verbose=False, verify=True) + assert dominant_filter(dst) == "gzip" + + def test_scope_does_not_affect_already_compressed(self, tmp_path): + src = _mk(tmp_path / "g.h5", "gzip") + for scope in ("source", "all"): + dst = str(tmp_path / f"{scope}.h5") + repack(src, compression="lzf", dst=dst, scope=scope, + verbose=False, verify=True) + assert dominant_filter(dst) == "lzf" + + def test_a_bad_scope_is_rejected(self, tmp_path): + src = _mk(tmp_path / "g.h5", "gzip") + with _pytest.raises(ValueError, match="scope"): + repack(src, compression="gzip", dst=str(tmp_path / "o.h5"), + scope="everything", verbose=False) From 821189fce38a1323017391d24ba06114cd7d5750 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 28/62] layout: compute one event's rows, ranges and ordering New module, separating what to write from how to write it. io_v3 does no derivation; everything below happens here. Point ordering is interaction > instance > is_LE > fragment > particle which makes three things true at once: an instance's points are one contiguous block split into a non-LE and an LE run; a fragment's points are exactly two runs, one per side, never more; and LE-ness is positional, so no per-point label is stored. The query training issues most often -- the non-LE voxels of instance X -- is then a slice rather than a mask. snapshot_groups has to be called at the right moment and the reason is worth stating: merge_em_showers mutates the surviving representatives in place and returns those same objects as instances, so after step 2 a representative's member_ids and sem_type describe its instance and the fragment values are gone. Reading fragment membership afterwards silently misattributes most particles -- 10,299 in one event before this was understood. Fragments are snapshotted before the merge, instances after. parent_id is redirected to the nearest *stored* ancestor, so walking the genealogy in the output always terminates; ancestor_id is then derived from the redirected chain so the two agree. parent_pdg describes whatever parent_id points at. Because only ~17% of particles are stored, the true Geant4 parent is unrecoverable from the file alone, so it is kept explicitly as geant4_parent_trackid / geant4_parent_pdg. The interactions table keeps live interactions only, renumbered densely, with interaction_id as the index particles refer to and vertex_id as the surviving link back to the EDepSim vertex row. --- pysupera/layout.py | 409 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 409 insertions(+) create mode 100644 pysupera/layout.py diff --git a/pysupera/layout.py b/pysupera/layout.py new file mode 100644 index 0000000..5bef693 --- /dev/null +++ b/pysupera/layout.py @@ -0,0 +1,409 @@ +""" +Layout computation for the 3.0.0 on-disk format. + +Format 3.0.0 replaces the three parallel particle tables of 2.x with a single +``particles`` table plus one flat point array. A particle row carries its own +identity and, when it represents a group, that group's columns as well: + + frag_id / inst_id the *particle id of the group's representative*. + A representative therefore satisfies ``frag_id == id``, + which is how the levels are enumerated. ``-1`` means + the particle heads no group at that level. + *_pc_start/end the group's slice of the point array + *_merge_count Geant4 particles merged into the group + +This module does the part that has to be right before any bytes are written: +deciding which particles to store, what order the points go in, and what those +ranges are. It is deliberately free of HDF5 so it can be tested directly. + +Two invariants it establishes +----------------------------- +*Nesting.* Points are ordered by ``(interaction, instance, fragment, +particle)``. Because instances partition into fragments and fragments into +particles -- verified against real events -- every level's points are then a +single contiguous slice, so one ``(start, end)`` pair per level suffices and no +per-point group labels are needed. + +*Walkable genealogy.* Only a fraction of Geant4 particles are stored, so a +stored particle's true parent may be absent. ``parent_id`` is therefore +redirected to the nearest *stored* ancestor, and a particle with no stored +ancestor is marked a primary (``parent_id == id``). Walking ``parent_id`` +always terminates on a stored row rather than dangling. +""" + +from __future__ import annotations + +from collections import namedtuple + +import numpy as np + +from .utils import SemanticType as _ST + +#: Low-energy scatter. Sorting on this splits each instance into a non-LE +#: block followed by an LE block; see build_layout. +_LE_SEM = _ST.kLEScatter + + +#: A group's identity captured at a point in time: which particle represents +#: it, which particles it contains, and how it was classified. +Group = namedtuple("Group", "rep_id member_ids sem_type") + + +def snapshot_groups(reps): + """ + Capture the membership and classification of *reps* right now. + + This must be called at the correct moment, and the reason is worth + spelling out: ``merge_em_showers`` mutates the surviving representatives + **in place** and returns those same objects as the instances -- every + instance object is also a fragment object. So after step 2 a + representative's ``member_ids`` and ``sem_type`` describe its *instance*, + and the fragment values it used to hold are gone. Reading fragment + membership after the merge silently attributes most particles to the wrong + fragment. + + Snapshot fragments before :func:`~pysupera.merge.merge_em_showers`, and + instances after it. + """ + return [Group(int(r.id), + [int(m) for m in (r.member_ids + if r.member_ids is not None else [r.id])], + int(r.sem_type.value)) + for r in reps] + + +class EventLayout: + """Result of :func:`build_layout` for one event.""" + + __slots__ = ("order", "rows", "pc_offsets", "columns", "n_points", + "interactions") + + def __init__(self, order, rows, pc_offsets, columns, n_points, + interactions=None): + #: every particle, in point order -- defines the point array + self.order = order + #: indices into *order* of the particles actually written + self.rows = rows + #: fencepost into the point array, one entry per particle in *order* + self.pc_offsets = pc_offsets + #: per-written-row arrays, keyed by column name + self.columns = columns + self.n_points = n_points + #: per-interaction arrays; ``particles.interaction_id`` indexes these + self.interactions = interactions or {} + + def __repr__(self): + return (f"EventLayout(order={len(self.order)}, rows={len(self.rows)}, " + f"points={self.n_points})") + + +def _group_of(groups): + """Map every member particle id to its group's representative id.""" + out = {} + for g in groups: + for m in g.member_ids: + out[m] = g.rep_id + return out + + +def build_layout(particles, frag_groups, inst_groups, vertices=None): + """ + Decide storage order, row selection and group ranges for one event. + + Parameters + ---------- + particles : list of Particle + Every particle in the event. All of their points are stored, whether + or not the particle itself gets a row -- the charge belongs to the + event regardless of which representative owns it. + frag_groups, inst_groups : list of Group + Step-1 and step-2 groups from :func:`snapshot_groups`, taken before + and after ``merge_em_showers`` respectively -- see that function for + why the timing matters. *inst_groups* should already be filtered to + the instances being written; ordering still uses every fragment, so + the nesting stays complete even when rows are dropped. + vertices : structured ndarray or None + The event's vertices, as the reader read them. Interactions that no + stored particle references are dropped and the survivors renumbered, + so ``interaction_id`` is a direct row index into the interactions + table rather than a value that has to be searched for. + + Returns + ------- + EventLayout + """ + by_id = {int(p.id): p for p in particles} + frag_of = _group_of(frag_groups) + inst_of = _group_of(inst_groups) + kept_inst_ids = {g.rep_id for g in inst_groups} + + # ---- ordering: interaction > instance > fragment > particle ---------- + # LE-ness sorts *between* instance and fragment, so each instance becomes + # a non-LE block followed by an LE block. That makes "the non-LE points of + # this instance" -- the query panoptic training issues most -- a single + # contiguous slice instead of a mask, and removes the need for any + # per-point label: LE-ness is implied by position relative to the split. + # The cost is that a fragment's points become two runs rather than one. + is_le = {int(p.id): (p.sem_type is _LE_SEM) for p in particles} + + def sort_key(p): + pid = int(p.id) + return (int(p._interaction_id), + inst_of.get(pid, pid), + is_le[pid], + frag_of.get(pid, pid), + pid) + + order = sorted(particles, key=sort_key) + pos = {int(p.id): k for k, p in enumerate(order)} + + n_pts = np.fromiter((len(p.point_cloud) for p in order), + dtype=np.int64, count=len(order)) + pc_offsets = np.zeros(len(order) + 1, dtype=np.int64) + np.cumsum(n_pts, out=pc_offsets[1:]) + + + # ---- group point ranges ---------------------------------------------- + # Contiguous by construction, so min(start) / max(end) over the members + # describes the block exactly. + def _span(members): + """Point range covering *members*, or (-1, -1) when there are none.""" + idx = [pos[m] for m in members if m in pos] + if not idx: + return (-1, -1) + lo, hi = min(idx), max(idx) + return (int(pc_offsets[lo]), int(pc_offsets[hi + 1])) + + def group_ranges(groups): + rng = {} + for g in groups: + r = _span(g.member_ids) + rng[g.rep_id] = (0, 0) if r == (-1, -1) else r + return rng + + frag_rng = group_ranges(frag_groups) + inst_rng = group_ranges(inst_groups) + + # Each side of a group is a single run under this ordering, so two pairs + # describe a fragment completely -- never three. + frag_nle = {g.rep_id: _span([m for m in g.member_ids if not is_le[m]]) + for g in frag_groups} + frag_le = {g.rep_id: _span([m for m in g.member_ids if is_le[m]]) + for g in frag_groups} + # Where the LE block starts inside an instance; == inst_pc_end when the + # instance has no LE points at all. + inst_split = {} + for g in inst_groups: + le_span = _span([m for m in g.member_ids if is_le[m]]) + inst_split[g.rep_id] = (inst_rng[g.rep_id][1] if le_span[0] < 0 + else le_span[0]) + frag_count = {g.rep_id: len(g.member_ids) for g in frag_groups} + inst_count = {g.rep_id: len(g.member_ids) for g in inst_groups} + + # ---- row selection ---------------------------------------------------- + # Representatives that carry points, every kept instance, and the full + # ancestry of those instances so their history is walkable. + frag_ids = {g.rep_id for g in frag_groups} + visible = {g.rep_id for g in frag_groups + if frag_rng[g.rep_id][1] > frag_rng[g.rep_id][0]} + visible |= {i for i in kept_inst_ids + if inst_rng[i][1] > inst_rng[i][0]} + + ancestry = set() + for iid in kept_inst_ids: + cur, seen = iid, set() + while cur in by_id and cur not in seen: + seen.add(cur) + ancestry.add(cur) + nxt = int(by_id[cur].parent_id) + if nxt == cur: + break + cur = nxt + + keep = (visible | kept_inst_ids | ancestry) & set(by_id) + rows = sorted(pos[i] for i in keep) + row_ids = [int(order[k].id) for k in rows] + row_set = set(row_ids) + + # ---- parent redirection ------------------------------------------------ + def nearest_stored_parent(pid): + cur, seen = int(by_id[pid].parent_id), set() + while cur in by_id and cur not in seen: + seen.add(cur) + if cur in row_set and cur != pid: + return cur + nxt = int(by_id[cur].parent_id) + if nxt == cur: + break + cur = nxt + return pid # no stored ancestor -> a primary here + + def g4_parent_trackid(pid): + """Track ID of the real direct parent, -1 when it is the particle itself.""" + q = by_id[pid] + par = int(q.parent_id) + if par == pid or par not in by_id: + return -1 + return int(getattr(by_id[par], "geant4_id", par)) + + def g4_parent_pdg(pid): + q = by_id[pid] + par = int(q.parent_id) + if par == pid or par not in by_id: + return -1 + return int(by_id[par].pdg) + + def col(fn, dtype=np.int32): + return np.fromiter((fn(int(order[k].id)) for k in rows), + dtype=dtype, count=len(rows)) + + frag_sem = {g.rep_id: g.sem_type for g in frag_groups} + inst_sem = {g.rep_id: g.sem_type for g in inst_groups} + sem = lambda gid, table: table.get(gid, -1) + + columns = { + "id": col(lambda i: i), + "geant4_trackid": col(lambda i: int(getattr(by_id[i], "geant4_id", i))), + "parent_id": col(nearest_stored_parent), + "pdg": col(lambda i: int(by_id[i].pdg)), + # parent_pdg describes whatever parent_id points at, so the two always + # name the same particle. The *true* Geant4 parent -- usually not + # stored, since only ~17% of particles are -- is kept alongside. + "parent_pdg": col(lambda i: int(by_id[nearest_stored_parent(i)].pdg)), + "geant4_parent_trackid": col(g4_parent_trackid), + "geant4_parent_pdg": col(g4_parent_pdg), + "interaction_id": col(lambda i: int(by_id[i]._interaction_id)), + "interaction_type": col(lambda i: int(by_id[i]._interaction_type)), + "sem_type": col(lambda i: int(by_id[i].sem_type.value), np.int8), + "pc_start": col(lambda i: int(pc_offsets[pos[i]])), + "pc_end": col(lambda i: int(pc_offsets[pos[i] + 1])), + # group columns: valid where *_id == id + "frag_id": col(lambda i: i if i in frag_ids else -1), + "frag_sem_type": col(lambda i: sem(i, frag_sem), np.int8), + "frag_pc_start": col(lambda i: frag_nle.get(i, (-1, -1))[0], np.int64), + "frag_pc_end": col(lambda i: frag_nle.get(i, (-1, -1))[1], np.int64), + "frag_pc_le_start": col(lambda i: frag_le.get(i, (-1, -1))[0], np.int64), + "frag_pc_le_end": col(lambda i: frag_le.get(i, (-1, -1))[1], np.int64), + "frag_merge_count": col(lambda i: frag_count.get(i, -1)), + "inst_id": col(lambda i: i if i in kept_inst_ids else -1), + "inst_sem_type": col(lambda i: sem(i, inst_sem), np.int8), + # Non-LE run then LE run. They are adjacent here, so + # inst_pc_end == inst_pc_le_start; the pair is spelled out anyway so + # instance and fragment level read identically. + "inst_pc_start": col(lambda i: inst_rng.get(i, (-1, -1))[0]), + "inst_pc_end": col(lambda i: inst_split.get(i, -1), np.int64), + "inst_pc_le_start": col(lambda i: inst_split.get(i, -1), np.int64), + "inst_pc_le_end": col(lambda i: inst_rng.get(i, (-1, -1))[1]), + "inst_merge_count": col(lambda i: inst_count.get(i, -1)), + } + # ancestor_id follows the *redirected* parents, so the chain a reader walks and + # the root it is told to expect agree. Memoised along each chain: O(n). + row_index = {pid: k for k, pid in enumerate(row_ids)} + parents = columns["parent_id"] + root: dict = {} + + def resolve_ancestor(pid): + chain, cur, seen = [], pid, set() + while True: + if cur in root: + answer = root[cur] + break + if cur in seen: + answer = cur # cycle: treat as its own root + break + seen.add(cur) + chain.append(cur) + nxt = int(parents[row_index[cur]]) + if nxt == cur: + answer = cur + break + cur = nxt + for c in chain: + root[c] = answer + return answer + + columns["ancestor_id"] = np.fromiter((resolve_ancestor(i) for i in row_ids), + dtype=np.int32, count=len(row_ids)) + + # ---- group parent pointers -------------------------------------------- + # The nearest *stored* ancestor that heads a group at this level, skipping + # the row's own group: when a rep's parent was merged into the rep's own + # shower, answering with itself would dead-end the production history. + # Self means "no parent group here", matching the primary convention. + def group_parent(level_col): + heads = {int(i): int(i) for i, g in zip(row_ids, columns[level_col]) + if int(g) == int(i)} + out = [] + for i in row_ids: + cur, seen, ans = int(columns["parent_id"][row_index[i]]), set(), i + while cur not in seen: + seen.add(cur) + if cur in heads and cur != i: + ans = cur + break + nxt = int(columns["parent_id"][row_index[cur]]) + if nxt == cur: + break + cur = nxt + out.append(ans) + return np.asarray(out, dtype=np.int32) + + columns["frag_parent_id"] = group_parent("frag_id") + columns["inst_parent_id"] = group_parent("inst_id") + + # ---- interactions ------------------------------------------------------ + # Keep only interactions some stored particle belongs to, renumber them, + # and rewrite interaction_id to the new row index. Points are ordered by + # interaction first, so each one's particles and points are contiguous. + live = sorted({int(v) for v in columns["interaction_id"] if v >= 0}) + remap = {old: new for new, old in enumerate(live)} + columns["interaction_id"] = np.fromiter( + (remap.get(int(v), -1) for v in columns["interaction_id"]), + dtype=np.int32, count=len(row_ids)) + + interactions: dict = {} + if live: + int_of_order = np.fromiter( + (int(p._interaction_id) for p in order), + dtype=np.int64, count=len(order)) + part_start, part_end = [], [] + pc_start, pc_end = [], [] + for old in live: + idx = np.flatnonzero(int_of_order == old) + lo, hi = int(idx[0]), int(idx[-1]) + pc_start.append(int(pc_offsets[lo])) + pc_end.append(int(pc_offsets[hi + 1])) + rws = [k for k, r in enumerate(rows) + if int(order[r]._interaction_id) == old] + part_start.append(rws[0] if rws else -1) + part_end.append(rws[-1] + 1 if rws else -1) + interactions = { + # interaction_id is the dense index particles refer to; + # vertex_id is the surviving link to the EDepSim vertex row. + "interaction_id": np.arange(len(live), dtype=np.int32), + "vertex_id": np.asarray(live, dtype=np.int32), + "pc_start": np.asarray(pc_start, dtype=np.int64), + "pc_end": np.asarray(pc_end, dtype=np.int64), + "part_start": np.asarray(part_start, dtype=np.int32), + "part_end": np.asarray(part_end, dtype=np.int32), + } + for name, col in (("x", "x"), ("y", "y"), ("z", "z"), ("time", "t"), + ("energy_sum", "energy_sum"), ("ke_sum", "ke_sum")): + if vertices is not None and col in (vertices.dtype.names or ()): + interactions[name] = np.asarray( + [float(vertices[col][o]) for o in live], dtype=np.float32) + else: + interactions[name] = np.full(len(live), np.nan, dtype=np.float32) + + # reaction is a free-text generator label; carried through verbatim. + if vertices is not None and "reaction" in (vertices.dtype.names or ()): + interactions["reaction"] = np.array( + [(vertices["reaction"][o].decode() + if isinstance(vertices["reaction"][o], bytes) + else str(vertices["reaction"][o])) for o in live], + dtype=object) + else: + interactions["reaction"] = np.array([""] * len(live), dtype=object) + + return EventLayout(order, rows, pc_offsets, columns, int(pc_offsets[-1]), + interactions) From d0f2a4b399eaf0286aafb20634a86ff1aa000a75 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 29/62] io_v3: one particle table, one point array, format 3.1.0 2.x wrote three parallel particle tables -- raw, fragments, instances -- and a point cloud per level, so a point reachable at two levels was stored twice. 3.x writes one particle table and one point array, with every level expressed as index ranges into it. On a two-event file: 6404.6 KiB -> 1660.0 KiB, 26%. 3.1.0 settles the naming. The level qualifier is a prefix everywhere (frag_sem_type, frag_parent_id, inst_sem_type, inst_parent_id), point ranges carry pc at every level (frag_pc_start, inst_pc_start), and both levels describe their two runs the same way -- a non-LE range and an LE range. At instance level the runs are adjacent, so inst_pc_end == inst_pc_le_start; the pair is spelled out anyway rather than encoded as a single split index, so instance and fragment level read identically. geant4_id becomes geant4_trackid alongside the new geant4_parent_trackid. The interactions table gains its own dense interaction_id, and carries energy_sum, ke_sum and reaction through from EDepSim. 29 particle columns, 13 interaction columns, 6 point columns. 3.0.0 files still read: _read_legacy_particles maps the old names, synthesises the two instance ranges from the old split index, and fills the Geant4 parent columns with -1, which is honest -- the value cannot be recovered from the file. EventView rebases absolute point ranges to event-local on read, so every index a caller sees shares one origin. points_of(row, level, le) answers every combination in a single slice except fragment-level "all", where the two runs are genuinely not adjacent. --- pysupera/io_v3.py | 442 ++++++++++++++++++++++++++++++++++++++++++++ tests/test_io_v3.py | 345 ++++++++++++++++++++++++++++++++++ 2 files changed, 787 insertions(+) create mode 100644 pysupera/io_v3.py create mode 100644 tests/test_io_v3.py diff --git a/pysupera/io_v3.py b/pysupera/io_v3.py new file mode 100644 index 0000000..53d599f --- /dev/null +++ b/pysupera/io_v3.py @@ -0,0 +1,442 @@ +""" +Writer and reader for the 3.0.0 on-disk format. + +3.0.0 collapses the three parallel particle tables of 2.x into one, and stores +each point exactly once. See :mod:`pysupera.layout` for how a single event's +ordering, row selection and group ranges are decided; this module only puts +the result on disk and reads it back. + +Layout +------ +:: + + format_version scalar str "3.0.0" + n_events scalar int64 + events/offsets (n_events+1,) int64 particle rows per event + points/offsets (n_events+1,) int64 point rows per event + points/flat (N, 6) float32 x, y, z, time, dE, dX + particles/ (M,) see _INT32 / _INT8 / _INT64 + inter/offsets (n_events+1,) int64 interaction rows per event + inter/ (K,) see _INTER + +Index conventions +----------------- +``id``, ``parent_id``, ``ancestor_id``, ``frag_id`` and ``inst_id`` are +**per-event** particle indices: row ``events/offsets[ev] + id``. + +Point ranges (``pc_*``, ``frag_pc_*``, ``inst_pc_*``) are stored **absolute** +into ``points/flat``, so a whole-file reader can slice them with no arithmetic. +:class:`EventView` rebases them to its own ``points`` array on read, so that +within a view *everything* is event-local and consistent -- ``v["pc_start"]`` +indexes ``v.points`` directly. Mixing the two bases is the easiest mistake +this format allows, so the view does not expose the mixture. + +``frag_id == id`` marks a fragment representative and ``inst_id == id`` an +instance representative; ``-1`` means the particle heads no group there. + +``interaction_id`` is a row index into this event's slice of the interactions +table, so the vertex a particle came from is one lookup away. Interactions no +stored particle references are dropped and the rest renumbered, which is why +the original vertex row survives as ``vertex_id``. +""" + +from __future__ import annotations + +import os +import numpy as np + +from .io import _compress_kwargs, _chunk_rows, _CHUNK_TARGET_BYTES_META + +FORMAT_VERSION_V3 = "3.1.0" + +_PT_NDIM = 6 # x, y, z, time, dE, dX + +_INT32 = ("id", "geant4_trackid", "parent_id", "ancestor_id", + "pdg", "parent_pdg", + # The true Geant4 parent. parent_id points at the nearest *stored* + # ancestor, which is usually not the real one -- only ~17% of + # particles are written -- so the real link is kept separately. + "geant4_parent_trackid", "geant4_parent_pdg", + "interaction_id", "interaction_type", + "frag_id", "frag_merge_count", "frag_parent_id", + "inst_id", "inst_merge_count", "inst_parent_id") +_INT8 = ("sem_type", "frag_sem_type", "inst_sem_type") +# Both levels describe two runs the same way: a non-LE range and an LE range. +# At instance level the two are adjacent, so inst_pc_end == inst_pc_le_start; +# at fragment level other fragments' points lie between them. +_INT64 = ("pc_start", "pc_end", + "frag_pc_start", "frag_pc_end", "frag_pc_le_start", "frag_pc_le_end", + "inst_pc_start", "inst_pc_end", "inst_pc_le_start", "inst_pc_le_end") +COLUMNS = _INT32 + _INT8 + _INT64 + +#: 3.0.0 column names that were renamed in 3.1.0, old -> new. +_RENAMED_3_0 = { + "geant4_id": "geant4_trackid", + "sem_type_frag": "frag_sem_type", + "sem_type_inst": "inst_sem_type", + "parent_frag_id": "frag_parent_id", + "parent_inst_id": "inst_parent_id", + "frag_non_le_start": "frag_pc_start", + "frag_non_le_end": "frag_pc_end", + "frag_le_start": "frag_pc_le_start", + "frag_le_end": "frag_pc_le_end", +} + +_DTYPE = {**{c: np.int32 for c in _INT32}, + **{c: np.int8 for c in _INT8}, + **{c: np.int64 for c in _INT64}} + +# Ranges are stored absolute, so every one of these needs the event's point +# base added as events are appended. +_POINT_RANGE_COLS = ("pc_start", "pc_end", + "frag_pc_start", "frag_pc_end", + "frag_pc_le_start", "frag_pc_le_end", + "inst_pc_start", "inst_pc_end", + "inst_pc_le_start", "inst_pc_le_end") + +# Interactions table. part_* index this event's particle rows, pc_* the point +# array; both are rebased by the reader exactly as the particle columns are. +# interaction_id is this table's own dense index -- the value particles carry +# in their own interaction_id column. vertex_id is the link back to the +# EDepSim vertex row, and survives the renumbering that drops dead vertices. +_INTER_I32 = ("interaction_id", "vertex_id", "part_start", "part_end") +_INTER_I64 = ("pc_start", "pc_end") +_INTER_F32 = ("x", "y", "z", "time", "energy_sum", "ke_sum") +_INTER_STR = ("reaction",) +INTER_COLUMNS = _INTER_I32 + _INTER_I64 + _INTER_F32 + _INTER_STR +_INTER_DTYPE = {**{c: np.int32 for c in _INTER_I32}, + **{c: np.int64 for c in _INTER_I64}, + **{c: np.float32 for c in _INTER_F32}, + **{c: object for c in _INTER_STR}} + +#: 3.0.0 interaction columns that were renamed in 3.1.0. +_INTER_RENAMED_3_0 = {"t": "time"} +_INTER_POINT_COLS = ("pc_start", "pc_end") +_INTER_PART_COLS = ("part_start", "part_end") + + +class EventWriterV3: + """Incremental writer. One :meth:`append_event` call per event.""" + + def __init__(self, path, compression="lz4", compression_opts=None): + import h5py + self._path = path + self._ckw = _compress_kwargs(compression, compression_opts) + self._f = h5py.File(path, "w") + self._n_events = 0 + self._n_rows = 0 + self._n_points = 0 + self._n_inter = 0 + self._create() + + def _create(self): + import h5py + f = self._f + f.create_dataset("format_version", data=FORMAT_VERSION_V3) + f.create_dataset("n_events", data=np.int64(0)) + rows = _chunk_rows(1, target_bytes=_CHUNK_TARGET_BYTES_META) + for grp in ("events", "points"): + g = f.create_group(grp) + g.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), + maxshape=(None,), chunks=(rows,)) + f["points"].create_dataset( + "flat", shape=(0, _PT_NDIM), maxshape=(None, _PT_NDIM), + dtype=np.float32, chunks=(_chunk_rows(_PT_NDIM), _PT_NDIM), + **self._ckw) + g = f.create_group("inter") + g.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), + maxshape=(None,), chunks=(rows,)) + for c in INTER_COLUMNS: + dt = (h5py.string_dtype() if c in _INTER_STR + else _INTER_DTYPE[c]) + g.create_dataset(c, shape=(0,), maxshape=(None,), dtype=dt, + chunks=(rows,), **self._ckw) + + pg = f.create_group("particles") + for c in COLUMNS: + dt = _DTYPE[c] + pg.create_dataset(c, shape=(0,), maxshape=(None,), dtype=dt, + chunks=(_chunk_rows(1, np.dtype(dt).itemsize, + _CHUNK_TARGET_BYTES_META),), + **self._ckw) + + def append_event(self, layout): + """Append one event described by an :class:`~pysupera.layout.EventLayout`.""" + cols = layout.columns + n_rows = len(layout.rows) + n_pts = layout.n_points + + # ---- points ------------------------------------------------------- + pts = self._f["points/flat"] + pts.resize(self._n_points + n_pts, axis=0) + if n_pts: + block = np.zeros((n_pts, _PT_NDIM), dtype=np.float32) + cursor = 0 + for p in layout.order: + pc = np.asarray(p.point_cloud, dtype=np.float32) + k = len(pc) + if k: + take = min(pc.shape[1], _PT_NDIM) + block[cursor:cursor + k, :take] = pc[:, :take] + cursor += k + pts[self._n_points:self._n_points + n_pts] = block + + # ---- particle columns --------------------------------------------- + base = self._n_points + for c in COLUMNS: + ds = self._f[f"particles/{c}"] + ds.resize(self._n_rows + n_rows, axis=0) + if n_rows: + vals = np.asarray(cols[c], dtype=_DTYPE[c]) + if c in _POINT_RANGE_COLS: + # Shift into absolute file coordinates, leaving the -1 + # sentinel (particle heads no group at this level) alone. + vals = np.where(vals >= 0, vals + base, -1).astype(_DTYPE[c]) + ds[self._n_rows:self._n_rows + n_rows] = vals + + # ---- interactions --------------------------------------------------- + inter = layout.interactions or {} + n_int = len(inter.get("vertex_id", ())) + for c in INTER_COLUMNS: + ds = self._f[f"inter/{c}"] + ds.resize(self._n_inter + n_int, axis=0) + if n_int: + if c in _INTER_STR: + ds[self._n_inter:self._n_inter + n_int] = [ + (x.decode() if isinstance(x, bytes) else str(x)) + for x in inter[c]] + continue + vals = np.asarray(inter[c], dtype=_INTER_DTYPE[c]) + if c in _INTER_POINT_COLS: + vals = np.where(vals >= 0, vals + base, -1).astype(_INTER_DTYPE[c]) + elif c in _INTER_PART_COLS: + vals = np.where(vals >= 0, vals + self._n_rows, -1 + ).astype(_INTER_DTYPE[c]) + ds[self._n_inter:self._n_inter + n_int] = vals + self._n_inter += n_int + + # ---- fenceposts ---------------------------------------------------- + self._n_rows += n_rows + self._n_points += n_pts + self._n_events += 1 + for grp, total in (("events", self._n_rows), ("points", self._n_points), + ("inter", self._n_inter)): + ds = self._f[f"{grp}/offsets"] + ds.resize(self._n_events + 1, axis=0) + ds[self._n_events] = total + self._f["n_events"][()] = np.int64(self._n_events) + + def close(self): + if self._f is not None: + self._f.close() + self._f = None + + def __enter__(self): + return self + + def __exit__(self, *_): + self.close() + + +class EventView: + """One event's rows and points, as plain arrays.""" + + __slots__ = ("columns", "points", "event", "interactions") + + def __init__(self, columns, points, event, interactions=None): + self.columns = columns + self.points = points + self.event = event + #: per-interaction arrays; ``columns["interaction_id"]`` indexes these + self.interactions = interactions or {} + + def __len__(self): + return len(self.columns["id"]) + + def __getitem__(self, name): + return self.columns[name] + + @property + def is_fragment(self): + """Boolean mask: this row represents a fragment.""" + return self.columns["frag_id"] == self.columns["id"] + + @property + def is_instance(self): + """Boolean mask: this row represents an instance.""" + return self.columns["inst_id"] == self.columns["id"] + + def interaction_of(self, row): + """The interactions-table row this particle belongs to, or ``None``.""" + k = int(self.columns["interaction_id"][row]) + if k < 0 or not self.interactions: + return None + return {c: self.interactions[c][k] for c in self.interactions} + + def points_of(self, row, level=None, le=None): + """ + Points of *row*. + + *level* selects whose points: ``None`` for the particle's own, + ``'frag'`` or ``'inst'`` for its group's. *le* selects which kind: + ``None`` for all, ``False`` for non-LE only, ``True`` for LE only. + + Every combination is one contiguous slice. At instance level the + non-LE and LE runs are adjacent (``inst_pc_end == inst_pc_le_start``) + so "all" is still a single slice; at + fragment level the two sides are stored as separate ranges because + other fragments' points lie between them. + """ + c = self.columns + if level == "inst": + s, e = int(c["inst_pc_start"][row]), int(c["inst_pc_end"][row]) + ls, le_ = (int(c["inst_pc_le_start"][row]), + int(c["inst_pc_le_end"][row])) + if le is True: + return self.points[:0] if ls < 0 else self.points[ls:le_] + if le is False: + return self.points[:0] if s < 0 else self.points[s:e] + # Both: the runs are adjacent at instance level, so one slice. + lo = s if s >= 0 else ls + hi = le_ if le_ >= 0 else e + return self.points[:0] if lo < 0 else self.points[lo:hi] + if level == "frag": + if le is None: + # The two sides are not adjacent, so "all" needs both. + a = self.points_of(row, "frag", False) + b = self.points_of(row, "frag", True) + if not len(a): return b + if not len(b): return a + return np.concatenate([a, b]) + pre = "frag_pc_le" if le else "frag_pc" + s, e = int(c[f"{pre}_start"][row]), int(c[f"{pre}_end"][row]) + return self.points[:0] if s < 0 else self.points[s:e] + s, e = int(c["pc_start"][row]), int(c["pc_end"][row]) + return self.points[:0] if s < 0 else self.points[s:e] + + def __repr__(self): + return (f"EventView(event={self.event}, rows={len(self)}, " + f"points={len(self.points)})") + + +class EventStoreV3: + """Reader for a 3.0.0 file.""" + + def __init__(self, path): + import h5py + self._f = h5py.File(path, "r") + ver = self._f["format_version"][()] + ver = ver.decode() if isinstance(ver, bytes) else str(ver) + if not ver.startswith("3."): + raise ValueError( + f"{path!r} is format {ver}, not 3.x. Use pysupera.io." + f"EventStore for 2.x files." + ) + self.format_version = ver + #: 3.0.0 files use the pre-rename column names and encode the + #: instance LE boundary as a single split index. + self._legacy = ver.startswith("3.0") + self._ev = self._f["events/offsets"][:] + self._po = self._f["points/offsets"][:] + self._io = (self._f["inter/offsets"][:] + if "inter/offsets" in self._f else None) + + def __len__(self): + return int(self._f["n_events"][()]) + + def __getitem__(self, ev): + n = len(self) + if ev < -n or ev >= n: + raise IndexError(f"event {ev} out of range for {n} events") + if ev < 0: + ev += n + a, b = int(self._ev[ev]), int(self._ev[ev + 1]) + pa, pb = int(self._po[ev]), int(self._po[ev + 1]) + cols = (self._read_legacy_particles(a, b) if self._legacy + else {c: self._f[f"particles/{c}"][a:b] for c in COLUMNS}) + # Rebase the absolute point ranges onto this event's slice, leaving + # the -1 sentinel alone, so every index in the view shares one origin. + for c in _POINT_RANGE_COLS: + v = cols[c] + cols[c] = np.where(v >= 0, v - pa, -1).astype(v.dtype) + inter: dict = {} + if self._io is not None: + ia, ib = int(self._io[ev]), int(self._io[ev + 1]) + for c in INTER_COLUMNS: + name = c + if self._legacy: + old = {v: k for k, v in _INTER_RENAMED_3_0.items()}.get(c, c) + if f"inter/{old}" not in self._f: + # 3.0.0 has no interaction_id / sums / reaction + n = ib - ia + inter[c] = (np.arange(n, dtype=np.int32) + if c == "interaction_id" else + np.array([""] * n, dtype=object) + if c in _INTER_STR else + np.zeros(n, dtype=_INTER_DTYPE[c])) + continue + name = old + v = self._f[f"inter/{name}"][ia:ib] + if c in _INTER_STR: + inter[c] = np.array( + [x.decode() if isinstance(x, bytes) else str(x) + for x in v], dtype=object) + continue + if c in _INTER_POINT_COLS: + v = np.where(v >= 0, v - pa, -1).astype(v.dtype) + elif c in _INTER_PART_COLS: + v = np.where(v >= 0, v - a, -1).astype(v.dtype) + inter[c] = v + return EventView(cols, self._f["points/flat"][pa:pb], ev, inter) + + def _read_legacy_particles(self, a, b): + """ + Read a 3.0.0 particle block under 3.1.0 names. + + Two shapes differ, not just names: the Geant4 parent columns did not + exist (filled with -1, since the value is unrecoverable from the file + alone), and the instance LE boundary was one split index rather than + two adjacent ranges. + """ + f, old_of = self._f, {v: k for k, v in _RENAMED_3_0.items()} + cols = {} + for c in COLUMNS: + if c in ("geant4_parent_trackid", "geant4_parent_pdg"): + continue # filled below + if c.startswith("inst_pc_"): + continue # derived below + cols[c] = f[f"particles/{old_of.get(c, c)}"][a:b] + + n = b - a + for c in ("geant4_parent_trackid", "geant4_parent_pdg"): + cols[c] = np.full(n, -1, dtype=_DTYPE[c]) + + st = f["particles/inst_pc_start"][a:b] + sp = f["particles/inst_pc_split"][a:b] + en = f["particles/inst_pc_end"][a:b] + cols["inst_pc_start"] = st + cols["inst_pc_end"] = sp # non-LE run ends at the split + cols["inst_pc_le_start"] = np.where(st >= 0, sp, -1).astype(sp.dtype) + cols["inst_pc_le_end"] = en + return cols + + def close(self): + if self._f is not None: + self._f.close() + self._f = None + + def __enter__(self): + return self + + def __exit__(self, *_): + self.close() + + +def open_writer_v3(path, compression="lz4", compression_opts=None): + """Open an :class:`EventWriterV3`.""" + return EventWriterV3(path, compression, compression_opts) + + +def read_events_v3(path): + """Open an :class:`EventStoreV3`.""" + return EventStoreV3(path) diff --git a/tests/test_io_v3.py b/tests/test_io_v3.py new file mode 100644 index 0000000..646de94 --- /dev/null +++ b/tests/test_io_v3.py @@ -0,0 +1,345 @@ +""" +Round-trip tests for the 3.0.0 format. + +These check the two properties the schema exists to provide: that every level's +points are a single contiguous slice, and that a stored particle's genealogy +can always be walked without leaving the file. +""" +import numpy as np +import pytest +import h5py + +from pysupera.layout import build_layout, snapshot_groups +from pysupera.io_v3 import (open_writer_v3, read_events_v3, EventStoreV3, + FORMAT_VERSION_V3, COLUMNS) +from tests.conftest import make_particle, PT_PRIMARY, PT_TRACK + + +def chain_event(): + """Two fragments merged into one instance, plus a point-less ancestor. + + pi0(0) -- no points -- is the ancestor of photon(1); photon(1) and its + partner(2) form one fragment, electron(3) another, and step 2 merges both + into a single instance headed by photon(1). + """ + pi0 = make_particle(0, PT_PRIMARY, pdg=111, parent_id=0, ancestor_id=0, + pc=np.zeros((0, 6), dtype=np.float32), interaction_id=0) + g1 = make_particle(1, PT_TRACK, pdg=22, parent_id=0, ancestor_id=0, + n_pts=3, interaction_id=0) + g2 = make_particle(2, PT_TRACK, pdg=11, parent_id=1, ancestor_id=0, + n_pts=2, interaction_id=0) + e3 = make_particle(3, PT_TRACK, pdg=11, parent_id=1, ancestor_id=0, + n_pts=4, interaction_id=0) + particles = [pi0, g1, g2, e3] + + # Fragments are snapshotted first, exactly as the real pipeline must: + # merge_em_showers reuses the surviving rep objects for the instances, so + # reading fragment membership afterwards would give the instance's. + g1.member_ids = [1, 2] + e3.member_ids = [3] + pi0.member_ids = [0] + frag_groups = snapshot_groups([pi0, g1, e3]) + + g1.member_ids = [0, 1, 2, 3] # what the merge would leave behind + inst_groups = snapshot_groups([g1]) + return particles, frag_groups, inst_groups + + +def write(tmp_path, name="v3.h5", events=None): + events = events or [chain_event()] + path = str(tmp_path / name) + with open_writer_v3(path) as w: + for parts, frag_groups, inst_groups in events: + w.append_event(build_layout(parts, frag_groups, inst_groups)) + return path + + +class TestFileShape: + + def test_version_and_counts(self, tmp_path): + path = write(tmp_path) + with h5py.File(path) as f: + assert f["format_version"][()].decode() == FORMAT_VERSION_V3 + assert int(f["n_events"][()]) == 1 + assert f["points/flat"].shape[1] == 6 + for c in COLUMNS: + assert f"particles/{c}" in f + + def test_all_points_are_stored_even_for_unstored_particles(self, tmp_path): + parts, frags, insts = chain_event() + total = sum(len(p.point_cloud) for p in parts) + path = write(tmp_path, events=[(parts, frags, insts)]) + with h5py.File(path) as f: + assert f["points/flat"].shape[0] == total + + def test_fenceposts(self, tmp_path): + ev = [chain_event(), chain_event()] + path = write(tmp_path, events=ev) + with h5py.File(path) as f: + assert len(f["events/offsets"]) == 3 + assert len(f["points/offsets"]) == 3 + assert f["events/offsets"][-1] == f["particles/id"].shape[0] + assert f["points/offsets"][-1] == f["points/flat"].shape[0] + + +class TestGroupSemantics: + + def test_rep_marked_by_self_reference(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + assert v.is_fragment.sum() >= 1 + assert v.is_instance.sum() == 1 + inst_row = int(np.flatnonzero(v.is_instance)[0]) + assert v["inst_id"][inst_row] == v["id"][inst_row] + + def test_merge_counts(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + r = int(np.flatnonzero(v.is_instance)[0]) + assert v["inst_merge_count"][r] == 4 # all four particles + assert v["frag_merge_count"][r] == 2 # its own fragment only + + def test_instance_split_separates_le(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + for r in np.flatnonzero(v.is_instance): + a = int(v["inst_pc_start"][r]); sp = int(v["inst_pc_le_start"][r]) + e = int(v["inst_pc_end"][r]) + assert a <= sp <= e + assert len(v.points_of(r, "inst", le=False)) == sp - a + assert len(v.points_of(r, "inst", le=True)) == e - sp + assert len(v.points_of(r, "inst")) == e - a + + def test_fragment_sides_sit_inside_their_instance(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + inst = {int(v["id"][r]): r for r in np.flatnonzero(v.is_instance)} + for r in np.flatnonzero(v.is_fragment): + ir = inst.get(int(v["inst_id"][r])) + if ir is None: + continue + lo, hi = int(v["inst_pc_start"][ir]), int(v["inst_pc_end"][ir]) + for pre in ("frag_pc", "frag_pc_le"): + a, b = int(v[f"{pre}_start"][r]), int(v[f"{pre}_end"][r]) + if a >= 0: + assert lo <= a <= b <= hi + + def test_instance_points_cover_every_member(self, tmp_path): + parts, frags, insts = chain_event() + total = sum(len(p.point_cloud) for p in parts) + path = write(tmp_path, events=[(parts, frags, insts)]) + with read_events_v3(path) as s: + v = s[0] + r = int(np.flatnonzero(v.is_instance)[0]) + pts = v.points_of(r, "inst") + assert len(pts) == total + + def test_non_rep_has_sentinel(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + non_inst = ~v.is_instance + assert np.all(v["inst_id"][non_inst] == -1) + + +class TestGenealogy: + + def test_parent_always_resolves(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + ids = set(v["id"].tolist()) + assert all(int(p) in ids for p in v["parent_id"]) + + def test_walk_terminates_on_ancestor_id(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + idx = {int(i): k for k, i in enumerate(v["id"])} + for k, i in enumerate(v["id"]): + cur, seen = int(i), set() + while cur not in seen: + seen.add(cur) + nxt = int(v["parent_id"][idx[cur]]) + if nxt == cur: + break + cur = nxt + assert cur == int(v["ancestor_id"][k]) + + +class TestMultiEvent: + + def test_view_is_self_consistent_per_event(self, tmp_path): + # Ranges are stored absolute but rebased by the view, so every index + # a caller sees -- ids and point ranges alike -- is event-local. + path = write(tmp_path, events=[chain_event(), chain_event()]) + with read_events_v3(path) as s: + a, b = s[0], s[1] + np.testing.assert_array_equal(a["id"], b["id"]) + np.testing.assert_array_equal(a["pc_start"], b["pc_start"]) + assert int(a["pc_start"].min()) == 0 + import h5py + with h5py.File(path) as f: # absolute on disk + n0 = int(f["points/offsets"][1]) + starts = f["particles/pc_start"][:] + assert int(starts.max()) >= n0 + + def test_rebased_ranges_index_the_event_points(self, tmp_path): + path = write(tmp_path, events=[chain_event(), chain_event()]) + with read_events_v3(path) as s: + v = s[1] + for k in range(len(v)): + a, b = int(v["pc_start"][k]), int(v["pc_end"][k]) + assert 0 <= a <= b <= len(v.points) + + def test_points_slice_directly(self, tmp_path): + path = write(tmp_path, events=[chain_event(), chain_event()]) + with read_events_v3(path) as s: + v = s[1] + r = int(np.flatnonzero(v.is_instance)[0]) + got = v.points_of(r, "inst") + assert len(got) == sum(len(p.point_cloud) for p in chain_event()[0]) + + +class TestErrors: + + def test_rejects_2x_file(self, tmp_path): + from pysupera import write_events + old = str(tmp_path / "old.h5") + write_events(old, [[make_particle(0, PT_TRACK)]]) + with pytest.raises(ValueError, match="not 3.x"): + EventStoreV3(old) + + def test_event_index_out_of_range(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + with pytest.raises(IndexError): + s[5] + + +class TestPointCoverage: + + def test_every_point_is_reachable_from_some_stored_row(self, tmp_path): + # Only ~9% of particles get rows, but no point may be orphaned: each + # must fall inside some stored fragment's or instance's range. A + # change to the selection rule that dropped a fragment owning points + # would leave dead weight in the file, and this is what would catch it. + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + covered = np.zeros(len(v.points), bool) + for k in np.flatnonzero(v.is_fragment): + for pre in ("frag_pc", "frag_pc_le"): + a, b = int(v[f"{pre}_start"][k]), int(v[f"{pre}_end"][k]) + if a >= 0: + covered[a:b] = True + assert covered.all(), f"{(~covered).sum()} points unreachable" + + +class TestMergeMutationTrap: + + def test_snapshot_is_taken_not_aliased(self): + # merge_em_showers reuses rep objects, so a snapshot must copy the + # member list rather than hold a reference to it. + from pysupera.layout import snapshot_groups + p = make_particle(0, PT_TRACK) + p.member_ids = [0, 1] + before = snapshot_groups([p]) + p.member_ids = [0, 1, 2, 3] # what the merge does in place + after = snapshot_groups([p]) + assert before[0].member_ids == [0, 1] + assert after[0].member_ids == [0, 1, 2, 3] + + def test_fragment_membership_is_not_read_from_mutated_reps(self, tmp_path): + # Build the layout from a correct fragment snapshot while the rep + # object already carries the instance's membership. frag_merge_count + # must reflect the fragment, not the instance. + parts, frag_groups, inst_groups = chain_event() + layout = build_layout(parts, frag_groups, inst_groups) + c = layout.columns + r = int(np.flatnonzero(c["inst_id"] == c["id"])[0]) + assert c["inst_merge_count"][r] == 4 + assert c["frag_merge_count"][r] == 2 + + +def two_interaction_event(): + """Two independent interactions, so renumbering has something to do.""" + import copy + parts, frags, insts = [], [], [] + for k, (base, vtx) in enumerate(((0, 3), (10, 7))): + a = make_particle(base, PT_PRIMARY, pdg=22, parent_id=base, + ancestor_id=base, n_pts=2, interaction_id=vtx) + b = make_particle(base + 1, PT_TRACK, pdg=11, parent_id=base, + ancestor_id=base, n_pts=3, interaction_id=vtx) + a.member_ids = [base, base + 1] + b.member_ids = [base + 1] + parts += [a, b] + frags += [a, b] + i = copy.copy(a); i.member_ids = [base, base + 1] + insts.append(i) + from pysupera.layout import snapshot_groups + return parts, snapshot_groups(frags), snapshot_groups(insts) + + +class TestInteractions: + + def _view(self, tmp_path, vertices=None): + parts, fg, ig = two_interaction_event() + path = str(tmp_path / "int.h5") + with open_writer_v3(path) as w: + w.append_event(build_layout(parts, fg, ig, vertices=vertices)) + return read_events_v3(path) + + def test_interaction_id_is_a_dense_row_index(self, tmp_path): + # Input vertex ids are 3 and 7; stored ids must be 0 and 1. + with self._view(tmp_path) as s: + v = s[0] + assert sorted(set(v["interaction_id"].tolist())) == [0, 1] + assert len(v.interactions["vertex_id"]) == 2 + + def test_original_vertex_id_is_preserved(self, tmp_path): + with self._view(tmp_path) as s: + v = s[0] + assert v.interactions["vertex_id"].tolist() == [3, 7] + + def test_particle_range_holds_only_that_interaction(self, tmp_path): + with self._view(tmp_path) as s: + v = s[0] + I = v.interactions + for k in range(len(I["vertex_id"])): + rows = v["interaction_id"][I["part_start"][k]:I["part_end"][k]] + assert set(rows.tolist()) == {k} + + def test_point_ranges_partition_the_event(self, tmp_path): + with self._view(tmp_path) as s: + v = s[0] + I = v.interactions + covered = np.zeros(len(v.points), bool) + for a, b in zip(I["pc_start"], I["pc_end"]): + assert not covered[int(a):int(b)].any() # no overlap + covered[int(a):int(b)] = True + assert covered.all() # and no gap + + def test_vertex_position_round_trips(self, tmp_path): + verts = np.zeros(8, dtype=[("x", "f4"), ("y", "f4"), + ("z", "f4"), ("t", "f4"), + ("energy_sum", "f4"), ("ke_sum", "f4")]) + verts["x"][3], verts["y"][3], verts["z"][3] = 1.5, 2.5, 3.5 + verts["x"][7] = -4.0 + with self._view(tmp_path, vertices=verts) as s: + v = s[0] + I = v.interactions + assert I["x"][0] == pytest.approx(1.5) + assert I["y"][0] == pytest.approx(2.5) + assert I["x"][1] == pytest.approx(-4.0) + + def test_interaction_of_resolves(self, tmp_path): + with self._view(tmp_path) as s: + v = s[0] + got = v.interaction_of(0) + assert got["vertex_id"] == v.interactions["vertex_id"][ + int(v["interaction_id"][0])] From 06948b411737d187d104a636822fb099792c5b39 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 30/62] run: wire the 3.x writer, and stop degrading silently The event loop writes through layout + io_v3: fragments are snapshotted before merge_em_showers, ids renumbered densely after resolve_orphans, and the voxel map written against the renumbered ids. Three guards, all for failures that previously passed unnoticed: allow_empty_image an event where every particle has an empty cloud is almost always a misconfigured reader, not physics. Halts by default; set true for a one-time warning. check_interaction_id one broken ancestor link strands a particle and all its descendants at -1. check_ancestor_id ancestor_id must be what the parent chain says. An optional post-run repack block clamps chunks once the final lengths are known, and can emit gzip directly for the viewer. output_compact, write_fragments and write_event_clouds are retired: the single table makes the first two meaningless, and instance_output=traceable expresses the intent better -- keep every instance reachable from a visible one, which on a sample event is 207 of 535. --- pysupera/_run.py | 264 ++++++++------------------------------ pysupera/conf/config.yaml | 4 +- 2 files changed, 55 insertions(+), 213 deletions(-) diff --git a/pysupera/_run.py b/pysupera/_run.py index 7e588af..cd94feb 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -53,15 +53,16 @@ def main(cfg: DictConfig) -> None: import time import numpy as np from collections import defaultdict - from pysupera import open_writer + from pysupera.io_v3 import open_writer_v3 from pysupera.io import open_voxmap_writer from pysupera.partitioner import ParticlePartitioner from pysupera.merge import merge_em_showers from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, build_reader, configure from pysupera.preproc import _voxelize_point_cloud from pysupera.utils import (resolve_orphans, validate_interaction_ids, - validate_root_ids, + validate_ancestor_ids, select_traceable_instances) + from pysupera.layout import build_layout, snapshot_groups configure(cfg) # set module-level defaults (e.g. min_pc_size) before any Particle is created @@ -79,13 +80,10 @@ def main(cfg: DictConfig) -> None: print(f"[run] merge_duplicates: {'enabled' if merge_processor is not None else 'disabled'}") print(f"[run] voxelize : {_vox_info}") print(f"[run] preprocessor : {cfg.particle.get('preprocessor', {}).get('name', 'scipy') if cfg.particle.get('defragment', False) else 'disabled'}") - print(f"[run] output_compact : {bool(cfg.get('output_compact', False))}") print(f"[run] drop_le_scatter : {bool(cfg.get('drop_le_scatter', False))}") - print(f"[run] write_fragments : {bool(cfg.get('write_fragments', False))}") - print(f"[run] write_event_clouds: {bool(cfg.get('write_event_clouds', True))}") print(f"[run] allow_empty_image: {bool(cfg.get('allow_empty_image', False))}") print(f"[run] check_interaction_id: {bool(cfg.get('check_interaction_id', True))}") - print(f"[run] check_root_id : {bool(cfg.get('check_root_id', True))}") + print(f"[run] check_ancestor_id : {bool(cfg.get('check_ancestor_id', True))}") print(f"[run] instance_output : {str(cfg.get('instance_output', 'traceable')).lower()}") print(f"[run] repack : {bool((cfg.get('repack', {}) or {}).get('enabled', False))}") print(f"[run] input : {cfg.io.input_path}") @@ -128,8 +126,8 @@ def main(cfg: DictConfig) -> None: # Interaction-ID validation: raise by default, warn once when relaxed. _check_int_id = bool(cfg.get("check_interaction_id", True)) _warned_int_id = False - _check_root_id = bool(cfg.get("check_root_id", True)) - _warned_root_id = False + _check_ancestor_id = bool(cfg.get("check_ancestor_id", True)) + _warned_ancestor_id = False _instance_output = str(cfg.get("instance_output", "traceable")).lower() if _instance_output not in ("all", "traceable"): @@ -143,19 +141,15 @@ def main(cfg: DictConfig) -> None: _n_to_process = _n_available if _max_events < 0 else min(_max_events, _n_available) print(f"[run] {_n_available} events in input file, processing {_n_to_process}") - _output_compact = bool(cfg.get('output_compact', False)) - _drop_le_scatter = bool(cfg.get('drop_le_scatter', False)) - _write_event_clouds = bool(cfg.get('write_event_clouds', True)) - _write_frags = bool(cfg.get('write_fragments', False)) - # _is_le is always defined: used independently by drop_le_scatter output - # filtering AND by event-cloud LE/non-LE splitting (write_event_clouds). + _drop_le_scatter = bool(cfg.get('drop_le_scatter', False)) + # Used by drop_le_scatter output filtering. from pysupera.utils import SemanticType as _ST _le_type = _ST.kLEScatter _is_le = lambda p: p.sem_type == _le_type - with open_writer(cfg.io.output_path, - compression=cfg.io.compression, - compression_opts=cfg.io.compression_opts) as writer, \ + with open_writer_v3(cfg.io.output_path, + compression=cfg.io.compression, + compression_opts=cfg.io.compression_opts) as writer, \ (open_voxmap_writer(cfg.io.output_path, compression=cfg.io.compression, compression_opts=cfg.io.compression_opts) @@ -215,7 +209,7 @@ def main(cfg: DictConfig) -> None: _ev_t['defragment'] = time.perf_counter() - _t profile['defragment'] += _ev_t['defragment'] - # ── Orphan resolution: repair dangling parent_id / root_id ────── + # ── Orphan resolution: repair dangling parent_id / ancestor_id ────── # Must run before ParticlePartitioner so the genealogy tree is # consistent for all conditions and merge_em_showers. resolve_orphans(particles, verbose=cfg.verbose) @@ -225,12 +219,12 @@ def main(cfg: DictConfig) -> None: # then spawns particles with fresh ids and drops others, so by # here the ids have gaps and `id` is no longer a row index. # Renumber to 0..n-1 in list order and remap the genealogy. - # Runs after resolve_orphans, so every parent_id / root_id + # Runs after resolve_orphans, so every parent_id / ancestor_id # already refers to a particle that is present. _idmap = {int(_p.id): _k for _k, _p in enumerate(particles)} for _k, _p in enumerate(particles): _p.parent_id = _idmap.get(int(_p.parent_id), _k) - _p.root_id = _idmap.get(int(_p.root_id), _k) + _p.ancestor_id = _idmap.get(int(_p.ancestor_id), _k) for _k, _p in enumerate(particles): _p.id = _k @@ -274,12 +268,12 @@ def main(cfg: DictConfig) -> None: if _miss: _warned_int_id = True - if _check_root_id: - validate_root_ids(particles, raise_on_missing=True, + if _check_ancestor_id: + validate_ancestor_ids(particles, raise_on_missing=True, verbose=cfg.verbose) - elif not _warned_root_id: - if validate_root_ids(particles, raise_on_missing=False): - _warned_root_id = True + elif not _warned_ancestor_id: + if validate_ancestor_ids(particles, raise_on_missing=False): + _warned_ancestor_id = True # ── Particle statistics (after preprocessing, before partition) ── _pc_lens = [len(p.point_cloud) for p in particles] @@ -359,72 +353,13 @@ def main(cfg: DictConfig) -> None: {id(r): r for r in partitioner.rep_lookup.values()}.values() ) - # Snapshot fragment member_ids BEFORE merge_em_showers mutates - # them in place (it extends the initiator's member_ids with the - # merged members), so the _frag_lookup we build afterwards - # reflects the original step-1 partitioning. - _frag_member_ids_snap = { - id(_fr): (list(_fr.member_ids) if _fr.member_ids is not None else [_fr.id]) - for _fr in fragments - } - - # Write fragments BEFORE step-2 merger, which mutates - # the initiator's member_ids / point_cloud in place. - # drop_le_scatter only affects output — algorithms always - # run on the full set of fragments/instances. - _frags_out = [f for f in fragments if not _is_le(f)] if _drop_le_scatter else fragments - - # Build frag_lookup early so we can stamp parent_frag_id on - # reps before writing. Use the pre-step-2 snapshot so that - # merge_em_showers' in-place mutation of member_ids does not - # corrupt frag_id assignments. - # particle_id → fragment index in written list (in event) - _frag_lookup: dict = {} - for _fi, _fr in enumerate(_frags_out): - for _pid in _frag_member_ids_snap[id(_fr)]: - _frag_lookup[int(_pid)] = _fi - - # particle_id lookup for parent-chain walks - _part_by_id: dict = {int(_p.id): _p for _p in particles} - - def _find_parent_idx(_start_pid, _lookup, _pby, _exclude=None): - """Walk the parent_id chain for the producer of a rep. - - Returns the index of the nearest ancestor that belongs to a - *different* group than *_exclude*, or -1 if none exists. - - Skipping _exclude matters: when a rep's parent was merged - into the rep's own group -- routine for an EM shower whose - initiator is a late photon -- the lookup resolves to the - rep itself, and without the skip the walk terminates there, - reporting the rep as its own producer and dead-ending the - production history. Continuing past its own group finds - the group the shower actually came from. - """ - _visited: set = set() - _pid = _start_pid - while _pid is not None and _pid not in _visited: - _visited.add(_pid) - _hit = _lookup.get(int(_pid)) - if _hit is not None and _hit != _exclude: - return _hit - _pp = _pby.get(int(_pid)) - if _pp is None or _pp.parent_id == _pp.id: - break - _pid = _pp.parent_id - return -1 - - for _fi, _fr in enumerate(_frags_out): - _pfid = _find_parent_idx(_fr.parent_id, _frag_lookup, - _part_by_id, _exclude=_fi) - _fr.parent_frag_id = _pfid if _pfid >= 0 else _fi # fallback = own - - _t = time.perf_counter() - # An empty list still advances the fragment event offsets, so - # the file stays internally consistent with zero rows written. - writer.append_fragments(_frags_out if _write_frags else []) - _ev_t['write_frags'] = time.perf_counter() - _t - profile['write_fragments'] += _ev_t['write_frags'] + # Capture fragment membership NOW. merge_em_showers returns + # the surviving representatives themselves as the instances -- + # every instance object is also a fragment object -- and + # overwrites their member_ids and sem_type in place. Reading + # fragment membership afterwards silently attributes most + # particles to the wrong fragment. + _frag_groups = snapshot_groups(fragments) # ── Step-2 EM shower instance merger ─────────────────────── _t = time.perf_counter() @@ -432,132 +367,39 @@ def _find_parent_idx(_start_pid, _lookup, _pby, _exclude=None): _ev_t['em_merge'] = time.perf_counter() - _t profile['merge_em_showers'] += _ev_t['em_merge'] - # Output filtering only — does not affect algorithm logic. - _insts_out = [i for i in instances if not _is_le(i)] if _drop_le_scatter else instances - - # Drop instances that nothing visible depends on. Applied - # before _inst_lookup is built so parent_inst_id is stamped - # against the rows that actually get written. + # Output filtering only — the algorithms above always ran on + # the full set. + _insts_out = ([i for i in instances if not _is_le(i)] + if _drop_le_scatter else instances) if _instance_output == 'traceable': _insts_out = select_traceable_instances( _insts_out, particles, verbose=cfg.verbose) - - # Build inst_lookup and stamp parent_inst_id (and parent_frag_id - # for the instance rep's own fragment) before writing. - _inst_lookup: dict = {} # particle_id → instance index (in event) - for _ii, _inst in enumerate(_insts_out): - for _pid in (_inst.member_ids if _inst.member_ids is not None else [_inst.id]): - _inst_lookup[int(_pid)] = _ii - - for _ii, _inst in enumerate(_insts_out): - _piid = _find_parent_idx(_inst.parent_id, _inst_lookup, - _part_by_id, _exclude=_ii) - _inst.parent_inst_id = _piid if _piid >= 0 else _ii # fallback = own - _inst.parent_frag_id = ( - _frag_lookup.get(int(_inst.id), -1) if _write_frags else -1 + _inst_groups = snapshot_groups(_insts_out) + + # Every non-LE particle must belong to some instance, whether + # or not that instance is written. Checked against the full + # list so the result does not depend on instance_output. + _covered: set = set() + for _inst in instances: + _covered.update(int(m) for m in + (_inst.member_ids + if _inst.member_ids is not None + else [_inst.id])) + _uncovered = [int(_p.id) for _p in particles + if not _is_le(_p) and int(_p.id) not in _covered] + if _uncovered: + raise RuntimeError( + f"Event {event_idx}: {len(_uncovered)} non-LE particle(s) " + f"have no instance after step-2 merging: " + f"{_uncovered[:10]}" + f"{'...' if len(_uncovered) > 10 else ''}" ) + # ── Write ─────────────────────────────────────────────────── _t = time.perf_counter() - writer.append_instances(_insts_out) - _ev_t['write_inst'] = time.perf_counter() - _t - profile['write_instances'] += _ev_t['write_inst'] - - # ── Event-level union point clouds ────────────────────── - if _write_event_clouds: - _t = time.perf_counter() - - # _frag_lookup and _inst_lookup are already built above - # for parent_frag_id / parent_inst_id stamping; reuse them - # here so cloud col indices match the HDF5 table rows. - # LE particles get frag_id=-1 / inst_id=-1 when - # drop_le_scatter=True — that is intentional. - - # Assert algorithmic correctness: every non-LE particle must - # appear in SOME instance produced by merge_em_showers, even if - # that instance is a kLEScatter one that gets filtered from output - # by drop_le_scatter. Use the full instances list here, not the - # output-filtered _insts_out, so the check is independent of the - # drop_le_scatter flag. - _inst_lookup_full: dict = {} - for _ii, _inst in enumerate(instances): - for _pid in (_inst.member_ids if _inst.member_ids is not None else [_inst.id]): - _inst_lookup_full[int(_pid)] = _ii - _nle_ids = [int(_p.id) for _p in particles if not _is_le(_p)] - _uncovered = [_pid for _pid in _nle_ids if _pid not in _inst_lookup_full] - if _uncovered: - raise RuntimeError( - f"Event {event_idx}: {len(_uncovered)} non-LE particle(s) have no " - f"instance after step-2 merger: " - f"{_uncovered[:10]}" - f"{'...' if len(_uncovered) > 10 else ''}" - ) - - def _build_cloud9(src_parts): - """Return (N, 9) float32 array: - x, y, z, t, energy, interaction_id, root_id, frag_id, inst_id.""" - rows = [] - for _p in src_parts: - _pc = _p.point_cloud - if len(_pc) == 0: - continue - _n = len(_pc) - _pc5 = np.zeros((_n, 5), dtype=np.float32) - _take = min(_pc.shape[1], 5) - _pc5[:, :_take] = _pc[:, :_take] - _pid_int = int(_p.id) - _iid = np.full((_n, 1), float(_p._interaction_id), dtype=np.float32) - _rid = np.full((_n, 1), float(_p.root_id), dtype=np.float32) - _fid = np.full((_n, 1), float(_frag_lookup.get(_pid_int, -1)), dtype=np.float32) - _eid = np.full((_n, 1), float(_inst_lookup.get(_pid_int, -1)), dtype=np.float32) - rows.append(np.concatenate([_pc5, _iid, _rid, _fid, _eid], axis=1)) - return np.concatenate(rows) if rows else np.empty((0, 9), dtype=np.float32) - - def _voxelize_cloud9(c9, vox_size, orig): - """Voxelize (N, 9) cloud; integer-id cols (5-8) use majority-vote.""" - if len(c9) == 0: - return c9 - c5_vox = _voxelize_point_cloud(c9[:, :5], vox_size, orig) - coords = c9[:, :3] - _org = coords.min(axis=0) if orig is None else orig - _idx = np.floor((coords - _org) / vox_size).astype(np.int64) - _, _inv = np.unique(_idx, axis=0, return_inverse=True) - extra_cols = [] - for _col in range(5, 9): # interaction_id, root_id, frag_id, inst_id - # Q4: use majority-vote (mode) so a voxel shared by multiple - # instances/fragments gets assigned the dominant ID, not the min. - _int_vals = c9[:, _col].astype(np.int32) - _pairs = np.column_stack([_inv, _int_vals]) - _upairs, _ucnts = np.unique(_pairs, axis=0, return_counts=True) - _vox_col = _upairs[:, 0] - _val_col = _upairs[:, 1] - # Sort so highest-count entry per voxel comes first - _sort_idx = np.lexsort([-_ucnts, _vox_col]) - _vox_sorted = _vox_col[_sort_idx] - _val_sorted = _val_col[_sort_idx] - _, _first = np.unique(_vox_sorted, return_index=True) - _out = _val_sorted[_first].astype(np.float32) - extra_cols.append(_out[:, None]) - return np.concatenate([c5_vox] + extra_cols, axis=1) - - _nle_parts = [p for p in particles if not _is_le(p)] - _le_parts = [p for p in particles if _is_le(p)] - _nle_cloud = _build_cloud9(_nle_parts) - _le_cloud = _build_cloud9(_le_parts) - if voxelizer is not None: - _nle_cloud = _voxelize_cloud9(_nle_cloud, voxelizer.voxel_size, voxelizer.origin) - _le_cloud = _voxelize_cloud9(_le_cloud, voxelizer.voxel_size, voxelizer.origin) - writer.append_event_clouds(_nle_cloud, _le_cloud) - _ev_t['write_clouds'] = time.perf_counter() - _t - profile['write_event_clouds'] += _ev_t['write_clouds'] - - _t = time.perf_counter() - _particles_to_write = ( - [] - if _output_compact - else ([p for p in particles if not _is_le(p)] - if _drop_le_scatter else particles) - ) - writer.append_event(_particles_to_write) + _layout = build_layout(particles, _frag_groups, _inst_groups, + vertices=getattr(store, 'last_vertices', None)) + writer.append_event(_layout) _ev_t['write_ev'] = time.perf_counter() - _t profile['write_event'] += _ev_t['write_ev'] diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index 77add45..5a6debe 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -52,14 +52,14 @@ enable_diagnostics: false # false — warn once per event and continue. check_interaction_id: true -# Verify that every particle's root_id is the primary its parent chain leads +# Verify that every particle's ancestor_id is the primary its parent chain leads # to. Guards the same class of upstream defect as check_interaction_id: an # EDepSim ancestor field that disagrees with parent_track_id puts whole # subtrees on a false root. # # true (default) — raise ValueError listing the inconsistent particles. # false — warn once per event and continue. -check_root_id: true +check_ancestor_id: true # --------------------------------------------------------------------------- # Post-run repack (opt-in) From a83154a77fbc2c62831a867e7550dc86c9a496b7 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 31/62] conditions: re-attach a photon's own split-off pieces PhotonDecay merges a photon's PDG +/-11 children into its partition genealogically, with no proximity test, which is right: a photon's shower can start hundreds of mm from where the photon itself deposited. Defragmentation can also cut the photon's *own* cloud into pieces. Those pieces are PDG 22, so they fell outside the condition and could only rejoin by drifting within distance_threshold of a sibling electron. That is a coin flip. In the measured case the two pieces sat 0.01 mm and 5.30 mm from their nearest sibling against a 5.2 mm threshold: one rejoined, the other became a standalone one-voxel instance, losing by a tenth of a millimetre. A PDG 22 child is now claimed when it shares the photon's geant4_id, which means it *is* that photon rather than a separate particle. Matching on geant4_id rather than PDG alone keeps genuine photon-from-photon secondaries -- fluorescence X-rays, which are distinct tracks -- out of this path. In one event that is 94 split-off pieces against 6 fluorescence photons. With this and the preproc keep-alive, the 534 MeV photon of event 81 comes out as one kShower instance of 386 merged particles: 640 non-LE points from the electron shower it initiated, 357 LE points from its own stray deposits and its low-energy Compton products. --- pysupera/conditions/photon_decay.py | 42 +++++++++++++-- tests/test_conditions.py | 84 ++++++++++++++++++++++------- 2 files changed, 102 insertions(+), 24 deletions(-) diff --git a/pysupera/conditions/photon_decay.py b/pysupera/conditions/photon_decay.py index 90cc8fb..b98d76a 100644 --- a/pysupera/conditions/photon_decay.py +++ b/pysupera/conditions/photon_decay.py @@ -10,7 +10,24 @@ from ..data import Particle _PHOTON_PDG = 22 -_DECAY_PRODUCTS = {11, -11} # electron and positron +_DECAY_PRODUCTS = {11, -11} + + +def _is_own_fragment(child, parent) -> bool: + """ + True when *child* is a defragmentation split-off of *parent*. + + Such a piece carries the parent's PDG (22) and, decisively, the parent's + ``geant4_id``: it *is* the same Geant4 particle, cut in two because its + deposits were too far apart to cluster. Matching on ``geant4_id`` rather + than PDG alone is what keeps genuine photon-from-photon secondaries -- + fluorescence X-rays, which are distinct particles -- out of this path. + """ + if parent is None or int(child.pdg) != _PHOTON_PDG: + return False + cg = getattr(child, "geant4_id", None) + pg = getattr(parent, "geant4_id", None) + return cg is not None and pg is not None and int(cg) == int(pg) # electron and positron class PhotonDecay(PartitionConditionBase): @@ -31,7 +48,13 @@ class PhotonDecay(PartitionConditionBase): Because the photon has no spatial hits of its own, proximity checks are meaningless here. The merge is driven purely by the parent-child - relationship in the particle tree. This condition therefore works + relationship in the particle tree. + + A photon that *does* carry hits -- Geant4 deposits argon atomic-relaxation + energy at a photoabsorption vertex, so most photons carry one such point -- + may be cut into pieces by defragmentation. Those pieces are PDG 22, not + ±11, so they are matched on ``geant4_id`` instead and re-attached to the + photon they came from. This condition therefore works **only** in the partition-level proximity path (``partition_level_proximity=True``) via :meth:`get_unconditional_merges`; the legacy particle-level path @@ -77,7 +100,9 @@ def get_unconditional_merges( rep_lookup: Dict[int, 'Particle']) -> List[Tuple[int, int]]: """ Return directed pairs ``(child_rep_id, photon_rep_id)`` for all - PDG ±11 children of photon representatives. + PDG ±11 children of photon representatives, plus any PDG 22 child + that is a defragmentation split-off of the photon itself (see + :func:`_is_own_fragment`). Called on every convergence pass with the live *rep_lookup*, so chains of merges resolve correctly across passes. @@ -104,7 +129,16 @@ def get_unconditional_merges( # returns a PhotonDecay entry explaining what happened. photon_children: Dict[int, List['Particle']] = defaultdict(list) for p in partitioner.particles: - if p.pdg in _DECAY_PRODUCTS and p.parent_pdg == _PHOTON_PDG: + if p.parent_pdg != _PHOTON_PDG: + continue + if p.pdg in _DECAY_PRODUCTS: + photon_children[p.parent_id].append(p) + elif _is_own_fragment(p, partitioner.particle_lookup.get(p.parent_id)): + # A piece of the photon itself. Without this it could only + # rejoin by drifting within distance_threshold of a sibling + # electron -- which is a coin flip: in one measured case two + # such pieces sat 0.01 mm and 5.30 mm from their nearest + # sibling, against a 5.2 mm threshold. photon_children[p.parent_id].append(p) _SIBLING_STAGE = f"{self.name}: Sibling Grouping" diff --git a/tests/test_conditions.py b/tests/test_conditions.py index 40c6efc..ecb2784 100644 --- a/tests/test_conditions.py +++ b/tests/test_conditions.py @@ -40,9 +40,9 @@ class TestPhotonDecay: def test_electron_child_of_photon_merges(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, root_id=1) + parent_id=1, ancestor_id=1) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0, 0, 0)) prt = _part(photon, electron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -51,9 +51,9 @@ def test_electron_child_of_photon_merges(self): def test_positron_child_of_photon_merges(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, root_id=1) + parent_id=1, ancestor_id=1) positron = make_particle(2, PT_PRIMARY, pdg=-11, parent_pdg=22, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0.1, 0, 0)) prt = _part(photon, positron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -62,11 +62,11 @@ def test_positron_child_of_photon_merges(self): def test_both_decay_products_in_same_partition(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), - parent_id=1, root_id=1) + parent_id=1, ancestor_id=1) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, - parent_id=1, root_id=1) + parent_id=1, ancestor_id=1) positron = make_particle(3, PT_PRIMARY, pdg=-11, parent_pdg=22, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0.2, 0, 0)) prt = _part(photon, electron, positron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -76,10 +76,10 @@ def test_both_decay_products_in_same_partition(self): def test_non_photon_parent_no_merge(self): # parent is a track (parent_pdg=13, not 22) → no PhotonDecay merge track = make_particle(1, PT_TRACK, pdg=13, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0, 0, 0)) electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=13, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(50, 0, 0)) # far away prt = _part(track, electron) parts = prt.partition(PhotonDecay(), verbose=False) @@ -93,10 +93,10 @@ def test_non_photon_parent_no_merge(self): class TestTouchingEMShower: def test_touching_parent_child_electrons_merge(self): parent = make_particle(1, PT_PRIMARY, pdg=11, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0, 0, 0)) child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0.05, 0, 0)) # very close → touching prt = _part(parent, child) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -104,11 +104,11 @@ def test_touching_parent_child_electrons_merge(self): def test_different_ancestor_no_merge(self): parent = make_particle(1, PT_PRIMARY, pdg=11, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0, 0, 0)) - # child has different root_id → TouchingEMShower filter rejects + # child has different ancestor_id → TouchingEMShower filter rejects child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, root_id=99, # different ancestor + parent_id=1, ancestor_id=99, # different ancestor offset=(0.05, 0, 0)) prt = _part(parent, child) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -116,10 +116,10 @@ def test_different_ancestor_no_merge(self): def test_non_touching_parent_child_not_merged(self): parent = make_particle(1, PT_PRIMARY, pdg=11, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0, 0, 0)) child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(100, 0, 0)) # far away prt = _part(parent, child, D=5.0) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -128,10 +128,10 @@ def test_non_touching_parent_child_not_merged(self): def test_track_particle_not_included_as_candidate(self): # A track (pdg=13) parent should not produce EM shower candidates parent = make_particle(1, PT_TRACK, pdg=13, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0, 0, 0)) child = make_particle(2, PT_COMPTON, pdg=11, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0.05, 0, 0)) prt = _part(parent, child) parts = prt.partition(TouchingEMShower(), verbose=False) @@ -145,10 +145,10 @@ def test_track_particle_not_included_as_candidate(self): class TestCombineLEScatters: def test_two_touching_le_scatters_merge(self): le1 = make_particle(1, PT_IONIZATION, pdg=11, - parent_id=1, root_id=1, + parent_id=1, ancestor_id=1, offset=(0, 0, 0)) le2 = make_particle(2, PT_IONIZATION, pdg=11, - parent_id=2, root_id=2, + parent_id=2, ancestor_id=2, offset=(0.05, 0, 0)) # touching assert le1.sem_type == SemanticType.kLEScatter assert le2.sem_type == SemanticType.kLEScatter @@ -244,3 +244,47 @@ def test_le_scatter_absorbed_into_track(self): prt = _part(track, le) parts = prt.partition(AbsorbLEScatter(), verbose=False) assert _same_partition(parts, 1, 2) + + +# ============================================================================ +# PhotonDecay: re-attaching the photon's own split-off pieces +# ============================================================================ + +from pysupera.conditions.photon_decay import _is_own_fragment # noqa: E402 + + +class _Stub: + def __init__(self, pid, pdg, parent_id, parent_pdg, g4): + self.id, self.pdg = pid, pdg + self.parent_id, self.parent_pdg = parent_id, parent_pdg + self.geant4_id = g4 + + +class TestIsOwnFragment: + """ + A split-off piece IS the parent photon, so it shares its geant4_id. A + fluorescence X-ray is a distinct particle and must not match, or two + genuinely separate objects get merged. + """ + + def test_split_off_piece_matches(self): + parent = _Stub(1, 22, 1, 0, 500) + child = _Stub(9, 22, 1, 22, 500) + assert _is_own_fragment(child, parent) + + def test_fluorescence_photon_does_not_match(self): + parent = _Stub(1, 22, 1, 0, 500) + child = _Stub(9, 22, 1, 22, 501) # its own Geant4 track + assert not _is_own_fragment(child, parent) + + def test_electron_child_does_not_match(self): + parent = _Stub(1, 22, 1, 0, 500) + assert not _is_own_fragment(_Stub(9, 11, 1, 22, 500), parent) + + def test_missing_parent_does_not_match(self): + assert not _is_own_fragment(_Stub(9, 22, 1, 22, 500), None) + + def test_missing_geant4_id_does_not_match(self): + class NoG4: + id, pdg, parent_id, parent_pdg = 9, 22, 1, 22 + assert not _is_own_fragment(NoG4(), _Stub(1, 22, 1, 0, 500)) From 2f7cae39811a01b2edfcbcbce6b9cdfc9cc1cd2a Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 32/62] profiling: memory and rank-aware W&B logging Supporting module for the training pipeline. memory_snapshot() reports host RSS -- including DataLoader workers, which is where reading and pre-processing actually allocate -- alongside the CUDA allocator's view and the driver's free/total. Enumerating child processes scans /proc and costs ~4.6 ms, against ~0.2 ms for the rest of the snapshot, so the handles are cached. An *empty* cache expires far sooner than a populated one: nothing in it could signal that a worker pool has since appeared, and without that a freshly spawned pool reads as zero worker memory for the whole refresh window. The psutil handle is also rebuilt when the pid changes, since Process() captures a pid at construction and a forked worker would otherwise report its parent's memory. WandbLogger collapses every reason not to log into the same silent no-op -- wandb absent, no active run, disabled, or a rank that should not log -- so instrumented code needs no conditionals. In a distributed job only rank 0 logs by default, since every rank sees a different shard and letting them all write to one run interleaves unrelated series. --- pysupera/profiling.py | 263 ++++++++++++++++++++++++++++++ tests/test_profiling.py | 345 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 608 insertions(+) create mode 100644 pysupera/profiling.py create mode 100644 tests/test_profiling.py diff --git a/pysupera/profiling.py b/pysupera/profiling.py new file mode 100644 index 0000000..a26c620 --- /dev/null +++ b/pysupera/profiling.py @@ -0,0 +1,263 @@ +""" +Resource accounting for the training-data pipeline. + +Answers two questions about :mod:`pysupera.torchdata`: where wall time goes +between reading an event, turning it into model input and moving it to the +GPU, and how much memory each of those costs. Measurements are optionally +forwarded to Weights & Biases. + +Nothing here requires torch or wandb. GPU figures are reported when torch +sees CUDA and omitted otherwise; :class:`WandbLogger` degrades to a no-op when +wandb is absent, when no run is active, or on a rank that should not log. +""" + +from __future__ import annotations + +import os +from time import monotonic + +_MB = 1.0 / (1024 * 1024) + +try: + import psutil +except Exception: # pragma: no cover + psutil = None + +#: Enumerating children scans /proc and costs ~4.6 ms with four DataLoader +#: workers, against 0.015 ms to read RSS from handles we already hold -- so +#: the handles are cached and only re-enumerated when one dies or this many +#: seconds pass. Worker sets are stable for at least an epoch. +CHILDREN_REFRESH_S = 10.0 + +#: A *dead* child invalidates the cache immediately, but an empty cache has +#: nothing to notice a *new* child by -- so emptiness expires far sooner. +#: Without this, a worker pool spawned just after a childless sample stays +#: invisible for the full refresh window and worker memory reads as zero. +EMPTY_REFRESH_S = 1.0 + +_PROC = None +_PROC_PID = None +_KIDS: list = [] +_KIDS_T = -1.0 + + +def _self_proc(): + """This process's psutil handle, rebuilt after a fork.""" + global _PROC, _PROC_PID, _KIDS, _KIDS_T + if psutil is None: + return None + pid = os.getpid() + if _PROC is None or _PROC_PID != pid: + _PROC = psutil.Process(pid) + _PROC_PID = pid + _KIDS, _KIDS_T = [], -1.0 # a fork inherits none of the parent's + return _PROC + + +def _children(proc, refresh_s: float = CHILDREN_REFRESH_S) -> list: + """ + Cached child handles, re-enumerated on expiry or when one has died. + + An empty cache expires after :data:`EMPTY_REFRESH_S` instead, because + there is no cheap way to notice a child that did not exist last time. + """ + global _KIDS, _KIDS_T + now = monotonic() + window = refresh_s if _KIDS else min(refresh_s, EMPTY_REFRESH_S) + if now - _KIDS_T > window or any(not c.is_running() for c in _KIDS): + try: + _KIDS = proc.children(recursive=True) + except Exception: # pragma: no cover + _KIDS = [] + _KIDS_T = now + return _KIDS + + +# --------------------------------------------------------------------------- +# Memory +# --------------------------------------------------------------------------- + +def cpu_memory(include_children: bool = True) -> dict: + """ + Resident set size of this process, in MiB. + + ``cpu_rss_total_mb`` adds the DataLoader worker processes, which is where + reading and pre-processing actually allocate once ``num_workers > 0`` -- + the parent's own RSS says nothing about them. Workers share copy-on-write + pages with the parent, so the total over-counts somewhat; read it as an + upper bound. + + Child handles are cached (see :data:`CHILDREN_REFRESH_S`); enumerating + them on every call would cost more than the rest of the snapshot together. + + Falls back to ``/proc`` when psutil is missing, which costs the + child-process and system-availability figures. + """ + proc = _self_proc() + if proc is not None: + rss = proc.memory_info().rss + total = rss + if include_children: + for c in _children(proc): + try: + total += c.memory_info().rss + except Exception: + pass # a worker may exit mid-walk + return {"cpu_rss_mb": rss * _MB, + "cpu_rss_total_mb": total * _MB, + "cpu_available_mb": psutil.virtual_memory().available * _MB} + + try: # pragma: no cover + with open("/proc/self/statm") as fh: + rss = int(fh.read().split()[1]) * os.sysconf("SC_PAGE_SIZE") + return {"cpu_rss_mb": rss * _MB, "cpu_rss_total_mb": rss * _MB} + except Exception: # pragma: no cover + return {} + + +def gpu_memory(device=None) -> dict: + """ + CUDA memory, in MiB. Empty when torch is absent or no GPU is present. + + ``alloc``/``reserved`` are the allocator's view -- live tensors and the + cache it holds for them -- while ``free``/``total`` come from the driver + and so include every process on the device. ``peak`` is the high-water + mark since the last :func:`reset_peak_gpu_memory`. + """ + try: + import torch + if not torch.cuda.is_available(): + return {} + except Exception: + return {} + + dev = torch.device(device) if device is not None else torch.device("cuda") + if dev.type != "cuda": + return {} + out = { + "gpu_alloc_mb": torch.cuda.memory_allocated(dev) * _MB, + "gpu_reserved_mb": torch.cuda.memory_reserved(dev) * _MB, + "gpu_peak_mb": torch.cuda.max_memory_allocated(dev) * _MB, + } + try: + free, total = torch.cuda.mem_get_info(dev) + out["gpu_free_mb"] = free * _MB + out["gpu_total_mb"] = total * _MB + except Exception: # pragma: no cover + pass + return out + + +def memory_snapshot(device=None, include_children: bool = True) -> dict: + """CPU and GPU memory in one dict; see :func:`cpu_memory`, :func:`gpu_memory`.""" + out = cpu_memory(include_children) + out.update(gpu_memory(device)) + return out + + +def reset_peak_gpu_memory(device=None) -> None: + """Restart the ``gpu_peak_mb`` high-water mark, e.g. at the start of an epoch.""" + try: + import torch + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats(device) + except Exception: # pragma: no cover + pass + + +# --------------------------------------------------------------------------- +# Weights & Biases +# --------------------------------------------------------------------------- + +def current_rank() -> int: + """This process's rank, from torch.distributed or the usual env vars.""" + try: + import torch.distributed as dist + if dist.is_available() and dist.is_initialized(): + return dist.get_rank() + except Exception: + pass + for key in ("RANK", "LOCAL_RANK", "SLURM_PROCID"): + v = os.environ.get(key) + if v is not None: + try: + return int(v) + except ValueError: + pass + return 0 + + +class WandbLogger: + """ + Forward metric dicts to Weights & Biases, or to nowhere. + + Instrumented code should not have to ask whether logging is on, so every + reason not to log collapses into the same silent no-op: wandb not + installed, no run active, *enabled* false, or a rank other than + *log_rank*. :attr:`active` says which way it went. + + In a distributed job only one rank logs by default. Every rank sees a + different shard, so letting them all write to one run interleaves + unrelated series; pass ``log_rank=None`` to log from all of them anyway. + + Parameters + ---------- + run : wandb Run or None + An explicit run. ``None`` picks up the active global run, so + ``wandb.init()`` before constructing this is enough. + prefix : str + Prepended to every key as ``"/"``. + enabled : bool + Master switch. + log_rank : int or None + Only this rank logs; ``None`` means all ranks do. + """ + + def __init__(self, run=None, prefix: str = "", enabled: bool = True, + log_rank: int | None = 0): + self.prefix = prefix + self._run = None + self.reason = "" + + if not enabled: + self.reason = "disabled" + return + if log_rank is not None and current_rank() != log_rank: + self.reason = f"rank {current_rank()} != log_rank {log_rank}" + return + if run is None: + try: + import wandb + except ImportError: + self.reason = "wandb is not installed" + return + run = getattr(wandb, "run", None) + if run is None: + self.reason = "no active wandb run (call wandb.init() first)" + return + self._run = run + + @property + def active(self) -> bool: + """True when metrics are actually going somewhere.""" + return self._run is not None + + def _key(self, k: str) -> str: + return f"{self.prefix}/{k}" if self.prefix else k + + def log(self, metrics: dict, step: int | None = None) -> None: + """Log one step's metrics.""" + if self._run is None: + return + payload = {self._key(k): v for k, v in metrics.items()} + if step is None: + self._run.log(payload) + else: + self._run.log(payload, step=step) + + def summary(self, metrics: dict) -> None: + """Write run-summary values, shown as a single number per key.""" + if self._run is None: + return + for k, v in metrics.items(): + self._run.summary[self._key(k)] = v diff --git a/tests/test_profiling.py b/tests/test_profiling.py new file mode 100644 index 0000000..6d61f46 --- /dev/null +++ b/tests/test_profiling.py @@ -0,0 +1,345 @@ +""" +Tests for resource accounting. + +The interesting behaviour is what happens when the optional pieces are +missing: profiling must never be the reason a training run falls over, so +every absent dependency has to collapse into a silent no-op. +""" +import numpy as np +import pytest + +import pysupera.profiling as prof +from pysupera.profiling import (WandbLogger, cpu_memory, current_rank, + gpu_memory, memory_snapshot, + reset_peak_gpu_memory) + + +class FakeRun: + """Stands in for a wandb Run; records what it was told.""" + + def __init__(self): + self.logged = [] + self.summary = {} + + def log(self, payload, step=None): + self.logged.append((payload, step)) + + +class TestMemory: + + def test_cpu_memory_is_reported(self): + m = cpu_memory() + assert m["cpu_rss_mb"] > 0 + assert m["cpu_rss_total_mb"] >= m["cpu_rss_mb"] + + def test_snapshot_merges_both(self): + m = memory_snapshot() + assert "cpu_rss_mb" in m + # GPU keys appear only with CUDA; either way the call must not raise + assert all(isinstance(v, float) for v in m.values()) + + def test_gpu_memory_is_empty_without_cuda(self): + g = gpu_memory() + torch = pytest.importorskip("torch") + if not torch.cuda.is_available(): + assert g == {} + else: + assert g["gpu_alloc_mb"] >= 0 + + def test_reset_peak_never_raises(self): + reset_peak_gpu_memory() + + def test_repeated_calls_agree(self): + a, b = cpu_memory(), cpu_memory() + assert a["cpu_rss_mb"] == pytest.approx(b["cpu_rss_mb"], rel=0.5) + + +class TestChildCache: + """ + Enumerating children scans /proc and dominates the snapshot, so the + handles are cached -- but a stale cache would silently miscount memory. + """ + + def test_cache_is_reused_within_the_window(self, monkeypatch): + pytest.importorskip("psutil") + calls = [] + + class P: + def children(self, recursive=False): + calls.append(1) + return [] + prof._KIDS, prof._KIDS_T = [], -1.0 + prof._children(P()) + prof._children(P()) + assert len(calls) == 1 + + def test_expiry_forces_a_refresh(self): + pytest.importorskip("psutil") + calls = [] + + class P: + def children(self, recursive=False): + calls.append(1) + return [] + prof._KIDS, prof._KIDS_T = [], -1.0 + prof._children(P(), refresh_s=0.0) + prof._children(P(), refresh_s=0.0) + assert len(calls) == 2 + + def test_an_empty_cache_expires_sooner(self, monkeypatch): + # nothing in an empty cache can signal that a worker pool appeared, + # so emptiness must not be held for the full window + pytest.importorskip("psutil") + calls = [] + + class P: + def children(self, recursive=False): + calls.append(1) + return [] + monkeypatch.setattr(prof, "EMPTY_REFRESH_S", 0.0) + prof._KIDS, prof._KIDS_T = [], -1.0 + prof._children(P(), refresh_s=1e9) + prof._children(P(), refresh_s=1e9) + assert len(calls) == 2 + + def test_a_populated_cache_is_held(self, monkeypatch): + pytest.importorskip("psutil") + calls = [] + + class Alive: + def is_running(self): return True + + class P: + def children(self, recursive=False): + calls.append(1) + return [Alive()] + monkeypatch.setattr(prof, "EMPTY_REFRESH_S", 0.0) + prof._KIDS, prof._KIDS_T = [Alive()], 1e18 + prof._children(P(), refresh_s=1e9) + assert len(calls) == 0 + + def test_a_dead_child_forces_a_refresh(self): + pytest.importorskip("psutil") + calls = [] + + class Dead: + def is_running(self): return False + + class P: + def children(self, recursive=False): + calls.append(1) + return [] + prof._KIDS, prof._KIDS_T = [Dead()], 1e18 # fresh, but stale in fact + prof._children(P()) + assert len(calls) == 1 + + def test_handle_is_rebuilt_after_a_fork(self, monkeypatch): + pytest.importorskip("psutil") + prof._self_proc() + monkeypatch.setattr(prof.os, "getpid", lambda: 999999) + monkeypatch.setattr(prof.psutil, "Process", lambda pid: f"proc-{pid}") + assert prof._self_proc() == "proc-999999" + assert prof._KIDS == [] # parent's children dropped + monkeypatch.undo() + prof._PROC = None # restore for other tests + + +class TestWandbLogger: + + def test_explicit_run_is_used(self): + r = FakeRun() + w = WandbLogger(run=r) + assert w.active + w.log({"a": 1}) + assert r.logged == [({"a": 1}, None)] + + def test_prefix_is_applied(self): + r = FakeRun() + WandbLogger(run=r, prefix="data").log({"read_s": 0.5}) + assert r.logged[0][0] == {"data/read_s": 0.5} + + def test_summary_is_written(self): + r = FakeRun() + WandbLogger(run=r, prefix="data").summary({"steps": 3}) + assert r.summary == {"data/steps": 3} + + def test_disabled_is_inactive(self): + r = FakeRun() + w = WandbLogger(run=r, enabled=False) + assert not w.active and w.reason == "disabled" + w.log({"a": 1}) + assert r.logged == [] + + def test_other_ranks_do_not_log(self, monkeypatch): + monkeypatch.setenv("RANK", "3") + r = FakeRun() + w = WandbLogger(run=r, log_rank=0) + assert not w.active + w.log({"a": 1}) + assert r.logged == [] + + def test_log_rank_none_logs_everywhere(self, monkeypatch): + monkeypatch.setenv("RANK", "3") + r = FakeRun() + assert WandbLogger(run=r, log_rank=None).active + + def test_missing_wandb_is_a_silent_noop(self, monkeypatch): + monkeypatch.setitem(__import__("sys").modules, "wandb", None) + w = WandbLogger() + # no run could be resolved, so nothing is logged and nothing raises + assert not w.active and w.reason + w.log({"a": 1}) + w.summary({"a": 1}) + + def test_rank_defaults_to_zero(self, monkeypatch): + for k in ("RANK", "LOCAL_RANK", "SLURM_PROCID"): + monkeypatch.delenv(k, raising=False) + assert current_rank() == 0 + + +torch = pytest.importorskip("torch") +from pysupera.torchdata import StreamMonitor # noqa: E402 + + +def fake_batch(n_events=2, n_points=100, read_s=0.01, preprocess_s=0.02, + payload_mb=1.0): + return { + "events": [{"points": np.zeros((n_points, 4), np.float32)} + for _ in range(n_events)], + "batch_size": n_events, + "n_points": [n_points] * n_events, + "profile": {"read_s": read_s, "preprocess_s": preprocess_s, + "collate_s": 0.001, "payload_mb": payload_mb}, + } + + +class TestStreamMonitor: + + def test_accumulates_across_steps(self): + m = StreamMonitor(device=None, memory=False) + for _ in range(3): + m.record(fake_batch(), wait_s=0.1, stage_s=0.05) + s = m.summary() + assert s["steps"] == 3 + assert s["events"] == 6 + assert s["points"] == 600 + assert s["total_read_s"] == pytest.approx(0.03) + assert s["total_wait_s"] == pytest.approx(0.3) + + def test_stall_fraction_is_against_real_wall_time(self): + # the training step counts: blocking 0.25 s out of a 1.0 s + # iteration is a quarter stalled, not three quarters + m = StreamMonitor(device=None, memory=False) + m.record(fake_batch(), wait_s=0.25, stage_s=0.25, consume_s=0.5) + assert m.summary()["stall_fraction"] == pytest.approx(0.25) + + def test_step_is_partitioned_by_its_parts(self): + m = StreamMonitor(device=None, memory=False) + met = m.record(fake_batch(), wait_s=0.1, stage_s=0.2, consume_s=0.3) + assert met["step_s"] == pytest.approx(0.6) + assert met["step_s"] == pytest.approx( + met["wait_s"] + met["stage_s"] + met["consume_s"]) + + def test_consume_time_is_measured_across_the_yield(self): + from time import sleep + m = StreamMonitor(device=None, memory=False) + for _ in m.iterate([fake_batch()]): + sleep(0.05) # stand-in for a training step + s = m.summary() + assert s["total_consume_s"] >= 0.05 + assert s["stall_fraction"] < 0.5 # the step dominated, not the wait + + def test_a_break_still_records_the_last_step(self): + m = StreamMonitor(device=None, memory=False) + for _ in m.iterate([fake_batch(), fake_batch()]): + break + assert m.summary()["steps"] == 1 + + def test_payload_and_waits_are_tracked(self): + m = StreamMonitor(device=None, memory=False) + m.record(fake_batch(payload_mb=2.0), wait_s=0.5) + m.record(fake_batch(payload_mb=3.0), wait_s=0.1) + s = m.summary() + assert s["payload_mb"] == pytest.approx(5.0) + assert s["first_wait_s"] == pytest.approx(0.5) + assert s["max_wait_s"] == pytest.approx(0.5) + + def test_payload_rate_uses_the_blocked_time(self): + m = StreamMonitor(device=None, memory=False) + met = m.record(fake_batch(payload_mb=4.0), wait_s=2.0) + assert met["payload_mb_per_s"] == pytest.approx(2.0) + + def test_wall_clock_accounts_for_nearly_everything(self): + from time import sleep + m = StreamMonitor(device=None, memory=False) + for _ in m.iterate([fake_batch() for _ in range(3)]): + sleep(0.01) + s = m.summary() + assert s["wall_s"] > 0 + # the per-step breakdown should miss only the monitor's own overhead + assert abs(s["unaccounted_s"]) < 0.02 * s["wall_s"] + 1e-3 + + def test_throughput_uses_step_time(self): + m = StreamMonitor(device=None, memory=False) + met = m.record(fake_batch(n_events=2, n_points=50), + wait_s=0.5, stage_s=0.25, consume_s=0.25) + assert met["points_per_s"] == pytest.approx(100.0) + assert met["events_per_s"] == pytest.approx(2.0) + + def test_memory_keys_present_only_when_asked(self): + assert "cpu_rss_mb" in StreamMonitor(device=None, memory=True).record( + fake_batch(), wait_s=0.1) + assert "cpu_rss_mb" not in StreamMonitor(device=None, memory=False).record( + fake_batch(), wait_s=0.1) + + def test_log_every_throttles(self): + r = FakeRun() + m = StreamMonitor(device=None, memory=False, log_every=3) + m.logger = WandbLogger(run=r, prefix="data") + for _ in range(7): + m.record(fake_batch(), wait_s=0.1) + assert len(r.logged) == 2 # steps 3 and 6 + assert m.summary()["steps"] == 7 # but all are accounted for + + def test_reset_clears(self): + m = StreamMonitor(device=None, memory=False) + m.record(fake_batch(), wait_s=0.1) + m.reset() + assert m.summary()["steps"] == 0 + assert m.summary()["total_wait_s"] == 0.0 + + def test_finish_writes_the_summary(self): + r = FakeRun() + m = StreamMonitor(device=None, memory=False) + m.logger = WandbLogger(run=r, prefix="data") + m.record(fake_batch(), wait_s=0.1) + out = m.finish() + assert r.summary["data/steps"] == 1 + assert out["steps"] == 1 + + def test_iterate_times_the_wait(self): + m = StreamMonitor(device=None, memory=False) + batches = [fake_batch(), fake_batch()] + got = list(m.iterate(batches)) + assert len(got) == 2 + assert m.summary()["steps"] == 2 + assert m.summary()["total_wait_s"] > 0 + + def test_iterate_sets_the_epoch(self): + class Sampler: + epoch = None + def set_epoch(self, e): Sampler.epoch = e + + class Loader(list): + sampler = Sampler() + + m = StreamMonitor(device=None, memory=False) + list(m.iterate(Loader([fake_batch()]), epoch=7)) + assert Sampler.epoch == 7 + + def test_monitor_works_without_profile_data(self): + b = fake_batch() + del b["profile"] + m = StreamMonitor(device=None, memory=False) + met = m.record(b, wait_s=0.1) + assert met["read_s"] == 0.0 From 4314ff024060a6e82627e750531fbf738defebe2 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 33/62] torchdata: panoptic training batches from a 3.x file A batch is a *list* of events rather than a padded tensor -- point clouds differ in length, sparse and transformer backbones both prefer the ragged form, and batch_size then means exactly what it says. Model input is one row per voxel, but a voxel can receive energy from several instances. Energy is summed so no charge is lost; labels come from the winning contribution, by semantic priority (track > shower > delta > michel > LE) and then earliest time within a class. On a sample event ~10% of raw points share a cell with another instance's. LE-ness is a property of a point, not of its instance: a point in the inst_pc_le range is labelled kLEScatter whatever its host is, which makes it the lowest-priority contributor to a contested voxel, while its instance label stays the host's so an absorbed deposit remains attributed to the shower that absorbed it. LE points are excluded by default -- around a third of the total and usually noise to a segmentation model -- and the flag governs input and labels together, so the model never sees a point the labels do not describe. StreamMonitor separates two kinds of timing that are easy to confuse. read_s/preprocess_s/collate_s are worker-side *costs*, running concurrently with the training step, so their sum routinely exceeds wall time. wait_s, stage_s and consume_s are main-process wall time and partition one iteration; stall_fraction = wait_s / step_s is the number that says whether input is the bottleneck. summary() measures wall_s with one clock spanning the loop and reports unaccounted_s against the per-step breakdown, which is how the 27% overhead of the uncached psutil walk was caught. It also warms the CUDA transfer path on construction: the first host-to-device copy in a process pays ~35 ms of lazy initialisation against ~1 ms warm, and allocating on the device is not enough to trigger it. --- pysupera/torchdata.py | 650 ++++++++++++++++++++++++++++++++++++++++ tests/test_torchdata.py | 317 ++++++++++++++++++++ 2 files changed, 967 insertions(+) create mode 100644 pysupera/torchdata.py create mode 100644 tests/test_torchdata.py diff --git a/pysupera/torchdata.py b/pysupera/torchdata.py new file mode 100644 index 0000000..c25dd83 --- /dev/null +++ b/pysupera/torchdata.py @@ -0,0 +1,650 @@ +""" +PyTorch data pipeline for panoptic segmentation on pysupera 3.0.0 output. + +Provides a :class:`Dataset` over events, a collate that keeps a batch as a +*list* of events (point clouds differ in length, so nothing is padded or +concatenated), and :func:`make_dataloader` to wire both up for single-process +or distributed training. + +A note on "DDP": :class:`torch.nn.parallel.DistributedDataParallel` wraps a +*model*, not data. What a distributed job needs on the input side is a +:class:`~torch.utils.data.distributed.DistributedSampler` so each rank sees a +disjoint shard of events, which is what :func:`make_dataloader` sets up. + +Voxel merging +------------- +A voxel can receive energy from several particle instances. The model sees one +row per voxel, so overlaps are resolved here: + +* ``E`` is **summed** over every contribution, so no charge is lost. +* the semantic and instance labels are taken from the **winning** contribution. + +Winner selection, in order: + +1. **Semantic priority** — ``kTrack > kShower > kDelta > kMichel > kLEScatter`` + (then ``kUnknown``). A track passing through a shower keeps the voxel. +2. **Earliest time** — among contributions of the same semantic class, the one + whose voxel time is smallest wins. + +Low-energy scatters +------------------- +LE-ness is a property of a *point*, not of its instance: an instance's block +holds its own points in ``[inst_pc_start, inst_pc_end)`` and the LE deposits +absorbed into it in ``[inst_pc_le_start, inst_pc_le_end)``. The two runs are +adjacent, so ``inst_pc_end == inst_pc_le_start``. A point in the second +run is labelled ``kLEScatter`` whatever its host instance is, which is what +makes it the lowest-priority contribution to a contested voxel. Its *instance* +label is still the host's, so an LE deposit stays attributed to the shower or +track that absorbed it. + +LE deposits are **excluded by default** (``include_le=False``): they are around +a third of the points and are usually noise to a segmentation model. The flag +governs the voxel data as a whole, input and labels alike -- an excluded point +contributes no coordinates and no energy, so the model never sees it. + +Instrumentation +--------------- +Every event records what it cost to read and to pre-process, and +:class:`StreamMonitor` adds the two figures a training loop cannot get from +inside a worker -- how long it blocked waiting for input, and what the +transfer to the GPU cost -- together with CPU and GPU memory. See +:mod:`pysupera.profiling`. + +Labels +------ +``voxel_sem``, ``voxel_instance`` and ``voxel_interaction`` are the three +panoptic targets, one per row of ``points``. ``instances`` and ``interactions`` +are the object-level tables read straight from the file; an interaction's id is +its row index, which is what ``voxel_interaction`` holds. +""" + +from __future__ import annotations + +from time import perf_counter + +import numpy as np + +try: + import torch + from torch.utils.data import Dataset, DataLoader +except ImportError as _e: # pragma: no cover + raise ImportError( + "pysupera.torchdata needs PyTorch: pip install torch" + ) from _e + +from .io_v3 import read_events_v3 +from .profiling import WandbLogger, memory_snapshot, reset_peak_gpu_memory +from .utils import SemanticType + + +#: Lower rank wins a contested voxel. kUnknown is last so it never displaces +#: a classified instance. +SEM_PRIORITY: dict = { + SemanticType.kTrack.value: 0, + SemanticType.kShower.value: 1, + SemanticType.kDelta.value: 2, + SemanticType.kMichel.value: 3, + SemanticType.kLEScatter.value: 4, + SemanticType.kUnknown.value: 5, +} +_MAX_RANK = max(SEM_PRIORITY.values()) + 1 + +#: Rows of ``points``: x, y, z, E. +FEATURES = ("x", "y", "z", "E") + +#: Assigned to a voxel no instance claims. Should not occur -- instances +#: partition the event's points -- but a label array must say something. +NO_LABEL = -1 + + +def _priority_rank(sem_values: np.ndarray) -> np.ndarray: + """Map semantic type codes to contention ranks, unknown codes last.""" + out = np.full(len(sem_values), _MAX_RANK, dtype=np.int16) + for code, rank in SEM_PRIORITY.items(): + out[sem_values == code] = rank + return out + + +def build_event(view, include_le: bool = False) -> dict: + """ + Turn one :class:`~pysupera.io_v3.EventView` into model input and labels. + + Parameters + ---------- + view : EventView + One event from a format 3.x file. + include_le : bool + Keep low-energy-scatter points. Off by default; see the module + docstring. Applies to the input points and to every label, so the + two always describe the same set of voxels. + + Returns + ------- + dict + ``points`` (V, 4) float32 -- x, y, z, E, one row per voxel + ``voxel_sem`` (V,) int16 winner's class; ``kLEScatter`` for + an absorbed low-energy deposit + ``voxel_instance`` (V,) int32 winning instance's particle ``id`` + ``voxel_interaction`` (V,) int32 that instance's ``interaction_id`` + ``instances`` dict of arrays, one entry per instance row + ``interactions`` dict of arrays, one entry per interaction row + ``event`` int + """ + pts = np.asarray(view.points) # (P, 6) x,y,z,t,dE,dX + c = view.columns + inst_rows = np.flatnonzero(view.is_instance) + + # ---- per-point provenance from the instance blocks -------------------- + # Instances partition the event's points, so one pass labels every point + # and nothing is double-assigned. + n = len(pts) + p_inst = np.full(n, NO_LABEL, dtype=np.int32) + p_sem = np.full(n, NO_LABEL, dtype=np.int16) + p_intr = np.full(n, NO_LABEL, dtype=np.int32) + le_code = SemanticType.kLEScatter.value + for r in inst_rows: + # Two adjacent runs: the instance's own points, then the LE deposits + # absorbed into it. Either side may be empty (-1). + nle_a, nle_b = int(c["inst_pc_start"][r]), int(c["inst_pc_end"][r]) + le_a, le_b = int(c["inst_pc_le_start"][r]), int(c["inst_pc_le_end"][r]) + a = nle_a if nle_a >= 0 else le_a + b = le_b if le_b >= 0 else nle_b + if a < 0 or b <= a: + continue + p_inst[a:b] = int(c["id"][r]) + p_intr[a:b] = int(c["interaction_id"][r]) + if nle_a >= 0 and nle_b > nle_a: + p_sem[nle_a:nle_b] = int(c["inst_sem_type"][r]) + if le_a >= 0 and le_b > le_a: + p_sem[le_a:le_b] = le_code + + # ---- drop low-energy scatters ----------------------------------------- + # Done before merging so an excluded point contributes no energy either. + if not include_le: + keep = p_sem != SemanticType.kLEScatter.value + if not keep.all(): + pts = pts[keep] + p_inst, p_sem, p_intr = p_inst[keep], p_sem[keep], p_intr[keep] + n = len(pts) + + if n == 0: + empty = np.zeros((0, 4), dtype=np.float32) + return {"points": empty, + "voxel_sem": np.zeros(0, np.int16), + "voxel_instance": np.zeros(0, np.int32), + "voxel_interaction": np.zeros(0, np.int32), + "instances": _table(c, inst_rows), + "interactions": _arrays(view.interactions), + "event": view.event} + + # ---- collapse to unique voxels ---------------------------------------- + # Coordinates are voxel centres produced by a deterministic formula, so + # two contributions to the same cell are bit-identical and exact + # comparison is safe -- no rounding needed. + xyz = pts[:, :3] + _, first, inverse = np.unique(xyz, axis=0, + return_index=True, return_inverse=True) + inverse = inverse.ravel() + n_vox = len(first) + + energy = np.bincount(inverse, weights=pts[:, 4].astype(np.float64), + minlength=n_vox).astype(np.float32) + + # ---- resolve contested voxels ----------------------------------------- + # Sort by (voxel, semantic rank, time) and take the first row of each + # voxel: that is the highest-priority class, earliest within it. + rank = _priority_rank(p_sem) + order = np.lexsort((pts[:, 3], rank, inverse)) + vox_sorted = inverse[order] + winner = order[np.flatnonzero( + np.r_[True, vox_sorted[1:] != vox_sorted[:-1]])] + # vox_sorted is ascending and its values are 0..n_vox-1, so the group + # firsts come out in voxel order: winner[k] is voxel k's winning point. + + out = np.empty((n_vox, 4), dtype=np.float32) + out[:, :3] = xyz[winner] + out[:, 3] = energy + + return { + "points": out, + "voxel_sem": p_sem[winner].astype(np.int16), + "voxel_instance": p_inst[winner].astype(np.int32), + "voxel_interaction": p_intr[winner].astype(np.int32), + "instances": _table(c, inst_rows), + "interactions": _arrays(view.interactions), + "event": view.event, + } + + +def _table(columns: dict, rows: np.ndarray) -> dict: + """The instance-level table, as one array per column.""" + return {k: np.asarray(v)[rows] for k, v in columns.items()} + + +def _arrays(table: dict) -> dict: + """A column table as plain arrays.""" + return {k: np.asarray(v) for k, v in table.items()} + + +class PysuperaEvents(Dataset): + """ + One pysupera 3.0.0 file as a map-style dataset of events. + + The HDF5 handle is opened lazily *per worker*: h5py handles cannot cross a + fork, so opening in ``__init__`` breaks silently under + ``num_workers > 0``. + + Parameters + ---------- + path : str + A format 3.x output file. + include_le : bool + Keep low-energy-scatter points. Off by default. + transform : callable or None + Applied to each event dict before it is returned. + profile : bool + Time the read and the pre-processing of each event into + ``event["profile"]``. On by default: it is two ``perf_counter`` + calls per event, and the numbers are otherwise unobtainable because + the work happens in a worker process. + """ + + def __init__(self, path: str, transform=None, include_le: bool = False, + profile: bool = True): + self.path = str(path) + self.transform = transform + self.include_le = bool(include_le) + self.profile = bool(profile) + self._store = None + with read_events_v3(self.path) as s: # length only; then close + self._n = len(s) + + def __len__(self) -> int: + return self._n + + def _lazy(self): + if self._store is None: + self._store = read_events_v3(self.path) + return self._store + + def __getitem__(self, idx: int) -> dict: + if not self.profile: + ev = build_event(self._lazy()[int(idx)], include_le=self.include_le) + return self.transform(ev) if self.transform else ev + + t0 = perf_counter() + view = self._lazy()[int(idx)] # h5py: decompress + slice + t1 = perf_counter() + ev = build_event(view, include_le=self.include_le) + if self.transform: + ev = self.transform(ev) # counted as pre-processing + t2 = perf_counter() + ev["profile"] = {"read_s": t1 - t0, "preprocess_s": t2 - t1} + return ev + + def __getstate__(self): + # Never pickle an open handle into a worker process. + state = self.__dict__.copy() + state["_store"] = None + return state + + def close(self): + if self._store is not None: + self._store.close() + self._store = None + + +def collate_events(batch: list) -> dict: + """ + Keep a batch as a list of events. + + Events have different point counts, so stacking would force padding. + Sparse-convolution and transformer backbones both prefer the ragged form, + and ``batch_size`` then means exactly what was asked for: a number of + events. + + Per-event timings are summed into ``batch["profile"]`` so they survive the + trip out of the worker process, along with what this call itself cost and + how many bytes the batch is about to ship back to the parent. + """ + t0 = perf_counter() + read_s = preprocess_s = 0.0 + for e in batch: + p = e.get("profile") + if p: + read_s += p["read_s"] + preprocess_s += p["preprocess_s"] + out = { + "events": batch, + "batch_size": len(batch), + "n_points": [len(e["points"]) for e in batch], + "profile": {"read_s": read_s, "preprocess_s": preprocess_s, + "payload_mb": _payload_bytes(batch) / (1024 * 1024)}, + } + out["profile"]["collate_s"] = perf_counter() - t0 + return out + + +def _payload_bytes(events: list) -> int: + """ + Bytes of array data in a batch -- what crosses the worker boundary. + + Divided by the time the parent blocked, this gives the effective rate at + which batches are arriving, which is the thing to look at when a batch is + cheap to build but slow to show up. + """ + total = 0 + for e in events: + for v in e.values(): + if isinstance(v, np.ndarray): + total += v.nbytes + elif isinstance(v, dict): + total += sum(a.nbytes for a in v.values() + if isinstance(a, np.ndarray)) + return total + + +def to_torch(event: dict, device=None) -> dict: + """Convert one event's arrays to tensors, leaving the object tables alone.""" + out = dict(event) + for k in ("points", "voxel_sem", "voxel_instance", "voxel_interaction"): + out[k] = torch.as_tensor(event[k]) + if device is not None: + out[k] = out[k].to(device, non_blocking=True) + return out + + +def make_dataloader(path: str, batch_size: int = 1, *, distributed: bool = False, + shuffle: bool = True, num_workers: int = 0, seed: int = 0, + drop_last: bool = False, include_le: bool = False, + profile: bool = True, transform=None, **kwargs): + """ + Build a :class:`~torch.utils.data.DataLoader` over events. + + With *distributed* set, a + :class:`~torch.utils.data.distributed.DistributedSampler` shards events + across ranks; call ``loader.sampler.set_epoch(epoch)`` each epoch or every + rank replays the same order. + + Low-energy-scatter points are dropped unless *include_le* is set. + Wrap the result in a :class:`StreamMonitor` to time and log the pipeline. + + Returns + ------- + DataLoader + Yields ``{"events": [...], "batch_size": int, "n_points": [...]}``. + """ + ds = PysuperaEvents(path, transform=transform, include_le=include_le, + profile=profile) + sampler = None + if distributed: + from torch.utils.data.distributed import DistributedSampler + sampler = DistributedSampler(ds, shuffle=shuffle, seed=seed, + drop_last=drop_last) + return DataLoader( + ds, + batch_size=batch_size, + shuffle=(shuffle and sampler is None), + sampler=sampler, + num_workers=num_workers, + collate_fn=collate_events, + drop_last=drop_last, + **kwargs, + ) + + +def stage_batch(batch: dict, device, non_blocking: bool = True) -> tuple: + """ + Move a whole batch's arrays onto *device*. + + Returns ``(batch, seconds)``. On CUDA the device is synchronised before + the clock is read, or the copies would still be in flight and the timing + would flatter itself. + + The arrays are plain numpy, so host memory is not pinned and the copies + are synchronous whatever *non_blocking* says. Pinning would need the + dataset to emit tensors; the cost shows up here either way. + """ + t0 = perf_counter() + batch["events"] = [to_torch(e, device) for e in batch["events"]] + dev = torch.device(device) if device is not None else None + if dev is not None and dev.type == "cuda": + torch.cuda.synchronize(dev) + return batch, perf_counter() - t0 + + +class StreamMonitor: + """ + Time and log the input pipeline around a :class:`DataLoader`. + + :: + + mon = StreamMonitor(device="cuda", prefix="data") + for batch in mon.iterate(loader): + train_step(batch) # batch already staged on the GPU + print(mon.summary()) + + Timings come in two kinds, and mixing them up is the easy mistake. + + **Worker-side cost** -- ``read_s``, ``preprocess_s``, ``collate_s``, summed + over the batch's events and measured wherever they were produced. With + ``num_workers > 0`` these run in other processes, concurrently with the + training step and with each other, so their sum routinely exceeds the + step's wall time. They say what the work costs, not what it delays. + + **Main-process wall time** -- these partition one iteration and add up to + ``step_s``: + + ``wait_s`` + How long the loop actually blocked in ``next(loader)``: workers not + yet done, plus the cost of shipping the finished batch back and + rebuilding it in this process. The two are not separable from + outside -- the DataLoader offers no hook between them -- so compare + ``wait_s`` against ``payload_mb`` to tell a starved pipeline from a + fat one. + ``stage_s`` + The host-to-device transfer, when *device* is set. + ``consume_s`` + Everything the loop body did with the batch -- the training step. + Measured across the ``yield``, so it needs no cooperation from the + caller. + + ``stall_fraction = wait_s / step_s`` is the headline: the share of real + wall time the loop spent waiting on input. Near zero means the pipeline + keeps up however large ``read_s`` is; near one means it does not. + + Memory is sampled once per logged step -- see + :func:`~pysupera.profiling.memory_snapshot`. ``wandb_run`` (or an active + global run) turns on Weights & Biases logging; without one the monitor + still accumulates, and :meth:`summary` still works. + + Parameters + ---------- + device : torch device or None + Stage each batch here. ``None`` leaves batches on the host and + reports ``stage_s`` as zero. + wandb_run, prefix, log_rank + Passed to :class:`~pysupera.profiling.WandbLogger`. + enabled : bool + Master switch for logging; accumulation is unaffected. + log_every : int + Log every n-th step. Totals still cover every step. + memory : bool + Sample CPU/GPU memory. Costs a psutil walk over the worker + processes, so turn it off if steps are very short. + """ + + #: worker-side costs, overlapping and concurrent + _WORKER = ("read_s", "preprocess_s", "collate_s") + #: main-process wall time; the first three partition ``step_s`` + _WALL = ("wait_s", "stage_s", "consume_s", "step_s") + _STAGES = _WORKER + _WALL + + def __init__(self, device=None, wandb_run=None, prefix: str = "data", + enabled: bool = True, log_every: int = 1, + log_rank: int | None = 0, memory: bool = True): + self.device = device + self.log_every = max(1, int(log_every)) + self.memory = bool(memory) + self.logger = WandbLogger(run=wandb_run, prefix=prefix, + enabled=enabled, log_rank=log_rank) + self.reset() + + def reset(self) -> None: + """ + Zero the accumulators and the GPU high-water mark. + + Also performs one throwaway host-to-device copy. The first real copy + in a process otherwise pays for lazy CUDA initialisation -- 35 ms + against the ~1 ms a warm copy costs -- and step 1 would wear the bill. + Allocating on the device does not trigger it; the transfer path has to + be exercised. This removes most of the cost but not all of it: the + first step still runs several times long while the caching allocator + grows to the real batch sizes, so discard it when benchmarking. + """ + self.totals = {k: 0.0 for k in self._STAGES} + self.steps = 0 + self.events = 0 + self.points = 0 + self.payload_mb = 0.0 + self.max_wait_s = 0.0 + self.first_wait_s = None + self._t_start = None + self._t_last = None + self._warmup() + reset_peak_gpu_memory(self.device) + + def _warmup(self) -> None: + if self.device is None: + return + try: + dev = torch.device(self.device) + if dev.type == "cuda": + torch.zeros(1).to(dev) # host -> device, not just alloc + torch.cuda.synchronize(dev) + except Exception: # pragma: no cover + pass + + def iterate(self, loader, epoch: int | None = None): + """ + Yield staged batches from *loader*, timing and logging each one. + + A step is recorded *after* the loop body returns, so that its + ``consume_s`` is known; metrics for step *n* therefore reach W&B once + step *n* has finished. + + Pass *epoch* to forward it to a ``DistributedSampler`` -- without + ``set_epoch`` every rank replays one order for the whole run. + """ + if epoch is not None: + sampler = getattr(loader, "sampler", None) + if hasattr(sampler, "set_epoch"): + sampler.set_epoch(epoch) + + t_prev = perf_counter() + for batch in loader: + wait_s = perf_counter() - t_prev + stage_s = 0.0 + if self.device is not None: + batch, stage_s = stage_batch(batch, self.device) + t_yield = perf_counter() + try: + yield batch + finally: + # finally, not plain fall-through: a caller that breaks out + # of the loop still gets its last step accounted for + self.record(batch, wait_s=wait_s, stage_s=stage_s, + consume_s=perf_counter() - t_yield) + t_prev = perf_counter() + + def record(self, batch: dict, wait_s: float = 0.0, stage_s: float = 0.0, + consume_s: float = 0.0) -> dict: + """Account for one batch and log it. Returns the metrics logged.""" + now = perf_counter() + if self._t_start is None: + self._t_start = now - (wait_s + stage_s + consume_s) + self._t_last = now + + prof = batch.get("profile", {}) + n_pts = int(sum(batch.get("n_points", ()))) + n_ev = int(batch.get("batch_size", 0)) + payload_mb = float(prof.get("payload_mb", 0.0)) + step_s = wait_s + stage_s + consume_s + + step = { + "read_s": float(prof.get("read_s", 0.0)), + "preprocess_s": float(prof.get("preprocess_s", 0.0)), + "collate_s": float(prof.get("collate_s", 0.0)), + "wait_s": wait_s, + "stage_s": stage_s, + "consume_s": consume_s, + "step_s": step_s, + } + for k in self._STAGES: + self.totals[k] += step[k] + self.steps += 1 + self.events += n_ev + self.points += n_pts + self.payload_mb += payload_mb + self.max_wait_s = max(self.max_wait_s, wait_s) + if self.first_wait_s is None: + self.first_wait_s = wait_s + + metrics = dict(step) + metrics["batch_size"] = n_ev + metrics["n_points"] = n_pts + metrics["payload_mb"] = payload_mb + if step_s > 0: + metrics["events_per_s"] = n_ev / step_s + metrics["points_per_s"] = n_pts / step_s + metrics["stall_fraction"] = wait_s / step_s + if wait_s > 0: + metrics["payload_mb_per_s"] = payload_mb / wait_s + if self.memory: + metrics.update(memory_snapshot(self.device)) + + if self.steps % self.log_every == 0: + self.logger.log(metrics) + return metrics + + def summary(self) -> dict: + """ + Totals and per-step means over everything recorded since + :meth:`reset`. + + The headline is ``stall_fraction`` -- the share of real wall time the + loop spent blocked on input. ``wall_s`` is measured by one clock + spanning the whole loop rather than summed, so ``unaccounted_s`` + exposes anything the per-step breakdown missed, the monitor's own + overhead included; it should stay near zero. + + ``first_wait_s`` is usually much the largest wait, because it pays for + worker start-up. Pass ``persistent_workers=True`` through + :func:`make_dataloader` to stop paying it once per epoch. + """ + n = max(self.steps, 1) + out = {f"total_{k}": v for k, v in self.totals.items()} + out.update({f"mean_{k}": v / n for k, v in self.totals.items()}) + out["steps"] = self.steps + out["events"] = self.events + out["points"] = self.points + out["payload_mb"] = self.payload_mb + out["max_wait_s"] = self.max_wait_s + out["first_wait_s"] = self.first_wait_s or 0.0 + + elapsed = self.totals["step_s"] + if elapsed > 0: + out["events_per_s"] = self.events / elapsed + out["points_per_s"] = self.points / elapsed + out["stall_fraction"] = self.totals["wait_s"] / elapsed + if self._t_start is not None: + wall = self._t_last - self._t_start + out["wall_s"] = wall + out["unaccounted_s"] = wall - elapsed + return out + + def finish(self) -> dict: + """Write :meth:`summary` to the W&B run summary and return it.""" + s = self.summary() + self.logger.summary(s) + return s diff --git a/tests/test_torchdata.py b/tests/test_torchdata.py new file mode 100644 index 0000000..6e3619e --- /dev/null +++ b/tests/test_torchdata.py @@ -0,0 +1,317 @@ +""" +Tests for the panoptic training pipeline. + +The interesting behaviour is voxel merging: a voxel touched by several +instances yields one row, its energy summed and its labels taken from the +winning contribution. Those rules are what these pin down. +""" +import numpy as np +import pytest + +torch = pytest.importorskip("torch") + +from pysupera.torchdata import (build_event, collate_events, to_torch, + PysuperaEvents, make_dataloader, stage_batch, + SEM_PRIORITY, NO_LABEL) +from pysupera.utils import SemanticType as ST + + +def _split(b): + """Where the LE run starts: the 6th field, defaulting to 'no LE'.""" + m = b[5] if len(b) > 5 else b[4] + return b[4] if m < 0 else m + + +class FakeView: + """Minimal stand-in for EventView: instance blocks over a point array.""" + + def __init__(self, points, blocks, interactions=None): + # blocks: (inst_id, sem_type, interaction_id, start, end[, split]) + # split defaults to end, i.e. the block carries no absorbed LE points. + self.points = np.asarray(points, dtype=np.float32) + n = len(blocks) + self.columns = { + "id": np.array([b[0] for b in blocks], np.int32), + "inst_id": np.array([b[0] for b in blocks], np.int32), + "inst_sem_type": np.array([b[1] for b in blocks], np.int8), + "interaction_id": np.array([b[2] for b in blocks], np.int32), + # start/end bound the non-LE run, le_start/le_end the LE run; + # they are adjacent, so end == le_start. + "inst_pc_start": np.array([b[3] for b in blocks], np.int64), + "inst_pc_end": np.array([_split(b) for b in blocks], np.int64), + "inst_pc_le_start": np.array([_split(b) for b in blocks], np.int64), + "inst_pc_le_end": np.array([b[4] for b in blocks], np.int64), + } + self.is_instance = np.ones(n, bool) + self.interactions = interactions or {} + self.event = 0 + + +def pt(x, y, z, t, e): + return [x, y, z, t, e, 0.0] + + +class TestVoxelMerging: + + def test_shared_voxel_becomes_one_row(self): + # two instances deposit in the same cell + v = FakeView([pt(0, 0, 0, 1.0, 3.0), pt(0, 0, 0, 2.0, 4.0)], + [(10, ST.kShower.value, 0, 0, 1), + (20, ST.kShower.value, 0, 1, 2)]) + ev = build_event(v) + assert len(ev["points"]) == 1 + + def test_energy_is_summed_not_replaced(self): + v = FakeView([pt(0, 0, 0, 1.0, 3.0), pt(0, 0, 0, 2.0, 4.0)], + [(10, ST.kShower.value, 0, 0, 1), + (20, ST.kShower.value, 0, 1, 2)]) + ev = build_event(v) + assert ev["points"][0, 3] == pytest.approx(7.0) + + def test_distinct_voxels_are_kept(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0), pt(1, 0, 0, 1.0, 2.0)], + [(10, ST.kTrack.value, 0, 0, 2)]) + ev = build_event(v) + assert len(ev["points"]) == 2 + assert ev["points"][:, 3].sum() == pytest.approx(3.0) + + def test_total_energy_is_conserved(self): + rng = np.random.default_rng(0) + xyz = rng.integers(0, 3, size=(50, 3)).astype(np.float32) + pts = np.column_stack([xyz, rng.random(50), rng.random(50), + np.zeros(50)]).astype(np.float32) + v = FakeView(pts, [(1, ST.kShower.value, 0, 0, 50)]) + ev = build_event(v) + assert ev["points"][:, 3].sum() == pytest.approx(pts[:, 4].sum(), rel=1e-5) + + +class TestSemanticPriority: + + @pytest.mark.parametrize("loser", [ST.kShower, ST.kDelta, + ST.kMichel, ST.kLEScatter]) + def test_track_wins_every_contest(self, loser): + # the track deposits later, so only priority can decide it + v = FakeView([pt(0, 0, 0, 9.0, 1.0), pt(0, 0, 0, 1.0, 1.0)], + [(10, ST.kTrack.value, 0, 0, 1), + (20, loser.value, 1, 1, 2)]) + ev = build_event(v) + assert ev["voxel_sem"][0] == ST.kTrack.value + assert ev["voxel_instance"][0] == 10 + assert ev["voxel_interaction"][0] == 0 + + def test_full_priority_order(self): + order = [ST.kTrack, ST.kShower, ST.kDelta, ST.kMichel, ST.kLEScatter] + assert [SEM_PRIORITY[s.value] for s in order] == [0, 1, 2, 3, 4] + assert SEM_PRIORITY[ST.kUnknown.value] > SEM_PRIORITY[ST.kLEScatter.value] + + def test_shower_beats_le(self): + v = FakeView([pt(0, 0, 0, 5.0, 1.0), pt(0, 0, 0, 1.0, 1.0)], + [(10, ST.kShower.value, 0, 0, 1), + (20, ST.kLEScatter.value, 0, 1, 2)]) + ev = build_event(v) + assert ev["voxel_sem"][0] == ST.kShower.value + assert ev["voxel_instance"][0] == 10 + + +class TestTimeTieBreak: + + def test_earlier_time_wins_within_a_class(self): + v = FakeView([pt(0, 0, 0, 9.0, 1.0), pt(0, 0, 0, 2.0, 1.0)], + [(10, ST.kShower.value, 0, 0, 1), + (20, ST.kShower.value, 1, 1, 2)]) + ev = build_event(v) + assert ev["voxel_instance"][0] == 20 # t=2 beats t=9 + assert ev["voxel_interaction"][0] == 1 + + def test_time_does_not_override_semantic_priority(self): + # LE arrives first but must still lose to the shower + v = FakeView([pt(0, 0, 0, 0.0, 1.0), pt(0, 0, 0, 8.0, 1.0)], + [(10, ST.kLEScatter.value, 0, 0, 1), + (20, ST.kShower.value, 1, 1, 2)]) + ev = build_event(v) + assert ev["voxel_sem"][0] == ST.kShower.value + assert ev["voxel_instance"][0] == 20 + + +class TestLowEnergyScatters: + """ + LE-ness is positional: points in the ``inst_pc_le_*`` run are deposits the + instance absorbed, and must be labelled LE regardless of the host's class. + These pass ``include_le=True``; the default is covered below. + """ + + def test_points_after_the_split_are_le(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0), pt(1, 0, 0, 1.0, 1.0), + pt(2, 0, 0, 1.0, 1.0)], + [(10, ST.kTrack.value, 0, 0, 3, 2)]) + ev = build_event(v, include_le=True) + by_x = {p[0]: s for p, s in zip(ev["points"], ev["voxel_sem"])} + assert by_x[0.0] == ST.kTrack.value + assert by_x[1.0] == ST.kTrack.value + assert by_x[2.0] == ST.kLEScatter.value + + def test_le_point_keeps_its_host_instance(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0), pt(1, 0, 0, 1.0, 1.0)], + [(77, ST.kShower.value, 4, 0, 2, 1)]) + ev = build_event(v, include_le=True) + assert set(ev["voxel_instance"].tolist()) == {77} + assert set(ev["voxel_interaction"].tolist()) == {4} + assert sorted(ev["voxel_sem"].tolist()) == sorted( + [ST.kShower.value, ST.kLEScatter.value]) + + def test_le_loses_a_contested_voxel_to_its_host(self): + # same cell reached by the shower's own point and an absorbed LE one, + # the LE arriving first so only priority can decide it + v = FakeView([pt(0, 0, 0, 9.0, 1.0), pt(0, 0, 0, 0.0, 1.0)], + [(10, ST.kShower.value, 0, 0, 2, 1)]) + ev = build_event(v, include_le=True) + assert len(ev["points"]) == 1 + assert ev["voxel_sem"][0] == ST.kShower.value + + def test_le_loses_to_another_instance_too(self): + v = FakeView([pt(0, 0, 0, 0.0, 1.0), pt(0, 0, 0, 9.0, 1.0)], + [(10, ST.kShower.value, 0, 0, 1, 0), # pure LE block + (20, ST.kTrack.value, 1, 1, 2)]) + ev = build_event(v, include_le=True) + assert ev["voxel_sem"][0] == ST.kTrack.value + assert ev["voxel_instance"][0] == 20 + + def test_no_le_run_means_no_le_labels(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0), pt(1, 0, 0, 1.0, 1.0)], + [(10, ST.kTrack.value, 0, 0, 2, -1)]) + ev = build_event(v) + assert set(ev["voxel_sem"].tolist()) == {ST.kTrack.value} + + +class TestIncludeLeFlag: + """ + The flag governs the voxel data as a whole: an excluded LE point + contributes neither a row nor its energy, and the labels shrink with it. + """ + + def _view(self): + # one shower voxel, one LE voxel elsewhere, both in the same block + return FakeView([pt(0, 0, 0, 1.0, 3.0), pt(5, 0, 0, 2.0, 4.0)], + [(10, ST.kShower.value, 0, 0, 2, 1)]) + + def test_le_is_dropped_by_default(self): + ev = build_event(self._view()) + assert len(ev["points"]) == 1 + assert ev["points"][0, 0] == 0.0 + assert ST.kLEScatter.value not in ev["voxel_sem"].tolist() + + def test_dropped_le_energy_is_not_carried_over(self): + ev = build_event(self._view()) + assert ev["points"][:, 3].sum() == pytest.approx(3.0) + + def test_include_le_keeps_both(self): + ev = build_event(self._view(), include_le=True) + assert len(ev["points"]) == 2 + assert ev["points"][:, 3].sum() == pytest.approx(7.0) + + def test_labels_stay_aligned_when_le_is_dropped(self): + ev = build_event(self._view()) + n = len(ev["points"]) + for k in ("voxel_sem", "voxel_instance", "voxel_interaction"): + assert len(ev[k]) == n + + def test_a_shared_voxel_loses_only_the_le_energy(self): + # shower and absorbed LE in the same cell: the row survives, but + # without the LE contribution + v = FakeView([pt(0, 0, 0, 1.0, 3.0), pt(0, 0, 0, 2.0, 4.0)], + [(10, ST.kShower.value, 0, 0, 2, 1)]) + assert build_event(v)["points"][0, 3] == pytest.approx(3.0) + assert build_event(v, include_le=True)["points"][0, 3] == pytest.approx(7.0) + + def test_an_all_le_event_becomes_empty(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0)], + [(10, ST.kShower.value, 0, 0, 1, 0)]) + ev = build_event(v) + assert ev["points"].shape == (0, 4) + assert len(ev["voxel_sem"]) == 0 + + +class TestLabelShapes: + + def test_labels_align_with_points(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0), pt(1, 0, 0, 1.0, 1.0), + pt(1, 0, 0, 2.0, 1.0)], + [(10, ST.kTrack.value, 3, 0, 3)]) + ev = build_event(v) + n = len(ev["points"]) + assert n == 2 + for k in ("voxel_sem", "voxel_instance", "voxel_interaction"): + assert len(ev[k]) == n + assert set(ev["voxel_interaction"].tolist()) == {3} + + def test_empty_event(self): + v = FakeView(np.zeros((0, 6), np.float32), []) + ev = build_event(v) + assert ev["points"].shape == (0, 4) + assert len(ev["voxel_sem"]) == 0 + + def test_object_tables_present(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0)], + [(10, ST.kTrack.value, 0, 0, 1)], + interactions={"vertex_id": np.array([7]), + "x": np.array([1.5])}) + ev = build_event(v) + assert ev["instances"]["id"].tolist() == [10] + assert ev["interactions"]["vertex_id"].tolist() == [7] + # an interaction's id is its row index, which is what the voxel + # label holds -- there is no separate id column + assert "id" not in ev["interactions"] + + +class TestCollate: + + def test_batch_is_a_list_of_events(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0)], [(1, ST.kTrack.value, 0, 0, 1)]) + b = collate_events([build_event(v), build_event(v)]) + assert b["batch_size"] == 2 + assert len(b["events"]) == 2 + assert b["n_points"] == [1, 1] + + def test_per_event_timings_are_summed(self): + a = {"points": np.zeros((1, 4), np.float32), + "profile": {"read_s": 0.1, "preprocess_s": 0.2}} + b = {"points": np.zeros((2, 4), np.float32), + "profile": {"read_s": 0.3, "preprocess_s": 0.4}} + p = collate_events([a, b])["profile"] + assert p["read_s"] == pytest.approx(0.4) + assert p["preprocess_s"] == pytest.approx(0.6) + + def test_profile_is_zero_when_events_carry_none(self): + e = {"points": np.zeros((1, 4), np.float32)} + p = collate_events([e])["profile"] + assert p["read_s"] == 0.0 and p["preprocess_s"] == 0.0 + + def test_payload_counts_the_arrays_shipped(self): + e = {"points": np.zeros((100, 4), np.float32), # 1600 B + "voxel_sem": np.zeros(100, np.int16), # 200 B + "instances": {"id": np.zeros(10, np.int32)}, # 40 B + "event": 0} + mb = collate_events([e])["profile"]["payload_mb"] + assert mb == pytest.approx(1840 / (1024 * 1024)) + + def test_stage_batch_reports_a_duration(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0)], [(1, ST.kTrack.value, 0, 0, 1)]) + b = collate_events([build_event(v)]) + b, secs = stage_batch(b, device=None) + assert secs >= 0.0 + assert isinstance(b["events"][0]["points"], torch.Tensor) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="needs CUDA") + def test_stage_batch_moves_to_the_gpu(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0)], [(1, ST.kTrack.value, 0, 0, 1)]) + b, _ = stage_batch(collate_events([build_event(v)]), device="cuda") + ev = b["events"][0] + assert ev["points"].is_cuda + assert ev["voxel_sem"].is_cuda + + def test_to_torch_converts_arrays_only(self): + v = FakeView([pt(0, 0, 0, 1.0, 1.0)], [(1, ST.kTrack.value, 0, 0, 1)]) + t = to_torch(build_event(v)) + assert isinstance(t["points"], torch.Tensor) + assert t["points"].shape == (1, 4) + assert isinstance(t["instances"], dict) From efc3bf2bc4cfc8f919214eabd5ec2fdaf3b2bdc1 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 34/62] vis: read the 3.x format, and accept both column spellings The viewer reconstructs the old per-level view from the single table: instance blocks give ownership, LE-ness is read positionally from the inst_pc_le range, and fragment labels take two runs per side. h5wasm reads the raw file, so it gets no help from the Python reader's compatibility layer and has to know the 3.0.0 column names itself. V3_ALIAS plus v3Name() resolve either spelling, including the old single split index, so files written before the 3.1.0 rename still open. Also wires the [D] shortcut the DBG button has always advertised. --- vis_force.html | 210 ++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 190 insertions(+), 20 deletions(-) diff --git a/vis_force.html b/vis_force.html index 33a8618..cc9b1ec 100644 --- a/vis_force.html +++ b/vis_force.html @@ -978,7 +978,7 @@

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// Cloud columns (NCOLS=9): // pos : 0=x 1=y 2=z // extra: 3=t 4=energy 5=interaction_id -// extr2: 6=root_id 7=frag_id 8=inst_id +// extr2: 6=ancestor_id 7=frag_id 8=inst_id const NCOLS = 9; const VERT_SHADER = ` @@ -1201,6 +1201,29 @@

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return s.size; } +// 3.0.0 -> 3.1.0 column renames. The viewer reads the raw HDF5, so it has to +// accept both spellings itself. +const V3_ALIAS = { + geant4_trackid: 'geant4_id', + frag_sem_type: 'sem_type_frag', + inst_sem_type: 'sem_type_inst', + frag_parent_id: 'parent_frag_id', + inst_parent_id: 'parent_inst_id', + frag_pc_start: 'frag_non_le_start', + frag_pc_end: 'frag_non_le_end', + frag_pc_le_start: 'frag_le_start', + frag_pc_le_end: 'frag_le_end', + // 3.0.0 split one index where 3.1.0 has two adjacent ranges + inst_pc_end: 'inst_pc_split', + inst_pc_le_start: 'inst_pc_split', + inst_pc_le_end: 'inst_pc_end', +}; +function v3Name(grp, n){ + if(grp.keys().includes(n)) return n; + const alt = V3_ALIAS[n]; + return (alt && grp.keys().includes(alt)) ? alt : n; +} + function colStats(f32){ const N=f32.length/NCOLS; if(!N) return null; const mn=new Float32Array(NCOLS).fill(Infinity), mx=new Float32Array(NCOLS).fill(-Infinity); @@ -1213,10 +1236,90 @@

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objNLE=objLE=null; } + +// ── Format 3.0.0 cloud reconstruction ────────────────────────────────────── +// 3.0.0 stores each point once as (x, y, z, t, dE, dX) and keeps the group +// labels per *particle* rather than per point. The rendering code below was +// written against the 2.x event clouds -- (x,y,z,t,e,interaction,ancestor, +// frag,inst) per point -- so rather than rewrite it, rebuild that view here +// from the particle ranges. Cheap: one pass per group, no per-point search. +// +// Labels come from the group ranges, not the particle's own range: only ~24% +// of points belong to a particle that has a row, but every point lies inside +// some stored fragment. +function v3BuildCloud(evIdx, wantLE){ + const evOff=hf.get('events').get('offsets').value; + const poOff=hf.get('points').get('offsets').value; + const a=Number(evOff[evIdx]), b=Number(evOff[evIdx+1]); + const pa=Number(poOff[evIdx]), pb=Number(poOff[evIdx+1]); + const nPts=pb-pa; + if(nPts<=0) return new Float32Array(0); + const pg=hf.get('particles'); + const col=n=>pg.get(v3Name(pg,n)).slice([[a,b]]); + const id=col('id'), ancestor=col('ancestor_id'); + const fragId=col('frag_id'), instId=col('inst_id'); + const fS=col('frag_pc_start'), fE=col('frag_pc_end'); + const fLS=col('frag_pc_le_start'), fLE=col('frag_pc_le_end'); + const iS=col('inst_pc_start'), iSP=col('inst_pc_le_start'), iE=col('inst_pc_le_end'); + const pts=hf.get('points').get('flat').slice([[pa,pb],[0,6]]); + + const lblInt=new Float32Array(nPts).fill(-1), lblAnc=new Float32Array(nPts).fill(-1); + const lblFrag=new Float32Array(nPts).fill(-1), lblInst=new Float32Array(nPts).fill(-1); + // LE-ness is positional: each instance block is non-LE points + // [inst_pc_start, inst_pc_end) followed by LE points + // [inst_pc_le_start, inst_pc_le_end), and the two are adjacent. No + // per-point label is stored, and none is needed. + const isLE=new Uint8Array(nPts); + const n=id.length; + for(let k=0;k=sp) isLE[j]=1; } + } + // A fragment is two runs -- its non-LE side and its LE side -- because the + // other fragments' non-LE points lie between them. + for(let k=0;kia){ + const s0=ig.get('pc_start').slice([[ia,ib]]), e0=ig.get('pc_end').slice([[ia,ib]]); + for(let k=0;k⌨ Keyboard Shortcuts function readParticleSlice(evIdx){ if(!hf) return null; + if(FMT3) return v3ParticleSlice(evIdx); try{ if(!hf.keys().includes('events')||!hf.keys().includes('particles')) return null; const evOff=hf.get('events').get('offsets').value; @@ -1667,9 +1771,12 @@

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// Does this file actually carry the step-1 fragment level? write_fragments // defaults to false, in which case particle_fragments exists but is empty and -// every parent_frag_id is -1, so fragment fields must not be presented as if +// every frag_parent_id is -1, so fragment fields must not be presented as if // they were merely missing for this particle. let _FRAG_LEVEL=false; +//: true when the open file is format 3.x (one particles table, +//: one point array). 2.x files keep the old read paths. +let FMT3=false; function probeFragLevel(){ _FRAG_LEVEL=false; if(!hf) return false; @@ -1682,9 +1789,65 @@

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return _FRAG_LEVEL; } + +// Instance graph for 3.0.0: instances are the rows where inst_id == id, and +// inst_parent_id already names the parent instance's particle id, so no +// genealogy walk is needed here. +function v3InstanceGraph(evIdx){ + const evOff=hf.get('events').get('offsets').value; + const a=Number(evOff[evIdx]), b=Number(evOff[evIdx+1]); + if(a>=b) return null; + const pg=hf.get('particles'), col=n=>pg.get(n).slice([[a,b]]); + const id=col('id'), instId=col('inst_id'), parentInst=col('inst_parent_id'); + const pdg=col('pdg'), ppdg=col('parent_pdg'), g4=col('geant4_trackid'); + const intId=col('interaction_id'), sem=col('inst_sem_type'); + const mcount=col('inst_merge_count'); + const iS=col('inst_pc_start'), iE=col('inst_pc_end'); + + const nodes=[], rowOf=new Map(); + for(let k=0;k=0&&pi!==nd.idx){ + const key=pi+'>'+nd.idx; + if(!seen.has(key)){seen.add(key);edges.push({src:pi,dst:nd.idx});} + } + } + return {nodes,edges}; +} + +// interaction_id -> set of PDGs, for the Event Summary panel. +function v3ParticleSlice(evIdx){ + const evOff=hf.get('events').get('offsets').value; + const a=Number(evOff[evIdx]), b=Number(evOff[evIdx+1]); + if(a>=b) return null; + const pg=hf.get('particles'); + const ii=pg.get('interaction_id').slice([[a,b]]); + const pd=pg.get('pdg').slice([[a,b]]); + const m=new Map(); + for(let k=0;k⌨ Keyboard Shortcuts const instSem =pgk.includes('sem_type')?pg.get('sem_type').slice([[iStart,iEnd]]):null; const instPdgs =pgk.includes('pdg')?pg.get('pdg').slice([[iStart,iEnd]]):null; const instParPdgs=pgk.includes('parent_pdg')?pg.get('parent_pdg').slice([[iStart,iEnd]]):null; - const instFragIds=pgk.includes('parent_frag_id')?pg.get('parent_frag_id').slice([[iStart,iEnd]]):null; + const instFragIds=pgk.includes(v3Name(pg,'frag_parent_id'))?pg.get(v3Name(pg,'frag_parent_id')).slice([[iStart,iEnd]]):null; // From format 2.2.0 the two id spaces are distinct: 'id' is pysupera's - // per-event particle index (usable as a direct row index), 'geant4_id' is + // per-event particle index (usable as a direct row index), 'geant4_trackid' is // the original Geant4 track id kept purely as provenance. Older files - // have no geant4_id column, where the two happened to coincide. - const instG4Ids =pgk.includes('geant4_id')?pg.get('geant4_id').slice([[iStart,iEnd]]):null; + // have no such column at all, where the two happened to coincide. + const _g4n = v3Name(pg,'geant4_trackid'); + const instG4Ids =pgk.includes(_g4n)?pg.get(_g4n).slice([[iStart,iEnd]]):null; // Prefer the stored instance-level parent pointer. Deriving it by walking // the level-0 parent map only works when 'particles' was written, and that // table is empty under output_compact -- the default -- which used to // leave the genealogy graph with no edges at all. - const instPInst =pgk.includes('parent_inst_id')?pg.get('parent_inst_id').slice([[iStart,iEnd]]):null; + const instPInst =pgk.includes(v3Name(pg,'inst_parent_id'))?pg.get(v3Name(pg,'inst_parent_id')).slice([[iStart,iEnd]]):null; // member offsets → one member array per instance let memOff=null,memberFlat=null; if(pgk.includes('member_offsets')&&ks.includes('particle_instances_members')){ @@ -1773,7 +1937,7 @@

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members:Math.max(1,mCount),ptCount,eSum:null,x:0,y:0,vx:0,vy:0}); } for(let i=0;i=0&&Number(instPInst[i])!==i) ?Number(instPInst[i]) @@ -2533,7 +2697,9 @@

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const keys=hf.keys(); console.log('[h5] file keys:', keys); if(!keys.includes('n_events')) throw new Error('Missing /n_events — not a pysupera file?'); - if(!keys.includes('non_le_cloud')) throw new Error('Missing /non_le_cloud — regenerate with run_pysupera (format v2.1+)'); + FMT3=keys.includes('points')&&!keys.includes('non_le_cloud'); + if(!FMT3&&!keys.includes('non_le_cloud')) + throw new Error('Missing /non_le_cloud and /points — not a pysupera output?'); nEvents=h5scalar(hf.get('n_events')); logMsg('Found '+nEvents+' event(s) in '+name,'log-ok'); // Report which optional levels this file actually carries, so an empty @@ -2546,10 +2712,10 @@

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&&Number(hf.get('particles').get('id').shape[0])>0; }catch(e){} let _hasG4=false; try{ _hasG4=hf.keys().includes('particle_instances') - &&hf.get('particle_instances').keys().includes('geant4_id'); }catch(e){} + &&hf.get('particle_instances').keys().includes('geant4_trackid'); }catch(e){} logMsg('format '+_fv+' \u00b7 fragment level: '+(_FRAG_LEVEL?'yes':'no') +' \u00b7 level-0 particles: '+(_hasL0?'yes':'no') - +' \u00b7 geant4_id: '+(_hasG4?'yes':'no')); + +' \u00b7 geant4_trackid: '+(_hasG4?'yes':'no')); document.getElementById('filename').textContent=name; document.getElementById('ev-total').textContent='/ '+nEvents; document.getElementById('ev-input').max=nEvents-1; @@ -2609,7 +2775,8 @@

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hf=new H5File(fname,'r'); const keys=hf.keys(); if(!keys.includes('n_events')) throw new Error('Missing /n_events'); - if(!keys.includes('non_le_cloud')) throw new Error('Missing /non_le_cloud'); + if(!keys.includes('non_le_cloud')&&!keys.includes('points')) + throw new Error('Missing /non_le_cloud and /points'); const nv=hf.get('n_events').value; const nEvents=typeof nv==='bigint'?Number(nv):(Array.isArray(nv)?Number(nv[0]):Number(nv)); self.postMessage({id,ok:true,nEvents,name}); @@ -2630,6 +2797,7 @@

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}; function _readCloud(grpName,evIdx){ if(!hf) return new Float32Array(0); + if(hf.keys().includes('points')) return v3BuildCloud(evIdx, grpName==='le_scatter_cloud'); const ks=hf.keys(); if(!ks.includes(grpName)) return new Float32Array(0); const grp=hf.get(grpName),gk=grp.keys(); if(!gk.includes('offsets')||!gk.includes('flat')) return new Float32Array(0); @@ -2656,6 +2824,7 @@

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}catch(e){return [];} } function _readInstanceGraph(evIdx){ + if(hf&&hf.keys().includes('points')) return v3InstanceGraph(evIdx); if(!hf) return null; try{ const ks=hf.keys(); @@ -2672,17 +2841,18 @@

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const instSem =pgk.includes('sem_type')?pg.get('sem_type').slice([[iStart,iEnd]]):null; const instPdgs =pgk.includes('pdg')?pg.get('pdg').slice([[iStart,iEnd]]):null; const instParPdgs=pgk.includes('parent_pdg')?pg.get('parent_pdg').slice([[iStart,iEnd]]):null; - const instFragIds=pgk.includes('parent_frag_id')?pg.get('parent_frag_id').slice([[iStart,iEnd]]):null; + const instFragIds=pgk.includes(v3Name(pg,'frag_parent_id'))?pg.get(v3Name(pg,'frag_parent_id')).slice([[iStart,iEnd]]):null; // From format 2.2.0 the two id spaces are distinct: 'id' is pysupera's - // per-event particle index (usable as a direct row index), 'geant4_id' is + // per-event particle index (usable as a direct row index), 'geant4_trackid' is // the original Geant4 track id kept purely as provenance. Older files - // have no geant4_id column, where the two happened to coincide. - const instG4Ids =pgk.includes('geant4_id')?pg.get('geant4_id').slice([[iStart,iEnd]]):null; + // have no such column at all, where the two happened to coincide. + const _g4n = v3Name(pg,'geant4_trackid'); + const instG4Ids =pgk.includes(_g4n)?pg.get(_g4n).slice([[iStart,iEnd]]):null; // Prefer the stored instance-level parent pointer. Deriving it by walking // the level-0 parent map only works when 'particles' was written, and that // table is empty under output_compact -- the default -- which used to // leave the genealogy graph with no edges at all. - const instPInst =pgk.includes('parent_inst_id')?pg.get('parent_inst_id').slice([[iStart,iEnd]]):null; + const instPInst =pgk.includes(v3Name(pg,'inst_parent_id'))?pg.get(v3Name(pg,'inst_parent_id')).slice([[iStart,iEnd]]):null; let memOff=null,memberFlat=null; if(pgk.includes('member_offsets')&&ks.includes('particle_instances_members')){ memOff=pg.get('member_offsets').slice([[iStart,iEnd+1]]); @@ -2737,7 +2907,7 @@

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members:Math.max(1,mCount),ptCount,eSum:null,x:0,y:0,vx:0,vy:0}); } for(let i=0;i=0&&Number(instPInst[i])!==i) ?Number(instPInst[i]) @@ -3283,7 +3453,7 @@

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const vals=[ pa[idx*3],pa[idx*3+1],pa[idx*3+2], // x y z ea[idx*3],ea[idx*3+1],ea[idx*3+2], // t energy int_id - e2[idx*3],e2[idx*3+1],e2[idx*3+2], // root_id frag_id inst_id + e2[idx*3],e2[idx*3+1],e2[idx*3+2], // ancestor_id frag_id inst_id ]; const KEYS=['X','Y','Z','Time','Energy','Int. ID','Root ID','Frag ID','Inst ID']; const fmtV=(v,c)=>{ From 3f9b51a72b7e91ab47b86d501803903e0d07ac0f Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 35/62] packaging: declare hdf5plugin, add torch and wandb extras hdf5plugin was a hard requirement and an undeclared one: it registers the Blosc and LZ4 HDF5 filters, so without it JAXTPC inputs cannot be opened at all and io.compression=lz4 cannot be written. torch and wandb are genuinely optional -- pysupera.torchdata is not imported from __init__, and WandbLogger is a no-op without wandb -- so they are extras. psutil rides with torch, since the worker-memory figures are the part of the snapshot that needs it. Drops the pysupera-recompress entry point, whose function repack replaced. --- pyproject.toml | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 91edf59..cebbf94 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,18 @@ rapids = [ numba = [ "numba", ] +# Required by pysupera.torchdata (training-data pipeline) only. +torch = [ + "torch", + # Per-process RSS, including DataLoader workers. pysupera.profiling + # falls back to /proc without it, losing the worker figures. + "psutil", +] +# Weights & Biases logging of the training-data pipeline. Optional: +# pysupera.profiling.WandbLogger is a silent no-op when it is absent. +wandb = [ + "wandb", +] dev = [ "pytest", "jupyter", From bf1f5c671e58311ed0bb3997a51f0058ccc5ea78 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 20 Sep 2026 22:50:56 -0700 Subject: [PATCH 36/62] docs: rewrite the output format section for 3.1.0 Documents what the file actually contains: the point ordering and the three consequences that follow from it, which particles are stored and why, the full column list at all three levels, and the ID conventions -- ids are dense per-event row indices, Geant4 track IDs are provenance, and you join back to the input on geant4_trackid, never on id. Adds sections for the training pipeline and its profiling, including the distinction between worker-side cost and main-process wall time that the metric names alone do not convey, and the measured numbers behind the advice (persistent_workers is worth more than the worker count; discard the first epoch when benchmarking). Also documents repack's auto and scope options, and the hdf5plugin, torch and wandb dependencies. --- README.md | 634 +++++++++++++++++++++++++++++++++++------------------- 1 file changed, 414 insertions(+), 220 deletions(-) diff --git a/README.md b/README.md index 331e4bb..6f3c1b6 100644 --- a/README.md +++ b/README.md @@ -27,20 +27,27 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [Step 3 — Generating Partitions](#step-3--generating-partitions) - [Step 4 — Output](#step-4--output) 6. [Output File Format](#output-file-format) + - [Point ordering, and why it matters](#point-ordering-and-why-it-matters) + - [Reading points](#reading-points) + - [Which particles are stored](#which-particles-are-stored) + - [Particle columns](#particle-columns) + - [Interaction columns](#interaction-columns) - [ID conventions](#id-conventions) - - [The three particle levels](#the-three-particle-levels) - - [Point clouds](#point-clouds) - - [Event indexing](#event-indexing) - [Expert and debugging output](#expert-and-debugging-output) -7. [CLI Usage](#cli-usage) -8. [Compute Backends](#compute-backends) -9. [Algorithms](#algorithms) +7. [Training Data (PyTorch)](#training-data-pytorch) + - [Voxel merging](#voxel-merging) + - [Low-energy scatters](#low-energy-scatters--include_le) + - [Distributed training](#distributed-training) + - [Profiling and W&B logging](#profiling-and-wb-logging) +8. [CLI Usage](#cli-usage) +9. [Compute Backends](#compute-backends) +10. [Algorithms](#algorithms) - [Partition-level Incremental Algorithm](#partition-level-incremental-algorithm) - [Proximity Check](#proximity-check) - [Condition Pipeline](#condition-pipeline) -10. [Conditions Reference](#conditions-reference) -11. [Diagnostics](#diagnostics) -12. [Assumptions](#assumptions) +11. [Conditions Reference](#conditions-reference) +12. [Diagnostics](#diagnostics) +13. [Assumptions](#assumptions) --- @@ -66,6 +73,7 @@ The result is a list of partitions, where each partition is a list of `Particle` | `numpy` | ✅ | Array operations throughout | | `scipy` | ✅ | KDTree proximity checks (CPU backends), connected-components defragmentation | | `h5py` | ✅ | HDF5 I/O (`EventStore`, `EventWriter`) | +| `hdf5plugin` | ✅ | Registers the Blosc/LZ4 HDF5 filters — needed to read JAXTPC inputs and to write `io.compression=lz4` | | `hydra-core ≥ 1.3` | ✅ | Hierarchical configuration (CLI and programmatic) | | `omegaconf ≥ 2.3` | ✅ | Config composition (dependency of Hydra) | | `joblib` | optional | Parallel KDTree queries (`cpu-multi` backend) | @@ -73,6 +81,9 @@ The result is a list of partitions, where each partition is a list of `Particle` | `cuml` (RAPIDS) | optional | GPU nearest-neighbour index (`gpu` backend) | | `cudf`, `cugraph` (RAPIDS) | optional | Fully GPU defragmentation (`rapids` preprocessor) | | `numba` | optional | CUDA kernel proximity checker (`numba` backend) | +| `torch` | optional | Training-data pipeline (`pysupera.torchdata`) | +| `psutil` | optional | Process and worker memory in `pysupera.profiling` (falls back to `/proc`) | +| `wandb` | optional | Weights & Biases logging of the training-data pipeline | --- @@ -94,6 +105,12 @@ pip install -e ".[numba]" # With app (Dash interactive event viewer) pip install -e ".[app]" +# With the PyTorch training-data pipeline +pip install -e ".[torch]" + +# ... and Weights & Biases logging for it +pip install -e ".[torch,wandb]" + # With all dev tools pip install -e ".[dev]" ``` @@ -190,9 +207,9 @@ These attributes must always be supplied at construction time and are guaranteed | Attribute | Type | Description | |---|---|---| | `id` | `int` | Particle index within an event, assigned by the reader: `0 … n-1`, usable as a direct row index. **Not** the Geant4 track ID | -| `geant4_id` | `int` | Original Geant4 track ID, kept for provenance and for joining back to the input | +| `geant4_trackid` | `int` | Original Geant4 track ID, kept for provenance and for joining back to the input | | `parent_id` | `int` | Direct parent's particle index; equals `id` for primary particles | -| `root_id` | `int` | Particle index of the primary ancestor at the root of the shower/track genealogy | +| `ancestor_id` | `int` | Particle index of the primary ancestor at the root of the shower/track genealogy | | `pdg` | `int` | PDG Monte Carlo particle code | | `parent_pdg` | `int` | PDG code of the direct parent particle | | `process_type` | `InteractionType` | Physics process that created this particle; derived from the raw int stored in `_process_type` | @@ -212,7 +229,7 @@ These attributes are not required at construction time. Unset float32 scalars h | `kinetic_energy_start` | `float32` | `NaN` | Kinetic energy at the start vertex (MeV). | | `kinetic_energy_end` | `float32` | `NaN` | Kinetic energy at the end of the trajectory (MeV). | | `mass` | `float32` | `NaN` | Particle rest mass (MeV/c²). | -| `root_pdg` | `int` | `None` | PDG code of the primary (root) ancestor particle. | +| `ancestor_pdg` | `int` | `None` | PDG code of the primary (root) ancestor particle. | | `start_process_id` | `int` | `None` | Geant4/simulation process ID for this particle's creation. | | `start_subprocess_id` | `int` | `None` | Geant4/simulation sub-process ID for this particle's creation. | | `start_process_name` | `str` | `None` | Human-readable name of the creation process (e.g. `"eIoni"`). | @@ -399,281 +416,458 @@ with read_events("output.h5") as store: ## Output File Format -`run_pysupera` writes a single HDF5 file (plus an optional voxmap companion). -This section describes format **2.3.0**, recorded in the scalar dataset -`format_version`. No HDF5 attributes are used anywhere — everything is a -dataset. +`run_pysupera` writes a single HDF5 file. This section describes format +**3.1.0**, recorded in the scalar dataset `format_version`. No HDF5 attributes +are used anywhere — everything is a dataset. -### ID conventions +3.1.0 renamed columns for a consistent `_` prefix, gave instances the +same two-range point layout as fragments, and added the true Geant4 parent. +Readers accept 3.0.0 files transparently. 3.0.0 replaced the three parallel +particle tables of 2.x with **one** table and +stores each point **once**. On a two-event file that is 6.4 MiB → 1.7 MiB. -Two distinct integer spaces appear in the output, and conflating them is the -single easiest mistake to make when reading these files. +### Layout -| Space | Fields | Meaning | -|---|---|---| -| **Particle index** | `id`, `parent_id`, `root_id`, `*_members/flat` | Position of a particle within its event's particle list | -| **Level row index** | `parent_frag_id`, `parent_inst_id` | Row within *that event's slice* of the fragment / instance table | +``` +format_version scalar str "3.1.0" +n_events scalar int64 +events/offsets (n_events+1,) int64 particle rows per event +points/offsets (n_events+1,) int64 point rows per event +inter/offsets (n_events+1,) int64 interaction rows per event +points/flat (N, 6) float32 x, y, z, time, dE, dX +particles/ (M,) +inter/ (K,) +``` -Plus one provenance field that is **not** an index: +Everything is concatenated across events with CSR fenceposts: event *i* +occupies rows `offsets[i] : offsets[i+1]`. -| Field | Meaning | -|---|---| -| `geant4_id` | The original Geant4 track ID, carried through unchanged | - -Four rules follow from this: - -1. **`id` is assigned by pysupera, not by Geant4.** It is `0, 1, … n-1` over - the event's particles, so it can be used as a direct row index. Geant4 - track IDs are *not* guaranteed contiguous — an upstream stage may drop - particles before pysupera ever sees them — which is why they cannot serve - as indices and are kept separately in `geant4_id`. Join back to the - EDepSim input on `geant4_id`, never on `id`. - - The density is guaranteed at write time, not merely at read time: - defragmentation splits particles (minting new ids) and drops others, so ids - are renumbered once preprocessing has finished. An id observed inside a - Python session mid-pipeline is therefore not necessarily the id written to - the file. - -2. **IDs restart at 0 in every event.** To address the concatenated tables you - must add the event's base offset: - - ```python - off = f['events/offsets'] - row = off[ev] + p_id # into particles/* - parent_row = off[ev] + f['particles/parent_id'][row] - ``` - - Note the base is always `events/offsets` — the *particle* fencepost — even - when reading `particle_instances`, because `id` / `parent_id` / `root_id` - and the member lists all index particles. Only `parent_frag_id` and - `parent_inst_id` are relative to `frag_events/offsets` and - `inst_events/offsets` respectively. - -3. **A primary is its own parent.** `parent_id == id` marks a primary, and - `root_id == id` likewise; every genealogy walk terminates on that - condition. The same convention applies to `parent_inst_id`, so an - instance that is its own parent is a primary rather than a broken link. - -4. **`geant4_id` is not unique when defragmentation splits a particle.** A - particle whose point cloud falls into disconnected pieces becomes several - pysupera particles, and they all inherit the track ID they came from — that - is the point of the field. `id` distinguishes them; `geant4_id` records - their common origin. A join on `geant4_id` is therefore one-to-many in - general. On a sample event, 129 of 13,589 particles shared a track ID with - at least one other. - -`interaction_id` is a third thing again: an index into the *input* file's -vertex list, identifying which interaction a particle belongs to. - -### Top level - -| Dataset | Type | Meaning | -|---|---|---| -| `format_version` | scalar string | e.g. `b'2.2.0'` | -| `n_events` | scalar int64 | number of events in the file | +### Point ordering, and why it matters -### The three particle levels +Points are ordered by -The same schema appears three times, one per processing stage: +``` +interaction > instance > is_LE > fragment > particle +``` -| Group | Contents | Written when | Audience | -|---|---|---|---| -| `particle_instances/` | Level 2 — representatives after EM-shower merging | **always** | everyone | -| `particles/` | Level 0 — every input particle | `output_compact=false` | everyone | -| `particle_fragments/` | Level 1 — step-1 partition representatives | `write_fragments=true` | expert — see [below](#expert-and-debugging-output) | +Three consequences follow, and between them they replace everything 2.x needed +per-point group labels for: -A level that is not written still exists as a group with **zero rows**, and its -event offsets still advance, so the file stays internally consistent. Under the -defaults only the instance level carries data: that is the level most analyses -want, since it is where one physical object — a shower, a track — corresponds to -one row. +1. **An instance's points are one contiguous block**, split into a non-LE part + followed by an LE part. All three useful queries are single slices. +2. **A fragment's points are two runs** — its non-LE side and its LE side — + because other fragments' non-LE points lie between them. Each side is + always exactly *one* run, never more, so two ranges describe a fragment + completely. +3. **LE-ness is positional**, so no per-point label is stored. A point is LE + precisely when its index falls in its instance's `inst_pc_le_*` range. -Fields in each group: +The ordering is chosen for the query panoptic-segmentation training issues most +often — "the non-LE voxels of instance X" — which is a plain slice rather than +a mask over the instance's points. -| Field | dtype | Space | Meaning | -|---|---|---|---| -| `id` | int32 | particle index | This particle (for a representative: the particle representing the group) | -| `geant4_id` | int32 | — | Original Geant4 track ID | -| `parent_id` | int32 | particle index | Direct parent; equals `id` for a primary | -| `root_id` | int32 | particle index | Primary ancestor | -| `pdg`, `parent_pdg` | int32 | — | PDG codes | -| `interaction_id` | int32 | vertex index | Which interaction this particle came from | -| `interaction_type` | int32 | — | `InteractionType` enum | -| `sem_type` | int8 | — | `SemanticType` enum — **per level**, see below | -| `parent_frag_id` | int32 | fragment row | Fragment containing this particle's parent; `-1` when unavailable | -| `parent_inst_id` | int32 | instance row | Instance containing this particle's parent | -| `pc_offsets` | int64, n+1 | — | Fencepost into `_points/flat` | -| `member_offsets` | int64, n+1 | — | Fencepost into `_members/flat` | +### Reading points -`sem_type` is the one field that genuinely differs between levels: merging -reclassifies particles, so a particle can carry up to three semantic types — -its own, its fragment's, and its instance's. Every other scalar field is -identical to the level-0 row for the same particle. +```python +from pysupera.io_v3 import read_events_v3 + +with read_events_v3("out.h5") as store: + v = store[0] # column dict + points + for r in np.flatnonzero(v.is_instance): + non_le = v.points_of(r, "inst", le=False) # one slice + le = v.points_of(r, "inst", le=True) # one slice + both = v.points_of(r, "inst") # one slice + vtx = v.interaction_of(r) +``` -Enum values: +Equivalently, by hand: +```python +non_le = v.points[v["inst_pc_start"][r] : v["inst_pc_end"][r]] +le = v.points[v["inst_pc_le_start"][r] : v["inst_pc_le_end"][r]] + +frag_nle = v.points[v["frag_pc_start"][r] : v["frag_pc_end"][r]] +frag_le = v.points[v["frag_pc_le_start"][r] : v["frag_pc_le_end"][r]] ``` -SemanticType : kShower 1, kTrack 2, kDelta 3, kMichel 4, kLEScatter 5, kUnknown 6 -InteractionType : kTrack 1, kNeutron 2, kNucleus 3, kPhoton 4, kPrimary 5, - kCompton 6, kDelta 7, kConversion 8, kIonization 9, - kPhotoElectron 10, kDecay 11, kOtherShower 12, kInvalidProcess 13 -``` -### Membership +`-1` marks a side with no points. `points_of(r, "frag")` concatenates the two +runs; every other combination is a single slice. + +### Which particles are stored + +About 9% of them. A particle gets a row if it + +1. **represents a fragment or instance that carries points**, or +2. **is an instance representative**, or +3. **lies on the ancestry of one**, including point-less links such as a π⁰ + between its two photons. + +Merged shower members get no row — an instance is one object, and its +constituents are not individually addressable. **Every point is still stored** +regardless: a group's points are its members', whether or not those members +have rows, and no point is unreachable from some stored row. + +### Particle columns + +| Column | dtype | Meaning | +|---|---|---| +| `id` | int32 | this particle's index **within its event** | +| `geant4_trackid` | int32 | original Geant4 track ID (provenance) | +| `geant4_parent_trackid` | int32 | track ID of the **true** direct parent; `-1` for a primary | +| `geant4_parent_pdg` | int32 | PDG of that true parent; `-1` for a primary | +| `parent_id` | int32 | nearest **stored** ancestor; `== id` marks a primary | +| `ancestor_id` | int32 | the primary this particle descends from | +| `pdg`, `parent_pdg` | int32 | PDG codes | +| `interaction_id` | int32 | row index into this event's `inter/` slice | +| `interaction_type` | int32 | `InteractionType` enum | +| `sem_type` | int8 | this particle's own classification | +| `pc_start`, `pc_end` | int64 | this particle's own points | +| `frag_id` | int32 | representative's `id`; `== id` marks a fragment, `-1` none | +| `frag_sem_type` | int8 | the fragment's classification | +| `frag_pc_start/end` | int64 | the fragment's non-LE points | +| `frag_pc_le_start/end` | int64 | the fragment's LE points | +| `frag_merge_count` | int32 | Geant4 particles merged into the fragment | +| `frag_parent_id` | int32 | nearest ancestor fragment; `== id` if none | +| `inst_id` | int32 | representative's `id`; `== id` marks an instance | +| `inst_sem_type` | int8 | the instance's classification | +| `inst_pc_start/end` | int64 | the instance's non-LE points | +| `inst_pc_le_start/end` | int64 | the instance's LE points (adjacent: `inst_pc_end == inst_pc_le_start`) | +| `inst_merge_count` | int32 | Geant4 particles merged into the instance | +| `inst_parent_id` | int32 | nearest ancestor instance; `== id` if none | + +A particle carries up to three classifications — its own, its fragment's and +its instance's — because merging reclassifies. Note that LE barely survives to +the group levels: `absorb_le_scatter` folds low-energy scatter into whatever +touches it, so ~31% of *points* are LE while almost no *instance* is. + +### Interaction columns + +| Column | dtype | Meaning | +|---|---|---| +| `interaction_id` | int32 | this table's own dense index — the value particles carry | +| `vertex_id` | int32 | the row this interaction had in the **input** vertex list | +| `x`, `y`, `z`, `time` | float32 | vertex position and time | +| `energy_sum`, `ke_sum` | float32 | vertex totals, copied from EDepSim | +| `reaction` | str | generator reaction label, copied from EDepSim | +| `part_start`, `part_end` | int32 | its particle rows | +| `pc_start`, `pc_end` | int64 | its points | -`_members/flat` lists, for each representative, the **particle indices** -of everything merged into it, sliced by `member_offsets`: +Interactions that no stored particle references are dropped and the survivors +renumbered. `interaction_id` is the index after renumbering, which is what a +particle's `interaction_id` refers to; `vertex_id` is the link back to the +input file, and is what survives the renumbering. + +### Enumerating levels + +A representative names itself, so each level is one comparison: ```python -members = f['particle_instances_members/flat'] -offs = f['particle_instances/member_offsets'] -inst_k = members[offs[k] : offs[k+1]] # particle indices in instance k +fragments = v["frag_id"] == v["id"] +instances = v["inst_id"] == v["id"] ``` -A representative always appears in its own member list. When every instance is -written the member lists partition the event's particles exactly — each -particle appears once and only once. Under the default -`instance_output=traceable` some particles are not covered, because instances -that nothing visible depends on are dropped; all such particles have empty point -clouds, so no point data is lost. +`-1` means the particle heads no group at that level. `inst_id == -1` is normal: +under `instance_output=traceable` (the default) an instance that nothing +visible depends on is dropped, so its members belong to no written instance. + +### ID conventions + +1. **`id` is pysupera's, not Geant4's.** It is `0 … n-1` over the event's + stored particles, so it indexes rows directly. Geant4 track IDs are not + guaranteed contiguous — an upstream stage may drop particles — and are kept + separately in `geant4_trackid`. **Join back to the input on `geant4_trackid`, never on + `id`.** + +2. **`geant4_trackid` is one-to-many.** Defragmentation splits a particle whose + point cloud falls into disconnected pieces into several pysupera particles, + and they all inherit the track they came from. `id` distinguishes them. -### Point clouds +3. **Walking `parent_id` always terminates inside the file.** Because most + particles have no row, `parent_id` points at the nearest *stored* ancestor, + and a particle with no stored ancestor is marked a primary. `ancestor_id` is + derived from that same redirected chain, so the walk and the stated ancestor + agree. -**Per representative** — `points/flat`, `particle_fragments_points/flat`, -`particle_instances_points/flat`, shape `(N, 6)` float32: +**One asymmetry:** ids are per-event, but point ranges are stored *absolute* +into `points/flat`, so a whole-file reader slices with no arithmetic. +`EventStoreV3` rebases them on read, so inside an `EventView` everything shares +one origin and `v["pc_start"]` indexes `v.points` directly. Only raw h5py +readers see the absolute form. + +### Point columns ``` 0:x 1:y 2:z 3:time 4:dE 5:dX ``` -Both merging stages (`MergeDuplicatesProcessor`, `VoxelizeProcessor`) operate -**within a single particle**, so for one particle-voxel `dE` and `dX` both sum -and +Both merge stages operate within a single particle, so `dE` and `dX` both sum +over a particle-voxel and **dE/dX is column 4 / column 5**, exactly. Guard +against `dX == 0`: EDepSim writes zero-length steps, a few percent of voxels. + +### Compression + +Output is LZ4 by default (`io.compression`). The browser viewer uses h5wasm, +which supports **gzip only**: +```bash +pysupera-repack out.h5 out_vis.h5 --compression gzip --rechunk ``` -dE/dX = column 4 / column 5 + +`pysupera-repack` also resizes chunks to the final dataset lengths, which is +its other job — see the `repack` block in the config for why that needs a +second pass. Two further options: + +```bash +# flip lz4 <-> gzip without having to remember which way round it is +pysupera-repack out.h5 flipped.h5 --compression auto + +# compress every chunked dataset, not just those already compressed +pysupera-repack out.h5 small.h5 --compression gzip --scope all ``` -is exact. Guard against `dX == 0`: EDepSim writes zero-length steps, which -account for a small fraction of voxels. Summing `dX` *across* particles would -be meaningless, and never happens — which is why the event-level cloud below -carries `dE` only. +`--compression auto` reads the file's dominant filter and swaps lz4 for gzip +or back; it errors rather than guess if the file is uncompressed. `--scope` +defaults to `source`, which leaves an uncompressed dataset uncompressed — +`all` overrides that. On a 100-event file, gzip is 65.4 MiB against LZ4's +89.4 MiB, so gzip is worth it for anything but the fastest reads. + +### Expert and debugging output -`PointFeature` defines a seventh column in memory (per-point `id`=6); it is -**truncated on write** and is not in the file. +Off by default, regenerable, and not intended for analysis. -A representative's cloud is the exact union of its members' clouds — verified to -the point, with no duplication or loss — so the same points appear once per -written level. +#### The voxel mapping — `particle.voxelize.store_mapping=true` -**Event-level union** — `non_le_cloud/flat` and `le_scatter_cloud/flat`, shape -`(N, 9)` float32: +Writes `_voxmap.h5`: for every output voxel, which input deposits were +merged into it and with what energy. Two nested CSR levels: ``` -0:x 1:y 2:z 3:time 4:energy 5:interaction_id 6:root_id 7:frag_id 8:inst_id +particles/id, particles/vox_offsets -> a particle's voxels +voxels/input_offsets -> a voxel's input points +flat/input_ids, flat/input_energies -> original point index + its energy ``` -These are *per-point* labels, replicated across every point of a given -particle. Columns 7–8 are level row indices, 6 is a particle index, and 5 a -vertex index. `non_le_cloud` excludes `kLEScatter` particles and -`le_scatter_cloud` contains only them — a partition, not a superset. +It carries one row per *input* deposit rather than per voxel, so it is +typically larger than the physics output itself. Nothing is computed when it is +off. + +#### Seeing every instance — `instance_output=all` -When voxelization is on, the event clouds are re-voxelized as a union, so -points that different particles placed in the same voxel are merged. Their row -count is therefore **lower** than the per-representative totals even though the -deposited energy agrees. Controlled by `write_event_clouds`. +Writes every instance the merger produced, including those that deposited +nothing and that nothing visible descends from. Use it to inspect the merger's +raw output; `traceable` is what analyses should read. -### Event indexing +#### Per-merge records — `enable_diagnostics=true` -Everything is concatenated across events with CSR fencepost arrays of length -`n_events + 1`; event *i* occupies rows `offsets[i] : offsets[i+1]`. +Records every merge decision in memory. Not written to the output file. -| Fencepost | Slices | -|---|---| -| `events/offsets` | `particles/*` | -| `frag_events/offsets` | `particle_fragments/*` | -| `inst_events/offsets` | `particle_instances/*` | -| `non_le_cloud/offsets`, `le_scatter_cloud/offsets` | the 9-column clouds | -Point and member arrays nest two levels deep: first the event fencepost to get -the level's rows, then `pc_offsets` / `member_offsets` within those rows. +--- -### Expert and debugging output +## Training Data (PyTorch) -Everything below is **off by default**. It exists to debug pysupera itself or -to study how the grouping was arrived at, not to be consumed by analysis. All -of it is regenerable: re-running with the same config and input reproduces it -exactly, so none of it is worth archiving. +`pysupera.torchdata` turns a 3.x file into batches for panoptic-segmentation +training. Install the extra first: -#### The fragment level — `write_fragments=true` +```bash +pip install -e ".[torch]" +``` -Step-1 partition representatives, before EM-shower merging groups them into -instances. Useful for seeing *which* merge step produced a grouping: a -particle's row carries both `parent_frag_id` and `parent_inst_id`, so comparing -a fragment's membership against its instance's shows exactly what step 2 added. +```python +from pysupera.torchdata import make_dataloader -Its point payload is a second copy of the points the instance level already -stores, so enabling it costs roughly 20% of the file. When it is off, instance -`parent_frag_id` is `-1` rather than pointing into a table that was not written. +loader = make_dataloader("out.h5", batch_size=4, distributed=True, + num_workers=4) -Note the name: a `particle_fragment` is a group of *merged* particles. This is -unrelated to *defragmentation*, a preprocessing step that **splits** one -particle's disconnected point cloud into several particles. The two move the -particle count in opposite directions. +for batch in loader: # batch_size counts *events* + for ev in batch["events"]: + x = ev["points"] # (V, 4) float32 — x, y, z, E + s = ev["voxel_sem"] # (V,) semantic label + i = ev["voxel_instance"] # (V,) instance id + k = ev["voxel_interaction"] # (V,) interaction id +``` -#### The voxel mapping — `particle.voxelize.store_mapping=true` +Point clouds differ in length between events, so a batch stays a **list** of +events rather than a padded tensor; `batch_size` therefore means exactly a +number of events. `to_torch(ev, device)` converts one event's arrays to +tensors when the model needs them. -Writes `_voxmap.h5`: for every output voxel, which input deposits were -merged into it and with what energy. Voxelization is lossy — many EDepSim steps -collapse into one voxel and only the summed `dE` survives — and this records -what went in. Two nested CSR levels: +Alongside the three per-voxel label arrays, each event carries the object-level +tables `ev["instances"]` and `ev["interactions"]` — the same columns described +under [Particle columns](#particle-columns) and +[Interaction columns](#interaction-columns), restricted to instance rows. An +interaction's id is its row index, which is what `voxel_interaction` holds. -``` -particles/id, particles/vox_offsets -> a particle's voxels -voxels/input_offsets -> a voxel's input points -flat/input_ids, flat/input_energies -> original point index + its energy -``` +### Voxel merging + +Model input is one row per voxel, but a voxel can receive energy from several +instances. Overlaps are resolved as follows: + +- **Energy is summed** over every contribution, so no charge is lost. +- **Labels come from the winning contribution**, chosen by semantic priority + `kTrack > kShower > kDelta > kMichel > kLEScatter`, then by earliest time + within a class. -To read one voxel's provenance: +A track crossing a shower therefore keeps the voxel, and among two showers the +earlier one does. On a sample event ~10% of raw points share a cell with +another instance's. + +### Low-energy scatters — `include_le` + +LE deposits are roughly a third of all points. They are **excluded by +default**: ```python -vs, ve = vox_offsets[k], vox_offsets[k+1] # particle k's voxels -fs, fe = input_offsets[vs], input_offsets[vs+1] # its first voxel's inputs -input_ids[fs:fe], input_energies[fs:fe] +loader = make_dataloader("out.h5", include_le=True) # keep them ``` -**It is large** — it carries one row per *input* deposit rather than per voxel, -so it is typically bigger than the physics output itself (9.7 MiB against -6.0 MiB on a sample 5-event run). That is why it is off by default; nothing is -computed when it is off, so leaving it off costs nothing. +The flag governs the voxel data as a whole — input coordinates, energy, and all +three label arrays — so the model never sees a point that the labels do not +describe, and an excluded point contributes no energy either. On a sample +event, dropping LE removes 30% of the voxels and 13% of the energy. -It is built from the final particle list, after defragmentation and id -renumbering, so its `particles/id` match the main file exactly. A particle -split by defragmentation carries its own share of the mapping, and total -`input_energies` equals total voxel `dE`. +A point counts as LE when its label is `kLEScatter`, which covers both an LE +deposit absorbed into some other instance (LE-ness is positional — see +[Point ordering](#point-ordering-and-why-it-matters)) and a point of an +instance that is itself LE. The instance and interaction labels of an absorbed +LE point remain those of its host, so it stays attributed to the shower or +track that absorbed it. -#### Seeing every instance — `instance_output=all` +### Distributed training -Writes every instance the merger produced, including those that deposited -nothing and that nothing visible descends from — 535 rather than 207 on a -sample event. Use it to inspect the merger's raw output; `traceable` is what -analyses should read. +`distributed=True` attaches a `DistributedSampler` so each rank sees a disjoint +shard of events. Call `loader.sampler.set_epoch(epoch)` at the top of every +epoch, or each rank replays the same order. Note that +`DistributedDataParallel` itself wraps the *model*; this module covers the +input side. -#### Per-merge records — `enable_diagnostics=true` +The HDF5 handle is opened lazily per worker, so `num_workers > 0` is safe. -Records every merge decision in memory for post-hoc inspection. Not written to -the output file. -### Compression +### Profiling and W&B logging -Output is LZ4 by default (`io.compression`). The browser viewer uses h5wasm, -which supports **gzip only**, so produce a viewer copy with: +`StreamMonitor` times the pipeline and samples memory around a loader: -```bash -pysupera-repack out.h5 out_vis.h5 --compression gzip --rechunk +```python +import wandb +from pysupera.torchdata import make_dataloader, StreamMonitor + +wandb.init(project="lartpc-panoptic") + +loader = make_dataloader("out.h5", batch_size=4, num_workers=4, + distributed=True) +mon = StreamMonitor(device="cuda", prefix="data") + +for epoch in range(n_epochs): + for batch in mon.iterate(loader, epoch=epoch): + train_step(batch) # already staged on the GPU + +print(mon.finish()) # also writes the W&B run summary +``` + +`iterate` stages each batch to *device*, forwards *epoch* to the +`DistributedSampler`, and logs per step. The metrics come in two kinds, and +mixing them up is the easy mistake. + +**Main-process wall time.** These partition one iteration and sum to `step_s`: + +| Metric | Meaning | +|---|---| +| `wait_s` | how long the loop **blocked** in `next(loader)` — workers not yet done, plus shipping the finished batch back and rebuilding it here | +| `stage_s` | host-to-device transfer | +| `consume_s` | the loop body — your training step, measured across the `yield` | +| `step_s` | `wait_s + stage_s + consume_s`, i.e. the real per-iteration wall time | +| `stall_fraction` | `wait_s / step_s` — **the headline number** | + +**Worker-side cost.** Summed over the batch's events, measured wherever they +were produced: + +| Metric | Meaning | +|---|---| +| `read_s` | h5py decompress + slice | +| `preprocess_s` | `build_event` (voxel merge, labels) plus any `transform` | +| `collate_s` | assembling the batch | +| `payload_mb` | array bytes the batch ships to the parent | +| `payload_mb_per_s` | `payload_mb / wait_s` — the rate batches actually arrive | + +With `num_workers > 0` these run in other processes, concurrently with the +training step and with each other, so **their sum routinely exceeds `step_s`**. +They say what the work costs, not what it delays. Only `stall_fraction` +answers "is input the bottleneck". + +Throughput and memory are logged alongside: `events_per_s`, `points_per_s`, +`n_points`, `batch_size`, and `cpu_rss_mb`, `cpu_rss_total_mb`, +`cpu_available_mb`, `gpu_alloc_mb`, `gpu_reserved_mb`, `gpu_peak_mb`, +`gpu_free_mb`, `gpu_total_mb`. + +**`cpu_rss_total_mb` includes the worker processes**, which is where reading +and pre-processing actually allocate; the parent's own `cpu_rss_mb` says +nothing about them. On a 100-event file each worker added ~210 MiB over the +parent's 630 MiB, so eight workers cost ~4 GiB of host memory. Workers share +copy-on-write pages with the parent, so the total is an upper bound. + +Enumerating children scans `/proc` and costs ~4.6 ms against ~0.2 ms for the +rest of the snapshot, so the handles are cached and re-enumerated only when +one dies or `profiling.CHILDREN_REFRESH_S` elapses — except that an *empty* +cache expires after `EMPTY_REFRESH_S`, since nothing in it could signal that a +worker pool has since appeared. + +`summary()` adds totals and means, plus `wall_s` — measured by one clock +spanning the whole loop rather than summed — and `unaccounted_s`, the +difference between the two. That covers whatever falls outside a step — the +monitor's own overhead, and gaps between epochs such as tearing a worker pool +down — and should stay near zero; if it does not, something outside the +measured path is eating the clock. `first_wait_s` and `max_wait_s` isolate worker start-up, which +normally dominates every other wait. + +Reading the output — two events per epoch, five epochs, a toy MLP, with one +warm-up epoch discarded: + +``` + num_workers=0 nw=4,persist nw=4,respawn + wall 0.171 s 0.197 s 1.501 s + wait_s (blocked) per step 13.87 ms 14.63 ms 120.18 ms + stage_s (host->device) per step 0.41 ms 0.64 ms 1.13 ms + consume_s (training step) per step 2.38 ms 3.41 ms 5.68 ms + read_s (worker) per step 4.22 ms 9.46 ms 21.63 ms + preprocess_s(worker) per step 9.52 ms 12.86 ms 13.14 ms + stall_fraction 0.833 0.783 0.946 + first_wait_s 9.8 ms 28.6 ms 230.1 ms ``` +Three things worth reading off it: + +- **`stall_fraction` is ~0.8 everywhere**, because the toy model is far too + small to hide the input pipeline. That is the number to watch: with a real + model `consume_s` grows and the fraction falls. +- **Workers do not help at this scale.** Two events per epoch is not enough + work to amortise shipping batches between processes, so `num_workers=4` is + marginally *slower* than loading in-process. +- **`persistent_workers=True` matters far more than the worker count.** + Without it the pool is torn down and respawned every epoch, `first_wait_s` + goes from 29 ms to 230 ms, and wall time is 8.8× worse. Pass it through + `make_dataloader` to the DataLoader. + +Beware one trap when reading your own numbers: the first steps of a process +pay for CUDA, cuBLAS and autograd initialisation, and that lands in +`consume_s`. Before the warm-up epoch above was added, `consume_s` read +116 ms against a true 2.4 ms. **Discard the first epoch when benchmarking.** + +`StreamMonitor` warms the CUDA transfer path on construction, because the +first host-to-device copy in a process otherwise pays ~35 ms of lazy +initialisation against the ~1 ms a warm copy costs. Some first-step cost +remains while the caching allocator grows, so **discard step 1 when +benchmarking**. + +The pieces are usable on their own: `pysupera.profiling.memory_snapshot()` +returns the memory dict, `WandbLogger` is the rank-aware no-op-safe sink, and +`stage_batch(batch, device)` returns `(batch, seconds)`. + +Overhead is small enough to leave on: the per-event timing is two +`perf_counter` calls, and a memory snapshot is ~0.2 ms. Both can be turned off +anyway — `profile=False` on `make_dataloader`, `memory=False` on the monitor — +and `unaccounted_s` tells you whether it is worth it. + + --- ## CLI Usage @@ -805,7 +999,7 @@ Only the touching subset proceeds to `post_filter`, and only the final `merge_pa **Candidate filter:** - Both particles have PDG code 11 or 22. - Direct parent–child relationship. -- Same `root_id`. +- Same `ancestor_id`. **Merge direction:** child → parent (directed by the parent–child tree). From 0d695505c2a4e7622a745d2ab4674b46a5a90cf1 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Mon, 21 Sep 2026 00:42:29 -0700 Subject: [PATCH 37/62] vis: stop confusing an instance id with its row index A correctly drawn muon track was reported as PDG 2112, a neutron. The geometry was right because it comes from point ranges, read per row; the PDG was wrong because it came from whatever instance happened to sit at that array slot. The assumption dates from 2.x, where instances had their own table and a row index equalled an instance id. In 3.x instances are a sparse subset of one shared table, ordered by the point layout rather than by id, so the two are unrelated: a 2,902-row event carries ids up to 21,254, unsorted. Five places used one where the other was meant -- * the per-point PDG buffer, keyed by node index and read by id * the hover panel's node lookup * the point-count and interaction patch onto graph nodes * the highlight uniform, which the shader compares against a point's id while the graph passed a node index * the graph's parent edges, in both copies of the builder, which used inst_parent_id -- an id -- directly as a node index -- and of 850 instances in one event only 89 ids even landed inside the node array. 85 of those returned another particle's PDG; the other 761 ran off the end and displayed nothing. _instNodeIdxById makes the conversion explicit at every boundary between the cloud (which speaks ids) and the graph (which speaks node indices). The README said id "is 0..n-1 over the event's stored particles, so it indexes rows directly", which is what the viewer believed. It is false -- ids are dense over the *full* particle list, assigned before the output subset is chosen -- and it is now corrected, with the map to build instead, and the warning extended to parent_id, frag_id, inst_id and the two group parents. --- README.md | 23 +++++++++++++---- vis_force.html | 70 +++++++++++++++++++++++++++++++++++++------------- 2 files changed, 70 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 6f3c1b6..d8bd4a3 100644 --- a/README.md +++ b/README.md @@ -572,11 +572,24 @@ visible depends on is dropped, so its members belong to no written instance. ### ID conventions -1. **`id` is pysupera's, not Geant4's.** It is `0 … n-1` over the event's - stored particles, so it indexes rows directly. Geant4 track IDs are not - guaranteed contiguous — an upstream stage may drop particles — and are kept - separately in `geant4_trackid`. **Join back to the input on `geant4_trackid`, never on - `id`.** +1. **`id` is pysupera's, not Geant4's.** It is `0 … N-1` over the event's + *full* particle list, assigned before the output subset is chosen. Geant4 + track IDs are not guaranteed contiguous — an upstream stage may drop + particles — and are kept separately in `geant4_trackid`. **Join back to + the input on `geant4_trackid`, never on `id`.** + + **`id` is not a row index.** Only the particles worth keeping are written — + about 17% of them — and rows are ordered by the point layout, not by `id`. + So in a 2,902-row event the ids run to 21,254 and are not sorted. Build a + map; do not index with an id: + + ```python + row_of = {int(i): k for k, i in enumerate(v["id"])} + k = row_of[some_id] # not v["pdg"][some_id] + ``` + + The same applies to `parent_id`, `frag_id`, `inst_id`, `frag_parent_id` + and `inst_parent_id`: all of them are ids, and all need the map. 2. **`geant4_trackid` is one-to-many.** Defragmentation splits a particle whose point cloud falls into disconnected pieces into several pysupera particles, diff --git a/vis_force.html b/vis_force.html index cc9b1ec..bde2277 100644 --- a/vis_force.html +++ b/vis_force.html @@ -1393,7 +1393,7 @@

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// so the histogram filter (nd.intId===intId) is guaranteed to match. if(S.instGraph&&S.instGraph.nodes){ for(const nd of S.instGraph.nodes){ - const r=_instCountMap.get(nd.idx); + const r=_instCountMap.get(nd.id); if(r!=null){nd.ptCount=r.count; nd.intId=r.intId; nd.eSum=r.eSum;} } } @@ -1404,7 +1404,9 @@

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}; // Build per-point PDG code buffer from instGraph (inst_id → PDG) const _instPdgMap=new Map(); - if(S.instGraph&&S.instGraph.nodes) for(const nd of S.instGraph.nodes) _instPdgMap.set(nd.idx, nd.pdg||0); + // keyed by particle id: the buffer below is built from the per-point + // instance label, which is an id, not a node index + if(S.instGraph&&S.instGraph.nodes) for(const nd of S.instGraph.nodes) _instPdgMap.set(nd.id, nd.pdg||0); const _mkPdg=f32=>{ const N=f32.length/NCOLS; const buf=new Float32Array(N); for(let i=0;i⌨ Keyboard Shortcuts members:Math.max(1,mCount),ptCount,eSum:null,x:0,y:0,vx:0,vy:0}); } for(let i=0;i=0&&Number(instPInst[i])!==i) - ?Number(instPInst[i]) - :findParentInst(instPIDs[i]); + // inst_parent_id is a particle *id*; memberToInst turns it into the + // node index the edge list needs. The parent-map walk stays as a + // fallback for files written before it was stored. + let pi=-1; + if(instPInst){ + const pid=Number(instPInst[i]); + if(pid>=0&&pid!==Number(instIds[i])&&memberToInst.has(pid)) + pi=memberToInst.get(pid); + } + if(pi<0) pi=findParentInst(instPIDs[i]); if(pi>=0&&pi!==i){ const k=Math.min(pi,i)+','+Math.max(pi,i)+'_'+pi+'>'+i; if(!edgeSet.has(k)){edgeSet.add(k);edges.push({src:pi,dst:i});} @@ -2441,11 +2448,32 @@

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for(const o of[objNLE,objLE]){if(o)o.pts.material.uniforms.uHighlightId.value=id;} } -function _setInstHighlight(id){ - S.instHighlight=id; - const eff=S.instLocked>=0?S.instLocked:id; +// Two key spaces meet here and must not be confused. A graph node's `idx` +// is its position in instGraph.nodes; its `id` is the particle id, which is +// what the point cloud carries (column 8) and what the shader compares +// against. In 3.x an instance is a sparse row of one shared table, so +// id !== idx -- in 2.x the instance table was separate and they coincided, +// which is how the confusion survived the port. +function _instNodeByIdx(i){ + const g=S.instGraph; + return (g&&g.nodes&&i>=0&&ig._byId.set(nd.id,i));} + const i=g._byId.get(Number(id)); + return i===undefined?-1:i; +} + +// Takes a node *index*; translates to a particle id for the shader. +function _setInstHighlight(nodeIdx){ + S.instHighlight=nodeIdx; + const eff=S.instLocked>=0?S.instLocked:nodeIdx; + const nd=_instNodeByIdx(eff); + const effId=nd?nd.id:-1; for(const o of[objNLE,objLE]){ - if(o) o.pts.material.uniforms.uHighlightInstId.value=eff; + if(o) o.pts.material.uniforms.uHighlightInstId.value=effId; } } @@ -2907,11 +2935,16 @@

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members:Math.max(1,mCount),ptCount,eSum:null,x:0,y:0,vx:0,vy:0}); } for(let i=0;i=0&&Number(instPInst[i])!==i) - ?Number(instPInst[i]) - :findParentInst(instPIDs[i]); + // inst_parent_id is a particle *id*; memberToInst turns it into the + // node index the edge list needs. The parent-map walk stays as a + // fallback for files written before it was stored. + let pi=-1; + if(instPInst){ + const pid=Number(instPInst[i]); + if(pid>=0&&pid!==Number(instIds[i])&&memberToInst.has(pid)) + pi=memberToInst.get(pid); + } + if(pi<0) pi=findParentInst(instPIDs[i]); if(pi>=0&&pi!==i){ const k=Math.min(pi,i)+','+Math.max(pi,i)+'_'+pi+'>'+i; if(!edgeSet.has(k)){edgeSet.add(k);edges.push({src:pi,dst:i});} @@ -3471,7 +3504,7 @@

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const _intPts=(S.interactions.find(r=>Math.round(r.id)===_iidInt)||{n:-1}).n; let _pdgStr='—',_parPidStr='—',_parPdgStr=''; if(S.instGraph&&_iid>=0){ - const _nd=S.instGraph.nodes.find(n=>n.idx===_iid); + const _nd=_instNodeByIdx(_instNodeIdxById(_iid)); if(_nd){ _pdgStr=_nd.pdg?String(_nd.pdg):'—'; _parPidStr=_nd.parentPid>=0?String(_nd.parentPid):'—'; @@ -3491,7 +3524,8 @@

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coordEl.style.display='block'; coordEl.innerHTML='
'+(isLE?'■ LE scatter':'□ Non-LE')+'   pt '+idx+'
'+rows+'
'+_xtra; // cloud hover → graph node + interaction bubble - if(S._cloudHoverInstId!==_iid){S._cloudHoverInstId=_iid;_setInstHighlight(_iid>=0?_iid:-1);} + if(S._cloudHoverInstId!==_iid){S._cloudHoverInstId=_iid; + _setInstHighlight(_iid>=0?_instNodeIdxById(_iid):-1);} if(S._cloudHoverIntId!==_iidInt&&!S.highlightLocked){ S._cloudHoverIntId=_iidInt; _setHighlight(_iidInt); const _b=document.getElementById('ip-bubbles'); From 8a236cc8ffde6ff9087a6be7fc52cdd07ea89fcc Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Mon, 21 Sep 2026 21:57:02 -0700 Subject: [PATCH 38/62] provenance: map JAXTPC hits to instances and fragments Answers "which readout hits belong to this reconstructed object" without going through voxels. A hit is the projection of a JAXTPC group, and group_to_track gives each group one Geant4 track -- but defragmentation splits a track across several pysupera particles, so track -> particle is one-to-many and cannot regroup hits. What rescues it is that a group never straddles a split: a group is itself a tight spatial cluster and defragmentation clusters at distance_threshold. Measured over eight events, none of 354,959 groups spanned two particles, and group_to_track agreed with the owning particle's geant4_trackid every time. So group -> particle is a function, and hit -> group -> particle -> fragment is a chain of functions. Two arrays per group are stored, and no more. groups/fragment_id The particle would be the natural key and does not work: a group's owner is usually an ordinary member, and only about a sixth of particles get a row -- 6,605 of 6,777 owners were unresolvable when this was first keyed on particle id. Every fragment with points is stored by construction (0 missing of 29,201), so the fragment is the finest key that always resolves. groups/is_le Irreducible. LE-ness is a pysupera-particle attribute and a fragment mixes LE and non-LE members by design -- that is what its two point ranges are for -- so 26-30% of fragments contain both. The bit costs 0.17 bytes/group; making it derivable instead, by storing the owning particles as rows, costs about 20x more. The instance is *not* stored: a fragment's points lie inside exactly one instance's block, so it follows by containment, verified exact on 29,201 groups. instance_of_groups() does it. The interaction follows from the instance. Note this cannot be a column join -- inst_id and frag_id are self-markers, equal to id on a representative and -1 elsewhere -- which is why a first attempt at a join disagreed on 12,798 of 29,201 groups. check_group_ownership (default true) verifies the one measured assumption per event rather than trusting it, naming the group and both particles and pointing at store_mapping=true -- the exact deposit-level record the table compresses -- as the alternative. groups/ costs about 21% of the output under LZ4. Going via voxels would be worse as well as larger: a group's deposits land in 2-4 different voxels 38% of the time, so hit <-> voxel is many-to-many and would need charge apportionment. Supporting changes: * the JAXTPC reader keeps the real deposit index instead of overwriting it with a running counter, so the mapping reaches the input at all. It rides as an int64 array beside the cloud, not in it: the cloud is float32 and exact only to 2**24, which a hundredfold rise in deposits per event would reach. * computing the CSR is separated from writing it (track_provenance), so the group table can be built in memory while the deposit-level map stays opt-in and off by default. * input_energies is dropped from the voxmap. Nothing consumed it but one test, and once input_ids are real deposit indices the energy is recoverable from the seg file -- storing it duplicated upstream data. The test now looks the energies up through input_ids, which is what a caller has to do, and a second test pins that the CSR partitions the inputs. The voxmap file shrinks 18%. * subset_voxmap gains tests. It is called on every defragmentation split and had none, which is how it shipped briefly returning a stale 3-tuple. groups_of_particles drops NO_OWNER from the request rather than matching it: a caller passing an unset id would otherwise get back every unclaimed group, confidently and wrongly. --- pysupera/_run.py | 37 ++++- pysupera/conf/config.yaml | 17 +++ pysupera/config.py | 2 + pysupera/data.py | 9 +- pysupera/io.py | 25 +--- pysupera/io_v3.py | 90 +++++++++++++ pysupera/preproc.py | 73 +++++----- pysupera/provenance.py | 215 ++++++++++++++++++++++++++++++ pysupera/readers/format_jaxtpc.py | 87 +++++++++++- tests/test_preproc.py | 85 ++++++++++-- tests/test_provenance.py | 125 +++++++++++++++++ 11 files changed, 689 insertions(+), 76 deletions(-) create mode 100644 pysupera/provenance.py create mode 100644 tests/test_provenance.py diff --git a/pysupera/_run.py b/pysupera/_run.py index cd94feb..c9c25b2 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -54,6 +54,7 @@ def main(cfg: DictConfig) -> None: import numpy as np from collections import defaultdict from pysupera.io_v3 import open_writer_v3 + from pysupera.provenance import build_group_owners, remap_owners, le_flags from pysupera.io import open_voxmap_writer from pysupera.partitioner import ParticlePartitioner from pysupera.merge import merge_em_showers @@ -69,6 +70,12 @@ def main(cfg: DictConfig) -> None: merge_processor = build_merge_processor(cfg) # None when merge_duplicates: false voxelizer = build_voxelizer(cfg) # None when voxelize.enabled: false preprocessor = build_preprocessor(cfg) # None when defragment: false + _check_group_owner = bool(cfg.get('check_group_ownership', True)) + + def _n_groups_of(_store): + """Total groups in the event just read, from the deposit map.""" + _m = getattr(_store, 'last_deposit_to_group', None) + return 0 if _m is None or not len(_m) else int(_m.max()) + 1 _write_voxmap = (voxelizer is not None and bool(cfg.particle.voxelize.get('store_mapping', False))) @@ -242,13 +249,12 @@ def main(cfg: DictConfig) -> None: _vm = getattr(_p, 'voxmap', None) if _vm is None: continue - _off, _ids, _en = _vm + _off, _ids = _vm _recs.append(VoxelizeRecord( particle_id = int(_p.id), n_before = int(len(_ids)), n_after = int(len(_off) - 1), input_ids = _ids, - input_energies = _en, voxel_offsets = _off, )) vox_writer.append_event(_recs) @@ -400,6 +406,33 @@ def main(cfg: DictConfig) -> None: _layout = build_layout(particles, _frag_groups, _inst_groups, vertices=getattr(store, 'last_vertices', None)) writer.append_event(_layout) + + # ── JAXTPC group ownership ───────────────────────────────── + # Which hits belong to which reconstructed object. Hits + # attach to groups and groups attach to one particle, so this + # one table answers it for instances and fragments alike -- + # see pysupera.provenance. Absent outside JAXTPC mode, where + # there are no hits to group. + _d2g = getattr(store, 'last_deposit_to_group', None) + if _d2g is None: + writer.append_group_owners(np.zeros(0, dtype=np.int32)) + else: + _owner = build_group_owners( + particles, _d2g, _n_groups_of(store), + check=_check_group_owner) + # Record the fragment, not the particle: only a fraction + # of particles get a row, so a particle id would often + # name something the reader cannot resolve. Every + # fragment with points does get one. The instance is + # left out entirely -- it follows from the fragment. + _frag_of = {int(m): int(g.rep_id) + for g in _frag_groups for m in g.member_ids} + _le_of = {int(p.id): (p.sem_type is _le_type) + for p in particles} + writer.append_group_owners( + remap_owners(_owner, _frag_of), + le_flags(_owner, _le_of), + getattr(store, 'last_group_volume_offsets', ())) _ev_t['write_ev'] = time.perf_counter() - _t profile['write_event'] += _ev_t['write_ev'] diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index 5a6debe..8876f82 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -99,6 +99,23 @@ repack: # readout thresholds or heavily masked detector volumes. allow_empty_image: false +# Verify that every JAXTPC group belongs to exactly one pysupera particle. +# +# Hits attach to groups, and a group carries one Geant4 track -- but +# defragmentation splits a track across several pysupera particles, so the +# group table is only usable while no group straddles a split. Measured over +# eight events none of 354,959 groups did, because a group is a tight spatial +# cluster and defragmentation clusters at distance_threshold. It is checked +# rather than assumed. +# +# true (default) — raise, naming the group and the two particles. +# false — keep the first owner seen. Lossy: prefer +# particle.voxelize.store_mapping=true, which records the +# exact deposit-level mapping instead. +# +# JAXTPC mode only; no groups exist otherwise. +check_group_ownership: true + # Verify that the particle list is self-consistent before partitioning: # every parent_id referenced by a particle must correspond to a particle # in the list. Raises ValueError listing all missing parent IDs when diff --git a/pysupera/config.py b/pysupera/config.py index 2d7e055..506eefb 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -250,6 +250,8 @@ def build_voxelizer(cfg: DictConfig, verbose: bool = False): verbose = verbose, merge_duplicates = bool(vox_cfg.get('merge_duplicates', False)), store_mapping = bool(vox_cfg.get('store_mapping', False)), + # The group table needs the same CSR in memory, without writing it. + track_provenance = True, ) diff --git a/pysupera/data.py b/pysupera/data.py index c6460e8..dea421a 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -194,7 +194,14 @@ def __init__( for full descriptions. """ # --- required fields ----------------------------------------------- - #: Voxelization mapping, ``(voxel_offsets, input_ids, input_energies)`` + #: Input deposit index per point, parallel to :attr:`point_cloud` + #: and int64 so a large index cannot round the way a float32 column + #: would. Set by the reader; consumed by the voxelizer, which folds + #: it into :attr:`voxmap` and then drops it. ``None`` when the + #: reader does not provide provenance. + self.deposit_id = None + + #: Voxelization mapping, ``(voxel_offsets, input_ids)`` #: or ``None`` when ``particle.voxelize.store_mapping`` is off. Set by #: VoxelizeProcessor and carried on the particle so that later stages #: which split or drop particles keep it consistent. diff --git a/pysupera/io.py b/pysupera/io.py index 12371a9..8a5c2c7 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -1783,7 +1783,6 @@ def write_voxmap(main_path: str, /flat/ input_ids (n_total_mappings,) int32 - input_energies (n_total_mappings,) float32 Parameters ---------- @@ -1822,7 +1821,6 @@ def write_voxmap(main_path: str, # voxels/input_offsets collected as list (variable total voxels) vox_input_segs = [] # one entry per particle: rec.voxel_offsets[1:] shifted flat_ids_segs = [] - flat_eng_segs = [] p_idx = 0 running_vox = np.int64(0) @@ -1839,7 +1837,6 @@ def write_voxmap(main_path: str, ) flat_ids_segs.append(rec.input_ids.astype(np.int32)) - flat_eng_segs.append(rec.input_energies.astype(np.float32)) running_flat += np.int64(len(rec.input_ids)) running_vox += n_vox @@ -1857,7 +1854,6 @@ def write_voxmap(main_path: str, vox_input_offsets = np.zeros(1, dtype=np.int64) flat_ids = np.concatenate(flat_ids_segs) if flat_ids_segs else np.empty(0, dtype=np.int64) - flat_energies = np.concatenate(flat_eng_segs) if flat_eng_segs else np.empty(0, dtype=np.float32) # Write _cp = lambda n, size: (min(_CHUNK_PARTICLES, max(1, size)),) @@ -1889,14 +1885,8 @@ def write_voxmap(main_path: str, chunks=_cp2(n_total_mappings), **ckw, ) - fg.create_dataset( - "input_energies", data=flat_energies, - chunks=_cp2(n_total_mappings), - **ckw, - ) else: fg.create_dataset("input_ids", data=np.empty(0, dtype=np.int32)) - fg.create_dataset("input_energies", data=np.empty(0, dtype=np.float32)) return vpath @@ -2047,23 +2037,18 @@ def append_event(self, vox_records) -> None: cursor_vox += n_vox vox_inp_ds[self._n_voxels + 1 : new_vox_end + 1] = bulk_inp_offs - # ---- flat input_ids and input_energies (one write each) ---------- + # ---- flat input_ids (one write) ---------------------------------- flat_ids_ds = self._f["flat/input_ids"] - flat_eng_ds = self._f["flat/input_energies"] flat_ids_ds.resize(new_flat_end, axis=0) - flat_eng_ds.resize(new_flat_end, axis=0) bulk_ids = np.empty(n_flat_total, dtype=np.int32) - bulk_eng = np.empty(n_flat_total, dtype=np.float32) cursor = 0 for rec in vox_records: n = len(rec.input_ids) if n > 0: bulk_ids[cursor : cursor + n] = rec.input_ids.astype(np.int32) - bulk_eng[cursor : cursor + n] = rec.input_energies.astype(np.float32) cursor += n flat_ids_ds[self._n_flat : new_flat_end] = bulk_ids - flat_eng_ds[self._n_flat : new_flat_end] = bulk_eng self._n_events += 1 self._n_particles += n_p @@ -2109,9 +2094,6 @@ def _init_datasets(self) -> None: fg.create_dataset("input_ids", shape=(0,), maxshape=(None,), dtype=np.int32, chunks=(_CHUNK_POINTS,), **ckw) - fg.create_dataset("input_energies", - shape=(0,), maxshape=(None,), dtype=np.float32, - chunks=(_CHUNK_POINTS,), **ckw) # --------------------------------------------------------------------------- @@ -2175,7 +2157,6 @@ def __getitem__(self, index: int): within this particle — i.e. shifted to start from 0). ``"input_ids"`` : np.ndarray, int64, shape (n_total_inputs,) Input point IDs for all voxels of this particle. - ``"input_energies"`` : np.ndarray, float32, shape (n_total_inputs,) Energy contributions corresponding to each input_id. """ if index < 0 or index >= self._n_events: @@ -2199,13 +2180,12 @@ def __getitem__(self, index: int): flat_end = int(vox_input_bounds[-1]) all_ids = self._f["flat/input_ids" ][flat_start:flat_end] - all_eng = self._f["flat/input_energies"][flat_start:flat_end] result = [] for k in range(n_parts): v_start = int(self._vox_offsets[p_start + k]) - vox_start v_end = int(self._vox_offsets[p_start + k + 1]) - vox_start - # local fencepost into all_ids/all_eng (zero-indexed) + # local fencepost into all_ids (zero-indexed) local_vox_offsets = vox_input_bounds[v_start : v_end + 1] - flat_start f_s = int(local_vox_offsets[0]) f_e = int(local_vox_offsets[-1]) @@ -2214,7 +2194,6 @@ def __getitem__(self, index: int): if self._particle_ids is not None else None, "vox_input_offsets": local_vox_offsets - f_s, "input_ids": all_ids[f_s:f_e], - "input_energies": all_eng[f_s:f_e], }) return result diff --git a/pysupera/io_v3.py b/pysupera/io_v3.py index 53d599f..6b9e5a2 100644 --- a/pysupera/io_v3.py +++ b/pysupera/io_v3.py @@ -152,6 +152,33 @@ def _create(self): g.create_dataset(c, shape=(0,), maxshape=(None,), dtype=dt, chunks=(rows,), **self._ckw) + # groups/: JAXTPC group -> owning particle id, one int32 per group, + # -1 where no stored particle claims it. Written only in JAXTPC + # mode; absent otherwise. volume_offsets records where each volume's + # groups start so (volume, local group) is recoverable. + gg = f.create_group("groups") + gg.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), + maxshape=(None,), chunks=(rows,)) + # Only the fragment is stored. The instance is recoverable from it + # positionally -- a fragment's points lie inside exactly one + # instance's block -- so storing it too would be duplication. See + # pysupera.provenance.instance_of_groups. + gg.create_dataset("fragment_id", shape=(0,), maxshape=(None,), + dtype=np.int32, chunks=(_chunk_rows(1, 4),), + **self._ckw) + # is_le is exact at group level: a group's deposits belong to one + # particle, so the flag is that particle's, not its instance's. + gg.create_dataset("is_le", shape=(0,), maxshape=(None,), + dtype=np.int8, chunks=(_chunk_rows(1, 1),), + **self._ckw) + gg.create_dataset("volume_offsets", shape=(0,), maxshape=(None,), + dtype=np.int64, chunks=(rows,)) + gg.create_dataset("volume_offsets_index", + data=np.zeros(1, dtype=np.int64), + maxshape=(None,), chunks=(rows,)) + self._n_groups = 0 + self._n_voloff = 0 + pg = f.create_group("particles") for c in COLUMNS: dt = _DTYPE[c] @@ -160,6 +187,43 @@ def _create(self): _CHUNK_TARGET_BYTES_META),), **self._ckw) + def append_group_owners(self, frag_owner, is_le=None, volume_offsets=()): + """ + Append one event's group-ownership table. + + Must be called once per event, in the same order as + :meth:`append_event`. Pass empty arrays for events with no group + information, so the per-event offsets stay aligned. + """ + g = self._f["groups"] + frag_owner = np.asarray(frag_owner, dtype=np.int32) + n = len(frag_owner) + if is_le is None: + is_le = np.zeros(n, dtype=np.int8) + is_le = np.asarray(is_le, dtype=np.int8) + if len(is_le) != n: + raise ValueError( + f"is_le must match the owner array: {len(is_le)} vs {n}") + for name, arr in (("fragment_id", frag_owner), ("is_le", is_le)): + ds = g[name] + ds.resize(self._n_groups + n, axis=0) + if n: + ds[self._n_groups:self._n_groups + n] = arr + self._n_groups += n + off = g["offsets"] + off.resize(off.shape[0] + 1, axis=0) + off[-1] = self._n_groups + + vo = np.asarray(volume_offsets, dtype=np.int64) + vds = g["volume_offsets"] + vds.resize(self._n_voloff + len(vo), axis=0) + if len(vo): + vds[self._n_voloff:self._n_voloff + len(vo)] = vo + self._n_voloff += len(vo) + vidx = g["volume_offsets_index"] + vidx.resize(vidx.shape[0] + 1, axis=0) + vidx[-1] = self._n_voloff + def append_event(self, layout): """Append one event described by an :class:`~pysupera.layout.EventLayout`.""" cols = layout.columns @@ -420,6 +484,32 @@ def _read_legacy_particles(self, a, b): cols["inst_pc_le_end"] = en return cols + def group_owners(self, ev): + """ + ``(instance_id, fragment_id, volume_offsets)`` for event *ev*, or + ``(None, None, None)`` when the file carries no group table + (non-JAXTPC runs). + + Indexed by event-global group number; ``fragment_id`` is -1 where no + stored fragment claims the group. The owning *instance* is not + stored -- derive it with + :func:`pysupera.provenance.instance_of_groups`. *volume_offsets* says where + each volume's groups begin, so a plane's local group number can be + shifted into this space. See :mod:`pysupera.provenance`. + """ + if "groups/fragment_id" not in self._f: + return None, None, None + go = self._f["groups/offsets"] + if ev + 1 >= go.shape[0]: + return None, None, None + a, b = int(go[ev]), int(go[ev + 1]) + vi = self._f["groups/volume_offsets_index"] + va, vb = int(vi[ev]), int(vi[ev + 1]) + le = (self._f["groups/is_le"][a:b] if "groups/is_le" in self._f + else np.zeros(b - a, dtype=np.int8)) + return (self._f["groups/fragment_id"][a:b], le, + self._f["groups/volume_offsets"][va:vb]) + def close(self): if self._f is not None: self._f.close() diff --git a/pysupera/preproc.py b/pysupera/preproc.py index e4068e4..197f9fd 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -599,7 +599,7 @@ def subset_voxmap(voxmap, mask): """ Restrict a particle's voxel mapping to a subset of its point-cloud rows. - *voxmap* is the ``(voxel_offsets, input_ids, input_energies)`` triple + *voxmap* is the ``(voxel_offsets, input_ids)`` pair attached by :class:`VoxelizeProcessor`, whose ``voxel_offsets`` is a CSR fencepost over the particle's voxels in point-cloud row order. *mask* is the boolean row selector used to split the cloud, so the same mask selects @@ -609,17 +609,16 @@ def subset_voxmap(voxmap, mask): """ if voxmap is None: return None - off, ids, en = voxmap + off, ids = voxmap sel = np.flatnonzero(mask) if len(sel) == 0: - return (np.zeros(1, dtype=np.int64), - ids[:0].copy(), en[:0].copy()) + return np.zeros(1, dtype=np.int64), ids[:0].copy() counts = (off[sel + 1] - off[sel]).astype(np.int64) new_off = np.zeros(len(sel) + 1, dtype=np.int64) np.cumsum(counts, out=new_off[1:]) take = np.concatenate([np.arange(off[i], off[i + 1]) for i in sel]) \ if len(sel) else np.zeros(0, dtype=np.int64) - return new_off, ids[take].copy(), en[take].copy() + return new_off, ids[take].copy() def _merge_point_cloud(pc: np.ndarray) -> np.ndarray: @@ -840,10 +839,6 @@ class VoxelizeRecord: grouping boundaries are given by :attr:`voxel_offsets`. ``None`` when :attr:`~VoxelizeProcessor.store_mapping` is ``False``. - input_energies : np.ndarray or None - Flat float32 array of length ``n_before`` holding the energy of - each input point in the same order as :attr:`input_ids`. - ``None`` when ``store_mapping`` is ``False``. voxel_offsets : np.ndarray or None Fencepost (CSR) int64 array of length ``n_after + 1``. For output voxel ``v`` (0-based within this particle), the @@ -857,7 +852,6 @@ class VoxelizeRecord: n_before : int n_after : int input_ids : Optional[np.ndarray] = field(default=None, repr=False) - input_energies : Optional[np.ndarray] = field(default=None, repr=False) voxel_offsets : Optional[np.ndarray] = field(default=None, repr=False) @property @@ -1003,6 +997,7 @@ def __init__( verbose: bool = False, merge_duplicates: bool = False, store_mapping: bool = False, + track_provenance: bool = False, ) -> None: vs = np.asarray(voxel_size, dtype=float) if vs.ndim == 0: @@ -1032,12 +1027,16 @@ def __init__( self.merge_duplicates: bool = bool(merge_duplicates) #: When ``True``, :meth:`process` populates the CSR mapping fields #: (:attr:`~VoxelizeRecord.input_ids`, - #: :attr:`~VoxelizeRecord.input_energies`, #: :attr:`~VoxelizeRecord.voxel_offsets`) on every #: :class:`VoxelizeRecord` in :attr:`last_diagnostics`, including #: unaffected particles (identity mapping). When ``False`` (default) #: only affected particles are recorded and mapping fields are ``None``. self.store_mapping: bool = bool(store_mapping) + #: Compute the same CSR but only attach it to the particle, for + #: callers that need provenance in memory without writing the + #: deposit-level map to disk -- the group-ownership table is built + #: this way. Implied by :attr:`store_mapping`. + self.track_provenance: bool = bool(track_provenance) #: :class:`VoxelizeRecord` list from the most recent :meth:`process` #: call. When :attr:`store_mapping` is ``False``, only particles #: that had at least one merge are included. When ``True``, every @@ -1089,6 +1088,18 @@ def process( all_pts = np.concatenate(all_pcs) # (total, n_cols) coords = all_pts[:, :3] + # Reader-supplied deposit provenance, concatenated in the same order + # as the clouds. All-or-nothing: if any contributing particle lacks + # it the array would be misaligned, so fall back to the id column. + _deps = [getattr(p, "deposit_id", None) for p in particles + if len(p.point_cloud) > 0] + if _deps and all(d is not None and len(d) == n for d, n in + zip(_deps, (len(p.point_cloud) for p in particles + if len(p.point_cloud) > 0))): + all_dep = np.concatenate(_deps).astype(np.int64) + else: + all_dep = None + # Per-particle origins (preserves per-particle grid alignment) if self.origin is None: # compute per-particle min; empty particles get zeros @@ -1145,25 +1156,23 @@ def process( # We sort input points by their assigned output voxel so all points # belonging to the same voxel are contiguous, then derive CSR offsets. # This reuses the already-computed inv array at O(total log total). - if self.store_mapping: + if self.store_mapping or self.track_provenance: sort_order = np.argsort(inv, kind='stable') # (total,) sorted_vox = inv[sort_order] # voxel indices, sorted # Fencepost offsets over all n_out voxels vox_off_all = np.searchsorted( sorted_vox, np.arange(n_out + 1) ).astype(np.int64) # (n_out + 1,) - # Flat input IDs and energies in sorted-voxel order - _en_col = int(PointFeature.energy) - flat_ids = ( - all_pts[sort_order, _id_col].astype(np.int64) - if n_cols > _id_col - else sort_order.astype(np.int64) - ) - flat_en = ( - all_pts[sort_order, _en_col].astype(np.float32) - if n_cols > _en_col - else np.zeros(total, dtype=np.float32) - ) + # Flat input deposit IDs in sorted-voxel order. + # Provenance comes from the reader's int64 deposit array when + # present. The float32 id column is the fallback: it is exact + # only to 2**24, which a large enough event would exceed. + if all_dep is not None: + flat_ids = all_dep[sort_order] + elif n_cols > _id_col: + flat_ids = all_pts[sort_order, _id_col].astype(np.int64) + else: + flat_ids = sort_order.astype(np.int64) for i, p in enumerate(particles): start, end = int(boundaries[i]), int(boundaries[i + 1]) @@ -1174,22 +1183,21 @@ def process( if affected: p.point_cloud = out[start:end] n_affected += 1 - elif self.store_mapping: + elif self.store_mapping or self.track_provenance: # When tracking the mapping, all particles get updated so that # p.point_cloud[:, PointFeature.id] reflects the new voxel IDs # that are also stored in the mapping record. p.point_cloud = out[start:end] # Build VoxelizeRecord when affected OR when tracking the mapping. - if affected or self.store_mapping: - if self.store_mapping: + if affected or self.store_mapping or self.track_provenance: + if self.store_mapping or self.track_provenance: inp_s = int(vox_off_all[start]) inp_e = int(vox_off_all[end]) p_off = (vox_off_all[start:end + 1] - inp_s).astype(np.int64) p_ids = flat_ids[inp_s:inp_e].copy() - p_en = flat_en[inp_s:inp_e].copy() else: - p_off = p_ids = p_en = None + p_off = p_ids = None # Attach the mapping to the particle, not just to the # diagnostics list. Defragmentation runs after this stage and @@ -1203,15 +1211,16 @@ def process( # voxel_offsets is a CSR over THIS particle's voxels, in the # same order as its point-cloud rows, which is what lets a # split reuse the very mask applied to the cloud. - if self.store_mapping: - p.voxmap = (p_off, p_ids, p_en) + if self.store_mapping or self.track_provenance: + p.voxmap = (p_off, p_ids) + + p.deposit_id = None # now carried by voxmap rec = VoxelizeRecord( particle_id = p.id, n_before = n_before, n_after = n_after, input_ids = p_ids, - input_energies = p_en, voxel_offsets = p_off, ) self.last_diagnostics.append(rec) diff --git a/pysupera/provenance.py b/pysupera/provenance.py new file mode 100644 index 0000000..27c77b6 --- /dev/null +++ b/pysupera/provenance.py @@ -0,0 +1,215 @@ +""" +Which JAXTPC hits belong to which reconstructed object. + +A hit is the readout projection of a *group* -- JAXTPC's cluster of energy +deposits -- and ``group_to_track`` gives each group exactly one Geant4 track. +That is not enough on its own to regroup hits by reconstructed object, because +defragmentation splits one track into several pysupera particles, so +``track -> particle`` is one-to-many. + +What makes this tractable is that a group never straddles a split. A group is +itself a tight spatial cluster and defragmentation clusters at +``distance_threshold``; measured over eight events, none of 354,959 groups +spanned two particles. So ``group -> pysupera particle`` *is* a function, and +one small table per event answers "which hits belong to instance X" without +going near voxels: + + groups = {g for g, pid in group_particle if pid in particles_of(X)} + hits = [h for h in plane if group_ids[h] in groups] + +The invariant is checked rather than assumed -- see :func:`build_group_owners`. +Going through voxels instead would be both larger and worse: a group's deposits +land in several voxels 38% of the time, so hit-to-voxel is many-to-many, while +hit-to-group-to-particle is a chain of functions. +""" + +from __future__ import annotations + +import numpy as np + +#: Written for groups that no stored particle claims. +NO_OWNER = -1 + + +class GroupOwnershipError(ValueError): + """A group's deposits landed in more than one pysupera particle.""" + + +def build_group_owners(particles, deposit_to_group, n_groups, check=True): + """ + Map every JAXTPC group to the pysupera particle that owns it. + + Parameters + ---------- + particles : list of Particle + The event's particles, after preprocessing, each carrying the + ``voxmap`` CSR whose ``input_ids`` are event-global deposit indices. + deposit_to_group : ndarray of int64 + Event-global deposit index -> event-global group number. + n_groups : int + Total groups in the event, so untouched ones can be marked. + check : bool + Raise :class:`GroupOwnershipError` when a group spans two particles. + With *check* false the first owner seen wins, which is a lossy + approximation -- enable the deposit-level map instead if this fires. + + Returns + ------- + ndarray of int32, shape (n_groups,) + Owning particle id per group, :data:`NO_OWNER` where none. + """ + owner = np.full(int(n_groups), NO_OWNER, dtype=np.int32) + clash = None + + for p in particles: + vm = getattr(p, "voxmap", None) + if vm is None: + continue + _off, ids = vm + if ids is None or not len(ids): + continue + groups = np.unique(deposit_to_group[np.asarray(ids, dtype=np.int64)]) + groups = groups[(groups >= 0) & (groups < len(owner))] + if not len(groups): + continue + taken = owner[groups] + held = groups[taken != NO_OWNER] + if len(held) and clash is None: + g0 = int(held[0]) + clash = (g0, int(owner[g0]), int(p.id)) + owner[groups[taken == NO_OWNER]] = int(p.id) + + if clash is not None and check: + g, a, b = clash + raise GroupOwnershipError( + f"group {g} has deposits in two pysupera particles ({a} and {b}), " + f"so hits cannot be attributed to one of them. This breaks the " + f"assumption the group table rests on. Set " + f"particle.voxelize.store_mapping=true for the exact " + f"deposit-level mapping, or check_group_ownership=false to " + f"accept the first owner." + ) + return owner + + +def remap_owners(owner, mapping): + """ + Translate a particle-level owner array into another id space. + + *mapping* is ``{particle_id: other_id}``. Particles absent from it -- + and unclaimed groups -- come out as :data:`NO_OWNER`. + + The particle id is the primitive the invariant is checked against, but it + is the wrong key to *store*: only about a sixth of particles get a row in + the output, so most owners would name something the reader cannot see. + Instance and fragment ids are always present, which is what makes the + join possible. + """ + out = np.full(len(owner), NO_OWNER, dtype=np.int32) + claimed = np.flatnonzero(owner != NO_OWNER) + for g in claimed: + v = mapping.get(int(owner[g])) + if v is not None: + out[g] = np.int32(v) + return out + + +def instance_of_groups(view, frag_owner): + """ + The instance owning each group, derived rather than stored. + + A fragment's points lie inside exactly one instance's block, so the + instance follows from the fragment by containment -- storing it as well + would be duplication. Verified over 29,201 groups with no mismatch. + + Note this cannot be done with a column join: ``inst_id`` marks a row as + *being* an instance representative (it equals ``id`` there and is -1 + everywhere else), it does not record membership. Membership is + positional, which is what this function reads. + + Parameters + ---------- + view : EventView + The event the groups belong to. + frag_owner : ndarray + ``groups/fragment_id`` for that event. + + Returns + ------- + ndarray of int32 + Owning instance id per group, :data:`NO_OWNER` where none. + """ + c = view.columns + row_of = {int(i): k for k, i in enumerate(c["id"])} + + inst = np.flatnonzero(view.is_instance) + lo = np.asarray(c["inst_pc_start"])[inst] + hi = np.maximum(np.asarray(c["inst_pc_le_end"])[inst], + np.asarray(c["inst_pc_end"])[inst]) + keep = lo >= 0 + inst, lo, hi = inst[keep], lo[keep], hi[keep] + order = np.argsort(lo) + inst, lo, hi = inst[order], lo[order], hi[order] + inst_id = np.asarray(c["id"])[inst] + + out = np.full(len(frag_owner), NO_OWNER, dtype=np.int32) + for g in np.flatnonzero(np.asarray(frag_owner) >= 0): + k = row_of.get(int(frag_owner[g])) + if k is None: + continue + p = int(c["frag_pc_start"][k]) + if p < 0: + p = int(c["frag_pc_le_start"][k]) + if p < 0: + continue + j = int(np.searchsorted(lo, p, side="right")) - 1 + if 0 <= j < len(lo) and p < hi[j]: + out[g] = inst_id[j] + return out + + +def le_flags(owner, is_le_of): + """ + Per-group low-energy flag, from the particle that owns the group. + + A group's deposits all belong to one particle, so LE-ness is exact at + this level -- unlike the instance, which normally mixes an LE and a + non-LE side. Without this the 2D view could only guess, and would have + to mask by the host instance, which is a different question. + + *is_le_of* is ``{particle_id: bool}``. Unclaimed groups come out 0. + """ + out = np.zeros(len(owner), dtype=np.int8) + for g in np.flatnonzero(owner != NO_OWNER): + if is_le_of.get(int(owner[g]), False): + out[g] = 1 + return out + + +def hits_of_groups(group_ids, wanted): + """ + Row indices of the hits belonging to *wanted*, on one readout plane. + + *group_ids* is a plane's per-hit group number; *wanted* any collection of + group numbers. + """ + wanted = np.asarray(list(wanted), dtype=np.int64) + if not len(wanted): + return np.zeros(0, dtype=np.int64) + return np.flatnonzero(np.isin(np.asarray(group_ids), wanted)) + + +def groups_of_particles(group_owner, particle_ids): + """ + Group numbers owned by any of *particle_ids*. + + :data:`NO_OWNER` is dropped from the request rather than matched: it + marks a group nothing claims, so asking for it would hand back every + unclaimed group. A caller passing an unset ``inst_id`` gets nothing, + which is the truthful answer. + """ + want = np.asarray(list(particle_ids), dtype=np.int64) + want = want[want != NO_OWNER] + if not len(want): + return np.zeros(0, dtype=np.int64) + return np.flatnonzero(np.isin(group_owner, want)) diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index 8014480..b3e3673 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -270,6 +270,12 @@ def _build_visible_point_cloud( assigned as a global 0-based integer across all visible segments in the event. + The returned ``deposit_id`` is the *input* deposit index -- the row in + the seg file that produced each point -- offset per volume so one + integer identifies a deposit across the whole event. It is carried as + int64 rather than in the float32 cloud, because the cloud is exact only + to 2**24 and a future hundredfold increase in deposits would reach that. + Parameters ---------- seg_volumes : list of dict @@ -290,12 +296,28 @@ def _build_visible_point_cloud( ``offsets[i] = [start, end)`` into ``point_cloud_flat`` for particle *i*. Particles with no visible segments get a zero-length slice (``start == end``). + deposit_id : np.ndarray, shape (M,), dtype int64 + Event-global input deposit index for each row of + *point_cloud_flat*. + volume_offsets : list of int + Where each volume's deposits start in that global index, so + ``(volume, local index)`` can be recovered. """ # Merge per-volume dicts into a single {track_id: list_of_segment_rows} # where each segment row is a 1-D float32 array of length 7. track_segs: dict[int, list[np.ndarray]] = {} - - for seg_vol, vol_visible in zip(seg_volumes, visible_by_track): + track_deps: dict[int, list[np.ndarray]] = {} + + # Deposit indices are per volume in the seg file; offset them so one + # integer names a deposit across the event. + volume_offsets: list[int] = [] + _cum = 0 + for sv in seg_volumes: + volume_offsets.append(_cum) + _cum += int(sv.get('n_actual', 0) or 0) + + for _vi, (seg_vol, vol_visible) in enumerate( + zip(seg_volumes, visible_by_track)): if seg_vol.get('n_actual', 0) == 0: continue pos = seg_vol['positions_mm'] # (N, 3) x, y, z in mm @@ -317,6 +339,8 @@ def _build_visible_point_cloud( chunk[:, PointFeature.dx] = dx[seg_idx] # id column left as zero; assigned globally below. track_segs.setdefault(track_id, []).append(chunk) + track_deps.setdefault(track_id, []).append( + volume_offsets[_vi] + np.asarray(seg_idx, dtype=np.int64)) # Concatenate chunks per track and build the flat array + offsets in the # same order as track_ids_edepsim so that Particle.from_flat_arrays @@ -324,6 +348,7 @@ def _build_visible_point_cloud( n_particles = len(track_ids_edepsim) offsets = np.zeros((n_particles, 2), dtype=np.int64) flat_chunks: list[np.ndarray] = [] + dep_chunks: list[np.ndarray] = [] cursor = 0 for i, tid in enumerate(track_ids_edepsim): @@ -335,19 +360,22 @@ def _build_visible_point_cloud( combined = np.concatenate(chunks, axis=0) # (M_i, 7) offsets[i] = [cursor, cursor + len(combined)] flat_chunks.append(combined) + dep_chunks.append(np.concatenate(track_deps[int(tid)], axis=0)) cursor += len(combined) if flat_chunks: point_cloud_flat = np.concatenate(flat_chunks, axis=0) + deposit_id = np.concatenate(dep_chunks, axis=0) else: point_cloud_flat = np.zeros((0, 7), dtype=np.float32) + deposit_id = np.zeros(0, dtype=np.int64) # Assign global, event-wide, 0-based point IDs. point_cloud_flat[:, PointFeature.id] = np.arange( len(point_cloud_flat), dtype=np.float32 ) - return point_cloud_flat, offsets + return point_cloud_flat, offsets, deposit_id, volume_offsets # --------------------------------------------------------------------------- @@ -497,7 +525,8 @@ def __getitem__(self, index: int) -> list[Particle]: # ── Step 4: build the flat point cloud (visible segments only) ──── # Segments are assembled in the same track order as `parts` so that # Particle.from_flat_arrays correctly matches metadata to point clouds. - point_cloud_flat, offsets = _build_visible_point_cloud( + (point_cloud_flat, offsets, + deposit_id, volume_offsets) = _build_visible_point_cloud( seg_volumes, visible_by_track, parts['track_id'] ) @@ -529,7 +558,15 @@ def __getitem__(self, index: int) -> list[Particle]: _ids, _par, _root, _g4 = _to_index_space(parts, _get_ancestor_id(parts)) - return Particle.from_flat_arrays( + # ── Deposit provenance for this event ──────────────────────────── + # deposit_to_group is per volume; concatenate it onto the same + # event-global index the point clouds now carry, and do the same for + # group_to_track so a group number is unambiguous event-wide. + self.last_deposit_to_group, self.last_group_volume_offsets = \ + self._load_deposit_groups(event_key, seg_volumes) + self.last_deposit_volume_offsets = volume_offsets + + _particles = Particle.from_flat_arrays( ids = _ids, parent_ids = _par, ancestor_ids = _root, @@ -543,6 +580,46 @@ def __getitem__(self, index: int) -> list[Particle]: geant4_ids = _g4, ) + # Attach each particle's slice of the deposit index. Kept beside the + # cloud rather than inside it: the cloud is float32 and exact only to + # 2**24, which a hundredfold rise in deposits per event would reach. + for _i, _p in enumerate(_particles): + _a, _b = int(offsets[_i][0]), int(offsets[_i][1]) + _p.deposit_id = deposit_id[_a:_b] + return _particles + + def _load_deposit_groups(self, event_key, seg_volumes): + """ + ``(deposit_to_group, group_volume_offsets)`` for one event. + + Both the deposit index and the group number are rebased onto an + event-global space, matching the deposit ids attached to the point + clouds, so a caller never has to know which volume a deposit came + from to look up its group. + """ + import numpy as _np + ev = (self._inst_file[event_key] + if event_key in self._inst_file else None) + if ev is None: + return None, [] + d2g, g_off, cum_g = [], [], 0 + for vol_name in self._volume_names(ev): + vg = ev[vol_name] + key = next((k for k in _SEG_TO_GROUP_KEYS if k in vg), None) + if key is None or 'group_to_track' not in vg: + continue + g_off.append(cum_g) + d2g.append(vg[key][:].astype(_np.int64) + cum_g) + cum_g += len(vg['group_to_track']) + if not d2g: + return None, [] + return _np.concatenate(d2g), g_off + + @staticmethod + def _volume_names(ev): + """Volume subgroups of one event, in the order the seg loader uses.""" + return sorted(k for k in ev.keys() if k.startswith('volume_')) + def close(self) -> None: """Close all open HDF5 file handles.""" for attr in ('_edepsim_file', '_seg_file', '_inst_file'): diff --git a/tests/test_preproc.py b/tests/test_preproc.py index 92b2b17..bc97c96 100644 --- a/tests/test_preproc.py +++ b/tests/test_preproc.py @@ -664,30 +664,46 @@ def test_inverse_mapping_correct_voxel_for_particle1(self): # ------------------------------------------------------------------ energy consistency - def test_energy_in_record_matches_input_energies(self): + def test_voxel_energy_is_the_sum_of_its_inputs(self): """ - For each voxel, the sum of rec.input_energies must equal the merged - output voxel's energy column. + Each voxel's energy must equal the sum over the inputs the CSR names. + + The energies are no longer stored beside the mapping -- they would + duplicate what the input already holds -- so they are looked up + through input_ids, which is also what a caller has to do. """ - _EN = int(PointFeature.energy) + _EN, _ID = int(PointFeature.energy), int(PointFeature.id) p1, p2 = self._make_particles() + # energy by input id, captured before process() replaces the clouds + by_id = {int(r[_ID]): float(r[_EN]) + for p in (p1, p2) for r in p.point_cloud} + vox = self._vox() results = vox.process([p1, p2]) - - # Build lookup: particle_id → output point cloud out_clouds = {p.id: p.point_cloud for p in results} + checked = 0 for rec in vox.last_diagnostics: cloud_out = out_clouds[rec.particle_id] for v in range(rec.n_after): - sl = slice(int(rec.voxel_offsets[v]), int(rec.voxel_offsets[v + 1])) - eng_sum = float(rec.input_energies[sl].sum()) - vox_eng = float(cloud_out[v, _EN]) + sl = slice(int(rec.voxel_offsets[v]), + int(rec.voxel_offsets[v + 1])) + eng_sum = sum(by_id[int(i)] for i in rec.input_ids[sl]) np.testing.assert_allclose( - eng_sum, vox_eng, rtol=1e-5, - err_msg=f"Particle {rec.particle_id} voxel {v}: " - f"input energy sum {eng_sum} != voxel energy {vox_eng}", - ) + eng_sum, float(cloud_out[v, _EN]), rtol=1e-5, + err_msg=f"Particle {rec.particle_id} voxel {v}") + checked += 1 + assert checked > 0, "no voxels were checked" + + def test_every_input_appears_in_exactly_one_voxel(self): + """The CSR must partition the inputs -- none lost, none doubled.""" + p1, p2 = self._make_particles() + vox = self._vox() + vox.process([p1, p2]) + for rec in vox.last_diagnostics: + assert int(rec.voxel_offsets[-1]) == rec.n_before + assert len(rec.input_ids) == rec.n_before + assert len(set(int(i) for i in rec.input_ids)) == rec.n_before def test_energy_known_values_particle1(self): """Explicit check: particle 1 voxel energies must be 3.0 and 7.0.""" @@ -825,3 +841,46 @@ def test_nothing_is_dropped(self): out = ScipyDefragmenter(distance_threshold=5.0, min_pc_size=5).process(ps) assert {1, 2, 3} <= {q.id for q in out} + + +# ============================================================================ +# subset_voxmap +# ============================================================================ + +from pysupera.preproc import subset_voxmap # noqa: E402 + + +class TestSubsetVoxmap: + """ + Splitting a cloud has to carry its provenance along. This path had no + test and shipped broken once: the mapping is consumed by every + defragmentation split, so a wrong shape here fails the whole run. + """ + + def _vm(self): + # 3 voxels: voxel 0 <- deposits 10,11 1 <- 12 2 <- 13,14,15 + return (np.array([0, 2, 3, 6], dtype=np.int64), + np.array([10, 11, 12, 13, 14, 15], dtype=np.int64)) + + def test_returns_a_pair(self): + assert len(subset_voxmap(self._vm(), np.array([True, False, True]))) == 2 + + def test_keeps_the_selected_voxels_deposits(self): + off, ids = subset_voxmap(self._vm(), np.array([True, False, True])) + assert off.tolist() == [0, 2, 5] + assert ids.tolist() == [10, 11, 13, 14, 15] + + def test_a_single_voxel(self): + off, ids = subset_voxmap(self._vm(), np.array([False, True, False])) + assert off.tolist() == [0, 1] and ids.tolist() == [12] + + def test_empty_selection(self): + off, ids = subset_voxmap(self._vm(), np.array([False, False, False])) + assert off.tolist() == [0] and len(ids) == 0 + + def test_none_passes_through(self): + assert subset_voxmap(None, np.array([True])) is None + + def test_offsets_stay_a_valid_fencepost(self): + off, ids = subset_voxmap(self._vm(), np.array([True, True, True])) + assert off[0] == 0 and off[-1] == len(ids) diff --git a/tests/test_provenance.py b/tests/test_provenance.py new file mode 100644 index 0000000..1d38e24 --- /dev/null +++ b/tests/test_provenance.py @@ -0,0 +1,125 @@ +""" +Tests for hit provenance. + +The table compresses a per-deposit truth into one owner per group, which is +only valid while no group straddles a defragmentation split. Most of what +matters here is that the compression refuses to lie when it cannot hold. +""" +import numpy as np +import pytest + +from pysupera.provenance import (build_group_owners, remap_owners, + hits_of_groups, groups_of_particles, + GroupOwnershipError, NO_OWNER) + + +class FakeParticle: + """Carries only what build_group_owners reads: id and voxmap.""" + + def __init__(self, pid, deposits): + self.id = pid + n = len(deposits) + self.voxmap = (np.arange(n + 1, dtype=np.int64), + np.asarray(deposits, dtype=np.int64)) + + +# deposits 0,1 -> group 0; 2,3 -> group 1; 4 -> group 2 +D2G = np.array([0, 0, 1, 1, 2], dtype=np.int64) + + +class TestBuildGroupOwners: + + def test_each_group_gets_its_particle(self): + ps = [FakeParticle(7, [0, 1]), FakeParticle(9, [2, 3])] + owner = build_group_owners(ps, D2G, 3) + assert owner[0] == 7 + assert owner[1] == 9 + + def test_untouched_groups_are_unowned(self): + owner = build_group_owners([FakeParticle(7, [0])], D2G, 3) + assert owner[1] == NO_OWNER and owner[2] == NO_OWNER + + def test_a_particle_may_own_several_groups(self): + owner = build_group_owners([FakeParticle(7, [0, 2, 4])], D2G, 3) + assert owner.tolist() == [7, 7, 7] + + def test_particles_without_a_voxmap_are_skipped(self): + p = FakeParticle(7, [0]); p.voxmap = None + assert (build_group_owners([p], D2G, 3) == NO_OWNER).all() + + def test_empty_event(self): + assert len(build_group_owners([], D2G, 3)) == 3 + + def test_out_of_range_groups_are_ignored(self): + # a deposit mapped past the end must not write out of bounds + owner = build_group_owners([FakeParticle(7, [4])], D2G, 2) + assert owner.tolist() == [NO_OWNER, NO_OWNER] + + +class TestInvariant: + """A straddling group means the table cannot express the truth.""" + + def _straddle(self): + # both particles hold a deposit of group 0 + return [FakeParticle(7, [0]), FakeParticle(9, [1])] + + def test_a_straddling_group_raises(self): + with pytest.raises(GroupOwnershipError, match="two pysupera particles"): + build_group_owners(self._straddle(), D2G, 3) + + def test_the_error_names_both_particles(self): + with pytest.raises(GroupOwnershipError) as e: + build_group_owners(self._straddle(), D2G, 3) + assert "7" in str(e.value) and "9" in str(e.value) + + def test_the_error_points_at_the_exact_alternative(self): + with pytest.raises(GroupOwnershipError) as e: + build_group_owners(self._straddle(), D2G, 3) + assert "store_mapping" in str(e.value) + + def test_check_false_keeps_the_first_owner(self): + owner = build_group_owners(self._straddle(), D2G, 3, check=False) + assert owner[0] == 7 + + +class TestRemap: + + def test_maps_into_the_other_id_space(self): + owner = np.array([7, 9, NO_OWNER], dtype=np.int32) + assert remap_owners(owner, {7: 100, 9: 200}).tolist() == [100, 200, -1] + + def test_unmapped_particles_become_unowned(self): + owner = np.array([7, 9], dtype=np.int32) + assert remap_owners(owner, {7: 100}).tolist() == [100, -1] + + def test_unowned_stays_unowned(self): + owner = np.full(3, NO_OWNER, dtype=np.int32) + assert (remap_owners(owner, {7: 1}) == NO_OWNER).all() + + def test_several_particles_may_share_an_owner(self): + # the usual case: many particles in one instance + owner = np.array([7, 9], dtype=np.int32) + assert remap_owners(owner, {7: 5, 9: 5}).tolist() == [5, 5] + + +class TestQueries: + + def test_hits_of_groups_selects_by_group(self): + gid = np.array([0, 1, 0, 2, 1]) + assert hits_of_groups(gid, [0]).tolist() == [0, 2] + assert hits_of_groups(gid, [1, 2]).tolist() == [1, 3, 4] + + def test_hits_of_no_groups_is_empty(self): + assert len(hits_of_groups(np.array([0, 1]), [])) == 0 + + def test_groups_of_particles(self): + owner = np.array([7, 9, 7, NO_OWNER], dtype=np.int32) + assert groups_of_particles(owner, [7]).tolist() == [0, 2] + assert groups_of_particles(owner, [7, 9]).tolist() == [0, 1, 2] + + def test_groups_of_no_particles_is_empty(self): + assert len(groups_of_particles(np.array([7]), [])) == 0 + + def test_unowned_is_never_selected(self): + owner = np.array([NO_OWNER, NO_OWNER], dtype=np.int32) + assert len(groups_of_particles(owner, [NO_OWNER])) == 0 From 5e1f3684e12c97d6c1a1c42a5c444b581b0775b2 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Mon, 21 Sep 2026 21:57:02 -0700 Subject: [PATCH 39/62] vis: a viewer that shows the hit-to-voxel mapping vis_hits.html puts the 3D voxels next to the 2D readout planes and colours both by the same owner id, so a colour means the same reconstructed object in either view. Hover a 3D voxel and its hits light up across every plane image; hover a 2D pixel and its voxels light up in 3D. That is the whole demonstration: the link is a single integer, and the two views agree. A separate file rather than an addition to vis_force.html, which is already 122k characters and answers a different question. Colour by instance, fragment or interaction. Only the fragment is read from the file -- the instance is derived by containment and the interaction from the instance -- so the viewer demonstrates the same derivation the format relies on rather than depending on stored duplicates. Unselected points are genuinely transparent, not merely darkened: the cloud uses RGBA vertex colours with depthWrite off, so the selection is never hidden behind the points being de-emphasised. A slider sets how transparent. "hide LE" drops low-energy points from both views, filtering at build time so the picker cannot select something hidden. A per-point readout shows x/y/z, time, dE, dX, dE/dX, LE and owner in 3D, and wire, time, peak charge, group, LE and owner in 2D. It refuses to draw rather than show something false. h5wasm decodes gzip and nothing else, and an LZ4 dataset yields garbage instead of raising -- which cost an afternoon: a viewer showing a muon track labelled PDG 2112, from a file that looked repacked but had kept its original filter. The viewer now checks the filter on open, and also banners a missing groups/ table or an event where every voxel resolves to one owner. The 3D canvas is absolutely positioned, sized from getBoundingClientRect with a window fallback, tracked by a ResizeObserver, and drawn by a rAF loop. A zero-sized canvas renders nothing *and* swallows every mouse event, which is indistinguishable from a broken viewer. pysupera-hits-subset prepares the second input: the JAXTPC hits file is 1.3 GB, almost all of it per-tick arrays the viewer never opens. Keeping the four arrays it does read gives 1279.7 MiB -> 2.73 MiB for three events. Verified against a three-event sample: 22,067 voxels and 87,268 hits, every one owned, all 196 instances present in both views. Rendering itself is unverified -- there is no browser in the build environment. The README gains a Browser viewers section documenting vis_force.html and vis_hits.html side by side -- what each is for, what it needs, its controls and shortcuts. vis_force.html had never been documented at all, only pysupera-app had. Writing it up turned up two labels still saying "Root Track ID" and "Root tracks" from before the root_id -> ancestor_id rename; both now say ancestor. --- README.md | 160 +++++++++ pyproject.toml | 1 + pysupera/hits_subset.py | 80 +++++ vis_force.html | 6 +- vis_hits.html | 713 ++++++++++++++++++++++++++++++++++++++++ 5 files changed, 957 insertions(+), 3 deletions(-) create mode 100644 pysupera/hits_subset.py create mode 100644 vis_hits.html diff --git a/README.md b/README.md index d8bd4a3..6a81f05 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,7 @@ A Python library for grouping simulated LArTPC particles into physics partitions 4. [Quick Start](#quick-start) - [Python API](#python-api) - [Interactive Event Viewer (`pysupera-app`)](#interactive-event-viewer-pysupera-app) + - [Browser viewers (`vis_force.html`, `vis_hits.html`)](#browser-viewers-vis_forcehtml-vis_hitshtml) 5. [Workflow](#workflow) - [Input: the `Particle` object](#input-the-particle-object) - [Step 0 — Configuration](#step-0--configuration) @@ -33,6 +34,8 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [Particle columns](#particle-columns) - [Interaction columns](#interaction-columns) - [ID conventions](#id-conventions) + - [Hit provenance (JAXTPC mode)](#hit-provenance-jaxtpc-mode) + - [Seeing it](#seeing-it) - [Expert and debugging output](#expert-and-debugging-output) 7. [Training Data (PyTorch)](#training-data-pytorch) - [Voxel merging](#voxel-merging) @@ -196,6 +199,72 @@ The **PARTICLE FILTER** and **show legend** checkbox take effect immediately wit --- +### Browser viewers (`vis_force.html`, `vis_hits.html`) + +Two standalone HTML files that read an output file directly in the browser — +no server, no install, nothing to launch. Open the file and pick your HDF5. +They answer different questions: + +| | `vis_force.html` | `vis_hits.html` | +|---|---|---| +| **question** | what is in this event, and how is it grouped? | which readout hits belong to which object? | +| **inputs** | the pysupera output | the output **and** the JAXTPC hits file | +| **views** | one 3-D cloud, plus an instance-genealogy graph | 3-D cloud beside the six 2-D readout planes | +| **needs** | any 3.x output | JAXTPC mode, so `groups/` exists | + +Both read HDF5 through **h5wasm, which decodes gzip only**, so the inputs have +to be repacked first — see [Compression](#compression). + +#### `vis_force.html` — exploring one event + +Colour the cloud by energy, time, interaction id, ancestor track id, fragment +id, instance id or PDG code, with a selectable colormap and optional log +scale. LE and non-LE points toggle independently, energy and time thresholds +slide, and a bounding box clips the view. An animation plays the event by +time, and an interaction panel and a force-directed instance-genealogy graph +open alongside. + +Shortcuts: `c` centre · `a` axes · `s` screenshot · `space` play/pause · +`r` reset animation · `o` auto-rotate · `i` interaction panel · `g` graph · +`d` debug log · `?` help. + +#### `vis_hits.html` — linking 3-D to the readout + +3-D voxels on the left, the 2-D planes built from hits on the right, and the +same colour meaning the same object in both. **Hover a 3-D voxel and its hits +light up across all six plane images; hover a 2-D pixel and its voxels light +up in 3-D.** The link is a single owner id, so the two views cannot disagree. + +| control | | +|---|---| +| **colour by** | instance, fragment or interaction — the last two derived, not read | +| **others** | opacity of everything not under the cursor. The cloud uses RGBA vertex colours with depth-write off, so unselected points are genuinely transparent and never hide the selection behind them | +| **hide LE** | drops low-energy points from both views. Filtering happens at build time, so the picker cannot select something hidden | +| **point info** | x/y/z, time, dE, dX, dE/dX, LE and owner in 3-D; wire, time, peak charge, group, LE and owner in 2-D | +| **c** | fit the view | + +Preparing its two inputs: + +```bash +pysupera-repack out.h5 out_gz.h5 -c gzip # the pysupera output +pysupera-hits-subset sim_wire_hits_0000.h5 hits_small.h5 -n 10 +``` + +`-c gzip` is not optional: omitting it keeps each dataset's existing filter, +leaving a file the browser cannot read. The second command exists because the +hits file is mostly per-tick arrays the viewer never opens — keeping only +`center_wires`, `center_times`, `peak_charges` and `group_ids` takes a +3-event sample from 1279.7 MiB to 2.73 MiB. + +`vis_hits.html` refuses to draw rather than show something false: a red +banner appears for a filter it cannot decode, a missing `groups/` table, or +an event where every voxel resolves to one owner. Files written before the +group table, or before `groups/is_le`, still load — it says what is missing +and shows what it can. The mapping it displays is described under +[Hit provenance](#hit-provenance-jaxtpc-mode). + +--- + ## Workflow ### Input: the `Particle` object @@ -617,6 +686,97 @@ Both merge stages operate within a single particle, so `dE` and `dX` both sum over a particle-voxel and **dE/dX is column 4 / column 5**, exactly. Guard against `dX == 0`: EDepSim writes zero-length steps, a few percent of voxels. +### Hit provenance (JAXTPC mode) + +Which readout hits belong to which reconstructed object. A hit is the +projection of a JAXTPC *group*, and `groups/` records the fragment that owns +each group, plus one bit saying whether that group is low-energy: + +```python +with read_events_v3("out.h5") as store: + frag_owner, is_le, vol_offsets = store.group_owners(ev) + +groups = np.flatnonzero(frag_owner == my_fragment_id) +gid = inst_file[f"event_{ev:03d}/volume_0/U/group_ids"][:] + vol_offsets[0] +hits = np.flatnonzero(np.isin(gid, groups)) +``` + +Both arrays are indexed by event-global group number, `-1` where nothing +claims the group; `vol_offsets` shifts a plane's local group number into that +space. `pysupera.provenance` has `hits_of_groups` and `groups_of_particles` +for the same joins. + +For instances, derive rather than read — `instance_of_groups(view, +frag_owner)` returns the owning instance per group, and the interaction +follows from the instance's `interaction_id`: + +```python +from pysupera.provenance import instance_of_groups +inst_owner = instance_of_groups(store[ev], frag_owner) +``` + +#### What is stored, and what is not + +| | stored? | why | +|---|---|---| +| fragment | yes | the only usable key — see below | +| instance | **no** | a fragment's points lie inside exactly one instance's block, so it follows by containment (verified exact on 29,201 groups) | +| interaction | **no** | follows from the instance's `interaction_id` | +| LE flag | yes | irreducible — see below | + +**Why the fragment and not the particle.** The particle is the natural key and +does not work: a group's owner is usually an ordinary member particle, and +only about a sixth of particles get a row — 6,605 of 6,777 owners were +unresolvable in one event. Every fragment *with points* is stored by +construction (0 missing of 29,201), so the fragment is the finest key that +always resolves. + +Note that `inst_id` and `frag_id` are **self-markers**, not membership: each +equals `id` on a representative row and is `-1` everywhere else. Membership is +positional, via the point ranges, which is why deriving the instance is a +containment search rather than a column join. + +**Why the LE flag cannot be derived.** LE-ness is an attribute of the pysupera +particle, and a fragment mixes LE and non-LE members by design — that is what +its two point ranges are for. Measured, 26–30% of fragments contain both LE +and non-LE groups, so the fragment cannot answer it. Deriving it would mean +knowing which *point rows* a group produced, which is the opt-in deposit map +below. The bit costs 0.17 bytes/group; making it derivable instead, by storing +the owning particles as rows, costs about 20× more. + +#### Why groups and not voxels + +`group_to_track` gives each group one Geant4 track, but defragmentation splits +a track across several pysupera particles, so `track → particle` is +one-to-many and cannot regroup hits. What rescues it is that a group never +straddles a split — a group is a tight spatial cluster and defragmentation +clusters at `distance_threshold`. Over eight events, none of 354,959 groups +spanned two particles. So `group → particle` *is* a function, and +`hit → group → particle → fragment` is a chain of functions. + +Going via voxels would be worse as well as larger: a group's deposits land in +2–4 different voxels 38% of the time, so `hit ↔ voxel` is many-to-many and +would need charge apportionment (`qs_fractions` in the inst file). + +The invariant is checked per event, not assumed — `check_group_ownership` +(default true) raises naming the group and the two particles. `groups/` costs +about 21% of the output under LZ4, less under gzip. + +#### Seeing it + +`vis_hits.html` shows this mapping directly — 3-D voxels beside the 2-D +readout planes, hover either side to light up the other. See +[Browser viewers](#browser-viewers-vis_forcehtml-vis_hitshtml) for its +controls and for preparing its two inputs. + +#### Exact per-voxel provenance — `particle.voxelize.store_mapping=true` + +Off by default. Writes `_voxmap.h5`, a CSR giving the input deposits +behind every output voxel. This is the exact record the group table +compresses, so it is the thing to enable if `check_group_ownership` ever +fires — and the only way to answer "which deposits made *this* voxel". It is +roughly ten times larger. + ### Compression Output is LZ4 by default (`io.compression`). The browser viewer uses h5wasm, diff --git a/pyproject.toml b/pyproject.toml index cebbf94..5f59126 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,7 @@ app = [ run_pysupera = "pysupera._run:main" pysupera-app = "pysupera.app:main" pysupera-repack = "pysupera.io:repack_cli" +pysupera-hits-subset = "pysupera.hits_subset:main" [tool.setuptools.packages.find] where = ["."] diff --git a/pysupera/hits_subset.py b/pysupera/hits_subset.py new file mode 100644 index 0000000..d46c65b --- /dev/null +++ b/pysupera/hits_subset.py @@ -0,0 +1,80 @@ +""" +Extract a viewer-sized slice of a JAXTPC hits file. + +``vis_hits.html`` reads four arrays per readout plane -- ``center_wires``, +``center_times``, ``peak_charges`` and ``group_ids`` -- and nothing else. The +per-tick arrays (``charges_u16``, ``delta_times``, ``delta_wires``) are the +bulk of the file and are never touched, so a handful of events comes to about +a megabyte against the original gigabyte. + +The output is gzip, because the browser reads HDF5 through h5wasm, which +supports gzip only. + + pysupera-hits-subset sim_wire_hits_0000.h5 hits_small.h5 --events 3 +""" + +from __future__ import annotations + +import argparse +import os + +#: What the viewer reads. Anything else is left behind. +KEEP = ("center_wires", "center_times", "peak_charges", "group_ids") + + +def extract(src: str, dst: str, n_events: int = 3, level: int = 4, + verbose: bool = True) -> dict: + """Copy *n_events* worth of the plane arrays from *src* into *dst*.""" + import h5py + try: + import hdf5plugin # noqa: F401 (registers Blosc/LZ4 for reading) + except ImportError: + pass + + n_ds = 0 + with h5py.File(src, "r") as fin, h5py.File(dst, "w") as fout: + events = sorted(k for k in fin.keys() if k.startswith("event_")) + if n_events > 0: + events = events[:n_events] + for ek in events: + for vol in sorted(k for k in fin[ek].keys() + if k.startswith("volume_")): + vg = fin[f"{ek}/{vol}"] + for plane in ("U", "V", "Y"): + if plane not in vg: + continue + for name in KEEP: + if name not in vg[plane]: + continue + fout.create_dataset( + f"{ek}/{vol}/{plane}/{name}", + data=vg[plane][name][:], + compression="gzip", compression_opts=level, + ) + n_ds += 1 + out = {"events": len(events), "datasets": n_ds, + "size_before": os.path.getsize(src), + "size_after": os.path.getsize(dst)} + if verbose: + print(f"[hits-subset] {src} -> {dst}: {len(events)} event(s), " + f"{n_ds} dataset(s), " + f"{out['size_before'] / 1048576:.1f} MiB -> " + f"{out['size_after'] / 1048576:.2f} MiB") + return out + + +def main() -> None: + """Entry point for ``pysupera-hits-subset``.""" + p = argparse.ArgumentParser( + prog="pysupera-hits-subset", + description="Extract a gzip, viewer-sized slice of a JAXTPC hits file.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + p.add_argument("src", help="JAXTPC hits (inst) HDF5 file") + p.add_argument("dst", help="Output file, gzip") + p.add_argument("-n", "--events", type=int, default=3, + help="Number of events to copy; -1 for all") + p.add_argument("-l", "--level", type=int, default=4, help="gzip level (1-9)") + a = p.parse_args() + if not 1 <= a.level <= 9: + p.error("--level must be between 1 and 9") + extract(a.src, a.dst, a.events, a.level) diff --git a/vis_force.html b/vis_force.html index bde2277..b898555 100644 --- a/vis_force.html +++ b/vis_force.html @@ -693,7 +693,7 @@ - + @@ -2434,7 +2434,7 @@

⌨ Keyboard Shortcuts

`
centroid x${d.xc}
`+ `
centroid y${d.yc}
`+ `
centroid z${d.zc}
`+ - `
Root tracks${d.nroots}
`+ + `
Ancestors${d.nroots}
`+ `
Cloud source${d.src}
`+ (d.pdg&&d.pdg!=='—'?`
PDG codes  ${d.pdg}
`:'')+ _instPtHistHTML(_intId); @@ -2502,7 +2502,7 @@

⌨ Keyboard Shortcuts

`
centroid x${d.xc}
`+ `
centroid y${d.yc}
`+ `
centroid z${d.zc}
`+ - `
Root tracks${d.nroots}
`+ + `
Ancestors${d.nroots}
`+ `
Cloud source${d.src}
`+ (d.pdg&&d.pdg!=='—'?`
PDG codes  ${d.pdg}
`:'')+ _instPtHistHTML(_intId); diff --git a/vis_hits.html b/vis_hits.html new file mode 100644 index 0000000..ddb4027 --- /dev/null +++ b/vis_hits.html @@ -0,0 +1,713 @@ + + +pysupera — hits ⇄ voxels + + +
+ + + + + + + + + + load both files — hover anywhere to link 2D ⇄ 3D +
+ +
+
no file loaded
+
+
+ + + + From 6f8258b3ea94b0c34cd608f038ae49ad7034836c Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Wed, 23 Sep 2026 11:02:41 -0700 Subject: [PATCH 40/62] jaxtpc: hit provenance, pixel readout, and the detected-image point source One change rather than seven because the pieces landed on the same files and cannot be separated after the fact. What follows is what is in it. Hit provenance ============== Answers "which readout hits belong to this reconstructed object" without going through voxels. A hit is the projection of a JAXTPC group, and group_to_track gives each group one Geant4 track -- but defragmentation splits a track across several pysupera particles, so track -> particle is one-to-many. What rescues it is that a group never straddles a split: measured over eight events, none of 354,959 groups spanned two particles. So hit -> group -> particle -> fragment is a chain of functions. Two arrays per group, and no more. groups/fragment_id, because the particle is the natural key and does not work -- only about a sixth of particles get a row, and 6,605 of 6,777 owners were unresolvable when this was first keyed on particle id, where every fragment with points is stored by construction. And groups/is_le, which is irreducible: a fragment mixes LE and non-LE members by design and 26-30% contain both. The instance is derived by containment and the interaction from the instance; neither is stored. That cannot be a column join -- inst_id and frag_id are self-markers, equal to id on a representative and -1 elsewhere -- which is why a first attempt disagreed on 12,798 of 29,201 groups. groups/ costs about 21% under LZ4. Supporting: the reader keeps the real deposit index instead of overwriting it with a running counter, riding beside the cloud as int64 rather than in it (float32 is exact only to 2**24). Computing the CSR is separated from writing it, so the group table can be built while the deposit-level map stays opt-in. input_energies is dropped from the voxmap -- nothing consumed it, and once input_ids are real deposit indices the energy is recoverable upstream; the file shrinks 18%. subset_voxmap gains the tests it never had, which is how it shipped briefly returning a stale 3-tuple. Wire and pixel readout ====================== JAXTPC simulates both LArTPC geometries: wire records three 2-D projections (U/V/Y planes), pixel records one natively 3-D image (a single Pixel plane). Only wire had ever been exercised. The reader needed nothing. Visibility is a question about a *group* -- a group is above threshold or it is not, whatever geometry saw it -- and the filter already discovered plane subgroups structurally and read only group_ids. Rather than add a branch that would have to be kept honest, this documents why there is none and tests it: the same groups fed through both geometries must give the same visible segments. What does differ is anything that draws a hit. pysupera-hits-subset hardcoded U/V/Y and center_wires, so handed a pixel file it wrote an empty one and said nothing; it now finds planes structurally and keeps whichever centres they store. vis_hits.html gains a second 3-D pane for pixel readout -- x = drift tick, y = py, z = pz -- in place of the 2-D canvases. Two bugs found there, both of which made the feature dead rather than merely wrong. The load gate tested groups/instance_id, a column groups/ has never had by design, so every output ever produced reported "no groups/ table" and drew nothing. And group numbers were shifted twice, gids + base - ga, where the owner array is already sliced to the event; event 0 survived because ga is 0 there, and event 1 resolved 1,686 of 12,450 hits instead of all of them. The detected image as the point source ====================================== reader.point_source=hits replaces each particle's point cloud with the pixel hits themselves. A point stops being a Geant4 deposit that survived the readout and becomes a fired pixel, so the cloud carries pixelation, diffusion, threshold and the drift-time ambiguity, and every algorithm downstream sees what the detector saw. The assignment stays exact: a CSR entry belongs to one group and group_to_track gives each group one track. y and z invert exactly. x is drift time, and a tick measures t_drift + t0 -- t0 being precisely what a real detector must determine separately. So hit_x_from chooses: 'nominal' assumes the beam time, leaving x offset from truth by drift_direction * v * t0; 'true_t0' puts the real t0 back. On this batch t0 is constant within an interaction (max spread 0.53 us) and differs across the interactions of one event by up to 1094 us -- 175 cm. So nominal is a rigid translation per interaction, and the two conventions partition almost identically (4,723 against 4,727 over two events), which is itself the answer to a question worth asking. points/true_x_shift keeps the truth anyway, as a displacement rather than a second coordinate: true_x = x + shift, zero under true_t0 and in deposit mode so the relation holds for every file. It costs 0.14% of the output, because it is drift_direction * t0 * v and takes one value per (interaction, volume) pair -- 28 in a test event against 4.18M hits. Per point rather than per particle because the two exceptions are cathode-crossing tracks carrying 15% of the points. Geometry is configured and then checked, never measured. An earlier round fitted the pitch, velocity, tick length and drift direction from the truth deposits and checked the result against the same deposits, which is circular in the direction that matters: a geometry wrong but linear cannot fail such a check. Now the anode comes from config/volume_ranges, the velocity and sampling period from the sensor file, and the pitch and drift direction from config. A wrong pitch of 4.0 instead of 4.32 shows up as a 248 mm residual; a mirrored drift direction as 277 mm. Before, all of them fitted cleanly. What the readout window hides ============================= num_time_steps is 2701 and a tick is 0.8 mm, so the recordable ticks span exactly one full drift and a nominal x cannot leave the volume. The t0 displacement instead pushes charge off the end of the window, and that turns out to be nearly the whole story of what "visible" means here: per event and volume, the fraction of masked deposits that the window never reached is 97.0%, 98.1%, 99.7%, 99.4%. A deposit is invisible because the readout had closed, not because it was below threshold. Reported per run. A group can also straddle a fragment split now -- 1.84%, against 0 of 354,959 for truth deposits -- because the readout leaves gaps the deposit cloud does not have. build_group_owners grows resolve= and report=, hit mode settles by majority, and check_group_ownership must be off. Classification by extent, not by row count ========================================== min_pc_size decides whether a particle is a low-energy scatter, and it counted rows. Row count is a property of the input sampling, not of the particle: deposits arrive every 0.3 mm and pixel hits on a 4.32 mm grid, so the same object is 1 row in one and 40 in the other. With point_source=hits the size rule stopped contributing almost entirely and 3,818 particles the truth input calls low-energy scatters came out as showers. It now counts occupied voxels, which is what defragmentation already splits on, so the two stages finally speak one unit. The value follows from a physical rule: a chunk is worth calling a trajectory only when its direction can be read off it, so the threshold is the cell count of a blob one track-width across. Truth deposits are 4.9 mm wide -> 5, which is the long-standing default and reassuring about where it came from. reader.hit_charge_threshold drops hits below a given induced charge in electrons, on the magnitude, matching JAXTPC's own encoder -- the response is bipolar and 16% of hits are negative signal. JAXTPC thresholds at 1 electron, which is none, so this is where a readout threshold enters. It interacts with the extent threshold, because cutting the halo thins the tracks: hit_charge_threshold track width min_pc_size agrees with truth@5 ---------------------------------------------------------------------- 0 e- 13.1 mm 85 99.0% 500 e- 6.5 mm 13 95.5% 500 e- 6.5 mm 85 (mixed) 85.2% Measured over ten events; the per-event best cut ranged 81-90 at threshold zero. Agreement means the same particles, not merely the same fraction. Mixing the pair is worse than either, and no single config group could express that they belong together -- min_pc_size is under particle, the threshold under reader, distance_threshold and check_group_ownership at the top level. Hence preset/, an optional group listed after _self_ so it can override the defaults while a command-line flag still overrides it. preset=cubic_pixel_hits carries the matched set and a sensor-hit run is now just file paths. Note this also moves truth-deposit labelling, 85.8% -> 95.3% LE, because a particle spread over a handful of rows inside one or two voxels is now measured as what it is. A correction, but a change. Configuration ============= conf/reader/ held one 197-line file covering three unrelated inputs, almost all of it nulls that mattered to one of them. It is now edepsim_h5 (plain EDepSim, and the base), jaxtpc_wire and jaxtpc_pixel, the latter two inheriting the first through a within-group defaults list. jaxtpc_wire carries no point_source or pixel-geometry keys at all: a wire plane has no 3-D image, and options that cannot work should not look as though they might. Selecting a JAXTPC config without its input paths used to fall through to plain EDepSim in silence -- OmegaConf's .get() returns None for an unfilled mandatory value and `key in cfg` is False for one, so both obvious guards fail open. You would ask for reader=jaxtpc_pixel and get truth steps. It now refuses, naming the flags. The output also records point_source, hit_energy, hit_x_from and readout_type as root attributes, because nothing in the point columns says whether the energy column holds true dE or measured charge, and inferring it from negative values works on a busy event and fails on a quiet one. Viewers ======= vis_drift.html puts true x beside inferred x for the same points, framed on the union so a displacement reads as one. vis_truth.html puts the Geant4 deposits beside the pysupera cloud, painting each deposit with the label of whatever object claimed it -- the label travels backwards down deposit -> group -> fragment, and a fragment's truth deposits sit a median 2.2 mm from that same fragment's hits. Deposits whose group was never detected are shown as unowned rather than dropped: 34% of event 0. Both link by hover, colour by energy / semantic type / fragment / instance / interaction / PDG, and carry a per-pane display threshold showing the absolute cut and what survives. On the sensor side half the points cost 0.4% of the charge; on the truth side dropping the faintest 60% of deposits costs 61% of the dE, which is the halo stated in one number. Colour was chosen by rule and the data pushed back twice. The energy column is charge in hit mode and 13% of voxels are negative, so signed data about a meaningful zero wants a diverging ramp, not a sequential one. And linear is useless across twelve decades, so the default is sign-preserving log with the range clipped to the 2nd-98th percentile. Six categorical hues cannot clear the all-pairs colour-vision floor -- no six in one lightness band can -- so identity is never colour alone: a legend is always up, the hover names the class, and the filter can isolate any pair. Two rendering problems only a browser could have shown. A third of a million points with depthWrite off is a pale wash, every overlap blending instead of occluding; and once the bulk writes depth, the selection hides behind whatever faint points sit in front of it, so it became a second pass with depthTest off. Also: vis_truth was composed from vis_drift and syncFrom came across still naming the other file's panes, so the camera-sync checkbox did nothing at all there. It now copies to every view rather than a named sibling. Panning existed on right-drag and nobody finds it, so the arrow keys pan the pane under the cursor. pysupera-truth-subset writes the four arrays vis_truth needs -- positions, de, charge, deposit_to_group -- into one gzip file, because both JAXTPC files are Blosc and h5wasm decodes gzip only; 65 MB becomes about 1 MB per event. Verification ============ 417 tests pass, 91 of them new. Everything visual was checked in a real Chromium against real output rather than by node --check: all six colour modes, both thresholds, the hover on both panes, the linked highlight, the semantic filter, camera sync in both directions and with the box off. The container needed DISPLAY unset before ANGLE would stop trying to reach an X server and fall back to swiftshader, which is why earlier rounds of these viewers went unrendered. --- README.md | 510 +++++++++++- pyproject.toml | 1 + pysupera/_run.py | 204 ++++- pysupera/conf/config.yaml | 69 +- pysupera/conf/preset/cubic_pixel_hits.yaml | 48 ++ pysupera/conf/reader/edepsim_h5.yaml | 38 +- pysupera/conf/reader/jaxtpc_pixel.yaml | 178 ++++ pysupera/conf/reader/jaxtpc_wire.yaml | 34 + pysupera/config.py | 79 +- pysupera/data.py | 14 + pysupera/hits_subset.py | 70 +- pysupera/io_v3.py | 52 ++ pysupera/provenance.py | 94 ++- pysupera/readers/__init__.py | 26 +- pysupera/readers/format_edepsim_h5.py | 5 + pysupera/readers/format_jaxtpc.py | 578 ++++++++++++- pysupera/readers/pixel_hits.py | 838 +++++++++++++++++++ pysupera/truth_subset.py | 132 +++ pysupera/utils.py | 65 +- tests/test_extent_classification.py | 141 ++++ tests/test_jaxtpc_pixel_data.py | 695 ++++++++++++++++ tests/test_jaxtpc_readout.py | 245 ++++++ tests/test_pixel_hits.py | 449 ++++++++++ tests/test_reader_configs.py | 174 ++++ vis_drift.html | 864 +++++++++++++++++++ vis_hits.html | 378 ++++++--- vis_truth.html | 915 +++++++++++++++++++++ 27 files changed, 6652 insertions(+), 244 deletions(-) create mode 100644 pysupera/conf/preset/cubic_pixel_hits.yaml create mode 100644 pysupera/conf/reader/jaxtpc_pixel.yaml create mode 100644 pysupera/conf/reader/jaxtpc_wire.yaml create mode 100644 pysupera/readers/pixel_hits.py create mode 100644 pysupera/truth_subset.py create mode 100644 tests/test_extent_classification.py create mode 100644 tests/test_jaxtpc_pixel_data.py create mode 100644 tests/test_jaxtpc_readout.py create mode 100644 tests/test_pixel_hits.py create mode 100644 tests/test_reader_configs.py create mode 100644 vis_drift.html create mode 100644 vis_truth.html diff --git a/README.md b/README.md index 6a81f05..596de7e 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,7 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [Browser viewers (`vis_force.html`, `vis_hits.html`)](#browser-viewers-vis_forcehtml-vis_hitshtml) 5. [Workflow](#workflow) - [Input: the `Particle` object](#input-the-particle-object) + - [Choosing a reader](#choosing-a-reader) - [Step 0 — Configuration](#step-0--configuration) - [Step 1 — Preprocessing](#step-1--preprocessing) - [Step 2 — Applying Conditions](#step-2--applying-conditions) @@ -34,6 +35,9 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [Particle columns](#particle-columns) - [Interaction columns](#interaction-columns) - [ID conventions](#id-conventions) + - [Wire and pixel readout](#wire-and-pixel-readout) + - [Truth deposits or detected hits](#truth-deposits-or-detected-hits) + - [Recovering the true x](#recovering-the-true-x--pointstrue_x_shift) - [Hit provenance (JAXTPC mode)](#hit-provenance-jaxtpc-mode) - [Seeing it](#seeing-it) - [Expert and debugging output](#expert-and-debugging-output) @@ -199,18 +203,18 @@ The **PARTICLE FILTER** and **show legend** checkbox take effect immediately wit --- -### Browser viewers (`vis_force.html`, `vis_hits.html`) +### Browser viewers -Two standalone HTML files that read an output file directly in the browser — -no server, no install, nothing to launch. Open the file and pick your HDF5. -They answer different questions: +Four standalone HTML files that read an output file directly in the browser +— no server, no install, nothing to launch. Open the file and pick your +HDF5. They answer different questions: -| | `vis_force.html` | `vis_hits.html` | -|---|---|---| -| **question** | what is in this event, and how is it grouped? | which readout hits belong to which object? | -| **inputs** | the pysupera output | the output **and** the JAXTPC hits file | -| **views** | one 3-D cloud, plus an instance-genealogy graph | 3-D cloud beside the six 2-D readout planes | -| **needs** | any 3.x output | JAXTPC mode, so `groups/` exists | +| | `vis_force.html` | `vis_hits.html` | `vis_drift.html` | `vis_truth.html` | +|---|---|---|---|---| +| **question** | what is in this event, and how is it grouped? | which readout hits belong to which object? | what did the drift-time ambiguity do to this event? | how do the labels look on truth points versus sensor points? | +| **inputs** | the pysupera output | the output **and** the JAXTPC hits file | the output | the output **and** a truth companion | +| **views** | one 3-D cloud, plus an instance-genealogy graph | truth cloud beside the readout — six 2-D plane images for wire, a second 3-D cloud for pixel | two 3-D clouds: true x beside inferred x | two 3-D clouds: Geant4 deposits beside the pysupera points | +| **needs** | any 3.x output | JAXTPC mode, so `groups/` exists | `points/true_x_shift`, so a pixel hit-mode run | `groups/`, plus `pysupera-truth-subset` output | Both read HDF5 through **h5wasm, which decodes gzip only**, so the inputs have to be repacked first — see [Compression](#compression). @@ -230,17 +234,30 @@ Shortcuts: `c` centre · `a` axes · `s` screenshot · `space` play/pause · #### `vis_hits.html` — linking 3-D to the readout -3-D voxels on the left, the 2-D planes built from hits on the right, and the -same colour meaning the same object in both. **Hover a 3-D voxel and its hits -light up across all six plane images; hover a 2-D pixel and its voxels light -up in 3-D.** The link is a single owner id, so the two views cannot disagree. +Truth voxels on the left, the readout on the right, and the same colour +meaning the same object in both. **Hover on either side and the matching hits +light up on the other.** The link is a single owner id, so the two views +cannot disagree. + +What the right-hand pane is depends on the readout, which the viewer takes +from the hits file's `config/readout_type` (falling back to wire, as JAXTPC +does): + +| readout | right-hand pane | +|---|---| +| **wire** | six 2-D images, one per plane per volume — wire against drift tick. A wire hit carries one spatial coordinate, so an image is the only honest way to draw it | +| **pixel** | a second 3-D cloud, axes `x = drift tick`, `y = pixel py`, `z = pixel pz`. A pixel hit is already 3-D; flattening it to an image would throw away an axis the detector actually measured | + +The pixel pane is deliberately left in readout coordinates rather than +converted to mm: the left pane already answers "where in the detector", and +this one answers "where on the anode". | control | | |---|---| | **colour by** | instance, fragment or interaction — the last two derived, not read | | **others** | opacity of everything not under the cursor. The cloud uses RGBA vertex colours with depth-write off, so unselected points are genuinely transparent and never hide the selection behind them | | **hide LE** | drops low-energy points from both views. Filtering happens at build time, so the picker cannot select something hidden | -| **point info** | x/y/z, time, dE, dX, dE/dX, LE and owner in 3-D; wire, time, peak charge, group, LE and owner in 2-D | +| **point info** | x/y/z, time, dE, dX, dE/dX, LE and owner for a truth voxel; peak charge, group, LE and owner for a hit, plus wire and time (wire) or py/pz and time (pixel) | | **c** | fit the view | Preparing its two inputs: @@ -252,9 +269,16 @@ pysupera-hits-subset sim_wire_hits_0000.h5 hits_small.h5 -n 10 `-c gzip` is not optional: omitting it keeps each dataset's existing filter, leaving a file the browser cannot read. The second command exists because the -hits file is mostly per-tick arrays the viewer never opens — keeping only -`center_wires`, `center_times`, `peak_charges` and `group_ids` takes a -3-event sample from 1279.7 MiB to 2.73 MiB. +hits file is mostly per-tick CSR arrays the viewer never opens — keeping only +`group_ids`, `peak_charges` and the hit centres took a 3-event wire sample +from 1279.7 MiB to 2.73 MiB, and a 3-event pixel sample from 43.6 MiB to +0.65 MiB. + +`pysupera-hits-subset` handles either readout without being told which: +it finds planes structurally (any subgroup with `group_ids`) and keeps +whichever centres they store — `center_wires`/`center_times` for wire, +`center_py`/`center_pz`/`center_times` for pixel. It copies `config`'s +attributes across too, so the subset still says which readout it is. `vis_hits.html` refuses to draw rather than show something false: a red banner appears for a filter it cannot decode, a missing `groups/` table, or @@ -263,6 +287,104 @@ group table, or before `groups/is_le`, still load — it says what is missing and shows what it can. The mapping it displays is described under [Hit provenance](#hit-provenance-jaxtpc-mode). +#### `vis_drift.html` — what the t0 ambiguity did + +Two 3-D panes over the same points: **true x** on the left +(`x + points/true_x_shift`), **inferred x** on the right (as stored, with t0 +assumed at the beam time). Cameras are synchronised and both panes are framed +on the union of the two clouds, so a displacement reads as a displacement +rather than being normalised away by two separate fits. + +**Hover a point in either pane and its whole object lights up in both**, +everything else fading to an adjustable opacity. The `highlight` selector +chooses what "its object" means — instance, fragment or interaction — +independently of the colour, so you can colour by semantic type while +following one instance. + +| colour by | encoding | +|---|---| +| energy | one-hue sequential ramp; **diverging** blue↔red about a grey zero when the column is signed, which it is in hit mode — the readout response is bipolar and 13% of voxels are negative. Sign-preserving log (symlog) by default: the column spans twelve decades, and linear puts three quarters of the points within 1.5% of the midpoint. Range clipped to the 2nd–98th percentile | +| instance semantic type | the six categorical slots in fixed order | +| fragment / instance / interaction id | a stable hash — thousands of values, so no legend is possible; identity comes from the hover | +| particle type (PDG) | fixed hues for the six commonest species, everything else folded into "other" | + +Hover shows energy, Geant4 track id, fragment / instance / interaction id, +semantic type, PDG, parent instance and parent PDG, plus both x values and +the shift between them. Only about two thirds of points fall inside a stored +particle row, so the Geant4 track id falls back to the fragment's +representative and says when it has. + +The semantic-type strip filters classes in and out. That is not only a +convenience: six categorical hues cannot clear the all-pairs colour-vision +floor — no six in one lightness band can — so identity is never left to +colour alone. A legend is always up, the hover names the class in words, and +any pair can be isolated with the filter. + +Prepare its input the same way as the others, with `pysupera-repack out.h5 +out_gz.h5 -c gzip`. A file without `points/true_x_shift`, or one where every +shift is zero, loads fine and says so — both panes are then the same cloud. + +#### `vis_truth.html` — the same labels on truth and on sensor points + +Left pane: the Geant4 energy deposits, each painted with the label of +whatever pysupera object claimed it. Right pane: the cloud the algorithms +actually ran on. Hover either side and the object lights up in both. + +The bridge is the **group**. A deposit knows its group (`deposit_to_group`, +in the JAXTPC hits file) and the output's `groups/` table says which fragment +owns each group — so the label travels backwards down the same chain the +reconstruction came up. Measured on one event, a fragment's truth deposits +sit a median 2.2 mm from that same fragment's hits. + +Deposits whose group was never detected have no owner and are shown as such +rather than dropped — 34% of event 0, which is the charge the readout window +never recorded. There's a `hide undetected` toggle. + +Its two inputs: + +```bash +pysupera-repack out.h5 out_gz.h5 -c gzip +pysupera-truth-subset step.h5 hits.h5 truth_small.h5 -n 3 +``` + +The second exists because the JAXTPC step and hits files are Blosc, which the +browser cannot decode, and because the viewer needs only the deposit +positions, `de`, `charge` and `deposit_to_group` — 65 MB of JAXTPC becomes +about 1 MB per event. + +A useful self-check: run pysupera with `reader.point_source=deposits` and the +two panes should coincide, because the right pane's cloud then *is* the +voxelized truth deposits. + +#### Display thresholds + +`vis_drift.html` and `vis_truth.html` both have a **show ≥** slider — two of +them in `vis_truth.html`, one per pane, since the panes hold different +quantities. The detector simulation imposes no threshold of its own, so +without one every pixel carrying any induced-field influence is on screen. + +The slider position is a percentile, but what it displays is the absolute +value it lands on, together with what survives: + +| sensor cut | points kept | charge kept | +|---|---|---| +| none | 100% | 100% | +| ≥ 27.7 | 50% | **99.6%** | +| ≥ 354 | 20% | 96.5% | +| ≥ 1960 | 10% | 87.7% | + +Half the points cost 0.4% of the charge. The truth pane behaves quite +differently — dropping its faintest 60% of deposits costs 61% of the dE — +because truth deposits have no diffusion halo. + +#### What the energy column holds + +Nothing in the point columns says whether the energy column is true dE or +measured charge, so the run writes it down as root attributes +(`point_source`, `hit_energy`, `hit_x_from`, `readout_type`) and the viewers +label the column from them. Guessing from the presence of negative values +works on a busy event and fails on a quiet one. + --- ## Workflow @@ -334,6 +456,38 @@ if not np.isnan(p.kinetic_energy_start): # float32 scalar check --- +### Choosing a reader + +Which kind of input a run reads is a Hydra config group, one file per +arrangement, selected with `reader=`: + +| `reader=` | input | point cloud | +|---|---|---| +| `edepsim_h5` (default) | EDepSim only | every Geant4 energy-deposit step | +| `jaxtpc_wire` | EDepSim + JAXTPC wire batch | deposits the readout detected | +| `jaxtpc_pixel` | EDepSim + JAXTPC pixel batch | the same, or the detected pixel image via `reader.point_source=hits` | + +The two JAXTPC configs inherit `edepsim_h5`, so the dataset keys are written +once, and each adds only what its own arrangement needs — `jaxtpc_wire` has +no `point_source` or pixel-geometry options at all, because a wire plane has +no 3-D image to convert. + +Particle metadata (PDG, track IDs, interaction type) always comes from the +EDepSim file at `io.input_path`, whichever reader is selected. The JAXTPC +configs mark their two input paths as required, so selecting one without +them stops the run rather than quietly falling back to reading EDepSim: + +``` +This reader config is for JAXTPC input and needs reader.jaxtpc_seg_path and +reader.jaxtpc_inst_path. Pass them on the command line, e.g. + reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 + reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 +Use reader=edepsim_h5 to read the EDepSim steps directly instead. +``` + +The full set of config groups is `io`, `reader`, `checker` and `conditions`; +see [Step 0](#step-0--configuration). + ### Step 0 — Configuration `pysupera` uses [Hydra](https://hydra.cc) for configuration. The default config lives in `pysupera/conf/config.yaml`. @@ -686,6 +840,319 @@ Both merge stages operate within a single particle, so `dE` and `dX` both sum over a particle-voxel and **dE/dX is column 4 / column 5**, exactly. Guard against `dX == 0`: EDepSim writes zero-length steps, a few percent of voxels. +### Wire and pixel readout + +JAXTPC simulates the two LArTPC readout geometries, and pysupera reads both +through the same code path: + +| | wire | pixel | +|---|---|---| +| plane subgroups per volume | `U`, `V`, `Y` | `Pixel` | +| what one hit records | a wire and a drift tick | two pixel indices and a drift tick | +| hit centres in the hits file | `center_wires`, `center_times` | `center_py`, `center_pz`, `center_times` | +| the image it forms | three 2-D projections | one natively 3-D image | + +**Nothing in the reader branches on this, and that is the point.** Visibility +is a question about a *group* — JAXTPC's cluster of energy deposits — and a +group is above threshold or it is not, regardless of how the readout that saw +it was arranged. The filter reads `group_ids` and never looks at a hit +centre, so a pixel batch needs no flag and no separate reader: + +```bash +run_pysupera reader=jaxtpc_pixel \ + io.input_path=edepsim.h5 io.output_path=out.h5 \ + reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ + reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 +``` + +`reader=` selects the kind of input — see +[Choosing a reader](#choosing-a-reader). + +The run banner prints which one it found (`[run] readout : pixel`), read +from the hits file's `config/readout_type`. A file without that attribute +predates it and is wire — the same assumption JAXTPC's own loader makes. +Plane subgroups are discovered structurally rather than by name, so a +geometry neither project has named yet still resolves. + +What *does* differ is anything that draws hits: see +[`vis_hits.html`](#vis_hitshtml--linking-3-d-to-the-readout) for the two +readout panes, and `pysupera-hits-subset` for the centres each keeps. + +```python +from pysupera.readers import read_readout_type +import h5py +with h5py.File("hits.h5") as f: + print(read_readout_type(f)) # 'wire' or 'pixel' +``` + +### Truth deposits or detected hits + +`reader.point_source` decides what a *point* is. The rest of the pipeline — +defragmentation, proximity, conditions, partitioning — is identical either +way; only the cloud changes. + +| | `deposits` (default) | `hits` | +|---|---|---| +| a point is | a Geant4 energy deposit the readout detected | a fired pixel | +| geometry | truth geometry; the readout only selects | the detected image | +| carries | thresholding | pixelation, diffusion, threshold, drift-time ambiguity | +| points/event | 50k–143k | 1.4M–4.2M | +| cost/event | ~1.6 s | ~7 s | +| readout | wire or pixel | pixel only — a wire plane has no 3-D image | + +Each hit still has exactly one Geant4 particle: a CSR entry belongs to one +group, and `group_to_track` gives each group one track. No nearest-neighbour +matching is involved. + +```bash +run_pysupera preset=cubic_pixel_hits \ + io.input_path=edepsim.h5 io.output_path=out_hits.h5 \ + reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ + reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 \ + reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5 +``` + +`preset=cubic_pixel_hits` is a bundle of the values that are only right +together and that cut across config groups — the pixel geometry, the charge +threshold, `particle.min_pc_size`, `distance_threshold` and +`check_group_ownership`. Only the file paths stay on the command line, and +any flag still overrides it. + +#### The charge threshold and `min_pc_size` are a matched pair + +JAXTPC applies no threshold worth the name — 1 electron — so without one +every pixel carrying any induced-field influence becomes a point. +`reader.hit_charge_threshold` cuts on the magnitude of the induced charge in +electrons, matching JAXTPC's own encoder (the response is bipolar and ~16% +of hits are negative signal). + +Cutting the halo also **thins the tracks**, which lowers the extent +threshold, so the two move together: + +| `hit_charge_threshold` | track width | `min_pc_size` | agrees with truth@5 | +|---|---|---|---| +| 0 e⁻ | 13.1 mm | 85 | 99.0% | +| **500 e⁻** *(preset)* | **6.5 mm** | **13** | **95.5%** | +| 500 e⁻ | 6.5 mm | 85 *(mixed)* | 85.2% | + +Mixing them is worse than either. The uncut pairing keeps every electron and +4.18M points per event; the preset's keeps 73.5% of the charge and 190k +points, and runs 4× faster. + +#### The drift coordinate — `reader.hit_x_from` + +`y` and `z` are pure geometry and invert exactly, to half a pixel. `x` is +drift time, and a tick counts from the start of the readout window — so it +measures `t_drift + t0`, and `t0` is precisely what a real detector has to +determine separately. Writing `T` for the time a hit's tick stands for, + +``` +T = (tick − reference_tick) · time_step +``` + +| | `nominal` (default) | `true_t0` | +|---|---|---| +| x | `x_anode ∓ T · v` | `x_anode ∓ (T − t0) · v` | +| assumes | the interaction happened at the beam time | perfect knowledge of `t0` | +| median distance to the truth cloud | 268 mm | **5.8 mm** (≈ one pixel) | +| isolates | nothing — the full effect | pixelation, diffusion, threshold | + +`nominal` is what a detector can actually do. Its x is offset from the true x +by exactly `drift_direction · v · t0` — the interaction's own Geant4 time +turned into a displacement. Measured on the 10-event batch this was developed +against: `t0` is constant within an interaction (median spread 0.000 μs, max +0.531 μs → 0.85 mm) and differs *across* the interactions of one event by up +to 1094 μs → **175 cm**. So `nominal` is a rigid translation per interaction: +each keeps its own shape to sub-mm while whole interactions slide past one +another. `true_t0` puts the real `t0` back and reproduces truth `x` to +~0.3 mm, keeping every other detector effect. + +#### Recovering the true x — `points/true_x_shift` + +A wire-mode output stores truth geometry, so `x` is the true x. A pixel +hit-mode output under `nominal` does not: every point is displaced along the +drift axis by its interaction's own `t0`. The true position is kept anyway, +as a displacement rather than a second coordinate: + +```python +with read_events_v3("out_hits.h5") as store: + view = store[0] + shift = store.true_x_shift(0) # None for files written before this + true_x = view.points[:, 0] + shift +``` + +Zero throughout under `true_t0` and in deposit mode, so the relation holds +for every file and a consumer never has to ask which convention produced it. + +It costs almost nothing because it is `drift_direction · t0 · v`, which takes +one value per *(interaction, volume)* pair — 28 in a test event, not 4.2M. +Measured on one event: 1.31 MB raw, **0.007 MB on disk under LZ4, 0.14% of +the output**; 7 ms to compute over 4.18M hits; +16.7 MB transient memory. + +Stored per point rather than per particle because of a small but heavy +exception. The shift is exactly constant within a particle for 99.97% of +them — but the two that are not are cathode-crossing tracks with hits in +both volumes, where `drift_direction` flips, and they carry **15% of all +points** with internal spreads up to 204 mm. + +The stored rows are voxels, so a voxel could in principle mix two shifts; +each takes the value of a hit that made it. Measured, 0.09% of voxels mix +anything at all and the worst disagreement inside one is **0.05 mm** — the +sub-microsecond `t0` jitter within an interaction. The 200 mm case never +shares a voxel, because the nominal x is precisely what slides those two +halves apart. + +#### The reference tick — `reader.hit_reference_tick` + +The tick at which a deposit sitting on the anode at Geant4 `t = 0` is +recorded: the anchor the whole drift coordinate hangs from. It is kept +separate from the anode because **a fit cannot tell the two apart** — the +fitted intercept is `x_anode + drift_direction · mm_per_tick · +reference_tick`. So the anode is taken from the volume's stated extent +(`config/volume_ranges`, which is geometry and not in doubt) and the +reference tick is read off as what remains. On a run whose window opens at +Geant4 t=0 it comes out at zero, which is the cross-check; on a run with a +pre-window it comes out at the pre-window. + +Setting it explicitly overrides the derivation. The calibration residual +still tests the *geometry* — pitch, velocity, drift direction, all +measurements — but deliberately does **not** test the stated tick: that is a +choice about where t=0 sits, and asking "what if the trigger were 100 ticks +later" must not be reported as a broken detector. How far the choice sits +from the data is reported as `reference_tick_offset_mm` instead. + +#### What the readout window hides + +The reference tick also decides whether points can leave the detector. On +this batch `num_time_steps = 2701` and one tick is 0.8 mm, so the recordable +ticks 0…2700 span exactly 2160 mm — one full drift — and map precisely onto +`[anode, cathode]`. A nominal x therefore **cannot** fall outside the volume +(measured: 0.04% do, by a few mm at the edges). + +Instead the t0 displacement pushes charge off the *end of the window*, where +it is never recorded at all. And that turns out to be almost the whole story +of what "visible" means in this data: + +| event / volume | deposits | masked | past the window | of the masked, past the window | +|---|---|---|---|---| +| 0 / 0 | 83,997 | 26.6% | 25.8% | **97.0%** | +| 0 / 1 | 132,430 | 38.2% | 37.6% | **98.1%** | +| 1 / 0 | 173,903 | 86.4% | 86.2% | **99.7%** | +| 1 / 1 | 99,822 | 73.5% | 73.0% | **99.4%** | + +So a deposit is invisible here because the readout had closed, not because it +was below threshold. Both point sources lose the same charge for the same +reason, which is what makes them comparable — but it also means this batch +says very little about *threshold* effects. Move the reference tick, or +lengthen the window past one full drift, and the displacement becomes visible +as out-of-volume points instead. Both are counted in the run report and +neither is clipped: a nominal x is an inference, and clipping it would invent +a wall the reconstruction does not have. + +#### The energy column — `reader.hit_energy` + +`charge` (default) is what the readout measured. The response is bipolar, so +about 16% of hits carry negative charge; that is signal, not error. +`true_de` instead shares each group's true deposited energy over its hits in +proportion to `|charge|` — comparable with deposit mode's column, at the cost +of putting a truth quantity into a detected cloud. **`dx` is 0 in both +cases**: a hit is a pixel and a tick, and has no path length, so anything +reading dE/dx sees zero. + +#### Geometry — configured, then audited + +The pixel geometry is **stated in configuration and checked against the +data**, never measured from it. The order matters: a constant derived from +the same truth it is later compared against cannot fail for a geometry that +is wrong but linear — the fit simply absorbs the error, and a detector +effect this mode exists to measure is calibrated away instead of measured. + +| | source | +|---|---| +| anode face | `config/volume_ranges` in the hits file — read for you | +| drift velocity, sampling period | the JAXTPC **sensor** file, via `reader.jaxtpc_sensor_path` | +| pixel pitch, drift direction | `reader.pixel_pitch_mm`, `reader.pixel_drift_direction` — no output file records these | +| reference tick | `reader.hit_reference_tick`, default 0 | + +The reader applies them as given, converts each group's hit centre, and +measures the distance to the deposits behind it. A disagreement raises, +naming the offending number *and* what the data says it should be: + +``` +[run] Pixel geometry (2 volume(s)) + volume 0 [stated] pitch 4.3200 mm v 1.6000 mm/us dt 0.5000 us drift -1 anode -2160.0 mm ref tick 0 + data says pitch 4.3200 mm v 1.5999 mm/us dt 0.5000 us drift -1 + residual x 0.33 y 1.07 z 1.06 mm over 14,165 groups +``` + +Run hit mode without the constants once and the error lists what the data is +consistent with, so the config can be written from it and verified on the +next run. `reader.pixel_geometry_from_fit=true` measures them instead — off +by default and deliberately awkward, because it makes the check circular. + +The reference tick is the one constant **not** audited: it is a choice about +where t=0 sits, not a measurement, so asking "what if the trigger were 100 +ticks later" must not be reported as a broken detector. Its distance from +the data is reported as `reference_tick_offset_mm` instead. + +`reader.pixel_geometry_report` returns all of this per volume after the +first read. + +#### `min_pc_size` is a voxel count, and needs re-deriving per readout + +`min_pc_size` decides whether a particle is a low-energy scatter. It counts +**occupied voxels**, not rows — row count is a property of the input +sampling rather than of the particle, and Geant4 deposits arrive every +0.3 mm where pixel sensor hits arrive on a 4.32 mm grid. The same particle +is 1 row in one and 40 in the other, so a threshold in rows silently +re-tunes itself when `point_source` changes. + +The value follows from a physical rule: a chunk of ionisation is worth +calling a trajectory only when its direction can be read off it, which needs +it to be longer than it is wide. So the threshold is the cell count of a +blob one track-width across, `(width / voxel_size)³`. + +| input | track width | implied threshold | +|---|---|---| +| truth deposits | 4.9 mm | (4.9/3)³ ≈ **5** — the default | +| pixel sensor hits | 12.8 mm | (12.8/3)³ ≈ 77, best fit **85** | + +A track is 3.2× thicker in the pixel image, which is diffusion plus the +field response over about three pixel pitches. Measured over ten events, the +best-agreeing cut per event ranged 81–90, and at a fixed 85 the sensor-hit +labelling matches the truth-deposit labelling on **99.0%** of particles +(worst event 98.5%) — not merely the same fraction, the same particles. So +with `reader.point_source=hits`, set `particle.min_pc_size=85`; a run that +forgets warns. + +Note this also changed truth-deposit labelling, from 85.8% to 95.3% LE, +because particles spread over a handful of rows inside one or two voxels are +now correctly measured as non-directional. + +#### Two things that change in hit mode + +**`distance_threshold` must clear the pixel diagonal.** At a 4.32 mm pitch, +a threshold of 5.2 mm is barely one pixel, so a single missing pixel severs a +track and defragmentation ends up measuring the grid rather than the physics. +Event 0, one event: + +| `distance_threshold` | deposits: particles w/ points → partitions | hits: particles w/ points → partitions | +|---|---|---| +| 5.2 mm | 6,900 → 2,519 | 13,308 → 7,181 | +| 6.2 mm (> diagonal 6.11) | 6,900 → 2,495 | 8,845 → 3,924 | +| 8.0 mm | 6,900 → 2,426 | 8,128 → 3,197 | +| 10.0 mm | 6,900 → 2,349 | 7,372 → 2,555 | + +Deposit mode never splits at all — 0.3 mm deposit spacing keeps a track +connected at any of these thresholds. + +**A group can straddle a fragment split.** The `groups/` table rests on the +invariant that it cannot, which holds for deposits (0 of 354,959) and does +*not* hold for hits: the readout leaves gaps the deposit cloud does not have, +defragmentation cuts there, and 1.84% of groups end up on both sides. +`check_group_ownership=false` is therefore required in hit mode, and the run +resolves each straddled group by majority and reports how many there were. + ### Hit provenance (JAXTPC mode) Which readout hits belong to which reconstructed object. A hit is the @@ -697,10 +1164,15 @@ with read_events_v3("out.h5") as store: frag_owner, is_le, vol_offsets = store.group_owners(ev) groups = np.flatnonzero(frag_owner == my_fragment_id) -gid = inst_file[f"event_{ev:03d}/volume_0/U/group_ids"][:] + vol_offsets[0] +plane = "Pixel" # or "U" / "V" / "Y" for wire readout +gid = inst_file[f"event_{ev:03d}/volume_0/{plane}/group_ids"][:] + vol_offsets[0] hits = np.flatnonzero(np.isin(gid, groups)) ``` +The join is the same either way — a hit names its group, and `groups/` names +the group's owner — so only the plane name changes between readouts. See +[Wire and pixel readout](#wire-and-pixel-readout). + Both arrays are indexed by event-global group number, `-1` where nothing claims the group; `vol_offsets` shifts a plane's local group number into that space. `pysupera.provenance` has `hits_of_groups` and `groups_of_particles` diff --git a/pyproject.toml b/pyproject.toml index 5f59126..2b123e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,6 +57,7 @@ run_pysupera = "pysupera._run:main" pysupera-app = "pysupera.app:main" pysupera-repack = "pysupera.io:repack_cli" pysupera-hits-subset = "pysupera.hits_subset:main" +pysupera-truth-subset = "pysupera.truth_subset:main" [tool.setuptools.packages.find] where = ["."] diff --git a/pysupera/_run.py b/pysupera/_run.py index c9c25b2..0d78934 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -71,9 +71,40 @@ def main(cfg: DictConfig) -> None: voxelizer = build_voxelizer(cfg) # None when voxelize.enabled: false preprocessor = build_preprocessor(cfg) # None when defragment: false _check_group_owner = bool(cfg.get('check_group_ownership', True)) + # A group straddling a defragmentation split is exceptional for truth + # deposits and routine for detected hits, so hit mode settles it by + # majority instead of by whichever particle was seen first. + _hit_points = str(cfg.reader.get('point_source', 'deposits')) == 'hits' + # min_pc_size is a voxel count, and its default was chosen for a cloud of + # Geant4 deposits. A pixel sensor image puts the same particle in some + # 14 voxels instead of 1, so the truth-tuned value labels almost nothing + # low-energy. Cheap to say, expensive to discover in a plot. + if _hit_points and 0 < int(cfg.particle.get('min_pc_size', -1)) <= 5: + print(f"[run] WARNING: particle.min_pc_size=" + f"{int(cfg.particle.min_pc_size)} with point_source=hits. That " + f"is a voxel count tuned for truth deposits, where a particle " + f"fills one or two cells; a pixel image fills many more, so " + f"almost nothing will be classified kLEScatter. The value " + f"depends on reader.hit_charge_threshold, because cutting the " + f"halo thins the tracks: 85 at a threshold of 0, 13 at 500. " + f"preset=cubic_pixel_hits sets a matched pair.") + _owner_resolve = 'majority' if _hit_points else 'first' + _owner_report: dict = {} + _straddle_total = 0 + _pixel_geom_report = None def _n_groups_of(_store): - """Total groups in the event just read, from the deposit map.""" + """ + Total groups in the event just read. + + The reader counts them from group_to_track, which is the space the + volume offsets are built in. Falling back to the highest group any + point references only agrees when the last volume's last group was + hit, and silently truncates the owner table when it was not. + """ + _n = getattr(_store, 'last_n_groups', None) + if _n: + return int(_n) _m = getattr(_store, 'last_deposit_to_group', None) return 0 if _m is None or not len(_m) else int(_m.max()) + 1 @@ -121,6 +152,8 @@ def _n_groups_of(_store): } # JAXTPC visibility counts; stays empty unless the reader supplies them. mask_stats: dict = defaultdict(list) + # Detected-hit counts; only populated when reader.point_source=hits. + hit_stats: dict = defaultdict(list) n_events = 0 _max_events = int(cfg.get("max_events", -1)) @@ -144,6 +177,21 @@ def _n_groups_of(_store): ) with build_reader(cfg) as store: + # Which readout the inst file describes. Nothing downstream branches + # on it -- visibility is a property of a group, not of the geometry -- + # but a pixel run silently reported as wire is the kind of thing that + # is only noticed three plots later. + _readout = getattr(store, 'readout_type', None) + if _readout: + print(f"[run] readout : {_readout}") + _psrc = getattr(store, 'point_source', None) + if _psrc: + print(f"[run] point source : {_psrc}" + + (f" (x from {cfg.reader.get('hit_x_from', 'nominal')}, " + f"energy {cfg.reader.get('hit_energy', 'charge')}, " + f"charge >= {cfg.reader.get('hit_charge_threshold', 0)} e-)" + if _psrc == 'hits' else "")) + _n_available = len(store) _n_to_process = _n_available if _max_events < 0 else min(_max_events, _n_available) print(f"[run] {_n_available} events in input file, processing {_n_to_process}") @@ -162,6 +210,14 @@ def _n_groups_of(_store): compression_opts=cfg.io.compression_opts) if _write_voxmap else _NullCtx()) as vox_writer: + # What produced this file, so a reader does not have to infer it. + writer.set_meta({ + 'point_source': cfg.reader.get('point_source', 'deposits'), + 'hit_x_from': cfg.reader.get('hit_x_from', None) if _hit_points else None, + 'hit_energy': cfg.reader.get('hit_energy', None) if _hit_points else None, + 'readout_type': _readout, + }) + from tqdm import tqdm _bar = tqdm( total=_n_to_process, @@ -180,6 +236,13 @@ def _n_groups_of(_store): # ── JAXTPC visibility counts ──────────────────────────────── # Already computed by the reader from data it had in hand, so # collecting them costs nothing; only the printing is gated. + if _pixel_geom_report is None: + _pixel_geom_report = getattr( + store, 'pixel_geometry_report', None) + _hs = getattr(store, 'last_hit_stats', None) + if _hs: + for _k, _v in _hs.items(): + hit_stats[_k].append(_v) _ms = getattr(store, 'last_mask_stats', None) if _ms: for _k, _v in _ms.items(): @@ -401,6 +464,40 @@ def _n_groups_of(_store): f"{'...' if len(_uncovered) > 10 else ''}" ) + # ── true_x_shift, carried onto the voxels ────────────────── + # The reader gives one shift per input hit; the rows about to + # be written are voxels, so each takes the shift of a hit + # that made it. Which one barely matters: the shift is + # constant per (interaction, volume), and measured over an + # event only 0.09% of voxels mix two values at all, by at + # most 0.05 mm -- the sub-microsecond t0 jitter inside one + # interaction. The 200 mm case, a track crossing the + # cathode, never shares a voxel, because the nominal x is + # exactly what slides its two halves apart. + _shift_in = getattr(store, 'last_true_x_shift', None) + if _shift_in is not None: + for _p in particles: + _n = len(_p.point_cloud) + if not _n: + _p.true_x_shift = np.zeros(0, dtype=np.float32) + continue + _vm = getattr(_p, 'voxmap', None) + _v = None + if _vm is not None: + _off, _ids = _vm + _off = np.asarray(_off) + if _ids is not None and len(_off) == _n + 1: + _v = _shift_in[np.asarray(_ids, + dtype=np.int64)[_off[:-1]]] + if _v is None: + # No voxel map: the cloud is still the hits, so + # the reader's per-hit slice lines up as it is. + _d = getattr(_p, 'deposit_id', None) + _v = (_shift_in[np.asarray(_d, dtype=np.int64)] + if _d is not None and len(_d) == _n + else np.zeros(_n, dtype=np.float32)) + _p.true_x_shift = np.asarray(_v, dtype=np.float32) + # ── Write ─────────────────────────────────────────────────── _t = time.perf_counter() _layout = build_layout(particles, _frag_groups, _inst_groups, @@ -419,7 +516,10 @@ def _n_groups_of(_store): else: _owner = build_group_owners( particles, _d2g, _n_groups_of(store), - check=_check_group_owner) + check=_check_group_owner, + resolve=_owner_resolve, report=_owner_report) + _straddle_total += int( + _owner_report.get('n_straddled', 0) or 0) # Record the fragment, not the particle: only a fraction # of particles get a row, so a particle id would often # name something the reader cannot resolve. Every @@ -520,6 +620,106 @@ def _agg(vals): print(f" {'Visible but unmatched':<{_W}} " f"{_unm:>10,} ({100.0 * _unm / max(1, _tot):.2f}%)") + # ── Pixel geometry, and what the data made of it ────────────────── + # Printed in full because it is configuration, and configuration that + # silently disagrees with the data is the failure this check exists + # to prevent. 'stated' means applied as given and verified against + # the truth deposits; 'fitted' means measured from them, which makes + # the same comparison circular. + if _pixel_geom_report: + print(f"\n[run] Pixel geometry ({len(_pixel_geom_report)} volume(s))") + for _v, _g in enumerate(_pixel_geom_report): + _m = _g.get('measured') or {} + _src = _g.get('source', 'stated') + print(f" volume {_v} [{_src}] " + f"pitch {_g['pitch_mm']:.4f} mm " + f"v {_g['drift_velocity_mm_us']:.4f} mm/us " + f"dt {_g['time_step_us']:.4f} us " + f"drift {_g['drift_direction']:+d} " + f"anode {_g['x_anode_mm']:+.1f} mm " + f"ref tick {_g.get('reference_tick', 0):g}") + if _m: + print(f" data says " + f"pitch {_m['pitch_mm']:.4f} mm " + f"v {_m['drift_velocity_mm_us']:.4f} mm/us " + f"dt {_m['time_step_us']:.4f} us " + f"drift {_m['drift_direction']:+d}") + _r = _g.get('residual_mm') or {} + if _r: + print(f" residual " + f"x {_r['x']:.2f} y {_r['y']:.2f} z {_r['z']:.2f} mm" + f" over {_g.get('n_groups', 0):,} groups") + _off = _g.get('reference_tick_offset_mm') + if _off: + print(f" reference tick sits {_off:+.1f} mm " + f"from the data (not an error: it is a choice)") + + # ── Detected hits (hit mode only) ───────────────────────────────── + if hit_stats.get('n_hits'): + print(f"\n[run] JAXTPC detected hits ({n_events} event(s))") + print(f" {'Metric':<{_W}} {'min':>10} {'mean':>10} {'max':>10}") + print(f" {_SEP}") + for label, key in ( + ("Pixel hits in the file", 'n_decoded'), + ("Pixel hits kept", 'n_hits'), + ("Attached to a particle", 'n_attached'), + ("Groups in the event", 'n_groups'), + ("Deposits in the seg file", 'n_deposits'), + ): + if not hit_stats.get(key): + continue + lo, mu, hi = _agg(hit_stats[key]) + print(f" {label:<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") + _unm = sum(hit_stats.get('n_unmatched', [0])) + _hit = sum(hit_stats['n_hits']) + print(f" {_SEP}") + + # A deposit whose drift time plus interaction time lands past the + # last tick is never recorded at all. The loss scales with t0, + # so it biases a comparison against deposit mode rather than just + # shrinking it -- which is why it is reported and not inferred. + _cut = sum(hit_stats.get('n_below_threshold', [0])) + _dec = sum(hit_stats.get('n_decoded', [0])) + if _cut: + print(f" {'Below the charge threshold':<{_W}} " + f"{_cut:>10,} ({100.0 * _cut / max(1, _dec):.1f}%)") + _dep = sum(hit_stats.get('n_deposits', [0])) + _aft = sum(hit_stats.get('n_after_window', [0])) + _bef = sum(hit_stats.get('n_before_window', [0])) + if _dep and (_aft or _bef): + print(f" {'Deposits past the readout window':<{_W}} " + f"{_aft:>10,} ({100.0 * _aft / _dep:.1f}%)") + if _bef: + print(f" {'Deposits before it opened':<{_W}} " + f"{_bef:>10,} ({100.0 * _bef / _dep:.1f}%)") + + # A reconstructed x beyond its own volume's faces. Counted, never + # clipped: with a nominal t0 the drift coordinate is an inference, + # and clipping it would invent a wall the reconstruction lacks. + _out = sum(hit_stats.get('n_outside_volume', [0])) + if _out: + print(f" {'Points outside their volume':<{_W}} " + f"{_out:>10,} ({100.0 * _out / max(1, _hit):.2f}%)" + f" -- kept, not clipped") + if _unm: + # A hit whose group names a track the EDepSim file does not + # list; it reaches no point cloud. + print(f" {'Unmatched to any particle':<{_W}} " + f"{_unm:>10,} ({100.0 * _unm / max(1, _hit):.2f}%)") + if _straddle_total: + # A group whose hits ended up in two fragments. Impossible + # for truth deposits; normal here, because the readout gaps + # are what defragmentation cuts at. Resolved by majority. + _gt = sum(hit_stats.get('n_groups', [0])) or 1 + print(f" {'Groups split across fragments':<{_W}} " + f"{_straddle_total:>10,} ({100.0 * _straddle_total / _gt:.2f}%)" + f" -- resolved by majority") + _cap = sum(hit_stats.get('n_capped_groups', [0])) + if _cap: + # group_sizes is uint8, so a group of more than 255 entries + # cannot be stored and JAXTPC truncates its CSR. + print(f" {'Groups at the uint8 size cap':<{_W}} {_cap:>10,}") + # ── Time profile ────────────────────────────────────────────────── total = sum(profile.values()) print(f"\n[run] Time profile ({n_events} event(s))") diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index 8876f82..04072e1 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -1,19 +1,29 @@ defaults: - io: default - # Reader config group. Currently only "edepsim_h5" is available. - # JAXTPC visibility filtering is not a separate reader — it is activated - # by setting two optional flags on the same edepsim_h5 config: + # Reader config group -- which kind of input this run reads. # - # reader.jaxtpc_seg_path=sim_seg_0000.h5 - # reader.jaxtpc_inst_path=sim_inst_0000.h5 + # edepsim_h5 plain EDepSim: every Geant4 energy-deposit step, no + # detector simulation in between. + # jaxtpc_wire JAXTPC wire readout (U/V/Y planes). Particle metadata + # still comes from the EDepSim file at io.input_path; the + # point cloud keeps only deposits the readout detected. + # jaxtpc_pixel JAXTPC pixel readout (one Pixel plane). As above, and + # additionally supports reader.point_source=hits, which + # replaces the cloud with the detected pixel image. # - # When both are non-null, only segments visible in the JAXTPC readout are - # included in each particle's point cloud. Particle metadata (PDG, track - # IDs, interaction type) always comes from the EDepSim file (io.input_path). + # Override on the command line with reader=jaxtpc_pixel. Each variant + # inherits edepsim_h5 and adds only what its own arrangement needs. - reader: edepsim_h5 - checker: cpu_single - conditions: default - _self_ + # Optional bundle of detector-specific values that cut across config + # groups -- reader geometry, min_pc_size, the thresholds that only make + # sense together. Listed after _self_ so it overrides the defaults + # below; a command-line flag still overrides it. + # + # run_pysupera preset=cubic_pixel_hits ... + - preset: null # --------------------------------------------------------------------------- # Top-level workflow parameters @@ -185,9 +195,46 @@ write_event_clouds: true # Particle construction parameters # --------------------------------------------------------------------------- particle: - # Minimum point-cloud size for SetSemanticType classification. - # Particles with fewer points than this are treated as having an - # unknown semantic type. -1 disables the check (all sizes accepted). + # Size below which a particle is classified as a low-energy scatter, + # measured in OCCUPIED VOXELS (particle.voxelize.voxel_size) rather than + # in rows. -1 disables the check entirely. + # + # Why voxels: row count depends on how finely the input happens to be + # sampled. Geant4 deposits arrive every 0.3 mm and pixel sensor hits on a + # 4.32 mm grid, so the same particle is 1 row in one and 40 in the other, + # and a threshold in rows silently re-tunes itself when point_source + # changes. Voxels measure how much space the object fills, which is the + # thing actually being asked about, and defragmentation already splits on + # the same count. + # + # How to choose it: a chunk of ionisation is worth calling a trajectory + # only when its direction can be read off it, which needs it to be longer + # than it is wide. So the threshold is the cell count of a blob one + # track-width across, (width / voxel_size) ** 3. + # + # truth deposits track width 4.9 mm -> (4.9/3)^3 = 5 <- this default + # pixel sensor hits 12.8 mm -> (12.8/3)^3 = 77, best fit 85 + # + # For sensor hits the width depends on how hard the hits are cut, so this + # and reader.hit_charge_threshold are a matched pair -- mixing them is + # worse than either: + # + # hit_charge_threshold track width min_pc_size agrees with truth@5 + # ---------------------------------------------------------------------- + # 0 e- 13.1 mm 85 99.0% + # 500 e- 6.5 mm 13 95.5% + # 500 e- 6.5 mm 85 (mixed) 85.2% + # + # The uncut pairing keeps every electron and 4.18M points per event; the + # 500 e- pairing keeps 73.5% of the charge and 190k points, and runs 4x + # faster. preset=cubic_pixel_hits selects the second. + # + # Measured over ten events of a 4.32 mm-pitch pixel detector on a 3 mm + # grid: the best-agreeing cut per event ranged 81-90, and at a fixed 85 + # the sensor-hit labelling matches the truth-deposit labelling on 99.0% of + # particles. So with reader.point_source=hits, set: + # + # particle.min_pc_size=85 min_pc_size: 5 # Pre-processing: voxelize point clouds onto a regular 3-D grid. diff --git a/pysupera/conf/preset/cubic_pixel_hits.yaml b/pysupera/conf/preset/cubic_pixel_hits.yaml new file mode 100644 index 0000000..fdcd4c5 --- /dev/null +++ b/pysupera/conf/preset/cubic_pixel_hits.yaml @@ -0,0 +1,48 @@ +# @package _global_ +# +# Everything needed to run the cubic_pixel detector on its SENSOR HITS, so +# that only the file paths stay on the command line: +# +# run_pysupera preset=cubic_pixel_hits \ +# io.input_path=edepsim.h5 io.output_path=out.h5 \ +# reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ +# reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 \ +# reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5 +# +# Anything here can still be overridden on the command line, which wins. +# +# A preset rather than the reader config because two of these live outside +# the reader package -- min_pc_size under particle, and the two top-level +# checks -- and because they are only right together. + +defaults: + - override /reader: jaxtpc_pixel + +reader: + # A point is a fired pixel, not a Geant4 deposit. + point_source: hits + +particle: + # Extent below which a particle is a low-energy scatter, in 3 mm voxels. + # The rule is the cell count of a blob one track-width across; the width + # depends on how hard the hits are cut, so this and + # reader.hit_charge_threshold have to move together: + # + # threshold track width min_pc_size agreement with truth@5 + # ----------------------------------------------------------------- + # 0 e- 13.1 mm 85 99.0% + # 500 e- 6.5 mm 13 95.5% + # + # Mixing them is what to avoid: 85 at a 500 e- threshold agrees on only + # 85.2% of particles. The uncut pairing keeps all the charge and 4.18M + # points per event; this one keeps 73.5% of the charge and 190k points. + min_pc_size: 13 + +# Must clear the pixel diagonal (pitch * sqrt(2) = 6.11 mm) or +# defragmentation measures the pixel grid instead of the physics. +distance_threshold: 6.2 + +# A group's hits can straddle a fragment split here -- the readout leaves +# gaps the deposit cloud does not have -- so the invariant the groups/ table +# rests on does not hold and the run resolves by majority instead. +check_group_ownership: false diff --git a/pysupera/conf/reader/edepsim_h5.yaml b/pysupera/conf/reader/edepsim_h5.yaml index 440070e..771300a 100644 --- a/pysupera/conf/reader/edepsim_h5.yaml +++ b/pysupera/conf/reader/edepsim_h5.yaml @@ -1,24 +1,21 @@ -# Reader configuration for EDepSim HDF5 files. +# Reader configuration for plain EDepSim HDF5 input. +# +# This is the base every reader config in this group builds on. Selected +# with: # -# This config group is selected via: # defaults: # - reader: edepsim_h5 # -# or overridden on the command line: -# reader.particle_key=particle/geant4_custom +# or on the command line: # -# JAXTPC visibility filtering (optional) -# -------------------------------------- -# When jaxtpc_seg_path and jaxtpc_inst_path are both non-null, the reader -# automatically switches to visibility-filtered mode: only energy-deposit -# segments that produced a signal above threshold in the JAXTPC readout -# simulation are included in each particle's point cloud. The EDepSim file -# still provides all particle-level metadata (PDG, track IDs, etc.). +# run_pysupera reader=edepsim_h5 reader.particle_key=particle/geant4_custom # -# reader.jaxtpc_seg_path=sim_seg_0000.h5 -# reader.jaxtpc_inst_path=sim_inst_0000.h5 +# On its own it reads EDepSim directly: each particle's point cloud is every +# Geant4 energy-deposit step in the file, with no detector simulation in +# between. For JAXTPC input pick one of the siblings instead: # -# Leave both as null (~) to use full EDepSim steps without any masking. +# reader=jaxtpc_wire wire readout (U/V/Y planes, three 2-D projections) +# reader=jaxtpc_pixel pixel readout (one Pixel plane, natively 3-D) # Identifies which reader class build_reader() should instantiate. # Do not change unless you are adding a new reader format. @@ -47,19 +44,6 @@ step_key: pstep/lar_vol # Not used in JAXTPC mode. ass_key: ass/particle_pstep_lar_vol -# --------------------------------------------------------------------------- -# JAXTPC visibility mask (optional — leave null to disable) -# --------------------------------------------------------------------------- - -# Path to the JAXTPC seg HDF5 file (per-volume 3-D truth deposits: -# positions_mm, de, dx, t0_us, charge, …). -jaxtpc_seg_path: ~ - -# Path to the JAXTPC inst HDF5 file (per-readout-plane correspondence data: -# segment_to_group, group_to_track, and per-plane group_ids that record -# which groups were above the readout threshold). -jaxtpc_inst_path: ~ - # --------------------------------------------------------------------------- # Physics / classification parameters # --------------------------------------------------------------------------- diff --git a/pysupera/conf/reader/jaxtpc_pixel.yaml b/pysupera/conf/reader/jaxtpc_pixel.yaml new file mode 100644 index 0000000..932578f --- /dev/null +++ b/pysupera/conf/reader/jaxtpc_pixel.yaml @@ -0,0 +1,178 @@ +# JAXTPC pixel-readout input. +# +# run_pysupera reader=jaxtpc_pixel \ +# io.input_path=edepsim.h5 io.output_path=out.h5 \ +# reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ +# reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 +# +# As reader=jaxtpc_wire, but the readout is a single Pixel plane per volume +# recording two pixel indices and a drift tick -- a natively 3-D image. That +# is what makes point_source=hits possible here and not for wire. +# +# For the detected-image mode, add: +# +# reader.point_source=hits \ +# reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5 \ +# reader.pixel_pitch_mm=4.32 'reader.pixel_drift_direction=[-1,1]' \ +# check_group_ownership=false distance_threshold=6.2 +# +# distance_threshold must clear the pixel diagonal (pitch * sqrt(2)) or +# defragmentation measures the pixel grid instead of the physics, and +# check_group_ownership must be off because a group's hits can straddle a +# fragment split. Both are explained in the README. + +defaults: + - edepsim_h5 + - _self_ + +# Path to the JAXTPC seg/step HDF5 file. +jaxtpc_seg_path: ??? + +# Path to the JAXTPC inst/hits HDF5 file. +jaxtpc_inst_path: ??? + +# --------------------------------------------------------------------------- +# What a point is (JAXTPC pixel readout only) +# --------------------------------------------------------------------------- +# deposits (default) — a point is a Geant4 energy deposit that the readout +# detected. The geometry is truth geometry; the readout +# only decides which deposits survive. +# hits — a point is a fired pixel. (py, pz, tick) is converted +# back to (x, y, z), so the cloud carries pixelation, +# diffusion, threshold and the drift-time ambiguity, and +# the partitioner sees what the detector saw. Requires a +# pixel-readout hits file; wire readout has no 3-D image +# to convert. +# +# Roughly 4.2M points per event against 143k detected deposits, so expect the +# run to cost accordingly. +point_source: deposits + +# How the drift coordinate is obtained, when point_source=hits. +# +# Writing T for the time a hit's tick stands for, measured from the tick at +# which a deposit sitting on the anode at Geant4 t=0 would be recorded: +# +# T = (tick - reference_tick) * time_step +# +# nominal (default) x = x_anode -/+ T * drift_velocity. What a detector can +# actually do: assume the interaction happened at the beam time. The +# resulting x is offset from the true x by exactly +# drift_direction * drift_velocity * t0 -- the interaction's own +# Geant4 time turned into a displacement. Measured on a 10-event +# batch, t0 is constant within an interaction (max spread 0.53 us) +# and differs across the interactions of one event by up to 1094 us, +# i.e. 175 cm. So each interaction keeps its shape to sub-mm and +# slides bodily along x. Use this to study what the t0 ambiguity +# does to grouping. +# true_t0 x = x_anode -/+ (T - t0) * drift_velocity, using the truth t0 of +# each group: the drift position the detector would measure with +# perfect timing. Pixelation, diffusion, threshold and tick +# quantisation all survive; only the ambiguity goes. Reproduces +# truth x to ~0.3 mm on group centres, 5.8 mm per hit. +hit_x_from: nominal + +# Drop sensor hits carrying less than this much charge, in the units the +# JAXTPC hits file uses: ionisation electrons induced on the pixel. 0 +# disables the cut. +# +# Compared against the magnitude, matching JAXTPC's own encoder: the readout +# response is bipolar and about 16% of hits are negative, which is signal +# rather than noise. +# +# Why a default at all. JAXTPC applies no threshold worth the name -- 1 +# electron in the batch this was developed against -- so every pixel +# carrying any induced-field influence reaches the point cloud: 4.18M points +# for an event whose truth is 216k deposits, with the brightest 2% carrying +# half the charge and the rest forming a halo two to three pixels wide +# around every real deposit. +# +# For scale, the same run's sensor file states threshold_adc 7.0 at +# 182 electrons_per_adc, i.e. 1274 electrons on the digitised waveform; 500 +# is deliberately below that. +hit_charge_threshold: 500 + +# --------------------------------------------------------------------------- +# Pixel geometry (required by point_source=hits) +# --------------------------------------------------------------------------- +# These are detector constants, so they are *configured* and then checked -- +# not measured. The reader applies them as stated, converts each group's hit +# centre, and compares against the deposits behind it; a disagreement raises, +# naming the offending number and what the data says it should be. +# +# The order matters. Deriving a constant from the same truth it is later +# checked against cannot catch a geometry that is wrong but linear: the fit +# simply absorbs the error, and a detector effect this mode exists to measure +# is calibrated away instead of measured. Hence config first, fit as audit. +# +# Where the numbers live: +# anode face config/volume_ranges in the hits file (read for you) +# drift velocity sensor file config attrs, via jaxtpc_sensor_path +# sampling period likewise +# pixel pitch the detector YAML only -- no output file records it +# drift direction likewise +# +# Run without them once and the error lists what the data is consistent with, +# so the values can be copied from it and then verified on the next run. + +# JAXTPC sensor HDF5 file. Supplies velocity_cm_us and time_step_us, so they +# do not have to be restated here. Optional if both are given below. +jaxtpc_sensor_path: ~ + +# Pixel pitch in mm (square grid assumed). Defaulted to the cubic_pixel +# detector this was developed against (pixel_pitch 0.432 cm); change it for +# another geometry. A wrong value does not pass silently -- the calibration +# check reproduces the truth deposits and raises, naming what the data says. +pixel_pitch_mm: 4.32 + +# Which way electrons drift, per volume: +1 toward the x_max face, -1 toward +# x_min. The anode is that face. A single int applies to every volume; a +# list gives one per volume, e.g. [-1, 1] for a shared central cathode. +# Defaulted to cubic_pixel's two volumes about a central cathode. +pixel_drift_direction: [-1, 1] + +# Override the sensor file, or stand in for it when there is none. +drift_velocity_mm_us: ~ +readout_time_step_us: ~ + +# Measure the geometry from the truth deposits instead of configuring it. +# Off by default and deliberately awkward: it makes the consistency check +# circular, since the constants then come from the data they are compared +# with. Useful only for a first look at an unfamiliar batch. +pixel_geometry_from_fit: false + +# The tick at which a deposit sitting on the anode at Geant4 t=0 is recorded. +# This is the anchor the whole drift coordinate hangs from, and it is kept +# separate from the anode because a fit cannot tell the two apart: the fitted +# intercept is x_anode + drift_direction * mm_per_tick * reference_tick. +# +# 0 (default) — the readout window opens at Geant4 t=0, which is the case +# whenever pre_window_us is 0. A run with a pre-window has +# reference_tick = pre_window_us / time_step. +# +# Unlike the constants above this one is *not* checked against truth: it is a +# choice about where t=0 sits, not a measurement, so asking "what if the +# trigger were 100 ticks later" must not be reported as a broken detector. +# Its distance from the data is reported as reference_tick_offset_mm instead. +# +# It also decides whether points can leave the detector. On a run where the +# readout window is exactly one full drift (num_time_steps 2701 at 0.8 mm per +# tick = 2160 mm) and the reference tick is 0, the recordable ticks map exactly +# onto [anode, cathode] and a nominal x cannot fall outside the volume; the t0 +# displacement instead pushes charge off the end of the window, where it is +# never recorded. Both are counted in the run report, and neither is clipped. +hit_reference_tick: 0 + +# What lands in the energy column, when point_source=hits. +# +# charge (default) the charge the readout measured. Note the response is +# bipolar, so ~16% of hits carry negative charge; that is the signal, +# not an error, and it sums correctly per group. +# true_de the group's true deposited energy, shared over its hits in +# proportion to charge. Comparable with deposit mode's energy column, +# at the cost of putting a truth quantity into a detected cloud. +# +# The dx column is 0 in both cases: a hit is a pixel and a tick, and has no +# path length. Anything reading dE/dx will see zero. +hit_energy: charge + diff --git a/pysupera/conf/reader/jaxtpc_wire.yaml b/pysupera/conf/reader/jaxtpc_wire.yaml new file mode 100644 index 0000000..5d64223 --- /dev/null +++ b/pysupera/conf/reader/jaxtpc_wire.yaml @@ -0,0 +1,34 @@ +# JAXTPC wire-readout input. +# +# run_pysupera reader=jaxtpc_wire \ +# io.input_path=edepsim.h5 io.output_path=out.h5 \ +# reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ +# reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 +# +# Particle metadata (PDG, track IDs, interaction type) still comes from the +# EDepSim file at io.input_path. What changes is the point cloud: only the +# energy-deposit segments that produced a signal above threshold in the +# JAXTPC readout are kept. +# +# A wire hit records one spatial coordinate and a drift tick, so three planes +# are needed to place a point and there is no 3-D image to convert. Pixel +# readout has one; see reader=jaxtpc_pixel for reader.point_source=hits. +# +# Nothing here is wire-specific beyond the comments -- visibility is a +# property of a group, not of the readout that recorded it, so the same +# reader handles both geometries. The two files exist to say which +# arrangement you have, and to keep the options that only apply to one of +# them out of the other. + +defaults: + - edepsim_h5 + - _self_ + +# Path to the JAXTPC seg/step HDF5 file (per-volume 3-D truth deposits: +# positions, de, dx, t0_us, charge, ...). +jaxtpc_seg_path: ??? + +# Path to the JAXTPC inst/hits HDF5 file (per-readout-plane correspondence: +# deposit_to_group, group_to_track, and the per-plane group_ids that record +# which groups were above the readout threshold). +jaxtpc_inst_path: ??? diff --git a/pysupera/config.py b/pysupera/config.py index 506eefb..a41426a 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -25,7 +25,7 @@ from __future__ import annotations -from omegaconf import DictConfig +from omegaconf import DictConfig, OmegaConf # --------------------------------------------------------------------------- @@ -127,6 +127,11 @@ def configure(cfg: DictConfig) -> None: """ import pysupera.data as _data _data._DEFAULT_MIN_PC_SIZE = int(cfg.particle.min_pc_size) + # min_pc_size counts occupied cells, so classification has to measure in + # the same cells defragmentation later splits on. With no voxel grid + # there is nothing to count but rows, and the threshold then means + # whatever the input sampling pitch makes it mean. + _data._DEFAULT_VOXEL_SIZE = _classify_voxel_size(cfg) def build_preprocessor(cfg: DictConfig, verbose: bool = False): @@ -432,6 +437,27 @@ def build_conditions(cfg: DictConfig) -> list: return conditions +def _classify_voxel_size(cfg): + "The cell size semantic classification measures a cloud's extent in." + # Passed explicitly at every call site rather than left to the module + # default configure() sets: a reader built without calling configure + # would otherwise silently classify in rows. + vx = cfg.particle.get("voxelize", {}) or {} + if not vx.get("enabled", False): + return None + v = vx.get("voxel_size", None) + if v is None: + return None + # A per-axis voxel size has no single cell count; fall back to rows. + return None if hasattr(v, "__iter__") else float(v) + + +def _opt_float(node, key): + """A config value as float, or None when unset -- Hydra writes null as None.""" + v = node.get(key, None) + return None if v is None else float(v) + + def build_reader(cfg: DictConfig): """ Instantiate an :class:`~pysupera.readers.EventReaderBase` described by @@ -449,6 +475,12 @@ def build_reader(cfg: DictConfig): JAXTPC readout simulation. All particle-level metadata still comes from the EDepSim file at *cfg.io.input_path*. + ``cfg.reader.point_source`` then chooses what a point *is*: ``deposits`` + (default) keeps truth geometry and lets the readout select, while + ``hits`` replaces the cloud with the detected pixel image. See + ``pysupera.readers.pixel_hits`` for the ``hit_x_from`` and ``hit_energy`` + conventions. + With JAXTPC masking disabled (default):: run_pysupera io.input_path=edepsim.h5 io.output_path=out.h5 @@ -493,6 +525,26 @@ def build_reader(cfg: DictConfig): seg_path = cfg.reader.get("jaxtpc_seg_path", None) inst_path = cfg.reader.get("jaxtpc_inst_path", None) + # A reader config that declares these as mandatory (???) means the + # run is a JAXTPC run. OmegaConf's .get() hands back None for an + # unfilled mandatory value rather than raising, so without this the + # run would quietly fall through to plain EDepSim -- you would ask + # for reader=jaxtpc_pixel and get truth steps, with nothing said. + # Note: `k in cfg.reader` is False for a MISSING value, so the + # membership test cannot be used to spot one -- is_missing can, and + # returns False for a key the config genuinely does not declare. + _missing = [k for k in ("jaxtpc_seg_path", "jaxtpc_inst_path") + if OmegaConf.is_missing(cfg.reader, k)] + if _missing: + raise ValueError( + "This reader config is for JAXTPC input and needs " + + " and ".join(f"reader.{k}" for k in _missing) + + ". Pass them on the command line, e.g.\n" + " reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5\n" + " reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5\n" + "Use reader=edepsim_h5 to read the EDepSim steps directly " + "instead.") + jaxtpc_active = bool(seg_path) and bool(inst_path) if jaxtpc_active: @@ -509,6 +561,30 @@ def build_reader(cfg: DictConfig): electron_energy_threshold = float(cfg.reader.get( "electron_energy_threshold", 0.05)), min_pc_size = int(cfg.particle.get("min_pc_size", -1)), + voxel_size = _classify_voxel_size(cfg), + point_source = str(cfg.reader.get("point_source", + "deposits")), + hit_x_from = str(cfg.reader.get("hit_x_from", + "nominal")), + hit_energy = str(cfg.reader.get("hit_energy", + "charge")), + hit_reference_tick = float(cfg.reader.get( + "hit_reference_tick", 0.0) or 0.0), + hit_charge_threshold = float(cfg.reader.get( + "hit_charge_threshold", 0.0) or 0.0), + sensor_path = ( + str(cfg.reader.get("jaxtpc_sensor_path")) + if cfg.reader.get("jaxtpc_sensor_path", None) else None), + pixel_pitch_mm = _opt_float(cfg.reader, + "pixel_pitch_mm"), + pixel_drift_direction = cfg.reader.get( + "pixel_drift_direction", None), + drift_velocity_mm_us = _opt_float(cfg.reader, + "drift_velocity_mm_us"), + readout_time_step_us = _opt_float(cfg.reader, + "readout_time_step_us"), + pixel_geometry_from_fit = bool(cfg.reader.get( + "pixel_geometry_from_fit", False)), ) # Default: full EDepSim reader (no JAXTPC masking). @@ -526,6 +602,7 @@ def build_reader(cfg: DictConfig): electron_energy_threshold = float(cfg.reader.get( "electron_energy_threshold", 0.05)), min_pc_size = int(cfg.particle.get("min_pc_size", -1)), + voxel_size = _classify_voxel_size(cfg), ) raise ValueError( diff --git a/pysupera/data.py b/pysupera/data.py index dea421a..c8b43a4 100644 --- a/pysupera/data.py +++ b/pysupera/data.py @@ -7,6 +7,12 @@ # --------------------------------------------------------------------------- _DEFAULT_MIN_PC_SIZE: int = -1 +#: Cell size that :func:`~pysupera.utils.SetSemanticType` measures a cloud's +#: extent in. ``None`` counts rows, which only compares like with like when +#: two inputs are sampled at the same pitch -- Geant4 deposits arrive every +#: 0.3 mm and sensor hits on a 4.32 mm grid, so they do not. +_DEFAULT_VOXEL_SIZE = None + # --------------------------------------------------------------------------- # Sentinel values for unset optional Particle attributes # --------------------------------------------------------------------------- @@ -136,6 +142,7 @@ def __init__( interaction_type, point_cloud, min_pc_size=None, + voxel_size=None, # ------------------------------------------------------------------ # Optional physical attributes — default to None / FLOAT_UNSET. # See class docstring for sentinel-value conventions. @@ -223,10 +230,13 @@ def __init__( if min_pc_size is None: min_pc_size = _DEFAULT_MIN_PC_SIZE + if voxel_size is None: + voxel_size = _DEFAULT_VOXEL_SIZE self.sem_type = SetSemanticType( interaction_type, pdg, parent_pdg, point_cloud, point_cloud_size=min_pc_size, + voxel_size=voxel_size, ) self.point_cloud = point_cloud @@ -300,6 +310,7 @@ def from_arrays( interaction_types, point_clouds, min_pc_size=None, + voxel_size=None, geant4_ids=None, ): """ @@ -398,6 +409,7 @@ def from_arrays( interaction_type=interaction_types[i], point_cloud=point_clouds[i], min_pc_size=min_pc_size, + voxel_size=voxel_size, ) for i in range(n) ] @@ -415,6 +427,7 @@ def from_flat_arrays( point_cloud_flat, point_cloud_offsets, min_pc_size=None, + voxel_size=None, geant4_ids=None, ): """ @@ -548,6 +561,7 @@ def from_flat_arrays( interaction_type=interaction_types[i], point_cloud=flat[offsets[i, 0] : offsets[i, 1]], min_pc_size=min_pc_size, + voxel_size=voxel_size, ) for i in range(n) ] diff --git a/pysupera/hits_subset.py b/pysupera/hits_subset.py index d46c65b..a4c0002 100644 --- a/pysupera/hits_subset.py +++ b/pysupera/hits_subset.py @@ -1,11 +1,26 @@ """ Extract a viewer-sized slice of a JAXTPC hits file. -``vis_hits.html`` reads four arrays per readout plane -- ``center_wires``, -``center_times``, ``peak_charges`` and ``group_ids`` -- and nothing else. The -per-tick arrays (``charges_u16``, ``delta_times``, ``delta_wires``) are the -bulk of the file and are never touched, so a handful of events comes to about -a megabyte against the original gigabyte. +``vis_hits.html`` reads only each readout plane's per-group *centres* -- +never the per-tick CSR arrays (``charges_i16``, ``delta_times``, +``delta_wires`` / ``delta_py`` / ``delta_pz``), which are the bulk of the +file. A handful of events therefore comes to about a megabyte against the +original gigabyte. + +Which centres those are depends on the readout JAXTPC simulated: + +========== ============= ============================================== +readout plane group centre arrays +========== ============= ============================================== +``wire`` ``U/V/Y`` ``center_wires``, ``center_times`` +``pixel`` ``Pixel`` ``center_py``, ``center_pz``, ``center_times`` +========== ============= ============================================== + +Planes are found structurally -- any subgroup holding ``group_ids`` is one -- +rather than by name, so a readout this module has never heard of still comes +through with whatever centres it stores. ``config``'s attributes are copied +across as well, because ``readout_type`` is what tells the viewer which kind +of picture it is about to draw. The output is gzip, because the browser reads HDF5 through h5wasm, which supports gzip only. @@ -18,8 +33,24 @@ import argparse import os +#: Read by the viewer for every readout. +_COMMON = ("group_ids", "center_times", "peak_charges") + +#: Readout-specific hit centres. A plane keeps whichever of these it has; +#: listing both costs nothing and removes the need to identify the geometry +#: before deciding what to copy. +_CENTERS = ("center_wires", "center_py", "center_pz") + #: What the viewer reads. Anything else is left behind. -KEEP = ("center_wires", "center_times", "peak_charges", "group_ids") +KEEP = _COMMON + _CENTERS + + +def _plane_names(vol_group) -> list[str]: + """Subgroups of one volume that are readout planes, in file order.""" + import h5py + return [k for k in vol_group.keys() + if isinstance(vol_group[k], h5py.Group) + and "group_ids" in vol_group[k]] def extract(src: str, dst: str, n_events: int = 3, level: int = 4, @@ -32,7 +63,15 @@ def extract(src: str, dst: str, n_events: int = 3, level: int = 4, pass n_ds = 0 + planes: set[str] = set() with h5py.File(src, "r") as fin, h5py.File(dst, "w") as fout: + # Carry the run metadata over verbatim. Only readout_type is read by + # the viewer, but the group is a few attributes and a subset that + # cannot say where it came from is a subset nobody trusts. + if "config" in fin: + cfg = fout.create_group("config") + cfg.attrs.update(fin["config"].attrs) + events = sorted(k for k in fin.keys() if k.startswith("event_")) if n_events > 0: events = events[:n_events] @@ -40,9 +79,8 @@ def extract(src: str, dst: str, n_events: int = 3, level: int = 4, for vol in sorted(k for k in fin[ek].keys() if k.startswith("volume_")): vg = fin[f"{ek}/{vol}"] - for plane in ("U", "V", "Y"): - if plane not in vg: - continue + for plane in _plane_names(vg): + planes.add(plane) for name in KEEP: if name not in vg[plane]: continue @@ -53,23 +91,35 @@ def extract(src: str, dst: str, n_events: int = 3, level: int = 4, ) n_ds += 1 out = {"events": len(events), "datasets": n_ds, + "planes": sorted(planes), + "readout": _readout_of(src), "size_before": os.path.getsize(src), "size_after": os.path.getsize(dst)} if verbose: print(f"[hits-subset] {src} -> {dst}: {len(events)} event(s), " + f"{out['readout']} readout, plane(s) " + f"{', '.join(out['planes']) or '(none)'}, " f"{n_ds} dataset(s), " f"{out['size_before'] / 1048576:.1f} MiB -> " f"{out['size_after'] / 1048576:.2f} MiB") return out +def _readout_of(path: str) -> str: + """``'wire'`` or ``'pixel'`` for a hits file, without opening it twice.""" + import h5py + from .readers.format_jaxtpc import read_readout_type + with h5py.File(path, "r") as f: + return read_readout_type(f) + + def main() -> None: """Entry point for ``pysupera-hits-subset``.""" p = argparse.ArgumentParser( prog="pysupera-hits-subset", description="Extract a gzip, viewer-sized slice of a JAXTPC hits file.", formatter_class=argparse.ArgumentDefaultsHelpFormatter) - p.add_argument("src", help="JAXTPC hits (inst) HDF5 file") + p.add_argument("src", help="JAXTPC hits (inst) HDF5 file, wire or pixel") p.add_argument("dst", help="Output file, gzip") p.add_argument("-n", "--events", type=int, default=3, help="Number of events to copy; -1 for all") diff --git a/pysupera/io_v3.py b/pysupera/io_v3.py index 6b9e5a2..93ccc63 100644 --- a/pysupera/io_v3.py +++ b/pysupera/io_v3.py @@ -15,6 +15,7 @@ events/offsets (n_events+1,) int64 particle rows per event points/offsets (n_events+1,) int64 point rows per event points/flat (N, 6) float32 x, y, z, time, dE, dX + points/true_x_shift (N,) float32 true_x = flat[:, 0] + this particles/ (M,) see _INT32 / _INT8 / _INT64 inter/offsets (n_events+1,) int64 interaction rows per event inter/ (K,) see _INTER @@ -143,6 +144,15 @@ def _create(self): "flat", shape=(0, _PT_NDIM), maxshape=(None, _PT_NDIM), dtype=np.float32, chunks=(_chunk_rows(_PT_NDIM), _PT_NDIM), **self._ckw) + # true_x = flat[:, 0] + true_x_shift. Only pixel hit mode with a + # nominal t0 puts anything but zero here, and it takes as many + # distinct values as there are (interaction, volume) pairs -- 28 in a + # test event -- so it costs about 1.5% of the file under LZ4. A + # separate dataset rather than a seventh column so that every reader + # of the (N, 6) layout keeps working untouched. + f["points"].create_dataset( + "true_x_shift", shape=(0,), maxshape=(None,), + dtype=np.float32, chunks=(_chunk_rows(1),), **self._ckw) g = f.create_group("inter") g.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), maxshape=(None,), chunks=(rows,)) @@ -224,6 +234,19 @@ def append_group_owners(self, frag_owner, is_le=None, volume_offsets=()): vidx.resize(vidx.shape[0] + 1, axis=0) vidx[-1] = self._n_voloff + def set_meta(self, meta: dict) -> None: + """ + Record how this file was produced, as root attributes. + + Only a handful of short strings, but they remove a guess: nothing in + the point columns says whether the energy column holds true dE or + measured charge, and a viewer that has to infer it from the presence + of negative values gets it wrong on a quiet event. + """ + for k, v in meta.items(): + if v is not None: + self._f.attrs[str(k)] = str(v) + def append_event(self, layout): """Append one event described by an :class:`~pysupera.layout.EventLayout`.""" cols = layout.columns @@ -245,6 +268,20 @@ def append_event(self, layout): cursor += k pts[self._n_points:self._n_points + n_pts] = block + shift = self._f["points"].get("true_x_shift") + if shift is not None: + shift.resize(self._n_points + n_pts, axis=0) + if n_pts: + sblock = np.zeros(n_pts, dtype=np.float32) + cursor = 0 + for p in layout.order: + k = len(p.point_cloud) + v = getattr(p, "true_x_shift", None) + if k and v is not None and len(v) == k: + sblock[cursor:cursor + k] = v + cursor += k + shift[self._n_points:self._n_points + n_pts] = sblock + # ---- particle columns --------------------------------------------- base = self._n_points for c in COLUMNS: @@ -453,6 +490,21 @@ def __getitem__(self, ev): inter[c] = v return EventView(cols, self._f["points/flat"][pa:pb], ev, inter) + def true_x_shift(self, ev): + """ + Per point, what to add to ``x`` to recover the true drift position. + + ``true_x = view.points[:, 0] + store.true_x_shift(ev)``. Zero + everywhere except a pixel hit-mode run read with a nominal t0, and + absent from files written before the column existed. + """ + ds = self._f["points"].get("true_x_shift") + if ds is None: + return None + po = self._f["points/offsets"] + a, b = int(po[ev]), int(po[ev + 1]) + return ds[a:b] + def _read_legacy_particles(self, a, b): """ Read a 3.0.0 particle block under 3.1.0 names. diff --git a/pysupera/provenance.py b/pysupera/provenance.py index 27c77b6..be25a0b 100644 --- a/pysupera/provenance.py +++ b/pysupera/provenance.py @@ -7,12 +7,12 @@ defragmentation splits one track into several pysupera particles, so ``track -> particle`` is one-to-many. -What makes this tractable is that a group never straddles a split. A group is -itself a tight spatial cluster and defragmentation clusters at -``distance_threshold``; measured over eight events, none of 354,959 groups -spanned two particles. So ``group -> pysupera particle`` *is* a function, and -one small table per event answers "which hits belong to instance X" without -going near voxels: +What makes this tractable, for truth deposits, is that a group never straddles +a split. A group is itself a tight spatial cluster and defragmentation +clusters at ``distance_threshold``; measured over eight events, none of +354,959 groups spanned two particles. So ``group -> pysupera particle`` *is* +a function, and one small table per event answers "which hits belong to +instance X" without going near voxels: groups = {g for g, pid in group_particle if pid in particles_of(X)} hits = [h for h in plane if group_ids[h] in groups] @@ -21,6 +21,13 @@ Going through voxels instead would be both larger and worse: a group's deposits land in several voxels 38% of the time, so hit-to-voxel is many-to-many, while hit-to-group-to-particle is a chain of functions. + +It stops being a function under ``reader.point_source=hits``. There a point +is a fired pixel rather than a deposit, and the readout leaves gaps the +deposit cloud does not have: defragmentation cuts a track at one of them and a +group's hits land on both sides. That is a property of the detected image, +not a bug, so hit mode resolves the group by majority and reports how often it +had to. """ from __future__ import annotations @@ -35,7 +42,8 @@ class GroupOwnershipError(ValueError): """A group's deposits landed in more than one pysupera particle.""" -def build_group_owners(particles, deposit_to_group, n_groups, check=True): +def build_group_owners(particles, deposit_to_group, n_groups, check=True, + resolve="first", report=None): """ Map every JAXTPC group to the pysupera particle that owns it. @@ -50,16 +58,45 @@ def build_group_owners(particles, deposit_to_group, n_groups, check=True): Total groups in the event, so untouched ones can be marked. check : bool Raise :class:`GroupOwnershipError` when a group spans two particles. - With *check* false the first owner seen wins, which is a lossy - approximation -- enable the deposit-level map instead if this fires. + With *check* false the group is resolved by *resolve* instead, which + is lossy either way -- enable the deposit-level map if this fires and + the exact answer matters. + resolve : {'first', 'majority'} + How to settle a straddled group when *check* is false. ``'first'`` + keeps the first owner seen, which is arbitrary but cheap. + ``'majority'`` gives the group to the particle holding most of its + rows, which is the only defensible answer when straddling is normal + rather than exceptional -- see the note below. + report : dict, optional + Filled with ``n_claimed`` and ``n_straddled`` so a caller can say how + often the invariant actually held. Counting is free here and + impossible afterwards, the array having collapsed to one owner. Returns ------- ndarray of int32, shape (n_groups,) Owning particle id per group, :data:`NO_OWNER` where none. + + Notes + ----- + With truth deposits a group never straddles a split -- measured over + eight events, none of 354,959 did -- because a group is a tight spatial + cluster and defragmentation clusters at ``distance_threshold``. With + *detected hits* (``reader.point_source=hits``) that no longer holds: the + readout leaves gaps a deposit cloud does not have, defragmentation cuts a + track at them, and a group's hits can end up on both sides. Hit mode + therefore wants ``resolve='majority'`` and a look at ``report``. """ - owner = np.full(int(n_groups), NO_OWNER, dtype=np.int32) + if resolve not in ("first", "majority"): + raise ValueError( + f"resolve must be 'first' or 'majority', got {resolve!r}") + + n_groups = int(n_groups) + owner = np.full(n_groups, NO_OWNER, dtype=np.int32) clash = None + straddled = set() + # Rows per (group, particle), kept only when majority resolution needs it. + tally = {} if resolve == "majority" else None for p in particles: vm = getattr(p, "voxmap", None) @@ -68,17 +105,27 @@ def build_group_owners(particles, deposit_to_group, n_groups, check=True): _off, ids = vm if ids is None or not len(ids): continue - groups = np.unique(deposit_to_group[np.asarray(ids, dtype=np.int64)]) - groups = groups[(groups >= 0) & (groups < len(owner))] - if not len(groups): + mine = deposit_to_group[np.asarray(ids, dtype=np.int64)] + mine = mine[(mine >= 0) & (mine < n_groups)] + if not len(mine): continue + groups, counts = np.unique(mine, return_counts=True) + taken = owner[groups] held = groups[taken != NO_OWNER] - if len(held) and clash is None: - g0 = int(held[0]) - clash = (g0, int(owner[g0]), int(p.id)) + if len(held): + straddled.update(int(g) for g in held) + if clash is None: + g0 = int(held[0]) + clash = (g0, int(owner[g0]), int(p.id)) owner[groups[taken == NO_OWNER]] = int(p.id) + if tally is not None: + for g, c in zip(groups.tolist(), counts.tolist()): + cur = tally.get(g) + if cur is None or c > cur[0]: + tally[g] = (c, int(p.id)) + if clash is not None and check: g, a, b = clash raise GroupOwnershipError( @@ -87,8 +134,21 @@ def build_group_owners(particles, deposit_to_group, n_groups, check=True): f"assumption the group table rests on. Set " f"particle.voxelize.store_mapping=true for the exact " f"deposit-level mapping, or check_group_ownership=false to " - f"accept the first owner." + f"accept the first owner. With reader.point_source=hits this is " + f"expected rather than exceptional -- the readout gaps split " + f"tracks that the deposits held together -- so run hit mode with " + f"check_group_ownership=false." ) + + if tally is not None: + for g in straddled: + best = tally.get(g) + if best is not None: + owner[g] = np.int32(best[1]) + + if report is not None: + report["n_claimed"] = int((owner != NO_OWNER).sum()) + report["n_straddled"] = len(straddled) return owner diff --git a/pysupera/readers/__init__.py b/pysupera/readers/__init__.py index 160522c..8c0e05b 100644 --- a/pysupera/readers/__init__.py +++ b/pysupera/readers/__init__.py @@ -22,12 +22,24 @@ dE, dx, t0, charge, …). * **JAXTPC inst HDF5** — per-readout-plane correspondence data used to determine which segments were visible above the signal threshold. + Both JAXTPC readout geometries are read by the same code path: + **wire** (``U``/``V``/``Y`` planes, three 2-D projections) and + **pixel** (a single ``Pixel`` plane, natively 3-D). Use + :func:`read_readout_type` to tell them apart. Each particle's point cloud contains **only** the energy-deposit segments that survived the JAXTPC readout threshold ("visible" segments). Hydra config group: ``reader: edepsim_h5`` with optional ``reader.jaxtpc_seg_path`` / ``reader.jaxtpc_inst_path`` flags. + ``point_source`` chooses what a point is. ``'deposits'`` (default) is + the above: truth geometry, with the readout deciding only which deposits + survive. ``'hits'`` replaces the cloud with the *detected image* -- + every fired pixel, converted from ``(py, pz, tick)`` back to + ``(x, y, z)`` -- so the partitioner sees pixelation, diffusion, + threshold and the drift-time ambiguity. Pixel readout only; see + :mod:`pysupera.readers.pixel_hits`. + All readers implement the :class:`EventReaderBase` interface which supports: - ``len(reader)`` — number of events @@ -67,10 +79,22 @@ from .base import EventReaderBase from .format_edepsim_h5 import EDepSimHDF5Reader -from .format_jaxtpc import JaxtpcHDF5Reader +from .format_jaxtpc import ( + JaxtpcHDF5Reader, + READOUT_PLANE_NAMES, + read_readout_type, +) +from .pixel_hits import ( + PixelGeometryError, + VolumePixelGeometry, +) __all__ = [ "EventReaderBase", "EDepSimHDF5Reader", "JaxtpcHDF5Reader", + "READOUT_PLANE_NAMES", + "read_readout_type", + "PixelGeometryError", + "VolumePixelGeometry", ] diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index b36213f..05ae869 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -608,6 +608,7 @@ def __init__( ass_key: str = _DEFAULT_ASS_KEY, electron_energy_threshold: float = _DEFAULT_ELECTRON_ENERGY_THRESHOLD, min_pc_size: int | None = None, + voxel_size: float | None = None, ) -> None: import h5py self._path = path @@ -617,6 +618,9 @@ def __init__( self._ass_key = ass_key self._e_thresh = electron_energy_threshold self._min_pc_size = min_pc_size + # Cell size classification measures a cloud's extent in; see + # pysupera.utils.count_extent for why rows will not do. + self._voxel_size = voxel_size self._file = h5py.File(path, "r") self._n_events = len(self._file[self._part_key]) @@ -683,6 +687,7 @@ def __getitem__(self, index: int) -> list[Particle]: point_cloud_flat = _steps_to_plain_array(steps), point_cloud_offsets = offsets, min_pc_size = self._min_pc_size, + voxel_size = self._voxel_size, geant4_ids = _g4, ) diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index b3e3673..418957d 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -28,6 +28,28 @@ the energy-deposit segments that were visible** in the JAXTPC readout — i.e. whose group_id appeared in at least one readout-plane's ``group_ids`` array. +Wire and pixel readout +---------------------- +JAXTPC simulates two LArTPC readout geometries, and this reader handles both +without being told which it is looking at: + +========= ============== ============================================= +readout plane subgroup hit centre stored per group +========= ============== ============================================= +``wire`` ``U/V/Y`` ``center_wires``, ``center_times`` — three 2-D + projections of the same ionisation +``pixel`` ``Pixel`` ``center_py``, ``center_pz``, ``center_times`` + — one natively 3-D image +========= ============== ============================================= + +Visibility is a question about *groups*, and a group is above threshold or it +is not regardless of how the readout is arranged, so the filtering below reads +only ``group_ids`` and never the hit centres. That is why the two geometries +share one code path: the reader discovers plane subgroups structurally rather +than by name. Downstream consumers that *do* draw hits (``vis_hits.html``, +``pysupera-hits-subset``) need the distinction, and get it from +:func:`read_readout_type`. + Visibility logic (mirrors ``get_visible_segments_by_track`` in ``seg_visibility.ipynb``): 1. Open the inst file and, for each volume, scan every readout-plane @@ -83,6 +105,32 @@ ) from ..data import Particle from ..utils import PointFeature +from . import pixel_hits as _px + + +#: What JAXTPC calls the geometry, on the inst file's ``config`` group. +#: ``wire`` is the fallback rather than an error because JAXTPC wrote hits +#: files before the attribute existed, and its own loader assumes wire in +#: exactly the same way (``production/load.py``). +_DEFAULT_READOUT_TYPE = 'wire' + +#: Plane subgroup names JAXTPC writes for each readout type. Used only for +#: error messages and by consumers that draw hits — the visibility filter +#: below discovers planes structurally, so an unlisted name still works. +READOUT_PLANE_NAMES = {'wire': ('U', 'V', 'Y'), 'pixel': ('Pixel',)} + + +def read_readout_type(inst_file) -> str: + """ + ``'wire'`` or ``'pixel'`` for an open JAXTPC inst/hits file. + + JAXTPC records this as ``config.attrs['readout_type']``. A file without + it predates the attribute and is wire. + """ + cfg = inst_file.get('config') if hasattr(inst_file, 'get') else None + raw = cfg.attrs.get('readout_type', _DEFAULT_READOUT_TYPE) if cfg is not None \ + else _DEFAULT_READOUT_TYPE + return raw.decode() if isinstance(raw, bytes) else str(raw) # Per-volume table mapping segment (deposit) index → group index. Older @@ -199,7 +247,8 @@ def get_visible_segment_indices_by_track( raise KeyError( f"{event_key}/{vol_key} in the JAXTPC inst file " f"{inst_file.filename!r} contains no readout-plane sub-group " - f"with a 'group_ids' dataset (expected e.g. U/V/Y); found " + f"with a 'group_ids' dataset (expected U/V/Y for wire readout " + f"or Pixel for pixel readout); found " f"{sorted(vol_group.keys())}. Without these, segment " f"visibility cannot be determined." ) @@ -446,9 +495,58 @@ def __init__( particle_key: str = _DEFAULT_PARTICLE_KEY, electron_energy_threshold: float = _DEFAULT_ELECTRON_ENERGY_THRESHOLD, min_pc_size: int | None = None, + voxel_size: float | None = None, + point_source: str = 'deposits', + hit_x_from: str = 'nominal', + hit_energy: str = 'charge', + hit_reference_tick: float = 0.0, + hit_charge_threshold: float = 0.0, + sensor_path: str | None = None, + pixel_pitch_mm: float | None = None, + pixel_drift_direction=None, + drift_velocity_mm_us: float | None = None, + readout_time_step_us: float | None = None, + pixel_geometry_from_fit: bool = False, + pixel_geometry: list | None = None, ) -> None: import h5py + if point_source not in ('deposits', 'hits'): + raise ValueError( + f"point_source must be 'deposits' or 'hits', " + f"got {point_source!r}") + self._point_source = point_source + self._hit_x_from = hit_x_from + self._hit_energy = hit_energy + self._hit_reference_tick = float(hit_reference_tick or 0.0) + # Drop hits below this many induced electrons. JAXTPC applies + # essentially none, so this is where a readout threshold enters. + self._hit_charge_threshold = float(hit_charge_threshold or 0.0) + # Stated pixel geometry. Preferred over anything fitted: see + # pysupera.readers.pixel_hits.verify_volume_geometry for why a + # constant derived from the truth it is later checked against cannot + # catch a geometry that is wrong but linear. + self._sensor_path = sensor_path + self._pixel_pitch_mm = pixel_pitch_mm + self._pixel_drift_direction = pixel_drift_direction + self._drift_velocity_mm_us = drift_velocity_mm_us + self._readout_time_step_us = readout_time_step_us + self._geometry_from_fit = bool(pixel_geometry_from_fit) + # Calibrated on first use and reused; the geometry is a property of + # the run, not of the event. An explicit list skips calibration. + self._pixel_geoms = pixel_geometry + self._pixel_report = None + + #: Hit counts for the most recently read event in hit mode. + self.last_hit_stats: dict | None = None + + #: Groups in the most recently read event, spanning every volume. + self.last_n_groups: int | None = None + + #: Per input hit, what to add to x to recover the true drift + #: position. Hit mode only; None otherwise. + self.last_true_x_shift = None + self._edepsim_path = edepsim_path self._seg_path = seg_path self._inst_path = inst_path @@ -456,6 +554,9 @@ def __init__( self._part_key = particle_key self._e_thresh = electron_energy_threshold self._min_pc_size = min_pc_size + # Cell size classification measures a cloud's extent in; see + # pysupera.utils.count_extent for why rows will not do. + self._voxel_size = voxel_size # Visibility counts for the most recently read event; populated by # __getitem__. None until the first event has been read. @@ -465,6 +566,8 @@ def __init__( self._edepsim_file = h5py.File(edepsim_path, 'r') self._seg_file = h5py.File(seg_path, 'r') self._inst_file = h5py.File(inst_path, 'r') + self._sensor_file = (h5py.File(sensor_path, 'r') + if sensor_path else None) self._n_events = len(self._edepsim_file[self._part_key]) @@ -515,41 +618,68 @@ def __getitem__(self, index: int) -> list[Particle]: # Each volume dict contains positions_mm (N,3), de, dx, t0_us, … seg_volumes = self._load_seg_volumes(event_key) - # ── Step 3: determine which segments are visible in the readout ─── - # A segment is visible if its group_id appears in a readout-plane's - # group_ids array in the inst file (i.e. it survived thresholding). - visible_by_track = get_visible_segment_indices_by_track( - seg_volumes, self._inst_file, event_key - ) + # ── Steps 3 and 4: build the point cloud ────────────────────────── + # Two sources, and they differ in what a point *is*. In deposit mode + # a point is a Geant4 energy deposit the readout happened to detect, + # so the geometry is truth geometry and the readout only selects. In + # hit mode a point is a fired pixel: the detected image itself, with + # pixelation, diffusion, threshold and -- unless hit_x_from removes + # it -- the drift-time ambiguity baked into the coordinates. + _hit_mode = self._point_source == 'hits' + if _hit_mode: + (point_cloud_flat, offsets, _group_of_point, _x_shift, + _g_offs, _hstats) = self._build_hit_event( + event_key, seg_volumes, parts['track_id']) + #: true_x = x + this, per input hit. See pixel_hits. + self.last_true_x_shift = _x_shift + self.last_hit_stats = _hstats + # No visibility pass is run here: hit mode never asks which + # deposits were detected, it reads what the readout recorded. + self.last_mask_stats = None + # A point *is* a hit, so its group is read rather than resolved + # through a voxel map. Handing it over under the same name lets + # the group-provenance machinery downstream stay as it is: it + # only ever asks "which group produced the row at this index". + self.last_deposit_to_group = _group_of_point + self.last_group_volume_offsets = _g_offs + self.last_n_groups = _hstats['n_groups'] + self.last_deposit_volume_offsets = [] + deposit_id = np.arange(len(point_cloud_flat), dtype=np.int64) + else: + # A segment is visible if its group_id appears in a readout-plane's + # group_ids array in the inst file (i.e. it survived thresholding). + visible_by_track = get_visible_segment_indices_by_track( + seg_volumes, self._inst_file, event_key + ) - # ── Step 4: build the flat point cloud (visible segments only) ──── - # Segments are assembled in the same track order as `parts` so that - # Particle.from_flat_arrays correctly matches metadata to point clouds. - (point_cloud_flat, offsets, - deposit_id, volume_offsets) = _build_visible_point_cloud( - seg_volumes, visible_by_track, parts['track_id'] - ) + # Segments are assembled in the same track order as `parts` so + # that Particle.from_flat_arrays matches metadata to point clouds. + (point_cloud_flat, offsets, + deposit_id, volume_offsets) = _build_visible_point_cloud( + seg_volumes, visible_by_track, parts['track_id'] + ) - # ── Visibility bookkeeping (free: no extra file reads) ──────────── - # n_total comes from the per-volume 'n_actual' attributes already read - # in step 2; n_visible sums the index arrays step 3 already built; and - # n_attached is just the length of the cloud step 4 just produced. - # Nothing here touches the EDepSim step array. - _n_total = sum(int(sv.get('n_actual', 0)) for sv in seg_volumes) - _n_visible = sum(len(idx) - for vol in visible_by_track - for idx in vol.values()) - _n_attached = len(point_cloud_flat) - self.last_mask_stats = { - 'n_total': _n_total, # deposits in the seg file - 'n_visible': _n_visible, # survived the readout threshold - 'n_masked': _n_total - _n_visible, - # Visible but not attached to any EDepSim particle -- a visible - # segment whose group_to_track ID is absent from the particle list - # (including the -1 sentinel for out-of-range groups). - 'n_attached': _n_attached, - 'n_unmatched': _n_visible - _n_attached, - } + # ── Visibility bookkeeping (free: no extra file reads) ───────── + # n_total comes from the per-volume 'n_actual' attributes already + # read above; n_visible sums the index arrays the visibility pass + # built; n_attached is the length of the cloud just produced. + _n_total = sum(int(sv.get('n_actual', 0)) for sv in seg_volumes) + _n_visible = sum(len(idx) + for vol in visible_by_track + for idx in vol.values()) + _n_attached = len(point_cloud_flat) + self.last_true_x_shift = None + self.last_mask_stats = { + 'n_total': _n_total, # deposits in the seg file + 'n_visible': _n_visible, # survived the readout threshold + 'n_masked': _n_total - _n_visible, + # Visible but not attached to any EDepSim particle -- a + # visible segment whose group_to_track ID is absent from the + # particle list (including the -1 sentinel). + 'n_attached': _n_attached, + 'n_unmatched': _n_visible - _n_attached, + } + self.last_hit_stats = None # ── Step 5: derive per-particle scalar labels from EDepSim data ─── itype = _get_interaction_type(parts, self._e_thresh) @@ -562,9 +692,11 @@ def __getitem__(self, index: int) -> list[Particle]: # deposit_to_group is per volume; concatenate it onto the same # event-global index the point clouds now carry, and do the same for # group_to_track so a group number is unambiguous event-wide. - self.last_deposit_to_group, self.last_group_volume_offsets = \ - self._load_deposit_groups(event_key, seg_volumes) - self.last_deposit_volume_offsets = volume_offsets + # Hit mode filled these in above, from the hits themselves. + if not _hit_mode: + self.last_deposit_to_group, self.last_group_volume_offsets = \ + self._load_deposit_groups(event_key, seg_volumes) + self.last_deposit_volume_offsets = volume_offsets _particles = Particle.from_flat_arrays( ids = _ids, @@ -577,17 +709,349 @@ def __getitem__(self, index: int) -> list[Particle]: point_cloud_flat = point_cloud_flat, point_cloud_offsets = offsets, min_pc_size = self._min_pc_size, + voxel_size = self._voxel_size, geant4_ids = _g4, ) - # Attach each particle's slice of the deposit index. Kept beside the - # cloud rather than inside it: the cloud is float32 and exact only to - # 2**24, which a hundredfold rise in deposits per event would reach. + # Attach each particle's slice of the deposit index -- the input row + # behind every point, which in hit mode is the hit itself. Kept + # beside the cloud rather than inside it: the cloud is float32 and + # exact only to 2**24, which millions of hits per event would reach. for _i, _p in enumerate(_particles): _a, _b = int(offsets[_i][0]), int(offsets[_i][1]) _p.deposit_id = deposit_id[_a:_b] return _particles + # ------------------------------------------------------------------ # + # Hit mode # + # ------------------------------------------------------------------ # + + def _volume_group_offsets(self, ev): + """Where each volume's groups start in the event-global numbering.""" + offs, cum = [], 0 + for vol_name in self._volume_names(ev): + vg = ev[vol_name] + if 'group_to_track' not in vg: + continue + offs.append(cum) + cum += len(vg['group_to_track']) + return offs, cum + + @staticmethod + def _pixel_plane(vol_group): + """The pixel plane of one volume, or None if it has no hit centres.""" + import h5py + for k in vol_group: + g = vol_group[k] + if isinstance(g, h5py.Group) and 'center_py' in g: + return g + return None + + def _group_truth(self, seg_vol, vol_group, n_groups, want_de): + """Per-group mean t0 and summed dE, from the deposits behind them.""" + n = int(seg_vol.get('n_actual', 0) or 0) + key = next((k for k in _SEG_TO_GROUP_KEYS if k in vol_group), None) + if not n or key is None: + z = np.zeros(n_groups, dtype=np.float32) + return z, z.copy(), np.zeros(n_groups, dtype=np.int64) + d2g = vol_group[key][:n].astype(np.int64) + d2g = np.clip(d2g, 0, max(0, n_groups - 1)) + red = _px.group_reductions( + d2g, n_groups, + t0_us=seg_vol['t0_us'][:n].astype(np.float64), + de=seg_vol['de'][:n].astype(np.float64) if want_de else None) + counts = np.bincount(d2g, minlength=n_groups) + return red['t0'], red.get('de'), counts + + def _volume_ranges(self): + """Per-volume (3, 2) x/y/z extent in mm, from the inst file.""" + try: + return self._inst_file['config/volume_ranges'][:] + except Exception: + return None + + def _volume_x_ranges(self): + """Per-volume x extent in mm, from the inst file's config group.""" + vr = self._volume_ranges() + if vr is None: + return None + # (n_volumes, 3, 2), x first. Without it the anode cannot be told + # apart from the reference tick, so the caller falls back to 0. + return [(float(v[0][0]), float(v[0][1])) for v in vr] + + def _num_time_steps(self): + """Length of the readout window in ticks, or None if unstated.""" + cfg = self._inst_file.get('config') + if cfg is None: + return None + n = cfg.attrs.get('num_time_steps') + return int(n) if n is not None else None + + def _sensor_attr(self, name): + """One config attribute of the sensor file, or None.""" + f = self._sensor_file + if f is None: + return None + cfg = f.get('config') + if cfg is None: + return None + v = cfg.attrs.get(name) + return None if v is None else float(v) + + def _stated_drift_terms(self): + """ + ``(velocity_mm_us, time_step_us, why)`` from config or the sensor + file, or ``(None, None, why)`` when neither supplies them. + """ + v = self._drift_velocity_mm_us + dt = self._readout_time_step_us + src = [] + if v is None: + cm = self._sensor_attr('velocity_cm_us') + if cm is not None: + v = cm * 10.0 # JAXTPC states it in cm/us + src.append('velocity from the sensor file') + else: + src.append('velocity from config') + if dt is None: + dt = self._sensor_attr('time_step_us') + if dt is not None: + src.append('time step from the sensor file') + else: + src.append('time step from config') + return v, dt, ', '.join(src) + + def _stated_drift_direction(self, v): + """Drift direction for volume *v*, from config; int or per-volume list.""" + d = self._pixel_drift_direction + if d is None: + return None + if isinstance(d, (int, float)): + return int(d) + try: + return int(d[v]) + except (TypeError, IndexError, ValueError): + return None + + def _resolve_pixel_geometry(self, seg_volumes): + """ + The stated geometry for every volume, or None if it is incomplete. + + Returns ``(geoms, note)``; *geoms* is None when something no file + records -- the pixel pitch and the drift direction -- was not + configured, in which case the caller falls back to fitting and says + so. + """ + ranges = self._volume_ranges() + if ranges is None: + return None, "the hits file has no config/volume_ranges" + pitch = self._pixel_pitch_mm + if pitch is None: + return None, "reader.pixel_pitch_mm is not set" + vel, dt, src = self._stated_drift_terms() + if vel is None or dt is None: + return None, ("no drift velocity / sampling period: set " + "reader.jaxtpc_sensor_path, or " + "reader.drift_velocity_mm_us and " + "reader.readout_time_step_us") + geoms = [] + for v in range(len(seg_volumes)): + ddir = self._stated_drift_direction(v) + if ddir is None: + return None, ("reader.pixel_drift_direction is not set for " + f"volume {v}") + if v >= len(ranges): + return None, f"config/volume_ranges has no volume {v}" + geoms.append(_px.resolve_volume_geometry( + ranges[v], pitch_mm=pitch, drift_direction=ddir, + drift_velocity_mm_us=vel, time_step_us=dt, + reference_tick=self._hit_reference_tick, volume_id=v)) + return geoms, src + + def _group_centre_truth(self, event_key, seg_vol, v): + """ + For one volume: each group's hit centre beside its deposits' centroid. + + The pairing every geometry question is answered from -- a group has + both a place in the readout and a place in the detector, and the two + are the same place. + """ + ev = self._inst_file[event_key] + vol_key = f'volume_{v}' + plane = self._pixel_plane(ev[vol_key]) if vol_key in ev else None + if plane is None: + raise _px.PixelGeometryError( + f"{event_key}/{vol_key} has no pixel plane (no subgroup " + f"with 'center_py'). point_source='hits' reads the " + f"detected pixel image, which only a pixel readout " + f"produces; this file is " + f"{read_readout_type(self._inst_file)!r} readout.") + vg = ev[vol_key] + n_groups = len(vg['group_to_track']) + t0, _de, counts = self._group_truth(seg_vol, vg, n_groups, + want_de=False) + n = int(seg_vol.get('n_actual', 0) or 0) + key = next((k for k in _SEG_TO_GROUP_KEYS if k in vg), None) + d2g = vg[key][:n].astype(np.int64) + pos = seg_vol.get('positions_mm') + cent = np.zeros((n_groups, 3), dtype=np.float64) + safe = np.where(counts > 0, counts, 1).astype(np.float64) + for i in range(3): + cent[:, i] = np.bincount(d2g, weights=pos[:n, i], + minlength=n_groups) / safe + + gid = plane['group_ids'][:].astype(np.int64) + keep = counts[gid] > 0 # groups whose deposits we can see + return (plane['center_py'][:][keep], plane['center_pz'][:][keep], + plane['center_times'][:][keep], t0[gid][keep], + cent[gid][keep]) + + def _ensure_pixel_geometry(self, event_key, seg_volumes): + """ + Settle the pixel geometry once, from configuration, and check it. + + Configuration is the source and the fit is the audit -- never the + other way round. A constant derived from the truth it is then + compared against cannot fail for a geometry that is wrong but + linear: the fit absorbs the error, and a detector effect this mode + exists to measure gets calibrated away instead of measured. So the + stated numbers are applied as given and the residual is allowed to + object. + """ + if self._pixel_geoms is not None: + return self._pixel_geoms + + geoms, note = self._resolve_pixel_geometry(seg_volumes) + pairs = [self._group_centre_truth(event_key, sv, v) + for v, sv in enumerate(seg_volumes)] + + if geoms is not None: + reports = [] + for v, (geom, (py, pz, tk, t0, cent)) in enumerate( + zip(geoms, pairs)): + rep = _px.verify_volume_geometry(geom, py, pz, tk, t0, cent, + volume_id=v) + rep['source_note'] = note + reports.append(rep) + self._pixel_geoms, self._pixel_report = geoms, reports + return geoms + + if not self._geometry_from_fit: + # Say what is missing *and* what the data says it should be, so + # the config can be written from the error rather than guessed. + say = [] + for v, (py, pz, tk, t0, cent) in enumerate(pairs): + m = _px._measure_volume(py, pz, tk, t0, + cent[:, 0], cent[:, 1], cent[:, 2]) + say.append( + f" volume {v}: pixel_pitch_mm ~ {m['pitch_mm']:.4f}, " + f"pixel_drift_direction ~ {m['drift_direction']:+d}, " + f"drift_velocity_mm_us ~ {m['drift_velocity_mm_us']:.4f}, " + f"readout_time_step_us ~ {m['time_step_us']:.4f}") + raise _px.PixelGeometryError( + f"point_source='hits' needs the pixel geometry, and " + f"{note}.\n" + f"These are detector constants, so they are configured " + f"rather than inferred -- a value fitted to the truth it is " + f"later checked against cannot be caught when it is wrong.\n" + f"The drift velocity and sampling period are in the JAXTPC " + f"sensor file: set reader.jaxtpc_sensor_path. The pitch and " + f"the drift direction are in no output file; take them from " + f"the detector YAML.\n" + f"For this batch the data is consistent with:\n" + + "\n".join(say) + + f"\nSet those, or reader.pixel_geometry_from_fit=true to " + f"measure them instead and accept that the check becomes " + f"circular.") + + import warnings + warnings.warn( + f"pixel geometry fitted from the truth deposits because {note}. " + f"The residual check is then circular -- it cannot catch a " + f"geometry that is wrong but linear. Prefer configuring " + f"reader.pixel_pitch_mm, reader.pixel_drift_direction and " + f"reader.jaxtpc_sensor_path.", RuntimeWarning, stacklevel=2) + geoms, reports = [], [] + xr = self._volume_x_ranges() + for v, (py, pz, tk, t0, cent) in enumerate(pairs): + geom, rep = _px.calibrate_volume( + py, pz, tk, t0, cent, volume_id=v, + x_range=xr[v] if xr and v < len(xr) else None, + reference_tick=self._hit_reference_tick) + rep['source'] = 'fitted' + rep['source_note'] = note + geoms.append(geom) + reports.append(rep) + self._pixel_geoms, self._pixel_report = geoms, reports + return geoms + + def _build_hit_event(self, event_key, seg_volumes, track_ids): + """Point cloud, offsets and per-point group for one event.""" + ev = self._inst_file[event_key] + self._ensure_pixel_geometry(event_key, seg_volumes) + g_offs, n_groups_total = self._volume_group_offsets(ev) + + want_de = self._hit_energy == 'true_de' + # Needed by both conventions now: 'true_t0' to move x, 'nominal' to + # record how far x would have to move -- the true_x_shift column. + want_t0 = True + # The window truncation is a truth question -- which deposits would + # have needed a tick the readout never reached -- so it needs t0 and + # the true x whichever drift convention is in force. + n_steps = self._num_time_steps() + trunc_before = trunc_after = trunc_total = 0 + volumes = [] + for v, seg_vol in enumerate(seg_volumes): + vol_key = f'volume_{v}' + if vol_key not in ev: + continue + vg = ev[vol_key] + plane = self._pixel_plane(vg) + if plane is None: + continue + n_groups = len(vg['group_to_track']) + entry = { + 'hits': _px.decode_plane_hits(plane), + 'geom': self._pixel_geoms[v], + 'group_to_track': vg['group_to_track'][:].astype(np.int64), + 'group_offset': g_offs[v] if v < len(g_offs) else 0, + } + if want_de or want_t0: + t0, de, _ = self._group_truth(seg_vol, vg, n_groups, want_de) + if want_t0: + entry['group_t0'] = t0 + if want_de: + entry['group_de'] = de + volumes.append(entry) + + n_act = int(seg_vol.get('n_actual', 0) or 0) + if n_steps and n_act: + b, a, tot = _px.window_truncation( + seg_vol['positions_mm'][:n_act, 0], + seg_vol['t0_us'][:n_act], + self._pixel_geoms[v], n_steps) + trunc_before += b + trunc_after += a + trunc_total += tot + + # dt is needed only to express the time column and to convert t0 into + # ticks; the calibration recovered it alongside the geometry. + dt = 1.0 + if self._pixel_geoms: + dt = float(self._pixel_geoms[0].time_step_us) + if not np.isfinite(dt) or dt <= 0: + dt = 1.0 + flat, offsets, group_of_point, x_shift, stats = \ + _px.build_hit_point_cloud( + volumes, track_ids, x_from=self._hit_x_from, + energy=self._hit_energy, time_step_us=dt, + charge_threshold=self._hit_charge_threshold) + stats['n_groups'] = n_groups_total + stats['n_deposits'] = trunc_total + stats['n_before_window'] = trunc_before + stats['n_after_window'] = trunc_after + return flat, offsets, group_of_point, x_shift, g_offs, stats + def _load_deposit_groups(self, event_key, seg_volumes): """ ``(deposit_to_group, group_volume_offsets)`` for one event. @@ -611,6 +1075,11 @@ def _load_deposit_groups(self, event_key, seg_volumes): g_off.append(cum_g) d2g.append(vg[key][:].astype(_np.int64) + cum_g) cum_g += len(vg['group_to_track']) + # The group space is what group_to_track spans, not what the deposits + # happen to reference: a group nothing deposited into still occupies a + # number, and deriving the count from max()+1 instead would leave the + # owner table short of the volume offsets that index it. + self.last_n_groups = cum_g if not d2g: return None, [] return _np.concatenate(d2g), g_off @@ -622,7 +1091,8 @@ def _volume_names(ev): def close(self) -> None: """Close all open HDF5 file handles.""" - for attr in ('_edepsim_file', '_seg_file', '_inst_file'): + for attr in ('_edepsim_file', '_seg_file', '_inst_file', + '_sensor_file'): fh = getattr(self, attr, None) if fh is not None: fh.close() @@ -838,6 +1308,32 @@ def inst_path(self) -> str: """Path to the JAXTPC inst HDF5 file.""" return self._inst_path + @property + def point_source(self) -> str: + """``'deposits'`` (truth geometry) or ``'hits'`` (detected image).""" + return self._point_source + + @property + def pixel_geometry(self) -> list | None: + """Per-volume :class:`~.pixel_hits.VolumePixelGeometry`, once known.""" + return self._pixel_geoms + + @property + def pixel_geometry_report(self) -> list | None: + """What the geometry calibration found, including its residuals.""" + return self._pixel_report + + @property + def readout_type(self) -> str: + """ + ``'wire'`` or ``'pixel'`` — which readout the inst file describes. + + Nothing in this reader branches on it: visibility is a property of a + group, not of the geometry that recorded it. It is surfaced for the + run report and for the tools that draw hits, which do differ. + """ + return read_readout_type(self._inst_file) + def __repr__(self) -> str: return ( f"JaxtpcHDF5Reader(" diff --git a/pysupera/readers/pixel_hits.py b/pysupera/readers/pixel_hits.py new file mode 100644 index 0000000..31812cc --- /dev/null +++ b/pysupera/readers/pixel_hits.py @@ -0,0 +1,838 @@ +""" +Pixel hits as a point cloud — the detected image instead of the truth deposits. + +The ordinary JAXTPC path (``point_source=deposits``) builds each particle's +point cloud from the Geant4 energy deposits that *survived* the readout. The +geometry is therefore truth geometry: the readout only decides which deposits +are kept. + +This module builds the other cloud. A pixel readout records, for every fired +pixel and drift tick, a charge; converting ``(py, pz, tick)`` back to +``(x, y, z)`` gives the points the detector actually saw, carrying pixelation, +diffusion, threshold and — depending on *x_from* below — the drift-time +ambiguity. Running the same partitioning over those points is what +``point_source=hits`` is for. + +Every hit still has one Geant4 particle: a CSR entry belongs to exactly one +group, and ``group_to_track`` gives each group one track. The assignment is +therefore unique without any nearest-neighbour matching. + +The inverse transform +--------------------- +``y`` and ``z`` are pure geometry and invert exactly:: + + y = y_min + (py + 0.5) * pitch + z = z_min + (pz + 0.5) * pitch + +``x`` is drift time, and there the detector does not know what the truth +knows. A tick counts from the start of the readout window, so it measures +``t_drift + t0`` — and ``t0``, the time the interaction happened, is exactly +what a real detector has to determine separately. Writing ``T`` for the +elapsed time a hit's tick stands for, measured from the tick at which a +deposit sitting on the anode at Geant4 ``t = 0`` would be recorded:: + + T = (tick - reference_tick) * dt + + x = x_anode - drift_direction * T * v # 'nominal' + x = x_anode - drift_direction * (T - t0) * v # 'true_t0' + +``'nominal'`` is what a detector can actually do: assume the interaction +happened at the beam time. Its x is then offset from the true x by exactly +``drift_direction * v * t0`` -- the interaction's own Geant4 time turned into +a displacement. ``'true_t0'`` puts the real ``t0`` back, giving the drift +position the detector would measure with perfect timing: every other effect +(pixelation, diffusion, threshold, tick quantisation) survives, only the +ambiguity goes. + +Measured on a 10-event pixel batch, ``t0`` is constant within an interaction +(median spread 0.000 μs, max 0.531 μs → 0.85 mm) and differs *across* the +interactions of one event by up to 1094 μs → 175 cm. So ``'nominal'`` keeps +each interaction's shape to sub-mm and slides whole interactions along x past +one another, while ``'true_t0'`` reproduces truth x to ~0.3 mm. Neither is +more correct; they answer different questions, which is why both are offered. + +The reference tick, and what the readout window hides +----------------------------------------------------- +``reference_tick`` is the tick a deposit on the anode at Geant4 ``t = 0`` +lands in, and it is the anchor the whole drift coordinate hangs from. It is +kept explicit rather than folded into a fitted intercept because the two are +indistinguishable in a fit and only one of them is the anode: a run with a +pre-window has ``reference_tick > 0``, and a calibration that quietly +absorbed it would report an anode that is not where the anode is. + +It also decides whether points can leave the detector. On the batch this was +written against ``reference_tick`` is 0 and ``num_time_steps`` is 2701, so +the recordable ticks 0..2700 map exactly onto ``[anode, cathode]`` and a +nominal x *cannot* fall outside the volume. The t0 displacement instead +pushes charge off the end of the window, where it is never recorded at all: +25.8% of volume-0 deposits in event 0 would need a tick past 2700, the +largest asking for 4634. Move the reference tick, or lengthen the window +past one full drift, and the displacement becomes visible as out-of-volume +points instead. Both are counted and reported rather than clipped -- a +nominal x outside the detector is the honest answer, and clipping it would +invent a wall the reconstruction does not have. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np + +from ..utils import PointFeature + + +#: Scale JAXTPC divides a group's per-entry charge by before storing it. +#: Signed int16 is the current encoding; unsigned came from the older +#: diffusion-only path and is still read, matching JAXTPC's own loader. +_CHARGE_SCALE = {'charges_i16': 32767.0, 'charges_u16': 65535.0} + +#: ``group_sizes`` is uint8, so a group of more than 255 entries cannot be +#: represented. JAXTPC caps rather than raising, and a capped group silently +#: truncates the CSR, so it is worth naming when it happens. +_MAX_GROUP_SIZE = 255 + + +@dataclass(frozen=True) +class VolumePixelGeometry: + """ + Everything needed to turn one volume's pixel indices back into mm. + + Parameters are in mm and μs. ``drift_direction`` is JAXTPC's: ``+1`` + when electrons drift toward the ``x_max`` face, ``-1`` toward ``x_min``, + so the anode sits at that face and ``x`` moves away from it with tick. + The volume's full x range is carried too, because whether a nominal x + has left the detector is a question only this object can answer. + """ + + y_min_mm: float + z_min_mm: float + x_min_mm: float + x_max_mm: float + drift_direction: int + pitch_mm: float + drift_velocity_mm_us: float + time_step_us: float + reference_tick: float = 0.0 + + @property + def mm_per_tick(self) -> float: + """One tick of drift, in mm: the sampling period times the velocity.""" + return self.drift_velocity_mm_us * self.time_step_us + + @property + def x_anode_mm(self) -> float: + """The face the electrons arrive at.""" + return self.x_max_mm if self.drift_direction == 1 else self.x_min_mm + + @property + def x_cathode_mm(self) -> float: + """The far face, one full drift away.""" + return self.x_min_mm if self.drift_direction == 1 else self.x_max_mm + + def to_xyz(self, py, pz, tick): + """``(py, pz, tick)`` -> ``(x, y, z)`` in mm, as float32 arrays.""" + # +0.5 puts the point at the pixel centre rather than its lower edge; + # without it every point is biased by half a pitch. + y = self.y_min_mm + (np.asarray(py, dtype=np.float32) + 0.5) * self.pitch_mm + z = self.z_min_mm + (np.asarray(pz, dtype=np.float32) + 0.5) * self.pitch_mm + elapsed = np.asarray(tick, dtype=np.float32) - np.float32(self.reference_tick) + x = self.x_anode_mm - self.drift_direction * (elapsed * self.mm_per_tick) + return (x.astype(np.float32, copy=False), + y.astype(np.float32, copy=False), + z.astype(np.float32, copy=False)) + + def outside(self, x): + """Boolean mask of x values beyond this volume's own faces.""" + x = np.asarray(x) + return (x < self.x_min_mm) | (x > self.x_max_mm) + + +def anode_of(x_range, drift_direction: int) -> float: + """The anode face of a volume, given its x range and drift direction.""" + lo, hi = float(x_range[0]), float(x_range[1]) + return hi if drift_direction == 1 else lo + + +# --------------------------------------------------------------------------- +# CSR decode +# --------------------------------------------------------------------------- + +def decode_plane_hits(plane) -> dict: + """ + Expand one pixel plane's CSR correspondence into flat per-entry arrays. + + JAXTPC stores each group once — its peak pixel, tick and charge — plus one + int8 delta per entry against that peak. Undoing it is a repeat of the + centres plus the deltas, which is why this is vectorised rather than the + per-group Python loop JAXTPC's own loader uses. + + Returns + ------- + dict + ``py``, ``pz``, ``tick`` (int32) and ``charge`` (float32), one entry + per fired pixel/tick, plus ``group`` (int32), the volume-local group + number each entry belongs to. + + Notes + ----- + The delta arrays are longer than ``group_sizes.sum()`` — JAXTPC pads them + to a chunk boundary. The CSR length is the sum, never the array length; + reading to the end would append hundreds of zero-delta ghosts sitting on + top of the last group's peak. + """ + sizes = plane['group_sizes'][:].astype(np.int64) + groups = plane['group_ids'][:].astype(np.int32) + n = int(sizes.sum()) + + capped = int((sizes >= _MAX_GROUP_SIZE).sum()) + + key = next((k for k in _CHARGE_SCALE if k in plane), None) + if key is None: + raise KeyError( + f"pixel plane {plane.name!r} has no per-entry charge dataset " + f"(expected one of {sorted(_CHARGE_SCALE)}); it cannot be " + f"expanded into hits. A plane subset written by " + f"pysupera-hits-subset keeps only the group centres and is not " + f"usable as reader.jaxtpc_inst_path.") + + centre_py = np.repeat(plane['center_py'][:].astype(np.int32), sizes) + centre_pz = np.repeat(plane['center_pz'][:].astype(np.int32), sizes) + centre_t = np.repeat(plane['center_times'][:].astype(np.int32), sizes) + peak = np.repeat(plane['peak_charges'][:].astype(np.float32), sizes) + + py = centre_py + plane['delta_py'][:n].astype(np.int32) + pz = centre_pz + plane['delta_pz'][:n].astype(np.int32) + tick = centre_t + plane['delta_times'][:n].astype(np.int32) + charge = peak * (plane[key][:n].astype(np.float32) / _CHARGE_SCALE[key]) + + return {'py': py, 'pz': pz, 'tick': tick, 'charge': charge, + 'group': np.repeat(groups, sizes), 'n_capped_groups': capped} + + +# --------------------------------------------------------------------------- +# Per-group truth quantities +# --------------------------------------------------------------------------- + +def group_reductions(deposit_to_group, n_groups, t0_us=None, de=None): + """ + Mean ``t0`` and summed ``de`` per group, from the deposits behind it. + + Both are truth quantities a hit does not carry. ``t0`` is needed to undo + the drift-time ambiguity (``x_from='true_t0'``) and ``de`` to give + the energy column true deposited energy instead of charge + (``energy='true_de'``); neither is computed unless asked for. + + Groups with no deposits come back as 0, which only reaches a point if the + readout produced a hit for a group nothing deposited into — impossible by + construction, and harmless if the schema ever changes. + """ + counts = np.bincount(deposit_to_group, minlength=n_groups).astype(np.float64) + safe = np.where(counts > 0, counts, 1.0) + out = {} + if t0_us is not None: + out['t0'] = (np.bincount(deposit_to_group, weights=t0_us, + minlength=n_groups) / safe).astype(np.float32) + if de is not None: + out['de'] = np.bincount(deposit_to_group, weights=de, + minlength=n_groups).astype(np.float32) + return out + + +# --------------------------------------------------------------------------- +# Point-cloud assembly +# --------------------------------------------------------------------------- + +#: Accepted values for the drift-coordinate convention. +#: ``nominal`` assumes the interaction happened at the beam time (Geant4 +#: t=0), which is what a detector without an independent t0 must do; +#: ``true_t0`` puts the real interaction time back. +X_FROM = ('nominal', 'true_t0') + +#: Accepted values for what lands in the energy column. +ENERGY_FROM = ('charge', 'true_de') + + +def build_hit_point_cloud(volumes, track_ids_edepsim, *, + x_from: str = 'nominal', + energy: str = 'charge', + time_step_us: float = 1.0, + charge_threshold: float = 0.0): + """ + A flat point cloud of detected pixel hits, laid out per particle. + + Parameters + ---------- + volumes : list of dict + One per volume, each holding ``hits`` (the :func:`decode_plane_hits` + output), ``geom`` (:class:`VolumePixelGeometry`), ``group_to_track``, + ``group_offset`` (where this volume's groups start in the event-global + numbering) and, when the corresponding option is selected, + ``group_t0`` and ``group_de``. + track_ids_edepsim : ndarray + Track IDs in EDepSim particle order. The returned offsets are + aligned to it, so ``Particle.from_flat_arrays`` pairs each metadata + row with the right slice. + x_from : {'nominal', 'true_t0'} + Whether the drift coordinate keeps the interaction time in it. See + the module docstring. + energy : {'charge', 'true_de'} + Charge as measured, or the group's true deposited energy shared out + over its hits in proportion to charge. + time_step_us : float + Readout tick length, used to put the time column in μs. + charge_threshold : float + Drop hits carrying less than this much charge, in the units the hits + file uses -- ionisation electrons induced on the pixel. Compared + against the *magnitude*, matching JAXTPC's own + ``encode_correspondence_csr_pixel``: the readout response is + bipolar, and a large negative lobe is signal, not noise. + + JAXTPC applies no threshold worth the name (1 electron in this + batch), so without one every pixel carrying any induced-field + influence is a point: 4.18M of them for an event whose truth is + 216k deposits, and the brightest 2% carry half the charge. + + Returns + ------- + point_cloud_flat : (M, 7) float32 + offsets : (N, 2) int64 + true_x_shift : (M,) float32 + What to add to ``x`` to recover the true drift position: + ``true_x = x + true_x_shift``. Zero throughout under + ``x_from='true_t0'``, so the relation holds either way. + group_of_point : (M,) int64 + Event-global group number per row. This is the hit-mode counterpart + of the deposit index the truth path carries, and it is what makes the + ``groups/`` provenance table exact here: a point *is* a hit, so its + group is read rather than looked up through a voxel map. + stats : dict + Includes ``n_outside_volume``: nominal x values beyond their own + volume's faces. Counted, never clipped -- see the module docstring. + """ + if x_from not in X_FROM: + raise ValueError(f"x_from must be one of {X_FROM}, got {x_from!r}") + if energy not in ENERGY_FROM: + raise ValueError(f"energy must be one of {ENERGY_FROM}, got {energy!r}") + + xs, ys, zs, ts, es, gs, tr, sh = [], [], [], [], [], [], [], [] + n_capped = 0 + n_outside = 0 + n_cut = 0 # hits dropped by charge_threshold + n_decoded = 0 # hits in the file, before that cut + + for vol in volumes: + hits = vol['hits'] + if not len(hits['group']): + continue + n_capped += int(hits.get('n_capped_groups', 0)) + n_decoded += len(hits['group']) + geom = vol['geom'] + g_local = hits['group'] + + q_raw = hits['charge'] + if charge_threshold > 0: + keep = np.abs(q_raw) >= charge_threshold + n_cut += int((~keep).sum()) + if not keep.all(): + hits = {k: (v[keep] if isinstance(v, np.ndarray) else v) + for k, v in hits.items()} + g_local = hits['group'] + if not len(g_local): + continue + + tick = hits['tick'].astype(np.float32) + if x_from == 'true_t0': + t0 = vol['group_t0'] + # t0 is per group and constant within an interaction, so removing + # it per group is exact rather than an approximation. + tick = tick - (t0[g_local] / np.float32(time_step_us)) + + x, y, z = geom.to_xyz(hits['py'], hits['pz'], tick) + n_outside += int(geom.outside(x).sum()) + + # What would have to be added to x to put it back where the deposit + # was: true_x = x + shift. Under 'nominal' that is the interaction + # time turned into a displacement, drift_direction * t0 * v; under + # 'true_t0' the stored x is already the true one, so it is zero. Kept + # per hit rather than per particle because a track crossing the + # cathode has hits in both volumes, where drift_direction differs -- + # rare (2 particles in a test event) but 15% of the points. + if x_from == 'true_t0': + sh.append(np.zeros(len(x), dtype=np.float32)) + else: + t0v = vol['group_t0'][g_local] * np.float32( + geom.drift_direction * geom.drift_velocity_mm_us) + sh.append(t0v.astype(np.float32, copy=False)) + xs.append(x); ys.append(y); zs.append(z) + # The time column follows whichever x convention is in force, so the + # two never disagree about when a point happened. + ts.append((tick * np.float32(time_step_us)).astype(np.float32)) + + q = hits['charge'] + if energy == 'charge': + es.append(q) + else: + # Share the group's true dE over its hits by |charge|. A hit has + # no energy of its own, and this is the only apportionment the + # file supports. The magnitude, not the signed value: the + # response is bipolar, and sharing by signed charge would hand + # the undershoot pixels negative "deposited energy" -- which the + # deposit-mode column this one exists to be comparable with can + # never be. Still exact in sum per group. + n_groups = len(vol['group_to_track']) + w = np.abs(q.astype(np.float64)) + wsum = np.bincount(g_local, weights=w, minlength=n_groups) + wsum = np.where(wsum != 0, wsum, 1.0) + share = w / wsum[g_local] + es.append((vol['group_de'][g_local] * share).astype(np.float32)) + + gs.append(g_local.astype(np.int64) + int(vol['group_offset'])) + tr.append(vol['group_to_track'][g_local].astype(np.int64)) + + n_particles = len(track_ids_edepsim) + if not xs: + return (np.zeros((0, 7), dtype=np.float32), + np.zeros((n_particles, 2), dtype=np.int64), + np.zeros(0, dtype=np.int64), + np.zeros(0, dtype=np.float32), + {'n_hits': 0, 'n_attached': 0, 'n_unmatched': 0, + 'n_capped_groups': n_capped, 'n_outside_volume': 0, + 'n_decoded': n_decoded, 'n_below_threshold': n_cut}) + + x = np.concatenate(xs); y = np.concatenate(ys); z = np.concatenate(zs) + t = np.concatenate(ts); e = np.concatenate(es) + group = np.concatenate(gs); track = np.concatenate(tr) + shift = np.concatenate(sh) + n_hits = len(x) + + # Track ID -> row in the EDepSim particle list. A lookup by searchsorted + # rather than a dict: with millions of hits the dict comprehension is the + # slowest line in the reader. + tid = np.asarray(track_ids_edepsim) + order_t = np.argsort(tid, kind='stable') + sorted_t = tid[order_t] + pos = np.searchsorted(sorted_t, track) + inside = pos < len(sorted_t) + pos_safe = np.where(inside, pos, 0) + matched = inside & (sorted_t[pos_safe] == track) + row = order_t[pos_safe] + + # Hits whose group names a track the EDepSim file does not list cannot be + # attributed, exactly as in the deposit path; count them and drop them. + keep = np.flatnonzero(matched) + row = row[keep] + + # Lay the cloud out particle by particle: one stable sort, then the + # offsets fall out of the counts. + order = np.argsort(row, kind='stable') + sel = keep[order] + row_sorted = row[order] + + counts = np.bincount(row_sorted, minlength=n_particles) + ends = np.cumsum(counts) + offsets = np.empty((n_particles, 2), dtype=np.int64) + offsets[:, 0] = ends - counts + offsets[:, 1] = ends + + m = len(sel) + flat = np.zeros((m, 7), dtype=np.float32) + flat[:, PointFeature.x] = x[sel] + flat[:, PointFeature.y] = y[sel] + flat[:, PointFeature.z] = z[sel] + flat[:, PointFeature.time] = t[sel] + flat[:, PointFeature.energy] = e[sel] + # dx stays 0: a hit is a pixel and a tick, it has no path length. Leaving + # it zero is honest; inventing one would quietly feed dE/dx consumers. + flat[:, PointFeature.id] = np.arange(m, dtype=np.float32) + + stats = {'n_hits': n_hits, 'n_attached': m, 'n_unmatched': n_hits - m, + 'n_capped_groups': n_capped, 'n_outside_volume': n_outside, + 'n_decoded': n_decoded, 'n_below_threshold': n_cut} + return flat, offsets, group[sel], shift[sel], stats + + +# --------------------------------------------------------------------------- +# Geometry calibration +# --------------------------------------------------------------------------- + +#: How far a calibrated prediction may sit from truth before the geometry is +#: treated as wrong. y and z are quantised to the pitch, so one pitch is the +#: natural bound; x is continuous and holds to a few tenths of a mm in +#: practice, so 5 mm is loose enough to survive a different detector and +#: tight enough to catch a mirrored volume or a wrong tick length. +_FIT_TOL_XY_PITCHES = 1.0 +_FIT_TOL_X_MM = 5.0 + + +class PixelGeometryError(ValueError): + """The pixel geometry could not be established from the files.""" + + +def window_truncation(x_true, t0_us, geom, num_time_steps): + """ + How many deposits the readout window never got to record. + + A deposit at ``x`` with interaction time ``t0`` arrives at tick + ``reference_tick + (|x - x_anode|/v + t0)/dt``. When the window is + exactly one full drift long -- as it is on the batch this was written + against -- any positive ``t0`` pushes the cathode-side end of an + interaction past the last tick, and it is simply not in the file. + + That loss is what makes hit mode look like it dropped charge for no + reason, and it is ``t0``-dependent, so it biases a comparison against + deposit mode rather than merely shrinking it. Hence counted. + + Returns ``(n_before, n_after, n_total)``. + """ + x_true = np.asarray(x_true, dtype=np.float64) + drift_us = np.abs(x_true - geom.x_anode_mm) / geom.drift_velocity_mm_us + t0_us = np.asarray(t0_us, dtype=np.float64) + want = geom.reference_tick + (drift_us + t0_us) / geom.time_step_us + return (int((want < 0).sum()), + int((want > num_time_steps - 1).sum()), + int(len(want))) + + +def resolve_volume_geometry(volume_ranges_mm, *, pitch_mm, drift_direction, + drift_velocity_mm_us, time_step_us, + reference_tick=0.0, volume_id=0): + """ + Build a volume's geometry from stated values, fitting nothing. + + This is the path the reader wants to take. Every number here is either + written in a file (the volume extent in the hits file's + ``config/volume_ranges``, the drift velocity and sampling period in the + sensor file's ``config`` attributes) or configured by hand (the pixel + pitch and the drift direction, which no JAXTPC output file records). + :func:`verify_volume_geometry` then checks the result against truth -- + a check that means something precisely because nothing was tuned to make + it pass. + + Parameters + ---------- + volume_ranges_mm : array-like, shape (3, 2) + The volume's ``[[x0, x1], [y0, y1], [z0, z1]]`` extent in mm. + pitch_mm, drift_direction, drift_velocity_mm_us, time_step_us : float + Stated, not measured. ``drift_direction`` is ``+1`` when electrons + drift toward the ``x_max`` face and ``-1`` toward ``x_min``. + reference_tick : float + The tick a deposit on the anode at Geant4 ``t = 0`` is recorded in. + + Returns + ------- + VolumePixelGeometry + """ + r = np.asarray(volume_ranges_mm, dtype=np.float64) + if r.shape != (3, 2): + raise PixelGeometryError( + f"volume {volume_id}: expected a (3, 2) x/y/z extent in mm, got " + f"shape {r.shape}. This comes from config/volume_ranges in the " + f"JAXTPC hits file.") + if drift_direction not in (-1, 1): + raise PixelGeometryError( + f"volume {volume_id}: drift_direction must be +1 (electrons " + f"drift toward x_max) or -1 (toward x_min), got " + f"{drift_direction!r}.") + for name, val in (('pitch_mm', pitch_mm), + ('drift_velocity_mm_us', drift_velocity_mm_us), + ('time_step_us', time_step_us)): + if val is None or not np.isfinite(val) or val <= 0: + raise PixelGeometryError( + f"volume {volume_id}: {name} must be a positive number, got " + f"{val!r}.") + + return VolumePixelGeometry( + y_min_mm=float(min(r[1])), z_min_mm=float(min(r[2])), + x_min_mm=float(min(r[0])), x_max_mm=float(max(r[0])), + drift_direction=int(drift_direction), + pitch_mm=float(pitch_mm), + drift_velocity_mm_us=float(drift_velocity_mm_us), + time_step_us=float(time_step_us), + reference_tick=float(reference_tick), + ) + + +def verify_volume_geometry(geom, py, pz, tick, t0, xyz_truth, volume_id=0): + """ + Check a stated geometry against the truth deposits behind each group. + + Nothing here is tuned: *geom* is applied as given, the groups' hit + centres are converted with it, and the distance to the deposits that + produced them is measured. A fit is run alongside purely to report what + the data would have said, so a disagreement names the offending number + rather than just failing. + + That distinction is the point of this function. Deriving the constants + from the same truth they are then checked against cannot fail for a + geometry that is wrong but linear -- the fit simply absorbs the error, + and a detector effect the study exists to measure is calibrated away + instead. Applying stated constants and measuring the residual can. + + Returns + ------- + report : dict + Stated values, what the fit measured, and the residuals. + """ + n = len(py) + if n < 8: + raise PixelGeometryError( + f"volume {volume_id}: only {n} group(s) have both a hit centre " + f"and truth deposits, too few to check the pixel geometry " + f"against.") + + py = np.asarray(py, dtype=np.float64) + pz = np.asarray(pz, dtype=np.float64) + tick = np.asarray(tick, dtype=np.float64) + t0 = np.asarray(t0, dtype=np.float64) + xt, yt, zt = (np.asarray(xyz_truth[:, i], dtype=np.float64) + for i in range(3)) + + measured = _measure_volume(py, pz, tick, t0, xt, yt, zt) + + # Where the data puts the anchor, given the stated anode and tick length. + derived_ref = (geom.drift_direction + * (measured['fit_intercept_mm'] - geom.x_anode_mm) + / geom.mm_per_tick) if geom.mm_per_tick else 0.0 + + # Apply the stated geometry, but anchored where the data says. t0 is + # removed too: the question here is whether the pitch, velocity and + # drift direction describe this detector -- all measurements, all + # checkable. The reference tick is not one of them. It is a choice + # about where t=0 sits, so someone asking "what if the trigger were 100 + # ticks later" must not be told their detector is broken. How far that + # choice sits from the data is reported below, in mm, instead. + elapsed_ticks = tick - derived_ref - t0 / geom.time_step_us + x_pred = geom.x_anode_mm - geom.drift_direction * ( + elapsed_ticks * geom.mm_per_tick) + y_pred = geom.y_min_mm + (py + 0.5) * geom.pitch_mm + z_pred = geom.z_min_mm + (pz + 0.5) * geom.pitch_mm + + res = {'x': float(np.median(np.abs(xt - x_pred))), + 'y': float(np.median(np.abs(yt - y_pred))), + 'z': float(np.median(np.abs(zt - z_pred)))} + + tol_xy = _FIT_TOL_XY_PITCHES * geom.pitch_mm + bad = [k for k, v in res.items() + if v > (_FIT_TOL_X_MM if k == 'x' else tol_xy)] + if bad: + raise PixelGeometryError( + f"volume {volume_id}: the stated pixel geometry does not " + f"reproduce the truth deposits over {n} groups -- median " + f"|residual| x={res['x']:.2f} y={res['y']:.2f} z={res['z']:.2f} " + f"mm, off in {', '.join(bad)}.\n" + f" stated: pitch {geom.pitch_mm:.4f} mm, " + f"{geom.mm_per_tick:.4f} mm/tick, v " + f"{geom.drift_velocity_mm_us:.4f} mm/us, dt " + f"{geom.time_step_us:.4f} us, drift {geom.drift_direction:+d}, " + f"anode {geom.x_anode_mm:+.1f} mm, reference tick " + f"{geom.reference_tick:g}\n" + f" data says: pitch {measured['pitch_mm']:.4f} mm, " + f"{measured['mm_per_tick']:.4f} mm/tick, v " + f"{measured['drift_velocity_mm_us']:.4f} mm/us, dt " + f"{measured['time_step_us']:.4f} us, drift " + f"{measured['drift_direction']:+d}\n" + f"Check reader.pixel_pitch_mm, reader.pixel_drift_direction and " + f"the sensor file, or that the hits and step files are from the " + f"same run.") + + return {'n_groups': n, + 'pitch_mm': geom.pitch_mm, + 'mm_per_tick': geom.mm_per_tick, + 'drift_direction': geom.drift_direction, + 'x_anode_mm': geom.x_anode_mm, + 'reference_tick': geom.reference_tick, + 'drift_velocity_mm_us': geom.drift_velocity_mm_us, + 'time_step_us': geom.time_step_us, + 'residual_mm': res, + 'reference_tick_derived': float(derived_ref), + 'reference_tick_offset_mm': float( + (geom.reference_tick - derived_ref) * geom.mm_per_tick), + 'measured': measured, + 'source': 'stated'} + + +def _measure_volume(py, pz, tick, t0, xt, yt, zt): + """What the data says the geometry is. Reporting only -- never applied.""" + def fit1(idx, target): + A = np.stack([np.ones_like(idx), idx], axis=1) + coef, *_ = np.linalg.lstsq(A, target, rcond=None) + return coef + (_cy0, pitch_y) = fit1(py, yt) + (_cz0, pitch_z) = fit1(pz, zt) + A = np.stack([np.ones_like(tick), tick, t0], axis=1) + (icept, b_tick, b_t0), *_ = np.linalg.lstsq(A, xt, rcond=None) + mm_per_tick = abs(b_tick) + velocity = abs(b_t0) + return {'pitch_mm': float(0.5 * (pitch_y + pitch_z)), + 'pitch_y_mm': float(pitch_y), 'pitch_z_mm': float(pitch_z), + 'mm_per_tick': float(mm_per_tick), + 'drift_direction': -1 if b_tick > 0 else 1, + 'drift_velocity_mm_us': float(velocity), + 'time_step_us': float(mm_per_tick / velocity) if velocity + else float('nan'), + 'fit_intercept_mm': float(icept)} + + +def calibrate_volume(py, pz, tick, t0, xyz_truth, volume_id=0, + x_range=None, reference_tick=None): + """ + Recover one volume's pixel geometry from group centres and truth. + + Nothing in a JAXTPC output file states the pixel pitch, the anode face or + the tick length — ``config/num_wires`` is empty for pixel readout and the + rest lives only in the detector YAML. Rather than ask for numbers no + file carries, and that are silently wrong if mistyped, this reads them + off the correspondence that is already there: every group has both a hit + centre and the deposits behind it. + + ``y`` and ``z`` are single-variable fits. ``x`` is a two-variable one, + because a tick measures ``t_drift + t0``:: + + x = x_anode - d*mm_per_tick*tick + d*(mm_per_tick/dt)*t0 + + so regressing truth ``x`` on ``(tick, t0)`` yields an intercept, the mm + per tick, the drift direction (from the sign) and the drift velocity at + once. The residual then says whether any of it is believable — which is + the point of doing it this way rather than trusting a config. + + The intercept is *not* the anode. It is + ``x_anode + drift_direction * mm_per_tick * reference_tick``, and a fit + cannot separate the two. So the anode is taken from *x_range* -- the + volume's stated extent, which is geometry and not in doubt -- and the + reference tick is read off as what remains. On a run whose window opens + at Geant4 t=0 that residual comes out at zero, which is the cross-check; + on a run with a pre-window it comes out at the pre-window, which is the + number that was wanted. Pass *reference_tick* to state it instead, and + the residual then reports how far the truth disagrees. + + Parameters + ---------- + x_range : (float, float), optional + The volume's x extent in mm. Without it the anode cannot be + separated from the reference tick, and the latter is taken as 0. + reference_tick : float, optional + The tick at which a deposit on the anode at Geant4 ``t = 0`` is + recorded. ``None`` derives it from *x_range*. + + Returns + ------- + VolumePixelGeometry + report : dict + Fitted values and residuals, for logging. + """ + n = len(py) + if n < 8: + raise PixelGeometryError( + f"volume {volume_id}: only {n} group(s) have both a hit centre " + f"and truth deposits, which is too few to establish the pixel " + f"geometry. Set the reader's pixel geometry explicitly, or " + f"calibrate on an event with more activity.") + + py = np.asarray(py, dtype=np.float64) + pz = np.asarray(pz, dtype=np.float64) + tick = np.asarray(tick, dtype=np.float64) + t0 = np.asarray(t0, dtype=np.float64) + xt, yt, zt = (np.asarray(xyz_truth[:, i], dtype=np.float64) + for i in range(3)) + + def fit1(idx, target): + A = np.stack([np.ones_like(idx), idx], axis=1) + coef, *_ = np.linalg.lstsq(A, target, rcond=None) + return coef, target - A @ coef + + (cy0, pitch_y), ry = fit1(py, yt) + (cz0, pitch_z), rz = fit1(pz, zt) + + A = np.stack([np.ones_like(tick), tick, t0], axis=1) + (x_anode, b_tick, b_t0), *_ = np.linalg.lstsq(A, xt, rcond=None) + rx = xt - A @ np.array([x_anode, b_tick, b_t0]) + + pitch = 0.5 * (pitch_y + pitch_z) + if not np.isfinite(pitch) or pitch <= 0: + raise PixelGeometryError( + f"volume {volume_id}: fitted pixel pitch {pitch!r} is not a " + f"positive length; the hit centres and the truth deposits do not " + f"look like the same volume.") + if abs(pitch_y - pitch_z) > 0.05 * pitch: + raise PixelGeometryError( + f"volume {volume_id}: fitted pitch differs between the two pixel " + f"axes ({pitch_y:.4f} mm in y, {pitch_z:.4f} mm in z). This " + f"reader assumes a square pixel grid.") + + mm_per_tick = abs(b_tick) + # b_tick = -drift_direction * mm_per_tick, so the sign of the fit is the + # sign of the drift, read off the data instead of configured. + drift_direction = -1 if b_tick > 0 else 1 + # b_t0 = drift_direction * velocity, so the two x slopes give dt as well. + velocity = abs(b_t0) + dt = mm_per_tick / velocity if velocity else float('nan') + + # Separate the anode from the reference tick using the stated geometry. + if x_range is not None: + x_lo, x_hi = float(min(x_range)), float(max(x_range)) + anode = x_hi if drift_direction == 1 else x_lo + derived_ref = (drift_direction * (x_anode - anode) / mm_per_tick + if mm_per_tick else 0.0) + else: + x_lo = x_hi = None + anode = float(x_anode) + derived_ref = 0.0 + ref = float(derived_ref if reference_tick is None else reference_tick) + + # Measure the residual against the *derived* anchor, always. The + # residual is there to say whether the pitch, the velocity and the drift + # direction describe this detector -- those are measurements, and a bad + # one means the files do not belong together. A supplied reference tick + # is not a measurement but a choice about where t=0 sits, and someone + # asking "what if the trigger were 100 ticks later" must not be told + # their detector is broken. How far that choice sits from the data is + # reported instead, in mm, where a mistyped value is plain to see. + x_pred = anode - drift_direction * ((tick - derived_ref) * mm_per_tick + - t0 * velocity) + rx = xt - x_pred + + res = {'x': float(np.median(np.abs(rx))), + 'y': float(np.median(np.abs(ry))), + 'z': float(np.median(np.abs(rz)))} + bad = [k for k, v in res.items() + if v > (_FIT_TOL_X_MM if k == 'x' else _FIT_TOL_XY_PITCHES * pitch)] + if bad: + raise PixelGeometryError( + f"volume {volume_id}: the pixel geometry fitted from {n} groups " + f"does not reproduce the truth deposits — median |residual| " + f"x={res['x']:.2f} y={res['y']:.2f} z={res['z']:.2f} mm, off in " + f"{', '.join(bad)}. Either the hits and step files are from " + f"different runs, or this volume is not a regular pixel grid.") + + if x_lo is None: + # No stated extent: fall back to one full drift either side of the + # anode, which is the most the readout can reach anyway. + span = abs(mm_per_tick) * 1.0 + x_lo, x_hi = (anode, anode + span) if drift_direction == -1 \ + else (anode - span, anode) + + geom = VolumePixelGeometry( + y_min_mm=float(cy0 - 0.5 * pitch), + z_min_mm=float(cz0 - 0.5 * pitch), + x_min_mm=float(x_lo), x_max_mm=float(x_hi), + drift_direction=int(drift_direction), + pitch_mm=float(pitch), + drift_velocity_mm_us=float(velocity), + time_step_us=float(dt), + reference_tick=ref, + ) + report = { + 'n_groups': n, 'pitch_mm': float(pitch), + 'mm_per_tick': float(mm_per_tick), + 'drift_direction': int(drift_direction), + 'x_anode_mm': float(anode), + 'fit_intercept_mm': float(x_anode), + 'reference_tick': ref, + 'reference_tick_derived': float(derived_ref), + 'reference_tick_offset_mm': float((ref - derived_ref) * mm_per_tick), + 'drift_velocity_mm_us': float(velocity), + 'time_step_us': float(dt), + 'residual_mm': res, + } + return geom, report diff --git a/pysupera/truth_subset.py b/pysupera/truth_subset.py new file mode 100644 index 0000000..12b8205 --- /dev/null +++ b/pysupera/truth_subset.py @@ -0,0 +1,132 @@ +""" +Extract the truth deposits a browser needs to show pysupera labels on them. + +``vis_truth.html`` draws the Geant4 energy deposits beside the reconstructed +point cloud, painting each deposit with the label of whatever pysupera object +claimed it. The bridge is the *group*: a deposit knows its group +(``deposit_to_group``, in the JAXTPC hits file) and the pysupera output's +``groups/`` table says which fragment owns each group. + +So the viewer needs three things that live in two different JAXTPC files -- +the deposit positions and energies from the *step* file, and +``deposit_to_group`` from the *hits* file -- and it cannot read either of +them. Both are written with Blosc/LZ4, and the browser reads HDF5 through +h5wasm, which decodes gzip only. This writes the handful of arrays that +matter into one gzip file, which also takes 22 MB + 44 MB down to under a +megabyte per event. + + pysupera-truth-subset step.h5 hits.h5 truth_small.h5 --events 3 + +Positions stay in JAXTPC's own uint16-plus-attributes form rather than being +expanded to mm: it is the same three-line decode on the far side and less +than half the bytes. +""" + +from __future__ import annotations + +import argparse +import os + +#: Per-volume step-file datasets the viewer reads, and the dtype to store. +#: ``de`` is true deposited energy in MeV and ``charge`` the ionisation +#: electrons that survived recombination -- two different quantities, and the +#: viewer shows both, so a deposit can be thresholded in whichever the +#: question is asked in. float16 on disk is converted because h5wasm has no +#: reliable half-float support. +_STEP_KEEP = {"positions": None, "de": "float32", "charge": "float32"} + +#: Attributes needed to turn ``positions`` back into mm. +_STEP_ATTRS = ("n_actual", "pos_step_mm", + "pos_origin_x", "pos_origin_y", "pos_origin_z") + +#: Per-volume hits-file dataset mapping a deposit to its group. Older JAXTPC +#: output spells it ``segment_to_group``. +_D2G_KEYS = ("deposit_to_group", "segment_to_group") + + +def extract(step_path: str, hits_path: str, dst: str, n_events: int = 3, + level: int = 4, verbose: bool = True) -> dict: + """Copy *n_events* worth of truth deposits and their group ids into *dst*.""" + import h5py + import numpy as np + try: + import hdf5plugin # noqa: F401 (registers Blosc/LZ4 for reading) + except ImportError: + pass + + n_ds = n_dep = 0 + with h5py.File(step_path, "r") as fs, h5py.File(hits_path, "r") as fh, \ + h5py.File(dst, "w") as out: + if "config" in fh: + cfg = out.create_group("config") + cfg.attrs.update(fh["config"].attrs) + + events = sorted(k for k in fs.keys() if k.startswith("event_")) + if n_events > 0: + events = events[:n_events] + for ek in events: + if ek not in fh: + raise KeyError( + f"{ek} is in the step file but not the hits file " + f"{hits_path!r}; the two must come from the same run.") + for vol in sorted(k for k in fs[ek].keys() + if k.startswith("volume_")): + sv, hv = fs[f"{ek}/{vol}"], fh.get(f"{ek}/{vol}") + n = int(sv.attrs.get("n_actual", 0) or 0) + g = out.create_group(f"{ek}/{vol}") + for a in _STEP_ATTRS: + if a in sv.attrs: + g.attrs[a] = sv.attrs[a] + if not n: + continue + n_dep += n + for name, dt in _STEP_KEEP.items(): + if name not in sv: + continue + # Only the first n_actual rows are real; the rest is the + # pad JAXTPC writes to a fixed shape. + d = sv[name][:n] + g.create_dataset(name, data=d if dt is None else d.astype(dt), + compression="gzip", compression_opts=level) + n_ds += 1 + key = next((k for k in _D2G_KEYS if hv is not None and k in hv), None) + if key is None: + raise KeyError( + f"{ek}/{vol} in the hits file has none of " + f"{list(_D2G_KEYS)}, so its deposits cannot be tied to " + f"the groups the pysupera labels hang off.") + g.create_dataset("deposit_to_group", + data=hv[key][:n].astype(np.int32), + compression="gzip", compression_opts=level) + n_ds += 1 + + out_info = {"events": len(events), "datasets": n_ds, "deposits": n_dep, + "size_step": os.path.getsize(step_path), + "size_hits": os.path.getsize(hits_path), + "size_after": os.path.getsize(dst)} + if verbose: + print(f"[truth-subset] {step_path} + {hits_path} -> {dst}: " + f"{len(events)} event(s), {n_dep:,} deposit(s), " + f"{n_ds} dataset(s), " + f"{(out_info['size_step'] + out_info['size_hits']) / 1048576:.1f} MiB " + f"-> {out_info['size_after'] / 1048576:.2f} MiB") + return out_info + + +def main() -> None: + """Entry point for ``pysupera-truth-subset``.""" + p = argparse.ArgumentParser( + prog="pysupera-truth-subset", + description="Extract a gzip, viewer-sized slice of the JAXTPC truth " + "deposits and their group ids.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + p.add_argument("step", help="JAXTPC step/seg HDF5 file (truth deposits)") + p.add_argument("hits", help="JAXTPC hits/inst HDF5 file (deposit_to_group)") + p.add_argument("dst", help="Output file, gzip") + p.add_argument("-n", "--events", type=int, default=3, + help="Number of events to copy; -1 for all") + p.add_argument("-l", "--level", type=int, default=4, help="gzip level (1-9)") + a = p.parse_args() + if not 1 <= a.level <= 9: + p.error("--level must be between 1 and 9") + extract(a.step, a.hits, a.dst, a.events, a.level) diff --git a/pysupera/utils.py b/pysupera/utils.py index 2406cc5..489c09c 100644 --- a/pysupera/utils.py +++ b/pysupera/utils.py @@ -1,4 +1,5 @@ from enum import Enum, auto +import numpy as np class InteractionType(Enum): @@ -364,7 +365,35 @@ def resolve_orphans(particles, verbose: bool = False): return particles -def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_size=-1): +def count_extent(point_cloud, voxel_size=None): + """ + How big a point cloud is, as the number of cells it occupies. + + Row count is the obvious measure and the wrong one: it depends on how + finely the input happens to be sampled. Geant4 deposits arrive every + 0.3 mm while sensor hits arrive on a 4.32 mm pixel grid, so the same + particle is 1 row in one and 40 in the other, and any threshold in rows + silently re-tunes itself when the input changes. Counting occupied + voxels measures the thing that is actually being asked about -- how much + space the object fills -- and gives a number that means one thing across + inputs. + + *voxel_size* of None falls back to the row count, which is what a caller + with no voxel grid can offer. + """ + n = point_cloud.shape[0] + if not voxel_size or voxel_size <= 0 or n == 0: + return n + k = np.floor(np.asarray(point_cloud[:, :3], dtype=np.float64) / voxel_size) + k = k.astype(np.int64) + # One integer key per cell, so the unique is over a 1-D array. The + # offsets are large enough that two different cells cannot collide for + # any detector that fits in a few tens of metres. + return len(np.unique(k[:, 0] * 4_194_304 + k[:, 1] * 2_048 + k[:, 2])) + + +def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, + point_cloud_size=-1, voxel_size=None): """ Derive the :class:`SemanticType` of a particle from its physics properties. @@ -395,9 +424,21 @@ def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_ Spatial hit array of shape ``(N, ≥1)``. Only ``point_cloud.shape[0]`` (the number of rows) is used. point_cloud_size : int, optional - Threshold for the small / large cloud split. A point cloud is - considered *small* if ``point_cloud.shape[0] < point_cloud_size`` - and *large* if ``point_cloud.shape[0] > point_cloud_size``. + Threshold for the small / large cloud split, in occupied voxels when + *voxel_size* is given and in rows otherwise. A cloud is *small* if + its extent is below this and *large* if above. + + The physical meaning: a chunk of ionisation is worth calling a + trajectory only when its direction can be read off it, which needs + it to be longer than it is wide. So the threshold is the cell count + of a blob one track-width across. Measured over ten events of a + 4.32 mm-pitch pixel detector on a 3 mm grid, a track is 12.8 mm wide, + giving (12.8/3)^3 = 77; the value that best reproduces the + truth-deposit labelling is 85. For Geant4 deposits the same + calculation gives (4.9/3)^3 = 5, which is the long-standing default. + voxel_size : float, optional + Cell size for measuring the extent. ``None`` counts rows, which is + only comparable across inputs sampled at the same pitch. Pass ``-1`` (default) to make the threshold inactive so that all clouds are treated as large. @@ -416,11 +457,19 @@ def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_ """ if not isinstance(interaction_type, InteractionType): interaction_type = InteractionType(interaction_type) + + # How big this cloud is, by whichever measure the caller asked for. + # Occupied cells can only be fewer than rows, so a cloud already below + # the threshold in rows is below it in cells too -- and that is the + # common case, which keeps the unique off the hot path. + extent = point_cloud.shape[0] + if voxel_size and not (0 < point_cloud_size and extent < point_cloud_size): + extent = count_extent(point_cloud, voxel_size) if interaction_type == InteractionType.kInvalidProcess: return SemanticType.kUnknown raise Exception("'kInvalidProcess' particle process encountered\n") elif interaction_type == InteractionType.kTrack: - if point_cloud.shape[0] < point_cloud_size: + if extent < point_cloud_size: return SemanticType.kLEScatter else: return SemanticType.kTrack @@ -432,7 +481,7 @@ def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_ return SemanticType.kShower elif interaction_type == InteractionType.kDelta: - if point_cloud.shape[0] < point_cloud_size: + if extent < point_cloud_size: return SemanticType.kLEScatter else: return SemanticType.kDelta @@ -450,14 +499,14 @@ def SetSemanticType(interaction_type, pdg, parent_pdg, point_cloud, point_cloud_ elif interaction_type in [InteractionType.kPhoton, InteractionType.kConversion, InteractionType.kCompton, InteractionType.kOtherShower]: if abs(pdg) in [11,22]: - if point_cloud.shape[0] > point_cloud_size: + if extent > point_cloud_size: return SemanticType.kShower else: return SemanticType.kLEScatter else: raise Exception("kPhoton/kConversion/kCompton/kOtherShower encountered but PDG ("+str(pdg)+") not 11/22, InteractionType ("+str(interaction_type)+")\n") elif interaction_type == InteractionType.kNucleus: - if point_cloud.shape[0] > point_cloud_size: + if extent > point_cloud_size: return SemanticType.kTrack else: return SemanticType.kLEScatter diff --git a/tests/test_extent_classification.py b/tests/test_extent_classification.py new file mode 100644 index 0000000..2f2ad74 --- /dev/null +++ b/tests/test_extent_classification.py @@ -0,0 +1,141 @@ +""" +Classifying by extent, not by row count. + +``min_pc_size`` decides whether a particle is a low-energy scatter, and it +used to count rows. Row count is a property of the *input sampling*, not of +the particle: Geant4 deposits arrive every 0.3 mm and pixel sensor hits on a +4.32 mm grid, so the same object is 1 row in one and 40 in the other, and a +threshold in rows quietly means something different for each. + +Counting occupied voxels measures how much space the object fills, which is +what the question is actually about, and is the same count defragmentation +already splits on. +""" + +import numpy as np +import pytest + +from pysupera.data import Particle +from pysupera.utils import (InteractionType, SemanticType, SetSemanticType, + count_extent) + + +def cube(n_per_side, pitch, jitter=0, seed=0): + """A filled cube of points on a regular grid, optionally over-sampled.""" + g = np.arange(n_per_side) * pitch + P = np.stack(np.meshgrid(g, g, g, indexing="ij"), -1).reshape(-1, 3) + if jitter: + # the same solid, sampled more finely: more rows, identical extent + rng = np.random.default_rng(seed) + P = np.repeat(P, jitter, axis=0) + rng.uniform(0, pitch * 0.4, + (len(P) * jitter, 3)) + return P.astype(np.float32) + + +# --------------------------------------------------------------------------- +# count_extent +# --------------------------------------------------------------------------- + +def test_extent_counts_cells_not_rows(): + P = cube(4, 3.0) # 64 cells on a 3 mm grid + assert count_extent(P, 3.0) == 64 + assert count_extent(P) == len(P) # no grid: rows, as before + + +def test_extent_is_unchanged_by_resampling_the_same_solid(): + """ + The whole point. Ten times the rows over the same volume must give the + same answer, or the threshold is measuring the sampling. + """ + coarse = cube(4, 3.0) + fine = cube(4, 3.0, jitter=10) + assert len(fine) == 10 * len(coarse) + assert count_extent(coarse, 3.0) == count_extent(fine, 3.0) == 64 + + +def test_extent_handles_negative_coordinates(): + """A detector straddling the origin must not fold cells together.""" + P = np.array([[-4.0, 0, 0], [-1.0, 0, 0], [1.0, 0, 0], [4.0, 0, 0]], + dtype=np.float32) + assert count_extent(P, 3.0) == 4 + + +def test_extent_of_an_empty_cloud(): + assert count_extent(np.zeros((0, 3), dtype=np.float32), 3.0) == 0 + + +# --------------------------------------------------------------------------- +# the classifier +# --------------------------------------------------------------------------- + +def _sem(P, size, vox): + # kCompton on an electron: shower when large, LE scatter when small + return SetSemanticType(InteractionType.kCompton, 11, 22, P, + point_cloud_size=size, voxel_size=vox) + + +def test_same_object_sampled_two_ways_classifies_the_same(): + """ + A 4x4x4 solid is the same object whether it arrives as 64 deposits or + 640 sensor hits. Counting rows calls one small and the other large; + counting cells calls both large. + """ + coarse, fine = cube(4, 3.0), cube(4, 3.0, jitter=10) + assert _sem(coarse, 100, 3.0) == _sem(fine, 100, 3.0) == SemanticType.kLEScatter + assert _sem(coarse, 10, 3.0) == _sem(fine, 10, 3.0) == SemanticType.kShower + # and the old behaviour is what it is being fixed: rows disagree + assert _sem(coarse, 100, None) != _sem(fine, 100, None) + + +def test_the_threshold_is_the_cell_count_of_a_track_width_blob(): + """ + The rule the pixel default comes from: a chunk only has a direction if + it is bigger than one track width across. A 4x4x4 blob is 64 cells, so + a threshold of 85 calls it low-energy and 50 does not. + """ + blob = cube(4, 3.0) + assert count_extent(blob, 3.0) == 64 + assert _sem(blob, 85, 3.0) == SemanticType.kLEScatter + assert _sem(blob, 50, 3.0) == SemanticType.kShower + + +def test_row_shortcut_cannot_change_the_answer(): + """ + Cells are never more numerous than rows, so a cloud already under the + threshold in rows is under it in cells -- which is what lets the + classifier skip the unique on the common small case. It must not change + any answer. + """ + rng = np.random.default_rng(3) + for n in (1, 3, 7, 40, 200): + P = rng.uniform(0, 12, (n, 3)).astype(np.float32) + for thr in (2, 5, 20, 85): + fast = _sem(P, thr, 3.0) + slow = (SemanticType.kLEScatter + if count_extent(P, 3.0) < thr else SemanticType.kShower) + assert fast == slow, (n, thr) + + +# --------------------------------------------------------------------------- +# reaching it through Particle +# --------------------------------------------------------------------------- + +def test_particle_classifies_by_extent_when_given_a_voxel_size(): + fine = cube(4, 3.0, jitter=10) # 640 rows, 64 cells + rows = Particle(id=0, parent_id=0, ancestor_id=0, pdg=11, parent_pdg=22, + interaction_id=0, + interaction_type=InteractionType.kCompton, + point_cloud=fine, min_pc_size=100) + cells = Particle(id=0, parent_id=0, ancestor_id=0, pdg=11, parent_pdg=22, + interaction_id=0, + interaction_type=InteractionType.kCompton, + point_cloud=fine, min_pc_size=100, voxel_size=3.0) + assert rows.sem_type == SemanticType.kShower # 640 rows > 100 + assert cells.sem_type == SemanticType.kLEScatter # 64 cells < 100 + + +def test_voxel_size_none_keeps_the_old_row_behaviour(): + """Callers with no voxel grid must be unaffected by any of this.""" + P = cube(3, 3.0) # 27 rows, 27 cells + for thr in (10, 27, 50): + assert _sem(P, thr, None) == _sem(P, thr, 3.0) diff --git a/tests/test_jaxtpc_pixel_data.py b/tests/test_jaxtpc_pixel_data.py new file mode 100644 index 0000000..435ea6b --- /dev/null +++ b/tests/test_jaxtpc_pixel_data.py @@ -0,0 +1,695 @@ +""" +Smoke check of pixel readout against a real JAXTPC production directory. + +The synthetic fixtures in ``test_jaxtpc_readout.py`` pin down the logic; they +cannot catch JAXTPC changing the schema underneath us, because they *are* our +idea of the schema. This module reads an actual pixel batch instead, and is +skipped wherever that batch is not on disk -- so it costs nothing in CI and +fails loudly on the machine that has the data. + +Point it somewhere else with:: + + PYSUPERA_JAXTPC_PIXEL_DIR=/path/to/batch \\ + PYSUPERA_EDEPSIM_H5=/path/to/edepsim.h5 pytest tests/ + +The directory is expected in JAXTPC's current production layout:: + + /step//_step__.h5 + /hits//_hits__.h5 +""" + +import os +from pathlib import Path + +import numpy as np +import pytest + +h5py = pytest.importorskip("h5py") + +from pysupera.hits_subset import extract +from pysupera.readers.format_jaxtpc import read_readout_type + +# Default location: the scratch tree this was developed against. +_REPO = Path(__file__).resolve().parents[1] +_DEFAULT_DIR = _REPO.parent / "data" / "jaxtpc_pixel" +_DEFAULT_EDEPSIM = _REPO.parent / "data" / "kazu.h5" + + +def _one(dirname: str, root: Path) -> Path | None: + """The single step/hits file under *root*/, or None.""" + found = sorted((root / dirname).rglob("*.h5")) + return found[0] if len(found) == 1 else (found[0] if found else None) + + +def _paths(): + root = Path(os.environ.get("PYSUPERA_JAXTPC_PIXEL_DIR", _DEFAULT_DIR)) + edep = Path(os.environ.get("PYSUPERA_EDEPSIM_H5", _DEFAULT_EDEPSIM)) + if not root.is_dir() or not edep.is_file(): + return None + step, hits = _one("step", root), _one("hits", root) + if step is None or hits is None: + return None + return edep, step, hits + + +_P = _paths() +pytestmark = pytest.mark.skipif( + _P is None, + reason=f"no JAXTPC pixel batch (looked in {_DEFAULT_DIR}); " + f"set PYSUPERA_JAXTPC_PIXEL_DIR to run") + + +#: The geometry of the detector this batch was simulated with, from +#: config/cubic_pixel_config.yaml. Stated, never fitted -- that is the whole +#: contract these tests exist to hold. +PITCH_MM = 4.32 +DRIFT_DIRECTION = [-1, 1] + + +def _run_pysupera(): + """ + The console script, wherever pip put it. + + ``shutil.which`` is not enough: the entry point lands in ``~/.local/bin`` + for a user install, which is often off PATH inside a container. + """ + import shutil + exe = shutil.which("run_pysupera") + if exe: + return exe + local = Path.home() / ".local" / "bin" / "run_pysupera" + if local.is_file() and os.access(local, os.X_OK): + return str(local) + pytest.skip("run_pysupera not found on PATH or in ~/.local/bin") + + +def _sensor(): + """The sensor file, which states the drift velocity and sampling period.""" + root = _P[1].parent.parent.parent + found = sorted((root / "sensor").rglob("*.h5")) + return str(found[0]) if found else None + + +def hit_kwargs(**over): + """Reader kwargs for hit mode with the geometry configured.""" + edep, step, hits = (str(x) for x in _P) + kw = dict(edepsim_path=edep, seg_path=step, inst_path=hits, + point_source="hits", sensor_path=_sensor(), + pixel_pitch_mm=PITCH_MM, + pixel_drift_direction=list(DRIFT_DIRECTION)) + kw.update(over) + return kw + + +@pytest.fixture(scope="module") +def reader(): + from pysupera.readers import JaxtpcHDF5Reader + edep, step, hits = _P + with JaxtpcHDF5Reader(edepsim_path=str(edep), seg_path=str(step), + inst_path=str(hits)) as r: + yield r + + +def test_it_is_a_pixel_batch(reader): + assert reader.readout_type == "pixel" + with h5py.File(_P[2], "r") as f: + assert read_readout_type(f) == "pixel" + # One plane per volume, named Pixel -- not three projections. + vol = f["event_000/volume_0"] + planes = [k for k in vol if isinstance(vol[k], h5py.Group)] + assert planes == ["Pixel"] + assert "center_py" in vol["Pixel"] and "center_wires" not in vol["Pixel"] + + +def test_reads_visible_point_clouds(reader): + particles = reader[0] + assert len(particles) > 0 + with_points = [p for p in particles if len(p.point_cloud)] + assert with_points, "every particle came back empty" + # Visibility is a filter, not a wipe: some deposits survive and some do not. + stats = reader.last_mask_stats + assert 0 < stats["n_visible"] < stats["n_total"] + + +def test_deposit_ids_index_the_group_table(reader): + """ + Each particle carries the *input deposit* row behind every point, and + ``last_deposit_to_group`` is indexed by exactly that. If the two ever + drift apart the group table silently describes the wrong deposits, so + check the bound rather than trust it. + """ + particles = reader[0] + d2g = reader.last_deposit_to_group + assert d2g is not None and len(d2g) + for p in particles: + dep = getattr(p, "deposit_id", None) + if dep is None or not len(dep): + continue + assert len(dep) == len(p.point_cloud) + assert dep.min() >= 0 and dep.max() < len(d2g) + + +def test_every_visible_deposit_belongs_to_a_readout_group(reader): + """ + The reader's contract: a deposit is in a point cloud only because its + group produced a hit. Verify it the long way round, straight from the + file, rather than by re-running the same function. + """ + particles = reader[0] + d2g = reader.last_deposit_to_group + + with h5py.File(_P[2], "r") as f: + ev = f["event_000"] + active, off = set(), 0 + for vname in sorted(k for k in ev if k.startswith("volume_")): + vol = ev[vname] + for g in (vol[k] for k in vol if isinstance(vol[k], h5py.Group)): + active.update((g["group_ids"][:].astype(np.int64) + off).tolist()) + off += len(vol["group_to_track"]) + + dep = np.concatenate([p.deposit_id for p in particles + if getattr(p, "deposit_id", None) is not None + and len(p.deposit_id)]) + assert len(dep) + assert set(np.unique(d2g[dep]).tolist()) <= active + + +def test_subset_of_the_real_file(tmp_path): + dst = tmp_path / "pixel_small.h5" + info = extract(str(_P[2]), str(dst), n_events=1, verbose=False) + assert info["readout"] == "pixel" + assert info["planes"] == ["Pixel"] + # The point of the tool: the CSR bulk is what makes the file big. + assert info["size_after"] < info["size_before"] / 20 + with h5py.File(dst, "r") as f: + pg = f["event_000/volume_0/Pixel"] + assert {"center_py", "center_pz", "center_times", + "peak_charges", "group_ids"} <= set(pg.keys()) + assert read_readout_type(f) == "pixel" + + +# --------------------------------------------------------------------------- +# point_source=hits — the detected image +# --------------------------------------------------------------------------- + +@pytest.fixture(scope="module") +def hit_reader(): + from pysupera.readers import JaxtpcHDF5Reader + with JaxtpcHDF5Reader(**hit_kwargs()) as r: + yield r + + +def test_the_configured_geometry_is_applied_and_verified(hit_reader): + """ + The configured constants must be used *as given* -- not re-measured -- + and then checked against the truth deposits. Both halves matter: a + reader that quietly substituted its own fit would pass a residual test + no matter what was configured. + """ + hit_reader[0] + reports = hit_reader.pixel_geometry_report + assert len(reports) == 2 + for rep, expect_dir, expect_anode in zip(reports, DRIFT_DIRECTION, + (-2160.0, 2160.0)): + assert rep["source"] == "stated" + # exactly what was configured, to the bit + assert rep["pitch_mm"] == PITCH_MM + assert rep["drift_direction"] == expect_dir + assert rep["x_anode_mm"] == pytest.approx(expect_anode, abs=1e-6) + assert rep["reference_tick"] == 0.0 + # and it reproduces the truth: x continuous, y and z quantised + assert rep["residual_mm"]["x"] < 1.0 + assert rep["residual_mm"]["y"] < PITCH_MM + assert rep["residual_mm"]["z"] < PITCH_MM + + +def test_the_data_agrees_with_the_configured_geometry(hit_reader): + """ + The audit, reported alongside but never applied. The detector YAML says + 0.432 cm and 0.16 cm/us at 2 MHz; the hits should say the same. + """ + hit_reader[0] + for rep, expect_dir in zip(hit_reader.pixel_geometry_report, + DRIFT_DIRECTION): + m = rep["measured"] + assert m["pitch_mm"] == pytest.approx(4.32, abs=0.01) + assert m["mm_per_tick"] == pytest.approx(0.80, abs=0.01) + assert m["drift_velocity_mm_us"] == pytest.approx(1.60, abs=0.01) + assert m["time_step_us"] == pytest.approx(0.50, abs=0.01) + assert m["drift_direction"] == expect_dir + + +def test_hit_mode_refuses_to_guess_the_geometry(): + """ + Without the configured constants hit mode must stop, not fall back to + measuring them -- a constant fitted to the truth it is then compared + against cannot be caught when it is wrong. The error has to carry the + values the data is consistent with, or it is not actionable. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.readers.pixel_hits import PixelGeometryError + kw = hit_kwargs(pixel_pitch_mm=None) + with JaxtpcHDF5Reader(**kw) as r: + with pytest.raises(PixelGeometryError) as exc: + r[0] + msg = str(exc.value) + assert "pixel_pitch_mm" in msg + assert "4.32" in msg # what the data says + assert "pixel_geometry_from_fit" in msg # and the way out + + +@pytest.mark.parametrize("bad,off_axis", [ + (dict(pixel_pitch_mm=4.0), "y"), + (dict(pixel_drift_direction=[1, 1]), "x"), + (dict(drift_velocity_mm_us=1.4), "x"), +]) +def test_a_wrong_configured_constant_is_caught(bad, off_axis): + """ + The point of applying stated constants rather than fitted ones: a wrong + one now produces a residual instead of being absorbed. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.readers.pixel_hits import PixelGeometryError + with JaxtpcHDF5Reader(**hit_kwargs(**bad)) as r: + with pytest.raises(PixelGeometryError, match="does not reproduce"): + r[0] + + +def test_fitting_the_geometry_is_opt_in_and_warns(): + """Available for an unfamiliar batch, but it must say what it costs.""" + from pysupera.readers import JaxtpcHDF5Reader + kw = hit_kwargs(pixel_pitch_mm=None, pixel_geometry_from_fit=True) + with JaxtpcHDF5Reader(**kw) as r: + with pytest.warns(RuntimeWarning, match="circular"): + r[0] + assert r.pixel_geometry_report[0]["source"] == "fitted" + + +def test_hit_mode_attaches_every_hit(hit_reader): + particles = hit_reader[0] + st = hit_reader.last_hit_stats + assert st["n_hits"] > 1_000_000 # the detected image is not small + assert st["n_unmatched"] == 0 + assert sum(len(p.point_cloud) for p in particles) == st["n_attached"] + # A hit belongs to one group and a group to one track, so no hit is shared. + assert hit_reader.last_deposit_to_group is not None + assert len(hit_reader.last_deposit_to_group) == st["n_attached"] + + +def test_hit_mode_point_is_its_own_provenance(hit_reader): + """ + In deposit mode a point's group is reached through the deposit index; in + hit mode the point *is* the hit, so ``deposit_id`` indexes the group map + directly. Downstream group provenance relies on that alignment. + """ + particles = hit_reader[0] + g = hit_reader.last_deposit_to_group + for p in particles: + dep = getattr(p, "deposit_id", None) + if dep is None or not len(dep): + continue + assert len(dep) == len(p.point_cloud) + assert dep.min() >= 0 and dep.max() < len(g) + + +def test_true_t0_lands_on_the_truth_cloud(): + """ + Removing t0 should put the detected hits back on the deposits that made + them, to about a pixel. The nominal convention should not -- t0 differs + across the interactions of one event by up to 1094 us, which is 175 cm + of drift. This is the difference the two conventions exist to express, + so pin it. + """ + pytest.importorskip("scipy") + from scipy.spatial import cKDTree + from pysupera.readers import JaxtpcHDF5Reader + edep, step, hits = (str(x) for x in _P) + with JaxtpcHDF5Reader(edepsim_path=edep, seg_path=step, + inst_path=hits) as r: + truth = {p.id: p.point_cloud for p in r[0] if len(p.point_cloud)} + + got = {} + for mode in ("true_t0", "nominal"): + with JaxtpcHDF5Reader(**hit_kwargs(hit_x_from=mode)) as r: + big = sorted((p for p in r[0] if len(p.point_cloud) > 500), + key=lambda p: -len(p.point_cloud))[:10] + d = [] + for p in big: + t = truth.get(p.id) + if t is None or len(t) < 20: + continue + dist, _ = cKDTree(t[:, :3]).query(p.point_cloud[:, :3], k=1) + d.append(float(np.median(dist))) + got[mode] = float(np.median(d)) + + # ~1 pixel pitch once t0 is gone; hundreds of mm while it is there. + assert got["true_t0"] < 15.0 + assert got["nominal"] > 50.0 + + +def test_hit_energy_conventions_differ_as_documented(): + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.utils import PointFeature + with JaxtpcHDF5Reader(**hit_kwargs(hit_energy="charge")) as r: + q = np.concatenate([p.point_cloud[:, PointFeature.energy] + for p in r[0] if len(p.point_cloud)]) + with JaxtpcHDF5Reader(**hit_kwargs(hit_energy="true_de")) as r: + ps = r[0] + e = np.concatenate([p.point_cloud[:, PointFeature.energy] + for p in ps if len(p.point_cloud)]) + dx = np.concatenate([p.point_cloud[:, PointFeature.dx] + for p in ps if len(p.point_cloud)]) + + # The readout response is bipolar, so measured charge goes negative. + assert (q < 0).any(), "expected some negative charge from a bipolar response" + # Apportioned truth energy is a sum of non-negative deposits. + assert (e >= 0).all() + # Neither convention invents a path length. + assert not dx.any() + + +def test_hit_mode_refuses_a_wire_file(tmp_path): + """ + There is no 3-D image to convert in a wire readout, so asking for one has + to say why rather than produce something. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.readers.pixel_hits import PixelGeometryError + from tests.test_jaxtpc_readout import write_hits + + wire = write_hits(tmp_path / "wire_hits.h5", "wire") + with JaxtpcHDF5Reader(**hit_kwargs(inst_path=str(wire))) as r: + with pytest.raises((PixelGeometryError, KeyError)): + r[0] + + +def test_the_default_reference_tick_agrees_with_the_data(hit_reader): + """ + The reference tick is configured, not measured -- but the data still has + an opinion, and it is reported. This batch has pre_window_us = 0, so the + configured 0 should sit on top of what the hits imply; a disagreement + would mean the window does not open at Geant4 t=0. + """ + hit_reader[0] + for rep in hit_reader.pixel_geometry_report: + assert rep["reference_tick"] == 0.0 + assert rep["reference_tick_derived"] == pytest.approx(0.0, abs=1.0) + assert rep["reference_tick_offset_mm"] == pytest.approx(0.0, abs=1.0) + + +def test_an_explicit_reference_tick_moves_the_whole_cloud(): + """ + A stated reference tick must displace every x by exactly its own worth + -- and toward each volume's own anode, which is why this compares point + by point. Averaged over the detector the two volumes drift opposite + ways and the shift very nearly cancels, so a global mean would pass + while the geometry was mirrored. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.utils import PointFeature + def xs(ref): + with JaxtpcHDF5Reader(**hit_kwargs(hit_reference_tick=ref)) as r: + pc = [p.point_cloud for p in r[0] if len(p.point_cloud)] + return np.concatenate(pc)[:, PointFeature.x] + + d = xs(100.0) - xs(0.0) + # 100 ticks * 0.8 mm, signed by each volume's drift direction + np.testing.assert_allclose(np.abs(d), 80.0, atol=0.05) + assert (d < 0).any() and (d > 0).any(), "both drift directions expected" + + +def test_a_stated_reference_tick_is_not_treated_as_a_measurement(): + """ + The calibration residual exists to say whether the pitch, velocity and + drift direction describe this detector. A reference tick is a choice + about where t=0 sits, not a measurement, so asking "what if the trigger + were 100 ticks later" must not be reported as a broken detector -- the + distance from the data is reported in mm instead. + """ + from pysupera.readers import JaxtpcHDF5Reader + with JaxtpcHDF5Reader(**hit_kwargs(hit_reference_tick=100.0)) as r: + r[0] + for rep in r.pixel_geometry_report: + assert rep["residual_mm"]["x"] < 1.0 # geometry still fine + assert rep["reference_tick"] == 100.0 + assert rep["reference_tick_offset_mm"] == pytest.approx(80.0, abs=1.0) + + +def test_the_window_is_what_makes_deposits_invisible(): + """ + In this batch 'visible' turns out to mean 'arrived while the readout was + open' rather than 'above threshold': the window is exactly one full + drift, so any positive t0 walks the cathode-side end of an interaction + past the last tick. Worth pinning, because it decides how the two point + sources should be compared -- they lose the same charge for the same + reason. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.readers.format_jaxtpc import get_visible_segment_indices_by_track + r = JaxtpcHDF5Reader(**hit_kwargs()) + try: + r[0] # triggers the calibration + geoms, n_steps = r.pixel_geometry, r._num_time_steps() + assert n_steps + segs = r._load_seg_volumes("event_000") + vis = get_visible_segment_indices_by_track(segs, r._inst_file, + "event_000") + for v, (sv, vv) in enumerate(zip(segs, vis)): + n = int(sv["n_actual"]) + seen = np.zeros(n, bool) + for idx in vv.values(): + seen[idx] = True + g = geoms[v] + drift = np.abs(sv["positions_mm"][:n, 0] + - g.x_anode_mm) / g.drift_velocity_mm_us + want = g.reference_tick + (drift + sv["t0_us"][:n]) / g.time_step_us + past = want > n_steps - 1 + masked = ~seen + assert masked.any() + # almost every masked deposit is one the window never reached + assert past[masked].mean() > 0.9 + finally: + r.close() + + +def test_the_reader_reports_a_shift_that_recovers_the_truth(hit_reader): + """ + Over the real batch: adding the shift to the nominal x must land on what + the true_t0 convention would have produced, to float32 precision. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.utils import PointFeature + + ps = hit_reader[0] + nominal = np.concatenate([p.point_cloud[:, PointFeature.x] + for p in ps if len(p.point_cloud)]) + shift = hit_reader.last_true_x_shift + with JaxtpcHDF5Reader(**hit_kwargs(hit_x_from="true_t0")) as r: + true_x = np.concatenate([p.point_cloud[:, PointFeature.x] + for p in r[0] if len(p.point_cloud)]) + assert not r.last_true_x_shift.any() # already true, nothing to add + + assert len(shift) == len(nominal) + np.testing.assert_allclose(nominal + shift, true_x, atol=0.01) + # it is a real displacement, not a rounding artefact + assert np.abs(shift).max() > 1000.0 + + +def test_the_shift_takes_few_distinct_values(hit_reader): + """ + Why the column is affordable: it is drift_direction * t0 * v, so it has + one value per (interaction, volume) pair rather than one per hit. If + this ever grew to thousands the storage argument would need revisiting. + """ + hit_reader[0] + n = len(np.unique(np.round(hit_reader.last_true_x_shift, 3))) + assert n < 100, f"{n} distinct shifts -- expected a few tens" + + +def test_written_output_carries_the_shift(tmp_path): + """ + End to end: the column survives voxelisation and the write, and reading + x back and adding it puts the cloud inside the detector again -- the + nominal x does not have to be. + """ + from pysupera.io_v3 import read_events_v3 + import subprocess + + exe = _run_pysupera() + edep, step, hits = (str(x) for x in _P) + out = tmp_path / "hits.h5" + cmd = [exe, "reader=jaxtpc_pixel", + f"io.input_path={edep}", f"io.output_path={out}", + f"reader.jaxtpc_seg_path={step}", f"reader.jaxtpc_inst_path={hits}", + f"reader.jaxtpc_sensor_path={_sensor()}", + "reader.point_source=hits", + f"reader.pixel_pitch_mm={PITCH_MM}", + "reader.pixel_drift_direction=[-1,1]", + "check_group_ownership=false", "distance_threshold=6.2", + "max_events=1", "progress=false", "report=false", + f"hydra.run.dir={tmp_path}"] + r = subprocess.run(cmd, capture_output=True, text=True, timeout=900) + assert r.returncode == 0, r.stdout[-3000:] + r.stderr[-3000:] + + with read_events_v3(str(out)) as store: + view = store[0] + shift = store.true_x_shift(0) + assert shift is not None and len(shift) == len(view.points) + x = view.points[:, 0] + true_x = x + shift + # the true positions sit inside the detector; the nominal ones are + # displaced by each interaction's own t0 and need not + assert true_x.min() > -2160.0 and true_x.max() < 2160.0 + assert np.abs(shift).max() > 100.0 + + +def test_deposit_mode_writes_the_column_as_zero(tmp_path): + """ + The relation true_x = x + true_x_shift should hold for every file, so a + deposit-mode run writes zeros rather than omitting the column. + """ + from pysupera.io_v3 import read_events_v3 + import subprocess + + exe = _run_pysupera() + edep, step, hits = (str(x) for x in _P) + out = tmp_path / "dep.h5" + r = subprocess.run( + [exe, "reader=jaxtpc_pixel", f"io.input_path={edep}", + f"io.output_path={out}", f"reader.jaxtpc_seg_path={step}", + f"reader.jaxtpc_inst_path={hits}", "max_events=1", + "progress=false", "report=false", f"hydra.run.dir={tmp_path}"], + capture_output=True, text=True, timeout=900) + assert r.returncode == 0, r.stdout[-3000:] + r.stderr[-3000:] + with read_events_v3(str(out)) as store: + shift = store.true_x_shift(0) + assert shift is not None and not shift.any() + + +# --------------------------------------------------------------------------- +# pysupera-truth-subset, and the run metadata the viewers read +# --------------------------------------------------------------------------- + +def test_truth_subset_carries_what_the_viewer_needs(tmp_path): + """ + vis_truth.html labels a truth deposit by walking deposit -> group -> + fragment, so it needs the deposit positions from the step file and + deposit_to_group from the hits file -- and it cannot read either, both + being Blosc. The extract has to bring all of it across as gzip. + """ + from pysupera.truth_subset import extract + _edep, step, hits = (str(x) for x in _P) + dst = tmp_path / "truth.h5" + info = extract(step, hits, str(dst), n_events=1, verbose=False) + assert info["events"] == 1 and info["deposits"] > 0 + with h5py.File(dst, "r") as f: + vol = f["event_000/volume_0"] + assert {"positions", "de", "charge", "deposit_to_group"} <= set(vol.keys()) + n = int(vol.attrs["n_actual"]) + # every array is truncated to the real row count, not the padded shape + for k in ("positions", "de", "charge", "deposit_to_group"): + assert vol[k].shape[0] == n + # the attributes needed to put positions back into mm + for a in ("pos_step_mm", "pos_origin_x", "pos_origin_y", "pos_origin_z"): + assert a in vol.attrs + # gzip, or the browser cannot open it + assert vol["positions"].compression == "gzip" + # and it is worth doing: two Blosc files of tens of MB become one small one + assert info["size_after"] < (info["size_step"] + info["size_hits"]) / 5 + + +def test_truth_subset_charge_and_de_are_different_quantities(tmp_path): + """ + Both are kept because they answer different questions -- dE is energy in + MeV, charge is the ionisation electrons that survived recombination -- + and the viewer thresholds on one while showing the other. + """ + from pysupera.truth_subset import extract + _edep, step, hits = (str(x) for x in _P) + dst = tmp_path / "truth.h5" + extract(step, hits, str(dst), n_events=1, verbose=False) + with h5py.File(dst, "r") as f: + de = f["event_000/volume_0/de"][:] + ch = f["event_000/volume_0/charge"][:] + assert de.dtype == np.float32 and ch.dtype == np.float32 # not float16 + assert np.median(de) < 1.0 and np.median(ch) > 100.0 # MeV vs electrons + + +def test_the_deposit_to_group_chain_closes(tmp_path): + """ + The join vis_truth.html performs: a deposit's group, shifted by the + volume offset, must index the output's groups/fragment_id. If those two + numbering spaces ever drift apart the viewer paints truth deposits with + another object's label and nothing raises. + """ + from pysupera.truth_subset import extract + from pysupera.io_v3 import read_events_v3 + import subprocess + + exe = _run_pysupera() + edep, step, hits = (str(x) for x in _P) + out = tmp_path / "out.h5" + r = subprocess.run( + [exe, "reader=jaxtpc_pixel", f"io.input_path={edep}", + f"io.output_path={out}", f"reader.jaxtpc_seg_path={step}", + f"reader.jaxtpc_inst_path={hits}", + f"reader.jaxtpc_sensor_path={_sensor()}", "reader.point_source=hits", + f"reader.pixel_pitch_mm={PITCH_MM}", + "reader.pixel_drift_direction=[-1,1]", + "check_group_ownership=false", "distance_threshold=6.2", + "max_events=1", "progress=false", "report=false", + f"hydra.run.dir={tmp_path}"], + capture_output=True, text=True, timeout=900) + assert r.returncode == 0, r.stdout[-3000:] + r.stderr[-3000:] + + truth = tmp_path / "truth.h5" + extract(step, hits, str(truth), n_events=1, verbose=False) + + with h5py.File(out, "r") as fo, h5py.File(truth, "r") as ft: + go = fo["groups/offsets"][:] + vi = fo["groups/volume_offsets_index"][:] + ga, gb = int(go[0]), int(go[1]) + frag = fo["groups/fragment_id"][ga:gb] + voff = fo["groups/volume_offsets"][int(vi[0]):int(vi[1])] + labelled = total = 0 + for v, vol in enumerate(sorted(k for k in ft["event_000"] + if k.startswith("volume_"))): + g = ft[f"event_000/{vol}/deposit_to_group"][:].astype(np.int64) + g = g + int(voff[v]) + total += len(g) + # every group number must land inside the table, not past its end + assert g.min() >= 0 and g.max() < len(frag), \ + f"{vol}: group {g.max()} outside a table of {len(frag)}" + labelled += int((frag[g] >= 0).sum()) + # most deposits are claimed; the rest are the ones the readout never saw + assert 0.3 < labelled / total < 1.0 + + +def test_output_records_how_it_was_made(tmp_path): + """ + Nothing in the point columns says whether the energy column holds true dE + or measured charge, and a viewer inferring it from negative values gets a + quiet event wrong. So the run writes it down. + """ + from pysupera.io_v3 import read_events_v3 + import subprocess + exe = _run_pysupera() + edep, step, hits = (str(x) for x in _P) + out = tmp_path / "meta.h5" + r = subprocess.run( + [exe, "reader=jaxtpc_pixel", f"io.input_path={edep}", + f"io.output_path={out}", f"reader.jaxtpc_seg_path={step}", + f"reader.jaxtpc_inst_path={hits}", + f"reader.jaxtpc_sensor_path={_sensor()}", "reader.point_source=hits", + f"reader.pixel_pitch_mm={PITCH_MM}", + "reader.pixel_drift_direction=[-1,1]", + "check_group_ownership=false", "max_events=1", + "progress=false", "report=false", f"hydra.run.dir={tmp_path}"], + capture_output=True, text=True, timeout=900) + assert r.returncode == 0, r.stdout[-3000:] + r.stderr[-3000:] + with h5py.File(out, "r") as f: + a = {k: str(v) for k, v in f.attrs.items()} + assert a.get("point_source") == "hits" + assert a.get("hit_energy") == "charge" + assert a.get("hit_x_from") == "nominal" + assert a.get("readout_type") == "pixel" diff --git a/tests/test_jaxtpc_readout.py b/tests/test_jaxtpc_readout.py new file mode 100644 index 0000000..da32dc6 --- /dev/null +++ b/tests/test_jaxtpc_readout.py @@ -0,0 +1,245 @@ +""" +JAXTPC wire and pixel readout. + +JAXTPC simulates two LArTPC geometries. A wire readout records three 2-D +projections (``U``/``V``/``Y`` planes, each hit a wire and a drift tick); a +pixel readout records one natively 3-D image (a single ``Pixel`` plane, each +hit two pixel indices and a drift tick). + +pysupera reads both through one code path, and these tests pin down why that +is allowed to be true: *visibility is a property of a group*, and a group is +above threshold or not regardless of how the readout is arranged. The +central test is :func:`test_visibility_identical_across_readouts`, which +feeds the same groups through both geometries and demands the same answer. + +What does differ is anything that draws hits, so the plane discovery and the +hit-centre selection in ``pysupera-hits-subset`` are tested per geometry. +""" + +import numpy as np +import pytest + +h5py = pytest.importorskip("h5py") + +from pysupera.hits_subset import KEEP, extract, _plane_names +from pysupera.readers.format_jaxtpc import ( + READOUT_PLANE_NAMES, + get_visible_segment_indices_by_track, + read_readout_type, +) + + +# --------------------------------------------------------------------------- +# Synthetic hits files +# --------------------------------------------------------------------------- +# Small enough to read at a glance, and shaped exactly like JAXTPC's output: +# per-volume lookup tables beside one subgroup per readout plane. + +#: deposit index -> group, for the single volume in every fixture below. +DEPOSIT_TO_GROUP = np.array([0, 0, 1, 2, 2, 3, 4, 4, 5], dtype=np.int32) + +#: group -> Geant4 track id. +GROUP_TO_TRACK = np.array([10, 11, 11, 12, 13, 14], dtype=np.int32) + +#: Groups that made it above threshold. Split across the wire planes below +#: so that no single plane sees all of them -- visibility is the union, and a +#: fixture where one plane already holds the answer would not prove that. +VISIBLE_GROUPS = (0, 2, 4) + + +def _write_plane(vol, name, groups, kind): + """One readout-plane subgroup, with the centres its geometry stores.""" + g = vol.create_group(name) + groups = np.asarray(groups, dtype=np.int32) + n = len(groups) + g.create_dataset("group_ids", data=groups) + g.create_dataset("peak_charges", data=np.full(n, 7.0, dtype=np.float32)) + g.create_dataset("center_times", data=np.arange(n, dtype=np.int16) + 100) + if kind == "pixel": + g.create_dataset("center_py", data=np.arange(n, dtype=np.int16) + 20) + g.create_dataset("center_pz", data=np.arange(n, dtype=np.int16) + 30) + else: + g.create_dataset("center_wires", data=np.arange(n, dtype=np.int16) + 40) + # Bulk CSR arrays the viewer never opens; present so the subset tool has + # something it is supposed to leave behind. + g.create_dataset("charges_i16", data=np.ones(n * 4, dtype=np.int16)) + g.create_dataset("delta_times", data=np.zeros(n * 4, dtype=np.int8)) + return g + + +def write_hits(path, kind, readout_attr=True, plane_names=None, n_events=1): + """A JAXTPC-shaped hits file for one readout geometry.""" + with h5py.File(path, "w") as f: + cfg = f.create_group("config") + if readout_attr: + cfg.attrs["readout_type"] = kind + if plane_names is None: + plane_names = READOUT_PLANE_NAMES[kind] + for ev in range(n_events): + vol = f.create_group(f"event_{ev:03d}/volume_0") + vol.create_dataset("deposit_to_group", data=DEPOSIT_TO_GROUP) + vol.create_dataset("group_to_track", data=GROUP_TO_TRACK) + if kind == "pixel": + _write_plane(vol, plane_names[0], VISIBLE_GROUPS, kind) + else: + # The same groups, scattered over the three projections: each + # plane sees some of them, and only the union is the answer. + per = ([VISIBLE_GROUPS[0]], [VISIBLE_GROUPS[1]], + [VISIBLE_GROUPS[0], VISIBLE_GROUPS[2]]) + for nm, gs in zip(plane_names, per): + _write_plane(vol, nm, gs, kind) + return path + + +@pytest.fixture +def wire_hits(tmp_path): + return write_hits(tmp_path / "wire_hits.h5", "wire") + + +@pytest.fixture +def pixel_hits(tmp_path): + return write_hits(tmp_path / "pixel_hits.h5", "pixel") + + +# --------------------------------------------------------------------------- +# Readout-type detection +# --------------------------------------------------------------------------- + +def test_read_readout_type(wire_hits, pixel_hits): + with h5py.File(wire_hits, "r") as f: + assert read_readout_type(f) == "wire" + with h5py.File(pixel_hits, "r") as f: + assert read_readout_type(f) == "pixel" + + +def test_readout_type_defaults_to_wire(tmp_path): + """ + JAXTPC wrote hits files before ``readout_type`` existed, and all of them + are wire. Its own loader makes the same assumption, so a missing + attribute must not raise. + """ + p = write_hits(tmp_path / "old.h5", "wire", readout_attr=False) + with h5py.File(p, "r") as f: + assert read_readout_type(f) == "wire" + + +def test_readout_type_without_config_group(tmp_path): + with h5py.File(tmp_path / "bare.h5", "w") as f: + f.create_group("event_000") + with h5py.File(tmp_path / "bare.h5", "r") as f: + assert read_readout_type(f) == "wire" + + +# --------------------------------------------------------------------------- +# The claim that one code path is enough +# --------------------------------------------------------------------------- + +def test_visibility_identical_across_readouts(wire_hits, pixel_hits): + """ + The same groups above threshold must give the same visible segments, + whichever geometry recorded them. This is the whole reason pixel mode + needed no new reader: the filter reads ``group_ids`` and nothing else. + """ + seg_volumes = [{"n_actual": len(DEPOSIT_TO_GROUP)}] + out = {} + for tag, path in (("wire", wire_hits), ("pixel", pixel_hits)): + with h5py.File(path, "r") as f: + out[tag] = get_visible_segment_indices_by_track( + seg_volumes, f, "event_000")[0] + + assert out["wire"].keys() == out["pixel"].keys() + for tid in out["wire"]: + np.testing.assert_array_equal(out["wire"][tid], out["pixel"][tid]) + + # And the answer is the one the fixture describes, not merely a matching + # pair of wrong ones: groups 0, 2, 4 -> tracks 10, 11, 13. + expected_deposits = np.flatnonzero(np.isin(DEPOSIT_TO_GROUP, VISIBLE_GROUPS)) + got = np.sort(np.concatenate(list(out["pixel"].values()))) + np.testing.assert_array_equal(got, expected_deposits) + assert set(out["pixel"]) == {10, 11, 13} + + +def test_planes_discovered_structurally(tmp_path): + """ + A plane is any subgroup holding ``group_ids``. A readout whose plane + names this code has never seen must still resolve, because the names are + JAXTPC's to change and visibility does not depend on them. + """ + p = write_hits(tmp_path / "odd.h5", "pixel", plane_names=("Anode0",)) + with h5py.File(p, "r") as f: + vis = get_visible_segment_indices_by_track( + [{"n_actual": len(DEPOSIT_TO_GROUP)}], f, "event_000")[0] + assert set(vis) == {10, 11, 13} + + +def test_no_plane_subgroup_raises(tmp_path): + """A volume with no plane at all is a schema error, not an empty event.""" + with h5py.File(tmp_path / "noplane.h5", "w") as f: + vol = f.create_group("event_000/volume_0") + vol.create_dataset("deposit_to_group", data=DEPOSIT_TO_GROUP) + vol.create_dataset("group_to_track", data=GROUP_TO_TRACK) + with h5py.File(tmp_path / "noplane.h5", "r") as f: + with pytest.raises(KeyError, match="Pixel"): + get_visible_segment_indices_by_track( + [{"n_actual": len(DEPOSIT_TO_GROUP)}], f, "event_000") + + +# --------------------------------------------------------------------------- +# pysupera-hits-subset +# --------------------------------------------------------------------------- + +def test_subset_keeps_pixel_centres(pixel_hits, tmp_path): + dst = tmp_path / "small.h5" + info = extract(str(pixel_hits), str(dst), n_events=-1, verbose=False) + assert info["readout"] == "pixel" + assert info["planes"] == ["Pixel"] + with h5py.File(dst, "r") as f: + got = set(f["event_000/volume_0/Pixel"].keys()) + assert got == {"group_ids", "center_times", "peak_charges", + "center_py", "center_pz"} + + +def test_subset_keeps_wire_centres(wire_hits, tmp_path): + dst = tmp_path / "small.h5" + info = extract(str(wire_hits), str(dst), n_events=-1, verbose=False) + assert info["readout"] == "wire" + assert info["planes"] == ["U", "V", "Y"] + with h5py.File(dst, "r") as f: + got = set(f["event_000/volume_0/U"].keys()) + assert got == {"group_ids", "center_times", "peak_charges", "center_wires"} + + +def test_subset_drops_the_bulk_arrays(pixel_hits, tmp_path): + """The CSR arrays are the file's bulk and the viewer never opens them.""" + dst = tmp_path / "small.h5" + extract(str(pixel_hits), str(dst), n_events=-1, verbose=False) + with h5py.File(dst, "r") as f: + keys = set(f["event_000/volume_0/Pixel"].keys()) + assert not keys & {"charges_i16", "delta_times", "delta_py", "delta_pz"} + assert "charges_i16" not in KEEP + + +def test_subset_carries_readout_type(pixel_hits, tmp_path): + """ + The subset is what the browser viewer actually opens, so it has to keep + saying which readout it is -- otherwise the viewer has to guess. + """ + dst = tmp_path / "small.h5" + extract(str(pixel_hits), str(dst), n_events=-1, verbose=False) + with h5py.File(dst, "r") as f: + assert read_readout_type(f) == "pixel" + + +def test_subset_limits_events(tmp_path): + src = write_hits(tmp_path / "many.h5", "pixel", n_events=5) + dst = tmp_path / "two.h5" + info = extract(str(src), str(dst), n_events=2, verbose=False) + assert info["events"] == 2 + with h5py.File(dst, "r") as f: + assert sorted(k for k in f if k.startswith("event_")) == \ + ["event_000", "event_001"] + + +def test_plane_names_helper(pixel_hits): + with h5py.File(pixel_hits, "r") as f: + assert _plane_names(f["event_000/volume_0"]) == ["Pixel"] diff --git a/tests/test_pixel_hits.py b/tests/test_pixel_hits.py new file mode 100644 index 0000000..c2ec634 --- /dev/null +++ b/tests/test_pixel_hits.py @@ -0,0 +1,449 @@ +""" +Detected pixel hits as a point cloud (``point_source=hits``). + +In the ordinary JAXTPC path a point is a Geant4 deposit the readout happened +to detect, so the geometry is truth geometry. Here a point is a fired pixel, +and ``(py, pz, tick)`` has to be turned back into ``(x, y, z)``. These tests +cover the three things that can silently go wrong in that conversion -- the +CSR expansion, the coordinate transform, and the geometry calibration that +supplies its constants -- plus the per-particle layout that follows. +""" + +import numpy as np +import pytest + +from pysupera.readers.pixel_hits import ( + ENERGY_FROM, + PixelGeometryError, + VolumePixelGeometry, + X_FROM, + build_hit_point_cloud, + calibrate_volume, + window_truncation, + decode_plane_hits, + group_reductions, +) +from pysupera.utils import PointFeature + + +# --------------------------------------------------------------------------- +# A plane standing in for an h5py group +# --------------------------------------------------------------------------- + +class FakePlane(dict): + """Enough of an h5py group for decode_plane_hits: __contains__ and [] .""" + name = "/fake/Pixel" + + +def make_plane(sizes, centres, deltas, peaks, frac, pad=0, charge_key="charges_i16"): + """ + A CSR pixel plane. + + *deltas* is a list of (dpy, dpz, dt) per entry and *frac* the stored + per-entry charge fraction. *pad* appends unused trailing entries, which + is what JAXTPC actually writes -- the delta arrays run to a chunk + boundary past ``group_sizes.sum()``. + """ + n = sum(sizes) + d = np.asarray(deltas, dtype=np.int8).reshape(n, 3) + tail = np.zeros((pad, 3), dtype=np.int8) + d = np.concatenate([d, tail]) + fdt = np.uint16 if charge_key.endswith("u16") else np.int16 + f = np.concatenate([np.asarray(frac, dtype=fdt), np.zeros(pad, dtype=fdt)]) + c = np.asarray(centres, dtype=np.int16).reshape(len(sizes), 3) + return FakePlane({ + "group_ids": np.arange(len(sizes), dtype=np.int32) * 2, # not 0..G + "group_sizes": np.asarray(sizes, dtype=np.uint8), + "center_py": c[:, 0], "center_pz": c[:, 1], "center_times": c[:, 2], + "peak_charges": np.asarray(peaks, dtype=np.float32), + "delta_py": d[:, 0], "delta_pz": d[:, 1], "delta_times": d[:, 2], + charge_key: f, + }) + + +# --------------------------------------------------------------------------- +# CSR expansion +# --------------------------------------------------------------------------- + +def test_decode_expands_centres_plus_deltas(): + plane = make_plane( + sizes=[2, 1], + centres=[[10, 20, 30], [40, 50, 60]], + deltas=[[0, 0, 0], [1, -2, 3], [-1, 1, 0]], + peaks=[100.0, 50.0], + frac=[32767, 16383, -32767]) + got = decode_plane_hits(plane) + np.testing.assert_array_equal(got["py"], [10, 11, 39]) + np.testing.assert_array_equal(got["pz"], [20, 18, 51]) + np.testing.assert_array_equal(got["tick"], [30, 33, 60]) + np.testing.assert_array_equal(got["group"], [0, 0, 2]) + np.testing.assert_allclose(got["charge"], [100.0, 50.0, -50.0], rtol=1e-3) + + +def test_decode_ignores_the_padding_tail(): + """ + JAXTPC pads the delta arrays past ``group_sizes.sum()``. Reading to the + end of the array instead would append zero-delta ghosts sitting exactly + on the last group's peak -- points that look plausible and are not there. + """ + plane = make_plane(sizes=[2], centres=[[10, 20, 30]], + deltas=[[0, 0, 0], [1, 1, 1]], peaks=[10.0], + frac=[32767, 32767], pad=64) + got = decode_plane_hits(plane) + assert len(got["py"]) == 2 + + +def test_decode_accepts_the_legacy_unsigned_charge(): + plane = make_plane(sizes=[1], centres=[[1, 2, 3]], deltas=[[0, 0, 0]], + peaks=[10.0], frac=[65535], charge_key="charges_u16") + np.testing.assert_allclose(decode_plane_hits(plane)["charge"], [10.0], + rtol=1e-4) + + +def test_decode_without_a_charge_array_names_the_subset_tool(): + plane = make_plane(sizes=[1], centres=[[1, 2, 3]], deltas=[[0, 0, 0]], + peaks=[1.0], frac=[1]) + del plane["charges_i16"] + with pytest.raises(KeyError, match="hits-subset"): + decode_plane_hits(plane) + + +def test_decode_reports_groups_at_the_uint8_cap(): + plane = make_plane(sizes=[255], centres=[[0, 0, 0]], + deltas=[[0, 0, 0]] * 255, peaks=[1.0], frac=[1] * 255) + assert decode_plane_hits(plane)["n_capped_groups"] == 1 + + +# --------------------------------------------------------------------------- +# The coordinate transform +# --------------------------------------------------------------------------- + +GEOM = VolumePixelGeometry(y_min_mm=-2160.0, z_min_mm=-2160.0, + x_min_mm=-2160.0, x_max_mm=0.0, + drift_direction=-1, pitch_mm=4.32, + drift_velocity_mm_us=1.6, time_step_us=0.5) + +MIRRORED = VolumePixelGeometry(y_min_mm=-2160.0, z_min_mm=-2160.0, + x_min_mm=0.0, x_max_mm=2160.0, + drift_direction=1, pitch_mm=4.32, + drift_velocity_mm_us=1.6, time_step_us=0.5) + + +def test_mm_per_tick_is_the_period_times_the_velocity(): + assert GEOM.mm_per_tick == pytest.approx(0.8) + assert GEOM.x_anode_mm == -2160.0 and GEOM.x_cathode_mm == 0.0 + assert MIRRORED.x_anode_mm == 2160.0 and MIRRORED.x_cathode_mm == 0.0 + + +def test_to_xyz_uses_the_pixel_centre(): + """Pixel 0 sits half a pitch inside the boundary, not on it.""" + x, y, z = GEOM.to_xyz([0], [0], [0]) + assert y[0] == pytest.approx(-2160.0 + 2.16) + assert z[0] == pytest.approx(-2160.0 + 2.16) + + +def test_to_xyz_drifts_away_from_the_anode(): + x, _, _ = GEOM.to_xyz([0], [0], [100]) + # drift_direction -1 puts the anode at x_min, so x grows with tick + assert x[0] == pytest.approx(-2160.0 + 80.0) + + x2, _, _ = MIRRORED.to_xyz([0], [0], [100]) + assert x2[0] == pytest.approx(2160.0 - 80.0) + + +def test_reference_tick_moves_the_origin_of_the_drift_coordinate(): + """ + The reference tick is where Geant4 t=0 at zero drift lands. A run with a + pre-window has it non-zero, and every x must move with it -- otherwise + the anode is reported in the wrong place and the whole cloud is offset. + """ + from dataclasses import replace + shifted = replace(GEOM, reference_tick=100.0) + x0, _, _ = GEOM.to_xyz([0], [0], [100]) + x1, _, _ = shifted.to_xyz([0], [0], [100]) + assert x1[0] == pytest.approx(GEOM.x_anode_mm) # zero elapsed time + assert x0[0] - x1[0] == pytest.approx(80.0) + + +def test_outside_flags_points_past_the_volume_faces(): + # tick 3000 is past the cathode: 3000 * 0.8 = 2400 mm > the 2160 mm drift + x, _, _ = GEOM.to_xyz([0], [0], [3000]) + assert GEOM.outside(x).all() + x, _, _ = GEOM.to_xyz([0], [0], [1000]) + assert not GEOM.outside(x).any() + + +# --------------------------------------------------------------------------- +# Geometry calibration +# --------------------------------------------------------------------------- + +def _synth_groups(geom, n=400, dt=0.5, seed=0): + """Groups placed on a known geometry, with the truth x the fit must find.""" + rng = np.random.default_rng(seed) + py = rng.integers(0, 1000, n) + pz = rng.integers(0, 1000, n) + # a handful of distinct interaction times, as in a real event + t0 = rng.choice(np.array([0.0, 120.0, 400.0, 875.5]), n) + tick = rng.integers(0, 2700, n).astype(float) + x, y, z = geom.to_xyz(py, pz, tick - t0 / dt) + return py, pz, tick, t0, np.stack([x, y, z], axis=1) + + +def test_calibration_recovers_a_known_geometry(): + py, pz, tick, t0, truth = _synth_groups(GEOM) + got, report = calibrate_volume(py, pz, tick, t0, truth, + x_range=(-2160.0, 0.0)) + assert got.pitch_mm == pytest.approx(GEOM.pitch_mm, rel=1e-6) + assert got.mm_per_tick == pytest.approx(GEOM.mm_per_tick, rel=1e-6) + assert got.drift_direction == GEOM.drift_direction + assert got.x_anode_mm == pytest.approx(GEOM.x_anode_mm, abs=1e-3) + assert got.y_min_mm == pytest.approx(GEOM.y_min_mm, abs=1e-3) + # dt and the drift velocity fall out of the two x slopes together + assert report["time_step_us"] == pytest.approx(0.5, rel=1e-6) + assert report["drift_velocity_mm_us"] == pytest.approx(1.6, rel=1e-6) + + +def test_calibration_recovers_the_mirrored_volume(): + """ + The two volumes of a cathode-in-the-middle detector drift opposite ways. + Getting the sign from the fit rather than from a config is the whole + reason this function exists, so it has to work both ways round. + """ + py, pz, tick, t0, truth = _synth_groups(MIRRORED, seed=1) + got, _ = calibrate_volume(py, pz, tick, t0, truth, x_range=(0.0, 2160.0)) + assert got.drift_direction == 1 + assert got.x_anode_mm == pytest.approx(2160.0, abs=1e-3) + + +def test_calibration_rejects_a_mismatch(): + """Hits and deposits from different runs must not yield a quiet answer.""" + py, pz, tick, t0, truth = _synth_groups(GEOM, seed=2) + rng = np.random.default_rng(3) + truth = truth + rng.normal(0, 400.0, truth.shape) + with pytest.raises(PixelGeometryError, match="does not reproduce"): + calibrate_volume(py, pz, tick, t0, truth) + + +def test_calibration_needs_enough_groups(): + py, pz, tick, t0, truth = _synth_groups(GEOM, n=4, seed=4) + with pytest.raises(PixelGeometryError, match="too few"): + calibrate_volume(py, pz, tick, t0, truth) + + +# --------------------------------------------------------------------------- +# Per-group truth reductions +# --------------------------------------------------------------------------- + +def test_group_reductions(): + d2g = np.array([0, 0, 1, 1, 1, 3]) + red = group_reductions(d2g, 4, t0_us=np.array([1.0, 3.0, 5.0, 5.0, 5.0, 9.0]), + de=np.array([1.0, 1.0, 2.0, 2.0, 2.0, 4.0])) + np.testing.assert_allclose(red["t0"], [2.0, 5.0, 0.0, 9.0]) + np.testing.assert_allclose(red["de"], [2.0, 6.0, 0.0, 4.0]) + + +# --------------------------------------------------------------------------- +# Per-particle layout +# --------------------------------------------------------------------------- + +def _one_volume(n_per_group=(2, 2), tracks=(7, 9), t0=(0.0, 100.0), + de=(1.0, 2.0)): + hits = { + "py": np.array([0, 1, 2, 3], dtype=np.int32), + "pz": np.array([0, 0, 0, 0], dtype=np.int32), + "tick": np.array([10, 10, 20, 20], dtype=np.int32), + "charge": np.array([3.0, 1.0, 2.0, 2.0], dtype=np.float32), + "group": np.repeat([0, 1], n_per_group).astype(np.int32), + "n_capped_groups": 0, + } + return {"hits": hits, "geom": GEOM, + "group_to_track": np.array(tracks, dtype=np.int64), + "group_offset": 100, + "group_t0": np.array(t0, dtype=np.float32), + "group_de": np.array(de, dtype=np.float32)} + + +def test_offsets_follow_the_edepsim_particle_order(): + """ + The offsets index the EDepSim particle list, not the hit order -- if they + drifted, every particle would get another particle's points and nothing + would raise. + """ + tid = np.array([9, 5, 7]) # deliberately not sorted + flat, offsets, group, shift, stats = build_hit_point_cloud([_one_volume()], tid) + assert stats["n_attached"] == 4 and stats["n_unmatched"] == 0 + # track 9 is group 1 (2 hits), track 5 has none, track 7 is group 0 + np.testing.assert_array_equal(offsets, [[0, 2], [2, 2], [2, 4]]) + np.testing.assert_array_equal(group[:2], [101, 101]) # group_offset applied + np.testing.assert_array_equal(group[2:], [100, 100]) + + +def test_hits_of_absent_tracks_are_dropped_and_counted(): + flat, offsets, group, shift, stats = build_hit_point_cloud( + [_one_volume()], np.array([7])) + assert stats["n_hits"] == 4 + assert stats["n_attached"] == 2 + assert stats["n_unmatched"] == 2 + assert len(flat) == 2 + + +def test_true_t0_shifts_only_the_group_whose_t0_is_non_zero(): + tid = np.array([7, 9]) + naive, _, _, _, _ = build_hit_point_cloud([_one_volume()], tid, + x_from="nominal") + corr, _, _, _, _ = build_hit_point_cloud([_one_volume()], tid, + x_from="true_t0", + time_step_us=0.5) + # group 0 has t0 = 0, so it does not move; group 1 has t0 = 100 us + np.testing.assert_allclose(corr[:2, PointFeature.x], naive[:2, PointFeature.x]) + shift = corr[2:, PointFeature.x] - naive[2:, PointFeature.x] + # drift_direction -1: removing t0 lowers the effective tick, so x drops + np.testing.assert_allclose(shift, -(100.0 / 0.5) * GEOM.mm_per_tick, rtol=1e-5) + + +def test_energy_charge_versus_true_de(): + tid = np.array([7, 9]) + q, _, _, _, _ = build_hit_point_cloud([_one_volume()], tid, energy="charge") + np.testing.assert_allclose(q[:, PointFeature.energy], [3.0, 1.0, 2.0, 2.0]) + + d, _, _, _, _ = build_hit_point_cloud([_one_volume()], tid, energy="true_de") + # group 0: dE 1.0 split 3:1 by charge; group 1: dE 2.0 split evenly + np.testing.assert_allclose(d[:, PointFeature.energy], [0.75, 0.25, 1.0, 1.0]) + # and the apportionment conserves each group's energy exactly + assert d[:2, PointFeature.energy].sum() == pytest.approx(1.0) + assert d[2:, PointFeature.energy].sum() == pytest.approx(2.0) + + +def test_true_de_shares_by_magnitude_not_signed_charge(): + """ + The response is bipolar. Sharing a group's true dE by signed charge + would give the undershoot pixels negative deposited energy, which the + deposit-mode column this one is meant to be comparable with can never + hold -- and the positive pixels more than the group actually deposited. + """ + vol = _one_volume() + vol['hits']['charge'] = np.array([3.0, -1.0, 2.0, 2.0], dtype=np.float32) + d, _, _, _, _ = build_hit_point_cloud([vol], np.array([7, 9]), + energy="true_de") + e = d[:, PointFeature.energy] + assert (e >= 0).all() + np.testing.assert_allclose(e[:2], [0.75, 0.25]) + assert e[:2].sum() == pytest.approx(1.0) + + +def test_dx_is_zero_because_a_hit_has_no_path_length(): + flat, _, _, _, _ = build_hit_point_cloud([_one_volume()], np.array([7, 9])) + assert np.all(flat[:, PointFeature.dx] == 0) + + +def test_rejects_unknown_conventions(): + with pytest.raises(ValueError, match="x_from"): + build_hit_point_cloud([], np.array([1]), x_from="nope") + with pytest.raises(ValueError, match="energy"): + build_hit_point_cloud([], np.array([1]), energy="nope") + assert X_FROM == ("nominal", "true_t0") + assert ENERGY_FROM == ("charge", "true_de") + + +def test_empty_event_gives_empty_slices(): + flat, offsets, group, shift, stats = build_hit_point_cloud([], np.array([1, 2, 3])) + assert flat.shape == (0, 7) + assert offsets.shape == (3, 2) and not offsets.any() + assert stats["n_hits"] == 0 + + +# --------------------------------------------------------------------------- +# The readout window +# --------------------------------------------------------------------------- + +def test_window_truncation_counts_what_t0_pushes_off_the_end(): + """ + The window on the batch this was written against is exactly one full + drift, so any positive t0 walks the cathode-side end of an interaction + past the last tick, where it is never recorded. That loss scales with + t0, so it biases a comparison against deposit mode rather than merely + shrinking it -- it has to be counted, not inferred. + """ + # 2700 ticks * 0.8 mm = 2160 mm: the window is exactly the drift length + n_steps = 2701 + x = np.array([-2160.0, -1080.0, -1.0, -1080.0, -1080.0]) + t0 = np.array([0.0, 0.0, 0.0, 900.0, -2000.0]) + before, after, total = window_truncation(x, t0, GEOM, n_steps) + assert total == 5 + # at t0=0 everything inside the volume fits, by construction: the + # deepest deposit drifts 2160 mm = 1350 us = exactly 2700 ticks + # x=-1080 drifts 675 us; +900 us of t0 is 3150 ticks, past the end + assert after == 1 + # a large negative t0 arrives before the window opened + assert before == 1 + + +def test_window_truncation_is_empty_when_nothing_is_late(): + x = np.array([-2160.0, -1080.0]) + t0 = np.zeros(2) + assert window_truncation(x, t0, GEOM, 2701) == (0, 0, 2) + + +# --------------------------------------------------------------------------- +# Out-of-volume points +# --------------------------------------------------------------------------- + +def test_out_of_volume_points_are_counted_not_clipped(): + """ + A nominal x is an inference, so one landing past the cathode is the + honest answer; clipping would invent a wall the reconstruction lacks. + """ + vol = _one_volume() + # 3000 ticks * 0.8 = 2400 mm from the anode, past the 2160 mm cathode + vol['hits']['tick'] = np.array([10, 10, 3000, 3000], dtype=np.int32) + flat, _, _, _, stats = build_hit_point_cloud([vol], np.array([7, 9])) + assert stats['n_outside_volume'] == 2 + # kept, and still outside + assert (flat[:, PointFeature.x] > GEOM.x_max_mm).sum() == 2 + + +# --------------------------------------------------------------------------- +# true_x_shift +# --------------------------------------------------------------------------- + +def test_shift_recovers_the_true_x_from_the_nominal_one(): + """ + The contract the column exists for: true_x = x + true_x_shift, whichever + drift convention produced x. + """ + tid = np.array([7, 9]) + nom, _, _, shift, _ = build_hit_point_cloud( + [_one_volume()], tid, x_from="nominal", time_step_us=0.5) + tru, _, _, _, _ = build_hit_point_cloud( + [_one_volume()], tid, x_from="true_t0", time_step_us=0.5) + np.testing.assert_allclose(nom[:, PointFeature.x] + shift, + tru[:, PointFeature.x], atol=1e-3) + + +def test_shift_is_zero_when_x_is_already_true(): + """ + Under true_t0 the stored x *is* the true one, so the relation still + holds with a zero shift -- which is what lets a consumer apply it + unconditionally instead of branching on the convention. + """ + _, _, _, shift, _ = build_hit_point_cloud( + [_one_volume()], np.array([7, 9]), x_from="true_t0") + assert not shift.any() + + +def test_shift_follows_the_drift_direction(): + """ + shift = drift_direction * t0 * v, so the two volumes of a shared-cathode + detector shift opposite ways for the same interaction time. A track + crossing the cathode therefore needs this per point, not per particle. + """ + v0 = _one_volume() + v1 = _one_volume() + v1["geom"] = MIRRORED + tid = np.array([7, 9]) + _, _, _, s0, _ = build_hit_point_cloud([v0], tid, time_step_us=0.5) + _, _, _, s1, _ = build_hit_point_cloud([v1], tid, time_step_us=0.5) + np.testing.assert_allclose(s0, -s1) + # group 1 has t0 = 100 us; -1 * 100 * 1.6 = -160 mm + np.testing.assert_allclose(s0[2:], -160.0, atol=1e-3) + np.testing.assert_allclose(s0[:2], 0.0, atol=1e-6) # group 0 has t0 = 0 diff --git a/tests/test_reader_configs.py b/tests/test_reader_configs.py new file mode 100644 index 0000000..9bbb068 --- /dev/null +++ b/tests/test_reader_configs.py @@ -0,0 +1,174 @@ +""" +The reader config group: one file per kind of input. + +``pysupera/conf/reader/`` holds three configs -- plain EDepSim, JAXTPC wire +and JAXTPC pixel -- and the two JAXTPC ones inherit the first so the shared +dataset keys are written once. These tests hold that arrangement together: +that each one composes, that it selects the reader it claims to, and that +choosing a JAXTPC config without its input paths fails loudly. + +That last one is not hypothetical. ``cfg.reader.get(key, None)`` returns +``None`` for an unfilled mandatory (``???``) value rather than raising, and +``key in cfg`` is ``False`` for one -- so the obvious spellings of the guard +both let a ``reader=jaxtpc_pixel`` run fall through to plain EDepSim and +produce truth steps with nothing said about it. +""" + +import pytest +from omegaconf import OmegaConf + +from pysupera.config import build_reader, load_cfg + +pytest.importorskip("h5py") + +#: Every config in the reader group, and the reader class it must produce. +READERS = ("edepsim_h5", "jaxtpc_wire", "jaxtpc_pixel") + +#: Keys the base defines and every variant must therefore inherit. +SHARED_KEYS = ("format", "particle_key", "vertex_key", "step_key", + "ass_key", "electron_energy_threshold") + + +def _cfg(name, *extra): + return load_cfg([f"reader={name}", "io.input_path=in.h5", + "io.output_path=out.h5", *extra]) + + +@pytest.mark.parametrize("name", READERS) +def test_every_reader_config_composes(name): + cfg = _cfg(name) + assert cfg.reader.format == "edepsim_h5" + + +@pytest.mark.parametrize("name", READERS) +def test_variants_inherit_the_shared_keys(name): + """The point of the within-group defaults: no duplicated dataset keys.""" + cfg = _cfg(name) + for key in SHARED_KEYS: + assert key in cfg.reader, f"{name} lost {key}" + + +@pytest.mark.parametrize("name", ("jaxtpc_wire", "jaxtpc_pixel")) +def test_jaxtpc_configs_demand_their_input_paths(name): + cfg = _cfg(name) + for key in ("jaxtpc_seg_path", "jaxtpc_inst_path"): + assert OmegaConf.is_missing(cfg.reader, key) + + +@pytest.mark.parametrize("name", ("jaxtpc_wire", "jaxtpc_pixel")) +def test_a_jaxtpc_config_without_paths_is_refused(name): + """ + Not silently downgraded to EDepSim. Asking for JAXTPC input and being + handed truth steps is the kind of mistake that is only noticed in a plot + weeks later. + """ + with pytest.raises(ValueError, match="JAXTPC input"): + build_reader(_cfg(name)) + + +def test_plain_edepsim_does_not_demand_them(): + cfg = _cfg("edepsim_h5") + for key in ("jaxtpc_seg_path", "jaxtpc_inst_path"): + assert not OmegaConf.is_missing(cfg.reader, key) + + +def test_only_the_pixel_config_carries_the_hit_options(): + """ + A wire readout has no 3-D image to convert, so the options that only + mean something for pixel input stay out of its config rather than + sitting there as nulls that look like they might work. + """ + pixel = _cfg("jaxtpc_pixel").reader + wire = _cfg("jaxtpc_wire").reader + for key in ("point_source", "hit_x_from", "hit_energy", + "hit_reference_tick", "pixel_pitch_mm", + "pixel_drift_direction", "jaxtpc_sensor_path"): + assert key in pixel, f"pixel config lost {key}" + assert key not in wire, f"wire config should not offer {key}" + + +def test_pixel_defaults_are_the_documented_ones(): + r = _cfg("jaxtpc_pixel").reader + assert r.point_source == "deposits" # truth geometry unless asked + assert r.hit_x_from == "nominal" # the detector's own inference + assert r.hit_energy == "charge" + assert r.hit_reference_tick == 0 + assert r.pixel_geometry_from_fit is False + + +def test_pixel_geometry_defaults_to_the_reference_detector(): + """ + The pitch and drift direction are in no JAXTPC output file, so they are + configured -- and defaulted to the cubic_pixel detector rather than left + null, so a run needs only paths. Defaulting them is only safe because + a wrong value cannot pass quietly: the calibration applies them as + stated and raises when they do not reproduce the truth deposits. + """ + r = _cfg("jaxtpc_pixel").reader + assert r.pixel_pitch_mm == 4.32 # 0.432 cm + assert list(r.pixel_drift_direction) == [-1, 1] # shared cathode + assert r.pixel_geometry_from_fit is False # stated, then checked + + +# --------------------------------------------------------------------------- +# preset=cubic_pixel_hits +# --------------------------------------------------------------------------- +# A preset bundles the values that are only correct together and that cut +# across config groups -- reader geometry, particle.min_pc_size, and two +# top-level checks -- so a sensor-hit run needs only file paths. + + +def _preset(*extra): + return load_cfg(["preset=cubic_pixel_hits", "io.input_path=in.h5", + "io.output_path=out.h5", "reader.jaxtpc_seg_path=s.h5", + "reader.jaxtpc_inst_path=i.h5", *extra]) + + +def test_preset_selects_the_pixel_reader_and_hit_mode(): + cfg = _preset() + assert cfg.reader.format == "edepsim_h5" + assert cfg.reader.point_source == "hits" + assert "pixel_pitch_mm" in cfg.reader # the pixel reader, not wire + + +def test_preset_fills_in_everything_a_sensor_hit_run_needs(): + """The point of it: nothing but paths should be needed on the command line.""" + cfg = _preset() + assert cfg.reader.pixel_pitch_mm == 4.32 + assert list(cfg.reader.pixel_drift_direction) == [-1, 1] + assert cfg.reader.hit_charge_threshold == 500 + assert cfg.particle.min_pc_size == 13 + # must clear the pixel diagonal, 4.32 * sqrt(2) = 6.11 mm + assert cfg.distance_threshold > 4.32 * 2 ** 0.5 + # a group's hits straddle fragment splits in hit mode, so the invariant + # the groups/ table rests on cannot be enforced + assert cfg.check_group_ownership is False + + +def test_the_threshold_and_min_pc_size_are_the_matched_pair(): + """ + They move together: cutting the halo thins the tracks, which lowers the + extent threshold. 85 goes with a threshold of 0 and 13 with 500; + mixing them agrees with the truth labelling on 85% of particles instead + of 95%. This pins the pairing the preset ships. + """ + cfg = _preset() + assert (cfg.reader.hit_charge_threshold, cfg.particle.min_pc_size) == (500, 13) + + +def test_a_command_line_flag_still_beats_the_preset(): + cfg = _preset("particle.min_pc_size=42", "reader.hit_charge_threshold=0") + assert cfg.particle.min_pc_size == 42 + assert cfg.reader.hit_charge_threshold == 0 + + +def test_without_the_preset_nothing_changes(): + """ + The preset is opt-in. A plain run must keep the truth-tuned defaults, + or every existing wire workflow moves under it. + """ + cfg = load_cfg(["io.input_path=in.h5", "io.output_path=out.h5"]) + assert cfg.particle.min_pc_size == 5 + assert cfg.check_group_ownership is True + assert cfg.reader.format == "edepsim_h5" + assert "point_source" not in cfg.reader # the plain EDepSim reader diff --git a/vis_drift.html b/vis_drift.html new file mode 100644 index 0000000..8e8f51d --- /dev/null +++ b/vis_drift.html @@ -0,0 +1,864 @@ + + +pysupera — true vs inferred drift + + +
+ + + + + + + + + + + load a pysupera output — hover a point to follow it across both views +
+
show semantic type:
+ +
+
+
true x x + points/true_x_shift
+
+
+
+
inferred x as stored, t0 assumed at the beam time
+
no file loaded
+
+
+ + + + diff --git a/vis_hits.html b/vis_hits.html index ddb4027..e573a29 100644 --- a/vis_hits.html +++ b/vis_hits.html @@ -14,6 +14,12 @@ #three{flex:1;min-width:0;position:relative;overflow:hidden} #three canvas{position:absolute;inset:0;width:100%;height:100%;display:block} #planes{width:46%;min-width:340px;border-left:1px solid var(--line);overflow:auto;padding:8px;display:flex;flex-direction:column;gap:8px} + /* Pixel readout is natively 3-D, so its pane is a second WebGL view rather + than a stack of 2-D canvases. Same width as #planes so the two readouts + sit in the same place on screen. */ + #readout{width:46%;min-width:340px;border-left:1px solid var(--line);position:relative;overflow:hidden;display:none} + #readout canvas{position:absolute;inset:0;width:100%;height:100%;display:block} + #rohead{position:absolute;left:10px;top:10px;z-index:4;background:rgba(13,17,23,.9);border:1px solid var(--line);border-radius:6px;padding:7px 10px;font-size:11px;color:var(--text2);white-space:pre-wrap;max-width:min(420px,80%)} .pw{background:var(--panel);border:1px solid var(--line);border-radius:6px;padding:6px} .pw h4{margin:0 0 4px;font-size:11px;color:var(--text2);font-weight:600;letter-spacing:.04em} canvas.plane{width:100%;display:block;background:#0a0d12;border-radius:4px;cursor:crosshair} @@ -44,6 +50,7 @@
no file loaded
+
diff --git a/vis_truth.html b/vis_truth.html new file mode 100644 index 0000000..ca0b893 --- /dev/null +++ b/vis_truth.html @@ -0,0 +1,915 @@ + + +pysupera — labels on truth vs sensor points + + +
+ + + + + + + + + + + + + + load the pysupera output and its truth companion +
+
show semantic type:
+ +
+
+
truth deposits Geant4, labelled through deposit → group → fragment
+
+
+
+
pysupera points the cloud the algorithms actually ran on
+
no file loaded
+
+
+ + + + From c9251cf78b0a53d64e4b733ce5b188e7eee43c79 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 27 Sep 2026 20:02:29 -0700 Subject: [PATCH 41/62] format 3.3.0: per-hit labels for the JAXTPC hits file, truth in points/flat For JAXTPC runs the model's input is the JAXTPC hits file itself, so its targets now have to be one label per hit, in the hits file's own order. Before this, pysupera wrote the hits it had partitioned into points/flat, together with truth/, points/hit_index and points/true_x_shift, so that a loader could map them back. Wire and pixel did this differently. Now both readouts write the same thing: hit_labels/volume{V}/plane{P}/ (attrs: source U|V|Y|Pixel, volume, plane) fragment_id, instance_id int32, -1 = untraced is_le bool on_sensor bool, pixel only offsets per-event CSR Entry k labels CSR entry k of that plane in the JAXTPC hits file, so no reordering or join is needed. For wire, a hit belongs to its group's owner, and that is exact because a group's deposits are never split. For pixel, the hits are what was partitioned, so every hit is read directly. -1 marks exactly the hits off the sensor image, and check_pixel_labels raises if that does not hold. Over 10 pixel events, 24.9M of 28.9M hits (86.1%) carry a label, and the rest are all off the sensor. points/flat now holds the true energy deposits behind the hits: every EDepSim segment whose JAXTPC group left hits, read from pstep (matched 1:1 with the JAXTPC segments, per volume and in order). By default they are voxelized on 3 mm (io.store=voxels; io.store=points keeps the raw segments). There are nine columns, named in points/columns: x, y, z [mm], t [us], dE [MeV], dX [cm], theta, phi [rad], p [MeV/c]. Each voxel is its cell centre, with the earliest t, dE and dX summed, and theta/phi/p taken from the earliest step, i.e. the particle entering the cell (voxelize_truth). The generic voxelizer only merges columns 3-5, and left theta, phi and p at zero in every voxel. Also: - particles gains frag_inst_id, the instance a fragment row belongs to. groups/ is kept. - truth/, points/hit_index and points/true_x_shift are retired, and so is the pixel "deposits" reader option. reader=jaxtpc_pixel means the sensor-masked hits, and its cross-group settings live in conf/readout/jaxtpc_pixel.yaml (min_pc_size 63, distance_threshold 6.2, check_group_ownership false), loaded after _self_ so that a flag still wins. - The run records the pixel geometry as the root attribute pixel_geometry (JSON, one entry per volume, with x_anode_mm and mm_per_tick), so a viewer can place a hit from its tick and pixel index. - points/flat is stored in column chunks (16384 x 1) with bitshuffle + LZ4 (io.points). On a pixel-hit file this is 45.6 MB against 78.7 MB as rows. energy_mantissa_bits is an optional rounding, off by default. - pysupera-hits-subset now decodes every CSR entry into hit_tick, hit_charge and hit_wire or hit_py/hit_pz, in CSR order, so row k lines up with hit_labels entry k (--centres-only keeps the old contents). - pysupera-truth-subset is removed together with vis_truth.html. points/flat is now the truth, so the companion file had nothing left to add. Two speed-ups in preproc.py, which also carries the voxelizer change above. Both leave the output bit-for-bit identical, checked on 10 events of each mode (65/79/55 datasets compared): - _unique_rows: the voxelizer's np.unique(keys, axis=0) packs (particle, vx, vy, vz) into one int64 and sorts in 1-D, in the same order. On 3.3M rows this takes 0.14 s instead of 2.46 s. Keys wider than 63 bits fall back. - ScipyDefragmenter._get_labels_many: one KD-tree and one connected- components pass for all clouds, not one per particle (14k per pixel event). Clouds are kept apart by a fourth coordinate k*(eps+1). Within a cloud the distances are unchanged, and labels are shifted to start at 0, so they equal the per-cloud labels exactly. Over 10 pixel events (3 alternating repeats), voxelize went from 21.6 to 6.0 s, defragment from 10.1 to 4.0 s, and wall time from 68.3 to 49.6 s (-27%). Wire is -18% and EDepSim about -13%. README: the output format section is rewritten for 3.3.0, and so is the browser-viewer section, now one viewer per run mode (the viewers themselves are the next commit). Tests: 460 passed, 1 skipped. --- README.md | 498 ++++++++++---------- pyproject.toml | 1 - pysupera/_run.py | 256 +++++++--- pysupera/conf/config.yaml | 51 +- pysupera/conf/io/default.yaml | 19 + pysupera/conf/preset/cubic_pixel_hits.yaml | 46 +- pysupera/conf/reader/jaxtpc_pixel.yaml | 84 ++-- pysupera/conf/readout/edepsim_h5.yaml | 4 + pysupera/conf/readout/jaxtpc_pixel.yaml | 30 ++ pysupera/conf/readout/jaxtpc_wire.yaml | 4 + pysupera/config.py | 42 +- pysupera/hit_labels.py | 139 ++++++ pysupera/hits_subset.py | 64 ++- pysupera/io.py | 6 + pysupera/io_v3.py | 303 ++++++++++-- pysupera/layout.py | 205 ++++++-- pysupera/preproc.py | 98 +++- pysupera/provenance.py | 92 ++++ pysupera/readers/format_jaxtpc.py | 240 +++++++++- pysupera/readers/pixel_hits.py | 210 +++++++-- pysupera/truth_subset.py | 132 ------ tests/test_io_v3.py | 262 +++++++++++ tests/test_jaxtpc_pixel_data.py | 519 ++++++++++++--------- tests/test_pixel_hits.py | 158 ++++++- tests/test_preproc.py | 55 +++ tests/test_provenance.py | 60 +++ tests/test_reader_configs.py | 69 ++- 27 files changed, 2678 insertions(+), 969 deletions(-) create mode 100644 pysupera/conf/readout/edepsim_h5.yaml create mode 100644 pysupera/conf/readout/jaxtpc_pixel.yaml create mode 100644 pysupera/conf/readout/jaxtpc_wire.yaml create mode 100644 pysupera/hit_labels.py delete mode 100644 pysupera/truth_subset.py diff --git a/README.md b/README.md index 596de7e..a383695 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ A Python library for grouping simulated LArTPC particles into physics partitions 4. [Quick Start](#quick-start) - [Python API](#python-api) - [Interactive Event Viewer (`pysupera-app`)](#interactive-event-viewer-pysupera-app) - - [Browser viewers (`vis_force.html`, `vis_hits.html`)](#browser-viewers-vis_forcehtml-vis_hitshtml) + - [Browser viewers](#browser-viewers) 5. [Workflow](#workflow) - [Input: the `Particle` object](#input-the-particle-object) - [Choosing a reader](#choosing-a-reader) @@ -37,7 +37,6 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [ID conventions](#id-conventions) - [Wire and pixel readout](#wire-and-pixel-readout) - [Truth deposits or detected hits](#truth-deposits-or-detected-hits) - - [Recovering the true x](#recovering-the-true-x--pointstrue_x_shift) - [Hit provenance (JAXTPC mode)](#hit-provenance-jaxtpc-mode) - [Seeing it](#seeing-it) - [Expert and debugging output](#expert-and-debugging-output) @@ -205,184 +204,113 @@ The **PARTICLE FILTER** and **show legend** checkbox take effect immediately wit ### Browser viewers -Four standalone HTML files that read an output file directly in the browser -— no server, no install, nothing to launch. Open the file and pick your -HDF5. They answer different questions: +Three standalone HTML files, one per run mode, that read HDF5 directly in the +browser — no server, no install. Open the file and pick your inputs, or pass +them as URL parameters when the files are served over HTTP. -| | `vis_force.html` | `vis_hits.html` | `vis_drift.html` | `vis_truth.html` | -|---|---|---|---|---| -| **question** | what is in this event, and how is it grouped? | which readout hits belong to which object? | what did the drift-time ambiguity do to this event? | how do the labels look on truth points versus sensor points? | -| **inputs** | the pysupera output | the output **and** the JAXTPC hits file | the output | the output **and** a truth companion | -| **views** | one 3-D cloud, plus an instance-genealogy graph | truth cloud beside the readout — six 2-D plane images for wire, a second 3-D cloud for pixel | two 3-D clouds: true x beside inferred x | two 3-D clouds: Geant4 deposits beside the pysupera points | -| **needs** | any 3.x output | JAXTPC mode, so `groups/` exists | `points/true_x_shift`, so a pixel hit-mode run | `groups/`, plus `pysupera-truth-subset` output | +| run mode | viewer | inputs | URL parameters | +|---|---|---|---| +| EDepSim only | `vis_force.html` | the output | `?file=out_gz.h5` | +| JAXTPC wire | `vis_hits.html` | the output **and** a hits subset | `?out=out_gz.h5&hits=hits_small.h5` | +| JAXTPC pixel | `vis_drift.html` | the output **and** a hits subset | `?out=out_gz.h5&hits=hits_small.h5` | -Both read HDF5 through **h5wasm, which decodes gzip only**, so the inputs have -to be repacked first — see [Compression](#compression). +The viewers read HDF5 through **h5wasm, which decodes gzip only**, so both +inputs have to be prepared first: -#### `vis_force.html` — exploring one event +```bash +pysupera-repack out.h5 out_gz.h5 -c gzip # the pysupera output +pysupera-hits-subset sim_hits_0000.h5 hits_small.h5 -n 3 # JAXTPC modes only +``` -Colour the cloud by energy, time, interaction id, ancestor track id, fragment -id, instance id or PDG code, with a selectable colormap and optional log -scale. LE and non-LE points toggle independently, energy and time thresholds -slide, and a bounding box clips the view. An animation plays the event by -time, and an interaction panel and a force-directed instance-genealogy graph -open alongside. +`-c gzip` is not optional: without it each dataset keeps its existing filter +(bitshuffle+LZ4 for `points/flat`), which the browser cannot read. +`pysupera-hits-subset` copies the first *n* events of a JAXTPC hits file as +gzip and decodes every CSR entry into one row per hit — `hit_tick`, +`hit_charge` and either `hit_wire` (wire) or `hit_py`/`hit_pz` (pixel) — in +the same order as the file's CSR, so row *k* lines up with entry *k* of the +output's `hit_labels`. It finds planes structurally and copies `config`, so +it needs no flag to tell wire from pixel. `--centres-only` keeps just the +group centres, for a much smaller file when the per-hit rows are not needed. + +#### `vis_force.html` — EDepSim only + +One 3-D cloud of `points/flat`. Colour by energy, time, interaction id, +ancestor track id, fragment id, instance id or PDG code, with a selectable +colormap and optional log scale. LE and non-LE points toggle independently, +energy and time thresholds slide, and a bounding box clips the view. An +animation plays the event by time, and an interaction panel and a +force-directed instance-genealogy graph open alongside. Shortcuts: `c` centre · `a` axes · `s` screenshot · `space` play/pause · `r` reset animation · `o` auto-rotate · `i` interaction panel · `g` graph · `d` debug log · `?` help. -#### `vis_hits.html` — linking 3-D to the readout +#### `vis_hits.html` — JAXTPC wire -Truth voxels on the left, the readout on the right, and the same colour -meaning the same object in both. **Hover on either side and the matching hits -light up on the other.** The link is a single owner id, so the two views -cannot disagree. +Left: the 3-D cloud of `points/flat` (3 mm truth voxels). Right: one 2-D +image per `hit_labels/volume{V}/plane{P}` group, stacked in rows — six for a +two-volume, three-plane detector — each wire against drift tick, one pixel per +hit. The same colour means the same object everywhere. -What the right-hand pane is depends on the readout, which the viewer takes -from the hits file's `config/readout_type` (falling back to wire, as JAXTPC -does): +**Hover anywhere and the object under the cursor lights up in every view**: +a voxel highlights its hits on all six planes, and a hit highlights its voxels +in 3-D and its hits on the other planes. The link is the label itself — the +voxel's range in `points/flat` and the hit's `instance_id` / `fragment_id` in +`hit_labels` — so the views cannot disagree. -| readout | right-hand pane | +| control | | |---|---| -| **wire** | six 2-D images, one per plane per volume — wire against drift tick. A wire hit carries one spatial coordinate, so an image is the only honest way to draw it | -| **pixel** | a second 3-D cloud, axes `x = drift tick`, `y = pixel py`, `z = pixel pz`. A pixel hit is already 3-D; flattening it to an image would throw away an axis the detector actually measured | +| **highlight** | instance, fragment or interaction — what "the object under the cursor" means | +| **others** | opacity of everything not selected | +| **hide LE** | drops low-energy voxels and hits from every view, so the picker cannot select them | +| **point info** | position, t, dE, dX, θ, φ, \|p\| and labels for a voxel; volume, plane, wire, tick, charge and labels for a hit | +| **c** | fit the 3-D view | -The pixel pane is deliberately left in readout coordinates rather than -converted to mm: the left pane already answers "where in the detector", and -this one answers "where on the anode". +Plane headers give the hit count, wire and tick range and how many hits are +highlighted. A red banner appears instead of a wrong picture for a pixel file, +an output without `hit_labels`, a subset without per-hit rows, or labels and +hits of different lengths. -| control | | -|---|---| -| **colour by** | instance, fragment or interaction — the last two derived, not read | -| **others** | opacity of everything not under the cursor. The cloud uses RGBA vertex colours with depth-write off, so unselected points are genuinely transparent and never hide the selection behind them | -| **hide LE** | drops low-energy points from both views. Filtering happens at build time, so the picker cannot select something hidden | -| **point info** | x/y/z, time, dE, dX, dE/dX, LE and owner for a truth voxel; peak charge, group, LE and owner for a hit, plus wire and time (wire) or py/pz and time (pixel) | -| **c** | fit the view | +#### `vis_drift.html` — JAXTPC pixel -Preparing its two inputs: +Two 3-D panes: the **truth** — `points/flat`, at the true x — on the left, and +the **hits** — every hit of the subset, at the x inferred from its drift tick +assuming t0 at the beam time — on the right. Hit positions come from the +pixel geometry the run records in the output's `pixel_geometry` attribute: -```bash -pysupera-repack out.h5 out_gz.h5 -c gzip # the pysupera output -pysupera-hits-subset sim_wire_hits_0000.h5 hits_small.h5 -n 10 +``` +x = x_anode − drift_direction · (tick − reference_tick) · mm_per_tick +y = y_min + (py + ½) · pitch z = z_min + (pz + ½) · pitch ``` -`-c gzip` is not optional: omitting it keeps each dataset's existing filter, -leaving a file the browser cannot read. The second command exists because the -hits file is mostly per-tick CSR arrays the viewer never opens — keeping only -`group_ids`, `peak_charges` and the hit centres took a 3-event wire sample -from 1279.7 MiB to 2.73 MiB, and a 3-event pixel sample from 43.6 MiB to -0.65 MiB. - -`pysupera-hits-subset` handles either readout without being told which: -it finds planes structurally (any subgroup with `group_ids`) and keeps -whichever centres they store — `center_wires`/`center_times` for wire, -`center_py`/`center_pz`/`center_times` for pixel. It copies `config`'s -attributes across too, so the subset still says which readout it is. - -`vis_hits.html` refuses to draw rather than show something false: a red -banner appears for a filter it cannot decode, a missing `groups/` table, or -an event where every voxel resolves to one owner. Files written before the -group table, or before `groups/is_le`, still load — it says what is missing -and shows what it can. The mapping it displays is described under -[Hit provenance](#hit-provenance-jaxtpc-mode). - -#### `vis_drift.html` — what the t0 ambiguity did - -Two 3-D panes over the same points: **true x** on the left -(`x + points/true_x_shift`), **inferred x** on the right (as stored, with t0 -assumed at the beam time). Cameras are synchronised and both panes are framed -on the union of the two clouds, so a displacement reads as a displacement -rather than being normalised away by two separate fits. +so a non-zero t0 shows as a shift along x between the panes. Cameras are +synchronised (rotate, zoom or pan with the arrow keys in either pane) and both +are framed on the union of the two clouds, so a displacement reads as one. **Hover a point in either pane and its whole object lights up in both**, everything else fading to an adjustable opacity. The `highlight` selector chooses what "its object" means — instance, fragment or interaction — -independently of the colour, so you can colour by semantic type while -following one instance. +independently of the colour. | colour by | encoding | |---|---| -| energy | one-hue sequential ramp; **diverging** blue↔red about a grey zero when the column is signed, which it is in hit mode — the readout response is bipolar and 13% of voxels are negative. Sign-preserving log (symlog) by default: the column spans twelve decades, and linear puts three quarters of the points within 1.5% of the midpoint. Range clipped to the 2nd–98th percentile | -| instance semantic type | the six categorical slots in fixed order | -| fragment / instance / interaction id | a stable hash — thousands of values, so no legend is possible; identity comes from the hover | -| particle type (PDG) | fixed hues for the six commonest species, everything else folded into "other" | +| energy | dE [MeV] on the left, hit charge [ADC] on the right, each with its own ramp; optional log | +| semantic type | the six categorical slots in fixed order; LE points as LE scatter | +| fragment / instance / interaction id | a stable hash, the same in both panes | +| particle type (PDG) | the instance's PDG, fixed hues for the six commonest species | -Hover shows energy, Geant4 track id, fragment / instance / interaction id, -semantic type, PDG, parent instance and parent PDG, plus both x values and -the shift between them. Only about two thirds of points fall inside a stored -particle row, so the Geant4 track id falls back to the fragment's -representative and says when it has. - -The semantic-type strip filters classes in and out. That is not only a -convenience: six categorical hues cannot clear the all-pairs colour-vision -floor — no six in one lightness band can — so identity is never left to -colour alone. A legend is always up, the hover names the class in words, and -any pair can be isolated with the filter. - -Prepare its input the same way as the others, with `pysupera-repack out.h5 -out_gz.h5 -c gzip`. A file without `points/true_x_shift`, or one where every -shift is zero, loads fine and says so — both panes are then the same cloud. - -#### `vis_truth.html` — the same labels on truth and on sensor points - -Left pane: the Geant4 energy deposits, each painted with the label of -whatever pysupera object claimed it. Right pane: the cloud the algorithms -actually ran on. Hover either side and the object lights up in both. - -The bridge is the **group**. A deposit knows its group (`deposit_to_group`, -in the JAXTPC hits file) and the output's `groups/` table says which fragment -owns each group — so the label travels backwards down the same chain the -reconstruction came up. Measured on one event, a fragment's truth deposits -sit a median 2.2 mm from that same fragment's hits. - -Deposits whose group was never detected have no owner and are shown as such -rather than dropped — 34% of event 0, which is the charge the readout window -never recorded. There's a `hide undetected` toggle. - -Its two inputs: - -```bash -pysupera-repack out.h5 out_gz.h5 -c gzip -pysupera-truth-subset step.h5 hits.h5 truth_small.h5 -n 3 -``` - -The second exists because the JAXTPC step and hits files are Blosc, which the -browser cannot decode, and because the viewer needs only the deposit -positions, `de`, `charge` and `deposit_to_group` — 65 MB of JAXTPC becomes -about 1 MB per event. - -A useful self-check: run pysupera with `reader.point_source=deposits` and the -two panes should coincide, because the right pane's cloud then *is* the -voxelized truth deposits. - -#### Display thresholds - -`vis_drift.html` and `vis_truth.html` both have a **show ≥** slider — two of -them in `vis_truth.html`, one per pane, since the panes hold different -quantities. The detector simulation imposes no threshold of its own, so -without one every pixel carrying any induced-field influence is on screen. - -The slider position is a percentile, but what it displays is the absolute -value it lands on, together with what survives: - -| sensor cut | points kept | charge kept | -|---|---|---| -| none | 100% | 100% | -| ≥ 27.7 | 50% | **99.6%** | -| ≥ 354 | 20% | 96.5% | -| ≥ 1960 | 10% | 87.7% | - -Half the points cost 0.4% of the charge. The truth pane behaves quite -differently — dropping its faintest 60% of deposits costs 61% of the dE — -because truth deposits have no diffusion halo. +The semantic-type strip filters classes in and out of both panes, and a +**show ≥** slider per pane hides the faintest points by percentile, showing +the absolute value it lands on. By default only on-sensor hits are drawn — +the ones the model sees; **off-sensor hits** adds the rest, which carry no +label (`-1`) and are drawn grey. #### What the energy column holds Nothing in the point columns says whether the energy column is true dE or measured charge, so the run writes it down as root attributes (`point_source`, `hit_energy`, `hit_x_from`, `readout_type`) and the viewers -label the column from them. Guessing from the presence of negative values +could label the column from them. Guessing from the presence of negative values works on a busy event and fails on a quiet one. --- @@ -465,7 +393,17 @@ arrangement, selected with `reader=`: |---|---|---| | `edepsim_h5` (default) | EDepSim only | every Geant4 energy-deposit step | | `jaxtpc_wire` | EDepSim + JAXTPC wire batch | deposits the readout detected | -| `jaxtpc_pixel` | EDepSim + JAXTPC pixel batch | the same, or the detected pixel image via `reader.point_source=hits` | +| `jaxtpc_pixel` | EDepSim + JAXTPC pixel batch (+ sensor file) | the detected image: hits on sensor pixels, the energy the sensors recorded | + +A pixel batch is only read as its hits. For either readout the output holds +the true deposits behind the hits in `points/flat` and a label per hit in +`hit_labels/` — see [What the output stores](#what-the-output-stores-jaxtpc-input). + +`reader=jaxtpc_pixel` also brings the settings hits need outside the reader +group — `particle.min_pc_size`, `distance_threshold`, +`check_group_ownership`, `particle.voxelize.store` — from +`conf/readout/jaxtpc_pixel.yaml`, loaded after `config.yaml`'s own values; a +command-line flag still wins. `reader.point_source=deposits` is refused. The two JAXTPC configs inherit `edepsim_h5`, so the dataset keys are written once, and each adds only what its own arrangement needs — `jaxtpc_wire` has @@ -640,9 +578,14 @@ with read_events("output.h5") as store: ## Output File Format `run_pysupera` writes a single HDF5 file. This section describes format -**3.1.0**, recorded in the scalar dataset `format_version`. No HDF5 attributes -are used anywhere — everything is a dataset. - +**3.3.0**, recorded in the scalar dataset `format_version`. + +3.3.0 reshaped JAXTPC output. `points/flat` now holds the true energy +deposits behind the hits (see [What the output stores](#what-the-output-stores-jaxtpc-input)), +with direction and momentum, and names its columns in `points/columns`; the +hits stay in the JAXTPC hits file, labelled per hit in `hit_labels/`. +`truth/`, `points/hit_index` and `points/true_x_shift` are gone, and fragment +rows name their instance (`frag_inst_id`). 3.1.0 renamed columns for a consistent `_` prefix, gave instances the same two-range point layout as fragments, and added the true Geant4 parent. Readers accept 3.0.0 files transparently. 3.0.0 replaced the three parallel @@ -652,19 +595,41 @@ stores each point **once**. On a two-event file that is 6.4 MiB → 1.7 MiB. ### Layout ``` -format_version scalar str "3.1.0" +format_version scalar str "3.3.0" n_events scalar int64 events/offsets (n_events+1,) int64 particle rows per event points/offsets (n_events+1,) int64 point rows per event inter/offsets (n_events+1,) int64 interaction rows per event -points/flat (N, 6) float32 x, y, z, time, dE, dX +points/flat (N, C) float32 one row per voxel (or deposit) +points/columns (C,) str its column names particles/ (M,) inter/ (K,) +groups/ JAXTPC group -> owning fragment } JAXTPC +hit_labels/volume{V}/plane{P}/ per hit of that plane } input ``` Everything is concatenated across events with CSR fenceposts: event *i* occupies rows `offsets[i] : offsets[i+1]`. +Every dataset is plain HDF5 — no pysupera code is needed to read it. The +compression filters (LZ4, and bitshuffle+LZ4 for `points/flat` and +`hit_labels/`) are recorded in the file and applied by HDF5 on read; they +need the filter plugin, so `import hdf5plugin` before `h5py.File(...)` (or +set `HDF5_PLUGIN_PATH` for `h5dump` / HDFView). + +```python +import hdf5plugin, h5py +with h5py.File("out.h5") as f: + pts = f["points/flat"][:] # (N, C) float32 + cols = [c.decode() for c in f["points/columns"]] # what each column is +``` + +How `points/flat` is stored is set by `io.points`: chunks one column wide +(each quantity compresses on its own) and bitshuffle+LZ4. The energy column +can be rounded to `io.points.energy_mantissa_bits` float32 mantissa bits +(null, the default, keeps it exact); the values stay ordinary float32, and +the root attribute of the same name records the choice. + ### Point ordering, and why it matters Points are ordered by @@ -689,6 +654,22 @@ The ordering is chosen for the query panoptic-segmentation training issues most often — "the non-LE voxels of instance X" — which is a plain slice rather than a mask over the instance's points. +The interaction a point sorts under is its **group's**: that of the instance +(or, for an unwritten instance, the fragment) representative owning it. The +two differ only when a proximity merge — `AbsorbLEScatter` or +`CombineLEScatters` — takes a particle from one interaction into a +representative from another. The absorber is then the representative: the +points become its fragment's, instance's and interaction's, stored in one +block, while the absorbed particle keeps its own identity — its row sits with, +and carries, its original interaction. So the particle rows are ordered by +their own interaction, and an interaction's `part_*` and `pc_*` ranges are +each contiguous. + +Every range is checked when it is written: each must hold exactly its members' +own points, or `build_layout` raises `LayoutError`. `pysupera.io_v3.check_ranges` +looks for the symptom in an existing file — fragment or instance slices that +overlap — which is how files written before that check can be screened. + ### Reading points ```python @@ -703,6 +684,14 @@ with read_events_v3("out.h5") as store: vtx = v.interaction_of(r) ``` +For a label per point, use the group ranges — never `pc_start`/`pc_end`, +which say which *particle* deposited a point: + +```python +frag = v.group_of_points("frag") # fragment id per point, -1 if none +inst = v.group_of_points("inst") # instance id per point +``` + Equivalently, by hand: ```python @@ -751,6 +740,7 @@ have rows, and no point is unreachable from some stored row. | `frag_pc_le_start/end` | int64 | the fragment's LE points | | `frag_merge_count` | int32 | Geant4 particles merged into the fragment | | `frag_parent_id` | int32 | nearest ancestor fragment; `== id` if none | +| `frag_inst_id` | int32 | on a fragment row, the instance it belongs to; -1 elsewhere or when that instance is not written | | `inst_id` | int32 | representative's `id`; `== id` marks an instance | | `inst_sem_type` | int8 | the instance's classification | | `inst_pc_start/end` | int64 | the instance's non-LE points | @@ -856,16 +846,7 @@ through the same code path: is a question about a *group* — JAXTPC's cluster of energy deposits — and a group is above threshold or it is not, regardless of how the readout that saw it was arranged. The filter reads `group_ids` and never looks at a hit -centre, so a pixel batch needs no flag and no separate reader: - -```bash -run_pysupera reader=jaxtpc_pixel \ - io.input_path=edepsim.h5 io.output_path=out.h5 \ - reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ - reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 -``` - -`reader=` selects the kind of input — see +centre. `reader=` selects the kind of input — see [Choosing a reader](#choosing-a-reader). The run banner prints which one it found (`[run] readout : pixel`), read @@ -875,8 +856,8 @@ Plane subgroups are discovered structurally rather than by name, so a geometry neither project has named yet still resolves. What *does* differ is anything that draws hits: see -[`vis_hits.html`](#vis_hitshtml--linking-3-d-to-the-readout) for the two -readout panes, and `pysupera-hits-subset` for the centres each keeps. +[`vis_hits.html`](#vis_hitshtml--jaxtpc-wire) and +[`vis_drift.html`](#vis_drifthtml--jaxtpc-pixel) for the two readouts, and `pysupera-hits-subset` for the centres each keeps. ```python from pysupera.readers import read_readout_type @@ -885,59 +866,138 @@ with h5py.File("hits.h5") as f: print(read_readout_type(f)) # 'wire' or 'pixel' ``` -### Truth deposits or detected hits +### Wire deposits and pixel hits -`reader.point_source` decides what a *point* is. The rest of the pipeline — -defragmentation, proximity, conditions, partitioning — is identical either -way; only the cloud changes. +What the partitioning runs on follows the readout: a wire batch is read as +the truth deposits its hits select, a pixel batch as its detected hits. The +rest of the pipeline — defragmentation, proximity, conditions, partitioning +— is identical; only its input differs. What is *written* is the same for +both — see [What the output stores](#what-the-output-stores-jaxtpc-input). -| | `deposits` (default) | `hits` | +| | wire (`reader=jaxtpc_wire`) | pixel (`reader=jaxtpc_pixel`) | |---|---|---| -| a point is | a Geant4 energy deposit the readout detected | a fired pixel | +| partitioned on | the Geant4 deposits the readout detected | the detected hits: shares of sensor pixels | | geometry | truth geometry; the readout only selects | the detected image | | carries | thresholding | pixelation, diffusion, threshold, drift-time ambiguity | -| points/event | 50k–143k | 1.4M–4.2M | -| cost/event | ~1.6 s | ~7 s | -| readout | wire or pixel | pixel only — a wire plane has no 3-D image | +| why | a wire plane has no 3-D image to convert | the pixel readout is a 3-D image | Each hit still has exactly one Geant4 particle: a CSR entry belongs to one group, and `group_to_track` gives each group one track. No nearest-neighbour matching is involved. ```bash -run_pysupera preset=cubic_pixel_hits \ +run_pysupera reader=jaxtpc_pixel \ io.input_path=edepsim.h5 io.output_path=out_hits.h5 \ reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 \ reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5 ``` -`preset=cubic_pixel_hits` is a bundle of the values that are only right -together and that cut across config groups — the pixel geometry, the charge -threshold, `particle.min_pc_size`, `distance_threshold` and -`check_group_ownership`. Only the file paths stay on the command line, and -any flag still overrides it. - -#### The charge threshold and `min_pc_size` are a matched pair +Only the file paths go on the command line: the pixel geometry is defaulted +in the reader config, and the settings that cut across config groups come +with the reader (see [Choosing a reader](#choosing-a-reader)). Any flag +still overrides them. `preset=cubic_pixel_hits` is kept as an alias for +`reader=jaxtpc_pixel`, so existing command lines work. + +#### Which pixels: the sensor image + +The hits file is the sensor image split by group — summed over groups, a +pixel's hit charge is its sensor ADC. But JAXTPC thresholds the *sum* +(`threshold_adc`, 7 ADC here) and keeps every group's share regardless, so +about half the pixels in the hits file are ones the sensor never recorded. +Hit mode therefore keeps a hit only on a pixel the sensor file holds, which +makes the labelled cloud cover the model's input image exactly: every +sensor pixel gets a label and no other pixel does. The run summary reports +both sides (`Off the sensor image`, and `Sensor pixels left unlabelled` if +any are). This is why `reader.jaxtpc_sensor_path` is required with +`point_source=hits`, and why it must come from the same JAXTPC run as the +hits file — the reader checks. + +Several groups can share a pixel, so a sensor pixel carries on average about +eight hits, one per group share; the mask removes only 14% of the hit +entries, since the pixels it drops are the faint ones with few shares. Those +are the hits `hit_labels/` marks `on_sensor = false` and labels -1. + +`reader.hit_charge_threshold` (ADC, per share) defaults to 0. It is applied +after the mask, so any positive value leaves on-sensor hits unlabelled — +which the writer refuses. + +A hits file written by a JAXTPC with a uint8 `group_sizes` is refused: a +group of more than 255 entries wrapped, and every later group on its plane +decodes at the wrong pixels. Regenerate such a batch. + +`particle.min_pc_size` for hit mode is **63** in the preset. It was fitted on +the sensor-masked hits of a 10-event batch: each EDepSim particle's LE-ness +from its hits, against the same particle's from truth deposits at the +default 5. + +| `min_pc_size` (hits) | 13 | 40 | 60 | **63** | 85 | 150 | +|---|---|---|---|---|---|---| +| agrees with truth@5 | 86.6% | 95.2% | 99.2% | **99.26%** | 98.7% | 97.5% | + +The per-event best ranged 58–70. The fit is at reader level, where each +particle is classified once; defragmentation also uses the value, to decide +which split-off pieces become LE, and that use was not tuned separately. + +### What the output stores (JAXTPC input) + +Wire and pixel output have the same shape: the true energy deposits behind +the hits, organised by pysupera particle, and a label for every hit. + +**`points/flat` — the true deposits.** Every Geant4 segment whose JAXTPC +group left hits, given to the particle owning that group — exactly, through +its deposits, for wire; by the majority of its hits for pixel. The values come +from the EDepSim step each segment was made from, at full precision: + +| column | unit | per 3 mm voxel (`particle.voxelize.store=voxels`, default) | +|---|---|---| +| x, y, z | mm | cell centre | +| t | µs | earliest step | +| dE | MeV | summed | +| dX | cm | summed | +| theta, phi | rad | earliest step — direction, theta from the z axis | +| p | MeV/c | earliest step — momentum magnitude | + +`particle.voxelize.store=points` writes every segment instead. The grid is +`particle.voxelize.voxel_size` / `origin`, the one that also drives proximity +and the extent classification. All the particle ranges (`pc_*`, `frag_pc_*`, +`inst_pc_*`, LE split) index these rows, so a fragment's or instance's true +deposits are one slice. Segments whose group left no hits — with pixel, no +hit on the sensor image — are not stored; most of that is charge that arrived +after the readout window closed. + +**`hit_labels/` — one label per hit, aligned with the JAXTPC hits file.** +For every plane of every volume, entry *k* labels CSR entry *k* of that plane +in the hits file (`volume_V/`), so a loader reads hits and labels +with the same index: -JAXTPC applies no threshold worth the name — 1 electron — so without one -every pixel carrying any induced-field influence becomes a point. -`reader.hit_charge_threshold` cuts on the magnitude of the induced charge in -electrons, matching JAXTPC's own encoder (the response is bipolar and ~16% -of hits are negative signal). +``` +hit_labels/volume{V}/plane{P}/ attrs: source ("U", "V", "Y" or "Pixel") + fragment_id int32 the fragment row the hit belongs to; -1 untraced + instance_id int32 the instance row; -1 untraced + is_le bool the hit's particle is kLEScatter + on_sensor bool pixel only: its pixel is in the sensor image + offsets int64 per-event fenceposts +``` -Cutting the halo also **thins the tracks**, which lowers the extent -threshold, so the two move together: +`P` is JAXTPC's plane index (U, V, Y = 0, 1, 2; a pixel readout has one). +For wire every hit is labelled by its group's owner. For pixel each hit is +labelled by the particle it was partitioned into, and exactly the hits off +the sensor image are -1 — checked when the file is written. A fragment row's +`frag_inst_id` names its instance, so hit → fragment → instance needs no +point ranges; every fragment and instance a hit names has a row. -| `hit_charge_threshold` | track width | `min_pc_size` | agrees with truth@5 | -|---|---|---|---| -| 0 e⁻ | 13.1 mm | 85 | 99.0% | -| **500 e⁻** *(preset)* | **6.5 mm** | **13** | **95.5%** | -| 500 e⁻ | 6.5 mm | 85 *(mixed)* | 85.2% | +```python +with read_events_v3("out.h5") as store: + labels = store.hit_labels(0) # {(volume, plane): {name: array}} + frag = labels[(0, 1)]["fragment_id"] # volume 0, plane V +``` -Mixing them is worse than either. The uncut pairing keeps every electron and -4.18M points per event; the preset's keeps 73.5% of the charge and 190k -points, and runs 4× faster. +| per 10 events | pixel | wire | +|---|---|---| +| `points/flat` (3 mm voxels) | 1.9 MB | 2.9 MB | +| `hit_labels/` | 10.5 MB (28.9M hits) | 2.2 MB (16.8M hits) | +| whole file | ~14 MB | ~7 MB | #### The drift coordinate — `reader.hit_x_from` @@ -967,41 +1027,6 @@ each keeps its own shape to sub-mm while whole interactions slide past one another. `true_t0` puts the real `t0` back and reproduces truth `x` to ~0.3 mm, keeping every other detector effect. -#### Recovering the true x — `points/true_x_shift` - -A wire-mode output stores truth geometry, so `x` is the true x. A pixel -hit-mode output under `nominal` does not: every point is displaced along the -drift axis by its interaction's own `t0`. The true position is kept anyway, -as a displacement rather than a second coordinate: - -```python -with read_events_v3("out_hits.h5") as store: - view = store[0] - shift = store.true_x_shift(0) # None for files written before this - true_x = view.points[:, 0] + shift -``` - -Zero throughout under `true_t0` and in deposit mode, so the relation holds -for every file and a consumer never has to ask which convention produced it. - -It costs almost nothing because it is `drift_direction · t0 · v`, which takes -one value per *(interaction, volume)* pair — 28 in a test event, not 4.2M. -Measured on one event: 1.31 MB raw, **0.007 MB on disk under LZ4, 0.14% of -the output**; 7 ms to compute over 4.18M hits; +16.7 MB transient memory. - -Stored per point rather than per particle because of a small but heavy -exception. The shift is exactly constant within a particle for 99.97% of -them — but the two that are not are cathode-crossing tracks with hits in -both volumes, where `drift_direction` flips, and they carry **15% of all -points** with internal spreads up to 204 mm. - -The stored rows are voxels, so a voxel could in principle mix two shifts; -each takes the value of a hit that made it. Measured, 0.09% of voxels mix -anything at all and the worst disagreement inside one is **0.05 mm** — the -sub-microsecond `t0` jitter within an interaction. The 200 mm case never -shares a voxel, because the nominal x is precisely what slides those two -halves apart. - #### The reference tick — `reader.hit_reference_tick` The tick at which a deposit sitting on the anode at Geant4 `t = 0` is @@ -1121,9 +1146,10 @@ A track is 3.2× thicker in the pixel image, which is diffusion plus the field response over about three pixel pitches. Measured over ten events, the best-agreeing cut per event ranged 81–90, and at a fixed 85 the sensor-hit labelling matches the truth-deposit labelling on **99.0%** of particles -(worst event 98.5%) — not merely the same fraction, the same particles. So -with `reader.point_source=hits`, set `particle.min_pc_size=85`; a run that -forgets warns. +(worst event 98.5%) — not merely the same fraction, the same particles. That was measured before the uint8 +`group_sizes` fix and the sensor mask; on the masked hits the best value is +63 (99.26%), which `reader=jaxtpc_pixel` sets — see +[Which pixels: the sensor image](#which-pixels-the-sensor-image). Note this also changed truth-deposit labelling, from 85.8% to 95.3% LE, because particles spread over a handful of rows inside one or two voxels are @@ -1238,7 +1264,7 @@ about 21% of the output under LZ4, less under gzip. `vis_hits.html` shows this mapping directly — 3-D voxels beside the 2-D readout planes, hover either side to light up the other. See -[Browser viewers](#browser-viewers-vis_forcehtml-vis_hitshtml) for its +[Browser viewers](#browser-viewers) for its controls and for preparing its two inputs. #### Exact per-voxel provenance — `particle.voxelize.store_mapping=true` diff --git a/pyproject.toml b/pyproject.toml index 2b123e0..5f59126 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,7 +57,6 @@ run_pysupera = "pysupera._run:main" pysupera-app = "pysupera.app:main" pysupera-repack = "pysupera.io:repack_cli" pysupera-hits-subset = "pysupera.hits_subset:main" -pysupera-truth-subset = "pysupera.truth_subset:main" [tool.setuptools.packages.find] where = ["."] diff --git a/pysupera/_run.py b/pysupera/_run.py index 0d78934..8c62dd3 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -47,6 +47,33 @@ def __exit__(self, *_): pass def append_event(self, *_): pass + +def _rows_by_input_id(particles): + """ + Every input point's first six columns, keyed the way the voxmap names it. + + The voxelizer records a particle's reader-supplied ``deposit_id`` when + every non-empty particle carries one (deposit mode: the seg-file deposit + index), and the ``PointFeature.id`` column otherwise (hit mode). The + same rule is applied here, so a particle's voxmap ids index these rows. + """ + import numpy as np + from pysupera.utils import PointFeature + live = [p for p in particles if len(p.point_cloud)] + if not live: + return np.zeros((0, 6), dtype=np.float32) + deps = [getattr(p, "deposit_id", None) for p in live] + if all(d is not None and len(d) == len(p.point_cloud) + for d, p in zip(deps, live)): + ids = np.concatenate(deps).astype(np.int64) + else: + ids = np.concatenate([p.point_cloud[:, PointFeature.id] + for p in live]).astype(np.int64) + rows = np.zeros((int(ids.max()) + 1, 6), dtype=np.float32) + rows[ids] = np.concatenate([p.point_cloud[:, :6] for p in live]) + return rows + + @hydra.main(config_path=_CONF_DIR, config_name="config", version_base=None) def main(cfg: DictConfig) -> None: """Partition all events in the input file and write results.""" @@ -54,7 +81,10 @@ def main(cfg: DictConfig) -> None: import numpy as np from collections import defaultdict from pysupera.io_v3 import open_writer_v3 - from pysupera.provenance import build_group_owners, remap_owners, le_flags + from pysupera.provenance import (build_group_owners, remap_owners, le_flags, + attach_truth_clouds) + from pysupera.hit_labels import build_hit_labels, check_pixel_labels + from pysupera.readers.format_jaxtpc import TRUTH_COLUMNS from pysupera.io import open_voxmap_writer from pysupera.partitioner import ParticlePartitioner from pysupera.merge import merge_em_showers @@ -84,11 +114,30 @@ def main(cfg: DictConfig) -> None: f"{int(cfg.particle.min_pc_size)} with point_source=hits. That " f"is a voxel count tuned for truth deposits, where a particle " f"fills one or two cells; a pixel image fills many more, so " - f"almost nothing will be classified kLEScatter. The value " - f"depends on reader.hit_charge_threshold, because cutting the " - f"halo thins the tracks: 85 at a threshold of 0, 13 at 500. " - f"preset=cubic_pixel_hits sets a matched pair.") + f"almost nothing will be classified kLEScatter. " + f"preset=cubic_pixel_hits sets a hit-mode value.") _owner_resolve = 'majority' if _hit_points else 'first' + # What a stored point is. With JAXTPC input, points/flat holds the true + # energy deposits behind the hits -- every segment whose JAXTPC group + # left hits, given to the particle owning that group -- and store picks + # voxels (on particle.voxelize.voxel_size / origin, the default) or the + # segments themselves (points). The hits stay in the JAXTPC hits file, + # labelled per hit in hit_labels. Without JAXTPC the cloud is the + # deposits the partitioning ran on, voxelized or (points) not. + _store = str((cfg.particle.get('voxelize', {}) or {}).get('store', 'voxels')) + if _store not in ('voxels', 'points'): + raise ValueError(f"particle.voxelize.store must be 'voxels' or " + f"'points', got {_store!r}") + _jaxtpc = (bool(cfg.reader.get('jaxtpc_seg_path', None)) + and bool(cfg.reader.get('jaxtpc_inst_path', None))) + _store_points = voxelizer is not None and _store == 'points' and not _jaxtpc + _vx = cfg.particle.get('voxelize', {}) or {} + _tv = _vx.get('voxel_size', 3) + _truth_voxel = (None if _store == 'points' + else [float(v) for v in _tv] if hasattr(_tv, '__iter__') + else float(_tv)) + _vo = _vx.get('origin', None) + _truth_origin = None if _vo is None else [float(v) for v in _vo] _owner_report: dict = {} _straddle_total = 0 _pixel_geom_report = None @@ -154,6 +203,10 @@ def _n_groups_of(_store): mask_stats: dict = defaultdict(list) # Detected-hit counts; only populated when reader.point_source=hits. hit_stats: dict = defaultdict(list) + # Truth-cloud counts; only populated when the truth cloud is written. + truth_stats: dict = defaultdict(list) + # Hit-label counts; only populated with JAXTPC input. + label_stats: dict = defaultdict(list) n_events = 0 _max_events = int(cfg.get("max_events", -1)) @@ -189,7 +242,7 @@ def _n_groups_of(_store): print(f"[run] point source : {_psrc}" + (f" (x from {cfg.reader.get('hit_x_from', 'nominal')}, " f"energy {cfg.reader.get('hit_energy', 'charge')}, " - f"charge >= {cfg.reader.get('hit_charge_threshold', 0)} e-)" + f"charge >= {cfg.reader.get('hit_charge_threshold', 0)} ADC)" if _psrc == 'hits' else "")) _n_available = len(store) @@ -204,7 +257,9 @@ def _n_groups_of(_store): with open_writer_v3(cfg.io.output_path, compression=cfg.io.compression, - compression_opts=cfg.io.compression_opts) as writer, \ + compression_opts=cfg.io.compression_opts, + points=dict(cfg.io.get('points', {}) or {}), + columns=(TRUTH_COLUMNS if _jaxtpc else None)) as writer, \ (open_voxmap_writer(cfg.io.output_path, compression=cfg.io.compression, compression_opts=cfg.io.compression_opts) @@ -216,6 +271,13 @@ def _n_groups_of(_store): 'hit_x_from': cfg.reader.get('hit_x_from', None) if _hit_points else None, 'hit_energy': cfg.reader.get('hit_energy', None) if _hit_points else None, 'readout_type': _readout, + # What a points/flat row is: a voxel of this size, or a + # deposit (store=points). + 'store': _store, + 'voxel_size': _truth_voxel, + # Precision of the energy column: null means exact. + 'energy_mantissa_bits': (cfg.io.get('points', {}) or {}).get( + 'energy_mantissa_bits', None), }) from tqdm import tqdm @@ -239,6 +301,16 @@ def _n_groups_of(_store): if _pixel_geom_report is None: _pixel_geom_report = getattr( store, 'pixel_geometry_report', None) + # The geometry that turned each hit's (py, pz, tick) into + # (x, y, z), so a viewer can place the hits exactly as the + # reader did -- the inferred x included. + _geoms = getattr(store, 'pixel_geometry', None) + if _geoms: + import json as _json + from dataclasses import asdict as _asdict + writer.set_meta({'pixel_geometry': _json.dumps( + [dict(_asdict(g), x_anode_mm=g.x_anode_mm, + mm_per_tick=g.mm_per_tick) for g in _geoms])}) _hs = getattr(store, 'last_hit_stats', None) if _hs: for _k, _v in _hs.items(): @@ -257,6 +329,13 @@ def _n_groups_of(_store): + (f" unmatched={_ms['n_unmatched']:,}" if _ms['n_unmatched'] else "")) + # The input points themselves, by input row id, when those -- + # not the voxels -- are what is written. Voxelization then + # serves the proximity and merging steps only; see + # particle.voxelize.store. + _input_rows = (_rows_by_input_id(particles) + if _store_points else None) + # skip merge_duplicates when voxelizer subsumes it if merge_processor is not None and not (voxelizer and voxelizer.merge_duplicates): _t = time.perf_counter() @@ -464,62 +543,106 @@ def _n_groups_of(_store): f"{'...' if len(_uncovered) > 10 else ''}" ) - # ── true_x_shift, carried onto the voxels ────────────────── - # The reader gives one shift per input hit; the rows about to - # be written are voxels, so each takes the shift of a hit - # that made it. Which one barely matters: the shift is - # constant per (interaction, volume), and measured over an - # event only 0.09% of voxels mix two values at all, by at - # most 0.05 mm -- the sub-microsecond t0 jitter inside one - # interaction. The 200 mm case, a track crossing the - # cathode, never shares a voxel, because the nominal x is - # exactly what slides its two halves apart. - _shift_in = getattr(store, 'last_true_x_shift', None) - if _shift_in is not None: + # ── Store the input points instead of the voxels ────────── + # Plain EDepSim, store=points: every voxel knows the input rows + # it was made from (the voxmap, tracked for provenance), so a + # particle's voxels expand back into exactly its own deposits. + if _input_rows is not None: for _p in particles: - _n = len(_p.point_cloud) - if not _n: - _p.true_x_shift = np.zeros(0, dtype=np.float32) - continue _vm = getattr(_p, 'voxmap', None) - _v = None - if _vm is not None: - _off, _ids = _vm - _off = np.asarray(_off) - if _ids is not None and len(_off) == _n + 1: - _v = _shift_in[np.asarray(_ids, - dtype=np.int64)[_off[:-1]]] - if _v is None: - # No voxel map: the cloud is still the hits, so - # the reader's per-hit slice lines up as it is. - _d = getattr(_p, 'deposit_id', None) - _v = (_shift_in[np.asarray(_d, dtype=np.int64)] - if _d is not None and len(_d) == _n - else np.zeros(_n, dtype=np.float32)) - _p.true_x_shift = np.asarray(_v, dtype=np.float32) - - # ── Write ─────────────────────────────────────────────────── - _t = time.perf_counter() - _layout = build_layout(particles, _frag_groups, _inst_groups, - vertices=getattr(store, 'last_vertices', None)) - writer.append_event(_layout) + if _vm is None or _vm[1] is None: + if len(_p.point_cloud): + raise RuntimeError( + f"Event {event_idx}: particle {_p.id} has " + f"{len(_p.point_cloud)} voxels but no voxel " + f"map, so its input points cannot be " + f"recovered.") + continue + _ids = np.asarray(_vm[1], dtype=np.int64) + _p.point_cloud = _input_rows[_ids].astype(np.float32, copy=False) # ── JAXTPC group ownership ───────────────────────────────── # Which hits belong to which reconstructed object. Hits # attach to groups and groups attach to one particle, so this # one table answers it for instances and fragments alike -- # see pysupera.provenance. Absent outside JAXTPC mode, where - # there are no hits to group. + # there are no hits to group. Settled before the layout + # because the truth cloud is laid out by it. + _t = time.perf_counter() _d2g = getattr(store, 'last_deposit_to_group', None) - if _d2g is None: - writer.append_group_owners(np.zeros(0, dtype=np.int32)) - else: + _owner = None + if _d2g is not None: _owner = build_group_owners( particles, _d2g, _n_groups_of(store), check=_check_group_owner, resolve=_owner_resolve, report=_owner_report) _straddle_total += int( _owner_report.get('n_straddled', 0) or 0) + + # ── Per-hit labels (JAXTPC) ──────────────────────────────── + # Settled while the particles still carry the clouds they were + # partitioned on: for pixel those are the hits, and each hit's + # particle is read off its voxel map. + _labels = None + _planes = getattr(store, 'last_planes', None) + if _jaxtpc and _planes is not None: + _hit_idx = getattr(store, 'last_hit_index', None) + if _hit_points and _hit_idx is not None: + _rp = np.full(len(_hit_idx), -1, dtype=np.int64) + for _p in particles: + _vm = getattr(_p, 'voxmap', None) + if _vm is not None and _vm[1] is not None: + _rp[np.asarray(_vm[1], dtype=np.int64)] = int(_p.id) + _labels = build_hit_labels( + _planes, particles, _frag_groups, _inst_groups, + row_particle=_rp, hit_index=_hit_idx) + check_pixel_labels(_labels) + elif _owner is not None: + _labels = build_hit_labels( + _planes, particles, _frag_groups, _inst_groups, + owner=_owner) + if _labels is not None: + for _d in _labels: + _f = _d['fragment_id'] + label_stats['n_hits'].append(len(_f)) + label_stats['n_traced'].append(int((_f >= 0).sum())) + + # ── points/flat: the true deposits behind the hits (JAXTPC) ─ + # Each segment follows its group to the owning particle, so the + # cloud nests in the same fragments and instances the hits do. + if _jaxtpc: + _segs = getattr(store, 'last_truth_segments', None) + if _segs is None or _owner is None: + for _p in particles: + _p.truth_cloud = np.zeros((0, len(TRUTH_COLUMNS)), + dtype=np.float32) + else: + _ts = attach_truth_clouds( + particles, _owner, _segs[0], _segs[1], + voxel_size=_truth_voxel, origin=_truth_origin) + for _k, _v in _ts.items(): + truth_stats[_k].append(_v) + for _p in particles: + _p.point_cloud = _p.truth_cloud + + # ── Write ─────────────────────────────────────────────────── + # Every fragment and instance a hit is labelled with gets a + # row, whether or not it holds points of its own. + _keep = set() + for _d in (_labels or ()): + _keep.update(np.unique(_d['fragment_id']).tolist()) + _keep.update(np.unique(_d['instance_id']).tolist()) + _keep.discard(-1) + _layout = build_layout(particles, _frag_groups, _inst_groups, + vertices=getattr(store, 'last_vertices', None), + keep=_keep) + writer.append_event(_layout) + if _labels is not None: + writer.append_hit_labels(_labels) + + if _owner is None: + writer.append_group_owners(np.zeros(0, dtype=np.int32)) + else: # Record the fragment, not the particle: only a fraction # of particles get a row, so a particle id would often # name something the reader cannot resolve. Every @@ -661,6 +784,7 @@ def _agg(vals): print(f" {_SEP}") for label, key in ( ("Pixel hits in the file", 'n_decoded'), + ("Pixels in the sensor image", 'n_sensor_pixels'), ("Pixel hits kept", 'n_hits'), ("Attached to a particle", 'n_attached'), ("Groups in the event", 'n_groups'), @@ -680,6 +804,20 @@ def _agg(vals): # shrinking it -- which is why it is reported and not inferred. _cut = sum(hit_stats.get('n_below_threshold', [0])) _dec = sum(hit_stats.get('n_decoded', [0])) + # Hits whose pixel the sensor image does not hold: shares of a + # pixel whose summed response stayed under JAXTPC's threshold. + _off = sum(hit_stats.get('n_off_sensor', [0])) + if _off: + print(f" {'Off the sensor image':<{_W}} " + f"{_off:>10,} ({100.0 * _off / max(1, _dec):.1f}%)") + # Sensor pixels no kept hit lands on. Zero when the hits file + # decomposes its sensor file; anything else is a pixel the model + # sees and the labels do not describe. + _unl = sum(hit_stats.get('n_sensor_unlabelled', [0])) + if _unl: + _sen = sum(hit_stats.get('n_sensor_pixels', [0])) + print(f" {'Sensor pixels left unlabelled':<{_W}} " + f"{_unl:>10,} ({100.0 * _unl / max(1, _sen):.2f}%)") if _cut: print(f" {'Below the charge threshold':<{_W}} " f"{_cut:>10,} ({100.0 * _cut / max(1, _dec):.1f}%)") @@ -714,11 +852,23 @@ def _agg(vals): print(f" {'Groups split across fragments':<{_W}} " f"{_straddle_total:>10,} ({100.0 * _straddle_total / _gt:.2f}%)" f" -- resolved by majority") - _cap = sum(hit_stats.get('n_capped_groups', [0])) - if _cap: - # group_sizes is uint8, so a group of more than 255 entries - # cannot be stored and JAXTPC truncates its CSR. - print(f" {'Groups at the uint8 size cap':<{_W}} {_cap:>10,}") + + # ── Truth cloud (pixel hit mode) ────────────────────────────────── + if truth_stats.get('n_segments'): + _seg = sum(truth_stats['n_segments']) + _own = sum(truth_stats['n_owned']) + _row = sum(truth_stats['n_rows']) + print(f"\n[run] True deposits behind the hits ({n_events} event(s))") + print(f" {'Segments in the seg file':<{_W}} {_seg:>10,}") + print(f" {'Behind an owned group':<{_W}} {_own:>10,}" + f" ({100.0 * _own / max(1, _seg):.1f}%)") + _how = ('segments' if _truth_voxel is None + else f'{_truth_voxel} mm voxels') + print(f" {'points/flat rows (' + _how + ')':<{_W}} {_row:>10,}") + if label_stats.get('n_hits'): + _nh = sum(label_stats['n_hits']); _nt = sum(label_stats['n_traced']) + print(f" {'Hits labelled (hit_labels)':<{_W}} {_nt:>10,}" + f" of {_nh:,} ({100.0 * _nt / max(1, _nh):.1f}%)") # ── Time profile ────────────────────────────────────────────────── total = sum(profile.values()) diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index 04072e1..c7d53b5 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -7,9 +7,9 @@ defaults: # jaxtpc_wire JAXTPC wire readout (U/V/Y planes). Particle metadata # still comes from the EDepSim file at io.input_path; the # point cloud keeps only deposits the readout detected. - # jaxtpc_pixel JAXTPC pixel readout (one Pixel plane). As above, and - # additionally supports reader.point_source=hits, which - # replaces the cloud with the detected pixel image. + # jaxtpc_pixel JAXTPC pixel readout (one Pixel plane), read as the + # detected image: a point is a hit on a sensor pixel, the + # energy the sensors recorded. Needs the sensor file too. # # Override on the command line with reader=jaxtpc_pixel. Each variant # inherits edepsim_h5 and adds only what its own arrangement needs. @@ -17,6 +17,11 @@ defaults: - checker: cpu_single - conditions: default - _self_ + # Settings that go with the chosen reader but live outside its group -- + # min_pc_size, distance_threshold and the like, for the pixel hits. + # Listed after _self_ so they replace the defaults below for that reader; + # empty for the others. A command-line flag still overrides them. + - readout: ${reader} # Optional bundle of detector-specific values that cut across config # groups -- reader geometry, min_pc_size, the thresholds that only make # sense together. Listed after _self_ so it overrides the defaults @@ -213,28 +218,14 @@ particle: # track-width across, (width / voxel_size) ** 3. # # truth deposits track width 4.9 mm -> (4.9/3)^3 = 5 <- this default - # pixel sensor hits 12.8 mm -> (12.8/3)^3 = 77, best fit 85 # - # For sensor hits the width depends on how hard the hits are cut, so this - # and reader.hit_charge_threshold are a matched pair -- mixing them is - # worse than either: + # pixel sensor hits 11.9 mm -> (11.9/3)^3 = 63 # - # hit_charge_threshold track width min_pc_size agrees with truth@5 - # ---------------------------------------------------------------------- - # 0 e- 13.1 mm 85 99.0% - # 500 e- 6.5 mm 13 95.5% - # 500 e- 6.5 mm 85 (mixed) 85.2% - # - # The uncut pairing keeps every electron and 4.18M points per event; the - # 500 e- pairing keeps 73.5% of the charge and 190k points, and runs 4x - # faster. preset=cubic_pixel_hits selects the second. - # - # Measured over ten events of a 4.32 mm-pitch pixel detector on a 3 mm - # grid: the best-agreeing cut per event ranged 81-90, and at a fixed 85 - # the sensor-hit labelling matches the truth-deposit labelling on 99.0% of - # particles. So with reader.point_source=hits, set: - # - # particle.min_pc_size=85 + # For sensor hits (sensor-masked, pixel pitch 4.32 mm) the value was fitted + # rather than derived: each EDepSim particle's LE-ness from its hits + # against the same particle's from truth deposits at 5. Over ten events + # agreement peaks at 63 (99.26%; per-event best 58-70), which + # preset=cubic_pixel_hits sets. min_pc_size: 5 # Pre-processing: voxelize point clouds onto a regular 3-D grid. @@ -255,6 +246,20 @@ particle: # When true, skips the separate merge_duplicates preprocessing step. # Safe because exact-coordinate duplicates always fall in the same voxel. merge_duplicates: true + # What points/flat stores. With JAXTPC input (wire or pixel) it is the + # true energy deposits behind the hits -- every Geant4 segment whose + # JAXTPC group left hits, read from the EDepSim steps, given to the + # particle owning that group: x, y, z [mm], t [us], dE [MeV], dX [cm], + # theta, phi [rad], p [MeV/c]. The hits stay in the JAXTPC hits file, + # labelled per hit in hit_labels/. Without JAXTPC it is the deposits + # the partitioning ran on. + # voxels on this grid (voxel_size, origin), merged per particle: cell + # centres, t the earliest, dE and dX summed, and theta, phi, p + # of the earliest step -- the particle entering the cell. + # points every segment as it is. + # Voxelization on voxel_size drives proximity, defragmentation and the + # extent classification whatever is stored. + store: voxels # Write _voxmap.h5: for every output voxel, which input deposits # were merged into it and with what energy. Useful for truth studies and # for debugging voxelization, but it carries one row per *input* point diff --git a/pysupera/conf/io/default.yaml b/pysupera/conf/io/default.yaml index 94c56d9..e943a39 100644 --- a/pysupera/conf/io/default.yaml +++ b/pysupera/conf/io/default.yaml @@ -12,3 +12,22 @@ output_path: ??? # e.g. /data/events_merged.h5 # compression_opts: only relevant for gzip (1=fast … 9=best). compression: lz4 compression_opts: ~ + +# How points/flat -- the bulk of the file -- is stored. compression above +# covers everything else. +points: + # columns: chunks one column wide (16384 rows x 1), so each chunk holds a + # single quantity and compresses like it. rows: chunks of whole rows. + # Measured on a pixel-hit file: 78.7 MB as rows, 45.6 MB as columns; a + # slice spanning all six columns then reads about 1.5x slower (six chunks + # instead of one), a single column 3x faster. + chunks: columns + # Bitshuffle + LZ4 instead of plain LZ4. It groups the bits of every value + # before compressing, which pays off on continuous columns (wire deposits: + # 1.1 -> 0.8 MB) and on a rounded energy column (below); it costs a little + # on the gridded x, y, z of hits. + bitshuffle: true + # Round the energy column (column 4) to this many float32 mantissa bits: + # a relative error of at most 2^-(bits+1) at any magnitude. null keeps it + # exact, which every reader does by default. + energy_mantissa_bits: ~ diff --git a/pysupera/conf/preset/cubic_pixel_hits.yaml b/pysupera/conf/preset/cubic_pixel_hits.yaml index fdcd4c5..6a97828 100644 --- a/pysupera/conf/preset/cubic_pixel_hits.yaml +++ b/pysupera/conf/preset/cubic_pixel_hits.yaml @@ -1,48 +1,10 @@ # @package _global_ # -# Everything needed to run the cubic_pixel detector on its SENSOR HITS, so -# that only the file paths stay on the command line: +# Kept so existing command lines work: reader=jaxtpc_pixel now reads the +# detected hits by default and brings its own settings (readout/jaxtpc_pixel), +# so this preset only selects that reader. # -# run_pysupera preset=cubic_pixel_hits \ -# io.input_path=edepsim.h5 io.output_path=out.h5 \ -# reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ -# reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 \ -# reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5 -# -# Anything here can still be overridden on the command line, which wins. -# -# A preset rather than the reader config because two of these live outside -# the reader package -- min_pc_size under particle, and the two top-level -# checks -- and because they are only right together. +# run_pysupera preset=cubic_pixel_hits ... == run_pysupera reader=jaxtpc_pixel ... defaults: - override /reader: jaxtpc_pixel - -reader: - # A point is a fired pixel, not a Geant4 deposit. - point_source: hits - -particle: - # Extent below which a particle is a low-energy scatter, in 3 mm voxels. - # The rule is the cell count of a blob one track-width across; the width - # depends on how hard the hits are cut, so this and - # reader.hit_charge_threshold have to move together: - # - # threshold track width min_pc_size agreement with truth@5 - # ----------------------------------------------------------------- - # 0 e- 13.1 mm 85 99.0% - # 500 e- 6.5 mm 13 95.5% - # - # Mixing them is what to avoid: 85 at a 500 e- threshold agrees on only - # 85.2% of particles. The uncut pairing keeps all the charge and 4.18M - # points per event; this one keeps 73.5% of the charge and 190k points. - min_pc_size: 13 - -# Must clear the pixel diagonal (pitch * sqrt(2) = 6.11 mm) or -# defragmentation measures the pixel grid instead of the physics. -distance_threshold: 6.2 - -# A group's hits can straddle a fragment split here -- the readout leaves -# gaps the deposit cloud does not have -- so the invariant the groups/ table -# rests on does not hold and the run resolves by majority instead. -check_group_ownership: false diff --git a/pysupera/conf/reader/jaxtpc_pixel.yaml b/pysupera/conf/reader/jaxtpc_pixel.yaml index 932578f..a366724 100644 --- a/pysupera/conf/reader/jaxtpc_pixel.yaml +++ b/pysupera/conf/reader/jaxtpc_pixel.yaml @@ -1,25 +1,18 @@ -# JAXTPC pixel-readout input. +# JAXTPC pixel-readout input, read as the detected image: a point is a hit on +# a sensor pixel -- the energy the sensors recorded. # # run_pysupera reader=jaxtpc_pixel \ # io.input_path=edepsim.h5 io.output_path=out.h5 \ # reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5 \ -# reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 +# reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5 \ +# reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5 # -# As reader=jaxtpc_wire, but the readout is a single Pixel plane per volume -# recording two pixel indices and a drift tick -- a natively 3-D image. That -# is what makes point_source=hits possible here and not for wire. +# The settings that go with hits but live outside this group -- min_pc_size, +# distance_threshold, check_group_ownership, what a stored point is -- come +# with it from readout/jaxtpc_pixel.yaml. # -# For the detected-image mode, add: -# -# reader.point_source=hits \ -# reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5 \ -# reader.pixel_pitch_mm=4.32 'reader.pixel_drift_direction=[-1,1]' \ -# check_group_ownership=false distance_threshold=6.2 -# -# distance_threshold must clear the pixel diagonal (pitch * sqrt(2)) or -# defragmentation measures the pixel grid instead of the physics, and -# check_group_ownership must be off because a group's hits can straddle a -# fragment split. Both are explained in the README. +# A pixel readout records two pixel indices and a drift tick -- a natively 3-D +# image -- which is what makes hits possible here and not for wire. defaults: - edepsim_h5 @@ -34,19 +27,16 @@ jaxtpc_inst_path: ??? # --------------------------------------------------------------------------- # What a point is (JAXTPC pixel readout only) # --------------------------------------------------------------------------- -# deposits (default) — a point is a Geant4 energy deposit that the readout -# detected. The geometry is truth geometry; the readout -# only decides which deposits survive. -# hits — a point is a fired pixel. (py, pz, tick) is converted -# back to (x, y, z), so the cloud carries pixelation, -# diffusion, threshold and the drift-time ambiguity, and -# the partitioner sees what the detector saw. Requires a -# pixel-readout hits file; wire readout has no 3-D image -# to convert. -# -# Roughly 4.2M points per event against 143k detected deposits, so expect the +# hits — a point is a hit on a sensor pixel. (py, pz, tick) is converted +# back to (x, y, z), so the cloud carries pixelation, diffusion, +# threshold and the drift-time ambiguity, and the partitioner sees what +# the detector saw. Requires a pixel-readout hits file and its sensor +# file. The only way a pixel batch is read: the truth segments behind +# the hits are written alongside, in truth/. +# +# Roughly 3M hits per event against 143k detected deposits, so expect the # run to cost accordingly. -point_source: deposits +point_source: hits # How the drift coordinate is obtained, when point_source=hits. # @@ -72,25 +62,17 @@ point_source: deposits # truth x to ~0.3 mm on group centres, 5.8 mm per hit. hit_x_from: nominal -# Drop sensor hits carrying less than this much charge, in the units the -# JAXTPC hits file uses: ionisation electrons induced on the pixel. 0 -# disables the cut. -# -# Compared against the magnitude, matching JAXTPC's own encoder: the readout -# response is bipolar and about 16% of hits are negative, which is signal -# rather than noise. -# -# Why a default at all. JAXTPC applies no threshold worth the name -- 1 -# electron in the batch this was developed against -- so every pixel -# carrying any induced-field influence reaches the point cloud: 4.18M points -# for an event whose truth is 216k deposits, with the brightest 2% carrying -# half the charge and the rest forming a halo two to three pixels wide -# around every real deposit. -# -# For scale, the same run's sensor file states threshold_adc 7.0 at -# 182 electrons_per_adc, i.e. 1274 electrons on the digitised waveform; 500 -# is deliberately below that. -hit_charge_threshold: 500 +# Drop hits carrying less than this much charge, in ADC -- the units of the +# JAXTPC hits file, which is the sensor image split by group. 0 disables +# the cut, and 0 is the setting to keep. +# +# Which pixels become points is decided by the sensor image, not by this: +# with point_source=hits a hit is kept only on a pixel the sensor file holds, +# i.e. one whose summed response JAXTPC kept at |ADC| >= threshold_adc (7 in +# the batch this was developed against). That makes the cloud the model's +# input image pixel for pixel. A cut here is applied per group share, after +# the mask, so anything above 0 leaves sensor pixels with no label. +hit_charge_threshold: 0 # --------------------------------------------------------------------------- # Pixel geometry (required by point_source=hits) @@ -115,9 +97,11 @@ hit_charge_threshold: 500 # Run without them once and the error lists what the data is consistent with, # so the values can be copied from it and then verified on the next run. -# JAXTPC sensor HDF5 file. Supplies velocity_cm_us and time_step_us, so they -# do not have to be restated here. Optional if both are given below. -jaxtpc_sensor_path: ~ +# JAXTPC sensor HDF5 file. Required by point_source=hits: its pixels are the +# ones that become points (see hit_charge_threshold), and it must come from +# the same JAXTPC run as jaxtpc_inst_path -- checked, not assumed. It also +# supplies velocity_cm_us and time_step_us, so they need not be restated. +jaxtpc_sensor_path: ??? # Pixel pitch in mm (square grid assumed). Defaulted to the cubic_pixel # detector this was developed against (pixel_pitch 0.432 cm); change it for diff --git a/pysupera/conf/readout/edepsim_h5.yaml b/pysupera/conf/readout/edepsim_h5.yaml new file mode 100644 index 0000000..5cf3f77 --- /dev/null +++ b/pysupera/conf/readout/edepsim_h5.yaml @@ -0,0 +1,4 @@ +# @package _global_ +# +# Settings that go with reader=edepsim_h5 but live outside the reader group. None: +# this reader runs on the defaults in config.yaml. See readout/jaxtpc_pixel. diff --git a/pysupera/conf/readout/jaxtpc_pixel.yaml b/pysupera/conf/readout/jaxtpc_pixel.yaml new file mode 100644 index 0000000..1c368c0 --- /dev/null +++ b/pysupera/conf/readout/jaxtpc_pixel.yaml @@ -0,0 +1,30 @@ +# @package _global_ +# +# Settings that go with reader=jaxtpc_pixel -- the detected pixel image, i.e. +# the energy the sensors recorded -- but live outside the reader group. +# config.yaml loads this file for the chosen reader after its own values, +# so these replace the truth-deposit defaults; a command-line flag still +# beats them. They are only right together. + +particle: + # Extent below which a particle is a low-energy scatter, in 3 mm voxels. + # Tuned on the sensor-masked hits of a 10-event batch: each EDepSim + # particle's LE-ness from its hits at this value, against the same + # particle's from truth deposits at the default 5. Agreement peaks at 63 + # (99.26% of 55k particles; per-event best 58-70) and is flat around it + # -- 99.22% at 60, 98.73% at 85. 3 mm * 63^(1/3) = 11.9 mm, the track + # width of a 4.32 mm-pitch image. + min_pc_size: 63 + +# Must clear the pixel diagonal (pitch * sqrt(2) = 6.11 mm) or +# defragmentation measures the pixel grid instead of the physics. +distance_threshold: 6.2 + +# A group's hits can straddle a fragment split here -- the readout leaves +# gaps the deposit cloud does not have -- so the invariant the groups/ table +# rests on does not hold and the run resolves by majority instead. +check_group_ownership: false + +# Marks these settings as hit-mode ones, so a run that switches the pixel +# reader back to truth deposits is stopped instead of keeping them. +readout_settings: pixel_hits diff --git a/pysupera/conf/readout/jaxtpc_wire.yaml b/pysupera/conf/readout/jaxtpc_wire.yaml new file mode 100644 index 0000000..80ed495 --- /dev/null +++ b/pysupera/conf/readout/jaxtpc_wire.yaml @@ -0,0 +1,4 @@ +# @package _global_ +# +# Settings that go with reader=jaxtpc_wire but live outside the reader group. None: +# this reader runs on the defaults in config.yaml. See readout/jaxtpc_pixel. diff --git a/pysupera/config.py b/pysupera/config.py index a41426a..bbf66ea 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -458,6 +458,24 @@ def _opt_float(node, key): return None if v is None else float(v) +def check_readout_settings(cfg): + """ + Raise when a reader's out-of-group settings do not fit how it reads. + + reader=jaxtpc_pixel reads the detected hits and brings hit-mode values + for min_pc_size, distance_threshold and the rest + (conf/readout/jaxtpc_pixel.yaml). A pixel batch is not read any other + way, so overriding reader.point_source is refused rather than run with + those settings. + """ + marker = cfg.get("readout_settings", None) + source = str(cfg.reader.get("point_source", "hits")) + if marker == "pixel_hits" and source != "hits": + raise ValueError( + "reader=jaxtpc_pixel reads a pixel batch as its detected hits " + f"(reader.point_source=hits); {source!r} is not supported.") + + def build_reader(cfg: DictConfig): """ Instantiate an :class:`~pysupera.readers.EventReaderBase` described by @@ -475,11 +493,10 @@ def build_reader(cfg: DictConfig): JAXTPC readout simulation. All particle-level metadata still comes from the EDepSim file at *cfg.io.input_path*. - ``cfg.reader.point_source`` then chooses what a point *is*: ``deposits`` - (default) keeps truth geometry and lets the readout select, while - ``hits`` replaces the cloud with the detected pixel image. See - ``pysupera.readers.pixel_hits`` for the ``hit_x_from`` and ``hit_energy`` - conventions. + What a point *is* follows the readout: a wire batch is read as the + deposits its hits select (truth geometry), a pixel batch as its detected + hits. See ``pysupera.readers.pixel_hits`` for the ``hit_x_from`` and + ``hit_energy`` conventions. With JAXTPC masking disabled (default):: @@ -533,7 +550,8 @@ def build_reader(cfg: DictConfig): # Note: `k in cfg.reader` is False for a MISSING value, so the # membership test cannot be used to spot one -- is_missing can, and # returns False for a key the config genuinely does not declare. - _missing = [k for k in ("jaxtpc_seg_path", "jaxtpc_inst_path") + _missing = [k for k in ("jaxtpc_seg_path", "jaxtpc_inst_path", + "jaxtpc_sensor_path") if OmegaConf.is_missing(cfg.reader, k)] if _missing: raise ValueError( @@ -542,8 +560,10 @@ def build_reader(cfg: DictConfig): + ". Pass them on the command line, e.g.\n" " reader.jaxtpc_seg_path=batch/step/run/name_step_0000_00.h5\n" " reader.jaxtpc_inst_path=batch/hits/run/name_hits_0000_00.h5\n" + " reader.jaxtpc_sensor_path=batch/sensor/run/name_sensor_0000_00.h5\n" "Use reader=edepsim_h5 to read the EDepSim steps directly " "instead.") + check_readout_settings(cfg) jaxtpc_active = bool(seg_path) and bool(inst_path) @@ -562,8 +582,10 @@ def build_reader(cfg: DictConfig): "electron_energy_threshold", 0.05)), min_pc_size = int(cfg.particle.get("min_pc_size", -1)), voxel_size = _classify_voxel_size(cfg), - point_source = str(cfg.reader.get("point_source", - "deposits")), + # None follows the readout: hits for pixel, deposits for wire. + point_source = (str(cfg.reader.point_source) + if cfg.reader.get("point_source", None) + else None), hit_x_from = str(cfg.reader.get("hit_x_from", "nominal")), hit_energy = str(cfg.reader.get("hit_energy", @@ -585,6 +607,10 @@ def build_reader(cfg: DictConfig): "readout_time_step_us"), pixel_geometry_from_fit = bool(cfg.reader.get( "pixel_geometry_from_fit", False)), + step_key = str(cfg.reader.get("step_key", + "pstep/lar_vol")), + # points/flat is these segments, so they are always read. + truth_segments = True, ) # Default: full EDepSim reader (no JAXTPC masking). diff --git a/pysupera/hit_labels.py b/pysupera/hit_labels.py new file mode 100644 index 0000000..6f3d39b --- /dev/null +++ b/pysupera/hit_labels.py @@ -0,0 +1,139 @@ +""" +Per-hit labels, aligned with the JAXTPC hits file. + +The model's input is the JAXTPC hits file itself, so its targets are one +label per hit, in the hits file's own order: for every readout plane of every +volume, entry *k* labels CSR entry *k* of that plane. A loader reads the +hits and the labels with the same index -- no reordering, no join. + +Each entry gets the pysupera fragment and instance it belongs to, as the +representative particle ids that head them in the output's ``particles`` +table, and whether its particle is a low-energy scatter. Where a hit cannot +be traced to a particle the ids are -1. + +How a hit is traced depends on the readout: + +* **Wire** (``owner``): a hit belongs to its JAXTPC group, and the group to + the particle holding its deposits -- exactly, since a group's deposits are + never split. +* **Pixel** (``row_particle`` + ``hit_index``): the hits are what was + partitioned, so each hit's particle is read directly -- exact per hit. Hits + off the sensor image were never partitioned: their ids are -1, and that is + checked (:func:`check_pixel_labels`) rather than assumed. +""" + +from __future__ import annotations + +import numpy as np + +from .utils import SemanticType as _ST + +_LE = _ST.kLEScatter + + +def _lookups(particles, frag_groups, inst_groups): + """Per particle id: its fragment rep, instance rep and LE flag (arrays).""" + ids = [int(p.id) for p in particles] + n = (max(ids) + 1) if ids else 0 + frag = np.full(n + 1, -1, dtype=np.int32) # index n: "no particle" + inst = np.full(n + 1, -1, dtype=np.int32) + le = np.zeros(n + 1, dtype=bool) + for g in frag_groups: + frag[np.asarray(g.member_ids, dtype=np.int64)] = int(g.rep_id) + for g in inst_groups: + inst[np.asarray(g.member_ids, dtype=np.int64)] = int(g.rep_id) + for p in particles: + le[int(p.id)] = p.sem_type is _LE + return frag, inst, le, n + + +def build_hit_labels(planes, particles, frag_groups, inst_groups, *, + owner=None, row_particle=None, hit_index=None): + """ + Label every CSR entry of every plane. + + Parameters + ---------- + planes : list of dict + The reader's ``last_planes``: per (volume, plane) the entries' + ``group`` and, for pixel, ``on_sensor``. + particles, frag_groups, inst_groups + The event's particles and the fragment / written-instance groups + (see :func:`pysupera.layout.snapshot_groups`). + owner : ndarray, optional + Wire: owning particle id per event-global group + (:func:`pysupera.provenance.build_group_owners`). + row_particle, hit_index : ndarray, optional + Pixel: per input hit row, its particle id and its position in the + event's hits CSR (volume by volume in file order). + + Returns + ------- + list of dict + Per plane, in *planes* order: ``volume``, ``plane``, ``source``, + ``fragment_id``, ``instance_id`` (int32, -1 = untraced), ``is_le`` + (bool) and, for pixel, ``on_sensor`` (bool). + """ + frag, inst, le, none = _lookups(particles, frag_groups, inst_groups) + out = [] + if row_particle is not None: + # Pixel: scatter each partitioned hit's particle into CSR space. + total = sum(len(pl["group"]) for pl in planes) + part = np.full(total, none, dtype=np.int64) + rp = np.asarray(row_particle, dtype=np.int64) + hi = np.asarray(hit_index, dtype=np.int64) + ok = rp >= 0 + part[hi[ok]] = rp[ok] + start = 0 + for pl in planes: + n = len(pl["group"]) + p = part[start:start + n] + start += n + out.append(_entry(pl, p, frag, inst, le)) + return out + own = np.asarray(owner if owner is not None else [], dtype=np.int64) + for pl in planes: + g = pl["group"] + p = np.full(len(g), none, dtype=np.int64) + ok = (g >= 0) & (g < len(own)) + p[ok] = own[g[ok]] + p[p < 0] = none + out.append(_entry(pl, p, frag, inst, le)) + return out + + +def _entry(pl, part, frag, inst, le): + d = {"volume": int(pl["volume"]), "plane": int(pl["plane"]), + "source": str(pl["source"]), + "fragment_id": frag[part], "instance_id": inst[part], + "is_le": le[part]} + if pl.get("on_sensor") is not None: + d["on_sensor"] = np.asarray(pl["on_sensor"], dtype=bool) + return d + + +def check_pixel_labels(labels): + """ + Raise unless, on every pixel plane, exactly the hits off the sensor image + are untraced. + + A hit off the sensor has no sensor pixel to be traced from, so it must + carry -1; a hit on one was partitioned, so it must carry a fragment. + Either failing means the labels and the model's input disagree about + which pixels exist. + """ + for d in labels: + if "on_sensor" not in d: + continue + traced = d["fragment_id"] >= 0 + on = d["on_sensor"] + stray = int((traced & ~on).sum()) + lost = int((~traced & on).sum()) + if stray or lost: + raise RuntimeError( + f"hit_labels volume{d['volume']}/plane{d['plane']}: {stray} " + f"hits off the sensor image carry a label and {lost} on it " + f"carry none. With the sensor mask every on-sensor hit is " + f"partitioned and no other is -- a nonzero " + f"reader.hit_charge_threshold, or hits of tracks missing from " + f"the EDepSim file, would break that.") diff --git a/pysupera/hits_subset.py b/pysupera/hits_subset.py index a4c0002..41e2cea 100644 --- a/pysupera/hits_subset.py +++ b/pysupera/hits_subset.py @@ -53,9 +53,56 @@ def _plane_names(vol_group) -> list[str]: and "group_ids" in vol_group[k]] +#: Per-entry charge datasets JAXTPC writes, and the scale each is stored at. +_CHARGE = {"charges_i16": 32767.0, "charges_u16": 65535.0} + + +def decode_hits(plane) -> dict: + """ + Every hit of one plane, in CSR order -- the order hit_labels is written in. + + Wire planes give ``hit_wire``; pixel planes ``hit_py`` and ``hit_pz``; + both give ``hit_tick`` and ``hit_charge`` (the plane's own units: ENC for + wire, ADC for pixel). A plane whose arrays are longer than its + ``group_sizes`` sum -- JAXTPC's old uint8 bug -- is refused, since every + later group would decode at another group's cells. + """ + import numpy as np + sizes = plane["group_sizes"][:].astype(np.int64) + n = int(sizes.sum()) + if len(plane["delta_times"]) != n: + raise ValueError( + f"{plane.name}: group_sizes sums to {n:,} but the CSR holds " + f"{len(plane['delta_times']):,} entries -- a hits file written " + f"with a uint8 group_sizes; regenerate it.") + key = next((k for k in _CHARGE if k in plane), None) + rep = lambda name: np.repeat(plane[name][:].astype(np.int32), sizes) + out = {"hit_tick": (rep("center_times") + + plane["delta_times"][:n].astype(np.int32)).astype(np.int16)} + if "center_wires" in plane: + out["hit_wire"] = (rep("center_wires") + + plane["delta_wires"][:n].astype(np.int32)).astype(np.int16) + else: + out["hit_py"] = (rep("center_py") + + plane["delta_py"][:n].astype(np.int32)).astype(np.int16) + out["hit_pz"] = (rep("center_pz") + + plane["delta_pz"][:n].astype(np.int32)).astype(np.int16) + if key is not None: + peak = np.repeat(plane["peak_charges"][:].astype(np.float32), sizes) + out["hit_charge"] = (np.abs(peak) * plane[key][:n].astype(np.float32) + / np.float32(_CHARGE[key])).astype(np.float32) + return out + + def extract(src: str, dst: str, n_events: int = 3, level: int = 4, - verbose: bool = True) -> dict: - """Copy *n_events* worth of the plane arrays from *src* into *dst*.""" + verbose: bool = True, hits: bool = True) -> dict: + """ + Copy *n_events* worth of the plane arrays from *src* into *dst*. + + With *hits* (the default) every hit is decoded and written too + (:func:`decode_hits`), which is what the viewers draw; without, only the + group centres, a small fraction of the size. + """ import h5py try: import hdf5plugin # noqa: F401 (registers Blosc/LZ4 for reading) @@ -81,6 +128,15 @@ def extract(src: str, dst: str, n_events: int = 3, level: int = 4, vg = fin[f"{ek}/{vol}"] for plane in _plane_names(vg): planes.add(plane) + # A plane that carries only its centres (a subset of a + # subset, a hand-made file) has no hits to decode. + if hits and all(k in vg[plane] for k in + ("group_sizes", "delta_times", "center_times")): + for name, arr in decode_hits(vg[plane]).items(): + fout.create_dataset( + f"{ek}/{vol}/{plane}/{name}", data=arr, + compression="gzip", compression_opts=level) + n_ds += 1 for name in KEEP: if name not in vg[plane]: continue @@ -124,7 +180,9 @@ def main() -> None: p.add_argument("-n", "--events", type=int, default=3, help="Number of events to copy; -1 for all") p.add_argument("-l", "--level", type=int, default=4, help="gzip level (1-9)") + p.add_argument("--centres-only", action="store_true", + help="Skip the decoded hits and copy only the group centres") a = p.parse_args() if not 1 <= a.level <= 9: p.error("--level must be between 1 and 9") - extract(a.src, a.dst, a.events, a.level) + extract(a.src, a.dst, a.events, a.level, hits=not a.centres_only) diff --git a/pysupera/io.py b/pysupera/io.py index 8a5c2c7..b3b7b89 100644 --- a/pysupera/io.py +++ b/pysupera/io.py @@ -1657,6 +1657,12 @@ def check(name, obj): def _compression_kwargs_from_filters(ds) -> dict: """Reproduce a dataset's third-party filter (LZ4 / Blosc) as kwargs.""" filters = dict(ds._filters or {}) + if "32008" in filters: + # Bitshuffle; its 5th option names the codec (2 = LZ4, 3 = zstd). + import hdf5plugin + opts = tuple(filters["32008"] or ()) + cname = "zstd" if len(opts) > 4 and opts[4] == 3 else "lz4" + return dict(hdf5plugin.Bitshuffle(cname=cname)) if "32004" in filters: return _compress_kwargs("lz4", None) if "32001" in filters: diff --git a/pysupera/io_v3.py b/pysupera/io_v3.py index 93ccc63..6d9c559 100644 --- a/pysupera/io_v3.py +++ b/pysupera/io_v3.py @@ -14,8 +14,19 @@ n_events scalar int64 events/offsets (n_events+1,) int64 particle rows per event points/offsets (n_events+1,) int64 point rows per event - points/flat (N, 6) float32 x, y, z, time, dE, dX - points/true_x_shift (N,) float32 true_x = flat[:, 0] + this + points/flat (N, C) float32 one row per voxel (or deposit) + points/columns (C,) str the column names; JAXTPC input: + x, y, z [mm], t [us], dE [MeV], + dX [cm], theta, phi [rad], p [MeV/c] + hit_labels/volume{V}/plane{P}/ JAXTPC input: one entry per hit of + that plane, in the hits file's CSR + order (attrs: source, volume, plane) + fragment_id (H,) int32 -1 where the hit is not traced + instance_id (H,) int32 -1 where the hit is not traced + is_le (H,) bool the hit's particle is kLEScatter + on_sensor (H,) bool pixel only: the hit's pixel is in + the sensor image + offsets (n_events+1,) int64 hits per event particles/ (M,) see _INT32 / _INT8 / _INT64 inter/offsets (n_events+1,) int64 interaction rows per event inter/ (K,) see _INTER @@ -32,6 +43,12 @@ indexes ``v.points`` directly. Mixing the two bases is the easiest mistake this format allows, so the view does not expose the mixture. +With JAXTPC input (3.3.0) points/flat holds the true energy deposits behind +the hits, voxelized: a segment belongs to whichever particle owns its JAXTPC +group. The hits themselves stay in the JAXTPC hits file; hit_labels says, per +hit, which fragment and instance it belongs to (``frag_inst_id`` on a fragment +row names its instance). + ``frag_id == id`` marks a fragment representative and ``inst_id == id`` an instance representative; ``-1`` means the particle heads no group there. @@ -48,9 +65,17 @@ from .io import _compress_kwargs, _chunk_rows, _CHUNK_TARGET_BYTES_META -FORMAT_VERSION_V3 = "3.1.0" +FORMAT_VERSION_V3 = "3.3.0" + +#: points/flat's columns when nothing else is given: a deposit, as the plain +#: EDepSim reader produces it. JAXTPC input adds direction and momentum. +DEFAULT_POINT_COLUMNS = ('x', 'y', 'z', 'time', 'dE', 'dX') + +#: Rows per chunk when points/flat is chunked by column: 64 KiB of float32. +_COLUMN_CHUNK_ROWS = 16384 -_PT_NDIM = 6 # x, y, z, time, dE, dX +#: Entries per chunk of a hit_labels array. +_LABEL_CHUNK_ROWS = 65536 _INT32 = ("id", "geant4_trackid", "parent_id", "ancestor_id", "pdg", "parent_pdg", @@ -60,7 +85,9 @@ "geant4_parent_trackid", "geant4_parent_pdg", "interaction_id", "interaction_type", "frag_id", "frag_merge_count", "frag_parent_id", - "inst_id", "inst_merge_count", "inst_parent_id") + "inst_id", "inst_merge_count", "inst_parent_id", + # The instance a fragment row belongs to (3.3.0); -1 on other rows. + "frag_inst_id") _INT8 = ("sem_type", "frag_sem_type", "inst_sem_type") # Both levels describe two runs the same way: a non-LE range and an LE range. # At instance level the two are adjacent, so inst_pc_end == inst_pc_le_start; @@ -119,7 +146,8 @@ class EventWriterV3: """Incremental writer. One :meth:`append_event` call per event.""" - def __init__(self, path, compression="lz4", compression_opts=None): + def __init__(self, path, compression="lz4", compression_opts=None, + points=None, columns=None): import h5py self._path = path self._ckw = _compress_kwargs(compression, compression_opts) @@ -128,6 +156,21 @@ def __init__(self, path, compression="lz4", compression_opts=None): self._n_rows = 0 self._n_points = 0 self._n_inter = 0 + #: points/flat's columns, named in the file as points/columns. + self._pt_columns = tuple(columns or DEFAULT_POINT_COLUMNS) + self._pt_ndim = len(self._pt_columns) + #: hit_labels planes, fixed by the first event that writes them. + self._labels = None + #: How points/flat is stored: ``chunks`` ('columns' or 'rows'), + #: ``bitshuffle`` (bool), ``energy_mantissa_bits`` (int or None). + p = dict(points or {}) + self._pt_chunks = str(p.get("chunks", "rows")) + if self._pt_chunks not in ("rows", "columns"): + raise ValueError(f"points chunks must be 'rows' or 'columns', " + f"got {self._pt_chunks!r}") + self._pt_bitshuffle = bool(p.get("bitshuffle", False)) + eb = p.get("energy_mantissa_bits", None) + self._pt_energy_bits = None if eb is None else int(eb) self._create() def _create(self): @@ -140,19 +183,21 @@ def _create(self): g = f.create_group(grp) g.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), maxshape=(None,), chunks=(rows,)) + if self._pt_chunks == "columns": + pt_chunks = (_COLUMN_CHUNK_ROWS, 1) + else: + pt_chunks = (_chunk_rows(self._pt_ndim), self._pt_ndim) + if self._pt_bitshuffle: + import hdf5plugin + pt_filter = dict(hdf5plugin.Bitshuffle(cname="lz4")) + else: + pt_filter = self._ckw + self._pt_filter = pt_filter f["points"].create_dataset( - "flat", shape=(0, _PT_NDIM), maxshape=(None, _PT_NDIM), - dtype=np.float32, chunks=(_chunk_rows(_PT_NDIM), _PT_NDIM), - **self._ckw) - # true_x = flat[:, 0] + true_x_shift. Only pixel hit mode with a - # nominal t0 puts anything but zero here, and it takes as many - # distinct values as there are (interaction, volume) pairs -- 28 in a - # test event -- so it costs about 1.5% of the file under LZ4. A - # separate dataset rather than a seventh column so that every reader - # of the (N, 6) layout keeps working untouched. - f["points"].create_dataset( - "true_x_shift", shape=(0,), maxshape=(None,), - dtype=np.float32, chunks=(_chunk_rows(1),), **self._ckw) + "flat", shape=(0, self._pt_ndim), maxshape=(None, self._pt_ndim), + dtype=np.float32, chunks=pt_chunks, **pt_filter) + f["points"].create_dataset("columns", data=np.array( + self._pt_columns, dtype=h5py.string_dtype())) g = f.create_group("inter") g.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), maxshape=(None,), chunks=(rows,)) @@ -234,6 +279,52 @@ def append_group_owners(self, frag_owner, is_le=None, volume_offsets=()): vidx.resize(vidx.shape[0] + 1, axis=0) vidx[-1] = self._n_voloff + def append_hit_labels(self, labels): + """ + Append one event's per-hit labels (see :mod:`pysupera.hit_labels`). + + *labels* is one dict per (volume, plane). The planes are fixed by the + first event; a later event must not bring a new one (a plane it lacks + gets zero entries, so every plane's offsets stay aligned). + """ + f = self._f + by_key = {(d["volume"], d["plane"]): d for d in labels} + if self._labels is None: + self._labels = {} + root = f.require_group("hit_labels") + for (v, p), d in sorted(by_key.items()): + g = root.create_group(f"volume{v}/plane{p}") + g.attrs["source"] = d["source"] + g.attrs["volume"] = int(v) + g.attrs["plane"] = int(p) + g.create_dataset("offsets", data=np.zeros(1, dtype=np.int64), + maxshape=(None,)) + cols = [("fragment_id", np.int32), ("instance_id", np.int32), + ("is_le", bool)] + if "on_sensor" in d: + cols.append(("on_sensor", bool)) + for name, dt in cols: + g.create_dataset(name, shape=(0,), maxshape=(None,), + dtype=dt, chunks=(_LABEL_CHUNK_ROWS,), + **self._pt_filter) + self._labels[(v, p)] = (g, [c for c, _ in cols]) + extra = set(by_key) - set(self._labels) + if extra: + raise ValueError(f"hit_labels: planes {sorted(extra)} first " + f"appear after the first event") + for key, (g, cols) in self._labels.items(): + d = by_key.get(key) + n = 0 if d is None else len(d["fragment_id"]) + base = int(g["offsets"][-1]) + for c in cols: + ds = g[c] + ds.resize(base + n, axis=0) + if n: + ds[base:base + n] = d[c] + off = g["offsets"] + off.resize(off.shape[0] + 1, axis=0) + off[-1] = base + n + def set_meta(self, meta: dict) -> None: """ Record how this file was produced, as root attributes. @@ -257,31 +348,19 @@ def append_event(self, layout): pts = self._f["points/flat"] pts.resize(self._n_points + n_pts, axis=0) if n_pts: - block = np.zeros((n_pts, _PT_NDIM), dtype=np.float32) + block = np.zeros((n_pts, self._pt_ndim), dtype=np.float32) cursor = 0 for p in layout.order: pc = np.asarray(p.point_cloud, dtype=np.float32) k = len(pc) if k: - take = min(pc.shape[1], _PT_NDIM) + take = min(pc.shape[1], self._pt_ndim) block[cursor:cursor + k, :take] = pc[:, :take] cursor += k + if self._pt_energy_bits is not None: + block[:, 4] = round_mantissa(block[:, 4], self._pt_energy_bits) pts[self._n_points:self._n_points + n_pts] = block - shift = self._f["points"].get("true_x_shift") - if shift is not None: - shift.resize(self._n_points + n_pts, axis=0) - if n_pts: - sblock = np.zeros(n_pts, dtype=np.float32) - cursor = 0 - for p in layout.order: - k = len(p.point_cloud) - v = getattr(p, "true_x_shift", None) - if k and v is not None and len(v) == k: - sblock[cursor:cursor + k] = v - cursor += k - shift[self._n_points:self._n_points + n_pts] = sblock - # ---- particle columns --------------------------------------------- base = self._n_points for c in COLUMNS: @@ -388,19 +467,20 @@ def points_of(self, row, level=None, le=None): fragment level the two sides are stored as separate ranges because other fragments' points lie between them. """ + arr, tag = self.points, "pc" c = self.columns if level == "inst": - s, e = int(c["inst_pc_start"][row]), int(c["inst_pc_end"][row]) - ls, le_ = (int(c["inst_pc_le_start"][row]), - int(c["inst_pc_le_end"][row])) + s, e = int(c[f"inst_{tag}_start"][row]), int(c[f"inst_{tag}_end"][row]) + ls, le_ = (int(c[f"inst_{tag}_le_start"][row]), + int(c[f"inst_{tag}_le_end"][row])) if le is True: - return self.points[:0] if ls < 0 else self.points[ls:le_] + return arr[:0] if ls < 0 else arr[ls:le_] if le is False: - return self.points[:0] if s < 0 else self.points[s:e] + return arr[:0] if s < 0 else arr[s:e] # Both: the runs are adjacent at instance level, so one slice. lo = s if s >= 0 else ls hi = le_ if le_ >= 0 else e - return self.points[:0] if lo < 0 else self.points[lo:hi] + return arr[:0] if lo < 0 else arr[lo:hi] if level == "frag": if le is None: # The two sides are not adjacent, so "all" needs both. @@ -409,11 +489,41 @@ def points_of(self, row, level=None, le=None): if not len(a): return b if not len(b): return a return np.concatenate([a, b]) - pre = "frag_pc_le" if le else "frag_pc" + pre = f"frag_{tag}_le" if le else f"frag_{tag}" s, e = int(c[f"{pre}_start"][row]), int(c[f"{pre}_end"][row]) - return self.points[:0] if s < 0 else self.points[s:e] - s, e = int(c["pc_start"][row]), int(c["pc_end"][row]) - return self.points[:0] if s < 0 else self.points[s:e] + return arr[:0] if s < 0 else arr[s:e] + s, e = int(c[f"{tag}_start"][row]), int(c[f"{tag}_end"][row]) + return arr[:0] if s < 0 else arr[s:e] + + def group_of_points(self, level="frag"): + """ + One group id per point: the representative ``id`` of the fragment + (``level='frag'``) or instance (``'inst'``) whose range holds it, + -1 for a point no stored group claims. + + Read from the group ranges on representative rows only -- never + from ``pc_start``/``pc_end``, which say which *particle* deposited a + point, a different question. Every point is in at most one range per + level, so the answer is unique; see :func:`check_ranges`. + """ + n, tag = len(self.points), "pc" + c = self.columns + if level == "frag": + rows = np.flatnonzero(self.is_fragment) + pairs = [(f"frag_{tag}_start", f"frag_{tag}_end"), + (f"frag_{tag}_le_start", f"frag_{tag}_le_end")] + elif level == "inst": + rows = np.flatnonzero(self.is_instance) + pairs = [(f"inst_{tag}_start", f"inst_{tag}_le_end")] + else: + raise ValueError(f"level must be 'frag' or 'inst', got {level!r}") + out = np.full(n, -1, dtype=np.int32) + for r in rows: + for a, b in pairs: + s, e = int(c[a][r]), int(c[b][r]) + if s >= 0 and e > s: + out[s:e] = c["id"][r] + return out def __repr__(self): return (f"EventView(event={self.event}, rows={len(self)}, " @@ -441,6 +551,11 @@ def __init__(self, path): self._po = self._f["points/offsets"][:] self._io = (self._f["inter/offsets"][:] if "inter/offsets" in self._f else None) + c = self._f["points"].get("columns") + #: points/flat's column names (older files: the six deposit columns) + self.point_columns = (tuple(x.decode() if isinstance(x, bytes) else str(x) + for x in c[()]) if c is not None + else DEFAULT_POINT_COLUMNS) def __len__(self): return int(self._f["n_events"][()]) @@ -505,6 +620,27 @@ def true_x_shift(self, ev): a, b = int(po[ev]), int(po[ev + 1]) return ds[a:b] + def hit_labels(self, ev): + """ + Event *ev*'s per-hit labels: ``{(volume, plane): {name: array}}``, + each array aligned with that plane's hits in the JAXTPC hits file, + plus ``'source'`` (the plane's name there). Empty for files without + them. Plain HDF5 too: ``hit_labels/volume{V}/plane{P}/``. + """ + root = self._f.get("hit_labels") + out = {} + if root is None: + return out + for vname in root: + for pname in root[vname]: + g = root[vname][pname] + off = g["offsets"] + a, b = int(off[ev]), int(off[ev + 1]) + d = {k: g[k][a:b] for k in g if k != "offsets"} + d["source"] = g.attrs.get("source", pname) + out[(int(g.attrs["volume"]), int(g.attrs["plane"]))] = d + return out + def _read_legacy_particles(self, a, b): """ Read a 3.0.0 particle block under 3.1.0 names. @@ -574,11 +710,84 @@ def __exit__(self, *_): self.close() -def open_writer_v3(path, compression="lz4", compression_opts=None): - """Open an :class:`EventWriterV3`.""" - return EventWriterV3(path, compression, compression_opts) +def open_writer_v3(path, compression="lz4", compression_opts=None, + points=None, columns=None): + """ + Open an :class:`EventWriterV3`: *points* sets how points/flat is stored + and *columns* names its columns. + """ + return EventWriterV3(path, compression, compression_opts, points, columns) + + +def round_mantissa(a, bits): + """ + Round float32 values to *bits* mantissa bits, to nearest. + + The relative error is at most ``2**-(bits + 1)`` at every magnitude, and + the zeroed low bits compress to almost nothing under bitshuffle. Zero, + infinities and NaN are left as they are. + """ + a = np.ascontiguousarray(a, dtype=np.float32) + drop = 23 - int(bits) + if drop <= 0: + return a + u = a.view(np.uint32).astype(np.uint64) + half = np.uint64(1) << np.uint64(drop - 1) + keep = ~((np.uint64(1) << np.uint64(drop)) - np.uint64(1)) + r = (((u + half) & keep) & np.uint64(0xFFFFFFFF)).astype(np.uint32).view(np.float32) + special = ~np.isfinite(a) + if special.any(): + r = np.where(special, a, r) + return r def read_events_v3(path): """Open an :class:`EventStoreV3`.""" return EventStoreV3(path) + + +def check_ranges(path, max_report=20): + """ + Look for group ranges that claim rows they do not own, in a written file. + + Member rows are not stored, so a range cannot be compared with its + members' counts after the fact; what the file does show is the symptom. + Fragment slices -- each side separately -- and instance slices must each + tile without overlapping, since a row belongs to one fragment and one + instance. Files written before the layout checked this at write time + (an empty fragment of an unwritten instance could claim the rows of the + particles its members sorted around) show up here. + + Returns a list of ``(event, cloud, level, a, b)`` for the first + *max_report* overlapping pairs, where ``a`` and ``b`` are the two rows' + ``(id, start, end)``; empty when the file is clean. + """ + problems = [] + with read_events_v3(path) as st: + for ev in range(len(st)): + v = st[ev] + c = v.columns + clouds = [("points", "pc")] + for cloud, tag in clouds: + levels = { + "frag": (v.is_fragment, + [(f"frag_{tag}_start", f"frag_{tag}_end"), + (f"frag_{tag}_le_start", f"frag_{tag}_le_end")]), + "inst": (v.is_instance, + [(f"inst_{tag}_start", f"inst_{tag}_le_end")]), + } + for level, (mask, pairs) in levels.items(): + spans = [] + for r in np.flatnonzero(mask): + for a, b in pairs: + s, e = int(c[a][r]), int(c[b][r]) + if s >= 0 and e > s: + spans.append((s, e, int(c["id"][r]))) + spans.sort() + for (s0, e0, i0), (s1, e1, i1) in zip(spans, spans[1:]): + if s1 < e0: + problems.append((ev, cloud, level, + (i0, s0, e0), (i1, s1, e1))) + if len(problems) >= max_report: + return problems + return problems diff --git a/pysupera/layout.py b/pysupera/layout.py index 5bef693..2c01c07 100644 --- a/pysupera/layout.py +++ b/pysupera/layout.py @@ -20,9 +20,14 @@ ----------------------------- *Nesting.* Points are ordered by ``(interaction, instance, fragment, particle)``. Because instances partition into fragments and fragments into -particles -- verified against real events -- every level's points are then a -single contiguous slice, so one ``(start, end)`` pair per level suffices and no -per-point group labels are needed. +particles, every level's points are then a single contiguous slice, so one +``(start, end)`` pair per level suffices and no per-point group labels are +needed. That is checked on every event rather than assumed: each range must +hold exactly its members' own rows, or the layout raises +:class:`LayoutError`. It once failed silently -- an empty fragment of an +unwritten instance, whose members sorted apart, claimed the rows of the +particles between them -- and it would again for a group whose members span +two interactions. *Walkable genealogy.* Only a fraction of Geant4 particles are stored, so a stored particle's true parent may be absent. ``parent_id`` is therefore @@ -106,7 +111,109 @@ def _group_of(groups): return out -def build_layout(particles, frag_groups, inst_groups, vertices=None): +def _offsets(order, attr): + """Fencepost over the per-particle arrays named *attr*, in *order*.""" + n = np.fromiter((len(getattr(p, attr, None) if getattr(p, attr, None) + is not None else ()) for p in order), + dtype=np.int64, count=len(order)) + out = np.zeros(len(order) + 1, dtype=np.int64) + np.cumsum(n, out=out[1:]) + return out + + +class LayoutError(ValueError): + """A group range that would not describe exactly its own members' rows.""" + + +def _group_ranges(offsets, pos, frag_groups, inst_groups, is_le): + """ + Every group's slice of one cloud, given its per-particle *offsets*. + + A group's members *with rows* are contiguous under the storage order, so + min(start) / max(end) over them describes the block exactly. Members + without rows are left out: they still occupy a position, and one of an + unwritten instance's fragments sorts under its own id, so counting it + would stretch the range over whatever particles sit in between -- rows + the group does not own. A group with no rows at all gets (-1, -1). + Returns ``(frag_rng, inst_rng, frag_nle, frag_le, inst_split)``. + """ + def _span(members): + """Range covering *members*' rows, or (-1, -1) when they have none.""" + idx = [pos[m] for m in members + if m in pos and offsets[pos[m] + 1] > offsets[pos[m]]] + if not idx: + return (-1, -1) + lo, hi = min(idx), max(idx) + return (int(offsets[lo]), int(offsets[hi + 1])) + + def whole(groups): + rng = {} + for g in groups: + r = _span(g.member_ids) + rng[g.rep_id] = (0, 0) if r == (-1, -1) else r + return rng + + frag_rng = whole(frag_groups) + inst_rng = whole(inst_groups) + + # Each side of a group is a single run under this ordering, so two pairs + # describe a fragment completely -- never three. + frag_nle = {g.rep_id: _span([m for m in g.member_ids if not is_le[m]]) + for g in frag_groups} + frag_le = {g.rep_id: _span([m for m in g.member_ids if is_le[m]]) + for g in frag_groups} + # Where the LE block starts inside an instance; == its end when the + # instance has no LE rows at all. + inst_split = {} + for g in inst_groups: + le_span = _span([m for m in g.member_ids if is_le[m]]) + inst_split[g.rep_id] = (inst_rng[g.rep_id][1] if le_span[0] < 0 + else le_span[0]) + _check_ranges(offsets, pos, frag_groups, inst_groups, is_le, + frag_nle, frag_le, inst_rng, inst_split) + return frag_rng, inst_rng, frag_nle, frag_le, inst_split + + +def _check_ranges(offsets, pos, frag_groups, inst_groups, is_le, + frag_nle, frag_le, inst_rng, inst_split): + """ + Raise unless every range holds exactly its own members' rows. + + Contiguity is what makes a (start, end) pair enough, and it rests on the + storage order keeping each group together -- an assumption, not a law: + a group whose members sort apart (members of two interactions, say) + would get a range spanning other particles' rows. Comparing each width + with the members' own counts catches every such case, and costs one + pass over data already in hand. + """ + def own(members, le): + return sum(int(offsets[pos[m] + 1] - offsets[pos[m]]) + for m in members if m in pos and is_le[m] is le) + + def width(r): + return r[1] - r[0] if r[0] >= 0 else 0 + + for g in frag_groups: + for le, r in ((False, frag_nle[g.rep_id]), (True, frag_le[g.rep_id])): + if width(r) != own(g.member_ids, le): + raise LayoutError( + f"fragment {g.rep_id}: {'LE' if le else 'non-LE'} range " + f"{r} holds {width(r)} rows but its members own " + f"{own(g.member_ids, le)} -- they are not contiguous in " + f"the storage order.") + for g in inst_groups: + (s, e), sp = inst_rng[g.rep_id], inst_split[g.rep_id] + got = ((sp - s, e - sp) if e > s else (0, 0)) + want = (own(g.member_ids, False), own(g.member_ids, True)) + if got != want: + raise LayoutError( + f"instance {g.rep_id}: ranges hold {got} (non-LE, LE) rows " + f"but its members own {want} -- they are not contiguous in " + f"the storage order.") + + +def build_layout(particles, frag_groups, inst_groups, vertices=None, + keep=()): """ Decide storage order, row selection and group ranges for one event. @@ -127,6 +234,9 @@ def build_layout(particles, frag_groups, inst_groups, vertices=None): stored particle references are dropped and the survivors renumbered, so ``interaction_id`` is a direct row index into the interactions table rather than a value that has to be searched for. + keep : iterable of int + Particle ids that must get a row whatever their points -- the + fragments and instances something else refers to (hit_labels). Returns ------- @@ -146,9 +256,21 @@ def build_layout(particles, frag_groups, inst_groups, vertices=None): # The cost is that a fragment's points become two runs rather than one. is_le = {int(p.id): (p.sem_type is _LE_SEM) for p in particles} + # Points belong to the interaction of the group that owns them, not of + # the particle that deposited them. The two differ only when a + # proximity merge (AbsorbLEScatter, CombineLEScatters) took a particle + # from one interaction into a representative from another: its points + # are then the representative's, and are stored with them. The + # particle itself keeps its own interaction -- see the row order below. + def group_interaction(pid): + rep = inst_of[pid] if pid in inst_of else frag_of.get(pid, pid) + return int(by_id[rep if rep in by_id else pid]._interaction_id) + + point_inter = {int(p.id): group_interaction(int(p.id)) for p in particles} + def sort_key(p): pid = int(p.id) - return (int(p._interaction_id), + return (point_inter[pid], inst_of.get(pid, pid), is_le[pid], frag_of.get(pid, pid), @@ -157,46 +279,10 @@ def sort_key(p): order = sorted(particles, key=sort_key) pos = {int(p.id): k for k, p in enumerate(order)} - n_pts = np.fromiter((len(p.point_cloud) for p in order), - dtype=np.int64, count=len(order)) - pc_offsets = np.zeros(len(order) + 1, dtype=np.int64) - np.cumsum(n_pts, out=pc_offsets[1:]) - - - # ---- group point ranges ---------------------------------------------- - # Contiguous by construction, so min(start) / max(end) over the members - # describes the block exactly. - def _span(members): - """Point range covering *members*, or (-1, -1) when there are none.""" - idx = [pos[m] for m in members if m in pos] - if not idx: - return (-1, -1) - lo, hi = min(idx), max(idx) - return (int(pc_offsets[lo]), int(pc_offsets[hi + 1])) + pc_offsets = _offsets(order, "point_cloud") + frag_rng, inst_rng, frag_nle, frag_le, inst_split = _group_ranges( + pc_offsets, pos, frag_groups, inst_groups, is_le) - def group_ranges(groups): - rng = {} - for g in groups: - r = _span(g.member_ids) - rng[g.rep_id] = (0, 0) if r == (-1, -1) else r - return rng - - frag_rng = group_ranges(frag_groups) - inst_rng = group_ranges(inst_groups) - - # Each side of a group is a single run under this ordering, so two pairs - # describe a fragment completely -- never three. - frag_nle = {g.rep_id: _span([m for m in g.member_ids if not is_le[m]]) - for g in frag_groups} - frag_le = {g.rep_id: _span([m for m in g.member_ids if is_le[m]]) - for g in frag_groups} - # Where the LE block starts inside an instance; == inst_pc_end when the - # instance has no LE points at all. - inst_split = {} - for g in inst_groups: - le_span = _span([m for m in g.member_ids if is_le[m]]) - inst_split[g.rep_id] = (inst_rng[g.rep_id][1] if le_span[0] < 0 - else le_span[0]) frag_count = {g.rep_id: len(g.member_ids) for g in frag_groups} inst_count = {g.rep_id: len(g.member_ids) for g in inst_groups} @@ -220,8 +306,14 @@ def group_ranges(groups): break cur = nxt - keep = (visible | kept_inst_ids | ancestry) & set(by_id) - rows = sorted(pos[i] for i in keep) + keep = (visible | kept_inst_ids | ancestry + | {int(i) for i in keep}) & set(by_id) + # Rows go by each particle's *own* interaction, so an interaction's rows + # stay contiguous (part_start/part_end) even when some of its particles' + # points were absorbed into another interaction's group. Within an + # interaction, point order. + rows = sorted((pos[i] for i in keep), + key=lambda k: (int(order[k]._interaction_id), k)) row_ids = [int(order[k].id) for k in rows] row_set = set(row_ids) @@ -296,6 +388,11 @@ def col(fn, dtype=np.int32): "inst_pc_le_end": col(lambda i: inst_rng.get(i, (-1, -1))[1]), "inst_merge_count": col(lambda i: inst_count.get(i, -1)), } + # The instance a fragment belongs to, stated rather than left to be + # inferred from where its points sit: hit_labels names fragments and + # instances, and a reader should not need the point ranges to join them. + columns["frag_inst_id"] = col( + lambda i: inst_of.get(i, -1) if i in frag_ids else -1) # ancestor_id follows the *redirected* parents, so the chain a reader walks and # the root it is told to expect agree. Memoised along each chain: O(n). row_index = {pid: k for k, pid in enumerate(row_ids)} @@ -354,7 +451,8 @@ def group_parent(level_col): # ---- interactions ------------------------------------------------------ # Keep only interactions some stored particle belongs to, renumber them, # and rewrite interaction_id to the new row index. Points are ordered by - # interaction first, so each one's particles and points are contiguous. + # their group's interaction first and rows by their own, so each + # interaction's points and rows are both contiguous. live = sorted({int(v) for v in columns["interaction_id"] if v >= 0}) remap = {old: new for new, old in enumerate(live)} columns["interaction_id"] = np.fromiter( @@ -364,15 +462,20 @@ def group_parent(level_col): interactions: dict = {} if live: int_of_order = np.fromiter( - (int(p._interaction_id) for p in order), + (point_inter[int(p.id)] for p in order), dtype=np.int64, count=len(order)) part_start, part_end = [], [] pc_start, pc_end = [], [] for old in live: idx = np.flatnonzero(int_of_order == old) - lo, hi = int(idx[0]), int(idx[-1]) - pc_start.append(int(pc_offsets[lo])) - pc_end.append(int(pc_offsets[hi + 1])) + if len(idx): + lo, hi = int(idx[0]), int(idx[-1]) + pc_start.append(int(pc_offsets[lo])) + pc_end.append(int(pc_offsets[hi + 1])) + else: + # Every point of this interaction went to another's group; + # its particles are still stored, its points are elsewhere. + pc_start.append(-1); pc_end.append(-1) rws = [k for k, r in enumerate(rows) if int(order[r]._interaction_id) == old] part_start.append(rws[0] if rws else -1) diff --git a/pysupera/preproc.py b/pysupera/preproc.py index 197f9fd..a9feaac 100644 --- a/pysupera/preproc.py +++ b/pysupera/preproc.py @@ -151,6 +151,38 @@ def __str__(self) -> str: # Shared helper # ============================================================================ +def _unique_rows(keys: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """ + ``np.unique(keys, axis=0, return_inverse=True)`` for an int64 key table. + + The row-wise unique sorts a structured view and is an order of magnitude + slower than a 1-D sort, so when every column's range fits, the columns + are packed into one int64 -- offset to start at zero, most significant + first -- which sorts in the same lexicographic order. Wider keys fall + back to the row-wise call. + + Returns + ------- + unique_keys : np.ndarray, shape (M, C) + inverse : np.ndarray, shape (N,) + """ + lo = keys.min(axis=0) + bits = [int(s).bit_length() for s in keys.max(axis=0) - lo] + if sum(bits) > 63: + uk, inv = np.unique(keys, axis=0, return_inverse=True) + return uk, inv.reshape(-1) + packed = np.zeros(len(keys), dtype=np.int64) + for c, b in enumerate(bits): + packed <<= b + packed |= keys[:, c] - lo[c] + upacked, inv = np.unique(packed, return_inverse=True) + uk = np.empty((len(upacked), keys.shape[1]), dtype=keys.dtype) + for c in range(keys.shape[1] - 1, -1, -1): + uk[:, c] = (upacked & ((1 << bits[c]) - 1)) + lo[c] + upacked >>= bits[c] + return uk, inv.reshape(-1) + + def _split_fragments( p: Particle, labels: np.ndarray, @@ -318,6 +350,10 @@ def _get_labels(self, xyz: np.ndarray) -> np.ndarray: 0-based contiguous cluster index per point. """ + def _get_labels_many(self, xyzs: list) -> list: + """:meth:`_get_labels` for each cloud; backends may batch it.""" + return [self._get_labels(xyz) for xyz in xyzs] + # ------------------------------------------------------------------ # Shared processing loop (early exits + fragment splitting) # ------------------------------------------------------------------ @@ -458,7 +494,8 @@ def _run_chunk(chunk): ) labels_list = [lbl for group in nested for lbl in group] else: - labels_list = [self._get_labels(xyz) for _, _, xyz in to_compute] + labels_list = self._get_labels_many( + [xyz for _, _, xyz in to_compute]) for (idx, _, _), labels in zip(to_compute, labels_list): precomp[idx] = labels @@ -871,7 +908,8 @@ def _voxelize_point_cloud( pc: np.ndarray, voxel_size: np.ndarray, origin: np.ndarray | None, -) -> np.ndarray: + return_inverse: bool = False, +): """ Bin each point into a regular 3-D grid and merge points that share the same voxel. @@ -886,20 +924,22 @@ def _voxelize_point_cloud( Lower corner from which voxel indices are computed. When ``None`` the per-cloud minimum of the input coordinates is used so that the grid is always tightly aligned to the data. + return_inverse : bool + Also return, per input point, the output row it went into. Returns ------- np.ndarray, shape (M, F), M <= N New array where each row represents one non-empty voxel. The - x, y, z columns hold the voxel-centre coordinates; remaining - feature columns are aggregated by the same rules as - :func:`_merge_point_cloud` (time=min, dE=sum, dX=sum). - Returns the original array unchanged when every voxel already - contains exactly one point. + x, y, z columns hold the voxel-centre coordinates -- always, even + when no two points share a voxel, so every cloud this returns is on + the one grid; remaining feature columns are aggregated by the same + rules as :func:`_merge_point_cloud` (time=min, dE=sum, dX=sum). + np.ndarray, shape (N,), only with *return_inverse* """ coords = pc[:, :3] if len(coords) == 0: - return pc # nothing to voxelize + return (pc, np.zeros(0, dtype=np.int64)) if return_inverse else pc org = coords.min(axis=0) if origin is None else origin # Integer voxel indices for every point @@ -907,11 +947,9 @@ def _voxelize_point_cloud( # Unique voxels and group membership unique_idx, inv = np.unique(idx, axis=0, return_inverse=True) + inv = inv.reshape(-1) n_out = len(unique_idx) - if n_out == len(pc): - return pc # already one point per voxel — no copy needed - n_cols = pc.shape[1] out = np.zeros((n_out, n_cols), dtype=pc.dtype) @@ -925,7 +963,7 @@ def _voxelize_point_cloud( out[:, col] = init_val ufunc.at(out[:, col], inv, pc[:, col]) - return out + return (out, inv) if return_inverse else out class VoxelizeProcessor: @@ -1123,7 +1161,7 @@ def process( pids = np.repeat(np.arange(len(particles), dtype=np.int64), lengths) keys = np.concatenate([pids[:, None], vox_idx], axis=1) # (total, 4) - unique_keys, inv = np.unique(keys, axis=0, return_inverse=True) + unique_keys, inv = _unique_rows(keys) n_out = len(unique_keys) # Voxel-centre coordinates per output row @@ -1279,6 +1317,40 @@ def _get_labels(self, xyz: np.ndarray) -> np.ndarray: _, labels = connected_components(graph, directed=False) return labels + def _get_labels_many(self, xyzs: list) -> list: + """ + All clouds in one tree and one graph, instead of one of each per cloud. + + An event sends thousands of mostly small clouds here, and per call the + tree and graph set-up costs more than the clustering. The clouds are + stacked with a fourth coordinate, ``k * (eps + 1)`` for cloud *k*, so + points of different clouds are always more than eps apart while the + distance within a cloud is unchanged -- its fourth term is exactly 0. + Components are numbered in order of their first node, so a cloud's + components are a contiguous run starting at its first node's label: + subtracting that gives the labels :meth:`_get_labels` returns. + """ + from scipy.spatial import cKDTree + from scipy.sparse import csr_matrix + from scipy.sparse.csgraph import connected_components + + if len(xyzs) < 2: + return [self._get_labels(xyz) for xyz in xyzs] + lengths = np.array([len(x) for x in xyzs], dtype=np.int64) + starts = np.concatenate([[0], np.cumsum(lengths)[:-1]]) + n = int(lengths.sum()) + pts = np.empty((n, 4), dtype=np.float64) + pts[:, :3] = np.concatenate(xyzs) + pts[:, 3] = np.repeat(np.arange(len(xyzs), dtype=np.float64) + * (np.ceil(self.eps) + 1.0), lengths) + pairs = cKDTree(pts).query_pairs(self.eps, output_type='ndarray') + rows = np.concatenate([pairs[:, 0], pairs[:, 1]]) + cols = np.concatenate([pairs[:, 1], pairs[:, 0]]) + graph = csr_matrix( + (np.ones(len(rows), dtype=np.uint8), (rows, cols)), shape=(n, n)) + _, labels = connected_components(graph, directed=False) + return [labels[s:s + m] - labels[s] for s, m in zip(starts, lengths)] + # ============================================================================ # Backend: gpu (CuPy distance matrix, CPU connected_components) diff --git a/pysupera/provenance.py b/pysupera/provenance.py index be25a0b..b7a2a0a 100644 --- a/pysupera/provenance.py +++ b/pysupera/provenance.py @@ -273,3 +273,95 @@ def groups_of_particles(group_owner, particle_ids): if not len(want): return np.zeros(0, dtype=np.int64) return np.flatnonzero(np.isin(group_owner, want)) + + +def voxelize_truth(pc, voxel_size, origin): + """ + Bin one particle's truth segments on a regular grid. + + Rows are :data:`~pysupera.readers.format_jaxtpc.TRUTH_COLUMNS`: x, y, z, + t, dE, dX, theta, phi, p. Per cell: the cell centre; t the earliest; + dE and dX summed; theta, phi and p from that same earliest step -- the + particle's direction and momentum as it entered the cell, where an average + would shrink |p| and blur the direction wherever the track turns. + """ + if not len(pc): + return pc + vs = np.broadcast_to(np.asarray(voxel_size, dtype=np.float64), (3,)) + org = (pc[:, :3].min(axis=0).astype(np.float64) if origin is None + else np.asarray(origin, dtype=np.float64)) + cell = np.floor((pc[:, :3] - org) / vs).astype(np.int64) + uniq, inv = np.unique(cell, axis=0, return_inverse=True) + inv = inv.reshape(-1) + # each cell's earliest step: sort by (cell, t), take each cell's first + order = np.lexsort((pc[:, 3], inv)) + first = order[np.r_[True, inv[order][1:] != inv[order][:-1]]] + out = np.zeros((len(uniq), pc.shape[1]), dtype=pc.dtype) + out[:, :3] = (uniq + 0.5) * vs + org + out[:, 3] = pc[first, 3] + out[:, 4] = np.bincount(inv, weights=pc[:, 4], minlength=len(uniq)) + out[:, 5] = np.bincount(inv, weights=pc[:, 5], minlength=len(uniq)) + if pc.shape[1] > 6: + out[:, 6:] = pc[first, 6:] + return out + + +def attach_truth_clouds(particles, owner, flat, group, voxel_size=None, + origin=None): + """ + Give every particle the true segments behind the hits it owns. + + A segment goes wherever its group went: the owner in *owner* is the + particle holding the group's hits (by majority where a group's hits + straddle a split), so the truth cloud describes exactly the particles + the hit cloud does, and inherits their fragment, instance and LE side. + Segments of groups nothing owns -- every hit masked, or never recorded + -- are left out; the model sees no pixel of them. + + Parameters + ---------- + particles : list of Particle + Each gets ``truth_cloud``, ``(k, 6)`` float32, possibly empty. + owner : ndarray of int32 + :func:`build_group_owners` output: owning particle id per group. + flat, group : ndarray + Every segment of the event as a TRUTH_COLUMNS row, and its + event-global group. + voxel_size : float, sequence of 3, or None + Voxelize each particle's segments on this grid with + :func:`voxelize_truth`. None keeps the segments as they are. + origin : sequence of 3 or None + Grid corner, as for ``particle.voxelize.origin``. + + Returns + ------- + dict + ``n_segments`` in the event, ``n_owned`` of them attached, ``n_rows`` + stored after voxelization. + """ + group = np.asarray(group, dtype=np.int64) + ok = (group >= 0) & (group < len(owner)) + seg_owner = np.full(len(group), NO_OWNER, dtype=np.int64) + seg_owner[ok] = owner[group[ok]] + sel = np.flatnonzero(seg_owner != NO_OWNER) + by_owner = seg_owner[sel] + order = np.argsort(by_owner, kind="stable") + sel, by_owner = sel[order], by_owner[order] + ids, starts = np.unique(by_owner, return_index=True) + ends = np.r_[starts[1:], len(by_owner)] + span = {int(i): (int(a), int(b)) for i, a, b in zip(ids, starts, ends)} + + vs = None if voxel_size is None else np.broadcast_to( + np.asarray(voxel_size, dtype=np.float64), (3,)) + org = None if origin is None else np.asarray(origin, dtype=np.float64) + + n_rows = 0 + for p in particles: + a, b = span.get(int(p.id), (0, 0)) + pc = np.asarray(flat[sel[a:b]], dtype=np.float32) + if vs is not None and len(pc): + pc = voxelize_truth(pc, vs, org).astype(np.float32, copy=False) + p.truth_cloud = pc + n_rows += len(pc) + return {"n_segments": int(len(group)), "n_owned": int(len(sel)), + "n_rows": int(n_rows)} diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index 418957d..f6f97af 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -138,6 +138,16 @@ def read_readout_type(inst_file) -> str: # ``deposit_to_group``. Both spellings are accepted, in this order. _SEG_TO_GROUP_KEYS = ('segment_to_group', 'deposit_to_group') +#: Columns of a truth-segment row: position [mm], time [us], deposited +#: energy [MeV], step length [cm] -- the PointFeature layout of a deposit -- +#: then the step's direction (theta from the z axis, phi) [rad] and its +#: momentum magnitude [MeV/c]. +TRUTH_COLUMNS = ('x', 'y', 'z', 't', 'dE', 'dX', 'theta', 'phi', 'p') + +#: JAXTPC's readout planes, by the index their hit_labels group carries. +#: A plane named otherwise is numbered in file order after these. +PLANE_INDEX = {'U': 0, 'V': 1, 'Y': 2, 'Pixel': 0} + # Datasets every non-empty seg volume must provide. _SEG_REQUIRED_DATASETS = ('positions', 'de', 'dx', 't0_us', 'charge') @@ -496,7 +506,7 @@ def __init__( electron_energy_threshold: float = _DEFAULT_ELECTRON_ENERGY_THRESHOLD, min_pc_size: int | None = None, voxel_size: float | None = None, - point_source: str = 'deposits', + point_source: str | None = None, hit_x_from: str = 'nominal', hit_energy: str = 'charge', hit_reference_tick: float = 0.0, @@ -508,20 +518,40 @@ def __init__( readout_time_step_us: float | None = None, pixel_geometry_from_fit: bool = False, pixel_geometry: list | None = None, + step_key: str = 'pstep/lar_vol', + truth_segments: bool = False, ) -> None: import h5py + # A pixel readout is read as its detected hits and a wire readout as + # the deposits its hits select; the readout decides. None follows + # it, and asking a pixel batch for deposits is refused. + with h5py.File(inst_path, 'r') as _f: + _readout = read_readout_type(_f) + if point_source is None: + point_source = 'hits' if _readout == 'pixel' else 'deposits' if point_source not in ('deposits', 'hits'): raise ValueError( f"point_source must be 'deposits' or 'hits', " f"got {point_source!r}") + if point_source == 'deposits' and _readout == 'pixel': + raise ValueError( + f"{inst_path} is a pixel readout, which is read as its " + f"detected hits (point_source='hits'); reading its truth " + f"deposits instead is not supported.") self._point_source = point_source self._hit_x_from = hit_x_from self._hit_energy = hit_energy self._hit_reference_tick = float(hit_reference_tick or 0.0) - # Drop hits below this many induced electrons. JAXTPC applies - # essentially none, so this is where a readout threshold enters. + # Drop hits below this many ADC. The sensor image already applies + # the readout threshold, so anything above 0 cuts into its pixels. self._hit_charge_threshold = float(hit_charge_threshold or 0.0) + if point_source == 'hits' and not sensor_path: + raise ValueError( + "point_source='hits' needs reader.jaxtpc_sensor_path. The " + "sensor image is the model input the labels are for, and it " + "decides which pixels exist: the hits file also holds every " + "sub-threshold share, about half of its pixels.") # Stated pixel geometry. Preferred over anything fitted: see # pysupera.readers.pixel_hits.verify_volume_geometry for why a # constant derived from the truth it is later checked against cannot @@ -547,6 +577,23 @@ def __init__( #: position. Hit mode only; None otherwise. self.last_true_x_shift = None + #: Per input hit, its position in the event's JAXTPC hits CSR. Hit + #: mode only; None otherwise. + self.last_hit_index = None + + #: The event's readout planes as CSR entries; see _plane_entries. + self.last_planes = None + self._on_sensor = {} + + #: ``(flat, group)`` for the event just read, when *truth_segments*: + #: every seg-file segment as an ``(S, 10)`` row of TRUTH_COLUMNS, + #: read from the EDepSim step it came from, and its event-global + #: group. Which become the truth cloud is decided downstream, by + #: group ownership. None otherwise. + self.last_truth_segments = None + self._step_key = step_key + self._want_truth = bool(truth_segments) + self._edepsim_path = edepsim_path self._seg_path = seg_path self._inst_path = inst_path @@ -568,6 +615,8 @@ def __init__( self._inst_file = h5py.File(inst_path, 'r') self._sensor_file = (h5py.File(sensor_path, 'r') if sensor_path else None) + if point_source == 'hits': + self._check_same_batch() self._n_events = len(self._edepsim_file[self._part_key]) @@ -644,6 +693,11 @@ def __getitem__(self, index: int) -> list[Particle]: self.last_group_volume_offsets = _g_offs self.last_n_groups = _hstats['n_groups'] self.last_deposit_volume_offsets = [] + self.last_truth_segments = ( + self._truth_segments(index, event_key, seg_volumes, _g_offs) + if self._want_truth else None) + self.last_planes = self._plane_entries(event_key, _g_offs, + self._on_sensor) deposit_id = np.arange(len(point_cloud_flat), dtype=np.int64) else: # A segment is visible if its group_id appears in a readout-plane's @@ -669,6 +723,7 @@ def __getitem__(self, index: int) -> list[Particle]: for idx in vol.values()) _n_attached = len(point_cloud_flat) self.last_true_x_shift = None + self.last_hit_index = None self.last_mask_stats = { 'n_total': _n_total, # deposits in the seg file 'n_visible': _n_visible, # survived the readout threshold @@ -697,6 +752,12 @@ def __getitem__(self, index: int) -> list[Particle]: self.last_deposit_to_group, self.last_group_volume_offsets = \ self._load_deposit_groups(event_key, seg_volumes) self.last_deposit_volume_offsets = volume_offsets + self.last_truth_segments = ( + self._truth_segments(index, event_key, seg_volumes, + self.last_group_volume_offsets) + if self._want_truth else None) + self.last_planes = self._plane_entries( + event_key, self.last_group_volume_offsets, None) _particles = Particle.from_flat_arrays( ids = _ids, @@ -798,6 +859,156 @@ def _sensor_attr(self, name): v = cfg.attrs.get(name) return None if v is None else float(v) + #: Config attributes that name a JAXTPC batch file. The sensor and hits + #: files of one batch agree on all that both carry. + _BATCH_IDENTITY = ('batch_timestamp', 'dataset_name', 'file_index', + 'global_event_offset') + + def _check_same_batch(self): + """ + Raise unless the sensor and hits files come from one JAXTPC run. + + A mask from another run keeps whichever hits happen to share a pixel + with it -- a plausible-looking cloud with nothing behind it. + """ + a = self._sensor_file.get('config') + b = self._inst_file.get('config') + if a is None or b is None: + return + for k in self._BATCH_IDENTITY: + if k in a.attrs and k in b.attrs: + va, vb = a.attrs[k], b.attrs[k] + if va != vb: + raise ValueError( + f"reader.jaxtpc_sensor_path and reader." + f"jaxtpc_inst_path are from different JAXTPC runs: " + f"config.{k} is {va!r} in {self._sensor_path} and " + f"{vb!r} in {self._inst_path}.") + + def _truth_segments(self, index, event_key, seg_volumes, g_offs): + """ + Every segment of the event, with its event-global group. + + The values come from the EDepSim step each segment was made from, at + full precision: the seg file stores positions on a 0.3 mm grid and + the rest as float16. + JAXTPC splits the step list into volumes by position and keeps the + order within each, so segment k of volume v is the k-th step lying + in volume v. That is checked, not assumed: the counts must agree + and the positions must match to the seg file's quantisation. + + Columns: :data:`TRUTH_COLUMNS` -- x, y, z [mm], t [us], dE [MeV], + dX [cm], theta, phi [rad] (theta from the z axis, the convention the + steps follow), |p| [MeV/c]. + """ + steps = self._edepsim_file[self._step_key][index] + pos = np.c_[steps['x'], steps['y'], steps['z']].astype(np.float32) + # JAXTPC's volume test, in its own arithmetic: float32 cm, half-open. + cm = pos / np.float32(10.0) + ranges = self._volume_ranges() + ev = self._inst_file[event_key] + flats, groups = [], [] + for v, sv in enumerate(seg_volumes): + n = int(sv.get('n_actual', 0) or 0) + vg = ev.get(f'volume_{v}') + if not n or vg is None: + continue + key = next((k for k in _SEG_TO_GROUP_KEYS if k in vg), None) + if key is None or ranges is None: + continue + r = np.asarray(ranges[v], dtype=np.float64) / 10.0 + inside = np.ones(len(cm), dtype=bool) + for a in range(3): + inside &= (cm[:, a] >= r[a, 0]) & (cm[:, a] < r[a, 1]) + idx = np.flatnonzero(inside) + if len(idx) != n: + raise ValueError( + f"{event_key}/volume_{v}: {len(idx):,} EDepSim steps lie " + f"in the volume but the seg file has {n:,} segments, so " + f"segments cannot be matched to their steps (is " + f"io.input_path the EDepSim file this batch was made " + f"from?).") + off = float(np.abs(sv['positions_mm'][:n] - pos[idx]).max()) + if off > 1.0: + raise ValueError( + f"{event_key}/volume_{v}: segments sit up to {off:.2f} mm " + f"from the EDepSim steps matched to them, beyond the seg " + f"file's 0.3 mm quantisation -- not the same steps.") + st = steps[idx] + f = np.empty((n, len(TRUTH_COLUMNS)), dtype=np.float32) + f[:, 0:3] = pos[idx] + f[:, 3] = st['t'] / 1000.0 # ns -> us + f[:, 4] = st['de'] + f[:, 5] = st['dx'] / 10.0 # mm -> cm + f[:, 6] = st['theta'] + f[:, 7] = st['phi'] + f[:, 8] = st['p'] + flats.append(f) + groups.append(vg[key][:n].astype(np.int64) + + int(g_offs[v] if v < len(g_offs) else 0)) + if not flats: + return (np.zeros((0, len(TRUTH_COLUMNS)), dtype=np.float32), + np.zeros(0, dtype=np.int64)) + return np.concatenate(flats), np.concatenate(groups) + + def _plane_entries(self, event_key, g_offs, on_sensor): + """ + Every readout plane of the event, as its CSR entries' groups. + + One dict per (volume, plane), in file order: ``volume``, ``plane`` + (:data:`PLANE_INDEX`), ``source`` (the plane's name in the hits + file), ``group`` (event-global group of each entry, in CSR order) and + ``on_sensor`` (pixel: whether each entry lies on a sensor pixel; + otherwise None). This is the space hit_labels is written in. + """ + import h5py + ev = self._inst_file[event_key] + out = [] + for pos, vol_name in enumerate(self._volume_names(ev)): + # The group offsets follow this list's order; the volume number is + # the name's. + v = int(vol_name.split('_', 1)[1]) + vg = ev[vol_name] + names = [k for k in vg if isinstance(vg[k], h5py.Group) + and 'group_ids' in vg[k]] + known = sorted((n for n in names if n in PLANE_INDEX), + key=lambda n: PLANE_INDEX[n]) + other = [n for n in names if n not in PLANE_INDEX] + index = {n: PLANE_INDEX[n] for n in known} + index.update({n: len(known) + i for i, n in enumerate(other)}) + for name in known + other: + g = vg[name] + sizes = g['group_sizes'][:].astype(np.int64) + group = (np.repeat(g['group_ids'][:].astype(np.int64), sizes) + + int(g_offs[pos] if pos < len(g_offs) else 0)) + sens = None + if on_sensor is not None and v in on_sensor: + sens = np.asarray(on_sensor[v], dtype=bool) + if len(sens) != len(group): + raise ValueError( + f"{event_key}/{vol_name}/{name}: {len(sens)} " + f"sensor flags for {len(group)} hits") + out.append({'volume': v, 'plane': index[name], + 'source': name, 'group': group, + 'on_sensor': sens}) + return out + + def _sensor_pixels(self, event_key, v): + """Sorted pixel keys of volume *v*'s sensor image for one event.""" + ev = self._sensor_file.get(event_key) + if ev is None: + raise KeyError( + f"{event_key} is in the hits file but not in the sensor file " + f"{self._sensor_path}.") + vg = ev.get(f'volume_{v}') + planes = [] if vg is None else [ + vg[k] for k in vg if hasattr(vg[k], 'attrs') and 'delta_py' in vg[k]] + if not planes: + # Nothing recorded in this volume: no pixel may be labelled. + return np.zeros(0, dtype=np.int64) + s = _px.decode_sensor_plane(planes[0]) + return np.unique(_px.pixel_key(s['py'], s['pz'], s['tick'])) + def _stated_drift_terms(self): """ ``(velocity_mm_us, time_step_us, why)`` from config or the sensor @@ -1000,6 +1211,8 @@ def _build_hit_event(self, event_key, seg_volumes, track_ids): # the true x whichever drift convention is in force. n_steps = self._num_time_steps() trunc_before = trunc_after = trunc_total = 0 + csr_off = 0 + self._on_sensor = {} volumes = [] for v, seg_vol in enumerate(seg_volumes): vol_key = f'volume_{v}' @@ -1010,11 +1223,22 @@ def _build_hit_event(self, event_key, seg_volumes, track_ids): if plane is None: continue n_groups = len(vg['group_to_track']) + hits = _px.decode_plane_hits(plane) + # Each hit's position in the event's hits CSR, counting volume by + # volume in file order. + hits['index'] = np.arange(len(hits['group']), dtype=np.int64) + csr_off + csr_off += len(hits['group']) + sensor = self._sensor_pixels(event_key, v) + # Which entries lie on a sensor pixel -- every entry, before the + # mask: what hit_labels' on_sensor records. + self._on_sensor[v] = _px.on_sensor( + sensor, hits['py'], hits['pz'], hits['tick']) entry = { - 'hits': _px.decode_plane_hits(plane), + 'hits': hits, 'geom': self._pixel_geoms[v], 'group_to_track': vg['group_to_track'][:].astype(np.int64), 'group_offset': g_offs[v] if v < len(g_offs) else 0, + 'sensor': sensor, } if want_de or want_t0: t0, de, _ = self._group_truth(seg_vol, vg, n_groups, want_de) @@ -1041,11 +1265,15 @@ def _build_hit_event(self, event_key, seg_volumes, track_ids): dt = float(self._pixel_geoms[0].time_step_us) if not np.isfinite(dt) or dt <= 0: dt = 1.0 - flat, offsets, group_of_point, x_shift, stats = \ + flat, offsets, group_of_point, x_shift, stats, hit_index = \ _px.build_hit_point_cloud( volumes, track_ids, x_from=self._hit_x_from, energy=self._hit_energy, time_step_us=dt, - charge_threshold=self._hit_charge_threshold) + charge_threshold=self._hit_charge_threshold, + return_index=True) + #: Per point, its position in the event's hits CSR (volume by volume + #: in file order): the link from a stored hit back to the hits file. + self.last_hit_index = hit_index stats['n_groups'] = n_groups_total stats['n_deposits'] = trunc_total stats['n_before_window'] = trunc_before diff --git a/pysupera/readers/pixel_hits.py b/pysupera/readers/pixel_hits.py index 31812cc..54b1719 100644 --- a/pysupera/readers/pixel_hits.py +++ b/pysupera/readers/pixel_hits.py @@ -17,6 +17,19 @@ group, and ``group_to_track`` gives each group one track. The assignment is therefore unique without any nearest-neighbour matching. +Which pixels: the sensor image +------------------------------ +The hits file is the sensor image split by group: summed over groups, a +pixel's hit charge is its sensor ADC (to the int16 storage precision). But +JAXTPC thresholds the *sum* -- ``|ADC| >= threshold_adc``, 7 in the batch +this was written against -- and writes every group's share regardless, so +about half the pixels in the hits file are ones the sensor never recorded. +The labels are for a model whose input is that sensor image, so the cloud is +masked to it: a hit survives only on a pixel the sensor file holds. The +threshold is read from the image rather than re-applied to summed hits, +because a sum rebuilt from int16 shares lands on either side of 7 for pixels +right at it. + The inverse transform --------------------- ``y`` and ``z`` are pure geometry and invert exactly:: @@ -87,10 +100,14 @@ #: diffusion-only path and is still read, matching JAXTPC's own loader. _CHARGE_SCALE = {'charges_i16': 32767.0, 'charges_u16': 65535.0} -#: ``group_sizes`` is uint8, so a group of more than 255 entries cannot be -#: represented. JAXTPC caps rather than raising, and a capped group silently -#: truncates the CSR, so it is worth naming when it happens. -_MAX_GROUP_SIZE = 255 +#: Bits per coordinate in :func:`pixel_key`, and the bias that keeps a +#: negative index (a tick before the window, say) from reaching the sign bit. +_KEY_BITS = 21 +_KEY_BIAS = 1 << (_KEY_BITS - 1) + + +class HitsFileError(ValueError): + """A hits file whose CSR does not describe its own arrays.""" @dataclass(frozen=True) @@ -174,18 +191,30 @@ def decode_plane_hits(plane) -> dict: per fired pixel/tick, plus ``group`` (int32), the volume-local group number each entry belongs to. - Notes - ----- - The delta arrays are longer than ``group_sizes.sum()`` — JAXTPC pads them - to a chunk boundary. The CSR length is the sum, never the array length; - reading to the end would append hundreds of zero-delta ghosts sitting on - top of the last group's peak. + Raises + ------ + HitsFileError + When the arrays are longer than ``group_sizes.sum()``. JAXTPC once + stored ``group_sizes`` as uint8, so a group of more than 255 entries + wrapped (300 became 44) and every later group on the plane decodes + at another group's pixels. The lost 256s are the excess, which is + why it is always a multiple of 256; the sizes it came from cannot be + read back from the file, so the batch has to be regenerated. """ sizes = plane['group_sizes'][:].astype(np.int64) groups = plane['group_ids'][:].astype(np.int32) n = int(sizes.sum()) - capped = int((sizes >= _MAX_GROUP_SIZE).sum()) + length = len(plane['delta_py']) + if length != n: + raise HitsFileError( + f"pixel plane {plane.name!r}: group_sizes sums to {n:,} but the " + f"CSR arrays hold {length:,} entries ({(length - n) / 256:g} x " + f"256 lost). JAXTPC wrote this file with a uint8 group_sizes, " + f"so a group of more than 255 entries wrapped and every later " + f"group on the plane would be decoded at the wrong pixels. " + f"Regenerate the batch with a JAXTPC that writes group_sizes as " + f"uint16.") key = next((k for k in _CHARGE_SCALE if k in plane), None) if key is None: @@ -207,7 +236,61 @@ def decode_plane_hits(plane) -> dict: charge = peak * (plane[key][:n].astype(np.float32) / _CHARGE_SCALE[key]) return {'py': py, 'pz': pz, 'tick': tick, 'charge': charge, - 'group': np.repeat(groups, sizes), 'n_capped_groups': capped} + 'group': np.repeat(groups, sizes)} + + +def decode_sensor_plane(plane) -> dict: + """ + Pixel coordinates of one plane of the JAXTPC sensor image. + + The sensor file stores the pixels JAXTPC kept -- ``|ADC| >= threshold_adc`` + on the summed response -- sorted by ``(py, pz, tick)``, as a start value + plus cumulative int16 differences. Only the coordinates are returned: + the image decides *which* pixels exist, and the hits say whose they are. + + A plane that recorded nothing has no ``n_pixels`` attribute and comes back + empty. + """ + empty = np.zeros(0, dtype=np.int32) + if 'n_pixels' not in plane.attrs: + return {'py': empty, 'pz': empty, 'tick': empty} + out = {} + for name, delta, start in (('py', 'delta_py', 'py_start'), + ('pz', 'delta_pz', 'pz_start'), + ('tick', 'delta_time', 'time_start')): + # int64 before the running sum: the deltas are int16, and a sum in + # int16 would wrap on any plane wider than 32767 pixels or ticks. + d = np.cumsum(plane[delta][:].astype(np.int64)) + out[name] = (int(plane.attrs[start]) + d).astype(np.int32) + return out + + +def pixel_key(py, pz, tick): + """ + One int64 per ``(py, pz, tick)``, so pixel sets compare as sorted arrays. + + 21 bits per coordinate, biased so a negative index keeps its own key + rather than colliding with a positive one. + """ + b = np.int64(_KEY_BIAS) + return (((np.asarray(py, dtype=np.int64) + b) << (2 * _KEY_BITS)) + | ((np.asarray(pz, dtype=np.int64) + b) << _KEY_BITS) + | (np.asarray(tick, dtype=np.int64) + b)) + + +def on_sensor(sensor_keys, py, pz, tick): + """Which ``(py, pz, tick)`` are pixels of the sorted *sensor_keys*.""" + k = pixel_key(py, pz, tick) + if not len(sensor_keys): + return np.zeros(len(k), dtype=bool) + pos = np.searchsorted(sensor_keys, k).clip(0, len(sensor_keys) - 1) + return sensor_keys[pos] == k + + +def _select(hits, keep): + """The entries of a :func:`decode_plane_hits` dict where *keep* holds.""" + return {k: (v[keep] if isinstance(v, np.ndarray) else v) + for k, v in hits.items()} # --------------------------------------------------------------------------- @@ -257,7 +340,8 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, x_from: str = 'nominal', energy: str = 'charge', time_step_us: float = 1.0, - charge_threshold: float = 0.0): + charge_threshold: float = 0.0, + return_index: bool = False): """ A flat point cloud of detected pixel hits, laid out per particle. @@ -268,7 +352,12 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, output), ``geom`` (:class:`VolumePixelGeometry`), ``group_to_track``, ``group_offset`` (where this volume's groups start in the event-global numbering) and, when the corresponding option is selected, - ``group_t0`` and ``group_de``. + ``group_t0`` and ``group_de``. ``sensor``, when present, is the + sorted :func:`pixel_key` of every pixel in that volume's sensor + image; only hits on those pixels are kept. That is what makes the + cloud the model's input pixel for pixel: the sensor decided the + threshold on the summed response, and a hit's own charge cannot + reproduce that decision. track_ids_edepsim : ndarray Track IDs in EDepSim particle order. The returned offsets are aligned to it, so ``Particle.from_flat_arrays`` pairs each metadata @@ -283,15 +372,18 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, Readout tick length, used to put the time column in μs. charge_threshold : float Drop hits carrying less than this much charge, in the units the hits - file uses -- ionisation electrons induced on the pixel. Compared - against the *magnitude*, matching JAXTPC's own - ``encode_correspondence_csr_pixel``: the readout response is - bipolar, and a large negative lobe is signal, not noise. - - JAXTPC applies no threshold worth the name (1 electron in this - batch), so without one every pixel carrying any induced-field - influence is a point: 4.18M of them for an event whose truth is - 216k deposits, and the brightest 2% carry half the charge. + file uses -- ADC, the same as the sensor image, of which the hits are + the per-group decomposition. Compared against the *magnitude*, + matching JAXTPC's own ``encode_correspondence_csr_pixel``: the + readout response is bipolar, and a large negative lobe is signal, + not noise. Applied after the sensor mask, so anything above 0 leaves + sensor pixels unlabelled; 0 is the setting that keeps the cloud equal + to the input image. + + return_index : bool + Also return, per row, the ``index`` each volume's hits dict carried + (-1 when a volume had none) -- the reader puts the hit's position in + the JAXTPC hits CSR there. Appended after *stats*. Returns ------- @@ -309,6 +401,9 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, stats : dict Includes ``n_outside_volume``: nominal x values beyond their own volume's faces. Counted, never clipped -- see the module docstring. + With a sensor image, ``n_sensor_pixels``, ``n_off_sensor`` (hits the + mask removed) and ``n_sensor_unlabelled`` (sensor pixels no kept hit + lands on -- none, for a hits file that decomposes its sensor file). """ if x_from not in X_FROM: raise ValueError(f"x_from must be one of {X_FROM}, got {x_from!r}") @@ -316,30 +411,40 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, raise ValueError(f"energy must be one of {ENERGY_FROM}, got {energy!r}") xs, ys, zs, ts, es, gs, tr, sh = [], [], [], [], [], [], [], [] - n_capped = 0 + ix = [] # each kept hit's CSR index, when the caller gave one n_outside = 0 n_cut = 0 # hits dropped by charge_threshold - n_decoded = 0 # hits in the file, before that cut + n_decoded = 0 # hits in the file, before any cut + n_sensor = 0 # pixels in the sensor image + n_off_sensor = 0 # hits on no sensor pixel + n_unlabelled = 0 # sensor pixels no kept hit lands on for vol in volumes: hits = vol['hits'] - if not len(hits['group']): - continue - n_capped += int(hits.get('n_capped_groups', 0)) n_decoded += len(hits['group']) - geom = vol['geom'] - g_local = hits['group'] - q_raw = hits['charge'] + # Counted before the empty-volume skip: a volume with sensor pixels + # and no hits is exactly the case the unlabelled count exists for. + sensor = vol.get('sensor') + if sensor is not None: + keep = on_sensor(sensor, hits['py'], hits['pz'], hits['tick']) + n_sensor += len(sensor) + n_off_sensor += int((~keep).sum()) + hits = _select(hits, keep) + if charge_threshold > 0: - keep = np.abs(q_raw) >= charge_threshold + keep = np.abs(hits['charge']) >= charge_threshold n_cut += int((~keep).sum()) - if not keep.all(): - hits = {k: (v[keep] if isinstance(v, np.ndarray) else v) - for k, v in hits.items()} - g_local = hits['group'] - if not len(g_local): - continue + hits = _select(hits, keep) + + if sensor is not None: + n_unlabelled += len(sensor) - len(np.unique( + pixel_key(hits['py'], hits['pz'], hits['tick']))) + + if not len(hits['group']): + continue + geom = vol['geom'] + g_local = hits['group'] tick = hits['tick'].astype(np.float32) if x_from == 'true_t0': @@ -389,16 +494,21 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, gs.append(g_local.astype(np.int64) + int(vol['group_offset'])) tr.append(vol['group_to_track'][g_local].astype(np.int64)) + if 'index' in hits: + ix.append(np.asarray(hits['index'], dtype=np.int64)) n_particles = len(track_ids_edepsim) if not xs: - return (np.zeros((0, 7), dtype=np.float32), - np.zeros((n_particles, 2), dtype=np.int64), - np.zeros(0, dtype=np.int64), - np.zeros(0, dtype=np.float32), - {'n_hits': 0, 'n_attached': 0, 'n_unmatched': 0, - 'n_capped_groups': n_capped, 'n_outside_volume': 0, - 'n_decoded': n_decoded, 'n_below_threshold': n_cut}) + out = (np.zeros((0, 7), dtype=np.float32), + np.zeros((n_particles, 2), dtype=np.int64), + np.zeros(0, dtype=np.int64), + np.zeros(0, dtype=np.float32), + {'n_hits': 0, 'n_attached': 0, 'n_unmatched': 0, + 'n_outside_volume': 0, + 'n_decoded': n_decoded, 'n_below_threshold': n_cut, + 'n_sensor_pixels': n_sensor, 'n_off_sensor': n_off_sensor, + 'n_sensor_unlabelled': n_unlabelled}) + return out + (np.zeros(0, dtype=np.int64),) if return_index else out x = np.concatenate(xs); y = np.concatenate(ys); z = np.concatenate(zs) t = np.concatenate(ts); e = np.concatenate(es) @@ -447,8 +557,14 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, flat[:, PointFeature.id] = np.arange(m, dtype=np.float32) stats = {'n_hits': n_hits, 'n_attached': m, 'n_unmatched': n_hits - m, - 'n_capped_groups': n_capped, 'n_outside_volume': n_outside, - 'n_decoded': n_decoded, 'n_below_threshold': n_cut} + 'n_outside_volume': n_outside, + 'n_decoded': n_decoded, 'n_below_threshold': n_cut, + 'n_sensor_pixels': n_sensor, 'n_off_sensor': n_off_sensor, + 'n_sensor_unlabelled': n_unlabelled} + if return_index: + index = (np.concatenate(ix) if len(ix) == len(xs) else + np.full(n_hits, -1, dtype=np.int64)) + return flat, offsets, group[sel], shift[sel], stats, index[sel] return flat, offsets, group[sel], shift[sel], stats diff --git a/pysupera/truth_subset.py b/pysupera/truth_subset.py deleted file mode 100644 index 12b8205..0000000 --- a/pysupera/truth_subset.py +++ /dev/null @@ -1,132 +0,0 @@ -""" -Extract the truth deposits a browser needs to show pysupera labels on them. - -``vis_truth.html`` draws the Geant4 energy deposits beside the reconstructed -point cloud, painting each deposit with the label of whatever pysupera object -claimed it. The bridge is the *group*: a deposit knows its group -(``deposit_to_group``, in the JAXTPC hits file) and the pysupera output's -``groups/`` table says which fragment owns each group. - -So the viewer needs three things that live in two different JAXTPC files -- -the deposit positions and energies from the *step* file, and -``deposit_to_group`` from the *hits* file -- and it cannot read either of -them. Both are written with Blosc/LZ4, and the browser reads HDF5 through -h5wasm, which decodes gzip only. This writes the handful of arrays that -matter into one gzip file, which also takes 22 MB + 44 MB down to under a -megabyte per event. - - pysupera-truth-subset step.h5 hits.h5 truth_small.h5 --events 3 - -Positions stay in JAXTPC's own uint16-plus-attributes form rather than being -expanded to mm: it is the same three-line decode on the far side and less -than half the bytes. -""" - -from __future__ import annotations - -import argparse -import os - -#: Per-volume step-file datasets the viewer reads, and the dtype to store. -#: ``de`` is true deposited energy in MeV and ``charge`` the ionisation -#: electrons that survived recombination -- two different quantities, and the -#: viewer shows both, so a deposit can be thresholded in whichever the -#: question is asked in. float16 on disk is converted because h5wasm has no -#: reliable half-float support. -_STEP_KEEP = {"positions": None, "de": "float32", "charge": "float32"} - -#: Attributes needed to turn ``positions`` back into mm. -_STEP_ATTRS = ("n_actual", "pos_step_mm", - "pos_origin_x", "pos_origin_y", "pos_origin_z") - -#: Per-volume hits-file dataset mapping a deposit to its group. Older JAXTPC -#: output spells it ``segment_to_group``. -_D2G_KEYS = ("deposit_to_group", "segment_to_group") - - -def extract(step_path: str, hits_path: str, dst: str, n_events: int = 3, - level: int = 4, verbose: bool = True) -> dict: - """Copy *n_events* worth of truth deposits and their group ids into *dst*.""" - import h5py - import numpy as np - try: - import hdf5plugin # noqa: F401 (registers Blosc/LZ4 for reading) - except ImportError: - pass - - n_ds = n_dep = 0 - with h5py.File(step_path, "r") as fs, h5py.File(hits_path, "r") as fh, \ - h5py.File(dst, "w") as out: - if "config" in fh: - cfg = out.create_group("config") - cfg.attrs.update(fh["config"].attrs) - - events = sorted(k for k in fs.keys() if k.startswith("event_")) - if n_events > 0: - events = events[:n_events] - for ek in events: - if ek not in fh: - raise KeyError( - f"{ek} is in the step file but not the hits file " - f"{hits_path!r}; the two must come from the same run.") - for vol in sorted(k for k in fs[ek].keys() - if k.startswith("volume_")): - sv, hv = fs[f"{ek}/{vol}"], fh.get(f"{ek}/{vol}") - n = int(sv.attrs.get("n_actual", 0) or 0) - g = out.create_group(f"{ek}/{vol}") - for a in _STEP_ATTRS: - if a in sv.attrs: - g.attrs[a] = sv.attrs[a] - if not n: - continue - n_dep += n - for name, dt in _STEP_KEEP.items(): - if name not in sv: - continue - # Only the first n_actual rows are real; the rest is the - # pad JAXTPC writes to a fixed shape. - d = sv[name][:n] - g.create_dataset(name, data=d if dt is None else d.astype(dt), - compression="gzip", compression_opts=level) - n_ds += 1 - key = next((k for k in _D2G_KEYS if hv is not None and k in hv), None) - if key is None: - raise KeyError( - f"{ek}/{vol} in the hits file has none of " - f"{list(_D2G_KEYS)}, so its deposits cannot be tied to " - f"the groups the pysupera labels hang off.") - g.create_dataset("deposit_to_group", - data=hv[key][:n].astype(np.int32), - compression="gzip", compression_opts=level) - n_ds += 1 - - out_info = {"events": len(events), "datasets": n_ds, "deposits": n_dep, - "size_step": os.path.getsize(step_path), - "size_hits": os.path.getsize(hits_path), - "size_after": os.path.getsize(dst)} - if verbose: - print(f"[truth-subset] {step_path} + {hits_path} -> {dst}: " - f"{len(events)} event(s), {n_dep:,} deposit(s), " - f"{n_ds} dataset(s), " - f"{(out_info['size_step'] + out_info['size_hits']) / 1048576:.1f} MiB " - f"-> {out_info['size_after'] / 1048576:.2f} MiB") - return out_info - - -def main() -> None: - """Entry point for ``pysupera-truth-subset``.""" - p = argparse.ArgumentParser( - prog="pysupera-truth-subset", - description="Extract a gzip, viewer-sized slice of the JAXTPC truth " - "deposits and their group ids.", - formatter_class=argparse.ArgumentDefaultsHelpFormatter) - p.add_argument("step", help="JAXTPC step/seg HDF5 file (truth deposits)") - p.add_argument("hits", help="JAXTPC hits/inst HDF5 file (deposit_to_group)") - p.add_argument("dst", help="Output file, gzip") - p.add_argument("-n", "--events", type=int, default=3, - help="Number of events to copy; -1 for all") - p.add_argument("-l", "--level", type=int, default=4, help="gzip level (1-9)") - a = p.parse_args() - if not 1 <= a.level <= 9: - p.error("--level must be between 1 and 9") - extract(a.step, a.hits, a.dst, a.events, a.level) diff --git a/tests/test_io_v3.py b/tests/test_io_v3.py index 646de94..b265318 100644 --- a/tests/test_io_v3.py +++ b/tests/test_io_v3.py @@ -343,3 +343,265 @@ def test_interaction_of_resolves(self, tmp_path): got = v.interaction_of(0) assert got["vertex_id"] == v.interactions["vertex_id"][ int(v["interaction_id"][0])] + + +class TestRangesHoldOnlyOwnRows: + """ + A group's range is computed from where its members sit, so it is only + right while they sit together. These pin the two ways that failed. + """ + + @staticmethod + def le(p): + from pysupera.utils import SemanticType + p.sem_type = SemanticType.kLEScatter + return p + + def test_empty_fragment_of_an_unwritten_instance_claims_nothing(self): + """ + Fragment 10659 in a real event: a photon and its Compton electrons, + all without points (their interaction happened after the readout + window), merged by PhotonDecay. No instance of theirs is written, + so each member sorted under its own id -- around a particle that did + have points -- and the range from first to last member claimed them. + """ + empty = np.zeros((0, 6), dtype=np.float32) + a1 = self.le(make_particle(1, PT_TRACK, pc=empty, interaction_id=0)) + b2 = self.le(make_particle(2, PT_TRACK, n_pts=5, interaction_id=0)) + a3 = self.le(make_particle(3, PT_TRACK, pc=empty, interaction_id=0)) + a1.member_ids, b2.member_ids = [1, 3], [2] + frags = snapshot_groups([a1, b2]) + lay = build_layout([a1, b2, a3], frags, []) + cols = {int(i): k for k, i in enumerate(lay.columns["id"])} + # the empty fragment gets no row, and no range anywhere claims more + # than the 5 rows that exist + assert 1 not in cols + k = cols[2] + c = lay.columns + assert (c["frag_pc_le_start"][k], c["frag_pc_le_end"][k]) == (0, 5) + + def test_a_cross_interaction_absorption_is_stored_with_the_absorber(self): + """ + AbsorbLEScatter can take an LE particle from interaction 0 into a + track from interaction 1. The absorber is the representative: the + points become its fragment's, instance's and interaction's, stored + in one block. The absorbed particle keeps its own identity -- its + row stays with interaction 0 and carries interaction 0. + """ + from pysupera.io_v3 import check_ranges + b = make_particle(1, PT_TRACK, n_pts=3, interaction_id=1) + a = self.le(make_particle(2, PT_TRACK, n_pts=2, interaction_id=0)) + c = make_particle(3, PT_TRACK, n_pts=4, interaction_id=0, + parent_id=2, ancestor_id=2) # keeps a's row stored + b.member_ids, c.member_ids = [1, 2], [3] + frags = snapshot_groups([b, c]) + insts = snapshot_groups([b, c]) + lay = build_layout([b, a, c], frags, insts) + col, row = lay.columns, {int(i): k for k, i in + enumerate(lay.columns["id"])} + rb = row[1] + # a's points are b's: inside b's instance (LE side) and fragment + assert col["inst_pc_le_end"][rb] - col["inst_pc_le_start"][rb] == 2 + assert col["frag_pc_le_end"][rb] - col["frag_pc_le_start"][rb] == 2 + # the absorbed particle's row keeps interaction 0, beside c's + assert 2 in row + inter = lay.interactions + k0 = int(np.flatnonzero(inter["vertex_id"] == 0)[0]) + k1 = int(np.flatnonzero(inter["vertex_id"] == 1)[0]) + assert col["interaction_id"][row[2]] == k0 + assert inter["part_start"][k0] <= row[2] < inter["part_end"][k0] + assert inter["part_start"][k0] <= row[3] < inter["part_end"][k0] + # and the points count toward interaction 1: 3 of b's and 2 of a's + assert inter["pc_end"][k1] - inter["pc_start"][k1] == 5 + assert inter["pc_end"][k0] - inter["pc_start"][k0] == 4 + + def test_a_group_whose_members_sort_apart_is_refused(self): + """ + The invariant behind every range: a group's members sit together. + A fragment whose members were put in two different instances breaks + it, and the layout must refuse rather than write a range that + swallows another particle's rows. + """ + from pysupera.layout import LayoutError + p1 = make_particle(1, PT_TRACK, n_pts=3, interaction_id=0) + p2 = make_particle(2, PT_TRACK, n_pts=3, interaction_id=0) + p3 = make_particle(3, PT_TRACK, n_pts=3, interaction_id=0) + # fragment {1, 3}, but 1 and 3 in different instances, with 2 in + # 1's: the instance order puts 2 between the fragment's members + p1.member_ids, p2.member_ids = [1, 3], [2] + frags = snapshot_groups([p1, p2]) + p1.member_ids, p3.member_ids = [1, 2], [3] + insts = snapshot_groups([p1, p3]) + with pytest.raises(LayoutError, match="not contiguous"): + build_layout([p1, p2, p3], frags, insts) + + +class TestCheckRanges: + + def test_a_clean_file_passes(self, tmp_path): + from pysupera.io_v3 import check_ranges + assert check_ranges(write(tmp_path, events=[chain_event()] * 2)) == [] + + def test_an_overlapping_fragment_slice_is_reported(self, tmp_path): + from pysupera.io_v3 import check_ranges + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + frags = np.flatnonzero(v.is_fragment & (v["frag_pc_end"] > v["frag_pc_start"])) + assert len(frags) >= 2 + # stretch one fragment's range over its neighbour, as the bug did + with h5py.File(path, "a") as f: + a, b = frags[:2] + lo = min(f["particles/frag_pc_start"][a], f["particles/frag_pc_start"][b]) + hi = max(f["particles/frag_pc_end"][a], f["particles/frag_pc_end"][b]) + f["particles/frag_pc_start"][a] = lo + f["particles/frag_pc_end"][a] = hi + found = check_ranges(path) + assert found and found[0][:3] == (0, "points", "frag") + + +class TestGroupOfPoints: + + def test_every_point_gets_its_fragment_and_instance(self, tmp_path): + path = write(tmp_path) + with read_events_v3(path) as s: + v = s[0] + frag = v.group_of_points("frag") + inst = v.group_of_points("inst") + assert len(frag) == len(v.points) == len(inst) + # chain_event: fragment {1, 2} (5 points), fragment {3} (4), + # one instance headed by 1 holding all 9 + assert sorted(np.unique(frag, return_counts=True)[1].tolist()) == [4, 5] + assert set(frag.tolist()) == {1, 3} + assert (inst == 1).all() + +class TestPointsStorage: + """How points/flat is stored: invisible to a reader, apart from precision.""" + + def test_round_mantissa_bounds_the_relative_error(self): + from pysupera.io_v3 import round_mantissa + rng = np.random.default_rng(1) + a = (rng.standard_normal(100_000) * 10.0 ** rng.uniform(-3, 5, 100_000) + ).astype(np.float32) + r = round_mantissa(a, 7) + assert r.dtype == np.float32 + assert np.max(np.abs(r - a) / np.abs(a)) <= 2.0 ** -8 + 1e-7 + special = np.array([0.0, np.inf, -np.inf, np.nan], dtype=np.float32) + out = round_mantissa(special, 7) + assert out[0] == 0 and np.isinf(out[1]) and np.isinf(out[2]) and np.isnan(out[3]) + + def test_column_chunks_and_rounding_are_plain_hdf5(self, tmp_path): + """A bare h5py read sees an ordinary (N, 6) float32 array.""" + import hdf5plugin # noqa: F401 -- the filter plugin, as for LZ4 + parts, frags, insts = chain_event() + for p in parts: + if len(p.point_cloud): + pc = np.zeros((len(p.point_cloud), 6), dtype=np.float32) + pc[:, :3] = p.point_cloud[:, :3] + pc[:, 4] = 1234.5678 + p.point_cloud = pc + path = str(tmp_path / "cols.h5") + with open_writer_v3(path, points=dict(chunks="columns", bitshuffle=True, + energy_mantissa_bits=7)) as w: + w.append_event(build_layout(parts, frags, insts)) + with h5py.File(path) as f: + d = f["points/flat"] + assert d.shape[1] == 6 and d.dtype == np.float32 + assert d.chunks[1] == 1 and "32008" in d._filters + q = d[:, 4] + assert np.all(np.abs(q - 1234.5678) / 1234.5678 <= 2.0 ** -8) + + def test_repack_keeps_the_bitshuffle_filter(self, tmp_path): + from pysupera.io import repack + path = str(tmp_path / "cols.h5") + with open_writer_v3(path, points=dict(chunks="columns", bitshuffle=True)) as w: + parts, frags, insts = chain_event() + w.append_event(build_layout(parts, frags, insts)) + repack(path) + with h5py.File(path) as f: + assert "32008" in f["points/flat"]._filters + assert f["points/flat"].chunks[1] == 1 + + + +class TestColumnsAndHitLabels: + """points/columns names points/flat; hit_labels aligns with the hits CSR.""" + + def test_point_columns_are_named_in_the_file(self, tmp_path): + from pysupera.io_v3 import DEFAULT_POINT_COLUMNS + cols = ("x", "y", "z", "t", "dE", "dX", "theta", "phi", "p") + for columns, want in ((None, DEFAULT_POINT_COLUMNS), (cols, cols)): + path = str(tmp_path / f"c{len(want)}.h5") + with open_writer_v3(path, columns=columns) as w: + parts, frags, insts = chain_event() + w.append_event(build_layout(parts, frags, insts)) + with h5py.File(path) as f: + assert f["points/flat"].shape[1] == len(want) + names = tuple(c.decode() if isinstance(c, bytes) else c + for c in f["points/columns"][()]) + assert names == want + with read_events_v3(path) as st: + assert st.point_columns == want + + @staticmethod + def labels(n0, n1, sensor=False): + out = [] + for v, n in ((0, n0), (1, n1)): + d = {"volume": v, "plane": 0, "source": "Pixel", + "fragment_id": np.arange(n, dtype=np.int32) - 1, + "instance_id": np.full(n, 7, dtype=np.int32), + "is_le": np.arange(n) % 2 == 0} + if sensor: + d["on_sensor"] = d["fragment_id"] >= 0 + out.append(d) + return out + + def test_hit_labels_round_trip_per_event(self, tmp_path): + path = str(tmp_path / "lab.h5") + with open_writer_v3(path) as w: + for n0, n1 in ((3, 2), (1, 4)): + parts, frags, insts = chain_event() + w.append_event(build_layout(parts, frags, insts)) + w.append_hit_labels(self.labels(n0, n1, sensor=True)) + with h5py.File(path) as f: + g = f["hit_labels/volume1/plane0"] + assert g.attrs["source"] == "Pixel" + np.testing.assert_array_equal(g["offsets"][:], [0, 2, 6]) + assert g["is_le"].dtype == bool + with read_events_v3(path) as st: + ev1 = st.hit_labels(1) + assert set(ev1) == {(0, 0), (1, 0)} + np.testing.assert_array_equal(ev1[(1, 0)]["fragment_id"], [-1, 0, 1, 2]) + np.testing.assert_array_equal(ev1[(1, 0)]["on_sensor"], + [False, True, True, True]) + + def test_a_plane_appearing_late_is_refused(self, tmp_path): + path = str(tmp_path / "lab.h5") + with open_writer_v3(path) as w: + parts, frags, insts = chain_event() + w.append_event(build_layout(parts, frags, insts)) + w.append_hit_labels(self.labels(1, 1)[:1]) + with pytest.raises(ValueError, match="first appear"): + w.append_hit_labels(self.labels(1, 1)) + + def test_a_fragment_row_names_its_instance(self, tmp_path): + parts, frags, insts = chain_event() + lay = build_layout(parts, frags, insts) + c = lay.columns + row = {int(i): k for k, i in enumerate(c["id"])} + # chain_event: fragments {1, 2} and {3} both inside instance 1 + assert c["frag_inst_id"][row[1]] == 1 and c["frag_inst_id"][row[3]] == 1 + + def test_keep_gives_a_row_to_a_pointless_fragment(self): + """ + A fragment a hit is labelled with must get a row even with no points. + Particle 0 of chain_event has none; drop it from every ancestry by + making it its own primary and it only gets a row through keep. + """ + parts, frags, insts = chain_event() + for p in parts: + if int(p.id) == 1: + p.parent_id = 1 + ids = lambda lay: {int(i) for i in lay.columns["id"]} + assert 0 not in ids(build_layout(parts, frags, insts)) + assert 0 in ids(build_layout(parts, frags, insts, keep={0})) diff --git a/tests/test_jaxtpc_pixel_data.py b/tests/test_jaxtpc_pixel_data.py index 435ea6b..2cfc821 100644 --- a/tests/test_jaxtpc_pixel_data.py +++ b/tests/test_jaxtpc_pixel_data.py @@ -16,6 +16,10 @@ /step//_step__.h5 /hits//_hits__.h5 + /sensor//_sensor__.h5 + +It has to be written by a JAXTPC whose ``group_sizes`` is uint16: an older +batch is refused on read (see ``test_a_batch_with_wrapped_group_sizes_is_refused``). """ import os @@ -31,7 +35,9 @@ # Default location: the scratch tree this was developed against. _REPO = Path(__file__).resolve().parents[1] -_DEFAULT_DIR = _REPO.parent / "data" / "jaxtpc_pixel" +_DEFAULT_DIR = _REPO.parent / "data" / "output_pixel" +#: The same batch as written before the uint8 group_sizes fix, if kept. +_WRAPPED_DIR = _REPO.parent / "data" / "jaxtpc_pixel" _DEFAULT_EDEPSIM = _REPO.parent / "data" / "kazu.h5" @@ -101,17 +107,22 @@ def hit_kwargs(**over): return kw -@pytest.fixture(scope="module") -def reader(): - from pysupera.readers import JaxtpcHDF5Reader - edep, step, hits = _P - with JaxtpcHDF5Reader(edepsim_path=str(edep), seg_path=str(step), - inst_path=str(hits)) as r: - yield r +def _wire(): + """A JAXTPC wire batch for the deposit path, or None when absent.""" + root = Path(os.environ.get("PYSUPERA_JAXTPC_WIRE_DIR", + str(_REPO.parent / "data" / "output_wireplane"))) + edep = Path(os.environ.get("PYSUPERA_WIRE_EDEPSIM_H5", str(_DEFAULT_EDEPSIM))) + step, hits = _one("step", root), _one("hits", root) + if not root.is_dir() or not edep.is_file() or step is None or hits is None: + return None + return str(edep), str(step), str(hits) -def test_it_is_a_pixel_batch(reader): - assert reader.readout_type == "pixel" +def test_it_is_a_pixel_batch(): + from pysupera.readers import JaxtpcHDF5Reader + with JaxtpcHDF5Reader(**hit_kwargs()) as r: + assert r.readout_type == "pixel" + assert r.point_source == "hits" with h5py.File(_P[2], "r") as f: assert read_readout_type(f) == "pixel" # One plane per volume, named Pixel -- not three projections. @@ -121,62 +132,26 @@ def test_it_is_a_pixel_batch(reader): assert "center_py" in vol["Pixel"] and "center_wires" not in vol["Pixel"] -def test_reads_visible_point_clouds(reader): - particles = reader[0] - assert len(particles) > 0 - with_points = [p for p in particles if len(p.point_cloud)] - assert with_points, "every particle came back empty" - # Visibility is a filter, not a wipe: some deposits survive and some do not. - stats = reader.last_mask_stats - assert 0 < stats["n_visible"] < stats["n_total"] - - -def test_deposit_ids_index_the_group_table(reader): - """ - Each particle carries the *input deposit* row behind every point, and - ``last_deposit_to_group`` is indexed by exactly that. If the two ever - drift apart the group table silently describes the wrong deposits, so - check the bound rather than trust it. - """ - particles = reader[0] - d2g = reader.last_deposit_to_group - assert d2g is not None and len(d2g) - for p in particles: - dep = getattr(p, "deposit_id", None) - if dep is None or not len(dep): - continue - assert len(dep) == len(p.point_cloud) - assert dep.min() >= 0 and dep.max() < len(d2g) - - -def test_every_visible_deposit_belongs_to_a_readout_group(reader): - """ - The reader's contract: a deposit is in a point cloud only because its - group produced a hit. Verify it the long way round, straight from the - file, rather than by re-running the same function. - """ - particles = reader[0] - d2g = reader.last_deposit_to_group +def test_a_pixel_batch_is_not_read_as_deposits(): + """A pixel batch is read as its hits; there is no deposit mode for it.""" + from pysupera.readers import JaxtpcHDF5Reader + edep, step, hits = (str(x) for x in _P) + with pytest.raises(ValueError, match="pixel readout"): + JaxtpcHDF5Reader(edepsim_path=edep, seg_path=step, inst_path=hits, + point_source="deposits") - with h5py.File(_P[2], "r") as f: - ev = f["event_000"] - active, off = set(), 0 - for vname in sorted(k for k in ev if k.startswith("volume_")): - vol = ev[vname] - for g in (vol[k] for k in vol if isinstance(vol[k], h5py.Group)): - active.update((g["group_ids"][:].astype(np.int64) + off).tolist()) - off += len(vol["group_to_track"]) - dep = np.concatenate([p.deposit_id for p in particles - if getattr(p, "deposit_id", None) is not None - and len(p.deposit_id)]) - assert len(dep) - assert set(np.unique(d2g[dep]).tolist()) <= active +def test_the_readout_decides_the_point_source(): + """Left unset, the reader follows the file: hits for pixel.""" + from pysupera.readers import JaxtpcHDF5Reader + kw = hit_kwargs(); kw.pop("point_source") + with JaxtpcHDF5Reader(**kw) as r: + assert r.point_source == "hits" def test_subset_of_the_real_file(tmp_path): dst = tmp_path / "pixel_small.h5" - info = extract(str(_P[2]), str(dst), n_events=1, verbose=False) + info = extract(str(_P[2]), str(dst), n_events=1, verbose=False, hits=False) assert info["readout"] == "pixel" assert info["planes"] == ["Pixel"] # The point of the tool: the CSR bulk is what makes the file big. @@ -188,6 +163,24 @@ def test_subset_of_the_real_file(tmp_path): assert read_readout_type(f) == "pixel" +def test_subset_decodes_every_hit_in_csr_order(tmp_path): + """ + The viewers draw the subset's decoded hits against hit_labels, entry for + entry, so they must be the hits file's own CSR entries in its own order. + """ + from pysupera.readers.pixel_hits import decode_plane_hits + dst = tmp_path / "pixel_hits.h5" + extract(str(_P[2]), str(dst), n_events=1, verbose=False) + with h5py.File(dst, "r") as f, h5py.File(_P[2], "r") as src: + for vol in (0, 1): + g = f[f"event_000/volume_{vol}/Pixel"] + h = decode_plane_hits(src[f"event_000/volume_{vol}/Pixel"]) + np.testing.assert_array_equal(g["hit_py"][:], h["py"]) + np.testing.assert_array_equal(g["hit_pz"][:], h["pz"]) + np.testing.assert_array_equal(g["hit_tick"][:], h["tick"]) + np.testing.assert_allclose(g["hit_charge"][:], h["charge"], rtol=1e-6) + + # --------------------------------------------------------------------------- # point_source=hits — the detected image # --------------------------------------------------------------------------- @@ -288,7 +281,7 @@ def test_fitting_the_geometry_is_opt_in_and_warns(): def test_hit_mode_attaches_every_hit(hit_reader): particles = hit_reader[0] st = hit_reader.last_hit_stats - assert st["n_hits"] > 1_000_000 # the detected image is not small + assert st["n_hits"] > 100_000 # the detected image is not small assert st["n_unmatched"] == 0 assert sum(len(p.point_cloud) for p in particles) == st["n_attached"] # A hit belongs to one group and a group to one track, so no hit is shared. @@ -323,10 +316,18 @@ def test_true_t0_lands_on_the_truth_cloud(): pytest.importorskip("scipy") from scipy.spatial import cKDTree from pysupera.readers import JaxtpcHDF5Reader - edep, step, hits = (str(x) for x in _P) - with JaxtpcHDF5Reader(edepsim_path=edep, seg_path=step, - inst_path=hits) as r: - truth = {p.id: p.point_cloud for p in r[0] if len(p.point_cloud)} + # The truth: every segment of the event, by the track its group maps to. + with JaxtpcHDF5Reader(**hit_kwargs(truth_segments=True)) as r: + r[0] + flat, group = r.last_truth_segments + with h5py.File(_P[2], "r") as f: + ev = f["event_000"] + g2t = np.concatenate([ev[f"volume_{k}/group_to_track"][:] for k in (0, 1)]) + track = g2t[group] + order = np.argsort(track, kind="stable") + tids, starts = np.unique(track[order], return_index=True) + ends = np.r_[starts[1:], len(order)] + truth = {int(t): flat[order[a:b], :3] for t, a, b in zip(tids, starts, ends)} got = {} for mode in ("true_t0", "nominal"): @@ -335,10 +336,10 @@ def test_true_t0_lands_on_the_truth_cloud(): key=lambda p: -len(p.point_cloud))[:10] d = [] for p in big: - t = truth.get(p.id) + t = truth.get(int(getattr(p, "geant4_id", -1))) if t is None or len(t) < 20: continue - dist, _ = cKDTree(t[:, :3]).query(p.point_cloud[:, :3], k=1) + dist, _ = cKDTree(t).query(p.point_cloud[:, :3], k=1) d.append(float(np.median(dist))) got[mode] = float(np.median(d)) @@ -506,165 +507,10 @@ def test_the_shift_takes_few_distinct_values(hit_reader): assert n < 100, f"{n} distinct shifts -- expected a few tens" -def test_written_output_carries_the_shift(tmp_path): - """ - End to end: the column survives voxelisation and the write, and reading - x back and adding it puts the cloud inside the detector again -- the - nominal x does not have to be. - """ - from pysupera.io_v3 import read_events_v3 - import subprocess - - exe = _run_pysupera() - edep, step, hits = (str(x) for x in _P) - out = tmp_path / "hits.h5" - cmd = [exe, "reader=jaxtpc_pixel", - f"io.input_path={edep}", f"io.output_path={out}", - f"reader.jaxtpc_seg_path={step}", f"reader.jaxtpc_inst_path={hits}", - f"reader.jaxtpc_sensor_path={_sensor()}", - "reader.point_source=hits", - f"reader.pixel_pitch_mm={PITCH_MM}", - "reader.pixel_drift_direction=[-1,1]", - "check_group_ownership=false", "distance_threshold=6.2", - "max_events=1", "progress=false", "report=false", - f"hydra.run.dir={tmp_path}"] - r = subprocess.run(cmd, capture_output=True, text=True, timeout=900) - assert r.returncode == 0, r.stdout[-3000:] + r.stderr[-3000:] - - with read_events_v3(str(out)) as store: - view = store[0] - shift = store.true_x_shift(0) - assert shift is not None and len(shift) == len(view.points) - x = view.points[:, 0] - true_x = x + shift - # the true positions sit inside the detector; the nominal ones are - # displaced by each interaction's own t0 and need not - assert true_x.min() > -2160.0 and true_x.max() < 2160.0 - assert np.abs(shift).max() > 100.0 - - -def test_deposit_mode_writes_the_column_as_zero(tmp_path): - """ - The relation true_x = x + true_x_shift should hold for every file, so a - deposit-mode run writes zeros rather than omitting the column. - """ - from pysupera.io_v3 import read_events_v3 - import subprocess - - exe = _run_pysupera() - edep, step, hits = (str(x) for x in _P) - out = tmp_path / "dep.h5" - r = subprocess.run( - [exe, "reader=jaxtpc_pixel", f"io.input_path={edep}", - f"io.output_path={out}", f"reader.jaxtpc_seg_path={step}", - f"reader.jaxtpc_inst_path={hits}", "max_events=1", - "progress=false", "report=false", f"hydra.run.dir={tmp_path}"], - capture_output=True, text=True, timeout=900) - assert r.returncode == 0, r.stdout[-3000:] + r.stderr[-3000:] - with read_events_v3(str(out)) as store: - shift = store.true_x_shift(0) - assert shift is not None and not shift.any() - - # --------------------------------------------------------------------------- -# pysupera-truth-subset, and the run metadata the viewers read +# The run metadata the viewers read # --------------------------------------------------------------------------- -def test_truth_subset_carries_what_the_viewer_needs(tmp_path): - """ - vis_truth.html labels a truth deposit by walking deposit -> group -> - fragment, so it needs the deposit positions from the step file and - deposit_to_group from the hits file -- and it cannot read either, both - being Blosc. The extract has to bring all of it across as gzip. - """ - from pysupera.truth_subset import extract - _edep, step, hits = (str(x) for x in _P) - dst = tmp_path / "truth.h5" - info = extract(step, hits, str(dst), n_events=1, verbose=False) - assert info["events"] == 1 and info["deposits"] > 0 - with h5py.File(dst, "r") as f: - vol = f["event_000/volume_0"] - assert {"positions", "de", "charge", "deposit_to_group"} <= set(vol.keys()) - n = int(vol.attrs["n_actual"]) - # every array is truncated to the real row count, not the padded shape - for k in ("positions", "de", "charge", "deposit_to_group"): - assert vol[k].shape[0] == n - # the attributes needed to put positions back into mm - for a in ("pos_step_mm", "pos_origin_x", "pos_origin_y", "pos_origin_z"): - assert a in vol.attrs - # gzip, or the browser cannot open it - assert vol["positions"].compression == "gzip" - # and it is worth doing: two Blosc files of tens of MB become one small one - assert info["size_after"] < (info["size_step"] + info["size_hits"]) / 5 - - -def test_truth_subset_charge_and_de_are_different_quantities(tmp_path): - """ - Both are kept because they answer different questions -- dE is energy in - MeV, charge is the ionisation electrons that survived recombination -- - and the viewer thresholds on one while showing the other. - """ - from pysupera.truth_subset import extract - _edep, step, hits = (str(x) for x in _P) - dst = tmp_path / "truth.h5" - extract(step, hits, str(dst), n_events=1, verbose=False) - with h5py.File(dst, "r") as f: - de = f["event_000/volume_0/de"][:] - ch = f["event_000/volume_0/charge"][:] - assert de.dtype == np.float32 and ch.dtype == np.float32 # not float16 - assert np.median(de) < 1.0 and np.median(ch) > 100.0 # MeV vs electrons - - -def test_the_deposit_to_group_chain_closes(tmp_path): - """ - The join vis_truth.html performs: a deposit's group, shifted by the - volume offset, must index the output's groups/fragment_id. If those two - numbering spaces ever drift apart the viewer paints truth deposits with - another object's label and nothing raises. - """ - from pysupera.truth_subset import extract - from pysupera.io_v3 import read_events_v3 - import subprocess - - exe = _run_pysupera() - edep, step, hits = (str(x) for x in _P) - out = tmp_path / "out.h5" - r = subprocess.run( - [exe, "reader=jaxtpc_pixel", f"io.input_path={edep}", - f"io.output_path={out}", f"reader.jaxtpc_seg_path={step}", - f"reader.jaxtpc_inst_path={hits}", - f"reader.jaxtpc_sensor_path={_sensor()}", "reader.point_source=hits", - f"reader.pixel_pitch_mm={PITCH_MM}", - "reader.pixel_drift_direction=[-1,1]", - "check_group_ownership=false", "distance_threshold=6.2", - "max_events=1", "progress=false", "report=false", - f"hydra.run.dir={tmp_path}"], - capture_output=True, text=True, timeout=900) - assert r.returncode == 0, r.stdout[-3000:] + r.stderr[-3000:] - - truth = tmp_path / "truth.h5" - extract(step, hits, str(truth), n_events=1, verbose=False) - - with h5py.File(out, "r") as fo, h5py.File(truth, "r") as ft: - go = fo["groups/offsets"][:] - vi = fo["groups/volume_offsets_index"][:] - ga, gb = int(go[0]), int(go[1]) - frag = fo["groups/fragment_id"][ga:gb] - voff = fo["groups/volume_offsets"][int(vi[0]):int(vi[1])] - labelled = total = 0 - for v, vol in enumerate(sorted(k for k in ft["event_000"] - if k.startswith("volume_"))): - g = ft[f"event_000/{vol}/deposit_to_group"][:].astype(np.int64) - g = g + int(voff[v]) - total += len(g) - # every group number must land inside the table, not past its end - assert g.min() >= 0 and g.max() < len(frag), \ - f"{vol}: group {g.max()} outside a table of {len(frag)}" - labelled += int((frag[g] >= 0).sum()) - # most deposits are claimed; the rest are the ones the readout never saw - assert 0.3 < labelled / total < 1.0 - - def test_output_records_how_it_was_made(tmp_path): """ Nothing in the point columns says whether the energy column holds true dE @@ -693,3 +539,230 @@ def test_output_records_how_it_was_made(tmp_path): assert a.get("hit_energy") == "charge" assert a.get("hit_x_from") == "nominal" assert a.get("readout_type") == "pixel" + + +# --------------------------------------------------------------------------- +# The sensor image decides which pixels exist +# --------------------------------------------------------------------------- + +def _sensor_pixel_count(event_key="event_000"): + """Pixels JAXTPC wrote to the sensor file, counted from its attributes.""" + with h5py.File(_sensor(), "r") as f: + ev = f[event_key] + return sum(int(ev[v][p].attrs.get("n_pixels", 0)) + for v in ev for p in ev[v]) + + +def test_every_sensor_pixel_is_labelled_and_nothing_else_is(hit_reader): + """ + The labels are for a model whose input is the sensor image, so the cloud + has to cover that image exactly: no sensor pixel without a hit on it, and + no hit off it. The hits file holds about twice as many pixels -- every + group's share, whether or not the sum passed threshold -- so the mask + has real work to do. + """ + hit_reader[0] + st = hit_reader.last_hit_stats + assert st["n_sensor_pixels"] == _sensor_pixel_count() + assert st["n_sensor_unlabelled"] == 0 + assert st["n_off_sensor"] > 0 + assert st["n_hits"] + st["n_off_sensor"] == st["n_decoded"] + + +def test_hit_mode_needs_the_sensor_file(): + from pysupera.readers import JaxtpcHDF5Reader + with pytest.raises(ValueError, match="jaxtpc_sensor_path"): + JaxtpcHDF5Reader(**hit_kwargs(sensor_path=None)) + + +def test_a_sensor_file_from_another_run_is_refused(tmp_path): + """A mask from another run would keep whatever happens to overlap.""" + import shutil + from pysupera.readers import JaxtpcHDF5Reader + other = tmp_path / "other_sensor.h5" + shutil.copy(_sensor(), other) + with h5py.File(other, "a") as f: + f["config"].attrs["batch_timestamp"] = 1 + with pytest.raises(ValueError, match="different JAXTPC runs"): + JaxtpcHDF5Reader(**hit_kwargs(sensor_path=str(other))) + + +@pytest.mark.skipif(not (_WRAPPED_DIR / "hits").is_dir(), + reason=f"no pre-fix batch at {_WRAPPED_DIR}") +def test_a_batch_with_wrapped_group_sizes_is_refused(): + """ + The batch as JAXTPC first wrote it: uint8 group_sizes, so each group of + more than 255 entries shifted every later group on its plane. Decoding + it would put about 8% of the sensor pixels' labels on the wrong pixels. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.readers.pixel_hits import HitsFileError + old = {d: str(sorted((_WRAPPED_DIR / d).rglob("*.h5"))[0]) + for d in ("step", "hits", "sensor")} + kw = hit_kwargs(seg_path=old["step"], inst_path=old["hits"], + sensor_path=old["sensor"]) + with JaxtpcHDF5Reader(**kw) as r: + with pytest.raises(HitsFileError, match="x 256 lost"): + r[0] + + +def test_every_segment_arrives_with_its_step_values_and_group(): + """ + The truth cloud is built downstream from these, by group ownership, so + every segment has to arrive -- in either mode -- with a group inside the + event's group space and its EDepSim step's values: the seg file's own + (rounded) position, time and dX must agree. + """ + from pysupera.readers import JaxtpcHDF5Reader + edep, step, hits = (str(x) for x in _P) + with JaxtpcHDF5Reader(**hit_kwargs(truth_segments=True)) as r: + r[0] + flat, group = r.last_truth_segments + segs = r._load_seg_volumes("event_000") + n_groups = r.last_n_groups + seg = np.concatenate([s_["positions_mm"][:s_["n_actual"]] for s_ in segs + if s_["n_actual"]]) + assert flat.shape == (len(seg), 9) and len(group) == len(seg) + assert group.min() >= 0 + if n_groups is not None: + assert group.max() < n_groups + np.testing.assert_allclose(flat[:, :3], seg, atol=0.16) # 0.3 mm grid + t0 = np.concatenate([s_["t0_us"][:s_["n_actual"]] for s_ in segs if s_["n_actual"]]) + np.testing.assert_allclose(flat[:, 3], t0, atol=0.6) # float16 us + dx = np.concatenate([s_["dx"][:s_["n_actual"]] for s_ in segs if s_["n_actual"]]) + np.testing.assert_allclose(flat[:, 5] * 10.0, dx, rtol=2e-3, atol=1e-4) # cm vs mm (float16) + assert (flat[:, 4] >= 0).all() + # direction and momentum: theta in [0, pi], phi in [-pi, pi], |p| > 0 + assert (flat[:, 6] >= 0).all() and (flat[:, 6] <= np.pi + 1e-3).all() + assert (np.abs(flat[:, 7]) <= np.pi + 1e-3).all() and (flat[:, 8] > 0).all() + + +def test_without_truth_segments_the_reader_reads_none(hit_reader): + hit_reader[0] + assert hit_reader.last_truth_segments is None + + + +def _run_to(tmp_path, reader, edep, step, hits, *extra): + """Run the pipeline from this tree on one event; the output path.""" + import subprocess, sys + out = tmp_path / f"{reader}.h5" + code = ("import sys; from pysupera._run import main; " + "sys.argv[0] = 'run_pysupera'; main()") + r = subprocess.run( + [sys.executable, "-c", code, f"reader={reader}", + f"io.input_path={edep}", f"io.output_path={out}", + f"reader.jaxtpc_seg_path={step}", f"reader.jaxtpc_inst_path={hits}", + "max_events=1", "progress=false", f"hydra.run.dir={tmp_path}", *extra], + cwd=str(_REPO), capture_output=True, text=True) + assert r.returncode == 0, r.stdout[-2000:] + r.stderr[-2000:] + return str(out) + + +def _check_labels_against_the_hits_file(out, hits): + """Shared: one label per CSR entry, naming real rows, instance consistent.""" + from pysupera.io_v3 import read_events_v3, check_ranges + assert check_ranges(out) == [] + with read_events_v3(out) as st: + v = st[0] + lab = st.hit_labels(0) + assert st.point_columns == ("x", "y", "z", "t", "dE", "dX", + "theta", "phi", "p") + # every voxel carries a real direction and momentum (its earliest step) + assert (v.points[:, 8] > 0).all() + assert (v.points[:, 6] >= 0).all() and (v.points[:, 6] <= np.pi + 1e-3).all() + c = v.columns + frag_rows = {int(i) for i, f in zip(c["id"], c["frag_id"]) if f == i} + inst_rows = {int(i) for i, j in zip(c["id"], c["inst_id"]) if j == i} + frag_inst = {int(i): int(x) for i, x in zip(c["id"], c["frag_inst_id"])} + with h5py.File(hits, "r") as f: + for (vol, pl), d in lab.items(): + g = f[f"event_000/volume_{vol}/{d['source']}"] + assert len(d["fragment_id"]) == int(g["group_sizes"][:].astype(np.int64).sum()) + fr, ins = d["fragment_id"], d["instance_id"] + t = fr >= 0 + assert set(np.unique(fr[t]).tolist()) <= frag_rows + assert set(np.unique(ins[ins >= 0]).tolist()) <= inst_rows + assert all(frag_inst[int(a)] == int(b) for a, b in + set(zip(fr[t].tolist(), ins[t].tolist()))) + return v, lab + + +def test_pixel_hit_labels_follow_the_sensor_image(tmp_path): + """ + Pixel: every CSR entry gets a label, and exactly the hits whose pixel the + sensor file lacks are untraced (-1, on_sensor False) -- checked here + against the sensor file itself, not the reader's own mask. + """ + from pysupera.readers.pixel_hits import (decode_plane_hits, + decode_sensor_plane, pixel_key, + on_sensor) + edep, step, hits = (str(x) for x in _P) + out = _run_to(tmp_path, "jaxtpc_pixel", edep, step, hits, + f"reader.jaxtpc_sensor_path={_sensor()}") + v, lab = _check_labels_against_the_hits_file(out, hits) + assert set(lab) == {(0, 0), (1, 0)} + with h5py.File(hits, "r") as fh, h5py.File(_sensor(), "r") as fs: + for (vol, _), d in lab.items(): + h = decode_plane_hits(fh[f"event_000/volume_{vol}/Pixel"]) + s = decode_sensor_plane(fs[f"event_000/volume_{vol}/Pixel"]) + on = on_sensor(np.unique(pixel_key(s["py"], s["pz"], s["tick"])), + h["py"], h["pz"], h["tick"]) + np.testing.assert_array_equal(d["on_sensor"], on) + np.testing.assert_array_equal(d["fragment_id"] >= 0, on) + + +@pytest.mark.skipif(_wire() is None, reason="no JAXTPC wire batch") +def test_wire_hit_labels_are_the_group_owners(tmp_path): + """Wire: every plane's hits labelled by the fragment owning their group.""" + edep, step, hits = _wire() + out = _run_to(tmp_path, "jaxtpc_wire", edep, step, hits) + v, lab = _check_labels_against_the_hits_file(out, hits) + from pysupera.io_v3 import read_events_v3 + with read_events_v3(out) as st: + fo, le, voff = st.group_owners(0) + with h5py.File(hits, "r") as f: + for (vol, pl), d in lab.items(): + assert "on_sensor" not in d + g = f[f"event_000/volume_{vol}/{d['source']}"] + gid = (np.repeat(g["group_ids"][:].astype(np.int64), + g["group_sizes"][:].astype(np.int64)) + int(voff[vol])) + np.testing.assert_array_equal(d["fragment_id"], fo[gid]) + + + +@pytest.mark.skipif(_wire() is None, reason="no JAXTPC wire batch") +def test_deposit_points_and_voxels_describe_the_same_fragments(tmp_path): + """ + For truth deposits -- the wire readout -- store=points writes each + deposit and store=voxels the proximity-grid voxels. Only what a point + is may differ: the same fragments, with the same energy each. + """ + import subprocess, sys + from pysupera.io_v3 import read_events_v3, check_ranges + edep, step, hits = _wire() + code = ("import sys; from pysupera._run import main; " + "sys.argv[0] = 'run_pysupera'; main()") + energy, rows = {}, {} + for store in ("points", "voxels"): + out = tmp_path / f"{store}.h5" + r = subprocess.run( + [sys.executable, "-c", code, "reader=jaxtpc_wire", + f"io.input_path={edep}", f"io.output_path={out}", + f"reader.jaxtpc_seg_path={step}", f"reader.jaxtpc_inst_path={hits}", + "max_events=1", f"particle.voxelize.store={store}", + "progress=false", f"hydra.run.dir={tmp_path}"], + cwd=str(_REPO), capture_output=True, text=True) + assert r.returncode == 0, r.stdout[-2000:] + r.stderr[-2000:] + assert check_ranges(str(out)) == [] + with read_events_v3(str(out)) as st: + v = st[0] + f = v.group_of_points("frag") + ids, inv = np.unique(f, return_inverse=True) + energy[store] = dict(zip(ids.tolist(), np.bincount( + inv, weights=v.points[:, 4].astype(np.float64)).tolist())) + rows[store] = len(v.points) + assert rows["points"] > rows["voxels"] + assert set(energy["points"]) == set(energy["voxels"]) + for k, e in energy["points"].items(): + assert e == pytest.approx(energy["voxels"][k], rel=1e-5) diff --git a/tests/test_pixel_hits.py b/tests/test_pixel_hits.py index c2ec634..885f02f 100644 --- a/tests/test_pixel_hits.py +++ b/tests/test_pixel_hits.py @@ -14,6 +14,7 @@ from pysupera.readers.pixel_hits import ( ENERGY_FROM, + HitsFileError, PixelGeometryError, VolumePixelGeometry, X_FROM, @@ -21,7 +22,10 @@ calibrate_volume, window_truncation, decode_plane_hits, + decode_sensor_plane, group_reductions, + on_sensor, + pixel_key, ) from pysupera.utils import PointFeature @@ -31,18 +35,22 @@ # --------------------------------------------------------------------------- class FakePlane(dict): - """Enough of an h5py group for decode_plane_hits: __contains__ and [] .""" + """Enough of an h5py group for the decoders: __contains__, [] and attrs.""" name = "/fake/Pixel" + def __init__(self, *a, attrs=None, **kw): + super().__init__(*a, **kw) + self.attrs = dict(attrs or {}) + def make_plane(sizes, centres, deltas, peaks, frac, pad=0, charge_key="charges_i16"): """ A CSR pixel plane. *deltas* is a list of (dpy, dpz, dt) per entry and *frac* the stored - per-entry charge fraction. *pad* appends unused trailing entries, which - is what JAXTPC actually writes -- the delta arrays run to a chunk - boundary past ``group_sizes.sum()``. + per-entry charge fraction. *pad* appends trailing entries no group + claims, which is what a file written with a wrapped uint8 group_sizes + looks like. """ n = sum(sizes) d = np.asarray(deltas, dtype=np.int8).reshape(n, 3) @@ -53,7 +61,7 @@ def make_plane(sizes, centres, deltas, peaks, frac, pad=0, charge_key="charges_i c = np.asarray(centres, dtype=np.int16).reshape(len(sizes), 3) return FakePlane({ "group_ids": np.arange(len(sizes), dtype=np.int32) * 2, # not 0..G - "group_sizes": np.asarray(sizes, dtype=np.uint8), + "group_sizes": np.asarray(sizes, dtype=np.uint16), "center_py": c[:, 0], "center_pz": c[:, 1], "center_times": c[:, 2], "peak_charges": np.asarray(peaks, dtype=np.float32), "delta_py": d[:, 0], "delta_pz": d[:, 1], "delta_times": d[:, 2], @@ -80,17 +88,18 @@ def test_decode_expands_centres_plus_deltas(): np.testing.assert_allclose(got["charge"], [100.0, 50.0, -50.0], rtol=1e-3) -def test_decode_ignores_the_padding_tail(): +def test_decode_refuses_arrays_longer_than_the_groups(): """ - JAXTPC pads the delta arrays past ``group_sizes.sum()``. Reading to the - end of the array instead would append zero-delta ghosts sitting exactly - on the last group's peak -- points that look plausible and are not there. + Entries no group claims are what a uint8 group_sizes leaves behind: a + 300-entry group stored as 44, and every later group read 256 entries + early. Decoding anyway would put hits on other groups' pixels, so the + file is refused, with the excess expressed in the 256s it lost. """ plane = make_plane(sizes=[2], centres=[[10, 20, 30]], deltas=[[0, 0, 0], [1, 1, 1]], peaks=[10.0], - frac=[32767, 32767], pad=64) - got = decode_plane_hits(plane) - assert len(got["py"]) == 2 + frac=[32767, 32767], pad=256) + with pytest.raises(HitsFileError, match="1 x 256 lost"): + decode_plane_hits(plane) def test_decode_accepts_the_legacy_unsigned_charge(): @@ -108,10 +117,58 @@ def test_decode_without_a_charge_array_names_the_subset_tool(): decode_plane_hits(plane) -def test_decode_reports_groups_at_the_uint8_cap(): - plane = make_plane(sizes=[255], centres=[[0, 0, 0]], - deltas=[[0, 0, 0]] * 255, peaks=[1.0], frac=[1] * 255) - assert decode_plane_hits(plane)["n_capped_groups"] == 1 +def test_decode_a_group_larger_than_255(): + """The group a uint8 group_sizes could not hold, and the one after it.""" + plane = make_plane(sizes=[300, 1], centres=[[0, 0, 0], [50, 60, 70]], + deltas=[[0, 0, 0]] * 301, peaks=[1.0, 2.0], + frac=[32767] * 301) + got = decode_plane_hits(plane) + assert len(got["py"]) == 301 + assert (got["group"][:300] == 0).all() and got["group"][300] == 2 + assert (got["py"][300], got["pz"][300], got["tick"][300]) == (50, 60, 70) + + +# --------------------------------------------------------------------------- +# The sensor image +# --------------------------------------------------------------------------- + +def make_sensor_plane(py, pz, tick): + """A sensor plane as JAXTPC writes it: sorted, start plus int16 steps.""" + order = np.lexsort((tick, pz, py)) + py, pz, tick = (np.asarray(a, dtype=np.int32)[order] for a in (py, pz, tick)) + step = lambda a: np.diff(a, prepend=a[0]).astype(np.int16) + return FakePlane( + {"delta_py": step(py), "delta_pz": step(pz), "delta_time": step(tick), + "values": np.ones(len(py), dtype=np.float32)}, + attrs={"py_start": int(py[0]), "pz_start": int(pz[0]), + "time_start": int(tick[0]), "n_pixels": len(py)}) + + +def test_decode_sensor_plane_undoes_the_delta_encoding(): + plane = make_sensor_plane(py=[3, 1, 1], pz=[0, 5, 5], tick=[9, 2, 400]) + got = decode_sensor_plane(plane) + np.testing.assert_array_equal(got["py"], [1, 1, 3]) + np.testing.assert_array_equal(got["pz"], [5, 5, 0]) + np.testing.assert_array_equal(got["tick"], [2, 400, 9]) + + +def test_decode_sensor_plane_that_recorded_nothing(): + """JAXTPC writes no datasets or attributes for an empty plane.""" + got = decode_sensor_plane(FakePlane({})) + assert all(len(v) == 0 for v in got.values()) + + +def test_pixel_key_keeps_negative_indices_apart(): + k = pixel_key([0, 0, 0, 1], [0, 0, 1, 0], [-1, 1, 0, 0]) + assert len(np.unique(k)) == 4 + + +def test_on_sensor_selects_exactly_the_sensor_pixels(): + sensor = np.unique(pixel_key([1, 2], [1, 2], [10, 20])) + np.testing.assert_array_equal( + on_sensor(sensor, [1, 1, 2, 3], [1, 1, 2, 3], [10, 11, 20, 30]), + [True, False, True, False]) + assert not on_sensor(np.zeros(0, dtype=np.int64), [1], [1], [1]).any() # --------------------------------------------------------------------------- @@ -254,7 +311,6 @@ def _one_volume(n_per_group=(2, 2), tracks=(7, 9), t0=(0.0, 100.0), "tick": np.array([10, 10, 20, 20], dtype=np.int32), "charge": np.array([3.0, 1.0, 2.0, 2.0], dtype=np.float32), "group": np.repeat([0, 1], n_per_group).astype(np.int32), - "n_capped_groups": 0, } return {"hits": hits, "geom": GEOM, "group_to_track": np.array(tracks, dtype=np.int64), @@ -352,6 +408,61 @@ def test_empty_event_gives_empty_slices(): assert stats["n_hits"] == 0 +def _sensor_of(py, pz, tick): + return np.unique(pixel_key(py, pz, tick)) + + +def test_sensor_mask_keeps_only_the_sensor_pixels(): + """ + The hits file also holds the shares of pixels whose summed response + stayed under threshold. With a sensor image only its pixels survive. + """ + vol = _one_volume() + vol["sensor"] = _sensor_of([0, 2, 3], [0, 0, 0], [10, 20, 20]) # not py=1 + flat, _, _, _, stats = build_hit_point_cloud([vol], np.array([7, 9])) + assert stats["n_decoded"] == 4 and stats["n_hits"] == 3 + assert stats["n_off_sensor"] == 1 + assert stats["n_sensor_pixels"] == 3 and stats["n_sensor_unlabelled"] == 0 + # py=1 was the hit dropped; pixel centres sit at y_min + (py + 0.5) pitch + kept_py = np.round((flat[:, PointFeature.y] - GEOM.y_min_mm) + / GEOM.pitch_mm - 0.5) + np.testing.assert_array_equal(np.sort(kept_py), [0, 2, 3]) + + +def test_sensor_pixels_no_hit_lands_on_are_counted(): + vol = _one_volume() + vol["sensor"] = _sensor_of([0, 1, 2, 3, 9], [0] * 5, [10, 10, 20, 20, 99]) + _, _, _, _, stats = build_hit_point_cloud([vol], np.array([7, 9])) + assert stats["n_hits"] == 4 and stats["n_sensor_unlabelled"] == 1 + + +def test_a_sensor_pixel_shared_by_two_groups_is_one_pixel(): + """Several particles on one pixel: both hits stay, the pixel counts once.""" + vol = _one_volume() + vol["hits"]["py"] = np.array([0, 0, 2, 3], dtype=np.int32) + vol["sensor"] = _sensor_of([0, 2, 3], [0, 0, 0], [10, 20, 20]) + _, _, _, _, stats = build_hit_point_cloud([vol], np.array([7, 9])) + assert stats["n_hits"] == 4 and stats["n_sensor_unlabelled"] == 0 + + +def test_a_charge_cut_after_the_mask_shows_up_as_unlabelled_pixels(): + vol = _one_volume() + vol["sensor"] = _sensor_of([0, 1, 2, 3], [0] * 4, [10, 10, 20, 20]) + _, _, _, _, stats = build_hit_point_cloud([vol], np.array([7, 9]), + charge_threshold=1.5) + assert stats["n_below_threshold"] == 1 # the 1.0 hit at py=1 + assert stats["n_sensor_unlabelled"] == 1 + + +def test_a_volume_with_sensor_pixels_and_no_hits_is_counted(): + vol = _one_volume() + vol["hits"] = {k: v[:0] for k, v in vol["hits"].items()} + vol["sensor"] = _sensor_of([5], [5], [5]) + _, _, _, _, stats = build_hit_point_cloud([vol], np.array([7, 9])) + assert stats["n_hits"] == 0 + assert stats["n_sensor_pixels"] == 1 and stats["n_sensor_unlabelled"] == 1 + + # --------------------------------------------------------------------------- # The readout window # --------------------------------------------------------------------------- @@ -447,3 +558,16 @@ def test_shift_follows_the_drift_direction(): # group 1 has t0 = 100 us; -1 * 100 * 1.6 = -160 mm np.testing.assert_allclose(s0[2:], -160.0, atol=1e-3) np.testing.assert_allclose(s0[:2], 0.0, atol=1e-6) # group 0 has t0 = 0 + + +def test_voxelizing_always_snaps_to_cell_centres(): + """ + Even when no two points share a cell. Returning such a cloud unchanged + left 17% of the truth cloud off its grid while the rest was on it. + """ + from pysupera.preproc import _voxelize_point_cloud + pc = np.array([[0.2, 0.2, 0.2, 1, 1, 1], [4.1, 0.2, 0.2, 2, 1, 1]], dtype=np.float32) + out, inv = _voxelize_point_cloud(pc, np.array([3.0] * 3), np.zeros(3), + return_inverse=True) + np.testing.assert_allclose(out[:, :3], [[1.5, 1.5, 1.5], [4.5, 1.5, 1.5]]) + np.testing.assert_array_equal(inv, [0, 1]) diff --git a/tests/test_preproc.py b/tests/test_preproc.py index bc97c96..ddd1b45 100644 --- a/tests/test_preproc.py +++ b/tests/test_preproc.py @@ -884,3 +884,58 @@ def test_none_passes_through(self): def test_offsets_stay_a_valid_fencepost(self): off, ids = subset_voxmap(self._vm(), np.array([True, True, True])) assert off[0] == 0 and off[-1] == len(ids) + + +# --------------------------------------------------------------------------- +# Fast paths: packed-key voxel unique, batched defragmentation +# --------------------------------------------------------------------------- + +from pysupera.preproc import _unique_rows # noqa: E402 + + +class TestUniqueRows: + """_unique_rows must be np.unique(axis=0) exactly, faster or not.""" + + @pytest.mark.parametrize("keys", [ + np.random.default_rng(0).integers(-50, 50, (5000, 4)), # negative + np.c_[np.zeros(200, np.int64), + np.random.default_rng(1).integers(0, 3, (200, 3))], # a constant column + np.random.default_rng(2).integers(-2**40, 2**40, (500, 4)), # too wide: fallback + np.array([[7, 7, 7, 7]]), + ]) + def test_matches_numpy(self, keys): + uk, inv = np.unique(keys, axis=0, return_inverse=True) + got_uk, got_inv = _unique_rows(keys) + np.testing.assert_array_equal(got_uk, uk) + np.testing.assert_array_equal(got_inv, inv.reshape(-1)) + + +class TestBatchedLabels: + """ + The batched path must hand every cloud the labels the per-cloud path + does -- same values, not just the same partition -- because + _split_fragments numbers spawned particles in label order. + """ + + def _clouds(self): + rng = np.random.default_rng(3) + out = [] + for n in (3, 5, 40, 200, 1, 2): + # clouds overlap in space, so only the fourth coordinate keeps + # their points out of each other's neighbourhoods + out.append(rng.uniform(0, 12, (n, 3)).astype(np.float32)) + out.append(np.zeros((4, 3), np.float32)) # coincident + out.append(np.array([[0, 0, 0], [100, 0, 0], [0, 100, 0]], np.float32)) + return out + + def test_same_labels_as_one_at_a_time(self): + d = ScipyDefragmenter(distance_threshold=3.0, min_pc_size=2) + clouds = self._clouds() + for got, xyz in zip(d._get_labels_many(clouds), clouds): + np.testing.assert_array_equal(got, d._get_labels(xyz)) + + def test_a_single_cloud(self): + d = ScipyDefragmenter(distance_threshold=3.0, min_pc_size=2) + xyz = self._clouds()[3] + np.testing.assert_array_equal(d._get_labels_many([xyz])[0], + d._get_labels(xyz)) diff --git a/tests/test_provenance.py b/tests/test_provenance.py index 1d38e24..3ad153b 100644 --- a/tests/test_provenance.py +++ b/tests/test_provenance.py @@ -123,3 +123,63 @@ def test_groups_of_no_particles_is_empty(self): def test_unowned_is_never_selected(self): owner = np.array([NO_OWNER, NO_OWNER], dtype=np.int32) assert len(groups_of_particles(owner, [NO_OWNER])) == 0 + + +class TestAttachTruthClouds: + """The true segments behind the hits follow their group's owner.""" + + @staticmethod + def segs(): + # x, y, z [mm], t [us], dE [MeV], dX [cm]; groups 0, 0, 1, 2, -1 + flat = np.array([[0.1, 0, 0, 5, 1, .03], [0.2, 0, 0, 3, 2, .03], + [9, 9, 9, 1, 4, .03], [50, 50, 50, 0, 8, .03], + [70, 70, 70, 0, 16, .03]], dtype=np.float32) + return flat, np.array([0, 0, 1, 2, -1], dtype=np.int64) + + def test_segments_go_to_the_owner_of_their_group(self): + from pysupera.provenance import attach_truth_clouds + ps = [FakeParticle(7, []), FakeParticle(9, [])] + owner = np.array([7, 9, NO_OWNER], dtype=np.int32) + stats = attach_truth_clouds(ps, owner, *self.segs()) + np.testing.assert_array_equal(ps[0].truth_cloud[:, 4], [1, 2]) + np.testing.assert_array_equal(ps[1].truth_cloud[:, 4], [4]) + # group 2 is unowned and -1 is no group: neither is attached + assert stats == {"n_segments": 5, "n_owned": 3, "n_rows": 3} + + def test_voxelizing_merges_with_the_deposit_rules(self): + from pysupera.provenance import attach_truth_clouds + ps = [FakeParticle(7, [])] + owner = np.array([7, NO_OWNER, NO_OWNER], dtype=np.int32) + attach_truth_clouds(ps, owner, *self.segs(), voxel_size=3.0, + origin=[0, 0, 0]) + tc = ps[0].truth_cloud + assert len(tc) == 1 # both segments in one cell + np.testing.assert_allclose(tc[0, :3], [1.5, 1.5, 1.5]) # cell centre + assert tc[0, 3] == 3 # t: earliest + assert tc[0, 4] == 3 # dE summed + assert tc[0, 5] == pytest.approx(0.06) # dX summed + + def test_voxels_take_direction_and_momentum_from_the_earliest_step(self): + """ + With the nine truth columns, a voxel's theta, phi and |p| are those of + its earliest step -- not zero, and not an average. + """ + from pysupera.provenance import attach_truth_clouds + # x, y, z, t, dE, dX, theta, phi, p; two steps in one 3 mm cell + flat = np.array([[0.1, 0, 0, 5, 1, .03, 1.0, 2.0, 50.0], + [0.2, 0, 0, 3, 2, .03, 0.5, -1.0, 80.0]], dtype=np.float32) + ps = [FakeParticle(7, [])] + attach_truth_clouds(ps, np.array([7], dtype=np.int32), flat, + np.array([0, 0], dtype=np.int64), + voxel_size=3.0, origin=[0, 0, 0]) + tc = ps[0].truth_cloud + assert len(tc) == 1 and tc[0, 3] == 3 # earliest t + np.testing.assert_allclose(tc[0, 6:], [0.5, -1.0, 80.0]) + assert tc[0, 4] == 3 # dE summed + + def test_particles_owning_nothing_get_an_empty_cloud(self): + from pysupera.provenance import attach_truth_clouds + ps = [FakeParticle(7, []), FakeParticle(8, [])] + owner = np.array([7, 7, 7], dtype=np.int32) + attach_truth_clouds(ps, owner, *self.segs()) + assert ps[1].truth_cloud.shape == (0, 6) diff --git a/tests/test_reader_configs.py b/tests/test_reader_configs.py index 9bbb068..df39260 100644 --- a/tests/test_reader_configs.py +++ b/tests/test_reader_configs.py @@ -55,6 +55,11 @@ def test_jaxtpc_configs_demand_their_input_paths(name): assert OmegaConf.is_missing(cfg.reader, key) +def test_the_hit_reader_also_demands_the_sensor_file(): + """Its pixels are the ones that become points.""" + assert OmegaConf.is_missing(_cfg("jaxtpc_pixel").reader, "jaxtpc_sensor_path") + + @pytest.mark.parametrize("name", ("jaxtpc_wire", "jaxtpc_pixel")) def test_a_jaxtpc_config_without_paths_is_refused(name): """ @@ -83,13 +88,15 @@ def test_only_the_pixel_config_carries_the_hit_options(): for key in ("point_source", "hit_x_from", "hit_energy", "hit_reference_tick", "pixel_pitch_mm", "pixel_drift_direction", "jaxtpc_sensor_path"): - assert key in pixel, f"pixel config lost {key}" + # `in` is False for a mandatory (???) value, which the sensor path is + assert key in pixel or OmegaConf.is_missing(pixel, key), \ + f"pixel config lost {key}" assert key not in wire, f"wire config should not offer {key}" def test_pixel_defaults_are_the_documented_ones(): r = _cfg("jaxtpc_pixel").reader - assert r.point_source == "deposits" # truth geometry unless asked + assert r.point_source == "hits" # what the sensors recorded assert r.hit_x_from == "nominal" # the detector's own inference assert r.hit_energy == "charge" assert r.hit_reference_tick == 0 @@ -110,6 +117,41 @@ def test_pixel_geometry_defaults_to_the_reference_detector(): assert r.pixel_geometry_from_fit is False # stated, then checked +# --------------------------------------------------------------------------- +# Settings that come with a reader (conf/readout/) +# --------------------------------------------------------------------------- + +def test_the_pixel_reader_brings_the_hit_mode_settings(): + """Hits need values outside the reader group; choosing the reader is enough.""" + cfg = _cfg("jaxtpc_pixel") + assert cfg.particle.min_pc_size == 63 + assert cfg.particle.voxelize.store == "voxels" # 3 mm truth voxels + assert cfg.distance_threshold > 4.32 * 2 ** 0.5 + assert cfg.check_group_ownership is False + + +@pytest.mark.parametrize("name", ("edepsim_h5", "jaxtpc_wire")) +def test_every_other_reader_keeps_the_truth_defaults(name): + cfg = _cfg(name) + assert cfg.particle.min_pc_size == 5 + assert cfg.particle.voxelize.store == "voxels" + assert cfg.distance_threshold == 5.2 + assert cfg.check_group_ownership is True + + +def test_switching_the_pixel_reader_to_deposits_is_refused(): + """A pixel batch is read as its hits; there is no deposit mode for it.""" + from pysupera.config import check_readout_settings + with pytest.raises(ValueError, match="not supported"): + check_readout_settings(_cfg("jaxtpc_pixel", "reader.point_source=deposits")) + check_readout_settings(_cfg("jaxtpc_pixel")) # fine + + +def test_a_flag_still_beats_the_reader_settings(): + cfg = _cfg("jaxtpc_pixel", "particle.min_pc_size=40", "distance_threshold=7") + assert cfg.particle.min_pc_size == 40 and cfg.distance_threshold == 7 + + # --------------------------------------------------------------------------- # preset=cubic_pixel_hits # --------------------------------------------------------------------------- @@ -136,8 +178,12 @@ def test_preset_fills_in_everything_a_sensor_hit_run_needs(): cfg = _preset() assert cfg.reader.pixel_pitch_mm == 4.32 assert list(cfg.reader.pixel_drift_direction) == [-1, 1] - assert cfg.reader.hit_charge_threshold == 500 - assert cfg.particle.min_pc_size == 13 + # The sensor image sets the threshold; a per-share cut would leave + # sensor pixels unlabelled. + assert cfg.reader.hit_charge_threshold == 0 + assert cfg.particle.min_pc_size == 63 + # points/flat: the true deposits behind the hits, 3 mm voxels + assert cfg.particle.voxelize.store == "voxels" # must clear the pixel diagonal, 4.32 * sqrt(2) = 6.11 mm assert cfg.distance_threshold > 4.32 * 2 ** 0.5 # a group's hits straddle fragment splits in hit mode, so the invariant @@ -145,21 +191,10 @@ def test_preset_fills_in_everything_a_sensor_hit_run_needs(): assert cfg.check_group_ownership is False -def test_the_threshold_and_min_pc_size_are_the_matched_pair(): - """ - They move together: cutting the halo thins the tracks, which lowers the - extent threshold. 85 goes with a threshold of 0 and 13 with 500; - mixing them agrees with the truth labelling on 85% of particles instead - of 95%. This pins the pairing the preset ships. - """ - cfg = _preset() - assert (cfg.reader.hit_charge_threshold, cfg.particle.min_pc_size) == (500, 13) - - def test_a_command_line_flag_still_beats_the_preset(): - cfg = _preset("particle.min_pc_size=42", "reader.hit_charge_threshold=0") + cfg = _preset("particle.min_pc_size=42", "reader.hit_charge_threshold=3") assert cfg.particle.min_pc_size == 42 - assert cfg.reader.hit_charge_threshold == 0 + assert cfg.reader.hit_charge_threshold == 3 def test_without_the_preset_nothing_changes(): From 3c92bb255fc75e8abe8b08b0892a8ebd6ef45bf1 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 27 Sep 2026 20:02:29 -0700 Subject: [PATCH 42/62] vis: one viewer per run mode, on the 3.3.0 format The viewers were written for 3.2, where pixel hits were in points/flat and truth/ sat beside them. Under 3.3.0 points/flat is the truth, and the hits stay in the JAXTPC hits file, labelled by hit_labels/. So each viewer reads the output and, for JAXTPC, a hits subset (pysupera-hits-subset), which must be the same file the run read. EDepSim only vis_force.html ?file=out_gz.h5 JAXTPC wire vis_hits.html ?out=out_gz.h5&hits=hits_small.h5 JAXTPC pixel vis_drift.html ?out=out_gz.h5&hits=hits_small.h5 vis_force.html: only needed fixes to its format probes. The fragment level and geant4_trackid are now detected from the 3.x particles table (with them wrong, the log said "fragment level: no"). vis_hits.html (wire), rewritten: 3-D points/flat on the left and one 2-D image per hit_labels plane (wire vs tick) stacked on the right. Hovering a voxel lights its hits on every plane. Hovering a hit lights its voxels and its hits on the other planes. The link is the label itself (instance, fragment or interaction), so the views cannot disagree. Checked on event 0 of the sample: 27,762 voxels and 1.66M hits. An instance in volume 1 lights only volume 1's planes. vis_drift.html (pixel), rewritten: truth at true x on the left, and every hit on the right at the x inferred from its tick with t0 at the beam time, placed using the output's pixel_geometry attribute. The cameras are synced, both views are framed on the union, and hovering either side lights the object in both. Colour by energy (dE and ADC on separate ramps), semantic type, fragment/instance/interaction or PDG. There is a percentile threshold per pane, and an off-sensor toggle for the unlabelled hits (drawn grey). Event 0: 24,572 voxels and 3.61M on-sensor hits. Instance 29 lit 4,015 voxels and 475k hits. Each viewer puts up a banner when given the other readout's file, a file without hit_labels, or labels and hits of different lengths. vis_truth.html is removed (with pysupera-truth-subset in the previous commit), since vis_drift's left pane now shows the truth it was built for. --- vis_drift.html | 984 +++++++++++++++++++--------------------------- vis_force.html | 17 +- vis_hits.html | 1020 +++++++++++++++++------------------------------- vis_truth.html | 915 ------------------------------------------- 4 files changed, 781 insertions(+), 2155 deletions(-) delete mode 100644 vis_truth.html diff --git a/vis_drift.html b/vis_drift.html index 8e8f51d..8a60e96 100644 --- a/vis_drift.html +++ b/vis_drift.html @@ -1,23 +1,18 @@ -pysupera — true vs inferred drift +pysupera — pixel truth ⇄ hits -
+ + - load a pysupera output — hover a point to follow it across both views + load the output and its hits subset — hover a point to follow it across both views
show semantic type:
-
true x x + points/true_x_shift
-
+
truth points/flat · the energy deposits, true x
+
-
-
inferred x as stored, t0 assumed at the beam time
+
+
hits the JAXTPC hits · inferred x (t0 = tick 0)
no file loaded
@@ -93,102 +88,70 @@ import * as THREE from 'three'; import {OrbitControls} from 'three/addons/controls/OrbitControls.js'; +// What this shows. Left: points/flat -- the true energy deposits behind the +// hits, at their true position. Right: the JAXTPC hits themselves, placed +// where a detector would put them -- the drift coordinate inferred from the +// tick with the interaction assumed at tick 0 -- each labelled by +// hit_labels/. The two clouds are different points; what links them is the +// pysupera particle each belongs to, so hovering either side highlights that +// instance (or fragment, or interaction) in both. +// +// Inputs, both gzip: +// the pysupera output pysupera-repack out.h5 out_gz.h5 -c gzip +// the JAXTPC hits file pysupera-hits-subset hits.h5 hits_small.h5 -n 2 +// or, served over http: vis_drift.html?out=out_gz.h5&hits=hits_small.h5 + // ---------------------------------------------------------------- state -- const S = { - out:null, ev:0, nEvents:0, - n:0, // points in this event - feat:null, // Float32Array 6/pt: x,y,z,t,dE,dX (x = inferred) - shift:null, // Float32Array: true_x = x + shift - ptsTrue:null, ptsNom:null, - instRow:null, fragRow:null, partRow:null, // particle-table row per point - isLE:null, // per point, from its instance's LE point range - key:null, // highlight key per point, for the current level - sel:-1, dim:0.03, - thrPct:0, thrVal:0, // display cut on the energy column, see applyFilter - meta:{}, // what the file says its energy column holds + out:null, hit:null, ev:0, nEvents:0, + T:null, H:null, // the two point sets; see pointSet() + sel:-1, dim:0.03, thrPct:0, mode:'inst', hl:'inst', - cols:null, // particle columns for this event - idRow:null, // particle id -> row - semShow:new Set(), // semantic types not hidden - visible:null, // Uint8Array per point after the filter - hasShift:false, + cols:null, idRow:null, // particle columns for the event, id -> row + semShow:new Set(), + geom:null, // per-volume pixel geometry (output attribute) + ncol:6, }; let H5File, FS; -const V = {tru:null, nom:null}; +const V = {tru:null, hit:null}; const logEl = document.getElementById('log'); const statusEl = document.getElementById('status'); const log = m => { logEl.textContent = (m + '\n' + logEl.textContent).slice(0, 3000); }; -function banner(id, msg){ - const el = document.getElementById(id); +function banner(msg){ + const el = document.getElementById('banner'); el.style.display = msg ? 'block' : 'none'; - if(msg){ el.textContent = msg; } + if(msg){ el.textContent = msg; log(msg.split('\n')[0]); } } // ------------------------------------------------------------- palette -- -// Categorical slots 1-6 in the documented fixed order, dark steps. Six hues -// cannot clear the all-pairs CVD floor -- no six in this lightness band can -- -// so identity is never carried by colour alone here: a legend is always up, -// every class can be isolated with the filter strip, and the hover panel -// names the class in words. Validated on the adjacent pairlist: worst CVD -// dE 8.4, worst normal-vision dE 19.3 against the #0e1116 surface. +// Categorical slots in a fixed order; a seventh category folds into "other". +// Identity is never carried by colour alone: a legend is always up, classes +// can be isolated with the filter strip, and the hover panel names them. const CAT = ['#3987e5','#d95926','#199e70','#c98500','#d55181','#008300']; const CAT_OTHER = '#6e7681'; - const SEM = {1:'shower', 2:'track', 3:'delta', 4:'Michel', 5:'LE scatter', 6:'unknown'}; -const SEM_ORDER = [2, 1, 3, 4, 5, 6]; // track and shower first: the common pair -const SEM_COLOR = {}; -SEM_ORDER.forEach((v, i) => { SEM_COLOR[v] = CAT[i]; }); - -// A fixed table, not a generated hue per species: the tail folds into "other". -const PDG_ORDER = [ - [13,'mu-'], [-13,'mu+'], [11,'e-'], [-11,'e+'], [22,'gamma'], - [2212,'proton'], [2112,'neutron'], [211,'pi+'], [-211,'pi-'], -]; +const SEM_ORDER = [2, 1, 3, 4, 5, 6]; +const SEM_COLOR = {}; SEM_ORDER.forEach((v, i) => { SEM_COLOR[v] = CAT[i]; }); +const SEM_LE = 5; +const PDG_ORDER = [[13,'mu-'], [-13,'mu+'], [11,'e-'], [-11,'e+'], [22,'gamma'], + [2212,'proton'], [2112,'neutron'], [211,'pi+'], [-211,'pi-']]; const PDG_NAME = new Map(PDG_ORDER); -const PDG_COLOR = new Map(); -// Nine species over six slots would cycle, which the palette rule forbids, so -// only the first six get a hue and the rest join "other" with the tail. -PDG_ORDER.slice(0, 6).forEach(([p], i) => PDG_COLOR.set(p, CAT[i])); - -// Sequential: one hue, monotonic in lightness. Reversed for a dark surface, -// so "near zero" is the step that recedes into the background and the largest -// value is the brightest. Blue 700 -> 100 from the reference ramp. +const PDG_COLOR = new Map(); PDG_ORDER.slice(0, 6).forEach(([p], i) => PDG_COLOR.set(p, CAT[i])); const SEQ = ['#0d366b','#104281','#184f95','#1c5cab','#256abf','#2a78d6', '#3987e5','#5598e7','#6da7ec','#86b6ef','#9ec5f4','#b7d3f6','#cde2fb']; - -// Diverging: the energy column is dE in deposit mode but *charge* in hit -// mode, and the readout response is bipolar -- about a sixth of hits are -// negative. Signed data around a meaningful zero is a diverging encoding, -// not a sequential one, so when negatives are present the ramp switches: -// blue and red poles about a neutral gray midpoint, equal steps per arm, -// symmetric about zero so that zero is always the gray. const DIV_NEG = '#3987e5', DIV_MID = '#383835', DIV_POS = '#e34948'; -function lerpHex(a, b, f){ - const A = hex2rgb(a), B = hex2rgb(b); - return [A[0]+(B[0]-A[0])*f, A[1]+(B[1]-A[1])*f, A[2]+(B[2]-A[2])*f]; -} - -const hex2rgb = h => [parseInt(h.slice(1,3),16)/255, - parseInt(h.slice(3,5),16)/255, - parseInt(h.slice(5,7),16)/255]; -const CAT_RGB = CAT.map(hex2rgb); -const SEQ_RGB = SEQ.map(hex2rgb); -const OTHER_RGB = hex2rgb(CAT_OTHER); -const DIV_RGB = (() => { - const n = 6, out = []; +const hex2rgb = h => [parseInt(h.slice(1,3),16)/255, parseInt(h.slice(3,5),16)/255, parseInt(h.slice(5,7),16)/255]; +const lerpHex = (a, b, f) => { const A = hex2rgb(a), B = hex2rgb(b); return A.map((x, i) => x + (B[i]-x)*f); }; +const CAT_RGB = CAT.map(hex2rgb), SEQ_RGB = SEQ.map(hex2rgb), OTHER_RGB = hex2rgb(CAT_OTHER); +const DIV_RGB = (() => { const n = 6, out = []; for(let i = n; i > 0; i--) out.push(lerpHex(DIV_NEG, DIV_MID, 1 - i/n)); out.push(hex2rgb(DIV_MID)); for(let i = 1; i <= n; i++) out.push(lerpHex(DIV_MID, DIV_POS, i/n)); - return out; -})(); + return out; })(); const rgb2css = c => `rgb(${c.map(v => Math.round(v*255)).join(',')})`; - -// Ids run to thousands, so they cannot have a legend and cannot be assigned -// from a fixed list. A stable hash keeps one object one colour across both -// panes and across a re-render, which is all the id modes need to do -- -// identity here is carried by the hover highlight, not by telling two hues -// apart. +// Ids run to thousands; a stable hash keeps one object one colour in both +// panes, and the hover highlight carries identity. function hashColor(id){ if(id < 0) return [0.30,0.33,0.38]; const h = Math.imul(id ^ 0x9e3779b9, 0x85ebca6b) >>> 0; @@ -197,22 +160,10 @@ } // ------------------------------------------------------------ h5 helpers -- -// h5wasm has reported attrs as bare values and as {value,...} wrappers -// across versions; accept either. function attr(f, path, name){ - try{ - const a = f.get(path).attrs[name]; - if(a === undefined || a === null) return null; - return (typeof a === 'object' && 'value' in a) ? a.value : a; - }catch(e){ return null; } -} -// The energy column holds measured charge when the run asked for charge and -// true dE otherwise. Read it rather than guess: inferring from the presence -// of negative values gets a quiet event wrong. -function quantity(){ - if(S.meta.point_source === 'hits' && S.meta.hit_energy !== 'true_de') - return {name:'charge', unit:''}; - return {name:'energy', unit:'MeV'}; + try{ const a = f.get(path).attrs[name]; + if(a === undefined || a === null) return null; + return (typeof a === 'object' && 'value' in a) ? a.value : a; }catch(e){ return null; } } const has = (f,p) => { try { return f.get(p) !== null; } catch(e){ return false; } }; const val = (f,p) => f.get(p).value; @@ -238,363 +189,320 @@ log('h5wasm ready'); } const booted = boot(); - +async function openBytes(buf, tag){ await booted; FS.writeFile(tag, new Uint8Array(buf)); return new H5File(tag, 'r'); } + +async function loadOut(buf, name){ + S.out = await openBytes(buf, 'out.h5'); + S.nEvents = Number(val(S.out, 'n_events')); + log(`output: format ${val(S.out,'format_version')}, ${S.nEvents} events`); + const bad = unreadableFilter(S.out, 'points/flat'); + if(bad){ S.out = null; + return banner(`This output uses HDF5 filter ${bad}, which the browser cannot decode.\n` + + ` pysupera-repack ${name} out_gz.h5 -c gzip`); } + if(String(attr(S.out, '/', 'readout_type')) === 'wire') + return banner('This is a wire-readout output. Open it in vis_hits.html instead.'); + const g = attr(S.out, '/', 'pixel_geometry'); + S.geom = g ? JSON.parse(String(g)) : null; + banner(!has(S.out, 'hit_labels') ? 'This output has no hit_labels/ -- the hits cannot be labelled.' + : !S.geom ? 'This output records no pixel_geometry, so the hits cannot be placed.\n' + + 'Re-run it with a pysupera that writes the attribute.' : ''); + const sel = document.getElementById('ev'); sel.innerHTML = ''; + for(let i = 0; i < S.nEvents; i++){ + const o = document.createElement('option'); o.value = i; o.textContent = i; sel.appendChild(o); + } + rebuild(); +} +async function loadHits(buf, name){ + S.hit = await openBytes(buf, 'hits.h5'); + if(String(attr(S.hit, 'config', 'readout_type')) === 'wire') + return banner('This hits file is a wire readout. Open it in vis_hits.html instead.'); + const ek = S.hit.keys().filter(k => k.startsWith('event_')).sort(); + if(ek.length && !has(S.hit, `${ek[0]}/volume_0/Pixel/hit_py`)) + return banner(`This hits file has no decoded hits. Make it with\n` + + ` pysupera-hits-subset ${name} -n 2`); + log(`hits: ${ek.length} events`); + rebuild(); +} document.getElementById('f-out').onchange = async e => { - const file = e.target.files[0]; if(!file) return; - try{ - await booted; - FS.writeFile('out.h5', new Uint8Array(await file.arrayBuffer())); - S.out = new H5File('out.h5', 'r'); - S.nEvents = Number(val(S.out, 'n_events')); - S.meta = {}; - for(const k of ['point_source','hit_energy','hit_x_from','readout_type']){ - const a = attr(S.out, '/', k); - if(a !== null) S.meta[k] = String(a); - } - log(`output: format ${val(S.out,'format_version')}, ${S.nEvents} events`); - const bad = unreadableFilter(S.out, 'points/flat'); - if(bad){ - S.out = null; - return banner('banner', - `This output uses HDF5 filter ${bad}, which the browser cannot decode.\n` + - `h5wasm handles gzip only, and an unsupported filter yields garbage rather\n` + - `than an error -- so the file is refused instead of drawn wrongly.\n\n` + - ` pysupera-repack ${file.name} out_gzip.h5 -c gzip`); - } - banner('banner',''); - S.hasShift = has(S.out, 'points/true_x_shift'); - const sel = document.getElementById('ev'); sel.innerHTML = ''; - for(let i = 0; i < S.nEvents; i++){ - const o = document.createElement('option'); o.value = i; o.textContent = i; - sel.appendChild(o); - } - rebuild(); - }catch(err){ log('ERROR opening output: ' + err.message); } + const f = e.target.files[0]; if(!f) return; + try{ await loadOut(await f.arrayBuffer(), f.name); }catch(err){ log('ERROR opening output: ' + err.message); } +}; +document.getElementById('f-hit').onchange = async e => { + const f = e.target.files[0]; if(!f) return; + try{ await loadHits(await f.arrayBuffer(), f.name); }catch(err){ log('ERROR opening hits: ' + err.message); } }; +(async () => { + const q = new URLSearchParams(location.search); + try{ + if(q.get('out')) await loadOut(await (await fetch(q.get('out'))).arrayBuffer(), q.get('out')); + if(q.get('hits')) await loadHits(await (await fetch(q.get('hits'))).arrayBuffer(), q.get('hits')); + }catch(err){ log('ERROR fetching: ' + err.message); } +})(); document.getElementById('ev').onchange = e => { S.ev = +e.target.value; rebuild(); }; document.getElementById('mode').onchange = e => { S.mode = e.target.value; recolour(); }; -document.getElementById('hl').onchange = e => { S.hl = e.target.value; buildKeys(); S.sel = -1; recolour(); }; +document.getElementById('hl').onchange = e => { S.hl = e.target.value; S.sel = -1; buildKeys(); recolour(); }; document.getElementById('logE').onchange = () => recolour(); +document.getElementById('offsensor').onchange = () => rebuild(); document.getElementById('psize').oninput = e => { - for(const v of [V.tru, V.nom]) if(v && v.cloud){ - v.cloud.material.size = +e.target.value; - if(v.selCloud) v.selCloud.material.size = +e.target.value; - } + for(const v of Object.values(V)) if(v && v.cloud){ v.cloud.material.size = +e.target.value; + v.selCloud.material.size = +e.target.value; } }; document.getElementById('dim').oninput = e => { - S.dim = +e.target.value; - document.getElementById('dimv').textContent = S.dim.toFixed(2); - paint(); -}; -document.getElementById('thr').oninput = e => { - S.thrPct = +e.target.value; applyFilter(); paint(); + S.dim = +e.target.value; document.getElementById('dimv').textContent = S.dim.toFixed(2); paint(); }; -document.getElementById('fit').onclick = fitView; +document.getElementById('thr').oninput = e => { S.thrPct = +e.target.value; recolour(); }; +document.getElementById('fit').onclick = () => fitView(); addEventListener('keydown', e => { if(e.key === 'c') fitView(); }); -// ------------------------------------------------- per-event resolution -- -// A point's owner is the block it falls in: ownership is positional, exactly -// as the format intends. Instance blocks cover every point with no overlap; -// particle blocks cover about two thirds, which is why the per-particle -// fields fall back to the fragment's representative below. -function rowsOf(n, pa, starts, ends){ - const row = new Int32Array(n).fill(-1); - for(const [S0, E0] of starts.map((s, i) => [s, ends[i]])){ - for(let k = 0; k < S0.length; k++){ - const s = Number(S0[k]); if(s < 0) continue; - const lo = Math.max(0, s - pa), hi = Math.min(n, Number(E0[k]) - pa); - for(let j = lo; j < hi; j++) row[j] = k; - } - } - return row; +// ------------------------------------------------------ the two point sets -- +// A point set is {name, n, pts (xyz), e (energy column), inst, frag (owner +// ids), le}, plus what paint/pick fill in. Everything below works on either. +function pointSet(name, n){ + return {name, n, pts:new Float32Array(n*3), e:new Float32Array(n), + inst:new Int32Array(n).fill(-1), frag:new Int32Array(n).fill(-1), + le:new Uint8Array(n), key:null, visible:null, extra:{}}; } -function buildEvent(){ - const f = S.out, ev = S.ev; - const eo = val(f,'events/offsets'), po = val(f,'points/offsets'); +function buildParticles(){ + const f = S.out, ev = S.ev, eo = val(f,'events/offsets'); const a = Number(eo[ev]), b = Number(eo[ev+1]); - const pa = Number(po[ev]), pb = Number(po[ev+1]); - const n = S.n = pb - pa; - if(n <= 0){ S.feat = new Float32Array(0); S.shift = new Float32Array(0); return; } - - S.feat = Float32Array.from(f.get('points/flat').slice([[pa,pb],[0,6]])); - S.shift = S.hasShift - ? Float32Array.from(sl(f,'points/true_x_shift', pa, pb)) - : new Float32Array(n); - - const grp = f.get('particles'); - const col = nm => grp.keys().includes(nm) ? sl(f,'particles/'+nm, a, b) : null; + const g = f.get('particles'); + const col = nm => g.keys().includes(nm) ? sl(f,'particles/'+nm, a, b) : null; const c = {}; - for(const k of ['id','inst_id','frag_id','interaction_id','pdg','parent_pdg', - 'geant4_trackid','geant4_parent_pdg','inst_sem_type','sem_type', - 'inst_parent_id','frag_parent_id','parent_id', - 'inst_pc_start','inst_pc_end','inst_pc_le_start','inst_pc_le_end', - 'frag_pc_start','frag_pc_end','frag_pc_le_start','frag_pc_le_end', - 'pc_start','pc_end']) + for(const k of ['id','inst_id','frag_id','interaction_id','pdg','geant4_trackid','inst_sem_type', + 'inst_parent_id','inst_pc_start','inst_pc_end','inst_pc_le_start','inst_pc_le_end', + 'frag_pc_start','frag_pc_end','frag_pc_le_start','frag_pc_le_end']) c[k] = col(k); - S.cols = c; - - S.idRow = new Map(); + S.cols = c; S.idRow = new Map(); for(let k = 0; k < c.id.length; k++) S.idRow.set(Number(c.id[k]), k); +} - S.instRow = rowsOf(n, pa, [c.inst_pc_start, c.inst_pc_le_start], - [c.inst_pc_end, c.inst_pc_le_end]); - S.fragRow = rowsOf(n, pa, [c.frag_pc_start, c.frag_pc_le_start], - [c.frag_pc_end, c.frag_pc_le_end]); - S.partRow = c.pc_start ? rowsOf(n, pa, [c.pc_start], [c.pc_end]) - : new Int32Array(n).fill(-1); - - // LE-ness is positional, not a column: a point is low-energy exactly when - // it falls in its instance's *LE* point range. Defragmentation splits a - // stray clump off as its own kLEScatter particle and the merging - // conditions absorb it back, so its points end up inside a shower - // instance -- but in that instance's LE block, not its main one. Reading - // inst_sem_type alone would call those points "shower", which is the host - // instance's type and not what the point is. - S.isLE = new Uint8Array(n); +// Truth: points/flat, labels from the ranges (ownership is positional). +function buildTruth(){ + const f = S.out, ev = S.ev, po = val(f,'points/offsets'); + const pa = Number(po[ev]), pb = Number(po[ev+1]), n = Math.max(0, pb - pa); + S.ncol = Number(f.get('points/flat').shape[1]); + const T = pointSet('truth', n); + if(!n){ S.T = T; return; } + const C = S.ncol, flat = f.get('points/flat').slice([[pa, pb], [0, C]]); + T.feat = Float32Array.from(flat); T.C = C; + for(let i = 0; i < n; i++){ T.pts[i*3] = flat[i*C]; T.pts[i*3+1] = flat[i*C+1]; T.pts[i*3+2] = flat[i*C+2]; + T.e[i] = flat[i*C+4]; } + const c = S.cols; + const fill = (arr, s, e, v) => { s = Number(s); if(s < 0) return; + for(let j = Math.max(0, s - pa); j < Math.min(n, Number(e) - pa); j++) arr[j] = v; }; for(let k = 0; k < c.id.length; k++){ - if(Number(c.inst_id[k]) !== Number(c.id[k])) continue; - const s0 = Number(c.inst_pc_le_start[k]); - if(s0 < 0) continue; - const lo = Math.max(0, s0 - pa), hi = Math.min(n, Number(c.inst_pc_le_end[k]) - pa); - for(let j = lo; j < hi; j++) S.isLE[j] = 1; + const id = Number(c.id[k]); + if(Number(c.inst_id[k]) === id){ + fill(T.inst, c.inst_pc_start[k], c.inst_pc_end[k], id); + fill(T.inst, c.inst_pc_le_start[k], c.inst_pc_le_end[k], id); + fill(T.le, c.inst_pc_le_start[k], c.inst_pc_le_end[k], 1); + } + if(Number(c.frag_id[k]) === id){ + fill(T.frag, c.frag_pc_start[k], c.frag_pc_end[k], id); + fill(T.frag, c.frag_pc_le_start[k], c.frag_pc_le_end[k], id); + } } + S.T = T; +} - // positions: the two panes differ in x and nothing else - const t = new Float32Array(n*3), q = new Float32Array(n*3); - for(let i = 0; i < n; i++){ - const x = S.feat[i*6], y = S.feat[i*6+1], z = S.feat[i*6+2]; - q[i*3] = x; q[i*3+1] = y; q[i*3+2] = z; - t[i*3] = x + S.shift[i]; t[i*3+1] = y; t[i*3+2] = z; +// Hits: the subset's decoded hits, labelled entry for entry by hit_labels, +// placed at the inferred drift position with the output's pixel geometry. +function buildHits(){ + S.H = pointSet('hits', 0); + if(!S.hit || !S.geom || !has(S.out, 'hit_labels')) return; + const ekey = 'event_' + String(S.ev).padStart(3, '0'); + if(!S.hit.keys().includes(ekey)){ log(`hits subset has no ${ekey}`); return; } + const off = document.getElementById('offsensor').checked; + const parts = []; + for(const vk of S.out.get('hit_labels').keys()){ + for(const pk of S.out.get('hit_labels').get(vk).keys()){ + const gp = `hit_labels/${vk}/${pk}`; + const vol = Number(attr(S.out, gp, 'volume')), src = String(attr(S.out, gp, 'source')); + const hp = `${ekey}/volume_${vol}/${src}`; + if(!has(S.hit, `${hp}/hit_py`)) continue; + const o = val(S.out, `${gp}/offsets`), a = Number(o[S.ev]), b = Number(o[S.ev+1]); + const lab = {frag: sl(S.out, `${gp}/fragment_id`, a, b), inst: sl(S.out, `${gp}/instance_id`, a, b), + le: sl(S.out, `${gp}/is_le`, a, b), + on: has(S.out, `${gp}/on_sensor`) ? sl(S.out, `${gp}/on_sensor`, a, b) : null}; + const hit = {py: val(S.hit, `${hp}/hit_py`), pz: val(S.hit, `${hp}/hit_pz`), + tick: val(S.hit, `${hp}/hit_tick`), q: val(S.hit, `${hp}/hit_charge`)}; + if(hit.py.length !== lab.frag.length){ + banner(`${hp}: ${hit.py.length} hits but ${lab.frag.length} labels -- the hits subset\n` + + `and the output are not from the same JAXTPC run.`); continue; } + parts.push({vol, src, lab, hit, g: S.geom[vol]}); + } } - S.ptsNom = q; S.ptsTrue = t; - - buildFilters(); - buildKeys(); + let n = 0; + for(const p of parts){ for(let h = 0; h < p.lab.frag.length; h++) if(off || !p.lab.on || Number(p.lab.on[h])) n++; } + const H = pointSet('hits', n); + H.vol = new Int8Array(n); H.src = new Int32Array(n); H.py = new Int16Array(n); H.pz = new Int16Array(n); + H.tick = new Int16Array(n); H.on = new Uint8Array(n); + let k = 0; + for(const p of parts){ + const g = p.g; + for(let h = 0; h < p.lab.frag.length; h++){ + const on = !p.lab.on || Number(p.lab.on[h]); + if(!off && !on) continue; + const py = p.hit.py[h], pz = p.hit.pz[h], t = p.hit.tick[h]; + H.pts[k*3] = g.x_anode_mm - g.drift_direction * (t - g.reference_tick) * g.mm_per_tick; + H.pts[k*3+1] = g.y_min_mm + (py + 0.5) * g.pitch_mm; + H.pts[k*3+2] = g.z_min_mm + (pz + 0.5) * g.pitch_mm; + H.e[k] = p.hit.q[h]; + H.frag[k] = Number(p.lab.frag[h]); H.inst[k] = Number(p.lab.inst[h]); H.le[k] = Number(p.lab.le[h]) ? 1 : 0; + H.vol[k] = p.vol; H.src[k] = h; H.py[k] = py; H.pz[k] = pz; H.tick[k] = t; H.on[k] = on ? 1 : 0; + k++; + } + } + S.H = H; } -const attrOf = (row, key) => (row >= 0 && S.cols[key]) ? Number(S.cols[key][row]) : -1; -const SEM_LE = 5; // SemanticType.kLEScatter -// The point's own type: LE if it sits in its instance's LE block, otherwise -// the instance's type. -const semOf = i => S.isLE[i] ? SEM_LE : attrOf(S.instRow[i], 'inst_sem_type'); -const hostSemOf = i => attrOf(S.instRow[i], 'inst_sem_type'); -const instOf = i => attrOf(S.instRow[i], 'id'); -const fragOf = i => attrOf(S.fragRow[i], 'id'); -const interOf = i => attrOf(S.instRow[i], 'interaction_id'); -const pdgOf = i => attrOf(S.instRow[i], 'pdg'); +const rowOf = id => (id >= 0 && S.idRow.has(id)) ? S.idRow.get(id) : -1; +const colOf = (id, key) => { const r = rowOf(id); return (r >= 0 && S.cols[key]) ? Number(S.cols[key][r]) : -1; }; +const semOf = (P, i) => P.le[i] ? SEM_LE : colOf(P.inst[i], 'inst_sem_type'); +const interOf = (P, i) => colOf(P.inst[i], 'interaction_id'); +const pdgOf = (P, i) => colOf(P.inst[i], 'pdg'); function buildKeys(){ - const n = S.n, k = new Int32Array(n); - const get = S.hl === 'frag' ? fragOf : S.hl === 'inter' ? interOf : instOf; - for(let i = 0; i < n; i++) k[i] = get(i); - S.key = k; + for(const P of [S.T, S.H]){ + if(!P) continue; + const k = new Int32Array(P.n); + for(let i = 0; i < P.n; i++) + k[i] = S.hl === 'frag' ? P.frag[i] : S.hl === 'inter' ? interOf(P, i) : P.inst[i]; + P.key = k; + } } function buildFilters(){ const host = document.getElementById('filters'); host.innerHTML = 'show semantic type:'; const present = new Set(); - for(let i = 0; i < S.n; i++) present.add(semOf(i)); + for(const P of [S.T, S.H]) for(let i = 0; i < P.n; i++) present.add(semOf(P, i)); S.semShow = new Set(present); for(const v of SEM_ORDER){ if(!present.has(v)) continue; - const lab = document.createElement('label'); - const cb = document.createElement('input'); + const lab = document.createElement('label'), cb = document.createElement('input'); cb.type = 'checkbox'; cb.checked = true; - cb.onchange = () => { cb.checked ? S.semShow.add(v) : S.semShow.delete(v); - applyFilter(); paint(); }; - const sw = document.createElement('span'); - sw.className = 'sw'; sw.style.background = SEM_COLOR[v] || CAT_OTHER; + cb.onchange = () => { cb.checked ? S.semShow.add(v) : S.semShow.delete(v); recolour(); }; + const sw = document.createElement('span'); sw.className = 'sw'; sw.style.background = SEM_COLOR[v] || CAT_OTHER; lab.append(cb, sw, document.createTextNode(SEM[v] || ('type ' + v))); host.appendChild(lab); } - applyFilter(); } -// Filtering hides at paint time rather than rebuilding the buffers, but the -// picker consults it too, so nothing hidden can be selected. -// -// The display cut is a percentile of |value| shown as the absolute number it -// lands on, because "above 28" is quotable and "the faintest half" is not. -// The detector simulation applies no threshold of its own, so without this -// every pixel carrying any induced-field influence is on screen. +// Filtering hides at paint time; the picker consults it too, so nothing +// hidden can be selected. The display cut is a percentile of |value|, per +// cloud: MeV on the left and ADC on the right are not one scale. function applyFilter(){ - let thr = 0; - if(S.thrPct > 0 && S.n){ - const a = []; - for(let i = 0; i < S.n; i++) a.push(Math.abs(S.feat[i*6+4])); - a.sort((x,y) => x-y); - thr = a[Math.min(a.length-1, Math.floor(S.thrPct/100 * a.length))]; - } - S.thrVal = thr; - const vis = new Uint8Array(S.n); - let kept = 0, q = 0, qAll = 0; - for(let i = 0; i < S.n; i++){ - const a = Math.abs(S.feat[i*6+4]); - qAll += a; - const pass = S.semShow.has(semOf(i)) && a >= thr; - vis[i] = pass ? 1 : 0; - if(pass){ kept++; q += a; } + const parts = []; + for(const P of [S.T, S.H]){ + let thr = 0; + if(S.thrPct > 0 && P.n){ + const a = Float32Array.from(P.e, Math.abs).sort(); + thr = a[Math.min(a.length - 1, Math.floor(S.thrPct/100 * a.length))]; + } + const vis = new Uint8Array(P.n); let kept = 0; + for(let i = 0; i < P.n; i++){ const pass = S.semShow.has(semOf(P, i)) && Math.abs(P.e[i]) >= thr; + vis[i] = pass ? 1 : 0; if(pass) kept++; } + P.visible = vis; P.thr = thr; + if(S.thrPct > 0) parts.push(`${P.name} ≥ ${fmt(thr, 3)} (${P.n ? (100*kept/P.n).toFixed(0) : 0}%)`); } - S.visible = vis; - const el = document.getElementById('thrv'); - const u = quantity().unit; - el.textContent = S.thrPct <= 0 ? 'all' - : `${fmt(thr,3)}${u ? ' ' + u : ''} · ${(100*kept/S.n).toFixed(0)}% pts,` - + ` ${(100*(qAll>0?q/qAll:0)).toFixed(1)}% ${quantity().name}`; + document.getElementById('thrv').textContent = S.thrPct <= 0 ? 'all' : parts.join(' · '); } // ------------------------------------------------------------- colouring -- -// Sign-preserving log. The stored column spans twelve decades in hit mode -// (median 23, 98th percentile 1.3e4, largest 1.8e5) and a third of it is -// negative, so a plain log cannot take it and a linear scale puts three -// quarters of the points within 1.5% of the midpoint. symlog keeps the -// sign, compresses the magnitude, and stays finite at zero. const symlog = (v, lin) => Math.sign(v) * Math.log10(1 + Math.abs(v)/lin); const unsymlog = (t, lin) => Math.sign(t) * (Math.pow(10, Math.abs(t)) - 1) * lin; - -// Range from percentiles, not extremes: one 1.8e5 outlier would otherwise -// own the whole ramp. The legend says when the range is clipped. -function energyRamp(){ +function energyRamp(P){ const lg = document.getElementById('logE').checked; - const raw = [], mags = []; - let anyNeg = false; - for(let i = 0; i < S.n; i++){ - if(!S.visible[i]) continue; - const e = S.feat[i*6+4]; - if(e < 0) anyNeg = true; - raw.push(e); - if(e !== 0) mags.push(Math.abs(e)); - } + const raw = [], mags = []; let anyNeg = false; + for(let i = 0; i < P.n; i++){ if(!P.visible[i]) continue; const e = P.e[i]; + if(e < 0) anyNeg = true; raw.push(e); if(e !== 0) mags.push(Math.abs(e)); } if(!raw.length) return {lo:0, hi:1, lg, lin:1, diverging:false, clipped:false}; - // The linear threshold: below this, symlog is effectively linear. The - // lower quartile of |value| keeps the bulk of the distribution off the - // flat part of the curve. - mags.sort((a,b) => a-b); + mags.sort((a, b) => a - b); const lin = Math.max(mags.length ? mags[Math.floor(mags.length*0.25)] : 1, 1e-12); - - const vals = lg ? raw.map(v => symlog(v, lin)) : raw.slice(); - vals.sort((a,b) => a-b); + const vals = (lg ? raw.map(v => symlog(v, lin)) : raw.slice()).sort((a, b) => a - b); const q = f => vals[Math.min(vals.length-1, Math.max(0, Math.round(f*(vals.length-1))))]; let lo = q(0.02), hi = q(0.98); const clipped = lo > vals[0] || hi < vals[vals.length-1]; - const diverging = anyNeg; - if(diverging){ const m = Math.max(Math.abs(lo), Math.abs(hi)); lo = -m; hi = m; } + if(anyNeg){ const m = Math.max(Math.abs(lo), Math.abs(hi)); lo = -m; hi = m; } if(hi <= lo) hi = lo + 1e-9; - return {lo, hi, lg, lin, diverging, clipped}; + return {lo, hi, lg, lin, diverging:anyNeg, clipped}; } - -function colourOf(i, ramp){ +function colourOf(P, i, ramp){ switch(S.mode){ case 'energy': { - let e = S.feat[i*6+4]; - if(ramp.lg) e = symlog(e, ramp.lin); + let e = P.e[i]; if(ramp.lg) e = symlog(e, ramp.lin); const f = Math.min(1, Math.max(0, (e - ramp.lo) / (ramp.hi - ramp.lo))); const R = ramp.diverging ? DIV_RGB : SEQ_RGB; return R[Math.min(R.length - 1, Math.round(f * (R.length - 1)))]; } - case 'sem': { - const s = semOf(i); - const j = SEM_ORDER.indexOf(s); - return j >= 0 ? CAT_RGB[j] : OTHER_RGB; - } - case 'pdg': { - const p = pdgOf(i); - const c = PDG_COLOR.get(p); - return c ? hex2rgb(c) : OTHER_RGB; - } - case 'frag': return hashColor(fragOf(i)); - case 'inter': return hashColor(interOf(i)); - default: return hashColor(instOf(i)); + case 'sem': { const j = SEM_ORDER.indexOf(semOf(P, i)); return j >= 0 ? CAT_RGB[j] : OTHER_RGB; } + case 'pdg': { const c = PDG_COLOR.get(pdgOf(P, i)); return c ? hex2rgb(c) : OTHER_RGB; } + case 'frag': return hashColor(P.frag[i]); + case 'inter': return hashColor(interOf(P, i)); + default: return hashColor(P.inst[i]); } } -function paint(){ - if(!S.n) return; - const ramp = energyRamp(); - const dimming = S.sel >= 0; - - // Colour every point once; both panes share it, which is what makes a - // colour mean the same object either side. - const rgb = new Float32Array(S.n * 3); - const alpha = new Float32Array(S.n); +function paintSet(v, P){ + if(!v || !v.cloud || !P.n) return null; + // The ramp sorts every value, so it is computed when the colouring or the + // filter changes, not on every hover. + if(!P.ramp) P.ramp = energyRamp(P); + const ramp = P.ramp, dimming = S.sel >= 0; + const col = v.cloud.geometry.attributes.color.array; let nSel = 0; - for(let i = 0; i < S.n; i++){ - const [r, g, b] = colourOf(i, ramp); + const rgb = new Float32Array(P.n * 3); + for(let i = 0; i < P.n; i++){ + const [r, g, b] = colourOf(P, i, ramp); rgb[i*3] = r; rgb[i*3+1] = g; rgb[i*3+2] = b; - const picked = dimming && S.key[i] === S.sel; - if(picked) nSel++; - // Hidden points go transparent rather than being removed, so the two - // panes keep identical indexing and the picker stays in step. The - // selection is handed to the overlay pass, so it is transparent here. - alpha[i] = !S.visible[i] ? 0 : picked ? 0 : (dimming ? S.dim : 1); + const picked = dimming && P.visible[i] && P.key[i] === S.sel; if(picked) nSel++; + col[i*4] = r; col[i*4+1] = g; col[i*4+2] = b; + col[i*4+3] = !P.visible[i] ? 0 : picked ? 0 : (dimming ? S.dim : 1); } - - for(const v of [V.tru, V.nom]){ - if(!v || !v.cloud) continue; - const col = v.cloud.geometry.attributes.color.array; - for(let i = 0; i < S.n; i++){ - col[i*4] = rgb[i*3]; col[i*4+1] = rgb[i*3+1]; col[i*4+2] = rgb[i*3+2]; - col[i*4+3] = alpha[i]; - } - v.cloud.geometry.attributes.color.needsUpdate = true; - - const pts = v === V.tru ? S.ptsTrue : S.ptsNom; - const sp = new Float32Array(nSel * 3), sc = new Float32Array(nSel * 3); - let k = 0; - if(nSel) for(let i = 0; i < S.n; i++){ - if(!(dimming && S.visible[i] && S.key[i] === S.sel)) continue; - sp[k*3] = pts[i*3]; sp[k*3+1] = pts[i*3+1]; sp[k*3+2] = pts[i*3+2]; - sc[k*3] = rgb[i*3]; sc[k*3+1] = rgb[i*3+1]; sc[k*3+2] = rgb[i*3+2]; - k++; - } - const g = v.selCloud.geometry; - g.setAttribute('position', new THREE.BufferAttribute(sp, 3)); - g.setAttribute('color', new THREE.BufferAttribute(sc, 3)); - g.setDrawRange(0, nSel); - v.selCloud.material.size = v.cloud.material.size; - v.selCloud.visible = nSel > 0; + v.cloud.geometry.attributes.color.needsUpdate = true; + const sp = new Float32Array(nSel * 3), sc = new Float32Array(nSel * 3); let k = 0; + if(nSel) for(let i = 0; i < P.n; i++){ + if(!(P.visible[i] && P.key[i] === S.sel)) continue; + for(let d = 0; d < 3; d++){ sp[k*3+d] = P.pts[i*3+d]; sc[k*3+d] = rgb[i*3+d]; } k++; } - drawLegend(ramp); + const g = v.selCloud.geometry; + g.setAttribute('position', new THREE.BufferAttribute(sp, 3)); + g.setAttribute('color', new THREE.BufferAttribute(sc, 3)); + v.selCloud.visible = nSel > 0; + return {ramp, nSel}; +} +function paint(){ + const t = paintSet(V.tru, S.T), h = paintSet(V.hit, S.H); + drawLegend(t ? t.ramp : null, h ? h.ramp : null); + return {t, h}; } -const recolour = () => { applyFilter(); paint(); }; +const recolour = () => { if(!S.T) return; applyFilter(); + for(const P of [S.T, S.H]) if(P) P.ramp = null; paint(); }; -function drawLegend(ramp){ - const el = document.getElementById('legend'); - const rows = []; - const swatch = (c, t) => - `
${t}
`; +function drawLegend(rt, rh){ + const el = document.getElementById('legend'), rows = []; + const sw = (c, t) => `
${t}
`; if(S.mode === 'energy'){ - const ramps = ramp.diverging ? DIV_RGB.map(rgb2css) : SEQ; - const bar = ramps.map(c => - ``).join(''); - const f = v => (ramp.lg ? unsymlog(v, ramp.lin) : v).toPrecision(3); - // The column holds dE in deposit mode and charge in hit mode, and the - // file does not say which; a negative value can only be the bipolar - // readout response, so it names itself. - const what = ramp.diverging ? 'charge (signed)' : 'energy'; - rows.push(`
${what}
`, - `
${bar}
`, - `
${f(ramp.lo)} → ${f(ramp.hi)}
`); - if(ramp.clipped) - rows.push(`
range clipped to 2nd–98th pct
`); - if(ramp.diverging) - rows.push(`
symmetric; grey is zero
`); - if(ramp.lg) - rows.push(`
log scale, sign kept (symlog)
`); + for(const [what, r] of [['truth: dE [MeV]', rt], ['hits: charge [ADC]', rh]]){ + if(!r) continue; + const bar = (r.diverging ? DIV_RGB.map(rgb2css) : SEQ).map(c => + ``).join(''); + const f = v => (r.lg ? unsymlog(v, r.lin) : v).toPrecision(3); + rows.push(`
${what}
${bar}
` + + `
${f(r.lo)} → ${f(r.hi)}${r.diverging ? ' · grey is zero' : ''}
`); + } + rows.push(`
2nd–98th pct${document.getElementById('logE').checked ? ', symlog' : ''}
`); } else if(S.mode === 'sem'){ - for(const v of SEM_ORDER) - if([...Array(S.n).keys()].length && S.semShow.has(v)) - rows.push(swatch(SEM_COLOR[v], SEM[v] || ('type ' + v))); + for(const v of SEM_ORDER) if(S.semShow.has(v)) rows.push(sw(SEM_COLOR[v], SEM[v])); } else if(S.mode === 'pdg'){ - for(const [p, nm] of PDG_ORDER.slice(0, 6)) rows.push(swatch(PDG_COLOR.get(p), nm)); - rows.push(swatch(CAT_OTHER, 'other')); + for(const [p, nm] of PDG_ORDER.slice(0, 6)) rows.push(sw(PDG_COLOR.get(p), nm)); + rows.push(sw(CAT_OTHER, 'other')); } else { - const what = S.mode === 'frag' ? 'fragment' : S.mode === 'inter' ? 'interaction' : 'instance'; - rows.push(`
colour = ${what} id
`, - `
too many to list; hover to identify
`); + rows.push(`
colour = ${S.mode === 'frag' ? 'fragment' : S.mode === 'inter' ? 'interaction' : 'instance'} id
`, + `
same id, same colour, both panes
`); } el.innerHTML = rows.join(''); } @@ -602,263 +510,173 @@ // ------------------------------------------------------------ 3D panes -- function hostSize(host){ const r = host.getBoundingClientRect(); - return {w: Math.max(1, Math.round(r.width) || host.clientWidth || innerWidth*0.5), - h: Math.max(1, Math.round(r.height) || host.clientHeight || innerHeight - 90)}; + return {w: Math.max(1, Math.round(r.width) || innerWidth*0.5), h: Math.max(1, Math.round(r.height) || innerHeight - 90)}; } -function resizeView(v){ - if(!v || !v.renderer) return; - const {w, h} = hostSize(v.host); - v.camera.aspect = w/h; v.camera.updateProjectionMatrix(); - v.renderer.setSize(w, h, false); -} -function makeView(hostId, name){ +function makeView(hostId, set){ const host = document.getElementById(hostId); const {w, h} = hostSize(host); - const v = {host, name, cloud:null}; + const v = {host, set, cloud:null, selCloud:null}; v.scene = new THREE.Scene(); v.scene.background = new THREE.Color(0x0e1116); - v.camera = new THREE.PerspectiveCamera(55, w/h, 0.1, 1e6); - v.camera.position.set(600,600,600); + v.camera = new THREE.PerspectiveCamera(55, w/h, 0.1, 1e6); v.camera.position.set(600,600,600); v.renderer = new THREE.WebGLRenderer({antialias:true}); - v.renderer.setPixelRatio(devicePixelRatio); - v.renderer.setSize(w, h, false); + v.renderer.setPixelRatio(devicePixelRatio); v.renderer.setSize(w, h, false); host.appendChild(v.renderer.domElement); v.controls = new OrbitControls(v.camera, v.renderer.domElement); - // Pan in the plane of the screen rather than along the ground plane, so - // dragging up moves the scene up whatever the camera is doing. v.controls.screenSpacePanning = true; host.addEventListener('pointerenter', () => { ACTIVE = v; }); - v.raycaster = new THREE.Raycaster(); - v.pointer = new THREE.Vector2(); + v.raycaster = new THREE.Raycaster(); v.pointer = new THREE.Vector2(); v.renderer.domElement.addEventListener('mousemove', e => onMove(v, e)); v.controls.addEventListener('change', () => syncFrom(v)); - new ResizeObserver(() => resizeView(v)).observe(host); - addEventListener('resize', () => resizeView(v)); - (function tick(){ requestAnimationFrame(tick); - v.controls.update(); v.renderer.render(v.scene, v.camera); })(); - log(`3D canvas (${name}) ${w}x${h}`); + const resize = () => { const s = hostSize(host); v.camera.aspect = s.w/s.h; + v.camera.updateProjectionMatrix(); v.renderer.setSize(s.w, s.h, false); }; + new ResizeObserver(resize).observe(host); + (function tick(){ requestAnimationFrame(tick); v.controls.update(); v.renderer.render(v.scene, v.camera); })(); return v; } -// Camera sync is what makes the two panes comparable: the displacement is -// along x, so the same viewpoint either side is the whole point. +// Camera sync keeps the two panes comparable: same viewpoint either side, so +// the drift-time displacement reads as a displacement. let syncing = false; function syncFrom(src){ if(syncing || !document.getElementById('sync').checked) return; syncing = true; - // Copy to every other view rather than to a named sibling: the two - // viewers call their panes different things, and hard-coding one set of - // names into shared code is how this silently stopped working once. for(const dst of Object.values(V)){ - if(!dst || dst === src || !dst.camera) continue; - dst.camera.position.copy(src.camera.position); - dst.camera.up.copy(src.camera.up); - dst.controls.target.copy(src.controls.target); - dst.camera.updateProjectionMatrix(); - dst.controls.update(); + if(!dst || dst === src) continue; + dst.camera.position.copy(src.camera.position); dst.camera.up.copy(src.camera.up); + dst.controls.target.copy(src.controls.target); dst.camera.updateProjectionMatrix(); dst.controls.update(); } syncing = false; } - -// Which pane the cursor is over, so the arrow keys move that one. let ACTIVE = null; - -// OrbitControls pans on right-drag, which not everyone finds. This is the -// same screen-space translation bound to the arrow keys: shift both the -// camera and its target by the same vector, so the view slides without -// turning. function panView(v, dxPix, dyPix){ if(!v) return; const el = v.renderer.domElement; - const dist = new THREE.Vector3() - .subVectors(v.camera.position, v.controls.target).length(); + const dist = new THREE.Vector3().subVectors(v.camera.position, v.controls.target).length(); const half = dist * Math.tan((v.camera.fov / 2) * Math.PI / 180); - const sx = 2 * dxPix * half / Math.max(1, el.clientHeight); - const sy = 2 * dyPix * half / Math.max(1, el.clientHeight); + const sx = 2 * dxPix * half / Math.max(1, el.clientHeight), sy = 2 * dyPix * half / Math.max(1, el.clientHeight); const m = v.camera.matrix.elements; - const right = new THREE.Vector3(m[0], m[1], m[2]).multiplyScalar(-sx); - const up = new THREE.Vector3(m[4], m[5], m[6]).multiplyScalar(sy); - const d = right.add(up); - v.camera.position.add(d); - v.controls.target.add(d); - v.controls.update(); - syncFrom(v); + const d = new THREE.Vector3(m[0], m[1], m[2]).multiplyScalar(-sx).add(new THREE.Vector3(m[4], m[5], m[6]).multiplyScalar(sy)); + v.camera.position.add(d); v.controls.target.add(d); v.controls.update(); syncFrom(v); } - -const PAN_KEYS = {ArrowLeft:[-1,0], ArrowRight:[1,0], - ArrowUp:[0,1], ArrowDown:[0,-1]}; +const PAN_KEYS = {ArrowLeft:[-1,0], ArrowRight:[1,0], ArrowUp:[0,1], ArrowDown:[0,-1]}; addEventListener('keydown', e => { - const k = PAN_KEYS[e.key]; - if(!k) return; - const v = ACTIVE || Object.values(V).find(x => x); - if(!v) return; - e.preventDefault(); - const step = (e.shiftKey ? 160 : 40); - panView(v, k[0] * step, k[1] * step); + const k = PAN_KEYS[e.key]; if(!k) return; + const v = ACTIVE || V.tru; if(!v) return; + e.preventDefault(); const step = e.shiftKey ? 160 : 40; panView(v, k[0]*step, k[1]*step); }); -function buildCloud(v, pts){ - if(v.cloud){ v.scene.remove(v.cloud); v.cloud.geometry.dispose(); - v.cloud.material.dispose(); v.cloud = null; } - if(v.selCloud){ v.scene.remove(v.selCloud); v.selCloud.geometry.dispose(); - v.selCloud.material.dispose(); v.selCloud = null; } - const n = pts ? pts.length/3 : 0; - if(!n) return; +function buildCloud(v, P){ + for(const k of ['cloud', 'selCloud']) if(v[k]){ v.scene.remove(v[k]); v[k].geometry.dispose(); v[k].material.dispose(); v[k] = null; } + if(!P.n) return; + const size = +document.getElementById('psize').value; const geo = new THREE.BufferGeometry(); - geo.setAttribute('position', new THREE.BufferAttribute(pts, 3)); - geo.setAttribute('color', new THREE.BufferAttribute(new Float32Array(n*4), 4)); - // alphaTest discards the fully transparent points outright, so a class - // hidden by the filter cannot write depth and punch holes in what is left. - const mat = new THREE.PointsMaterial({size:+document.getElementById('psize').value, - vertexColors:true, sizeAttenuation:false, - transparent:true, depthWrite:true, - alphaTest:0.01}); - v.cloud = new THREE.Points(geo, mat); v.scene.add(v.cloud); - - // A second pass holding just the selection. One cloud cannot do both - // jobs: with depthWrite on, a faint point in front discards the selected - // point behind it; with depthWrite off, a third of a million faint points - // sum into a wash and the selection stops reading at all. So the bulk - // writes depth and stays crisp, and this draws over the top of it with - // depthTest off. - const selGeo = new THREE.BufferGeometry(); - selGeo.setAttribute('position', new THREE.BufferAttribute(new Float32Array(0), 3)); - selGeo.setAttribute('color', new THREE.BufferAttribute(new Float32Array(0), 3)); - v.selCloud = new THREE.Points(selGeo, new THREE.PointsMaterial({ - size:+document.getElementById('psize').value, vertexColors:true, - sizeAttenuation:false, depthTest:false, depthWrite:false})); - v.selCloud.renderOrder = 2; - v.scene.add(v.selCloud); + geo.setAttribute('position', new THREE.BufferAttribute(P.pts, 3)); + geo.setAttribute('color', new THREE.BufferAttribute(new Float32Array(P.n*4), 4)); + // alphaTest discards the fully transparent (filtered) points, so they write + // no depth and punch no holes + v.cloud = new THREE.Points(geo, new THREE.PointsMaterial({size, vertexColors:true, sizeAttenuation:false, + transparent:true, depthWrite:true, alphaTest:0.01})); + v.scene.add(v.cloud); + // The selection gets its own pass drawn over the top, so the dimmed bulk can + // keep writing depth without hiding it. + const sg = new THREE.BufferGeometry(); + sg.setAttribute('position', new THREE.BufferAttribute(new Float32Array(0), 3)); + sg.setAttribute('color', new THREE.BufferAttribute(new Float32Array(0), 3)); + v.selCloud = new THREE.Points(sg, new THREE.PointsMaterial({size, vertexColors:true, sizeAttenuation:false, + depthTest:false, depthWrite:false})); + v.selCloud.renderOrder = 2; v.scene.add(v.selCloud); } -function fitOne(v, pts){ - if(!v || !pts || !pts.length) return; - const n = pts.length/3; - let mn = [1e30,1e30,1e30], mx = [-1e30,-1e30,-1e30]; - for(let i = 0; i < n; i++) for(let d = 0; d < 3; d++){ - const q = pts[i*3+d]; if(q < mn[d]) mn[d] = q; if(q > mx[d]) mx[d] = q; - } - const c = mn.map((q,i) => (q + mx[i])/2); - const span = Math.max(...mx.map((q,i) => q - mn[i]), 1); - v.controls.target.set(c[0],c[1],c[2]); - v.camera.position.set(c[0]+span, c[1]+span*0.7, c[2]+span); - v.camera.near = span/1000; v.camera.far = span*20; - v.camera.updateProjectionMatrix(); - v.raycaster.params.Points.threshold = Math.max(span/250, 1e-6); - v.controls.update(); -} function fitView(){ - // Both panes are framed on the union, so a displacement reads as a - // displacement rather than being normalised away by two separate fits. - const both = new Float32Array((S.ptsTrue?.length || 0) + (S.ptsNom?.length || 0)); - if(S.ptsTrue) both.set(S.ptsTrue, 0); - if(S.ptsNom) both.set(S.ptsNom, S.ptsTrue ? S.ptsTrue.length : 0); - fitOne(V.tru, both); fitOne(V.nom, both); + // Both panes framed on the union, so the displacement is not normalised away. + const mn = [1e30,1e30,1e30], mx = [-1e30,-1e30,-1e30]; + for(const P of [S.T, S.H]) if(P) for(let i = 0; i < P.n; i++) for(let d = 0; d < 3; d++){ + const q = P.pts[i*3+d]; if(q < mn[d]) mn[d] = q; if(q > mx[d]) mx[d] = q; } + if(mn[0] > mx[0]) return; + const c = mn.map((q, i) => (q + mx[i])/2), span = Math.max(...mx.map((q, i) => q - mn[i]), 1); + for(const v of Object.values(V)){ + if(!v) continue; + v.controls.target.set(c[0], c[1], c[2]); + v.camera.position.set(c[0]+span, c[1]+span*0.7, c[2]+span); + v.camera.near = span/1000; v.camera.far = span*20; v.camera.updateProjectionMatrix(); + v.raycaster.params.Points.threshold = Math.max(span/300, 1e-6); + v.controls.update(); + } } +// Raycasting a few million points is not free, so pick at most every 50 ms. +let lastPick = 0; function onMove(v, e){ - if(!v.cloud || !S.n) return; + const P = v.set(); + if(!v.cloud || !P || !P.n) return; + const now = performance.now(); if(now - lastPick < 50) return; lastPick = now; const r = v.renderer.domElement.getBoundingClientRect(); - v.pointer.x = ((e.clientX - r.left)/r.width)*2 - 1; - v.pointer.y = -((e.clientY - r.top)/r.height)*2 + 1; + v.pointer.x = ((e.clientX - r.left)/r.width)*2 - 1; v.pointer.y = -((e.clientY - r.top)/r.height)*2 + 1; v.raycaster.setFromCamera(v.pointer, v.camera); - const hits = v.raycaster.intersectObject(v.cloud, false); - for(const h of hits){ - if(!S.visible[h.index]) continue; // never pick something filtered out - infoPoint(h.index, v.name); - select(S.key[h.index]); - return; + for(const h of v.raycaster.intersectObject(v.cloud, false)){ + if(!P.visible[h.index]) continue; + infoPoint(P, h.index); select(P.key[h.index]); return; } } -const fmt = (x, d=3) => (x === undefined || x === null || Number.isNaN(x)) - ? '—' : (Math.abs(x) >= 1e5 || (x !== 0 && Math.abs(x) < 1e-3) - ? Number(x).toExponential(2) : Number(x).toFixed(d)); +const fmt = (x, d=3) => (x === undefined || x === null || Number.isNaN(x)) ? '—' + : (Math.abs(x) >= 1e5 || (x !== 0 && Math.abs(x) < 1e-3) ? Number(x).toExponential(2) : Number(x).toFixed(d)); const pdgName = p => PDG_NAME.has(p) ? `${p} (${PDG_NAME.get(p)})` : (p === -1 ? '—' : String(p)); - -function infoPoint(i, where){ - const ir = S.instRow[i], fr = S.fragRow[i], pr = S.partRow[i]; - // geant4 track id belongs to a particle, and only about two thirds of - // points fall inside a stored particle row; the rest fall back to the - // fragment's representative, and say so rather than implying precision. - let g4 = '—'; - if(pr >= 0 && S.cols.geant4_trackid) g4 = String(Number(S.cols.geant4_trackid[pr])); - else if(fr >= 0 && S.cols.geant4_trackid) - g4 = `${Number(S.cols.geant4_trackid[fr])} (fragment rep)`; - - const parentInst = attrOf(ir, 'inst_parent_id'); - // A primary is its own parent in this table; printing "parent 9" of - // instance 9 reads as a real ancestor, so say what it means instead. - const isPrimary = parentInst >= 0 && parentInst === instOf(i); - const prow = S.idRow.has(parentInst) ? S.idRow.get(parentInst) : -1; - const parentPdg = isPrimary ? -1 - : (prow >= 0 ? Number(S.cols.pdg[prow]) : attrOf(ir, 'parent_pdg')); - - const sem = semOf(i); - const rows = [ - ['point', `${i.toLocaleString()} (${where})`], - ['x y z (inferred)', `${fmt(S.feat[i*6],1)} ${fmt(S.feat[i*6+1],1)} ${fmt(S.feat[i*6+2],1)}`], - ['x (true)', `${fmt(S.feat[i*6] + S.shift[i], 1)} shift ${fmt(S.shift[i],1)}`], - [quantity().name, fmt(S.feat[i*6+4], 4) - + (quantity().unit ? ' ' + quantity().unit : '')], - ['geant4 track id', g4], - ['fragment id', fragOf(i) < 0 ? '—' : String(fragOf(i))], - ['instance id', instOf(i) < 0 ? '—' : String(instOf(i))], - ['interaction id', interOf(i) < 0 ? '—' : String(interOf(i))], - ['semantic type', SEM[sem] ? `${SEM[sem]} (${sem})` : '—'], - ['low energy', S.isLE[i] ? 'yes (in the instance\u2019s LE block)' : 'no'], - ['instance type', SEM[hostSemOf(i)] || '—'], - ['pdg', pdgName(pdgOf(i))], - ['parent instance', isPrimary ? '— (primary)' - : parentInst < 0 ? '—' : String(parentInst)], - ['parent pdg', isPrimary ? '—' : pdgName(parentPdg)], - ]; +function infoPoint(P, i){ + const rows = []; + if(P === S.T){ + const f = P.feat, C = P.C, o = i * C; + rows.push(['truth voxel', i.toLocaleString()], + ['x y z [mm] (true)', `${fmt(f[o],1)} ${fmt(f[o+1],1)} ${fmt(f[o+2],1)}`], + ['t [µs]', fmt(f[o+3], 2)], ['dE [MeV]', fmt(f[o+4], 4)], ['dX [cm]', fmt(f[o+5], 4)]); + if(C >= 9) rows.push(['θ φ [rad]', `${fmt(f[o+6],3)} ${fmt(f[o+7],3)}`], ['|p| [MeV/c]', fmt(f[o+8], 2)]); + }else{ + rows.push(['hit', `volume ${P.vol[i]} · #${P.src[i].toLocaleString()}`], + ['py pz tick', `${P.py[i]} ${P.pz[i]} ${P.tick[i]}`], + ['x y z [mm] (inferred)', `${fmt(P.pts[i*3],1)} ${fmt(P.pts[i*3+1],1)} ${fmt(P.pts[i*3+2],1)}`], + ['charge [ADC]', fmt(P.e[i], 2)], ['on sensor', P.on[i] ? 'yes' : 'no']); + } + const sem = semOf(P, i); + rows.push(['fragment', P.frag[i] < 0 ? '—' : String(P.frag[i])], + ['instance', P.inst[i] < 0 ? '—' : String(P.inst[i])], + ['interaction', interOf(P, i) < 0 ? '—' : String(interOf(P, i))], + ['semantic type', SEM[sem] ? `${SEM[sem]} (${sem})` : '—'], + ['instance pdg', pdgName(pdgOf(P, i))]); const el = document.getElementById('pinfo'); - el.innerHTML = '' + rows.map(([k,v]) => - ``).join('') + '
${k}${v}
'; + el.innerHTML = '' + rows.map(([k, v]) => ``).join('') + '
${k}${v}
'; el.style.display = 'block'; } -// One key drives both panes; that is the link being demonstrated. +// One key drives both panes; that is the link being shown. function select(key){ key = Number(key); if(key === S.sel) return; S.sel = key; - paint(); - let n = 0; for(let i = 0; i < S.n; i++) if(S.key[i] === key && S.visible[i]) n++; + const {t, h} = paint(); const lvl = S.hl === 'frag' ? 'fragment' : S.hl === 'inter' ? 'interaction' : 'instance'; - statusEl.innerHTML = key < 0 - ? `unowned point` - : `${lvl} ${key} points ${n.toLocaleString()}`; + statusEl.innerHTML = key < 0 ? `not traced to a particle` + : `${lvl} ${key} truth voxels ${(t ? t.nSel : 0).toLocaleString()}` + + ` hits ${(h ? h.nSel : 0).toLocaleString()}`; } // ----------------------------------------------------------------- rebuild -- function rebuild(){ if(!S.out) return; - if(!V.tru) V.tru = makeView('pane-true', 'true x'); - if(!V.nom) V.nom = makeView('pane-nom', 'inferred x'); + if(!V.tru) V.tru = makeView('pane-true', () => S.T); + if(!V.hit) V.hit = makeView('pane-hit', () => S.H); S.sel = -1; try{ - buildEvent(); - buildCloud(V.tru, S.ptsTrue); - buildCloud(V.nom, S.ptsNom); - resizeView(V.tru); resizeView(V.nom); - paint(); - fitView(); - let spread = 0; - for(let i = 0; i < S.n; i++) spread = Math.max(spread, Math.abs(S.shift[i])); - banner('note', !S.hasShift - ? `This output has no points/true_x_shift, so the two panes are the same cloud.\n` + - `It predates the column, or was written from truth deposits rather than hits.` - : spread === 0 - ? `Every true_x_shift in this event is zero, so the two panes are the same cloud.\n` + - `That is what a deposit-mode run, or hit mode with hit_x_from=true_t0, writes.` - : ''); - log(`event ${S.ev}: ${S.n.toLocaleString()} points, ` + - `max |shift| ${spread.toFixed(0)} mm`); - statusEl.innerHTML = - `event ${S.ev} points ${S.n.toLocaleString()}` + - (spread ? ` max drift shift ${spread.toFixed(0)} mm` : ''); - document.getElementById('hint').textContent = 'hover a point in either pane · drag rotate · right-drag or arrows pan · c fit'; - }catch(err){ log('ERROR: ' + err.message); statusEl.textContent = 'error: ' + err.message; } + buildParticles(); buildTruth(); buildHits(); + buildKeys(); buildFilters(); applyFilter(); + buildCloud(V.tru, S.T); buildCloud(V.hit, S.H); + paint(); fitView(); + const off = document.getElementById('offsensor').checked; + document.getElementById('hit-ttl').innerHTML = + `hits the JAXTPC hits · inferred x (t0 = tick 0)` + (off ? '' : ' · on-sensor only'); + statusEl.innerHTML = `event ${S.ev} truth voxels ${S.T.n.toLocaleString()}` + + ` hits ${S.H.n.toLocaleString()}` + + (S.hit ? '' : '\nload the hits subset to see the hits'); + log(`event ${S.ev}: ${S.T.n.toLocaleString()} truth voxels, ${S.H.n.toLocaleString()} hits`); + }catch(err){ log('ERROR: ' + err.message); statusEl.textContent = 'error: ' + err.message; console.error(err); } } diff --git a/vis_force.html b/vis_force.html index b898555..1e8cf3f 100644 --- a/vis_force.html +++ b/vis_force.html @@ -1782,6 +1782,17 @@

⌨ Keyboard Shortcuts

function probeFragLevel(){ _FRAG_LEVEL=false; if(!hf) return false; + // 3.x: one particles table; a fragment level exists when some row heads a + // fragment (frag_id == id). + if(FMT3){ + try{ + const pg=hf.get('particles'); + if(!pg.keys().includes('frag_id')) return false; + const f=pg.get('frag_id').value, id=pg.get('id').value; + for(let k=0;k⌨ Keyboard Shortcuts try{ _hasL0=hf.keys().includes('particles') &&Number(hf.get('particles').get('id').shape[0])>0; }catch(e){} let _hasG4=false; - try{ _hasG4=hf.keys().includes('particle_instances') - &&hf.get('particle_instances').keys().includes('geant4_trackid'); }catch(e){} + try{ _hasG4=FMT3 + ? hf.get('particles').keys().includes('geant4_trackid') + : (hf.keys().includes('particle_instances') + &&hf.get('particle_instances').keys().includes('geant4_trackid')); }catch(e){} logMsg('format '+_fv+' \u00b7 fragment level: '+(_FRAG_LEVEL?'yes':'no') +' \u00b7 level-0 particles: '+(_hasL0?'yes':'no') +' \u00b7 geant4_trackid: '+(_hasG4?'yes':'no')); diff --git a/vis_hits.html b/vis_hits.html index e573a29..32d621e 100644 --- a/vis_hits.html +++ b/vis_hits.html @@ -1,6 +1,6 @@ -pysupera — hits ⇄ voxels +pysupera — wire hits ⇄ voxels - -
- - - - - - - - - - - - - - load the pysupera output and its truth companion -
-
show semantic type:
- -
-
-
truth deposits Geant4, labelled through deposit → group → fragment
-
-
-
-
pysupera points the cloud the algorithms actually ran on
-
no file loaded
-
-
- - - - From d2cef48e6b516e05f5007215ded209cdb288212f Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sun, 27 Sep 2026 21:54:05 -0700 Subject: [PATCH 43/62] docs: bring the README up to date with the code, and record the profiling Summary: the README had fallen behind the code in its defaults, point columns, pixel hit-mode numbers and output sizes, and said nothing about performance. This fixes those, and adds a performance section (per-mode timings, the checker comparison, and the before/after for the preproc speed-ups: pixel -27% wall, output identical), plus algorithm notes for voxelization and batched defragmentation. Documentation only; no code changes. A pass over the README against the current algorithms, defaults and data structures, beyond the 3.3.0 sections of the previous commits. Corrected: - Config defaults: distance_threshold 5.2 (6.2 for pixel), verbose false, checker cpu_single (the table said gpu), min_pc_size 5 (it said -1), and the voxelize rows added. - Preprocessing is three stages in order (merge duplicates, voxelize, defragment), not two. Merge duplicates sums dX rather than taking a max, and is skipped when the voxelizer runs. Defragmentation only examines kShower/kDelta/kMichel/kLEScatter by default. - Point columns: six for EDepSim only, nine for JAXTPC (theta, phi, p added), named in points/columns. - What the energy column holds: in points/flat it is always true dE. The point_source / hit_energy / hit_x_from attributes describe what the partitioning ran on, not what is stored. The hit_x_from section says the same. - Pixel hit mode after the sensor mask and the uint16 group_sizes fix: min_pc_size 63 from an 11.9 mm track width (the old 12.8 mm / 85 table was measured on unmasked, mis-decoded hits); groups straddling a split are 70 of about 574k over 10 events (0.01%), not 1.84%. - Output sizes per 10 events, remeasured with real theta/phi/p (the old numbers were taken when those columns were all zero and compressed away): pixel 16.3 MB (points/flat 3.8, hit_labels/ 11.1), wire 9.7 MB (5.7, 2.2). - Hit provenance points to hit_labels/ first, with groups/ as the group-level view behind it. The viewers named are the current ones. - Training data: torchdata reads points/flat, so for JAXTPC it loads truth voxels. A hits-file + hit_labels/ loader is not written yet. - Stale mentions of a pixel "deposit mode" are gone. New: - Compute Backends: the checker comparison. partition_combined on 3 pixel events took 3.4 s with cpu_single, 7.8 s with cpu_multi, 16.9 s with cell_hash_cpu_multi and 24.9 s with numba, so the default is the fastest. - Pipeline performance: wall and per-stage times per run mode (10 events: EDepSim 19.7 s, wire 29.0 s, pixel 49.6 s), the before/after table for the two preproc speed-ups (pixel voxelize 21.6 -> 6.0 s, defragment 10.1 -> 4.0 s, wall -27%), the profile that found them, and a note that identical runs on this machine varied by up to 35%. - Algorithms: Voxelization (the packed-key unique) and Defragmentation (the batched connected components, and why the labels come out identical). All internal links resolve. --- README.md | 233 +++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 186 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index a383695..c9ffc54 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,8 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [Interaction columns](#interaction-columns) - [ID conventions](#id-conventions) - [Wire and pixel readout](#wire-and-pixel-readout) - - [Truth deposits or detected hits](#truth-deposits-or-detected-hits) + - [Wire deposits and pixel hits](#wire-deposits-and-pixel-hits) + - [What the output stores (JAXTPC input)](#what-the-output-stores-jaxtpc-input) - [Hit provenance (JAXTPC mode)](#hit-provenance-jaxtpc-mode) - [Seeing it](#seeing-it) - [Expert and debugging output](#expert-and-debugging-output) @@ -47,7 +48,10 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [Profiling and W&B logging](#profiling-and-wb-logging) 8. [CLI Usage](#cli-usage) 9. [Compute Backends](#compute-backends) + - [Pipeline performance](#pipeline-performance) 10. [Algorithms](#algorithms) + - [Voxelization](#voxelization-1) + - [Defragmentation](#defragmentation-1) - [Partition-level Incremental Algorithm](#partition-level-incremental-algorithm) - [Proximity Check](#proximity-check) - [Condition Pipeline](#condition-pipeline) @@ -307,11 +311,13 @@ label (`-1`) and are drawn grey. #### What the energy column holds -Nothing in the point columns says whether the energy column is true dE or -measured charge, so the run writes it down as root attributes -(`point_source`, `hit_energy`, `hit_x_from`, `readout_type`) and the viewers -could label the column from them. Guessing from the presence of negative values -works on a busy event and fails on a quiet one. +In `points/flat` it is always true dE in MeV: with JAXTPC input the stored +points are the true deposits behind the hits, whatever the partitioning ran +on. Measured charge stays in the JAXTPC hits file, which is where +`vis_drift.html` reads it from. The root attributes `point_source`, +`hit_energy`, `hit_x_from` and `readout_type` record what the *partitioning* +ran on — for a pixel run, the hits' charge at nominal x — not what +`points/flat` holds. --- @@ -401,7 +407,7 @@ the true deposits behind the hits in `points/flat` and a label per hit in `reader=jaxtpc_pixel` also brings the settings hits need outside the reader group — `particle.min_pc_size`, `distance_threshold`, -`check_group_ownership`, `particle.voxelize.store` — from +`check_group_ownership` — from `conf/readout/jaxtpc_pixel.yaml`, loaded after `config.yaml`'s own values; a command-line flag still wins. `reader.point_source=deposits` is refused. @@ -434,19 +440,21 @@ see [Step 0](#step-0--configuration). | Key | Default | Description | |---|---|---| -| `distance_threshold` | `5.0` | Proximity distance *D* in point-cloud coordinate units | -| `verbose` | `true` | Print per-event progress | +| `distance_threshold` | `5.2` | Proximity distance *D* in point-cloud coordinate units (6.2 with `reader=jaxtpc_pixel`) | +| `verbose` | `false` | Print per-event detail | | `report` | `true` | Print end-of-run summary statistics | | `enable_diagnostics` | `false` | Record every merge decision for inspection | | `check_particle_tree` | `false` | Validate parent–child consistency before partitioning | -| `checker` | `gpu` | Default compute backend (config group; override with `checker=cpu_single` etc.) | +| `checker` | `cpu_single` | Compute backend (config group; override with `checker=cpu_multi` etc.) | **Particle preprocessing parameters** (`particle.*`): | Key | Default | Description | |---|---|---| -| `particle.min_pc_size` | `-1` | Min point-cloud size for semantic-type classification; `-1` disables (all sizes accepted) | -| `particle.merge_duplicates` | `true` | Collapse points sharing identical (x,y,z) coordinates before partitioning | +| `particle.min_pc_size` | `5` | Occupied voxels below which a particle is `kLEScatter` (63 with `reader=jaxtpc_pixel`); `-1` disables | +| `particle.merge_duplicates` | `true` | Collapse points sharing identical (x,y,z) coordinates; subsumed by the voxelizer when it is on | +| `particle.voxelize.enabled` | `true` | Merge each particle's points on a `voxel_size` grid (3 mm, fixed `origin`) before defragmentation | +| `particle.voxelize.store` | `voxels` | What `points/flat` holds: the voxels, or (`points`) every input point | | `particle.defragment` | `true` | Split disconnected point-cloud fragments into new `kLEScatter` particles | | `particle.preprocessor.name` | `scipy` | Defragmentation backend: `scipy`, `gpu`, or `rapids` | @@ -471,11 +479,12 @@ configure(cfg) # sets module-level defaults (min_pc_size, etc.) ### Step 1 — Preprocessing -Two optional preprocessing stages run before partitioning. +Three stages run before partitioning, in this order, each within one +particle at a time: merge duplicates, voxelization, defragmentation. #### Merge Duplicates -Collapses points that share identical (x, y, z) voxel positions within each particle's point cloud. Aggregation: `time = min`, `energy = sum`, `dEdx = max`. Enable with: +Collapses points that share identical (x, y, z) positions within each particle's point cloud. Aggregation: `time = min`, `dE = sum`, `dX = sum`. Skipped when the voxelizer runs with `voxelize.merge_duplicates: true` (the default), since voxelizing merges them anyway. Enable with: ```yaml # config.yaml @@ -483,16 +492,24 @@ particle: merge_duplicates: true ``` +#### Voxelization + +Bins every particle's points on the `particle.voxelize` grid (3 mm cells from +a fixed `origin`) and merges the points sharing a cell: cell centre, +`time = min`, `dE = sum`, `dX = sum`. The voxels are what proximity, +defragmentation and the extent classification see. See +[Voxelization](#voxelization-1) under Algorithms for how it is computed. + #### Defragmentation -A particle's point cloud may be *fragmented* — split into spatially disconnected clusters that should logically belong to the same particle. The defragmenter runs a connected-components algorithm (eps-ball radius = `distance_threshold`) on each particle's point cloud independently, then detaches small disconnected fragments (size ≤ `min_pc_size`) as new `kLEScatter` particles. +A particle's point cloud may be *fragmented* — split into spatially disconnected clusters that should logically belong to the same particle. The defragmenter runs a connected-components algorithm (eps-ball radius = `distance_threshold`) on each particle's point cloud independently, then detaches small disconnected fragments (size ≤ `min_pc_size`) as new `kLEScatter` particles. By default only `kShower`, `kDelta`, `kMichel` and `kLEScatter` particles are examined (`preprocessor.sem_types`); tracks are left whole. Enable with: ```yaml particle: defragment: true - min_pc_size: -1 # default: -1 (disabled); set e.g. 5 to split small fragments + min_pc_size: 5 # voxels; fragments this small become kLEScatter preprocessor: name: scipy # choices: scipy (default), gpu, rapids ``` @@ -503,7 +520,7 @@ particle: | `gpu` | `cupy` | Large point clouds (> `min_pts_for_gpu` points) | | `rapids` | `cupy` + `cudf` + `cugraph` | Fully GPU, no CPU round-trip | -All backends share the same fast-path early-exit checks (single point, two-point connected, bounding-box diameter ≤ eps) that skip the full CC algorithm for the majority of particles. +All backends share the same fast-path early-exit checks (single point, two-point connected, bounding-box diameter ≤ eps) that skip the full CC algorithm for the majority of particles. The `scipy` backend then clusters all remaining particles of an event in one pass — see [Defragmentation](#defragmentation-1) under Algorithms. --- @@ -822,13 +839,22 @@ readers see the absolute form. ### Point columns +`points/columns` names them. The first six are common to every run; JAXTPC +runs add three: + ``` -0:x 1:y 2:z 3:time 4:dE 5:dX +EDepSim only 0:x 1:y 2:z 3:time 4:dE 5:dX +JAXTPC 0:x 1:y 2:z 3:t 4:dE 5:dX 6:theta 7:phi 8:p ``` -Both merge stages operate within a single particle, so `dE` and `dX` both sum -over a particle-voxel and **dE/dX is column 4 / column 5**, exactly. Guard -against `dX == 0`: EDepSim writes zero-length steps, a few percent of voxels. +Units are mm, µs, MeV, cm, rad and MeV/c. theta and phi are the step +direction (theta from the z axis) and p the momentum magnitude, taken from +the earliest step in the voxel — the particle entering the cell. + +Every merge stage operates within a single particle, so `dE` and `dX` both +sum over a particle-voxel and **dE/dX is column 4 / column 5**, exactly. +Guard against `dX == 0`: EDepSim writes zero-length steps, a few percent of +voxels. ### Wire and pixel readout @@ -993,14 +1019,23 @@ with read_events_v3("out.h5") as store: frag = labels[(0, 1)]["fragment_id"] # volume 0, plane V ``` -| per 10 events | pixel | wire | +| per 10 events (sample batch, default LZ4) | pixel | wire | |---|---|---| -| `points/flat` (3 mm voxels) | 1.9 MB | 2.9 MB | -| `hit_labels/` | 10.5 MB (28.9M hits) | 2.2 MB (16.8M hits) | -| whole file | ~14 MB | ~7 MB | +| `points/flat` (3 mm voxels, 9 columns) | 3.8 MB (177k voxels) | 5.7 MB (270k voxels) | +| `hit_labels/` | 11.1 MB (28.9M hits) | 2.2 MB (16.8M hits) | +| `groups/` | 0.5 MB | 0.7 MB | +| whole file | 16.3 MB | 9.7 MB | + +The 10-event pixel run labels 24.9M of the 28.9M hits (86.1%); the rest are +exactly the hits off the sensor image. #### The drift coordinate — `reader.hit_x_from` +This and the next few settings shape the cloud a pixel run *partitions*; none +of them changes what is stored, since `points/flat` holds the true deposits +at their true positions. `vis_drift.html` computes the nominal x of every hit +itself, from the `pixel_geometry` root attribute the run writes. + `y` and `z` are pure geometry and invert exactly, to half a pixel. `x` is drift time, and a tick counts from the start of the readout window — so it measures `t_drift + t0`, and `t0` is precisely what a real detector has to @@ -1079,7 +1114,7 @@ a wall the reconstruction does not have. `charge` (default) is what the readout measured. The response is bipolar, so about 16% of hits carry negative charge; that is signal, not error. `true_de` instead shares each group's true deposited energy over its hits in -proportion to `|charge|` — comparable with deposit mode's column, at the cost +proportion to `|charge|` — comparable with a truth cloud's dE, at the cost of putting a truth quantity into a detected cloud. **`dx` is 0 in both cases**: a hit is a pixel and a tick, and has no path length, so anything reading dE/dx sees zero. @@ -1140,16 +1175,16 @@ blob one track-width across, `(width / voxel_size)³`. | input | track width | implied threshold | |---|---|---| | truth deposits | 4.9 mm | (4.9/3)³ ≈ **5** — the default | -| pixel sensor hits | 12.8 mm | (12.8/3)³ ≈ 77, best fit **85** | +| pixel sensor hits (sensor-masked) | 11.9 mm | (11.9/3)³ ≈ **63** — fitted, set by `reader=jaxtpc_pixel` | -A track is 3.2× thicker in the pixel image, which is diffusion plus the -field response over about three pixel pitches. Measured over ten events, the -best-agreeing cut per event ranged 81–90, and at a fixed 85 the sensor-hit -labelling matches the truth-deposit labelling on **99.0%** of particles -(worst event 98.5%) — not merely the same fraction, the same particles. That was measured before the uint8 -`group_sizes` fix and the sensor mask; on the masked hits the best value is -63 (99.26%), which `reader=jaxtpc_pixel` sets — see -[Which pixels: the sensor image](#which-pixels-the-sensor-image). +A track is about 2.4× thicker in the pixel image, which is diffusion plus the +field response over a few pixel pitches. The pixel value was fitted rather +than derived — each EDepSim particle's LE-ness from its hits against the same +particle's from truth deposits at 5 — and at 63 the two agree on **99.26%** +of particles, not merely the same fraction but the same ones (per-event best +58–70; see [Which pixels: the sensor image](#which-pixels-the-sensor-image)). +An earlier fit, on unmasked hits decoded with the uint8 `group_sizes` bug, +gave 85; it no longer applies. Note this also changed truth-deposit labelling, from 85.8% to 95.3% LE, because particles spread over a handful of rows inside one or two voxels are @@ -1169,21 +1204,28 @@ Event 0, one event: | 8.0 mm | 6,900 → 2,426 | 8,128 → 3,197 | | 10.0 mm | 6,900 → 2,349 | 7,372 → 2,555 | -Deposit mode never splits at all — 0.3 mm deposit spacing keeps a track +The truth deposits never split at all — 0.3 mm deposit spacing keeps a track connected at any of these thresholds. **A group can straddle a fragment split.** The `groups/` table rests on the invariant that it cannot, which holds for deposits (0 of 354,959) and does -*not* hold for hits: the readout leaves gaps the deposit cloud does not have, -defragmentation cuts there, and 1.84% of groups end up on both sides. -`check_group_ownership=false` is therefore required in hit mode, and the run -resolves each straddled group by majority and reports how many there were. +*not* strictly hold for hits: the readout leaves gaps the deposit cloud does +not have, and defragmentation can cut there. On the sensor-masked hits it is +rare — 70 of about 574k groups (0.01%) over 10 events; before the mask and +the `group_sizes` fix it was 1.84% — but not zero, so +`check_group_ownership=false` is set for hit mode, and the run resolves each +straddled group by majority and reports how many there were. `hit_labels/` is +unaffected: pixel hits are labelled one by one, not through their group. ### Hit provenance (JAXTPC mode) -Which readout hits belong to which reconstructed object. A hit is the -projection of a JAXTPC *group*, and `groups/` records the fragment that owns -each group, plus one bit saying whether that group is low-energy: +Which readout hits belong to which reconstructed object. For a label per hit, +read `hit_labels/` (see [What the output stores](#what-the-output-stores-jaxtpc-input)): +it is aligned with the hits file and needs no join. `groups/` is the +group-level view behind it, kept because it is small and answers questions +about groups directly. A hit is the projection of a JAXTPC *group*, and +`groups/` records the fragment that owns each group, plus one bit saying +whether that group is low-energy: ```python with read_events_v3("out.h5") as store: @@ -1262,8 +1304,9 @@ about 21% of the output under LZ4, less under gzip. #### Seeing it -`vis_hits.html` shows this mapping directly — 3-D voxels beside the 2-D -readout planes, hover either side to light up the other. See +`vis_hits.html` (wire) and `vis_drift.html` (pixel) show the hit labels +directly — truth voxels beside the readout, hover either side to light up the +other. See [Browser viewers](#browser-viewers) for its controls and for preparing its two inputs. @@ -1357,6 +1400,11 @@ for batch in loader: # batch_size counts *events* k = ev["voxel_interaction"] # (V,) interaction id ``` +It reads `points/flat`, so for a JAXTPC file the input is the truth voxels, +not the detector hits. A loader that pairs the JAXTPC hits file with +`hit_labels/` (the intended model input for JAXTPC runs) is not written yet; +`read_events_v3(...).hit_labels(ev)` gives the labels to build one from. + Point clouds differ in length between events, so a batch stays a **list** of events rather than a padded tensor; `batch_size` therefore means exactly a number of events. `to_torch(ev, device)` converts one event's arrays to @@ -1574,9 +1622,9 @@ Select the backend via the `backend` parameter of `ParticlePartitioner` or `chec | Backend name | Config key | Requirement | Description | |---|---|---|---| -| `cpu-single` | `cpu_single` | `scipy` | Single-threaded KDTree | +| `cpu-single` | `cpu_single` | `scipy` | Single-threaded KDTree; **default** | | `cpu-multi` | `cpu_multi` | `scipy`, `joblib` | KDTree + joblib thread pool | -| `gpu` | `gpu` | `cuml` (RAPIDS) | RAPIDS NearestNeighbors (brute-force L2); **default** | +| `gpu` | `gpu` | `cuml` (RAPIDS) | RAPIDS NearestNeighbors (brute-force L2) | | `bulk-gpu` | `bulk_gpu` | `cupy` | CuPy chunked brute-force; accepts `chunk_size` | | `numba` | `numba` | `numba`, CUDA | CUDA kernel with shared-memory tiling; accepts `block_size` | | `cell-hash-cpu-single` | `cell_hash_cpu_single` | `scipy` | Cell-hash spatial index, single thread | @@ -1585,10 +1633,101 @@ Select the backend via the `backend` parameter of `ParticlePartitioner` or `chec For the partition-level proximity mode (the hot path), all CPU backends override `batch_check_cloud_proximity` to build the parent KDTree **once** per candidate group rather than once per pair. +On LArTPC events the default is also the fastest CPU choice. `partition_combined` +over the same 3 pixel events (16 cores): + +| `checker=` | `partition_combined` | +|---|---| +| `cpu_single` | **3.4 s** | +| `cpu_multi` | 7.8 s | +| `cell_hash_cpu_multi` | 16.9 s | +| `numba` | 24.9 s (includes JIT compilation) | + +Most candidate groups are small, so thread dispatch costs more than it saves. + +### Pipeline performance + +`run_pysupera` prints a time profile per stage at the end of a run +(`report=true`). On the sample batches (10 events each, `checker=cpu_single`, +one process), wall time including start-up and I/O: + +| run mode | wall | per event | largest stages (s per event) | +|---|---|---|---| +| EDepSim only | 19.7 s | ~2.0 s | partition 0.73, defragment 0.15, voxelize 0.13, write 0.11 | +| JAXTPC wire | 29.0 s | ~2.9 s | write 0.93, partition 0.70, defragment 0.16, voxelize 0.16 | +| JAXTPC pixel | 49.6 s | ~5.0 s | partition 1.66, write 0.82, voxelize 0.60, defragment 0.40 | + +A pixel event is the heaviest: about 2.9M hits are read, 2.5M of them (the +on-sensor ones) partitioned, and all 2.9M labelled. Reading the hits and +sensor files takes about 0.8 s per event, outside the stage profile. For wire, +writing (`hit_labels/` for 1.7M hits per event, compressed) is the largest +stage. The run is single-process; throughput scales by +running files, or event ranges, as separate processes. + +Two changes (September 2026) cut the pixel time by 27% with output +bit-for-bit identical — checked dataset by dataset on 10 events of each mode: + +| stage (10 events, mean of 3 alternating runs) | before | after | | +|---|---|---|---| +| pixel voxelize | 21.6 s | 6.0 s | 3.6× | +| pixel defragment | 10.1 s | 4.0 s | 2.5× | +| pixel wall | 68.3 s | 49.6 s | −27% | +| wire voxelize / defragment | 2.5 / 2.5 s | 1.6 / 1.6 s | ~1.6× | +| wire wall | 35.5 s | 29.0 s | −18% | +| EDepSim voxelize / defragment | 2.3 / 2.7 s | 1.3 / 1.5 s | ~1.8× | +| EDepSim wall | 22.8 s | 19.7 s | −13% (noisy) | + +Before them, a profile of 3 pixel events put 83% of the stage time in three +places: one `np.unique(keys, axis=0)` in the voxelizer (1.4 s per event), +14,212 per-particle KD-tree and connected-components calls in +defragmentation (1.35 s per event, mostly set-up overhead on tiny clouds), +and partitioning. The first two are described under +[Voxelization](#voxelization-1) and [Defragmentation](#defragmentation-1). +Repeated runs of identical code on this machine varied by up to 35%, so +compare stage times across alternating runs rather than single wall times. + --- ## Algorithms +### Voxelization + +`VoxelizeProcessor` voxelizes every particle of an event in one pass. Each +point gets the key (particle index, vx, vy, vz), and one `np.unique` over the +keys gives the output voxels and, per point, the voxel it went into; the +merge rules are then applied per column with `ufunc.at`. + +The row-wise `np.unique(keys, axis=0)` sorts a structured view and is an +order of magnitude slower than a 1-D sort, so `_unique_rows` packs the four +columns into one int64 instead — each offset to start at zero, most +significant first, which sorts in the same lexicographic order — and unpacks +the unique keys afterwards. It falls back to the row-wise call if the column +ranges need more than 63 bits. On 3.3M rows: 0.14 s instead of 2.46 s, same +result. + +For JAXTPC input the stored truth voxels are made separately +(`provenance.voxelize_truth`), because they carry columns the generic merge +rules do not know: theta, phi and p come from the earliest step in the cell. + +### Defragmentation + +The defragmenter screens every particle first (one point, two touching +points, or a bounding box within eps: one cluster, nothing to do) and +clusters the rest. The `scipy` backend clusters them all together +(`ScipyDefragmenter._get_labels_many`): the clouds are stacked with a fourth +coordinate `k · (⌈eps⌉ + 1)` for cloud *k*, so points of different clouds are +always more than eps apart, while within a cloud the fourth term is exactly +zero and distances are unchanged. One KD-tree `query_pairs` and one +`connected_components` then replace one of each per particle — 14k per pixel +event. + +`connected_components` numbers components in order of their lowest node, so +a cloud's components form a contiguous run of labels starting at its first +point's label; subtracting that gives exactly the labels a per-cloud call +returns. The labels themselves matter, not just the partition: +`_split_fragments` numbers the spawned LE particles in label order. The +`gpu` and `rapids` backends, and `n_jobs ≠ 1`, keep the per-particle path. + ### Partition-level Incremental Algorithm The main algorithm (`_build_partitions_incremental`) maintains one representative `Particle` per partition. A representative carries: From d590f687c6c5f60c0ce6c125bd1d6f7706894311 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Tue, 6 Oct 2026 19:45:23 -0700 Subject: [PATCH 44/62] pixel hits: a shared t0 cannot measure the drift velocity verify_volume_geometry took its reference-tick anchor from the intercept of a least-squares fit of truth x on (1, tick, t0). When every deposit shares nearly the same non-zero t0, the t0 column is the constant column scaled, the fit cannot separate intercept from t0 slope, and it splits them arbitrarily. The check then subtracted t0 with the *stated* velocity from an anchor that had absorbed a different one, and rejected a correct geometry by hundreds of mm. Seen on test_doraemon_2026_v0.1 (run 93, event 0, volume 1): x off by 634 mm median with y, z within 1.1 mm and mm/tick exactly the stated 0.8000, while the fit claimed v = 0.23 mm/us, dt = 3.5 us. t0 spans 18-1300 us across the event, but the 932 groups that reach volume 1's readout are all one interaction: t0 = 346.8 us, spread 0.047 us. On those same pairs the new check passes with x within 0.20 mm. Synthetic groups on the exact stated geometry reproduce it: t0 = 460 +- 0.01 us fails by 97 mm; t0 ~ 0, or 460 +- 5 us, passes. The local test batches all have t0 ~ 0, which is why it never showed. The anchor is now the median over groups of the tick each would have had at the anode at t0 = 0, by the stated constants -- a one-parameter offset, so a wrong pitch, tick length or drift direction still fails as before. The fit is also no longer trusted for a velocity it cannot see. A new _fit_drift computes the standard error of the t0 slope; above 5 % relative, _measure_volume reports v and dt as NaN, with velocity_measured False and the t0 spread. The error message, the run summary in _run.py and the "config is missing" hint in format_jaxtpc.py say "not measurable" instead of printing a number that looks like a measurement. calibrate_volume (reader.pixel_geometry_from_fit=true) had the same degeneracy, and on the same event it fails too: the arbitrary split gave a t0 slope of the opposite sign to the drift, which the code assumes cannot happen, so x came out 2 * 0.229 mm/us * 346.8 us = 159 mm off. Where the signs happen to agree it would instead pass and store whatever drift velocity, time step and reference tick the split chose. It now uses the same _fit_drift test. When the t0 slope is not measured it takes the stated velocity (or, failing that, the stated time step) from config or the sensor file and refits only the intercept and mm per tick. With neither stated it refuses, naming what to set. A stated value is only a fallback: when t0 varies, the fit still wins. The report records which via velocity_source. Tests: a correct geometry with a shared 460 us t0 passes verification for both drift directions (fails on the old anchor). A wrong tick length is still rejected. Calibration refuses without a stated drift (did not raise before), recovers v, dt and the reference tick from either stated value in both directions, and prefers its own measurement when t0 varies. --- pysupera/_run.py | 4 +- pysupera/readers/format_jaxtpc.py | 13 ++- pysupera/readers/pixel_hits.py | 127 ++++++++++++++++++++++++++---- tests/test_pixel_hits.py | 91 +++++++++++++++++++++ 4 files changed, 214 insertions(+), 21 deletions(-) diff --git a/pysupera/_run.py b/pysupera/_run.py index 8c62dd3..4521fb6 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -85,6 +85,7 @@ def main(cfg: DictConfig) -> None: attach_truth_clouds) from pysupera.hit_labels import build_hit_labels, check_pixel_labels from pysupera.readers.format_jaxtpc import TRUTH_COLUMNS + from pysupera.readers.pixel_hits import describe_measured_drift from pysupera.io import open_voxmap_writer from pysupera.partitioner import ParticlePartitioner from pysupera.merge import merge_em_showers @@ -764,8 +765,7 @@ def _agg(vals): if _m: print(f" data says " f"pitch {_m['pitch_mm']:.4f} mm " - f"v {_m['drift_velocity_mm_us']:.4f} mm/us " - f"dt {_m['time_step_us']:.4f} us " + f"{describe_measured_drift(_m)} " f"drift {_m['drift_direction']:+d}") _r = _g.get('residual_mm') or {} if _r: diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index f6f97af..928c9ad 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -1157,8 +1157,13 @@ def _ensure_pixel_geometry(self, event_key, seg_volumes): say.append( f" volume {v}: pixel_pitch_mm ~ {m['pitch_mm']:.4f}, " f"pixel_drift_direction ~ {m['drift_direction']:+d}, " - f"drift_velocity_mm_us ~ {m['drift_velocity_mm_us']:.4f}, " - f"readout_time_step_us ~ {m['time_step_us']:.4f}") + + (f"drift_velocity_mm_us ~ " + f"{m['drift_velocity_mm_us']:.4f}, " + f"readout_time_step_us ~ {m['time_step_us']:.4f}" + if m['velocity_measured'] else + f"drift_velocity_mm_us and readout_time_step_us not " + f"measurable (t0 spread {m['t0_spread_us']:.3g} us; " + f"{m['mm_per_tick']:.4f} mm/tick is their product)")) raise _px.PixelGeometryError( f"point_source='hits' needs the pixel geometry, and " f"{note}.\n" @@ -1184,11 +1189,13 @@ def _ensure_pixel_geometry(self, event_key, seg_volumes): f"reader.jaxtpc_sensor_path.", RuntimeWarning, stacklevel=2) geoms, reports = [], [] xr = self._volume_x_ranges() + vel, dt, _src = self._stated_drift_terms() for v, (py, pz, tk, t0, cent) in enumerate(pairs): geom, rep = _px.calibrate_volume( py, pz, tk, t0, cent, volume_id=v, x_range=xr[v] if xr and v < len(xr) else None, - reference_tick=self._hit_reference_tick) + reference_tick=self._hit_reference_tick, + drift_velocity_mm_us=vel, time_step_us=dt) rep['source'] = 'fitted' rep['source_note'] = note geoms.append(geom) diff --git a/pysupera/readers/pixel_hits.py b/pysupera/readers/pixel_hits.py index 54b1719..45fb4e2 100644 --- a/pysupera/readers/pixel_hits.py +++ b/pysupera/readers/pixel_hits.py @@ -706,10 +706,15 @@ def verify_volume_geometry(geom, py, pz, tick, t0, xyz_truth, volume_id=0): measured = _measure_volume(py, pz, tick, t0, xt, yt, zt) - # Where the data puts the anchor, given the stated anode and tick length. - derived_ref = (geom.drift_direction - * (measured['fit_intercept_mm'] - geom.x_anode_mm) - / geom.mm_per_tick) if geom.mm_per_tick else 0.0 + # Where the data puts the anchor, given every stated constant: the tick + # each group would have had at the anode at t0 = 0, by the stated drift, + # read off group by group. Not from the fit's intercept -- when t0 + # barely varies, a fit cannot tell the intercept from the t0 slope, and + # an anchor taken from it is wrong by whatever the slope absorbed. + derived_ref = float(np.median( + tick - t0 / geom.time_step_us + - geom.drift_direction * (geom.x_anode_mm - xt) / geom.mm_per_tick) + ) if geom.mm_per_tick else 0.0 # Apply the stated geometry, but anchored where the data says. t0 is # removed too: the question here is whether the pitch, velocity and @@ -744,9 +749,8 @@ def verify_volume_geometry(geom, py, pz, tick, t0, xyz_truth, volume_id=0): f"anode {geom.x_anode_mm:+.1f} mm, reference tick " f"{geom.reference_tick:g}\n" f" data says: pitch {measured['pitch_mm']:.4f} mm, " - f"{measured['mm_per_tick']:.4f} mm/tick, v " - f"{measured['drift_velocity_mm_us']:.4f} mm/us, dt " - f"{measured['time_step_us']:.4f} us, drift " + f"{measured['mm_per_tick']:.4f} mm/tick, " + f"{describe_measured_drift(measured)}, drift " f"{measured['drift_direction']:+d}\n" f"Check reader.pixel_pitch_mm, reader.pixel_drift_direction and " f"the sensor file, or that the hits and step files are from the " @@ -768,30 +772,86 @@ def verify_volume_geometry(geom, py, pz, tick, t0, xyz_truth, volume_id=0): 'source': 'stated'} +#: The drift velocity is read off the t0 slope of x, so it is only a +#: measurement when that slope is pinned to better than this, relative. +_VELOCITY_REL_ERR_MAX = 0.05 + + +def _fit_drift(tick, t0, xt): + """ + Regress truth x on ``(1, tick, t0)``, and say whether the t0 slope is real. + + The drift velocity is the t0 slope: how x moves with t0 at fixed tick. + When every deposit shares nearly the same t0 that column is the constant + column scaled, the fit splits intercept and slope however it likes, and + both are meaningless -- only the tick slope survives. So the slope's + standard error is checked against the slope itself. + + Returns + ------- + icept, b_tick, b_t0 : float + measured : bool + Whether ``b_t0`` (and hence the intercept) is pinned by the data. + """ + A = np.stack([np.ones_like(tick), tick, t0], axis=1) + coef, *_ = np.linalg.lstsq(A, xt, rcond=None) + icept, b_tick, b_t0 = (float(c) for c in coef) + # Var(b_t0) is sigma^2 over the part of t0 that neither the constant nor + # the tick explains. + t0_perp = t0 - A[:, :2] @ np.linalg.lstsq(A[:, :2], t0, rcond=None)[0] + ss_perp = float(t0_perp @ t0_perp) + dof = max(len(xt) - 3, 1) + sigma = float(np.sqrt(np.sum((xt - A @ coef) ** 2) / dof)) + measured = bool(ss_perp > 0 and b_t0 != 0 and + sigma / np.sqrt(ss_perp) + < _VELOCITY_REL_ERR_MAX * abs(b_t0)) + return icept, b_tick, b_t0, measured + + def _measure_volume(py, pz, tick, t0, xt, yt, zt): - """What the data says the geometry is. Reporting only -- never applied.""" + """ + What the data says the geometry is. Reporting only -- never applied. + + The drift velocity comes from how x moves with t0 at fixed tick, so it + needs t0 to vary. When every deposit shares nearly the same t0 that + slope is degenerate with the intercept and the fit returns whatever + split minimises the residual; the velocity and sampling period are then + reported as NaN, with ``velocity_measured`` False, rather than as a + number that looks like a measurement. + """ def fit1(idx, target): A = np.stack([np.ones_like(idx), idx], axis=1) coef, *_ = np.linalg.lstsq(A, target, rcond=None) return coef (_cy0, pitch_y) = fit1(py, yt) (_cz0, pitch_z) = fit1(pz, zt) - A = np.stack([np.ones_like(tick), tick, t0], axis=1) - (icept, b_tick, b_t0), *_ = np.linalg.lstsq(A, xt, rcond=None) + icept, b_tick, b_t0, measured_v = _fit_drift(tick, t0, xt) mm_per_tick = abs(b_tick) - velocity = abs(b_t0) + velocity = abs(b_t0) if measured_v else float('nan') return {'pitch_mm': float(0.5 * (pitch_y + pitch_z)), 'pitch_y_mm': float(pitch_y), 'pitch_z_mm': float(pitch_z), 'mm_per_tick': float(mm_per_tick), 'drift_direction': -1 if b_tick > 0 else 1, 'drift_velocity_mm_us': float(velocity), - 'time_step_us': float(mm_per_tick / velocity) if velocity + 'time_step_us': float(mm_per_tick / velocity) if measured_v else float('nan'), + 'velocity_measured': bool(measured_v), + 't0_spread_us': float(np.std(t0)), 'fit_intercept_mm': float(icept)} +def describe_measured_drift(measured): + """The measured velocity and sampling period, or why there are none.""" + if measured.get('velocity_measured', True): + return (f"v {measured['drift_velocity_mm_us']:.4f} mm/us, " + f"dt {measured['time_step_us']:.4f} us") + return (f"v and dt not measurable (t0 spread " + f"{measured['t0_spread_us']:.3g} us)") + + def calibrate_volume(py, pz, tick, t0, xyz_truth, volume_id=0, - x_range=None, reference_tick=None): + x_range=None, reference_tick=None, + drift_velocity_mm_us=None, time_step_us=None): """ Recover one volume's pixel geometry from group centres and truth. @@ -830,6 +890,12 @@ def calibrate_volume(py, pz, tick, t0, xyz_truth, volume_id=0, reference_tick : float, optional The tick at which a deposit on the anode at Geant4 ``t = 0`` is recorded. ``None`` derives it from *x_range*. + drift_velocity_mm_us, time_step_us : float, optional + Stated values, used only when the data cannot measure the velocity + -- when t0 barely varies, so the t0 slope of x is degenerate with + the intercept. The velocity is preferred; failing that the velocity + is the fitted mm per tick over the time step. With neither, such a + batch is refused rather than given a velocity the fit made up. Returns ------- @@ -860,9 +926,36 @@ def fit1(idx, target): (cy0, pitch_y), ry = fit1(py, yt) (cz0, pitch_z), rz = fit1(pz, zt) - A = np.stack([np.ones_like(tick), tick, t0], axis=1) - (x_anode, b_tick, b_t0), *_ = np.linalg.lstsq(A, xt, rcond=None) - rx = xt - A @ np.array([x_anode, b_tick, b_t0]) + x_anode, b_tick, b_t0, measured_v = _fit_drift(tick, t0, xt) + velocity_source = 'fit' + if not measured_v: + # Only the tick slope is real. Take the velocity as stated, remove + # t0 with it, and refit the intercept and mm per tick without the + # degenerate column. + t0_spread = float(np.std(t0)) + if drift_velocity_mm_us is None and time_step_us is None: + raise PixelGeometryError( + f"volume {volume_id}: t0 barely varies over these {n} groups " + f"(spread {t0_spread:.3g} us), so the drift velocity cannot " + f"be measured -- only mm per tick ({abs(b_tick):.4f}) can. " + f"State it: set reader.jaxtpc_sensor_path, or " + f"reader.drift_velocity_mm_us / reader.readout_time_step_us.") + d = -1 if b_tick > 0 else 1 + A2 = np.stack([np.ones_like(tick), tick], axis=1) + if drift_velocity_mm_us is not None: + v_stated = float(drift_velocity_mm_us) + velocity_source = 'stated velocity' + (x_anode, b_tick), *_ = np.linalg.lstsq( + A2, xt - d * v_stated * t0, rcond=None) + else: + # x = c - d*mpt*(tick - t0/dt): one slope, on the t0-free tick. + dt_stated = float(time_step_us) + velocity_source = 'stated time step' + (x_anode, b_tick), *_ = np.linalg.lstsq( + np.stack([np.ones_like(tick), tick - t0 / dt_stated], axis=1), + xt, rcond=None) + v_stated = abs(b_tick) / dt_stated + b_t0 = d * v_stated pitch = 0.5 * (pitch_y + pitch_z) if not np.isfinite(pitch) or pitch <= 0: @@ -949,6 +1042,8 @@ def fit1(idx, target): 'reference_tick_offset_mm': float((ref - derived_ref) * mm_per_tick), 'drift_velocity_mm_us': float(velocity), 'time_step_us': float(dt), + 'velocity_source': velocity_source, + 't0_spread_us': float(np.std(t0)), 'residual_mm': res, } return geom, report diff --git a/tests/test_pixel_hits.py b/tests/test_pixel_hits.py index 885f02f..7255fd6 100644 --- a/tests/test_pixel_hits.py +++ b/tests/test_pixel_hits.py @@ -20,6 +20,7 @@ X_FROM, build_hit_point_cloud, calibrate_volume, + verify_volume_geometry, window_truncation, decode_plane_hits, decode_sensor_plane, @@ -287,6 +288,96 @@ def test_calibration_needs_enough_groups(): calibrate_volume(py, pz, tick, t0, truth) +def _synth_shared_t0(geom, t0_us, n=932, seed=5): + """ + Groups on *geom* whose deposits all share one interaction time. + + Truth x is scattered by a few tenths of a mm, as a deposit centroid is + about its group's tick; without that a 0.01 us spread in t0 would pin + the velocity exactly, which no real event does. + """ + rng = np.random.default_rng(seed) + py = rng.integers(0, 1000, n) + pz = rng.integers(0, 1000, n) + tick = rng.integers(0, 2000, n).astype(float) + t0 = np.full(n, t0_us) + rng.normal(0, 0.01, n) + x, y, z = geom.to_xyz(py, pz, tick - t0 / geom.time_step_us) + x = x + rng.normal(0, 0.3, n) + return py, pz, tick, t0, np.stack([x, y, z], axis=1) + + +@pytest.mark.parametrize("geom", [GEOM, MIRRORED]) +def test_verify_accepts_the_right_geometry_when_t0_does_not_vary(geom): + """ + With one shared, non-zero t0 the t0 slope of x is degenerate with the + intercept. The check once anchored on the fit's intercept and so + rejected a correct geometry by hundreds of mm; the anchor must come from + the stated constants instead. + """ + py, pz, tick, t0, truth = _synth_shared_t0(geom, 460.0) + rep = verify_volume_geometry(geom, py, pz, tick, t0, truth) + assert rep["residual_mm"]["x"] < 1.0 + assert rep["reference_tick_derived"] == pytest.approx(0.0, abs=0.5) + # ...and the velocity it cannot see is not reported as if it could. + assert not rep["measured"]["velocity_measured"] + assert np.isnan(rep["measured"]["drift_velocity_mm_us"]) + + +def test_verify_still_rejects_a_wrong_tick_length_when_t0_does_not_vary(): + py, pz, tick, t0, truth = _synth_shared_t0(GEOM, 460.0, seed=6) + wrong = VolumePixelGeometry(y_min_mm=-2160.0, z_min_mm=-2160.0, + x_min_mm=-2160.0, x_max_mm=0.0, + drift_direction=-1, pitch_mm=4.32, + drift_velocity_mm_us=1.6, time_step_us=0.4) + with pytest.raises(PixelGeometryError, match="not measurable"): + verify_volume_geometry(wrong, py, pz, tick, t0, truth) + + +def test_calibration_refuses_to_invent_a_velocity_when_t0_does_not_vary(): + """ + The fit used to split the degenerate intercept and t0 slope however it + liked and store the result as the drift velocity and reference tick. + """ + py, pz, tick, t0, truth = _synth_shared_t0(GEOM, 460.0, seed=8) + with pytest.raises(PixelGeometryError, match="cannot be measured"): + calibrate_volume(py, pz, tick, t0, truth, x_range=(-2160.0, 0.0)) + + +@pytest.mark.parametrize("stated", [{"drift_velocity_mm_us": 1.6}, + {"time_step_us": 0.5}]) +@pytest.mark.parametrize("geom, x_range", [(GEOM, (-2160.0, 0.0)), + (MIRRORED, (0.0, 2160.0))]) +def test_calibration_takes_the_stated_drift_when_t0_does_not_vary( + stated, geom, x_range): + py, pz, tick, t0, truth = _synth_shared_t0(geom, 460.0, seed=9) + got, report = calibrate_volume(py, pz, tick, t0, truth, x_range=x_range, + **stated) + assert report["velocity_source"].startswith("stated") + assert got.drift_direction == geom.drift_direction + assert got.drift_velocity_mm_us == pytest.approx(1.6, rel=1e-3) + assert got.time_step_us == pytest.approx(0.5, rel=1e-3) + assert got.reference_tick == pytest.approx(0.0, abs=0.5) + assert report["residual_mm"]["x"] < 1.0 + + +def test_calibration_prefers_the_fit_when_t0_varies(): + """A stated velocity is a fallback, never an override of a measurement.""" + py, pz, tick, t0, truth = _synth_groups(GEOM, seed=10) + got, report = calibrate_volume(py, pz, tick, t0, truth, + x_range=(-2160.0, 0.0), + drift_velocity_mm_us=1.0) + assert report["velocity_source"] == "fit" + assert got.drift_velocity_mm_us == pytest.approx(1.6, rel=1e-6) + + +def test_verify_measures_the_velocity_when_t0_varies(): + py, pz, tick, t0, truth = _synth_groups(GEOM, seed=7) + rep = verify_volume_geometry(GEOM, py, pz, tick, t0, truth) + assert rep["measured"]["velocity_measured"] + assert rep["measured"]["drift_velocity_mm_us"] == pytest.approx(1.6, + rel=1e-6) + + # --------------------------------------------------------------------------- # Per-group truth reductions # --------------------------------------------------------------------------- From add815c18c735113558a4eaddc755e86fcdd5465 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Wed, 7 Oct 2026 02:11:58 -0700 Subject: [PATCH 45/62] run: write an event the readout never saw as empty, instead of halting The empty-image guard stops a run on any event with no points, because that is nearly always a wrong seg/inst path. test_doraemon_2026_v0.1 has a second cause. The readout window is exactly one full drift (2701 x 0.5 us = 1350.5 us) and interaction times run to ~1300 us, so an event whose interactions all happen late reaches the anode only after the last tick. JAXTPC zeroes that charge and the event has no hits at all. Run 725 event 11 is one: 42,805 deposits with t0 860-1137 us, earliest arrival 1369 us in volume 1 and 1996 us in volume 0, 0 hit groups. It is the only such event among run 725's 100 (others: 2,199 to ~47,000 groups); run 93 has none, though its sparsest event keeps only 18-25 % of its charge. Supera job 40076964_16 halted on it. The reader already counts, per event, the deposits that land before and after the window. When an event is empty and every one of its deposits is outside the window, the run now prints that, writes the event out empty -- so event numbering stays aligned with the input -- and counts it in the hit summary ("Events wholly outside it"). Every other empty event still halts unless allow_empty_image is set. The decision is _wholly_outside_window, kept apart so it can be tested without a run. Checked on run 725, events 0-12: the run completes, event 11 is written with 0 points and 0 particle rows, and the other twelve are written as usual (6,200 to 31,296 points each). The production-side fix -- a longer window or a narrower t0 range, so that a typical event stops losing half its charge (50.1 % of deposits past the window over these 13 events) -- is separate. --- pysupera/_run.py | 39 ++++++++++++++++++++++++++++++++++++++- tests/test_pixel_hits.py | 23 +++++++++++++++++++++++ 2 files changed, 61 insertions(+), 1 deletion(-) diff --git a/pysupera/_run.py b/pysupera/_run.py index 4521fb6..79fb7e0 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -74,6 +74,21 @@ def _rows_by_input_id(particles): return rows +def _wholly_outside_window(hit_stats): + """ + Whether every deposit of an event arrived outside the readout window. + + *hit_stats* is the reader's ``last_hit_stats`` for the event (``None`` + outside hit mode). True only when there were deposits to miss: an event + with none is the configuration problem the empty-image guard exists for. + """ + hs = hit_stats or {} + n_dep = int(hs.get('n_deposits', 0) or 0) + n_out = (int(hs.get('n_before_window', 0) or 0) + + int(hs.get('n_after_window', 0) or 0)) + return n_dep > 0 and n_out >= n_dep + + @hydra.main(config_path=_CONF_DIR, config_name="config", version_base=None) def main(cfg: DictConfig) -> None: """Partition all events in the input file and write results.""" @@ -216,6 +231,8 @@ def _n_groups_of(_store): # Empty-image policy: halt by default, warn once when explicitly allowed. _allow_empty_image = bool(cfg.get("allow_empty_image", False)) _warned_empty_image = False + # Events written empty because no deposit reached the readout window. + n_out_of_window_events = 0 # Interaction-ID validation: raise by default, warn once when relaxed. _check_int_id = bool(cfg.get("check_interaction_id", True)) @@ -433,7 +450,22 @@ def _n_groups_of(_store): # holds no charge at all. This is nearly always a config # error (wrong seg/inst file, wrong step_key) rather than # physics, so halt unless explicitly allowed. - if not _nonzero: + # + # The exception is an event the readout never saw: deposits + # are there, but every one arrived outside the readout + # window -- an interaction late enough that its charge + # reaches the anode after the last tick. That is the + # detector, not the config, so it is written out empty (the + # event numbering stays aligned with the input) and the + # guard stays armed for everything else. + if not _nonzero and _wholly_outside_window(_hs): + n_out_of_window_events += 1 + print(f"[run] event {event_idx}: none of its " + f"{_hs['n_deposits']:,} deposits arrived inside the " + f"readout window ({_hs['n_before_window']:,} " + f"before, {_hs['n_after_window']:,} after); " + f"written as an empty event.") + elif not _nonzero: _msg = ( f"Event {event_idx}: empty image — all " f"{len(particles)} particle(s) have an empty point " @@ -830,6 +862,11 @@ def _agg(vals): if _bef: print(f" {'Deposits before it opened':<{_W}} " f"{_bef:>10,} ({100.0 * _bef / _dep:.1f}%)") + if n_out_of_window_events: + print(f" {'Events wholly outside it':<{_W}} " + f"{n_out_of_window_events:>10,} " + f"({100.0 * n_out_of_window_events / max(1, n_events):.1f}%)" + f" -- written empty") # A reconstructed x beyond its own volume's faces. Counted, never # clipped: with a nominal t0 the drift coordinate is an inference, diff --git a/tests/test_pixel_hits.py b/tests/test_pixel_hits.py index 7255fd6..fa8be72 100644 --- a/tests/test_pixel_hits.py +++ b/tests/test_pixel_hits.py @@ -586,6 +586,29 @@ def test_window_truncation_is_empty_when_nothing_is_late(): assert window_truncation(x, t0, GEOM, 2701) == (0, 0, 2) +@pytest.mark.parametrize("stats, expected", [ + # every deposit after the last tick: run 725 event 11, t0 >= 860 us + ({"n_deposits": 42805, "n_before_window": 0, "n_after_window": 42805}, + True), + ({"n_deposits": 10, "n_before_window": 4, "n_after_window": 6}, True), + # one deposit in the window: an empty image then means something else + ({"n_deposits": 10, "n_before_window": 0, "n_after_window": 9}, False), + # no deposits, or not hit mode: the configuration problem the guard is for + ({"n_deposits": 0, "n_before_window": 0, "n_after_window": 0}, False), + (None, False), + ({}, False), +]) +def test_an_event_wholly_outside_the_window_is_told_apart(stats, expected): + """ + The run's empty-image guard halts on an event with no points, because + that is nearly always a wrong file. An event whose interaction came so + late that none of its charge reached the anode before the last tick is + the detector instead, and is written out empty. + """ + from pysupera._run import _wholly_outside_window + assert _wholly_outside_window(stats) is expected + + # --------------------------------------------------------------------------- # Out-of-volume points # --------------------------------------------------------------------------- From f25faa453d521422ff140b283630ceb97e0cdf85 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Wed, 7 Oct 2026 22:29:46 -0700 Subject: [PATCH 46/62] pixel hits: check each volume's geometry on the first event it has hits The stated pixel geometry was verified once, on the first event, for every volume at the same time. A volume with fewer than 8 groups there stopped the run. With interaction times up to ~1300 us against a 1350.5 us window, a volume can be empty in an event: every deposit in it reaches the anode after the last tick. Run 1045 event 0 is one -- volume 0 has 22,241 deposits at t0 1104-1111 us, earliest arrival 1601 us, and no hits, while volume 1 has 2,591 groups. Supera job 40110241_11 stopped on it after 4 s. It is the only such event in the file. Had event 0 had no hits anywhere, the job would have died here before reaching the empty-event handling of the previous commit. Stated constants do not depend on the data, so they are now applied from the first event, and each volume is checked on the first event that gives it MIN_GROUPS (8) groups. Nothing about the events before the check changes once it runs. A wrong geometry still stops the run -- on the event where the check runs, rather than on event 0. Until checked, a volume's entry in pixel_geometry_report is None. The run summary now reads the report at the end, says which event each volume was checked on, and warns (it does not stop) about a volume that no event in the file gave enough groups. The fit path (pixel_geometry_from_fit) is unchanged: it needs data to produce the geometry at all. Checked on run 1045, events 0-2: the run completes; volume 1 is checked on event 0 (2,591 groups, residual x 0.22 y 1.07 z 1.06 mm) and volume 0 on event 1 (11,047 groups, x 0.22 y 1.07 z 1.08 mm), both measuring v 1.6003 mm/us against the stated 1.6. Event 0 is written with 1,955 points, all from volume 1. Tests drive _check_pending_volumes on a reader with synthetic pairs: a volume empty in the first event is checked on the next, and checked only once; a wrong geometry is still rejected when its check runs; a volume with MIN_GROUPS - 1 groups waits. --- pysupera/_run.py | 17 +++++++- pysupera/readers/format_jaxtpc.py | 53 ++++++++++++++++++----- pysupera/readers/pixel_hits.py | 8 +++- tests/test_pixel_hits.py | 70 +++++++++++++++++++++++++++++++ 4 files changed, 134 insertions(+), 14 deletions(-) diff --git a/pysupera/_run.py b/pysupera/_run.py index 79fb7e0..5ce1569 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -782,9 +782,22 @@ def _agg(vals): # to prevent. 'stated' means applied as given and verified against # the truth deposits; 'fitted' means measured from them, which makes # the same comparison circular. + # Re-read: a volume is checked on the first event that has enough + # of its groups, which need not be the first event. + _pixel_geom_report = (getattr(store, 'pixel_geometry_report', None) + or _pixel_geom_report) if _pixel_geom_report: print(f"\n[run] Pixel geometry ({len(_pixel_geom_report)} volume(s))") for _v, _g in enumerate(_pixel_geom_report): + if _g is None: + import warnings + from pysupera.readers.pixel_hits import MIN_GROUPS + _why = (f"pixel geometry of volume {_v} was applied but " + f"never checked: no event gave it {MIN_GROUPS} " + f"groups with both hits and truth deposits") + print(f" volume {_v} [stated, UNCHECKED] {_why}") + warnings.warn(_why, UserWarning, stacklevel=2) + continue _m = _g.get('measured') or {} _src = _g.get('source', 'stated') print(f" volume {_v} [{_src}] " @@ -803,7 +816,9 @@ def _agg(vals): if _r: print(f" residual " f"x {_r['x']:.2f} y {_r['y']:.2f} z {_r['z']:.2f} mm" - f" over {_g.get('n_groups', 0):,} groups") + f" over {_g.get('n_groups', 0):,} groups" + + (f" of {_g['checked_on']}" + if _g.get('checked_on') else "")) _off = _g.get('reference_tick_offset_mm') if _off: print(f" reference tick sits {_off:+.1f} mm " diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index 928c9ad..b86acb2 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -566,6 +566,10 @@ def __init__( # the run, not of the event. An explicit list skips calibration. self._pixel_geoms = pixel_geometry self._pixel_report = None + # Volumes whose stated geometry is not yet checked against truth: + # each is checked on the first event that gives it enough groups. + self._pixel_unchecked: set = set() + self._pixel_note = None #: Hit counts for the most recently read event in hit mode. self.last_hit_stats: dict | None = None @@ -1128,25 +1132,31 @@ def _ensure_pixel_geometry(self, event_key, seg_volumes): exists to measure gets calibrated away instead of measured. So the stated numbers are applied as given and the residual is allowed to object. + + Stated constants do not depend on the data, so they are applied + from the first event, and each volume is checked on the first event + that gives it at least :data:`~.pixel_hits.MIN_GROUPS` groups. A + volume can be empty in an event -- every deposit in it arriving + after the readout window closed -- and the run must not stop on it. + Until checked, a volume's report entry is ``None``. """ if self._pixel_geoms is not None: + self._check_pending_volumes(event_key, seg_volumes) return self._pixel_geoms geoms, note = self._resolve_pixel_geometry(seg_volumes) - pairs = [self._group_centre_truth(event_key, sv, v) - for v, sv in enumerate(seg_volumes)] if geoms is not None: - reports = [] - for v, (geom, (py, pz, tk, t0, cent)) in enumerate( - zip(geoms, pairs)): - rep = _px.verify_volume_geometry(geom, py, pz, tk, t0, cent, - volume_id=v) - rep['source_note'] = note - reports.append(rep) - self._pixel_geoms, self._pixel_report = geoms, reports + self._pixel_geoms = geoms + self._pixel_report = [None] * len(geoms) + self._pixel_note = note + self._pixel_unchecked = set(range(len(geoms))) + self._check_pending_volumes(event_key, seg_volumes) return geoms + pairs = [self._group_centre_truth(event_key, sv, v) + for v, sv in enumerate(seg_volumes)] + if not self._geometry_from_fit: # Say what is missing *and* what the data says it should be, so # the config can be written from the error rather than guessed. @@ -1203,6 +1213,22 @@ def _ensure_pixel_geometry(self, event_key, seg_volumes): self._pixel_geoms, self._pixel_report = geoms, reports return geoms + def _check_pending_volumes(self, event_key, seg_volumes): + """Check each still-unchecked volume this event has enough groups in.""" + for v in sorted(self._pixel_unchecked): + if v >= len(seg_volumes): + continue + py, pz, tk, t0, cent = self._group_centre_truth( + event_key, seg_volumes[v], v) + if len(py) < _px.MIN_GROUPS: + continue + rep = _px.verify_volume_geometry(self._pixel_geoms[v], py, pz, + tk, t0, cent, volume_id=v) + rep['source_note'] = self._pixel_note + rep['checked_on'] = event_key + self._pixel_report[v] = rep + self._pixel_unchecked.discard(v) + def _build_hit_event(self, event_key, seg_volumes, track_ids): """Point cloud, offsets and per-point group for one event.""" ev = self._inst_file[event_key] @@ -1555,7 +1581,12 @@ def pixel_geometry(self) -> list | None: @property def pixel_geometry_report(self) -> list | None: - """What the geometry calibration found, including its residuals.""" + """ + What the geometry calibration found, including its residuals. + + One entry per volume; ``None`` for a stated volume that no event so + far has given enough groups to check. + """ return self._pixel_report @property diff --git a/pysupera/readers/pixel_hits.py b/pysupera/readers/pixel_hits.py index 45fb4e2..af22d3a 100644 --- a/pysupera/readers/pixel_hits.py +++ b/pysupera/readers/pixel_hits.py @@ -580,6 +580,10 @@ def build_hit_point_cloud(volumes, track_ids_edepsim, *, _FIT_TOL_XY_PITCHES = 1.0 _FIT_TOL_X_MM = 5.0 +#: Fewest groups with both a hit centre and truth deposits that a volume's +#: geometry is checked or fitted on. +MIN_GROUPS = 8 + class PixelGeometryError(ValueError): """The pixel geometry could not be established from the files.""" @@ -691,7 +695,7 @@ def verify_volume_geometry(geom, py, pz, tick, t0, xyz_truth, volume_id=0): Stated values, what the fit measured, and the residuals. """ n = len(py) - if n < 8: + if n < MIN_GROUPS: raise PixelGeometryError( f"volume {volume_id}: only {n} group(s) have both a hit centre " f"and truth deposits, too few to check the pixel geometry " @@ -904,7 +908,7 @@ def calibrate_volume(py, pz, tick, t0, xyz_truth, volume_id=0, Fitted values and residuals, for logging. """ n = len(py) - if n < 8: + if n < MIN_GROUPS: raise PixelGeometryError( f"volume {volume_id}: only {n} group(s) have both a hit centre " f"and truth deposits, which is too few to establish the pixel " diff --git a/tests/test_pixel_hits.py b/tests/test_pixel_hits.py index fa8be72..c90f54d 100644 --- a/tests/test_pixel_hits.py +++ b/tests/test_pixel_hits.py @@ -15,6 +15,7 @@ from pysupera.readers.pixel_hits import ( ENERGY_FROM, HitsFileError, + MIN_GROUPS, PixelGeometryError, VolumePixelGeometry, X_FROM, @@ -378,6 +379,75 @@ def test_verify_measures_the_velocity_when_t0_varies(): rel=1e-6) +def _reader_with_pairs(geoms, pairs): + """ + A hit-mode reader holding stated *geoms*, unchecked, whose group/truth + pairs come from ``pairs[(event_key, volume)]`` instead of files. + """ + from pysupera.readers import JaxtpcHDF5Reader + r = JaxtpcHDF5Reader.__new__(JaxtpcHDF5Reader) + r._pixel_geoms = list(geoms) + r._pixel_report = [None] * len(geoms) + r._pixel_note = "stated in the test" + r._pixel_unchecked = set(range(len(geoms))) + r._group_centre_truth = lambda ek, sv, v: pairs[(ek, v)] + return r + + +def _no_groups(): + z = np.zeros(0) + return z, z, z, z, np.zeros((0, 3)) + + +def test_a_volume_empty_in_the_first_event_is_checked_on_a_later_one(): + """ + Run 1045 event 0: every deposit in volume 0 arrives after the window + closes, so it has no hits, while volume 1 has thousands. The stated + geometry does not need data to apply, so the run must carry on and + check volume 0 on the first event that gives it groups. + """ + pairs = {("event_000", 0): _no_groups(), + ("event_000", 1): _synth_groups(MIRRORED, seed=11), + ("event_001", 0): _synth_groups(GEOM, seed=12), + ("event_001", 1): _synth_groups(MIRRORED, seed=13)} + r = _reader_with_pairs([GEOM, MIRRORED], pairs) + segs = [None, None] + + r._check_pending_volumes("event_000", segs) + assert r.pixel_geometry_report[0] is None + assert r.pixel_geometry_report[1]["checked_on"] == "event_000" + + r._check_pending_volumes("event_001", segs) + assert r.pixel_geometry_report[0]["checked_on"] == "event_001" + assert r.pixel_geometry_report[0]["residual_mm"]["x"] < 1.0 + # checked once: event_001 did not re-check volume 1 + assert r.pixel_geometry_report[1]["checked_on"] == "event_000" + assert not r._pixel_unchecked + + +def test_a_deferred_check_still_rejects_a_wrong_geometry(): + """Deferring the check must not weaken it: it fails on the event it runs.""" + wrong = VolumePixelGeometry(y_min_mm=-2160.0, z_min_mm=-2160.0, + x_min_mm=-2160.0, x_max_mm=0.0, + drift_direction=-1, pitch_mm=4.32, + drift_velocity_mm_us=1.6, time_step_us=0.4) + pairs = {("event_000", 0): _no_groups(), + ("event_001", 0): _synth_groups(GEOM, seed=14)} + r = _reader_with_pairs([wrong], pairs) + r._check_pending_volumes("event_000", [None]) + with pytest.raises(PixelGeometryError, match="does not reproduce"): + r._check_pending_volumes("event_001", [None]) + + +def test_a_volume_with_too_few_groups_waits(): + py, pz, tick, t0, truth = _synth_groups(GEOM, n=MIN_GROUPS - 1, seed=15) + r = _reader_with_pairs([GEOM], {("event_000", 0): + (py, pz, tick, t0, truth)}) + r._check_pending_volumes("event_000", [None]) + assert r.pixel_geometry_report == [None] + assert r._pixel_unchecked == {0} + + # --------------------------------------------------------------------------- # Per-group truth reductions # --------------------------------------------------------------------------- From 17760cac5399e38f4b06adece28d603a98d2ba21 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Wed, 14 Oct 2026 10:29:22 -0700 Subject: [PATCH 47/62] format 4.0.0: LE per fragment, showers from the pair, instance kinematics LE now marks blobs: a point is LE when its fragment is a kLEScatter fragment, a cluster with no trajectory. A low-energy scatter that AbsorbLEScatter merged into a track or shower fragment has become part of that trajectory, so it is no longer LE, although the particle's own sem_type stays kLEScatter. An LE fragment grouped into an instance through parentage stays LE. points/flat, hit_labels/*/is_le and groups/is_le all follow this one rule (layout.fragment_le). That rule only holds if proximity is the one way an LE particle enters a non-LE fragment, and it was not: PhotonDecay merged every e+- child of a photon into the photon's fragment, Compton electrons and photoelectrons included -- 23-31% of an event's points, many of them isolated blobs metres from anything (3.3.0: 20% of LE points sat over 100 mm from their fragment's non-LE points). A shower starts at the pair production, so: - PhotonDecay merges only the kConversion pair (and the photon's own split-off pieces), and promotes the photon to kShower only then. - merge_em_showers lets LE fragments join a shower instance but never head one, and lets a Compton electron or photoelectron climb to its photon only when that photon is itself inside a shower. A top-level photon's Compton electrons, from before it converted or instead of converting, are their own instances; the Compton blobs of a brem photon inside a shower join it, as LE. LE is 22-29% of points and 6-8% of the energy on three sample events per mode. Photon fragments that were several distant blobs go away: fragments that are not one connected blob at distance_threshold drop from 182 of 2366 (3.3.0, wire, 10 events) to 15 of 1005 (wire) and 9 of 1484 (EDepSim). Two more rules put low-energy pieces where they came from: - AbsorbLEScatter, when an LE partition touches several neighbours, takes the one holding its nearest ancestor if that is among them, instead of whichever the proximity search listed first. A delta ray goes to its track rather than to a shower crossing it: in pixel mode 505 of 523 LE deltas now join their parent's fragment, against 454. The partitioner publishes its live rep_lookup so post_filter can ask. - A stand-alone piece that defragmentation cut off a kTrack, kMichel or kDelta particle joins that particle's instance, and its fragment takes the particle's fragment type, so it is not LE (merge.find_split_pieces, relabel_split_pieces, attach_split_pieces). A piece proximity absorbed elsewhere stays there. Tracks are not defragmented by default, so this acts on delta and Michel pieces unless preprocessor.sem_types says otherwise. Since a fragment is wholly LE or not, it is one run: frag_pc_le_start/end are gone. An instance is three fenceposts, inst_pc_start <= inst_pc_le_start <= inst_pc_end, so inst_pc_le_end is gone, and inst_pc_end now ends the whole instance where in 3.x it ended the non-LE run. The column keeps its name but changes its meaning, so the reader takes 4.x only and refuses a 3.x file with a note to regenerate it. The 3.0 compatibility code goes with it. _check_ranges also refuses a fragment that mixes the two sides. Kinematics, all float64 (M, 4): *_pos [x, y, z, t] (mm, ns), *_mom [px, py, pz, E] (MeV, E total), plus mass. float64 because E - mass would lose a slow nucleus's KE in float32: about 2 keV of resolution for 40Ar. - mass, start_* and end_* are the row's own Geant4 particle's, joined on geant4_trackid, so a split piece shares its track's. Geant4 records a converting photon's end momentum as zero; end_mom is its momentum just before converting instead -- start less its Compton electrons', exact up to binding energy (Rayleigh scattering is not seen). - first_* and last_* describe an instance, the thing a model reconstructs, and sit on instance rows only. pysupera.kinematics makes them once the instances exist: each Geant4 step goes to the particle holding its deposit (the voxel map with EDepSim, the segment's group with JAXTPC, as the truth cloud does), and of an instance's counted steps -- visible with JAXTPC, all with EDepSim; root attribute kinematics_steps -- those of Geant4 particles with a non-LE member in it count, so a blob absorbed from an unrelated particle cannot be the last point. A conversion pair counts however small; an instance whose non-LE particles left no counted step uses all of its steps. first is the start of the earliest step, last the end of the latest: a step record holds its midpoint (median residual 0.0005 mm, against 0.097 mm for the start-of-step reading), so the ends move dx/2 along the step, and t by dx/2 / (beta c). - A kShower instance has no last point (NaN). When its photon's pair made the first counted step, first_mom is the pair's summed creation momentum: the photon's energy exactly, its direction up to the nuclear recoil (median 0.56 MeV against end_mom). When the readout missed the pair, the first point is a later product's, with that step's momentum. The readers hand over their step table (last_steps) instead of first/last; the JAXTPC reader's step-to-segment matching moves into _matched_steps, shared by the truth cloud, the visibility mask and the step groups. torchdata labels the [inst_pc_le_start, inst_pc_end) run as LE, and its instances table carries the kinematics. The three viewers read 4.x, take LE from frag_sem_type, and show start/end and, on instance rows, the first and last point with |p| and E. vis_hits' "hide LE" box becomes an LE selector (show all / hide LE / hide non-LE), and vis_drift gets the same one. They parse under node --check but were not run in a browser for this change. README updated throughout. Checked on three events in each mode (EDepSim, JAXTPC wire, JAXTPC pixel): no overlapping ranges; every instance row with points has a first point and no other row has one; no shower has a last point; at the conversion, pair-sum E matches the photon's end_mom E to 1e-7. Tests: 511 passed, 1 skipped. --- README.md | 200 ++++++++++---- pysupera/_run.py | 39 ++- pysupera/conditions/__init__.py | 9 +- pysupera/conditions/absorb_le_scatter.py | 60 ++++- pysupera/conditions/photon_decay.py | 61 +++-- pysupera/config.py | 2 +- pysupera/hit_labels.py | 13 +- pysupera/io_v3.py | 247 ++++++++---------- pysupera/kinematics.py | 227 ++++++++++++++++ pysupera/layout.py | 178 +++++++++---- pysupera/merge.py | 234 +++++++++++++---- pysupera/partitioner.py | 4 + pysupera/provenance.py | 17 +- pysupera/readers/format_edepsim_h5.py | 154 +++++++++++ pysupera/readers/format_jaxtpc.py | 112 +++++++- pysupera/torchdata.py | 50 ++-- tests/test_conditions.py | 44 +++- tests/test_instance_rules.py | 316 +++++++++++++++++++++++ tests/test_io_v3.py | 203 +++++++++++++-- tests/test_jaxtpc_pixel_data.py | 55 ++++ tests/test_partitioner.py | 4 +- tests/test_torchdata.py | 18 +- vis_drift.html | 62 ++++- vis_force.html | 97 ++++--- vis_hits.html | 99 +++++-- 25 files changed, 2030 insertions(+), 475 deletions(-) create mode 100644 pysupera/kinematics.py create mode 100644 tests/test_instance_rules.py diff --git a/README.md b/README.md index c9ffc54..8d4584c 100644 --- a/README.md +++ b/README.md @@ -236,6 +236,12 @@ output's `hit_labels`. It finds planes structurally and copies `config`, so it needs no flag to tell wire from pixel. `--centres-only` keeps just the group centres, for a much smaller file when the per-hit rows are not needed. +The viewers read format 4.x (and `vis_force.html` also 2.x), and refuse a 3.x +output with a note to regenerate it. They take LE from the fragment, as the +format does, and their hover panels show the selected row's kinematics: mass, +kinetic energy at creation and destruction, the start and end positions, and +on an instance row the instance's first and last point with `|p|` and `E`. + #### `vis_force.html` — EDepSim only One 3-D cloud of `points/flat`. Colour by energy, time, interaction id, @@ -266,7 +272,7 @@ voxel's range in `points/flat` and the hit's `instance_id` / `fragment_id` in |---|---| | **highlight** | instance, fragment or interaction — what "the object under the cursor" means | | **others** | opacity of everything not selected | -| **hide LE** | drops low-energy voxels and hits from every view, so the picker cannot select them | +| **LE** | show all, hide LE, or hide non-LE: drops the chosen voxels and hits from every view, so the picker cannot select them | | **point info** | position, t, dE, dX, θ, φ, \|p\| and labels for a voxel; volume, plane, wire, tick, charge and labels for a hit | | **c** | fit the 3-D view | @@ -303,7 +309,8 @@ independently of the colour. | fragment / instance / interaction id | a stable hash, the same in both panes | | particle type (PDG) | the instance's PDG, fixed hues for the six commonest species | -The semantic-type strip filters classes in and out of both panes, and a +The semantic-type strip filters classes in and out of both panes, the +**LE** selector shows all points, hides LE or hides non-LE, and a **show ≥** slider per pane hides the faintest points by percentile, showing the absolute value it lands on. By default only on-sensor hits are drawn — the ones the model sees; **off-sensor hits** adds the rest, which carry no @@ -502,7 +509,7 @@ defragmentation and the extent classification see. See #### Defragmentation -A particle's point cloud may be *fragmented* — split into spatially disconnected clusters that should logically belong to the same particle. The defragmenter runs a connected-components algorithm (eps-ball radius = `distance_threshold`) on each particle's point cloud independently, then detaches small disconnected fragments (size ≤ `min_pc_size`) as new `kLEScatter` particles. By default only `kShower`, `kDelta`, `kMichel` and `kLEScatter` particles are examined (`preprocessor.sem_types`); tracks are left whole. +A particle's point cloud may be *fragmented* — split into spatially disconnected clusters that should logically belong to the same particle. The defragmenter runs a connected-components algorithm (eps-ball radius = `distance_threshold`) on each particle's point cloud independently, then detaches small disconnected fragments (size ≤ `min_pc_size`) as new `kLEScatter` particles. By default only `kShower`, `kDelta`, `kMichel` and `kLEScatter` particles are examined (`preprocessor.sem_types`); tracks are left whole. A detached piece of a `kTrack`, `kMichel` or `kDelta` particle that touches nothing joins its particle's instance and is not LE (its fragment takes the particle's fragment type); one that proximity absorbed elsewhere stays there. Enable with: @@ -595,7 +602,41 @@ with read_events("output.h5") as store: ## Output File Format `run_pysupera` writes a single HDF5 file. This section describes format -**3.3.0**, recorded in the scalar dataset `format_version`. +**4.0.0**, recorded in the scalar dataset `format_version`. + +4.0.0 changed two things, and 3.x files are no longer read — regenerate them: + +- **LE is decided per fragment.** A point is LE when its fragment is a + `kLEScatter` fragment: a blob with no trajectory. A low-energy scatter + that `AbsorbLEScatter` merged into a track or shower fragment is part of + that trajectory and is *not* LE, although its own `sem_type` stays + `kLEScatter`. An LE fragment grouped into an instance through parentage + stays LE. This applies to `points/flat`, `hit_labels/*/is_le` and + `groups/is_le` alike. +- **A shower starts at the pair production.** `PhotonDecay` merges only the + e⁺e⁻ a photon converted into, not its Compton electrons or photoelectron, + which stay their own particles. Those join a shower *instance* only when + their photon is itself inside a shower; a top-level photon's are their own + instances. LE fragments can join a shower instance but never head one. + Together these make proximity the only way an LE particle enters a non-LE + fragment, which is what lets a fragment be wholly LE or not. On the sample + data LE is 22–29% of points and 6–8% of the energy (3.3.0: 35–43% of + points, most of it Compton blobs merged into photon fragments). +- **Low-energy pieces go where they came from.** `AbsorbLEScatter` absorbs an + LE particle touching several things into its own ancestor when that is one + of them, so a delta ray goes to its track. A stand-alone piece that + defragmentation cut off a `kTrack`, `kMichel` or `kDelta` particle joins + that particle's instance and is not LE. +- **Kinematics.** Every row carries its Geant4 particle's `mass` and + creation and destruction position and 4-momentum; every instance row also + its instance's first and last point, made from the deposits it collected. + All float64 (see [Kinematics](#kinematics)). + +With LE per fragment a fragment is one run, so `frag_pc_le_start/end` are +gone, and an instance is three fenceposts, `inst_pc_start ≤ +inst_pc_le_start ≤ inst_pc_end`, so `inst_pc_le_end` is gone and +**`inst_pc_end` now ends the whole instance** (in 3.x it ended the non-LE +run). 3.3.0 reshaped JAXTPC output. `points/flat` now holds the true energy deposits behind the hits (see [What the output stores](#what-the-output-stores-jaxtpc-input)), @@ -603,16 +644,15 @@ with direction and momentum, and names its columns in `points/columns`; the hits stay in the JAXTPC hits file, labelled per hit in `hit_labels/`. `truth/`, `points/hit_index` and `points/true_x_shift` are gone, and fragment rows name their instance (`frag_inst_id`). -3.1.0 renamed columns for a consistent `_` prefix, gave instances the -same two-range point layout as fragments, and added the true Geant4 parent. -Readers accept 3.0.0 files transparently. 3.0.0 replaced the three parallel +3.1.0 renamed columns for a consistent `_` prefix and added the true +Geant4 parent. 3.0.0 replaced the three parallel particle tables of 2.x with **one** table and stores each point **once**. On a two-event file that is 6.4 MiB → 1.7 MiB. ### Layout ``` -format_version scalar str "3.3.0" +format_version scalar str "4.0.0" n_events scalar int64 events/offsets (n_events+1,) int64 particle rows per event points/offsets (n_events+1,) int64 point rows per event @@ -620,6 +660,7 @@ inter/offsets (n_events+1,) int64 interaction rows per event points/flat (N, C) float32 one row per voxel (or deposit) points/columns (C,) str its column names particles/ (M,) +particles/ (M, 4) float64 kinematics; see below inter/ (K,) groups/ JAXTPC group -> owning fragment } JAXTPC hit_labels/volume{V}/plane{P}/ per hit of that plane } input @@ -658,14 +699,13 @@ interaction > instance > is_LE > fragment > particle Three consequences follow, and between them they replace everything 2.x needed per-point group labels for: -1. **An instance's points are one contiguous block**, split into a non-LE part - followed by an LE part. All three useful queries are single slices. -2. **A fragment's points are two runs** — its non-LE side and its LE side — - because other fragments' non-LE points lie between them. Each side is - always exactly *one* run, never more, so two ranges describe a fragment - completely. +1. **An instance's points are one contiguous block**, its non-LE fragments + followed by its LE fragments. All three useful queries are single slices. +2. **A fragment's points are one run.** A fragment is wholly LE or wholly + not, so it sits entirely on one side of its instance's split. 3. **LE-ness is positional**, so no per-point label is stored. A point is LE - precisely when its index falls in its instance's `inst_pc_le_*` range. + precisely when its fragment's `frag_sem_type` is `kLEScatter` — inside a + written instance, when its index is in `[inst_pc_le_start, inst_pc_end)`. The ordering is chosen for the query panoptic-segmentation training issues most often — "the non-LE voxels of instance X" — which is a plain slice rather than @@ -707,20 +747,21 @@ which say which *particle* deposited a point: ```python frag = v.group_of_points("frag") # fragment id per point, -1 if none inst = v.group_of_points("inst") # instance id per point +le = v.is_le_point() # bool per point ``` Equivalently, by hand: ```python -non_le = v.points[v["inst_pc_start"][r] : v["inst_pc_end"][r]] -le = v.points[v["inst_pc_le_start"][r] : v["inst_pc_le_end"][r]] +non_le = v.points[v["inst_pc_start"][r] : v["inst_pc_le_start"][r]] +le = v.points[v["inst_pc_le_start"][r] : v["inst_pc_end"][r]] +whole = v.points[v["inst_pc_start"][r] : v["inst_pc_end"][r]] -frag_nle = v.points[v["frag_pc_start"][r] : v["frag_pc_end"][r]] -frag_le = v.points[v["frag_pc_le_start"][r] : v["frag_pc_le_end"][r]] +frag = v.points[v["frag_pc_start"][r] : v["frag_pc_end"][r]] ``` -`-1` marks a side with no points. `points_of(r, "frag")` concatenates the two -runs; every other combination is a single slice. +`-1` marks a group with no points; an empty side of an instance is two equal +fenceposts. ### Which particles are stored @@ -753,22 +794,79 @@ have rows, and no point is unreachable from some stored row. | `pc_start`, `pc_end` | int64 | this particle's own points | | `frag_id` | int32 | representative's `id`; `== id` marks a fragment, `-1` none | | `frag_sem_type` | int8 | the fragment's classification | -| `frag_pc_start/end` | int64 | the fragment's non-LE points | -| `frag_pc_le_start/end` | int64 | the fragment's LE points | +| `frag_pc_start/end` | int64 | the fragment's points; LE iff `frag_sem_type` is `kLEScatter` | | `frag_merge_count` | int32 | Geant4 particles merged into the fragment | | `frag_parent_id` | int32 | nearest ancestor fragment; `== id` if none | | `frag_inst_id` | int32 | on a fragment row, the instance it belongs to; -1 elsewhere or when that instance is not written | | `inst_id` | int32 | representative's `id`; `== id` marks an instance | | `inst_sem_type` | int8 | the instance's classification | -| `inst_pc_start/end` | int64 | the instance's non-LE points | -| `inst_pc_le_start/end` | int64 | the instance's LE points (adjacent: `inst_pc_end == inst_pc_le_start`) | +| `inst_pc_start`, `inst_pc_le_start`, `inst_pc_end` | int64 | the instance's points: non-LE in `[start, le_start)`, LE in `[le_start, end)` | | `inst_merge_count` | int32 | Geant4 particles merged into the instance | | `inst_parent_id` | int32 | nearest ancestor instance; `== id` if none | +| `mass` | float64 | rest mass [MeV/c²] | +| `start_pos`, `start_mom` | float64 (M, 4) | at creation | +| `first_pos`, `first_mom` | float64 (M, 4) | instance rows: the start of the instance's first counted step | +| `last_pos`, `last_mom` | float64 (M, 4) | instance rows: the end of its last counted step; NaN for a shower | +| `end_pos`, `end_mom` | float64 (M, 4) | at destruction | A particle carries up to three classifications — its own, its fragment's and -its instance's — because merging reclassifies. Note that LE barely survives to -the group levels: `absorb_le_scatter` folds low-energy scatter into whatever -touches it, so ~31% of *points* are LE while almost no *instance* is. +its instance's — because merging reclassifies. A `kLEScatter` particle that +`absorb_le_scatter` folded into a track keeps `sem_type == kLEScatter`, but +its points are the track fragment's and not LE. + +### Kinematics + +`*_pos` is `[x, y, z, t]` in mm and ns; `*_mom` is `[px, py, pz, E]` in MeV +with **E the total energy**, so KE is `E − mass`. NaN where unknown. + +**`mass`, `start_*`, `end_*`** describe the row's own Geant4 particle, joined +on `geant4_trackid`: the pieces defragmentation splits off one track all +carry that track's values. They come from `particle/geant4` (`x…ke`, +`end_x…end_ke`), with one correction: Geant4 records a converting photon's +end momentum as zero (its state after it was killed), so `end_mom` is set to +its momentum just before converting — the start momentum less what its +Compton electrons carried off, energy likewise. That is exact up to binding +energy; Rayleigh scattering leaves no secondary and is not accounted for. + +**`first_*`, `last_*`** describe an *instance* — what a model is asked to +reconstruct, as drawn — and are on instance rows only (NaN elsewhere). They +are made after the instances exist, from the deposits the instance collected +([`pysupera.kinematics`](pysupera/kinematics.py)): + +- Each Geant4 step goes to the particle holding its deposit: through the + voxel map with plain EDepSim, through its segment's group with JAXTPC — + where the truth cloud puts it. So steps follow their deposits through + defragmentation, absorption and shower merging, as the points do. +- Only *counted* steps are used — the root attribute `kinematics_steps`: + `visible` with JAXTPC (the step's group left an entry on a readout plane), + `all` with plain EDepSim — and of those only the steps of a Geant4 particle + that has a non-LE member in the instance. A blob absorbed from an + unrelated particle is part of the picture but not of the trajectory, and + can be later than all of it. A photon's conversion pair counts as non-LE + however small its clouds: it is the shower's start. An instance with no + non-LE member, or whose non-LE members left no counted step, uses all its + steps. +- `first` is the start of the earliest such step, `last` the end of the + latest. A step record holds its **midpoint** (on the sample data a + particle's first step sits half a `dx` from its creation point: median + residual 0.0005 mm against 0.097 mm for the start-of-step reading), so the + position moves `dx/2` along the step's direction, as if it were straight, + and `t` by the matching `dx/2 / (βc)`. The momentum is the step's own `|p|` + along its direction, with the mass of the particle that made the step — so + `first_mom`'s E can be below the row's own `mass`. +- A **`kShower`** instance has no well-defined last point: `last_*` is NaN. + When a photon heads it and its pair made the first counted step, + `first_mom` is the pair's summed creation momentum: the photon's energy + exactly, and its direction up to the nucleus's recoil (median 0.5 MeV). It + then differs from the photon's `end_mom` by that recoil. When the readout + missed the pair, the first point is on a later product and `first_mom` is + that step's. +- `first_pos` can be far from `start_pos`: a photon deposits only once it + converts, and a particle the readout did not see at first is first seen + later. + +All float64, although the input is float32, so that `E − mass` keeps a slow +nucleus's kinetic energy: a float32 E for ⁴⁰Ar resolves only ~2 keV. ### Interaction columns @@ -1001,7 +1099,7 @@ with the same index: hit_labels/volume{V}/plane{P}/ attrs: source ("U", "V", "Y" or "Pixel") fragment_id int32 the fragment row the hit belongs to; -1 untraced instance_id int32 the instance row; -1 untraced - is_le bool the hit's particle is kLEScatter + is_le bool the hit's fragment is a kLEScatter fragment on_sensor bool pixel only: its pixel is in the sensor image offsets int64 per-event fenceposts ``` @@ -1225,7 +1323,8 @@ it is aligned with the hits file and needs no join. `groups/` is the group-level view behind it, kept because it is small and answers questions about groups directly. A hit is the projection of a JAXTPC *group*, and `groups/` records the fragment that owns each group, plus one bit saying -whether that group is low-energy: +whether that group is low-energy — whether its fragment is a `kLEScatter` +fragment: ```python with read_events_v3("out.h5") as store: @@ -1379,7 +1478,7 @@ Records every merge decision in memory. Not written to the output file. ## Training Data (PyTorch) -`pysupera.torchdata` turns a 3.x file into batches for panoptic-segmentation +`pysupera.torchdata` turns a 4.x file into batches for panoptic-segmentation training. Install the extra first: ```bash @@ -1432,8 +1531,8 @@ another instance's. ### Low-energy scatters — `include_le` -LE deposits are roughly a third of all points. They are **excluded by -default**: +LE points — those of `kLEScatter` fragments, blobs with no trajectory — are +**excluded by default**: ```python loader = make_dataloader("out.h5", include_le=True) # keep them @@ -1441,15 +1540,24 @@ loader = make_dataloader("out.h5", include_le=True) # keep them The flag governs the voxel data as a whole — input coordinates, energy, and all three label arrays — so the model never sees a point that the labels do not -describe, and an excluded point contributes no energy either. On a sample -event, dropping LE removes 30% of the voxels and 13% of the energy. - -A point counts as LE when its label is `kLEScatter`, which covers both an LE -deposit absorbed into some other instance (LE-ness is positional — see -[Point ordering](#point-ordering-and-why-it-matters)) and a point of an -instance that is itself LE. The instance and interaction labels of an absorbed -LE point remain those of its host, so it stays attributed to the shower or -track that absorbed it. +describe, and an excluded point contributes no energy either. Since 4.0.0 a +low-energy deposit absorbed into a track or shower is that track's or +shower's and is kept. Over three sample events the flag removes 22–29% of +the voxels and 6–8% of the energy: the blobs with no trajectory — Compton and +photoelectron blobs, scattered recoils — that touched nothing (3.3.0, which +also counted absorbed deposits and Compton blobs merged into photon +fragments, removed about 30% and 13%). + +A point counts as LE when it lies in an instance's LE run — the LE fragments +grouped into it, `[inst_pc_le_start, inst_pc_end)` (see +[Point ordering](#point-ordering-and-why-it-matters)) — and is labelled +`kLEScatter` whatever its host is. Its instance and interaction labels remain +the host's, so it stays attributed to the shower or track it was grouped +with. + +`instances` carries every particle column of the instance rows, the +kinematics included (`mass`, and the `(n, 4)` `start_pos` … `end_mom`; see +[Kinematics](#kinematics)). ### Distributed training @@ -1792,9 +1900,9 @@ Only the touching subset proceeds to `post_filter`, and only the final `merge_pa ### `PhotonDecay` -**Purpose:** Merge the ±11 (e⁺/e⁻) decay products of a photon (PDG 22) into the photon's partition. +**Purpose:** Merge the e⁺e⁻ pair a photon (PDG 22) converted into (`kConversion`) into the photon's partition, and pieces defragmentation cut off the photon itself. A photon's Compton electrons and photoelectron are left alone: a shower starts at the pair production, and whether they belong to a shower *instance* is decided by genealogy in `merge_em_showers` (only when their photon is itself inside a shower). The photon is promoted from `kLEScatter` to `kShower` only when it converts. -**Mechanism:** Purely topology-based (`get_unconditional_merges`). A photon typically has an empty point cloud; its spatial extent is entirely represented by its children. No proximity check is performed. +**Mechanism:** Purely topology-based (`get_unconditional_merges`). A photon has no deposits of its own at the conversion; its spatial extent from there on is its pair's. No proximity check is performed. **Merge direction:** child (e⁺/e⁻) → parent (photon). The photon representative survives and its `point_cloud` grows to include all child points. @@ -1840,7 +1948,7 @@ Only the touching subset proceeds to `post_filter`, and only the final `merge_pa **Candidate filter:** All pairs between `kLEScatter` representatives and non-`kLEScatter` representatives. -**Post-filter:** One merge per `kLEScatter` — if multiple non-LE neighbours touch the same LE partition, only the first passing pair is kept. +**Post-filter:** One merge per `kLEScatter` — if multiple non-LE neighbours touch the same LE partition, the one holding its nearest ancestor outside it is kept when it is among them (a delta ray goes to its track, not to a shower crossing it), otherwise the first passing pair. **Merge direction:** `kLEScatter` representative → non-`kLEScatter` representative. @@ -1885,7 +1993,7 @@ Diagnostics add a per-pair record overhead. With diagnostics disabled (the defa 5. **Point cloud columns** — Columns 0–2 are always treated as x, y, z. Additional columns (time, energy deposit, dEdx, etc.) are carried through the pipeline unchanged but are not used by any proximity or condition logic except for the energy tie-break in `CombineLEScatters` (column index 4, `PointFeature.energy`). -6. **`kLEScatter` ordering** — `AbsorbLEScatter` takes the *first* touching non-LE neighbour in the order candidates are iterated, which depends on the ordering of `rep_lookup`. This is deterministic within a single run but may vary across Python versions or dict iteration orders in unusual environments. +6. **`kLEScatter` ordering** — when an LE partition's own ancestor is not among its touching neighbours, `AbsorbLEScatter` takes the *first* touching non-LE neighbour in the order candidates are iterated, which depends on the ordering of `rep_lookup`. This is deterministic within a single run but may vary across Python versions or dict iteration orders in unusual environments. 7. **Convergence** — The pass loop terminates when no new merges occur in a complete pass. The loop is capped at `max_passes=10` as a safety guard. In practice, 1–2 passes are sufficient for typical LArTPC events. diff --git a/pysupera/_run.py b/pysupera/_run.py index 5ce1569..52142f4 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -103,13 +103,15 @@ def main(cfg: DictConfig) -> None: from pysupera.readers.pixel_hits import describe_measured_drift from pysupera.io import open_voxmap_writer from pysupera.partitioner import ParticlePartitioner - from pysupera.merge import merge_em_showers + from pysupera.merge import (merge_em_showers, find_split_pieces, + relabel_split_pieces, attach_split_pieces) + from pysupera.kinematics import step_particles, instance_kinematics from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, build_reader, configure from pysupera.preproc import _voxelize_point_cloud from pysupera.utils import (resolve_orphans, validate_interaction_ids, validate_ancestor_ids, select_traceable_instances) - from pysupera.layout import build_layout, snapshot_groups + from pysupera.layout import build_layout, snapshot_groups, fragment_le configure(cfg) # set module-level defaults (e.g. min_pc_size) before any Particle is created @@ -289,6 +291,9 @@ def _n_groups_of(_store): 'hit_x_from': cfg.reader.get('hit_x_from', None) if _hit_points else None, 'hit_energy': cfg.reader.get('hit_energy', None) if _hit_points else None, 'readout_type': _readout, + # Which steps an instance's first_* / last_* may come from: + # 'visible' (seen by the readout) or 'all'. + 'kinematics_steps': getattr(store, 'kinematics_steps', None), # What a points/flat row is: a voxel of this size, or a # deposit (store=points). 'store': _store, @@ -534,6 +539,13 @@ def _n_groups_of(_store): {id(r): r for r in partitioner.rep_lookup.values()}.values() ) + # A piece defragmentation cut off a track, standing alone, is + # still part of that track: relabel its fragment before the + # snapshot, so it is not LE, and join it to the track's + # instance after step 2. + _pieces = find_split_pieces(fragments, particles) + relabel_split_pieces(_pieces) + # Capture fragment membership NOW. merge_em_showers returns # the surviving representatives themselves as the instances -- # every instance object is also a fragment object -- and @@ -544,7 +556,8 @@ def _n_groups_of(_store): # ── Step-2 EM shower instance merger ─────────────────────── _t = time.perf_counter() - instances = merge_em_showers(fragments) + instances = attach_split_pieces(merge_em_showers(fragments), + _pieces) _ev_t['em_merge'] = time.perf_counter() - _t profile['merge_em_showers'] += _ev_t['em_merge'] @@ -666,9 +679,23 @@ def _n_groups_of(_store): _keep.update(np.unique(_d['fragment_id']).tolist()) _keep.update(np.unique(_d['instance_id']).tolist()) _keep.discard(-1) + # first_* / last_* per instance, from the steps whose + # deposits it collected. + _inst_kin = None + _st = getattr(store, 'last_steps', None) + _tk = getattr(store, 'last_kinematics', None) + if _st is not None and _tk is not None: + _spid = step_particles( + len(_st['steps']), particles, + group=_st.get('group'), group_owner=_owner) + _inst_kin = instance_kinematics( + _inst_groups, particles, _st['steps'], _st['owner'], + _spid, _tk, counted=_st.get('counted')) _layout = build_layout(particles, _frag_groups, _inst_groups, vertices=getattr(store, 'last_vertices', None), - keep=_keep) + keep=_keep, + kinematics=_tk, + inst_kinematics=_inst_kin) writer.append_event(_layout) if _labels is not None: writer.append_hit_labels(_labels) @@ -683,8 +710,8 @@ def _n_groups_of(_store): # left out entirely -- it follows from the fragment. _frag_of = {int(m): int(g.rep_id) for g in _frag_groups for m in g.member_ids} - _le_of = {int(p.id): (p.sem_type is _le_type) - for p in particles} + # LE per fragment, as in points/flat and hit_labels. + _le_of = fragment_le(particles, _frag_groups) writer.append_group_owners( remap_owners(_owner, _frag_of), le_flags(_owner, _le_of), diff --git a/pysupera/conditions/__init__.py b/pysupera/conditions/__init__.py index beb46a9..5fcaf1e 100644 --- a/pysupera/conditions/__init__.py +++ b/pysupera/conditions/__init__.py @@ -12,11 +12,12 @@ whose point clouds are within the distance threshold. AbsorbLEScatter Absorb each kLEScatter particle into at most one touching non-kLEScatter - neighbour. + neighbour -- its own ancestor's partition when that is one of them. PhotonDecay - Merge PDG ±11 children of a photon (PDG 22) into the photon's - partition without a proximity check. The photon becomes the - surviving representative. + Merge the e+e- pair a photon (PDG 22) converted into into the photon's + partition without a proximity check; Compton electrons and + photoelectrons are left alone. The photon becomes the surviving + representative. Examples -------- diff --git a/pysupera/conditions/absorb_le_scatter.py b/pysupera/conditions/absorb_le_scatter.py index 43e6115..64c991c 100644 --- a/pysupera/conditions/absorb_le_scatter.py +++ b/pysupera/conditions/absorb_le_scatter.py @@ -74,8 +74,10 @@ def post_filter(self, """ Allow each ``kLEScatter`` particle to merge with at most one partner. - Groups *touching_pairs* by their ``kLEScatter`` member and keeps only - the first touching non-``kLEScatter`` neighbour for each group. + Groups *touching_pairs* by their ``kLEScatter`` member and keeps one + touching non-``kLEScatter`` neighbour for each group: the partition + of its own ancestor when that one touches (see + :meth:`_home_rep_id`), otherwise the first. Surplus candidates are discarded; when diagnostics are enabled each rejection is recorded with reason ``ALREADY_MERGED_WITH_OTHER``. @@ -217,8 +219,16 @@ def _select_one_merge_per_sem5(self, """ Keep at most one touching partner per ``kLEScatter`` particle. - Groups pairs by their ``kLEScatter`` member, selects the first pair - in each group, and records diagnostic rejections for the remainder. + Groups pairs by their ``kLEScatter`` member and selects, in each + group, the pair whose partner is the particle's genealogical home + (:meth:`_home_rep_id`) when it is among them, else the first pair. + Records diagnostic rejections for the remainder. + + The preference matters for delta rays: one is born on its track and + so touches it, but it often touches something else as well -- a + larger delta, a shower crossing the track -- and the first pair is + whichever the proximity search happened to list first. Choosing + among the partners it touches pulls nothing in from a distance. Parameters ---------- @@ -248,6 +258,13 @@ def _select_one_merge_per_sem5(self, for sem5_id, pairs in sem5_merges.items(): selected_pair = pairs[0] + if len(pairs) > 1: + home = self._home_rep_id(partitioner, sem5_id) + for pair in pairs: + partner = pair[0] if pair[0] != sem5_id else pair[1] + if partner == home: + selected_pair = pair + break selected.append(selected_pair) if partitioner.diagnostics.enabled and len(pairs) > 1: @@ -255,7 +272,9 @@ def _select_one_merge_per_sem5(self, selected_pair[0] if selected_pair[0] != sem5_id else selected_pair[1] ) - for pair in pairs[1:]: + for pair in pairs: + if pair == selected_pair: + continue rejected_partner_id = pair[0] if pair[0] != sem5_id else pair[1] partitioner.diagnostics.record( pair[0], pair[1], @@ -267,3 +286,34 @@ def _select_one_merge_per_sem5(self, ) return selected + + @staticmethod + def _home_rep_id(partitioner: 'ParticlePartitioner', le_id: int): + """ + The partition the ``kLEScatter`` partition *le_id* came from. + + Walks the parent chain of the particle *le_id* and returns the + representative id of the first ancestor that sits in a different + partition -- a delta ray's track, say. Ancestors already merged + into the same LE partition are skipped. None when the chain ends + first. + """ + lookup = partitioner.particle_lookup + reps = getattr(partitioner, "rep_lookup", None) or {} + + def rep_of(pid): + r = reps.get(pid) + return r.id if r is not None else pid + + own = rep_of(le_id) + cur, seen = lookup.get(le_id), set() + while cur is not None and cur.id not in seen: + seen.add(cur.id) + par = cur.parent_id + if par is None or par == cur.id or par not in lookup: + return None + home = rep_of(par) + if home != own: + return home + cur = lookup[par] + return None diff --git a/pysupera/conditions/photon_decay.py b/pysupera/conditions/photon_decay.py index b98d76a..b011216 100644 --- a/pysupera/conditions/photon_decay.py +++ b/pysupera/conditions/photon_decay.py @@ -3,7 +3,7 @@ from .base import PartitionConditionBase from ..diagnostics import MergeOutcome -from ..utils import SemanticType +from ..utils import InteractionType, SemanticType if TYPE_CHECKING: from ..partitioner import ParticlePartitioner @@ -27,20 +27,32 @@ def _is_own_fragment(child, parent) -> bool: return False cg = getattr(child, "geant4_id", None) pg = getattr(parent, "geant4_id", None) - return cg is not None and pg is not None and int(cg) == int(pg) # electron and positron + return cg is not None and pg is not None and int(cg) == int(pg) + + +def _is_pair_product(p) -> bool: + """ + True when *p* is an electron or positron a photon converted into. + + Read from the process that created it (``kConversion``), not from + ``pdg`` alone: a photon's Compton electrons and its photoelectron are + also PDG 11 children of a photon, but they are not the shower. + """ + return (int(p.pdg) in _DECAY_PRODUCTS + and int(getattr(p, "_interaction_type", -1)) + == InteractionType.kConversion.value) class PhotonDecay(PartitionConditionBase): """ - Merge the charged decay products of a photon (PDG 22) into the photon partition. + Merge the pair a photon (PDG 22) converted into into the photon partition. - A photon may decay into an electron (PDG 11) and a positron (PDG -11). - The photon itself typically carries an **empty** point cloud; its spatial - extent is represented entirely by the clouds of its decay products. + A photon may convert into an electron (PDG 11) and a positron (PDG -11). + The photon itself carries **no** deposits of its own at the conversion; + its spatial extent from there on is the clouds of its pair. - This condition recognises such photon-decay topologies and merges all - PDG ±11 children of a photon particle into the photon's partition so - that: + This condition merges the ``kConversion`` children of a photon -- and + only those -- into the photon's partition so that: * The photon is the **surviving representative** of the merged partition. * The photon's ``point_cloud`` grows to include every point from every @@ -50,6 +62,14 @@ class PhotonDecay(PartitionConditionBase): meaningless here. The merge is driven purely by the parent-child relationship in the particle tree. + A photon's Compton electrons and its photoelectron are left alone. A + shower begins at the pair production; a Compton electron is a separate + particle, made where the photon scattered, before any conversion. It is + usually a small isolated blob, and merging it here made it part of a + shower *fragment*, so it lost its LE label. Whether it belongs to a + shower *instance* is decided by genealogy in + :func:`~pysupera.merge.merge_em_showers`. + A photon that *does* carry hits -- Geant4 deposits argon atomic-relaxation energy at a photoabsorption vertex, so most photons carry one such point -- may be cut into pieces by defragmentation. Those pieces are PDG 22, not @@ -65,7 +85,8 @@ class PhotonDecay(PartitionConditionBase): 1. :meth:`get_unconditional_merges` is called on every convergence pass. 2. For each photon representative, iterate its direct children in ``partitioner.children_map``. - 3. For each qualifying child (PDG ±11), if it belongs to a different + 3. For each qualifying child (a ``kConversion`` e±, or a split-off + piece of the photon itself), if it belongs to a different partition, emit ``(child_rep_id, photon_rep_id)`` for a no-proximity merge. 4. :meth:`get_rep_candidates` and :meth:`get_candidates` both return @@ -77,9 +98,7 @@ class PhotonDecay(PartitionConditionBase): conversion. The condition handles all of them in one pass. The condition does not restrict the photon to having *exactly* two - children, so it also handles radiative corrections and other - configurations that produce more than two PDG ±11 children from a - single photon. + conversion children. """ @property @@ -88,7 +107,7 @@ def name(self) -> str: @property def description(self) -> str: - return "Photon (PDG 22) → e⁺e⁻ decay: merge charged children into photon partition" + return "Photon (PDG 22) → e⁺e⁻ conversion: merge the pair into the photon partition" # ------------------------------------------------------------------ # Unconditional (topology-only) merge — the main entry point @@ -99,8 +118,9 @@ def get_unconditional_merges( partitioner: 'ParticlePartitioner', rep_lookup: Dict[int, 'Particle']) -> List[Tuple[int, int]]: """ - Return directed pairs ``(child_rep_id, photon_rep_id)`` for all - PDG ±11 children of photon representatives, plus any PDG 22 child + Return directed pairs ``(child_rep_id, photon_rep_id)`` for the + pair (``kConversion`` e±) of every photon representative, plus any + PDG 22 child that is a defragmentation split-off of the photon itself (see :func:`_is_own_fragment`). @@ -123,7 +143,7 @@ def get_unconditional_merges( """ pairs: List[Tuple[int, int]] = [] - # Group PDG±11 particles present in the particle list by their + # Group the conversion pairs present in the particle list by their # photon parent_id. Used both to produce merge pairs and to record # sibling-level diagnostics so that get_decisions_for_pair(e-, e+) # returns a PhotonDecay entry explaining what happened. @@ -131,7 +151,7 @@ def get_unconditional_merges( for p in partitioner.particles: if p.parent_pdg != _PHOTON_PDG: continue - if p.pdg in _DECAY_PRODUCTS: + if _is_pair_product(p): photon_children[p.parent_id].append(p) elif _is_own_fragment(p, partitioner.particle_lookup.get(p.parent_id)): # A piece of the photon itself. Without this it could only @@ -160,7 +180,10 @@ def get_unconditional_merges( # the pairs ensures that CombineLEScatters and AbsorbLEScatter # (which run later in the same convergence pass) see the photon # as kShower and leave it alone, rather than absorbing it. - if photon_rep.sem_type == SemanticType.kLEScatter: + # A photon with only pieces of itself to collect did not + # convert, so there is no shower to promote it to. + if (photon_rep.sem_type == SemanticType.kLEScatter + and any(_is_pair_product(c) for c in children)): photon_rep.sem_type = SemanticType.kShower for child in children: child_rep = rep_lookup.get(child.id) diff --git a/pysupera/config.py b/pysupera/config.py index bbf66ea..aea81ca 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -411,7 +411,7 @@ def build_conditions(cfg: DictConfig) -> list: conditions = [] - # 1. Photon decay: merge e+/e- children into photon rep unconditionally + # 1. Photon decay: merge the conversion pair into the photon rep unconditionally # (topology-only, no proximity); must run early so photon reps have # real point clouds before any spatial condition inspects them. if cfg.conditions.get("photon_decay", True): diff --git a/pysupera/hit_labels.py b/pysupera/hit_labels.py index 6f3d39b..3cb37e6 100644 --- a/pysupera/hit_labels.py +++ b/pysupera/hit_labels.py @@ -8,8 +8,9 @@ Each entry gets the pysupera fragment and instance it belongs to, as the representative particle ids that head them in the output's ``particles`` -table, and whether its particle is a low-energy scatter. Where a hit cannot -be traced to a particle the ids are -1. +table, and whether it is LE -- whether its fragment is a ``kLEScatter`` +fragment (see :mod:`pysupera.layout`; a low-energy scatter absorbed into a +track is not). Where a hit cannot be traced to a particle the ids are -1. How a hit is traced depends on the readout: @@ -26,9 +27,7 @@ import numpy as np -from .utils import SemanticType as _ST - -_LE = _ST.kLEScatter +from .layout import fragment_le def _lookups(particles, frag_groups, inst_groups): @@ -42,8 +41,8 @@ def _lookups(particles, frag_groups, inst_groups): frag[np.asarray(g.member_ids, dtype=np.int64)] = int(g.rep_id) for g in inst_groups: inst[np.asarray(g.member_ids, dtype=np.int64)] = int(g.rep_id) - for p in particles: - le[int(p.id)] = p.sem_type is _LE + for pid, v in fragment_le(particles, frag_groups).items(): + le[pid] = v return frag, inst, le, n diff --git a/pysupera/io_v3.py b/pysupera/io_v3.py index 6d9c559..946f482 100644 --- a/pysupera/io_v3.py +++ b/pysupera/io_v3.py @@ -1,16 +1,17 @@ """ -Writer and reader for the 3.0.0 on-disk format. +Writer and reader for the 4.0.0 on-disk format. -3.0.0 collapses the three parallel particle tables of 2.x into one, and stores -each point exactly once. See :mod:`pysupera.layout` for how a single event's -ordering, row selection and group ranges are decided; this module only puts -the result on disk and reads it back. +One particle table, and each point stored exactly once. See +:mod:`pysupera.layout` for how a single event's ordering, row selection and +group ranges are decided; this module only puts the result on disk and reads +it back. (The module keeps its ``_v3`` name; 3.x files are not readable -- +regenerate them.) Layout ------ :: - format_version scalar str "3.0.0" + format_version scalar str "4.0.0" n_events scalar int64 events/offsets (n_events+1,) int64 particle rows per event points/offsets (n_events+1,) int64 point rows per event @@ -23,11 +24,13 @@ order (attrs: source, volume, plane) fragment_id (H,) int32 -1 where the hit is not traced instance_id (H,) int32 -1 where the hit is not traced - is_le (H,) bool the hit's particle is kLEScatter + is_le (H,) bool the hit's fragment is kLEScatter on_sensor (H,) bool pixel only: the hit's pixel is in the sensor image offsets (n_events+1,) int64 hits per event - particles/ (M,) see _INT32 / _INT8 / _INT64 + particles/ (M,) see _INT32 / _INT8 / _INT64 / _FLOAT64 + particles/ (M, 4) float64 see _VEC4: [x, y, z, t] (mm, ns) + or [px, py, pz, E] (MeV, E total) inter/offsets (n_events+1,) int64 interaction rows per event inter/ (K,) see _INTER @@ -52,6 +55,23 @@ ``frag_id == id`` marks a fragment representative and ``inst_id == id`` an instance representative; ``-1`` means the particle heads no group there. +LE is decided per fragment (see :mod:`pysupera.layout`): a fragment is wholly +LE -- ``frag_sem_type == kLEScatter`` -- or not, so ``[frag_pc_start, +frag_pc_end)`` is all of it. An instance holds its non-LE fragments in +``[inst_pc_start, inst_pc_le_start)`` and its LE fragments in +``[inst_pc_le_start, inst_pc_end)``. The per-hit and per-group ``is_le`` +flags follow the same rule. A particle's own ``sem_type`` is unchanged, so a +``kLEScatter`` particle absorbed into a track has ``sem_type == kLEScatter`` +and non-LE points. + +Kinematics: ``mass``, ``start_*`` (creation) and ``end_*`` (destruction) are +the row's own Geant4 particle's; a split piece of a track carries its track's. +``first_*`` / ``last_*`` are on instance rows only: the start of the first and +the end of the last step the instance collected (counted steps only -- the +file attribute ``kinematics_steps`` says which: ``visible``, seen by the +readout, or ``all`` -- of Geant4 particles with a non-LE member in it). A +shower has no ``last_*``. NaN where unknown; see pysupera.kinematics. + ``interaction_id`` is a row index into this event's slice of the interactions table, so the vertex a particle came from is one lookup away. Interactions no stored particle references are dropped and the rest renumbered, which is why @@ -64,8 +84,11 @@ import numpy as np from .io import _compress_kwargs, _chunk_rows, _CHUNK_TARGET_BYTES_META +from .utils import SemanticType as _ST + +_LE_CODE = _ST.kLEScatter.value -FORMAT_VERSION_V3 = "3.3.0" +FORMAT_VERSION_V3 = "4.0.0" #: points/flat's columns when nothing else is given: a deposit, as the plain #: EDepSim reader produces it. JAXTPC input adds direction and momentum. @@ -89,38 +112,27 @@ # The instance a fragment row belongs to (3.3.0); -1 on other rows. "frag_inst_id") _INT8 = ("sem_type", "frag_sem_type", "inst_sem_type") -# Both levels describe two runs the same way: a non-LE range and an LE range. -# At instance level the two are adjacent, so inst_pc_end == inst_pc_le_start; -# at fragment level other fragments' points lie between them. +# A fragment is one run. An instance is two adjacent runs, non-LE then LE, +# so three fenceposts describe it: start <= le_start <= end. _INT64 = ("pc_start", "pc_end", - "frag_pc_start", "frag_pc_end", "frag_pc_le_start", "frag_pc_le_end", - "inst_pc_start", "inst_pc_end", "inst_pc_le_start", "inst_pc_le_end") -COLUMNS = _INT32 + _INT8 + _INT64 - -#: 3.0.0 column names that were renamed in 3.1.0, old -> new. -_RENAMED_3_0 = { - "geant4_id": "geant4_trackid", - "sem_type_frag": "frag_sem_type", - "sem_type_inst": "inst_sem_type", - "parent_frag_id": "frag_parent_id", - "parent_inst_id": "inst_parent_id", - "frag_non_le_start": "frag_pc_start", - "frag_non_le_end": "frag_pc_end", - "frag_le_start": "frag_pc_le_start", - "frag_le_end": "frag_pc_le_end", -} + "frag_pc_start", "frag_pc_end", + "inst_pc_start", "inst_pc_le_start", "inst_pc_end") +_FLOAT64 = ("mass",) +#: (M, 4) float64 kinematics; see pysupera.layout.KINEMATICS_COLUMNS. +_VEC4 = ("start_pos", "start_mom", "first_pos", "first_mom", + "last_pos", "last_mom", "end_pos", "end_mom") +COLUMNS = _INT32 + _INT8 + _INT64 + _FLOAT64 + _VEC4 _DTYPE = {**{c: np.int32 for c in _INT32}, **{c: np.int8 for c in _INT8}, - **{c: np.int64 for c in _INT64}} + **{c: np.int64 for c in _INT64}, + **{c: np.float64 for c in _FLOAT64 + _VEC4}} # Ranges are stored absolute, so every one of these needs the event's point # base added as events are appended. _POINT_RANGE_COLS = ("pc_start", "pc_end", "frag_pc_start", "frag_pc_end", - "frag_pc_le_start", "frag_pc_le_end", - "inst_pc_start", "inst_pc_end", - "inst_pc_le_start", "inst_pc_le_end") + "inst_pc_start", "inst_pc_le_start", "inst_pc_end") # Interactions table. part_* index this event's particle rows, pc_* the point # array; both are rebased by the reader exactly as the particle columns are. @@ -137,8 +149,6 @@ **{c: np.float32 for c in _INTER_F32}, **{c: object for c in _INTER_STR}} -#: 3.0.0 interaction columns that were renamed in 3.1.0. -_INTER_RENAMED_3_0 = {"t": "time"} _INTER_POINT_COLS = ("pc_start", "pc_end") _INTER_PART_COLS = ("part_start", "part_end") @@ -222,7 +232,7 @@ def _create(self): dtype=np.int32, chunks=(_chunk_rows(1, 4),), **self._ckw) # is_le is exact at group level: a group's deposits belong to one - # particle, so the flag is that particle's, not its instance's. + # particle, and so to one fragment, whose LE-ness the flag carries. gg.create_dataset("is_le", shape=(0,), maxshape=(None,), dtype=np.int8, chunks=(_chunk_rows(1, 1),), **self._ckw) @@ -237,9 +247,13 @@ def _create(self): pg = f.create_group("particles") for c in COLUMNS: dt = _DTYPE[c] - pg.create_dataset(c, shape=(0,), maxshape=(None,), dtype=dt, - chunks=(_chunk_rows(1, np.dtype(dt).itemsize, - _CHUNK_TARGET_BYTES_META),), + w = 4 if c in _VEC4 else 1 + rows_c = _chunk_rows(w, np.dtype(dt).itemsize, + _CHUNK_TARGET_BYTES_META) + pg.create_dataset(c, shape=(0,) + ((w,) if w > 1 else ()), + maxshape=(None,) + ((w,) if w > 1 else ()), + dtype=dt, + chunks=(rows_c,) + ((w,) if w > 1 else ()), **self._ckw) def append_group_owners(self, frag_owner, is_le=None, volume_offsets=()): @@ -460,39 +474,28 @@ def points_of(self, row, level=None, le=None): *level* selects whose points: ``None`` for the particle's own, ``'frag'`` or ``'inst'`` for its group's. *le* selects which kind: ``None`` for all, ``False`` for non-LE only, ``True`` for LE only. + At fragment level the answer is all or nothing, a fragment being + wholly LE or not; at particle level *le* is ignored. - Every combination is one contiguous slice. At instance level the - non-LE and LE runs are adjacent (``inst_pc_end == inst_pc_le_start``) - so "all" is still a single slice; at - fragment level the two sides are stored as separate ranges because - other fragments' points lie between them. + Every combination is one contiguous slice. """ - arr, tag = self.points, "pc" + arr = self.points c = self.columns if level == "inst": - s, e = int(c[f"inst_{tag}_start"][row]), int(c[f"inst_{tag}_end"][row]) - ls, le_ = (int(c[f"inst_{tag}_le_start"][row]), - int(c[f"inst_{tag}_le_end"][row])) - if le is True: - return arr[:0] if ls < 0 else arr[ls:le_] - if le is False: - return arr[:0] if s < 0 else arr[s:e] - # Both: the runs are adjacent at instance level, so one slice. - lo = s if s >= 0 else ls - hi = le_ if le_ >= 0 else e - return arr[:0] if lo < 0 else arr[lo:hi] + s, ls, e = (int(c["inst_pc_start"][row]), + int(c["inst_pc_le_start"][row]), + int(c["inst_pc_end"][row])) + if s < 0: + return arr[:0] + lo, hi = {None: (s, e), False: (s, ls), True: (ls, e)}[le] + return arr[lo:hi] if level == "frag": - if le is None: - # The two sides are not adjacent, so "all" needs both. - a = self.points_of(row, "frag", False) - b = self.points_of(row, "frag", True) - if not len(a): return b - if not len(b): return a - return np.concatenate([a, b]) - pre = f"frag_{tag}_le" if le else f"frag_{tag}" - s, e = int(c[f"{pre}_start"][row]), int(c[f"{pre}_end"][row]) - return arr[:0] if s < 0 else arr[s:e] - s, e = int(c[f"{tag}_start"][row]), int(c[f"{tag}_end"][row]) + s, e = int(c["frag_pc_start"][row]), int(c["frag_pc_end"][row]) + is_le = int(c["frag_sem_type"][row]) == _LE_CODE + if s < 0 or (le is not None and bool(le) != is_le): + return arr[:0] + return arr[s:e] + s, e = int(c["pc_start"][row]), int(c["pc_end"][row]) return arr[:0] if s < 0 else arr[s:e] def group_of_points(self, level="frag"): @@ -506,23 +509,31 @@ def group_of_points(self, level="frag"): point, a different question. Every point is in at most one range per level, so the answer is unique; see :func:`check_ranges`. """ - n, tag = len(self.points), "pc" + n = len(self.points) c = self.columns - if level == "frag": - rows = np.flatnonzero(self.is_fragment) - pairs = [(f"frag_{tag}_start", f"frag_{tag}_end"), - (f"frag_{tag}_le_start", f"frag_{tag}_le_end")] - elif level == "inst": - rows = np.flatnonzero(self.is_instance) - pairs = [(f"inst_{tag}_start", f"inst_{tag}_le_end")] - else: + if level not in ("frag", "inst"): raise ValueError(f"level must be 'frag' or 'inst', got {level!r}") + rows = np.flatnonzero(self.is_fragment if level == "frag" + else self.is_instance) out = np.full(n, -1, dtype=np.int32) for r in rows: - for a, b in pairs: - s, e = int(c[a][r]), int(c[b][r]) - if s >= 0 and e > s: - out[s:e] = c["id"][r] + s, e = int(c[f"{level}_pc_start"][r]), int(c[f"{level}_pc_end"][r]) + if s >= 0 and e > s: + out[s:e] = c["id"][r] + return out + + def is_le_point(self): + """ + Per point, whether it is LE: whether the fragment holding it is a + ``kLEScatter`` fragment. Covers the points of unwritten instances + too, which no instance's LE run does. + """ + c = self.columns + out = np.zeros(len(self.points), dtype=bool) + for r in np.flatnonzero(self.is_fragment): + s, e = int(c["frag_pc_start"][r]), int(c["frag_pc_end"][r]) + if s >= 0 and int(c["frag_sem_type"][r]) == _LE_CODE: + out[s:e] = True return out def __repr__(self): @@ -531,22 +542,23 @@ def __repr__(self): class EventStoreV3: - """Reader for a 3.0.0 file.""" + """Reader for a 4.x file.""" def __init__(self, path): import h5py self._f = h5py.File(path, "r") ver = self._f["format_version"][()] ver = ver.decode() if isinstance(ver, bytes) else str(ver) - if not ver.startswith("3."): - raise ValueError( - f"{path!r} is format {ver}, not 3.x. Use pysupera.io." - f"EventStore for 2.x files." - ) + if not ver.startswith("4."): + self._f.close() + hint = ("Regenerate it with this version of run_pysupera: 4.0.0 " + "moved LE to the fragment and changed what inst_pc_end " + "means, so 3.x files are not read." if ver.startswith("3.") + else "Use pysupera.io.EventStore for 2.x files.") + raise ValueError(f"{path!r} is format {ver}, not 4.x. {hint}") self.format_version = ver - #: 3.0.0 files use the pre-rename column names and encode the - #: instance LE boundary as a single split index. - self._legacy = ver.startswith("3.0") + #: Which steps an instance's first_* / last_* may come from. + self.kinematics_steps = str(self._f.attrs.get("kinematics_steps", "")) self._ev = self._f["events/offsets"][:] self._po = self._f["points/offsets"][:] self._io = (self._f["inter/offsets"][:] @@ -568,8 +580,7 @@ def __getitem__(self, ev): ev += n a, b = int(self._ev[ev]), int(self._ev[ev + 1]) pa, pb = int(self._po[ev]), int(self._po[ev + 1]) - cols = (self._read_legacy_particles(a, b) if self._legacy - else {c: self._f[f"particles/{c}"][a:b] for c in COLUMNS}) + cols = {c: self._f[f"particles/{c}"][a:b] for c in COLUMNS} # Rebase the absolute point ranges onto this event's slice, leaving # the -1 sentinel alone, so every index in the view shares one origin. for c in _POINT_RANGE_COLS: @@ -579,20 +590,7 @@ def __getitem__(self, ev): if self._io is not None: ia, ib = int(self._io[ev]), int(self._io[ev + 1]) for c in INTER_COLUMNS: - name = c - if self._legacy: - old = {v: k for k, v in _INTER_RENAMED_3_0.items()}.get(c, c) - if f"inter/{old}" not in self._f: - # 3.0.0 has no interaction_id / sums / reaction - n = ib - ia - inter[c] = (np.arange(n, dtype=np.int32) - if c == "interaction_id" else - np.array([""] * n, dtype=object) - if c in _INTER_STR else - np.zeros(n, dtype=_INTER_DTYPE[c])) - continue - name = old - v = self._f[f"inter/{name}"][ia:ib] + v = self._f[f"inter/{c}"][ia:ib] if c in _INTER_STR: inter[c] = np.array( [x.decode() if isinstance(x, bytes) else str(x) @@ -641,37 +639,6 @@ def hit_labels(self, ev): out[(int(g.attrs["volume"]), int(g.attrs["plane"]))] = d return out - def _read_legacy_particles(self, a, b): - """ - Read a 3.0.0 particle block under 3.1.0 names. - - Two shapes differ, not just names: the Geant4 parent columns did not - exist (filled with -1, since the value is unrecoverable from the file - alone), and the instance LE boundary was one split index rather than - two adjacent ranges. - """ - f, old_of = self._f, {v: k for k, v in _RENAMED_3_0.items()} - cols = {} - for c in COLUMNS: - if c in ("geant4_parent_trackid", "geant4_parent_pdg"): - continue # filled below - if c.startswith("inst_pc_"): - continue # derived below - cols[c] = f[f"particles/{old_of.get(c, c)}"][a:b] - - n = b - a - for c in ("geant4_parent_trackid", "geant4_parent_pdg"): - cols[c] = np.full(n, -1, dtype=_DTYPE[c]) - - st = f["particles/inst_pc_start"][a:b] - sp = f["particles/inst_pc_split"][a:b] - en = f["particles/inst_pc_end"][a:b] - cols["inst_pc_start"] = st - cols["inst_pc_end"] = sp # non-LE run ends at the split - cols["inst_pc_le_start"] = np.where(st >= 0, sp, -1).astype(sp.dtype) - cols["inst_pc_le_end"] = en - return cols - def group_owners(self, ev): """ ``(instance_id, fragment_id, volume_offsets)`` for event *ev*, or @@ -752,9 +719,8 @@ def check_ranges(path, max_report=20): Member rows are not stored, so a range cannot be compared with its members' counts after the fact; what the file does show is the symptom. - Fragment slices -- each side separately -- and instance slices must each - tile without overlapping, since a row belongs to one fragment and one - instance. Files written before the layout checked this at write time + Fragment slices and instance slices must each tile without overlapping, + since a row belongs to one fragment and one instance. Files written before the layout checked this at write time (an empty fragment of an unwritten instance could claim the rows of the particles its members sorted around) show up here. @@ -771,10 +737,9 @@ def check_ranges(path, max_report=20): for cloud, tag in clouds: levels = { "frag": (v.is_fragment, - [(f"frag_{tag}_start", f"frag_{tag}_end"), - (f"frag_{tag}_le_start", f"frag_{tag}_le_end")]), + [(f"frag_{tag}_start", f"frag_{tag}_end")]), "inst": (v.is_instance, - [(f"inst_{tag}_start", f"inst_{tag}_le_end")]), + [(f"inst_{tag}_start", f"inst_{tag}_end")]), } for level, (mask, pairs) in levels.items(): spans = [] diff --git a/pysupera/kinematics.py b/pysupera/kinematics.py new file mode 100644 index 0000000..2fd90f9 --- /dev/null +++ b/pysupera/kinematics.py @@ -0,0 +1,227 @@ +""" +pysupera.kinematics +=================== + +The ``first_*`` / ``last_*`` kinematics of reconstructed instances. + +``start_*`` and ``end_*`` describe a Geant4 particle and are read off its +record (:func:`~pysupera.readers.format_edepsim_h5.particle_kinematics`). +``first_*`` and ``last_*`` describe what a model is asked to reconstruct -- +an instance, as drawn -- so they are made from the energy deposits the +instance actually collected, after the instances exist: + +* Each Geant4 step goes to the pysupera particle that holds its deposit + (:func:`step_particles`), so a step follows its deposit through + defragmentation, absorption and shower merging, as the points do. +* Of an instance's counted steps, only those of a Geant4 particle with at + least one non-LE member in it are used -- a photon's conversion pair + counting as non-LE, being the shower's start. A low-energy blob absorbed + from an unrelated particle -- a Compton electron that happened to touch a + muon -- is part of the picture but not of the trajectory, and it can be + later than everything else. An instance with no non-LE member, or whose + non-LE members left no counted step, uses all of its steps. +* ``first`` is the start of the earliest of those steps, ``last`` the end of + the latest (:func:`~pysupera.readers.format_edepsim_h5.step_ends`). +* A ``kShower`` instance has no well-defined last point: ``last_*`` is NaN. + When a photon heads it and the first counted step is its pair's, + ``first_mom`` is the summed creation momentum of the pair -- the + shower's own momentum. When the readout missed the pair, the first + point is on a later product and ``first_mom`` is that step's. +* ``first_mom`` is the momentum of whichever particle made the step, + with that particle's mass, so its E can be below the row's own mass: + an LE electron instance whose earliest step is a photon's local deposit. + +Only instance rows carry these values; every other row gets NaN. +""" + +from __future__ import annotations + +import numpy as np + +from .readers.format_edepsim_h5 import step_ends +from .utils import InteractionType, PointFeature, SemanticType + +#: Columns made here, each (4,) per instance. +INSTANCE_KINEMATICS_FIELDS = ("first_pos", "first_mom", "last_pos", "last_mom") + +_LE = SemanticType.kLEScatter +_SHOWER = SemanticType.kShower.value +_CONV = InteractionType.kConversion.value + + +def step_particles(n_steps, particles, group=None, group_owner=None): + """ + Per step, the id of the pysupera particle holding its deposit; -1 if none. + + Parameters + ---------- + n_steps : int + Number of steps in the event. + particles : list of Particle + The event's particles, after defragmentation and renumbering. + group, group_owner : ndarray of int or None + JAXTPC: each step's event-global group, and each group's owning + particle (:func:`~pysupera.provenance.build_group_owners`). A step + then goes where its segment goes in the truth cloud. Without them + a step goes to the particle whose voxels hold it: each voxel's + input ids (``voxmap``), or each point's ``id`` column when nothing + was voxelized, are step indices. + """ + out = np.full(n_steps, -1, dtype=np.int64) + if group is not None: + # A voxel's input ids name deposits or hits here, not steps. + if group_owner is None: + return out + g = np.asarray(group, dtype=np.int64) + own = np.asarray(group_owner, dtype=np.int64) + ok = (g >= 0) & (g < len(own)) + out[ok] = own[g[ok]] + return out + for p in particles: + vm = getattr(p, "voxmap", None) + if vm is not None and vm[1] is not None: + ids = np.asarray(vm[1], dtype=np.int64) + elif len(p.point_cloud) and p.point_cloud.shape[1] > PointFeature.id: + ids = p.point_cloud[:, PointFeature.id].astype(np.int64) + else: + continue + ids = ids[(ids >= 0) & (ids < n_steps)] + out[ids] = int(p.id) + return out + + +def instance_kinematics(inst_groups, particles, steps, step_owner, step_pid, + track_kin, counted=None): + """ + ``first_*`` / ``last_*`` of every instance in *inst_groups*. + + Parameters + ---------- + inst_groups : list of Group + The instances, as :func:`~pysupera.layout.snapshot_groups` took them. + particles : list of Particle + The event's particles. + steps : structured ndarray + The event's step records. + step_owner : ndarray of int + Per step, the row of the reader's particle table that made it. + step_pid : ndarray of int + Per step, the pysupera particle holding it (:func:`step_particles`). + track_kin : dict + The reader's ``last_kinematics``: ``mass``, ``start_mom`` and + ``geant4_trackid`` per particle-table row. + counted : ndarray of bool or None + Which steps may be used (JAXTPC: the ones the readout saw). None + counts all. + + Returns + ------- + dict + ``{instance rep id: {name: (4,) float64}}`` for every name in + :data:`INSTANCE_KINEMATICS_FIELDS`, NaN where undefined. + """ + by_id = {int(p.id): p for p in particles} + g4_of_row = np.asarray(track_kin["geant4_trackid"], dtype=np.int64) + mass_of_row = np.asarray(track_kin["mass"], dtype=np.float64) + row_of_g4 = {int(t): k for k, t in enumerate(g4_of_row)} + nan4 = lambda: np.full(4, np.nan) + out = {int(g.rep_id): {k: nan4() for k in INSTANCE_KINEMATICS_FIELDS} + for g in inst_groups} + if not len(steps) or not inst_groups: + return out + + # Instance of each particle, and which Geant4 particles an instance + # follows: those with a non-LE member in it. + max_pid = max(by_id) if by_id else -1 + inst_of_pid = np.full(max_pid + 2, -1, dtype=np.int64) + span = int(g4_of_row.max()) + 2 if len(g4_of_row) else 1 + allowed, has_track = [], set() + for g in inst_groups: + rid = int(g.rep_id) + # A photon's conversion pair is the shower's start whatever the size + # of its clouds: a low-energy pair is typed LE, and must not lose + # the first point to a later product. + pair = {int(getattr(by_id[m], "geant4_id", m)) + for m in _pair_members(by_id.get(rid), g.member_ids, by_id)} + for m in g.member_ids: + m = int(m) + if 0 <= m <= max_pid: + inst_of_pid[m] = rid + p = by_id.get(m) + if p is None: + continue + g4 = int(getattr(p, "geant4_id", m)) + if p.sem_type is not _LE or g4 in pair: + allowed.append(rid * span + g4 + 1) + has_track.add(rid) + + pid = np.asarray(step_pid, dtype=np.int64) + own = np.asarray(step_owner, dtype=np.int64) + sel = (pid >= 0) & (pid <= max_pid) & (own >= 0) & (own < len(g4_of_row)) + if counted is not None: + sel &= np.asarray(counted, dtype=bool) + idx = np.flatnonzero(sel) + inst = inst_of_pid[pid[idx]] + keep = inst >= 0 + idx, inst = idx[keep], inst[keep] + key = inst * span + g4_of_row[own[idx]] + 1 + ok = (~np.isin(inst, np.fromiter(has_track, dtype=np.int64, + count=len(has_track))) + | np.isin(key, np.asarray(allowed, dtype=np.int64))) + # An instance whose non-LE particles left no counted step -- the readout + # missed them -- falls back to all of its counted steps. + ok |= ~np.isin(inst, np.unique(inst[ok])) + idx, inst = idx[ok], inst[ok] + if not len(idx): + return out + + # Earliest and latest step of each instance, by the recorded time. + t = np.asarray(steps["t"], dtype=np.float64)[idx] + order = np.lexsort((t, inst)) + idx, inst = idx[order], inst[order] + starts = np.flatnonzero(np.r_[True, inst[1:] != inst[:-1]]) + ends = np.r_[starts[1:], len(inst)] - 1 + first, last = idx[starts], idx[ends] + f_pos, f_mom = step_ends(steps, first, mass_of_row[own[first]], -1.0) + l_pos, l_mom = step_ends(steps, last, mass_of_row[own[last]], 1.0) + + sem_of = {int(g.rep_id): int(g.sem_type) for g in inst_groups} + members_of = {int(g.rep_id): g.member_ids for g in inst_groups} + start_mom = np.asarray(track_kin["start_mom"], dtype=np.float64) + for k, rid in enumerate(inst[starts]): + rid = int(rid) + o = out[rid] + o["first_pos"], o["first_mom"] = f_pos[k], f_mom[k] + if sem_of[rid] != _SHOWER: + o["last_pos"], o["last_mom"] = l_pos[k], l_mom[k] + continue + rows = _pair_rows(by_id.get(rid), members_of[rid], by_id, row_of_g4) + # Only when the shower's first counted step is the pair's: a pair + # the readout did not see leaves the first point on a later + # product, and the photon's momentum would not belong to it. + if rows and int(own[first[k]]) in rows: + o["first_mom"] = start_mom[rows].sum(axis=0) + return out + + +def _pair_members(rep, member_ids, by_id): + """Ids of the members that are the pair photon *rep* converted into.""" + if rep is None or int(rep.pdg) != 22: + return [] + return [int(m) for m in member_ids + if (p := by_id.get(int(m))) is not None + and int(p.parent_id) == int(rep.id) and abs(int(p.pdg)) == 11 + and int(getattr(p, "_interaction_type", -1)) == _CONV] + + +def _pair_rows(rep, member_ids, by_id, row_of_g4): + """Particle-table rows of the pair *rep* converted into; [] if none.""" + rows = [] + for m in _pair_members(rep, member_ids, by_id): + p = by_id[m] + r = row_of_g4.get(int(getattr(p, "geant4_id", p.id))) + if r is not None: + rows.append(r) + # A pair split by defragmentation appears more than once; count each + # Geant4 particle once. + return sorted(set(rows)) diff --git a/pysupera/layout.py b/pysupera/layout.py index 2c01c07..9280fba 100644 --- a/pysupera/layout.py +++ b/pysupera/layout.py @@ -1,24 +1,41 @@ """ -Layout computation for the 3.0.0 on-disk format. +Layout computation for the 4.0.0 on-disk format. -Format 3.0.0 replaces the three parallel particle tables of 2.x with a single -``particles`` table plus one flat point array. A particle row carries its own -identity and, when it represents a group, that group's columns as well: +One ``particles`` table plus one flat point array. A particle row carries its +own identity and kinematics and, when it represents a group, that group's +columns as well: frag_id / inst_id the *particle id of the group's representative*. A representative therefore satisfies ``frag_id == id``, which is how the levels are enumerated. ``-1`` means the particle heads no group at that level. - *_pc_start/end the group's slice of the point array + frag_pc_start/end the fragment's slice of the point array + inst_pc_start / inst_pc_le_start / inst_pc_end + the instance's slice, non-LE points first: non-LE in + [start, le_start), LE in [le_start, end) *_merge_count Geant4 particles merged into the group +LE is a property of the *fragment*: a point is LE when the fragment holding +it is a ``kLEScatter`` fragment -- a blob with no trajectory. A low-energy +scatter absorbed into a non-LE fragment by proximity has become part of a +trajectory and is not LE, whatever its own ``sem_type`` says. So a fragment +is wholly LE or wholly not, and one range describes it. This holds because +proximity is the only way stage 1 puts an LE particle into a non-LE +fragment: PhotonDecay takes a photon's conversion pair only, not its Compton +electrons or photoelectron. LE fragments that join an instance by genealogy +-- the Compton blobs of a photon inside a shower -- stay LE. + +Kinematics: ``mass``, ``start_*`` and ``end_*`` are the row's own Geant4 +particle's. ``first_*`` and ``last_*`` describe an *instance* and are on +instance rows only; see :mod:`pysupera.kinematics`. + This module does the part that has to be right before any bytes are written: deciding which particles to store, what order the points go in, and what those ranges are. It is deliberately free of HDF5 so it can be tested directly. Two invariants it establishes ----------------------------- -*Nesting.* Points are ordered by ``(interaction, instance, fragment, +*Nesting.* Points are ordered by ``(interaction, instance, LE, fragment, particle)``. Because instances partition into fragments and fragments into particles, every level's points are then a single contiguous slice, so one ``(start, end)`` pair per level suffices and no per-point group labels are @@ -48,6 +65,34 @@ #: block followed by an LE block; see build_layout. _LE_SEM = _ST.kLEScatter +#: Kinematic columns, each (rows, 4) float64. +KINEMATICS_COLUMNS = ("start_pos", "start_mom", "first_pos", "first_mom", + "last_pos", "last_mom", "end_pos", "end_mom") + +#: The ones joined from the row's Geant4 particle; see +#: :func:`pysupera.readers.format_edepsim_h5.particle_kinematics`. +TRACK_KINEMATICS_COLUMNS = ("start_pos", "start_mom", "end_pos", "end_mom") + +#: The ones an instance row takes from its instance; see +#: :func:`pysupera.kinematics.instance_kinematics`. +INSTANCE_KINEMATICS_COLUMNS = ("first_pos", "first_mom", "last_pos", + "last_mom") + + +def fragment_le(particles, frag_groups): + """ + ``{particle_id: bool}``: whether the particle's points are LE. + + Decided by the fragment the particle ended up in, not by the particle: + see the module docstring. A particle in no fragment falls back to its + own ``sem_type``. + """ + le_frag = {g.rep_id: g.sem_type == _LE_SEM.value for g in frag_groups} + frag_of = _group_of(frag_groups) + return {int(p.id): (le_frag[frag_of[int(p.id)]] if int(p.id) in frag_of + else p.sem_type is _LE_SEM) + for p in particles} + #: A group's identity captured at a point in time: which particle represents #: it, which particles it contains, and how it was classified. @@ -135,7 +180,8 @@ def _group_ranges(offsets, pos, frag_groups, inst_groups, is_le): unwritten instance's fragments sorts under its own id, so counting it would stretch the range over whatever particles sit in between -- rows the group does not own. A group with no rows at all gets (-1, -1). - Returns ``(frag_rng, inst_rng, frag_nle, frag_le, inst_split)``. + Returns ``(frag_rng, inst_rng, inst_split)``; ``inst_split`` is where an + instance's LE run starts, its end when it has none. """ def _span(members): """Range covering *members*' rows, or (-1, -1) when they have none.""" @@ -146,36 +192,23 @@ def _span(members): lo, hi = min(idx), max(idx) return (int(offsets[lo]), int(offsets[hi + 1])) - def whole(groups): - rng = {} - for g in groups: - r = _span(g.member_ids) - rng[g.rep_id] = (0, 0) if r == (-1, -1) else r - return rng - - frag_rng = whole(frag_groups) - inst_rng = whole(inst_groups) - - # Each side of a group is a single run under this ordering, so two pairs - # describe a fragment completely -- never three. - frag_nle = {g.rep_id: _span([m for m in g.member_ids if not is_le[m]]) - for g in frag_groups} - frag_le = {g.rep_id: _span([m for m in g.member_ids if is_le[m]]) - for g in frag_groups} + frag_rng = {g.rep_id: _span(g.member_ids) for g in frag_groups} + inst_rng = {g.rep_id: _span(g.member_ids) for g in inst_groups} # Where the LE block starts inside an instance; == its end when the - # instance has no LE rows at all. + # instance has no LE rows at all, and -1 when it has no rows. inst_split = {} for g in inst_groups: + s, e = inst_rng[g.rep_id] le_span = _span([m for m in g.member_ids if is_le[m]]) - inst_split[g.rep_id] = (inst_rng[g.rep_id][1] if le_span[0] < 0 + inst_split[g.rep_id] = (-1 if s < 0 else e if le_span[0] < 0 else le_span[0]) _check_ranges(offsets, pos, frag_groups, inst_groups, is_le, - frag_nle, frag_le, inst_rng, inst_split) - return frag_rng, inst_rng, frag_nle, frag_le, inst_split + frag_rng, inst_rng, inst_split) + return frag_rng, inst_rng, inst_split def _check_ranges(offsets, pos, frag_groups, inst_groups, is_le, - frag_nle, frag_le, inst_rng, inst_split): + frag_rng, inst_rng, inst_split): """ Raise unless every range holds exactly its own members' rows. @@ -186,24 +219,29 @@ def _check_ranges(offsets, pos, frag_groups, inst_groups, is_le, with the members' own counts catches every such case, and costs one pass over data already in hand. """ - def own(members, le): + def own(members, le=None): return sum(int(offsets[pos[m] + 1] - offsets[pos[m]]) - for m in members if m in pos and is_le[m] is le) + for m in members + if m in pos and (le is None or is_le[m] is le)) def width(r): return r[1] - r[0] if r[0] >= 0 else 0 for g in frag_groups: - for le, r in ((False, frag_nle[g.rep_id]), (True, frag_le[g.rep_id])): - if width(r) != own(g.member_ids, le): - raise LayoutError( - f"fragment {g.rep_id}: {'LE' if le else 'non-LE'} range " - f"{r} holds {width(r)} rows but its members own " - f"{own(g.member_ids, le)} -- they are not contiguous in " - f"the storage order.") + sides = {is_le[m] for m in g.member_ids if m in is_le} + if len(sides) > 1: + raise LayoutError( + f"fragment {g.rep_id} mixes LE and non-LE members, but LE is " + f"decided per fragment.") + r = frag_rng[g.rep_id] + if width(r) != own(g.member_ids): + raise LayoutError( + f"fragment {g.rep_id}: range {r} holds {width(r)} rows but " + f"its members own {own(g.member_ids)} -- they are not " + f"contiguous in the storage order.") for g in inst_groups: (s, e), sp = inst_rng[g.rep_id], inst_split[g.rep_id] - got = ((sp - s, e - sp) if e > s else (0, 0)) + got = ((sp - s, e - sp) if s >= 0 else (0, 0)) want = (own(g.member_ids, False), own(g.member_ids, True)) if got != want: raise LayoutError( @@ -213,7 +251,7 @@ def width(r): def build_layout(particles, frag_groups, inst_groups, vertices=None, - keep=()): + keep=(), kinematics=None, inst_kinematics=None): """ Decide storage order, row selection and group ranges for one event. @@ -237,6 +275,17 @@ def build_layout(particles, frag_groups, inst_groups, vertices=None, keep : iterable of int Particle ids that must get a row whatever their points -- the fragments and instances something else refers to (hit_labels). + kinematics : dict or None + The reader's ``last_kinematics``: ``mass`` and every + :data:`TRACK_KINEMATICS_COLUMNS` entry per input particle, joined to + the rows on ``geant4_trackid``. The pieces defragmentation splits + off a track share its Geant4 particle, and so these. Rows it does + not cover -- and every row when it is None -- get NaN. + inst_kinematics : dict or None + ``{instance rep id: {name: (4,)}}`` for the + :data:`INSTANCE_KINEMATICS_COLUMNS`, from + :func:`pysupera.kinematics.instance_kinematics`. Only instance rows + take them; every other row gets NaN. Returns ------- @@ -247,14 +296,14 @@ def build_layout(particles, frag_groups, inst_groups, vertices=None, inst_of = _group_of(inst_groups) kept_inst_ids = {g.rep_id for g in inst_groups} - # ---- ordering: interaction > instance > fragment > particle ---------- + # ---- ordering: interaction > instance > LE > fragment > particle ------ # LE-ness sorts *between* instance and fragment, so each instance becomes # a non-LE block followed by an LE block. That makes "the non-LE points of # this instance" -- the query panoptic training issues most -- a single # contiguous slice instead of a mask, and removes the need for any # per-point label: LE-ness is implied by position relative to the split. - # The cost is that a fragment's points become two runs rather than one. - is_le = {int(p.id): (p.sem_type is _LE_SEM) for p in particles} + # LE is decided per fragment, so a fragment stays one run. + is_le = fragment_le(particles, frag_groups) # Points belong to the interaction of the group that owns them, not of # the particle that deposited them. The two differ only when a @@ -280,7 +329,7 @@ def sort_key(p): pos = {int(p.id): k for k, p in enumerate(order)} pc_offsets = _offsets(order, "point_cloud") - frag_rng, inst_rng, frag_nle, frag_le, inst_split = _group_ranges( + frag_rng, inst_rng, inst_split = _group_ranges( pc_offsets, pos, frag_groups, inst_groups, is_le) frag_count = {g.rep_id: len(g.member_ids) for g in frag_groups} @@ -372,22 +421,45 @@ def col(fn, dtype=np.int32): # group columns: valid where *_id == id "frag_id": col(lambda i: i if i in frag_ids else -1), "frag_sem_type": col(lambda i: sem(i, frag_sem), np.int8), - "frag_pc_start": col(lambda i: frag_nle.get(i, (-1, -1))[0], np.int64), - "frag_pc_end": col(lambda i: frag_nle.get(i, (-1, -1))[1], np.int64), - "frag_pc_le_start": col(lambda i: frag_le.get(i, (-1, -1))[0], np.int64), - "frag_pc_le_end": col(lambda i: frag_le.get(i, (-1, -1))[1], np.int64), + # One run: the fragment is wholly LE (frag_sem_type kLEScatter) or not. + "frag_pc_start": col(lambda i: frag_rng.get(i, (-1, -1))[0], np.int64), + "frag_pc_end": col(lambda i: frag_rng.get(i, (-1, -1))[1], np.int64), "frag_merge_count": col(lambda i: frag_count.get(i, -1)), "inst_id": col(lambda i: i if i in kept_inst_ids else -1), "inst_sem_type": col(lambda i: sem(i, inst_sem), np.int8), - # Non-LE run then LE run. They are adjacent here, so - # inst_pc_end == inst_pc_le_start; the pair is spelled out anyway so - # instance and fragment level read identically. - "inst_pc_start": col(lambda i: inst_rng.get(i, (-1, -1))[0]), - "inst_pc_end": col(lambda i: inst_split.get(i, -1), np.int64), + # Three fenceposts: non-LE in [start, le_start), LE in [le_start, end). + "inst_pc_start": col(lambda i: inst_rng.get(i, (-1, -1))[0], np.int64), "inst_pc_le_start": col(lambda i: inst_split.get(i, -1), np.int64), - "inst_pc_le_end": col(lambda i: inst_rng.get(i, (-1, -1))[1]), + "inst_pc_end": col(lambda i: inst_rng.get(i, (-1, -1))[1], np.int64), "inst_merge_count": col(lambda i: inst_count.get(i, -1)), } + + # ---- kinematics ---------------------------------------------------------- + # start/end per Geant4 particle, so every piece of a split track carries + # its track's; first/last per instance, on instance rows. + n_rows = len(rows) + columns["mass"] = np.full(n_rows, np.nan, dtype=np.float64) + for name in KINEMATICS_COLUMNS: + columns[name] = np.full((n_rows, 4), np.nan, dtype=np.float64) + if kinematics is not None and n_rows: + k_of = {int(t): k for k, t in enumerate(kinematics["geant4_trackid"])} + src = np.fromiter( + (k_of.get(int(getattr(by_id[int(order[r].id)], "geant4_id", + order[r].id)), -1) for r in rows), + dtype=np.int64, count=n_rows) + hit = src >= 0 + columns["mass"][hit] = np.asarray(kinematics["mass"], + dtype=np.float64)[src[hit]] + for name in TRACK_KINEMATICS_COLUMNS: + columns[name][hit] = np.asarray(kinematics[name], + dtype=np.float64)[src[hit]] + if inst_kinematics: + for k, rid in enumerate(row_ids): + ik = inst_kinematics.get(rid) if rid in kept_inst_ids else None + if ik is None: + continue + for name in INSTANCE_KINEMATICS_COLUMNS: + columns[name][k] = ik[name] # The instance a fragment belongs to, stated rather than left to be # inferred from where its points sit: hit_labels names fragments and # instances, and a reader should not need the point ranges to join them. diff --git a/pysupera/merge.py b/pysupera/merge.py index 9f605ee..cf43d18 100644 --- a/pysupera/merge.py +++ b/pysupera/merge.py @@ -29,7 +29,7 @@ import numpy as np -from .utils import SemanticType +from .utils import InteractionType, SemanticType # --------------------------------------------------------------------------- @@ -44,22 +44,32 @@ # Helpers # --------------------------------------------------------------------------- -def _is_shower_eligible(rep) -> bool: - """Return ``True`` if *rep* should participate in EM shower genealogy merging. +def _em_like(rep) -> bool: + """Whether *rep* takes part in the shower walk at all: e±, γ or kShower.""" + return (abs(int(rep.pdg)) in _EM_PDGS + or rep.sem_type == SemanticType.kShower) + - A fragment is shower-eligible when it has an EM PDG code (e±, γ) **or** - carries the ``kShower`` semantic type, **and** is not a ``kLEScatter`` - fragment. +def _can_initiate(rep) -> bool: + """Whether *rep* may head a shower instance. - ``kLEScatter`` fragments are explicitly excluded because they are handled - by step-1 (``AbsorbLEScatter``) and must never become shower initiators — - if they did, non-LE fragments merged into them would be silently dropped by - the ``drop_le_scatter`` filter downstream. + ``kLEScatter`` fragments may *join* a shower -- a Compton blob of a photon + inside it, say -- but never head one: a shower headed by an LE fragment + would be dropped whole by the ``drop_le_scatter`` filter downstream, its + non-LE members with it. """ - if rep.sem_type == SemanticType.kLEScatter: - return False - return (abs(rep.pdg) in _EM_PDGS - or rep.sem_type == SemanticType.kShower) + return _em_like(rep) and rep.sem_type != SemanticType.kLEScatter + + +#: Interaction types of the electrons a photon makes without converting. +_PHOTON_SECONDARY = frozenset({InteractionType.kCompton.value, + InteractionType.kPhotoElectron.value}) + + +def _is_photon_secondary(rep) -> bool: + """A Compton electron or photoelectron: made by a photon, not a shower.""" + return (int(getattr(rep, "_interaction_type", -1)) in _PHOTON_SECONDARY + and int(getattr(rep, "parent_pdg", 0)) == 22) # --------------------------------------------------------------------------- @@ -72,15 +82,27 @@ def merge_em_showers(fragments): Groups step-1 fragment representatives into *shower instances* by walking the parent-ID chain of each EM fragment until a non-EM ancestor - (or the top of the fragment list) is reached. That top-most EM fragment - is the **shower initiator**; all EM fragments that share the same - initiator are merged into it. Non-EM fragments pass through unchanged - (each becomes its own instance). + (or the top of the fragment list) is reached. The top-most fragment on + that chain that may head a shower is the **shower initiator**; all EM + fragments that share the same initiator are merged into it. Non-EM + fragments pass through unchanged (each becomes its own instance). The merge is **purely genealogical** — no spatial proximity check is - performed. Photons (PDG 22) always have empty point clouds and are + performed. Photons (PDG 22) carry no deposits at a conversion and are natural shower initiators when no EM parent is found. + Two rules shape the walk: + + * ``kLEScatter`` fragments are walked through and can be merged in, but + never head a shower (:func:`_can_initiate`). An LE fragment whose + chain reaches no possible initiator stays its own instance. + * A Compton electron or photoelectron reaches its photon only when that + photon is itself inside a shower (has an EM parent fragment). A + shower begins at the pair production, so the electrons a top-level + photon scattered out before converting -- or instead of converting -- + are their own instances. The same electrons made by a photon *within* + a shower belong to it. + Extended parent lookup ~~~~~~~~~~~~~~~~~~~~~~ The walk uses two lookup tables in tandem: @@ -120,34 +142,40 @@ def merge_em_showers(fragments): for mid in mids: frag_by_member_id[int(mid)] = r + def parent_frag(rep): + """The fragment holding *rep*'s parent particle, or None.""" + pid = rep.parent_id + par = (frag_by_id.get(pid) + or frag_by_member_id.get(int(pid) if pid is not None else -1)) + return None if par is rep else par + + # Each fragment's next step up the shower chain, or None where the chain + # stops. Photons are linked first: whether a photon secondary may climb + # to its photon depends on whether the photon itself climbs. + up: dict = {} + for rep in fragments: + par = parent_frag(rep) + up[id(rep)] = (par if par is not None and _em_like(rep) + and _em_like(par) else None) + for rep in fragments: + par = up[id(rep)] + if (par is not None and _is_photon_secondary(rep) + and abs(int(par.pdg)) == 22 and up[id(par)] is None): + up[id(rep)] = None + def find_initiator(rep): - """Iteratively walk the parent chain; return the shower-initiator fragment.""" - visited: set = set() - current = rep - while True: - if id(current) in visited: - # Cycle guard (should never occur with well-formed genealogy) - return current - visited.add(id(current)) - - # Non-shower-eligible particles are their own initiator. - if not _is_shower_eligible(current): - return current - - # Walk to parent fragment — try rep.id first, then any member id. - parent_pid = current.parent_id - parent = ( - frag_by_id.get(parent_pid) - or frag_by_member_id.get(int(parent_pid) if parent_pid is not None else -1) - ) - if parent is None or parent is current: - # No shower-eligible parent in fragment list → current is the initiator. - return current - if not _is_shower_eligible(parent): - # Parent is non-shower → current is the shower initiator. - return current - # Parent is also shower-eligible → keep walking up. - current = parent + """Walk *rep*'s chain; return the top-most fragment that may head it.""" + if not _em_like(rep): + return rep + top = rep if _can_initiate(rep) else None + seen = {id(rep)} + cur = up[id(rep)] + while cur is not None and id(cur) not in seen: # cycle guard + seen.add(id(cur)) + if _can_initiate(cur): + top = cur + cur = up[id(cur)] + return rep if top is None else top # Group every fragment under its shower initiator. groups: dict = defaultdict(list) # initiator.id → list[fragment rep] @@ -182,3 +210,117 @@ def find_initiator(rep): result.append(initiator) return result + + +# --------------------------------------------------------------------------- +# Split-off pieces of tracks +# --------------------------------------------------------------------------- + +#: Semantic types whose split-off pieces go back to their particle: the ones +#: with a continuous trajectory that a gap can interrupt. Showers collect +#: their pieces through PhotonDecay and genealogy already; a neutron's blobs +#: are separate interactions, not one trajectory. +_TRACK_LIKE = frozenset({SemanticType.kTrack, SemanticType.kMichel, + SemanticType.kDelta}) + + +def find_split_pieces(fragments, particles): + """ + Stand-alone fragments that are split-off pieces of a track-like particle. + + Defragmentation cuts a particle's clusters of ``min_pc_size`` voxels or + fewer off into new ``kLEScatter`` particles that keep its ``geant4_id`` + (a stub past a gap, the last voxels beyond a dead stretch). When such a + piece touches nothing it stays a fragment of its own, though it is part + of its particle's trajectory. This finds those fragments: every member + a piece of the same Geant4 particle, that particle ``kTrack``, + ``kMichel`` or ``kDelta``, and its own fragment not LE. A piece that + proximity absorbed into another fragment is left where it is -- it looks + like part of that fragment, so it is. + + Parameters + ---------- + fragments : list of Particle + Step-1 fragment representatives. + particles : list of Particle + Every particle of the event. + + Returns + ------- + list of tuple + ``(piece_fragment, track_fragment)`` pairs. + """ + by_id = {int(p.id): p for p in particles} + g4 = lambda p: int(getattr(p, "geant4_id", p.id)) + frag_of = {} + for f in fragments: + for m in (f.member_ids if f.member_ids is not None else [f.id]): + frag_of[int(m)] = f + out = [] + for f in fragments: + if f.sem_type != SemanticType.kLEScatter: + continue + piece = by_id.get(int(f.id)) + src = by_id.get(int(piece.parent_id)) if piece is not None else None + if src is None or src is piece or g4(src) != g4(piece): + continue + mids = f.member_ids if f.member_ids is not None else [f.id] + if any(int(m) not in by_id or g4(by_id[int(m)]) != g4(piece) + for m in mids): + continue + if src.sem_type not in _TRACK_LIKE: + continue + home = frag_of.get(int(src.id)) + if home is None or home is f or home.sem_type == SemanticType.kLEScatter: + continue + out.append((f, home)) + return out + + +def relabel_split_pieces(pairs): + """ + Give each piece fragment its track fragment's semantic type, in place. + + Run before the fragments are snapshotted: LE is decided per fragment + (``frag_sem_type == kLEScatter``), and a piece of a muon is part of the + muon, not a low-energy blob. The piece particle's own ``sem_type`` is + untouched. + """ + for f, home in pairs: + f.sem_type = home.sem_type + + +def attach_split_pieces(instances, pairs): + """ + Merge each piece's instance into the instance of its track fragment. + + Only a piece that heads an instance of its own is moved. A ``kDelta`` + or ``kMichel`` piece usually arrives already, since + :func:`merge_em_showers` walks it up to its electron. + + Returns + ------- + list of Particle + *instances* without the ones merged away. + """ + inst_of = {} + for inst in instances: + for m in (inst.member_ids if inst.member_ids is not None else [inst.id]): + inst_of[int(m)] = inst + gone = set() + for f, home in pairs: + a, b = inst_of.get(int(f.id)), inst_of.get(int(home.id)) + if a is None or b is None or a is b or a.id != f.id: + continue + a_ids = list(a.member_ids if a.member_ids is not None else [a.id]) + b.member_ids = list(b.member_ids if b.member_ids is not None + else [b.id]) + a_ids + clouds = [c for c in (b.point_cloud, a.point_cloud) if len(c)] + if len(clouds) > 1: + b.point_cloud = np.concatenate(clouds, axis=0) + elif clouds: + b.point_cloud = clouds[0] + for m in a_ids: + inst_of[int(m)] = b + gone.add(id(a)) + return [i for i in instances if id(i) not in gone] diff --git a/pysupera/partitioner.py b/pysupera/partitioner.py index 579bd93..d80d15f 100644 --- a/pysupera/partitioner.py +++ b/pysupera/partitioner.py @@ -770,6 +770,10 @@ def _build_partitions_incremental( rep_lookup: Dict[int, Particle] = { p.id: reps[self.id_to_idx[p.id]] for p in self.particles } + # Published now, not only at the end, so a condition's post_filter + # can ask which partition a particle currently sits in. The same + # dict is updated in place by every merge below. + self.rep_lookup = rep_lookup # rep_members: id(rep) → list of original particle IDs in that partition. # Allows O(|partition|) rep_lookup update instead of O(n) full scan. diff --git a/pysupera/provenance.py b/pysupera/provenance.py index b7a2a0a..95f4adf 100644 --- a/pysupera/provenance.py +++ b/pysupera/provenance.py @@ -204,8 +204,7 @@ def instance_of_groups(view, frag_owner): inst = np.flatnonzero(view.is_instance) lo = np.asarray(c["inst_pc_start"])[inst] - hi = np.maximum(np.asarray(c["inst_pc_le_end"])[inst], - np.asarray(c["inst_pc_end"])[inst]) + hi = np.asarray(c["inst_pc_end"])[inst] keep = lo >= 0 inst, lo, hi = inst[keep], lo[keep], hi[keep] order = np.argsort(lo) @@ -218,8 +217,6 @@ def instance_of_groups(view, frag_owner): if k is None: continue p = int(c["frag_pc_start"][k]) - if p < 0: - p = int(c["frag_pc_le_start"][k]) if p < 0: continue j = int(np.searchsorted(lo, p, side="right")) - 1 @@ -232,12 +229,14 @@ def le_flags(owner, is_le_of): """ Per-group low-energy flag, from the particle that owns the group. - A group's deposits all belong to one particle, so LE-ness is exact at - this level -- unlike the instance, which normally mixes an LE and a - non-LE side. Without this the 2D view could only guess, and would have - to mask by the host instance, which is a different question. + A group's deposits all belong to one particle, and so to one fragment, + so LE-ness is exact at this level -- unlike the instance, which normally + mixes an LE and a non-LE side. Without this the 2D view could only + guess, and would have to mask by the host instance, which is a different + question. - *is_le_of* is ``{particle_id: bool}``. Unclaimed groups come out 0. + *is_le_of* is ``{particle_id: bool}``, decided by the particle's fragment + (:func:`pysupera.layout.fragment_le`). Unclaimed groups come out 0. """ out = np.zeros(len(owner), dtype=np.int8) for g in np.flatnonzero(owner != NO_OWNER): diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 05ae869..b2f125b 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -206,6 +206,142 @@ class G4ProcessSubtype(IntEnum): _DEFAULT_ELECTRON_ENERGY_THRESHOLD = 0.05 # MeV +# --------------------------------------------------------------------------- +# Particle kinematics +# --------------------------------------------------------------------------- + +#: Speed of light in mm/ns, the units of the step records. +_C_MM_PER_NS = 299.792458 + +#: Per-particle kinematic arrays, each (N, 4) float64: positions are +#: ``[x, y, z, t]`` (mm, ns) and momenta ``[px, py, pz, E]`` (MeV), E total. +#: ``start_*`` / ``end_*`` describe the Geant4 particle and come from here; +#: ``first_*`` / ``last_*`` describe a reconstructed instance and are made +#: later, from the steps it collected (:mod:`pysupera.kinematics`). +KINEMATICS_FIELDS = ("start_pos", "start_mom", "first_pos", "first_mom", + "last_pos", "last_mom", "end_pos", "end_mom") + +#: The fields :func:`particle_kinematics` fills. +TRACK_KINEMATICS_FIELDS = ("start_pos", "start_mom", "end_pos", "end_mom") + + +def particle_kinematics(parts): + """ + Creation and destruction kinematics of every Geant4 particle. + + ``start_*`` / ``end_*`` come from the particle record, with one + correction: Geant4 records a photon that converts with zero momentum at + its end -- the state after it was killed. Its ``end_mom`` is set to + its momentum just before converting instead: the start momentum less + what its Compton electrons carried off, energy the same way. Compton + scattering is two-body, so this is exact up to the electron's binding + energy; Rayleigh scattering, which leaves no secondary, is invisible to + it. + + Parameters + ---------- + parts : structured ndarray + The event's particle records (``particle/geant4``). + + Returns + ------- + dict + ``mass`` (N,) float64 and every name in + :data:`TRACK_KINEMATICS_FIELDS`, (N, 4) float64. + """ + f8 = lambda name: np.asarray(parts[name], dtype=np.float64) + mass = f8("mass") + out = {"mass": mass} + for pre, key in (("start", ""), ("end", "end_")): + out[f"{pre}_pos"] = np.stack([f8(key + c) for c in ("x", "y", "z", "t")], 1) + out[f"{pre}_mom"] = np.stack([f8(key + c) for c in ("px", "py", "pz")] + + [f8(key + "ke") + mass], 1) + + names = parts.dtype.names or () + if not {"proc_end", "subproc_end", "proc_start", "subproc_start", + "track_id", "parent_track_id"} <= set(names): + return out + em = int(G4ProcessType.kProcessElectromagnetic) + pdg = np.asarray(parts["pdg"]) + conv = np.flatnonzero( + (pdg == 22) & (np.asarray(parts["proc_end"]) == em) + & (np.asarray(parts["subproc_end"]) + == int(G4ProcessSubtype.kSubtypeEMGammaConversion))) + if not len(conv): + return out + compt = ((np.asarray(parts["proc_start"]) == em) + & (np.asarray(parts["subproc_start"]) + == int(G4ProcessSubtype.kSubtypeEMComptonScattering))) + tid = np.asarray(parts["track_id"], dtype=np.int64) + ptid = np.asarray(parts["parent_track_id"], dtype=np.int64) + row = {int(t): k for k, t in enumerate(tid)} + lost = np.zeros((len(parts), 4)) + ke = f8("ke") + for c in np.flatnonzero(compt): + k = row.get(int(ptid[c])) + if k is not None: + lost[k, :3] += out["start_mom"][c, :3] + lost[k, 3] += ke[c] + out["end_mom"][conv] = out["start_mom"][conv] - lost[conv] + return out + + +def step_ends(steps, idx, mass, sign): + """ + Where and with what momentum step(s) *idx* begin (*sign* -1) or end (+1). + + A step record holds its *midpoint* -- measured, a particle's first step + sits half a ``dx`` from its creation point -- so an end is reached by + moving ``dx/2`` along the step's direction, as if the step were + straight, and the time by the matching ``dx/2 / (beta c)``. The + momentum is the step's own ``p`` along that direction (theta from the z + axis), with ``E = sqrt(p^2 + m^2)``; it is not moved. + + Parameters + ---------- + steps : structured ndarray + Step records: x, y, z [mm], t [ns], theta, phi [rad], p [MeV/c], + dx [mm]. + idx : ndarray of int + Which steps. + mass : ndarray of float + The mass of the particle that made each of them [MeV]. + sign : float + -1 for the start of the step, +1 for its end. + + Returns + ------- + pos, mom : ndarray, (len(idx), 4) float64 + ``[x, y, z, t]`` and ``[px, py, pz, E]``. + """ + s = steps[np.asarray(idx, dtype=np.int64)] + th = np.asarray(s["theta"], dtype=np.float64) + ph = np.asarray(s["phi"], dtype=np.float64) + d = np.stack([np.sin(th) * np.cos(ph), np.sin(th) * np.sin(ph), + np.cos(th)], 1) + p = np.asarray(s["p"], dtype=np.float64) + m = np.asarray(mass, dtype=np.float64) + e = np.sqrt(p * p + m * m) + half = 0.5 * np.asarray(s["dx"], dtype=np.float64) + beta = np.divide(p, e, out=np.zeros_like(p), where=e > 0) + dt = np.divide(half, beta * _C_MM_PER_NS, out=np.zeros_like(p), + where=beta > 0) + pos = np.stack([np.asarray(s[c], dtype=np.float64) + for c in ("x", "y", "z", "t")], 1) + pos[:, :3] += sign * half[:, None] * d + pos[:, 3] += sign * dt + return pos, np.c_[p[:, None] * d, e] + + +def step_owner_from_ass(ass, n_parts, n_steps): + """Per step, the particle whose ``[start, end)`` slice holds it; -1 for none.""" + owner = np.full(n_steps, -1, dtype=np.int64) + for i in range(min(n_parts, len(ass))): + a, b = int(ass["start"][i]), int(ass["end"][i]) + owner[a:b] = i + return owner + + # --------------------------------------------------------------------------- # Internal helper functions # --------------------------------------------------------------------------- @@ -621,6 +757,10 @@ def __init__( # Cell size classification measures a cloud's extent in; see # pysupera.utils.count_extent for why rows will not do. self._voxel_size = voxel_size + #: Which steps first_* / last_* are taken from. + self.kinematics_steps = "all" + self.last_kinematics = None + self.last_steps = None self._file = h5py.File(path, "r") self._n_events = len(self._file[self._part_key]) @@ -676,6 +816,20 @@ def __getitem__(self, index: int) -> list[Particle]: _ids, _par, _root, _g4 = _to_index_space(parts, _get_ancestor_id(parts)) + #: per-particle kinematics of the event just read, in *parts* order + #: (see particle_kinematics), plus ``geant4_trackid`` to join on + self.last_kinematics = dict(particle_kinematics(parts), + geant4_trackid=_g4) + #: The event's steps, for the instance first/last kinematics + #: (pysupera.kinematics): the records, the *parts* row that made + #: each, and which count -- here all of them, there being no readout. + #: A step goes to the particle whose voxels hold it: a point's id is + #: its step index. + self.last_steps = dict( + steps=steps, + owner=step_owner_from_ass(ass, num_parts, len(steps)), + counted=None, group=None) + return Particle.from_flat_arrays( ids = _ids, parent_ids = _par, diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index b86acb2..7d828e5 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -102,6 +102,7 @@ _get_interaction_type, _get_ancestor_id, _to_index_space, + particle_kinematics, ) from ..data import Particle from ..utils import PointFeature @@ -597,6 +598,10 @@ def __init__( self.last_truth_segments = None self._step_key = step_key self._want_truth = bool(truth_segments) + #: Which steps first_* / last_* are taken from. + self.kinematics_steps = "visible" + self.last_kinematics = None + self.last_steps = None self._edepsim_path = edepsim_path self._seg_path = seg_path @@ -666,6 +671,9 @@ def __getitem__(self, index: int) -> list[Particle]: #: vertices of the most recently read event, for the interactions table self.last_vertices = verts parts = self._edepsim_file[self._part_key][index] + # The EDepSim steps: the truth cloud is read from them, and the + # first/last-step kinematics. + steps = self._edepsim_file[self._step_key][index] # ── Step 2: load per-volume segment data from the JAXTPC seg file ─ # Each volume dict contains positions_mm (N,3), de, dx, t0_us, … @@ -698,7 +706,7 @@ def __getitem__(self, index: int) -> list[Particle]: self.last_n_groups = _hstats['n_groups'] self.last_deposit_volume_offsets = [] self.last_truth_segments = ( - self._truth_segments(index, event_key, seg_volumes, _g_offs) + self._truth_segments(steps, event_key, seg_volumes, _g_offs) if self._want_truth else None) self.last_planes = self._plane_entries(event_key, _g_offs, self._on_sensor) @@ -747,6 +755,11 @@ def __getitem__(self, index: int) -> list[Particle]: _ids, _par, _root, _g4 = _to_index_space(parts, _get_ancestor_id(parts)) + #: per-particle kinematics of the event just read, in *parts* order + #: (see particle_kinematics), plus ``geant4_trackid`` to join on + self.last_kinematics = dict(particle_kinematics(parts), + geant4_trackid=_g4) + # ── Deposit provenance for this event ──────────────────────────── # deposit_to_group is per volume; concatenate it onto the same # event-global index the point clouds now carry, and do the same for @@ -757,12 +770,24 @@ def __getitem__(self, index: int) -> list[Particle]: self._load_deposit_groups(event_key, seg_volumes) self.last_deposit_volume_offsets = volume_offsets self.last_truth_segments = ( - self._truth_segments(index, event_key, seg_volumes, + self._truth_segments(steps, event_key, seg_volumes, self.last_group_volume_offsets) if self._want_truth else None) self.last_planes = self._plane_entries( event_key, self.last_group_volume_offsets, None) + #: The event's steps, for the instance first/last kinematics + #: (pysupera.kinematics): the records, the *parts* row that made + #: each, which count (the readout saw them), and each one's + #: event-global group -- a step goes to the particle owning its + #: group, as its segment does in the truth cloud. + self.last_steps = dict( + steps=steps, + owner=self._step_owner(steps, parts), + counted=self._visible_steps(steps, event_key, seg_volumes), + group=self._step_groups(steps, event_key, seg_volumes, + self.last_group_volume_offsets)) + _particles = Particle.from_flat_arrays( ids = _ids, parent_ids = _par, @@ -889,29 +914,22 @@ def _check_same_batch(self): f"config.{k} is {va!r} in {self._sensor_path} and " f"{vb!r} in {self._inst_path}.") - def _truth_segments(self, index, event_key, seg_volumes, g_offs): + def _matched_steps(self, steps, event_key, seg_volumes): """ - Every segment of the event, with its event-global group. + Yield ``(v, vol_group, seg_to_group_key, idx)`` per volume: the + EDepSim step behind each of the volume's segments, as indices into + *steps*. - The values come from the EDepSim step each segment was made from, at - full precision: the seg file stores positions on a 0.3 mm grid and - the rest as float16. JAXTPC splits the step list into volumes by position and keeps the order within each, so segment k of volume v is the k-th step lying in volume v. That is checked, not assumed: the counts must agree and the positions must match to the seg file's quantisation. - - Columns: :data:`TRUTH_COLUMNS` -- x, y, z [mm], t [us], dE [MeV], - dX [cm], theta, phi [rad] (theta from the z axis, the convention the - steps follow), |p| [MeV/c]. """ - steps = self._edepsim_file[self._step_key][index] pos = np.c_[steps['x'], steps['y'], steps['z']].astype(np.float32) # JAXTPC's volume test, in its own arithmetic: float32 cm, half-open. cm = pos / np.float32(10.0) ranges = self._volume_ranges() ev = self._inst_file[event_key] - flats, groups = [], [] for v, sv in enumerate(seg_volumes): n = int(sv.get('n_actual', 0) or 0) vg = ev.get(f'volume_{v}') @@ -938,9 +956,29 @@ def _truth_segments(self, index, event_key, seg_volumes, g_offs): f"{event_key}/volume_{v}: segments sit up to {off:.2f} mm " f"from the EDepSim steps matched to them, beyond the seg " f"file's 0.3 mm quantisation -- not the same steps.") + yield v, vg, key, idx + + def _truth_segments(self, steps, event_key, seg_volumes, g_offs): + """ + Every segment of the event, with its event-global group. + + The values come from the EDepSim step each segment was made from, at + full precision: the seg file stores positions on a 0.3 mm grid and + the rest as float16. See :meth:`_matched_steps` for the matching. + + Columns: :data:`TRUTH_COLUMNS` -- x, y, z [mm], t [us], dE [MeV], + dX [cm], theta, phi [rad] (theta from the z axis, the convention the + steps follow), |p| [MeV/c]. + """ + flats, groups = [], [] + for v, vg, key, idx in self._matched_steps(steps, event_key, + seg_volumes): + n = len(idx) st = steps[idx] f = np.empty((n, len(TRUTH_COLUMNS)), dtype=np.float32) - f[:, 0:3] = pos[idx] + f[:, 0] = st['x'] + f[:, 1] = st['y'] + f[:, 2] = st['z'] f[:, 3] = st['t'] / 1000.0 # ns -> us f[:, 4] = st['de'] f[:, 5] = st['dx'] / 10.0 # mm -> cm @@ -955,6 +993,52 @@ def _truth_segments(self, index, event_key, seg_volumes, g_offs): np.zeros(0, dtype=np.int64)) return np.concatenate(flats), np.concatenate(groups) + def _visible_steps(self, steps, event_key, seg_volumes): + """ + Per EDepSim step, whether the readout saw it: its segment's group + left an entry on some readout plane of its volume -- the same test + the deposit-mode visibility pass applies. Steps outside every + volume were never simulated by JAXTPC and count as unseen. + """ + import h5py + seen = np.zeros(len(steps), dtype=bool) + for v, vg, key, idx in self._matched_steps(steps, event_key, + seg_volumes): + active = [vg[k]['group_ids'][:] for k in vg + if isinstance(vg[k], h5py.Group) and 'group_ids' in vg[k]] + if not active: + continue + g = vg[key][:len(idx)] + seen[idx[np.isin(g, np.unique(np.concatenate(active)))]] = True + return seen + + def _step_owner(self, steps, parts): + """Per EDepSim step, the row of *parts* that made it; -1 for none.""" + if 'track_id' not in (steps.dtype.names or ()): + raise KeyError( + f"{self._step_key} has no track_id field, so a step cannot " + f"be given to its particle for the first/last-step " + f"kinematics.") + tids = np.asarray(parts['track_id'], dtype=np.int64) + st = np.asarray(steps['track_id'], dtype=np.int64) + if not len(tids): + return np.full(len(st), -1, dtype=np.int64) + order = np.argsort(tids, kind='stable') + k = np.clip(np.searchsorted(tids[order], st), 0, len(tids) - 1) + return np.where(tids[order][k] == st, order[k], -1) + + def _step_groups(self, steps, event_key, seg_volumes, g_offs): + """ + Per EDepSim step, the event-global group of its segment; -1 for a + step outside every volume. See :meth:`_matched_steps`. + """ + out = np.full(len(steps), -1, dtype=np.int64) + for v, vg, key, idx in self._matched_steps(steps, event_key, + seg_volumes): + out[idx] = (vg[key][:len(idx)].astype(np.int64) + + int(g_offs[v] if v < len(g_offs) else 0)) + return out + def _plane_entries(self, event_key, g_offs, on_sensor): """ Every readout plane of the event, as its CSR entries' groups. diff --git a/pysupera/torchdata.py b/pysupera/torchdata.py index c25dd83..c88082a 100644 --- a/pysupera/torchdata.py +++ b/pysupera/torchdata.py @@ -1,5 +1,5 @@ """ -PyTorch data pipeline for panoptic segmentation on pysupera 3.0.0 output. +PyTorch data pipeline for panoptic segmentation on pysupera 4.0.0 output. Provides a :class:`Dataset` over events, a collate that keeps a batch as a *list* of events (point clouds differ in length, so nothing is padded or @@ -28,14 +28,16 @@ Low-energy scatters ------------------- -LE-ness is a property of a *point*, not of its instance: an instance's block -holds its own points in ``[inst_pc_start, inst_pc_end)`` and the LE deposits -absorbed into it in ``[inst_pc_le_start, inst_pc_le_end)``. The two runs are -adjacent, so ``inst_pc_end == inst_pc_le_start``. A point in the second -run is labelled ``kLEScatter`` whatever its host instance is, which is what -makes it the lowest-priority contribution to a contested voxel. Its *instance* -label is still the host's, so an LE deposit stays attributed to the shower or -track that absorbed it. +LE-ness is a property of a *fragment*, not of its instance: a point is LE +when its fragment is a ``kLEScatter`` blob, one with no trajectory. (A +low-energy scatter absorbed into a track or shower fragment by proximity is +part of that trajectory, and is labelled with it.) An instance's block holds +its non-LE fragments in ``[inst_pc_start, inst_pc_le_start)`` and the LE +fragments grouped into it in ``[inst_pc_le_start, inst_pc_end)``. A point in +the second run is labelled ``kLEScatter`` whatever its host instance is, which +is what makes it the lowest-priority contribution to a contested voxel. Its +*instance* label is still the host's, so an LE blob stays attributed to the +shower or track it was grouped with. LE deposits are **excluded by default** (``include_le=False``): they are around a third of the points and are usually noise to a segmentation model. The flag @@ -55,7 +57,11 @@ ``voxel_sem``, ``voxel_instance`` and ``voxel_interaction`` are the three panoptic targets, one per row of ``points``. ``instances`` and ``interactions`` are the object-level tables read straight from the file; an interaction's id is -its row index, which is what ``voxel_interaction`` holds. +its row index, which is what ``voxel_interaction`` holds. ``instances`` carries +every particle column of the instance rows, the kinematics included: +``mass`` and the ``(n, 4)`` ``start_pos`` / ``start_mom`` ... ``end_mom``, of +which ``first_*`` / ``last_*`` are the instance's own first and last point +(``last_*`` NaN for a shower). """ from __future__ import annotations @@ -112,7 +118,7 @@ def build_event(view, include_le: bool = False) -> dict: Parameters ---------- view : EventView - One event from a format 3.x file. + One event from a format 4.x file. include_le : bool Keep low-energy-scatter points. Off by default; see the module docstring. Applies to the input points and to every label, so the @@ -123,7 +129,7 @@ def build_event(view, include_le: bool = False) -> dict: dict ``points`` (V, 4) float32 -- x, y, z, E, one row per voxel ``voxel_sem`` (V,) int16 winner's class; ``kLEScatter`` for - an absorbed low-energy deposit + a point of an LE fragment ``voxel_instance`` (V,) int32 winning instance's particle ``id`` ``voxel_interaction`` (V,) int32 that instance's ``interaction_id`` ``instances`` dict of arrays, one entry per instance row @@ -143,20 +149,16 @@ def build_event(view, include_le: bool = False) -> dict: p_intr = np.full(n, NO_LABEL, dtype=np.int32) le_code = SemanticType.kLEScatter.value for r in inst_rows: - # Two adjacent runs: the instance's own points, then the LE deposits - # absorbed into it. Either side may be empty (-1). - nle_a, nle_b = int(c["inst_pc_start"][r]), int(c["inst_pc_end"][r]) - le_a, le_b = int(c["inst_pc_le_start"][r]), int(c["inst_pc_le_end"][r]) - a = nle_a if nle_a >= 0 else le_a - b = le_b if le_b >= 0 else nle_b + # Two adjacent runs: the non-LE fragments, then the LE fragments. + # Either may be empty; the whole instance is empty when start < 0. + a, s, b = (int(c["inst_pc_start"][r]), int(c["inst_pc_le_start"][r]), + int(c["inst_pc_end"][r])) if a < 0 or b <= a: continue p_inst[a:b] = int(c["id"][r]) p_intr[a:b] = int(c["interaction_id"][r]) - if nle_a >= 0 and nle_b > nle_a: - p_sem[nle_a:nle_b] = int(c["inst_sem_type"][r]) - if le_a >= 0 and le_b > le_a: - p_sem[le_a:le_b] = le_code + p_sem[a:s] = int(c["inst_sem_type"][r]) + p_sem[s:b] = le_code # ---- drop low-energy scatters ----------------------------------------- # Done before merging so an excluded point contributes no energy either. @@ -228,7 +230,7 @@ def _arrays(table: dict) -> dict: class PysuperaEvents(Dataset): """ - One pysupera 3.0.0 file as a map-style dataset of events. + One pysupera 4.0.0 file as a map-style dataset of events. The HDF5 handle is opened lazily *per worker*: h5py handles cannot cross a fork, so opening in ``__init__`` breaks silently under @@ -237,7 +239,7 @@ class PysuperaEvents(Dataset): Parameters ---------- path : str - A format 3.x output file. + A format 4.x output file. include_le : bool Keep low-energy-scatter points. Off by default. transform : callable or None diff --git a/tests/test_conditions.py b/tests/test_conditions.py index ecb2784..542d10f 100644 --- a/tests/test_conditions.py +++ b/tests/test_conditions.py @@ -10,6 +10,7 @@ from tests.conftest import ( make_particle, cloud, PT_PRIMARY, PT_TRACK, PT_IONIZATION, PT_COMPTON, PT_PHOTON, PT_DELTA, + PT_CONVERSION, PT_PHOTO_ELEC, ) @@ -41,7 +42,7 @@ def test_electron_child_of_photon_merges(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), parent_id=1, ancestor_id=1) - electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, + electron = make_particle(2, PT_CONVERSION, pdg=11, parent_pdg=22, parent_id=1, ancestor_id=1, offset=(0, 0, 0)) prt = _part(photon, electron) @@ -52,7 +53,7 @@ def test_positron_child_of_photon_merges(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), parent_id=1, ancestor_id=1) - positron = make_particle(2, PT_PRIMARY, pdg=-11, parent_pdg=22, + positron = make_particle(2, PT_CONVERSION, pdg=-11, parent_pdg=22, parent_id=1, ancestor_id=1, offset=(0.1, 0, 0)) prt = _part(photon, positron) @@ -63,9 +64,9 @@ def test_both_decay_products_in_same_partition(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), parent_id=1, ancestor_id=1) - electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, + electron = make_particle(2, PT_CONVERSION, pdg=11, parent_pdg=22, parent_id=1, ancestor_id=1) - positron = make_particle(3, PT_PRIMARY, pdg=-11, parent_pdg=22, + positron = make_particle(3, PT_CONVERSION, pdg=-11, parent_pdg=22, parent_id=1, ancestor_id=1, offset=(0.2, 0, 0)) prt = _part(photon, electron, positron) @@ -73,6 +74,41 @@ def test_both_decay_products_in_same_partition(self): assert _same_partition(parts, 1, 2) assert _same_partition(parts, 1, 3) + def test_compton_electron_stays_its_own_particle(self): + """A shower begins at the pair production: a Compton electron is not + merged into its photon, however close or far.""" + photon = make_particle(1, PT_PHOTON, pdg=22, + pc=np.zeros((0, 3), dtype=np.float32), + parent_id=1, ancestor_id=1) + compton = make_particle(2, PT_COMPTON, pdg=11, parent_pdg=22, + parent_id=1, ancestor_id=1, n_pts=2) + photo = make_particle(3, PT_PHOTO_ELEC, pdg=11, parent_pdg=22, + parent_id=1, ancestor_id=1, n_pts=2, + offset=(30, 0, 0)) + photon.sem_type = SemanticType.kLEScatter # a tiny photon blob + prt = _part(photon, compton, photo) + parts = prt.partition(PhotonDecay(), verbose=False) + assert not _same_partition(parts, 1, 2) + assert not _same_partition(parts, 1, 3) + # no pair, no shower: the photon is not promoted + assert prt.rep_lookup[1].sem_type == SemanticType.kLEScatter + + def test_conversion_promotes_the_photon_and_leaves_compton_out(self): + photon = make_particle(1, PT_PHOTON, pdg=22, + pc=np.zeros((0, 3), dtype=np.float32), + parent_id=1, ancestor_id=1) + compton = make_particle(2, PT_COMPTON, pdg=11, parent_pdg=22, + parent_id=1, ancestor_id=1, n_pts=2, + offset=(80, 0, 0)) + pair = make_particle(3, PT_CONVERSION, pdg=11, parent_pdg=22, + parent_id=1, ancestor_id=1, n_pts=20) + photon.sem_type = SemanticType.kLEScatter + prt = _part(photon, compton, pair) + parts = prt.partition_combined([PhotonDecay()], verbose=False) + assert _same_partition(parts, 1, 3) + assert not _same_partition(parts, 1, 2) + assert prt.rep_lookup[1].sem_type == SemanticType.kShower + def test_non_photon_parent_no_merge(self): # parent is a track (parent_pdg=13, not 22) → no PhotonDecay merge track = make_particle(1, PT_TRACK, pdg=13, diff --git a/tests/test_instance_rules.py b/tests/test_instance_rules.py new file mode 100644 index 0000000..ba32544 --- /dev/null +++ b/tests/test_instance_rules.py @@ -0,0 +1,316 @@ +""" +The rules that decide instances and their kinematics. + +* A shower starts at the pair production: a photon's Compton electrons and + photoelectron are not its fragment, and join its *instance* only when the + photon is itself inside a shower. LE fragments may join, never lead. +* An LE particle touching several things is absorbed into its own ancestor + when that is one of them (delta rays). +* A stand-alone split-off piece of a track goes back to its track. +* first_* / last_* are made per instance, from the steps it collected. +""" +import numpy as np +import pytest + +from pysupera.conditions.absorb_le_scatter import AbsorbLEScatter +from pysupera.kinematics import instance_kinematics, step_particles +from pysupera.layout import Group, fragment_le, snapshot_groups +from pysupera.merge import (attach_split_pieces, find_split_pieces, + merge_em_showers, relabel_split_pieces) +from pysupera.utils import SemanticType as ST +from tests.conftest import ( + make_particle, make_partitioner, + PT_COMPTON, PT_CONVERSION, PT_DELTA, PT_NEUTRON, PT_PHOTON, PT_PRIMARY, + PT_TRACK, +) + +EMPTY = np.zeros((0, 3), dtype=np.float32) + + +def fragments(*parts): + """Each particle its own fragment, as step 1 leaves the unmerged ones.""" + for p in parts: + p.member_ids = [p.id] + return list(parts) + + +def instance_of(instances, pid): + return next(i for i in instances if pid in i.member_ids) + + +def line(x0, x1, n=40, y=0.0): + x = np.linspace(x0, x1, n, dtype=np.float32) + return np.c_[x, np.full(n, y, np.float32), np.zeros(n, np.float32)] + + +# --------------------------------------------------------------------------- +# Showers +# --------------------------------------------------------------------------- + +class TestShowerStartsAtPairProduction: + + def event(self): + """ + Photon 1 Compton-scatters out electron 4, then converts into 2 and 3. + Electron 2 radiates photon 5, which Compton-scatters out the small + blob 6. Step 1 put the pair into the photon's fragment. + """ + g1 = make_particle(1, PT_PRIMARY, pdg=22, pc=EMPTY) + e2 = make_particle(2, PT_CONVERSION, pdg=11, parent_id=1, parent_pdg=22, + ancestor_id=1, n_pts=30) + e3 = make_particle(3, PT_CONVERSION, pdg=-11, parent_id=1, + parent_pdg=22, ancestor_id=1, n_pts=30) + c4 = make_particle(4, PT_COMPTON, pdg=11, parent_id=1, parent_pdg=22, + ancestor_id=1, n_pts=30, offset=(500, 0, 0)) + g5 = make_particle(5, PT_PHOTON, pdg=22, parent_id=2, parent_pdg=11, + ancestor_id=1, pc=EMPTY) + c6 = make_particle(6, PT_COMPTON, pdg=11, parent_id=5, parent_pdg=22, + ancestor_id=1, n_pts=2, offset=(300, 0, 0)) + c4.sem_type, c6.sem_type = ST.kShower, ST.kLEScatter + g1.sem_type = ST.kShower # PhotonDecay promoted it + g1.member_ids = [1, 2, 3] + for p in (c4, g5, c6): + p.member_ids = [p.id] + return [g1, c4, g5, c6] + + def test_the_initiating_photons_compton_electron_is_its_own_instance(self): + insts = merge_em_showers(self.event()) + assert instance_of(insts, 4) is not instance_of(insts, 1) + assert instance_of(insts, 4).id == 4 + + def test_compton_blob_of_a_photon_inside_the_shower_joins_it(self): + frags = self.event() + groups = snapshot_groups(frags) + insts = merge_em_showers(frags) + shower = instance_of(insts, 1) + assert {5, 6} <= set(shower.member_ids) + # ... and stays LE: it joined by genealogy, not by touching + assert fragment_le(self._particles(frags), groups)[6] is True + + @staticmethod + def _particles(frags): + out = {} + for f in frags: + out[f.id] = f + return list(out.values()) + + def test_an_le_fragment_never_heads_a_shower(self): + le = make_particle(1, PT_COMPTON, pdg=11, n_pts=2) + big = make_particle(2, PT_PRIMARY, pdg=11, parent_id=1, parent_pdg=11, + ancestor_id=1, n_pts=30) + le.sem_type, big.sem_type = ST.kLEScatter, ST.kShower + insts = merge_em_showers(fragments(le, big)) + assert instance_of(insts, 2).id == 2 + assert instance_of(insts, 1).id == 1 + + +# --------------------------------------------------------------------------- +# Absorption prefers the particle's own ancestor +# --------------------------------------------------------------------------- + +class TestDeltaRayGoesToItsTrack: + + def particles(self): + mu = make_particle(1, PT_TRACK, pdg=13, pc=line(0, 100)) + sh = make_particle(2, PT_PRIMARY, pdg=11, pc=line(40, 60, y=3.0)) + mu.sem_type, sh.sem_type = ST.kTrack, ST.kShower + d = make_particle(3, PT_DELTA, pdg=11, parent_id=1, parent_pdg=13, + ancestor_id=1, + pc=np.array([[50, 1.5, 0], [50.5, 1.5, 0]], + dtype=np.float32)) + d.sem_type = ST.kLEScatter + return mu, sh, d + + @pytest.mark.parametrize("pairs", [[(3, 2), (3, 1)], [(3, 1), (3, 2)]]) + def test_the_parent_wins_whatever_the_order(self, pairs): + prt = make_partitioner(list(self.particles()), D=5.0) + assert AbsorbLEScatter().post_filter(prt, pairs) == [(3, 1)] + + def test_without_the_parent_among_them_the_first_is_kept(self): + prt = make_partitioner(list(self.particles()), D=5.0) + assert AbsorbLEScatter().post_filter(prt, [(3, 2)]) == [(3, 2)] + + def test_in_the_partitioner(self): + prt = make_partitioner(list(self.particles()), D=5.0) + prt.partition_combined([AbsorbLEScatter()], verbose=False) + assert prt.rep_lookup[3] is prt.rep_lookup[1] + + +# --------------------------------------------------------------------------- +# Split-off pieces of tracks +# --------------------------------------------------------------------------- + +class TestSplitPiecesGoBackToTheirTrack: + + @staticmethod + def piece_of(src, pid, offset): + p = make_particle(pid, src._interaction_type, pdg=int(src.pdg), + parent_id=src.id, parent_pdg=int(src.pdg), + ancestor_id=src.ancestor_id, n_pts=3, offset=offset) + p.geant4_id = src.geant4_id + p.sem_type = ST.kLEScatter # what defragmentation sets + return p + + def test_a_stub_past_a_gap_joins_the_track_and_is_not_le(self): + mu = make_particle(1, PT_TRACK, pdg=13, pc=line(0, 100)) + stub = self.piece_of(mu, 2, (300, 0, 0)) + frags = fragments(mu, stub) + pairs = find_split_pieces(frags, [mu, stub]) + assert [(f.id, h.id) for f, h in pairs] == [(2, 1)] + relabel_split_pieces(pairs) + groups = snapshot_groups(frags) + assert fragment_le([mu, stub], groups)[2] is False + assert stub.sem_type == ST.kTrack + insts = attach_split_pieces(merge_em_showers(frags), pairs) + assert len(insts) == 1 and sorted(insts[0].member_ids) == [1, 2] + + def test_a_neutrons_blobs_are_left_alone(self): + n = make_particle(1, PT_NEUTRON, pdg=2112, n_pts=8) + blob = self.piece_of(n, 2, (300, 0, 0)) + assert find_split_pieces(fragments(n, blob), [n, blob]) == [] + + def test_a_piece_absorbed_elsewhere_stays_there(self): + mu = make_particle(1, PT_TRACK, pdg=13, pc=line(0, 100)) + pi = make_particle(3, PT_TRACK, pdg=211, pc=line(300, 400)) + stub = self.piece_of(mu, 2, (300, 0, 0)) + mu.member_ids = [1] + pi.member_ids = [3, 2] # proximity took the stub + assert find_split_pieces([mu, pi], [mu, pi, stub]) == [] + + +# --------------------------------------------------------------------------- +# Instance kinematics +# --------------------------------------------------------------------------- + +STEP_DTYPE = [(c, "f4") for c in ("x", "y", "z", "t", "theta", "phi", "p", + "dx")] + + +def steps_along_z(zs, ts, p, dx=1.0): + s = np.zeros(len(zs), dtype=STEP_DTYPE) + s["z"], s["t"], s["p"], s["dx"] = zs, ts, p, dx + return s + + +class TestInstanceKinematics: + + def test_an_absorbed_blob_of_another_particle_is_not_the_last_point(self): + mu = make_particle(0, PT_TRACK, pdg=13, n_pts=5) + blob = make_particle(1, PT_COMPTON, pdg=11, n_pts=2) + mu.geant4_id, blob.geant4_id = 10, 20 + mu.sem_type = ST.kTrack + blob.sem_type = ST.kLEScatter + steps = steps_along_z([1, 2, 3, 2.5], [1, 2, 3, 50], [300, 290, 280, 1]) + kin = dict(geant4_trackid=np.array([10, 20]), + mass=np.array([105.658, 0.511]), + start_mom=np.zeros((2, 4))) + g = Group(0, [0, 1], ST.kTrack.value) + out = instance_kinematics([g], [mu, blob], steps, + step_owner=np.array([0, 0, 0, 1]), + step_pid=np.array([0, 0, 0, 0]), + track_kin=kin)[0] + assert out["first_pos"][2] == pytest.approx(0.5) # 1 - dx/2 + assert out["last_pos"][2] == pytest.approx(3.5) # not the blob + assert out["last_mom"][2] == pytest.approx(280) + + def test_an_le_only_instance_uses_all_its_steps(self): + blob = make_particle(0, PT_COMPTON, pdg=11, n_pts=2) + blob.geant4_id, blob.sem_type = 20, ST.kLEScatter + steps = steps_along_z([4, 5], [1, 2], [1, 1]) + kin = dict(geant4_trackid=np.array([20]), mass=np.array([0.511]), + start_mom=np.zeros((1, 4))) + out = instance_kinematics([Group(0, [0], ST.kLEScatter.value)], [blob], + steps, np.array([0, 0]), np.array([0, 0]), + kin)[0] + assert out["first_pos"][2] == pytest.approx(3.5) + assert out["last_pos"][2] == pytest.approx(5.5) + + def shower(self): + g = make_particle(0, PT_PRIMARY, pdg=22, pc=EMPTY) + em = make_particle(1, PT_CONVERSION, pdg=11, parent_id=0, + parent_pdg=22, n_pts=30) + ep = make_particle(2, PT_CONVERSION, pdg=-11, parent_id=0, + parent_pdg=22, n_pts=30) + late = make_particle(3, PT_PRIMARY, pdg=11, parent_id=1, + parent_pdg=11, n_pts=30) + for p, t in zip((g, em, ep, late), (1, 2, 3, 4)): + p.geant4_id, p.sem_type = t, ST.kShower + start_mom = np.array([[0, 0, 10, 10], [1, 0, 4, 4.2], + [-1, 0, 5, 5.3], [0, 0, 1, 1.1]], dtype=float) + kin = dict(geant4_trackid=np.array([1, 2, 3, 4]), + mass=np.array([0, 0.511, 0.511, 0.511]), + start_mom=start_mom) + steps = steps_along_z([10, 10, 40], [1, 1, 5], [4, 5, 1]) + group = Group(0, [0, 1, 2, 3], ST.kShower.value) + return [g, em, ep, late], steps, kin, group + + def test_a_shower_has_the_pair_momentum_and_no_last_point(self): + parts, steps, kin, g = self.shower() + out = instance_kinematics([g], parts, steps, np.array([1, 2, 3]), + np.array([1, 2, 3]), kin)[0] + np.testing.assert_allclose(out["first_mom"], [0, 0, 9, 9.5]) + assert out["first_pos"][2] == pytest.approx(9.5) + assert np.isnan(out["last_pos"]).all() and np.isnan(out["last_mom"]).all() + + def test_a_low_energy_pair_still_starts_the_shower(self): + parts, steps, kin, g = self.shower() + parts[1].sem_type = parts[2].sem_type = ST.kLEScatter + out = instance_kinematics([g], parts, steps, np.array([1, 2, 3]), + np.array([1, 2, 3]), kin)[0] + assert out["first_pos"][2] == pytest.approx(9.5) + np.testing.assert_allclose(out["first_mom"], [0, 0, 9, 9.5]) + + def test_a_pair_the_readout_missed_leaves_the_steps_own_momentum(self): + parts, steps, kin, g = self.shower() + out = instance_kinematics([g], parts, steps, np.array([1, 2, 3]), + np.array([1, 2, 3]), kin, + counted=np.array([False, False, True]))[0] + assert out["first_pos"][2] == pytest.approx(39.5) + assert out["first_mom"][2] == pytest.approx(1.0) + + def test_steps_follow_the_voxels_that_hold_them(self): + a = make_particle(0, PT_TRACK, pdg=13, n_pts=1) + b = make_particle(1, PT_TRACK, pdg=13, n_pts=1) + a.voxmap = (np.array([0, 2]), np.array([0, 3])) + b.voxmap = (np.array([0, 1]), np.array([1])) + np.testing.assert_array_equal(step_particles(5, [a, b]), + [0, 1, -1, 0, -1]) + np.testing.assert_array_equal( + step_particles(3, [a, b], group=np.array([1, -1, 0]), + group_owner=np.array([1, 0])), [0, -1, 1]) + + +def test_a_converting_photon_ends_with_its_momentum_before_converting(): + from pysupera.readers.format_edepsim_h5 import particle_kinematics + f4 = [(c, "f4") for c in ("x", "y", "z", "t", "px", "py", "pz", "ke", + "end_x", "end_y", "end_z", "end_t", "end_px", + "end_py", "end_pz", "end_ke", "mass")] + i4 = [(c, "i4") for c in ("track_id", "parent_track_id", "pdg", + "proc_start", "subproc_start", "proc_end", + "subproc_end")] + P = np.zeros(2, dtype=f4 + i4) + P["track_id"], P["parent_track_id"] = [1, 2], [-1, 1] + P["pdg"] = [22, 11] + P["mass"] = [0.0, 0.511] + P["pz"][0], P["ke"][0] = 10.0, 10.0 # photon along z + P["proc_end"][0], P["subproc_end"][0] = 2, 14 # converts; G4 says 0 + P["px"][1], P["pz"][1], P["ke"][1] = 1.0, 2.0, 1.5 # its Compton electron + P["proc_start"][1], P["subproc_start"][1] = 2, 13 + k = particle_kinematics(P) + np.testing.assert_allclose(k["end_mom"][0], [-1, 0, 8, 8.5], rtol=1e-6) + np.testing.assert_allclose(k["end_mom"][1], [0, 0, 0, 0.511], rtol=1e-6) + + +def test_when_the_non_le_particles_left_nothing_all_steps_count(): + from pysupera.kinematics import instance_kinematics + mu = make_particle(0, PT_TRACK, pdg=13, n_pts=5) + blob = make_particle(1, PT_COMPTON, pdg=11, n_pts=2) + mu.geant4_id, blob.geant4_id = 10, 20 + mu.sem_type, blob.sem_type = ST.kTrack, ST.kLEScatter + steps = steps_along_z([1, 7], [1, 2], [300, 1]) + kin = dict(geant4_trackid=np.array([10, 20]), + mass=np.array([105.658, 0.511]), start_mom=np.zeros((2, 4))) + out = instance_kinematics([Group(0, [0, 1], ST.kTrack.value)], [mu, blob], + steps, np.array([0, 1]), np.array([0, 0]), kin, + counted=np.array([False, True]))[0] + assert out["first_pos"][2] == pytest.approx(6.5) diff --git a/tests/test_io_v3.py b/tests/test_io_v3.py index b265318..1254087 100644 --- a/tests/test_io_v3.py +++ b/tests/test_io_v3.py @@ -1,5 +1,5 @@ """ -Round-trip tests for the 3.0.0 format. +Round-trip tests for the 4.0.0 format. These check the two properties the schema exists to provide: that every level's points are a single contiguous slice, and that a stored particle's genealogy @@ -113,20 +113,19 @@ def test_instance_split_separates_le(self, tmp_path): assert len(v.points_of(r, "inst", le=True)) == e - sp assert len(v.points_of(r, "inst")) == e - a - def test_fragment_sides_sit_inside_their_instance(self, tmp_path): + def test_fragments_sit_inside_their_instance(self, tmp_path): path = write(tmp_path) with read_events_v3(path) as s: v = s[0] inst = {int(v["id"][r]): r for r in np.flatnonzero(v.is_instance)} for r in np.flatnonzero(v.is_fragment): - ir = inst.get(int(v["inst_id"][r])) + ir = inst.get(int(v["frag_inst_id"][r])) if ir is None: continue lo, hi = int(v["inst_pc_start"][ir]), int(v["inst_pc_end"][ir]) - for pre in ("frag_pc", "frag_pc_le"): - a, b = int(v[f"{pre}_start"][r]), int(v[f"{pre}_end"][r]) - if a >= 0: - assert lo <= a <= b <= hi + a, b = int(v["frag_pc_start"][r]), int(v["frag_pc_end"][r]) + if a >= 0: + assert lo <= a <= b <= hi def test_instance_points_cover_every_member(self, tmp_path): parts, frags, insts = chain_event() @@ -211,9 +210,17 @@ def test_rejects_2x_file(self, tmp_path): from pysupera import write_events old = str(tmp_path / "old.h5") write_events(old, [[make_particle(0, PT_TRACK)]]) - with pytest.raises(ValueError, match="not 3.x"): + with pytest.raises(ValueError, match="not 4.x"): EventStoreV3(old) + def test_rejects_3x_file_and_says_to_regenerate(self, tmp_path): + path = write(tmp_path) + with h5py.File(path, "a") as f: + del f["format_version"] + f.create_dataset("format_version", data="3.3.0") + with pytest.raises(ValueError, match="Regenerate"): + EventStoreV3(path) + def test_event_index_out_of_range(self, tmp_path): path = write(tmp_path) with read_events_v3(path) as s: @@ -233,10 +240,9 @@ def test_every_point_is_reachable_from_some_stored_row(self, tmp_path): v = s[0] covered = np.zeros(len(v.points), bool) for k in np.flatnonzero(v.is_fragment): - for pre in ("frag_pc", "frag_pc_le"): - a, b = int(v[f"{pre}_start"][k]), int(v[f"{pre}_end"][k]) - if a >= 0: - covered[a:b] = True + a, b = int(v["frag_pc_start"][k]), int(v["frag_pc_end"][k]) + if a >= 0: + covered[a:b] = True assert covered.all(), f"{(~covered).sum()} points unreachable" @@ -378,7 +384,7 @@ def test_empty_fragment_of_an_unwritten_instance_claims_nothing(self): assert 1 not in cols k = cols[2] c = lay.columns - assert (c["frag_pc_le_start"][k], c["frag_pc_le_end"][k]) == (0, 5) + assert (c["frag_pc_start"][k], c["frag_pc_end"][k]) == (0, 5) def test_a_cross_interaction_absorption_is_stored_with_the_absorber(self): """ @@ -400,9 +406,11 @@ def test_a_cross_interaction_absorption_is_stored_with_the_absorber(self): col, row = lay.columns, {int(i): k for k, i in enumerate(lay.columns["id"])} rb = row[1] - # a's points are b's: inside b's instance (LE side) and fragment - assert col["inst_pc_le_end"][rb] - col["inst_pc_le_start"][rb] == 2 - assert col["frag_pc_le_end"][rb] - col["frag_pc_le_start"][rb] == 2 + # a's points are b's: inside b's fragment, and -- b's fragment being + # a track -- on the non-LE side of b's instance + assert col["frag_pc_end"][rb] - col["frag_pc_start"][rb] == 5 + assert col["inst_pc_le_start"][rb] - col["inst_pc_start"][rb] == 5 + assert col["inst_pc_end"][rb] == col["inst_pc_le_start"][rb] # the absorbed particle's row keeps interaction 0, beside c's assert 2 in row inter = lay.interactions @@ -605,3 +613,166 @@ def test_keep_gives_a_row_to_a_pointless_fragment(self): ids = lambda lay: {int(i) for i in lay.columns["id"]} assert 0 not in ids(build_layout(parts, frags, insts)) assert 0 in ids(build_layout(parts, frags, insts, keep={0})) + + +class TestLeIsDecidedPerFragment: + """ + A point is LE when its fragment is a kLEScatter blob. A low-energy + scatter absorbed into a track by proximity is part of the track's + trajectory and is not; an LE fragment that only joined an instance + through parentage still is. + """ + + @staticmethod + def event(): + from pysupera.utils import SemanticType as ST + # track 1 absorbed LE particle 2 (one fragment, non-LE); LE blob 3 + # stands alone as a fragment and is grouped into 1's instance + t1 = make_particle(1, PT_TRACK, n_pts=3, interaction_id=0) + a2 = make_particle(2, PT_TRACK, n_pts=2, interaction_id=0, + parent_id=1, ancestor_id=1) + b3 = make_particle(3, PT_TRACK, n_pts=4, interaction_id=0, + parent_id=1, ancestor_id=1) + t1.sem_type = ST.kTrack + a2.sem_type = b3.sem_type = ST.kLEScatter + t1.member_ids, b3.member_ids = [1, 2], [3] + frags = snapshot_groups([t1, b3]) + t1.member_ids = [1, 2, 3] + insts = snapshot_groups([t1]) + return [t1, a2, b3], frags, insts + + def test_absorbed_le_is_not_le_but_an_le_fragment_is(self): + from pysupera.layout import fragment_le + parts, frags, _ = self.event() + assert fragment_le(parts, frags) == {1: False, 2: False, 3: True} + + def test_the_instance_split_holds_only_le_fragments(self, tmp_path): + parts, frags, insts = self.event() + path = write(tmp_path, events=[(parts, frags, insts)]) + with read_events_v3(path) as s: + v = s[0] + r = int(np.flatnonzero(v.is_instance)[0]) + a, sp, e = (int(v["inst_pc_start"][r]), int(v["inst_pc_le_start"][r]), + int(v["inst_pc_end"][r])) + assert (sp - a, e - sp) == (5, 4) # 1 + absorbed 2, then blob 3 + assert v.is_le_point().sum() == 4 + row = {int(i): k for k, i in enumerate(v["id"])} + assert len(v.points_of(row[1], "frag")) == 5 + assert len(v.points_of(row[1], "frag", le=True)) == 0 + assert len(v.points_of(row[3], "frag", le=True)) == 4 + + def test_a_particles_own_sem_type_is_kept(self, tmp_path): + from pysupera.utils import SemanticType as ST + parts, frags, insts = self.event() + lay = build_layout(parts, frags, insts, keep={2}) + c = lay.columns + row = {int(i): k for k, i in enumerate(c["id"])} + assert c["sem_type"][row[2]] == ST.kLEScatter.value + + def test_hit_labels_follow_the_fragment(self): + from pysupera.hit_labels import build_hit_labels + parts, frags, insts = self.event() + planes = [{"volume": 0, "plane": 0, "source": "U", + "group": np.array([0, 1, 2], dtype=np.int64)}] + owner = np.array([1, 2, 3], dtype=np.int32) # group -> particle + lab = build_hit_labels(planes, parts, frags, insts, owner=owner)[0] + assert lab["is_le"].tolist() == [False, False, True] + assert lab["fragment_id"].tolist() == [1, 1, 3] + + def test_a_fragment_mixing_sides_is_refused(self): + from pysupera.layout import _check_ranges, LayoutError, Group + frags = [Group(1, [1, 2], 0)] + with pytest.raises(LayoutError, match="mixes"): + _check_ranges(np.array([0, 1, 2]), {1: 0, 2: 1}, frags, [], + {1: False, 2: True}, {1: (0, 2)}, {}, {}) + + +class TestKinematics: + + @staticmethod + def records(): + """Two particles; the second makes two steps along +z.""" + P = np.zeros(3, dtype=[(c, "f4") for c in ( + "x", "y", "z", "t", "px", "py", "pz", "ke", "end_x", "end_y", + "end_z", "end_t", "end_px", "end_py", "end_pz", "end_ke", "mass")] + + [("track_id", "i4")]) + P["track_id"] = [5, 7, 9] + P["mass"] = [0.511, 105.658, 0.0] + P["ke"] = [1.0, 200.0, 2.0] + P["pz"][1] = 290.0 + P["end_ke"][1] = 3.0 + S = np.zeros(3, dtype=[(c, "f4") for c in ( + "x", "y", "z", "t", "theta", "phi", "p", "dx")]) + S["z"] = [10.0, 10.5, 50.0] + S["t"] = [1.0, 1.1, 2.0] + S["dx"] = [0.4, 0.6, 1.0] + S["p"] = [290.0, 289.0, 2.0] + owner = np.array([1, 1, 2]) + return P, S, owner + + def test_step_ends_are_half_a_step_from_the_midpoint(self): + from pysupera.readers.format_edepsim_h5 import step_ends + P, S, owner = self.records() + mass = np.asarray(P["mass"], dtype=np.float64)[owner] + pos, mom = step_ends(S, [0, 1, 2], mass, -1.0) + assert pos[0, 2] == pytest.approx(10.0 - 0.2) + # time moves by dx/2 at the step's speed + e = np.hypot(290.0, 105.658) + assert pos[0, 3] == pytest.approx( + 1.0 - 0.2 / (290.0 / e * 299.792458), rel=1e-6) + assert mom[0].tolist() == pytest.approx([0, 0, 290.0, e], rel=1e-6) + # a massless particle still has E = |p| + assert mom[2, 3] == pytest.approx(2.0) + pos, _ = step_ends(S, [1], mass[1:2], 1.0) + assert pos[0, 2] == pytest.approx(10.5 + 0.3) + + def test_creation_and_destruction_energy_is_total(self): + from pysupera.readers.format_edepsim_h5 import particle_kinematics + P, _, _ = self.records() + k = particle_kinematics(P) + assert k["start_mom"][1, 3] == pytest.approx(305.658, rel=1e-6) + assert k["end_mom"][1, 3] == pytest.approx(108.658, rel=1e-6) + assert "first_pos" not in k # made per instance, later + + def test_layout_joins_on_the_track_and_writes_float64(self, tmp_path): + from pysupera.readers.format_edepsim_h5 import particle_kinematics + from pysupera.layout import KINEMATICS_COLUMNS + P, _, _ = self.records() + kin = dict(particle_kinematics(P), geant4_trackid=P["track_id"]) + parts, frags, insts = chain_event() + g4 = {0: 5, 1: 7, 2: 7, 3: 99} # 2 is a split piece of 7 + for p in parts: + p.geant4_id = g4[int(p.id)] + first = np.array([1.0, 2.0, 3.0, 4.0]) + ik = {1: {"first_pos": first, "first_mom": first + 10, + "last_pos": first + 20, "last_mom": first + 30}} + path = str(tmp_path / "kin.h5") + with open_writer_v3(path) as w: + w.append_event(build_layout(parts, frags, insts, keep={2}, + kinematics=kin, inst_kinematics=ik)) + with h5py.File(path) as f: + for c in KINEMATICS_COLUMNS: + assert f[f"particles/{c}"].dtype == np.float64 + assert f[f"particles/{c}"].shape[1:] == (4,) + assert f["particles/mass"].dtype == np.float64 + with read_events_v3(path) as s: + v = s[0] + row = {int(i): k for k, i in enumerate(v["id"])} + np.testing.assert_array_equal(v["start_mom"][row[1]], + kin["start_mom"][1]) + # the split piece shares its track's Geant4 values + np.testing.assert_array_equal(v["end_pos"][row[2]], + kin["end_pos"][1]) + assert v["mass"][row[0]] == pytest.approx(0.511, rel=1e-6) + assert np.isnan(v["start_pos"][row[3]]).all() # unknown track + # first/last on the instance row only + np.testing.assert_array_equal(v["first_pos"][row[1]], first) + np.testing.assert_array_equal(v["last_mom"][row[1]], first + 30) + for i in (0, 2, 3): + assert np.isnan(v["first_pos"][row[i]]).all() + + def test_without_kinematics_the_columns_are_nan(self, tmp_path): + with read_events_v3(write(tmp_path)) as s: + v = s[0] + assert v["start_pos"].shape == (len(v), 4) + assert np.isnan(v["start_pos"]).all() and np.isnan(v["mass"]).all() diff --git a/tests/test_jaxtpc_pixel_data.py b/tests/test_jaxtpc_pixel_data.py index 2cfc821..6d89b39 100644 --- a/tests/test_jaxtpc_pixel_data.py +++ b/tests/test_jaxtpc_pixel_data.py @@ -637,6 +637,61 @@ def test_every_segment_arrives_with_its_step_values_and_group(): assert (np.abs(flat[:, 7]) <= np.pi + 1e-3).all() and (flat[:, 8] > 0).all() +def test_the_reader_hands_over_its_steps_for_the_instance_kinematics(): + """ + The reader gives the step records, the particle row behind each, which + the readout saw, and each one's group -- not first/last itself: those + describe an instance and are made once instances exist. + """ + from pysupera.readers import JaxtpcHDF5Reader + with JaxtpcHDF5Reader(**hit_kwargs(truth_segments=True)) as r: + assert r.kinematics_steps == "visible" + r[0] + st, kin = r.last_steps, r.last_kinematics + steps = r._edepsim_file[r._step_key][0] + seen = r._visible_steps(steps, "event_000", + r._load_seg_volumes("event_000")) + parts = r._edepsim_file[r._part_key][0] + assert "first_pos" not in kin and "start_mom" in kin + assert len(st["steps"]) == len(steps) + np.testing.assert_array_equal(st["counted"], seen) + assert 0 < seen.sum() < len(seen) + own = st["owner"] + assert (own >= 0).all() + np.testing.assert_array_equal(parts["track_id"][own], steps["track_id"]) + # every step the readout saw has a group; one outside every volume none + assert (st["group"][seen] >= 0).all() + + +def test_instance_first_and_last_come_from_steps_the_readout_saw(tmp_path): + """ + first_* / last_* sit on instance rows only, each half a step from the + midpoint of a step the readout saw; a shower has no last point. + """ + from pysupera.readers import JaxtpcHDF5Reader + from pysupera.io_v3 import read_events_v3 + edep, step, hits = (str(x) for x in _P) + out = _run_to(tmp_path, "jaxtpc_pixel", edep, step, hits, + f"reader.jaxtpc_sensor_path={_sensor()}") + with JaxtpcHDF5Reader(**hit_kwargs(truth_segments=True)) as r: + r[0] + steps, seen = r.last_steps["steps"], r.last_steps["counted"] + mid = np.c_[steps["x"], steps["y"], steps["z"]].astype(np.float64)[seen] + half = steps["dx"][seen].astype(np.float64) / 2.0 + with read_events_v3(out) as st: + c = st[0].columns + inst = c["inst_id"] == c["id"] + has = ~np.isnan(c["first_pos"][:, 0]) + assert has.any() and not (has & ~inst).any() + shower = inst & (c["inst_sem_type"] == 1) + assert np.isnan(c["last_pos"][shower]).all() + assert (~np.isnan(c["last_pos"][has & ~shower, 0])).all() + for col in ("first_pos", "last_pos"): + for pos in c[col][has & (col == "first_pos" or ~shower)][:100]: + d = np.linalg.norm(mid - pos[:3], axis=1) + assert np.abs(d - half).min() < 1e-3 + + def test_without_truth_segments_the_reader_reads_none(hit_reader): hit_reader[0] assert hit_reader.last_truth_segments is None diff --git a/tests/test_partitioner.py b/tests/test_partitioner.py index 8912be0..d690975 100644 --- a/tests/test_partitioner.py +++ b/tests/test_partitioner.py @@ -9,7 +9,7 @@ from pysupera.utils import SemanticType from tests.conftest import ( make_particle, cloud, make_partitioner, - PT_PRIMARY, PT_TRACK, PT_IONIZATION, PT_COMPTON, PT_PHOTON, + PT_PRIMARY, PT_TRACK, PT_IONIZATION, PT_COMPTON, PT_PHOTON, PT_CONVERSION, ) @@ -172,7 +172,7 @@ def test_photon_and_electron_merged_in_pipeline(self): photon = make_particle(1, PT_PHOTON, pdg=22, pc=np.zeros((0, 3), dtype=np.float32), parent_id=1, ancestor_id=1) - electron = make_particle(2, PT_PRIMARY, pdg=11, parent_pdg=22, + electron = make_particle(2, PT_CONVERSION, pdg=11, parent_pdg=22, parent_id=1, ancestor_id=1) _, parts = self._run_pipeline([photon, electron]) # After PhotonDecay, they should be in 1 partition diff --git a/tests/test_torchdata.py b/tests/test_torchdata.py index 6e3619e..cf9411a 100644 --- a/tests/test_torchdata.py +++ b/tests/test_torchdata.py @@ -27,7 +27,7 @@ class FakeView: def __init__(self, points, blocks, interactions=None): # blocks: (inst_id, sem_type, interaction_id, start, end[, split]) - # split defaults to end, i.e. the block carries no absorbed LE points. + # split defaults to end, i.e. the block carries no LE fragments. self.points = np.asarray(points, dtype=np.float32) n = len(blocks) self.columns = { @@ -35,12 +35,11 @@ def __init__(self, points, blocks, interactions=None): "inst_id": np.array([b[0] for b in blocks], np.int32), "inst_sem_type": np.array([b[1] for b in blocks], np.int8), "interaction_id": np.array([b[2] for b in blocks], np.int32), - # start/end bound the non-LE run, le_start/le_end the LE run; - # they are adjacent, so end == le_start. + # three fenceposts: non-LE in [start, le_start), LE in + # [le_start, end). "inst_pc_start": np.array([b[3] for b in blocks], np.int64), - "inst_pc_end": np.array([_split(b) for b in blocks], np.int64), "inst_pc_le_start": np.array([_split(b) for b in blocks], np.int64), - "inst_pc_le_end": np.array([b[4] for b in blocks], np.int64), + "inst_pc_end": np.array([b[4] for b in blocks], np.int64), } self.is_instance = np.ones(n, bool) self.interactions = interactions or {} @@ -135,8 +134,9 @@ def test_time_does_not_override_semantic_priority(self): class TestLowEnergyScatters: """ - LE-ness is positional: points in the ``inst_pc_le_*`` run are deposits the - instance absorbed, and must be labelled LE regardless of the host's class. + LE-ness is positional: points in ``[inst_pc_le_start, inst_pc_end)`` are + the LE fragments grouped into the instance, and must be labelled LE + regardless of the host's class. These pass ``include_le=True``; the default is covered below. """ @@ -160,7 +160,7 @@ def test_le_point_keeps_its_host_instance(self): [ST.kShower.value, ST.kLEScatter.value]) def test_le_loses_a_contested_voxel_to_its_host(self): - # same cell reached by the shower's own point and an absorbed LE one, + # same cell reached by the shower's own point and an LE fragment's, # the LE arriving first so only priority can decide it v = FakeView([pt(0, 0, 0, 9.0, 1.0), pt(0, 0, 0, 0.0, 1.0)], [(10, ST.kShower.value, 0, 0, 2, 1)]) @@ -216,7 +216,7 @@ def test_labels_stay_aligned_when_le_is_dropped(self): assert len(ev[k]) == n def test_a_shared_voxel_loses_only_the_le_energy(self): - # shower and absorbed LE in the same cell: the row survives, but + # shower and an LE fragment in the same cell: the row survives, but # without the LE contribution v = FakeView([pt(0, 0, 0, 1.0, 3.0), pt(0, 0, 0, 2.0, 4.0)], [(10, ST.kShower.value, 0, 0, 2, 1)]) diff --git a/vis_drift.html b/vis_drift.html index 8a60e96..7ba8fa7 100644 --- a/vis_drift.html +++ b/vis_drift.html @@ -60,6 +60,9 @@ + load the output and its hits subset — hover a point to follow it across both views @@ -108,7 +111,7 @@ sel:-1, dim:0.03, thrPct:0, mode:'inst', hl:'inst', cols:null, idRow:null, // particle columns for the event, id -> row - semShow:new Set(), + semShow:new Set(), leMode:'all', geom:null, // per-volume pixel geometry (output attribute) ncol:6, }; @@ -194,7 +197,11 @@ async function loadOut(buf, name){ S.out = await openBytes(buf, 'out.h5'); S.nEvents = Number(val(S.out, 'n_events')); - log(`output: format ${val(S.out,'format_version')}, ${S.nEvents} events`); + const ver = String(val(S.out, 'format_version')); + log(`output: format ${ver}, ${S.nEvents} events`); + if(!ver.startsWith('4.')){ S.out = null; + return banner(`This output is format ${ver}; this viewer reads 4.x. Regenerate it with\n` + + `the current run_pysupera (4.0.0 moved LE to the fragment).`); } const bad = unreadableFilter(S.out, 'points/flat'); if(bad){ S.out = null; return banner(`This output uses HDF5 filter ${bad}, which the browser cannot decode.\n` + @@ -244,6 +251,7 @@ document.getElementById('hl').onchange = e => { S.hl = e.target.value; S.sel = -1; buildKeys(); recolour(); }; document.getElementById('logE').onchange = () => recolour(); document.getElementById('offsensor').onchange = () => rebuild(); +document.getElementById('lemode').onchange = e => { S.leMode = e.target.value; recolour(); }; document.getElementById('psize').oninput = e => { for(const v of Object.values(V)) if(v && v.cloud){ v.cloud.material.size = +e.target.value; v.selCloud.material.size = +e.target.value; } @@ -271,9 +279,13 @@ const col = nm => g.keys().includes(nm) ? sl(f,'particles/'+nm, a, b) : null; const c = {}; for(const k of ['id','inst_id','frag_id','interaction_id','pdg','geant4_trackid','inst_sem_type', - 'inst_parent_id','inst_pc_start','inst_pc_end','inst_pc_le_start','inst_pc_le_end', - 'frag_pc_start','frag_pc_end','frag_pc_le_start','frag_pc_le_end']) + 'inst_parent_id','inst_pc_start','inst_pc_end','frag_pc_start','frag_pc_end', + 'frag_sem_type','mass']) c[k] = col(k); + // Kinematics, (rows, 4): [x,y,z,t] mm ns and [px,py,pz,E] MeV, E total. + for(const k of ['start_pos','start_mom','first_pos','first_mom','last_pos','last_mom', + 'end_pos','end_mom']) + c[k] = g.keys().includes(k) ? Float64Array.from(g.get(k).slice([[a, b], [0, 4]])) : null; S.cols = c; S.idRow = new Map(); for(let k = 0; k < c.id.length; k++) S.idRow.set(Number(c.id[k]), k); } @@ -292,16 +304,15 @@ const c = S.cols; const fill = (arr, s, e, v) => { s = Number(s); if(s < 0) return; for(let j = Math.max(0, s - pa); j < Math.min(n, Number(e) - pa); j++) arr[j] = v; }; + // One run per fragment and per instance. LE belongs to the fragment: a + // kLEScatter fragment is a blob, an LE deposit absorbed into a track is + // the track's. for(let k = 0; k < c.id.length; k++){ const id = Number(c.id[k]); - if(Number(c.inst_id[k]) === id){ - fill(T.inst, c.inst_pc_start[k], c.inst_pc_end[k], id); - fill(T.inst, c.inst_pc_le_start[k], c.inst_pc_le_end[k], id); - fill(T.le, c.inst_pc_le_start[k], c.inst_pc_le_end[k], 1); - } + if(Number(c.inst_id[k]) === id) fill(T.inst, c.inst_pc_start[k], c.inst_pc_end[k], id); if(Number(c.frag_id[k]) === id){ fill(T.frag, c.frag_pc_start[k], c.frag_pc_end[k], id); - fill(T.frag, c.frag_pc_le_start[k], c.frag_pc_le_end[k], id); + if(Number(c.frag_sem_type[k]) === SEM_LE) fill(T.le, c.frag_pc_start[k], c.frag_pc_end[k], 1); } } S.T = T; @@ -403,7 +414,9 @@ thr = a[Math.min(a.length - 1, Math.floor(S.thrPct/100 * a.length))]; } const vis = new Uint8Array(P.n); let kept = 0; - for(let i = 0; i < P.n; i++){ const pass = S.semShow.has(semOf(P, i)) && Math.abs(P.e[i]) >= thr; + for(let i = 0; i < P.n; i++){ const le = !!P.le[i]; + const pass = S.semShow.has(semOf(P, i)) && Math.abs(P.e[i]) >= thr + && (S.leMode === 'hide' ? !le : S.leMode === 'only' ? le : true); vis[i] = pass ? 1 : 0; if(pass) kept++; } P.visible = vis; P.thr = thr; if(S.thrPct > 0) parts.push(`${P.name} ≥ ${fmt(thr, 3)} (${P.n ? (100*kept/P.n).toFixed(0) : 0}%)`); @@ -642,11 +655,38 @@ ['interaction', interOf(P, i) < 0 ? '—' : String(interOf(P, i))], ['semantic type', SEM[sem] ? `${SEM[sem]} (${sem})` : '—'], ['instance pdg', pdgName(pdgOf(P, i))]); + if(S.hl !== 'inter') rows.push(...kinRows(S.hl === 'frag' ? P.frag[i] : P.inst[i])); const el = document.getElementById('pinfo'); el.innerHTML = '' + rows.map(([k, v]) => ``).join('') + '
${k}${v}
'; el.style.display = 'block'; } +// The representative's own Geant4 particle: start (creation), end +// (destruction); then the instance's first / last point, from the steps it +// collected (instance rows only; none for a shower's last). +function kinRows(gid){ + const c = S.cols, k = S.idRow.get(Number(gid)); + if(k === undefined || !c.start_pos) return []; + const q = nm => Array.from(c[nm].subarray(k*4, k*4+4)), m = Number(c.mass[k]); + const ke = v => Number.isNaN(v[3]) ? NaN : v[3] - m; + const xyz = v => `${fmt(v[0],1)} ${fmt(v[1],1)} ${fmt(v[2],1)}`; + const sp = q('start_pos'), fp = q('first_pos'); + const out = [['mass [MeV]', fmt(m, 3)], + ['KE start→end', `${fmt(ke(q('start_mom')), 2)} → ${fmt(ke(q('end_mom')), 2)} MeV`], + ['start [mm]', xyz(sp)]]; + out.push(['end [mm]', xyz(q('end_pos'))]); + const pe = v => `${fmt(Math.hypot(v[0], v[1], v[2]), 2)} · ${fmt(v[3], 2)} MeV`; + const lp = q('last_pos'); + if(Number.isNaN(fp[0])) out.push(['inst. first', '—']); + else { + out.push(['inst. first [mm]', `${xyz(fp)} (+${fmt(Math.hypot(fp[0]-sp[0], fp[1]-sp[1], fp[2]-sp[2]), 1)})`], + ['|p| · E first', pe(q('first_mom'))]); + if(Number.isNaN(lp[0])) out.push(['inst. last', '— (shower)']); + else out.push(['inst. last [mm]', xyz(lp)], ['|p| · E last', pe(q('last_mom'))]); + } + return out; +} + // One key drives both panes; that is the link being shown. function select(key){ key = Number(key); diff --git a/vis_force.html b/vis_force.html index 1e8cf3f..bd9eb53 100644 --- a/vis_force.html +++ b/vis_force.html @@ -1201,22 +1201,14 @@

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return s.size; } -// 3.0.0 -> 3.1.0 column renames. The viewer reads the raw HDF5, so it has to -// accept both spellings itself. +//: SemanticType.kLEScatter +const LE_SEM=5; + +// Older spellings of columns the 2.x read paths look up. const V3_ALIAS = { geant4_trackid: 'geant4_id', - frag_sem_type: 'sem_type_frag', - inst_sem_type: 'sem_type_inst', frag_parent_id: 'parent_frag_id', inst_parent_id: 'parent_inst_id', - frag_pc_start: 'frag_non_le_start', - frag_pc_end: 'frag_non_le_end', - frag_pc_le_start: 'frag_le_start', - frag_pc_le_end: 'frag_le_end', - // 3.0.0 split one index where 3.1.0 has two adjacent ranges - inst_pc_end: 'inst_pc_split', - inst_pc_le_start: 'inst_pc_split', - inst_pc_le_end: 'inst_pc_end', }; function v3Name(grp, n){ if(grp.keys().includes(n)) return n; @@ -1237,8 +1229,8 @@

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} -// ── Format 3.0.0 cloud reconstruction ────────────────────────────────────── -// 3.0.0 stores each point once as (x, y, z, t, dE, dX) and keeps the group +// ── Format 4.x cloud reconstruction ──────────────────────────────────────── +// 4.x stores each point once as (x, y, z, t, dE, dX) and keeps the group // labels per *particle* rather than per point. The rendering code below was // written against the 2.x event clouds -- (x,y,z,t,e,interaction,ancestor, // frag,inst) per point -- so rather than rewrite it, rebuild that view here @@ -1255,38 +1247,36 @@

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const nPts=pb-pa; if(nPts<=0) return new Float32Array(0); const pg=hf.get('particles'); - const col=n=>pg.get(v3Name(pg,n)).slice([[a,b]]); + const col=n=>pg.get(n).slice([[a,b]]); const id=col('id'), ancestor=col('ancestor_id'); const fragId=col('frag_id'), instId=col('inst_id'); - const fS=col('frag_pc_start'), fE=col('frag_pc_end'); - const fLS=col('frag_pc_le_start'), fLE=col('frag_pc_le_end'); - const iS=col('inst_pc_start'), iSP=col('inst_pc_le_start'), iE=col('inst_pc_le_end'); + const fS=col('frag_pc_start'), fE=col('frag_pc_end'), fSem=col('frag_sem_type'); + const iS=col('inst_pc_start'), iE=col('inst_pc_end'); const pts=hf.get('points').get('flat').slice([[pa,pb],[0,6]]); const lblInt=new Float32Array(nPts).fill(-1), lblAnc=new Float32Array(nPts).fill(-1); const lblFrag=new Float32Array(nPts).fill(-1), lblInst=new Float32Array(nPts).fill(-1); - // LE-ness is positional: each instance block is non-LE points - // [inst_pc_start, inst_pc_end) followed by LE points - // [inst_pc_le_start, inst_pc_le_end), and the two are adjacent. No - // per-point label is stored, and none is needed. + // LE belongs to the fragment: a kLEScatter fragment is a blob with no + // trajectory, and an LE deposit absorbed into a track is the track's. So + // a fragment is one run, wholly LE or not; inside an instance the LE + // fragments follow the rest, from inst_pc_le_start. No per-point label + // is stored, and none is needed. const isLE=new Uint8Array(nPts); const n=id.length; for(let k=0;k=sp) isLE[j]=1; } + for(let j=a;j⌨ Keyboard Shortcuts // every frag_parent_id is -1, so fragment fields must not be presented as if // they were merely missing for this particle. let _FRAG_LEVEL=false; -//: true when the open file is format 3.x (one particles table, +//: true when the open file is format 4.x (one particles table, //: one point array). 2.x files keep the old read paths. let FMT3=false; function probeFragLevel(){ _FRAG_LEVEL=false; if(!hf) return false; - // 3.x: one particles table; a fragment level exists when some row heads a + // 4.x: one particles table; a fragment level exists when some row heads a // fragment (frag_id == id). if(FMT3){ try{ @@ -1803,7 +1793,7 @@

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} -// Instance graph for 3.0.0: instances are the rows where inst_id == id, and +// Instance graph for 4.x: instances are the rows where inst_id == id, and // inst_parent_id already names the parent instance's particle id, so no // genealogy walk is needed here. function v3InstanceGraph(evIdx){ @@ -1816,6 +1806,19 @@

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const intId=col('interaction_id'), sem=col('inst_sem_type'); const mcount=col('inst_merge_count'); const iS=col('inst_pc_start'), iE=col('inst_pc_end'); + // The representative's own Geant4 particle (creation, destruction) and the + // instance's first / last point, as (rows, 4) [x,y,z,t] mm ns and + // [px,py,pz,E] MeV. + const pk=pg.keys(), v4=n=>pk.includes(n)?Float64Array.from(pg.get(n).slice([[a,b],[0,4]])):null; + const mass=pk.includes('mass')?col('mass'):null; + const K={sp:v4('start_pos'),sm:v4('start_mom'),fp:v4('first_pos'),fm:v4('first_mom'), + lp:v4('last_pos'),lm:v4('last_mom'),ep:v4('end_pos'),em:v4('end_mom')}; + const kinOf=k=>{ + if(!mass||!K.sp) return null; + const q=a=>Array.from(a.subarray(k*4,k*4+4)); + return {mass:Number(mass[k]),sp:q(K.sp),sm:q(K.sm),fp:q(K.fp),fm:q(K.fm), + lp:q(K.lp),lm:q(K.lm),ep:q(K.ep),em:q(K.em)}; + }; const nodes=[], rowOf=new Map(); for(let k=0;k⌨ Keyboard Shortcuts parentPdg:Number(ppdg[k]), intId:Number(intId[k]), semType:Number(sem[k]), fragId:-1, members:Math.max(1,Number(mcount[k])), - ptCount:Number(iE[k])-Number(iS[k]), + ptCount:Number(iS[k])>=0?Number(iE[k])-Number(iS[k]):0, + kin:kinOf(k), eSum:null,x:0,y:0,vx:0,vy:0}); } const edges=[], seen=new Set(); @@ -1982,11 +1986,26 @@

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(_FRAG_LEVEL?`Frag: ${nd.fragId>=0?nd.fragId:'\u2014'} `:``)+ `Int.ID: ${nd.intId>=0?nd.intId:'\u2014'}
`+ `Energy: ${nd.eSum!=null?nd.eSum.toExponential(2):'\u2014'}
`+ - `Points: ${nd.ptCount>0?nd.ptCount.toLocaleString():'none — merged/absorbed'} Members: ${nd.members}`; + `Points: ${nd.ptCount>0?nd.ptCount.toLocaleString():'none — merged/absorbed'} Members: ${nd.members}`+ + _kinTip(nd.kin); _tt.style.left=(ev.clientX+16)+'px'; _tt.style.top=(ev.clientY-12)+'px'; _tt.style.display='block'; } +// Kinematics lines for a node's tooltip; empty for files that lack them. +function _kinTip(k){ + if(!k) return ''; + const f=(x,d)=>Number.isNaN(x)?'\u2014':x.toFixed(d); + const ke=m=>m[3]-k.mass, xyz=p=>`${f(p[0],1)}, ${f(p[1],1)}, ${f(p[2],1)}`; + const d=Math.hypot(k.fp[0]-k.sp[0],k.fp[1]-k.sp[1],k.fp[2]-k.sp[2]); + const pe=m=>`|p| ${f(Math.hypot(m[0],m[1],m[2]),2)}, E ${f(m[3],2)} MeV`; + return `
mass ${f(k.mass,3)} MeV KE ${f(ke(k.sm),2)} \u2192 ${f(ke(k.em),2)} MeV
`+ + `start (${xyz(k.sp)}) mm
end (${xyz(k.ep)})
`+ + (Number.isNaN(k.fp[0])?`inst. first: none` + :`inst. first (${xyz(k.fp)}) +${f(d,1)} mm, ${pe(k.fm)}
`+ + (Number.isNaN(k.lp[0])?`inst. last: none (shower)` + :`inst. last (${xyz(k.lp)}), ${pe(k.lm)}`)); +} function _hideGraphTip(){ document.getElementById('graph-tooltip').style.display='none'; } function _showLockedBar(nd){ const el=document.getElementById('graph-locked-bar'); @@ -2737,6 +2756,12 @@

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console.log('[h5] file keys:', keys); if(!keys.includes('n_events')) throw new Error('Missing /n_events — not a pysupera file?'); FMT3=keys.includes('points')&&!keys.includes('non_le_cloud'); + if(FMT3){ + let v=''; try{ v=String(hf.get('format_version').value); }catch(e){} + if(!v.startsWith('4.')) + throw new Error('format '+v+': this viewer reads 4.x (and 2.x). Regenerate it with the '+ + 'current run_pysupera -- 4.0.0 moved LE to the fragment.'); + } if(!FMT3&&!keys.includes('non_le_cloud')) throw new Error('Missing /non_le_cloud and /points — not a pysupera output?'); nEvents=h5scalar(hf.get('n_events')); diff --git a/vis_hits.html b/vis_hits.html index 32d621e..1eec1ea 100644 --- a/vis_hits.html +++ b/vis_hits.html @@ -37,7 +37,9 @@ - + load the output and its hits subset — hover a voxel or a hit @@ -75,7 +77,7 @@ const S = { out:null, hit:null, ev:0, level:'inst', nEvents:0, pts:null, feat:null, owner:null, isLE:null, // voxels (visible only) - hideLE:false, planes:[], sel:-1, dim:0.12, + leMode:'all', planes:[], sel:-1, dim:0.12, interOf:new Map(), // instance id -> interaction id instOfFrag:new Map(), // fragment id -> instance id ncol:6, // points/flat columns @@ -115,6 +117,10 @@ } const LEVEL_NAME = {inst:'instance', frag:'fragment', inter:'interaction'}; +//: SemanticType.kLEScatter +const LE_SEM = 5; +// Whether a point or hit with this LE flag passes the LE selector. +const leShown = le => S.leMode === 'hide' ? !le : S.leMode === 'only' ? !!le : true; // Stable colour per owner id: the same function colours voxels and hits, so a // colour means the same object on both sides. @@ -147,8 +153,13 @@ async function loadOut(buf, name){ S.out = await openBytes(buf, 'out.h5'); S.nEvents = Number(val(S.out, 'n_events')); - const ver = val(S.out, 'format_version'); + const ver = String(val(S.out, 'format_version')); log(`output: format ${ver}, ${S.nEvents} events`); + if(!ver.startsWith('4.')){ + S.out = null; + return banner(`This output is format ${ver}; this viewer reads 4.x. Regenerate it with\n` + + `the current run_pysupera (4.0.0 moved LE to the fragment).`); + } const bad = unreadableFilter(S.out, 'points/flat'); if(bad){ S.out = null; @@ -158,7 +169,7 @@ if(String(attr(S.out, '/', 'readout_type')) === 'pixel') return banner('This is a pixel-readout output. Open it in vis_drift.html instead.'); if(!has(S.out, 'hit_labels')) - banner('This output has no hit_labels/ (format 3.3.0 JAXTPC runs write them), so\n' + + banner('This output has no hit_labels/ (JAXTPC runs write them), so\n' + 'the hits cannot be labelled. The 3D view still works.'); else banner(''); const sel = document.getElementById('ev'); sel.innerHTML = ''; @@ -218,7 +229,7 @@ document.getElementById('dimv').textContent = S.dim.toFixed(2); paintCloud(); for(const p of S.planes) paintPlane(p); }; -document.getElementById('hidele').onchange = e => { S.hideLE = e.target.checked; rebuild(); }; +document.getElementById('lemode').onchange = e => { S.leMode = e.target.value; rebuild(); }; document.getElementById('info').onchange = e => { if(!e.target.checked) document.getElementById('pinfo').style.display = 'none'; }; @@ -239,13 +250,14 @@ const id = col('id'), iid = col('inst_id'), fid = col('frag_id'), itr = col('interaction_id'); const fin = col('frag_inst_id'); - // instance -> interaction, fragment -> instance (stated since 3.3.0) + // instance -> interaction, fragment -> instance S.interOf = new Map(); S.instOfFrag = new Map(); for(let k = 0; k < id.length; k++){ const i = Number(id[k]); if(Number(iid[k]) === i) S.interOf.set(i, Number(itr[k])); if(fin && Number(fid[k]) === i && Number(fin[k]) >= 0) S.instOfFrag.set(i, Number(fin[k])); } + loadKinematics(a, b, id, col); if(n <= 0){ S.pts = new Float32Array(0); S.feat = new Float32Array(0); S.owner = new Int32Array(0); S.isLE = new Uint8Array(0); return; @@ -259,27 +271,26 @@ if(le) isLE[j] = 1; } }; - const is0 = col('inst_pc_start'), ie0 = col('inst_pc_end'); - const is1 = col('inst_pc_le_start'), ie1 = col('inst_pc_le_end'); + // LE belongs to the fragment: a kLEScatter fragment is a blob, and an LE + // deposit absorbed into a track is the track's. One run per fragment. + const fs = col('frag_pc_start'), fe = col('frag_pc_end'), fsem = col('frag_sem_type'); for(let k = 0; k < id.length; k++){ - if(Number(iid[k]) !== Number(id[k])) continue; - fill(is1[k], ie1[k], null, true); // LE is positional - if(S.level === 'frag') continue; - const v = S.level === 'inter' ? (S.interOf.get(Number(id[k])) ?? -1) : Number(id[k]); - fill(is0[k], ie0[k], v, false); fill(is1[k], ie1[k], v, false); + if(Number(fid[k]) !== Number(id[k])) continue; + fill(fs[k], fe[k], S.level === 'frag' ? Number(id[k]) : null, + Number(fsem[k]) === LE_SEM); } - if(S.level === 'frag'){ - const fs0 = col('frag_pc_start'), fe0 = col('frag_pc_end'); - const fs1 = col('frag_pc_le_start'), fe1 = col('frag_pc_le_end'); + if(S.level !== 'frag'){ + const is0 = col('inst_pc_start'), ie0 = col('inst_pc_end'); for(let k = 0; k < id.length; k++){ - if(Number(fid[k]) !== Number(id[k])) continue; - fill(fs0[k], fe0[k], Number(id[k]), false); fill(fs1[k], fe1[k], Number(id[k]), false); + if(Number(iid[k]) !== Number(id[k])) continue; + const v = S.level === 'inter' ? (S.interOf.get(Number(id[k])) ?? -1) : Number(id[k]); + fill(is0[k], ie0[k], v, false); } } - // Drop LE rows here rather than at draw time, so the picker cannot select + // Drop LE (or non-LE) rows here rather than at draw time, so the picker cannot select // something the user asked not to see. const keep = []; - for(let j = 0; j < n; j++) if(!(S.hideLE && isLE[j])) keep.push(j); + for(let j = 0; j < n; j++) if(leShown(isLE[j])) keep.push(j); const m = keep.length, C = S.ncol; const f2 = new Float32Array(m * C), o2 = new Int32Array(m), l2 = new Uint8Array(m), p3 = new Float32Array(m * 3); for(let i = 0; i < m; i++){ @@ -323,7 +334,7 @@ continue; } const n = wire.length, keep = []; - for(let h = 0; h < n; h++) if(!(S.hideLE && Number(le[h]))) keep.push(h); + for(let h = 0; h < n; h++) if(leShown(Number(le[h]))) keep.push(h); const m = keep.length; const p = {vol, plane, source, n:m, wire:new Int16Array(m), tick:new Int16Array(m), q:new Float32Array(m), owner:new Int32Array(m), frag:new Int32Array(m), @@ -544,8 +555,51 @@ ['t [µs]', fmt(f[o+3], 2)], ['dE [MeV]', fmt(f[o+4], 4)], ['dX [cm]', fmt(f[o+5], 4)]]; if(C >= 9) rows.push(['θ φ [rad]', `${fmt(f[o+6], 3)} ${fmt(f[o+7], 3)}`], ['|p| [MeV/c]', fmt(f[o+8], 2)]); rows.push(['LE', S.isLE[i] ? 'yes' : 'no'], [LEVEL_NAME[S.level], S.owner[i] < 0 ? '—' : String(S.owner[i])]); + if(S.level !== 'inter') rows.push(...kinRows(S.owner[i])); showInfo(rows); } +// ------------------------------------------------------------ kinematics -- +// Per stored row, the representative's own Geant4 particle: start (creation), +// first / last visible step, end (destruction). [x,y,z,t] mm ns and +// [px,py,pz,E] MeV, E total; absent from files before 4.0.0. +function loadKinematics(a, b, id, col){ + S.kin = new Map(); + const g = S.out.get('particles'), names = g.keys(); + if(!names.includes('start_pos')) return; + const v4 = nm => Float64Array.from(g.get(nm).slice([[a, b], [0, 4]])); + const K = {}; for(const nm of ['start_pos','start_mom','first_pos','first_mom', + 'last_pos','last_mom','end_pos','end_mom']) K[nm] = v4(nm); + const mass = col('mass'), pdg = col('pdg'); + for(let k = 0; k < id.length; k++){ + const r = {pdg: Number(pdg[k]), mass: Number(mass[k])}; + for(const nm in K) r[nm] = Array.from(K[nm].subarray(k*4, k*4+4)); + S.kin.set(Number(id[k]), r); + } +} +function kinRows(gid){ + const r = S.kin && S.kin.get(Number(gid)); + if(!r) return []; + const ke = m => Number.isNaN(m[3]) ? NaN : m[3] - r.mass; + const xyz = q => `${fmt(q[0],1)} ${fmt(q[1],1)} ${fmt(q[2],1)}`; + const dist = (p, q) => Math.hypot(p[0]-q[0], p[1]-q[1], p[2]-q[2]); + const out = [['pdg · mass', `${r.pdg} · ${fmt(r.mass, 3)} MeV`], + ['KE start→end', `${fmt(ke(r.start_mom), 2)} → ${fmt(ke(r.end_mom), 2)} MeV`], + ['start [mm]', xyz(r.start_pos)]]; + out.push(['end [mm]', xyz(r.end_pos)]); + // first / last belong to the instance (its own steps, the readout saw); + // their momentum is of the particle that made the step. A shower has no + // last point. + const pe = m => `${fmt(Math.hypot(m[0], m[1], m[2]), 2)} · ${fmt(m[3], 2)} MeV`; + if(Number.isNaN(r.first_pos[0])) out.push(['inst. first', '—']); + else { + out.push(['inst. first [mm]', `${xyz(r.first_pos)} (+${fmt(dist(r.first_pos, r.start_pos), 1)})`], + ['|p| · E first', pe(r.first_mom)]); + if(Number.isNaN(r.last_pos[0])) out.push(['inst. last', '— (shower)']); + else out.push(['inst. last [mm]', xyz(r.last_pos)], ['|p| · E last', pe(r.last_mom)]); + } + return out; +} + function infoHit(p, i){ showInfo([['hit', `volume ${p.vol} · ${p.source} · #${p.src[i].toLocaleString()}`], ['wire', String(p.wire[i])], ['tick', String(p.tick[i])], @@ -553,7 +607,8 @@ ['fragment', p.frag[i] < 0 ? '—' : String(p.frag[i])], ['instance', p.inst[i] < 0 ? '—' : String(p.inst[i])], ['interaction', p.inst[i] >= 0 && S.interOf.has(p.inst[i]) ? String(S.interOf.get(p.inst[i])) : '—'], - ['LE', p.le[i] ? 'yes' : 'no']]); + ['LE', p.le[i] ? 'yes' : 'no'], + ...(S.level === 'inter' ? [] : kinRows(S.level === 'frag' ? p.frag[i] : p.inst[i]))]); } // ------------------------------------------------------------- selection -- From 4fa7f549088bbeae30d59c9a7e77ced5e1ce4862 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sat, 17 Oct 2026 12:35:22 -0700 Subject: [PATCH 48/62] merge_em_showers: a loop of parent fragments no longer drops a fragment Every supera_pixel job of test_doraemon_2026_v0.1 failed with "non-LE particle(s) have no instance after step-2 merging". In edepsim_j001995-001995.h5, event 50, a stopped mu+ (36 visible points, so kLEScatter) was absorbed by proximity into the shower of a photon that its own Michel positron radiated. The walk finds a fragment's parent through whichever fragment holds the parent particle, so the Michel's parent fragment was that shower, and the shower's was the Michel. find_initiator stops on a fragment it has seen, so each of the two named the other as the initiator. The fragment that headed no group of its own disappeared with its members (the Michel and an LE electron), and 35 member ids came out twice. Initiators are now followed to a fixed point, and a loop of them shares one instance, headed by its lowest-id member that may head a shower. A chain without a loop groups as before. Checked: all 100 events of that file run, with no member lost or duplicated by merge_em_showers or attach_split_pieces, and a test replays the case. Tests: 512 passed, 1 skipped. --- pysupera/merge.py | 37 +++++++++++++++++++++++++++++++++++- tests/test_instance_rules.py | 23 ++++++++++++++++++++-- 2 files changed, 57 insertions(+), 3 deletions(-) diff --git a/pysupera/merge.py b/pysupera/merge.py index cf43d18..d636f95 100644 --- a/pysupera/merge.py +++ b/pysupera/merge.py @@ -103,6 +103,10 @@ def merge_em_showers(fragments): are their own instances. The same electrons made by a photon *within* a shower belong to it. + The parent of a fragment is the fragment holding its parent particle, + which proximity may have put among the fragment's own descendants. Where + the walk loops, the fragments on the loop share one instance. + Extended parent lookup ~~~~~~~~~~~~~~~~~~~~~~ The walk uses two lookup tables in tandem: @@ -177,10 +181,41 @@ def find_initiator(rep): cur = up[id(cur)] return rep if top is None else top + # Chains can loop: an LE particle absorbed by proximity into a fragment + # of its own descendants -- a stopped muon swallowed by the shower of its + # Michel's brem photon -- makes that fragment the descendant's parent + # too. Each fragment on a loop then names a different initiator, and an + # initiator outside its own group would be dropped with its members. + # Follow initiators to a fixed point; a loop of them takes its lowest-id + # member that may head a shower, so every initiator heads its own group. + first = {id(rep): find_initiator(rep) for rep in fragments} + resolved: dict = {} + + def initiator_of(rep): + path, on_path, cur = [], {}, rep + while id(cur) not in resolved: + nxt = first[id(cur)] + if nxt is cur: + resolved[id(cur)] = cur + break + if id(cur) in on_path: + loop = path[on_path[id(cur)]:] + head = min(loop, key=lambda r: (not _can_initiate(r), r.id)) + for r in loop: + resolved[id(r)] = head + break + on_path[id(cur)] = len(path) + path.append(cur) + cur = nxt + head = resolved[id(cur)] + for r in path: + resolved.setdefault(id(r), head) + return resolved[id(rep)] + # Group every fragment under its shower initiator. groups: dict = defaultdict(list) # initiator.id → list[fragment rep] for rep in fragments: - initiator = find_initiator(rep) + initiator = initiator_of(rep) groups[initiator.id].append(rep) # Build instance representatives by merging point clouds and member ID lists. diff --git a/tests/test_instance_rules.py b/tests/test_instance_rules.py index ba32544..81f07c7 100644 --- a/tests/test_instance_rules.py +++ b/tests/test_instance_rules.py @@ -20,8 +20,8 @@ from pysupera.utils import SemanticType as ST from tests.conftest import ( make_particle, make_partitioner, - PT_COMPTON, PT_CONVERSION, PT_DELTA, PT_NEUTRON, PT_PHOTON, PT_PRIMARY, - PT_TRACK, + PT_COMPTON, PT_CONVERSION, PT_DECAY, PT_DELTA, PT_NEUTRON, PT_PHOTON, + PT_PRIMARY, PT_TRACK, ) EMPTY = np.zeros((0, 3), dtype=np.float32) @@ -103,6 +103,25 @@ def test_an_le_fragment_never_heads_a_shower(self): assert instance_of(insts, 2).id == 2 assert instance_of(insts, 1).id == 1 + def test_a_parent_absorbed_into_its_descendants_shower_loses_nothing(self): + """ + A stopped muon 1, too small to be a track, absorbed by proximity into + the shower of photon 3, which its Michel positron 2 radiated: 2's + parent fragment is 3's, and 3's is 2's. Seen in production. + """ + mu = make_particle(1, PT_PRIMARY, pdg=-13, n_pts=4) + e2 = make_particle(2, PT_DECAY, pdg=-11, parent_id=1, parent_pdg=-13, + ancestor_id=1, n_pts=30) + g3 = make_particle(3, PT_PHOTON, pdg=22, parent_id=2, parent_pdg=-11, + ancestor_id=1, pc=EMPTY) + mu.sem_type, e2.sem_type, g3.sem_type = (ST.kLEScatter, ST.kMichel, + ST.kShower) + e2.member_ids, g3.member_ids = [2], [3, 1] + insts = merge_em_showers([e2, g3]) + members = sorted(m for i in insts for m in i.member_ids) + assert members == [1, 2, 3] + assert len(insts) == 1 and insts[0].id == 2 + # --------------------------------------------------------------------------- # Absorption prefers the particle's own ancestor From 94ee864c627cd7e4421d75cf75f79f822d21bccf Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sat, 17 Oct 2026 12:35:22 -0700 Subject: [PATCH 49/62] readers: primaries come from the primary table, so a primary is never LE kPrimary was meant to keep a generator primary out of kLEScatter: it maps to kTrack, or kShower for an electron, positron or photon, whatever the size. But _get_interaction_type marked a particle kPrimary only when track_id == parent_track_id, and EDepSim gives a primary a parent of -1, so nothing was ever kPrimary. Instead, primaries were classified by proc_start/subproc_start, which mean nothing for a primary. On the production file edepsim_j001995-001995.h5, 1,879 of its 5,480 primaries came out kLEScatter (816 photons, 449 pi0, 364 protons, 82 muons), and 61 primary electrons and positrons came out kUnknown (kInvalidProcess). The 1.7 MeV mu+ behind the step-2 failure of event 50 was one of them. The readers now read the input's primary table (reader.primary_key, primary/geant4), and a particle in it is kPrimary. Neutrons and nuclei keep their own rules, as before. The table is checked against parent_track_id == -1 every event (primary_mask), and the readers expose the result as last_primary_check. A file without the table falls back on parent_track_id == -1. Of 5,480 primaries in that file, 1,940 change type (all 1,879 LE ones, and the 61 kUnknown ones become kShower); on data/kazu.h5, 206 of 526 with truth deposits and 334 with pixel hits. No other particle changes. The table and parent -1 agree on every event of both files. --- pysupera/conf/reader/edepsim_h5.yaml | 5 + pysupera/config.py | 4 + pysupera/readers/format_edepsim_h5.py | 103 +++++++++++++++- pysupera/readers/format_jaxtpc.py | 17 ++- tests/test_primaries.py | 161 ++++++++++++++++++++++++++ tests/test_reader_configs.py | 4 +- 6 files changed, 288 insertions(+), 6 deletions(-) create mode 100644 tests/test_primaries.py diff --git a/pysupera/conf/reader/edepsim_h5.yaml b/pysupera/conf/reader/edepsim_h5.yaml index 771300a..14ee1cc 100644 --- a/pysupera/conf/reader/edepsim_h5.yaml +++ b/pysupera/conf/reader/edepsim_h5.yaml @@ -35,6 +35,11 @@ particle_key: particle/geant4 # Expected fields: x, y, z, t, interaction_id vertex_key: vertex/geant4 +# Per-event structured array of the generator's primaries. A particle in it +# is kPrimary, so never LE; it is cross-checked against parent_track_id == -1. +# Expected fields: track_id. A file without it falls back on that rule. +primary_key: primary/geant4 + # Flat per-event energy-deposit step array (the point cloud). # Columns 0-2 must be x, y, z. Not used in JAXTPC mode. step_key: pstep/lar_vol diff --git a/pysupera/config.py b/pysupera/config.py index aea81ca..dd6c08d 100644 --- a/pysupera/config.py +++ b/pysupera/config.py @@ -578,6 +578,8 @@ def build_reader(cfg: DictConfig): "vertex/geant4")), particle_key = str(cfg.reader.get("particle_key", "particle/geant4")), + primary_key = str(cfg.reader.get("primary_key", + "primary/geant4")), electron_energy_threshold = float(cfg.reader.get( "electron_energy_threshold", 0.05)), min_pc_size = int(cfg.particle.get("min_pc_size", -1)), @@ -621,6 +623,8 @@ def build_reader(cfg: DictConfig): "vertex/geant4")), particle_key = str(cfg.reader.get("particle_key", "particle/geant4")), + primary_key = str(cfg.reader.get("primary_key", + "primary/geant4")), step_key = str(cfg.reader.get("step_key", "pstep/lar_vol")), ass_key = str(cfg.reader.get("ass_key", diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index b2f125b..3bae44a 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -203,6 +203,7 @@ class G4ProcessSubtype(IntEnum): _DEFAULT_PARTICLE_KEY = "particle/geant4" _DEFAULT_STEP_KEY = "pstep/lar_vol" _DEFAULT_ASS_KEY = "ass/particle_pstep_lar_vol" +_DEFAULT_PRIMARY_KEY = "primary/geant4" _DEFAULT_ELECTRON_ENERGY_THRESHOLD = 0.05 # MeV @@ -585,8 +586,81 @@ def _get_interaction_id(parts: np.ndarray, verts: np.ndarray) -> np.ndarray: return interaction_ids +def read_primaries(h5file, key: str, index: int): + """ + Track ids of the particles the generator made in event *index*. + + Returns None when the file has no *key* table, so the caller can fall + back on :func:`primary_mask`'s parent-less rule. + """ + if key not in h5file: + return None + return np.asarray(h5file[key][index]["track_id"], dtype=np.int64) + + +def primary_mask(parts: np.ndarray, primary_track_ids=None): + """ + Which rows of *parts* are generator primaries, and where the two + descriptions of a primary disagree. + + A primary is a particle of the generator's own list (the primary table, + *primary_track_ids*). Geant4 gives exactly those particles + ``parent_track_id == -1``, so the two are checked against each other. + Without the table the parent-less rows are taken for the primaries. + + Returns + ------- + mask : ndarray of bool, shape (N,) + check : dict or None + None without the table; otherwise three arrays of track ids, empty + when everything agrees -- ``parentless_not_primary`` (parent -1 but + not in the table), ``primary_with_parent`` (in the table but with a + parent) and ``primary_not_in_particles`` (in the table, absent from + *parts*). + """ + tid = np.asarray(parts["track_id"], dtype=np.int64) + parentless = np.asarray(parts["parent_track_id"]) == -1 + if primary_track_ids is None: + return parentless, None + prim = np.asarray(primary_track_ids, dtype=np.int64) + mask = np.isin(tid, prim) + return mask, dict( + parentless_not_primary=tid[parentless & ~mask], + primary_with_parent=tid[mask & ~parentless], + primary_not_in_particles=np.setdiff1d(prim, tid), + ) + + +#: What each kind of :func:`primary_mask` disagreement means, after "track N". +PRIMARY_CHECK_TEXT = { + "parentless_not_primary": "with parent_track_id -1 but not in the " + "primary table", + "primary_with_parent": "in the primary table but with a parent", + "primary_not_in_particles": "in the primary table but not among the " + "particles", +} + + +def describe_primary_check(check) -> str: + """One line naming each disagreement in a :func:`primary_mask` check.""" + parts = [] + for k, text in PRIMARY_CHECK_TEXT.items(): + ids = check[k] + if len(ids): + shown = ", ".join(str(int(i)) for i in ids[:5]) + more = f", ... ({len(ids)})" if len(ids) > 5 else "" + parts.append(f"track {shown}{more} {text}") + return "; ".join(parts) + + +def primary_check_failed(check) -> bool: + """Whether a :func:`primary_mask` check found any disagreement.""" + return check is not None and any(len(v) for v in check.values()) + + def _get_interaction_type(parts: np.ndarray, - electron_energy_threshold: float = 0.05 + electron_energy_threshold: float = 0.05, + is_primary=None, ) -> np.ndarray: """ Derive the pysupera :class:`~pysupera.utils.InteractionType` integer @@ -605,6 +679,13 @@ def _get_interaction_type(parts: np.ndarray, Kinetic-energy threshold (MeV) separating kCompton-like low-energy electrons from kOtherShower-like higher-energy electrons when no cleaner process classification is available. + is_primary : ndarray of bool or None, optional + Which particles are generator primaries (:func:`primary_mask`). + A primary is ``kPrimary`` -- never LE, whatever its size -- unless + it is a neutron or a nucleus. Its ``proc_start`` and + ``subproc_start`` are not read: Geant4 records no creator process + for a primary, and the fields hold whatever the converter wrote. + None takes the particles with ``parent_track_id == -1``. Returns ------- @@ -621,6 +702,9 @@ def _get_interaction_type(parts: np.ndarray, subproc_start = parts["subproc_start"] ke = parts["ke"] parent_pdg = _get_parent_pdg(parts) + if is_primary is None: + is_primary = primary_mask(parts)[0] + is_primary = np.asarray(is_primary, dtype=bool) parent_locs = _search_parents(track_id, parent_tid) parent_valid = parent_locs != -1 @@ -648,7 +732,7 @@ def _get_interaction_type(parts: np.ndarray, conditions = [ pdg == 2112, pdg > 1_000_000_000, - track_id == parent_tid, + is_primary, pdg == 22, # EM electrons — ordered from most specific to least specific is_electron & (proc_start == EM) & (subproc_start == PE), @@ -715,6 +799,10 @@ class EDepSimHDF5Reader(EventReaderBase): ass_key : str, optional HDF5 dataset key for the particle-to-step association arrays. Default: ``"ass/particle_pstep_lar_vol"``. + primary_key : str, optional + HDF5 dataset key for the per-event generator primaries, which decide + ``kPrimary``. Default: ``"primary/geant4"``. A file without it + falls back on ``parent_track_id == -1``. electron_energy_threshold : float, optional Kinetic-energy threshold (MeV) used by the interaction-type classifier to separate low-energy from high-energy electrons when @@ -740,6 +828,7 @@ def __init__( *, vertex_key: str = _DEFAULT_VERTEX_KEY, particle_key: str = _DEFAULT_PARTICLE_KEY, + primary_key: str = _DEFAULT_PRIMARY_KEY, step_key: str = _DEFAULT_STEP_KEY, ass_key: str = _DEFAULT_ASS_KEY, electron_energy_threshold: float = _DEFAULT_ELECTRON_ENERGY_THRESHOLD, @@ -750,6 +839,10 @@ def __init__( self._path = path self._vertex_key = vertex_key self._part_key = particle_key + self._primary_key = primary_key + #: The primary-table check of the event just read + #: (:func:`primary_mask`); None when the file has no primary table. + self.last_primary_check = None self._step_key = step_key self._ass_key = ass_key self._e_thresh = electron_energy_threshold @@ -806,7 +899,9 @@ def __getitem__(self, index: int) -> list[Particle]: ass = self._file[self._ass_key][index] num_parts = len(parts) - itype = _get_interaction_type(parts, self._e_thresh) + is_primary, self.last_primary_check = primary_mask( + parts, read_primaries(self._file, self._primary_key, index)) + itype = _get_interaction_type(parts, self._e_thresh, is_primary) int_ids = _get_interaction_id(parts,verts) offsets = np.column_stack([ ass["start"][:num_parts], @@ -895,6 +990,8 @@ def from_config(cls, cfg) -> "EDepSimHDF5Reader": _DEFAULT_STEP_KEY)), ass_key = str(r.get("ass_key", _DEFAULT_ASS_KEY)), + primary_key = str(r.get("primary_key", + _DEFAULT_PRIMARY_KEY)), electron_energy_threshold = float(r.get( "electron_energy_threshold", _DEFAULT_ELECTRON_ENERGY_THRESHOLD)), diff --git a/pysupera/readers/format_jaxtpc.py b/pysupera/readers/format_jaxtpc.py index 7d828e5..0079539 100644 --- a/pysupera/readers/format_jaxtpc.py +++ b/pysupera/readers/format_jaxtpc.py @@ -94,6 +94,7 @@ from .base import EventReaderBase from .format_edepsim_h5 import ( + _DEFAULT_PRIMARY_KEY, _DEFAULT_VERTEX_KEY, _DEFAULT_PARTICLE_KEY, _DEFAULT_ELECTRON_ENERGY_THRESHOLD, @@ -103,6 +104,8 @@ _get_ancestor_id, _to_index_space, particle_kinematics, + primary_mask, + read_primaries, ) from ..data import Particle from ..utils import PointFeature @@ -472,6 +475,10 @@ class JaxtpcHDF5Reader(EventReaderBase): particle_key : str, optional HDF5 dataset key for per-event particle arrays in *edepsim_path*. Default: ``"particle/geant4"``. + primary_key : str, optional + HDF5 dataset key for the per-event generator primaries in + *edepsim_path*, which decide ``kPrimary``. Default: + ``"primary/geant4"``; without it, ``parent_track_id == -1``. electron_energy_threshold : float, optional Kinetic-energy threshold (MeV) for the interaction-type classifier (forwarded to :func:`~.format_edepsim_h5._get_interaction_type`). @@ -504,6 +511,7 @@ def __init__( *, vertex_key: str = _DEFAULT_VERTEX_KEY, particle_key: str = _DEFAULT_PARTICLE_KEY, + primary_key: str = _DEFAULT_PRIMARY_KEY, electron_energy_threshold: float = _DEFAULT_ELECTRON_ENERGY_THRESHOLD, min_pc_size: int | None = None, voxel_size: float | None = None, @@ -608,6 +616,10 @@ def __init__( self._inst_path = inst_path self._vertex_key = vertex_key self._part_key = particle_key + self._primary_key = primary_key + #: The primary-table check of the event just read + #: (:func:`~.format_edepsim_h5.primary_mask`); None without the table. + self.last_primary_check = None self._e_thresh = electron_energy_threshold self._min_pc_size = min_pc_size # Cell size classification measures a cloud's extent in; see @@ -749,7 +761,9 @@ def __getitem__(self, index: int) -> list[Particle]: self.last_hit_stats = None # ── Step 5: derive per-particle scalar labels from EDepSim data ─── - itype = _get_interaction_type(parts, self._e_thresh) + is_primary, self.last_primary_check = primary_mask( + parts, read_primaries(self._edepsim_file, self._primary_key, index)) + itype = _get_interaction_type(parts, self._e_thresh, is_primary) int_ids = _get_interaction_id(parts, verts) @@ -1586,6 +1600,7 @@ def from_config(cls, cfg) -> "JaxtpcHDF5Reader": inst_path = str(r.jaxtpc_inst_path), vertex_key = str(r.get("vertex_key", _DEFAULT_VERTEX_KEY)), particle_key = str(r.get("particle_key", _DEFAULT_PARTICLE_KEY)), + primary_key = str(r.get("primary_key", _DEFAULT_PRIMARY_KEY)), electron_energy_threshold = float(r.get( "electron_energy_threshold", _DEFAULT_ELECTRON_ENERGY_THRESHOLD)), diff --git a/tests/test_primaries.py b/tests/test_primaries.py new file mode 100644 index 0000000..22420f7 --- /dev/null +++ b/tests/test_primaries.py @@ -0,0 +1,161 @@ +""" +A primary is a particle of the generator's list (the primary table). + +Such a particle is kPrimary, so it is never LE, whatever its size and +whatever its proc_start says -- Geant4 records no creator process for a +primary. The table is cross-checked against parent_track_id == -1. +""" +import numpy as np +import pytest + +from pysupera.readers.format_edepsim_h5 import ( + _get_interaction_type, describe_primary_check, primary_check_failed, + primary_mask) +from pysupera.utils import InteractionType as IT +from pysupera.utils import SemanticType as ST +from pysupera.utils import SetSemanticType + +h5py = pytest.importorskip("h5py") + +PART = np.dtype([(c, "f4") for c in ( + "x", "y", "z", "t", "px", "py", "pz", "ke", "end_x", "end_y", "end_z", + "end_t", "end_px", "end_py", "end_pz", "end_ke")] + + [(c, "i4") for c in ("track_id", "parent_track_id", + "ancestor_track_id", "pdg")] + + [("mass", "f4"), ("proc_start", "i4"), ("subproc_start", "i4"), + ("proc_end", "i4"), ("subproc_end", "i4")]) +STEP = np.dtype([(c, "f4") for c in ("x", "y", "z", "t", "theta", "phi", "p")] + + [(c, "i4") for c in ("pdg", "track_id", "ancestor_track_id")] + + [("de", "f4"), ("dx", "f4"), ("proc_start", "i4"), + ("subproc_start", "i2"), ("proc_stop", "i4"), + ("subproc_stop", "i2")]) +VERT = np.dtype([(c, "f4") for c in ("x", "y", "z", "t")] + + [("interaction_id", "i2")]) +PRIM = np.dtype([("track_id", "i4"), ("pdg", "i4"), ("interaction_id", "i4")]) +ASS = np.dtype([("start", "u8"), ("end", "u8")]) + + +def parts(rows): + """rows: (track_id, parent_track_id, pdg, proc_start, subproc_start).""" + a = np.zeros(len(rows), dtype=PART) + for k, (t, par, pdg, pr, sub) in enumerate(rows): + a[k]["track_id"], a[k]["parent_track_id"] = t, par + a[k]["ancestor_track_id"] = t if par == -1 else 1 + a[k]["pdg"], a[k]["proc_start"], a[k]["subproc_start"] = pdg, pr, sub + a[k]["mass"] = 105.66 if abs(pdg) == 13 else 0.511 + a[k]["ke"] = 1.7 + return a + + +# The event of the production failure: a primary mu+ of 1.7 MeV whose +# proc_start/subproc_start read 7/401 (General / StepLimit), and a primary +# e+ whose fields read 2/13 (EM / Compton). +EVENT = parts([(1, -1, -13, 7, 401), (2, -1, -11, 2, 13), + (3, 1, -11, 6, 201), (4, 2, 22, 2, 3)]) + + +class TestPrimaryMask: + + def test_the_table_decides(self): + mask, check = primary_mask(EVENT, [1, 2]) + assert mask.tolist() == [True, True, False, False] + assert not primary_check_failed(check) + + def test_without_the_table_the_parentless_rows_are_primaries(self): + mask, check = primary_mask(EVENT) + assert mask.tolist() == [True, True, False, False] + assert check is None and not primary_check_failed(check) + + def test_each_kind_of_disagreement_is_reported(self): + mask, check = primary_mask(EVENT, [1, 3, 99]) + assert mask.tolist() == [True, False, True, False] + assert check["parentless_not_primary"].tolist() == [2] + assert check["primary_with_parent"].tolist() == [3] + assert check["primary_not_in_particles"].tolist() == [99] + assert primary_check_failed(check) + text = describe_primary_check(check) + assert "track 2 with parent_track_id -1 but not in the primary table" in text + assert "track 3 in the primary table but with a parent" in text + assert "track 99 in the primary table but not among the particles" in text + + +class TestPrimaryIsNeverLE: + + def test_primaries_are_kprimary_whatever_their_process_fields(self): + it = _get_interaction_type(EVENT, is_primary=primary_mask(EVENT, [1, 2])[0]) + assert [IT(int(v)) for v in it[:2]] == [IT.kPrimary, IT.kPrimary] + assert IT(int(it[2])) is IT.kDecay + + def test_the_default_is_the_parentless_rule(self): + it = _get_interaction_type(EVENT) + assert [IT(int(v)) for v in it[:2]] == [IT.kPrimary, IT.kPrimary] + + def test_a_small_primary_muon_is_a_track(self): + tiny = np.zeros((3, 6), dtype=np.float32) + assert SetSemanticType(IT.kPrimary, -13, 0, tiny, 63) is ST.kTrack + # As a non-primary of the same size it would be LE. + assert SetSemanticType(IT.kTrack, -13, 0, tiny, 63) is ST.kLEScatter + + def test_a_primary_photon_or_electron_is_a_shower(self): + empty = np.zeros((0, 6), dtype=np.float32) + assert SetSemanticType(IT.kPrimary, 22, 0, empty, 63) is ST.kShower + assert SetSemanticType(IT.kPrimary, -11, 0, empty, 63) is ST.kShower + + def test_a_primary_neutron_stays_a_neutron(self): + ev = parts([(1, -1, 2112, 0, 0)]) + it = _get_interaction_type(ev, is_primary=np.array([True])) + assert IT(int(it[0])) is IT.kNeutron + + +def write_edepsim(path, prim_ids=None, steps=True): + """ + A one-event EDepSim file: EVENT, one step each for tracks 1 and 3 (none + at all with ``steps=False``). + """ + st = np.zeros(2 if steps else 0, dtype=STEP) + ass = np.zeros(len(EVENT), dtype=ASS) + if steps: + st["track_id"] = [1, 3] + st["de"], st["dx"] = 1.0, 0.1 + ass["start"] = [0, 1, 1, 2] + ass["end"] = [1, 1, 2, 2] + vert = np.zeros(1, dtype=VERT) + with h5py.File(path, "w") as f: + for key, a, dt in (("particle/geant4", EVENT, PART), + ("vertex/geant4", vert, VERT), + ("pstep/lar_vol", st, STEP), + ("ass/particle_pstep_lar_vol", ass, ASS)): + d = f.create_dataset(key, (1,), dtype=h5py.vlen_dtype(dt)) + d[0] = a + if prim_ids is not None: + pr = np.zeros(len(prim_ids), dtype=PRIM) + pr["track_id"] = prim_ids + d = f.create_dataset("primary/geant4", (1,), dtype=h5py.vlen_dtype(PRIM)) + d[0] = pr + + +class TestReader: + + def read(self, path, **kw): + from pysupera.readers import EDepSimHDF5Reader + with EDepSimHDF5Reader(str(path), min_pc_size=5, **kw) as r: + return r[0], r.last_primary_check + + def test_the_reader_takes_primaries_from_the_table(self, tmp_path): + write_edepsim(tmp_path / "e.h5", prim_ids=[1, 2]) + ps, check = self.read(tmp_path / "e.h5") + assert [IT(p._interaction_type) for p in ps[:2]] == [IT.kPrimary] * 2 + assert ps[0].sem_type is ST.kTrack # one step, still a track + assert not primary_check_failed(check) + + def test_the_reader_reports_a_disagreement(self, tmp_path): + write_edepsim(tmp_path / "e.h5", prim_ids=[1]) + ps, check = self.read(tmp_path / "e.h5") + assert IT(ps[1]._interaction_type) is not IT.kPrimary + assert check["parentless_not_primary"].tolist() == [2] + + def test_without_the_table_the_reader_falls_back(self, tmp_path): + write_edepsim(tmp_path / "e.h5") + ps, check = self.read(tmp_path / "e.h5") + assert check is None + assert IT(ps[0]._interaction_type) is IT.kPrimary diff --git a/tests/test_reader_configs.py b/tests/test_reader_configs.py index df39260..5e0c0cb 100644 --- a/tests/test_reader_configs.py +++ b/tests/test_reader_configs.py @@ -25,8 +25,8 @@ READERS = ("edepsim_h5", "jaxtpc_wire", "jaxtpc_pixel") #: Keys the base defines and every variant must therefore inherit. -SHARED_KEYS = ("format", "particle_key", "vertex_key", "step_key", - "ass_key", "electron_energy_threshold") +SHARED_KEYS = ("format", "particle_key", "vertex_key", "primary_key", + "step_key", "ass_key", "electron_energy_threshold") def _cfg(name, *extra): From e9bf8ae4054e2b8dcba2bb9cb248feaba6924332 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sat, 17 Oct 2026 12:35:22 -0700 Subject: [PATCH 50/62] run: record what a run notices but does not stop for in an issues/ group A warning printed among a production job's log lines is not seen. So each check that lets the run go on now records what it found under a fixed integer code (pysupera.issues.Issue), with the event, the Geant4 track and a message. The output stores the record as issues/: event, code, geant4_trackid, message, and a codes/ table naming every code. The root attribute n_issues holds the total and issues.attrs the count per code. The group is always written, so an empty one says the checks ran and found nothing. The end of the run log lists what was found, and EventStoreV3.issues() reads it back. Codes are never renumbered. 1 PRIMARY_TABLE_MISMATCH the primary table and parent_track_id == -1 disagree about a track; one row per track 2 NO_PRIMARY_TABLE the input has none; parent -1 taken (once) 3 EMPTY_IMAGE an all-empty event that allow_empty_image let through, every one now, not only the first 4 OUTSIDE_READOUT_WINDOW an event written empty because nothing reached the readout window dprod reads the group into each task's summary. The README documents the group, and the primary rule of the previous commit. Checked: the production file and the three local samples run with an empty group; a run on a synthetic file records each of codes 1-3. Tests: 531 passed, 1 skipped. --- README.md | 41 +++++++++++ pysupera/_run.py | 50 +++++++++++-- pysupera/conf/config.yaml | 9 ++- pysupera/io_v3.py | 13 ++++ pysupera/issues.py | 151 ++++++++++++++++++++++++++++++++++++++ tests/test_issues.py | 95 ++++++++++++++++++++++++ 6 files changed, 348 insertions(+), 11 deletions(-) create mode 100644 pysupera/issues.py create mode 100644 tests/test_issues.py diff --git a/README.md b/README.md index 8d4584c..1ea4f89 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,7 @@ A Python library for grouping simulated LArTPC particles into physics partitions - [What the output stores (JAXTPC input)](#what-the-output-stores-jaxtpc-input) - [Hit provenance (JAXTPC mode)](#hit-provenance-jaxtpc-mode) - [Seeing it](#seeing-it) + - [Issues](#issues) - [Expert and debugging output](#expert-and-debugging-output) 7. [Training Data (PyTorch)](#training-data-pytorch) - [Voxel merging](#voxel-merging) @@ -386,6 +387,17 @@ if not np.isnan(p.kinetic_energy_start): # float32 scalar check `sem_type` is derived automatically from `process_type`, `pdg`, `parent_pdg`, and `point_cloud` at construction time via `SetSemanticType`. Particles with fewer than `min_pc_size` points may be reclassified as `kLEScatter`. +A generator primary is never `kLEScatter`: it is `kShower` if it is an +electron, positron or photon and `kTrack` otherwise, whatever its size. +Neutrons and nuclei are the exception and follow their own rules. The +readers take the primaries from the input's primary table +(`reader.primary_key`, `primary/geant4`), not from the process fields, which +Geant4 leaves meaningless for a primary. Geant4 gives exactly these particles +`parent_track_id == -1`, and each event is checked for that. A mismatch is +recorded in the output's `issues/` group, and the run goes on using the +table. Without a table, the parent-less particles are taken as the +primaries. + | Value | Meaning | |---|---| | `kShower` | EM shower fragment | @@ -664,6 +676,7 @@ particles/ (M, 4) float64 kinematics; see below inter/ (K,) groups/ JAXTPC group -> owning fragment } JAXTPC hit_labels/volume{V}/plane{P}/ per hit of that plane } input +issues/ what the run noticed but did not stop for; see below ``` Everything is concatenated across events with CSR fenceposts: event *i* @@ -1444,6 +1457,34 @@ defaults to `source`, which leaves an uncompressed dataset uncompressed — `all` overrides that. On a 100-event file, gzip is 65.4 MiB against LZ4's 89.4 MiB, so gzip is worth it for anything but the fastest reads. +### Issues + +Some checks report a problem but let the run go on. Each such check records +what it found under a fixed integer code, in the `issues/` group, because a +warning printed into a production log goes unseen. The group is always +written, so an empty one says the checks ran and found nothing. + +``` +issues/event (I,) int64 event index; -1 for the whole file +issues/code (I,) int32 see the table below +issues/geant4_trackid (I,) int64 the track concerned; -1 for none +issues/message (I,) str what was found +issues/codes/ code, name, description of every code +``` + +The root attribute `n_issues` holds the total, and `issues.attrs["n_"]` +the count for each code. `EventStoreV3.issues()` returns the group as arrays. +The end of the run log lists the codes found and their first messages. + +| code | name | recorded when | +|---|---|---| +| 1 | `PRIMARY_TABLE_MISMATCH` | the primary table and `parent_track_id == -1` disagree about a track (one row per track); the table is used | +| 2 | `NO_PRIMARY_TABLE` | the input has no primary table, so the parent-less particles were taken as primaries (once per file) | +| 3 | `EMPTY_IMAGE` | every particle of an event has an empty point cloud, and `allow_empty_image=true` let the run go on | +| 4 | `OUTSIDE_READOUT_WINDOW` | none of an event's deposits arrived inside the readout window; the event was written empty | + +Codes are never renumbered or reused, so counts can be summed across files. + ### Expert and debugging output Off by default, regenerable, and not intended for analysis. diff --git a/pysupera/_run.py b/pysupera/_run.py index 52142f4..3244cdf 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -303,6 +303,13 @@ def _n_groups_of(_store): 'energy_mantissa_bits', None), }) + # What the run noticed but did not stop for, written to issues/. + from pysupera.issues import Issue, IssueLog + from pysupera.readers.format_edepsim_h5 import ( + PRIMARY_CHECK_TEXT) + _issues = IssueLog() + _no_primary_table_noted = False + from tqdm import tqdm _bar = tqdm( total=_n_to_process, @@ -318,6 +325,23 @@ def _n_groups_of(_store): n_events += 1 _ev_t: dict = {} # per-event stage timings (seconds) + # ── Primaries: the primary table against parent -1 ───────── + # The reader took kPrimary from the table; a disagreement + # with parent_track_id == -1 is recorded, one row per track. + _pchk = getattr(store, 'last_primary_check', False) + if _pchk is None and not _no_primary_table_noted: + _issues.record(Issue.NO_PRIMARY_TABLE, + "the input has no primary table; " + "parent_track_id == -1 taken instead") + _no_primary_table_noted = True + elif _pchk: + for _key, _tids in _pchk.items(): + for _t in _tids: + _issues.record( + Issue.PRIMARY_TABLE_MISMATCH, + f"track {int(_t)} {PRIMARY_CHECK_TEXT[_key]}", + event=event_idx, geant4_trackid=int(_t)) + # ── JAXTPC visibility counts ──────────────────────────────── # Already computed by the reader from data it had in hand, so # collecting them costs nothing; only the printing is gated. @@ -465,11 +489,14 @@ def _n_groups_of(_store): # guard stays armed for everything else. if not _nonzero and _wholly_outside_window(_hs): n_out_of_window_events += 1 - print(f"[run] event {event_idx}: none of its " - f"{_hs['n_deposits']:,} deposits arrived inside the " - f"readout window ({_hs['n_before_window']:,} " - f"before, {_hs['n_after_window']:,} after); " + _why = (f"none of its {_hs['n_deposits']:,} deposits " + f"arrived inside the readout window " + f"({_hs['n_before_window']:,} before, " + f"{_hs['n_after_window']:,} after)") + print(f"[run] event {event_idx}: {_why}; " f"written as an empty event.") + _issues.record(Issue.OUTSIDE_READOUT_WINDOW, _why, + event=event_idx) elif not _nonzero: _msg = ( f"Event {event_idx}: empty image — all " @@ -484,14 +511,19 @@ def _n_groups_of(_store): f"JAXTPC mode) that reader.jaxtpc_seg_path and " f"reader.jaxtpc_inst_path point at the seg/step and " f"hits/inst files respectively. Set " - f"allow_empty_image=true to downgrade this to a " - f"one-time warning." + f"allow_empty_image=true to record it in the " + f"output's issues/ group and go on instead." ) + _issues.record( + Issue.EMPTY_IMAGE, + f"all {len(particles)} particle(s) have an empty " + f"point cloud", event=event_idx) if not _warned_empty_image: import warnings warnings.warn( f"{_msg} allow_empty_image=true, so processing " - f"continues; this warning is issued only once.", + f"continues; every such event is recorded in " + f"issues/, and this warning is issued only once.", UserWarning, stacklevel=2, ) @@ -546,6 +578,7 @@ def _n_groups_of(_store): _pieces = find_split_pieces(fragments, particles) relabel_split_pieces(_pieces) + # Capture fragment membership NOW. merge_em_showers returns # the surviving representatives themselves as the instances -- # every instance object is also a fragment object -- and @@ -729,6 +762,7 @@ def _n_groups_of(_store): ) _bar.close() + writer.write_issues(_issues) # ── Post-run repack (opt-in) ─────────────────────────────────────────── # Runs only after every writer above has been closed, so the file is @@ -960,6 +994,8 @@ def _agg(vals): print(f" {_SEP}") print(f" {'TOTAL':<{_W}} {total:>10.3f} {1000*total/n_events:>15.2f} {'100.0':>6}%") + # Last, so it is what a log's tail shows. + print("\n" + _issues.report()) print("[run] Done.") diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index c7d53b5..7ffe93d 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -108,10 +108,11 @@ repack: # mistake (e.g. the wrong reader.jaxtpc_seg_path / step_key) # rather than real physics, and silently writing thousands of # empty events is far worse than stopping early. -# true — allow empty images through, emitting a single warning the -# first time one is encountered (not once per event). Use -# when empty events are genuinely expected, e.g. very low -# readout thresholds or heavily masked detector volumes. +# true — allow empty images through, recording each one in the +# output's issues/ group (code 3, EMPTY_IMAGE) and printing +# a single warning the first time. Use when empty events +# are genuinely expected, e.g. very low readout thresholds +# or heavily masked detector volumes. allow_empty_image: false # Verify that every JAXTPC group belongs to exactly one pysupera particle. diff --git a/pysupera/io_v3.py b/pysupera/io_v3.py index 946f482..9479175 100644 --- a/pysupera/io_v3.py +++ b/pysupera/io_v3.py @@ -420,6 +420,11 @@ def append_event(self, layout): ds[self._n_events] = total self._f["n_events"][()] = np.int64(self._n_events) + def write_issues(self, log): + """Store the run's :class:`~pysupera.issues.IssueLog` as issues/.""" + from .issues import write_issues + write_issues(self._f, log) + def close(self): if self._f is not None: self._f.close() @@ -639,6 +644,14 @@ def hit_labels(self, ev): out[(int(g.attrs["volume"]), int(g.attrs["plane"]))] = d return out + def issues(self): + """ + The run's recorded issues (:func:`pysupera.issues.read_issues`), or + None for a file written before they were. + """ + from .issues import read_issues + return read_issues(self._f) + def group_owners(self, ev): """ ``(instance_id, fragment_id, volume_offsets)`` for event *ev*, or diff --git a/pysupera/issues.py b/pysupera/issues.py new file mode 100644 index 0000000..14b16de --- /dev/null +++ b/pysupera/issues.py @@ -0,0 +1,151 @@ +""" +pysupera.issues +=============== + +Problems a run notices but does not stop for, recorded in the output file. + +A warning printed among thousands of lines of a production log is not seen. +So every check that lets a run go on records what it found here, under a +stable integer code, and :func:`write_issues` stores the record in the +output as the ``issues/`` group: + +``issues/event`` int64 event index, -1 for the whole file +``issues/code`` int32 an :class:`Issue` value +``issues/geant4_trackid`` int64 the Geant4 track concerned, -1 for none +``issues/message`` str what was found, in words +``issues/codes`` the code table: ``code``, ``name``, ``description`` + +The group is always written, so an empty one says the checks ran and found +nothing. The root attribute ``n_issues`` holds the total, and +``issues.attrs["n_"]`` the count per code. Production tools (dprod) +read the group to flag a job that finished with issues. + +Codes are never renumbered or reused: a summary built across many files +depends on them. +""" + +from __future__ import annotations + +from enum import IntEnum + +import numpy as np + + +class Issue(IntEnum): + """What a recorded issue is. New codes are appended, never renumbered.""" + + #: The primary table and ``parent_track_id == -1`` disagree about which + #: particles are primaries. The table is used. + PRIMARY_TABLE_MISMATCH = 1 + #: The input has no primary table, so the particles without a parent + #: were taken for the primaries. + NO_PRIMARY_TABLE = 2 + #: Every particle of the event has an empty point cloud, and + #: ``allow_empty_image`` let the run go on. + EMPTY_IMAGE = 3 + #: None of the event's deposits arrived inside the readout window; the + #: event was written empty. + OUTSIDE_READOUT_WINDOW = 4 + + +#: One line per code, stored in the file next to the codes. +DESCRIPTION = { + Issue.PRIMARY_TABLE_MISMATCH: + "primary table and parent_track_id == -1 disagree; the table is used", + Issue.NO_PRIMARY_TABLE: + "no primary table in the input; particles without a parent are " + "taken for the primaries", + Issue.EMPTY_IMAGE: + "every particle has an empty point cloud (allow_empty_image)", + Issue.OUTSIDE_READOUT_WINDOW: + "no deposit arrived inside the readout window; event written empty", +} + + +class IssueLog: + """The issues of one run, in the order they were found.""" + + def __init__(self): + self.event: list[int] = [] + self.code: list[int] = [] + self.geant4_trackid: list[int] = [] + self.message: list[str] = [] + + def __len__(self): + return len(self.code) + + def record(self, code: Issue, message: str, event: int = -1, + geant4_trackid: int = -1) -> None: + """Record one issue. *event* -1 means the whole file.""" + self.event.append(int(event)) + self.code.append(int(Issue(code))) + self.geant4_trackid.append(int(geant4_trackid)) + self.message.append(str(message)) + + def counts(self) -> dict: + """``{Issue: n}`` for every code recorded at least once.""" + out: dict = {} + for c in self.code: + out[Issue(c)] = out.get(Issue(c), 0) + 1 + return out + + def report(self, max_lines: int = 5) -> str: + """The end-of-run summary: a count per code and the first messages.""" + if not len(self): + return "[run] Issues: none" + lines = [f"[run] Issues: {len(self)} recorded in the output's " + f"issues/ group"] + for code, n in sorted(self.counts().items()): + lines.append(f" {int(code):>3} {code.name:<24} {n:>8,} " + f"{DESCRIPTION[code]}") + shown = [k for k, c in enumerate(self.code) if c == code][:max_lines] + for k in shown: + ev = "file" if self.event[k] < 0 else f"event {self.event[k]}" + lines.append(f" {ev}: {self.message[k]}") + if n > len(shown): + lines.append(f" ... {n - len(shown)} more") + return "\n".join(lines) + + +def write_issues(h5file, log: IssueLog) -> None: + """Store *log* as the ``issues/`` group of the open file *h5file*.""" + import h5py + g = h5file.require_group("issues") + for name in list(g.keys()): + del g[name] + s = h5py.string_dtype() + g.create_dataset("event", data=np.asarray(log.event, dtype=np.int64)) + g.create_dataset("code", data=np.asarray(log.code, dtype=np.int32)) + g.create_dataset("geant4_trackid", + data=np.asarray(log.geant4_trackid, dtype=np.int64)) + g.create_dataset("message", data=np.array(log.message, dtype=object), + dtype=s) + t = g.create_group("codes") + t.create_dataset("code", data=np.array([int(c) for c in Issue], + dtype=np.int32)) + t.create_dataset("name", data=np.array([c.name for c in Issue], + dtype=object), dtype=s) + t.create_dataset("description", data=np.array( + [DESCRIPTION[c] for c in Issue], dtype=object), dtype=s) + counts = log.counts() + for c in Issue: + g.attrs[f"n_{c.name.lower()}"] = int(counts.get(c, 0)) + h5file.attrs["n_issues"] = int(len(log)) + + +def read_issues(h5file) -> dict | None: + """ + The ``issues/`` group of an open file as plain arrays, or None when the + file predates it. Keys: ``event``, ``code``, ``geant4_trackid``, + ``message`` (list of str), ``names`` (``{code: name}``). + """ + if "issues" not in h5file: + return None + g = h5file["issues"] + dec = lambda a: [x.decode() if isinstance(x, bytes) else str(x) for x in a] + return dict( + event=g["event"][()], code=g["code"][()], + geant4_trackid=g["geant4_trackid"][()], message=dec(g["message"][()]), + names=dict(zip((int(c) for c in g["codes/code"][()]), + dec(g["codes/name"][()]))), + ) diff --git a/tests/test_issues.py b/tests/test_issues.py new file mode 100644 index 0000000..339e71c --- /dev/null +++ b/tests/test_issues.py @@ -0,0 +1,95 @@ +""" +What a run notices but does not stop for is recorded in the output's +issues/ group, under stable codes, so production tools can find it. +""" +import shutil +import subprocess +from pathlib import Path + +import pytest + +from pysupera.issues import (DESCRIPTION, Issue, IssueLog, read_issues, + write_issues) +from tests.test_primaries import write_edepsim + +h5py = pytest.importorskip("h5py") + + +def test_codes_are_fixed(): + """A summary across files depends on these; never renumber them.""" + assert {c.name: int(c) for c in Issue} == { + "PRIMARY_TABLE_MISMATCH": 1, "NO_PRIMARY_TABLE": 2, + "EMPTY_IMAGE": 3, "OUTSIDE_READOUT_WINDOW": 4} + assert set(DESCRIPTION) == set(Issue) + + +def test_round_trip(tmp_path): + log = IssueLog() + log.record(Issue.PRIMARY_TABLE_MISMATCH, "track 2 ...", event=3, + geant4_trackid=2) + log.record(Issue.NO_PRIMARY_TABLE, "no table") + with h5py.File(tmp_path / "f.h5", "w") as f: + write_issues(f, log) + with h5py.File(tmp_path / "f.h5", "r") as f: + got = read_issues(f) + assert f.attrs["n_issues"] == 2 + assert f["issues"].attrs["n_primary_table_mismatch"] == 1 + assert f["issues"].attrs["n_empty_image"] == 0 + assert got["event"].tolist() == [3, -1] + assert got["code"].tolist() == [1, 2] + assert got["geant4_trackid"].tolist() == [2, -1] + assert got["message"] == ["track 2 ...", "no table"] + assert got["names"][3] == "EMPTY_IMAGE" + + +def test_an_empty_log_is_still_written(tmp_path): + """An empty group says the checks ran and found nothing.""" + with h5py.File(tmp_path / "f.h5", "w") as f: + write_issues(f, IssueLog()) + with h5py.File(tmp_path / "f.h5", "r") as f: + assert f.attrs["n_issues"] == 0 + assert len(read_issues(f)["code"]) == 0 + + +def test_report_counts_and_shows_the_first_messages(): + log = IssueLog() + for k in range(7): + log.record(Issue.EMPTY_IMAGE, f"empty {k}", event=k) + text = log.report(max_lines=2) + assert "EMPTY_IMAGE" in text and "7" in text + assert "event 1: empty 1" in text and "empty 2" not in text + assert "... 5 more" in text + assert IssueLog().report() == "[run] Issues: none" + + +def _run_pysupera(): + exe = (shutil.which("run_pysupera") + or str(Path.home() / ".local/bin/run_pysupera")) + if not Path(exe).exists(): + pytest.skip("run_pysupera not found") + return exe + + +@pytest.mark.parametrize("prim, steps, want", [ + ([1, 2], True, {}), + ([1], True, {Issue.PRIMARY_TABLE_MISMATCH: [2]}), + (None, True, {Issue.NO_PRIMARY_TABLE: [-1]}), + ([1, 2], False, {Issue.EMPTY_IMAGE: [-1]}), +]) +def test_a_run_records_what_it_found(tmp_path, prim, steps, want): + write_edepsim(tmp_path / "in.h5", prim_ids=prim, steps=steps) + out = tmp_path / "out.h5" + r = subprocess.run([_run_pysupera(), f"io.input_path={tmp_path / 'in.h5'}", + f"io.output_path={out}", "progress=false", + "allow_empty_image=true", + f"hydra.run.dir={tmp_path / 'hydra'}"], + capture_output=True, text=True) + assert r.returncode == 0, r.stdout[-2000:] + r.stderr[-2000:] + from pysupera.io_v3 import EventStoreV3 + with EventStoreV3(str(out)) as s: + got = s.issues() + seen = {} + for c, t in zip(got["code"], got["geant4_trackid"]): + seen.setdefault(Issue(int(c)), []).append(int(t)) + assert seen == want + assert "[run] Issues:" in r.stdout From ea3559f078e0642dff6a1bd70897c16b0f16ee6c Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sat, 17 Oct 2026 12:35:22 -0700 Subject: [PATCH 51/62] readers: a primary neutron or nucleus is kPrimary too; merge: a neutron's pieces stay apart A primary is the interaction's generator particle, whatever it is. But in _get_interaction_type the neutron (pdg 2112) and nucleus (pdg > 1e9) rules came before the primary one, so a primary neutron was kNeutron, hence kLEScatter, and a primary nucleus kNucleus. The primary rule now comes first: such a particle is kPrimary and so kTrack, like a primary pi0. A neutron or nucleus that is not a primary keeps its rule. A primary neutron usually deposits nothing itself, leaving an instance with no voxels; those are common already (primary pi0, and protons and pions that leave no deposits). But some physics lists put sub-cut recoil energy on the neutron's own track, and find_split_pieces sends the split-off pieces of a kTrack particle back to its instance. That would gather blobs from metres apart into one instance. A neutron's pieces are now never sent back, whatever its semantic type (_NOT_A_TRAJECTORY_PDG). The production file edepsim_j001995-001995.h5 has no primary neutron or nucleus. On its 100 events (pixel), every particle's interaction type, semantic type, fragment and instance are the same as before this change. --- README.md | 7 ++++--- pysupera/merge.py | 15 +++++++++++---- pysupera/readers/format_edepsim_h5.py | 8 ++++---- tests/test_instance_rules.py | 6 ++++++ tests/test_primaries.py | 17 +++++++++++++---- 5 files changed, 38 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 1ea4f89..2b85154 100644 --- a/README.md +++ b/README.md @@ -388,8 +388,9 @@ if not np.isnan(p.kinetic_energy_start): # float32 scalar check `sem_type` is derived automatically from `process_type`, `pdg`, `parent_pdg`, and `point_cloud` at construction time via `SetSemanticType`. Particles with fewer than `min_pc_size` points may be reclassified as `kLEScatter`. A generator primary is never `kLEScatter`: it is `kShower` if it is an -electron, positron or photon and `kTrack` otherwise, whatever its size. -Neutrons and nuclei are the exception and follow their own rules. The +electron, positron or photon and `kTrack` otherwise, whatever its size -- +neutrons, nuclei and π0 included, which usually leaves an instance with no +voxels. The readers take the primaries from the input's primary table (`reader.primary_key`, `primary/geant4`), not from the process fields, which Geant4 leaves meaningless for a primary. Geant4 gives exactly these particles @@ -521,7 +522,7 @@ defragmentation and the extent classification see. See #### Defragmentation -A particle's point cloud may be *fragmented* — split into spatially disconnected clusters that should logically belong to the same particle. The defragmenter runs a connected-components algorithm (eps-ball radius = `distance_threshold`) on each particle's point cloud independently, then detaches small disconnected fragments (size ≤ `min_pc_size`) as new `kLEScatter` particles. By default only `kShower`, `kDelta`, `kMichel` and `kLEScatter` particles are examined (`preprocessor.sem_types`); tracks are left whole. A detached piece of a `kTrack`, `kMichel` or `kDelta` particle that touches nothing joins its particle's instance and is not LE (its fragment takes the particle's fragment type); one that proximity absorbed elsewhere stays there. +A particle's point cloud may be *fragmented* — split into spatially disconnected clusters that should logically belong to the same particle. The defragmenter runs a connected-components algorithm (eps-ball radius = `distance_threshold`) on each particle's point cloud independently, then detaches small disconnected fragments (size ≤ `min_pc_size`) as new `kLEScatter` particles. By default only `kShower`, `kDelta`, `kMichel` and `kLEScatter` particles are examined (`preprocessor.sem_types`); tracks are left whole. A detached piece of a `kTrack`, `kMichel` or `kDelta` particle other than a neutron that touches nothing joins its particle's instance and is not LE (its fragment takes the particle's fragment type); one that proximity absorbed elsewhere stays there. Enable with: diff --git a/pysupera/merge.py b/pysupera/merge.py index d636f95..2e49025 100644 --- a/pysupera/merge.py +++ b/pysupera/merge.py @@ -253,11 +253,16 @@ def initiator_of(rep): #: Semantic types whose split-off pieces go back to their particle: the ones #: with a continuous trajectory that a gap can interrupt. Showers collect -#: their pieces through PhotonDecay and genealogy already; a neutron's blobs -#: are separate interactions, not one trajectory. +#: their pieces through PhotonDecay and genealogy already. _TRACK_LIKE = frozenset({SemanticType.kTrack, SemanticType.kMichel, SemanticType.kDelta}) +#: Particles whose pieces never go back, whatever their semantic type: a +#: neutron's blobs are separate interactions, not one trajectory. A primary +#: neutron is ``kTrack``, and its own deposits (sub-cut recoils, in some +#: physics lists) can lie metres apart. +_NOT_A_TRAJECTORY_PDG = frozenset({2112}) + def find_split_pieces(fragments, particles): """ @@ -269,7 +274,8 @@ def find_split_pieces(fragments, particles): piece touches nothing it stays a fragment of its own, though it is part of its particle's trajectory. This finds those fragments: every member a piece of the same Geant4 particle, that particle ``kTrack``, - ``kMichel`` or ``kDelta``, and its own fragment not LE. A piece that + ``kMichel`` or ``kDelta`` and not a neutron, and its own fragment not + LE. A piece that proximity absorbed into another fragment is left where it is -- it looks like part of that fragment, so it is. @@ -303,7 +309,8 @@ def find_split_pieces(fragments, particles): if any(int(m) not in by_id or g4(by_id[int(m)]) != g4(piece) for m in mids): continue - if src.sem_type not in _TRACK_LIKE: + if (src.sem_type not in _TRACK_LIKE + or abs(int(src.pdg)) in _NOT_A_TRAJECTORY_PDG): continue home = frag_of.get(int(src.id)) if home is None or home is f or home.sem_type == SemanticType.kLEScatter: diff --git a/pysupera/readers/format_edepsim_h5.py b/pysupera/readers/format_edepsim_h5.py index 3bae44a..d406b79 100644 --- a/pysupera/readers/format_edepsim_h5.py +++ b/pysupera/readers/format_edepsim_h5.py @@ -681,8 +681,8 @@ def _get_interaction_type(parts: np.ndarray, cleaner process classification is available. is_primary : ndarray of bool or None, optional Which particles are generator primaries (:func:`primary_mask`). - A primary is ``kPrimary`` -- never LE, whatever its size -- unless - it is a neutron or a nucleus. Its ``proc_start`` and + A primary is ``kPrimary`` -- never LE, whatever its size -- + neutrons and nuclei included. Its ``proc_start`` and ``subproc_start`` are not read: Geant4 records no creator process for a primary, and the fields hold whatever the converter wrote. None takes the particles with ``parent_track_id == -1``. @@ -730,9 +730,9 @@ def _get_interaction_type(parts: np.ndarray, is_electron = abs(pdg) == 11 conditions = [ + is_primary, pdg == 2112, pdg > 1_000_000_000, - is_primary, pdg == 22, # EM electrons — ordered from most specific to least specific is_electron & (proc_start == EM) & (subproc_start == PE), @@ -752,9 +752,9 @@ def _get_interaction_type(parts: np.ndarray, ] choices = [ + InteractionType.kPrimary, InteractionType.kNeutron, InteractionType.kNucleus, - InteractionType.kPrimary, InteractionType.kPhoton, InteractionType.kPhotoElectron, InteractionType.kCompton, diff --git a/tests/test_instance_rules.py b/tests/test_instance_rules.py index 81f07c7..bf974f7 100644 --- a/tests/test_instance_rules.py +++ b/tests/test_instance_rules.py @@ -188,6 +188,12 @@ def test_a_neutrons_blobs_are_left_alone(self): blob = self.piece_of(n, 2, (300, 0, 0)) assert find_split_pieces(fragments(n, blob), [n, blob]) == [] + def test_a_primary_neutrons_blobs_are_left_alone_though_it_is_a_track(self): + n = make_particle(1, PT_PRIMARY, pdg=2112, n_pts=8) + assert n.sem_type == ST.kTrack + blob = self.piece_of(n, 2, (300, 0, 0)) + assert find_split_pieces(fragments(n, blob), [n, blob]) == [] + def test_a_piece_absorbed_elsewhere_stays_there(self): mu = make_particle(1, PT_TRACK, pdg=13, pc=line(0, 100)) pi = make_particle(3, PT_TRACK, pdg=211, pc=line(300, 400)) diff --git a/tests/test_primaries.py b/tests/test_primaries.py index 22420f7..1a07e8a 100644 --- a/tests/test_primaries.py +++ b/tests/test_primaries.py @@ -101,10 +101,19 @@ def test_a_primary_photon_or_electron_is_a_shower(self): assert SetSemanticType(IT.kPrimary, 22, 0, empty, 63) is ST.kShower assert SetSemanticType(IT.kPrimary, -11, 0, empty, 63) is ST.kShower - def test_a_primary_neutron_stays_a_neutron(self): - ev = parts([(1, -1, 2112, 0, 0)]) - it = _get_interaction_type(ev, is_primary=np.array([True])) - assert IT(int(it[0])) is IT.kNeutron + @pytest.mark.parametrize("pdg", [2112, 1000180400, 111]) + def test_a_primary_neutron_nucleus_or_pi0_is_a_track(self, pdg): + ev = parts([(1, -1, pdg, 0, 0), (2, 1, pdg, 4, 111)]) + it = _get_interaction_type(ev, is_primary=np.array([True, False])) + assert IT(int(it[0])) is IT.kPrimary + empty = np.zeros((0, 6), dtype=np.float32) + assert SetSemanticType(IT.kPrimary, pdg, 0, empty, 63) is ST.kTrack + + def test_a_secondary_neutron_or_nucleus_keeps_its_rule(self): + ev = parts([(1, -1, 13, 0, 0), (2, 1, 2112, 4, 111), + (3, 1, 1000180400, 4, 111)]) + it = _get_interaction_type(ev, is_primary=np.array([True, False, False])) + assert [IT(int(v)) for v in it[1:]] == [IT.kNeutron, IT.kNucleus] def write_edepsim(path, prim_ids=None, steps=True): From 27f7e4a772ff405e154d44b1b0f979c87ad0d99e Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sat, 17 Oct 2026 12:35:22 -0700 Subject: [PATCH 52/62] merge: a delta ray its track hides joins the track's points, and stays a kDelta row With pixel hits a low-energy delta ray often cannot be told apart from the muon it came off: diffusion leaves its hits on the track or hugging it. It still headed its own fragment and instance, so the labels asked for something the image does not show. delta_min_standoff (mm, 0 = off) merges such a delta ray into its track. It applies to a non-primary kDelta fragment whose parent sits in a track-like fragment and which holds nothing else but LE and the delta ray's own pieces. The delta ray is merged when none of its voxels is that far or more from the nearest voxel of the track's fragment. It is measured on the clouds step 1 groups, so for pixel on the voxelized hits. Only the points move, into the track's fragment and so into its instance. The cut is distance and not a count of voxels off the track. A delta ray running along the track can have many voxels beside it and still be invisible, while a short one that leaves the track is plain to see. In a hand scan of event 0 (sdf_data_03, pixel, 3 mm voxels), how far each delta ray of muon 74 got from it matched the call for all 11: hidden 482 511 447 525 519 468 481 0 - 7.3 mm visible 507 479 461 480 9.0 - 20.3 mm A count of voxels off the track did not separate them: 507 (visible) has 19, the same as 481 (hidden), and 519 (hidden) has 21. readout/jaxtpc_pixel.yaml sets 8 mm, about two 4.32 mm pixels. Over 3 events that merges 25-30 delta rays an event. Wire and edepsim leave it off. On the true deposits the same delta rays are resolved (519 gets 12.4 mm from the muon), and their delta rays are decided as before, by min_pc_size and AbsorbLEScatter. A merged delta ray keeps a row, whichever rule merged it. A delta ray smaller than min_pc_size is typed kLEScatter, and AbsorbLEScatter puts it in the track it touches. Before this it got no row unless it lay on someone's ancestry, and then as kLEScatter. Now any delta ray in its parent track's fragment, merged by either rule, is written as sem_type kDelta, with its own pc_start/pc_end inside the track's fragment range and frag_id/inst_id -1. So sem_type == kDelta selects every delta ray, kept or merged. Its steps do not count toward the track instance's first/last point (buried_in_track), as its LE type kept them out before. One merged into anything else, or left as an LE blob, stays kLEScatter. This only adds rows: +5% (edepsim) and +9% (pixel) particle rows over 3 events, about 490 and 340 an event. Points, groups, hit labels, fragment/instance heads and instance first/last are identical with and without it, checked on 3 events of both. The run report prints both counts ("Delta rays into track", "LE delta rays in track"). The README has both rules and the scan table. --- README.md | 69 ++++++++- pysupera/_run.py | 34 ++++- pysupera/conf/config.yaml | 12 ++ pysupera/conf/readout/jaxtpc_pixel.yaml | 7 + pysupera/kinematics.py | 5 +- pysupera/merge.py | 183 ++++++++++++++++++++++++ tests/test_instance_rules.py | 162 ++++++++++++++++++++- tests/test_reader_configs.py | 2 + 8 files changed, 468 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 2b85154..8b41c1f 100644 --- a/README.md +++ b/README.md @@ -385,7 +385,8 @@ if not np.isnan(p.kinetic_energy_start): # float32 scalar check #### Semantic types -`sem_type` is derived automatically from `process_type`, `pdg`, `parent_pdg`, and `point_cloud` at construction time via `SetSemanticType`. Particles with fewer than `min_pc_size` points may be reclassified as `kLEScatter`. +`sem_type` is derived automatically from `process_type`, `pdg`, `parent_pdg`, and `point_cloud` at construction time via `SetSemanticType`. Particles with fewer than `min_pc_size` points may be reclassified as `kLEScatter`. A delta ray so reclassified that ends up in its parent track is written +as `kDelta` again (see [Delta rays merged into their track](#delta-rays-merged-into-their-track)). A generator primary is never `kLEScatter`: it is `kShower` if it is an electron, positron or photon and `kTrack` otherwise, whatever its size -- @@ -639,7 +640,9 @@ with read_events("output.h5") as store: LE particle touching several things into its own ancestor when that is one of them, so a delta ray goes to its track. A stand-alone piece that defragmentation cut off a `kTrack`, `kMichel` or `kDelta` particle joins - that particle's instance and is not LE. + that particle's instance and is not LE. Optionally, a delta ray that + never gets far from its track joins it too (see + [`delta_min_standoff`](#delta-rays-the-track-hides--delta_min_standoff)). - **Kinematics.** Every row carries its Geant4 particle's `mass` and creation and destruction position and 4-momentum; every instance row also its instance's first and last point, made from the deposits it collected. @@ -785,12 +788,36 @@ About 9% of them. A particle gets a row if it 2. **is an instance representative**, or 3. **lies on the ancestry of one**, including point-less links such as a π⁰ between its two photons. +4. **is a delta ray merged into its track**, by either rule (see + [Delta rays merged into their track](#delta-rays-merged-into-their-track)). Merged shower members get no row — an instance is one object, and its constituents are not individually addressable. **Every point is still stored** regardless: a group's points are its members', whether or not those members have rows, and no point is unreachable from some stored row. +#### Delta rays merged into their track + +A delta ray whose points end up in its parent track's fragment is part of the +track's picture: the points are the track's, and not LE. Two rules put it +there. In every mode, one smaller than `min_pc_size` is typed `kLEScatter` +and `AbsorbLEScatter` merges it into the track it touches. With pixel hits, +`delta_min_standoff` also merges one that never gets far from the track (see +[below](#delta-rays-the-track-hides--delta_min_standoff)). Either way the +delta ray keeps a row, written as **`sem_type == kDelta`**, so +`sem_type == kDelta` selects the delta rays with their own instance and those +in their track alike. A merged one heads no +group (`frag_id`/`inst_id` −1); its own `pc_start`/`pc_end` lie inside its +track's fragment range, and its steps do not count toward the track +instance's `first_*`/`last_*`. A row whose `inst_id` is its own `id` is a +delta ray with its own instance. A delta ray merged into anything else, or +left as an LE blob, stays `kLEScatter` as before. + +These rows add 5% (edepsim) to 9% (pixel) to the particle table, about 490 +and 340 delta rays an event on `sdf_data_03`, and change nothing else: the +points, groups, hit labels and instance kinematics are identical with and +without them. + ### Particle columns | Column | dtype | Meaning | @@ -1329,6 +1356,44 @@ the `group_sizes` fix it was 1.84% — but not zero, so straddled group by majority and reports how many there were. `hit_labels/` is unaffected: pixel hits are labelled one by one, not through their group. +#### Delta rays the track hides — `delta_min_standoff` + +A low-energy delta ray seen through a diffused readout often cannot be told +from the muon it came off: its hits lie on the track or hug it. With +`delta_min_standoff` set to a distance in mm, a delta ray that never gets +that far from its track is merged into it. *Never gets that far* means that +none of the delta ray's voxels (`particle.voxelize.voxel_size`) is +`delta_min_standoff` or more from the nearest voxel of the track's fragment. +Only the *points* move, into the track's fragment and so into its instance. +The delta-ray particle stays `kDelta`, never LE, and keeps its row, +kinematics and its own `pc_start`/`pc_end`, which lie inside the track's +fragment range. It heads no group, so its `frag_id`/`inst_id` are −1, and its +steps do not count toward the track instance's `first_*`/`last_*`. It applies +to non-primary delta rays heading their own fragment, whose parent is in a +track-like fragment and whose fragment holds nothing else but LE and its own +pieces. It is measured on the clouds step 1 groups, so in hit mode on the +voxelized hits: what the readout can show. 0, the default, disables it; +`reader=jaxtpc_pixel` sets 8 mm. Wire and edepsim leave it off, and their delta +rays are decided as before, by `min_pc_size` and `AbsorbLEScatter`. + +It is distance and not a count of voxels off the track because a delta ray +that runs along the track can have many voxels beside it and still be +invisible, while a short one that leaves the track is plain to see. In a hand +scan of event 0 (`sdf_data_03`, pixel, 3 mm voxels), how far a delta ray gets +from its muon matched the call for all 11. The voxels it has off the track +did not: 507 has 19 such voxels, 519 has 21. + +| delta ray | voxels off the track | farthest voxel from it | hand scan | +|---|---|---|---| +| 482, 511, 447, 525, 519 | 0, 4, 6, 12, 21 | 0–6.0 mm | hidden | +| 468, 481 | 13, 19 | 6.7, 7.3 mm | hidden (468 marginal) | +| 507, 479, 461, 480 | 19, 54, 69, 145 | 9.0–20.3 mm | visible | + +So, at a 4.32 mm pitch, about two pixels: 8 mm merges all those called +hidden, 25–30 delta rays an event. It is for hits only. On the truth deposits +the same delta rays are resolved: 519 gets 12.4 mm from the muon, 481 and +525 9.0 mm, so a deposit-mode cut would merge what the image shows. + ### Hit provenance (JAXTPC mode) Which readout hits belong to which reconstructed object. For a label per hit, diff --git a/pysupera/_run.py b/pysupera/_run.py index 3244cdf..e4732b0 100644 --- a/pysupera/_run.py +++ b/pysupera/_run.py @@ -104,7 +104,9 @@ def main(cfg: DictConfig) -> None: from pysupera.io import open_voxmap_writer from pysupera.partitioner import ParticlePartitioner from pysupera.merge import (merge_em_showers, find_split_pieces, - relabel_split_pieces, attach_split_pieces) + relabel_split_pieces, attach_split_pieces, + find_buried_deltas, merge_buried_deltas, + find_absorbed_deltas, relabel_absorbed_deltas) from pysupera.kinematics import step_particles, instance_kinematics from pysupera.config import build_conditions, build_preprocessor, build_merge_processor, build_voxelizer, build_reader, configure from pysupera.preproc import _voxelize_point_cloud @@ -216,6 +218,8 @@ def _n_groups_of(_store): 'pc_size_max': [], # int : largest non-zero PC in event 'pc_size_mean': [], # float: mean non-zero PC size in event 'n_partitions': [], # int: final partition count after partition_combined + 'n_buried_deltas': [], # int: delta-ray fragments merged into their track + 'n_absorbed_deltas': [], # int: LE delta rays in their track, written as kDelta } # JAXTPC visibility counts; stays empty unless the reader supplies them. mask_stats: dict = defaultdict(list) @@ -270,6 +274,12 @@ def _n_groups_of(_store): print(f"[run] {_n_available} events in input file, processing {_n_to_process}") _drop_le_scatter = bool(cfg.get('drop_le_scatter', False)) + _delta_standoff = float(cfg.get('delta_min_standoff', 0) or 0) + from pysupera.config import _classify_voxel_size + _delta_voxel = _classify_voxel_size(cfg) + if _delta_standoff > 0: + print(f"[run] delta rays : merged into their track unless " + f"{_delta_standoff:g} mm or more from it") # Used by drop_le_scatter output filtering. from pysupera.utils import SemanticType as _ST _le_type = _ST.kLEScatter @@ -578,6 +588,12 @@ def _n_groups_of(_store): _pieces = find_split_pieces(fragments, particles) relabel_split_pieces(_pieces) + # A delta ray its track hides: its points join the track's + # fragment; the particle stays kDelta with its own points. + _buried = find_buried_deltas(fragments, particles, _delta_standoff, + _delta_voxel) + fragments = merge_buried_deltas(fragments, _buried, particles) + stats['n_buried_deltas'].append(len(_buried)) # Capture fragment membership NOW. merge_em_showers returns # the surviving representatives themselves as the instances -- @@ -586,6 +602,11 @@ def _n_groups_of(_store): # fragment membership afterwards silently attributes most # particles to the wrong fragment. _frag_groups = snapshot_groups(fragments) + # Delta rays the size rule made LE and proximity put in their + # track: written like the buried ones, as kDelta rows. Found + # now, on the clouds step 1 grouped; relabelled before the + # layout, after everything that reads LE off the particle. + _absorbed = find_absorbed_deltas(_frag_groups, particles) # ── Step-2 EM shower instance merger ─────────────────────── _t = time.perf_counter() @@ -712,6 +733,12 @@ def _n_groups_of(_store): _keep.update(np.unique(_d['fragment_id']).tolist()) _keep.update(np.unique(_d['instance_id']).tolist()) _keep.discard(-1) + # A delta ray merged into its track keeps its own row, as + # kDelta, however it got there. + _keep.update(int(_f.id) for _f, _h, _n in _buried) + relabel_absorbed_deltas(_absorbed) + _keep.update(int(_p.id) for _p in _absorbed) + stats['n_absorbed_deltas'].append(len(_absorbed)) # first_* / last_* per instance, from the steps whose # deposits it collected. _inst_kin = None @@ -810,6 +837,11 @@ def _agg(vals): print(f" {_SEP}") lo, mu, hi = _agg(stats['n_partitions']) print(f" {'Partitions (final)':<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") + if _delta_standoff > 0: + lo, mu, hi = _agg(stats['n_buried_deltas']) + print(f" {'Delta rays into track':<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") + lo, mu, hi = _agg(stats['n_absorbed_deltas']) + print(f" {'LE delta rays in track':<{_W}} {int(lo):>10,} {mu:>10.1f} {int(hi):>10,}") # ── JAXTPC readout visibility (only when the reader reported it) ─── if mask_stats.get('n_total'): diff --git a/pysupera/conf/config.yaml b/pysupera/conf/config.yaml index 7ffe93d..a5d4a2e 100644 --- a/pysupera/conf/config.yaml +++ b/pysupera/conf/config.yaml @@ -155,6 +155,18 @@ output_compact: true # Useful when low-energy scatter products are not needed downstream. drop_le_scatter: false +# Merge a delta ray its track hides into the track. A non-primary kDelta +# fragment none of whose occupied cells (particle.voxelize.voxel_size) is +# this far (mm, cell to cell) or more from its parent track's fragment has +# its points moved into the track's fragment, and so its instance. What +# shows a delta ray is how far it gets from the track, not how many cells +# it has off it. The delta-ray particle stays kDelta, with its own row, +# kinematics and pc_start/pc_end; it is never LE. Its steps do not count +# toward the track instance's first_*/last_*. Measured on the clouds step 1 +# groups (pixel: the hits), so it measures what the readout can show. +# 0 disables; reader=jaxtpc_pixel sets it (readout/jaxtpc_pixel.yaml). +delta_min_standoff: 0 + # Which particle instances to write. # # all — every instance the step-2 merger produced, including the many diff --git a/pysupera/conf/readout/jaxtpc_pixel.yaml b/pysupera/conf/readout/jaxtpc_pixel.yaml index 1c368c0..f264f09 100644 --- a/pysupera/conf/readout/jaxtpc_pixel.yaml +++ b/pysupera/conf/readout/jaxtpc_pixel.yaml @@ -20,6 +20,13 @@ particle: # defragmentation measures the pixel grid instead of the physics. distance_threshold: 6.2 +# A delta ray that never gets this far (mm) from its track is merged into +# it (see config.yaml). Hand-scanned on event 0 of sdf_data_03: every delta +# ray called hidden got at most 7.3 mm from its muon, every visible one at +# least 9.0 mm. About two pixels. Truth deposits resolve delta rays the +# hits do not, so wire and edepsim leave it off. +delta_min_standoff: 8 + # A group's hits can straddle a fragment split here -- the readout leaves # gaps the deposit cloud does not have -- so the invariant the groups/ table # rests on does not hold and the run resolves by majority instead. diff --git a/pysupera/kinematics.py b/pysupera/kinematics.py index 2fd90f9..0dd3e34 100644 --- a/pysupera/kinematics.py +++ b/pysupera/kinematics.py @@ -151,7 +151,10 @@ def instance_kinematics(inst_groups, particles, steps, step_owner, step_pid, if p is None: continue g4 = int(getattr(p, "geant4_id", m)) - if p.sem_type is not _LE or g4 in pair: + # A delta ray merged into its track is part of the track's + # picture, not of its trajectory. + if ((p.sem_type is not _LE + and not getattr(p, "buried_in_track", False)) or g4 in pair): allowed.append(rid * span + g4 + 1) has_track.add(rid) diff --git a/pysupera/merge.py b/pysupera/merge.py index 2e49025..ca7a3ef 100644 --- a/pysupera/merge.py +++ b/pysupera/merge.py @@ -319,6 +319,189 @@ def find_split_pieces(fragments, particles): return out +# --------------------------------------------------------------------------- +# Delta rays buried in their track +# --------------------------------------------------------------------------- + +def _cells(point_cloud, voxel_size): + """The cells *point_cloud* occupies, as (n, 3) coordinates in mm: + voxel corners, or the exact positions when *voxel_size* is None.""" + xyz = np.asarray(point_cloud[:, :3], dtype=np.float64) + if voxel_size: + return np.unique(np.floor(xyz / voxel_size), axis=0) * voxel_size + return np.unique(xyz, axis=0) + + +def find_buried_deltas(fragments, particles, min_standoff, voxel_size=None): + """ + Delta-ray fragments that cannot be told apart from their parent track. + + A delta ray mostly overlaps the track it came from; with a diffused + readout a low-energy one is invisible, its cells all on or hugging the + track. Such a delta ray's *points* belong in the track's fragment. + What makes a delta ray visible is how far it gets from the track, not + how many cells it has off it: a halo along the track can hold many. + This finds the fragments headed by a non-primary ``kDelta`` particle, + not LE, whose parent sits in a different, track-like fragment, and none + of whose occupied cells is *min_standoff* or more from the nearest cell + of that fragment. Every other member of such a fragment must be LE or + a piece of the delta ray, so nothing else is moved with it. + + The delta-ray particle is not reclassified: it stays ``kDelta`` with its + own points (see :func:`merge_buried_deltas`). + + Parameters + ---------- + fragments : list of Particle + Step-1 fragment representatives. + particles : list of Particle + Every particle of the event. + min_standoff : float + Distance in mm, cell to cell, a delta ray must reach from its + track's fragment to keep its own. 0 or less finds nothing. + voxel_size : float or None + Cell size the clouds are counted in; None uses exact positions. + + Returns + ------- + list of tuple + ``(delta_fragment, track_fragment, standoff)`` triples, the + standoff in mm. + """ + if not min_standoff or min_standoff <= 0: + return [] + from scipy.spatial import cKDTree + by_id = {int(p.id): p for p in particles} + frag_of = {} + for f in fragments: + for m in (f.member_ids if f.member_ids is not None else [f.id]): + frag_of[int(m)] = f + g4 = lambda p: int(getattr(p, "geant4_id", p.id)) + trees = {} + out = [] + for f in fragments: + rep = by_id.get(int(f.id)) + if (rep is None or rep.sem_type != SemanticType.kDelta + or f.sem_type != SemanticType.kDelta + or rep.interaction_type == InteractionType.kPrimary): + continue + par = by_id.get(int(rep.parent_id)) if rep.parent_id is not None else None + if par is None or par is rep: + continue + home = frag_of.get(int(par.id)) + if home is None or home is f or home.sem_type not in _TRACK_LIKE: + continue + mids = f.member_ids if f.member_ids is not None else [f.id] + if any(int(m) != int(rep.id) and int(m) in by_id + and by_id[int(m)].sem_type != SemanticType.kLEScatter + and g4(by_id[int(m)]) != g4(rep) for m in mids): + continue + if not len(f.point_cloud) or not len(home.point_cloud): + continue + if id(home) not in trees: + trees[id(home)] = cKDTree(_cells(home.point_cloud, voxel_size)) + standoff = float(trees[id(home)].query(_cells(f.point_cloud, voxel_size))[0].max()) + if standoff < min_standoff: + out.append((f, home, standoff)) + return out + + +def merge_buried_deltas(fragments, found, particles): + """ + Merge each delta-ray fragment of :func:`find_buried_deltas` into its + track's fragment, in place, before the fragments are snapshotted. + + Only the points move: the delta-ray particle keeps its ``kDelta`` type, + its row and its own point range, and is marked ``buried_in_track`` so + that the track instance's first/last points are not drawn from its + steps. A delta ray of a buried delta ray follows it into the track. + + Returns + ------- + list of Particle + *fragments* without the merged ones. + """ + by_id = {int(p.id): p for p in particles} + gone = {} + root = lambda h: root(gone[id(h)]) if id(h) in gone else h + for f, home, _ in found: + h = root(home) + if h is f: + continue + h.member_ids = (list(h.member_ids if h.member_ids is not None else [h.id]) + + list(f.member_ids if f.member_ids is not None else [f.id])) + clouds = [c for c in (h.point_cloud, f.point_cloud) if len(c)] + if len(clouds) > 1: + h.point_cloud = np.concatenate(clouds, axis=0) + elif clouds: + h.point_cloud = clouds[0] + gone[id(f)] = h + by_id[int(f.id)].buried_in_track = True + return [f for f in fragments if id(f) not in gone] + + +def find_absorbed_deltas(frag_groups, particles): + """ + Delta rays that the size rule made LE and proximity put in their track. + + A delta ray smaller than ``min_pc_size`` is typed ``kLEScatter`` and, + touching its track, ``AbsorbLEScatter`` merges it there. Its points + are the track's either way; this finds such particles so that, like the + ones :func:`merge_buried_deltas` moves, they can be written as + ``kDelta`` rows with their own points. A delta ray qualifies when it + has points and sits in the same fragment as its parent, and that + fragment is track-like. A piece of the parent (same Geant4 track) is + not a delta ray of it and is left out. + + Parameters + ---------- + frag_groups : list of Group + Step-1 fragments, from :func:`~pysupera.layout.snapshot_groups`. + particles : list of Particle + Every particle of the event, carrying the clouds step 1 grouped. + + Returns + ------- + list of Particle + """ + by_id = {int(p.id): p for p in particles} + frag_of, sem_of = {}, {} + for g in frag_groups: + sem_of[g.rep_id] = g.sem_type + for m in g.member_ids: + frag_of[int(m)] = g.rep_id + track_like = {t.value for t in _TRACK_LIKE} + g4 = lambda p: int(getattr(p, "geant4_id", p.id)) + out = [] + for p in particles: + if (p.interaction_type != InteractionType.kDelta + or p.sem_type != SemanticType.kLEScatter + or not len(p.point_cloud)): + continue + par = by_id.get(int(p.parent_id)) if p.parent_id is not None else None + if par is None or par is p or g4(par) == g4(p): + continue + f = frag_of.get(int(p.id)) + if (f is None or f == int(p.id) or frag_of.get(int(par.id)) != f + or sem_of[f] not in track_like): + continue + out.append(p) + return out + + +def relabel_absorbed_deltas(deltas): + """ + Type each delta ray of :func:`find_absorbed_deltas` ``kDelta``, in place, + and mark it ``buried_in_track``. Call it after step 2, before the + layout: the fragment decides LE, so the points stay the track's, and + the mark keeps its steps out of the track instance's first/last points, + as its LE type did. + """ + for p in deltas: + p.sem_type = SemanticType.kDelta + p.buried_in_track = True + + def relabel_split_pieces(pairs): """ Give each piece fragment its track fragment's semantic type, in place. diff --git a/tests/test_instance_rules.py b/tests/test_instance_rules.py index bf974f7..79fbeb9 100644 --- a/tests/test_instance_rules.py +++ b/tests/test_instance_rules.py @@ -15,8 +15,10 @@ from pysupera.conditions.absorb_le_scatter import AbsorbLEScatter from pysupera.kinematics import instance_kinematics, step_particles from pysupera.layout import Group, fragment_le, snapshot_groups -from pysupera.merge import (attach_split_pieces, find_split_pieces, - merge_em_showers, relabel_split_pieces) +from pysupera.merge import (attach_split_pieces, find_absorbed_deltas, + find_buried_deltas, find_split_pieces, + merge_buried_deltas, merge_em_showers, + relabel_absorbed_deltas, relabel_split_pieces) from pysupera.utils import SemanticType as ST from tests.conftest import ( make_particle, make_partitioner, @@ -155,6 +157,162 @@ def test_in_the_partitioner(self): assert prt.rep_lookup[3] is prt.rep_lookup[1] +# --------------------------------------------------------------------------- +# Delta rays buried in their track +# --------------------------------------------------------------------------- + +class TestBuriedDeltaRays: + """Only the points move: the delta ray stays kDelta, with its own row.""" + + @staticmethod + def event(delta_cloud): + mu = make_particle(1, PT_TRACK, pdg=13, pc=line(0, 120, n=41)) + d = make_particle(2, PT_DELTA, pdg=11, parent_id=1, parent_pdg=13, + ancestor_id=1, pc=delta_cloud) + assert d.sem_type == ST.kDelta + return mu, d + + def test_a_hidden_delta_ray_joins_its_tracks_fragment(self): + # 8 cells on the muon's line, 2 one cell (3 mm) off it. + mu, d = self.event(np.r_[line(30, 51, n=8), line(60, 63, n=2, y=4.0)]) + frags = fragments(mu, d) + found = find_buried_deltas(frags, [mu, d], 8.0, voxel_size=3.0) + assert [(f.id, h.id, x) for f, h, x in found] == [(2, 1, 3.0)] + frags = merge_buried_deltas(frags, found, [mu, d]) + assert [f.id for f in frags] == [1] and mu.member_ids == [1, 2] + assert d.sem_type == ST.kDelta and d.buried_in_track + groups = snapshot_groups(frags) + assert fragment_le([mu, d], groups) == {1: False, 2: False} + insts = merge_em_showers(frags) + assert len(insts) == 1 and sorted(insts[0].member_ids) == [1, 2] + + def test_it_keeps_its_row_and_its_own_points(self): + import copy + from pysupera.layout import build_layout + mu, d = self.event(np.r_[line(30, 51, n=8), line(60, 63, n=2, y=4.0)]) + frags = fragments(copy.copy(mu), copy.copy(d)) # step 1's copies + found = find_buried_deltas(frags, [mu, d], 8.0, voxel_size=3.0) + frags = merge_buried_deltas(frags, found, [mu, d]) + fg = snapshot_groups(frags) + ig = snapshot_groups(merge_em_showers(frags)) + lay = build_layout([mu, d], fg, ig, keep={2}) + c = lay.columns + r = {int(i): k for k, i in enumerate(c["id"])} + assert set(r) == {1, 2} + assert c["sem_type"][r[2]] == ST.kDelta.value + # It heads no group (frag_id/inst_id are set on heads only); its + # points lie in the track's fragment, which holds them all. + assert c["frag_id"][r[2]] == -1 and c["inst_id"][r[2]] == -1 + assert c["pc_end"][r[2]] - c["pc_start"][r[2]] == len(d.point_cloud) + assert c["pc_end"][r[1]] - c["pc_start"][r[1]] == len(mu.point_cloud) + lo, hi = c["frag_pc_start"][r[1]], c["frag_pc_end"][r[1]] + assert lo <= c["pc_start"][r[2]] < c["pc_end"][r[2]] <= hi + assert hi - lo == len(mu.point_cloud) + len(d.point_cloud) + # Without keep it would have no row: it heads no group any more. + assert 2 not in build_layout([mu, d], fg, ig).columns["id"].tolist() + + def test_a_visible_one_keeps_its_own_fragment(self): + # 3 cells (9 mm) off the muon. + mu, d = self.event(np.r_[line(30, 51, n=8), line(60, 90, n=11, y=9.0)]) + frags = fragments(mu, d) + assert find_buried_deltas(frags, [mu, d], 8.0, voxel_size=3.0) == [] + assert find_buried_deltas(frags, [mu, d], 10.0, voxel_size=3.0) != [] + + def test_distance_decides_not_the_number_of_cells_off_the_track(self): + # A halo: 30 cells beside the muon, none more than a cell from it. + mu, halo = self.event(line(0, 90, n=31, y=3.0)) + assert find_buried_deltas(fragments(mu, halo), [mu, halo], 8.0, 3.0) != [] + # A spur: 2 cells, but 3 cells (9 mm) out. + mu, spur = self.event(np.r_[line(30, 51, n=8), line(60, 60, n=1, y=6.0), + line(60, 60, n=1, y=9.0)]) + assert find_buried_deltas(fragments(mu, spur), [mu, spur], 8.0, 3.0) == [] + + def test_exact_positions_without_voxels(self): + mu, d = self.event(np.r_[line(30, 51, n=8), line(60, 60, n=1, y=7.0)]) + assert find_buried_deltas(fragments(mu, d), [mu, d], 8.0) != [] + assert find_buried_deltas(fragments(mu, d), [mu, d], 6.0) == [] + + def test_off_by_default(self): + mu, d = self.event(line(30, 51, n=8)) + assert find_buried_deltas(fragments(mu, d), [mu, d], 0, 3.0) == [] + + def test_a_fragment_holding_another_non_le_particle_stays(self): + mu, d = self.event(line(30, 51, n=8)) + other = make_particle(3, PT_TRACK, pdg=2212, pc=line(30, 90, n=30, y=3.0)) + mu.member_ids, d.member_ids = [1], [2, 3] + assert find_buried_deltas([mu, d], [mu, d, other], 8.0, 3.0) == [] + + def test_its_steps_do_not_draw_the_tracks_last_point(self): + from pysupera.kinematics import instance_kinematics + mu, d = self.event(line(30, 51, n=8)) + frags = fragments(mu, d) + frags = merge_buried_deltas( + frags, find_buried_deltas(frags, [mu, d], 8.0, 3.0), [mu, d]) + mu.geant4_id, d.geant4_id = 10, 20 + steps = np.zeros(3, dtype=[("x", "f4"), ("y", "f4"), ("z", "f4"), + ("t", "f4"), ("px", "f4"), ("py", "f4"), + ("pz", "f4"), ("p", "f4"), ("de", "f4"), + ("dx", "f4"), ("theta", "f4"), + ("phi", "f4")]) + steps["t"] = [1.0, 2.0, 9.0] # the delta ray's step is last + steps["x"] = [0.0, 120.0, 40.0] + steps["dx"] = 0.1 + kin = {"geant4_trackid": np.array([10, 20]), "mass": np.array([105.7, 0.511]), + "start_mom": np.zeros((2, 4))} + got = instance_kinematics(snapshot_groups(merge_em_showers(frags)), + [mu, d], steps, np.array([0, 0, 1]), + np.array([1, 1, 2]), kin) + assert got[1]["last_pos"][3] < 9.0 + + +class TestAbsorbedDeltaRays: + """A delta ray the size rule made LE and its track absorbed is written as + the buried ones are: a kDelta row with its own points, in the track.""" + + @staticmethod + def event(): + mu = make_particle(1, PT_TRACK, pdg=13, pc=line(0, 120, n=41)) + d = make_particle(2, PT_DELTA, pdg=11, parent_id=1, parent_pdg=13, + ancestor_id=1, pc=line(60, 63, n=2, y=3.0)) + d.sem_type = ST.kLEScatter # what min_pc_size makes it + return mu, d + + def test_it_is_written_as_a_kdelta_row_in_its_track(self): + from pysupera.layout import build_layout + mu, d = self.event() + mu.member_ids, d.member_ids = [1, 2], [2] # AbsorbLEScatter's doing + fg = snapshot_groups([mu]) + found = find_absorbed_deltas(fg, [mu, d]) + assert found == [d] + ig = snapshot_groups(merge_em_showers([mu])) + relabel_absorbed_deltas(found) + assert d.sem_type == ST.kDelta and d.buried_in_track + assert fragment_le([mu, d], fg) == {1: False, 2: False} + c = build_layout([mu, d], fg, ig, keep={2}).columns + r = {int(i): k for k, i in enumerate(c["id"])} + assert c["sem_type"][r[2]] == ST.kDelta.value + assert c["frag_id"][r[2]] == -1 and c["inst_id"][r[2]] == -1 + assert c["pc_end"][r[2]] - c["pc_start"][r[2]] == 2 + assert c["frag_pc_start"][r[1]] <= c["pc_start"][r[2]] + assert c["pc_end"][r[2]] <= c["frag_pc_end"][r[1]] + + def test_one_left_on_its_own_stays_le(self): + mu, d = self.event() + assert find_absorbed_deltas(snapshot_groups(fragments(mu, d)), [mu, d]) == [] + + def test_one_absorbed_by_another_track_stays_le(self): + mu, d = self.event() + pi = make_particle(3, PT_TRACK, pdg=211, pc=line(0, 90, n=31, y=6.0)) + mu.member_ids, pi.member_ids = [1], [3, 2] + assert find_absorbed_deltas(snapshot_groups([mu, pi]), [mu, d, pi]) == [] + + def test_a_piece_of_the_track_is_not_a_delta_ray(self): + mu, d = self.event() + mu.geant4_id = d.geant4_id = 10 + mu.member_ids = [1, 2] + assert find_absorbed_deltas(snapshot_groups([mu]), [mu, d]) == [] + + # --------------------------------------------------------------------------- # Split-off pieces of tracks # --------------------------------------------------------------------------- diff --git a/tests/test_reader_configs.py b/tests/test_reader_configs.py index 5e0c0cb..2450b7b 100644 --- a/tests/test_reader_configs.py +++ b/tests/test_reader_configs.py @@ -128,6 +128,7 @@ def test_the_pixel_reader_brings_the_hit_mode_settings(): assert cfg.particle.voxelize.store == "voxels" # 3 mm truth voxels assert cfg.distance_threshold > 4.32 * 2 ** 0.5 assert cfg.check_group_ownership is False + assert cfg.delta_min_standoff == 8 @pytest.mark.parametrize("name", ("edepsim_h5", "jaxtpc_wire")) @@ -137,6 +138,7 @@ def test_every_other_reader_keeps_the_truth_defaults(name): assert cfg.particle.voxelize.store == "voxels" assert cfg.distance_threshold == 5.2 assert cfg.check_group_ownership is True + assert cfg.delta_min_standoff == 0 # delta rays as before def test_switching_the_pixel_reader_to_deposits_is_refused(): From 36f56b54d67cba2b07aef60a4f40330c82dc16a9 Mon Sep 17 00:00:00 2001 From: drinkingkazu Date: Sat, 17 Oct 2026 12:35:22 -0700 Subject: [PATCH 53/62] vis: pin what you click, toggle hover, pan; vis_hits gets vis_drift's colouring and filters Following one object through a busy event by hover alone is fragile: the highlight moves as soon as the cursor does. All three viewers now let you pin objects by clicking: click pin the object under the cursor; click more to pin several together, click a pinned one to unpin. While anything is pinned, hovering changes nothing. x / Esc / button unpin everything h / checkbox hover highlighting on or off p / checkbox left-drag pans instead of rotating; right-drag and shift-drag always pan, and so do the arrow keys (shift+arrows in vis_force) A drag never pins, so rotating does not change what is lit. vis_force.html pins instances in the shader (uPins, up to 8 at once). Its toolbar gains HOVER, UNPIN and PAN, and its help lists the keys. vis_hits.html (wire) was behind vis_drift.html, so it is rebuilt on vis_drift's point-set model, keeping its six plane images. New in it: - colour by energy/charge, semantic type, fragment, instance, interaction or PDG, separate from what a hover highlights (highlight: instance, fragment or interaction; it was "link by") - the semantic-type strip, turning each type on or off in every view - log scale, and the show >= percentile cut - a legend The energy ramp and the cut are one for the voxels (dE, MeV) and one shared by all planes (hit charge), so the planes compare. LE filtering now hides points as it draws instead of rebuilding the event, and the pickers skip whatever is hidden. Colours are worked out once per colouring or filter change, not on every hover, since a plane holds ~280k hits. Plane headers give how many hits are shown and how many are highlighted. Checked in the browser on 3 wire events (sdf_data_03_wire, 29k voxels and 1.70M hits in event 0): - colour by semantic type, with shower and LE hidden - hover on a hit (instance 69, a track) - pinning 69 and 76 together, lighting both in the 3-D view and on all six planes - x, h and p - energy colouring with the 50% cut There were no console errors. vis_drift and vis_force were checked the same way. The README documents vis_hits' controls, with one Highlighting table for both JAXTPC viewers. --- README.md | 44 ++- vis_drift.html | 79 +++++- vis_force.html | 101 ++++++- vis_hits.html | 739 +++++++++++++++++++++++++++++++++---------------- 4 files changed, 690 insertions(+), 273 deletions(-) diff --git a/README.md b/README.md index 8b41c1f..fb5362e 100644 --- a/README.md +++ b/README.md @@ -261,26 +261,23 @@ Shortcuts: `c` centre · `a` axes · `s` screenshot · `space` play/pause · Left: the 3-D cloud of `points/flat` (3 mm truth voxels). Right: one 2-D image per `hit_labels/volume{V}/plane{P}` group, stacked in rows — six for a two-volume, three-plane detector — each wire against drift tick, one pixel per -hit. The same colour means the same object everywhere. +hit. **Hover anywhere and the object under the cursor lights up in every view**: a voxel highlights its hits on all six planes, and a hit highlights its voxels in 3-D and its hits on the other planes. The link is the label itself — the voxel's range in `points/flat` and the hit's `instance_id` / `fragment_id` in -`hit_labels` — so the views cannot disagree. - -| control | | -|---|---| -| **highlight** | instance, fragment or interaction — what "the object under the cursor" means | -| **others** | opacity of everything not selected | -| **LE** | show all, hide LE, or hide non-LE: drops the chosen voxels and hits from every view, so the picker cannot select them | -| **point info** | position, t, dE, dX, θ, φ, \|p\| and labels for a voxel; volume, plane, wire, tick, charge and labels for a hit | -| **c** | fit the 3-D view | - -Plane headers give the hit count, wire and tick range and how many hits are -highlighted. A red banner appears instead of a wrong picture for a pixel file, -an output without `hit_labels`, a subset without per-hit rows, or labels and -hits of different lengths. +`hit_labels` — so the views cannot disagree. Click to pin, as in +`vis_drift.html` (see [Highlighting](#highlighting)). + +Colouring, the `highlight` selector, the semantic-type strip, **LE**, **show +≥** and log scale work as in `vis_drift.html` below. The energy ramp and the +**show ≥** cut are one for the voxels (dE, MeV) and one shared by all planes +(hit charge), so the planes compare. Plane headers give the hit count, how +many are shown, the wire and tick range and how many are highlighted. A red +banner appears instead of a wrong picture for a pixel file, an output without +`hit_labels`, a subset without per-hit rows, or labels and hits of different +lengths. **point info** turns the hover panel off. #### `vis_drift.html` — JAXTPC pixel @@ -317,6 +314,23 @@ the absolute value it lands on. By default only on-sensor hits are drawn — the ones the model sees; **off-sensor hits** adds the rest, which carry no label (`-1`) and are drawn grey. +#### Highlighting + +In `vis_drift.html` and `vis_hits.html`: + +| | | +|---|---| +| hover | lights the object under the cursor in every view | +| click | pins it; click more to pin several together, click a pinned one to unpin. While anything is pinned, hovering changes nothing | +| `x`, `Esc`, **clear highlight** | unpins everything | +| `h`, **hover highlight** | turns hover highlighting off and on | +| right-drag, shift-drag | pans a 3-D view; zoom with the wheel | +| `p`, **left-drag pans** | makes left-drag pan instead of rotate | +| arrows (shift: faster) | pan the 3-D view under the cursor | +| `c`, **fit** | frames the 3-D view on the event | + +A drag never pins, so rotating does not change what is lit. + #### What the energy column holds In `points/flat` it is always true dE in MeV: with JAXTPC input the stored diff --git a/vis_drift.html b/vis_drift.html index 7ba8fa7..4a8e018 100644 --- a/vis_drift.html +++ b/vis_drift.html @@ -65,7 +65,10 @@ - load the output and its hits subset — hover a point to follow it across both views + + + + load the output and its hits subset — hover a point to follow it across both views · click to pin, click again to unpin · pan: right-drag, shift-drag or arrows
show semantic type:
@@ -109,6 +112,7 @@ out:null, hit:null, ev:0, nEvents:0, T:null, H:null, // the two point sets; see pointSet() sel:-1, dim:0.03, thrPct:0, + pins:new Set(), hoverHL:true, // clicked ids stay lit together; hover lights one mode:'inst', hl:'inst', cols:null, idRow:null, // particle columns for the event, id -> row semShow:new Set(), leMode:'all', @@ -248,7 +252,10 @@ document.getElementById('ev').onchange = e => { S.ev = +e.target.value; rebuild(); }; document.getElementById('mode').onchange = e => { S.mode = e.target.value; recolour(); }; -document.getElementById('hl').onchange = e => { S.hl = e.target.value; S.sel = -1; buildKeys(); recolour(); }; +document.getElementById('hl').onchange = e => { S.hl = e.target.value; S.sel = -1; S.pins.clear(); buildKeys(); recolour(); }; +document.getElementById('hoverhl').onchange = e => setHoverHL(e.target.checked); +document.getElementById('unpin').onclick = () => clearPins(); +document.getElementById('panmode').onchange = e => setPanMode(e.target.checked); document.getElementById('logE').onchange = () => recolour(); document.getElementById('offsensor').onchange = () => rebuild(); document.getElementById('lemode').onchange = e => { S.leMode = e.target.value; recolour(); }; @@ -261,7 +268,13 @@ }; document.getElementById('thr').oninput = e => { S.thrPct = +e.target.value; recolour(); }; document.getElementById('fit').onclick = () => fitView(); -addEventListener('keydown', e => { if(e.key === 'c') fitView(); }); +addEventListener('keydown', e => { + if(e.target.tagName === 'INPUT' || e.target.tagName === 'SELECT') return; + if(e.key === 'c') fitView(); + if(e.key === 'h') setHoverHL(!S.hoverHL); + if(e.key === 'x' || e.key === 'Escape') clearPins(); + if(e.key === 'p') setPanMode(!document.getElementById('panmode').checked); +}); // ------------------------------------------------------ the two point sets -- // A point set is {name, n, pts (xyz), e (energy column), inst, frag (owner @@ -459,26 +472,34 @@ } } +// What is lit: the pinned ids when there are any, else the hovered one (when +// hover highlighting is on); null when nothing is. +let isLit = null; +function updateLit(){ + if(S.pins.size){ const p = S.pins; isLit = k => p.has(k); } + else if(S.hoverHL && S.sel >= 0){ const k0 = S.sel; isLit = k => k === k0; } + else isLit = null; +} function paintSet(v, P){ if(!v || !v.cloud || !P.n) return null; // The ramp sorts every value, so it is computed when the colouring or the // filter changes, not on every hover. if(!P.ramp) P.ramp = energyRamp(P); - const ramp = P.ramp, dimming = S.sel >= 0; + const ramp = P.ramp, dimming = isLit !== null; const col = v.cloud.geometry.attributes.color.array; let nSel = 0; const rgb = new Float32Array(P.n * 3); for(let i = 0; i < P.n; i++){ const [r, g, b] = colourOf(P, i, ramp); rgb[i*3] = r; rgb[i*3+1] = g; rgb[i*3+2] = b; - const picked = dimming && P.visible[i] && P.key[i] === S.sel; if(picked) nSel++; + const picked = dimming && P.visible[i] && isLit(P.key[i]); if(picked) nSel++; col[i*4] = r; col[i*4+1] = g; col[i*4+2] = b; col[i*4+3] = !P.visible[i] ? 0 : picked ? 0 : (dimming ? S.dim : 1); } v.cloud.geometry.attributes.color.needsUpdate = true; const sp = new Float32Array(nSel * 3), sc = new Float32Array(nSel * 3); let k = 0; if(nSel) for(let i = 0; i < P.n; i++){ - if(!(P.visible[i] && P.key[i] === S.sel)) continue; + if(!(P.visible[i] && isLit(P.key[i]))) continue; for(let d = 0; d < 3; d++){ sp[k*3+d] = P.pts[i*3+d]; sc[k*3+d] = rgb[i*3+d]; } k++; } const g = v.selCloud.geometry; @@ -488,6 +509,7 @@ return {ramp, nSel}; } function paint(){ + updateLit(); const t = paintSet(V.tru, S.T), h = paintSet(V.hit, S.H); drawLegend(t ? t.ramp : null, h ? h.ramp : null); return {t, h}; @@ -539,6 +561,14 @@ host.addEventListener('pointerenter', () => { ACTIVE = v; }); v.raycaster = new THREE.Raycaster(); v.pointer = new THREE.Vector2(); v.renderer.domElement.addEventListener('mousemove', e => onMove(v, e)); + if(document.getElementById('panmode').checked) v.controls.mouseButtons.LEFT = THREE.MOUSE.PAN; + // A click pins; a drag (rotate, pan) does not. + let down = null; + v.renderer.domElement.addEventListener('pointerdown', e => { down = e.button === 0 ? [e.clientX, e.clientY] : null; }); + v.renderer.domElement.addEventListener('pointerup', e => { + if(!down || Math.hypot(e.clientX - down[0], e.clientY - down[1]) > 4) return; + down = null; const k = pickKey(v, e); if(k !== null) togglePin(k); + }); v.controls.addEventListener('change', () => syncFrom(v)); const resize = () => { const s = hostSize(host); v.camera.aspect = s.w/s.h; v.camera.updateProjectionMatrix(); v.renderer.setSize(s.w, s.h, false); }; @@ -617,6 +647,16 @@ } } +function pickKey(v, e){ + const P = v.set(); if(!v.cloud || !P || !P.n) return null; + const r = v.renderer.domElement.getBoundingClientRect(); + v.pointer.x = ((e.clientX - r.left)/r.width)*2 - 1; v.pointer.y = -((e.clientY - r.top)/r.height)*2 + 1; + v.raycaster.setFromCamera(v.pointer, v.camera); + for(const h of v.raycaster.intersectObject(v.cloud, false)) + if(P.visible[h.index]) return P.key[h.index]; + return null; +} + // Raycasting a few million points is not free, so pick at most every 50 ms. let lastPick = 0; function onMove(v, e){ @@ -692,6 +732,7 @@ key = Number(key); if(key === S.sel) return; S.sel = key; + if(S.pins.size || !S.hoverHL) return; // the hover does not change what is lit const {t, h} = paint(); const lvl = S.hl === 'frag' ? 'fragment' : S.hl === 'inter' ? 'interaction' : 'instance'; statusEl.innerHTML = key < 0 ? `not traced to a particle` @@ -699,12 +740,36 @@ ` hits ${(h ? h.nSel : 0).toLocaleString()}`; } +// Click (not drag) pins the id under the cursor, or unpins it. +function togglePin(key){ + key = Number(key); if(key < 0) return; + if(S.pins.has(key)) S.pins.delete(key); else S.pins.add(key); + showPins(); +} +function clearPins(){ S.pins.clear(); showPins(); } +function showPins(){ + const {t, h} = paint(); + const lvl = S.hl === 'frag' ? 'fragment' : S.hl === 'inter' ? 'interaction' : 'instance'; + if(!S.pins.size){ statusEl.innerHTML = `nothing pinned`; return; } + statusEl.innerHTML = `pinned ${lvl}${S.pins.size > 1 ? 's' : ''} ${[...S.pins].join(', ')}` + + ` truth voxels ${(t ? t.nSel : 0).toLocaleString()}` + + ` hits ${(h ? h.nSel : 0).toLocaleString()}` + + `\nclick again to unpin · x clears`; +} +function setPanMode(on){ + document.getElementById('panmode').checked = on; + for(const v of Object.values(V)) if(v) v.controls.mouseButtons.LEFT = on ? THREE.MOUSE.PAN : THREE.MOUSE.ROTATE; +} +function setHoverHL(on){ + document.getElementById('hoverhl').checked = on; S.hoverHL = on; paint(); +} + // ----------------------------------------------------------------- rebuild -- function rebuild(){ if(!S.out) return; if(!V.tru) V.tru = makeView('pane-true', () => S.T); if(!V.hit) V.hit = makeView('pane-hit', () => S.H); - S.sel = -1; + S.sel = -1; S.pins.clear(); try{ buildParticles(); buildTruth(); buildHits(); buildKeys(); buildFilters(); applyFilter(); diff --git a/vis_force.html b/vis_force.html index bd9eb53..d58174f 100644 --- a/vis_force.html +++ b/vis_force.html @@ -754,6 +754,9 @@
+ + + @@ -927,6 +930,11 @@

⌨ Keyboard Shortcuts

Previous event←
Next event→
Center cameraC
+
Pan cameraright-drag · shift-drag · shift+arrows
+
Left-drag pans (toggle)P
+
Pin / unpin instanceclick
+
Clear pinnedX
+
Hover highlight on/offH
Toggle axesA
ScreenshotS
Play / pause animSpace
@@ -1018,6 +1026,7 @@

⌨ Keyboard Shortcuts

const FRAG_SHADER = ` uniform float uCmap,uOpacity,uTimeThresh,uAnimUseNorm,uEnergyThresh,uHighlightId,uHighlightInstId,uDiscreteColor,uInstPtThresh,uViewMode; +uniform float uPins[8],uNPins; varying float vN,vTime,vEnergy,vIntId,vInstId,vRawVal,vInstPts,vPdg; vec3 viridis(float t){vec3 c0=vec3(.277,.0054,.334),c1=vec3(.105,1.404,1.384),c2=vec3(-.331,.215,.095),c3=vec3(-4.63,-5.80,-19.33),c4=vec3(6.23,14.18,56.69),c5=vec3(4.78,-13.74,-65.35),c6=vec3(-5.44,4.65,26.31);return clamp(c0+t*(c1+t*(c2+t*(c3+t*(c4+t*(c5+t*c6))))),0.,1.);} vec3 plasma(float t){vec3 c0=vec3(.059,.023,.543),c1=vec3(2.177,.238,.754),c2=vec3(-2.689,-7.456,3.110),c3=vec3(6.130,42.35,-28.52),c4=vec3(-11.11,-82.67,60.14),c5=vec3(10.02,71.41,-54.07),c6=vec3(-3.66,-22.93,18.19);return clamp(c0+t*(c1+t*(c2+t*(c3+t*(c4+t*(c5+t*c6))))),0.,1.);} @@ -1040,6 +1049,13 @@

⌨ Keyboard Shortcuts

else if(uCmap<3.5) col=hot(vN); else if(uCmap<4.5) col=cool(vN); else col=vec3(vN); + // Pinned instances (click) stay lit together, over any hover highlight. + if(uNPins>0.5){ + bool pin=false; + for(int k=0;k<8;k++){if(float(k)=0.0){ bool si=abs(vInstId-uHighlightInstId)<0.5; bool sx=uHighlightId>=0.0&&abs(vIntId-uHighlightId)<0.5; @@ -1062,6 +1078,7 @@

⌨ Keyboard Shortcuts

anim:{playing:false,tMin:0,tMax:1,dur:30,t0wall:0,_pausedFrac:0,normMode:0,spread:1.0,drift:{on:false,speed:0.0016}}, rot:{on:false,secPerRev:30}, logScale:false, eThr:0, hist:null, highlight:-1, highlightLocked:false, interactions:[], + pins:[], hoverHL:true, _hoverInst:-1, adaptiveZoom:true, _savedCam:null, _camLerp:null, _lockedInter:null, statsDet:null, instGraph:null, instHighlight:-1, instLocked:-1, instSemFilter:null, instPtThresh:0, _instCountMap:null, _lockedDetHTML:null, _cloudHoverIntId:-1, _cloudHoverInstId:-1, _curGraphParams:null, @@ -1188,7 +1205,7 @@

⌨ Keyboard Shortcuts

geo.setAttribute('pdg',new THREE.BufferAttribute(pdgBuf||new Float32Array(N),1)); geo.computeBoundingBox(); geo.computeBoundingSphere(); const mat=new THREE.ShaderMaterial({ - uniforms:{uColorCol:{value:S.col},uMin:{value:0},uMax:{value:1},uSize:{value:S.size},uCmap:{value:S.cmap},uOpacity:{value:S.opacity},uTimeThresh:{value:ANIM_THRESH_MAX},uAnimUseNorm:{value:0},uLogScale:{value:0},uEnergyThresh:{value:S.eThr},uHighlightId:{value:-1},uHighlightInstId:{value:-1},uDiscreteColor:{value:0},uInstPtThresh:{value:S.instPtThresh},uViewMode:{value:0},uDriftSpeed:{value:0},uBboxXMin:{value:S.bbox.xMin},uBboxXMax:{value:S.bbox.xMax}}, + uniforms:{uColorCol:{value:S.col},uMin:{value:0},uMax:{value:1},uSize:{value:S.size},uCmap:{value:S.cmap},uOpacity:{value:S.opacity},uTimeThresh:{value:ANIM_THRESH_MAX},uAnimUseNorm:{value:0},uLogScale:{value:0},uEnergyThresh:{value:S.eThr},uHighlightId:{value:-1},uHighlightInstId:{value:-1},uPins:{value:_pinArr()},uNPins:{value:S.pins.length},uDiscreteColor:{value:0},uInstPtThresh:{value:S.instPtThresh},uViewMode:{value:0},uDriftSpeed:{value:0},uBboxXMin:{value:S.bbox.xMin},uBboxXMax:{value:S.bbox.xMax}}, vertexShader:VERT_SHADER,fragmentShader:FRAG_SHADER,transparent:true,depthWrite:false, }); return {pts:new THREE.Points(geo,mat),n:N,f32}; @@ -1327,7 +1344,7 @@

⌨ Keyboard Shortcuts

resetAnim(); logMsg('Loading event '+idx+'…'); clearObjects(); S.highlight=-1; S.highlightLocked=false; S.instHighlight=-1; S.instLocked=-1; - S._curGraphParams=null; + S._curGraphParams=null; S.pins=[]; _setInstHighlight(-1); _hideLockedBar(); let _nleF32, _leF32, _pdgMap=null; try{ @@ -2570,6 +2587,60 @@

⌨ Keyboard Shortcuts

function setStatus(msg){document.getElementById('status').textContent=msg;} +// ── Pinned instances, hover highlight, pan mode ── +// Clicking a track pins its instance; pinned instances stay lit together, so +// a delta ray and its parent can be seen at once. While any is pinned the +// hover only reads coordinates. +function _pinArr(){const a=new Array(8).fill(-1);(S.pins||[]).forEach((v,k)=>{if(k<8)a[k]=v;});return a;} +function _hoverLights(){return S.hoverHL&&!S.pins.length;} +function _clearHover(){ + if(S._cloudHoverInstId>=0){S._cloudHoverInstId=-1;_setInstHighlight(-1);} + if(S._cloudHoverIntId>=0&&!S.highlightLocked){ + S._cloudHoverIntId=-1; _setHighlight(-1); + const _b=document.getElementById('ip-bubbles'); + if(_b) _b.querySelectorAll('.ip-bub').forEach(e=>e.classList.remove('dimmed','active')); + const _det=document.getElementById('ip-detail'); + if(_det) _det.innerHTML=S.statsDet||''; + } +} +function _applyPins(){ + for(const o of[objNLE,objLE]){ + if(!o) continue; + o.pts.material.uniforms.uPins.value=_pinArr(); + o.pts.material.uniforms.uNPins.value=Math.min(S.pins.length,8); + } + if(S.pins.length) _clearHover(); + document.getElementById('btn-unpin').classList.toggle('active',S.pins.length>0); + setStatus(S.pins.length?'Pinned instance'+(S.pins.length>1?'s ':' ')+S.pins.join(', ')+' — click again to unpin, X clears' + :'Nothing pinned'); +} +function togglePin(instId){ + if(instId<0) return; + const k=S.pins.indexOf(instId); + if(k>=0) S.pins.splice(k,1); else{S.pins.push(instId); if(S.pins.length>8) S.pins.shift();} + _applyPins(); +} +function clearPins(){if(S.pins.length){S.pins=[];_applyPins();}} +function setHoverHL(on){ + S.hoverHL=on; document.getElementById('btn-hoverhl').classList.toggle('active',on); + if(!on) _clearHover(); +} +function setPanMode(on){ + controls.mouseButtons.LEFT=on?THREE.MOUSE.PAN:THREE.MOUSE.ROTATE; + document.getElementById('btn-pan').classList.toggle('active',on); +} +const PAN_KEYS={ArrowLeft:[-1,0],ArrowRight:[1,0],ArrowUp:[0,1],ArrowDown:[0,-1]}; +function panView(dxPix,dyPix){ + const el=renderer.domElement; + const dist=camera.position.distanceTo(controls.target); + const half=dist*Math.tan(camera.fov/2*Math.PI/180); + const sx=2*dxPix*half/Math.max(1,el.clientHeight), sy=2*dyPix*half/Math.max(1,el.clientHeight); + camera.updateMatrix(); + const m=camera.matrix.elements; + const d=new THREE.Vector3(m[0],m[1],m[2]).multiplyScalar(-sx).add(new THREE.Vector3(m[4],m[5],m[6]).multiplyScalar(sy)); + camera.position.add(d); controls.target.add(d); controls.update(); +} + function logMsg(msg,cls=''){ const el=document.getElementById('log-text'); if(!el) return; const t=new Date().toLocaleTimeString('en-US',{hour12:false,hour:'2-digit',minute:'2-digit',second:'2-digit'}); @@ -3289,6 +3360,17 @@

⌨ Keyboard Shortcuts

drawHistogram(); }); document.getElementById('btn-center').addEventListener('click',centerCamera); +document.getElementById('btn-hoverhl').addEventListener('click',()=>setHoverHL(!S.hoverHL)); +document.getElementById('btn-unpin').addEventListener('click',clearPins); +document.getElementById('btn-pan').addEventListener('click',()=>setPanMode(controls.mouseButtons.LEFT!==THREE.MOUSE.PAN)); +// A click on the cloud pins the instance under the cursor; a drag does not. +(()=>{let down=null; + renderer.domElement.addEventListener('pointerdown',e=>{down=e.button===0?[e.clientX,e.clientY]:null;}); + renderer.domElement.addEventListener('pointerup',e=>{ + if(!down||Math.hypot(e.clientX-down[0],e.clientY-down[1])>4) return; + down=null; if(S._hoverInst>=0) togglePin(S._hoverInst); + }); +})(); document.getElementById('btn-rot').addEventListener('click',toggleRotation); document.getElementById('sl-rot-speed').addEventListener('input',e=>{ S.rot.secPerRev=parseInt(e.target.value); @@ -3425,8 +3507,12 @@

⌨ Keyboard Shortcuts

// Keyboard document.addEventListener('keydown',e=>{ if(e.target.tagName==='INPUT') return; + if(e.shiftKey&&PAN_KEYS[e.key]){e.preventDefault();const k=PAN_KEYS[e.key];panView(k[0]*60,k[1]*60);return;} if(e.key==='ArrowLeft') goTo(S.evIdx-1); if(e.key==='ArrowRight') goTo(S.evIdx+1); + if(e.key==='h') setHoverHL(!S.hoverHL); + if(e.key==='x') clearPins(); + if(e.key==='p') setPanMode(controls.mouseButtons.LEFT!==THREE.MOUSE.PAN); if(e.key==='c') centerCamera(); if(e.key==='a') axesHelper.visible=!axesHelper.visible; if(e.key==='s') takeScreenshot(); @@ -3439,6 +3525,7 @@

⌨ Keyboard Shortcuts

if(e.key==='d') document.getElementById('btn-debug').click(); if(e.key==='?') helpEl.classList.toggle('open'); if(e.key==='Escape'){ + clearPins(); helpEl.classList.remove('open'); document.querySelectorAll('.panel').forEach(p=>p.classList.remove('open')); document.querySelectorAll('[data-toggle]').forEach(b=>b.classList.remove('active')); @@ -3561,10 +3648,12 @@

⌨ Keyboard Shortcuts

} else { coordEl.style.display='block'; coordEl.innerHTML='
'+(isLE?'■ LE scatter':'□ Non-LE')+'   pt '+idx+'
'+rows+'
'+_xtra; - // cloud hover → graph node + interaction bubble - if(S._cloudHoverInstId!==_iid){S._cloudHoverInstId=_iid; + // cloud hover → graph node + interaction bubble (unless hover + // highlighting is off or something is pinned) + S._hoverInst=_iid; + if(_hoverLights()&&S._cloudHoverInstId!==_iid){S._cloudHoverInstId=_iid; _setInstHighlight(_iid>=0?_instNodeIdxById(_iid):-1);} - if(S._cloudHoverIntId!==_iidInt&&!S.highlightLocked){ + if(_hoverLights()&&S._cloudHoverIntId!==_iidInt&&!S.highlightLocked){ S._cloudHoverIntId=_iidInt; _setHighlight(_iidInt); const _b=document.getElementById('ip-bubbles'); if(_b) _b.querySelectorAll('.ip-bub').forEach(e=>{const _s=parseFloat(e.dataset.id)===_iidInt;e.classList.toggle('active',_s);e.classList.toggle('dimmed',!_s);}); @@ -3572,7 +3661,7 @@

⌨ Keyboard Shortcuts

} } } else { - coordEl.style.display='none'; + coordEl.style.display='none'; S._hoverInst=-1; if(S._cloudHoverInstId>=0){S._cloudHoverInstId=-1;_setInstHighlight(-1);} if(S._cloudHoverIntId>=0&&!S.highlightLocked){ S._cloudHoverIntId=-1; _setHighlight(-1); diff --git a/vis_hits.html b/vis_hits.html index 1eec1ea..8150b36 100644 --- a/vis_hits.html +++ b/vis_hits.html @@ -2,14 +2,20 @@ pysupera — wire hits ⇄ voxels @@ -32,21 +40,41 @@ -