Python API Reference

The nereids Python package is a PyO3 layer over the Rust pipeline. This page is a curated narrative reference covering the typed APIs Python users reach for most often, with argument tables, array-shape contracts, and dispatch rules.

For the exhaustive auto-generated reference (every function, class, and attribute exported by the package), see the generated Python API reference built by pdoc from the installed wheel and the shipped nereids/__init__.pyi type stubs.

Install the base package with:

pip install nereids

Optional extras are:

pip install "nereids[mcp]"  # MCP server console script
pip install "nereids[gui]"  # GUI wheel dependency, when available for your platform

Data Objects

ResonanceData

Returned by load_endf(...), load_endf_file(...), and create_resonance_data(...).

Important properties:

FitResult

Returned by fit_spectrum_typed(...) and fit_counts_spectrum_typed(...).

InputData

Opaque typed 3D input for spatial mapping. Create it with:

data = nereids.from_transmission(transmission, uncertainty)
data = nereids.from_counts(sample_counts, open_beam_counts)

The spectral axis is always axis 0, so arrays have shape (n_energy, height, width).

SpatialResult

Returned by spatial_map_typed(...).

NexusData

Returned by load_nexus_histogram(...) and load_nexus_events(...).

ENDF Loading

u238 = nereids.load_endf(92, 238, library="endf8.1")
u238_local = nereids.load_endf_file("examples/data/u238_ex027.endf")

load_endf(...) fetches and caches evaluated nuclear data. Supported library names include endf8.0, endf8.1, jeff3.3, jendl5, tendl2023, and cendl3.2. First use can require network access; cached files are reused afterwards. load_endf_file(...) parses a local ENDF file and does not download data.

Forward Modeling

import numpy as np
import nereids

u238 = nereids.load_endf(92, 238)
energies = np.linspace(1.0, 30.0, 2000)

transmission = nereids.forward_model(
    energies,
    [(u238, 0.001)],
    temperature_k=300.0,
    flight_path_m=25.0,
    delta_t_us=5.0,
    delta_l_m=0.005,
)

forward_model(...) returns a 1D float64 transmission spectrum on the input energy grid. Pass either isotopes=[(ResonanceData, density), ...] or groups=[(IsotopeGroup, density), ...], but not both. Gaussian resolution is enabled by the flight_path_m, delta_t_us, and delta_l_m parameters. Tabulated resolution can be supplied with resolution=load_resolution(...).

Single-Spectrum Fitting

Transmission Data

result = nereids.fit_spectrum_typed(
    transmission,
    uncertainty,
    energies,
    [(u238, 0.0005)],
    temperature_k=300.0,
    solver="lm",
)

Shape contract:

• transmission, uncertainty, and energies are 1D arrays with the same length.
• energies is in eV and should be ascending.
• isotopes supplies (ResonanceData, initial_density) pairs.

Keyword arguments:

Raw Counts

result = nereids.fit_counts_spectrum_typed(
    sample_counts,
    open_beam_counts,
    energies,
    [(u238, 0.0005)],
    solver="auto",
    c=1.0,
)

solver="auto", "kl", "poisson", and "joint_poisson" all route counts data to the counts-KL dispatch. Use c=Q_s / Q_ob when sample and open-beam counts have different proton charge or dwell-time normalization. The primary GOF for this path is FitResult.deviance_per_dof.

Counts fitting accepts the same temperature, background, group, resolution, energy-scale, and fit_energy_range options as transmission fitting. Counts specific options are:

Spatial Mapping

Pre-Normalized Transmission Cubes

data = nereids.from_transmission(transmission_3d, uncertainty_3d)
result = nereids.spatial_map_typed(
    data,
    energies,
    [u238],
    initial_densities=[0.0005],
    solver="auto",
)

For from_transmission(...) inputs the default solver="lm" and solver="auto" both route to LM (this is the dispatcher contract in __init__.pyi: “from_transmission + solver="lm" (default for transmission) → LM”). The explicit solver="kl" opt-in for from_transmission runs the legacy Poisson-NLL-on-transmission path. density_maps[0] is the fitted U-238 map.

Raw Count Cubes

data = nereids.from_counts(sample_counts_3d, open_beam_counts_3d)
result = nereids.spatial_map_typed(
    data,
    energies,
    [u238],
    initial_densities=[0.0005],
    solver="auto",
    c=1.0,
)

solver="auto" uses counts-KL for from_counts(...) data and populates deviance_per_dof_map.

Shape contract:

• sample_counts_3d, open_beam_counts_3d, transmission_3d, and uncertainty_3d use shape (n_energy, height, width).
• energies.shape == (n_energy,).
• dead_pixels, when supplied, uses shape (height, width) with True marking pixels to skip.

Keyword arguments:

Pixel Masks

Pixel masks exist only to exclude pipeline-corrupting pixels (dead or hot/railed detector defects). They are not a data-quality or coverage filter: low-count pixels are alive and must be kept (the KL-domain fitters handle them), and coverage/thickness inhomogeneity is a model concern, not a masking concern. Downstream, a masked pixel is hard-excluded — never fitted, NaN in the result maps. Masks are (height, width) boolean arrays, True = exclude, and feed directly into spatial_map(dead_pixels=...).

# Recommended entry point: dead ∪ hot over sample AND open beam.
mask = nereids.detect_bad_pixels(sample, open_beam=open_beam)

# Individual criteria:
dead = nereids.detect_dead_pixels(sample)             # exactly zero in every TOF bin
hot  = nereids.detect_hot_pixels(sample, k_mad=6.0)   # railed/hot point defects
gone = nereids.detect_dead_pixels_chunked([chunk_a, chunk_b])  # intermittent deadness

result = nereids.spatial_map(cube, energies, isotopes, dead_pixels=mask)

detect_bad_pixels(sample, open_beam=None, hot_k_mad=6.0)

Union mask dead(sample) ∪ hot(sample) [∪ dead(ob) ∪ hot(ob)] — deadness and hotness are per-acquisition, so a mask built from one stack alone misses failures in the other. This is the validating entry point (rejects non-finite/negative counts and empty TOF axes with ValueError). hot_k_mad=None disables the hot screen (dead-only mask).

detect_dead_pixels(data)

Legacy single-stack detector: flags pixels that are exactly 0.0 in every TOF bin. Assumes counts already validated finite and non-negative; prefer detect_bad_pixels for new code.

detect_hot_pixels(data, k_mad=6.0)

Two-stage hot/railed screen on raw counts: a global robust cut on log total counts (median + k_mad · max(1.4826 · MAD, Poisson floor)) nominates candidates, and a local 8-neighbor confirmation (≥ 10× the neighbors’ median, iterated to a fixpoint) keeps contiguous bright scene regions — open-beam areas, slit apertures — unmasked while catching isolated point, line, and small-cluster defects. Pass raw detected counts, not proton-charge-normalized rates or transmission ratios: scaling breaks the Poisson floor.

detect_dead_pixels_chunked(chunks)

Intermittent deadness: a pixel dead for part of the acquisition is invisible in a summed stack (uniformly reduced counts, no zeros). Given per-chunk stacks (e.g. per-run splits or event data re-histogrammed in time windows), flags pixels all-zero in any chunk. Chunk so that live pixels expect λ ≥ 20 counts each (false-flag probability ≤ m·e^(−λ)). Spatial dims must match across chunks; the TOF axis may differ.

TIFF and NeXus I/O

stack = nereids.load_tiff_stack("transmission_stack.tif", pixel_policy="allow")
folder_stack, info = nereids.load_tiff_folder(
    "frames",
    pattern="frame_*.tif",
    sum_chunks=True,        # sum chunked VENUS runs element-wise (default)
    pixel_policy="reject",  # "reject" | "clip" | "allow" (default "reject")
    return_info=True,       # also return the load-provenance dict
)
info["n_chunks"]            # DAQ chunks detected (1 if not chunked)
info["chunks_summed"]       # True when they were summed element-wise
info["n_clipped_pixels"]    # pixels clamped under pixel_policy="clip"
# full key set: n_files, n_chunks, chunk_ids, chunks_summed,
# n_clipped_pixels, chunk_inconsistent, n_unrecognized_files,
# unrecognized_examples
edges_us = nereids.read_tof_sidecar(
    "run_764/run_764_Spectra.txt",
    n_frames=folder_stack.shape[0],
)

sample = nereids.load_nexus_histogram("sample.nxs")
open_beam = nereids.load_nexus_histogram("open_beam.nxs")
energies = nereids.tof_to_energy_centers(
    sample.tof_edges_us,
    sample.flight_path_m or 25.0,
)
health = nereids.run_health("sample.nxs")   # RunHealth: pause/beam-dip fractions

load_tiff_folder detects chunked VENUS folders (<prefix>_<chunk>_<frame>.tif) and sums chunks element-wise by default, emitting a UserWarning naming the summed chunks (and one with the clipped-pixel count under pixel_policy="clip"); return_info=True returns the load provenance as a second value. read_tof_sidecar converts a VENUS *_Spectra.txt sidecar (frame start times in seconds — the left bin edges, verified on measured autoreduce output) into the N+1 ascending microsecond TOF bin edges that tof_to_energy_centers expects. Negative or non-finite pixels are rejected at load time unless pixel_policy says otherwise. run_health returns a RunHealth summary of the /entry/DASlogs pause and beam-power logs using last-value-held time-weighted integration (SNS PV-name defaults).

See Data I/O and NeXus/TOF for ordering and pairing rules, chunk semantics, the pixel-value policy, and run health.

Beam-State Filtering (DASlogs and Event Banks)

Facility NeXus files record slow-control PVs under /entry/DASlogs/<pv> as transition logs: each value takes effect at its timestamp and persists until the next entry. Averaging the value array directly is wrong whenever entries are unevenly spaced — on a real VENUS run the entry-mean of the pause log read 0.43 while the time-weighted pause fraction was 0.90.

read_run_log(path, pv)

Returns a RunLog with times (seconds since run start), values, duration_s, offset_iso (ISO-8601 epoch of the clock), and n_dropped_corrupt — the number of corrupt device-reconnect records (backward time jumps or subnormal garbage payloads, both seen in real SNS files) dropped from the log.

intervals_where(times, values, duration_s, min_value=None, max_value=None)

Derives (t_start, t_end) intervals where the PV satisfies the bounds, under correct step-function semantics: the last value persists to duration_s (padded one f32 ULP — SNS records duration in float32 while pulse times are float64, and the final pulse of about half of real runs is stamped just beyond it), time before the first entry never matches, NaN never matches, and adjacent segments merge.

intervals_intersect(a, b)

Composes conditions across PVs (e.g. not-paused AND beam power above threshold). Inputs are validated and normalised (sorted, merged).

load_nexus_bank_spectrum(path, bank, n_bins, tof_min_us, tof_max_us, keep_intervals=None)

Loads one NXevent_data bank (e.g. "monitor1") as a BankSpectrum — a 1-D TOF spectrum (tof_edges_us, counts) with retention statistics (pulses_total/pulses_kept, events_total/events_kept, drop counters, pulse_time_offset_iso). With keep_intervals, only pulses whose event_time_zero falls inside the intervals (half-open, DASlogs clock) are histogrammed. units attributes are required on both event datasets (never guess a scale); a bank with zero events loads gracefully to a zero spectrum — on VENUS every imaging-detector bank is empty because tpx1 is frame-mode, and only monitors carry events.

pause = nereids.read_run_log("run.nxs.h5", "pause")
live = nereids.intervals_where(
    pause.times, pause.values, pause.duration_s, max_value=0.5
)
power = nereids.read_run_log("run.nxs.h5", "BL10:Det:rtdl:BeamPowerAvg")
stable = nereids.intervals_where(
    power.times, power.values, power.duration_s, min_value=1.5
)
keep = nereids.intervals_intersect(live, stable)

mon = nereids.load_nexus_bank_spectrum(
    "run.nxs.h5", "monitor1",
    n_bins=500, tof_min_us=0.0, tof_max_us=16667.0,
    keep_intervals=keep,
)
print(mon.pulses_kept, "/", mon.pulses_total, "pulses in stable beam")

Element and Utility APIs

nereids.element_symbol(92)        # "U"
nereids.element_name(92)          # "Uranium"
nereids.parse_isotope_str("U-238") # (92, 238)
nereids.natural_abundance(92, 238)
nereids.natural_isotopes(26)
nereids.tof_to_energy(tof_us, flight_path_m)
nereids.energy_to_tof(energy_ev, flight_path_m)

How This Page Is Generated

The published docs site renders three things side by side:

To rebuild the whole site locally:

pixi run doc-build   # depends on: doc-guide, doc-api, doc-python
pixi run doc         # serves target/book/ at http://localhost:8000

doc-python invokes pdoc -o target/book/python --no-show-source nereids after pixi run build has produced an importable wheel. Whenever bindings/python/python/nereids/__init__.pyi or PyO3 docstrings in bindings/python/src/lib.rs change, both the auto-generated python/ reference and any affected sections of this curated page should be reviewed in the same PR.

This page does not execute notebooks or compile-test Python snippets. The Rust quickstart on this site IS compile-tested by cargo check --workspace --examples (see crates/nereids-fitting/examples/quickstart.rs).