From 981d5f049249b9d380d08c313d8ae0e6ce111a4b Mon Sep 17 00:00:00 2001 From: Paullo9 Date: Thu, 5 Nov 2026 19:42:50 +0100 Subject: [PATCH 1/5] wip: origin from Bragg peaks (py4DSTEM measure_origin), parked Started for mac4DSTEM#138 and parked on 2026-11-05 at Paul's request (for later, not before the App Store launch). Untested draft: BraggOrigin.swift, its fixture and the pin script. Not for merge as is. Done: draft port parked on this branch. Next: finish, parity test, review. Co-Authored-By: Claude Opus 5.5 --- Sources/CuboBragg/BraggOrigin.swift | 192 ++++++++++++++++++ .../CuboBraggTests/Fixtures/braggorigin.json | 1 + tools/bragg-origin/pins.py | 130 ++++++++++++ 3 files changed, 323 insertions(+) create mode 100644 Sources/CuboBragg/BraggOrigin.swift create mode 100644 Tests/CuboBraggTests/Fixtures/braggorigin.json create mode 100644 tools/bragg-origin/pins.py diff --git a/Sources/CuboBragg/BraggOrigin.swift b/Sources/CuboBragg/BraggOrigin.swift new file mode 100644 index 0000000..7cc6219 --- /dev/null +++ b/Sources/CuboBragg/BraggOrigin.swift @@ -0,0 +1,192 @@ +// +// BraggOrigin.swift +// py4DSTEM's origin from the Bragg peaks: `BraggVectors.measure_origin` (braggvectors/braggvector_methods.py 320 at +// f050d207) and `BraggVectors.fit_origin` (513, which calls process/calibration/origin.py's `fit_origin`). The direct +// beam is picked among each position's detected peaks, so the origin follows the unscattered beam where a centre of mass +// of the whole pattern is pulled by strong reflections. Rows and columns: py4DSTEM's qx is the row. +// + +import Foundation +import CuboCore +import CuboVirtual + +/// How `BraggOrigin.measure` picks the direct beam among a position's peaks (py4DSTEM's `score_method`). +public enum BraggOriginScore: Sendable, Hashable, CaseIterable { + /// `"intensity"`: the brightest peak. py4DSTEM's default when no centre guess is given. + case intensity + /// `"distance"`: the peak closest to the centre guess. py4DSTEM's default when a guess is given. + case distance + /// `"intensity weighted distance"`: the largest intensity / (1 + squared distance to the guess). + case intensityWeightedDistance +} + +/// The per-position direct-beam positions of `BraggOrigin.measure`, row-major `[scanRow][scanColumn]`. +public struct BraggOriginMeasurement: Sendable, Equatable { + public let scanRows: Int + public let scanColumns: Int + /// py4DSTEM's `qx0`: the chosen peak's row, or the guess's row at a position without peaks. + public let row: [Double] + /// py4DSTEM's `qy0`: the chosen peak's column, or the guess's column at a position without peaks. + public let column: [Double] + /// True where the position had at least one peak (py4DSTEM's intended `origin_meas_mask`). + public let found: [Bool] + /// The centre guess: the one given, or the brightest pixel of the smoothed raw Bragg vector map. + public let guess: BraggDetectorCenter + /// The score method used (the default resolved). + public let score: BraggOriginScore + + public init(scanRows: Int, scanColumns: Int, row: [Double], column: [Double], found: [Bool], + guess: BraggDetectorCenter, score: BraggOriginScore) { + precondition(row.count == scanRows * scanColumns && column.count == row.count && found.count == row.count, + "BraggOriginMeasurement: arrays do not match \(scanRows) × \(scanColumns)") + self.scanRows = scanRows + self.scanColumns = scanColumns + self.row = row + self.column = column + self.found = found + self.guess = guess + self.score = score + } +} + +/// The smooth origin of `BraggOrigin.fit`, row-major like the measurement. +public struct BraggOriginFit: Sendable, Equatable { + public let fittedRow: [Double] + public let fittedColumn: [Double] + /// Measured minus fitted, at every position (py4DSTEM's `qx0_residuals`, `qy0_residuals`). + public let residualRow: [Double] + public let residualColumn: [Double] + /// The positions that entered the fit: a peak was found there and its row is not 0. + public let used: [Bool] + + public init(fittedRow: [Double], fittedColumn: [Double], residualRow: [Double], residualColumn: [Double], used: [Bool]) { + self.fittedRow = fittedRow + self.fittedColumn = fittedColumn + self.residualRow = residualRow + self.residualColumn = residualColumn + self.used = used + } +} + +public enum BraggOrigin { + /// py4DSTEM's `BraggVectors.measure_origin(center_guess, score_method, findcenter="max")` on the raw (uncalibrated) + /// peaks of a `detectorRows × detectorColumns` detector. + /// + /// Without `centerGuess` the guess is the brightest pixel (first in raster order) of the raw Bragg vector map + /// (`histogram(mode="raw")`, bilinear deposition) smoothed by scipy's `gaussian_filter(σ = 10)`, and `score` defaults to + /// `.intensity`; with a guess it defaults to `.distance`. Each position's origin is its best-scoring peak (the first of + /// equal scores, as `np.argmax`); a position without peaks gets the guess and `found` false. + /// + /// DEVIATION from py4DSTEM f050d207: (1) `measure_origin` hands the map object, not its array, to `gaussian_filter`, + /// which returns a 0-d array, so its guess is always (0, 0); here it is the map's brightest smoothed pixel, as the + /// docstring describes. Only a distance score and an empty position read the guess. (2) Its returned mask becomes the + /// scalar `False` once any position is empty; `found` is per position. (3) `findcenter="CoM"` raises upstream + /// (`get_CoM` of the map object) and is not offered. (4) A non-finite peak is refused (NumPy's argmax would pick it). + public static func measure(_ vectors: BraggVectors, detectorRows: Int, detectorColumns: Int, + centerGuess: BraggDetectorCenter? = nil, + score: BraggOriginScore? = nil) throws -> BraggOriginMeasurement { + guard detectorRows > 0, detectorColumns > 0 else { + throw CuboBraggError.shapeMismatch("the detector must be at least 1 × 1 (\(detectorRows) × \(detectorColumns))") + } + for list in vectors.peaks { + for p in list where !(p.row.isFinite && p.column.isFinite && p.intensity.isFinite) { + throw CuboBraggError.invalidOption("a Bragg peak is not finite (\(p.row), \(p.column), \(p.intensity))") + } + } + let guess: BraggDetectorCenter + if let centerGuess { + guard centerGuess.row.isFinite, centerGuess.column.isFinite else { + throw CuboBraggError.invalidOption("the centre guess must be finite") + } + guess = centerGuess + } else { + let map = rawHistogram(vectors, rows: detectorRows, columns: detectorColumns) + let smooth = try GaussianFilter(sigma: 10).apply(map, rows: detectorRows, columns: detectorColumns, edge: .reflect) + var best = 0 + for i in smooth.indices where smooth[i] > smooth[best] { best = i } + guess = BraggDetectorCenter(row: Double(best / detectorColumns), column: Double(best % detectorColumns)) + } + let method = score ?? (centerGuess == nil ? .intensity : .distance) + let n = vectors.scanRows * vectors.scanColumns + var row = [Double](repeating: guess.row, count: n) + var column = [Double](repeating: guess.column, count: n) + var found = [Bool](repeating: false, count: n) + for k in 0.. Double { + let dr = p.row - guess.row, dc = p.column - guess.column + switch method { + case .intensity: return p.intensity + case .distance: return -(dr * dr + dc * dc) // argmin of r² is argmax of −r², first of equals kept + case .intensityWeightedDistance: return p.intensity / (1 + (dr * dr + dc * dc)) + } + } + var best = 0, bestValue = value(list[0]) + for i in 1.. bestValue { best = i; bestValue = v } + } + row[k] = list[best].row + column[k] = list[best].column + found[k] = true + } + return BraggOriginMeasurement(scanRows: vectors.scanRows, scanColumns: vectors.scanColumns, row: row, column: column, + found: found, guess: guess, score: method) + } + + /// py4DSTEM's `BraggVectors.fit_origin(fitfunction, robust, robust_steps, robust_thresh)` with its default + /// `mask_check_data=True` and `mask` = the found positions: the surface is fitted over the positions where a peak was + /// found and whose measured row is not 0 (`used`), and evaluated everywhere. The fit is `OriginFit.fit`. + /// + /// DEVIATION from py4DSTEM f050d207: upstream fits every position its mask lets through, so an empty position enters + /// with the guess it was given unless that guess's row is 0 (as upstream's always-(0, 0) guess is, so its default path + /// and this one fit the same positions). Its `"constant"` model ignores the mask and averages every position; here it + /// averages the `used` ones. + public static func fit(_ measurement: BraggOriginMeasurement, model: OriginFitModel = .plane, robust: Bool = false, + robustSteps: Int = 3, robustThreshold: Double = 2) throws -> BraggOriginFit { + let m = measurement + let used = m.found.indices.map { m.found[$0] && m.row[$0] != 0 } + guard used.contains(true) else { throw OriginFitError.underdetermined(model) } + let fittedRow: [Double], fittedColumn: [Double] + if model == .constant { + let r = NumPySum.mean(m.row.indices.filter { used[$0] }.map { m.row[$0] }) + let c = NumPySum.mean(m.column.indices.filter { used[$0] }.map { m.column[$0] }) + fittedRow = Array(repeating: r, count: used.count) + fittedColumn = Array(repeating: c, count: used.count) + } else { + let rowValues = m.row.indices.map { used[$0] ? m.row[$0] : .nan } + let columnValues = m.column.indices.map { used[$0] ? m.column[$0] : .nan } + fittedRow = try OriginFit.fit(rowValues, rows: m.scanRows, columns: m.scanColumns, model: model, robust: robust, + robustSteps: robustSteps, robustThreshold: robustThreshold) + fittedColumn = try OriginFit.fit(columnValues, rows: m.scanRows, columns: m.scanColumns, model: model, + robust: robust, robustSteps: robustSteps, robustThreshold: robustThreshold) + } + return BraggOriginFit(fittedRow: fittedRow, fittedColumn: fittedColumn, + residualRow: m.row.indices.map { m.row[$0] - fittedRow[$0] }, + residualColumn: m.column.indices.map { m.column[$0] - fittedColumn[$0] }, + used: used) + } + + /// py4DSTEM's `BraggVectors.histogram(mode="raw", sampling=1)`: every peak's intensity shared bilinearly between the four + /// cells around it (floor and ceil of each coordinate); a peak with a neighbour outside the grid is dropped. The four + /// `np.bincount` sums are added cell by cell, as upstream. + static func rawHistogram(_ vectors: BraggVectors, rows: Int, columns: Int) -> [Double] { + let size = rows * columns + var b00 = [Double](repeating: 0, count: size), b01 = b00, b10 = b00, b11 = b00 + for list in vectors.peaks { + for p in list { + guard p.row > -1e9, p.row < 1e9, p.column > -1e9, p.column < 1e9 else { continue } + let fr = Int(p.row.rounded(.down)), cr = Int(p.row.rounded(.up)) + let fc = Int(p.column.rounded(.down)), cc = Int(p.column.rounded(.up)) + guard fr >= 0, fc >= 0, cr < rows, cc < columns else { continue } + let dr = p.row - Double(fr), dc = p.column - Double(fc) + b00[fr * columns + fc] += p.intensity * (1.0 - dr) * (1.0 - dc) + b01[fr * columns + cc] += p.intensity * (1.0 - dr) * dc + b10[cr * columns + fc] += p.intensity * dr * (1.0 - dc) + b11[cr * columns + cc] += p.intensity * dr * dc + } + } + return (0.. 0 for c in range(R[1])] for r in range(R[0])]) + +measures = [] +for label, kwargs, expected_guess in [ + ("default", {}, None), + ("max-distance", dict(center_guess=guess, score_method="distance"), "max"), + ("max-iwd", dict(center_guess=guess, score_method="intensity weighted distance"), "max"), + ("guess-default", dict(center_guess=(24.0, 31.0)), None), + ("guess-intensity", dict(center_guess=(24.0, 31.0), score_method="intensity"), None), + ("guess-iwd", dict(center_guess=(24.0, 31.0), score_method="intensity weighted distance"), None), +]: + bv = fresh() + qx0, qy0, mask = bv.measure_origin(**kwargs) + case = dict(label=label, row=flat(qx0), column=flat(qy0), maskScalar=bool(np.ndim(mask) == 0)) + # "max" cases are run by the kit with findCenter .maximum and no guess; the others with the guess given + case["centerGuess"] = None if expected_guess == "max" or "center_guess" not in kwargs else [float(v) for v in kwargs["center_guess"]] + case["score"] = kwargs.get("score_method") + fits = [] + for fitlabel, fk in [ + ("plane", dict(fitfunction="plane")), + ("parabola", dict(fitfunction="parabola")), + ("plane-robust", dict(fitfunction="plane", robust=True, robust_steps=3, robust_thresh=2)), + ]: + if label not in ("default", "guess-default") and fitlabel != "plane": + continue + # mask = the positions that had peaks: the kit's fit leaves an empty position out (upstream would fit the guess it + # wrote there, unless its row is 0, as the (0, 0) guess of the default path is) + xf, yf, xr, yr = bv.fit_origin(plot=False, mask=found.copy(), **fk) + fits.append(dict(label=fitlabel, fittedRow=flat(xf), fittedColumn=flat(yf), residualRow=flat(xr), residualColumn=flat(yr))) + if label == "default": + bv2 = fresh() + bv2.measure_origin() + xf, yf, _, _ = bv2.fit_origin(plot=False) + assert np.array_equal(xf, np.asarray(fits[0]["fittedRow"]).reshape(R)), "default fit without a mask differs" + case["fits"] = fits + measures.append(case) + +out = dict( + reference="py4DSTEM f050d207 / 0.14.19", + scan=list(R), + detector=list(Q), + peaks=[[list(map(float, p)) for p in pts] for pts in peaks], + histogram=flat(np.asarray(bvm.data)), + maxGuess=list(guess), + measures=measures, +) +dest = pinenv.fixtures("CuboBraggTests") / "braggorigin.json" +dest.write_text(json.dumps(out, separators=(",", ":"), allow_nan=True) + "\n") +print("fixture bytes", dest.stat().st_size, "guess", guess) From c58e4e79f497dd6384ff7a72adb78495efcdffcd Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 5 Nov 2026 19:04:39 +0000 Subject: [PATCH 2/5] feat: origin from the Bragg peaks (py4DSTEM measure_origin, fit_origin) and a symmetry centre guess BraggOrigin.measure and BraggOrigin.fit port py4DSTEM's BraggVectors.measure_origin and fit_origin, pinned on planted peaks (tools/bragg-origin/pins.py). OriginFit.fit takes fit_origin's boolean mask. BraggOrigin.symmetryCenterGuess is a mac4DSTEM variant: the centre about which the map of peak counts is most point-symmetric, so a distance score finds the direct beam where a reflection is brighter than it (py4DSTEM's Si/SiGe tutorial cube). Refs mac4DSTEM/mac4DSTEM#138 Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01VkKZEUZ2RdEgWryyPgjZzR --- Sources/CuboBragg/BraggOrigin.swift | 71 +++++++++- Sources/CuboCore/OriginFit.swift | 11 +- Tests/CuboBraggTests/BraggOriginTests.swift | 133 ++++++++++++++++++ .../CuboBraggTests/Fixtures/braggorigin.json | 2 +- docs/parity.md | 6 +- tools/bragg-origin/pins.py | 74 +++++++++- 6 files changed, 284 insertions(+), 13 deletions(-) create mode 100644 Tests/CuboBraggTests/BraggOriginTests.swift diff --git a/Sources/CuboBragg/BraggOrigin.swift b/Sources/CuboBragg/BraggOrigin.swift index 7cc6219..679b4c7 100644 --- a/Sources/CuboBragg/BraggOrigin.swift +++ b/Sources/CuboBragg/BraggOrigin.swift @@ -142,7 +142,7 @@ public enum BraggOrigin { /// DEVIATION from py4DSTEM f050d207: upstream fits every position its mask lets through, so an empty position enters /// with the guess it was given unless that guess's row is 0 (as upstream's always-(0, 0) guess is, so its default path /// and this one fit the same positions). Its `"constant"` model ignores the mask and averages every position; here it - /// averages the `used` ones. + /// averages the `used` ones. The robust loop scores every position, unused ones included, as upstream's `fit_2D`. public static func fit(_ measurement: BraggOriginMeasurement, model: OriginFitModel = .plane, robust: Bool = false, robustSteps: Int = 3, robustThreshold: Double = 2) throws -> BraggOriginFit { let m = measurement @@ -155,11 +155,9 @@ public enum BraggOrigin { fittedRow = Array(repeating: r, count: used.count) fittedColumn = Array(repeating: c, count: used.count) } else { - let rowValues = m.row.indices.map { used[$0] ? m.row[$0] : .nan } - let columnValues = m.column.indices.map { used[$0] ? m.column[$0] : .nan } - fittedRow = try OriginFit.fit(rowValues, rows: m.scanRows, columns: m.scanColumns, model: model, robust: robust, - robustSteps: robustSteps, robustThreshold: robustThreshold) - fittedColumn = try OriginFit.fit(columnValues, rows: m.scanRows, columns: m.scanColumns, model: model, + fittedRow = try OriginFit.fit(m.row, rows: m.scanRows, columns: m.scanColumns, model: model, mask: used, + robust: robust, robustSteps: robustSteps, robustThreshold: robustThreshold) + fittedColumn = try OriginFit.fit(m.column, rows: m.scanRows, columns: m.scanColumns, model: model, mask: used, robust: robust, robustSteps: robustSteps, robustThreshold: robustThreshold) } return BraggOriginFit(fittedRow: fittedRow, fittedColumn: fittedColumn, @@ -168,6 +166,67 @@ public enum BraggOrigin { used: used) } + /// mac4DSTEM variant (not in py4DSTEM): a centre guess for `measure(centerGuess:score: .distance)` that finds the direct + /// beam where a diffracted beam is brighter than it, the case where py4DSTEM's guess (the brightest point of the map) and + /// its `.intensity` score both pick a reflection. A diffraction pattern is centrosymmetric about the direct beam + /// (Friedel's law), and the extent of the detected disks is too, so the guess is the candidate about which the map of + /// peak counts best matches its own point reflection. + /// + /// The map: every peak counted once (intensity 1), deposited as `rawHistogram`, smoothed by scipy's + /// `gaussian_filter(σ = 1, mode="reflect")`. The candidates: its local maxima (equal to the maximum of the 5 × 5 window, + /// scipy's `maximum_filter(size=5)`) above 5% of the largest value, the `candidates` highest (12 by default; ties in + /// raster order). The score of a candidate pixel c: Σ min(M(p), M(2c − p)) over the pixels p whose mirror is on the + /// detector. The best score wins, the first of equal scores. Peak counts, not intensities, so one strong reflection cannot + /// outweigh the symmetry of all the others. On py4DSTEM's Si/SiGe tutorial cube (128 × 128 detector) it returns the + /// direct beam at (64, 64) where the brightest point of the map is a first-order reflection at (49, 69). + /// + /// nil when no position has a peak. + public static func symmetryCenterGuess(_ vectors: BraggVectors, detectorRows: Int, detectorColumns: Int, + candidates: Int = 12) throws -> BraggDetectorCenter? { + guard detectorRows > 0, detectorColumns > 0 else { + throw CuboBraggError.shapeMismatch("the detector must be at least 1 × 1 (\(detectorRows) × \(detectorColumns))") + } + guard candidates > 0 else { throw CuboBraggError.invalidOption("at least one candidate (\(candidates))") } + let counted = BraggVectors(scanRows: vectors.scanRows, scanColumns: vectors.scanColumns, + peaks: vectors.peaks.map { $0.map { BraggPeak(row: $0.row, column: $0.column, intensity: 1) } }) + let rows = detectorRows, columns = detectorColumns + let map = try GaussianFilter(sigma: 1).apply(rawHistogram(counted, rows: rows, columns: columns), rows: rows, + columns: columns, edge: .reflect) + guard let top = map.max(), top > 0 else { return nil } + var maxima: [(index: Int, value: Double)] = [] + for r in 0.. 0.05 * top else { continue } + var isMaximum = true + for rr in max(0, r - 2)...min(rows - 1, r + 2) where isMaximum { + for cc in max(0, c - 2)...min(columns - 1, c + 2) where map[rr * columns + cc] > v { + isMaximum = false + break + } + } + if isMaximum { maxima.append((r * columns + c, v)) } + } + } + // Stable: equal values keep raster order, as np.argsort(-values, kind="stable"). + let chosen = maxima.enumerated().sorted { $0.element.value != $1.element.value ? $0.element.value > $1.element.value + : $0.offset < $1.offset } + .prefix(candidates).map(\.element.index) + var best: (score: Double, index: Int)? + for index in chosen { + let r0 = index / columns, c0 = index % columns + var score = 0.0 + for r in max(0, 2 * r0 - (rows - 1))...min(rows - 1, 2 * r0) { + for c in max(0, 2 * c0 - (columns - 1))...min(columns - 1, 2 * c0) { + score += min(map[r * columns + c], map[(2 * r0 - r) * columns + (2 * c0 - c)]) + } + } + if best == nil || score > best!.score { best = (score, index) } + } + guard let best else { return nil } + return BraggDetectorCenter(row: Double(best.index / columns), column: Double(best.index % columns)) + } + /// py4DSTEM's `BraggVectors.histogram(mode="raw", sampling=1)`: every peak's intensity shared bilinearly between the four /// cells around it (floor and ceil of each coordinate); a peak with a neighbour outside the grid is dropped. The four /// `np.bincount` sums are added cell by cell, as upstream. diff --git a/Sources/CuboCore/OriginFit.swift b/Sources/CuboCore/OriginFit.swift index 5002fdd..20cd75b 100644 --- a/Sources/CuboCore/OriginFit.swift +++ b/Sources/CuboCore/OriginFit.swift @@ -24,7 +24,11 @@ public enum OriginFit { /// The fitted surface at every position of a `rows × columns` map (row-major), over its finite values. /// `rankTolerance`: a design column whose remaining norm is at most this fraction of the largest column norm is left /// out (the rank-deficient projection, as NumPy's lstsq); 1e-12 by default. - public static func fit(_ values: [Double], rows: Int, columns: Int, model: OriginFitModel, robust: Bool = false, + /// `mask`: fit_origin's `mask` (fit_2D's `data_mask`): a position where it is false is left out of the first fit; the + /// robust loop still scores every position, masked ones included, as upstream does. nil fits every finite value. Like + /// upstream, the `.constant` model ignores it and averages every value. + public static func fit(_ values: [Double], rows: Int, columns: Int, model: OriginFitModel, mask dataMask: [Bool]? = nil, + robust: Bool = false, robustSteps: Int = 3, robustThreshold: Double = 2, rankTolerance: Double = 1e-12) throws -> [Double] { guard rankTolerance >= 0, rankTolerance.isFinite else { @@ -33,10 +37,13 @@ public enum OriginFit { guard rows > 0, columns > 0, values.count == rows * columns else { throw OriginFitError.invalidMap("\(values.count) values for \(rows) × \(columns)") } + if let dataMask, dataMask.count != values.count { + throw OriginFitError.invalidMap("the mask has \(dataMask.count) entries for \(values.count) values") + } if model == .constant { return Array(repeating: NumPySum.mean(values), count: values.count) } let design = Self.design(rows: rows, columns: columns, model: model) let p = design.count / values.count - var mask = values.map(\.isFinite) + var mask = values.indices.map { values[$0].isFinite && (dataMask?[$0] ?? true) } var coefficients = try solve(design, values, mask, p, model, rankTolerance) if robust { for _ in 0.. Double { + guard got.count == want.count else { return .infinity } + return zip(got, want).reduce(0.0) { max($0, abs($1.0 - $1.1) / (atol + rtol * abs($1.1))) } +} + +private func score(_ name: String?) -> BraggOriginScore? { + switch name { + case "distance": .distance + case "intensity": .intensity + case "intensity weighted distance": .intensityWeightedDistance + default: nil + } +} + +private func model(_ label: String) -> (OriginFitModel, Bool) { + switch label { + case "parabola": (.parabola, false) + case "plane-robust": (.plane, true) + default: (.plane, false) + } +} + +@Suite struct BraggOriginTests { + @Test func rawHistogramMatchesPy4DSTEM() { + let map = BraggOrigin.rawHistogram(vectors, rows: pin.detector[0], columns: pin.detector[1]) + let w = worst(map, pin.histogram) + print("raw Bragg vector map: worst gap / bound = \(w)") + #expect(w <= 1) + } + + @Test func measureAndFitMatchPy4DSTEM() throws { + for c in pin.measures { + let guess = c.centerGuess.map { BraggDetectorCenter(row: $0[0], column: $0[1]) } + let m = try BraggOrigin.measure(vectors, detectorRows: pin.detector[0], detectorColumns: pin.detector[1], + centerGuess: guess, score: score(c.score)) + if guess == nil { + #expect(m.guess == BraggDetectorCenter(row: pin.maxGuess[0], column: pin.maxGuess[1]), "\(c.label)") + } + #expect(m.row == c.row, "\(c.label) row") + #expect(m.column == c.column, "\(c.label) column") + #expect(m.found.filter { !$0 }.count == 1, "\(c.label): one empty position") + for f in c.fits { + let (fitModel, robust) = model(f.label) + let fit = try BraggOrigin.fit(m, model: fitModel, robust: robust) + let gaps = [worst(fit.fittedRow, f.fittedRow), worst(fit.fittedColumn, f.fittedColumn), + worst(fit.residualRow, f.residualRow), worst(fit.residualColumn, f.residualColumn)] + print("Bragg origin \(c.label) / \(f.label): worst gap / bound = \(gaps.max()!)") + #expect(gaps.max()! <= 1, "\(c.label) / \(f.label)") + } + } + } + + @Test func positionsWithoutPeaksOrAtRowZeroStayOutOfTheFit() throws { + let m = try BraggOrigin.measure(vectors, detectorRows: pin.detector[0], detectorColumns: pin.detector[1]) + let fit = try BraggOrigin.fit(m) + #expect(!fit.used[13] && !m.found[13]) + #expect(m.row[40] == 0 && !fit.used[40]) + #expect(fit.used.filter { $0 }.count == m.row.count - 2) + } + + @Test func refusesBadInput() { + let bad = BraggVectors(scanRows: 1, scanColumns: 1, peaks: [[BraggPeak(row: .nan, column: 1, intensity: 1)]]) + #expect(throws: CuboBraggError.self) { try BraggOrigin.measure(bad, detectorRows: 8, detectorColumns: 8) } + #expect(throws: CuboBraggError.self) { try BraggOrigin.measure(vectors, detectorRows: 0, detectorColumns: 8) } + let empty = BraggVectors(scanRows: 2, scanColumns: 2, peaks: [[], [], [], []]) + #expect(throws: OriginFitError.self) { + try BraggOrigin.fit(try BraggOrigin.measure(empty, detectorRows: 8, detectorColumns: 8)) + } + } + + @Test func symmetryGuessFindsTheBeamUnderABrighterReflection() throws { + let bright = BraggVectors(scanRows: pin.scan[0], scanColumns: pin.scan[1], + peaks: pin.symmetry.peaks.map { $0.map { BraggPeak(row: $0[0], column: $0[1], intensity: $0[2]) } }) + let guess = try #require(try BraggOrigin.symmetryCenterGuess(bright, detectorRows: pin.detector[0], + detectorColumns: pin.detector[1])) + #expect(guess == BraggDetectorCenter(row: pin.symmetry.guess[0], column: pin.symmetry.guess[1])) + let m = try BraggOrigin.measure(bright, detectorRows: pin.detector[0], detectorColumns: pin.detector[1], + centerGuess: guess, score: .distance) + #expect(m.row == pin.symmetry.row && m.column == pin.symmetry.column) + // py4DSTEM's default picks the brighter reflection at every position here. + let byIntensity = try BraggOrigin.measure(bright, detectorRows: pin.detector[0], detectorColumns: pin.detector[1]) + #expect(zip(byIntensity.row, m.row).allSatisfy { abs($0 - $1) > 5 }) + let planted = try #require(try BraggOrigin.symmetryCenterGuess(vectors, detectorRows: pin.detector[0], + detectorColumns: pin.detector[1])) + #expect(planted == BraggDetectorCenter(row: pin.symmetry.planedGuess[0], column: pin.symmetry.planedGuess[1])) + let none = BraggVectors(scanRows: 1, scanColumns: 2, peaks: [[], []]) + #expect(try BraggOrigin.symmetryCenterGuess(none, detectorRows: 8, detectorColumns: 8) == nil) + } + + @Test func originFitMaskLeavesPositionsOut() throws { + // A plane with one wild value: masked out, the fit is the plane exactly. + var values = (0..<12).map { k in 2.0 + 0.5 * Double(k / 4) - 0.25 * Double(k % 4) } + let clean = values + values[5] = 100 + var mask = [Bool](repeating: true, count: 12) + mask[5] = false + let fit = try OriginFit.fit(values, rows: 3, columns: 4, model: .plane, mask: mask) + #expect(worst(fit, clean, rtol: 1e-12, atol: 1e-12) <= 1) + #expect(throws: OriginFitError.self) { try OriginFit.fit(values, rows: 3, columns: 4, model: .plane, mask: [true]) } + } +} diff --git a/Tests/CuboBraggTests/Fixtures/braggorigin.json b/Tests/CuboBraggTests/Fixtures/braggorigin.json index c358db7..94dcb7a 100644 --- a/Tests/CuboBraggTests/Fixtures/braggorigin.json +++ b/Tests/CuboBraggTests/Fixtures/braggorigin.json @@ -1 +1 @@ -{"reference":"py4DSTEM f050d207 / 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diff --git a/docs/parity.md b/docs/parity.md index 26b4b45..9c9f722 100644 --- a/docs/parity.md +++ b/docs/parity.md @@ -151,7 +151,7 @@ CuboKit has no stateful `Calibration` object yet; its measurements return plain | `fit_origin(fitfunction="parabola")` | `OriginFit.fit(model: .parabola)` | present | useful | no upstream test; np.allclose defaults on the Float64 reference | | `fit_origin(fitfunction="bezier_two")` | `OriginFit.fit(model: .bezierTwo)` | present | useful | no upstream test; np.allclose defaults on the Float64 reference | | `fit_origin(robust, robust_steps, robust_thresh)` | `OriginFit.fit(robust:robustSteps:robustThreshold:)` | present | useful | no upstream test; np.allclose defaults on the Float64 reference | -| `fit_origin(mask=)` | `OriginFit.fit` | partial: NaN entries are left out of the fit; no boolean mask argument | useful | no upstream test; np.allclose defaults on the Float64 reference | +| `fit_origin(mask=)` | `OriginFit.fit(mask:)` | present (0.11.0; NaN entries are left out too; the robust loop scores masked positions, as upstream) | useful | no upstream test; np.allclose defaults on the Float64 reference | | mac4DSTEM trimmed fit (`fitOriginTrimmed`, MAD cut, up to three rounds) | `OriginFit.fitTrimmed` | named option beside `fit(robust:)`; mask and surfaces pinned to a Float32 transcription of the app source, the final fit also to `fit_origin(mask=kept)` (not counted twice) | useful | no upstream test; np.allclose defaults on the Float64 reference | | `fit_origin(returnfitp=True)` | — | missing (the fit coefficients are not returned) | useful | no upstream test; np.allclose defaults on the Float64 reference | | `get_probe_size` (`process/calibration/probe.py`) | `ProbeSize.estimate` | present (exact today) | study | no upstream test; np.allclose defaults on the Float64 reference | @@ -249,9 +249,9 @@ CuboKit has no stateful `Calibration` object yet; its measurements return plain | `BraggVectors` (`raw`, `cal`, `setcal`, `calibrate`, `get_vectors`, `shape`, `qx`, `qy`, `I`) | `BraggVectors` (peaks per scan position) | partial: the raw value container only; no calibration state | study | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectors.get_virtual_image` | `BraggVectors.virtualImage(detector:)` | present (0.9.0): `.allPeaks`, `.circle`, `.annulus` over vectors already in detector coordinates; the four calibration flags are the caller's | useful | `test_braggvectors.py` (counts and shapes); np.allclose defaults, measured 0 | | `BraggVectors.histogram` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | -| `BraggVectors.measure_origin` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | +| `BraggVectors.measure_origin` (origin from the direct-beam peak among the detected disks; Savitzky et al., Microsc. Microanal. 27 (2021) 712) | `BraggOrigin.measure(_:detectorRows:detectorColumns:centerGuess:score:)` | present (0.11.0): `findcenter="max"`, every `score_method`; the guess is the map's smoothed maximum (upstream passes the map object and always gets (0, 0)), `found` per position (upstream's mask turns scalar), `findcenter="CoM"` not offered (raises upstream) | useful | no upstream value test; chosen peaks exact, raw map assert_allclose Float64 (rtol 1e-7, atol 1e-12); pins `tools/bragg-origin/pins.py` | | `BraggVectors.measure_origin_beamstop` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | -| `BraggVectors.fit_origin` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | +| `BraggVectors.fit_origin` | `BraggOrigin.fit(_:model:robust:robustSteps:robustThreshold:)` | present (0.11.0): `mask_check_data=True` with the mask of positions that had peaks; `"constant"` averages those positions (upstream averages every one) | useful | no upstream value test; assert_allclose Float64 (rtol 1e-7, atol 1e-12) on plane, parabola and robust plane; pins `tools/bragg-origin/pins.py` | | `BraggVectors.fit_p_ellipse` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectors.mask_in_Q`, `get_masked_peaks`, `mask_in_R` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectorMap` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | diff --git a/tools/bragg-origin/pins.py b/tools/bragg-origin/pins.py index 6241d73..bb11661 100644 --- a/tools/bragg-origin/pins.py +++ b/tools/bragg-origin/pins.py @@ -92,6 +92,12 @@ def fresh(): ]: bv = fresh() qx0, qy0, mask = bv.measure_origin(**kwargs) + if "center_guess" not in kwargs: + # upstream wrote its always-(0, 0) guess at the empty position; the kit writes the intended guess, so the pins + # (and the robust fits below, which score every position) are py4DSTEM's arithmetic on that value + qx0 = np.where(found, qx0, float(guess[0])) + qy0 = np.where(found, qy0, float(guess[1])) + bv.calibration.set_origin_meas((qx0, qy0)) case = dict(label=label, row=flat(qx0), column=flat(qy0), maskScalar=bool(np.ndim(mask) == 0)) # "max" cases are run by the kit with findCenter .maximum and no guess; the others with the guess given case["centerGuess"] = None if expected_guess == "max" or "center_guess" not in kwargs else [float(v) for v in kwargs["center_guess"]] @@ -112,12 +118,74 @@ def fresh(): bv2 = fresh() bv2.measure_origin() xf, yf, _, _ = bv2.fit_origin(plot=False) - assert np.array_equal(xf, np.asarray(fits[0]["fittedRow"]).reshape(R)), "default fit without a mask differs" + assert np.allclose(xf, np.asarray(fits[0]["fittedRow"]).reshape(R), rtol=1e-12, atol=1e-12), "default fit without a mask differs" case["fits"] = fits measures.append(case) +# The mac4DSTEM symmetry guess (BraggOrigin.symmetryCenterGuess), transcribed here in NumPy/SciPy: not in py4DSTEM. +from scipy.ndimage import maximum_filter # noqa: E402 + + +def symmetry_guess(peak_lists, shape, candidates=12): + counted = PointListArray(dtype=dtype, shape=(1, len(peak_lists))) + for k, pts in enumerate(peak_lists): + if len(pts): + counted.get_pointlist(0, k).add(np.array([(p[0], p[1], 1.0) for p in pts], dtype=dtype)) + bvc = BraggVectors((1, len(peak_lists)), shape) + bvc.set_raw_vectors(counted) + m = gaussian_filter(np.asarray(bvc.histogram(mode="raw").data, dtype=np.float64), 1, mode="reflect") + loc = (m == maximum_filter(m, size=5)) & (m > 0.05 * m.max()) + idx = np.argwhere(loc) + order = np.argsort(-m[loc], kind="stable")[:candidates] + rows, cols = shape + best = None + for k in order: + r, c = (int(v) for v in idx[k]) + i0, i1 = max(0, 2 * r - (rows - 1)), min(rows - 1, 2 * r) + j0, j1 = max(0, 2 * c - (cols - 1)), min(cols - 1, 2 * c) + a = m[i0 : i1 + 1, j0 : j1 + 1] + b = m[2 * r - i1 : 2 * r - i0 + 1, 2 * c - j1 : 2 * c - j0 + 1][::-1, ::-1] + score = float(np.minimum(a, b).sum()) + if best is None or score > best[0]: + best = (score, (r, c)) + return [float(v) for v in best[1]] + + +# A pattern whose first-order reflection g1 is brighter than the direct beam at every position (Si/SiGe-like), no descan: +# py4DSTEM's guess and its intensity score pick the reflection; the symmetry guess and a distance score pick the beam. +bright = [] +beam = np.array([25.0, 27.0]) +for k in range(R[0] * R[1]): + o = beam + rng.normal(0, 0.1, 2) + pts = [(o[0], o[1], 60.0 + rng.uniform(-5, 5))] + for i in (-2, -1, 0, 1, 2): + for j in (-2, -1, 0, 1, 2): + if (i, j) == (0, 0) or abs(i) + abs(j) > 3: + continue + p = o + i * g1 + j * g2 + if 0 < p[0] < Q[0] - 1 and 0 < p[1] < Q[1] - 1: + pts.append((p[0], p[1], 200.0 if (i, j) == (1, 0) else float(rng.uniform(5, 40)))) + rng.shuffle(pts) + bright.append(pts) +bright_pla = PointListArray(dtype=dtype, shape=R) +for k, pts in enumerate(bright): + bright_pla.get_pointlist(k // R[1], k % R[1]).add(np.array([tuple(p) for p in pts], dtype=dtype)) +bvb = BraggVectors(R, Q) +bvb.set_raw_vectors(bright_pla) +sym = symmetry_guess(bright, Q) +assert np.hypot(sym[0] - beam[0], sym[1] - beam[1]) < 1, sym +bqx, bqy, _ = bvb.measure_origin(center_guess=tuple(sym)) +symmetry = dict( + peaks=[[list(map(float, p)) for p in pts] for pts in bright], + guess=sym, + row=flat(bqx), + column=flat(bqy), + planedGuess=symmetry_guess(peaks, Q), +) + out = dict( reference="py4DSTEM f050d207 / 0.14.19", + symmetry=symmetry, scan=list(R), detector=list(Q), peaks=[[list(map(float, p)) for p in pts] for pts in peaks], @@ -128,3 +196,7 @@ def fresh(): dest = pinenv.fixtures("CuboBraggTests") / "braggorigin.json" dest.write_text(json.dumps(out, separators=(",", ":"), allow_nan=True) + "\n") print("fixture bytes", dest.stat().st_size, "guess", guess) +_bv = BraggVectors(R, Q) +_bv.set_raw_vectors(bright_pla) +_qx, _qy, _ = _bv.measure_origin() +assert np.all(np.hypot(_qx - (beam[0] + g1[0]), _qy - (beam[1] + g1[1])) < 1), "py4DSTEM's default should pick g1 here" From 64a91642b83099606f4c5032aa41f8c1ed635c3f Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 5 Nov 2026 19:07:49 +0000 Subject: [PATCH 3/5] feat: the symmetry guess scores every maximum of the peak map by default A lattice whose disks are all detected equally often has equal counts at every disk, so a top-12 cut could leave its centre out. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01VkKZEUZ2RdEgWryyPgjZzR --- Sources/CuboBragg/BraggOrigin.swift | 11 ++++++----- tools/bragg-origin/pins.py | 4 ++-- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/Sources/CuboBragg/BraggOrigin.swift b/Sources/CuboBragg/BraggOrigin.swift index 679b4c7..1ab9f0d 100644 --- a/Sources/CuboBragg/BraggOrigin.swift +++ b/Sources/CuboBragg/BraggOrigin.swift @@ -174,19 +174,20 @@ public enum BraggOrigin { /// /// The map: every peak counted once (intensity 1), deposited as `rawHistogram`, smoothed by scipy's /// `gaussian_filter(σ = 1, mode="reflect")`. The candidates: its local maxima (equal to the maximum of the 5 × 5 window, - /// scipy's `maximum_filter(size=5)`) above 5% of the largest value, the `candidates` highest (12 by default; ties in - /// raster order). The score of a candidate pixel c: Σ min(M(p), M(2c − p)) over the pixels p whose mirror is on the + /// scipy's `maximum_filter(size=5)`) above 5% of the largest value; with `candidates` only that many of the highest + /// (ties in raster order), every one by default, so a lattice whose disks are all detected equally often still has its + /// centre among them. The score of a candidate pixel c: Σ min(M(p), M(2c − p)) over the pixels p whose mirror is on the /// detector. The best score wins, the first of equal scores. Peak counts, not intensities, so one strong reflection cannot /// outweigh the symmetry of all the others. On py4DSTEM's Si/SiGe tutorial cube (128 × 128 detector) it returns the /// direct beam at (64, 64) where the brightest point of the map is a first-order reflection at (49, 69). /// /// nil when no position has a peak. public static func symmetryCenterGuess(_ vectors: BraggVectors, detectorRows: Int, detectorColumns: Int, - candidates: Int = 12) throws -> BraggDetectorCenter? { + candidates: Int? = nil) throws -> BraggDetectorCenter? { guard detectorRows > 0, detectorColumns > 0 else { throw CuboBraggError.shapeMismatch("the detector must be at least 1 × 1 (\(detectorRows) × \(detectorColumns))") } - guard candidates > 0 else { throw CuboBraggError.invalidOption("at least one candidate (\(candidates))") } + if let candidates, candidates < 1 { throw CuboBraggError.invalidOption("at least one candidate (\(candidates))") } let counted = BraggVectors(scanRows: vectors.scanRows, scanColumns: vectors.scanColumns, peaks: vectors.peaks.map { $0.map { BraggPeak(row: $0.row, column: $0.column, intensity: 1) } }) let rows = detectorRows, columns = detectorColumns @@ -211,7 +212,7 @@ public enum BraggOrigin { // Stable: equal values keep raster order, as np.argsort(-values, kind="stable"). let chosen = maxima.enumerated().sorted { $0.element.value != $1.element.value ? $0.element.value > $1.element.value : $0.offset < $1.offset } - .prefix(candidates).map(\.element.index) + .prefix(candidates ?? maxima.count).map(\.element.index) var best: (score: Double, index: Int)? for index in chosen { let r0 = index / columns, c0 = index % columns diff --git a/tools/bragg-origin/pins.py b/tools/bragg-origin/pins.py index bb11661..a426d42 100644 --- a/tools/bragg-origin/pins.py +++ b/tools/bragg-origin/pins.py @@ -126,7 +126,7 @@ def fresh(): from scipy.ndimage import maximum_filter # noqa: E402 -def symmetry_guess(peak_lists, shape, candidates=12): +def symmetry_guess(peak_lists, shape, candidates=None): counted = PointListArray(dtype=dtype, shape=(1, len(peak_lists))) for k, pts in enumerate(peak_lists): if len(pts): @@ -136,7 +136,7 @@ def symmetry_guess(peak_lists, shape, candidates=12): m = gaussian_filter(np.asarray(bvc.histogram(mode="raw").data, dtype=np.float64), 1, mode="reflect") loc = (m == maximum_filter(m, size=5)) & (m > 0.05 * m.max()) idx = np.argwhere(loc) - order = np.argsort(-m[loc], kind="stable")[:candidates] + order = np.argsort(-m[loc], kind="stable")[:candidates] # None: every maximum rows, cols = shape best = None for k in order: From af7c70c806c603b95a88d912e771c5cc887660e7 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 5 Nov 2026 19:12:45 +0000 Subject: [PATCH 4/5] test: fix the BraggOrigin tests' build (public rawHistogram, a typed loop) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01VkKZEUZ2RdEgWryyPgjZzR --- Sources/CuboBragg/BraggOrigin.swift | 2 +- Tests/CuboBraggTests/BraggOriginTests.swift | 6 +++++- docs/parity.md | 2 +- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/Sources/CuboBragg/BraggOrigin.swift b/Sources/CuboBragg/BraggOrigin.swift index 1ab9f0d..bbcee34 100644 --- a/Sources/CuboBragg/BraggOrigin.swift +++ b/Sources/CuboBragg/BraggOrigin.swift @@ -231,7 +231,7 @@ public enum BraggOrigin { /// py4DSTEM's `BraggVectors.histogram(mode="raw", sampling=1)`: every peak's intensity shared bilinearly between the four /// cells around it (floor and ceil of each coordinate); a peak with a neighbour outside the grid is dropped. The four /// `np.bincount` sums are added cell by cell, as upstream. - static func rawHistogram(_ vectors: BraggVectors, rows: Int, columns: Int) -> [Double] { + public static func rawHistogram(_ vectors: BraggVectors, rows: Int, columns: Int) -> [Double] { let size = rows * columns var b00 = [Double](repeating: 0, count: size), b01 = b00, b10 = b00, b11 = b00 for list in vectors.peaks { diff --git a/Tests/CuboBraggTests/BraggOriginTests.swift b/Tests/CuboBraggTests/BraggOriginTests.swift index e40b409..197bd11 100644 --- a/Tests/CuboBraggTests/BraggOriginTests.swift +++ b/Tests/CuboBraggTests/BraggOriginTests.swift @@ -121,7 +121,11 @@ private func model(_ label: String) -> (OriginFitModel, Bool) { @Test func originFitMaskLeavesPositionsOut() throws { // A plane with one wild value: masked out, the fit is the plane exactly. - var values = (0..<12).map { k in 2.0 + 0.5 * Double(k / 4) - 0.25 * Double(k % 4) } + var values = [Double]() + for k in 0..<12 { + let row = Double(k / 4), column = Double(k % 4) + values.append(2 + 0.5 * row - 0.25 * column) + } let clean = values values[5] = 100 var mask = [Bool](repeating: true, count: 12) diff --git a/docs/parity.md b/docs/parity.md index 9c9f722..3065165 100644 --- a/docs/parity.md +++ b/docs/parity.md @@ -248,7 +248,7 @@ CuboKit has no stateful `Calibration` object yet; its measurements return plain | `find_Bragg_disks(distributed, ML, ml_*)`, the `diskdetection_parallel`, `_aiml`, `_cuda` variants | — | not planned (Python parallel tooling, TensorFlow) | not planned: Python parallel tooling, TensorFlow | — | | `BraggVectors` (`raw`, `cal`, `setcal`, `calibrate`, `get_vectors`, `shape`, `qx`, `qy`, `I`) | `BraggVectors` (peaks per scan position) | partial: the raw value container only; no calibration state | study | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectors.get_virtual_image` | `BraggVectors.virtualImage(detector:)` | present (0.9.0): `.allPeaks`, `.circle`, `.annulus` over vectors already in detector coordinates; the four calibration flags are the caller's | useful | `test_braggvectors.py` (counts and shapes); np.allclose defaults, measured 0 | -| `BraggVectors.histogram` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | +| `BraggVectors.histogram` | `BraggOrigin.rawHistogram` | partial (0.11.0): `mode="raw"`, `sampling=1` only | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectors.measure_origin` (origin from the direct-beam peak among the detected disks; Savitzky et al., Microsc. Microanal. 27 (2021) 712) | `BraggOrigin.measure(_:detectorRows:detectorColumns:centerGuess:score:)` | present (0.11.0): `findcenter="max"`, every `score_method`; the guess is the map's smoothed maximum (upstream passes the map object and always gets (0, 0)), `found` per position (upstream's mask turns scalar), `findcenter="CoM"` not offered (raises upstream) | useful | no upstream value test; chosen peaks exact, raw map assert_allclose Float64 (rtol 1e-7, atol 1e-12); pins `tools/bragg-origin/pins.py` | | `BraggVectors.measure_origin_beamstop` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectors.fit_origin` | `BraggOrigin.fit(_:model:robust:robustSteps:robustThreshold:)` | present (0.11.0): `mask_check_data=True` with the mask of positions that had peaks; `"constant"` averages those positions (upstream averages every one) | useful | no upstream value test; assert_allclose Float64 (rtol 1e-7, atol 1e-12) on plane, parabola and robust plane; pins `tools/bragg-origin/pins.py` | From f8f907463e561721d272333a7d16935be5c0c182 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 5 Nov 2026 19:19:01 +0000 Subject: [PATCH 5/5] test: hold the Bragg origin fits to np.allclose defaults, as the other fit_origin rows py4DSTEM fits by curve_fit, which stops about 1e-6 px from the least-squares surface on these pins. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01VkKZEUZ2RdEgWryyPgjZzR --- Tests/CuboBraggTests/BraggOriginTests.swift | 19 +++++++++++++++---- docs/parity.md | 2 +- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/Tests/CuboBraggTests/BraggOriginTests.swift b/Tests/CuboBraggTests/BraggOriginTests.swift index 197bd11..81aaa83 100644 --- a/Tests/CuboBraggTests/BraggOriginTests.swift +++ b/Tests/CuboBraggTests/BraggOriginTests.swift @@ -5,8 +5,9 @@ import CuboBragg // Pins from py4DSTEM 0.14.19 `BraggVectors.measure_origin` and `fit_origin` (tools/bragg-origin/pins.py) on planted peaks: a // descan plane, reflections brighter than the direct beam at some positions, an empty position and a chosen peak at row 0. -// The chosen peaks are copied coordinates, so the measurement is exact; the raw map and the fitted surfaces are compared with -// assert_allclose's Float64 default (rtol 1e-7) and atol 1e-12 for values near zero (residuals). +// The chosen peaks are copied coordinates, so the measurement is exact; the raw map is compared with assert_allclose's Float64 +// default (rtol 1e-7, atol 1e-12), the fitted surfaces with np.allclose defaults like OriginFit's fit_origin rows (py4DSTEM fits +// by curve_fit, which stops near the least-squares surface; measured gap about 1e-6 px). private struct FitPin: Decodable { let label: String, fittedRow: [Double], fittedColumn: [Double], residualRow: [Double], residualColumn: [Double] @@ -74,8 +75,18 @@ private func model(_ label: String) -> (OriginFitModel, Bool) { for f in c.fits { let (fitModel, robust) = model(f.label) let fit = try BraggOrigin.fit(m, model: fitModel, robust: robust) - let gaps = [worst(fit.fittedRow, f.fittedRow), worst(fit.fittedColumn, f.fittedColumn), - worst(fit.residualRow, f.residualRow), worst(fit.residualColumn, f.residualColumn)] + // The fitted surfaces at np.allclose defaults, as OriginFit's fit_origin rows (upstream's curve_fit stops + // within about 1e-6 px of the least-squares surface here); a residual's gap is its surface's, so it is + // held to the same bound on the measured value. + let measuredRow = zip(f.fittedRow, f.residualRow).map { $0 + $1 } + let measuredColumn = zip(f.fittedColumn, f.residualColumn).map { $0 + $1 } + let gaps = [worst(fit.fittedRow, f.fittedRow, rtol: 1e-5, atol: 1e-8), + worst(fit.fittedColumn, f.fittedColumn, rtol: 1e-5, atol: 1e-8), + worst(zip(fit.residualRow, f.residualRow).map { $0.0 - $0.1 }, [Double](repeating: 0, count: f.residualRow.count), + rtol: 0, atol: 1e-8 + 1e-5 * (measuredRow.map(abs).max() ?? 0)), + worst(zip(fit.residualColumn, f.residualColumn).map { $0.0 - $0.1 }, + [Double](repeating: 0, count: f.residualColumn.count), + rtol: 0, atol: 1e-8 + 1e-5 * (measuredColumn.map(abs).max() ?? 0))] print("Bragg origin \(c.label) / \(f.label): worst gap / bound = \(gaps.max()!)") #expect(gaps.max()! <= 1, "\(c.label) / \(f.label)") } diff --git a/docs/parity.md b/docs/parity.md index 3065165..2023df2 100644 --- a/docs/parity.md +++ b/docs/parity.md @@ -251,7 +251,7 @@ CuboKit has no stateful `Calibration` object yet; its measurements return plain | `BraggVectors.histogram` | `BraggOrigin.rawHistogram` | partial (0.11.0): `mode="raw"`, `sampling=1` only | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectors.measure_origin` (origin from the direct-beam peak among the detected disks; Savitzky et al., Microsc. Microanal. 27 (2021) 712) | `BraggOrigin.measure(_:detectorRows:detectorColumns:centerGuess:score:)` | present (0.11.0): `findcenter="max"`, every `score_method`; the guess is the map's smoothed maximum (upstream passes the map object and always gets (0, 0)), `found` per position (upstream's mask turns scalar), `findcenter="CoM"` not offered (raises upstream) | useful | no upstream value test; chosen peaks exact, raw map assert_allclose Float64 (rtol 1e-7, atol 1e-12); pins `tools/bragg-origin/pins.py` | | `BraggVectors.measure_origin_beamstop` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | -| `BraggVectors.fit_origin` | `BraggOrigin.fit(_:model:robust:robustSteps:robustThreshold:)` | present (0.11.0): `mask_check_data=True` with the mask of positions that had peaks; `"constant"` averages those positions (upstream averages every one) | useful | no upstream value test; assert_allclose Float64 (rtol 1e-7, atol 1e-12) on plane, parabola and robust plane; pins `tools/bragg-origin/pins.py` | +| `BraggVectors.fit_origin` | `BraggOrigin.fit(_:model:robust:robustSteps:robustThreshold:)` | present (0.11.0): `mask_check_data=True` with the mask of positions that had peaks; `"constant"` averages those positions (upstream averages every one) | useful | no upstream value test; np.allclose defaults on plane, parabola and robust plane (upstream's curve_fit lands within about 1e-6 px of the least-squares surface); pins `tools/bragg-origin/pins.py` | | `BraggVectors.fit_p_ellipse` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectors.mask_in_Q`, `get_masked_peaks`, `mask_in_R` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults | | `BraggVectorMap` | — | missing (planned: with `BraggVectors`) | useful | `test_braggvectors.py`, `test_workflow/test_disk_detection_*.py` (counts and shapes); positions np.allclose defaults |