Version 5.1.1 — available now
+Version 5.2.0 — available now
@@ -148,7 +148,7 @@ -
@@ -362,7 +362,7 @@
-
Download
-Version 5.1.1 is available as a signed, notarized disk image. Open it and drag mac4DSTEM to your Applications folder — it launches without a security warning, and needs nothing else installed.
+Version 5.2.0 is available as a signed, notarized disk image. Open it and drag mac4DSTEM to your Applications folder — it launches without a security warning, and needs nothing else installed.
-
Source release
@@ -418,7 +418,7 @@Enquiries, issue reports, and collaboration
"url": "https://mac4dstem.com/", "applicationCategory": "ScientificApplication", "operatingSystem": "macOS 26 or later, Apple silicon", - "softwareVersion": "5.1.1", + "softwareVersion": "5.2.0", "downloadUrl": "https://github.com/mac4DSTEM/mac4DSTEM/releases/latest/download/mac4DSTEM.dmg", "fileSize": "17.7 MB", "license": "https://www.gnu.org/licenses/gpl-3.0.html", diff --git a/suite/benchmarks.html b/suite/benchmarks.html index cbddb5a..9380728 100644 --- a/suite/benchmarks.html +++ b/suite/benchmarks.html @@ -105,8 +105,8 @@Results
Sizes: quantification small 8 x 8, typical 256 x 256 (3 elements), large 1024 x 1024; thickness small 8 x 8, typical 64 x 64 (2048 channels), large 256 x 256. Large: not measured yet (
largein the size list adds it).Backend decision per function (CPU float64 reference, Metal float32 where size pays)
-function decision why quantification maps, all methods (the Python column converts to weight percent only; Swift returns both) CPU only (parallel over pixels since 0.8.0) 9 to 40 ms for 65 536 pixels; per pixel the work is 10 to 500 ns of arithmetic, so a Metal dispatch pays only above about 1e6 pixels; the break-even is not measured (the large size is not measured yet). Not a work item until a large-size run shows a slow large image t/lambda log ratio, fixed threshold CPU only; slower than Python on the typical size (0.49x), first in docs/parity.md(known limits)single-threaded scalar passes against NumPy's vectorised axis sums t/lambda log ratio, estimated threshold CPU only 5x faster than Python at the typical size (Python's per-pixel Savitzky-Golay search dominates) EDX line fit, auto ID, EELS background fit, edge quantification not benchmarked in 0.8.0 need a Velox spectrum image or a planted EELS image on both sides; listed in docs/parity.md(known limits)CuboKit 0.10.0
- +CuboKit 0.11.0
+Kit against py4DSTEM, same inputs, Float32 cubes, one run of
tools/benchmarkson 2026-10-21. Harness:Tests/CuboVirtualTests/BenchmarkHarness.swift(Swift, built withswift test -c release, skipped unlessCUBOKIT_BENCH_DIRis set) andtools/benchmarks/bench.py(the py4DSTEM side and the merge). Both read the same.npycubes. Best of five after one warm-up; cells are best / median (spread = slowest - best) in milliseconds; "CPU vs py4DSTEM" is py4DSTEM's best divided by the kit CPU's best (above 1 the kit is faster). The cubes are held in memory (MemoryCube;DataCube(data=ndarray)). The Metal columns copy each tile to the GPU on every call; the_residentrows hold the cube in aResidentCube(one Metal buffer) first.Sizes (scan rows x columns x detector rows x columns): small 32x32x32x32 (1.0 M values), mid 64x64x32x32 (4.2 M), typical 100x100x64x64 (41.9 M, 168 MB). The large size (330x330x64x64, 1.8 GB) and the Bragg, strain, DPC, parallax and ptychography functions are not measured yet. The machine was shared during the run (load average 5 to 11 from other builds), so the spreads are wider than on a quiet Mac.
Re-run from a fresh clone of the kit, on a quiet Mac, one benchmark at a time (the typical cube is 168 MB on disk and in memory):
diff --git a/suite/index.html b/suite/index.html index f92f717..506f7de 100644 --- a/suite/index.html +++ b/suite/index.html @@ -40,10 +40,10 @@The macEM suite
ImagoKit Image numerics: FFT, profiles, filters, alignment, measurement, segmentation. NumPy, SciPy, scikit-image 0.9.1 36 of 93 API DiffractoKit Crystallography, diffraction geometry, ring and ellipse fits, indexing, orientation and ACOM. pymatgen, py4DSTEM, orix 0.12.0 22 of 148 API SpectroKit EDX quantification and EELS thickness, background and edges. HyperSpy, exSpy 0.13.2 58 of 100 API CuboKit The 4D-STEM library: virtual imaging, centre of mass, DPC, strain, Bragg disks, parallax, ptychography. py4DSTEM 0.10.0 135 of 215 API CuboKit The 4D-STEM library: virtual imaging, centre of mass, DPC, strain, Bragg disks, parallax, ptychography. py4DSTEM 0.11.0 135 of 215 API SimulatoKit Multislice TEM/STEM simulation: potentials, frozen phonons, scans, detectors, CTF. abTEM 0.5.0 38 of 129 API