docs: mac4DSTEM 5.1.1 and macTEM 1.1.1, macOS 26

index.html

@@ -130,7 +130,7 @@
 
   <div class="wrap">
     <section class="hero">
-      <p class="eyebrow">Version 5.0.1 — available now</p>
+      <p class="eyebrow">Version 5.1.1 — available now</p>
 
       <div class="hero-head">
         <img class="hero-logo" src="/assets/icon-512.png" alt="" width="512" height="512">
@@ -148,11 +148,11 @@ <h1 class="wordmark">mac<em>4D</em>STEM</h1>
         <a class="get-btn" href="https://github.com/mac4DSTEM/mac4DSTEM/releases/latest/download/mac4DSTEM.dmg">
           <span aria-hidden="true">↓</span> Download for macOS
         </a>
-        <p class="get-meta">Version 5.0.1 · 11.6 MB · signed and notarized</p>
+        <p class="get-meta">Version 5.1.1 · 17.7 MB · signed and notarized</p>
       </div>
 
       <ul class="specs">
-        <li>macOS 27+</li>
+        <li>macOS 26+</li>
         <li>Apple silicon</li>
         <li>Metal &amp; Neural Engine</li>
         <li>GPL-3.0</li>
@@ -357,12 +357,12 @@ <h2 class="section-label" id="availability-h">Availability</h2>
 
       <ol class="steps">
         <li data-state="done">
-          <h3>Version 5.0.1 complete</h3>
+          <h3>Version 5.1 complete</h3>
           <p>The analysis workflow is finished and covered by automated tests, including parity harnesses measured against py4DSTEM on experimental data.</p>
         </li>
         <li data-state="done">
           <h3>Download</h3>
-          <p>Version 5.0.1 is <a href="https://github.com/mac4DSTEM/mac4DSTEM/releases/latest/download/mac4DSTEM.dmg">available as a signed, notarized disk image</a>. Open it and drag mac4DSTEM to your Applications folder — it launches without a security warning, and needs nothing else installed.</p>
+          <p>Version 5.1.1 is <a href="https://github.com/mac4DSTEM/mac4DSTEM/releases/latest/download/mac4DSTEM.dmg">available as a signed, notarized disk image</a>. Open it and drag mac4DSTEM to your Applications folder — it launches without a security warning, and needs nothing else installed.</p>
         </li>
         <li data-state="done">
           <h3>Source release</h3>
@@ -375,7 +375,7 @@ <h3>Distribution</h3>
       </ol>
 
       <p class="band-note">
-        Requires macOS 27 or later on Apple silicon (for macOS 14–26, <a href="https://github.com/mac4DSTEM/mac4DSTEM/releases/tag/v3.0.0">version 3.0.0</a> remains available). No external runtime, toolchain, or library
+        Requires macOS 26 or later on Apple silicon (for macOS 14–25, <a href="https://github.com/mac4DSTEM/mac4DSTEM/releases/tag/v3.0.0">version 3.0.0</a> remains available). No external runtime, toolchain, or library
         installation is needed — all dependencies are contained within the application. The Merlin
         MIB and EMPAD RAW readers are preview-grade pending further vendor data.
       </p>
@@ -417,10 +417,10 @@ <h2 id="contact-h">Enquiries, issue reports, and collaboration</h2>
   "name": "mac4DSTEM",
   "url": "https://mac4dstem.com/",
   "applicationCategory": "ScientificApplication",
-  "operatingSystem": "macOS 27 or later, Apple silicon",
-  "softwareVersion": "5.0.1",
+  "operatingSystem": "macOS 26 or later, Apple silicon",
+  "softwareVersion": "5.1.1",
   "downloadUrl": "https://github.com/mac4DSTEM/mac4DSTEM/releases/latest/download/mac4DSTEM.dmg",
-  "fileSize": "11.6 MB",
+  "fileSize": "17.7 MB",
   "license": "https://www.gnu.org/licenses/gpl-3.0.html",
   "description": "A free, native macOS application for interactive 4D-STEM analysis: virtual imaging, Bragg disk detection, strain and orientation mapping, and phase reconstruction, computed natively on Apple silicon.",
   "screenshot": "https://mac4dstem.com/assets/workspace.png",

mactem/index.html

@@ -48,7 +48,7 @@
 
   <div class="wrap">
     <section class="hero">
-      <p class="eyebrow">Version 1.0.0 · part of the macEM suite, beside <a class="u-link" href="/">mac4DSTEM</a></p>
+      <p class="eyebrow">Version 1.1.1 · part of the macEM suite, beside <a class="u-link" href="/">mac4DSTEM</a></p>
 
       <div class="hero-head">
         <h1 class="wordmark">mac<em>TEM</em></h1>
@@ -65,7 +65,7 @@ <h1 class="wordmark">mac<em>TEM</em></h1>
         <a class="get-btn" href="https://github.com/mac4DSTEM/macTEM/releases/latest/download/macTEM.dmg">
           <span aria-hidden="true">↓</span> Download for macOS
         </a>
-        <p class="get-meta">Version 1.0.0 · 19.7 MB · signed and notarized · macOS 27+ on Apple silicon</p>
+        <p class="get-meta">Version 1.1.1 · 17.9 MB · signed and notarized · macOS 26+ on Apple silicon</p>
       </div>
 
       <ul class="specs">
@@ -155,7 +155,7 @@ <h2 class="section-label" id="validation-h">Provenance and validation</h2>
         </div>
 
         <dl class="facts">
-          <div><dt>Requires</dt><dd>macOS 27, Apple silicon</dd></div>
+          <div><dt>Requires</dt><dd>macOS 26, Apple silicon</dd></div>
           <div><dt>Built on</dt><dd>FormatoKit, ImagoKit, DiffractoKit, SpectroKit</dd></div>
           <div><dt>Runtime</dt><dd>Self-contained</dd></div>
           <div><dt>Licence</dt><dd>GPL-3.0</dd></div>
@@ -195,10 +195,10 @@ <h2 id="contact-h">Questions, issues, collaboration</h2>
   "name": "macTEM",
   "url": "https://mac4dstem.com/mactem/",
   "applicationCategory": "ScientificApplication",
-  "operatingSystem": "macOS 27 or later, Apple silicon",
-  "softwareVersion": "1.0.0",
+  "operatingSystem": "macOS 26 or later, Apple silicon",
+  "softwareVersion": "1.1.1",
   "downloadUrl": "https://github.com/mac4DSTEM/macTEM/releases/latest/download/macTEM.dmg",
-  "fileSize": "19.7 MB",
+  "fileSize": "17.9 MB",
   "license": "https://www.gnu.org/licenses/gpl-3.0.html",
   "description": "A native macOS application for TEM: calibrated images and FFTs, EDS and EELS spectrum images with quantification, and electron diffraction, computed natively on Apple silicon.",
   "offers": {

suite/benchmarks.html

@@ -68,10 +68,10 @@ <h4>FFT work of 2026-10-22 (real-input transform, mixed radix): before and after
 <h4>Backend decision per function</h4>
 <p>Today every function above is CPU (Accelerate, Float64 reference plus Float32 where it exists). <strong>No function has a Metal path, so no Metal break-even has been measured</strong>; the column says what the CPU table implies and what a large-size run must measure. A guess is not recorded as a measurement.</p>
 <div class="tablewrap"><table><thead><tr><th>function</th><th>backend today</th><th>decision</th></tr></thead><tbody><tr><td><code>fft2</code> Float32/Float64</td><td>CPU</td><td>CPU only at 128² to 512² (0.02 to 2 ms: a Metal dispatch cannot win). Float64 lengths of prime factors 2, 3, 5, 7 run on the mixed-radix plan. Work item: a Metal Float32 FFT prototype, break-even not measured yet (2048², 4096², 8192²)</td></tr><tr><td><code>rfft2</code>, <code>irfft2</code>, <code>butterworth</code>, masked registration</td><td>CPU</td><td>CPU; real-input transform since 2026-10-22 (0.7 ms Float64 at 512², 1.1 times NumPy); a Metal path would have to beat 0.4 to 0.7 ms at 512², which a dispatch cannot, so the break-even is again a question for 2048² and up, not measured yet</td></tr><tr><td><code>gaussian_filter</code>, <code>uniform_filter</code>, <code>difference_of_gaussians</code></td><td>CPU</td><td>CPU only at 128² to 512² (1 to 4 ms, memory bound). Metal candidate above 2048² for large sigma; not measured yet</td></tr><tr><td><code>shift</code> order 1</td><td>CPU</td><td>CPU only: 0.65 ms at 512²</td></tr><tr><td><code>shift</code> order 3 (prefilter plus taps)</td><td>CPU</td><td>CPU; the prefilter is sequential along lines, the tap sum is the Metal-friendly part: candidate above 2048², not measured</td></tr><tr><td><code>phase_cross_correlation</code></td><td>CPU</td><td>CPU; FFT-bound, follows <code>fft2</code></td></tr><tr><td><code>profile_line</code></td><td>CPU</td><td>CPU: small arrays stay on the CPU</td></tr><tr><td><code>label</code></td><td>CPU</td><td>CPU: union-find is not a Metal workload</td></tr><tr><td><code>peak_local_max</code></td><td>CPU</td><td>CPU; the window maximum is a Metal candidate only above 4096²</td></tr></tbody></table></div>
-<h2 id="diffractokit">DiffractoKit 0.11.0</h2>
-<p><a class="u-link" href="https://github.com/mac4DSTEM/DiffractoKit/blob/0.11.0/docs/benchmarks.md">Full file and method</a></p>
+<h2 id="diffractokit">DiffractoKit 0.12.0</h2>
+<p><a class="u-link" href="https://github.com/mac4DSTEM/DiffractoKit/blob/0.12.0/docs/benchmarks.md">Full file and method</a></p>
 <p>Measured 2026-10-21. Small and typical sizes only; the <strong>large</strong> rows are not measured yet (they run with <code>DIFFRACTO_BENCH_LARGE=1</code>). Best of five after one warm-up: the median is the figure, the spread (min-max) is in brackets. Same inputs on both sides (<code>tools/benchmarks/gen_inputs.py</code> writes them), Float64 unless the function is Float32 by design (ACOM, ellipse pattern pixels).</p>
-<div class="tablewrap"><table><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Machine</td><td>Apple M5 Pro, 64 GB, macOS 27.0.1 (Darwin 27.0.0)</td></tr><tr><td>Toolchain</td><td>Apple Swift 6.4, <code>swift build -c release</code> (separate package <code>tools/benchmarks</code>)</td></tr><tr><td>Python</td><td>3.12.14, NumPy 2.5.4, SciPy 1.18.1, in two virtual environments: py4DSTEM 0.14.19 in one; pymatgen-core 2026.10.16 (the code; its <code>pymatgen</code> metapackage dist-info reads 2026.9.24), orix 0.15.0 and spglib 2.8.0 in the other</td></tr><tr><td>Load</td><td><strong>10.5 on a 12-core machine while other builds ran</strong> (uptime 17:39): every figure carries that noise, the single-core rows least. Re-run when the machine is quiet before quoting a ratio.</td></tr><tr><td>Pin note</td><td>The orix environment runs pymatgen-core <strong>2026.10.16</strong> under a <code>pymatgen</code> 2026.9.24 metapackage; the fixtures record <code>pymatgen</code> 2026.9.24 and, except <code>cif-symmetry-pins.json</code> (generated 2026-10-21 on this install, byte-identical on regeneration), <code>pymatgen-core</code> 2026.9.23. Nothing here compares numbers, only time</td></tr><tr><td>0.9.1</td><td>The <code>symmetry_detection</code> and <code>cif_import</code> rows above are the 2026-10-21 measurement of 0.9.0. 0.9.1 replaced the closure check that took 95 % of them (see <code>docs/open-items.md</code>); measured in-process on the same <code>al_super2.cif</code> (machine load 3.6): <code>cif_import</code> typical median 67 ms (min 67, max 77), against 297 ms (one run at load 9) before the change and the table&#x27;s 351 ms. Not yet re-run through <code>run.sh</code></td></tr></tbody></table></div>
+<div class="tablewrap"><table><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Machine</td><td>Apple M5 Pro, 64 GB, macOS 27.0.1 (Darwin 27.0.0)</td></tr><tr><td>Toolchain</td><td>Apple Swift 6.4, <code>swift build -c release</code> (separate package <code>tools/benchmarks</code>)</td></tr><tr><td>Python</td><td>3.12.14, NumPy 2.5.4, SciPy 1.18.1, in two virtual environments: py4DSTEM 0.14.19 in one; pymatgen-core 2026.10.16 (the code; its <code>pymatgen</code> metapackage dist-info reads 2026.9.24), orix 0.15.0 and spglib 2.8.0 in the other</td></tr><tr><td>Load</td><td><strong>10.5 on a 12-core machine while other builds ran</strong> (uptime 17:39): every figure carries that noise, the single-core rows least. Re-run when the machine is quiet before quoting a ratio.</td></tr><tr><td>Pin note</td><td>The pymatgen environment runs pymatgen-core <strong>2026.10.16</strong> under a <code>pymatgen</code> 2026.9.24 metapackage, and every fixture that records pymatgen-core records 2026.10.16 (<code>tools/pins/requirements/pymatgen.txt</code>). Nothing here compares numbers, only time</td></tr><tr><td>0.9.1</td><td>The <code>symmetry_detection</code> and <code>cif_import</code> rows above are the 2026-10-21 measurement of 0.9.0. 0.9.1 replaced the closure check that took 95 % of them (see <code>docs/parity.md</code>, known limits); measured in-process on the same <code>al_super2.cif</code> (machine load 3.6): <code>cif_import</code> typical median 67 ms (min 67, max 77), against 297 ms (one run at load 9) before the change and the table&#x27;s 351 ms. Not yet re-run through <code>run.sh</code></td></tr></tbody></table></div>
 <p>Re-run from a fresh clone of the kit, on a quiet Mac: <code>gen_inputs.py</code> writes the shared inputs, <code>run.sh</code> builds the benchmark package (<code>tools/benchmarks</code>, release) and prints the kit&#x27;s JSON lines, <code>bench_python.py</code> prints the Python lines, and <code>merge.py</code> turns them into the table below. Add <code>DIFFRACTO_BENCH_LARGE=1</code> to <code>run.sh</code> and <code>bench_python.py</code> for the large rows.</p>
 <p>The raw lines of the run below are <code>tools/benchmarks/results/2026-10-21.jsonl</code>.</p>
 <h4>Per-function table (ms per call; <code>acom_match_scan</code> is ms per pattern)</h4>
@@ -84,7 +84,7 @@ <h4>What the rows compare, and what they do not</h4>
 <p><code>kinematic_reflections</code>: the kit&#x27;s <code>kinematicReflections(kMax:retainingAll:false)</code> against py4DSTEM&#x27;s</p>
 <p><code>Crystal.calculate_structure_factors</code> on the same C2/m cell; the kit is 1.8 to 4.5 times faster. Both use the Lobato-Van Dyck table.</p>
 <p><code>symmetry_detection</code> and <code>cif_import</code>: the kit&#x27;s <code>CIFImport.read</code> always detects the symmetry of the cell it reads;</p>
-<p>pymatgen&#x27;s <code>CifParser</code> only parses and <code>SpacegroupAnalyzer</code> is spglib in C. On a 32-atom P1 cell (an fcc supercell with 1 536 operations) the kit took 361 ms against 13 ms, <strong>28 times slower</strong>; on a four-atom cell they are level. The cause was the closure check, not the symmetry search (<code>docs/open-items.md</code>); 0.9.1 made the check about five times faster, the rest stays listed there.</p>
+<p>pymatgen&#x27;s <code>CifParser</code> only parses and <code>SpacegroupAnalyzer</code> is spglib in C. On a 32-atom P1 cell (an fcc supercell with 1 536 operations) the kit took 361 ms against 13 ms, <strong>28 times slower</strong>; on a four-atom cell they are level. The cause was the closure check, not the symmetry search (<code>docs/parity.md</code>, known limits); 0.9.1 made the check about five times faster, the rest stays listed there.</p>
 <p><code>orientation_reduce</code>: the kit reduces one matrix at a time (24 proper operators); orix reduces a vectorised batch</p>
 <p>(<code>Orientation.from_matrix</code> + <code>map_into_symmetry_reduced_zone</code>). The kit is 2.5 to 12 times faster at these sizes.</p>
 <p><code>ellipse_fit_1d</code>: level with py4DSTEM&#x27;s <code>fit_ellipse_1D</code> (0.94 and 1.1).</p>
@@ -93,7 +93,7 @@ <h4>What the rows compare, and what they do not</h4>
 <p><code>acom_match_scan</code>: the kit&#x27;s <code>matchAll</code> over the whole scan against py4DSTEM&#x27;s <code>match_single_pattern</code> per pattern (one</p>
 <p>core; Python&#x27;s per-pattern time includes its own refinements, so this is a time, not a like-for-like algorithm). Per pattern the kit on one core is 14 times (8 x 8 scan) and 9 times (32 x 32) faster than py4DSTEM on one core, and on all cores 105 and 81 times.</p>
 <h4>Backend decision per function</h4>
-<div class="tablewrap"><table><thead><tr><th>Function</th><th>Backend</th><th>Why / break-even</th></tr></thead><tbody><tr><td><code>niggli_reduce</code>, <code>reflections</code>, <code>kinematic_reflections</code>, <code>symmetry_detection</code>, <code>cif_import</code></td><td>CPU only</td><td>small arrays, branchy integer logic, readers; the kit is already faster than Python except where <code>docs/open-items.md</code> lists it</td></tr><tr><td><code>orientation_reduce</code></td><td>CPU only</td><td>24 small matrix products per rotation; the batch (100 000 in 0.2 s) is bound by memory traffic, a GPU would pay the transfer for no gain at these sizes</td></tr><tr><td><code>ellipse_fit_1d</code></td><td>CPU only</td><td>one 1D least-squares fit per pattern; level with Python</td></tr><tr><td><code>acom_library</code></td><td>CPU only</td><td>built once per phase (60 ms for 600 axes), not per pattern</td></tr><tr><td><code>acom_match_scan</code></td><td><strong>CPU all cores; Metal not above any measured N</strong></td><td>Metal 0.294 ms per pattern at 64 patterns, 0.236 at 1 024, against 0.245 and 0.216 on all CPU cores: the break-even is <strong>not reached up to 1 024 patterns</strong> (the Metal path is 7 to 22 % slower); the 128 x 128 row (16 384 patterns) is not measured yet, and the Metal path stays an option (<code>ACOMBackend.metal</code>), not the default. A larger library (2 400 axes) is the other axis still to measure</td></tr></tbody></table></div>
+<div class="tablewrap"><table><thead><tr><th>Function</th><th>Backend</th><th>Why / break-even</th></tr></thead><tbody><tr><td><code>niggli_reduce</code>, <code>reflections</code>, <code>kinematic_reflections</code>, <code>symmetry_detection</code>, <code>cif_import</code></td><td>CPU only</td><td>small arrays, branchy integer logic, readers; the kit is already faster than Python except where <code>docs/parity.md</code> (known limits) lists it</td></tr><tr><td><code>orientation_reduce</code></td><td>CPU only</td><td>24 small matrix products per rotation; the batch (100 000 in 0.2 s) is bound by memory traffic, a GPU would pay the transfer for no gain at these sizes</td></tr><tr><td><code>ellipse_fit_1d</code></td><td>CPU only</td><td>one 1D least-squares fit per pattern; level with Python</td></tr><tr><td><code>acom_library</code></td><td>CPU only</td><td>built once per phase (60 ms for 600 axes), not per pattern</td></tr><tr><td><code>acom_match_scan</code></td><td><strong>CPU all cores; Metal not above any measured N</strong></td><td>Metal 0.294 ms per pattern at 64 patterns, 0.236 at 1 024, against 0.245 and 0.216 on all CPU cores: the break-even is <strong>not reached up to 1 024 patterns</strong> (the Metal path is 7 to 22 % slower); the 128 x 128 row (16 384 patterns) is not measured yet, and the Metal path stays an option (<code>ACOMBackend.metal</code>), not the default. A larger library (2 400 axes) is the other axis still to measure</td></tr></tbody></table></div>
 <p>Numbers in <code>docs/parity.md</code> are untouched by this table (time only).</p>
 <h2 id="spectrokit">SpectroKit 0.13.2</h2>
 <p><a class="u-link" href="https://github.com/mac4DSTEM/SpectroKit/blob/0.13.2/docs/benchmarks.md">Full file and method</a></p>