Data update

.claude/settings.local.json

@@ -2,7 +2,83 @@
   "permissions": {
     "allow": [
       "Read(///wsl.localhost/ubuntu/home/alvaradovictor/HuggingFace/Datasets/**)",
-      "Read(//mnt/wsl/ubuntu/home/alvaradovictor/HuggingFace/Datasets/**)"
+      "Read(//mnt/wsl/ubuntu/home/alvaradovictor/HuggingFace/Datasets/**)",
+      "Bash(find 'C:\\\\Users\\\\valva\\\\Downloads\\\\Info\\\\info' -not -path '*/\\\\.*')",
+      "Bash(git -c safe.directory='*' log main..pr/4 --oneline)",
+      "Bash(git -c safe.directory='*' diff main...pr/4 --stat)",
+      "Bash(git -c safe.directory='*' diff main...pr/4 -- scripts/ README.md)",
+      "Bash(git -c safe.directory='*' diff main...pr/4 -- .gitignore)",
+      "Bash(git -c safe.directory='*' branch -vv)",
+      "Bash(git -c safe.directory='*' log --oneline -1 main)",
+      "Bash(git -c safe.directory='*' log --oneline -1 pr/4)",
+      "Bash(git -c safe.directory='*' branch -r)",
+      "Bash(git -c safe.directory='*' rev-parse origin/pr/4)",
+      "Bash(git -c safe.directory='*' rev-parse pr/4)",
+      "Bash(git -c safe.directory='*' remote -v)",
+      "Bash(gh pr *)",
+      "Bash(git -c safe.directory='*' ls-remote origin 'refs/pr/*')",
+      "Bash(git -c safe.directory='*' for-each-ref)",
+      "Bash(git -c safe.directory='*' config --get branch.pr/4.remote)",
+      "Bash(git -c safe.directory='*' config --get branch.pr/4.merge)",
+      "Bash(git -c safe.directory='*' status)",
+      "Bash(git -c safe.directory='*' status --porcelain=v1 -- source/2026_06_25 source/2026_06_26 source/2026_06_30)",
+      "Bash(python3 -c ' *)",
+      "PowerShell(Get-Command uv -ErrorAction SilentlyContinue)",
+      "PowerShell(Get-Command python -ErrorAction SilentlyContinue)",
+      "PowerShell(Get-Command python3 -ErrorAction SilentlyContinue)",
+      "PowerShell(python -c \"import h5py; print\\(h5py.__version__\\)\" 2>&1)",
+      "PowerShell(python -c \"import openpyxl; print\\(openpyxl.__version__\\)\" 2>&1)",
+      "PowerShell(pip install h5py --quiet)",
+      "PowerShell(python -c \"import h5py; print\\(h5py.__version__\\)\")",
+      "Bash(export HDF5_USE_FILE_LOCKING=FALSE)",
+      "Bash(.venv/bin/python3 --version)",
+      "Bash(python3 -c \"print\\('ok'\\)\")",
+      "PowerShell(python -c \"import matplotlib; print\\(matplotlib.__version__\\)\" 2>&1)",
+      "Bash(.venv/bin/python3 -c \"print\\(1\\)\")",
+      "PowerShell(python scripts/plots/01_composite.py)",
+      "Bash(git -c safe.directory='*' status --porcelain=v1)",
+      "Bash(git -c safe.directory=* add CLAUDE.md README.md assets/D638_composite.png assets/D790_composite.png scripts/plots/01_composite.py scripts/specimens/_lib.py scripts/specimens/10_2026_06_25_tensile.py scripts/specimens/11_2026_06_25_flex.py scripts/specimens/12_2026_06_26_tensile.py scripts/specimens/13_2026_06_26_flex.py scripts/specimens/14_2026_06_30_tensile_h.py scripts/specimens/15_2026_06_30_tensile_i.py scripts/specimens/16_2026_06_30_flex_i.py scripts/specimens/17_2026_06_30_flex_h.py data/D638/F1.jsonl data/D638/F2.jsonl data/D638/F3.jsonl data/D638/F4.jsonl data/D638/F5.jsonl data/D638/G1.jsonl data/D638/G2.jsonl data/D638/G3.jsonl data/D638/G4.jsonl data/D638/G5.jsonl data/D638/H1.jsonl data/D638/H2.jsonl data/D638/H3.jsonl data/D638/I1.jsonl data/D638/I2.jsonl data/D638/I3.jsonl data/D638/I4.jsonl data/D638/I5.jsonl data/D790/F1.jsonl data/D790/F2.jsonl data/D790/F3.jsonl data/D790/F4.jsonl data/D790/F5.jsonl data/D790/F6.jsonl data/D790/F7.jsonl data/D790/F8.jsonl data/D790/F9.jsonl data/D790/G1.jsonl data/D790/G2.jsonl data/D790/G3.jsonl data/D790/G4.jsonl data/D790/H1.jsonl data/D790/H2.jsonl data/D790/H3.jsonl data/D790/H4.jsonl data/D790/H5.jsonl data/D790/I1.jsonl data/D790/I2.jsonl data/D790/I3.jsonl data/D790/I4.jsonl data/D790/I5.jsonl source/2026_06_25 source/2026_06_26 source/2026_06_30)",
+      "Bash(echo \"---exit $?---\")",
+      "Bash(git -c safe.directory=* commit -m ' *)",
+      "Bash(git -c safe.directory='*' config --global user.name)",
+      "Bash(git -c safe.directory='*' config --global user.email)",
+      "Bash(git -c safe.directory='*' config user.name)",
+      "Bash(git -c safe.directory='*' config user.email)",
+      "Bash(git -c safe.directory='*' config user.email \"valvarad@andrew.cmu.edu\")",
+      "Bash(git -c safe.directory='*' config user.name \"Victor Alvarado\")",
+      "Bash(git -c safe.directory='*' log --oneline -3)",
+      "Bash(git lfs *)",
+      "Bash(git -c safe.directory='*' show --stat bc0f96d -- \"*.ico\" \"*.ini\")",
+      "Bash(xargs ls -la)",
+      "Bash(git -c safe.directory='*' add .gitattributes)",
+      "Bash(git -c safe.directory='*' add --renormalize \"source/2026_06_30/Flex 6-30/MTS Flexure \\(3-Point Bend\\) 27-100 21.TWTemplate\")",
+      "Bash(git -c safe.directory='*' check-attr filter -- \"source/2026_06_30/Flex 6-30/MTS Flexure \\(3-Point Bend\\) 27-100 21.TWTemplate\")",
+      "Bash(git -c safe.directory='*' show \":source/2026_06_30/Flex 6-30/MTS Flexure \\(3-Point Bend\\) 27-100 21.TWTemplate\")",
+      "Bash(git -c safe.directory='*' commit --amend --no-edit)",
+      "Bash(git -c safe.directory='*' log --oneline -1)",
+      "Bash(git -c safe.directory='*' show HEAD:\"source/2026_06_30/Flex 6-30/MTS Flexure \\(3-Point Bend\\) 27-100 21.TWTemplate\")",
+      "Bash(git -c safe.directory='*' diff bc0f96d HEAD --stat)",
+      "Bash(git -c safe.directory='*' fsck --no-progress)",
+      "Bash(git -c safe.directory='*' diff bc0f96d cc03336 --stat)",
+      "Bash(git -c safe.directory='*' lfs ls-files)",
+      "Read(///wsl.localhost/ubuntu//**)",
+      "Bash(where.exe uv *)",
+      "Bash(wsl.exe -e bash -lc \"cd /home/alvaradovictor/HuggingFace/Datasets/Inova-Mk1-ASTM && which uv && uv run python -c 'print\\(1\\)'\")",
+      "Bash(wsl.exe -e bash -lc \"cd /home/alvaradovictor/HuggingFace/Datasets/Inova-Mk1-ASTM && which uv; echo EXIT:\\\\$?\")",
+      "Bash(wsl.exe -e bash -lc 'ls -la ~/.local/bin/ 2>&1 | head; echo ---; ls -la ~/.cargo/bin 2>&1 | head; echo ---; echo $PATH')",
+      "Bash(wsl.exe -e bash -lc \"find / -maxdepth 6 -iname 'uv' -type f 2>/dev/null\")",
+      "Bash(python -c ' *)",
+      "Bash(python -c \"import openpyxl; print\\(openpyxl.__version__\\)\")",
+      "Bash(python -c \"import h5py; print\\(h5py.__version__\\)\")",
+      "Bash(git -c safe.directory='*' status --short)",
+      "Bash(git -c safe.directory='*' status --short -- source/2026_07_06 source/2026_07_08)",
+      "Bash(python 18_2026_07_06_tensile_j.py)",
+      "Bash(python 19_2026_07_06_flex_j.py)",
+      "Bash(python 20_2026_07_08_tensile_j_mb.py)",
+      "Bash(python 21_2026_07_08_flex_j_mb.py)",
+      "Bash(cd \"scripts/specimens\")",
+      "Bash(python scripts/plots/01_composite.py)",
+      "Bash(git -c safe.directory='*' status --short -- source/2026_07_06 source/2026_07_08 scripts/specimens/18_2026_07_06_tensile_j.py scripts/specimens/19_2026_07_06_flex_j.py scripts/specimens/20_2026_07_08_tensile_j_mb.py scripts/specimens/21_2026_07_08_flex_j_mb.py data/D638 data/D790 README.md CLAUDE.md scripts/plots/01_composite.py assets/D638_composite.png assets/D790_composite.png)"
     ]
   }
 }

CLAUDE.md

@@ -42,7 +42,8 @@ The composite plots in `assets/` were regenerated and **do** reflect the current
 - **`xlsx_only` sessions for specimens whose raw h5 didn't survive.** Batch H's 5 flex specimens (`2026_06_30/Flex 6-30/`) have no matching TestRuns folder — it looks like TestWorks overwrote it when the Batch I project reused the same default `TST1.Test` name in the same directory. `_lib.build_row_from_xlsx` (used when a `SESSION` dict sets `"xlsx_only": True`) reads the raw load/extension curve straight from xlsx cols A-B and whatever scalar metrics that export happened to include, converting units via `convert_unit()`. Curves/metrics not present in that export (full strain/stress arrays, modulus, yield — they need the support span) stay null/empty rather than being approximated. Prefer fixing the underlying data (re-export from TestWorks) over reusing this path if it happens again; it's a fallback, not the default.
 - **`notes` is a free-text per-row field** for exceptions that don't fit elsewhere in the schema (e.g. "printed vertically" for FormLabs, or the xlsx-only sourcing caveat for Batch H flex). Empty string when there's nothing to flag.
 - **One JSONL file per specimen.** Outputs land under `data/{standard}/{sample_id}.jsonl` (or `data/D638/{material_class}_TSR{n}.jsonl` for non-SLS controls without a sample_id). HF configs glob these via `path: data/D638/*.jsonl`. This keeps git diffs scoped to a single specimen when a row is regenerated and makes it easy to delete or replace individual rows.
-- **Domain dependencies, not stdlib-only.** Unlike `Inova-Mk1-Database` (stdlib only), the ETL here needs `h5py`, `openpyxl`, and `matplotlib` (for the composite plots). They're declared in `pyproject.toml`. Python 3.13.
+- **Domain dependencies, not stdlib-only.** Unlike `Inova-Mk1-Database` (stdlib only), the ETL here needs `h5py`, `openpyxl`, `matplotlib`, and `numpy` (the latter two for `scripts/plots/`). They're declared in `pyproject.toml`. Python 3.13.
+- **`scripts/plots/` follows the same shared-lib pattern as `scripts/specimens/`.** `_lib.py` holds `BATCH_COLORS`, `load_specimen`/`load_standard`, and styling helpers shared by `01_composite.py` (raw overlaid curves) and `02_batch_averages.py` (per-batch mean ± 1 SD, SLS-only — see its docstring for the interpolation/truncation method and why degenerate curves get dropped per-batch).
 
 ## Directory layout
 
@@ -80,13 +81,17 @@ scripts/specimens/
   15_2026_06_30_tensile_i.py            # Batch I tensile (TST2.Test, same session_folder as H)
   16_2026_06_30_flex_i.py               # Batch I flex
   17_2026_06_30_flex_h.py               # Batch H flex — xlsx_only, no TestRuns folder
+  18_2026_07_06_tensile_j.py            # Batch J tensile (not media blasted)
+  19_2026_07_06_flex_j.py               # Batch J flex (not media blasted)
+  20_2026_07_08_tensile_j_mb.py         # Batch J_MB tensile (media blasted, same print as J)
+  21_2026_07_08_flex_j_mb.py            # Batch J_MB flex (media blasted, same print as J)
 
 data/
   D638/                                 # one JSONL per specimen (HF glob: data/D638/*.jsonl)
-    A1.jsonl ... I5.jsonl               # SLS specimens, named by sample_id
+    A1.jsonl ... J_MB5.jsonl            # SLS specimens, named by sample_id
     PLA_TSR6.jsonl ... PA12GF_FL_TSR5.jsonl  # non-SLS controls (no sample_id)
   D790/
-    C1.jsonl ... I6.jsonl               # SLS specimens, named by sample_id
+    C1.jsonl ... J_MB2.jsonl            # SLS specimens, named by sample_id
 ```
 
 ## File format notes — TestWorks 4.1 output
@@ -123,11 +128,12 @@ Same TestWorks framework, different schema. Notable renames you have to handle:
 
 ## Current state
 
-- `D638` config — 56 rows (45 SLS across batches A-I + 3 PLA + 3 PETG + 5 FormLabs PA12GF). Batches F-I (18 rows) have null FKs pending Database backfill.
-- `D790` config — 55 rows (50 SLS across batches C-I + 5 FormLabs PA12GF). Batches E-I (33 rows) have null FKs pending Database backfill.
-- Total: 111 specimens, produced by 18 per-session scripts (some date folders — 2026_06_10, 2026_06_30 — contain more than one Test project/batch).
+- `D638` config — 65 rows (54 SLS across batches A-I, J, J_MB + 3 PLA + 3 PETG + 5 FormLabs PA12GF). Batches F-I, J, J_MB (27 rows) have null FKs pending Database backfill.
+- `D790` config — 59 rows (54 SLS across batches C-I, J, J_MB + 5 FormLabs PA12GF). Batches E-I, J, J_MB (37 rows) have null FKs pending Database backfill.
+- Total: 124 specimens, produced by 22 per-session scripts (some date folders — 2026_06_10, 2026_06_30 — contain more than one Test project/batch).
 - Batch I flex is missing TSR4 (excluded: `test_end_reason` is "Test Stopped", not a detected break) — sample_ids I1-I5 map to TSR{1,2,3,5,6}.
 - Batch H's 5 flex rows are `xlsx_only` (no raw TestRuns folder survived — see architectural decisions above); every other row has full h5-derived curves.
+- **Batch J / J_MB (2026-07-06 print, tested 07/06 and 07/08) is the first split-batch label.** Both are the same physical print batch; `J` specimens were tested as-printed, `J_MB` specimens were media blasted post-print before testing. This deviates from the single-letter `batch_label` convention (see architectural decisions above) — deliberately, per user instruction, to keep the surface-treatment distinction visible without adding a new schema field. If more media-blast-vs-not comparisons get added, consider promoting this to a real `surface_treatment` field instead of continuing to overload `batch_label`.
 
 ## Running the extract
 
@@ -154,11 +160,11 @@ uv run scripts/specimens/01_2026_05_26.py
    - If a TestRuns folder is missing/overwritten and only an xlsx export survives, set `"xlsx_only": True` instead of `test_folder` — this is a fallback (see architectural decisions above), not something to reach for by default.
 4. Run that one script → new files land in `data/{standard}/`.
 5. Update the README's "Test sessions covered" table (and the "Current state" section here).
-6. Regenerate the composite figures: `uv run scripts/plots/01_composite.py`. If it's a new batch letter, add a color for it to `BATCH_COLORS` in `scripts/plots/01_composite.py` first.
+6. Regenerate the figures: `uv run scripts/plots/01_composite.py && uv run scripts/plots/02_batch_averages.py`. If it's a new batch letter, add a color for it to `BATCH_COLORS` in `scripts/plots/_lib.py` first.
 
-## Backfilling batches E–I
+## Backfilling batches E–I, J, J_MB
 
-Batches E (D790 only), F, G, H, and I all currently pass `db_print_date=None` because their print jobs aren't in `Inova-Mk1-Database/data/jobs.jsonl` yet. When one lands:
+Batches E (D790 only), F, G, H, I, J, and J_MB all currently pass `db_print_date=None` because their print jobs aren't in `Inova-Mk1-Database/data/jobs.jsonl` yet. J and J_MB share the same 2026-07-06 print job, so both batches' scripts get the same `db_print_date` once it's backfilled. When one lands:
 1. In the matching `scripts/specimens/{NN}_*.py`, change `(i, "SLS", None)` to `(i, "SLS", "<print_date>")` for that batch's `test_runs`.
 2. Re-run that one script. Its JSONLs get FKs and `print_profile_snapshot` filled in. No schema change needed.
 

README.md

@@ -35,26 +35,40 @@ flex    = load_dataset("ppak10/Inova-Mk1-ASTM", "D790", split="train")
 
 ## Composite stress–strain
 
-Every specimen overlaid on one plot per standard. Colors are batch labels (A–I); dashed and dotted gray lines in D638 are PLA/PETG filament controls, and solid gray lines are FormLabs PA12GF benchtop controls — they print at much higher stress and strain than the SLS PA12 specimens, which is why the SLS curves cluster near the origin.
+Every specimen overlaid on one plot per standard. Colors are batch labels (A–I, J, J_MB); solid gray lines are FormLabs PA12GF benchtop controls. PLA/PETG filament controls (D638 only) print at much higher stress and strain than either PA12 material, so they're broken out into their own figure to keep the main composite readable.
 
 | D638 (tensile) | D790 (three-point flex) |
 |---|---|
 | ![D638 composite](assets/D638_composite.png) | ![D790 composite](assets/D790_composite.png) |
 
+PLA/PETG controls (D638 only — D790 has no filament controls):
+
+![D638 PLA/PETG controls](assets/D638_controls.png)
+
 Regenerate with `uv run scripts/plots/01_composite.py`.
 
+## Batch averages (SLS Nylon 12 GF only)
+
+One mean stress-strain curve per SLS batch, ± 1 sample standard deviation across specimens (error bars), for our own Inova Mk1 SLS Nylon 12 GF prints — PLA/PETG/FormLabs benchtop controls are excluded (they have no `batch_label`). Each specimen's curve is linearly interpolated onto a common strain grid bounded by the *shortest* specimen's max strain in that batch, so every point in the mean is backed by the same specimen count. See `scripts/plots/02_batch_averages.py` for the full averaging method (including why one D790 specimen — a degenerate 3-point curve — is dropped from its batch's average).
+
+| D638 (tensile) | D790 (three-point flex) |
+|---|---|
+| ![D638 batch averages](assets/D638_batch_averages.png) | ![D790 batch averages](assets/D790_batch_averages.png) |
+
+Regenerate with `uv run scripts/plots/02_batch_averages.py`.
+
 ---
 
 ## Configs
 
 | Config | Description | Files |
 |---|---|---|
-| `D638` (default) | Tensile specimens (ASTM D638 Type I). 45 SLS (batches A–I) + 3 PLA + 3 PETG + 5 FormLabs PA12GF = 56 rows. Batches F–I (18 rows) have null FKs — their print jobs aren't in Database yet. | `data/D638/*.jsonl` (one row per file) |
-| `D790` | Three-point flex specimens (ASTM D790 Procedure A). 50 SLS rows (batches C–I) + 5 FormLabs PA12GF = 55 rows. Batches E–I (33 rows) have null FKs — their print jobs aren't in Database yet. | `data/D790/*.jsonl` (one row per file) |
+| `D638` (default) | Tensile specimens (ASTM D638 Type I). 54 SLS (batches A–I, J, J_MB) + 3 PLA + 3 PETG + 5 FormLabs PA12GF = 65 rows. Batches F–I, J, J_MB (27 rows) have null FKs — their print jobs aren't in Database yet. | `data/D638/*.jsonl` (one row per file) |
+| `D790` | Three-point flex specimens (ASTM D790 Procedure A). 54 SLS rows (batches C–I, J, J_MB) + 5 FormLabs PA12GF = 59 rows. Batches E–I, J, J_MB (37 rows) have null FKs — their print jobs aren't in Database yet. | `data/D790/*.jsonl` (one row per file) |
 
 One JSONL file per specimen — SLS rows are named after their `sample_id` (e.g. `data/D638/A1.jsonl`, `data/D790/E10.jsonl`); non-SLS controls use `{material}_TSR{n}.jsonl` (e.g. `data/D638/PLA_TSR6.jsonl`, `data/D790/PA12GF_FL_TSR1.jsonl`). Both configs share the same row schema and are produced by the per-session scripts under `scripts/specimens/`.
 
-Specimens are labeled with a `sample_id` of the form `{batch_letter}{seq}` (e.g. `A1`, `C7`), where the batch letter identifies the print batch they came from. The same `sample_id` may appear in both configs — `C1` in `D638` and `C1` in `D790` are **different physical specimens** that came from the same Batch C print.
+Specimens are labeled with a `sample_id` of the form `{batch_label}{seq}` (e.g. `A1`, `C7`), where the batch label identifies the print batch they came from. Batch labels are usually a single letter, but `J` / `J_MB` are the same 2026-07-06 print batch split by post-processing: `J` specimens were tested as-printed, `J_MB` specimens were media blasted before testing. The same `sample_id` may appear in both configs — `C1` in `D638` and `C1` in `D790` are **different physical specimens** that came from the same Batch C print.
 
 ## Source Layout
 
@@ -93,6 +107,10 @@ Test sessions covered:
 | `2026_06_30/Tensile/` (TST2.Test) | 5 | D638 | I | print job **not yet in Database** — FKs null until backfilled |
 | `2026_06_30/Flex 6-30/` (TST1.Test) | 5 | D790 | I | print job **not yet in Database** — FKs null until backfilled |
 | `2026_06_30/Flex 6-30/` (xlsx only, no TestRuns folder) | 5 | D790 | H | print job **not yet in Database** — FKs null until backfilled |
+| `2026_07_06/Tensile/` | 4 | D638 | J (not media blasted) | print job **not yet in Database** — FKs null until backfilled |
+| `2026_07_06/Flex/` | 2 | D790 | J (not media blasted) | print job **not yet in Database** — FKs null until backfilled |
+| `2026_07_08/Tensile/` | 5 | D638 | J_MB (media blasted) | same 2026-07-06 print batch as J; print job **not yet in Database** — FKs null until backfilled |
+| `2026_07_08/Flex/` | 2 | D790 | J_MB (media blasted) | same 2026-07-06 print batch as J; print job **not yet in Database** — FKs null until backfilled |
 
 TSR11 in `Batch E 3pt test/` exists on disk but has empty `Data/` (aborted run, no DAQ scans) and no corresponding xlsx sheet — it is excluded from the JSONL. TSR4 in Batch I flex (`Flex 6-30/`) is excluded for a similar reason: `test_end_reason` is "Test Stopped" rather than a detected break.
 

data/D638/J1.jsonl

@@ -0,0 +1 @@
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data/D638/J2.jsonl

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data/D638/J3.jsonl

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data/D638/J4.jsonl

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data/D638/J_MB1.jsonl

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data/D638/J_MB2.jsonl

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data/D638/J_MB3.jsonl

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data/D638/J_MB4.jsonl

@@ -0,0 +1 @@
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data/D638/J_MB5.jsonl

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712.7884521484375, 714.4645385742188, 714.7916870117188, 716.4805908203125, 717.8689575195312, 718.4432983398438, 719.7650146484375, 720.058837890625, 721.3255615234375, 721.9465942382812, 722.8473510742188, 722.9557495117188, 725.1244506835938, 725.3662109375, 725.8007202148438, 726.8114624023438, 727.6497192382812, 728.3919677734375, 728.9202270507812, 730.0626831054688, 730.2257690429688, 731.0841064453125, 731.0526123046875, 732.312744140625, 732.2230834960938, 732.0554809570312, 731.244384765625, 13.27147102355957], "strain": [0.00011772564697338345, 0.00020085671217222765, 0.00027974018987251414, 0.0003580169021560333, 0.0004429683342455445, 0.0005182110605044057, 0.0006019489617603823, 0.0006826528240114271, 0.0007603227532180877, 0.0008446673492504664, 0.0009223372784571271, 0.0010054683436559713, 0.001086172205907016, 0.0011638421351136768, 0.0012487936025233706, 0.0013246431295191815, 0.0014065605226037609, 0.0014890848577060205, 0.0015661479861757313, 0.00165049258220811, 0.0017287694534324509, 0.0018100800457800802, 0.0018919974388646595, 0.00196966736807132, 0.002054011964103699, 0.0021310750925734095, 0.0022117790254648196, 0.0022961237627779287, 0.0023713663477560594, 0.0024551041430514882, 0.0025352012752059487, 0.0026146917479041893, 0.002697822742462668, 0.002775492389107868, 0.0028592303256840274, 0.0029381138563645878, 0.003016997387045148, 0.0031013422656389874, 0.003177191651354068, 0.0032603226459125474, 0.003341633379540907, 0.0034193033087475677, 0.0035030412453237268, 0.0035807111745303874, 0.0036638421690888667, 0.003744545960699546, 0.0038222161724676676, 0.0039071674279562655, 0.003983016813671346, 0.004064934206755926, 0.004147458541858186, 0.004224521529047166, 0.004308259465623325, 0.004387749938321565, 0.004468454012493706, 0.004550371123016824, 0.004628041334784946, 0.004712385930817325, 0.004789449200567766, 0.004870152709616984, 0.004953890928754604, 0.005030346973925905, 0.005114085193063524, 0.005193575383200305, 0.005273065573337085, 0.005356803227351783, 0.005434473156558444, 0.005518210810573142, 0.005597094623815163, 0.005675977871934263, 0.005760322467966641, 0.005836172136243183, 0.005919303413363122, 0.006000007487535263, 0.0060776768516190025, 0.0061620214476513805, 0.006240298883998643, 0.006322822653977982, 0.006402919786132441, 0.006481196092233861, 0.006566148195406841, 0.006642604805701063, 0.006724521633662721, 0.0068064390267473, 0.006884109521076882, 0.0069672402330739, 0.0070461240463159215, 0.007127434497382821, 0.00720995883248508, 0.007287021819674061, 0.00737136641570644, 0.007448429968018341, 0.00752913347706756, 0.00761287169620518, 0.0076887207993588, 0.007773065395391179, 0.007852555585527958, 0.007932652717682419, 0.008015783994802359, 0.008093453924009019, 0.008177191578023718, 0.008255468449248059, 0.008334958639384838, 0.008419303235417216, 0.008494546526798999, 0.008578283615690777, 0.00865898825498584, 0.008737264561087258, 0.008821609157119636, 0.008899279086326298, 0.008983016740340995, 0.009062507495600697, 0.009140783801702116, 0.009225128397734495, 0.009301585008028718, 0.009384109343130977, 0.009466026736215556, 0.009542482781386858, 0.009626827377419236, 0.009705710625538336, 0.009787021641728157, 0.009868332657917976, 0.009946002022001715, 0.010030346618034095, 0.010107410170345997, 0.010188720056289974, 0.010271851898532835, 0.010348308508827057, 0.010432046162841754, 0.010511536352978535, 0.010591633485132996, 0.010674764197130013, 0.010751827749441915, 0.010836172345474295, 0.010913842839803876, 0.010993939971958336, 0.011078284567990714, 0.011153526164003733, 0.011237870760036113, 0.011317967892190571, 0.011396852270555514, 0.011479982982552532, 0.011558259288653953, 0.011641391130896813, 0.011720881321033593, 0.011799764569152693, 0.011884109165185073, 0.01196056464523345, 0.012042482603440952, 0.012124400561648452, 0.012201464113960354, 0.01228641452176457, 0.012364691958111832, 0.01244660878607349, 0.01252791980226331, 0.01260558916634705, 0.01268993376237943, 0.01276699731469133, 0.01284770138886347, 0.01293083210086049, 0.013006681769137031, 0.01309163330718709, 0.013170517685552031, 0.01325122062947833, 0.013333745529703511, 0.013411414893787249, 0.013495759489819629, 0.01357342998414921, 0.01365352711630367, 0.013737264770318369, 0.01381311443859491, 0.013896850962363766, 0.013976342282746388, 0.014055832472883169, 0.014140177068915547, 0.014217239490981606, 0.014300978275242147, 0.014379861523361247, 0.014458744771480347, 0.014543696309530407, 0.014619545977806948, 0.014702677820049808, 0.014783380763976107, 0.014861657070077526, 0.014945395854338068, 0.015023672160439486, 0.015106195930418825, 0.015186293062573285, 0.015255468629147029], "stress_pa": [243146.3394781097, 445244.6743074238, 650763.3596696703, 840066.049740358, 1044377.724218655, 1259570.1909558845, 1446280.758220651, 1631734.9373045806, 1823514.1350470143, 2030709.0545741378, 2218863.791972444, 2411424.318273879, 2609425.24070478, 2776427.5964076063, 2979152.619921946, 3169318.023083184, 3328620.1144008785, 3533729.514776918, 3688086.46517933, 3872027.9202841353, 4055552.8644568482, 4231116.66906988, 4424489.280520498, 4595356.220307661, 4768980.877493966, 4942833.800458002, 5111753.811030561, 5280269.539194834, 5436500.047669008, 5600383.018117069, 5766045.42405971, 5924612.321703675, 6089852.658294313, 6252685.828501608, 6418834.41090591, 6582558.781105807, 6730037.742708775, 6888012.48335142, 7040139.025123701, 7191319.894388243, 7355354.794980572, 7498281.781236718, 7660341.589953554, 7797040.181417339, 7945774.604797826, 8112331.545785015, 8256897.895353954, 8409076.315712085, 8554828.461528974, 8694985.131300846, 8839003.791227764, 8973864.398507224, 9121850.288006192, 9269000.93227301, 9398113.392240528, 9538676.938349977, 9676831.094708122, 9816597.933963481, 9943953.192116067, 10075359.426700715, 10216563.30278406, 10341116.376178207, 10463340.84219044, 10595624.565998573, 10709411.35058398, 10844493.553537278, 10953829.155456938, 11080790.877479736, 11212040.735184187, 11321588.29818317, 11430040.481898108, 11548372.571726443, 11661876.988580307, 11786355.208586302, 11883316.28553594, 12005688.976079168, 12127280.523344059, 12227580.357854335, 12345873.168181956, 12440987.36747541, 12539283.947449304, 12650722.114341114, 12741918.7392804, 12855824.844613258, 12947910.816738509, 13046686.161947513, 13139156.035608036, 13239635.96292347, 13329600.842010198, 13423339.51765603, 13502611.47907686, 13601115.573394146, 13690228.16142766, 13774501.218524223, 13853782.073416913, 13946020.716809088, 14032004.28499332, 14111252.530489191, 14194467.264434459, 14260905.94596184, 14355436.140603181, 14430725.308876205, 14498443.168106064, 14582908.91709292, 14649424.675376417, 14720553.181064446, 14794287.474007346, 14852773.909446906, 14930944.562602451, 15003263.311274359, 15047650.629325783, 15141414.503141884, 15198528.379424442, 15257652.180347275, 15322266.217898123, 15378146.866082812, 15459523.615843754, 15492221.947381001, 15561390.92476929, 15626987.690960627, 15673113.682760607, 15756417.351424472, 15788063.288791662, 15850408.008772993, 15888895.990490861, 15956001.682407714, 15994962.500379454, 16058417.422097929, 16103960.891491104, 16155136.893062048, 16196501.912926506, 16249547.02583328, 16282768.589964937, 16347514.547348369, 16380155.071318528, 16422397.58040647, 16469516.67656411, 16514273.07369771, 16557425.681405954, 16598300.078072824, 16626232.990938619, 16661337.006613895, 16713091.083855715, 16753056.86414759, 16804232.865718532, 16810120.344089612, 16860567.080968063, 16892450.177584846, 16931903.100999482, 16968038.75941048, 16994038.824392114, 17023830.47287657, 17053678.446682803, 17073822.16044487, 17137391.21505221, 17149763.516654268, 17173098.504568633, 17226036.895813093, 17227104.11243625, 17275570.569580626, 17292070.924370863, 17310143.941434946, 17350847.879891172, 17358792.714752443, 17399807.924723756, 17433524.55878893, 17447472.487155452, 17479570.509342175, 17486706.03826422, 17517468.557426687, 17532550.403455313, 17554425.37973939, 17557057.84740984, 17609724.98776258, 17615596.16143525, 17626148.265796702, 17650694.248129286, 17671051.405215982, 17689076.99043015, 17701905.823587667, 17729650.4912991, 17733611.050767258, 17754455.866560925, 17753691.027980994, 17784293.46464999, 17782116.04628969, 17778045.800668597, 17758348.242744822, 322299.095951905]}, "notes": "Media blasted post-print; same print batch as Batch J (printed 2026-07-06).", "source_paths": {"persistent_h5": "source\\2026_07_08\\Tensile\\TST1.Test\\TestRuns\\TSR5.TestRun\\AnalysisRuns\\ANR1.AnalysisRun\\persistent.h5", "daq_h5": "source\\2026_07_08\\Tensile\\TST1.Test\\TestRuns\\TSR5.TestRun\\Data\\DaqTaskActivity1.h5", "xlsx": "source\\2026_07_08\\Tensile\\tensile_J_media_blast.xlsx", "xlsx_sheet": "Sheet5"}}

pyproject.toml

@@ -8,4 +8,5 @@ dependencies = [
     "h5py>=3.10",
     "openpyxl>=3.1",
     "matplotlib>=3.8",
+    "numpy>=1.26",
 ]

scripts/plots/01_composite.py

@@ -1,62 +1,20 @@
 #!/usr/bin/env python3
 """Render composite stress-strain plots for the D638 and D790 configs.
 
-Reads every specimen JSONL under data/{standard}/*.jsonl, overlays all curves
-onto one figure per standard, color-codes by batch_label, and uses linestyle
-to distinguish SLS from PLA/PETG controls. Writes to assets/{standard}_composite.png.
+Reads every specimen JSONL under data/{standard}/*.jsonl and splits it into
+two figures per standard: the main composite (SLS batches + FormLabs PA12GF
+benchtop control) at assets/{standard}_composite.png, and — only if the
+standard has any — a separate figure for the PLA/PETG filament controls at
+assets/{standard}_controls.png. PLA/PETG print at much higher stress/strain
+than the SLS specimens, which squashed the SLS curves near the origin when
+they shared an axis with the main composite.
 """
-import json
 from pathlib import Path
 
 import matplotlib.pyplot as plt
 from matplotlib.lines import Line2D
 
-ROOT = Path(__file__).parent.parent.parent
-DATA_DIR = ROOT / "data"
-OUT_DIR = ROOT / "assets"
-
-# Tab10 palette — kept consistent across both plots so batch C in the D638
-# plot matches batch C in the D790 plot.
-BATCH_COLORS = {
-    "A": "#1f77b4",  # blue
-    "B": "#ff7f0e",  # orange
-    "C": "#2ca02c",  # green
-    "D": "#d62728",  # red
-    "E": "#9467bd",  # purple
-    "F": "#8c564b",  # brown
-    "G": "#e377c2",  # pink
-    "H": "#bcbd22",  # olive
-    "I": "#17becf",  # cyan
-}
-CONTROL_COLOR = "#7f7f7f"  # gray for PLA/PETG
-
-MATERIAL_STYLES = {"SLS": "-", "PLA": "--", "PETG": ":"}
-
-
-def load_specimen(path: Path) -> dict | None:
-    with path.open() as f:
-        row = json.loads(f.readline())
-    pairs = [
-        (s, t)
-        for s, t in zip(row["curves"]["strain"], row["curves"]["stress_pa"])
-        if s is not None and t is not None
-    ]
-    if not pairs:
-        return None
-    strain, stress_pa = zip(*pairs)
-    return {
-        "row": row,
-        "strain": list(strain),
-        "stress_mpa": [t / 1e6 for t in stress_pa],
-    }
-
-
-def style_for(row: dict) -> tuple[str, str]:
-    material = row["material_class"]
-    batch = row["batch_label"]
-    color = BATCH_COLORS.get(batch, CONTROL_COLOR) if batch else CONTROL_COLOR
-    linestyle = MATERIAL_STYLES.get(material, "-")
-    return color, linestyle
+from _lib import OUT_DIR, ROOT, FILAMENT_CONTROLS, load_standard, style_for
 
 
 def legend_handles(rows: list[dict]) -> list[Line2D]:
@@ -80,11 +38,9 @@ def legend_handles(rows: list[dict]) -> list[Line2D]:
     return [h for _, h in sorted(seen.items(), key=sort_key)]
 
 
-def plot_standard(standard: str) -> Path:
-    paths = sorted((DATA_DIR / standard).glob("*.jsonl"))
-    specs = [s for p in paths if (s := load_specimen(p))]
+def render(standard: str, specs: list[dict], title: str, out_name: str) -> Path | None:
     if not specs:
-        raise RuntimeError(f"No specimens found in data/{standard}/")
+        return None
 
     fig, ax = plt.subplots(figsize=(9, 6))
     for s in specs:
@@ -94,25 +50,40 @@ def plot_standard(standard: str) -> Path:
 
     ax.set_xlabel("Strain (mm/mm)")
     ax.set_ylabel("Stress (MPa)")
-    title_map = {"D638": "ASTM D638 — tensile composite",
-                 "D790": "ASTM D790 — three-point flex composite"}
-    ax.set_title(f"{title_map.get(standard, standard)}  ({len(specs)} specimens)")
+    ax.set_title(f"{title}  ({len(specs)} specimens)")
     ax.grid(True, alpha=0.3)
     ax.set_xlim(left=0)
     ax.set_ylim(bottom=0)
 
     ax.legend(handles=legend_handles([s["row"] for s in specs]),
-              loc="lower right", fontsize=9, framealpha=0.9)
+              loc="upper left", bbox_to_anchor=(1.02, 1.0), borderaxespad=0,
+              fontsize=9, framealpha=0.9)
 
     OUT_DIR.mkdir(parents=True, exist_ok=True)
-    out_path = OUT_DIR / f"{standard}_composite.png"
-    fig.tight_layout()
-    fig.savefig(out_path, dpi=150)
+    out_path = OUT_DIR / f"{standard}_{out_name}.png"
+    fig.savefig(out_path, dpi=150, bbox_inches="tight")
     plt.close(fig)
     print(f"wrote {out_path.relative_to(ROOT)}  ({len(specs)} curves)")
     return out_path
 
 
+def plot_standard(standard: str) -> None:
+    specs = load_standard(standard)
+    if not specs:
+        raise RuntimeError(f"No specimens found in data/{standard}/")
+
+    main_specs = [s for s in specs if s["row"]["material_class"] not in FILAMENT_CONTROLS]
+    control_specs = [s for s in specs if s["row"]["material_class"] in FILAMENT_CONTROLS]
+
+    title_map = {"D638": "ASTM D638 — tensile composite",
+                 "D790": "ASTM D790 — three-point flex composite"}
+    render(standard, main_specs, title_map.get(standard, standard), "composite")
+
+    controls_title_map = {"D638": "ASTM D638 — PLA/PETG filament controls",
+                           "D790": "ASTM D790 — PLA/PETG filament controls"}
+    render(standard, control_specs, controls_title_map.get(standard, standard), "controls")
+
+
 if __name__ == "__main__":
     plot_standard("D638")
     plot_standard("D790")

scripts/plots/02_batch_averages.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+"""Render per-batch mean stress-strain curves with +/- 1 SD error *channels*
+(shaded bands) for our SLS Nylon 12 GF prints plus the FormLabs PA12GF
+benchtop control, one figure per standard.
+
+Groups plotted: every SLS batch (rows with a non-null batch_label) and the
+FormLabs PA12GF_FL control, since it's the same nominal material (Nylon 12
+GF) printed on different hardware and is the reference point the SLS batches
+are being compared against. PLA/PETG filament controls are still excluded —
+different material entirely, and they already have their own figure
+(assets/D638_controls.png).
+
+Each specimen's analyzed stress-strain curve is resampled onto a common
+strain grid via linear interpolation (`np.interp`; strain is monotonically
+increasing in every specimen, so this is safe), then averaged pointwise
+across the group. The grid runs from 0 to the *shortest* specimen's max
+strain in that group, so every point in the mean curve is backed by the same
+number of specimens — n doesn't quietly shrink as strain increases past where
+some specimens have already broken.
+
+The shaded band is +/- 1 sample standard deviation across specimens at each
+strain value (ddof=1). This is specimen-to-specimen variability (print
+placement, powder packing, sintering, etc.), which dominates over
+DAQ/instrument noise here — that's the source of scatter worth showing, so a
+per-point stddev across replicates is more informative than propagating
+instrument measurement uncertainty through the curve.
+
+With this many groups, overlapping fills of the same low alpha turn to mud
+where two bands cover the same region, so each band's own upper/lower edge is
+also traced with a thin, more opaque line in the group's color — that gives
+every band a visible boundary to follow even where fills stack.
+
+A specimen with a degenerate curve (too few points to be a real stress-strain
+trace, e.g. D790 E6 at 3 points vs. ~2000 for its batch-mates) is excluded
+from its group's average — otherwise the shared strain grid (bounded by the
+*shortest* max strain in the group, so every point has full sample size)
+collapses to near-zero width for the whole group.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+
+from _lib import BATCH_COLORS, CONTROL_COLOR, OUT_DIR, ROOT, load_standard
+
+N_POINTS = 100
+BAND_ALPHA = 0.12
+EDGE_ALPHA = 0.75
+MIN_CURVE_POINTS = 20  # below this a curve is degenerate, not just short
+
+FORMLABS_LABEL = "FormLabs PA12GF"
+# Ordered group -> (color, legend label). SLS batches first, FormLabs last.
+GROUP_STYLE = {batch: (color, f"Batch {batch}") for batch, color in BATCH_COLORS.items()}
+GROUP_STYLE["PA12GF_FL"] = (CONTROL_COLOR, FORMLABS_LABEL)
+
+
+def group_key(row: dict) -> str | None:
+    if row["batch_label"]:
+        return row["batch_label"]
+    if row["material_class"] == "PA12GF_FL":
+        return "PA12GF_FL"
+    return None  # PLA/PETG controls — excluded, see module docstring
+
+
+def group_average(specs: list[dict]) -> tuple[np.ndarray, np.ndarray, np.ndarray, int]:
+    """specs: per-specimen {"strain": [...], "stress_mpa": [...]}.
+    Returns (strain_grid, mean_stress, std_stress, n_specimens)."""
+    max_strain = min(max(s["strain"]) for s in specs)
+    grid = np.linspace(0, max_strain, N_POINTS)
+    curves = np.array([np.interp(grid, s["strain"], s["stress_mpa"]) for s in specs])
+    n = len(specs)
+    std = curves.std(axis=0, ddof=1) if n > 1 else np.zeros_like(grid)
+    return grid, curves.mean(axis=0), std, n
+
+
+def plot_standard(standard: str) -> None:
+    specs = load_standard(standard)
+
+    groups: dict[str, list[dict]] = {}
+    for s in specs:
+        key = group_key(s["row"])
+        if key is None:
+            continue
+        if len(s["strain"]) < MIN_CURVE_POINTS:
+            print(f"  skipping {s['row']['specimen_id']} (group {key}): "
+                  f"degenerate curve, only {len(s['strain'])} points")
+            continue
+        groups.setdefault(key, []).append(s)
+
+    fig, ax = plt.subplots(figsize=(9, 6))
+    handles = []
+    for key, (color, label) in GROUP_STYLE.items():
+        group = groups.get(key)
+        if not group:
+            continue
+        grid, mean, std, n = group_average(group)
+        ax.plot(grid, mean, color=color, linewidth=1.6, alpha=0.95)
+        ax.fill_between(grid, mean - std, mean + std, color=color, alpha=BAND_ALPHA,
+                         linewidth=0)
+        # Trace each band's own edges so overlapping fills don't turn to mud.
+        ax.plot(grid, mean + std, color=color, linewidth=0.8, alpha=EDGE_ALPHA)
+        ax.plot(grid, mean - std, color=color, linewidth=0.8, alpha=EDGE_ALPHA)
+        handles.append(Patch(facecolor=color, edgecolor=color, alpha=0.6,
+                              label=f"{label} (n={n})"))
+
+    ax.set_xlabel("Strain (mm/mm)")
+    ax.set_ylabel("Stress (MPa)")
+    title_map = {"D638": "ASTM D638 — tensile batch averages (± 1 SD)",
+                 "D790": "ASTM D790 — three-point flex batch averages (± 1 SD)"}
+    ax.set_title(title_map.get(standard, standard))
+    ax.grid(True, alpha=0.3)
+    ax.set_xlim(left=0)
+    ax.set_ylim(bottom=0)
+    ax.legend(handles=handles, loc="upper left", bbox_to_anchor=(1.02, 1.0),
+              borderaxespad=0, fontsize=9, framealpha=0.9)
+
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    out_path = OUT_DIR / f"{standard}_batch_averages.png"
+    fig.savefig(out_path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    print(f"wrote {out_path.relative_to(ROOT)}  ({len(handles)} groups)")
+
+
+if __name__ == "__main__":
+    plot_standard("D638")
+    plot_standard("D790")

scripts/plots/_lib.py

@@ -0,0 +1,59 @@
+"""Shared helpers for scripts/plots/*.py."""
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).parent.parent.parent
+DATA_DIR = ROOT / "data"
+OUT_DIR = ROOT / "assets"
+
+# Kept consistent across figures so e.g. batch C is the same color everywhere.
+BATCH_COLORS = {
+    "A": "#1f77b4",  # blue
+    "B": "#ff7f0e",  # orange
+    "C": "#2ca02c",  # green
+    "D": "#d62728",  # red
+    "E": "#9467bd",  # purple
+    "F": "#8c564b",  # brown
+    "G": "#e377c2",  # pink
+    "H": "#bcbd22",  # olive
+    "I": "#17becf",  # cyan
+    "J": "#aec7e8",  # light blue
+    "J_MB": "#08519c",  # dark blue (media blasted variant of Batch J)
+}
+CONTROL_COLOR = "#7f7f7f"  # gray for PLA/PETG
+
+MATERIAL_STYLES = {"SLS": "-", "PLA": "--", "PETG": ":"}
+
+FILAMENT_CONTROLS = {"PLA", "PETG"}
+
+
+def load_specimen(path: Path) -> dict | None:
+    with path.open() as f:
+        row = json.loads(f.readline())
+    pairs = [
+        (s, t)
+        for s, t in zip(row["curves"]["strain"], row["curves"]["stress_pa"])
+        if s is not None and t is not None
+    ]
+    if not pairs:
+        return None
+    strain, stress_pa = zip(*pairs)
+    return {
+        "row": row,
+        "strain": list(strain),
+        "stress_mpa": [t / 1e6 for t in stress_pa],
+    }
+
+
+def load_standard(standard: str) -> list[dict]:
+    """Load every specimen with a non-empty curve for a config."""
+    paths = sorted((DATA_DIR / standard).glob("*.jsonl"))
+    return [s for p in paths if (s := load_specimen(p))]
+
+
+def style_for(row: dict) -> tuple[str, str]:
+    material = row["material_class"]
+    batch = row["batch_label"]
+    color = BATCH_COLORS.get(batch, CONTROL_COLOR) if batch else CONTROL_COLOR
+    linestyle = MATERIAL_STYLES.get(material, "-")
+    return color, linestyle

scripts/specimens/18_2026_07_06_tensile_j.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+"""Batch J tensile — D638, tested 2026-07-06 (4 specimens, not media blasted, no Database job yet)."""
+from _lib import process_session
+
+SESSION = {
+    "session_folder": "2026_07_06/Tensile",
+    "test_folder":    "TST1.Test",
+    "xlsx_name":      "7.6_tensile_J.xlsx",
+    "astm":           {"standard": "D638", "type": "Type I", "year": "2022"},
+    "batch_label":    "J",
+    "test_runs":      [(i, "SLS", None) for i in range(1, 5)],
+}
+
+if __name__ == "__main__":
+    process_session(SESSION)

scripts/specimens/19_2026_07_06_flex_j.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+"""Batch J flex — D790, tested 2026-07-06 (2 specimens, not media blasted, no Database job yet)."""
+from _lib import process_session
+
+SESSION = {
+    "session_folder": "2026_07_06/Flex",
+    "test_folder":    "TST1.Test",
+    "xlsx_name":      "7.6_flex_J.xlsx",
+    "astm":           {"standard": "D790", "type": "Procedure A", "year": "2017"},
+    "batch_label":    "J",
+    "test_runs":      [(i, "SLS", None) for i in range(1, 3)],
+}
+
+if __name__ == "__main__":
+    process_session(SESSION)

scripts/specimens/20_2026_07_08_tensile_j_mb.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+"""Batch J_MB tensile — D638, tested 2026-07-08 (5 specimens, media blasted,
+same 2026-07-06 print batch as Batch J, no Database job yet)."""
+from _lib import process_session
+
+SESSION = {
+    "session_folder": "2026_07_08/Tensile",
+    "test_folder":    "TST1.Test",
+    "xlsx_name":      "tensile_J_media_blast.xlsx",
+    "astm":           {"standard": "D638", "type": "Type I", "year": "2022"},
+    "batch_label":    "J_MB",
+    "test_runs":      [(i, "SLS", None) for i in range(1, 6)],
+    "notes":          "Media blasted post-print; same print batch as Batch J (printed 2026-07-06).",
+}
+
+if __name__ == "__main__":
+    process_session(SESSION)

scripts/specimens/21_2026_07_08_flex_j_mb.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+"""Batch J_MB flex — D790, tested 2026-07-08 (2 specimens, media blasted,
+same 2026-07-06 print batch as Batch J, no Database job yet)."""
+from _lib import process_session
+
+SESSION = {
+    "session_folder": "2026_07_08/Flex",
+    "test_folder":    "TST1.Test",
+    "xlsx_name":      "flex_J_mediablast.xlsx",
+    "astm":           {"standard": "D790", "type": "Procedure A", "year": "2017"},
+    "batch_label":    "J_MB",
+    "test_runs":      [(i, "SLS", None) for i in range(1, 3)],
+    "notes":          "Media blasted post-print; same print batch as Batch J (printed 2026-07-06).",
+}
+
+if __name__ == "__main__":
+    process_session(SESSION)

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source/2026_07_06/Flex/TST1.Test/AnalysisRunSets/ARS1.AnalysisRunSet/delta.xml

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+<AnalysisDelta>
+  <OverriddenVariables />
+</AnalysisDelta>

source/2026_07_06/Flex/TST1.Test/ReportTemplates/RET1.ReportTemplate/ReportTemplate.Traits

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+    </KVP>
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5HtfD9F5pQ/XrpJDPJ0lT8tsp3vV0CVP0oMAiunkZU/mG4Sg+A1lT+Nl24So1iVP9nO91PzxZU/K4cW2Z7VlT8Sg8DKsZiVPyGwcmiBpJU/CtejcF2ElT/hehSuF3qVPzeJQWCldZU/xSCwcuh0lT/8qfHSvcCVP+kmMQj8qJU/eekmMSjLlT/Xo3A96t2VP6RwPQqXiZU/ZmZmZsbHlT9I4XoU7gGVP+kmMQjM5pU/7nw/NR6ulT8X2c73wzGWPxSuR+EK8ZU/MQisHAoslj9cj8L1+HiWPylcj8IlNpY/pHA9Cuellj81XrpJfFOWPyUGgZUTlJY/L90kBjFflj8X2c738wqWP1YOLbKtCJY/ukkMArvylj/P91PjRQ2WP1K4HoXrc5Y/O99Pjeenlj8nMQisHCGWP+f7qfGylZY/AAAAADDKlj/wp8ZLN/CWP/CnxkuH0JY/9ihcj4Khlj/NzMzMTH+WP+58PzVu35Y/eekmMXh/lj+gGi/dVCCXP6wcWmRLt5Y/XI/C9Vjolj/6fmq8dHSWP166SQzCVZc/i2zn+9l3lj9YObTIFoGWP6abxCBQEpc/lkOLbAeylj/NzMzM/AWXP76fGi8NKZc/8KfGS6cmlz/Xo3A9mp6WPyUGgZWDLpc/ZmZmZqbIlz8lBoGVQyGXP1TjpZvkBZc/O99PjZdclz/dJAaBFYSXPxSuR+HaS5c/N4lBYPWrlz9OYhBYKQuXP7ByaJEthZc/PQrXo+Allz/ZzvdT02CXP+f7qfHSwJc/AiuHFlltlz8lBoGVU1aXP6JFtvMt/Jc/AiuHFtl9lz8X2c73wyqXP+58PzVO3Jc/zczMzAwTmD8fhetRCNSXP2Dl0CLrRJc/c2iR7QzBlz+yne+nliqYP4ts5/v5Cpg/cT0K19P1lz8tsp3vJzuYP9ejcD36Mpg/mpmZmckAmD8X2c730wCYP0w3iUHQSJg/exSuR5H8lz8X2c73I0+YP/YoXI+Cb5g/xSCwcggimD9/arx0MxKYP8uhRbbDBpg/9ihcjzJCmD8X2c73k5uYP6rx0k3Si5g/ObTIdh5DmD/Xo3A9mpqYP2IQWDl0bZg/YhBYOYRhmD/NzMzMrB6YP/yp8dIN75g/pHA9CqcXmD+e76fG28KXP8dLN4khgpg/AiuHFvlbmD+HFtnO93CYP6abxCAwo5g/xSCwcljWmD91kxgENnCYP8uhRbYTu5g/j8L1KJx6mD/hehSup6CYP4lBYOUADZk/x0s3icG7mD9zaJHtTOqYPycxCKws8Zg/Rrbz/YScmT+oxks3GbCYP90kBoHFJJk/WDm0yHZ0mT9SuB6FG0qZP8P1KFxvDZk/j8L1KPymmD8GgZVD67aYPxSuR+FKWpk/1XjpJvFEmT8EVg4t8huZPzm0yHZebJk/dZMYBDYkmT9Ei2znO6aZP4lBYOUA1Zg/L90kBlFnmT9U46WbRAyZP/LSTWJwz5g/5dAi25mCmT9aZDvfr9qYP8/3U+NVDJk/IbByaHHVmD8zMzMzM5aZPwwCK4fm45g/aJHtfI+DmT8MAiuHZgqZP42XbhIDn5k/O99PjXegmT8tsp3vd7CZP2q8dJOoaJk/SgwCK3fgmT8X2c73c7qZP/p+aryUApo/vp8aLw08mT/hehSux+KZP6jGSzfpl5k/EFg5tGhumT8rhxbZ3hCZP4XrUbjeg5k/Vg4tsq2xmT+wcmiRvaaZPwrXo3CtzZk/okW2891YmT/b+X5qDNKZP2q8dJNI8Jk/g8DKoUV4mT/TTWIQuPWZPxkEVg7NrJk//tR46faOmT/8qfHSrZqZP2q8dJNI25k/CtejcE2Omj8dWmQ73yWaP2ZmZmZW5Zk/oBov3fTzmT99PzVe+oaaPx+F61GYIpo/sp3vp5Z+mT/0/dR42eSZP2Q730/9QJo/vp8aL72LmT/wp8ZLNyqaP1YOLbKd4pk/uB6F65Eamj9WDi2yHduZP1yPwvUoM5o/ke18P2UCmj8rhxbZHiOaP1g5tMgGF5o/YOXQIivtmT8fhetRuISaP6JFtvMNKJo/Gy/dJFZLmT/LoUW2E2iaP2ZmZma2d5o/FK5H4fovmj+6SQwCm+6aP9nO91PTbJo/16NwPZpGmj+DwMqhNe+aP57vp8bLa5o/I9v5fgrSmj8CK4cWmb+aP0w3iUGAZJo/z/dT4zXrmj9U46WbhJmaP4cW2c4n45o/bxKDwDq9mj9iEFg5RCGaP5qZmZkZkZo/fT81Xtp1mj+PwvUoPAybP/p+arxE+po/0SLb+e5Nmz9aZDvfv6eaP2Q7308d5po/WmQ7358Qmz9eukkMEsiaP5qZmZl5sZo/iUFg5WDxmj9aZDvfryWbP0a28/1UC5s/Vg4tsn0Mmz/6fmq89EibP7x0kxgUsZo/L90kBoGVmz++nxovnaKbP+58PzWO6Zo/jZduEqPymj/l0CLbaUybP7gehesRlps/MzMzM3N0mz+WQ4tsJxucPyPb+X4645o/z/dT43WMmz9YObTIJkSbP8l2vp+aj5o/SOF6FB4Gmz+uR+F6lFmbP6rx0k3SwJo/f2q8dBNwmz/6fmq8dHGbPxkEVg7db5s/1XjpJpGcmz9eukkMMtKbP1TjpZuku5s/O99Pjaehmz/ZzvdTA4CbP/YoXI+yNpw/H4XrUei5mz81XrpJzD2cP/hT46XrOJw/f2q8dGPLmz+kcD0KR8ObPx+F61E4pJs/TmIQWHmhmz++nxovzeCbP0Jg5dDSjps/30+Nl67Bmz8pXI/Cdf+bP0jhehS+tJw/qMZLNxnemz/l0CLbWSacPylcj8KFeZw/EoPAysFWnD/Jdr6fioubPzMzMzMzPZw/KVyPwiVUmz+DwMqh5UucP6wcWmRbhZs/iUFg5ZDOmz99PzVeeuubP/CnxktHv5s/PzVeunkunD9aZDvfXwacPwisHFrkKJw/UI2XbuKemz/VeOkmsc+bP/hT46V7+Zs/2c73U/P+mz/D9Shcr2qcP2iR7Xxv05s/ke18PzUQnD/y0k1ioI+bP1yPwvUohZs/001iEOh+nD9/arx04/6bP/Cnxks3lJs/9P3UeCnXmz8X2c73Y+mcP5HtfD91YZw/CtejcJ2tnD/Xo3A9+k2cPyuHFtlu8ps/kxgEVi5InD9kO99PfZycP6AaL91UmJw/hxbZzueZnD8IrBxaVHScP5HtfD/laZw/5dAi2xnAnD+q8dJNgkycP8P1KFwPt5w/O99PjadAnD/l0CLbWaKcPx1aZDtfrpw/vHSTGKTHnD8AAAAAQKecPxBYObR4Q50/EoPAykFynD8OLbKd/5+cP+XQItv5gJw/ZmZmZpZJnD+F61G4PoScPwIrhxapuZw/2c73U3PxnD/Xo3A9ut2cP1TjpZuE5pw/30+Nl94NnT/ByqFFBoycP1TjpZt0/5w/UI2XbqJ9nD+oxks3uRydP39qvHTjGJ0/sp3vp5bjnD9mZmZm5tycP/T91HiJP50/EFg5tEh3nT9t5/upQcOcP76fGi/tXJ0/x0s3icEwnT8hsHJocRCdP+58PzXug50/LbKd75c1nT+mm8QgkESdP5zEILAiNJ0/BFYOLWIdnT8IrBxaNEOdP/7UeOnG8Jw/oBov3WT8nD8fhetRyFWdP2Q73089kp0/bxKDwHp7nT+JQWDlYGqdP7gehevhgZ0/lkOLbDewnT+uR+F69OOcP2ZmZmbGRp0/vHSTGNTBnD/FILByCLydPzVeukn8O50//tR46QbJnT9mZmZmhp+dP8l2vp/Ktp0/SgwCK8cwnT8j2/l+mtWdPyUGgZUzZ50/VOOlm6R1nT8dWmQ7L/KdP2Dl0CKL+p0/ke18P/WXnT956SYxGEydPxfZzveTl50/ukkMAju5nT+0yHa+/1ydP+xRuB4Vz50/BoGVQ1ssnj89CtejAJ6dP30/NV5KcZ0/XI/C9ejYnT9xPQrX8wieP+F6FK6HHJ4/8tJNYoCEnT/ZzvdTYxmeP0a28/2k050/O99PjTf8nT/FILByyBOeP7Kd76fGL54/HVpkO1+Jnj9KDAIrB92dP3sUrkfBmp0/rkfhelTmnT9U46Wb1PSdPy/dJAYR2J0/9P3UeGnMnT9KDAIrFweeP0a28/3kRp4/30+Nlz76nT9vEoPASoaeP4/C9ShcP54//tR46YaEnj9I4XoU/p2eP2iR7Xz/K54/bxKDwKoHnj+wcmiR/SqePwAAAAAw2p0/Rrbz/dRgnj8730+Np5aeP90kBoFlfZ4//Knx0n27nT8nMQis/GWeP8/3U+NFIJ4/CKwcWvRmnj+YbhKDILWeP1pkO98fk54/O99PjUd3nj+HFtnONwefP42XbhKzYZ4/TmIQWBmunj9zaJHtfJWeP4XrUbguH58/16NwPSomnz8730+Nl3iePxkEVg6N354/nMQgsNIunz9MN4lBcNyeP3sUrkdx4p4/tMh2vl8fnz/Jdr6fuhefP/YoXI+CDp8/TDeJQVDJnj8dWmQ7P42ePxBYObS4554/L90kBhF7nz/0/dR4Cf2eP9v5fmo8Tp8/QmDl0IKOnj97FK5HsT+fP7x0kxhU2p4/5dAi22n2nj/FILByGH6fP8dLN4nB9J4/AiuHFomonz9YObTI5gyfPwrXo3CddZ8/ukkMAquanz9iEFg59CmfP2iR7XzvBJ8/g8DKobWTnj/D9Shcb9+eP3E9CtcjB58/f2q8dCNcnz/b+X5qnGOfP5zEILBCIZ8/WmQ739/tnz8dWmQ7D4yfPxKDwMoRNZ8/kxgEVg6nnz9YObTItqSfP0a28/3U6J4/qMZLN2nTnj/D9Shcn6WfPy2yne/fBKA/pHA9Csdmnz+HFtnOhx+fP3npJjH4ZJ8/UI2XbgIunz+e76fG27WfP+XQItupxJ8/5dAi2zn0nz8MAiuHFr6fP3sUrkdh3J8/pHA9CvcmoD+LbOf7Sa2fP5qZmZkJ3p8/Di2ynacAoD9eukkMElOfP166SQxSvJ8/iUFg5eDRnz/ufD81HpyfPxkEVg7lEKA/YhBYOQTznz8tsp3vZ+CfP39qvHQbB6A/Gy/dJKabnz/sUbge5bqfPwaBlUM7vZ8/YhBYOQTmnz/Xo3A96gWgP9NNYhAIGqA/dZMYBDa3nz9I4XoUPhyfPwisHFpUBJ8/NV66ScQWoD89CtejsAWgPzeJQWAV3p8/0SLb+c4yoD9cj8L1yPufP3e+nxqf1Z8/1XjpJmkdoD/FILByOFifP8dLN4lx158/iUFg5RgKoD+wcmiRxTOgPxsv3ST+IqA/WmQ73w/znz8MAiuHRlGgP4cW2c7/E6A/KVyPwr0QoD/wp8ZLPySgP+F6FK73TqA/K4cW2f4MoD9zaJHtpEigPyuHFtnWOaA/JzEIrMwOoD8X2c73U0agPwwCK4cGFqA/16NwPWIBoD+gGi/dtDagP4PAyqFF6J8/9P3UeLltoD/pJjEILGagPyuHFtk2EKA/2/l+akRYoD+R7Xw/5TSgP83MzMx8Z6A/SgwCK6e8oD89CtejAJigPx+F61HgWKA/aJHtfO88oD8v3SQGqVqgPwrXo3CVBKA/hxbZzj9JoD8AAAAAYF6gP8HKoUUGnKA/rkfheoRloD/D9ShcT0igP6abxCDQXaA//tR46c5VoD/jpZvEUGqgP0Jg5dCiJ6A/vp8aLw2MoD8xCKwciligP+XQItvZj6A/PQrXoxhMoD/6fmq85G+gP8dLN4npWaA/K4cW2d5voD956SYxSGSgPwAAAAAAiKA/MQisHFqGoD/ZzvdTo7KgP1g5tMgGj6A/9P3UeClwoD9zaJHtnIKgP+kmMQgsmqA/gZVDi+RLoD97FK5HscWgP30/NV4KdaA/nMQgsCLBoD+6SQwCq7agP9V46SYhkaA/iUFg5WDMoD8GgZVDm9qgPxkEVg79g6A/okW2833aoD/jpZvEEIqgP76fGi9N0qA/XrpJDKLGoD/HSzeJ4aqgP/yp8dLdyqA/kxgEVn7foD+oxks3SaGgP+F6FK4HhqA/1XjpJqGioD+kcD0KZ4mgP2Dl0CJ786A/bef7qVGNoD9KDAIrZ5KgP9nO91NDp6A/MQisHCqAoD/0/dR46YKgP/LSTWKgtaA/EFg5tOigoD/FILByiGegP7bz/dRodqA/vp8aL42boD+BlUOLXKygPzEIrByamaA/JQaBlRNtoD/0/dR4GfOgP1yPwvWod6A/exSuR3GVoD/jpZvEYM6gP1pkO98vC6E/g8DKoaXPoD8/NV66uZygP8l2vp/q96A/I9v5fpq7oD/RItv5fr+gP3e+nxofhqA/x0s3iRHSoD8xCKwcSsagP/CnxkunGaE/sp3vp+bjoD8AAAAAwLCgP/YoXI/S66A/mpmZmZngoD8QWDm0CPKgP+f7qfFCjKA/nu+nxlujoD+LbOf72b+gP5MYBFYeQqE/UI2XbnKaoD+TGARWjvWgP+XQIttptKA/xSCwcojqoD9WDi2yreSgPyUGgZUzzqA/QmDl0FLtoD8nMQisTBmhP/hT46U756A/sHJokf3noD9zaJHtzMugP7Kd76f296A/3SQGgbVAoT+iRbbzfRmhP6abxCDA6KA/CKwcWuQqoT8j2/l+elyhP+kmMQhM2aA/gZVDi0xToT8rhxbZ7imhP7ByaJHtC6E/JzEIrDwgoT+yne+nRkehP+58PzWeBaE/bxKDwIodoT91kxgExu2gP42XbhITTKE/qMZLN5kwoT8730+Nt0GhP7ByaJHtCqE/AiuHFrkAoT81XrpJ3BahPxKDwMqBXKE/bef7qQEnoT9oke18P9ygP/p+arzEM6E/bef7qXH2oD+iRbbzXSOhP+58PzUO8qA/f2q8dDMHoT97FK5HsSqhP7gehetxJKE/5/up8cIToT8pXI/CxQ2hPylcj8JFI6E/CKwcWhRZoT8ZBFYO7ROhP1K4HoUba6E/BoGVQ6tPoT+kcD0K9x+hPx1aZDsfJ6E/f2q8dONFoT/n+6nxkh+hPzVeukn8W6E/7FG4HgUyoT+e76fG6zihPwwCK4fWSKE/iUFg5aBmoT9vEoPAWg2hP2IQWDl0dKE/UI2XblJToT9/arx0A16hP9nO91OzBKE/mpmZmekwoT8zMzMzw1KhP6wcWmS7RqE/YOXQIntMoT+R7Xw/ZXyhP/YoXI/CbqE/JzEIrDwgoT+uR+F6xE6hP3WTGATWRaE/Gy/dJNZToT+6SQwCayqhPwIrhxYJZqE/dZMYBMZioT8tsp3vF1yhP1pkO98vnKE/I9v5flpEoT8zMzMzwzahP8dLN4nxUaE/DAIrh3YyoT/LoUW2M3uhP6wcWmSrhqE/okW2891hoT/fT42XTjKhPzVeuknsXaE/arx0k/jGoT+WQ4tsBzihPzMzMzNja6E//Knx0j1SoT83iUFgZUGhP28Sg8BaE6E/exSuR+FGoT/8qfHSDYuhPxKDwMpRUaE/6SYxCByOoT/wp8ZLV4qhPz0K16OAeqE/NV66SQxwoT8Sg8DKkZihP0w3iUFgkaE/BoGVQ2tqoT85tMh2bm2hP6abxCDQg6E/tMh2vo9loT8EVg4tUnmhP76fGi89gqE/7FG4HvWMoT+iRbbz/XOhP42XbhLza6E/L90kBrGyoT81XrpJ/HehP/CnxksXmaE/CtejcL2soT9mZmZmxm6hP1g5tMgmmqE/PzVeutmyoT+oxks3qXGhPzvfT433o6E/cT0K13ORoT+yne+nZmWhPzvfT40ntaE/HVpkO09loT/6fmq8xGehP8P1KFy/o6E/ppvEILBwoT8dWmQ7Lw6iP90kBoFV26E/PQrXo4CcoT85tMh23q6hPzm0yHZ+iKE/0SLb+c64oT8tsp3v56ShP1CNl26SwaE/qMZLN7mRoT9kO99PvcqhPxKDwMpx1KE/mG4Sg9CNoT/Jdr6fOoahP2q8dJN4maE/AAAAABCwoT+wcmiRjdGhP7Kd76f2laE/FK5H4ap+oT83iUFgNYahP65H4XpUeqE/7FG4HlXboT/y0k1iMH2hP9nO91Pz9KE/dZMYBAa7oT/wp8ZLp+WhP7bz/dTYmKE/ukkMAlutoT+LbOf7WWmhPy2yne/nmqE/VOOlmxSGoT+28/3UiLmhP8/3U+PF/qE/RIts5xsIoj/n+6nxYsqhP8l2vp9q5KE/vp8aL73MoT++nxovjdOhPxkEVg4NL6I/c2iR7Xz2oT8K16NwHdihP8/3U+PFqaE/QmDl0FLZoT/LoUW2A+ChPyUGgZVj+KE/K4cW2T7loT+DwMqhFeehP2ZmZmYWv6E/x0s3ieHFoT+WQ4tsR+ShP1yPwvX4GqI/2/l+aszFoT/sUbge5UmiPwaBlUP7BKI/rkfhesQJoj/P91PjxRqiP7TIdr5v8aE/CKwcWlTAoT8GgZVDSwuiP4XrUbjOBqI/zczMzKyhoT+BlUOLHMqhP2q8dJNY0KE/SgwCKwcZoj++nxovLfChP76fGi+tF6I/TmIQWKk7oj8xCKwcav6hP6RwPQqH4qE/tMh2vi/6oT+BlUOLXAWiP/T91HgJ4qE/BFYOLULQoT++nxovbQSiPz0K16MgXaI/1XjpJsEIoj/FILByKCeiP2IQWDn0DKI/I9v5frpuoj+8dJMYNAyiP6RwPQqH/6E/4XoUrifloT8nMQisnFaiP1K4HoUbE6I/lkOLbPd2oj9zaJHtnEWiPylcj8KVIqI/lkOLbDdpoj+amZmZeZKiP/p+arwkWaI/arx0k5hOoj/HSzeJcUqiP1K4HoVLRqI/DAIrhyZ6oj++nxov3R2iP76fGi9NY6I/001iEIhIoj+wcmiRXXyiP+58PzXeVaI/ppvEIBCVoj/RItv53kmiP+xRuB7lP6I/UrgehZuEoj8IrBxapICiP42XbhIzoKI/bef7qXFUoj/LoUW2s1uiPzeJQWBVnKI/nu+nxitMoj/2KFyPQmGiP2ZmZmbmYaI/JQaBlQMboj/wp8ZL1zqiP4/C9Sg8EKI/MzMzM9M9oj/hehSuh6SiP65H4XoEiqI/8KfGS2dwoj9I4XoUrm+iP8UgsHIIeqI/46WbxEBooj8v3SQGQWmiP+kmMQjceKI/j8L1KCymoj/Jdr6f2n+iP4lBYOUAkaI/H4XrUQiToj/ufD81TqGiP9ejcD06iaI/CKwcWqSXoj9xPQrXM1qiP9ejcD0an6I/mG4SgzCAoj+e76fGW1aiP6wcWmTLY6I/AAAAALBloj/dJAaB9UyiP3WTGATGsKI/AAAAAEAroj+sHFpkm3CiPwrXo3A9VaI/MzMzM/Ncoj/TTWIQeIKiP28Sg8BKkKI/mpmZmTlXoj/jpZvEcG6iP5HtfD+1naI/KVyPwqVToj+YbhKDYIuiP65H4Xp0m6I/tvP91HjWoj+LbOf7ab+iPxSuR+F6qaI/Rrbz/eSsoj+iRbbzXaSiP0jhehT+yaI/y6FFtsOYoj/FILByqJ6iP1TjpZt0sKI/mpmZmTlooj8EVg4t8rCiP1YOLbK9aKI/46WbxKDNoj8EVg4two6iP2Q730+9eaI/hxbZzleioj9OYhBYWVaiP8UgsHK4uKI/sHJokR2noj8730+Nd6eiP2ZmZmamPqI/nMQgsHKHoj8EVg4tksKiP7TIdr4v0aI/AiuHFvmwoj8fhetRyJyiPwrXo3DdoKI/okW2852Doj8IrBxaRIiiPwaBlUNbrqI/fT81Xvqgoj+amZmZ2b2iPx1aZDtvX6I/arx0kwhtoj+BlUOL/HyiP8UgsHJIcqI/PzVeuilGoj/4U+Ole5KiP4cW2c4nhqI/UI2XbvKWoj8hsHJo8WKiPxkEVg7dTKI/exSuR3Enoj89CtejgGKiPwaBlUNbX6I/O99PjSfJoj8nMQisLEKiP166SQxijqI/3SQGgQVtoj9KDAIrV5yiPycxCKxci6I/nMQgsGJ4oj9/arx0c4WiP90kBoEFZ6I//Knx0s1ooj9g5dAiW4OiP1pkO9//bqI/F9nO9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+  </OKVP>
+</ControlState>

source/2026_07_06/Flex/TST1.Test/TestRuns/TSR1.TestRun/CS/1/ctls.xml

@@ -0,0 +1,3 @@
+<Indexes>
+  <Control Key="Array-Variable Chart 1" StateFile="\CS\1\ctl1.xml" AssemblyQualifiedName="MTS.TestSuite.Common.TMC.RuntimeUI.ControlState, MTS.TestSuite.Common.TMC, Version=4.1.7.918, Culture=neutral, PublicKeyToken=null" />
+</Indexes>

source/2026_07_06/Flex/TST1.Test/TestRuns/TSR1.TestRun/Data/DaqTaskActivity1.h5

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