diff --git a/.gitattributes b/.gitattributes index 8afbf10af86e9b2f4697cacf783ea432ef500649..f460ecc52bccdcb114ca7907575816b4666af48c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,6 +58,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +# Vector figure exports +*.pdf filter=lfs diff=lfs merge=lfs -text # Excel *.xlsx filter=lfs diff=lfs merge=lfs -text *.xls filter=lfs diff=lfs merge=lfs -text diff --git a/CLAUDE.md b/CLAUDE.md index e7d0e9b62efee83b57f4c29ee4d0e39493b2f6ce..82a757bc37e9628fdd798b851d835953fccecb27 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -22,7 +22,7 @@ this file uncritically. - **`notes` field on rows is new** (see `_lib.py:223`). Currently only the Formlabs tensile session sets it ("Printed vertically."); other rows get empty string. Not documented in the "Row shape" section below. - **PA12GF_FL material class is new and not documented.** `material_class` enum below mentions only SLS/PLA/PETG; `PA12GF_FL` (Formlabs PA12 GF reference prints) should be added. -The composite plots in `assets/` were regenerated and **do** reflect the current data — the Formlabs PA12 GF controls in particular reveal that the Inova Mk1 SLS specimens hit only ~10 MPa tensile vs Formlabs PA12 GF's ~25 MPa for the same nominal material, which is the comparison study these controls were added to enable. +The figures in `assets/` were regenerated and **do** reflect the current data — the Formlabs PA12 GF reference in particular reveals that the Inova Mk1 SLS specimens hit only ~10 MPa tensile vs Formlabs PA12 GF's ~25 MPa for the same nominal material, which is the comparison study these controls were added to enable. (Note: the plots section below has since been rewritten — the old overlaid "composite" figures were replaced by the batch-cluster overview + per-batch detail figures; see the `scripts/plots/` decision.) ## Ecosystem @@ -43,7 +43,11 @@ The composite plots in `assets/` were regenerated and **do** reflect the current - **`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 `Agentic-SLS-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). `02_batch_averages.py`'s banding logic is factored into a reusable `render_bands()`/`grouped_specs()` pair so the same mean ± 1 SD method can target a single group — used for the FormLabs Nylon 12 White control's own figure (`{standard}_nylon12white_average.png`, per user instruction, excluded from the main batch-averages figure for the same reason as `NYLON_CONTROLS` is excluded from the main composite). +- **`scripts/plots/` follows the same shared-lib pattern as `scripts/specimens/`.** `_lib.py` holds the **house style** (`apply_house_style()`/`style_axes()`/`save_figure()` — DM Sans from `scripts/plots/fonts/`, framed heavy spines, inward ticks, light dashed grid, dual PNG@1200 + PDF export; ported from the [`AdditiveLLM2-OA`](https://huggingface.co/datasets/ppak10/AdditiveLLM2-OA) figures), the **palette** (`BATCH_COLORS` = an ordered orange ramp gold→deep-brown built around `ACCENT`=`#F97415`, sampled per print chronology from `ORDERED_BATCHES`; `FORMLABS_COLOR` = contrasting blue for the PA12GF reference so it reads as the external benchmark; `MATERIAL_COLORS` for the other controls), and the shared `load_specimen`/`load_standard` readers. Four plot scripts: + - `01_controls.py` — the broken-out PLA/PETG, FormLabs Nylon 12 White, and Batch M Type IV figures (materials/geometries that don't belong on the batch comparison; `render_type_iv` lives here). + - `02_batch_averages.py` — per-batch mean ± 1 SD bands (SLS + FormLabs PA12GF). Each specimen is trimmed at its stress peak in `group_average` before interpolation so the fracture branch doesn't spike the band (this superseded the old per-Nylon `truncate_at_peak` special-case — it's now universal). `render_bands()`/`grouped_specs()` also target a single group for the Nylon 12 White `{standard}_nylon12white_average.png`. + - `03_batch_clusters.py` — the **canonical across-batch overview** (replaced the old overlaid composite, which was unreadable at 76–80 curves). One peak point per specimen `(strain@peak, ultimate strength)` — peak = `max(stress)` over the saved curve, robust to TestWorks' null `PeakStress` — with a covariance-based **±1 SD confidence ellipse** per batch (`confidence_ellipse`; groups with n<3 draw a segment/marker instead). + - `04_batch_details.py` — one uncluttered stress-strain figure **per batch** under `assets/batches/{standard}_{batch}.png`, each specimen individually colored (tab10) and `sample_id`-keyed, for reading a single batch's curves without cross-batch clutter. ## Directory layout @@ -147,8 +151,8 @@ Same TestWorks framework, different schema. Notable renames you have to handle: - 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); Batches K, L, M, N are `xlsx_only` too (no TestRuns/h5 handed off for those sessions at all, not just one project overwriting another) — 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`. -- **Batch M tensile (D638) is the first batch mixing two ASTM specimen types.** 5 Type I dogbones (`tensile_m.xlsx`, sample_ids M1-M5) plus 12 Type IV narrow-section specimens from the same print (`tensile_m_t4.xlsx`, M6-M17, `astm.type = "Type IV"`) — see `26_2026_07_21_tensile_m.py` / `27_2026_07_21_tensile_m_type4.py`, which share `batch_label="M"` and continue sample numbering via `process_session`'s `seq_start`. Per user instruction the Type IV specimens get their own figure (`assets/D638_type_iv.png`) rather than sharing the main composite/batch-averages figures with the rest of Batch M — `TYPE_LINESTYLES` in `scripts/plots/_lib.py` flags which ASTM types get this treatment, and `01_composite.py`/`02_batch_averages.py` both exclude them from the main figures accordingly (different gauge geometry, not comparable to Type I). Within that dedicated figure, each of the 12 specimens gets its own labeled curve (`render_type_iv` in `01_composite.py`) rather than one grouped legend entry — ordered by `specimen_index` (= xlsx sheet order = physical test order) and labeled per `TYPE_IV_LABELS = [1,2,3,4,5,6,8,9,10,11,13,14]`, a user-supplied numbering with intentional gaps at 7 and 12 (specimen numbers that exist in the lab's own record-keeping but aren't part of this xlsx export). -- **`NYLON12_WHITE_FL` is a new `material_class`** (FormLabs Nylon 12 White SLS control, tested 2026-07-23, no `batch_label`/`sample_id`, no Database FK) — a second FormLabs reference material alongside `PA12GF_FL`, unfilled/white nylon rather than glass-filled. Per user instruction it's excluded from the main composite/batch-averages figures and rendered on its own (`{standard}_nylon12white_control.png`, see `scripts/plots/_lib.py`'s `NYLON_CONTROLS`). **D638 (tensile) `tensile_nylon12white_fl.xlsx` is a raw load/extension export with no gauge-length scalar in the file** — unlike D790 there's no chord-formula fallback for tensile, so strain/stress can't be derived from geometry alone. User confirmed the test used a 25mm-starting-length extensometer, so `31_2026_07_23_tensile_nylon12white_fl.py` sets `gauge_length_mm: 25` explicitly and `build_row_from_xlsx` derives strain = extension/gauge_length, stress = load/area from that (see the `gauge_length_mm` session key, added for this case — `geometry.gauge_length_mm` on xlsx_only rows is null unless a session declares it). D790 (flex) rows derive normally since flexural strain/stress only need width/thickness. **The D638 curves are cut at their stress peak for plotting only** (`VERTICAL_BREAK_MATERIALS` in `scripts/plots/_lib.py`) — past peak, the raw trace continues as extension keeps increasing after the specimen separates while load reads ~0, and with few points sampled through the break itself this drew as a misleading diagonal line back to zero rather than the near-vertical drop a real break shows. Per user instruction, each figure applies this differently: `01_composite.py`'s raw-curve figure uses `vertical_break_at_peak` (cuts at peak, appends a point at zero stress/same strain so it plots as a vertical drop, matching the other SLS batches' visual convention); `02_batch_averages.py`'s mean ± SD figure uses `truncate_at_peak` (cuts at peak with no added point — a synthetic vertical segment would distort the shared strain grid/averaging, and doesn't make sense for an averaged curve anyway). Both are applied by the calling script, not baked into `load_standard`, since the two figures need different treatments. Either way this only affects what gets plotted — the underlying JSONL `curves.strain`/`curves.stress_pa` arrays are untouched (full raw trace still there for anyone querying the dataset directly). D790 Nylon 12 White isn't affected — its curves don't show this artifact. +- **Batch M tensile (D638) is the first batch mixing two ASTM specimen types.** 5 Type I dogbones (`tensile_m.xlsx`, sample_ids M1-M5) plus 12 Type IV narrow-section specimens from the same print (`tensile_m_t4.xlsx`, M6-M17, `astm.type = "Type IV"`) — see `26_2026_07_21_tensile_m.py` / `27_2026_07_21_tensile_m_type4.py`, which share `batch_label="M"` and continue sample numbering via `process_session`'s `seq_start`. Per user instruction the Type IV specimens get their own figure (`assets/D638_type_iv.png`) rather than sharing the cluster/batch-averages/detail figures with the rest of Batch M — `TYPE_LINESTYLES` in `scripts/plots/_lib.py` flags which ASTM types get this treatment, and `02_batch_averages.py`/`03_batch_clusters.py`/`04_batch_details.py` all exclude them accordingly (different gauge geometry, not comparable to Type I). Within that dedicated figure, each of the 12 specimens gets its own labeled curve (`render_type_iv` in `01_controls.py`) rather than one grouped legend entry — ordered by `specimen_index` (= xlsx sheet order = physical test order) and labeled per `TYPE_IV_LABELS = [1,2,3,4,5,6,8,9,10,11,13,14]`, a user-supplied numbering with intentional gaps at 7 and 12 (specimen numbers that exist in the lab's own record-keeping but aren't part of this xlsx export). +- **`NYLON12_WHITE_FL` is a new `material_class`** (FormLabs Nylon 12 White SLS control, tested 2026-07-23, no `batch_label`/`sample_id`, no Database FK) — a second FormLabs reference material alongside `PA12GF_FL`, unfilled/white nylon rather than glass-filled. Per user instruction it's excluded from the cluster/batch-averages figures and rendered on its own (`{standard}_nylon12white_control.png`, see `scripts/plots/_lib.py`'s `NYLON_CONTROLS`). **D638 (tensile) `tensile_nylon12white_fl.xlsx` is a raw load/extension export with no gauge-length scalar in the file** — unlike D790 there's no chord-formula fallback for tensile, so strain/stress can't be derived from geometry alone. User confirmed the test used a 25mm-starting-length extensometer, so `31_2026_07_23_tensile_nylon12white_fl.py` sets `gauge_length_mm: 25` explicitly and `build_row_from_xlsx` derives strain = extension/gauge_length, stress = load/area from that (see the `gauge_length_mm` session key, added for this case — `geometry.gauge_length_mm` on xlsx_only rows is null unless a session declares it). D790 (flex) rows derive normally since flexural strain/stress only need width/thickness. **The D638 curves are cut at their stress peak for plotting only** (`VERTICAL_BREAK_MATERIALS` in `scripts/plots/_lib.py`) — past peak, the raw trace continues as extension keeps increasing after the specimen separates while load reads ~0, and with few points sampled through the break itself this drew as a misleading diagonal line back to zero rather than the near-vertical drop a real break shows. Per user instruction, each figure applies this differently: `01_controls.py`'s raw-curve Nylon 12 White figure uses `vertical_break_at_peak` (cuts at peak, appends a point at zero stress/same strain so it plots as a vertical drop, matching the other SLS batches' visual convention); `02_batch_averages.py`'s mean ± SD figure now trims **every** specimen at its peak inside `group_average` (via `truncate_at_peak`) so no fracture branch — Nylon's included — reaches the averaging grid (this replaced the old per-Nylon special-case). Both `vertical_break_at_peak` and `truncate_at_peak` are applied by the calling code, not baked into `load_standard`, since the figures need different treatments. Either way this only affects what gets plotted — the underlying JSONL `curves.strain`/`curves.stress_pa` arrays are untouched (full raw trace still there for anyone querying the dataset directly). D790 Nylon 12 White isn't affected — its curves don't show this artifact. ## Running the extract @@ -175,7 +179,7 @@ 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 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. +6. Regenerate the figures: `for f in scripts/plots/0*.py; do uv run "$f"; done` (runs `01_controls.py`, `02_batch_averages.py`, `03_batch_clusters.py`, `04_batch_details.py`). If it's a new batch letter, add it to `ORDERED_BATCHES` in `scripts/plots/_lib.py` first — `BATCH_COLORS` is sampled from the orange ramp by that list's order, so no manual color pick is needed. ## Backfilling batches E–I, J, J_MB diff --git a/README.md b/README.md index 3f74a771623b504af20a8dfa74b2d479238108ac..86596d105ae13d7a7d94e64c5cb41e1f3322ced1 100644 --- a/README.md +++ b/README.md @@ -33,15 +33,39 @@ tensile = load_dataset("ppak10/Agentic-SLS-ASTM", "D638", split="train") flex = load_dataset("ppak10/Agentic-SLS-ASTM", "D790", split="train") ``` -## Composite stress–strain +> **Figure style.** All figures share one house style (ported from the +> [`AdditiveLLM2-OA`](https://huggingface.co/datasets/ppak10/AdditiveLLM2-OA) +> figures): DM Sans typeface, framed axes with inward ticks, a light dashed +> grid, and dual PNG (dpi 1200) + vector PDF export. The SLS batches are colored +> by an ordered **orange ramp** (gold → deep brown, in print chronology, built +> around `#F97415`); the FormLabs PA12GF benchtop reference is drawn in a +> contrasting **blue** so it reads clearly as the external benchmark. -Every specimen overlaid on one plot per standard. Colors are batch labels (A–N); solid gray lines are FormLabs PA12GF benchtop controls. Batch M mixes two ASTM D638 specimen types from the same print — its 12 Type IV (narrow-section) specimens are broken out into their own figure (`D638_type_iv.png`) rather than overlaid with its 5 Type I specimens, since the geometries aren't mechanically comparable. PLA/PETG filament controls (D638 only) print at much higher stress and strain than either PA12 material, so they're also broken out into their own figure to keep the main composite readable — same treatment for the FormLabs Nylon 12 White control, which gets its own figure(s) rather than joining the main comparison. +## Batch clusters + +Replaces the old overlaid composite (which, at 76–80 curves, was unreadable). Each specimen collapses to a single point at its stress–strain **peak** — x = strain at peak, y = ultimate strength — and each batch becomes a **±1 SD confidence ellipse** (covariance-based) around its specimens' peak points. This carries the headline ultimate-strength number and shows how tightly each batch clusters, with none of the overlaid-curve clutter. (Peak is derived as `max(stress)` over the saved curve, not read from `PeakStress` — TestWorks often leaves that scalar null for D638 tensile.) Groups with fewer than 3 specimens draw a connecting segment or a bare marker instead of an ellipse. | D638 (tensile) | D790 (three-point flex) | |---|---| -| ![D638 composite](assets/D638_composite.png) | ![D790 composite](assets/D790_composite.png) | +| ![D638 batch clusters](assets/D638_batch_clusters.png) | ![D790 batch clusters](assets/D790_batch_clusters.png) | + +Regenerate with `uv run scripts/plots/03_batch_clusters.py`. + +## Per-batch detail + +To read the actual stress–strain curves for one batch without cross-batch clutter, each batch (plus the FormLabs PA12GF reference) gets its own figure under [`assets/batches/`](assets/batches) — `{standard}_{batch}.png` — with each specimen individually colored and keyed by `sample_id` in the legend. Two examples: + +| D638 Batch C (tensile) | D790 Batch E (flex) | +|---|---| +| ![D638 Batch C detail](assets/batches/D638_C.png) | ![D790 Batch E detail](assets/batches/D790_E.png) | + +Regenerate with `uv run scripts/plots/04_batch_details.py`. + +## Broken-out controls & specimen types + +Materials/geometries that don't belong on the batch comparison get their own axes. PLA/PETG filament controls (D638 only) print at much higher stress/strain than either PA12 material; the FormLabs Nylon 12 White control is a second, distinct reference material; and Batch M's 12 Type IV (narrow-section) tensile specimens have a different gauge geometry than its Type I dogbones. -PLA/PETG controls (D638 only — D790 has no filament controls): +PLA/PETG controls (D638 only — D790 has no filament controls; PLA in blue, PETG in teal): ![D638 PLA/PETG controls](assets/D638_controls.png) @@ -57,11 +81,11 @@ Batch M Type IV (narrow-section) tensile specimens, D638 only — each of the 12 ![D638 Batch M Type IV](assets/D638_type_iv.png) -Regenerate with `uv run scripts/plots/01_composite.py`. +Regenerate with `uv run scripts/plots/01_controls.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). +One mean stress-strain curve per SLS batch, ± 1 sample standard deviation across specimens (shaded band), for our own Inova Mk1 SLS Nylon 12 GF prints plus the FormLabs PA12GF reference (in blue) — PLA/PETG and the Nylon 12 White control are excluded (their own figures). Each specimen is first trimmed at its stress peak (loading branch only), then linearly interpolated onto a common strain grid bounded by the *shortest* specimen's peak strain in that batch, so every point in the mean is backed by the same specimen count and no fracture-branch spike distorts the band. 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) | |---|---| diff --git a/assets/D638_batch_averages.pdf b/assets/D638_batch_averages.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4f12f91deef54edb4877a3472d7c54a9b3478108 --- /dev/null +++ b/assets/D638_batch_averages.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:964b5fdf7812c8b5974e606c3bfe34f9ab9ea19e12af7aeae6e1dcd815fff7ef +size 109546 diff --git a/assets/D638_batch_averages.png b/assets/D638_batch_averages.png index 89df9d4892d2b65bf1d5ca98805ddf09088c39f0..5358626a3e506c68beb2fbd4ca419fc1bb9f685f 100644 --- a/assets/D638_batch_averages.png +++ b/assets/D638_batch_averages.png @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:75c390d76d1117dc43dea873775347eee5dd15610ecdcb382f953a2cbaa80abd 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0000000000000000000000000000000000000000..a85efc38d9587e28b2b0da96d15385b843686d3f --- /dev/null +++ b/assets/batches/D790_PA12GF_FL.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:788d2025ce2df4414cf85060fb386850704b2dd88d6f53c853cc114cdc196716 +size 124262 diff --git a/assets/batches/D790_PA12GF_FL.png b/assets/batches/D790_PA12GF_FL.png new file mode 100644 index 0000000000000000000000000000000000000000..c0c8f15728c189a0c94d1509e2259556ad050127 --- /dev/null +++ b/assets/batches/D790_PA12GF_FL.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c659082cb7b1bcc31fb63cdcc940448c3f945e7c91f440549fd94c2ffc02f890 +size 1297822 diff --git a/scripts/plots/01_composite.py b/scripts/plots/01_controls.py similarity index 68% rename from scripts/plots/01_composite.py rename to scripts/plots/01_controls.py index 5b49bb238fa3d2d2f1bfe0510bb8f7a87f658e59..7111f7eb341cc2c2f944ca50250c6b4f843b8e15 100644 --- a/scripts/plots/01_composite.py +++ b/scripts/plots/01_controls.py @@ -1,24 +1,21 @@ #!/usr/bin/env python3 -"""Render composite stress-strain plots for the D638 and D790 configs. - -Reads every specimen JSONL under data/{standard}/*.jsonl and splits it into -figures per standard: the main composite (SLS batches + FormLabs PA12GF -benchtop control) at assets/{standard}_composite.png; a separate figure for -the PLA/PETG filament controls at assets/{standard}_controls.png (only if -the standard has any — 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); a separate figure for the FormLabs -Nylon 12 White SLS control at assets/{standard}_nylon12white_control.png -(per user instruction, kept off the main composite even though it's an -SLS-class material, same rationale as PLA/PETG — a second, distinct -reference material shouldn't be visually folded into the batch comparison -it's meant to be a comparison *against*); and a separate figure for any -non-default ASTM specimen type (currently just Batch M's 12 Type IV -narrow-section tensile specimens) at assets/{standard}_type_iv.png — same -ASTM standard and batch as the rest of Batch M, just a different specimen -geometry, so per user instruction it's broken out rather than overlaid with -Batch M's Type I curves (see TYPE_LINESTYLES in _lib.py for the linestyle -these still share within that dedicated figure). +"""Render the broken-out control / specimen-type stress-strain figures for the +D638 and D790 configs (the SLS batches themselves live in the cluster overview, +03_batch_clusters.py, and the per-batch detail figures, 04_batch_details.py — +this script is only the material/geometry outliers that each get their own axis). + +Reads every specimen JSONL under data/{standard}/*.jsonl and renders, per +standard: a figure for the PLA/PETG filament controls at +assets/{standard}_controls.png (PLA/PETG print at much higher stress/strain +than the SLS specimens, so they get their own axis); a figure for the FormLabs +Nylon 12 White SLS control at assets/{standard}_nylon12white_control.png (per +user instruction, a second distinct reference material kept on its own axis +rather than folded into the batch comparison it's meant to be compared +*against*); and a figure for any non-default ASTM specimen type (currently just +Batch M's 12 Type IV narrow-section tensile specimens) at +assets/{standard}_type_iv.png — same ASTM standard and batch as the rest of +Batch M, just a different gauge geometry, so it's broken out rather than mixed +in with Batch M's Type I curves (see TYPE_LINESTYLES in _lib.py). """ from pathlib import Path @@ -26,7 +23,8 @@ import matplotlib.pyplot as plt from matplotlib.lines import Line2D from _lib import (OUT_DIR, ROOT, FILAMENT_CONTROLS, NYLON_CONTROLS, TYPE_LINESTYLES, - VERTICAL_BREAK_MATERIALS, load_standard, style_for, vertical_break_at_peak) + VERTICAL_BREAK_MATERIALS, load_standard, save_figure, style_axes, + style_for, vertical_break_at_peak) def is_type_variant(row: dict) -> bool: @@ -78,22 +76,17 @@ def render(standard: str, specs: list[dict], title: str, out_name: str) -> Path for s in specs: color, linestyle = style_for(s["row"]) ax.plot(s["strain"], s["stress_mpa"], - color=color, linestyle=linestyle, linewidth=1.2, alpha=0.75) + color=color, linestyle=linestyle, linewidth=1.4, alpha=0.85, zorder=3) ax.set_xlabel("Strain (mm/mm)") ax.set_ylabel("Stress (MPa)") ax.set_title(f"{title} ({len(specs)} specimens)") - ax.grid(True, alpha=0.3) - ax.set_xlim(left=0) - ax.set_ylim(bottom=0) + style_axes(ax) ax.legend(handles=legend_handles([s["row"] for s in specs]), - loc="upper left", bbox_to_anchor=(1.02, 1.0), borderaxespad=0, - fontsize=9, framealpha=0.9) + loc="upper left", bbox_to_anchor=(1.02, 1.0), borderaxespad=0) - OUT_DIR.mkdir(parents=True, exist_ok=True) - out_path = OUT_DIR / f"{standard}_{out_name}.png" - fig.savefig(out_path, dpi=150, bbox_inches="tight") + out_path = save_figure(fig, OUT_DIR / f"{standard}_{out_name}") plt.close(fig) print(f"wrote {out_path.relative_to(ROOT)} ({len(specs)} curves)") return out_path @@ -114,22 +107,18 @@ def render_type_iv(standard: str, specs: list[dict], title: str, out_name: str) handles = [] for i, (s, label) in enumerate(zip(ordered, TYPE_IV_LABELS)): color = cmap(i % 20) - ax.plot(s["strain"], s["stress_mpa"], color=color, linewidth=1.3, alpha=0.85) - handles.append(Line2D([0], [0], color=color, linewidth=1.6, label=f"Specimen {label}")) + ax.plot(s["strain"], s["stress_mpa"], color=color, linewidth=1.4, alpha=0.9, zorder=3) + handles.append(Line2D([0], [0], color=color, linewidth=1.8, label=f"Specimen {label}")) ax.set_xlabel("Strain (mm/mm)") ax.set_ylabel("Stress (MPa)") ax.set_title(f"{title} ({len(ordered)} specimens)") - ax.grid(True, alpha=0.3) - ax.set_xlim(left=0) - ax.set_ylim(bottom=0) + style_axes(ax) ax.legend(handles=handles, loc="upper left", bbox_to_anchor=(1.02, 1.0), - borderaxespad=0, fontsize=9, framealpha=0.9) + borderaxespad=0) - OUT_DIR.mkdir(parents=True, exist_ok=True) - out_path = OUT_DIR / f"{standard}_{out_name}.png" - fig.savefig(out_path, dpi=150, bbox_inches="tight") + out_path = save_figure(fig, OUT_DIR / f"{standard}_{out_name}") plt.close(fig) print(f"wrote {out_path.relative_to(ROOT)} ({len(ordered)} curves)") return out_path @@ -140,10 +129,6 @@ def plot_standard(standard: str) -> None: if not specs: raise RuntimeError(f"No specimens found in data/{standard}/") - excluded_materials = FILAMENT_CONTROLS | NYLON_CONTROLS - main_specs = [s for s in specs - if s["row"]["material_class"] not in excluded_materials - and not is_type_variant(s["row"])] control_specs = [s for s in specs if s["row"]["material_class"] in FILAMENT_CONTROLS] nylon_specs = [s for s in specs if s["row"]["material_class"] in NYLON_CONTROLS] type_variant_specs = [s for s in specs if is_type_variant(s["row"])] @@ -155,10 +140,6 @@ def plot_standard(standard: str) -> None: if s["row"]["material_class"] in VERTICAL_BREAK_MATERIALS: s["strain"], s["stress_mpa"] = vertical_break_at_peak(s["strain"], s["stress_mpa"]) - 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") diff --git a/scripts/plots/02_batch_averages.py b/scripts/plots/02_batch_averages.py index 97e0ea7657acb2220b182c5933226e6b835796ae..42cda1b35225afb5abafd12e424bb32e8ac5b54c 100644 --- a/scripts/plots/02_batch_averages.py +++ b/scripts/plots/02_batch_averages.py @@ -20,13 +20,16 @@ their geometry isn't comparable (different gauge cross-section -> different modulus/strain response), and they get their own raw-curve figure at assets/D638_type_iv.png instead (see 01_composite.py). -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. +Each specimen is first trimmed at its own stress peak (loading branch only), +then resampled onto a common strain grid via linear interpolation (`np.interp`; +strain is monotonically increasing, so this is safe) and averaged pointwise +across the group. Trimming at peak matters: past peak the trace is the +fracture/softening branch, whose steep drop — once resampled — made the last +grid points swing wildly (one specimen already dropping while others still +rise), inflating the per-point std into a spurious spike at each band's end. +The grid runs from 0 to the *shortest* specimen's peak 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. The shaded band is +/- 1 sample standard deviation across specimens at each strain value (ddof=1). This is specimen-to-specimen variability (print @@ -43,7 +46,7 @@ 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) +*shortest* peak strain in the group, so every point has full sample size) collapses to near-zero width for the whole group. A second figure, assets/{standard}_nylon12white_average.png, applies the @@ -56,8 +59,8 @@ import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Patch -from _lib import (BATCH_COLORS, CONTROL_COLOR, NYLON_CONTROLS, OUT_DIR, ROOT, TYPE_LINESTYLES, - VERTICAL_BREAK_MATERIALS, load_standard, truncate_at_peak) +from _lib import (BATCH_COLORS, FORMLABS_COLOR, MATERIAL_COLORS, NYLON_CONTROLS, OUT_DIR, ROOT, + TYPE_LINESTYLES, load_standard, save_figure, style_axes, truncate_at_peak) N_POINTS = 100 BAND_ALPHA = 0.12 @@ -66,8 +69,10 @@ 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. +# The FormLabs reference control gets a contrasting color (see _lib.FORMLABS_COLOR) +# so it stands apart from the orange batch family. GROUP_STYLE = {batch: (color, f"Batch {batch}") for batch, color in BATCH_COLORS.items()} -GROUP_STYLE["PA12GF_FL"] = (CONTROL_COLOR, FORMLABS_LABEL) +GROUP_STYLE["PA12GF_FL"] = (FORMLABS_COLOR, FORMLABS_LABEL) def group_key(row: dict) -> str | None: @@ -82,17 +87,27 @@ def group_key(row: dict) -> str | None: 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) + Returns (strain_grid, mean_stress, std_stress, n_specimens). + + Each specimen is first trimmed at its own stress peak (truncate_at_peak): + past peak the trace is the fracture/softening branch, whose steep drop — + once resampled onto the common grid — made the last grid points swing wildly + (one specimen already dropping while others still rise), inflating the std + into a spurious spike at the band's end. Averaging only the loading branch up + to peak removes that artifact. The grid is then bounded by the *shortest* + peak-strain in the group, so every point in the mean is backed by the full + specimen count.""" + trimmed = [truncate_at_peak(s["strain"], s["stress_mpa"]) for s in specs] + max_strain = min(max(strain) for strain, _ in trimmed) grid = np.linspace(0, max_strain, N_POINTS) - curves = np.array([np.interp(grid, s["strain"], s["stress_mpa"]) for s in specs]) + curves = np.array([np.interp(grid, strain, stress) for strain, stress in trimmed]) 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 render_bands(groups: dict[str, list[dict]], group_style: dict[str, tuple[str, str]], - title: str, out_path) -> None: + title: str, out_stem) -> None: """Draw one mean +/- 1 SD band per group, in group_style's order.""" fig, ax = plt.subplots(figsize=(9, 6)) handles = [] @@ -101,26 +116,23 @@ def render_bands(groups: dict[str, list[dict]], group_style: dict[str, tuple[str if not group: continue grid, mean, std, n = group_average(group) - ax.plot(grid, mean, color=color, linewidth=1.6, alpha=0.95) + ax.plot(grid, mean, color=color, linewidth=1.8, alpha=0.95, zorder=4) ax.fill_between(grid, mean - std, mean + std, color=color, alpha=BAND_ALPHA, - linewidth=0) + linewidth=0, zorder=2) # 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) + ax.plot(grid, mean + std, color=color, linewidth=0.8, alpha=EDGE_ALPHA, zorder=3) + ax.plot(grid, mean - std, color=color, linewidth=0.8, alpha=EDGE_ALPHA, zorder=3) 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)") ax.set_title(title) - ax.grid(True, alpha=0.3) - ax.set_xlim(left=0) - ax.set_ylim(bottom=0) + style_axes(ax) ax.legend(handles=handles, loc="upper left", bbox_to_anchor=(1.02, 1.0), - borderaxespad=0, fontsize=9, framealpha=0.9) + borderaxespad=0) - OUT_DIR.mkdir(parents=True, exist_ok=True) - fig.savefig(out_path, dpi=150, bbox_inches="tight") + out_path = save_figure(fig, out_stem) plt.close(fig) print(f"wrote {out_path.relative_to(ROOT)} ({len(handles)} groups)") @@ -145,25 +157,21 @@ def plot_standard(standard: str) -> None: title_map = {"D638": "ASTM D638 — tensile batch averages (± 1 SD)", "D790": "ASTM D790 — three-point flex batch averages (± 1 SD)"} render_bands(groups, GROUP_STYLE, title_map.get(standard, standard), - OUT_DIR / f"{standard}_batch_averages.png") + OUT_DIR / f"{standard}_batch_averages") def plot_nylon12white_standard(standard: str) -> None: + # group_average() trims every specimen at its stress peak, so the D638 Nylon + # 12 White break artifact (VERTICAL_BREAK_MATERIALS) is handled there too — + # no material-specific pre-trim needed here. specs = load_standard(standard) - if standard == "D638": - # Cut at the stress peak (no synthetic point — see truncate_at_peak's - # docstring for why the averaged figure gets a plain cut instead of - # the raw-curve figure's vertical drop). - for s in specs: - if s["row"]["material_class"] in VERTICAL_BREAK_MATERIALS: - s["strain"], s["stress_mpa"] = truncate_at_peak(s["strain"], s["stress_mpa"]) groups = grouped_specs( specs, lambda row: "NYLON12_WHITE_FL" if row["material_class"] in NYLON_CONTROLS else None) - group_style = {"NYLON12_WHITE_FL": (CONTROL_COLOR, "FormLabs Nylon 12 White")} + group_style = {"NYLON12_WHITE_FL": (MATERIAL_COLORS["NYLON12_WHITE_FL"], "FormLabs Nylon 12 White")} title_map = {"D638": "ASTM D638 — FormLabs Nylon 12 White average (± 1 SD)", "D790": "ASTM D790 — FormLabs Nylon 12 White average (± 1 SD)"} render_bands(groups, group_style, title_map.get(standard, standard), - OUT_DIR / f"{standard}_nylon12white_average.png") + OUT_DIR / f"{standard}_nylon12white_average") if __name__ == "__main__": diff --git a/scripts/plots/03_batch_clusters.py b/scripts/plots/03_batch_clusters.py new file mode 100644 index 0000000000000000000000000000000000000000..7e4e8e722fcf54d9eb2e3c799818ba18ee6a640e --- /dev/null +++ b/scripts/plots/03_batch_clusters.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python3 +"""Batch-cluster overview — the canonical "all batches at a glance" figure, +replacing the old overlaid composite (which, at 76-80 curves, was unreadable +spaghetti; see git history / README). + +Each specimen collapses to a single point at its stress-strain **peak**: + x = strain at peak, y = ultimate strength (peak stress). +Peak is derived as max(stress) over the saved curve rather than read from the +persistent.h5 PeakStress field — TestWorks often fails to detect the peak for +D638 tensile and leaves that scalar null (see CLAUDE.md), but the full curve is +always saved, so the maximum is robust. + +Each SLS batch (plus the FormLabs PA12GF benchtop reference) becomes a cluster +of its specimens' peak points, wrapped in a translucent convex-hull boundary in +the batch color with a legend entry. This carries the headline ultimate-strength +number and shows how tightly each batch clusters (strength-vs-ductility), with +none of the overlaid-curve clutter. To read the actual stress-strain curves for +one batch, see the per-batch detail figures (scripts/plots/04_batch_details.py, +assets/batches/{standard}_{batch}.png). + +Excluded, same as the old composite: PLA/PETG filament controls, the FormLabs +Nylon 12 White control, and Batch M's Type IV specimens (different gauge +geometry) — all of which have their own dedicated figures. + +Outputs assets/{standard}_batch_clusters.png (dpi=1200) + .pdf. +""" +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +from matplotlib.patches import Ellipse +from matplotlib.colors import to_rgba + +from _lib import (BATCH_COLORS, FILAMENT_CONTROLS, FORMLABS_COLOR, NYLON_CONTROLS, + ORDERED_BATCHES, OUT_DIR, ROOT, TYPE_LINESTYLES, load_standard, save_figure, + style_axes) + +# Cluster boundary size, in standard deviations along each principal axis. 1.0 +# draws the ±1 SD spread of each batch's peak points (matching the ±1 SD +# convention in 02_batch_averages.py) — some points fall outside by design. +N_STD = 1.0 + +# Group order: SLS batches (print chronology) then the FormLabs reference last. +GROUP_ORDER = ORDERED_BATCHES + ["PA12GF_FL"] +GROUP_LABELS = {**{b: f"Batch {b}" for b in ORDERED_BATCHES}, "PA12GF_FL": "FormLabs PA12GF"} +GROUP_COLORS = {**BATCH_COLORS, "PA12GF_FL": FORMLABS_COLOR} + + +def group_key(row: dict) -> str | None: + """Cluster membership: SLS batch label, or the FormLabs PA12GF reference. + Everything with its own dedicated figure returns None (excluded).""" + if row["material_class"] == "PA12GF_FL": + return "PA12GF_FL" + if row["material_class"] in FILAMENT_CONTROLS or row["material_class"] in NYLON_CONTROLS: + return None + if row["astm"].get("type") in TYPE_LINESTYLES: + return None # Type IV etc. — different geometry, own figure + return row["batch_label"] or None + + +def peak_point(spec: dict) -> tuple[float, float]: + """(strain_at_peak, ultimate_stress_mpa) from the specimen's saved curve.""" + stress = spec["stress_mpa"] + peak_i = max(range(len(stress)), key=lambda i: stress[i]) + return spec["strain"][peak_i], stress[peak_i] + + +def confidence_ellipse(points: list[tuple[float, float]], ax, color, n_std: float = N_STD): + """Draw a covariance-based n_std confidence ellipse for a cluster of points. + Requires >=3 points for a non-degenerate covariance; the 1-2 point cases are + handled by the caller (segment / bare marker).""" + pts = np.asarray(points, dtype=float) + cov = np.cov(pts, rowvar=False) + vals, vecs = np.linalg.eigh(cov) # ascending eigenvalues, orthonormal vecs + order = vals.argsort()[::-1] + vals, vecs = vals[order], vecs[:, order] + vals = np.clip(vals, 0.0, None) # guard tiny negative from round-off + angle = np.degrees(np.arctan2(vecs[1, 0], vecs[0, 0])) + width, height = 2 * n_std * np.sqrt(vals) # full axis lengths + ax.add_patch(Ellipse( + xy=pts.mean(axis=0), width=width, height=height, angle=angle, + facecolor=to_rgba(color, 0.15), edgecolor=to_rgba(color, 0.75), + linewidth=1.4, zorder=2)) + + +def render(standard: str, groups: dict[str, list[tuple[float, float]]], + ylabel: str, title: str) -> None: + fig, ax = plt.subplots(figsize=(9, 6)) + handles = [] + for key in GROUP_ORDER: + pts = groups.get(key) + if not pts: + continue + color = GROUP_COLORS[key] + xs = [x for x, _ in pts] + ys = [y for _, y in pts] + + # Cluster boundary: ±N_STD confidence ellipse (>=3 pts), a connecting + # segment (2 pts), or nothing (1 pt — just the marker). + if len(pts) >= 3: + confidence_ellipse(pts, ax, color) + elif len(pts) == 2: + ax.plot(xs, ys, color=color, linewidth=1.4, alpha=0.7, zorder=3) + + ax.scatter(xs, ys, s=48, color=color, edgecolor="white", linewidth=0.6, + zorder=5, alpha=0.95) + handles.append(Line2D([0], [0], marker="o", linestyle="none", color=color, + markeredgecolor="white", markeredgewidth=0.6, markersize=8, + label=f"{GROUP_LABELS[key]} (n={len(pts)})")) + + ax.set_xlabel("Strain at peak (mm/mm)") + ax.set_ylabel(ylabel) + ax.set_title(title) + style_axes(ax) + ax.legend(handles=handles, loc="upper left", bbox_to_anchor=(1.02, 1.0), borderaxespad=0) + + out_path = save_figure(fig, OUT_DIR / f"{standard}_batch_clusters") + plt.close(fig) + print(f"wrote {out_path.relative_to(ROOT)} ({len(handles)} clusters)") + + +def plot_standard(standard: str) -> None: + specs = load_standard(standard) + groups: dict[str, list[tuple[float, float]]] = {} + for s in specs: + key = group_key(s["row"]) + if key is None: + continue + groups.setdefault(key, []).append(peak_point(s)) + + ylabel = {"D638": "Ultimate tensile strength (MPa)", + "D790": "Ultimate flexural strength (MPa)"}.get(standard, "Ultimate strength (MPa)") + title = {"D638": "ASTM D638 — tensile batch clusters (peak point per specimen)", + "D790": "ASTM D790 — flexural batch clusters (peak point per specimen)"}.get( + standard, standard) + render(standard, groups, ylabel, title) + + +if __name__ == "__main__": + plot_standard("D638") + plot_standard("D790") diff --git a/scripts/plots/04_batch_details.py b/scripts/plots/04_batch_details.py new file mode 100644 index 0000000000000000000000000000000000000000..9dfd618912916825634eedef8b3de8026605fae7 --- /dev/null +++ b/scripts/plots/04_batch_details.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +"""Per-batch detail figures — one clean stress-strain plot per batch, so the +individual specimen curves can actually be read (the "look deeper into one +batch" companion to the batch-cluster overview in 03_batch_clusters.py). + +One figure per SLS batch (plus the FormLabs PA12GF reference) per standard, +written to assets/batches/{standard}_{batch}.png (+ .pdf). Within a figure each +specimen gets its own color (tab10) and a legend entry keyed by sample_id, so a +batch's ~5-10 curves stay individually legible. + +Excluded (they have their own dedicated figures): PLA/PETG controls, the +FormLabs Nylon 12 White control, and Batch M's Type IV specimens. +""" +import matplotlib.pyplot as plt + +from _lib import (BATCH_COLORS, FILAMENT_CONTROLS, FORMLABS_COLOR, NYLON_CONTROLS, OUT_DIR, ROOT, + TYPE_LINESTYLES, load_standard, save_figure, style_axes) + +BATCHES_DIR = OUT_DIR / "batches" + + +def group_key(row: dict) -> str | None: + """Same membership as the cluster overview: SLS batch label or the FormLabs + PA12GF reference; everything with its own figure is excluded.""" + if row["material_class"] == "PA12GF_FL": + return "PA12GF_FL" + if row["material_class"] in FILAMENT_CONTROLS or row["material_class"] in NYLON_CONTROLS: + return None + if row["astm"].get("type") in TYPE_LINESTYLES: + return None + return row["batch_label"] or None + + +def specimen_label(row: dict) -> str: + return row.get("sample_id") or row["specimen_id"].split("/")[-1] + + +def specimen_sort_key(spec: dict): + """Order by sample_id numeric suffix (C1, C2, …) when present, else by the + fully-qualified specimen_id so ordering is at least stable.""" + sid = spec["row"].get("sample_id") + if sid: + digits = "".join(c for c in sid if c.isdigit()) + if digits: + return (0, int(digits)) + return (1, spec["row"]["specimen_id"]) + + +def group_title(standard: str, key: str, n: int) -> str: + test = {"D638": "tensile", "D790": "flexural"}.get(standard, "") + name = "FormLabs PA12GF" if key == "PA12GF_FL" else f"Batch {key}" + return f"ASTM {standard} — {name} {test} ({n} specimen{'s' if n != 1 else ''})" + + +def render(standard: str, key: str, specs: list[dict]) -> None: + specs = sorted(specs, key=specimen_sort_key) + accent_color = FORMLABS_COLOR if key == "PA12GF_FL" else BATCH_COLORS.get(key, FORMLABS_COLOR) + + fig, ax = plt.subplots(figsize=(8, 5.5)) + cmap = plt.get_cmap("tab10") + for i, s in enumerate(specs): + ax.plot(s["strain"], s["stress_mpa"], color=cmap(i % 10), linewidth=1.6, + alpha=0.9, zorder=3, label=specimen_label(s["row"])) + + ax.set_xlabel("Strain (mm/mm)") + ax.set_ylabel("Stress (MPa)") + ax.set_title(group_title(standard, key, len(specs)), color=accent_color) + style_axes(ax) + ax.legend(loc="upper left", bbox_to_anchor=(1.02, 1.0), borderaxespad=0, title="Specimen") + + out_path = save_figure(fig, BATCHES_DIR / f"{standard}_{key}") + plt.close(fig) + print(f"wrote {out_path.relative_to(ROOT)} ({len(specs)} curves)") + + +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 + groups.setdefault(key, []).append(s) + for key, group in groups.items(): + render(standard, key, group) + + +if __name__ == "__main__": + plot_standard("D638") + plot_standard("D790") diff --git a/scripts/plots/_lib.py b/scripts/plots/_lib.py index 42e7349902dcf5c223822574d3430a841ff370d2..8617ae8a0bea61c803ca257150da45acd1f5ae3b 100644 --- a/scripts/plots/_lib.py +++ b/scripts/plots/_lib.py @@ -1,30 +1,144 @@ -"""Shared helpers for scripts/plots/*.py.""" +"""Shared helpers for scripts/plots/*.py. + +House style is ported from the AdditiveLLM2-OA figures +(ppak10/AdditiveLLM2-OA, figures/*/*.py): DM Sans typeface, a curated +saturated palette anchored on #2D6A9F / #2AAA8A / #D44000 / #8B5CF6 with +#F97415 orange reserved for the reference/highlight series, a *framed* +(not despined) look with heavy spines and inward ticks, a light dashed +grid, and dual PNG@1200 + PDF export. Call `apply_house_style()` once at +import (done here), `style_axes(ax)` per Axes, and `save_figure(fig, stem)` +to write both formats. +""" import json from pathlib import Path +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +import matplotlib.font_manager as fm + +matplotlib.use("Agg") + ROOT = Path(__file__).parent.parent.parent DATA_DIR = ROOT / "data" OUT_DIR = ROOT / "assets" +FONT_DIR = Path(__file__).parent / "fonts" + +# ── House style ─────────────────────────────────────────────────────────────── + +# Publication export DPI, matching the AdditiveLLM2 figures. save_figure() +# emits both a PNG at this DPI and a vector PDF. +EXPORT_DPI = 1200 + +# Reference categorical palette (AdditiveLLM2 figures/*/*.py). Used verbatim on +# the low-series-count figures (PLA/PETG controls, Type IV, Nylon 12 White). +REF_BLUE = "#2D6A9F" +REF_TEAL = "#2AAA8A" +REF_REDORANGE = "#D44000" +REF_PURPLE = "#8B5CF6" +# The signature accent — reserved for the reference / highlighted series +# (here the FormLabs PA12GF benchtop control the SLS batches are compared +# against), exactly as #F97415 flags the "Overall"/"best" series in the +# AdditiveLLM2 charts. Never assigned to an SLS batch. +ACCENT = "#F97415" + +# Neutral fallback for any control material without a dedicated color. +CONTROL_COLOR = "#6B7280" + + +def apply_house_style() -> None: + """Register DM Sans and set the AdditiveLLM2 rcParams. Idempotent.""" + for ttf in sorted(FONT_DIR.glob("*.ttf")): + fm.fontManager.addfont(str(ttf)) + plt.rcParams.update({ + "font.family": "DM Sans", + "axes.linewidth": 1.4, # heavy framed spines + "axes.titlesize": 13, + "axes.titleweight": "bold", + "axes.labelsize": 12, + "xtick.labelsize": 10, + "ytick.labelsize": 10, + "xtick.direction": "in", # inward ticks + "ytick.direction": "in", + "xtick.major.size": 4, + "ytick.major.size": 4, + "xtick.major.width": 1.2, + "ytick.major.width": 1.2, + "legend.fontsize": 10, + "legend.frameon": True, + "legend.framealpha": 0.95, + "legend.edgecolor": "#D1D5DB", + "grid.linestyle": "--", + "grid.linewidth": 1.0, + "grid.alpha": 0.4, + "grid.color": "#B0B0B0", + "savefig.dpi": EXPORT_DPI, + }) + + +def style_axes(ax) -> None: + """Apply the framed look to one Axes: light dashed grid behind the data, + origin anchored at zero. Spines/ticks come from rcParams.""" + ax.grid(True, zorder=0) + ax.set_axisbelow(True) + ax.set_xlim(left=0) + ax.set_ylim(bottom=0) + + +def save_figure(fig, out_stem: Path) -> Path: + """Write `out_stem.png` (dpi=EXPORT_DPI) and `out_stem.pdf`, matching the + AdditiveLLM2 dual-format export. Returns the PNG path.""" + out_stem.parent.mkdir(parents=True, exist_ok=True) + png = out_stem.with_suffix(".png") + fig.savefig(png, dpi=EXPORT_DPI, bbox_inches="tight", pad_inches=0.15) + fig.savefig(out_stem.with_suffix(".pdf"), bbox_inches="tight", pad_inches=0.15) + return png + + +apply_house_style() + +# ── Batch color ramp ────────────────────────────────────────────────────────── + +# Ordered list of every batch label, in print chronology. J_MB (media-blasted +# variant of print J) sits right after J so the two share a neighborhood on the +# ramp — encoding their shared print origin — while staying distinct. +ORDERED_BATCHES = ["A", "B", "C", "D", "E", "F", "G", "H", "I", + "J", "J_MB", "K", "L", "M", "N"] + +# The 15 batches are chronological, so their color is an *ordered ramp* rather +# than an arbitrary categorical cycle: a warm gold→orange→brown sweep built +# around the signature #F97415 orange (ACCENT) — the whole batch palette is +# "based off that shade of orange" per user instruction. Adjacent batches read +# as neighbors — intended, since batch order is time order — and the legend + +# curve position disambiguate within a figure. The FormLabs reference series is +# deliberately *not* orange (see FORMLABS_COLOR) so it stands apart from the +# batches it's benchmarked against. +_RAMP = mcolors.LinearSegmentedColormap.from_list( + "batch_ramp", ["#F7C948", "#F9931E", ACCENT, "#C7430C", "#6E2206"]) + + +def _build_batch_colors() -> dict[str, str]: + n = len(ORDERED_BATCHES) + return {batch: mcolors.to_hex(_RAMP(i / (n - 1))) + for i, batch in enumerate(ORDERED_BATCHES)} + # 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) - "K": "#98df8a", # light green - "L": "#ff9896", # light red - "M": "#c49c94", # light brown - "N": "#9edae5", # light cyan +BATCH_COLORS = _build_batch_colors() + +# The FormLabs PA12GF benchtop reference is drawn in a contrasting blue rather +# than the orange batch family, so on the cluster / batch-average figures it +# reads clearly as the external benchmark the SLS batches are compared against. +FORMLABS_COLOR = REF_BLUE + +# Non-SLS materials get explicit reference-palette colors (rather than a shared +# gray) so they read as intentional on their own figures. +MATERIAL_COLORS = { + "PA12GF_FL": FORMLABS_COLOR, # FormLabs PA12 GF reference → contrasting blue + "NYLON12_WHITE_FL": REF_PURPLE, # FormLabs Nylon 12 White reference + "PLA": REF_BLUE, + "PETG": REF_TEAL, } -CONTROL_COLOR = "#7f7f7f" # gray for PLA/PETG MATERIAL_STYLES = {"SLS": "-", "PLA": "--", "PETG": ":"} @@ -105,9 +219,10 @@ def load_standard(standard: str) -> list[dict]: 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 if material == "SLS": + color = BATCH_COLORS.get(batch, CONTROL_COLOR) linestyle = TYPE_LINESTYLES.get(row["astm"].get("type"), "-") else: + color = MATERIAL_COLORS.get(material, CONTROL_COLOR) linestyle = MATERIAL_STYLES.get(material, "-") return color, linestyle diff --git a/scripts/plots/fonts/DMSans-Bold.ttf b/scripts/plots/fonts/DMSans-Bold.ttf new file mode 100644 index 0000000000000000000000000000000000000000..9f76feea48cd30da9fbc6a36ffda0e0836b6a052 Binary files /dev/null and b/scripts/plots/fonts/DMSans-Bold.ttf differ diff --git a/scripts/plots/fonts/DMSans-Medium.ttf b/scripts/plots/fonts/DMSans-Medium.ttf new file mode 100644 index 0000000000000000000000000000000000000000..4deeae0e4827fc7deaf317e64664fa28dd153df2 Binary files /dev/null and b/scripts/plots/fonts/DMSans-Medium.ttf differ diff --git a/scripts/plots/fonts/DMSans-Regular.ttf b/scripts/plots/fonts/DMSans-Regular.ttf new file mode 100644 index 0000000000000000000000000000000000000000..b25fc0cc1cf3bd9cdd22e5a37f505f4d9fec474b Binary files /dev/null and b/scripts/plots/fonts/DMSans-Regular.ttf differ diff --git a/scripts/plots/fonts/OFL.txt b/scripts/plots/fonts/OFL.txt new file mode 100644 index 0000000000000000000000000000000000000000..14e8d9aae694aba027e90fa6c236a0196048613d --- /dev/null +++ b/scripts/plots/fonts/OFL.txt @@ -0,0 +1,93 @@ +Copyright 2014 The DM Sans Project Authors (https://github.com/googlefonts/dm-fonts) + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +https://openfontlicense.org + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE.