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UNNS SPECTRAL TRAJECTORY EXTRACTION PIPELINE
Completed Stages
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PROJECT CONTEXT
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Objective:
Transform large heterogeneous spectral-material corpora
into admissibility-compatible structural trajectories
usable by STRUC-PERC-I.

Primary source:
SciGlass / SpectralParam corpus exported from MDB.

Source file:
spectralparam_full.TXT

Domain:
Boundary-mediated structural continuity
within the UNNS Substrate framework.

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STAGE 1 — MDB CORPUS ACQUISITION
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Initial state:
Data existed only inside MDB database structure.

Actions completed:
- Inspected MDB schema
- Identified SpectralParam table
- Identified critical property families:
    100 → density
    130 → TEC
    400 → thermal transitions
    410 → Littleton / annealing
    420 → Tg
    500 → mechanical
    700 → electrical
    950 → characteristic temperatures
    954 → melting/liquid transitions

Critical realization:
Direct MDB access is unnecessary after export.

Result:
Full SpectralParam corpus exported into flat TXT format.

Produced artifact:
spectralparam_full.TXT

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STAGE 2 — RAW CORPUS INSPECTION
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Actions completed:
- Inspected actual TXT headers
- Detected separator format
- Verified coordinate fields
- Verified spectral fields
- Verified oxide/material identifiers

Critical realization:
Actual TXT structure differs from MDB visual layout.

Key structural fields identified:
- Colorant
- ColorantWtPct
- Nd
- Density
- GlassNo

Critical realization:
Trajectory extraction must rely on actual TXT headers,
not MDB visual assumptions.

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STAGE 3 — INITIAL TRAJECTORY EXTRACTION
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Initial implementation:
spectral_ladder_generator.py

Initial grouping strategy:
oxide + GlassNo

Result:
Tiny 2–4 point pseudo-ladders.

Critical realization:
Grouping by glass fragments trajectories.

Structural correction:
Trajectories must represent:
    oxide evolution across realizability space

NOT:
    oxide behavior inside one glass.

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STAGE 4 — GLOBAL TRAJECTORY EXTRACTION
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Generator revised.

New grouping strategy:
group by oxide only.

Pipeline:
TXT corpus
    ↓
oxide grouping
    ↓
coordinate sorting
    ↓
trajectory extraction

Result:
Large admissibility trajectories emerged.

Typical trajectory sizes:
- 10+
- 50+
- 100+ ordered points

Produced artifact:
ladders/

Trajectory format:
index coordinate spectral

Critical realization:
The corpus now resembles a realizability manifold
rather than isolated measurements.

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STAGE 5 — TRAJECTORY CONDITIONING
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Problem:
Raw ladders still contained:
- duplicated coordinates
- sparse sampling
- incompatible scales
- discontinuous jumps
- outlier spikes
- mixed spectral regimes

Solution:
trajectory_normalizer.py

Normalization operations:
1. Ladder parsing
2. Coordinate deduplication
3. Spectral averaging
4. Outlier removal
5. Local smoothing
6. Coordinate normalization → [0,1]
7. Spectral normalization → [0,1]

Result:
188 admissibility-compatible normalized trajectories.

Produced artifact:
normalized_ladders/

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CURRENT PIPELINE STATE
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Completed:

MDB export
    ↓
TXT corpus acquisition
    ↓
header inspection
    ↓
global trajectory extraction
    ↓
trajectory conditioning
    ↓
normalized admissibility trajectories

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CURRENT STATUS
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The corpus has successfully transitioned from:

database records

into:

ordered structural trajectories.

The normalized ladders now possess:
- coordinate continuity
- realizability ordering
- normalized admissibility geometry
- κ-compatible scaling

The corpus is now structurally suitable for:
- STRUC-PERC-I ingestion
- κ-field extraction
- admissibility analysis
- regime transition detection
- structural continuity studies

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NEXT STAGE
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Next required pipeline stage:

STRUC-PERC-I ingestion
using:

normalized_ladders/

Target analyses:
- admissibility fields
- κ-trajectories
- boundary transitions
- spectral regime clustering
- structural continuity collapse regions

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