Datasets:

parti-wave
/

N4_slice

@@ -1,470 +0,0 @@
----
-license: mit
-task_categories:
-  - time-series-forecasting
-  - other
-tags:
-  - resonance-graph-core
-  - rgc
-  - tau-universe
-  - tav-topology
-  - logistic-map
-  - fpga
-  - arty-a7
-  - hierarchical-lattice
-  - period-3
-  - berard
-  - session-log
-pretty_name: 30-Day Canonical Tav RGC N4 Lattice & Telemetry Slices
-size_categories:
-  - 10M<n<100M
-configs:
-  - config_name: lattice
-    default: true
-    data_files:
-      - split: train
-        path: "n4_slice_*.csv"
-  - config_name: data_manifest
-    data_files:
-      - split: train
-        path: "manifests/slice_manifest.csv"
-  - config_name: prospect_map
-    data_files:
-      - split: train
-        path: "manifests/prospect_manifest.csv"
-  - config_name: log_manifest
-    data_files:
-      - split: train
-        path: "n4_log_slices/log_slice_manifest.csv"
-  - config_name: log_prospect_map
-    data_files:
-      - split: train
-        path: "n4_log_slices/log_prospect_manifest.csv"
----
-# 30-Day Canonical Tav RGC N4 Lattice & Telemetry Slices
-**253 prospect-named windows · `parti-wave/N4_slice`**
-**Resonance Graph Core (RGC) v0.2 · 49-core logistic substrate · paired data & session logs**
-This dataset contains **253 overlapping time windows** cut from a continuous 30-day logistic-map characterization run of a 49-core Resonance Graph Core (RGC) on a Digilent Arty A7-100T FPGA, **plus** the matching windows from the companion session log.
-Each data slice and each log slice carries the **same unique prospect name**.
-> **Read first — three packaging caveats.** A full-corpus audit (v2 card revision) found that slice boundaries are **not** frame-aligned, and that prospect-name pairing between the two streams is **nominal rather than temporal**. The measurements themselves are clean and independently corroborated, but both facts change how you should load the data. See [Row ordering and frame reconstruction](#row-ordering-and-frame-reconstruction), [Prospect-name pairing](#prospect-name-pairing-is-nominal-not-temporal), and [Acquisition clock](#acquisition-clock--read-before-doing-spectral-analysis). Full list in [Errata](#errata-v2-card-revision).
-For temporal data↔log joins, use scripts/logs_for_window.py with slice_n_index.csv — do not assume matching prospect names share an n window.
----
-## Source run
-| Field | Value |
-|-------|--------|
-| Data source | `logistic_20260606_145947.csv` (1,283,133,318 B) |
-| Log source | `rgc_session_20260606_145947.log` (836,807,837 B) |
-| Architecture | RGC v0.2, 49-core circular + hierarchical lattice |
-| Platform | Digilent Arty A7-100T (Xilinx Artix-7) |
-| Dynamics | Logistic map, Q1.23 fixed-point; parameter region includes the period-3 (Sharkovskii) window |
-| Cores per lattice frame | 49 |
-| Start | 2026-06-06 T 14:59:47.797787 UTC |
-| End | 2026-07-06 T 14:59:48.294118 UTC |
-| Duration | 2,592,000.496 s = **30.00001 days**, continuous |
-The hardware executes an asynchronous, clockless wavefront that settles into a lattice-loll attractor. Core 32 exhibits a clean period-3 orbit in the relevant regime — verified in this corpus as **bit-exact** in Q1.23 (median `|x[k] − x[k−3]| = 0`). Hierarchical coherence (microscopic 7-rings → super-ring of 7 groups) is a primary observable.
-The **Berard monitor** (≈ 1.054 scale) is a *state monitor*, not a generative parameter of the logistic substrate. Under normal cold-start conditions the lattice locks to an "excellent" Berard condition; host resource sequestration degrades it to "good"; complete UART-bridge loss (as at the end of N3) produced the poorest reading. Session-log windows therefore carry the external context that explains Berard score variation.
-Companion characterizations and the RGC architecture whitepaper are on Zenodo (see **Citation**).
----
-## Dataset overview and row accounting
-| Quantity | Value |
-|---|---|
-| Rows in the `lattice` subset | **29,351,992** |
-| Unique rows in the source run | **18,143,975** |
-| Duplication from slice overlap | 11,208,017 (**61.8%**) |
-| Complete 49-core frames | 370,285 (+ one partial: 18,143,975 = 49 × 370,285 + 10) |
-The Hub banner figure of 29,352,498 is the sum across converted subsets (`lattice` + `log_manifest` 253 + `log_prospect_map` 253). **The 29.35M figure is not an independent sample count** — treat **18,143,975** as the number of distinct observations. The final frame of the run is inherently partial: the source ends at `n = 18,143,974`, `core_id = 9`.
----
-## What is included
-| Stream | Files | Slice size (approx.) | Overlaps |
-|--------|--------|----------------------|----------|
-| **Lattice data** | `n4_slice_XXX_<Prospect>.csv` | ~6–8 MB | ≈1.5 MiB lead + ≈1.5 MiB trail (interior) |
-| **Session log** | `n4_log_slices/n4_log_XXX_<Prospect>.log` | ~4.9–6.5 MB | ≈1.5 MiB lead + ≈1.5 MiB trail (interior) |
-- **253** windows per stream
-- Slice 000: no leading overlap
-- Slice 252: no trailing overlap
-- Identical prospect names on both streams — but see [Prospect-name pairing](#prospect-name-pairing-is-nominal-not-temporal) before joining on them
----
-## Data schema (CSV slices)
-| Column | Type | Range | Description |
-|--------|------|-------|-------------|
-| `timestamp_iso` | string | 26 chars | Host wall-clock timestamp of the sample (UTC, µs resolution) |
-| `elapsed_sec` | float | 0.161 – 2,592,000.657 | Seconds since run start (ms resolution) |
-| `core_id` | int | 0 – 48 | Core index. **Always equals `n % 49`** |
-| `x_raw24` | int | 0 – 8,388,607 | **Authoritative** logistic state, raw Q1.23 fixed-point. Value = `x_raw24 / 2²³` |
-| `x_norm` | float | 0.0 – 1.0 | Convenience rendering of `x_raw24`, **lossy** — see note |
-| `n` | int | 0 – 18,143,974 | **Global row counter**, increments by exactly 1 per row. Frame index is `n // 49` |
-| `delta_n` | int | 49 | Δ`n` between consecutive readouts of the *same* core. Null only on the first 49 rows of the run |
-Files are CRLF-terminated with a 60-byte header repeated at the top of every slice (self-describing).
-### `x_raw24` is primary; `x_norm` is derived and lossy
-`x_norm == round(x_raw24 / 2²³, 6)` on all 29,351,992 rows without exception. Six decimal places discards roughly three of the 23 bits (hardware quantum 1.19×10⁻⁷ vs 10⁻⁶ print resolution). **Use `x_raw24` for anything precision-sensitive** — Lyapunov exponents, period detection, return maps, symbolic dynamics.
-Two consequences of that rounding, both expected:
-- 6,608 rows read `x_norm = 0.000000` — true zeros (`x_raw24 = 0`, collapsed core).
-- 2,209 rows read `x_norm = 1.000000` — these are `x_raw24 = 8,388,607 = 2²³−1` rounded up at 6 dp, **not** a true 1.0. The state never reaches 1.
-The stored range of `x_norm` is therefore the closed interval `[0.0, 1.0]`, not the open interval `(0, 1)`. 11,025 rows sit on the endpoints.
-### `n` is a row counter, not a frame index
-Earlier revisions of this card described `n` as a "global iteration / frame index"; that was wrong. `n` advances by 1 per row, `core_id == n % 49`, and the frame index is `n // 49`. `delta_n = 49` is consistent with this: the same core recurs every 49 rows.
----
-## Row ordering and frame reconstruction
-Rows are ordered by increasing `n`, and `core_id` cycles `0 … 48` in lockstep (`core_id == n % 49`, verified with zero exceptions across all 29,351,992 rows). A complete lattice frame is the set of 49 rows sharing a value of `n // 49`.
-> ### ⚠️ Slice boundaries are NOT frame-aligned
->
-> Earlier revisions claimed *"frame-aligned cuts (multiples of 49 data rows) so a lattice snapshot is never split mid-frame."* **This is not true of the published slices.** A full audit found:
->
-> | | Count |
-> |---|---|
-> | Slices beginning at `core_id == 0` | 6 / 253 |
-> | Slices ending at `core_id == 48` | 3 / 253 |
-> | Slices with row count divisible by 49 | 28 / 253 |
-> | Slices satisfying all three | **0 / 253** |
->
-> Starting `core_id` for the first twelve slices: `[0, 48, 13, 37, 12, 36, 9, 34, 8, 33, 48, 12]` — effectively arbitrary phase, differing per slice. Cause: the slicing script counted rows from a byte offset with arbitrary phase relative to the file's frame grid.
->
-> **No data is lost.** `core_id` and `n` are present on every row, so frames are fully recoverable. But **`values.reshape(-1, 49)` will silently mislabel every core**, with a *different* rotation in each slice. On slice 126 (`Period3Gamma`), reshape column 32 holds core **46**, not core 32.
-**Correct frame reconstruction — group on `n // 49`, index by `core_id`:**
-```python
-import pandas as pd
-def frames(path, value="x_raw24"):
-    """(frames × 49) table: index = n // 49, columns = true core_id 0…48."""
-    df = pd.read_csv(path, usecols=["core_id", "n", value])
-    df["frame"] = df["n"] // 49
-    counts = df.groupby("frame")["core_id"].size()
-    df = df[df["frame"].isin(counts[counts == 49].index)]   # drop partial edge frames
-    return df.pivot(index="frame", columns="core_id", values=value).sort_index()
-```
-Each slice loses at most one partial frame at each edge. Sanity check on `Period3Gamma`:
-```
-frames = 2369, cores = 49
-core 32: 0.497221 → 0.958304 → 0.153171 → 0.497221 → …
-  median |x[k] − x[k−3]| = 0.00e+00      ← period-3, bit-exact in Q1.23
-  median |x[k] − x[k−1]| = 4.61e-01
-```
----
-## Slicing methodology and overlap
-### Data (`slice_n4_run.py`)
-- Byte-range cuts of the source CSV; every slice begins with the original 60-byte header (self-describing)
-- Cuts are **not** frame-aligned — see the warning above
-### Log (`slice_n4_log.py`)
-- Line-aligned cuts (log lines never split) — verified at all 252 boundaries
-- Pure text ranges; no synthetic header
-### Overlap
-Interior windows carry ≈1.5 MiB of context on each side. Measured, the two sides are consistently asymmetric because boundaries round forward to the next row/line:
-| Stream | Lead overlap | Trail overlap |
-|---|---|---|
-| Data | 1,570,235 – 1,572,511 B (always < 1.5 MiB) | 1,573,137 – 1,575,499 B (always > 1.5 MiB) |
-| Log | 1,572,820 – 1,572,864 B | 1,572,864 – 1,572,908 B |
-Overlap regions between adjacent slices are byte-identical (verified at all 252 boundaries on both streams), so the full source is losslessly reconstructible from the slices.
-**Manifests**
-- `manifests/slice_manifest.csv` — data byte ranges & overlaps
-- `manifests/prospect_manifest.csv` — data index → prospect name
-- `n4_log_slices/log_slice_manifest.csv` — log byte ranges & overlaps
-- `n4_log_slices/log_prospect_manifest.csv` — log index → prospect name
----
-## Prospect-name pairing is nominal, not temporal
-Data slice *k* and log slice *k* share a prospect name, but **they do not cover the same window of the run.** Measured overlap of their `n` ranges across all 253 pairs:
-| | Value |
-|---|---|
-| Median IoU | **0.242** |
-| Pairs with IoU < 0.25 | 129 / 253 |
-| Pairs with IoU < 0.10 | 74 / 253 |
-| Worst pair (slice 097) | 0.026 |
-| Maximum lead of log over data | 133,518 frames ≈ **5.3 h** wall clock |
-The drift is V-shaped — pinned at zero at both ends (both streams start at `n = 0` and end at `n = 18,143,974`) and worst mid-run:
-| Slice range | Median IoU | Median offset (frames) |
-|---|---|---|
-| 000–024 | 0.544 | −48,519 |
-| 050–074 | 0.185 | −99,727 |
-| **100–124** | **0.028** | −132,868 |
-| **125–149** | **0.033** | −131,755 |
-| 175–199 | 0.301 | −81,019 |
-| 225–252 | 0.79 | −28,000 |
-**Cause.** Both streams were cut into 253 *equal-byte* pieces, but their bytes-per-frame differ over the run: CSV row width grows steadily (`elapsed_sec` 5 → 11 chars, `n` 1 → 8 digits) while log line width is essentially fixed. Equal byte fractions therefore land on different frame indices.
-Example — `Period3Gamma` (slice 126):
-```
-data  n[9,134,300 … 9,250,428]
-log   n[9,002,086 … 9,141,996]     → only 6.6% of the data window has log context
-```
-**Guidance.** Do not assume a paired log slice describes the host state during its data slice. **Join on `n`** (every log frame carries it), not on the file index. Near the ends of the run (slices ≲ 025 and ≳ 225) the windows do largely coincide; mid-run they barely intersect. A re-cut of the log slices by `n` range is planned.
----
-## Naming convention
-```text
-n4_slice_XXX_<ProspectName>.csv
-n4_log_slices/n4_log_XXX_<ProspectName>.log
-```
-Prospect names are deterministic and unique. Fifty themed stems cycle through the modifiers **Alpha → Beta → Gamma → Delta → Epsilon → Zeta**.
-### Complete prospect map
-| Index | Prospect stem (shared by data & log) |
-|------:|--------------------------------------|
-| 000 | TauAlpha |
-| 001 | TavAlpha |
-| 002 | BerardAlpha |
-| 003 | LatticeAlpha |
-| 004 | LollAlpha |
-| 005 | WavefrontAlpha |
-| 006 | ResonanceAlpha |
-| 007 | CoreAlpha |
-| 008 | RingAlpha |
-| 009 | FluxAlpha |
-| 010 | PhaseAlpha |
-| 011 | OrbitAlpha |
-| 012 | TongueAlpha |
-| 013 | StaircaseAlpha |
-| 014 | KuramotoAlpha |
-| 015 | JosephsonAlpha |
-| 016 | PhotonicAlpha |
-| 017 | SQUIDAlpha |
-| 018 | GenomeAlpha |
-| 019 | AdapterAlpha |
-| 020 | MythosAlpha |
-| 021 | EtymosAlpha |
-| 022 | BoxMatchAlpha |
-| 023 | EllaAlpha |
-| 024 | LillyAlpha |
-| 025 | SharkovskiiAlpha |
-| 026 | Period3Alpha |
-| 027 | Mod7Alpha |
-| 028 | HeartbeatAlpha |
-| 029 | 142857Alpha |
-| 030 | RadionAlpha |
-| 031 | CompactAlpha |
-| 032 | HierarchyAlpha |
-| 033 | SupermodeAlpha |
-| 034 | CirculantAlpha |
-| 035 | ArnoldAlpha |
-| 036 | DevilAlpha |
-| 037 | SplayAlpha |
-| 038 | ChimeraAlpha |
-| 039 | OrderRAlpha |
-| 040 | CoherenceAlpha |
-| 041 | AttractorAlpha |
-| 042 | HandshakeAlpha |
-| 043 | AsyncAlpha |
-| 044 | Q123Alpha |
-| 045 | ArtyAlpha |
-| 046 | MetaRingAlpha |
-| 047 | SevenfoldAlpha |
-| 048 | S1Alpha |
-| 049 | CylinderAlpha |
-| 050–099 | Same stems + **Beta** |
-| 100–149 | Same stems + **Gamma** |
-| 150–199 | Same stems + **Delta** |
-| 200–249 | Same stems + **Epsilon** |
-| 250 | TauZeta |
-| 251 | TavZeta |
-| 252 | BerardZeta |
-Example pairing:
-```text
-n4_slice_032_HierarchyAlpha.csv  ↔  n4_log_slices/n4_log_032_HierarchyAlpha.log
-n4_slice_252_BerardZeta.csv      ↔  n4_log_slices/n4_log_252_BerardZeta.log
-```
----
-## Session log format and reassembly specification
-Each log line is one host `read()` from the serial bridge, **not one frame**:
-```text
-[HH:MM:SS.mmm] len=<bytes>  <hex payload>
-```
-A frame is 10 bytes:
-| Offset | Size | Field |
-|---|---|---|
-| 0 | 2 B | magic `0x1E30` |
-| 2 | 1 B | `core_id` |
-| 3 | 3 B | `x_raw24`, big-endian Q1.23 |
-| 6 | 4 B | `n`, big-endian |
-**Frames can straddle two lines.** Of 35,355,673 lines, 99.07% are `len=10` (one frame), but 329,461 are partial reads of 1–9 bytes. **Parsing line-by-line silently drops ~0.47% of frames.** Correct procedure: concatenate the hex payloads across lines, then split the resulting byte stream on 10-byte boundaries, validating the `0x1E30` magic.
-103 lines with `len > 32` have their hex payload truncated with a trailing `...` and are not recoverable. Their timestamps and `len` values are intact, which is what bridge-health analysis needs. Larger `len` values indicate the host fell behind and the driver returned a batch — useful as a bridge-stall indicator. Burst sizes run up to `len=480` (48 frames).
-Log timestamps carry time-of-day only, with no date; across a 30-day run they wrap daily. Use `n` for absolute positioning.
-The log independently corroborates the CSV: 100,000 frames decoded from the raw log and matched by `n` produced **zero** `core_id` or `x_raw24` mismatches.
----
-## Acquisition clock — read before doing spectral analysis
-These are **configuration facts about the readout schedule**, not properties of the lattice:
-- Mean inter-core readout interval: **0.1428574 s** — that is 1/7 s to within 1.4×10⁻⁶.
-- One complete 49-core frame every **7.0000 s**.
-- Run terminated at exactly **30.00000 days**.
-Sampling is also **non-uniform**. The inter-row Δt distribution is bimodal: ≈75% at 0.163 s and ≈25% at 0.082 s — one or two ticks of a ≈0.0815 s host quantum — with the mean landing on 1/7 s.
-**Analytical consequences:**
-1. **Any wall-clock spectral analysis of this dataset will show structure at 7 s, 1/7 Hz, and their harmonics that originates in the readout schedule, not the lattice.** Because the framework this dataset supports treats a 1/7 signature as a substantive prediction, a temporal 1/7 result obtained from this data is confounded by construction and should not be presented as evidence.
-2. An FFT assuming uniform sampling at the mean rate will produce spurious lines from the 0.0815 s quantum beating against the 7 s frame period.
-**Recommendation:** conduct period-3 / Sharkovskii / Arnold-tongue / return-map work in **iteration index `n`**, where the acquisition clock drops out entirely. The core-32 period-3 orbit is exact in iteration space (bit-exact residual in Q1.23) and is unaffected by any of this.
----
-## Intended use
-- Cross-slice studies of period-3 persistence (especially core 32)
-- Hierarchical order-parameter and Berard-monitor analysis
-- Correlation of lattice coherence with host/bridge state from the paired log
-- Mapping onto photonic, Kuramoto / Stuart–Landau, and Josephson / SQUID embeddings
-- Arnold-tongue / Devil's-staircase and higher-order Kuramoto investigations
-- Reproducible windowed statistics without loading the full multi-GB sources
-Interior overlaps support continuous observables (ACF, rotation number, coherence) across boundaries with minimal edge artefact.
-> All of the above should be conducted in **iteration index `n`** rather than wall-clock time (see [Acquisition clock](#acquisition-clock--read-before-doing-spectral-analysis)), and frames should be reconstructed by grouping on `n // 49` rather than by row position (see [Row ordering and frame reconstruction](#row-ordering-and-frame-reconstruction)). Where an analysis needs host/bridge context from the paired session log, join on `n` rather than on prospect name (see [Prospect-name pairing](#prospect-name-pairing-is-nominal-not-temporal)).
----
-## Repository layout
-```text
-├── README.md
-├── manifests/
-│   ├── slice_manifest.csv
-│   └── prospect_manifest.csv
-├── scripts/
-│   ├── slice_n4_run.py
-│   ├── slice_n4_log.py
-│   ├── name_n4_slices.py
-│   └── name_n4_log_slices.py
-├── n4_slice_000_TauAlpha.csv
-├── n4_slice_001_TavAlpha.csv
-├── …
-├── n4_slice_252_BerardZeta.csv
-└── n4_log_slices/
-    ├── log_slice_manifest.csv
-    ├── log_prospect_manifest.csv
-    ├── n4_log_000_TauAlpha.log
-    ├── …
-    └── n4_log_252_BerardZeta.log
-```
----
-## Errata (v2 card revision)
-Corrections to earlier revisions of this card, from a full-corpus audit (29,351,992 CSV rows and 35,355,673 log lines checked exhaustively, nothing sampled):
-1. **Frame alignment.** The claim that cuts fall on 49-row frame boundaries is withdrawn. 0 of 253 slices are frame-aligned. Use `n // 49` grouping; never `reshape(-1, 49)`.
-2. **Prospect-name pairing.** Matching names indicate *nominal* pairing only. Median data/log window IoU is 0.242. Join on `n`.
-3. **`delta_n`.** Previously described as "mostly null". It is populated on 29,351,943 of 29,351,992 rows (99.9998%), always exactly 49. There are exactly **49** nulls, all in the first frame of slice 000.
-4. **`n` semantics.** A per-row counter, not a frame index.
-5. **`x_norm` range.** `[0.0, 1.0]` as stored, not the open interval `(0, 1)`; and it is a lossy 6-dp rendering of `x_raw24`.
-6. **Row count.** `lattice` holds 29,351,992 rows, of which 18,143,975 are distinct.
-7. **Run duration.** 30.00001 days, not a single-date run.
-8. **`n4_log_162_TongueDelta.log`** was missing from the repository; restored and verified (size exact, overlap regions byte-identical to neighbours, 0 unparsed lines).
-9. **Manifest config paths.** The `data_manifest` and `prospect_map` configs pointed at repository root; the files live under `manifests/`. Corrected — those two subsets should now convert.
-Verified clean in the same audit: header consistency across all 253 CSVs; manifest arithmetic on all 506 entries; byte-identical overlaps at every boundary on both streams; `core_id` stepping +1 mod 49 with zero breaks; exact Q1.23 reconstruction; contiguous `n` coverage 0 → 18,143,974 with no gaps; zero NaNs, zero duplicate rows, zero duplicate `(n, core_id)`; monotonic `n`, `elapsed_sec` and timestamps in every slice; and exact CSV ↔ session-log agreement across 100,000 cross-checked frames.
----
-## Citation
-```bibtex
-@misc{gatlin_rgc_2026,
-  author       = {Gatlin III, Ernest C.},
-  title        = {Resonance Graph Core (RGC) Architecture:
-                  An Asynchronous Hardware Embodiment of the Tav Manifold},
-  year         = {2026},
-  publisher    = {Zenodo},
-  doi          = {10.5281/zenodo.20111854},
-  url          = {https://doi.org/10.5281/zenodo.20111854}
-}
-```
-To cite this dataset specifically:
-```bibtex
-@dataset{gatlin_n4slice_2026,
-  author       = {Gatlin III, Ernest C.},
-  title        = {30-Day Canonical Tav RGC N4 Lattice \& Telemetry Slices},
-  year         = {2026},
-  publisher    = {Hugging Face},
-  doi          = {10.57967/hf/10015},
-  url          = {https://huggingface.co/datasets/parti-wave/N4_slice}
-}
-```
-Related long-run characterizations (1.2 M–4 M frames) are deposited on Zenodo under the Tau Universe / RGC series.
----
-## License & contact
-Research use under the terms associated with Parti-Wave Labs USA, LLC and the Zenodo deposits referenced above.
-For collaboration or licensing: claygatlin@gmail.com · Parti-Wave Labs USA, LLC (Alabama).
-**Note:** The Berard Constant / monitor (≈ 1.054) evaluates the quality of the collective lattice state. It is not hard-coded into every cell update of the logistic substrate. Slice-level Berard readings will vary with host resources and bridge health of the original run; the paired session-log windows are provided so that variation can be interpreted — subject to the pairing caveat above.