Add SlidingEuclideanDistanceNode for FFT-based sliding window distance computation (closes #78)

gQuant/plugins/gquant_plugin/greenflow_gquant_plugin/Sliding Window Euclidean Distance Node readme.md

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+# Sliding Window Euclidean Distance Node
+
+## Overview
+
+`SlidingEuclideanDistanceNode` is a gQuant transform node that compares the **shape** of a short query signal against **every position** in a longer time series stream, using Euclidean distance. It produces a *distance profile* — one distance value per possible alignment of the query within the stream — which is the core primitive behind pattern search, motif discovery, and anomaly detection on time series (e.g. price, volume, or tick data).
+
+## Problem
+
+There was previously no gQuant node for computing the Euclidean distance between a query signal and every sliding window of a stream. Doing this with a naive loop is `O(n·m)` (`n` = stream length, `m` = query length), which does not scale for large or frequently-updated streams, and any hand-rolled version would sit outside the TaskGraph, losing gQuant's column/type validation, GPU dataframe support, and gQuantLab UI integration.
+
+## Solution
+
+This node computes the distance profile in `O(n log n)` using the FFT-based sliding dot-product trick (Mueen's Algorithm for Similarity Search, MASS), instead of the naive `O(n·m)` loop:
+
+```
+dist(i)^2 = Σ T[i+j]^2 − 2·Σ T[i+j]·Q[j] + Σ Q[j]^2
+```
+
+- `Σ Q[j]^2` — constant, computed once
+- `Σ T[i+j]^2` — moving sum of squares over the stream, via cumulative sums, `O(n)`
+- `Σ T[i+j]·Q[j]` — a sliding dot product, computed via FFT cross-correlation, `O(n log n)`
+
+An optional `normalize` flag applies z-normalization to each window, making the comparison invariant to amplitude and offset — useful when you care about matching the *shape* of the query rather than its absolute scale.
+
+## Usage
+
+### Node configuration
+
+| Key | Type | Description |
+|---|---|---|
+| `stream_col` | `str` | Column name of the longer time series to search |
+| `query_col` | `str` | Column name of the short query/reference signal |
+| `normalize` | `bool` (optional, default `False`) | Z-normalize each window for shape-only comparison |
+
+### Output
+
+Adds a new column, `distance_profile`, of the same length as the stream, front-padded with `NaN` for the first `m - 1` positions (where a full-length window isn't yet available), so it aligns with the original stream index.
+
+### Example
+
+```python
+from greenflow_gquant_plugin.transform import SlidingEuclideanDistanceNode
+
+node = SlidingEuclideanDistanceNode(
+    "distance_node",
+    {"stream_col": "price", "query_col": "pattern", "normalize": True}
+)
+result_df = node.process([df])
+
+# position of the best (closest) match:
+best_match_index = result_df["distance_profile"].idxmin()
+```
+
+## File location
+
+```
+gQuant/plugins/gquant_plugin/greenflow_gquant_plugin/transform/slidingDistanceNode.py
+```
+
+Registered in `transform/__init__.py`:
+
+```python
+try:
+    from .slidingDistanceNode import SlidingEuclideanDistanceNode  # noqa: F401
+except ImportError:
+    from slidingDistanceNode import SlidingEuclideanDistanceNode  # noqa: F401
+```
+
+and added to that file's `__all__` list.
+
+## Alternatives considered
+
+1. **Naive `O(n·m)` sliding loop** — simple, useful as a correctness baseline for tests, but too slow at scale.
+2. **`numpy.correlate` / `scipy.signal.correlate` alone** — computes the cross-correlation term but not the moving sum-of-squares term needed for true Euclidean distance; would still need to be paired with the cumulative-sum trick above.
+3. **Skipping normalization** — simpler, but loses invariance to amplitude/offset differences, which matters when comparing shapes at different price levels.
+
+## Testing
+
+- Unit test: compare FFT-based output against the naive `O(n·m)` loop on random data (`atol=1e-6`).
+- Benchmark: measure speedup vs. the naive loop at realistic stream sizes (10k–1M points).
+- GPU/CPU dispatch: verify consistent results whether inputs come from `cudf`/`cupy` or plain `numpy`.
+
+## Reference
+
+Mueen's Algorithm for Similarity Search (MASS) — the standard method for computing an all-subsequence Euclidean distance profile efficiently, and the basis for Matrix Profile motif discovery.

gQuant/plugins/gquant_plugin/greenflow_gquant_plugin/slidingDistanceNode.py

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+import numpy as np
+
+def sliding_euclidean_distance(T: np.ndarray, Q: np.ndarray) -> np.ndarray:
+    n, m = len(T), len(Q)
+    if m > n:
+        raise ValueError("Query longer than stream")
+
+    q_rev = Q[::-1]
+    fft_size = 1
+    while fft_size < n + m - 1:
+        fft_size *= 2
+
+    T_fft = np.fft.rfft(T, fft_size)
+    Q_fft = np.fft.rfft(q_rev, fft_size)
+    cross_corr = np.fft.irfft(T_fft * Q_fft, fft_size)[m - 1:n]
+
+    T_sq = T ** 2
+    cumsum = np.concatenate(([0], np.cumsum(T_sq)))
+    T_sq_win = cumsum[m:] - cumsum[:-m]
+
+    q_sq_sum = np.sum(Q ** 2)
+    dist_sq = T_sq_win - 2 * cross_corr + q_sq_sum
+    return np.sqrt(np.maximum(dist_sq, 0))