Upload 10 files (#1)

- Upload 10 files (58e4e812f57cbe57b3d3ded45c9001af6114566e)


Co-authored-by: Tom Freeman <TomFreeman3@users.noreply.huggingface.co>

.gitattributes

@@ -57,3 +57,12 @@ 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
+np_extract_part_1.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_2.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_3.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_4.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_5.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_6.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_7.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_8.csv filter=lfs diff=lfs merge=lfs -text
+np_extract_part_9.csv filter=lfs diff=lfs merge=lfs -text

np_extract_part_1.csv

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np_extract_part_2.csv

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np_extract_part_3.csv

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np_extract_part_4.csv

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np_extract_part_5.csv

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np_extract_part_6.csv

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np_extract_part_7.csv

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np_extract_part_8.csv

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np_extract_part_9.csv

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+size 364490746

obfs_np.py

@@ -0,0 +1,248 @@
+import gc
+import logging
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from gomez_cloud.utils.date_utils import iterate_days
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+)
+log = logging.getLogger(__name__)
+
+
+def _haversine_m(lat1, lon1, lat2, lon2):
+    """Vectorised haversine distance in meters."""
+    R = 6371000.0
+    phi1 = np.radians(lat1); phi2 = np.radians(lat2)
+    dphi = np.radians(lat2 - lat1); dl = np.radians(lon2 - lon1)
+    a = np.sin(dphi/2)**2 + np.cos(phi1)*np.cos(phi2)*np.sin(dl/2)**2
+    return 2*R*np.arcsin(np.sqrt(a))
+
+
+def _meters_to_deg(lat_deg, dx_m, dy_m):
+    """Convert local meter offsets (east=dx, north=dy) to lon/lat degrees at given latitude."""
+    lat_rad = np.radians(lat_deg)
+    m_per_deg_lat = 111_320.0
+    m_per_deg_lon = 111_320.0 * np.cos(lat_rad)
+    dlat = dy_m / m_per_deg_lat
+    dlon = dx_m / m_per_deg_lon
+    return dlat, dlon
+
+
+def jitter_points(df, lat_col, lon_col, radius_m=50, seed=42, shuffle=True):
+    """
+    Add uniform random jitter within a circle of radius_m meters.
+    Returns a new DataFrame with columns <lat_col>_jit, <lon_col>_jit.
+    """
+    rng = np.random.default_rng(seed)
+    n = len(df)
+    # uniform in disk: r = R*sqrt(u), theta ~ U[0,2pi)
+    u = rng.random(n)
+    r = radius_m * np.sqrt(u)
+    theta = rng.random(n) * 2*np.pi
+    dx = r * np.cos(theta)
+    dy = r * np.sin(theta)
+
+    lat = df[lat_col].to_numpy(dtype=float, copy=False)
+    dlat, dlon = _meters_to_deg(lat, dx, dy)
+
+    out = df.copy()
+    out[f"{lat_col}_jit"] = lat + dlat
+    out[f"{lon_col}_jit"] = df[lon_col].to_numpy(dtype=float, copy=False) + dlon
+
+    if shuffle:
+        out = out.sample(frac=1.0, random_state=seed).reset_index(drop=True)
+    return out
+
+
+def jitter_signal(series, sigma_db=2.0, seed=42, clip=(-120, -20)):
+    """
+    Add small Gaussian noise (dBm) to signal, robust to strings like '-083'.
+    """
+    rng = np.random.default_rng(seed)
+    sig = pd.to_numeric(series, errors="coerce")  # "-083" -> -83
+    noise = rng.normal(0.0, sigma_db, size=len(sig))
+    out = (sig + noise).clip(clip[0], clip[1])
+    return out
+
+
+# ----- difference metrics -----
+def displacement_stats(orig_lat, orig_lon, jit_lat, jit_lon):
+    d = _haversine_m(orig_lat, orig_lon, jit_lat, jit_lon)
+    return {
+        "n": d.size,
+        "mean_m": float(np.nanmean(d)),
+        "p50_m": float(np.nanpercentile(d, 50)),
+        "p90_m": float(np.nanpercentile(d, 90)),
+        "p95_m": float(np.nanpercentile(d, 95)),
+        "max_m":  float(np.nanmax(d)),
+    }
+
+
+def js_distance_2d(orig_lat, orig_lon, jit_lat, jit_lon, bins=100, eps=1e-12):
+    """
+    Jensen–Shannon distance between 2D (lat,lon) distributions via hist2d.
+    Range: 0 identical … 1 very different (we return sqrt(JS divergence)).
+    """
+    lat_all = np.concatenate([orig_lat, jit_lat])
+    lon_all = np.concatenate([orig_lon, jit_lon])
+    lat_edges = np.linspace(lat_all.min(), lat_all.max(), bins+1)
+    lon_edges = np.linspace(lon_all.min(), lon_all.max(), bins+1)
+
+    H1, _, _ = np.histogram2d(orig_lat, orig_lon, bins=[lat_edges, lon_edges])
+    H2, _, _ = np.histogram2d(jit_lat,  jit_lon,  bins=[lat_edges, lon_edges])
+
+    P = (H1.ravel() + eps); P /= P.sum()
+    Q = (H2.ravel() + eps); Q /= Q.sum()
+    M = 0.5*(P+Q)
+
+    def kl(p, q):  # both already have eps
+        return np.sum(p * np.log(p/q))
+    js_div = 0.5*kl(P, M) + 0.5*kl(Q, M)
+    return float(np.sqrt(js_div))
+
+
+def ks_1d_marginals(orig, jit):
+    """Kolmogorov–Smirnov D for 1D arrays (simple numpy implementation)."""
+    x = np.sort(np.asarray(orig))
+    y = np.sort(np.asarray(jit))
+    # empirical CDFs on merged support
+    grid = np.sort(np.unique(np.concatenate([x, y])))
+    Fx = np.searchsorted(x, grid, side='right') / x.size
+    Fy = np.searchsorted(y, grid, side='right') / y.size
+    return float(np.max(np.abs(Fx - Fy)))
+
+
+def main() -> None:
+    start_all = time.time()
+
+    partitions = iterate_days(first="2025-03-01", last="2025-06-30")
+
+    output_dir = Path(...)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    base_uri = ...
+    storage_opts = {"token": "cloud"}  # ADC via gcsfs
+
+    week_buffers = []
+    week_idx = 1
+    day_idx = 0
+    total_rows_written = 0
+    total_days_processed = 0
+
+    for day in partitions:
+        t0 = time.time()
+        uri = f"{base_uri}/day={day}"
+
+        try:
+            df = pd.read_parquet(uri, storage_options=storage_opts)
+        except FileNotFoundError:
+            log.warning("Partition not found (skipping): %s", uri)
+            continue
+        except Exception as e:
+            log.exception("Failed to read partition %s: %s", uri, e)
+            continue
+
+        log.info("Loaded %s rows x %s cols from %s", len(df), len(df.columns), uri)
+
+        try:
+            # Jitter location
+            pings_jit = jitter_points(
+                df,
+                lat_col="latitude",
+                lon_col="longitude",
+                radius_m=20,
+                seed=456,
+                shuffle=True,
+            )
+            # Jitter signal
+            pings_jit["signal_level_jit"] = jitter_signal(
+                df["signal_level"], sigma_db=2.0, seed=456
+            )
+
+            # Replace originals with jittered
+            pings_jit = (
+                pings_jit.drop(columns=["latitude", "longitude", "signal_level"])
+                .rename(
+                    columns={
+                        "latitude_jit": "latitude",
+                        "longitude_jit": "longitude",
+                        "signal_level_jit": "signal_level",
+                    }
+                )
+            )
+
+            # Keep only 'Full Service Loss (>120s)', else set to None
+            if "measurement_type_name" in pings_jit.columns:
+                pings_jit["measurement_type_name"] = pings_jit["measurement_type_name"].apply(
+                    lambda x: x if x == "Full Service Loss (>120s)" else None
+                )
+            else:
+                log.warning("Column 'measurement_type_name' missing in partition %s", day)
+
+            log.info(
+                "Transformed day=%s → %s rows", day, len(pings_jit)
+            )
+
+            week_buffers.append(pings_jit)
+            total_days_processed += 1
+            day_idx += 1
+
+        except Exception as e:
+            log.exception("Transform failed for day=%s: %s", day, e)
+            # Drop heavy refs before moving on
+            del df
+            gc.collect()
+            continue
+        finally:
+            # free the original df ASAP
+            del df
+            gc.collect()
+
+        # Flush every 14 days
+        if day_idx % 14 == 0:
+            try:
+                week_df = pd.concat(week_buffers, ignore_index=True)
+                out_path = output_dir / f"np_extract_part_{week_idx}.csv"
+                week_df.to_csv(out_path, index=False)
+                total_rows_written += len(week_df)
+                log.info(
+                    "Wrote week %d: %s rows to %s (elapsed %.2fs)",
+                    week_idx, len(week_df), out_path, time.time() - t0
+                )
+            finally:
+                week_buffers.clear()
+                week_idx += 1
+                # encourage memory to return
+                del week_df
+                gc.collect()
+
+        log.info("Processed day=%s in %.2fs", day, time.time() - t0)
+
+    # Final partial week flush
+    if week_buffers:
+        week_df = pd.concat(week_buffers, ignore_index=True)
+
+        week_df.to_csv(f"/home/tom_freeman_vodafone_com/tom-foolery/data/np_extractions/part_{week_idx}.csv", index=False)
+
+        total_rows_written += len(week_df)
+        log.info(
+            "Wrote FINAL part %d: %s rows to %s",
+            week_idx, len(week_df), out_path
+        )
+        week_buffers.clear()
+        del week_df
+        gc.collect()
+
+    log.info(
+        "Done. Days processed: %d | Rows written: %d | Total time: %.2fs",
+        total_days_processed, total_rows_written, time.time() - start_all
+    )
+
+
+if __name__ == "__main__":
+    main()