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chore: reformat

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This commit is contained in:
can1357
2026-07-15 00:08:50 +02:00
parent 7d02778c60
commit 55f5ebec49
97 changed files with 12237 additions and 3179 deletions
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scripts/session-stats/plot_read_summarizer.py
+86 -28
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@@ -15,6 +15,7 @@ thinking + user messages. That removes the "I worked harder that day" effect.
Outputs to scripts/session-stats/out/read-summarizer-*.png.
"""
from __future__ import annotations
import argparse
@@ -44,6 +45,7 @@ COHORT_COLORS = {
# --------------------------------------------------------------------------- #
# Classification
def has_selector(path: str) -> bool:
"""True iff `path` carries a read selector (`:50-200`, `:raw`, ...)."""
if not path:
@@ -76,6 +78,7 @@ def cohort_of(arg_json: str | None) -> str | None:
# --------------------------------------------------------------------------- #
# Data
def fetch_read_calls(conn) -> dict[str, dict[str, np.ndarray]]:
sql = """
SELECT c.timestamp,
@@ -97,7 +100,10 @@ def fetch_read_calls(conn) -> dict[str, dict[str, np.ndarray]]:
out: dict[str, dict[str, np.ndarray]] = {}
for c, rows in by.items():
if not rows:
out[c] = {"ts": np.array([], dtype=np.int64), "tok": np.array([], dtype=np.int64)}
out[c] = {
"ts": np.array([], dtype=np.int64),
"tok": np.array([], dtype=np.int64),
}
continue
ts = np.fromiter((r[0] for r in rows), dtype=np.int64, count=len(rows))
tok = np.fromiter((r[1] for r in rows), dtype=np.int64, count=len(rows))
@@ -152,7 +158,9 @@ def daily_sum(ts_ms: np.ndarray, tok: np.ndarray, day_axis: np.ndarray) -> np.nd
return out
def daily_percentile(ts_ms: np.ndarray, tok: np.ndarray, q: float) -> tuple[np.ndarray, np.ndarray]:
def daily_percentile(
ts_ms: np.ndarray, tok: np.ndarray, q: float
) -> tuple[np.ndarray, np.ndarray]:
if ts_ms.size == 0:
return np.array([]), np.array([])
day_idx = ts_ms // DAY_MS
@@ -164,7 +172,9 @@ def daily_percentile(ts_ms: np.ndarray, tok: np.ndarray, q: float) -> tuple[np.n
lo, hi = order[i], order[i + 1]
if hi > lo:
pct[i] = np.percentile(tok[lo:hi], q)
dates = np.array([datetime.fromtimestamp(int(d) * DAY_MS / 1000, tz=timezone.utc) for d in days])
dates = np.array(
[datetime.fromtimestamp(int(d) * DAY_MS / 1000, tz=timezone.utc) for d in days]
)
return dates, pct
@@ -183,9 +193,10 @@ def smooth_nan(y: np.ndarray, w: int) -> np.ndarray:
# --------------------------------------------------------------------------- #
# Plot helpers
def thousands(x: float, _p=0) -> str:
if x >= 1000:
return f"{x/1000:.1f}k"
return f"{x / 1000:.1f}k"
return f"{x:.0f}"
@@ -195,11 +206,20 @@ def style_time(ax: plt.Axes, deploy: datetime) -> None:
ax.grid(True, alpha=0.25, linestyle="--")
ax.axvline(deploy, color="#dc2626", linestyle="--", linewidth=1.2, alpha=0.8)
y1 = ax.get_ylim()[1] if ax.get_ylim()[1] > 0 else 1
ax.text(deploy, y1, " summarizer\n deploy", color="#dc2626",
va="top", ha="left", fontsize=9)
ax.text(
deploy,
y1,
" summarizer\n deploy",
color="#dc2626",
va="top",
ha="left",
fontsize=9,
)
def panel_share_stacked(ax: plt.Axes, reads, denom_dates, denom, deploy: datetime) -> None:
def panel_share_stacked(
ax: plt.Axes, reads, denom_dates, denom, deploy: datetime
) -> None:
"""Stacked area: per-day read-cohort share of total tokens."""
series = []
labels = []
@@ -212,11 +232,13 @@ def panel_share_stacked(ax: plt.Axes, reads, denom_dates, denom, deploy: datetim
series.append(smooth_nan(share, 7))
labels.append(cohort)
colors.append(color)
x = np.array([datetime.fromtimestamp(int(d) / 1000, tz=timezone.utc) for d in denom_dates])
x = np.array(
[datetime.fromtimestamp(int(d) / 1000, tz=timezone.utc) for d in denom_dates]
)
ax.stackplot(x, series, labels=labels, colors=colors, alpha=0.85)
ax.set_title("read share of daily token spend (7d MA)")
ax.set_ylabel("share of all tokens that day")
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda v, _: f"{v*100:.0f}%"))
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda v, _: f"{v * 100:.0f}%"))
ax.set_ylim(0, None)
ax.legend(loc="upper left", frameon=False)
style_time(ax, deploy)
@@ -224,7 +246,9 @@ def panel_share_stacked(ax: plt.Axes, reads, denom_dates, denom, deploy: datetim
def panel_share_line(ax: plt.Axes, reads, denom_dates, denom, deploy: datetime) -> None:
"""Lines: each cohort's share, plus the combined total."""
x = np.array([datetime.fromtimestamp(int(d) / 1000, tz=timezone.utc) for d in denom_dates])
x = np.array(
[datetime.fromtimestamp(int(d) / 1000, tz=timezone.utc) for d in denom_dates]
)
total = np.zeros(denom_dates.size, dtype=np.int64)
for cohort, color in COHORT_COLORS.items():
d = reads[cohort]
@@ -235,22 +259,37 @@ def panel_share_line(ax: plt.Axes, reads, denom_dates, denom, deploy: datetime)
ax.plot(x, smooth_nan(share, 7), label=cohort, color=color, linewidth=1.7)
with np.errstate(divide="ignore", invalid="ignore"):
combined = np.where(denom > 0, total / denom, 0.0)
ax.plot(x, smooth_nan(combined, 7), label="all reads", color="#111111", linewidth=2.2, linestyle="-")
ax.plot(
x,
smooth_nan(combined, 7),
label="all reads",
color="#111111",
linewidth=2.2,
linestyle="-",
)
ax.set_title("read share by cohort (7d MA)")
ax.set_ylabel("share of daily tokens")
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda v, _: f"{v*100:.0f}%"))
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda v, _: f"{v * 100:.0f}%"))
ax.set_ylim(0, None)
ax.legend(loc="upper left", frameon=False)
style_time(ax, deploy)
def panel_per_call(ax: plt.Axes, reads, deploy: datetime, q: float, label_q: str) -> None:
def panel_per_call(
ax: plt.Axes, reads, deploy: datetime, q: float, label_q: str
) -> None:
for cohort, color in COHORT_COLORS.items():
d = reads[cohort]
if d["ts"].size == 0:
continue
dates, pct = daily_percentile(d["ts"], d["tok"], q)
ax.plot(dates, smooth_nan(pct, 7), label=f"{cohort} ({label_q})", color=color, linewidth=1.9)
ax.plot(
dates,
smooth_nan(pct, 7),
label=f"{cohort} ({label_q})",
color=color,
linewidth=1.9,
)
ax.set_title(f"daily {label_q} tokens per read call (7d MA)")
ax.set_ylabel("tokens / call")
ax.set_yscale("log")
@@ -262,14 +301,19 @@ def panel_per_call(ax: plt.Axes, reads, deploy: datetime, q: float, label_q: str
# --------------------------------------------------------------------------- #
# Stats
def share_stats(reads, denom_dates, denom, deploy_ms: int) -> None:
pre_mask = denom_dates < deploy_ms
post_mask = denom_dates >= deploy_ms
pre_total = int(denom[pre_mask].sum())
post_total = int(denom[post_mask].sum())
print(f"\nshare-of-day (pre vs post deploy):")
print(f" denominator pre = {pre_total:>14,} tokens across {int(pre_mask.sum())} days")
print(f" denominator post = {post_total:>14,} tokens across {int(post_mask.sum())} days")
print(
f" denominator pre = {pre_total:>14,} tokens across {int(pre_mask.sum())} days"
)
print(
f" denominator post = {post_total:>14,} tokens across {int(post_mask.sum())} days"
)
print(f" {'cohort':<22} {'pre share':>10} {'post share':>11} {'delta':>10}")
grand_pre = 0
grand_post = 0
@@ -282,17 +326,23 @@ def share_stats(reads, denom_dates, denom, deploy_ms: int) -> None:
grand_post += post
pre_share = pre / pre_total if pre_total else 0
post_share = post / post_total if post_total else 0
print(f" {cohort:<22} {pre_share*100:>9.2f}% {post_share*100:>10.2f}% "
f"{(post_share-pre_share)*100:>+9.2f}pp")
print(
f" {cohort:<22} {pre_share * 100:>9.2f}% {post_share * 100:>10.2f}% "
f"{(post_share - pre_share) * 100:>+9.2f}pp"
)
pre_share = grand_pre / pre_total if pre_total else 0
post_share = grand_post / post_total if post_total else 0
print(f" {'all reads':<22} {pre_share*100:>9.2f}% {post_share*100:>10.2f}% "
f"{(post_share-pre_share)*100:>+9.2f}pp")
print(
f" {'all reads':<22} {pre_share * 100:>9.2f}% {post_share * 100:>10.2f}% "
f"{(post_share - pre_share) * 100:>+9.2f}pp"
)
def per_call_stats(reads, deploy_ms: int) -> None:
print(f"\nper-call stats (pre vs post deploy):")
print(f" {'cohort':<22} {'window':<6} {'n':>9} {'p50':>7} {'p90':>7} {'mean':>8}")
print(
f" {'cohort':<22} {'window':<6} {'n':>9} {'p50':>7} {'p90':>7} {'mean':>8}"
)
for cohort in COHORT_COLORS:
d = reads[cohort]
if d["ts"].size == 0:
@@ -302,19 +352,25 @@ def per_call_stats(reads, deploy_ms: int) -> None:
for name, arr in (("pre", pre), ("post", post)):
if arr.size == 0:
continue
print(f" {cohort:<22} {name:<6} {arr.size:>9,} "
f"{int(np.percentile(arr,50)):>7,} "
f"{int(np.percentile(arr,90)):>7,} "
f"{int(arr.mean()):>8,}")
print(
f" {cohort:<22} {name:<6} {arr.size:>9,} "
f"{int(np.percentile(arr, 50)):>7,} "
f"{int(np.percentile(arr, 90)):>7,} "
f"{int(arr.mean()):>8,}"
)
# --------------------------------------------------------------------------- #
# Entry
def main() -> int:
ap = argparse.ArgumentParser(description="read summarizer impact analysis")
ap.add_argument("--deploy", default=DEFAULT_DEPLOY,
help=f"deploy date YYYY-MM-DD (default {DEFAULT_DEPLOY})")
ap.add_argument(
"--deploy",
default=DEFAULT_DEPLOY,
help=f"deploy date YYYY-MM-DD (default {DEFAULT_DEPLOY})",
)
args = ap.parse_args()
deploy = datetime.strptime(args.deploy, "%Y-%m-%d").replace(tzinfo=timezone.utc)
@@ -343,7 +399,9 @@ def main() -> int:
panel_share_line(axes[0, 1], reads, denom_dates, denom, deploy)
panel_per_call(axes[1, 0], reads, deploy, q=50, label_q="p50")
panel_per_call(axes[1, 1], reads, deploy, q=90, label_q="p90")
fig.suptitle(f"read summarizer impact — deploy = {args.deploy}", fontsize=13, y=0.995)
fig.suptitle(
f"read summarizer impact — deploy = {args.deploy}", fontsize=13, y=0.995
)
fig.tight_layout()
p = OUT_DIR / "read-summarizer.png"
fig.savefig(p, bbox_inches="tight")