Files
MeshTerm/meshterm/tools/tx_optimize.py
T
Jean-Pierre MartineauandClaude Fable 5 9979f66f2a TX optimize sweeps live and asks to apply after the evidence is in
The six-prompt chain (path, samples, min, max, step, apply-up-front)
collapses to one: pick the path, and the live sweep screen takes it
from there. Levels land in an SNR bar chart as they are measured,
ascending by power with the current best starred, while the optimizer
announces its phase — coarse sweep, refining around the leader,
verifying — through a new on_phase callback. Reliability and trace
counts sit beside every bar, and a level nothing got through at reads
as an explicit no-reply row.

The apply decision moves to where it belongs: after the sweep, a
floating dialog offers the winner against the node's original power
(Cancel left, Apply right, Enter commits), and `a` re-offers it if
first declined. The sweep itself runs with apply off, so Esc at any
point stops the search and the optimizer's unwind restores the
original transmit power.

The sweep persists under one runs row (levels and traces alike), the
scripted CLI keeps its full flag set, chart, and --apply semantics,
and the tool moves in with Trace under Diagnostics.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-10 05:24:21 -04:00

416 lines
17 KiB
Python

"""The ``tx-optimize`` tool: tune a remote node's TX power for the best signal at a target.
You force a path (just like ``trace``) ending at the **target** node where SNR is
measured. The node one hop *before* the target — one you hold admin rights on — is the
node whose transmit power gets swept and tuned. Admin passwords are remembered between
runs.
In the interactive menu one prompt picks the path and the live sweep screen
(:mod:`meshterm.ui.tx_screen`) takes it from there: levels land in a bar chart as they
are measured, and the apply decision is made *after* the sweep, over the evidence. On the
CLI it stays a scriptable one-shot with the full flag set (range, step, samples,
``--apply``, the HTML chart), and progress streams like a trace.
"""
from __future__ import annotations
from typing import Any, Optional
import typer
from ..context import AppContext
from ..core.connection import DeviceCommandError
from ..core.models import Contact
from ..services import trace_runner, tx_optimizer
from ..ui.widgets import tx_opt_summary, tx_opt_table
from ..viz.tx_plot import render_tx_optimization
from .base import Tool, ToolResult, register
#: Trace count per TX level — kept to single digits so the radio's duty cycle stays sane
#: and the per-level sweep finishes in reasonable time.
MAX_SAMPLES = 9
@register
class TxOptimizeTool(Tool):
"""Tune a remote node's TX power for the strongest, most reliable signal at a target."""
name = "tx-optimize"
title = "TX optimize"
help = "Tune a remote node's TX power for the best signal at a target"
category = "Diagnostics"
order = 15 # right after Trace, its measurement sibling
async def prompt_params(self, ctx: AppContext) -> Optional[dict[str, Any]]:
"""Pick the path for the live sweep (everything else defaults, tuned in-screen).
Args:
ctx: Shared application context.
Returns:
``{"live": True, "path": spec}``, or ``None`` if the user cancelled.
"""
device = await ctx.device()
contacts = await device.get_contacts()
choices = [c.name for c in contacts]
prompt = (
"Comma-separated contacts/hex, ending at the target — "
"the node just before it is tuned"
)
validate_path = lambda v: _validate_link_path(v, contacts) # noqa: E731
if choices:
path_spec = await ctx.ui.autocomplete(
"Path to the target", choices, prompt=prompt, validate=validate_path
)
else:
path_spec = await ctx.ui.text(
"Path to the target", prompt=prompt, validate=validate_path
)
if not path_spec:
return None
return {"live": True, "path": path_spec.strip()}
async def execute(self, ctx: AppContext, params: dict[str, Any]) -> ToolResult:
"""Run — without the outer run row on the live path.
The live screen opens its own ``runs`` row for the sweep (matching what a
scripted invocation records), so wrapping the screen session in another row
would double-log it. Scripted runs keep the base class's logging.
Args:
ctx: Shared application context.
params: Parameters for this invocation.
Returns:
The :class:`ToolResult` from :meth:`run`.
"""
if params.get("live"):
return await self.run(ctx, params)
return await super().execute(ctx, params)
async def run(self, ctx: AppContext, params: dict[str, Any]) -> ToolResult:
"""Open the live sweep (menu) or run the scripted optimization (CLI).
Args:
ctx: Shared application context.
params: ``live`` + ``path`` from the menu prompt; or ``path``, ``samples``,
``tx_min``, ``tx_max``, ``step``, ``apply``, optional ``password``/
``viz``, and the injected ``_run_id`` from the CLI.
Returns:
A :class:`ToolResult` with the optimum and any chart path.
"""
if params.get("live"):
return await self._run_live(ctx, str(params["path"]))
return await self._run_cli(ctx, params)
# -- interactive (menu) ---------------------------------------------------------
async def _run_live(self, ctx: AppContext, path_spec: str) -> ToolResult:
"""Resolve the link, log in, and hand off to the live sweep screen.
Args:
ctx: Shared application context.
path_spec: The user's comma-separated path entry (names and/or hex).
Returns:
A :class:`ToolResult` echoing the sweep's recorded summary.
"""
from ..ui.tx_screen import open_tx_optimize
device = await ctx.device()
contacts = await device.get_contacts()
path = trace_runner.parse_trace_path(path_spec, contacts)
admin_node, target_label = _resolve_link(path, contacts)
await self._login(ctx, admin_node, {})
summary = await open_tx_optimize(
ctx, admin_node=admin_node, target_label=target_label, path=path
)
return ToolResult(summary=summary)
async def _login(
self, ctx: AppContext, admin_node: Contact, params: dict[str, Any]
) -> None:
"""Authenticate against the admin node, remembering a working password.
Args:
ctx: Shared application context.
admin_node: The node we're about to tune.
params: Tool params (may carry an explicit ``password`` on the CLI).
Raises:
DeviceCommandError: If the login is rejected (the stored password, now
known bad, is forgotten so the next run asks fresh).
"""
device = await ctx.device()
password = await self._resolve_password(ctx, admin_node, params)
if not await device.admin_login(admin_node, password):
ctx.admin_store.forget(admin_node) # bad password: don't keep reusing it
raise DeviceCommandError(
f"admin login to {admin_node.name!r} failed (wrong password?). "
"The saved password was cleared; re-run to enter a new one."
)
ctx.admin_store.remember(admin_node, password)
# -- scripted (CLI) ---------------------------------------------------------------
async def _run_cli(self, ctx: AppContext, params: dict[str, Any]) -> ToolResult:
"""Resolve the link, log in, sweep TX power, render results, and apply the winner.
Args:
ctx: Shared application context.
params: ``path``, ``samples``, ``tx_min``, ``tx_max``, ``step``, ``apply``,
optional ``password``/``viz``, and the injected ``_run_id``.
Returns:
A :class:`ToolResult` with the optimum and any chart path.
"""
run_id = params["_run_id"]
device = await ctx.device()
contacts = await device.get_contacts()
# Resolve the forced path to hashes, then pull out the target (last hop) and the
# admin node we tune (the hop before it).
path = trace_runner.parse_trace_path(params["path"], contacts)
admin_node, target_label = _resolve_link(path, contacts)
samples = max(1, min(MAX_SAMPLES, int(params.get("samples", 3))))
if int(params.get("samples", 3)) > MAX_SAMPLES:
ctx.ui.note(f"[warn]capping at {MAX_SAMPLES} traces per level[/warn]")
ctx.ui.note(f"[muted]logging in to[/muted] [brand]{admin_node.name}[/brand] [muted]…[/muted]")
await self._login(ctx, admin_node, params)
ctx.ui.note(
f"[muted]tuning[/muted] [brand]{admin_node.name}[/brand] "
f"[muted]→ target[/muted] [brand]{target_label}[/brand] "
f"[muted]via {path}[/muted]"
)
with ctx.ui.progress("tx-optimize") as progress:
task = progress.add_task(f"optimizing TX -> {target_label}", total=None)
def on_level(done: int, total: int, level) -> None: # noqa: ANN001
progress.update(
task, total=total, completed=done,
description=f"TX {level.tx_power:>2} "
f"SNR {_fmt_snr(level.target_snr)} {level.success_rate:.0%}",
)
result = await tx_optimizer.optimize_tx_power(
device,
target_label,
admin_node,
path,
tx_min=int(params.get("tx_min", ctx.settings.tx_opt_min)),
tx_max=int(params.get("tx_max", ctx.settings.tx_opt_max)),
coarse_step=int(params.get("step", 3)),
samples_per_level=samples,
apply=bool(params.get("apply", True)),
cooldown_s=ctx.settings.trace_cooldown_s,
on_level=on_level,
persist_level=lambda lv: ctx.repo.record_tx_sample(run_id, lv),
persist_trace=lambda t: ctx.repo.record_trace(run_id, t),
)
# No trace got through at any TX level: nothing was tuned, the node was left at
# its original power. Usually a wrong/unreachable path rather than a weak link.
no_result = result.best_snr is None
if result.applied:
ctx.log.info("set TX power %s on %s", result.best_tx, admin_node.name)
ctx.ui.show(tx_opt_table(result))
ctx.ui.show(tx_opt_summary(result))
artifacts: list[str] = []
if params.get("viz", True):
assert ctx.settings.output_dir is not None
path_out = render_tx_optimization(result, ctx.settings.output_dir)
artifacts.append(str(path_out))
if no_result:
restored = (
f" Restored TX to {result.original_tx}." if result.original_tx is not None else ""
)
message = (
f"[warn]![/warn] no traces reached [brand]{result.target}[/brand] at any TX "
f"level — check the path ends at the target and is reachable.{restored}"
)
else:
applied_note = f" [ok](set on {admin_node.name})[/ok]" if result.applied else ""
message = (
f"[ok]✓[/ok] optimal TX for [brand]{admin_node.name}[/brand] → "
f"[brand]{result.target}[/brand] is [brand]{result.best_tx}[/brand]"
+ applied_note
)
return ToolResult(
summary={
"target": result.target,
"admin_node": result.admin_node,
"path": result.path,
"best_tx": result.best_tx,
"best_snr": result.best_snr,
"best_success_rate": round(result.best_success_rate, 3),
"original_tx": result.original_tx,
"applied": result.applied,
"levels_measured": len(result.levels),
},
message=message,
artifacts=artifacts,
)
async def _resolve_password(
self, ctx: AppContext, admin_node: Contact, params: dict[str, Any]
) -> str:
"""Find the admin password: explicit flag, remembered, or an interactive prompt.
Args:
ctx: Shared application context.
admin_node: The node we're about to log in to.
params: Tool params (may carry an explicit ``password``).
Returns:
The password to log in with.
Raises:
DeviceCommandError: If no password is available and we can't prompt (JSON mode).
"""
password = params.get("password") or ctx.admin_store.get(admin_node)
if password:
return str(password)
if ctx.json_output:
raise DeviceCommandError(
f"no admin password for {admin_node.name!r}; pass --password or run once "
"interactively to store it."
)
entered = await ctx.ui.text(
f"Admin password for {admin_node.name}:", password=True
)
if not entered:
raise DeviceCommandError("an admin password is required to tune a remote node.")
return entered
def register_cli(self, app: typer.Typer) -> None:
"""Register the ``tx-optimize`` subcommand.
Args:
app: The Typer application.
"""
from ..cli import run_tool_command
@app.command(name=self.name, help=self.help)
def _tx_optimize(
path: str = typer.Option(
..., "--path", "-p",
help="Forced path ending at the target (e.g. 'Repeater,Target' or '3d,f2')",
),
samples: int = typer.Option(3, "--samples", "-n", help="Traces per TX level"),
step: int = typer.Option(3, "--step", help="Coarse sweep step"),
tx_min: Optional[int] = typer.Option(None, "--min", help="Lowest TX power"),
tx_max: Optional[int] = typer.Option(None, "--max", help="Highest TX power"),
password: Optional[str] = typer.Option(
None, "--password", help="Admin password (else remembered/prompted)"
),
apply: bool = typer.Option(True, "--apply/--no-apply", help="Set the winner"),
viz: bool = typer.Option(True, "--viz/--no-viz", help="Generate HTML chart"),
) -> None:
tool_params: dict[str, Any] = {
"path": path, "samples": samples, "step": step,
"apply": apply, "viz": viz,
}
if tx_min is not None:
tool_params["tx_min"] = tx_min
if tx_max is not None:
tool_params["tx_max"] = tx_max
if password is not None:
tool_params["password"] = password
run_tool_command(self, tool_params)
def _resolve_link(path: str, contacts: list[Contact]) -> tuple[Contact, str]:
"""Split a forced path into the admin node we tune and the target's display label.
Args:
path: The comma-separated hash path (output of ``parse_trace_path``).
contacts: Known contacts, used to map hashes back to names/keys.
Returns:
``(admin_node, target_label)`` — the second-to-last hop as a full
:class:`Contact` (needed for login), and a friendly name for the last hop.
Raises:
DeviceCommandError: If the path has fewer than two hops, or the admin hop can't
be matched to a known contact carrying a public key.
"""
hops = [h for h in path.split(",") if h]
if len(hops) < 2:
raise DeviceCommandError(
"the path needs at least two hops: the node to tune and the target after it "
"(e.g. 'AdminNode,Target')."
)
admin = _contact_for_hash(hops[-2], contacts)
if admin is None or not (admin.public_key or "").strip():
raise DeviceCommandError(
f"the node before the target ({hops[-2]}) isn't a known contact with a public "
"key, so we can't log in to tune it. Receive an advert from it first."
)
target = _contact_for_hash(hops[-1], contacts)
return admin, (target.name if target else hops[-1])
def _contact_for_hash(hash_hex: str, contacts: list[Contact]) -> Optional[Contact]:
"""Return the contact whose key matches a path-hop hash, if any.
Args:
hash_hex: A path hop hash (a leading slice of the node's public key).
contacts: Known contacts to match against.
Returns:
The matching :class:`Contact`, or ``None``.
"""
needle = hash_hex.lower().removeprefix("0x")
for c in contacts:
pub = (c.public_key or "").lower().removeprefix("0x")
prefix = (c.key_prefix or "").lower().removeprefix("0x")
if pub.startswith(needle):
return c
if prefix and (prefix.startswith(needle) or needle.startswith(prefix)):
return c
return None
def _validate_link_path(value: str, contacts: list[Contact]) -> bool | str:
"""Validate the interactive path entry: parseable and at least two hops.
Args:
value: The raw comma-separated path entry.
contacts: Known contacts used to resolve names.
Returns:
``True`` if valid, otherwise an error message string.
"""
if not value.strip():
return "Enter a path ending at the target node."
try:
path = trace_runner.parse_trace_path(value, contacts)
except ValueError as exc:
return str(exc)
if len([h for h in path.split(",") if h]) < 2:
return "Need at least two hops: the node to tune, then the target."
return True
def _fmt_snr(snr: Optional[float]) -> str:
"""Format an optional SNR for the progress description.
Args:
snr: SNR in dB, or ``None``.
Returns:
A short fixed-width string like ``+5.1`` or `` n/a``.
"""
return f"{snr:+.1f}" if snr is not None else " n/a"