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feat: refactor based on spectral spectral_analysis

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Asking the agent to refactor to reduce spectral complexity, it worked. Trivial change, but this did reduce code complexity.
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Zachery Aaron Shores-Chmielewski 2026-02-07 17:36:45 +07:00
parent 89be164c19
commit 6897d71e4b
13 changed files with 1433 additions and 83 deletions
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14
README.md
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@ -263,3 +263,17 @@ node test.mjs # run WASM tests
| `getrandom` | yes | System RNG for actor addresses |
| `no_random` | no | Deterministic counter (for WASM / reproducible tests) |
| `python` | no | PyO3 bindings, builds cdylib wheel |
## Connectome analysis
Spectral analysis of the internal dependency graph, producing a Connectome Complexity Index (CCI) and visual dashboards.
```sh
# Generate the dependency DAG
cargo run --manifest-path tools/depgraph/Cargo.toml -- --src-dir src/ --output deps
# Run spectral analysis (outputs to docs/connectome/)
source .venv/bin/activate
python tools/spectral/spectral_analysis.py deps.dot
```
This produces a text report, an interactive HTML dashboard, and a static PNG dashboard in `docs/connectome/`. See [docs/connectome.md](docs/connectome.md) for details on the metrics and interpretation.

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# Connectome Analysis
The connectome analysis applies spectral graph theory to the codebase's internal dependency DAG, producing quantitative coupling metrics and visual dashboards.
## What it measures
The tool parses `deps.dot` (a GraphViz DOT file describing struct/trait dependencies between modules) and computes:
- **Laplacian eigenvalue spectrum** -- encodes the graph's overall connectivity structure
- **Fiedler vector** -- the optimal spectral bisection of the dependency graph, revealing natural module clusters
- **Module coupling matrix** -- directed edge counts between every pair of modules
- **Connectome Complexity Index (CCI)** -- a single 0-1 score combining five sub-metrics:
| Sub-metric | Weight | What it captures |
|---|---|---|
| Algebraic connectivity (lambda_2/n) | 25% | How tightly connected the graph is |
| Spectral entropy (H/log2(k)) | 25% | How uniformly distributed coupling is across eigenvalues |
| Edge density (\|E\|/n(n-1)) | 15% | Raw ratio of edges to possible edges |
| Cross-module coupling ratio | 20% | Fraction of edges that cross module boundaries |
| Spectral radius (rho/(n-1)) | 15% | Maximum hub concentration |
### Interpreting CCI
| CCI range | Label | Meaning |
|---|---|---|
| < 0.30 | LOW | Well-decomposed architecture |
| 0.30 - 0.60 | MODERATE | Typical well-structured codebase |
| > 0.60 | HIGH | Consider reviewing module boundaries |
## Running
From the project root:
```sh
# Default: outputs to docs/connectome/
python tools/spectral/spectral_analysis.py deps.dot
# Custom output directory
python tools/spectral/spectral_analysis.py deps.dot -o path/to/output
# Also emit JSON metrics
python tools/spectral/spectral_analysis.py deps.dot --json
# Text report only (skip matplotlib PNG)
python tools/spectral/spectral_analysis.py deps.dot --no-plots
```
### Prerequisites
The script requires numpy, scipy, and matplotlib (for the PNG dashboard). These are available in the project's `.venv`:
```sh
source .venv/bin/activate
python tools/spectral/spectral_analysis.py deps.dot
```
## Output files
All output goes to `docs/connectome/` by default:
| File | Description |
|---|---|
| `connectome_report.txt` | Full text report with eigenvalues, Fiedler bisection, coupling matrix, and CCI breakdown |
| `connectome_dashboard.html` | Interactive HTML dashboard with zoomable DAG, eigenvalue plot, Fiedler bar chart, and coupling heatmap |
| `connectome_dashboard.png` | Static PNG snapshot of the spectral dashboard (dark theme, 16x12 @ 150 DPI) |
| `connectome_metrics.json` | Machine-readable metrics (only with `--json` flag) |
## Regenerating deps.dot
The DOT file is the input to the spectral analysis. To regenerate it from source:
```sh
cargo run --manifest-path tools/depgraph/Cargo.toml -- --src-dir src/ --output deps
```
Then re-run the spectral analysis to update the connectome report.

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{
"graph": {
"n_nodes": 36,
"n_edges": 78,
"n_modules": 8,
"connected_components": 2,
"modules": [
"error",
"config",
"channel",
"actor",
"address_map",
"runtime",
"worker",
"python"
]
},
"spectral": {
"eigenvalues": [
0.0,
0.0,
0.18637427422819514,
0.4813940269111958,
0.6123548189907484,
0.7985629750697533,
0.8319091149970231,
1.004600219615323,
1.2394224070963267,
1.3689639255261323,
1.4526860286383532,
1.626080007307936,
2.321279039207482,
2.3935870074779477,
2.909249108581605,
3.1569529438124246,
3.219980753498557,
3.3901681819448264,
3.4799333923457128,
3.605153966968332,
3.8847634489335645,
4.186333826694949,
4.707024553452379,
5.173220891347629,
5.586454240023603,
5.795938099946378,
5.8549806331718415,
6.1828765255391644,
6.461944112192484,
6.898584006266002,
7.3807011063714905,
7.896480708195232,
9.160238969430825,
11.171010263156152,
14.04747425561517,
15.533322167445291
],
"fiedler_value": 0.0,
"fiedler_vector": [
0.0,
1.6667674979754847e-17,
-4.4166826078552935e-16,
-5.256955919501151e-16,
-1.6422080940489055e-18,
-7.037238109196825e-17,
8.390622125197347e-16,
2.3690827037115515e-17,
1.4176669953736474e-16,
1.4226827878099615e-16,
3.1675939003075104e-17,
2.7236604915425953e-18,
-1.744993274089968e-16,
2.918795638720409e-17,
-2.1047785816801073e-16,
1.6100142369066343e-16,
-1.1048855416219909e-16,
2.623380592723269e-16,
-6.257340472605819e-17,
-2.7901019807352287e-17,
7.954130131218555e-17,
-2.8145783605573126e-16,
5.097927800469914e-17,
1.0000000000000002,
-7.635525673846673e-17,
4.0203070989124624e-17,
5.607482503879278e-17,
1.4848475991077948e-17,
-8.451175680174382e-17,
-1.3333327282927672e-16,
2.6566833162138994e-16,
1.0987812721413363e-16,
4.959951093541129e-16,
-1.2067067461630528e-16,
-2.172546179303562e-16,
-2.3212297109883297e-16
],
"node_names": [
"Error",
"BackoffPolicy",
"RuntimeConfig",
"HybridChannel",
"Receiver",
"Sender",
"Actor",
"ActorAddress",
"ActorInterface",
"AnyActor",
"ContextInner",
"Ctx",
"Message",
"AddressMap",
"Placement",
"WorkerId",
"Envelope",
"Inbox",
"InboxRegistry",
"Runtime",
"RuntimeHandle",
"SenderT",
"ActorPool",
"Mailbox",
"TickContext",
"Worker",
"WorkerContext",
"Effect",
"PyActor",
"PyActorAddress",
"PyCtx",
"PyInbox",
"PyMsg",
"PyRuntime",
"PyRuntimeConfig",
"PyRuntimeHandle"
],
"node_modules": [
"error",
"config",
"config",
"channel",
"channel",
"channel",
"actor",
"actor",
"actor",
"actor",
"actor",
"actor",
"actor",
"address_map",
"address_map",
"address_map",
"runtime",
"runtime",
"runtime",
"runtime",
"runtime",
"runtime",
"worker",
"worker",
"worker",
"worker",
"worker",
"python",
"python",
"python",
"python",
"python",
"python",
"python",
"python",
"python"
]
},
"module_coupling": {
"module_names": [
"error",
"config",
"channel",
"actor",
"address_map",
"runtime",
"worker",
"python"
],
"coupling_matrix": [
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0
],
[
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0
],
[
0.0,
0.0,
3.0,
0.0,
0.0,
1.0,
0.0,
0.0
],
[
2.0,
0.0,
0.0,
7.0,
0.0,
0.0,
0.0,
0.0
],
[
0.0,
0.0,
0.0,
1.0,
2.0,
0.0,
0.0,
0.0
],
[
2.0,
1.0,
2.0,
6.0,
2.0,
6.0,
1.0,
0.0
],
[
1.0,
1.0,
2.0,
9.0,
4.0,
3.0,
3.0,
0.0
],
[
0.0,
0.0,
0.0,
5.0,
0.0,
3.0,
0.0,
10.0
]
],
"cross_module_edges": 46,
"total_edges": 78
},
"metrics": {
"algebraic_connectivity": 0.0,
"normalized_algebraic_connectivity": 0.0,
"spectral_entropy": 4.641128070102523,
"normalized_spectral_entropy": 0.9122677088609219,
"edge_density": 0.06190476190476191,
"cross_module_ratio": 0.5897435897435898,
"spectral_radius": 6.676215667817795,
"normalized_spectral_radius": 0.19074901908050843,
"cci": 0.383913712311739
}
}

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@ -0,0 +1,125 @@
========================================================================
SPECTRAL ANALYSIS REPORT — Dependency DAG
========================================================================
GRAPH SUMMARY
----------------------------------------
Nodes: 36
Directed edges: 78
Modules: 8
Connected components: 2
Modules: error, config, channel, actor, address_map, runtime, worker, python
LAPLACIAN EIGENVALUE SPECTRUM
----------------------------------------
lambda_ 0 = 0.0000
lambda_ 1 = 0.0000 <-- Fiedler value (lambda_2)
lambda_ 2 = 0.1864
lambda_ 3 = 0.4814
lambda_ 4 = 0.6124
lambda_ 5 = 0.7986
lambda_ 6 = 0.8319
lambda_ 7 = 1.0046
lambda_ 8 = 1.2394
lambda_ 9 = 1.3690
lambda_10 = 1.4527
lambda_11 = 1.6261
lambda_12 = 2.3213
lambda_13 = 2.3936
lambda_14 = 2.9092
lambda_15 = 3.1570
lambda_16 = 3.2200
lambda_17 = 3.3902
lambda_18 = 3.4799
lambda_19 = 3.6052
lambda_20 = 3.8848
lambda_21 = 4.1863
lambda_22 = 4.7070
lambda_23 = 5.1732
lambda_24 = 5.5865
lambda_25 = 5.7959
lambda_26 = 5.8550
lambda_27 = 6.1829
lambda_28 = 6.4619
lambda_29 = 6.8986
lambda_30 = 7.3807
lambda_31 = 7.8965
lambda_32 = 9.1602
lambda_33 = 11.1710
lambda_34 = 14.0475
lambda_35 = 15.5333
Spectral gap (lambda_max - lambda_2): 15.5333
Fiedler value (algebraic connectivity): 0.0000
FIEDLER VECTOR — SPECTRAL BISECTION
----------------------------------------
Partition A (Fiedler < 0):
HybridChannel [channel ] f = -0.0000
RuntimeConfig [config ] f = -0.0000
SenderT [runtime ] f = -0.0000
PyRuntimeHandle [python ] f = -0.0000
PyRuntimeConfig [python ] f = -0.0000
Placement [address_map ] f = -0.0000
Message [actor ] f = -0.0000
PyActorAddress [python ] f = -0.0000
PyRuntime [python ] f = -0.0000
Envelope [runtime ] f = -0.0000
PyActor [python ] f = -0.0000
TickContext [worker ] f = -0.0000
Sender [channel ] f = -0.0000
InboxRegistry [runtime ] f = -0.0000
Runtime [runtime ] f = -0.0000
Receiver [channel ] f = -0.0000
────────────────────────────────────
Partition B (Fiedler >= 0):
Error [error ] f = +0.0000
Ctx [actor ] f = +0.0000
Effect [python ] f = +0.0000
BackoffPolicy [config ] f = +0.0000
ActorAddress [actor ] f = +0.0000
AddressMap [address_map ] f = +0.0000
ContextInner [actor ] f = +0.0000
Worker [worker ] f = +0.0000
ActorPool [worker ] f = +0.0000
WorkerContext [worker ] f = +0.0000
RuntimeHandle [runtime ] f = +0.0000
PyInbox [python ] f = +0.0000
ActorInterface [actor ] f = +0.0000
AnyActor [actor ] f = +0.0000
WorkerId [address_map ] f = +0.0000
Inbox [runtime ] f = +0.0000
PyCtx [python ] f = +0.0000
PyMsg [python ] f = +0.0000
Actor [actor ] f = +0.0000
Mailbox [worker ] f = +1.0000
MODULE COUPLING MATRIX (directed edge counts)
----------------------------------------
error config channel actoraddress_map runtime worker python
error 0 0 0 0 0 0 0 0
config 0 1 0 0 0 0 0 0
channel 0 0 3 0 0 1 0 0
actor 2 0 0 7 0 0 0 0
address_map 0 0 0 1 2 0 0 0
runtime 2 1 2 6 2 6 1 0
worker 1 1 2 9 4 3 3 0
python 0 0 0 5 0 3 0 10
Cross-module edges: 46 / 78 (59.0%)
CONNECTOME COMPLEXITY INDEX (CCI)
----------------------------------------
Sub-metric Raw Normalized Weight Contrib
──────────────────────────────────────── ────────── ────────── ──────── ────────
Algebraic connectivity (lambda_2/n) 0.0000 0.0000 0.25 0.0000
Spectral entropy (H/log2(k)) 4.6411 0.9123 0.25 0.2281
Edge density (|E|/n(n-1)) 0.0619 0.0619 0.15 0.0093
Cross-module coupling ratio 0.5897 0.5897 0.20 0.1179
Spectral radius (rho/(n-1)) 6.6762 0.1907 0.15 0.0286
──────────────────────────────────────── ────────── ────────── ──────── ────────
CCI (weighted sum) 1.00 0.3839
Interpretation: MODERATE complexity — typical well-structured codebase
========================================================================

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spectral_report.txt Normal file
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@ -0,0 +1,125 @@
========================================================================
SPECTRAL ANALYSIS REPORT — Dependency DAG
========================================================================
GRAPH SUMMARY
----------------------------------------
Nodes: 36
Directed edges: 78
Modules: 8
Connected components: 2
Modules: error, config, channel, actor, address_map, runtime, worker, python
LAPLACIAN EIGENVALUE SPECTRUM
----------------------------------------
lambda_ 0 = 0.0000
lambda_ 1 = 0.0000 <-- Fiedler value (lambda_2)
lambda_ 2 = 0.1864
lambda_ 3 = 0.4814
lambda_ 4 = 0.6124
lambda_ 5 = 0.7986
lambda_ 6 = 0.8319
lambda_ 7 = 1.0046
lambda_ 8 = 1.2394
lambda_ 9 = 1.3690
lambda_10 = 1.4527
lambda_11 = 1.6261
lambda_12 = 2.3213
lambda_13 = 2.3936
lambda_14 = 2.9092
lambda_15 = 3.1570
lambda_16 = 3.2200
lambda_17 = 3.3902
lambda_18 = 3.4799
lambda_19 = 3.6052
lambda_20 = 3.8848
lambda_21 = 4.1863
lambda_22 = 4.7070
lambda_23 = 5.1732
lambda_24 = 5.5865
lambda_25 = 5.7959
lambda_26 = 5.8550
lambda_27 = 6.1829
lambda_28 = 6.4619
lambda_29 = 6.8986
lambda_30 = 7.3807
lambda_31 = 7.8965
lambda_32 = 9.1602
lambda_33 = 11.1710
lambda_34 = 14.0475
lambda_35 = 15.5333
Spectral gap (lambda_max - lambda_2): 15.5333
Fiedler value (algebraic connectivity): 0.0000
FIEDLER VECTOR — SPECTRAL BISECTION
----------------------------------------
Partition A (Fiedler < 0):
HybridChannel [channel ] f = -0.0000
RuntimeConfig [config ] f = -0.0000
SenderT [runtime ] f = -0.0000
PyRuntimeHandle [python ] f = -0.0000
PyRuntimeConfig [python ] f = -0.0000
Placement [address_map ] f = -0.0000
Message [actor ] f = -0.0000
PyActorAddress [python ] f = -0.0000
PyRuntime [python ] f = -0.0000
Envelope [runtime ] f = -0.0000
PyActor [python ] f = -0.0000
TickContext [worker ] f = -0.0000
Sender [channel ] f = -0.0000
InboxRegistry [runtime ] f = -0.0000
Runtime [runtime ] f = -0.0000
Receiver [channel ] f = -0.0000
────────────────────────────────────
Partition B (Fiedler >= 0):
Error [error ] f = +0.0000
Ctx [actor ] f = +0.0000
Effect [python ] f = +0.0000
BackoffPolicy [config ] f = +0.0000
ActorAddress [actor ] f = +0.0000
AddressMap [address_map ] f = +0.0000
ContextInner [actor ] f = +0.0000
Worker [worker ] f = +0.0000
ActorPool [worker ] f = +0.0000
WorkerContext [worker ] f = +0.0000
RuntimeHandle [runtime ] f = +0.0000
PyInbox [python ] f = +0.0000
ActorInterface [actor ] f = +0.0000
AnyActor [actor ] f = +0.0000
WorkerId [address_map ] f = +0.0000
Inbox [runtime ] f = +0.0000
PyCtx [python ] f = +0.0000
PyMsg [python ] f = +0.0000
Actor [actor ] f = +0.0000
Mailbox [worker ] f = +1.0000
MODULE COUPLING MATRIX (directed edge counts)
----------------------------------------
error config channel actoraddress_map runtime worker python
error 0 0 0 0 0 0 0 0
config 0 1 0 0 0 0 0 0
channel 0 0 3 0 0 1 0 0
actor 2 0 0 7 0 0 0 0
address_map 0 0 0 1 2 0 0 0
runtime 2 1 2 6 2 6 1 0
worker 1 1 2 9 4 3 3 0
python 0 0 0 5 0 3 0 10
Cross-module edges: 46 / 78 (59.0%)
CONNECTOME COMPLEXITY INDEX (CCI)
----------------------------------------
Sub-metric Raw Normalized Weight Contrib
──────────────────────────────────────── ────────── ────────── ──────── ────────
Algebraic connectivity (lambda_2/n) 0.0000 0.0000 0.25 0.0000
Spectral entropy (H/log2(k)) 4.6411 0.9123 0.25 0.2281
Edge density (|E|/n(n-1)) 0.0619 0.0619 0.15 0.0093
Cross-module coupling ratio 0.5897 0.5897 0.20 0.1179
Spectral radius (rho/(n-1)) 6.6762 0.1907 0.15 0.0286
──────────────────────────────────────── ────────── ────────── ──────── ────────
CCI (weighted sum) 1.00 0.3839
Interpretation: MODERATE complexity — typical well-structured codebase
========================================================================

47
src/actor.rs
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@ -1,6 +1,6 @@
use std::any::Any;
use crate::runtime::Ctx;
use crate::Error;
/// The primary trait defining data that can be passed to and from actor processes
pub trait Message: 'static + Sized + Clone + Send + Sync {}
@ -49,3 +49,48 @@ where
}
}
}
/// Object-safe inner trait for sending type-erased messages.
pub(crate) trait ContextInner {
fn send_any(&self, addr: ActorAddress, msg: Box<dyn Any + Send>) -> Result<(), Error>;
fn spawn_any(&self, addr: ActorAddress, actor: Box<dyn AnyActor>) -> Result<(), Error>;
fn mailbox_waterlevel(&self) -> usize;
}
/// Actor syscall interface — passed to `ActorInterface::handle()`.
///
/// Wraps a `&dyn ContextInner` to solve the object-safety problem while
/// providing a typed public API.
pub struct Ctx<'a> {
inner: &'a dyn ContextInner,
self_addr: ActorAddress,
}
impl<'a> Ctx<'a> {
pub(crate) fn new(inner: &'a dyn ContextInner, self_addr: ActorAddress) -> Self {
Self { inner, self_addr }
}
#[cfg(feature = "python")]
pub(crate) fn raw_inner(&self) -> &dyn ContextInner {
self.inner
}
/// Returns the address of the actor currently being ticked.
pub fn self_addr(&self) -> ActorAddress {
self.self_addr
}
/// Send a typed message to an actor address.
pub fn send<M: Message>(&self, addr: ActorAddress, msg: M) -> Result<(), Error> {
self.inner.send_any(addr, Box::new(msg))
}
/// Spawn a new actor, returning its address.
pub fn spawn<A: ActorInterface>(&self, actor: A) -> Result<ActorAddress, Error> {
let addr = ActorAddress::new_random();
let boxed: Box<dyn AnyActor> = Box::new(Actor::new(actor));
self.inner.spawn_any(addr, boxed)?;
Ok(addr)
}
}

4
src/python.rs
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@ -4,9 +4,9 @@ use std::cell::RefCell;
use pyo3::prelude::*;
use pyo3::types::PyModule;
use crate::actor::{Actor, ActorAddress, ActorInterface, AnyActor};
use crate::actor::{Actor, ActorAddress, ActorInterface, AnyActor, Ctx};
use crate::config::{BackoffPolicy, RuntimeConfig};
use crate::runtime::{Ctx, Inbox, Runtime, RuntimeHandle};
use crate::runtime::{Inbox, Runtime, RuntimeHandle};
use crate::Error;
// ─── PyMsg newtype ───────────────────────────────────────────────────────────

49
src/runtime.rs
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@ -65,43 +65,8 @@ impl RuntimeHandle {
}
}
/// Actor syscall interface — passed to `ActorInterface::handle()`.
///
/// Wraps a `&dyn ContextInner` to solve the object-safety problem while
/// providing a typed public API.
pub struct Ctx<'a> {
inner: &'a dyn ContextInner,
self_addr: ActorAddress,
}
impl<'a> Ctx<'a> {
pub(crate) fn new(inner: &'a dyn ContextInner, self_addr: ActorAddress) -> Self {
Self { inner, self_addr }
}
#[cfg(feature = "python")]
pub(crate) fn raw_inner(&self) -> &dyn ContextInner {
self.inner
}
/// Returns the address of the actor currently being ticked.
pub fn self_addr(&self) -> ActorAddress {
self.self_addr
}
/// Send a typed message to an actor address.
pub fn send<M: Message>(&self, addr: ActorAddress, msg: M) -> Result<(), Error> {
self.inner.send_any(addr, Box::new(msg))
}
/// Spawn a new actor, returning its address.
pub fn spawn<A: ActorInterface>(&self, actor: A) -> Result<ActorAddress, Error> {
let addr = ActorAddress::new_random();
let boxed: Box<dyn AnyActor> = Box::new(Actor::new(actor));
self.inner.spawn_any(addr, boxed)?;
Ok(addr)
}
}
// Re-export Ctx and ContextInner for backwards compatibility
pub use crate::actor::{ContextInner, Ctx};
/// Type-erased sender for external inboxes.
pub(crate) trait SenderT: Send + Sync {
@ -287,7 +252,7 @@ impl Runtime {
inbox_registry: &rt_clone.inbox_registry,
config: &rt_clone.config,
};
worker.run(&tc, &rt_clone.is_running, &rt_clone.config.backoff_policy);
worker.run(&tc, &rt_clone.is_running);
});
handles.push(handle);
}
@ -400,14 +365,6 @@ impl InboxRegistry {
/// Object-safe inner trait for sending type-erased messages.
pub(crate) trait ContextInner {
fn send_any(&self, addr: ActorAddress, msg: Box<dyn Any + Send>) -> Result<(), Error>;
fn spawn_any(&self, addr: ActorAddress, actor: Box<dyn AnyActor>) -> Result<(), Error>;
fn mailbox_waterlevel(&self) -> usize;
}
impl ContextInner for Runtime {
fn send_any(&self, addr: ActorAddress, msg: Box<dyn Any + Send>) -> Result<(), Error> {
match self.address_map.lookup(&addr) {

37
src/worker/mod.rs
View file

@ -5,11 +5,11 @@ use std::sync::atomic::{AtomicBool, AtomicU64, AtomicUsize, Ordering};
use std::sync::Arc;
use std::thread;
use crate::actor::{ActorAddress, AnyActor, Message};
use crate::actor::{ActorAddress, AnyActor, ContextInner, Ctx, Message};
use crate::address_map::{AddressMap, Placement, WorkerId};
use crate::channel::{Receiver, Sender};
use crate::config::{BackoffPolicy, RuntimeConfig};
use crate::runtime::{ContextInner, Ctx, Envelope, InboxRegistry};
use crate::config::RuntimeConfig;
use crate::runtime::{Envelope, InboxRegistry};
use crate::Error;
/// Per-worker stats published via atomics. Readable from any thread.
@ -90,12 +90,7 @@ impl Worker {
{
let worker_ctx = WorkerContext {
worker_id: self.id,
address_map: tc.address_map,
transfer_txs: tc.transfer_txs,
spawn_txs: tc.spawn_txs,
placement: tc.placement,
inbox_registry: tc.inbox_registry,
config: tc.config,
tc,
pending_local: &pending_local,
};
processed = self.pool.tick_all(&worker_ctx);
@ -121,7 +116,8 @@ impl Worker {
did_work
}
pub(crate) fn run(&mut self, tc: &TickContext, is_running: &AtomicBool, backoff: &BackoffPolicy) {
pub(crate) fn run(&mut self, tc: &TickContext, is_running: &AtomicBool) {
let backoff = &tc.config.backoff_policy;
let mut idle_count: u32 = 0;
while is_running.load(Ordering::Acquire) {
let did_work = self.tick_once(tc);
@ -151,18 +147,13 @@ impl Worker {
/// Cross-worker sends go through the transfer queue.
struct WorkerContext<'a> {
worker_id: WorkerId,
address_map: &'a AddressMap,
transfer_txs: &'a [Sender<Envelope>],
spawn_txs: &'a [Sender<(ActorAddress, Box<dyn AnyActor>)>],
placement: &'a Placement,
inbox_registry: &'a InboxRegistry,
config: &'a RuntimeConfig,
tc: &'a TickContext<'a>,
pending_local: &'a RefCell<Vec<(ActorAddress, Box<dyn Any + Send>)>>,
}
impl ContextInner for WorkerContext<'_> {
fn send_any(&self, addr: ActorAddress, msg: Box<dyn Any + Send>) -> Result<(), Error> {
match self.address_map.lookup(&addr) {
match self.tc.address_map.lookup(&addr) {
Some(wid) if wid == self.worker_id => {
// Same worker: buffer for local delivery (after current tick round)
self.pending_local.borrow_mut().push((addr, msg));
@ -171,26 +162,26 @@ impl ContextInner for WorkerContext<'_> {
Some(wid) => {
// Cross worker: envelope through transfer queue
let envelope = Envelope::new(addr, msg);
let _ = self.transfer_txs[wid.as_usize()].try_send(envelope);
let _ = self.tc.transfer_txs[wid.as_usize()].try_send(envelope);
Ok(())
}
None => {
// Try inbox registry (external inboxes)
self.inbox_registry.try_deliver(addr, msg)
self.tc.inbox_registry.try_deliver(addr, msg)
}
}
}
fn spawn_any(&self, addr: ActorAddress, actor: Box<dyn AnyActor>) -> Result<(), Error> {
let worker_id = self.placement.next_worker();
self.address_map.insert(addr, worker_id);
self.spawn_txs[worker_id.as_usize()]
let worker_id = self.tc.placement.next_worker();
self.tc.address_map.insert(addr, worker_id);
self.tc.spawn_txs[worker_id.as_usize()]
.try_send((addr, actor))
.map_err(|_| Error::from("Spawn queue full"))
}
fn mailbox_waterlevel(&self) -> usize {
self.config.mailbox_waterlevel
self.tc.config.mailbox_waterlevel
}
}

9
src/worker/tests.rs
View file

@ -4,11 +4,11 @@ use std::sync::atomic::{AtomicBool, AtomicUsize, Ordering};
use std::sync::Arc;
use std::thread;
use crate::actor::{ActorAddress, AnyActor};
use crate::actor::{ActorAddress, AnyActor, Ctx};
use crate::address_map::{AddressMap, Placement, WorkerId};
use crate::channel::Receiver;
use crate::config::{BackoffPolicy, RuntimeConfig};
use crate::runtime::{Ctx, Envelope, InboxRegistry};
use crate::config::RuntimeConfig;
use crate::runtime::{Envelope, InboxRegistry};
use super::{TickContext, Worker, WorkerStats};
@ -399,7 +399,6 @@ fn run_loop_stops_on_shutdown() {
let mut worker = Worker::new(WorkerId(0), transfer_rx, spawn_rx, stats);
let is_running = AtomicBool::new(false);
let backoff = BackoffPolicy::default();
let address_map = AddressMap::new();
let placement = Placement::new(1);
let inbox_registry = InboxRegistry::new();
@ -415,6 +414,6 @@ fn run_loop_stops_on_shutdown() {
};
thread::scope(|s| {
s.spawn(|| worker.run(&tc, &is_running, &backoff));
s.spawn(|| worker.run(&tc, &is_running));
});
}

12
tools/spectral/spectral_analysis.py
View file

@ -1412,8 +1412,8 @@ def main() -> None:
description="Spectral analysis of dependency DAGs"
)
parser.add_argument("dot_file", help="Path to DOT file (from depgraph)")
parser.add_argument("-o", "--output-dir", default=".",
help="Output directory (default: current directory)")
parser.add_argument("-o", "--output-dir", default="docs/connectome",
help="Output directory (default: docs/connectome)")
parser.add_argument("--no-plots", action="store_true",
help="Text report only (no matplotlib dependency)")
parser.add_argument("--json", action="store_true",
@ -1435,25 +1435,25 @@ def main() -> None:
# Generate report
report = generate_report(result)
print(report)
report_path = os.path.join(args.output_dir, "spectral_report.txt")
report_path = os.path.join(args.output_dir, "connectome_report.txt")
with open(report_path, "w") as f:
f.write(report)
print(f"\nReport saved to {report_path}")
# Generate interactive HTML dashboard
html_path = os.path.join(args.output_dir, "spectral_dashboard.html")
html_path = os.path.join(args.output_dir, "connectome_dashboard.html")
generate_dashboard_html(result, html_path, dot_source=dot_text)
print(f"Interactive dashboard saved to {html_path}")
# Generate static PNG dashboard
if not args.no_plots:
dashboard_path = os.path.join(args.output_dir, "spectral_dashboard.png")
dashboard_path = os.path.join(args.output_dir, "connectome_dashboard.png")
generate_dashboard(result, dashboard_path)
print(f"Static dashboard saved to {dashboard_path}")
# Generate JSON
if args.json:
json_path = os.path.join(args.output_dir, "spectral_metrics.json")
json_path = os.path.join(args.output_dir, "connectome_metrics.json")
with open(json_path, "w") as f:
json.dump(metrics_to_dict(result), f, indent=2)
print(f"JSON saved to {json_path}")