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### Scheduler End-to-End Flowchart
This document shows the high-level flow across modules using Mermaid. It covers input data preparation, ripeness classification, scheduling, overrides, allocation, outputs, and optional simulation and dashboard flows.
```mermaid
flowchart TD
%% Inputs
subgraph Data_Layer
DCFG[configs/*.toml]
DLOAD[scheduler/data/param_loader.py<br/>scheduler/data/config.py]
DDEFS[scheduler/data/defaults/*]
DCASES[Data/cases.parquet<br/>reports/figures/*/cases_clean.parquet]
DHEAR[Data/hearings.parquet<br/>reports/figures/*/hearings_clean.parquet]
end
%% Core Domain
subgraph Core_Domain
CASE[scheduler/core/case.py]
RIPEN[scheduler/core/ripeness.py]
HEAR[scheduler/core/hearing.py]
ROOM[scheduler/core/courtroom.py]
JUDGE[scheduler/core/judge.py]
POLICY[scheduler/core/policy.py]
ALGO[scheduler/core/algorithm.py]
end
%% Control
subgraph Control_and_Overrides
OV[scheduler/control/overrides.py]
EXP[scheduler/control/explainability.py]
end
%% Allocation/Simulation
subgraph Simulation_Engine
SIM[scheduler/simulation/engine.py]
ALLOC[scheduler/simulation/allocator.py]
POLS[scheduler/simulation/policies/*]
end
%% UI
subgraph Dashboard_UI
APP[scheduler/dashboard/app.py]
PAGES[scheduler/dashboard/pages/*]
DUTIL[scheduler/dashboard/utils/*]
end
%% Metrics and Monitoring
subgraph Metrics_and_Monitoring
MET[scheduler/metrics/basic.py]
MONCAL[scheduler/monitoring/ripeness_calibrator.py]
MONMET[scheduler/monitoring/ripeness_metrics.py]
end
%% Outputs
subgraph Outputs
OUTCL[scheduler/output/cause_list.py]
OUTFILES[outputs/simulation_runs/*<br/>reports/*]
end
%% Data flow
DCFG --> DLOAD
DDEFS --> DLOAD
DCASES --> DLOAD
DHEAR --> DLOAD
DLOAD --> CASE
CASE --> RIPEN
RIPEN --> ALGO
HEAR --> ALGO
ROOM --> ALGO
JUDGE --> ALGO
POLICY --> ALGO
%% Scheduling path for a real day
ALGO -->|filters by ripeness| RIPEN
ALGO -->|eligibility and priority| CASE
ALGO -->|manual overrides| OV
ALGO -->|explanations| EXP
ALGO -->|allocation| ALLOC
ALLOC --> OUTCL
OUTCL --> OUTFILES
%% Simulation path (optional)
DLOAD --> SIM
SIM --> RIPEN
SIM --> POLS
SIM --> ALLOC
SIM --> MET
SIM --> OUTFILES
%% Dashboard path
DUTIL --> APP
APP --> PAGES
PAGES --> ALGO
PAGES --> SIM
PAGES --> OUTFILES
PAGES --> EXP
PAGES --> OV
%% Monitoring (future integration)
ALGO -. record predictions .-> MONMET
MONMET -. calibrate thresholds .-> MONCAL
MONCAL -. update ripeness thresholds .-> RIPEN
```
#### Narrative
1) Data layer assembles parameters and input datasets via `param_loader.py` and `config.py`, pulling defaults from `scheduler/data/defaults/*` and optionally real case/hearing data.
2) Core domain models (`case.py`, `hearing.py`, `courtroom.py`, `judge.py`) define the state. Ripeness (`ripeness.py`) classifies cases as ripe/unripe and provides reasons and thresholds.
3) The scheduling algorithm (`algorithm.py`) orchestrates: ripeness filtering, priority computation, manual overrides, and courtroom allocation. Explanations are available through `control/explainability.py`.
4) Allocations produce cause lists (`output/cause_list.py`) and artifacts.
5) The simulation engine (`simulation/engine.py`) uses the same domain and policies to stress‑test scheduling strategies at scale, producing metrics and reports.
6) The dashboard (`scheduler/dashboard/*`) provides a UI to run data explorations, inspect ripeness, run simulations, and export cause lists.
7) Monitoring components track classification accuracy and enable future threshold calibration; currently not wired into the live flow.
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