PROMPTS:

#1: Build me a Python simulation using Pygame that demonstrates a smart AI traffic light system.

There should be 4 lanes (like a crossroad) with moving cars represented as rectangles.
Each lane should have a traffic signal (red, yellow, green).
Cars should stop when the light is red and move when it is green.
Green light duration should be dynamic: based on vehicle count in each lane (randomly generated).
Add an “emergency vehicle” feature: if an ambulance appears in a lane, the light should immediately switch to green for that lane.
Show real-time updates: vehicle count, current green lane, and timer countdown on the screen.

#2: Build me a web-based simulation using HTML, CSS, and JavaScript (Canvas).

Simulate a 4-way intersection with small cars (colored rectangles or icons) moving along roads.
Each road has a traffic light (red, yellow, green).
Cars should stop at red and move on green.
Traffic light timing should be dynamic: adjust green time based on number of cars waiting.
Randomly spawn cars and occasionally spawn an ambulance that must get priority green.
Show a timer above each signal and highlight which signal is currently green.
Keep the interface colorful, simple, and easy to present to non-technical people.

#3: TASK: Implement a scheduling layer for a traffic intersection simulation. This module will be used by the simulation to produce phase schedules (green cycles + durations). Implement the following:

Algorithms to implement: a) Round Robin (RR): cycles through directions/phases in fixed order. Duration per phase can be proportional to queue length or fixed. b) Shortest Job First (SJF): choose the next phase which will clear the smallest expected remaining 'job' (e.g., vehicles waiting + expected arrivals in short horizon) to reduce average waiting time. c) Priority Scheduling: assign priorities by vehicle type + waiting time. Emergency vehicles have highest priority and must preempt schedule. Public transport (if present) higher than private cars; else use waiting time to break ties. d) Meta-Scheduler: chooses among RR, SJF, Priority dynamically based on heuristics or rule set: - If emergency vehicles present -> always use Priority (immediate preemption). - If overall load low (avg queue < low_threshold) -> use RR for simplicity. - If high variance in queue lengths -> prefer SJF to reduce backlog. - Expose
```
policy_params
```
to tune thresholds, minimum green, max green.
Interfaces & types (Python):
- Input type:
```
IntersectionState
```
  (dict or dataclass) containing: queues: Dict[str,int] # counts per approach: "N","E","S","W" waiting_times: Dict[str,List[float]] # per-vehicle waiting times (seconds) arrival_rates: Dict[str,float] # estimated λ for short horizon (vehicles/sec) emergency: List[EmergencyVehicle] # each has direction, time_to_intersection (ETA seconds), id current_phase: str # e.g., "NS_green" or "EW_green" or "all_red" sim_time: float # current sim time in seconds
- Output type:
```
ActionPlan
```
  -> List[Phase], where Phase = {"phase":str, "duration":float, "preemptable":bool}
- Config:
```
policy_params
```
  dict containing: min_green: float (e.g., 7s) max_green: float (e.g., 60s) yellow_duration: float (default 3s) rr_cycle_order: ["NS_green","EW_green"] low_load_threshold: float high_variance_threshold: float sjf_horizon: float (seconds to forecast arrivals) emergency_preempt_buffer: float (seconds to safely transition)
Emergency handling:
- If
```
emergency
```
  list not empty and any EV ETA <= emergency_preempt_buffer OR already in queue, produce a plan that: a) Immediately schedules green for EV direction (with all-red + yellow transitions as required). b) Limit preemption duration to clear EVs (configurable). c) After EV clears, smoothly resume previously-in-progress scheduling algorithm (remember prior phase and remaining duration).
Safety & constraints:
- Always include yellow/all-red before phase switches (yellow_duration param).
- Respect min_green & max_green.
- Avoid frequent oscillations: do not allow same approach to be toggled on/off faster than
```
min_switch_interval
```
  (configurable).
- Return deterministic results for same state + params (pure function).
Unit tests (pytest):
- test_rr_basic: verifies RR cycles through phases in the defined order and durations are within limits.
- test_sjf_reduces_avg_wait: small simulated input where SJF picks shorter job and reduces average waiting time compared to RR.
- test_emergency_preempt: EV arriving in 4s forces immediate preemption to EV direction; check first phase of action_plan is EV direction.
- test_meta_switch: when queue variance is high, meta_scheduler selects SJF; when low, selects RR.
Logging & metrics:
- Add optional debug logging (toggle by
```
debug=True
```
  ) that prints chosen policy, reasons (e.g., "high variance -> SJF"), and emergency actions.
- Provide small helper
```
explain_decision(state, policy)
```
  that returns a short string explanation for the selection.
Integration hooks:
- Provide
```
apply_action_plan(sim, action_plan)
```
  stub that shows how the simulation should consume the plan (calls sim.set_phase(phase, duration)).
- Provide
```
simulate_one_step(state, action_plan)
```
  example to validate behavior inside unit tests.
Code quality:
- Clear type hints, docstrings, no global state.
- Keep core logic in functions/classes so Copilot can suggest tests and further optimizations.

Output:

A single Python module
```
schedulers.py
```
with implementations and docstrings
A
```
test_schedulers.py
```
with the 4 unit tests using pytest and a minimal deterministic simulation helper.

Edge cases:

All queues empty: produce a minimal cyclical RR plan or an empty plan with a small
```
all_red
```
pause.
Multiple simultaneous emergency vehicles from different directions: prioritize the one with smallest ETA; if ETAs equal, pick the one closest to intersection by ETA + direction priority tie-breaker.

Notes for Copilot: produce readable, well-documented code with small helper functions (compute_queue_variance, estimate_jobs_in_horizon, schedule_transition_with_yellow). Use simple deterministic math; no ML required in this module.

PROMPTS:

#1: Build me a Python simulation using Pygame that demonstrates a smart AI traffic light system.

There should be 4 lanes (like a crossroad) with moving cars represented as rectangles.
Each lane should have a traffic signal (red, yellow, green).
Cars should stop when the light is red and move when it is green.
Green light duration should be dynamic: based on vehicle count in each lane (randomly generated).
Add an “emergency vehicle” feature: if an ambulance appears in a lane, the light should immediately switch to green for that lane.
Show real-time updates: vehicle count, current green lane, and timer countdown on the screen.

#2: Build me a web-based simulation using HTML, CSS, and JavaScript (Canvas).

Simulate a 4-way intersection with small cars (colored rectangles or icons) moving along roads.
Each road has a traffic light (red, yellow, green).
Cars should stop at red and move on green.
Traffic light timing should be dynamic: adjust green time based on number of cars waiting.
Randomly spawn cars and occasionally spawn an ambulance that must get priority green.
Show a timer above each signal and highlight which signal is currently green.
Keep the interface colorful, simple, and easy to present to non-technical people.

Algorithms to implement: a) Round Robin (RR): cycles through directions/phases in fixed order. Duration per phase can be proportional to queue length or fixed. b) Shortest Job First (SJF): choose the next phase which will clear the smallest expected remaining 'job' (e.g., vehicles waiting + expected arrivals in short horizon) to reduce average waiting time. c) Priority Scheduling: assign priorities by vehicle type + waiting time. Emergency vehicles have highest priority and must preempt schedule. Public transport (if present) higher than private cars; else use waiting time to break ties. d) Meta-Scheduler: chooses among RR, SJF, Priority dynamically based on heuristics or rule set: - If emergency vehicles present -> always use Priority (immediate preemption). - If overall load low (avg queue < low_threshold) -> use RR for simplicity. - If high variance in queue lengths -> prefer SJF to reduce backlog. - Expose
```
policy_params
```
to tune thresholds, minimum green, max green.
Interfaces & types (Python):
- Input type:
```
IntersectionState
```
  (dict or dataclass) containing: queues: Dict[str,int] # counts per approach: "N","E","S","W" waiting_times: Dict[str,List[float]] # per-vehicle waiting times (seconds) arrival_rates: Dict[str,float] # estimated λ for short horizon (vehicles/sec) emergency: List[EmergencyVehicle] # each has direction, time_to_intersection (ETA seconds), id current_phase: str # e.g., "NS_green" or "EW_green" or "all_red" sim_time: float # current sim time in seconds
- Output type:
```
ActionPlan
```
  -> List[Phase], where Phase = {"phase":str, "duration":float, "preemptable":bool}
- Config:
```
policy_params
```
  dict containing: min_green: float (e.g., 7s) max_green: float (e.g., 60s) yellow_duration: float (default 3s) rr_cycle_order: ["NS_green","EW_green"] low_load_threshold: float high_variance_threshold: float sjf_horizon: float (seconds to forecast arrivals) emergency_preempt_buffer: float (seconds to safely transition)
Emergency handling:
- If
```
emergency
```
  list not empty and any EV ETA <= emergency_preempt_buffer OR already in queue, produce a plan that: a) Immediately schedules green for EV direction (with all-red + yellow transitions as required). b) Limit preemption duration to clear EVs (configurable). c) After EV clears, smoothly resume previously-in-progress scheduling algorithm (remember prior phase and remaining duration).
Safety & constraints:
- Always include yellow/all-red before phase switches (yellow_duration param).
- Respect min_green & max_green.
- Avoid frequent oscillations: do not allow same approach to be toggled on/off faster than
```
min_switch_interval
```
  (configurable).
- Return deterministic results for same state + params (pure function).
Unit tests (pytest):
- test_rr_basic: verifies RR cycles through phases in the defined order and durations are within limits.
- test_sjf_reduces_avg_wait: small simulated input where SJF picks shorter job and reduces average waiting time compared to RR.
- test_emergency_preempt: EV arriving in 4s forces immediate preemption to EV direction; check first phase of action_plan is EV direction.
- test_meta_switch: when queue variance is high, meta_scheduler selects SJF; when low, selects RR.
Logging & metrics:
- Add optional debug logging (toggle by
```
debug=True
```
  ) that prints chosen policy, reasons (e.g., "high variance -> SJF"), and emergency actions.
- Provide small helper
```
explain_decision(state, policy)
```
  that returns a short string explanation for the selection.
Integration hooks:
- Provide
```
apply_action_plan(sim, action_plan)
```
  stub that shows how the simulation should consume the plan (calls sim.set_phase(phase, duration)).
- Provide
```
simulate_one_step(state, action_plan)
```
  example to validate behavior inside unit tests.
Code quality:
- Clear type hints, docstrings, no global state.
- Keep core logic in functions/classes so Copilot can suggest tests and further optimizations.

Output:

A single Python module
```
schedulers.py
```
with implementations and docstrings
A
```
test_schedulers.py
```
with the 4 unit tests using pytest and a minimal deterministic simulation helper.

Edge cases:

All queues empty: produce a minimal cyclical RR plan or an empty plan with a small
```
all_red
```
pause.
Multiple simultaneous emergency vehicles from different directions: prioritize the one with smallest ETA; if ETAs equal, pick the one closest to intersection by ETA + direction priority tie-breaker.

PROMPTS:

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